Phenology of First and Peak Emergence of Aphthona Lacertosa

Biological Control 37 (2006) 382–391 www.elsevier.com/locate/ybcon

Phenology of ﬁrst and peak emergence of Aphthona lacertosa and A. nigriscutis: Two ﬂea beetles introduced for biological control of leafy spurge, Euphorbia esula L.

Luke C. Skinner a,b,*, David W. Ragsdale b, Richard W. Hansen c, Monika A. Chandler d, Greg Spoden a

a Minnesota Department of Natural Resources, 500 Lafayette Road, St. Paul, MN 55155, USA b Department of Entomology, 1980 Folwell Ave., University of Minnesota, St. Paul, MN 55108, USA c National Weed Management Laboratory, USDA-APHIS-PPQ-CPHST, 2301 Research Blvd., Ft. Collins, CO 80526, USA d Minnesota Department of Agriculture, 90 West Plato Blvd., St. Paul, MN 55107, USA

Received 13 September 2005; accepted 13 January 2006 Available online 24 February 2006

Abstract

Nonlinear models were used to estimate first emergence and peak abundance dates for Aphthona lacertosa Rosenhauer and A. nigriscutis Foudras, two flea beetles introduced to control leafy spurge, Euphorbia esula L., in North America. For model development, 26 field sites were sampled for flea beetle abundance at weekly intervals for eight weeks in three western Minnesota counties in 2000, 2001, and 2002. A three-parameter Weibull function, fit to observed cumulative probability distributions, were used to predict accumulated degree-days (ADD) to first emergence. Bias testing indicated the Weibull function provided a useful estimate of first emergence for A. lacertosa (304 ADD, lower developmental threshold 7.5 °C), but failed to produce a useful estimate for A. nigriscutis. A third-order polynomial was used to approximate seasonal abundance and predict peak abundance for each species. Estimated ADD to peak abundance of A. lacertosa was 594 ± 24 (DD > 7.5 °C) and 670 ± 15 (DD > 9.3 °C) for A. nigriscutis. Models were validated with additional data sets from Minnesota, Montana, and North Dakota. Estimated date of peak emergence provided useful predictions of peak emergence for Minnesota and North Dakota, but failed to predict peak emergence in Montana. We speculate that variation in climate and environmental conditions between Midwestern states and Montana were responsible for differing emergence patterns. We conclude that phenology models should be developed regionally to provide useful predictions of peak emergence for land managers. Maps were developed for Minnesota to spatially display predicted dates of peak abundance for A. lacertosa and A. nigriscutis. Ó 2006 Elsevier Inc. All rights reserved.

Keywords: Aphthona lacertosa; Aphthona nigriscutis; Biological control; Leafy spurge; Euphorbia esula; Phenology

1. Introduction 1979) and reduced carrying capacity of grazing lands (Hein and Miller, 1992; Kronberg et al., 1993; Trammell and But- Leafy spurge, Euphorbia esula L., is an invasive perenni- ler, 1995), leading to an economic loss of $130 million al plant that has become established on more than 535,000 annually (Leitch et al., 1994). hectares of rangeland in the upper Great Plains of the Unit- Management of leafy spurge with chemical, cultural, ed States (Dunn, 1979; Leitch et al., 1994). Impacts caused and mechanical control methods usually do not provide by leafy spurge include reductions of native plant species long-term control and are very expensive (Lym and Mes- (Belcher and Wilson, 1989; Steenhagen and Zimdahl, sersmith, 1987, 1994). A biological control program began for leafy spurge in the 1960’s, culminating in 12 insect spe- * Corresponding author. Fax: +1 651 296 1811. cies introduced into the United States and 17 insect species E-mail address: [email protected] (L.C. Skinner). introduced into Canada (Nowierski and Pemberton, 2002).

