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Made available in DSpace on 2022-04-28T19:52:06Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-03-18 Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale. Department of Biology University of Toronto Mississauga ON Centre for Urban Environments University of Toronto Mississauga ON Department of Biology University of North Carolina, Chapel Hill Department of Biology University of Louisiana LA Department of Biology Queen's University ON Department of Biology Concordia University QC Department of Biological Sciences DePaul University Department of Biology DePauw University IN Department of Urban Design and Planning, University of Washington, Seattle, WA, USA Colegio de Ciencias Biológicas y Ambientales Universidad San Francisco de Quito USFQ Department of Genetics University of Georgia Department of Ecology and Genetics Evolutionary Biology Centre Uppsala University Field Science Center for Northern Biosphere Hokkaido University Natural History Museum Zoology University of New England NSW Programa de Pós-Graduação em Geografia da UFMT campus de Rondonópolis Department of Botany and Biodiversity Research Centre University of British Columbia Graduate Program in Genome Sciences and Technology Genome Sciences Centre University of British Columbia Department of Microbiology and Immunology University of British Columbia Red de Biología Evolutiva Instituto de Ecología A. C. School of the Environment Yale University CT Departamento de Ciencias Ecológicas Universidad de Chile, Facultad de Ciencias Instituto de Ecología y Biodiversidad Universidad de Chile Department of Biology Mount Allison University Red de Ecoetología Instituto de Ecología A. C. Department of Biology University of Ottawa ON Department of Zoology University of Cambridge Department of Biology, Washington University in St. Louis, St. Louis, MO, USA Department of Biology University of Miami FL Centro de Investigación en Recursos Naturales y Sustentabilidad (CIRENYS) Universidad Bernardo O'Higgins Department of Biology, University of La Verne, La Verne, CA, USA Département des sciences du bois et de la forêt Université Laval QC Evolution & Ecology Research Centre School of Biological Earth and Environmental Sciences UNSW Sydney NSW Department of Biology Ghent University Department of Biology Monmouth University West Long Branch Centre for Ecology Evolution and Environmental Changes Faculdade de Ciências Universidade de Lisboa Lisboa Department of Biology KU Leuven School of Agriculture and Environment Wildlife and Ecology group Massey University, Palmerston North Department of Biological Sciences University of Cape Town Institute of Landscape Ecology University of Münster Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, USA Department of Biological Sciences University of Alberta AB Louis Calder Center and Department of Biological Sciences, Fordham University, Armonk, NY, USA Departamento de Ecología Tropical, Universidad Autónoma de Yucatán, Mérida, Yucatán, México School of Life Sciences University of Sussex Department of Ecology Environment and Plant Sciences Stockholm University iBIOTROP Instituto de Biodiversidad Tropical Universidad San Francisco de Quito Department of Biology, San Francisco State University, San Francisco, CA, USA Unidad de Recursos Naturales, Centro de Investigación Científica de Yucatán AC, Mérida, Yucatán, México School of Ecological and Environmental Sciences East China Normal University Shanghai Engineering Research Center of Sustainable Plant Innovation Centre for Ecosystem Science School of Biological Earth and Environmental Sciences UNSW Sydney NSW Department of Ecology and Evolutionary Biology University of Michigan MI Department of Biosciences Rice University TX IFEVA Universidad de Buenos Aires Facultad de Agronomía, CONICET Biology Department Saint Mary's University NS Department of Biological Sciences, Universidad de los Andes Department of Biology and Biochemistry University of Houston TX Université de Rennes Department of Zoology and Biodiversity Research Centre University of British Columbia Department of Environmental Studies Dordt University Sioux Center IA Department of Biology Minneapolis Community and Technical College MN Department of Natural Sciences Ecology and Environment Research Centre Manchester Metropolitan University Instituto de Investigaciones en Ecosistemas y Sustentabilidad UNAM Department of Botany School of Biology Aristotle University of Thessaloniki Faculty of Biological and Environmental Science Organismal & Evolutionary Biology Research Programme University of Helsinki Institute of Biodiversity Animal Health and Comparative Medicine University of Glasgow Department of Biology Hendrix College Department of Ecological Science Vrije Universiteit Amsterdam Departamento de Ciencias Biológicas y Agropecuarias Universidad Técnica Particular de Loja Departamento de Biologia Universidade Federal de Santa Maria (UFSM) Department of Plant Sciences School of Biology College of Science University of Tehran NTNU University Museum Norwegian University of Science and Technology Red de Estudios Moleculares Avanzados Instituto de Ecología A. C. School of Biological Sciences, University of Reading, Whiteknights Park, Reading, Berkshire, UK Department of Biology Northern Arizona University AZ Department of Biological Sciences MacEwan University AB Max Planck Institute for Plant Breeding Research Departamento de Ecología Evolutiva Instituto de Ecología Universidad Nacional Autónoma de México Max Planck Institute of Molecular Plant Physiology Potsdam-Golm BIO5 Institute University of Arizona AZ Alaska Center for Conservation Science University of Alaska Anchorage Tropical Diversity, Royal Botanical Garden of Edinburgh Département de biologie Université de Moncton Department of Biological Sciences University of Manitoba MB Departments of Microbiology & Statistics University of Manitoba MB Department of Biology University of New Brunswick Department of Biology Kalamazoo College MI BioProtection Research Centre Lincoln University Departamento de Ciencias Facultad de Artes Liberales Universidad Adolfo Ibáñez Department of Ecology Evolution Behaviour University of Minnesota MN Department of Biological Sciences Brock University Department of Environmental Toxicology, University of California, Davis, CA, USA ICB - University of Talca School of Molecular and Life Science Curtin University College of Science Health Engineering and Education Murdoch University, Murdoch School of Life and Environmental Sciences University of Sydney NSW School of Biological Sciences, Monash University, Melbourne, VIC, Australia Department of Biological Sciences Wayne State University MI Department of Biology Western Oregon University, OR School of Natural Resources and the Environment University of Arizona AZ Departamento de Ecología Humana, Cinvestav Mérida Departamento de Ciencias Biológicas y Departamento de Ecología y Biodiversidad Facultad de Ciencias de la Vida, Universidad Andrés Bello Institute of Ecology and Biodiversity (IEB) Department of Biology Lund University Department of Biology Norwegian University of Science and Technology Escuela Superiro de Desarrollo Sustentable Universidad Autónoma de Guerrero -CONACYT Clarkson Secondary School Peel District School Board ON Homelands Sr. Public School Peel District School Board ON Department of Biological Sciences University of Illinois at Chicago Dufferin-Peel Catholic District School Board, St. James Catholic Global Learning Centre Department of Biosciences University of Calgary AB Ecological Processes Branch U.S. Army ERDC-CERL Department of Biology, Oberlin College, Oberlin, OH, USA Escuela Nacional de Estudios Superiores Unidad Morelia UNAM Institute of Evolution and Ecology University of Tübingen Tübingen Department of Evolutionary Biology and Environmental Studies University of Zurich, Winterthurerstrasse Urban Wildlife Institute Department of Conservation and Science, Lincoln Park Zoo Departamento de Ecología Universidad Católica de la Santísima Concepción Department of Biological Sciences University of Denver CO Department of Biological Sciences Mississippi State University MS Department of Biology Center for Computational & Integrative Biology Rutgers University-Camden Kunming Institute of Botany Chinese Academy of Sciences Department of Chemistry & Biochemistry Laurentian University ON Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering College of Life Sciences Beijing Normal University School of BioSciences, University of Melbourne, Melbourne, VIC, Australia Posgrado en Ciencias Biológicas Universidad Nacional Autónoma de México Department of Biological Sciences, Auburn University, Auburn, AL, USA Department of Entomology and Nematology, University of California, Davis, CA, USA Department of Biology University of New Mexico NM Department of Biology University of Wisconsin - Eau Claire Agriculture Institute Iranian Research Organization for Science and Technology (IROST) Department of Biology Colby College ME Instituto de Biología Universidad de Antioquia Department of Biology University of Massachusetts Boston MA Agricultural Biology Colorado State University CO Departamento de Biología Vegetal y Ecología Facultad de Biología Universidad de Sevilla, Av. Reina Mercedes s/n Facultad de Estudios Interdisciplinarios Centro GEMA- Genómica Universidad Mayor Evolutionary Ecology Group Naturalis Biodiversity Center Department of Biology and Chemistry Nipissing University ON, North Bay Center for Ecological Research Kyoto University Bonanza Creek Long Term Ecological Research Program University of Alaska Fairbanks Department of Botany and Molecular Evolution Senckenberg Research Institute and Natural History Museum Frankfurt Departamento de Biodiversidade Instituto de Biociências Univ Estadual Paulista - UNESP Nelson Institute for Environmental Studies University of Wisconsin-Madison WI Department of Biology, California State University, Northridge, Los Angeles, CA, USA Department of Ecology Swedish University of Agricultural Sciences Facultad de Ciencias y Biotecnologia Universidad CES Department of Biology Hofstra University Faculty of Biosciences and Aquaculture Nord University, Bodø Division of Biological Sciences, University of California San Diego, San Diego, CA, USA Department of Biology University of Richmond VA Estación de Biodiversidad Tiputini Colegio de Ciencias Biológicas y Ambientales Universidad San Francisco de Quito USFQ Department of Biological Sciences Institute of Environment Florida International University FL Agronomy Department University of Almería Department of Biological Sciences and Center for Urban Ecology and Sustainability Butler University IN Department of Biological Sciences Louisiana State University LA Faculty of Biological Sciences, Goethe University Frankfurt Institute of Biology Leiden Leiden University Department of Biological and Environmental Science University of Jyväskylä Jyväskylä Department of Biology University of Louisville KY Organization for Programs on Environmental Science University of Tokyo CNRS AgroParisTech Ecologie Systématique et Evolution, Université Paris-Saclay Department of Biology, Providence College, Providence, RI, USA General Zoology Institute for Biology Martin Luther University Halle-Wittenberg International Arctic Research Center University of Alaska Fairbanks Science, Technology and Society Department, Rochester Institute of Technology, Rochester, NY, USA SLU Swedish Species Information Centre Swedish University of Agricultural Sciences Department of Biology Westfield State University MA Centre of New Technologies University of Warsaw Department of Biology, Stanford University, Stanford, CA, USA Plant Biology Department Michigan State University MI Biology Department Davidson College College of Horticulture and Forestry Sciences/ Hubei Engineering Technology Research Center for Forestry Information Huazhong Agricultural University School of Life Sciences Technical University of Munich School of Life Sciences Lanzhou University Institute of Ecology and Evolution University of Bern Department of Evolution Ecology and Behaviour University of Liverpool Departamento de Biodiversidade Instituto de Biociências Univ Estadual Paulista - UNESP

Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.

The metacommunity concept provides a theoretical framework that aims at explaining organism distributions by a combination of environmental filtering, dispersal, and drift. However, few works have attempted a multitaxon approach and even fewer have compared two distant biogeographical regions using the same methodology. We tested the expectation that temperate (mediterranean-climate) pond metacommunities would be more influenced by environmental and spatial processes than tropical ones, because of stronger environmental gradients and a greater isolation of waterbodies. However, the pattern should be different among groups of organisms depending on their dispersal abilities. We surveyed 30 tropical and 32 mediterranean temporary ponds from Costa Rica and Spain, respectively, and obtained data on 49 environmental variables. We characterized the biological communities of bacteria and archaea (from the water column and the sediments), phytoplankton, zooplankton, benthic invertebrates, amphibians and birds, and estimated the relative role of space and environment on metacommunity organization for each group and region, by means of variation partitioning using generalized additive models. Purely environmental effects were important in both tropical and mediterranean ponds, but stronger in the latter, probably due to their larger limnological heterogeneity. Spatially correlated environment and pure spatial effects were greater in the tropics, related to higher climatic heterogeneity and dispersal processes (e.g., restriction, surplus) acting at different scales. The variability between taxonomic groups in the contribution of spatial and environmental factors to metacommunity variation was very wide, but higher in active, compared with passive, dispersers. Higher environmental effects were observed in mediterranean passive dispersers, and higher spatial effects in tropical passive dispersers. The unexplained variation was larger in the tropical setting, suggesting a higher role for stochastic processes, unmeasured environmental factors, or biotic interactions in the tropics, although this difference affected some actively dispersing groups (insects and birds) more than passive dispersers. These results, despite our limitations in comparing only two regions, provide support, for a wide variety of aquatic organisms, for the classic view of stronger abiotic niche constraints in temperate areas compared with the tropics. The heterogeneous response of taxonomic groups between regions also points to a stronger influence of regional context than organism adaptations on metacommunity organization.

Considerable efforts have been made over the last decades to improve the robustness of clustering algorithms against noise features and outliers, known to be important sources of error in clustering. Outliers dominate the sum-of-the-squares calculations and generate cluster overlap, thus leading to unreliable clustering results. They can be particularly detrimental in computational biology, e.g., when determining the number of clusters in gene expression data related to cancer or when inferring phylogenetic trees and networks. While the issue of feature weighting has been studied in detail, no clustering methods using object weighting have been proposed yet. Here we describe a new general data partitioning method that includes an object-weighting step to assign higher weights to outliers and objects that cause cluster overlap. Different object weighting schemes, based on the Silhouette cluster validity index, the median and two intercluster distances, are defined. We compare our novel technique to a number of popular and efficient clustering algorithms, such as K-means, X-means, DAPC and Prediction Strength. In the presence of outliers and cluster overlap, our method largely outperforms X-means, DAPC and Prediction Strength as well as the K-means algorithm based on feature weighting.

Environmental and geographical factors are known to influence the number, distribution, and combination of species that coexist within ecological communities. This, in turn, should influence ecosystem functions such as biomass conservation, or the ability of a community to sustain biomass from small to large organisms. We tested this hypothesis by assessing the role of environmental factors in determining how biomass is conserved in over 600 limnetic fish communities spread across a broad geographic gradient in Canada. Comprehensive and accurate information on water conditions and community characteristics such as taxonomy, abundance, biomass, and size distributions were used in our assessment. Results showed that species combinations emerge as one of the main predictors of biomass conservation among the effects of individual species and abiotic factors. Our study highlights the strong role that geographic patterns in the distribution of species can play in shaping key ecosystem functions, with consequences for ecosystem services such as the provision of harvestable fish biomass.

