570 Biowissenschaften; Biologie
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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.
Questions Has plant species richness in semi-natural grasslands changed over recent decades? Do the temporal trends of habitat specialists differ from those of habitat generalists? Has there been a homogenization of the grassland vegetation? Location Different regions in Germany and the UK. Methods We conducted a formal meta-analysis of re-survey vegetation studies of semi-natural grasslands. In total, 23 data sets were compiled, spanning up to 75 years between the surveys, including 13 data sets from wet grasslands, six from dry grasslands and four from other grassland types. Edaphic conditions were assessed using mean Ellenberg indicator values for soil moisture, nitrogen and pH. Changes in species richness and environmental variables were evaluated using response ratios. Results In most wet grasslands, total species richness declined over time, while habitat specialists almost completely vanished. The number of species losses increased with increasing time between the surveys and were associated with a strong decrease in soil moisture and higher soil nutrient contents. Wet grasslands in nature reserves showed no such changes or even opposite trends. In dry grasslands and other grassland types, total species richness did not consistently change, but the number or proportions of habitat specialists declined. There were also considerable changes in species composition, especially in wet grasslands that often have been converted into intensively managed, highly productive meadows or pastures. We did not find a general homogenization of the vegetation in any of the grassland types. Conclusions The results document the widespread deterioration of semi-natural grasslands, especially of those types that can easily be transformed to high production grasslands. The main causes for the loss of grassland specialists are changed management in combination with increased fertilization and nitrogen deposition. Dry grasslands are most resistant to change, but also show a long-term trend towards an increase in more mesotrophic species.