Metabarcoding fecal samples to investigate spatiotemporal variation in the diet of the endangered Westland Petrel ( Procellaria westlandica )
Bibliographic record
Abstract
As top predators, seabirds can be indirectly impacted by climate variability and commercial fishing activities through changes in marine communities. However, high mobility and foraging behavior enable seabirds to exploit prey distributed patchily in time and space. Despite this environmental adaptability, seabirds are the world’s most threatened birds and there is, therefore, an urge to acquire information about their ecological and foraging requirements through the study of their diet. Traditionally, the diet of seabirds is assessed through the morphological identification of prey remains in regurgitates. This sampling method is invasive for the bird and limited in terms of taxonomic resolution. However, the recent progress in DNA-based approaches is now providing a noninvasive means to more comprehensively and accurately characterize animal diets. We used a noninvasive metabarcoding approach to characterize the diet of the Westland Petrel (<em>Procellaria westlandica</em>), which is an endangered burrowing species, endemic to the South Island of New Zealand. We collected 99 fresh fecal samples in 2 different seasons and in 2 different subcolonies. Our aim was to describe the Westland Petrel’s diet and to investigate seasonal and spatial variations to their diet to understand the feeding requirements of this species. We also specifically investigated potential links with the New Zealand fishery industry to inform efficient conservation practices. Our noninvasive dietary DNA (dDNA) approach also highlighted significant differences in diet between seasons (incubation vs. chick-rearing season) and between sampling sites (two subcolonies 1.5 km apart). This suggests plasticity in the foraging strategy of the Westland Petrel. We found that amphipods were the most common prey, followed by cephalopods, and fish. Our findings could be the result of natural foraging behavior but also suggest a close link between the composition of prey items and New Zealand’s commercial fishing activities. In particular, the high abundance of amphipods could be the result of Westland Petrels feeding on discarded fisheries waste (fish guts). This close relationship to New Zealand’s fisheries may put stress on the resilience of the Westland Petrel. This valuable knowledge about Westland Petrel foraging needs is key to design efficient conservation plans for this iconic species. We illustrate how dDNA can inform the conservation of endangered or at-risk species that have elusive foraging behaviors.
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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".