Economic injury levei and sequential sampling plan for Bemisiatabaci in outdoor tomatoM. R. Gusmãol, M. C. Pícanço", R. N. C. Geedes', T. L. Galvan1,2 and E. J. G. Pereiral,3
'Departamento de Biologia Animal, Universidade Federal de Viçosa, Viçosa, MG, Brazil; 2Departrnent ofEntornology, University of Minnesota, St Paul, MN, USA; 3Departrnent of Entornology, University ofNebraska-Lincoln, Lincoln, NE, USA
Ms. received: Ju/y 4, 2005; accepted: November 24, 2005
Abstract: This work aimed to determine the economic injury levels and to establish sequential sampling plans fornymphs and adults of the whitefly Bemisia tabaci Genn. (Sternorrhyncha: Aleyrodidae) in tomato fields. Densities ofnymphs and adults, as well as crop yield were evaluated in 13 commercial tomato fields to determine the economicinjury levels.The whitefly nymphs were sampled by direct counting in a leaf from the lower part of the canopy and theadults were sampled by beating an apical leaf against a white plastic tray. The sequential sampling plan was based ondata collected in eight commercial tomato fields. The validation of the sequential sampling plan was carried out basedon the curves of operational characteristics and average sample numbers. The decisions reached with the conventionaland the sequential sampling plans in 21 commercial fields were compared for the intended validation of the sequentialplan. The economic injury levels were four nymphs per leaf and one aduIt per tray. The decisions taken based on thesequential sampling plan were similar to those obtained through the conventional sampling plan. Most of the decisionstaken with the sequential sampling plan were obtained through the minimum number of seven samples per field fornymphs and 1I samples per field for aduIts, with reductions of 84.44% and 54.17% in the number or samples requiredto reach a decision with the sequential sampling plan compared with the conventional sampling plan.
1 IntroductionThe whitefly, Bemisia tabaci Genn. (Sternorrhyncha:Aleyrodidae), is an irnportant agricultural pestthroughout the world (Naranjo and Ellsworth, 2001).The nymph and adult stages are critical because oftheir ability to feed on sap (Lourenção and Nagai,1994), to inject toxins into the vascular systern ofplants, to induce irregular ripening of fruits (Powellet aI., 1998) and to transmit viral diseases to tornatoplants (Czosnek et aI., 1988). The damage potential ofwhitefly is very high and farmers usually spray higharnount of insecticides for controlling this pest intropical tornato fields. The low susceptibility of thewhitefly to rnany insecticides and the reduction of itsnatural enerny populations, because of applications ofnon-selective cornpounds, are factors that have con-tributed to the increase in the whitefly populationin crops (Lourenção and Nagai, 1994; Byrne andDevonshire, 1997).
The extensive use of insecticides has resulted inheavy econornic and environrnental losses (Irneneset aI., 1992; Stansly et aI., 1998). Therefore, the devel-opment and implementation of an integrated whiteflyrnanagernent programrne are necessary for a more
sustainable approach to control this pest speciesreducing insecticide use and rninirnizing the existentenvironrnental problerns besides rnaintaining or enhan-cing econornic crop viability. It is necessary to estimatethe damage caused by this insect on the tornato fieldand to determine the population density capable ofcausing econornic loss for the irnplernentation of anintegrated rnanagernent programrne. Economic dam-age is defined as the econornic value of losses necessaryto equal the economic costs of managernent (Stone andPedigo, 1972). The ratio between the rnanagement costand crop value is the expression of yield loss necessaryto justify the rnanagernent, which is called gainthreshold. The insect numbers necessary to produceequivalent losses to the gain threshold is the economicinjury levei (Higley and Pedigo, 1996). Although greatyieId losses are reported as resulting frorn whitefiyattack, there is a lack of studies quantifying theselosses. Such studies are necessary for the determinationof decision-making pararneters to control B. tabaci.
