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Nonlinear Time Series Analysis Unravels Underlying Mechanisms of Interspecific Synchrony Among Foliage-Feeding Forest Lepidoptera Species

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Nonlinear Time Series Analysis Unravels Underlying Mechanisms of Interspecific Synchrony Among Foliage-Feeding Forest Lepidoptera Species Received: 28 November 2018 Revised: 5 August 2019 Accepted: 16 August 2019 Published on: 5 November 2019 DOI: 10.1002/1438-390X.12025 ORIGINAL ARTICLE - INVITED Nonlinear time series analysis unravels underlying mechanisms of interspecific synchrony among foliage-feeding forest Lepidoptera species Kazutaka Kawatsu1 | Takehiko Yamanaka2 | Jan Patoèka3† | Andrew M. Liebhold3,4 1Graduate School of Life Sciences, Tohoku University, Sendai, Japan Abstract 2Institute for Agro-Environmental Sciences, Interspecific synchrony, that is, synchrony in population dynamics among sympatric NARO (NIAES), Tsukuba, Japan populations of different species can arise via several possible mechanisms, including com- 3Faculty of Forestry and Wood Sciences, mon environmental effects, direct interactions between species, and shared trophic interac- Czech University of Life Sciences Prague, tions, so that distinguishing the relative importance of these causes can be challenging. In Praha, Czech Republic 4USDA Forest Service Northeastern this study, to overcome this difficulty, we combine traditional correlation analysis with a Research Station, Morgantown, West novel framework of nonlinear time series analysis, empirical dynamic modeling (EDM). Virginia The EDM is an analytical framework to identify causal relationships and measure chang- Correspondence ing interaction strength from time series. We apply this approach to time series of sympat- Kazutaka Kawatsu, Graduate School of Life ric foliage-feeding forest Lepidoptera species in the Slovak Republic and yearly mean Sciences, Tohoku University, Sendai temperature, precipitation and North Atlantic Oscillation Index. These Lepidoptera species 980-8578, Japan. Email: [email protected] include both free-feeding and leaf-roller larval life histories: the former are hypothesized to be more strongly affected by similar exogenous environments, while the latter are iso- Funding information lated from such pressures. Correlation analysis showed that interspecific synchrony is gen- Operational Programme Research, Development and Education, Grant/Award erally strongest between species within same feeding guild. In addition, the convergent Number: CZ.02.1.01/0.0/0.0/16_019/ cross mapping analysis detected causal effects of meteorological factors on most of the 0000803; JSPS KAKENHI, Grant/Award Numbers: 15H04613, 18K14797, free-feeding species while such effects were not observed in the leaf-rolling species. How- 16K18625, 16H04846 ever, there were fewer causal relationships among species. The multivariate S-map analy- sis showed that meteorological factors tend to affect similar free-feeding species that are synchronous with each other. These results indicate that shared meteorological factors are key drivers of interspecific synchrony among members of the free-feeding guild, but do not play the same role in synchronizing species within the leaf-roller guild. KEYWORDS convergent cross mapping, cross-correlation coefficient, empirical dynamic modeling, Moran effect, multivariate S-map 1 | INTRODUCTION Synchrony among spatially disjunct populations of the same †Deceased. species is known to occur in diverse taxa (e.g., Liebhold & This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2019 The Authors. Population Ecology published by John Wiley & Sons Australia, Ltd on behalf of The Society of Population Ecology Population Ecology. 2020;62:5–14. wileyonlinelibrary.com/journal/pope 5 6 KAWATSU ET AL. Kamata, 2000; Peltonen, Liebhold, Bjørnstad, & Williams, unknown. Thus, we still cannot rule out the possibility of the 2002). Such spatially synchronous pattern can be caused by other factors, such as shared environmental factors and the movement of individuals among populations and/or direct interactions between lepidopteran species. shared exogenous abiotic/biotic drivers (e.g., weather condi- A recently developed approach to nonlinear time series tions and common predator impacts, respectively) among the analysis, empirical dynamic modeling (EDM), is a promising populations (Blasius, Huppert, & Stone, 1999; Liebhold, method for overcoming the inference problems described Koenig, & Bjørnstad, 2004; Moran, 1953). Synchronous above. The approach of EDM is based on theories of dynamics can also occur among populations of different spe- nonlinear dynamical systems (e.g., Deyle & Sugihara, 2011; cies coexisting across a common area. In fact, interspecific, Takens, 1981), and enables testing for causal relationships synchronous population fluctuations have been observed