Department of Entomology, Purdue University, Smith Hall, 901 W. State St., West Lafayette, IN Environ. Entomol. 35(5): 1342Ð1349 (2006)

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1 BIOLOGICAL CONTROLÐPARASITES AND PREDATORS Suppression of Population Growth of the Soybean Aphid, Aphis glycines Matsumura, by Predators: The Identification of a Key Predator and the Effects of Prey Dispersion, Predator Abundance, and Temperature NICOLAS DESNEUX, 1 ROBERT J. O NEIL, AND HO JUNG S. YOO Department of Entomology, Purdue University, Smith Hall, 901 W. State St., West Lafayette, IN Environ. Entomol. 35(5): 1342Ð1349 (2006) ABSTRACT The soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), has recently invaded North America from Asia and has become a major pest in soybean. Using Þeld surveys and cage exclusion techniques, we identiþed the effect of natural enemies and abiotic factors on the growth of soybean aphid populations in 2004 and The soybean aphid population was signiþcantly limited by natural enemies in the Þeld. Generalist predators dominated the natural enemy community. One species, Orius insidiosus Say (Hemiptera: Anthocoridae) represented 85Ð90% of predators found. There was a signiþcant negative relationship between aphid population growth and O. insidiosus abundance. For other predators, there were no relationships between abundance and aphid population growth. The spatial distribution of aphids among plants affected the impact of O. insidiosus on aphid population growth. When aphids were distributed in a clumped manner, increases in O. insidiosus numbers resulted in lower aphid growth rates. For randomly distributed aphids, there was no effect of O. insidiosus abundance on aphid population growth. Finally, we found no relationship between aphid population growth and degree-day accumulations. The potential of O. insidiosus to suppress soybean aphid population growth at low aphid numbers and the importance of the predator to soybean aphid integrated pest management are discussed. KEY WORDS PredatorÐprey dynamics, prey distribution, soybean aphid, Orius insidiosus The soybean aphid Aphis glycines Matsumura (Hemiptera: Aphididae), a pest of soybean in Asia, has recently invaded North America. Soybean aphid was discovered infesting soybean Þelds in the Midwest in the summer of 2000, and by 2003, it had been found in 20 states and parts of eastern Canada (Venette and Ragsdale 2004). In Wisconsin, high densities of soybean aphids in 2003 were associated with substantial yield losses (Myers et al. 2005). Similar aphid densityð damage relationships have been noted in Minnesota, Michigan (DiFonzo and Hines 2002), Indiana (R.J.O., unpublished data), and in the aphidõs native range, Asia (Wang et al. 1994). In the Midwest, yield reductions by soybean aphid have prompted insecticide applications on numerous acres of soybeans. Generalist predators typically dominate the natural enemy community of soybean aphid in North America (Rutledge et al. 2004). Predators can signiþcantly impact populations of soybean aphid, particularly early in the season when the aphid is colonizing the crop (Fox et al. 2004, Rutledge et al. 2004, Rutledge and 1 Corresponding author: Department of Entomology, University of Minnesota, 1980 Folwell Ave., St. Paul, MN ( desne001@ umn.edu). OÕNeil 2005). Although predators have been implicated in early-season suppression of soybean aphid population growth, the effects of aphid and predator abundance, plant size, prey dispersion, temperature, and other factors known to inßuence predation in other predatorðprey systems (Wiedenmann and OÕNeil 1992) have not been documented for soybean aphid. In this paper, we report measures of the growth of soybean aphid populations in soybeans at low aphid numbers that are characteristic of the initial infestation in the early season. We identify a predator that signiþcantly limits soybean aphid population growth and evaluate the impact of aphid distribution, predator abundance, and temperature on predation in the Þeld. This research is part of a larger effort to describe how various biotic and abiotic factors affect predation of soybean aphid. Ultimately, we wish to predict the impact of predators on soybean aphid dynamics to better use predators in soybean aphid management. Materials and Methods Experiments were conducted in 2004 and 2005 at the Purdue University Agronomy Center for Research X/06/1342Ð1349$04.00/ Entomological Society of America

2 October 2006 DESNEUX ET AL.: SOYBEAN APHID PREDATION 1343 and Education (ACRE) in Tippecanoe County, IN. In 2004, we used Beck 355RR (Beck Hybrids, Atlanta, IN), and in 2005, we used Asgrow 3602RR soybeans (Monsanto Co., St. Louis, MO), cultivated using standard agronomic techniques and 76.2-cm spacing between rows. In 2004, experiments were conducted within a 7.5-ha Þeld planted at a rate of 444,600 seeds/ha on 10 May. In 2005, we used an 11.9-ha Þeld planted on 7 May at a rate of 370,500 seeds/ha. Experiments were conducted in JulyÐAugust in 2004 and JuneÐJuly Experimental plots were established using a 2 by 2 factorial design. The Þrst factor varied soybean aphid exposure to predators using exclusion cages or uncaged, Þeld-release plots. The second factor varied the initial between-plant distribution of soybean aphid as either clumped or random. Exclusion cages were 1.5 by 1 by 1.5 m (L by W by H). Cages were constructed using PVC pipe (Silver-Line Plastics, Asheville, NC) and nylon mesh netting (540 m; Service Textiles, Chicago, IL). The cage was divided in the center by additional mesh to