Spatial and behavioral scales of habitat selection and activity by river otters at latrine sites
Bibliographic record
Abstract
Animals interact with their environment at multiple spatial, temporal, and behavioral scales. Few studies of selection for latrine sites by river otters (Lontra canadensis) have considered spatial scale, and no studies have integrated scales of behavior. We used an information theoretic model comparison approach to identify elements of otter habitat that influence the presence, consistency, and intensity of latrine-site activity at 2 spatial scales. We identified and monitored 73 latrine sites in central British Columbia, Canada, during the open-water season in 2007 and 2008. We inventoried latrines and randomly selected sites along the adjacent shoreline, and used those data in the form of a binary resource selection function to model fine-scale selection of latrine sites. At the scale of the landscape, we used a resource selection function and data from geographic information systems to model coarse-scale selection of latrine sites. Drawing on those same data, we used binary and count models to quantify factors that contributed to the consistency (high versus low use) and intensity (number of scats) of otter activity at latrine sites. Fine-scale habitat characteristics were better at predicting the presence of latrine sites when compared to coarse-scale geographic information system data. In general, the presence, consistency, and intensity of latrine activity at the fine scale were influenced by visual obscurity, larger trees, and characteristics of conifer trees. The presence of latrine sites at the coarse scale could not be accurately described by any of the models. The consistency and intensity of activity of otters at latrine sites at the coarse scale, however, was best predicted by habitat characteristics beneficial to fish. These results provide insight into the spatial and behavioral scales of latrine-site activity by river otters that can be incorporated into management, monitoring, and conservation strategies.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".