Bats are hosts to a range of zoonotic and potentially zoonotic pathogens. Human activities that increase exposure to bats will likely increase the opportunity for infections to spill over in the future. Ecological drivers of pathogen spillover and emergence in novel hosts, including humans, involve a complex mixture of processes, and understanding these complexities may aid in predicting spillover. In particular, only once the pathogen and host ecologies are known can the impacts of anthropogenic changes be fully appreciated. Cross-disciplinary approaches are required to understand how host and pathogen ecology interact. Bats differ from other sylvatic disease reservoirs because of their unique and diverse lifestyles, including their ability to fly, often highly gregarious social structures, long lifespans and low fecundity rates. We highlight how these traits may affect infection dynamics and how both host and pathogen traits may interact to affect infection dynamics. We identify key questions relating to the ecology of infectious diseases in bats and propose that a combination of field and laboratory studies are needed to create data-driven mechanistic models to elucidate those aspects of bat ecology that are most critical to the dynamics of emerging bat viruses. If commonalities can be found, then predicting the dynamics of newly emerging diseases may be possible. This modelling approach will be particularly important in scenarios when population surveillance data are unavailable and when it is unclear which aspects of host ecology are driving infection dynamics.
The stream network temperature model (SNTEMP): A decade of results
Bartholow, J. M
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Workshop on Computer Application in Water Management
Quantitative and Statistical Research Collaboration
Mathematical and statistical models are powerful research tools that play several important roles in conceptualizing and understanding the structure and dynamics of complicated ecological systems, including developing mechanistic hypotheses pertaining to ecological systems, designing studies that elucidate ecosystem structure and function, and extracting information from data. The complex nature of ecological systems and the data arising from studies of these systems often require the development of specialized and sophisticated models so that progress can be made in understanding these systems. The objective under this task is to develop mathematical or statistical models that abstract and accommodate the unique characteristics of ecological systems and data, while allowing for maximum extraction of information about those systems. This is accomplished through collaboration with field biologists having unique or unusual data analysis questions or circumstances, and with mathematicians and statisticians able to creatively apply powerful mathematical or statistical methods to difficult, real-world problems.