Multi-taxa integrated landscape genetics for zoonotic infectious diseases: deciphering variables influencing disease emergence

Abstract

Zoonotic disease transmission systems involve sets of species interacting with each other and their environment. This complexity impedes development of disease monitoring and control programs that require reliable identification of spatial and biotic variables and mechanisms facilitating disease emergence. To overcome this difficulty, we propose a framework that simultaneously examines all species involved in disease emergence by integrating concepts and methods from population genetics, landscape ecology, and spatial statistics. Multi-taxa integrated landscape genetics (MTILG) can reveal how interspecific interactions and landscape variables influence disease emergence patterns. We test the potential of our MTILG-based framework by modelling the emergence of a disease system across multiple species dispersal, interspecific interaction, and landscape scenarios. Our simulations showed that both interspecific-dependent dispersal patterns and landscape characteristics significantly influenced disease spread. Using our framework, we were able to detect statistically similar inter-population genetic differences and highly correlated spatial genetic patterns that imply species-dependent dispersal. Additionally, species that were assigned coupled-dispersal patterns were affected to the same degree by similar landscape variables. This study underlines the importance of an integrated approach to investigating emergence of disease systems. MTILG is a robust approach for such studies and can identify potential avenues for targeted disease management strategies.

Pathogens in space: Advancing understanding of pathogen dynamics and disease ecology through landscape genetics

“…In addition, with the need to develop further landscape genetic frameworks for the study of pathogens, simulation modelling can prove useful in testing and validating these techniques, as it has done in the broader landscape genetics field (Cushman et al., ; Zeller et al., ). For example, Leo, Gonzalez, Millien, and Cristescu () used landscape genetic simulations to validate their multitaxa integrated landscape genetic framework, which appears to be a promising solution to the challenge of studying pathogens with multiple hosts and/or vectors. Landscape genetic simulations may also include epidemiological parameters such as mortality or activity responses to infection, or limited infectious periods, which may otherwise confound conventional (i.e., nonsimulation) landscape genetic approaches.…”

“…Leo et al. () developed a multitaxa integrated landscape genetic framework for diseases, which simultaneously quantifies the effects of both landscape variables and interspecific codispersal on pathogen gene flow in multi‐host‐vector systems. Few studies include both host and pathogen genetic data in landscape analyses.…”

The contribution of genetics and genomics to understanding the ecology of the mountain pine beetle system

“…Finally, on the methodological front, their integrated approach to landscape genetics using one species as a "resistance" surface to model connectivity of an associated species was unique. This approach has been taken up for the analysis of other systems, including Lyme disease in eastern Canada (Leo et al 2016).…”

Prediction and Prevention of Parasitic Diseases Using a Landscape Genomics Framework

“…Pioneered by work on rabies [11] and chronic wasting disease [12], research has targeted a handful of viruses (reviewed in [13]; see also [14]) and microbes (notably Batrachochytrium dendrobatidis [15]), helminths with direct life cycles [16,17] and their hosts. Systems involving vector-borne pathogens [18–21] or several intermediate hosts [22] have been mostly spared from investigation. We believe the application of landscape genetics to vector-borne disease agents, especially including landscape genetic simulation modelling [23] (see Glossary), has significant, underappreciated potential to inform targeted disease control strategies.…”

“…With the exception of recent theoretical work in the context of Lyme disease [21], essentially all landscape genetic studies applied to parasitic diseases to date have considered a single level of transmission, focusing primarily on landscape resistance hypotheses that influence movement processes and thus, gene flow, of principal reservoir hosts. For complex, multi-species disease systems, we find that today’s landscape genomic methods warrant a more inclusive, multi-level approach.…”