Management of the Diamondback Moth and Other Crucifer Insect ...

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Predicting outbreaks of amigratory pest: ananalysis of DBMdistribution andabundance revisitedZalucki, M.P.SCHOOL OF BIOLOGICAL SCIENCES, THE UNIVERSITY OFQUEENSLAND, ST LUCIA 4072, QUEENSLAND, AUSTRALIA.m.zalucki@uq.edu.auFurlong, M.J.SCHOOL OF BIOLOGICAL SCIENCES, THE UNIVERSITY OFQUEENSLAND, ST LUCIA 4072, QUEENSLAND, AUSTRALIA.m.furlong@uq.edu.auABSTRACTOur estimates of the effect of climate on the worldwidedistribution and relative abundance of diamondbackmoth have been revised. Using the known limits of thespecies’ range, experimental observations and theknown and inferred responses of the pest to temperatureand moisture as the base data, we have parameterized aCLIMEX model to predict temporal abundance andspatial distributions of diamondback moth. We test ourmodel by comparing the predicted diamondback mothdistribution with its “known” distribution; for such amajor pest the latter is very poorly defined indeed. Wefurther analyze changes in relative abundance amongyears due to variable climatic conditions, using a seriesof long-term diamondback moth population data setsfrom China and England and associated weather data.Models such as this are crucial to our understanding ofthe reasons for changes in DBM abundance. They arealso essential for the development of long-term pestpressure forecasts and allow us to begin to disentanglethe effects of various management practices from thenormal variation in abundance due to climate alone.KeywordsBioclimatic modelling, forecasts, prediction, populationoutbreaks, Plutella xylostellaINTRODUCTIONThe diamondback moth, Plutella xylostella L.(Lepidoptera: Plutellidae), or DBM, is the most widelydistributed major pest of crucifers. For such a majorpest surprisingly little research has been conducted onits long term population dynamics and the forecasting ofoutbreaks (but see Zalucki and Furlong 2008). For apest manager, being able to predict pest speciesabundance and distribution (‘pest pressure’), and itstiming and level, is crucial to both strategic planningand tactical decision-making (e.g. Maelzer et al. 1996).Phenological models, based on insect physiological timescales, have been relatively successful at predicting thetiming of insect population peaks (e.g. Mohandass andZalucki 2004; Collier and Finch 2004) and may beuseful for strategic timing of sampling and controlmeasures. Forecasting pest levels is more problematicbecause many factors influence abundance (see e.g.Yonow et al. 2004; Zalucki and Furlong 2005;Muthuthantri et al. 2010). For all major pest species,effective and timely forecasts would be useful fordetermining insecticide budgets, hiring additional cropscouts or making strategic decisions on which crops toplant. Long-term forecasts of abundance would beespecially useful for major pest species such as DBM.Abundance can vary greatly among years. Outbreaks ofthe increasingly insecticide-resistant DBM can makepest management difficult; a situation which can beexacerbated if there are insufficient supplies ofinsecticide for distribution (Zalucki et al. 2009).Zalucki & Furlong (2008) analyzed the seasonalabundance of DBM in Gatton, Australia and relativelyrecent outbreaks of the species in the British Isles anddeveloped a CLIMEX model which predicted theworldwide geographic distribution of DBM. Here werefine that model and make more specific predictions ofthe poorly known geographic distribution for what issuch a ‘well-known’ insect pest. We predict the speciesseasonal phenology for a greater number of sites acrossthe species range and present a simple analysis of longtermabundance data for Hangzhou, China. Wellconstructedmodels that realistically describe the effectsof climate on abundance are not only useful forforecasting, but also aid in the interpretation ofpopulation changes and impacts of managementpractices on pest populations.If climate is the main determinant of where a species islikely to be found, we might expect a strong influence ofclimate variability or weather on the temporal variationin abundance at a given site (Zalucki and Furlong 2005,2008; Zalucki and van Klinken 2006; Lawson et al.2010). CLIMEX is generally used to predict thesuitability of a site for a species based on long-termaverage conditions and estimated responses of thespecies to seasonal variation in temperature andmoisture. If we have the long-term daily weather datafor a site (these