Life history and habitat explain variation among insect ... - ESA Journals

Life history and habitat explain variation among insect pest populations subject to global change JONATHAN A. WALTER,1,2,5, ANTHONY R. IVES,3 JOHN F. TOOKER,4 AND DEREK M. JOHNSON1 1

Department of Biology, Virginia Commonwealth University, 1000 W. Cary Street, Richmond, Virginia 23284 USA Department of Ecology and Evolutionary Biology and Kansas Biological Survey, University of Kansas, 2101 Constant Avenue, Lawrence, Kansas 66047 USA 3 Department of Zoology, University of Wisconsin, 430 Lincoln Way, Madison, Wisconsin 53706 USA 4 Department of Entomology, The Pennsylvania State University, 501 ASI Building, University Park, Pennsylvania 16802 USA 2

Citation: Walter, J. A., A. R. Ives, J. F. Tooker, and D. M. Johnson. 2018. Life history and habitat explain variation among insect pest populations subject to global change. Ecosphere 9(5):e02274. 10.1002/ecs2.2274

Abstract. Population dynamic responses to global change have varied widely among taxa. Most studies of population dynamics of insect pests focus on one or a few species, leaving open the question of whether changes in outbreak patterns are species-specific or reveal predictable responses to global change, and what factors explain differences among populations. We analyzed 64 multi-decadal time series of agricultural and forest pest insect populations in the United States. We first characterized populations according to long-term trends, strength of population regulation, and cycle presence and length. We then asked whether these attributes could be predicted by geography, taxonomy, and life-history traits. Roughly half of time series exhibited a long-term trend, and agricultural pests were more likely to be declining, while forest pests were more likely to be increasing. Approximately one quarter of records exhibited periodic oscillations, and we used their statistical properties to infer whether the oscillations were environmentally forced or arose from density dependence. Insects hatching in early spring may be more strongly influenced by environmental forcing, for example, early springs or late spring frosts, than species hatching later. Our findings suggest roles of climate change, forest compositional change, and agricultural practices in driving long-term change in pest populations. Key words: agriculture; climate change; Coleoptera; Lepidoptera; pest; population cycles. Received 29 November 2017; revised 15 March 2018; accepted 24 April 2018. Corresponding Editor: Robert R. Parmenter. Copyright: © 2018 The Authors. 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. 5

Present address: Department of Environmental Sciences, University of Virginia, 291 McCormick Road, Charlottesville,

Virginia 22904 USA. E-mail: [email protected]

INTRODUCTION

1200 yr of regular 8- to 10-yr outbreak cycles (Johnson et al. 2010). On the other hand, Ouyang et al. (2014) attributed increased abundance and outbreaks of cotton bollworm (Helicoverpa armigera) in North China to climate change and agricultural intensification. Climate change is not the only aspect of global change driving pest dynamics: Agricultural practices, notably the advent of transgenic, insect-resistant crops (also known as Bt crops), can suppress pests (Carriere et al. 2003, Hutchison et al. 2010, Bohnenblust et al. 2014) and dampen outbreak cycles (Bell et al. 2012). However, the future balance between

Populations of insect pests have exhibited varied responses to global change. In a study of five forest-defoliating species in central Europe, climate change was associated with high-frequency outbreak cycles in Bupalus piniarius and Panolis flammea in the late 1900s, but over the same time period a collapse of Lymantria monacha and Dendrolimus pini outbreak cycles (Haynes et al. 2014). Similarly, climate change has been invoked for the collapse during the 1980s of outbreaks of the larch budmoth in the European Alps, after ❖ www.esajournals.org

1

May 2018

❖ Volume 9(5) ❖ Article e02274

WALTER ET AL.

damage to characterize increasing or decreasing trends, the strength of population regulation, and periodic behavior of a suite of agricultural and forest pests in the United States. We further asked whether aspects of geography, taxonomy, and life history could explain similarities and differences in the dynamics of the pest time series. Aspects of global change—notably, climate change and developments in agricultural technology—may contribute to the observed dynamics, and we discuss our findings in this light. Temporal trends, strength of population regulation, and periodicity of oscillations provide general characterizations of population dynamics that can shed light on ecology. The tendency of a population to increase or decrease through time signals changes that, in this context, could pinpoint a pest of growing management concern, or a pest for which managers can scale back interventions. The strength of population regulation, a subject of longstanding debate, relates to the degree to which population fluctuations are driven by density dependence vs. environmental fluctuations (Ziebarth et al. 2010). Periodic or cyclic behavior is a feature of many pest outbreaks (Cooke et al. 2007) and can result from complex density-dependent factors (Volterra 1926, Turchin 1990, Kendall et al. 1999, Haynes et al. 2009) or environmental forcing (Nelson et al. 2013, Reddy et al. 2015). Although our time series are generally not long enough to detect temporal changes in periodic behavior—such time series are rare in ecology—we gain insight into how global change can affect periodic insect outbreaks by separating cycles arising via density dependence from those driven or altered by environmental forcing. Adopting a general characterization of population dynamics, while admittedly opaque to mechanisms driving any given population, enables statistical comparisons across populations, which were the focus of this study. Though general population dynamic responses to global change have proven elusive, several factors could explain differences in population dynamics. Global change is geographically heterogeneous (Thomas et al. 2004), suggesting that organisms in the same region could be more similarly affected than those in different regions. Taxonomy can explain in part differences in insect outbreak behavior, but in such cases, taxonomy may serve as a proxy for differences in other

