Movescapes and eco‐evolutionary movement strategies in marine fish: Assessing a connectivity hotspot
Bibliographic record
Abstract
Abstract Data from the Integrated Tracking of Aquatic Animals in the Gulf of Mexico (iTAG) network, and sister networks, were used to evaluate fish movements in the Florida Keys—an extensive reef fish ecosystem just north of Cuba connecting the Atlantic Ocean and Gulf of Mexico. We analysed ~2 million detections for 23 species, ranging from reef fish such as Nassau grouper ( Epinephelus striatus , Serranidae) to migratory apex predators such as white sharks ( Carcharodon carcharias , Lamnidae). To facilitate comparisons across species, we used an eco‐evolutionary movement strategy framework that identified measurable movement traits and their proximate and ultimate drivers. Detectability was species‐specific and quantified with a detection potential index. Life stages detected in the study area varied by species and residency varied with life stage. Four annual movement types were identified as follows: high site‐fidelity residents, range residents, seasonal migrants and general migrants. The endangered smalltooth sawfish ( Pristis pectinata , Pristidae), a seasonal migrant, exhibited the greatest within‐ecosystem connectivity. Site attachment, stopover and deep‐water migration behaviours differed between individuals, species and annual movement types. All apex predators were migratory. General migrants were significantly larger than fish in the other movement types, a life‐history and movement trait combination that is common but not exclusive, as many small pelagics also migrate. Most teleosts exhibited movements associated with spawning. As concerns grow over habitat and biodiversity loss, multispecies movescapes, such as presented here, are expected to play an increasingly important role in informing ecosystem‐based and non‐extractive fisheries management strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".