Single‐visit dynamic occupancy models: an approach to account for imperfect detection with Atlas data
Bibliographic record
Abstract
Summary Atlas data provide biodiversity information at a relatively fine spatial grain over a broad spatial extent and, increasingly, at multiple points in time, which make them invaluable for understanding processes that affect species distributions over time. The effect of survey effort on species detection has long been appreciated and Atlases typically include survey standards and records of effort, but challenges remain in analysing Atlas data that have not been collected using a repeated sampling protocol designed to correct for imperfect detection. We developed a single‐visit dynamic occupancy model to quantify the effects of climatic and land‐use drivers on local species extinction and colonization while accounting for imperfect detection using repeat Atlas data. We evaluated model stability using data simulated under alternative scenarios and, ultimately, applied the model to empirical data for Canada warbler Cardellina canadensis , a wide‐spread species exhibiting a long‐term population decline. At sample sizes that are realistic for many Atlases ( n = 1000–10 000 independent survey blocks), our models produced unbiased estimates of detection, occupancy, colonization and extinction parameters. Slope estimates for explanatory covariates were somewhat less stable than overall occupancy, colonization and extinction rates, with covariate effects being sensitive to the total number of, and relationships among, explanatory variables. In comparison to other analyses of Canada warbler distributions that indicated minor changes over time, our approach identified a widespread decline in occupancy probability across New York, consistent with the broader population trend, particularly in the areas where it was initially more likely to occur. Synthesis and applications . A single‐visit dynamic occupancy model is a novel method for analysing common, ecologically valuable datasets, such as Atlases, that lack repeated sampling necessary to correct for imperfect detection using alternative multi‐season occupancy modelling approaches. As a result, using this method can improve understanding of species distributions and factors that shape them over time, thereby providing more accurate information to guide conservation and management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".