climate and management effects is unclear: Yamamura et al. (2005) document contemporary (1949–2001) declines in abundance of three rice pests but indicate potential for major increases in damage under future climate conditions. This range of observed outcomes contrasts with earlier predictions of general increases in the frequency and severity of insect pest outbreaks under climate change (Mooney 1996, Cannon 1998, Simberloff 2000, Harrington et al. 2001, for a contrasting view see Ims et al. 2008). While general poleward shifts of crop pests and pathogens have been documented (Bebber et al. 2013), system-specific factors appear to mediate population dynamic effects of climate change, and it is unclear what, if any, generalizations can be made about the effects of climate change on insect pest populations. This is in part because the long-term population dynamics of insect pests are often poorly documented. Notable exceptions are mainly forest pests (Yamamura et al. 2005) such as the gypsy moth (Elkinton and Liebhold 1990, Allstadt et al. 2013), Eastern and Western spruce budworms (Williams and Liebhold 2000, Flower et al. 2014), and the larch budmoth (Esper et al. 2007), the latter of which is a species whose millennia-long defoliation patterns in the Alps have been documented through tree ring analysis. Notwithstanding a small number of exemplar long-term monitoring programs for agricultural pests such as the Rothamsted Insect Survey (Bell et al. 2015, Sheppard et al. 2016), the overall rarity of long-term studies of agricultural pests has prompted calls for more concerted efforts (Ingram et al. 2008, Gregory et al. 2009, IPCC 2014). Absent continuous experiments, analyses of timeseries records of abundance or damage can yield inference regarding long-term trends and other aspects of population dynamics (Turchin 1990, Woiwod and Hanski 1992, Turchin et al. 2003, Sparks et al. 2005, Abbott et al. 2009). Understanding the long-term population dynamics of insect pests, and the potential for global change to alter their dynamics, has major potential ramifications given the economic and ecological significance of insect pests (Pimentel 2005, Aukema et al. 2011, Lovett et al. 2016). The present study represents a first step to cataloguing the diverse responses of pest populations to global change. We analyzed a collection of 64 longterm (≥20 yr) time series of pest abundance or ❖ www.esajournals.org

2

May 2018


WALTER ET AL.

life-history traits (Økland et al. 2005). For example, species that feed externally (e.g., many foliagefeeding insects) may be more strongly regulated by natural enemies than internal feeders (e.g., bark beetles), which could be less apparent to predators and parasitoids (Gross 1993) and may also be somewhat buffered from weather fluctuations. Phenology could also explain population dynamic differences given that populations active at different times of the year are subject to different environmental conditions. For example, the seasonal activity patterns of an herbivorous insect may be critically important if it must match the phenology of its host plant species (Pureswaran et al. 2015). Altered phenology is a prominent signature of climate change (Parmesan and Yohe 2003) sometimes leading to demographic change (van Asch and Visser 2007, Yang and Rudolf 2010); however, the degree to which these factors explain differences among insect population dynamics is still largely unknown.

Fig. 1. Map of study regions. Code to abbreviations: AK is Alaska; EA is east; MW is midwest; NW is northwest; SW is southwest.

METHODS aggregated due to inconsistencies in data type (abundance or damage) and sampling methodology. In addition, we excluded invasive species if establishment of self-sustaining populations could not be confirmed, and time series for which >1/3 of its length equaled zero. The latter restriction was imposed to ensure the appropriateness of our statistical methods for the data. After filtering, our dataset consisted of 64 time series of 43 unique taxa, ranging from 20 to 91 yr long (mean = 38.9 yr). Forty-six time series (27 unique taxa) represented forest pests, and 18 time series (16 unique taxa) were agricultural pests. By region, 21 time series were in the east, four in the midwest, 22 in the northwest, nine in the southwest, and eight in Alaska. Thirty-six time series were Lepidoptera, 23 Coleoptera, three Hemiptera, and one Hymenoptera; henceforth, the Hemiptera and Hymenoptera were combined as “Other” taxa due to the small number of records. Our dataset is available online on the Knowledge Network for Biocomplexity database, record knb.1373.2.

Database assembly We assembled a database of pest population time series from sources including: United States Department of Agriculture (USDA) Forest Service Aerial Detection Surveys; state agricultural extensions and other agencies; published literature; and online open-access repositories. The time series represent a mix of records of abundance (such as from trapping programs) or damage. Damage records predominantly reflect area defoliated or killed by forest pests, sampled over areas of 10s to 100s of km2. We take such records as indices of abundance, as is convention (Williams and Liebhold 2000, Økland et al. 2005, Allstadt et al. 2013). Records were collected from five regions of the United States: the east, midwest, northwest, southwest, and Alaska (Fig. 1). Over 100 time series were acquired, which were then filtered for quality. If time series were obtained for the same species in different regions, the regions were assumed to be independent. If a species was represented by >1 time series in a given region, and the time series were uncorrelated, both were retained. If the time series were significantly correlated, the best example (generally, the longest record) was selected. Time series from multiple sources were not ❖ www.esajournals.org

