Location Is Everything: Evaluating the Effects of Terrestrial and Marine Resource Subsidies on an Estuarine Bivalve
Bibliographic record
Abstract
Estuaries are amongst the world's most productive ecosystems, lying at the intersection between terrestrial and marine environments. They receive substantial inputs from adjacent landscapes but the importance of resource subsidies is not well understood. Here, we test hypotheses for the effects of both terrestrial- and salmon-derived resource subsidies on the diet (inferred from stable isotopes of muscle tissue), size and percent nitrogen of the soft-shell clam (Mya arenaria), a sedentary estuarine consumer. We examine how these relationships shift across natural gradients among 14 estuaries that vary in upstream watershed size and salmon density on the central coast of British Columbia, Canada. We also test how assimilation and response to subsidies vary at smaller spatial scales within estuaries. The depletion and enrichment of stable isotope ratios in soft-shell clam muscle tissue correlated with increasing upstream watershed size and salmon density, respectively. The effects of terrestrial- and salmon-derived subsidies were also strongest at locations near stream outlets. When we controlled for age of individual clams, there were larger individuals with higher percent nitrogen content in estuaries below larger watersheds, though this effect was limited to the depositional zones below river mouths. Pink salmon exhibited a stronger effect on isotope ratios of clams than chum salmon, which could reflect increased habitat overlap as spawning pink salmon concentrate in lower stream reaches, closer to intertidal clam beds. However, there were smaller clams in estuaries that had higher upstream pink salmon densities, possibly due to differences in habitat requirements. Our study highlights the importance of upstream resource subsidies to this bivalve species, but that individual responses to subsidies can vary at smaller scales within estuaries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".