Spatial heterogeneity of climate and land‐cover constraints on distributions of tick‐borne pathogens

Abstract

Aim To compare the geographical distributions of two tick-borne pathogens vectored by different tick species, to examine the relative importance of climate, land cover and host density in structuring these distributions, and to assess the spatial variability of these environmental constraints across the species ranges.Location South-central and south-eastern North America.Methods Presence/absence data for two tick-borne pathogens, Ehrlichia chaffeensis and Anaplasma phagocytophilum , were obtained for 567 counties from a regional data base based on white-tailed deer ( Odocoileus virginianus ) serology. Environmental variables describing climate, land cover and deer density were calculated for these counties. Global logistic regression analysis was used to screen the environmental variables and select a parsimonious subset of predictors. Local analysis was carried out using geographically weighted regression (GWR) to explore spatial variability in the parameters of the regression models. Cluster analysis was applied to the GWR output to identify zones with distinctive species-habitat relationships.Results Global habitat models for E. chaffeensis and A. phagocytophilum included temperature, humidity, precipitation and forest cover as explanatory variables. The E. chaffeensis model also included forest fragmentation, whereas the A. phagocytophilum model included deer density. Local analyses revealed that climate was the primary correlate of pathogen presence in the eastern portion of the study area, whereas forest cover and fragmentation constrained the western range boundaries. Habitat relationships for all variables were weak in and around the Mississippi Delta.Main conclusions Efforts to model pathogen and disease ranges, and to predict shifts in response to global change should consider future scenarios of land-cover change as well as climate change, and should address the possibility of spatial heterogeneity in species-habitat relationships. The methods presented here outline an approach for objectively delineating geographical zones with similar speciesenvironment relationships, which can then be used to stratify landscapes for the purposes of further explanatory and predictive modelling.

Ecological Factors Characterizing the Prevalence of Bacterial Tick-Borne Pathogens in Ixodes ricinus Ticks in Pastures and Woodlands

“…Similar results were found in the United States, where patch size was found to be predictive of the B. burgdorferi sensu lato infection prevalence of I. scapularis because of the increased abundance of the reservoir host for B. burgdorferi sensu lato (1,12). Another possible explanation for the increase in the A. phagocytophilum infection prevalence in relation to the patch perimeter is that an increased availability of forest edge favors roe deer abundance (67). Roe deer are a reservoir host for this bacterium (22) and are known to prefer forest edges (64).…”

“…As vectors and wildlife are very sensitive to environmental conditions, ecological changes are expected to have a particular impact on the epidemiology of vector-borne diseases and those with a wildlife origin (29,48). Several studies have highlighted the influence of factors such as climate change and habitat fragmentation on the risk of tick-borne diseases (20,67). The risk of a tick-borne disease being transmitted to humans or to animals is closely linked to the prevalence of pathogens in ticks questing for hosts (38,58).…”

“…The increased fragmentation of deciduous forests, for example, favors infection prevalence in ticks that are the agents of Lyme disease in the eastern United States because this fragmentation pattern favors the abundance of a highly competent host reservoir, the white-footed mouse (Peromyscus leucopus) (1,12). However, studies of the effect of habitat fragmentation on different tick-borne pathogens are scarce (25,40,67). Most only report on the infection prevalence of pathogens in ticks according to sampling locations, the stage of tick development, and their sex (18,56); few studies take into account the interplay or simultaneous effects of explanatory environmental factors (2).…”

Statistic-empirical modelling of mountain permafrost distribution in the Abisko region, sub-Arctic northern Sweden

“…A fixed (Gaussian) kernel was applied as weighting function with a large bandwidth with the size of the study region. Here, an adaptive bi-squared kernel may have been more appropriate (Atkinson et al 2003;Wimberly et al 2008). The logistic model can, however, be problematic to run when the data points are either 0 or 1, because locally all points may be 0, which will generate an error and stop the model run.…”

Spatial variations in the associations of birth weight with socioeconomic, environmental, and behavioral factors in Georgia, USA

“…Spatial variations of the t-test values for parameter estimates are used to analyze how the significance of the relationships between birth weight and its factors change over space in this study. Compared with the hypothesis testing process in OLS, the t-test in GWR has been criticized to have the multiple comparison problem because statistical inferences are made across multiple locations (Wimberly, Yabsley, Baer, Dugan, & Davidson, 2008). However, the t-test has still been widely used as an exploratory tool in GWR applications to analyze the significance of parameter estimates (Harms et al, 2009;Helbich & Leitner, 2009;Jaimes et al, 2010;Malczewski & Poetz, 2005;Wimberly et al, 2008).…”