Current analyses of metacommunity data largely focus on global attributes across the entire metacommunity, such as mean alpha, beta, and gamma diversity, as well as the partitioning of compositional variation into single estimates of contributions of space and environmental effects and, more recently, possible contributions of species interactions. However, this view neglects the fact that different species and sites in the landscape can vary widely in how they contribute to these metacommunity-wide attributes. We argue for a new conceptual framework with matched analytics with the goals of studying the complex and interactive relations between process and pattern in metacommunities that is focused on the variation among species and among sites which we call the ‘internal structure’ of the metacommunity. To demonstrate how the internal structure could be studied, we create synthetic data using a process-based colonization-extinction metacommunity model. We then use Joint Species Distribution Models to estimate how the contributions of space, environment and biotic interactions driving metacommunity assembly differ among species and sites. We find that this approach to the internal structure of metacommunities provides useful information about the distinct ways that different species and different sites contribute to metacommunity structure. Although it has limitations, our work points at a more general approach to understand how other possible complexities might affect internal structure and might thus be incorporated into a more cohesive metacommunity theory.

The metacommunity concept provides a theoretical framework that aims at explaining organism distributions by a combination of environmental filtering, dispersal and drift. With the development of statistical tools to quantify and partially isolate the role of each of these processes, empirical metacommunity studies have multiplied worldwide. However, few works attempt a multi-taxon approach and even fewer compare two distant biogeographical regions using the same methodology. Under this framework, we tested the expectation that temperate (mediterranean-climate) pond metacommunities would be more influenced by environmental and spatial processes than tropical ones, because of stronger environmental gradients and greater isolation of waterbodies.We surveyed 30 tropical and 32 mediterranean temporary ponds from Costa Rica and Spain, respectively, and obtained data on 49 environmental variables (including limnological, hydrogeomorphological, biotic, climatic, and landscape variables). We characterized the biological communities of Bacteria and Archaea (from both the water column and the sediments), phytoplankton, zooplankton, benthic invertebrates, amphibians and birds, and estimated the relative role of space and environment on metacommunity organization for each group and region, by means of variation partitioning using Generalized Additive Models (GAMs).Environmental selection was important in both tropical and mediterranean ponds, but markedly stronger in the latter, probably due to their larger limnological heterogeneity. Spatialized environment and pure spatial effects were greater in the tropics, related to higher climatic heterogeneity and dispersal processes (e.g. restriction, surplus) acting at different scales. The variability between taxonomic groups in spatial and environmental contributions was very wide. Effects on passive and active dispersers were similar within regions but different across regions, with higher environmental effects in mediterranean active dispersers. The residual (unexplained) variation was larger in tropical pond metacommunities, suggesting a higher role for stochastic processes and/or effects of biotic interactions in the tropics. Overall, these results provide support, for a wide variety of organisms related to aquatic habitats, for the classical view of stronger abiotic niche constraints in temperate areas compared to the tropics.

SummaryCanonical analysis, a generalization of multiple regression to multiple response variables, is widely used in ecology. Because these models often involve large amounts of parameters (one slope per response per predictor), they pose challenges to model interpretation. Currently, multi-response canonical analysis is constrained by two major challenges. Firstly, we lack quantitative frameworks for estimating the overall importance of single predictors. Secondly, although the commonly used variation partitioning framework to estimate the importance of groups of multiple predictors can be used to estimate the importance of single predictors, it is currently computationally constrained to a maximum of four predictor matrices.We established that commonality analysis and hierarchical partitioning, widely used for both estimating predictor importance and improving the interpretation of single-response regression models, are related and complementary frameworks that can be expanded for the analysis of multiple-response models.In this application, we aim at: a) demonstrating the mathematical links between commonality analysis, variation and hierarchical partitioning; b) generalizing these frameworks to allow the analysis of any number of responses, predictor variables or groups of predictor variables in the case of variation partitioning; and c) introducing and demonstrating the usage of the R package rdacca.hp that implements these generalized frameworks.

Emigration propensity (i.e., the tendency to leave undisturbed patches) is a key life-history trait of organisms in metapopulations with local extinctions and colonizations. Metapopulation models of dispersal evolution typically assume that patch disturbance kills all individuals within the patch, thus causing local extinction. However, individuals may instead be able to leave a patch when it is disturbed, either by fleeing before being killed or simply because the disturbance destroys the patch without causing mortality. This scenario may pertain to a wide range of organisms from horizontally transmitted symbionts, to aquatic insects inhabiting temporary ponds, to vertebrates living in fragmented forests. We generalized a Levins-type metapopulation model of dispersal evolution by adding a new parameter of disturbance escape probability, which incorporates a second source of dispersal into the model: disturbance-induced emigration. We show that disturbance escape expands the domain of metapopulation viability and selects for lower rates of emigration propensity when disturbance rates are high. The fitness gains from disturbance-induced emigration are generally moderate, suggesting that disturbance escape might act more as a complementary dispersal strategy rather than a replacement to emigration propensity, at least for metapopulations that meet the assumptions of the Levins-type model. Yet disturbance-induced emigration may in some circumstances rescue a metapopulation from long-term extinction when the combination of high disturbance rates and low local population growth rates compromises its viability. Further, a metapopulation could persist exclusively by disturbance escape if local carrying capacities are large enough to counterbalance two sources of mortality: mortality driven by disturbance and mortality during dispersal. This study opens two promising research lines: (1) the investigation of disturbance escape in metapopulations of ephemeral habitats with unsaturated populations and non-equilibrium dynamics and (2) the incorporation of information costs to investigate the joint evolution of disturbance escape and emigration propensity.

International audience; The processes involved in shaping latitudinal-diversity gradients (LDGs) have been a longstanding source of debate and research. Climatic, historical and evolutionary factors have all been shown to contribute to the formation of LDGs. However, meta-analyses have shown that different clades have LDG slopes that may vary in more than one order of magnitude. Such large variation cannot be explained solely by climatic or historical factors (e.g. difference in surface area between temperate and tropical zones) given that all clades within a geographic region are subject to the same conditions. Therefore, biotic processes intrinsic to each taxonomic group could be relevant in explaining rate differences in diversity decline across latitudinal gradients among groups. In this study, we developed a model simulating multiple competing species subjected (or not) to a demographic Allee effect. We simulated the range expansion of these species across an environmental gradient to show how these two overlooked factors (competition and Allee effects) are capable of modulating LDGs. Allee effects resulted in a steeper LDG given a higher probability of local extinction and lower colonization capacity compared to species without Allee effects. Likewise, stronger competition also led to a steeper decline in species diversity compared to scenarios with weaker species antagonistic interactions. This pattern occurred mostly due to the strength of priority effects, wherein scenarios with strong competition, species that dispersed earlier in the landscape were able to secure many patches whereas late-arriving species were progressively precluded from expanding their ranges. Overall, our results suggest that the effect of biotic processes in shaping macroecological patterns could be more important than it is currently appreciated.

