Habitat selection and the spatial distribution of forage fish and marine predators in Atlantic Canada and the California Current
Bibliographic record
Abstract
Many marine food webs are “wasp-waist” in which energy funnels through one, or few, intermediate trophic level prey species, such as forage fish. However, marine prey are patchy and ephemeral, a challenge for marine predators. Ideal Free Distribution posits that individuals will disperse “ideally” among patches, whereby patches persistently supporting more individuals can be inferred to be high quality. Understanding the habitat types that support high abundances of animals is critical for effective marine conservation efforts. In this thesis, I focused from the bottom up on areas of high forage fish (capelin, Mallotus villosus) density in Newfoundland and from the top down on areas of high marine predator density in the California Current Ecosystem (CCE). To understand habitat associations of forage fish in Atlantic Canada, I interviewed fishers to map the spatial extent of subtidal spawning sites of capelin, a key foraging area for marine predators and used the interview results to conduct at-sea surveys. In the CCE, I used archived marine bird and mammal count surveys and a variety of statistical and spatial methods, such as zero-inflated negative binomial models and canonical correlation analyses, to examine predator habitat associations and niche partitioning. My findings provide an increased understanding of the factors that influence, and limit, habitat selection of key marine predators and their prey during especially vulnerable life-history stages, including breeding and over-wintering. To continue monitoring capelin subtidal spawning dynamics, future studies in Newfoundland will greatly benefit from establishing professional relationships with fishers as citizen scientists. In the CCE, my results emphasize the need for survey coverage with in-situ oceanography monitors in all four seasons in the highly seasonal CCE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".