Bibliographic record
Abstract
Aim It is commonly asserted that climate presents the primary constraint on species’ geographic distributions, and therefore, that species' ranges shift in response to changing climate given their specific climatic tolerances. However, supporting evidence is surprisingly inconsistent. Alternatively, spatially structured processes (e.g., dispersal) could more strongly determine species’ geographic distributions. Is climate the primary determinant of species’ geographic distributions, or might non-climatic, spatial processes constitute a stronger influence, such that the effect of climate is indirect? This study tests a number of predictions made by each of these hypotheses, during a single period of time. Location Contiguous United States and southern Canada. Methods We used 19 species of passerine birds whose distributions fall entirely within the area sampled by the North American Breeding Bird Survey from 1990-2000. We related these distributions to the mean breeding season climate, geographic locations and neighbourhood effects. Two spatial scales were addressed to assess the geographic location of species’ ranges and species' distributions within ranges. Results On average, geographic coordinates and a model representing neighbourhood occupancy outperform a simple climatic model. After controlling for geographic coordinates, species occupancy is poorly related to climate. A neighbourhood model on average accounts for the majority of variance captured by geographic coordinates within ranges, and more for the continental placement of ranges. Spatially explicit variables are more important than macroclimatic variables in a predictive model of species occupancy on average. Main Conclusions The geographic distributions of wide-spread North American passerine birds appear not to be primarily determined by climate. Our results are consistent with the hypothesis that localized spatial processes such as dispersal are stronger determinants of both continental range placement and within-range distributions of North American birds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".