<scp>DNA</scp> metabarcoding and video camera collars yield different inferences about the summer diet of an arctic ungulate
Bibliographic record
Abstract
Abstract The diets of wild ungulates are a foundational component of their ecology, influencing their behavior, body condition, and demography. With changing environmental conditions, there is a significant need to identify important forage items for ungulates, but this has often proved challenging. Declines in several barren‐ground caribou herds across the North American Arctic have raised concerns about the influence of climate change on caribou forage conditions. Shifts in plant phenology, biomass, quality, and composition may be influencing caribou diets and subsequently affecting their body condition and demographic rates. Although forage is a primary driver of barren‐ground caribou behavior and population dynamics, there is limited recent information about the specific foods they consume, and uncertainty about appropriate methods for identifying those foods. Investigators are increasingly using fecal DNA metabarcoding and video camera collars to assess ungulate diets, but comparative studies of these approaches are lacking. To examine the summer diets of barren‐ground caribou, we used both fecal metabarcoding and video camera collars to identify forage used by the Porcupine caribou herd, which spans the Alaska–Yukon border. In 2021, we sampled the diets of adult females by collecting fecal samples and observing collar videos during 4 sampling occasions. We found that caribou consumed very specific forage items, and those items varied markedly across the growing season. Caribou predominantly consumed graminoids and lichens during early summer, and shrubs and forbs later in the season. Metabarcoding and video data provided significantly different estimates of diet for all taxonomic levels we evaluated, and inferences from the two approaches were often disparate. Metabarcoding failed to detect some items frequently consumed in videos, such as lichens, and indicated high use of other items rarely consumed, such as mosses. We found that video data provided greater taxonomic diversity and resolution for vascular plants and lichens, and more closely aligned with past research and biological expectations than data from fecal metabarcoding. Additional research is needed to be able to use these methods to identify the biomass of different forage 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".