Population dynamics of Frankliniella species (Thysanoptera: Thripidae) thrips and progress of spotted wilt in tomato fields

Crop

0261-2194(95)OOOOO-O

l’rorrcrron

Vol.

14. No. 7. pp. 577-583, 1995 Elsevier Science Ltd Prmted in Great Britam 0261.2194/95 $10.00 + o.ou

Population dynamics of Frankliniella species (Thysanoptera: Thripidae) thrips and progress of spotted wilt in tomato fields* H. Puche,+* R. D. Berge? and J. E. Funderburk” fEntomology

and Nematology

§Plant Pathology

Department,

Department,

University of Florida, Gainesville,

FL 32611,

USA,

University of Florida and !North Florida Research and Education

Center, Quincy, FL 32351, USA

The population of Franklini& thrips, as monitored by captures on sticky traps, developed quite differently in 1990 compared to 1989. The first thrips in 1990 arrived 10 days earlier, increased at a 22% faster rate, and reached an asymptotic level that was nearly twice as high as the thrips in 1989. Despite the substantial differences in the thrips populations between the 2 years, the incidence of spotted wilt in tomatoes was low (<9%) both years. Although the thrips were present daily in the crop, the disease seemingly developed in only one-three separate monocycles. These monocycles were not correlated with

apparent generations of thrips nor with specific weather events. The sigmoidal curves for the cumulative number of trapped thrips over time were fit well by the Weibull model with shape parameters of c = 2.16 and 2.55 for fields 1 and 2 in 1989 and c = 3.32 and 2.81 for the two fields in 1990. Three generations of migrating thrips apparently occurred in each season as detected by waves of increased numbers of thrips when the population curves were linearized. The diseased plants occurred mostly at random in the fields, which was evidence that the primary source of dispersal was by immigrating thrips. Polycyclic development undoubtedly was prevented in these commercial fields by weekly application of insecticides. Additional tactics are needed to prevent movement into tomato fields of viruliferous thrips that develop on hosts outside the fields.

Keywords:

insect trapping; insect population curves; spatial analysis

Epidemics of spotted wilt caused by the tomato spotted wilt virus (TSWV) have been observed on tomatoes, peanuts, and other crops in northern Florida since 1986 (Best, 1968; Cho, Mau, German, Hartman, Yudin, Gonsalves and Provvidenti, 1989; Olson and Funderburk, 1986). In tomatoes, the symptoms of the disease include brown ring-spotting of leaves and fruit, and yellowing and general wilting of plants (Cho et al., 1989). Although the incidence of spotted wilt in tomatoes has been less than 2% (Salguero Navas, Funderburk, Beshear, Olson and Mack, 1991), incidences of more than 20% have been found in tobacco in Georgia (Culbreath, Csinos, Bertrand and Demski, 1991). Thus, TSWV represents a potentially serious problem for various crops in the southeastern United States. More than 200 species of plants are hosts of the TSWV (Cho, Mau, Gonsalves and Mitchell, 1986; Yudin, Cho and Mitchell, 1986). In northern Florida, the virus is transmitted exclusively by two species of thrips, Frankliniella occidentalis (Pergande) and F, fuscu (Hinds). Both species frequent the flowers of 31 plant species in northern Florida (Chellemi, Funderburk and Hall, 1994). The transmission of TSWV on thrips vectors is *Portion of a Ph.D. dissertation submitted by the senior author to the University of Florida Graduate School. ‘Author to whom correspondence should be addressed

