Seasonal Patterns of Aster Leafhopper (Hemiptera: Cicadellidae) Abundance and Aster Yellows Phytoplasma Infectivity in Wisconsin Carrot Fields

Home , Callistephus, Leafhopper, Macrosteles

PLANT-INSECT INTERACTIONS Seasonal Patterns of Aster Leafhopper (Hemiptera: Cicadellidae) Abundance and Aster Yellows Phytoplasma Infectivity in Wisconsin Carrot Fields

1 2 3 3 4 K. E. FROST, P. D. ESKER, R. VAN HAREN, L. KOTOLSKI, AND R. L. GROVES

Environ. Entomol. 42(3): 491Ð502 (2013); DOI: http://dx.doi.org/10.1603/EN12240 ABSTRACT In Wisconsin, vegetable crops are threatened annually by the aster yellows phytoplasma (AYp), which is obligately transmitted by the aster leafhopper. Using a multiyear, multilocation data set, seasonal patterns of leafhopper abundance and infectivity were modeled. A seasonal aster yellows index (AYI) was deduced from the model abundance and infectivity predictions to represent the expected seasonal risk of pathogen transmission by infectious aster leafhoppers. The primary goal of this study was to identify periods of time during the growing season when crop protection practices could be targeted to reduce the risk of AYp spread. Based on abundance and infectivity, the annual exposure of the carrot crop to infectious leafhoppers varied by 16- and 70-fold, respectively. Together, this corresponded to an estimated 1,000-fold difference in exposure to infectious leafhoppers. Within a season, exposure of the crop to infectious aster leafhoppers (Macrosteles quadrilineatus Forbes), varied threefold because of abundance and ninefold because of infectivity. Periods of above average aster leafhopper abundance occurred between 11 June and 2 August and above average infectivity occurred between 27 May and 13 July. A more comprehensive description of the temporal trends of aster leafhopper abundance and infectivity provides new information deÞning when the aster leafhopper moves into susceptible crop Þelds and when they transmit the pathogen to susceptible crops.

KEY WORDS Macrosteles quadrilineatus, aster yellows phytoplasma, aster leafhopper, migration, dispersal

Aster yellows (AY) is a widespread disease of plants ciÞcally, processing problems can result from an caused by the aster yellows phytoplasma (AYp), a inability to obtain clean raw product because of ad- small, wall-less prokaryotic organism that is currently ventitious root growth and associated Þeld soil con- placed in the provisional genus ÔCandidatus Phyto- tamination. plasmaÕ (Lee et al. 2000, IRPCM Phytoplasma/Spiro- AYp has been reported to be circulative and prop- plasma working team Ð Phytoplasma taxonomy group agative in the aster leafhopper (Maramorosch 1952, 2004). The AYp has an extensive and diverse host Sinha and Chiykowski 1967, Lee et al. 2000), and range infecting over 350 plant species including many vector competence involves acquisition, pathogen common vegetable, ornamental, and agronomically replication, and circulation to result in successful important Þeld crops, and several noncrop plant spe- transmission to a susceptible host (Matthews 1991). cies (Kunkle 1926; Chiykowski 1965, 1967; Chiykowski Plant-to-plant spread of AYp in the Þeld can occur as and Chapman 1965; Westdal and Richardson 1969; a result of transmission by more than 24 leafhopper Peterson 1973; Lee et al. 1998, 2000, 2003; Holling- species (Mahr 1989, Christensen et al. 2005). How- sworth et al. 2008). The most common disease phe- ever, the aster leafhopper, Macrosteles quadrilineatus notypes are vein clearing, chlorosis, stunting, twisting Forbes, is considered to be the primary vector of the and proliferation of plant stems, and the development AYp because of its prevalence in Midwestern suscep- of adventitious roots (Kunkle 1926, Bloomquist 2002). tible crops (Drake and Chapman 1965, Hoy et al. In vegetable crops, these symptoms can lead to direct 1992). yield and quality losses and, for root vegetables spe- Similar to most plant pathogens spread by arthro- pods, risk for spread of AY to a susceptible crop is a function of aster leafhopper abundance and transmis- 1 Department of Plant Pathology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706. sion capability, or infectivity (Madden et al. 2000, 2 Centro de Investigaciones en Proteccio´n de Cultivos, Universidad Jeger et al. 2004). In Wisconsin, aster yellows man- de Costa Rica, San Jose´, Costa Rica. agement has focused primarily on controlling the in- 3 Pest Pros, Inc., PlainÞeld, WI 54966. sect vector, the aster leafhopper, and an AY risk index, 4 Corresponding author: Russell L. Groves, Department of Ento- mology, University of Wisconsin-Madison, 1630 Linden Drive, Mad- known as the aster yellows index (AYI), was devel- ison, WI 53706 (e-mail: [email protected]). oped to describe the maximum allowable numbers of

