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Layout of the research apple orchard planted on M.26 rootstock in Frederick County, VA, showing tree varieties (Buckeye Gala and Idared) and trunk treatments.
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The temporal and small-scale spatial patterns of infestation by larval dogwood borer, Synanthedon scitula (Harris) were studied from 2002 to 2004 in two newly planted apple orchards in West Virginia and Virginia. Rootstock, tree variety, and cultural management practice were significantly associated with the presence and extent of dogwood borer inf...
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Context 1
... Design. Data on infestation of burr knots by dogwood borer larvae were collected in two experimental apple orchards located in Jefferson County, WV ( Fig. 1) and Frederick County, VA (Fig. 2) from 2002 to 2004. The West Virginia orchard consisted of two 0.625-ha plots separated by a spacing of 7.6 m. ÔGale GalaÕ and ÔSun FujiÕ trees were planted on size-controlling rootstocks in each plot in December 2001. The northern-most plot consisted of 12 rows of 20 trees planted on M.7 rootstock. Tree varieties were separated into ...
Context 2
... Virginia orchard (Fig. 2) consisted of a 0.65-ha plot (six rows 60 trees) of ÔIdaredÕ and ÔBuckeye GalaÕ trees planted on M.26 rootstock in March 2002. Tree varieties were separated into two neighboring blocks consisting of 60 trees in three rows with 6.1 m between rows and 2.4 m between trees. Orchard Management. The orchard drive rows were mowed frequently ...
Context 3
... dogwood borer infestation (Leskey and Bergh 2005), data presented in Riedl et al. (1985) suggest that the mere presence of burr knot tissue on individual trees does not guarantee that affected trees will be attacked by the insect. The precise reasons for this are unclear, but might be related to factors such as the size of adult dogwood borer populations in the orchard vicinity and variability of within-orchard factors (e.g., temperature, humidity, and management practices) that affect the temporal and spatial occurrences of burr knot tissue and probability of dogwood borer attack. An assessment of the temporal changes in in- sectÐ host plant interaction, therefore, is needed to better understand the dynamics of localized dogwood borer populations and infestations of burr knot tissue by the insect. It is to be expected that temporal changes in insect density in an area will be inßuenced by the spatial dynamics of the population (Schotzko and Knudsen 1992). As such, our understanding of the factors that could potentially inßuence species dynamics is usually strengthened by knowledge of the spatial structure of the population within the management unit (Strother and Steelman 2001, Tobin and Pitts 2002). Currently, however, very little is known about the spatial structure of dogwood borer populations and infestations of burr knot tissue within apple orchards. Several approaches can be used to describe the spatial structure of plant and animal populations within deÞned areas (Isaaks and Srivastava 1989, Cressie 1993, Perry 1995, Dale 1998, Haining 2003). One approach that has been applied to insect populations is geostatistics (Schotzko and OÕKeeffe 1989, Midgarden et al. 1993). Geostatistical analysis can provide valuable information on the spatial dependence of individuals in relation to their resources within a deÞned area. This information can be used in developing efÞcient sampling programs (Kemp et al. 1989, Schotzko and OÕKeeffe 1989, Midgarden et al. 1993) and for obtaining spatially independent samples that satisfy the assumption of random sampling for the design and analysis of experimental data (Williams et al. 1992, Wright et al. 2002). In addition, knowledge of the spatial structure of the insect, or of its effects, can be used to create distribution maps to develop management support systems. Such systems may lead to optimized insecticide spray programs via spatially precise and targeted applications for current infestations, future infestations, or both within the target area. This type of spatially referenced approach to insect management, formerly referred to as precision integrated pest management (IPM) (PIPM), has been described for several insect pests (Weisz et al. 1995, Midgarden et al. 1997, Ellisbury et al. 1998, Blom and Fleischer 2001, Blom et al. 2002). We investigated the factors associated