Jan 23, 1994 - Xavier Fauvergue* and Keith R. Hopper**. * Biology Department .... Anthony Fordham-Skelton for reviewing this paper. We thank Barry Ogg ...
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Spatial distribution of Diuraphis noxia and one of its parasitoids, Diaeretiella rapae Xavier Fauvergue* and Keith R. Hopper**
* Biology Department, Colorado State University, Fort ColI ins CO 80523, USA ** European Biological Control Laboratory, ARS UDSA, BP 4168 Agropolis, 34092 MontpelIier, France Proceedings of the Sixth Russian Wheat Aphid Worlcshop January 23-25, 1994 Fort Collins, Colorado
Introduction
Spatial distribution - or how abundances vary through space - is an important trait of insect populations. Knowledge of the spatial distribution provides a basis for understanding various aspects of population biology including resource exploitation, intraspecific competition or the dynamics of predators and parasitoids. For insect pests, information on the spatial structure of populations may help in developing rational chemical control and release strategies for natural enemies. For parasitoids, spatial density dependence (a correlation between parasitism rate and host density) may stabilize otherwise unstable population models (Hassel & May 1973) and maximize parasitoid oviposition rate, at least in theory (Charnov 1976, Cook & Hubbard 1977). However in nature, density-dependence occurs in about only half of the species studied (Walde & Murdoch 1988). Here, we have analyzed spatial distribution and abundance of the Russian wheat aphid (RWA) Diuraphis noxia (Hemiptera: Aphididae) and one of its parasitoids, Diaeretiella rapae (Hymenoptera: Braconidae). Two questions are addressed: (1) How are RWA and D. rapae distributed in space? (2) Does parasitism of RWA by D. rapae depend on RWA density?
Materials and Methods
Sampling During 21-25 June 1993, we sampled 10 wheat fields (variety "Tarn 107") located half-way between Carr and Nunn (Weld County, Colorado) and encompassing a rectangle of 4 by 7 km. In each field, we counted the number of RWA-infested tillers in circular quadrats (0.8 m 2) at 5 m interval in a 45x45 m grid for a total of 100 samples per field.
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To get information at a smaller spatial scale, we collected 5 additional samples between members of a randomly selected group of 4 quad rats (thus in each field, 9 samples were 2.5 m apart). The area sampled was at least 100 m from any edges of the field, and presented some symptoms of RW A. Infested tillers were clipped and placed in an emergence canister which consisted of cardboard mailing tubes (20x11 cm) capped with plastic translucent funnels which ended in inverted transparent plastic vials. Parasitoids emerging from mummified aphids were caught in the vials. These emergence canisters permitted us to rapidly assess the number of parasitoids emerging from several thousand RWA-infested tillers which were obtained from 1050 quadrats (100 + 5 quadrats per field x 10 fields).
Analysis Spatial distribution of insects is often approached by statistical methods based on the frequency distribution of numbers per sample or on the mean-variance ratio (Southwood 1978). Because theses measures ignore the spatial arrangement of abundances, they often fail to distinguish different spatial structures (Schotzko & Smith 1991). For this reason, we plotted abundances (number of RWA-infested tillers and number of parasitoids) at each x-y coordinate using SAS/GRAPH (SAS Institute Inc. 1988) and used geostatistics, a method to analyze the degree of spatial dependence among samples (Liebhold et al. 1993). We evaluated spatial patterns of abundance by means of the semivariance y(h), calculated for each specific distance h:
where z(xJ is the abundance at point Xi' Z(Xi+J is the abundance at point Xi+h, and N(h) is the total number of sample pairs that are separated by a distance h. The resulting plot of y(h) versus all possible distances between sample pairs is called the semivariogram and describes the spatial structure of the population sampled. We constructed semivariograms for each field with and without the 5 additional points, using the software GS+ 2.11. We used Spearman's rank correlation coefficient to test the association between parasitism (100 x [number of D. mpae] + [number of RWA-infested tillers]) and RWA abundance (number of RWA-infested tillers). We carried out this analysis at 2 different spatial scales: within field and between field. For the within-field scale, we used abundances for each quadrat. For the between-field scale, we used the cumulative abundances for entire plots. We used SAS/STAT (SAS Institute Inc. 1988) in these analysis.
Results
Spatial distribution of RW A At low densities «1 % to 2% RW A-infested tillers), there were many samples with zero abundance in contrast to samples with relatively high values. Spatially, this was
3 reflected in a few small areas with high levels of RWA. As densities increased, the number of samples with no infested tillers decreased rapidly and local peaks became higher (Fig. 1). The number of RWA-infested tillers was spatially unpredictable. That is, high density at one location did not correspond to a similar level 5 m away. In fact, it was often the contrary (Fig. 1). Without the closely-spaced quadrats, the semivariograms were similar among the 10 wheat fields. In all cases, the semivariance slightly increased with distance between sample pairs (i.e. lag interval) (Fig. 2). Often, "localized discontinuity" (intercept of the curve to the y-axis) was relatively high compared to the "sill" (the asymptotic value of the semivariance), suggesting that most of the spatial dependence between samples occurred under 5 m (Fig. 2). In geostatistics, the "range" refers to the point at which no more spatial dependence exists. In our study, this was between 10 and 30 m in some fields. In others, samples still showed some interdependence even after 33 m (Fig. 2). With the addition of the closely-spaced quadrats, we saw 2 types of spatial structures. In half of the fields, "localized discontinuity" was much smaller, sometimes zero, confirming that most of the spatial dependence occurred at short distances (0 to 5 m) between samples (Fig. 3). However, in the other half of the fields, adding the closelyspaced quadrats resulted in a pattern where samples closest to one another were less related than those further apart. Such a spatial structure could be caused by localized high densities surrounded by very low densities; that is, hot spots of RWA abundance. However, because only 9 points were responsible for this "hyperlocaIized discontinuity", it could also result from small sample size.
