Understanding of spatial synchrony of forest insects
The spatial synchrony of forest insect populations refers to geographically disjunct population densities that tend to be temporally correlated (Liebhold et al 2012). Synchronous insect population growth was hypothesized as a cause of the occurrence of an insect outbreak, and the spatial extent of synchronous insect population increases was believed as the most crucial evidence to classify this insect as noxious pests or not (Liebhold et al 2012). Furthermore, understanding the causal mechanisms behind large-scale synchrony of forest insect populations was believed as one of the key insights for outbreak onset forecasting, although predicting potential insect outbreaks would remain a challenge accounting for climate changes as well as habitat modifications (Bouchard et al. 2018).
Causes of spatial synchrony (hypothesis)
Generally, there were three theories hypothesized to understand the driving factors of insect population synchrony and the occurrence of an insect outbreak. The three hypotheses were regional stochasticity, dispersal effects, and trophic interactions including bottom-up and top-down effects (Myers and Cory 2013).
Regional stochasticity (Moran effect)
Since as early as the 1950s, researchers have been looking for a reasonable explanation for population synchrony in regional scales. Moran (1953) proposed that spatially correlated forces (climate perturbations), which were the density-independent factors, and able to turn local population fluctuations into synchrony (known as the “Moran effect”). Following the theory of the Moran effect, an investigation of gypsy moth (Lymantria dispar [L.]) outbreaks were carried out by analyzing weather variables and gypsy defoliation residuals across the New England states, indicating the reliability of the Moran effect and suggesting that spatially correlated weather conditions can lead to synchronous local populations (Williams and Liebhold 1995). A review of the literature over 140 outbreaks of 26 forest insect species took place between 1932 and 1992 in the Northern Hemisphere concluded that exogenous climate forces, acting as the Moran effect, had the chance to synchronizing oscillating populations over large areas (Myers 1998). In the case of SBW, spatial synchrony of outbreaks in eastern North America from 1945 and 1988 was studied, and the results of the spatially explicit lattice model declared that both local and regional Moran effects were present (Williams and Liebhold 2000). They also suggested that SBW increasing populations were synchronized by a high dispersal rate as well. However, some other research doubted the Moran effect theory more or less. It was indicated that regional outbreak synchrony would be limited by geographic variation of populations resulted from different forest stand characteristics (Zhang and Alfaro 2003). Also, Turchin 2003 indicated that synchronized population dynamics would not follow a second-order log-linear model which was proposed by Moran (1953), and nonlinearities in population dynamics can influence synchrony profoundly.
Dispersal
Dispersal has been proposed to result in synchronous dynamics of forest insects over large areas, although some studies showed that there was little relationship between the dispersal capability of an insect species and the distance over which insect populations would show synchrony (Peltonen et al 2002). However, as Myers and Cory (2013) mentioned, dispersal was the least well understood and studied characteristic of cyclic forest insect outbreaks. What is more, the dispersal capability leading to spatial synchrony tends to be affected by the positive or negative relationships between dispersal and density (Hanski 1999). In terms of SBW, Regniere and Lysyk (1995) declared that moth dispersal was a strong mechanism for SBW population synchronization especially when local insect populations fluctuate at slightly different frequencies. It was also believed that a high rate of moth dispersal, combined with the Moran effect, would result in spatially synchronized SBW outbreaks (Williams and Liebhold 2000).
