Ancient and contemporary shellfish cultivation practices bolster bivalve communities and diversity‐biomass relationships
Bibliographic record
Abstract
Abstract Marine ecosystems provide humans with access to nutritious food. The discussion surrounding the cultivation of intertidal resources currently focuses on recent decades, disregarding a robust history of complex social‐ecological interactions and environmental stewardship. Here, we use the co‐occurrence of Indigenous clam gardens, shellfish aquaculture farms, and unmodified beaches along Canada's West Coast to test the hypothesis that two different resource management practices, one engineered over millennia and the other decades, bolster contemporary bivalve communities. We quantified the diversity, density and estimated biomass of the bivalve communities within 24 intertidal sites and evaluated the communities' association with the habitat's substrate composition, sediment characteristics and physical complexity. We examined these data using a combination of diversity indices, structural complexity metrics, non‐parametric multivariate statistics and supervised machine learning algorithms. We show that both cultivation methods create distinct biological communities, with community composition proportional to how each cultivation practice alters the habitats' substrate, sediment and physical complexity. Increases in bivalve biomass were comparable across cultivation methods but varied considerably among taxa. We identify a previously undocumented correlation between bivalve biomass and species richness, which is highly influenced by habitat complexity. Our work contributes to the mounting evidence that shellfish cultivation fosters ecologically diverse communities while enhancing food production. We propose that an ecosystem‐wide management approach that considers multiple bivalve species, ecological processes and socio‐cultural practices will elevate the conservation and cultivation of marine resources. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".