Local temperature and ecological similarity drive distributional dynamics of tropical mammals worldwide
Bibliographic record
Abstract
Abstract Aim Identifying the underlying drivers of species’ distributional dynamics is critical for predicting change and managing biological diversity. While anthropogenic factors such as climate change can affect species distributions through time, other naturally occurring ecological processes can also have an influence. Theory predicts that interactions between species can influence distributional dynamics, yet empirical evidence remains sparse. A powerful approach is to monitor and model local colonization and extinction—the processes that generate change in distributions over time—and to identify their abiotic and biotic associations. Intensive camera‐trap monitoring provides an opportunity to assess the role of temperature and species interactions in the colonization and extinction dynamics of tropical mammals, many of which are species of conservation concern. Using data from a pan‐tropical monitoring network, we examined how short‐term local temperature change and ecological similarity between species (a proxy for the strength of species interactions) influenced the processes that drive distributional shifts. Location Tropical forests worldwide. Time period 2007–2016. Major taxa studied Terrestrial mammals. Methods We used dynamic occupancy models to assess the influence of the abiotic and biotic environment on the distributional dynamics of 42 mammal populations from 36 species on 7 tropical elevation gradients around the world. Results Overall, temperature, ecological similarity, or both, were linked to colonization or extinction dynamics in 29 populations. For six species, the effect of temperature depended upon the local mammal community similarity. This result suggests that the way in which temperature influences local colonization and extinction dynamics depends on local mammal community composition. Main conclusions These results indicate that varying temperatures influence tropical mammal distributions in surprising ways and suggest that interactions between species mediate distributional dynamics.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".