0046-225X/13/0491Ð0502$04.00/0 ᭧ 2013 Entomological Society of America 492 ENVIRONMENTAL ENTOMOLOGY Vol. 42, no. 3 infectious leafhoppers and deÞne periods of time with those factors. An outcome of our previous anal- when plant protection is most needed (Chapman ysis noted signiÞcant residual trends in the seasonal 1971). The AYI metric is the product of aster leafhop- patterns of aster leafhopper abundance and infectivity per infectivity (percent of infectious aster leafhop- that could be directly modeled. Nonparametric re- pers) and the magnitude of the aster leafhopper pop- gression and additive models are extensions of linear ulation (number of aster leafhoppers in 100 pendulum models that allow for nonlinear relationships between sweeps by using a standard 38-cm sweep net). Orig- the response variable and multiple predictor variables. inally, the AYI was used to make insecticide spray In these models, the goal is primarily to describe the recommendations based on a series of early season data in a way in which complex functions serve as best leafhopper collections (Chapman 1971, 1973). How- predictors making the models ßexible and preserving ever, following the observations that aster leafhopper model interpretability (Hastie et al. 2009). Recently abundance and infectivity, in and around carrot Þelds, their application to examine trends in long-term data varied spatially and temporally (Mahr et al. 1993), sets has become useful. For example, these method- efforts were made to reÞne AYI estimates for a speciÞc ologies have been used to examine environmental date and Þeld. However, even with the availability of drivers affecting long-term trends in water quality contemporary tools, signiÞcant annual and site-spe- (Ferguson et al. 2008), sperm whale habitat prefer- ciÞc variation in pathogen detection in the insect ence (Pirotta et al. 2011), shifts in insect phenology vector frequently occurs. In most cases, the relation- because of climatic variation (Hodgson et al. 2011), ship between pathogen presence in the vector and the and seasonal trends of aphid dispersal into agricultural vectorÕs ability to successfully transmit the pathogen is Þelds (Nault et al. 2009). Our primary goal in the not known. In turn, many producers avoid risk of current study is to identify periods of time in the pathogen spread by using inexpensive, prophylactic growing season when crop protection is most needed. insecticide applications, a management practice that In this paper, generalized additive mixed models are circumvents the utility of the AYI. used to: 1) describe the pattern that represents the In Wisconsin, aster yellows typically has been con- expected seasonal aster leafhopper abundance and trolled using repetitive applications of insecticidal infectivity in the carrot crop, 2) predict the expected compounds in the synthetic pyrethroid group. Al- seasonal aster leafhopper abundance and infectivity to though successful from the perspective of managing deduce a seasonal AYI that best represents the periods insect pests in a cost-effective manner, this approach of risk for exposure to infectious leafhopper, and 3) presents considerable risk, because these insecticides retrospectively quantify the frequency and magnitude are older, broad-spectrum compounds with docu- of aster leafhopper infestation events. For integrated mented mammalian toxicity (Wolansky and Harrill pest management (IPM) practitioners, the identiÞca- 2008). The chemicals in this group are also harmful to tion of temporal trends of abundance and infectivity aquatic organisms, are lipophilic, and in aquatic envi- can improve our knowledge of where leafhoppers ronments, tend to adsorb to organic sediments (Gan acquire the pathogen, when they move into suscep- et al. 2008). Monitoring surveys in the United States tible Þelds, and when they spread the pathogen to have detected the presence of synthetic pyrethroid susceptible crops. residues in the sediment of both agricultural and urban dominated waterways (Werner et al. 2002, Weston et Materials and Methods al. 2004). These Þndings have prompted concerns about pyrethroid exposure to nontarget areas, espe- Data Description. A detailed description of the data cially ecologically sensitive areas such as wetlands, set, sample sites, and sampling methodologies, previ- which include the lowland muck soils where the ma- ously has been reported (Frost et al. 2012). Brießy, jority of Wisconsin carrot is grown. Thus, it has been Þeld sampling was conducted using sweep nets in our goal to reduce the nearly exclusive reliance on commercial carrot Þelds to monitor the relative abun- synthetic pyrethroid insecticides for the management dance of aster leafhopper in the common carrot pro- of aster yellows in carrot. We hypothesize that an duction areas of Wisconsin from 2001 to 2011. In total, improved understanding of the expected AY risk 237 Þelds were sampled over the 11-yr span of this based on historical data, combined with an under- survey. Abundance was estimated for an average of 31 standing of the “window” of time during which these Þelds per year with several Þelds resampled in mul- events occur, will improve aster yellows management. tiple years because of crop rotation practices. Abun- SpeciÞcally, grower adoption of reduced-risk (RR) dance was determined by sweep net sampling along insecticides may be improved by targeting insecticide two to 18 transects that were walked into the carrot applications to periods during which AY risk is high, crop toward the middle of the Þeld. Twenty-Þve to 100 thereby reducing the number of applications of the (pendulum) sweeps per transect were conducted us- more expensive RR insecticides necessary to control ing a standard sweep net (38 cm in diameter) and aster aster yellows, improving the cost-efÞciency of these leafhoppers were counted and enumerated as aster newer tools. leafhoppers per 25 sweeps. Decimal values were We have previously examined factors that contrib- rounded to the nearest integer. Aster leafhopper in- ute to the variability of aster leafhopper abundance fectivity was monitored using an infectivity bioassay in and infectivity (Frost et al. 2012). However, we did not the commercial carrot production areas and records of directly model the patterns of variation associated infectivity were available from 1994 to 2007. June 2013 FROST ET AL.: PATTERNS OF LEAFHOPPER ABUNDANCE AND INFECTIVITY 493