with and their inßuence on the temporal pattern of dogwood borer infestations within apple orchards. The data we examined were collected as part of a larger effort to study the initiation and level of infestation by dogwood borer in two newly planted apple orchards grown under different cultural management regimes (Leskey and Bergh 2005). In addition, we use geostatistics to study the small-scale spatial variability of dogwood borer infestations so as to understand the the seasonal spatial patterns of larval populations across years. Orchard Design. Data on infestation of burr knots by dogwood borer larvae were collected in two experimental apple orchards located in Jefferson County, WV (Fig. 1) and Frederick County, VA (Fig. 2) from 2002 to 2004. The West Virginia orchard consisted of two 0.625-ha plots separated by a spacing of 7.6 m. ÔGale GalaÕ and ÔSun FujiÕ trees were planted on size-controlling rootstocks in each plot in December 2001. The northern-most plot consisted of 12 rows of 20 trees planted on M.7 rootstock. Tree varieties were separated into two equal blocks consisting of 10 trees in six rows with 4.9 m between rows and 3.7 m between trees. The southwestern plot consisted of 12 rows of 30 trees planted on M.26 rootstock. Tree varieties were separated into two equal blocks consisting of 15 trees in six rows with 4.9 m between rows and 2.4 m between trees. Crabapple ( Malus spp.) pollenizers were planted in both plots, which consisted of ÔManchurianÕ on M.7 rootstock planted every two trees in every other row in the northeastern plot, and ÔSnow DriftÕ on M.26 rootstock planted every three trees in every other row in the southwestern plot. The Virginia orchard (Fig. 2) consisted of a 0.65-ha plot (six rows ϫ 60 trees) of ÔIdaredÕ and ÔBuckeye GalaÕ trees planted on M.26 rootstock in March 2002. Tree varieties were separated into two neighboring blocks consisting of 60 trees in three rows with 6.1 m between rows and 2.4 m between ...
Context 4
... dogwood borer infestation (Leskey and Bergh 2005), data presented in Riedl et al. (1985) suggest that the mere presence of burr knot tissue on individual trees does not guarantee that affected trees will be attacked by the insect. The precise reasons for this are unclear, but might be related to factors such as the size of adult dogwood borer populations in the orchard vicinity and variability of within-orchard factors (e.g., temperature, humidity, and management practices) that affect the temporal and spatial occurrences of burr knot tissue and probability of dogwood borer attack. An assessment of the temporal changes in in- sectÐ host plant interaction, therefore, is needed to better understand the dynamics of localized dogwood borer populations and infestations of burr knot tissue by the insect. It is to be expected that temporal changes in insect density in an area will be inßuenced by the spatial dynamics of the population (Schotzko and Knudsen 1992). As such, our understanding of the factors that could potentially inßuence species dynamics is usually strengthened by knowledge of the spatial structure of the population within the management unit (Strother and Steelman 2001, Tobin and Pitts 2002). Currently, however, very little is known about the spatial structure of dogwood borer populations and infestations of burr knot tissue within apple orchards. Several approaches can be used to describe the spatial structure of plant and animal populations within deÞned areas (Isaaks and Srivastava 1989, Cressie 1993, Perry 1995, Dale 1998, Haining 2003). One approach that has been applied to insect populations is geostatistics (Schotzko and OÕKeeffe 1989, Midgarden et al. 1993). Geostatistical analysis can provide valuable information on the spatial dependence of individuals in relation to their resources within a deÞned area. This information can be used in developing efÞcient sampling programs (Kemp et al. 1989, Schotzko and OÕKeeffe 1989, Midgarden et al. 1993) and for obtaining spatially independent samples that satisfy the assumption of random sampling for the design and analysis of experimental data (Williams et al. 1992, Wright et al. 2002). In addition, knowledge of the spatial structure of the insect, or of its effects, can be used to create distribution maps to develop management support systems. Such systems may lead to optimized insecticide spray programs via spatially precise and targeted applications for current