Linking resource selection and step selection models for habitat preferences in animals

Abstract Background New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods. Methods We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing. Results We demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs. Conclusions Based on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.

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Sea ice resource selection models for polar bears in the Barents Sea subpopulation

Ecography ◽

10.1111/ecog.03020 ◽

2017 ◽

Vol 41 (4) ◽

pp. 567-578 ◽

Cited By ~ 16

Author(s):

Karen Lone ◽

Benjamin Merkel ◽

Christian Lydersen ◽

Kit M. Kovacs ◽

Jon Aars

Keyword(s):

Sea Ice ◽

Resource Selection ◽

Barents Sea ◽

Polar Bears ◽

Selection Models ◽

The Barents Sea

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Resource selection by the Endangered Arabian tahr: identifying critical habitats for conservation and climate change adaptation

10.1101/2021.01.26.428299 ◽

2021 ◽

Author(s):

S Ross ◽

H. Rawahi ◽

M.H. Jahdhami

Keyword(s):

Climate Change ◽

Habitat Selection ◽

Resource Selection ◽

Wildlife Conservation ◽

Habitat Preferences ◽

Wildlife Habitat ◽

Geographical Information ◽

Future Climate Change ◽

Adaptation To Climate Change ◽

Thermal Refuge

AbstractThe Arabian tahr is an Endangered mountain ungulate endemic to the Hajar Mountains of Arabia. The Arabian tahr population is in decline and threats to tahr habitat are intensifying, in addition new potential challenges from climate change are emerging. Fundamental to future conservation planning is understanding tahr habitat selection patterns, so we can prioritise habitat protection, and understand how habitat may be used to provide thermal refuge and allow adaptation to climate change impacts. We used GPS collars and resource selection functions to characterise Arabian tahr habitat preferences in Wadi Sareen Nature Reserve, Oman. We found tahr habitat selection was dependent on scale, sex and season. Vegetation resources were only selected at the smallest scales of selection and avoided at other scales. Habitat providing low heat load and thermal refuge were intensely selected at small and medium scales, by both sexes and in both seasons, suggesting the importance of thermal refuges in facilitating thermoregulation. Higher elevations, steep slopes and rugged habitats were selected across all scales tested here, and in previous landscape-scale studies, indicating the fundamental importance of these habitats in supporting Arabian tahr populations. Our results identified critical habitats required to sustain Arabian tahr, and demonstrated the importance of thermal refuges to species living in the hot climates such as the Arabian Peninsula. Given the accessibility of habitat layers, and ease in which the identified habitats can be mapped using a geographical information system, understanding the habitat selection of tahr and other species is a crucial step to increasing conservation management capacity of threatened species. Given our uncertainty of how to conserve wildlife under future climate change, understanding the availability and distribution of wildlife habitat is an important baseline from where we can plan, connect and preserve the resources necessary for wildlife conservation.

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Modelling wildlife-human relationships for social species with mixed-effects resource selection models

Journal of Applied Ecology ◽

10.1111/j.1365-2664.2008.01466.x ◽

2007 ◽

Vol 45 (3) ◽

pp. 834-844 ◽

Cited By ~ 218

Author(s):

Mark Hebblewhite ◽

Evelyn Merrill

Keyword(s):

Resource Selection ◽

Mixed Effects ◽

Selection Models ◽

Human Relationships ◽

Social Species

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Assessing Risk to Birds from Industrial Wind Energy Development via Paired Resource Selection Models

Conservation Biology ◽

10.1111/cobi.12227 ◽

2014 ◽

Vol 28 (3) ◽

pp. 745-755 ◽

Cited By ~ 35

Author(s):

TRICIA A. MILLER ◽

ROBERT P. BROOKS ◽

MICHAEL LANZONE ◽

DAVID BRANDES ◽

JEFF COOPER ◽

...

