Seasonal Variation in the Spatial Distribution of Basking Sharks (Cetorhinus maximus) in the Lower Bay of Fundy, Canada
Bibliographic record
Abstract
The local distribution of basking sharks in the Bay of Fundy (BoF) is unknown despite frequent occurrences in the area from May to November. Defining this species' spatial habitat use is critical for accurately assessing its Special Concern conservation status in Atlantic Canada. We developed maximum entropy distribution models for the lower BoF and the northeast Gulf of Maine (GoM) to describe spatiotemporal variation in habitat use of basking sharks. Under the Maxent framework, we assessed model responses and distribution shifts in relation to known migratory behavior and local prey dynamics. We used 10 years (2002-2011) of basking shark surface sightings from July-October acquired during boat-based surveys in relation to chlorophyll-a concentration, sea surface temperature, bathymetric features, and distance to seafloor contours to assess habitat suitability. Maximum entropy estimations were selected based on AICc criterion and used to predict habitat utilizing three model-fitting routines as well as converted to binary suitable/non-suitable habitat using the maximum sensitivity and specificity threshold. All models predicted habitat better than random (AUC values >0.796). From July-September, a majority of habitat was in the BoF, in waters >100 m deep, and in the Grand Manan Basin. In October, a majority of the habitat shifted southward into the GoM and to areas >200 m deep. Model responses suggest that suitable habitat from July - October is dependent on a mix of distance to the 0, 100, 150, and 200 m contours but in some models on sea surface temperature (July) and chlorophyll-a (August and September). Our results reveal temporally dynamic habitat use of basking sharks within the BoF and GoM. The relative importance of predictor variables suggests that prey dynamics constrained the species distribution in the BoF. Also, suitable habitat shifted minimally from July-September providing opportunities to conserve the species during peak abundance in the region.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".