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Seminar on BioSceneMada project at Kew (July 2015)

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Kew – July, 21st 2015 BioSceneMada Biodiversity scenarios under the effect of climate change and future deforestation in Madagascar Ghislain Vieilledent1 Tom Allnutt2 Clovis Grinand3 Miguel Pedrono4 Jean-Roger Rakotoarijaona 5 Dimby Razafimpahanana2 [1] Cirad BSEF, [2] WCS, [3] ETC Terra, [4] Cirad AGIR, [5] ONE
FRB BioSceneMada 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Project summary 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats An unparralleled biodiversity Madagascar: top 3 of the countries with mega-diversity Vascular plants: 12000 species, endemism=85% Trees: endemism=96% Invertebrates: 5800 species, endemism=86%
FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Concentrated in forests Tropical forests >50% of the terrestrial species
FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Deforestation and demography 10 to 15% of original forest Deforestation rate: ∼1%·yr−1 1950–2000: 10% of species committed to extinction Demographic rate: >3%·yr−1 Doubling-time: 25 years
FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Predicted climate change Mean temperature increase: +1.1 to +2.7◦ C Wetter summer (up to +200 mm.yr−1 ) Drier winter in the SE (down to -100 mm.yr−1 ) and wetter winter elsewhere (up to +100 mm.yr−1 )
FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Predicted climate change Andriamasimanana 2013, Vieilledent 2013, Raxworthy 2008
FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Predicted climate change
FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Predicted climate change
FRB BioSceneMada Project summary Objectives: conservation planning 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Project summary Objectives: conservation planning Objectives Biodiversity conservation Anticipating climate change and deforestation Conservation planning Adapting the protected area network Biodiversity safeguards for REDD+ projects
FRB BioSceneMada Project summary Objectives: conservation planning Deliverables Id Deliverables 1 Biodiversity map 2 Maps of future deforestation (2050, 2100) 3 Maps of future biodiversity under climate change (refugea areas, areas with high risk of biodiversity loss) 4 Maps showing the overlap between future refugea areas for biodiversity and areas with high risk of deforestation
FRB BioSceneMada Project summary Organization: partners and funding 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Project summary Organization: partners and funding Project partners Id Name Institution Tasks 1 Ghislain Vieilledent Cirad UMR BSEF Coordination 2 Tom Allnut WCS Biodiversity map and GDM 3 Clovis Grinand ETC Terra Deforestation model 4 Miguel Pedrono Cirad UMR AGIR Conservation planning 5 Jean-Roger Rakotoarijaona ONE Madagascar Coordination with stakeholders 6 Dimby Razafimpahanana WCS Biodiversity data
Timetable
FRB BioSceneMada Project summary Organization: partners and funding Timetable After one year of work: project advances First results for the deforestation model Biodiversity data-set Modelling β diversity Perspectives
FRB BioSceneMada Project summary Organization: partners and funding Funding FRB: Fondation pour la Recherche sur la Biodiversit´e FFEM: Fond Fran¸cais pour l’Environnement Mondial
FRB BioSceneMada Deforestation model 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Deforestation model Objectives Modelling deforestation Estimating deforestation intensity Spatial factors explaining location Forecasting deforestation Various scenarios of intensity On the long term: 2050-2100
FRB BioSceneMada Deforestation model Maps of past deforestation 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Deforestation model Maps of past deforestation Maps of past deforestation 1 1. Harper et al. 2007, Environmental Conservation Forest map c.1973-1990-2000 with clouds 2 2. Hansen et al. 2013, Science Tree cover map in 2000 without clouds Deforestation map 2000-2012
