Predator regulation of sedimentary fauna in a sub-arctic fjord ecosystem
Bibliographic record
Abstract
Historical changes in predator composition in the Newfoundland ecosystem as a result of \nover-fishing have resulted in a switch from a cod-dominated system to one with abundant \ndecapod crustaceans. In order to understand the consequences of this switch to benthic \necosystems, it is critical to evaluate how epifaunal crustaceans regulate sedimentary \ncommunities. An array of exploratory and experimental studies was undertaken in Bonne \nBay, a sub-arctic Newfoundland fjord, in order to document predator and prey spatial \nvariation and community responses to predator manipulation. \nThe distribution of snow crab and at least one shrimp species in the main arms of Bonne \nBay fjord were found to be related to planktonic larval supply, particularly, late larval \nstages. The distribution of infaunal prey varied in parallel with predator patterns and, as \nshown by detailed analysis of the dominant taxon (polychaetes), was related to habitat \nquality and distribution. Sandy and muddy habitats supported different infaunal \ncommunities, and species that occupied a variety of substrates were more broadly \ndistributed inside the fjord and the region. Field exclusion and inclusion experiments \ncarried out in the two main arms of the fjord were complemented with laboratory \nexperiments using the main predators of the fjord: snow crab (Chionoecetes opilio), rock \ncrab (Cancer irroratus) and toad crab (Hyas spp). Results suggest that i) crustacean \npredation regulates benthic composition, density, and sometimes diversity, ii) predator \neffects vary spatially, iii) the same infaunal species were important in describing predator exclusion treatments both in the field and in the laboratory experiments, and iv) snow \ncrab and rock crab are the predators that have the strongest effects on infaunal \ncommunities. Given that both predators are targeted by the fishery, these results also \nsuggest that the potential impacts of fishing may be even broader than expected through \ncascading effects on infauna. Finally, the effects of predation on benthic infauna were \nexamined using surrogates or taxonomic categories coarser than species. Although results \nobtained with data at the family level resemble those with data at the species level, the \nlack of generality in surrogate performance suggests a cautious use of surrogates in \nexperimental and biodiversity studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".