User-Friendly Methods for Timing Integrated Pest Management Strategies

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

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

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.

Dedication

Dedicated to my parents, Steven and Jamie Font and my Husband, Jason.

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

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 43 pest species made by a standardized degree-day model (January 1, 10°C), customized species-specific degree-day models, and average calendar date models. Accuracy of models presented as the deviation in days from the actual event occurrence (observed- predicted shown as o-p). Event occurrence in 2002 is also listed...…...…...... 35

3.1: Plant species in each garden of The Ohio State University Phenology Garden Network.……………………………………………..……………………...... 51

3.2: Overall average blooming sequence for The Ohio State University Phenology Garden Network (2005-2008) was generated by averaging the cumulative degree-days on the date of occurrence for each of the 32 phenological events (16 species of woody ornamental plants first and full bloom) across all 34 gardens and all four years. The averages were ranked from 1 to 32, with 1 being the first event on average and 32 being the last event on average. Average of the cumulative degree-days ( ±SD) required for first and full bloom for each of the 16 species are shown by year and for all four years combined………………………………………………………………….…...57

3.3: Spearman‟s bivariate correlation coefficients generated from correlations of phenological sequence from location-to-location across four years in The Ohio State University Phenology Garden Network. All correlations are significant at after applying Bonferroni correction with one exception of Trumbull 2005 v.s. 2007 (indicated in bold). Cells with asterisks indicate comparisons that could not be made because no data were recorded that year or because n<10…………………………………………………………………………...59

3.4: Velocity of the phenological wave of blooming (expressed as km north per day) for each phenological event for 16 species of woody ornamental plants in The Ohio State University Phenology Garden Network. Values in bold indicate significant regression relationships (P<0.05)………………….………..…….66

3.5: Slopes (dd/km) of the lines generated by regression analyses comparing cumulative degree-days required for a blooming event to occur and the latitude of the gardens, relative to distance (km) from the southernmost city in Ohio. A significant slope would indicate variation in cumulative degree-days by location. Degree-days were calculated by Allen (1976) double sine wave with a January 1 starting date and 10°C base temperature for all the phenological events of 16 species of woody ornamental plants in The Ohio State University Phenology Garden Network…………………………………..…………...….70

A.1: The gardens by county of The Ohio State University Phenology Garden Network………………………………………………………………….…….90

List of Figures

Figure

2.1: Accumulation of growing degree-days over the course of the year for 1997 to 2002. Degree-days were calculated using a modified double sine wave (Allen 1976), a starting date of January 1 and a base temperature of 10°C……...... 36

3.1: Distribution of gardens within The Ohio State University Phenology Garden Network as of the 2008 growing season. Stars represent new gardens added for the 2010 growing season……………...…………………………………….…51

3.2: Percentage of gardens in The Ohio State University Phenology Garden Network reporting phenology data for 16 species of woody ornamental plants (32 phenophases total) throughout the four year (2005-2008) study...... 56

3.3: Frequency distribution of Spearman‟s bivariate correlation coefficients (r- values) generated by correlating phenological sequences for 29 gardens in The Ohio State University Phenology Garden Network from year-to-year. All correlations with r>0.75 are significant at after applying Bonferroni correction for multiple comparisons.…………...……...……………………...58

3.4: Frequency of Spearman‟s bivariate correlation coefficients generated from correlations of phenological sequences from location-to-location in The Ohio State University Phenology Garden Network in a) 2005, b) 2006, c) 2007, and d) 2008.…………………………………………………………………..……61

3.5: Representative examples of variation in location-to-location comparisons of phenological sequences from different gardens in The Ohio State University Phenology Garden Network during a year. The line bisecting each graph represents perfect correspondence (y=x) between the blooming sequences of each garden. Graphs a, c, and e are examples of significant correlations (P<0.05 after Bonferroni correction for multiple comparisons). Graphs b, d, and f are examples of non-significant correlations…………………….……...62

xii

3.6: Representative examples of correlations made between the overall phenological sequence and the phenological sequences from Wayne County garden for each year of the study (2005-2008). The overall phenological sequence for The OSU Phenology Garden Network was generated by averaging the cumulative growing degree-days for each phenological event across all gardens and all years and ranking the averaged events from 1 to 32. Diagonal lines bisecting each graph represent perfect correspondence (y=x)…………………………...63

3 7: Regression analysis comparing the distance between the garden in Ashtabula County and all other gardens in The Ohio State University Phenology Garden Network and the corresponding Spearman‟s correlation coefficient of the phenological sequences of the two gardens from 2005-2008. 2005-2007 (P>0.05), 2008 (P=0.011)…………………….………………….……....…....65

3.8: Velocity (km/day) of the phenological wave for (a) Goldtide forsythia and (b) Oakleaf hydrangea, expressed as a slope of the regression between distance north from South Point, Ohio and the date of occurrence A) Lines represent fitted trend lines for each year. (a) 2005: F=62.76, P=0.000; 2006: F=46.37, P=0.000 (b) 2006: F=1.21, P=0.29; 2008: F=0.053, P=0.88..…………..……67

3.9: Average velocity (km/day) of the phenological wave of first bloom expressed as a slope of the regression between distance north from South Point, Ohio and the date of occurrence. Plants are listed along x-axis in sequence from earliest to latest phenological event based on the four-year averaged Julian date of occurrence for Ohio…………………………………………………………...68

3.10: Average slopes (dd/km) resulting from the regression analyses of cumulative degree-days required for the occurrence of first bloom to occur of 16 species of woody ornamental plants in The Ohio State University Phenology Garden Network and distance north from South Point, Ohio. Degree-days were calculated using modified double sine wave (Allen 1976) with a base temperature of 10°C and a starting date of January 1. Plants are listed on the x- axis in the order in which they bloomed based on the average of Julian date of occurrence across all gardens and all years…………...………………………71

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Chapter 1

A History of Phenology:

A Review of an Agricultural Prediction Tool: Past, Present, and Future

Phenology: A Historical Perspective

Phenology is the study of recurring biological events and their relationship to weather. Observations of bird migration, flower blossom, leaf abscission, insect emergence, and budburst all fall under the umbrella of phenological studies. Phenology found its first uses in the earliest civilizations. Devoid of modern technology, early humans used phenology to predict seasonal changes by observing the annual appearance and development of plants and animals. For example, the Aboriginals of Australia relied on seasonal indicators such as the change in rain, temperature, wind, and the occurrences of plants and animals to define their seasons, which helped to ensure their survival

(Keatley and Fletcher 2003). Early hunter and gatherer societies would have observed early on the link between seasonal events and when their food sources would be available, such as when specific berries and nuts were ripe to eat (Defila and Clot 2003).

Knowledge of when and where game animals migrate would ensure successfully planned hunts. In these ways, phenology is one of the oldest fields of science (Huberman 1941).

Phenology as the science we know today was first described by Carolus Linnaeus in 1751 (Huberman 1941, Hopp 1974, Koch et al. 2008) and was first coined by the botanist Charles Morren in 1853 (Hopp 1974). However, records indicate that observations of recurring seasonal events have been kept and utilized for many years before phenology was conceptualized. Many early naturalists, such as Linnaeus, can be considered the first “phenologists” for their diligently-kept journals and notes on seasonal occurrences and their relation to weather conditions. For example, Chen (2003) states there are roughly “200 kinds of archaic personal diaries in the Chinese literature, in which 10-20% of the diaries recorded phenological data”. In Europe, there is also a long tradition of recording phenological events, including one of the oldest phenology records, which was kept by Robert Marsham and his family from 1736 to 1947 (Sparks and Carey

1995, Menzel 2003). Other ancient records include those from Japan, which include records of cherry blossom bloom since the 14th century (Chen 2003). Even the famous poet Henry David Thoreau was a naturalist whose phenology journals have received renewed attention as tools for documenting biological effects of climate change (Miller-

Rushing and Primack 2008, Willis et al. 2008). Records such as these have inspired more organized endeavors into researching phenology, including the foundation of phenological networks.

Phenology networks are not merely groups of people who casually collect data from various locations. They often consist of networks of replicate gardens with the same plant species, often clones that minimize genetic variation among plants at different locations, that ensure the same individuals are documented yearly (Schnelle and Volkert

1974). Some phenology gardens have been established to monitor the growing season to

2 predict pests (Ellsworth and Herms 2005), to develop biological calendars (Chen 2003,

Ellsworth and Herms 2005), and to monitor weather, climate, and environmental changes

(Chen 2003, Keatley and Fletcher 2003, Menzel 2003). These networks aim to catalog data systematically and uniformly to ensure that the data can be compared across geographic locations and years (Chmielewski 2008).

Current phenological networks include the expansive International Phenological

Gardens (IPG) of Europe

(http://www.agrar.hu-berlin.de/struktur/institute/nptw/agrarmet/phaenologie/ipg), The

National Phenology Network (NPN) of The United States (http://www.usanpn.org/), as well as regional groups such as The Ohio State University Phenology Garden Network

(http://phenology.osu.edu/). Far from an exhaustive list, these are some examples of many small to large-scale networks of volunteers, citizens, and researchers now actively participating in phenological observations. Data from networks such as these have been summarized and analyzed in many recent studies (Schwartz 1994, Herms 1998, Menzel and Fabian 1999, Menzel et al. 2001, Herms 2003).

Phenology and Agriculture

As one of the oldest fields of science, phenological observation has informed and guided some of the earliest agricultural practices. Before the advent of modern methods, farmers relied on seasonal phenology events as indicators for timing management activities (Hopp and Lieth 1974, Wielgolaski 1974, Chen 2003, Chmielewski 2003,

Schwartz 2003). It is important to accurately time tilling, planting, harvesting, and pest management practices. Some of the oldest evidence of early phenological observation

3 includes old proverbial sayings such as this from the Apache Indians: “The early appearance of insects indicates an early spring and good crops” (Inwards 1898). Other traditional weather lore includes: “Frost will not occur after the dogwood blossoms”

(Inwards 1898), and “When oak puts on its gosling grey, „Tis time to sow barley, night or day” (Inwards 1898). These proverbs confirm the historic use of phenological tools for timing.

Accurate timing of agronomic practices results in efficient utilization of the full growing season (Waggoner 1974, Wielgolaski 1974), avoidance of frost damage to susceptible crops such as fruiting trees (Chmielewski 2008), harvesting of crops at proper times (Waggoner 1974, Wielgolaski 1974), and timing of weeding practices to limit competition (Ghersa & Holt 1995). Phenology also aids in the analysis of geographic areas to determine whether they are suitable for growing specific crops, or which crop varieties are best suited for commercial use (Wielgolaski 1974). Further benefits of phenology come from using prediction tools to schedule pest management practices for maximum effectiveness and to reduce the amount and frequency of pesticide applications (and especially unnecessary costly reapplications) (Waggoner 1974, Ascerno

1991, Swanton and Weise 1991, Raupp et al. 1992, Mussey and Potter 1997, Herms

1998). A survey by Burrows (1983) found that proper timing of pesticides as a component of an IPM program will decrease their use. Proper timing of these events can also reduce the injury caused by pests or disease, ensuring a good crop while minimizing environmental impact (Chmielewski 2003).

Yet, for many modern day growers, the use of phenological indicators and proverbs may not be the preferred tools for making management decisions when yield

4 and profit are on the line. Today, farmers and nursery managers often rely on forecasts and calendar dates predetermined from past experience to make management decisions.

As technology and science have advanced, so have agricultural practices, and the older methods may be considered less reliable or simply forgotten, and the timing of planting, harvesting, and pest management practices are often based on degree-day models rather than phenological indicators (Delahaut 2003).

Most phenological indicators used to develop biological calendars for predictions

(e.g. Herms 1990, Mussey and Potter 1997, Herms 2003, Cardina et al. 2007) are based upon data from single locations. However, many phenological indicators such as these prove to be specific to the locations where they were originally observed, and may not be useful over large areas (Schwartz 2003). While some phenological indicators hold true over wider areas than others, the use of indicators in multiple regions needs to be studied on a case-by-case basis in order to develop confidence in their use (Herms 1998).

Degree-Day Models

While still in use today, the practice of using pre-planned calendar dates for planning agricultural practices is considered inaccurate due to the variation in weather by year and by location (Ascerno 1991, Herms 1998, 2003, 2004). Therefore, degree-day models have been advocated as a more accurate tool for making agricultural predictions.

Degree-day models are formulas used to predict temperature-dependent biological events based on how much heat has accumulated above a base temperature over a given period of time. Because development of insects and plants is temperature-dependent (Huberman

1941, Wagner et al. 1984, Worner 1992, Mussey and Potter 1997), the goal is to use

5 degree-days to track this development (Huberman 1941, Mussey and Potter 1997).

Initially, degree-day models were used primarily for the prediction and timing of crop events such as corn silking (Gilmore and Rogers 1958). However, they were eventually suggested as a means for predicting phenological events of insect pests such as egg hatch

(Pruess 1983).

The concept of temperature-dependent development, which is foundational to the degree-day model concept, was first suggested by Reaumur in 1735, who proposed that

“plant development is proportional to the sum of temperature over time rather than to temperature during the phenological event itself” (Chuine et al. 2003). Research has shown that the biophysical explanation for this temperature-development relationship is that enzymes control reaction rates (Howe 1967, Sharpe and DeMichele 1977, Higley et al. 1986, Bonhomme 2000). Enzymes catalyze reactions such as those responsible for development within organisms, and temperature affects rates of enzyme activity

(Bonhomme 2000). Heat increases and cold decreases the rate of these reactions thereby affecting developmental rates overall (Bonhomme 2000).

Three main classes of degree-day models are commonly used today (Pruess

1983). The first and simplest model is known as the rectangle method. It requires the maximum and minimum daily temperature over the course of a 24-hour day to attain the average daily temperature from which a base temperature is subtracted (Gilmore and

Rogers 1958). This generates the number of degree-days (DD) that have accumulated over the 24-hour period according to the following formula:

The total degree-days necessary for an event to occur is generated by summing daily degree-day accumulations over a specified time interval, beginning with a biofix (such as the date eggs are laid or the end of diapause) or arbitrary starting date (such as January 1) and ending with the event‟s occurrence. This generates the cumulative degree-days on which a particular phenological event occurred.

Modifications of this method often include the addition of a maximum temperature threshold such as 86°F, such as is used in the “weather bureau method”

(Pruess 1983). This maximum threshold acts as an upper boundary above which degree- days are no longer calculated, and is based on the assumption that insect and plant development slows or stops when temperatures exceed this threshold (Sharpe and

DeMichele 1977, Higley et al. 1986).

The triangle method is another degree-day calculation commonly used. This method acknowledges that temperatures fluctuate throughout the day. By imposing a triangle over the daily temperature curve, one may estimate the area under the curve by measuring the area within the triangle (Lindsey and Newman 1956, Higley et al. 1986).

A common modification of this method is to measure the area in half-day increments from the minimum daily temperature in the morning to the maximum mid-day temperature, and from that mid-day temperature to the next lowest temperature of the 24 hour day during the night (Sevacherian et al. 1977, Higley et al 1986).

A third method often used (e.g. Akers and Nielsen 1984, Ascerno and Moon

1989, Herms 2004) is the modified Allen sine wave method (Allen 1976), which takes all of the above-mentioned criteria into consideration by including a maximum and minimum temperature as well as calculating degree-days in half-day increments using a

7 sine wave to approximate the daily temperature curve. The modified Allen sine wave is the most complicated of the three formulas to calculate.

More complex non-linear models for generating degree-day units have also been developed but are often reserved for research and are generally not put to practical use in the field (e.g. Parton and Logan 1981, Cesaraccio et al. 2001). These formulations often require additional information or necessitate the use of a computer. Other complex models include the use of hourly temperature data rather than just the daily maximum and minimum. Some researchers consider temperature data recorded hourly to be more representative of the daily temperature accumulation than minimum and maximum alone

(Raworth 1994, Roltsch et al. 1999, Cesaraccio et al. 2001). However, they can be less user-friendly if practitioners do not have proper equipment with which to collect hourly data.

A caveat of degree-day models is that lower physiological temperature thresholds, which ideally would be used as the base temperature, are not known for the majority of the species for which degree-day models have been developed. Therefore, many models utilize a single arbitrary starting date for beginning calculations (January 1) and a standard base temperature such as 10°C (Pruess 1983). This standardized method presents a convenient and manageable approach to calculating heat units for multiple pest species. For example, The Ohio State University Degree-Day and Biological Calendar

Website (http://oardc.ohio-state.edu/gdd) employs this standardized model to predict the phenology of nearly 50 key arthropod pests of woody ornamental plants.

Limitations of Degree-Day Models

There are concerns that degree-day models do not precisely simulate rates of pest development, as there are many assumptions upon which degree-day models are constructed that introduce sources of error that can lead to inaccurate predictions (Pruess

1983, Higley et al. 1986). The primary assumption is that temperature is an important factor regulating development, and because of this, heat sums accurately reflect rates of developmental processes (Wagner et al. 1984, Higley et al. 1986, Raworth 1994,

Bonhomme 2000, Herms 2001), although the best way to calculate the heat sum or degree-days is widely argued (e.g. Lindsey and Newman 1956, Arnold 1959, Allen 1976,

Raworth 1994, Roltsch et al. 1999, Cesaraccio et al. 2001). By focusing on the methods for estimating heat unit accumulation, degree-day models fail to incorporate or quantify the importance of many other factors that affect rates of development (Higley et al.

1986). While enzymatic reactions are indeed accelerated as temperatures increase, factors such substrate availability, water, and photoperiod may also be necessary triggers that signal the enzymes that are crucial to development (Higley et al. 1986). To what extent these additional factors must be incorporated into models to sufficiently predict a phenological event is generally unknown.

Many models calculate degree-days based on the assumption that development rate is linear when it actually resembles a sigmoidal relationship (Woerner 1992). At high and low temperature extremes the development rate of plants and insects is slower relative to mid-range temperatures. At the molecular level, extreme temperatures affect enzymes by changing their conformation, in some cases denaturing the proteins that

9 regulate the biophysical processes of development (Sharpe and DeMichele 1977) and stunt developmental rates.

