Macroecological approaches can provide valuable insight into the epidemiology of globally distributed, multi-host pathogens.
Toxoplasma gondii
is a zoonotic protozoan that infects any warm-blooded animal, including humans, in almost every ecosystem worldwide. There is substantial geographical variation in
T. gondii
prevalence in wildlife populations and the mechanisms driving this variation are poorly understood. We implemented Bayesian phylogenetic mixed models to determine the association between species’ ecology, phylogeny and climatic and anthropogenic factors on
T. gondii
prevalence.
Toxoplasma gondii
prevalence data were compiled for free-ranging wild mammal species from 202 published studies, encompassing 45 079 individuals from 54 taxonomic families and 238 species. We found that
T. gondii
prevalence was positively associated with human population density and warmer temperatures at the sampling location. Terrestrial species had a lower overall prevalence, but there were no consistent patterns between trophic level and prevalence. The relationship between human density and
T. gondii
prevalence is probably mediated by higher domestic cat abundance and landscape degradation leading to increased environmental oocyst contamination. Landscape restoration and limiting free-roaming in domestic cats could synergistically increase the resiliency of wildlife populations and reduce wildlife and human infection risks from one of the world's most common parasitic infections.
The prairie region of Canada is a dynamically changing landscape in relation to past and present anthropogenic activities and recent climate change. Improving our understanding of the rate, timing, and distribution of landscape change is needed to determine the impact on wildlife populations and biodiversity, ultimately leading to better-informed management regarding requirements for habitat amount and its connectedness. In this research, we assessed the viability of an approach to detect from–to class changes designed to be scalable to the prairie region with the capacity for local refinement. It employed a deep-learning convolution neural network to model general land covers and examined class memberships to identify land-cover conversions. For this implementation, eight land-cover categories were derived from the Agriculture and Agri-Food Canada Annual Space-Based Crop Inventory. Change was assessed in three study areas that contained different mixes of grassland, pasture, and forest cover. Results showed that the deep-learning method produced the highest accuracy across all classes relative to an implementation of random forest that included some first-order texture measures. Overall accuracy was 4% greater with the deep-learning classifier and class accuracies were more balanced. Evaluation of change accuracy suggested good performance for many conversions such as grassland to crop, forest to crop, water to dryland covers, and most bare/developed-related changes. Changes involving pasture with grassland or cropland were more difficult to detect due to spectral confusion among classes. Similarly, conversion to forests in some cases was poorly detected due to gradual and subtle change characteristics combined with confusion between forest, shrub, and croplands. The proposed framework involved several processing steps that can be explored to enhance the thematic content and accuracy for large regional implementation. Evaluation for understanding connectivity in natural land covers and related declines in species at risk is planned for future research.
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