Winners and losers in a changing climate: how will protected areas conserve red list species under climate change?
Bibliographic record
Abstract
Abstract Aim A fundamental challenge to protected area‐based conservation is that protected areas are typically established under assumptions of environmental stationarity. With a rapidly changing climate, this assumption of stationarity is violated, and climate change could push some species beyond the bounds of currently protected areas. Here, we evaluate the efficacy of protected areas in conserving threatened plant biodiversity under future climate projections. Location South Africa. Methods We use ensemble species distribution modelling to map the projected distribution of South Africa's ~1200 threatened endemic plant species under present‐day and projected climate scenarios for 2050. We quantify the performance of the existing protected area network by examining changes in the relative proportion of species’ projected geographic extents within protected areas. We then examine whether current IUCN Red List status is a good predictor of climate ‘winners’ and 'losers.' Results We find that 56%–66% of species may have a greater proportion of their projected range extent falling within protected areas in 2050 under climate scenarios of mitigated and upsurge greenhouse gas emissions (Representative Concertation Pathways 4.5 and 8.5). However, this increase in the proportion of range protected is frequently associated with range contraction outside of protected areas. We also show that current threat intensity is not a good indicator of which species will lose versus gain increased protection. Main conclusions Our results suggest that the existing reserve network is surprisingly robust to projected range shifts; however, we also identify regions where species protection needs to be improved. In addition, we suggest that there is an urgent need to better incorporate future climate threats into the assessment of species extinction risks.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".