Waves of larch budmoth outbreaks in the European alps

A Time for Every Purpose: Using Time-Dependent Sensitivity Analysis to Help Understand and Manage Dynamic Ecological Systems

AbstractSensitivity analysis is often used to help understand and manage ecological systems by assessing how a constant change in vital rates or other model parameters might affect the management outcome. This allows the manager to identify the most favorable course of action. However, realistic changes are often localized in time-for example, a short period of culling leads to a temporary increase in the mortality rate over the period. Hence, knowing when to act may be just as important as knowing what to act on. In this article, we introduce the method of time-dependent sensitivity analysis (TDSA) that simultaneously addresses both questions. We illustrate TDSA using three case studies: transient dynamics in static disease transmission networks, disease dynamics in a reservoir species with seasonal life history events, and endogenously driven population cycles in herbivorous invertebrate forest pests. We demonstrate how TDSA often provides useful biological insights, which are understandable on hindsight but would not have been easily discovered without the help of TDSA. However, as a caution, we also show how TDSA can produce results that mainly reflect uncertain modeling choices and are therefore potentially misleading. We provide guidelines to help users maximize the utility of TDSA while avoiding pitfalls.

Tree-ring isotopes from the Swiss Alps reveal non-climatic fingerprints of cyclic insect population outbreaks over the past 700 years

Recent experiments have underlined the potential of δ2H in tree-ring cellulose as a physiological indicator of shifts in autotrophic versus heterotrophic processes (i.e., the use of fresh versus stored non-structural carbohydrates). However, the impact of these processes has not yet been quantified under natural conditions. Defoliator outbreaks disrupt tree functioning and carbon assimilation, stimulating remobilization, therefore providing a unique opportunity to improve our understanding of changes in δ2H. By exploring a 700-year tree-ring isotope chronology from Switzerland, we assessed the impact of 79 larch budmoth (LBM, Zeiraphera griseana [Hübner]) outbreaks on the growth of its host tree species, Larix decidua [Mill]. The LBM outbreaks significantly altered the tree-ring isotopic signature, creating a 2H-enrichment and an 18O- and 13C-depletion. Changes in tree physiological functioning in outbreak years are shown by the decoupling of δ2H and δ18O (O-H relationship), in contrast to the positive correlation in non-outbreak years. Across the centuries, the O-H relationship in outbreak years was not significantly affected by temperature, indicating that non-climatic physiological processes dominate over climate in determining δ2H. We conclude that the combination of these isotopic parameters can serve as a metric for assessing changes in physiological mechanisms over time.

A time for every purpose: using time-dependent sensitivity analysis to help understand and manage dynamic ecological systems

Sensitivity analysis is often used to help understand and manage ecological systems, by assessing how a constant change in vital rates or other model parameters might affect the management outcome. This allows the manager to identify the most favorable course of action. However, realistic changes are often localized in time-for example, a short period of culling leads to a temporary increase in the mortality rate over the period. Hence, knowing when to act may be just as important as knowing what to act upon. In this article, we introduce the method of time-dependent sensitivity analysis (TDSA) that simultaneously addresses both questions. We illustrate TDSA using three case studies: transient dynamics in static disease transmission networks, disease dynamics in a reservoir species with seasonal life-history events, and endogenously-driven population cycles in herbivorous invertebrate forest pests. We demonstrate how TDSA often provides useful biological insights, which are understandable on hindsight but would not have been easily discovered without the help of TDSA. However, as a caution, we also show how TDSA can produce results that mainly reflect uncertain modeling choices and are therefore potentially misleading. We provide guidelines to help users maximize the utility of TDSA while avoiding pitfalls.

Mechanisms driving interspecific variation in regional synchrony of trees reproduction

Seed production in many plants is characterized by large interannual variation, which is synchronized at subcontinental scales in some species but local in others. The reproductive synchrony affects animal migrations, trophic responses to resource pulses and the planning of management and conservation. Spatial synchrony of reproduction is typically attributed to the Moran effect, but this alone is unable to explain interspecific differences in synchrony. We show that interspecific differences in the conservation of seed production-weather relationships combine with the Moran effect to explain variation in reproductive synchrony. Conservative timing of weather cues that trigger masting allows populations to be synchronized at distances >1000 km. Conversely, if populations respond to variable weather signals, synchrony cannot be achieved. Our study shows that species vary in the extent to which their weather cueing is spatiotemporally conserved, with important consequences, including an interspecific variation of masting vulnerability to climate change.

