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Landscape Context Affects Aphid Parasitism by Lysiphlebus testaceipes (Hymenoptera: Aphidiinae) in Wheat Fields

Landscape Context Affects Aphid Parasitism by Lysiphlebus testaceipes (Hymenoptera: Aphidiinae)... Winter wheat is Oklahoma’s most widely grown crop, and is planted during September and October, grows from fall through spring, and is harvested in June. Winter wheat fields are typically interspersed in a mosaic of habitats in other uses, and we hypothesized that the spatial and temporal composition and configuration of landscape elements, which contribute to agroecosystem diversity also influence biological control of common aphid pests. The parasitoid Lysiphlebus testaceipes (Cresson; Hymenoptera: Aphidiinae) is highly effective at reducing aphid populations in wheat in Oklahoma, and though a great deal is known about the biology and ecology of L. testaceipes, there are gaps in knowledge that limit predicting when and where it will be effective at controlling aphid infestations in wheat. Our objective was to determine the influence of landscape structure on parasitism of cereal aphids by L. testaceipes in wheat fields early in the growing season when aphid and parasitoid colonization occurs and later in the growing season when aphid and parasitoid populations are established in wheat fields. Seventy fields were studied during the three growing seasons. Significant correlations between parasitism by L. testaceipes and landscape variables existed for patch density, fractal dimension, Shannon’s patch diversity index, percent wheat, percent summer crops, and percent wooded land. Correlations between parasitism and landscape variables were generally greatest at a 3.2 km radius surrounding the wheat field. Correlations between parasitism and landscape variables that would be expected to increase with increasing landscape diversity were usually positive. Subsequent regression models for L.  testaceipes parasitism in wheat fields in autumn and spring showed that landscape variables influenced parasitism and indicated that parasitism increased with increasing landscape diversity. Overall, results indicate that L. testaceipes utilizes multiple habitats throughout the year depending on their availability and acceptability, and frequently disperses among habitats. Colonization of wheat fields by L. testaceipes in autumn appears to be enhanced by proximity to fields of summer crops and semi-natural habitats other than grasslands. Key words: cereal aphid, biological control, Hymenoptera, parasitoid Winter wheat is Oklahoma’s most widely grown crop, with more insects such as fall armyworm in autumn, and cereal aphids, primar- than 2 million ha planted annually (Epplin et al. 1998, USDA NASS ily greenbug, in autumn or spring (Royer et al. 2015). 2017). Winter wheat in Oklahoma is planted during September During the wheat growing season, winter wheat fields are typ - and October, grows from fall through spring, and is harvested in ically interspersed in a mosaic of lands in other uses. Grasslands June. Several aphid species infest wheat fields in this region, the (pasture and rangeland) with varying levels of management are the most common and important being greenbug, Schizaphis graminum most abundant land use type. Grasslands range from semi-natural (Rondani); bird cherry-oat aphid, Rhopalosiphum padi (L.); and lands that have never been cultivated and have high plant species English grain aphid, Sitobion avenae (F.) (Heteroptera: Aphididae). diversity to highly managed lands planted to a single grass species. Apart from the use of insecticidal seed treatments, which are used Fallow fields will mostly be planted to summer crops (soybean, corn, preventively in Oklahoma by an increasing number of producers, sorghum, and cotton) in spring, and fields may also be planted to insecticide applications are infrequent, and primarily used to control other winter crops, such as canola and barley. Riparian areas and Published by Oxford University Press on behalf of Entomological Society of America 2018. This work is writ- ten by (a) US Government employee(s) and is in the public domain in the US. 803 Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 804 Environmental Entomology, 2018, Vol. 47, No. 4 other semi-natural lands are also present in the landscape. Based on has specifically addressed effects of landscape context on L.  testa- previous studies that investigated pest suppression in agricultural ceipes. Brewer et al. (2008) demonstrated that adding sunflower to landscapes, the spatial and temporal configuration of landscape a strip cropping system including wheat in southeastern Wyoming elements, which contribute to agroecosystem diversity are likely to increased parasitism levels of Russian wheat aphid by L. testaceipes. influence populations of natural enemies, and possibly, biological Our objective was to determine the relative influence of land - control of insect pests by determining the availability of resources scape structure on parasitism of cereal aphids by L.  testaceipes in for beneficial insects ( Rusch et al. 2016). wheat fields early in the growing season when aphid and parasitoid In Oklahoma the parasitoid Lysiphlebus testaceipes (Cresson; colonization occurs, and later in the growing season when within Hymenoptera: Aphidiinae) is highly effective at reducing aphid field aphid and parasitoid population processes may predominate populations in winter wheat (Webster and Phillips 1912, Giles et al. over colonization. Our hypothesis was that the landscape context 2003, Jones et al. 2007, Royer et al. 2015). The parasitoid’s effect- within which a wheat field is embedded influences the level of para - iveness is thought to be the result of features of its biology and ecol- sitism in a wheat field through its presumed effect on resource avail - ogy. It attacks multiple aphid species during cold but non-lethal fall ability to L.  testaceipes in other habitats, and this influence would and winter months where daytime temperatures often exceed the be most pronounced early in the growing season (autumn) when species’ activity threshold (Jones et al. 2007), it has high attack and colonization of wheat fields by aphids and L. testaceipes from other reproductive rates (Jones et al. 2003, Giles et al. 2003), it sterilizes habitats primarily determines abundance and parasitism. Landscape the aphids it attacks (Hight et al. 1972, Eikenbary and Rogers 1974), context would be predicted to have less influence later in the grow - and it dislodges aphids from the plant as it forages, making them ing season (spring) when aphid and parasitoid population dynamics subject to mortality from predation and the environment (Losey and within wheat fields predominates over colonization in determining Denno 1998). L.  testaceipes has been observed to keep greenbug abundance and parasitism. populations below the economic injury level in wheat (Eikenbary and Rogers 1974, Giles et  al. 2003), and its impact has been suc- Materials and Methods cessfully incorporated in pest sampling programs and management Field Study guidelines for greenbug (Giles et al. 2003; Royer et al. 2004, 2015). Even though a great deal is known about the biology and ecology We utilized aphid-infested sentinel barley plants in pots to quan- of the species, there are gaps in knowledge that limit understanding tify aphid parasitism within wheat fields in north central Oklahoma of the L.  testaceipes/aphid system and predicting when and where over a 3-yr period (Fig.  1). Potted wheat plants grown in a green- L.  testaceipes will be effective at controlling infestations in wheat. house and infested with parasitoid free bird cherry-oat aphids were The structure of the landscape surrounding a particular agricultural set out in commercial wheat fields during autumn and spring. The field has been shown to influence populations of predatory insects in technique has been successfully used to compare relative parasitism wheat fields ( Elliott et al. 1998), and parasitoid abundance and para- rates within and between wheat fields ( Brewer et  al. 2008). Each sitism levels of herbivorous insects in other agricultural ecosystems year, approximately 24 wheat fields were selected to achieve broad (Schmidt et  al. 2004, Thies et  al. 2005). However, only one study coverage of the area with randomization partially restricted by the Fig. 1. Approximate boundaries of the geographic area where field studies were conducted during the 2008–2009, 2009–2010, and 2010–2011 wheat growing seasons. Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 Environmental Entomology, 2018, Vol. 47, No. 4 805 availability of cooperating farmers and that study fields were sepa - fitted with a standard 33  cm diameter collecting unit, fine mesh rated by a minimum of 5 km. organdy collecting bag, and fiberglass collar. Sampling by D-Vac To determine if landscape effects on parasitism existed during has been shown to provide useful estimates of aphid abundance in autumn and spring, the field study described below was repeated cereals (Hand 1986). A  sample from each field consisting of three in autumn of three consecutive years, 2008–2010, and in spring subsamples was taken within the area where the sentinel plants were of two consecutive years, 2009 and 2010. Studies were initiated in deployed by walking three equally spaced linear transects. The first October, soon after wheat plants emerged from the soil, and during transect was situated parallel to and about 5 m to the left or right of mid-March when aphid and parasitoid activity is common (Giles the transect along which five of the seven sentinel plants had been et al. 2003). stationed. The other two transects were parallel to and approxi- Approximately 10 barley seeds (variety Eight Twelve) were mately 25 m to the right and left of the first transect. Approximately planted in a 2:1 mixture of peat moss and fritted clay in 15.2  cm every 5 m the D-vac collecting unit was placed straight down over diameter plastic pots, and a 14 cm diameter by 35 cm high circular growing wheat plants to just above the soil surface until 20 such clear plastic cage was placed on each pot. Each cage was vented in placements had been made. Each time the collecting unit was placed the side and top with fine muslin cloth and pressed about 2.5  cm down it was held in position slightly above the soil surface for 5 s. into the planting mixture. Fifteen pots containing caged barley After 20 stops, the sampling bag was removed from the D-vac and plants (hereafter referred to as sentinel plants) were placed in each all arthropods in it were transferred to a labeled plastic bag. Bags of 12 cubicle shaped cages (90 × 90 × 40 cm) covered with fine mesh were brought to the laboratory and placed in a freezer. Aphids were screen on the four sides, a plywood bottom, and a clear plastic top. counted at a later date. Frozen aphids are difficult to identify to spe - The double caging was done to ensure that aphids on sentinel plants cies so we did not distinguish among cereal aphid species. The mean remained parasitoid free until they were uncaged in the field. Two- number of aphids for the three transects sampled per field, each