calculang essen&al biodiversity variables (ebv) for ... · calculang essen&al biodiversity...

Calcula&ngEssen&alBiodiversityVariables(EBV)forspeciespopula&onabundance–acasestudyfrom

planktonmonitoringdataintheBal&cSea

Ma#hiasObst1,BengtKarlson2,ChristosArvandi:s3,KlaasDeneudt4

Contact:[email protected]

1DepartementofMarineSciences,GöteborgUniversity2SwedishMeteorologicalandHydrologicalIns&tute

3HellenicCentreforMarineResearch,Greece4FlandersMarineIns&tute(VLIZ),Belgium

WhatisanEBV?

Essen:alBiodiversityVariables(EBVs)aredefinedasderivedmeasurementsrequiredtostudy,report,andmanagebiodiversitychange,focusingonstatusandtrendinelementsofbiodiversity.

Source:Avanza.se

Canwemeasuretrendsfornaturalcapitalinthesamewayaswemeasuretrendsforeconomiccapital?

AmajorproblemisthatspeciesdonotreporttheirperformancedataCanwecalculatesuchtrendswiththedatacollec&onmethodsweusetoday?

Assets(species,habitats,ecosystems)

Performanceofasingleassetsover&me

Performanceofallassetsinaregionover&me

http://geobon.org

Collect & publish data

Integrate & process data

Build models

Test & validate

Visualize trends

EBV data package

M1 M2

M1 M2 M3

M1

M2 M1

Abu

ndan

ce /

dist

ribut

ion

Usable data set

Monitoring

Observations

Taxonomy

Indicators

Group on Earth Observations Biodiversity Observation Network

Calcula&ngEssen&alBiodiversityVariables(EBV) Taking the pulse of the biosphere

Collections

18 JANUARY 2013 VOL 339 SCIENCE www.sciencemag.org 278

POLICYFORUM

tion. However, EBVs relating to species popu-lations or traits and to genetic or community composition require representative sampling across taxonomic groups or community types. These EBVs need to balance specifi city and generality, enabling valid aggregation of data from multiple monitoring programs, while allowing for fl exibility in the species or taxo-nomic groups addressed by these programs.

Variables selected as EBVs fi ll a niche not covered by global observation initiatives looking at environmental pressures [e.g., GCOS ( 8), Essential Ocean Variables ( 18)]. An EBV such as species abundance provides data for indicators such as the Living Planet, Wild Bird, and Red List indices (LPI, WBI, and RLI) (see the table). Assessing ecosystem services (ES) requires knowledge of changes in benefi cial species, functional groups, or ecosystem processes; additional physical, social, and economic data (fi g. S1) can be obtained from valuation studies, surveys, and national statistics ( 19). Complemen-tary spatial information on responses imple-mentation (e.g., coverage of protected areas) can inform indicators of the effectiveness of policy and management (fi g. S1). This fun-damental, but fl exible, role of EBVs confers robustness to the system: EBVs are insulated from changing technologies at the observa-tion level and from changing approaches at the indicator level.

Building Consensus and Capacity

Identifi cation of EBVs and defi nition of sam-pling protocols are done by an open process that requires engagement of scientifi c, pol-icy, and other communities. Major roles can be played by IPBES, national biodiversity authorities, space agencies, nongovernmen-tal organizations, and citizen-science com-munities. Information on the EBV process is updated at ( 11); written contributions can be sent to GEO BON. Side events will be organized in scientifi c and policy meetings over the next year. This will refi ne the EBV list, which, once stable, will periodically be updated by GEO BON in a process similar to that used for ECVs ( 8).

Coordination of sampling schemes by GEO BON across countries and scales can minimize costs and improve spatial repre-sentativeness. Developing suitable fi nancial mechanisms to share costs between develop-ing countries, where most biodiversity occurs, and developed countries, which share in the benefi ts but drive many of the pressures ( 20), will play a key role in the development of a truly global system. We hope that EBVs will catalyze investment in biodiversity observa-tions, as ECVs have done for climate.

References and Notes

1. S. H. M. Butchart et al., Science 328, 1164 (2010). 2. CBD, Decision X/2, The Strategic Plan for Biodiversity

2011–2020 and the Aichi Biodiversity Targets, Nagoya,

Japan, 18 to 29 October 2010. 3. H. M. Pereira, L. M. Navarro, I. S. Martins, Annu. Rev.

