Niche centrality and human influence predict rangewide variation in population abundance of a widespread mammal: The collared peccary (<i>Pecari tajacu</i>)
Bibliographic record
Abstract
Abstract Aim (1) To evaluate whether geographic variation in population abundance of a widespread mammal ( Pecari tajacu ) is related to its location with respect to the centroid of its ecological niche or to the centroid of its geographic range. (2) To assess whether the abundance–niche centrality relationship defines the maximum expected abundance at any location, rather than the realized abundance. (3) To test whether including human impacts improves the abundance–niche centrality relationship, and therefore the prediction of geographic variation in population abundance. Location The Americas. Methods We modelled the ecological niche of the species using occurrence and environmental data and created spatial models of distance to the niche centroid ( DNC ) and to the geographic centroid ( DGC ). We tested the relationships between population abundance and DNC and between abundance and DGC . We evaluated whether the rate of change in the abundance– DNC relationship was steeper near the upper boundary of quantile regressions. We tested whether the human influence index ( HII ) contributed to improve niche‐based predictions of population abundance. Finally, we generated broad‐scale predictions of collared peccary population abundances. Results We found a negative relationship between abundance and DNC and a non‐significant relationship between abundance and DGC . The abundance– DNC relationship was wedge‐shaped, steeper in the upper quantile boundary than in the median. HII also had a negative effect on abundance. The model including DNC and HII was best supported for predicting the median abundance, while DNC alone was the best to predict the upper boundary of population abundances. Main conclusions Population abundances are associated with the structure of the ecological niche, especially the maximum abundance expected in an area. Thus, the DNC approach can be useful in obtaining a spatial approximation of potential abundance patterns at biogeographic extents. To achieve a better prediction of realized abundances, it is critical to consider the human influence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".