Forward selection of explanatory variables

This paper proposes a new way of using forward selection of explanatory variables in regression or canonical redundancy analysis. The classical forward selection method presents two problems: a highly inflated Type I error and an overestimation of the amount of explained variance. Correcting these problems will greatly improve the performance of this very useful method in ecological modeling. To prevent the first problem, we propose a two-step procedure. First, a global test using all explanatory variables is carried out. If, and only if, the global test is significant, one can proceed with forward selection. To prevent overestimation of the explained variance, the forward selection has to be carried out with two stopping criteria: (1) the usual alpha significance level and (2) the adjusted coefficient of multiple determination (Ra(2)) calculated using all explanatory variables. When forward selection identifies a variable that brings one or the other criterion over the fixed threshold, that variable is rejected, and the procedure is stopped. This improved method is validated by simulations involving univariate and multivariate response data. An ecological example is presented using data from the Bryce Canyon National Park, Utah, U.S.A.     

Does interspecific competition influence relationships between heterozygosity and fitness-related behaviors in juvenile Atlantic salmon (<Emphasis Type="Italic">Salmo salar</Emphasis>)?

Very few studies have investigated the effect of genetic diversity on the behavioral and phenotypic traits linked to the competitive ability of individuals. In this study, we reared juvenile Atlantic salmon (Salmo salar) alone or with the competitive rainbow trout (Oncorhynchus mykiss) in order to: (1) to assess correlations between heterozygosity and traits related to individual competitive ability [i.e., heterozygosity–fitness correlations (HFCs)] in Atlantic salmon, and (2) to evaluate the effect of the competitive rainbow trout on any such HFCs. We also tested whether a few loci had a disproportionately large effect (i.e., the local effect hypothesis) or, on the contrary, if all loci contributed equally (i.e., the global effect hypothesis) in explaining the observed HFCs. We found significant HFCs for phenotypic traits related to the competitive ability of juvenile Atlantic salmon, i.e., the growth rate and the distance to the feeding source. Some HFCs were nonlinear, suggesting that individuals with intermediate levels of heterozygosity were favored. In addition, we found that the competition exerted by rainbow trout only weakly modified these HFCs as the relationships were highly consistent across treatments. We demonstrated that the local-effect hypothesis best explained both linear and nonlinear HFCs. Overall, our results illustrated the importance of genetic diversity in explaining the behavioral variability observed within populations. Moreover, we provide evidence that, even if a competitive species can have strong ecological effects, the relationships between genetic diversity and fitness-related traits in juvenile Atlantic salmon were not influenced by such effects.     

Modelling directional spatial processes in ecological data

Distributions of species, animals or plants, terrestrial or aquatic, are influenced by numerous factors such as physical and biogeographical gradients. Dominant wind and current directions cause the appearance of gradients in physical conditions whereas biogeographical gradients can be the result of historical events (e.g. glaciations). No spatial modelling technique has been developed to this day that considers the direction of an asymmetric process controlling species distributions along a gradient or network. This paper presents a new method that can model species spatial distributions generated by a hypothesized asymmetric, directional physical process. This method is an eigenfunction-based spatial filtering technique that offers as much flexibility as the Moran's eigenvector maps (MEM) framework; it is called asymmetric eigenvector maps (AEM) modelling. Information needed to construct eigenfunctions through the AEM framework are the spatial coordinates of the sampling or experimental sites, a connexion diagram linking the sites to one another, prior information about the direction of the hypothesized asymmetric process influencing the response variable(s), and optionally, weights attached to the edges (links). To illustrate how this new method works, AEM is compared to MEM analysis through simulations and in the analysis of an ecological example where a known asymmetric forcing is present. The ecological example reanalyses the dietary habits of brook trout (Salvelinus fontinalis) sampled in 42 lakes of the Mastigouche Reserve, Québec.     

Determination of In Situ Esterification Parameters of Citric Acid-Glycerol Based Polymers for Wood Impregnation

The development of wood treatments is of increasing industrial importance. A novel technique for improving the properties of lodgepole pine and white pine through modification of the microstructure is described. The present investigation is devoted to the synthesis and determination of in situ parameters of citric acid and glycerol based polymers for wood impregnation. This solvent free approach is environmentally friendly and achieved through an esterification condensation reaction under acidic conditions. Crude glycerol and citric acid reactants were cross-linked via a curing process at 160?°C creating a polymer with only water as the byproduct. The ester bonds and crosslinking levels were controlled using different catalysts and citric acid contents and related to the reaction time and temperature. The nature of bonding within the polymers and at the wood cell walls was determined by FT-IR analysis. The thermal properties such as glass transition temperature (T_g) were studied using TGA/DSC and the effect of citric acid content and catalyst type determined. Dimensional stability of impregnated wood samples improved above 50% for each sample with HCl and p-TSA catalysts compared to control samples. FTIR spectra were studied to show the presence of the ester linkages of the polymer in situ at the wood cell walls. Bonding between the polymer and wood macromolecules were observed by scanning electron microscopy and interpreted as evidence of chemical bonds at the wood cells. When prepared using a catalyst, the polymer was intimately incorporated into wood structure significantly improving the substrate dimensional stability. Enhanced stability makes this approach of particular interest for exterior wood products especially as a green renewable option for the wood industry.