Integrated pest management (IPM) has been defined as a control

Transcription

1 RESEARCH Geostatistical Analysis of Cydia funebrana (Lepidoptera: Tortricidae) Pheromone Trap Catches at Two Spatial Scales A. Sciarretta, P. Trematerra, and J. Baumgärtner ABSTRACT In the Molise region of central Italy, spatial relationships between pheromone catches of male Cydia funebrana (Treitschke) were studied at local scale inside and around a single plum orchard as well as at regional scale comprising selected sites including orchards. From these relationships we infer adult movement patterns with particular relevance to pest control operations. At local scale, kriging procedures applied to pheromone trap catches showed that the number of male C. funebrana was lowest in the interior of a plum orchard and highest around a ravine, near irrigation channels, and along hedgerows in the orchard surroundings. The degree of aggregation of trap catches was influenced by daily temperature minima during the first of three flights. The correlation coefficients of trap catches inside and outside the orchard significantly decreased with increasing distance. Moran s I index revealed no significant autocorrelation and, therefore, no influence from neighboring traps. However, correlograms showed slightly positive values (i.e., slight aggregations at low distances, decreasing values with increasing distance up to 400 m, and even more decreasing values thereafter). At regional scale, a cluster analysis of the data of the first year grouped the sites according to host plants. The same analysis of the data of the second year grouped the sites primarily by altitudes and distance from the sea and suggested a slight effect of between site distances. Among others, the selection of distant sites and the influence of host plant type as well as the geographical location may confound the effect of distance: the distance did not have a significant effect on correlation coefficients for site trap catches. The results are discussed with respect to orchard-specific and regional monitoring, pest control operations, and planning of future research work. Integrated pest management (IPM) has been defined as a control system that, in the context of the associated environment and the population dynamics of the pest species, uses all suitable techniques and methods in as compatible manner as possible and maintains the pest population at levels below those causing economic injury (Flint and van den Bosch 1981). Traditionally, emphasis has been on development and implementation of field-specific IPM systems. Recently, however, Kogan et al. (1999) observed an extension from single-species control systems at local levels to multispecies management systems at higher levels such as regions. Without the previous space constraints, the design of more comprehensive IPM systems becomes feasible. In addition, traditional IPM research has focused more on temporal than on spatial dynamics. This is justified in many cases but also may reflect limitations in the available methodology for investigating spatial processes in comparison with the one developed for temporal processes (Di Cola et al. 1998, 1999). The recent elaboration of scale concepts and the introduction of geostatistics in applied entomology open new possibilities for study and management of spatial processes. Scale denotes the resolution within the range of a measured quality (Schneider 1994), whereas geostatistical analysis involves two main steps (Brenner et al. 1998); the first step combines spatial autocorrelations on the basis of correlograms with the computation and interpretation of covariance functions or semivariograms and the second step involves the development of spatial interpolation models. The widely applied kriging method uses a semivariogram that describes the relation between observed variance and distance (i.e., lag distance among pairs of observations). The function obtained is used to estimate the values of points not supported by experimental data. Geostatistics were introduced into applied entomology at the end of the 1980s in North America. At local scales, Arbogast et al. (1998), for example, used kriging to locate pest infestations in silos. Brenner et al. (1998) identified the initial points of cockroach infestations in buildings and verified IPM programs. The dispersion and movements of the pink bollworm, Pectinophora gossypiella (Saunders), and western tarnished plant bug, Lygus hesperus Knight, adults and nymphs were studied in cotton and in lentil fields, respectively (Borth and Huber 1987, Schotzko and O Keeffe 1989), whereas the colonization and spread of leafhoppers in Israel s apple and stone fruit orchards were investigated by Nestel and Klein (1995). At wider spatial scales, Kemp et al. (1989) and Johnson (1989) investigated the distribution of acridids inhabiting uncultivated areas. A wide spatial scale was also used to analyze the distribution of sugarbeet wireworm, Limonius californicus (Mannerheim), in the northwestern United States (Williams et al. 1992). The distributions of the western corn rootworm, Diabrotica virginifera LeConte, and the northern corn rootworm, Diabrotica barberi Smith & Lawrence, were investigated at local and regional scales: distributions of both pests were studied in cereal fields subjected to different crop rotation schemes and in different agricultural areas of northwestern Iowa (Midgarden et al. 1993, Rossi et al. 1993, Ellsbury et al. 1998). At regional scale, geostatistical techniques were applied to pheromone trap catches to delimit the area of distribution and the expansion rate of the gypsy moth, Lymantria dispar (L.), which was introduced into the United States from Europe in the last century (Sharov et al. 174 AMERICAN ENTOMOLOGIST Fall 2001