As a group, the flea beetles have been the most success- accumulated degree-days to peak emergence for A. nigri- ful agents in terms of establishment and control. Two flea scutis, also based on air temperature. Their results again beetles, Aphthona lacertosa and A. nigriscutis (Coleoptera: confirm that the historical and sine wave methods provide Chrysomelidae), have become widely established and have acceptable predictions of peak emergence of A. nigriscutis. contributed to the reduction of leafy spurge populations Currently, the FLEA BEETLE program has been updated (Kalischuk et al., 2004; Kirby et al., 2000; Larson and (Version 2.0) to provide an estimate of degree-days accu- Grace, 2004; Lym and Nelson, 2000). These two species mulated towards peak emergence (http://w3.uwyo.edu/ are commonly collected and redistributed to other leafy ~dlegg/fleabeetle.html). spurge infested land throughout the United States and Alternatively, Skinner et al. (2004) experimentally deter- Canada (Bourchier et al., 2002; Hansen et al., 1997). mined actual lower developmental thresholds for A. lacer- Phenological models can provide an understanding of tosa (7.5 °C) and A. nigriscutis (9.3 °C) in laboratory key events in an insects life cycle that are important to experiments. The lower developmental threshold was deter- researchers and land managers alike. Phenological events mined by measuring developmental time required for third such as first emergence and peak abundance, enable instars (diapausing) to emerge as adults at various constant researchers to assess insect populations effectively as well temperatures. Legg et al. (2002a) suggest that controlled as provide land managers with information on the best laboratory experiments are the preferred method for esti- time to collect biological control agents for redistribution, mating lower developmental thresholds and required or to make other management decisions such as when to degree-days if insects can be reared in captivity. In burn, graze or apply herbicides if multiple control methods addition, Skinner et al. (2004) developed accumulated are to be utilized. degree-day models using the actual lower developmental Insect phenology models are commonly used to predict thresholds to predict first emergence for A. nigriscutis and key events in both pests and beneficial insects (Higley et al., A. lacertosa based on soil temperature. 1986; Legg et al., 2002a; Pruess, 1983; Wagner et al., Previous A. nigriscutis phenological models have used air 1984a). Phenology models use daily accumulation of heat temperature and functional lower developmental thresholds units above a lower developmental threshold; the latter to predict first and peak emergence. No phenological mod- being unique for each insect species. The sum of daily heat els exist based on experimentally measured lower develop- units until the phenological event occurs is defined as the mental thresholds coupled with field collected data of ‘‘accumulated degree-days.’’ Aphthona spp. abundance. Here, we report the first pheno- For Aphthona nigriscutis, Legg et al. (2002a) used simula- logical model to predict peak emergence of A. lacertosa. tion models to estimate the functional lower developmental One objective was to determine whether acceptable phe- threshold and accumulated degree-days to first emergence. nological models to predict first and peak emergence of A. A functional lower developmental threshold is an estimate lacertosa and A. nigriscutis could be developed based on of the lower developmental threshold and is derived depen- experimentally determined lower developmental thresholds dently with accumulated degree-day model estimates using estimates (Skinner et al., 2004). Second, we compare and field collected data over multiple years or locations. Func- contrast our A. nigriscutis phenology model with model tional lower developmental thresholds may be biased, i.e., results developed by Legg et al. (2002a, 2003). Our third lower or higher than the true developmental threshold since objective was to develop maps that spatially display aver- they are not derived experimentally. It is often impractical to age dates of peak abundance for A. lacertosa and A. nigri- measure the actual developmental threshold and as such, scutis using historical weather data (30-year average) as a functional lower developmental thresholds are used in devel- resource for land managers. opment of accumulated degree-day models to estimate when specific phenological events occur. Legg et al. (2002a) com- 2. Materials and methods pared five different methods of calculating degree-days and suggested that the historical and sine wave methods provided 2.1. Field sampling the best estimates of accumulated degree-days to first emergence for A. nigriscutis. In choosing research sites, we wanted two locations Legg et al. (2002a) incorporated their estimates of medi- within Minnesota that were separated by enough distance an functional lower developmental threshold (2.56 °C) to experience different temperature regimes captured by and median required degree-days (1189.2 °C) to first emer- separate local weather stations. Second, we wanted each gence (historical method) into a computer program called location to have multiple sites with leafy spurge where A. FLEA BEETLE. Data needed for the FLEA BEETLE lacertosa and A. nigriscutis had been released and estab- program is air temperature. The program calculates and lished. When data is pooled from multiple sampling sites sums accumulated degree-days and displays the results as within a single location, this reduces location variability a percentage of degree-days that has accumulated toward caused by microclimate. Based on these two criteria, 26 first emergence of A. nigriuscutis. sites were chosen in three western Minnesota counties, Similarly, Legg et al. (2003) used simulation models to including 16 sites in Clay County, eight sites in Otter Tail estimate functional lower developmental thresholds and County, and two sites in Becker County. The Becker and 384 L.C. Skinner et al. / Biological Control 37 (2006) 382–391

