Global conservation scenarios for mammals

Habitat loss is the main threat to mammals (Schipper et al. Science, 2008). Agriculture expansion is the main driver of habitat loss, and, worryingly, it is expected to continue in the future. Demand for agricultural products is predicted to increase by up to 50% with most expansion in tropical countries (Food and Agriculture Organization of the United Nations 2009).
An assessment of the projected impacts of agricultural expansion on mammals is of utmost urgency to ensure pre-emptive and effective conservation actions. We have estimates the impact of future scenarios of expanding agricultural land on the world’s terrestrial mammals in four scenarios of land use change (Visconti et al. Phil Trans B Series 2011). We found that most of the future decline in suitable habitat for terrestrial mammals will occur in Sub-Saharan Africa and South America, irrespective of the human development scenario analyzed, more detail on the project can be found here.

Figure 1. Global percentage decline in terrestrial mammals due to land use change

To account for the interactive effects of climate change and land use change we are modeling the predicted distribution of the world’s mammals accounting for the species-specific effects of habitat loss in isolation and in combination with the effects of climate change using SRES-AR4 scenarios of future land cover and climate. We consider both altitudinal and geographic range shifts due to climate change and  we estimate the amount of suitable habitat within projected ranges using the habitat suitability models from Rondinini et al. 2011 and Visconti et al. 2011.

The next step  will be to use the projected changes in extent of suitable habitat and extent of occurrence to estimate the future Red List Status of the world terrestrial mammals and aggregate the information to calculate the Red List Index, a synthetic measure of the risk of extinction of a group of species. The Red List Index will be calculated for all scenarios of human development of the UNEP project Rio+20

Data structure and availability

Aknowledgment
We would like to thank CINECA for providing access to its supercomputing facility