Substratum as a driver of variation in periphyton chlorophyll and productivity in lakes
Bibliographic record
Abstract
Quantifying periphyton (attached algal) contributions to autotrophic production in lakes is confounded by properties of substratum that affect community biomass (as chlorophyll content) and productivity. We compared chlorophyll content and productivity of natural algal communities (phytoplankton, epipelon, epilithon, epixylon, and epiphyton) experiencing high (>10%) incident radiation in lakes in the US, Greenland, and Quebec, Canada. Chlorophyll content and productivity differed significantly among regions, but they also differed consistently among communities independent of region. Chlorophyll content of periphyton on hard substrata (rocks and wood) was positively related to water-column total P (TP), whereas chlorophyll content of algae on sediment (epipelon) and TP were not significantly related. Chlorophyll content was up to 100× higher on sediments than on hard substrata. Within regions, chlorophyll-specific primary productivity was highest for phytoplankton and lowest for epipelon. Periphyton on hard substrata and on macrophytes (epiphyton) had similar rates of chlorophyll-specific productivity that were intermediate to those of epipelon and phytoplankton. Area-specific productivity of epipelon was 5 to 10× higher than area-specific productivity of periphyton on hard substrata. This broad geographic comparison indicates that, in low to moderately productive lakes under high-light conditions, algal communities have predictable differences in area-specific and chlorophyll-specific productivity based on substratum. As such, chlorophyll alone is an inadequate predictor of the relative contributions of different algal communities to total primary production. Our results highlight the importance of the relative abundance and spatial distributions of substrata in determining the role of the littoral zones in nutrient and energy cycles in 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".