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Record W3177906111 · doi:10.51492/cfwj.cesasi.27

Predation impedes recovery of Sierra Nevada bighorn sheep

2021· article· en· W3177906111 on OpenAlexaff
Daniel J. Gammons, Jeffrey L. Davis, David W. German, Kristin Denryter, John D. Wehausen, Thomas R. Stephenson

Bibliographic record

VenueCalifornia Fish and Wildlife Journal · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsBishop's University
Fundersnot available
KeywordsOvis canadensisPredationPopulationEndangered speciesBiologyHerdEcologyHabitatGeographyDemography

Abstract

fetched live from OpenAlex

Translocation of animals into formerly occupied habitat is a key element of the recovery plan for Sierra Nevada bighorn sheep (Ovis canadensis sierrae), which are state (California) and federally listed as endangered. However, implementing Sierra bighorn translocations is a significant conservation challenge because of the small size of the extant population and the limited number of herds available to donate translocation stock. One such herd, the Mt. Langley herd, recently became unusable as a translocation source following a substantial population decline. At the time of listing in 1999, predation by mountain lions (Puma concolor; hereafter lion) was considered a primary threat to Sierra bighorn, and since then lion predation may have continued to limit the ability of source herds to provide translocation stock. We evaluated the relationship between lion predation and ewe survival rates within three source herds of the Southern Recovery Unit, compared lion abundance and ewe survival among years of varying predation levels, provided a range of estimated times for the Mt. Langley herd to recover to its former status as a translocation source, and determined if the rates lions have been removed to mitigate Sierra bighorn predation exceeded sustainable harvest guidelines. We found compelling evidence that lion predation has impeded the recovery of Sierra bighorn by reducing survival rates of adult ewes (and consequently, population growth) and by preying upon individuals that could have otherwise been translocated. Ewe survival was poor during years of extreme predation but even during years of typical predation, survival rates were below a level needed to ensure population growth, indicating that years with little or no lion predation may be necessary for the population to grow and meet recovery goals. Because the intensity of predation was related to lion abundance, monitoring lion populations could provide managers with advance warning of periods of extreme predation. We found that following a period of particularly extreme predation, the Mt. Langley herd decreased in abundance far below the threshold needed to be considered a source of translocation stock, resulting in the loss of approximately 25% of the recovery program’s capacity for translocations. It is unclear how many years it will take for this herd to recover, but management actions to reduce lion predation are likely needed for this herd to grow to a size that can afford to donate individuals to translocation efforts in the near future, even when optimistic growth rates are assumed. We found that lion removal may also be needed to prevent predation from leading to Sierra bighorn population decline. Lion removal rates that have been implemented thus far are well below what would be needed to reduce the abundance the eastern Sierra lion population itself. We recommend continued monitoring of Sierra bighorn and sympatric lions and note that lion removal may be required to facilitate bighorn recovery for the foreseeable future.

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)0.002
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 score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.001
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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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