separate two experimental arenas. Each cage was set over a single row of soybean plants. Five adjacent plants centered in each arena of the cage were selected. The remaining plants in the cage were uprooted and discarded. The plants were at the same growth stage, had overlapping foliage, and were not touching either the edges of the cage or the center partition. The plants were cleaned of all arthropods using a modiþed leaf-blower and a mouth aspirator. To exclude ground-dwelling predators, the mesh was buried to a depth of 10 cm after the plants were cleaned. Each arena of the cage was considered as one plot in the exclusion cage treatment: one random and one clumped treatment plot per exclusion cage. In uncaged treatments, randomly selected groups of Þve plants (in the same row) were delineated with ßags and left uncaged. Plants were cleaned of preexisting aphids, using a manual aspirator. A two-plant buffer on either side of each plot was cleaned of aphids to minimize colonization of soybean aphid from adjoining plants. Both exclusion cages and Þeld-release plots were contained within a 0.3-ha section of the soybean Þeld. After preparation of the caged and open plots, 10 greenhouse-reared soybean aphid adults were added to each plot (between 1600 and 1800 hours on each day the experiment was set up). Aphids were placed on the highest central leaßet of the plants using a camelõs-hair brush. For the clumped distribution treatment, all aphids were placed on the central plant within the plot giving an aphid per plant distribution of 0, 0, 10, 0, and 0, respectively. For the random distribution treatment, aphids were placed according to a Poisson distribution with a mean of two (i.e., 10 aphids per Þve plants), giving a per plant aphid distribution of 2, 0, 1, 6, and 1, respectively. After placement of aphids, exclusion cages were closed, and aphids in all treatments were left to reproduce for 7 d. At the end of this period, we counted the number of aphids on each of the Þve plants. Two independent experiments were conducted per week, set up 3Ð4 d apart throughout the season until the plants began to senesce (in 2004) or until the background aphid population reached 10 aphids per plant (in 2005) (H.J.S.Y., unpublished data), which posed an unacceptable contamination risk to the experiment. In 2004, we used 28Ð30 replicates (plots) per distribution in the open Þeld and 10 replicates per distribution in exclusion cages. In 2005, we used 17Ð20 replicates per distribution in the open Þeld and 9Ð10 replicates per distribution in exclusion cages. Fewer replicates were used for exclusion cages as we expected lower variance in cages than in open Þeld plots. The abundance of natural enemies and aphids was surveyed twice weekly in an adjacent area in the same soybean Þeld to avoid disruption of the experimental study. In 2004, the survey area consisted of Þve plots of 0.12 ha each, randomly located within the soybean Þeld. At each sampling date, 12 plants per plot were sampled systematically at 10-m intervals. Each plant was pulled from the ground and all arthropods were recorded by species. MummiÞed and (fungal) infected aphids were also recorded. Predator densities are reported as mean counts per plant for the Þeld on each sampling date (n 60 for all dates). In 2005, sampling was similar except that four plots of 0.2 ha each were used. At each survey date, 20 plants were sampled per plot (n 80). In addition, ground-dwelling predator abundance and composition was assessed using 10 pitfall traps randomly distributed in the same part of the Þeld containing the exclusion cages. Each trap was 8.5 cm in diameter at the soil surface and contained ethylene glycol as the killing and preservative agent. Pitfall samples were collected every 7 d, and predators were identiþed from the species to ordinal level depending on the relative abundance of taxa. Specimens have been deposited in the Robert Meyer Biological Control collection in the laboratory of R. J. OÕNeil at Purdue University. Temperature was monitored daily at the ACRE weather station. We computed the weekly accumulated degree-days (WDD) using the formula: 7 Tmax Tmin WDD 2 t 1 K where Tmax and Tmin are the daily maximum and minimal temperatures, respectively, and K is the lower developmental threshold set at 8.6 C for A. glycines (McCornack et al. 2004). We used weekly accumulated degree-days to estimate the aphidõs expected survivorship (l x ) and fecundity (m x ) as reported by McCornack et al. (2004). To test whether the exclusion cage had an effect on temperature, we measured temperatures at top-canopy height within and outside an exclusion cage using HOBO temperature data loggers (Onset Computer, Pocasset, MA) from 16 July 2004 to 26 August 2004 and 6 July 2005 to 18 July, We tested the effects of exclusion cage, aphid distribution, and date on the Þnal number of aphids using a generalized linear model based on a Poisson distribution and a log-link function (Proc Genmod; SAS Institute 1999). Simple linear regressions were used to

3 1344 ENVIRONMENTAL ENTOMOLOGY Vol. 35, no. 5 Fig. 1. Mean Þnal number ( SE) of aphids per plot (Þve plants each) in exclusion cages (solid line, random and clumped treatments pooled; 2004: n 10 per date; 2005: n 9Ð10 per date) and in open plots (dashed line, random and clumped treatments pooled; 2004: n 28Ð30 per date; 2005: n 17Ð20 per date). Populations started with 10 adults per plot and were counted 1 wk later. The experiment was repeated 16 times during the summer of 2004 (A) and 11 times during the summer of 2005 (B).

