Harnessing Community Science for Seahorse Population Monitoring: Insights From the iSeahorse Programme in Tampa Bay
Bibliographic record
Abstract
ABSTRACT Community science provides a valuable approach for population monitoring, offering benefits such as public engagement, cost‐effectiveness and broad geographic coverage. Seahorses are excellent candidates for community science monitoring because they are iconic and sedentary and because their cryptic nature and patchy distribution hampers formal professional research. We analysed data collected by a non‐profit organization for the iSeahorse programme. Data were collected by community members over a 5‐year period in two locations in Tampa Bay, Florida, using otter trawls and seines. Their data for the two local seahorse species ( Hippocampus erectus and Hippocampus zosterae ) were valuable in complementing professional science. Densities found in community monitoring were orders of magnitude lower than those found professionally, at least partly because the areas differed. However, sex ratios were similar in both areas, being predominantly female‐biased. Community data on timing of pregnancy confirmed professional findings but also extended the season. Usefully, community science provided the first published torso lengths of H. zosterae anywhere and of H. erectus in Tampa Bay. Beyond the biological, we interviewed the project leaders for their opinions on the programme's impact, challenges and areas for improvement, to give a societal context to the study. It became clear that there should had been more and ongoing communication between the non‐profit organization and the iSeahorse programme during the course of the surveys. Overall, our analysis endorses the strong potential of community science for population monitoring and its complementarity with professional science.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".