A third concern arises because the degree-day concept ignores the ability of plants and insects to thermoregulate (Higley et al. 1986). Without the ability to maintain internal temperature, insect and plant development is proportional to the temperature of the environment. However, insects can control internal temperature through behavioral actions such as moving into shade or sun, moving under brush, and by aggregating

(Knapp and Casey 1986). For both plants and insects, the manner in which temperature data are collected can introduce another source of error (Higley et al. 1986, Bonhomme

2000), as it may not reflect the microclimate in which the insect or plant inhabits. To truly measure the temperatures which organisms are experiencing would require sensors attached directly to the organism, which is of course impractical for growers (Higley et al. 1986).

In order to adjust for these sources of error, researchers have looked for empirical ways to improve predictions without addressing the underlying assumptions (Higley et al.

1986). Variation can be reduced and predictive power increased by altering the starting date of calculations (Wielgolaski 1999), determining appropriate base temperatures

(Arnold 1959, Yang et al. 1995, Snyder et al. 1999), and incorporating upper temperature thresholds into the models (Gilmore and Rogers 1958, Baskerville and Emin 1968). In addition, researchers have created increasingly complex degree-day models (Parton and

Logan 1981, Cesaraccio et al. 2001).

The multitude of available degree-day models that often utilize different starting dates and thresholds, can present logistical challenges to practitioners, especially for

10 those working in diverse habitats with multiple pests. Which method works best? Which is most user-friendly? The endless selection can be overwhelming, even to a researcher.

Many papers continue to be published, thus compounding the issue. In some cases when cumulative degree-days required for a species‟ phenological event are reported, the exact degree-day model used and how it was executed may be vague, which causes inaccuracies when comparing studies (McMaster and Wilhelm 1997). In an effort to categorize these data, an online database of species-specific base temperatures and starting dates for degree-day calculations has been made available for download

(Nietschke et al. 2007). But despite the assumptions governing the degree-day and the challenges they pose, the method continues to be used with success (Woerner 1992).

And so in the pursuit of accurate predictions and user-friendly methodology, what is next?

Biological Calendars as a Phenological Tool

Phenological indicators could be resurrected as a useful prediction tool for agriculture if applied properly. Just as ancient humans used plant phenology as signs of the seasons, practitioners today can monitor specific indicator plants to alert them when to spray for insects or harvest a crop. Instead of using increasingly complex mathematical models to make predictions, practitioners can use phenological events such as budburst and flower bloom to help time and predict pest activity (Herms 2003).

Because development of plants and insects are both temperature-dependent, plant phenology can be used to track the environmental factors that govern the development of

11 both plants and insects which normally would be monitored via degree-day and other timing methods (Herms 1990).

Phenology gardens can be a useful tool for this purpose. Phenology gardens are collections of specific plants that can be monitored for the occurrence of phenological events such as budburst, flower bloom and leaf fall. Networks of phenology gardens have been established in Asia (Chen 2003), the United States (Hopp 1974, Vittum and

Hopp 1978, Koch et al. 2008), and across Europe (Chmielewski 2008, Koch et al. 2008) to coordinate the collection of massive amounts of phenology data across great distances.

Some of the oldest and longest running networks are in Europe (Schwartz 2003, Koch et al. 2008, Primack and Miller-Rushing 2009). One such historic network was formed by the Royal Meteorological Society in 1883 to observe the effect of climate and weather on animals and plants (Huberman 1941). Botanical gardens also have diligently kept records of blooming, which can be useful sources of data (Primack and Miller-Rushing

2009).

Plants in phenology gardens should be easy to identify and have distinct blooming phases that can be easily monitored (Delahaut 2003). Instead of continually recording temperatures and calculating degree-days, practitioners need only observe plant bloom.

By noting the phenological sequence in which plants bloom and by tracking what phenological events precede and follow the blooming events, a biological calendar can be generated. A biological calendar of phenological events represents the order in which seasonal events will occur, and therefore can be a useful method for predicting the occurrence of agriculturally significant events such as insect emergence and for timing

12 management practices yearly. If the sequence remains consistent from year-to-year despite variation in weather, the biological calendars can be a reliable tool (Herms 2004).

Recent research has confirmed the potential of plant phenology to predict pest activity in order to plan management decisions. Cardina et al. (2007) showed that phenological indicator plants (non-target plants used as visual cues for events of interest) can be used to predict giant foxtail emergence in agricultural fields. Masin et al. (2005) similarly showed that indicator species could reliably predict the occurrence of four annual weed species in Europe. Hodges and Braman (2004) monitored 40 species of plant in Georgia to determine indicator species which could be used to time the activity of certain species of scale insects. Mussey and Potter (1997) and Herms (1990), both showed that plant sequences could be used as indicators for predicting insect emergence, and that the biological calendars used were reliable from year-to-year.

Because both weed seedlings and insects are small, hard to identify, and perhaps unfamiliar to many practitioners (Herms 2003, Masin et al. 2005, Cardina et al. 2007), the use of indicator plants and phenology gardens are more practical visual cues to use as tools for making predictions. The use of phenological indicators is also simpler than degree-day models because it relies on observations of common plants rather than calculations based on daily temperature data or computers (Delahaut 2003).

Furthermore, using easily recognized plants makes phenology an accessible science to growers, pest managers, climate researchers, and laymen alike.

Nursery growers could likely benefit from an increased use of phenological observations and indicator plants. Already stocked with ornamental plants, nurseries would have to invest relatively little to establish a phenology garden. There are at least

13 three benefits to using indicator plants in nurseries. First, by monitoring and recording the phenological events of plants such as budburst, first bloom, and duration of flowering, nurseries could easily help customers choose plants that will bloom at various times through the season. Second, nurseries could use records of blooming to create a biological calendar to help predict when to scout for common pests, and finally, nurseries could plan when to spray for these pests as well as advise clients of these practices.

The use of phenology also presents a useful approach for climatology research.

Earth‟s climate has increased in temperature by a 1.4°C over recent decades (a projected increase of 0.2°C per decade; IPCC 2007). Phenology records can be used to document biological impacts of this warming. Because the journals of some naturalists and weather data have been stored for centuries, phenology records present an increasingly utilized source of data to study the effects of climate change. A recent example is the data of poet and naturalist Henry David Thoreau, whose personal journals and phenological observations at Concord, Massachusetts are now receiving close analysis (Miller-Rushing and Primack 2008, Willis et al. 2008). Phenological studies such as these have helped document earlier springs and later autumns, which are characteristic of lengthened growing seasons resulting from global climate change (Schwartz 1994, Menzel & Fabian

1999, Menzel et al. 2001, Khanduri et al. 2008), as well as other ecological effects of global warming trends.

The ease of recognizing and observing seasonal events has also created a niche for the public to participate in science, and phenology projects have proven to be well suited for citizen science. Citizen science projects that involve phenology studies include

“Project Budburst” (http://www.windows.ucar.edu/citizen_science/budburst/), which

14 monitors plant budburst and blooming events across the United States for use in climate change research; “Monarch Watch” (http://www.monarchwatch.org/), which invites citizens to report monarch sightings in order to document monarch migration; and the

USA National Phenology Network (http://www.usanpn.org/), which has citizens reporting on plant bloom for climate research nationwide. The Ohio State Phenology

Garden Network (http://phenology.osu.edu/) incorporates Master Gardener volunteers as citizen scientists to collect phenological data across Ohio. Some of this data is also reported to the National Phenology Network.

Historical uses of phenology may be found in folklore, but its future potential resides in the revitalization of seasonal observations as a reliable tool to aid in many practical applications and in multiple fields of research. Research has shown that phenology still holds great promise and provides a unique tool for solving a myriad of research questions. Climatologists are able to use the ample supply of historic observations as data for climate change research. Citizen science projects are growing in number and in size as more volunteers give their time towards educating themselves and others about phenology. With so many potential benefits of phenology, it is time that it reclaims a prominent role as a foundational science of agriculture.

Research Objectives

Thesis Statement: The use of calendar dates as a prediction method is considered inaccurate, but the complexity of degree-day models may deter their use by growers. An alternative suggested here is that the use of seasonal phenological indicators and The

Ohio State University Growing Degree-Day and Biological Calendar Website

(http://www.oardc.ohio-state.edu/gdd/) can be accurate tools for predicting pest phenology and scheduling pest management strategies. Managers can use phenological indicators as a complement to standardized degree-day models. Blooming sequences or other plant events used as biological calendars can be an accurate method to track heat accumulation. It is hypothesized that (a) standardized degree-day models will generate sufficiently accurate predictions of multiple pests (Chapter 2), (b) phenological sequences will be consistent from year-to-year and from location-to-location (Chapter 3), (c) the phenological wave of blooming will predictably traverse northward across Ohio (Chapter

3), and (d) degree-days calculated for first bloom of 16 ornamental plant species by a standardized degree-day model (starting date January 1, base temperature 10°C) will not vary by location (Chapter 3).

References

Akers, R.C. and D.G. Nielsen. 1984. Predicting Agrilus anxius Gory (Coleoptera: Buprestidae) adult emergence by heat unit accumulation. J. Econ. Entomol. 77: 1459-1463.

Allen, J.C. 1976. A modified sine wave method for calculating degree-days. Environ. Entomol. 5: 388-396.

Arnold, C.Y. 1959. The determination and significance of the base temperature in a linear heat unit system. Proc. Am. Soc. Hortic. Sci.74: 430-445.

Ascerno, M.E., and R.D. Moon. 1989. Forecaster: predicting biological phenomena based on daily temperatures. Minnesota Extension Service. AG-CS-3029. V.1.0.

Ascerno, M.E. 1991. Insect phenology and integrated pest management. J. Arbor. 17: 13- 15.

Baskerville, G.L., and P. Emin. 1969. Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology 50: 514-517.

Bonhomme, R. 2000. Bases and limits to using „degree.day‟ units. Eur. J. Agron. 13: 1- 10.

Burrows, T.M. 1984. Pesticide demand and integrated pest management: a limited dependent variable analysis. Am. J. Agr. Econ. 65: 806-810.

Cardina, J., Herms, C.P., Herms, D.A., and F. Forcella. 2007. Evaluating phenological indicators for predicting giant foxtail (Setaria faberi) emergence. Weed Sci. 55: 455-464.

Cesaraccio, C., Spano, D., Duce, P., and R.L. Snyder . 2001. An improved model for determining degree-day values from daily temperature data. Int. J. Biometeorol. 45: 161-169.

Chen, X. 2003. East Asia, pp. 11-25. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Chuine, I., Kramer, K., and H. Hänninen. 2003. Plant development model, pp. 217-235. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Chmielewski, F.M. 2003. Phenology and agriculture, pp. 505-522. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands. 17

Chmielewski, F.M. 2008. The international phenological gardens in Europe, pp. 17-21. In: J. Nekorvář, E. Koch, E. Kubin, P. Nejedlik, T. Sparks, and F. Wielgolaski (eds.), COST Action 725- the history and current status of plant phenology in Europe.Vammalan Kirjapaino Oy, Finland.

Defila, C., and B. Clot. 2003. Long-term urban-rural comparisons, pp. 541-554. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Delhaut, K. 2003. Insects, pp. 405-420. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Ellsworth, D., and D.A. Herms. 2005. Biological calendars: the state-wide network of OSU Phenology Gardens, pp. 111-115. In: J.A. Chatfield, J.F. Boggs, and E.A. Draper (eds.), Ornamental plants: annual reports and research reviews 2004. Ohio Agricultural Research and Development Center and The Ohio State University Extension Special Circular 195.

Ghersa, C.M., and J.S. Holt. 1995. Using phenology prediction in weed management: a review. Weed Res. 35: 461-470.

Gilmore, E.C., and J.S. Rogers. 1958. Heat units as a method of measuring maturity in corn. Agron. J. 50: 611-615.

Herms, D.A. 1990. Biological clocks: using plant phenology to predict insect activity. Am. Nurseryman. 162: 56-63.

Herms, D.A. 1998. The flowering sequence of ornamental plants as a tool for predicting the phenology of insect pests, pp. 77-88. In: M. Rose and J.A. Chaffield (eds.), Ornamental plants: annual reports and research reviews 1997. Ohio Agricultural Research and Development Center and The Ohio State University Extension Special Circular 157.

Herms, D.A. 2001. Degrees of separation. Am. Nurseryman. 194: 34-40.

Herms, D.A. 2003. A biological calendar for predicting pest activity: six years of plant and insect phenology in Secrest Arboretum, pp.40-49. In: J.A. Chatfield, J.F. Boggs, E.A. Draper, and P.J. Bennett (eds.), Ornamental plants: annual reports and research reviews 2002. Ohio Agricultural Research and Development Center and The Ohio State University Extension Special Circular 189.

Higley, L.G., Pedigo, L.P., and K.R. Ostlie. 1986. DEGDAY: a program for calculating degree-days, and assumptions behind the degree-day approach. Environ. Entomol. 15: 999-1016.

Hodges, G.S. and S.K. Braman. 2004. Seasonal occurrence, phenological indicators and mortality factors affecting five scale insect species (Hemiptera: Diaspididae, Coccidae) in the urban landscape setting. J. Entomol. Sci. 39: 611-622.

Hopp, R.J. 1974. Plant phenology observation networks, pp.25-43. In: H. Lieth (ed.), Phenology and seasonality modeling. Springer-Verlag. New York, New York.

Hopp, R.J. and H. Lieth. 1974. Introduction, pp. 367-368. In: H. Lieth (ed.), Phenology and seasonality modeling. Springer-Verlag. New York, New York.

Howe, R.W. 1967. Temperature effects on embryonic development in insects. Annu. Rev. Entomol. 12: 15-42.

Huberman, M.A. 1941. Why phenology? J. Forestry 39: 1007-1013.

Inwards, R. 1898. Weather lore: a collection of proverbs, sayings, and rules concerning the weather. Elliot Stock, London, England.

IPCC. 2007: Climate change 2007: the physical science basis. Contribution of WorkingGroup I to the fourth assessment report of the Intergovernmental Panel on Climate Change. [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Keatley M.R., and T.D. Fletcher. 2003. Australia, pp. 27-44. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Khanduri, V.P., Sharma, C.M., and S.P. Singh. 2008. The effects of climate change on plant phenology. Environmentalist 28: 143-147.

Knapp, R., and T.M. Casey. 1986. Thermal ecology, behavior, and growth of gypsy moth and eastern tent caterpillars. Ecology 67: 598-608.

Koch, E., Demairée, G., Lipa, W., Zach, S., and K. Zimmermann. 2008. History of international phenology networks, pp. 12-16. In: J. Nekorvář, E. Koch, E. Kubin, P. Nejedlik, T. Sparks, and F. Wielgolaski (eds.), COST Action 725- the history and current status of plant phenology in Europe.Vammalan Kirjapaino Oy, Finland.

Lindsey, A.A and J.E. Newman. 1956. Use of official weather data in spring time: temperature analysis of an Indiana phenological record. Ecology 37: 812-823.

Masin, R., Zuin, M., and G. Zanin. 2005. Phenological observations on shrubs to predict weed emergence in turf. Int. J. Biometeorol. 50: 23-32.

McMaster, G.S. and W.W. Wilhelm. 1997. Growing degree-days: one equation, two interpretations. Agr. Forest Meteorol. 87: 291-300.

Menzel, A. and P. Fabian. 1999. Growing season extended in Europe. Nature 397: 397: 659.

Menzel, A., Estrella, N., and P. Fabian. 2001. Spatial and temporal variability of the phenological seasons in Germany from 1951 to 1996. Glob. Change Biol. 7: 657- 666.

Menzel, A. 2003. Europe, pp. 45-56. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Miller-Rushing, A.J. and R.B. Primack. 2008. Global warming and flowering times in Thoreau‟s Concord: a community perspective. Ecology 89: 332-341.

Mussey, G.J., and D.A. Potter. 1997. Phenological correlations between flowering plants and activity of urban landscape pests in Kentucky. J. Econ. Entomol. 90: 1615- 1627

Nietschke, B.S., Magarey, R.D., Borchert, D.M., Calvin, D.D., and E. Jones. 2007. A developmental database to support insect phenology models. Crop Prot. 26: 1444- 1448.

Parton, W.J., and J.A. Logan. 1981. A model for diurnal variation in soil and air temperature. Agric. For. Meteorol. 23: 205-216.

Primack, R.B., and A.J. Miller-Rushing. 2009. The role of botanical gardens in climate change research. New Phytol. 182: 303-313.

Pruess, K.P. 1983. Day-degree methods for pest management. Environ. Entomol. 12: 613-619.

Raupp, M.J., Koehler C.S., and J.A. Davidson. 1992. Advances in implementing integrated pest management for woody landscape plants. Annu. Rev. Entomol. 37: 561-585.

Raworth, D.A. 1994. Estimation of degree-days using temperature data recorded at regular intervals. Environ. Entomol. 23: 893-899.

Roltsch, W.J., Zalom F.G., Strawn A.J., Strand J.F., and M.J. Pitcairn. 1999. Evaluation of several degree-day estimation methods in California climates. Int. J. Biometeorol. 42: 169-176.

Schnelle F., and E. Volkert. 1974. International phenological gardens in Europe the basic network for international phenological observations, pp. 383-387. In: H. Lieth (ed.), Phenology and seasonality modeling. Springer-Verlag New York, New York.

Schwartz, M.D. 1994. Monitoring global change with phenology: the case of the spring green wave. Int. J. Biometeorol. 38: 18-22.

Schwartz, M.D. 2003. Phenology: An integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Sevacherian, V., Stern N.M., and A.J. Muellers. 1977. Heat accumulation for timing Lygus control measures in a safflower-cotton complex. J. Econ. Entomol. 70: 399- 402.

Sharpe, P.J.H., and D.W. DeMichele 1977. Reaction kinetics of poikilotherm development. Theor. Biol. 64: 649-670.

Snyder, R.L., Spano D., Cesaraccio C., and P. Duce. 1999. Determining degree-day thresholds from field observations. Int. J. Biometeorol. 42: 177-182.

