An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo‐absence data

Top Cited Papers
Open Access
Publisher Website
Google Scholar
Abstract
Summary: Few examples of habitat‐modelling studies of rare and endangered species exist in the literature, although from a conservation perspective predicting their distribution would prove particularly useful. Paucity of data and lack of valid absences are the probable reasons for this shortcoming. Analytic solutions to accommodate the lack of absence include the ecological niche factor analysis (ENFA) and the use of generalized linear models (GLM) with simulated pseudo‐absences. In this study we tested a new approach to generating pseudo‐absences, based on a preliminary ENFA habitat suitability (HS) map, for the endangered species Eryngium alpinum. This method of generating pseudo‐absences was compared with two others: (i) use of a GLM with pseudo‐absences generated totally at random, and (ii) use of an ENFA only. The influence of two different spatial resolutions (i.e. grain) was also assessed for tackling the dilemma of quality (grain) vs. quantity (number of occurrences). Each combination of the three above‐mentioned methods with the two grains generated a distinct HS map. Four evaluation measures were used for comparing these HS maps: total deviance explained, best kappa, Gini coefficient and minimal predicted area (MPA). The last is a new evaluation criterion proposed in this study. Results showed that (i) GLM models using ENFA‐weighted pseudo‐absence provide better results, except for the MPA value, and that (ii) quality (spatial resolution and locational accuracy) of the data appears to be more important than quantity (number of occurrences). Furthermore, the proposed MPA value is suggested as a useful measure of model evaluation when used to complement classical statistical measures. Synthesis and applications. We suggest that the use of ENFA‐weighted pseudo‐absence is a possible way to enhance the quality of GLM‐based potential distribution maps and that data quality (i.e. spatial resolution) prevails over quantity (i.e. number of data). Increased accuracy of potential distribution maps could help to define better suitable areas for species protection and reintroduction.