Fine-scale farming features drive resource selection of a small carnivore of conservation concern
Bibliographic record
Abstract
Anthropogenic factors are accelerating species extinction, with small mammalian carnivores among the most affected. These species play vital ecological roles, yet their conservation needs are often overlooked. Our study focused on the plains spotted skunk ( Spilogale interrupta (Rafinesque, 1820)), a small carnivore that has experienced population declines. We hypothesized that their resource selection was influenced by factors expected to influence prey availability, protection from predators, and human activity. We tracked 14 plains spotted skunks in east-central South Dakota, USA, over 2 years during spring and summer. Using mixed-effects logistic regression, we identified seasonal habitat associations. In spring, plains spotted skunks selected areas near farming structures and human development, avoiding high wetland density and crop cover. In summer, they continued to select areas near farming structures and low human development, but also high wetland density and pasture, while avoiding hay bales and crop cover. Our first analysis of the species’ resource use in the Great Plains indicates that plains spotted skunks select habitats with permanent small-scale agricultural features and varying levels of human development across seasons. Our findings suggest that species’ persistence in the region may depend on conservation strategies that account for seasonal planning, habitat heterogeneity, and key agricultural structures.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".