Data are one of the primary outputs of science. Although certain subdisciplines of biology have pioneered efforts to ensure their long-term preservation and facilitate collaborations, data continue to disappear, owing mostly to technological, regulatory, and ideological hurdles. In this article, we describe the important steps toward proper data management and archiving and provide a critical discussion on the importance of long-term data conservation. We then illustrate the rise in data archiving through the Joint Data Archiving Policy and the Dryad Digital Repository. In particular, we discuss data integration and how the limited availability of large-scale data sets can hinder new discoveries. Finally, we propose solutions to increase the rate of data preservation, for example by generating mechanisms insuring proper data management and archiving, by providing training in data management, and by transforming the traditional role of research institutions and libraries as data generators toward managers and archivers.

Understanding the processes that underlie species fluctuations is crucial to the development of efficient management strategies for outbreaks of destructive forest pests. Yet, the role of biotic and abiotic factors as well as their interactions in synchronizing outbreaks is not understood, despite many empirical and theoretical studies of species fluctuations. Here, we use a combined statistical–simulation model to investigate how defoliation spread and autocorrelated weather affect outbreaks of a major defoliator of North American boreal forest, the spruce budworm (Choristoneura fumiferana (Clemens)). We modelled the regional dynamics of spruce budworm and based the model on data collected from spatiotemporal aerial surveys of defoliation from 1968–2015 in Quebec, Canada. The effects of weather on local forest stand defoliation and mortality transitions, along with defoliation spread probability and distance, were estimated statistically. Simulations were run with these estimates to identify the effects of spatiotemporal weather autocorrelation on synchronicity of outbreaks. Defoliation spread together with weather variables was found to best fit the observed outbreak size. Simulation models suggest that positive temporal autocorrelation in weather promotes outbreaks, indicating that a series of suitable years could encourage outbreaks.

Phylogenetic imputation has recently emerged as a potentially powerful tool for predicting missing data in functional traits datasets. As such, understanding the limitations of phylogenetic modelling in predicting trait values is critical if we are to use them in subsequent analyses. Previous studies have focused on the relationship between phylogenetic signal and clade‐level prediction accuracy, yet variability in prediction accuracy among individual tips of phylogenies remains largely unexplored. Here, we used simulations of trait evolution along the branches of phylogenetic trees to show how the accuracy of phylogenetic imputations is influenced by the combined effects of 1) the amount of phylogenetic signal in the traits and 2) the branch length of the tips to be imputed. Specifically, we conducted cross‐validation trials to estimate the variability in prediction accuracy among individual tips on the phylogenies (hereafter ‘tip‐level accuracy’). We found that under a Brownian motion model of evolution (BM, Pagel't λ = 1), tip‐level accuracy rapidly decreased with increasing tip branch‐lengths, and only tips of approximately 10% or less of the total height of the trees showed consistently accurate predictions (i.e. cross‐validation R‐squared >0.75). When phylogenetic signal was weak, the effect of tip branch‐length was reduced, becoming negligible for traits simulated with λ

This work was assisted through participation in “Next Generation Genetic Monitoring” Investigative Workshop at the National Institute for Mathematical and Biological Synthesis, sponsored by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Hawaiian fish community data were provided by the NOAA Pacific Islands Fisheries Science Center's Coral Reef Ecosystem Division (CRED) with funding from NOAA Coral Reef Conservation Program. O.E.G. was supported by the Marine Alliance for Science and Technology for Scotland (MASTS). A. C. and C. H. C. were supported by the Ministry of Science and Technology, Taiwan. P.P.-N. was supported by a Canada Research Chair in Spatial Modelling and Biodiversity. K.A.S. was supported by National Science Foundation (BioOCE Award Number 1260169) and the National Center for Ecological Analysis and Synthesis. All data used in this manuscript are available in DRYAD (https://doi.org/dx.doi.org/10.5061/dryad.qm288) and BCO-DMO (http://www.bco-dmo.org/project/552879). Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organisation (genes, population, species, ecological communities and ecosystems), a diversity index that behaves consistently across these different levels has so far been lacking, hindering the development of truly integrative biodiversity studies. To fill this important knowledge gap we present a unifying framework for the measurement of biodiversity across hierarchical levels of organisation. Our weighted, information-based decomposition framework is based on a Hill number of order q = 1, which weights all elements in proportion to their frequency and leads to diversity measures based on Shannon’s entropy. We investigated the numerical behaviour of our approach with simulations and showed that it can accurately describe complex spatial hierarchical structures. To demonstrate the intuitive and straightforward interpretation of our diversity measures in terms of effective number of components (alleles, species, etc.) we applied the framework to a real dataset on coral reef biodiversity. We expect our framework will have multiple applications covering the fields of conservation biology, community genetics, and eco-evolutionary dynamics. Publisher PDF

Question: The CWM approach is an easy way of analysing trait–environment association by regressing (or correlating) the mean trait per plot against an environmental variable and assessing the statistical significance of the slope or the associated correlation coefficient. However, the CWM approach does not yield valid tests, as random traits (or random indicator values) are far too often judged significantly related to the environmental variable, even when the trait and environmental variable are extrinsic to (not derived from) the community data. Existing solutions are the ZS-modified test (Zelený & Schaffers,) and the max (or sequential) test based on the fourth-corner correlation. Both tests are based on permutations which become cumbersome when many tests need to be carried out and many permutations are required, as in methods that correct for multiple testing. The main goal of this study was to compare these existing permutation-based solutions and to develop a quick and easy parametric test that can replace them. Methods: This study decomposes the fourth-corner correlation in two ways, which suggests a simple parametric approach consisting of assessing the significances of two linear regressions, one plot-level test as in the CWM approach and one species-level test, the reverse of the CWM approach, that regresses the environmental mean per species (i.e. the species niche centroid) on to the trait. The tests are combined by taking the maximum p-value. The type I error rates and power of this parametric max test are examined by simulation of one- and two-dimensional Gaussian models and log-linear models. Results: The ZS-modified test and the fourth-corner max test are conservative in different scenarios, the ZS-modified test being even more conservative than the fourth-corner. The new parametric max test is shown to control the type I error and has equal or even higher power than permutation tests based on the fourth-corner, the ZS-modified test and variants thereof. A weighted version of the new test showed inflated type I error. Conclusion: The combination of two simple regressions is a good alternative to the fourth-corner and the ZS-modified test. This combination is also applicable when multiple trait measurements are made per plot.

Drawing upon the data deposited in publicly shared archives has the potential to transform the way we conduct ecological research. For this transformation to happen, we argue that data need to be more interoperable and easier to discover. One way to achieve these goals is to adopt domain-specific data representations.

Forest insect outbreaks can have large impacts on ecosystems and understanding the underlying ecological processes is critical for their management. Current process-based modeling approaches of insect outbreaks are often based on population processes operating at small spatial scales (i.e. within individual forest stands). As such, they are difficult to parameterize and offer limited applicability when modeling and predicting outbreaks at the landscape level where management actions take place. In this paper, we propose a new process-based landscape model of forest insect outbreaks that is based on stand defoliation, the Forest-Infected-Recovering-Forest (FIRF) model. We explore both spatially-implicit (mean field equations with global dispersal) and spatially-explicit (cellular automata with limited dispersal between neighboring stands) versions of this model to assess the role of dispersal in the landscape dynamics of outbreaks. We show that density-dependent dispersal is necessary to generate cyclic outbreaks in the spatially-implicit version of the model. The spatially-explicit FIRF model with local and stochastic dispersal displays cyclic outbreaks at the landscape scale and patchy outbreaks in space, even without density-dependence. Our simple, process-based FIRF model reproduces large scale outbreaks and can provide an innovative approach to model and manage forest pests at the landscape scale.

