Article Flight Synchrony among the Major Pests of Cranberries in the Upper Midwest, USA

Shawn A. Steffan 1,2,*,†, Merritt E. Singleton 2,†, Jayne Sojka 3,†, Elissa M. Chasen 1,2, Annie E. Deutsch 4, Juan E. Zalapa 1 and Christelle Guédot 2

1 Department of Agriculture, Agricultural Research Service, 1575 Linden Dr., Madison, WI 53706, USA; [email protected] (E.M.C.); [email protected] (J.E.Z.) 2 Department of Entomology, University of , 1630 Linden Drive, Madison, WI 53706, USA; [email protected] (M.E.S.); [email protected] (C.G.) 3 Lady Bug IPM, LLC, 10107 State Hwy 54, Pittsville, WI 54466, USA; [email protected] 4 University of Wisconsin-Extension, Door County, 421 Nebraska St., Sturgeon Bay, WI 54235, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-608-890-1281 † These authors contributed equally to this work.

Academic Editors: Jeffrey A. Davis, Michael J. Stout, Rodrigo Diaz and Julien M. Beuzelin Received: 9 December 2016; Accepted: 21 February 2017; Published: 26 February 2017

Abstract: The cranberry fruitworm ( vaccinii Riley), fruitworm ( Clemens), and blackheaded fireworm ( naevana Hübner) are historically significant pests of cranberries ( macrocarpon Aiton) in the Upper Midwest (Wisconsin), USA. Their respective natural histories are well documented but correlations between developmental benchmarks (e.g., larval eclosion) and degree-day accruals are not yet known. Treatment timings are critical to the optimization of any given control tactic, and degree-day accrual facilitates optimization by quantifying the developmental status of pest populations. When key developmental benchmarks in the pest life cycle are linked to degree-days, real-time weather data can be used to predict precise treatment timings. Here, we provide the degree-day accumulations associated with discrete biological events (i.e., initiation of flight and peak flight) for the three most consistent moth pests of cranberries in Wisconsin. were trapped each spring and summer from 2003 to 2011. To characterize flight dynamics and average timing of flight initiation, pheromone-baited trap-catch data were tallied for all three pest species within each of seven growing seasons. These flight dynamics were then associated with the corresponding degree-day accumulations generated using the cranberry plant’s developmental thresholds. Finally, models were fit to the data in order to determine the peak flight of each species. The initiation of the spring flight among all three moth species was highly synchronous, aiding in the timing of control tactics; however, there were substantial differences in the timing of peak flight among the moth species. Characterization of the relationship between temperature and pest development allows pest management professionals to target specific life stages, improving the efficacy of any given pest control tactic.

Keywords: ; blackheaded fireworm; cranberry fruitworm; degree-day; IPM; ; Sparganothis sulfureana

1. Introduction One of the central tenets of any integrated pest management (IPM) program is that pest management decisions should be based not only on knowledge of pest identity and abundance, but also on accurate estimates of pest ontogeny (development) and stage-specific damage potential [1–4]. Generating information on pest ontogeny and damage potential is facilitated by the use of degree-day

