Prioritizing Species for Conservation Planning

CONVENORS: Caroline Lees, Onnie Byers, Phil Miller

AIM: To explore the need for and potential solutions to, prioritizing species for conservation planning

within the SSC.

BACKGROUND: The IUCN’s Species Survival Commission recently launched a new initiative aimed at

increasing its participation and effectiveness in species conservation through species conservation

planning. Of the 85,604 species assessed through the IUCN Global Red List, 24,307 are considered

threatened with extinction. Resources are finite and decisions will need to be taken about which of

these species to focus on, or at least which to focus on first. A number of initiatives, including some led

by IUCN member countries and organizations, have considered the issue of species prioritization for

conservation attention and have developed their own approaches. A sample is described in Table 1. We

can learn much from these initiatives. In particular:

1) That there is no universally “right” outcome of species prioritization for conservation.

Different prioritization goals and contexts will necessarily give rise to different priorities – that

is, different priorities can be “right” for different circumstances.

2) That despite the necessary subjectivity embedded within specific prioritization schemes, it is

possible to design systematic approaches that are transparent about where this subjectivity lies.

3) That those prioritizing species for conservation attention, though working in different

contexts and towards different goals, will often cover some of the same ground and draw the

same conclusions about what is important.

4) That where prioritization criteria require the de novo assembly or analysis of large amounts

of data, the use of those criteria is likely to be limited to well-understood taxa.

5) That developing from scratch a prioritization scheme acceptable to a large group of

stakeholders can take much time, energy and resources.

In this workshop we will explore the species prioritization needs of the SSC community in the area of

conservation planning. We will invite participants to share their experiences of developing or

implementing prioritization schemes and use this as a basis for discussing how we might move forward

with one or more tools to assist prioritization for use within the SSC, and potentially beyond.

PROCESS:

Presentations:

• Summary of different approaches to prioritizing species for conservation attention.

Discussion:

• Prioritization needs: what kinds of species planning prioritization problems are there, or

might there be within the SSC?

• Pros and cons of different schemes: from participants’ experiences of developing or

implementing species prioritization schemes, what has or is working well and why? What

has not and why? What were the biggest challenges? What else can we learn?

• For the prioritization needs identified, are existing tools adequate? If not, what kinds of new

tools might be most useful? Is an expert system an option? Would written guidelines be a

better approach?

Some of the results of this session will be used to inform the subsequent workshop on multi-species

planning.

PREPARATION: Participants are asked to familiarize themselves with Mace et al., 2007 and to come

equipped with details and insights into any species prioritization schemes or tools with which they are

familiar.

Table 1. Example schemes for prioritizing species for conservation attention

Note that all of the schemes exemplified here use the IUCN Red List or equivalent as a criterion, which

automatically excludes the many species not yet assessed.

Overarching goal List of alternatives Criteria for selection/scoring Resulting priorities

Initiative: Alliance for Zero Extinction (AZE) (88 NGOs. National Alliances now also exist.) See

http://www.zeroextinction.org To defend against the most predictable species losses.

All species for which endangerment and distribution are known.

Endangered or Critically Endangered (IUCN) AND restricted to a single remaining site

920 species prioritised to date - mammals, birds, amphibians, reptiles, conifers, and reef-building corals.

Asian Species Action Partnership (ASAP) http://www.speciesonthebrink.org/

Reversing declines in the wild of Asian species on the brink of extinction.

Southeast Asian species

Critically Endangered, freshwater and terrestrial vertebrates, occurring regularly in Southeast Asia

174 species

Initiative: Evolutionarily Distinct and Globally Endangered (EDGE) (Zoological Society of London) (see Isaac et al., 2007)

To maximise conservation of phylogenetic diversity.

All species for which phylogenetic uniqueness has been assessed: mammals, amphibians, corals and birds.

Score = Evolutionary Distinctiveness (ED) X Global Endangerment (IUCN). EDGE species have a greater than average score (Isaac et al., 2007)

Portfolio approach includes the top 100 scoring species in each of the major taxonomic groups considered

Initiative: Method for the Assessment of Priorities for International Species Conservation (MAPISCo) To identify species for which targeted conservation action would have the broadest co-benefits for other species, habitats, wider ecosystems, and ecosystem

Species in the IUCN Red List database for which sufficient data exist to allow assessment against

Ability to contribute to: (1) habitat and area conservation (2) sustainable harvesting of

?

Overarching goal List of alternatives Criteria for selection/scoring Resulting priorities

services. the criteria (?). fish, invertebrates and aquatic plants, (3) conservation of genetic diversity of wild relatives of cultivated plants and domesticated animals, (4) protection of the provisioning of ecosystem services (5) the prevention of species extinctions.

Initiative: National prioritisation scheme for conservation action planning (New Zealand Dept. of Conservation) (NZ DOC) (see Joseph et al., 2009)

To optimise allocation of conservation planning resources towards the goal of ensuring the persistence of all New Zealand species somewhere.

All species native to New Zealand.

Assessed (using NZ RL-equivalent) as conservation dependent OR as threatened and declining, with threats understood and conservation action considered feasible.

≈700 species prioritised for management planning out of ≈10,000 assessed.

READING:

Isaac N.J., Turvey S.T., Collen B., Waterman C., Baillie J.E. 2007. Mammals on the EDGE: Conservation

Priorities Based on Threat and Phylogeny. PLoS ONE 2(3): e296.

https://doi.org/10.1371/journal.pone.0000296

Joseph, L. N., R. F. Maloney, J. E. M. Watson, and H. P. Possingham. 2011. Securing nonflagship species

from extinction. Conservation Letters 4:324-325.

Mace, G.M., Possingham, H.P., and Leader-Williams, N. 2007. Prioritizing Choices in Conservation. In:

MacDonald, D.W. and Service, K. (Eds) Key Topics in Conservation Biology. Blackwell Publishing Ltd.

MAPISCo Project Team (2013) Method for the assessment of priorities for international species

conservation. Newcastle University, Newcastle upon Tyne, UK.

http://www.zeroextinction.org

Mammals on the EDGE: Conservation Priorities Based onThreat and PhylogenyNick J. B. Isaac*, Samuel T. Turvey, Ben Collen, Carly Waterman, Jonathan E. M. Baillie

Institute of Zoology, Zoological Society of London, London, United Kingdom

Conservation priority setting based on phylogenetic diversity has frequently been proposed but rarely implemented. Here, wedefine a simple index that measures the contribution made by different species to phylogenetic diversity and show how theindex might contribute towards species-based conservation priorities. We describe procedures to control for missing species,incomplete phylogenetic resolution and uncertainty in node ages that make it possible to apply the method in poorly knownclades. We also show that the index is independent of clade size in phylogenies of more than 100 species, indicating thatscores from unrelated taxonomic groups are likely to be comparable. Similar scores are returned under two different speciesconcepts, suggesting that the index is robust to taxonomic changes. The approach is applied to a near-complete species-levelphylogeny of the Mammalia to generate a global priority list incorporating both phylogenetic diversity and extinction risk. The100 highest-ranking species represent a high proportion of total mammalian diversity and include many species not usuallyrecognised as conservation priorities. Many species that are both evolutionarily distinct and globally endangered (EDGEspecies) do not benefit from existing conservation projects or protected areas. The results suggest that global conservationpriorities may have to be reassessed in order to prevent a disproportionately large amount of mammalian evolutionary historybecoming extinct in the near future.

Citation: Isaac NJB, Turvey ST, Collen B, Waterman C, Baillie JEM (2007) Mammals on the EDGE: Conservation Priorities Based on Threat andPhylogeny. PLoS ONE 2(3): e296. doi:10.1371/journal.pone.0000296

INTRODUCTIONOur planet is currently experiencing a severe anthropogenically

driven extinction event, comparable in magnitude to prehistoric

mass extinctions. Global extinction rates are now elevated up to

a thousand times higher than the background extinction rates

shown by the fossil record, and may climb another order of

magnitude in the near future [1–3]. The resources currently

available for conservation are, unfortunately, insufficient to

prevent the loss of much of the world’s threatened biodiversity

during this crisis, and conservation planners have been forced into

the unenviable situation of having to prioritise which species

should receive the most protection–this is ‘the agony of choice’ [4]

or the ‘Noah’s Ark problem’ [5].

A range of methods for setting species-based conservation

priorities have been advocated by different researchers or

organisations, focusing variously on threatened species, restrict-

ed-range endemics, ‘flagship’, ‘umbrella’, ‘keystone’, ‘landscape’ or

‘indicator’ species, or species with significant economic, ecological,

scientific or cultural value [6–8]. To date, global priority-setting

exercises have tended to focus on endemic (or restricted range)

species [6,9,10], presumably because endemism is easier to

measure than competing methods. However, recent data show

that endemism is a poor predictor of total species richness or the

number of threatened species [11].

It has also been argued that maximising Phylogenetic Diversity

(PD) should be a key component of conservation priority setting

[4,12–14]. Species represent different amounts of evolutionary

history, reflecting the tempo and mode of divergence across the

Tree of Life. The extinction of a species in an old, monotypic or

species-poor clade would therefore result in a greater loss of

biodiversity than that of a young species with many close relatives

[15,16]. However, conserving such lineages may be difficult, since

there is some evidence that they are more likely to be threatened

with extinction than expected by chance [17]. This clumping of

extinction risk in species-poor clades greatly increases the loss of

PD compared with a null model of random extinction [18] and

suggests that entire vertebrate orders may be lost within centuries

[19]. Among mammals alone, at least 14 genera and three families

have gone extinct since AD 1500 [20], and all members of a further

19 families and three orders are considered to be in imminent

danger of extinction [2]. Many academic papers have suggested

ways to maximise the conservation of PD [e.g. 12,13,21–23] and

measure species’ contributions to PD [e.g. 4,23–25], but these

have rarely been incorporated into conservation strategies.

Therefore, it is possible that evolutionary history is being rapidly

lost, yet the most distinct species are not being identified as high

priorities in existing conservation frameworks.

There are several reasons why PD has not gained wider accept-

ance in the conservation community. First, although evolutionary

history consists of two distinct components (the branching pattern

of a phylogenetic tree and the length of its branches), complete

dated species-level phylogenies for large taxonomic groups have

only recently become available [26]. Early implementations of PD-

based approaches were therefore unable to incorporate branch

length data, and focused solely on measurements of branching

pattern [4]. Second, PD removes the focus from species and so

may lack wider tangible appeal to the public; conserving PD may

be seen as less important than the protection of endemic or

Academic Editor: Walt Reid, The David and Lucile Packard Foundation,Conservation and Science Program, United States of America

Received January 16, 2007; Accepted February 19, 2007; Published March 14,2007

Copyright: � 2007 Isaac et al. This is an open-access article distributed underthe terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided theoriginal author and source are credited.

Funding: The authors did not receive any funding to conduct the researchdescribed in this paper.

Competing Interests: The authors have declared that no competing interestsexist.

* To whom correspondence should be addressed. E-mail: nick.isaac@ioz.ac.uk

PLoS ONE | www.plosone.org 1 March 2007 | Issue 3 | e296

threatened species [16]. However, the current instability in species

taxonomy [27] means that decisions based on PD might be more

objective than those based on different species concepts

[13,16,27]. Combining species’ conservation status with a measure

of their contribution to PD is therefore desirable, because species

can be retained as units but weighted appropriately [5,22]. This

would generate a useful and transparent means for setting global

priorities for species-based conservation [25].

This paper describes a new method for measuring species’

relative contributions to phylogenetic diversity [the ‘originality’ of

species: ref 24]. We explore the statistical properties of the

resulting measure, which we call Evolutionary Distinctiveness

(ED), and test its robustness to changing species concepts. ED

scores are calculated for the Class Mammalia, and combined with

values for species’ extinction risk to generate a list of species that

are both evolutionarily distinct and globally endangered (‘EDGE

species’). The resultant list provides a set of priorities for

mammalian conservation based not only on the likelihood that

a species will be lost, but also on its irreplaceability.

Evolutionary Distinctiveness and its use in

priority-settingIn order to calculate ED scores for each species, we divide the total

phylogenetic diversity of a clade amongst its members. This is

achieved by applying a value to each branch equal to its length

divided by the number of species subtending the branch. The ED

of a species is simply the sum of these values for all branches from

which the species is descended, to the root of the phylogeny. For

the examples in this paper, we have measured ED in units of time,

such that each million years of evolution receives equal weighting

and the branches terminate at the same point (i.e. the phylogeny is

ultrametric). The method could be applied to non-ultrametric

phylogenies if the conservation of other units [e.g. character

diversity 28,29] was prioritised [although see ref 30].

The basic procedure for calculating ED scores is illustrated in

figure 1, which describes a clade of seven species (A–G). The ED

score of species A is given by the sum of the ED scores for each of

the four branches between A and the root. The terminal branch

contains just one species (A) and is 1 million years (MY) long, so

receives a score of 1 MY. The next two branches are both 1 MY

long and contain two and three species, so each daughter species

(A, B and C) receives 1/2 and 1/3 MY respectively. The deepest

branch that is ancestral to species A is 2 MY long and is shared

among five species (A to E), so the total ED score for species A is

given by (1/1+1/2+1/3+2/5) = 2.23 MY. Species B is the sister

taxon of A, so receives the same score. By the same arithmetic, C

has a score of (2/1+1/3+2/5) = 2.73 MY, both D and E receive

(1/1+2/2+2/5) = 2.4 MY, and both F and G receive (0.5/1+4.5/

2) = 2.75 MY. The example illustrates that ED is not solely

determined by a species’ unique PD (i.e. the length of the terminal

branch). Species F and G are the top-ranked species based on their

ED scores, even though each represents just a small amount of

unique evolutionary history (0.5 MY). This suggests that the

conservation of both F and G should be prioritised, because the

extinction of either would leave a single descendant of the oldest

and most unusual lineage in the phylogeny [c.f. 15,24]. The ED

calculation is similar to the Equal Splits measure [25], which

apportions branch length equally among daughter clades, rather

than among descendent species.

In order to represent a useful tool in priority setting, ED scores

must be applicable in real phylogenies of large taxonomic groups.

To do this, we modified the basic procedure described above to

control for missing species, incomplete phylogenetic resolution and

uncertainty in node ages (see Materials and Methods). The

approach is implemented using a dated phylogeny of the Class

Mammalia that is nearly complete (.99%) at the species level

[31]. We then combined ED and extinction risk to identify species

that are both evolutionarily distinct and globally endangered

(‘EDGE species’). We measured extinction risk using the

quantitative and objective framework provided by the World

Conservation Union (IUCN) Red List Categories [2]. We follow

previous researchers in treating the Red List categories as intervals

of extinction risk and by assuming equivalence among criteria

[32,33, but see 34]. The resulting list of conservation priorities

(‘EDGE scores’) was calculated as follows:

EDGE~ln(1zED)zGE � ln(2) ð1Þ

where GE is the Red List category weight [Least Concern = 0,

Near Threatened and Conservation Dependent = 1, Vulnera-

ble = 2, Endangered = 3, Critically Endangered = 4, ref 32], here

representing extinction risk on a log scale. EDGE scores are

therefore equivalent to a loge-transformation of the species-specific

expected loss of evolutionary history [5,25] in which each

increment of Red List category represents a doubling (eln(2)) of

extinction risk. For the purposes of these analyses, we did not

calculate EDGE scores for species listed as Extinct in the Wild

(n = 4), domesticated populations of threatened species and 34

species (mostly of dubious taxonomic status) for which an

evaluation has not been made.

Figure 1. Hypothetical phylogeny of seven species (A–G) withEvolutionary Distinctiveness (ED) scores. Numbers above each branchindicate the length; numbers below show the number of descendentspecies. MYBP, millions of years before present.doi:10.1371/journal.pone.0000296.g001

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RESULTS

Statistical properties of EDWe measured ED in clades of different sizes to test whether ED

scores from different taxonomic groups are likely to be

comparable. We found that most ED is derived from a few

branches near the tips (i.e. those shared with few other species) and

that virtually no ED is gained in clades above ,180 species

(figure 2). Median ED in clades of 60 species is 88% of the total

accumulated using the whole tree (n = 10, figure 2). Moreover, the

rank order of ED scores is unaffected by the size of the clade under

consideration, except in very small clades and among species with

low overall ED (i.e. few of the lines in figure 2 cross one another).

These findings suggest that ED scores of different taxonomic

groups measured on separate phylogenies (i.e. with no nodes in

common) will be comparable, so long as each phylogeny is larger

than a threshold size. Based on the scaling observed in figure 2, we

suggest a minimum species richness of 100 as a useful rule of

thumb to ensure comparability among taxa.

Although most species (90% in figure 2) derive at least two-

thirds of their total ED from the terminal branch (which is not

shared with others), this branch length is a poor predictor of total

ED (r2 = 0.03 on a log-log scale). For species on short branches,

there is an order of magnitude difference between the length of the

terminal branch and ED. For example, the pale-throated and

brown-throated three-toed sloths (Bradypus tridactylus and B.

variegatus) share a common ancestor thought to be just over

a million years old, but the total ED of both species is 20.4 MY

(Table S1) since they have few close living relatives.

ED scores are also robust to taxonomic changes. For example,

ED scores in primates under the biological species concept [35]

are tightly correlated with ED scores under the phylogenetic

species concept [36] (r2 = 0.65 on a log-log scale), in spite of the

fact that there are substantial differences between the two: the

number of primate species differs by 50%. Furthermore, the

highest-ranking species do not change their identity: 45 of 58

biological species in the upper quartile of ED scores are also in the

upper quartile as phylogenetic species. However, species that have

been split into three or more species do tend to lose a large portion

of their ED. For example, the fork-marked lemur (Phaner furcifer) is

the second most distinct biological species of primate, with an ED

score of 38.33. It was split into four phylogenetic species [36] with

an ED score of 10.45 (Table S2), which is just inside the upper

quartile.

ED and EDGE scores in mammalsMammal ED scores range from 0.0582 MY (19 murid rodents) to

97.6 MY (duck-billed platypus, Ornithorhynchus anatinus). Scores are

approximately log-normally distributed, with a median of 7.86

MY and geometric mean of 6.28 MY.

Evolutionary Distinctiveness is not evenly distributed among the

Red List categories. Least Concern species have significantly lower

ED than the other categories (F1,4180 = 26.3, p,0.0001, using loge

transformed scores); there are no significant differences among the

remaining categories. This suggests that species with low ED

scores tend to suffer from low levels of extinction risk, although the

explanatory power of this model is extremely low (r2 = 0.006).

EDGE scores range from 0.0565 (10 murid rodents) to 6.48

(Yangtze River dolphin or baiji, Lipotes vexillifer) and are

approximately normally distributed around a mean of 2.63

(60.017; figure 3). The 100 highest priority (EDGE) species

includes several large-bodied and charismatic mammals, including

the giant and lesser pandas, the orang-utan, African and Asian

Figure 2. Scaling of ED scores with clade size for ten Critically Endangered mammal species. ED scores were calculated at each node between the tipsand root for ten species in different orders. Species chosen are: the baiji (Lipotes vexillifer), sumatran rhino (Dicerorhinus sumatrensis), northern hairy-nosed wombat (Lasiorhinus krefftii), persian mole (Talpa streeti), Omiltemi rabbit (Sylvilagus insonus), Przewalski’s gazelle (Procapra przewalskii), black-faced lion tamarin (Leontopithecus caissara), Livingstone’s flying fox (Pteropus livingstonii), red wolf (Canis rufus) and northern Luzon shrew rat(Crunomys fallax). See Materials and Methods for further details.doi:10.1371/journal.pone.0000296.g002

Mammals on the EDGE

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elephants, four rhinoceroses, two tapirs, two baleen whales,

a dugong and a manatee. However, many smaller and less

appreciated species also receive high priority, including sixteen

rodents, thirteen eulipotyphlans, twelve bats, four lagomorphs and

an elephant shrew (Table S1). The top 100 also includes at least 37

species that would not qualify for most area-based definitions of

endemism, since they are listed as threatened under Red List

criterion A (reduction in population size) without qualifying for

criteria B–D, which are based on population size or geographical

range. Whilst the highest-ranked species, by definition, are all

highly threatened (44 of the top 100 species are Critically

Endangered, a further 47 are Endangered), threat status alone

does not guarantee a high priority. For example, 10 Critically

Endangered species (in the genera Gerbillus, Peromyscus and

Crocidura), as well as 32 Endangered species, fail to make the top

1000, whilst 130 Near Threatened species do.

DISCUSSIONIt is important that conservation priority-setting approaches are

able to satisfy two conditions: they capture biodiversity and are

robust to uncertainty. The method described herein satisfies the

first condition because EDGE scores incorporate species value (in

terms of originality, or irreplaceability) weighted by urgency of

action (i.e. risk of extinction). Our approach satisfies the second

condition because the scores are also robust to clade size, missing

species and poor phylogenetic resolution. EDGE scores are also

easy to calculate, as all that is required is a set of Red List

assessments and a near-complete phylogeny containing at least

100 species.

In particular, EDGE priorities are much less sensitive to

taxonomic uncertainty than alternate methods. The current trend

towards the adoption of the phylogenetic species concept among

biologists [27] is likely to produce a large number of ‘new’

threatened and endemic species [37], potentially altering the

distribution of hotspots [38] and distorting other biodiversity

patterns [27]. The EDGE approach is robust to such distortion

because any increase in extinction risk due to splitting is balanced

by a decrease in ED. A good example is that of the ruffed lemurs

(Varecia spp.), which consist of one Endangered biological species

(ED = 19.8; EDGE = 5.11) or two phylogenetic species (Endan-

gered and Critically Endangered; ED = 10.3; EDGE = 4.50 and

5.20). Using the same approach, we estimate that the long-beaked

echidna (Zaglossus bruijni) would fall from the second-ranked

priority to the 20th after the addition of two new congeners

[suggested by 39]. Thus, EDGE scores for existing species are

robust to the ongoing discovery of new species.

EDGE priorities are also robust to several other forms of

uncertainty. Like all phylogenetic methods, the precise EDGE

scores are dependent on the topology and branch lengths of the

phylogeny. However, errors in the phylogeny are unlikely to alter

the identity of high-ranking species, particularly for clades of

several hundred species. Topological uncertainty is usually

expressed in supertrees as polytomies, which are accounted for

using simple correction factors. Likewise, branch length un-

certainty has been incorporated into the scoring system to down-

weight the priority of species descended from nodes with

imprecisely estimated ages (see Materials and Methods). These

developments make it possible to estimate robustly the contribu-

tion to phylogenetic diversity of species in poorly known clades.

The other major source of uncertainty is in estimating extinction

risk: most recent changes in Red List category have come about

through improvements in knowledge, rather than genuine changes

in status [32]. EDGE scores will inevitably be affected by future

changes in extinction risk, although no more so than other

approaches using the Red List categories.

A minority of mammal species could not be assigned EDGE

scores. Around 300 species are classified as Data Deficient and

could not be meaningfully included, although in reality they may

have a high risk of extinction [17]. By far the most likely candidate

for high EDGE status following future Red List re-assessment is

the franciscana or La Plata River dolphin Pontoporia blainvillei

(ED = 36.3 MY). In addition, fifty extant species are missing from

the phylogeny. The highest ranked of these are probably a pair of

Critically Endangered shrews (Sorex cansulus and S. kizlovi); median

and maximum ED scores for the genus are 4.55 and 14.6 MY,

giving potential respective EDGE scores of 4.49 and 5.52 for these

species (cf. figure 3). A further 260 species have been described

since the chosen taxonomy was published [40]. Of these, the

recently described Annamite striped rabbit Nesolagus timminsi [41] is

the sister species to the tenth-ranked Sumatran rabbit N. netscheri,

so would be a high priority if similarly threatened.

It has been suggested that species with few close relatives (i.e.

high ED) are ‘relicts’ or ‘living fossils’ that have limited ability to

generate novel diversity. This view implies that conservation

efforts should instead be focused on recent radiations containing

species with low ED scores (e.g. murid rodents), which represent

‘cradles’ rather than ‘museums’ of diversity [e.g. 16,42]. However,

the assumption that we are able to predict future evolutionary

potential is dubious and no general relationships between

phylogeny and diversity over geological time have yet been

established [43,44]. Furthermore, phylogenetic diversity is clearly

related to character diversity [30], and so ED may be a useful

predictor of divergent properties and hence potential utilitarian

value [14]. Moreover, because species with low ED scores tend to

suffer from low levels of extinction risk, phylogenetic cradles of

mammalian diversity are likely to survive the current extinction

crisis even without specific interventions. Focusing on lower risk

species, at the expense of EDGE priorities, would therefore result

in a severe pruning of major branches of the Tree of Life

comparable to that seen in previous mass extinction events

[45,46].

Figure 3. Histogram of EDGE scores for 4182 mammal species, by threatcategory. Colours indicate the Red List category: Least Concern (green),Near Threatened and Conservation Dependent (brown), Vulnerable(yellow), Endangered (orange) and Critically Endangered (red).doi:10.1371/journal.pone.0000296.g003

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The top 100 EDGE species span all the major mammalian

clades [being distributed among 18 orders and 52 families

recognised by ref 35] and display a comparable range of

morphological and ecological disparity, including the largest and

smallest mammals, most of the world’s freshwater cetaceans, an

oviparous mammal and the only species capable of injecting

venom using their teeth. However, around three-quarters of

species-based mammal conservation projects are specifically aimed

at charismatic megafauna [47], so conventional priority-setting

tools may not be sufficient to protect high priority EDGE species.

This concern is supported by two additional lines of evidence.

First, we found that species not found in protected areas [‘gap

species’ defined by ref 48] tended to have higher EDGE scores

than those found inside protected areas (logistic regression:

x21,3994 = 69.46, p,0.0001). Second, an assessment of published

conservation strategies and recommendations (including IUCN

Specialist Group Conservation Action Plans, captive breeding

protocols and the wider scientific literature listed in the 1978–2005

Zoological Record database) reveals that no species-specific

conservation actions have even been suggested for 42 of the top

100 EDGE species. Most of these species are from poorly known

regions or taxonomic groups and until now have rarely been

highlighted as conservation priorities. Little conservation action is

actually being implemented for many other top EDGE species,

despite frequent recommendations in the conservation literature.

