@article{RasigrafWagner2022,
  author    = {Rasigraf, Olivia and Wagner, Dirk},
  title     = {Landslides},
  series = {Earth science reviews : the international geological journal bridging the gap between research articles and textbooks},
  volume    = {231},
  journal   = {Earth science reviews : the international geological journal bridging the gap between research articles and textbooks},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0012-8252},
  doi       = {10.1016/j.earscirev.2022.104064},
  pages     = {16},
  year      = {2022},
  abstract  = {Erosion by landslides is a common phenomenon in mountain regions around the globe, affecting all climatic zones. Landslides facilitate bedrock weathering, pedogenesis and ecological succession, being key drivers of biodiversity. Landslide chronosequences have long been used for studies of vegetation succession in initial ecosystems, but they further offer ideal model systems for studies of soil development and microbial community succession. In this review we synthesize the state of knowledge on the role of landslides in ecosystems, their influence on element cycles and interactions with biota. Further, we discuss feedback mechanisms between global warming, landslide activity and greenhouse gas emissions. In the view of increasing anthropogenic influence and climate change, soils are becoming a critical resource. Due to their ubiquity, landslide chronosequences have the potential to provide critical insights into soil development under different climates and thereby contribute to future soil restoration efforts.},
  language  = {en}
}
@article{FournierSteinerBrochetetal.2022,
  author    = {Fournier, Bertrand and Steiner, Magdalena and Brochet, Xavier and Degrune, Florine and Mammeri, Jibril and Carvalho, Diogo Leite and Siliceo, Sara Leal and Bacher, Sven and Pe{\~n}a-Reyes, Carlos Andr{\´e}s and Heger, Thierry Jean},
  title     = {Toward the use of protists as bioindicators of multiple stresses in agricultural soils},
  series = {Ecological indicators : integrating monitoring, assessment and management},
  volume    = {139},
  journal   = {Ecological indicators : integrating monitoring, assessment and management},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1470-160X},
  doi       = {10.1016/j.ecolind.2022.108955},
  pages     = {8},
  year      = {2022},
  abstract  = {Management of agricultural soil quality requires fast and cost-efficient methods to identify multiple stressors that can affect soil organisms and associated ecological processes. Here, we propose to use soil protists which have a great yet poorly explored potential for bioindication. They are ubiquitous, highly diverse, and respond to various stresses to agricultural soils caused by frequent management or environmental changes. We test an approach that combines metabarcoding data and machine learning algorithms to identify potential stressors of soil protist community composition and diversity. We measured 17 key variables that reflect various potential stresses on soil protists across 132 plots in 28 Swiss vineyards over 2 years. We identified the taxa showing strong responses to the selected soil variables (potential bioindicator taxa) and tested for their predictive power. Changes in protist taxa occurrence and, to a lesser extent, diversity metrics exhibited great predictive power for the considered soil variables. Soil copper concentration, moisture, pH, and basal respiration were the best predicted soil variables, suggesting that protists are particularly responsive to stresses caused by these variables. The most responsive taxa were found within the clades Rhizaria and Alveolata. Our results also reveal that a majority of the potential bioindicators identified in this study can be used across years, in different regions and across different grape varieties. Altogether, soil protist metabarcoding data combined with machine learning can help identifying specific abiotic stresses on microbial communities caused by agricultural management. Such an approach provides complementary information to existing soil monitoring tools that can help manage the impact of agricultural practices on soil biodiversity and quality.},
  language  = {en}
}