Gypsy moth dynamics

TREE vol. 6, no.

8,

August

1991

4 Shorrocks, B., Rosewell, J., Edwards, K. and Atkinson, W. (1984) Nature 310, 310-312 5 Shorrocks, B., Atkinson, W.D. and Charlesworth, P. (1979) J. Anim. Ecol. 48, 899-908 6 Pianka, E.R. (1981) in Theoretical

Eco/ogy(May, R.M., ed.), pp. 114-141, Blackwells 7 Shorrocks, B. (1990) in Living in a Patchy Environment (Shorrocks, 8 and Swingland, I., eds), pp. 91-106, Oxford University Press 6 Rosewell, J., Shorrocks, B. and

Edwards, K. (19901 J. Anim. Ecol. 59, 977-l 001 9 Shorrocks, B., Rosewell, J. and Edwards, K. (1990) J. Aim. Ecol. 59, 1003-1017 10 Shorrocks, B. Rio/. J. Linn. Sot. (in press)

specific roles in community ecology for life history traits, behavior and physiology that hitherto have been mostly of concern in more strictly evolutionary settings’r3. However, I do not agree with everything in Bryan Shorrocks’ letter. I did not say that the Atkinson and Shorrocks model4 assumed species to be identical but that the presumption was that it does not require ‘any differences in oviposition behaviour of individuals of different species’. The article by Shorrocks etaL5, which uses the phrase ‘no traditional resource partitioning’, does not explain the use of the term but makes the claim that the model and data together support the notion that ‘interspecific competition is not a major organizing force in many insect communities’. My point is that the data and model are compatible with interspecific competition placing a limit to

similarity of competitors and this limit being realized in some insect communities through differences in oviposition behavior.

R[ln(/V,+,INr)]. He concluded that the apparent slow, cyclical oscillation of the phase diagram at low densities is indicative of a system driven by a second-order (delayed densitydependent) process. We do not agree that this is compelling evidence of delayed density dependence; it only shows that population densities gradually rose, and then declined. We performed an analysis of the Melrose data that we feel provides a more critical test for delayed density dependence. First, we used data from the individual plots, rather than yearly means of all plots. Berryman’s yearly means represent aggregations of data from populations separated by as much as 100 km; it has been shown that populations over this area often behave quite asynchronously”. Thus, Berryman’s yearly means do not represent the dynamics of any actual populations. Secondly, we applied a more rigorous analytical approach. Instead of relying on qualitative interpretations of population trajectories, we adopted the statistical techniques

used by Turchin7, who investigated the delayed density-dependent dynamics of 14 forest defoliators: delayed density dependence was detected by significant inverse autocorrelation at lag 2 in the partial autocorrelation function (PACF18 and by a significant increase in the f value when adding an Nf..2 term to the regression R = a + b, N,-, . Using this analytical approach, we were unable to detect delayed density dependence in the Melrose census data. Though most of the PACFs exhibited a significant autocorrelation at lag order 1, none had a significant lag 2 value. Furthermore, addition of an Nr.* term did not significantly contribute to the regressions of R on N,., at any of the plots. Restriction of these analyses to years where N,-,
- Replyfrom PeterChesson I welcome Bryan Shorrocks’ discussion of points raised in my recent article. Models of coexistence involving patchiness and environmental variability have indeed demonstrated important new ways in which species may coexist, a fact that Nancy Huntly and I have emphasized elsewhere’r2. The purpose of my article was to clear up confusion about what these models do say. In general, they do not imply a lesser role for interspecific competition in community structure. They continue to say that species must differ in ecologically significant ways if they are to coexist. But, as Bryan Shorrocks points out, it would be just as wrong to assume that these models had nothing new to say, as they suggest a focus on new kinds of ecological differences between species associated with environmental fluctuations and spatial patchiness. In addition, they suggest

Peter Chesson EcosystemDynamicsGroup, ResearchSchool of Biological Sciences, Australian National University, GPO Box 475, Canberra, ACT 2601, Australia

References 1 Chesson. P.L and Huntlv. N. (1988) Ann.

Zool.‘Fenn.

25, 5-16”

2 Chesson, P. and Huntly, N. (1989) Trends Ecol. ho/. 10,29-$298 3 Chesson. P. (1990) /W/OS. Trans. Sot. London, Ser. s330,165-173

4 Atkinson, (1981)

W.D. and Shorrocks,

J. Anim.

R.

B.

Ecol.