Indeed, the top-scoring EDGE species, the Yangtze River dolphin

(Lipotes vexillifer), is now possibly the world’s most threatened

mammal despite two decades of debate over a potential ex situ

breeding programme, and may number fewer than 13 surviving

individuals [49]. The lack of conservation attention for priority

EDGE species is a serious problem for mammalian biodiversity

and suggests that large amounts of evolutionary history are likely

to be lost in the near future. This phenomenon of diversity slipping

quietly towards extinction is likely to be much more severe in less

charismatic groups than mammals.

The approach described in this paper can be used for

conservation in a number of ways. First, conservation managers

with limited resources at their disposal typically need to conserve

populations of several threatened species. If all other factors were

equal, the management of the most evolutionarily distinct species

should be prioritized. Second, a list of high-priority species

requiring urgent conservation action can be generated easily. In

this paper, we have selected the 100 highest-ranking species, but

one might equally choose all threatened (Vulnerable and above)

species with above average ED. This would result in a list of 521

(using median) or 630 (using geometric mean) ‘EDGE species’ that

are both evolutionarily distinct and globally endangered. Third,

EDGE scores could also be used to weight species’ importance in

selecting reserve networks, building on previous studies that have

used phylogenetic diversity [50–52] or threatened species [11] to

identify priority areas for conservation. The statistical properties of

EDGE scores (they are both normally-distributed and bounded at

zero) make them especially suitable for these kinds of analysis. In

this way, the EDGE approach is not an alternative to existing

conservation frameworks [e.g. 6] but complements them.

The EDGE approach identifies the species representing most

evolutionary history from among those in imminent danger of

extinction. Our methods extend the application of PD-based

conservation to a wider range of taxa and situations than previous

approaches [4,5,13,22,24,25]. Future work might incorporate

socioeconomic considerations [5,14] and the fact that a species’

value depends also on the extinction risk of its close relatives [53].

However, our results suggest that large numbers of evolutionarily

distinct species are inadequately served by existing conservation

measures, and that more work is carried out to prevent the

imminent loss of large quantities of our evolutionary heritage. It is

hoped that this approach will serve to highlight their importance

to biodiversity and emphasize the need for urgent conservation

action.

MATERIALS AND METHODS

Implementing ED scores for mammalsWe used a composite ‘supertree’ phylogeny [31] to calculate ED

scores for mammals. The supertree presents several challenges to

the estimation of ED when compared with the (unknown) true

phylogeny: poor resolution, missing species and uncertainty in

node ages. Accordingly, we modified the basic procedure to

control for these problems.

Phylogenetic information is poor in many mammalian clades

(especially bats and rodents, which together make up .60% of

species) and the whole supertree contains only 47% of all possible

nodes, many of which are polytomies (nodes with more than two

daughter branches). Across the whole phylogeny, ,40% of species

are immediately descended from bifurcations, ,20% from small

polytomies (3–5 daughters), ,15% from medium-sized polytomies

(6–10 daughters) and the remainder from large polytomies with

.10 daughters. Polytomies in supertrees result from poor or

conflicting data rather than a true representation of the speciation

process, so the distinctiveness of branches subtending them is

overestimated [54], thus leading to biased ED scores. For example,

the common ancestor of species X, Y and Z is believed to be 1 MY

old, but the branching pattern within the clade is unknown. The

polytomy appears to show that each species represents 1 MY of

unique evolutionary history. In reality, the phylogeny is bi-

furcating, with one species aged 1 MY and the others sharing

a more recent common ancestor. The bias induced by polytomies

can be corrected by estimating the expected ED of descendant

species under an appropriate null model of diversification. We

achieved this by applying a scaling factor based on the empirical

distribution of ED scores in a randomly generated phylogeny of

5000 species grown under constant rates of speciation (0.1) and

extinction (0.08). The mean ED score of species in 819 clades of

three species was 0.81 of the clade age; ED scores for nodes of 2–

20 species scale according to (branch length) * (1.081–0.267 *

ln{d}), where d is the number of descendent branches (n = 2873

clades, r2 = 0.69). Quantitatively similar values were obtained in

bifurcating clades of primates [1.117–0.246 * ln{d}, n = 78, ref 55]

and carnivores [1.139–0.269 * ln{d}, n = 101, ref 56].

The mammal supertree contains 4510 of the 4548 (.99%)

extant species listed in Wilson & Reeder [35]. Although few in

number, the missing species need to be taken into account because

their absence will tend to inflate the ED scores of close relatives.

For example, omitting species A from the phylogeny in figure 1

would elevate B from the joint lowest ranking species (with A) to

the joint highest-ranking (with C), with an ED score of (2/1+1/

2+2/4) = 3.5 MY. The problem is acute in real datasets since

missing species tend not to be a random sample: 22 of the 38

missing mammals are from the genus Sorex. We account for this

problem using a simple correction factor that allocates the missing

species among their presumed closest relatives. For example, we

correct for the omission of the bare-bellied hedgehog (Hemiechinus

nudiventris) by treating the other five Hemiechinus spp. as 6/5 = 1.2

species, and we correct for the omission of both Cryptochloris species

by spreading the two missing species evenly between other

Chrysochloridae.

Variation among morphological and molecular estimates of

divergence times (node ages) can lead to considerable uncertainty

Mammals on the EDGE

PLoS ONE | www.plosone.org 5 March 2007 | Issue 3 | e296

in ED scores. To reduce the effects of this uncertainty, we

estimated ED using three sets of branch lengths. One set was based

on the best (i.e. mean) estimates of node age; the others were

derived from the upper and lower 95% confidence intervals

around these dates. Species values of ED were calculated as the

geometric mean of scores under the three sets of branch lengths.

The geometric mean was preferred since it down-weights species

whose scores are based on nodes with symmetrical but wide

confidence intervals in estimate age, and is therefore more

conservative than the arithmetic mean.

Tests of robustnessTo test whether ED scores are comparable among taxonomic

groups, we examined how species’ ED accumulates as pro-

gressively larger clades are considered. If ED scores are truly

comparable, their rank order will be independent of the size of the

clade considered. We randomly selected one Critically Endan-

gered species from each of ten mammal orders and measured the

cumulative ED score at each node between the species and the

root of the mammal supertree, thus redefining and enlarging the

clade (and so increasing the number of species it contained) at each

step.

Taxonomic changes have the potential to dramatically alter the

ED scores of individual species. Splitting a species in two reduces

the distinctiveness of all branches ancestral to the split, particularly

those near the tips. If ED scores are highly sensitive to taxonomic

changes then it may be meaningless to apply them in setting

conservation priorities. The effects of taxonomic changes on ED

scores were therefore investigated in the primates, which have

recently experienced considerable taxonomic inflation [27]. We

compared primate ED scores under a biological species concept

[35: 233 species] and a phylogenetic species concept [36: 358

species]. We employed a single phylogeny [31], but changed the

number of species represented by each tip. We calculated the

expected ED for multi-species tips by treating them as if they were

descended from a polytomy of {n+r+1} descendent branches,

where n is the actual number of descendent branches and r is the

number of species represented by the tip.

SUPPORTING INFORMATION

Table S1 Evolutionary Distinctiveness and EDGE scores for

mammals. This table shows Evolutionary Distinctiveness (ED) and

EDGE scores for all species included in the mammal supertree

[31] ranked by their EDGE score. Species that could not be

assigned EDGE scores are appended to the bottom of the list,

sorted by status and ED score. Species taxonomy follows Wilson &

Reeder [35]. Red List categories follow the 2006 IUCN Red List

[2]: CR = Critically Endangered, EN = Endangered, VU = Vul-

nerable, NT = Near Threatened, LC = Least Concern, CD = Con-

servation Dependent, DD = Data Deficient, NE = Not Evaluated.

The NE category includes species in Wilson & Reeder [35] that

could not be matched with any species or subspecies names in the

Red List.

Found at: doi:10.1371/journal.pone.0000296.s001 (0.42 MB

PDF)

Table S2 Evolutionary Distinctiveness for primates under two

species concepts. This table lists ED scores for primates under the

biological species concept i[.e. the taxonomy of ref 35], the

number of phylogenetic species into which the biological species

was split [36] and the estimated ED score of each phylogenetic

species. See Materials and Methods for further information. ED

scores are lower for phylogenetic species than biological species,

even for taxa whose taxonomic status is the same under both

concepts (i.e. the number of phylogenetic species is one). This

occurs because the total number of species in the phylogeny is

greater, so each receives a smaller share of the distinctiveness of

ancestral branches. ED scores were calculated using just one set of

branch lengths (the ‘best’ set), so differ from those in table S1.

Found at: doi:10.1371/journal.pone.0000296.s002 (0.05 MB

PDF)

ACKNOWLEDGMENTSWe thank the PanTHERIA consortium for use of the mammal supertree.

In particular, Olaf Bininda-Emonds generously provided us with electronic

copies of the topology and alternate branch lengths. We also thank Ana

Rodrigues for providing a list of species found in protected areas. We are

also grateful to Guy Cowlishaw, Sarah Durant, Dan Faith, John Gittleman,

Rich Grenyer, Kate Jones, Georgina Mace, Arne Mooers, Andy Purvis

and three anonymous referees for useful comments and discussion.

Author Contributions

Conceived and designed the experiments: JB BC NI ST. Performed the

experiments: NI. Analyzed the data: NI. Contributed reagents/materials/

analysis tools: NI ST CW. Wrote the paper: JB BC NI ST.

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CORRESPONDENCE

Securing nonflagship species from extinctionLiana N. Joseph1, Richard F. Maloney2, James E.M. Watson1, & Hugh P. Possingham3

1 Wildlife Conservation Society, 2300 Southern Boulevard, Bronx, New York 10460, USA2 Threatened Species Development, Research and Development Group, Department of Conservation, Christchurch, New Zealand3 ARC Centre of Excellence for Environmental Decisions, The Ecology Centre, University of Queensland, St Lucia 4072, Australia

Received17 December 2010

Accepted6 February 2011

EditorJames Blignaut

doi: 10.1111/j.1755-263X.2011.00174.x

Introduction

A recent article in Conservation Letters by Verissimoand colleagues provides clarity with respect to theconcept of flagship species. As the authors state, theuse of flagship species can offer a powerful tool forenvironmental organizations to raise money and raisepublic awareness generally. Regrettably, in many cases,the money that is raised for flagship species is tied tospending solely on that species. Consequently, other non-flagship threatened species are unlikely to benefit. Theuse of flagship species creates a conundrum for those or-ganizations that aim to secure the greatest number ofthreatened species from extinction. This goal will notbe achievable if the limited conservation budget is con-strained to specific actions that only assist the few flagshipspecies. The authors make a brief reference to this weak-ness of the flagship-species approach and suggest thatsolutions may include using the funds to pay for over-heads that benefit multiple species or declaring upfrontthat funding will be spent on other species. We suggestthat there is another option: a marketing tool that maybe attractive to donors and result in funding that is nottied to a single species.

We believe that it is possible to raise funds by focus-ing on the task of securing large numbers of threatenedspecies rather than a single flagship species. We illustratethe potential power of this type of marketing tool with aspecies prioritization exercise recently undertaken by theNew Zealand Department of Conservation. In this plan-

ning exercise, priority actions, and costs and feasibility forthose actions, were identified for securing each of ∼660of New Zealand’s most threatened species (Joseph et al.2009; O’Conner et al. 2009). The New Zealand govern-ment is now in the position to state how much it will costto secure all or a selection of these species from extinc-tion. With this kind of information, it is possible to cal-culate the exact amount required to secure species andmake statements like: “. . . as little as $x million is neededto secure a given number of the most threatened speciesand $y million would secure a greater number.” Simi-larly, these data can be used to demonstrate the expectedgains of additional funding for threatened species. Thesefigures give the Department of Conservation a powerfultool for seeking wider support for managing threatenedspecies in New Zealand.

The concept of saving large numbers of endangeredspecies is commonly used to “sell” priority landscapes orregions for conservation NGOs (e.g., Conservation Inter-national’s Biodiversity Hotspots, Myers et al. 2000; Al-liance for Zero Extinction sites, Ricketts et al. 2005). Yet,the example that we present here illustrates a methodfor proving clear and fully costed opportunities to raisefunds for priority actions that will result in the recoveryof threatened species specifically. We suggest that mar-keting the ability to secure from extinction of large num-bers of species is an effective complementary tool to theflagship-species approach that can be particularly usefulfor securing threatened species that will never be poten-tial flagship species.

324 Conservation Letters 4 (2011) 324–325 Copyright and Photocopying: c©2011 Wiley Periodicals, Inc.

L. N. Joseph et al. Protecting non-flagship species

References

Joseph, L.N., Maloney R.F., Possingham H.P. (2009)

Optimal allocation of resources among threatened

species: a project prioritization protocol. Conserv Biol 23,

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Myers, N., Mittermeier R.A., Mittermeier C.G., da Fonseca

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O’Conner, S., Maloney R., Newman D., Joseph L.,

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Conservation Letters 4 (2011) 324–325 Copyright and Photocopying: c©2011 Wiley Periodicals, Inc. 325

2

Prioritizing choices inconservation

__________

Georgina M. Mace, Hugh P. Possingham and NigelLeader-Williams

The last word in ignorance is the man who says of an animal or plant: ‘What good is it?’ If the landmechanism as a whole is good, then every part is good, whether we understand it or not. If the biota, in thecourse of aeons, has built something we like but do not understand, then who but a fool would discardseemingly useless parts? To keep every cog and wheel is the first precaution of intelligent tinkering.(Aldo Leopold, Round River, Oxford University Press, New York, 1993, pp. 145–6.)

Introduction

We are in the midst of a mass extinction in

which at least 10%, and may be as much as

50%, of the world’s biodiversity may disappear

over the next few hundred years. Conservation

practitioners face the dilemma that the cost of

maintaining global biodiversity far exceeds the

available financial and human resources. Esti-

mates suggest that in the late twentieth century

only US$6 billion per year was spent globally

on protecting biodiversity (James et al. 1999),

even though an estimated US$33 trillion per

year of direct and indirect benefits were derived

from ecosystem services provided by biodiver-

sity, implying an asset worth US$330 trillion

(Costanza et al. 1997). Together these crude

estimates suggest that there could be a 500-

fold underinvestment in conserving the world’s

biodiversity. However, even if these estimates

are wildly wrong, the imbalance of funding is

seriously inconsistent with best business prac-

tice in other sectors. In business, many com-

panies spend about 10% of the value of their

capital assets each year on maintaining those

assets, although the figure varies depending on

the type of asset. For example, 30% might be

spent for computers compared with 5% for

buildings: contrast that with 0.02% for bio-

diversity! Furthermore, the scale of underin-

vestment in biodiversity may be exaggerated

by the effects of poor governance, sometimes

even corruption, on achieving success in

conservation (Smith et al. 2003). Given such

problems, conservation scientists and non-

government organizations (NGOs) supporting

international conservation efforts are begin-

ning to develop systems to more effectively

target investment in biodiversity conservation

(Johnson 1995; Kershaw et al. 1995; Olson &

Dinerstein 1998; Myers et al. 2000; Possingham

et al. 2001; Wilson et al. in press).

One fundamental resource allocation question

facing conservation scientists and practitioners is

whether conservation goals are best met by man-

aging single species as opposed to whole ecosys-

Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 17 6.5.2006 11:07pm

tems (Simberloff 1998). Efforts in conservation

priority setting have historically concentrated on

ecosystem-based priorities – determining where

and when to acquire protected areas (Ferrier et

al. 2000; Margules & Pressey 2000; Pressey &

Taffs 2001; Meir et al. 2004). There has been

comparatively little work on the question of

how to allocate conservation effort between

species. Despite the tension between ecosystem-

based and species-based conservation, we believe

there is merit in considering the issue of resource

allocation between species because:

1. a ‘fuzzy’ idea such as ecosystem manage-

ment holds little appeal for the general pub-

lic, who prefer to grasp simpler messages

conveyed by charismatic species such as

tigers (Leader-Williams & Dublin 2000);

2. data on species, whether through direct

counts of indicator species (Heywood

1995), or through assessments of threat

(Butchart et al. 2005), provide some of the

most readily available, repeatable and expli-

cit monitoring and analytic systems with

which to assess the success or otherwise of

conservation efforts (Balmford et al. 2005).

3. in practice, almost regardless of their ultim-

ate goal, conservation bodies often end up

directing conservation actions to species

and species communities (see e.g. figure 1

of Redford et al. 2003), probably because

these are tangible and manageable compon-

ents of ecosystems.

The topic of setting priorities for conservation is

immense, so here we restrict ourselves to dif-

ferent methods for setting priorities between

species. We explore the issues that a systematic

approach should consider, and we show how

simple scoring systems may lead to unintended

consequences. We also recommend an explicit

discussion of attributes of the species that make

them desirable targets for conservation effort.

Using a case study, we show how different

perspectives will affect the outcome, and so as

an alternative we present a method based

on economic optimization. Ultimately, any

decisions about ‘what to save first’ should in-

clude judgments that cannot be made by scien-

tists or managers alone. Involving wider

societal and political decision-making processes

is vital to gain local support for, and ensure the

ultimate success of, all conservation planning.

Single species approaches

Species-based conservation management ap-

proaches have, until fairly recently, concentrated

on a single species, such as keystone species,

umbrella species, indicator species or flagship

species (see Leader-Williams & Dublin 2000).

Keystone and umbrella species differ in the im-

portance of their ecological role in an ecosystem:

1. keystone species have a disproportionate

effect on their ecosystem, due to their size or

activity, and any change in their population

will have correspondingly large effects on

their ecosystem (e.g. the sole fruit disperser

of many species of tree);

2. umbrella species have such demanding

habitat and/or area requirements that, if we

can conserve enough land to ensure their

viability, the viability of smaller and more

abundant species is almost guaranteed.

In contrast, ‘flagship species’ encompass

purely strategic objectives:

3. flagship species are chosen strategically to

raise public awareness or financial support

for conservation action.

Furthermore, definitions for indicator species

can encompass both ecological and strategic

roles:

4. indicator species are intended either to

represent community composition or to re-

flect environmental change. With respect to

the latter, indicator species must respond to

the particular environmental change of con-

Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 18 6.5.2006 11:07pm

18 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS

cern and demonstrate that change when

monitored.

One species may be a priority species for

more than one reason, depending on the situ-

ation or context in which the term is used.

However, the terms ‘keystone’ and ‘umbrella’

are likely to remain more of a fixed character-

istic or property of that species. In contrast, the

term ‘flagship’ and, possibly to a lesser extent,

‘indicator’ may be more context-specific.

Promoting the conservation of a specific focal

species may help to identify potential areas for

conservation that satisfy the needs of other spe-

cies and species assemblages (Leader-Williams

& Dublin 2000). For example, the umbrella

species concept (Simberloff 1998) can represent

an efficient first step to protect other species. In

addition, minimizing the number of species

that must be monitored once a protected area

has been created will reduce the time and

money that must be devoted to its maintenance

(Berger 1997).

Alternatively, conservation managers and

international NGOs may choose to focus on

the most charismatic ‘flagship’ species, which

stimulate public support for conservation ac-

tion, and that in turn may have spin-off bene-

fits for other species. For example, use of the

giant panda (Ailuropoda melanoleuca) as a logo

by the World Wildlife Fund (WWF) has been

widely accepted (Dietz et al. 1994) as a success-

ful mechanism for conserving many other spe-

cies across a wide variety of taxonomic groups.

Furthermore, other mammalian and avian

‘flagships’ have been used to promote the con-

servation of large natural ecosystems (i.e. Mit-

termeier 1986; Goldspink et al. 1998; Downer

1996; Johnsingh & Joshua 1994; Western

1987; Dietz et al. 1994).

Nevertheless, the context of what may con-

stitute a charismatic species can differ widely

across stakeholders. For example, the tiger

(Panthera tigris) is among the most popular flag-

ship species in developed countries, but those

in developing countries who suffer loss of life

and livelihood because of tigers or other large

predators have a different view (Leader-

Williams & Dublin 2000). Such dissonance is

best avoided by promoting locally supported

flagship species (Entwistle 2000). For example,

the discovery of a new species of an uncharis-

matic, but virus-resistant, wild maize, with its

possible utilitarian value for human food pro-

duction, highlighted the conservation value of

the Mexican mountains in which it was found

(Iltis 1988). This increase in local public aware-

ness led to the establishment of a protected area

that conserves parrots and jaguars (Panthera

onca), orchids and ocelots (Leopardus pardalis),

species that many consider charismatic. Hence

this species of wild maize served as a strategic-

ally astute local flagship species. Another way

of promoting local flagships is to prioritize those

species that bring significant and obvious local

benefits (Goodwin & Leader-Williams 2000),

such as the Komodo dragon, Varanus komodoen-

sis (Walpole & Leader-Williams 2001), which

generates tourism. Similarly, species that can

be hunted for sport, such as the African ele-

phants (Loxodonta africana), may contribute dir-

ectly to community conservation programmes

(Bond 1994).

Several questions can arise from promoting

conservation through single species (Simberloff

1998). One of these is how should individual

species be prioritized? The common response is

to begin with species that are most at risk of

extinction, the critically endangered species.

Many countries and agencies take this ap-

proach. However, there may be no known

management for some of these species, and if

there is, it may be risky and/or expensive. This

can lead to a large share of limited conservation

resources being expended with negligible or

uncertain benefit (Possingham et al. 2002).

On the other hand, when taking an ecosystem

approach, managers might choose to focus on

the keystone species that play the most signifi-

cant role in the ecosystem. Unfortunately in

many ecosystems we do not know the identity

of keystone species. Often, after intensive

study, they turn out to be invertebrates or

fungi (Paine 1995), groups that are unlikely to

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PRIORITIZING CHOICES IN CONSERVATION 19

attract public or government support unless

ways can be found to make them locally

relevant.

Another problem with single species conser-

vation arises when the management of one focal

species is detrimental to the management of

another focal species. For example, in the Ever-

glades of Florida, management plans for two

charismatic, federally listed birds are in conflict.

One species, the Everglades snail kite (Rostrha-

mus sociabilis plumbeus), has been reduced to

some 600 individuals by wetland degradation

and agricultural and residential development.

It feeds almost exclusively on freshwater snails

of the genus Pomacea and requires high water

levels, which increase snail production. The

snail kite is thus an extreme habitat specialist

(Ehrlich et al. 1992). The other species, the

wood stork Mycteria americana, has been reduced

to about 10,000 pairs by swamp drainage, habi-

tat modification and altered water regimes.

Ironically, the US Fish and Wildlife Service op-

posed a proposal by the Everglades National

Park to modify water flow to improve stork

habitat on the grounds that the change would

be detrimental to the kite (Ehrlich et al. 1992)

(an added thought-provoking detail is that both

species are common in South America).

Another issue is that few studies have been

carried out to assess the effectiveness of one

focal species in adequately protecting viable

populations of other species (Caro et al. 2004).

For example, the umbrella-species concept is

often applied in management yet rarely tested.

The grizzly bear (Ursus arctos) has been recog-

nized as an umbrella species but, had a pro-

posed conservation plan for the grizzly bear in

Idaho been implemented, taxa such as reptiles

would have been underrepresented (Noss et al.

1996). Similarly, in a smaller scale study, the

areas where flagship species, such as jaguar,

tapir (Tapirus terrestris) and white-lipped pec-

cary (Tayassu pecari), were most commonly

seen did not coincide with areas of vertebrate

species richness or abundance (Caro et al. 2004).

Although these results may not hold true for all

other protected areas based around flagship spe-

cies, it does highlight the need for more field-

based studies to determine the most appropriate

approach for conserving the most biodiversity.

As a result of problems associated with single

species management, focus has been turning

towards multiple species approaches.

Multispecies approaches

Methods based on several focal species, or pro-

tecting a specific habitat type, might be a more

appropriate means of prioritizing protected

areas (Lambeck 1997; Fleishman et al. 2000;

Sanderson et al. 2002b). A frequent criticism of

setting conservation priorities based on a single

focal species is that it is improbable that the

requirements of one species would encapsulate

those of all other species (Noss et al. 1996; Basset

et al. 2000; Hess & King 2002; Lindenmayer et al.

2002). Hence, there is a need for multispecies

strategies to broaden the coverage of the pro-

tective umbrella (e.g. Miller et al. 1999; Fleish-

man et al. 2000, 2001; Carroll et al. 2001).

Among the different multispecies ap-

proaches, Lambeck’s (1997) ‘focal species’ ap-

proach seems the most promising because it

provides a systematic procedure for selecting

several focal species (Lambeck 1997; Watson

et al. 2001; Bani et al. 2002; Brooker 2002;

Hess & King 2002). In Lambeck’s (1997) in-

novative approach, a suite of focal species are

identified and used to define the spatial, com-

positional and functional attributes that must

be present in a landscape. The process involves

identifying the main threats to biodiversity and

selecting the species that is most sensitive to

each threat. The requirements of this small

and manageable suite of focal species guide

conservation actions. The approach was

extended by Sanderson et al. (2002a), who

proposed the ‘landscape species approach’.

They defined landscape species by their ‘use of

large, ecologically diverse areas and their im-

pacts on the structure and function of natural

ecosystems . . . their requirements in time and

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20 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS

space make them particularly susceptible to

human alteration and use of wild landscapes’.

Because landscape species require large, wild

areas, they could potentially serve an umbrella

function (sensu Caro & O’Doherty 1999) –

meeting their needs would provide substantial

protection for the species with which they co-

occur. Like other focal-species approaches, this

method of setting priorities carries inherent

biases (Lindenmayer et al. 2002), and may be

constrained by incomplete or inconsistent data.

Ecosystem and habitat-basedapproaches

Some conservation scientists believe that set-

ting conservation priorities at the scale of eco-

systems and habitats is more appropriate for

developing countries with limited resources

for conservation, inadequate information about

single species and pressing threats such as

habitat destruction. Logically, how much effort

we place in conserving a particular ecosystem

should take into account factors such as: how

threatened it is, how well represented that

ecosystem is in that country’s protected area

network, the number of species restricted to

that ecosystem (endemic species), the cost of

conserving the ecosystem and the likelihood

that conservation actions will work. One can

debate the relative importance of each of these

factors – for example, some consider the the

number of endemic species is paramount,

whereas others prefer the notion of ‘equal rep-

resentation’ whereby a fixed percentage of

every habitat type is conserved.

The main goals of an ecosystem approach

are to:

1. maintain viable populations of all native

species in situ;

2. represent, within protected areas, all native

ecosystem types across their natural range

of variation;

3. maintain evolutionaryand ecological processes;

4. manage over periods of time long enough

to maintain the evolutionary potential of

species;

5. accommodate human use and occupancy

within these constraints (Grumbine 1994).

