Ecotypic variation in the context of global climate change: revisiting the rules
Bibliographic record
Abstract
Patterns of ecotypic variation constitute some of the few 'rules' known to modern biology. Here, we examine several well-known ecogeographical rules, especially those pertaining to body size in contemporary, historical and fossil taxa. We review the evidence showing that rules of geographical variation in response to variation in the local environment can also apply to morphological changes through time in response to climate change. These rules hold at various time scales, ranging from contemporary to geological time scales. Patterns of body size variation in response to climate change at the individual species level may also be detected at the community level. The patterns underlying ecotypic variation are complex and highly context-dependent, reducing the 'predictive-power' of ecogeographical rules. This is especially true when considering the increasing impact of human activities on the environment. Nonetheless, ecogeographical rules may help interpret the likely influences of anthropogenic climate change on ecosystems. Global climate change has already influenced the body size of several contemporary species, and will likely have an even greater impact on animal communities in the future. For this reason, we highlight and emphasise the importance of museum specimens and the continued need for documenting the earth's biological diversity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".