TY - JOUR A1 - Frommhold, Martin A1 - Heim, Arend A1 - Barabanov, Mikhail A1 - Maier, Franziska A1 - Mühle, Ralf-Udo A1 - Smirenski, Sergei M. A1 - Heim, Wieland T1 - Breeding habitat and nest-site selection by an obligatory "nest-cleptoparasite", the Amur Falcon Falco amurensis JF - Ecology and evolution N2 - The selection of a nest site is crucial for successful reproduction of birds. Animals which re-use or occupy nest sites constructed by other species often have limited choice. Little is known about the criteria of nest-stealing species to choose suitable nesting sites and habitats. Here, we analyze breeding-site selection of an obligatory "nest-cleptoparasite", the Amur Falcon Falco amurensis. We collected data on nest sites at Muraviovka Park in the Russian Far East, where the species breeds exclusively in nests of the Eurasian Magpie Pica pica. We sampled 117 Eurasian Magpie nests, 38 of which were occupied by Amur Falcons. Nest-specific variables were assessed, and a recently developed habitat classification map was used to derive landscape metrics. We found that Amur Falcons chose a wide range of nesting sites, but significantly preferred nests with a domed roof. Breeding pairs of Eurasian Hobby Falco subbuteo and Eurasian Magpie were often found to breed near the nest in about the same distance as neighboring Amur Falcon pairs. Additionally, the occurrence of the species was positively associated with bare soil cover, forest cover, and shrub patches within their home range and negatively with the distance to wetlands. Areas of wetlands and fallow land might be used for foraging since Amur Falcons mostly depend on an insect diet. Additionally, we found that rarely burned habitats were preferred. Overall, the effect of landscape variables on the choice of actual nest sites appeared to be rather small. We used different classification methods to predict the probability of occurrence, of which the Random forest method showed the highest accuracy. The areas determined as suitable habitat showed a high concordance with the actual nest locations. We conclude that Amur Falcons prefer to occupy newly built (domed) nests to ensure high nest quality, as well as nests surrounded by available feeding habitats. KW - cleptoparasitism KW - fire KW - habitat use KW - machine learning KW - magpie KW - nest-site selection KW - random forest Y1 - 2019 U6 - https://doi.org/10.1002/ece3.5878 SN - 2045-7758 VL - 9 IS - 24 SP - 14430 EP - 14441 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Lehmann, Anika A1 - Zheng, Weishuang A1 - Ryo, Masahiro A1 - Soutschek, Katharina A1 - Roy, Julien A1 - Rongstock, Rebecca A1 - Maaß, Stefanie A1 - Rillig, Matthias C. T1 - Fungal traits important for soil aggregation JF - Frontiers in microbiology N2 - Soil structure, the complex arrangement of soil into aggregates and pore spaces, is a key feature of soils and soil biota. Among them, filamentous saprobic fungi have well-documented effects on soil aggregation. However, it is unclear what properties, or traits, determine the overall positive effect of fungi on soil aggregation. To achieve progress, it would be helpful to systematically investigate a broad suite of fungal species for their trait expression and the relation of these traits to soil aggregation. Here, we apply a trait-based approach to a set of 15 traits measured under standardized conditions on 31 fungal strains including Ascomycota, Basidiomycota, and Mucoromycota, all isolated from the same soil. We find large differences among these fungi in their ability to aggregate soil, including neutral to positive effects, and we document large differences in trait expression among strains. We identify biomass density, i.e., the density with which a mycelium grows (positive effects), leucine aminopeptidase activity (negative effects) and phylogeny as important factors explaining differences in soil aggregate formation (SAF) among fungal strains; importantly, growth rate was not among the important traits. Our results point to a typical suite of traits characterizing fungi that are good soil aggregators, and our findings illustrate the power of employing a trait-based approach to unravel biological mechanisms underpinning soil aggregation. Such an approach could now be extended also to other soil biota groups. In an applied context of restoration and agriculture, such trait information can inform management, for example to prioritize practices that favor the expression of more desirable fungal traits. KW - soil aggregation KW - traits KW - saprobic fungi KW - random forest KW - biomass KW - density KW - leucine amino peptidases Y1 - 2020 U6 - https://doi.org/10.3389/fmicb.2019.02904 SN - 1664-302X VL - 10 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Hempel, Sabrina A1 - Adolphs, Julian A1 - Landwehr, Niels A1 - Janke, David A1 - Amon, Thomas T1 - How the selection of training data and modeling approach affects the estimation of ammonia emissions from a naturally ventilated dairy barn—classical statistics versus machine learning JF - Sustainability N2 - Environmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days. KW - livestock KW - air pollutant KW - emission modeling KW - emission inventory KW - regression KW - artificial neural network KW - random forest KW - gradient boosting KW - Gaussian process KW - training sample Y1 - 2020 U6 - https://doi.org/10.3390/su12031030 SN - 2071-1050 VL - 12 IS - 3 PB - MDPI CY - Basel 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 -