An assessment of the efficacy of camera traps for studying demographic composition and variation in chimpanzees (<i>Pan troglodytes</i>)
Bibliographic record
Abstract
Demographic factors can strongly influence patterns of behavioral variation in animal societies. Traditionally, these factors are measured using longitudinal observation of habituated social groups, particularly in social animals like primates. Alternatively, noninvasive biomonitoring methods such as camera trapping can allow researchers to assess species occupancy, estimate population abundance, and study rare behaviors. However, measures of fine-scale demographic variation, such as those related to age and sex structure or subgrouping patterns, pose a greater challenge. Here, we compare demographic data collected from a community of habituated chimpanzees (Pan troglodytes verus) in the Taï Forest using two methods: camera trap videos and observational data from long-term records. By matching data on party size, seasonal variation in party size, measures of demographic composition, and changes over the study period from both sources, we compared the accuracy of camera trap records and long-term data to assess whether camera trap data could be used to assess such variables in populations of unhabituated chimpanzees. When compared to observational data, camera trap data tended to underestimate measures of party size, but revealed similar patterns of seasonal variation as well as similar community demographic composition (age/sex proportions) and dynamics (particularly emigration and deaths) during the study period. Our findings highlight the potential and limitations of camera trap surveys for estimating fine-scale demographic composition and variation in primates. Continuing development of field and statistical methods will further improve the usability of camera traps for demographic studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".