Environ. Resour. 37, 25 (2012). 4. R. J. Scholes et al., Curr. Opin. Environ. Sustain. 4, 139

(2012). 5. R. P. Guralnick et al., Ecol. Lett. 10, 663 (2007). 6. L. J. Martin et al., Front. Ecol. Environ 10, 195 (2012). 7. C. K. Feld et al., Oikos 118, 1862 (2009). 8. GCOS, Implementation Plan for the Global Observing

System for Climate in Support of the UNFCCC (2010 Update) (World Meteorological Organization, Geneva, 2010), p. 180.

9. Secretariat of the CBD, Report of the Ad Hoc Technical Expert Group on indicators for the Strategic Plan for Bio-diversity 2011–2020 (SCBD, Montreal, Canada, 2011).

10. P. Bubb et al., National Indicators, Monitoring and Reporting for the Strategic Plan for Biodiversity 2011–2020 (UNEP-WCMC, Cambridge, 2011).

11. GEO BON, EBVs; www.earthobservations.org/geobon_ebv.shtml.

12. R. D. Gregory et al., Philos. Trans. R. Soc. London Ser. B 360, 269 (2005).

13. D. P. Turner, Front. Ecol. Environ 9, 111 (2011). 14. S. Ferrier, Bioscience 61, 96 (2011). 15. W. Jetz et al., Trends Ecol. Evol. 27, 151 (2012). 16. H. M. Pereira et al., Science 330, 1496 (2010). 17. E. O. Wilson, Science 289, 2279 (2000). 18. IOC, A framework for ocean observing—Consultative

draft v.7 (UNESCO, Paris, 2011), p. 26. 19. H. Tallis et al., Bioscience 62, 977 (2012). 20. M. Lenzen et al., Nature 486, 109 (2012).

Acknowledgments: NASA, DIVERSITAS, GEO, ESA-ESRIN, and the Department of Science and Technology (South Africa) pro-vided resources. H.M.P. was supported by FCT grant PTDC/AAC-AMB/114522/2009. M. Paganini helped organize a workshop. H.M.P., S.F., M. Walters, G.N.G., R.H.G.J., R.J.S., D.P.F., C.H., R.H., R.S., S.N.S., and M. Walpole are on the GEO BON steering committee. Abbreviations spelled out in SM.

Examples of candidate Essential Biodiversity Variables

Allelic diversityGenetic

composition

EBV

class

EBV

examples

Measurement and scalability Temporal

sensitivity

Feasibility Relevance for CBD targets

and indicators (1,9)

Generationtime

1 to 5 years

Genotypes of selected species (e.g., endangered, domesticated) at representative locations.

Data available for many species and for several locations, but little global systematic sampling.

Targets: 12, 13.Indicators: Trends in genetic diversity of selected species and of domesticated animals and cultivated plants; RLI.

Taxonomicdiversity

Communitycomposition

5 to >10years

Ongoing at intensive monitoring sites (opportunities for expansion). Metagenomics and hyperspectral RS emerging.

Targets: 8, 10, 14. Indicators: Trends in condition and vulnerability of ecosystems; trends in climatic impacts on community composition.

Consistent multitaxa surveys and metagenomics at select locations.

Habitatstructure

Ecosystemstructure

RS of cover (or biomass) by height (or depth) globally or regionally.

Global terrestrial maps available with RS (e.g., Light Detection and Ranging). Marine and freshwater habitats mapped by combining RS and in situ data.

Targets: 5, 11, 14, 15.Indicators: Extent of forest and forest types; mangrove extent; seagrass extent; extent of habitats that provide carbon storage.

Abundancesand distributions

Species populations

PhenologySpecies traits

1 to >10years

Counts or presence surveys for groups of species easy to monitor or important for ES, over an extensive network of sites, complemented with incidental data.

Standardized counts under way for some taxa but geographically restricted. Presence data collected for more taxa. Ongoing data integration efforts (Global Biodiversity Information Facility, Map of Life).

Targets: 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15.Indicators: LPI; WBI; RLI; population and extinction risk trends of target species, forest specialists in forests under restoration, and species that provide ES; trends in invasive alien species; trends in climatic impacts on populations.

1 yearTiming of leaf coloration by RS, with in situ validation.