FRB BioSceneMada Deforestation model Maps of past deforestation Maps of past deforestation Clouds were remove from 1990 and 2000 Harper’s forest map Assuming a tree cover >75% in moist forest for 2000 Hansen’s map Deforestation using Hansen’s data on the period 2000-2010
FRB BioSceneMada Deforestation model Maps of past deforestation Maps of past deforestation Results: 1990-2000-2010 deforestation maps Without clouds At 30m resolution http://bioscenemada.net/ forestmaps
FRB BioSceneMada Deforestation model Maps of past deforestation Maps of past deforestation
FRB BioSceneMada Deforestation model Maps of past deforestation Maps of past deforestation
FRB BioSceneMada Deforestation model Intensity 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Deforestation model Intensity Deforestation intensity Deforestation intensity varies at regional scale 120 × 120 km cells Deforestation intensity by cell Surfaces (ha.yr−1 ), rates (%.yr−1 ) ? National 1990-2010: ∼73,000 ha.yr−1
FRB BioSceneMada Deforestation model Intensity Deforestation intensity
FRB BioSceneMada Deforestation model Intensity Deforestation intensity No sampling for estimating deforestation intensity Wall-to-wall approach Surfaces (ha.yr−1 )
FRB BioSceneMada Deforestation model Intensity Deforestation intensity
FRB BioSceneMada Deforestation model Intensity Deforestation intensity
FRB BioSceneMada Deforestation model Location 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Deforestation model Location Deforestation location Objectives Which pixels are going to be deforested first in a cell ? What are the spatial factors explaining the deforestation location ?
FRB BioSceneMada Deforestation model Location Deforestation location Type of spatial factors Landscape factors: dist. to forest edge, dist. to past deforestation Accessibility factors: altitude, dist. to road, town, river Land-policy factors: protected area network Data Period 2000-2010 Sampling: 20,000 points (10,000 deforested) By ecoregion
FRB BioSceneMada Deforestation model Location Deforestation location Model Random Forest algorithm deforestation = f(spatial factors)
FRB BioSceneMada Deforestation model Location Deforestation location Variable effects for moist forest ecoregion 0 1 SAPM Probability −0.4−0.20.00.2 0 500 1000 1500 2000 −0.4−0.20.00.2 Altitude 0 5000 10000 15000 20000 25000 −0.40.00.4 Distance to past deforestation Probability 0 2000 4000 6000 8000 10000 −0.6−0.20.2 Distance to forest edge 0 20000 40000 60000 80000 −0.20−0.100.00 Distance to river 0 20000 40000 60000 80000 −0.15−0.050.00 Distance to road Moist forest Model performance 0 1 Error 0 6471 3506 0.35 1 1865 8114 0.19 Variable importance %IncMSE sapm 107 altitude 133 dist.patch 97 dist.edge 105 dist.river 91 dist.road 84
FRB BioSceneMada Deforestation model Location Deforestation location Variable effects for dry forest ecoregion 0 1 SAPM Probability −0.20−0.05 0 500 1000 1500 −0.6−0.30.0 Altitude 0 20000 40000 60000 −0.8−0.20.4 Distance to past deforestation Probability 0 1000 2000 3000 4000 5000 6000 −0.20−0.05 Distance to forest edge 0 20000 40000 60000 −0.14−0.08 Distance to river 0 50000 100000 150000 200000 −0.5−0.20.0 Distance to road 0 20000 40000 60000 80000 100000 120000 −0.20−0.100.00 Distance to town Dry forest Model performance 0 1 Error 0 7067 2889 0.29 1 2163 7820 0.22 Variable importance %IncMSE sapm 65 altitude 133 dist.patch 209 dist.edge 101 dist.river 114 dist.road 122 dist.town 116
FRB BioSceneMada Deforestation model Location Deforestation location Variable effects for spiny forest ecoregion 0 1 SAPM Probability 0.00.51.01.52.02.53.0 0 10000 20000 30000 40000 −1012345 Distance to past deforestation 0 1000 2000 3000 4000 5000 0246 Distance to forest edge Probability Spiny forest Model performance 0 1 Error 0 5694 4299 0.43 1 2180 7820 0.22 Variable importance %IncMSE sapm 28 dist.patch 51 dist.edge 33
