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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.
Biochar is being discussed as a soil amendment to improve soil fertility and mitigate climate change. While biochar interactions with soil microbial biota have been frequently studied, interactions with soil mesofauna are understudied. We here present an experiment in which we tested if the collembolan Folsomia candida I) can transport biochar particles, II) if yes, how far the particles are distributed within 10 days, and III) if it shows a preference among biochars made from different feedstocks, i.e. pine wood, pine bark and spelt husks. In general, biochar particles based on pine bark and pine wood were consistently distributed significantly more than those made of spelt husks, but all types were transported more than 4cm within 10 days. Additionally, we provide evidence that biochar particles can become readily attached to the cuticle of collembolans and hence be transported, potentially even over large distances. Our study shows that the soil mesofauna can indeed act as a vector for the transport of biochar particles and show clear preferences depending on the respective feedstock, which would need to be studied in more detail in the future.