Although the financial efficiencies inherent

in managing an ecosystem rather than several

single species are attractive, this approach is

also not without its problems. First, compared

with a species, ecosystem boundaries are

harder to define, so determining the location,

size, connectivity and spacing of protected areas

to conserve the full range of ecosystems, and

variation within those ecosystems, is more dif-

ficult (Possingham et al. 2005). Second, indi-

vidual species are more interesting to people

and will attract greater emotional and financial

investments than ecosystems. Third, although

ecological services are provided by ecosystems,

individual species often play pivotal roles in the

provision of these services, particularly for dir-

ect uses such as tourism or harvesting. Finally,

the main problem faced by managers wishing

to implement an ecosystem approach is the lack

of data available on how ecosystems function.

This manifests itself in confusion about how

much of each ecosystem needs to be conserved

to protect biodiversity adequately in a region.

In contrast, for the better known single species,

the issue of adequacy can be dealt with using

population viability analysis and/or harvesting

models (Beissinger & Westphal 1998; this vol-

ume, Chapter 15).

Systematic conservation planning

Systematic conservation planning (or gap-an-

alysis in the USA: Scott et al. 1993) focuses on

locating and designing protected areas that

comprehensively represent the biodiversity of

each region. Without a systematic approach,

protected area networks have the tendency to

occur in economically unproductive areas

(Leader-Williams et al. 1990), leaving many

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PRIORITIZING CHOICES IN CONSERVATION 21

habitats or ecosystems with little or no protec-

tion (Pressey 1994). The systematic conserva-

tion planning approach can be divided into six

stages (Margules & Pressey 2000).

1. Compile biodiversity data in the region of

concern. This includes collating existing

data, along with collecting new data if ne-

cessary, and if time and funds permit.

Where biodiversity data, such as habitat

maps and species distributions, are limited

more readily available biophysical data may

be used that reflect variation in biodiversity,

such as mean annual rainfall or soil type.

2. Identify conservation goals for the region,

including setting conservation targets for

species and habitats, and principles for pro-

tected area design, such as maximizing con-

nectivity and minimizing the edge-to-area

ratio.

3. Review existing conservation areas, includ-

ing determining the extent to which they

already meet quantitative targets, and miti-

gate threats.

4. Select additional conservation areas in the

region using systematic conservation plan-

ning software.

5. Implement conservation action, including

decisions on the most appropriate form of

management to be applied.

6. Maintain the required values of the conser-

vation areas. This includes setting conserva-

tion goals for each area, and monitoring key

indicators that will reflect the success of

management (see below).

Ultimately, conservation planning is riddled

with uncertainty, so managers must learn to

deal explicitly with uncertainty in ways that

minimize the chances for major mistakes (Mar-

gules & Pressey 2000; Araujo & Williams 2000,

Wilson et al 2005), and be prepared to modify

their management goals appropriately through

adaptive management.

Systematic conservation planning can com-

plement species-based approaches because it

focuses on removing the threat of development

and it compliments a long tradition of species

recovery plans that concentrate on mitigating

threats. The degree to which different countries

use species-based planning as opposed to sys-

tematic conservation planning depends on his-

torical, cultural and legislative influences. Even

with systematic conservation planning, how-

ever, the better surveyed species or species

groups often feature as the units for assessment.

In other words, the conservation value of dif-

ferent areas is often assessed on the presence or

conservation status of the species within it,

simply because these are the best known elem-

ents of biodiversity. Systematic conservation

planning approaches have become popular

and widespread, partly because they are sup-

ported by several decision-support software

packages (Possingham et al 2000, Pressey et al

1995, Williams et al 2000, Garson et al 2002).

Methods for setting conservationpriorities of species

Prioritizing species, habitats and ecosystems by

their perceived level of endangerment has be-

come a standard practice in the field of conser-

vation biology (Rabinowitz 1981; Master 1991;

Mace & Collar 1995; Carter et al. 2000; Stein

et al. 2000). The need for a priority-setting

process is driven by limited conservation re-

sources that necessitate choices among a subset

of all possible species in any given geographical

area, and distinct differences among species in

their apparent vulnerability to extinction or

need for conservation action. This need has

led to the development of practical systems

for categorizing and assessing the degree of vul-

nerability of various components of biodiver-

sity, particularly vertebrates (e.g. Millsap et al.

1990; Mace & Lande 1991; Master 1991; Reed

1992; Stotz et al. 1996), and more recently

ecoregions (Hoekstra et al. 2005).

Methods used for assessing the conservation

status of species are varied but follow three

general styles (Regan et al. 2004), rule-based,

point scoring and qualitative judgement. Per-

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22 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS

haps the best known system is that developed

by the IUCN (International Union for the Con-

servation of Nature and Natural Resources) –

The World Conservation Union – which uses a

set of five quantitative rules with explicit

thresholds to assign a risk of extinction (Mace

& Lande 1991; IUCN 2001). Other methods

adopt point-scoring approaches where points

are assigned for a number of attributes and

summed to indicate conservation priority (Mill-

sap et al. 1990; Lunney et al. 1996; Carter et al.

2000). Other methods assess conservation sta-

tus using qualitative criteria; judgements about

a species’ status are determined intuitively

based on available information and expert

opinion (Master 1991). One widely applied sys-

tem is the biodiversity status-ranking system

developed and used by the Natural Heritage

Network and The Nature Conservancy (Master

1991; Morse 1993). This ranking system has

been designed to evaluate the biological and

conservation status of plant and animal species

and within-species taxa, as well as of ecological

communities.

Rule-based methods

Quantitative rule-based methods can be used to

estimate the extinction risk of a species and

thus contribute to determining priority areas

for conservation action. For example, the

IUCN Red List places species in one of the fol-

lowing categories: extinct (EX), extinct in the

wild (EW), critically endangered (CR), endan-

gered (EN), vulnerable (V), near threatened

(NT) or least concern (LC), based on quantita-

tive information for known life history, habitat

requirements, abundance, distribution, threats

and any specified management options of that

species, and in a data deficient (DD) category if

there are insufficient data to make an assess-

ment (IUCN 2001). The IUCN system is based

around five criteria (A to E) which reflect dif-

ferent ways in which a species might qualify for

any of the threat categories (CR, EN, VU). A

species is placed in a category if it meets one or

more of the criteria – for example because there

are less than 250 mature individuals of the

Norfolk Island green parrot (Cyanoramphus coo-

kii) in the wild it is immediately listed as endan-

gered under criterion D of the IUCN Red List

protocol. A similar species can meet a higher

category of threat if it meets alternative cri-

teria. For example, the orange-bellied parrot

(Neophema chrysogaster) also has less than 250

mature individuals but it is listed as critically

endangered, under criterion C2b, because the

population is also in decline and all the individ-

uals are in a single subpopulation. One concep-

tual problem with rule-based methods is that a

species that just missed out on being listed as,

say, endangered on several criteria would be

ranked as vulnerable, equal with a species that

may have only just met the criteria for being

vulnerable.

The rule-based methods have the advantage

that they are completely explicit about what

feature of the species led to it being listed as

threatened. In the IUCN system, assessors have

to list the criteria whereby the species qualified

for a particular category of threat, and also have

to provide documentation to support this infor-

mation – usually in the form of scientific sur-

veys or field reports that detail the information

used. As a result, listings may be continually

updated and improved as new data become

available. Normally this will allow a new con-

sensus among experts, but in the exceptional

cases where this is not agreed, the IUCN have a

petitions and appeals process to resolve matters.

For example, in 2001 some of the listings of

marine turtle species were disputed among ex-

perts. On this occasion, IUCN implemented

their appeal procedure and provided a new

assessment (http://www.iucn.org/themes/ssc/

redlists/petitions.html). The wide use of the

IUCN system also means that there is an ever

increasing resource of best-practice documen-

tation and guidelines, which aid consistent and

comparable approaches by different species

assessors (see http://www.iucn.org/themes/ssc/red-

lists.htm).

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PRIORITIZING CHOICES IN CONSERVATION 23

Point scoring method

The point scoring method for assigning conser-

vation priority involves assigning a series of

scores to each species based on different param-

eters relating to their ecology or conservation

status, which together will determine their

relative priority. One method of dealing with

the scores is then to simply sum them to give an

overall conservation priority, although this can

be misleading. Beissinger et al. (2000) suggest

that a categorical approach based on a combin-

ation of scores might be more accurate in

determining overall conservation priority.

An example of a point scoring system is that

developed by Partners in Flight (PIF) in 1995 in

an effort to conserve non-game birds and their

habitats throughout the USA (Carter et al.

2000). The PIF system involves assigning a series

of scores to each species ranging from 1 (low

priority) to 5 (high priority) for seven param-

eters that reflect different degrees of need for

conservation attention. The scores are assigned

within physiographical areas and the seven

parameters are based on global and local infor-

mation. Three of the parameters are strictly

global and are assigned for the entire range of

the bird: breeding distribution (BD), non-breed-

ing distribution (ND) and relative abundance

(AR). Other parameters are threats to breeding

(TB), threats to non-breeding (TN), population

trend (PT) and, locally, area importance (AI).

The scores for each of these seven parameters

are obtained independently (Carter et al. 2000).

The PIF then uses a combination of approaches,

including the summing of scores, to determine

an overall conservation priority (Carter et al.

2000), with species that score highly on several

parameters achieving high priority. Although

this method of defining bird species of high con-

servation priority is thought to be reliable, like

other methodologies, it is hindered by the lack

of data on species distribution, abundance and

populations trends, particularly in areas outside

the USA to which many of these species migrate

(Carter et al. 2000).

A problem with some point-scoring methods

is that there is no explicit link to extinction risk,

the weightings of each criteria, from 1 to 5 in

the example above, are completely arbitrary,

and there is an infinity of ways in which the

scores could be combined: adding, multiplying,

taking the product of the largest three values,

and so forth. A related problem is that point-

scoring methods can generate an artificially

high ranking for a species when criteria are

interrelated. For example, a system that priori-

tized species because they needed large home

ranges, had slow reproductive rates and small

litter sizes might end up allocating unreason-

ably high scores to any large-bodied species.

All three of these traits are associated with

relatively large body size, but they are not

necessarily so much more vulnerable.

Conservation status ranks method

Status ranks are based primarily on objective

factors relating to a species’ rarity, population

trends and threats. Four aspects of rarity are

typically considered: the number of individuals,

number of populations or occurrences, rarity of

habitat, and size of geographic range. Ranking

is based on an approximately logarithmic scale,

ranging from 1 (critically imperiled) to 5 (dem-

onstrably secure). Typically species with ranks

from 1 to 3 would be considered of conserva-

tion concern and broadly overlap with species

that might be considered for review under the

Endangered Species Act or similar state or

international statutes.

The NatureServe system (Master 1991) is one

example of a system that uses status ranks.

Developed initially by The Nature Conservancy

(TNC) and applied throughout North America,

the NatureServe system uses trained experts

who evaluate quantitative data and make in-

tuitive judgements about species vulnerability.

The aim of the NatureServe system is to deter-

mine the relative susceptibility of a species or

ecological community to extinction or extirpa-

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24 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS

tion. To achieve this, assessments consider both

deterministic and stochastic processes that can

lead to extinction. Deterministic factors include

habitat destruction or alteration, non-indigen-

ous predators, competitors, or parasites, over-

harvesting and environmental shifts such as

climate change. Stochastic factors include, en-

vironmental and demographic stochasticity,

natural catastrophes and genetic effects (Shaffer

1981).

NatureServe assessments are performed on a

basic unit called an element. An element can

be any plant or animal species or infraspecific

taxon (subspecies or variety), ecological com-

munity, or other non-taxonomic biological

entity, such as a distinctive population (e.g.

evolutionarily significant unit or distinct popu-

lation segment, as defined by some agencies) or

a consistently occurring mixed species aggrega-

tion of migratory species (e.g. shorebird migra-

tory concentration area) (Regan et al. 2004).

Defining elements in this way ensures that a

broad spectrum of biodiversity and ecological

processes are identified and targeted for conser-

vation (Stein et al. 2000). This approach is be-

lieved to be an efficient and effective approach

to capturing biodiversity in a network of

reserves (e.g. Jenkins 1976, 1996). Assessment

results in a numeric code or rank that reflects

an element’s relative degree of imperilment or

risk of extinction at either the global, national

or subnational scale (Master et al. 2000).

Back to basics – extinction risk versussetting priorities

The discussion above has reviewed methods for

categorizing species according to their conser-

vation priority. Running throughout is a ten-

dency to equate conservation priority with

extinction risk; yet these are clearly not the

same thing (Mace & Lande 1991). Extinction

risk is only one of a range of considerations that

determine priorities for action or for conserva-

tion funding. The threat assessment is really an

assessment of urgency, and an answer to the

question of how quickly action needs to be

taken. Hence, all other things being equal, the

critically endangered species will be most likely

to become extinct first if nothing is done. How-

ever, this is by no means the only consideration

that should be used by a conservation planner.

How then should extinction risk be used for

priority-setting? It may be easier to make the

analogy with a different system altogether. For

example, the priority-setting systems used by

Triage nurses in hospital emergency depart-

ments categorize people according to how ur-

gently they need to be seen; those seen first are

the ones that appear to have the most urgent

and threatening symptoms. The symptoms can

be very diverse, however, and some may turn

out upon inspection and diagnosis to be less

serious than might have been expected. Medical

planning across the board would not use the

triage system to allocate resources. The same is

true for conservation planning. As with ill and

injured people, our first sorting of cases should

be according to urgency, and should also be

precautionary (i.e. take more risks with listing

species that are in fact not threatened than with

failing to list those that really are). However,

once the diagnosis is made, and the manager

is reasonably sure that most critical cases are

now known and diagnosed, a more systematic

planning process should follow.

Variables other than risk

Now we consider a whole range of new variables

other than risk. Table 2.1 shows a range of vari-

ables – grouped under headings of biological

value (i.e. what biologists would consider), eco-

nomic value, social and cultural value, urgency

and practical issues. Under each of these head-

ings are a range of attributes that might contrib-

ute to a species priority. The first three columns

concern values, but the last two are rather dif-

ferent. Urgency is a measure that can be compli-

cated to implement – i.e. high urgency may

indicate that if nothing is done now, then it

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PRIORITIZING CHOICES IN CONSERVATION 25

will be too late. This measure is not a value score

that can easily be added to the others, and a

moderate score has little meaning. Practical

issues are also rather different, and will vary

greatly in their nature and importance depend-

ing on the context. Some species that are con-

sidered urgent cases may be extremely

impractical and/or costly to attend to. This set

of considerations is probably not complete, but it

does illustrate the point that there are more

things to think about than extinction risk.

This initial classification by the value type is

hard to manage in a priority-setting system.

Therefore, in Table 2.2, we classify these into

six criteria reflecting the nature of the attribute

(importance, feasibility, biological benefits,

economic benefits, urgency and chance of suc-

cess). This classification has the advantage that

the different questions are more or less inde-

pendent of one another, and each addresses a

question that public, policy-makers and scien-

tists can all address, and for which they can

provide at least relative scores.

Interestingly, the criteria that biologists com-

monly consider, and which form the basis of

most formal decision-processes, fall under one

heading (biological benefits). Yet in practice,

the other five criteria (Table 2.2) also influence

real decisions. Would it not, therefore, be pref-

erable to incorporate these other criteria expli-

citly in the process of setting priorities?

Turning criterion-based ranksinto priorities

A potential next step would be to add the scores

from Table 2.2. By allocating a score of 1, 2 or 3

to each criterion and then adding the ranks, an

overall priority could be calculated. We advise

against this for several reasons. First, the differ-

ent variables are not equal; we might for ex-

ample wish to weight the biological issues

more highly. Second, they are not additive: as

mentioned earlier both urgency and chance

of success are all or nothing decisions. For

Table 2.1 Classes and kinds of issues that are considered in priority-setting exercises for single-species

recovery

Biological value Economic valueSocial andcultural value Urgency Practical issues

Degree of endemism Cost of management

or recovery

Scientific and

educational benefits

Threat status

¼ extinction risk

Feasibility and logistics

Relictual status Direct economic

benefits

Cultural status

(e.g. ceremonial)

Time limitation,

i.e. opportunities

will be lost later

Recoverability, i.e.

reversibility of threats,

rate of species response

Evolutionary

uniqueness

Indirect economic

benefit

Political status

(e.g. symbolic or

emblematic)

Timeliness, i.e.

likelihood of

success varies

with time

Popularity – will there

be support from the

community?

Collateral benefits to

other species

Ecological services Popularity Responsibility, i.e. how

much is this also someone

else’s responsibility?

Collateral costs to

other species

Local or regional

significance

Land tenure

Ecological uniqueness Governmental/agency

jurisdictions

Keystone species status

Umbrella species status

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26 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS

example, if chance of success is nil we would

not wish to invest in that species at all, so it

would seem more logical to multiply other

scores by the chance of success. Third, although

we have sorted the issues into more-or-less

independent categories, there still are associ-

ations between them. For example, the feasibil-

ity and chance of success are likely to be

positively correlated, as are biological benefits

and importance. Hence, simple scoring can lead

to double-counting, which is not what was in-

tended.

Multicriteria decision-analysis is one decision-

making tool for choosing between priorities that

rate differently for separate criteria. There are

innumerable ways of carrying out a multicriteria

analysis, and the process can be complex and

may lead to ambiguous results. An expedient

process at this stage is to invite a range of experts

representing different perspectives to rate the

priorities explicitly. For example, given the pos-

sible set of scores in Table 2.2, what set would

they most wish to see in the top priorities versus

those lower down? This sounds complicated but

in practice we think it is feasible.

A good example of this approach was devel-

oped for UK birds by the Royal Society for the

Protection of Birds (Avery et al. 1995). Three

criteria were used: global threat, national de-

cline rates and national responsibility, and each

was rated high, medium or low. However, by

simply adding these scores, globally endan-

gered species that are stable, and for which

the UK has medium responsibility, had the

Table 2.2 Criteria for setting prorities. The different kinds of considerations from Table 2.1 are classified into

six criteria (rows), each of which can be qualitatively assessed for a particular species

Criterion Explanation Subcriteria Scores

Importance ‘Does anyone care?’ A

measure of how much

support there is likely to be

Social and cultural importance

(including charisma)

Responsibility –

how much of the species status

depends on this project?

Important (I)

Moderately important (M)

Unimportant (U)

Feasibility ‘How easy is this to achieve?’

An assessment of the difficulty

associated with this project

Logistical and political, source of

funds, community attitudes

Biological

Feasible (F)

Moderately difficult (M)

Difficult (D)

Benefits ‘What good will it do?’ A

measure of how much good

will result from the project.

Reduction in extinction risk,

increase in population size, extent

of occurrence

Collateral biological

benefits, to other species or processes

Highly beneficial (H)

Moderately beneficial (M)

Unclear benefits (U)

Costs ‘What will it cost?’ An assessment

of the relative economic costs

of the project (or gains). In this

criterion there are both postive

and negative aspects which have

to be weighed against each other

Direct and indirect costs of project

Direct and indirect social and

economic costs and benefits that will

flow from the project

Expensive

Moderately costly

Inexpensive

Urgency ‘Can it be delayed?’ A measure

of whether the project is time-

limited, or whether it can be

delayed

Extinction risk, potential for loss of

opportunity if delayed

Urgent

Moderately urgent

Less urgent

Chance of

success

‘Will it work?’ An assessment of

whether or not the project will

work

Will it meet its specified objectives? Achievable

Uncertain

Highly uncertain

Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 27 6.5.2006 11:07pm

PRIORITIZING CHOICES IN CONSERVATION 27

same priority as globally secure species exhibit-

ing slow decline in the UK. This would not be

most people’s choice; whatever their status in

the UK, a globally endangered species probably

should be in the category of highest priority.

Hence, Avery et al. (1995) set priorities using

their conservation cube (Fig. 2.1). Here they

evaluated each of the 27 possible circumstances

into three categories for priority. In their

system, any globally threatened species and

any species declining at a high rate nationally

are the highest priority.

This approach can be taken more generally

using the six criteria in Table 2.2. By asking

what would be the criteria associated with top

priority species, it is possible to assemble a pro-

file. For example, whereas a species conserva-

tion ‘idealist’ might choose to ignore

importance, feasibility, economic benefits and

chance of success, and to focus just on the most

urgent and most threatened forms, a more ‘pol-

itical’ approach would be to maximize import-

ance and economic benefits and minimize risk

of failure. Hence the two profiles would look

quite different (Fig. 2.2). Figure 2.2 illustrates

the different approaches – see how you would

score the criteria in Table 2.2 to make your own

set!

Here we are effectively creating a complex

rule set that maps any species into one of

three categories without adding or multiplying

the scores for different criteria. The method

suffers from its somewhat arbitrary nature.

Below we suggest that optimal allocation of

funds between species can be achieved more

rigorously if we place the problem within an

explicit framework in which we can apply

decision theory.

A decision theory approach – optimalallocation

A major problem with using scores or ranks for

threatened species to determine funding and

action priorities is that these methods were not

designed for that task – they were designed to

determine the relative level of threat to a suite of

species (Possingham et al. 2002). Hence they

cannot provide the solution to the problem of

optimal resource allocation between species –

this problem should be formulated then solved

properly (Possingham et al. 2001).

Optimal allocation is one simple and attract-

ive approach to prioritization that could inform

decisions about how to allocate resources be-

tween species. It requires information about

National decline

Responsibility

Conservation priority set

1

2 2 22

2233

2

2

1 11

11

1

1

Global threat

Fig. 2.1 The conservation cube. (From Avery et al.

1995.)

Importance

Manager 1

H M L

Manager 2

H M L

Idealist

H M L

Politician

H M L

Feasibility

Biological benefits

Economic benefits

Urgency

Chance of success

Fig. 2.2 Priority sets for four different people. The blocked out cells indicate the conditions under which

assessors would choose to include species in their priority set, according to how they scored on the variables

in Table 2.2 as H, high; M, medium; L, low.

Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 28 6.5.2006 11:07pm

28 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS

the relationship between the resources allo-

cated to the species and the reduction in prob-

ability of extinction. Here we use expert

opinion and/or population models to estimate

the relationship between percentage recovery

(measured, for example, in terms of probability

of not becoming extinct) and the funds allo-

cated to that species.

For poorly known taxa the curves showing

this relationship would very much be a reflec-

tion of expert opinion, garnered by asking

questions about how much it might cost to

give a particular species a 90% chance of not

becoming extinct in the long term. Given a set

amount of money for a set period in the con-

servation budget, the optimal allocation of

100

90

80

70

60

50

40

30

20

10

00 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000

$ spent

Rec

over

y (%

)

Scheme to maximise the total amount of recovery, given $ amount

16

14

12

10

8

6

4

2

00.E+00 1.E+07 2.E+07 3.E+07 4.E+07 5.E+07 6.E+07 7.E+07 8.E+07 9.E+07 1.E+08

$ in budget

Exp

ecte

d sp

ecie

s re

cove

red

Species accumulation

(a)

(b)

Fig. 2.3 Optimal allocation. (a) Three curves show the expected recovery for three different species given

certain amounts of investment. If the manager has a specified budget (in this case $1 million), the optimal

allocation among species that achieves the greatest total amount of recovery will result if funds are allocated

as shown by the vertical dotted lines (see Possingham et al. 2002). (b) Increasing investment leads to

gradually increasing numbers of species recovered.

Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 29 6.5.2006 11:07pm

PRIORITIZING CHOICES IN CONSERVATION 29

funds can be determined between species. This

occurs when the rate of gain of recovery for

each species is equal, such that there is no

advantage in shifting resources from one spe-

cies to another (see Fig. 2.3 and Possingham

et al. 2002). The implicit objective is to maxi-

mize the mean number of recovered species

given a fixed budget and assuming all species

are of equal ‘value’.

Using the set of species plotted in Fig. 2.3, we

estimate the costs of recovery, and then find

the optimal allocation of funds per species.

The species accumulation curve shows the

total expected number of species that can be

recovered given a conservation budget. The al-

gorithm will tend first to select species that

show large recovery for relatively low costs.

Slow responders will be conserved later. Given

an annual budget basis, the more intractable

conservation problems may never be funded

because the selection process will always favour

allocation of resources to the species that pro-

vide the greatest gains for the smallest costs

(the low-hanging fruit).

So how would these two approaches: cri-

teria-based prioritization and optimal allocation

of resources differ in practice? Obviously there

is no general answer to this, other than a priori

we do not expect them to be the same. The

outcome of a small case study, based on real

species and the expertise of two real conserva-

tion managers is shown in Fig. 2.4.

When species are rated highly by the criteria

the two approaches give similar results, but at

low criterion scores there can be much variabil-

ity. Perhaps the only general conclusion here is

that inevitably the optimal allocation approach

will favour some species that, on the basis of the

criteria, would not be given high priority. In

practice, sensible management could use both

approaches – the criteria to select high-priority

sets and the financial algorithm to then maxi-

mize the benefits from the finite resources avail-

able to conservation.

Conclusions

Priority setting needs to consider a range of

variables, and although this undoubtedly oc-

curs, it is not always transparent. Although

much effort has gone into biologically based

systems, in practice other societal value judg-

ments are often included. We suggest that, if

conservation goals are to be achieved, it is vital

to be explicit about what these are, and to

decide upon them in an open and consultative

manner before choices are made.

Different people and organizations, and differ-

ent sectors in society, will make different choices

in their value judgments. Approaches to under-

standing these choices are important so we can

interpret the differences in setting priorities.

4.5

4

3.5

3

2.5

2

1.5

1

0.5

00 1 2 3 4 5

Opt

imis

atio

n ra

nk

Criterion rank

Fig. 2.4 Comparison of priority ranks for18 species using the criteria-based method versus optimal allocation

of funds.

Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 30 6.5.2006 11:07pm

30 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS

We recommend using more than one

method to set priorities, and the comparison

can be informative. We also recommend that

decisions about resource allocation be formu-

lated more explicitly in terms of objectives,

constraints and costs.

For if one link in nature’s chain might be lost, another might be lost, until the whole of things will vanish bypiecemeal.(Thomas Jefferson (1743–1826) in Charles Miller, Jefferson and Nature, 1993.)