Characterizing time series patterns Temporal patterns in population time series were characterized using a combination of autoregressive moving-average [ARMA(p, q)] model 3

May 2018


WALTER ET AL.

fitting (Box et al. 1994, Ives et al. 2010) and wavelet analysis (Cazelles et al. 2008). Autoregressive moving-average (p, q) models, where p indicates the order of the autoregressive (AR) component and q that of the moving average (MA) component, identified the following characteristics: presence/absence of linear trends; ||k||, a measure of population regulation strength related to the return time to a stochastic equilibrium; presence/ absence of intrinsic cycles; and the length of intrinsic cycles. Wavelet analysis was used to detect the presence and length of periodic oscillations that were not explained by the intrinsic cycles found in the ARMA(p, q) analyses and hence could have been driven by extrinsic environmental factors. Time-series characteristics were derived from the top-performing model from a candidate set including all combinations of AR orders p = {1, 2, 3} and MA orders q = {0, 1, 2, 3}, and models with or without a linear trend through time. The value ||k||, the magnitude of the inverse of the minimum root of the characteristic equation (Box et al. 1994), is bounded between 0 and 1 for stationary processes; small values of ||k|| indicate rapid return times to the mean of the stationary distribution and strong population regulation, while large values of ||k|| indicate weak regulation. This value depends only on the AR components of the model. The AR components of the model also give the presence and length of intrinsic, density-dependent cycles. If k is complex, the density-dependent components of the model generate quasi-cyclic dynamics with characteristic period 2p/ci, where ci is the imaginary component of k. These are called quasi-cycles because, in the absence of stochasticity, ARMA models cannot produce sustained cycles; nonetheless, natural systems are stochastic, and we will refer to these patterns simply as cycles. If in any case the estimated characteristic cycle length was longer than half the length of the time series, this was deemed to lack adequate support from the data, and the time series was classified as non-cyclic. Because the MA components of an ARMA model absorb measurement error and environmental variation (Abbott et al. 2009, Ives et al. 2010), we consider cyclic patterns from ARMA(p, q) model fitting to reflect intrinsic cycles arising from density dependence. We fit ARMA(p, q) models using simulated annealing to find parameter values maximizing ❖ www.esajournals.org

the likelihood function (Ives et al. 2010). We selected the best ARMA(p, q) model using the Akaike information criterion with a correction for small sample sizes (AICc; Hurvich and Tsai 1989). The best model was identified by comparing the top-performing (lowest AICc) models with and without a trend; the model with a trend was accepted if its AICc value was lower than the model without a trend by an increment of 2 or more (Burnham and Anderson 2002). Prior to analysis, time series were log-transformed and standardized to have mean = 0 and variance = 1. Autoregressive moving-average (p, q) model fitting was conducted in MATLAB version 2012b using code described in Ives et al. (2010). We also used wavelet analysis to identify periodic oscillations in our time series. Wavelet power identifies periodic content in a time series, regardless of generating mechanism (i.e., intrinsic to system dynamics or arising from external forcing). However, periodic dynamics that differ from those produced by intrinsic ARMA-type processes can be distinguished via statistical testing procedures. Non-ARMA cyclicity could come from two sources. First, it could be driven by cyclicity in extrinsic environmental forcing of the system. Second, it could be caused by changes in the intrinsic ARMA processes that drive cyclicity; our ARMA procedure assumes that the processes governing the time-series dynamics do not change through time, while wavelet analysis can identify changes in the cyclicity of time series. Either of these two sources of non-ARMA cyclicity picked up by wavelet analysis implies a role of external forcing to the time series: External forcing could either itself be cyclic, or it could change the intrinsic properties that drive cyclicity. We determined the statistical significance of wavelet power through comparison with null models (Cazelles et al. 2014) that include ARMA dynamics. We compared the empirical wavelet power of a time series to a distribution of wavelet power from n = 5000 surrogate time series generated from an AR process, using the fitted AR coefficients estimated from the ARMA fitting. Thus, the null model was that periodic oscillations in the time series were generated by intrinsic factors (density dependence); as such, deviations from the null model indicate a role of environmental forcing in generating periodic behavior. We determined that a time series 4

May 2018


WALTER ET AL.

create shelter such as leaf rollers. Migrants were those species unable or rarely able to overwinter in the area covered by the time series, and thus, records represented dispersers from overwintering habitats. All migrants in our dataset were agricultural pests. Insect species was included as a random effect in all analyses because some species were represented by multiple time series, and observations were weighted by time series length because longer time series tend to enable greater certainty in population dynamic patterns. Binary variables were analyzed using a binomial distribution and logit link function (i.e., logistic regression). Continuous variables were analyzed using a Gaussian distribution on the natural scale. For intrinsic cycle length, and timescale of forced periodicity response variables, time series not exhibiting the respective pattern were excluded. Preliminary analyses indicated that response variables were associated with time series length (years) and data type (abundance or damage index): In our database, time series of damage indices had larger trend coefficients, were less likely to exhibit intrinsic cycles, and were more likely to exhibit externally forced periodicity than time series of abundance. Longer time series exhibited longer intrinsic cycles and dominant timescales externally forced periodic behavior. Consequently, data type was included as a fixed effect in subsequent analyses of trend coefficients and the presence of intrinsic cycles and forced periodicity. Data type, however, was not independent of other factors of interest (Appendix S1), and these associations likely underpin its explanatory power. It was not possible to use time series length as both a covariate and a weighting variable due to model identifiability problems. Our model fitting strategy sought the most parsimonious model for explaining differences in time-series characteristics. For binary response variables, we tested each predictor individually, while controlling for data type. The absence of some variable combinations in our dataset created model identifiability issues that led us not to consider models containing >1 focal predictor (while controlling for data type). To address the absence of variable combinations (i.e., complete separability), we assigned to the fixed effects a normal prior distribution with mean = 0 and

