Thu, Aug 05, 2021:On Demand
Background/Question/Methods
Linking ecological communities to the environment has been done by constrained multivariate analysis. Various methods have been proposed to integrate information on functional traits, phylogenetic relationships, and spatial variability. Among these, the double-constrained correspondence analyses (dc-CA) and its linear counterpart (dc-PCA) integrate environmental variables and traits as predictors. We expand this framework, proposing a dc-CA-based algorithm for decomposing the total variation in community composition (and not only the trait- or environmentally structured variation) and testing the simple and conditional effects of four sets of predictors, organized in two data tables related to sites (environment and space) and two related to species (functional traits and ecological niche). Usually, traits and niche parameters are used interchangeably. In our approach, we use separate matrices: a table of species-by-traits and another of resources-by-species, the last being further described by newly introduced niche dissimilarity metrics. We consider that ecological niches differ from traits in that the latter are distinguished on and characterize the individual level, while niches are measured on the species level, and when compared, they are characteristics of communities and should be used as separate predictors.
Results/Conclusions The novelties are the introduction of new niche parameters, the development of an algorithm for the full variation decomposition and testing of the CENTS (Community-Environment-Niche-Traits-Space) space by dc-CA with and without covariates, and the new types of diagrams (denoted VADOC), a synthetically display of simple and conditional effects of all explanatory variables and their interactions. Applying these methods to a dataset of freshwater mollusk communities in Olt River (Romania), we learned that niche predictors may be as important as the traits in explaining community structure and are not redundant, overweighting the effects of environmental and spatial predictors. We defined a standardized niche overlap or similarity index that considers the availability of resources in the environment, conceived in two variants: for categorical and continuous resources, and its complement, the niche dissimilarity index. We used this metric to define and measure the species' uniqueness and one more aspect of the community diversity, termed as niche-based diversity. We explored relationships between taxonomic, functional, and niche-based diversity measures, showing their use in explaining different facets of ecological communities' features. By analyzing their relationships with environmental variables, we have emphasized the value of niche-based diversity in human impact assessment. Our algorithm opens new pathways for developing integrative methods linking life and environment, both in theoretical and practical applications.
Results/Conclusions The novelties are the introduction of new niche parameters, the development of an algorithm for the full variation decomposition and testing of the CENTS (Community-Environment-Niche-Traits-Space) space by dc-CA with and without covariates, and the new types of diagrams (denoted VADOC), a synthetically display of simple and conditional effects of all explanatory variables and their interactions. Applying these methods to a dataset of freshwater mollusk communities in Olt River (Romania), we learned that niche predictors may be as important as the traits in explaining community structure and are not redundant, overweighting the effects of environmental and spatial predictors. We defined a standardized niche overlap or similarity index that considers the availability of resources in the environment, conceived in two variants: for categorical and continuous resources, and its complement, the niche dissimilarity index. We used this metric to define and measure the species' uniqueness and one more aspect of the community diversity, termed as niche-based diversity. We explored relationships between taxonomic, functional, and niche-based diversity measures, showing their use in explaining different facets of ecological communities' features. By analyzing their relationships with environmental variables, we have emphasized the value of niche-based diversity in human impact assessment. Our algorithm opens new pathways for developing integrative methods linking life and environment, both in theoretical and practical applications.