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Background: Individual variations in the use of the species niche are an important component of diversity in trophic interactions. A challenge in testing consistency of individual foraging strategy is the repeated collection of information on the same individuals. Methodology/Principal Findings: The foraging strategies of sympatric fur seals (Arctocephalus gazella and A. tropicalis) were examined using the stable isotope signature of serially sampled whiskers. Most whiskers exhibited synchronous delta C-13 and delta N-15 oscillations that correspond to the seal annual movements over the long term (up to 8 years). delta C-13 and delta N-15 values were spread over large ranges, with differences between species, sexes and individuals. The main segregating mechanism operates at the spatial scale. Most seals favored foraging in subantarctic waters (where the Crozet Islands are located) where they fed on myctophids. However, A. gazella dispersed in the Antarctic Zone and A. tropicalis more in the subtropics. Gender differences in annual time budget shape the seal movements. Males that do not perform any parental care exhibited large isotopic oscillations reflecting broad annual migrations, while isotopic values of females confined to a limited foraging range during lactation exhibited smaller changes. Limited inter-individual isotopic variations occurred in female seals and in male A. tropicalis. In contrast, male A. gazella showed large inter-individual variations, with some males migrating repeatedly to high-Antarctic waters where they fed on krill, thus meaning that individual specialization occurred over years. Conclusions/Significance: Whisker isotopic signature yields unique long-term information on individual behaviour that integrates the spatial, trophic and temporal dimensions of the ecological niche. The method allows depicting the entire realized niche of the species, including some of its less well-known components such as age-, sex-, individual- and migration-related changes. It highlights intrapopulation heterogeneity in foraging strategies that could have important implications for likely demographic responses to environmental variability.

Carrying out statistical analysis over an extensive dataset of human plague reports in Chinese villages from 1772 to 1964, we identified plague endemic territories in China (i.e., plague foci). Analyses rely on ( i ) a clustering method that groups time series based on their time-frequency resemblances and ( ii ) an ecological niche model that helps identify plague suitable territories characterized by value ranges for a set of predefined environmental variables. Results from both statistical tools indicate the existence of two disconnected plague territories corresponding to Northern and Southern China. Altogether, at least four well defined independent foci are identified. Their contours compare favorably with field observations. Potential and limitations of inferring plague foci and dynamics using epidemiological data is discussed.

We adapt a recent model from the Framingham study (Circulation 1991; 83: 356-362) to predict CHD in France for both sexes over a large age range. Calculations were based on data from the French PCV-METRA study. In France, the Paris Prospective Study model could predict CHD but only for men aged 43-53 years. Applied to men 43-53 years from the PCV-METRA, the Framingham model estimated a 5-year CHD risk (4%) lower than the risk reported in the Framingham sample, but significantly higher than the risk estimated by the French model (2%). Differences in estimated CHD risk between the Framingham and the PCV-METRA samples were explained for only 30% by adjustment on major CHD risk factors (mainly HDL-cholesterol and tobacco). Modifying the intercept in the Framingham model, agreement with estimated risk by the French model was improved from 29 to 80%. By an appropriate change of the intercept, the Framingham model might be used to estimate CHD risk in other populations.

International audience; Plague is a vector-borne, highly virulent zoonotic disease caused by the bacterium Yersinia pestis. It persists in nature through transmission between its hosts (wild rodents) and vectors (fleas). During epizootics, the disease expands and spills over to other host species such as humans living in or close to affected areas. Here, we investigate the effect of large-scale climate variability on the dynamics of human plague in the western United States using a 56-year time series of plague reports (1950-2005). We found that El Niño Southern Oscillation and Pacific Decadal Oscillation in combination affect the dynamics of human plague over the western United States. The underlying mechanism could involve changes in precipitation and temperatures that impact both hosts and vectors. It is suggested that snow also may play a key role, possibly through its effects on summer soil moisture, which is known to be instrumental for flea survival and development and sustained growth of vegetation for rodents.

