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Data from: Disentangling invasion processes in a dynamic shipping - boating network
Lacoursière-Roussel, Anaïs; McKindsey, Christopher W.; Bock, Dan G.; Cristescu, Melania E.; Guichard, Frédéric; Girard, Philippe; Legendre, Pierre — 2012-09-25 The relative importance of multiple vectors to the initial establishment, spread, and population dynamics of invasive species remains poorly understood. This study used molecular methods to clarify the roles of commercial shipping and recreational boating in the invasion by the cosmopolitan tunicate, Botryllus schlosseri. We evaluated i) single vs. multiple introduction scenarios, ii) the relative importance of shipping and boating to primary introductions, iii) the interaction between these vectors for spread (i.e., the presence of a shipping-boating network), and iv) the role of boating in determining population similarity. Tunicates were sampled from 26 populations along the Nova Scotia, Canada, coast that were exposed to either shipping (i.e., ports), or boating (i.e., marinas) activities. A total of 874 individuals (~30 per population) from 5 ports and 21 marinas was collected and analyzed using both mitochondrial cytochrome c oxidase subunit I gene (COI) and 10 nuclear microsatellite markers. The geographical location of multiple hotspot populations indicates that multiple invasions have occurred in Nova Scotia. A loss of genetic diversity from port to marina populations suggests a stronger influence of ships than recreational boats on primary coastal introductions. Population similarity analysis reveals a clear dependence of marina populations on those that had been previously established in ports and connectivity due to a boating network better explains patterns in population similarities than does natural spread. We conclude that frequent primary introductions arise by ships and that secondary spread occurs gradually thereafter around individual ports, facilitated by recreational boating.
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Title: Partitioning plant spectral diversity into alpha and beta components
Laliberté, Etienne; Schweiger, Anna; Legendre, Pierre — 2020-11-18 <p style="border:none;text-align:start;text-indent:0px;margin-bottom:8px;"><span style="font-style:normal;"><span><span style="font-weight:normal;"><span style="letter-spacing:normal;"><span><span><span style="white-space:normal;"><span><span><span>Plant spectral diversity — how plants differentially interact with solar radiation — is an integrator of plant chemical, structural, and taxonomic diversity that can be remotely sensed. We propose to measure spectral diversity as spectral variance, which allows the partitioning of the spectral diversity of a region, called spectral <i>gamma</i> (<i>γ</i>) diversity, into additive alpha (<i>α</i>; within communities) and beta (<i>β</i>; among communities) components. Our method calculates the contributions of individual bands or spectral features to spectral <i>γ</i>-, <i>β</i>-, and <i>α</i>-diversity, as well as the contributions of individual plant communities to spectral diversity. We present two case studies illustrating how our approach can identify “hotspots” of spectral <i>α</i>-diversity within a region, and discover spectrally unique areas that contribute strongly to <i>β</i>-diversity. Partitioning spectral diversity and mapping its spatial components has many applications for conservation since high local diversity and distinctiveness in composition are two key criteria used to determine the ecological value of ecosystems.</span></span></span></span></span></span></span></span></span></span></p>
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Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales
Guénard, Guillaume; Legendre, Pierre — 2018-08-02 1. Multiscale codependence analysis (MCA) quantifies the joint spatial distribution of a pair of variables in order to provide a spatially-explicit assessment of their relationships to one another. For the sake of simplicity, the original definition of MCA only considered a single response variable (e.g. a single species). However, that definition would limit the application of MCA when many response variables are studied jointly, for example when one wants to study the effect of the environment on the spatial organisation of a multi-species community in an explicit manner. 2. In the present paper, we generalize MCA to multiple response variables. We conducted a simulation study to assess the statistical properties (i.e. type I error rate and statistical power) of multivariate MCA (mMCA) and found that it had honest type I error rate and sufficient statistical power for practical purposes, even with modest sample sizes. We also exemplified mMCA by applying it to two ecological data sets. 3. The simulation study confirmed the adequacy of mMCA from a statistical standpoint: it has honest type I error rates and sufficient power to be useful in practice. Using mMCA, we were able to detect variation in fish community structure along the Doubs River (in France), which was associated with large spatial structures in the variation of physical and chemical variables related to water quality. Also, mMCA usefully described the spatial variation of an Oribatid mite community structure associated with a gradient of water content superimposed on various smaller-scale spatial features associated with vegetation cover in the peat blanket surrounding Lac Geai (in Québec, Canada). 4. In addition to demonstrating the soundness of mMCA in theory and practice, we further discuss the strengths and assumptions of mMCA and describe other potential scenarios where it would be helpful to biologists interested in assessing influence of environmental conditions on community structure in a spatially-explicit way. https://creativecommons.org/publicdomain/zero/1.0/
