Factors influencing invertebrate communities in prairie wetlands: a multivariate approach
Bibliographic record
Abstract
We examined the relationships between invertebrate community structure and a number of biotic and abiotic variables in 19 semipermanent prairie wetlands. We tested whether aquatic invertebrate communities differed (i) between wetlands with and without fathead minnows (Pimephales promelas) and (ii) according to drainage history of wetlands (restored versus natural, nondrained). We also evaluated influences of other environmental variables on invertebrate community structure, including abundance of aquatic macrophytes and amphibians and wetland depth and surface area. Invertebrate communities differed significantly between wetlands with and without fathead minnows, largely due to lower relative abundance of 19 invertebrate taxa (of 32 taxa analyzed) in wetlands with fathead minnows. In contrast, we found no differences in these taxa between natural and restored wetlands. Canonical correspondence analysis indicated that invertebrate community structure was affected by abundance of fathead minnows, abundance of aquatic macrophytes, and wetland depth, with fathead minnows the most influential variable measured. Many studies have documented the effects of fish predation on zooplankton communities, but our results show that fathead minnows in prairie wetlands affect a large number of diverse invertebrate taxa. The presence of these fish results in an invertebrate community distinctly different from that found in fishless wetlands.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".