Background Dengue is a major global public health problem with increasing incidence and geographic spread. The epidemiology is complex with long inter-epidemic intervals and endemic with seasonal fluctuations. This study was initiated to investigate dengue transmission dynamics in Binh Thuan province, southern Vietnam. Methodology Wavelet analyses were performed on time series of monthly notified dengue cases from January 1994 to June 2009 (i) to detect and quantify dengue periodicity, (ii) to describe synchrony patterns in both time and space, (iii) to investigate the spatio-temporal waves and (iv) to associate the relationship between dengue incidence and El Niño-Southern Oscillation (ENSO) indices in Binh Thuan province, southern Vietnam. Principal Findings We demonstrate a continuous annual mode of oscillation and a multi-annual cycle of around 2–3-years was solely observed from 1996–2001. Synchrony in time and between districts was detected for both the annual and 2–3-year cycle. Phase differences used to describe the spatio-temporal patterns suggested that the seasonal wave of infection was either synchronous among all districts or moving away from Phan Thiet district. The 2–3-year periodic wave was moving towards, rather than away from Phan Thiet district. A strong non-stationary association between ENSO indices and climate variables with dengue incidence in the 2–3-year periodic band was found. Conclusions A multi-annual mode of oscillation was observed and these 2–3-year waves of infection probably started outside Binh Thuan province. Associations with climatic variables were observed with dengue incidence. Here, we have provided insight in dengue population transmission dynamics over the past 14.5 years. Further studies on an extensive time series dataset are needed to test the hypothesis that epidemics emanate from larger cities in southern Vietnam., Author Summary Dengue has become a major international public health problem due to increasing geographic distribution and a transition from epidemic transmission with long inter-epidemic intervals to endemic transmission with seasonal fluctuation. Seasonal and multi-annual cycles in dengue incidence vary over time and space. We performed wavelet analyses on time series of monthly notified dengue cases in Binh Thuan province, southern Vietnam, from January 1994 to June 2009. We observed a continuous annual mode of oscillation with a non-stationary 2–3-year multi-annual cycle. We used phase differences to describe the spatio-temporal patterns which suggest that the seasonal wave of infection was either synchronous with all districts or moving away from Phan Thiet district, while the multi-annual wave of infection was moving towards Phan Thiet district. We also found a strong non-stationary association between ENSO indices and climate variables with dengue incidence. We provided insight in dengue population transmission dynamics over the past 14.5 years. Further studies on an extensive time series dataset are needed to test the hypothesis that epidemics emanate from larger cities in southern Vietnam.

Catch data of large pelagic fish such as tuna, swordfish and billfish are highly variable ranging from short to long term. Based on fisheries data, these time series are noisy and reflect mixed information on exploitation (targeting, strategy, fishing power), population dynamics (recruitment, growth, mortality, migration, etc.), and environmental forcing (local conditions or dominant climate patterns). In this work, we investigated patterns of variation of large pelagic fish (i.e. yellowfin tuna, bigeye tuna, swordfish and blue marlin) in Japanese longliners catch data from 1960 to 2004. We performed wavelet analyses on the yearly time series of each fish species in each biogeographic province of the tropical Indian and Atlantic Oceans. In addition, we carried out cross-wavelet analyses between these biological time series and a large-scale climatic index, i.e. the Southern Oscillation Index (Sol). Results showed that the biogeographic province was the most important factor structuring the patterns of variability of Japanese catch time series. Relationships between the SOI and the fish catches in the Indian and Atlantic Oceans also pointed out the role of climatic variability for structuring patterns of variation of catch time series. This work finally confirmed that Japanese longline CPUE data poorly reflect the underlying population dynamics of tunas. (C) 2010 Elsevier Ltd. All rights reserved.

Although 50 years of scientific work has been invested in building retrospective economic time series, their reliability is still debated, a good example being the two competing nineteenth century French GNP series. Instead of trying to bring up some new details to gauge their respective accuracy, we propose a different route, i.e. testing the intrinsic features of these two series, in absolute terms first, then by benchmarking them to a non-retrospective time series. In order to do that, we rely on new mathematical tools-wavelet spectrum analysis-developed in signal processing. This leads to a new approach, which separates the accuracy of a series between amplitude and time variations, and brings nuanced conclusions as to which of the two series tested is the best: indeed, since a trade-off is almost inescapable between the two criterions of accuracy, the statistical quality of one retrospective time series tends to linger either on one side (amplitude level) or the other (time variations). Our study also shows that variance distribution along the time axis is a good proxy for complex retrospective series accuracy.

