Mapping the Azores Marine Park Vulnerability to Temperature Changes
Bibliographic record
Abstract
ABSTRACT Aim Identifying highly vulnerable regions to climate change is increasingly incorporated in marine management planning given the expected redistribution of species with latitude, longitude, and depth following temperature changes. Here, we developed a spatially explicit vulnerability framework incorporating sensitivity, exposure, and adaptive capacity of species living in one of the largest networks of Marine Protected Areas (MPAs) within the EU. Location Azores Marine Park, North Atlantic. Methods We quantified benthic, benthopelagic, and pelagic species sensitivity to temperature changes based on adult thermal affinity and georeferenced their distribution with quality‐controlled records from various data compilators. To assess their exposure, we extracted historical (1995–2020) temperatures across latitudes, longitudes, and depths and calculated mean interannual change (i.e., increase or decrease) and variability. We estimated the adaptive capacity of species with traits related to relocation ability during adult and early life stages (i.e., “Motility” and “Developmental Mechanism”) using the FUN Azores trait database. To map the results, we pooled the species into 3D‐regions of 0.25° × 0.25° resolution and 50 and 500 m depth bands at shallow and deep areas, respectively. We assigned a sensitivity, exposure, and adaptive capacity score to each region based on species scores and combined them into a final vulnerability class (i.e., “Highly Vulnerable” (HV), “Advisable Monitoring” (AM), “Expected Relocation” (ER), and “Least Concern” (LC)). Results HV and AM regions exist only in the benthic environment across various MPAs and depths. Increased mobility of species explains the absence of the most vulnerable categories in the benthopelagic and pelagic environments. Main Conclusions We advise strong conservation measures in HV areas and to maintain connectivity with climate refugia and monitoring of environmental variables and populations in areas classified as AM and ER, respectively. Our results suggest that the Azores deep‐sea benthos is the most vulnerable environment to both warming and temperature variations.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.004 |
| 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".