Novel methods improve prediction of species’ distributions from occurrence data

Abstract

. Novel methods improve prediction of species' distributions from occurrence data. Á/ Ecography 29: 129 Á/151.Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve. J. Elith

Ecology and Geography of Human Monkeypox Case Occurrences Across Africa

“…Maxent is used to estimate the probability distribution for species' occurrences by identifying the distribution of maximum entropy (i.e., a probability distribution closest to uniform), subject to the constraint that the expected value of each environmental variable within the estimated distribution should match its empirical average . Maxent builds niche models based on environmental characteristics of presence-only occurrence data and 10,000 randomly chosen background points representing areas of nonoccurrence (pseudoabsence) across the study area (Elith et al, 2006).…”

“…Our ENMs are based on human case occurrence data collected by the CDC andWHO during 1970-1987. Biases in raw case data are well known and include sampling bias, detection and reporting biases, and other factors that may distort the picture of actual distributions of species with respect to ecologic and environmental factors (Elith et al, 2006). In this respect, the niche modeling step employed in both studies-to some degree-allows a lessbiased and more-objective view of the environmental distributions of species.…”

Detecting the potential sympatric range and niche divergence between Asian endemic ungulates of Procapra

“…Maxent is a recently developed presence-background technique (Phillips et al 2006;Phillips and Dudík 2008) that has been performed well in recent comparative studies with good statistical performance (Elith et al 2006). For estimating the target distribution, Maxent satisfies a set of constraints representing the incomplete information on the distribution and, subject to those constraints, maximizes the entropy of the probability distribution (Phillips et al 2006).…”

Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal

“…A commonly adopted approach is to use a range of thresholds and calculate the corresponding kappa value for each one of them (e.g. Elith et al, 2006) adopting the maximum value achieved as the predictive performance of the model. All three models were partitioned in 20 intervals of equal amplitude (0.05) and the kappa value calculated for each.…”