International audience; Wavelet analysis is a powerful tool that is already in use throughout science and engineering. The versatility and attractiveness of the wavelet approach lie in its decomposition properties, principally its time-scale localization. It is especially relevant to the analysis of non-stationary systems, i.e., systems with short-lived transient components, like those observed in ecological systems. Here, we review the basic properties of the wavelet approach for time-series analysis from an ecological perspective. Wavelet decomposition offers several advantages that are discussed in this paper and illustrated by appropriate synthetic and ecological examples. Wavelet analysis is notably free from the assumption of stationarity that makes most methods unsuitable for many ecological time series. Wavelet analysis also permits analysis of the relationships between two signals, and it is especially appropriate for following gradual change in forcing by exogenous variables.

Norwegian lemmings (Lemmus lemmus) are well known for their population cycles, which are thought, at their peak, to influence other ecosystem components. In fact the role of the physical environment — climate included — in determining rodent cycle dynamics has remained largely a matter of conjecture. Now from a combination of long-term (1970–2007) data on rodent density, bird densities and field estimates of snow pack conditions together with meteorological data, a clearer picture of the lemming cycle has been obtained. What emerges is a marked shift away from the familiar 3–5-year rodent cycles to an aperiodic, mostly low-amplitude state, which can be explained and predicted by the between-year variations in winter climate. There is strong evidence for the hypothesis that climate effects on rodent dynamics are transmitted to other parts of the ecosystem. The population cycles of rodents at northern latitudes have puzzled people for centuries1,2, and their impact is manifest throughout the alpine ecosystem2,3. Climate change is known to be able to drive animal population dynamics between stable and cyclic phases4,5, and has been suggested to cause the recent changes in cyclic dynamics of rodents and their predators3,6,7,8,9. But although predator–rodent interactions are commonly argued to be the cause of the Fennoscandian rodent cycles1,10,11,12,13, the role of the environment in the modulation of such dynamics is often poorly understood in natural systems8,9,14. Hence, quantitative links between climate-driven processes and rodent dynamics have so far been lacking. Here we show that winter weather and snow conditions, together with density dependence in the net population growth rate, account for the observed population dynamics of the rodent community dominated by lemmings (Lemmus lemmus) in an alpine Norwegian core habitat between 1970 and 1997, and predict the observed absence of rodent peak years after 1994. These local rodent dynamics are coherent with alpine bird dynamics both locally and over all of southern Norway, consistent with the influence of large-scale fluctuations in winter conditions. The relationship between commonly available meteorological data and snow conditions indicates that changes in temperature and humidity, and thus conditions in the subnivean space, seem to markedly affect the dynamics of alpine rodents and their linked groups. The pattern of less regular rodent peaks, and corresponding changes in the overall dynamics of the alpine ecosystem, thus seems likely to prevail over a growing area under projected climate change.

In nature, non-stationarity is rather typical, but the number of statistical tools allowing for non-stationarity remains rather limited. Wavelet analysis is such a tool allowing for non-stationarity but the lack of an appropriate test for statistical inference as well as the difficulty to deal with multiple time series are 2 important shortcomings that limits its use in ecology. We present 2 approaches to deal with these shortcomings. First, we used 1/f beta models to test cycles in the wavelet spectrum against a null hypothesis that takes into account the highly autocorrelated nature of ecological time series. To illustrate the approach, we investigated the fluctuations in bluefin tuna trap catches with a set of different null models. The 1/f beta models approach proved to be the most consistent to discriminate significant cycles. Second, we used the maximum covariance analysis to compare, in a quantitative way, the time-frequency patterns (i.e. the wavelet spectra) of numerous time series. This approach built cluster trees that grouped the wavelet spectra according to their time-frequency patterns. Controlled signals and time series of sea surface temperature (SST) in the Mediterranean Sea were used to test the ability and power of this approach. The results were satisfactory and clusters on the SST time series displayed a hierarchical division of the Mediterranean into a few homogeneous areas that are known to display different hydrological and oceanic patterns. We discuss the limits and potentialities of these methods to study the associations between ecological and environmental fluctuations.

Background The relationship between cholera and climate was explored in Africa, the continent with the most reported cases, by analyzing monthly 20-year cholera time series for five coastal adjoining West African countries: Côte d'Ivoire, Ghana, Togo, Benin and Nigeria. Methods We used wavelet analyses and derived methods because these are useful mathematical tools to provide information on the evolution of the periodic component over time and allow quantification of non-stationary associations between time series. Results The temporal variability of cholera incidence exhibits an interannual component, and a significant synchrony in cholera epidemics is highlighted at the end of the 1980's. This observed synchrony across countries, even if transient through time, is also coherent with both the local variability of rainfall and the global climate variability quantified by the Indian Oscillation Index. Conclusion Results of this study suggest that large and regional scale climate variability influence both the temporal dynamics and the spatial synchrony of cholera epidemics in human populations in the Gulf of Guinea, as has been described for two other tropical regions of the world, western South America and Bangladesh.

