The importance of independence in unmarked spatial capture-recapture analysis
Bibliographic record
Abstract
Wildlife populations can be unmarked, meaning individuals lack visually distinguishing features for identification; populations may also exhibit non-independent movements, meaning individuals move together. For either unmarked or non-independent individuals, models based on spatial capture-recapture (SCR) approaches estimate abundance, density, and other population parameters critical for monitoring, management, and conservation. However, when individuals are both unmarked and non-independent, few model options exist. One approach has been to apply unmarked models and not address the non-independence despite unquantified impacts of overdispersion on bias, precision, and the ability to make robust ecological inferences. We conducted a simulation study to quantify the impact of non-independence on the performance of spatial count (SC) and spatial partial identity models (SPIM), two SCR-based unmarked modeling approaches, and used the performance of fully marked and independent SCR as a reference. We varied the levels of non-independence (aggregation and cohesion), detection probability, and the number of partial identity covariates used to resolve identities in SPIM estimation. We expected estimates of abundance and sigma (the spatial scale of individual movement) to be increasingly biased and less precise as aggregation and cohesion increased. Results showed that models indeed became less robust to increasing non-independence, especially for abundance, but importantly suggested that only SPIM could be reliably applied under low levels of cohesion when sufficient partial identity covariates are available. SC yielded consistently biased estimates with inflated precision that could not be corrected to nominal levels of coverage. SCR was the most robust across all combinations of aggregation and cohesion, as expected. We therefore advise against the use of SC models for estimating population parameters when individuals are known to be non-independent, caution that SPIM may be used under narrow ecological conditions, and encourage continued investigations into sampling design and methods development for populations of unmarked and non-independent individuals.
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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 | 0.049 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".