4 October 2006 DESNEUX ET AL.: SOYBEAN APHID PREDATION 1345 Table 1. Statistics from the GLM used to analyze the no. of aphids after 1 wk of growth under experimental treatments (clumped and random distributions in open plots and exclusion cages) for 2004 and 2005 Source of variation df 2 P df 2 P Effects of exclusion cages and distribution on avg aphid no. Cage 1 5, Distribution Date 15 1, , Cage distribution Date distribution Date was used to represent the variability in aphid population growth over the course of the study. determine relationships between Þnal aphid numbers and natural enemy abundance at the beginning of each trial, as well as average aphid population growth and total degree-days per week (SPSS, Chicago, IL). Average daily maximum and minimum temperatures within cages and the Þeld were compared using paired sample t-tests. Results SigniÞcantly more aphids were found in exclusion cages than in the open Þeld plots (Fig. 1A and B; Table 1). In 2004, aphid numbers in exclusion cages increased on average by 2.0- to 5.6-fold (max-min), whereas in the open Þeld, aphid numbers increased on average by 0- to 1.9-fold. In 2005, aphid numbers increased by 1.5- to 7.7-fold in exclusion cages and 1.5- to 2.9-fold in the open Þeld (Fig. 1B). The initial distribution of aphids impacted the growth of aphid populations in both 2004 and 2005 (Table 1). There was a signiþcant interaction between distribution and cage factors (Table 1), speciþcally initial aphid distribution affected population growth in open plots (2004 data: , df 1, P 0.002; 2005 data: , df 1, P 0.009) but not in exclusion cages (2004 data: , df 1, P 0.311; 2005 data: , df 1, P 0.339). Date had a signiþcant effect indicating that aphid population growth varied throughout the season (Table 1). The distribution factor interacted with date suggesting that during the season the distribution effect on aphid population growth changed over time (Table 1). Whole plant samples of natural enemies in 2004 contained only predators; diseased and parasitized aphids (mummies) were absent. Adults and nymphs of the anthocorid, Orius insidiosus (Say), represented 85% of predators collected (Fig. 2A). Other predators found included coccinellids, spiders, and syrphid larvae, which represented 1.8, 3.2, and 6.4% of the predators found, respectively. There was a signiþcant negative relationship between Þnal aphid counts in the open Þeld plots and the number of O. insidiosus at the start of each experimental replicate (Fig. 3A; R , F 8.10, df 30, P 0.008). There were no signiþcant relationships between Þnal aphid numbers and the number of pooled (excluding O. insidiosus) predators (R , F 1.43, df 30, P 0.241) or between aphid number and the number of spiders (R , F 2.51, df 30, P 0.124) or syrphid larvae (R , F 2.87, df 30, P 0.101). For coccinellids, we found a signiþcant positive relationship with Þnal aphid counts (R , F 9.43, df 30, P 0.005). Surveys of natural enemies in 2005 showed the same lack of parasitized or diseased aphids and predominance of O. insidiosus in the natural enemy community (Fig. 2B). O. insidiosus accounted for 89.6% of the natural enemies found. Other predators were sparse, and consisted of coccinellids (0.2%), spiders (3.3%), syrphid larvae (6.2%), and lacewing larvae (0.7%). There was a signiþcant negative relationship between Þnal aphid counts in the open Þeld and the number of O. insidiosus at the start of each experimental replicate (Fig. 3B; R , F 5.76, df 20, P 0.026). No signiþcant regressions were found for pooled (excluding O. insidiosus) predator numbers (R , F 1.76, df 20, P 0.200), spiders (R , F 0.68, df 20, P 0.423), syrphid larvae (R , F 1.32, df 20, P 0.269), or coccinellids (R , F 2.31, df 20, P 0.151). In open plots, there was a signiþcant negative relationship between Þnal aphid count and O. insidiosus numbers for clumped aphids (Fig. 3A and B; 2004: R , F 15.65, df 14, P 0.001; 2005: R , F 8.01, df 9, P 0.020) but not for randomly distributed aphids (2004: R , F 0.29, df 14, P 0.596; 2005: R , F 0.82, df 9, P 0.389). Most ground-dwelling predators were carabids (69.4%), spiders (19.9%), or Opiliones (10.7%). We found no signiþcant relationships between aphid numbers and the numbers of carabid beetles (R , F 1.06, df 20, P 0.316) or Opiliones (R , F 0.01, df 20, P 0.907). Weekly totals of degree-days were similar for both years and varied between 62Ð100 (2004) and 67Ð125 (2005). There were no signiþcant relationships between total degree-days per week and aphid population growth in exclusion cages in 2004 (clumped: R , F 0.38, df 14, P 0.546; random: R , F 0.73, df 14, P 0.406) or 2005 (clumped: R , F 1.85, df 9, P 0.207; random: R , F 1.86, df 9, P 0.206). Likewise, no signiþcant relationships were found in open Þeld plots in 2004 (clumped: R , F 1.61, df 14, P 0.225; random: R , F 2.08, df 14, P 0.172) or 2005 (clumped: R , F 0.008, df 9, P 0.931;