We analysed the influence of climatic oscillations [based on the Indian Oscillation Index (IOI)] on monthly catch rates of two tropical tuna species in the equatorial Indian Ocean. We carried out wavelet analysis, an efficient method of time series analysis to study non-stationary data. Catch per unit of effort (CPUE) of bigeye tuna was computed from Japanese longline statistics from 1955 to 2002 in the equatorial Indian Ocean and CPUE of yellowfin tuna was derived from industrial purse seine statistics from 1984 to 2003 in the Western Indian Ocean. Wavelet analyses allowed us to quantify both the pattern of variability in the time series and non-stationary associations between tuna and climatic signals. Phase analyses were carried out to investigate dependency between the two signals. We reported strong associations between tuna and climate series for the 4- and 5-yr periodic modes, i.e. the periodic band of the El Nino Southern Oscillation signal propagation in the Indian Ocean. These associations were non-stationary, evidenced from 1970 to 1990 for bigeye, and from 1984 to 1991 and then from 1993 to 2001 for yellowfin. Warm episodes (low negative IOI values) matched increases of longline catch rates of bigeye during the 1970–1990 time frame, whereas the strong 1997–1998 warm event matched a decrease of purse seine catch rates of yellowfin. We discussed these results in terms of changes in catchability for purse seine and longline.

In the current context of global infectious disease risks, a better understanding of the dynamics of major epidemics is urgently needed. Time-series analysis has appeared as an interesting approach to explore the dynamics of numerous diseases. Classical time-series methods can only be used for stationary time-series (in which the statistical properties do not vary with time). However, epidemiological time-series are typically noisy, complex and strongly non-stationary. Given this specific nature, wavelet analysis appears particularly attractive because it is well suited to the analysis of non-stationary signals. Here, we review the basic properties of the wavelet approach as an appropriate and elegant method for time-series analysis in epidemiological studies. The wavelet decomposition offers several advantages that are discussed in this paper based on epidemiological examples. In particular, the wavelet approach permits analysis of transient relationships between two signals and is especially suitable for gradual change in force by exogenous variables.

International audience; Cholera remains a major public health problem with significant mortality, and numerous re-emergences have been observed in the last decade in many developing countries. Previous analyses have suggested a complex process for cholera disease emergence and spread, particularly related to environmental factors associated to aquatic habitats. To disentangle the complexity of these ecological and epidemiological processes and to understand the dynamics of cholera epidemics, mathematical models are needed. Here we review the state-of-the-art of cholera models before proposing a modified model that integrates some influential environmental drivers. Particularly, as statistical analyses have revealed that chlorophyll a concentration had a significant influence over cholera epidemics our model incorporates this association, we suggest a new model where the disease starts with a bloom of phytoplankton, and then spreads in human community.; Le choléra demeure un grave problème de santé publique avec une mortalité impor-tante et avec de nombreux phénomènes de ré-émergences ces dix dernières années dans les pays en développement. Les analyses antérieures ontévoqué des processus complexesà l'origine de la dynamique de la maladie et plus particulièrement des relations avec lesécosystèmes aquatiques et les facteurs environnementaux associés. Les modèles mathématiques sont nécessaires pour améliorer notre compréhension de la complexité de ces processusécologiques etépidémiologiques, et pour mieux appréhender la dynamique desépidémies de choléra. Ici, nous passons en revue l'état de l'art des modèles sur le choléra avant de proposer un modèle modifié intégrant des forçages environnementaux. En particulier, des analyses statistiques ayant révélé que la concentration en chlorophylle-a avait une influence significative sur lesépidémies de choléra, notre modèle intègre cette association et nous pro-posons un nouveau modèle dans lequel la maladie est initiée par un bloom phytoplanctonique puis se propage dans la population humaine.

