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Record W3183322328 · doi:10.1093/jmammal/gyab081

Determinants of migration trajectory and movement rate in a long-distance terrestrial mammal

2021· article· en· W3183322328 on OpenAlexaffabout
Martin Leclerc, Mathieu Leblond, Maël Le Corre, Christian Dussault, Steeve D. Côté

Bibliographic record

VenueJournal of Mammalogy · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMinistère des Ressources naturelles et des ForêtsEnvironment and Climate Change CanadaUniversité Laval
Fundersnot available
KeywordsTundraHabitatGeographyWildlifeEcologySnowSpring (device)Elevation (ballistics)Physical geographyClimate changePrecipitationEnvironmental scienceArcticBiologyMeteorology

Abstract

fetched live from OpenAlex

Abstract Animal migrations occur in many taxa and are considered an adaptive response to spatial or temporal variations in resources. Human activities can influence the cost-benefit trade-offs of animal migrations, but evaluating the determinants of migration trajectory and movement rate in declining populations facing relatively low levels of human disturbance can provide new and valuable insights on the behavior of wildlife in natural environments. Here, we used an adapted version of path selection functions and quantified the effects of habitat type, topography, and weather, on 313 spring migrations by migratory caribou (Rangifer tarandus) in northern Québec, Canada, from 2011 to 2018. Our results showed that during spring migration, caribou selected tundra and avoided water bodies, forest, and higher elevation. Higher precipitation and deeper snow were linked to lower movement rates. Weather variables had a stronger effect on the migration trajectories and movement rates of females than males. Duration of caribou spring migration (mean of 48 days) and length (mean of 587 km) were similar in males and females, but females started (22 April) and ended (10 June) spring migrations ca. 6 days earlier than males. Caribou spring migration was influenced by habitat type, topography, and weather, but we also observed that caribou migrations were not spatially constrained. Better knowledge on where and when animals move between their winter and summer ranges can help inform management and land planning decisions. Our results could be used to model future migration trajectories and speed of caribou under different climate change scenarios.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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 teacher head, 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

Citations18
Published2021
Admission routes2
Has abstractyes

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