Mussels can both outweigh and interact with the effects of terrestrial to freshwater resource subsidies on littoral benthic communities
Bibliographic record
Abstract
Litterfall is an important resource subsidy for lake ecosystems that primarily accumulates in littoral zones. Bivalves are abundant within littoral zones and may modify the effects of terrestrial resource subsidies through trophic interactions and engineering their surrounding habitat. Leaf inputs to lakes and freshwater mussel abundances are changing throughout the boreal ecoregion so we set out to investigate how the co-occurring benthic community might respond. We set up an in situ mesocosm experiment in Ramsey Lake, Sudbury, ON, Canada. Mesocosms contained sediments of either 5% or 35% terrestrial organic matter (tOM), into which we placed mussels (Elliptio complanata) at differing densities (0, 0.4 and 2 mussels m− 2, with a sham mussel treatment at 0.4 mussels m− 2). Over one month we recorded the sediment chemistry (dissolved organic carbon, nitrogen and phosphorus), littoral organisms (benthic algae and zooplankton) and mussel growth. At high mussel densities we recorded a 90%, 80%, 45% and 40% reduction in phosphorus, dissolved organic carbon, nitrogen and benthic diatoms, respectively, whereas at low mussel densities we observed a 3-fold increase in zooplankton. We discuss that these results were caused by a combination of bioturbation and trophic interactions. Benthic diatom concentrations were also reduced by 20% in sediments of 35% tOM, likely due to shading and competition with bacteria. Mussel growth increased at high mussel densities but was offset at high tOM, likely due to the organic matter interfering with filter feeding. Our results suggest that mussels can alter the geochemical composition of sediments and abundances of associated littoral organisms, in some cases regardless of tOM quantity. Therefore, the dominant top-down control exerted by freshwater mussels may outweigh bottom-up effects of tOM additions. Generally, our study reveals the importance of considering dominant species when studying the effects of cross-ecosystem resource fluxes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".