From pattern to process? Dual travelling waves, with contrasting propagation speeds, best describe a self-organised spatio-temporal pattern in population growth of a cyclic rodent

The dynamics of cyclic populations distributed in space result from the relative strength of synchronising influences and the limited dispersal of destabilising factors (activators and inhibitors), known to cause multi‐annual population cycles. However, while each of these have been well studied in isolation, there is limited empirical evidence of how the processes of synchronisation and activation–inhibition act together, largely owing to the scarcity of datasets with sufficient spatial and temporal scale and resolution. We assessed a variety of models that could be underlying the spatio‐temporal pattern, designed to capture both theoretical and empirical understandings of travelling waves using large‐scale (>35,000 km²), multi‐year (2011–2017) field monitoring data on abundances of common vole (Microtus arvalis), a cyclic agricultural rodent pest. We found most support for a pattern formed from the summation of two radial travelling waves with contrasting speeds that together describe population growth rates across the region.

Environmental variation drives continental-scale synchrony of European beech reproduction

Spatial synchrony is the tendency of spatially separated populations to display similar temporal fluctuations. Synchrony affects regional ecosystem functioning, but it remains difficult to disentangle its underlying mechanisms. We leveraged regression on distance matrices and geography of synchrony to understand the processes driving synchrony of European beech masting over the European continent. Masting in beech shows distance-decay, but significant synchrony is maintained at spatial scales of up to 1,500 km. The spatial synchrony of the weather cues that drive interannual variation in reproduction also explains the regional spatial synchrony of masting. Proximity played no apparent role in influencing beech masting synchrony after controlling for synchrony in environmental variation. Synchrony of beech reproduction shows a clear biogeographical pattern, decreasing from the northwest to southeast Europe. Synchrony networks for weather cues resemble networks for beech masting, indicating that the geographical structure of weather synchrony underlies the biogeography of masting synchrony. Our results support the hypothesis that environmental factors, the Moran effect, are key drivers of spatial synchrony in beech seed production at regional scales. The geographical patterns of regional synchronization of masting have implications for regional forest production, gene flow, carbon cycling, disease dynamics, biodiversity, and conservation.

A practical guide to selecting models for exploration, inference, and prediction in ecology

Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions.

Variation in synchrony of production among species, sites, and intertidal zones in coastal marshes

Spatially synchronous population dynamics are important to ecosystem functioning and have several potential causes. By looking at synchrony in plant productivity over 18 yr across two elevations in three types of coastal marsh habitat dominated by different clonal plant species in Georgia, USA, we were able to explore the importance of plant species and different habitat conditions to synchrony. Synchrony was highest when comparing within a plant species and within a marsh zone, and decreased across species, with increasing distance, and with increasing elevational differences. Abiotic conditions that were measured at individual sites (water column temperature and salinity) also showed high synchrony among sites, and in one case (salinity) decreased with increasing distance among sites. The Moran effect (synchronous abiotic conditions among sites) is the most plausible explanation for our findings. Decreased synchrony between creekbank and mid-marsh zones, and among habitat types (tidal fresh, brackish, and salt marsh) was likely due in part to different exposure to abiotic conditions and in part to variation in sensitivity of dominant plant species to these abiotic conditions. We found no evidence for asynchrony among species, sites or zones, indicating that one habitat type or zone will not compensate for poor production in another during years with low productivity; however, tidal fresh, brackish. and salt marsh sites were also not highly synchronous with each other, which will moderate productivity variation among years at the landscape level due to the portfolio effect. We identified the creekbank zone as more sensitive than the mid-marsh to abiotic variation and therefore as a priority for monitoring and management.

