Describing the seasonal and spatial distribution of Calanus prey and North Atlantic Right Whale potential foraging habitats in Canadian waters using species distribution models
Bibliographic record
Abstract
The goal of this study was to describe the seasonal and spatial variations of Calanus species abundance and North Atlantic Right Whale (henceforth NARW) potential foraging habitat in Canadian waters during 1999-2020. We took advantage of oceanographic monitoring programs in Canada and USA to develop an integrated modelling approach including the following elements: (1) Species Distribution Models (SDMs) of Calanus finmarchicus, C. glacialis and C. hyperboreus to predict their abundance, (2) predicted abundance converted in biomass to account for differences in body size among Calanus species, (3) Generalized Additive Models (GAMs) describing the seasonal variations in Calanus vertical distribution, (4) a multispecies 3- D prey layer combining species-specific water column biomass and vertical distribution, and (5) a right whales bioenergetic model to assess prey suitability and describe the seasonal and spatial distribution of potential foraging habitat. Using GAMs, we built a suite of SDMs based on different mechanistic assumptions about the drivers of Calanus species populations across Canadian and US waters. The best performing models included a seascape ‘connectivity’ term in addition to other key covariates (temperature, bathymetry) and assumed that Calanus species responses to covariates were generally the same across the domain (no local adaptation) with strong influence of transport in specific locations. The temperature and ’connectivity’ terms captured realistic patterns of influence of different temperature regimes and waters masses on Calanus across Canadian waters. Our integrated modelling approach successfully identified known (ex: Roseway Basin) and newly identified (ex: southern GSL) NARW foraging habitats as well as other potential foraging habitats across Canadian waters. Our results showed that NARW potential foraging areas in Canadian waters are determined by an assemblage of multiple Calanus species that varies across space and time. Therefore, inferences about past, current and future resilience of NARW foraging habitats to variations in environmental conditions and climate change should be carefully made due to species-specific responses to these changes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".