Collinearity refers to the non independence of predictor variables, usually in a regression‐type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold‐based pre‐selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor‐response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine‐learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold‐based pre‐selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold‐based pre‐selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the ‘folk lore’‐thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre‐analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.

Summary1. Phylogenetic signal is the tendency of related species to resemble each other more than species drawn at random from the same tree. This pattern is of considerable interest in a range of ecological and evolutionary research areas, and various indices have been proposed for quantifying it. Unfortunately, these indices often lead to contrasting results, and guidelines for choosing the most appropriate index are lacking. 2. Here, we compare the performance of four commonly used indices using simulated data. Data were generated with numerical simulations of trait evolution along phylogenetic trees under a variety of evolutionary models. We investigated the sensitivity of the approaches to the size of phylogenies, the resolution of tree structure and the availability of branch length information, examining both the response of the selected indices and the power of the associated statistical tests. 3. We found that under a Brownian motion (BM) model of trait evolution, Abouheif's C mean and Pagel's k performed well and substantially better than Moran's I and Blomberg's K. Pagel's k provided a reliable effect size measure and performed better for discriminating between more complex models of trait evolution, but was computationally more demanding than Abouheif's C mean . Blomberg's K was most suitable to capture the effects of changing evolutionary rates in simulation experiments. 4. Interestingly, sample size influenced not only the uncertainty but also the expected values of most indices, while polytomies and missing branch length information had only negligible impacts. 5. We propose guidelines for choosing among indices, depending on (a) their sensitivity to true underlying patterns of phylogenetic signal, (b) whether a test or a quantitative measure is required and (c) their sensitivities to different topologies of phylogenies. 6. These guidelines aim to better assess phylogenetic signal and distinguish it from random trait distributions. They were developed under the assumption of BM, and additional simulations with more complex trait evolution models show that they are to a certain degree generalizable. They are particularly useful in comparative analyses, when requiring a proxy for niche similarity, and in conservation studies that explore phylogenetic loss associated with extinction risks of specific clades.

Ecophylogenetics can be viewed as an emerging fusion of ecology, biogeography and macroevolution. This new and fastgrowing field is promoting the incorporation of evolution and historical contingencies into the ecological research agenda through the widespread use of phylogenetic data. Including phylogeny into ecological thinking represents an opportunity for biologists from different fields to collaborate and has provided promising avenues of research in both theoretical and empirical ecology, towards a better understanding of the assembly of communities, the functioning of ecosystems and their responses to environmental changes. The time is ripe to assess critically the extent to which the integration of phylogeny into these different fields of ecology has delivered on its promise. Here we review how phylogenetic information has been used to identify better the key components of species interactions with their biotic and abiotic environments, to determine the relationships between diversity and ecosystem functioning and ultimately to establish good management practices to protect overall biodiversity in the face of global change. We evaluate the relevance of information provided by phylogenies to ecologists, highlighting current potential weaknesses and needs for future developments. We suggest * Address for correspondence (E-mail: nmouquet@univ-montp2.fr). † These authors contributed equally to this work. that despite the strong progress that has been made, a consistent unified framework is still missing to link local ecological dynamics to macroevolution. This is a necessary step in order to interpret observed phylogenetic patterns in a wider ecological context. Beyond the fundamental question of how evolutionary history contributes to shape communities, ecophylogenetics will help ecology to become a better integrative and predictive science.

Aim  The study of biological invasions has long considered species invasiveness and community invasibility as separate questions. Only recently, there is an increasing recognition that integrating these two questions offers new insights into the mechanisms of biological invasions. This recognition has renewed the interest in two long‐standing and seemingly contradictory hypotheses proposed by Darwin: phylogenetic relatedness of invaders to native communities is predicted to promote naturalization because of appropriate niche‐adaptation but is at the same time predicted to hamper naturalization because of niche overlap with native species. The latter is known as Darwin’s naturalization hypothesis. Location  Global. Methods and Results  We review the studies that have tested these hypotheses and summarize their largely inconsistent outcomes. We argue that most of the inconsistency arises from discrepancies in the applied conceptual frameworks and analytical approaches and not from different model organisms and different ecological contexts. First, observed patterns and results can be seriously flawed by different spatial and phylogenetic scales, which do not equally reveal community assembly mechanisms. Second, different studies have used different metrics, which may test for different specific hypotheses. Thus, we propose a set of metrics derived from the alpha niche concept to measure invaders relatedness to native communities. Finally, approximating species niche differentiation from phylogenetic relatedness is not exempt of assumptions, and invasive species naturalization may result from various ecological mechanisms of biotic resistance that are not necessarily revealed by species phylogeny alone. Main conclusions  The quest for resolving the conundrum of Darwin’s naturalization hypothesis will only be successful if appropriate scales, metrics and analytical tests are thoroughly considered. We give several recommendations and suggest, whenever possible, to use trait‐based measurements of species dissimilarity as the most promising avenue to unravel the mechanisms driving alien species invasions.