The prevalence of infectious aster leafhoppers was sented as a random effect in the model. This GAMM estimated for 378 insect populations in total from 1994 could be represented as: through 2008. Approximately 25 populations were col- Ϸ ␮ lected and assayed per year with multiple geographic Yij Poisson(g[ ij͑abc͒]) (model 1) ͑␮ ϭ ␮ ϭ ϩ ␤ locations and several dates represented throughout g ij[ab]) loge( ij[abc]) offset(lNt) i each growing season. At each sample location and ϩ ϩ ␧ ϩ ␧ ϩ ␧ date, aster leafhoppers were collected in sweep nets f(dj) (a) (b) (c) and placed onto oat (Avena sativa L.) seedlings for ␧ Ϸ N(0, ␴2 ), transport back to the laboratory. When possible, 204 (a) farm ␧ Ϸ ␴2 leafhoppers were placed, in pairs, onto 102 Chinese (b) N(0, Þeld), aster [Callistephus chinensis (L.) Nees], plants and ␧ Ϸ ␴2 allowed to feed for the duration of the experiment. (c) N(0, c), ␤ Disease symptoms were assessed after a 2-wk incuba- where i corresponded to the average abundance es- tion period and percent infectivity was calculated as: timate for each year (i) and f(dj) was a smoothing function (penalized cubic regression spline) of the ϭ infectivity number of diseased plants/total calendar day covariate (di). The model contains an number of leafhoppers offset corresponding to the log-transformed number of transects (lNt) used to estimate leafhopper abun- ␧ ␧ ␧ The total number of leafhoppers was used as the de- dance, and (a), (b), and (c) are the random effects nominator because infectivity levels are low, usually for each farm, Þeld, and observation, respectively. The between 0 and 3%, and a diseased plant was more model was speciÞed using the gamm4 function in the likely because of a single infective leafhopper rather gamm4 package (Wood 2006) of R (version 2.15.0; R than the presence of two infective leafhoppers on the Development Core Team 2012) and generalized cross same plant. validation was used to estimate the value of the Statistical Analysis. We used generalized additive smoothing parameter for the unknown scale param- mixed models (GAMM) to describe the annual and eter (Wood 2004, 2006; Zuur et al. 2009).

seasonal trends of aster leafhopper abundance and The component smooth function f(di), centered on infectivity. A GAMM is an extension of a generalized the scale of the linear predictor, was plotted versus

additive model that relaxes the underlying assumption calendar date (di) together with the partial residual of that the data are independent by allowing observa- the model to represent the seasonal trend of aster tions to be correlated (Zuur et al. 2009). One advan- leafhopper abundance. Thus, the period of elevated tage of using a GAMM to describe seasonal leafhopper leafhopper abundance was estimated by visual exam-

abundance is that we can estimate the underlying ination of the plotted data (i.e., f1[di] versus di). Pre- trend from the data without assuming the trend has dictions (PA) of leafhopper abundance, and associ- any speciÞc functional form (Wood 2006). In these ated standard errors (SE) of the predictions, were models, the goal is primarily to describe the data in a obtained from the Þtted GAMM for each calendar day way in which complex functions serve as best predic- in low, typical, and high abundance years. ConÞdence Ϯ torsÑwithout needing to understand the complex intervals for each day were estimated as PA 2*SE. mathematical representation of the model; trends are ConÞdence intervals were back-transformed to the represented by mathematical functions, but those response scale and multiplied by four, to obtain leaf- mathematical representations are not particularly in- hoppers per 100 sweeps, before being used in the tuitive when written down (Wood 2006, Agresti 2007). calculation of the seasonal AYI. Therefore, to examine the form of the function, or ALH Infectivity. A GAMM also was used to examine trend in the data, we produced model predictions seasonal trends of leafhopper infectivity. This model

given a new set of data containing all model covariates. was used to Þt aster leafhopper infectivity (Yi; sqrt- From our models, the seasonal predictions of leafhop- transformed) to calendar day (xi) with year repre- per abundance and infectivity were used to deduce sented as a random effect in the model. This GAMM the expected AYI for the average growing season to could be represented as follows: deÞne the interval in which elevated risk for crop Ϸ ␮ ␴2 exposure to infectious leafhoppers was greatest. In Yi N(g[ i], ) (model 2) addition, the strategy we used to confront the pres- g(␮ ) ϭ ␮ ϭ ␤ ϩ f(d ) ϩ ␧ ϩ ␧ ence of annual variability associated with leafhopper ij ij i j i ij ␧ Ϸ ␴2 abundance and infectivity was to include seasonal i N(0, i) estimates for the years with the highest and lowest ␧ Ϸ N(0, ␴2 ), observed annual abundance and infectivity. This ap- ij r ␤ proach is in contrast to our previous study where we where i corresponded to the mean infectivity esti- chose to characterize the distributions for each level mate for each year (i) and f(di) was a smoothing of grouping in our data. function (penalized cubic regression spline) of cal- ␧ Aster Leafhopper Abundance. A GAMM was used endar day (di). In this model, a corresponded to the ␧ to describe the seasonal pattern of aster leafhopper random effects term for farm and ij represented the abundance (Yi) as a function of calendar day (xi) with residual error. The method used to Þt this function was each ÞeldÐyear combination and observation repre- the same as the one described previously. 494 ENVIRONMENTAL ENTOMOLOGY Vol. 42, no. 3

The component smooth function, f(di), of the resent the phase shift between leafhopper abundance model Þt was plotted versus calendar date (di) rep- and infectivity within the growing season. resenting the seasonal trend of aster leafhopper in- Quantifying the Frequency of Leafhopper Inﬂuxes. fectivity. Again, the period of elevated infectivity was We deÞned an inßux event as an occurrence when the estimated by visual examination of the plotted data observed number of aster leafhoppers at a Þeld was