Insects 2017, 8, 26; doi:10.3390/insects8010026 www.mdpi.com/journal/insects Insects 2017, 8, 26 2 of 9 models. Such models refine predictions of development using ambient temperatures [5–7]. Because insects are poikilothermic, daily temperature data can be used to predict development via the calculation of heat units, or degree-days (DD). To date, phenological models using DDs have been determined for over five hundred pests, across five insect families and several landscape systems [8]. DD accrual is typically initiated after a discrete biological event, referred to as a ‘biofix,’ or a calendar date. DD accumulations can then be linked to specific developmental benchmarks, such as the start of oviposition, larval eclosion, or adult emergence, which create useful, predictive models to better time pest management tactics [9–11]. In the cranberry system, there have been relatively few studies producing reliable, predictive models of pest development [12,13], although recent work has established the temperature-mediated development thresholds for two key cranberry insect pests [10,14,15]. The American cranberry, Aiton, is a high-value crop, and is one of the very few plants native to North America that is grown commercially [16]. Cranberries have been cultivated in the US since the early 1800s [17] and are currently grown almost exclusively in the US and Canada [16]. Cranberry growers in Wisconsin commonly report that insects are their most significant pest group [18]. Given that Wisconsin represents more cranberry production than all other US states combined [19], significant crop losses due to insect feeding can affect global cranberry supplies. The insect pests of Wisconsin marshes are diverse, but have historically been dominated by three moth species: (1) cranberry fruitworm, Acrobasis vaccinii Riley (: ); (2) sparganothis fruitworm, Sparganothis sulfureana Clemens (Lepidoptera: ); (3) blackheaded fireworm, Rhopobota naevana Hübner (Lepidoptera: Tortricidae). Crop loss attributable to A. vaccinii can be as high as 50% in untreated beds [20], and Wisconsin growers have consistently ranked this insect as their top pest [18]. A. vaccinii has a single generation in the Upper Midwest, with adults beginning to emerge during bloom (mid/late June) and continuing through July. Control of this pest at any stage is challenging, and it is made particularly troublesome because larvae target fruit and remain within berries while feeding. Inside the berry, larvae are protected from predation and spray treatments, and thus require precisely timed insecticide applications to suppress populations before they enter the fruit. Co-occurrence of peak flight and the cranberry bloom presents yet another obstacle to the management of this pest. The cranberry bloom attracts many pollinators, and these pollinators are key to fruit set, so any insecticide treatment applied during bloom can have consequences for both pollinators and fruit set. Finally, overwintering prepupae and/or pupae of A. vaccinii appear to be tolerant of spring flooding as practiced in Wisconsin [21], and biological control does not confer adequate pest suppression in conventional spray programs, given the low parasitism rates commonly observed [22]. Optimization of treatment timing, therefore, is critical for this pest species. Similarly, S. sulfureana populations often require a well-timed treatment in the spring to intercept larvae before they can create protective galleries in which to feed. In Wisconsin, overwintered S. sulfureana neonates emerge in May and begin feeding on the new succulent growth. There is a second generation each season [23], and these larvae feed on developing berries, as well as the foliage. In both generations, S. sulfureana larvae use silk to web together leaves, stems, and berries, forming a tight, protective gallery [16]. This underscores the need for proper timing of insecticide applications to target this species during narrow windows of opportunity. Like A. vaccinii, S. sulfureana mortality is not consistently attributable to flooding as it is practiced in Wisconsin, presumably due to the hydrophobic, semi-permeable refuge they build out of silk and plant material [24]. While significant levels of biological control of S. sulfureana have been demonstrated in growing regions outside of Wisconsin [25], it is not clear how management tactics impact specific natural enemies. R. naevana also has two generations per season in Wisconsin [26]. It overwinters as an egg, and first-generation larvae begin feeding on foliage late May through early June. Like S. sulfureana, R. naevana webs together cranberry uprights to create a refuge in which to feed. First-generation adults fly from June to July. Second-generation larvae emerge between mid-June through August, feeding Insects 2017, 8, 26 3 of 9 primarily on foliage but also fruit, to some extent. Biological control has not been shown to provide significant control of R. naevana [22] but flooding can be effective [27]. To refine pest management strategies, it is important to have a means of predicting the start of the mating flight, as well as the peak of this flight. Peak flight is not only the midway point of the flight, but it serves to approximate the relative size of a population. Our objectives focused on gathering trapping data on all three moth species over many years, allowing us to model trap-catch data as a function of DD accrual. With predictive, temperature-based models, it is then possible to provide cranberry growers with the means to create their own real-time pest development information and allow IPM professionals to better assess the developmental status of multiple pest populations within a cranberry bed, marsh, or region. Specifically, we have determined the DD accumulation at which flight initiation and peak flight occur for A. vaccinii, S. sulfureana, and R. naevana.

2. Materials and Methods

2.1. Trapping Moths Populations of Acrobasis vaccinii, Sparganothis sulfureana, and Rhopobota naevana were monitored at 85 commercial cranberry marshes in the Upper Midwest, distributed across the three major cranberry-growing regions of Wisconsin (i.e., Wood, Monroe, Jackson, Juneau, and Adams Counties). The southernmost area of this growing region included the towns of Tomah and Warrens; the central area of the growing region included the towns of Cranmoor, City Point, Nekoosa, and Babcock; the eastern area represented marshes east of the Wisconsin River, including Adams County. Trapping data were compiled during the growing seasons of 2003, 2005, 2007, 2008, 2009, 2010 and 2011. Moth populations were assessed using “P-2” traps (Great Lakes IPM, Vestaburg, MI, USA) and commercially available species-specific pheromone-baited lures (Scentry Biologicals, Inc. (Billings, MT, USA), Great Lakes IPM (Vestaburg, MI, USA), Agbio, Inc. (Westminster, CO, USA)). At each site, traps were staked into cranberry beds directly above the cranberry canopy, with approximately one trap per four hectares. Traps were deployed in May, collected every week, and adults were tallied over the course of approximately ten weeks. Pheromone lures were changed biweekly. Mean per-trap counts were derived for each site every week.

