Degree of aquatic ecosystem fragmentation predicts population characteristics of gray snapper (<i>Lutjanus griseus</i>) in Caribbean tidal creeks
Bibliographic record
Abstract
Ecosystem fragmentation is one of the most damaging anthropogenic impacts to aquatic and terrestrial ecosystems worldwide. In this study, we analyzed population-level characteristics of a resilient top predator, the gray snapper (Lutjanus griseus), across a gradient of fragmentation in Bahamian tidal creeks. Analysis of hydrologic connectivity (the inverse of fragmentation) and site-specific population parameters revealed that varying degrees of connectivity translated into predictable characteristics in fish populations. Less-fragmented systems produced fast-growing snapper with high condition factors, whereas more fragmented tidal creeks yielded slow-growing snapper with low condition factors. Underlying reasons for growth rate differences likely originated from structural and functional shifts in tidal creek ecosystems following fragmentation. Such ecosystem-level shifts were reflected by low per-individual volumes of consumed prey, as well as increased parasite infestations. Results indicated that aquatic ecosystem fragmentation may affect long-term fitness and viability of resilient species capable of persisting in fragmented habitats. Our results from estuarine tidal creeks compare well with studies on fragmentation of freshwater streams by dams and suggest that population-level consequences following ecosystem fragmentation may be generalizable between seemingly disparate aquatic environments.
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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 | 0.000 | 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".