in between observed time series. In particular, we focused on several taxa, including tetraonid birds (Lindström, Ranta, & the two methods of EDM, convergent cross mapping (CCM) Lindén, 1996; Ranta, Lindström, & Lindén, 1995), small and the multivariate S-map procedure. The CCM method is mammals (Norrdahl & Korpimäki, 1996) and insects used to determine the direction of causal effect (Sugihara (Raimondo, Liebhold, Strazanac, & Bulter, 2004; Raimondo, et al., 2012; Ye et al., 2015), and the S-map analysis is used Turcáni, Patoéka, & Liebhold, 2004). Possible mechanisms to measure the strength of the causal interaction (Deyle, of “interspecific” synchrony are analogous to but slightly May, Munch, & Sugihara, 2016). The EDM is applicable to different from those of intraspecific synchrony. Specifically, a wide variety of problems involving inference of causal intraspecific immigration/migration among populations can- relationships from ecological time series, and its application not generate synchronous dynamics among species and in ecology is rapidly increasing (e.g., Kawatsu & Kishi, instead interspecific synchrony may arise when sympatric 2018; Sugihara et al., 2012; Ushio et al., 2018). For example, populations of different species share at least one common Kawatsu and Kishi (2018) leveraged the CCM method and abiotic or biotic influence, including environmental (e.g., the multivariate S-map analysis to identify critical interac- meteorological) effects, direct interactions, shared hosts/ tions in the competition dynamics between two bean beetle shared predators and parasitoids/pathogens. Because these species, where two different interspecific interactions, that is, exogenous factors are capable of generating similar patterns resource competition between larvae and reproductive inter- of synchrony, distinguishing the relative importance among ference between adults, can co-occur. Similarly, application them can be challenging (Vesseur & Fox, 2009). of EDM approach to lepidopteran time series and candidate Here we explore the causes of interspecific synchrony causal series (e.g., weather conditions and predators) could using a system studied by Raimondo, Liebhold, et al. (2004) be expected to provide more definitive identification of the and Raimondo, Turcáni, et al. (2004) consisting of sympatric drivers of interspecific synchrony. For example, since free- foliage-feeding forest Lepidoptera species in the Slovak feeding larvae are likely exposed to similar environmental Republic. This insect community can be categorized into conditions while leaf-rolling larvae are likely more isolated two groups based on larval feeding biology, that is, free- from exogenous environments (Raimondo, Turcáni, et al., feeding species and leaf-rollers (Raimondo, Liebhold, et al., 2004), we hypothesize that the free-feeding species may be 2004; Raimondo, Turcáni, et al., 2004). These two species more strongly synchronized by shared meteorological condi- guilds are likely to be differently influenced by environmen- tions (i.e., the direction of causality and the sign of climate tal conditions and/or predators that use search images within same feeding group. Applying cross-correlation analysis to effects) than would leaf-rollers. In this study, we reanalyze the dynamics of the foliage- lepidopteran abundance time series and using a model analy- sis of prey dynamics with shared predators, they found that feeding forest Lepidoptera community studied by Raimondo, (a) significant synchrony occurs more frequently among spe- Turcáni, et al. (2004) applying EDM analysis. The analysis cies exhibiting similar feeding strategies, and (b) pairs of consists of the following four steps. First, to confirm the prey species yielding similar search images to predators are degree of interspecific synchrony, we quantify cross- easily synchronized compared to species with different correlation between each species. Second, to test the impor- images (Raimondo, Turcáni, et al., 2004). Based on these tance of the weather conditions on interspecific synchrony, results they concluded that generalist predators are likely we performed CCM analysis between the lepidopteran time causal drivers of observed interspecific synchrony among series and meteorological time series. Third, to explore the forest Lepidoptera (Raimondo, Turcáni, et al., 2004). How- possibility of direct interactions, we performed the CCM ever, their conclusion dependent upon inference from the analysis between the lepidopteran time series. Finally, to correlation, which is not necessarily a good indicator of cau- infer the sign of the detected causal effects of climatic factors sality (Sugihara et al., 2012), and inference from the behav- on lepidopteran species, we applied multivariate S-map anal- ior of a population model, whose validity in the real world is ysis. The results
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