The merger of phylogenies with ecology has given rise to the field of “community phylogenetics” predicated on the assumption that ecological differences among species can be estimated from phylogenetic relationships (the phylogenetic distance/ecological difference, PDED, hypothesis). A number of studies have failed to find strong support for this assumption, thus challenging the utility of phylogenetic approaches. This gap might highlight the fact that the PDED relationship is not useful for community assembly, but it is difficult to know because the lack of a relationship might also be due to a number of biological or methodological reasons, including inappropriate phylogenies, skewed distributions of phylogenetic distances, the lack of consideration of models of trait evolution, or the absence of sufficient niche space in experimental and observational venues. Each of these limitations, separately or combined, may confound recent experimental or observational results that examine relationships between phylogenetic distance and ecological differences. Notably, common evolutionary models can support alternative conclusions about the relationship between evolutionary distances and ecological differences than typically assumed and can change interpretations of community-based phylogenetic analyses. Here we review a number of issues that may lead to confounded effects in community phylogenetic analyses. In light of these potential pitfalls, we provide a number of guidelines for researchers to follow and stress that they need to address methodological shortcomings before concluding that ecological differences are unrelated to phylogenetic distances. This article is protected by copyright. All rights reserved.

Spatial and temporal heterogeneity within landscapes influences the distribution and phenotypic diversity of individuals both within and across populations. Phenotype–habitat correlations arise either through phenotypes within an environment altering through the process of natural selection or plasticity, or phenotypes remaining constant but individuals altering their distribution across environments. The mechanisms of non-random movement and phenotype-dependent habitat choice may account for associations within highly heterogeneous systems, such as streams, where local adaptation may be negated, plasticity too costly and movement is particularly important. Despite growing attention, however, few empirical tests have yet to be conducted. Here we provide a test of phenotype-dependent habitat choice and ask: 1) if individuals collected from a single habitat type continue to select original habitat; 2) if decisions are phenotype-dependent and functionally related to habitat requirements; and 3) if phenotypic-sorting continues despite increasing population density. To do so we both conducted experimental trials manipulating the density of four stream-fish species collected from either a single riffle or pool and developed a game-theoretical model exploring the influence of individuals’ growth rate, sampling and competitive abilities as well as interference on distribution across two habitats as a function of density. Our experimental trials show individuals selecting original versus alternative habitats differed in their morphologies, that morphologies were functionally related to habitat-type swimming demands, and that phenotypic-sorting remained significant (although decreased) as density increased. According to our model this only occurs when phenotypes have contrasting habitat preferences and only one phenotype disperses (i.e. selects alternatives) in response to density pressures. This supports our explanation that empirical habitat selection was due to a combination of collecting a fraction of mobile individuals with different habitat preferences and the exclusion of individuals via scramble competition at increased densities. Phenotype-dependent habitat choice can thereby account for observed patterns of natural stream-fish distribution. This article is protected by copyright. All rights reserved.

Aim We present a novel quantitative framework that combines information on phylogeny and the spatial distributions of related species to enhance the single-species distributional models commonly used in ecology. Innovation While species distribution models (SDMs) are becoming increasingly sophisticated, they rarely take into consideration the shared evolutionary histories of species. Species are not independent entities, and phylogenies may capture how species have configured their spatial distributions as a response to their ecological similarities and interactions in space and through evolutionary time. Our framework provides a flexible approach to include phylogenies as a surrogate for missing trait data within SDMs, and may be particularly valuable for disentangling current and historical drivers of species distributions and modelling data-poor species when their close relatives are better sampled. Main conclusions Using both simulations and empirical examples, we demonstrate how the inclusion of phylogenetic information can significantly improve the fit of species distribution models by up to 30% for certain species. We show that the potential of phylogenies to improve model fit directly relates to the phylogenetic structure of species distributions and suggest that our framework has the potential to reconcile the apparent conflict between current and historical drivers of biodiversity patterns.

Perhaps the most widely used quantitative approach in metacommunity ecology is the estimation of the importance of local environment versus spatial structuring using the variation partitioning framework. Contrary to metapopulation models, however, current empirical studies of metacommunity structure using variation partitioning assume a space-for-dispersal substitution due to the lack of analytical frameworks that incorporate patch connectivity predictors of dispersal dynamics. Here, a method is presented that allows estimating the relative importance of environment, spatial variation and patch connectivity in driving community composition variation within metacommunities. The proposed approach is illustrated by a study designed to understand the factors driving the structure of a soft-bottom marine polychaete metacommunity. Using a standard variation partitioning scheme (i.e., where only environmental and spatial predictors are used), only about 13% of the variation in metacommunity structure was explained. With the connectivity set of predictors, the total amount of explained variation increased up to 51% of the variation. These results highlight the importance of considering predictors of patch connectivity rather than just spatial predictors. Given that information on connectivity can be estimated by commonly available data on species distributions for a number of taxa, the framework presented here can be readily applied to past studies as well, facilitating a more robust evaluation of the factors contributing to metacommunity structure. This article is protected by copyright. All rights reserved.

Establishing trait-environment relationships has become routine in community ecology. Here, we demonstrate that the community weighted means correlation (CWM) and its parallel approach in linking trait variation to the environment, the species niche centroid correlation (SNC), have important shortcomings, arguing against their continuing application. Using mathematical derivations and simulations, we show that the two major issues are inconsistent parameter estimation and unacceptable significance rates when only the environment or only traits are structuring species distributions, but they themselves are not linked. We show how both CWM and SNC are related to the fourth-corner correlation and propose to replace all by the Chessel fourth-corner correlation, which is the fourth-corner correlation divided by its maximum attainable value. We propose an appropriate hypothesis testing procedure that is not only unbiased but also has much greater statistical power in detecting trait-environmental relationships. We derive an additive framework in which trait variation is partitioned among and within communities, which can be then modeled against the environment. We finish by presenting a contrast between methods and an application of our proposed framework across 85 lake-fish metacommunities.

Aim Community phylogenetic studies use information about the evolutionary relationships of species to understand the ecological processes of community assembly. A central premise of the field is that the evolution of species maps onto ecological patterns, and phylogeny reveals something more than species traits alone about the ecological mechanisms structuring communities, such as environmental filtering, competition, and facilitation. We argue, therefore, that there is a need for better understanding and modelling of the interaction of phylogeny with species traits and community composition. Innovation We outline a new approach that identifies clades that are ecophylogenetically clustered or overdispersed and assesses whether those clades have different rates of trait evolution. Ecophylogenetic theory would predict that the traits of clustered or overdispersed clades might have evolved differently, in terms of either tempo (fast or slow) or mode (e.g., under constraint or neutrally). We suggest that modelling the evolution of independent trait data in these clades represents a strong test of whether there is an association between the ecological co‐occurrence patterns of a species and its evolutionary history. Main conclusions Using an empirical dataset of mammals from around the world, we identify two clades of rodents whose species tend not to co‐occur in the same local assemblages (are phylogenetically overdispersed) and find independent evidence of slower rates of body mass evolution in these clades. Our approach, which assumes nothing about the mode of species trait evolution but instead seeks to explain it using ecological information, presents a new way to examine ecophylogenetic structure.