Several ongoing initiatives (Phenological Eyes Network, PhenoCam, etc.)

Targets: 10, 15.Indicators: Trends in extent and rate of shifts of boundaries of vulnerable ecosystems.

Nutrientretention

Ecosystemfunction

1 yearNutrient output/input ratios measured at select locations. Combine with RS to model regionally.

Intensive monitoring sites exist for N saturation in acid-deposition areas and P retention in affected rivers.

Targets: 5, 8, 14. Indicators: Trends in delivery of multiple ES; trends in condition and vulnerability of ecosystems.

10.1126/science.1229931

Supplementary Materials

www.sciencemag.org/cgi/content/full/339/6117/277/DC1

Published by AAAS

on

Janu

ary

17, 2

013

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

Pereiraetal(2013)Science

2013). Version 20 of the ENM workflow was used in our

analyses. The ENM workflow uses occurrence and environ-

mental data to model ecological niches using a web service

based on openModeller, a library that provides a variety of

algorithms to model species distribution patterns (http://

openmodeller.sf.net/) (Mu~noz et al., 2011). The second

workflow, the ENM Statistical Difference Workflow (ESW

DIFF) (http://purl.ox.ac.uk/workflow/myexp-3959.2; accessed

23 January 2014) allows the spatial computation of changes

in PD maps by calculating the differences between two raster

layers using the R statistical environment 3.0.2 (R Core

Team, 2013).

Occurrence data

Occurrence data for all species were extracted from GBIF

(Global Biodiversity Information Facility; http://gbif.org/)

during spring 2013 (see Appendix S1 in Supporting Informa-

tion) (Table 1). For the Idotea spp., additional occurrence

records were gathered through an extensive literature survey

and manually geo-referenced (Appendix S2) as well as

obtained from museum collections (FMNH Helsinki; GOM

Stralsund; SMF Frankfurt; SMNH Stockholm; ZIN St. Peters-

burg; ZMB Berlin; ZMH Hamburg) and through our own

sampling (Appendix S3). For F. vesiculosus and F. radicans,

GBIF records were either concentrated in a small area of the

Baltic that is not representative of the species’ full distribution

range, or were too few (< 50). Consequently, additional occur-

rence points were created by geo-referencing in the known dis-

tribution range from the literature (Bonsdorff, 2006;

Schagerstr€om, 2013) (Table 1, Appendix S2). All occurrence

data collected for this study have been submitted to the OBIS

database (http://www.iobis.org/) (http://www.vliz.be/nl/imis?

module=dataset&dasid=4607; title of data set ‘Observations of

three Idotea species (I. balthica, I. chelipes and I. granulosa) in

northern Europe, including the Baltic Sea’).

Environmental data

Environmental layers that are likely to affect the distribution

of the species were chosen based on the literature (Table 1).

Global marine layers came from Bio-Oracle (http://www.

bio-oracle.ugent.be/; data downloaded 14 August 2013) at a

resolution of 5 arc-minutes (Tyberghein et al., 2012), and

from AquaMaps (http://www.aquamaps.org/download/main.

php; data downloaded 1 April 2008) at a resolution of 30

arc-minutes (Kaschner et al., 2010). Layers for mean annual

sea-surface salinity (SSS) and sea-surface temperature (SST)

were available at a resolution of 5 arc-minutes for the pres-

ent only, so we combined them with 30 arc-minute layers

from AquaMaps for sea ice concentration (SIC), mean dis-

tance to land (DL) and maximum depth (MD) (Table 1).

For the 2050 projection, only 30 arc-minute layers from

AquaMaps were used. Present-day datasets from AquaMaps

were built from long-term averages of temporally varying

environmental variables (Ready et al., 2010), whereas BioO-

racle layers were based on monthly level-3 pre-processed

satellite data from NASA (Tyberghein et al., 2012). For the

PD under 2050 climate conditions, the AquaMaps layers

were derived from the ECHAM5 A1B climate change sce-

nario (Jungclaus et al., 2006; IPCC et al., 2007).

To address the question of which environmental factors

mostly influence the distribution range of the grazers, we used

a jackknife leave-one-out procedure (Peterson et al., 2011)

based on area under the curve of a receiver operating charac-

teristic plot (AUC) values for SIC, SST and SSS for the Baltic

and the known distribution area of the species (Table 2). In

this procedure, for each environmental variable a model was

created without it, and then model assessments were com-

pared across the different layer sets. The most influential vari-

able was considered the one that, when not included in the

model, produced the lowest assessment value.