infestations, future infestations, or both within the target area. This type of spatially referenced approach to insect management, formerly referred to as precision integrated pest management (IPM) (PIPM), has been described for several insect pests (Weisz et al. 1995, Midgarden et al. 1997, Ellisbury et al. 1998, Blom and Fleischer 2001, Blom et al. 2002). We investigated the factors associated with and their inßuence on the temporal pattern of dogwood borer infestations within apple orchards. The data we examined were collected as part of a larger effort to study the initiation and level of infestation by dogwood borer in two newly planted apple orchards grown under different cultural management regimes (Leskey and Bergh 2005). In addition, we use geostatistics to study the small-scale spatial variability of dogwood borer infestations so as to understand the the seasonal spatial patterns of larval populations across years. Orchard Design. Data on infestation of burr knots by dogwood borer larvae were collected in two experimental apple orchards located in Jefferson County, WV (Fig. 1) and Frederick County, VA (Fig. 2) from 2002 to 2004. The West Virginia orchard consisted of two 0.625-ha plots separated by a spacing of 7.6 m. ÔGale GalaÕ and ÔSun FujiÕ trees were planted on size-controlling rootstocks in each plot in December 2001. The northern-most plot consisted of 12 rows of 20 trees planted on M.7 rootstock. Tree varieties were separated into two equal blocks consisting of 10 trees in six rows with 4.9 m between rows and 3.7 m between trees. The southwestern plot consisted of 12 rows of 30 trees planted on M.26 rootstock. Tree varieties were separated into two equal blocks consisting of 15 trees in six rows with 4.9 m between rows and 2.4 m between trees. Crabapple ( Malus spp.) pollenizers were planted in both plots, which consisted of ÔManchurianÕ on M.7 rootstock planted every two trees in every other row in the northeastern plot, and ÔSnow DriftÕ on M.26 rootstock planted every three trees in every other row in the southwestern plot. The Virginia orchard (Fig. 2) consisted of a 0.65-ha plot (six rows ϫ 60 trees) of ÔIdaredÕ and ÔBuckeye GalaÕ trees planted on M.26 rootstock in March 2002. Tree varieties were separated into two neighboring blocks consisting of 60 trees in three rows with 6.1 m between rows and 2.4 m between ...
Citations
... nivel de dependencia espacial para determinar el nivel de relación entre los datos obtenidos en los muestreos; este valor se obtuvo al dividir el efecto pepita y la meseta, expresado en porcentaje: con menos de 25 por ciento es alto; entre 26 y 75 por ciento es moderado, y mayor a 76 por ciento es bajo (Frank et al. 2011). ...
The sugar cane agroecosystem of central Mexico is affected by gumming disease, which is caused by Xanthomonas vasicola pv. vasculorum, a systemic bacterium that reduces the formation of sucrose and forms a gummy substance, which, when mixed with cane juice, slows its crystallization.
Integrated and sustainable management is supported by spatial-temporal models for
decision making. The objective was to generate a spatial-temporal model of X. vasicola pv. vasculorum around the Emiliano Zapata sugar mill, in Morelos, Mexico. In 2016 and 2017, 80 points were randomly and regionally georeferenced to determine the incidence and to carry out statistical analyses. The spatial pattern obtained was aggregated, fitting a spherical model in 2016 with an incidence of 19.12%. In 2017, it was fitted to a linear model, with an incidence of 38.08%. The maps allow the location of points of infestation to direct management measures; they also make it possible to visualize changes in the location and dispersion through time and space.
... The placement of wooden sticks allowed us to find the same sampling points sampled previously. Sampling was performed every week: the first sampling took place at 44 Meteorological Standard Weeks (MSW) (29 October to 4 November); the second at 45 MSW (5)(6)(7)(8)(9)(10)(11); third at 46 MSW (12)(13)(14)(15)(16)(17)(18); fourth at 47 MSW (19)(20)(21)(22)(23)(24)(25); fifth at 48 MSW (26 November to 2 December); sixth at 49 MSW (3-9 December); seventh at 50 MSW (10)(11)(12)(13)(14)(15)(16); eighth at 51 MSW (17)(18)(19)(20)(21)(22)(23); and ninth at 52 MSW (24)(25)(26)(27)(28)(29)(30)(31). In each crop season, a total of 252 samplings were undertaken in the 28 pigeonpea fields (nine sampling weeks in each field). ...
