FISHES AS INTEGRATORS OF BENTHIC AND PELAGIC FOOD WEBS IN LAKES
Bibliographic record
Abstract
Studies of lake ecosystems generally focus on pelagic food chains and processes. Recently, there has been an emerging recognition of the importance of benthic production and processes to whole-lake ecosystems. To examine the extent to which zoobenthos contribute to higher trophic level production in lakes, we synthesized diet data from 470 fish populations (15 species) and stable isotope data from 90 fish populations (11 species), all of which are common inhabitants of north-temperate lakes. Across all species considered, zoobenthos averaged 50% of total prey consumption. Indirect consumption of zoobenthos (i.e., feeding on zoobenthos-supported fishes) contributed another 15%, for a total of 65% reliance on benthic secondary production. Stable isotopes provided estimates of mean zoobenthivory ranging from 43% to 59%. For most fish species, consumption of zoobenthos was highly variable among populations. The overwhelming concern of ecologists with pelagic food chains and processes contrasts sharply with our finding that benthic secondary production plays a central role in supporting higher trophic level production. This extensive zoobenthivory can subsidize fish populations, leading to apparent competition and otherwise altering trophic dynamics and ecosystem processes in the pelagic zone. We argue for a more integrated view of lake ecosystems that recognizes the duality of benthic and pelagic production pathways. Food web models that explicitly consider energy flow from pelagic and benthic sources will provide a more realistic energy flow template for understanding the regulation of lake ecosystem functioning.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".