Application of the Random Encounter Model in citizen science projects to monitor animal densities
Bibliographic record
Abstract
Abstract Abundance and density are vital metrics for assessing a species’ conservation status and for developing effective management strategies. Remote‐sensing cameras are being used increasingly as part of citizen science projects to monitor wildlife, but current methodologies to monitor densities pose challenges when animals are not individually recognizable. We investigated the use of camera traps and the Random Encounter Model ( REM ) for estimating the density of West European hedgehogs ( Erinaceus europaeus ) within a citizen science framework. We evaluated the use of a simplified version of the REM in terms of the parameters’ estimation (averaged vs. survey‐specific) and assessed its potential application as part of a large‐scale, long‐term citizen science project. We compared averaged REM estimates to those obtained via spatial capture–recapture ( SCR ) using data from nocturnal spotlight surveys. There was a high degree of concordance in REM ‐derived density estimates from averaged parameters versus those derived from survey‐specific parameters. Averaged REM density estimates were also comparable to those produced by SCR at eight out of nine sites; hedgehog density was 7.5 times higher in urban (32.3 km −2 ) versus rural (4.3 km 2 ) sites. Power analyses indicated that the averaged REM approach would be able to detect a 25% change in hedgehog density in both habitats with >90% power. Furthermore, despite the high start‐up costs associated with the REM method, it would be cost‐effective in the long term. The averaged REM approach is a promising solution to the challenge of large‐scale and long‐term species monitoring. We suggest including the REM as part of a citizen science monitoring project, where participants collect data and researchers verify and implement the required analysis.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".