spatial distribution and movement patterns of stored

Biology, Behavior, and Pest Detection on Stored Grain

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KPS5-1

Spatial distribution and movement patterns of stored-product insects

J.F. Campbell1,*, G.P. Ching’oma2, M.D. Toews1, S.B. Ramaswamy2,3

1 United States Department of Agriculture, Agricultural Research Service, Grain Marketing and Production ResearchCenter, 1515 College Ave, Manhattan, KS, USA 66502-2736.

2 Department of Entomology, 123 W. Waters Hall, Kansas State University, Manhattan, KS, USA 66506-4004.3 Current address: College of Agriculture, Purdue University, 615W. State St., West Lafayette, IN, USA 47907-2053.* Corresponding author: fax: 785-537-5584, e-mail: [email protected].

Abstract

If the foundation of an effective pestmanagement program is an understanding of pestecology and behavior, then this understandingmust be at an appropriate spatial and temporalscale for the pest species and the environment.This is because the structure of the landscapemosaic in which an organism lives influencesecological processes such as population dynamics,movement patterns, and spatial distribution.Stored-product pests occupy spatially andtemporally fragmented landscapes that can haveprofound impacts not only on their populationdynamics, but also our ability to monitorpopulations and effectively target pestmanagement. Recent research findings regardingthese broader landscape issues in relation tostored-product pest spatial distribution andmovement patterns will be presented. The focuswill be on Rhyzopertha dominica, the lesser grainborer, temporal and spatial patterns in flightactivity and dispersal, and the influence of land-cover and land-use patterns on flight activity.How this data has generated new hypothesesabout the ecology and behavior of this pest andprovided insight into more targeted pestmanagement approaches will also be discussed.

Key words: Behavior, ecology, landscape,Rhyzopertha dominica, lesser grain borer.

Introduction

The importance of landscape structure and howorganisms interact with spatial and temporallandscape heterogeneity has come to the forefrontof ecology, and this perspective is making inroadsinto pest management as well. If, as it has beenargued, the foundation of an effective integrated pestmanagement (IPM) program is an understanding ofpest ecology, then this understanding needs to bedeveloped within the appropriate landscapestructure and at the appropriate scale for that pest.This may be especially true for post harvest IPM,where the focus on sanitation, structural modification,and targeted pest control tactics can be summarizedin ecological terms as manipulating the landscapestructure to make it less favorable for pest populationestablishment, growth, and persistence.

Environments created or modified by humanstend to be highly fragmented landscapes consistingof a mosaic of resource patches that are separatedfrom each other by barriers to movement or by amatrix of less hospitable habitat (Wiens, 1976).Landscape structure and dynamics influenceecological processes (e.g., population dynamics,spatial distribution) of the organisms living in thelandscape (Turner, 1989; Wiens, 1976; Wiens etal., 1993). For example, spatial heterogeneity affectsindividual movement behavior, which in turninfluences dispersal success and the distribution andpersistence of populations (With and Crist, 1995;

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With et al., 1997). Populations can be made up ofinterconnected subpopulations occupying differentresource patches [metapopulations (Hanski andSimberloff, 1997)], and the degree of movementamong patches is what defines the type ofmetapopulation structure and the scale at whichindividuals can be considered to be from the samepopulation. Through its influence on resource patches(size, number, and distribution) and the movementof organisms, landscapes structure can haveimportant influences on metapopulations. When thereis considerable movement among patches andpopulation trends within patches are correlated witheach other, this is considered just a single patchilydistributed population; but, as the level of recruitmentfrom within a patch increases relative to immigrationfrom other patches, the population becomes moreof a true metapopulation (Harrison and Taylor,1997). A range of intermediate scenarios may beespecially relevant to stored-product insects,particularly source-sink metapopulations wherepopulations in higher quality (source) patches persistand produce individuals that immigrate into lowerquality (sink) patches and enable subpopulations topersist in these less favorable locations. Onlyrecently have metapopulation models begun to takeinto account the spatial structure of the landscape(DeWoody et al., 2005), and stored-product insectshave served as useful models (Bancroft and Turchin,2003).