2 1995, 1996, 1997). Also, at regional scale, Odulaja et al. (2001) inferred from trap catches the movement patterns of tsetse flies, Glossina spp., in western Kenya and used the results to propose trap deployment strategies. The work by Odulaja et al. (2001) serves as a basis and provides details for many of the methodologies used in this investigation. In our work, we focused on the spatial distribution of male adults of the tortricid moth Cydia funebrana (Treitschke) in central Italy. Besides being the main pest of plum and damson fruits throughout the Palearctic region, C. funebrana also infests other Prunus species such blackthorn (Prunus spinosa L.), which is thought to be the most important wild host plant (Popova 1971, Emmet 1988). Cydia funebrana adults emerge in the morning. Sexual activity begins about 2 h before sunrise and ends at sunrise, whereas eggs are laid mainly in the afternoon and evening (Bovey 1966, Charmillot et al. 1979). The number of flight per year varies depending on climate; the first flight occurs from April to June and peaks in May, a second flight period occurs later in the summer and, in warmer areas, a third flight is possible. In southern Europe, including central Italy, C. funebrana has three generations per year (Batinica 1970, Popova 1971, Molinari 1995). The threshold for development is 10 C and the complete life cycle takes 420 day-degrees centigrade (Charmillot et al. 1979). The diapause of C. funebrana, which occurs at the end of the summer, is determined primarily by the photoperiodic conditions prevailing during the second and third larval stadium; the critical photophase for inducing diapause is between 14 and 15 h (Sáringer 1967). The species overwinters as mature larva and pupation occurs during February or March. We carried out geostatistical analyses at local and regional scales and, from the determined relationships, infer knowledge on spatial processes with relevance to pest control. At the local scale we address, with the resolution of a tree area, an orchard and its surroundings, whereas at the regional scale we consider systematically selected ecological sites with some composed of a single orchard. The analyses are expected to further improve existing IPM systems and to generate hypothesis in future research work. Materials and Methods Study Area. Local Scale Investigations. In 1998 and 1999, we made observations in a 12-ha plum orchard (Stanley cultivars) located 50 m above sea level in a hilly landscape of the Molise region in central Italy. The 5-yr-old orchard is located on a hill facing southeast, at a distance of 5 km from the Adriatic sea. In 1998, the research was conducted inside the orchard; in 1999, it was extended to the surrounding area of the orchard and covered a zone of about 250 ha. The location of the pheromone traps is shown in Fig. 1. The orchard is rectangular with the longest border extending from southwest to northeast. Cereal and sunflower fields are grown in the northwest, and vegetables are grown between the orchard and a ravine with riparian vegetation including shrubs and blackthorn trees in the southeast. There are irrigation channels and hedgerows in the southwestern and northeastern borders, beyond which there is arable land. Regional Scale Investigations. The investigations were carried out during 1998 and 1999 in the coastal area of the Molise region. In 1998, observations were made on eight sites: three for plum orchards (B, D, G), one for peach (C), one for cherry (H), and one for apricot (L) as well as in uncultivated land with blackthorns (AI) (Fig. 2). In 1999, data were collected in six plum orchards (B, D, E, F, G, N) and in three uncultivated sites with blackthorns (A, I, M). In both years, the experimental sites were selected with a 1-ha surface area and at minimum distances of m from others orchards. The area of about 250,000 ha is divided into a flat sector following the sea and a neighboring hilly zone with altitudes increasing Fig. 1. Experimental plum orchard (po) (12 ha) surrounded by field crops (fc), vegetables (ve), a vineyard (vi), and important landscape elements (bold line = road, white stripe = ravine and irrigation channels, hr = hedgerow including riparian vegetation) with the location of Cydia funebrana pheromone traps (Î = traps operating in 1998, = traps operating in 1999). with augmenting distance from the sea. In general, the temperatures decrease with increasing distance from the sea. In 1998, all sites were located in the flat