Temporal variation and scale in movement-based resource selection functions

Abstract

a b s t r a c tA common population characteristic of interest in animal ecology studies pertains to the selection of resources. That is, given the resources available to animals, what do they ultimately choose to use? A variety of statistical approaches have been employed to examine this question and each has advantages and disadvantages with respect to the form of available data and the properties of estimators given model assumptions. A wealth of high resolution telemetry data are now being collected to study animal population movement and space use and these data present both challenges and opportunities for statistical inference. We summarize traditional methods for resource selection and then describe several extensions to deal with measurement uncertainty and an explicit movement process that exists in studies involving high-resolution telemetry data. Our approach uses a correlated random walk movement model to obtain temporally varying use and availability distributions that are employed in a weighted distribution context to estimate selection coefficients. The temporally varying coefficients are then weighted by their contribution to selection and combined to provide inference at the population level. The result is an intuitive and accessible statistical procedure that uses readily available software and is computationally feasible for large datasets. These methods are *

Practical guidance on characterizing availability in resource selection functions under a use–availability design

“…With the exception of Johnson et al (2008), the issue of autocorrelation in habitat selection studies only has been addressed in terms of model assumptions (i.e., independence of errors [Fieberg et al 2010]). When animal locations are sampled at high resolution, the habitat available to be selected also is autocorrelated (Hooten et al 2013), an issue that has been largely overlooked. Despite this autocorrelation, inference can be obtained at the desired scale through thinning of autocorrelated data, or accounting for autocorrelation explicitly in the model (Hooten et al 2013).…”

“…The use-availability framework and important considerations.-For RSFs fit under a use-availability design, the used locations are a realization from the used distribution f U (x) (see Table 1), which can be written as a weighted version of the availability distribution f A (x) (Johnson et al 2006, Lele and Keim 2006, Hooten et al 2013:…”

Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”

“…Johnson (1980) is often credited with developing a framework for thinking about spatial scale in studies of space use and resource selection, and others have tackled similar scale issues in the temporal domain (e.g., Hooten et al 2014). However, temporal multi-scale specifications in a state-switching hidden Markov model framework have not been addressed, until now.…”

When to be discrete: the importance of time formulation in understanding animal movement

“…The 30-minute analysis could reveal coarse-grain properties, but would completely miss finegrain properties. The specification of time step length in a discrete-time analysis is therefore critical and requires very careful consideration [41][42][43], and it is particularly important that the time step is chosen to match the scale at which behavioral decisions are made [40]. A major advantage of continuous-time models is that they avoid dependence on a particular timescale.…”