The insect population should be determined bysarnpling plans and compared with the economic injuryleveI for decision-making regarding insect controI.Therefore, it is necessary to determine a fast and precisesampling plan to estimate the whitefly populations for
Sequential sampling for whitefly in tomato 161
2 Materiais and Methodsthe implementation of a sound pest managementprogramme (Fowler and Lynch, 1987). There are twotypes of sampling plans for the relative estimation ofinsect populations ~ the conventional and the sequen-tial sampling plans. The conventional sampling planrepresents the initial generation of a decision-makingsystem in integrated pest management programmesbecause of its easy adoption. It allows the determin-ation of essential parameters that are important for theestaoúsument of a sequenÜa\ sampling p\an, sucu aseconomic injury levei, sampling unit and samplingtechnique (Pedigo et el., 1982). Gusmão et al. (2005)determined conventional sampling plans for nymphsand adults of whitefly in outdoor tomato field, whichwere characterized by the direct counting ofthe nymphson a basalleaf of the plant canopy and by counting theadults after beating the apicalleaf ofthe tomato canopyin a plastic tray. The required sample numbers were 45and 24 samples per field for sampling nymphs andadults respectively.
The sequential plan was developed as a rapid qualitycontrol evaluation tool during World War 11 (Wald,1945) and it was subsequently adapted for use inintegrated pest management programmes because ofthe lesser effort required than programmes of fixednumber of samples, as in the conventional samplingplans (Waters, 1955; Ruesink and Kogan, 1982). Thereare several methodologies for the determination of thesequential sampling plan. Wald (1945,1947) developedthe sequential probability ratio test based on theprobability ratio of Newman-Pearson to obtain thedecision boundaries. Green (1970) presented a methodusing derived parameters of Taylor's power law(Taylor, 1961) to determine a critical stop line. Iwao(1975) described a method using Lloyd's index ofmean crowding (Lloyd, 1967) and linear regressiontechniques recognized as Iwao's confidence intervalmethod.
Considering the importance of whitefly controlguided by population estimations, this work aimed todetermine the economic injury levels and to establishsequential sampling plans for nymphs and adults ofB. tabaci in outdoor tomato crop.
2.1 Determination of the economic injury levei
The populations of nymphs and adults of whitefly wereevaluated from February to July 1999, in 13 commercialtomato fields, variety Santa Clara Asgrow, with a1.0 x 0.50 m spacing and in the reproductive phase (about70 days after seedling transplantation) (table 1). The cropfields were located at Coimbra and Guidoval counties, Stateof Minas Gerais, Brazil. The fruits were manually pickedtwice a week and the yields were recorded as tons per hectare.The whitefly nymphs were sampled by direct counting in aleaf from the lower part of the canopy and the aduíts weresampled by beating an apical leaf against a white plastic tray(30 em width x 45 em length x 5 em depth) (Gusmão et al.,2005).
The curves of yield loss were obtained by regressionanalysis with nymphs per leaf, adults per tray and yield asindependent variables. The data were adjusted to theexponential, linear, quadratic, cubic and inverse regressionmodels. The choice ofthe best regression model was based onthe significance of the regression (P < 0.05), determinationcoefficient (R2) and significance of the equation coefficients(P < 0.05).
The estimation of the economic damage was based onthe value of the losses caused by an insect populationwhich equals its control cost (CC) and the expensesincurred by three insecticide applications (number usuallyused by the producers) for the control of whitefly duringthe tomato cultivation. The recommended insecticide wasimidacloprid [70 WG] [200 active ingredient (a.i.)jhaapplied with a volume of 500 Ljha], the price of whichduring lhe research period was US$0.28 per gramo Thelabour expense was US$3.20 per day, based on the wage oflhe spraying worker with an operational capacity of 120 Lof solution per day in a workday of 8 h. The CC in US$per hectare was transformed in tomato tons per hectare(tjha) using the averaged market value of US$181.78 perton, which was the tomato commercialization value duringthe period of data collection at the State Center ofAgricultural Provision (CEASA-MG). The potential yield(pY; tonjha) was estimated from the yield loss equations,considering the null density of insects. The real yield (RY)of tomato in tonjha was estimated by subtracting the costof insect control from the value of the potential yield withinsect damage. The economic injury levels were the insectdensities (x-value) in the regression equations which
Table 1. Area, number of plants, yield and whitefiy densities in lhe sampled fields
Whitefly densities (mean ± SE)
Field County Area (ha) Number of plants Yield (ton/ha) Adults per tray Nymphs per leaf
corresponded to the yield values (v-values) equal to realyield.