sector; in 1999, three sites were located in the hilly zone and six in the flat sector. Data collection. Monitoring of C. funebrana adults was carried out using pheromone traps (delta types) baited with synthetic sexual blend containing dodecyl acetate (50%), Z8-dodecenyl acetate (49%), and E8-dodecenyl acetate (1%) (Novapher, Italy). The traps were placed in trees at m above ground and surveys conducted from the beginning of April until the end of September in both years. The adults trapped were counted weekly and pheromone dispensers were replaced by new ones every 6 wk and sticky boards by new ones at intervals of 2-4 wk according to the sticky surface efficiency. Local Scale Investigations. In 1998, 10 traps were placed inside the orchard; in 1999, 10 traps were placed inside and 5 additional traps outside the orchard. Mean distances between two adjacent traps were about 125 m in 1998 and 170 m in The position of the traps was first defined by the distance, measured along the rows, from the orchard boarder (Fig. 1). Next, we assigned the trap position to coordinates x,y used in the geostatistical analysis. Regional Scale Investigations. During 1998 and 1999, two traps were placed at each site (A, B, C, D, E, F, G, H, I, L, M) (Fig. 2) with openings either perpendicular or parallel to tree rows to reduce the wind influence on average trap catches. The geo-referenced Universal Transversal Mercator (U.T.M.) coordinates of latitude and longitude and the altitude in each experimental site were assigned. Insects counts and trap maintenance were made as described above. Geostatistical Analysis at Local Scale. Kriging. Spatial analysis was carried out using Surfer version 6.02 (Golden Software, Golden, CO) with x,y representing the coordinates and z the weekly trap counts transformed as described below. By interpolating z values, Surfer produces a continuous grid of values. The interpolation algorithm used is linear kriging with zero nugget, which is considered here as sufficiently effective as it was in the analysis of Brenner et al. (1998). The interpolation grid obtained is represented graphically by a contour map, which shows the configuration of the surface by means of isolines representing equal z-values. A base map showing AMERICAN ENTOMOLOGIST Volume 47, Number 3 175

3 The important meteorological variables are weekly maximum and minimum temperatures ( C) and maximum and minimum relative humidity and rainfall (mm of rain) (Pitcairn et al. 1990) calculated from daily recordings by a meteorological station positioned in the orchard during the two experimental years (Fig. 3). A stepwise regression procedure in determining Ia was used to select the most important variables with slopes different from zero (P < 0.05). To allow for curvilinear effects, the square of the variables and the product of temperature and rainfall were also considered as independent variables. The dependent variable was subjected to logarithmic transformation of Ia, as suggested by Perry and Hewitt (1991): Ia N = Ia 1 Ia log 10 [2] Fig. 2. Regional scale investigation area and experimental sites (A, I, M = blackthorns; B, D, E, F, G, N = plum orchards; C = peach orchard; H = cherry orchard; L = apricot orchard). the orchard and surroundings, with the same coordinate system, was placed on top of the contour map. The z variable is obtained by converting weekly trap catches in catch probability by means of a kriging indicator, following Brenner et al. (1998). This procedure enables us to focus on initial infestations and allows the identification of areas with important insect densities by minimizing the effect of an unusual trap count. The weekly trap counts were ordered in descending manner and expressed as proportions of the pooled weekly counts. An indicator score of 1 is given to all traps with catches that exceed a critical proportion; a score of 0 is given to the remaining traps. The critical proportion was set at 80% but modified to assure that equal catch numbers were assigned to the same indicator score. The interpolation of scores yields a contour map with isolines ranging from 0 to 1. Evaluation of Environmental Variables. The aggregation index for each weekly count was calculated according to Perry and Hewitt (1991). The calculation is based on the effort needed by single individuals to reach maximum crowding (mtc) compared with that needed to reach maximum randomness (mtr). The aggregation index is obtained from Among others, the evaluation of environmental factors yields the range at which Ia passes from a random distribution to weak aggregation, (i.e., for 0.3 < Ia < 0.5) (Perry and Hewitt 1991). Assessment of Distances. The weekly trap catches n were subjected to the log 10 (n+1) transformation, and Pearson correlation coefficients between each pair of traps were computed. The correlation matrix yielded 45 pairs of traps ( 10 C 2 = 10 9/2) in 1998 and 105 ( 15 C 2 = 15 14/2) in 1999; each pair consisted of 25 observations in 1998 (from 14 April to 25 September) and 27 in 1999 (from 26 March to 24 September ). The distance between trap locations was calculated; distances between two adjacent traps ranged