unusual in that only the larvae can become infected when feeding on a viriliferous plant. The virus multiplies in the thrips and is passed transtadially to the adults. Adult thrips transmit the virus when feeding on plants. Non-infective adults that acquire the virus through plant feeding do not become viriliferous due to a midgut barrier (Ullman, Cho, Mau, Wescot and Custer, 1992). The location and source of viruliferous vectors may affect the distribution and activity of thrips. The pattern of diseased plants within a field may be affected as a result. Insect vectors coming from distant sources usually produce a random pattern of diseased plants, while an in-field population of viruliferous vectors would produce a clumped pattern of plants with disease. Vectors coming from weed hosts at the margins of fields often produce a gradient of diseased plants from the edge of fields. The spatial dynamics of spotted wilt has been studied on peanut (Culbreath, Demski and Todd, 1990), tobacco (Culbreath, Bertrand and Csinos, 1990; Tsakiridis, 1980), and tomatoes (Bald, 1937). The patterns of diseased plants have been clumped (Culbreath, Demski and Todd, 1990; Tsakiridis, 1980) or random (Bald, 1937; Culbreath, Bertrand and Csinos, 1990), depending on the host. Tsakiridis (1980) interpreted the clumped distribution of TSWV-diseased tobacco plants to be from infections within the field.

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Dynamics of thrips in tomatoes: H. Puche et al.

Tresh (1983) and Vanderplank (1982) considered the progress of spotted wilt in a field to be monocyclic with all infections arising from TSWV that was vectored by migrating thrips, since secondary spread of TSWV by the chance movement of viruliferous larvae has been observed rarely (e.g. Bald, 1937). There has been insufficient information on the population dynamics of thrips within a field to determine if polycyclic progress of the disease could occur. The purpose of this study was to determine the population dynamics of thrips in commercial fields of tomato and the effect of these dynamics on the temporal and spatial pattern of diseased plants in commercial fields. Materials

and methods

Crop culture

The experiments were conducted in two commercial fields of tomatoes in Gadsden County, Florida during the spring of 1989 and 1990. The rows are oriented east-west in both fields in both years, with a row spacing of 180 cm and a plant spacing of 50 cm. Sixweek-old tomato plants (Lycupersicon esculentum Mill. cv ‘Sunny’), were transplanted into the fields in midMarch. Plant density was approximately 67,450 in the 7.5 ha field 1, and 45,860 in the 5.1 ha field 2. A 60 cm wide band of black polyethylene mulch was laid down the center of each row. Standard commercial production practices were followed (Hochmuth, 1988). Each field was sprayed, usually weekly, with label rates of esfenvalerate 0.66 (E. I. duPont de Nemours and Co., Wilmington, DE), methamidophos 4E (Valent USA Corp., Walnut Creek, CA), or Bacillus thuringiensis (Abbott Laboratories, North Chicago, IL), or a combination of these products. Numerous insect pests damage tomatoes in northern Florida, and applications of insecticides are a needed production practice. The materials of the foregoing that are labeled to suppress populations of thrips are esfenvalerate and met hamidophos . Insect trapping

The movement of adult thrips was monitored in each field with yellow and blue 12 X 25 cm plastic sticky cards. Both blue and yellow cards were used to assure that at least one of the two colors was highly attractive to Frankliniella spp. in tomatoes (Gillespie and Vernon, 1990; Puche, Funderburk and Olson, 1993). Each card was sprayed front and back with a clear insect trapping adhesive (‘Tanglefoot@ Insect Trap Coating’, Tanglefoot Company, Grand Rapids, MI). The cards were stapled to wooden stakes with the base of the card 79 cm above the ground. Paired yellow and blue cards were set at a similar location in an adjacent row. Ten cards were placed in each field; one blue card and one yellow card were placed at each of the four cardinal compass directions, 6 m from the edges, and in the center of each field (five in-field locations X two colors of cards X two fields = 20 traps per sampling date). The sticky cards were collected at weekly intervals and new cards were placed between 0600 and 1200 h EST. The collected cards were covered with transparent plastic wrap and frozen until processed.

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The number of thrips on both sides of the cards was determined under 6.5 X magnification. The results of trap placement on the numbers of captured thrips were reported in Puche et al. (1993).

