The relative role of environmental, spatial, and land-use patterns in explaining aquatic macrophyte community composition
Bibliographic record
Abstract
Quantifying the relative role of environmental and spatial factors to understand patterns in community composition is a fundamental goal of community ecology. We applied a tested and repeatable point-intercept sampling method to aquatic macrophyte assemblages in 225 Wisconsin lakes to understand the ability of environmental, land-use, and spatial patterns to explain aquatic plant distribution and abundance. Using a variation partitioning framework in conjunction with Moran eigenvector maps we found that environmental, land-use, and spatial patterns explained 31% of total adjusted variation in aquatic macrophyte assemblages across the landscape. Environmental factors were the most important (contributing 34% of the total explained variation), but all sources of variation were statistically significant. Community composition varied from north to south along a gradient of alkalinity and from disturbed to undisturbed lakes, diverging according to whether disturbance was urban or agricultural. The large amount of shared variation among predictor variables suggests causal relationships are complex and emphasizes the importance of considering space and land-use in addition to environmental factors when characterizing macrophyte assemblages. This work is the first to examine the joint and unique effects of environment, land-use, and spatial patterns on aquatic plant communities.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| 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".