Tensor decomposition for infectious disease incidence data

Many demographic and ecological processes generate seasonal and other periodicities. Seasonality in infectious disease transmission can result from climatic forces such as temperature and humidity; variation in contact rates as a result of migration or school calendar; or temporary surges in birth rates. Seasonal drivers of acute immunizing infections can also drive longer-term fluctuations.Tensor decomposition has been used in many disciplines to uncover dominant trends in multi-dimensional data. We introduce tensors as a novel method for decomposing oscillatory infectious disease time series.We illustrate the reliability of the method by applying it to simulated data. We then present decompositions of measles data from England and Wales. This paper leverages simulations as well as much-studied data to illustrate the power of tensor decomposition to uncover dominant epidemic signals as well as variation in space and time. We then use tensor decomposition to uncover new findings and demonstrate the potential power of the method for disease incidence data. In particular, we are able to distinguish between annual and biennial signals across locations and shifts in these signals over time.Tensor decomposition is able to isolate variation in disease seasonality as a result of variation in demographic rates. The method allows us to discern variation in the strength of such signals by space and population size. Tensors provide an opportunity for a concise approach to uncovering heterogeneity in disease transmission across space and time in large datasets.

Structure, space and size: competing drivers of variation in urban and rural measles transmission

A key concern in public health is whether disparities exist between urban and rural areas. One dimension of potential variation is the transmission of infectious diseases. In addition to potential differences between urban and rural local dynamics, the question of whether urban and rural areas participate equally in national dynamics remains unanswered. Specifically, urban and rural areas may diverge in local transmission as well as spatial connectivity, and thus risk for receiving imported cases. Finally, the potential confounding relationship of spatial proximity with size and urban/rural district type has not been addressed by previous research. It is rare to have sufficient data to explore these questions thoroughly. We use exhaustive weekly case reports of measles in 954 urban and 468 rural districts of the UK (1944–1965) to compare both local disease dynamics as well as regional transmission. We employ the time-series susceptible–infected–recovered model to estimate disease transmission, epidemic severity, seasonality and import dependence. Congruent with past results, we observe a clear dependence on population size for the majority of these measures. We use a matched-pair strategy to compare proximate urban and rural districts and control for possible spatial confounders. This analytical strategy reveals a modest difference between urban and rural areas. Rural areas tend to be characterized by more frequent, smaller outbreaks compared to urban counterparts. The magnitude of the difference is slight and the results primarily reinforce the importance of population size, both in terms of local and regional transmission. In sum, urban and rural areas demonstrate remarkable epidemiological similarity in this recent UK context.

Return of the moth: rethinking the effect of climate on insect outbreaks

The sudden interruption of recurring larch budmoth (LBM; Zeiraphera diniana or griseana Gn.) outbreaks across the European Alps after 1982 was surprising, because populations had regularly oscillated every 8–9 years for the past 1200 years or more. Although ecophysiological evidence was limited and underlying processes remained uncertain, climate change has been indicated as a possible driver of this disruption. An unexpected, recent return of LBM population peaks in 2017 and 2018 provides insight into this insect’s climate sensitivity. Here, we combine meteorological and dendrochronological data to explore the influence of temperature variation and atmospheric circulation on cyclic LBM outbreaks since the early 1950s. Anomalous cold European winters, associated with a persistent negative phase of the North Atlantic Oscillation, coincide with four consecutive epidemics between 1953 and 1982, and any of three warming-induced mechanisms could explain the system’s failure thereafter: (1) high egg mortality, (2) asynchrony between egg hatch and foliage growth, and (3) upward shifts of outbreak epicentres. In demonstrating that LBM populations continued to oscillate every 8–9 years at sub-outbreak levels, this study emphasizes the relevance of winter temperatures on trophic interactions between insects and their host trees, as well as the importance of separating natural from anthropogenic climate forcing on population behaviour.

Spatial synchrony in sub-arctic geometrid moth outbreaks reflects dispersal in larval and adult life cycle stages