exhibited forced periodic dynamics at a given period if the global (i.e., time-averaged) wavelet power at that period exceeded the 95th percentile of surrogates, and if the peak wavelet power over statistically significant periods exceeded 2. The latter threshold was imposed based on inspection of the data to exclude cases in which wavelet power deviated from the null model, but oscillations were weak or transient. For time series meeting these criteria, we estimated the dominant timescale of forced periodic behavior as the timescale having the largest peak wavelet power that also met our criteria of statistical significance and minimum power. Prior to wavelet analyses, all time series were log-transformed, linear trends were removed, and the time series was scaled to unit variance. Wavelet analyses were conducted using the “biwavelet” package in R version 3.3.3 (R Core Team 2017).

Trait effects on time series patterns We next asked whether differences in our characterization of insect pest population dynamics could be explained by geography, taxonomy, and life-history traits. We used a series of generalized linear mixed-effects models to evaluate patterns in six response variables: trend coefficient; strength of population regulation (||k||); presence of intrinsic cycles; length of intrinsic cycles; presence of forced periodicity; and dominant timescale of forced periodic behavior. Fixed-effect predictor variables considered were as follows: region; habitat (agriculture or forest); taxonomic order (Lepidoptera, Coleoptera, or other); generations per year (1); insect phenology (early, late, or broad); feeding habit (internal or external); native or exotic; and migrant or resident. The number of generations per year was assigned based on what is typical for the region represented by the time series. Phenology was classified based on the feeding life stage. Species with early phenology exhibit activity peaks early in the growing season, while those with late phenology peak toward the end of the growing season. Species classified as having broad phenology feed actively throughout the growing season, or even beyond the growing season in the case of some bark beetle species. Internal feeders included bark beetles and other species that feed inside plant structures, or that ❖ www.esajournals.org

5

May 2018


WALTER ET AL.

standard deviation = 4. For continuous response variables, we fit all combinations of up to three predictors, plus controlling as needed for data type. No more than three predictors were used to minimize overfitting. Moreover, restricting the number of predictors limits opportunities for hidden correlations between variables to influence results. When we restricted the data to only time series exhibiting a particular pattern (e.g., we analyzed only time series exhibiting intrinsic cycles when modeling variability in intrinsic cycle length), some factors were represented by 0.99), while controlling for data type (deviance explained = 99.9%; wAICc > 0.99). Time series exhibiting intrinsic cycles were most common in the east, followed by the northwest, southwest,

RESULTS Thirty-two of 64 time series (50%) showed evidence of temporal trends based on AICc model selection; of these, 17 were increasing and 15 were decreasing. Seventeen time series (27% of 64) showed intrinsic cycles arising from density dependence as detected by fitting ARMA models; the mean characteristic length of intrinsic cycles was 14.2 4.4 (standard deviation) years. Nineteen time series (30% of 64) exhibited cyclicity identified by the wavelet analysis that involved external forcing, either periodic forcing or forcing that changed the intrinsic cyclicity of the time series. The mean dominant timescale of oscillations in the wavelet analyses was 8.15 3.28 yr. Although roughly the same number of species exhibited periodicity in the ARMA and wavelet analyses, only 5 (8% of 64) showed evidence of both. For these five time series, the ❖ www.esajournals.org

Fig. 2. Effects of predictor variables from the top model on the trend coefficient. Solid horizontal lines indicate the median; the box spans the interquartile range; whiskers extend to the most extreme data point that is 0.99), while controlling for data type (deviance explained = 88%, wAICc > 0.99). Populations in the southwest and midwest were more likely to display evidence of extrinsically forced periodic oscillations (Fig. 6). The model that best explained the dominant timescale of forced periodicity contained taxonomic order (variable importance >0.99), phenology (variable importance = 0.98), and feeding habit (variable importance = 0.96) (deviance explained = 99%, wAICc = 0.49). While controlling for other factors, the dominant timescale of forced periodic behavior was longer in Coleoptera than Lepidoptera (Fig. 7a). The timescale of

Fig. 5. Effects of predictors from the top model on intrinsic cycle length.

❖ www.esajournals.org

7

May 2018


WALTER ET AL.

