The habitat functional response links seasonal third‐order selection to second‐order landscape characteristics

Abstract

Determining how animals respond to differences in resource availabilities across spatiotemporal extents is critical to our understanding of organism distributions. Variations in resource distribution leading to changes in spatial arrangements across landscapes are indicative of a habitat functional response. Our goal was to assess how resource availabilities influenced both second‐order (i.e., home ranging behavior) and third‐order (i.e., habitat or resource selection) selection by feral pigs ( Sus scrofa ) in an agricultural landscape. We defined agriculturally based seasons to estimate home range characteristics using autocorrelated kernel density estimation within each season. We then modeled home range size as a function of resource availability (i.e., resource selection analyses) to determine whether individual behaviors were predicted by shifts in home ranging behavior. Both home range analyses and resource selection analyses indicated seasonal differences in selection for agricultural resources as availabilities changed, suggesting second‐ and third‐order selection is mechanistically linked through a habitat functional response.

Context-dependent foraging habitat selection in a farmland raptor along an agricultural intensification gradient

“…Since landscape patterns drive resource distribution, often determining uneven resource availability (Boyce et al, 2003;Mayor et al, 2007), habitat selection may be influenced by landscape composition and configuration (Paolini et al, 2019;Sánchez-Clavijo et al, 2016). For instance, landscape composition at a wider scale could heavily affect fine-scale habitat selection (e.g., the selection of foraging habitat within the individual home-range) by shaping food availability and accessibility (Beatty et al, 2014;Chambers et al, 2016).…”

Habitat selection patterns are density dependent under the ideal free distribution

“…Such reaction norms are thought to stem from a nonlinear relationship between landscape‐scale resource availability and its relative utility to the consumer (due to saturation, threshold or trade‐off effects), and are often assumed to be monotonic, an assumption that lacks formal theoretical grounding (Holbrook et al., 2019; Matthiopoulos et al., 2011). In fact, the mechanisms invoked in explaining availability dependencies are often vague with regard to the nature of the shift in ‘resource availability’, which may refer to either a shift in the relative frequency of habitat classes, or a shift in the intrinsic value of these habitat classes, with their relative frequency remaining unchanged (Duparc et al., 2019; Paolini et al., 2019; Gaudry et al., 2018).…”

“…Because of the potential impact on the observed spatial relationship between consumers and their resources, our inability to account for these density dependencies clouds our interpretation of empirically parameterised models of animal space‐use patterns, particularly the widely‐used Habitat Selection Functions (HSFs; Manly et al., 2002). Consequently, ecological insights gained from HSFs may be site‐ and time specific, hindering their utility for predicting outcomes in other ecosystems or even the same ecosystem at a later time (Beyer et al., 2010; Bledsoe & Ernest, 2019; Matthiopoulos et al., 2011; Paolini et al., 2019; Street et al., 2017; Zurell et al., 2018).…”

Population‐level inference for home‐range areas

“…At the individual level, space‐use requirements are typically described by an animal’s home range (Burt, 1943), which is formalized by the probability distribution of the animal’s locations (Worton, 1995). Population‐level inference on space‐use parameters is also important—both for quantifying the area requirements of a typical organism and for quantifying the effect of covariates, such as species or taxa (Habel et al, 2019; Matley et al, 2019; Poessel et al, 2020; Rehm et al, 2018), sex (D’haen et al, 2019; Desbiez et al, 2019; Morato et al, 2016; Naveda‐Rodríguez et al, 2018), body size (Bašić et al, 2019; Desbiez et al, 2019; Naveda‐Rodríguez et al, 2018), age (Averill‐Murray et al, 2020; Goldenberg et al, 2018; Kays et al, 2020; Mirski et al, 2020), movement characteristics (Bowman et al, 2002; Desbiez et al, 2019; Swihart et al, 1988), conspecific density (Erlinge et al, 1990; Massei et al, 1997; Trewhella et al, 1988), resource density (Herfindal et al, 2005; Loveridge et al, 2009; Massei et al, 1997), habitat or biome (McBride Jr & Thompson, 2019; Morato et al, 2016; Paolini et al, 2019; Tonra et al, 2019), human influences (Hansen et al, 2020; McBride Jr & Thompson, 2019; Rutt et al, 2020; Ullmann et al, 2020), weather (Kay et al, 2017; Matley et al, 2019; Mirski et al, 2020) and season or time (Bašić et al, 2019; Goldenberg et al, 2018; Matley et al, 2019; Roffler & Gregovich, 2018). Both the mean response and population variation have been studied as important regressors for biological inference (Seigle‐Ferrand et al, 2021).…”