Clay County sites were considered part of the same loca- accumulated degree-days to date of first emergence. This tion due to their close proximity of less than 20 km. The was carried out by regressing cumulative relative insect fre- eight sites in Otter Tail County were considered a separate quency on accumulated degree-days for combined loca- location as they were more than 130 km from the Clay and tion-years for A. lacertosa and A. nigriscutis. A separate Becker County sites and used a different local weather three-parameter Weibull function (Eq. 2) was fit to the station. cumulative probability distribution for A. lacertosa and Aphthona spp. were sampled weekly beginning the first A. nigriscutis. week of June for 8 weeks during 2000, 2001, and 2002 to capture seasonal abundance, including first and peak emer- F ¼ 1 expðððx cÞ=gÞbÞ; ð2Þ gence. Sites were systematically sampled by walking a predetermined ‘‘W’’ pattern (Buntin, 1994) throughout leafy where F = cumulative insect frequency at accumulated spurge infestations, and taking a sweep net sample every degree-days (x), gamma (c) = the expected accumulated 20 paces. A different direction of the predetermined pattern degree-days at the onset of first emergence, eta (g) = con- was taken on each successive sampling date to reduce sam- stant for rate of emergence, and beta (b) = parameter for pling bias. All sites were sampled between 10 a.m. and 4 shape. Estimated parameters and standard errors were cal- p.m. to reduce the variability in patterns of Aphthona culated through nonlinear regression (SAS Institute, 1999). spp. activity due to light, temperature, wind, etc. Using a We used the PROC NLIN, DUD method, to carry out the standard 38-cm diameter sweep net and 10 sweeps per sam- analysis as described by Wagner et al. (1984b) and Nowa- ple, a designated number of samples (6, 8 or 10) were con- tzki et al. (2002). Estimated values of gamma (c) were used sistently taken at each site based on the size of the spurge as first emergence model predictions. infestation at each site. More samples were taken at large sites to adequately cover the area infested with leafy 2.3. Peak emergence model spurge. To accurately identify and count each A. lacertosa and A. nigriscutis, collected beetles were placed in plastic For each species, data from the six location-years were bags and frozen until they could be separated by species pooled to plot relative frequency of capture versus accumu- and counted. The relative frequency of capture for each lated degree-days. A curvilinear, third-order polynomial species was calculated for each site and sampling date in was fit to approximate seasonal abundance. The accumu- each year. Relative frequency of capture was averaged lated degree-days to peak abundance were then calculated for each sampling date, by year and county. The 2 Becker to estimate for the maximum values (relative frequencies of and 16 Clay county sites were pooled as one location called capture) on the fitted curves. Regression coefficients, esti- ‘‘Clay’’ and the 8 Otter Tail county sites were pooled and mates, and standard errors were all calculated using Arc called ‘‘Otter Tail.’’ This produced 6 location-years as statistical regression software (Cook and Weisberg, 1999). described by Legg et al. (2002b), and are designated as Clay Regression parameters were tested for significance from 2000, Clay 2001, Clay 2002, Otter Tail 2000, Otter Tail zero using a t test. 2001, and Otter Tail 2002. Accumulated degree-days were calculated using previ- 2.4. Evaluation of model predictions ously determined lower developmental threshold (LDT) for A. lacertosa (7.5 °C) and A. nigriscutis (9.3 °C) (Skinner A total of 12 additional data sets for A. lacertosa and et al., 2004), local weather station daily temperature data, seven for A. nigriscutis were obtained from Minnesota, and calculated using the historical method (Eq. 1). Montana, and North Dakota. Six sites in Minnesota were X sampled in 2002 to evaluate the models and included one DD ¼ððMaxTemp MinTemp=2ÞLDTÞ; ð1Þ site in six different Minnesota counties including Crow P Wing, Hennepin, Otter Tail, Pope, Redwood, and Scott where DD is the sum or accumulation of degree-days Counties. For the Minnesota sites, the closest weather sta- over time, MaxTemp and MinTemp are daily maximum and tion was the source of daily temperature data. Each site minimum temperatures, respectively, and LDT was the was sweep-sampled weekly for 8 weeks, beginning the first experimentally derived lower developmental threshold. week of June. A. nigriscutis was present in only three of the Daily maximum and minimum temperatures were obtained six locations in Minnesota. from weather stations based on geographic region and year In Montana, data sets were obtained from two locations sampled. One weather station was used for the Clay and over a several years. Two data sets for A. lacertosa were Becker County sites combined and a second used for the collected Story Hills in Gallatin County. The second loca- Otter Tail County sites. tion, the Webber site in Sweet Grass County, was sampled in 1994–1995 for A. lacertosa and 1993–1995 for A. nigri- 2.2. Model for first emergence scutis. For both Montana sites, Omnidata weather stations recorded air temperatures during sampling years. Each For each species, a three-parameter Weibull function fit site was sweep-sampled weekly from June through to a cumulative probability distribution was used to relate September. L.C. Skinner et al. / Biological Control 37 (2006) 382–391 385

