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Global dominance of lianas over trees is driven by forest disturbance, climate and topography.

Alain Senghor K NguteDavid S SchoemanMarion PfeiferGeertje M F van der HeijdenOliver L PhillipsMichiel Van BreugelMason J CampbellChris J ChandlerBrian J EnquistRachael V GallagherChristoph GehringJefferson S HallSusan G W LauranceWilliam F LauranceSusan G LetcherWen-Yao LiuMartin J P SullivanS Joseph WrightChunming YuanAndrew R Marshall
Published in: Global change biology (2024)
Growing evidence suggests that liana competition with trees is threatening the global carbon sink by slowing the recovery of forests following disturbance. A recent theory based on local and regional evidence further proposes that the competitive success of lianas over trees is driven by interactions between forest disturbance and climate. We present the first global assessment of liana-tree relative performance in response to forest disturbance and climate drivers. Using an unprecedented dataset, we analysed 651 vegetation samples representing 26,538 lianas and 82,802 trees from 556 unique locations worldwide, derived from 83 publications. Results show that lianas perform better relative to trees (increasing liana-to-tree ratio) when forests are disturbed, under warmer temperatures and lower precipitation and towards the tropical lowlands. We also found that lianas can be a critical factor hindering forest recovery in disturbed forests experiencing liana-favourable climates, as chronosequence data show that high competitive success of lianas over trees can persist for decades following disturbances, especially when the annual mean temperature exceeds 27.8°C, precipitation is less than 1614 mm and climatic water deficit is more than 829 mm. These findings reveal that degraded tropical forests with environmental conditions favouring lianas are disproportionately more vulnerable to liana dominance and thus can potentially stall succession, with important implications for the global carbon sink, and hence should be the highest priority to consider for restoration management.
Keyphrases
  • climate change
  • human health
  • microbial community
  • dna methylation
  • gene expression
  • machine learning
  • genome wide
  • deep learning
  • life cycle