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34 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS

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Matthew Grainger (Newcastle University)

Phil McGowan (Newcastle University)

Ailsa McKenzie (Newcastle University)

Jeroen Minderman (Newcastle University)

Jon-Paul Rodriguez (IUCN-SSC)

Alison Rosser (UNEP-WCMC)

Andy South (Freelance)

Selina Stead (Newcastle University)

Mark Whittingham (Newcastle University)

Steering Group

Vin Fleming (JNCC)

Noel McGough (RBG Kew)

Trevor Salmon (Defra)

Michael Sigsworth (Defra)

Andy Stott (Defra)

Dominic Whitmee (Defra)

METHOD FOR THE ASSESSMENT OF PRIORITIES FOR

INTERNATIONAL SPECIES CONSERVATION

(MAPISCo)

Final Report

March 2013

MAPISCo Final report: Table of contents

2

Table of contents

1. EXECUTIVE SUMMARY ........................................................................................................ 4

2. INTRODUCTION .................................................................................................................. 9

2.1. Background ............................................................................................................................................................................... 9

2.2. Why is this method necessary? The link between policy demands and scientific capability ................. 10

2.3. Outline of the proposed project ..................................................................................................................................... 11

3. DEVELOPMENT OF THE METHOD ...................................................................................... 12

3.1. Selection of co-benefits ...................................................................................................................................................... 12

3.2. Focal co-benefits, data sets used and sources & category scoring..................................................................... 13 3.2.1. Habitat and area conservation (Aichi Targets 5 and 7) .................................................................................................... 13 3.2.2. Sustainable harvesting (Aichi Target 6) .................................................................................................................................. 15 3.2.3. Conservation of genetic diversity (Aichi Target 13) .......................................................................................................... 17 3.2.4. Protection of ecosystem services (Aichi Target 14) .......................................................................................................... 18 3.2.5. Preventing species extinction (Aichi Target 12) ................................................................................................................. 20

3.3. Database & prioritisation ................................................................................................................................................. 22 3.3.1. Database building .............................................................................................................................................................................. 22 3.3.2. Co-benefit weighting, re-scaling and priority score calculation ................................................................................... 22

3.4. Inclusion of red list threat classification data ........................................................................................................... 24

4. RESULTS - EXAMPLE PRIORITY LISTS .................................................................................. 27

4.1. Example 1: All species ........................................................................................................................................................ 30 4.1.1. Summary findings ............................................................................................................................................................................. 30 4.1.2. Taxonomic composition ................................................................................................................................................................. 30 4.1.3. Geographic composition ................................................................................................................................................................. 30 4.1.4. IUCN threat categories and classifications ............................................................................................................................. 30 4.1.5. Co-benefits ............................................................................................................................................................................................ 31

4.2. Example 2: Taxonomic case study – birds .................................................................................................................. 36 4.2.1. Summary findings ............................................................................................................................................................................. 36 4.2.2. Orders and Families.......................................................................................................................................................................... 36 4.2.3. Country/region ................................................................................................................................................................................... 36 4.2.4. IUCN Red List threat categories and classifications ........................................................................................................... 36 4.2.5. Co-benefits ............................................................................................................................................................................................ 37 4.2.6. Key findings and how they relate to policy ............................................................................................................................ 37

4.3. Example 3: Geographic case study – SE Asia .............................................................................................................. 44 4.3.1. General findings ................................................................................................................................................................................. 44 4.3.2. Taxonomic composition ................................................................................................................................................................. 44 4.3.3. Geographic composition ................................................................................................................................................................. 44 4.3.4. IUCN Red List threat categories and classifications ........................................................................................................... 44 4.3.5. Co-benefits ............................................................................................................................................................................................ 45 4.3.6. Key findings and how they relate to policy ............................................................................................................................ 45

5. USING THE METHOD ............................................................................................................. 50

MAPISCo Final report: Table of contents

3

5.1. Expandable –How does the does the priority list respond to the inclusion of additional co-benefit data? A plant example. ............................................................................................................................................................... 50

5.2. Adaptable - How does the priority list respond to changes in co-benefit weightings? .............................. 51 5.2.1. Sensitivity analysis ............................................................................................................................................................................ 51 5.2.2. Worked examples – threat status and ecosystem services ............................................................................................. 51

5.3. Usable - Development of Graphic User Interface (GUI) ......................................................................................... 54 5.3.1. GUI Development ............................................................................................................................................................................... 54 5.3.2. Constraints and legacy .................................................................................................................................................................... 55 5.3.3. Future development options ........................................................................................................................................................ 55 5.3.4. Future hosting options .................................................................................................................................................................... 56

6. DISCUSSION ......................................................................................................................... 57

6.1. Fit to original project brief ............................................................................................................................................... 57

6.2. How does the method compare with ‘business as usual’? .................................................................................... 58

6.3 How do co-benefits relate to IUCN threat status? ..................................................................................................... 58

6.4. Operating constraints ........................................................................................................................................................ 59

6.5. Integration of MAPISCo into decision-making – next steps ................................................................................. 60 6.5.1. SCIENCE. Ensuring that the methodology fully accounts for scientific advances .................................................... 61 6.5.2. PRACTICAL - Maintenance of database and incorporation of additional data. Where will the database be housed? ............................................................................................................................................................................................................... 61 6.5.3. POLICY - Integrating MAPISCo into policy and resource allocation decisions .......................................................... 63

6.6. Concluding remarks ............................................................................................................................................................ 64

7. REFERENCES ......................................................................................................................... 65

MAPISCo Final report: 1. Executive summary

4

1. Executive summary

Context. Biodiversity is declining at unprecedented levels globally, and meeting international

targets aimed at halting these declines requires conservation efforts targeted not only at species

but also at other aspects of biodiversity such as habitats, cultural values and ecosystem services.

In spite of this wide range of targets requiring investment, resources available are declining.

Against this backdrop, the UK Department for Environment, Food and Rural Affairs (Defra) sought,

via this project, to develop a methodology for identifying species for which targeted conservation

action would have the broadest consequential benefits (hereafter co-benefits) on other species,

habitats, wider ecosystems, and ecosystem services.

Agreed scope and aims. The objective of the MAPISCo project was to develop a scoring method

that enables species to be ranked based on their combined contribution to a selection of co-

benefits linked to conservation targets (Aichi Targets). Concentration of conservation on these

high-ranking species would, in theory, result in the largest associated biodiversity benefit. This

methodology would be expandable, able to include further datasets should they become available,

adaptable, with the weighting of co-benefits able to be altered in line with varying policy

aspirations, and usable, ultimately able to be used by non-expert practitioners.

Selection of co-benefits. Five co-benefits were selected for inclusion in the methodology- (1)

habitat and area conservation (Aichi Targets 5 and 7), sustainable harvesting of fish, invertebrates

and aquatic plants (Aichi Target 6), (3) conservation of genetic diversity of wild relatives of

cultivated plants and domesticated animals (Aichi Target 13), (4) protection of the provisioning of

ecosystem services (Aichi Target 14) and (5) the prevention of species extinctions (Aichi Target

12). The selection of these co-benefits was based on the Aichi Targets of policy interest to Defra.

They could also be linked in a scientifically defensible way with conservation effort on a species

level AND have adequate data associated with them (from preliminary searches) to be related to

species conservation lists, incurring as few taxonomic and geographic constraints as possible.

However, it should be stressed that this methodological framework can be extended to include

more co-benefits in the future, given, for example, specific policy needs (expandable).

Scoring. The methodology proposed here produces species lists ranked by their expected value in

contributing to each of the five focal co-benefits under consideration. First, reliable data sources

were identified that could be used to quantify the value of a given species to each of the five co-

benefits. Species from these data sources were then added to a database and given a score for

each of the five co-benefits. Details of how this was done are explained briefly in Table S1. The

scores from each database were then combined to obtain an overall value that corresponds to

species rank (see Box S1). Twelve data sources were found to be suitable for inclusion in the

database at this stage. Many more were identified but later disregarded because of issues with

data coverage and compatibility. The weighting, or importance, of each co-benefit can be adjusted

in response to policy aspirations – this makes the methodology highly adaptable.

Results – example priority lists. The results generated by the database in its current format are

constrained by data availability (e.g. only around 3% of all plant species have been categorised on

the IUCN Red List while almost all bird species are included). For this reason in this section we

present three different sets of results 1. All species, 2.Birds only (taxonomic case study) and 3. SE

Asia only (geographic case study). The use of case studies allows us to focus on discrete sets of

data, which, while not eliminating the constraints completely, allows a more meaningful

MAPISCo Final report: 1. Executive summary

5

demonstration of how the database can be used. For each set of lists we have outlined the main

findings from the method followed by a discussion of how some of the key findings could be related

to policy actions.

Generally, the results indicate that “politically interesting” or flagship species often championed by

interest groups do not generally rank highly (e.g. Polar Bear Ursus maritimus 45625th and White

Rhinoceros Ceratotherium simum 51510th), because they are associated with only a small number

of the co-benefits considered here. There are 1064 species which occur in the top 500 regardless

of changes in the co-benefit weightings, these include 502 birds, 161 mammals, 158 amphibians,

151 fish, 54 plants, 20 reptiles and 18 “other” species. This reflects the need for more data on

plants, reptiles and invertebrates. Both the Habitat and Ecosystem Services co-benefits are

significantly negatively correlated to Threat Status, meaning that more traditional approaches to

conservation (based on extinction risk- the IUCN Red List) do not capture more recent concerns

about protecting a range of co-benefits from each species. The MAPISCo methodology

successfully prioritises both extinction risk and contribution to co-benefits.

Using the method. Expandable. We demonstrate how additional species or co-benefit data can

be added to the database, and outline how such changes impact on the ranking of priority lists.

Adaptable. We examine the effect changing individual co-benefit weightings (i.e. making certain

co-benefits “more important” in the calculation of priority lists than others) has on priority list

ranking. Usable. Here we outline the development of a web-based interface, which, using a variety

of tabs and graphics, allows users to fully explore the priority lists created by the methodology

under a number of different scenarios. Importantly, it also enables user to investigate how varying

individual co-benefit weightings impact upon rankings. We view this as a critical feature of the

Graphical User Interface (GUI), as it makes it extremely adaptable to policy aspirations. Further

investment in this project could see this tool becoming available (open source) to interested parties

Discussion and project legacy. We conclude that we have delivered a methodology that can

prioritise species for conservation based on their expected contributions to a selection of co-

benefits. Thus, higher-ranking species should make greater contributions to meeting relevant Aichi

Targets (5-8 and 12-14). This methodology is expandable – additional datasets can be added to it

should they become available, adaptable – co-benefit weightings can be altered to fit with

individual policy aims and usable – the development of a graphic user interface will allow non-

technical users to use the method.

The original ambitious conceptual development of MAPISCo was rooted in the desire to embed

science firmly in international species policy. The core issue was that biodiversity spending can

tend towards projects focussed on charismatic animals with little evidence scientific justification for

such action. The method we present here yields priority lists based on available scientific evidence

but there are major caveats. The most important is the paucity of data available for some taxa

(especially plants). Whilst our analysis based on a well-known taxon (birds) for which all species

are assessed on the Red List does yield potentially usable results other prioritisation results based

on combining taxa are inevitably strongly constrained by data availability.

As a consequence of this we therefore outline a road-map to overcoming the challenges of linking

science and policy effectively in biodiversity governance in a way that will help ensure that

MAPISCo strengthens the UK’s ability to maximise the wider value to biodiversity of its spend on

international species conservation.

MAPISCo Final report: 1. Executive summary

6

Conclusion. We have developed a methodology which provides a broad-brush mechanism for

identifying species conservation priorities based on a selection of co-benefits. These co-benefits

are based on currently available and accessible data that are accepted to be good quality and

have the potential to be expanded as new data emerges. The project has demonstrated that the

choice of co-benefits, the importance given to them and the data sources used has a strong effect

on which species are identified as being higher priorities. Therefore, explicit policy decisions are

required (and need to be documented) throughout the prioritisation process. This finding alone is a

significant contribution to increasing engagement at the science-policy interface, because it shows

how closely intertwined the two spheres are. This feature of MAPISCo is likely to make it more

policy relevant than other prioritisation processes which are less sensitive to the practicalities of

policy-making. Implied in the original project brief is an assumption of a relatively straightforward

and linear science-policy interface, where policy asks a question, science answers it and then

policy decides what action should be taken. In practice, while this assumption has proved broadly

accurate, this must go hand in hand with meaningful dialogue between policy-makers and

scientists so that the best information available is used to inform policy as soundly as possible.

There is clear scope for Defra to build on the progress made in this project to allow scientific

knowledge and practice to better support UK government objectives. Overall, there is significant

potential for the methodology we have developed to become part of an iterative process where

conservation science and policy continually inform each other to produce evidence-based scientific

policy that is more relevant to society.

MAPISCo Final report: 1. Executive summary

7

Table S1. The data sources and scoring format used for each of the five co-benefits currently included in

the methodology. With further development we envisage being able to include a greater number of data

sources.

*These scores were rescaled to between 0 and 1 for the final ranking process and then standardized to give equal weighting between scores.

Co-Benefit Which data source do scores come

from?

How were species scored? *

1. Habitat/area

conservation

1) Important Bird Area (IBA)

2) Alliance for Zero Extinction (AZE)

1) Mean (average) number of co-

occurring species of conservation

concern (e.g. at high risk of

extinction) in all of the IBA’s in which

a species occurs.

2) Total number of species of

conservation concern co-occurring

with the target species in an AZE

2. Sustainable

harvesting

3) “FishBase” data on commercial

value in fisheries

4) IUCN Red List listed as affected

by aquaculture

5) “FishBase” for species used in

aquaculture

3) 1-6 (1=no interest, 6=highly

commercial

4) 1-3 (1=unknown, 3=industrial)

5) 1 or 0 (1 if listed, 0 if not)

3. Conservation

of genetic

diversity

6) Database of crop wild relatives

7) Lists of wild relatives of

domesticated animals

8) Plants listed as of medicinal use

6) 1 or 0 (1 if listed, 0 if not)

7) 1 or 0 (1 if listed, 0 if not)

8) 1-3 (least to most use)

4. Protection of

ecosystem

services

9) Carbon loss through

deforestation (country-level)

10) Freshwater availability (country-

level)

9) Estimate of loss of carbon through

deforestation (tonnes/year)

10) Availability of freshwater per

capita per year (m3/capita/year).

5. The

prevention of

extinctions

11) IUCN Red List (for animals)

12) SRLI (for plants)

11) & 12) 1-9 (1= extinct, 2= least

concern, 9=critically endangered)

MAPISCo Final report: 1. Executive summary

8

2) The mean score calculated in 1) is then standardised by taking away from it

the mean of all the values in the entire co-benefit column, then dividing it by

the standard deviation of that co-benefit mean (calculation of a “z score”).

The resultant score will be positive if the individual species score is greater

than the mean score and negative if the individual species score is smaller

than the mean score.

Box S1: A worked example of the final priority score calculation

The final priority score for a species is the sum of the scores given for the five co-benefits. The method for

calculating co-benefits scores is outlined below.

Species Habitat Harvesting Genetic diversity Ecosystem Service

Provisioning

Threat status Final Score

Francolinus camerunensis

(0.136+0.789)/2

=0.462

0.462-0.07 0.08

= 5.05

5.05*1

(0+0+0)/3

= 0

0-0.25 0.10

=-2.58

-2.58*1

(0+0+3)/3=

0.333

0.333- 0.24 0.11 =0.82

0.82*1

(0.323+0.975)/2

=0.649

0.462-0.55 0.11

=0.77

0.77*1

max(0.778,0)

= 0.778

0.462-0.45 0.25

=1.35

1.35*1

((5.05)+(-2.58)+ (0.82)+(0.77)

+(1.35)=

5.42

1) Mean taken of the scores assigned from original individual datasets (in this example, two different datasets).

4) Final score calculated by adding together the 5 co-benefit scores. This score is then used to rank species in the priority list.

3) The new co-benefit score is then multiplied by a weighting factor (in this case all co-benefits are weighted equally (i.e. weighting set to 1).

2) The mean score calculated in step 1 is then standardised by taking away from it the mean of all the values in the entire co-benefit column, then dividing it by the standard deviation of that co-benefit mean (calculation of a “z score”). The resultant score will be positive if the individual species score is greater than the mean score and negative if the individual species score is smaller than the mean score.

MAPISCo Final report: 2. Introduction

9

2. Introduction

2.1. Background

Biodiversity is declining at unprecedented levels globally: rates of species extinctions are

increasing while natural habitats are declining. This is largely as a result of anthropogenic

pressures (Butchart et al. 2010; Hoffmann et al. 2010). As a consequence, negative impacts on

humans accrue, not only through intrinsic loss of wildlife but also as a result of declines in and loss

of the ecosystem services healthy natural systems underpin and provide (Millennium Ecosystem

Assessment 2005; Cardinale et al. 2012). The choice of where and how to invest biodiversity

conservation effort is becoming increasingly difficult as available resources are shrinking and the

number of targets to which to contribute is growing. Against this backdrop, the UK Department for

Environment, Food and Rural Affairs (Defra) seeks through this project to develop a scientifically

robust and repeatable method to identify species for which targeted conservation action by

the UK Government would have the broadest consequential benefits (hereafter termed co-

benefits) for other species (or taxa), habitats, wider ecosystems, and ecosystem services. Key

conservation action aimed at such species will maximise contributions to international species

conservation treaties such as the Convention on Biological Diversity’s (CBD) Strategic Plan for

Biodiversity 2011-2020, which established twenty international targets to safeguard global

biodiversity. These are known as the Aichi Targets (COP 10 Decision X/2, see

http://www.cbd.int/decision/cop/?id=12268), and aim to safeguard biodiversity in its broadest sense

and at different levels. The targets of interest include preventing extinctions, conserving

habitats, controlling invasive species, sustainable harvesting, and protection of ecosystem

services.

As described in the original project brief (see Appendix 1), this method would involve the

development of a scoring system where individual species are linked, via existing data sources

(e.g. IUCN Red List, FishBase), to their expected contribution to various co-benefits (such as

ecosystem service provision or genetic relatedness to domesticated plants and animals). Species

recorded in the FishBase database, for example, as being important food sources would receive a

high score for a “sustainable harvesting” co-benefit, whereas a species recorded as having little or

no importance in harvesting would receive a low, or zero, score. This would enable individual

species to be ranked within an overall priority list based on their contribution to all the co-benefits

added together (the score for each co-benefit summed). Conservation action aimed at species

ranked at the top of this list would, therefore, be expected to have their greatest co-

Capsule.

With biodiversity declining at unprecedented levels globally, the choice of where and how to

invest biodiversity conservation effort is becoming increasingly difficult.

This project seeks to develop a methodology by which species can be prioritised for

conservation based not only on individual species benefits, but also on the contribution their

conservation may make to other species, habitats, wider ecosystems, and ecosystem

services.

This project aims to help bridge the gap between the contrasting spheres of science and

policy.

MAPISCo Final report: 2. Introduction

10

sequential benefits to the environment (based on the co-benefits selected for inclusion in the

method).

This methodology should be 1) expandable allowing the incorporation of future data, 2) adaptable

to changing policy aims and 3) usable by non-technical practitioners. This methodology would

then be available to and usable by a range of practitioners, and be adaptable to a wide range of

policy and conservation goals.

2.2. Why is this method necessary? The link between policy demands and scientific

capability

One key goal of this project is to link policy goals (i.e. the addressing of Aichi Targets) to real

conservation actions via sound scientific method. The fulfilment of this goal requires a smooth

transition from policy to science and back to policy - that scientifically robust findings can be used

to explore policy aspirations, and that the resulting policy is based on sound science and clear

decisions (Figure 1).

However, as the science and policy “spheres” tend to have very different rationales, time-lines and

objectives, the transition between them is often far from straightforward. Koetz et al. (2008)

synthesise several authors in arriving at their view of the issues at the heart of the science-policy

interface. They suggest that while science objectively deals with the generation of knowledge,

policy tends to be concerned with making subjective choices between different arguments, often

tackling interests and values that ultimately conflict (see also Appendix 5. Rapid Assessment

Report to support development of a Methodology for the Assessment of Priorities for International

Species Conservation, a report commissioned by this project, subcontracted to UNEP-WCMC).

This project aims to help bridge the gap between science and policy spheres by taking an

integrated approach. While the inputs of the methodology will be policy-driven (i.e. the selection

of co-benefits to which individual species conservation will be related and how these co-benefits

are individually weighted), the methodology used to address these policy questions will be based

on the best available scientific evidence. The development of a usable “front end” for this

methodology should enable the scientific finding to be used by policy makers and applied directly

to the policy sphere.

Figure 1. Classic view of the science-policy interface

1) Scope defined by policy aspirations

(e.g. Aichi Targets)

2) Scientific knowledge and data availability used to identify 'priorities'

3) Resources available and

deployed by policy makers

MAPISCo Final report: 2. Introduction

11

2.3. Outline of the proposed project

The project brief underwent considerable development in the early phase of this project (see

Appendix 1 and 2 for full details of the original brief and how it was amended). The final agreed

aims of the project are as follows:

To develop a scoring method which enables species to be ranked based on their

contribution to a selection of conservation targets (or co-benefits), that would be

expandable allowing the incorporation of future data, adaptable to changing policy

aims and usable by non-technical practitioners.

To test the results and usability of this methodology using case studies (taxonomic and

geographic), testing the expandability and adaptability of the database with the

addition of extra data sources and changes to co-benefit weighting.

To develop a web-based tool (a graphical user interface GUI) so that the methodology

can be demonstrated to and used by non-technical practitioners - usability.

To consider the broader science-policy context in which MAPISCo sits and propose

how it may become fully integrated in the future (the project legacy).

MAPISCo Final report: 3. Development of the method

12

3. Development of the method

3.1. Selection of co-benefits

The original aim of this project was to link the conservation of individual species to a large suite of

ecosystem co-benefits that could be related directly to the relevant Aichi Targets. The targets of

interest included preventing extinctions, conserving habitats, controlling invasive species,

sustainable harvesting, and protection of ecosystem services. However, preliminary work indicated

that for many species groups, sufficient data linking their conservation to many of the suggested

co-benefits are either not available or not easily accessible. Moreover, the full range of co-benefits

set out in the original brief was likely too large for the project timeframe.

For these reasons, a subset of five co-benefits was selected for inclusion in the development of the

methodology (see Box 1). This selection was made based on the contribution these co-benefits

made to Aichi Targets of policy interest to Defra, their links with conservation effort on a species

level and adequate data being available (from preliminary searches) to link them to species

conservation lists.

However, it should be stressed that further co-benefits could be included in the future to

incorporate specific policy needs (expandable).

Capsule.

Co-benefits selected (by the steering group) for inclusion in the method: (1) Habitat and

area conservation, (2) Sustainable harvesting of fish, invertebrates and aquatic plants, (3)

Conservation of genetic diversity of wild relatives of cultivated plants and domesticated

animals, (4) Protection of the provisioning of ecosystem services, and (5) Prevention of

species extinctions.

These co-benefits can be changed or added to - this makes the method expandable.

Scoring method developed which produces lists in which species are ranked by their

expected value in contributing to each of the five co-benefits above.

The weighting, or importance, of each co-benefit can be adjusted in response to policy.

aspirations. This makes the method adaptable.

Box 1. The five co-benefits selected for inclusion in the development of the methodology, and the

Aichi Targets to which they contribute

1. Habitat and area conservation (Targets 5 and 7; hereafter termed “Habitat co-benefit”)

2. Sustainable harvesting of fish, invertebrates and aquatic plants (Target 6; hereafter “Harvesting co-benefit”)

3. Conservation of genetic diversity, in particular of wild relatives of cultivated plants and domesticated animals (Target 13; hereafter “Genetic Diversity co-benefit”)

4. Conservation of the provisioning of ecosystem services (Target 14; hereafter “ES co-benefit”)

5. Prevention of species extinctions (Target 12; hereafter “species extinction co-benefit”).

MAPISCo Final report: 3. Development of the method

13

3.2. Focal co-benefits, data sets used and sources & category scoring

In the following sections, for each of the five co-benefits selected for inclusion we discuss (i) the

rationale for links to conservation effort on a species level, (ii) data sets chosen to make this link

and (iii) quantitative scoring used to integrate each data source in the prioritisation methodology.

A note on data sources

The majority of data sources were identified through discussions with experts at the project

workshop (see Appendix 4). Many suggested data sources were unsuitable for use in the final

methodology due to taxonomic or geographic biases in datasets, or general data accessibility

issues. For example, for the habitat co-benefit, Biodiversity Hotpots (Myers et al. 2000) and the

Global “200” Ecoregions project data (Olsen & Dinerstein 2002) could not be used as species

associations made in the lists were taxonomically biased and the data were not easily available.

For the harvest co-benefit, the Seas Around Us project (www.seaaroundus.org) was also rejected

due to the data not being publicly accessible. For the genetic diversity co-benefit, the SEPASAL

database (www.kew.org/ceb/sepasal) was rejected as data were both taxonomically and regionally

biased as well as being difficult to access. Further examples of investigated but unsuitable

databases are listed in Appendix 8, Table A8-1.

3.2.1. Habitat and area conservation (Aichi Targets 5 and 7)

Rationale

Target 5: “By 2020, the rate of loss of all natural habitats, including forests, is at least halved and

where feasible brought close to zero, and degradation and fragmentation is significantly reduced.”

Target 7: “By 2020 areas under agriculture, aquaculture and forestry are managed sustainably,

ensuring conservation of biodiversity.”

Both Aichi Target 5 and 7 relate to the conservation or sustainable management of natural and

semi-natural habitats. Although conservation effort directed at species usually involves a degree of

protection of the habitat(s) (Mace & Collar 2002), such contribution to habitat conservation is likely

to be greater for some species than for others.

One way to link species-level conservation to habitat conservation is to focus on those species that

are thought to be of disproportionate importance to their habitats or co-occurring species (i.e.

“surrogate species” such as umbrella, keystone or indicator species; (Caro & O’Doherty 1999;

Caro & Girling 2010). However, the effectiveness of surrogate species for conservation is widely

criticised (e.g. Lindenmayer et al. 2002; Saetersdal & Gjerde 2011). More importantly, concrete

evidence for the effectiveness of species as surrogates for habitats is limited and often highly

context-dependent (Andelman & Fagan 2000), which means globally applicable lists of appropriate

habitat surrogate species are not available.

