Landscape Features and Spatial Distribution of Adult Northern Corn Rootworm (Coleoptera: Chrysomelidae) in the South Dakota Areawide Management Site

FIELD AND FORAGE CROPS Landscape Features and Spatial Distribution of Adult Northern Corn Rootworm (Coleoptera: Chrysomelidae) in the South Dakota Areawide Management Site

1 1 2 B. WADE FRENCH, AMBER A. BECKLER, AND LAURENCE D. CHANDLER

J. Econ. Entomol. 97(6): 1943Ð1957 (2004) ABSTRACT The northern corn rootworm, Diabrotica barberi Smith & Lawrence (Coleoptera: Chrysomelidae), creates economic and environmental concerns in the Corn Belt. To supplement the population control tactics of the Areawide Pest Management Program in Brookings, SD, geographic information systems were used from 1997 to 2001 to examine the spatial relationships between D. barberi population dynamics and habitat structure, soil texture, and elevation. Using the inverse distance weighted interpolation technique, D. barberi population density maps were created from georeferenced emergence and postemergence traps placed in maize, Zea mays L., Þelds. For each year, these maps were overlaid with vegetation, soil, and elevation maps to search for quantitative relationships between pest numbers and landscape structure. Through visual interpretation and correlation analysis, shifts in landscape structure, such as size, number, and arrangement of patches were shown to associate with D. barberi population abundance and distribution in varying degrees. D. barberi were found in greater proportions than expected on loam and silty clay loam soils and on elevations between 500 and 509 m. An understanding of the interactions between D. barberi population dynamics and landscape variables provides information to pest managers, which can be used to identify patterns in the landscape that promote high insect population density patches to improve pest management strategies.

KEY WORDS geographic information systems, landscape metrics, population dynamics, spatial analysis, Diabrotica