Sparks, T.H., and P.D. Carey. 1995. The responses of species to climate over two centuries: an analysis of the Marsham phenological record, 1736-1947. J. Ecol. 83: 321-329.

Swanton, C.J., and S.F. Weise. 1991. Integrated weed management: the rational and approach. Weed Technol. 5:657-663.

Taylor, F. 1981. Ecology and evolution of physiological time in insects. Am. Nat. 117: 1- 23.

Vittum, M.T., and R.J. Hopp. 1978. The NE-95 lilac phenology network, pp.1-5. In: R.J. Hopp (Ed.), Phenology: an aid to agricultural technology. Agricultural Experiment Station-University of Vermont Bulletin 684.

Waggoner, P.E. 1974. Using models of seasonality, pp. 401-405. In: H. Lieth (ed.), Phenology and seasonality modeling. Springer-Verlag New York, New York.

Wagner, T.L., Wu, H.I., Sharpe, P.J.H., Schoolfield, R.T., and R.N. Coulson. 1984. Modeling insect development rates: a literature review and application of a biophysical model. Ann. Entomol. Soc. Am. 77: 208-225.

Wielgolaski, F.1974. Phenology in agriculture, pp. 369-381. In: H. Lieth (ed.), Phenology and seasonality modeling. Springer-Verlag New York., New York.

Wielgolaski, F.1999. Starting dates and basic temperatures in phenological observations of plants. Int. J. Biometeorol. 42: 158-168.

Willis, C.G., Ruhfel, B., Primack, R.B., Miller-Rushing, A.J., and C.C. Davis. 2008. Phylogenetic patterns of species loss in Thoreau‟s woods are driven by climate change. Proc. Nat. Acad. Sci. 105: 17029-17033.

Worner, S.P. 1992. Performance of phenological models under variable temperature regimes: consequences of the Kaufmann or rate summation effect. Environ. Entomol. 21: 689-699.

Yang, S., Logan, J., and D.L.Coffey. 1995. Mathematical formulae for calculating the base temperature for growing degree days. Agr. Forest Meteorol. 74: 61-74.

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

Abstract

Degree-day models are valuable tools for predicting pest activity to accurately time pest management tactics. However, in practice there are logistical challenges associated with their use. Degree-day models are typically developed for a particular pest species by evaluating combinations of starting dates and base temperatures to identify the one that has the greatest predictive power. However, when managing a large complex of pests, such as the case in highly diverse ornamental landscapes and nurseries, the use of different models for each pest species is not logistically practical. Consequently, a standardized model based on a single starting date and base temperature is often used.

For example, The Ohio State University Degree-Day and Phenology Website

(http://www.oardc.ohio-state.edu/gdd) employs a standardized model utilizing a starting date of January 1 and a base temperature of 10°C to predict the phenology of nearly 50 key arthropod pests of woody ornamental plants. This standardized method presumably compromises predictive accuracy in favor of simplicity. The objective of this study was

23 to quantify the magnitude of error in the standardized model relative to species-specific models and more simplified calendar date predictions developed individually for each of the key pests included on The Ohio State University Degree-Day and Biological Calendar

Website to determine the degree to which standardized models have utility for timing pest management decisions. A standardized and customized model was developed for 45 phenological events (such as first egg hatch) of 43 species based on five years (1997-

2001) of phenological and degree-day data. A sixth year of data (2002) was used to compare the accuracy of predictions of the standardized model relative to customized models and calendar date averages. Analysis of variance found no significant difference in the accuracy of predictions among the three models based on their deviation in days

(observed-predicted) from the actual date of occurrence of the phenological event in 2002

(F = 2.153; df = 2, 132; P=0.120). Surprisingly, the standardized model most accurately predicted 28 of 45 phenological events. These results indicate that standardized degree- day models have practical application for use in integrated pest management.

Introduction

Degree-day models, which are based on the concept of temperature-dependent development, are valuable tools for predicting pest phenology. Considered to be more accurate than calendar dates for timing pest management practices, degree-days models have been advocated for use in integrated pest management (IPM) programs (Ascerno

1991, Raupp et al.1992). Most degree-day models are developed from variations of the basic model that subtracts a predetermined base temperature from the daily average ambient temperature (Pruess 1983, Higley et al. 1986), with the base temperature ideally

24 representing a temperature threshold below which no considerable development will occur (Herms 2003).

Because all degree-day models specify a lower base temperature and a starting date

(Bonhomme 2000, Herms 2001), modifications to degree-day models can be made by altering the starting date on which to begin calculating degree-day accumulation

(Wielgolaski 1999, Jones et al. 2008), or by altering the base temperature used in the model (Wielgolaski 1999). Such alterations can be made to customize degree-day models for individual species to improve accuracy. These variables are experimentally determined for their biological relevance when possible. Starting dates should correspond to a biologically significant event (such as the date eggs are laid or the end of diapause), but often are arbitrarily chosen such as January 1 (Pruess 1983, Wielgolaski

1999). Ideally the lower physiological threshold for development is used as the base temperature in the model; however, these thresholds are unknown for a majority of pest species (Herms 2001), so an arbitrary or statistically determined value is often used.

These values can be calculated in labs under controlled conditions (Higley et al.

1986, Burden & Hart 1989), or by using field data to estimate values that provide the best fit to observed data (Arnold, 1959; Akers & Nielsen, 1984; Ascerno & Moon 1989). Due to the difficulty associated with determining physiological thresholds for individual species experimentally, alternative methods to determining appropriate base temperatures and starting dates are often employed. Arnold (1959) describes methods for selecting base temperatures and starting dates stating: “Heat unit summations from a series of plantings are calculated on a number of base temperatures and the one giving the least variation is found by the process of elimination”. The model with the least variation can

25 be estimated by measuring the coefficient of variation in degree-day accumulation over several years, a method shown to be as good as alternative methods such as regression coefficient and x-intercept (Arnold 1959). This method has been widely used (e.g. Potter and Timmons 1983, Akers & Nielsen 1984, Ascerno & Moon 1989, Mussey & Potter

1997, Masin et al. 2005, Cardina et al. 2007).

By determining these values on a species-by-species basis, practitioners should be able to improve accuracy of pest prediction models (Arnold 1959, Yang et al. 1995,

Snyder et al. 1999). However, despite the lure of improved accuracy, there are logistical challenges associated with utilizing different base temperatures and starting dates for individual species (Pruess 1983), especially when confronted with large pest complexes such as those associated with ornamental landscapes or nurseries. Researchers have developed increasingly complex degree-day models which appear to be more accurate

(Allen 1976, Cesaraccio et al. 2001) as well. However, these too tend to sacrifice simplicity in favor of more precise calculations which may not be practical for use as a heuristic tool for pest management.

A single standardized model that employs a common base temperature and starting date used for all pest species is a more convenient and manageable approach for predicting pest phenology. For example, The Ohio State University Degree-Day and

Phenology Website (http://www.oardc.ohio-state.edu/gdd) employs a standardized model that uses January 1 and 10ºC to predict the phenology of nearly 50 key arthropod pests of woody ornamental plants.

Although “customized models” (i.e. models with individual base temperature and starting date for each species) may achieve increased accuracy (e.g. Tobin et al 2001), it

26 is not known if they significantly improve accuracy when used in the field. While studies have compared the accuracy of standardized and customized degree-day models for individual pests (e.g. Tobin et al. 2001, Hodges and Braman 2004, Jones et al. 2008), to our knowledge there have been no studies comparing standardized and customized models for the overall accuracy of large pest complexes. Averaged calendar dates are often used to predict pests and plan sprays as well, but are considered the least accurate

(Ascerno 1991, Herms 2004) due to geographic and temporal weather variation (Herms

2003), although Pruess (1983) claimed that often degree day models are generally not compared to predictions based on calendar dates. Therefore, the objective of this study was to compare the accuracy of predictions for 45 phenological events from 43 key pests of ornamental plants made with (1) a standardized degree-day model, (2) customized species-specific degree-day model, and (3) average calendar date model.

Methods

Phenology and Temperature Dataset

Accuracy of predictions was assessed using data for 45 phenological events (e.g. first egg hatch, first adult emergence) from 43 arthropod pest species of woody ornamental plants (Table 2.1). Data were collected over six growing seasons at the

Secrest Arboretum in Wooster, Ohio [40.77949, -81.931204] from 1997 to 2002 (Herms

2003). Daily temperature data were recorded by the Ohio Agricultural Research and

Development Center (OARDC) Weather System, located within Secrest Arboretum.

Common Name Scientific Name Phenological Event Defoliators Eastern tent caterpillar Malacosoma americanum (F.) 1st observed egg hatch Gypsy moth Lymantria dispar (L.) 1st observed egg hatch Mimosa webworm 1st generation Homadaula anisocentra Meyrick 1st observed egg hatch Larch casebearer Coleophora laricella (Hübner) 1st larval feeding activity Fall webworm Hyphantria cunea (Drury) 1st feeding larva Japanese beetle Popilla japonica Newman 1st observed adult emergence Imported willow leaf beetle Plagiodera versicolora (Laicharting) 1st observed adult emergence European pine sawfly Neodiprion sertifer (Geoffroy) 1st observed egg hatch Wood-borers Lilac borer Podosesia syringae (Harris) 1st observed adult emergence Peachtree borer Synanthedon exitiosa (Say) 1st observed adult emergence Lesser peachtree borer Synanthedon pictipes (Grote & Robinson) 1st observed adult emergence Rhododendron borer Synanthedon rhododendri (Beutenmüller) 1st observed adult emergence Dogwood borer Synanthedon scitula (Harris) 1st observed adult emergence Banded ash clearwing borer Podesisia aureocincta Purrington & Nielsen 1st observed adult emergence Bronze birch borer Agrilus anxius Gory 1st observed adult emergence Sucking Arthropods Pine needle scale 1st & 2nd generation Chionaspis pinifoliae (Fitch) 1st observed egg hatch Euonymus scale 1st & 2nd generation Unaspis euonymi (Comstock) 1st observed egg hatch Oystershell scale Lepidosaphes ulmi (L.) 1st observed egg hatch Juniper scale Carulaspis juniperi (Bouché) 1st observed egg hatch Calico scale Eulecanium cerasorum (Cockerell) 1st observed egg hatch Striped pine scale Toumeyella pini (King) 1st observed egg hatch European fruit fecanium scale Parthenolecanium corni (Bouché) 1st observed egg hatch Cottony maple scale Pulvinaria innumerabilis (Rathvon) 1st observed egg hatch Spruce bud scale Physokermes piceae (Schrank) 1st observed egg hatch Azalea bark scale Acanthococcus azaleae (Comstock) 1st observed egg hatch Magnolia scale Neolecanium cornuparvum (Thro) 1st observed egg hatch Winged euonymus scale* Lepidosaphes yanagicola Kuwana 1st observed egg hatch Cooley spruce gall adelgid Adelges cooleyi (Gillette) 1st observed egg hatch Eastern spruce gall adelgid Adelges abeitis (L.) 1st observed egg hatch Boxwood psyllid Cacopsylla buxi (L.) 1st observed egg hatch Honeylocust spider mite Platytetranychus multidigituli (Ewing) 1st observed egg hatch Spruce spider mite Oligonychus ununguis (Jacobi) 1st observed egg hatch Andromeda lace bug* Stephanitis takeyai Drake & Maa 1st observed egg hatch Potatoleafhopper Empoasca fabae (Harris) 1st adult appearance Honeylocust plant bug Diaphnocoris chlorionis (Say) 1st observed egg hatch Hawthorn lace bug Corythucha cydoniae (Fitch) 1st observed adult emergence Leafminers Inkberry leafminer* Phytomyza glabricola Kulp 1st observed adult emergence Hawthorn leafminer* Profenusa canadensis (Marlatt) 1st observed adult emergence European alder leafminer Fenusa dohrnii (Tishbein) 1st observed adult emergence Elm leafminer Kaliofenusa ulmi (Sundevall) 1st observed adult emergence Native holly leafminer Phytomyza ilicicola Loew 1st observed adult emergence Birch leafminer Fenusa pusilla (Lepeletier) 1st observed adult emergence Others Black vine weevil Otiorhynchus sulcatus (F.) 1st observed adult emergence * Common name is not recognized by the Entomological Society of America

Table 2.1: The 43 species of arthropod pests of woody ornamental plants and the corresponding phenophases that were observed from 1997-2002.

Insect Monitoring

Arthropod species were monitored via visual inspection or traps. Host plants and traps were checked at least three times a week, but up to four to five times per week as necessary to make the first observation of the phenological event.

Defoliators. First egg hatch of five species of lepidopteran defoliators was monitored: eastern tent caterpillar, gypsy moth, mimosa webworm, and fall webworm and first larval activity of larch casebearer. At least 10 eastern tent caterpillar egg masses were collected during the winter season and re-attached to conveniently located host plants for ease of observation. At least 50 gypsy moth egg masses were monitored each year to observe first egg hatch. The egg hatch of first generation mimosa webworm was monitored on at least five honeylocust trees by checking for larval feeding as indicated by first signs of webbing made by first instars. Similarly, fall webworm was observed by searching for webbing initiated by first instars throughout Secrest Arboretum until the first colony was found. To monitor first feeding by overwintered larch casebearer larvae,

10 twigs on five host plants (Larix spp.) were observed for the first sign of actively feeding larvae.

Other defoliators included imported willow leaf beetle which was monitored by scouting at least 50 branches across 5 host plants for the first signs of feeding by overwintered adults. First emergence of adult Japanese beetle was monitored using four standard Japanese beetle pheromone traps with attractants containing floral (1:1.1:2.2 ratio of geraniol, phenethyl propionate, and eugenol) and sex pheromone [(R,Z)-5(1- decenyl) dihydro-2(3H) furanone] as described in Mussey and Potter (1997). First egg

29 hatch of European pine sawfly was monitored by observing 10 egg masses on at least 5 host plants.

Wood-borers. We monitored six species of clearwing borers (Lepidoptera:

Sesiidae) for adult emergence, as indicated by first flight of adult males, using methods described in Mussey and Potter (1997). Four wing-type pheromone traps were deployed per species being monitored. They were baited with pheromone lures containing various ratios of isomers of 3,13-octadecadien-1-ol acetate and were deployed in respective host plants to monitor flight of lilac borer, peach tree borer, lesser peach tree borer, rhododendron borer, dogwood borer, and banded ash clearwing borer. Lilac borer, peach tree borer, and banded ash clear wing borer were attracted by lures containing a 97:3 mixture of Z,Z- and E,Z-octadecadien-1-ol acetate (Mussey and Potter 1997). Lures for dogwood borer and rhododendron borer contained >99% Z,Z-octadecadien-1-ol acetate, while lesser peach tree borer lures contained >99% E,Z-octadecadien-1-ol acetate

(Mussey and Potter 1997). Pheromone traps were checked three times weekly until the first capture of adult males was observed. First bronze birch borer adult emergence was documented by monitoring at least 10 infested European white birch trees (Betula pendula F. Ehrhart) at least three times per week until new emergence holes were observed. Old emergence holes were marked each year with a red wax pencil and unmarked exit holes signaled commencement of current year emergence.

Sucking Arthropods. Scouting for first egg hatch of 12 species of scale insects entailed microscopic examination of at least 50 females across at least five host plants at least three times per week until first egg hatch was observed. This procedure was also followed for two adelgid species: cooley spruce gall adelgid and eastern spruce gall

30 adelgid. First egg hatch of boxwood psyllid was monitored by inspecting at least 50 branches on at least five host plants for first signs of nymphal activity. Spruce spider mite egg hatch was monitored by observing 50 eggs microscopically on twigs collected from at least five trees. First egg hatch of honeylocust spider mite was inferred by searching for first nymphs on at least 10 twigs of five host plants. Andromeda lace bugs were inspected on at least 50 branches for egg masses on at least 5 host plants (Pieris spp.) to find signs of first nymphal activity. At least 10 twigs of five host plants were monitored for honeylocust plant bug and hawthorn lace bug to determine first egg hatch and adult emergence, respectively. Potato leafhopper adult arrival was monitored with yellow sticky card traps hung on four host plants known to be infested in previous years.

Leafminers. Two species of dipteran leafminers and four species of hymenopteran leafminers were monitored. First adult emergence of inkberry leafminer and holly leafminer were monitored by examining at least 50 pupae across at least five host plants, the inkberry, Ilex glabra (L.) A.Gray, and American holly, Ilex opaca Aiton, respectively. Emergence of birch leafminer, alder leafminer, elm leafminer, and hawthorn leafminer were all monitored via yellow sticky cards. One card was hung on each of four host plants known to be infested the previous year.

Others. Adult emergence of black vine weevil was monitored by examining at least 10 evergreen host plants with feeding on previous years‟ foliage. The first signs of characteristic leaf notching on current years‟s foliage indicated the onset of maturation feeding by newly emerged adults.

Degree-Day Models

The predictive accuracy of (1) a standardized model, (2) customized species- specific models, and (3) an average calendar day model was compared in this study. The standardized model was generated by calculating daily cumulative degree-days using a base temperature of 10°C and a starting date of January 1 for each of 45 phenological events for 43 arthropod species from 1997-2001. Cumulative degree-days were calculated using the program Forecaster v 1.0 (Ascerno and Moon 1989), which employs a modified double sine wave approach (Allen 1976).

Customized degree-day models were constructed for each of the 45 individual phenological events by determining the base temperature and starting date that minimized the coefficient of variation. First, total degree-days of individual events for 1997-2001 were calculated using eight starting dates (September 1, October 1, November 1,

December 1 of the previous year, and January 1, February 1, March 1, and April 1 of the current year) in combination with one of four base temperatures (0°C, 5°C, 10°C, &

15°C). The coefficient of variation was then calculated for the five year average with each combination of starting date and base temperature. The customized species-specific model was determined to be the model with the combination of starting date and base temperature that resulted in the lowest coefficient of variation for that phenological event.

This process was repeated for each of the 45 phenological events. The average calendar date of occurrence for each phenological event was determined by averaging the actual

Julian date of occurrence for each event from 1997 to 2001.

