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Record W3029174271 · doi:10.1002/jwmg.21886

Annual Pronghorn Survival of a Partially Migratory Population

2020· article· en· W3029174271 on OpenAlexafffund
Paul F. Jones, Andrew F. Jakes, Daniel R. Eacker, Mark Hebblewhite

Bibliographic record

VenueJournal of Wildlife Management · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsAlberta Conservation Association
FundersMitacsUniversity of MontanaUniversity of CalgaryAlberta Conservation AssociationNational Fish and Wildlife FoundationWorld Wildlife Fund
KeywordsUngulateWildlifeGeographyPopulationEcologySurvivorship curveDemographyHabitatBiology

Abstract

fetched live from OpenAlex

ABSTRACT The importance of conserving migratory populations is recognized across a variety of ungulate taxa, yet the demographic benefits of migration remain uncertain for ungulate populations that exhibit partial migration. We hypothesized that migratory pronghorn ( Antilocapra americana ) would experience greater survival compared to residents by moving longer distances to avoid severe winter weather and access higher quality forage. We used a Bayesian time‐to‐event approach to analyze the fates of 175 radio‐collared adult female pronghorn monitored over 8 biological years (2004–2011) in the Northern Sagebrush Steppe ecosystem. Annual survivorship of migratory pronghorn was 7% higher on average compared to residents but not statistically different. Migratory pronghorn had higher survivorship in summer and winter compared to residents, and few mortalities were observed during the short autumn and spring migration periods. Mortality risk for both movement tactics intensified under more severe winter weather; winter weather severity alone best explained annual pronghorn mortality risk. The top model predicted survival rates to decline on average by 56% over the range of observed winter climatic conditions. To minimize human impacts to pronghorn during extreme climatic events, we recommend working with transportation departments and land managers to enhance pronghorn crossings of roads and railroads, and landholders to modify fences to wildlife‐friendly standards. © 2020 The Authors. Journal of Wildlife Management published by Wiley Periodicals, LLC on behalf of The Wildlife Society.

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 score0.074
Threshold uncertainty score0.473

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 head0.013
GPT teacher head0.223
Teacher spread0.209 · 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

Citations21
Published2020
Admission routes2
Has abstractyes

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