THE NORTHERN CORN ROOTWORM. Diabrotica barberi To minimize the use of insecticides and protect the Smith & Lawrence, is a serious pest of maize, Zea mays environment, the United States Department of Agri- L., in the Corn Belt (Kantack et al. 1970, Metcalf 1986). cultureÐAgricultural Research Service implemented a Traditional control methods include crop rotation and corn rootworm areawide pest management program soil insecticides applied at planting. However, insec- in 1996 (Chandler and Faust 1998, Chandler et al. ticides often are used unnecessarily, and this indis- 2000) in Þve geographic locations, including four in criminant use has promoted environmental (e.g., run- the Corn Belt and one in Texas (Chandler and Faust off and groundwater alterations), safety (e.g., 1998, Tollefson 1998, Wilde et al. 1998). The areawide handling), and economic concerns (Pimentel et al. approach relies on management of adult rootworm 1991, Gray et al. 1993). D. barberi also have circum- populations over a wide geographic area applying ac- vented crop rotation as a control strategy. Instead of tion thresholds to determine need and appropriate the typical 1-yr diapause, many eggs overwinter for timing for aerial applications of toxic bait formulations two or more years (Chiang 1965; Krysan et al. 1984, that substantially diminish insecticide use (Chandler 1986; Levine et al. 1992). Consequently, in a 2-yr crop and Faust 1998). To supplement the areawide ap- rotation of maize and another crop, D. barberi eggs proach and to document interactions of edaphic and hatch after 2 yr and larvae then feed on the maize landscape factors with D. barberi, this study focused roots. Therefore, the indiscriminant use of insecticides on analyzing spatial relationships between D. barberi and failure of crop rotation as a consistent and eco- population dynamics and habitat structure, soil tex- nomical means of managing D. barberi populations ture, and elevation. Understanding these relation- prompted a need for new management tactics. ships is important for directing future pest management decisions at a landscape scale (Landis 1994). For Mention of a proprietary product does not constitute an endorse- analysis of these relationships, we used geographic ment or a recommendation by the USDA for its use. information systems to create map layers for visual 1 Northern Grain Insects Research Laboratory, USDAÐARS, NPA, interpretation and geostatistical analysis of spatial in- 2923 Medary Ave., Brookings, South Dakota 57006. 2 Red River Valley Agricultural Research Center, USDAÐARS, teractions at larger scales (Roberts et al. 1993, Stow NPA, P.O. Box 5677, Fargo, North Dakota 58105. 1993). Our objectives were to 1) document shifts 1944 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 97, no. 6 in maize patches from 1997 to 2001 in relation to tures (e.g., trap type). For each year, we imported the D. barberi population dynamics and 2) identify the variables of Þeld locations, Þeld sizes, crop types, trap relationships of soil texture and elevation with D. bar- locations, trap types, and the number of D. barberi beri populations. adults captured into the attribute tables. Crop types were classiÞed as continuous maize, Þrst-year maize, mixed maize, other maize, and soybean. The soybean Materials and Methods classiÞcation was deemed important because of the Study Area Description. The South Dakota corn root- prominence of maizeÐsoybean rotations in the man- worm areawide management site was located in the agement site. Continuous maize Þelds were Þelds that western portion of the Corn Belt (Universal Transverse were planted to maize for two or more consecutive Mercator coordinates in meters for zone 14N, northwest years. First-year maize Þelds were Þelds planted to a corner 681127e 4911981n, northeast corner 687618e crop other than maize the previous year (usually soy- 4912125n, southwest corner 681317e 4905524w, and bean). Mixed maize Þelds were those that contained southeast corner 687768e 4905671n) in Brookings a portion of continuous maize and Þrst-year maize. County, South Dakota. The study site was within the tall Other maize Þelds included sweet maize or maize test grass prairie region of the northern Great Plains (Kaul plots. Mixed and other maize Þelds made up only a 1986), encompassed 41.4 km2 and was dominated by a small portion of the maize planted in the management mosaic of maizeÐsoybean, Glycine max (L.), cropping site and were used only in the “all maize” class-level systems. The site contained Ϸ60 maize Þelds and 60 analyses, and not used in the “patch-level” analyses soybean Þelds of varying size (5Ð50 ha), depending on (see below). Trap types were classiÞed into emer- the year. We used this management area to characterize gence and postemergence traps. the spatial dynamics of D. barberi over a 5-yr period Interpolation Techniques. Areas of abundance (1997Ð2001). and distribution of D. barberi based on emergence Trap Collection. Emergence of D. barberi populations and postemergence trapping in maize were charac- terized as raster map layers with a cell size of 26.4 m was monitored weekly by using adult emergence traps per side by using Inverse Distance Weighted inter- (Hein and Tollefson 1985), and Pherocon AM yellow polation techniques. In contrast to vector maps, sticky traps (Trece Inc., Salinas, CA) were used to mon- ´ ´ raster maps portray spatial features as a matrix of itor postemergence adult activity (Tollefson 1986). Both equal-area grid cells containing unique values trap types were placed in the maize Þelds 30Ð60 m apart (Bonham-Carter 1994, Johnston 1998). Interpolation along either one or two transects during late June to early methods calculate predicted variable values for un- July of each year, depending on weather conditions. sampled areas by using georeferenced point sample Postemergence sticky traps were placed in 55Ð62 maize locations (McCoy and Johnston 2002). The Inverse Þelds, whereas emergence traps were placed only in Distance Weighted method of interpolation estimates 11Ð16 randomly chosen maize Þelds, depending on the the values of sample data points in the vicinity of year. The number of transects, emergence traps, and each cell of the surface map. The closer the point is postemergence traps used in each Þeld varied with Þeld to the cell center being estimated, the more inßuence Ն size. For both trap types, 12 were placed in Þelds 47 ha, it has in the averaging process. Also with the Inverse nine in Þelds 25Ð46 ha, six in Þelds 10Ð24 ha, and three Distance Weighted technique, the exponent or Յ Յ in Þelds 9 ha. One transect was used in Þelds 24 ha, power value controls the signiÞcance of known geo- Ͼ whereas two transects were used in Þelds 25 ha and referenced points upon the interpolated values, based Ϸ were separated by 80 m. The emergence traps (1.0 by upon the distance from the output point (McCoy and 0.6 m) were placed lengthwise directly over Þve cut Johnston 2002). A high exponent (3Ð5) emphasizes plants in the maize row to capture beetles as they points nearer to the interpolated value, whereas a emerged from the soil. Postemergence traps (0.2 by low exponent (0Ð2) emphasizes points further away 0.3 m) were clamped onto wooden lath strips placed the interpolated value. Using high exponents results between maize rows in each Þeld. Each postemergence in a more detailed, less smooth output surface, trap was positioned at ear height of the maize plant, or whereas using low exponents result in a smoother Ϸ1 m above the soil surface. As beetles ßew through the surface with less detail, respectively (Krajewski and Þeld, they were captured on the adhesive surface of the Gibbs 2001). The most