... 26,27 The extent of H. armigera larval aggregation was defined by nugget-to-sill ratios (C 0 /C 0 + C), 43 where ratios of less than 0.25, 0.25-0.75 and more than 0.75 indicate strong, moderate and weak aggregation, respectively. 13,[27][28][29] A Shapiro-Wilk test demonstrated that larval count data for all MSWs did not follow the normality assumption. Therefore, mean larval count data were transformed using Turkey's ladders of power before variogram analysis. ...
... If the purpose of sampling is towards an ETL-based management advisory, the sampling distance should be more than the average range of the variogram. 13,14,28 The current spatial distribution sampling plan may be adapted for practical use in the management of H. armigera on pigeonpea. ...
BACKGROUND
The gram pod borer, Helicoverpa armigera (Lepidoptera: Noctuidae) is an economically important pest of pigeonpea crop in India. Fixed plot surveys for H. armigera larvae were carried out at 28 pigeonpea fields located in the Southern Plateau and Hills agro‐climatic zone of India for three crop seasons (nine sampling weeks per season). The spatiotemporal dynamics of H. armigera larvae in the experimental area (=Hanamkonda) was analysed using geostatistics tools, namely a variogram and Voronoi diagram, and H. armigera larval distribution patterns were further characterized and mapped.
RESULTS
A significant difference in H. armigera larval incidence was noticed between sampling weeks, with greater larval incidence observed between 26 November and 2 December. Pod formation phenophase (Meteorological Standard Week 44) of pigeonpea favoured the initial H. armigera larval incidence. Variogram analysis revealed moderate to strong larval aggregation (spatial dependence) of H. armigera in all nine sampling weeks. Based on the range value of the variogram, the average aggregation distance of H. armigera larvae in pigeonpea was estimated to be 2425.48 m. Voronoi diagrams illustrated the spatial heterogeneity of H. armigera larva between sampling weeks, which can be linked to availability of food sources.
CONCLUSION
This study witnessed intrapopulation variability in H. armigera larvae associated with geographical space and temporal patterns. Based on our findings, a sampling distance of 2425.48 m may be used in larger pigeonpea fields (experimental area) to reduce scouting fatigue. The interpolated maps generated in this study may be of value in developing effective H. armigera larva monitoring and management tools in pigeonpea crop.
... Due to the lack of two-dimensional information for individual sample locations, the information derived from these mean-variance methods lacks many ecological interactions [13,14]. Another benefit of spatial distribution sampling is to develop a visual representation of pest infestations in the field by creating prediction maps and kriging maps in variograms [6,15,16] and "red and blue" maps in SADIE [17,18]. This type of visual representation can be useful for site-specific pest management efforts. ...
... All models with evidence of spatial dependency have an additional parameter called "range". Range is the maximum distance between samples below which spatial autocorrelation is present [34,38], and the range value plays a critical role in determining the adequate sampling distance for an unbiased, independent sampling plan [6,11,15,25,39]. The nugget-to-sill ratio (C 0 /C 0 + C) and nugget were used to determine the degree of aggregation [40], where ratios <0.25, 0.25-0.75, and >0.75 indicated strong, moderate, and weak aggregation, respectively [11,[41][42][43]. ...
... However, developing distribution maps may not be feasible for sod growers because they require many sample points and substantial technical skills to process the raw data for map construction [20,59]. When the range values are used to obtain unbiased samples, the distances between two sampling points should be greater than the average range values for both larvae and adults [15,26,58]. S. venatus vestitus larvae are hidden in the soil, and thus, their infestations in soils can be determined if soil samples are collected using a hole cutter at 4.0 m (average range value = 3.9 m) distances to capture larvae. ...