FRB BioSceneMada Deforestation model Location Deforestation location Model Spatial probability of deforestation Coherent patterns
FRB BioSceneMada Deforestation model Location Deforestation location
FRB BioSceneMada Deforestation model Location Deforestation location
FRB BioSceneMada Deforestation model Location Deforestation location
FRB BioSceneMada Deforestation model Location Deforestation location
FRB BioSceneMada Deforestation model Forecast 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Deforestation model Forecast Forecasting deforestation: 2050 2050 Method 1 Deforested surface → number of forest pixels to remove 2 Map of probabilities: first pixels to be deforested in a grid cell Forest map in 2050 Remaining forest in protected areas Forest 2010: 9.3 Mha / Forest 2050: 6.4 Mha
FRB BioSceneMada Deforestation model Forecast Forecasting deforestation: 2050
FRB BioSceneMada Deforestation model Forecast Forecasting deforestation: 2050
FRB BioSceneMada Deforestation model Forecast Forecasting deforestation: 2050
FRB BioSceneMada Deforestation model Forecast Forecasting deforestation: 2100 2100 Forest map in 2100 Deforestation in protected areas Remaining forest in remote areas (Masoala, Tsaratanana) Forest 2010: 9.3 Mha / Forest 2100: 2.8 Mha !!
FRB BioSceneMada Deforestation model Forecast Forecasting deforestation: 2100
FRB BioSceneMada Deforestation model Forecast Forecasting deforestation: 2100
FRB BioSceneMada Deforestation model Forecast Forecasting deforestation: 2100
FRB BioSceneMada Deforestation model Forecast Perspectives Testing several scenarios 1 Constant surfaces 2 Increasing surfaces (link with demography)
FRB BioSceneMada Biodiversity data 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Biodiversity data Objectives 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Biodiversity data Objectives Objectives 1. Compiling biodiversity data Occurrence data (with spatial coordinates) For a maximal number of species In a maximal number of taxonomic groups
FRB BioSceneMada Biodiversity data Objectives Objectives 2. Deriving biodiversity maps α diversity: species diversity (richness, Shannon, Simpson) β diversity: differentiation among habitats
FRB BioSceneMada Biodiversity data Objectives Objectives 3. Vulnerability to climate change At the species level At the community level Refugia for biodiversity Loss of habitats
FRB BioSceneMada Biodiversity data Sources and data-set compilation 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Biodiversity data Sources and data-set compilation Sources Data portals: Rebioma, BirdLife, AntWeb Data from published scientific articles Private data: Kew, Universities, Cirad, ONE, MEF
FRB BioSceneMada Biodiversity data Sources and data-set compilation Data cleaning Checking taxonomy taxize R package Plant data: TNRS (Taxonomic Name Resolution Service) Animal data: GNR (Global Names Resolver) Removing data Incomplete observations (coordinates) Unresolved taxonomic name
FRB BioSceneMada Biodiversity data Biodiversity data-set 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Biodiversity data Biodiversity data-set Biodiversity data Group Species Genus Obs. Main source Plants Trees 557 329 85236 IEFN Palms 201 17 5456 Kew (M. Rakotoarinivo, W. Baker) Ferns 651 76 10544 MNHN (F. Rakotondrainibe) Legumes 846 151 22693 Kew (J. Moat), MNHN (J.-N. Labat) Grasses 338 144 9933 Kew (M. Voronstova) Vertebrates Mammals (−lemurs) 318 50 2390 Rebioma, Vahatra Lemurs 64 15 3136 ONE Birds 214 147 40955 eBird, Vahatra Reptiles 448 70 5080 M. Vences, R. Pearson Amphibians 336 28 2550 M. Vences Invertebrates Snails 618 68 2560 T. Pearce Ants 513 103 68845 AntWeb Butterflies 407 112 13287 D. Lees Diptera 72 21 1595 Rebioma Coleoptera 30 16 164 Rebioma TOTAL= 5613 1347 274424
