A re‐evaluation of a case–control model with contaminated controls for resource selection studies

Abstract

Summary 1.A common sampling design in resource selection studies involves measuring resource attributes at sample units used by an animal and at sample units considered available for use. Few models can estimate the absolute probability of using a sample unit from such data, but such approaches are generally preferred over statistical methods that estimate a relative probability of use. 2. The case-control model that allows for contaminated controls, proposed by Lancaster & Imbens (1996) and Lele (2009), can estimate the absolute probability of using a sample unit from use-availability data. However, numerous misconceptions have likely prevented the widespread application of this model to resource selection studies. We address common misconceptions regarding the case-control model with contaminated controls and demonstrate its ability to estimate the absolute probability of use, prevalence and parameters associated with categorical covariates from use-availability data. 3. We fit the case-control model with contaminated controls to simulated data with varying prevalence (defined as the average probability of use across all sample units) and sample sizes (n 1 = 500 used and n a = 500 available samples; n 1 = 1000 used and n a = 1000 available samples). We then applied this model to estimate the probability Ozark hellbenders (Cryptobranchus alleganiensis bishopi) would use a location within a stream as a function of covariates. 4. The case-control model with contaminated controls provided unbiased estimates of all parameters at N = 2000 sample size simulation scenarios, particularly at low prevalence. However, this model produced increasingly variable maximum likelihood estimates of parameters as prevalence increased, particularly at N = 1000 sample size scenarios. We thus recommend at least 500-1000 used samples when fitting the case-control model with contaminated controls to use-availability data. Our application to hellbender data revealed selection for locations with coarse substrate that are close to potential sources of cover. 5. This study unites a disparate literature, addresses and clarifies many commonly held misconceptions and demonstrates that the case-control model with contaminated controls is a viable alternative for estimating the absolute probability of use from use-availability data.

Foraging habitat selection by California spotted owls after fire

“…Resource‐selection studies quantify specific factors that increase or decrease the probability that an animal will use a particular resource, for example, a late‐seral or severely burned forest stand (Rota et al ). A habitat feature is considered to be selected if it is used disproportionately more than it is available (Johnson , Beyer et al ), and a variety of statistical methods can quantify which specific habitat features are preferred or avoided relative to other types (Rettie and McLoughlin , Aarts et al ).…”

Location‐only and use‐availability data: analysis methods converge

“…He also argues that RSFs require fewer assumptions and are more useful than functions which attempt to estimate true probability of use. This position is somewhat at odds with others, that is, Lele et al (2013) and Rota et al (2013) pun intended because of the use of logistic models in the latter two papers. Rota et al (2013) and others have promoted case-control models (Lancaster & Imbens 1996) allowing for contaminated controls (i.e.…”

“…() and Rota et al . () – pun intended because of the use of logistic models in the latter two papers.…”

“…Rota et al . () and others have promoted case–control models (Lancaster & Imbens ) allowing for contaminated controls (i.e. available units may have been selected by study animals) to estimate the absolute probability a sample unit being used from use‐availability data.…”

Evaluating the individuality of animal‐habitat relationships

“…In this way, RSF models estimate the relative probability that a habitat unit is used, given the resources present, in relation to available or unused habitat units (Manly et al., ). While an RSF is assumed to be proportional to a resource selection probability function (RSPF) up to an arbitrary constant, this proportionality is not guaranteed (Keating & Cherry, ; Rota et al., ; Royle, Chandler, Yackulic, & Nichols, ). Thus, given reasons of convenience, precedence, and evident misconceptions regarding RSPF parameter estimation, RSF models remain a widely used framework for assessing animal‐habitat relationships (Manly et al., ; Rota et al., ; Thomas & Taylor, , ).…”