Habitat selection patterns are density dependent under the ideal free distribution

Avgar, Tal; Betini, Gustavo S.; Fryxell, John M.

doi:10.1111/1365-2656.13352

Cited by 69 publications

(82 citation statements)

References 108 publications

(233 reference statements)

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“…Such context dependencies are in fact so common that we do not know of a single instance where researchers were looking for them and failed to find them. Recently, Avgar et al (2020) showed that such context dependencies in habitat-selection patterns are expected to emerge even under the simplest theoretical model of an Ideal Free Distribution (Fretwell, 1969).…”

Section: Sensitivity Of Selection Coefficients To Species Population Density and Habitat Availabilitymentioning

confidence: 99%

A ‘How to’ guide for interpreting parameters in habitat‐selection analyses

Fieberg

Signer

Smith

et al. 2021

Journal of Animal Ecology

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New technologies (e.g. improved Global Positioning System [GPS] collars) and advances in remote sensing have made it possible to collect animal location data on unprecedented spatial and temporal scales (Kays et al., 2015;Robinson et al., 2020), which in turn has fuelled the development of new methods for modelling animal movement and for linking individuals to their environments (Guisan et al., 2017;Hooten et al., 2017). Two of the most popular approaches for analysing telemetry data, habitat-selection functions (HSFs; Box 1) and step-selection functions (SSFs), compare environmental covariates at locations visited by an animal ('used locations') to environmental covariates at a set of locations assumed available to the animal ('available locations') using logistic and conditional logistic regression re-

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Section: Sensitivity Of Selection Coefficients To Species Population Density and Habitat Availabilitymentioning

confidence: 99%

A ‘How to’ guide for interpreting parameters in habitat‐selection analyses

Fieberg

Signer

Smith

et al. 2021

Journal of Animal Ecology

Self Cite

186

187

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show abstract

“…Such context dependencies are in fact so common that we do not know of a single instance where researchers were looking for them and failed to find them. Recently, Avgar et al [85] showed that such context dependencies in habitat-selection patterns are expected to emerge even under the simplest theoretical model of an Ideal Free Distribution [86]. Thus, habitat-selection models often have poor predictive capacity when transferred across different study areas, or even within the same area over time [45].…”

Section: Sensitivity Of Selection Coefficients To Species Population mentioning

confidence: 99%

A ‘How-to’ Guide for Interpreting Parameters in Habitat-Selection Analyses

Fieberg

Signer

Smith

et al. 2020

Preprint

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111

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Resource-selection and step-selection analyses allow researchers to link animals to their environment and are commonly used to address questions related to wildlife management and conservation efforts. Step-selection analyses that incorporate movement characteristics, referred to as integrated step-selection analyses, are particularly appealing because they allow modeling of both movement and habitat-selection processes.Despite their popularity, many users struggle with interpreting parameters in resource-selection and step-selection functions. Integrated step-selection analyses also require several additional steps to translate model parameters into a full-fledged movement model, and the mathematics supporting this approach can be challenging for biologists to understand.Using simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point-process model can facilitate parameter interpretation in resource-selection and step-selection analyses. Further, we provide a “how to” guide illustrating the steps required to implement integrated step-selection analyses using the amt package.By providing clear examples with open-source code, we hope to make resource-selection and integrated step-selection analyses more understandable and accessible to end users.

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“…The estimated roe deer population density was low, ranging between 1-3 animals/km 2 (48). Hence, density was not expected to affect habitat selection behaviour (25,27). Between 2005 and 2012, 168 roe deer individuals were monitored (see Details on roe deer capture and data processing in Appendix S1a).…”

Section: Study Area and Datamentioning

confidence: 99%

“…Functional response is usually presented as the effect of availability on odds ratios (13,15) or on the selection coefficients (10,23,25). Although the relationships revealed by RSFs may indicate the presence of a functional response, they are difficult to interpret and to quantify (4,26,27) as they represent selection for or against a certain habitat but do not directly depict the relation between the availability and the actual use of a habitat (e.g. 15,28).…”

Section: Introductionmentioning

confidence: 99%

Holling Meets Habitat Selection - Functional Response of Large Herbivores Revisited

Dupke

Peters

Morellet

et al. 2021

Preprint

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Background: Holling (1959) was the first to describe a functional response between a predator’s consumption-rate and the density of its prey. The same concept may be applied to the habitat selection of herbivores, by considering the change in relative habitat use with the change in habitat availability. Functional responses in habitat selection at a home-range scale has been reported for several large herbivores. However, a link to Holling’s original functional response types has never been drawn despite its potential to understand availability dependence in habitat selection more profoundly. Methods: Discrete choice models were implemented as mixed-effects baseline-category logit models to analyze the variation in habitat selection of a large herbivore over seasonal and diurnal scales. Specifically, changes in habitat use with respect to habitat availability were investigated by monitoring 11 habitat 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 habitat types. The shape of the functional response curves varied depending on the season, 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 could be applied to describe general patterns in habitat selection behaviour of herbivores. Functional response in habitat selection may occur in situations of trade-off in the selection of habitats offering different resources, due to temporally varying physiological needs of herbivores. Moreover, the two associated parameters defining the curves helps to identify the temporal variations and clarify how strongly the cost-to-benefit ratio is pronounced for a specific habitat. The presented novel approach of using Holling’s equations to describe functional response in habitat selection of herbivores could be used for assigning general habitat attraction values, independent of habitat availability, which might facilitate the identification of suitable habitats.

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Habitat selection patterns are density dependent under the ideal free distribution

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 69 publications

References 108 publications

A ‘How to’ guide for interpreting parameters in habitat‐selection analyses

A ‘How to’ guide for interpreting parameters in habitat‐selection analyses

A ‘How-to’ Guide for Interpreting Parameters in Habitat-Selection Analyses

Holling Meets Habitat Selection - Functional Response of Large Herbivores Revisited

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