@misc{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 = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1171},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-51764},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517643},
  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}
}
@misc{SchittkoBernardVerdierHegeretal.2020,
  author    = {Schittko, Conrad and Bernard-Verdier, Maud and Heger, Tina and Buchholz, Sascha and Kowarik, Ingo and von der Lippe, Moritz and Seitz, Birgit and Joshi, Jasmin Radha and Jeschke, Jonathan M.},
  title     = {A multidimensional framework for measuring biotic novelty: How novel is a community?},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {8},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-52565},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-525657},
  pages     = {19},
  year      = {2020},
  abstract  = {Anthropogenic changes in climate, land use, and disturbance regimes, as well as introductions of non-native species can lead to the transformation of many ecosystems. The resulting novel ecosystems are usually characterized by species assemblages that have not occurred previously in a given area. Quantifying the ecological novelty of communities (i.e., biotic novelty) would enhance the understanding of environmental change. However, quantification remains challenging since current novelty metrics, such as the number and/or proportion of non-native species in a community, fall short of considering both functional and evolutionary aspects of biotic novelty. Here, we propose the Biotic Novelty Index (BNI), an intuitive and flexible multidimensional measure that combines (a) functional differences between native and non-native introduced species with (b) temporal dynamics of species introductions. We show that the BNI is an additive partition of Rao's quadratic entropy, capturing the novel interaction component of the community's functional diversity. Simulations show that the index varies predictably with the relative amount of functional novelty added by recently arrived species, and they illustrate the need to provide an additional standardized version of the index. We present a detailed R code and two applications of the BNI by (a) measuring changes of biotic novelty of dry grassland plant communities along an urbanization gradient in a metropolitan region and (b) determining the biotic novelty of plant species assemblages at a national scale. The results illustrate the applicability of the index across scales and its flexibility in the use of data of different quality. Both case studies revealed strong connections between biotic novelty and increasing urbanization, a measure of abiotic novelty. We conclude that the BNI framework may help building a basis for better understanding the ecological and evolutionary consequences of global change.},
  language  = {en}
}