It can be argued that stored-product pests ofthe grain and food industries are pests in large partdue to their effectiveness at finding and exploitingthe temporally and spatially fragmented landscapeswithin food facilities and within which food facilitiesare located. Targeting pest management to locationswhere pests are present can increase the probabilityof suppressing the pest population, and reduce thecost of management and risk of negative nontargetconsequences (Brenner et al., 1998). In evaluatingtreatment efficacy, it is important to consider thespatial scale over which pest subpopulations interact,because this determines the proportion of individualsexposed to treatment and the potential forrecolonization. For example, when pestsubpopulations are interacting over spatial scaleslarger than an individual structure, rapid pest

resurgence after treatments are applied to thatstructure could occur due to recolonization from othersource patches. Broad spatial scale processes maybe driving population dynamics within food storageand processing structures, but for most stored-product pests, with the notable exception ofProstephanus truncatus (Horn) (Coleoptera:Bostrichidae) (Hill et al., 2002), our understanding ofecology and behavior at broader landscape scalesis limited.

Multiple studies have found that stored-productinsects have spatially and temporally patchydistributions inside structures (Arbogast et al., 2000;Arbogast et al., 1998; Campbell et al., 2002;Nansen et al., 2004), and even around the outsideof structures (Campbell and Mullen, 2004).Although studies measuring stored-product insectdispersal ability are limited, they indicate that manystored-product insects are highly mobile and capableof moving among food patches (Campbell andArbogast, 2004; Campbell et al., 2002; Chesnut,1972; Fadamiro, 1997; Hagstrum and Davis, 1980).Stored-product pests are often trapped outside grainstorage and processing structures (Campbell andArbogast, 2004; Campbell and Mullen, 2004; Doudand Phillips, 2000; Dowdy and McGaughey, 1994;Fields et al., 1993; Throne and Cline, 1989, 1991)and sometimes far away from these structures(Cogburn and Vick, 1981; Sinclair and Haddrell,1985; Strong, 1970; Vick et al., 1987), whichsuggests the capability for long distance flight.However, these captures may also indicate localizedmovement of feral populations. Studies have alsoshown that insects can immigrate into facilities(Campbell and Arbogast, 2004; Campbell andMullen, 2004; Hagstrum, 2001). These patternssuggest the potential for stored-product insects tobe interacting with broader landscape patterns thanjust those inside food facilities.

A variety of approaches can be taken to studythe influence of landscape structure on stored-product pest spatial distribution and movement. Forsome species, locations and questions, fine scalespatial patterns may be most important in impactingmovement and distribution. For investigation of thesepatterns, field studies can be performed and, asdiscussed above, many studies of pest distribution


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within food facilities have shown temporally andspatially patchy distributions. Another approach thatcan be used is to create experimental landscapes inwhich the influence of landscape features onbehavior and ecology can be examined in moredetail. Studies of animal movement on theselandscapes can provide insight into the relevantspatial scale, and ultimately into how organismsperceive landscape structure and the scale(s) at whichthey interact with spatial complexity (With, 1994).The experimental landscape approach has been usedfor stored-product insects (Bancroft and Turchin,2003), and a number of current research projectsare exploring the impact of landscape structure onred flour beetle behavior and ecology. However,many stored-product species are likely to interactwith landscape structure over much broader spatialscales then can be fully understood in the laboratory.This seems to be the case for the P. truncatus (Hillet al., 2002) and may also be the case with its relativethe lesser grain borer, Rhyzopertha dominica F.(Coleoptera: Bostrichidae). Here, some recentresearch findings on R. dominica are presented toshow how these broader landscape issues might beimportant in understanding and managing this pest.

Rhyzopertha dominica is a devastatingcosmopolitan pest of stored grain, grain products,and other materials (Potter, 1935). There is littleevidence that R. dominica infests wheat in the field(Hagstrum, 2001). Therefore, infestation in storageresults from either a failure to remove residualpopulations from storage structures or fromdispersing individuals finding and exploiting storedgrain. Rhyzopertha dominica is a strong flier thatis commonly captured near grain elevators andfarm storage bins (Dowdy and McGaughey,1994, 1998; Edde et al., 2005; Throne and Cline,1991), but also far away from farm storagestructures (Edde et al., 2005; Fields et al., 1993;Sinclair and Hadrell, 1985). There is also someevidence suggesting that R. dominica can survivein the wild on other hosts such as wood twigsand acorns (Potter, 1935; Wright et al., 1990). Therelative importance of these sources is not wellunderstood, but generally regarded as low. Hagstrum(2001) showed that R. dominica can immigrateinto grain bins through the eaves, vents, and poorly

sealed bin bottoms. There is limited informationon long-term outdoor monitoring of R. dominicaaround grain storage facilities (Dowdy andMcGaughey, 1998; Throne and Cline, 1991).