Occurrence point filtering

Occurrence data were filtered for environmentally unique

points by running an initial BioClim workflow also based on

the openModeller web service, using the same environmen-

tal layers as in the ENM workflow. This procedure avoids

passing redundant information to niche modelling algo-

rithms later. Besides filtering the points, the workflow gener-

ated a BioClim model (Busby, 1986; Nix, 1986) to calculate

the environmental range for each variable (Table 3). The

Figure 1 Study area of the meso-grazer guild. Mean sea-surfacesalinity values show the characteristic salinity gradient of theBaltic Sea. psu = practical salinity unit.

ª 2014 The Authors. Journal of BiogeographyPublished by John Wiley & Sons Ltd.

3

Ecological niche modelling for a guild of meso-grazers

EBVclass•  SpeciesabundanceDatatype•  monitoringdata,basedondetailed

andoYenharmonizedprotocolsThestudyregion•  differenthydrologicalcondi&ons•  strongsalinitygradientSpa&allevel•  Regional(Bal&c)•  Sub-regional(KaAegaA-Skagerrak,

Bal&cProper,UpperBothnianBay,LowerBothnianBay)

Bal&cEBVtestcase

Weusedthesedataandanalysedabundancetrendsacrossspaceand&me.

Thedataset-  26monitoringsta&ons-  1979-2013-  4sub-regions-  60,511observa&onrecords

Samplingvarianceacross&me(no.ofsamples/yr)

LowerBothnianBay UpperBothnianBay

● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ●

●

●

●

● ● ● ●

●

● ● ● ●

●

● ●

1980 1985 1990 1995 2000 2005 2010

05

1015

2025

30

B3

year

no o

f sam

ples

● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ●

●

●

● ● ● ● ● ● ●

●

● ● ●

● ● ●

1980 1985 1990 1995 2000 2005 2010

05

1015

2025

30

A13

year

no o

f sam

ples

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ●

●

●

●

●

●

●

●

1980 1985 1990 1995 2000 2005 2010

05

1015

2025

30

BY15

year

no o

f sam

ples

Bal&cProperKaAegaA/Skagerrak

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

●

●

●

●

●

●

1980 1985 1990 1995 2000 2005 2010

05

1015

2025

30

A17

year

no o

f sam

ples

Samplingvarianceacrossseasons(no.ofsamples/month)

●

● ●

●

●

●

●

●

●

●

●

●

2 4 6 8 10 12

05

1015

2025

3035

A13

month

no o

f sam

ples

●

●

● ● ●

●

●

●

●

● ●

●

2 4 6 8 10 12

05

1015

2025

3035

A17

month

no o

f sam

ples

● ●

●

●

●

●

●

●

●

●

●

●

2 4 6 8 10 12

05

1015

2025

3035

B3

month

no o

f sam

ples

● ●

● ●

●

●

●

●

●

●

●

●

2 4 6 8 10 12

05

1015

2025

3035

BY15

month

no o

f sam

ples

UpperBothnianBay

Bal&cProperKaAegaA/Skagerrak

LowerBothnianBay

Overallsamlingday&me

Baltic

daytimenu

mbe

r of o

bser

vatio

ns0 5 10 15 20

010

0020

0030

0040

0050

0060

00

Almosthalfofthesamplesdidnothave&mestampsWethereforeincludedthediurnalvarianceintheabundancecalcula&ons,butcheckedthattherewasnosystema&cbiasinsamplingday&meacrosssta&ons

Annualvariance.Inordertobeabletocomparetrendsamongthesub-regions,wechosetoincludeonlydatafrom2006-2013.Seasonalvariance.Becauseoftheseasonalvaria&onwecomparedonlymeasurementsfromtheearlysummerandlumpedallabundancerecordsfromApril-July.Diurnalvariance.Weincludeddiurnalvariance.Depthvariance.Welumpedalldepthslices.Taxonomicvariance.WeusedtheWoRMStaxonomicnameservicetosynonymiseallspeciesnames.Lifehistoryvaria:on.Weonlyincludedadultlifestages.Visualisa:on.WeploAedboxplotsfromallabundancevalues(numberofindividuals/m3).

