Climate-smart prioritisation of tropical Key Biodiversity Areas for protection in response to widespread temperature novelty.
Trew, B. T.; Lees, A. C.; Edwards, D. P.; Early, R.; Maclean, I.
Show abstract
Key Biodiversity Areas (KBAs) are a cornerstone of 21st century area-based conservation targets. In tropical KBAs, biodiversity is potentially at high risk from climate change, because most species reside within or beneath the canopy, where small increases in temperature can lead to novel climate regimes. We quantify novelty in temperature regimes by modelling hourly temperatures below the forest canopy across tropical KBAs between 1990 and 2019. We find that up to 66% of KBAs with tropical forest are likely to have transitioned to novel temperature regimes. Nevertheless, 34% of KBAs are providing refuge from novelty, 58% of which are not protected. By conducting the first pan-tropical analyses of changes in below-canopy temperatures, we identify KBAs that are acting as climate refugia and should be prioritised as candidates for expansion of the conservation network in response to the post-2020 Global Biodiversity Framework target to conserve 30% of land area by 2030.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- The values of ecosystem services inside and outside of protected areas in Eastern and Southern Africa 93%
- Tailoring evidence into action: using a codesign approach for biodiversity information in the Tropical Andes 93%
- A framework for linking hemispheric, full annual cycle prioritizations to local conservation actions for migratory birds 93%
Similar papers in this journal
- An operational methodology to identify Critical Ecosystem Areas to help nations achieve the Kunming-Montreal Global Biodiversity Framework 94%
- Identifying key federal, state, and private lands strategies for achieving 30x30 in the US 94%
- Acoustic indices predict recovery of tropical bird communities for taxonomic and functional composition 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.