responding to evolving threats using innovative tools, technologies and datasets - kathy willis
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Professor Kathy Willis, Biodiversity Institute, University of OxfordResponding to evolving threats using innovative tools, technologies and datasetsEvolving threats
Increasing demand on landGlobal population most likely to peak ~9B
20002050210012B8B4BPopulation projection (Lutz & Samir 2010) 20%60%95%People will be richer and demand higher quality dietChinaIndiaAfrica1970198019902000Livestock consumptionDeveloped nationsLivestock consumption (FAO 2009) 333
Hwange National Park, ZimbabweProtected (12%)Not protected (88%)Biodiversity declines
Stokard 2010. Despite progress, biodiversity declines. Science. 329: 1272-1273.6Is all lost for biodiversity?
Shift away from traditional protection recommendations to one that attempts to incorporate people, value the ecosystem services and create sustainable system8Convention of Biological Diversity targets (2011)Target 5 By 2020, the rate of loss of all natural habitats, including forests, is at least halved and where feasible brought close to zero, and degradation and fragmentation is significantly reduced.
Target 14 By 2020, ecosystems that provide essential services, including services related to water, and contribute to health, livelihoods and well-being, are restored and safeguarded
Target 15By 2020, ecosystem resilience and the contribution of biodiversity to carbon stocks has been enhanced, through conservation and restoration
What innovative tools, technologies and datasets do we need to:
Identify and reduce loss of natural habitats?Enhance ecosystem resilience?Conserve ecosystems that provide essential services related to human well-being?
Talk outlineCase study:Determining the ecological value of landscapes beyond protected areasWhat tools are available to Identify and reduce loss of natural habitats?Willis, K.J. et al., 2012, Biological Conservation, 147, 3-12
Where can we damage?
Points arising from workshops with StatoilNeed a tool that provides estimation of ecological value of land outside of protected areasTo produce landscape information at a spatial scale less than 500m;Use existing available web-based databases;Produce simplified displays preferably maps;Simple user input;Able to assess any region in world;
Global vegetation cover at 300m pixel size resolution (GLOBCOVER (Bicheron et al. 2009)What is the finest spatial resolution (pixel size)?What data are needed to provide an spatial distribution of ecological value on a landscape?Need data on:Key ecological properties of the landscape (e.g. biodiversity, threatened species)Key features for supporting ecosystem functions (e.g. connectivity (migration routes, wetlands) habitat integrity, resilience)Their spatial configuration on the landscape.
Biodiversity dataFor most regions in the world will rarely be enough detailed species data to obtain clear pictureNecessary to model predictive diversity across landscape (generalised dissimilarity modelling)Can then use combination of point species occurrences + environmental variables to predict diversity (spatial heterogeneity) across landscape
Global Biodiversity (GBIF):
Data Portal (http://data.gbif.org) that provides access to more than 330 million records of species occurrence worldwide
Biodiversity species occurrence dataGBIF network Data CoverageLast updated: 2010-09-16
>330 million occurrence records from >8,500 datasets from >360 publishers and spanning a wide range of geospatial, temporal and taxonomic coverages being shared through distributed networkData sources for environmental variables
BIOCLIM/WORLDCLIM Annual mean temperature (BIO1) Temperature seasonality (BIO4) Annual precipitation (BIO12) Precipitation seasonality (BIO15)Global Lakes and Wetlands Database Distance to lakes, rivers, wetlands, etc. FAO Soil data % nitrogen % water in soil (soil/water holding capacity)
19Beta-diversity for Canadian site measured using Generalised Dissimilarity modelling
Value provided for every 300m pixelThreatened species data sources2010 IUCN Red List of Threatened Species
Assessments for ~56,000 species, of which about 28,000 have spatial data.