Summaryofdataprocessing

Replicates and depth slices. Field codes in the original dataset suggested that at some sites more than one replicate was taken. We standardized all measurements by dividing the abundance counts by the number of replicates. Taxonomic variance. Taxonomic inconsistencies were removed manually after inspecting the species abundances across stations. We used the World Register of Marine Species (WoRMS) taxonomic name service to synonymise all species names. Life history variation. The data set included both measurements of adult and non-adult stages (eggs, juveniles, larvae). We excluded all records of non-adult life stages from the analysis. Visualisation. We plotted boxplots from all abundance values (number of individuals/m3) during April-July at the stations in the sub-region for all years. The mean abundance values were connected in the graph with a line over the years 2007–2013. The same approach was used for creating boxplots for the entire region. Data storage. All input files, output files, R code, and geographic coordinates of the stations are stored at https://ecds.se/ under file identifier: ccc84507-49c1-43df-9887-97d2232bcb89 (https://ecds.se/dataset/calculating-essential-biodiversity-variables-for-species-population-abundance-in-the-baltic-sea). Table S1. Principal steps of the information supply chain to build Essential Biodiversity Variable (EBV) datasets as applied to the Baltic Sea zooplankton monitoring (BALTIC) dataset with indication of abundance records and taxa available for trend analysis.

Generic steps

Specific data processing steps applied to BALTIC

No. of abundance records

No. of taxa

Sampling (raw observations)

Assembly and integration of raw data from different plankton monitoring programs in the Baltic region

60,511 217

EBV-usable dataset

Taxonomic refinement and basic cleaning, e.g. making location and species names consistent

59,374 181

EBV-ready dataset

Cropping the dataset to relevant seasons and years

18,856 135

Derived & modelled EBV data

Cropping the dataset to comparable species observations with >100 measurements (no modelling)

7,067 33

Summary of workflows On the following pages we summarize how the mentioned projects (i.e., eBird, TEAM, LPI, and BALTIC) relate to the workflow steps presented in the main text.

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1400

0

Cyclopoida.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

Eurytemora.affinis

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0015

000

2500

035

000

Eurytemora.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

2500

0

Evadne.nordmanni

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

2500

0

Gastropoda.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0020

0030

0040

0050

00


year

abun

danc

e in

ind/

m3

2006 2009 2012

0e+0

02e

+04

4e+0

46e

+04

8e+0

41e

+05

Keratella.quadrata

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

0040

0060

0080

00


year

abun

danc

e in

ind/

m3

2006 2009 2012

020

0060

0010

000

1400

0

Cyclopoida.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

Eurytemora.affinis

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0015

000

2500

035

000

Eurytemora.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

2500

0

Evadne.nordmanni

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

2500

0

Gastropoda.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0020

0030

0040

0050

00


year

abun

danc

e in

ind/

m3

2006 2009 2012

0e+0

02e

+04

4e+0

46e

+04

8e+0

41e

+05

Keratella.quadrata

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

0040

0060

0080

00


year

abun

danc

e in

ind/

m3

2006 2009 2012

020

0060

0010

000

1400

0

Cyclopoida.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

Eurytemora.affinis

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0015

000

2500

035

000

Eurytemora.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

2500

0

Evadne.nordmanni

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

2500

0

Gastropoda.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0020

0030

0040

0050

00


year

abun

danc

e in

ind/

m3

2006 2009 2012

0e+0

02e

+04

4e+0

46e

+04

8e+0

41e

+05

Keratella.quadrata

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

0040

0060

0080

00


year

abun

danc

e in

ind/

m3

Cyclopoidcopepod.Source:Wikipedia

KaAegat/Skagerrak

TrendsofdecreasingabundanceHarpac&coidscopedopds

2006 2009 2012

050

100

150

200

250

300

Cyclopoida.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Eurytemora.affinis

year

abun

danc

e in

ind/

m3

2006 2009 2012

05

1015

20

Eurytemora.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

2500

0

Evadne.nordmanni

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

2500

0

Gastropoda.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0020

0030

0040

0050

00


year

abun

danc

e in

ind/

m3

2006 2009 2012

010

020

030

040

050

0

Keratella.quadrata

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2006 2009 2012

020

0060

0010

000

1400

0

Cyclopoida.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Eurytemora.affinis

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

Eurytemora.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

010

0015

0020

00

Evadne.nordmanni

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Gastropoda.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

