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      Change in the trade‐off between aboveground and belowground biomass of alpine grassland: Implications for the land degradation process

      1 , 2 , 3 , 1 , 1 , 4 , 5 , 1 , 2 , 3
      Land Degradation & Development
      Wiley

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          A working guide to boosted regression trees.

          1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
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            Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control.

            We quantified the biomass allocation patterns to leaves, stems and roots in vegetative plants, and how this is influenced by the growth environment, plant size, evolutionary history and competition. Dose-response curves of allocation were constructed by means of a meta-analysis from a wide array of experimental data. They show that the fraction of whole-plant mass represented by leaves (LMF) increases most strongly with nutrients and decreases most strongly with light. Correction for size-induced allocation patterns diminishes the LMF-response to light, but makes the effect of temperature on LMF more apparent. There is a clear phylogenetic effect on allocation, as eudicots invest relatively more than monocots in leaves, as do gymnosperms compared with woody angiosperms. Plants grown at high densities show a clear increase in the stem fraction. However, in most comparisons across species groups or environmental factors, the variation in LMF is smaller than the variation in one of the other components of the growth analysis equation: the leaf area : leaf mass ratio (SLA). In competitive situations, the stem mass fraction increases to a smaller extent than the specific stem length (stem length : stem mass). Thus, we conclude that plants generally are less able to adjust allocation than to alter organ morphology. © 2011 The Authors. New Phytologist © 2011 New Phytologist Trust.
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              A CRITICAL EXAMINATION OF A RAPID METHOD FOR DETERMINING ORGANIC CARBON IN SOILS—EFFECT OF VARIATIONS IN DIGESTION CONDITIONS AND OF INORGANIC SOIL CONSTITUENTS

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                Author and article information

                Contributors
                Journal
                Land Degradation & Development
                Land Degrad Dev
                Wiley
                1085-3278
                1099-145X
                October 31 2019
                January 15 2020
                October 28 2019
                January 15 2020
                : 31
                : 1
                : 105-117
                Affiliations
                [1 ]Key Laboratory of Desert and Desertification, Northwest Institute of Eco‐Environment and ResourceChinese Academy of Sciences Lanzhou 730000 PR China
                [2 ]International Platform for Dryland Research and EducationTottori University Tottori 680‐0001 Japan
                [3 ]Arid Land Research CenterTottori University Tottori 680‐0001 Japan
                [4 ]Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences Beijing 100101 PR China
                [5 ]Institute of Mountain Hazards and EnvironmentChinese Academy of Sciences Chengdu 610041 PR China
                Article
                10.1002/ldr.3432
                5f45f953-9630-445f-b0b0-767f97e85337
                © 2020

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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