Temporal

analysis

of thrips populations

The numbers of captured thrips in the weekly collections were cumulated over time and plotted. The Weibull function, because of its flexibility to fit various curve shapes, was chosen as a model to be fitted to the data. The Weibull function can be expressed as Yt = Y,,,,, (1 - exp{-[ (t - a)k]“} ); m which Y, is the cumulative number of thrips, Y,,,,, is the estimated maximum number of thrips that could be captured, k is a measure of the daily rate of increase in the popuIation of captured thrips, t is time, a is a position parameter, and c is the shape parameter for the function (Stauffer, 1979; Weibull, 1951). The position parameter, a, represents the starting point of the curve on the x-axis (time) relative to the magnitude of the initial observation (Campbell and Madden, 1990). The shape parameter c controls the shape of the rate curve and the inflection point. The values for the parameters Y,,,, k, a, and c were estimated pairwise at first, and then singly (by setting the other parameters to appropriate values) with a nonlinear, least-squares, curve-fitting program (Berger, 1989). The cumuIated weekly number of trapped thrips were also transformed with the Gompertz equation (gompit (Y) = -ln(-ln( Y/YmBX))). The date on which the thrips population began in each field and season was arbitrarily set as the first day on which 0.1% of the maximum number of thrips would have been captured. This initial day and the day on which 50% of the maximum number of trapped thrips occurred were estimated by linear regression of gompit values vs time. This approach to curve analysis has been used for decades for disease progress (e.g. Berger, 1981; Campbell and Madden, 1990; Zadoks and Schein, 1979) and for number of trapped fungal spores (e.g. Jowett, Browning and Hanning, 1974). Correlation analysis (Gauch, 1984) was also performed between the number of thrips captured each week and the weekly accumulation of rainfall. Rainfall was recorded at a nearby meteorological station located about 3 km from the commercial fields. Disease assessment The incidences of spotted wilt were determined

in each of two fields from 30 April to 30 June in both 1989 and 1990. Naturally occurring sources of infection were responsible for initiation of the epidemics in both years. In 1989, a stratified, systematic, sampling plan was used to assess the incidence of spotted wilt in the tomato fields. At weekly intervals, disease incidence was rated on 11 plants spaced 11 steps from each other within a row for each of three rows chosen at random on each sampling date, and for each field. Therefore, disease incidence was not rated on the same plants from week to week. Because of the variability associated with the sampling of the low incidence of spotted wilt in 1989, the disease progress curve could not be fully characterized. Consequently, in 1990, disease incidence was

Dynamics of thrips

In each of two fields in both years, the numbers of captured thrips increased early in May and remained high until early or mid-June. Over 97% of the captured thrips were Frunklinielfa spp., but deterioration of the insects due to weather and the sticky condition of the collected thrips precluded accurate estimates of the frequency of individual species. The predominant species from samples of flowers in these fields was F. occidentalis, although F. tritici (Fitch) was common in samples taken in 1989 (Salguero Navas et al., 1991). The predominant species in 1990 was again F. occidentalis, but F. tritici and F. bispinosa (Morgan) were sometimes present. The numbers of F. fusca were very low in both years. For each field in 1989 and 1990, the statistical fit of the Weibull function to each of the curves for the cumulative number of captured thrips over time was very good (R2 > 0.97) (Table I). In 1989, the values of the shape parameter of the Weibull function were c = 2.16 and 2.55 for fields 1 and 2, respectively. In 1990, the curves for the cumulative number of thrips had shape values of c = 3.32 and 2.81 for fields 1 and 2. Values of c near 2.2 occur when the Weibull model is fit to asymmetrically sigmoidal curves that are characterized by the Gompertz model (Berger, 1989). Since three of the four curves had shape values somewhat typical of Gompertz-type curves, the Gompertz transformation was used for linearization. The position parameter a of the Weibull function was estimated at -8 to -10 days in 1989 which is indicative that trapping was initiated more than 1 week after the time of arrival of the first thrips of the seasonal population. The day for 0.1% of the captured thrips estimated by regression of the Gompertz-transformed values was calendar day 105 or 106 (Table 2). This day was about 3 weeks before the first sticky traps were collected in 1989 (day 125). In 1990, the value of a was estimated at -1 to -2 days, which means that the traps collected on day 93 were placed out as the first thrips were arriving. The estimates for the day of 0.1% captured thrips made with the regression of gompit values was similar. The rate parameter k for the increase in the population of thrips over time was similar and slow for both years; k = 0.02 in 1989 and k