FRB BioSceneMada Biodiversity data Biodiversity data-set Representativity regarding known biodiversity Group BSM Goodman 2005 Plants Trees/Palms 758 2625 Ferns 651 586 Legumes 846 573 Grasses 338 34 Vertebrates Mammals 382 131 Birds 214 209 Reptiles 448 345 Amphibians 336 199 Invertebrates Snails 618 671 Ants 513 583 Butterflies 407 300 Diptera 72 1796 Coleoptera 30 351 Others others 0 6790 TOTAL= 5613 15373
FRB BioSceneMada Biodiversity data Biodiversity data-set Originality: comparison with other studies Comparison Allnutt et al. 2008: 2843 Kremen et al. 2008: 2315 (Ants, Butterflies, Frogs, Geckos, Lemurs and Plants) BioSceneMada: 5613
FRB BioSceneMada Modelling biodiversity 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Modelling biodiversity SDM: Species Distribution Models 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Modelling biodiversity SDM: Species Distribution Models Species distribution models At the species level (> 5613 SDMs !) Species presence = f(present climate + other factors) Climate: Temp, TSeas, Precip, CWD, dry months Ensemble modelling approach: Biomod R package GLM, GAM, Random Forests, MaxEnt Bioclimatic niche of each species
FRB BioSceneMada Modelling biodiversity SDM: Species Distribution Models Species distribution models Prediction of future species distribution Future climate: MadaClim (IPPC Fifth Assessment) Ensemble forecasting approach (3 GCMs, 2 RCPs) http://madaclim.org
FRB BioSceneMada Modelling biodiversity SDM: Species Distribution Models Species distribution models At the species level R script written for Baobab species Run the R script for the 5613 species Atlas of Madagascar biodiversity Bioclimatic niche of each species Vulnerability of species to climate change Automatic report generation (new data, new climate models) (At the community level) Compute biodiversity indices (α and β diversity) Identify refugia for biodiversity under climate change
FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: Generalized Dissimilarity Models Habitat conservation (not only hotspots of biodiversity) Identifying the environmental factors determining β diversity How communities will answer to climate change?
FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: Generalized Dissimilarity Models Environmental factors determining β diversity Climate gradient hypothesis Retreat-dispersion watersheds hypothesis
FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: Generalized Dissimilarity Models Retreat-dispersion watersheds hypothesis Demonstrated for some groups: Lemurs (Wilm´e 2006), Reptiles (Pearson 2009), Legumes (Buerki 2015)
FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: Generalized Dissimilarity Models At the community level Dissimilarity between pairs of locations Bray-Curtis index One single model for all the 5613 species β diversity R package gdm
FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: plants Id Models Deviance 1 NULL Deviance 634 2 Clim only 24.5% 3 Watersheds only 15.8% 4 Clim and Spatial 28.6% 5 Clim and Watersheds 30.3% 6 Clim, Spatial and Watersheds 30.7%
FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: animals Id Models Deviance 1 NULL Deviance 2125 2 Clim only 5.5% 3 Watersheds only 1.6% 4 Clim and Spatial 5.6% 5 Clim and Watersheds 6.1% 6 Clim, Spatial and Watersheds 6.1%
FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: impact of watersheds on projections In the past, no anthropogenic disturbance: watersheds → retreat-dispersion pathways → local endemism Nowadays: degraded landscape + absence of dispersers → zero-colonization hypothesis Likely no effect of watersheds in the future Disentangling effects of climate and watersheds in explaining present biodiversity
FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models Perspectives Future species distribution (SDM) Identify climate refuge for biodiversity (SDM + GDM) Overlap biodiversity maps and deforestation maps Identify high priority areas for conservation
FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models . . . Thank you for attention . . .

