TY - JOUR A1 - Ryo, Masahiro A1 - Jeschke, Jonathan M. A1 - Rillig, Matthias C. A1 - Heger, Tina T1 - Machine learning with the hierarchy-of-hypotheses (HoH) approach discovers novel pattern in studies on biological invasions JF - Research synthesis methods N2 - 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. KW - artificial intelligence KW - hierarchy-of-hypotheses approach KW - machine learning KW - meta-analysis KW - synthesis KW - systematic review Y1 - 2019 U6 - https://doi.org/10.1002/jrsm.1363 SN - 1759-2879 SN - 1759-2887 VL - 11 IS - 1 SP - 66 EP - 73 PB - Wiley CY - Hoboken ER - TY - GEN A1 - Ryo, Masahiro A1 - Jeschke, Jonathan M. A1 - Rillig, Matthias C. A1 - Heger, Tina T1 - Machine learning with the hierarchy-of-hypotheses (HoH) approach discovers novel pattern in studies on biological invasions T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1171 KW - artificial intelligence KW - hierarchy-of-hypotheses approach KW - machine learning KW - meta-analysis KW - synthesis KW - systematic review Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-517643 SN - 1866-8372 IS - 1171 SP - 66 EP - 73 ER - TY - JOUR A1 - Diekmann, Martin A1 - Andres, Christian A1 - Becker, Thomas A1 - Bennie, Jonathan A1 - Blueml, Volker A1 - Bullock, James M. A1 - Culmsee, Heike A1 - Fanigliulo, Miriam A1 - Hahn, Annett A1 - Heinken, Thilo A1 - Leuschner, Christoph A1 - Luka, Stefanie A1 - Meissner, Justus A1 - Müller, Josef A1 - Newton, Adrian A1 - Peppler-Lisbach, Cord A1 - Rosenthal, Gert A1 - van den Berg, Leon J. L. A1 - Vergeer, Philippine A1 - Wesche, Karsten T1 - Patterns of long-term vegetation change vary between different types of semi-natural grasslands in Western and Central Europe JF - Journal of vegetation science N2 - 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. KW - dry grasslands KW - fragmentation KW - homogenization KW - management KW - meta-analysis KW - nitrogen deposition KW - quasi-permanent plot KW - re-survey KW - species richness KW - wet grasslands Y1 - 2019 U6 - https://doi.org/10.1111/jvs.12727 SN - 1100-9233 SN - 1654-1103 VL - 30 IS - 2 SP - 187 EP - 202 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - De Frenne, Pieter A1 - Baeten, Lander A1 - Graae, Bente J. A1 - Brunet, Jorg A1 - Wulf, Monika A1 - Orczewska, Anna A1 - Kolb, Annette A1 - Jansen, Ivy A1 - Jamoneau, Aurelien A1 - Jacquemyn, Hans A1 - Hermy, Martin A1 - Diekmann, Martin A1 - De Schrijver, An A1 - De Sanctis, Michele A1 - Decocq, Guillaume A1 - Cousins, Sara A. O. A1 - Verheyen, Kris T1 - Interregional variation in the floristic recovery of post-agricultural forests JF - The journal of ecology N2 - 1. Worldwide, the floristic composition of temperate forests bears the imprint of past land use for decades to centuries as forests regrow on agricultural land. Many species, however, display significant interregional variation in their ability to (re)colonize post-agricultural forests. This variation in colonization across regions and the underlying factors remain largely unexplored. 2. We compiled data on 90 species and 812 species x study combinations from 18 studies across Europe that determined species' distribution patterns in ancient (i.e. continuously forested since the first available land use maps) and post-agricultural forests. The recovery rate (RR) of species in each landscape was quantified as the log-response ratio of the percentage occurrence in post-agricultural over ancient forest and related to the species-specific life-history traits and local (soil characteristics and light availability) and regional factors (landscape properties as habitat availability, time available for colonization, and climate). 3. For the herb species, we demonstrate a strong (interactive) effect of species' life-history traits and forest habitat availability on the RR of post-agricultural forest. In graminoids, however, none of the investigated variables were significantly related to the RR. 4. The better colonizing species that mainly belonged to the short-lived herbs group showed the largest interregional variability. Their recovery significantly increased with the amount of forest habitat within the landscape, whereas, surprisingly, the time available for colonization, climate, soil characteristics and light availability had no effect. 5. Synthesis. By analysing 18 independent studies across Europe, we clearly showed for the first time on a continental scale that the recovery of short-lived forest herbs increased with the forest habitat availability in the landscape. Small perennial forest herbs, however, were generally unsuccessful in colonizing post-agricultural forest even in relatively densely forested landscapes. Hence, our results stress the need to avoid ancient forest clearance to preserve the typical woodland flora. KW - ancient forest KW - colonization capacity KW - forest herbs KW - functional traits KW - habitat fragmentation KW - habitat loss KW - life-history traits KW - meta-analysis KW - plant population and community dynamics KW - secondary succession Y1 - 2011 U6 - https://doi.org/10.1111/j.1365-2745.2010.01768.x SN - 0022-0477 VL - 99 IS - 2 SP - 600 EP - 609 PB - Wiley-Blackwell CY - Malden ER -