Monitoring human impacts and ecosystem function in the Eastern Arc biodiversity hotspot of Tanzania

and Kenya.

Photo © info: www.easternarc.or.tz

Rob Marchant, Antje Ahrends, Andrew Balmford, Neil Burgess, Jemma Finch, Alistair Jump, Jon Lovett, Colin McClean, Amos Majule, Cassian Mumbi; Phil Platts,

Carsten Rahbek, Stephen Rucina, Pius Yanda

Environment Department University of York

•Palaeoecology, Biogeography, Phylogenetics•Modelling: developing bioclimatic approach•What are controls on Eastern Arc ecosystems•How are biodiverse areas formed •How will they change in the future•Use information to aid in valuation of services•Scenario and capacity development

Managing Dynamic Ecosystems - understanding ecosystem dynamics

The Eastern Arc Mountains: ideal for studying environmental change impacts

Ancient: In excess of 30 MY old (Kilimanjaro 1-2 MY) Forests ‘are’ remnants of Miocene pan-African tropical forest

Diverse geography: Upland areas vary in size and connectivity

Biodiversity hotspot: High proportion of endemic species

Socially important: 80% reduction in forest cover, important watershed, HEP, produce, tourism.Palaeoecological ‘desert’: a single site to understand long term ecology

The IOD: the unsung driver of climate change in Eastern Africa. Marchant et al., 2007

IOD: El Niño-like coupled ocean-atmosphere system – only differentiated from El Niño in 1997. Character and periodicity still being researched.

Climate controls

•ITCZ•Trade winds (NE/SW)•Altitude – lapse rate•Atlantic Ocean•Pacific Ocean•Indian Ocean

• Pollen

• Stable isotope

• Phytoliths

• Charcoal

• Plant & Insect Macrofossils

Palaeoecological Indicators

Tropical areas are highly responsive to environmental changes – indeed numerous times the tropical records have been precursors of environmental shifts ‘recorded’ at high-latitudes

Stager et al., 2007; Vershuren et al., 2005

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

Dep

th

23177

>42606

3870

37726

200

9008

20 40 20 40 60 20 40 20 40 60 80 20 20 20 40 60

Zone

Deva 3-b

Deva 3-a

Deva 2

Deva 1-b

Deva 1-a

Lowland forestMontane forestUpper montane forest

Eastern Arc stability?

Deva-DevaUluguru Mtns

Dama Swamp

Udzungwa Mtnsvs.

Biogeographical Research

Are we stating conservation priority areas in a circular fashion while potentially overlooking other important areas and basing theories on distribution of species richness on a biased pattern?

Using plant distribution data (2500 plots) to extrapolate forest types and character. Initially need to check for artefacts within the data

Impatiens sp. (Menegon)Nectarinia loveridgei (Finch)

Data Species data:

W3Tropicos from Missouri Botanical Gardens > 26,000 specimens Records in KITE database

Funding data: ODI Tropical Forests Information System, Critical Ecosystem

Partnership Fund, Other multi- and bi-lateral donors and indiv.

Environmental data: Forest area and altitudinal range, 8 climate variables (CRESS)

.69

Species richness

Other

PC productivity PC precipitation PC heterogeneity& area

-.23 .80-.05

Funding

.92

Climate Surfaces – for regional modeling

•0.05 dd CRES African surfaces. (Hutchinson)

•8 Climatic Variables:

-Mean temp warmest month-Mean temp coldest month-Monthly temp range -Mean total annual precipitation-Moisture index -Mean total precipitation wettest month-Mean total precipitation driest month-Mean total precipitation warmest month

Slope angleAspect

Elevation Species presence/absence

Detect spatialautocorrelation Calibrate generalised additive model

Elevation model(USGS)

Topographicalpredictors

Clean data

Validate site locations

Test for correlations

Weight absences

Fieldwork in Tz

Speciespresence/absence

Determine complexity of species-environment response curves

Climaticpredictors

Climate model(CRES)

Predict habitat suitability

Guide fieldwork / ground truth

Cross-validation

Cross-validation

Refine model

Probability distribution: Myrsine

melanophloeos

1.0

0.0

0.5

Iden

tify

poo

rly r

epr

esen

ted

resp

onse

cur

ves

Modelling the Eastern Arc September 17, 2007

Ground-truthingGround-truthing

Newtonia buchananii

North Pare Mountains

Podocarpus milianjanus (Habitat Suitability / Probability of occurrence)

Present 2025 2055 2085

Species richness (habitat suitability scores summed over 120 tree species)

Present 2025 2055 2085

Platts, 2007

Linking science with Linking science with stakeholders to sustain natural stakeholders to sustain natural

capitalcapital

Our vision: building a robust, scientifically credible and practical framework which

captures the true value of natural capital in development decisions for the Eastern Arc

1. Inventory services, people & landscapes

2. Model & map service production & use

3. Model & map service values

5. Map governance structures

6. Map winners & losers

8. Design mechanisms to capture service values

7. Explore plausible scenarios

4. Measure & map conservation costs

Valuing the Arc concept

• biodiversitybiodiversity• hydrologyhydrology• carboncarbon• timbertimber• non-timber non-timber • pollinationpollination• tourismtourism

Preliminary map of water provisionPreliminary map of water provision

ClimateTopographyLanduseWater abstraction / useSWAT InVest

Preliminary map of carbon storage

ClimateTopographyLandusePermanent plotsID and measurementRe-measure in 2010REED

Needs and gapsData needs•Inventories from under researched areas (BREAM)•Land use map and land use change map – ‘complicated’•Climate / environmental data•Ground-truthing of generated data

Methodological needs•Accounting for potentially bias in existing data•Model development (dispersal, animal interactions, land use)•Climate model development – inapprporiate climate models•Incorporation of socioeconomic trends within scenarios•Linking of results to policy (international to village)•Move through space and time scaling issues

Capacity needs•Teaching and direct dissemination of information (resource) within Governmental, Intergovernmental and NGO organizations•Portrayal of results in an appropriate manner•Linking exploratory tools such a models, INVEST, MARXAN•Generation of mechanisms (e.g. REDD) and appropriate governance to maximize opportunities