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Seminar kew

  • 1. Kew – July, 21st 2015 BioSceneMada Biodiversity scenarios under the effect of climate change and future deforestation in Madagascar Ghislain Vieilledent1 Tom Allnutt2 Clovis Grinand3 Miguel Pedrono4 Jean-Roger Rakotoarijaona 5 Dimby Razafimpahanana2 [1] Cirad BSEF, [2] WCS, [3] ETC Terra, [4] Cirad AGIR, [5] ONE
  • 2. FRB BioSceneMada 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 3. FRB BioSceneMada Project summary 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 4. FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 5. FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats An unparralleled biodiversity Madagascar: top 3 of the countries with mega-diversity Vascular plants: 12000 species, endemism=85% Trees: endemism=96% Invertebrates: 5800 species, endemism=86%
  • 6. FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Concentrated in forests Tropical forests >50% of the terrestrial species
  • 7. FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Deforestation and demography 10 to 15% of original forest Deforestation rate: ∼1%·yr−1 1950–2000: 10% of species committed to extinction Demographic rate: >3%·yr−1 Doubling-time: 25 years
  • 8. FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Predicted climate change Mean temperature increase: +1.1 to +2.7◦ C Wetter summer (up to +200 mm.yr−1 ) Drier winter in the SE (down to -100 mm.yr−1 ) and wetter winter elsewhere (up to +100 mm.yr−1 )
  • 9. FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Predicted climate change Andriamasimanana 2013, Vieilledent 2013, Raxworthy 2008
  • 10. FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Predicted climate change
  • 11. FRB BioSceneMada Project summary Context: Madagascar biodiversity and threats Predicted climate change
  • 12. FRB BioSceneMada Project summary Objectives: conservation planning 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 13. FRB BioSceneMada Project summary Objectives: conservation planning Objectives Biodiversity conservation Anticipating climate change and deforestation Conservation planning Adapting the protected area network Biodiversity safeguards for REDD+ projects
  • 14. FRB BioSceneMada Project summary Objectives: conservation planning Deliverables Id Deliverables 1 Biodiversity map 2 Maps of future deforestation (2050, 2100) 3 Maps of future biodiversity under climate change (refugea areas, areas with high risk of biodiversity loss) 4 Maps showing the overlap between future refugea areas for biodiversity and areas with high risk of deforestation
  • 15. FRB BioSceneMada Project summary Organization: partners and funding 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 16. FRB BioSceneMada Project summary Organization: partners and funding Project partners Id Name Institution Tasks 1 Ghislain Vieilledent Cirad UMR BSEF Coordination 2 Tom Allnut WCS Biodiversity map and GDM 3 Clovis Grinand ETC Terra Deforestation model 4 Miguel Pedrono Cirad UMR AGIR Conservation planning 5 Jean-Roger Rakotoarijaona ONE Madagascar Coordination with stakeholders 6 Dimby Razafimpahanana WCS Biodiversity data
  • 18. FRB BioSceneMada Project summary Organization: partners and funding Timetable After one year of work: project advances First results for the deforestation model Biodiversity data-set Modelling β diversity Perspectives
  • 19. FRB BioSceneMada Project summary Organization: partners and funding Funding FRB: Fondation pour la Recherche sur la Biodiversit´e FFEM: Fond Fran¸cais pour l’Environnement Mondial
  • 20. FRB BioSceneMada Deforestation model 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 21. FRB BioSceneMada Deforestation model Objectives Modelling deforestation Estimating deforestation intensity Spatial factors explaining location Forecasting deforestation Various scenarios of intensity On the long term: 2050-2100
  • 22. FRB BioSceneMada Deforestation model Maps of past deforestation 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 23. FRB BioSceneMada Deforestation model Maps of past deforestation Maps of past deforestation 1 1. Harper et al. 2007, Environmental Conservation Forest map c.1973-1990-2000 with clouds 2 2. Hansen et al. 2013, Science Tree cover map in 2000 without clouds Deforestation map 2000-2012
  • 24. FRB BioSceneMada Deforestation model Maps of past deforestation Maps of past deforestation Clouds were remove from 1990 and 2000 Harper’s forest map Assuming a tree cover >75% in moist forest for 2000 Hansen’s map Deforestation using Hansen’s data on the period 2000-2010
