Assessing habitat-suitability models with a virtual species

Abstract

This paper compares two habitat-suitability assessing methods, the Ecological Niche Factor Analysis (ENFA) and the Generalised Linear Model (GLM), to see how well they cope with three different scenarios. The main difference between these two analyses is that GLM is based on species presence/absence data while ENFA on presence data only. A virtual species was created and then dispatched in a geographic information system model of a real landscape following three historic scenarios: (1) spreading, (2) at equilibrium, and (3) overabundant species. In each situation, the virtual species was sampled and these simulated data sets were used as input for the ENFA and GLM to reconstruct the habitat suitability model. The results showed that ENFA is very robust to the quality and quantity of the data, giving good results in the three scenarios. GLM was badly affected in the case of the spreading species but produced slightly better results than ENFA when the species was overabundant; at equilibrium, both methods produced equivalent results. The use of a virtual species proved to be a very efficient method, allowing one to fully control the quality of the input data as well as to accurately evaluate the predictive power of both analyses.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

“…Consequently, use of these tools to identify environmental factors responsible for the distribution of species that are, for example, still expanding their range in the study area can lead to biased results like truncated ecological response curves (Hirzel et al, 2001). GLMs and GAMs, the focus of this collection of papers, effectively model ecological (realized) rather than fundamental niches due to their intrinsic empirical nature.…”

“…Models are prepared for 43 native species of ferns in New Zealand, using presence-only data from a data set including presence Á/absence data gathered at nearly twenty thousand sites. They compared their approach with another, the ecological niche factor analysis (ENFA; Hirzel et al, 2001), which is also used for modeling presence */only data. Because logistic models require a binary response variable, 'pseudo' absences were generated according to different methods.…”

Climate Change and Biodiversity: Some Considerations in Forecasting Shifts in Species' Potential Distributions

“…Of course, the former methods are more powerful, but interpretation of 'absence' information is complex-absences of species will frequently not coincide with absence of appropriate niche conditions, making the meaning of the absence data difficult to interpret (Soberón and Peterson 2005). In instances in which true absence data are lacking and sampling from non-presence areas is performed to obtain 'pseudo-absence' points (Engler et al 2004;Hirzel et al 2001;Zaniewski et al 2002), these same concerns are relevant.…”

Habitat suitability modelling for Gaur (Bos gaurus) using multiple logistic regression, remote sensing and GIS

“…Logistic regression was also used by Houts (2003) to model probable dispersion of wolves in near future, which were reintroduced into Yellowstone National Park and Central Idaho in 1995, whereas, Glenz et al (2001) predicted suitable habitat for wolf in Valais (Switzerland) using logistic regression. Hirzel et al (2001) used this geospatial technology to assess habitat suitability models for a virtual species in Bern Alps (Switzerland). In 1999, Palma et al used logistic regression to analyse Iberian lynx habitat and distribution.…”