Species occurrence data reflect the magnitude of animal movements better than the proximity of animal space use
Bibliographic record
Abstract
Abstract Animal ecologists often use stationary point‐count surveys, such as camera traps, to collect presence–absence data and infer distribution, abundance, and density of species. Rarely do these surveys explicitly consider variations in the magnitude of animal movement despite movement assumptions being implicit in their interpretation. For example, ecologists assume the frequency of species detections at a site is associated with the intensity of local space use, but it may be more indicative of transit through that point en route to other areas. This assumption remains untested, and a resolution is critical to accurate interpretation of species occurrence data. We compared fisher (Pekania pennanti) detections collected from a camera trap array with detailed Global Positioning System‐telemetry data to test whether, at the population level, the spatial and temporal patterns of detections reflected the proximity of space use to sampling sites, or variability in the magnitude of animal movement across the study area. We also used an occupancy modeling framework to quantify the relative contributions of space use proximity and movement magnitude to estimated probabilities of site occupancy and detectability. We demonstrate that, at the population level, detection frequency and estimates of detection probability and occupancy are more closely associated with the magnitude of animal movement around a survey device than the proximity of animal space use. Variations in the magnitude of animal movement within and between species should receive greater consideration when interpreting occurrence data to correctly infer ecological processes. Not accounting for species movement, especially in multi‐species surveys, may bias inferences of ecologic processes and result in misspecified management recommendations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".