2.2. Degree-Day Calculations Site-specific DDs were calculated using daily minimum and maximum temperatures for one location representing each region: Volk (Southern), Wisconsin Rapids (Central), and Hancock (Eastern). Weather data for the Southern and Central regions were reported from local weather stations (accessed via http://www.wunderground.com) and for the Eastern region from the Hancock Agricultural Research station (http://www.soils.wisc.edu/uwex_agwx/awon/daily_clim). For any missing weather data from the two previous sources, daily data was used from volunteer weather observers who transmit their data to the National Weather Forecast Service office in Sullivan, WI (UW Extension 2014b). DD calculations were determined using an online DD calculator (UC-IPM Online 2014) using the single sine calculation method and a horizontal cutoff. DD accrual began 1 March of each year (this start date is used because any accrual prior to 1 March would be minimal in the Upper Midwest, but would allow for the inclusion of occasional warm springs in March/April). To standardize the DD accrual for each insect species, a single pair of temperature thresholds were used, and we chose to use the lower and upper thresholds of the cranberry plant. These thresholds were 5 ◦C (41 ◦F) and 29 ◦C (85 ◦F) [28].

2.3. Modeling of Flight Dynamics Flight dynamics were analyzed as moths per time-step (i.e., week), and these dynamics were modeled using parabolic functions (second-order polynomial functions; y = ax2 + bx + c) (SigmaPlot 12.3) because these functions closely approximate the bell-shaped curve of the moth flights. Parabolic Insects 2017, 8, 26 4 of 9 models were fit to the trap-catch data of any given flight. Peak flight was determined by solving for the xInsects-value 2017 associated, 8, 26 with the peak of the parabola. This was determined by setting the first-derivative4 of of 9 the parabolic equation equal to zero (y’ = 0 = 2ax + b) and solving for x. This x-value corresponds to thecorresponds point on theto curvethe point at which on the the curve tangent at which would the have tangent a slope would = 0, which have is a the slope maximum = 0, which value is ofthey (peak)maximum for the value parabola. of y (peak) Although for theS. sulfureanaparabola. andAlthoughR. naevana S. sulfureanaare bivoltine, and R. analyses naevana were are bivoltine, confined toanalyses the first-generation were confined flights to the for fi thoserst-generation two species, flights which for is those when two pest species, management which efforts is when tend pest to bemanagement focused. efforts tend to be focused.

3.3. ResultsResults andand DiscussionDiscussion

3.1.3.1. FlightFlight Initiation,Initiation, byby SpeciesSpecies TheThe springspring flightsflights S.S. sulfureanasulfureana,, R.R. naevananaevana,, andand A.A. vacciniivaccinii werewere initiatedinitiated withinwithin aa narrownarrow rangerange ofof DDDD accumulationsaccumulations inin eacheach ofof Wisconsin’sWisconsin’s majormajor growinggrowing regionsregions (Figure(Figure1 1A–C),A–C), as as well well as as among among regionsregions (Figure(Figure2 2).). The The first first moth moth species species to to begin begin its its spring spring flight flight was was S. sulfureana,, appearingappearing inin trapstraps typicallytypically byby 846846 DD.DD. TheThe flightflight ofof R.R. naevananaevana began soon thereafter atat 934 DD, followed by A. vaccinii, flyingflying atat 997997 DD.DD. Thus,Thus, thesethese threethree mothmoth speciesspecies cancan bebe expectedexpected toto initiateinitiate theirtheir springspring flightsflights withinwithin 160160 DDDD ofof oneone another,another, whichwhich wouldwould correspondcorrespond toto approximatelyapproximately oneone week,week, givengiven typicaltypical JuneJune temperaturestemperatures inin WisconsinWisconsin (20–25(20–25 DDsDDs cancan bebe expectedexpected perper day).day). ThisThis predictablepredictable synchronysynchrony inin thethe initiationinitiation ofof flightflight amongamong thesethese threethree mothmoth speciesspecies providesprovides keykey informationinformation forfor IPMIPM practitioners,practitioners, particularlyparticularly whenwhen weatherweather oror otherother logisticslogistics maymay precludepreclude adequateadequate scouting.scouting.

(A) 80

BHFW CFW 60 SFW

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0 500 1000 1500 2000 2500 3000 3500 FigureFigure 1.1. Cont.Cont.

Insects 2017, 8, 26 5 of 9 Insects 2017, 8, 26 5 of 9 Insects 2017, 8, 26 5 of 9 (C) (C) 80 80

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0 500 1000 1500 2000 2500 3000 3500 0 500 1000 1500 2000 2500 3000 3500 Degree Day Accumulation Degree Day Accumulation Figure 1. Flights of cranberry fruitworm (CFW), sparganothis fruitworm (SFW), and blackheaded FigureFigure 1. 1.Flights Flights of of cranberry cranberry fruitworm fruitworm (CFW),(CFW), sparganothis fruitworm fruitworm (SFW), (SFW), and and blackheaded blackheaded fireworm (BHFW) in the (A) southern; (B) central; and (C) eastern growing regions of Wisconsin. firewormfireworm (BHFW) (BHFW) in in the the (A (A) southern;) southern; ( B(B)) central;central; and ( (CC)) eastern eastern growing growing regions regions of ofWisconsin. Wisconsin. 70 70