Spatial synchrony in population dynamics can be caused by dispersal or spatially correlated variation in environmental factors like weather (Moran effect). Distinguishing between these mechanisms is challenging for natural populations, and the study of dispersal-induced synchrony in particular has been dominated by theoretical modelling and laboratory experiments. The goal of the present study was to evaluate the evidence for dispersal as a cause of meso-scale (distances of tens of kilometres) spatial synchrony in natural populations of the two cyclic geometrid moths Epirrita autumnata and Operophtera brumata in sub-arctic mountain birch forest in northern Norway. To infer the role of dispersal in geometrid synchrony, we applied three complementary approaches, namely estimating the effect of design-based dispersal barriers (open sea) on synchrony, comparing the strength of synchrony between E. autumnata (winged adults) and the less dispersive O. brumata (wingless adult females), and relating the directionality (anisotropy) of synchrony to the predominant wind directions during spring, when geometrid larvae engage in windborne dispersal (ballooning). The estimated effect of dispersal barriers on synchrony was almost three times stronger for the less dispersive O. brumata than E. autumnata. Inter-site synchrony was also weakest for O. brumata at all spatial lags. Both observations argue for adult dispersal as an important synchronizing mechanism at the spatial scales considered. Further, synchrony in both moth species showed distinct anisotropy and was most spatially extensive parallel to the east-west axis, coinciding closely to the overall dominant wind direction. This argues for a synchronizing effect of windborne larval dispersal. Congruent with most extensive dispersal along the east-west axis, E. autumnata also showed evidence for a travelling wave moving southwards at a speed of 50-80 km/year. Our results suggest that dispersal processes can leave clear signatures in both the strength and directionality of synchrony in field populations, and highlight wind-driven dispersal as promising avenue for further research on spatial synchrony in natural insect populations.

Consequences of Long-Distance Dispersal for Epidemic Spread: Patterns, Scaling, and Mitigation

Epidemics caused by long-distance dispersed pathogens result in some of the most explosive and difficult to control diseases of both plants and animals (including humans). Yet the factors influencing disease spread, especially in the early stages of the outbreak, are not well-understood. We present scaling relationships, of potentially widespread relevance, that were developed from more than 15 years of field and in silico single focus studies of wheat stripe rust spread. These relationships emerged as a consequence of accounting for a greater proportion of the fat-tailed disease gradient that may be frequently underestimated in disease spread studies. Leptokurtic dispersal gradients (highly peaked and fat-tailed) are relatively common in nature and they can be represented by power law functions. Power law scale invariance properties generate patterns that repeat over multiple spatial scales, suggesting important and predictable scaling relationships between disease levels during the first generation of disease outbreaks and subsequent epidemic spread. Experimental wheat stripe rust outbreaks and disease spread simulations support theoretical scaling relationships from power law properties and suggest that relatively straightforward scaling approximations may be useful for projecting the spread of disease caused by long-distance dispersed pathogens. Our results suggest that, when actual dispersal/disease data are lacking, an inverse power law with exponent = 2 may provide a reasonable approximation for modeling disease spread. Furthermore, our experiments and simulations strongly suggest that early control treatments with small spatial extent are likely to be more effective at suppressing an outbreak caused by a long-distance dispersed pathogen than would delayed treatment of a larger area. The scaling relationships we detail and the associated consequences for disease control may be broadly applicable to plant and animal pathogens characterized by non-exponentially bound, fat-tailed dispersal gradients.

Delayed Chemical Defense: Timely Expulsion of Herbivores Can Reduce Competition with Neighboring Plants

Time delays in plant responses to insect herbivory are thought to be the principal disadvantage of induced over constitutive defenses, suggesting that there should be strong selection for rapid responses. However, observed time delays between the onset of herbivory and defense induction vary considerably among plants. We postulate that strong competition with conspecifics is an important codeterminant of the cost-benefit balance for induced responses. There may be a benefit to the plant to delay mounting a full defense response until the herbivore larvae are mobile enough to leave and large enough to cause severe damage to neighboring plants. Thus, delayed responses could reduce the competitive pressure on the focal plant. To explore this idea, we developed an individual-based model using data from wild tobacco, Nicotiana attenuata, and its specialized herbivore, Manduca sexta. Chemical defense was assumed to be costly in terms of reduced plant growth. We used a genetic algorithm with the plant's delay time as a heritable trait. A stationary distribution of delay times emerged, which under high herbivore densities peaked at higher values, which were related to the time larvae need to grow large enough to severely damage neighboring plants. Plants may thus tip the competitive balance by expelling insect herbivores to move to adjacent plants when the herbivores are most damaging. Thus, herbivores become part of a plant's strategy for reducing competition and increasing fitness.