commonly used exponent trap. Beetles from postemergence traps were collected value of two was used, thereby resulting in a smooth weekly for Ϸ10 wk, whereas beetles from emergence raster surface. A variable search radius, with 12 input traps were collected weekly for Ϸ7 wk. points was used to allow for variable search Georeferencing and Crop Association. Trap and neighborhoods, depending on the density of measured Þeld locations were georeferenced and crop or veg- points near the interpolated cell (McCoy and etation types were documented yearly. Vector map Johnston 2002). Although we smoothed over nonpre- layers were created for the study site by using a dif- ferred habitat, allowing the distance of the search ferential global positioning system. Vector maps por- radius to vary to include the input points reduces their tray spatial features as points (traps), lines (roads), effects with increasing distance on the interpolated and polygons (crop Þelds) (Bonham-Carter 1994, cell values. There were several reasons that the In- Johnston 1998). Attribute tables associated with vec- verse Distance Weighted method was used to estimate tor maps contain information about the spatial fea- beetle abundance. The Inverse Distance Weighted December 2004 FRENCH ET AL.: SPATIAL DISTRIBUTION OF NORTHERN CORN ROOTWORM 1945 algorithm allows for rapid calculations, is appropriate The proximity index considers size and proximity of for aggregated data and for analyzing short-range vari- all patches whose edges are within a speciÞed search ability between scattered data points, and generates radius of the focal patch (McGarigal et al. 2002). We quick contour plots for relatively smooth data values used a search radius of 800 m to reach beyond the (Krajewski and Gibbs 2001). boundary of some large Þelds. The proximity index We used the D. barberi interpolated maps to visually determines the spatial arrangement of a habitat patch analyze their spatial relationships with the vegetation in relation to its neighbors of the same class; speciÞ- maps. However, describing general relationships in cally, the index differentiates sparse distributions of quantitative terms, or mapping the distribution of small habitat patches from a complex cluster of larger varying degrees of correspondence between maps patches. Landscapes with aggregated large patches would be impossible from visual inspection alone. have higher proximity values than landscapes with Therefore, interpolated maps, classiÞed maps of veg- dispersed smaller patches (Hargis et al. 1998). etation and the landscape metrics were compared To determine relationships between landscape met- with determine relationships between habitat struc- rics and D. barberi population dynamics, we used correlation analyses between class-level metrics with the ture and D. barberi population dynamics. seasonal totals and mean number (capture per week per Landscape Metrics. Landscape metrics were cal- trap) of D. barberi captured in postemergence traps, and culated using FRAGSTATS software (University of Mas- patch-level metrics with mean number (capture per sachusetts, Amherst, MA) to examine relationships be- patch per trap) of D. barberi captured in postemergence tween changes in landscape structure and D. barberi traps. These analyses were conducted on continuous population dynamics across the management site. Veg- maize, Þrst-year maize, and all maize classes. For patch- etation (vector) maps were converted into raster maps level metrics the all maize class included continuous and from which FRAGSTATS calculated class- and patch- Þrst-year maize Þelds, whereas for class-level metrics the level landscape metrics. Patch-level metrics focus on all maize class included continuous, Þrst-year maize, and individual Þelds (i.e., patches) of a particular vegetation other maize Þelds that contained postemergence traps. type (e.g., all maize or continuous maize), whereas class- ϩ All data were transformed to log10 (n 1) to normalize level metrics focus on groups of Þelds of a particular distributions and equalize variances (Zar 1984). Signif- vegetation type (McGarigal et al. 2002). The patch-level icance of each correlation coefÞcient was determined metrics included patch area, proximity index of each from a Fisher r to z transformation (SAS Institute 1998). patch, and the nearest neighbor distance of each patch. Emergence and Postemergence Correlation. D. The class-level metrics included total class area, percent- barberi emergence probably correlates with optimal age of landscape occupied by each class, number of conditions for oviposition and larval survival and patches in each class, mean patch size of each class, mean should associate with edaphic factors such as soil tex- proximity index of each class, and mean nearest neighbor ture and topography (Tollefson and Calvin 1994). distance of each class. However, because much fewer emergence than poste- At the class-level, area and percentage of landscape mergence traps were placed in the management site, occupied by patches are indicators of landscape com- and postemergence traps encompassed all elevation position; speciÞcally, how much of the landscape consists classes and soil types, we believe the postemergence of a particular patch type (McGarigal et al. 2002). In interpolated maps would provide more accurate esti- addition, the number and arrangement of patches of a mates of adult D. barberi spatial associations with soil particular habitat type may inßuence ecological and evo- texture and elevation. To use postemergence inter- lutionary processes within a landscape. For example, the polated maps to analyze relationships of D. barberi number of patches may determine the number of sub- numbers with soil texture, and elevation, we Þrst populations for species exclusively associated with that wanted to determine the relationship between D. barberi emergence and postemergence. Two correlation habitat type, whereas the arrangement of patches may methods were used to evaluate this relationship. The affect dispersal rates and gene ßow among the preferred Þrst correlation was computed within ArcInfo soft- habitats (McCauley 1995, Wiens 1997). ware (ESRI, Redlands, CA) by overlaying the inter- We used nearest neighbor and proximity analyses to polated emergence and postemergence map layers determine patch arrangement (McGarigal et al. 2002). (Chou 1997). The second correlation was computed Nearest neighbor analysis is calculated by measuring by using the mean number of D. barberi beetles cap- the shortest straight-line distance between the focal tured in emergence and postemergence traps in each patch and its nearest neighbor of the same habitat patch. This allowed a direct comparison between type. As a measure of patch dispersion, a large stan- Þelds that contained both emergence and postemer- dard deviation relative to the mean implies an uneven gence traps. These data also were transformed to log10 or irregular distribution, whereas a small standard de- (n ϩ 1) (Zar 1984). SigniÞcance of each correlation viation about the mean indicates a uniform or regular coefÞcient from interpolated map analysis was deter- distribution (McGarigal et al. 2002). However, the mined from a table of critical values of the correlation measure only applies to distances between patches in coefÞcient (Zar 1984). SigniÞcance of each correla- a cluster, thus ignoring the potentially vast distance tion coefÞcient from the second analysis (tabular between the edge of the cluster and the edge of the data) was determined from a Fisher r to z transfor- map (Hargis et al. 1998). mation (SAS Institute 1998). 1946 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 97, no. 6