The proportions of diseased plants (y) were plotted over time (t). For the 1990 season, several of the common disease-progression models (Berger, 1981; Campbell and Madden, 1990) were fitted to these values. Since a discontinuous infection process was assumed, a model to handle discrete monomolecular curves was needed. The von Bertalanffy model was chosen because this equation has easy provision for the initial level of disease (yc), the asymptotic, maximum level of disease y,,,, and the rate (k) when the curves are of the monomolecular type. The model equation is y, = y,,, (1 - b exp (-k,)); where b = 1 - yc)y,,. For each of several segments of the total curve, the von Bertalanffy equation was fitted by using a non-linear, least-squares, curve-fitting program (Berger, 1989). Spatial analyses The spatial aspects of tomato plants with symptoms of spotted wilt for the two plots in 1990 were determined using runs analysis. A run is defined as a succession of one or more diseased or healthy plants within a row. The total number of runs expected (E(U)) under the null hypothesis of randomness is described as E(U)) = [l + 2m (N - m)]lN, where N is the total number of plants in a row and m is the total number of diseased plants (Madden, Louie, Abt and Knobe, 1982). A Ztest was performed to detect clustering, and this test was done for each row individually and for the 300 plant populations of each of the two plots in 1990. This analysis was performed to detect clustering of diseased plants for each sampling date in 1990.

Table 1. Characterization of the increase of four populations of weekly captures on sticky traps in tomato fields

Year

Field

1989

1 2

I

1990

days were

estimated

with

Calendar

of determination

day*

Weibull

Y = 0.1%

Y = 50%

-7.03 -6.72

+ 0.048X + 0.046X

0.99 0.92

106 105

154 155

-8.0 -10.0

-8.00 -7.52

+ 0.062X + 0.059X

0.97 0.97

97 95

134 134

-2.5 -1.2

the stated

regression

equations:

the Gompertz

with the Weibull function the Yrw.x had been estimated ‘The cumulated weekly numbers of thrips were used to fit the Weibull coefficients

over time based on the cumulative number of

R2

2 *The

frankliniellaspecies

vs time

Gompits

were

et a/.

Temporal analysis of thrips populations

Temporal analysis of disease

eq.

H. Puche

Results

determined by monitoring a plot of 300 plants in each of two fields twice a week. In field 1, the plot was six rows wide and 29 m long; in field 2, the plot was four rows wide and 44 m long. When a tomato plant was found with symptoms of spotted wilt, it was flagged and noted on a map of the plot. The presence of TSWV was confirmed with an ELISA test in randomly selected, diseased plants, but not all plants with symptoms were tested. Some nonsymptomatic plants were tested with an ELISA test as well. Fourteen censuses for spotted wilt were conducted in the designated area of each field during the period 30 April to 15 June 1990.

Regression

in tomatoes:

transformation

function,

Y, =

Y,,,,,

(gompit

a

(Y)

(I-exp{-((r

= -In -

o)k)‘}),

parameters+ k

Y,,,

2.16 2.55

0.021 0.020

2750 2300

3.32 2.81

0.018 0.018

4200 4000

C

(-In

(Y/Y,,,,,))) by nonlinear,

was used prior least-squares

to analysis regression.

in which The

four

RZ > 0.97

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Dynamics of thrips in tomatoes: H. Puche et al. = 0.018 in 1990. However, the rates calculated with the Gompertz-transformed values were more than 20% faster in 1990, 0.61 vs 0.47 (Table 1). The greatest differences between the populations of thrips for the 2 years were: (i) the time when the first thrips were estimated to have arrived as calendar day 105 in 1989 and day 96 in 1990; (ii) the time to 50% of the total number of thrips that could be trapped as calendar day 155 in 1989 and day 134 in 1990; and (iii) the estimated maximum numbers of thrips to be captured which were 2500 in 1989 and >4000 in 1990. When the cumulative numbers of captured thrips were transformed with the Gompertz equation and then plotted, two or three waves of increased numbers were detected (Figure I). These wavesmost likely corresponded to distinct generations of immigrating thrips. Since trapping in 1989 began later in the season compared to 1990, much of the earliest generation was undetected. Twice as much rain fell in 1990 compared to 1989, and the amount of rain on any day in both years, more thrips were captured in the dry periods between rainfalls. Correlation coefficients between the amount of rainfall and the number of thrips captured one week later were not significant (P > 0.05).

