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Record W2126653606 · doi:10.1002/jwmg.625

Identifying hidden sinks in growing populations from individual fates and movements: The feral horses of Sable Island

2013· article· en· W2126653606 on OpenAlexafffundabout
Adrienne L. Contasti, Floris M. van Beest, Eric Vander Wal, Philip D. McLoughlin

Bibliographic record

VenueJournal of Wildlife Management · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversité de SherbrookeUniversity of Saskatchewan
FundersParks Canada
KeywordsBiological dispersalPopulationWildlifeHabitatImmigrationPopulation growthEcologyGeographyPopulation densityNational parkOccupancyPopulation sizeBiologyDemography

Abstract

fetched live from OpenAlex

ABSTRACT Identifying the existence of population sinks is critical for conservation and management. However, because of density‐dependent dispersal, sinks can sometimes be masked by immigration events, especially during phases of population growth. We present a large‐scale, empirical demonstration of within‐population source‐sink dynamics using the feral horses ( Equus ferus caballus ) of Sable Island National Park Reserve, Nova Scotia, Canada, as a model. We tracked the fates and movements of 98.7% of the female population ( n = 190–237) across 3 demographic clusters (subunits) during a period of rapid population growth (2008–2010; 24.7% increase in density). All subunits experienced increases in population size each year (λ > 1.0). Our individual‐based analysis showed that western Sable Island, where water availability was greatest, behaved as a source and would have grown with or without immigration in all years. However, the central (and fastest growing subunit) would have declined from 2008–2009 (λ = 0.951) without immigration. Further, the eastern subunit would have declined in 2 intervals (λ = 0.932, 0.999) without immigration. Our study demonstrates that the propensity of habitat to act as a sink can be masked during a period of population growth because of density‐dependent immigration from adjacent habitats. These findings present a caution to managers charged with conserving wide‐ranging species with long population cycles for which effects of immigration on local population growth rate can be difficult to isolate using standard methods of enumeration. © 2013 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 score0.010
Threshold uncertainty score0.563

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 communication0.0000.001
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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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

Citations25
Published2013
Admission routes3
Has abstractyes

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