Subtopic Deep Dive

Habitat Selection by Large Herbivores
Research Guide

What is Habitat Selection by Large Herbivores?

Habitat selection by large herbivores is the process by which species like elk, bison, and deer choose specific environmental features based on forage quality, predation risk, and disturbance factors.

Researchers use resource selection functions (RSFs) and step-selection functions (SSFs) to analyze GPS telemetry data from large herbivores. These models quantify preferences for vegetation types, topography, and human proximity. Over 10 key papers, including Thurfjell et al. (2014) with 610 citations, establish SSF applications in ecology.

Curated Papers

Key Challenges

Why It Matters

Habitat selection models inform protected area design by identifying critical foraging zones for bison and elk, as shown in Laundré et al. (2001) reestablishing predation-driven fear landscapes in Yellowstone (1124 citations). NDVI metrics from Pettorelli et al. (2010) link remote sensing to forage selection, aiding conservation amid anthropogenic pressures (767 citations). These insights prevent human-wildlife conflicts and optimize grazing management, per McNaughton (1984) on herd dynamics (972 citations).

Key Research Challenges

Modeling Fine-Scale Movement

Step-selection functions struggle with high-resolution telemetry data variability across herd sizes. Thurfjell et al. (2014) highlight bias in SSF availability estimation (610 citations). Integrating NDVI introduces scale mismatches with animal perception.

Quantifying Predation Risk

Linking telemetry to dynamic wolf-elk interactions requires spatiotemporal models beyond static RSFs. Laundré et al. (2001) demonstrate fear landscape shifts post-wolf reintroduction but note measurement gaps (1124 citations). Behavioral responses vary by sex and season.

Anthropogenic Disturbance Effects

Roads and development alter selection nonlinearly, complicating RSF predictions. Ellis (2015) frames human biosphere impacts on herbivore ecology (610 citations). Long-term data scarcity hinders causal inference.

Essential Papers

2016 Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education:

Robert S. Sikes · 2016 · Journal of Mammalogy · 3.1K citations

Guidelines for use of wild mammal species in research are updated from Sikes et al. (2011) . These guidelines cover current professional techniques and regulations involving the use of mammals in r...

Guidelines of the American Society of Mammalogists for the use of wild mammals in research

Robert S. Sikes, William L. Gannon · 2011 · Journal of Mammalogy · 2.3K citations

Abstract Guidelines for use of wild mammal species are updated from the American Society of Mammalogists (ASM) 2007 publication. These revised guidelines cover current professional techniques and r...

Wolves, elk, and bison: reestablishing the "landscape of fear" in Yellowstone National Park, U.S.A.

John W. Laundré, Lucina Hernández, Kelly B. Altendorf · 2001 · Canadian Journal of Zoology · 1.1K citations

The elk or wapiti (Cervus elaphus) and bison (Bison bison) of Yellowstone National Park have lived in an environment free of wolves (Canis lupus) for the last 50 years. In the winter of 1994-1995, ...

Grazing Lawns: Animals in Herds, Plant Form, and Coevolution

S. J. McNaughton · 1984 · The American Naturalist · 972 citations

Many grazing animals in both terrestrial and aquatic ecosystems form dense herds that maintain the vegetation in their concentration areas at very low statures. Studies of the effects of large ungu...

The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology

Nathalie Pettorelli, Sadie J. Ryan, Thomas Mueller et al. · 2010 · Climate Research · 767 citations

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 46:15-27 (2011) - DOI: htt...

Ecology in an anthropogenic biosphere

Erle C. Ellis · 2015 · Ecological Monographs · 610 citations

Humans, unlike any other multicellular species in Earth's history, have emerged as a global force that is transforming the ecology of an entire planet. It is no longer possible to understand, predi...

Applications of step-selection functions in ecology and conservation

Henrik Thurfjell, Simone Ciuti, Mark S. Boyce · 2014 · Movement Ecology · 610 citations

Reading Guide

Foundational Papers

Start with Laundré et al. (2001) for predation-driven selection in elk and bison (1124 citations), then Sikes and Gannon (2011) for telemetry ethics (2341 citations), and McNaughton (1984) for grazing coevolution (972 citations).

Recent Advances

Thurfjell et al. (2014) on SSF applications (610 citations); Ellis (2015) on anthropogenic ecology (610 citations); Tuia et al. (2022) for ML in conservation (587 citations).

Core Methods

Resource/step-selection functions on GPS data; NDVI for vegetation; logistic regression for preference coefficients.

How PapersFlow Helps You Research Habitat Selection by Large Herbivores

Discover & Search

Research Agent uses searchPapers('step-selection functions large herbivores') to find Thurfjell et al. (2014), then citationGraph reveals 200+ citing works on bison telemetry, while findSimilarPapers uncovers RSF extensions and exaSearch pulls NDVI applications from Pettorelli et al. (2010).

Analyze & Verify

Analysis Agent applies readPaperContent on Laundré et al. (2001) to extract Yellowstone elk telemetry stats, verifyResponse with CoVe cross-checks predation risk claims against Sikes et al. (2011) guidelines, and runPythonAnalysis simulates SSF models via pandas on sample GPS data with GRADE scoring for model fit.

Synthesize & Write

Synthesis Agent detects gaps in predation-foraging tradeoffs across papers, flagging contradictions between McNaughton (1984) grazing lawns and Ellis (2015) anthroposphere effects; Writing Agent uses latexEditText for RSF equations, latexSyncCitations for 20-paper bibliography, latexCompile for figures, and exportMermaid for selection flowcharts.

Use Cases

"Analyze GPS data for elk habitat selection in Yellowstone using SSFs"

Research Agent → searchPapers('elk SSF Yellowstone') → Analysis Agent → runPythonAnalysis(pandas GPS simulation, matplotlib selection curves) → GRADE verification → output: Verified SSF coefficients and risk maps.

"Draft LaTeX review on NDVI in herbivore habitat models"

Synthesis Agent → gap detection(NDVI forage) → Writing Agent → latexGenerateFigure(NDVI plots), latexSyncCitations(Pettorelli 2010 et al.), latexCompile → output: Compiled PDF with equations and 15 synced references.

"Find GitHub code for resource selection functions in R"

Research Agent → searchPapers('RSF large herbivores code') → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect → output: Curated repos with SSF scripts tested in Python sandbox.

Automated Workflows

Deep Research workflow scans 50+ papers via searchPapers on 'bison habitat selection telemetry', structures SSF evolution report with citationGraph. DeepScan applies 7-step CoVe to verify Laundré et al. (2001) claims against modern data. Theorizer generates hypotheses linking NDVI trends to predation from Pettorelli et al. (2010) and Thurfjell et al. (2014).

Try Doxa for Habitat Selection by Large Herbivores Research

Frequently Asked Questions

What defines habitat selection in large herbivores?

It is the non-random use of environmental features like forage and cover by species such as elk and bison, modeled via RSFs and SSFs on telemetry data.

What are key methods used?

Resource selection functions compare used vs. available habitats; step-selection functions analyze movement steps, as detailed in Thurfjell et al. (2014).

What are foundational papers?

Sikes and Gannon (2011, 2341 citations) provide mammal research guidelines; Laundré et al. (2001, 1124 citations) shows predation effects on elk-bison.

What open problems remain?

Dynamic modeling of multi-scale anthropogenic effects and integrating genetics with selection, per Soulé and Simberloff (1986) reserve design challenges.