Additional data sets from two sites in North Dakota were used as input to the models. Adjusted normal (30-year were obtained from (Jordan, 1999). These two locations average) air temperature data were available from the in Barnes County were sweep-sampled every 5–7 days National Oceanic and Atmospheric Administration throughout the growing season in 1997. Air temperature (NOAA) for 172 weather stations across Minnesota. Utiliz- data from the closest weather station in Jamestown, North ing a methodology developed by the Minnesota State Dakota, were used for both sites. Climatology Office (unpublished adaptation of Thom, To evaluate date of first emergence, we compared the 1954) normal monthly air temperature data were used to observed date of first capture with predicted dates of first calculate normal ADD. The average Julian date of peak emergence. For each species, predicted date of first emer- abundance was determined for each weather station based gence was determined by the date the first emergence esti- on our estimates of ADD to peak abundance for each mate was reached for each location, based on ADD from species. Using Surfer Geographic Information System air temperature. Departures were calculated by subtracting software (Keckler, 1994), we incorporated the Julian date observed dates from the predicted dates. We used the values and associated weather station coordinates to MEANS procedure (paired t test) (SAS Institute, 1999)to display average peak abundance dates in mapped form. test whether average days of departure were significantly different from zero for each state (Legg et al., 2002a). For each 3. Results species, we also compared mean departures between states using GLM and REGWQ procedures (SAS Institute, 1999). A total of 365,000 A. lacertosa and 36,000 A. nigriscutis To evaluate peak emergence estimates, we compared the individuals were collected from all locations over the 3-year observed date of peak capture with predicted dates of peak period. Sufficient populations (>1 insect per sweep at peak abundance. For each species, predicted date of peak abun- emergence) of A. lacertosa were collected from all 26 sites. dance was determined by the date the peak abundance esti- Only the Clay location (12 sites) had sufficient populations mate was reached for each location, based on ADD from of A. nigriscutis. With 3 years of sampling, there were six air temperature. Departures were calculated by subtracting location-years for A. lacertosa and three for A. nigriscutis. observed dates from the predicted dates. We used the MEANS procedure(pairedttest)(SASInstitute,1999)totest 3.1. First emergence model whetheraverage daysofdeparture were significantly different from zero for each state (Legg et al., 2002a). For each species, Cumulative probability distributions of flea beetle abun- we also compared mean departure days between states using dance versus ADD are shown for A. lacertosa and A. nig- GLM and REGWQ procedures (SAS Institute, 1999). riscutis in Fig. 1. The Weibull function parameters including gamma (c), the estimate for first emergence, are 2.5. Model comparison summarized in Table 1. Estimated degree-days to first emergence for A. lacertosa and A. nigriscutis were We compared our prediction of A. nigriscutis peak emer- 304 ± 54 and 127 ± 119, respectively. The fits of the non- gence with predictions made by Legg et al. (2003). We used linear curves were similar with R2 values of 0.98 and 0.99 the authors’ published estimates (referred to as the Legg for A. lacertosa and A. nigriscutis, respectively. model) of median functional lower developmental threshold (4.79 °C) and accumulated degree-days to peak emer- 3.2. Peak emergence model gence (775.8, historical method). These estimates were shown to be acceptable and the program FLEA BEETLE Pooled location-year data and derived nonlinear models (Ver. 2.0) currently uses the historical method in calculat- describing proportional seasonal abundance versus ADD ing its predictions. We compared the observed date of peak for A. lacertosa and A. nigriscutis are shown in Fig. 2. capture with predicted dates of peak abundance base on Six location-year data sets were used in A. lacertosa model the Legg model using the same data sets described in Sec- development, but because of low abundance in some sam- tion 2.4. We used the MEANS procedure (paired t test) pling sites only three location-years were used for A. nigri- (SAS Institute, 1999) to test whether average days of depar- scutis. Model equations and R2 values for curvilinear ture were significantly different from zero for each state. models are provided in Table 2. Estimated ADD to peak We also compared mean departure days between our mod- abundance of A. lacertosa was 594 ± 24 above a LDT of el and the Legg model for Montana and Minnesota using 7.5 °C. The estimated accumulated degree-days to peak GLM and REGWQ procedures (SAS Institute, 1999). abundance of A. nigriscutis was 670 ± 15 above a LDT of 9.3 °C. Estimates and standard errors for predictions 2.6. Model application using historical climate data of ADD at peak abundance are listed in Table 2.