Climatic conditions vary in spatial frequency globally. Spatially rare climatic conditions provide fewer suitable environments than common ones and should impose constraints on the types of species present locally and regionally. We used data on 467 North American angiosperms to test the effects of the spatial frequency of climatic conditions on ecological niche specialisation and functional diversity. We predicted that rare climates should favour generalist species that are able to inhabit a broader range of climatic conditions. Our results show that climate frequency filters species that differ in niche breadths and rare environments host species combinations with greater functional diversity. The proposed analytical approaches and hypotheses can be adapted to investigate different aspects of ecological assemblies and their biodiversity. We discuss different mechanisms regarding how spatial frequency of environments can affect niche composition and functional diversity. These should be useful while developing theoretical frameworks for generating a deeper understanding of its underpinnings.

Aim We investigated how freshwater microcrustaceans with different susceptibilities to Allee effects differ in the distribution of their geographical range size (GRS) and diversity along latitudinal gradients, evaluating the importance of climatic and historical factors in explaining these differences. We hypothesized that sexual copepods would have a smaller GRS and that their distribution would be linked to historical processes due to mate-finding Allee effects during colonization. Given that cyclic parthenogenetic cladocerans avoid these Allee effects, we predicted that they would exhibit a larger GRS and their distribution would be related to climatic factors rather than dispersal limitation. Location Canadian watersheds, North America. Methods We used a database containing the presence–absence of freshwater zooplankton across 1665 Canadian lakes along a latitudinal gradient of 40°. We computed GRS using minimum convex polygons encompassing all lakes in which each species was present. We pooled the diversity of lakes within watersheds, and computed linear regressions models between watershed diversity and average GRS with the average latitude, distance from a glacial refugium and environmental variables of the watershed. All analyses were performed separately for cladocerans and copepods. Results Cladocerans exhibited, on average, a GRS 70% larger than that of copepods. We found a strong relationship between diversity (negative) and average GRS (positive) with latitude for cladocerans but not for copepods. Cladoceran macroecological patterns were mainly explained by climatic factors, whereas the lack of latitudinal gradients in copepods was potentially due to the influence of a northern glacial refuge and dispersal limitation. Main conclusions Our results show that Allee effects are strongly and negatively associated with GRS, influencing the relative importance of environmental filtering and dispersal limitation on species diversity patterns. We suggest that studies should avoid lumping species with large differences in their susceptibility to Allee effects in order to better disentangle the multiple processes affecting large-scale patterns.

The classical view of peer review is that it is our primary process for assessing and judging whether research results should be published in a scholarly journal. However, the increased pressure to publish and technological developments are transforming peer review such that it is becoming a system that judges where work is published rather than whether the research is publishable (a ‘where rather than if’ process). Ecology is a field in which publication numbers puts a particular pressure on the review system. In this forum piece, I summarize the issues with the current publication system and discuss how technology is changing it, while suggesting solutions for important prior and ongoing issues with the peer review system. The view explored here is that technological developments (e.g. ease of creating journals, internet sites, storage, data generation, sharing of data and analytical code) will not eliminate peer review per se but will allow for a new set of parameters in which ethics and the optimal use of public funding will play a vital role in the evolution of the review process. Synthesis The number of papers and journals in Ecology has increased dramatically in the past decade. I present a critical overview of our review system and proposes that pressure to publish and technological developments have transformed peer review into a system that decides “where rather than if” papers are publishable. While reviewing the current pressures and factors playing a vital role in the evolution of the review and publication systems, I propose potential solutions to deal with current and future challenges to the peer review and publication systems.

1AbstractAimCommunity phylogenetic studies use information about species’ evolutionary relationships to understand the processes of community ecological assembly. A central premise of the field is that species’ evolution maps onto ecological patterns, and phylogeny reveals something more than species’ traits alone. We argue, therefore, that there is a need to better understand and model the interaction of phylogeny with species’ traits and community composition.InnovationWe outline a new method that identifies clades with unusual ecological structures, based around partitioning the variation of species’ site occupancies (β-diversity). Eco-phylogenetic theory would predict that these clades should also demonstrate distinct evolutionary trajectories. We suggest that modelling the evolution of independent trait data in these clades represents a strong test of whether there is an association between species’ ecological structure and evolutionary history.Main conclusionsUsing an empirical dataset of mammals from around the world, we identify two clades of rodents that tend not to co-occur (are phylogenetically overdispersed), and then find independent evidence of slower rates of body mass evolution in these clades. We suggest that our approach, which assumes nothing about the mode of species’ trait evolution but rather seeks to explain it using ecological information, presents a new way to examine eco-phylogenetic structure.

The methods of direct gradient analysis and variation partitioning are the most widely used frameworks to evaluate the contributions of species sorting to metacommunity structure. In many cases, however, species are also driven by spatial processes that are independent of environmental heterogeneity (e.g., neutral dynamics). As such, spatial autocorrelation can occur independently in both species (due to limited dispersal) and the environmental data, leading to spurious correlations between species distributions and the spatialized (i.e., spatially autocorrelated) environment. In these cases, the method of variation partitioning may present high Type I error rates (i.e., reject the null hypothesis more often than the pre-established critical level) and inflated estimates regarding the environmental component that is used to estimate the importance of species sorting. In this paper, we (1) demonstrate that metacommunities driven by neutral dynamics (via limited dispersal) alone or in combination with species sorting leads to inflated estimates and Type I error rates when testing for the importance of species sorting; and (2) propose a general and flexible new variation partitioning procedure to adjust for spurious contributions due to spatial autocorrelation from the environmental fraction. We used simulated metacommunity data driven by pure neutral, pure species sorting, and mixed (i.e., neutral + species sorting dynamics) processes to evaluate the performances of our new methodological framework. We also demonstrate the utility of the proposed framework with an empirical plant dataset in which we show that half of the variation initially due to the environment by the standard variation partitioning framework was due to spurious correlations.

Phenotypic variability represents an important factor allowing species to adapt to local environmental conditions, but mechanisms underlying such variation are incompletely understood. This study investigated whether habitat-specific demands on swimming performance or difference in trophic relationships in lakes (pelagic, littoral) and streams (riffle, pool) were significant predictors of phenotypic variation exhibited by brook char (Salvelinus fontinalis), the only fish in the study habitats. Specifically, we hypothesized that pelagic and riffle habitats would impose greater selective pressures associated with swimming, resulting in body morphologies that were dorsoventrally compressed, anterior–posteriorly elongated, and that exhibited a long, narrow caudal peduncle. Geometric morphometrics was applied in a quantitative analysis of body morphology among habitats, whereas stable isotope analysis was used to differentiate between food sources. Analyses revealed that while body morphology differed between lake and stream habitats, there was convergence between the pelagic and riffle habitats, as well as among littoral and riffle and pool environments. The littoral and pool habitats were thought to be more structurally complex, thereby selecting for increased maneuverability but lower sustained swimming and correspondingly deeper bodies with shorter, dorsoventrally expanded caudal peduncles. Carbon source and trophic position did not differ among habitats with a system, suggesting that feeding was not the main influence on morphological plasticity; however, fish in the stream were feeding at a higher trophic position than fish in the lake. These findings suggest that individual species may take advantage of morphological variation to better adapt local surroundings.