Model Validation and Comparison of Accuracy

To compare model accuracy, each model was used to predict the occurrence of the 45 phenological events in 2002. Using the actual dates of occurrence in 2002, we calculated the deviation in days between the observed date and the predicted date for each model. Analysis of variance (SPSS Inc. 2007) was used to assess whether the mean deviation in days (observed-predicted) generated by each model was significantly different (P<0.05).

Results

The starting date and base temperature determined for the optimized models generated from the 1997-2001 data for each phenological event are listed in Table 2.2.

Predictions made for 2002 by the optimized models, the generalized model, and average calendar date model are listed in Table 2.3. The three models generated predictions that deviated (observed-predicted) from 0 to 30 days from the actual occurrence of the phenological events. The range of these deviations was smallest among the predictions generated by the standardized model, with predictions ranging from 0-17 days from the actual date of occurrence. The average calendar date model generated predictions that deviated from the observed event by 0-23 days and prediction of the customized models deviated from the date of occurrence from 0-30 days.

The accumulation of degree-days over the course of a year varied between 1997 and 2002 (Figure 2.1). For example, 1997 was a colder year, resulting in fewer cumulative degree-days than warmer years, such as 1998. Despite the yearly weather variation and the predictions deviating so widely (0-30 days), analysis of variance

Common name Starting date Base Temp°C CV Eastern tent caterpillar February 1 0°C 4.4 Larch casebearer September 1 0°C 6.0 Inkberry leafminer* March 1 0°C 4.6 European pine sawfly February 1 0°C 3.8 Spruce spider mite September 1 0°C 6.4 Boxwood psyllid September 1 15°C 6.2 Gypsy moth October 1 15°C 5.5 Birch leafminer March 1 5°C 5.1 Andromeda lace bug* September 1 10°C 0.2 Alder leafminer September 1 10°C 4.7 Elm leafminer March 1 0°C 4.5 Honeylocust spider mite September 1 10°C 3.8 Honeylocust plant bug January 1 10°C 4.5 Hawthorn lace bug* October 1 15°C 3.8 Imported willow leaf beetle September 1 0°C 7.2 Hawthorn leafminer March 1 10°C 3.1 Pine needle scale 1st generation March 1 5°C 5.4 Cooley spruce gall adelgid March 1 5°C 3.8 Eastern spruce gall adelgid March 1 5°C 3.8 Lilac borer March 1 10°C 4.4 Holly leafminer March 1 5°C 3.6 Lesser peachtree borer September 1 5°C 5.6 Euonymus scale 1st generation September 1 5°C 5.1 Oystershell scale March 1 5°C 4.8 Bronze birch borer September 1 0°C 7.5 Potato leafhopper April 1 10°C 4.3 Juniper scale October 1 5°C 5.2 Black vine weevil September 1 0°C 9.6 Greater peachtree borer September 1 10°C 5.0 Calico scale January 1 0°C 0.9 European fruit lecanium scale January 1 5°C 1.6 Striped pine scale February 1 0°C 0.8 Rhododendron borer October 1 15°C 3.2 Cottony maple scale October 1 5°C 2.6 Fall webworm March 1 0°C 4.2 Mimosa webworm April 1 0°C 3.9 Dogwood borer September 1 0°C 5.6 Winged euonymus scale* January 1 5°C 0.4 Spruce bud scale February 1 5°C 3.2 Azalea bark scale March 1 5°C 1.6 Japanese beetle March 1 5°C 1.7 Pine needle scale 2nd generation January 1 0°C 1.9 Euonymus scale 2nd generation September 1 5°C 3.8 Magnolia scale October 1 10°C 4.1 Banded ash clearwing borer March 1 0°C 2.2 * Indicates common name not recognized by the Entomological Society of America.

Table 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.

Observed Standardized Customized Calendar day Phenological 2002 model prediction model prediction prediction Common Names Event Date Date (o-p) Date (o-p) Date (o-p) Eastern tent caterpillar 1st egg hatch 10-Apr 9-Apr ( 1) 2-Apr ( 8) 1-Apr ( 9) Larch casebearer 1st larval activity 8-Apr 13-Apr (-5) 9-Mar (-1) 6-Apr ( 2) Inkberry leafminer* 1st adult emergence 8-Apr 15-Apr (-7) 13-Apr (-5) 11-Apr (-3) European pine sawfly 1st egg hatch 14-Apr 14-Apr ( 0) 13-Apr ( 1) 10-Apr ( 4) Spruce spider mite 1st egg hatch 12-Apr 16-Apr (-4) 13-Apr (-1) 13-Apr (-1) Boxwood psyllid 1st egg hatch 14-Apr 16-Apr (-2) 18-Apr (-4) 17-Apr (-3) Gypsy moth 1st egg hatch 18-Apr 17-Apr ( 1) 12-Apr ( 6) 23-Apr (-5) Birch leafminer 1st adult emergence 18-Apr 18-Apr ( 0) 20-Apr (-2) 26-Apr (-8) Andromeda lace bug* 1st egg hatch 24-Apr 18-Apr ( 6) 17-Apr ( 7) 23-Apr ( 1) European alder leafminer 1st adult emergence 18-Apr 18-Apr ( 0) 17-Apr ( 1) 25-Apr (-7) Elm leafminer 1st adult emergence 19-Apr 18-Apr ( 1) 20-Apr (-1) 24-Apr (-5) Honeylocust spider mite 1st egg hatch 19-Apr 18-Apr ( 1) 18-Apr ( 1) 27-Apr (-8) Honeylocust plant bug 1st egg hatch 20-Apr 19-Apr ( 1) 19-Apr ( 1) 30-Apr (-10) Hawthorn lace bug 1st adult emergence 1-May 19-Apr (12) 16-Apr (12) 29-Apr ( 2) Imported willow leaf beetle 1st adult emergence 18-Apr 28-Apr (-10) 17-Apr ( 1) 5-May (-17) Hawthorn leafminer* 1st adult emergence 29-Apr 24-Apr ( 5) 20-Apr ( 9) 1-May (-2) Pine needle scale 1st egg hatch 30-Apr 5-May (-5) 7-May (-7) 8-May (-8) Cooley spruce gall adelgid 1st egg hatch 30-Apr 5-May (-5) 7-May (-7) 8-May (-8) Eastern spruce gall adelgid 1st egg hatch 30-Apr 5-May (-5) 7-May (-7) 8-May (-8) Lilac borer 1st adult emergence 16-May 7-May ( 9) 7-May ( 9) 11-May ( 5) Native holly leafminer 1st adult emergence 16-May 13-May ( 3) 14-May ( 2) 13-May ( 3) Lesser peach tree borer 1st adult emergence 23-May 12-May (11) 2-May (21) 14-May ( 9) Euonymus scale 1st egg hatch 23-May 17-May ( 6) 7-May (16) 16-May ( 7) Oystershell scale 1st egg hatch 29-May 29-May ( 0) 29-May ( 0) 24-May ( 5) Bronze birch borer 1st adult emergence 2-Jun 1-Jun ( 1) 31-May ( 2) 28-May ( 4) Potato leafhopper 1st adult arrival 29-May 1-Jun ( 2) 1-Jun ( 2) 29-May ( 0) Juniper scale 1st egg hatch 2-Jun 1-Jun ( 1) 23-May (10) 29-May ( 4) Black vine weevil 1st adult emergence 29-May 9-Jun (-10) 29-May ( 0) 30-May (-1) Greater peach tree borer 1st adult emergence 10-Jun 10-Jun ( 0) 8-Jun ( 2) 5-Jun ( 5) Calico scale 1st egg hatch 11-Jun 11-Jun ( 0) 10-Jun ( 1) 6-Jun ( 5) European fruit lecanium scale 1st egg hatch 11-Jun 12-Jun (-1) 12-Jun (-1) 7-Jun ( 4) Striped pine scale 1st egg hatch 11-Jun 13-Jun (-2) 13-Jun (-2) 8-Jun ( 3) Rhododendron borer 1st adult emergence 24-Jun 14-Jun (10) 11-Jun (13) 10-Jun (14) Cottony maple scale 1st egg hatch 20-Jun 17-Jun ( 3) 10-Jun (10) 12-Jun ( 8) Fall webworm 1st feeding larva 18-Jun 18-Jun ( 0) 18-Jun ( 0) 16-Jun ( 2) Mimosa webworm 1st egg hatch 20-Jun 18-Jun ( 2) 18-Jun ( 2) 13-Jun ( 7) Dogwood borer 1st adult emergence 3-Jul 16-Jun (17) 8-Jun (25) 10-Jun (23) Winged euonymus scale* 1st egg hatch 24-Jun 19-Jun ( 5) 19-Jun ( 5) 14-Jun (10) Spruce bud scale 1st egg hatch 25-Jun 24-Jun ( 1) 20-Jun (-5) 17-Jun ( 8) Azalea bark scale 1st egg hatch 24-Jun 22-Jun ( 2) 22-Jun ( 2) 18-Jun ( 6) Japanese beetle 1st adult emergence 24-Jun 27-Jun (-3) 23-Jun ( 1) 21-Jun ( 3) Pine needle scale (2nd gen) 1st egg hatch 16-Jul 7-Jul ( 9) 8-Jul ( 8) 6-Jul (10) Euonymus scale (2nd gen) 1st egg hatch 27-Jul 25-Jul ( 2) 22-Jul ( 5) 26-Jul ( 1) Magnolia scale 1st egg hatch 4-Aug 31-Jul ( 4) 30-Jul ( 5) 4-Aug ( 0) Banded ash clearwing borer 1st adult emergence 14-Aug 11-Aug ( 3) 2-Aug (12) 14-Aug ( 0) * Common names not recognized by the Entomological Society of America.

Table 2.3: Predictions for 2002 of 45 phenological events of 43 pest species made by a standardized degree-day model (January 1, 10°C), customized species-specific degree- day models, and average calendar date models. Accuracy of models presented as the deviation in days from the actual event occurrence (observed-predicted shown as o-p). Event occurrence in 2002 is also listed.

Figure 2.1: Accumulation of growing degree-days over the course of the year for 1997 to 2002. Degree-days were calculated using a modified double sine wave (Allen 1976), a starting date of January 1 and a base temperature of 10°C.

36 revealed that the average deviation in days between models was not significantly different (F = 2.153; df = 2, 132; P = 0.120). Tukey‟s HSD test further showed that the standardized model had the smallest mean deviation of four days (4.00 ± 0.59).

Customized models deviated on average by six days (6.13 ± 1.00), and the calendar date model deviated by about five days, on average (5.76 ± 0.68).

Discussion

There was no overall difference in the relative accuracy of predictions made by the three models. The accuracy of the three models did, however, vary by individual species in 2002. For example, in 2002, the standardized model most accurately predicted first egg hatch of eastern tent caterpillar and first adult emergence of bronze birch borer

(both were predicted within one day of the actual event). However, the customized model most accurately predicted first adult emergence of imported willow leaf beetle

(predicted within one day of the actual event) and black vine weevil (predicted the exact date of occurrence). Although some species were most accurately predicted by standardized degree-day models and other species were most accurately predicted by the customized or calendar date models, there was no significant difference in the mean deviation of predictions made by the three models overall.

Surprisingly, the standardized model was as good as, or better, at predicting the occurrence of each phenological event than the other two models for 28 of the 45 phenological events in 2002, while the customized model most accurately predicted five phenological events, and the averaged calendar date method most accurately predicted 11 events (with one event being equally predicted by both custom and calendar method).

Contrary to the findings of this research, many studies report that customized degree-day models improve prediction accuracy (Johnson et al. 1983, Potter and Timmons 1983,

Burden and Hart 1989, Neal et al. 1997, Tobin et al. 2001). However, the customized models were most accurate for only five events in 2002 and, unexpectedly, the average calendar date model was most accurate for 11 phenological events in 2002. Both the customized and the average calendar date method predicted spruce spider mite first egg hatch within one day of the actual event. Perhaps the average calendar date method was so successful in 2002 because degree-day accumulation that year was very close to the average of 1997-2001 during the early growing season (Figure 2.1). It is reasonable that averaged dates would align well with average weather; however, it is likely that the average calendar dates would not be as accurate in predicting phenological events in years during which degree-day accumulation deviated substantially from average.

Degree-day models have been developed for several species monitored here, including pine needle scale (Burden and Hart 1989), European fruit lecanium scale, euonymous scale (Hodges and Braman 2004), gypsy moth (Johnson et al. 1983), eastern tent caterpillar (Neal et al 1997), bronze birch borer (Akers and Nielsen 1984), and lilac borer (Potter and Timmons 1983). However, the customized degree-day models developed in this study were sometimes consistent with those reported in previous studies, sometimes not. For example, Burden and Hart (1989) determined the developmental threshold for 50% egg eclosion of overwintering eggs of pine needle scale to be 11°C. This study was consistent with their results, as the standardized degree-day model using a 10°C base temperature and January 1 starting date was found to be the most accurate predictor for pine needle first egg hatch in this study.

Akers and Nielsen (1984) found different models to be most accurate for predicting bronze birch borer adult emergence at two different locations in Ohio. The most accurate degree-day model for predicting bronze birch borer adult emergence in

Columbus, Ohio was determined to be base temperature = 8°C, starting date = May 1, while a base temperature = 10°C, starting date = April 1 generated the most accurate predictions in Wooster, Ohio (Akers and Nielsen 1984). In this study, however, the standardized model (starting date = January 1, base temperature = 10°C) was the best predictor of first adult emergence of bronze birch borer. This is a further example of how two studies using the same methods for constructing degree-day models can reach different conclusions about which model is most accurate for predicting the phenology of a particular species.

The discrepancy between degree-day models found to be most accurate here and those reported in previous studies could be due to differences in weather, location, degree-day formula used (i.e. sine wave vs. rectangle method), method used to select base temperatures and starting dates (i.e. coefficient of variation vs. x-intercept), or phenological event being predicted (i.e. first egg hatch vs. 50% egg hatch). These potential sources of variation further illustrate the complexity involved in utilizing a customized, species-specific degree-day model for each pest, and further support the use of a standardized approach for multiple pests.

This study suggests that a standardized degree-day model employing January 1 as a starting date and 10°C as the base temperature may be sufficiently accurate for timing pest management decisions for large pest complexes, as our study did not detect a statistical increase in predictive accuracy from the use of customized models. These

39 results validate the accuracy of the user-friendly Ohio State University Degree-Day and

Biological Calendar Website (http://www.oardc.ohio-state.edu/gdd) (which utilizes the standardized degree-day model) for predicting the phenology of a large pest complex.

Accuracy was not sacrificed in favor of simplicity.

References

Akers, R.C. and D.G. Nielsen 1984. Predicting Agrilus anxius Gory (Coleoptera: Buprestidae) adult emergence by heat unit accumulation. J. Econ. Entomol. 77: 1459-1463.

Allen, J.C. 1976. A modified sine wave method for calculating degree-days. Environ. Entomol. 5: 388-396.

Arnold, C.Y. 1959. The determination and significance of the base temperature in a linear heat unit system. Proc. Amer. Soc. Hort. Sci. 74: 430-445.

Ascerno, M.E. 1991. Insect phenology and integrated pest management. J. Arbor. 17: 13- 15.

Ascerno, M.E. and R.D. Moon. 1989. Forecaster: predicting biological phenomena based on daily temperatures. Minnesota Extension Service. AG-CS-3029. V.1.0.

Bonhomme, R. 2000. Bases and limits to using „degree-day‟ units. Eur. J. Agron. 13:1- 10.

Burden, D.J, and E.R. Hart. 1989. Degree-day model for egg eclosion of the pine needle scale (Hemiptera: Diaspididae). Environ. Entomol. 18: 223-227.

Cardina, J., Herms, C.P., Herms, D.A., and F. Forcella. 2007. Evaluating phenological indicators for predicting giant foxtail (Setaria faberi) emergence. Weed Sci. 55: 455-464.

Cesaraccio, C., Spano, D., Duce, P., R.L. Snyder. 2001. An improved model for determining degree-day values from daily temperature data. Int. J. Biometeorol. 45: 161-169.

Herms, D.A. 2001. Degrees of separation. Am. Nurseryman. 194: 34-40.

Herms, D.A. 2003. A biological calendar for predicting pest activity: six years of plant and insect phenology in Secrest Arboretum, pp. 40-49. In: J.A. Chatfield, J.F. Boggs, E.A. Draper, and P.J. Bennett (eds.), Ornamental plants: annual reports and research reviews 2002. Ohio Agricultural Research and Development Center and The Ohio State University Extension Special Circular 189.

Herms, D.A. 2004. Using degree-days and plant phenology to predict pest activity, pp. 49-59. In: V. Krischik, and J. Davidson (eds.), IPM (Integrated Pest Management) of Midwest Landscapes. Regents of the University of Minnesota. Minnesota Agricultural Experiment Station Publication SB-07645.

Higley, L.G., Pedigo P.P., and K.R. Pstlie. 1986. DEGDAY: a program for calculating degree-day and assumptions behind the degree-day approach. Environ. Entomol. 15: 999-1016.

Johnson, P.C., Mason, D.P., Radke, S.L., and K.T. Tracewski. 1983. Gypsy moth, Lymantria dispar L. (Lepidoptera Lymantriidae), egg eclosion: degree-day accumulation. Environ. Entomol. 12: 929-932.

Jones, V.P., Doerr, M., and J.F.Brunner . 2008. Is biofix necessary for predicting codling moth (Lepidoptera: Tortricidae) emergence in Washington state apple orchards? Hort. Entomol. 101: 1651-1657.

Masin, R., Zuin, M.C., and G. Zarin. 2005. Phenological observations on shrubs to predict weed emergence in turf. Int. J. Biometeorol. 50: 23-32.

Mussey, G.J., and D.A. Potter. 1997. Phenological correlations between flowering plants and activity of urban landscape pests in Kentucky. J. Econ. Entomol. 90: 1615- 1627

Neal, J.W. Jr., Chittams, J.L., and J.Bentz. 1997. Spring emergence by larvae of the eastern tent caterpillar (Lepidoptera: Lasiocampidae): a hedge against high-risk conditions. Ann. Entomol. Soc. 90: 596-603.

