Chimpanzees (<i>Pan troglodytes</i>) Indicate Mammalian Abundance Across Broad Spatial Scales
Bibliographic record
Abstract
Ongoing ecosystem change and biodiversity decline across the Afrotropics call for tools to monitor the state of biodiversity or ecosystem elements across extensive spatial and temporal scales. We assessed relationships in the co-occurrence patterns between great apes and other medium to large-bodied mammals to evaluate whether ape abundance serves as a proxy for mammal diversity across broad spatial scales. We used camera trap footage recorded at 22 research sites, each known to harbor a population of chimpanzees, and some additionally a population of gorillas, across 12 sub-Saharan African countries. From ~350,000 1-min camera trap videos recorded between 2010 and 2016, we estimated mammalian community metrics, including species richness, Shannon diversity, and mean animal mass. We then fitted Bayesian Regression Models to assess potential relationships between ape detection rates (as proxy for ape abundance) and these metrics. We included site-level protection status, human footprint, and precipitation variance as control variables. We found that relationships between detection rates of great apes and other mammal species, as well as animal mass were largely positive. In contrast, relationships between ape detection rate and mammal species richness were less clear and differed according to site protection and human impact context. We found no clear association between ape detection rate and mammal diversity. Our findings suggest that chimpanzees hold potential as indicators of specific elements of mammalian communities, especially population-level and composition-related characteristics. Declines in chimpanzee populations may indicate associated declines of sympatric medium to large-bodied mammal species and highlight the need for improved conservation interventions.Changes in chimpanzee abundance likely precede extirpation of sympatric mammals.
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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 | 0.001 | 0.001 |
| 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".