Drivers of extinction risk in African mammals: the interplay of distribution state, human pressure, conservation response and species biology

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.

FactorsR: An RWizard Application for Identifying the Most Likely Causal Factors in Controlling Species Richness

“…Area of occupancy (AOO) is the spatial distribution of known, inferred or projected sites of occurrence expressed in km 2 and was estimated with ModestR as described by [34]. We estimated AOO for each species with ModestR using rasterized maps with a resolution of 1′ × 1′ cells, in which the occupied area was estimated using the following equation:…”

“…The identification of the main factors affecting species richness or extension risk is an important topic in macroecological, biogeographical, and conservation studies [1,2]. Multiple regression (MR) has been frequently used to identify factors influencing response variables such as species richness or occurrence [3], but predictor variables are frequently significantly intercorrelated (multicollinearity), and the most frequently used stepwise-selection techniques (e.g., Akaike Information Criterion (AIC), quasi-Akaike Information Criterion (QAIC), Bayesian Information Criterion (BIC), etc.)…”

“…are not the most effective way of identifying those variables most likely to influence variation in species richness, because these techniques are focused on finding a single best predictive function and not drawing inferences about the likely causality of variables [4]. Hierarchical partitioning (HP) [2] has been suggested as a possible solution to identify the most likely causal factors in an MR, because it considers all models in an MR setting jointly [5].…”

Large and medium-sized mammals of Buenaventura Reserve, southwestern Ecuador

“…The ecological importance and elevated degree of threats to large and medium-sized mammals (Di Marco et al 2014) show the need to ensure their protection. It is broadly accepted that mammal conservation begins with accurate, up-to-date information on which species are present in an area (Antos and Yuen 2014).…”

Migration timing and stopover selection for barnacle geese Branta leucopsis

“…Satellite remote sensing is increasingly being used in ecological studies (Di Marco et al, 2014;Madritch et al, 2014;Pettorelli et al, 2005;St-Louis et al, 2014) and some new systems are facilitating the use of satellite data in ecological studies. For example, the Environmental-Data Automated Track Annotation (Env-DATA) System enables the processing of a large array of remote sensing weather and geographical data to analyze spatio-temporal patterns of animal movement tracks (Dodge et al, 2013).…”