60 BHFW 60 BHFWCFW 50 CFWSFW 50 SFW 40 40

30 30

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Mean Moths/Trap Mean 20 Mean Moths/Trap Mean 10 10

0 0

0 500 1000 1500 2000 2500 3000 3500 0 500 1000 1500 2000 2500 3000 3500 Degree-Day Accumulation Degree-Day Accumulation Figure 2. Flights of cranberry fruitworm (CFW), sparganothis fruitworm (SFW), and blackheaded Figure 2. Flights of cranberry fruitworm (CFW), sparganothis fruitworm (SFW), and blackheaded Figurefireworm 2. Flights (BHFW) of cranberry averaged across fruitworm all three (CFW), major sparganothisgrowing regions fruitworm of Wisconsin. (SFW), and blackheaded firewormfireworm (BHFW) (BHFW) averaged averaged across across all all three three majormajor growing regions regions of of Wisconsin. Wisconsin. 3.2. Peak Flight, by Species 3.2. Peak Flight, by Species 3.2. PeakDespite Flight, byour Species observations that all three moth flights were relatively synchronous in their respective Despite our observations that all three moth flights were relatively synchronous in their respective commencements, and that the flights overlapped to a high degree (Figure 2), each species exhibited commencements,Despite our observations and that the that flights all three overlapped moth flights to a high were degree relatively (Figure synchronous 2), each species in their exhibited respective markedly different peaks (Table 1). R. naevana was generally the first species to reach its peak. On commencements,markedly different and peaks that the(Table flights 1). R. overlapped naevana was to generally a high degree the first (Figure species2), to each reach species its peak. exhibited On average, this flight peaked around 1360 DDs (95% CI: 1331–1384 DDs), meaning that 95% of the time, markedlyaverage, differentthis flight peakspeaked (Table around1). 1360R. DDs naevana (95% CI:was 1331–1384 generally DDs), the meaning first species that 95% to reach of the itstime, peak. the R. naevana peak can be expected to occur between 1331 and 1384 DDs (Table 1 and Figure 3). Onthe average, R. naevana this peak flight can peaked be expected around to occur 1360 between DDs (95% 1331 CI: and 1331–1384 1384 DDs DDs), (Table meaning 1 and Figure that 3). 95% of the time, the R. naevana peak can be expected to occur between 1331 and 1384 DDs (Table1 and Figure3). Table 1. Degree-day accumulation (mean ± 95% CI) associated with the peak of each species’ Table 1. Degree-day accumulation (mean ± 95% CI) associated with the peak of each species’ respective flight. Degree-day values are derived from the x-maxima of the models listed in Tables S1– Tablerespective 1. Degree-day flight. Degree-day accumulation values (mean are derived± 95% CI)from associated the x-maxima with of the the peak models of each listed species’ in Tables respective S1– S3. The corresponding graphical representations of these models are presented in Figures S1-S6. flight.S3. The Degree-day corresponding values graphical are derived representations from the x of-maxima these models of the are models presented listed in Figures in Tables S1-S6. S1–S3. The correspondingMoth species graphical representations South of these Central models are presented East in Figures All S1–S6. Regions Moth species South Central East All Regions A. vaccinii 1714.9 1776.3 1801.7 1760.9 ± 44.0 A. vaccinii 1714.9 1776.3 1801.7 1760.9 ± 44.0 MothS. sulfureana Species South 1571.7 Central 1622.9 East 1638.3 All 1608.9 Regions ± 59.8 S. sulfureana 1571.7 1622.9 1638.3 1608.9 ± 59.8 R. naevana 1335.7 1402.3 1350.2 1357.8 ± 26.5 R.A. naevana vaccinii 1714.9 1335.7 1776.3 1402.3 1801.7 1350.2 1760.9 1357.8 ±± 26.544.0 S. sulfureana 1571.7 1622.9 1638.3 1608.9 ± 59.8 R. naevana 1335.7 1402.3 1350.2 1357.8 ± 26.5

Insects 2017, 8, 26 6 of 9 Insects 2017, 8, 26 6 of 9 Insects 2017, 8, 26 6 of 9 120 12 120 Mean BHFW trap-catch 12 % BHFW emergence vs. Degree-days Mean BHFW trap-catch 100 Peak population (50%) 10 % BHFW emergence vs. Degree-days 100 Peak population (50%) 10

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40 4 trap-catch BHFW Mean % BHFW population emerged population BHFW % 20 2 trap-catch BHFW Mean % BHFW population emerged population BHFW % 20 2