of week old sentinel plants in cages were infested with ca. 50 parasitoid which consisted of 20 D-vac placement subsamples (n = 3) provided free bird cherry-oat aphids. Approximately 10 d later, when aphid our estimate of aphid abundance for each wheat field. counts in cages averaged ca. 750 per pot, sentinel plants were trans- Landscape Data ported to wheat fields where they were stationed as described below. One of the 15 sentinel plants from each cage (total of 12 plants) Landscape context for each field was quantified for each of three was retained in the greenhouse as a check to ensure that parasitoids circular areas centered on the focal wheat field with radii of 0.8, 1.6, had not entered cages and parasitized aphids while they were being and 3.2 km extracted from the USDA NASS Cropland Data Layer grown in the greenhouse. from the appropriate year. The Cropland Data Layer was acquired In each wheat field, seven uncaged sentinel plants were placed for the year in which winter wheat was present in the layer for the into the soil and arranged 25 m apart in a T-shaped pattern. Sentinel study fields for that year, and was used to quantify the amount and plants were placed 5 m from a field edge, and every 25 m along distribution of all land use types. The Cropland Data Layer differ- a transect perpendicular to the field edge until five sentinel plants entiates crop types with accuracy rates typically above 85% (https:// had been placed. Sentinel plants were also placed 25 m to the right www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2. and left of the fourth plant at 90-degree angles. Sentinel plants were php date accessed 5 March 2017). The crop-specific data is avail - left in fields for 3 d and then caged, returned to the greenhouse able at https://nassgeodata.gmu.edu/CropScape/ (date accessed 5 and maintained for 7 d at 21°C (±5°C), 16:8 (L:D) h, and ambient March 2017). Distances probably encompassed the range of poten- (uncontrolled) humidity to allow any parasitoids to undergo devel- tial dispersal ability of L. testaceipes (Thies et al. 2005). In addition opment to the pupal stage. After 7 d the barley plants from each pot to NASS data, we collected fine spatial scale ground survey data were cut, separately placed in an emergence canister, and held for an of the grass species in the wheat field boundaries. All grass species additional 7 d to allow parasitoids to emerge as adults. Adult par- within one 15-m transect at an arbitrary location within each of two asitoids in each emergence canister were counted and identified to accessible field edges (e.g., adjacent to a road) were recorded for each species. In wheat fields in Oklahoma, L. testaceipes is the dominant field. One of the edges sampled was the one adjacent to where sen - cereal aphid parasitoid, usually accounting for over 95% of cereal tinel plants were deployed in the field. In addition, percent cover by aphid parasitism. During our study L. testaceipes accounted for over Johnson grass, Sorghum halepense (L.), was estimated visually for an 98% of total parasitism in all fields in autumn and spring. Thus, the area of approximately 200 m in the boundary adjacent to the field. number of adult L.  testaceipes per sentinel plant provided a useful The cropland data layer was re-classified to retain eight land use measure of parasitism in a field. categories: wheat, summer crops, winter crops other than wheat, Check plants that remained under greenhouse conditions were fallow, grassland (pasture and rangeland), wooded, built areas and processed identically to the experimental sentinel plants. No parasi- roads, and water. Aggregating land uses into fewer categories than toids were recovered from check plants during the 3-yr study indi- represented in the original NASS data was desirable for calculating cating that it was very unlikely that contamination of aphid infested meaningful landscape metrics because many categories would have sentinel plants by parasitoids occurred in the greenhouse. been represented by very small areas and metrics would be subject We recorded several attributes for each wheat field. We recorded to high variability. We quantified landscape structure relative to each whether insecticidal seed treatment was used on the seed planted in field in each year using the following landscape metrics: the propor - a field, whether the field was in a no-till or conventional till system, tion of the total area in each land use type, patch density, perimeter whether the field was in a crop rotational system or in continuous to area fractal dimension, Shannon’s patch diversity, and contagion wheat, the wheat plant growth stage, and aphid abundance in the (McGarigal and Marks 1995, McGarigal 2014). field at the time sentinel plants were deployed. Wheat plant growth Landscape metrics quantify various characteristics of landscape stage for each field was estimated using a 0–10 scale ( Zadoks et al. structure that can be ecologically significant ( O’Neill et al. 1988). 1974) on the day that sentinel plants were deployed. Aphid abun- Patch density, Shannon’s patch diversity, contagion, and perime- dance in each field was estimated by sampling with a Backpack ter to area fractal dimension are four among dozens of metrics. Model 24 D-Vac (Rincon-Vitova Insectaries, Inc., Ventura, CA) These four metrics have straight-forward interpretations, and the Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 806 Environmental Entomology, 2018, Vol. 47, No. 4 latter three were found by Ritters et al. (1995) in a study of sev- based on magnitudes of component loadings on the original var- eral landscapes to be good quantitative descriptors of landscape iables. Stepwise multiple regression was used to construct models structure that were relatively independent of one another. For the using the components as regressors. Component loadings were central Oklahoma landscapes in our study the three metrics were entered as independent variables in stepwise multiple regression correlated among themselves, and were also correlated with patch models with the mean number of L.  testaceipes per sentinel plant density. Patch density measures the number of patches per km and for each field as the dependent variable. F-tests were used to deter- indicates average patch size for a landscape. The perimeter to area mine the significance of regression models with α for inclusion of a fractal dimension is dimensionless and increases with increasing regressor in a model set at 0.15. The α = 0.15 level for inclusion was patch boundary curvilinearity. Contagion, measures the amount chosen so that moderately influential regressors were not overlooked of clumping of patch types within a landscape as a percentage of during model selection. Significance of the overall regression model the maximum. Maximum contagion for a given landscape would was maintained at α ≤ 0.05. Regression modeling was accomplished be achieved when each landscape element type occurred as a sin- using PROC REG (SAS Institute 2004). gle contiguous patch. High contagion indicates highly aggregated and poorly interspersed patches. Shannon’s patch diversity index Results is based on information theory (Shannon and Weaver 1949) and General Patterns measures landscape composition, not shape or configuration. Large values of Shannon’s diversity index indicate a greater number of Seventy-one fields were studied in autumn during the three growing landscape element types present (patch richness) in the landscape, seasons, however data for one field was not used because all but one greater evenness in area of the patch types present, or both. The sentinel plant were destroyed by wildlife. For autumn, the propor- number of patch types present did not vary much among land- tion of the seven sentinel plants from each field that had one or more scapes in our study because most patch types were present. The L. testaceipes per sentinel plant ranged from 0 to 1 among fields with minimal variation observed for patch richness indicates that varia- a mean of 0.28, and the mean number of L. testaceipes per sentinel tion in Shannon’s patch diversity index in our landscapes primarily plant ranged 0 to 100.4 among fields with a mean of 11.6 ( Table 1). reflected variation in the evenness in percent of area of various During spring both the average number of L. testaceipes per senti- patch types. Large values for fractal dimension, patch density, and nel plant and the proportion of sentinel plants with L.  testaceipes Shannon’s patch diversity, and small values for contagion generally were greater by 10-fold and threefold, respectively, than in autumn indicate high landscape diversity (see Turner et al. 2001 for more (Table  1). Aphid abundance in fields was also higher (greater than information). sevenfold) in spring than in autumn (Table 1) as was expected since Measured attributes of vegetation in field edges and landscape sentinel plants were placed in wheat fields in autumn as soon as metrics calculated using Fragstats Version 4 were used to quantify possible after emergence of wheat plants in the field from the soil. It landscape structure. Fragstats derived variables were calculated at was not possible to identify 24 fields each year with identical plant - each of the three hierarchically increasing spatial extents (0.8, 1.6, ing dates, so there was variation in wheat plant growth stage among and 3.2 km radii) for each field each year. GIS operations were fields. Variation in planting date probably accounted for a major accomplished using ERDAS Imagine version 2014 including export- portion of the variation in aphid abundance among fields. ing data for calculating landscape metrics with Fragstats. Approximately 32% of fields studied were no till and 68% were conventional till (Table  1). Sixty-six percent of fields were grazed Data Analysis by cattle during winter months (a common practice in Oklahoma; Correlation was used to evaluate pairwise relationships among vari- Epplin et al. 1998). Grazing is associated with use of conventional ables. When one (or both) variables were categorical, such as tillage tillage, because grazing cattle on no-till fields causes high levels of type, which was coded numerically as zero for no-till and one for soil compaction. Therefore, the percent of fields in no-till systems conventional tillage, spearman rank correlation coefficients were cal - was inversely related to grazing. The number of grass species in culated. Pearson correlation coefficients were calculated when both field edges ranged widely among fields, as did the percentage cover variables were continuous. The magnitude of correlation coefficients by Johnson grass in field edges. Landscape metrics varied substan - was used to determine the spatial extent to account for the greatest tially among the 70 fields for land areas in the three radii meas - amount of variation in parasitism. SAS PROC CORR (SAS Institute ured (Table 2). For example, the perimeter to area fractal dimension 2004) was used to calculate correlation coefficients. ranged from 1.20 to 1.45 for a radius of 0.8 km and from 1.31 to Many of the landscape variables were correlated so principal 1.45 for areas with a radius of 3.2 km. Percent of total