We propose here some refinements of the ecological-niche factor analysis (ENFA) to describe precisely one organism’s habitat selection. The ENFA is based on the concept of the ecological niche, and provides a measure of the realised niche within the available space from the computation of two parameters, the marginality and the specialization. By measuring the departure of the ecological niche from the average available habitat, the marginality identifies the preference of the individual, population, or species for specific conditions of the environment among the whole set of possibilities. The specialization appears as a consequence of the narrowness of the niche on some environmental variables. The ENFA is a factorial analysis that extracts one axis of marginality and several axes of specialization. We present here the use of biplots (i.e., the projection of both the pixels of the map and the environmental variables in the subspace extracted by the ENFA) as a way to identify the key-variables for management, assessing which habitat features are of prime importance and should be preserved or reinforced. With the help of this tool, we are now able to describe much more precisely the habitat selection of the organism under focus. In our application to the lynx in the Vosges mountains, based on sightings as well as other indices of lynx presence, we thus underlined a strong avoidance of agricultural areas by the lynx. We also highlighted the relative indifference of the lynx to the proximity of artificial areas and at the opposite, the sensitivity to the proximity of highways. The ENFA provides a suitable way to measure habitat use/selection under a large range of ecological contexts and should be used to define precisely the ecological niche and therefore identify the characteristics searched for by the organism under study.
Reference: Basille M., Calenge C., Marboutin É., Andersen R. & Gaillard J.-M. (2008) Assessing habitat selection using multivariate statistics: Some refinements of the Ecological-Niche Factor Analysis. Ecological Modelling, 211:233–240. DOI: 10.1016/j.ecolmodel.2007.09.006