Species‐specific differences in detection and occupancy probabilities help drive ability to detect trends in occupancy
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Occupancy‐based surveys are increasingly used to monitor wildlife populations because they can be more cost‐effective than abundance surveys and because they may track multiple species, simultaneously. The design of these multi‐species occupancy surveys affects statistical power to detect trends in occupancy because individual species vary in resource selection, detection probability, and rarity. We tested for differences in the ability of a large‐scale monitoring program to detect changes in single‐species occupancy of 13 medium–large mammal species captured on n = 183 cameras systematically placed across five national parks in the Canadian Rockies (~21,000 km 2 ). We focus the interpretation of our findings on three species at risk: grizzly bear, wolverine, and caribou. We found that statistical power to monitor trends in occupancy depends not only on the established elements associated with power (sampling size, effect size, and variation in estimates), but also on species‐specific detection and occupancy probabilities. These two probabilities, however, affected power differently. For most species in our study, power is insensitive to detection probability. Increasing replicate‐specific detection probability only improved power when the cumulative detection probability was below 0.80. Therefore, efficient species monitoring must consider that power no longer improves by increasing sample size or the replicate‐specific detection probability once this threshold is reached. On the other hand, species with occupancy probabilities close to 0.5 had lower statistical power than those with higher or lower occupancy, that is, power was higher for both rare and very common species. This pattern is due to the heretofore‐underappreciated effect of the binomial variation in occupancy. The implications of these findings are species‐specific. Grizzly bears, for example, had high detection and occupancy probabilities, resulting in high power to detect a population change. Conversely, wolverines had low detection probability and the power to detect change could be improved if detection probability was increased using lure or complimentary survey techniques. Caribou, however, with both low detection and occupancy probabilities, were likely too rare on the landscape to rely on camera‐based occupancy for monitoring. Practitioners should be aware of these species‐specific trade‐offs and may need to tailor monitoring programs to prioritize particular species of conservation concern.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 it