The importance of spatial heterogeneity and spatial scales (at a village or neighbourhood scale) has been explored with individual-based models. Our reasoning is based on the Chilean Easter Island (EI) case, where a first dengue epidemic occurred in 2002 among the relatively small population localized in one village. Even in this simple situation, the real epidemic is not consistent with homogeneous models. Conversely, including contact heterogeneity on different scales (intra-households, inter-house, inter-areas) allows the recovery of not only the EI epidemiological curve but also the qualitative patterns of Brazilian urban dengue epidemic in more complex situations.

Environmental factors and their interactions are likely to have shaped specific breeding and survival strategies in top predators. Understanding how climatic factors affect populations requires detailed investigation of the demographic parameters and population modelling. Here, we focus on the modelling of a southern fulmar population over a 39 year period in Terre Ade´lie, Antarctica, using Leslie matrix models to understand from a prospective and retrospective point of view, how vital rates and their variations, affect the cyclic population dynamics. The elasticity of population growth rate to adult survival was very high (0.95), as predicted by a slow/fast continuum in avian life histories. However, adult survival varied little between years (mean9/SD: 0.929/0.07), and could not explain the strong fluctuations observed in the number of breeders and chicks. The high temporal fluctuations of the proportion of breeders (0.579/0.22) and breeding success (0.709/0.14) had the strongest impact on population dynamics, despite their weak elasticities (0.05). Before the 1980s, population fluctuations were mainly explained by a direct impact of sea-ice extent (SIE) anomalies during summer (by a threshold effect) on the proportion of breeders. After 1980s, 3 years periodic population fluctuations were best predicted by 3 years cyclic variations in the proportion of breeders. SIE showed a marked change of periodicity during the 1980s, and SIE during winter fluctuated with a 3 years periodicity during 1980/1995. The marked change in population dynamics, through a change of the variations of the proportion of breeders, may be explained in the light of a regime shift that probably occurred around the 1980s, and which affected the sea ice environment, the availability of prey, and thus the demographic parameters and population dynamics of southern fulmars.

Using North American porcupine (Erethizon dorsatum) feeding scars on trees as an index of past porcupine abundance, we have found that porcupine populations have fluctuated regularly over the past 130 years in the Bas St. Laurent region of eastern Quebec, with superimposed periodicities of 11 and 22 years. Coherency and phase analyses showed that this porcupine population cycle has closely followed the 11- and 22-year solar activity cycles. Fluctuations in local precipitation and temperature were also cyclic and closely related to both the solar cycle and the porcupine cycle. Our results suggest that the solar cycle indirectly sets the rhythm of population fluctuations of the most abundant vertebrate herbivore in the ecosystem we studied. We hypothesize that the solar cycle has sufficiently important effects on the climate along the southern shore of the St. Lawrence estuary to locally influence terrestrial ecosystem functioning. This constitutes strong evidence for the possibility of a causal link between solar variability and terrestrial ecology at the decadal timescale and local spatial scale, which confirms results obtained at greater temporal and spatial scales.

Ecological and biological systems provide us with many striking examples of synchronization phenomena. Here we discuss a number of intriguing cases and attempt to explain them taking advantage of a modelling framework. One main focus will concern synchronized ecological end epidemiological cycles which have Uniform Phase growth associated with their regular recurrence, and Chaotic Amplitudes ‐ a feature we term UPCA. Examples come from different areas and include decadal cycles of small mammals, recurrent viral epidemics such as childhood infections (eg., measles), and seasonally driven phytoplankton blooms observed in lakes and the oceans. A more detailed theoretical analysis of seasonally synchronized chaotic population cycles is presented.

The effects of migration in a network of patch populations, or metapopulation, are extremely important for predicting the possibility of extinctions both at a local and a global scale. Migration between patches synchronizes local populations and bestows upon them identical dynamics (coherent or synchronous oscillations), a feature that is understood to enhance the risk of global extinctions. This is one of the central theoretical arguments in the literature associated with conservation ecology. Here, rather than restricting ourselves to the study of coherent oscillations, we examine other types of synchronization phenomena that we consider to be equally important. Intermittent and out-of-phase synchronization are but two examples that force us to reinterpret some classical results of the metapopulation theory. In addition, we discuss how asynchronous processes (for example, random timing of dispersal) can paradoxically generate metapopulation synchronization, another non-intuitive result that cannot easily be explained by the standard theory.

