Discrete-Choice Modeling in Wildlife Studies Exemplified by Northern Spotted Owl Nighttime Habitat Selection

Abstract

Discrete-choice models are a powerful and flexible method for studying habitat selection, in part because they allow resource availability to change at every choice. Here, we consider application of discrete-choice models to data typically collected in wildlife science because different discrete-choice data are usually collected in other disciplines. We generalize the classic discrete-choice model to the situation in which multiple choices are made from 1 or more choice sets, and only 1 random sample from each choice set is available. We discuss analysis using 1) logistic regression, 2) maximum likelihood when choices are made with replacement, 3) maximum likelihood when the temporal order of selection is known, and 4) maximum likelihood when the order of selection is unknown. We show that 1) provides a good approximation to discrete choice models if the expected number of uses is much <1 for all units. We show that 2) and 3) can be fit using stratified Cox proportional hazards software. Analysis 4) must be fit using special purpose maximization routine such as Newton-Raphson. Finally, we demonstrate 2) on a case study of nightime habitat selection by 28 northern spotted owls (Strix occidentalis caurina), and conclude that these owls selected for locations low on the slope in stands >41 years old with high levels of hardwoods adjacent to stands 6–20 yrs old.