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https://www.selleckchem.com/products/bay-1000394.html y are especially intriguing and may play an important role in species and individual response to moisture conditions. Given the importance of roots in the uptake of resources, and in carbon and nutrient turnover, it is vital that we establish patterns of root trait variation across environmental gradients. One of the challenges of research on root traits has been considerable intraspecific variation. Here we show that part of intraspecific root trait variation is structured by a fine-scale hydrological gradient, and that the variation aligns with among-species trends in some cases. Patterns in root tissue density are especially intriguing and may play an important role in species and individual response to moisture conditions. Given the importance of roots in the uptake of resources, and in carbon and nutrient turnover, it is vital that we establish patterns of root trait variation across environmental gradients. The recently proposed knockoff filter is a general framework for controlling the false discovery rate when performing variable selection. This powerful new approach generates a "knockoff" of each variable tested for exact false discovery rate control. Imitation variables that mimic the correlation structure found within the original variables serve as negative controls for statistical inference. Current applications of knockoff methods use linear regression models and conduct variable selection only for variables existing in model functions. Here, we extend the use of knockoffs for machine learning with boosted trees, which are successful and widely used in problems where no prior knowledge of model function is required. However, currently available importance scores in tree models are insufficient for variable selection with false discovery rate control. We propose a novel strategy for conducting variable selection without prior model topology knowledge using the knockoff method with boosted tree models

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