Potter, D.A., and G.M. Timmons. 1983. Forecasting emergence and flight of the lilac borer (Lepidoptera: Sessiidae) based on pheromone trapping and degree-day accumulations. Environ. Entomol. 12: 400-403.

Pruess, K. 1983. Day-degree methods for pest management. Environ. Entomol. 12: 613- 619.

Raupp, M.J., Koehler, C.S., and J.A. Davidson. 1992. Advances in implementing integrated pest management for woody landscape plants. Annu. Rev. Entomol. 37: 561-585.

Snyder, R.L., Spano, D., Cesaraccio, C., and P. Duce. 1999. Determining degree-day thresholds from field observations. Int. J. Biometeorol. 42: 177-182.

SPSS 16.0 for Windows. 2007. Rel. 16.0.1 Chicago: SPSS Inc

Tobin, P.C., Nagarkatti, S., and M.C. Saunders. 2001. Modeling development in grape berry moth (Lepidoptera: Tortricidae). Environ. Entomol. 30: 692-699.

Yang, S., Logan, J., and D.L.Coffey. 1995. Mathematical formulae for calculating the base temperature for growing degree days. Agr. Forest Meteorol. 74: 61-74.

Chapter 3:

The Ohio State Phenology Garden Network:

Consistency of a Phenological Sequence Across Years and Locations

Abstract

Accurate prediction of pest activity is crucial for the successful implementation of pest management practices. While current tools for timing pest management are considered suitably accurate, they may be logistically difficult for practitioners to implement. Since plants and insects both exhibit temperature-dependent development, phenological sequences can be used as biological calendars as a practical tool to accurately time pest management decisions. However, most biological calendars are developed from phenological sequences at one location, and it is not known if these sequences can be extrapolated to other regions. Therefore, The Ohio State University

Phenology Garden Network, consisting of 34 gardens across the state of Ohio, was established in 2004 in order to monitor the consistency of plant phenological sequences over time and space. This network is operated by volunteer Master Gardeners acting as citizen scientists. The phenological blooming sequence of 16 species of woody ornamental plants was analyzed over four years within each garden in The Ohio State

University Phenology Garden Network with the following objectives: (a) to determine whether the sequence of bloom was consistent year-to-year; (b) to determine whether the

44 sequence of bloom was consistent from location-to-location across the state of Ohio within a given year; (c) to quantify the velocity (km/day) of the phenological wave of first bloom of each species as it progressed northward across Ohio from South Point,

Ohio (the southernmost city in Ohio) during the growing season, and (d) to determine if there is any latitudinal variation in the cumulative degree-days required for a phenological event to occur.

All phenological sequences from individual gardens were significantly correlated from year-to-year (P<0.05 after Bonferroni correction) with one exception, which was attributed to a combination of observational error and a late frost. Phenological sequences were also highly correlated from location-to-location within a given year

(P<0.05 after Bonferroni correction), with only 9.5% of all sequence comparisons resulting in non-significant correlations. The northward velocity of the phenological wave of first blooming (km/day) varied by plant species, by year, and by phenophase, which challenges the practice of forecasting phenological events based on calendar dates.

Surprisingly, regression analyses indicated there was a negative (although often non- significant) relationship between latitude of garden location and cumulative degree-days required for a phenological event to occur, with a trend towards fewer degree-days required with increasing latitude. This latitudinal gradient represents a previously undocumented source of variation in degree-day models for predicting plant phenology.

The consistency in the rank sequence of phenological events from year-to-year and location-to-location confirms that phenological sequences can be used reliably as biological calendars for predicting pest phenology and other temperature-dependent phenomenon.

Introduction

Accurate prediction of pest phenology is crucial for the successful implementation of integrated pest management programs. For example, maximum effectiveness of pesticide treatments is achieved when timing of pesticide applications corresponds with the most vulnerable stage of pest development (Ascerno 1991, Carey and Kells 1995,

Ghersa and Holt 1995). Many newer pesticides are short-lived in the environment, which further stresses the importance of accurate timing (Ascerno 1991, Raupp et al. 1992).

Improperly timed pesticide applications are ineffectiveness, expensive, and can adversely affect the environment (Herms 1990, 2004).

However, accurate pest prediction can require frequent monitoring of landscapes, nurseries, and fields for pests, which can be time consuming and requires expertise in pest identification (Raupp et al. 1992, Mussey and Potter 1997). Degree-day models require access to daily temperature data, and many models can be complicated to use

(Cardina et al. 2007), particularly when addressing multiple pest species (Preuss 1983).

Weather variation may further complicate prediction of pests based on calendar dates or degree-day models. Thus, phenological sequences used as biological calendars can be user-friendly alternatives for accurately predicting pest phenology, with the potential to save time and effort while achieving comparable accuracy for predicting pest phenology and planning pest management programs (Herms 1990, Mussey and Potter 1997, Shetlar and Herms 2003). Though weather variation may change the date on which events occur from year-to-year, if the sequence in which phenological events occur does not fluctuate, the biological calendar should be a reliable tool (Herms 2004).

Phenology, the study of recurring biological events and their relationship to weather, is the science upon which biological calendars are constructed. Biological calendars are generated by monitoring phenological events, or phenophases, of plants and pests and by recording environmental and climatological conditions throughout the year

(Ahas and Aasa 2003). Examples of phenophases that may be observed include flower bloom, budburst, leaf abscission, animal migration, and insect emergence. Biological calendars are useful in timing pest management because insects and plants both exhibit temperature-dependent development. Therefore the developmental phenology of plants may represent a reliable indicator for the development of insects (Huberman 1941, Herms

1990, Mussey and Potter 1997). Rather than record daily temperatures and use complex models, practitioners need only monitor the sequence in which plants bloom throughout the growing season in order to predict the associated sequence of pest phenology, allowing them to plan management decisions accordingly (Herms 1990). This could ease some of the logistical challenges associated with monitoring, scouting, and timing pests, particularly when dealing with large pest complexes such as those associated with landscapes and nurseries (Herms 1990, 2003).

The concept of biological calendars is appealing. Published literature on biological calendars has proven phenological sequences to be consistent from year-to- year. For example, Herms (1990) constructed a biological calendar by monitoring 47 plants and 24 insect species over five years at The Dow Gardens in Midland, Michigan.

Despite yearly variation in weather, the biological sequence in which the phenological events occurred was virtually the same. Other published works report similar consistency

(Mussey and Potter 1997, Herms 2003, Hodges and Braman 2004, Cardina et al. 2007).

However, most of the phenological sequences used to develop biological calendars are based upon data from a single location. While all of these studies have found that biological sequences are exceptionally consistent at their respective locations from year- to-year, consistency of a sequence from location-to-location has not been as extensively tested.

Biological calendars created from phenological data collected in Kentucky

(Mussey and Potter 1997) and Michigan (Herms 1990) were highly correlated despite temporal variation of up to six weeks on average (Mussey and Potter 1997). However, some exceptions were noted, such as the phenological order of blooming of corneliancherry dogwood, Cornus mas L. and the hatching of eastern tent caterpillar,

Malacosoma americanum (Mussey and Potter 1997). The Michigan phenological sequence (Herms 1990) was also significantly correlated with that of Ohio (Herms 1998), but also with some notable exceptions. For example, “oystershell scale followed first bloom of Vanhoutte spirea in Michigan, but did not occur until more than two weeks after first bloom in Ohio” (Herms 1998). These findings suggest research is necessary to determine the degree to which a phenological sequence developed in one location is applicable to other regions.

The Ohio State University Growing Degree-Day and Biological Calendar Website

(http://www.oardc.ohio-state.edu/gdd) was developed from multiple years of phenological data for 43 arthropod pests and 91 ornamental plant species at The Secrest

Arboretum in Wooster, Ohio (Herms 2003). The website provides a biological calendar for predicting pest phenology and estimates degree-day accumulation in real time for any location in Ohio using the modified double sine wave degree-day model (Allen 1976)

48 with a base temperature of 10°C and a starting date of January 1. Upon entering any zip code of Ohio, the website predicts which phenological events will occur based on degree- day estimates for that location.

To further assess the reliability of biological calendars, such as The OSU

Growing Degree-Day and Biological Calendar Website, as a prediction tool, The Ohio

State Phenology Garden Network was established in 2004. The goal of the OSU

Phenology Garden Network is to analyze the consistency of a plant phenological sequence from year-to-year across the state of Ohio (Ellsworth and Herms 2005). The 34 gardens within the network consist of 16 clonal cultivars of woody ornamental plant species that comprise a subset of the phenological events which are listed on The Ohio

State University Growing Degree-Day and Biological Calendar Website

(http://www.oardc.ohio-state.edu/gdd). The 16 species have been monitored for the flowering phenophases of first and full bloom since 2005 by volunteer Master Gardeners acting as citizen scientists.

The Ohio State Phenology Garden Network has taken a citizen science approach, recruiting volunteer Master Gardeners, to maintain the gardens and collect data. Citizen science projects have grown in popularity for science research due to the availability of technology with which to organize citizen projects, the need for volunteer-based labor, and the need to collect large amounts of data across time and wide geographic regions

(Cooper et al. 2007, Silvertown 2009). Data is reported online

(http://phenology.osu.edu/) and made available to researchers and volunteers. Volunteer participation is maintained through annual meetings at which updates are presented to volunteers and allow for interaction between citizen scientists and university researchers.

The objectives of this study were to analyze and synthesize the phenology data collected by the Master Gardeners across the state of Ohio with the following objectives:

(a) to determine whether the sequence of bloom was consistent year-to-year; (b) to determine whether the sequence of bloom was consistent from location-to-location across the state of Ohio within a given year; (c) to quantify the velocity (km/day) of the phenological wave of first bloom of each species as it progressed northward across Ohio from South Point, Ohio (the southernmost city in Ohio) during the growing season, and

(d) to determine if there is any latitudinal variation in the cumulative degree-days required for a phenological event to occur.

Methods

The OSU Phenology Garden Network and Phenological Data

The Ohio State University Phenology Garden Network was established in 2004 consisting of 28 replicate gardens comprised of 16 clonal cultivars of woody ornamental plant species (Table 3.1) in 24 counties across Ohio (Ellsworth and Herms 2005). By

2008, it had expanded to 36 gardens in 28 Ohio counties (Figure 3.1) as well as two gardens in Kentucky and Minnesota, which were not included among the 34 analyzed in this study. These gardens traverse the state of Ohio from north to south with gardens in

Ashtabula and Lucas counties being most northerly (41.884637,-80.697409 and

41.662001, -83.672994 respectively), and the garden located in Pike County being most southerly (39.051202, -82.995784) (See Appendix A1 for garden locations).

Common Name Scientific Name Cultivar

Gold Tide™ Forsythia Forsythia x intermedia „Courtasol‟ Star Magnolia Magnolia stellata „Royal Star‟ PJM Rhododendron Rhododendron x PJM „PJM‟ Koreanspice Viburnum Viburnum carlesii Coralburst™ Crabapple Malus x Coralcole „Coralcole‟ Common Lilac Syringa vulgaris „Charles Joly‟ Vanhoutte Spirea Spriraea x vanhouttei Miss Kim Lilac Syringa patula „Miss Kim‟ Redosier Dogwood Cornus sericea f. baileyi Red Prince Weigela Weigela florida „Red Prince‟ Autumn Jazz Arrowwood Viburnum dentatum „Ralph Senior‟ BumaldViburnum Spirea Spiraea x bumalda „Golflame‟ Abottswood Potentilla Potentilla fruticosa „Abbottswood‟ Oakleaf Hydrangea Hydrangea quercifolia Cutleaf Elderberry Sambucus canadensis „Laciniata‟ Rose-of-Sharon Hibiscus syriacus „Blushing Bride‟

Table 3.1: Plant species in each garden of The Ohio State University Phenology Garden Network

Figure 3.1: Distribution of gardens within The Ohio State University Phenology Garden Network as of the 2008 growing season. Stars represent new gardens added for the 2010 growing season.

After transplanting, plants acclimated to their garden site for one year before data collection began in 2005. Master Gardener volunteers collected phenological data weekly after being trained on how to identify two phenophases: first bloom and full bloom of each cultivar in the gardens. “First bloom is defined as the date on which the flower bud on the plant opens, revealing pistils and/or stamens, and full bloom is the date on which 95% of the flower buds have opened (i.e., one bud out of 20 has yet to open)”

(Ellsworth and Herms 2005). Master Gardeners were instructed to prevent drought stress and weed competition by maintaining gardens as needed. Data were submitted online

(http://phenology.osu.edu) from 2005 to 2008. Fifteen weather stations operated throughout Ohio by The Ohio Agricultural Research and Development Center were used to record weather data and interpolate the degree-day accumulation for each phenological event at each of the gardens (Ellsworth and Herms 2005). Cumulative degree-days were calculated using the double sine wave method (Allen 1976) with a 10°C base temperature and a January 1 starting date.

Analyzing the Phenological Sequence

For each garden site, the phenological blooming sequence was analyzed by ranking the order of the 32 phenophase events, with one being the first event of the season and 32 being the last (e.g., Goldtide forsythia first bloom=1, Goldtide forsythia full bloom=2, etc.). In cases where two or more events occurred on the same day, rank was assigned as the average occurrence of the two events (i.e., if the 2nd and 3rd event occurred on the same day, the rank assigned was 2.5). In cases where an event was not recorded (e.g., a plant died or an event did not occur because of frost), that phenological event was not included in the sequence for the garden. When comparing one sequence to

52 another, only events included in both phenological sequences were analyzed. In cases where sequences to be compared had a sample size of less than 10 phenological events the garden was removed from the analysis for that year. For all statistical analyses, significance was determined at . However, in specific cases, it was necessary to utilize the Bonferroni correction to maintain a Type I error ratio for multiple comparisons at

Year-to-Year Consistency of Phenological Sequences at Individual Gardens

Spearman‟s bivariate correlations and simple linear regression analysis (SAS

Institute 2002) were used to determine if blooming sequences correlated from year-to- year at individual garden locations. The slope and intercept of lines generated by the regression analyses were compared using one-sample t-test statistic (SPSS Inc. 2007) to determine if the slopes deviated significantly from a value of one and if the intercepts deviated significantly from zero, with the slope of one and an intercept of zero indicating perfect correspondence.

Location-to-Location Consistency of Phenological Sequences Within a Year

Spearman‟s bivariate correlations and simple linear regression analysis (SAS

Institute 2002) were also used to test if blooming sequences correlated from one location to another within the same year. The slope and intercept of lines generated by the regression analyses were compared using one-sample t-test statistics (SPSS 2007) to determine if the slopes deviated significantly from a value of one and if the intercepts deviated significantly from zero. A slope of one and an intercept of zero would indicate the two sequences had perfect correspondence between locations. Variation from location-to-location was further analyzed by regressing the Spearman‟s bivariate

53 correlation coefficients (r-values) against the distance in kilometers between the two compared locations. A zero slope in these analyses indicates that distance between gardens does not affect the strength of correlation between phenological sequences from different garden locations.

The Phenological Wave

The velocity of the phenological wave of first bloom events as they progressed northward across Ohio was determined by performing simple linear regression (SAS institute 2002) relating the latitude of each garden, relative to distance (km) north from the southernmost city in Ohio (South Point, Ohio -82.580579, 38.419289), on the x-axis to the Julian date on which each of the 16 species‟ first bloom event occurred in each year on the y-axis. The slopes of the regression lines generated the velocity of the phenological wave of blooming (km/day) as it progressed from the south of Ohio to the north through the course of the growing season.

Latitudinal Variation in the Cumulative Degree-Days

Simple linear regression analyses (SAS institute 2002) were used to test for any latitudinal gradient in cumulative degree-days required for phenological events to occur by plotting the latitude, relative to distance (km) north from the southernmost city in

Ohio (South Point, Ohio -82.580579, 38.419289) on the x-axis and the cumulative degree-days required for each event to occur on the y-axis. Cumulative degree-days were calculated using the Allen (1976) double sine wave method with a starting date of

January 1 and a base temperature of 10°C for all 16 species of plants. A significant regression slope (dd/km) would be indicative of a latitudinal gradient in cumulative degree-days.

Results

Volunteer Participation

Participation of Master Gardener volunteers varied throughout the study. Master

Gardeners from only 18 gardens reported data in all four years of the study, with some volunteers reporting less data than others. Some gardens were relocated within a county and some became inactive. Overall, data was reported for 26 of 28 gardens in 2005, 29 of 36 gardens in 2006 and 2007, and 32 of 36 gardens in 2008. The consistency of data reported for each species also varied. Figure 3.2 illustrates the percentage of gardens for which data was reported for the 16 plant species in each of the four years, while Table 3.2 documents substantial variation among gardens in cumulative degree-days required for occurrence of specific phenophases. Full bloom of species that produce flowers on indeterminate growth had particularly high standard deviations.

Year-to-Year Variation in Phenological Sequences at Individual Gardens

Twenty-nine of 36 gardens had data of sufficient sample size (n≥10) to compare sequences across years at individual gardens. Figure 3.3 reports a frequency distribution of Spearman‟s bivariate correlation coefficients (r-values) generated from correlating individual gardens‟ phenological sequences from year-to-year. After applying

Bonferroni correction for multiple comparisons, sequences at all gardens were significantly correlated in all years with one exception (Table 3.3). The phenological sequence for Trumbull County in 2005 did not correlate significantly with that of 2007

(r=0.66, after Bonferroni correction, n=11). One-sample t-tests of the slopes and intercepts of lines generated by regression analyses of the sequences were not statistically different from one (P=0.45) or zero (P=0.35), respectively.

nology data for 16 for data nology

2008) study.

