Generalized spatial mark–resight models with an application to grizzly bears
Bibliographic record
Abstract
Abstract The high cost associated with capture–recapture studies presents a major challenge when monitoring and managing wildlife populations. Recently developed spatial mark–resight (SMR) models were proposed as a cost‐effective alternative because they only require a single marking event. However, existingSMRmodels ignore the marking process and make the tenuous assumption that marked and unmarked populations have the same encounter probabilities. This assumption will be violated in most situations because the marking process results in different spatial distributions of marked and unmarked animals. We developed a generalizedSMRmodel that includes sub‐models for the marking and resighting processes, thereby relaxing the assumption that marked and unmarked populations have the same spatial distributions and encounter probabilities. Our simulation study demonstrated that conventionalSMRmodels produce biased density estimates with low credible interval coverage (CIC) when marked and unmarked animals had differing spatial distributions. In contrast, generalizedSMRmodels produced unbiased density estimates with correct CIC in all scenarios. We applied ourSMRmodel to grizzly bear (Ursus arctos) data where the marking process occurred along a transportation route through Banff and Yoho National Parks, Canada. Twenty‐two grizzly bears were trapped, fitted with radiocollars and then detected along with unmarked bears on 214 remote cameras. Closed population density estimates (posterior median ± 1SD) averaged from 2012 to 2014 were much lower for conventionalSMRmodels (7.4 ± 1.0 bears per 1,000 km2) than for generalizedSMRmodels (12.4 ± 1.5). When compared to previousDNA‐based estimates, conventionalSMRestimates erroneously suggested a 51% decline in density. Conversely, generalizedSMRestimates were similar to previous estimates, indicating that the grizzly bear population was relatively stable. Synthesis and applications. Mark–resight studies often cost less than capture–recapture studies, but require that marked and unmarked animals have equal encounter rates. Generalized spatial mark–resight models relax this assumption by including sub‐models for both the marking and resighting processes. They produce unbiased density estimates even when marked and unmarked animals have differing spatial distributions and encounter rates. They thus provide a cost‐effective and widely applicable approach for estimating the density of wildlife populations.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".