Alabama Phenology Garden Project: Using Degree-days and Plant Phenology to Predict Pest Activity by Raymond Albert Young A thesis submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Master of Science Auburn, Alabama August 4, 2012 Keywords: phenology, growing degree days, urban integrated pest management Copyright 2012 by Raymond Young Approved by David Held, Chair, Associate Professor of Entomology Gary Keever, Dodd Endowed Professor of Horticulture Bill Klingeman, Associate Professor of Entomology, University of Tennessee Knoxville ii Abstract Accurate prediction of pest activity is crucial crucial for maintaining a successful urban integrated pest management program. Plant phenology and growing degree days can be useful tools in tracking important pest stages thus signaling the most critical treatment time. Because plant and insect development is mostly dependent upon temperature, biological calendars can be developed to help monitor key stages. The main objectives of this research were to (1) establish phenology gardens containing common taxa throughout Alabama, (2) compare the emergence, flight, or appearance of insect pests with the progression of ornamental plant bloom stages in each garden, and (3) produce a website with key phenological indicators and pest correlates. Phenological data collected from two sentinel insect species, dogwood borer and crape myrtle aphid on five sites and eight additional landscape pests in Auburn from 2010 to 2011 were used to establish phenological bloom sequence-based prediction models for 12 plant species for landscaper management personnel, growers, and laypersons. Growing degree-days models were implemented and compared to test accuracy of each. A total of 32 phenological events were studied in correlation with key insect life stage events such as first appearance and peak stages. The rank order of phenological events showed that there was significant correlation of bloom stages from year to year and site to site based on results of the Spearman?s bivariate correlation and regression analysis. A common phenophase was not found to be consistent with activity statewide of the two sentinel pests. Variations in cumulative growing degree day and iii Julian date proved no reliable statewide indicator for pest prediction. However, In future studies, recommendations could possibly be on a broader latitudinal area such as region or USDA hardiness zone. iv Dedication I would like to dedicate this thesis and its work to my parents, Charles and Becky, and brother Scott. Thanks for your unlimited support throughout this project and graduate school. v Acknowledgments I would like to thank everyone involved in my project throughout the project in helping me complete my research, thesis, and classes. First and foremost I would like to thank Dr. Held for never giving up on me and pushing me along. He provided the skills and support necessary to complete this project and graduate. I would also like to thank my student advisory committee, Dr. Keever and Dr. Klingeman for support, guidance, and patience from start to finish. They provided challenging questions and asserted positive yet insightful leadership throughout. I would also like to give a special thanks to Shane Parker for his dedication in assisting me with organization of five field sites. We logged many a miles across Alabama and shared numerous 14 hour days throughout those two and a half years. I would also like to thank my other lab mates who helped me plant and maintain gardens and collect data. I would like to thank the Alabama Master Gardeners for their dedication, hard work, and enthusiasm in the project. I would also like to thank Harvey Cotton and the Huntsville Botanical Garden, Fred Kapp and Oak Mountain Middle School, Larry Wells, Ed Turner and Wiregrass Extension Center, and MaryJo Broussard and Mobile Botanical Garden and anyone else I may have failed to mention for collaboration in providing field sites and resources. Special thanks to Chazz Hesselein, John Olive, Ken Creel, and Kerry Smith for organization on the project. Field prep and maintenance was provided by Robert Hensarling and the Ag Land and Resource Management team. For them I am grateful. I would also like to thank the nurseries whom vi donated plant materials and Regal Chemical Company for herbicide donation. I would like to thank Ajun Zhang for pheromone lures. Additionally I would like to thank Mark Bransby for website construction and maintenance. Lastly, I would like to thank the Alabama Agriculture and Experiment Station (AAES) for funding the project. vii Table of Contents Abstract ......................................................................................................................................... ii Dedication .................................................................................................................................... iv Acknowledgments......................................................................................................................... v List of Tables ............................................................................................................................... ix List of Figures ............................................................................................................................... x Chapter 1 ...................................................................................................................................... 1 Importance of Landscape Pest Management .................................................................... 1 Growing degree days ............................................................................................ 2 Phenology ............................................................................................................. 4 Phenology gardens ................................................................................................ 5 Biology of the Experimental System ................................................................................ 6 Lepidopteran borers .............................................................................................. 6 Mandibulate folivores ......................................................................................... 10 Haustellate folivores ........................................................................................... 13 Objectives ....................................................................................................................... 14 Chapter 2 ................................................................................................................................... 16 Introduction ..................................................................................................................... 17 Objectives ....................................................................................................................... 18 Materials and Methods .................................................................................................... 18 Study sites ........................................................................................................... 18 Plant materials ..................................................................................................... 21 Phenophase recording ......................................................................................... 22 viii Degree-day accumulations .................................................................................. 23 Data collection .................................................................................................... 24 Statistical analyses .............................................................................................. 25 Results ............................................................................................................................ 25 Variation in Julian day and growing-degree day ............................................... 25 Discussion ....................................................................................................................... 40 Chapter 3 ................................................................................................................................... 42 Introduction ..................................................................................................................... 43 Objective ......................................................................................................................... 44 Materials and Methods .................................................................................................... 44 Statistical Analyses ............................................................................................. 49 Results ............................................................................................................................ 49 Sentinel pests first emergence, two year average ............................................... 49 Auburn temperature averages ............................................................................. 57 Auburn biological calendar ................................................................................. 60 Discussion ....................................................................................................................... 65 Chapter 4 ..................................................................................................................................... 67 Final conclusions ............................................................................................................ 67 References ................................................................................................................................. 70 ix List of Tables Table 2.1 Garden locations in Alabama Phenology Garden Network ........................................ 19 Table 2.2 Twelve plants established in the phenology gardens .................................................. 22 Table 2.3 Julian days to event of plant flowering sequences in Alabama Phenology Garden Network, five sites 2010-2011 .................................................................................................... 26 Table 2.4 Spearman?s bivariate correlation coefficients generated from phenological sequence Table 2.5 Julian date conversion for common year .................................................................... 29 Table 2.6 GDD and Julian date for flowering events for Yoshino cherry, Prunus ?yedoensis . 31 Table 2.7 GDD and JD for flowering events for flowering for daffodil, Narcissus 'Ice Follies' 32 Table 2.8 GDD and JD for flowering events for forsythia, Forsythia ?intermedia ?Lynwood Gold? ........................................................................................................................................... 33 Table 2.9 GDD and JD for flowering events for loropetalum, Loropetalum chinense ?Ruby? .. 34 Table 2.10 GDD and JD for flowering events for Indian hawthorn, Rhaphiolepis indica Eleanor Tabor? .......................................................................................................................... 35 Table 2.11 GDD and JD for flowering events for oakleaf hydrangea, Hydrangea quercifolia 'Ellen Huff' .................................................................................................................................. 36 Table 2.12 GDD and JD for flowering events for flowering for ?Happy Returns? daylily, Hemerocallis 'Happy Returns' .................................................................................................... 37 Table 2.13 GDD and JD for flowering events for ?Natchez? crapemyrtle, Lagerstroemia indica ?fauriei 'Natchez' ............................................................................................................. 38 Table 2.14 GDD and JD for flowering events for Majestic liriope, Liriope muscari 'Majestic' 39 Table 3.1 Arthropods monitored in Auburn, AL, 2010-2011 ..................................................... 48 Table 3.2 Sentinel insect species data across 5 sites and 2 years comparing GDD(C) and Calendar date .............................................................................................................................. 54 x Table 3.3 DWB first emergence, 2 year average and 2 year date with plant correlate, 5 sites statewide, 2010-2011 .................................................................................................................. 56 Table 3.4 CMA first emergence, 2 year average and 2 year date with plant correlate, 5 sites statewide, 2010, 2011 ................................................................................................................. 56 Table 3.4 Mean monthly temperatures for monitoring period 2010-2011 & 30-year average for Auburn, AL ......................................................................................................................... 57 Table 3.6 Comparison of degree-day requirements for first capture of CMA in five sites in Alabama; calculated from Jan 1 using eight potential base temperatures .................................. 58 Table 3.7 Comparison of degree-day requirements for first capture of DWB in five sites in Alabama; calculated from Jan 1 using eight potential base temperatures .................................. 59 Table 3.8 Biological calendar for Huntsville, AL 2010-2011 .................................................... 60 Table 3.9 Biological calendar for Birmingham, AL 2010-2011 ................................................. 61 Table 3.10 Biological calendar for Auburn, AL 2010-2011 ....................................................... 62 Table 3.11 Biological calendar for Headland, AL 2010-2011 .................................................... 63 Table 3.12 Biological calendar for Mobile, AL 2010-2011 ....................................................... 64 xi List of Figures Figure 1.1 Adult Synathedon scitula ............................................................................................. 