2.2 Determination of the sequential sampling plan
The sequential sampling plan had the tomato leaf as thesample unit and the quantified variables were the numberof nymphs per leaf and number of adults per tray. Thedata were adjusted to the frequency distribution models ofPoisson and negative binomial, which described the distri-butions of the variables, allowing the construction of theplan. The whitefly data coIlected by Gusmão et al, (2005)for the conventional sampling plan were used to generatethe sequential sampling plan. The data were collected fromeight commercial outdoor tomato fields, variety SantaClara Asgrow, with 1.0 x 0.5 m spacing during the repro-ductive phase (about 70 days after seedling transplanta-tion). The data were adjusted to the nega tive binomialmodel with the common K-values of 0.737 for nymphs and1.098 for adults. The sequential sampling plans weredetermined by the Wald's sequential probability ratio test(Wald, 1945, 1947; Fowler and Lynch, 1987; Bates et a!.,1991; Nault and Kennedy, 1996; Boeve and Weiss, 1997;Naranjo et aI., 1997), where the intercept values of lower(ho) and upper (h.) boundaries and the inclination valuesof these limits of decision (S) were obtained through theequations described by Pedigo and Zeiss (1996).
Two critical densities (mo ~ critical density at the lowerboundary, equal to one-third of the economic injury leveI;m, ~ critical density of the upper boundary, equal to two-thirds of the economic injury leveI) were estimated from theeconomic injury levels (Hammond and Pedigo, 1976). Eco-nomic damage will not take place when the insect densitystays below the lower boundary (null hypothesis) and it wil\take place when the insect density surpasses the upperboundary (alternative hypothesis). The maximum probabilitylevels of making mistakes in estimating insect densities [i.e.probability of predicting an insect density as non-harmfulwhen it is so (a type I error), and the probability of predictingan insect density as harrnful when it is not so (a type II error)]were IX = f3 = 10%.
The sample size in the sequential plan depended on thevalues ofthe observations made. The decision to accept or toreject the null hypothesis leading to the decision to stopsampling or to control the insect by applying insecticides wasmade after each observation, and the observations werecarried on until having sufficient data to make one of thedecisions (Ruesink and Kogan, 1982; Fowler and Lynch,1987).
2.3 Validation of the sequential sampling plan
Curves of operational characteristics and average samplenumbers were determined for validation of the sequentialsampling plan according to the methodology described byFowler and Lynch (1987). The curve of operational charac-
teristics shows the probability of deciding not to control theinsects as a function of the insect density. The curve of theaverage sample number indicates the required sample num-ber for decision-making as a function of the insect density.
Besides these analyses, the densities of whitefiy nymphsand adults present in 21 commercial tomato fields weredetermined using the conventional sampling plans ofGusmão et a\. (2005) and also using the sequential samplingplans reported here. The decision-making for managingwhitefly nymphs and adults from both sampling plans werecompared and the economy obtained by the reduction of therequested sample number was determined.
3 ResultsThe increase of whitefly nymph and adult densities ledto a reduction in yie!d (table 2 and figo 1). Theexponential mode! was the best for adjusting the dataas it is sufficient to explain a large part of thevariability and fits best to the biological background
(a) 125 oo
100
'2~ 75'"s
:s1 50.!l>-25
OO ISO
(b) 140
o120 o
100 o'2
~ 80g~ 60:;::
40
20
OO 20
600300 4SONymphs per leaf
750
40 60 80Adults per leaf
100 120
Fig.1. Yíeld as a functíon of the densíty of nymphs(a) and adults (b ) ofB. tabaci. Symbols represent meanof 65 replícates
Table 2. The best curve fitting models of yield as a function of whitefly densities after testing five regression models(linear, quadratic, cubic, exponential and inverse )
Model Coefficient Value Standard error P P-value of regression model R2
NymphsExponential (y = a * e-h') a 91.74 9.95 9.22 < 0.0001 0.0018 0.60
b 0.0025 0.0011 2.39 0.036AdultsExponential (y = a * e-h') a 95.52 9.0970 10.50 <0.0001 0.0008 0.65
third decision zone is represented by the interrnediaryinsect densities in which sampling should continueuntil reaching one of the decision boundaries. There isa 90% probability (IX = f3 = 10%), based on figo 2,that the accumulated density of nymphs and adults willbe placed below or above the economic damage, andtherefore one of the decisions (to stop the samplingand not controlling the insects, to continue thesampling or to make the decision of controlling theinsects) should be taken.