5 1346 ENVIRONMENTAL ENTOMOLOGY Vol. 35, no. 5 Fig. 2. Mean number per plant ( SE) of O. insidiosus (solid line) and other predators including coccinellids, spiders, syrphid larvae, and lacewing larvae (dashed line). Results are from sampling done during the summer of 2004 (A; n 60 per date) and the summer of 2005 (B; n 80 per date). random: R , F 2.26, df 9, P 0.167). Expected values of survivorship (l x ) based on our weekly accumulated degree-days measurements ranged from 0.8 to 0.83 (2004) and 0.75 to 0.8 (2005) (McCornack et al. 2004). Expected fecundity (m x ) varied between 0.48Ð0.55 (2004) and 0.5Ð0.55 (2005). Temperatures inside and outside of the exclusion cages were similar, with temperatures differing by 1 C in 2004 and In 2004, no signiþcant differences were found for either average maximum (t

6 October 2006 DESNEUX ET AL.: SOYBEAN APHID PREDATION 1347 Fig. 3. Linear regression of the number of aphids per plant after 1 wk of growth in the open Þeld plots as a function of O. insidiosus number per plant in 2004 (A) and 2005 (B). Average initial density was two aphids per plant. F, Þnal numbers of aphids distributed in a clumped fashion; E, randomly distributed aphids; continuous lines, regression on all data (clumped and random); dashed lines, regression on clumped data only , df 41, P 0.141) or minimum temperatures (t 0.756, df 41, P 0.454). Similarly, in 2005, no signiþcant differences were found for either average maximum (t 1.142, df 12, P 0.276) or minimum temperatures (t 1.453, df 12, P 0.172). Discussion Temperature had little effect on aphid population growth. The low range in accumulated degree-day values for both 2004 and 2005 resulted in relatively low variability in the population growth parameters l x and m x. The low variability in these parameters was not reßected in the population growth of aphids in exclusion cages (Fig. 1), which varied up to 7.7-fold over the course of the study. It is not clear why aphid population growth varied over time in exclusion cages, although one factor, plant age, does not seem to be important. Rutledge and OÕNeil (2006) have shown no effect of plant age on soybean aphid population dynamics or on aphid survival (l x ) or fecundity (m x ). Regardless of the factor(s) that may have inßuenced aphid growth within cages, the similarity in temperatures between cages and the open Þeld argue that temperature per se could not be the factor responsible for the signiþcant differences in aphid growth in exclusion cages and open Þeld plots. Our study provides an estimate of the contribution of natural enemies, speciþcally predators, to soybean aphid population growth and management. Predators were the only natural enemies observed in the Þeld, and comparisons of aphid population growth within cages and in open Þeld plots showed a signiþcant impact throughout the colonization period of the crop. In the absence of predators, soybean aphids were able to increase up to a maximum of 7.7-fold, whereas in the presence of predators, aphids only achieved a maximum increase of 2.9-fold. These differences in aphid population growth have signiþcant consequences for aphid management. Using the current economic threshold of 250 soybean aphids per plant (Ragsdale et al. 2006) and an exponential growth model, the aphid population growth rates as measured in cages would result in the 10 aphids per plot exceeding the threshold in 3 wk (assuming no immigration and emigration). In contrast, aphids exposed to predators would require 8 wk to reach the threshold. Depending on the arrival date and number of colonizing aphids, the delay induced by predation could result in reduced damage to the crop. Our study conþrms the importance of O. insidiosus to the population growth of