Diversity and Function of Algal Biofilms in the Laurentian Great Lakes
Bibliographic record
Abstract
Attached algae are ubiquitous components of lake benthic habitats wherever sufficient light reaches submerged surfaces. Attached algae interact with heterotrophic bacteria and fungi to form complex biofilms (“periphyton”) that provide a nutritious food source for consumers and influence biogeochemical cycling by regulating redox potential at the sediment-water interface. Despite their ecological importance, there are limited data on the role of periphyton in the Laurentian Great Lakes. I quantified wave exposure and light availability in rocky nearshore habitats in Lake Erie and Lake Huron. Periphyton biomass and productivity in nearshore Lake Erie was very high while algal biomass and productivity in Lake Huron were uniformly low irrespective of depth. Regression modeling demonstrated that wave disturbance and light availability control periphyton biomass and productivity in nearshore areas of the Great Lakes. To better understand how attached algal diversity and abundance vary with depth and substrate, I measured the biomass and composition of sediment algae and periphyton growing on Dreissena across broad depth gradients in Lake Ontario and Lake Erie. Sediment and mussel shell algal biomass were greatest around 20 m and declined with depth. Algal photosynthesis on sediments and mussels declined with depth down to approximately 40 m in both lakes. I found that sediments from both lakes were dominated by benthic diatoms and settled phytoplankton. In contrast, mussel shells harbored diverse filamentous algal assemblages. I analyzed the stable isotope signatures of Dreissena tissue and biofilms collected in Lake Ontario and Lake Erie, discovering enrichment of nitrogen isotopic signatures in both organisms with depth. DNA metabarcoding data from Lake Erie revealed that Dreissena biofilms harbor greater abundances of putative nitrifying and denitrifying bacteria than surrounding sediments, suggesting that Dreissena may be hotspots for nitrogen cycling in the Great Lakes. This work provides the first spatially extensive surveys of periphyton in the Great Lakes and reveals the composition, distribution, and function of these ecologically important assemblages. By encompassing perspectives on periphyton function and diversity from small to broad scales and across different physical conditions, this work is a critical step in understanding the role of periphyton in the Great Lakes.
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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 | 0.000 |
| 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 | 0.000 | 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 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".