from 57 to 229 m in 1998 and 71 to 362 m in The maximum distance between traps was 538 m in 1998 and 930 m in To evaluate effects of distances, the correlation coefficients were expressed as a linear function of trap distances. For 1998 and 1999, regression analysis was carried out for all flights denoted flights I+II (April-July) and flight III (July-September) as well as for the combined flights. Effect of Orchard Surroundings. Spatial autocorrelation was measured by Moran s I index (Cliff and Ord 1981), which was computed by the SAAP (Exeter Software, Setauket, NY). n S w z z ij i j I = [3] 2 zi with zi = ( xi + x), where n is the number of traps, x i represents the sum of observations at the i-th trap, w ij is a weight indicating the connection between traps i and j, and S is the sum of the weights. A significant I indicates spatial autocorrelation between traps separated by the cor- Ia mtr mtr + mtc = [1] with mtc = S i x i -x max, where x i is the number of individuals from the i-th trap and x max is the maximum catch in the i-th trap. To obtain mtr, the media (m) and variance (S 2 ) of all trap catches were calculated. Thereafter, a randomly selected individual is taken away from the highest catch number and added to the trap with the lowest catch number. This procedure continues until S 2 m. The number of times this process is repeated represents the mtr value. Fig. 3. Average weekly temperature maxima (temp max) and minima (temp min.), relative humidity maxima and minima (RH min., RH max), and rainfall (mm of rain). Data were obtained from daily recordings of a meteorological station located in the experimental orchard in 1998 and AMERICAN ENTOMOLOGIST Fall 2001

4 Fig. 4. Experimental plum orchard and surroundings with probability contour lines showing Cydia funebrana trap catch distribution obtained by kriging procedures applied to indicators for various population levels. Number of insect trapped: 12 (30 April), 59 (7 May), 31 (14 May), 20 (21 May), 14 (28 May), and 15 (4 June); the cumulative frequency distributions (cfd), expressed as percentages, are given in boxes. Contours are not shown for zones with catch probabilities <0.5. White triangles show trap locations, whereas the yellow dotted line represents the approximate area of sample points. responding lag distance; otherwise no spatial autocorrelation exists (Odulaja et al. 2001). Autocorrelograms were constructed for each week. Subsequently, mean values were calculated for flight I (April-May), flight II (JuneJuly), and flight III (July-September), as well as for the combined flights. For the traps placed inside the orchard, four distance classes of equal intervals were used. In 1999, the traps placed outside the orchard were added and five distance classes of equal intervals were defined. Geostatistical Analyses at Regional Scale. Classification of Orchards. Following Perry et al. (1981), the combined catches n of the two traps data were log10 (n+1) transformed. To classify the sites, a AMERICAN ENTOMOLOGIST Volume 47, Number 3 hierarchical cluster analysis was carried out using the Nearest Neighbor method with chi-square measures. The dendrograms for 1998 and 1999 were represented on a map depicting the area under study. Assessment of Distances. The distances between each pair of sites were obtained from geo-referenced coordinates; they ranged from 593 m (sites A and B) to 17,797 m (sites B and L) in 1998 and from 593 m (sites A and B) to 24,586 m (sites B and N) in Pearson correlation coefficient between each couple of sites was computed using log-transformed data. Subsequently, a linear regression of correlation coefficients between sites with their corresponding distances was carried out for the 1998 and 1999 data sets. 177

5 Fig. 5. Experimental plum orchard and surroundings with probability contour lines showing Cydia funebrana trap catch distribution obtained by kriging procedures applied to indicators for various population levels. Number of insect trapped: 47 (11 June), 80 (19 June), 19 (26 June), 32 (2 July), 31 (9 July), and 30 (16 July); the cumulative frequency distributions (cfd), expressed as percentages, are given in boxes. Contours are not shown for zones with catch probabilities <0.5. White triangles show trap locations, whereas the yellow dotted line represent the approximate area of sample points. Results Local Scale Investigation. Kriging. Figures 4-6 show the infestation patterns for Throughout the year, a high variability between trap catches inside the orchard was observed. High levels of infestations occurred almost always near the same zones of the orchards (i.e., in the southwest/southeast corner [7, 14 May; 19, 26 June; 9, 16, 30 July; 7, 13, 20, 27 August], in the southeast/northeast corner [30 April; 14, 21 May; 4 June; 16, 23, 30 July; 7, 13, 20, 27 August], and half way along the northwestern border zone [7 May; 9, 16, 23 July; 13, 27 August]). The lowest catches were obtained in the interior part of the field, mainly towards the southwestern side (7, 21, 28 May; 4, 11, 26 June; 9, 16, 23, 30 July; 7, 13, August). The zones of highest C. funebrana male catches seem to have had a more limited extension during flights I and II (Figs. 4 and 5) than during flight III (Figs. 5 and 6). Considering both the orchard and the surroundings, the catches sometimes were higher inside (21 May; 11, 19 June; 7 August) and sometimes higher outside (7, 14, 21 May; 16, 23 July; 27 August) the orchard. The zones outside the orchard with highest trap catches are located in and around the ravine with shrubs, including blackthorn (30 April; 7, 14, 28 May; 4 June; 9, 16, 23, 30 July; 27 August). These high catches zones follow irrigation channels and hedgerows (7, 14, 21, 28 May; 4, 26 June; 16, 23 July; 13, 20, 27 August) and arrive at the contact between irrigation channels and AMERICAN ENTOMOLOGIST Fall 2001

