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The influence of climatic legacies on the distribution of dryland biocrust communities.

David J EldridgeManuel Delgado-Baquerizo
Published in: Global change biology (2018)
Predicting the distribution of biocrust species, mosses, lichens and liverworts associated with surface soils is difficult, but climatic legacies (changes in climate over the last 20 k years) can improve our prediction of the distribution of biocrust species. To provide empirical support for this hypothesis, we used a combination of network analyses and structural equation modelling to identify the role of climatic legacies in predicting the distribution of ecological clusters formed by species of mosses, lichens and liverworts using data from 282 large sites distributed across 0.6 million km2 of eastern Australia. Two ecological clusters contained 87% of the 120 moss, lichen and liverwort species. Both clusters contained lichen, moss and liverwort species, but were dominated by different families. Sites where the air temperature increased the most over 20k years (positive temperature legacies) were associated with reductions in the relative abundance of species from the lichen (Peltulaceae and Teloschistaceae) and moss (Bryaceae) families (Cluster A species), greater groundstorey plant cover and lower soil pH. Sites where precipitation has increased over the past 20k years (positive precipitation legacy) were associated with increases in the relative abundance of lichen (Cladoniaceae, Lecideaceae and Thelotremataceae) and moss (Pottiaceae) families (Cluster B species) and lower levels of soil pH. Sites where temperatures have increased the most in the past 20k years suppressed the negative effects of plant cover on Cluster B by reducing plant cover. Increased intensity of grazing suppressed the negative effect of soil pH and the positive effect of soil carbon, on the relative abundance of Cluster B taxa. Finally, increasing temperature and precipitation legacies reduced the negative effect of soil pH on Cluster B. Understanding of the importance of climatic legacies improves our ability to predict how biocrust assemblies might respond to ongoing global environmental change associated with increasing land use intensification, increasing temperature and reduced rainfall.
Keyphrases
  • genetic diversity
  • climate change
  • plant growth
  • human health
  • machine learning
  • south africa
  • big data
  • antibiotic resistance genes
  • risk assessment
  • electronic health record
  • artificial intelligence
  • data analysis