To investigate broader landscape scalequestions about R. dominica populations inKansas, we addressed the following questions.First, how far is this species capable of dispersingin the field? This would provide an estimation ofthe spatial scale over which landscape featuresmight be interconnected. Second, how patchy isthe distribution of beetles within the landscape andwhat are the important landscape features thatimpact spatial distribution? Beetle spatialdistribution may by influenced primarily by thegrain storage sites on the landscape or by otherlandscape features. The family Bostrichidaecontains many species of wood borers that areassociated with forests and the other majorstored-product pest in this family, P. truncatus,has been found to be attracted to wood, to feedand reproduce on certain types of wood, and tobe associated with forests habitats in some partsof the world (Hill et al., 2002). Forested areas inan agricultural landscape may also contribute tothe spatial mosaic of resource patches for R.dominica. Finally, how important are thesebroader landscape patterns in flight activity interms of impacting populations within food facilities?

Dispersal capability of R. dominicain the field (Ching’oma, 2006)

Materials and methods

Mark-release-recapture studies in the fieldwere used to assess the dispersal ability of R.dominica adults. Releases were conducted in anative tallgrass prairie landscape in Kansas USAthat previous studies have shown contains R.dominica, but in which there was no grainproduction or storage. Beetles for release werecollected from the field within the previous year and,after the first generation, reared under conditionsthat have been previously demonstrated to maximizeflight initiation (Perez-Mendoza et al., 1999).


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Releases were made in the center of a circulargrid (radius of 1 km) of four-funnel Lindgren traps(Phero-Tech. Inc., British Columbia, Canada)containing a R. dominica aggregation pheromonelure (Trécé, Inc., Adair, OK USA). Traps wereplaced at various distances from the center releasepoint (Turchin and Thoeny, 1993). Prior to release,adults were marked so that recaptured beetles couldbe identified. Beetles were released approximatelytwo h before sunset. In 2003, 15,900 markedbeetles were released over two release dates (27August and 16 September). In 2004, 44,820 beetleswere released over seven dates in June. Traps werechecked at different time intervals depending on therelease date. All collected beetles were inspectedfor the presence of the marking agent and assigned

to a release date or classified as feral beetles.

Results

The average dispersal distance of recapturedbeetles over all the release dates combined(n = 872) was 380.4 ± 10.5 m and ranged fromthe minimum to the maximum trap distance of 1,000m (Figure 1). There was no difference between thesexes in dispersal distance in either 2003 (t-test:t = 0.78, df = 36, P = 0.44) or 2004 (t = 0.23,df = 410, P = 0.23). Diffusion coefficients rangedfrom 102.7 to 172.8 m/day for beetles captured inthe first four d after release during which over 80 %of beetles were recaptured. Some beetles wererecaptured 1 km away from the release point withinone (n = 1) or two d (n = 11). Marked beetles werealso captured in sticky traps placed outside therecapture grid at distances up to 3.6 km from therelease point.

Impact of landscape features onspatial distribution of R. dominica inan agricultural landscape(Ching’oma, 2006)


Flight activity was monitored in an agriculturallandscape in central Kansas USA that contained amosaic of agricultural fields (primarily wheat, corn,and sorghum); pasture; woodland; farm, residential,and commercial buildings; grain storage bins; and acounty grain elevator. Beetle flight activity wasmonitored between May and November 2003 andApril and November 2004 using a grid of 203 Deltasticky traps (Scentry Biologicals Inc., Billings, MTUSA) baited with pheromone lures. Traps wereevenly spaced along roadsides within a 10.7 by 9.3km area, with traps also placed on farms with steelbins for storing wheat. The geographic coordinatesof all pheromone trap locations and all grain storagebins within the monitoring area were recorded. Trapswere replaced at approximately two-week intervalsand returned to the laboratory to count the numberof trapped beetles.

Figure 1. (A) Mean (+ SEM) number of lesser grainborers, Rhyzopertha dominica, recaptured(n = 872) at various distances at Konza Prairie,Kansas, for all releases in 2003 and 2004. (B)Mean number of beetles per trap adjusted forcircumference distance (m) between traps at differentradii from release point.


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Temporal patterns in spatial distribution wereevaluated for each year. Spatial autocorrelation ofnumber of beetles captured on trap locations wasassessed by computing Moran’s I Index. Contourmaps using Inverse Distance Weighting (IDW) fornumber of beetles captured in traps were developedusing the Geostatistical Analyst extension in Arc GIS9.1 computer software (ESRI, Redlands, CA). Asan indirect method of quantifying dispersal, ellipsesaround the calculated mean center distribution thatencompassed 60 % of total number of beetlescaptured were calculated in Arc GIS 9.1.