With the models developed for predicting peak emer- 3.3. Evaluation of first emergence estimates gence, we applied the models using Minnesota historical climate data, in order to present long-term average peak Observed dates of first capture, predicted dates of first abundance dates statewide. Historical air temperature data emergence, and days of departure in first emergence for 386 L.C. Skinner et al. / Biological Control 37 (2006) 382–391

Fig. 2. Seasonal abundance in relation to accumulated degree-days for A. lacertosa (A) and A. nigriscutis (B). Location-year data pooled for each species. Solid line is predicted seasonal abundance from fit of third-order order polynomial in Table 2. Fig. 1. Effect of accumulated degree-days on proportional capture of A. lacertosa (A) and A. nigriscutis (B) from individual location-years. Solid line is the predicted proportional capture from fit of Weibull function to Table 2 combined location-years (Table1). Regression statistics (±SE) for fit of the third-order polynomial, 2 3 Y = b0 + b1X + b2X + b3X , where Y = the proportion of insects captured at accumulated degree-days (x), b0 = y-intercept, and b1, b2, and b3 a Table 1 are parameter estimates for A. lacertosa and A. nigriscutis Regression statistics (±SE) for fit of the cumulative Weibull function, Statistics Aphthona lacertosa Aphthona nigriscutis F =1 exp(((x c)/g)b), where F = cumulative insect frequency at nb 46 23 accumulated degree-days (x), gamma (c) = is the expected normalized b 1.1514 ± 0.3191 0.4183 ± 0.3179 time at onset of first emergence, eta (g) = constant for rate of emergence, 0 b 0.0056 ± 0.0016 0.0038 ± 0.0020 and beta (b) = parameter for shape 1 b2 7.289 ± 2.6E6 0.00001 ± 4.0E6 Statistic Aphthona lacertosa Aphthona nigriscutis b3 2.8803 ± 1.3E9 7.29E9 ± 2.5E9 2 na 46 23 R 0.53 0.78 a g 321.9 ± 59.21 491.5 ± 120.9 ADD at peak capture 594 ± 24 671 ± 15 b 1.88 ± 0.42 4.02 ± 1.13 Parameters b1, b2, and b3 significantly different from zero (P < .035). c 304 ± 54 127 ± 119 a Equation used to estimate accumulated degree-days (ADD) at peak 2 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R 0.98 0.99 2 P <0.0001 <0.0001 capture is ADD ¼ ðð2 b2Þ ðð2 b2Þ 2 b1 b3ÞÞ=ð6 b3Þ. b Number of observations in each model. a Number of observations in each model.

