Community-Level Waterbird Responses to Water Hyacinth (<i>Eichhornia crassipes</i>)
Bibliographic record
Abstract
Water hyacinth is among the most widespread invasive plants worldwide; however, its effects on waterbirds are largely undocumented. We monitored site use by waterbirds at Lake Chapala, the largest lake in Mexico and recently designated Ramsar site, to evaluate the potential influence of water hyacinth cover on species composition and aggregate measures of the waterbird community, including waterbird density, species richness, and Simpson's index of diversity. We examined the response of waterbirds to changes in percent water hyacinth cover at 22 independent sites around the lake during six study seasons from May 2006 to February 2008. We found little evidence to suggest that percent water hyacinth cover affected aggregate community measures; however, multivariate analysis of relative species composition suggested that water hyacinth cover corresponded with seasonal species composition (Canonical Correspondence r = 0.66, P = 0.007) when seasonal site cover averaged 17.7 ± 4.67% (winter 2007). Several migratory species were not observed during this season, which could suggest that some small-bodied migratory species avoided Lake Chapala during the winter of high water hyacinth cover. We suspect that observed changes in the waterbird community are in response to species-specific tolerances for water hyacinth and indirect abiotic and biotic effects of its presence (e.g., invertebrate and fish composition).
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".