5 Shorrocks, B., K. and Atkinson,

50,461-471 Rosewell, J., Edwards, W. (1984) Nature 310,

310-312

GypsyMoth Dynamics In a recent TREE news article, Berryman’ argues that low-density North American gypsy moth populations are ‘under the control’ of insect parasitoids. We agree that parasitoids may play a more important role in the dynamics of North American gypsy moth populations than was previously believed. However, there is no significant evidence that parasitoids are indeed regulating these populations. Berryman bases his comments partly on a previously unpublished analysis of the Melrose Highlands gypsy moth census data. These data have been studied extensively by others2,3 and consist of yearly counts of egg masses in 83 plots (of 0.18 ha) between 1910 and 1931. It is the largest single set of gypsy moth census data from North America. Berryman’ used yearly means of all plots to argue that year-to-year fluctuations in densities indicate that dynamics are governed by a multiple equilibrium mode14f5. Berryman plotted density, N,, against the replacement rate,

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Berryman’ cited elevated parasitism in gypsy moth populations following experimental elevation of densitiesg*10 as evidence that parasitoids are controlling gypsy moth populations. We agree that the strong spatially density-dependent response of parasitoids in these experiments indicates that parasitoids may play a more important role in the dynamics of low-density gypsy moth populations than was previously believed. However, we feel that this pronounced spatial density dependence does not necessarily translate into temporal density dependence and subsequent population regulation”. Indeed, subsequent work12 demonstrated that these experimental elevations produced no detectable between-generation numerical effect on parasitoid populations. Further-

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more, the gradations of density created in these experimental releases were much greater than is found in natural populations; these patterns of spatially density-dependent mortality have not been detected in natural populations10~12. Previous research on parasitoid biology presents further evidence that casts doubt on regulation of gypsy moth populations by these agents. The numerical response of Cotesia melanoscela is highly constrained by the action of hyperparasitoids13. Similarly, Compsilura concinnata is limited in its numerical response between generations by its dependence on alternate hosts14. This agent caused the strongly densitydependent mortality observed in experimental manipulationsg~lO. In conclusion, we support

Berryman’s contention that the role of parasitoids in the dynamics of gypsy moth populations has been underestimated. However, we disagree with his conclusion that parasitoids are ‘controlling’ the dynamics of low-density populations. The lack of a delayed density-dependent response indicates that parasitoids are not playing the regulatory role proposed by Berryman.

(2) Simple graphical procedures are sometimes superior to more complicated methods. I agree that statistical autocorrelation analysis, if used properly and cautiously, is a powerful diagnostic tool. However, its use is incumbent on the series having a stationary meat?. All the Melrose data, including the individual plots, show strong trends or discontinuities and, therefore, autocorrelation analysis on the untrended data is meaningless. Since writing my paper I became aware of three additional years of data from Melrose Highlandsl’j, which demonstrate that a second cycle occurred over the years 193234. Autocorrelation analysis7 of the stationary low-density series (192234) indicates a highly significant second-order effect (r = 0.885, P
albeit with a different mechanism. This is not strictly true. I argued that North American gypsy moths could be regulated, like their European relatives, by generalist parasitoids at sparse densities but that outbreak dynamics could be driven either by numerical interactions with specific insect parasitoids, in which case we do not require a multiple-equilibrium hypothesis (see Fig. 1 in Ref. I), or by interactions with food supplies and/ or viruses, which could produce bistable dynamics. My point that recent observations support the former hypothesis was based on discussion with gypsy moth researchers, including Liebhold and Elkinton.

A.M. Liebhold US Oept of Agriculture,

Forest Service, Morgantown, WV 26505, USA

J.S. Elkinton Dept of Entomology,

University of Massachusetts, Amherst, MA 01003, USA

Replyfrom Alan Berryman Liebhold and Elkinton criticize my interpretation of gypsy moth dynamics in North America’ on the following grounds: (I) I used the mean of all the data, representing 81 plots scattered over 100 km2, to calculate egg-mass denwas too sities; i.e. my resolution coarse to show local asynchronous population fluctuations. (2) I used simple graphical procedures to detect changes in population behavior. (3) Gould et a/.‘sg convergence experiment, which demonstrated extremely strong spatial density dependence, is insufficient evidence to support the hypothesis that generalist parasitoids regulated gypsy moth populations at sparse densities. I have the following responses to these criticisms: (1) The matter of scale (or resolution) was not approached casually by me or by others. It was apparent to me, as it was to Campbel12, that the individual 0.18 ha plots were much too small to represent the dynamics of a population that can disperse many kilometers as small larvae15. Campbell solved this problem by grouping the data by area. When one does this, it is evident that populations from all over the region remained high until 1922 and then declined suddenly to a much lower density, at which they remained thereafter (see Fig. 5 in Ref. 2). Hence, unlike time series captured in later year@, all the Melrose populations behaved in a similar manner, justifying their treatment as a single population.

Alan A. Berryman Dept of Entomology,

Washington State University, Pullman, WA 99163, USA

1 Berryman, A.A. (1991) Trends fcol. Evol. 6,110-111 2 Campbell, R.W. (1967) For. SC;. Monogr. 15, l-33 3 Campbell, R.W. and Sloan, R.J. (1978) Environ. Entomol. 7, 389-395 4 Campbell, R.W. and Sloan, R.J. (1978) Environ. Entomol. 7, 641-646 5 Southwood, T.R.E. and Comins, H.N (1976) J. Anim. Ecol. 65.949-965 6 Liedhold, A.M. and Elkinton, J.S. (1989) For. Sci. 35, 557-568 7 Turchin, P. (1990) Nature 344, 660-663 8 Box, G.E.P. and Jenkins, G.M. (1976) Time Series Analysis, Forecasting and Control, Holden Day 9 Gould, J.R., Elkinton, J.S. and Wallner, W.E. (1990) J. Anim. Ecol. 59, 213-233