The hunting billbug, Sphenophorus venatus vestitus Chittenden (Coleoptera: Curculionidae), is an important turfgrass pest, especially in sod farms. S. venatus vestitus larvae feed on the stems and roots of turfgrass. Damaged turfgrass is loosely held together and poses a challenge for machine harvesting. Additionally, the normal growth of turfgrass is affected, especially after winter dormancy. Because S. venatus vestitus larvae are hidden inside the stems or under the soil, larval management is challenging. To improve sampling and management, the spatial distribution patterns of S. venatus vestitus larvae and adults were assessed at four sod farm sites with a history of S. venatus vestitus infestation in central Georgia (USA). The larvae were sampled by soil cores using a hole cutter, whereas adults were collected using pitfall traps for 7 d. The spatial distributions of larvae and adults was analyzed using SADIE and variograms. The SADIE and variogram analyses revealed a significant aggregation pattern for adults, whereas aggregated distributions were detected for larvae with variogram analyses. The average ranges of spatial dependence for larval and adult samples were 3.9 m and 5.4 m, respectively. Interpolated distribution maps were created to visually depict S. venatus vestitus infestation hotspots within the sod farms.
... 33,53,57 The best fitted omnidirectional variograms were selected based on the greatest r 2 value. 30,33,35,58 Nugget-to-sill ratios (C 0 /C 0 + C) describe the extent of aggregation, 59 where ratios of < 0.25, 0.25-0.75, and > 0.75 indicate strong, moderate, and weak aggregation, respectively. 33,35,43,58,60 2.5 Spatial analysis by distance indices (SADIE) SADIE is a geospatial technique that can be used to determine the spatial distribution pattern of arthropod pests and plant diseases using spatially referenced count data. ...
... 30,33,35,58 Nugget-to-sill ratios (C 0 /C 0 + C) describe the extent of aggregation, 59 where ratios of < 0.25, 0.25-0.75, and > 0.75 indicate strong, moderate, and weak aggregation, respectively. 33,35,43,58,60 2.5 Spatial analysis by distance indices (SADIE) SADIE is a geospatial technique that can be used to determine the spatial distribution pattern of arthropod pests and plant diseases using spatially referenced count data. 33,42,43 SADIE measures the overall aggregation based on the distance to regularity (D), which represents the minimum total distance that individual samples would need to move in order to obtain the same number (i.e. ...
... If the intended use of the sampling is to take independent samples in order to determine the threshold values for insecticide treatment, the sampling distance should be higher than the average range value of the variogram. 34,35,58,77 This may be the most practical utility of the spatial-distribution of a sampling plan for H. postica management in alfalfa fields. ...
BACKGROUND
Understanding the spatio‐temporal dynamics of prey and predator distributions can provide valuable insights into pest management strategies and conservation of natural enemies in agro‐ecosystems. The alfalfa weevil, Hypera postica (Gyllenhal), is an economically important pest of alfalfa throughout the western United States. Coccinellids and nabids are among the most important natural enemies of this species, contributing to the biological control of H. postica in alfalfa fields. The spatio‐temporal dynamics of H. postica and these two predator groups were investigated using 81 (= 9 × 9 grid) sample points in each of five alfalfa fields in north‐central Montana. The data were analyzed using variogram and spatial analysis by distance indices (SADIE).
RESULTS
Variogram analysis revealed the spatial dependence (aggregation) of H. postica in 17 of 19 sampling times for larvae, and three of 12 sampling times for adults. Using SADIE, statistically significant aggregation distribution was evident in four of 19 sampling times for larvae, and five of 12 sampling times for adults of H. postica. Combined variogram and SADIE showed strong evidence of spatial aggregation of H. postica larval population (~95%) while a moderate level of aggregation in the adult population (~67%) of the sampling times analyzed. The average aggregation distances based on the range value of the variogram were 22.3 m and 14.7 m for larvae and adults, respectively. Based on variogram results, populations of natural enemies, coccinellids and Nabis spp. were found spatially aggregated in 57.9% and 5.6% of the sampling times, respectively. SADIE further supported the variogram results as coccinellid populations (52.6% of sampling times) were highly aggregated in contrast with the Nabis spp. populations (5.6% of sampling times) in alfalfa fields. There was no evidence of significant spatial synchrony between H. postica and its predators, coccinellids and Nabis spp.
CONCLUSION
Our study was able to determine the spatial and temporal distribution of H. postica and its two natural enemies (coccinellids and nabids) in irrigated alfalfa fields. The possible implications of these findings for integrated pest management (IPM) of alfalfa weevil populations are discussed.
