A climate-smart spatial planning framework
Bibliographic record
Abstract
Abstract 1. Climate change is already having profound effects on biodiversity, but climate change adaptation has yet to be fully incorporated into area-based management tools used to conserve biodiversity, such as protected areas. One main obstacle to its inclusion is the lack of consensus regarding how impacts of climate change can be included in spatial conservation plans.2. We propose a climate-smart framework that prioritizes the protection of climate refugia—areas of low climate exposure and high biodiversity retention—identified using climate metrics. We explore four aspects of climate-smart spatial planning in the proposed framework: i) climate model ensembles; ii) multiple emission scenarios; iii) climate metrics; and iv) approaches to identifying climate refugia. We illustrate this framework in the Western Pacific Ocean, but it is equally applicable to terrestrial systems.3. All aspects of climate-smart spatial planning considered affected the configuration of spatial plans. The choice of climate metrics and approaches to identifying refugia result in large differences in climate-smart spatial plans, whereas the choice of climate models and emission scenarios have smaller effects. As configuration of spatial plans depended on climate metrics used, a spatial plan based on a single measure of climate change (e.g., warming) will not necessarily be robust against other measures of climate change (e.g., ocean acidification). We recommend including climate metrics most relevant for the biodiversity considered. To include the uncertainty associated with different climate futures, we recommend using multiple climate models (i.e., an ensemble) and emission scenarios. Finally, we show that the approaches we used to identify climate refugia come with trade-offs between the degree to which they are climate-smart and their efficiency in meeting conservation targets. Hence, the choice of approach will depend on the relative value stakeholders place on climate change adaptation.4. By using this framework, protected areas can be designed with improved longevity and thus safeguard biodiversity against current and future climate change. We hope that the proposed climate-smart framework helps transition conservation planning towards climate-smart approaches.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".