Tree-ring proxies of larch bud moth defoliation: latewood width and blue intensity are more precise than tree-ring width

Reconstructions of defoliation by larch bud moth (LBM, Zeiraphera diniana Gn.) based on European larch (Larix decidua Mill.) tree rings have unraveled outbreak patterns over exceptional temporal and spatial scales. In this study, we conducted tree-ring analyses on 105 increment cores of European larch from the Valais Alps, Switzerland. The well-documented history of LBM outbreaks in Valais provided a solid baseline for evaluating the LBM defoliation signal in multiple tree-ring parameters. First, we used tree-ring width measurements along with regional records of LBM outbreaks to reconstruct the occurrence of these events at two sites within the Swiss Alps. Second, we measured earlywood width, latewood width and blue intensity, and compared these parameters with tree-ring width to assess the capacity of each proxy to detect LBM defoliation. A total of six LBM outbreaks were reconstructed for the two sites between AD 1850 and 2000. Growth suppression induced by LBM was, on average, highest in latewood width (59%), followed by total ring width (54%), earlywood width (51%) and blue intensity (26%). We show that latewood width and blue intensity can improve the temporal accuracy of LBM outbreak reconstructions, as both proxies systematically detected LBM defoliation in the first year it occurred, as well as the differentiation between defoliation and non-defoliation years. This study introduces blue intensity as a promising new proxy of insect defoliation and encourages its use in conjunction with latewood width.

Climate change-related regime shifts have altered spatial synchrony of plankton dynamics in the North Sea

During the 1980s, the North Sea plankton community underwent a well-documented ecosystem regime shift, including both spatial changes (northward species range shifts) and temporal changes (increases in the total abundances of warmer water species). This regime shift has been attributed to climate change. Plankton provide a link between climate and higher trophic-level organisms, which can forage on large spatial and temporal scales. It is therefore important to understand not only whether climate change affects purely spatial or temporal aspects of plankton dynamics, but also whether it affects spatiotemporal aspects such as metapopulation synchrony. If plankton synchrony is altered, higher trophic-level feeding patterns may be modified. A second motivation for investigating changes in synchrony is that the possibility of such alterations has been examined for few organisms, in spite of the fact that synchrony is ubiquitous and of major importance in ecology. This study uses correlation coefficients and spectral analysis to investigate whether synchrony changed between the periods 1959-1980 and 1989-2010. Twenty-three plankton taxa, sea surface temperature (SST), and wind speed were examined. Results revealed that synchrony in SST and plankton was altered. Changes were idiosyncratic, and were not explained by changes in abundance. Changes in the synchrony of Calanus helgolandicus and Para-pseudocalanus spp appeared to be driven by changes in SST synchrony. This study is one of few to document alterations of synchrony and climate-change impacts on synchrony. We discuss why climate-change impacts on synchrony may well be more common and consequential than previously recognized.

Migration, Prospecting, Dispersal? What Host Movement Matters for Infectious Agent Circulation?

Spatial disease ecology is emerging as a new field that requires the integration of complementary approaches to address how the distribution and movements of hosts and parasites may condition the dynamics of their interactions. In this context, migration, the seasonal movement of animals to different zones of their distribution, is assumed to play a key role in the broad scale circulation of parasites and pathogens. Nevertheless, migration is not the only type of host movement that can influence the spatial ecology, evolution, and epidemiology of infectious diseases. Dispersal, the movement of individuals between the location where they were born or bred to a location where they breed, has attracted attention as another important type of movement for the spatial dynamics of infectious diseases. Host dispersal has notably been identified as a key factor for the evolution of host-parasite interactions as it implies gene flow among local host populations and thus can alter patterns of coevolution with infectious agents across spatial scales. However, not all movements between host populations lead to dispersal per se. One type of host movement that has been neglected, but that may also play a role in parasite spread is prospecting, i.e., movements targeted at selecting and securing new habitat for future breeding. Prospecting movements, which have been studied in detail in certain social species, could result in the dispersal of infectious agents among different host populations without necessarily involving host dispersal. In this article, we outline how these various types of host movements might influence the circulation of infectious disease agents and discuss methodological approaches that could be used to assess their importance. We specifically focus on examples from work on colonial seabirds, ticks, and tick-borne infectious agents. These are convenient biological models because they are strongly spatially structured and involve relatively simple communities of interacting species. Overall, this review emphasizes that explicit consideration of the behavioral and population ecology of hosts and parasites is required to disentangle the relative roles of different types of movement for the spread of infectious diseases.