6 Fig. 6. Experimental plum orchard and surroundings with probability contour lines showing Cydia funebrana trap catch distribution obtained by kriging procedures applied to indicators for various population levels. Number of insect trapped: 74 (23 July), 37 (30 July), 144 (7 August), 176 (13 August), 134 (20 August), and 64 (27 August); the cumulative frequency distributions (cfd), expressed as percentage, are given in boxes. Contours are not shown for zones with catch probabilities <0.5. White triangles show trap locations, whereas the yellow dotted line represent the approximate area of sample points. orchard; the lowest trap catches occurred on the opposite side of the orchard (Figs. 4-6). Evaluation of Environmental Variables. Weekly minimum temperature was the only single variable considered that significantly (P < 0.05) influenced the aggregation pattern in both years (Table 1). Fig. 7 illustrates the decreasing value of Ia with increasing minimum temperatures. During the survey period, minimum temperature fluctuated from 0.7 C in the middle of April to 25.4 C at the end of July in 1998, and from 2.2 C in the middle of April to 22.2 C in the middle of August in In 1998, the passage from a random to a weak aggregation occurred between 1.6 and 6.4 C (i.e., in April and in the coldest days AMERICAN ENTOMOLOGIST Volume 47, Number 3 of May). In 1999, however, the range was from 1.6 and 7.l C, temperatures recorded in some cold days of April. Assessment of Distance. In 1998 and 1999, the correlation coefficient decreased with increasing distance between trap pairs. However, in 1998, the regression coefficient was significantly different from 0 at the P < 0.05 level in flight III only (Table 2). In 1999, when we considered inside traps only, the regression coefficient was insignificant. However, if the surroundings also were included in the analyses, then the distance effect on the correlation coefficient was significant (P < 0.05) in all three flights, with lowest R2 obtained from flights I+II (R2 = 0.058) and highest R2 from the three flights combined (R2 = 0.152). Fig. 8 shows the two significant relationships for flight III. 179

7 Fig. 7. Linear relation between the log transformed aggregation index of Cydia funebrana weekly trap catches and weekly temperature minima recorded in 1998 and According to Table 1, the weekly temperature minimum (Fig. 3) is the only independent environmental variable with significant effect on the aggregation index in a linear model (slope is different from 0 at P < 0.05). Table. 1. Aggregation index described by weekly minimum temperature as only independent environmental variable with significant effect (slope is different from 0 with P < 0.05 Year Intercept Slope R (0.576) (0.040) (0.721) (0.049) Numbers in parentheses are standard errors of the corresponding parameters. Fig. 8. Correlations between Cydia funebrana trap catches and distances between traps for the third flight (in 1998, the traps were placed within the experimental plum orchard whereas in 1999, traps were placed inside and outside the orchard). According to Table 2, the correlation coefficients are linearly related to distance as an independent variable (slope is different from 0 at P < 0.05). Effect of Orchard Surroundings. The autocorrelation analysis yielded distance class intervals from 138 m in 1998 and from 148 and 186 m in In the latter year, trap catches from inside and outside the orchard were considered. Moran s I index, calculated weekly, generally showed no significant autocorrelation, indicating that the catch distribution of C. funebrana was random for all flight periods and the number of male caught in a trap was independent of neighboring traps. However, correlograms calculated for each flight between traps inside the orchard (1998 and 1999) reveal a more detailed pattern (i.e., values slightly positive at low distances, decreasing with increasing distance as far as 400 m, and decreasing further thereafter). This trend is most conspicuous in flight I (Fig. 9 a and b). Correlograms that also consider traps around the orchard (Fig. 9c) gave a similar trend for flight III only, whereas flight I and flight II values tend to increase after 500 m. This trend also is most conspicuous in flight II. Regional Scale Investigations. Classification of Orchards. In the 1998 analysis, a cluster was formed by plum orchards and blackthorn sites (A, B, D, G, and I) and separated from peach (C), apricot (L), and cherry (H) orchards (Fig. 10). The 1999 investigation yielded Table. 2. Correlation coefficients, calculated for III flight from the 1998 and 1999 trap catches, described by a linear function with distances as independent variable (slope is different from 0 with P < 0.05) Year Intercept Slope R 2 F test x (0.046) (1.52 x 10-4 ) x (0.031) (6.94 x 10-5 ) Numbers in parentheses are standard errors of the corresponding parameters. 180 AMERICAN ENTOMOLOGIST Fall 2001