H

.2.0-

3

, , ,(, , ,,,, , , ,,,,,,,(,,,,,,

In both years, initial symptoms of spotted wilt started as concentric necrotic rings and leaf spots about 40 days after tomato seedlings had been transplanted into the field (calendar day 120). The presence of TSWV was confirmed in the leaves from all symptomatic tomato plants that were collected at random using ELISA tests. The maximum incidence of disease in both years was low (<9%) and occurred in mid-May and mid-June. After early June, no additional plants with symptoms of spotted wilt were found. Because of the problems with sampling for the disease in 1989, the progress curves for spotted wilt in the two fields were nondescript and were not fitted to the model. In 1990, the curves for the overall increase of spotted wilt over time were near linear for both fields, but the curves had several perturbations (Figure 2). Consequently, for field 1, the von Bertalanffy equation was fitted to three curve segments, calendar days 118-126,

1; Ymax 2: Ymox

,,,,,,,,

= 2750 = 2300

,,G

4200 4000

-2.5

1,,,,‘,,,,,,,,,,,--,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,~ 85 99 113 127 141 155 Calendar

Disease progress

Field

W Field , ,, ,,,,,,,,

169

4 _ ,

q

183

days

Figure 1. The cumulative number of thrips captured on sticky traps in tomato fields in northern Florida during 1989 (A) and 1990 (B). Each data point is the average of captures on 20 traps. The values were transformed with the Gompertz equation (gompit (Y) = -1 n(-1 n(Y/Y,,,)))

126-145 and 145-166 (Figure 2A). The parameters for the three segments were y. = 0.0, y,,, = 0.0133, and k = 0.678 for segment 1, y. = 0.0133, y,,, = 0.05, and k = 0.209 for segment 2, and y, = 0.05, y,, = 0.0833, and k 0.23 for segment 3. In field 2, the progress of spotted wilt was slow and the maximum incidence was very low (1.33%) on calendar day 166. Nonetheless, two cycles of spotted wilt occurred (Figure 2B). The von Bertalanffy equation was fitted to these cycles with the parameters y. = 0.0, = 0.0133, and k = 0.338 for the first cycle (days YIXl&+X 123-149), and y. = 0.0133, ymax = 0.0167, and k = 1.0 for the second cycle (days 149-166). The second ‘cycle’

Table 2. Analysis of ordinary runs to determine the spatial pattern of spotted wilt in a 300 plant plot for a tomato field in 1990 Calendar day

m

u

120 121 131 134 138 145 149 152 156

4 8 10 13 1.5 17 19 21 25

15 17 23 27 31 32 36 42

8

E(U) 8.5-9.3 15.7-17.4 19.2-21.4 24.5-27.3 27.9-31.1 31.2-34.9 34.6-38.6 37.8-42.3 44.2-49.4

ZY -0.92 -1.23 -2.60* -1.68* -1.24 0.86 -2.02* -1.60 -1.65*

Pattern random random clustered random/clustered random random clustered random random/clustered

*Expected number of runs (E(U)) calculated as E(U) = 1 + 2m(N-m)/N where m = number of diseased plants (I = observed number of runs and N = 3CKl.The standard deviation (s(u)) of E( Cl) was calculated as s(u) = (2m(N-m)-N/M (N-l))“-’ ‘khe Z-statistic was calculated from the observed number of runs (U) as Z = (U + 0.5 - e(L’))/s(U); where 0.5 is a continuity correction (see (5)). A value of Z more negative than -1.64 indicates clustering (P = 0.05)

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Dynamics of thrips in tomatoes: 'Om

1 0

Observed.

