Biogeographic determinants of Australian freshwater fish life-history indices assessed within a spatio-phylogenetic framework

AimThis study aims (1) to quantify the broad-scale patterns of functional diversity and life-history strategies of freshwater fish in relation to environmental variation across Australian river basins and (2) to identify key life-history traits associated with species extinction risk in order to determine how fish communities and extinction-prone species may respond to future environmental change. LocationOne hundred and twenty-three river basins across eastern Australia. MethodsBased on 10 key life-history traits for 194 freshwater fish we used a novel analytical approach to quantify multivariate life-history indices in relation to environmental variation within a spatio-phylogenetic framework. We assessed the utility of our analytical framework by contrasting final models against all candidate models, both with and without an eigenvector filtering procedure, and quantified the degree of autocorrelation in all model residuals. ResultsTemperature, habitat heterogeneity/availability, flow variability and primary productivity accounted for between 55 and 80% of the variation in life-history indices. Best-performing models were all derived from the addition of spatial and phylogenetic covariates to the analytical framework which consistently produced more parsimonious final models with higher explanatory power and insignificant levels of autocorrelation in the model residuals. Main conclusionThe life-history functional diversity of fish assemblages and the composition of life-history strategies across Australian river basins is in part determined by environmental variability, stability and seasonality, highlighting both the importance of environmentally driven community assembly processes and the potential changes to freshwater fish biodiversity in response to climate change. A spatio-phylogenetic analytical framework is a key component in the effective management of autocorrelation in ecological data and the derivation of more rigorous trait-environment relationships.