@article{LehmannZhengRyoetal.2020,
  author    = {Lehmann, Anika and Zheng, Weishuang and Ryo, Masahiro and Soutschek, Katharina and Roy, Julien and Rongstock, Rebecca and Maaß, Stefanie and Rillig, Matthias C.},
  title     = {Fungal traits important for soil aggregation},
  series = {Frontiers in microbiology},
  volume    = {10},
  journal   = {Frontiers in microbiology},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-302X},
  doi       = {10.3389/fmicb.2019.02904},
  pages     = {13},
  year      = {2020},
  abstract  = {Soil structure, the complex arrangement of soil into aggregates and pore spaces, is a key feature of soils and soil biota. Among them, filamentous saprobic fungi have well-documented effects on soil aggregation. However, it is unclear what properties, or traits, determine the overall positive effect of fungi on soil aggregation. To achieve progress, it would be helpful to systematically investigate a broad suite of fungal species for their trait expression and the relation of these traits to soil aggregation. Here, we apply a trait-based approach to a set of 15 traits measured under standardized conditions on 31 fungal strains including Ascomycota, Basidiomycota, and Mucoromycota, all isolated from the same soil. We find large differences among these fungi in their ability to aggregate soil, including neutral to positive effects, and we document large differences in trait expression among strains. We identify biomass density, i.e., the density with which a mycelium grows (positive effects), leucine aminopeptidase activity (negative effects) and phylogeny as important factors explaining differences in soil aggregate formation (SAF) among fungal strains; importantly, growth rate was not among the important traits. Our results point to a typical suite of traits characterizing fungi that are good soil aggregators, and our findings illustrate the power of employing a trait-based approach to unravel biological mechanisms underpinning soil aggregation. Such an approach could now be extended also to other soil biota groups. In an applied context of restoration and agriculture, such trait information can inform management, for example to prioritize practices that favor the expression of more desirable fungal traits.},
  language  = {en}
}
@article{RilligBielcikChaudharyetal.2020,
  author    = {Rillig, Matthias C. and Bielcik, Milos and Chaudhary, Veer Bala and Gr{\"u}nfeld, Leonie and Maass, Stefanie and Mansour, India and Ryo, Masahiro and Veresoglou, Stavros D.},
  title     = {Ten simple rules for increased lab resilience},
  series = {PLoS Computational Biology : a new community journal},
  volume    = {16},
  journal   = {PLoS Computational Biology : a new community journal},
  number    = {11},
  publisher = {PLoS},
  address   = {San Fransisco},
  issn      = {1553-734X},
  doi       = {10.1371/journal.pcbi.1008313},
  pages     = {5},
  year      = {2020},
  abstract  = {When running a lab we do not think about calamities, since they are rare events for which we cannot plan while we are busy with the day-to-day management and intellectual challenges of a research lab. No lab team can be prepared for something like a pandemic such as COVID-19, which has led to shuttered labs around the globe. But many other types of crises can also arise that labs may have to weather during their lifetime. What can researchers do to make a lab more resilient in the face of such exterior forces? What systems or behaviors could we adjust in 'normal' times that promote lab success, and increase the chances that the lab will stay on its trajectory? We offer 10 rules, based on our current experiences as a lab group adapting to crisis.},
  language  = {en}
}
@article{RyoJeschkeRilligetal.2020,
  author    = {Ryo, Masahiro and Jeschke, Jonathan M. and Rillig, Matthias C. and Heger, Tina},
  title     = {Machine learning with the hierarchy-of-hypotheses (HoH) approach discovers novel pattern in studies on biological invasions},
  series = {Research synthesis methods},
  volume    = {11},
  journal   = {Research synthesis methods},
  number    = {1},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {1759-2879},
  doi       = {10.1002/jrsm.1363},
  pages     = {66 -- 73},
  year      = {2020},
  abstract  = {Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.},
  language  = {en}
}