Integrating biodiversity, catch reconstruction and ecosystem modeling to inform sustainable fisheries in Saint Pierre and Miquelon (France) : a multi-scale approach
Bibliographic record
Abstract
Saint Pierre and Miquelon (SPM) is a small French archipelago in the Northwest Atlantic. Its historic dependence on fisheries for local livelihoods gives it heightened socio-ecological vulnerability under changing environmental and anthropogenic pressures. To better understand this vulnerability, this thesis investigates SPM’s marine biodiversity, historical fisheries, and ecosystem dynamics through a macroecological and modeling-based approach, linking global biogeographic patterns with local management relevance. Three main objectives guided this work: (1) positioning SPM within global insular gradients of marine biodiversity and testing the application of Island Biogeography Theory (IBT) to marine species; (2) reconstructing SPM’s total marine catches from 1950 to 2022 and the evolution of fishing pressures; and (3) evaluating the effects of historical and projected fishing activity on the surrounding ecosystem (NAFO Subdivision 3Ps) using Ecopath with Ecosim (EwE) modeling. This work established a biodiversity baseline for SPM, and also showed that latitude and continental shelf surface area are significant predictors of marine species richness in 40 archipelagos and islands around the world, consistent with IBT principles. The reconstructed marine catch of SPM revealed that actual removals exceeded official statistics by more than 20%. Over seven decades, catches shifted from high-trophic level predators such as Atlantic cod toward lower-trophic level invertebrates, notably sea cucumber, leading to a 37% decline in the mean trophic level of the catch. These data were integrated into EwE mass-balance models for two time periods (1960–1969 and 2000–2009) and dynamic Ecosim and Ecospace simulations were conducted. Results indicated shifts in fishing mortality and ecosystem structure, with increased effort projected to reduce biomass of invertebrate target species and modestly affect higher-trophic fishes like Atlantic halibut. By linking biodiversity gradients, reconstructed fisheries data, and ecosystem modeling, this research provides the first comprehensive ecological framework for SPM and the first EwE models for NAFO 3Ps. This demonstrates the value of integrating biogeographic theory and trophic modeling to inform local management and emphasizes the vulnerability of small, data-limited island ecosystems. Ultimately, this work contributes to assessing SPM’s long-term capacity to sustain ecological integrity and fishery productivity in a changing climate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".