Rapid systematic literature review: Camera trap sampling in ecological studies: Considerations of wildlife welfare
Bibliographic record
Abstract
The use of camera traps in wildlife conservation and ecological research is a popular method of data capture due in large part to the perceived low interference levels for the animals being studied. However, evidence exists that some species alter their behaviour when exposed to this technology. The primary aim of this study was to address whether researchers working with this technology in the ecology and forestry fields are making considerations for the possible impacts of cameras on animal behaviour. A secondary aim was to investigate how the use of this technology is framed in recent publications. In this rapid systematic literature review, we conducted a search on Web of Science and we identified 267 papers published in the last five years, in the fields of ecology and forestry, that met our inclusion criteria. We screened the studies for mentions of the impact of camera traps on the welfare of wildlife. Surprisingly, only 7.5% of the papers considered the possible animal welfare impacts of camera use on the wildlife species of interest in their study, with most comparing it to invasive methods and therefore framing this technology positively. We strongly encourage researchers working in this field to consider the impact of this technology on the specific species being studied. Whilst we recognise that the use of camera traps avoids direct handling of the animals, the short- and long-term effects of using this technology should not be ignored and should, at a minimum, be acknowledged in the limitations.
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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 | 0.022 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".