(t = 0.77, df =6; P = 0.47), Montana (t = 1.59, df =3; A. lacertosa and A. nigriscutis are shown in Tables 3 and 4, P = 0.21), and North Dakota (t = 2.75, df =2;P = 0.11). respectively. Observed and predicted dates of ﬁrst emer- Mean departures were similar in each state (F = 2.93, gence were in agreement for A. lacertosa in Minnesota df = 2, 11; P = 0.10). Departures for A. lacertosa ranged L.C. Skinner et al. / Biological Control 37 (2006) 382–391 387

Table 3 significantly different among states (F = 5.94, df =2,5; Observed and predicted dates of first emergence and accumulated degree- A P = 0.05). Mean departures were significantly smaller in days (ADD) for A. lacertosa above the threshold temperature of 7.5 °C Montana than in Minnesota (Table 4). Departures for A. Site Julian date ADD at Julian date Observed B nigriscutis first emergence estimates ranged from of first capture first capture of predicted Predicted, d 18.3 ± 4.3 days early in Minnesota to 0.3 ± 4.1 days late ADD at first emergence in Montana (Table 5). MN 168 314 167 1 MN 164 294 165 1 3.4. Evaluation of peak emergence estimates MN 161 354 158 3 MN 171 401 163 8 Observed dates of peak abundance, predicted dates of MN 157 255 160 3 peak abundance, and days of departure for A. lacertosa MN 157 255 160 3 and A. nigriscutis are shown in Tables 5 and 6, respec- MN 169 350 166 3 Mean ± SE 318 ± 21 1.1 ± 1.5a tively. We detected no bias in peak emergence predictions for A. lacertosa within Minnesota (t = 3.00, ND 160 247 165 5 ND 160 247 165 5 df =5; P = 0.14) and North Dakota (t = 1.20, df =1; ND 164 296 165 1 P = 0.44), while predictions for Montana were late Mean ± SE 263 ± 16 3.7 ± 1.3a (t = 5.31, df =3; P = 0.01). Mean departures were signif- MT 167 167 188 21 icantly different between states (F = 14.21, df = 2,9; MT 175 330 173 2 P = 0.001). Departure days for peak emergence of A. lac- MT 168 264 176 8 ertosa first emergence estimates ranged from 1.3 ± 0.8 MT 172 285 176 4 days late in Minnesota to 17.3 ± 3.3 days late in Mon- Mean ± SE 262 ± 34 7.8 ± 4.9a tana (Table 5). For A. nigriscutis, we detected no bias A Predicted ADD at first emergence is 304 ± 54. of peak emergence predictions for locations in Minnesota B Means for Observed Predicted followed by the same letter are not (t = 0.36, df =2;P = 0.75), while predictions in Montana significantly different as determined by Ryan–Einot–Gabriel–Welsch Multiple Range Test (P < 0.05). were late (t = 20.79, df =2; P = 0.002). North Dakota could not be tested with data from just one location available. Mean departures were significantly different Table 4 among states (F = 13.27, df = 2,4; P = 0.02). Mean Observed and predicted dates of first emergence and accumulated degree- departures in Minnesota were significantly smaller than A days (ADD) for A. nigriscutis above the threshold temperature of 9.3 °C in Montana (Table 6). Departures for peak emergence Site Julian date ADD at Julian date Observed of first capture first capture of predicted predicted, dB ADD at first emergence Table 5 MN 179 467 152 27 Observed and predicted dates of peak abundance and accumulated degree- MN 164 263 150 14 days (ADD) for A. lacertosa above the threshold temperature of 7.5 CA MN 164 263 150 14 ° Mean ± SE 331 ± 68 18.3 ± 4.3a Site Julian date of ADD at Julian date Observed peak capture peak capture of predicted predicted, dB ND 169 258 155 14 ADD at ND 164 222 155 9 peak abundance Mean ± SE 240 ± 18 11.5 ± 2.5ab MN 186 579 187 1 MT 167 101 174 7 MN 184 581 185 1 MT 161 145 154 7 MN 176 558 178 2 MT 169 120 170 1 MN 184 626 182 2 Mean ± SE 122 ± 13 0.3 ± 4.1b MN 177 523 180 3 A Predicted ADD at first emergence is 127 ± 119. MN 177 533 180 3 B Means for Observed Predicted followed by the same letter are not Mean ± SE 567 ± 15 1.3 ± 0.8a significantly different as determined by Ryan–Einot–Gabriel–Welsch ND 191 578 192 1 Multiple Range Test (P< 0.05). ND 181 449 192 11 Mean ± SE 518 ± 70 6.0 ± 5.0a MT 202 435 220 18 from 1.1 ± 1.5 days early in Minnesota to 7.8 ± 4.9 days MT 187 421 201 14 late in Montana (Table 3). For A. nigriscutis, observed MT 194 491 205 11 MT 191 328 217 26 and predicted dates of first emergence were generally Mean ± SE 419 ± 34 17.3 ± 3.3b acceptable for Montana locations (t = 0.08, df =2; A Predicted ADD at peak abundance is 594 ± 24. P = 0.94) and North Dakota (t = 4.60, df =1; B Means for Observed Predicted followed by the same letter are not P = 0.14), while emergence was generally earlier in Minne- significantly different as determined by Ryan–Einot–Gabriel–Welsch sota (t = 4.236, df =2;P = 0.05). Mean departures were Multiple Range Test (P < 0.05). 388 L.C. Skinner et al. / Biological Control 37 (2006) 382–391