02

46

8


year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0020

0030

0040

0050

0060

00

Keratella.quadrata

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2006 2009 2012

010

020

030

040

050

060

0

Cyclopoida.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

4060

8010

0

Eurytemora.affinis

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0015

000

2500

035

000

Eurytemora.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0030

0050

0070

00

Evadne.nordmanni

year

abun

danc

e in

ind/

m3

2006 2009 2012

0.0

0.2

0.4

0.6

0.8

Gastropoda.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

4060

80


year

abun

danc

e in

ind/

m3

2006 2009 2012

0e+0

02e

+04

4e+0

46e

+04

8e+0

41e

+05

Keratella.quadrata

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2007 2010 2012

020

040

060

080

0

Cyclopoida.indet.

year

abun

danc

e in

ind/

m3

2007 2010 2012

050

0010

000

1500

020

000

Eurytemora.affinis

year

abun

danc

e in

ind/

m3

2007 2010 2012

−1.0

−0.5

0.0

0.5

1.0

Eurytemora.indet.

year

abun

danc

e in

ind/

m3

2007 2010 2012

010

0020

0030

0040

0050

00

Evadne.nordmanni

year

abun

danc

e in

ind/

m3

2007 2010 2012

050

010

0015

00

Gastropoda.indet.

year

abun

danc

e in

ind/

m3

2007 2010 2012

050

010

0015

00


year

abun

danc

e in

ind/

m3

2007 2010 2012

050

0010

000

1500

0

Keratella.quadrata

year

abun

danc

e in

ind/

m3

2007 2010 2012

020

0040

0060

0080

00


year

abun

danc

e in

ind/

m3

UpperBothnian

LowerBothnian

Bal&cProper

Harpac:coidcopepod.Source:wikipedia

2006 2009 2012

020

0060

0010

000

1400

0

Cyclopoida.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

Eurytemora.affinis

yearab

unda

nce

in in

d/m

3

2006 2009 2012

050

0015

000

2500

035

000

Eurytemora.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

2500

0

Evadne.nordmanni

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

2500

0

Gastropoda.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0020

0030

0040

0050

00Harpacticoida.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

0e+0

02e

+04

4e+0

46e

+04

8e+0

41e

+05

Keratella.quadrata

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

0040

0060

0080

00


year

abun

danc

e in

ind/

m3

Bal&c

LowerBothnian

Bal&cProperKaAegat/Skagerrak

RegionalvssubregionaltrendsLimnocalanusmacrurus(Copepoda)

2006 2009 2012

050

010

0015

00

Limnocalanus.macrurus

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Oithona.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

040

060

080

010

00

Podon.polyphemoides

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Pseudocalanus.elongatus

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Pseudocalanus.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

0040

0060

0080

00

Synchaeta.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Temora.longicornis

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

010

0015

0020

0025

00


year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Oithona.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

0

Podon.polyphemoides

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2006 2009 2012

020

000

4000

060

000

Synchaeta.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Temora.longicornis

year

abun

danc

e in

ind/

m3

2007 2010 2012

050

010

0015

0020

0025

00


year

abun

danc

e in

ind/

m3

2007 2010 2012

−1.0

−0.5

0.0

0.5

1.0

Oithona.indet.

year

abun

danc

e in

ind/

m3

2007 2010 2012

020

0040

0060

0080

00

Podon.polyphemoides

year

abun

danc

e in

ind/

m3

2007 2010 2012

010

000

2000

030

000


year

abun

danc

e in

ind/

m3

2007 2010 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2007 2010 20120

5000

010

0000

1500

00

Synchaeta.indet.

year

abun

danc

e in

ind/

m3

2007 2010 2012

010

000

3000

050

000

Temora.longicornis

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2006 2009 2012

0e+0

01e

+05

2e+0

53e

+05

4e+0

5

Oithona.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Podon.polyphemoides

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2006 2009 2012

050

000

1500

0025

0000


year

abun

danc

e in

ind/

m3

2006 2009 2012

010

000

3000

050

000

Synchaeta.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

Temora.longicornis

year

abun

danc

e in

ind/

m3

UpperBothnian

Sub-regionaltrendsinUpper&LowerBothnianBaydissapearonaregionalscale.

