Prediction and validation of the potential global distribution of a problematic alien invasive species — the American bullfrog

Predicting the probability of successful establishment and invasion of alien species at global scale, by matching climatic and land use data, is a priority for the risk assessment. Both large- and local-scale factors contribute to the outcome of invasions, and should be integrated to improve the predictions. At global scale, we used climatic and land use layers to evaluate the habitat suitability for the American bullfrog Rana catesbeiana , a major invasive species that is among the causes of amphibian decline. Environmental models were built by using Maxent, a machine learning method. Then, we integrated global data with information on richness of native communities and hunting pressure collected at the local scale. Global-scale data allowed us to delineate the areas with the highest suitability for this species. Predicted suitability was significantly related to the invasiveness observed for bullfrog populations historically introduced in Europe, but did not explain a large portion of variability in invasion success. The integration of data at the global and local scales greatly improved the performance of models, and explained > 57% of the variance in introduction success: bullfrogs were more invasive in areas with high suitability and low hunting pressure over frogs. Our study identified the climatic factors entailing the risk of invasion by bullfrogs, and stresses the importance of the integration of biotic and abiotic data collected at different spatial scales, to evaluate the areas where monitoring and management efforts need to be focused.     

Conditionally autoregressive models improve occupancy analyses of autocorrelated data: An example with environmental DNA

Site occupancy‐detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false‐positive and false‐negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true‐/false‐positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true‐/false‐positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs.     

Phylogenetic profiles: a graphical method for detecting genetic recombinations in homologous sequences

Phylogenetic profiles constitute a novel way of graphically displaying the coherence of the sequence relationships over the entire length of a set of aligned homologous sequences. Using a sliding-window technique, this method determines the pairwise distances of all sequences in the windows and evaluates, for each sequence, the degree to which the patterns of distances in these regions agree. This method is suited for exploring data consistency as well as detecting recombinant sequences. A computer program implementing the algorithm has been developed, and examples with simulated and natural sequences are given to demonstrate the sensitivity and accuracy of the method for identifying recombinant sequences and their recombination junctions as well as detecting hot spots of recombinational activity.      

Spatial autocorrelation and the analysis of invasion processes from distribution data: a study with the crayfish <Emphasis Type="Italic">Procambarus clarkii</Emphasis>

Complex spatial dynamics are frequent in invasive species; analyzing distribution patterns can help to understand the mechanisms driving invasions. We used different spatial regression techniques to evaluate processes determining the invasion of the red swamp crayfish Procambarus clarkii. We evaluated four a priori hypotheses on processes that may determine crayfish invasion: landscape alteration, connectivity, wetland suitability for abiotic and biotic features. We assessed the distribution of P. clarkii in 119 waterbodies in a recently invaded area. We used spatially explicit statistical techniques (spatial eigenvector mapping, generalized additive models, Bayesian intrinsic conditional autoregressive models) within an information-theoretic framework to assess the support of hypotheses; we also analyzed the pattern of spatial autocorrelation of data, model residuals, and eigenvectors. We found strong agreement between the results of spatial eigenvector mapping and Bayesian autoregressive models. Procambarus clarkii was significantly associated with the largest, permanent wetlands. Additive models suggested also association with human-dominated landscapes, but tended to overfit data. The results indicate that abiotic wetlands features and landscape alteration are major drivers of the species’ distribution. Species distribution data, residuals of ordinary least squares regression, and spatial eigenvectors all showed positive and significant spatial autocorrelation at distances up to 2,500 m; this may be caused by the dispersal ability of the species. Our analyses help to understand the processes determining the invasion and to identify the areas most at risk where screening and early management efforts can be focused. The comparison of multiple spatial techniques allows a robust assessment of factors determining complex distribution patterns.     

Raised by aliens: constant exposure to an invasive predator triggers morphological but not behavioural plasticity in a threatened species tadpoles

Biological Invasions - During biotic invasions, native communities are abruptly exposed to novel and often severe selective pressures. The lack of common evolutionary history with invasive...