The influence of grain storage structures on R.dominica distribution in the landscape was analyzedusing two approaches. First, for the eight monitoredfarm bin locations, the number of beetles capturedin the traps within 5 km radii of the farm locationswas regressed against the distance from the farmbins. Second, to extend the evaluation to all storagelocations in the study area, pheromone trap locationswere sorted by the number of farm bin locationswithin a 1 km radius of the trap and the averagenumber of beetles captured was calculated andcompared using PROC GLM (SAS Institute, 1999).

An analysis of the impact of landscape featureson the number of beetles captured was performedusing the Spatial Analyst feature in ARC GIS 9.1. Anational land cover database (NLCD) and land useraster map (USGS, 1992) covering the study areawas imported as a data layer. The tabulate areaoption was used to create circle polygons aroundtrap locations, and the area of the various land usetypes within 100, 500, and 1,000 m radial distancesfrom each trap location were calculated. These areaswere converted to percentages, and the effect ofthe percentage land use on total number of beetlescaptured for each year was assessed using stepwiseregression (SAS Institute, 1999).

Results

The seasonal patterns of insect captures in bothyears indicated there was an early season flight peak,and, in one of the two years, a late season flightpeak occurred (Figure 2A). In 2003 and 2004,Moran’s I values ranged from 0.01 to 0.16, indicatingsignificant positive spatial autocorrelation in trap

captures. In 2004, trap captures during the first flightactivity in the spring (15-29 April) were clusterednear the center of the landscape, with most of thelandscape, particularly in the eastern half having lowtrap captures (Figure 2B). In the previous year(2003) the pattern was similar, but trapping started

Figure 2. (A) Mean (± SEM) number of lesser grainborers, Rhyzopertha dominica, captured in 2003and 2004. (B) Contour maps (Inverse DistanceWeighting) of trap captures in the study area for eachsampling interval in 2003 and (C) 2004. Ellipses oncontour maps indicate area in which 60 % of thebeetles were captured and are centered on thecalculated mean center of distribution.


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later in the spring so that the initial foci of flight activitywas missed. Interestingly, these early season trapcaptures tended to occur in traps in or near a forestedriparian area, not near farm locations. By the lateMay to early June sampling periods, the numberscaptured had increased across the whole landscape,but this was occurring before wheat was harvestedand stored (late June to early July) and when mostfarm storage bins were likely to be empty. Trapcaptures stayed relatively high and widely distributedthroughout the season, until dropping in Oct to afew scattered hot spots. In 2003, in the fall after aninitial drop in number captured per trap on 24 Septto 15 Oct (as observed in 2004) a large late seasonflight occurred during the 15 Oct to 5 Nov sampleperiod.

The hypothesis that beetle captures would tendto be associated with grain storage sites was notsupported by the data from either 2003 or 2004. Ifbeetles tend to be associated with grain storage, itwas hypothesized that trap captures should decreaseas distance from bins increased. However, linearregression of trap captures with distance for eachsampling interval indicated that for most locationsand sampling periods there was not a significant linearrelationship. When the model was significant, trapcaptures tended to increase with distance (31 outof 136 location/sampling period combinations),rather than decrease with distance (3 out of 136location/sampling period combinations). The numberof farm bin locations within 1 km of trap locationsranged from 0 to 4, but there was no significantdifference in total beetle captures among traplocations with different numbers of bins in the vicinityof the trap in either 2003 (GLM: F = 0.96,df = 4,196, P = 0.43) or 2004 (F = 1.66,df = 4,196, P = 0.16).

To determine the influence of other landscapefeatures on trap captures, stepwise regression wasperformed and three different radii around the trapswas tested because it was not known a priori thespatial scale of any landscape influence. Percentagedeciduous forest area was the most significantlandscape feature affecting the total number ofbeetles captured for the combined data and for mostof the individual sampling intervals in each year.More beetles were captured with increasing

percentage deciduous forest area within all three sizecircles around trap locations in 2003 (100 m:F = 27.03, P < 0.01, R2 = 0.12; 500 m:F = 35.84, P < 0.01, R2 = 0.16; and 1000 m:F = 23.47, P < 0.01, R2 = 0.11) and 2004 (100 m:F = 29.16, P < 0.01, R2 = 0.13; 500 m:F = 47.04, P < 0.01, R2 = 0.19; and 1000 m:F = 33.33, P < 0.01, R2 = 0.14). Other prominentlandscape features such as pasture/grassland, row-crop land, water, and residential land were notsignificant in the stepwise regression.

