Determinants of zoogeographical boundaries differ between vertebrate groups
Bibliographic record
Abstract
Abstract Aim Worldwide distribution patterns of living animals are structured in multiple zoogeographical regions, characterized by faunas with homogeneous composition that are separated by sharp boundaries. These zoogeographical regions can differ depending on the considered animal group, probably because they have distinct characteristics such as dispersal, metabolism, or evolutionary history, and thus divergent responses to major biogeographical drivers, such as tectonic movements, abrupt climate transitions and orographic barriers. Here, we tested if the drivers of biogeographical boundaries are different between vertebrate classes with strongly divergent traits and evolutionary history. Location Global. Time period Present. Major taxa studied Amphibians, birds and mammals. Methods We focused on terrestrial biogeographical boundaries, considering multiple potential drivers: spatial heterogeneity of present‐day climate, altitudinal variation, long‐term tectonic movements and past climate change (temperature). We used spatially explicit regression models and geographically weighted regressions to select and quantify the factors explaining the position of the biogeographical boundaries between vertebrate classes. Results For mammals, tectonic movements, abrupt climatic transitions and orographic barriers jointly determined extant biogeographical boundaries, with tectonic movements being the most important. For birds, abrupt climatic transitions played the strongest role, while the effect of orographic barriers was weak. For amphibians, biogeographical boundaries mostly corresponded to areas with abrupt climatic transitions. The strongest transitions of amphibian faunas occur in areas with abrupt shifts of temperature and precipitation regimes. Main conclusions Our analyses confirmed that different drivers have jointly shaped the global vertebrate biogeographical regions, and highlight that taxa with different features show heterogeneous responses across the globe. Eco‐physiological constraints likely increase the importance of spatial heterogeneity of climate, while dispersal limitations magnify the relevance of physical barriers (mountain chains and long‐term tectonic instability). Integrating among‐taxa heterogeneity into analyses thus provides a more complete view of how different processes determine biodiversity variation across the globe.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".