(i.e., f1[di] versus di). Predictions (PI) of leafhopper greater than the allowable number of leafhoppers cal- infectivity, and associated standard errors (SE) of the culated using predetermined AYI values of 25, 50, 75, predictions, were obtained from the Þtted GAMM for and 100, corresponding to high, medium, medium-low, each day in low, typical, and high infectivity years. and low susceptibility, respectively. These AYI values Similar to abundance, infectivity conÞdence intervals represented the nominal thresholds that have been Ϯ were estimated for infectivity as PI 2*SE. Predic- determined based on professional experience and ex- tions and conÞdence intervals were squared and ex- periments examining host plant resistance to aster pressed as percentages before being used in the cal- yellows (Pedigo1989, Foster and Flood 2005). For this culation of the seasonal AYI. calculation, infectivity was allowed to vary across the Aster Yellows Index. The number of aster leafhop- growing season as predicted by model 2 for the typical pers and the associated rate of infectious individuals in growing season. Allowable leafhoppers were calcu- the leafhopper population affect the exposure of a lated as follows: crop to infection by a pathogen and subsequent dis- ϭ ease development. Therefore, the seasonal AY risk was allowable leafhoppersi AYI/PIi deduced using the daily model predictions of aster where i indexes calendar day. Indicator variables were leafhopper abundance (PAi; model 1 above) and in- used to code the occurrence of an event or nonevent fectivity (P ; model 2) into an AYI metric as follows: Ͻ ϭ Ii (allowable leafhoppersi observed leafhoppersi 1; Ͼ ϭ AYI ϭ P * P (model 3) allowable leafhoppersi observed leafhoppersi 0). i Ai Ii The total number and proportion of events in each where i indexes calendar day. The seasonal AYI was magnitude category was calculated for each year, calculated for low, typical, and high abundance and week, farm, and Þeld. A GAMM (family ϭ binomial) infectivity years to represent the range of the observed was used to examine the seasonal probability of de- data. Because the calendar day range of the infectivity tecting leafhopper abundances above an AYI thresh- data set was shorter than the abundance data set, old value given an average seasonal infectivity. These predictions of infectivity after calendar day 228 were models could be represented as follows: estimated as the prediction on day 228. The AYI is Ϸ ␮ essentially an assessment of the potential annual and Yi͑abc͒ Binomial( i[abc],ni[abc]) (model 4) seasonal risk of crop exposure to aster leafhoppers “ ” g(␮ )ϭlogit(␮ ) ϭ f(d ) ϩ ␧ ϩ ␧ ϩ ␧ capable of transmitting AYp. In the results section, we i[abc] i[abc] i i(a) i(b) i(c) 2 deÞne the relative exposure “risk” as the exposure of ␧i͑a͒ Ϸ N͑0, ␴ a͒, the carrot crop to infectious leafhoppers relative to the ␧ Ϸ ͑ ␴2 ͒ exposure of some reference group, usually the low- i͑b͒ N 0, b , exposure AYI. For example, holding infectivity con- 2 ␧ ͑ ͒ Ϸ N͑0, ␴ ͒, stant, a Þeld with an abundance of two leafhoppers i c c

(per 100 sweeps) has a twofold greater exposure to where f(di) was a smoothing function (penalized cu- infectious leafhoppers than a Þeld with an abundance bic regression spline) of calendar day (di). In this ␧ ␧ ␧ of one leafhopper (per 100 sweeps). Similarly, a Þeld model, i(a), i(b), and i(c) corresponded to the ran- in which the rate of infectious leafhoppers is 3% has a dom effects term year, farm, and Þeld, respectively. As threefold greater exposure to infectious leafhoppers described above, this model was speciÞed using the than a Þeld in which only 1% of the leafhoppers are gamm4 function in the gamm4 package (Wood 2006).

capable of transmitting AYp. The component smooth function, f1(di), on the scale Correlation of Abundance and Infectivity. The cor- of the response, was plotted versus calendar date (di) relation between annual estimates of leafhopper to represent the expected seasonal probability of ob- abundance and infectivity was calculated for the years serving aster leafhopper abundance that would in which the data overlapped (2001Ð2008). To exam- prompt a control practice. ine if the average seasonal abundance and infectivity were correlated, a preliminary examination of the Results cross-correlation between leafhopper abundance and infectivity model predictions was conducted. Because Abundance Trends. Annual. Here we chose to pres- infectivity was only measured weekly, model predic- ent year modeled as a Þxed effect because we were tions were extracted for the weeks in which infectivity primarily interested in characterizing the within sea- estimates were available. The cross-correlations were son trends, and the annual trends also can be inferred calculated and plotted versus the weekly time lags. For from the graphical presentation of the annual means example, a time lag of Ϫ2 represents the correlation of (Fig. 1). Overall, the average annual aster leafhopper the weekly abundance predictions with weekly infec- abundance decreased from 2001 to 2011, but also var- tivity predictions from 2 wk earlier in the growing ied greatly among years with average abundance rang- season. Thus, the peaks in the cross-correlations rep- ing from 0.08 to 1.27 insects per 25 pendulum sweeps June 2013 FROST ET AL.: PATTERNS OF LEAFHOPPER ABUNDANCE AND INFECTIVITY 495

Table 1. Model coefficients (model 1) estimated by fitting fixed effects for year and a smooth function of calendar day (day of year) to aster leafhopper abundance

Abundance Parameters ͉z-value͉ P value estimate Ϯ SE Fixed ␤ 2001 0.17 (0.20) 0.85 0.39 ␤ 2002 0.24 (0.20) 1.17 0.24 ␤ Ϫ Ͻ 2003 0.75 (0.21) 3.49 0.001 ␤ 2004 0.14 (0.20) 0.72 0.47 ␤ Ϫ Ͻ 2005 0.58 (0.22) 2.66 0.008 ␤ Ϫ Ͻ 2006 0.69 (0.20) 3.35 0.001 ␤ Ϫ 2007 0.52 (0.21) 2.48 0.013 ␤ Ϫ Ͻ 2008 1.04 (0.21) 4.97 0.001 ␤ Ϫ Ͻ 2009 2.28 (0.22) 9.91 0.001 ␤ Ϫ Ͻ 2010 2.55 (0.21) 11.36 0.001 ␤ Ϫ 2011 1.03 (0.21) 2.32 0.02 Smooth df Chi-square P value s(calendar day) 6.57 109 Ͻ0.001 Random ␴ a 0.38 ␴ b 0.19 ␴ a,b 0.99 ␴ c 0.93

