Abstract
Globally, no species is exempt from the constraints associated with limited available habitat or resources, and endangered species in particular warrant critical examination. In most cases, these species are restricted to limited locations, and the relative likelihood of resource use within the space they can access is important. Using Gambelia sila, one of the first vertebrate species listed as endangered, we used resource selection function analysis of telemetry and remotely sensed data to identity key drivers of selected versus available locations for this species in Carrizo Plain National Monument, USA. We examined the probability of selection given different resource types. Increasing shrub cover, lower and relatively more flat sites, increasing normalized difference vegetation index, and solar radiation all significantly predicted likelihood of observed selection within the area sampled. Imagery data were also validated with fine-scale field data showing that large-scale contrasts of selection relative to available location patterns for animal species are a useful lens for potential habitat. Key environmental infrastructure such as foundation plant species including shrubs or local differences in the physical attributes were relevant to this endangered species.
Highlights
No species is exempt from the constraints associated with limited available habitat or resources, and endangered species in particular warrant critical examination
We show that shrub cover is a crucial habitat type used by an endangered species of lizard in Carrizo Plain National Monument, California
Resource selection functions contrast ‘used’ and ‘available’ locations within a region by a set of individuals for a species. This tool explores the relevant landscape attribute classifications of each set to build predictions of the realized patterns of selection for the individuals examined in a study for their habitat at fine scales. We examined the latter approach for an endangered species of lizard Gambelia sila (G. sila) because it was in the first group of species listed as endangered by the USA in 196714 and because it is an excellent indicator of ecosystem health regionally in these drylands[5]
Summary
AIC scores were calculated for models[6], and 95% confidence intervals for the variable coefficients were computed using the confint R function We repeated this second workflow splitting the dataset into mesohabitat (shrub vs open) and ground use (above vs below). As these dataset splits have fewer points than the complete relocation dataset, we sampled two available points for every used telemetry point to capture environmental variation within the study area to stabilize the coefficient estimates. To describe the potential distribution of lizard populations, we used the ‘predict’ function over a 5 km[2] map around the study site showing a greater extent of Carrizo Plain National Monument This is not the same as a resource selection function but is an excellent alternative mechanism to estimate likelihoods with the same key features. The simple resource selection function R package tests are available here: https://cjlortie.github.io/Resource_selection_Carrizo/
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