Finding the sweet spot in camera trapping: A global synthesis and meta‐analysis of minimum sampling effort
Bibliographic record
Abstract
Summary Camera traps are one of the most common tools in wildlife and conservation biology. Sampling can document and measure animal presence and activity. Captures can be used to estimate population parameters such as presence, abundance, habitat suitability, and resident species richness of specific populations. Effective camera trapping is relevant to conservation for many reasons. For instance, they can be used to inform pre‐ and post‐restoration efforts, monitor the use of artificial structures by species and assess behaviours like predator–prey interactions. This sampling approach can aid in assessing diversity change, habitat change, pre‐ and post‐restoration efforts, artificial structure effects, species presence, and animal behaviour. We reviewed the literature to collect data and estimate incidence effect size measures for both vertebrate abundance and vertebrate richness to examine the relative efficacy of deploying more camera traps for a given period in different ecosystems. Increasing sampling effort through an increased number of cameras significantly increased net positive abundance detection rates in grasslands and mixed ecosystems. Net richness detection rates in mixed, tropical, deciduous, and grassland ecosystems similarly increased with the number of cameras deployed. The total number of days, however, was not a significant predictor of abundance or richness rates detected in any ecosystem. These findings suggest that deploying relatively more cameras for relatively fewer days provides the most effective estimates of vertebrate abundance and richness for a region.
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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 | 0.032 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".