0 0 600 800 1000 1200 1400 1600 1800 2000 2200 0 0 600 800 1000Degree-day 1200 1400 accumulation 1600 1800 2000 2200 Degree-day accumulation Figure 3. Blackheaded fireworm (BHFW) flight (% emergence, trap-catch, and peak flight) plotted as Figure 3. Blackheaded fireworm (BHFW) flight (% emergence, trap-catch, and peak flight) plotted a function of degree-day accumulation. The peak flight interval (the halfway point, indicated by the asFigure a function 3. Blackheaded of degree-day fireworm accumulation. (BHFW) flight The (% peak emerge flightnce, interval trap-catch, (the halfwayand peak point,flight) indicatedplotted as by ablack function circle, of ±95% degree-day CI) represents accumulation. the range The of peak degree- flightdays interval within (the which halfway peak point, flight indicatedis most likely by the to the black circle, ±95% CI) represents the range of degree-days within which peak flight is most likely blackoccur. circle, ±95% CI) represents the range of degree-days within which peak flight is most likely to to occur. occur. The next moth flight to reach its peak was S. sulfureana, occurring at approximately 1610 DDs (95%TheThe CI: next 1549–1669next moth moth flight DDs)flight to(Table toreach reach 1 and itsits peak peakFigure was 4), S.which sulfureana was on, ,occurring occurringaverage 250at at approximately DD approximately later than R.1610 naevana 1610 DDs DDs (95%(95%(~10–14 CI: CI: 1549–1669 days 1549–1669 later in DDs) DDs) typical (Table (Table June1 1 andor and July Figure Figure temperatures4 ),4), which which in waswas central onon averageaverage Wisconsin). 250 250 DD DDAgain, later later there than than will R. R.naevana always naevana (~10–14(~10–14be year-to-year days days later later and in in typical site-to-sitetypical JuneJune variabi oror JulyJulylity, temperatures but the majority in in central central of the Wisconsin). Wisconsin). time, the Again,S. Again, sulfureana there there willpeak will always flight always bebecan year-to-year year-to-year be expected and andto site-to-siteoccur site-to-site between variability, variabi 1549lity, and but but1669 the the DDs majority majority (Figure of of the4). the time, time, the theS. S. sulfureana sulfureanapeak peak flight flight can becan expected be expected to occur to occur between between 1549 1549 and and 1669 1669 DDs DDs (Figure (Figure4). 4). 120 70 SFW trap-catch 120 % SFW emergence vs. Degree-days 70 SFW trap-catch Peak population (50%) 60 100 % SFW emergence vs. Degree-days 60 100 Peak population (50%) 50 80 50 80 40 60 40 60 30

40 30

20 SFW trap-catch Mean 40 % SFW population emerged SFW population % 20 SFW trap-catch Mean 20

% SFW population emerged SFW population % 10 20 10 0 0 0800 1000 1200 1400 1600 1800 2000 2200 24000 800 1000 1200Degree-day 1400 1600 accumulation 1800 2000 2200 2400 Degree-day accumulation Figure 4. Sparganothis fruitworm (SFW) flight (% emergence, trap-catch, and peak flight) plotted as a function of degree-day accumulation. The peak flight interval (the halfway point, indicated by the FigureFigure 4. 4.Sparganothis Sparganothis fruitworm fruitworm (SFW)(SFW) flightflight (% emerge emergence,nce, trap-catch, trap-catch, and and peak peak flight) flight) plotted plotted as as black circle, ±95% CI) represents the range of degree-days within which peak flight is likely to occur. a functiona function of of degree-day degree-day accumulation. accumulation. The peak flig flightht interval interval (the (the halfway halfway point, point, indicated indicated by bythe the black circle, ±95% CI) represents the range of degree-days within which peak flight is likely to occur. black circle, ±95% CI) represents the range of degree-days within which peak flight is likely to occur. The A. vaccinii flight was last to peak, and this tended to occur around 1760 DD (95% CI: 1717– 1805)The (Table A. vaccinii 1 and Figure flight 5),was approximately last to peak, and 400 thisDDs tend aftered R. to naevana occur ,around and 150 1760 DDs DD after (95% S. sulfureana CI: 1717–. 1805)ThisThe long (TableA. vacciniidelay 1 and inflight theFigure A. was vaccinii 5), lastapproximately topeak peak, flight and 400could this DDstended be afteras much toR. occur naevana as three around, and weeks, 150 1760 DDscomplicating DD after (95% S. CI: sulfureana efforts 1717–1805) to. (TableThiseffectively1 long and delay Figuretarget in 5the the), approximatelyadult A. vaccinii (the egg-laying peak 400 flight DDs stage) could after of be all R.as three naevanamuch species as, three and with 150 weeks, DDsa single complicating after spray.S. sulfureana As efforts growers. to This longeffectivelyand delay pest control in target the professionalsA.the vaccinii adult (thepeak plan egg-laying flight their treatment could stage) be of asopalltions, muchthree they species as threecan withexpect weeks, a single that complicating 95% spray. of the As time,growers efforts the to effectivelyand pest control target theprofessionals adult (the plan egg-laying their treatment stage) of op alltions, three they species can expect with athat single 95% spray. of the Astime, growers the