8 different results, emphasizing two main clusters (Fig. 10): the first one is formed by hilly sites where M and N sites are more closely linked than E; the second one is formed by the six sites located in the stretch of land close to the sea. In this case, two more related clusters can be identified. The first one is formed by two plum orchards located in the southern part of the study area and separated by about 600 m (F and G), the other is formed by two plum orchards (B and D) and two blackthorn sites (A and I). Assessment of Distances. The regression coefficient of the correlation distance relationship was in 1998 and in In both years, R 2 was not significant (P < 0.05). This indicates that the catches from different sites are not related. Discussion The pheromone traps are catching males only. Hence, the results obtained and discussed below are valid only for adult males, and the following tentative extensions for females may require revision once relevant observations become available. In general, the results presented here confirm and complement the ones obtained from the same data by Sciarretta (2000). At local scale, the distribution of C. funebrana obtained by kriging indicate high dispersal capability throughout the whole period flight. In particular, irrigation channels and hedgerows appear to serve as ecological corridors, where adults move from one zone to another. Fig. 10. Regional scale investigation of Cydia funebrana trap catches. The clusters are given for the pooled 1998 and 1999 trap catch data obtained in different sites (A, I, M = blackthorns; B, D, E, F, G, N = plum orchards; C = peach orchard; H = cherry orchard; L = apricot orchard). Fig. 9. Correlograms carried out in 1998 for traps placed within the orchard (a) and in 1999 for Cydia funebrana traps placed within the experimental plum orchard (b), and traps placed inside and outside the orchard (c). The autocorrelation trend is depicted for the three flights combined and for the three single annual flights. In fact, the highest trap catches inside the orchard occur at the contact zones of the orchard with corridors. Often, the highest catches inside the orchard are at the border of zones outside the orchard with high catch numbers. These data clearly show the movements between the ravine and the plum orchard. On the opposite side of the orchard, traps located in arable fields are near to the zero catch density during the whole survey period (except for 16 and 23 July in 1999). Therefore, open fields without any host plants seem to be a barrier to the movements of the pest, which prefers to follow wild vegetation with bushes and trees. These results suggest that the movements of C. funebrana occur under the influence of host plants close to the ground, which is a widespread behavior in insects (Loxdale and Lushai 1999). The flight occurs mainly in the early morning and less in the evening (Charmillot et al. 1979) (i.e., at the time of minimum tem- AMERICAN ENTOMOLOGIST Volume 47, Number 3 181

9 perature occurrence). This may explain the dependency on temperature minima rather than temperature maxima. However, low R 2 values suggest that other important environmental parameters may have an influence on Ia. Also, the decrease of Moran s index, more accentuated during the first than the other two flights, is well in agreement with the observation that aggregations occur at low minimum temperatures, whereas high dispersal may take place at high minimum temperatures. Autocorrelation analysis shows that this trend occurs in the center of the orchard but decreases towards the borders. However, after some distance beyond the border, the index increases again to reach the level of the center. A border effect is shown by a decrease in Moran aggregation index in the zone between the orchard and the surroundings; this effect is particularly conspicuous during flight II. The analysis of distance effects on trap catch correlation shows that different results are obtained when considering flights and zones located within and around orchard. This may be due to border effects and to a wider range of distances obtained when considering orchard surroundings. The lack of significant correlation between neighboring trap catches and a decreasing trend of correlation with trap distances may be an indication of factors having increasing effects with increasing distances. This analysis does not allow their identification, but we suspect that landscape features combined with wind patterns are among the most important ones. At a regional scale, some factors affect the flights in various collecting sites. Host plants influence the behavior