Instead, in the current project we have chosen to link species to habitats by focussing on

species that have previously been associated with, or used as “triggers” for the

designation of Key Biodiversity Areas (KBAs) (Eken et al. 2004). Defined as “sites of global

significance for biodiversity conservation “(Eken et al. 2004), KBAs are designated based on the

conservation of habitat within them being important or even vital for the persistence of one or more

MAPISCo Final report: 3. Development of the method

14

target species (Eken et al. 2004). However, conservation effort directed at these target species is

likely to benefit the wider habitat, making KBAs important areas for a wide and varied number of

species. There is also a general consensus that the conservation of KBA sites has many wider

benefits (e.g. in terms of cultural value or provisioning of ES (e.g. Butchart et al. 2012; Larsen,

Turner & Brooks 2012).

Theoretically, conservation effort directed at a species recorded in a KBA will also benefit non-

target species co-occurring in that KBA, as well as having other localised benefits (e.g. ecosystem

service provision). Therefore, conservation effort directed at species recorded in species-rich KBAs

is likely to produce higher levels of habitat co-benefits than conservation of a species that occurs in

a species poor KBA.

A “habitat score” for each individual species is calculated as follows: for each species associated

with a KBA, we use the mean number of other species known to co-occur in all KBAs in which that

individual species occurs. This is, in effect, a proxy measure of the expected contribution a given

species may make to habitat conservation overall.

Data sources

The linking of species and habitats necessary in this approach relies upon the availability of

species inventories for individual KBAs. However, these inventories are not always available for all

types of KBA, or for all species groups. For this reason we have decided to include data from the

two types of KBA for which most data is readily available: Important Bird Areas and Alliance for

Zero Extinction sites.

Important Bird Areas (IBAs) are sites identified as being globally important for the

persistence of one or more populations of endangered bird species. Identified using

standardised criteria (http://www.birdlife.org/datazone/info/bacritglob [Accessed 22 August

2012]), a site qualifies for IBA status if it holds or is thought to hold significant populations

(or parts of populations) of bird species (1) listed as endangered (Critically Endangered,

Endangered or Vulnerable) on the IUCN Red List, (2) with a restricted range (e.g.

endemics), (3) that are (largely) restricted to a single biome, or (4) that are migratory or

congregatory and for which the site is important during particular parts of the year (BirdLife

International 2008, http://www.birdlife.org/datazone/info/ibacriteria [Accessed 22 August

2012]). To date, over 10,000 IBAs have been identified globally

(http://www.birdlife.org/datazone/site [Accessed 22 August 2012]), in which 4847 species

are listed to occur. The number of bird species listed per IBA site ranges from 1 to 247, with

an average of 9 per site.

Alliance for Zero Extinction (AZE) sites are sites which hold the last remaining population(s)

of highly threatened species of mammals, birds and/or selected reptiles, amphibians and

conifers. To qualify as an AZE site, a site must (1) hold at least one species listed as

Critically Endangered or Endangered on the IUCN Red List, (2) hold all or the majority

(>95%) of the known population of the species, and (3) must be geographically and

politically discrete (Ricketts et al. 2005). To date, 587 AZE sites containing 920 species

have been identified. The number of species listed per AZE site ranges from 1 to 22, with

an average 1.6 species per site.

MAPISCo Final report: 3. Development of the method

15

Species-level score

All IBA and AZE inventories were sourced and the species included on them listed in a database in

preparation for the assignment of a score.

For species listed as occurring in an IBA (or listed as a “trigger” species used to delineate an

IBA), individual species scores were equal to the mean number of species recorded as co-

occurring with that individual species across all IBAs for which there was a record. For example, if

species A was recorded on three IBA inventories, and co-occurred with five species on one

inventory, ten on another and twenty on the third, the score species A received would be mean of

5, 10 and 20 = 11.67. This resulted in each species receiving a value between 1 and 246, with a

mean of 34.3.

Species listed as occurring in an AZE were attributed a score equal to the total number of

species co-occurring with that species at the site in which it was recorded (not an average because,

by definition, a given species occurs in only a single AZE site). This resulted in a score ranging

from 1 to 22 (22 being the maximum number of species listed for one site) with a mean of 3.5.

Species listed as occurring in both an IBA and an AZE were given the mean score. Those not

listed as occurring in either an IBA or an AZE site were not allocated any score.1

For both scores, higher values indicate that, on average, a species occurs in AZE or IBA sites that

hold larger numbers of other species. Conservation effort directed at species with such higher

scores is therefore likely to both benefit their wider habitat as well as a larger number of co-

occurring species.

3.2.2. Sustainable harvesting (Aichi Target 6)

Rationale

“By 2020 all fish and invertebrate stocks and aquatic plants are managed and harvested

sustainably, legally and applying ecosystem based approaches, so that overfishing is avoided,

recovery plans and measures are in place for all depleted species, fisheries have no significant

adverse impacts on threatened species and vulnerable ecosystems and the impacts of fisheries on

stocks, species and ecosystems are within safe ecological limits.”

It was assumed (in discussion and agreement with the steering group) that conservation effort

directed at harvested fish species or species involved in aquaculture production would contribute

to this target. Moreover, we assumed that such contributions would be stronger for species that are

seen as having greater economic value.

Data sources

Thus, we used three data sources to identify species relevant to this target:

1) Commercial value of a species to fisheries. FishBase (Froese & Pauly 2012) provides a

qualitative assessment of the economic value of 3111 fish species across countries in

which they are harvested. The overall economic value or importance of each species is

categorised in one of six categories, ranging from “no interest” to “highly commercial”. The

definitions of the categories are given in Table 1.

MAPISCo Final report: 3. Development of the method

16

2) Species listed as affected by aquaculture on the IUCN Red List (IUCN 2012). As well as

conservation status and extinction risk, the IUCN Red List (2012.1) records types of threats

faced by species. One of these threat classifications is aquaculture. We identified 269

species listed under Threat Classification (v.3.1) 2.4 (Marine & freshwater aquaculture),

which distinguishes between species impacted by “Industrial” (Classification 2.4.2),

“Subsistence/artisanal” (Classification 2.4.1.) and “Unknown” levels of aquaculture

(Classification 2.4.3).

3) Species listed as used in aquaculture production on FishBase (204 species, C. McOwen

pers. comm.).

Table 1. FishBase commercial harvesting categories and scores attributed.

Category No. spp. % spp.

Category

score Definition

Highly commercial 207 6.7 6 The species is very important to the

capture fisheries (or aquaculture) of

a country

Commercial 1416 45.5 5 The species is regularly taken in the

capture fisheries or regularly found

in aquaculture activities of a country

Minor commercial 1233 39.6 4 The species is of comparatively less

importance in capture fisheries or

aquaculture in a given country

Subsistence fisheries 210 6.8 3 The species is consumed locally only,

mostly by the fishers themselves

Of potential interest 2 0.1 2

Of no interest 43 1.4 1

Species-level scores

All species were attributed a score for each of the three data sources listed above. First, species

listed as commercially harvested in FishBase were attributed a score between 1 (for “no interest”)

to 6 (for “highly commercial”), reflecting increasing conservation priority for species of greater

economic interest (Table 1). Second, species listed as threatened by aquaculture on the IUCN Red

List were attributed a score between 1 (for “unknown scale”) and 3 (for “industrial”) reflecting the

increasing intensity of aquaculture threat and therefore the increasing potential for conservation

effort directed at such species to benefit the target in question (Table 2). Third, species listed as

used in aquaculture production on FishBase were attributed a score of 1, whereas other species

were not attributed any value.

MAPISCo Final report: 3. Development of the method

17

Table 2. Species and classifications listed under Threat Classification (v.3.1) 2.4 (Marine & freshwater aquaculture) on the IUCN Red List (2012.1), and category scores attributed.

Category No. spp. % of spp.

Category

score

Industrial 27 10 3

Subsistence/artisanal 16 5.9 2

Scale unknown 226 84 1

3.2.3. Conservation of genetic diversity (Aichi Target 13)

Rationale

“By 2020, the genetic diversity of cultivated plants and farmed and domesticated animals and of

wild relatives, including other socio-economically as well as culturally valuable species, is

maintained, and strategies have been developed and implemented for minimising genetic erosion

and safeguarding their genetic diversity.”

It was assumed (in discussion and agreement with the steering group) that conservation effort

directed at species extant in the wild will contribute to the preservation of (unique) genetic diversity

of the targeted species. Following the central tenet of this target, we chose to focus on wild

relatives of crops and domestic animals, and further expanded our consideration into plant species

with a known medicinal use.

Data sources

We used the following data sources to identify species relevant to this target:

1) A database of wild relatives of plant crop species. This database (Vincent et al. in prep.)

lists 1385 high priority crop wild relative (CWR) species. CWR are wild species closely

related to crop species which have the potential to contribute valuable traits (e.g. disease

resistance) to crops in the future. Vincent et al. define CWR as those species which are

sufficiently similar genetically to allow crossing (either naturally or in the laboratory; the

“gene pool concept”) or in some cases those species belonging to the same genus (the

“taxon concept”).

2) Lists of wild relatives of domesticated animal species, compiled from the FAO World Watch

List for Domestic Animal Diversity (FAO 2000) and from (McGowan 2010). The former

document identified avian and mammal species representing domestic animal genetic

resources at risk of loss, based on a range of survey- and monitoring- efforts. From these,

we identified those species extant in the wild and/or listed as at risk from hybridisation on

the IUCN Red List (IUCN 2012, in total 210 species). Among birds, Galliformes are

particularly important economically and we therefore added species from family and genera

identified in McGowan 2010 and listed on the IUCN Red List (IUCN 2012) as relatives of

domesticated animals (in total 323 species). It should be noted that in light of a recent

review (Owens & McGowan in prep), neither Cracidae (chachalacas, guans and curassows

MAPISCo Final report: 3. Development of the method

18

from Central and South America) nor Megapodidae (mound-builders from the Australasian

region) receive scores for this co-benefit. This is because no successful hybrids between

species from these families and domesticated poultry have been recorded (McCarthy 2006).

3) Plants species listed as used for medicinal purposes in the BGCI PlantSearch database

(http://www.bgci.org/plant_search.php/ [Accessed 22 August 2012]) and Hawkins (2008).

The BGCI PlantSearch database is compiled from information supplied by botanical

gardens worldwide, including whether a given species has a medicinal use. The database

holds 1788 species records listed as having medicinal use. Hawkins (2008) compiled

information on many medicinal plant species from a range of sources and expert opinion

questionnaires, and lists 429 medicinal plant priorities.

Species-level scores

All species were attributed a numerical score for each of the data sources outlined above. First,

reflecting their status as CWR, species occurring in the CWR database were attributed a score of 1.

Second, species occurring on our compiled list of relatives of domesticated animals were also

attributed a score of one. Species not on either list were not attributed any score. The resulting

binary scores reflect our limited ability using these data sources to distinguish further between the

relevant species in terms of priority (e.g. a species either is a CWR or not). Third, species listed in

the top 35 of Annex 5 of Hawkins (2008) were attributed a score of 3, remaining species in the

same list were scored as 2 and other species with a known medical use (listed in the PlantSearch

database) were scored as 1.

3.2.4. Protection of ecosystem services (Aichi Target 14)

Rationale

“By 2020, ecosystems that provide essential services, including services related to water, and

contribute to health, livelihoods and well-being, are restored and safeguarded, taking into account

the needs of women, indigenous and local communities, and the poor and vulnerable.”

To maximise contributions to the protection of ecosystem services (ES) by conservation effort

directed at a species level, evidence is required that shows the relative importance of species to

the provisioning of ES. While it is widely recognised that ecosystems provide a range of services

and benefits to humans, and indeed that groups of species can be associated with broad service

provision (Millenium Ecosystem Assessment 2005; UK NEA 2011), the evidence base linking

individual species to particular services is limited and evidence showing the relative value of

different species for a given service even more so. Where such evidence is available, it is often

limited to a single species in a particular context (e.g. Vira & Adams 2009; MAPISCo Project Team

2012). Such context-dependent examples do not constitute the solid evidence base necessary for

the taxonomic and geographic scope required for the present methodology. Moreover, examples of

broad species groups providing essential services are prevalent and often cited. However, in such

cases, often large numbers of species are involved, and their relative importance to the provision

of the service in different contexts is unclear. For example, although it is well known that many

insects are vital as pollinators of economically important crop species, the value of individual

pollinating species has, in the vast majority of cases, not been quantified. This inability to

MAPISCo Final report: 3. Development of the method

19

distinguish between such species in terms of their relative value to the service in question limits the

use of such data in a species prioritisation methodology.

By contrast, there is a growing consensus that priorities for biodiversity conservation and for ES

can be reconciled using area-based (as opposed to species-based) approaches, for example

freshwater provisioning or carbon sequestration along with levels of biodiversity (Lamoreux et al.

2005; Goldman et al. 2008; Naidoo et al. 2008; Egoh et al. 2011; Fisher et al. 2011; Butchart et al.

2012). Thus, here we chose to make the link between species and the provisioning of a selection

of ES by focusing on a larger, habitat- and country scale. The advantage of focusing on a country

level is that broad measures of the provisioning of some ES are available at country level, and

species occurrence data within broad habitats in countries is more readily available (and potentially

more reliable) than finer-scale measures of distribution.

Data sources

We focus on two example ES – 1) estimated carbon loss through deforestation and 2) freshwater

availability. We used the following data sets to link country-level measures of these two services

to species level:

1) Carbon loss through deforestation (tonnes/year). To estimate this, we multiplied country-

level estimates of (1) the stock of carbon in living forest biomass in 2010 (tonnes/hectare)

with (2) the trend of the extent of primary forest between 2005-2010 (change in hectares),

as available in the FAO Global Forest Resources Assessment 2010 (FAO 2010) database

(http://countrystat.org/index.asp?ctry=for&HomeFor=for [Accessed 22 August 2012]).

2) Freshwater availability. As a measure of the availability of freshwater to people, we used

the country-level estimated total renewable per capita freshwater supply in 2010, as

obtained from the FAO AQUASTAT database

(http://www.fao.org/nr/water/aquastat/data/query/index.html [Accessed 22 August 2012]).

Lower values (<1000 m3/capita) indicate water scarcity (UNEP Vital Water Graphics:

www.unep.org/geo/geo4/report/Glossary.pdf [Accessed 22 August 2012]).

3) Habitat- and country- occurrence. We used the data in the IUCN Red List (IUCN 2012)

augmented by the Sampled Red List Index (SRLI) for plants

(http://threatenedplants.myspecies.info/ [Accessed 22 August 2012], and S. Bachman pers.

comm.) (to increase representation of plant species) to identify species occurrence in

countries, and in (1) forest habitats (Habitat Classification 1) and wetland (inland) habitats

(Habitat Classification 5). See section 3.2.5 (page 20) for more information on the data

used from the Red List and SRLI for plants.

Species-level scores

We assumed that conservation effort directed at forest species occurring in countries with higher

estimated rates of carbon loss through deforestation is more likely to make contributions to targets

to reduce carbon loss or increase carbon sequestration. Similarly, because wetland and forest

habitats are particularly important in controlling both the supply and quality of freshwater (Millenium

Ecosystem Assessment 2005; Larsen, Turner, & Brooks 2012), we assumed that conservation

effort directed at forest or wetland species occurring in countries with lower levels of per capita

MAPISCo Final report: 3. Development of the method

20

freshwater supply is likely to make greater contributions to targets aiming to alleviate water scarcity

or stress.

Accordingly, species occurring in forest habitats (IUCN Red List Habitat Classification 1) were

attributed a score for the estimated carbon loss through deforestation, calculated as the average

estimated carbon loss through deforestation across all countries in which the species occurs.

Lower values indicate an association with higher rates of loss and therefore higher conservation

priority. Similarly, species occurring in forest- or wetland habitats (IUCN Red List Habitat

Classifications 1 and 5) were attributed a score for freshwater supply calculated as the average

estimated supply across countries in which it occurs. Lower values indicate a greater association

with higher levels of water scarcity, and therefore higher conservation priority.

3.2.5. Preventing species extinction (Aichi Target 12)

Rationale

“By 2020 the extinction of known threatened species has been prevented and their conservation

status, particularly of those most in decline, has been improved and sustained.”

As the present prioritisation methodology aims to maximise co-benefits of the conservation of

species, we considered Aichi Target 12 to be our “focal” target and assumed that conservation

effort directed at more highly threatened species would contribute most to it.

Since revisions from the IUCN Red Data Books (Mace & Lande 1991), the IUCN Red List has

grown to become not only the most comprehensive data set on the extinction risk of a wide range

of species from various taxonomic groups, but also represents an effective data source for species

occurrence and habitat classifications (IUCN 2012). Red List threat status assessments are made

by experts according to well-documented standards (IUCN 2001)

(http://www.iucnredlist.org/technical-documents/categories-and-criteria/2001-categories-criteria

[Accessed 22 August 2012]). Species are placed into one of nine threat categories representing

increasing extinction risk, based on a range of criteria including (1) declines in population size, (2)

restrictions in geographic range, (3) small absolute population size or (4) analytical evidence of

high extinction risk.

Data sources and species-level scores

For each species listed on the IUCN Red List v. 2012.1 (IUCN 2012) and/or the SRLI for plants

(http://threatenedplants.myspecies.info/ [Accessed 22 August 2012], and S. Bachman pers.

comm.), the most recent threat status assessment was obtained. Each category was attributed a

default numerical score on a linear scale, from 1 for the lowest category (Extinct) to 9 for Critically

Endangered (this scale was set by the larger number of categories in the Red List data which has

a “Lower Risk” category not present in the SRLI plant data), so that higher scores represent a

greater risk of extinction. Species not occurring on either list were not attributed any score. The

SRLI data had a “Not Evaluated” category, which was attributed a score of zero. Extinct in the Wild

was treated as a higher priority category by attributing the second-to-highest score in both cases,

with the view that the key goal of the prioritisation methodology is to achieve in situ conservation

and species currently only persisting ex situ therefore require substantial conservation effort. For

precautionary reasons, Data Deficient species were scored between Near Threatened and Least

MAPISCo Final report: 3. Development of the method

21

Concern, which in both the Red List and SRLI scoring is near the middle of the score distributions.

See Tables 3 and 4 for the default numerical scoring for the Red List and SRLI for plants,

respectively.

Table 3. Threat categories and number of species in the IUCN Red List (2012.1), and scores attributed. Species “Not Evaluated”(NE) were scored 0.

Table 4. Threat categories and number of species in the Sampled Red List Index (SRLI) for Plants, and scores attributed.

Although both the relative scores among species and the scale used (e.g. linear, exponential) are

inevitably largely subjective, the translation of threat categories into numerical scores used here is

similar to that used in previous prioritisation studies (e.g. Rodriguez et al. 2004, Butchart et al.

2012). Because of the qualitative nature of these scores, we suggest that in the final version of the

present prioritisation methodology these scores can be altered to suit changing expert opinion or

policy aspirations.

In addition to conservation status assessments, from the RL and SRLI for plants we obtained lists

of countries in which each species occurs, and lists of species occurring in forest habitats (IUCN

Red List Habitat Classification 1) and wetlands (Classification 5).

Category code Category No. spp. % of spp. Category score

CR Critically Endangered 3947 6.412 9

EW Extinct in the Wild 63 0.102 8

EN Endangered 5766 9.368 7

VU Vulnerable 10105 16.417 6

NT Near Threatened 3452 5.608 5

DD Data Deficient 10497 17.054 4

LC Least Concern 26922 43.738 2

EX Extinct 801 1.301 1

Category code Category No. spp. % of spp.

Category

score

CR Critically Endangered 1813 10.930 9

EW Extinct in the Wild 31 0.187 8

EN Endangered 2688 16.204 7

VU Vulnerable 4998 30.130 6

NT Near Threatened 752 4.533 5

DD Data Deficient 1244 7.499 4

LR/cd Lower Risk 223 1.344 3

LR/lc Lower Risk 909 5.480 3

LR/nt Lower Risk 677 4.081 3

LC Least Concern 3162 19.062 2

EX Extinct 91 0.549 1

MAPISCo Final report: 3. Development of the method

22

3.3. Database & prioritisation

3.3.1. Database building

For a full explanation of the database structure see Appendix 6. Briefly, data from the sources

described in section 3.2, page 13, were cleaned (errors removed) and compiled using R (v. 2.15.0,

R Development Core Team 2012) and resulting tables stored in a SQLite relational database (v.

3.7.11). The resulting main output table contained one row per species, with either a relevant value

for each data source, or a “blank” indicating the species does not occur in that data set.

3.3.2. Co-benefit weighting, re-scaling and priority score calculation

A worked example of how the final priority score for each species was calculated is provided in

Box 2). Broadly, the score was calculated by following these steps

1) Individual species scores from each data set were rescaled to allow them to be compared

on the same scale. First, each individual dataset score was divided by the maximum value

for scores from that particular dataset. This allowed all scores to be assigned a value

between 0 and 1. For example, a species receiving a score of 7 for the species extinction

co-benefit (i.e. a threatened species) was rescored to 0.778 (raw score of 7 divided by the

maximum score for that database of 9 [the score given to Critically Endangered species]),

while a species scoring 2 for extinction risk (Least Concern) was rescored to 0.222 (raw

score of 2 divided by the maximum score of 9). For database scores listed only as 0 or 1

(binary scores, such as those for “Aquaculture use” obtained from FishBase and species

listed as “Crop Wild Relatives”) this transformation had no effect. For both ES data sources

(estimated carbon loss through deforestation and freshwater availability) lower scores were

associated with higher priorities, so their scales were inverted as well as rescaled.

2) Scores attaining to each co-benefit were then combined to create a “score per co-benefit”.

For the prevention of species extinction (section 3.2.5, page 20), this co-benefit score

equalled either the Red List conservation status score or the plant SRLI score, whichever

was greater. All other co-benefits scores (Habitat, Harvesting, Genetic Diversity and ES

Provisioning) were calculated by taking the mean of the individual dataset scores

contributing to the co-benefit.

3) The overall co-benefit score for each species was then standardised. This was necessary

because while the individual database scores were “rescaled” to between zero and one as

described in 1), their position along this 0-1 scale was arbitrary. For example, for a species

in receipt of a co-benefit score of 1 for harvesting (of which there could only be a score of 1

or 0 due to limitations in the data) and a score of 0.56 for extinction list, the harvesting

score is not “twice” as important as the habitat extinction score – it is only twice as large as

a result of the scoring method of the individual datasets. For scores to be standardised a z-

score calculation was made – this is an accepted standardising technique. The quantity z

represents the distance between the raw score and the population mean in units of the

standard deviation. z is negative when the raw score is below the mean, positive when

above. This means that the co-benefit scores will be related in terms of the overall mean

score.

MAPISCo Final report: 3. Development of the method

23

a. First, the mean and standard deviation of all the scores given to individual species

was calculated for each co-benefit. It is important to note that this mean is

calculated with empty cells being treated as missing data rather than as zero data

(i.e. not included in the calculation of the mean). This is important, as the database

contain a large percentage of “missing data” given few species receive scores in all

databases. Treating them as “zeros” biases the database towards species that

have scores for more co-benefits. By treating them as missing data this is a more

accurate representation of what is known – i.e. it is not known if there is a

relationship, rather than there is no relationship (p 45).

b. Each co-benefit score was then standardised by taking this mean score away from

it, and dividing it by the standard deviation of this mean. The “z-score” was negative

when the raw score was below the mean and positive when it was above.

4) The new score per co-benefit was then able to be modulated further by multiplying each by

a weighting factor between 0 (no contribution) and 1 (maximum contribution). These

weights can be modified based on policy decisions regarding the importance of each co-

benefit. The default (as set in the database) is that all are equally important.

5) The final composite priority score per species equalled the sum of the weighted co-benefit

scores.

The resultant list was sorted by decreasing final priority score. We used seven broad taxonomic

groups to present the results below: amphibians (class Amphibia), birds (class Aves), fish (classes

Actinopterygii, Cephalaspidomorphi, Chondrichthyes, Myxini and Sarcopterygii), mammals (class

Mammalia), plants (kingdom Plantae), and reptiles (class Reptilia). These groups were chosen

because they represent a wide range of taxonomic groups and are relatively well represented in

the combined data sources used (e.g. as opposed to insects). The Red List was used as the

primary source of taxonomic data, although for species not included on the Red List additional

taxonomic data was used from the other data sets, where available, or inferred from the type of

data. Species not belonging to any of the above groups were grouped as “Other” (mainly

invertebrates).

MAPISCo Final report: 3. Development of the method

24

3.4. Inclusion of red list threat classification data

While Red List “threat category” (e.g. Critically Endangered, Near Threatened etc.) was included

as a co-benefit in the methodology (from the Red List for animals and from the SRLI list for plants),

the nature of that threat was not. The Red List “Threats Classification scheme” assigns a threat

type to each of the species listed within it. The threats take a hierarchical format, with main threat

categories subdivided into a number of subcategories, some of which are subdivided further (Table

5 and Appendix 8, Table A8-2 for full explanation of categories). The addition of this data to the

overall database would allow the production of a) lists containing major threat classifications for

particular species groups or geographical regions, and/or b) lists of species per threat classification.

2) The mean score calculated in 1) is then standardised by taking away from it

the mean of all the values in the entire co-benefit column, then dividing it by

the standard deviation of that co-benefit mean (calculation of a “z score”).

The resultant score will be positive if the individual species score is greater

than the mean score and negative if the individual species score is smaller

than the mean score.

Box 2: A worked example of the final priority score calculation

The final priority score for a species is the sum of the scores given for the five co-benefits. The method for

calculating co-benefits scores is outlined below.

Species Habitat Harvesting Genetic diversity Ecosystem Service

Provisioning

Threat status Final Score

Francolinus camerunensis

(0.136+0.789)/2

=0.462

0.462-0.07 0.08

= 5.05

5.05*1

(0+0+0)/3

= 0

0-0.25 0.10

=-2.58

-2.58*1

(0+0+3)/3=

0.333

0.333- 0.24 0.11 =0.82

0.82*1

(0.323+0.975)/2

=0.649

0.462-0.55 0.11

=0.77

0.77*1

max(0.778,0)

= 0.778

0.462-0.45 0.25

=1.35

1.35*1

((5.05)+(-2.58)+ (0.82)+(0.77)

+(1.35)=

5.42

1) Mean taken of the scores assigned from original individual datasets (in this example, two different datasets)

4) Final score calculated by adding together the five co-benefit scores. This score is then used to rank species in the priority list.

3) The new co-benefit score is then multiplied by a weighting factor (in this case all co-benefits are weighted equally (i.e. weighting set to 1)

2) The mean score calculated in step 1 is then standardised by taking away from it the mean of all the values in the entire co-benefit column, then dividing it by the standard deviation of that co-benefit mean (calculation of a “z score”). The resultant score will be positive if the individual species score is greater than the mean score and negative if the individual species score is smaller than the mean score.

