Coal‐mining intensity influences species and trait distributions of stream fishes in two Central Appalachian watersheds

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.

Understanding the processes shaping biological communities under multiple disturbances is a core challenge in ecology and conservation science. Traditionally, ecologists have explored linkages between the severity and type of disturbance and the taxonomic structure of communities. Recent advances in the application of species traits, to assess the functional structure of communities, have provided an alternative approach that responds rapidly and consistently across taxa and ecosystems to multiple disturbances. Importantly, trait-based metrics may provide advanced warning of disturbance to ecosystems because they do not need species loss to be reactive. Here, we synthesize empirical evidence and present a theoretical framework, based on species positions in a functional space, as a tool to reveal the complex nature of change in disturbed ecosystems. Copyright © 2012 Elsevier Ltd. All rights reserved.