Table 6 3.6. Maps of average dates of peak abundance Observed and predicted dates of peak emergence and accumulated degree- A days (ADD) for A. nigriscutis above the threshold temperature of 9.3 °C Estimated average dates to peak abundance for A. lacer- Site Julian date ADD at Julian date Observed B tosa and A. nigriscutis are displayed in Fig. 4. Average of peak capture peak capture of predicted predicted, d dates to peak abundance for A. lacertosa ranged from 17 ADD at peak abundance June to 22 July, depending on location in the state. Average date to peak abundance for A. nigriscutis ranged from 27 MN 195 691 193 2 MN 193 772 187 6 June to 8 August, approximately 10 days later statewide MN 178 432 193 15 than A. lacertosa in similar geographic locations. Peak Mean ± SE 632 ± 103 2.3 ± 6.4a emergence occurred earlier in the year in the southern part ND 189 439 206 17ab of the state for both species and became progressively later at points further north and east. MT 185 375 225 40 MT 194 350 228 34 MT 204 240 240 36 4. Discussion Mean ± SE 328 ± 42 36.7 ± 1.8b A Predicted ADD at peak abundance is 671 ± 15. 4.1. First emergence estimates B Means for Observed Predicted followed by the same letter are not significantly different as determined by Ryan–Einot–Gabriel–Welsch Cumulative probability distributions, when fit with the Multiple Range Test (P < 0.05). nonlinear Weibull function, can provide a useful method of examining and predicting emergence patterns of insects (Nowatzki et al., 2002; Wagner et al., 1984b). The Weibull of A. nigriscutis ranged from 2.3 ± 6.4 days late in Min- function, in this study, provided contradictory results for nesota to 36.7 ± 1.8 days late in Montana (Table 6). first emergence estimates of A. lacertosa and A. nigriscutis. The gamma (c) parameter provided a useful estimate of 3.5. Model comparison ADD at first emergence for A. lacertosa. No bias was detected for first emergence in any of the three states sam- For the Legg model, we detected no bias in peak emer- pled, nor were departure days among states significantly gence predictions for A. nigriscutis within Minnesota different, suggesting first emergence predictions can be used (t = 1.67, df =2;P = 0.24) but predictions for Montana in all three states. This was not the case, however, for were late (t = 13.98, df =2; P = 0.005). Departure days A. nigriscutis. Although the fit of the Weibull function to for peak emergence of A. nigriscuits estimates were cumulative probability distributions of A. nigriscutis was 9.7 ± 5.8 days early in Minnesota and 12.3 ± 0.9 days late strong (R2 = 0.99), the model failed to predict ADD at first in Montana. Comparison of mean departures between our emergence in Minnesota. Since the model predictions were model and the Legg model were significantly different for developed from Minnesota locations, it would be expected Montana (F = 152.26, df = 1,4; P = 0.0002), but not for that departures should be smaller in Minnesota than in Minnesota (F = 1.91, df = 1,4; P = 0.24). Comparisons of North Dakota and Montana. The results, however, were departure days for our model (Skinner model) and the just the opposite. Legg model are found in Fig. 3. The slow emergence rate of A. nigriscutis for the first five percent of emerging adults (Fig. 1B) may be the cause for the underestimation of predicted first emergence. This is in contrast to A. lacertosa, which emerged at a faster rate initially after first the emerging adult (Fig. 1A). In comparison to A. lacertosa, A. nigriscutis abundance was distinctly lower at nearly every site sampled and thus smaller sample size may have affected estimates of emergence rates and consequently, the fit of the Weibull function. While the Weibull function provided useful descriptions of first emergence for A. lacertosa and A. nigriscutis when held at constant temperatures (Skinner et al., 2004), the Weibull function may not be appropriate for predicting A. nigriscutis emergence with field-collected data.

