User-Friendly Methods for Timing Integrated Pest Management Strategies: An Analysis of Degree-Day Models and Biological Calendars Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Ashley Kulhanek, B.A. Graduate Program in Entomology The Ohio State University 2009 Thesis Committee: Daniel A. Herms, advisor Casey Hoy John Cardina Denise Ellsworth Copyright by Ashley Lynn Kulhanek 2009 Abstract Accurate prediction of pest phenology is crucial for successful implementation of integrated pest management strategies. Because plants and insects both exhibit temperature-dependent development, biological calendars based on plant phenological sequences can be practical, user-friendly alternatives to degree-day models for accurately predicting pest phenology. The primary objectives of this research were (1) to compare the accuracy of degree-day models and calendar date predictions of pest activity to determine if increased accuracy can be attained with customized, species-specific models, or if one standardized model, such as that used by The Ohio State University Growing Degree-Day and Biological Calendar Website (http://www.oardc.ohio-state.edu/gdd/), can suffice for accurately predicting large pest complexes; and (2) to analyze and synthesize data from The Ohio State University Phenology Garden Network to assess the consistency from location-to-location and year-to-year of phenological sequences for use as biological calendars for predicting pest activity. Phenological data collected for 43 arthropod pest species of woody ornamental plants from 1997 to 2002 were used to develop degree-day based prediction models and evaluate their accuracy. A standardized degree-day model, customized degree-day model, and averaged calendar date model were developed for 45 phenological events for the 43 species such as first egg hatch or first adult emergence based on five years of ii phenological and degree-day data. In the sixth year, the overall accuracy of the phenological predictions made by the standardized model was compared to predictions based on customized models and averaged calendar date. For each phenological event, the magnitude of error in the standardized model relative to the customized and calendar date model was quantified (observed date – predicted date in days) to determine the degree to which standardized models have utility for timing pest management decisions. Analysis of variance found no difference in the relative accuracy of the three models based on the deviation of their predictions from actual date of occurrence in 2002 (F=2.153; df=2, 132; P=0.120). Surprisingly, the standardized model most accurately predicted 28 of the 45 phenological events. To further develop and assess user-friendly prediction tools, The Ohio State University Phenology Garden Network was established in 2004. The network consists of 34 replicate gardens across Ohio, plus two gardens in Kentucky and Minnesota, each containing 16 clonal cultivars of woody ornamental plants which are tended by Master Gardener volunteers. Phenological sequences were constructed by ranking the chronological order of first and full bloom of each species. Consistency of the sequences from year-to-year and location-to-location was assessed using Spearman‟s bivariate correlation and regression analysis. The velocity (km/day) of the phenological wave of bloom as it progressed from south to north across Ohio over the course of the growing season was quantified using regression analysis between latitude, relative to the distance north from South Point, Ohio, and the day of the year of first blooming event for each of the 16 species. The iii slopes of regression lines defined the rate at which blooming dates migrated north for each species. To determine whether there was any latitudinal variation in cumulative degree- days required for a particular phenological event to occur, the relationship between the independent variable, latitude (relative to the distance north from South Point, Ohio in km), and the dependent variable, cumulative degree-days required for first bloom, was analyzed via regression analysis. The rank order of phenological sequences observed at individual gardens were significantly correlated from year-to-year (P<0.05 after Bonferroni correction) with one exception, and from location-to-location within a given year (P<0.05 after Bonferroni correction), with only 124 non-significant correlations out of 1301 total comparisons (P>0.05). The velocity (km/day) of the phenological wave of bloom as it progressed north varied by plant species, year, and phenophase, which challenges the use of calendar days for predicting pest phenology. The relationship between cumulative degree-days required for occurrence of phenological events and latitude of the gardens was not significant for the majority of phenological events in all four years, although there was a trend for most slopes to be negative. For all but one significant regression, the slope describing the relationship between location of garden and cumulative degree-days required for phenological events to occur was negative. This latitudinal gradient represents a previously undocumented source of variation in degree-day models for predicting plant phenology. It is concluded that a standardized degree-day model consisting of a January 1 starting date and a 10°C base temperature is suitably accurate for predicting the iv phenology of a pest complex consisting of multiple species. Overall, customized models for individual species did not improve accuracy. In The OSU Phenology Garden Network, the sequence in which phenological events occurred was consistent from year- to-year and from location-to-location. Collectively, these results indicate that biological calendars developed from phenological sequences can be accurate alternatives to complex degree-day models. Furthermore, these results validate the utility of The Ohio State University Growing Degree-Day and Biological Calendar Website (http://www.oardc.ohio-state.edu/gdd/), developed from phenological and temperature data at Wooster, Ohio, as a user-friendly tool for predicting pest phenology throughout Ohio. v Dedication Dedicated to my parents, Steven and Jamie Font and my Husband, Jason. vi Acknowledgments I would like to thank my advisor, Dan Herms, for never giving up on me or this project. I appreciate his faith in my ability to “swim” and for telling me to “stand up; you‟re in the shallow end”. I also wish to thank the members of my student advisory committee, Casey Hoy, John Cardina, and Denise Ellsworth for their support and patience throughout. I am grateful for the constant support of my colleagues, Vanessa Muilenburg, Sunny Park, Glené Mynhardt and Priyadarshani Loess for their input and friendship during this project. I would also like to thank Shirley Holmes and Brenda Franks for their unending dedication to helping graduate students succeed. I especially wish to thank the dedicated Master Gardeners of The Ohio State Phenology Garden Network for their hard work, diligence, and enthusiasm for this project. Special thanks goes to David Lohnes for developing The Ohio State University Phenology Garden Website used to collect, organize, and archive the data for this project. This material is based, in part, upon work supported by the National Science Foundation under grant DGE-0638669. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. vii Vita 2002………………………………………....Buckeye High School 2006…………………………………………B.A. Sociology, Baldwin-Wallace College 2006 to present……………………………...Graduate Student, Department of Entomology, The Ohio State University 2008 to 2009 ………………………………..National Science Foundation GK-12 Fellow Field of Study Major Field: Entomology viii Table of Content Abstract……………………………………………………………………………….......ii Dedication………………………………………………………………………..............vi Acknowledgements…………………………………………………………….….....….vii Vita……………………………………………………………………………………...viii List of Tables……………………………………………………………………....…..….x List of Figures ……………………………………………………………………….….xii Chapter 1: A History of Phenology: A Review of an Agricultural Prediction Tool: Past, Present, and Future...…………...…………………………………..…………...……...…1 Chapter 2: Degree-Day Models for 43 Pests of Ornamental Plants: Comparing the Accuracy of Phenological Predictions Based on a Standardized model, Customized Species-Specific Models, and Calendar Dates……………………………………..........23 Chapter 3: The Ohio State Phenology Garden Network: Consistency of a Phenological Sequence Across Years and Locations…………...……………………………...………44 Bibliography……………………………………………………………………….…….81 Appendix A: The Ohio State University Phenology Garden Network Information...…..89 ix List of Tables Table 2.1: The 43 species of arthropod pests of woody ornamental plants and the corresponding phenophases that were observed from 1997 to 2002……….…28 2.2: Starting dates and lower base temperatures used in the customized species- specific degree-day models to calculate growing degree-days for 45 phenological events of 43 pest species of woody ornamental plants. The lowest coefficient of variation in degree-days (shown) indicated which combination of starting date and base temperature was selected for use in the customized models…………………………………………………………………………34 2.3: Predictions for 2002 of 45 phenological events of