Comparing demersal fish and large mobile decapod crustacean assemblages in nearshore marine habitats across a boreal-sub-Arctic gradient using baited cameras
Bibliographic record
Abstract
Arctic and sub-Arctic ecosystems are experiencing changes in environmental conditions, altering marine biodiversity through shifts in species distributions and composition. Coastal ecosystems in northern environments are vulnerable to continued environmental change, but the remoteness of these areas and challenges associated with sampling shallow, structurally complex habitats have limited studies on nearshore communities. We compared the composition and relative abundances of nearshore assemblages in 7 coastal locations spanning 10° latitude of boreal and sub-Arctic habitats in Newfoundland and Labrador, Canada, using baited remote underwater video (BRUV). We identified 14 taxa, including 11 fish species and 3 decapod crustaceans. Species richness and diversity was generally higher in southern relative to northern locations, and spatial distributions differed across taxa. Greenland cod Gadus macrocephalus ogac and large cottids Myoxocephalus spp. were the most common taxa in northern areas and the only species observed across the entire environmental gradient. In contrast, we observed Atlantic cod G. morhua, winter flounder Pseudopleuronectes americanus, and cunner Tautogolabrus adspersus exclusively in southern locations. In addition to community variability across locations, habitat differences contributed more to variation in community-level abundances than to the abundances of most individual taxa. BRUVs provided an effective method for comparing nearshore assemblages across northern coastal habitats that are challenging to other common sampling methods. Further studies incorporating BRUVs could track variability in nearshore assemblages over longer time scales and offer an accessible method for coastal communities to monitor change across habitats.
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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 | 0.001 | 0.001 |
| 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 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".