land area components analysis was used to derive a set of linearly independ- planted to wheat ranged from 2.7 to 86.6 for the 0.8 km radius and ent regressors for use in regression modeling. Principal components from 10.1 to 68.5% for the 3.2 km radius. Although landscape met- were rotated using varimax rotation (Dillon and Goldstein 1984). rics varied considerably for each landscape extent, the range of each The number of rotated standardized principal components retained metric was similar across the 0.8 to 3.2 km spatial extents. for use as independent variables in regressions was determined by Many landscape metrics were correlated. Correlations among the scree method. The scree method involves plotting the eigenvalue metrics for 3.2 km radius land areas (Table 3) were similar to corre- associated with each principal component in successive order and lations for the two smaller radii (not shown). Of particular note, the determining the point beyond which the smaller eigenvalues form correlation between Shannon’s patch diversity index and contagion an approximately straight line. The components retained are those was −0.90, which indicates that evenness in the proportion of each associated with eigenvalues that fall above the straight line formed patch type was strongly negatively related to the extent of aggrega- by the smaller eigenvalues (Dillon and Goldstein 1984). The com- tion of particular patch types in the landscape. The presence of cor- ponents that were retained were used as regressors in models relat- relation among the majority of landscape metrics indicates that their ing parasitism to landscape context. Only linear (first order terms) use as independent variables in regression modeling would result in were included in models. Standardized components were interpreted multicollinearity. Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 Environmental Entomology, 2018, Vol. 47, No. 4 807 Table 1. Summary statistics (mean, SE, minimum, and maximum) for parasitism by L. testaceipes, aphid abundance, and other variables measured for n = 70 wheat fields in north central Oklahoma, for 2008, 2009, and 2010 Variable Mean SE Minimum Maximum Autumn Parasitism Number of L. testaceipes / sentinel plant 11.6 2.62 0.0 100.2 Prop. sentinel plants with L. testaceipes 0.28 0.03 0.0 1.0 Aphids (no. per 20 D-vac placements) 27.7 15.06 0.0 1040.0 Spring Parasitism Number of L. testaceipes / sentinel plant 179.9 22.64 0.0 576.7 Prop. sentinel plants with L. testaceipes 0.88 0.03 0.0 1.0 Aphids (no. per 20 D-vac placements) 195.6 47.48 12.3 1680.3 Within-field & Field Edge Tillage (0 = conventional, 1 = no till) 0.32 0.06 0.0 1.0 Crop rotation (0 = no, 1 = yes) 0.26 0.05 0.0 1.0 Grazed (0 = no, 1 = yes) 0.67 0.06 0.0 1.0 Grass species Richness 7.7 0.27 4.0 13.0 % Johnson grass coverage 34.1 2.66 0.0 75.0 Table 2. Summary statistics (mean, SE, minimum, and maximum) Aphid Abundance in Wheat Fields for landscape metrics measured at three radii centered on each Cereal aphid abundance was estimated for each wheat field at the of n = 70 wheat fields in north central Oklahoma, for 2008, 2009, times sentinel plants were deployed. Aphid abundance in autumn was and 2010 correlated with some landscape and within field variables. Autumn aphid abundance was correlated with whether the field was used for Variable Mean SE Minimum Maximum cattle grazing (r = 0.38; n = 70; P = 0.001), but not with any other Radius 0.8 km of the within-field variables measured. Since cattle were not placed Patch density 26.3 1.56 7.7 57.3 on fields until after our autumn study was complete, this effect may Shannon’s patch diversity 1.31 0.03 0.61 1.88 have been due to planting date, which is earlier for dual purpose Fractal dimension 1.33 0.01 1.20 1.45 wheat fields than for fields used only for grain production ( Epplin Contagion 53.0 1.22 32.5 77.5 et al. 1998). Aphid abundance in autumn was positively correlated % Wheat 40.8 2.36 2.7 86.6 % Summer crops 24.7 2.63 0.11 82.6 with % grassland in the landscape (r = 0.28; n = 70; P = 0.02), but % Winter crops (other than wheat) 0.2 0.07 0.0 4.1 not with any other landscape metric. For spring, aphid abundance % Fallow 1.9 0.24 0.0 10.1 was negatively correlated with both tillage type (conventional vs. % Grassland 23.0 1.69 1.7 52.4 no-till) (r = −0.47; n = 46; P = 0.001) and crop rotation (r = −0.58; % wooded 2.4 0.42 0.0 16.2 n = 46; P < 0.001). Since no-till is commonly practiced in conjunc- % manmade (built areas and roads) 6.5 0.53 2.0 24.3 tion with crop rotation in central Oklahoma, the two variables were % water 0.4 0.10 0.0 5.0 Radius 1.6 km highly correlated (r = 0.84; n = 70; P < 0.001). Aphid abundance in Patch density 24.9 1.28 7.8 48.3 wheat fields in spring was positively correlated with fractal dimen - Shannon’s patch diversity 1.46 0.03 0.87 1.87 sion (r = 0.29; n = 46; P = 0.05) and negatively correlated with % Fractal dimension 1.37 0.004 1.30 1.46 summer crops (r = −0.30; n = 46; P = 0.04); the correlation with % Contagion 51.5 0.89 34.5 69.4 grassland for spring was positive (r = 0.24) but not significant. % Wheat 35.9 1.77 11.6 78.1 % Summer crops 24.5 2.08 0.99 68.4 % Winter crops (other than wheat) 0.3 0.05 0.0 2.9 Parasitism of Bird Cherry–Oat Aphids by % Fallow 2.6 0.20 0.3 8.4 L. testaceipes % Grassland 27.0 1.89 6.5 67.9 Both the average number of L.  testaceipes per sentinel plant and % Wooded 2.8 0.33 0.0 9.9 the proportion of sentinel plants with L. testaceipes were correlated % Manmade (built areas and roads) 6.2 0.31 2.7 13.4 with landscape variables for autumn and spring. Correlations for the % Water 0.7 0.12 0.0 5.9 Radius 3.2 km number of L. testaceipes per sentinel plant were generally greater in Patch density 24.8 1.26 6.0 52.4 magnitude and more often significant than correlations for the pro - Shannon’s patch diversity 1.51 0.23 1.07 1.90 portion of sentinel plants with L. testaceipes. This was not surprising Fractal dimension 1.38 0.003 1.31 1.45 since only seven sentinel plants were deployed per field which limits Contagion 50.6 0.78 35.4 66.2 interpretation of this measure. Therefore, we limit further analysis of % Wheat 34.7 1.51 10.1 68.5 the relationship of parasitism with landscape variables to the num- % Summer crops 22.4 1.71 2.34 59.7 % Winter crops (other than wheat) 0.2 0.04 0.01 2.0 ber of L. testaceipes per sentinel plant. % Fallow 2.9 0.17 0.6 7.1 Neither aphid abundance nor any within field or field edge var - % Grassland 29.6 1.74 9.2 66.8 iable was correlated with the number of L.  testaceipes per sentinel % Wooded 3.1 0.26 0.1 10.5 plant in autumn (not shown). In spring, there was a significant cor - % Manmade (built areas and roads) 6.1 0.23 3.4 11.7 relation between aphid abundance in wheat fields and number of % Water 0.8 0.10 0.02 3.8 L. testaceipes per sentinel plant (r = 0.35; n = 46; P = 0.02). None of Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 808 Environmental Entomology, 2018, Vol. 47, No. 4 Table 3. Correlation among variables describing landscape context within 3.2 km radius areas centered on each of n = 70 wheat fields in north central Oklahoma, for 2008, 2009, and 2010 Variable Shannon diversity Fractal dimension Contagion % Wheat % Summer crops % Grass % Wooded Patch density 0.60* 0.26* −0.32* −0.25* 0.50* −0.36* 0.28* Shannon diversity −0.38* −0.90* −0.45* 0.77* −0.52* 0.45* Fractal dimension −0.50* −0.09 0.10 −0.16 0.49* Contagion 0.46* −0.56* 0.33* −0.59* % Wheat −0.36* −0.35* −0.48* % Summer crops −0.72* 0.10 % Grass 0.13 the other within field or field edge variables was significantly corre - the autumn regression model (Eq. 1) four factors, F1, F3, F4, and F5 lated with the number of L. testaceipes per sentinel plant in spring. were entered into the model using the selection criterion of P < 0.15 for For landscape metrics, significant correlations occurred for the inclusion of a variable. The regression coefficient for F1 was positive, number of L. testaceipes per sentinel plant with patch density, fractal and the largest loadings on F1 were for % summer crops (0.75) and dimension, Shannon’s patch diversity, percent wheat, percent summer % grassland (−0.71), indicating that the presence of high acreage of crops, and percent wooded land (Table 4). Correlations with percent summer crops increased parasitism in wheat in autumn, whereas the wheat were negative for all spatial extents in autumn, but were not presence of large amounts of grassland was detrimental to parasitism. significant in spring. Correlations with patch density were positive in Factor F3 had a negative regression coefficient and was dominated by a autumn but negative in spring (Fig. 2). Correlations with percent sum- large loading on % wheat (1.23), indicating a negative effect of wheat mer crops and fractal dimension were positive for all spatial extents mono-cropping on parasitism in autumn. The positive regression coef- and often significant in both autumn and spring ( Fig. 2). Correlations ficient for F4 combined with the large factor loading on patch density for contagion were negative for all spatial extents in both seasons, and (1.18) indicates a positive influence of small patch (field) size in the were significant in spring. Correlations with Shannon’s patch diversity landscape on parasitism. Finally, the positive regression coefficient for index were positive for all spatial extents in spring, but significant F5 combined with the dominant positive loading on fractal dimension only at 1.6 km. The correlation with percent wooded land was posi- (1.19) indicates a positive effect of curvilinear patch boundaries, char- tive and significant at 3.2 km, but not at finer spatial extents. acteristic of semi-natural and natural lands, on parasitism. Correlations between the number of L.  testaceipes per sentinel In spring, three factors were entered into the regression, F1, F4, plant and landscape variables for autumn were generally greatest at and F5 (Eq. 2). In contrast to autumn, F1 had a negative regression the 3.2 km radius and correlations in spring were similar at 1.6 and coefficient indicating a reversal of the relationship between percent 3.2 km (Table 4), therefore, we used landscape data measured at the of landscape planted to summer crops and the percent grassland 3.2 km scale in regression modeling for autumn and spring. Aphid from that in autumn. F4 had a positive regression coefficient indicat - abundance in autumn, and other variables measured in wheat fields ing a positive response of parasitism to increasing fractal dimension. and field edges, were uncorrelated with the number of L. testaceipes The coefficient of F5 was negative indicating a negative response per sentinel plant and therefore were not used as predictors in regres- of parasitism to increasing patch density, which is opposite of the sion models. Aphid abundance in wheat fields in spring was corre - response observed in autumn. lated with parasitism and was included as a predictor in stepwise regressions. Based on the scree method, we included five of eight factors Discussion (Supplementary Appendix 1) as predictors of the number of L. tes- Variables representing landscape composition as well as spatial config - taceipes per sentinel plant in stepwise regression models for autumn uration were correlated with parasitism levels by L. testaceipes in wheat and spring. The best fitting first-order regression model for the num - fields. Correlations were observed both in autumn when colonization ber of L. testaceipes in autumn (F = 6.72; df = 4, 65; P = 0.0001) was of wheat fields by cereal aphids and L. testaceipes first occurs, and in spring when within field processes might be expected to predominate Lt =+ 11.. 