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Record W2773353633 · doi:10.1038/s41598-017-17105-7

Heterogeneous responses of temperate-zone amphibian populations to climate change complicates conservation planning

2017· article· en· W2773353633 on OpenAlexaff
Erin Muths, Thierry Chambert, Benedikt R. Schmidt, Douglas A. Miller, Blake R. Hossack, Pierre Joly, Odile Grolet, David M. Green, David S. Pilliod, Marc Cheylan, Robert N. Fisher, Rebecca McCaffery, Michael J. Adams, Wendy J. Palen, Jan W. Arntzen, Justin M. Garwood, Gary M. Fellers, Jean‐Marc Thirion, Aurélien Besnard, Evan H. Campbell Grant

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsBurnaby HospitalSimon Fraser UniversityMcGill University
FundersU.S. Geological Survey
KeywordsTemperate climateTaxonEcologyClimate changeAmphibianPopulationBiologyGeographyTemperate rainforestEnvironmental resource managementEcosystemEnvironmental scienceDemography

Abstract

fetched live from OpenAlex

The pervasive and unabated nature of global amphibian declines suggests common demographic responses to a given driver, and quantification of major drivers and responses could inform broad-scale conservation actions. We explored the influence of climate on demographic parameters (i.e., changes in the probabilities of survival and recruitment) using 31 datasets from temperate zone amphibian populations (North America and Europe) with more than a decade of observations each. There was evidence for an influence of climate on population demographic rates, but the direction and magnitude of responses to climate drivers was highly variable among taxa and among populations within taxa. These results reveal that climate drivers interact with variation in life-history traits and population-specific attributes resulting in a diversity of responses. This heterogeneity complicates the identification of conservation 'rules of thumb' for these taxa, and supports the notion of local focus as the most effective approach to overcome global-scale conservation challenges.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.138
GPT teacher head0.338
Teacher spread0.200 · 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

Citations76
Published2017
Admission routes1
Has abstractyes

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Same venueScientific ReportsSame topicSpecies Distribution and Climate ChangeFrench-language works237,207