Peer Review #1 of "Explaining detection heterogeneity with finite mixture and non-Euclidean movement in spatially explicit capture-recapture models (v0.1)"
Bibliographic record
Abstract
Landscape structure affects animal movement.Differences between landscapes may induce heterogeneity in home range size and movement rates among individuals within a population.These types of heterogeneity can cause bias when estimating population size or density and are seldom considered during analyses.Individual heterogeneity attributable to unknown or unobserved covariates is often modelled using latent mixture distributions, but these are demanding of data and abundance estimates are sensitive to the parameters of the mixture distribution.A recent extension of spatially explicit capturerecapture models allows landscape structure to be modelled explicitly by incorporating landscape connectivity using non-Euclidean least-cost paths, improving inference, especially in highly structured (riparian & mountainous) landscapes.Our objective was to investigate whether these novel models could improve inference about black bear (Ursus americanus) density.We fit spatially explicit capture-recapture models with standard and complex structures to black bear data on from 51 separate study areas.We found that non-Euclidean models were supported in over half of our study areas.Associated density estimates were higher and less precise than those from simple models and only slightly more precise than those from finite mixture models.Estimates were sensitive to the scale (pixel resolution) at which least-cost paths were calculated, but there was no consistent pattern across covariates or resolutions.Our results indicate that negative bias associated with ignoring heterogeneity is potentially severe.However, the most popular method for dealing with this heterogeneity (finite mixtures) yielded potentially unreliable point estimates of abundance that may not be comparable across surveys, even in data sets with 136 -350 total detections, 3 -5 detections per individual, 97 -283 recaptures, and 80 -254 spatial recaptures.In these same study areas with high sample sizes, we expected that landscape features would not severely constrain animal movements and modelling non-Euclidian distance would not consistently improve inference.Our results
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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 | 0.016 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.349 | 0.195 |
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".