- : Percentage of gardens in The Ohio State University Phenology Garden Network in Phenology The University of State Ohio reportingNetwork phe Percentage : Garden gardens

Figure 3.2 Figure (2005 year ornamental woody four the throughout total) of phenophases (32 plants species

Average Cumulative Degree-Days Mean for Cultivar Bloom 2005 2006 2007 2008 2005-2008 1 Goldtide forsythia first 72.7±11 63.9±16 94.4±19 68.8±18 74.9±14 2 Goldtide forsythia full 96.5±16 86.3±19 119.9±32 92.6±19 98.8±15 3 Royalstar magnolia first 93.9±17 100.0±26 114.3±22 95.3±16 100.9±9 4 Royalstar Magnolia full 114.7±21 130.6±31 150.2±30 125.4±21 130.2±15 5 PJM Rhododendron first 152.8±21 140.9±21 167.8±25 142.6±39 151.0±12 6 PJM Rhododendron full 174.9±22 172.5±19 211.5±25 185.5±22 186.1±18 7 Koreanspice viburnum first 188.7±35 186.6±29 200.8±36 183.2±30 189.8±8 8 Koreanspice viburnum full 212.9±35 213.2±37 231.7±36 215.5±32 218.3±9 9 Coralburst Crabapple first 214.7±32 236.0±24 258.2±49 238.1±20 236.8±18 10 Charles Joly Lilac first 230.6±42 278.3±35 278.3±65 258.8±47 261.5±23 11 Coralburst Crabapple full 253.7±30 273.5±23 314.9±70 276.8±22 279.7±26 12 Charles Joly Lilac full 288.7±43 336.3±49 349.9±70 309.6±43 321.1±27 13 Vanhoutte Spirea first 312.6±45 355.7±77 418.5±122 325.5±35 353.1±47 14 Miss Kim Lilac first 380.3±48 409.1±38 439.9±54 387.9±28 404.3±26 15 Redoiser Dogwood first 411.1±300 380.2±46 469.2±276 364.0±32 406.1±46 16 Vanhoutte Spirea full 360.9±47 410.0±133 499.8±142 376.3±46 411.8±62 17 Abbotswood potentilla first 361.8±80 405.7±82 552.9±202 424.8±219 436.3±82 18 Miss Kim Lilac full 442.0±57 479.5±50 503.0±60 444.1±35 467.2±29 19 RedPrince Weigela first 471.8±108 485.9±89 567.8±149 453.1±75 494.7±51 20 Redoiser Dogwood full 481.9±320 498.5±223 533.6±298 561.2±539 518.8±36 21 Arrowwood viburnum first 515.6±83 543.7±69 540.7±61 526.6±56 531.7±13 22 Arrowwood viburnum full 598.6±101 622.4±80 629.6±57 609.1±72 614.9±14 23 RedPrince Weigela full 612.0±98 582.7±98 713.4±148 588.7±101 624.2±61 24 Bumald Spirea first 629.1±167 664.6±96 661.2±91 626.2±78 645.3±20 25 Abbotswood potentilla full 840.0±851 679.4±553 701.6±228 587.5±331 702.1±104 26 Oakleaf hydrangea first 857.7±225 739.3±114 838.6±102 739.3±131 793.7±63 27 Cutleaf elderberry first 843.4±163 809.9±134 823.8±66 787.6±56 816.2±24 28 Bumald Spirea full 804.3±190 869.9±167 816.0±95 787.9±94 819.5±36 29 Oakleaf hydrangea full 1073.3±69 1037.1±161 1002.1±200 961.9±130 1018.6±48 30 Cutleaf elderberry full 1122.1±277 1047.3±162 998.6±107 983.4±75 1037.9±62 31 Rose of Sharon first 1608.9±199 1531.4±290 1563.8±135 1417.9±125 1530.5±82 32 Rose of Sharon full 2410.8±438 2090.3±559 2236.4±522 1883.6±338 2155.3±224

Table 3.2: Overall average blooming sequence for The Ohio State University Phenology Garden Network (2005-2008) was generated by averaging the cumulative degree-days on the date of occurrence for each of the 32 phenological events (16 species of woody ornamental plants first and full bloom) across all 34 gardens and all four years. The averages were ranked from 1 to 32, with 1 being the first event on average and 32 being the last event on average. Average of the cumulative degree-days ( ±SD) required for first and full bloom for each of the 16 species are shown by year and for all four years combined.

Figure 3.3: Frequency distribution of Spearman‟s bivariate correlation coefficients (r- values) generated by correlating phenological sequences for 29 gardens in The Ohio State University Phenology Garden Network from year-to-year. All correlations with r>0.75 are significant at after applying Bonferroni correction for multiple comparisons.

Spearman’s Correlation Coefficients Location 2005 v.s. 2005 v.s. 2005 v.s. 2006 v.s. 2006 v.s. 2007 v.s. 2006 2007 2008 2007 2008 2008 Ashtabula 0.877 0.870 0.775 0.995 0.956 0.959 Athens 0.996 0.991 0.984 0.998 0.997 0.994 Clark * 0.945 * * * * Coshocton 0.959 0.866 0.982 0.825 0.973 0.907 Erie Osborn 0.997 0.996 0.995 0.996 0.994 0.999 Erie Willoway * * * * * 0.996 Franklin 0.914 0.944 0.984 0.934 0.975 0.963 Fayette * * * 0.929 0.978 0.916 Geauga 0.955 0.995 0.996 0.900 0.965 0.946 Green 0.975 0.945 0.981 0.959 0.988 0.980 Hancock 0.989 0.981 0.984 0.973 0.979 0.989 Huron 0.995 0.989 0.995 0.991 0.995 0.996 Licking Dawes 0.982 * 0.979 * 0.982 * Licking OSUE * * * * 0.883 * Lorain * * * * * 0.996 Lucas 0.994 0.982 0.990 0.974 0.959 0.972 Lake * * * 0.926 0.936 0.966 Mahoning 0.896 * 0.872 * 0.950 * Pickaway 0.970 0.996 0.960 1.00 0.916 0.983 Pike 0.981 0.846 * 0.846 * * Portage 0.967 0.963 * 0.925 * * Richland 0.968 0.853 0.849 0.786 0.967 0.772 Ross 0.968 0.960 0.934 0.908 0.991 0.845 Trumbull 0.829 0.662 0.869 0.951 0.979 0.982 Stark Canton 0.991 0.921 0.946 0.953 0.979 0.940 Stark OSUE 0.992 0.985 0.993 0.939 0.983 0.958 Summit Adele * 0.910 0.927 * * 0.967 Summit F.A.S. 0.963 0.956 0.950 0.988 0.993 0.976 Wayne 0.996 0.980 0.998 0.980 0.995 0.976

Table 3.3: Spearman‟s bivariate correlation coefficients generated from correlations of phenological sequence from location-to-location across four years in The Ohio State University Phenology Garden Network. All correlations are significant at after applying Bonferroni correction with one exception of Trumbull 2005 v.s. 2007 (indicated in bold). Cells with asterisks indicate comparisons that could not be made because no data were recorded that year or because n<10.

Location-to-Location Variation in Phenological Sequences within a Year

Over 90% of comparisons of phenological sequences from location-to-location resulted in significant correlations, with many showing perfect correspondence (r=1.00).

After applying Bonferroni correction for multiple comparisons, only 124 of 1301 correlations were not correlated. The majority of these comparisons were from the 2007 dataset. Thirty-four of the 124 uncorrelated comparisons ultimately had less than 10 of the same phenological events to compare due to the exclusion of phenological events that were not reported for both gardens. Correlations from location-to-location were strongest in 2005 (n=325), 2006 (n=325), and 2008 (n=351), the majority of which had r-values greater than 0.90 (Figure 3.4). Weaker correlations occurred in 2007 (n=325), including comparison which had an r-value less than 0.35 (Figure 3.4). Figure 3.5 displays representative examples of both strong and weak correlations of phenological sequences from location-to-location. Three (Figure 3.5a,c,e) were strongly correlated (r>0.90), and three (Figure 3.5b,d,f) were weakly correlated and non-significant.

An overall phenological sequence for Ohio was determined (see Table 3.2) by averaging the cumulative growing degree-days of blooming events for the 16 species of plants from the 36 gardens across all four years. The overall phenological sequence for

The Ohio State University Phenology Garden Network was then compared to the individual garden phenological sequences in all four years using Spearman‟s bivariate correlation. All comparisons were significantly correlated (r=0.80 to 1.00, P=<0.0001).

Figure 3.6 shows examples of the overall phenological sequence (Table 3.2) compared to that of the Wayne County garden, which was highly correlated each of the four years

(r=0.98 to 0.99).

location in The Ohio State University Phenology Garden Network in a) 2005, b) 2005, 2008. d) a) in and location2006, University 2007, State Ohio Network The in c) Phenology Garden

- Frequency of Spearman‟s bivariate correlation coefficients generated sequences correlations coefficients phenological bivariate from correlation of of Frequency Spearman‟s

Figure 3.4: 3.4: Figure location from

Figure 3.5: Representative examples of variation in location-to-location comparisons of phenological sequences from different gardens in The Ohio State University Phenology Garden Network during a year. The line bisecting each graph represents perfect correspondence (y=x) between the blooming sequences of each garden. Graphs a, c, and e are examples of significant correlations (P<0.05 after Bonferroni correction for multiple comparisons). Graphs b, d, and f are examples of non-significant correlations.

Figure 3.6: Representative examples of correlations made between the overall phenological sequence and the phenological sequences from Wayne County garden for each year of the study (2005-2008). The overall phenological sequence for The OSU Phenology Garden Network was generated by averaging the cumulative growing degree- days for each phenological event across all gardens and all years and ranking the averaged events from 1 to 32. Diagonal lines bisecting each graph represent perfect correspondence (y=x).

One-sample t-tests were used to further examine the consistency of phenological sequences from location-to-location by determining whether the slope and intercept of lines generated from the regression analyses varied significantly from a value of 1.0, and whether the y-intercept varied significantly from zero. Slopes of one and y-intercepts of zero would indicate perfect correspondence between sequences. Using the regression comparisons from all four years (1301 total comparisons), the mean slope (0.974 ± 0.011) and mean intercept (0.577 ± 0.063) were found to differ significantly (P>0.05) from one and zero, respectively.

However, distance between gardens did not seem to affect the strength of the correlations of phenological sequences compared. Regression analysis relating

Spearman‟s correlation coefficients and the corresponding distance (km) between the gardens compared revealed near zero slopes for all four years (Figure 3.7) and non- significant results (P>0.05) in 2005, 2006, and 2007. A significant relationship between distance and coefficients occurred in 2008 (P=0.011).

The Phenological Wave

The velocity of the phenological wave (generated by regressing latitude, relative to distance (km) from the southernmost city of Ohio, and date of bloom) varied by plant species, by year, and by phenophase (Table 3.4). For example, first bloom of GoldtideTM

Forsythia progressed north at a rate of 15.9 km per day in 2005, but only 6.9 km per day in 2006 (Figure 3.8). A trend emerged showing faster northward progression for earlier blooming plants and slower rates for late blooming plants, with more variation during mid-growing season (Figure 3.9). Non-significant regressions were observed between distance and day in at least one year for seven species (Table 3.4).

gardens in The in The gardens Ohio

=0.011).

>0.05), 2008 >0.05), (

2007 ( 2007

2008. 2005 2008. -

Regression analysis comparing the distance between the garden in Ashtabula County and all other garden the all County between and Ashtabula in distance the analysis comparing Regression

State University Phenology Garden Network and the corresponding Spearman‟s correlation coefficient of the phenological coefficient phenological the of correlation Spearman‟s corresponding the and Network Phenology Garden University State of gardens two 2005 the from sequences 65 3.7: Figure

Velocity (km/day) Plant Phenophase 2005 2006 2007 2008 Goldtide Forsythia first bloom 15.87 6.90 20.55 11.97 Goldtide Forsythia full bloom 8.59 7.42 16.17 22.11 „Royalstar‟ Star Magnolia first bloom 17.70 11.80 2.88 26.77 „Royalstar‟ Star Magnolia full bloom 13.41 11.20 8.42 23.38 „PJM‟ Rhododendron first bloom 10.38 20.96 8.47 21.57 „PJM‟ Rhododendron full bloom 10.69 22.63 9.77 24.35 Koreanspice Viburnum first bloom 8.44 16.50 11.97 24.40 Koreanspice Viburnum full bloom 8.96 13.58 11.03 19.82 Coralburst Crabapple first bloom 8.99 15.51 12.40 27.05 Coralburst Crabapple full bloom 9.43 14.70 11.63 26.36 „Charles Joly‟ Common Lilac first bloom 8.46 13.39 9.16 8.33 „Charles Joly‟ Common Lilac full bloom 9.13 8.76 10.78 8.53 Vanhoutte Spirea first bloom 10.00 6.35 0.18 18.24 Vanhoutte Spirea full bloom 8.15 4.99 1.20 14.52 „Miss Kim‟ Lilac first bloom 9.41 10.04 15.78 16.15 „Miss Kim‟ Lilac full bloom 10.26 9.90 18.76 16.65 Redoiser Dogwood first bloom 3.20 7.48 14.86 13.04 Redoiser Dogwood full bloom 3.29 3.65 14.46 15.94 „RedPrince‟ Weigela first bloom 6.63 4.99 3.72 13.51 „RedPrince‟ Weigela full bloom 4.32 6.83 5.05 5.55 „Arrowwood‟ Viburnum first bloom 9.11 12.65 15.75 23.84 „Arrowwood‟ Viburnum full bloom 9.99 11.18 18.40 18.37 Bumald Spirea first bloom 4.16 7.31 10.83 17.25 Bumald Spirea full bloom 0.12 5.28 11.16 6.93 Abbotswood Potentilla first bloom 6.45 5.34 0.92 5.77 Abbotswood Potentilla full bloom 0.18 0.50 2.73 6.67 Oakleaf Hydrangea first bloom 3.92 3.56 6.98 1.48 Oakleaf Hydrangea full bloom 12.18 9.73 2.23 1.15 Cutleaf elderberry first bloom 5.79 5.36 6.07 23.50 Cutleaf elderberry full bloom 3.92 7.87 9.56 15.79 „Blushing Bride‟ Rose of Sharon first bloom 5.19 5.65 10.92 10.26 „Blushing Bride‟ Rose of Sharon full bloom 1.28 1.83 0.23 -2.24

Table 3.4: Velocity of the phenological wave of blooming (expressed as km north per day) for each phenological event for 16 species of woody ornamental plants in The Ohio State University Phenology Garden Network. Values in bold indicate significant regression relationships (P<0.05).

Figure 3.8: Velocity (km/day) of the phenological wave for (a) Goldtide forsythia and (b) Oakleaf hydrangea, expressed as a slope of the regression between distance north from South Point, Ohio and the date of occurrence A) Lines represent fitted trend lines for each year. (a) 2005: F=62.76, P=0.000; 2006: F=46.37, P=0.000 (b) 2006: F=1.21, P=0.29; 2008: F=0.053, P=0.88.

Figure 3.9: Average velocity (km/day) of the phenological wave of first bloom expressed as a slope of the regression between distance north from South Point, Ohio and the date of occurrence. Plants are listed along x-axis in sequence from earliest to latest phenological event based on the four-year averaged Julian date of occurrence for Ohio.

Latitudinal Variation in Cumulative Degree-Days

Comparisons of the cumulative degree-days required for specific phenological events to occur at different locations revealed mixed results (Table 3.6). Of 128 regressions comparing cumulative degree-days against location over four years, 38 (or approximately 30%) generated a significant relationship between the variables (P<0.05).

For both first and full bloom in all years reported, regressions for cutleaf elderberry and bumald spirea were insignificant (P>0.05) indicating no latitudinal variation in the cumulative degree-days required for a phenological event by location. For the other 14 species, there was at least one significant relationship between location and cumulative degree-days in one or more years, indicating that, in some cases, cumulative degree-days required for specific phenological events to occur varies by location. No single species had significant regression results in all four years.

However, there was a trend for slopes of the regression analysis of cumulative degree-days and distance to the north to be negative (Figure 3.9). This suggests that the further north a plant is located, the fewer degree-days were required for a blooming event to occur. Only 26 (approximately 20%) of 128 comparison slopes were positive (Table

3.6). However, the average of all regression slopes (dd/km) of the first bloom of each of the 16 species was negative (Figure 3.9) and, of the averaged regression slopes of full blooming events, only two species were positive (oakleaf hydrangea and cutleaf elderberry).

Plant Phenophase Regression Slopes (dd/km) 2005 2006 2007 2008 Goldtide Forsythia first bloom 0.04 -0.04 -0.10 -0.05 Goldtide Forsythia full bloom -0.03 -0.05 -0.18 -0.10 „Royalstar‟ Star Magnolia first bloom -0.01 -0.09 -0.10 -0.10 „Royalstar‟ Star Magnolia full bloom -0.11 -0.04 -0.16 0.01 „PJM‟ Rhododendron first bloom -0.13 0.01 -0.19 0.09 „PJM‟ Rhododendron full bloom -0.18 -0.10 -0.11 0.06 Koreanspice Viburnum first bloom -0.29 -0.09 -0.35 -0.08 Koreanspice Viburnum full bloom -0.32 -0.11 -0.34 -0.02 Coralburst Crabapple first bloom -0.23 -0.10 -0.31 -0.02 Coralburst Crabapple full bloom -0.17 -0.12 -0.27 -0.04 „Charles Joly‟ Common Lilac first bloom -0.23 -0.16 -0.50 -0.21 „Charles Joly‟ Common Lilac full bloom -0.19 -0.22 -0.52 -0.21 Vanhoutte Spirea first bloom -0.24 0.04 -0.93 -0.21 Vanhoutte Spirea full bloom -0.36 -0.35 -0.83 -0.33 „Miss Kim‟ Lilac first bloom -0.24 -0.27 -0.48 -0.10 „Miss Kim‟ Lilac full bloom -0.20 0.01 -0.50 -0.08 Redoiser Dogwood first bloom 0.80 -0.31 -0.59 -0.10 Redoiser Dogwood full bloom 0.99 0.49 -0.62 -1.47 „RedPrince‟ Weigela first bloom -0.12 0.49 -0.90 -0.15 „RedPrince‟ Weigela full bloom 0.12 -0.03 -0.77 0.37 „Arrowwood‟ Viburnum first bloom -0.35 -0.13 -0.22 -0.27 „Arrowwood‟ Viburnum full bloom -0.39 -0.14 -0.32 -0.19 Bumald Spirea first bloom -0.33 -0.09 0.38 -0.20 Bumald Spirea full bloom -0.41 0.46 0.19 -0.32 Abbotswood Potentilla first bloom -0.21 -0.27 -0.91 -0.96 Abbotswood Potentilla full bloom -1.13 0.01 -0.50 -1.08 Oakleaf Hydrangea first bloom 0.50 -0.34 -0.56 -0.49 Oakleaf Hydrangea full bloom 0.63 0.56 -0.72 -0.39 Cutleaf elderberry first bloom 0.36 -0.35 0.10 -0.23 Cutleaf elderberry full bloom 0.85 0.18 0.36 -0.05 „Blushing Bride‟ Rose of Sharon first bloom -0.17 0.97 -1.08 -0.03 „Blushing Bride‟ Rose of Sharon full bloom -0.31 1.23 -2.68 -0.17

Table 3.5: Slopes (dd/km) of the lines generated by regression analyses comparing cumulative degree-days required for a blooming event to occur and the latitude of the gardens, relative to distance (km) from the southernmost city in Ohio. A significant slope would indicate variation in cumulative degree-days by location. Degree-days were calculated by Allen (1976) double sine wave with a January 1 starting date and 10°C base temperature for all the phenological events of 16 species of woody ornamental plants in The Ohio State University Phenology Garden Network.

