Partitioning the impact of environmental drivers and species interactions in dynamic aquatic communities

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Musters, C. J. M., Ieromina, O., Barmentlo, S. H., Hunting, E. R., Schrama, M., Cieraad, E., Vijver, M. G., & van Bodegom, P. M. Partitioning the impact of environmental drivers and species interactions in dynamic aquatic communities. Ecosphere, 10(11), (2019): e02910, doi:10.1002/ecs2.2910.Temperate aquatic communities are highly diverse and seasonally variable, due to internal biotic processes and environmental drivers, including human‐induced stressors. The impact of drivers on species abundance is supposed to differ fundamentally depending on whether populations are experiencing limitations, which may shift over the season. However, an integrated understanding of how drivers structure communities seasonally is currently lacking. In order to partition the effect of drivers, we used random forests to quantify interactions between all taxa and environmental factors using macrofaunal data from 18 agricultural ditches sampled over two years. We found that, over the agricultural season, taxon abundance became increasingly better predicted by the abundances of co‐occurring taxa and nutrients compared to other abiotic factors, including pesticides. Our approach provides fundamental insights in community dynamics and highlights the need to consider changes in species interactions to understand the effects of anthropogenic stressors.The authors are grateful to B. Schaub of Water Board Rijnland for his help, E. Gertenaar for assistance in the fieldwork, M. Wouterse for DOC measurements, and B. Koese for help with taxonomic identification of macrofaunal samples. CM designed the study, did the statistical modeling and analyses, and wrote the draft paper; OI did field sampling and taxonomic identification and constructed the datasets; OI and HB structured the data; EH, MS, ES, MV, and PvB contributed to the study design and the conceptual improvement of the manuscript; all authors substantially revised the subsequent drafts

Narrowing the Gap: Random Forests In Theory and In Practice

Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoretically tractable variant of random regression forests and prove that our algorithm is consistent. We also provide an empirical evaluation, comparing our algorithm and other theoretically tractable random forest models to the random forest algorithm used in practice. Our experiments provide insight into the relative importance of different simplifications that theoreticians have made to obtain tractable models for analysis.Comment: Under review by the International Conference on Machine Learning (ICML) 201

NightShift: NMR shift inference by general hybrid model training - a framework for NMR chemical shift prediction

BACKGROUND: NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model. A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore. RESULTS: In this work, we present the framework NightShift (NMR Shift Inference by General Hybrid Model Training), which enables automated data set generation as well as model training and evaluation of protein NMR chemical shift prediction. In addition to this main result – the NightShift framework itself – we describe the resulting, automatically generated, data set and, as a proof-of-concept, a random forest model called Spinster that was built using the pipeline. CONCLUSION: By demonstrating that the performance of the automatically generated predictors is at least en par with the state of the art, we conclude that automated data set and predictor generation is well-suited for the design of NMR chemical shift estimators. The framework can be downloaded from https://bitbucket.org/akdehof/nightshift. It requires the open source Biochemical Algorithms Library (BALL), and is available under the conditions of the GNU Lesser General Public License (LGPL). We additionally offer a browser-based user interface to our NightShift instance employing the Galaxy framework via https://ballaxy.bioinf.uni-sb.de/