Latitudinal‐diversity gradients can be shaped by biotic processes: new insights from an eco‐evolutionary model
Bibliographic record
Abstract
The processes involved in shaping latitudinal‐diversity gradients (LDGs) have been a longstanding source of debate and research. Climatic, historical and evolutionary factors have all been shown to contribute to the formation of LDGs. However, meta‐analyses have shown that different clades have LDG slopes that may vary in more than one order of magnitude. Such large variation cannot be explained solely by climatic or historical factors (e.g. difference in surface area between temperate and tropical zones) given that all clades within a geographic region are subject to the same conditions. Therefore, biotic processes intrinsic to each taxonomic group could be relevant in explaining rate differences in diversity decline across latitudinal gradients among groups. In this study, we developed a model simulating multiple competing species subjected (or not) to a demographic Allee effect. We simulated the range expansion of these species across an environmental gradient to show how these two overlooked factors (competition and Allee effects) are capable of modulating LDGs. Allee effects resulted in a steeper LDG given a higher probability of local extinction and lower colonization capacity compared to species without Allee effects. Likewise, stronger competition also led to a steeper decline in species diversity compared to scenarios with weaker species antagonistic interactions. This pattern occurred mostly due to the strength of priority effects, wherein scenarios with strong competition, species that dispersed earlier in the landscape were able to secure many patches whereas late‐arriving species were progressively precluded from expanding their ranges. Overall, our results suggest that the effect of biotic processes in shaping macroecological patterns could be more important than it is currently appreciated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".