of C. funebrana adults. In particular, plum and blackthorn are more related than other stone fruits, such as cherry, apricot, and peach. This is because these plants are known to be differentially infested (i.e., plum and blackthorn are considered primary host plants, whereas other stone fruits are secondary or occasional hosts and are infested mainly when there are few fruits of the primary ones available) (Bovey 1966, Popova 1971, Emmet 1988). This result shows the need to assess the role of primary and secondary host plant sites in the regional dynamics of C. funebrana. The two main clusters obtained in 1999 indicate a separation of the area in two sectors the stretch of land close to the sea and the inner hilly landscape. The wind and the decreasing temperatures with increasing distances from the sea may have an effect on population development and adult movements, which is reflected in this grouping pattern. At the center of each sector, there is a weak distance influence between sites, in part attenuated by the effects of host plants. At the regional level, the selection of a limited number of distant orchards and the possible existence of additional landscape elements relevant to movements are likely to confound distance effects further. In fact, spatial influence on distance is only conspicuous if zones with highly different landscape attributes are separated. The existing IPM programs should be modified to account for the influence of orchard size and surroundings on adult movements and possible infestation patterns. During the first flight periods, adults aggregate and, if uncontrolled, may build up high local population densities. High dispersal occurs at relatively high temperatures during the subsequent two flight periods along prominent landscape features such as ravines. These factors should be considered in sampling and monitoring programs and may assist in pest control operations. For the latter purpose, however, models on spatial processes should be combined with models on the temporal dynamics to combine control techniques for keeping the populations at levels below those causing injury (Flint and van den Bosch 1981). Adequate knowledge on spatial processes may allow, for example, the design of optimum trap deployment strategies. At regional scales, the planning of mass-trapping operations, including the establishment of barrier systems, may be facilitated (Odulaja et al. 2001). The results obtained at the regional level are instructive but not sufficient for such purpose, and further research on the 182 AMERICAN ENTOMOLOGIST Fall 2001

10 influence of different factors including relevant landscape attributes for population movements and development are required. Geographic information systems can be used efficiently to establish correlative relationships between georeferenced trap-catch data, meteorological phenomena and trends, and categorical variables such as land use and orchard type for the purpose of predicting pest distribution and dispersal. This would permit the development of a decision-making system that includes proactive strategies detecting incipient problems while they still are environmentally and economically manageable. 7 Acknowledgements We are grateful to D. Odulaja (International Centre of Insect Physiology and Ecology, Nairobi) for statistical advice and to V. Baumgärtner for editorial help. The comments and suggestions made by unknown reviewers are highly appreciated. References Cited Arbogast, R. T., D. K. Weaver, P. E. Kendra, and R. J. Brenner Implication of spatial distribution of insect populations in storage ecosystems. Environ. Entomol. 27: Batinica, J Contribution à la connaissance des éléments qui déterminent le seuil économique de tolerance pour Grapholitha funebrana Tr. Zastita Bilja. 21: Borth, P. W., and R. T. Huber Modeling pink bollworm establishment and dispersion in cotton with the Kriging technique, pp In Proceedings, Beltwide Cotton Production Res. Conference, Dallas, TX. 4-8 January. National Cotton Council of America, Memphis, TN. Bovey, P Super-famille des Tortricoidea, pp In A. S. Balachoswky [ed.], Entomologie Appliquée a l agriculture. Tome II. Lépidoptères, vol. 1. Masson, Paris. Brenner, R. J., D. A. Focks, R. T. Arbogast, D. K. Weaver, and D. Shuman Practical use of spatial analysis in precision targeting for integrated pest management. Am. Entomol. 44: Charmillot, P. J., R. Vallier, and S. Tagini-Rosset Carpocapse des prunes (Grapholita funebrana Tr.): étude du cycle de développement en fonction des sommes de température et considérations sur l activité des papillons. Bull. Soc. Entomol. Suisse 52: Cliff, A. D., and J. K. Ord Spatial processes: models and applications. Pion, London. Di Cola, G., G. Gilioli, and J. Baumgärtner Mathematical models for age-structured population dynamics: an overview, pp In J. Baumgärtner, P. Brandmayr and B.F.J. Manly [eds.]. Population and community ecology for insect management and conservation. Balkema, Rotterdam, The Netherlands. Di Cola, G., G. Gilioli, and J. Baumgärtner Mathematical models for age-structured population dynamics, pp In C. B. Huffaker and A. P. Gutierrez [eds.]