  • 25. FRB BioSceneMada Deforestation model Maps of past deforestation Maps of past deforestation Results: 1990-2000-2010 deforestation maps Without clouds At 30m resolution http://bioscenemada.net/ forestmaps
  • 26. FRB BioSceneMada Deforestation model Maps of past deforestation Maps of past deforestation
  • 27. FRB BioSceneMada Deforestation model Maps of past deforestation Maps of past deforestation
  • 28. FRB BioSceneMada Deforestation model Intensity 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 29. FRB BioSceneMada Deforestation model Intensity Deforestation intensity Deforestation intensity varies at regional scale 120 × 120 km cells Deforestation intensity by cell Surfaces (ha.yr−1 ), rates (%.yr−1 ) ? National 1990-2010: ∼73,000 ha.yr−1
  • 31. FRB BioSceneMada Deforestation model Intensity Deforestation intensity No sampling for estimating deforestation intensity Wall-to-wall approach Surfaces (ha.yr−1 )
  • 34. FRB BioSceneMada Deforestation model Location 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 35. FRB BioSceneMada Deforestation model Location Deforestation location Objectives Which pixels are going to be deforested first in a cell ? What are the spatial factors explaining the deforestation location ?
  • 36. FRB BioSceneMada Deforestation model Location Deforestation location Type of spatial factors Landscape factors: dist. to forest edge, dist. to past deforestation Accessibility factors: altitude, dist. to road, town, river Land-policy factors: protected area network Data Period 2000-2010 Sampling: 20,000 points (10,000 deforested) By ecoregion
  • 37. FRB BioSceneMada Deforestation model Location Deforestation location Model Random Forest algorithm deforestation = f(spatial factors)
  • 38. FRB BioSceneMada Deforestation model Location Deforestation location Variable effects for moist forest ecoregion 0 1 SAPM Probability −0.4−0.20.00.2 0 500 1000 1500 2000 −0.4−0.20.00.2 Altitude 0 5000 10000 15000 20000 25000 −0.40.00.4 Distance to past deforestation Probability 0 2000 4000 6000 8000 10000 −0.6−0.20.2 Distance to forest edge 0 20000 40000 60000 80000 −0.20−0.100.00 Distance to river 0 20000 40000 60000 80000 −0.15−0.050.00 Distance to road Moist forest Model performance 0 1 Error 0 6471 3506 0.35 1 1865 8114 0.19 Variable importance %IncMSE sapm 107 altitude 133 dist.patch 97 dist.edge 105 dist.river 91 dist.road 84
  • 39. FRB BioSceneMada Deforestation model Location Deforestation location Variable effects for dry forest ecoregion 0 1 SAPM Probability −0.20−0.05 0 500 1000 1500 −0.6−0.30.0 Altitude 0 20000 40000 60000 −0.8−0.20.4 Distance to past deforestation Probability 0 1000 2000 3000 4000 5000 6000 −0.20−0.05 Distance to forest edge 0 20000 40000 60000 −0.14−0.08 Distance to river 0 50000 100000 150000 200000 −0.5−0.20.0 Distance to road 0 20000 40000 60000 80000 100000 120000 −0.20−0.100.00 Distance to town Dry forest Model performance 0 1 Error 0 7067 2889 0.29 1 2163 7820 0.22 Variable importance %IncMSE sapm 65 altitude 133 dist.patch 209 dist.edge 101 dist.river 114 dist.road 122 dist.town 116
  • 40. FRB BioSceneMada Deforestation model Location Deforestation location Variable effects for spiny forest ecoregion 0 1 SAPM Probability 0.00.51.01.52.02.53.0 0 10000 20000 30000 40000 −1012345 Distance to past deforestation 0 1000 2000 3000 4000 5000 0246 Distance to forest edge Probability Spiny forest Model performance 0 1 Error 0 5694 4299 0.43 1 2180 7820 0.22 Variable importance %IncMSE sapm 28 dist.patch 51 dist.edge 33
  • 41. FRB BioSceneMada Deforestation model Location Deforestation location Model Spatial probability of deforestation Coherent patterns
  • 46. FRB BioSceneMada Deforestation model Forecast 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 47. FRB BioSceneMada Deforestation model Forecast Forecasting deforestation: 2050 2050 Method 1 Deforested surface → number of forest pixels to remove 2 Map of probabilities: first pixels to be deforested in a grid cell Forest map in 2050 Remaining forest in protected areas Forest 2010: 9.3 Mha / Forest 2050: 6.4 Mha
  • 51. FRB BioSceneMada Deforestation model Forecast Forecasting deforestation: 2100 2100 Forest map in 2100 Deforestation in protected areas Remaining forest in remote areas (Masoala, Tsaratanana) Forest 2010: 9.3 Mha / Forest 2100: 2.8 Mha !!