... La evaluación del nivel de agregación de cada especie se calculó considerando el valor cociente que resulte de dividir el valor del nugget (C0) entre la meseta (C0+C), donde <0.25, 0.25-0.75 y >0.75 indican fuerte, moderada o débil agregación ( Frank et al. 2011). Después de seleccionar el modelo que menor error presentó, se procedió a la interpolación con kriging. ...
Columnar cacti are distributed naturally in arid and semi-arid zones of America. Almost 50% are found in Mexico, were 45 species have been used for 8,500 years and currently are commercialized in regional or international markets. Rot damage in monopodic stems or branches was observed recently in columnar cacti of Central Mexico. Previous reports suggested Cactophagus spinolae (Gyllenhal) and Scyphophorus acupunctactus Gyllenhal as the main causes of damage, and both were feeding on new hosts. This paper recorded organisms associated with the process of rot damage in some columnar cacti of Central Mexico. In 2012, field trips during dry and rainy seasons documented damage and collected insects in five columnar cacti of Central Mexico. The presence of the organisms varied in relation to the host and damage stage: primary, intermediate, and late. The primary stage when rot damage begins is characterized by C. spinolae, as well as Chalcolepidius approximatus Erichson, and larvae of some Lepidoptera. The other stages are characterized by saprophages or parasitoids. Most insects found in the study were new records of species of Cactaceae. Knowledge of ecological interactions and dynamics of plant communities during rot damage is necessary to propose control and sanitation measures, and understand the effect of disturbance in the presence of these herbivores.
... In all seasons, repeated measures multivariate analysis of variance was first used to establish whether treatment, sampling date, and the interaction of treatment and sampling date were significant. The two main factors (sampling date and treatment) were analyzed with time as a repeated measures factor (Ott & Longnecker, 2001;Norman & Streiner, 2008;Frank et al., 2011). When no significant interaction of sampling date and treatment was observed, the data were pooled and a one-way analysis of variance (anova) was used to determine the treatment effect. ...
... C), and nugget (C 0 ) that determine the shape of the variogram. The maximum distance within which the spatial autocorrelation exists is the range ( Liebhold et al. 1993;Fortin and Dale 2005). The semivariance value at which the variogram plot of ^ c h ð Þ reaches a saturation point is the sill, and semivariance at zero lag distance is the nugget ( Liebhold et al. 1993). ...
... Because of insufficient sample points to detect anisotropy ( Liebhold et al. 1991;Robinson and Metternicht 2006), and also no significant ecological relevancy of using the directional variograms to study mite distributions, we used all isotropic variograms for the study reported here. The best fitted isotropic variogram models of T. urticae infestation in different sampling dates were selected based on the greatest r 2 value (Park and Tollefson 2005;Frank et al. 2011;Rijal et al. 2014), although the lowest residual sum of square is another criteria to select fitted variograms (Robertson 2008) The lag distance used to generate the best fitted variograms ranged from 11.00 to 13.50 m with uniform lag interval of 2 m. Nugget-to-sill ratio (C 0 /C 0 ? ...
... and[0.75 indicate strong, moderate, and weak aggregation, respectively (Farias et al. 2002;Frank et al. 2011;Rijal et al. 2014). ...
Twospotted spider mite, Tetranychus urticae Koch, is an important pest of peppermint in California, USA. Spider mite feeding on peppermint leaves causes physiological changes in the plant, which coupling with the favorable environmental condition can lead to increased mite infestations. Significant yield loss can occur in absence of pest monitoring and timely management. Understating the within-field spatial distribution of T. urticae is critical for the development of reliable sampling plan. The study reported here aims to characterize the spatial distribution of mite infestation in four commercial peppermint fields in northern California using spatial techniques, variogram and Spatial Analysis by Distance IndicEs (SADIE). Variogram analysis revealed that there was a strong evidence for spatially dependent (aggregated) mite population in 13 of 17 sampling dates and the physical distance of the aggregation reached maximum to 7 m in peppermint fields. Using SADIE, 11 of 17 sampling dates showed aggregated distribution pattern of mite infestation. Combining results from variogram and SADIE analysis, the spatial aggregation of T. urticae was evident in all four fields for all 17 sampling dates evaluated. Comparing spatial association using SADIE, ca. 62 % of the total sampling pairs showed a positive association of mite spatial distribution patterns between two consecutive sampling dates, which indicates a strong spatial and temporal stability of mite infestation in peppermint fields. These results are discussed in relation to behavior of spider mite distribution within field, and its implications for improving sampling guidelines that are essential for effective pest monitoring and management.