are the data on which a site’s climaticcharacteristics are based) we can use the estimatedspecies response to climate variables to infer thevariation in suitability (fluctuations in the variousindices) at the site over time, based on the observedclimate record (see also Zalucki and van Klinken 2006).Essentially we generate a model of likely seasonalpopulation growth and mortality rates based on climatealone, using information on a species’ geographicdistribution and its seasonal phenology at given sites toderive the species responses to climatic conditions, or itsclimatic “niche”.MATERIALS AND METHODSThe rationale behind CLIMEX has been described manytimes and we do not repeat it in detail here (see e.g.Yonow and Sutherst 1998; Zalucki and Furlong 2008).8 AVRDC - The World Vegetable Center
CLIMEX calculates a growth index (GI), analogous topopulation growth rate, which describes the potential ofthe population to increase at a location. The growthindex is calculated weekly and is a product oftemperature (TI) and moisture (MI) indices. For both TIand MI there is a range of conditions of temperature andmoisture over which growth is maximal. Either side ofthe optimum range growth rate decreases while aboveupper thresholds and below lower thresholds growthceases.A measure of the relative suitability of a location for aspecies to persist is summarized in a single annual ecoclimaticindex (EI), which is scaled to 100.EI=100 ∑ GI/52*SI*SXWhere,52 is the number of weeks in a year; SI are four stressindices, describing the species response to the extremesof cold (CS=Cold Stress), heat (HS), dry (DS) and wet(WS) conditions; SX, if needed, are interactions betweenextremes of these conditions; cold-dry (CDS), cold-wet(CWS), hot-dry (HDS) and hot-wet (HWS) stresses. Thestress indices (SI and SX) are accumulated at a specifiedrate whenever conditions exceed a specified threshold.Indices are calculated on a weekly basis for each locationusing standard meteorological information. A location’slong term monthly average maximum and minimumtemperatures, rainfall, and humidity values are used asinputs to calculate the various indices. Areas withpositive values for EI are suitable for species persistence;the larger the value of EI the more suitable the location.Generally, an EI of less than 20 for a location indicatesthat persistence of the given species at that location ismarginal. The values for parameters that describe the TIand MI components of growth, and the various stressindices (SI) and their interactions (SX) can be estimatedfrom laboratory or field studies (Zalucki and vanKlinken 2006). Unknown or poorly measured values areestimated by an iterative procedure that involvescomparing the predicted distribution with the actualdistribution and adjusting parameter values. Comparingthe predicted and observed species distribution in an area(e.g. a continent) not used for the procedure can test theparameter tuning; see Maywald and Sutherst (1991) fordetails.The CLIMEX model for DBM in Zalucki and Furlong(2008) was developed using anecdotal distribution data,the seasonal prevalence of DBM in the CameronHighlands of Malaysia and published values for variousparameters (see Table 1 for sources). Most of thetemperature related parameters were taken from thestudy by Liu et al. (2002). Here we mainly adjusted themoisture related parameters (Table 1); essentiallymaking the species more dry tolerant. This requiredconcomitant adjustments in the dry and wet stressthresholds. Similar adjustments were made for thedegree-day accumulation of cold and heat stressparameters (Table 1). We added a hot-wet interactionstress to reduce EI for tropical regions in Africa (Congoand West Africa). In the current paper we use CLIMEXversion 3 (Sutherst and Maywald 2005), whereas weused version 1 in the earlier paper (Zalucki and Furlong2008).RESULTS AND DISCUSSIONTest of the CLIMEX modelPredicted and actual geographicdistributionsThe predicted worldwide distribution, mapped as EIvalues (Fig 1a) and GI values (Fig 1b) based on this setof parameters (Table 1) agrees quite well with the knownworldwide distribution (Fig 1c). Regions with values ofEI below ca 20 are probably marginal for long-termpersistence of a species; essentially such conditions areassociated with the edges of the species core distribution(Fig 1a). Note the increase in the species range when GIvalues are plotted (Fig 1b). This essentially representsthe maximal range that DBM can exploit by migratingfrom its core