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Record W4402030491 · doi:10.1093/jmammal/gyae086

Predation risk for hoary marmots in Washington’s North Cascades Mountains

2024· article· en· W4402030491 on OpenAlexaffabout
Logan Whiles, Lisa A. Shipley, Jocelyn R. Akins, Roger G. Christophersen, Jason I. Ransom, Taal Levi, Daniel H. Thornton

Bibliographic record

VenueJournal of Mammalogy · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsCascades (Canada)
FundersNational Institute of Food and AgricultureU.S. Geological SurveyU.S. Department of Agriculture
KeywordsPredationGeographyEcologyBiology

Abstract

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Abstract Declines in populations of small mammals associated with high elevations, e.g., marmots (Marmota spp.) and pikas (Ochotona spp.), have been attributed to both direct and indirect effects of environmental changes caused by humans. For example, populations of Olympic marmots (M. olympus) and Vancouver Island marmots (M. vancouverensis) have declined in response to increased predator access to high-elevation marmot habitats. In the North Cascades National Park Service Complex (NOCA), observed mean abundance of hoary marmots (M. caligata) declined by 74% from 2007 to 2016. Although these declines have been linked to harsh winter conditions, the role of predation and its association with decreasing snowpack has yet to be explored in this system. We noninvasively examined these predator–prey dynamics by coupling behavioral surveys of hoary marmots with indices of predation risk. We conducted 145 vigilance samples and 39 flight initiation distance trials to measure Hoary Marmot antipredator behavior. We derived a risk index for each predator by weighting its probability of use estimates (the probability of a predator–prey encounter) with its observed proportion of Hoary Marmot predation (probability of prey death given an encounter). We used genetic metabarcoding of vertebrate DNA from 413 predator scat samples to quantify predation on hoary marmots. We surveyed 9,930 trap-nights using 130 remote cameras to model predator probability of use near Hoary Marmot colonies. From camera traps and scat DNA, we detected 10 predator species in the study area, and we detected hoary marmots in the scats of 5 of these species. The proportion of observed Hoary Marmot predation was highest for coyotes (Canis latrans) at 50%. To our knowledge, we also report the first record of hoary marmots being consumed by Pacific martens (Martes caurina), which were also significant predators (proportion of observed predation = 32%). We expected predators with low-elevation habits to use sites with lesser snow persistence; this prediction was supported for coyotes but not for other typically low-elevation predators. We found estimated risk experienced by hoary marmots was highest from Pacific martens and lowest from Canada Lynx (Lynx canadensis). Contrary to our predictions and despite hoary marmots allocating >50% of their time to vigilance, neither estimates of predator risk nor human presence explained variation in marmot antipredator behavior. Based on the results from both our dietary and probability of use analyses, we expect coyotes to have an increasing effect on NOCA’s Hoary Marmot population as the climate warms and Coyote range continues to expand. Thus, our work highlights the need to better understand the effect of climate-driven shifts in predator–prey ecology in high-elevation systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.260
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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