7 Figure 2.1 Map of 5 sites in the Alabama Phenology Garden Network ..................................... 19 Figure 2.2 One replicate in the phenology arden at the Mobile Botanical Gardens. .................. 20 Figure 2.3 A flowering cherry tree in the phenology garden that shows the flagged shoots used to record flowering phenology on certain woody plants. ................................................................ 23 Figure 2.4 Data loggers were used on all sites to record weather data loggers were housed in radiation shields. ......................................................................................................................... 24 Figure 3.1 Sooty mold on crapemyrtle ....................................................................................... 46 Figure 3.2 Cherry twig with an egg mass of Malacosoma americanum ................................... 47 Figure 3.3 DWB Cumulative GDD trap capture for 2010, 5 sites statewide.............................. 50 Figure 3.4 DWB Cumulative GDD trap capture for 2011, 5 sites statewide.............................. 50 Figure 3.5 DWB Cumulative JD trap capture for 2010, 5 sites statewide. ................................. 51 Figure 3.6 DWB Cumulative JD trap capture for 2011, 5 sites statewide. ................................. 51 Figure 3.7 CMA Cumulative GDD trap capture for 2010, 5 sites statewide. ............................. 52 Figure 3.8 CMA Cumulative GDD trap capture for 2011, 5 sites statewide. ............................. 52 Figure 3.9 CMA Cumulative JD trap capture for 2010, 5 sites statewide. ................................. 53 Figure 3.10 CMA Cumulative JD trap capture for 2011, 5 sites statewide. ............................... 53 1 Chapter 1 Introduction Importance of Landscape Pest Management The Green Industry is a major source of U.S. commerce with an estimated $175 billion (2007) in sales and employment of approximately two million persons (Hodges et al. 2011). More than 90 million American households partake in gardening as a recreational hobby (NGA 2006). Alabama?s Green Industry increased from $1.9 billion in 2003 to $2.9 billion in 2007, ranking as the state?s 3rd largest commodity behind poultry and cattle and employing over 20,000 people with more than 2,500 firms (Hodges et al. 2011). As urban areas increase, so does the demand for public, commercial, and residential green spaces including lawns and landscapes. Along with the increase in plant species comes an increase in the number of arthropod pests (Raupp et al. 2001). As insect pest populations increase, potential damage to landscape ornamental plants also increases. Consequently, significant amounts of pesticides are used for pest control in landscapes. Annually, more than $172 million worth of damages occur in Georgia landscapes and costs of control to pests attacking ornamentals plants in nurseries and landscapes (Oetting et al. 2004). Landscapers, nursery personnel, and laypersons alike traditionally rely on calendar based application for predicting pest activity, often with immense failure rates in controlling landscape pests due to inconsistencies in pest activity from year to year and variations such as warmer or cooler spring temperatures. The annual timing of plant events (budding, leafing, flowering) and insect 2 development is driven by temperature; therefore, we can use growing degree-days to track important developmental phases (Huberman 1941). Growing degree days (GDD) account for the accumulation of heat units in a 24-hour period. The use of phenology and degree-day models can aid in forecasting key pest events, ultimately saving money in pest management programs. Degree-day models and plant phenological indicators can be used for predicting pest activity. Growing Degree Days Temperature can be a valid tool in predicting insect development rate (Herms 2004). The idea of using a growing-degree day model was first applied in 1735 by R?aumur, stating ?plant development is proportional to the sum of temperature over time rather than to temperature during the phenological event itself? (cited by Chuine et al. 2003). Degree-day models can be used to predict temperature-driven events such as insect development. Thresholds are the upper and lower limits at which development occurs and above and below these limits, no development takes place (Pedigo and Rice 2009). Base temperature is the point at which insect development is optimal. Base temperatures are generally determined by insect development thresholds for each life stage. When developmental thresholds are unknown, the base temperature is typically determined as the lowest temperature using coefficient of variation among growing degree data (Arnold 1959). A biofix is an important date signaling when to begin recording growing degree days for a pest (Herms 2004). For some insect species, January 1 may not be the optimal starting date because some insects can start developing in late summer and fall. For example, October 1 is commonly used for dogwood borer (Bergh et al. 2009). One method to determine optimal biofix would be to monitor different starting dates, for recording temperatures, over numerous 3 years (Herms 2004). Growing degree-days can be calculated several ways. The average method, also known as the modified average method is calculated as: (maximum temp + minimum temp) / 2 = mean temperature for the day The modified sine-wave (Bakersville-Emin) method, which takes into account days when the minimum temperature falls below the base temperature, is often used. The base temperature is a lower limit at which development occurs. When low temperatures do not fall below base temperatures, GDD are calculated as follows: GDD = ((W * Cos(A)) ? ((BASE ? TAVE) * ((3.14/2)-A)))/3.14 Where: GDD = Average Temperature ? Base Temperature W = (Max. temperature ? Min. temperature) / 2 A = Arcsin ((BASE ? TAVE) / W The modified sine-wave method is typically more precise than the average method because it uses daily maximum and minimum temperatures and takes into account developmental thresholds (Bakersville and Emin 1968). There are some limiting factors to using growing degree days. Degree-day models fail to take into account other factors that affect rates of development such as environmental influences because models focus strictly on heat accumulation (Higley et al. 1986). Growing degree-day models assume insect growth is a linear relationship in time. However, insect development rate may be non-linear (Allen 1976, Stinner et al. 1974) although traditionally believed to be linear, in response to temperature. Nonlinear development simply means there are cutoff temperatures where insect development stops or where temperatures are lethal. For this reason, certain degree day models include a high or low cutoff temperature. High and low temperature extremes can present problems for accurate prediction of pest activity. (Stinner et al. 1974). The actual 4 temperature that pests experience is influenced by behavior (i.e. thermoregulation) and microenvironments. For example, leaf surface temperature may be 10?C higher than ambient temperature under certain conditions like sunny days, leading to an underestimation of insect growth (Ferro et al. 1979). Also, developmental temperatures for endophagous insects like wood borers can vary depending on the location within the plant where they are developing or overwintering. Model temperatures have been recorded in both sunny and shaded conditions (Herms 2004). Insects on the south side of trees, for example, could experience different heat accumulation than insects on the north side, or shady side (Mussey and Potter 1997). Insects may also thermoregulate, moving to darker or lighter surfaces to cool or warm their bodies (Herms 2004). The overall rate of development is also influenced by additional abiotic factors. Heat increases development rates and cold decreases development rate (Bonhomme 2000). Additionally, insect growth rates can be influenced by plant feeding regimens based on fertility and drought effects as in holometabolous insects (insects that have four life stages: egg, larva, pupa, adult) (Herms 2004). Other abiotic factors that could influence insect development include wind effects in relation to heat loss, as well as humidity in relation to insect moisture (Ferro et al. 1979). Most landscape pests do not have established base temperatures required for accurate optimal growth rate calculation (Mussey and Potter 1997, Herms 2004). Furthermore, accurate local temperature data and GDD calculations can often be aggravating and unavailable to landscapers and laypeople. Phenology Phenology is the study of natural phenomena between weather and the annual timing of biological events (Huberman 1941). Naturally occurring events such as animal migrations and 5 hibernations, insect activity, plant flowering, and agricultural crop stages are all examples of phenology. Phenology has been used for thousands of years to predict till, plant, and harvest dates (Schwartz 2010). Written phenology records from China date back to around 974 B.C. (Gardiner 2009). Use of phenological indicators can be valuable tools for pest managers (Mussey and Potter 1997, Hoover 2002). Geographic location will affect the occurrence of phenological events. Mussey and Potter (1997) reported up to 28 d difference between the average data of a phenological event in Kentucky compared to the average date for the same phenological event in MI. Data from different. regional sites has the potential to predict pest activity statewide (Mussey and Potter 1997, Herms 2004, Kulhanek 2009). In a 6-year study of 34 sites across Ohio, the phenological sequence of 43 arthropod pests of woody ornamentals and plants consistently correlated between years and between sites (Kulhanek 2009). Phenology Gardens Plant enthusiasts usually find it easier to track ?indicator plants? to correlate specific insect pests rather than study the pest solely (Schnelle and Volkert 1974). Phenology gardens can be implemented to track this phenomenon. Phenology garden networks are often composed of groups of people growing the same plants from the same sources in different locations (Chen 2003). Phenology gardens have multiple uses ranging from predicting pest activity to monitoring climatic events (Kulhanek 2009). Phenology gardens exist throughout Asia (Schwartz 2010), Europe (Chmielewski 2008, Koch et al. 2008), and the U.S. In Europe, a phenology garden network of 13 gardens in seven countries has been monitored for 54 years. These gardens 6 contain 14 clonal plant species that were asexually propagated with recognizable phenophases, developmental stages that are sensitive to air temperature, and phenophases that cover the majority of the growing season (Bruns et al. 2003). The main focus of the aforementioned systems is to collect and compare phenotype data over a wide range of locations and years (Chmielewski 2008). Phenology gardens consist of certain flowering plants that can be used to track occurrences like flower and leaf development and flowering stages. Plants are monitored and recorded yearly for first sequence of flowering (Schnelle and Volkert 1974). Biology of the Experimental System Lepidopteran borers: Dogwood borer (Synanthedon scitula): Dogwood borer (DWB), Synanthedon scitula (Lepidoptera: Sesiidae), is a multi-voltine pest of dogwoods but also develops in callus or gall tissue on other plant species including oaks and apples (Eliason and Potter 2000, Bergh and Leskey 2003). Dogwood borer has a wide host range including beech, willow, chestnut, blueberry, hickory, pecan, pine, ash, oak, and elm (Johnson and Lyon 1991). Despite the common name, DWB has the most expansive host range of any sesiid in North America (Potter and Timmons 1981). Dogwood borer emerges from inside the plant in the spring to lay eggs on the bark. Within 8-9 d, the eggs hatch and first instar larvae enter the plant and form large feeding galleries. In certain areas of the United States, it takes approximately a year for larvae to pass through seven instars (Neal and Eichlin 1983, Bergh and Leskey 2003). In other areas, it completes several generations in a year. Overwintering occurs below the bark and spring 7 temperatures of 7-10?C trigger feeding the following spring. Larvae then create cocoons close to the exterior of the plant in order to pupate. Pupal cases can be seen on the bark at exit sites (Gyelthsen and Hodges 2006). Adult male flight activity can be monitored with the sex pheromone Z, Z-3,13-ODDA (Bergh et al. 2009). Growing degree days for dogwood borer are calculated using 4 and 10?C base temperatures (Potter and Timmons 1983 Bergh et al. 2009 and Mussey and Potter 1997, Herms 2004, respectively) and using a biofix of either Jan 1 (Mussey and Potter 1997, Herms 2004) or Oct 1 (Timmons and Potter 1983, Bergh et al. 2009). October 1 biofix was used to account for the overwintering egg population. First emergence of DWB in central Kentucky occurs at about 95% flower of Ilex opaca (American holly) and first flower of Crataegus phaenopyrum (hawthorn) or 531 degree-days Celsius (DDC) with a biofix of January 1 (Mussey and Potter 1997). In Ohio, first emergence occurs with full flower of Kalmia latifolia (mountain laurel) or 830 DDF with a biofix of January 1 (Herms 2004). In the same year over a larger geographic area, GDD for first emergence ranged from 818 DDC in New York to Tennessee 1579 DDF with a base temperature of 4?C and a biofix of October 1 (Bergh et al. 2009). Figure 1.1. Adult Synathedon scitula 8 Lesser peachtree borer (Synanthedon pictipes): Adult lesser peachtree borer, (LPTB) Synanthedon pictipes (Lepidoptera: Sesiidae), emerge in the spring then females lay eggs in the fall. Eggs hatch and larvae enter the trunk and feed in the sapwood. Larval damage creates weak spots in the trunk and branches and exposes the plant to various other pest attacks. Lesser peachtree borer has at least 2 generations per year with a possible third generation reported from Georgia (Yonce et al. 1977). Adult emergence occurs as early as March but adults peak in May (Yonce et al. 