(Fig. 2), even after controlling for data type, which was non-randomly distributed among habitat types (Appendix S1). Several agricultural pest species exhibited population declines (eight time series decreasing to three increasing), while forest pest species more frequently increased in abundance (14 increasing to seven decreasing) over the period of record. One plausible hypothesis is that changes in agricultural practices, particularly the development and popularization of transgenic, insect-resistant corn and cotton, have suppressed many agricultural pests (Hutchison

Fig. 6. Effects of region on the presence of periodic oscillations seemingly driven by external forcing.

forced periodicity also tended to be longer in broad phenology than early-phenology species (Fig. 7b) and was also longer in internal than external feeders (Fig. 7c).

DISCUSSION Long-term pest population dynamics, particularly in agriculture, have tended to be studied in isolation. Here, we have gathered many datasets together to develop a better overall understanding of factors influencing pest population dynamics. During a time period when pest populations experienced a wide variety of environmental changes, we observed in many time series temporal trends and externally forced periodic behavior that suggest the dynamics of these populations were influenced by global change. Moreover, geography, taxonomy, and life history characteristics largely explained differences among the dynamics of these species. While we found no evidence for a one-size-fits-all response of insect pests to global change, populations that co-occurred in space and time, or that shared similar life-history traits, appeared to respond similarly to global change. We consider this work just a first step to elucidating broad responses of insect pests to global change, and we hope it will serve as a springboard for more detailed studies addressing specific mechanisms of change. We found it particularly interesting that habitat best explained differences in long-term trends ❖ www.esajournals.org

Fig. 7. Effects of predictors from the top model on the dominant timescale of forced periodic oscillations.

8

May 2018


WALTER ET AL.

pest populations we studied may be susceptible to or already responding to environmental change. While our time series were generally too short to permit robust inferences regarding changes in cycling through time, roughly a quarter of time series exhibited periodic oscillations. The few time series (5) for which both ARMA and wavelet analyses detected cycles suggest that the wavelet analysis was primarily picking up cyclicity induced by external forcing. External forces may include direct effects of climate, climate effects mediated through intrinsic processes (e.g., species interactions), or other factors; we did not attempt to distinguish between these. Forced periodic behavior from wavelet analyses tended to occur on shorter timescales (higher frequencies) than intrinsic cycles from ARMA analyses, and where we detected both intrinsic and forced periodic oscillations, environmental forcing appeared to speed up oscillations of time series that exhibited intrinsically cyclic dynamics. Climate cycles can drive population fluctuations (Post and Forchhammer 2002), and the frequency of oscillations in temperature variables is increasing (Garcıa-Carreras and Reuman 2011), suggesting that climate change could speed population oscillations in some cases. Life-history traits of species may determine their susceptibility to changes in periodic behavior driven by climatic variability. In our database, the timescale of forced periodicity tended to be shorter in species with early phenology (Fig. 7b) and in external feeders (Fig. 7c). The population dynamics of species hatching and feeding early in the growing season could plausibly respond relatively strongly to climate if conditions (e.g., temperature) affect the degree of (mis)match between food availability and emergence of feeding life stages (Bewick et al. 2016), or if early emergence risks mortality from spring freezes. Whether a species feeds internally or externally may also be related to its exposure to climatic variability. In our database, internal feeders were mainly bark beetles, which may be better buffered from climate variability than less concealed feeders, such as leaf rollers. Among populations in our dataset, the presence of both intrinsic cycles and externally forced periodicity was strongly associated with data type and region (Figs. 4, 6). This analysis, however, is limited for several reasons. First, despite

et al. 2010, Bell et al. 2012, Bohnenblust et al. 2014). Indeed, seven of eight declining agricultural pest populations came from six species that feed on corn or cotton and are the targets of transgenic Bt seed varieties: Onstrinia nubalis, Agrotis ipsilon, Anagrapha falcifera, Helicoverpa zea, Trichoplusia ni, and Pseudaletia unipunctata. Notably, populations of A. falcifera and H. zea in the United States have exhibited some degree of fieldevolved resistance to toxins in Bt crops (Tabashnik et al. 2013), raising some concern for future control. Yamamura et al. (2005) predicted climaterelated future increases in damage by three rice pests, despite observing declining abundance in 50-yr historical records, raising the question whether pest suppression in agricultural settings can compensate for climatic changes potentially favoring population growth. Although forest pests are also subject to management efforts, these might be less effective than those applied in agriculture. Evidence indicates that insecticide treatments, which are applied over a small percentage of a range, have little effect on large scale measures of population dynamics (Allstadt et al. 2013, Haynes et al. 2014), Consequently, forest pests may respond more freely to climate change or other factors, such as changes in forest area (Hansen et al. 2013) and composition (Nowacki and Abrams 2015) occurring over the study period. In particular, the over-mature, under-managed stands currently dominating US forests are susceptible to insect disturbance (Nowacki and Abrams 2015). Natural enemies provide another possible explanation for the contrasting trends between agricultural and forest pests. If global change favors natural enemies of agricultural pests more than the pests themselves, then global change could reduce agricultural pest abundance. For example, warmer temperatures lead to higher parasitism rates by Aphidius ervi on pea aphids, which leads to greater suppression and morerapid host–parasitoid cycles (Meisner et al. 2014). Thus, the impacts of global change might depend not just on the response of pests themselves, but also on those of the other species with which they interact (Gilman et al. 2010, Hoekman 2010, Post 2013, Miller et al. 2014). Global change has been shown to alter population cycling (Johnson et al. 2010, Bell et al. 2012, Haynes et al. 2014), and cycles in many of the ❖ www.esajournals.org

