Preferential habitats prediction in syngnathids using species distribution models
Bibliographic record
Abstract
Syngnathids are considered as flagship species for marine conservation. Seahorses and pipefish are highly vulnerable to anthropogenic and environmental disturbances, but most species are currently considered Data Deficient by IUCN, requiring more biological and ecological research. Although syngnathids are well known for their unusual breeding biology, some aspects on the ecology of this family have rarely received attention. The knowledge on the factors governing syngnathids distribution is limited to some species and geographical regions. The present study is the first approach to predict syngnathid habitat preference in Spanish coasts, particularly in a marine National Park. In this study, Species Distribution Models (SDMs) were implemented to investigate the preferential habitat and distribution of the pipefish Syngnathus acus in Cíes Archipelago (Atlantic Islands of Galicia National Park, PNIA). Occurrence data of the species obtained from 2016 to 2018 surveys in PNIA were modeled as a function of bathymetric (depth, slope), substrate (sediment texture) and oceanographic (waves exposure) variables, using GAM, Random Forest and Maxent algorithms. From those SDMs, prediction models were built and the ensemble map of predictions was performed. The variables that most determined the distribution of the species were depth and wave exposure. The results of this study provide information on (1) habitat preference in the most dominant species in PNIA, the pipefish S. acus, towards sustainable management of this species in the National Park, and (2) predictive statistical tools for proper spatial conservation plans of this syngnathid species.
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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 | 0.000 | 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) | 0.002 | 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 teacher head, 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".