MAPISCo Final report: 3. Development of the method

25

Species data were downloaded from the IUCN Red List website for each major threat category

(categories 1-12 Table 5). Only “first tier” threat classifications were used, rather than sub-

categories, as these data were more robust. These data were then integrated into the main

MAPISCo database using the unique binomial species name and or species identification number

as a link between data tables. This enabled us to produce lists of threat data as shown in Table 6.

Threat data was not scored in the current incarnation of the database because of uncertainties in

the IUCN threat classification process for each focal taxon. Where data are better standardised

(such as for the birds) there is the potential for these threats to be ranked and scored in

accordance with policy aspirations (expandable).

Table 5. The IUCN threat classification scheme categories (see Appendix Table A8-2 for full list and

definitions)

Main threat category Sub-category (number of further sub-categories)

1 Residential & commercial development

1.1 Housing & urban areas 1.2 Commercial & industrial areas 1.3 Tourism & recreation area

2 Agriculture & aquaculture

2.1 Annual & perennial non-timber crops (4) 2.2 Wood & pulp plantations (3*) 2.3 Livestock farming & ranching (4) 2.4 Marine & freshwater aquaculture (3)

3 Energy production & mining

3.1 Oil & gas drilling 3.2 Mining & quarrying 3.3 Renewable energy

4 Transportation & service corridors

4.1 Roads & railroads 4.2 Utility & service lines 4.3 Shipping lanes 4.4 Flight paths

5 Biological resource use

5.1 Hunting & collecting terrestrial animals (4) 5.2 Gathering terrestrial plants (4) 5.3 Logging & wood harvesting (5) 5.4 Fishing & harvesting aquatic resources (6)

6 Human intrusions & disturbance

6.1 Recreational activities 6.2 War, civil unrest & military exercises 6.3 Work & other activities

7 Natural system modifications

7.1 Fire & fire suppression (3) 7.2 Dams & water management/use (11) 7.3 Other ecosystem modifications

8 Invasive & other problematic species, genes & diseases

8.1 Invasive non-native/alien species/diseases (2) 8.2 Problematic native species/diseases (2) 8.3 Introduced genetic material 8.4 Problematic species/diseases of unknown origin (2) 8.5 Viral/prion-induced diseases (2) 8.6 Diseases of unknown cause

9 Pollution

9.1 Domestic & urban waste water (3) 9.2 Industrial & military effluents (3) 9.3 Agricultural & forestry effluents (4) 9.4 Garbage & solid waste 9.5 Air-borne pollutants (4) 9.6 Excess energy (4)

10 Geological events

10.1 Volcanoes 10.2 Earthquakes/tsunamis 10.3 Avalanches/landslides

11 Climate change & severe weather

11.1 Habitat shifting & alteration 11.2 Droughts 11.3 Temperature extremes 11.4 Storms & flooding 11.5 Other impacts

12 Other options 12.1 Other threat

MAPISCo Final report: 3. Development of the method

26

Table 6. Example output of the MAPISCo database with threat classifications added

Species Taxonomic

group

1. R

esid

en

tial

2. A

gric

ult

ure

3.E

ner

gy

4. T

ran

spo

rt

5. R

eso

urc

e u

se

6. D

istu

rban

ce

7. S

yste

m m

od

ific

atio

ns

8. I

nva

sive

sp

ecie

s

9. P

ollu

tio

n

10

. Geo

logi

cal e

ven

ts

11

. Clim

ate

chan

ge

12

. Oth

er

Thre

at s

tatu

s

Hab

itat

Har

vest

ing

Gen

. div

ersi

ty

ES p

rovi

sio

nin

g

Sco

re

Ran

k

Francolinus

camerunensis birds 1 0 0 0 1 0 0 0 1 0 1 0 0.78 0.46 0.33 0.65 5.42 1

Caprimulgus

prigoginei birds 0 0 0 0 0 0 0 0 0 0 1 0 0.78 0.57 0.66 3.88 2

Afropavo

congensis birds 1 0 0 0 1 0 0 1 0 0 1 0 0.67 0.36 0.33 0.66 3.73 3

Craugastor

polymniae amphibians 1 0 0 0 0 1 0 0 0 0 1 0 1 0.50 0.62 3.58 4

Ecnomiohyla

echinata amphibians 1 0 0 0 0 1 0 1 0 0 1 0 1 0.50 0.62 3.58 4

MAPISCo Final report: 4. Results - Example priority lists

27

4. Results - Example priority lists

In this section we present species priority lists generated using the method outlined in section 3.

However, we do so with an important caveat - priority lists generated by the current version of

the method are limited by the data used to calculate species scores.

As described in section 3.2 (page 13), only 12 data sources were deemed suitable for inclusion in

the method. This has resulted in constraints on the database, both in terms of which species

have been able to be included on the lists, and in terms of the numbers of co-benefits on which

species on the lists can be scored. There are also geographical biases, with many more species

being recorded from some regions than others.

If this method is to become fully integrated into conservation policy in the UK, these constraints

must be addressed. The focus of further development should be on sourcing data for taxa and

from regions which are under-represented by the current version of the database, and also on

identifying those species groups within the lists which have small numbers of co-benefit scores

(see Box 3, page 28, for full description of constraints in the database). This topic is further

discussed in both sections 5 (page 50) and 6.4 (page 59).

For these reasons, in this section we have presented three different priority lists.

1. A list for all species included on this list

2. A list containing only bird species (taxonomic case study)

3. A list containing only data from SE Asia (geographic case study)

The use of case studies allows us to focus on discrete sets of data, which, while not eliminating

constraints completely, allows a more meaningful demonstration of how the database can be used.

Capsule.

The results generated by the database in its current format are constrained by data (e.g. only

around 3% of all plant species have been categorised on the IUCN Red List while almost all bird

species are included).

For this reason in this section we present three different sets of results

1. All species

2. Birds only (taxonomic case study)

3. SE Asia only (geographic case study)

These case studies allow us to focus on discrete sets of data, which, while not eliminating

constraints completely, allows a more meaningful demonstration of how the database can be used.

These results are then linked to policy actions.

MAPISCo Final report: 4. Results - Example priority lists

28

The case studies were selected, based on a range of different criteria, outlined below -

Taxonomic case study. Birds were selected for number of reasons: 1) birds are a very

well-studied and understood group of species; 2) unlike for other taxa, there is only one

Red List authority responsible for the prioritisation of all bird species (BirdLife International),

which means the classification of species for the Red List is more uniform and more robust

than for other taxa; 3) for birds, unlike for other taxa, all species are evaluated on the Red

List. This is not the case for other taxonomic groups.

Geographic case study. SE Asia. Given the constraints in data availability between taxa it

is important to note that any region will be biased in its taxonomic coverage. However, we

Box 3. Description of constraints incurred in the “all species” database

The coverage of the database (the number of species on the database as a percentage of all the species

currently described globally) varies greatly between taxa. It is very good for birds (100.64%4), mammals

(100.22%4) and amphibians (94.10%; Table B3-1), but poor for reptiles (38.39%) and fish (37.82%) and poorer

still for plants (6.30%) and “other” species (1.02%; Table B3-1).

Table B3-1 (from RL Stats Table 2 2012 IUCN Red List website)

Taxonomic group Estimated number of

described species

Number of species on the

database

Coverage of the

database (%)

Amphibians 6771 6371 94.10

Birds 10064 10128 100.644

Fish 32400 12252 37.82

Mammals 5501 5513 100.224

Other 1305250 13298 1.02

Plants 307674 19398 6.30

Reptiles 9547 3665 38.39

* note, the coverage for the database is greater than 100% for mammals and birds because some species listed on the database are not

considered full species by all authorities, particularly those species that have been domesticated.

The database is also constrained by the number of co-benefits for which individual species have received

scores (mean 2.29 for birds and 1.25 for plants; Table B3-2). This is significant because missing scores do not

result from a “zero impact”, but from missing data. There are also large differences for the individual co-

benefits, with birds receiving by far the most scores for habitat, and fish for harvesting (Table B3-2).

Taxonomic group

Mean number of

co-benefits scored

(max = 5)

Proportion of species in each taxon scored on each co-benefit (%)

Threat status Habitat Harvesting Genetic Diversity

Ecosystem

Services

Birds 2.29 99.37 48.36 0.32 2.38 78.65

Amphibians 1.98 99.98 7.97 0.09 0 90.03

Mammals 1.77 99.78 3.01 0.33 3.66 69.73

Reptiles 1.63 99.97 0.46 0.08 0.49 61.47

Fish 1.57 84.57 0 26.03 0 46.89

Other 1.57 100.00 0.02 0.44 0 56.64

Plants 1.25 85.66 0.13 0.42 15.83 22.71

MAPISCo Final report: 4. Results - Example priority lists

29

present an example based on one region as an example of the applicability of the method.

We selected SE Asia as the case study region because large numbers of species on the

“all species” database were recorded from this region. There was also good taxonomic

coverage for these species (Figure 2). A further case study using UKOTs was also carried

out (as described in the original contract specification), which is detailed in Appendix 7.

For each set of lists we have outlined the main findings from the method. This is followed, for the

case studies (sections 4.2, page 36 and 4.3, page 44), by a discussion of how some of the key

findings could be related to policy actions. This section has not been included for the “all species”

section (section 4.1, page 30), as we believe there to be too much uncertainty in these results for

them to be related directly to policy actions.

Figure 2. The percentage contribution of each taxon (amphibians, birds, fish, mammals, plants, reptiles

and other) to the MAPISCo database for each region (not including European regions). Regions are ordered

by the number of species on the database they contain (shown in brackets). See Appendix 8, Table A8-3 for

how countries were assigned to regions.

MAPISCo Final report: 4. Results - Example priority lists

30

4.1. Example 1: All species 4.1.1. Summary findings

Combining all data sources, and with all co-benefit weighting set to 1, the final output list consisted

of 70625 species. The top 500 species from the list is shown in Appendix 9, Table A9-1.The top

ten species in the list are Francolinus camerunensis, Caprimulgus prigoginei, Afropavo congensis

(bird species), Craugastor polymniae, Ecnomiohyla echinata, Megastomatohyla mixe, Plectrohyla

calvicollina, Plectrohyla celata, Plectrohyla cyanomma, Plectrohyla sabrina (amphibians) with

priority scores ranging from 5.43 to 3.59 (Table 7). The score resolution1 for the full list is 18.55%

(13255 unique ranks for 70625 species).

4.1.2. Taxonomic composition

The overall list is made up of 14.34% birds, 27.47% plants, 7.81% mammals, 5.19% reptiles, 9.02%

amphibians, 17.35% fish and 18.83% “other” species (Table 7). The highest scoring species for

each taxon are shown in Table 7. This top 500 list consists of 116 (23.2%) amphibians, 242

(48.4%) birds, 36 (7.2%) fish, 79 (15.8%) mammals, 10 (2%) plants, 13 (2.6%) reptiles and 4

(0.8%) other species (Table 8).

4.1.3. Geographic composition

In the overall list 241 countries are represented, and 80 in the top 500. The ten countries for which

the largest percentage of bird species on the list have been recorded are shown in Table 9. In the

overall list, Indonesia has the largest percentage of species on the global list (2.05%) and Brazil

has the largest percentage of species (19.12%) in the top 500 list.

4.1.4. IUCN threat categories and classifications

The threat status of species in the overall and top 500 lists are shown in Table 10. The makeup of

the overall list mirrors the Red List, apart for species that have not been assessed by it (6.72%).

The majority of species on the list are classed as Least Concern (40.01%), followed by Data

Deficient (15.22%), Vulnerable (14.50), Endangered (8.33%) Critically Endangered (5.68%) and

Near Threatened (5.14%). The remaining categories make up less than 6%. In the top 500,

species classed as Critically Endangered contribute the biggest proportion (30.00%), followed by

species classed as Endangered (26.20%), Vulnerable (21.20%), Least Concern and Near

Threatened (10.40% each). Species in the remaining categories make up less than 2%. When the

numbers of species falling in each Red List major threat category were compared with the top 500

list, 3.74% of all the bird species listed as Critically Endangered, 3.17% of species listed as Extinct

in the Wild, 2.23% of species listed Endangered, 1.03% of species listed as Vulnerable and 1.43%

of species listed as Near Threatened were also in the top 500.

1 “Score resolution” refers to the ability of a given output list (priority list) to distinguish between species in terms of priorities: for example, some species receive the same final priority score, which results in the same rank (and therefore equal priority) for all these species. The score resolution is calculated as the number of unique ranks divided by the total number of species on a given list.

MAPISCo Final report: 4. Results - Example priority lists

31

The threat classification which occurred most frequently in both the overall and top 500 lists was

Biological resource use (23.94% and 46.0% of species respectively). This category includes

hunting, fishing and logging activities. Agriculture and aquaculture was the threat category next

most frequently recorded (17.02% and 40.8%), followed by Natural system modifications (10.86%

and 19.4%), Residential and commercial development (10.43% and 16.2%), Pollution (10.41% and

10.2 %) and Invasive species (8.38% and 16.2%).The remaining six classifications made up less

than 7% each (Table 11 and Figure 3). Looking at each species groups individually, the patterns

are remarkably similar (Table 11). The top two threats across all species groups are Biological

resource use and Agriculture and aquaculture. Beyond the top two, there are individual taxa

variations. Birds, for example, are more threatened by Climate change than any other taxa in the

list (9.36% vs. 7.16% and below); fish are more threatened by Pollution than any other taxa (13.49%

vs. 11.81% and below) and amphibians by Residential and commercial developments (13.22% vs.

11.79% and below).

4.1.5. Co-benefits

In the overall list, 56.84% are scored for threat status, 32.34% for ES Provisioning, 4.84% for

Habitat and Area Conservation, 2.92% for Sustainable Harvesting and 3.04% for Genetic Diversity.

In the top 500 list, 33.53% of species are scored for conservation status and ES Provisioning,

24.28% for Habitat and Area Conservation, 2.62% for Sustainable Harvesting and 6.04% for

Genetic Diversity.

MAPISCo Final report: 4. Results - Example priority lists

32

Table 7. Top 20 species listed in the database

Species name Taxonomic Group Thre

at s

tatu

s

Hab

itat

Har

vest

ing

Gen

. div

ersi

ty

ES p

rovi

sio

nin

g

Sco

re

Ran

k

Francolinus camerunensis birds 0.78 0.46 0.33 0.65 5.43 1

Caprimulgus prigoginei birds 0.78 0.57 0.66 3.89 2

Afropavo congensis birds 0.67 0.36 0.33 0.66 3.74 3

Craugastor polymniae amphibians 1.00 0.50 0.62 3.59 4

Ecnomiohyla echinata amphibians 1.00 0.50 0.62 3.59 4

Megastomatohyla mixe amphibians 1.00 0.50 0.62 3.59 4

Plectrohyla calvicollina amphibians 1.00 0.50 0.62 3.59 4

Plectrohyla celata amphibians 1.00 0.50 0.62 3.59 4

Plectrohyla cyanomma amphibians 1.00 0.50 0.62 3.59 4

Plectrohyla sabrina amphibians 1.00 0.50 0.62 3.59 4

Pseudoeurycea saltator amphibians 1.00 0.50 0.62 3.59 4

Pseudoeurycea smithi amphibians 1.00 0.50 0.62 3.59 4

Pseudoeurycea unguidentis amphibians 1.00 0.50 0.62 3.59 4

Thorius aureus amphibians 1.00 0.50 0.62 3.59 4

Thorius smithi amphibians 1.00 0.50 0.62 3.59 4

Habromys chinanteco mammals 1.00 0.50 0.62 3.59 4

Habromys ixtlani mammals 1.00 0.50 0.62 3.59 4

Habromys lepturus mammals 1.00 0.50 0.62 3.59 4

Calyptura cristata birds 1.00 0.24 0.96 3.14 19

Duellmanohyla ignicolor amphibians 0.78 0.50 0.62 2.68 20

Table 8. Number, percentage, highest ranks and scores and highest-ranking species in the complete and top 500 species lists.

Taxonomic

group No. spp. % spp.

No. spp. in

top 500

% spp. In

top 500

Highest

rank

Highest

score Highest scoring species

Amphibians 6371 9.02 116 23.2 4 3.59

Craugastor polymniae, Ecnomiohyla echinata,

Megastomatohyla mixe, Plectrohyla calvicollina, Plectrohyla

celata, Plectrohyla cyanomma, Plectrohyla sabrina,

Pseudoeurycea saltator, P. Smithi, P. Unguidentis, Thorius

aureus, T. smithi

Birds 10128 14.34 242 48.4 1 5.43 Francolinus camerunensis

Fish 12252 17.35 36 7.2 30 2.05 Acipenser sturio

Mammals 5513 7.81 79 15.8 4 3.59 Habromys chinanteco

Other 13298 18.83 4 0.8 149 0.08 Elga newtonsantosi

Plants 19398 27.47 10 2 149 0.08 Devillea flagelliformis

Reptiles 3665 5.19 13 2.6 89 0.69 Ctenosaura oaxacana

MAPISCo Final report: 4. Results - Example priority lists

33

Table 9. The top 10 countries in the overall and top 500 lists and the number and percentage of species which have been recorded as occurring within them.

Overall list Top 500 list

Country rank

Country No. spp.

% spp.

Country No. spp.

% spp.

1 Indonesia 6298 2.05 Brazil 528 19.12

2 Ecuador 6116 1.99 Indonesia 99 3.59

3 India 5340 1.74 Mexico 85 3.08

4 United States 5245 1.71 India 62 2.25

5 China 5059 1.65 Congo, The Democratic Republic of the 55 1.99

6 Brazil 4867 1.59 China 54 1.96

7 Malaysia 4842 1.58 Cameroon 53 1.92

8 Mexico 4782 1.56 Thailand 44 1.59

9 Colombia 4651 1.52 Myanmar 43 1.56

10 Thailand 4522 1.47 Malaysia and Argentina 39 1.41

Table 10. The proportion of species in the overall and top 500 lists and the IUCN threat category in which they are listed. The end column shows the proportion of bird species listed from each Red List category which occur in the top 500 list.

Overall list Top 500

Threat Status No. spp. % spp. No. spp. % spp.

% of all spp. in threat category on red list in top 500

CR 4009 5.68 150 30.00 3.74

DD 10672 15.11 6 1.20 0.06

EN 5882 8.33 131 26.20 2.23

EW 63 0.09 2 0.40 3.17

EX 801 1.13 0 0 0

LC 28258 40.01 52 10.40 0.18

LR/cd 255 0.36 0 0 0

LR/lc 1018 1.44 1 0.20 0.10

LR/nt 1015 1.44 0 0 0

NE 29 0.04 0 0 0

NT 3631 5.14 52 10.40 1.43

VU 10243 14.50 106 21.20 1.03

Not Assessed 4749 6.72 0 0 0

MAPISCo Final report: 4. Results - Example priority lists

34

Table 11. Percentage of species classified as threatened by each of the 12 IUCN threat classification categories, for the overall list and for each species

group individually.

Percentage of species classified as threatened by each of the 12 categories

overall list top 500 mammals plants birds fish reptiles other amphibians

Biological resource use 23.94 46.00 26.21 23.52 22.88 28.71 23.91 23.38 23.71

Agriculture /aquaculture 17.02 40.80 18.84 17.89 18.69 14.23 19.16 14.86 21.33

Natural system modification 10.86 19.40 10.51 12.28 12.60 11.29 9.90 10.73 8.56

Residential/commercial development 10.43 16.20 10.69 11.62 8.03 9.08 11.79 11.07 13.22

Pollution 10.18 10.20 8.90 9.91 7.44 13.49 10.01 11.81 10.03

Invasive species 8.39 16.20 7.87 8.31 8.98 8.12 8.58 9.17 8.55

Climate change 6.25 10.40 4.81 5.36 9.36 5.85 5.66 7.16 5.10

Human intrusions/disturbance 4.21 5.40 4.23 4.20 4.11 3.58 4.08 5.35 4.05

Energy production/mining 3.68 6.60 4.63 3.83 4.34 3.59 3.88 3.52 2.88

Transportation/service corridors 2.27 4.20 2.63 2.37 3.03 1.68 2.38 2.32 1.95

Geological events 0.48 1.20 0.59 0.57 0.51 0.27 0.49 0.51 0.55

Other threats 0.10 0.60 0.11 0.14 0.03 0.13 0.16 0.11 0.07

MAPISCo Final report: 4. Results - Example priority lists

35

Figure 3. Percentage of species classified as threatened by each of the 12 IUCN threat classification

categories a) the overall list; b) the top 500. Full names for each threat category are given in Table 11.

MAPISCo Final report: 4. Results - Example priority lists

36

4.2. Example 2: Taxonomic case study – birds

4.2.1. Summary findings

The bird list contained 10128 species, 14.34% of the overall database (for the top 500 list see

Appendix 9, Table A9-2). The score resolution for the overall list was 61.57% (6326 unique ranks

for species) and for the top 500 list 89% (445 unique ranks). The top five species on the list are all

Galliformes; Cameroon Francolin Francolinus camerunensis (scoring 6.53), Congo Peacock

Afropavo congensis (4.62), Nahan's Francolin Francolinus nahani (3.39), White-breasted

Guineafowl Agelastes meleagrides (2.30) and Swierstra’s Francolin Francolinus swierstra (1.44)

(for top 20 see Table 12).

4.2.2. Orders and Families

The overall list consisted of birds from 25 orders. Passeriformes were the most represented in the

list at 57.73%, followed by Apodiformes (4.96%), Piciformes (4.63%), Psittaciformes (4.13%), and

Galliformes (3.49%) (Table 13).

The top 500 list consisted of species from 19 orders. Of these, two made up almost three-quarters

of the species listed – Galliformes (44.8%) and Passeriformes (33.6%), (Table 13). Sixty-eight

individual families made up the top 500, with Phasianidae, making up the largest percentage

(36.2%). The highest ranking species in each order is also shown in Table 13.

4.2.3. Country/region

Two hundred and thirty four countries are represented in the overall list, and 80 in the top 500. The

ten countries for which the largest percentage of bird species on the list have been recorded are

shown in Table 14. In the overall list, Colombia has the largest percentage of species on the global

list (1.71%) and Brazil has the largest percentage of species (29.40%) in the top 500 list. The

number and proportion of species recorded in each country, for both the overall and top 500 lists,

is detailed in Appendix 9, Table A9-3.

4.2.4. IUCN Red List threat categories and classifications

The threat status of species in the overall and top 500 lists is shown in Table 15. The makeup of

the overall list mirrors the Red List, apart for species that have not been assessed by it. The

majority of species are classed as Least Concern (75.8%), followed by Near Threatened (8.69%),

Vulnerable (7.18%), Endangered (3.84%) and Critically Endangered (1.95%). The remaining

categories make up less than 3%. In the top 500, again species classed as Least Concern

contribute the biggest proportion (36.6%), followed by species classed as Vulnerable (20.2%),

Near Threatened (19.2%), Endangered (11.8%) and Critically Endangered (10.8%) followed by

species in the remaining categories making up less than 2%. When the numbers in each category

on the Red List were compared with the top 500 list, 27.41% of all the bird species listed as

Critically Endangered by the Red List were in that top 500, 25% of species listed as Extinct in the

Wild, 15.17% of species listed Endangered, 13.89% of species listed as Vulnerable and 10.91% of

species listed as Near Threatened (Table 15).

MAPISCo Final report: 4. Results - Example priority lists

37

In the overall list the threat classification against which the majority of species are listed is

Biological resource use (22.33%). Agriculture and aquaculture (21.56%), Natural system

modifications (14.28%), Climate change (12.42%), Invasive species (9.45%) and

Residential/commercial development (4.79%) make up the next largest proportions. The remaining

6 categories make up the remaining 15% (see Table 16 for full list). For the top 500 list, the threat

classification against which the majority of species are listed is also Biological resource use

(23.90%) followed by Agriculture and aquaculture (22.54%), Natural system modifications

(12.10%), Invasive species (9.53), Climate change (9.23%), Energy production and mining (5.59%),

Residential and commercial development (4.99%) and Transportation and service corridors

(4.54%). The remaining four categories made up less than 8% of the threats listed Table 16).

4.2.5. Co-benefits

In the overall list, 43.29% are scored for Threat Status, 33.67% for ES Provisioning, 20.62% for

Habitat and Area Conservation, 0.68% for Sustainable Harvesting and 1.73% for Genetic Diversity.

In the top 500 list, 31.83% of species are scored for Conservation Status and ES Provisioning,

29.79% for Habitat and Area Conservation, 0.57% for Sustainable Harvesting and 5.98% for

Genetic Diversity.

4.2.6. Key findings and how they relate to policy

Species from the order Galliformes made up the majority of the top 500 bird species on the priority

list. This is not surprising considering that the majority of Galliformes are scored on three co-

benefits more (mean number per species = 3.10). As one of the most threatened group of birds,

with over 25% of species in the group being classified as Vulnerable, Endangered or Critically

Endangered, Galliformes score highly on Threat Status. Over two-thirds of the Galliformes in the

top 500 have a score for Genetic Diversity; this reflects their close genetic relationship to the

domesticated chicken, guineafowl, pheasant and quail. Over two-thirds have a score for

Ecosystem Services and over half for Habitat, which reflects forest being the predominant habitat

of Galliformes on the top 500 list. These characteristics are scored highly by the current co-benefit

scoring system.

In order to maximise conservation benefit for Galliformes species (if we accept the inherent

constraints in the MAPISCo prioritisation) policy-makers could target resources to countries with a

high number of Galliformes species from the top 500 list. These countries (shown in Figure 4)

include Indonesia, Brazil, The Democratic Republic of the Congo and Malaysia.

MAPISCo Final report: 4. Results - Example priority lists

38

Table 12. The top 20 highest scoring species, all in the order Galliformes, the country(ies) in which they have been recorded, scores each of the five co-benefits as well as resultant priority score and rank. Note that all values have been rounded to three decimal points. All co-benefit weighting factors set to 1 (default).