range and colonizing these locations whenprevailing conditions result in a positive GI. However,such migration will be highly variable from year to year(see below). Our CLIMEX prediction provides a lotmore information on the distribution of DBM than theold CAB map (Fig 1c) which is likely an amalgam of EI,GI and anecdotal reports.Figure 1. Predicted worldwide distribution of DBM.a) Values where GI is positive denote the core of thespecies range and year-round occupation b)Regions where GI is positive indicate the potentialmigratory seasonal range c) The ‘actual” distributionredrawn from the CAB map.The 6th International Workshop on Management of the Diamondback Moth and Other Crucifer Insect Pests 9
Page 1 and 2: PROCEEDINGSThe Sixth International
Page 3 and 4: PROCEEDINGSThe Sixth International
Page 5 and 6: Table of ContentsForewordixAcknowle
Page 7 and 8: Myco‐Jaal® - A novel formulation
Page 9 and 10: ForewordVegetable brassicas such as
Page 11: AcknowledgementsThe Sixth Internati
Page 15 and 16: Behavioral and geneticcomponents of
Page 17 and 18: more wandering behavior in the pres
Page 19: influenced whether larvae remained
Page 23 and 24: Table 1 (continued)LimitingprocessW
Page 25 and 26: Figure 5. Observed DBM light trap c
Page 27 and 28: Rising to the challenge: anational
Page 29 and 30: Table 1. List of insecticides for w
Page 31 and 32: Progress and challengesin the Bt br
Page 33 and 34: mixtures. One third of farmers were
Page 35 and 36: Table 1. Mortality of lepidopteran
Page 37 and 38: o The Genetics Engineering Approval
Page 39 and 40: AVRDC; A. Rauf of Bogor University
Page 41: SESSION 2Biology, ecology and behav
Page 44 and 45: fields of Thondamuthur village, Coi
Page 46 and 47: Table 2. Morphometrics of C. binota
Page 48 and 49: On the basis of LC 50 values, the s
Page 50 and 51: Natural mortality ofPlutella xylost
Page 52 and 53: (x10 magnification) and excess eggs
Page 54 and 55: Figure 2. Lifetables for insect coh
Page 56 and 57: Diadegma semiclausum is well establ
Page 58 and 59: Diurnal behavior ofnaturally micros
Page 60 and 61: changed after the eggs were counted
Page 62 and 63: Geden, C. J., Long, S. J., Rutz, D.
Page 64 and 65: Scaptomyza flava (GP Walker, unpubl
Page 66 and 67: longer distance migrations into the
Page 68 and 69: FIGURESFigures 1A & B. Mean number
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Monitoring of cabbagelooper, Tricho
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populations of British Columbia col
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Importance ofglucosinolates indeter
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the importance of glucosinolates in
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Olfactory responses ofPlutella xylo
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Figure. 1: Percentage of adult P. x
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Moorthy, 1992) have reported that p
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P
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egg masses and more eggs in total o
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SESSION 4Biological and non‐chemi
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on cabbage cultivation in one seaso
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four stages at day 7 post treatment
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Table 5. Biological effects of 17 e
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Kelly A Cook. K.a., S.T. Ratcliffe.
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applied only once every three weeks
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South Africa was assigned to supply
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Waladde SM, Leutle MF, Villet MH. 2
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MATERIALS AND METHODSThe study site
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Several studies have demonstrated t
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approach would continue until it ev
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ecorded in Hod 3 (19.1%), followed
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plutellae. Many adults of C. plutel
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litura (SL) which normally live in
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Table 2. Sizes of m-PX and m-SL fro
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helping us with the in vivo study.
