Oceanic islands and climate: using a multi-criteria model of drivers of change to select key conservation areas in Galapagos
Bibliographic record
Abstract
Abstract The unique marine and terrestrial ecosystems of the Galapagos Islands are highly vulnerable to human-based drivers of change, including the introduction of invasive species, unsustainable tourism, illegal fishing, overexploitation of ecosystem services, and climate change. These drivers can interact with climate-based drivers such as El Niño-Southern Oscillation (ENSO) at multiple temporal and spatial scales, exacerbating their negative impacts on already fragile ecosystems and the socioeconomic system of the Archipelago. In this review, we performed a literature review based on published literature from 1945 to 2020 and local and global climate databases to analyze drivers of change in the Galapagos. We developed and applied a spatial impact assessment model to identify high-ecological value areas with high sensitivity and exposure scores to environmental change drivers. We identified 13 priority HEVA that encompass ca. 23% (14,715 km2) of the Galapagos Archipelago, distributed in nearly 3% of the Galapagos Marine Reserve and 20% Galapagos National Park. Current and future impacts are likely to concentrate on the inhabited islands’ highlands, whereas marine impacts concentrate along most of the Galapagos Islands’ shorelines. These results are important for guiding the design and implementation of adaptation measures aimed at increasing ecosystem resilience and human adaptive capacity in the face of global environmental change. Overall, these results will be valuable in their application for preserving Galapagos biota, securing the provision of vital ecosystem services for resident human populations, and sustaining the nature-based tourism industry.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".