Insects 2017, 8, x 26 7 of 9

A. vaccinii peak will occur between 1717 and 1805 DDs (Figure 5). Interestingly, despite rather large differencesand pest control in DD professionals accumulations plan among their treatmentthe three mo options,th species, they canthis expect pattern that was 95% consistent of the time, among the A.the vaccinii three southern,peak will central, occur between and eastern 1717 andgrowing 1805 DDsregions (Figure of Wisconsin5). Interestingly, (Figures despite S7–S9). rather All model large parameterdifferences values in DD and accumulations figures, by year among and the region three, are moth archived species, within this pattern the Supplementary was consistent Materials among (Tablesthe three S1–S3). southern, central, and eastern growing regions of Wisconsin (Figures S7–S9). All model parameter values and figures, by year and region, are archived within the Supplementary Materials (Tables S1–S3).

120 35 CFW trap-catch % CFW emergence vs. Degree-days 30 100 Peak population (50%)

25 80

20 60 15

40

10 CFW trap-catch Mean % CFW population emerged population CFW % 20 5

0 0 500 1000 1500 2000 2500 3000 Degree-day accumulation

Figure 5. Cranberry fruitworm (CFW) flight flight (% emergenc emergence,e, trap-catch, and peak flight) flight) plotted as a function of degree-day accumulation. The The peak peak flight flight interval interval (the (the halfway point, indicated by the black circle, ±95%±95% CI) CI) represents represents the the range range of of degree-days degree-days within within which which peak peak flight flight is is likely likely to occur.

4. Conclusions Understanding the relationship between insect pest development and ambient temperature improves the timing of scouting efforts and pest management tactics.tactics. Historically, Historically, there have been three moth species ( A. vaccinii , S. sulfureana sulfureana,, R.R. naevana naevana)) that that are are particularly particularly problematic problematic for for cranberry cranberry growers in the Upper Midwest of the USA, and whilewhile much is known of their respective natural histories, there have been few studies of the associationsassociations between degree-day (DD) accumulations and key biological events (e.g., larval eclosion, initiation of flight, flight, and peak flight). flight). Here, we use the developmental thresholds of the cranberry plant plant as as a a standardized standardized proxy proxy for for the the three three insect insect species. species. We talliedtallied pheromone-baitedpheromone-baited trap-catch data across seven years and 85 sites throughout central Wisconsin to determine the initiation of flight. flight. We then modeled the flight flight dynamics, demonstrating that moth trap-catch over time represented a predictable,predictable, parabolic function. Finally, Finally, the historical peak flightsflights ofof thesethese threethree mothmoth speciesspecies were were calculated calculated for for each each of of the the three three major major growing growing regions regions in Wisconsin.in Wisconsin. Our Our findings findings reveal reveal that that these these moth moth species species tend tend to initiate to initiate their their spring spring flights flights within within days daysof one of another, one another, and that and there that isthere a high is a degree high degr of synchronyee of synchrony (overlap) (overlap) among among the flights the offlights these of species. these species.However, However, the peak the flights peak amongflights among these species these spec tendies to tend be different to be different from onefrom another. one another. Mid-flight Mid- flighttreatment treatment timings, timings, therefore, therefore, may need may to need be tailored to be tailored to each to species, each species, depending depending on the typeon the of type control of controltactic and tactic stage and of stage the targetedof the targeted species. species. Using Using DD accumulations DD accumulations for key for biological key biological events events for each for eachspecies, species, pest controlpest control programs programs can be can tailored be tailored to suit to the suit particular the particular needs needs of any of given any given marsh. marsh.

Supplementary Materials:Materials: TheThe followingfollowing areare available available online online at at http://www.mdpi.com/2075-4450/8/1/26/ http://www.mdpi.com/2075-4450/8/1/26/s1, Figures1, Figure S1: Modeled S1: Modeled flight flight dynamics dynamics of cranberry of cranberry fruitworm fruitworm (A. vaccinii (A. vaccinii) in the) in growing the growing seasons seasons of 2003, of 2005, 2003, 2007;2005, Figure 2007; Figure S2: Modeled S2: Modeled flight dy flightnamics dynamics of cranberry of cranberry fruitworm fruitworm (A. vaccinii (A. vaccinii) in the) ingrowing the growing seasons seasons of 2008– of 2011;2008–2011; Figure Figure S3: Modeled S3: Modeled flight flight dyna dynamicsmics of sparganothis of sparganothis fruitworm fruitworm (S. sulfureana (S. sulfureana) in )the in thegrowing growing seasons seasons of of 2003, 2005, 2007; Figure S4: Modeled flight dynamics of sparganothis fruitworm (S. sulfureana) in the growing 2003, 2005, 2007; Figure S4: Modeled flight dynamics of sparganothis fruitworm (S. sulfureana) in the growing seasons of 2008–2011; Figure S5: Modeled flight dynamics of blackheaded fireworm (R. naevana) in the 2003,

