Monitoring human impacts and ecosystem function in the Eastern Arc biodiversity hotspot of Tanzania...
-
Upload
esmond-godfrey-riley -
Category
Documents
-
view
226 -
download
0
Transcript of Monitoring human impacts and ecosystem function in the Eastern Arc biodiversity hotspot of Tanzania...
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