the soybean aphid. The life history characteristics and ecology of O. insidiosus make it a particularly useful natural enemy against soybean aphid. The predator is commonly found in soybean Þelds early in the season at the time of establishment of the aphid and can attack aphids during their initial colonization of the crop (see also Rutledge et al. 2004, Rutledge and OÕNeil 2005). As a generalist, O. insidiosus can feed on a variety of prey, including spider mites, thrips, leafhoppers, and other small insects, as well as plants (Isenhour and Yeargan 1981, Kiman and Yeargan 1985, Coll and Guershon 2002). O. insidiosus is not reliant on soybean aphids to establish populations in soybeans (Anderson and Yeargan 1998) and can use alternative foods to survive periods of low aphid (or other prey) availability as do other generalist predators (Legaspi et al. 1996, Murdoch et

7 1348 ENVIRONMENTAL ENTOMOLOGY Vol. 35, no. 5 al. 1985, Wiedenmann and Smith 1997). Previous laboratory research (Rutledge and OÕNeil 2005) suggested that, like generalist predators in other systems (Chang and Kareiva 1999, Symondson et al. 2002), O. insidiosus is able to suppress soybean aphid population growth when aphid populations are small. Our veri- Þcation of the suppressive capability of O. insidiosus in Þeld research indicates the need for pest management strategies to incorporate the impact of this indigenous natural enemy on soybean aphid growth and pest status. As O. insidiosus numbers increased, there was a signiþcant decline in Þnal aphid numbers for clumped but not randomly distributed aphids. The effect of prey distribution on predation depends on a number of factors including the relative density of prey (Wiedenmann and OÕNeil 1992), the use of chemical cues by searching predators (Vet and Dicke 1992, Turlings and Wäckers 2004), the searching behavior of predators among and between patches of prey (Casas and Djemai 2002), and their learning capacity (van Alphen and Jervis 1996). When soybean aphids Þrst arrive in a soybean Þeld, their distribution is patchy, and it is common to Þnd several aphids on one plant and none on neighboring plants (Ragsdale et al. 2004). Predator effectiveness in suppressing aphid growth will depend on its ability to Þnd plants with aphids and how many aphids it attacks once aphids are encountered. In laboratory research, O. insidiosus can attack up to 12 soybean aphids per day when searching requirements are minimal, i.e., when both O. insidiosus and soybean aphids are enclosed in a microcosm (Rutledge and OÕNeil 2005). How O. insidiosus locates soybean aphids in soybeans is not known, but Orius spp. in other systems use a combination of visual (Henaut et al. 1999) and chemical cues (Venzon et al. 1999), as well as modiþcations to search behavior near high densities of prey (Tuda and Shima 2002). The soybean plant emits synomones after attack by the soybean aphid (Zhu and Park 2005). Potentially, these synomones could impact predation by O. insidiosus, particularly if the plants respond to aphid densities characteristic of the colonization period. Likewise, putative effects of other factors inßuencing predation by O. insidiosus should be studied under conditions that mimic those during the early season when aphid numbers are relatively low and predators can suppress aphid growth and prevent aphid outbreaks. The importance of predators early in the season presents a challenge to pest managers to develop sampling protocols, predictive models, and conservation techniques that take advantage of predator-mediated aphid dynamics before the aphid reaches pest status, and are readily observed by farmers. Likewise, for ecologists studying predatorðprey interactions in soybeans early in the season, the low numbers of both predator and prey present logistical challenges to experimentation and quantitative analyses. However, to develop an ecologically based management program for the soybean aphid, we will need to understand the mechanisms that drive its dynamics during the colonization of soybeans and initial population growth in the crop. Acknowledgments We thank C. Chango, P. Cisneros, J. Borjas, J. Head, C. Butler, K. Wyckhuys, A. Harrod, H. Hauck, J. OÕNeil, N. OÕNeil, B. Dimmitt, and E. Adley for Þeld assistance. 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