Insects 2017, 8, 26 8 of 9 seasons of 2008–2011; Figure S5: Modeled flight dynamics of blackheaded fireworm (R. naevana) in the 2003, 2005, and 2007 growing seasons; Figure S6: Modeled flight dynamics of blackheaded fireworm (R. naevana) in the growing seasons of 2008–2011; Figure S7: Flight dynamics of cranberry fruitworm (CFW), sparganothis fruitworm (SFW), and blackheaded fireworm (BHFW) in the Tomah area (southern region); Figure S8: Flight dynamics of cranberry fruitworm (CFW), sparganothis fruitworm (SFW), and blackheaded fireworm (BHFW) in the Hancock area (central region); Figure S9: Flight dynamics of cranberry fruitworm (CFW), sparganothis fruitworm (SFW), and blackheaded fireworm (BHFW) in the Wisconsin Rapids area (eastern region); Table S1: Cranberry fruitworm (A. vaccinii) peak flight, by region and year (model parameter values); Table S2: Sparganothis fruitworm (S. sulfureana) peak flight, by region and year (model parameter values); Table S3: Blackheaded fireworm (R. naevana) peak flight, by region and year (model parameter values). Acknowledgments: The authors wish to thank the cranberry-grower collaborators of Wisconsin, as well as the staff of Lady Bug IPM, Inc. (Pitsville, WI, USA). Funding was provided by the US Department of Agriculture, Agricultural Research Service (Current Research Information System #3655-21220-001, appropriated to Shawn Steffan). Author Contributions: Shawn Steffan conceived of and designed the project; Merritt Singleton, Jayne Sojka, Shawn Steffan, and Annie Deutsch performed the experiments; Merritt Singleton, and Elissa Chasen analyzed the data; Jayne Sojka, Juan Zalapa, and Christelle Guédot contributed materials and/or analytical tools; Shawn Steffan, Merritt Singleton, Elissa Chasen, Juan Zalapa, and Christelle Guédot wrote the paper. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Stern, V.; Smith, R.; Van Den Bosch, R.; Hagen, K. The integration of chemical and biological control of the spotted alfalfa aphid: The integrated control concept. Hilgardia 1959, 29, 81–101. [CrossRef] 2. Prokopy, R.; Kogan, M. Integrated Pest Management. In Encyclopedia of INSECTS; Resh, V.H., Carde, R.T., Eds.; Elsevier Inc.: Burlinton, VT, USA, 2009; pp. 523–528. 3. Hagen, A.F. The biology and control of the western bean cutworm in dent corn in Nebraska. J. Econ. Entomol. 1962, 55, 628–631. [CrossRef] 4. Moon, R.D.; Wilson, L.T. Sampling for detection, estimation and IPM decision making. In Integrated Pest Management: Concepts, Tactics, Strategies and Case Studies; Radcliffe, E.B., Hutchison, W.D., Cancelado, R.E., Eds.; Cambridge University Press: New York, NY, USA, 2009; pp. 75–89. 5. Legg, D.E. The relevance of modelling in successful implementation of IPM. In Integrated Pest Management: Potential, Constraints, and Challenges; Koul, O., Dhaliwal, G.S., Cuperus, G.W., Eds.; CABI Publishing: Cambridge, MA, USA, 2004; pp. 39–54. 6. Damos, P.T.; Savopoulou-Soultani, M. Development and statistical evaluation of models in forecasting moth phenology of major lepidopterous peach pest complex for integrated pest management programs. Crop Prot. 2010, 29, 1190–1199. [CrossRef] 7. Pruess, K.P. Day-degree methods for pest management. Environ. Entomol. 1983, 12, 613–619. [CrossRef] 8. Nietschke, B.S.; Magarey, R.D.; Borchert, D.M.; Calvin, D.D.; Jones, E. A developmental database to support insect phenology models. Crop Prot. 2007, 26, 1444–1448. [CrossRef] 9. Miller, J.; West, K.J.; Hanson, P.E. Temperature requirements for development of Autographa californica (Lepidoptera: ). Environ. Entomol. 1984, 13, 593–594. [CrossRef] 10. Deutsch, A.E.; Rodriguez-Saona, C.R.; Zalapa, J.E.; Steffan, S.A. Temperature-mediated development thresholds of Sparganothis sulfureana (Lepidoptera: Tortricidae) in cranberries. Environ. Entomol. 2015, 44, 400–405. [CrossRef][PubMed] 11. Roberts, S.; Mahr, D.L. Development of cranberry girdler, Chrysoteuchia topiaria (Lepidoptera: Pyralidae) in relation to temperature. Gt. Lakes Entomol. 1986, 19, 85–90. 12. Cockfield, S.D.; Butkewhich, S.L.; Samoil, K.S.; Mahr, D.L. Forecasting flight activity of Sparganothis sulfureana (Lepidoptera: Tortricidae) in cranberries. J. Econ. Entomol. 1994, 87, 193–196. [CrossRef] 13. Cockfield, S.D.; Fitzpatrick, S.M.; Patten, K.; Henderson, D.; Dittl, T.; Poole, A.; Shanks, C.; Mahr, D.L. Modeling of blackheaded fireworm (Lepidoptera: Tortricidae) oviposition and pheromone-trap catches. J. Econ. Entomol. 1994, 87, 787–792. [CrossRef] 14. Deutsch, A.E.; Rodriguez-saona, C.R.; Kyryczenko-roth, V.; Sojka, J.; Zalapa, J.E.; Steffan, S.A. Degree-day benchmarks for Sparganothis sulfureana (Lepidoptera: Tortricidae) development in cranberries. J. Econ. Entomol. 2014, 107, 2130–2136. [CrossRef][PubMed] Insects 2017, 8, 26 9 of 9