1977) followed by another peak of adults in July-August (Yonce et al. 1977). Frass is usually found at exit holes on the bark when larvae are feeding (Welty 2000) and pupal skins present with each adult emergence (Yonce et al. 1977). The capture of the first male, lesser peachtree borer in traps is phenologically correlated with Kousa dogwood (Cornus kousa) at 224 DDC, with a base 10?C and January 1 as a biofix in central Kentucky (Mussey and Potter 1997). In Midland, MI, first male capture occurs with an average of 362 DDF and a base temperature of 50?F and is correlated with full flower of Blackhaw viburnum (Viburnum prunifolium). In Ohio, male capture occurs or at 372 DDF or with full flower of horse chestnut (Aesculus hippocastanum) (Herms 2004). Lilac ash borer (Podosesia syringae): The lilac ash borer (LAB), Podosesia syringae (Lepidoptera: Sesiidae), is a univoltine moth whose larvae tunnel into lilac, privet, ash, and several other similar species. Adults emerge in the spring (February in FL) with peak flights in Apr-May in southern states and June in Ohio (Purrington and Nielsen 1977). Females deposit tan eggs (0.77 mm long) into crevices and wound sites in spring where larvae enter the tree, feed, and overwinter as fully grown larvae (Purrington and Nielsen 1977). The next year, larvae enter the cambium layer and exit the 9 following year. Damage occurs through tunneling, therefore causing weak spots in the trunks that are easily broken. Larvae usually burrow at or just below the soil surface (Westcott 1946). Daily male flight occurs from 9 a.m. until early afternoon (Taft et al. 2004). Male moths are monitored using the sex pheromone (Z,Z)-3,13-ODDA (Purrington and Nielsen, 1977). In Lexington and Louisville (KY), first flight occurs from April 13 to May 6, consistent with first flower of Tatarian honeysuckle (Lonicera tatarica) and cumulative 426 DDC (Timmons and Potter 1983, Mussey and Potter 1997). In Midland, MI, LAB first emergence is concurrent with Common lilac (Syringia vulgaris) full flower and 324 DDF base temp 50?F. Similarly flight occurs at 330 DDF in Ohio and is correlated with first flower of ?Winter King? Indian hawthorn (Crataegus viridus ?Winter King?) or 330 DDF (Herms 2004). Lilac ash borer first emerges in IL coinciding with Vanhoutte spirea (Spirea ?vanhouttei) (Orton 1989). Oak clearwing borer (Paranthrene simulans): The oak clearwing borer (Lepidoptera: Sesiidae) is an important multivoltine pest of white oaks throughout the Eastern and Southern U.S. causing damage to the root flange, often leading to degradation and decay. First sign of injury includes sap spots and frass around the base of the trunk. Later, entrance holes, 9-15 mm diameter, appear due to mining below the bark. Galleries 9 mm x 10 cm are made my tunneling. Upon emergence from wood, females lay eggs in bark crevices in the summer (Solomon et al. 1987). Male P. simulans can be monitored with traps containing (Z,Z)-3,13 octadecadien-l-01 acetate (Z,ZODDA) as well as Z,Z ODDA with minor components, Z,E and E,Z ODDA (Sharp et al. 1978, Neal and Eichlin 1983, Rogers and Grant 1991). Activity begins in early May and lasts mid?July (peak mid to late May) in TN and MD (Neal and Eichlin 1983, Rogers and Grant 1991), but males are trapped from April?July in FL with April as the peak month (Sharp et al. 1978). Activity seems to alternate between 10 years with odd-numbered years producing a greater numbers of males in TN and MD (Neal and Eichlin 1983, Rogers and Grant 1991) or in even-numbered years in FL (Sharp et al. 1978). This is attributed to a life cycle that requires ? 2 yr (Sharp et al. 1978). Male traps captures that peak in late June?July in TN (Rogers and Grant 1991) may indicate emerging adults from different species of oaks (Solomon 1995). Mandibulate folivores: Japanese beetle (Popillia japonica): Japanese beetle, Popillia japonica (Coleoptera: Scarabaeidae), has a host range of about 300 plant species in 79 families and the grubs feed on roots of variety of turfgrasses, trees, shrubs, and vegetables (Potter and Held 2002). Around late May, adult beetles emerge from the soil in search of a food source. After feeding, females burrow into the ground to lay eggs. They then emerge again in search of food and a mate. This cycle of feeding and egg laying repeats throughout the summer. Egg hatch normally takes 8-9 d at an average temperature of 29?C. The larvae complete three instars and feed on plant roots of mainly grasses. Grubs overwinter in the soil and feed during the following spring. Grub feeding removes significant root mass and often turfgrass can be peeled back. During the summer months (May-Aug), adults are leaf skeletonizers and also consume flowers and fruit. Japanese beetle has a 1-year life cycle. Adult Japanese beetles are typically monitored with a trap baited with a food lure (phenethyl- propionate and eugenol plus geraniol) and a sex lure (Potter and Held 2002). Adults emerge coincident with 50% flower of Little-leaf linden (Tilia cordata) and a three-year average 697 DDC (Mussey and Potter 1997). Emergence in Ohio is coincident with Panicled goldenraintree (Koelreuteria paniculata) or 970 DDF (Herms 2004). First adult emergence in IL occurs concurrently with Hills-of-snow hydrangea (Hydrangea arborescens 11 ?Grandiflora?) full flower (Orton 1989). Japanese beetle prepupal stage occurs with an average 1757 GDD (June 17) in College Park, MD (Schlar & Bergquist, Unpublished data). Lesser canna leafroller (Geshna cannalis): Lesser canna leafroller, Geshna cannalis (Lepidoptera: Pyralidae), is a leaf-rolling moth that attacks canna (Canna sp.) as larvae. Upon egg hatch, larvae enter leaves and mine throughout to feed until exiting inner leaves to feed on the upper side of the leaf. One week after hatch, groups of larvae begin rolling leaves with as many as six per leaf roll. The final instar makes a silk web before pupation. Overwintering occurs in canna leaf litter. There is very little literature on the seasonal phenology of lesser canna leafroller. Damage normally occurs on leaves that have not completely unfolded (McAuslane 2000). There are no identified pheromones for this pest but activity is generally measured by the occurrence of foliar damage. Black cutworm (Agrotis ipsilon): Black cutworm (Agrotis ipsilon) (Lepidoptera: Noctuidae) (BCW) is an important pest of turfgrass and at least 48 other species of cultivated plants including wheat, corn, and tobacco. Larvae chew stalks, roots, bulbs, and tubers causing major damage. Early larvae feed on foliage without actually cutting stems or leaves from its host. Black cutworm has three generations per year (Sherrod et al. 1979). Adult male moths are monitored with sticky trap or Texas cone traps (Hong and Williamson 2004) baited with the sex pheromone (Z)-7-dodenen-1-yl acetate and (Z)- 9-tetradecen-1-yl acetate. In KY, first male capture averaged 557 DDC, concurrent with 95% flower of Common lilac (Syringa vulgaris) (Mussey and Potter 1997). However, BCW overwinters in southern states (Showers 1997), which may significantly alter the seasonal phenology in the southeast relative to northern and midwestern records (Mussey and Potter 1997). 12 Fall armyworm (Spodoptera frugiperda): Fall armyworm, (Lepidoptera: Noctuidae) (FAW), is a major pest of all types of grasses, mainly corn and small grain, as well as legumes. These caterpillars consume foliage, and small plants can be eaten entirely. The insect has three generations per year with the last generation typically causing the most damage and forming ?armies? of gregarious larvae (Nagoshi and Meagher 2004). Third instar larvae overwinter in the soil in tropical areas of North America (Texas and Florida) and the adults migrate north during the spring. Egg hatch takes 2-10 days and full development of larvae takes roughly 20 days before burrowing into the soil. Pupation then usually takes 10 days (Sparks 1979). Adult male flight is monitored with traps baited with the sex pheromone (Z)-7-dodecen-l-ol-acetete (Z7-12:AC) (Mitchell et al. 1985). Eastern Tent Caterpillar (Malacosoma americanum): Eastern tent caterpillar, (Lepidoptera: Lasiocampidae), is an important defoliator of trees and shrubs native to North America. Preferred hosts are plants in the family Rosaceae, and host range includes Prunus, Malus, Crataegus, Pyrus, and a number of additional hardwood species. Overwintering occurs in spumaline coated egg masses encircled on the plants that normally contain 150-350 eggs. Full-grown larvae are 50?55 mm in length (Johnson and Lyon 1991, Hyche 1996). Larvae spin webs or tent-like structures on forked branches of trees and devour foliage of host trees. Feeding occurs only during the daytime and larvae accumulate in tents during nighttime and periods of inclement weather. Molt takes between 5?10 weeks, depending on temperature. After molting, larvae find nearby sites for pupation. In 3 to 4 weeks, moths pupate from silken cocoons. Eastern tent caterpillar has one generation per year in Auburn, AL and hatch occurs from mid-February to mid-March (Hyche 1996). 13 Adult male moths can be monitored using pheromone traps. However moths fly in one season and females defoliate trees the following spring. Therefore, spring larval activity is gauged by monitoring egg hatch (Potter et al. 2005). Egg hatch of ETC in central Kentucky occurs with 50% flower Border forsythia (Forsythia ?intermedia) and a three-year average of 18 DDC (Mussey and Potter 1997). In Midland, MI, egg hatch occurs at 47 DDF, in correlation with first flower of Acer rubrum (Herms 2004). In IL, egg hatch occurs when Saucer magnolia (Magnolia ?soulangiana) is in pink bud to early flower (Orton, 1989). Egg hatch in Ohio occurs on average date April 1 or with 92 GDD (Schlar & Bergquist, unpublished data). Haustellate folivores: Crapemyrtle aphid (Tinocallis kahawaluokalani): Crapemrytle aphids (CMA), Tinocallis (Sarucallis) kahawaluokalani, are introduced pests of crapemyrtles that have spread throughout the southeast (Mizell and Schiffhauer 2007). Adults and nymphs have yellow-green bodies with black projections on their abdomens. All adults CMA are winged (Alverson and Allen 1992). Length ranges from 0.4?0.6 cm (Ong 2010). Crapemyrtle aphids are specific mainly to crapemyrtle. Damage from CMA is both direct and indirect. Direct damage occurs via distortion and stunting of new growth through feeding, and indirect damage occurs through production of honeydew. CMA typically feeds on the bottom of leaves. They use piercing-sucking mouthparts to remove plant sap (Alverson and Allen 1992). Cultivars such as ?Biloxi?, ?Hopi?, ?Apalache?, ?Zuni?, and ?Comanche? are more susceptible to CMA, while ?Natchez?, ?Potomac?, and ?Victor? are nearly resistant (Mizell and Knox 1993). Damage includes deformed leaves and stunted growth. Additionally, production of 14 honeydew leaves plants aesthetically displeasing (Alverson and Allen 1992). Ultimately, the black soot shades the leaf, inhibiting photosynthesis (Mizell and Knox 1993). Aphids reproduce parthenogenically throughout the summer but sexual stages and mating occurs in fall. Eggs are deposited on the bark in fall on terminal growth up to 100 cm from the terminal. Eggs hatch in spring typically coincident with bud break (Alverson and Allen 1992). In Georgia, activity of CMA ranges from May 6 to September 8 (Stewart et al. 2002). In Texas, CMA is found from May through September, with peak populations during July and early August (Ong 2010). There are no pheromones available for CMA so alates and apterous forms can be monitored using sticky traps or beat samples (Mizell and Schifflauer 2007). Alverson and Allen (1992) found activity of alates captured on sticky traps was generally coincident with infestations on plants. In general, sticky cards may still record activity after the peak leaf density (Alverson and Allen 1992). Adults, all winged, readily disperse especially when air temperatures are ?30 C (Alverson and Allen 1991). Recommendations for the aforementioned phenology correlates vary from site to site because they came from different states or regions. The method used to calculate growing degree days for each insect event applied various biofix dates and base temperatures. For example, some calculations used a 55?F base temperature, some used several years worth of temperature data from a single location, and others were calculated using data from many locations. To be able to report a consistent recommendation with each pest species and plant would require original temperature datasets. This variation is due to the lack of a standard system for calculating growing degree day, therefore making each study site unique. Ultimately, it is difficult to compare GDD information from region to region because of sampling technique. 15 Objectives The use of the common calendar as a guide for predicting landscape insect pests is unreliable. The application of degree-day models and plant phenological indicators can be a more accurate tool to pinpoint control measures of these pests. The objectives of this study were to establish five phenology gardens containing 15 plant taxa across Alabama, to monitor insect activity in relation to plant phenology and growing degree-days, to compare plant phenological indicators and arthropod data collected in Auburn to occurrence in other gardens and to train Master Gardeners and other citizen scientists about phenology via meetings and a website. Ultimately a biological calendar was produced indicating growing degree data and plant phenological indicators for nursery managers, landscapers, pest control personnel, and laypersons to pinpoint key stages of landscape insect pests, producing recommendations for improved integrated pest management strategies. 