Estimating occupancy using spatially and temporally replicated snow surveys
Bibliographic record
Abstract
Abstract Occupancy modelling is increasingly used to monitor changes in the spatial distribution of rare and threatened species. Occupancy methods have traditionally relied upon temporally replicated surveys to estimate detection probability. Recently, occupancy models with spatial replication have been used to estimate detection probabilities over large geographical areas that are difficult to survey repeatedly. We developed occupancy models that combine spatially and temporally replicated data and applied them to snow‐tracking surveys of six species, including wolverine Gulo gulo and Canadian lynx Lynx canadensis. We surveyed thirty‐nine 100‐km2 cells and used 1‐km trail segments within cells as spatial replicates. We surveyed 56% of the cells once and 44% of the cells between 2 and 14 times, resulting in a total of 872 km surveyed. We compared four occupancy models that incorporated spatial correlation in detection probability and hierarchically estimated occupancy at two spatial scales: cell occupancy and segment presence. We detected strong serial correlation in probability of detection for all species. Our models with serial correlation had higher occupancy estimates with larger confidence intervals than models assuming segments were independent and exchangeable. Spatial and temporal replicates have identical power to detect decreases in occupancy when survey segments are independent, but spatial correlation in detection probability can reduce the power of spatial replicates. The effects of spatial correlation are more pronounced when detection probability is low. Application of temporal replicates to spatial replicated surveys increases the precision of occupancy estimates, but sampling design trade‐offs between number of sites and spatial versus temporal replicates need to balance levels of spatial correlation in detection probability with costs to visit sites.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".