... C), and nugget (C 0 ) that determine the shape of the variogram. The maximum distance within which the spatial autocorrelation exists is the range (Liebhold et al. 1993; Fortin and Dale 2005). The semivariance value at which the variogram plot of ^ c h ð Þ reaches a saturation point is the sill, and semivariance at zero lag distance is the nugget (Liebhold et al. 1993). ...
... Because of insufficient sample points to detect anisotropy (Liebhold et al. 1991; Robinson and Metternicht 2006 ), and also no significant ecological relevancy of using the directional variograms to study mite distributions, we used all isotropic variograms for the study reported here. The best fitted isotropic variogram models of T. urticae infestation in different sampling dates were selected based on the greatest r 2 value (Park and Tollefson 2005; Frank et al. 2011; Rijal et al. 2014), although the lowest residual sum of square is another criteria to select fitted variograms (Robertson 2008) The lag distance used to generate the best fitted variograms ranged from 11.00 to 13.50 m with uniform lag interval of 2 m. Nugget-to-sill ratio (C 0 /C 0 ? ...
... and[0.75 indicate strong, moderate, and weak aggregation, respectively (Farias et al. 2002; Frank et al. 2011; Rijal et al. 2014). ...
... A signiÞcant regression (P Ͻ 0.05) indicated the presence of a trend in the data, which were then subjected to median polishing. The median polishing technique extracts large-scale variation from the data so that the remnant or median polished residuals can be used in variogram analysis to model small-scale variation (Bakhsh et al. 2000, Costa 2009, Frank et al. 2011). The median polished residuals for the pupal exuviae data were derived using code written in MATLAB (Math-Works Inc., Natick, MA) and were then used to develop the semivariograms. ...
... The best Þtted omnidirectional variogram model for the pupal exuviae data in each of the nine blocks was selected based on the smallest value of residual sum of squares (RSS) and the greatest r 2 value Tollefson 2005, Frank et al. 2011). Evaluation of the degree of aggregation in pupal exuviae counts was based on the nugget-to-sill ratio (C0/C0 ϩ C; Trangmar et al. 1986), where Ͻ0.25, 0.25Ð 0.75, and Ͼ0.75 indicate strong, moderate, and weak aggregation, respectively (Farias et al. 2002, Frank et al. 2011. ...
... Understanding the spatial distribution of an insect species is a key component for developing a quantitative and efÞcient sampling scheme (Legendre and Fortin 1989, Fortin and Dale 2005, Park and Tollefson 2006. Sampling schemes developed for other pest insects that were based on spatial distributions used an appropriate sampling method (systematic, random, or stratiÞed) in a gridded (square, triangular, or hexagonal) sampling space (Schotzko and OÕKeeffe 1990, Williams et al. 1992, Liebhold et al. 1993, Wright et al. 2002, Frank et al. 2011. Sampling distance can differ according to the intended purpose. ...
