Estimating densities for sympatric kit foxes (<i>Vulpes macrotis</i>) and coyotes (<i>Canis latrans</i>) using noninvasive genetic sampling
Bibliographic record
Abstract
Kit fox (Vulpes macrotis Merriam, 1888) populations in the Great Basin Desert have declined and are of increasing concern for managers. Increasing coyote (Canis latrans Say, 1823) abundance and subsequent intraguild interactions may be one cause for this decline. Concurrent monitoring of carnivores is challenging and therefore rarely conducted. One possible solution for monitoring elusive carnivores is using noninvasive genetic sampling. We used noninvasive genetic sampling to collect fecal DNA from kit foxes and coyotes and estimate their densities from 2013–2014 in Utah, USA. We identified individuals based on microsatellite genotypes and estimated density with multisession spatially explicit capture–recapture models. Mean kit fox density was 0.02 foxes·km−2, while coyote densities were up to four times greater (0.07–0.08 coyotes·km−2). Kit fox densities were significantly lower than densities in the 1950s but were comparable with estimates from the late 1990s, suggesting that populations may be stabilizing after a precipitous decline. Our kit fox density estimates were among the lowest documented for the species. Our coyote density estimate was the first reported in our region and revealed that despite seemingly high abundance, densities are low compared with other regions. Our results suggested that kit foxes may be able to coexist with coyotes.
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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 | 0.001 | 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) | 0.001 | 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".