. Ecological entomology, 2nd ed. Wiley, New York. Ellsbury, M. M., W. D. Woodson, S. A. Clay, D. Malo, J. Schumacher, D. E. Clay, and C. G. Carlson Geostatistical characterization of the spatial distribution of adult Corn Rootworm (Coleoptera: Chrysomelidae) emergence. Environ. Entomol. 27: Emmet, A. M A field guide to the smaller British Lepidoptera, 2nd ed. The British Entomological and Natural History Society, London. Flint, M.L., and R. van den Bosch, Introduction to integrated pest management. Plenum, New York. Johnson, D. L Spatial autocorrelation, spatial modelling, and improvements in grasshopper survey methodology. Can. Entomol. 121: Kemp, W. P., T. M. Kalaris, and W. F. Quimby Rangeland grasshopper (Orthoptera: Acrididae) spatial variability: macroscale population assessment. J. Econ. Entomol. 82: Kogan, M., B. A. Croft, and R. F. Sutherst Applications of ecology for integrated pest management, pp In C. B. Huffaker and A. P. Gutierrez [eds.]. Ecological entomology, 2nd ed. Wiley, New York. Loxdale, H. D., and G. Lushai Slaves of the environment: the movement of herbivorous insects in relation to their ecology and genotype. Philos. Trans. R. Soc. Lond. B 354: Midgarden, D. G., R. R. Youngman, and S. J. Fleischer Spatial analysis of counts of western corn rootworm (Coleoptera: PICKUP VOL. 47 #2 PAGE 72 AMERICAN ENTOMOLOGIST Volume 47, Number 3 183

11 Chrysomelidae) adults on yellow sticky traps in corn: geostatistics and dispersion indices. Environ. Entomol. 22: Molinari, F Notes on biology and monitoring of Cydia funebrana (Treitschke). IOBC/WPRS Bull. 18: Nestel, D., and M. Klein Geostatistical analysis of leafhopper (Homoptera: Cicadellidae) colonization and spread in deciduous orchards. Environ. Entomol. 24: Odulaja, A., J. Baumgärtner, S. Mihok, and I. M. Abu-Zinid Spatial and temporal distribution of tsetse fly trap catches at Nguruman, southwest Kenya. Bull. Entomol. Res. (in press). Perry, J. N., and M. Hewitt A new index of aggregation for animal counts. Biometrics 47: Perry, J. N., E.D.M. Macaulay, and B. J. Emmett Phenological and geographical relationships between catches of pea moth in sex-attractant traps. Ann. Appl. Biol. 97: Pitcairn, M. J., F. G. Zalom, and W. J. Bentley Weather factors influencing capture of Cydia pomonella (Lepidoptera: Tortricidae) in pheromone traps during overwintering flight in California. Environ. Entomol. 19: Popova, A. I Biology of the plum fruit moth Grapholitha funebrana Tr. (Lepidoptera, Tortricidae) on the Black Sea Coast of the Krasnodar Territory. Entomol. Rev. 50: Rossi, R. R., P. W. Borth, and J. J. Tollefson Stochastic simulation for characterizing ecological spatial patterns and appraising risk. Ecol. Appl. 3: Sáringer, G Studies on the diapause of plum moth (Grapholitha funebrana Tr.). Acta Phytopathol. Hung. 2: Schneider, D. C Quantitative ecology. Spatial and temporal scaling. Academic, San Diego, CA. Schotzko, D. J., and L. E. O Keefe Geostatistical description of the spatial distribution of Lygus hesperus (Heteroptera: Miridae) in lentils. J. Econ. Entomol. 82: Sciarretta, A Geostatistical analysis of Cydia funebrana (Treitschke) adults dispersal and distribution (Lepidoptera Tortricidae). Ph.D. dissertation, University of Molise, Campobasso, Italy. Sharov, A. A., E. A. Roberts, A. M. Liebhold, and F. W. Ravlin Gypsy moth (Lepidoptera: Lymantriidae) spread in the central Appalachians: three methods for species boundary estimation. Environ. Entomol. 24: Sharov, A. A., A. M. Liebhold, and E. A. Roberts Spread of gypsy moth (Lepidoptera: Lymantriidae) in the central Appalachians: comparison of population boundaries obtained from male moth capture, egg mass counts, and defoliation records. Environ. Entomol. 25: Sharov, A. A., A. M. Liebhold, and E. A. Roberts Methods for monitoring the spread of Gypsy Moth (Lepidoptera: Lymantriidae) populations in the Appalachian Mountains. J. Econ. Entomol. 90: Williams, I., III, D. J. Schotzko, and J. P. Mccaffrey Geostatistical description of the spatial distribution Limonius californicus (Coleoptera: Elateridae) wireworms in the northwestern United States, with comment on sampling. Environ. Entomol. 21: Andrea Sciarretta and Pasquale Trematerra ( trema@ sunimol.it) are, respectively, research entomologist and professor of General and Applied Entomology in the Department of Animal, Plant and Environmental Science in the University of Molise (Via De Sanctis, Campobasso, Italy). Their research interests are in ecological and behavioral aspects of pest insects and in Integrated Pest Management applications. Johann Baumgärtner is Head of the Population Ecology and Ecosystem Science Department at the International Centre of Insect Physiology and Ecology (ICIPE, P.O. Box 30772, Nairobi, Kenya). He recently moved to Ethiopia to initiate projects, in collaboration with national institutions, in the areas of animal and human disease vector control, integrated crop and pest management, and integrated disease/natural resource management for poverty alleviation and restorative development. PICKUP VOL. 47 #2 PAGE AMERICAN ENTOMOLOGIST Fall 2001

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