16 Chapter II COMPARING THE PHENOLOGY AND DEGREE-DAY MODELS FOR 12 ORNAMENTAL LANDSCAPE PLANTS Abstract Ornamental plant phenophases can be important tools in tracking pest events. Degree day models are often used to predict activity of specific species pest life stages. A suite of the same 15 plants replicated four times was planted at five locations across Alabama and replicated four times at each site. Flower sequences were monitored and a standardized base temperature and biofix were used to calculate GDD for each plant species in two years. The phenological sequence was significantly correlated between years and all locations across Alabama. These data indicate a plant phenological sequence even across large geographic area may be useful to predict other phenological events such as insect activity. 17 Introduction Accurate predictions using phenological indicators are critical for pest management (Chmielewski 2008). Phenological methods have been used in the U.S. with over 200 agricultural and horticultural pest species (Delahaut 2010). Phenology has a useful application to pest management for predicting the timing of pest emergence and peak stages because the urban landscape contains a diversity of flowering plant species. Several studies conducted in northern states (e.g., KY, OH, MI) have documented the emergence or activity of key pests and correlated these events with flowering stage of common ornamental plants (Mussey and Potter 1997, Hoover 2002, Herms 2004). Phenology gardens have grown popular since the 1990s. Concern about climate change and global warming has led citizens to monitor this phenology phenomenon and use plants to gauge insect activity by monitoring easily recognizable growth stages (e.g., Project Budbreak). Phenological events such as bud break, flowering, and leaf expansion are recorded coincident with the date of occurrence. These events are reported categorically using either the BBCH (Biologische Bundesanstalt, Bundessortenamt, Chemische Industrie) scale (Meier 2010) or event specific categories such as first and full flower (Herms 2004). These categories are intended to provide conspicuous, observable events that can be compared between years in the context of climate change, or used to correlate with activity of insects (Delahaut 2010). Biological calendars are the most common application of phenology to agronomic and horticultural pests (Herms 2004, Mussey and Potter 1997, Kulhaneck 2009, Delahaut 2010). Herms (2004) gathered data and developed a calendar, monitoring 47 landscape plant species and 24 insect pests for 5 years in Midland, MI. Additionally, Mussey and Potter (1997) monitored 34 ornamental plant species and 33 insect pest species in Kentucky, with some of the 18 same plant species as the Herms 2004 study. Hodges and Braman (2004) studied seasonal abundance and phenological indicators of five scale species (Hemiptera: Diaspididae, Coccidae) in the landscape environment in Georgia. Kulhanek (2009) analyzed phenological data collected for 43 arthropod landscape pests from 1997?2002 to develop a model of 45 phenological events for the insect pest species. However, there is some perception that phenology is not important in southern states due to the milder climate (Jim Reinert, personal communication). In order to make accurate control recommendations for landscape pests, we monitored activity of plants and pests in each of the garden sites. Objectives The objective of this chapter was to record and compare Julian date and growing degree days of 12 ornamental landscape plants for 5 sites in Alabama over two years. Materials and Methods Study sites. Five study sites (Fig. 2.1) were established across Alabama including Huntsville Botanical Garden (Huntsville), Oak Mountain Middle School (Birmingham), Auburn University Campus (Auburn), Wiregrass Extension Center (Headland), and Mobile Botanical Garden (Mobile) (Table 2.1). In accordance with the standards of the European Phenology Network, which utilizes 13 gardens in seven countries, gardens should be planted in optimal growing conditions, on level ground, with even sun exposure, and free of obstacles or influence by man- made structures or highways that could potentially give microclimate variation (van Vliet et al. 2003, Bruns et al. 2003). Each garden contained four plot replicates approximately 0.16 ha each, spaced to avoid shading. Gardens were planted in November and December 2010, and mulched 19 annually with pine straw (Mobile only) (Fig. 2.2) or shredded hardwood mulch. All plants were fertilized in spring 2011 with a granular 18-8-12 (Regal Chemical, Alpharetta, GA) at label rate according to plant size. Plant species that were studied from February 2010 to October 2011 are listed in Table 1. Plant phenology was monitored at each site. Figure 2.1. Map of five sites in Alabama Phenology Garden network Table 2.1. Garden locations in Alabama Phenology Garden Network Garden # Location Nearest city County Latitude Longitud e Elevatio n USDA Hardines s Zone 1 Mobile Botanical Garden Mobile Mobile 30.7002 -88.1596 54m 8B 2 Wiregrass Extension Center Headland Henry 31.3575 -85.3217 109m 8A 3 Plant Science Research Center Auburn Lee 32.5908 -85.487 210m 8A 4 Oak Mountain Middle School Hoover Shelby 33.3705 -86.713 159m 8A 5 Huntsville Botanical Garden Huntsville Madison 34.7129 -86.6357 200m 7B *GPS coordinates and elevations from www.earthtools.com 20 Figure 2.2. One replicate in the phenology garden at the Mobile Botanical Gardens. An annual management program was implemented at each garden to control weeds. All garden plots were treated in February with oxadiazon + prodiamine (RegalStar, oxadiazon 1% ai + prodiamine 0.2% ai G, Regal Chemical, Alpharetta, GA) at 2.17kg/100 m2 or prodiamine (RegalKade, 0.5% ai G, Regal Chemical, Alpharetta, GA) at 39.18kg/ha. Garden plots were sprayed for control of annual and perennial weeds periodically throughout the growing season with post-emergent herbicide glyphosate (Eraser, 41% ai, L, Control Solutions, Inc., Pasadena, TX) at 15.64 ml/L. The Auburn garden was treated in summer 2011 with halosulfuron-methyl (SedgeHammer, 75% ai, G, Gowan Company, Yuma, AZ) at 0.9g/100 m2 s for selective control of yellow nutsedge (Cyperus esculentus). Additionally, fire ant (Solenopsis invicta) and varmit management strategies were used at some sites. Each garden was also treated for fire ants annually, and deer browsing caused vast damage to plants in the Huntsville garden. The Auburn and Huntsville gardens were treated with hydramethylnon (Amdro Fire Ant Bait, 0.73% ai G, BASF, Research Triangle Park, NC) for control of Solenopsis invicta at 1.7kg/ha in April 2011. In 2010, the Auburn garden was treated with fipronil (TopChoice, 0.0143% ai G, Bayer Environmental Science, Research Triangle Park, 21 NC) at 39kg/ha, for Solenopsis invicta. Plots were treated twice with dehydrated coyote urine (PredaScent, Landscape Control Products 1.5% ai, Kennesaw Georgia) at 1 capsule/m2 in spring 2011 for varmit and nuisance animal deterrent because we could not construct a fence around the garden. Also, bars of bath soap (Ivory, P&G, Cincinnati, OH) were hung in the trees in and surrounding the garden to discourage deer (Odocoileus sp.) browsing because our options for control were limited. Despite preventative measures to combat deer, several plants species were lost at the Huntsville site. Plant materials A consistent suite of 12 plants (Table 2.2) was selected to provide a continuum of flowers from February to November. Plants were chosen based on their landscape importance and the presence of easily identifiable phenological phases in the flower stage (Delahaut 2010). Plants were also selected based on their ability to survive and thrive throughout Alabama with a reasonably short, well-defined flower period. ?Natchez? crapemyrtle (Lagerstroemia indica ?fauriei ?Natchez?) is resistant to crapemyrtle aphid, therefore ?Biloxi? crapemyrtle (Lagerstroemia ?Biloxi?) was also planted at Mobile and Headland since a susceptible species was not previously on site (Mizell and Knox 1993). 22 Table 2.2. Twelve plants established in the phenology gardens Common Name Scientific Name Phenophase Recorded ?Lynwood Gold? Border Forsythia Forsythia ?intermedia ?Lynwood Gold? First flower 50% flower Full flower ?Ice Follies? Daffodil Narcissus ?Ice Follies? Bud tight Shepherd?s crook 1st petal open Flower fully open Yoshino Cherry Prunus ?yedoensis First flower 50% flower Full flower ?Ruby? Loropetalum Loropetalum chinense ?Ruby? First flower Full flower ?Eleanor Tabor? Indian Hawthorn Raphiolepis indica Eleanor Tabor? First flower 50% flower Full flower ?Ellen Huff? Oakleaf Hydrangea Hydrangea quercifolia ?Ellen Huff? First flower 50% flower Full flower ?Natchez? Crapemyrtle Lagerstroemia indica ?fauriei ?Natchez? First flower Full flower ?Happy Returns? Daylily Hemerocallis ?Happy Returns? Bud tight Shepherd?s crook 1st petal open Flower fully open ?Hummingbird? Clethra Clethra alnifolia ?Hummingbird? First flower Full flower ?Majestic? Liriope Liriope muscari ?Majestic? First flower Full flower ?Crown of Rays? Goldenrod Solidago canadensis ?Crown of Rays? First flower Full flower Swamp Sunflower Helianthus angustifolius ?Swamp Sunflower? First flower Full flower Phenophase recording To determine dates of first flower, 50% flower, and full flower, 10 randomly selected branches per plant were selected and the number of open flower buds was counted for plants such as forsythia, cherry, and indian hawthorn (Figure 2.3). First flower was recorded when one flower on any of the flagged branches was open. The next phase was determined by counting open flowers on each branch and when half of the branches had a single open flower, 50% 23 flower was recorded. Full flower was recorded when each flagged branch had an open flower. For plants such as hydrangea and chrysanthemum, which did not have the appropriate branching structure to flag shoots, first flower and full flower were recorded. Additionally, plants with no apparent 50% flower period, (loropetalum, crapemyrtle, liriope, goldenrod, and swamp sunflower), first flower and full flower classifications were used. Daylily and daffodil produce a funnelform flower, so four phenophase description terms were used; 1) bud tight & upright, 2) shepherd?s crook, 3) first petal open, and 4) fully open. Each plant species was represented by four replicates on each site. On each site, an average Julian date and growing degree date for each phenophase was determined. The phenological flowering sequence was determined by assigning the 32 plant events based on the average date per site. Plant phenological events were given a value and ranked, sequenced, and compared by year and site. There were cases in which we had missing data. In the case of missing variables, an M was placed in the sequence. Spearman?s bivariate correlation coefficients (Statistix 2009) were used to determine correlation between years and sites. Degree-day Accumulations Air temperature was recorded at each of the five gardens using an on-site weather station (Fig. 2.4) (HOBO, model # U23-003, Onset Computer Corporation, Bourne, MA), similar to Figure 2.3. A flowering cherry tree in the phenology garden that shows the flagged shoots used to record flowering phenology on certain woody plants 24 Herms (2004). Ambient temperature was recorded above ground at a height of 15?30 cm inside a radiation shield. A base temperature of 10?C was used to calculate growing degree day accumulations (Klein 2002) coincident with the phenological events of the concurrent plant species, consistent with similar studies. The average method was primarily used because ambient temperature typically did not fall below base temperature for the majority of the year (Herms 2004). Growing degree-day calculations for all plants and insects were obtained with a biofix of January 1. Data collection Volunteer citizen scientists and members of the Alabama Master Gardeners Association collected data at three sites (Mobile, Huntsville, Birmingham) for two growing seasons from February 2010 to November 2011. Volunteers were trained through on-site meetings in each year of the study. These meetings included yearly presentations on the importance of phenology applications to pest management, scouting and trapping procedures, and methods of rating plant flower stages. Also each participant was given a color manual with detailed information on monitoring pests and plants and a map depicting plants and traps in the garden (www.auburn.edu/phenology). Each plant in the garden was labeled and mapped, and gardens were checked three times each week from February to November of each year. Master Gardeners also did routine maintenance and watering of their local gardens. The Auburn and Headland gardens were monitored and maintained by R. Young and Agricultural Experiment Station staff. Figure 2.4. Data loggers were used on all sites to record weather data loggers were housed in radiation shields. 25 Statistical analyses Julian date and GDD for each phenological event were compared among sites and between years using an ANOVA (Stastix 2009). Following the ANOVA, the mean JD and GDD were compared using Tukey?s HSD test (P< 0.05). Spearman?s bivaraiate correlation coeffiecients were used to determine differences among plant phonological events across all sites over two years. Results Location-to-Location variation in phenological sequences over two years Some plants failed to produce adequate flower data due to a lack of flowering, plant death, or severe damage or death from deer browsing. In Huntsville, Indian hawthorn, hydrangea, liriope, sedum, and forsythia were severely damaged in both years by deer, which led to little or no flower stage data. All preventative measures (PredaScent and soap bars) were unsuccessful. Plants were replaced but deer continued to damage plants at that location. Hydrangeas at Headland phenology garden died likely due to lack of shade in 2010 and were not replaced. The phenological sequences statewide were significantly correlated, (P < 0.001) between sites and years with most sites (75%) occurring at greater than 0.90 and lowest value was 0.85. Spearman?s bivariate correlation coefficients was used to determine these correlates (Table 2.4). 