  • 55. FRB BioSceneMada Deforestation model Forecast Perspectives Testing several scenarios 1 Constant surfaces 2 Increasing surfaces (link with demography)
  • 56. FRB BioSceneMada Biodiversity data 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 57. FRB BioSceneMada Biodiversity data Objectives 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 58. FRB BioSceneMada Biodiversity data Objectives Objectives 1. Compiling biodiversity data Occurrence data (with spatial coordinates) For a maximal number of species In a maximal number of taxonomic groups
  • 59. FRB BioSceneMada Biodiversity data Objectives Objectives 2. Deriving biodiversity maps α diversity: species diversity (richness, Shannon, Simpson) β diversity: differentiation among habitats
  • 60. FRB BioSceneMada Biodiversity data Objectives Objectives 3. Vulnerability to climate change At the species level At the community level Refugia for biodiversity Loss of habitats
  • 61. FRB BioSceneMada Biodiversity data Sources and data-set compilation 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 62. FRB BioSceneMada Biodiversity data Sources and data-set compilation Sources Data portals: Rebioma, BirdLife, AntWeb Data from published scientific articles Private data: Kew, Universities, Cirad, ONE, MEF
  • 63. FRB BioSceneMada Biodiversity data Sources and data-set compilation Data cleaning Checking taxonomy taxize R package Plant data: TNRS (Taxonomic Name Resolution Service) Animal data: GNR (Global Names Resolver) Removing data Incomplete observations (coordinates) Unresolved taxonomic name
  • 64. FRB BioSceneMada Biodiversity data Biodiversity data-set 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 65. FRB BioSceneMada Biodiversity data Biodiversity data-set Biodiversity data Group Species Genus Obs. Main source Plants Trees 557 329 85236 IEFN Palms 201 17 5456 Kew (M. Rakotoarinivo, W. Baker) Ferns 651 76 10544 MNHN (F. Rakotondrainibe) Legumes 846 151 22693 Kew (J. Moat), MNHN (J.-N. Labat) Grasses 338 144 9933 Kew (M. Voronstova) Vertebrates Mammals (−lemurs) 318 50 2390 Rebioma, Vahatra Lemurs 64 15 3136 ONE Birds 214 147 40955 eBird, Vahatra Reptiles 448 70 5080 M. Vences, R. Pearson Amphibians 336 28 2550 M. Vences Invertebrates Snails 618 68 2560 T. Pearce Ants 513 103 68845 AntWeb Butterflies 407 112 13287 D. Lees Diptera 72 21 1595 Rebioma Coleoptera 30 16 164 Rebioma TOTAL= 5613 1347 274424
  • 66. FRB BioSceneMada Biodiversity data Biodiversity data-set Representativity regarding known biodiversity Group BSM Goodman 2005 Plants Trees/Palms 758 2625 Ferns 651 586 Legumes 846 573 Grasses 338 34 Vertebrates Mammals 382 131 Birds 214 209 Reptiles 448 345 Amphibians 336 199 Invertebrates Snails 618 671 Ants 513 583 Butterflies 407 300 Diptera 72 1796 Coleoptera 30 351 Others others 0 6790 TOTAL= 5613 15373
  • 67. FRB BioSceneMada Biodiversity data Biodiversity data-set Originality: comparison with other studies Comparison Allnutt et al. 2008: 2843 Kremen et al. 2008: 2315 (Ants, Butterflies, Frogs, Geckos, Lemurs and Plants) BioSceneMada: 5613
  • 68. FRB BioSceneMada Modelling biodiversity 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 69. FRB BioSceneMada Modelling biodiversity SDM: Species Distribution Models 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 70. FRB BioSceneMada Modelling biodiversity SDM: Species Distribution Models Species distribution models At the species level (> 5613 SDMs !) Species presence = f(present climate + other factors) Climate: Temp, TSeas, Precip, CWD, dry months Ensemble modelling approach: Biomod R package GLM, GAM, Random Forests, MaxEnt Bioclimatic niche of each species