Year effects represent the average annual leafhopper abundance (per 25 sweeps) on the scale of the linear predictor (i.e. loge). P values are approximate. R-sq. (adj.) ϭ 0.15.

each year (not shown); however, the overall pattern observed by year was described by the simpler model. In addition, we presented the simpler model here because it was more consistent with the objectives of our study to determine if, on average, there was a critical time interval when crop protection is most Fig. 1. A) Model coefÞcients (Ϯ2 x SE) representing the needed. average annual leafhopper abundance (log-scale) for 2001 Infectivity Trends.Annual. Similar to abundance, through 2011. B) Component smooth function representing aster leafhopper infectivity varied among years and the trend of aster leafhopper abundance as a smooth function the average annual infectivity ranged between 0.09 of calendar day plotted with partial residuals from model 1. and 6.25% (Table 2). Thus, the exposure of the crop to Partial residuals for a well-Þtting model should be scattered evenly around the curve to which they relate. Table 2. Model coefficients (model 2) estimated by fitting fixed effects for year and a smooth function of calendar day (or day of (Table 1; Fig. 1A). This variation implies that, for the year to square root-transformed aster leafhopper infectivity esti- year with the highest observed aster leafhopper abun- mates dance, the exposure of the crop to infectious individuals would be Ϸ16-fold greater than the year with the Infectivity ͉ ͉ Parameters Ϯ t-value P value lowest leafhopper abundance (i.e., holding infectivity estimate SE constant). Fixed ␤ Ͻ 1994 0.12 (0.01) 11.7 0.001 Seasonal. Within year, a plot of the component ␤ Ͻ 1995 0.14 (0.01) 14.4 0.001 smooth function for abundance versus calendar date ␤ Ͻ 1996 0.19 (0.01) 17.8 0.001 ␤ Ͻ indicated that periods of above average aster leafhop- 1997 0.18 (0.02) 11.1 0.001 ␤ Ͻ per abundance occurred between 11 June and 2 1998 0.18 (0.01) 13.2 0.001 ␤ Ͻ 1999 0.10 (0.01) 8.9 0.001 August (Fig. 1B). Holding infectivity constant, the ␤ Ͻ 2000 0.22 (0.01) 19.5 0.001 component smooth function for aster leafhopper ␤ Ͻ 2001 0.13 (0.01) 10.1 0.001 Ϫ ␤ Ͻ abundance ranged from 0.6 to 1.0 (loge-scale), 2002 0.13 (0.01) 10.1 0.001 ␤ Ͻ which represents a Þvefold change in the exposure of 2003 0.08 (0.02) 5.5 0.001 ␤ Ͻ 2004 0.20 (0.01) 13.7 0.001 the crop to infectious leafhoppers over the entire ␤ 2005 0.03 (0.01) 2.8 0.005 growing season and a threefold change in the exposure ␤ Ͻ 2006 0.09 (0.02) 4.2 0.001 ␤ Ͻ of the crop to infectious leafhoppers for the period in 2007 0.10 (0.02) 4.8 0.001 ␤ Ͻ which a pest management practice would be imple- 2008 0.25 (0.03) 8.4 0.001 mented, 1 June through 31 August. Residual plots Smooth df F P value s(calendar day) 5.41 6.6 Ͻ0.001 indicated that some seasonal (within year) trends remained after Þtting model 1. These trends could be P values are approximate. removed by Þtting separate smoothing functions for R-sq. (adj.) ϭ 0.36. 496 ENVIRONMENTAL ENTOMOLOGY Vol. 42, no. 3