4.2. Peak emergence estimates

For A. lacertosa, dates of predicted and observed peak Fig. 3. Mean departure days (±SE) for A. nigriscutis peak emergence emergence in Minnesota and North Dakota ﬁeld sites were predictions in Minnesota and Montana for two models. consistent. However, the A. lacertosa peak abundance L.C. Skinner et al. / Biological Control 37 (2006) 382–391 389

Fig. 4. Average date of peak abundance for A. nigriscutis (A) and A. lacertosa (B) using normal air temperatures. A. nigriscutis prediction based on accumulating 671 degree-days (based on lower developmental threshold of 9.3 °C). A. lacertosa prediction based on accumulating 594 degree-days (based on lower developmental threshold of 7.5 °C). model estimate failed to predict A. lacertosa peak emer- slope and aspect are not important criteria because the ter- gence in Montana which occurred on average 17 days later rain is mostly flat. While in Montana, slope and aspect than our model predicted. Results were similar for A. nig- along with other physical characteristics of the soil (rocky riscutis, where peak abundance estimates were not biased versus deep top soil) also plays a role in how fast soil tem- for Minnesota, but were biased for Montana. No conclu- peratures climb and thus air temperatures may be a more sions could be drawn for North Dakota, where data from variable predictor of soil temperatures. Also data sets col- only one A. nigriscutis location was obtained. Even more lected in Minnesota and North Dakota were within 320 km striking, the model estimate failed to predict A. nigriscutis of each other and were likely experiencing similar climatic peak emergence in Montana and on average was 37 days conditions including rainfall and temperatures. In Mon- late. Because sites were visited at weekly intervals, our esti- tana there is less annual rainfall and higher elevations typ- mates of first capture or peak emergence are by default ± 7 ically accumulate fewer degree-days during the growing days. Even given this inherent variability, estimates of peak season than does Minnesota and North Dakota. Possible emergence of both species using air temperature from local adaptation may have occurred in Aphthona popula- Montana ranges from 17 to 37 days late. tions due to these distinctly different climates and environ- In comparison with the Legg model of A. nigriscutis mental conditions. peak emergence, both our model and the Legg model were late (biased) in predicting peak emergence in Montana. 4.3. Spatial display of models However, the Legg model was less biased than our model (Fig. 3). For Minnesota, both models provide acceptable We used Minnesota as an example how prediction of predictions of first emergence and departure days between phenological events can be spatially displayed (Fig. 4). models were not significantly different. Although the num- Maps were developed to spatially display average dates ber of departure days for the Legg model are similar for to peak abundance for A. lacertosa and A. nigriscutis. Montana and Minnesota, they differ in that the predictions Maps are based on 30-year normal temperatures, which were early in Minnesota and late in Montana. This may be does not account for year-to-year variation in degree-day due in part that the Legg model was developed with data accumulations or the varying microclimates at each site. sets from Wyoming, Montana and Minnesota incorporat- However, density contour maps can provide estimates for ing more variability. Model results are most reliable within planning management efforts such as when to collect bio- the region where the model was developed. control agents for redistribution. Our predictions suggest It is unknown why differences in emergence patterns that A. lacertosa emerges approximately 2 weeks earlier between states exist. One possible explanation may be the than A. nigriscutis. 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