Stephen B. Heard

Sampling procedures and adult sex ratios in spruce budworm

Unbiased estimates of sex ratios that reflect local abundance of adult insects are practically difficult to obtain because many gender‐specific behavioural adaptations differentially influence the catchability of males and females in commonly applied sampling procedures. Historic data on outbreak populations of spruce budworm, Choristoneura fumiferana Clemens (Lepidoptera: Tortricidae), the major pest of conifers in Nearctic boreal forests, include dozens of sex ratio observations for 10 different sampling procedures; these data illustrate the importance of understanding the reproductive ecology of adults to contextualize sex ratio assessments. Sex ratios of resident adults (assessed by rearing field‐collected pupae to adulthood or fogging host trees with insecticide) were not different from 1:1. Sex ratios of in‐flight adults collected using Malaise traps or light traps deployed in tree canopies were consistently male‐biased, which presumably reflects the higher level of flight activity for males relative to females. Sex ratios of moths captured outside the forest canopy (presumed migrants), in contrast, were consistently female‐biased, a trend which is expected because females seeking oviposition sites are more likely to undergo migration than are males. The sex ratio among adults that died from natural causes (collected on drop trays) was not distinguishable from 1:1. In pre‐outbreak (endemic) populations, sex ratios estimated by light trapping were much more strongly male‐biased than in outbreak populations. This surprising result should, however, be interpreted with caution because little is known of reproductive ecology in endemic budworm populations.

Paradigms in Eastern Spruce Budworm (Lepidoptera: Tortricidae) Population Ecology: A Century of Debate

Abstract Three main hypotheses have been postulated over the past century to explain the outbreaking population dynamics of eastern spruce budworm, Choristoneura fumiferana (Clemens). The Silviculture Hypothesis first arose in the 1920s, with the idea that outbreaks were driven by forestry practices favoring susceptible softwood species. In the 1960s, it was proposed that populations were governed by Multiple Equilibria, with warm weather conditions releasing low-density populations from the regulatory control of natural enemies. Dispersal from outbreak foci, or “epicenters,” was seen as causing widespread outbreaks that eventually collapsed following resource depletion. However, in the 1980s, following the re-analysis of data from the 1940s outbreak in New Brunswick, this interpretation was challenged. The alternative Oscillatory Hypothesis proposed that budworm population dynamics were governed by a second-order density-dependent process, with oscillations being driven by natural enemy–victim interactions. Under this hypothesis, weather and resource availability contribute to secondary fluctuations around the main oscillation, and weather and moth dispersal serve to synchronize population cycles regionally. Intensive, independent population studies during the peak and declining phases of the 1980s outbreak supported the principal tenet of the Oscillatory Hypothesis, but concluded that host plant quality played a more important role than this hypothesis proposed. More recent research on the early phase of spruce budworm cycles suggests that mate-finding and natural-enemy-driven Allee effects in low-density populations might be overcome by immigration of moths, which can facilitate the onset of outbreaks. Even more recent research has supported components of all three hypotheses attempting to explain spruce budworm dynamics. In the midst of a new rising outbreak (2006-present), we discuss the evolution of debates surrounding these hypotheses from a historic perspective, examine gaps in current knowledge, and suggest avenues for future research (e.g., intensive studies on low-density populations) to better understand and manage spruce budworm populations.

Evidence for mate-encounter Allee effect in an invasive longhorn beetle (Coleoptera: Cerambycidae)

1. Limited empirical support is available for mate‐encounter Allee effects in invasive insects due to the logistical challenges of studying demographic trends in low‐density populations.

A simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.

Estimation of pest density is a basic requirement for integrated pest management in agriculture and forestry, and efficiency in density estimation is a common goal. Sequential sampling techniques promise efficient sampling, but their application can involve cumbersome mathematics and/or intensive warm-up sampling when pests have complex within- or between-site distributions. We provide tools for assessing the efficiency of sequential sampling and of alternative, simpler sampling plans, using computer simulation with “pre-sampling” data. We illustrate our approach using data for balsam gall midge (Paradiplosis tumifex) attack in Christmas tree farms. Paradiplosis tumifex proved recalcitrant to sequential sampling techniques. Midge distributions could not be fit by a common negative binomial distribution across sites. Local parameterization, using warm-up samples to estimate the clumping parameter k for each site, performed poorly: k estimates were unreliable even for samples of n∼100 trees. These methods were further confounded by significant within-site spatial autocorrelation. Much simpler sampling schemes, involving random or belt-transect sampling to preset sample sizes, were effective and efficient for P. tumifex. Sampling via belt transects (through the longest dimension of a stand) was the most efficient, with sample means converging on true mean density for sample sizes of n∼25–40 trees. Pre-sampling and simulation techniques provide a simple method for assessing sampling strategies for estimating insect infestation. We suspect that many pests will resemble P. tumifex in challenging the assumptions of sequential sampling methods. Our software will allow practitioners to optimize sampling strategies before they are brought to real-world applications, while potentially avoiding the need for the cumbersome calculations required for sequential sampling methods.