26 Table 2.3. Julian days to event of plant flowering sequences in Alabama Phenology Garden network, five sites 2010-2011 PLANT PHEN EVENT AUB10 AUB11 HVL10 HVL11 BHA10 BHA11 HEA10 HEA11 MOB10 MOB11 Yoshino cherry 1st flower 83 76 88 73 M 77 82 77 84 75 Yoshino cherry 50% flower 85 79 90 76 M 80 86 80 88 80 Yoshino cherry Full flower 88 82 93 81 91 84 91 83 92 84 Daffodil Bud tight 79 63 81 60 94 61 62 57 68 60 Daffodil Shep. crook 84 66 88 62 99 65 66 60 72 62 Daffodil 1st petal open 85 69 91 66 103 70 71 62 76 65 Daffodil Full flower 88 70 92 73 106 76 77 65 82 68 Forsythia 1st flower 70 53 88 56 M 57 82 52 68 61 Forsythia 50% flower 73 56 92 59 M 64 84 60 77 67 Forsythia Full flower 77 59 99 62 91 77 90 63 92 71 Loropetulum 1st flower 78 62 90 60 92 58 59 55 54 61 Loropetulum Full flower 88 69 100 80 99 80 67 61 92 63 Indian hawthorn 1st flower 105 91 M M 113 96 102 83 96 82 Indian hawthorn 50% flower 108 95 M M 116 100 106 85 99 84 Indian hawthorn Full flower 111 98 M M 120 107 113 87 105 87 Hydrangea 1st flower 124 111 M M 121 109 M M 118 101 27 Hydrangea 50% flower 129 115 M M 132 117 M M 121 106 Hydrangea Full flower 132 117 M M 144 122 M M 129 112 Daylily Bud tight 132 125 133 123 124 117 125 114 125 107 Daylily Shep. crook 141 128 140 131 132 124 128 118 131 117 Daylily 1st petal open 144 130 146 136 138 129 132 123 134 121 Daylily Full flower 147 132 155 141 143 132 137 126 140 125 Crapemyrtle 1st flower 151 143 162 148 148 142 144 128 143 132 Crapemyrtle Full flower 170 153 207 162 172 155 153 146 161 143 Clethra 1st flower 179 186 M M 176 184 144 182 164 M Clethra Full flower 186 197 M M 191 195 160 193 176 M Liriope 1st flower M 187 199 213 218 192 199 175 224 191 Liriope Full flower M 195 210 220 222 199 210 193 235 204 Sunflower 1st flower 177 193 205 159 185 161 199 209 M M Sunflower Full flower 186 200 216 184 220 180 214 223 M M Goldenrod 1st flower 148 184 174 176 M 140 177 M M M Goldenrod Full flower 173 190 181 206 M 148 M M M M 28 Table 2.4. Spearman?s bivariate correlation coefficients generated from phenological sequence of five sites, two years. AUB10 AUB 11 BHA10 BHA11 HEA10 HEA11 HUN10 HUN11 MOB10 AUB10 0.99 BHA10 0.90 0.88 BHA11 0.92 0.91 0.78 HEA10 0.88 0.90 0.78 0.97 HEA11 0.91 0.93 0.81 0.96 0.99 HUN10 0.88 0.86 0.78 0.97 0.93 0.92 HUN10 0.97 0.97 0.84 0.98 0.95 0.96 0.94 MOB10 0.90 0.89 0.78 0.99 0.97 0.96 0.98 0.96 MOB11 0.88 0.90 0.78 0.96 0.99 0.99 0.94 0.95 0.96 *All p-values are <0.001 29 In general, JD and GDD for each phenophase were greater in 2010 than in 2011 (Tables 2.6?2.14). Phenophases of spring flowering species cherry, daffodil, forsythia, loropetalum, indian hawthorn, and daylily occurred at significantly different growing degree days and JD at each site. Interestingly, first flower (JD) for these same plants didn?t occur first in Mobile (the southernmost location) was not the first site for most events to occur in the spring. Phenophases occurred at different Julian dates between each site in both years. When there was sufficient data for three plants, we used the data, noting the occurrence in the appropriate table. However, in instances that there were only two plants on a site, that plant was thrown out of the dataset. Table 2.5. Julian date conversion for common year (Excludes leap years) Month Calendar Date JD Range Jan 1-30 1-30 Feb 1-28 31-59 Mar 1-31 60-90 Apr 1-30 91-120 May 1-31 121-151 Jun 1-30 152-181 Jul 1-31 182-212 Aug 1-31 213-243 Sept 1-30 244-273 Oct 1-31 274-304 Nov 1-30 305-334 Dec 1-31 335-365 30 For each site in 2010, the first event in the sequence was bud tight of daffodil or first bloom of forsythia. In 2011, there was no significant difference among Julian day for daffodil for all sites. In Headland, bud tight of daffodil was the first phenological event in both years. Daffodil bloom period from bud tight to full bloom range was 8-15 days. In 2010, Headland and Mobile were more advanced until full bloom of daffodil. Huntsville and Birmingham were always different from the other sites. Daylily averaged 11 days from bud tight to full bloom. In 2010, forsythia first bloom was earliest, similar to Headland, Huntsville, and Birmingham. This event occurred significantly later in Mobile. In 2010, each site had the same phenological indicator. For Huntsville, 50% bloom was significantly later than all other sites. For full bloom, Auburn, Mobile, and Headland had consistent bloom stages and length of blooms was 7 days on average. Huntsville averaged 21 days from first to full. The cherry first bloom to full bloom range was 5-10 days with the southern-most sites at the long range, shortening as progression occurred North throughout the state. The average full sequence was 7.3 days. Cherry at Headland in 2010 bloomed first of all plant phenophases at the site. Headland and Auburn had very similar bloom occurrences in 2010 and Huntsville was the last to bloom. Auburn site had the fastest bloom progression from first to 50% bloom. The loropetulum first flowered in Mobile and last bloomed in Huntsville. Mobile had significantly earlier first bloom than Huntsville. Auburn differed significantly from all other sites for full flower in 2010. Mobile differed significantly from Huntsville. This plant showed spurious blooms throughtout the 2-year study. Loropetulum had a 3-36 day blooming period, which averaged 17.3 days. The first flower through 50% bloom of Indian hawthorn occurred in Mobile and Headland consistently. The 4- 11 day bloom period for sites averaged 7 days first to full bloom. Full bloom did not differ statewide, therefore an excellent statewide correlate. 31 TABLE 2.6. GDD and Julian date for flowering events for Yoshino cherry, Prunus ?yedoensis 2011 FIRST FLOWER 50% FULL SITE JD DDC JD DDC JD DDC Huntsville 72.5 ? 1.5a 146.25 ? 5.75a 76.25 ? 0.75b 167 ? 5d 80 ? 0.0e 203 ? 0.0b Birmingham 77 ? 0.0a 208 ? 0.0a 80.5 ? 0.5a 240 ? 0.0c 84 ? 0.0d 274 ? 0.0a Auburn 76.25 ? 1.25a 235.75 ? 11.75a 79 ? 1.22ab 261.5 ? 13.34c 82 ? 0.81c 292 ? 7.54ab Headland 55 ? 0.0b 289 ? 0.0b 60.25 ? 0.75c 324 ? 0.0b 63 ? 1.0b 361 ? 0.0c Mobile 74.5 ? 0.95a 308.25 ? 8.1a 80.5 ? 0.5ab 369.25 ? 6.25a 84 ? 0.0a 405 ? 0.0a SITE F= 72.36 df= 4 P < 0.001 F= 84.89 df= 4 P < 0.001 F= 111.74 df= 4 P < 0.001 F= 121.22 df= 4 P < 0.001 F= 236.4 df= 4 P < 0.001 F= 540.57 df= 4 P < 0.001 2010 FIRST FLOWER 50% FULL SITE JD DDC JD DDC JD DDC Huntsville 88 ? 0.0a 170d ? 0.0 90.5 ? 0.50ab 186.75 ? 4.75d 93.5 ? 0.86a 216.5 ? 8.94c Birmingham na na na na 91 ? 0.0b 224 ? 0.0c Auburn 83 ? 0.0bc 196 ? 0.0b 85 ? 0.0b 202 ? 0.0c 88.5 ? 0.5c 220 ? 3c Headland 82 ? 0.0c 180 ? 0.0c 86.5 ? 0.86c 208 ? 6.92b 91 ? 0.0b 245 ? 0.0b Mobile 83.75 ? 0.75b 265.5 ? 4.5a 87.5 ? 0.5a 290.25 ? 3.75a 92 ? 0.0ab 325 ? 0.0a SITE F= 49.59 df= 3 P < 0.001 F= 366.96 df= 3 P < 0.001 F= 18.07 df= 3 P < 0.001 F= 103.94 df= 3 P < 0.001 F= 19 df= 4 P < 0.001 F= 127.13 df= 4 P < 0.001 Means within column followed by the same letter were not significantly different (P < 0.05, Tukey?s HSD test). 32 TABLE 2.7. GDD and JD for flowering events for flowering for daffodil, Narcissus 'Ice Follies' 2011 BUD TIGHT SHEPHERDS CROOK FIRST PETAL OPEN FULL SITE JD DDC JD DDC JD DDC JD DDC Huntsville 59 ? 1de 100.5 ? 5.5d 62.5 ? 0.5d 118 ? 2ef 66.25 ? 0.75ef 125.5 ? 0.5d 73.5 ? 3.2de 159.75 ? 18.63c Birmingham 61.5 ? 0.86de 144.5 ? 5.48c 65.5 ? 1.65cd 159 ? 3.31de 70.75 ? 2.28def 176 ? 10.83cd 76 ? 2.12cde 206 ? 14.74bc Auburn 63 ? 0.0d 175 ? 0.0b 65.5 ? 0.5cd 182.25 ? 0.75cd 67.25 ? 0.47def 189 ? 2.44c 69 ? 0.57e 195 ? 1.15bc Headland 57.75 ? 1.1e 169 ? 10.55bc 60.5 ? 0.5d 192 ? 3bcd 61.5 ? 0.5f 198.75 ? 3.25bc 65 ? 1e 214 ? 4bc Mobile 60.5 ? 0.95de 221.5 ? 6.18a 62.75 ? 1.18d 234 ? 6.27ab 65.5 ? 1.65ef 249.75 ? 8.25ab 68.75 ? 1.7e 267.25 ? 11.01ab SITE F= 5.44 df= 4 P < 0.001 F= 46.73 df= 4 P < 0.001 F= 4.71 df= 4 P < 0.001 F= 142.94 df= 4 P < 0.001 F= 6.15 df= 4 P < 0.001 F= 49.33 df= 4 P < 0.001 F= 4.93 df= 4 P < 0.001 F= 10.75 df= 4 P < 0.001 2010 BUD TIGHT SHEPHERDS CROOK FIRST PETAL OPEN FULL SITE JD DDC JD DDC JD DDC JD DDC Huntsville 81 ? 0.0b 145 ? 0.0c 87.5 ? 1.25ab 169.75 ? 5.1cd 90.25 ? 0.85ab 186 ? 6.59c 92.25 ? 1.03ab 204 ? 10.35bc Birmingham 91 ? 1a 226.75 ? 6.25a 94.75 ? 1.49a 260.5 ? 14.37a 98.5 ? 2.5a 299.5 ? 22.5a 102.75 ? 3.75a 334.5 ? 33.5a Auburn 79.5 ? 0.86b 185 ? 3.46b 83 ? 0.81b 196.25 ? 2.25bcd 85 ? 0.4bc 202.25 ? 1.43 87.25 ? 0.75bc 213.25 ? 3.75bc Headland 60.5 ? 1.5de 78.5 ? 0.5d 68.25 ? 4.69cd 104.5 ? 25.16f 72.5 ? 4.17de 127.75 ? 22.9d 75.75 ? 4.4cde 147.5 ? 28.42c Mobile 68 ? 0.57c 187 ? 2.3b 72.5 ? 0.86c 214 ? 4.04abc 76 ? 0.81cd 229.25 ? 3.47bc 82 ? 2.3bcd 258.5 ? 11.83ab SITE F= 163.41 df= 4 P < 0.001 F= 277.55 df= 4 P < 0.001 F= 21.52 df= 4 P < 0.001 F= 18.73 df= 4 P < 0.001 F= 22.08 df= 4 P < 0.001 F= 18.05 df= 4 P < 0.001 F= 13.06 df= 4 P < 0.001 F= 11.17 df= 4 P < 0.001 Means within column followed by the same letter were not significantly different (P < 0.05, Tukey?s HSD test). 33 TABLE 2.8. GDD and JD for flowering events for forsythia, Forsythia ?intermedia ?Lynwood gold? 2011 FIRST FLOWER 50% FULL SITE JD DDC JD DDC JD DDC Huntsville 56 ? 0.0b 84 ? 0.0d 60 ? 0.0bc 106d ? 0.0d 62 ? 0.0c 116 ? 0.0d Birmingham 57 ? 0.0b 110 ? 0.0cd 66.25 ? 1.43ab 160.5 ? 2.87cd 77 ? 0.0cd 208 ? 0.0b Auburn 53 ? 0.57b 106 ? 4.61c 56.25 ? 1.03c 131.25 ? 8.06bc 59.5 ? 0.5c 156 ? 3c Headland 55 ? 0.0b 143 ? 0.0b 60.25 ? 0.75c 189 ? 3.46b 63 ? 1bc 206.5 ? 4.5b Mobile 64 ? 2.17a 242.75 ? 11.8a 67.5 ? 2.59a 260.75 ? 15.43a 68.5 ? 2.59b 266.5 ? 15.16a SITE F= 18.98 df= 4 P < 0.001 F= 122.37 df= 4 P < 0.001 F= 10.59 df= 4 P < 0.001 F= 55.18 df= 4 P < 0.001 F= 30.39 df= 4 P < 0.001 F= 63.04 df= 4 P < 0.001 2010 FIRST FLOWER 50% FULL SITE JD DDC JD DDC JD DDC Huntsville 88.33 ? 4.37a 189.67 ? 33.68a 98.33 ? 4.48a 262.67 ? 42.04a 109.33 ? 6.06a 353.67 ? 49.36a Birmingham na na na na 91 ? 0.0b 224 ? 0.0b Auburn 70.75 ? 2.68a 154.75 ? 8.6a 73.5 ? 2.1b 165 ? 6.36b 77 ? 2.12c 176.5 ? 5.1b Headland 76 ? 3.46a 144.5 ? 20.49ab 80 ? 2.67b 171 ? 12.92ab 83.75 ? 2.65bc 193.5 ? 16.19b Mobile 68 ? 1a 189 ? 6b 77.25 ? 3.72b 237.75 ? 19.29ab 81.75 ? 3.88bc 193.5 ? 22.77ab SITE F= 8.26 df= 3 P < 0.001 F= 1.69 df= 3 P < 0.001 F= 16.2 df= 3 P < 0.001 F= 5.28 df= 3 P < 0.001 F= 13.66 df= 4 P < 0.001 F= 9.51 df= 4 P < 0.001 Means within column followed by the same letter were not significantly different (P < 0.05, Tukey?s HSD test). 34 Means within column followed by the same letter were not significantly different (P < 0.05, Tukey?s HSD test). TABLE 2.9. GDD and JD for flowering events for loropetalum, Loropetalum chinense ?Ruby? 2011 FIRST FLOWER FULL FLOWER SITE JD DDC JD DDC Huntsville 80 ? 0.0b 203 ? 0.0ab 106 ? 0.0a 357 ? 0.0a Birmingham 57.5 ? 2.06cd 115.25 ? 14ef 84.83 ? bc 238 ? 30abc Auburn 62 ? 2.48c 165.5 ? 13.47cd 70.25 ? 3.4cde 203.5 ? 15.05c Headland 55 ? 0.0d 143 ? 0.0de 60.75 ? 1.75cde 195.25 ? 8.72c Mobile 61 ? 0.0c 224 ? 0.0a 63.75 ? 0.75de 241 ? 3bc year*site F= 46.48 df= 4 P < 0.001 F= 25.65 df= 4 P < 0.001 F= 51.42 df= 4 P < 0.001 F= 17.32 df= 4 P < 0.001 2010 FIRST FLOWER FULL FLOWER SITE JD DDC JD DDC Huntsville 90.5 ? 0.95a 188.5 ? 7.62bc 99.75 ? 0.75ab 273.75 ? 5.75abc Birmingham 92.25 ? 1.97a 238 ? 19.69a 99 ? 0.0ab 301 ? 0.0ab Auburn 78 ? 0.0b 179 ? 0.0bcd 87 ? 1.22cde 214 ? 7.03bc Headland 59 ? 1.77cd 78 ? 0.57g 68 ? 1.22abc 94.75 ? 9.72d Mobile 45.75 ? 4.81e 91.25 ? 4.87fg 81.5 ? 3.86bcd 259.5 ? 20.9abc year*site F= 65.7 df= 4 P < 0.001 F= 49.4 df= 4 P < 0.001 F= 47.2 df= 4 P < 0.001 F= 53.67 df= 4 P < 0.001 35 TABLE 2.10. GDD and JD for flowering events for indian hawthorn, Rhaphiolepis indica Eleanor Tabor? 2011 FIRST FLOWER 50% FULL SITE JD DDC JD DDC JD DDC Huntsville na na na na na na Birmingham 96 ? 0.0a 330 ? 0.0a 100.33 ? 0.66a 384.67 ? 6.38ab 105 ? 0.0a 426 ? 0.0ab Auburn 91.75 ? 0.75b 350 ? 6b 95.5 ? 0.95b 376.5 ? 9.64b 98.5 ? 1.44b 405 ? 17.32ab Headland 83 ? 0.0c 361 ? 0.0c 85 ? 0.0c 377 ? 0.0b 87 ? 0.0c 396 ? 0.0b Mobile 82 ? 0.0c 388 ? 0.0c 84 ? 0.0c 405 ? 0.0a 87 ? 0.0c 445 ? 0.0a SITE F= 206.6 df= 3 P < 0.001 F= 44.97 df= 3 P < 0.001 F= 71.60 df= 3 P < 0.001 F= 6.88 df= 3 P < 0.001 F= 96.87 df= 3 P < 0.001 F= 4.97 df= 3 P < 0.001 2010 FIRST FLOWER 50% FULL SITE JD DDC JD DDC JD DDC Huntsville na na na na na na Birmingham 114.75 ? 1.75a 438.25 ? 13.25a 118 ? 2a 471.5 ? 20.5a 121.5 ? 1.5a 498 ? 20a Auburn 105 ? 0.57b 366 ? 6.35a 107.75 ? 0.75ab 395.25 ? 7.28a 110 ? 0.57a 414 ? 4.04ab Headland 102.5 ? 4.33b 364.5 ? 43.59a 106 ? 4.04b 400 ? 41.59a 113 ? 4.52a 480.5 ? 54.74ab Mobile 96 ? 0.0b 365 ? 0.0a 98.75 ? 0.75b 390.5 ? 5.5a 104.5 ? 0.5a 441 ? 5b SITE F= 1.68 df= 3 P < 0.001 F= 2.25 df= 3 P < 0.001 F= 9.6 df= 3 P < 0.001 F= 2.18 df= 3 P < 0.001 F= 7.19 df= 3 P < 0.001 F= 1.39 df= 3 P < 0.001 Means within column followed by the same letter were not significantly different (P < 0.05, Tukey?s HSD test). 36 TABLE 2.11. GDD and JD for flowering events for oakleaf hydrangea, Hydrangea quercifolia 'Ellen Huff' 2011 FIRST FLOWER 50% FULL SITE JD DDC JD DDC JD DDC Huntsville na na na na na na Birmingham 107.67 ? 1.33a 448.67 ? 11.33c 117 ? 1b 554.33 ? 9.66b 122 ? 0.0a 552.67 ? 63.33b Auburn 111 ? 0.57a 522.5 ? 7.79b 114.75 ? 1.03b 570.75 ? 0.0a 117.5 ? 0.95b 601.5 ? 10.13b Headland na na na na na na Mobile 101.5 ? 0.5b 601 ? 5a 106 ? 1.77a 650.25 ? 20.89a 112 ? 0.0ac 732 ? 0.0a SITE F= 36.32 df= 2 P < 0.001 F= 82.04 df= 2 P < 0.001 F= 17.54 df= 2 P < 0.01 F= 9.85 df= 2 P = 0.007 F= 63.7 df= 2 P < 0.001 F= 9.87 df= 2 P =0.06 2010 FIRST FLOWER 50% FULL SITE JD DDC JD DDC JD DDC Huntsville na na na na na na Birmingham 120c ? 0.0c 478c ? 0.0c 131.5 ? 1.5a 619.25 ? 15.33a 143.5 ? 2.02a 774 ? 26.55a Auburn 124 ? 0.57b 555.5 ? 7.79b 128.33 ? 0.67ab 602.67 ? 15.33a 129.5 ? 1.65b 624.25 ? 20.37a Headland na na na na na na Mobile 118 ? 0.0a 578 ? 0.0a 121.75 ? 1.75b 623.25 ? 26.25a 129.5 ? 1.44b 733.5 ? 18.18b SITE F= 0.5 df= 2 P < 0.001 F= 135.91 df= 2 P < 0.001 F= 10.6 df= 2 P < 0.001 F= 0.21 df= 2 P = 0.81 F= 0.05 df= 2 P < 0.001 F= 11.92 df= 2 P < 0.001 Means within column followed by the same letter were not significantly different (P < 0.05, Tukey?s HSD test). 37 TABLE 2.12. GDD and JD for flowering events for flowering for ?Happy Returns? daylily, Hemerocallis 'Happy Returns' 2011 BUD TIGHT SHEPHERDS CROOK FIRST PETAL OPEN FULL SITE JD DDC JD DDC JD DDC JD DDC Huntsville 123 ? 0.0abc 536 ? 0.0bc 131 ? 1abc 615 ? 13bc 136 ? 1.95abc 665.75 ? 