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Data from: Modelling habitat distributions for multiple species using phylogenetics
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Guénard, Guillaume; Lanthier, Gabriel; Harvey-Lavoie, Simonne; Macnaughton, Camille; Senay, Caroline; Lapointe, Michel; Legendre, Pierre; Boisclair, Daniel — 2016-08-24 In this paper, we describe an empirical approach to model community structure using phylogenetic signals. That approach combines information about the species (i.e. traits and phylogeny) with information about the habitat (i.e. environmental conditions and spatial distribution of sampling sites) and their interactions to predict the species responses (e.g. the local densities). As an application, we use the approach to model fish densities in rivers. In the model, the different species and size classes were described using a functional trait, body length, and phylogenetic eigenvectors maps whereas the sites were described using water velocity, depth, substrate composition, macrophyte cover, degree-days, total phosphorus, and spatial eigenvector maps. The model (estimated using a regularised Poisson-family Generalised Linear Modelling approach) fitted the data well (likelihood-based R2adj=0.512) and showed fair predictive power (likelihood-based cross-validation R2=0.283) to predict the density of fish pertaining to 48 species totalling 143 combinations of species and size classes in 15 unregulated Canadian rivers. Using the model as a baseline to estimate the effect of flow regulation on community composition, we found that, with few exceptions, the densities of most fish species were lower in regulated than in unregulated rivers. Phylogenetics have been proposed to study community structure, but this is, to our knowledge, the first time phylogenetic information is used explicitly for numerical habitat modelling. We expect that models of that type will be in increasing demand now that development projects are routinely assessed through impact studies.
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Large-scale multi-trophic co-response models and environmental control of pelagic food webs in Québec lakes
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Taranu, Zofia; Pinel-Alloul, Bernadette; Legendre, Pierre — 2021-03-04 <p>Environmental heterogeneity plays a fundamental role in driving species distributions by, for one, fostering niche dimensionality. Within lake ecosystems, species distributions and concordance patterns are driven by both local and regional heterogeneity, though their relative importance across trophic levels has rarely been explored. We developed a statistical framework to compare responses of taxa from different trophic levels to abiotic factors and determine how thisaffected multi-trophic network structures.In particular, we used multi-species concordance modelling (Concordance Analysis and RV coefficient) to determine species associations and correlations within and among three trophic levels (phytoplankton, zooplankton and fish communities sampled across 49 southern Québeclakes, covering eight hydrological regions). We then used Multiple Factor Analysis, Latent Variable Modelling and Local Contributions of sites to Beta Diversity to assess the relative importance of major environmental gradients in structuring species co-responses and species interaction turnover across the landscape. Our analyses confirmed that concordant species within each trophic level varied jointly or segregated into different pelagic food webs in Québec lakes where important acidification and eutrophication took place. Somekeynote species were indicators of different food web compartments and distinguished groups of lakes along multiple environmental niche dimensions. Among the three trophic levels examined, zooplankton depicted the highest proportion of species concordance and appeared to act as a trophic linkage between phytoplankton and fish. Ultimately, the losses or gains in species richness and species interactions were strongly driven by environmental gradients.This study provides for the first time a combined analysis of the effects of environmental heterogeneity on ecological communities belonging to three trophic levels sampled near simultaneously across an 800 km broad lacustrine landscape. The new framework developed in this study has a great potential to better understand the complex response of aquatic ecosystems in a world increasingly affected by multiple, cumulative stressors.</p> <p>The file provided herein is an RStudio project containing all data files and R script to run the statistical analyses and reproduce figures of the study. </p>

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(Author: Legendre, Pierre)

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