Aquatic connectivity and fish metacommunities in wetlands of the lower Great Lakes
Bibliographic record
Abstract
Although many local and regional variables structure fish assemblage composition, few studies have assessed the effects of aquatic connectivity on fish assemblages in wetlands. Fish and habitat surveys were conducted in 12 wetlands across the lower Great Lakes basin in the spring and fall of 2003 and 2004. Spatial and temporal connectivity were classified into four connectivity classes to evaluate the interaction between aquatic connectivity and fish assemblage structure. Sequential, nested analysis of covariance was used to model the effect of habitat area and connectivity at long- and short-term time scales on aggregate descriptors of assemblage structure (i.e., species richness, piscivore richness, abundance, and diversity). Although no species–area relationship was detected, increases in connectivity were shown to positively affect species richness and piscivore richness. A variation decomposition method indicated that a combination of aquatic connectivity, followed by environmental and area variables, was most influential in structuring fish assemblages at short-term time scales. Connectivity thus influences both the local species pool present, as well as the abundance of these species within a wetland. Future fundamental and applied studies (e.g., climate change predictions, impact of humans on water budget, wetland management) on wetland fish assemblages should include connectivity as an important structuring process.
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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 | 0.000 | 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) | 0.001 | 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".