7.75bc 142 ? 2.48abc 729 ? 34.41ab Birmingham 117.5 ? 0.86bcd 562 ? 9.24abc 124.75 ? 2.25cd 633.25 ? 21.75bc 129.5 ? 0.5cd 692.75 ? 7.75bc 132 ? 0.57cde 728.5 ? 7.21ab Auburn 125.75 ? 1.18ab 684 ? 11.05a 127.75 ? 1.37bcd 704.25 ? 17.39ab 129.75 ? 1.49bcd 730.25 ? 20.48ab 132.25 ? 1.49cde 759.75 ? 17.98ab Headland 114.75 ? 1.84cd 493.75 ? 19.72c 118.5 ? 2.9d 539 ? 37.66c 123 ? 2.44d 598.5 ? 33.98c 126.75 ? 2.83de 650.5 ? 42.07b Mobile 107.67 ? 5.69d 680 ? 74.22a 117.33 ? 2.33d 807.33 ? 31.33a 121.67 ? 1.45d 862 ? 17.76a 125 ? 2.08e 902.67 ? 24.23a SITE F= 9.41 df= 4 P < 0.001 F= 9.68 df= 4 P < 0.001 F= 10.18 df= 4 P < 0.001 F= 7.89 df= 4 P < 0.001 F= 18.44 df= 4 P < 0.001 F= 10.82 df= 4 P < 0.001 F= 10.11 df= 4 P < 0.001 F= 9.12 df= 4 P < 0.001 2010 BUD TIGHT SHEPHERDS CROOK FIRST PETAL OPEN FULL SITE JD DDC JD DDC JD DDC JD DDC Huntsville 132.75 ? 2.13a 558.5 ? 27.79a 140 ? 40.2a 690 ? 55.64ab 146 ? 4.26a 772.75 ? 65ab 151.25 ? 4.67a 857.25 ? 76.7a Birmingham 124.25 ? 3.25abc 532.5 ? 39.5bc 133 ? 2.12abc 641.75 ? 25.27bc 138 ? 2.12abc 707 ? 29.69bc 143.5 ? 2.72ab 784.5 ? 40.18ab Auburn 132 ? 0.0a 655 ? 0.0ab 136.25 ? 0.75ab 719 ? 11ab 140.5 ? 1.5ab 779.25 ? 21.63ab 145.5 ? 0.95ab 853.75 ? 14.5a Headland 125.75 ? 1.49ab 635 ? 22.15ab 129.25 ? 1.43bc 689 ? 20.74ab 133 ? 1.77bc 746.5 ? 28.84ab 137.5 ? 0.5bcd 819.5 ? 7.5b Mobile 125.25 ? 1.75ab 675.75 ? 26.25a 131.25 ? 1.49abc 748.25 ? 18.94ab 134.75 ? 1.49abc 800.5 ? 23.89ab 140 ? 0.57e 880.5 ? 9.52a SITE F= 3.99 df= 4 P < 0.001 F= 4.69 df= 4 P < 0.001 F= 4.09 df= 4 P < 0.001 F= 1.25 df= 4 P < 0.001 F= 4.32 df= 4 P < 0.001 F= 0.92 df= 4 P < 0.001 F= 4.56 df= 4 P < 0.001 F= 0.89 df= 4 P < 0.001 Means within column followed by the same letter were not significantly different (P < 0.05, Tukey?s HSD test). 38 TABLE 2.13. GDD and JD for flowering events for ?Natchez? crapemyrtle, Lagerstroemia indica ?fourieri 'Natchez' 2011 FIRST FLOWER FULL FLOWER SITE JD DDC JD DDC Huntsville 148 ? 0.0bc 813 ? 0.0cd 162.25 ? 1.25bcd 1078 ? 23b Birmingham 142.5 ? 2.46c 840.5 ? 33.48cd 155.75 ? 4.64cde 1069.3 ? 88.75b Auburn 143 ? 1.77c 880.25 ? 26.78bcd 157.5 ? 3.01def 1143.8 ? 60.91b Headland 128 ? 1.15d 850.5 ? 13.56cd 146.75 ? 1.18def 1114.3 ? 22.44b Mobile 132.75 ? 1.75d 1003.3 ? 19.25ab 142.5 ? 0.5f 1149 ? 0.0b SITE F= 11.53 df= 4 P < 0.001 F= 24.66 df= 4 P < 0.001 F= 9.7 df= 4 P < 0.001 F= 0.53 df= 4 P = 0.71 2010 FIRST FLOWER FULL FLOWER SITE JD DDC JD DDC Huntsville 162.5 ? 2.5a 1039.8 ? 43.25a 207.25 ? 5.48a 1033.8 ? 235.66a Birmingham 148 ? 0.0bc 785.75 ? 66.25d 172.5 ? 2.02b 1297.5 ? 41.85b Auburn 151.25 ? 1.18b 966.5 ? 30.63abc 167 ? 2.54bc 1264.8 ? 46.65b Headland 144 ? 0.0bc 931 ? 0.0abcd 153.5 ? 0.5def 1092.8 ? 8.75b Mobile 143.5 ? 1.44c 939.5 ? 24.53abcd 161 ? 0.57bcd 1231.5 ? 10.68b SITE F= 5.49 df= 4 P < 0.001 F= 30.83 df= 4 P < 0.001 F= 52.47 df= 4 P < 0.001 F= 11.57 df= 4 P < 0.001 Means within column followed by the same letter were not significantly different (P < 0.05, Tukey?s HSD test). 39 TABLE 2.14. GDD and JD for flowering events for Majestic liriope, Liriope muscari 'Majestic' 2011 FIRST FLOWER FULL FLOWER SITE JD DDC JD DDC Huntsville 213 ? 0.0a 1999 ? 0.0ab 220 ? 0.0a 2134 ? 0.0a Birmingham 192.25 ? 2.25ab 1708.5 ? 44.5b 198 ? 4a 1838 ? 95.75a Auburn 188.75 ? 1.18ab 1717.5 ? 23.17b 195.25 ? 1.49a 1857.3 ? 40.12a Headland 175 ? 1b 1740 ? 22ab 193 ? 0.0a 2119 ? 0.0a Mobile 191.33 ? 9.82ab 2114.3 ? 193.23a 204 ? 9.68a 2208 ? 191a SITE F= 5.8 df= 4 P < 0.001 F= 4.32 df= 4 P < 0.001 F= 2.46 df= 4 P < 0.001 F= 4.25 df= 3 P = 0.71 2010 FIRST FLOWER FULL FLOWER SITE JD DDC JD DDC Huntsville 199.25 ? 3.88b 1735.8 ? 79.09c 210.5 ? 2.46c 1965.3 ? 49.02d Birmingham 218 ? 0.0a 2585 ? 0.0a 222 ? 0.0b 2893 ? 0.0a Auburn na na na na Headland 199.5 ? 0.5b 1996 ? 10b 209.25 ? 0.75c 2207 ? 16c Mobile 224.75 ? 1.75a 2487.3 ? 34.25a 235.5 ? 2.1a 2698 ? 40.15b SITE F= 36.78 df= 3 P < 0.001 F= 86.4 df= 3 P < 0.001 F= 54.02 df= 3 P < 0.001 F= 172.12 df= 3 P < 0.001 Means within column followed by the same letter were not significantly different (P < 0.05, Tukey?s HSD test). 40 Discussion For virtually all phenophases evaluated, there was significant variation in Julian data and GDD among sites and between years. In many instances, a phenophase was significantly different among sites in one year but not another (e.g., crapemyrtle). With crapemyrtle (Table 2.13), there was a significant difference at first flower but not at full flower. It is clear that the bloom period (first to full) also varied widely by site. With crapemyrtle, likely warmer temperatures at one site may have enabled flowers on that plant to ?catch up? phenologically to plants that bloomed earlier. In Mobile, for example, first to full flower was much faster (10 d compared to 14?18 d) at other sites in 2011 (Table 2.13). Interestingly, the sequence of flowering did not necessarily progress from south to north as would be expected (Tables 2.6-2.14). For many phenophases, Mobile or Headland was the first site but others such as Auburn were often not significantly different from Mobile. It was common, though, that a particular event in Mobile was significantly different from the same event in Huntsville. In Ohio, the progression of spring was measured a 7?16 km per day from south to north by tracking the occurrence of first bloom of forsythia in Ohio phenology garden sites (Kulhanek 2009). The southern sites (Mobile, Headland, and Auburn) were likely more phenologically similar due to similarity in temperatures. Despite variation in JD and GDD for each event, plant phenology across five sites showed a significant correlation in flower sequence from site-to-site and from year-to-year. This suggests that the sequence statewide progresses similarly irrespective of the absolute date or GDD for the event. These data reinforce the relative utility of phenology compared to GDD or calendar date for use in pest management (Mussey and Potter 1997, Herms 2004). Mobile 41 Alabama, the southern part of the state, is coastal and in USDA hardiness zone 8b (United States National Arboretum, 2012). However, the data presented here suggest that the sequence of bloom in Mobile is no different than the sequence at other locations in the state. This would indicate that plant phenological indicators identified from work at Auburn may be applicable for pests in other parts of the state. This hypothesis will be further investigated in Chapter 3. 42 Chapter III PHENOLOGY AND DEGREE-DAY MODELS IN ALABAMA Abstract Plant phenophases of 12 plants were monitored in five replicated gardens established in Mobile, Headland, Auburn, Birmingham, and Huntsville, Alabama, the main climate regions of the state. Coincident with phenophase recording, first capture and seasonal flight periods were monitored. Two sentinel landscape pests, crapemyrtle aphid (CMA) and dogwood borer (DWB) were tracked across the state, and flower stages were compared with important pest activities. No single phenological indicator existed for all sites. Emergence of DWB or CMA was consistent statewide. First emergence and\or seasonal flight period for an additional eight pest species were monitored in Auburn. First peak and seasonal trap capture of CMA and male DWB were graphed for each site relative to GDD and JD for two years. The sequence of phenophases to pests was not consistent statewide. However, the order in which plants flowered maintained a consistent pattern from Mobile to Huntsville. 43 Introduction As urban areas increase, so does the demand for public, commercial, and residential green spaces including lawns and landscapes. Along with the increase in plant species comes an increase in the number of arthropod pests (Raupp et al. 2001). As insect pest populations increase, potential damage to landscape ornamental plants also increases. Consequently, significant amounts of pesticides are used for pest control in landscapes. Annually, more than $172 million worth of damages occur in Georgia landscapes and costs of control to pests attacking ornamentals plants in nurseries and landscapes (Oetting et al. 2004). Landscapers, nursery personnel, and laypersons alike traditionally rely on calendar based application for predicting pest activity, often with immense failure rates in controlling landscape pests due to inconsistencies in pest activity from year to year and variations such as warmer or cooler spring temperatures. Plant and insect development is based on temperature; therefore, we can use growing degree-days to track important developmental phases (Huberman 1941). The use of phenology and degree-day models can aid in forecasting key pest events, ultimately saving money in pest management programs. Degree-day models and plant phenological indicators can be used for predicting pest activity. When available to pest managers, degree-day information to pinpoint control measures, has led to a 28% reduction in pesticide use over two years (Suchanic and Vorodi 1993) and a 41% decrease in pesticides over an 8-year period (Hoover 2002). Borers are among the most important pests of ornamental plants in production or in the landscape. At least 151 species in 19 genera of clearwing borers (Lepidoptera: Sesiidae) exist in North America, North of Mexico (Heppner 1987). Of these, 22 species in 8 genera have significant economic importance (Rogers and Grant 1990). Dogwood borer, Sinathedon scitula, 44 occurs from SE Canada to Eastern U.S. with one of the longest reproductive activity periods of clearwings (Drooz 1985, Davidson et al. 1992). This species also has the broadest host range of any North American clearwing borer (Rogers and Grant 1990). Crapemyrtle has grown to be one of the most prevalent ornamental landscape plants in the Southeastern U.S. (USDA hardiness zones 7-10). Crapemyrtles offer wide array of aesthetic attributes with its lustrous foliage, long-lasting flowers, extravagant fall color, and unique showy bark (Dirr 1998). However, all varieties are vulnerable to sooty mold (Fig. 3.1) due to crapemyrtle aphid infestations (Allen and Alverson 1991, Mizell and Knox 1993). Crapemyrtle aphid, Tinocallis kahawaluokalani is the only key insect pest of crapemyrtles. All adult crapemyrtle aphids are winged and on tree activity is correlated with captives on sticky cards (Allen and Alverson 1991). Objective The objective of this work was to determine the phenological phases of 12 ornamental plants for five sites in Alabama in two years. Materials and Methods Pests were monitored in five phenology gardens established as part of the Auburn Phenology Garden Project (Chapter 2). Plant phenophases were recorded as described in Figure 3.1. Sooty mold on crapemyrtle 45 Chapter 2. Also on these sites, the seasonal phenology of two key landscape pests (crapemyrlte aphid and dogwood borer) was monitored at all five sites statewide in 2010 and 2011. Flight activity of male dogwood borer was monitored with two 1C wing traps (Pherocon, Inc. Adair, OK) hung 1.5-1.8m high in baited rubber septa containing a trinary blend of purified dogwood borer pheromone (<0.05% Z, Z-3, 13-ODDA) obtained from Dr. A. Zhang (USDA- ARS) (Bergh et al. 2004). Two traps were hung 1.5-1.8m off the ground within the garden and lures were replaced every 4 wk. Wing traps at satellite sites with captured moths were sent to the lab biweekly, stored in the freezer, and labeled with the location and Julian date. All traps were inspected three times per week from March to late October. The first male capture and cumulative seasonal capture were recorded. Crapemyrtle aphids were monitored with two 7.6 cm x 12.7 cm yellow sticky cards (Olson Products, Medina, OH) hung 1.5-1.8 m above the ground on susceptible crapemyrtle species, beginning in March and sampled weekly at Auburn and biweekly at other sites. A crapemyrtle variety susceptible to crapemyrtle aphids were not present at Mobile and Headland so four ?Biloxi? crapemyrtles were planted within 5 m of each garden at the time the gardens were established in 2010. Sticky cards from remote sites were harvested from the trees and stuck to thin clear acetate transparency sheets then mailed to the lab for identification and counting. First occurrence and duration of activity on sticky cards was recorded for CMA at all sites. At the Auburn garden in 2010 and 2011, additional pests were monitored to establish their activity in relation to plant phenological indicators (Table 3.1). Flight activity of lesser peachtree borer, lilac ash borer, and oak clearwing was monitored with 1C traps as mentioned for dogwood borer (Pherocon, Inc. Adair, OK). Traps were hung 1.5-1.8 m high within 15 m of the garden. Traps for lesser peachtree borer were baited with commercially available lure 46 (LPTB3140 and LILA3224, Trece, Adair, OK). Lilac ash borer and oak clearwing borer were both monitored with the commercial lure for lilac ash borer (LILA3224, Trece, Adair, OK). Traps were inspected three times per week from March to late October and lures were replaced every 4 weeks. Captured moths were stored in the freezer and labeled with the location and Julian date. The first male capture and cumulative seasonal capture were recorded. The flight activity of black cutworm and fall armyworm, two turf-infesting moths, were monitored at the Auburn site. First emergence was monitored with one 1C wing trap (Pherocon, Inc. Adair, OK) hung 1.5?1.8m high near the garden. Two traps were in each garden. Commercially-available pheromones (3141 or BCW and 3143 for FAW, Trece) were used in traps and replaced every 4 wk. Traps were inspected coincident with monitoring of the other moths. BCW was monitored from January?May and FAW from Mar?October and the Julian date for first moth capture of each species recorded. Adult Japanese beetles were monitored using a Japanese beetle trap (Trece) baited with the food and the pheromone lures. Traps were inspected coincident with servicing the other traps. First trap capture and seasonal abundance were recorded. First emergence of adult Canna leafroller moth was recorded using caged Canna ?Tropicana? plants infested. Five plants with signs of previous damage and infested with immature canna leafrollers were planted into the garden in 2009 (Fig. 3.2). In January 2010 metal, screen cages (61 x 61 x 61 cm, Bioquip Products, Rancho Dominguez, CA) were used to cover each plant to trap emerging adults. First adult emerged and emergence period were recorded as adults were caught fluttering around in the cages. Infested twigs of Malacosoma americanum (F.) were obtained at sites in Auburn and surrounding areas from wild and cultivated cherry (Prunus) and crabapple (Malus) trees. In 47 spring 2010, twig collections included: AL CR. 30, near Auburn University Field Crops Research Farm, Tallassee, AL ? 