DNA barcoding increases resolution and changes structure in Canadian boreal shield lake food webs
Bibliographic record
Abstract
Abstract Food webs are important in understanding the structure, function, and behaviour of ecosystems, but, due to methodological limitations, are often poorly resolved in ways that impact food-web properties. Although DNA barcoding has proven useful in determining the diet of consumers, few studies have used this technique to determine food-web structure. These studies report mixed impacts on various food-web properties, but are limited by their taxonomic focus and their failure to evaluate DNA barcoding for both diet analysis and food-web structure. In this study, we show that, when compared to a morphological approach, DNA barcoding increases foodweb resolution by increasing the number and frequency of prey species identified in the stomach contents of eight species of Canadian boreal shield predatory fishes. In addition, we observed differences in food-web structure, such as increased generalism, habitat coupling, and omnivory, that have strong implications for food-web stability and dynamics. We conclude that DNA barcoding is a powerful tool to evaluate how resolution impacts foodweb properties and can help further our understanding of how food webs are structured by identifying feeding interactions in an unprecedented and highly detailed manner.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".