15. Chasen, E.M.; Steffan, S.A. Temperature-mediated growth thresholds of Acrobasis vaccinii (Lepidoptera: Pyralidae). Environ. Entomol. 2016, 45, 732–736. [CrossRef][PubMed] 16. Eck, P. The American Cranberry; Rutgers University Press: New Brunswick, NJ, USA, 1990. 17. Peltier, G.L. A History of the Cranberry Industry in Wisconsin; Harlo Press: Detroit, MI, USA, 1970. 18. Guédot, C.; Lippert, M.; McManus. Cranberry School Grower Survey Results. Available online: https://fruit. wisc.edu/wp-content/uploads/sites/36/2016/10/2014-Cranberry-School-Proceedings-red.pdf (accessed on 13 November 2016). 19. USDA-NASS United States Department of Agriculture—National Agricultural Statistics Service 2013 Wisconsin Agricultural Statistics. Available online: https://www.nass.usda.gov/Statistics_by_State/ Wisconsin/Publications/Annual_Statistical_Bulletin/bulletin2013_web.pdf (accessed on 9 May 2016). 20. Mahr, D.L. Cranberry Fruitworm. Available online: https://fruit.wisc.edu/wp-content/uploads/sites/36/ 2011/05/Cranberry-Fruitworm.pdf (accessed on 28 September 2015). 21. Demoranville, C.J.; Sandler, H.A.; Shumaker, D.E.; Averill, A.L.; Caruso, F.L.; Sylvia, M.M.; Pober, D.M. Fall flooding for management of cranberry fruitworm (Acrobasis vaccinii) and dewberry (Rubus hispidus) in Massachusetts cranberry production. Crop Prot. 2005, 24, 999–1006. [CrossRef] 22. Mahr, D.L. Biological control in a high-value native crop: Status, opportunities, and constraints in cranberry. In Biological Control of Native or Indigenous Pests: Challenges, Constraints, and Potential; Entomological Society of America: Lanham, MD, USA, 1999. 23. Mahr, D.L. Sparganothis Fruitworm. Available online: https://fruit.wisc.edu/wp-content/uploads/sites/ 36/2011/05/Sparganothis-Fruitworm.pdf (accessed on 7 November 2016). 24. Teixeira, L.A.F.; Averill, A.L.; Teixeira, L.S. Evaluation of flooding for cultural control of Sparganothis sulfureana (Lepidoptera: Tortricidae) in cranberry bogs. Environ. Entomol. 2006, 35, 670–675. [CrossRef] 25. Marucci, E.P.; Moulter, J.H. Biological contgrol of sparganothis fruitworm on cranberries in New Jersey. Cranberries 1992, 56, 11–15. 26. Mahr, D.L. Blackheaded Fireworm. Available online: https://fruit.wisc.edu/wp-content/uploads/sites/36/ 2011/05/Blackheaded-Fireworm.pdf (accessed on 7 November 2016). 27. Cockfield, S.D.; Mahr, D.L.; Blackheaded Fireworm, T.H.E. Flooding cranberry beds to control blackheaded fireworm (Lepidoptera: Tortricidae). J. Econ. Entomol. 1992, 85, 2383–2388. [CrossRef] 28. DeMoranville, C.J. Cranberry Nutrients, Phenology, and N-P-K Fertilization; University of Massachusetts: Amherst, MA, USA, 1992.

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