Guillaume Guénard

Multiscale codependence analysis: an integrated approach to analyze relationships across scales.

The spatial and temporal organization of ecological processes and features and the scales at which they occur are central topics to landscape ecology and metapopulation dynamics, and increasingly regarded as a cornerstone paradigm for understanding ecological processes. Hence, there is need for computational approaches which allow the identification of the proper spatial or temporal scales of ecological processes and the explicit integration of that information in models. For that purpose, we propose a new method (multiscale codependence analysis, MCA) to test the statistical significance of the correlations between two variables at particular spatial or temporal scales. Validation of the method (using Monte Carlo simulations) included the study of type I error rate, under five statistical significance thresholds, and of type II error rate and statistical power. The method was found to be valid, in terms of type I error rate, and to have sufficient statistical power to be useful in practice. MCA has assumptions that are met in a wide range of circumstances. When applied to model the river habitat of juvenile Atlantic salmon, MCA revealed that variables describing substrate composition of the river bed were the most influential predictors of parr abundance at 0.4-4.1 km scales whereas mean channel depth was more influential at 200-300 m scales. When properly assessed, the spatial structuring observed in nature may be used purposefully to refine our understanding of natural processes and enhance model representativeness.

Evolutionary patterns and physicochemical properties explain macroinvertebrate sensitivity to heavy metals

Ecological risk assessment depends strongly on species sensitivity data. Typically, sensitivity data are based on laboratory toxicity bioassays, which for practical constraints cannot be exhaustively performed for all species and chemicals available. Bilinear models integrating phylogenetic information of species and physicochemical properties of compounds allow to predict species sensitivity to chemicals. Combining the molecular information (DNA sequences) of 31 invertebrate species with the physicochemical properties of six bivalent metals, we built bilinear models that explained 70-80% of the variability in species sensitivity to heavy metals. Phylogeny was the most important component of the bilinear models, as it explained the major part of the explained variance (> 40%). Predicted values from bilinear modeling were in agreement with experimental values (> 50%); therefore, this approach is a good starting point to build statistical models which can potentially predict heavy metal toxicity for untested invertebrate species based on empirical values for similar species. Despite their good performance, development of the presented bilinear models would benefit from improved phylogenetic and toxicological datasets. Our analysis is an example for linking evolutionary biology with applied ecotoxicology. Its future applications may encompass other stress factors or traits influencing the survival of aquatic organisms in polluted environments.

Phylogenetics to help predict active metabolism

This paper shows how to build predictive models involving phylogenetic information to estimate metabolic traits such as active metabolic costs. Fish swimming cost is often estimated from body mass and swimming speed. The parameters of the relationships between these variables and swimming cost vary among species because each species has its own morphology and physiology. It is now widely recognized that traits are phylogenetically structured. Using new statistical approaches, it is possible to both correct swimming cost models for statistical phylogenetic non-independence and use the inherent phylogenetic signal to improve models. With these models one can extend, to a larger set of species, empirical knowledge about traits that are difficult to obtain; swimming cost is one such trait. Swimming cost accounts for a large and variable component of a fish energy budget, yet models have only been developed from observations performed on a few species, thereby constraining the scope of bioenergetic models. Her...

An experimental study of the multiple effects of brown trout Salmo trutta on the bioenergetics of two Arctic charr Salvelinus alpinus morphs.

This study investigated the importance of competition with brown trout Salmo trutta as a driver of the morphological and behavioural divergence of two morphs of Arctic charr Salvelinus alpinus. The morphs originated from two lakes differing in absence or presence of the competitor. The bioenergetics and behaviour of S. alpinus were quantified in replicate experimental enclosures (mean volume: 150 m(3)) stocked with 15 S. alpinus of one morph or the other and in the absence or presence of nine S. trutta. The presence of S. trutta decreased growth rate, affected food consumption and increased activity costs in S. alpinus, but provided little support for the hypothesis that competition with S. trutta is a major driver of the divergence of the two S. alpinus morphs. Both morphs responded similarly in terms of mean growth and consumption rates per enclosure, but the association between individual morphology and growth rate reversed between allopatric and sympatric enclosures. While the activity patterns of the two morphs were unaffected by the presence of S. trutta, their swimming speed and activity rate differed. Since the profound differences in the structure of the physical habitat of the source lakes provided a more likely explanation for the difference observed among these two morphs than interspecific competition, it is hypothesized that physical habitat may sometimes be a significant driving force of the phenotypic divergence.

Bringing multivariate support to multiscale codependence analysis: Assessing the drivers of community structure across spatial scales

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. 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. 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 Quebec, Canada). 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. This article is protected by copyright. All rights reserved.

Modelling habitat distributions for multiple species using phylogenetics

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.