9

May 2018


WALTER ET AL.

the impressive lengths of the multi-decadal time series, they are still relatively short compared to the average period length of 14 yr. Second, how these associations reflect ecological mechanisms is unclear. Data types were non-randomly distributed among habitats, regions, taxonomic orders, internal vs. external feeders, and overwintering residents vs. populations that must migrate north most or every year (Appendix S1). Although regions reflect broad geographic differences in environmental conditions, imbalance in the number of records obtained for each region makes comparison challenging. The aim of this study was to generate broad inferences about the internal and environmental factors that drive population dynamics of pest insects with different geographic, taxonomic, and life history characteristics. A strength of our approach is that, by applying consistent methods to many time series, we are able to make statistical comparisons among many populations, a number of which represent species whose longterm population dynamics have received little attention. We interpreted specific findings of our explanatory models with caution because in our dataset, some variable combinations were represented many times and others little or not at all (Appendices S1 and S2); consequently, underlying associations and imbalances will curtail the predictive power of some variables. Thus, this study does not explicitly link insect pest population dynamics to specific mechanisms; doing so convincingly would require additional data that are not consistently available and adjusting methodologies on a case-by-case basis. Our study, however, does provide a basis for inference about the underlying mechanisms driving population dynamics of specific pest species.

species that could emerge as important pests in the future. Our findings suggest effects of climate change and agricultural practices on long-term population trends and highlight another potential effect of climate change on pest populations —namely, altered population cycling (Johnson et al. 2010, Haynes et al. 2014). Taking into account the strengths and weaknesses of this work, we use our findings to advance the following hypotheses: (1) The population dynamics of species that rely on timing the emergence of feeding life stages with resource availability tend to be more sensitive to climate variability than those with vulnerable stages active later in the year; (2) general declines in agricultural pest populations are driven by agricultural practices. We anticipate future work addressing these hypotheses directly.

ACKNOWLEDGMENTS We thank Richard Bean (Maryland Department of Agriculture), Galen Dively (University of Maryland), Sean Malone (Virginia Tech), and Ames Herbert (Virginia Tech) for providing access to datasets used in this research. Thanks to Daniel Reuman for hosting JAW at the University of Kansas while this work was completed. This work was supported by USDA-NIFA Grant 2016-67012-24694 and NSF Dimensions of Biodiversity program (1240804).

LITERATURE CITED Abbott, K. C., J. Ripa, and A. R. Ives. 2009. Environmental variation in ecological communities and inferences from single-species data. Ecology 90: 1268–1278. Allstadt, A. J., K. J. Haynes, A. M. Liebhold, and D. M. Johnson. 2013. Long-term shifts in the cyclicity of outbreaks of a forest-defoliating insect. Oecologia 172:141–151. Aukema, J. E., et al. 2011. Economic impacts of nonnative forest insects in the continental United States. PLoS ONE 6:e24587. Bates, D. M., M. Maechler, B. Bolker, and S. Walker. 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67:1–48. Bebber, D. P., M. A. T. Ramotowski, and S. J. Gurr. 2013. Crop pests and pathogens move polewards in a warming world. Nature Climate Change 3:985–988. Bell, J. R., L. Alderson, D. Izera, T. Kruger, S. Parker, J. Pickup, C. Shortall, M. S. Taylor, P. Verrier, and R. Harrington. 2015. Long-term phenological

CONCLUSIONS This study documented long-term population dynamic patterns in a suite of insect pests of forest and agricultural systems in the United States, showing that geography, taxonomy, and life history can explain differences among populations in their dynamics. Haynes et al. (2014) also related life-history traits to among-species differences in population dynamics under climate change, indicating that such traits may predict population patterns and perhaps even suggest ❖ www.esajournals.org

10

May 2018


WALTER ET AL. € ntgen, D. C. Frank, D. Nievergelt, and Esper, J., U. Bu A. M. Liebhold. 2007. 1200 years of regular outbreaks in alpine insects. Proceedings of the Royal Society B-Biological Sciences 274:671–679. Flower, A., D. G. Gavin, E. K. Heyerdahl, R. A. Parsons, and G. M. Cohn. 2014. Western spruce budworm outbreaks did not increase fire risk over the last three centuries: a dendrochronological analysis of inter-disturbance synergism. PLoS ONE 9:e114282. Garcıa-Carreras, B., and D. C. Reuman. 2011. An empirical link between the spectral colour of climate and the spectral colour of field populations in the context of climate change. Journal of Animal Ecology 80:1042–1048. Gilman, S. E., M. C. Urban, J. Tewksbury, G. W. Gilchrist, and R. D. Holt. 2010. A framework for community interactions under climate change. Trends in Ecology and Evolution 25:325–331. Gregory, P. J., S. N. Johnson, A. C. Newton, and J. S. I. Ingram. 2009. Integrating pests and pathogens into the climate change/food security debate. Journal of Experimental Botany 60:2827–2838. Gross, P. 1993. Insect behavioral and morphological defenses against parasitoids. Annual Review of Entomology 38:251–273. Hansen, M. C., et al. 2013. High-resolution global maps of 21st-century forest cover change. Science 342:850–854. Harrington, R., R. A. Fleming, and I. P. Woiwod. 2001. Climate change impacts on insect management and conservation in temperate regions: Can they be predicted? Agricultural and Forest Entomology 3:233–240. Haynes, K. J., A. J. Allstadt, and D. Klimetzek. 2014. Forest defoliator outbreaks under climate change: effects on the frequency and severity of outbreaks of five pine insect pests. Global Change Biology 20:2004–2018. Haynes, K. J., A. M. Liebhold, T. M. Fearer, G. Wang, G. W. Norman, and D. M. Johnson. 2009. Spatial synchrony propagates through a forest food web via consumer-resource interactions. Ecology 90: 2974–2983. Hoekman, D. 2010. Turning up the heat: Temperature influences the relative importance of top-down and bottom-up effects. Ecology 91:2819–2825. Hurvich, C. M., and C. L. Tsai. 1989. Regression and time series model selection in small samples. Biometrika 76:297–307. Hutchison, W. D., et al. 2010. Areawide suppression of European corn borer with Bt maize reaps savings to non-Bt maize growers. Science 330:222–225. Ims, R. A., J. A. Henden, and S. T. Killengreen. 2008. Collapsing population cycles. Trends in Ecology and Evolution 23:79–86.