2006 2009 2012

050

010

0015

0020

0025

00


year

abun

danc

e in

ind/

m3

2006 2009 2012

0e+0

01e

+05

2e+0

53e

+05

4e+0

5

Oithona.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

0

Podon.polyphemoides

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

000

2000

030

000


year

abun

danc

e in

ind/

m3

2006 2009 2012

050

000

1500

0025

0000


year

abun

danc

e in

ind/

m3

2006 2009 2012

050

000

1000

0015

0000

Synchaeta.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

000

3000

050

000

Temora.longicornis

year

abun

danc

e in

ind/

m3

Limnocalanusmacrurus.Source:hAp://ci.unh.edu

Bal&c

2006 2009 2012

050

0010

000

1500

0

Acartia bifilosa

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

2000

0

Acartia.indet.

year

abun

danc

e in

ind/

m3

2006 2009 20120

2000

6000

1000

014

000

Acartia.longiremis

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0030

0050

00

Appendicularia.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

000

6000

010

0000

1400

00

Bivalvia.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

000

3000

050

000

Bosmina..Eubosmina..coregoni.maritima

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

0

Centropages.hamatus

year

abun

danc

e in

ind/

m3

2006 2009 2012

0e+0

01e

+05

2e+0

53e

+05

Copepoda.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

02

46

8

Acartia.bifilosa

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

100

150

Acartia.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Acartia.longiremis

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2006 2009 2012

010

2030

Bivalvia.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

000

3000

050

000


year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Centropages.hamatus

year

abun

danc

e in

ind/

m3

2006 2009 2012

01

23

45

6

Copepoda.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0020

0030

0040

0050

00

Acartia.bifilosa

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

0040

0060

0080

00

Acartia.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Acartia.longiremis

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

0

Bivalvia.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

2000

0


year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0

Centropages.hamatus

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

2030

4050

Copepoda.indet.

year

abun

danc

e in

ind/

m3

2007 2010 2012

050

0010

000

1500

0

Acartia.bifilosa

year

abun

danc

e in

ind/

m3

2007 2010 2012

050

0010

000

2000

0

Acartia.indet.

year

abun

danc

e in

ind/

m3

2007 2010 2012

020

0060

0010

000

1400

0

Acartia.longiremis

year

abun

danc

e in

ind/

m3

2007 2010 2012

010

0030

0050

00


year

abun

danc

e in

ind/

m3

2007 2010 2012

−1.0

−0.5

0.0

0.5

1.0

Bivalvia.indet.

year

abun

danc

e in

ind/

m3

2007 2010 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2007 2010 2012

050

0010

000

1500

0

Centropages.hamatus

year

abun

danc

e in

ind/

m3

2007 2010 2012

−1.0

−0.5

0.0

0.5

1.0

Copepoda.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

010

0015

0020

0025

0030

00

Acartia.bifilosa

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

020

000

2500

0

Acartia.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0020

0030

0040

00

Acartia.longiremis

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

040

060

080

010

00


year

abun

danc

e in

ind/

m3

2006 2009 2012

020

000

6000

010

0000

1400

00

Bivalvia.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

−0.5

0.0

0.5

1.0


year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0030

0050

0070

00

Centropages.hamatus

year

abun

danc

e in

ind/

m3

2006 2009 2012

0e+0

01e

+05

2e+0

53e

+05

Copepoda.indet.

year

abun

danc

e in

ind/

m3

TaxonomicproblemsAcar&aspp.

Acar:alongiremis.Source:Arc:cOceanDiversity

UpperBothnian

LowerBothnian

Bal&cProper

KaAegat/Skagerrak

A.bifilosa A.longiremisA.indet.

A.bifilosa A.longiremisA.indet.

Bal&c

2006 2009 2012

050

0010

000

1500

0

Acartia bifilosa

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

2000

0

Acartia.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

0060

0010

000

1400

0

Acartia.longiremis

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0030

0050

00


year

abun

danc

e in

ind/

m3

2006 2009 2012

020

000

6000

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00

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year

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danc

e in

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2006 2009 2012

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000

3000

050

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year

abun

danc

e in

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m3

2006 2009 2012

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1500

0

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year

abun

danc

e in

ind/

m3

2006 2009 2012

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+05

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53e

+05

Copepoda.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

02

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8

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year

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e in

ind/

m3

2006 2009 2012

050

100

150

Acartia.indet.

