Climate, history and neutrality as drivers of mammal beta diversity in Europe: insights from multiscale deconstruction
Bibliographic record
Abstract
1. Environmental sorting, historical factors and neutral dynamics may all drive beta diversity (change in species composition across space), but their relative importance remains unresolved. In the case of European mammals, key potential drivers of large-scale beta diversity include current climate, neutral dynamics and two historical factors: Pleistocene glaciations and peninsular dynamics (immigration from extra-regional eastern faunal source areas and inter-linked relictual survival and evolutionary differentiation in isolated areas). 2. We assessed the relative importance of these drivers using a novel analytical framework to deconstruct beta diversity of non-volant mammals in Europe (138 species) into its turnover (change in species composition because of species replacements) and nestedness components (change in species composition because of species richness differences) at continental and regional (250,000 km(2) ) scales. 3. We found continental-scale mammal beta diversity to be mainly caused by spatial turnover (99·9%), with only a small contribution (0·1%) from nestedness. 4. Current climate emerged as an important driver of beta diversity, given the strong continental-scale turnover, particularly in north-south direction, i.e., in line with the latitudinal climate gradient, and, more directly, the strong correlation of climate with spatial turnover at both continental and regional scales. 5. However, there was also evidence for the importance of non-climatic drivers. Notably, the compositional variation purely accounted for by space was greater than that purely accounted for by environment for both the turnover and the nestedness component of beta diversity. Furthermore, the strong longitudinal turnover within Southern Europe is in accordance with the region's long-term climatic stability having allowed multiple refugia and local evolutionary diversification. As expected from peninsular dynamics, there was increasing dissimilarity with geographic distance in an east-west direction because of nestedness, but only in Central and Northern Europe. 6. In conclusion, European mammal beta diversity mainly reflects spatial turnover and only to a limited extent nestedness and is driven by current climate in combination with historical - and perhaps, neutral - dynamics. These findings suggest that a key challenge for climate-change predictive studies will be taking the influence of non-climatic factors into account.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".