pected leafhopper abundance varied between 3 and 9 leafhoppers per 100 sweeps. Similarly, the typical infectivity between May and mid-August ranged from 0.3 to 2.6%, representing a ninefold increase in exposure of carrot to infectious individuals because of seasonality (Fig. 3B). The deduced AYI for the state of Wisconsin incorporates the seasonal variability of leafhopper abundance and infectivity estimates (Fig. 3C). In a typical year, the seasonal AYI ranged from approximately one to eight. In years when both leafhopper abundance and infectivity are high, the expected AYI peaks at 60, although an AYI of 130 is not unex- pected given the error associated with the seasonal trend. Taken together, estimates of a critical “risk window” or timing interval in which aster leafhopper management could be focused, were similar to previous estimates (Table 3; Frost et al. 2012). Correlation of Abundance and Infectivity. There was no correlation between annual estimates of leafhopper abundance and infectivity for the time interval 2001 through 2008 (R ϭ 0.05, P ϭ 0.90). Within a season, the correlation between the aster leafhopper abundance and infectivity was examined using a cross- correlogram (Fig. 4). On average, over the 11-yr term of this data set, within-season leafhopper abundance and infectivity estimates were negatively correlated at time lags that ranged between Ϫ3 and Ϫ5 wk. In contrast, leafhopper abundance and infectivity were positively correlated at time lags of 0Ð3 wk. Frequency of Occurrence of Aster Leafhopper In- ﬂuxes. Over the 11 yr of this data set, there were 583 occasions when the observed aster leafhopper abundance (an “inßux” event) was greater than the allow- Fig. 2. A) Model coefÞcients (Ϯ2 x SE) representing the able abundance (established AYI thresholds) given an average annual leafhopper infectivity (square root-trans- average expected seasonal infectivity (Table 4). This formed) for 1994 through 2008. B) Component smooth func- corresponds to Ϸ1.8 events per Þeld across all years. tion representing the trend of aster leafhopper infectivity as However, the highest number of inßux events (146) a smooth function of calendar day plotted with partial residuals. occurred in 2002, corresponding to an average of 4.4 events per Þeld. In contrast, only one inßux event occurred in 2009 for all 25 Þelds sampled correspond- infectious leafhoppers in the year with the highest rate ing to an estimated 0.04 events per Þeld. In general, the of infectivity was 75-fold higher than the year with the number of inßux events has decreased since 2001, lowest rate of infectivity (i.e., if leafhopper abundance which is consistent with the trend of the annual leaf- was held constant). Visually, there was no overt trend hopper abundance over the same time, and the mag- in the average annual leafhopper infectivity from 1994 nitude of the aster leafhopper events also has de- to 2008 (Fig. 2A). creased. Among farms, inßux events, determined as a Seasonal. Within year, plots of the component smooth function of infectivity versus calendar date proportion of total observations, were not evenly dis- indicated that periods of above average infectivity tributed and ranged from 3.5 to 29.5% (Table 5). occurred between 27 May and 13 July (Fig. 2B). Sim- Moreover, predicted inßux events were not evenly ilar to leafhopper abundance, residual plots indicated distributed throughout the growing season (Fig. 5). that some seasonal (within year) trends remained The seasonal dynamics of the probability of observing after Þtting model 2. These trends were removed by an inßux event was similar to the pattern observed for Þtting separate smoothing functions for each year (not the expected seasonal abundance with the peak prob- shown). Our rational for presenting the simpler model ability (14.8%) of observing any event occurring on 2 remains as stated previously. July (184). However, the timing of the peak proba- Seasonal Aster Yellows Risk. From May to August, bility of observing an inßux event was earlier for larger the typical (or expected) seasonal aster leafhopper inßux events. For example, the peak probability of abundance ranged from 1 to 3 leafhoppers per 100 observing an event with an associated AYI of 25, 50, 75, sweeps (Fig. 3A). However, for the year with the and 100, occurred at 11 July (193), 5 July (187), 1 July highest observed leafhopper population sizes, the ex- (183), and 26 June (178), respectively. June 2013 FROST ET AL.: PATTERNS OF LEAFHOPPER ABUNDANCE AND INFECTIVITY 497

Fig. 3. A) Model 1 prediction representing the average seasonal leafhopper abundance. Predictions are on the data scale and have been multiplied by four to obtain an estimate of abundance per 100 pendulum sweeps. y (square root-transformed) for 1994 through 2008. B) Component smooth function representing the trend of aster leafhopper infectivity as a smooth function of calendar day plotted with partial residuals. C) Expected aster yellows index (AYI) values in example seasons with high, typical, and low estimated AYI values computed from abundance and infectivity estimates. 498 ENVIRONMENTAL ENTOMOLOGY Vol. 42, no. 3

Table 3. Windows of risk for aster yellows phytoplasma spread

Calendar day Window length Model source Start End Aster leafhopper abundance 163 214 51 Frost et al. (2012) 11 June 1 Aug. 163 214 51 Current paper (model 1) 11 June 1 Aug. Aster leafhopper infectivity 140 197 57 Frost et al. (2012) 19 May 15 July 145 196 51 Current paper (model 2) 24 May 14 July AYI 155 210 55 Current paper (model 2) 3 June 28 July

Discussion Þtness is increased in aster leafhopper individuals infected by AYp (Beanland et al. 2000, Sugio et al. 2011), For aster yellows management in Wisconsin, the but it is not known how the increase in Þtness trans- aster yellows index combines insect vector abundance lates to the in-Þeld population dynamics of infectious and transmission capability to describe the maximum insects. Our correlations were calculated using the 8 allowable numbers of infectious leafhoppers that can be tolerated on a susceptible crop, providing a an yr of data for which we had estimates of both abun- indication of when crop protection is most needed dance and infectivity. However, it is likely that a Þt- (Chapman 1971, 1973). In this paper, we used a mul- ness effect may appear in the Þeld as a lagged corre- tiyear data set, and multilocation modeling approach, lation (i.e., high abundance lags high infectivity by a to generate reliable estimates for the expected sea- year or two) and this would require many more paired sonal patterns of aster leafhopper abundance and annual estimates of leafhopper abundance and infec- transmission capability, or infectivity. The predicted tivity than are currently available. seasonal leafhopper abundance and infectivity then Nevertheless, a year when the aster leafhopper is were used to deduce a seasonal aster yellows index to abundant and a high proportion of the population is represent the expected seasonal exposure to infec- capable of transmitting AYp may occur with a rela- tious aster leafhoppers that may spread AYp to a sus- tively low frequency, and many years may pass before ceptible crop. In addition, we used the expected sea- the two conditions coincide (Magnuson 1990). As- sonal infectivity to retrospectively determine the suming independent events and using previous esti- frequency at which the observed insect abundance mates of among year variability, i.e., loge(abun- Ϸ Ϫ 2 Ϸ would have exceeded the established AYI thresholds, dance) N( 0.82, 0.860 ) and sqrt(infectivity) 2 resulting in an insecticide application. N(0.14, 0.047 ); (Frost et al. 2012), we simulated val- Annual Trends of Abundance and Infectivity. The ues for the average annual leafhopper abundance and exposure of a carrot crop to infectious aster leafhop- infectivity. These abundance and infectivity values pers was Ϸ16-fold greater in the year with the highest then were back-transformed and used to calculate AYI observed aster leafhopper abundance than the year values to represent 1,000 yr. We found that an average with the lowest leafhopper abundance. Similarly, the annual AYI that was 2-, 3-, 5-, and 10-fold greater than exposure of the crop to infectious leafhoppers was the median annual AYI would occur, on average, every 70-fold higher in the year with the highest rate of 4, 6, 13, and 53 yr, respectively. These time estimates infectivity when compared with the year with the may represent a null model from which the assump- lowest rate of infectivity. Taken together, these ob- tion of independence among years may be tested. servations suggest that years in which high aster leaf- Finally, the high among year variability of abundance hopper abundance occurs co-incidentally with high and infectivity is also consistent with the hypothesis infectivity can result in 1000-fold greater exposure of that the spring migration of the aster leafhopper con- the carrot crop to infectious leafhoppers when com- tributes to the annual risk of AY epidemics in Wis- pared with years in which low leafhopper abundance consin. However, the large annual variability in the is co-incident with low infectivity. These observations rate of infectious leafhoppers further suggests that, in are consistent with the large annual variability of aster addition to weather events affecting insect trajectories yellows pressure reported previously in Wisconsin and deposition (Hurd 1920, Huff 1963, Westbrook and (Chapman 1971, 1973; Mahr et al. 1993) and consistent Isard 1999, Zhu et al. 2006), the prevalence and het- with our previous Þndings of large annual variability of erogeneity of AYp-infected feeding hosts of the leaf- leafhopper abundance and infectivity (Frost et al. hopper in the landscape along the migratory route, or 2012). acquisition trajectory, may also be important factor We found a low correlation among annual estimates contributing to annual infectivity (Carter 1961, Lee et of aster leafhopper abundance and infectivity, which al. 2003). would support the hypothesis that aster leafhopper Seasonality of Abundance and Infectivity. Aster abundance and infectivity are independent quantities leafhopper abundance varied by approximately three- varying among years. It has been determined that fold during the period of the growing season in which June 2013 FROST ET AL.: PATTERNS OF LEAFHOPPER ABUNDANCE AND INFECTIVITY 499