trends, species accumulation rates, aphid traits and climate: five decades of change in migrating aphids. Journal of Animal Ecology 84:21–34. Bell, J. R., E. C. Burkness, A. E. Milne, D. W. Onstad, M. Abrahamson, K. L. Hamilton, and W. D. Hutchison. 2012. Putting the brakes on a cycle: Bottomup effects damp cycle amplitude. Ecology Letters 15:310–318. Bewick, S., R. S. Cantrell, C. Cosner, and W. F. Fagan. 2016. How resource phenology affects consumer population dynamics. American Naturalist 187:151–166. Bohnenblust, E. W., J. A. Breining, J. A. Shaffer, S. J. Fleischer, G. W. Roth, and J. F. Tooker. 2014. Current European corn borer, Ostrinia nubilalis, injury levels in the northeastern United States and the value of Bt field corn. Pest Management Science 70:1711–1719. Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. 1994. Time series analysis: forecasting and control. Third edition. Prentice Hall, Englewood Cliffs, New Jersey, USA. Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference. Second edition. Springer, New York, New York, USA. Cannon, R. J. C. 1998. The implications of predicted climate change for insect pests in the UK, with emphasis on non-indigenous species. Global Change Biology 4:785–796. Carriere, Y., C. Ellers-Kirk, M. Sisterson, L. Antilla, M. Whitlow, T. J. Dennehy, and B. E. Tabashnik. 2003. Long-term regional suppression of pink bollworm by Bacillus thuringiensis cotton. Proceedings of the National Academy of Sciences of the United States of America 100:1519–1523. Cazelles, B., M. Chavez, D. Berteaux, F. Menard, J. O. Vik, S. Jenouvrier, and N. C. Stenseth. 2008. Wavelet analysis of ecological time series. Oecologia 156: 287–304. Cazelles, B., M. Chavez, K. Cazelles, M. Chavez, and K. Cazelles. 2014. Wavelet analysis in ecology and epidemiology: impact of statistical tests. Journal of the Royal Society Interface 11:20130585. Chung, Y., S. Rabe-Hesketh, V. Dorie, A. Gelman, and J. Liu. 2013. A nondegenerate penalized likelihood estimator for variance parameters in multilevel models. Psychometrika 78:685–709. Cooke, B. J., V. G. Nealis, and J. Regniere. 2007. Insect defoliators as periodic disturbances in northern forest ecosystems. Pages 487–525 in E. A. Johnson and K. Miyanashi, editors. Plant disturbance ecology: the process and the response. Elsevier, Burlington, Massachusetts, USA. Elkinton, J. S., and A. M. Liebhold. 1990. Population dynamics of gypsy moth in North America. Annual Review of Entomology 35:571–596.


