Human density modulates spatial associations among tropical forest terrestrial mammal species
Bibliographic record
Abstract
The spatial aggregation of species pairs often increases with the ecological similarity of the species involved. However, the way in which environmental conditions and anthropogenic activity affect the relationship between spatial aggregation and ecological similarity remains unknown despite the potential for spatial associations to affect species interactions, ecosystem function, and extinction risk. Given that human disturbance has been shown to both increase and decrease spatial associations among species pairs, ecological similarity may have a role in mediating these patterns. Here, we test the influences of habitat diversity, primary productivity, human population density, and species' ecological similarity based on functional traits (i.e., functional trait similarity) on spatial associations among tropical forest mammals. Large mammals are highly sensitive to anthropogenic change and therefore susceptible to changes in interspecific spatial associations. Using two-species occupancy models and camera trap data, we quantified the spatial overlap of 1216 species pairs from 13 tropical forest protected areas around the world. We found that the association between ecological similarity and interspecific species associations depended on surrounding human density. Specifically, aggregation of ecologically similar species was more than an order of magnitude stronger in landscapes with the highest human density compared to those with the lowest human density, even though all populations occurred within protected areas. Human-induced changes in interspecific spatial associations have been shown to alter top-down control by predators, increase disease transmission and increase local extinction rates. Our results indicate that anthropogenic effects on the distribution of wildlife within protected areas are already occurring and that impacts on species interactions, ecosystem functions, and extinction risk warrant further investigation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".