No abstract

Although conservation intervention has reversed the decline of some species, our success is outweighed by a much larger number of species moving towards extinction. Extinction risk modelling can identify correlates of risk and species not yet recognized to be threatened. Here, we use machine learning models to identify correlates of extinction risk in African terrestrial mammals using a set of variables belonging to four classes: species distribution state, human pressures, conservation response and species biology. We derived information on distribution state and human pressure from satellite-borne imagery. Variables in all four classes were identified as important predictors of extinction risk, and interactions were observed among variables in different classes (e.g. level of protection, human threats, species distribution ranges). Species biology had a key role in mediating the effect of external variables. The model was 90% accurate in classifying extinction risk status of species, but in a few cases the observed and modelled extinction risk mismatched. Species in this condition might suffer from an incorrect classification of extinction risk (hence require reassessment). An increased availability of satellite imagery combined with improved resolution and classification accuracy of the resulting maps will play a progressively greater role in conservation monitoring.

The growing access to Earth Observations and processing capabilities have stimulated the production of global and regional products that are commonly used to assess tree-covered habitats and their changes. The popularity of these products has led to their use for defining baselines and to assess progress in conserving natural habitats, in particular, in the context of the conservation targets to 2020 set by the UN Convention on Biological Diversity. In this paper, we reviewed three tree cover products commonly used over SubSaharan Africa: (1) MODIS Vegetation Continuous Field percent tree cover map, (2) Global Forest Change map and (3) TREES product. Over a systematic sample of 2045 map subsets, each having a size of 10 9 10 km², we calculated the extent and change of tree cover from each product for the period between 2000 and 2010. Our statistical and spatial comparison shows noticeable discrepancies between the three products, which lead to uncertainties when assessing tree cover across varying ecosystems. These differences are highest in habitats where tree cover is fragmented or reaches medium density levels and overlap with areas of high economic development potential, where habitat changes are likely to occur in the near future. We discuss these findings in the context of using these remotely sensed tree cover products to support current global biodiversity conservation policies.

No abstract