A group of 12 samples per field was necessary for thedecision of stopping the sampling and not controllingthe whitefly when the densities of nymphs were dose tothe critical density ofthe lower boundary (1.33 nymphsper leat). In contrast, for densities of nymphs dose tothe critical density of the upper boundary (2.67nymphs per leat), about 18 samples per field werenecessary for the decision of stopping the sampling andcontrolling the whitefly. For densities dose to theeconomic injury levei (four nymphs per leat), thenecessary number of samples to reach a decision waslower than 10 samples per field (fig. 3a).
The probability of deciding not to control thewhitefly when the density of nymphs was dose to thecritical density of the lower boundary (1.33 nymphsper leat) was 100%. In contrast, for densities ofnymphs above the critical density of the upperboundary (> 2.67 nymphs per leat), the probabilityof not controlling was lower than 50%. The probab-
parameters (R2 = 0.60, P = 0.002 for nymphs andR2 = 0.65, P = 0.001 for adults) (table 2 and figo 1).
The CC for whitefly nymphs and adults using threesprayings of imidacloprid were US$207 .27lha or about0.95 tons of toma to per hectare. The yields were 91.74and 95.51 ton/ha in the absence of whitefly nymphsand adults, respectively, based on the regressionequations of yield losses as a function of whiteflydensity. However, the real yields were 90.79 ton/ha dueto the damage caused by nymphs and 94.56 ton/ha dueto the damage caused by adults considering theeconomic damage caused by these insects, whichcorresponds to its CCs. Thus, the whitefly densitiescapable of causing damage equivalent to its CCs werefour nymphs per leaf and one adult per tray. Thesedensities are the values of economic injury levels fornymphs and adults ofwhitefly in outdoor tomato fields(fig. I and table 3).
The critical densities in the lower (mo) and upper(m.) boundaries of the sequential sampling plan fornymphs were mo = 1.33 nymphs per leaf andm, = 2.67 nymphs per leaf. The slope of the decisionboundary of the sampling plan for nymphs wasS = 1.87 and the intercepts were h, = -11.14 for thelower boundary and h, = 11.14 for the upper bound-ary. The rninimum number of samples for the decisionsof not controlling, keep sampling or controlling basedon nymph density was seven samp\es (fig. 2).
The critical densities in the lower (mo) and upper(m.) boundaries of the sequential sampling plan foradults were mo = 0.33 adults per tray and m, = 0.67adults per tray. The slope of the decision boundary ofthe sampling plan for adults was S = 0.47 and theintercepts were h, = -4.44 for the lower boundary andhl = 4.44 for the upper boundary. The minimumnum ber of samples for the decisions of not controIling,keep sampling or controlling based on adult densitywas 1i samples (fig. 2).
Three decision zones are defined by the criticalboundaries for decision-making shown in the figo 2.The first represents the insect density below which thesampling should be interrupted and the decision of notcontrolling the insects is made (it accepts the nullhypothesis). The second is deterrnined by the insectdensity above which insect control is necessary (itaccepts the alternative hypothesis), that is, densitiesabove this limit cause economic damages. Finally, the
Yield (US$ per ton) 218.18Number of insecticide sprayings to control whitefly 3Control cost for whitefly (tonjha) 0.95Potential yield as function of whitefly nymphs (tonjha) 91.74Potential yield as function of whitefly adults (tonjha) 95.51Real yield as function of whitefly nymphs (ton/ha) 90.79Real yield as function of whitefly nymphs (ton/ha) 94.56Economic injury levei for whitefly nymphs 4(nymphs per leal)
Economic injury levei for whitefíy adults(adults per tray)
(b) 16....----------------~
14
12
Keep sampling
4
2
12 1614 18 20 22 24
Sample number
Fig.2. Decision boundaries of the sequentia/ samplingp/an for whitefly nymphs (a) and adu/ts (b ) in outdoortomato
164
(a) 1.00 20/ - ...• -- Curve of operational cltaracteristics
(b) 1.00 35-- Curve of operational characteristics- - - Curve of average sample number 30g --<, Il
0.75 / " ~" 25 §O / -,og / " "/ -, .!l
"õ 0.50 / " 20~Ê
/ " -,~~
-,15<, '"~ 0.25
<, Il~
<, ><, •... \0<•...