Bottom-up trophic interactions
As Myers and Cory (2013) stated, host tree species undoubtedly have an impact on the synchronous dynamics of insects. Relationships between host plants and insect population dynamics have been demonstrated not only on SBW, but also on gypsy moth (Lymantria dispar; Bjørnstad et al. 2010), forest tent caterpillar (Malacosoma disstria; Cooke et al. 2011), and autumnal moth (Epirrita autwnna; Klemola et al. 2006). Recently, some researchers declaimed the bottom-up trophic effects, i.e., food web interactions, as part of the driving forces underlying the Moran effect. At first, the Moran effect was believed as a direct result of climate influence. Nonetheless, more and more scientists suggested that weather, as an exogenous effect, indirectly synchronize insect populations by directly affect the resource synchrony (Haynes et al. 2009). A recent study on bottom-up factors influencing SBW population synchrony in Quebec demonstrated that the insect population oscillations were affected by food availability as well as phenological mismatch (Bouchard et al. 2018). Furthermore, the study also indicated that food resources (white spruce cone production in this study) were itself correlated with low precipitation in the preceding summer. Therefore, it was emphasized that bottom-up trophic factors can contribute to spatial synchrony of SBW populations at the regional scale, and it was also stressed that biological processes including natural enemy’s abundance (Royama 1984), dispersal (Royama et al 2005, Regniere and Lys 1995), phenological mismatch, and food production (Bouchard et al. 2018) were all influenced by climate-related factors, which complicated the outbreak forecast in the context of climate change.
Top-down trophic interactions
Some studies suggested that insect population synchrony would be also achieved by top-down trophic interactions, i.e. nomadic predators, parasitoids, and pathogens (Walde and Murdoch 1988, Myers and Cory 2013). However, although parasitoids and pathogens were known to potentially influence the insect population, the dispersal ability of which may not be strong enough to cause synchrony of insect populations over large scales (Abbott and Dwyer 2008). Microsporidian infection was found common among SBW, but there was still not sufficient evidence for indicating the synchronizing function of the pathogen infection in the case of SBW (Myers and Cory 2013). Royama (1984) demonstrated that the basic oscillation of SBW population dynamics was influenced mainly by parasitoids and diseases, but the study area was not large enough to draw a regional-scale conclusion in this research. Nevertheless, the top-down trophic effect could be a new way to understand forest insect population dynamics at various stages of the cycle (Myers and Cory 2013).
Climate? Ultimately?
Although different hypotheses have been proposed to explain the mechanism underlying the forest insect population dynamics, climate, including temperature-related factors as well as precipitation, was frequently determined to directly or indirectly influence insect population via food availability, disease vulnerability, migration activities, and trophic relationships between host plants and natural enemies (Regniere and Lys 1995, Royama 1984, Myers and Cory 2013, Bouchard et al. 2018). Generally, outbreak cycles were more apparent in areas with higher latitudes, which may lead people to predict that warm climates tend to disrupt population dynamics at large scales (Myers and Cory 2013). Relationships between climate factors and insect outbreaks are undoubtedly complicated. Studies on such relationships were not able to achieve an agreement. According to Myers (1998), outbreaks of forest insects were associated with cool temperatures, whereas some studies suggested that egg recruitment has a positive relationship with warm temperatures during flight seasons (Régnière and Nealis 2007). Furthermore, dry weather conditions were indicated to be positively associated with the population increasing of insects (Greenbank 1957). In the case of SBW, several functional pathways have been detected for weather conditions that have an impact on population dynamics. Firstly, the temperature and precipitation of the current summer influenced the abundance of natural enemies and posed a top-down effect on SBW populations (Royama 1984). Secondly, precipitation of the preceding summer and May temperature were suggested to have an impact on food resources and generate a phenological mismatch, which posed a bottom-up effect on SBW populations (Bouchard et al. 2018). Thirdly, the temperature in July had an influence on SBW dispersal activities (Regnier and Lys 1995). What is more, the length of the growing season was determined as a constraint on boundaries within which outbreaks would potentially occur (Régnière et al 2012). These complex relationships contribute to challenges of understanding the mechanism resulting in large-scale SBW population dynamics and forecasting SBW outbreak occurrence. In conclusion, the relationships between climate and insect population synchrony are more complicated than what was proposed by Moran (1953), not only because of the spatial heterogeneities and nonlinearities which can influence the relationships but also because of the trophic chains affected both directly and indirectly by spatially correlated weather conditions.
References
Abbott, K.C. and Dwyer, G., 2008. Using mechanistic models to understand synchrony in forest insect populations: the North American gypsy moth as a case study. The American Naturalist, 172(5), pp.613-624.
