1. Determining Factors that Reflect Aphid Presence Eco-Informatics Summer Institute 2007 Genevieve Layman Sean Moore Elizabeth Borer

2. Background Barley Yellow Dwarf Virus (BYDV) is extensive and economically important Includes Virus, vector, and host Disease epidemics affect ecosystems and biodiversity California’s perennial grasslands serves as an example

3. Introduction www.ipm.ucdavis.edu/.../englishgrai naphid.html www.dewarcropprotection.co.uk/images/sitobion... This project focuses on vector behavior as indication of disease movement and structure BYDV cannot be transmitted directly or intergenerationally Determined largely by aphids! Objective: To study characteristic factors that reflect aphid presence to gain a better idea of BYDV’s spatial epidemiology in the HJ Andrews Experimental Forest

4. Methods Study design created by Sean Moore 15 study sites –5 Meadow, 5 Forest, and 5 Road Habitat Sites Selection Requirements -20 Elymus Glaucus -20 Bromus of Carinatus or Vulgaris species Meadow and Forest from Carpenter Mtn. Road from Lookout Mtn.

5. Methods Continued 6 Vegetation Quadrats per Site 1 Pan trap per Site Area sq meters Elevation by GPS Soil Samples, % canopy cover, and biomass samples taken as well although not used in analysis

6. Statistical Analysis Log-linear Poisson regression Appropriate for this model because of the distinct dependent variable being count based and non-continuous Tests for significance Aphid Count = %BV + %BC + %EG + %Total Grasses + Site Area + FMR + Elevation Aphid Count = %BC + FMR + Elevation

7. Results 4 explanitory variables were found to be significant- Road, Meadow, Elevation, and Bromus carinatus

8. Results for Elevation Overall slight downward trend in Elevation for Aphid Count Increase in elevation with road habitat has a distinct correlation with the increase in aphid count

9. Results for Bromus vulgaris Bromus vulgaris significant with outlier When the outlier is removed, P-Value drops and becomes no longer significant This gives more weight to %Total Grasses and it then becomes significance

10. Discussion Roadsite habitat variable confounded Mean number of aphids found in the Meadows was 3.8 compared to the 1.4 mean of aphids in the forested sites Site size had no correlation Elevation results may be influenced by temperature Outputs suggest more local interactions are influencing aphid feeding preference The presence of Bromus vulgaris does not necessarily reflect aphid presence

11. Assumptions and Limitations Small sample size Subjectivity in defining Site area Seasonality of aphids and grasses All aphids grass feeders Site selection requirements hard to find

12. Conclusions Road and meadow habitat as well as grass populace all are good indication of aphid presence Elevation is indirectly reflective of aphid population Broad, large-scale factors that provide insight for general trends in aphid populations This research will be used in Sean Moore’s dissertation testing the role of local community interactions and the effects of landscape structure and regional processes on the dynamics of plant pathogens Can also be used in consideration with the effects of land management

13. Future Studies Lab analysis Aphid collection Multiple Mountain Comparison Road habitat and exotic species Temperature Aspect Explanatory variable interactions

14. Acknowledgments A big thank you to Desiree Tullos who created the Eco-Informatic Summer Institute program and made this research possible. Thank you to Sean Moore for mentoring this project, helping with field data collection, and for aiding with brainstorm ideas for statistical analysis. Also a great thanks to Elizabeth Borer and Eric Seabloom who suggested the topic of review and aided in the statistical analysis process.

15. Eco-Informatics Eco-Informatics is combining ecology and mathematical interpretation of research based problems to provide a more holistic view of an environmental system. It is also a technique to visualize some of these problems and communicate them in a clear manner I approached this research from a largely ecological angle but used the data I collected in statistical analysis to view it from a different perspective I would not have been able to recognize these findings from exclusively observational field work.

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