Consider all categories in concession area except least concerned and extinct
More threatened species in pixel, higher its value
Threatened species distribution in Canadian concession area
Fragmentation dataSpatial continuity of natural vegetation based on the size (ha) of each continuous patch
Computer programme FRAGSTATS (McGarigal and Marks, 1995) defines individual patches and calculates patch size
Apply FRAGSTATS to vegetation cover
Greater the patch size, higher the ecological value
Fragmentation map Canadian concession areas
Global Register of Migratory Species
Contains list of 2,880 migratory vertebrate species in digital format
Also their threat status according to the International Red List 2000,
Digital maps for 545 species
Sum the number of migratory ranges occurring in each per pixel
www.groms.de Connectivity (1) Migratory routesConnectivity (2) Migration processesPrioritize pixels that support migratory processes:
Rivers, wetlands and lakes (at 300m resolution)
Adjacent pixels to rivers (so as to allow migratory corridors)
Data source: HYDROSHEDS (USGS), Global lakes & wetlands database (WWF)Water bodies and drainage networks for Canadian concession area
Global Lakes and Wetlands Database, HYDROSHEDS; 30m pixel resolutionResilienceAreas of landscape that are particularly resistant to climate change/disturbanceAreas of landscape that are able to recover from disturbance quicker than others
Resilience: measured through ability of vegetation to maintain relatively high levels of productivity despite low levels of rainfall
Rainfall (mm) in driest monthAnnualized NPPVegetation TypeScoring Rule:
1, if highest quartile of productivity & lowest quartile of rainfall
0.5, if highest quartile of productivity & next lowest quartile of rainfall
Assessed per vegetation typeResilience, Canadian concession area
Factors and data sources used in LEFT
Willis, K.J. et al., 2012, Biological Conservation, 147, 3-12Final indexFinal index: Local ecological footprint valuation Species richnessVulnerabilityConnectivityFragmentationResilience++++
How accurate in comparison to field data?Cusuco, Honduras
Montane tropical moist forestSurveyed 2004-2010Extensive datasets e.g >50,000 records of terrestrial vertebrates in database
Field data on distributions of globally threatened vertebrates were collected from the two case study sites. The data were used to make and validate distribution models and hence map relative numbers of threatened species. The results were compared with LEFT results from queries on the same study areas.
The key point to make when you show these slides is that LEFT does quite a good job of predicting the set of threatened species present and their general distribution in the landscape. Commision errors seem to be more prevalent than omission errors for species and land (at least in these sites), but this makes LEFT err on the side of caution which is what we and responsible resource extraction companies would want. The migratory species also exhibit this pattern of more commision than ommision errors in the species set, although I haven't made comparison maps for these yet. 35Cusuco national park, HondurasCan LEFT correctly identify which globally threatened terrestrial vertebrates are present in a study site?Threatened birdsThreatened mammalsThreatened reptilesThreatened amphibiansAll threatened terrestrial vertebratesField dataWeb dataLEFT correctLEFT omission error(detected byfieldwork, but missed by LEFT)LEFT commission error(not detected by fieldwork, yet included in LEFT) 2617531014211910601
Cusuco normalised number of threatened speciesCan LEFT correctly identify which locations in a study site are most important for threatened species?Difference mapWhite = agreement. Red = LEFT predicts relatively more threatened species than field data (commission error)Blue = LEFT predicts relatively fewer threatened species than field data (omission error)
The IUCN method is very straightforward: they asked experts to draw polygons on maps representing the ranges of each globally threatened terrestrial vertebrates species. At broad scale, this works very well, but has limitations at very fine spatial scale for species with patchy areas of occupancy within their range. To generate the maps of relative numbers of globally threatened terrestrial vertebrates in Cusuco and Mahamavo we used a spatial sampling framework stratified with respect to land cover types and elevation to collect large numbers of spatially unique records of threatened species presences. We then generated equal numbers of pseudo absences for each species with the same sampling framework. I )then made and validated with ROC plots GLM distribution models for each species as a function of a common set of environemental covariates: tasseled cap (TC) brightness, TC greenness, TC moistness, elevation, slope, sin(aspect), cos(aspect), topographic wetness, distance to roads and distance to villages. The habitat suitability maps for each species were thresholded by the Kappa-maximising threshold, then the thresholded maps were added to make a map of estimated number of threatened species and then normalised before comaprison to the LEFT vulenerability map (to account for the fact that both analyses used a different set of species). 37Cusuco, Aves Beta-diversity based on GB