Aim Ecological maps are increasingly used to support marine management and conservation. However, the biological datasets used to produce these maps are typically limited to taxonomic groups identified to the specific taxonomic levels available. Ecological units should, however, reflect the broader marine ecosystem, independent of the datasets used. This study assessed the influence of taxonomic groups and taxonomic resolution on the process of ecological mapping. Location Estuary and Gulf of St Lawrence (EGSL), Canada. Methods A dataset of more than 200 taxa of benthic macrofauna was used to create a set of biological matrices corresponding to different taxonomic groups (i.e. vertebrates, invertebrates, arthropods, echinoderms, molluscs, all taxa) and different taxonomic levels from species to class. Multivariate regression trees (MRTs) were used to identify environmental drivers of taxa distribution and to create ecological maps. Similarity between maps was assessed using pairwise comparisons. First, the relationships between the two classification legends were assessed using association plots on the partitions in the corresponding trees. Then, the spatial agreement of ecological units believed to represent the same habitat types was quantified and mapped. Results The comparison across different taxonomic groups showed a substantial level of similarity between ecological maps, indicating that ecological units defined for a specific taxonomic group can be considered to some extent as representative of the entire benthic macrofauna. Moreover, little information was lost when working at the family rather than species level, and common patterns of community distribution could still be distinguished at the class level. Main conclusions Using a novel spatially explicit approach for comparing ecological maps, this study demonstrates that datasets limited by taxonomic breadth or resolution can perform nearly as well as more extensive datasets. These simplifications should improve our ability to manage marine ecosystems.

Differences in species composition across ecological communities are the result of multiple interacting mechanisms. Gradient analysis has been perhaps the most widely used statistical framework in describing how ecological communities vary in space, and aiding in determining the causes underlying these patterns. Direct gradient analysis allows the use of predictors such as environmental factors and spatial descriptors to directly estimate their contributions in explaining common and independent patterns of species distributions. In recent years, ecologists have started to explore how evolutionary history is associated with community patterns given the observation that species that share a common phylogenetic history tend also to have similar niches. Although ecological phylogenetics is among the fastest-growing fields in ecology, gradient analysis has not yet been fully integrated in this field. In this paper, we show and adapt the versatility of gradient analysis in describing and interpreting patterns of ecological communities based on their patterns of phylogenetic structure. Describing phylogenetic patterns across communities presents additional challenges regarding statistical inference in contrast to classic direct gradient analysis that are described and tackled here. We investigate the performance of our phylogenetic gradient analysis frameworks using simulations and provide a detailed example using a grassland community dataset.

Aim Multiple spatial and non-spatial processes are involved in determining the complex patterns underlying the spatial variation of individual species and their assemblages. This complexity, and the logistical challenges involved in following dispersal for multiple species across multiple sites, make it challenging to infer the processes underlying metacommunity spatial heterogeneity. The goal of our paper is to present a robust quantitative framework for inferring spatial patterns across multiple ecological communities. Innovation Unlike numerous metapopulation studies that have inferred migration rates based on landscape connectivity metrics which take into account the spatial positioning of occupied and empty patches, metacommunity studies have relied on spatial predictors built without considering such information. Here, we introduce a novel method called the multi-species spatial network (MSSN) to detect and explain spatial variability in community assemblies using a graph-theoretical approach. The MSSN approach can be best described as a reconciliation between the spatial positioning of sites and their patterns of patch occupation. Main conclusions Our simulation and real data analyses showed that our MSSN approach was better at detecting spatial patterns within metacommunities than the commonly used MEM method (Moran's eigenvector maps). In addition, our proposed framework is also useful in estimating the levels of spatial connectivity for each local community. Finally, our framework is flexible enough to incorporate different types of functions, metrics and algorithms to detect complex spatial patterns.

Competition with non-native species may impede the restoration of native species, but differences in competitive abilities among intraspecific native populations may make some populations more suitable for reintroduction than others. Here, juvenile Atlantic salmon (Salmo salar) from two allopatric populations (LaHave and Sebago) being used for reintroduction into Lake Ontario were placed into two natural stream sites differing in the presence of ecologically similar rainbow trout (Oncorhynchus mykiss). We assessed the effects of competition in the natural streams on fitness-related traits and habitat use of the Atlantic salmon. We then compared these effects to those observed in artificial streams from a previous study. Atlantic salmon in natural streams had reduced fitness-related traits that were associated with suboptimal microhabitats in the presence of rainbow trout, but utilised optimal microhabitats in their absence. In the presence of rainbow trout, the two Atlantic salmon populations exhibited comparable recapture proportions to each other, but the individuals from the Sebago population had better performance (body size and condition) than those from the LaHave population. Responses of both Atlantic salmon populations to competition with rainbow trout were generally similar in both direction and magnitude when compared to results from the artificial stream study. The combined results suggest that native species restoration efforts should be focused on candidate populations that are ecologically suitable to reintroduction environments, as well as on suitable habitats that do not contain exotic competitors. Moreover, this study highlights the value of controlled experiments in artificial environments for predicting fitness-related performance in natural environments.

Summary 1. Landscape genetics studies using neutral markers have focused on the relationship between gene flow and landscape features. Spatial patterns in the genetic distances among individuals may reflect spatially uneven patterns of gene flow caused by landscape features that influence movement and dispersal. 2. We present a method and software for identifying spatial neighbourhoods in genetic distance data that adopts a regression framework where the predictors are generated using Moran’s eigenvectors maps (MEM), a multivariate technique developed for spatial ecological analyses and recommended for genetic applications. 3. Using simulated genetic data, we show that our MEMGENE method can recover patterns reflecting the landscape features that influenced gene flow. We also apply MEMGENE to genetic data from a highly vagile ungulate population and demonstrate spatial genetic neighbourhoods aligned with a river likely to reduce, but not eliminate, gene flow. 4. We developed the MEMGENE package for R in order to detect and visualize relatively weak or cryptic spatial genetic patterns and aid researchers in generating hypotheses about the ecological processes that may underlie these patterns. MEMGENE provides a flexible set of R functions that can be used to modify the analysis. Detailed supplementary documentation and tutorials are provided.