Impact of broader landscape patternon flight activity and populationdynamics within food facilities(Toews et al., 2006)


In and around a Foundation seed warehouselocated at Manhattan, Kansas USA, Lindgren trapswere placed in cardinal directions at distances of 0,50, 100, and 150 m from the warehouse and threeadditional traps among the grain bins. Two Lindgrentraps and six Storgard II sticky traps (Trécé Inc.,Adair, OK, USA) were placed inside thewarehouse. All traps were baited with R. dominicapheromone lures. Indoor traps were serviced weeklyand outdoor traps were serviced each weekdayfrom late April through November 2004. To identifypotential routes of entry into the warehouse, smallgaps between warehouse overhead doors and thedoorjamb were monitored using unbaited glue boards(Trapper® MAX, Bell Laboratories Inc., Madison,WI, USA). Traps were fastened to the interior sideof the doorjamb such that insects moving throughthe gap might become trapped. For each of twooverhead doors, on each side of the door one trapwas placed flat on the floor and ten successive trapswere placed one above the other to a height of 200cm from the ground, and three additional traps wereplaced along the top of the door. Sticky traps werereplaced at least monthly depending on trapcondition. Indoor R. dominica captures inpheromone traps and on unbaited glue boards wererelated to mean outdoor captures by week using


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correlation analyses (SAS Institute, 1999).

Results

Distinct seasonal peaks in R. dominica capturesoutside were observed, with a large spring peak inearly and mid-May and then the rate of capturestailed off to a fairly consistent level between 1 Juneand 15 August. From late August through Octoberrelatively large numbers of captures were recordedintermittently before ending in mid-November. Asignificant effect attributed to trap distance from thewarehouse was observed in the north-south transect(F = 7.00, df = 1,285; P < 0.01) and in the east-westtransect (F = 6.70, df = 1,285; P = 0.01): capturesincreased with distance from the warehouse.

Although fewer R. dominica were capturedinside the warehouses than outside, the trends weresimilar to those of outdoor traps. There was a strongpositive correlation between mean outdoor capturesand indoor captures in Lindgren traps (r = 0.99,P < 0.01; n = 28) and in Storgard II traps (r = 0.97,P < 0.01; n = 28). Captures of stored-productColeoptera on unbaited glue boards positionedadjacent to overhead doors suggest that this is animportant route of entry for insect infestations. Thetemporal distribution of R. dominica captures onunbaited glue boards was highly correlated withweekly capture of R. dominica in outdoorpheromone-baited traps (r = 0.92, P < 0.01;n = 27). More than three times the number of R.dominica were captured at floor level than all otherlocations combined.

Discussion

Our understanding of the influence of landscapestructure on stored-product insects is still limited,but results reported here illustrate how broader scaletemporal and spatial patterns may have a stronginfluence on pest populations. For the highly mobilepest, R. dominica, in the agricultural landscape inKansas USA presented here, we originallyhypothesized that beetles would be primarilyassociated with grain storage sites and theimportance of pest immigration into bins would

depend on dispersal ability and the distance tosurrounding infested storage locations. Our findingsdid not support this hypothesis, but instead suggestthat depending on the time of year beetles are activeacross the whole landscape and deciduouswoodlands may be strongly influencing beetledistribution patterns. Woodlands may be animportant reservoir for pests moving into grainstorage by serving as an alternative resource patchin which they reproduce and/or as overwinteringsites. Because pheromone trapping data is an indirectmeasure of pest populations and influenced by arange of factors, at this point we do not know ifeither of these hypotheses is supported, or if trapcaptures are being influenced by some otherconfounding factor(s). Peaks of trap captures in thefall and spring, raise the possibility that beetles havea dispersal phase prior to overwintering and that theycould be overwintering in non-grain storage habitatsand this may serve as a source of beetles to infestnewly stored wheat in the spring. It is likely thatdifferent patterns of spatial distribution, movement,and population structure will occur in otherlandscapes, in other geographic areas, and certainlywith other pest species. Understanding theseprocesses could help in the implementation andinterpretation of monitoring programs and theselection and targeting of pest management tacticsto make IPM programs more effective.

Acknowledgements

We thank Jim Throne and Paul Flinn (USDA-ARS GMPRC) for reviewing an earlier version ofthis manuscript, and Richard Hammel, Brian Barnett,and an army of undergraduate students for theirexcellent technical assistance. This research wasfunded in part by USA EPA Region VII underagreement No. X8-98732401-1 and USDA/CSREES (RAMP) under Agreement No. 2000-51101-9674 and 2004-51101-02226. Mention oftrade names or commercial products in thispublication is solely for the purpose of providingspecific information and does not implyrecommendation or endorsement by the U.S.Department of Agriculture. This article is contribution


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number 07-21-A of the Kansas AgriculturalExperiment Station.

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