3 twigs; Dr. Mike William?s property, Notasulga, AL ? 3 twigs; Intersection of Gateway Dr. & Thompson Dr., Opelika, AL ? 4 twigs; Memorial Park Cemetery ? 2 twigs; Moore?s Mill Rd. near Stonehenge Dr. ? 1 twig; Keesal Park, Auburn ? 1 twig. In spring 2011, 12 egg masses were collected from five large cherry trees (Prunus serotina) growing in a fencerow in Sharpsburg, GA. Additionally, three egg masses were collected from two large cherry trees (Prunus serotina) growing in a fencerow in Auburn, AL. Infested twigs were cut and placed in 1:3 antifreeze/water solution in plastic cups to prevent water from freezing (473 ml, Solo Cup Company, Lake Forest, IL) in the Auburn phenology garden in January (Figure 3.2). Cups with twigs were placed in metal screen cages (61 x 61 x 61 cm, Bioquip Products, Rancho Dominguez, CA) to prevent damage from wildlife. Samples were monitored and the number of larvae that hatched was recorded daily. Eight additional arthropod species were monitored in the Auburn garden (Table 3.1). Figure 3.2. Cherry twig with an egg mass of Malacosoma americanum 48 Table 3.1. Arthropods monitored in Auburn, AL, 2010-2011 Common Name Scientific Name Order: Family Life Cycle Stage Monitored Lepidopteran borers Dogwood borer Synanthedon scitula Lepidoptera: Sesiidae typically bivoltine generalist on many trees & shrubs 1 st emergencea, 1st peak Lesser peachtree borer Synanthedon pictipes Lepidoptera: Sesiidae univoltine specialist on ornamental trees & shrubs 1st emergencea, 50% flight Lilac ash borer Podosesia syringae Lepidoptera: Sesiidae univoltine specialist of Fraxinus 1st emergencea Oak clearwing borer Paranthrene simulans Lepidoptera: Sesiidae semivoltine generalist, primarily oaks of red & white groups, elm, & American chestnut 1st emergencea Mandibulate folivores Lesser canna leafroller Geshna cannalis Lepidoptera: Pyralidae univoltine specialist of Canna 1 st emergenceb Japanese beetle Popillia japonica Coleoptera: Scarabaeidae univoltine generalist on >79 plant families 1 st emergencea, peak Eatern tent caterpillar Malacosoma americanum Lepidoptera: Lymantridae univoltine specialist on Malus sp. & Prunus sp. (Rosaceae family) 1 st emergenceb Black cutworm Agrotis ipsilon Lepidoptera: Noctuidae multivoltine generalist of turfgrass, nearly all vegetable crops 1st emergencea Fall armyworm Spodoptera frugiperda Lepidoptera: Noctuidae univoltine generalist of turgrass, and over 80 other plants 1st emergencea Haustellate folivores Crapemyrtle aphid Sarucallis kahawaluokalani Hemiptera: Aphididae multivoltine specialist on crapemyrtle 1st emergencea, 50% flight, peak a - data for trap collections b - data for direct observation 49 Statistical analyses Five potential base temperatures, 35?F (1.6?C), 40?F (4.4?C), 45?F (7.2?C), 50?F (10?C) and 55?F (12.7?C), were used to determine the most appropriate base temperature for dogwood borer and crapemyrtle aphid. For the additional insect species, a base temperature of 1.6?C and a biofix of January 1 were used for GDD calculations (Klein 2002) for all plants and the additional insect species. Cumulative GDDC for first, last, and peak occurrence of CMA was analyzed for each location using ANOVA with Tukey?s LSD test to show variation in GDD among sites. Also, cumulative abundance of CMA was compared with cumulative GDD. Similar to Mussey and Potter (1997), the 2-year average was calculated for first emergence. Results Sentinel pests first emergence, two year average Analysis of total cumulative capture for dogwood borer from first peak using GDD (Figure 3.3) proved Mobile was not significantly correlated with the four other sites in 2010. In 2011, Huntsville showed no significant correlation compared to four other sites that were correlated (Figure 3.3). In JD analysis, for 2010 (Figure 3.4) showed no significant correlation with the four other sites as well as 2011 analysis (Figure 3.5). GDDC and JD monitored CMA first emergence and seasonal abundance in 2010 and 2011 at all garden sites over two years. In 2010 (Figure 3.6) GDD for Mobile were not significantly correlated with the other four sites; however in 2011 (Figure 3.7) were better correlated among five sites. 50 Figure 3.3. DWB Cumulative GDD trap capture for 2010, 5 sites statewide Figure 3.4. DWB Cumulative GDD trap capture for 2011, 5 sites statewide 0 10 20 30 40 50 60 70 80 90 100 0 500 1000 1500 2000 2500 3000 3500 4000 # c ap tur e GDDC 2010 DWB Total Capture (GDDC) Huntsville Birmingham Auburn Headland Mobile 0 20 40 60 80 100 120 0 500 1000 1500 2000 2500 3000 3500 4000 # c ap tur e GDD 2011 DWB Total Capture (GDDC) Huntsville Birmingham Auburn Headland Mobile 51 Figure 3.5. DWB Cumulative JD trap capture for 2010, 5 sites statewide Figure 3.6. DWB Cumulative JD trap capture for 2011, 5 sites statewide 0 10 20 30 40 50 60 70 80 90 100 0 50 100 150 200 250 300 350 # c ap tur e JD 2010 DWB Total Capture (JD) Huntsville Birmingham Auburn Headland Mobile 0 20 40 60 80 100 120 0 50 100 150 200 250 300 350 # c ap tur e JD 2011 DWB Total Capture (JD) Huntsville Birmingham Auburn Headland Mobile 52 Figure 3.7. CMA Cumulative GDD trap capture for 2010, 5 sites statewide Figure 3.8. CMA Cumulative GDD trap capture for 2011, 5 sites statewide 0 10 20 30 40 50 60 70 0 500 1000 1500 2000 2500 3000 3500 4000 4500 # c ap tur e GDDC 2010 CMA Total Capture (GDDC) Huntsville Birmingham Auburn Headland Mobile 0 10 20 30 40 50 60 70 0 500 1000 1500 2000 2500 3000 3500 4000 4500 # c ap tur e GDDC 2011 CMA Total Capture (GDDC) Huntsville Birmingham Auburn Headland Mobile 53 Figure 3.9. CMA Cumulative JD trap capture for 2010, 5 sites statewide Figure 3.10. CMA Cumulative JD trap capture for 2011, 5 sites statewide 0 10 20 30 40 50 60 70 80 0 50 100 150 200 250 300 # c ap tur e Julian days 2010 CMA Total Capture (JD) Huntsville Birmingham Auburn Headland Mobile 0 10 20 30 40 50 60 70 0 50 100 150 200 250 300 # c ap tur e Julian Days 2011 CMA Total Capture (JD) Huntsville Birmingham Auburn Headland Mobile 54 Table 3.2. Sentinel insect species data across 5 sites and 2 years comparing GDD(C) and calendar date Huntsville Insect Insect event GDD 2010 GDD 2011 GDD 2 yr avg Calendar date 2010 Calendar date 2011 Calendar date 2 yr avg CMA 1st emergence 536 367 451 7-Apr 15-May 26-Apr DWB 1st emergence 478 670 574 3-May 23-Apr 28-Apr DWB 1st peak 803 1334 1068 28-May 1-Jun 30-May Birmingham Insect Insect event GDD 2010 GDD 2011 GDD 2 yr avg Calendar date 2010 Calendar date 2011 Calendar date 2 yr avg CMA 1st emergence 335 286 310 7-Apr 1-Apr 4-Apr DWB 1st emergence 399 351 375 20-Apr 8-Apr 14-Apr DWB 1st peak 666 633 649 15-May 5-May 10-May Auburn Insect Insect event GDD 2010 GDD 2011 GDD 2 yr avg Calendar date 2010 Calendar date 2011 Calendar date 2 yr avg CMA 1st emergence 912 442 677 29-May 12-Apr 4-May DWB 1st emergence 441 244 342 23-Apr 4-Apr 13-Apr DWB 1st peak 628 681 654 10-May 6-May 8-May Headland Insect Insect event GDD 2010 GDD 2011 GDD 2 yr avg Calendar date 2010 Calendar date 2011 Calendar date 2 yr avg CMA 1st emergence 315 955 635 7-Apr 15-May 26-Apr DWB 1st emergence 245 419 664 1-Apr 5-Apr 3-Apr DWB 1st peak 674 925 799 8-May 13-May 10-May Mobile Insect Insect event GDD 2010 GDD 2011 GDD 2 yr avg Calendar date 2010 Calendar date 2011 Calendar date 2 yr avg CMA 1st emergence 733 828 780 10-May 16-May 13-May DWB 1st emergence 417 659 538 12-Apr 17-Apr 14-Apr DWB 1st peak 572 828 700 27-Apr 29-Apr 28-Apr 55 Growing degree day and calendar date for 5 sites over the two year study were noted and averaged (Table 3.2). Base temperatures for the two sentinel insects were determined using the lowest coefficient of variation for each site (Tables 3.6, 3.7). Base temperature of 1.6?C was consistently the lowest predictor for CMA in Auburn and Birmingham. Coefficients varied for all other sites and there was no consistent base temperature for DWB or the CMA in Huntsville, Headland, and Mobile. First emergence of DWB in both years was earlier in Headland and Birmingham than all other sites (Table 3.3). No single plant correlate was consistent statewide for first emergence; however, Indian hawthorn, daffodil, and daylily were three plants that pinpointed the occurrence. For DWB, the range of first flower to full flower of Indian hawthorn was coincident with five of the ten emergence dates for 3 sites over the two year study (Table 3.3). Huntsville had missing data for Indian hawthorn due to deer damage, no bloom stage was recorded in 2011. Average first emergence of CMA was greatly earlier in Birmingham than all other sites (Table 3.2). Plant correlates included daylily, Indian hawthorn, daffodil, and crapemyrtle. Various stages of Indian hawthorn were the most common phenological indicator, for CMA. Huntsville had missing data due to deer damage, no bloom stage was recorded in 2010. 56 Table 3.3. DWB first emergence, 2 year average and 2 year date with plant correlate, 5 sites statewide, 2010, 2011 Site 2 year average (2010, 2011) Plant correlate 2010 Plant correlate 2011 Mobile April 14 (April 12, April 17) Full flower indian hawthorn Daylily bud tight, upright Headland April 3 (April 1, April 5) 1st flower indian hawthorn Full flower indian hawthorn Auburn April 13 (April 23, April 4) 1st flower indian hawthorn 1st flower indian hawthorn Birmingham April 4 (April 7, April 1) Daffodil tight, upright Daffodil tight, upright Huntsville April 28 (April 23, May 5) Daylily Shepherd?s crook N/A Table 3.4. CMA first emergence, 2 year average and 2 year date with plant correlate, 5 sites statewide, 2010, 2011 Site 2 year average (2010, 2011) Plant correlate 2010 Plant correlate 2011 Mobile May 13 (May 10, May 16) Bud tight, daylily 1st flower crapemyrtle Headland April 26 (April 7, May 15) First flower indian hawthorn Full flower daylily Auburn May 5 (May 29, April 12) Full flower daylily Full flower indian hawthorn Birmingham April 14 (April 20, April 8) Full flower daffodil 1st flower indian hawthorn Huntsville April 29 (April 7, April 11) N/A Daffodil tight, upright 57 Auburn temperature averages Mean monthly temperatures in Auburn were recorded from January to October in both years of the study. January to March 2010 was much cooler than the 30-year average. Most phenological events occurred earlier in 2010 than 2011, probably due to the unseasonably warmer temperatures in February 2010. Table 3.5. Mean monthly temperatures for monitoring period 2010-2011 & 30-year average for Auburn, AL 2010 2011 *30-yr avg. Month ?F (?C) ?F (?C) ?F (?C) Jan. 39.2 (4) 42.8 (6) 44.7 (7.1) Feb. 42.8 (6) 51.8 (11) 48.4 (9.1) March 51.8 (11) 59 (15) 55.8 (13.2) Apr. 66.2 (19) 66.2 (19) 62.5 (17) May 75.5 (23) 72.2 (22) 70.6 (21.4) June 82.4 (28) 83.7 (28) 77.2 (25.1) July 84.2 (29) 83.1 (28) 79.9 (26.9) Aug. 86 (30) 83.3 (29) 79.9 (26.6) Sept. 80.6 (27) 72.5 (23) 74.7 (23.7) Oct. 67.5 (20) 60.9 (16) 64.5 (18.1) *30-yr avg. data from National Climatic Data Center. 58 Table 3.6. Comparison of degree-day requirements for first capture of CMA in five sites in Alabama; calculated from Jan 1 using eight potential base temperatures Base temperature ( C) Year Date 0 1.6 2 4 6 8 10 12.7 Mobile 2010 10 May 1820 1630 1583 1351 1128 920 733 516 2011 16 May 2309 2101 2051 1802 1566 1342 1132 876 CV 16.78 17.85 18.21 20.23 22.99 26.38 30.26 36.57 Headland 2010 7 Apr 1057 916 881 712 554 421 315 211 2011 15 May 2094 1889 1839 1594 1363 1147 947 708 CV 46.54 49.06 49.81 54.09 59.68 65.48 70.82 76.48 Auburn 2010 29 May 2021 1816 1767 1525 1298 1093 912 702 2011 12 Apr 1545 1374 1333 1132 944 771 616 441 CV 18.88 19.60 19.80 20.92 22.33 24.43 27.40 32.29 Birmingham 2010 7 Apr 824 710 683 559 453 363 290 216 2011 1 Apr 902 784 756 621 499 391 298 200 CV 6.39 7.00 7.17 7.43 6.83 5.25 1.92 5.44 Huntsville 2010 7 May 982 854 824 683 559 454 367 280 2011 21 Apr 1172 1023 987 816 662 530 416 287 CV 12.47 12.73 12.73 12.55 11.93 10.92 8.85 1.75 59 Table 3.7. Comparison of degree-day requirements for first capture of DWB in five sites in Alabama; calculated from Jan 1 using eight potential base temperatures Base temperature ( C) Year Date 0 1.6 2 4 6 8 10 12.7 Mobile 2010 12 Apr 1225 1080 1044 868 701 549 417 274 2011 17 Apr 1547 1386 1346 1156 977 811 659 477 CV 16.43 17.54 17.87 20.12 23.26 27.24 31.81 38.23 Headland 2010 1 Apr 927 785 763 605 460 339 245 155 2011 5 Apr 1131 997 964 807 664 534 419 287 CV 14.02 16.82 16.46 20.32 25.67 31.59 37.06 42.23 Auburn 2010 24 Apr 1213 1051 1029 858 701 565 453 333 2011 4 Apr 1057 926 894 742 603 477 368 248 CV 9.72 *8.94 9.93 10.25 10.63 11.94 14.64 20.69 Birmingham 2010 20 Apr 1055 921 889 739 608 494 399 298 2011 8 Apr 1016 888 857 709 574 454 351 241 CV 2.66 *2.57 2.59 2.93 4.07 5.97 9.05 14.96 Huntsville 2010 3 May 1209 1062 1027 862 715 586 478 362 2011 23 Apr 1196 1045 1009 836 680 546 430 298 CV 0.76 1.14 1.25 2.17 3.55 5.00 7.48 13.71 *Denotes base temperature with the lowest Coefficient of Variation 60 Auburn biological calendar Phenophases of 15 landscape ornamental plants were correlated with pest activity of nine ornamental landscape plant pests, and data were organized into a biological calendar for each site (Tables 3.7 ? 