Presentation on theme: "Community ecology of disease: Barley Yellow Dwarf viruses in west coast grasslands Eric Seabloom Elizabeth Borer Parviez Hosseini Dept. of Zoology Oregon."— Presentation transcript:
Community ecology of disease: Barley Yellow Dwarf viruses in west coast grasslands Eric Seabloom Elizabeth Borer Parviez Hosseini Dept. of Zoology Oregon State University
Host-pathogen interactions are often studied in isolation HostPathogenVector
However, pathogens act within a community context Resource Host Pathogen Vector
Community context of host-pathogen interactions Resource supply: Abiotic resources can alter host, vector, and community dynamics. Host Community: Most emerging human and plant diseases are host generalists Vector Community: Many emerging animal and plant diseases are vector transmitted Pathogen Community: Most host species are co-infected by multiple pathogens and co-infection can alter transmission and host mortality rates
Community context of host-pathogen interactions: B/CYDV in California grasslands 1. Host Community: How do changes in the host community affect host-pathogen dynamics? 2. Resource supply: How does nitrogen alter the host community and pathogen prevalence?
System Overview: Invasion of California Grasslands 9.2 million ha of California grasslands invaded by exotic annual grasses for at least 150 years Persistence and abundance of annuals generally interpreted as evidence of competitive superiority However, recent work shows native perennial grasses are better competitors and can invade stands of exotic annual grasses 1 1 Seabloom et al PNAS,
The conundrum: How did competitively inferior species invade and dominate in this system? Recent experiments show that exotic annual grasses can increase prevalence of Barley Yellow Dwarf Virus in native grasses 1,2 Can disease allow persistence of competitively-inferior exotic annual grasses? 1 Malmstrom et al Oecologia, 2 Malmstrom et al New Phytologist.
Barley and Cereal Yellow Dwarf Viruses One of the most economically important diseases of grasses worldwide One of the most prevalent of all viral diseases Over 150 hosts including cereal crops, exotic grasses, and native grasses RNA luteoviruses that infect sieve tubes of grasses Causes stunting, yellowing, increased mortality, and decreased fecundity Obligate vectors are aphids Irwin and Thresh Annual Review of Phytopathology. D'Arcy and Burnett 1995.
B/CYDV Strain and Vector Communities Sitobion avenae I. SGV I. MAV I. PAV II. RPV II. RMV Rhopalosiphum padi Schizaphis graminum Rhopalosiphum maidis
Community context of host-pathogen interactions: BYDV in California grasslands 1. Host Community: How do changes in the host community affect host-pathogen dynamics? 2. Resource supply: How does nitrogen alter the host community and pathogen prevalence? 3. Vector and Pathogen Community: How do vector and pathogen interactions affect pathogen prevalence and coexistence?
1. How do changes in the host community affect host pathogen dynamics? Resource Host Pathogen Vector
Disease Effects on Competition Conceptual Model Discrete Time –Survival between seasons –Reset of system Loss of Disease Continuous Time –Growing (Winter Rainy) Season –Ongoing infection processes (SI model) –Competition (Lotka-Volterra) Integro-Difference Equations Parameterized with data from field experiments
The Ugly Math: Between seasons
The Ugly Math: Growing season
Dominance mediated by Disease Without Disease –Perennials invade –And replace annuals Healthy Annuals Healthy Perennials Diseased Annuals Diseased Perennials With Disease –Annuals invade –Dominate system –High disease prevalence Borer et al PNAS
2. How does nitrogen alter host community and pathogen prevalence? Resource Host Pathogen Vector
Host community Annual dominated Perennial dominated Nitrogen addition Control 4 g N m -2 yr -1 Treatments started in 2000 BYDV prevalence exotic annual B. hordeaceus monitored from native perennial E. glaucus monitored from Experimental manipulation of host community composition and nitrogen
Nitrogen increased annual grass abundance and BYDV prevalence in the native perennial, Elymus glaucus
Community context of host-pathogen interactions: BYDV in California grasslands 1. Host Community: BYDV presence was a necessary precondition to allow invasion by exotic annual grasses in California 2. Resource supply: Nitrogen increases both exotic annual invasion and BYDV prevalence in native grasses
Community context of host-pathogen interactions: general implications 1. Host Community: there can be strong feedbacks between changes in the host community and pathogen dynamics. 2. Resource supply: changing supply of abiotic resources can indirectly affect pathogens via effects on host communities All this is well and good, but we are still lacking a large-scale experimental test of the model predictions
Hopland McLaughlin Finley Basket Slough Sierra Foothills Experimental Test of Resource Supply and Host Community effects on Disease Replicated at five Sites with two replicates per site N and P added in factorial combination Monitor Disease incidence in 3 congeneric pairs of annual and perennial grasses We have good estimates for end of season status for discrete time portion of model A key challenge is to track within season growth rates used in disease model as sites are widely scattered.
Hopland McLaughlin Finley Basket Slough Sierra Foothills Experimental Test of Resource Supply effects on Disease Remote meteorological stations will allow us to track changes in light interception, a surrogate for plant biomass. These continuous data will allow us to estimate the effects of resource supply (rainfall, nitrogen, phosphorous) on the phenology of annual and perennial grasslands.
Sensor setup for one reserve (site) NP plot Neon Wireless Platform: light, soil moisture, rainfall, temperature senors and compactrio, wireless, and power (solar & battery),… Radio Relay: may be necessary to compete connection to internet point of contact Internet point of contact: Cheap CPU with wireless antenna. This point will have power available. Ground-level light and soil moisture sensors in and out of N&P fertilization plot with ambient light, rain, and temp sensors at platform
Example workflow for BYDV models 1.Collection of raw sensor data from met stations 2.Data processing in R 3.Model fitting and parameter estimation of growth models in R 4.Run disease model simulations in R 5.Produce graphics from model output in R
General features for REAP to consider from BYDV case study 1.Need for continuous data from remote locations 2.Well-documented workflows between raw data, data processing, parameter fitting with statistical models, and analytical/simulation models 3.Ability to use a wide range of sensor configurations. Considerations not well-represented in current case study 1.Mobile sensor/data loggers as in the case with GPS radio collars 2.High density networks of sensor spatial sensor grids 3.Need for adaptive real-time reconfiguration of sensor networks
Funding NSF/NIH EID (Borer, Dobson, Mitchell, Power, and Seabloom) Murdock Foundation (Borer and Engler) USDA NRICGP (Briggs and Borer) NSF DEB (Reichman, Seabloom, and Schimel) NSF DEB (Power and Mitchell) People Field work: T. Yoshida, A. Borcher, J. Quinn, T. Mizerek, G. Creager, A. Brandt, B. Martin ELISAs: Kai Blaisdell and Craig Kahlke