1. Candidate KBA Identification: Modeling Techniques for Field Survey Prioritization Species Distribution Modeling: approximation of species ecological niche projected into geographic space –realized niche may be smaller than fundamental or “theoretical” niche –due to many possible factors (such as geographic barriers to dispersal, biotic interactions, and human modification of the environment), few species occupy all areas that satisfy their niche requirements

2. Limitations of Species Modeling for KBA Delineation Presence data rarely accompanied by absence data Models often overestimate species extent (errors of commission), which may lead to “protection” where a target species does not actually occur Environmental data associated with samples may not fully represent a species’ fundamental niche

3. Problems with presence-only species data Sampling bias False negatives Spatial auto-correlation Variation in sampling intensity and sampling methods used Errors in occurrence locality Errors in the recording associated environmental variables

4. Environmental Variables Should be both temporal and spatial (scale) relationships between variables and species requirements. –climatic variables such as temperature and precipitation are appropriate at global and meso-scales –topographic variables (e.g., elevation and aspect) likely affect species distributions at meso- and topo-scales –land-cover variables (e.g., percent canopy cover) influence species distributions at the micro-scale –certain variables generalize well over large, regional scales (bioclimatic and soil-type); others do not (elevation and latitude)

5. Environmental Data Sources Global –The usual suspects (Landsat, SRTM) –GLCF % tree cover –IPCC/CRI (1961-1990) precip. data –UNESCO soils –annual cloud cover; annual diurnal temperature range; annual vapor pressure; January, April, July, October and annual precipitation; minimum, maximum and mean annual temperature –potential vegetation Site/landscape –ASTER (high quality relative DEM’s)

6. Models BIOCLIM DOMAIN Generalized Linear Models (GLM) and Generalized Additive Models (GAM) Genetic Algorithm for Rule-set Prediction (GARP / OM- GARP) Maximum Entropy (Maxent)

7. Model Comparison AUC Eliith et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. ECOGRAPHY vol 29.

8. Raxworthy et al. 2003. Predicting distributions of known and unknown reptile species in Madagascar. Nature vol 426 Model Comparison Darker areas represent greater inter-model agreement; circles represent areas of over-estimation of ecological niche distribution.

9. Additional Prioritization Options for Field Surveys Identification of unstudied areas of greatest potential biological diversity (via beta diversity modeling) Areas that have experienced highest rate and extent of habitat loss Areas vulnerable to future threats (e.g., scenarios models of infrastructure development) –Areas of greatest climate change risk (El Niño and long-term)

10. Future Research Questions Modeling irreplaceability –Limited spatial options (e.g., large waterfalls in the Lower Kihansi) linked to unique species life history Modeling vulnerability –Climate change and inherent species resiliency –Prioritizing by opportunities: Ecosystem services (carbon e.g., value)