year

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e in

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m3

2006 2009 2012

−1.0

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year

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e in

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−1.0

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year

abun

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e in

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m3

2006 2009 2012

010

2030

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year

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2006 2009 2012

010

000

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nce

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−1.0

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year

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e in

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2006 2009 2012

01

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45

6

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year

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e in

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m3

2006 2009 2012

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0020

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0050

00

Acartia.bifilosa

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

0040

0060

0080

00

Acartia.indet.

yearab

unda

nce

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32006 2009 2012

0.0

0.2

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0.6

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Acartia.longiremis

year

abun

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e in

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2006 2009 2012

−1.0

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1.0


year

abun

danc

e in

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2006 2009 2012

050

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year

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e in

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2006 2009 2012

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year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

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Centropages.hamatus

year

abun

danc

e in

ind/

m3

2006 2009 2012

010

2030

4050

Copepoda.indet.

year

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e in

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2007 2010 2012

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000

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year

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2007 2010 2012

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year

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e in

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2007 2010 2012

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year

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e in

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year

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e in

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m3

2007 2010 2012

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1.0

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year

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2007 2010 2012

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year

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e in

ind/

m3

2007 2010 2012

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1500

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year

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e in

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2007 2010 2012

−1.0

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Copepoda.indet.

year

abun

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e in

ind/

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2006 2009 2012

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year

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2006 2009 2012

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year

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year

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year

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e in

ind/

m3

2006 2009 2012

020

000

6000

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00

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year

abun

danc

e in

ind/

m3

2006 2009 2012

−1.0

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0.5

1.0


year

abun

danc

e in

ind/

m3

2006 2009 2012

010

0030

0050

0070

00

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year

abun

danc

e in

ind/

m3

2006 2009 2012

0e+0

01e

+05

2e+0

53e

+05

Copepoda.indet.

year

abun

danc

e in

ind/

m3

UpperBothnian

LowerBothnian

Bal&cProperKaAegat/Skagerrak

Taxawithvaryingcoun;ngeffortamongsta;onsAppendiculariaspp.

2006 2009 2012

050

0010

000

1500

0

Acartia bifilosa

year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

2000

0

Acartia.indet.

year

abun

danc

e in

ind/

m3

2006 2009 2012

020

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000

1400

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year

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e in

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year

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danc

e in

ind/

m3

2006 2009 2012

020

000

6000

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year

abun

danc

e in

ind/

m3

2006 2009 2012

010

000

3000

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000


year

abun

danc

e in

ind/

m3

2006 2009 2012

050

0010

000

1500

0

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year

abun

danc

e in

ind/

m3

2006 2009 2012

0e+0

01e

+05

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53e

+05

Copepoda.indet.

year

abun

danc

e in

ind/

m3

JuvenileAppendicularian.Source:scripps.ucsd.edu

Bal&c

Results&Conclusion

•  InprincipleitispossibletoobtainEBVdataproductsfromaggregatedmarinedatasets,butonlyforafewabundantandtypicalindicatorspecies

•  MonitoringdataareneithersufficientnorconsistentenoughtocalculateEBVsforrarespecies,specieswithtaxonomicuncertain&es,andspeciesrequiringspecialistsforiden&fica&on

•  WiththecurrentlyavailabledataEBV’smayonlybecalculatedforfewwell-

recognisedandabundantspecies.

•  EBVsneedtobecalculatedatseveralspa&alscalessimultaneously,becausedifferentsub-regionaltrendsmaycounteractanotheronahigherspa&allevel.

•  amajorproblemisthelack&inconsistencyofbasictaxonomicknowledgeacrossspaceand&me,makingaggregateddatasetslesscomparable.

•  Itisworthtoretroac&velyimprovedocumenta&onandmetadataformonitoringmeasurements(e.g.add&mestamps).ThiswillenhancethequalityofEBVs.

For Review Only

Page 1

Figure 5 1737

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Kisslingetal(2017)Buildingessen&albiodiversityvariables(EBVs)ofspeciesdistribu&onandabundanceataglobalscale.BiologicalReviews.InReview.

Canwecalculatetrendswiththedatacollec&onmethodsweusetoday?

No, we need to implement new methods & technologies for data collection

Thank you for your attention!

calculang essen&al biodiversity variables (ebv) for ... · calculang essen&al biodiversity...

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