TY - JOUR A1 - Rana, Kamal A1 - Öztürk, Ugur A1 - Malik, Nishant T1 - Landslide geometry reveals its trigger JF - Geophysical research letters : GRL / American Geophysical Union N2 - Electronic databases of landslides seldom include the triggering mechanisms, rendering these inventories unusable for landslide hazard modeling. We present a method for classifying the triggering mechanisms of landslides in existing inventories, thus, allowing these inventories to aid in landslide hazard modeling corresponding to the correct event chain. Our method uses various geometric characteristics of landslides as the feature space for the machine-learning classifier random forest, resulting in accurate and robust classifications of landslide triggers. We applied the method to six landslide inventories spread over the Japanese archipelago in several different tests and training configurations to demonstrate the effectiveness of our approach. We achieved mean accuracy ranging from 67% to 92%. We also provide an illustrative example of a real-world usage scenario for our method using an additional inventory with unknown ground truth. Furthermore, our feature importance analysis indicates that landslides having identical trigger mechanisms exhibit similar geometric properties. KW - databases KW - Japan | landslides KW - random forest Y1 - 2021 U6 - https://doi.org/10.1029/2020GL090848 SN - 0094-8276 SN - 1944-8007 VL - 48 IS - 4 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Ceulemans, Ruben A1 - Guill, Christian A1 - Gaedke, Ursula T1 - Top predators govern multitrophic diversity effects in tritrophic food webs JF - Ecology : a publication of the Ecological Society of America N2 - It is well known that functional diversity strongly affects ecosystem functioning. However, even in rather simple model communities consisting of only two or, at best, three trophic levels, the relationship between multitrophic functional diversity and ecosystem functioning appears difficult to generalize, because of its high contextuality. In this study, we considered several differently structured tritrophic food webs, in which the amount of functional diversity was varied independently on each trophic level. To achieve generalizable results, largely independent of parametrization, we examined the outcomes of 128,000 parameter combinations sampled from ecologically plausible intervals, with each tested for 200 randomly sampled initial conditions. Analysis of our data was done by training a random forest model. This method enables the identification of complex patterns in the data through partial dependence graphs, and the comparison of the relative influence of model parameters, including the degree of diversity, on food-web properties. We found that bottom-up and top-down effects cascade simultaneously throughout the food web, intimately linking the effects of functional diversity of any trophic level to the amount of diversity of other trophic levels, which may explain the difficulty in unifying results from previous studies. Strikingly, only with high diversity throughout the whole food web, different interactions synergize to ensure efficient exploitation of the available nutrients and efficient biomass transfer to higher trophic levels, ultimately leading to a high biomass and production on the top level. The temporal variation of biomass showed a more complex pattern with increasing multitrophic diversity: while the system initially became less variable, eventually the temporal variation rose again because of the increasingly complex dynamical patterns. Importantly, top predator diversity and food-web parameters affecting the top trophic level were of highest importance to determine the biomass and temporal variability of any trophic level. Overall, our study reveals that the mechanisms by which diversity influences ecosystem functioning are affected by every part of the food web, hampering the extrapolation of insights from simple monotrophic or bitrophic systems to complex natural food webs. KW - food-web efficiency KW - functional diversity KW - machine learning KW - nutrient KW - exploitation KW - production KW - random forest KW - temporal variability KW - top KW - predator KW - trait diversity Y1 - 2021 U6 - https://doi.org/10.1002/ecy.3379 SN - 0012-9658 SN - 1939-9170 VL - 102 IS - 7 PB - Wiley CY - Hoboken ER -