Using the Formozov–Malyshev–Pereleshin formula to convert mammal spoor counts into density estimates for long‐term community‐level monitoring
Bibliographic record
Abstract
Abstract Finding an appropriate method to monitor a wide range of mammal species simultaneously is notoriously difficult, as each method has its limitations. Here, we examine a formula, known as the Formozov–Malyshev–Pereleshin (FMP), which uses mean daily travel distances (day ranges) to convert spoor counts into density estimates. Availability of accurate estimates of day ranges is a limitation of the FMP formula. Here, we used allometry to estimate day ranges for those species that lacked empirical movement data and general additive models (GAM) to assess trends in density estimates. With this approach, we derived density estimates for 10 mammal species, regardless of whether they were abundant, or rare and elusive (e.g. carnivores). General additive models suggest that six species are stable or increasing, and four declining, although all nonsignificantly. Use of allometric estimates in lieu of empirical estimates led to falsely increased precision in density estimates, highlighting the need to fill the knowledge gap in movement ecology for certain species. Simulations were used to examine error introduced into trend estimates by this bias. We conclude that the FMP formula, when properly employed, can be an efficient method for simultaneous monitoring of multispecies in different functional groups.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".