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Abstract While the negative effects that pathogens have on their hosts are well-documented in humans and agricultural systems, direct evidence of pathogen-driven impacts in wild host populations is scarce and mixed. Here, to determine how the strength of pathogen-imposed selection depends on spatial structure, we analyze growth rates across approximately 4000 host populations of a perennial plant through time coupled with data on pathogen presence-absence. We find that infection decreases growth more in the isolated than well-connected host populations. Our inoculation study reveals isolated populations to be highly susceptible to disease while connected host populations support the highest levels of resistance diversity, regardless of their disease history. A spatial eco-evolutionary model predicts that non-linearity in the costs to resistance may be critical in determining this pattern. Overall, evolutionary feedbacks define the ecological impacts of disease in spatially structured systems with host gene flow being more important than disease history in determining the outcome.more » « less
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Bacterial spot is an endemic seedborne disease responsible for recurring outbreaks on tomato and pepper around the world. The disease is caused by four diverse species, Xanthomonas gardneri, Xanthomonas euvesicatoria, Xanthomonas perforans, and Xanthomonas vesicatoria. There are no commercially available disease-resistant tomato varieties, and the disease is managed by chemical/biological control options, although these have not reduced the incidence of outbreaks. The disease on peppers is managed by disease-resistant cultivars that are effective against X. euvesicatoria but not X. gardneri. A significant shift in composition and prevalence of different species and races of the pathogen has occurred over the past century. Here, I attempt to review ecological and evolutionary processes associated with the population dynamics leading to disease emergence and spread. The goal of this review is to integrate the knowledge on population genomics and molecular plant–microbe interactions for this pathosystem to tailor disease management strategies.more » « less
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Abstract Understanding the community ecology of vector-borne and zoonotic diseases, and how it may shift transmission risk as it responds to environmental change, has become a central focus in disease ecology. Yet, it has been challenging to link the ecology of disease with reported human incidence. Here, we bridge the gap between local-scale community ecology and large-scale disease epidemiology, drawing from a priori knowledge of tick-pathogen-host ecology to model spatially-explicit Lyme disease (LD) risk, and human Lyme disease incidence (LDI) in California. We first use a species distribution modeling approach to model disease risk with variables capturing climate, vegetation, and ecology of key reservoir host species, and host species richness. We then use our modeled disease risk to predict human disease incidence at the zip code level across California. Our results suggest the ecology of key reservoir hosts—particularly dusky-footed woodrats—is central to disease risk posed by ticks, but that host community richness is not strongly associated with tick infection. Predicted disease risk, which is most strongly influenced by the ecology of dusky-footed woodrats, in turn is a strong predictor of human LDI. This relationship holds in the Wildland-Urban Interface, but not in open access public lands, and is stronger in northern California than in the state as a whole. This suggests peridomestic exposure to infected ticks may be more important to LD epidemiology in California than recreational exposure, and underlines the importance of the community ecology of LD in determining human transmission risk throughout this LD endemic region of far western North America. More targeted tick and pathogen surveillance, coupled with studies of human and tick behavior could improve understanding of key risk factors and inform public health interventions. Moreover, longitudinal surveillance data could further improve forecasts of disease risk in response to global environmental change.more » « less
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Abstract Molecular technologies have revolutionized the field of wildlife disease ecology, allowing the detection of outbreaks, novel pathogens, and invasive strains. In particular, metabarcoding approaches, defined here as tools used to amplify and sequence universal barcodes from a single sample (e.g., 16S rRNA for bacteria, ITS for fungi, 18S rRNA for eukaryotes), are expanding our traditional view of host–pathogen dynamics by integrating microbial interactions that modulate disease outcome. Here, I provide an analysis from the perspective of the field of amphibian disease ecology, where the emergence of multi-host pathogens has caused global declines and species extinctions. I reanalyzed an experimental mesocosm dataset to infer the functional profiles of the skin microbiomes of coqui frogs (Eleutherodactylus coqui), an amphibian species that is consistently found infected with the fungal pathogen Batrachochytrium dendrobatidis and has high turnover of skin bacteria driven by seasonal shifts. I found that the metabolic activities of microbiomes operate at different capacities depending on the season. Global enrichment of predicted functions was more prominent during the warm-wet season, indicating that microbiomes during the cool-dry season were either depauperate, resistant to new bacterial colonization, or that their functional space was more saturated. These findings suggest important avenues to investigate how microbes regulate population growth and contribute to host physiological processes. Overall, this study highlights the current challenges and future opportunities in the application of metabarcoding to investigate the causes and consequences of disease in wild systems.