Bjørnstad, O.N., Robinet, C., and Liebhold, A.M. 2010. Geographic variation in North American gypsy moth cycles: subharmonics, generalist predators, and spatial coupling. Ecology 91(1): 106-118.
Bouchard, M., Régnière, J., and Therrien, P. 2018. Bottom-up factors contribute to large-scale synchrony in spruce budworm populations. Can. J. For. Res. 48(3): 277-284.
Cooke, B.J., MacQuarrie, C.J.K., and Lorenzetti, F. 2011. The dynamics of forest tent caterpillar outbreaks across east-central Canada. Ecography 34: 001-014.
Greenbank, D.O. 1957. The role of climate and dispersal in the initation of outbreaks of the spruce budworm in New Brunswick II. The role of dispersal. Can. J. Zool. 35: 385-403.
Hanski. I. 1999. Metapopuluation Ecology. Oxford University Press, Oxford.
Haynes, K.J., Liebhold, A.M., Fearer, T.M., Wang, G., Norman, G.W., and Johnson, D.M. 2009. Spatial synchrony propagates through a forest food web via consumer-resource interactions. Ecology 90(11): 2974-2983.
Klemola, T., Huitu, O., and Ruohomaki, K. 2006. Geographically partitioned spatial synchrony among cyclic moth populations. Oikos 114(2): 349-359.
Liebhold, A.M., Haynes, K.J., and Bjørnstad, O.N. 2012. Spatial synchrony of insect outbreaks. In Insect outbreaks revisited. Edited by P. Barbosa, D.K. Letourneau, and A.A. Agrawal. Blackwell Publishing Ltd., Chichester, UK. pp. 113-125.
Moran, P.A.P. 1950. Notes on continuous stochastic phenomena. Biometrika 37: 17-23.
Myers, J.H. 1998. Synchrony in outbreaks of forest Lepidoptera: a possible example of the Moran effect. Ecology 79(3): 1111-1117.
Myers, J.H. and Cory, J.S. 2013. Population cycles in forest Lepidoptera revisited. Annu. Rev. Ecol. Evol. Syst. 44: 565-592.
Peltonen, M., Liebhold, A.M., O.N. Bjornstad, and D.W Williams. 2002 Spatial synchrony in forest insect outbreaks: roles of regional stochasticity and dispersal. Ecology 83:3120-3129.
Turchin, P., 2003. Evolution in population dynamics. Nature, 424(6946), pp.257-258.
Régnière, J. and Lysyk, T. 1995. Population dynamics of the spruce budworm, Choristoneura fumiferana. In Forest Insect Pests in Canada. Edited by J.A. Armstrong and W.G.H. Ives. Canadian Forest Service, Science and Sustainable Development Directorate, Ottawa, ON, Canada. pp. 95-105.
Régnière, J. and Nealis, V.G. 2007. Ecological mechanisms of population change during outbreaks of the spruce budworm. Ecol. Entomol. 32: 461-477.
Régnière, J., Powell, J., Bentz, B. and Nealis, V., 2012. Effects of temperature on development, survival and reproduction of insects: experimental design, data analysis and modeling. Journal of Insect Physiology, 58(5), pp.634-647.
Royama, T. 1984. Population dynamics of the spruce budworm Choristoneura fumiferana. Ecol. Monogr. 54(4): 429-462.
Walde, S.J. and Murdoch, W.W., 1988. Spatial density dependence in parasitoids. Annual Review of entomology, 33(1), pp.441-466.
Williams, D.W. and Liebhold, A.M. 1995. Influence of weather on the synchrony of gypsy moth (Lepidoptera: Lymantriidae) outbreaks in New England. Environ. Entomol. 24(5): 987-995.
Williams, D.W. and Liebhold, A.M. 2000. Spatial synchrony of spruce budworm outbreaks in eastern North America. Ecology 81(10): 2753-2766.
Zhang, Q.B., and R.I. Alfaro. 2003. Spatial synchrony of the two-year cycle budworm outbreaks in central British Columbia. Canada. Oikos 102:146-154.