Using Relative Capacity to Measure Habitat Suitability

Abstract

Habitat suitability (HS) modeling relates a species' potential presence/absence to a set of environmental variables. because of stochastic or demographic population fluctuations, relating abundance to environment is difficult and generally requires time-series of population-density data. Here, I propose an approach to compute relative capacity of a set of habitat classes. I defined relative capacity of a habitat class as being proportional to its carrying capacity, and replaced time-series of density measures with spatial replicates. A hypothetical environment was first divided into several habitat classes and population densities were measured in a sample of these habitats. Three methods of computing relative capacity were compared: (1) maximum density per habitat class, (2) coefficients of a Generalized Linear Model (GLM) of the habitat densities, and (3) slopes of isodars computed for all pairs of habitat classes. Accuracy of these methods was evaluated using spatially-explicit demographic simulations. I investigated biological assumptions (species' sensitivity, reactivity and selectivity, regional stochasticity) and sampling design (sample size, distance between pairs of habitat classes, number of habitat classes, uncertainty over abundance). GLM and maximum-density methods provided accurate results when at least five site-pairs were available. Isodars outperformed other methods in landscapes with many habitat classes, but are best suited to large or noise-free data sets. Optimal performance was reached with more than 50 site-pairs and five habitat classes.

Habitat Choices of Nubian Ibex (Capra Nubiana) Evaluated with A habitat Suitability Modeling and Isodar Analysis

“…Habitat models in general, and habitat suitability index (HSI) models in particular, are among the most widely used tools for wildlife management (Verner et al, 1986;Fabricius and Mentis, 1991;Van Horne and Wiens, 1991;Hirzel, 2008;Jonźen, 2008). Habitat suitability modeling (HSM) is a tool for predicting the suitability of habitat or determining the quality of habitat for the target species based on known affinities of environmental or habitat parameters (Van Horne and Wiens, 1991).…”

Adaptation and habitat selection in the eco-evolutionary process

“…[58]), as well as measurement error [58,59]. If habitats are dramatically different from one another, then betweenhabitat differences can be expected to predominate and yield relatively homogeneous isodars.…”

“…Temporal stochasticity raises numerous questions about the appropriate measure of fitness (e.g. [59]), which complicates decisions by habitat selectors. Choices maximizing current population growth rate may not maximize longterm fitness that depends critically on the pattern of temporal variation [93].…”

“…The effect of such heterogeneity on habitat isodars will depend on the magnitude of the (often stochastic) variation within and among states and habitats (e.g. [58]), as well as measurement error [58,59]. If habitats are dramatically different from one another, then betweenhabitat differences can be expected to predominate and yield relatively homogeneous isodars.…”

“…Stochasticity can also influence the evolution of habitat preferences through acquisition of discriminating cues [91]. Many other forms of stochasticity have been explored by Jonzén et al, including temporal differences in reproductive rates coupled with variation in sampling efficiency [59,92] and random temporal variation in fitness coupled with probabilistic habitat selection in correlated environments [58]. Temporal stochasticity raises numerous questions about the appropriate measure of fitness (e.g.…”