M. R. Gusmão et aI.
0.00 50.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Adults per tray
Fig. 3. Curves of operational characteristics and aver-age sample number for the sampling of whitefly nymphs(a) and adults (b) in outdoor tomato
ility of deciding not to control the whitefiy when thedensities of nymphs were similar to critical density ofthe economic injury levei (four nymphs per leat) waslower than 10% (fig. 3a).
A group of 26 samples per field was necessary todecide stopping the sampling and not controlling theinsects when the adult density was dose to the criticaldensity of the lower boundary (0.33 adults per tray).For adult densities dose to the critical density of theupper boundary (0.67 adults per tray), about 15samples per field were necessary for deciding to stopthe sampling and controlling the whitefiy. For densitiesclose to the economic injury levei (one adult per tray),the necessary number of samples to reach a decisionwas smaller than 10 samples per field (fig. 3b).
The probability of deciding not to control thewhitefiy when the densities of adults were close to thecritical density of the lower boundary (0.33 adults pertray) was larger than 90%. ln contrast, for adultdensities above the critical density of the upperboundary (> 0.67 adults per tray), the probability ofnot controlling was smaIler than 10%. A nuIl probab-ility was verified for the decision of not controIling thewhitefiy when the densities of adults were similar to thecritical density of the economic injury levei (one adultper tray) (fig. 3b).
The decisions obtained with the conventional samp-ling plans were similar to those obtained with thesequential sampling plans for both nymphs and adultsof whitefiy (table 4). The only decisions which did notmatch in both sampling plans were those of notcontrolling the insects obtained with conventional
sampling plan of adults at the fields 10, 18 and 19(table 4). Decisions were usually reached with theminimum number of samples (seven samples per fieldin the sampling of nymphs and 11 samples per field inthe sampling of adults) for most of the sampled fieldsusing the sequential sampling plans. Therefore if thefarmers adopt the sampling plan, they will makecontrol decisions with a number of samples about84.44% and 54.17% smaller than in the conventionalplan for sampling of nymphs and adults, respectively(table 4). ln other words, the farmers who adopt thesequential plan will reduce their cost and time ofsampling.
4 DiscussionThe lower economic mjury levei for adults whencompared with nymphs is probably because of thegreater adult capacity of reducing the yield. This fact isreinforced by the significant ditIerence between theslopes of the yield loss curves, obtained through thenega tive exponential model for adults and nymphs.The slope of the regression curve for adults(-0.0132 ± 0.0042) is about five times larger thanthe slope of the regression curve for nymphs(-0.0025 ± 0.0011) (table 2). Therefore, the damagepotential of adults is about five times larger than thatof nymphs because of an enhanced probability of virustransmission by dispersing adults (Jovel et aI.,2000a,b).
Although whitefiy nymph or adult populations canreach densities that justify a decision in the firstsamples, a minimum of seven and 11 samples isnecessary per field for a sound decision regarding thesampling of nymphs and adults respectively. How-ever, if the accumulated insect density during thesampling remains between the lower and upperboundaries until reaching a maximum number of45 and 24 samples per field for nymphs and adults,respectively (number of samples determined for theconventional plan, in agreement with Gusmão et aI.,2005), the sampling should be stopped and thegrowers should return to the field 4 days later torepeat sampling. This time interval for re-sampling isbased on the developmental time necessary for thewhitefiy to reach the third instar and adult stages(Villas Boas et aI., 2002).
The curves of operational characteristics andaverage sample number for nymphs and adultsprovide further insight into its value. When thepopulations of nymphs and adults are either low orhigh, decisions can be made more rapidly and with ahigh degree of probability of making a correctdecision. A reduction of 73.33% in the requirednumber of samples would be obtained with thesequential plan (12 samples per field) instead ofthe conventional plan (45 samples per field) based onthe critical density of nymphs at the lower boundary(1.33 nymphs per leaf). For critical densities close tothe upper boundaries (2.67 nymphs per leaf and 0.67adults per tray), the reduction reaches 60% in thenumber of samples for nymphs and 37.5% in the
Table 4. Validation of lhe sequential sarnpling plans for whitefly nyrnphs and adu/ts as a function of lhe decisionreached using conventional sarnpling plans in 21 tomato fields.