Delay driven spatiotemporal chaos in single species population dynamics models

Questions surrounding the prevalence of complex population dynamics form one of the central themes in ecology. Limit cycles and spatiotemporal chaos are examples that have been widely recognised theoretically, although their importance and applicability to natural populations remains debatable. The ecological processes underlying such dynamics are thought to be numerous, though there seems to be consent as to delayed density dependence being one of the main driving forces. Indeed, time delay is a common feature of many ecological systems and can significantly influence population dynamics. In general, time delays may arise from inter- and intra-specific trophic interactions or population structure, however in the context of single species populations they are linked to more intrinsic biological phenomena such as gestation or resource regeneration. In this paper, we consider theoretically the spatiotemporal dynamics of a single species population using two different mathematical formulations. Firstly, we revisit the diffusive logistic equation in which the per capita growth is a function of some specified delayed argument. We then modify the model by incorporating a spatial convolution which results in a biologically more viable integro-differential model. Using the combination of analytical and numerical techniques, we investigate the effect of time delay on pattern formation. In particular, we show that for sufficiently large values of time delay the system's dynamics are indicative to spatiotemporal chaos. The chaotic dynamics arising in the wake of a travelling population front can be preceded by either a plateau corresponding to dynamical stabilisation of the unstable equilibrium or by periodic oscillations.

Dissecting geographic variation in population synchrony using the common vole in central Europe as a test bed

Spatial synchrony of population fluctuations is ubiquitous in nature. Theoretical models suggest that correlated environmental stochasticity, dispersal, and trophic interactions are important promoters of synchrony in nature to leave characteristic signatures of distance‐dependent decays in synchrony. Recent refinements of this theory have clarified how distance‐decay curves may steepen if local dynamics are governed by different density‐dependent feedbacks and how synchrony should vary regionally if the importance and correlation of environmental stochasticity is location‐specific. We analysed spatiotemporal data for the common vole, Microtus arvalis from 49 districts in the Czech Republic to examine the pattern of population synchrony between 2000 and 2014. By extending the nonparametric covariation function, we develop a quantitative method that allows a dissection of the effects of distance and additional variables such as altitude on synchrony. To examine the pattern of local synchrony, we apply the noncentered local‐indicators of spatial association (ncLISA) which highlights areas with different degrees of synchrony than expected by the region‐wide average. Additionally, in order to understand the obtained pattern of local spatial correlations, we have regressed LISA results against the proportion of forest in each district. The common vole abundances fluctuated strongly and exhibited synchronous dynamics with the typical tendency for a decline of synchrony with increasing distance but, not with altitude. The correlation between the neighbor districts decreases as the proportion of forest increases. Forested areas are suboptimum habitats and are strongly avoided by common voles. The investigation of spatiotemporal dynamics in animal populations is a key issue in ecology. Although the majority of studies are focused on testing hypotheses about which mechanisms are involved in shaping this dynamics it is crucial to understand the sources of variation involved in order to understand the underlying processes.

Emergent long-range synchronization of oscillating ecological populations without external forcing described by Ising universality

Understanding the synchronization of oscillations across space is fundamentally important to many scientific disciplines. In ecology, long-range synchronization of oscillations in spatial populations may elevate extinction risk and signal an impending catastrophe. The prevailing assumption is that synchronization on distances longer than the dispersal scale can only be due to environmental correlation (the Moran effect). In contrast, we show how long-range synchronization can emerge over distances much longer than the length scales of either dispersal or environmental correlation. In particular, we demonstrate that the transition from incoherence to long-range synchronization of two-cycle oscillations in noisy spatial population models is described by the Ising universality class of statistical physics. This result shows, in contrast to all previous work, how the Ising critical transition can emerge directly from the dynamics of ecological populations.