Grape root borer, Vitacea polistiformis (Harris) (Lepidoptera: Sesiidae) is a potentially destructive pest of grape vines, Vitis spp. in the eastern United States. After feeding on grape roots for ≈2 yr in Virginia, larvae pupate beneath the soil surface around the vine base. Adults emerge during July and August, leaving empty pupal exuviae on or protruding from the soil. Weekly collections of pupal exuviae from an ≈1-m-diameter weed-free zone around the base of a grid of sample vines in Virginia vineyards were conducted in July and August, 2008-2012, and their distribution was characterized using both nonspatial (dispersion) and spatial techniques. Taylor's power law showed a significant aggregation of pupal exuviae, based on data from 19 vineyard blocks. Combined use of geostatistical and Spatial Analysis by Distance IndicEs methods indicated evidence of an aggregated pupal exuviae distribution pattern in seven of the nine blocks used for those analyses. Grape root borer pupal exuviae exhibited spatial dependency within a mean distance of 8.8 m, based on the range values of best-fitted variograms. Interpolated and clustering index-based infestation distribution maps were developed to show the spatial pattern of the insect within the vineyard blocks. The temporal distribution of pupal exuviae showed that the majority of moths emerged during the 3-wk period spanning the third week of July and the first week of August. The spatial distribution of grape root borer pupal exuviae was used in combination with temporal moth emergence patterns to develop a quantitative and efficient sampling scheme to assess infestations.
... Understanding the spatial distribution of an insect species is a key component for developing a quantitative and efficient sampling scheme (Legendre and Fortin 1989, Fortin and Dale 2005, Park and Tollefson 2006. Sampling schemes developed for other pest insects that were based on spatial distributions employed an appropriate sampling method (systematic, random, or stratified) in a gridded (square, triangular, or hexagonal) sampling space (Schotzko and O'Keeffe 1990, Williams et al. 1992, Liebhold et al. 1993, Wright et al. 2002, Frank et al. 2011). Sampling distance can differ according to the intended purpose. ...
... Sampling distance can differ according to the intended purpose. If the intention is to develop infestation density maps for site-specific management (Fleischer et al. 1999), as has been used for some agricultural pests (Weisz et al. 1996, Blom et al. 2002, sampling should be within the distance of the range value of the variogram (8.8 m for grape root borer exuviae) (Weisz et al. 1995, Park and Tollefson 2005, Frank et al. 2011. However, there are several reasons why a map-based precision approach for grape root borer management may not be a pragmatic option for growers. ...
... A significant regression (P < 0.05) indicated the presence of a trend in the data, which were then subjected to median polishing. The median polishing technique extracts large scale variation from the data so that the remnant or median polished residuals can be used in variogram analysis to model small-scale variation(Bakhsh et al. 2000, Costa 2009, Frank et al. 2011). The median polished residuals for the pupal exuviae data were derived using code written in Matlab (MathWorks Inc, Natick, MA) and were then used to develop the semivariograms. ...
Grape root borer, Vitacea polistiformis (Harris), is an oligophagous pest of grapevines in the eastern USA. Neonates must burrow into the soil to find grape roots. In Virginia, larvae feed on roots for ~2 years, then pupate just beneath the soil surface. Emerging adults leave an empty pupal exuviae at the soil surface around the vine base. There was no relationship between weekly captures in pheromone traps and pupal exuviae counts, indicating that exuviae sampling is most appropriate to assess infestations. Exuviae sampling in Virginia vineyards revealed infestations that ranged from light to very heavy. Eighteen biotic and abiotic variables were measured and used in analyses that assessed their relative contributions to differences in exuviae density. Water holding capacity and clay/sand ratio were most strongly associated with pupal exuviae density; these variables were used to develop a model for predicting the extent of infestation of individual vineyards. The spatial distribution of pupal exuviae was characterized using non-spatial and geospatial techniques. Although the non-spatial method (Taylor's Power Law) indicated that exuviae showed an aggregated distribution in all blocks, spatial methods (variograms, SADIE) revealed aggregated distributions only in blocks with >= 0.5 pupal exuviae per vine. Independent pupal exuviae samples for population assessment in vineyards can be achieved using sampling points separated by >8.8 m. Combined results from geospatial analyses and the temporal distribution of pupal exuviae within years enabled the development of a practical and quantitative sampling protocol. Bioassays used to measure the behavioral response of larvae to host stimuli revealed that neonates were attracted to grape root volatiles. In soil column bioassays, larvae moved vertically and horizontally over distances of up to 120 cm and apparently perceived the presence of grape roots from a distance of 5 cm in soil. Results are discussed in relation to their potential implications for monitoring and managing grape root borer.
