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...     

Infectious clones of cotton leafroll dwarf virus strains used for genotype evaluation in cotton

Cotton blue disease (CBD) and atypical-CBD are the most important viral diseases of cotton plants in the southern region of South America. Common and atypical strains of cotton leafroll dwarf virus (CLRDV and CLRDV-at, respectively) are thought to be causative agents of CBD and atypical-CBD, respectively. Inoculation of test plants via aphid vectors is difficult, as is determining strains via molecular diagnosis; accordingly, it is difficult for breeders to evaluate the effects of blue disease-associated virus infections in cotton lineages. In the present study, we attempted to circumvent these difficulties by producing six full-length cDNA infectious clones from CLRDV and CLRDV-at strains using the Gibson Assembly protocol. For inoculation of the infectious clones, a vacuum chamber-mediated agroinfiltration protocol was adapted and applied. Using this protocol, 90%–100% of cotton plants became infected with the clones, which was not possible via syringe-based agroinfiltration. A genotyping protocol based on RT-qPCR targeting a specific region of the virus P0 protein was also developed, allowing rapid differentiation of CLRDV and CLRDV-at. Applying this protocol to 68 field samples revealed that CLRDV-at was dominant (50%) over CLRDV (5.8%) in single virus infections. These preliminary results imply that CLRDV-at might occupy the ecological niche of CLRDV in the cotton fields of Brazil.