56 4 9 01 ⋅− FF 77 . 6 ⋅ 3 over colonization in determining L. testaceipes population density and +⋅ 4.. 93 FF 45 +⋅ 66 5 R = 00 .3 () (1) the resulting cereal aphid parasitism. It is notable that L.  testaceipes responds to landscape composition (e.g., % wheat and Shannon’s patch diversity index) as has been observed for braconid parasitoids of aphids where Lt is the number of L. testaceipes per sentinel plant, and F1, in several studies (e.g., Roschewitz et al. 2005, Thies et al. 2005, Plecas F3, F4, and F5 are factors derived from principal components ana- et al. 2014, Zhao et al. 2015), but also to landscape configuration (e.g., lysis (Supplementary Appendix 1). For spring, the best fitting regres - fractal dimension and contagion), which has been less frequently meas- sion model (F = 13.34; df = 42, 3; P = 0.0001) was ured (but see Plecas et al. 2014), and may be important in determining arthropod population processes in agroecosystems. Lt =− 179.. 95 2743 ⋅+ FF 10 43 . 9 ⋅ 4 2 Large acreage of wheat surrounding a focal wheat field was neg - −⋅ 955 .. 2 FR 50 = 49 () (2) atively correlated with cereal aphid parasitism in autumn, but was uncorrelated with parasitism in spring (Table  4). Acreage of sum- where Lt is the number of L.  testaceipes per sentinel plant and F1, mer crops was positively correlated with parasitism in autumn, but F4, and F5 are factors. Regression models for L.  testaceipes parasit- negatively correlated in spring (Table  4). The autumn and spring ism in wheat fields in autumn and spring were interpreted based on regression models (Eqs. 1 and 2) reflect these differing associations standardized factor loadings on the original landscape variables. For of parasitism with landscape variables for autumn and spring. Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 Environmental Entomology, 2018, Vol. 47, No. 4 809 Table 4. Correlation of landscape metrics with the number of L. testaceipes per sentinel plant and the percent of sentinel plants with L. tes- taceipes present for autumn (n = 70) and spring (n = 46) Autumn Spring Metric No. L. testaceipes % with L. testaceipes No. L. testaceipes % with L. testaceipes Radius 0.8 km Patch density 0.26* 0.23 −0.43* −0.05 Shannon’s patch diversity 0.13 0.07 0.01 0.22 Contagion −0.10 −0.09 −0.29* −0.26 Fractal dimension 0.10 0.06 0.11 0.11 % Wheat −0.34* −0.25* 0.01 −0.15 % Summer crops 0.39* 0.18 0.17 0.12 % Grassland −0.13 0.01 0.24 0.04 % Wooded −0.04 −0.04 0.09 0.15 Radius 1.6 km Patch density 0.32* 0.30* −0.50* −0.12 Shannon’s patch diversity 0.21 0.17 0.33* 0.10 Contagion −0.12 −0.15 −0.29* −0.43* Fractal dimension 0.18 0.18 0.39* 0.25 % Wheat −0.31* −0.35* 0.01 −0.17 % Summer crops 0.29* 0.16 0.27 0.10 % Grassland −0.08 0.08 0.20 0.00 % Wooded 0.07 0.08 0.22 0.22 Radius 3.2 km Patch density 0.32* 0.29* −0.61* −0.21 Shannon’s patch diversity 0.20 0.15 0.26 0.16 Contagion −0.14 −0.18 −0.30* −0.28 Fractal dimension 0.26* 0.31* 0.29* 0.20 % Wheat −0.34* −0.40* −0.01 −0.20 % Summer crops 0.31* 0.13 0.27 0.10 % Grassland −0.07 0.12 0.25 0.05 % Wooded 0.24* 0.19 0.07 0.18 *Statistically significant correlations ( P < 0.05). Additionally, patch density had a positive effect on parasitism in The varying effect of increasing patch density on parasitism autumn, but a negative effect in spring. Conversely, fractal dimen- from positive in fall to negative in spring is difficult to interpret. If sion had a positive effect in both seasons (Eqs. 1 and 2). In the agri- small patch size (high patch density) increased aphid parasitism by cultural landscapes of north central Oklahoma, both cropland and L. testaceipes in wheat fields it’s effect would most likely result from semi-natural land habitats contribute to the landscape scale popula- increasing access to habitats containing hosts or other resources. tion dynamics of L. testaceipes. The magnitude of this effect might vary seasonally but would not The autumn and spring regression models reflect the differing be expected to shift from positive to negative. The influence of patch associations of parasitism with landscape variables for autumn and density on parasitism is probably indirect and related to other land- spring. Differences in effects of landscape context among seasons sug- scape characteristics, such as those captured by Shannon’s diversity gest that the response of L.  testaceipes to landscape context is very index, that influence resource availability to L. testaceipes, are corre- rapid, within a single growing season. L. testaceipes dynamics within lated with patch density (Table 3), and are not explicity accounted for a wheat field, and correspondingly, capacity for cereal aphid suppres - in regression models. This result highlights the difficulty in teasing out sion, is partially dependent on population dynamics of the parasitoid the effects of numerous interacting factors on L. testaceipes ecology in the landscape surrounding a wheat field during the course of the using multivariate methods. Even with this limitation the study has growing season. In the heavily agricultural landscapes of north central highlighted several landscape characteristics that, through their influ - Oklahoma, cropland and semi-natural lands both contribute to deter- ence on resource availability influence the success of L. testaceipes as mining the landscape scale population dynamics of L.  testaceipes. a biological control agent in wheat agreoecosystems in Oklahoma. Considered in this context, the change in regression coefficient (posi - In Oklahoma, the correlation of aphid parasitism by L.  testa- tive to negative) of parasitism to presence of summer crops in autumn ceipes with landscape variables in wheat fields did not decrease with and spring is likely due the fact that summer crops serve as habitat increasing spatial extent, contrary to observations for braconid par- for L. testaceipes in early autumn when wheat is first planted, but are asitoids of cereal aphids in European wheat agroecosystems (Thies fallow fields in spring. The negative effect of amount of grassland on et al. 2005). L. testaceipes was not recorded in their study even though parasitism may be a consequence of the extremely arid and high tem- it is widely established in European fauna (Stary 1988, Zikic et  al. perature summer conditions in Oklahoma, which result in mostly dor- 2015). Interestingly, our finest spatial extent 0.8 km radius (1.6 km mant grasslands that harbor extremely low numbers of aphid hosts diameter) was closest to the maximum spatial extent (2.0 km diam- and therefore of parasitoids (Anstead 2000). Variation in the effect eter) at which landscape complexity significantly explained variation of wheat acreage on parasitism can be viewed similarly, where wheat in parasitism in wheat fields in Europe ( Thies et al. 2005). Our results fields are essentially devoid of cereal aphid hosts in early autumn but suggest that L.  testaceipes responds to landscape variation over a serve as habitat for cereal aphids and L. testaceipes in spring. broad spatial extent. L. testaceipes shows a very limited response to Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 810 Environmental Entomology, 2018, Vol. 47, No. 4 Fig. 2. Number of L. testaceipes per seven sentinel plants versus patch density and fractal dimension for autumn (top) and spring (bottom). plant volatiles (Lo Pinto et al. 2004, Fauvergue et al. 2006), and is rapid colonization of fields ( Bortoloto et al. 2015) and high attack and considered to be both a habitat and host generalist with potential reproductive rates (Jones 2005). Colonization of wheat fields in autumn for very rapid population growth (Mackauer and Stary 1967, Jones is enhanced by proximity to fields of summer crops and semi-natural et  al. 2003). These life history characteristics might be expected to habitats other than grasslands. Based on this study, the optimal land- be associated with high levels of dispersal. Although life histories of scape to promote biological control of cereal aphids in wheat by L. tes- most braconid aphid parasitoid species are not well known, L.  tes- taceipes is one where summer crops, wheat, and semi-natural habitats taceipes is probably exceptional in relation to breadth of host and exist in significant amounts, and are well interspersed. Future emphasis habitat range (Stary et al. 1988, Pike et al. 2000). Because of its life should be given to determining the specific resources within these habi - history characteristics L. testaceipes appears to be well adapted to the tats that are utilized by L. testaceipes. agroecosystems of the Southern Plains, where agricultural landscapes are coarse grained and habitats are poorly interspersed. Supplementary Data L. testaceipes is a key natural enemy of cereal aphids in wheat (Jones et al. 2007, Giles et al. 2017), and effective biological control frequently Supplementary data are available at Environmental Entomology occurs within wheat fields prior to the aphid infestation reaching the online. economic threshold (Giles et al. 2003). L. testaceipes does not appear to disperse among a set of essential habitats seasonally and therefore does not exhibit the characteristics of a cyclic colonizer (Wissinger 1997). Acknowledgments Rather L. testaceipes appears to utilize multiple habitats throughout the We thank Tim Johnson for technical assistance with the project and for coor- year depending on their availability and acceptability, and frequently dinating data collection and processing activities. Mention of trade names or disperses among habitats (Jessie 2017). In this sense L.  testaceipes is commercial products in this publication is solely for the purpose of providing better described as an r-selected species, with high reproductive and dis- specific information and does not imply recommendation or endorsement by persal rates (Ehler and Miller 1978, Price and Waldbauer 1994). The the U.S. Department of Agriculture (USDA). USDA is an equal opportunity provider and employer. ability of L.  testaceipes to control aphids in wheat fields depends on Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 Environmental Entomology, 2018, Vol. 47, No. 4 811 Technical Report PNWGTR-351. USDA Forest Service, Pacific Northwest References Cited Research Station, Portland, OR. Anstead, J. A. 2000. Genetic and biotypic diversity of greenbug Schizaphis O’Neill, R. V., J. R.  Krummel, R. H.  Gardner, G.  Sugihara, B.  Jackson, D. graminum (Rondani) populations on non-cultivated hosts. Doctoral L. DeAngelis, B. 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J. Zool. 11: 97–101. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Entomology Oxford University Press

Landscape Context Affects Aphid Parasitism by Lysiphlebus testaceipes (Hymenoptera: Aphidiinae) in Wheat Fields