Figure 3.10: Average slopes (dd/km) resulting from the regression analyses of cumulative degree-days required for the occurrence of first bloom to occur of 16 species of woody ornamental plants in The Ohio State University Phenology Garden Network and distance north from South Point, Ohio. Degree-days were calculated using modified double sine wave (Allen 1976) with a base temperature of 10°C and a starting date of January 1. Plants are listed on the x-axis in the order in which they bloomed based on the average of Julian date of occurrence across all gardens and all years.

Discussion

Many biological calendars have been constructed from phenological sequences,

(Herms 1990, Mussey and Potter 1997, Herms 2003), including The Ohio State

University Growing Degree-Day and Biological Calendar Website

(http://www.oardc.ohio-state.edu/gdd/); but these are typically based on data collected at one location. This study confirms the consistency of sequences from year-to-year and location-to-location within Ohio using the 34 gardens that comprise The Ohio State

University Phenology Garden Network (http://phenology.osu.edu/), finding that the vast majority of phenological sequences were highly correlated. This further supports the reliability of phenological sequences for use as biological calendars across Ohio.

While the phenological sequences are consistent from location-to-location and year-to-year, the velocity of phenological wave of blooming varied by species, by year, and even by phenophase as it progressed from the south to the north across Ohio (Table

3.4), which challenges the practice of forecasting phenological events based on calendar dates. Rules of thumb used by extension specialists in Ohio such as, “spraying one week earlier in southern Ohio than in central Ohio, and two weeks later in northern Ohio” (D.J.

Shetlar, personal communication) may be inaccurate if weather conditions cause blooming to occur faster or slower than predicted across the state. If the velocity of the phenological wave is slower or faster than “average,” timing recommendations will be too early or too late.

A latitudinal gradient found to exist in the required number of cumulative degree- days for blooming of some species represents a previously undocumented source of variation in degree-day models for predicting plant phenology (Table 3.5). This further

complicates the practice of using degree-days to accurately make predictions of plant and pest phenology. A negative association was documented between the latitude of a garden

(relative to the distance from the southernmost city in Ohio) and cumulative degree-days required for first bloom for several species, with a trend towards fewer degree-days required at northern locations in Ohio. Though most slopes were not statistically significant, almost all were negative and this trend suggests that degree-day values used to predict southern pest activity may be too high for northern predictions. This suggests another limitation in the use of degree-day models, along with other concerns about model ability to simulate actual development (Pruess, 1983, Higley et al. 1986), and the primary assumption that temperature is the major factor in development (Higley et al.

1986).

There were very few inconsistencies in the phenological sequences, which were thought to be due to extreme weather events such as frost, and limitations associated with inexperienced citizen scientists who collected data. For example, phenological sequences reported for the Trumbull County garden from 2005 and 2007 were not consistent with one another. A late frost in April 2007 killed flower buds, decreasing the total number of plants included in phenological sequences for 2007.

Throughout the four years, Master Gardeners were not always able to report on all

16 species at their assigned garden (Figure 3.2) due to plant mortality (reported to be caused by frost, deer, other pests, etc.). A key source of variation noted by many volunteers was difficulty in precise identification of full bloom of plants that produced flowers late into the season on indeterminate growth (e.g. „Blushing Bride‟ Rose-of-

Sharon and „Abbottswood‟ potentilla), suggesting that this phenophase may not be as

useful of a phenological indicator as first bloom of these species. Also, some gardens were discontinued or relocated, which also generated variation in the amount of data reported from year-to-year.

Phenology gardens in Austria (Koch et al. 2008), North America (Vittum and

Hopp 1978), Czechoslovakia (Nekovář 2008), Denmark (Heidmann and Olesen 2008),

Finland (Kubin et al.2008), and France (Chuine and Seguin 2008), among others in

Europe, have also reported similar fluctuations in participation due to issues such as garden deactivation, variability in volunteer initiative, or availability of replacement plants (Vittum and Hopp 1978, Chmielewski 2008).

It is unlikely that the variation in consistency of phenological sequences were due to differences in their geographic location and/or local climates. For example, Trumbull

County is surrounded by nearby gardens that did not experience similar inconsistencies among sequences. Furthermore, the consistency of phenology sequences was not affected by the distance between gardens (Figure 3.7), which suggests that operator error and not geography was the most probable source of variation. While studies have shown that certain geographic factors, such as higher elevation, which is associated with lower temperatures, can affect the time of year blooming begins (Menzel et al. 2001,

Richardson et al. 2006, Migliavacca et al. 2008, Scleip et al. 2009), they have not been investigated as sources of variation in phenological sequences.

Other environmental variables could have impacted blooming sequence in the phenology gardens. Photoperiod differences from south to north are known to have an impact on how flowers trigger blooming (Garner & Allard 1920, Kobayashi & Weigel

1997, Jarillo et al. 2008). There are “long-day,” “short-day” and “day-neutral” plants for

which blooming responds to the duration of light exposure. For example, Hybiscus syriacus (Rose-of-Sharon), one of the 16 species monitored in The OSU Phenology

Garden Network, is a long-day plant (Warner and Erwin 2001). However, the distinction between whether a plant is long-day or short-day can vary by species and by varieties within a species (Garner & Allard 1920, Kobayashi & Weigel 2008). Garner and Allard

(1920) found that, within soybeans, the Biloxi variety was a short-day bloomer while the

Mandarin variety was a long-day bloomer. In the past it has been asserted that photoperiod plays less of a role in blooming because “if flowering date were controlled only by the astronomically determined day-length, an individual would flower on the same date every year” (Lindsey & Newman 1956). The variation in photoperiod from south to north should be studied further to determine if it is a factor that may affect the phenological sequence of bloom across latitudinal gradients.

The Ohio State University Growing Degree-Day and Biological Calendar Website

(http://www.oardc.ohio-state.edu/gdd/) generates phenological predictions for a large number of arthropod pests and provides degree-day data in real time for any location in

Ohio. However, these models were generated from data from only one location (Secrest

Arboretum, Wooster, Ohio). This study found that a subset of the phenological sequence was consistent from location-to-location and year-to-year, which supports the hypothesis that the biological calendar is consistent throughout Ohio. Collectively, the results of this study validate the use of a single sequence developed at one location, such as The Ohio

State University Growing Degree-Day and Biological Calendar Website

(http://www.oardc.ohio-state.edu/gdd/), for predicting phenology in other regions despite geographic and year-to-year variation in climate and weather. These findings suggest

biological calendars can be regionally applicable, but similar studies should be conducted to confirm the regional consistency of other phenological sequences, particularly those applied across wide elevation and photoperiod gradients. Future studies of biological calendars should compare predictions made from degree-day models and calendar dates to predictions made by phenological sequences. Field tests should be performed to determine the utility of such sequences in various settings such as nurseries, home landscapes, and agricultural fields. The Ohio State Phenology Network will facilitate such investigations, as well as long-term studies of affects of climate on plant phenology.

Surveys of IPM practitioners would reveal their perceptions of phenology gardens as a tool for timing pest management decisions.

References

Ahas, R., and A. Aasa. 2003. Developing comparative phenological calendars, pp.301- 318. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Allen, J.C. 1976. A modified sine wave method for calculating degree days. Environ. Entomol. 5: 388-396.

Ascerno, M.E. 1991. Insect phenology and integrated pest management. J. Arbor. 17: 13- 15.

Cardina, J., Herms, C.P., Herms, D.A., and F. Forcella. 2007. Evaluating phenological indicators for predicting giant foxtail (Setaria faberi) emergence. Weed Sci. 55: 455-464.

Carey, J.B., and J.J. Kells. 1995. Timing of total postemergence herbicide applications to maximize weed control and corn (Zea mays) yield. Weed Technol. 9: 356-361.

Chmielewski, F.M. 2008. The International Phenological Gardens in Europe, pp. 17-21. In: J. Nekovář, E. Koch, E. Kubin, P. Nejedlik, T.Sparks, and F. Wielgolaski (eds.), COST Action 725: the history and current status of plant phenology in Europe. Vammalan Kirjapaino Oy, Finland.

Chuine, I., and B. Seguin. 2008. The history and current status of the French phenology networks, pp.76-79. In: J. Nekovář, E. Koch, E. Kubin, P. Nejedlik, T.Sparks, and F. Wielgolaski (eds.), COST Action 725: the history and current status of plant phenology in Europe. Vammalan Kirjapaino Oy, Finland.

Cooper, C.B., Dickinson, J., Phillips, T., and R. Bonney. 2007. Citizen science as a tool for conservation in residential ecosystems. Ecology and Society. 12: 11-21.

Ellsworth, D., and D.A. Herms. 2005. Biological calendars: the state-wide network of OSU Phenology Gardens, pp. 111-115. In: J.A. Chatfield, J.F. Boggs, and E.A. Draper (eds.), Ornamental plants annual reports and research reviews 2004, Ohio Agricultural Research and Development Center and The Ohio State University Extension Special Circular 195.

Garner, W.W., and H.A. Allard. 1920. Effect of the relative length of day and night and other factors of the environment on growth and reproduction of plants. J. Agric. Res. 18: 553-606.

Ghersa, C.M., and J.S. Holt. 1995. Using phenology prediction in weed management: a review. Weed Res. 35: 461-470.

Herms, D.A. 1990. Biological clocks: using plant phenology to predict insect activity. Am. Nurseryman 162: 56-63.

Herms, D.A. 2003. A biological calendar for predicting pest activity: six years of plant and insect phenology in Secrest Arboretum, pp. 40-49. In: J.A. Chatfield, J.F. Boggs, E.A. Draper, and P.J. Bennett (eds.), Ornamental plants: annual reports and research reviews 2002. Ohio Agricultural Research and Development Center and The Ohio State University Extension Special Circular 189.

Herms, D.A. 2004. Using degree-days and plant phenology to predict pest activity, pp. 49-59. In: V. Krischik, and J. Davidson (eds.), IPM (integrated pest management) of midwest landscapes. Regents of the University of Minnesota. Minnesota Agricultural Experiment Station Publication SB-07645.

Heidmann, T., and J.E. Olesen. 2008. History and present observations in Danish plant phenology, pp.58-63. In: J. Nekovář, E. Koch, E. Kubin, P. Nejedlik, T.Sparks, and F. Wielgolaski (eds.), COST Action 725: the history and current status of plant phenology in Europe. Vammalan Kirjapaino Oy, Finland.

Higley, L.G., Pedigo, L.P., and K.R. Ostlie. 1986. DEGDAY: a program for calculating degree-days, and assumptions behind the degree-day approach. Environ. Entomol. 15: 999-1016.

Hodges, G.S., and S.K.Braman. 2004. Seasonal occurrence, phenological indicators and mortality factors affecting five scale insect species (Hemiptera: Diaspididae, Coccidae) in urban landscape setting. J. Entomol. Sci. 4: 611-623.

Jarillo, J.A., del Olmo, I., Gómez-Zambrano, A., Lázaro, A., López-González, L., Miguel, E., Narro-Diego, L., Sáez, D., and M. Piñeiro. 2008. Review: photoperiodic control of flowering time. Spanish J. Agr. Res. 6: 221-244.

Kobayashi, Y. and D. Weigel. 2007. Move on up, it‟s time for change-mobile signals controlling photoperiod-dependent flowering. Genes Dev. 21: 2371-2384.

Koch, E., Lipa, W., Neumcke, R., and S. Zach. 2008. The history and current status of the Austrian phenology network, pp. 22-28. In: J. Nekovář, E. Koch, E. Kubin, P. Nejedlik, T.Sparks, and F. Wielgolaski (eds.), COST Action 725: the history and current status of plant phenology in Europe. Vammalan Kirjapaino Oy, Finland.

Kubin, E., Pudas, E., Venäläinen, A., and J. Terhivuo. 2008. Phenological recording in Finland, pp. 68-75. In: J. Nekovář, E. Koch, E. Kubin, P. Nejedlik, T.Sparks, and F. Wielgolaski (eds.), COST Action 725: the history and current status of plant phenology in Europe. Vammalan Kirjapaino Oy, Finland.

Lindsey, A.A., and J.E. Newman. 1956. Use of official weather data in spring time: temperature analysis of an Indiana phenological record. Ecology 37: 812-823.

Menzel, A., Estrella, N., and P. Fabian. 2001. Spatial and temporal variability of the phenological seasons in Germany from 1951 to 1996. Glob. Change Biol. 7: 657- 666.

Migliavacca, M., Cremonese, E., Colombo, R., Busetto, L., Galvagno, M., Ganis, L., Meroni, M., Pari, E., Rossini, M., Siniscalco, C., and U. Morra di Cella. 2008. European larch phenology in the Alps: can we grasp the role of ecological factors by combining field observations and inverse modeling? Int. J. Biometeorol. 52: 587-605.

Mussey, G.J., and D.A. Potter. 1997. Phenological correlations between flowering plants and activity of urban landscape pests in Kentucky. J. Econ. Entomol. 90: 1615- 1627.

Nekovář, J. 2008. The history and current status of Czech plant phenology, pp. 51-57. In: J. Nekovář, E. Koch, E. Kubin, P. Nejedlik, T.Sparks, and F. Wielgolaski (eds.), COST Action 725: the history and current status of plant phenology in Europe. Vammalan Kirjapaino Oy, Finland.

Pruess K.P. 1983. Day-degree methods for pest management. Environ. Entomol. 12: 613-619.

Raupp, M.J., Koehler, C.S., and J.A. Davidson. 1992. Advances in implementing integrated pest management for woody landscape plants. Annu. Rev. Entomol. 37: 561-585.

Richardson, A.D., Bailey, A.S., Denny, E.G., Martin, C.W., and J. O‟keefe. 2006. Phenology of a northern hardwood forest canopy. Glob. Change Biol. 12: 1174- 1188.

SAS Institute. 2002. SAS user‟s procedures guide, version 9.1.3. SAS Institute Inc., Cary, NC.

Schleip, C., Sparks, T.H., Estrella, N., and A. Menzel. 2009. Spatial variation in onset dates and trends in phenology across Europe. Clim. Res. 39: 249-260.

Shetlar D.J., and D.A. Herms. 2003. Insect and mite control on woody ornamentals and herbaceous perennials. Ohio Agricultural Research and Development Center and The Ohio State University Extension Bulletin 504.

Shetlar, D.J. 2009 November 29. Personal Communication.

Silvertown, J. 2009. A new dawn for citizen science. Trends Ecol. Evol. 24: 467-471.

SPSS 16.0 for Windows. 2007. Rel. 16.0.1 Chicago: SPSS Inc.

Vittum, M.T., and R.J. Hopp. 1978. The NE-95 lilac phenology network, pp.1-5. In: R.J. Hopp (ed.), Phenology: an aid to agricultural technology. Agricultural Experiment Station-University of Vermont Bulletin 684.

Warner, R.M., and J.E. Erwin. 2001. Variation in floral induction requirements of Hibiscus sp. J. Amer. Soc. Hort. Sci. 126: 262-268.

Bibliography

Ahas R., and A. Aasa. 2003. Developing comparative phenological calendars, pp.301- 318. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Akers, R.C., and D.G. Nielsen. 1984. Predicting Agrilus anxius Gory (Coleoptera: Buprestidae) adult emergence by heat unit accumulation. J. Econ. Entomol. 77: 1459-1463.

Allen, J.C. 1976. A modified sine wave method for calculating degree days. Environ. Entomol. 5: 388-396.

Arnold, C.Y. 1959. The determination and significance of the base temperature in a linear heat unit system. Proc. Am. Soc. Hortic. Sci.74: 430-445.

Ascerno, M.E., and R.D. Moon. 1989. Forecaster: predicting biological phenomena based on daily temperatures. Minnesota Extension Service. AG-CS-3029. V.1.0.

Ascerno, M.E. 1991. Insect phenology and integrated pest management. J. Arbor. 17: 13- 15.

Baskerville, G.L., and P. Emin. 1969. Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology 50: 514-517.

Bonhomme, R. 2000. Bases and limits to using „degree.day‟ units. Eur. J. Agron. 13: 1- 10.

Burden, D.J., and E.R. Hart. 1989. Degree-day model for egg eclosion of the pine needle scale (Hemiptera: Diaspididae). Environ. Entomol. 18: 223-227.

Burrows, T.M. 1984. Pesticide demand and integrated pest management: a limited dependent variable analysis. Am. J. Agr. Econ. 65: 806-810.

Cardina, J., Herms, C.P., Herms, D.A., and F. Forcella. 2007. Evaluating phenological indicators for predicting giant foxtail (Setaria faberi) emergence. Weed Sci. 55: 455-464.

Carey, J.B., and J.J. Kells. 1995. Timing of total postemergence herbicide applications to maximize weed control and corn (Zea mays) yield. Weed Technol. 9:356-361.

Cesaraccio, C., Spano, D., Duce, P., and R.L. Snyder. 2001. An improved model for determining degree-day values from daily temperature data. Int. J. Biometeorol. 45: 161-169.