Rank Species name English name Country

Thre

at

stat

us

Hab

itat

Har

vest

i

ng

Gen

.

div

ersi

ty

ES

pro

visi

o

nin

g

Sco

re

1 Francolinus camerunensis Cameroon

Francolin Cameroon 0.778 0.462 0 0.333 0.649 6.539

2 Afropavo congensis Congo Peacock Democratic Republic of Congo 0.667 0.361 0 0.333 0.659 4.625

3 Francolinus nahani Nahan's Francolin Uganda & Democratic Republic of Congo 0.778 0.245 0 0.333 0.640 3.389

4 Agelastes meleagrides White-breasted

Guineafowl Cote d' Ivoire, Ghana, Liberia & Sierra Leone 0.667 0.230 0 0.333 0.606 2.301

5 Francolinus swierstrai Swierstra's

Francolin Angola 0.778 0.116 0 0.333 0.626 1.436

6 Guttera plumifera Plumed

Guineafowl

Angola, Cameroon, Central African Republic, Congo, Democratic Republic of Congo,

Equatorial Guinea & Gabon 0.222 0.340 0 0.333 0.584 1.400

7 Agelastes niger Black Guineafowl Angola, Cameroon, Central African Republic, Congo, Democratic Republic Congo, Equatorial

Guinea, Gabon, Nigeria 0.222 0.330 0 0.333 0.597 1.380

8 Tragopan satyra Crimson Horned-

pheasant Bhutan, China, India, Nepal 0.556 0.231 0 0.333 0.561 1.347

9 Francolinus ochropectus Djibouti Francolin Djibouti 1 0.026 0 0.333 0.619 1.233

10 Lophura edwardsi Edwards's

Pheasant Vietnam 1 0.007 0 0.333 0.595 0.754

11 Odontophorus capueira Spot-winged

Wood-quail Argentina, Brazil, Paraguay 0.222 0.151 0 0.333 0.795 0.642

12 Lophura hoogerwerfi Aceh Pheasant Indonesia 0.667 0.016 0 0.333 0.747 0.564

MAPISCo Final report: 4. Results - Example priority lists

39

13 Polyplectron schleiermacheri Bornean Peacock-

pheasant Indonesia, Malaysia 0.778 0.013 0 0.333 0.684 0.514

14 Lophura inornata Salvadori's

Pheasant Indonesia 0.667 0.012 0 0.333 0.747 0.501

15 Arborophila orientalis Grey-breasted

Partridge Indonesia 0.667 0.007 0 0.333 0.747 0.434

16 Odontophorus melanonotus Dark-backed

Wood-quail Colombia, Ecuador 0.667 0.098 0 0.333 0.603 0.411

17 Francolinus lathami Forest Francolin

Angola, Cameroon, Central African Republic, Congo, The Democratic Republic of Congo, Cote

d' Ivoire, Equatorial Guinea, Gabon, Ghana, Guinea, Liberia, Nigeria, Sierra Leone, Sudan,

Tanzania, Togo, Uganda

0.222 0.249 0 0.333 0.608 0.329

18 Francolinus nobilis Handsome

Francolin Burundi, Democratic Republic of Congo, Rwanda, Uganda 0.222 0.225 0 0.333 0.629 0.177

19 Cyrtonyx ocellatus Ocellated Quail El Salvador, Guatemala, Honduras, Mexico, Nicaragua 0.667 0.069 0 0.333 0.617 0.124

20 Odontophorus dialeucos Tacarcuna Wood-

quail Colombia, Panama 0.667 0.079 0 0.333 0.590 0.027

MAPISCo Final report: 4. Results - Example priority lists

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Table 13. Proportion of species in the overall and top 500 species lists by Order, along with the highest rank and score per order and highest scoring species.

Order Overall list Top 500 Highest rank Highest score Highest scoring species Red List status

No. spp. % spp. No. spp. % spp.

PASSERIFORMES 4763 57.73 168 33.60 207 -8.230 Calyptura cristata CR

APODIFORMES 409 4.96 11 2.20 242 -8.960 Schoutedenapus schoutedeni VU

PICIFORMES 382 4.63 8 1.60 306 -11.920 Indicator pumilio NT

PSITTACIFORMES 341 4.13 30 6.00 249 -10.210 Touit melanonotus EN

GALLIFORMES 288 3.49 224 44.80 1 6.530 Francolinus camerunensis EN

FALCONIFORMES 275 3.33 5 1.00 261 -10.923 Leptodon forbesi CR

COLUMBIFORMES 270 3.27 3 0.60 415 -12.990 Columba albinucha NT

CHARADRIIFORMES 214 2.59 1 0.20 498 -13.560 Charadrius thoracicus VU

CORACIIFORMES 207 2.51 3 0.60 447 -13.220 Bycanistes cylindricus VU

STRIGIFORMES 181 2.19 10 2.00 245 -9.660 Phodilus prigoginei EN

GRUIFORMES 177 2.15 3 0.60 295 -11.740 Psophia viridis EN

ANSERIFORMES 159 1.93 18 3.60 21 0.001 Cairina scutulata EN

CUCULIFORMES 152 1.84 2 0.40 422 -13.060 Neomorphus squamiger VU

CICONIIFORMES 111 1.35 1 0.20 450 -13.212 Bostrychia bocagei CR

CAPRIMULGIFORMES 100 1.21 2 0.40 201 -7.435 Caprimulgus prigoginei EN

PROCELLARIIFORMES 55 0.67 3 0.60 340 -12.374 Pterodroma magentae CR

TROGONIFORMES 44 0.53 0 0.00 587 -14.253 Apaloderma aequatoriale LC

PELECANIFORMES 35 0.42 1 0.20 458 -13.300 Fregata andrewsi CR

TINAMIFORMES 32 0.39 1 0.20 401 -12.895 Crypturellus noctivagus NT

PODICIPEDIFORMES 22 0.27 0 0.00 821 -14.956 Podiceps taczanowskii CR

STRUTHIONIFORMES 12 0.15 6 1.20 29 -0.238 Casuarius casuarius VU

SPHENISCIFORMES 10 0.12 0 0.00 1548 -16.062 Eudyptes robustus VU

PHOENICOPTERIFORMES 5 0.06 0 0.00 2663 -17.468 Phoeniconaias minor NT

GAVIIFORMES 5 0.06 0 0.00 3509 -17.928 Gavia adamsii NT

COLIIFORMES 2 0.02 0 0.00 3105 -17.689 Colius castanotus LC

MAPISCo Final report: 4. Results - Example priority lists

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Table 14. The top 10 countries in the overall and top 500 lists and the number and percentage of species that have been recorded as occurring within them.

Overall list Top 500 list

Country

rank Country No. spp. % spp. Country No. spp. % spp.

1 Colombia 1835 1.71 Brazil 147 29.40

2 Peru 1814 1.69 DR Congo 27 5.40

3 Brazil 1766 1.65 Cameroon 18 3.60

4 Ecuador 1647 1.54 Indonesia 15 3.00

5 Indonesia 1600 1.49 India, Uganda 11 2.20

6 China 1269 1.19 Argentina, Liberia, Malaysia, Nigeria

10 2.00

7 India 1225 1.14 Colombia, Cote d' Ivoire, Ghana, Paraguay

9 1.80

8 DR Congo 1129 1.05 Gabon, Myanmar, Sierra Leone

8 1.60

9 Mexico 1103 1.03

Central African Republic, China, Congo, Equatorial Guinea

7 1.40

10 Kenya 1098 1.03 Angola, Guinea, Nepal, Thailand, Viet Nam

6 1.20

MAPISCo Final report: 4. Results - Example priority lists

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Table 15. The proportion of species in the overall and top 500 lists and the IUCN threat category in which

they are listed. The end column shows the proportion of bird species listed from each Red List category that

occur in the top 500 list.

Overall list Top 500

Threat Status No. spp. % spp. No. spp. % spp.

% of all spp. in threat category on red list in top 500

CR 197 1.95 54 10.8 27.41 DD 60 0.59 4 0.8 6.67 EN 389 3.84 59 11.8 15.17 EW 4 0.04 1 0.2 25.00 EX 130 1.28 1 0.2 0.77 LC 7677 75.8 183 36.6 2.38 NT 880 8.69 96 19.2 10.91 VU 727 7.18 101 20.2 13.89 not classified 64 0.63 1 0.2 1.56

Table 16. Red List threat classifications of the species in the overall and top 500 lists.

Overall list Top 500

Threats No. spp. % spp. No. spp. % spp.

Biological Resource use 1847 22.33 158 23.90

Transportation and service corridors 296 3.58 30 4.54

Pollution 294 3.55 26 3.93

Other threats 0 0 0 0

Natural system modifications 1181 14.28 80 12.10

Invasive species 782 9.45 63 9.53

Human intrusions and disturbance 281 3.40 21 3.177

Residential and commercial

development 396 4.79 33 4.99

Geological events 35 0.42 3 0.45

Energy production and mining 349 4.22 37 5.59

Agriculture and aquaculture 1783 21.56 149 22.54

Climate Change 1027 12.42 61 9.23

MAPISCo Final report: 4. Results - Example priority lists

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Figure 4. The global distribution of Galliformes species in the top 500 birds list. The categories relate to the

number of Galliformes species (in the top 500) that are found in each country. Countries coloured white

have no Galliformes listed in the top 500. (The map was produced using the package rworldmap South, A.

(2011) rworldmap: A New R package for Mapping Global Data. The R Journal Vol. 3/1: 35-43.)

category

1

2

3

4

5

6

7

8

Galliformes

MAPISCo Final report: 4. Results - Example priority lists

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4.3. Example 3: Geographic case study – SE Asia 4.3.1. General findings

The final output list for SE Asia contains 12496 species, which is 17.7% of the overall database.

The score resolution for the full list is 23.66% (2956 unique ranks for 12496 species) and for the

top 500 is 49.20% (246 unique ranks). The five highest priority species are the Togian Islands

Babirus Babyrousa togeanensis (0.782), Anoa Bubalus depressicornis (0.782), Mountain Anoa B.

quarlesi (0.782), Javan pig Sus verrucosus (0.782) and Aceh pheasant Lophura hoogerwerfi

(0.503) (see Table 17 for the top 20 species).

4.3.2. Taxonomic composition

The overall list consists of 20.42% “other” species (2552 spp.), 20.27% plants (2533 spp.), 20.09%

birds (2510 spp.), 18.17% fish (2271 spp.), 8.86% mammals (1107 spp.), 6.24% reptiles (780 spp.)

and 5.95% amphibians (743 spp.) (Table 18). The top 500 list has a rather different composition,

with birds and mammals contributing the largest proportions (36.8 %, 184 spp. and 35.0%, 175 spp.

respectively), followed by plants (10.4%, 52 spp.), “other” species (6.8%, 34 spp.), amphibians

(4.8%, 24 spp.), fish (4.2%, 21 spp.) and reptiles (2.0%, 10 spp.). The highest scoring species in

each taxon are shown in Table 18. The highest scoring mammals, birds and fish are all in the top

20, while the highest scoring amphibian Duttaphrynus sumatranus is ranked at 86, “other” species

Protosticta gracilis at 107, plant Taxus wallichiana at 44 and reptile Emoia ruficauda 193.

4.3.3. Geographic composition

The species in the overall and top 500 lists have been recorded in 11 countries - Brunei, Cambodia,

Indonesia, Lao, Malaysia, Myanmar, Philippines, Singapore, Thailand, East Timor and Viet Nam.

In the overall list, the greatest proportion of species have been recorded in Indonesia (18.28%)

followed by Malaysia (14.05%), Thailand (13.13%), Viet Nam (11.42%), Myanmar (10.15%),

Philippines (9.72%), Cambodia (6.40%), PDR Lao (6.39%), Singapore (5.64%), Brunei (3.55%)

and East Timor (1.27%) (Table 19). In the top 500 list the country in which the largest proportion of

species have been recorded is Indonesia (32.41%), followed by Malaysia (11.18%), Myanmar

(10.15%), Thailand and Viet Nam (both 9.44%), Philippines (7.28%), Cambodia (6.46%),

Myanmar (6.83%), Lao PDR (6.36%), Brunei (2.77%), Singapore (2.67%) and East Timor (1.85%)

(Table 19). The top ranked species in each country are listed in Table 20.

4.3.4. IUCN Red List threat categories and classifications

The threat status of the overall and top 500 lists is shown in Table 21. The majority of species in

the overall list are classed as Least Concern (47.7%), followed by Data Deficient (18.55%),

Vulnerable (13.43%), Near Threatened (6.55%), Endangered (4.64%) and Critically Endangered

(4.03%). The remaining categories make up less than 6%. In the top 500 list, species classed as

Vulnerable made up the largest proportion (29%), followed by Critically Endangered (27%),

Endangered (22%), Least Concern (14.2%), Near Threatened (6.6%), and Data Deficient (1.2%).

No species in the top 500 were classed in the any of the remaining categories. In the overall list,

the threat classification against which the majority of species are listed is Biological resource use

(29.90%) followed by Agriculture and aquaculture (13.25%), Pollution (11.95%),

Residential/commercial development (11.82%), Natural system modifications (8.46%), Climate

change (7.34%), Human intrusions/disturbance (6.55%), Invasive species (6.18%), Energy

MAPISCo Final report: 4. Results - Example priority lists

45

production and mining (2.74%). The remaining four categories make up less than 4% (see Table

22 for full list). For the top 500 list, the threat classification against which the majority of species

were listed is also Biological resource use (25.6%), followed by Agriculture and aquaculture

(20.79%), Natural system modifications (11.60%), Residential and commercial development and

invasive species (both 8.35%), Climate change (7.64%), Pollution (5.20%), Energy production and

mining (4.95%), Human intrusions and disturbance (3.68%), Transportation and service corridors

(3.11%) and Geological events (0.71%) (Table 22).

4.3.5. Co-benefits

In the overall list, 100% are scored for Threat Status, 66.12% for ES Provisioning, 7.18% for

Habitat and Area Conservation, 5.27% for Sustainable Harvesting and 1.58% for Genetic Diversity.

In the top 500 list, 100% of species are scored for Conservation Status, 99.8% for ES Provisioning,

28.6% for Habitat and Area Conservation, 8.8% for Sustainable Harvesting and 13.6% for Genetic

Diversity.

4.3.6. Key findings and how they relate to policy

In the top 500 list, the country in which the largest proportions of species have been recorded is

Indonesia (32.41%). These 316 species (138 mammals, 112 birds, 23 plants, 20 amphibians, 11

fish, 9 “other” species and 3 reptiles) share similar threats (37.34% of these species are threatened

by Biological resource use and 29.43% by Agriculture and aquaculture). For the highest ranked 10

species in the top 500 list (all from Indonesia), hunting and/or habitat destruction are the major

threats listed by the IUCN Red List. Conservation actions that reduce habitat destruction and target

unsustainable hunting of species in Indonesia should therefore benefit these priority species.

Several species on the list, e.g. Javan warty pig Sus verrucosus and Sulawesi babirusa Babyrousa

celebensis are illegally hunted in protected areas and therefore better community engagement and

conservation law enforcement would benefit these species.

MAPISCo Final report: 4. Results - Example priority lists

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Table 17. The top 20 highest scoring species, the order to which they belong, the country(ies) in which they

have been recorded, scores each of the five co-benefits as well as resultant priority score and rank. Note

that all values have been rounded to three decimal points. All co-benefit weighting factors set to 1 (default).

Rank Species English name Taxon Country

Thre

at s

tatu

s

Hab

itat

Har

vest

ing

Gen

. div

ersi

ty

ES

pro

visi

on

ing

Sco

re

1 Babyrousa

togeanensis

Togian Islands

Babirusa mammals Indonesia 0.778 0.333 0.747 0.782

1 Bubalus

depressicornis Anoa mammals Indonesia 0.778 0.333 0.747 0.782

1 Bubalus quarlesi Mountain Anoa mammals Indonesia 0.778 0.333 0.747 0.782

1 Sus verrucosus Javan Pig mammals Indonesia 0.778 0.333 0.747 0.782

5 Lophura

hoogerwerfi Aceh Pheasant Birds Indonesia 0.667 0.016 0.333 0.747 0.503

6 Lophura inornata Salvadori's Pheasant Birds Indonesia 0.667 0.012 0.333 0.747 0.441

7 Arborophila

orientalis

Grey-breasted

Partridge Birds Indonesia 0.667 0.007 0.333 0.747 0.374

8 Babyrousa

babyrussa Babiroussa mammals Indonesia 0.667 0.333 0.747 0.275

8 Babyrousa

celebensis Sulawesi Babirusa mammals Indonesia 0.667 0.333 0.747 0.275

8 Callosciurus

melanogaster Mentawai Squirrel mammals Indonesia 0.667 0.333 0.747 0.275

11 Polyplectron

schleiermacheri

Bornean Peacock-

pheasant Birds Indonesia, Malaysia 0.778 0.013 0.333 0.684 0.251

12 Bubalus

mindorensis

Mindoro Dwarf

Buffalo mammals Philippines 1.000 0.333 0.608 0.208

12 Sus cebifrons Visayan Warty Pig mammals Philippines 1.000 0.333 0.608 0.208

14 Lophura edwardsi Edwards's Pheasant Birds Viet Nam 1.000 0.007 0.333 0.595 0.172

15 Bos sauveli Grey Ox mammals Cambodia, Lao PDR,

Thailand, Viet Nam 1.000 0.333 0.597 0.082

16 Epinephelus

coioides Estuary Cod Fish

Brunei Darussalam,

Cambodia, Indonesia,

Malaysia, Myanmar,

Philippines, Singapore,

Thailand, Viet Nam

0.556 0.611 0.614 -0.119

17 Sus celebensis Celebes Pig mammals Indonesia 0.556 0.333 0.747 -0.233

18 Cairina scutulata White-winged Duck Birds

Cambodia, Indonesia,

Lao PDR, Malaysia,

Myanmar, Thailand,

Viet Nam

0.778 0.017 0.333 0.622 -0.407

19 Melanoperdix niger Black Partridge Birds

Brunei Darussalam,

Indonesia, Malaysia,

Singapore

0.667 0.020 0.333 0.660 -0.441

20 Lophura

erythrophthalma Crestless Fireback Birds

Brunei Darussalam,

Indonesia, Malaysia,

Singapore

0.667 0.019 0.333 0.660 -0.448

MAPISCo Final report: 4. Results - Example priority lists

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Table 18. Proportion of species in the overall and top 500 species lists by taxonomic group, along with the highest rank and score per group, highest scoring

species and the country in which it is found.

Taxonomic group

Overall list Top 500

Highest rank Highest score Highest scoring species Country (ies)

No. spp. % spp.

No.

spp. % spp.

Amphibians 743 5.95 24 4.8 86 -2.056 Duttaphrynus sumatranus

Sumartrian toad Indonesia

Birds 2510 20.09 184 36.8 5 0.503 Lophura hoogerwerfi Aceh

pheasant Indonesia

Fish 2271 18.17 21 4.2 16 -0.119 Epinephelus coioides

Estuary Cod

Brunei Darussalam; Cambodia;

Indonesia; Malaysia; Myanmar;

Philippines; Singapore; Thailand;

Viet Nam

Mammals 1107 8.86 175 35 1 0.782 Babyrousa togeanensis

Togian Islands Babirusa Indonesia

Other 2552 20.42 34 6.8 107 -2.375 Protosticta gracilis

Arthropod Indonesia

Plants 2533 20.27 52 10.4 44 -0.842 Taxus wallichiana

Himalayan Yew

Indonesia; Myanmar;

Philippines; Viet Nam

Reptiles 780 6.24 10 2 193 -3.334 Emoia ruficauda Red-tailed

Swamp Skink Philippines

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Table 19. The 11 countries which feature in the overall and top 500 lists and the number and percentage of species which have been recorded as occurring within them.

Overall list Top 500 list

Country

rank Country No. spp. % spp. Country No. spp. % spp.

1 Indonesia 6298 18.28 Indonesia 316 32.41

2 Malaysia 4842 14.05 Malaysia 109 11.18

3 Thailand 4522 13.13 Myanmar 99 10.15

4 Viet Nam 3934 11.42 Thailand 92 9.44

5 Myanmar 3498 10.15 Viet Nam 92 9.44

6 Philippines 3347 9.72 Philippines 71 7.28

7 Cambodia 2205 6.40 Cambodia 63 6.46

8 Lao PDR 2202 6.39 Lao PDR 62 6.36

9 Singapore 1944 5.64 Brunei 27 2.77

10 Brunei 1223 3.55 Singapore 26 2.67

11 East Timor 436 1.27 East Timor 18 1.85

Table 20. The top ranking species in each country.

Country

Highest Priority

score Rank Species

Brunei -0.119 16 Epinephelus coioides, Estuary Cod

Cambodia 0.081 15 Bos sauveli, Grey Ox

Indonesia 0.782 1 Babyrousa togeanensis, Togian

Islands Babirusa

People's Democratic Republic of Lao 0.081 15 Bos sauveli, Grey Ox

Malaysia 0.251 11 Polyplectron schleiermacheri,

Bornean Peacock-pheasant

Myanmar 0.356 28 Epinephelus coioides, Estuary Cod

Philippines 0.207 12 Bubalus mindorensis, Mindoro

Dwarf Buffalo

MAPISCo Final report: 4. Results - Example priority lists

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Table 21. The proportion of species in the overall and top 500 lists and the IUCN threat category in which

they are listed.

Table 22. Red List threat classifications of the species in the overall and top 500 lists.

Overall list Top 500

Threat Category No. spp. % spp. No. spp. % spp.

CR 503 4.03 135 27.00

DD 2318 18.55 6 1.20

EN 580 4.64 110 22.00

EW 2 0.02 0 0.00

EX 7 0.06 0 0.00

LC 5960 47.70 71 14.20

NT 819 6.55 33 6.60

VU 1678 13.43 145 29.00

not evaluated 122 0.98 0 0.00

LR/cd 122 0.98 0 0.00

LR/lc 378 3.02 0 0.00

LR/nt 129 1.03 0 0.00

Overall list Top 500

Threats No. spp. % spp. No. spp. % spp.

Biological Resource use 4371 29.90 181 25.60

Transportation and service corridors 224 1.53 22 3.11

Pollution 1747 11.95 37 5.23

Other threats 6 0.04 0 0.00

Natural system modifications 1236 8.46 82 11.60

Invasive species 903 6.18 59 8.35

Human intrusions and disturbance 957 6.55 26 3.68

Residential/commercial development 1728 11.82 59 8.35

Geological events 34 0.23 5 0.71

Energy production and mining 401 2.74 35 4.95

Agriculture and aquaculture 1937 13.25 147 20.79

Climate Change 1073 7.34 54 7.64

MAPISCo Final report: 5. Using the method

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5. Using the method

5.1. Expandable –How does the does the priority list respond to the inclusion of additional co-benefit data? A plant example.

The method developed in this project has the capacity for additional datasets (either taxonomic or

co-benefit) to be included within it should they become available. We see this expansion as a

critical element of the method because at present, the number of datasets that contribute to the

overall priority scores is relatively small (12 data sources). As discussed in section 4 (page 27),

this has resulted in taxonomic and geographic constraints on the lists, which must be considered

before this method can become fully integrated in conservation policy. One way to address these

constraints is to add new co-benefit data to the database. At present, we believe we have included

all currently available, verified data, by focussing on either the transcribing of existing datasets into

a format usable by the method, or on the collection of new data, additional data would make a

huge improvement to the database. As described in section 4 (page 27) this should focus on the

taxa that are underrepresented in the current database - plants for example, are vastly

underrepresented in the database in its current form – just 6.3% of all plants species currently

described worldwide are on the priority list. This is not the case for other taxa, birds, the coverage

for mammals and amphibians all approaching 100% (see Box 3 Table B3-1; page 28).

For this reason, we have chosen to investigate the effect additional plant data will have on the

composition of priority lists

Method: To assess the effect of inclusion of further co-benefit data, we asked the IUCN SSC Palm

Specialist Group (Bill Baker, Kew Gardens & IUCN-SSC Palm Specialist Group, pers. comm.) to

use its specialist knowledge to score a selection of palm species based on their contribution to one

of the five co-benefits – Harvesting. The group selected 64 species for inclusion in this assessment,

based on the flagship species for palm conservation. However, only 52 of these species were

already on the overall list. As this exercise was to address the addition of data to the list, we

concentrated on these 52 species. The group were asked to score species on a scale of 0 to 2

where 0 is a species of zero value to harvesting and 2 is a species of the maximum value to

harvesting. These values were then rescaled to fit with the original harvesting data. New co-benefit

scores for harvesting were then calculated using these rescaled data set scores, following the

procedure outlined in section 3 (page 12). Final priority scores for the full list were then calculated.

Capsule.

Expandable. We demonstrate how additional species or co-benefit data can be added to the database,

and outline how such changes impact on the ranking of priority lists.

Adaptable. We examine the effect changing individual co-benefit weightings (i.e. making certain co-

benefits “more important” in the calculation of priority lists than others) has on priority list ranking.

Usable. Here we outline the development of a web-based interface, which, using a variety of tabs and

graphics, allows users to fully explore the priority lists created by the methodology under a number of

different scenarios. We view this as a critical feature of the GUI, as it makes it adaptable to policy

aspirations.

MAPISCo Final report: 5. Using the method

51

Results: For the 52 palm species previously included on the priority list, inclusion of the new

harvesting co-benefit scores results in harvesting co-benefit scores being increased (mean value

from 0.111 to 0.414). The mean final priority score for these species in the overall list also

increased, from -7.043 to -1.224. This had a significant change in the overall species ranking within

the list. Firstly, two palm species now ranked equal first at the top of the overall list - Carpoxylon

macrospermum and Ceroxylon sasaimae (both previously ranked 37816th). Secondly, inclusion of

this new data set increased the representation of plants in the top 500 by 4% (from 10 to 30

species), largely at the expense of fish (-2.4%) and amphibians (-1.8%).

Discussion: These results show clearly that the expansion of the database through the addition of

new information will have considerable effects on species priority lists.

5.2. Adaptable - How does the priority list respond to changes in co-benefit weightings? The facility to change the relative weight given to of each co-benefit in is built into the database.

This allows the priority lists to be adapted to explore policy scenarios. If, for example, a

policy wished to prioritise species based on their contribution to ecosystem services, the weighting

this co-benefit was given in the methodology could be increased in relation to the other co-benefits.

This would then give an ecosystem service-centric list. We illustrate this adaptability using a

sensitivity analysis and by carrying out a worked example.

5.2.1. Sensitivity analysis

The species represented in the top 500 list changes as the co-benefit weightings are varied. We

can illustrate the strength of this effect across the five co-benefits by decreasing the weight of one

co-benefit by 0.1 intervals from 1 to 0 whilst keeping all others constant (at 1). This gives a total of

51 combinations (10 decreases in weighting by 0.1, for each of the 5 co-benefits, in addition to all

co-benefit weights set to 1).

