Revisiting two sympatric European seahorse species: apparent decline in the absence of exploitation
Bibliographic record
Abstract
ABSTRACT Seahorses are marine fish with several life history characteristics hypothesized to make them resilient but are of conservation concern because of their international trade and habitat loss. Surveys of two unexploited European seahorse species ( Hippocampus guttulatus and Hippocampus hippocampus ) in Ria Formosa lagoon, Portugal, were repeated seven years after their populations in the lagoon were found to be among the densest in the world. Population densities of both species declined significantly between 2001/2002 and 2008/2009 surveys (94% and 73% for H. guttulatus and H. hippocampus respectively). H. guttulatus declines were not associated with any environmental changes measured (i.e. percentage live benthic habitat cover, depth, temperature, water current speed, horizontal visibility). H. hippocampus declined more where current speed had decreased. At the low densities found in 2008/2009, occurrence for both species was best predicted by depth: seahorses were found in deeper locations throughout the lagoon. Other important predictors were temperature for H. guttulatus (found at sites warmer than average) and current speed for H. hippocampus (found in locations with faster currents). The large declines in seahorse densities made it difficult to compare results over time. Presence–absence and abundance modelling at multiple scales can help to ensure that data are comparable even when populations fluctuate drastically. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".