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Oxford University Press
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Copyright © 2022 Entomological Society of America
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0046-225X
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1938-2936
DOI
10.1093/ee/nvy035
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Abstract

Winter wheat is Oklahoma’s most widely grown crop, and is planted during September and October, grows from fall through spring, and is harvested in June. Winter wheat fields are typically interspersed in a mosaic of habitats in other uses, and we hypothesized that the spatial and temporal composition and configuration of landscape elements, which contribute to agroecosystem diversity also influence biological control of common aphid pests. The parasitoid Lysiphlebus testaceipes (Cresson; Hymenoptera: Aphidiinae) is highly effective at reducing aphid populations in wheat in Oklahoma, and though a great deal is known about the biology and ecology of L. testaceipes, there are gaps in knowledge that limit predicting when and where it will be effective at controlling aphid infestations in wheat. Our objective was to determine the influence of landscape structure on parasitism of cereal aphids by L. testaceipes in wheat fields early in the growing season when aphid and parasitoid colonization occurs and later in the growing season when aphid and parasitoid populations are established in wheat fields. Seventy fields were studied during the three growing seasons. Significant correlations between parasitism by L. testaceipes and landscape variables existed for patch density, fractal dimension, Shannon’s patch diversity index, percent wheat, percent summer crops, and percent wooded land. Correlations between parasitism and landscape variables were generally greatest at a 3.2 km radius surrounding the wheat field. Correlations between parasitism and landscape variables that would be expected to increase with increasing landscape diversity were usually positive. Subsequent regression models for L.  testaceipes parasitism in wheat fields in autumn and spring showed that landscape variables influenced parasitism and indicated that parasitism increased with increasing landscape diversity. Overall, results indicate that L. testaceipes utilizes multiple habitats throughout the year depending on their availability and acceptability, and frequently disperses among habitats. Colonization of wheat fields by L. testaceipes in autumn appears to be enhanced by proximity to fields of summer crops and semi-natural habitats other than grasslands. Key words: cereal aphid, biological control, Hymenoptera, parasitoid Winter wheat is Oklahoma’s most widely grown crop, with more insects such as fall armyworm in autumn, and cereal aphids, primar- than 2 million ha planted annually (Epplin et al. 1998, USDA NASS ily greenbug, in autumn or spring (Royer et al. 2015). 2017). Winter wheat in Oklahoma is planted during September During the wheat growing season, winter wheat fields are typ - and October, grows from fall through spring, and is harvested in ically interspersed in a mosaic of lands in other uses. Grasslands June. Several aphid species infest wheat fields in this region, the (pasture and rangeland) with varying levels of management are the most common and important being greenbug, Schizaphis graminum most abundant land use type. Grasslands range from semi-natural (Rondani); bird cherry-oat aphid, Rhopalosiphum padi (L.); and lands that have never been cultivated and have high plant species English grain aphid, Sitobion avenae (F.) (Heteroptera: Aphididae). diversity to highly managed lands planted to a single grass species. Apart from the use of insecticidal seed treatments, which are used Fallow fields will mostly be planted to summer crops (soybean, corn, preventively in Oklahoma by an increasing number of producers, sorghum, and cotton) in spring, and fields may also be planted to insecticide applications are infrequent, and primarily used to control other winter crops, such as canola and barley. Riparian areas and Published by Oxford University Press on behalf of Entomological Society of America 2018. This work is writ- ten by (a) US Government employee(s) and is in the public domain in the US. 803 Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 804 Environmental Entomology, 2018, Vol. 47, No. 4 other semi-natural lands are also present in the landscape. Based on has specifically addressed effects of landscape context on L.  testa- previous studies that investigated pest suppression in agricultural ceipes. Brewer et al. (2008) demonstrated that adding sunflower to landscapes, the spatial and temporal configuration of landscape a strip cropping system including wheat in southeastern Wyoming elements, which contribute to agroecosystem diversity are likely to increased parasitism levels of Russian wheat aphid by L. testaceipes. influence populations of natural enemies, and possibly, biological Our objective was to determine the relative influence of land - control of insect pests by determining the availability of resources scape structure on parasitism of cereal aphids by L.  testaceipes in for beneficial insects ( Rusch et al. 2016). wheat fields early in the growing season when aphid and parasitoid In Oklahoma the parasitoid Lysiphlebus testaceipes (Cresson; colonization occurs, and later in the growing season when within Hymenoptera: Aphidiinae) is highly effective at reducing aphid field aphid and parasitoid population processes may predominate populations in winter wheat (Webster and Phillips 1912, Giles et al. over colonization. Our hypothesis was that the landscape context 2003, Jones et al. 2007, Royer et al. 2015). The parasitoid’s effect- within which a wheat field is embedded influences the level of para - iveness is thought to be the result of features of its biology and ecol- sitism in a wheat field through its presumed effect on resource avail - ogy. It attacks multiple aphid species during cold but non-lethal fall ability to L.  testaceipes in other habitats, and this influence would and winter months where daytime temperatures often exceed the be most pronounced early in the growing season (autumn) when species’ activity threshold (Jones et al. 2007), it has high attack and colonization of wheat fields by aphids and L. testaceipes from other reproductive rates (Jones et al. 2003, Giles et al. 2003), it sterilizes habitats primarily determines abundance and parasitism. Landscape the aphids it attacks (Hight et al. 1972, Eikenbary and Rogers 1974), context would be predicted to have less influence later in the grow - and it dislodges aphids from the plant as it forages, making them ing season (spring) when aphid and parasitoid population dynamics subject to mortality from predation and the environment (Losey and within wheat fields predominates over colonization in determining Denno 1998). L.  testaceipes has been observed to keep greenbug abundance and parasitism. populations below the economic injury level in wheat (Eikenbary and Rogers 1974, Giles et  al. 2003), and its impact has been suc- Materials and Methods cessfully incorporated in pest sampling programs and management Field Study guidelines for greenbug (Giles et al. 2003; Royer et al. 2004, 2015). Even though a great deal is known about the biology and ecology We utilized aphid-infested sentinel barley plants in pots to quan- of the species, there are gaps in knowledge that limit understanding tify aphid parasitism within wheat fields in north central Oklahoma of the L.  testaceipes/aphid system and predicting when and where over a 3-yr period (Fig.  1). Potted wheat plants grown in a green- L.  testaceipes will be effective at controlling infestations in wheat. house and infested with parasitoid free bird cherry-oat aphids were The structure of the landscape surrounding a particular agricultural set out in commercial wheat fields during autumn and spring. The field has been shown to influence populations of predatory insects in technique has been successfully used to compare relative parasitism wheat fields ( Elliott et al. 1998), and parasitoid abundance and para- rates within and between wheat fields ( Brewer et  al. 2008). Each sitism levels of herbivorous insects in other agricultural ecosystems year, approximately 24 wheat fields were selected to achieve broad (Schmidt et  al. 2004, Thies et  al. 2005). However, only one study coverage of the area with randomization partially restricted by the Fig. 1. Approximate boundaries of the geographic area where field studies were conducted during the 2008–2009, 2009–2010, and 2010–2011 wheat growing seasons. Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 Environmental Entomology, 2018, Vol. 47, No. 4 805 availability of cooperating farmers and that study fields were sepa - fitted with a standard 33  cm diameter collecting unit, fine mesh rated by a minimum of 5 km. organdy collecting bag, and fiberglass collar. Sampling by D-Vac To determine if landscape effects on parasitism existed during has been shown to provide useful estimates of aphid abundance in autumn and spring, the field study described below was repeated cereals (Hand 1986). A  sample from each field consisting of three in autumn of three consecutive years, 2008–2010, and in spring subsamples was taken within the area where the sentinel plants were of two consecutive years, 2009 and 2010. Studies were initiated in deployed by walking three equally spaced linear transects. The first October, soon after wheat plants emerged from the soil, and during transect was situated parallel to and about 5 m to the left or right of mid-March when aphid and parasitoid activity is common (Giles the transect along which five of the seven sentinel plants had been et al. 2003). stationed. The other two transects were parallel to and approxi- Approximately 10 barley seeds (variety Eight Twelve) were mately 25 m to the right and left of the first transect. Approximately planted in a 2:1 mixture of peat moss and fritted clay in 15.2  cm every 5 m the D-vac collecting unit was placed straight down over diameter plastic pots, and a 14 cm diameter by 35 cm high circular growing wheat plants to just above the soil surface until 20 such clear plastic cage was placed on each pot. Each cage was vented in placements had been made. Each time the collecting unit was placed the side and top with fine muslin cloth and pressed about 2.5  cm down it was held in position slightly above the soil surface for 5 s. into the planting mixture. Fifteen pots containing caged barley After 20 stops, the sampling bag was removed from the D-vac and plants (hereafter referred to as sentinel plants) were placed in each all arthropods in it were transferred to a labeled plastic bag. Bags of 12 cubicle shaped cages (90 × 90 × 40 cm) covered with fine mesh were brought to the laboratory and placed in a freezer. Aphids were screen on the four sides, a plywood bottom, and a clear plastic top. counted at a later date. Frozen aphids are difficult to identify to spe - The double caging was done to ensure that aphids on sentinel plants cies so we did not distinguish among cereal aphid species. The mean remained parasitoid