Conserving pelagic habitats: seascape modelling of an oceanic top predator
Bibliographic record
Abstract
Summary 1. Currently pelagic ecosystems are changing significantly due to multiple threats. An important management policy is to establish marine protected areas, until now overlooked due to the difficulty of declaring ‘high seas’ protected areas, obtaining long‐term distribution data on indicator species and the dynamic nature of these ecosystems. 2. Within this framework, we developed predictive habitat suitability models of an oceanic predator, the vulnerable wandering albatross Diomedea exulans , in the highly dynamic Southern Ocean. Based on a long‐term tracking database (1998–2008), we estimated three quantitative ecological indices that complementarily describe the hierarchical habitat use of the species at multiple spatial scales: where the species (i) spent more time (the seascape, based on the time spent per area), (ii) searched for prey (the foraging habitat, based on zones of increased foraging intensity using first passage time), and (iii) fed (the feeding habitat, based on prey capture data). 3. Predictive habitat models reasonably matched the observed distribution patterns and described albatross multi‐scale habitat use as a hierarchical arrangement: albatrosses foraged over topographic features in subtropical waters, nested within the wider seascape due to the constraint imposed by the colony effect, whereas feeding occurred nested over the continental shelf and seamounts in areas of low oceanographic variability within the Polar Frontal Zone. 4. Within the current oceanographic conditions, the location of key pelagic habitats for albatrosses breeding in the southern Indian Ocean encompassed certain topographic features such as pelagic areas surrounding main breeding sites, seamounts and submarine mountain ranges. The placement of these pelagic hotspots depends on the current sea surface temperature conditions. 5. Synthesis and applications . The present study provides two key conservation and management tools. First, we provide the first map to support the development of a prospective network of priority conservation zones across the southern Indian Ocean based on habitat predictions of an oceanic indicator species. This could be used not only to support conservation of top predators but also the underlying biodiversity associated with pelagic key habitats. Secondly, the developed habitat modelling procedure is widely applicable and could be used to track changes in species distribution in both marine and terrestrial environments within the current global change scenario.
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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) | 0.001 | 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 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".