-

A

Field #l

...

Second

- - -

Third

_

._***---

First cycle

,*

cycle

/ ‘0

cycle

rC Y.-C

5’ -1.

.a’ ..‘. r

l

2.0

-

Observed.

Field #2

B

First cycle Second

cycle

,. *- .-* *1.5 1.0/

F

3

1::; 110

120

130

140

Calendar

150

160

170

180

days

Figure 2. The incidence of spotted wilt in northern Florida during 1990 for field 1 (A), and field 2 (8). The fitted curves were generated with the von Bertalanffy equation with the parameters for the various curve segments given in the text

was simply the addition plant on day 152.

of a single,

newly diseased

Spatial analysis of spread of spotted wilt In 1990, the mapping of plants as they exhibited symptoms of spotted wilt allowed for the analysis of the spatial pattern of diseased plants in the two fields. Overall, the pattern of diseased plants in field 1 was considered to be random (Table 2). However, two of the six rows in this field had patterns that could be considered slightly clustered based on the value of the Z-statistic. In both of these rows, there were only two diseased plants, and for each row, one diseased plant occurred as the end plant in the row. This position caused the observed runs to be four, rather than the expected five, and was sufficient to make the Z-statistic significant. In field 2, two of the five diseased plants occurred as a doublet, and this also caused the Zstatistic to be significant. Because of the low incidence of disease, the chance occurrence of a single, newly diseased plant that was adjacent to another diseased plant was sufficient to alter the Z-statistic so that the resulting pattern would be interpreted as clustered, rather than random. Thus, the interpretation of spatial pattern as clustered by runs analysis at these low incidences of disease must be viewed with considerable caution.

Discussion This is the first time that curves of the cumulative number of captured thrips over time have been used to monitor the population dynamics of thrips in tomato fields. In this study, the curves for the cumulative number of captured thrips were asymmetrically sig-

H. Puche et al.

moidal (Gompertz-type) or with shapes approaching the symmetrical logistic curves. After the Gompertzlinearization equation was applied to the cumulative numbers, two or three waves of increased numbers of thrips were detected. Plant pathologists commonly trap air-borne spores above a crop canopy to monitor the progress of disease caused by a fungal pathogen. The curves for the cumulative numbers of trapped spores over time are usually sigmoidal, and the curves are frequently parametrized by fitting various growth functions to the data (e.g. Jowett et al., 1974). When disease-progress values are linearized, the waves of disease in the early stages of polycyclic epidemics are the generations of the pathogen (Zadoks, 1979). Therefore, the waves found for the thrips populations most likely represent different generations of flower thrips species. The early portions of these transformed curves for the cumulative number of captures thrips may have great value in estimating the future insect population. In the case of TSWV, the early estimation of the future abundance of the thrips vectors should allow an advanced forecast of the incidence of spotted wilt if one considers the source strength of TSWV in the vegetation that surrounds the crop field. Most winged, adult thrips probably do not travel great distances (Cho et al., 1989, Puche et al., 1993). The common sources of the thrips are nearby crops or wild vegetation. Numerous hosts in adjacent weedy hedgerows and wooded areas are known sources of thrips that migrate into tomato fields (Chellemi et al., 1994). Therefore, the signoidal curves for the increase of thrips over time can be interpreted as the thrips probably originating from several generations in the immediate locale with some asynchrony of the interstadial processes. The dynamics of the populations of thrips (as monitored by captures on sticky traps) were quite different over the 2 years of the study. Compared to 1989, the populations of thrips in 1990 began 10 days earlier, increased in numbers by a 22% faster rate, reached the 50% level of total numbers sooner by 20 days, and approached an asymptote almost twice as high. Despite these differences in the populations of thrips in 1989 and 1990, the final incidences of spotted wilt in the tomato fields were similar for the 2 years. In 1990, the first thrips were found on sticky traps in late March and the first tomato plants with spotted wilt were observed during the last days of April. If an incubation period of 7-10 days for the TSWV in the plants is assumed (Bald, 1937), then the earliest arriving thrips likely were non-viruliferous. Also, the last tomato plants with new symptoms of spotted wilt were found on calendar day 155 in 1990, yet thrips were trapped until calendar day 171. Thus, the thrips arriving after calendar day 147 were either (i) nonviruliferous vector species, (ii) non-vector species, (iii) these thrips fed too briefly on infected plants to transmit the virus or, (iv) the tomato plants became resistant to infection by TSWV. To determine the reasons for the lack of virus transmission by the earlyand late-appearing thrips would require analysis of the number and species of thrips and their viral transmissiveness on a daily basis. If the thrips species known to be vectors were non-viruliferous, then the reasons for this condition should be determined. Perhaps the early-