Life-stage differences in spatial genetic structure in an irruptive forest insect: implications for dispersal and spatial synchrony

Dispersal determines the flux of individuals, energy and information and is therefore a key determinant of ecological and evolutionary dynamics. Yet, it remains difficult to quantify its importance relative to other factors. This is particularly true in cyclic populations in which demography, drift and dispersal contribute to spatio-temporal variability in genetic structure. Improved understanding of how dispersal influences spatial genetic structure is needed to disentangle the multiple processes that give rise to spatial synchrony in irruptive species. In this study, we examined spatial genetic structure in an economically important irruptive forest insect, the spruce budworm (Choristoneura fumiferana) to better characterize how dispersal, demography and ecological context interact to influence spatial synchrony in a localized outbreak. We characterized spatial variation in microsatellite allele frequencies using 231 individuals and seven geographic locations. We show that (i) gene flow among populations is likely very high (Fst  ≈ 0); (ii) despite an overall low level of genetic structure, important differences exist between adult (moth) and juvenile (larvae) life stages; and (iii) the localized outbreak is the likely source of moths captured elsewhere in our study area. This study demonstrates the potential of using molecular methods to distinguish residents from migrants and for understanding how dispersal contributes to spatial synchronization. In irruptive populations, the strength of genetic structure depends on the timing of data collection (e.g. trough vs. peak), location and dispersal. Taking into account this ecological context allows us to make more general characterizations of how dispersal can affect spatial synchrony in irruptive populations.

Initial epidemic area is strongly associated with the yearly extent of soybean rust spread in North America

Hosts of soybean rust (Phakopsora pachyrhizi) are sensitive to low temperatures, limiting this obligate parasite in the United States to overwintering sites in a restricted area along the Gulf Coast. This temperature sensitivity of soybean rust hosts allowed us to study spatial spread of epidemic invasions over similar territory for seven sequential years, 2005-2011. The epidemic front expanded slowly from early April through July, with the majority of expansion occurring from August through November. There was a 7.4-fold range of final epidemic extent (0.4 to 3.0 million km2) from the year of smallest final disease extent (2011) to that of the largest (2007). The final epidemic area of each year was regressed against epidemic areas recorded at one-week intervals to determine the association of final epidemic extent with current epidemic extent. Coefficients of determination for these regressions varied between 0.44 to 0.62 during April and May. The correlation coefficients varied between 0.70 and 0.96 from early June through October, and then increased monotonically to 1.0 by year's end. Thus, the spatial extent of disease when the epidemics began rapid expansion may have been a crucial contributor to subsequent spread of soybean rust. Our analyses used presence/absence data at the county level to evaluate the spread of the epidemic front only; the subsequent local intensification of disease could be strongly influenced by other factors, including weather.

Generation of periodic travelling waves in cyclic populations by hostile boundaries

Many recent datasets on cyclic populations reveal spatial patterns with the form of periodic travelling waves (wavetrains). Mathematical modelling has identified a number of potential causes of this spatial organization, one of which is a hostile habitat boundary. In this paper, the author investigates the member of the periodic travelling wave family selected by such a boundary in models of reaction–diffusion type. Using a predator–prey model as a case study, the author presents numerical evidence that the wave generated by a hostile (zero-Dirichlet) boundary condition is the same as that generated by fixing the population densities at their coexistence steady-state levels. The author then presents analysis showing that the two waves are the same, in general, for oscillatory reaction–diffusion models with scalar diffusion close to Hopf bifurcation. This calculation yields a general formula for the amplitude, speed and wavelength of these waves. By combining this formula with established results on periodic travelling wave stability, the author presents a division of parameter space into regions in which a hostile boundary will generate periodic travelling waves, spatio-temporal disorder or a mixture of the two.

Scaling Up in Ecology: Mechanistic Approaches

Ecologists have long grappled with the problem of scaling up from tractable, small-scale observations and experiments to the prediction of large-scale patterns. Although there are multiple approaches to this formidable task, there is a common underpinning in the formulation, testing, and use of mechanistic response functions to describe how phenomena interact across scales. Here, we review the principles of response functions to illustrate how they provide a means to guide research, extrapolate beyond measured data, and simplify our conceptual grasp of reality. We illustrate these principles with examples of mechanistic approaches ranging from explorations of the ecological niche, random walks, and macrophysiology to theories dealing with scale transition, self-organization, and the prediction of extremes.