Keyword(s):

Wind Energy ◽

Resource Selection ◽

Energy Development ◽

Selection Models ◽

Wind Energy Development

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The importance of seasonal resource selection when managing a threatened species: targeting conservation actions within critical habitat designations for the Gunnison sage-grouse

Wildlife Research ◽

10.1071/wr17027 ◽

2017 ◽

Vol 44 (5) ◽

pp. 407 ◽

Cited By ~ 6

Author(s):

M. B. Rice ◽

A. D. Apa ◽

L. A. Wiechman

Keyword(s):

Resource Selection ◽

Radio Telemetry ◽

Sage Grouse ◽

Isolated Populations ◽

Critical Habitat ◽

Selection Models ◽

Management Actions ◽

The Us ◽

Conservation Actions ◽

Priority Habitat

Context The ability to identify priority habitat is critical for species of conservation concern. The designation of critical habitat under the US Endangered Species Act 1973 identifies areas occupied by the species that are important for conservation and may need special management or protection. However, relatively few species’ critical habitats designations incorporate habitat suitability models or seasonal specificity, even when that information exists. Gunnison sage-grouse (GUSG) have declined substantially from their historical range and were listed as threatened by the US Fish and Wildlife Service (USFWS) in November 2014. GUSG are distributed into eight isolated populations in Colorado and Utah, and one population, the Gunnison Basin (GB), has been the focus of much research. Aims To provide season-specific resource selection models to improve targeted conservation actions within the designated critical habitat in the GB. Methods We utilised radio-telemetry data from GUSG captured and monitored from 2004 to 2010. We were able to estimate resource selection models for the breeding (1 April–15 July) and summer (16 July–30 September) seasons in the GB using vegetation, topographical and anthropogenic variables. We compared the seasonal models with the existing critical habitat to investigate whether the more specific seasonal models helped identify priority habitat for GUSG. Key results The predictive surface for the breeding model indicated higher use of large areas of sagebrush, whereas the predictive surface for the summer model predicted use of more diverse habitats. The breeding and summer models (combined) matched the current critical habitat designation 68.5% of the time. We found that although the overall habitat was similar between the critical habitat designation and our combined models, the pattern and configuration of the habitat were very different. Conclusions These models highlight areas with favourable environmental variables and spatial juxtaposition to establish priority habitat within the critical habitat designated by USFWS. More seasonally specific resource selection models will assist in identifying specific areas within the critical habitat designation to concentrate habitat improvements, conservation and restoration within the GB. Implications This information can be used to provide insight into the patterns of seasonal habitat selection and can identify priority GUSG habitat to incorporate into critical habitat designation for targeted management actions.

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Erratum: Validation of Elk resource selection models with spatially independent data

Journal of Wildlife Management ◽

10.1002/jwmg.350 ◽

2012 ◽

Vol 76 (3) ◽

pp. 656-657

Author(s):

Priscilla K. Coe ◽

Bruce K. Johnson ◽

Michael J. Wisdom ◽

John G. Cook ◽

Martin Vavra ◽

...

Keyword(s):

Resource Selection ◽

Independent Data ◽

Selection Models

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Validation of elk resource selection models with spatially independent data

Journal of Wildlife Management ◽

10.1002/jwmg.10 ◽

2011 ◽

Vol 75 (1) ◽

pp. 159-170 ◽

Cited By ~ 21

Author(s):

Priscilla K. Coe ◽

Bruce K. Johnson ◽

Michael J. Wisdom ◽

John G. Cook ◽

Martin Vavra ◽

...

Keyword(s):

Resource Selection ◽

Independent Data ◽

Selection Models

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A ‘How-to’ Guide for Interpreting Parameters in Resource- and Step-Selection Analyses

10.1101/2020.11.12.379834 ◽

2020 ◽

Author(s):

John Fieberg ◽

Johannes Signer ◽

Brian Smith ◽

Tal Avgar

Keyword(s):

Resource Selection ◽

Process Model ◽

Model Parameters ◽

List Type ◽

Movement Model ◽

Selection Processes ◽

Movement Characteristics ◽

Step Selection ◽

Inhomogeneous Poisson Point Process ◽

Management And Conservation

AbstractResource-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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