Changing variability is an overlooked aspect of protected area planning
Bibliographic record
Abstract
Abstract Protected areas are widely used management tools designed to support the long-term conservation of biodiversity. The effectiveness of protected areas is being challenged by human-induced climate change, however, which is causing three broad shifts away from the current distribution of climate trends: a change in mean conditions, a change in the variance around the mean, and/or a change in symmetry. Though changes in average conditions are certainly important, the second behaviour, a change in variance, brings a unique set of challenges that species must respond to. As conditions become more variable, phenological events become less predictable, extreme weather events become more frequent, food security and ecosystem stability are compromised, and extinction risk increases. It therefore stands to reason that changes in the variance of local conditions should be a core consideration when designing protected areas. Here, we reviewed the literature to determine the extent to which changes in variance are being incorporated into protected area planning. Worryingly, we found that fewer than a quarter of the 100 studies we surveyed formally considered how climate change might change mean conditions, and only four considered climate change-induced changes in the variance around the mean. Our evaluation reveals an alarming gap in protected area research. The majority of researchers continue to make recommendations for protected areas without acknowledging that the area(s) they are recommending for protection may have markedly different conditions in the future. Whether variability is considered or not, stochastic events represent a serious threat to the persistence of species and complex ecosystems. Effective conservation requires actively considering how the stability of conditions within protected areas will be impacted by future climate change. As global climate patterns tend towards increasing unpredictability, protecting less variable habitat should be a priority to ensure local populations are not exposed to elevated extinction risk.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".