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Record W3205143329 · doi:10.1111/1365-2656.13615

Integrating advances in population and evolutionary ecology with conservation strategy through long‐term studies of red‐billed choughs

2021· article· en· W3205143329 on OpenAlexfundno aff
Jane M. Reid, Eric M. Bignal, Sue Bignal, D. I. McCracken, Sarah R. Fenn, Amanda E. Trask, Pat Monaghan

Bibliographic record

VenueJournal of Animal Ecology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
FundersNatural Environment Research CouncilKillam TrustsUniversity of AberdeenUniversity of GlasgowSight Research UKJesus College, University of CambridgeScottish Natural HeritageRoyal SocietyLeverhulme Trust
KeywordsPopulationEcologyPopulation viability analysisBiologyEvolutionary ecologyPopulation growthVital ratesDemographyEndangered speciesHost (biology)

Abstract

fetched live from OpenAlex

Conceptual and methodological advances in population and evolutionary ecology are often pursued with the ambition that they will help identify demographic, ecological and genetic constraints on population growth rate (λ), and ultimately facilitate evidence-based conservation. However, such advances are often decoupled from conservation practice, impeding translation of scientific understanding into effective conservation and of conservation-motivated research into wider conceptual understanding. We summarise key outcomes from long-term studies of a red-billed chough Pyrrhocorax pyrrhocorax population of conservation concern, where we proactively aimed to achieve the dual and interacting objectives of advancing population and evolutionary ecology and advancing effective conservation. Estimation of means, variances and covariances in key vital rates from individual-based demographic data identified temporal and spatial variation in subadult survival as key constraints on λ, and simultaneously provided new insights into how vital rates can vary as functions of demographic structure, natal conditions and parental life history. Targeted analyses showed that first-year survival increased with prey abundance, implying that food limitation may constrain λ. First-year survival then decreased dramatically, threatening population viability and prompting emergency supplementary feeding interventions. Detailed evaluations suggested that the interventions successfully increased first-year survival in some years and additionally increased adult survival and successful reproduction, thereby feeding back to inform intervention refinements and understanding of complex ecological constraints on λ. Genetic analyses revealed novel evidence of expression of a lethal recessive allele, and demonstrated how critically small effective population size can arise, thereby increasing inbreeding and loss of genetic variation. Population viability analyses parameterised with all available demographic and genetic data showed how ecological and genetic constraints can interact to limit population viability, and identified ecological management as of primacy over genetic management to ensure short-term persistence of the focal population. This case study demonstrates a full iteration through the sequence of primary science, evidence-based intervention, quantitative evaluation and feedback that is advocated in conservation science but still infrequently achieved. It thereby illustrates how pure science advances informed conservation actions to ensure the (short-term) stability of the target population, and how conservation-motivated analyses fed back to advance fundamental understanding of population processes.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.020
GPT teacher head0.298
Teacher spread0.278 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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