Table 1. Total and mean (؎SD) number of D. barberi captured per week per trap in postemergence sticky traps and class-level landscape metrics computed for selected vegetation types in the South Dakota corn rootworm areawide management site for years 1997–2001

Landscape metrics D. barberi Vegetation type CA % NP Area Ϯ SD Prox Ϯ SD NN Ϯ SD Total Mean Ϯ SD 1997 Continuous maize 316 8 12 26.3 Ϯ 18.6 28.0 Ϯ 35.9 555.2 Ϯ 510.4 6,230 7.7 Ϯ 9.2 First-year maize 938 23 41 22.9 Ϯ 18.1 70.8 Ϯ 75.2 132.1 Ϯ 149.7 17,448 7.1 Ϯ 9.1 All maize 1352 33 60 22.3 Ϯ 18.4 151.6 Ϯ 139.6 102.9 Ϯ 113.4 25,765 7.5 Ϯ 9.3 Soybean 1173 28 55 21.3 Ϯ 16.8 121.3 Ϯ 109.6 101.9 Ϯ 86.9 1998 Continuous maize 282 7 10 28.2 Ϯ 21.9 130.3 Ϯ 129.5 455.1 Ϯ 670.2 12,899 21.4 Ϯ 29.1 First-year maize 962 23 49 19.6 Ϯ 15.6 87.2 Ϯ 81.4 115.0 Ϯ 127.2 20,016 8.8 Ϯ 13.1 All maize 1370 33 73 18.1 Ϯ 16.4 142.0 Ϯ 130.6 78.2 Ϯ 57.2 35,751 11.6 Ϯ 18.6 Soybean 1226 30 56 21.9 Ϯ 17.1 117.5 Ϯ 94.7 98.8 Ϯ 113.9 1999 Continuous maize 243 6 11 22.1 Ϯ 20.4 53.0 Ϯ 82.0 585.5 Ϯ 793.8 11,117 19.9 Ϯ 24.5 First-year maize 989 24 43 23.0 Ϯ 14.4 93.0 Ϯ 84.9 111.7 Ϯ 121.1 49,893 22.5 Ϯ 29.8 All maize 1278 31 60 21.0 Ϯ 16.1 121.4 Ϯ 116.9 98.5 Ϯ 114.5 62,138 21.9 Ϯ 28.7 Soybean 1211 29 57 21.3 Ϯ 16.0 113.3 Ϯ 92.9 89.0 Ϯ 80.3 2000 Continuous maize 158 4 8 19.8 Ϯ 19.0 8.4 Ϯ 11.8 439.4 Ϯ 336.9 4,834 11.3 Ϯ 12.0 First-year maize 1088 26 48 22.7 Ϯ 16.0 101.9 Ϯ 87.0 93.4 Ϯ 82.3 28,625 10.5 Ϯ 12.5 All maize 1303 31 59 21.8 Ϯ 16.1 127.4 Ϯ 92.1 87.4 Ϯ 78.5 34,260 10.7 Ϯ 12.5 Soybean 1291 31 53 24.3 Ϯ 18.4 149.5 Ϯ 111.3 80.7 Ϯ 76.1 2001 Continuous maize 78 2 4 19.6 Ϯ 20.2 0.0 Ϯ 0.0 979.6 Ϯ 2.8 2,560 14.3 Ϯ 13.8 First-year maize 1048 25 42 24.9 Ϯ 15.8 98.2 Ϯ 83.9 100.1 Ϯ 108.0 30,269 13.8 Ϯ 16.3 All maize 1133 27 48 23.5 Ϯ 16.5 97.8 Ϯ 85.2 119.6 Ϯ 138.8 32,829 13.9 Ϯ 16.1 Soybean 1225 30 60 20.4 Ϯ 16.3 113.6 Ϯ 83.5 72.9 Ϯ 57.7

Landscape metrics include cumulative class area (CA), percentage of the landscape occupied (%), number of patches (NP), mean patch size or area (Area Ϯ SD), mean proximity index (Prox Ϯ SD), and mean nearest neighbor distance (NN Ϯ SD).

