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Record W2784220428 · doi:10.1111/geb.12708

Are North American bird species' geographic ranges mainly determined by climate?

2018· article· en· W2784220428 on OpenAlexafffundabout
Johnathan L. Rich, David J. Currie

Bibliographic record

VenueGlobal Ecology and Biogeography · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOccupancyBiological dispersalBreeding bird surveyGeographyPasserineEcologySpecies distributionHabitatClimate changePhysical geographyPopulationBiology

Abstract

fetched live from OpenAlex

Abstract Aim It is commonly asserted that climate presents the primary constraint on species’ geographic distributions, and that species’ distributions should therefore shift to track changing climate. However, the evidence is surprisingly mixed about the causal link between species’ distributions and climate. Correlations between distributions and climate may be indirect, reflecting influences of other spatially structured habitat variables (e.g., land cover) and/or population processes (e.g., dispersal). Here, we ask if species’ geographic distributions are more strongly related to climate, or to other spatially structured variables. Location The contiguous United States and southern Canada. Time period 1990–2000. Major taxa studied North American passerine birds. Methods We used the breeding‐season distributions of 19 widespread species of passerine birds whose breeding‐season distributions fall entirely within the area sampled by the North American Breeding Bird Survey. We related these distributions to temperature and precipitation, geographic coordinates, and the degree of occupation of neighboring sites by conspecifics. Two spatial scales were examined: the geographic location of species’ ranges within North America, and site occupancy within species' ranges. We examine these relationships using generalized linear models, structural equation modeling, random forest models and spatial correlograms. Results On average, geographic coordinates and a model of neighborhood occupancy outperform a simple climatic model. After controlling for geographic coordinates, species occupancy is poorly related to climate. Neighborhood occupancy accounts for the majority of variance captured by geographic coordinates within ranges, and more for the continental placement of ranges. On average, species’ distributions are more strongly and more directly related to spatial coordinates, and neighborhood occupancy, than to climate. Main conclusions Our results are inconsistent with the hypothesis that climate is the primary, direct determinant of species’ geographic distributions. Rather, other spatially structured factors appear to be stronger determinants of both continental range placement and within‐range distributions of this sample of North American birds.

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)0.001
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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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

Citations41
Published2018
Admission routes3
Has abstractyes

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