Table 5. Number of inﬂux events, on each farm, when the observed ALH abundance was greater than estimated allowable abundance calculated using an AYI of 25, 50, 75 and 100, given the average seasonal infectivity

Location AYI 25 AYI 50 AYI 75 AYI 100 No. events No. obs. Farm 1 142 44 14 11 211 2,060 Farm 2 18 6 8 11 43 146 Farm 3 120 47 23 53 243 1,029 Farm 4 2 1 0 0 3 17 Farm 5 32 11 10 6 59 409 Farm 6 30 3 0 1 34 970 Totals 334 112 55 82 583 4,631

senophonus phytopathogenicus,Õ in the population. A key difference between the system that Bressan et al. Fig. 4. Cross-correlation of the weekly abundance and (2011) described and our system is that transovarial, rate of infectious leafhoppers for the average season. or vertical, transmission is not known to occur in our system. Bressan et al. (2011) also measured the proportion of insects carrying the pathogen, which may pest management normally would be implemented. In be different than the proportion of infectious plan- addition, the exposure of the carrot crop to infectious thoppers. leafhoppers within a year varied by as much as nine- In Wisconsin, a dilution of aster leafhopper infec- fold. Taken together, carrot growers can expect the tivity may occur as aster leafhoppers that overwinter relative exposure to infectious aster leafhoppers to in Wisconsin as eggs (local population) begin to vary by as much as 30-fold throughout the growing emerge from their habitats in early June, although we season. However, the periods during which aster leaf- have no direct evidence for this phenomenon (Drake hopper abundance and infectivity tend to be above and Chapman 1965). A preliminary examination of the average often overlap within the growing season. cross-correlation of the average within season dynam- Thus, without information about insect abundance ics of aster leafhopper abundance and infectivity and infectivity for a speciÞc Þeld, the coincidence of showed that infectivity was negatively correlated with these expected periods of high population sizes and abundance with a Ϫ3toϪ5 wk lag. This examination increased infectivity represent a timing interval in is counterintuitive given that leafhopper Þtness is in- which management of the insect could be focused to creased by AYp infection (Beanland et al. 2000, Sugio limit pathogen spread. et al. 2011). However, the negative correlation could Within a season, however, it is unlikely that aster be because of grower management resulting from leafhopper abundance and infectivity are indepen- plant protection practices implemented when infec- dent. In theory, the mixing of aster leafhopper populations with differing proportions of infectious aster leafhoppers can result in variable proportions of infectious leafhoppers in the population. Recently, Bres- san et al. (2011) described a pathosystem in which the abundance of the planthopper, Pentastiridius leporinus (Linnaeus), was directly related to the proportion of individuals carrying the pathogen, ÔCandidatus Ar-

Table 4. Number of inﬂux events, in each year, when the observed ALH abundance was greater than the allowable abundance as calculated using an AYI of 25, 50, 75 and 100, provided an average seasonal infectivity estimate