Contingency Analysis. The soils data covering each year, we classiÞed D. barberi populations as low, Brookings County were acquired as a vector map lowÐmedium, medium, mediumÐhigh, and high. Be- from the United States Geological Survey online Soil cause of the variation in number of D. barberi captured SurveyGeographicdatabase(www.ftw.nrcs.usda.gov/ _ ssur data.html). The soil texture map with a scale of Table 2. Correlation coefﬁcients (r) and probabilities (P) for 1:24,000 was converted into a raster map with a cell the relationships between class-level landscape metrics, D. barberi size of 26.4 m per side to match the cell size of the postemergence numbers expressed as seasonal totals, and mean D. barberi interpolated maps. The soil map was then capture per week per trap by maize type from the South Dakota classiÞed into Þve classes that corresponded to soil corn rootworm areawide management site for years 1997–2001 textures found within the management site. Soil tex- Total Mean Landscape ture classes ranged from least porous to most porous Maize type D. barberi D. barberi metric based on the composition of particle size associated rPrP with the general soil texture triangle (Buckman and Continuous Cumulative 0.85 0.08 -0.02 0.98 Brady 1969). The texture classes included silty clay, area silty clay loam, silt loam, loam, and sandy loam. First-year 0.43 0.52 0.31 0.65 The United States Geological Survey Digital Eleva- All -0.10 0.89 -0.41 0.53 tion model covering Brookings County was acquired Continuous % landscape 0.83 0.09 -0.05 0.95 First-year 0.46 0.48 0.33 0.63 as a raster map from an online database (www. All -0.08 0.91 -0.40 0.55 gisdatadepot.com). The data consist of georeferenced Continuous No. patches 0.82 0.10 -0.06 0.93 digital map and attribute data in a quadrangle format First-year -0.12 0.86 -0.19 0.78 with a scale of 1:24,000 (30 m per side cell size). This All 0.11 0.88 -0.16 0.82 Continuous Mean patch 0.71 0.21 0.09 0.90 elevation map was resampled with 26.4 m per side cell area size to match the cell size of the D. barberi interpo- First-year 0.41 0.53 0.39 0.56 lated maps. The elevation map was then reclassiÞed All -0.25 0.72 -0.08 0.91 into a new raster map with Þve equal-interval eleva- Continuous Mean proximity 0.98 Ͻ0.002 0.33 0.63 index tion classes from 494 to 519 m. First-year 0.64 0.28 0.58 0.35 Interpolated raster maps were classiÞed by year into All -0.50 0.43 -0.73 0.19 Þve classes (showing the number of D. barberi adults Continuous Mean nearest -0.67 0.25 -0.01 0.99 captured per trap). The natural breaks classiÞcation neighbor First-year -0.48 0.47 -0.38 0.58 scheme was used to identify natural groupings of data All -0.11 0.87 0.13 0.85 based on breakpoints inherent in the data (Abler et al. 1971, Monmonier 1977, Hatakeyama et al. 2000). For For each correlation, N is 5 yr. December 2004 FRENCH ET AL.: SPATIAL DISTRIBUTION OF NORTHERN CORN ROOTWORM 1947

Fig. 1. (A) Vector map illustrating roads and maize Þelds found in the South Dakota areawide management site in 1997. (B) Raster map illustrating interpolated values of D. barberi postemergence trap catches in the South Dakota areawide management site in 1997. 1948 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 97, no. 6 among years, the within class values also varied. Over and varied in the four preceding years. For continuous the 5 yr, low values ranged from 1Ð54 to 6Ð99, low- maize, a signiÞcant correlation was found only be- to-medium values ranged from 42Ð69 to 99Ð155, me- tween mean proximity and total number of D. barberi dium values ranged from 69Ð97 to 155Ð226, medium- captured (Table 2). The mean patch size of Þrst-year to-high values ranged from 97Ð129 to 226Ð325, and maize remained relatively constant over the 5-yr pe- high values ranged from 129Ð276 to 325Ð655. Each of riod, with a mean maximum difference of 5.3 ha be- the map layers, soils, elevation, and population, were tween 1998 and 2001. The mean proximity value for imported into Idrisi GIS software (Clark Labs-Clark Þrst-year maize was highest in 2000 and least in 1997, University, Worcester, MA) and masked to display whereas the mean nearest neighbor value was highest only the soil texture classes, elevation classes, and in 1997 and least in 2001 (Table 1). Regarding Þrst- interpolated values found in maize Þelds. This was year maize, no signiÞcant correlations were found necessary because we were concerned only with the between mean patch size, mean proximity, and mean statistical relationships in D. barberi preferred habitat nearest neighbor with total and mean number of and environmental variables. D. barberi captured (Table 2). Similar to Þrst-year Within Idrisi, contingency analysis compares the maize, the mean patch size for all maize also remained categories of one image (i.e., raster map layer) with relatively constant over the 5-yr period, with a mean those of a second image. We tabulated the frequency maximum difference of 5.4 ha between 1998 and 2001 of cells in each possible combination of both variables (Table 1). The mean proximity value for all maize was (i.e., population and soil texture or population and highest in 1997 and least in 2001, whereas the mean elevation) and computed measures of association be- nearest neighbor value was highest in 2001 and least in tween the images (Eastman 2001). These measures 1998 (Table 1). For all maize, no signiÞcant correla- included ␹2 statistics and CramerÕs V coefÞcients, tions were found between mean patch size, mean which indicate the degree of association between the proximity, and mean nearest neighbor with total and variables (Ott et al. 1983, Siegel and Castellan 1988, mean number of D. barberi captured (Table 2). Bonham-Carter 1994). SigniÞcance of each ␹2 statistic By visually inspecting and comparing the interpo- was determined from a table of critical values of the ␹2 lated D. barberi population raster maps to the classi- distribution (Zar 1984). Þed vegetation maps over the 5-yr period, it is evident that the spatial distribution of D. barberi in the management site was concentrated in large maize patches, especially those adjacent to other maize patches Results (Figs. 1Ð5). Note that as the distribution of maize patches shifted in the management site over the 5-yr Landscape Metrics. The total class area, percentage period, the corresponding distribution of D. barberi of landscape, and number of patches occupied by also shifted. This is partially corroborated by the sig- continuous maize decreased from 1997 to 2001. The niÞcant class-level correlation between mean proxim- fewest D. barberi captures occurred in 1997, whereas ity values and total number of D. barberi captured in the most were caught in 1999 (Table 1). In regard to continuous maize (Table 2). Further evidence is in- continuous maize, neither total nor mean number of dicated by the signiÞcant patch-level correlations be- D. barberi captured correlated signiÞcantly with class tween postemergence captures in continuous maize, area, percentage of landscape, or number of patches Þrst-year maize, and all maize with patch area (Table 2). With the decrease in continuous maize over (Table 3). the 5 yr of our study, there was an associated increase Contingency Analysis. We found signiÞcant corre- in crop rotation. This resulted in an overall increase lations between D. barberi emergence and postemer- in class area, percent occupied, and number of patches gence interpolated maps for all 5 yr (Table 4). We of Þrst-year maize and soybean (Table 1). Regarding also found signiÞcant correlations between mean Þrst-year maize, there were no signiÞcant correlations numbers of D. barberi captured from emergence and between class area, percentage of landscape, and postemergence traps for each of the 5 yr (Table 4). number of patches with total and mean numbers of The correlation coefÞcient also was signiÞcant over all D. barberi captured (Table 2). In addition, the total 5yr(r ϭ 0.72, df ϭ 60, P ϭϽ0.0001). Therefore, we and mean number of D. barberi captured in Þrst-year used postemergence interpolated maps to determine corn peaked in 1999 but varied greatly over the 5-yr relationships of soil texture and elevation with beetle period (Table 1). The class area, percentage of land- numbers. scape, and number of patches occupied by all maize Loam was the most common soil texture class peaked in 1998 and were least in 2001 (Table 1). No (47.7%) in the management site, followed by silty clay signiÞcant correlations were found between class loam (31.4%), silty loam (17.7%), sandy loam (2.9%), area, percentage of landscape, and number of patches and silty clay (0.3%). These percentage soil classes with total and mean number of D. barberi captured in were similar for maize growing areas over the 5-yr all maize (Table 2). management period (Fig. 6). Populations of D. barberi Mean patch size and mean proximity values for occurred most frequently on loam soils, followed by continuous maize peaked in 1998, and then steadily silty clay loam and silt loam (Table 5). Contingency declined through 2001 (Table 1). The mean nearest analysis revealed highly signiÞcant associations be- neighbor values for continuous maize peaked in 2001 tween soil texture and D. barberi abundance for each December 2004 FRENCH ET AL.: SPATIAL DISTRIBUTION OF NORTHERN CORN ROOTWORM 1949