  • 71. FRB BioSceneMada Modelling biodiversity SDM: Species Distribution Models Species distribution models Prediction of future species distribution Future climate: MadaClim (IPPC Fifth Assessment) Ensemble forecasting approach (3 GCMs, 2 RCPs) http://madaclim.org
  • 72. FRB BioSceneMada Modelling biodiversity SDM: Species Distribution Models Species distribution models At the species level R script written for Baobab species Run the R script for the 5613 species Atlas of Madagascar biodiversity Bioclimatic niche of each species Vulnerability of species to climate change Automatic report generation (new data, new climate models) (At the community level) Compute biodiversity indices (α and β diversity) Identify refugia for biodiversity under climate change
  • 73. FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models 1 Project summary Context: Madagascar biodiversity and threats Objectives: conservation planning Organization: partners and funding 2 Deforestation model Maps of past deforestation Intensity Location Forecast 3 Biodiversity data Objectives Sources and data-set compilation Biodiversity data-set 4 Modelling biodiversity SDM: Species Distribution Models GDM: Generalized Dissimilarity Models
  • 74. FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: Generalized Dissimilarity Models Habitat conservation (not only hotspots of biodiversity) Identifying the environmental factors determining β diversity How communities will answer to climate change?
  • 75. FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: Generalized Dissimilarity Models Environmental factors determining β diversity Climate gradient hypothesis Retreat-dispersion watersheds hypothesis
  • 76. FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: Generalized Dissimilarity Models Retreat-dispersion watersheds hypothesis Demonstrated for some groups: Lemurs (Wilm´e 2006), Reptiles (Pearson 2009), Legumes (Buerki 2015)
  • 77. FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: Generalized Dissimilarity Models At the community level Dissimilarity between pairs of locations Bray-Curtis index One single model for all the 5613 species β diversity R package gdm
  • 78. FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: plants Id Models Deviance 1 NULL Deviance 634 2 Clim only 24.5% 3 Watersheds only 15.8% 4 Clim and Spatial 28.6% 5 Clim and Watersheds 30.3% 6 Clim, Spatial and Watersheds 30.7%
  • 79. FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: animals Id Models Deviance 1 NULL Deviance 2125 2 Clim only 5.5% 3 Watersheds only 1.6% 4 Clim and Spatial 5.6% 5 Clim and Watersheds 6.1% 6 Clim, Spatial and Watersheds 6.1%
  • 80. FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models GDM: impact of watersheds on projections In the past, no anthropogenic disturbance: watersheds → retreat-dispersion pathways → local endemism Nowadays: degraded landscape + absence of dispersers → zero-colonization hypothesis Likely no effect of watersheds in the future Disentangling effects of climate and watersheds in explaining present biodiversity
  • 81. FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models Perspectives Future species distribution (SDM) Identify climate refuge for biodiversity (SDM + GDM) Overlap biodiversity maps and deforestation maps Identify high priority areas for conservation
  • 82. FRB BioSceneMada Modelling biodiversity GDM: Generalized Dissimilarity Models . . . Thank you for attention . . .