3.11). Phenological events for plants and insect pests in both years are presented and organized by the 2 year average for each event (Mussey and Potter, 1997). Notable plant events such as first flower can be associated with key insect activities like first appearance to more accurately time control measures. Important life stages of the additional 8 pests that were monitored in the Auburn phenology garden are also included in the calendar (Table 3.9). First emergence of lesser canna leafroller was observed and recorded in 2010 but no moths were captured in 2011. First emergence of fall armyworm, black cutworm, lesser peachtree borer, oak clearwing borer, and Japanese beetle was monitored in 2010 and 2011. Bagworms were also collected and monitored in both years but no emergence was detected. The unseasonably cold temperatures of the 2010 winter killed the overwintering population of Florida wax scale on Foster?s #2 hollies in the garden. A biological calendar was created for the Auburn phenology garden (Table 3.9), which states average dates of plant and insect phenological events for 2010 and 2011 and each year independently. Table 3.8. Biological calendar for Huntsville, AL, 2010-2011 Plant/Insect Phenological Event 2010 2011 2 year Average Average Average Daffodil Bud tight, upright 22-Mar 1-Mar 11-Mar Forsythia 1st flower 29-Mar 25-Feb 13-Mar Daffodil Shepherd?s crook 29-Mar 3-Mar 16-Mar Forsythia 50% flower 2-Apr 28-Feb 16-Mar Loropetulum 1st flower 31-Mar 1-Mar 16-Mar Daffodil 1st petal open 1-Apr 7-Mar 19-Mar Loropetulum Full flower 9-Apr 2-Mar 21-Mar Yoshino Cherry 1st flower 29-Mar 14-Mar 22-Mar Daffodil Full flower 2-Apr 14-Mar 23-Mar 61 Yoshino Cherry 50% flower 31-Mar 17-Mar 24-Mar Yoshino Cherry Full flower 3-Apr 21-Mar 28-Mar Dogwood borer 1st emergence 3-May 23-Apr 28-Apr Crapemyrtle aphid 1st emergence 7-May 21-Apr 29-Apr Daylily Bud tight, upright 13-May 3-May 8-May Daylily Shepherd?s crook 20-May 11-May 15-May Daylily 1st petal open 26-May 16-May 21-May Daylily Full flower 4-Jun 21-May 28-May Crape myrtle 1st flower 12-Jun 28-May 4-Jun Goldenrod 1st flower 23-Jun 25-June 24-Jun Sunflower 1st flower 25-Jul 8-Jun 24-Jun Sunflower Full flower 26-Jul 11-Jun 3-Jul Liriope 1st flower 18-Jul 10-Sep 14-Aug Liriope Full flower 29-Jul N/A N/A Table 3.9. Biological calendar for Birmingham, AL, 2010-2011 Plant/Insect Phenological Event 2010 2011 2 year Average Average Average Forsythia 1st flower N/A 26-Feb N/A Forsythia 50% flower N/A 5-Mar N/A Yoshino Cherry 1st flower N/A 18-Mar N/A Yoshino Cherry 50% flower N/A 21-Mar N/A Goldenrod 1st flower N/A 21-May N/A Goldenrod Full flower N/A 28-May N/A Loropetulum 1st flower 2-Apr 27-Feb 16-Mar Daffodil Bud tight, upright 4-Apr 2-Mar 18-Mar Daffodil Shepherd's crook 9-Apr 6-Mar 23-Mar Forsythia Full flower 1-Apr 18-Mar 25-Mar Daffodil 1st petal open 13-Apr 11-Mar 27-Mar Yoshino Cherry Full flower 1-Apr 25-Mar 28-Mar Loropetulum Full flower 9-Apr 21-Mar 30-Mar Daffodil Full flower 16-Apr 17-Mar 1-Apr Indian Hawthorn 1st flower 23-Apr 6-Apr 4-Apr Crapemyrtle aphid 1st emergence 7-Apr 1-Apr 4-Apr Dogwood borer 1st emergence 20-Apr 8-Apr 14-Apr Indian Hawthorn 50% flower 26-Apr 10-Apr 18-Apr Indian Hawthorn Full flower 30-Apr 16-Apr 23-Apr Hydrangea 1st flower 1-May 19-Apr 25-Apr Daylily Bud tight, upright 4-May 27-Apr 30-Apr Hydrangea 50% flower 12-May 27-Apr 4-May Daylily Shepherd's crook 12-May 2-May 8-May Dogwood borer 1st peak 15-May 5-May 10-May 62 Hydrangea Full flower 25-May 2-May 13-May Daylily 1st petal open 18-May 9-May 13-May Daylily Full flower 24-May 12-May 18-May Crapemyrtle aphid 1st peak 8-Apr 1-Jul 21-May Crape Myrtle 1st flower 28-May 22-May 25-May Crape Myrtle Full flower 22-Jun 4-Jun 13-Jun Sunflower 1st flower 4-Jul 10-Jun 22-Jun Clethra 1st flower 25-Jun 3-Jul 29-Jun Clethra Full flower 10-Jul 14-Jul 12-Jul Goldlace Full flower 8-Aug 29-Jun 19-Jul Liriope 1st flower 6-Aug 11-Jul 25-Jul Liriope Full flower 11-Aug 18-Jul 29-Jul Table 3.10. Biological calendar for Auburn, AL, 2010-2011 Plant/Insect Phenological Event 2010 2011 2 year Average Average Average Eastern tent caterpillar 1st emergence 8-Mar 24-Feb 2-Mar Forsythia 1st flower 11-Mar 22-Feb 2-Mar Forsythia 50% flower 14-Mar 25-Feb 5-Mar Forsythia Full flower 18-Mar 28-Feb 9-Mar Loropetulum 1st flower 19-Mar 3-Mar 11-Mar Daffodil Bud tight, upright 21-Mar 4-Mar 12-Mar Daffodil Shepherd's crook 25-Mar 7-Mar 16-Mar Daffodil 1st petal open 26-Mar 9-Mar 17-Mar Daffodil Full flower 29-Mar 10-Mar 19-Mar Loropetulum Full flower 29-Mar 10-Mar 19-Mar Yoshino Cherry 1st flower 24-Mar 17-Mar 20-Mar Yoshino Cherry 50% flower 26-Mar 20-Mar 23-Mar Yoshino Cherry Full flower 29-Mar 23-Mar 26-Mar Black cutworm 1st emergence 10-Apr 14-Mar 27-Mar Lesser canna leafroller 1st emergence 7-Apr N/A N/A Fall armyworm 1st emergence 16-Apr 18-Mar 1-Apr Indian Hawthorn 1st flower 15-Apr 1-Apr 8-Apr Indian Hawthorn 50% flower 18-Apr 5-Apr 11-Apr Dogwood borer 1st emergence 23-Apr 4-Apr 13-Apr Indian Hawthorn Full flower 21-Apr 8-Apr 14-Apr Oakleaf hydrangea 1st flower 4-May 21-Apr 27-Apr Hydrangea 50% flower 9-May 25-Apr 2-May Hydrangea Full flower 12-May 27-Apr 4-May Crapemyrtle aphid 1st emergence 29-May 12-Apr 5-May Daylily Bud tight, upright 12-May 5-May 8-May 63 Dogwood borer 1st peak 10-May 6-May 8-May Oak clearwing borer 1st emergence 14-May 11-May 12-May Daylily Shepherd's crook 21-May 8-May 14-May Daylily 1st petal open 24-May 10-May 17-May Lesser peachtree borer 1st emergence 11-May 23-May 17-May Daylily Full flower 27-May 12-May 19-May Japanese beetle 1st emergence 24-May 18-May 21-May Crapemyrtle 1st flower 31-May 23-May 27-May Crapemyrtle aphid 1st peak 6-Jun 17-May 27-May Crapemyrtle Full flower 19-Jun 2-Jun 10-Jun Goldenrod 1st flower 28-May 4-Jul 15-Jun Goldenrod Full flower 22-Jun 22-Jun 22-Jun Clethra 1st flower 28-Jun 7-Jul 1-Jul Sunflower 1st flower 26-Jun 12-Jul 5-Jul Liriope 1st flower N/A 7-Jul N/A Goldlace Sunflower Full flower 5-Jul 19-Jul 12-Jul Clethra Full flower 6-Jul 16-Jul 18-Jul Liriope Full flower N/A 14-Jul N/A Table 3.11. Biological calendar for Headland, AL, 2010-2011 Plant/Insect Phenological Event 2010 2011 2 year Average Average Average Loropetulum 1st flower 28-Feb 24-Feb 26-Feb Daffodil Bud tight, upright 3-Mar 26-Feb 28-Feb Daffodil Shepherd's crook 7-Mar 1-Mar 3-Mar Loropetulum Full flower 8-Mar 2-Mar 5-Mar Daffodil 1st petal open 12-Mar 3-Mar 7-Mar Forsythia 1st flower 23-Mar 21-Feb 8-Mar Daffodil Full flower 19-Mar 6-Mar 13-Mar Forsythia 50% flower 25-Mar 1-Mar 13-Mar Forsythia Full flower 1-Apr 4-Mar 17-Mar Yoshino Cherry First flower 23-Mar 18-Mar 20-Mar Yoshino Cherry 50% flower 27-Mar 20-Mar 23-Mar Yoshino Cherry Full flower 1-Apr 24-Mar 28-Mar Indian Hawthorn 1st flower 12-Apr 24-Mar 2-Apr Dogwood borer 1st emergence 1-Apr 5-Apr 3-Apr Indian Hawthorn 50% flower 16-Apr 26-Mar 5-Apr Indian Hawthorn Full flower 23-Apr 28-Mar 10-Apr Indian Hawthorn Full flower 21-Apr 8-Apr 14-Apr Crapemyrtle aphid 1st emergence 7-Apr 15-May 26-Apr Daylily Bud tight, upright 5-May 24-Apr 29-Apr 64 Daylily Shepherd's crook 8-May 28-Apr 3-May Daylily 1st petal open 12-May 2-May 7-May Dogwood borer 1st emergence 8-May 13-May 10-May Daylily Full flower 17-May 6-May 11-May Crapemyrtle 1st flower 24-May 8-May 16-May Crapemyrtle Full flower 2-Jun 26-May 29-May Crapemyrtle aphid 1st peak 7-May 21-Jul 13-Jun Liriope 1st flower 18-Jul 24-Jun 6-Jul Liriope Full flower 29-Jul 12-Jul 20-Jul Table 3.12. Biological calendar for Mobile, AL, 2010-2011 Plant/Insect Phenological Event 2010 2011 2 year Average Average Average Loropetulum 1st flower 23-Feb 2-Mar 26-Feb Daffodil Bud tight, upright 9-Mar 1-Mar 5-Mar Forsythia 1st flower 9-Mar 2-Mar 5-Mar Daffodil Shepherd?s crook 14-Mar 3-Mar 8-Mar Daffodil 1st petal open 17-Mar 6-Mar 11-Mar Forsythia 1st flower 23-Mar 21-Feb 8-Mar Daffodil Full flower 23-Mar 9-Mar 16-Mar Loropetulum Full flower 2-Apr 4-Mar 18-Mar Yoshino Cherry First flower 25-Mar 16-Mar 20-Mar Forsythia Full flower 22-Mar 12-Mar 22-Mar Yoshino Cherry 50% flower 29-Mar 21-Mar 25-Mar Yoshino Cherry Full flower 22-Apr 25-Mar 29-Mar Indian Hawthorn First flower 6-Apr 23-Mar 30-Mar Indian Hawthorn 50% flower 9-Apr 25-Mar 4-Apr Indian Hawthorn Full flower 15-Apr 28-Mar 6-Apr Dogwood borer 1st emergence 12-Apr 17-Apr 14-Apr Hydrandea 1st flower 28-Apr 11-Apr 19-Apr Hydrandea 50% flower 1-May 16-Apr 23-Apr Daylily Bud tight, upright 5-May 22-Apr 28-Apr Dogwood borer 1st emergence 27-Apr 29-Apr 28-Apr Hydrangea Full flower 9-May 22-Apr 30-Apr Daylily 1st petal open 12-May 2-May 7-May Dogwood borer 1st peak 8-May 13-May 10-May Daylily Full flower 17-May 6-May 11-May Crapemyrtle aphid 1st emergence 10-May 16-May 13-May Crapemyrtle 1st flower 24-May 8-May 16-May Crapemyrtle Full flower 2-Jun 26-May 29-May Crapemyrtle aphid 1st peak 7-May 21-Jul 13-Jun 65 Liriope 1st flower 12-Aug 10-Jul 26-Jul Liriope Full flower 23-Aug 23-Jul 7-Aug Discussion The sequence of phenophases to pests was not consistent statewide. However, the order in which plants flowered maintained a consistent pattern from Mobile to Huntsville. No common plant phenological indicator was correlated with pest activity from location to location. In further studies, perhaps recommendations could be based on regional phenophases or possibly categorized by USDA Hardiness Zone. Sequence of plants from location to location and year to year showed significant consistencies according to Spearman?s bivariate correlation and regression analysis, (Chapter 2) similar to Kulhanek (2009), which had significant correlation from year to year for all phenological sequences and location to location. Some of the sequences of plants flowering to pests emerging from year-to-year in the Auburn garden can be extrapolated statewide. Hydrangea data was inadequate in Headland and Huntsville due to plant loss. The pest emergences occurred before first flower of indian hawthorn, with lesser canna leafroller emergence very close to indian hawthorn flower. Additionally, dogwood borer first emergence always preceded crapemyrtle aphid first emergence at each site, statewide. Eastern tent caterpillar emergence was similar to Mussey and Potter (1997), with consistent correlation to first flower to 50% flower of Forsythia. In 2011, additional landscape plants with similar phenophase sequences to the plants in the Auburn Phenology Garden were observed. The similar plants were established in the landscape throughout Auburn, Alabama. Bridal wreath spiraea first flower (Spiraea prunifolia Siebold & Zucc.) and flowering dogwood (Cornus florida) first flower were consistent with Yoshino cherry (Prunun ?yedoensis) first flower. Chinese fringetree (Chionanthus retusus) was 66 consistent with ?Ellen Tabor? indian hawthorn (Raphiolepis indica Eleanor Tabor?) first flower. Glossy abelia (Abelia x grandiflora) was consistent with 50% flower of ?Ellen Tabor? indian hawthorn (Raphiolepis indica Eleanor Tabor?) indian hawthorn. Southern catalpa (Catalpa bignonioides) first flower was coincident with dogwood borer first peak. 67 Chapter IV FINAL CONCLUSIONS Phenological data provide a practical method for predicting pest control measures, and many biological calendars have been produced from the sequences, (Mussey and Potter 1997, Herms 2004) including the Auburn University Phenology Network website. The Auburn study compiles data collected from five garden sites, while some of the aforementioned studies typically use data from one location. Few inconsistencies existed in the flower sequences from year-to-year and location-to-location. When considering the consistency of phenological indicators, a number of environmental factors can play a role in plant and insect development (Orton 1989, Mussey and Potter 1997, Herms 2004). One factor to consider when comparing statewide plant correlates in regard to climate is the geographic variation from Mobile to Huntsville. Mobile and Headland tend to be more temperate than north Alabama field sites, or what is often referred to as a Maritime Climate. Characteristics of this factor may include warmer winters, warmer periods during flower and development, more annual rainfall, more constant cloud cover, lower radiation intensity, lower rate of evapotranspiration, and less weather variation from year to year. This in essence provides a more diffuse life cycle (Orton 1989). Auburn, Birmingham, and Huntsville tend to be more moderate in climate with characteristics of a Continental Climate like colder winters, warmer temperatures during flower and development, less annual rainfall, less constant cloud cover, higher radiation intensity, higher rates of evapotranspiration, and more variation in 68 weather from year to year. These factors therefore provide more brief and variable life cycles (Orton 1989). As previously mentioned plant and insect development can also be influenced by soil moisture and temperature, plant fertility, photoperiod, and atmospheric composition (Broome 2011) (as well as elevation and wind influence influencing plant development) (Orton 1989). Additionally, plants? response to environmental influences may also affect the accuracy of regional recommendations for pest predicitons (Mussey and Potter 1997). Master Gardener participation varied over the two years of the study. For example, the Master Gardener that recorded data on Monday may have been a different Master Gardener than showed up on Wednesday, possibly leading to some variation due their interpretation of in flower stage (Kulhanek 2009). Plants in Auburn and Headland progressed faster from first flower to full flower. This is probably due to the fact that one person monitored phases at each of these gardens, versus multiple participants at the other three garden sites. Phenology gardens worldwide have reported similar fluctuations in data due to variations in volunteer participation and lack of adequate data due to issues such as plant death (Kulhanek 2009). Differences in methodology probably give various results among similar studies of Herms (2004) and Mussey and Potter (1997). For example, differences in biofix and base temperatures among each of the 3 studies produces different growing degree day accumulations. Much of the variability among data collection can be attributed to the number of personnel collecting data. Two-year averages are more closely matched with each individual site with less people recording data. In order to implement necessary control of key landscape pests using phenology and growing degree days, knowledge of the life history and biology of the pest will be important. 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