Spatial distribution and density-dependence in Diaeretiella rapae D. rapae occurred at very low densities (from a few individuals to 73 adults per field) and in small patches of 1-8 individuals per sample (Fig. 4). We constructed semivariograms for the 5 fields having the highest density of parasitoids. Without the closely-spaced quadrats, the semivariance varied about a horizontal line, suggesting a complete lack of spatial dependence at that scale (random distribution). With the closely spaced quadrats, we often observed greater similarity between distant samples than between those closer together, suggesting isolated groups of individuals. However, as with RWA, these results suggest the need for a. smaller spatial resolution in further sampling efforts. Parasitism showed a weak but significant rank correlation with RWA density at two spatial scales (Fig. 5). At the within field scale, many samples had no D. rapae, but more zero values were associated with low RWA abundance.
Discussion
RWA had a clumped distribution in all fields. The spatial structure was characterized by hot spots often surrounded by relatively low densities. Thus, at the scale we measured (5 m between sample points), the density of RWA was rather unpredictable from one quadrat to the next. As a result, most semivariograms were almost flat: using 100 points in the analysis (5 m as the shortest lag interval) the localized discontinuity was nearly equal to the sill, indicating that most of the spatial dependence occurred below 5 ID.
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Use of the 2.5 m as the shortest lag intervals sometimes resulted in a clumped spatial structure with sample points 2.5 m apart having a lower variance than sample points further apart. Unfortunately, because only 9 quadrats at one location in each field contributed to this analysis, this pattern did not hold for all fields. Two main hypotheses could explain the spatial structure of RWA. First, heterogeneity among wheat plants may result in aggregation of individual aphids, or faster reproductive rate of local populations, on preferred plants. Cultivated wheat is genetically uniform, regularly distributed, and receives uniform rain at the scale we sampled. However, variation in soil quality might explain phenotypic heterogeneity among plants. An alternative hypothesis arises from the low dispersal rate of apterous RW A. On variety "Tarn 107" in the laboratory, RW A disperses only when host plants are over-exploited. Given this behavior, we could predict that locations where RW A overwintered or colonized after the winter would have very high densities at the end of the season. This hypothesis seems more likely than variation in soil quality given that the scale of the spatial pattern is less than 5 m. Further studies of RW A spatial distribution should concentrate on spatial scale smaller than 5 m. We suggest that samples should be taken every meter, a scale at which casual observation suggested that most of the spatial dependence occurred in our study. Only a few samples every 5 or 10 meters should be enough to describe low increases of the semivariance beyond 5 m. A different spatial structure at a much larger scale (i.e., over an entire field) is also likely because in many RWA semivariograms, the "range" was not reached, even after 30 m. The very clumped spatial distribution of D. rapae is more difficult to explain. Females may aggregate and lay more eggs at locations with high densities of RWA (within-field analysis). Also, the population reproductive rate of D. rapae seems to be affected by overall level of RWA abundance (between-field analysis). However, density dependence does not explain all the spatial distribution of D. rapae. Thus, there may be other reasons than host abundance to explain parasitoid aggregations.
Acknowledgements We are grateful to Thomas Crist, Frank Peairs, Ty Vaughn, Michael Antolin and Anthony Fordham-Skelton for reviewing this paper. We thank Barry Ogg and the Department of Entomology at Colorado State University for their collaboration during the field season, Ieanette Boylan, Ionathan Bowser, Alicia Cepaitis, Terri Randolph, and Sam Sharp for their active involvement in field sampling.
References Charnov, E. L. 1976. Optimal foraging, the marginal value theorem. Theor. Populo BioI. 9: 129-136. Cook, R. M. and Hubbard, S. F. 1977. Adaptative searching strategies in insect parasites. J Anim. Ecol. 46: 115-125. Harder, R. L. and Desmarais, R. N. 1972. Interpolation using surface splines. J Aircraft, 9: 189-191.
5 Hassel, M. P. and May, R. M. 1973. Stability in insect host-parasite models. J Anim. Ecol. 43: 567-594. Liebhold, A. M., Rossi, R. E. and Kemp, W. P. 1993. Geostatistics and geographic information systems in applied insect ecology. Annu. Rev. En tom 01. 38: 303-327. SAS institute Inc. 1988. SAS/STAT iII and SAS/GRAPHiII User's guides, release 6.03 Edition. Cary, NC: SAS Institute Inc.: 1028 pp. Schotzko, D. J and Smith, C. M. 1991. Effects of host plant on the between-plant spatial distribution of the Russian wheat aphid (Homoptera: Aphididae). J Econ. En tom 01. 84(6): 1725-1734. Southwood, T. R. E. 1978. Ecological methods. 2nd ed. Chapman & Hall, New York. Walde, S. 1. and Murdoch, W. W. 1988. Spatial density dependence in parasitoids. Ann. Rev. Entomol. 33: 441-466.
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