Fig. 2. (A) Vector map illustrating roads and maize Þelds found in the South Dakota areawide management site in 1998. (B) Raster map illustrating interpolated values of D. barberi postemergence trap catches in the South Dakota areawide management site in 1998. 1950 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 97, no. 6

Fig. 3. (A) Vector map illustrating roads and maize Þelds found in the South Dakota areawide management site in 1999. (B) Raster map illustrating interpolated values of D. barberi postemergence trap catches in the South Dakota areawide management site in 1999. December 2004 FRENCH ET AL.: SPATIAL DISTRIBUTION OF NORTHERN CORN ROOTWORM 1951

Fig. 4. (A) Vector map illustrating roads and maize Þelds found in the South Dakota areawide management site in 2000. (B) Raster map illustrating interpolated values of D. barberi postemergence trap catches in the South Dakota areawide management site in 2000. 1952 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 97, no. 6

Fig. 5. (A) Vector map illustrating roads and maize Þelds found in the South Dakota areawide management site in 2001. (B) Raster map illustrating interpolated values of D. barberi postemergence trap catches in the South Dakota areawide management site in 2001. December 2004 FRENCH ET AL.: SPATIAL DISTRIBUTION OF NORTHERN CORN ROOTWORM 1953

Table 3. Correlation coefﬁcients (r) and probabilities (P) for the relationships between patch-level landscape metrics with mean no. of D. barberi postemergence captured per patch per trap by maize type from the South Dakota corn rootworm areawide management site for years 1997–2001

Maize type Landscape metric Nr P Continuous Patch Area 44 0.33 0.03 First-year 220 0.27 Ͻ0.001 All 264 0.28 Ͻ0.001 Continuous Proximity index 44 0.02 0.88 First-year 220 0.05 0.46 All 264 0.05 0.45 Continuous Nearest neighbor 44 Ϫ0.03 0.85 First-year 220 Ͻ0.01 0.93 All 264 Ͻ0.01 0.97

N is number of Þelds from 1997 to 2001.