free until they were uncaged in the field. Two- number of aphids for the three transects sampled per field, each of week old sentinel plants in cages were infested with ca. 50 parasitoid which consisted of 20 D-vac placement subsamples (n = 3) provided free bird cherry-oat aphids. Approximately 10 d later, when aphid our estimate of aphid abundance for each wheat field. counts in cages averaged ca. 750 per pot, sentinel plants were trans- Landscape Data ported to wheat fields where they were stationed as described below. One of the 15 sentinel plants from each cage (total of 12 plants) Landscape context for each field was quantified for each of three was retained in the greenhouse as a check to ensure that parasitoids circular areas centered on the focal wheat field with radii of 0.8, 1.6, had not entered cages and parasitized aphids while they were being and 3.2 km extracted from the USDA NASS Cropland Data Layer grown in the greenhouse. from the appropriate year. The Cropland Data Layer was acquired In each wheat field, seven uncaged sentinel plants were placed for the year in which winter wheat was present in the layer for the into the soil and arranged 25 m apart in a T-shaped pattern. Sentinel study fields for that year, and was used to quantify the amount and plants were placed 5 m from a field edge, and every 25 m along distribution of all land use types. The Cropland Data Layer differ- a transect perpendicular to the field edge until five sentinel plants entiates crop types with accuracy rates typically above 85% (https:// had been placed. Sentinel plants were also placed 25 m to the right www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2. and left of the fourth plant at 90-degree angles. Sentinel plants were php date accessed 5 March 2017). The crop-specific data is avail - left in fields for 3 d and then caged, returned to the greenhouse able at https://nassgeodata.gmu.edu/CropScape/ (date accessed 5 and maintained for 7 d at 21°C (±5°C), 16:8 (L:D) h, and ambient March 2017). Distances probably encompassed the range of poten- (uncontrolled) humidity to allow any parasitoids to undergo devel- tial dispersal ability of L. testaceipes (Thies et al. 2005). In addition opment to the pupal stage. After 7 d the barley plants from each pot to NASS data, we collected fine spatial scale ground survey data were cut, separately placed in an emergence canister, and held for an of the grass species in the wheat field boundaries. All grass species additional 7 d to allow parasitoids to emerge as adults. Adult par- within one 15-m transect at an arbitrary location within each of two asitoids in each emergence canister were counted and identified to accessible field edges (e.g., adjacent to a road) were recorded for each species. In wheat fields in Oklahoma, L. testaceipes is the dominant field. One of the edges sampled was the one adjacent to where sen - cereal aphid parasitoid, usually accounting for over 95% of cereal tinel plants were deployed in the field. In addition, percent cover by aphid parasitism. During our study L. testaceipes accounted for over Johnson grass, Sorghum halepense (L.), was estimated visually for an 98% of total parasitism in all fields in autumn and spring. Thus, the area of approximately 200 m in the boundary adjacent to the field. number of adult L.  testaceipes per sentinel plant provided a useful The cropland data layer was re-classified to retain eight land use measure of parasitism in a field. categories: wheat, summer crops, winter crops other than wheat, Check plants that remained under greenhouse conditions were fallow, grassland (pasture and rangeland), wooded, built areas and processed identically to the experimental sentinel plants. No parasi- roads, and water. Aggregating land uses into fewer categories than toids were recovered from check plants during the 3-yr study indi- represented in the original NASS data was desirable for calculating cating that it was very unlikely that contamination of aphid infested meaningful landscape metrics because many categories would have sentinel plants by parasitoids occurred in the greenhouse. been represented by very small areas and metrics would be subject We recorded several attributes for each wheat field. We recorded to high variability. We quantified landscape structure relative to each whether insecticidal seed treatment was used on the seed planted in field in each year using the following landscape metrics: the propor - a field, whether the field was in a no-till or conventional till system, tion of the total area in each land use type, patch density, perimeter whether the field was in a crop rotational system or in continuous to area fractal dimension, Shannon’s patch diversity, and contagion wheat, the wheat plant growth stage, and aphid abundance in the (McGarigal and Marks 1995, McGarigal 2014). field at the time sentinel plants were deployed. Wheat plant growth Landscape metrics quantify various characteristics of landscape stage for each field was estimated using a 0–10 scale ( Zadoks et al. structure that can be ecologically significant ( O’Neill et al. 1988). 1974) on the day that sentinel plants were deployed. Aphid abun- Patch density, Shannon’s patch diversity, contagion, and perime- dance in each field was estimated by sampling with a Backpack ter to area fractal dimension are four among dozens of metrics. Model 24 D-Vac (Rincon-Vitova Insectaries, Inc., Ventura, CA) These four metrics have straight-forward interpretations, and the Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 806 Environmental Entomology, 2018, Vol. 47, No. 4 latter three were found by Ritters et al. (1995) in a study of sev- based on magnitudes of component loadings on the original var- eral landscapes to be good quantitative descriptors of landscape iables. Stepwise multiple regression was used to construct models structure that were relatively independent of one another. For the using the components as regressors. Component loadings were central Oklahoma landscapes in our study the three metrics were entered as independent variables in stepwise multiple regression correlated among themselves, and were also correlated with patch models with the mean number of L.  testaceipes per sentinel plant density. Patch density measures the number of patches per km and for each field as the dependent variable. F-tests were used to deter- indicates average patch size for a landscape. The perimeter to area mine the significance of regression models with α for inclusion of a fractal dimension is dimensionless and increases with increasing regressor in a model set at 0.15. The α = 0.15 level for inclusion was patch boundary curvilinearity. Contagion, measures the amount chosen so that moderately influential regressors were not overlooked of clumping of patch types within a landscape as a percentage of during model selection. Significance of the overall regression model the maximum. Maximum contagion for a given landscape would was maintained at α ≤ 0.05. Regression modeling was accomplished be achieved when each landscape element type occurred as a sin- using PROC REG (SAS Institute 2004). gle contiguous patch. High contagion indicates highly aggregated and poorly interspersed patches. Shannon’s patch diversity index Results is based on information theory (Shannon and Weaver 1949) and General Patterns measures landscape composition, not shape or configuration. Large values of Shannon’s diversity index indicate a greater number of Seventy-one fields were studied in autumn during the three growing landscape element types present (patch richness) in the landscape, seasons, however data for one field was not used because all but one greater evenness in area of the patch types present, or both. The sentinel plant were destroyed by wildlife. For autumn, the propor- number of patch types present did not vary much among land- tion of the seven sentinel plants from each field that had one or more scapes in our study because most patch types were present. The L. testaceipes per sentinel plant ranged from 0 to 1 among fields with minimal variation observed for patch richness indicates that varia- a mean of 0.28, and the mean number of L. testaceipes per sentinel tion in Shannon’s patch diversity index in our landscapes primarily plant ranged 0 to 100.4 among fields with a mean of 11.6 ( Table 1). reflected variation in the evenness in percent of area of various During spring both the average number of L. testaceipes per senti- patch types. Large values for fractal dimension, patch density, and nel plant and the proportion of sentinel plants with L.  testaceipes Shannon’s patch diversity, and small values for contagion generally were greater by 10-fold and threefold, respectively, than in autumn indicate high landscape diversity (see Turner et al. 2001 for more (Table  1). Aphid abundance in fields was also higher (greater than information). sevenfold) in spring than in autumn (Table 1) as was expected since Measured attributes of vegetation in field edges and landscape sentinel plants were placed in wheat fields in autumn as soon as metrics calculated using Fragstats Version 4 were used to quantify possible after emergence of wheat plants in the field from the soil. It landscape structure. Fragstats derived variables were calculated at was not possible to identify 24 fields each year with identical plant - each of the three hierarchically increasing spatial extents (0.8, 1.6, ing dates, so there was variation in wheat plant growth stage among and 3.2 km radii) for each field each year. GIS operations were fields. Variation in planting date probably accounted for a major accomplished using ERDAS Imagine version 2014 including export- portion of the variation in aphid abundance among fields. ing data for calculating landscape metrics with Fragstats. Approximately 32% of fields studied were no till and 68% were conventional till (Table  1). Sixty-six percent of fields were grazed Data Analysis by cattle during winter months (a common practice in Oklahoma; Correlation was used to evaluate pairwise relationships among vari- Epplin et al. 1998). Grazing is associated with use of conventional ables. When one (or both) variables were categorical, such as tillage tillage, because grazing cattle on no-till fields causes high levels of type, which was coded numerically as zero for no-till and one for soil compaction. Therefore, the percent of fields in no-till systems conventional tillage, spearman rank correlation coefficients were cal - was inversely related to grazing. The number of grass species in culated. Pearson correlation coefficients were calculated when both field edges ranged widely among fields, as did the percentage cover variables were continuous. The magnitude of correlation coefficients by Johnson grass in field edges. Landscape metrics varied substan - was used to determine the spatial extent to account for the greatest tially among the 70 fields for land areas in the three radii meas - amount of variation in parasitism. SAS PROC CORR (SAS Institute ured (Table 2). For example, the perimeter to area fractal dimension 2004) was used to calculate correlation coefficients. ranged from 1.20 to 1.45 for a radius of 0.8 km and from 1.31 to Many of