Distinctive macroinvertebrate communities in a subtropical river network
Bibliographic record
Abstract
Macroinvertebrates are widely used as bio-indicators in streams and rivers, and it is usually assumed that their community composition is primarily controlled by local environmental conditions. We examined the distribution of macroinvertebrates within the Guadalupe River basin (3256 km2) in Central Texas across physiographic gradients. Spatial analysis with variables that considers flow direction, connectivity and distances between sites (asymmetric eigenvector maps, AEM) detected distinctive communities in the lower reaches of the mainstem, in spring-influenced reaches, and in a tributary with intermittent reaches. Variation partitioning with redundancy analysis showed that large-scale factors, i.e. riverine network patterns (large-scale AEM variables), climatic variation and ecoregion explained a significant proportion (28%) of the variation in community composition within a river basin. The riverine network patterns were the most important factor, explaining 12% alone. Local environmental factors were significant, but completely confounded within these spatial patterns. We propose that there are distinctive macroinvertebrate communities depending on the location in the river network and this may apply to other (subtropical) rivers, which should be tested by future studies. We recommend spatial analysis that considers distances and connectivity within a river network as a powerful tool to recognize multiscale riverine network patterns, which can help to identify priority areas for conservation and to develop sound monitoring programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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; both teacher heads agree on what is shown here.
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".