Use of faecal genotyping to determine individual diet
Bibliographic record
Abstract
Faecal genotyping has been proposed as a method to examine the diets of individuals, but this application has been virtually unexplored by wildlife biologists. We used faecal genotyping and conventional scat analysis to determine the diets of 42 coyotes Canis latrans belonging to nine social groups in Alaska. We use rarefaction to examine the effect of scat sample size on the accuracy and precision of individual diets, and we simulate diets from scats to determine how diet richness and evenness affect sample size requirements. We then demonstrate the utility of this technique by examining variation in diet among individual coyotes and social groups in relation to prey availability. Estimates of diet diversity and composition were highly variable when <10 scats were used to construct the diet. Diets simulated with a uniform (i.e. even) distribution of prey items required generally smaller sample sizes to estimate diet diversity and richness than diets with exponentially distributed items; however, items in actual scats were exponentially distributed. We found moderate dietary variability among individuals in our study area, and diet overlap was higher among coyotes within social groups than between groups. As predicted by optimal foraging theory, the niche widths of all coyote groups expanded as their primary prey (the snowshoe hare Lepus americanus) became scarce during our three-year study. Despite increased niche width, diet overlap among groups remained constant, suggesting that coyotes selected differing alternative prey. Spatiotemporal variation in snowshoe hare availability explained 70% of the variation in hare consumption among groups, indicating that variation in local prey availability may be the primary cause of diet variation among coyotes. Although faecal genotyping can be used to address ecological questions at the individual level, studies should be designed specifically for this purpose so that sufficient numbers of faeces can be obtained.
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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".