Holling meets habitat selection: functional response of large herbivores revisited

Abstract

Background Holling (Can Entomol 91(5):293–320, 1959) was the first to describe a functional response between a predator’s consumption-rate and the density of its prey. The same concept can be applied to the habitat selection of herbivores, specifically, the change in relative habitat use with the change in habitat availability. Functional responses in habitat selection at a home-range scale have been reported for several large herbivores. However, a link to Holling’s original functional response types has never been drawn, although it could replace the current phenomenological view with a more mechanistically based understanding of functional responses. Methods In this study, discrete choice models were implemented as mixed-effects baseline-category logit models to analyze the variation in habitat selection of a large herbivore at seasonal and diurnal scales. Thus, changes in the use of land cover types with respect to their availability were investigated by monitoring 11 land cover types commonly used by roe deer (Capreolus capreolus) in the Bavarian Forest National Park, Germany. Functional response curves were then fitted using Holling’s formulas. Results Strong evidence of non-linear functional responses was obtained for almost all of the examined land cover types. The shape of the functional response curves varied depending on the season, the time of day, and in some cases between sexes. These responses could be referenced to Holling’s types, with a predominance of type II. Conclusions Our results indicate that Holling’s types can be applied to describe general patterns of the habitat selection behavior of herbivores. Functional responses in habitat selection may occur in situations requiring a trade-off in the selection of land cover types offering different resources, such as due to the temporally varying physiological needs of herbivores. Moreover, two associated parameters defining the curves (prey density and predation rate) can aid in the identification of temporal variations and in determinations of the strength of the cost-benefit ratio for a specific land cover type. Application of our novel approach, using Holling’s equations to describe functional responses in the habitat selection of herbivores, will allow the assignment of general land cover attraction values, independent of availability, thus facilitating the identification of suitable habitats.

Global knowledge gaps of herptile responses to land transformation

“…To estimate model parameters, we used the R package minpack.lm (Elzhov et al 2013). Holling's equations were first proposed to describe the consumption rate of a predator as a function of the density of its prey (Holling 1965), but more recently have been applied in other fields to model other phenomena characterized by saturation, such as enzyme kinetics and habitat selection by herbivores (Dupke et al 2021). Here we used Holling's equations to examine the relationships between (1) total richness of amphibians versus the number of amphibian species with at least one record of abundance in Cordier, (2) total reptile richness versus reptile species with at least one record in Cordier, (3) total amphibian richness versus amphibian species with at least one record in PREDICTS, and (4) total reptile richness versus reptile species with at least one record in PREDICTS.…”

Spatial scale selection for informing species conservation in a changing landscape

“…Greater distances between scales could also reduce analysis time but may increase uncertainty in the scale of effect estimate (Jackson & Fahrig, 2015; Miguet et al, 2017) and create abrupt changes between scales when landscape features are patchy (Stuber & Gruber, 2020). Additionally, influence of landscape features may vary seasonally (Dupke et al, 2021; Fedy et al, 2014) and effects of habitat change at broader scales for other components of life history (such as wintering areas) were likely more difficult to detect with a lek‐centric response. Indeed, trends in abundance may not reflect spatial scales for other biological responses such as genetic diversity or long‐term persistence influenced by patterns at broader scales (Jackson & Fahrig, 2014; Miguet et al, 2016).…”

A taste of space: Remote animal observations and discrete‐choice models provide new insights into foraging and density dynamics for a large subarctic herbivore

“…However, if all choices are available to, and similar for a caribou, we can estimate the probability of choices using a set of n − 1 logistic regression models (Cooper & Millspaugh, 1999), where n is the number of categories to be evaluated (e.g. following Dupke et al., 2021). Therefore, we approximated the standard discrete‐choice equation using two sets of nested generalized linear models to fit models to caribou choice for (i) behaviours and (ii) food types nested within the foraging behaviour.…”

“…We built five independent logistic regression models that included spatial covariates thought to influence behaviour (e.g. Dupke et al., 2021).…”

“…Discrete‐choice models account for the decision‐making process of the animal when selecting a resource, as resource availability is allowed to vary over time, across space, and among individuals (Cooper & Millspaugh, 2001; McCracken et al., 1998). Discrete‐choice models can also explicitly identify foraging functional responses (Cooper & Millspaugh, 1999; Dupke et al., 2021; van Beest et al., 2016). In this framework, animal behaviour can be directly observed from GPS video camera collars.…”