The influence of habitat complexity on littoral invertebrate distributions: patterns differ in shallow prairie lakes with and without fish
Bibliographic record
Abstract
Relationships between littoral habitat complexity and invertebrate distributions in fishless lakes are not well understood compared with well-documented relationships in lakes with fish. We examined littoral invertebrate distributions over fine-scale gradients of weed-bed habitat complexity and contrasted these patterns in four shallow prairie lakes two with fish and two without. The above-sediment portion of submerged macrophytes and associated invertebrates was sampled from three littoral microhabitats: weed-bed centres (highly complex), weed-bed edges (moderately complex), and single plants that grew apart from distinct weed beds (least complex). Total invertebrate densities in fishless lakes did not differ between littoral microhabitats, nor were they correlated with macrophyte biomass. In contrast, total invertebrate densities in lakes with fish increased with microhabitat complexity and were positively correlated with macrophyte biomass. Weed-bed complexity also affected littoral invertebrate community structure; in all lakes, the proportion of filter-feeders decreased with increasing microhabitat complexity, but the proportion of predatory invertebrates was greater overall in fishless lakes than in lakes with fish. Our results demonstrate that small-scale variation in littoral microhabitat complexity can lead to specific patterns of invertebrate distribution that systematically differ between lakes with and without fish, and that these systematic differences may be mediated through top-down mechanisms.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".