Mean no. of nymphs per leaf Number of samples Decision-rnaking
Fields Conventional plan Sequential plan Conventional plan Sequential plan Conventional plan Seq uential plan Economy (%)*
NymphsI 104.00 126.29 45 7 Control Control 84.442 111.00 147.43 45 7 Control Control 84.443 29.00 81.14 45 7 Control Control 84.444 25.00 44.00 45 7 Control Control 84.445 24.00 32.86 45 7 Control Control 84.446 0.71 0.00 45 7 No-control No-control 84.447 0.78 0.67 45 9 No-control No-control 80.008 440.00 180.71 45 7 Control Control 84.449 477.00 642.43 45 7 Control Control 84.44
10 1.56 1.25 45 20 No-control No-control 55.56II 1.89 1.48 45 31 No-control No-control 31.1112 6.47 15.29 45 7 Control Control 84.4413 6.07 3.33 45 9 Control Control 80.0014 0.04 0.00 45 7 No-control No-control 84.4415 0.02 0.14 45 7 No-control No-control 84.4416 8.53 3.43 45 7 Contro1 Control 84.4417 11.38 11.57 45 7 Control Control 84.4418 38.44 35.29 45 7 Control Control 84.4419 39.40 28.71 45 7 Control Control 84.4420 54.60 46.71 45 7 Control Control 84.4421 47.44 46.00 45 7 Control Control 84.44
Mean no. of adults Number ofFields per tray samples Decision-rnaking Economy (%)
AdultsI 34.00 36.09 24 II Control Control 54.172 40.00 37.91 24 )) Control Control 54.173 33.00 31.27 24 II Control Control 54.174 0.88 1.09 24 1I Control Control 54.175 1.54 1.27 24 1I Control Control 54.176 1.29 1.45 24 II Control Control 54.177 1.42 1.00 24 15 Control Control 37.508 0.96 1.18 24 II Control Control 54.179 0.21 0.24 24 17 No-control No-control 29.17
10 0.42 0.42 24 24 No-control re-sampling 0.00II 3.46 0.75 24 16 Control Control 33.3312 32.00 42.27 24 11 Control Control 54.1713 29.00 38.64 24 11 Control Control 54.1714 23.00 29.64 24 I1 Control Control 54.1715 0.46 0.15 24 13 No-control No-control 45.8316 0.83 0.74 24 19 Control Control 20.8317 1.38 0.45 24 22 Control Control 8.3318 0.42 0.42 24 24 No-control re-sarnpling 0.0019 0.54 0.54 24 24 No-control re-sampling 0.0020 0.92 0.94 24 18 Control Control 25.0021 0.21 0.24 24 17 No-control No-control 29.17
number of samples for aduIts in favor of thesequential sampling plan compared with the conven-tional sampling plan. When the densities of nymphsand adults were similar to critical density of theeconomic injury levei (four nymphs per leaf and oneaduIt per tray) the reduction in the required numberof samples reaches 77.77% for nymphs and 58.33%for adults in favour of the sequential sampling plancompared with the conventional sampling plan.
The similar decisions obtained here with thesequential sampling plans and the conventional samp-ling plans determined by Gusmão et aI. (2005) confirmthe precision of the sequential sampling estimations.The no-control decisions, with the conventional
sampling plan, and re-sampling decisions, with thesequential sampling for adults in the fields 10, 18 and19 were due to the densities of adults in those fieldsbeing dose to the critical densities of the decisionboundaries (mo and m.). As a consequence, thesefields required larger number of samples to reach adecision with the sequential sampling plan. However,for the other fields where the decision was similar forboth plans, the sequential sampling showed a redu c-tion of about 84.44% in the time spent in the samplingof nymphs and 54.17% in the time spent in thesampling of adults compared with the fixed number ofsamples established for the conventional samplingplan.
We would like to express our gratitude to the tomato growerswho allowed us to use their fields for this study, which wasfinancially supported by the CAPES Foundation (BrazilianMinistry of Education), the National Council of Scientificand Technological Development (CNPq, Brazilian Ministryof Science and Technology) and the Minas Gerais StateAgency for Research Aid (FAPEMIG).
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Author's address: Marcelo Coutinho Picanço (correspondingauthor), Departamento de Biologia Animal, UniversidadeFederal de Viçosa, Viçosa, MG 36571-000, Brazil, E-mail:[email protected]
