Long-term trends in fish community composition across coastal bays and lakes in the Lavaca–Colorado Estuary
Bibliographic record
Abstract
Human impacts continue to alter community structure, emphasizing the need to understand how spatial and temporal variability in disturbance and conservation affect ecological communities to optimize management strategies. Here, we quantify fish species richness, diversity, and community structure across five coastal bays and lakes in the Lavaca–Colorado Estuary, Texas, over 30 years to investigate spatial and temporal variability in species assemblages, and the potential effects of resource management. Results suggest that fish communities varied both spatially and temporally from 1976 to 2008, with greater temporal shifts in habitats more proximate to the Gulf of Mexico and removed from human residential areas — diversity increased in Powderhorn Lake and spotted seatrout (Cynoscion nebulosus (Cuvier, 1830)) and red drum (Sciaenops ocellatus (L., 1766)) abundances increased in Oyster Lake following changes in fishing regulations. Natural fluctuations in environmental conditions coupled with limited access to lakes by geographic restraints may have led to more pronounced changes in community structure. However, the effects of fishing management on fish communities within small lakes and bays within the Lavaca–Colorado Estuary is likely habitat- and context-specific, and continued monitoring, especially among ecologically and economically important species, will provide insight into how environmental change and anthropogenic disturbance may affect long-term trends in coastal community 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".