Agonistic interactions and island biogeography as drivers of carnivore spatial and temporal activity at multiple scales
Bibliographic record
Abstract
Carnivore communities can be diverse and complex, and lack of knowledge regarding intraguild interactions and alternative drivers of carnivore distributions can preclude effective conservation of co-occurring species. As such, our objectives were to evaluate the relative importance of intraguild interactions and island biogeography to carnivore community spatiotemporal activity at multiple spatial scales. We monitored the carnivore community of the Apostle Islands National Lakeshore (Wisconsin, USA) using a grid of camera traps from 2014 to 2018. We used generalized linear mixed-effects models and information-theoretic model selection to evaluate whether subordinate carnivore presence was related to dominant carnivore relative abundance (interactions) or to island biogeography at the island level and camera site level, and we calculated temporal overlap between each pair of species to determine whether subordinate carnivores were using temporal segregation. At the island level, the relative importance of interactions and island biogeography was species dependent. At the site level, relative abundance of dominant carnivores was not a significant predictor of subordinate carnivore presence, and all pairs exhibited high or neutral temporal overlap. At the island level, island biogeography and interactions may both impact species distributions; however, at finer spatial scales, the carnivore community may be using alternative segregation strategies, or the island system may preclude segregation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".