Spatial variation of communities composition (metacommunities) results from multiple assembly mechanisms, including environmental filtering and dispersal; however, whether and why the relative importance of the assembly mechanisms in shaping bacterial metacommunity changes through time in marine pelagic systems remains poorly studied. Here, we applied the elements of metacommunity structure framework and the variation partitioning framework to examine whether temporal variation of hydrographic conditions influences bacterioplankton metacommunity dynamics in the southern East China Sea (ECS). The spatiotemporal variation of bacterial communities composition was revealed using 454 pyrosequencing of 16S rDNA. In addition to the whole bacterial community, we analyzed four dominant taxonomic groups (Cyanobacteria, Alphaproteobacteria, Gammaproteobacteria, and Actinobacteria) separately. Our analyses indicate that, considering the whole community level, the determinism of metacommunity structure varied among seasons. When the degree of connectivity was low (December), the metacommunity exhibited random distribution and was explained mainly by the environmental component. However, Clementsian metacommunity was found at intermediate connectivity (May), during which the environmental and spatial predictors were both significant. When connectivity was high (August), a random distribution pattern was found and no significant effect of environmental filtering or dispersal limitation was detected. Nevertheless, when considering different taxonomic groups, the differences in metacommunity dynamics among groups were found. Our results suggest that the driving forces of metacommunity dynamics varied depending on hydrography, as the degrees of environmental heterogeneity and connectivity among habitat patches were determined by circulation pattern. Moreover, mechanisms varied among different taxonomic groups, suggesting that differential dispersal capacity among taxonomic groups should be integrated into community assembly studies.

edu Qu� ebecontr� eal, CP 8888, Succ. Centre-Ville, MontrQC H3C 3P8, Canada Summary 1. Phylogenetic signals are the legacy related to evolutionary processes shaping trait variation among species. Biologists can use these signals to tackle questions related to the evolutionary processes underlying trait evolu- tion, estimate the ancestral state of a trait and predict unknown trait values from those of related species (i.e. 'phylogenetic modelling'). Approaches to model phylogenetic signals rely on quantitative descriptors of the structures representing the consequences of evolution on trait differences among species. 2. Here, we propose a novel framework to model phylogenetic signals: Phylogenetic Eigenvectors Maps (PEM). PEM are a set of eigenfunctions obtained from the structure of a phylogenetic graph, which can be a standard phylogenetic tree or a phylogenetic tree with added reticulations. These eigenfunctions depict a set of potential patterns of phenotype variation among species from the structure of the phylogenetic graph. A subset of eigen- functions from a PEM is selected for the purpose of predicting the phenotypic values of traits for species that are represented in a tree, but for which trait data are otherwise lacking. This paper introduces a comprehensive view and the computational details of the PEM framework (with calculation examples), a simulation study to demon- strate the ability of PEM to predict trait values and four real data examples of the use of the framework. 3. Simulation results show that PEM are robust in representing phylogenetic signal and in estimating trait values. 4. The method also performed well when applied to the real-world data: prediction coefficients were high (0� 76-0� 88), and no notable model biases were found. 5. Phylogenetic modelling using PEM is shown to be a useful methodological asset to disciplines such as ecology, ecophysiology, ecotoxicology, pharmaceutical botany, among others, which can benefit from estimating trait values that are laborious and often expensive to obtain.

Understanding the various processes contributing to community assembly is among the central aims of ecology. As a means of exploring this topic we quantified the relative influences of habitat filtering and competition in establishing patterns of community functional trait diversity across a landscape of lakes. Habitat filtering has been invoked in shaping community structure when co-occurring taxa are more similar in their traits than expected by chance (under-dispersion), and competition has been inferred as a structuring agent when co-occurring taxa are less similar (over-dispersion). We tested these hypotheses in crustacean zooplankton communities using a functional trait-based approach based on five traits defining zooplankton feeding and habitat preferences across 51 lakes spanning several large limnological gradients. In general, zooplankton communities were functionally less diverse than random assemblages created from the same regional species pool. Furthermore, functional diversity was strongly correlated with variables related to lake productivity, suggesting that access to resources was the chief habitat filtering process constraining zooplankton functional diversity. This pattern was driven by the predominantly herbivorous cladocerans as opposed to the more commonly omnivorous, and sometimes carnivorous, copepods.

Ecological communities show extremely complex patterns of variation in space, and quantifying the relative importance of spatial and environmental factors underpinning patterns of species distributions is one of the main goals of community ecology. Although we have accumulated good knowledge about the processes driving species distributions within metacommunities, we have few insights about whether (and how) environmental and spatial features can actually generate consistent species distributional patterns across multiple metacommunities. In this paper we applied the elements of metacommunity structure (EMS) framework to identify and classify metacommunities according to multiple but discrete patterns of species distributions. Given that each pattern has unique underlying structuring mechanisms, exploring and comparing such patterns across multiple metacommunities spanning large geographical areas provides a way to test the existence of general principles underlying species distributions within metacommunities. In this study, we applied the EMS framework into a data set containing about 9000 lakes distributed across 85 fish metacommunities across Ontario, Canada, and estimated the relative importance of local and spatial factors in explaining their distributional patterns. Nested and Clementsian gradients were the patterns that fitted most metacommunities; nested metacommunities were distributed throughout the province, while Clementsian gradient metacommunities were concentrated in the southeastern region. Sixty-five percent of nested metacommunities were located in low-energy watersheds (i.e., colder climate and shorter growing season), whereas metacommunities representing Clementsian gradients were present in high-energy watersheds (i.e., relatively warmer climate and longer growing season). Taken together, our results reveal that the environmental and spatial properties in which metacommunities are embedded are at least partially responsible for their species distributional patterns.

Phylogenetic Generalized Least Square (PGLS) is the tool of choice among phylogenetic comparative methods to measure the correlation between species features such as morphological and life-history traits or niche characteristics. In its usual form, it assumes that the residual variation follows a homogenous model of evolution across the branches of the phylogenetic tree. Since a homogenous model of evolution is unlikely to be realistic in nature, we explored the robustness of the phylogenetic regression when this assumption is violated. We did so by simulating a set of traits under various heterogeneous models of evolution, and evaluating the statistical performance (type I error [the percentage of tests based on samples that incorrectly rejected a true null hypothesis] and power [the percentage of tests that correctly rejected a false null hypothesis]) of classical phylogenetic regression. We found that PGLS has good power but unacceptable type I error rates. This finding is important since this method has been increasingly used in comparative analyses over the last decade. To address this issue, we propose a simple solution based on transforming the underlying variance-covariance matrix to adjust for model heterogeneity within PGLS. We suggest that heterogeneous rates of evolution might be particularly prevalent in large phylogenetic trees, while most current approaches assume a homogenous rate of evolution. Our analysis demonstrates that overlooking rate heterogeneity can result in inflated type I errors, thus misleading comparative analyses. We show that it is possible to correct for this bias even when the underlying model of evolution is not known a priori.

While interspecific competition is prevalent in natural systems, we do not yet understand how it can influence an individual’s phenotype within its lifetime and how this might affect performance. Morphology and swimming performance are two important fitness-related traits in fishes. Both traits are essential in acquiring and defending resources as well as avoiding predation. Here, we examined if interspecific competition could induce changes in morphology and affect the swimming performance of two strains of juvenile Atlantic salmon (Salmo salar). We imposed competitive scenarios on the fish using artificial streams containing different combinations of four interspecific competitors. Exposure to interspecific competitors induced morphological changes over time, through the development of deeper bodies, whereas controls free of interspecific competitors developed more fusiform body shapes. Furthermore, swimming performance was correlated to fusiform morphologies and was weaker for Atlantic salmon in competitive scenarios vs. controls. This implies that interspecific competition has direct effects on these fitness-related traits in Atlantic salmon. To the best of our knowledge, this is the first time that morphology, an important fitness-related trait linked to swimming performance, has been shown to be negatively impacted through interactions with an interspecific competitor.