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and late-appearing thrips were developing at those times on hosts that did not harbor TSWV. The few apparent monocycles of increase of spotted wilt could not be directly associated with the two or three specific generations of thrips populations. The occurrence of true polycyclic development of spotted wilt in tomato fields seems unlikely, especially since insecticidal sprays are applied weekly to the tomato fields. The final incidences of spotted wilt in the tomato fields of northern Florida in both years was low, i.e. 2-9%. Since hundreds of thrips may visit many of the individual tomato flowers during the bloom period (Salguero Navas ef al., 1991), the rate of transmission of TSWV must be very low. Evidence of low rates of transmission of TSWV by thrips has been reported (Paliwal, 1974). Low rates of transmission may occur if the thrips are spending more time travelling and they are not remaining on the plants long enough to transmit the virus. This is the case for aphids, where higher rates of movement led to lower incidence of barley yellow dwarf on oats (Power, Seaman and Gray, 1991). The transmission eficiency can also be affected by virus titer and age of the infected leaves on which the vectors feed (Gray, Power, Smith, Seaman and Altman, 1991; Power, 1991; Tresh, 1974). In this study, the spatial pattern of local spread of TSWV among tomato plants was examined. The diseased plants occurred randomly in most cases. This distribution of diseased plants is interpreted as the inoculum arising from the influx of viruliferous thrips from sources outside the field (Bald, 1937; Tresh, 1983; Tsakiridis, 1980). Occasional doublets of diseased plants were found in some rows at the end of the season. A minimal amount of secondary spread of the virus may have occurred late in the season, perhaps by the chance movement of viruliferous adults to neighboring plants (Bald, 1937; Ullman et al., 1992). Based on these findings, the incidence of spotted wilt in the tomato fields in northern Florida is expected to increase in the future with the increasing presence of the thrips vectors. There are many species of plants present in the area that are preferred hosts of the thrips (Chellemi et al., 1994). As thrips increase in prominence, the incidence of TSWV in the many wild host plants should also increase. The management of spotted wilt in susceptible crops will be difficult via vector control alone, and identification of wild host reservoirs is especially important in the development of management programs in northern Florida. In addition to the management measures of selecting preferred sites for crop fields, sanitation procedures, and application of insecticides as suggested by Cho et al. (1989), the growers may utilize buffer zones and strips of trap crops (e.g. tall millet) around the fields to restrict the immigration of thrips. Monitoring, of the thrips populations by sticky traps or by other means (e.g. Puche and Funderburk, 1992; Salguero Navas et al., 1991) may prove useful to time the initial application of insecticide. References Bald, J. G. (1937) Investigations on ‘spotted wilt’ of tomatoes. Ill. Infection in field plots. Commonwealth on Australia, Council for Scientific and Industrial Research, Bull. No. 106. Melbourne

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Berger, R. D. (1981) Comparison of the Gomperz and logistic equations to describe plant disease progress. Phytopathology 71,716719 Berger, R. D. (1989) The analysis of effects of control measures on the development of epidemics. In: Experimental Techniques in Plant Disease Epidemiology (Ed. by J. Kranz and J. Rotem), pp. 137151. Springer-Verlag, Berlin Best, R. J. (1968) Tomato 145

spotted wilt virus. Adv. Virus Res. 13, 65-

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Received 23 May 1994 Revised 29 December 1994 Accepted 11 February 1995

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