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Since temperature is one of the mos
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Figure 4. Effect of different conce
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Predators in early seasonbrassica c
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Table 2. Predators encountered duri
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Table 5. Sticky trap captures for c
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dwelling predators, although it is
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Predators per trap109876543Lycosida
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Mortality was calculated using Abbo
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DBM fed on the leaf discs treated w
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Quality aspects of Bacillusthuringi
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five applications one week apart fo
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Antifeedant effect ofAcorus calamus
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Figure 1. Concentration-antifeedant
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CONCLUSIONA. calamus extracted in f
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of D. grandiflora on the growth kin
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etween antifeedant activity and con
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Table 2. Antifeedant index (AFI) an
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Field evaluation of insectexclusion
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Three main Lepidoptera pests were o
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price (SBD)30.0025.0020.0015.0010.0
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STATISTICAL ANALYSISThe data collec
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Myco-Jaal: a novelformulation of Be
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asexual conidium. The conidia adher
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Table 1. Efficacy of Myco-Jaal on y
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Susceptibility ofdiamondback moth a
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Table 1. Toxicity of Cry1Ac to fiel
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ReferencesAbbott WS. 1925. A method
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76°58′17″E), Tamil Nadu, India
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International Workshop, Talekar NS,
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Table 3. Effect of Neem limonoids o
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Figure 1. Structure of neem limonoi
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catch up with the farming community
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All treatments were imposed by usin
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The above results indicate that the
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A strong negative relationship obse
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ReferencesAnonymous 2000. Package o
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egularly causes epizootics in popul
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treatments. It is evident that DBM
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Table 1. Effects of three aqueous s
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egion (Adams, 1959); it is a native
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Effects of Cry toxins and Bt formul
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Hilbeck A, Baumgartner M, Fried MP,
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Table 3. Effect of Cry 1Ba2 on Cote
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SESSION 5Insecticides and insectici
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time. Within four years of the firs
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Update on DBM diamideresistance fro
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In year 2011, Sai Noi and Tha Muang
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Lahm GP, Cordova D, Barry JD. 2009.
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Fipronil acts by blocking chloride
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Table 2. Slope, LC 50 (ppm) and let
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Figure 2. Lethal dose ratios for fi
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y the inherent lower susceptibility
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Diamondback mothresistance to commo
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of the relative potency of each ins
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Table 3: Susceptibility of six fiel
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Spinetoram, a newspinosyn insectici
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Spinetoram is also highly effective
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Norte del Estado areas of Guanajuat
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Insecticide resistancemanagement: s
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still exist. The second source of i
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they organized the so called “Dia
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Recent developments inmanagement of
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is the reciprocal of the relative p
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developing thresholds for new brass
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Table 3: Spinosad (Success®) and i
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Insecticide sprays are generally ap
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may still benefit from doing so, ir
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generations” to Group 28 insectic
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Management of insecticideresistance
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Stop cultivation of crucifer crops
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252 AVRDC - The World Vegetable Cen
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Impact of reduced-riskinsecticides
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mines) on the 10 plants in each plo
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Table 4. Follow up with growers aft
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Guided by such objective, the Thai
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establishment rapidly achieved by D
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Malaysian Agricultural Research and
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the farmers. We report the developm
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Table 3. Number of DBM larvae in th
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Pest status and damage potentialThe
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Life tableLife table of DBM reveals
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Bacillus thuringiensis (Bt)Currentl
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CHEMICAL INSECTICIDESPeter et al. (
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Manjunath TM. 1972. Biological stud
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Armyworm, Spodoptera litura Armywo
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Adoption and perceptionTable 5 show
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Control of brassica pests -an examp
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Table 2. A Comparison of IPM and Pe
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Occurrence and control ofPlutella x
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Apopul at i on dynami c ( l ar va/
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Diadegma semiclausumD. semiclausum,
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Diamondback moth(Plutella xylostell
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SESSION 7Genomic and other novel ap
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management (IPM) systems (Shelton e
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Kalra VK, Sharma SS, Chauhan R, Bha
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Table 2. Segregation analysis based
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current paper provides detail on th
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Proteins do not act antagonisticall
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CONCLUSIONThe transformation events
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Enhancement of the sterileinsect te
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optimisation of transformation prot
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Workshop PresidentsDr. Jacqueline d
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Participant e‐mail Organization C
Page 332 and 333:
Author IndexAbuzid, I..............
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