No. No. No. Year AYI 25 AYI 50 AYI 75 AYI 100 events Þelds obs. 2001 78 28 15 23 144 34 438 2002 69 31 16 30 146 33 478 2003 25 6 2 2 35 21 309 2004 52 13 7 14 86 34 548 2005 33 1 2 1 37 26 386 2006 28 15 8 4 55 35 477 2007 28 6 2 2 38 31 415 2008 15 9 2 5 31 34 420 2009 1 0 0 0 1 25 315 2010 3 0 0 0 3 28 403 Fig. 5. The seasonal probability of detecting leafhopper 2011 12 3 1 1 17 35 442 abundances above an AYI of 25, 50 75, or 100 given an average Total 344 112 55 82 593 336 4,631 seasonal infectivity. 500 ENVIRONMENTAL ENTOMOLOGY Vol. 42, no. 3 tivity is known to be high. Leafhopper abundance was The number of occurrences when a spray would be positively correlated with infectivity at a 0Ð3-wk lag, recommended has decreased since 2001, which is con- suggesting that higher vector abundance results in sistent with the overall decrease in the annual aster AYp spread and higher rates of infectious leafhoppers leafhopper abundance occurring over this time pe- in subsequent generations, an observation consistent riod. In addition, the number of occurrences when a with the Þndings of Sisterson (2009). In general, more spray would be recommended varied among farm (or research is needed to examine the within season re- farm locations). This variation could be because of the lationship between leafhopper abundance and infec- differential inßuence of the landscape surrounding tivity in the Þeld to determine the reasons for the each farm and its effect upon the reproductive capa- correlations at time lags of 3Ð4 wk, which is approx- bility of the aster leafhopper in these local environ- imately the generation time of the aster leafhopper ments. For example, the aster leafhopper uses over 300 (Mahr 1989). Thus, the emergence of local popula- different plant species for food, oviposition, and shel- tions, or offspring from early migratory populations, ter (Lee and Robinson 1958, Wallis 1962, Peterson variability in birth and death rates between inocula- 1973), and the distribution and abundance of these tive and noninoculative individuals, and insect man- species surrounding each farm likely differs. It could agement practices could all contribute to the observed also be due, in part, to differences in grower manage- phase shifts between abundance and infectivity within ment of their crop where some growers tolerate the growing season. Frequencies and Magnitudes of Aster Leafhopper higher insect abundances before applying an insecti- Inﬂuxes. The arrival of large numbers of insects into cide; some growers tolerate a modest, but ephemeral a Þeld (or an inßux) has the potential to affect epi- leafhopper population prior an insecticide applica- demic progression, and the spring migration of the tion, and Þnally other growers may prefer to apply aster leafhopper has long been considered the prin- more regular, prophylactic insecticide treatments that ciple source of early season infectious aster leafhop- functionally maintain low leafhopper populations. pers (Chiykowski and Chapman 1965, Hoy et al. 1992). Management Implications. Control of pathogens We have deÞned a leafhopper inßux as an aster leaf- transmitted by insects in a persistent manner tends to hopper abundance that exceeds a predetermined AYI be less difÞcult than pathogens transmitted nonper- threshold, given the expected seasonal aster leafhop- sistently (Chapman 1973, Madden et al. 2000). The per infectivity. The selected AYI thresholds are used decision to intercede and implement a pest control by growers to make spray decisions and are nominal action requires an understanding of the level of insect thresholds determined by practitioner experience and infestation a crop can tolerate without incurring eco- known cultivar resistance to aster yellows (Pedigo nomic loss (Pedigo 1989). In Wisconsin, growers cur- 1989, Foster and Flood 2005). We found that the rently achieve adequate control of aster yellows in occurrence of insect inßuxes that would elicit an in- their crops using repetitive applications of insecticidal secticide application coincided with the period of time compounds in the synthetic pyrethroid group. In a when higher numbers of leafhoppers are expected. given year, it is common for as many as Þve to seven Our approach did not distinguish between an increase applications of an insecticide to be applied on a 7Ð10-d of insect numbers occurring because of population calendar schedule on the same crop. In part, the ra- growth, local insect movements, or long-distance in- tionale behind these repetitive spray applications re- sect movements. However, the probability of inßux sults from the fact that a single insecticide application events occurring in May is lower than the probability is inexpensive when compared with newer, reduced- of inßuxes occurring in mid to late-June. A similar risk, and less broad spectrum insecticides that target phenology has been observed for Circulifer tenellus fewer hemipterous pests. (Baker) (beet leafhopper), Psammotettix alienus (Da- In this study, we identiÞed a timing interval in which hib) (European grass-feeding leafhopper), and Gra- management of the aster leafhopper could be focused. phocephala atropunctata (Signoret) (blue-green This interval, or risk window results from the coin- sharpshooter), all of which achieve peak abundance in “ ” cidence of above average leafhopper abundance and June followed by population declines in late July and August (Lindblad and Areno 2002, Munyaneza et al. higher observed infectivity. In addition, because this 2010, Gruber and Daugherty 2012). It may be that timing interval occurs early in the season, there exists these leafhoppers all overwinter, or diapause, in the the opportunity to deploy newer, reduced-risk, sys- same life stage (i.e., eggs) and have to develop through temic insecticides with ßexible application methods a similar number of instar stadia (i.e., four to Þve (i.e., seed treatments, in-furrow, or layby incorpora- instars), leading to a reasonably synchronous emer- tion) as potential pest management alternatives for gence as adults among species. Alternatively, the oc- long-term control of the aster leafhopper. When com- currence of the observed leafhopper inßuxes begin- pared with current management practices (and if suc- ning in early June may be because of the emergence cessful), the use of these new insecticides and asso- and dispersal of the local leafhopper population from ciated delivery systems have the potential to increase their overwintering host (i.e., winter wheat, Triticum the sustainability and proÞtability of carrot produc- aestivum L.) to more succulent irrigated vegetable tion, enhance natural enemy populations and biolog- crops present in the landscape at that time (Carter ical control, and reduce adverse effects on farm work- 1961). ers and applicators, as well as the local environment. June 2013 FROST ET AL.: PATTERNS OF LEAFHOPPER ABUNDANCE AND INFECTIVITY 501

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