Decreasing the weighting of each co-benefit (relative to all others held at a constant weight of 1)

from 1 to 0.1 results in a similar pattern of absolute change in rank for each co-benefit (see Figure

5 below). The greatest absolute change in species in the top 500 list occurs when Ecosystem

services is reduced to a weight of 0.1 (the mean change in rank is 1588.01). In the 51

combinations of weightings tested, a total of 1064 different species occur in the top 500, with some

species occurring in many or all iterations (up to 51 times). The distribution of species occurrence

in the top 500 is distinctly bimodal: 504 (41.6%) species occurred in the top 500 in more than 30

iterations and 602 (49.4%) occurred fewer than 10 times. This suggests a surprising degree of

stability of species representation in the top of the list irrespective of modest levels of variations in

weighting. This most likely reflects the non-independence of the co-benefits outlined above.

Appendix 9, Table A9-4 shows the list of species occurring more than 30 times in the top 500.

5.2.2. Worked examples – threat status and ecosystem services

If policy-makers wish to prioritise threat status above all other co-benefits then they could adjust

the weight applied to it allowing it to have a greater influence on the final priority score. By setting

the weight of Threat Status to 1.0 and all other co-benefits to 0.5, this changes the priority list in

the following ways;

MAPISCo Final report: 5. Using the method

52

1. The percentage of Critically Endangered species in the top 500 increases by 24.2%

compared to when all co-benefits are equally weighted (1.0). The percentage of Vulnerable,

Near Threatened or Least Concern species decreases, but the number of Endangered

species remains constant (see Table 23).

2. The taxonomic focus of the top 500 does not change extensively, with birds continuing to

contribute the largest proportion of species, followed by amphibians, mammals and fish

(Table 23)

If policy-makers wish to prioritise Ecosystem service provision and adjust the weight applied to

it as described above, this changes the priority list in the following ways;

1. The percentage of Critically Endangered species in the top 500 decreases by 14.2%,

compared to when all co-benefits are equally weighted (1.0). The percentage of

Endangered and Vulnerable species also decreases, while the number of Near Threatened

and Data Deficient species increase. The number of Least Concern species remains

relatively constant (see Table 24).

2. The taxonomic focus of the top 500 list shifts from birds (48.4 % of species when all co-

benefits are weighted equally) to amphibians (42.4 %) (see Table 24).

MAPISCo Final report: 5. Using the method

53

Figure 5. Mean absolute change in the rank of species in the top 500, following decreases in co-benefit weightings.

Table 23. The proportion of species in the top 500 lists and the IUCN threat category in which they are

listed when Threat Status or Ecosystem Services are given priority over other co-benefits (CBs).

IUCN Red list Threat

Status categories

Threat status weighted 1 (all other

CBs weighted 0.5)

Ecosystem Services weighted 1 (all

other CBs weighted 0.5)

All CBs

weighted 1

CR 54.2 % 15.8% 30%

EN 26.6% 14.2% 26.2%

VU 12.6% 20.6% 21.2%

NT 5.6% 14.6% 10.4%

EW 0.4% 0.2% 0.4%

LC 0.4% 9% 10.4%

DD 0.2% 25.6% 1.2%

0

500

1000

1500

-0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 -0.9

Change in weight (from 1)

Me

an

ab

so

lute

ch

an

ge

in

ra

nk

Status

Hab

Har

GenD

ES

MAPISCo Final report: 5. Using the method

54

Table 24. The proportion of species in the top 500 lists in each taxonomic group when Threat status or

Ecosystem services are given priority over other co-benefits (CBs).

Taxonomic

group

Threat status weighted 1 (all

other CBs weighted 0.5)

Ecosystem Services weighted 1 (all

other CBs weighted 0.5)

All CBs

weighted 1

amphibians 29.8 42.4 23.2

birds 40.6 35.2 48.4

fish 6.4 0.6 7.2

mammals 17.2 14 16

other 1.6 1.6 0.8

plants 2.2 4.6 1.8

reptiles 2.2 1.6 2.6

5.3. Usable - Development of Graphic User Interface (GUI)

A GUI is defined as “a type of user interface that allows users to interact with electronic devices

using images rather than text commands”. In the context of the MAPISCo database, we see a

GUI functioning as a way of enabling non-technical users to explore the data within it without

having to individually alter each component manually. We envisaged the GUI using a

combination of lists, graphs and maps to display the data in an easily interpreted form, allowing

users to investigate questions such as: What are the highest priority species? Where in the world

does these species occur? What effect does altering particular co-benefit scores have on the

overall ranking of the lists? Where does a particular species fall in the ranking?

5.3.1. GUI Development

The GUI was developed using the same open-source statistical environment as the original

database - R. This allowed user interface and analysis routines to be integrated easily. R also has

rich graphical routines, a rapid development time, good transparency of method, and the potential

for modification by other team members. R is freely available for all common operating systems,

relatively easy to install, used in universities worldwide and increasingly by major commercial

organisations such as Google and the New York Times. Using the new R Package ‘Shiny’,

released in November 2012 for user interface development allows user interfaces to be run locally

as well as on a web server. In the latter case users do not need to have R installed.

An initial prototype user interface was presented by the developer Andy South at the MAPISCo

steering group meeting in November 2012.The initial prototype allowed users to select a species

from the list and then choose one of the following display options (tabs):

Graphic: the position of this species in the MAPISCo priority ranking (with all weighting factors set to 1), as a bar chart with each individual species represented by one bar. Bars are coloured according to taxonomic group.

Map: a world map showing which countries this species occurs in and an option to label the countries with their names.

MAPISCo Final report: 5. Using the method

55

Score co-benefits: shows how the priority score for this species is calculated from the five co-benefits. This allows the user to see, for example, whether a particular species receives a high priority score because of its values for Threat Status, Habitat or Harvesting.

A subsequent version was made available in time for the CITES COP at the start of March 2013.

The new version added the following functionality requested at the steering group meeting.

Two stage selection process: first allowing users to select a taxonomic group, continent and country. A species list is displayed based on these selections allowing the user to then select an individual species and view the outputs outlined above.

Weightings sliders for each of the five co-benefits. The sliders can be moved between zero and one (with one being the default starting value). Changing the weightings leads to re-calculation of the priority scores and all other UI components update with the re-calculated rankings.

Rank Table tab: displays a table of the selected species list ranked by priority scores. The table contains the scientific name, taxonomic group, English name and scores for each of the five co-benefits as well as the overall priority score.

About tab: gives a brief outline of the project, contact details for project participants and acknowledgements.

The current, development version, of the GUI can be viewed and tried out at: www.mapisco.org.uk.

5.3.2. Constraints and legacy

The resources available for this part of the project have been limited relative to the usual resources

required to develop a fully featured, robust, useable software product. 14 days of developer time

for GUI development were available from project funds. Therefore, the GUI that has been

produced should be seen as a prototype to be developed further. We are keen to develop the user

interface when funds can be sourced.

5.3.3. Future development options

With relatively few extra days development time the following options can be added to the GUI in

the short term. Costed proposals for these options were provided to the project steering committee

in February 2013 (options that were taken up at that stage and are included in the implementation

described above are not included here).

Outputting priority lists as a PDF or CSV file including metadata detailing the user, time of

creation and weighting options selected.

Produce a version of the user interface targeted specifically at Overseas Territories (OTs),

only including species occurring in OTs and allowing specific OTs to be selected.

Allow output of a single reference page for a species chosen, giving text, map and

graphics, including specifying the co-benefit values and which weightings options chosen.

MAPISCo Final report: 5. Using the method

56

Creation of a reference document/atlas containing a page for all species subject to a

maximum PDF size of ~2GB. The page would have text, map, and a scorings graph.

Provide a button that will link to other databases (WCMC, IUCN Red List) and/or image

search for the selected species.

Creation of an R package containing the MAPISCo database and analysis routines,

documentation and helpfiles. Submission to international repository. This will make the

database and methods easily accessible to researchers worldwide and will help to ensure

project legacy.

These short term development options could provide a bridge to longer term developments for

which there is considerable potential.

5.3.4. Future hosting options

The beta-test version of the GUI is currently hosted on a test server and redirected from the

www.mapisco.org.uk domain name. There is no guarantee how long this test server will remain

available. To make the user interface freely available online in the long term there are two options.

The first option is to make it available on a project specific server running R and shiny thus

incurring no extra costs beyond hosting. The second option is to use the Shiny hosting service,

which would incur an as yet unknown monthly fee. The related issue of where the database should

be hosted is considered in section 6.52, page 61.

MAPISCo Final Report: 6. Discussion

57

6. Discussion

Capsule

The method developed allows the prioritisation of species based on their contribution to five co-benefits - conservation effort directed at high ranking species is expected, therefore, to contribute most to biodiversity, via the selected co-benefits.

The database that we have compiled contains information on 70000 species that has been consolidated from a suite of databases held by other organisations. Creating, curating and updating primary databases is time-consuming and expensive and so the coverage of species and co-benefit scores is variable across major species-groups. For example, birds are well-covered, plants much less so.

There is clear scope for Defra to build on the progress made to date so that scientific knowledge and practice can better support UK government objectives. In order to do this, the database requires modest technical development and a permanent home, the scientific rationale linking species and co-benefits should be strengthened, and the policy arenas where it can be used should be defined more closely.

6.1. Fit to original project brief

The original contract specification for this project required the creation of a methodology to

prioritise species conservation effort for the greatest contribution to “consequential benefits for

other species (or taxa), habitats, wider ecosystems, and ecosystem services”. The methodology

was to be: expandable allowing the incorporation of future data, adaptable to changing policy

aims and usable by non-technical practitioners.

The methodology presented in this report meets the specification outlined above by focusing on a

selection of five priority co-benefits (habitat conservation, genetic diversity, harvesting, species

extinction risk and ecosystem services). The steps involved in developing a priority list of species

for conservation investment included: (i) identifying 2-3 data sources which could be used to

quantify the value of a given species to each co-benefit, (ii) computing standardised scores for

each species on each co-benefit (across data sources), (iii) summing these scores to create a final

ranked priority score, weighted as required. In theory, conservation effort directed at species

ranked highly on the priority list could be expected to contribute most to the selected co-

benefits. This would permit greater contributions to:

1) the prevention of species extinctions by on average focusing effort on more highly

threatened species (Aichi Target 12),

2) the conservation of habitats by focusing effort on those species used to identify a

selection of Key Biodiversity Areas in which larger number of species co-occur

(Aichi Targets 5 and 7),

3) the promotion of sustainable harvesting by focusing on harvested species of the

greatest economic value (Aichi Target 6),

MAPISCo Final Report: 6. Discussion

58

4) the conservation of genetic diversity of species of economic or social value, by

focusing on wild relatives of crops and domesticated animals, and medicinal plant

species (Aichi Target 13) and

5) the protection of ecosystem service provisioning by focusing on species occurring in

forest- and wetland habitats in countries with higher estimated rates of carbon loss

through deforestation or lower freshwater availability (Aichi Target 14).

Therefore, conservation of the species highlighted by our approach presents the greatest

potential to contribute to Aichi 5-7 and 12-14, as well as being an effective way to help

direct conservation policy to contribute to international conservation agreements.

6.2. How does the method compare with ‘business as usual’?

One of the original drivers for this project was the perceived view that resources were often

directed towards a few charismatic species. In the current methodology “politically interesting”

or flagship species often championed by interest groups do not generally rank highly (e.g. Asian

Elephant Elephas maximus is ranked 397th, African Elephant 553rd, Tiger 1759th, Giant Panda

Ailuropoda melanoleuca 9473rd, African Lion Panthera leo 6724th, Eastern Gorilla G. beringei

3819th, Lowland Gorilla Gorilla gorilla 2741st, Black Rhinoceros Diceros bicornis 37816th, Polar

Bear Ursus maritimus 45625th and White Rhinoceros Ceratotherium simum 51510th). This is

because they are associated with only a small number, if any, of the co-benefits considered here.

The method we have devised is based on objective criteria which can be transparently

adapted as policy aspirations change. This can be done by putting more or less value on each

co-benefit by varying the associated co-benefit weighting. For example, clearly using objective

criteria based on a range of co-benefits places a few of the charismatic species illustrated here in

context: they are less likely to fulfil a range of goals as set out in the Aichi Targets which we focus

on than a large number of other species.

6.3 How do co-benefits relate to IUCN threat status?

An aim of the MAPISCo approach was to prioritise species based on the consequential benefits

associated with their survival in addition to their IUCN Red List threat status. We would therefore

expect that resulting priority lists are not simply a reflection of threat status.

To test this expectation we subdivided the overall database into four sub-databases – 1) one

containing all species which score on Red List Threat Status AND Habitat Conservation, 2) one

containing all species which score on Red List Threat Status AND Harvesting, 3) one containing all

species which score on Red List Threat Status AND Genetic Diversity, and 4) one containing all

species which score on Red List Threat Status AND Ecosystem Services. This meant that each

sub database contained data only for species that score on Red List Threat Status and each of the

other four co-benefits in turn. For each of these four databases we created three new priority lists

based on -. 1) Red List threat category scores alone (for which the scores will be 1 for Critically

Endangered, 0.88 for Endangered etc). This represents how prioritisation decisions could be made

if the Red List alone was used to rank species. 2) Scores from the co-benefit in that database

MAPISCo Final Report: 6. Discussion

59

alone (e.g. scores for Genetic Diversity). 3) Scores from the threat category and co-benefit

combined (the average of the two). The results of this are shown in Table 25 below.

Both the Habitat and Ecosystem Services co-benefits are significantly negatively related to Threat

Status, meaning that more traditional approaches to conservation (based on extinction risk- the

IUCN Red List) do not capture more recent concerns about protecting a range of co-benefits from

each species.

When threat status and each co-benefit are combined, three of them (Habitat, Harvesting and

Ecosystem Services) are positively correlated to threat status suggesting that the MAPISCo

database does encompass extinction risk and these co-benefits as well. The exception is Genetic

Diversity, which is negatively correlated to both IUCN status (although not significantly) and to

IUCN status and Genetic Diversity suggesting that the relationship with this score and others is not

straightforward. This is probably due to the scoring for this co-benefit which tends to be binary

(either not related at all or quite highly related – section 3.2.3., page 17).

Table 25. Spearman’s rank correlation coefficient between IUCN status and each co-benefit (* = p < 0.05).

Priority score IUCN status

Habitat (n = 5553)

Harvesting (n = 1504)

Genetic diversity (n = 811)

Ecosystem services (n = 38946)

Habitat -0.154*

Harvesting 0.013

Genetic diversity -0.066

Ecosystem services -0.60*

IUCN status + Habitat 0.890* 0.246*

IUCN status + Harvesting 0.862* 0.450*

IUCN status + Genetic Diversity 0.902* -0.328*

IUCN status + Ecosystem services 0.863* 0.389*

6.4. Operating constraints It is important to bear in mind that, in its current format, the method developed by this project is

biased towards those taxa that have the greatest representation in the databases used to calculate

priority scores. This is because some species and some co-benefits have been subject to more

study and data collation than others. This issue is particularly acute for plants, which have a very

low proportional representation in the current version of the database (this is discussed in full in

section 4, page 27). This will inevitably result in the relative (overall mean) downgrading of plants

in any species prioritisation process until more plants have been assessed on the Red List and in

other databases. Therefore, we urge caution when using the method with all taxa: it is better to

currently use it to ask specific questions such as prioritisation within well studied groups, such as

birds.

MAPISCo Final Report: 6. Discussion

60

6.5. Integration of MAPISCo into decision-making – next steps

As biodiversity issues become mainstream in political processes there is a recognition that the

interface between science and policy must be strengthened (e.g. Koetz et al. 2008). Drawing on

experience with the Intergovernmental Panel on Climate Change (IPCC), the necessary elements

include research produced by bodies external to the policy body; a structure for collecting new data

and observations; and an assessment body to make information and knowledge accessible for

policy makers (Lariguaderie & Mooney 2010a;b). Perhaps most importantly, it is vital to recognise

that developing science-based policy is an iterative and adaptive process that relies on improving

knowledge so as to reduce uncertainty coupled with dialogue between the research and policy

community (Koetz, Farrell & Bridgewater (2011).

Thus, use of the MAPISCo methodology should be seen as an iterative process (Figure 6). As

discussed in sections 4 and 5, the use of data sources to inform the co-benefits should be

continually assessed, improved and expanded, in response to (changing) expert opinion. Because

the lists generated by the current method are based on a relatively small number of data sources

(12), this expansion is crucial to give greater robustness to decision-making. It would be sensible,

therefore, for Defra to give priority to supporting the efficient collation and curation (and even

collection) of such data. It may also be possible to develop formal means to incorporate expert

opinion in the way in which data sets are used and scored (e.g. Howes, Maron, & Mcalpine 2010;

Aguilera et al. 2011) (as with the Palms example, section 5.1, page 50).

Figure 6. Non-linear science-policy interface showing how MAPISCo may benefit from stronger dialogue

between these two fields. Black arrows form part of the traditional “linear” interface, red arrows are the

feedback required.

MAPISCo Final Report: 6. Discussion

61

To allow MAPISCo to be used successfully in the longer term, three areas require further attention:

1) Science, 2) Practical and 3) Policy. This will put MAPISCo on a sustainable footing and

enable it to contribute to policy development.

6.5.1. SCIENCE. Ensuring that the methodology fully accounts for scientific advances

Linking species data to co-benefits

Information on the links between conservation of individual species and co-benefits is varied. Thus

the ability to prioritise conservation on the basis of some co-benefits will be more limited in some

species groups than others (see section 4, page 27). However, the knowledge base relating

species conservation to co-benefits may improve in the future as interest in work on ecosystem

services gains ground (including a thorough review commissioned by this project, see Appendix 3).

If Defra is to progress with the MAPISCo method, further research may be appropriate refine the

concepts and framework.

Conceptual advances

The analytical field of prioritisation in biodiversity conservation is moving rapidly and there are

frequent advances in standardising variables and dealing with unknowns and uncertainty. It is

important that relevant advances are tracked so that any necessary adjustments to the MAPISCo

methodology can be made. Defra could achieve this by establishing a MAPSICo Secretariat, that

undertakes the necessary surveillance or by commissioning regular updates.

Links to other Aichi Targets

The project has taken place within the context of Defra’s desire to maximise overall conservation

benefit from its spend on species. Currently five co-benefits, linked to four Aichi Targets are

included in the methodology so the priority lists are only relevant within the context of these

particular targets. Different species would be ranked more highly if contributions to Target 9

(control of invasive species) or others were to be included.

Non-independence of data sets

There is a degree of overlap in type of data used for the calculation of co-benefit scores (e.g.

extinction risk as based on the IUCN Red List categories is used for Threat Status calculations, but

is also used in the identification of e.g. IBA and AZE species). In statistical analyses, such

interdependence would be considered a problem. However, in this case, this interdependence

results from the co-benefits themselves (and the Aichi Targets from which they are derived) being

non-independent. For example, by addressing Aichi Target 12 (preventing species extinctions),

many species relevant to Target 13 (Genetic Diversity) would also be covered (as many of these

are listed on the IUCN Red List).

6.5.2. PRACTICAL - Maintenance of database and incorporation of additional data. Where will the database be housed?

There is an immediate need to determine where the database will be housed and what form

technical support will be required. There are several options for hosting the database either within

MAPISCo Final Report: 6. Discussion

62

Defra, or with external hosts, such as the IUCN Red List Office (Cambridge), Newcastle University

or UNEP-WCMC. The suitability of these options will depend on an assessment of the following

factors:

Cost;

Capability;

Ability to provide scientific support (see below);

Understanding of Defra’s policy needs and way of working; and

Duration of hosting contract that Defra proposes.

Scientific support needs

Some level of ongoing scientific support to Defra may be required to allow the MAPISCO

methodology to be fully operational. It may be that the standards and other documentation (see

below) together with training in the application and use of the methodology to provide prioritisation

lists would be sufficient, rather than an ongoing ‘help desk’ approach. Defra will need to consider

what support it is likely to require, for how long and in what form.

Define standards and documentation

To ensure that the database and methodology are used appropriately and to best effect it is

desirable to develop technical documents that define the standards to be used and specify in what

format any outputs should appear. This is also important to show that all queries run in MAPISCo

are transparent and that the decisions on weightings are documented fully. Good examples of how

this can be done and how useful it may be can be drawn from the IUCN Red List which has

standard definitions and classifications (see http://www.iucnredlist.org/technical-

documents/classification-schemes and links therein) and also produces outputs of searches in a

standardised way with a recommended citation. With some consideration, it would be possible to

produce a similarly standardised output from the graphical user interface that gives all decisions

made on weighting and identifies the person running the query as the author.

Incorporating new data and updating existing data

Data availability and accessibility is a concern. A very limited number of datasets exist which

contain the information required for the MAPSICo methodology. Fewer still have been brought

together, been adequately assembled and documented, and then made accessible. This is

important because the availability and choice of data sources included in the database has

consequences for the ranking of species. Thus, the ability to prioritise conservation on the basis of

some co-benefits will be more limited in some species groups than others.

The paucity of data, for some taxa such as plants, means that small improvements in the

availability of data can have a large impact on the resulting species priority list. The inclusion of

specialist data on palm tree species into the database resulted in a considerable change in their

place in the species ranking from no species in the top 2000; to two in the top five (see section 5.2,

page 51). By exploring other scenarios where small improvements in the availability and/or quality

of data linking species to co-benefits, it will be possible to focus resource investment in the

MAPISCo Final Report: 6. Discussion

63

gathering and/or collation of data that can maximise impact on prioritisation ability. In addition, the

method derived here for ‘within-group’ prioritisation could be a useful way forward.

All suitable datasets have been incorporated in the current database. Addressing remaining gaps

may require various approaches. For example, taxonomic coverage is not uniform, the specific

content of the database (the data fields) vary and there is substantial need to improve the curation

and accessibility of many datasets before they could be considered for inclusion in the MAPSICo

database. Finally, there will be varying motivations of data holders to share their data with Defra on

a gratis basis. A practical first step would be to look at institutions close to Defra (and current/future

partners) and produce a detailed analysis of what their data holdings are and how they can be

made accessible to MAPSICo. A strong candidate here would be Kew Gardens and the data

currently being assessed by the IUCN Red List in Cambridge. Access to these sources may add

significantly to the MAPSICo coverage. Filling other gaps would require a more strategic approach

and this would depend on immediate Defra priorities.

6.5.3. POLICY - Integrating MAPISCo into policy and resource allocation decisions Demonstrating the potential of MAPISCo

MAPISCo would benefit from external review by scientists and policy-makers. Therefore, it is now

very important to demonstrate the method to policy makers and senior officials in Defra and other

potential users. This could involve a demonstration of how lists can be generated, the sorts of

decisions that can be taken on co-benefit weightings and the impact varying these may have, and

how the outputs can be used. One or more workshops with potential end users would likely be the

best way to promote the method. If this could be combined with working through one or more

current Defra species issues, it would be a very strong demonstration of the method.

Strengthening the ability of MAPISCo to underpin policy

The strengthening of links between both policy requirements and the framing of the scientific inputs,

and between MAPSICo’s outputs and the impact they have on policy decision-making will be key

integrating MAPISCo into working policy. Providing a broader array of policy situations in which to

demonstrate how the method may be used would be helpful, as this would allow the range of

assumptions and decisions to be assessed through the range of weightings applied by policy

makers to MAPISCo at the input stage. At the other, output, end of the process, it would allow

much greater understanding of the range of uses to which the priority lists (and associated data)

generated would then inform the decisions that have to be taken. Exploring this with a range of

users in a variety of contexts would allow the potential and the limits of the method to be defined

more clearly.

A further exploration and demonstration of the value of MAPISCo would be a clearly defined

project in which priority lists would be generated with various weightings (reflecting different policy

demands) and comparing these against existing Defra priorities. This would permit assessment of

both how well aligned they are and, perhaps more informatively, reasons for variation between

current priorities and various MAPISCo outputs. Such an assessment should help bring into sharp

focus unstated assumptions or other factors that may need to be incorporated into the

methodology to account for the full range of contexts in which it may be used.

MAPISCo Final Report: 6. Discussion

64

Ease of use

The methodology is far more likely to be used if it is intuitive and clear. Therefore the interface

(graphical user interface: GUI see section 5.3, page 54 for further discussion of GUI legacy) and

the explanatory documentation needs to be easy to understand. The need here is for a short

period of testing documentation and the GUI are a good fit to the users. There may be a need for

refinement based on this testing.

6.6. Concluding remarks

We have delivered a first version of a methodology that can identify priorities for species

conservation efforts based on expected contributions to a selection of five co-benefits. Our finding

that around 1064 species are commonly ranked highly irrespective of variations in how the co-

benefits are weighted, suggests that the proposed methodology does provide a blunt tool for

identifying species where conservation effort could be expected to make significant contributions to

the Aichi Targets.

This project is at the cutting edge of the science-policy interface. Although species prioritisation

efforts are common, the vast majority of previous efforts are either geographically or taxonomically

limited (e.g. Dunn, Hussell, & Welsh 1999; Knapp, Russell, & Swihart 2003; Rodríguez, Rojas-

Suárez, & Sharpe 2004; Jimenez-Alfaro, Colubi, & Gonzalez-Rodriguez 2010), and are often

limited to biological considerations only (Mace & Collar 2002; Mace, Possingham, & Leader-

Williams 2006).

Although this project set out to establish clear objective criteria to determine how to prioritise

species conservation investments, we have also shown that the choice of both co-benefit

weighting and the data sources has a strong effect on which species are identified as higher

priorities. As a result, the development of this methodology has brought the mismatch between the

data requirements and data availability/accessibility for ambitious species prioritisation exercises

into sharp focus.

Implied in the original project brief is an assumption of a relatively straightforward and linear

science-policy interface, where science can directly meet policy needs and inform policy changes.

Both the mismatch between the data required to fully service the original project brief and our

results presented here highlight the need for a re-evaluation of this interface. As we have outlined

above, at this stage of the methodology, further guidance and refinement of policy aims are

required for science to make progress. In other words, a number of feedbacks from science into

policy and back again need to be incorporated into a non-linear interface. As a result, the

methodology described here becomes part of an iterative process where conservation science and

policy meet and continuously refine each other’s needs, rather than a final answer to global

species prioritisation problems.

MAPISCo Final Report: 7. References

65

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