11

May 2018


WALTER ET AL. density dependence from climate change and agricultural intensification triggers pest outbreaks: A 37-year observation of cotton bollworms. Ecology and Evolution 4:3362–3374. Parmesan, C., and G. Yohe. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42. Pimentel, D. 2005. Environmental and economic costs of the application of pesticides primarily in the United States. Environment, Development and Sustainability 7:229–252. Post, E. 2013. Ecology of climate change: the importance of biotic interactions. Princeton University Press, Princeton, New Jersey, USA. Post, E., and M. C. Forchhammer. 2002. Synchronization of animal population dynamics by large-scale climate. Nature 420:168–171. Pureswaran, D. S., L. De Grandpre, D. Pare, A. Taylor, M. Barrette, H. Morin, J. Regniere, and D. D. Kneeshaw. 2015. Climate-induced changes in host treeinsect phenology may drive ecological state-shift in boreal forests. Ecology 96:1480–1491. R Core Team. 2017. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Reddy, G. V. P., P. Shi, C. Hui, X. Cheng, F. Ouyang, and F. Ge. 2015. The seesaw effect of winter temperature change on the recruitment of cotton bollworms Helicoverpa armigera through mismatched phenology. Ecology and Evolution 5:5652–5661. Sheppard, L. W., J. R. Bell, R. Harrington, and D. C. Reuman. 2016. Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Climate Change 6:610–613. Simberloff, D. 2000. Global climate change and introduced species in United States forests. Science of the Total Environment 262:253–261. Sparks, T. H., D. B. Roy, and R. L. H. Dennis. 2005. The influence of temperature on migration of Lepidoptera into Britain. Global Change Biology 11:507–514. Tabashnik, B. E., T. Brevault, and Y. Carriere. 2013. Insect resistance to Bt crops: lessons from the first billion acres. Nature Biotechnology 31:510–521. Thomas, C. D., et al. 2004. Extinction risk from climate change. Nature 427:145–148. Turchin, P. 1990. Rarity of density dependence or population regulation with lags? Nature 344:660–663. Turchin, P., S. N. Wood, S. P. Ellner, B. E. Kendall, W. William, A. Fischlin, J. Casas, E. Mccauley, and C. J. Briggs. 2003. Dynamical effects of plant quality and parasitism on population cycles of larch budmoth. Ecology 84:1207–1214. van Asch, M., and M. E. Visser. 2007. Phenology of forest caterpillars and their host trees: the importance of synchrony. Annual Review of Entomology 52:37–55.

Ingram, J. S. I., P. J. Gregory, and A.-M. Izac. 2008. The role of agronomic research in climate change and food security policy. Agriculture, Ecosystems & Environment 126:4–12. IPCC. 2014. Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. In Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (C. B. Field, et al., editors). Cambridge University Press, Cambridge, UK and New York, New York, USA. Ives, A. R., K. C. Abbott, and N. L. Ziebarth. 2010. Analysis of ecological time series with ARMA(p, q) models. Ecology 91:858–871. € ntgen, D. C. Frank, K. Kausrud, K. Johnson, D. M., U. Bu J. Haynes, and A. M. Liebhold. 2010. Climatic warming disrupts recurrent Alpine insect outbreaks. Proceedings of the National Academy of Sciences of the United States of America 107:20576–20581. Kendall, B. E., C. J. Briggs, W. W. Murdoch, P. Turchin, S. P. Ellner, E. McCauley, R. M. Nisbet, and S. N. Wood. 1999. Why do populations cycle? A synthesis of statistical and mechanistic approaches. Ecology 80:1789–1805. Lovett, G. M., et al. 2016. Nonnative forest insects and pathogens in the United States: impacts and policy options. Ecological Applications 26:1437–1455. Meisner, M. H., J. P. Harmon, and A. R. Ives. 2014. Temperature effects on long-term population dynamics in a parasitoid-host system. Ecological Monographs 84:457–476. Miller, L. P., C. M. Matassa, and G. C. Trussell. 2014. Climate change enhances the negative effects of predation risk on an intermediate consumer. Global Change Biology 20:3834–3844. Mooney, H. A. 1996. Biological invasions and global change. Pages 123–126 in O. T. Sandlund, S. P. J, and A. Viken, editors. Proceedings of the Norway/ UN Conference on Alien Species. Directorate for Nature Management and Norwegian Institute for Nature Research, Trondheim, Norway. Nelson, W. A., O. N. Bjørnstad, and T. Yamanaka. 2013. Recurrent insect outbreaks caused by temperature-driven changes in system stability. Science 341:796–799. Nowacki, G. J., and M. D. Abrams. 2015. Is climate an important driver of post-European vegetation change in the Eastern United States? Global Change Biology 21:314–334. Økland, B., A. M. Liebhold, O. N. Bjørnstad, N. Erbilgin, and P. Krokene. 2005. Are bark beetle outbreaks less synchronous than forest Lepidoptera outbreaks? Oecologia 146:365–372. Ouyang, F., C. Hui, S. Ge, X. Y. Men, Z. H. Zhao, P. J. Shi, Y. S. Zhang, and B. L. Li. 2014. Weakening


12

May 2018


WALTER ET AL. insect population dynamics under climate change: 50-year data of three insect pests in paddy fields. Population Ecology 48:31–48. Yang, L. H., and V. H. W. Rudolf. 2010. Phenology, ontogeny and the effects of climate change on the timing of species interactions. Ecology Letters 13:1–10. Ziebarth, N. L., K. C. Abbott, and A. R. Ives. 2010. Weak population regulation in ecological time series. Ecology Letters 13:21–31.

Volterra, V. 1926. Fluctuations in the abundance of a species considered mathematically. Nature 118:558–560. Williams, D. W., and A. M. Liebhold. 2000. Spatial synchrony of spruce budworm outbreaks in eastern North America. Ecology 81:2753–2766. Woiwod, I. P., and I. Hanski. 1992. Patterns of density dependence in moths and aphids. Journal of Animal Ecology 61:619–629. Yamamura, K., M. Yokozawa, M. Nishimori, Y. Ueda, and T. Yokosuka. 2005. How to analyze long-term

DATA ACCESSIBILITY Datasets supporting this article can be found in the Knowledge Network for Biocomplexity knb.1373.2.

SUPPORTING INFORMATION Additional Supporting Information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/ecs2. 2274/full


13

May 2018
