Applying global criteria to tracking data to define important areas for marine conservation
Bibliographic record
Abstract
Abstract Aim Enhanced management of areas important for marine biodiversity are now obligations under a range of international treaties. Tracking data provide unparalleled information on the distribution of marine taxa, but there are no agreed guidelines that ensure these data are used consistently to identify biodiversity hotspots and inform marine management decisions. Here, we develop methods to standardize the analysis of tracking data to identify sites of conservation importance at global and regional scales. Location We applied these methods to the largest available compilation of seabird tracking data, covering 60 species, collected from 55 deployment locations ranging from the poles to the tropics. Methods Key developments include a test for pseudo‐replication to assess the independence of two groups of tracking data, an objective approach to define species‐specific smoothing parameters ( h values) for kernel density estimation based on area‐restricted search behaviour, and an analysis to determine whether sites identified from tracked individuals are also representative for the wider population. Results This analysis delineated priority sites for marine conservation for 52 of the 60 species assessed. We compiled 252 data groupings and defined 1052 polygons, between them meeting Important Bird and Biodiversity Area criteria over 1500 times. Other results showed 13% of data groups were inadequate for site definition and 10% showed some level of pseudo‐replication. Between 25 and 50 trips were needed within a data group for data to be considered at least partially representative of the respective population. Main conclusions Our approach provides a consistent framework for using animal tracking data to delineate areas of global conservation importance, allowing greater integration into marine spatial planning and policy. The approaches we describe are exemplified for pelagic seabirds, but are applicable to a range of taxonomic groups. Covering 4.3% of the oceans, the sites identified would benefit from enhanced protection to better safeguard the threatened species populations they contain.
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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 | 0.032 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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 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".