Landscape heterogeneity and disease spread: experimental approaches with a plant pathogen

Understanding landscape effects on disease spread can contribute to the prediction and control of epidemic invasions. We conducted large-scale field experiments with wheat stripe rust, which is caused by a wind-dispersed rust fungus. Three landscape heterogeneity variables were altered: host frequency (mixtures of susceptible and resistant plants), host patch size (different plot sizes), and size of initial disease focus (attained by artificial inoculation). Assessments of disease prevalence at different distances from the disease foci were used to quantify effects of landscape variables. We expected that a low frequency of susceptible hosts, small host patch sizes, and small initial disease foci would reduce secondary inoculum levels and thus suppress disease spread. Low host frequency and small initial disease foci greatly reduced epidemic spread. We did not detect an effect of host patch size on disease spread, though artificial inoculations did not allow us to measure the potential for small patches to escape infection under conditions of random deposition of initial inoculum. Our results suggest that, for diseases epidemiologically similar to wheat stripe rust, epidemic invasions may be suppressed by decreasing host frequency, limiting the size of initial outbreak foci, and applying control measures soon after epidemic establishment.

Climatic warming disrupts recurrent Alpine insect outbreaks

Climate change has been identified as a causal factor for diverse ecological changes worldwide. Warming trends over the last couple of decades have coincided with the collapse of long-term population cycles in a broad range of taxa, although causal mechanisms are not well-understood. Larch budmoth (LBM) population dynamics across the European Alps, a classic example of regular outbreaks, inexplicably changed sometime during the 1980s after 1,200 y of nearly uninterrupted periodic outbreak cycles. Herein, analysis of perhaps the most extensive spatiotemporal dataset of population dynamics and reconstructed Alpine-wide LBM defoliation records reveals elevational shifts in LBM outbreak epicenters that coincide with temperature fluctuations over two centuries. A population model supports the hypothesis that temperature-mediated shifting of the optimal elevation for LBM population growth is the mechanism for elevational epicenter changes. Increases in the optimal elevation for population growth over the warming period of the last century to near the distributional limit of host larch likely dampened population cycles, thereby causing the collapse of a millennium-long outbreak cycle. The threshold-like change in LBM outbreak pattern highlights how interacting species with differential response rates to climate change can result in dramatic ecological changes.

Spatially mismatched trophic dynamics: cyclically outbreaking geometrids and their larval parasitoids

For trophic interactions to generate population cycles and complex spatio-temporal patterns, like travelling waves, the spatial dynamics must be matched across trophic levels. Here, we propose a spatial methodological approach for detecting such spatial match-mismatch and apply it to geometrid moths and their larval parasitoids in northern Norway, where outbreak cycles and travelling waves occur. We found clear evidence of spatial mismatch, suggesting that the spatially patterned moth cycles in this system are probably ruled by trophic interactions involving other agents than larval parasitoids.

Ecological processes can synchronize marine population dynamics over continental scales

Determining the relative importance of local and regional processes for the distribution of population abundance is a fundamental but contentious issue in ecology. In marine systems, classical theory holds that the influence of demographic processes and dispersal is confined to local populations whereas the environment controls regional patterns of abundance. Here, we use spatial synchrony to compare the distribution of population abundance of the dominant mussel Mytilus californianus observed along the West Coast of the United States to that predicted by dynamical models undergoing different dispersal and environmental treatments to infer the relative influence of local and regional processes. We reveal synchronized fluctuations in the abundance of mussel populations across a whole continent despite limited larval dispersal and strong environmental forcing. We show that dispersal among neighboring populations interacts with local demographic processes to generate characteristic patterns of spatial synchrony that can govern the dynamic distribution of mussel abundance over 1,800 km of coastline. Our study emphasizes the importance of dispersal and local dynamics for the distribution of abundance at the continental scale. It further highlights potential limits to the use of "climate envelope" models for predicting the response of large-scale ecosystems to global climate change.

Noise-induced suppression of periodic travelling waves in oscillatory reaction–diffusion systems

Ecological field data suggest that some species show periodic changes in abundance over time and in a specific spatial direction. Periodic travelling waves as solutions to reaction–diffusion equations have helped to identify possible scenarios, by which such spatio-temporal patterns may arise. In this paper, such solutions are tested for their robustness against an irregular temporal forcing, since most natural populations can be expected to be subject to erratic fluctuations imposed by the environment. It is found that small environmental noise is able to suppress periodic travelling waves in stochastic variants of oscillatory reaction–diffusion systems. Irregular spatio-temporal oscillations, however, appear to be more robust and persist under the same stochastic forcing.