the landscape variables were correlated so principal 1.45 for areas with a radius of 3.2 km. Percent of total land area components analysis was used to derive a set of linearly independ- planted to wheat ranged from 2.7 to 86.6 for the 0.8 km radius and ent regressors for use in regression modeling. Principal components from 10.1 to 68.5% for the 3.2 km radius. Although landscape met- were rotated using varimax rotation (Dillon and Goldstein 1984). rics varied considerably for each landscape extent, the range of each The number of rotated standardized principal components retained metric was similar across the 0.8 to 3.2 km spatial extents. for use as independent variables in regressions was determined by Many landscape metrics were correlated. Correlations among the scree method. The scree method involves plotting the eigenvalue metrics for 3.2 km radius land areas (Table 3) were similar to corre- associated with each principal component in successive order and lations for the two smaller radii (not shown). Of particular note, the determining the point beyond which the smaller eigenvalues form correlation between Shannon’s patch diversity index and contagion an approximately straight line. The components retained are those was −0.90, which indicates that evenness in the proportion of each associated with eigenvalues that fall above the straight line formed patch type was strongly negatively related to the extent of aggrega- by the smaller eigenvalues (Dillon and Goldstein 1984). The com- tion of particular patch types in the landscape. The presence of cor- ponents that were retained were used as regressors in models relat- relation among the majority of landscape metrics indicates that their ing parasitism to landscape context. Only linear (first order terms) use as independent variables in regression modeling would result in were included in models. Standardized components were interpreted multicollinearity. Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 Environmental Entomology, 2018, Vol. 47, No. 4 807 Table 1. Summary statistics (mean, SE, minimum, and maximum) for parasitism by L. testaceipes, aphid abundance, and other variables measured for n = 70 wheat fields in north central Oklahoma, for 2008, 2009, and 2010 Variable Mean SE Minimum Maximum Autumn Parasitism Number of L. testaceipes / sentinel plant 11.6 2.62 0.0 100.2 Prop. sentinel plants with L. testaceipes 0.28 0.03 0.0 1.0 Aphids (no. per 20 D-vac placements) 27.7 15.06 0.0 1040.0 Spring Parasitism Number of L. testaceipes / sentinel plant 179.9 22.64 0.0 576.7 Prop. sentinel plants with L. testaceipes 0.88 0.03 0.0 1.0 Aphids (no. per 20 D-vac placements) 195.6 47.48 12.3 1680.3 Within-field & Field Edge Tillage (0 = conventional, 1 = no till) 0.32 0.06 0.0 1.0 Crop rotation (0 = no, 1 = yes) 0.26 0.05 0.0 1.0 Grazed (0 = no, 1 = yes) 0.67 0.06 0.0 1.0 Grass species Richness 7.7 0.27 4.0 13.0 % Johnson grass coverage 34.1 2.66 0.0 75.0 Table 2. Summary statistics (mean, SE, minimum, and maximum) Aphid Abundance in Wheat Fields for landscape metrics measured at three radii centered on each Cereal aphid abundance was estimated for each wheat field at the of n = 70 wheat fields in north central Oklahoma, for 2008, 2009, times sentinel plants were deployed. Aphid abundance in autumn was and 2010 correlated with some landscape and within field variables. Autumn aphid abundance was correlated with whether the field was used for Variable Mean SE Minimum Maximum cattle grazing (r = 0.38; n = 70; P = 0.001), but not with any other Radius 0.8 km of the within-field variables measured. Since cattle were not placed Patch density 26.3 1.56 7.7 57.3 on fields until after our autumn study was complete, this effect may Shannon’s patch diversity 1.31 0.03 0.61 1.88 have been due to planting date, which is earlier for dual purpose Fractal dimension 1.33 0.01 1.20 1.45 wheat fields than for fields used only for grain production ( Epplin Contagion 53.0 1.22 32.5 77.5 et al. 1998). Aphid abundance in autumn was positively correlated % Wheat 40.8 2.36 2.7 86.6 % Summer crops 24.7 2.63 0.11 82.6 with % grassland in the landscape (r = 0.28; n = 70; P = 0.02), but % Winter crops (other than wheat) 0.2 0.07 0.0 4.1 not with any other landscape metric. For spring, aphid abundance % Fallow 1.9 0.24 0.0 10.1 was negatively correlated with both tillage type (conventional vs. % Grassland 23.0 1.69 1.7 52.4 no-till) (r = −0.47; n = 46; P = 0.001) and crop rotation (r = −0.58; % wooded 2.4 0.42 0.0 16.2 n = 46; P < 0.001). Since no-till is commonly practiced in conjunc- % manmade (built areas and roads) 6.5 0.53 2.0 24.3 tion with crop rotation in central Oklahoma, the two variables were % water 0.4 0.10 0.0 5.0 Radius 1.6 km highly correlated (r = 0.84; n = 70; P < 0.001). Aphid abundance in Patch density 24.9 1.28 7.8 48.3 wheat fields in spring was positively correlated with fractal dimen - Shannon’s patch diversity 1.46 0.03 0.87 1.87 sion (r = 0.29; n = 46; P = 0.05) and negatively correlated with % Fractal dimension 1.37 0.004 1.30 1.46 summer crops (r = −0.30; n = 46; P = 0.04); the correlation with % Contagion 51.5 0.89 34.5 69.4 grassland for spring was positive (r = 0.24) but not significant. % Wheat 35.9 1.77 11.6 78.1 % Summer crops 24.5 2.08 0.99 68.4 % Winter crops (other than wheat) 0.3 0.05 0.0 2.9 Parasitism of Bird Cherry–Oat Aphids by % Fallow 2.6 0.20 0.3 8.4 L. testaceipes % Grassland 27.0 1.89 6.5 67.9 Both the average number of L.  testaceipes per sentinel plant and % Wooded 2.8 0.33 0.0 9.9 the proportion of sentinel plants with L. testaceipes were correlated % Manmade (built areas and roads) 6.2 0.31 2.7 13.4 with landscape variables for autumn and spring. Correlations for the % Water 0.7 0.12 0.0 5.9 Radius 3.2 km number of L. testaceipes per sentinel plant were generally greater in Patch density 24.8 1.26 6.0 52.4 magnitude and more often significant than correlations for the pro - Shannon’s patch diversity 1.51 0.23 1.07 1.90 portion of sentinel plants with L. testaceipes. This was not surprising Fractal dimension 1.38 0.003 1.31 1.45 since only seven sentinel plants were deployed per field which limits Contagion 50.6 0.78 35.4 66.2 interpretation of this measure. Therefore, we limit further analysis of % Wheat 34.7 1.51 10.1 68.5 the relationship of parasitism with landscape variables to the num- % Summer crops 22.4 1.71 2.34 59.7 % Winter crops (other than wheat) 0.2 0.04 0.01 2.0 ber of L. testaceipes per sentinel plant. % Fallow 2.9 0.17 0.6 7.1 Neither aphid abundance nor any within field or field edge var - % Grassland 29.6 1.74 9.2 66.8 iable was correlated with the number of L.  testaceipes per sentinel % Wooded 3.1 0.26 0.1 10.5 plant in autumn (not shown). In spring, there was a significant cor - % Manmade (built areas and roads) 6.1 0.23 3.4 11.7 relation between aphid abundance in wheat fields and number of % Water 0.8 0.10 0.02 3.8 L. testaceipes per sentinel plant (r = 0.35; n = 46; P = 0.02). None of Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 808 Environmental Entomology, 2018, Vol. 47, No. 4 Table 3. Correlation among variables describing landscape context within 3.2 km radius areas centered on each of n = 70 wheat fields in north central Oklahoma, for 2008, 2009, and 2010 Variable Shannon diversity Fractal dimension Contagion % Wheat % Summer crops % Grass % Wooded Patch density 0.60* 0.26* −0.32* −0.25* 0.50* −0.36* 0.28* Shannon diversity −0.38* −0.90* −0.45* 0.77* −0.52* 0.45* Fractal dimension −0.50* −0.09 0.10 −0.16 0.49* Contagion 0.46* −0.56* 0.33* −0.59* % Wheat −0.36* −0.35* −0.48* % Summer crops −0.72* 0.10 % Grass 0.13 the other within field or field edge variables was significantly corre - the autumn regression model (Eq. 1) four factors, F1, F3, F4, and F5 lated with the number of L. testaceipes per sentinel plant in spring. were entered into the model using the selection criterion of P < 0.15 for For landscape metrics, significant correlations occurred for the inclusion of a variable. The regression coefficient for F1 was positive, number of L. testaceipes per sentinel plant with patch density, fractal and the largest loadings on F1 were for % summer crops (0.75) and dimension, Shannon’s patch diversity, percent wheat, percent summer % grassland (−0.71), indicating that the presence of high acreage of crops, and percent wooded land (Table 4). Correlations with percent summer crops increased parasitism in wheat in autumn, whereas the wheat were negative for all spatial extents in autumn, but were not presence of large amounts of grassland was detrimental to parasitism. significant in spring. Correlations with patch density were positive in Factor F3 had a negative regression coefficient and was dominated by a autumn but negative in spring (Fig. 2). Correlations with percent sum- large loading on % wheat (1.23), indicating a negative effect of wheat mer crops and fractal dimension were positive for all spatial extents mono-cropping on parasitism in autumn. The positive regression coef- and often significant in both autumn and spring ( Fig. 2). Correlations ficient for F4 combined with the large factor loading on patch density for contagion were negative for all spatial extents in both seasons, and (1.18) indicates a positive influence of small patch (field) size in the were significant in spring. Correlations with Shannon’s patch diversity landscape on parasitism. Finally, the positive regression coefficient for index were positive for all spatial extents in spring, but significant F5 combined with the dominant positive loading on fractal dimension only at 1.6 km. The correlation with percent wooded land was posi- (1.19) indicates a positive effect of curvilinear patch boundaries, char- tive and significant at 3.2 km, but not at finer spatial extents. acteristic of semi-natural and natural lands, on parasitism. Correlations between the number of L.  testaceipes per sentinel In spring, three factors were entered into the regression, F1, F4, plant and landscape variables for autumn were generally greatest at and F5 (Eq. 2). In contrast to autumn, F1 had a negative regression the 3.2 km radius and correlations in spring were similar at 1.6 and coefficient indicating a reversal of the relationship between percent 3.2 km (Table 4), therefore, we used landscape data measured at the of landscape planted to summer crops and the percent grassland 3.2 km scale in regression modeling for autumn and spring. Aphid from that in autumn. F4 had a positive regression coefficient indicat - abundance in autumn, and other variables measured in wheat fields ing a positive response of parasitism to increasing fractal dimension. and field edges, were uncorrelated with the number of L. testaceipes The coefficient of F5 was negative indicating a negative response per sentinel plant and therefore were not used as predictors in regres- of parasitism to increasing patch density, which is opposite of the sion models. Aphid abundance in wheat fields in spring was corre - response observed in autumn. lated with parasitism and was included as a predictor in stepwise regressions. Based on the scree method, we included five of eight factors Discussion (Supplementary Appendix 1) as predictors of the number of L. tes- Variables representing landscape composition as well as spatial config - taceipes per sentinel plant in stepwise regression models for autumn uration were correlated with parasitism levels by L. testaceipes in wheat and spring. The best fitting first-order regression model for the num - fields. Correlations were observed both in autumn when colonization ber of L. testaceipes in autumn (F = 6.72; df = 4, 65; P = 0.0001) was of wheat fields by cereal aphids and L. testaceipes first occurs, and in spring when within field processes might be expected to predominate Lt =+ 11.. 