Chen, X. 2003. East Asia, pp. 11-25. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Chmieloewski, F.M. 2003. Phenology and agriculture, pp.505-522. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands

Chmielewski, F.M. 2008. The international phenological gardens in Europe, pp.17-21. In: J. Nekorvář, E. Koch, E. Kubin, P. Nejedlik, T. Sparks, and F. Wielgolaski (eds.), COST Action 725-the history and current status of plant phenology in Europe. Vammalan Kirjapaino Oy, Finland.

Chuine, I., and B. Seguin. 2008. The history and current status of the French phenology networks, pp.76-79. In: J. Nekorvář, E. Koch, E. Kubin, P. Nejedlik, T. Sparks, and F. Wielgolaski (eds.), COST Action 725-the history and current status of plant phenology in Europe. Vammalan Kirjapaino Oy, Finland.

Cooper, C.B., Dickinson, J., Phillips, T., and R. Bonney. 2007. Citizen science as a tool for conservation in residential ecosystems. Ecology and Society 12: 11-21.

Defila, C., and B. Clot. 2003. Long-term urban-rural comparisons, pp.541-554. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Delhaut, K. 2003. Insects, pp.405-420. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Ellsworth, D., and D.A. Herms. 2005. Biological calendars: the statewide network of OSU Phenology Gardens, pp.111-115. In: J.A. Chatfield, J.F. Boggs, and E.A. Draper, (eds.). Ornamental Plants Annual Reports and Research Reviews 2004. OARDC and OSUE Special Circular 195.

Garner, W.W., and H.A. Allard. 1920. Effect of the relative length of day and night and other factors of the environment on growth and reproduction of plants. J. Agric. Res. 18: 553-606.

Ghersa, C.M., and J.S. Holt. 1995. Using phenology prediction in weed management: a review. Weed Res. 35: 461-470

Gilmore, E.C., and J.S. Rogers. 1958. Heat units as a method of measuring maturity in corn. Agron. J. 50: 611-615.

Herms, D.A. 1990. Biological clocks: using plant phenology to predict insect activity. Am. Nurseryman. 162: 56-63.

Herms, D.A. 1998. The flowering sequence of ornamental plants as a tool for predicting the phenology of insect pests, pp.77-88. In: M. Rose and J.A. Chatfield (eds.), Ornamental plants: annual reports and research reviews 1997. Ohio Agricultural Research and Development Center and The Ohio State University Extension Special Circular 157.

Herms, D.A. 2001. Degrees of separation. Am. Nurseryman 194: 34-40.

Herms, D.A. 2003. A biological calendar for predicting pest activity: six years of plant and insect phenology in Secrest Arboretum, pp.40-49. In: J.A. Chatfield, J.F. Boggs, E.A. Draper, and P.J. Bennett (eds.), Ornamental plants: annual reports and research reviews 2002. Ohio Agricultural Research Development Center and The Ohio State University Extension Special Circular 189.

Herms, D.A. 2004. Using degree-days and plant phenology to predict pest activity, pp.49- 59. In: V.Kriscik and J.Davidson (eds.), IPM (integrated pest management) of midwest landscapes. Regents of the University of Minnesota. Minnesota Agricultural Experiment Station Publication SB-07645.

Higley, L.G., Pedigo, L.P., and K.R. Ostlie. 1986. DEGDAY: a program for calculating degree-days, and assumptions behind the degree-day approach. Environ. Entomol. 15: 999-1016.

Hodges, G.S., and S.K. Braman. 2004. Seasonal occurrence, phenological indicators and mortality factors affecting five scale insect species (Hemiptera: Diaspididae, Coccidae) in the urban landscape setting. J. Entomol. Sci. 39: 611-622.

Hopp, R.J. 1974. Plant phenology observation networks, pp. In: H. Lieth (ed.), Phenology and seasonality modeling. Springer-Verlag. New York, New York.

Hopp, R.J., and H. Lieth. 1974. Introduction, pp.367-368. In: H. Lieth (ed.), Phenology and seasonality modeling. Springer-Verlag. New York, New York.

Howe, R.W. 1967. Temperature effects on embryonic development in insects. Annu. Rev. Entomol. 12: 15-42.

Huberman, M.A. 1941. Why phenology? J. Forestry 39: 1007-1013.

Inwards, R. 1898. Weather lore: a collection of proverbs, sayings, and rules concerning the weather. Elliot Stock, London, England.

IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of WorkingGroupI to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Jarillo, J.A., Del Olmo, I., Gómez-Zambrano, A., Lázaro, A., López-González, L., Miguel, E., Narro-Diego, L., Sáez, D., and M. Piñeiro. 2008. Review: photoperiodic control of flowering time. Spanish J. Agr. Res. 6: 221-244.

Johnson, P.C., Mason, D.P., Radke, S.L., and K.T. Tracewski. 1983. Gypsy moth Lymantria dispar L. (Lepidoptera Lymantriidae) egg eclosion degree-day accumulation. Environ. Entomol. 12: 929-932.

Jones, V.P., Doerr, M., and J.F.Brunner. 2008. Is biofix necessary for predicting codling moth (Lepidoptera: Tortricidae) emergence in Washington state apple orchards? Hort. Entomol. 101: 1651-1657.

Keatley, M.R., and T.D. Fletcher. 2003. Australia, pp. 27-44. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Neatherlands.

Khanduri, V.P., Sharma, C.M., and S.P. Singh. 2008. The effects of climate change on plant phenology. Environmentalist 28: 143-147.

Knapp, R. and T.M. Casey. 1986. Thermal ecology, behavior, and growth of gypsy moth and eastern tent caterpillars. Ecology 67:598-608.

Kobayashi, Y. and D. Weigel. 2007. Move on up, it‟s time for change-mobile signals controlling photoperiod-dependent flowering. Genes Dev. 21:2371-2384.

Koch, E., Demairée, G., Lipa, W., Zach, S., and K. Zimmermann. 2008. History of international phenology networks, pp. 12-16. In: J Nekorvář, E. Koch, E. Kubin, P.Nejedlik, T. Sparks, and F. Wielgolaski (eds.), COST Action 725-the history and current status of plant phenology in Europe. Vammalan Kirjapaino Oy, Finland.

Kubin, E., Pudas, E., Venäläinen, A., and J. Terhivuo. 2008. Phenological recording in Finland, pp. 68-75. In: J Nekovář, E. Koch, E. Kubin, P.Nejedlik, T. Sparks, and F. Wielgolaski (eds.), COST Action 725-the history and current status of plant phenology in Europe. Vammalan Kirjapaino Oy, Finland.

Lindsey, A.A and J.E. Newman. 1956. Use of official weather data in spring time: temperature analysis of an Indiana phenological record. Ecology 37: 812-823.

Masin, R., Zuin, M.C., and G. Zanin. 2005. Phenological observations on shrubs to predict weed emergence in turf. Int. J. Biometeorol. 50: 23-32.

McMaster, G.S. and W.W. Wilhelm. 1997. Growing degree-days: one equation, two interpretations. Agr. Forest Meteorol. 87: 291-300.

Menzel, A. and P. Fabian. 1999. Growing season extended in Europe. Nature 397: 397:659.

Menzel, A., Estrella, N., and P. Fabian. 2001. Spatial and temporal variability of the phenological seasons in Germany from 1951 to 1996. Glob. Change Biol. 7: 657- 666.

Menzel, A. 2003. Europe, pp. 45-56. In: M.D. Schwartz (ed.), Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands

Miller-Rushing, A.J. and R.B. Primack. 2008. Global warming and flowering times in Thoreau‟s Concord: a community perspective. Ecology 89: 332-341.

Mussey G.J., and D.A. Potter. 1997. Phenological correlations between flowering plants and activity of urban landscape pests in Kentucky. J. Econ. Entomol. 90: 1615- 1627.

Neal, J.W. Jr., Chittams, J.L., and J. Bentz. 1997. Spring emergence by larvae of the eastern tent caterpillar (Lepidoptera: Lasiocampidae): a hedge against high-risk conditions. Ann. Entomol. Soc. 90: 596-603. 85

Nekovář, J. 2008. The history and current status of Czech plant phenology, pp. 51-57. In: J Nekovář, E. Koch, E. Kubin, P.Nejedlik, T. Sparks, and F. Wielgolaski (eds.), COST Action 725-the history and current status of plant phenology in Europe. Vammalan Kirjapaino Oy, Finland.

Nietschke, B.S., Magarey, R.D., Borchert, D.M., Calvin, D.D., and E. Jones. 2007. A developmental database to support insect phenology models. Crop Prot. 26: 1444- 1448.

Parton, W.J., and J.A. Logan. 1981. A model for diurnal variation in soil and air temperature. Agric. For. Meteorol. 23: 205-216.

Potter, D.A. & G.M. Timmons. 1983. Forecasting emergence and flight of the lilac borer (Lepidoptera: Sessiidae) based on pheromone trapping and degree-day accumulations. Environ. Entomol. 12: 400-403

Primack, R.B., and A.J. Miller-Rushing. 2009. The role of botanical gardens in climate change research. New Phyto. 182: 303-313.

Pruess, K.P. 1983. Day-degree methods for pest management. Environ. Entomol. 12: 613-619.

Raupp, M.J., Koehler, C.S., and J.A. Davidson. 1992. Advances in implementing integrated pest management for woody landscape plants. Annu. Rev. Entomol. 37: 561-585.

Raworth, D.A. 1994. Estimation of degree-days using temperature data recorded at regular intervals. Environ. Entomol. 23: 893-899.

Richardson, A.D., Bailey, A.S., Denny, E.G., Martin, C.W., and J. O‟Keefe. 2006. Phenology of a northern hardwood forest canopy. Glob. Change Biol. 12: 1174- 1188.

Roltsch W.J., Zalom F.G., Strawn A.J., Strand J.F., and M.J. Pitcairn. 1999. Evaluation of several degree-day estimation methods in California climates. Int. J. Biometeorol. 42: 169-176.

SAS Institute. 2002. SAS user‟s procedures guide, version 9.1.3. SAS Institute Inc., Cary, NC.

Schleip, C., Sparks, T.H., Estrella, N., and A. Menzel. 2009. Spatial variation in onset dates and trends in phenology across Europe. Clim. Res. 39: 249-260.

Schwartz M.D. 1994. Monitoring global change with phenology: the case of the spring green wave. Int. J. Biometeorol. 38: 18-22.

Schwartz M.D. 2003. Phenology: an integrative environmental science. Kluwer Academic Publ., Dordrecht, The Netherlands.

Sevacherian, V., Stern, N.M., and A.J. Muellers. 1977. Heat accumulation for timing Lygus control measures in a safflower-cotton complex. J. Econ. Entomol. 70: 399- 402.

Sharpe P.J.H., and D.W. DeMichele. 1977. Reaction kinetics of poikilotherm development. Theor. Biol. 64: 649-670.

Shetlar, D.J. 2009 November 29. Personal Communication.

Silvertown, J. 2009. A new dawn for citizen science. Trends Ecol. Evol. 24:467-471.

Snyder R.L., Spano D., Cesaraccio C., and P. Duce. 1999. Determining degree-day thresholds from field observations. Int. J. Biometeorol. 42: 177-182.

Sparks, T.H., and P.D. Carey. 1995. The responses of species to climate over two centuries: an analysis of the Marsham phenological record, 1736-1947. J. Ecol. 83: 321-329.

SPSS 16.0 for Windows. 2007. Rel. 16.0.1 Chicago: SPSS Inc.

Swanton C.J. and S.F. Weise. 1991. Integrated weed management: the rational and approach. Weed Technol. 5:657-663.

Taylor, F. 1981. Ecology and evolution of physiological time in insects. Am. Nat. 117: 1-23.

Tobin, P.C., Nagarkatti, S., and M.C. Saunders. 2001. Modeling development in grape berry moth (Lepidoptera: Tortricidae). Environ. Entomol. 30: 692-699.

Waggoner, P.E. 1974. Using models of seasonality, pp. 401-405. In: H. Lieth (ed.), Phenology and seasonality modeling. Springer-Verlag New York, New York.

Warner R.M., and J.E. Erwin. 2001. Variation in floral induction requirements of Hibiscus sp. J. Amer. Soc. Hort. Sci. 126: 262-268.

Wielgolaski F.1974. Phenology in agriculture, pp. 369-381. In: H. Lieth (ed.), Phenology and seasonality modeling. Springer-Verlag New York, New York.

Wielgolaski, F. 1999. Starting dates and basic temperatures in phenological observations of plants. Int. J. Biometeorol. 42: 158-168.

Worner, S.P. 1992. Performance of phenological models under variable temperature regimes: consequences of the Kaufmann or rate summation effect. Environ. Entomol. 21: 689-699.

Yang, S., Logan, J., and D.L.Coffey. 1995. Mathematical formulae for calculating the base temperature for growing degree days. Agr. Forest Meteorol. 74: 61-74.

Appendix A:

The Ohio State University Phenology Garden Network Information

County Garden Site Current Address Latitude Longitude

Ashtabula Ashtabula Agricultural Research Station, 2625 S Ridge Road East 41.88464 -80.6974 Ohio Agriculture Research and Kingsville, Ohio 44048 Development Center

Athens Ohio University 204 Botanical Research Env. & 39.3217 -82.1009 Plant Biology Dept. Greenhouse and Garden, Athens, Ohio 45701

Boone, Kentucky Boone County Arboretum 9190 Camp Ernst Road 38.96438 -84.7232 Union, Kentucky 41091

90 Clark The Ohio State University Extension, 4400 Gateway Blvd. 39.90652 -83.7275

Gateway Learning Garden Springfield, Ohio 45502

Clark Northridge Elementary and Middle School 531 W. Harding Rd. 39.94616 -83.8183 (2005-07) Springfield, Ohio 45504 and Snowhill Elementary (2008)

Clinton The Ohio State University Extension, 111 S. Nelson Rd. 39.44379 -83.8515 County Extension Office Wilmington, Ohio 45177

Continued Table A.1: The gardens by county of The Ohio State University Phenology Garden Network.

Table A.1 continued

County Garden Site Current Address Latitude Longitude

Coshocton Lake Park, Coshocton Park District 23253 SR 83 40.27281 -81.8618 Coshocton, Ohio 43812

Delaware The Ohio State University Extension, 149 North Sandusky -83.0763 40.18198 County Extension Office Delaware, Ohio 43015

Erie Osborn Park 3910 East Perkins Ave Erie Metro 41.42023 -82.6346 Parks

91 Huron, Ohio 44839

Erie Willoway Nurseries, Huron Farm 7712 Hahn Road 41.37458 -82.4719 Huron, Ohio 44839

Fayette Master Gardener Memorial Garden, 1714 Washington Ave. 39.54032 -83.4171 Washington Cemetery Washington CH, Ohio 43160

Franklin Chadwick Arboretum, The Ohio State 2001 Fyffe Court 40.0013 -83.0182 University Columbus, Ohio 43210 Continued

Table A.1 continued

County Garden Site Current Address Latitude Longitude

Greene James Ranch Park 177 Fairgrounds Rd. 39.69852 -83.9405 Xenia, Ohio 45385

Hancock Hancock County Demonstration Gardens 7868 CR 140 41.05727 -83.6886 Findlay, Ohio 45840

Hennepin, MN UFI: Minneapolis Uptown W36th Street and Colfax Ave. 44.938 -93.293 Minneapolis, MN 55426

Huron Shady Lane Park Shady Lane 41.23961 -82.6047 Norwalk, Ohio 44857

Lake The Holden Arboretum 9500 Sperry Rd. 41.61543 -81.2985 Kirtland, Ohio 44094

Licking The Ohio State University Extension, 771 East Main Street 40.06 -82.3714 County Extension Office Newark, Ohio 43055 Continued

Table A.1 continued

County Garden Site Current Address Latitude Longitude

Licking The Dawes Arboretum 7770 Jacksontown Rd. SE 39.97897 -82.4114 Newark, Ohio 43056

Lorain Willoway Nurseries, Long Road Farm 3540 Long Road 41.43463 -82.0448 Avon, Ohio 44011

Lucas The Ohio State University Extension, 5526 West Bancroft 41.662 -83.673 Toledo Botanical Garden Toledo, Ohio 43615

Mahoning Millcreek Metropark 7574 Columbiana Canfield Rd 41.01273 -80.7608 Canfield, Ohio 44406

Pickaway Monroe Township Community Park, Mt. Sterling, Ohio 43143 39.72273 -83.2859 Five Points Pike

Pike The Ohio State University Extension, 1864 Shyville Rd 39.0512 -82.9958 South Center Piketon, Ohio 45661 Continued

Table A.1 continued

County Garden Site Current Address Latitude Longitude

Portage The Ohio State University Extension, 6970 SR 88 41.15962 -81.2397 County Extension Office Ravenna, Ohio 44266

Richland The Ohio State University Extension, 1495 W. Longview Ave. 40.77604 -82.5646 County Extension Office Mansfield, Ohio 44906

Ross Canal Gardens, Camp Sherman Chillicothe, Ohio 45601 39.33261 -82.9867

Stark The Ohio State University Extension, 2650 Richville Dr. 40.78139 -81.503 County Extension Office Massillon, Ohio 44646

Stark Canton County Day School 4000 Demmington Ave. 40.81383 -81.4255 Canton, Ohio 44708

Summit F.A. Seiberling Nature Realm 1828 Smith Rd. 41.14106 -81.5746 Akron, Ohio 44313 Continued

Table A.1 continued

County Garden Site Current Address Latitude Longitude

Summit Adell Durbin Arboretum, Stow State Rt. 91 41.17521 -81.4384 Stow, Ohio 44224

Trumbull Trumbull Agriculture and Family 520 W. Main St. 41.32997 -80.7415 Education Center Cortland, Ohio 44410

Washington The Ohio State University Extension, 202 Davis Ave. 39.43991 -81.4588 Memorial Garden Marietta, Ohio 45750

Wayne Secrest Arboretum, 1680 Madison Ave. 40.77865 -81.9308 Ohio Agricultural Research and Wooster, Ohio 44691 Development Center