Fig. 6. Mean (ϮSD) percentage of cell frequencies for soil texture classes where maize was grown in the South year, as indicated by ␹2 values (Table 6). The strength Dakota areawide management site from 1997 to 2001. of the association was greatest in both 1997 and 1999 and least in 1998 as indicated by CramerÕs V coefÞ- cients (Table 6). correlated with the total area and number of patches The most common elevation class in the manage- of preferred habitat within the context of the land- ment site was 500Ð504 m (34.6%) followed by 494Ð499 scape (Forman and Godron 1986, Robbins et al. 1989, m (24.1%), 505Ð509 m (22.4%), 510Ð514 m (17.7%), Andre´n 1994, Gustafson et al. 1994, Forman 1995, Mc- and 515Ð519 m (1.2%). However, the percentage of Garigal and McComb 1995). In this study, total and these elevation classes varied for maize growing areas mean D. barberi abundance did not correlate with over the 5-yr management period (Fig. 7). Maize- class area, percentage of landscape, or number of growing areas occurred less frequently at elevations patches for continuous maize, Þrst-year maize, and all between 494 and 499 m, and more frequently between maize. However, because probability values were 505 and 514 m. Beetle densities were greatest at ele- close to signiÞcance, total D. barberi abundance may vation classes 500Ð504 m and 505Ð509 m (Table 7). have correlated with these landscape metrics for con- Contingency analysis revealed highly signiÞcant asso- tinuous maize had more years been sampled. If so, this ciations between elevation class and D. barberi abun- would be similar to the signiÞcant relationships be- dance for each year, as indicated by ␹2 values tween these landscape metrics and total abundance (Table 8). The strength of the association was greatest of Diabrotica virgifera virgifera LeConte for continu- in both 1997 and 1999 and least in 1998 as indicated by ous maize (Beckler et al. 2004). Unlike D. barberi, CramerÕs V coefÞcients (Table 8). however, Beckler et al. (2004) found that D. v. virgifera total and mean abundance correlated with class area, percentage of landscape, and number of patches for all maize. They suggested that D. v. virgifera are Discussion dispersing from continuous maize, which acts as a primary source for D. v. virgifera populations, into Basic structural characteristics of the landscape can Þrst-year maize or sink habitats. These differences affect species abundance and distribution (Turner between D. barberi and D. v. virgifera may be due, in 1989, Wiens 1997, McGarigal et al. 2002). The abun- part, to extended diapause of D. barberi eggs (Chiang dance and distribution of many species can be strongly 1965, Krysan et al. 1984, Levine et al. 1992). Up to 40% of the eggs of these beetles are capable of overwin- tering for 2 yr, instead of the typical single year egg Table 4. Correlation coefﬁcients (r) and probabilities (P) for diapause (Krysan et al. 1984). Thus, in areas where the relationships between D. barberi emergence and postemergence interpolated map layers and between D. barberi mean emer- extended diapause is prominent, a 2-yr crop rotation gence and mean postemergence (Tabular) from the South Dakota has proven ineffective as a population control strategy corn rootworm areawide management site for years 1997–2001 for this insect. Consequently, we do not Þnd the variability in numbers of D. barberi associated with all Interpolated maps Tabular Year maize Þelds to correlate with these descriptive class- Nr PNrPlevel metrics. 1997 62,750 0.93 Ͻ0.001 10 0.89 Ͻ0.001 Large aggregated patches may support higher den- 1998 62,750 0.86 Ͻ0.001 11 0.82 0.001 sities than smaller dispersed patches (Harrison and 1999 62,750 0.70 Ͻ0.001 14 0.72 Ͻ0.003 Taylor 1997, Wiens 1997). For example, Thomas and 2000 62,750 0.88 Ͻ0.001 11 0.80 Ͻ0.002 2001 62,750 0.43 Ͻ0.001 15 0.81 Ͻ0.001 Hanski (1997) found that large habitat patches in proximity had a higher frequency of the skipper but- Degrees of freedom is number of observations (N) Ϫ 2. terßy Hesperia comma L. than smaller, more isolated 1954 JOURNAL OF ECONOMIC ENTOMOLOGY Vol. 97, no. 6

Table 5. Percentage of cell frequencies for contingency ␹2 analyses of the relationships between soil texture and interpolated D. barberi abundance maps from the South Dakota corn rootworm areawide management site for years 1997–2001

Soil texture class Abundance Silty clay Silty clay loam Silty loam Loam Sandy loam 1997 (19,240) Low 0.01 4.56 6.19 23.93 2.38 LowÐmedium 0.06 7.70 5.83 14.50 0.50 Medium 0.04 9.38 2.23 3.75 0.01 MediumÐhigh 0 11.46 2.26 0.84 0 High 0 3.16 0.46 0.22 0 1998 (19,654) Low 0.14 10.21 6.92 21.86 1.58 LowÐmedium 0 4.75 5.22 18.53 0.61 Medium 0.38 4.39 3.85 6.36 0.19 MediumÐhigh 0.19 4.98 1.37 3.46 0 High 0 1.69 0.83 2.48 0 1999 (18,214) Low 0.42 2.31 5.76 19.37 4.08 LowÐmedium 0.26 12.20 7.66 12.57 0.19 Medium 0.01 12.74 1.80 6.00 0.01 MediumÐhigh 0 8.59 0.21 3.41 0 High 0 1.86 0.02 0.55 0 2000 (18,677) Low 0.12 5.28 9.29 23.22 1.29 LowÐmedium 0.41 7.96 2.10 12.35 0.34 Medium 0 8.52 0.67 11.69 0.13 MediumÐhigh 0 4.20 1.65 6.32 0.01 High 0 1.30 0.71 2.45 0 2001 (16,209) Low 0 18.00 1.14 7.16 0.69 LowÐmedium 0 8.45 7.63 17.60 2.35 Medium 0 5.26 3.84 11.32 1.22 MediumÐhigh 0.32 1.24 2.92 10.24 0.24 High 0 0.04 0.04 0.32 0