56 4 9 01 ⋅− FF 77 . 6 ⋅ 3 over colonization in determining L. testaceipes population density and +⋅ 4.. 93 FF 45 +⋅ 66 5 R = 00 .3 () (1) the resulting cereal aphid parasitism. It is notable that L.  testaceipes responds to landscape composition (e.g., % wheat and Shannon’s patch diversity index) as has been observed for braconid parasitoids of aphids where Lt is the number of L. testaceipes per sentinel plant, and F1, in several studies (e.g., Roschewitz et al. 2005, Thies et al. 2005, Plecas F3, F4, and F5 are factors derived from principal components ana- et al. 2014, Zhao et al. 2015), but also to landscape configuration (e.g., lysis (Supplementary Appendix 1). For spring, the best fitting regres - fractal dimension and contagion), which has been less frequently meas- sion model (F = 13.34; df = 42, 3; P = 0.0001) was ured (but see Plecas et al. 2014), and may be important in determining arthropod population processes in agroecosystems. Lt =− 179.. 95 2743 ⋅+ FF 10 43 . 9 ⋅ 4 2 Large acreage of wheat surrounding a focal wheat field was neg - −⋅ 955 .. 2 FR 50 = 49 () (2) atively correlated with cereal aphid parasitism in autumn, but was uncorrelated with parasitism in spring (Table  4). Acreage of sum- where Lt is the number of L.  testaceipes per sentinel plant and F1, mer crops was positively correlated with parasitism in autumn, but F4, and F5 are factors. Regression models for L.  testaceipes parasit- negatively correlated in spring (Table  4). The autumn and spring ism in wheat fields in autumn and spring were interpreted based on regression models (Eqs. 1 and 2) reflect these differing associations standardized factor loadings on the original landscape variables. For of parasitism with landscape variables for autumn and spring. Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 Environmental Entomology, 2018, Vol. 47, No. 4 809 Table 4. Correlation of landscape metrics with the number of L. testaceipes per sentinel plant and the percent of sentinel plants with L. tes- taceipes present for autumn (n = 70) and spring (n = 46) Autumn Spring Metric No. L. testaceipes % with L. testaceipes No. L. testaceipes % with L. testaceipes Radius 0.8 km Patch density 0.26* 0.23 −0.43* −0.05 Shannon’s patch diversity 0.13 0.07 0.01 0.22 Contagion −0.10 −0.09 −0.29* −0.26 Fractal dimension 0.10 0.06 0.11 0.11 % Wheat −0.34* −0.25* 0.01 −0.15 % Summer crops 0.39* 0.18 0.17 0.12 % Grassland −0.13 0.01 0.24 0.04 % Wooded −0.04 −0.04 0.09 0.15 Radius 1.6 km Patch density 0.32* 0.30* −0.50* −0.12 Shannon’s patch diversity 0.21 0.17 0.33* 0.10 Contagion −0.12 −0.15 −0.29* −0.43* Fractal dimension 0.18 0.18 0.39* 0.25 % Wheat −0.31* −0.35* 0.01 −0.17 % Summer crops 0.29* 0.16 0.27 0.10 % Grassland −0.08 0.08 0.20 0.00 % Wooded 0.07 0.08 0.22 0.22 Radius 3.2 km Patch density 0.32* 0.29* −0.61* −0.21 Shannon’s patch diversity 0.20 0.15 0.26 0.16 Contagion −0.14 −0.18 −0.30* −0.28 Fractal dimension 0.26* 0.31* 0.29* 0.20 % Wheat −0.34* −0.40* −0.01 −0.20 % Summer crops 0.31* 0.13 0.27 0.10 % Grassland −0.07 0.12 0.25 0.05 % Wooded 0.24* 0.19 0.07 0.18 *Statistically significant correlations ( P < 0.05). Additionally, patch density had a positive effect on parasitism in The varying effect of increasing patch density on parasitism autumn, but a negative effect in spring. Conversely, fractal dimen- from positive in fall to negative in spring is difficult to interpret. If sion had a positive effect in both seasons (Eqs. 1 and 2). In the agri- small patch size (high patch density) increased aphid parasitism by cultural landscapes of north central Oklahoma, both cropland and L. testaceipes in wheat fields it’s effect would most likely result from semi-natural land habitats contribute to the landscape scale popula- increasing access to habitats containing hosts or other resources. tion dynamics of L. testaceipes. The magnitude of this effect might vary seasonally but would not The autumn and spring regression models reflect the differing be expected to shift from positive to negative. The influence of patch associations of parasitism with landscape variables for autumn and density on parasitism is probably indirect and related to other land- spring. Differences in effects of landscape context among seasons sug- scape characteristics, such as those captured by Shannon’s diversity gest that the response of L.  testaceipes to landscape context is very index, that influence resource availability to L. testaceipes, are corre- rapid, within a single growing season. L. testaceipes dynamics within lated with patch density (Table 3), and are not explicity accounted for a wheat field, and correspondingly, capacity for cereal aphid suppres - in regression models. This result highlights the difficulty in teasing out sion, is partially dependent on population dynamics of the parasitoid the effects of numerous interacting factors on L. testaceipes ecology in the landscape surrounding a wheat field during the course of the using multivariate methods. Even with this limitation the study has growing season. In the heavily agricultural landscapes of north central highlighted several landscape characteristics that, through their influ - Oklahoma, cropland and semi-natural lands both contribute to deter- ence on resource availability influence the success of L. testaceipes as mining the landscape scale population dynamics of L.  testaceipes. a biological control agent in wheat agreoecosystems in Oklahoma. Considered in this context, the change in regression coefficient (posi - In Oklahoma, the correlation of aphid parasitism by L.  testa- tive to negative) of parasitism to presence of summer crops in autumn ceipes with landscape variables in wheat fields did not decrease with and spring is likely due the fact that summer crops serve as habitat increasing spatial extent, contrary to observations for braconid par- for L. testaceipes in early autumn when wheat is first planted, but are asitoids of cereal aphids in European wheat agroecosystems (Thies fallow fields in spring. The negative effect of amount of grassland on et al. 2005). L. testaceipes was not recorded in their study even though parasitism may be a consequence of the extremely arid and high tem- it is widely established in European fauna (Stary 1988, Zikic et  al. perature summer conditions in Oklahoma, which result in mostly dor- 2015). Interestingly, our finest spatial extent 0.8 km radius (1.6 km mant grasslands that harbor extremely low numbers of aphid hosts diameter) was closest to the maximum spatial extent (2.0 km diam- and therefore of parasitoids (Anstead 2000). Variation in the effect eter) at which landscape complexity significantly explained variation of wheat acreage on parasitism can be viewed similarly, where wheat in parasitism in wheat fields in Europe ( Thies et al. 2005). Our results fields are essentially devoid of cereal aphid hosts in early autumn but suggest that L.  testaceipes responds to landscape variation over a serve as habitat for cereal aphids and L. testaceipes in spring. broad spatial extent. L. testaceipes shows a very limited response to Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 810 Environmental Entomology, 2018, Vol. 47, No. 4 Fig. 2. Number of L. testaceipes per seven sentinel plants versus patch density and fractal dimension for autumn (top) and spring (bottom). plant volatiles (Lo Pinto et al. 2004, Fauvergue et al. 2006), and is rapid colonization of fields ( Bortoloto et al. 2015) and high attack and considered to be both a habitat and host generalist with potential reproductive rates (Jones 2005). Colonization of wheat fields in autumn for very rapid population growth (Mackauer and Stary 1967, Jones is enhanced by proximity to fields of summer crops and semi-natural et  al. 2003). These life history characteristics might be expected to habitats other than grasslands. Based on this study, the optimal land- be associated with high levels of dispersal. Although life histories of scape to promote biological control of cereal aphids in wheat by L. tes- most braconid aphid parasitoid species are not well known, L.  tes- taceipes is one where summer crops, wheat, and semi-natural habitats taceipes is probably exceptional in relation to breadth of host and exist in significant amounts, and are well interspersed. Future emphasis habitat range (Stary et al. 1988, Pike et al. 2000). Because of its life should be given to determining the specific resources within these habi - history characteristics L. testaceipes appears to be well adapted to the tats that are utilized by L. testaceipes. agroecosystems of the Southern Plains, where agricultural landscapes are coarse grained and habitats are poorly interspersed. Supplementary Data L. testaceipes is a key natural enemy of cereal aphids in wheat (Jones et al. 2007, Giles et al. 2017), and effective biological control frequently Supplementary data are available at Environmental Entomology occurs within wheat fields prior to the aphid infestation reaching the online. economic threshold (Giles et al. 2003). L. testaceipes does not appear to disperse among a set of essential habitats seasonally and therefore does not exhibit the characteristics of a cyclic colonizer (Wissinger 1997). Acknowledgments Rather L. testaceipes appears to utilize multiple habitats throughout the We thank Tim Johnson for technical assistance with the project and for coor- year depending on their availability and acceptability, and frequently dinating data collection and processing activities. Mention of trade names or disperses among habitats (Jessie 2017). In this sense L.  testaceipes is commercial products in this publication is solely for the purpose of providing better described as an r-selected species, with high reproductive and dis- specific information and does not imply recommendation or endorsement by persal rates (Ehler and Miller 1978, Price and Waldbauer 1994). The the U.S. Department of Agriculture (USDA). USDA is an equal opportunity provider and employer. ability of L.  testaceipes to control aphids in wheat fields depends on Downloaded from https://academic.oup.com/ee/article/47/4/803/4969233 by DeepDyve user on 13 July 2022 Environmental Entomology, 2018, Vol. 47, No. 4 811 Technical Report PNWGTR-351. USDA Forest Service, Pacific Northwest References Cited Research Station, Portland, OR. Anstead, J. A. 2000. Genetic and biotypic diversity of greenbug Schizaphis O’Neill, R. V., J. R.  Krummel, R. H.  Gardner, G.  Sugihara, B.  Jackson, D. graminum (Rondani) populations on non-cultivated hosts. Doctoral L. DeAngelis, B. 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Published: Aug 11, 2018

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