A comparison of morphological and molecular diet analyses of predator scats
Bibliographic record
Abstract
Abstract An understanding of a species’ diet is required to make sound conservation and management decisions. Traditionally, morphological analyses of undigested hard parts from food items remaining in scats have been used to assess diets. More recently, molecular analyses of scats have been used to identify plant and prey species’ DNA, but no studies have compared morphological and molecular diet analyses for large, terrestrial carnivores. We used molecular tools to determine the percentage of black bear and coyote scats that contained 3 common prey species (caribou, moose, and snowshoe hares) in Newfoundland and compared the results to a traditional morphological analysis. We found that a ranking of relative prey frequencies was consistent between the 2 methods, but molecular methods tended to detect prey species in a greater percentage of scats for all prey species. However, there were individual scats in which a prey species was detected by morphological methods only, and we provide evidence that molecular methods could result in false negatives if prey DNA is not uniformly distributed throughout a scat or as a result of PCR inconsistency. We also found that the per sample cost comparison between morphological and molecular analyses was dependent upon whether or not a molecular test was needed to identify scats to the predator species, the cost of developing molecular methods, and the number of samples being processed. We recommend that controlled feeding studies be performed to validate molecular methods and investigate the utility of molecular techniques to estimate the proportions of food items consumed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".