Total number of cells is in parenthesis by year. patches. Similarly, Smith and Gilpin (1997) found that D. barberi total abundance and proximity indices of in all cases for the American pika, Ochtona princeps continuous maize. Richardson, the average size of occupied patches was In addition to structural characteristics of landscape greater than the average size of vacant patches. Maize conÞguration, edaphic factors can inßuence the dis- Þelds in the areawide management site of our study tribution of populations. For example, the microhabi- varied in size and arrangement over the 5-yr period. tat of a preferred oviposition site for female corn More D. barberi were captured on postemergence rootworms is inßuenced by soil properties such as traps in the larger maize patches. Also, through visual type, texture, moisture content, and compaction (Kirk map interpretation, nearest neighbor analyses, and et al. 1968, Ruesink 1986). The distribution and abun- proximity analyses, we showed that maize patches shifted in size and dispersion patterns in the management site over the 5-yr period. Also through visual map interpretation, D. barberi abundance shifted with the availability of maize patches. Similar to D. v. virgifera (Beckler et al. 2004), this association seemed especially true for large continuous maize patches, as sup- ported by the positive class-level correlation between

Table 6. Contingency ␹2 analyses of the relationship between soil texture and D. barberi abundance from the South Dakota corn rootworm areawide management site for years 1997–2001

Year ␹2 CramerÕs V P 1997 6607 0.29 Ͻ0.001 1998 1506 0.14 Ͻ0.001 1999 5845 0.28 Ͻ0.001 2000 2131 0.17 Ͻ0.001 2001 3839 0.24 Ͻ0.001 Fig. 7. Mean (ϮSD) percentage of cell frequencies for elevation classes where maize was grown in the South Dakota For each ␹2,dfϭ 16. areawide management site from 1997 to 2001. December 2004 FRENCH ET AL.: SPATIAL DISTRIBUTION OF NORTHERN CORN ROOTWORM 1955

Table 7. Percentage of cell frequencies for contingency ␹2 populations incorporated the broader landscape ap- analyses of the relationships between elevation and interpolated D. proach to pest management. Even though only a few barberi abundance maps from the South Dakota corn rootworm areawide management site for years 1997–2001 landscape metrics were computed in this study, a small set of landscape indices captured signiÞcant as- Elevation (m) pects of shifting patterns of vegetation and D. barberi Abundance 494Ð499 500Ð504 505Ð509 510Ð514 515Ð519 abundance. In addition, we could have underesti- mated some of the relationships of landscape compo- 1997 (19,240) Low 10.37 12.30 11.38 3.01 0 sition and D. barberi distribution because mortality LowÐmedium 1.66 8.03 11.99 7.32 0.11 from insecticide application was not accounted for. Medium 0.18 3.42 4.73 6.68 0.40 Further research at other areawide sites that vary in MediumÐhigh 0.03 2.95 3.67 7.36 0.56 landscape structure, soil textures, and elevation may High 0 0.50 1.48 1.81 0.05 1998 (19,654) be useful to determine the signiÞcance of these vari- Low 6.41 14.21 11.25 7.52 1.33 ables on D. barberi and D. v. virgifera metapopulation LowÐmedium 6.85 12.23 7.12 2.78 0.13 dynamics across the Corn Belt. Overall, our research Medium 1.19 4.77 5.84 3.04 0.34 emphasizes the potential role for geographic informa- MediumÐhigh 0.04 1.65 4.52 3.77 0.03 High 0 1.14 2.29 1.56 0 tion systems to provide information on interactions 1999 (18,214) between landscape structural characteristics, edaphic Low 10.26 8.80 9.59 3.30 0 factors, and insect population dynamics. Geographic LowÐmedium 2.31 10.08 11.36 8.94 0.19 Information Systems can be used in research to Þnd Medium 0.34 5.50 6.32 7.48 0.91 MediumÐhigh 0.07 2.75 2.84 6.46 0.09 patterns in the landscape that promote high insect High 0 1.55 0.43 0.45 0 population density patches and ultimately to improve 2000 (18,677) pest management strategies. Landscape planners, ag- Low 4.70 14.16 15.68 4.60 0.05 ricultural managers, and producers can beneÞt from LowÐmedium 3.77 6.17 5.85 6.13 1.24 Medium 6.14 6.21 3.54 4.66 0.47 this research by understanding the complex interac- MediumÐhigh 2.06 5.84 0.79 3.45 0.04 tions of D. barberi population dynamics and landscape High 0.43 2.74 0.31 0.98 0 variables. 2001 (16,209) Low 2.26 2.66 10.68 10.05 1.33 LowÐmedium 7.69 11.04 10.90 6.38 0.01 Acknowledgments Medium 2.40 12.17 4.37 2.69 0 MediumÐhigh 2.16 5.92 6.51 0.37 0 We thank the landowners of the South Dakota Corn Root- High 0.19 0.10 0.07 0.04 0 worm Areawide Pest Management Program, Deb Hartman (Project Coordinator), and support staff for the wealth of Total number of cells is in parenthesis by year. data and additional resources. Dave A. Beck georeferenced landscape features. 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