Harrison et al. [Phys. Rev. E $63,$ 051905 (2001)] recently proposed that the temporal synchronization and desynchronization between populations in different habitats leads to species coexistence and occurs through a pattern of on-off intermittency. As noise is unavoidable in ecological systems, we show, using the same example, that ``attractor bubbling'' appears to be another plausible hypothesis to explain the scenario leading to species coexistence. The interest of attractor bubbling in ecology is confirmed by other examples concerning the evolution of intermittent rare species.

Many factors, including therapy and behavioral changes, have modified the course of the HIV/AIDS epidemic in recent years. To include these modifications in HIV/AIDS models, in the absence of appropriate external data sources, changes over time in the parameters can be incorporated by a recursive estimation technique such as the Kalman filter. The Kalman filter accounts for stochastic fluctuations in both the model and the data and provides a means to assess any parameter modifications included in new observations. The Kalman filter approach was applied to a simple differential model to describe the observed HIV/AIDS epidemic in the homo/bisexual male community in Paris (France). This approach gave quantitative information on the time-evolution of some parameters of major epidemiological significance (average transmission rate, mean incubation rate, and basic reproduction rate), which appears quite consistent with the recent epidemiological literature.

Ordered categorical (or semi-quantitative) data are frequently encountered in ecology (e.g., Steen et al. 1990; Menard et al. 1993). Researchers often resort to semi-quantitative measures (to describe abundance patterns, age or stage structures, environmental factors, etc.) to reduce processing time and/or because of financial constraints, while retaining an acceptable level of accuracy.

Background Several factors, including environmental and climatic factors, influence the transmission of vector-borne diseases. Nevertheless, the identification and relative importance of climatic factors for vector-borne diseases remain controversial. Dengue is the world's most important viral vector-borne disease, and the controversy about climatic effects also applies in this case. Here we address the role of climate variability in shaping the interannual pattern of dengue epidemics. Methods and Findings We have analysed monthly data for Thailand from 1983 to 1997 using wavelet approaches that can describe nonstationary phenomena and that also allow the quantification of nonstationary associations between time series. We report a strong association between monthly dengue incidence in Thailand and the dynamics of El Niño for the 2–3-y periodic mode. This association is nonstationary, seen only from 1986 to 1992, and appears to have a major influence on the synchrony of dengue epidemics in Thailand. Conclusion The underlying mechanism for the synchronisation of dengue epidemics may resemble that of a pacemaker, in which intrinsic disease dynamics interact with climate variations driven by El Niño to propagate travelling waves of infection. When association with El Niño is strong in the 2–3-y periodic mode, one observes high synchrony of dengue epidemics over Thailand. When this association is absent, the seasonal dynamics become dominant and the synchrony initiated in Bangkok collapses., In Thailand, dengue transmission patterns are complex, with El Nino showing an association with some epidemics only; other reasons will need to be found for the initiation of other outbreaks

Dengue transmission in urban areas is strongly influenced by a range of biological and environmental factors, yet the key drivers still need further exploration. To better understand mechanisms of environment–mosquito–urban dengue transmission, we propose an empirical model parameterized and cross-validated from a unique dataset including viral gene sequences, vector dynamics and human dengue cases in Guangzhou, China, together with a 36-year urban environmental change maps investigated by spatiotemporal satellite image fusion. The dengue epidemics in Guangzhou are highly episodic and were not associated with annual rainfall over time. Our results indicate that urban environmental changes, especially variations in surface area covered by water in urban areas, can substantially alter the virus population and dengue transmission. The recent severe dengue outbreaks in Guangzhou may be due to the surge in an artificial lake construction, which could increase infection force between vector (mainly Aedes albopictus) and host when urban water area significantly increased. Impacts of urban environmental change on dengue dynamics may not have been thoroughly investigated in the past studies and more work needs to be done to better understand the consequences of urbanization processes in our changing world.

Interesting dynamic behaviors have been associated with the transverse stability of an invariant subspace (e.g., riddled basins or on-off intermittency). Recently, these complex behaviors have been generalized to cases of models with non-normal parameters showing that these behaviors might be widespread. In this Brief Report I demonstrate this type of complex dynamics in models of interacting populations for which the invariant subspace is defined by species extinction. In the context of the current biodiversity crisis, implications of these behaviors for population biology are discussed.

The temporal dynamics of many populations involve intermittent rarity, that is, the alternation, over variable periods of time, of phases of extremely low abundance, and short outbreaks. In this paper we show that intermittent rarity can arise in simple community models as a result of competitive interactions within and between species. Intermittently rare species are typified as weak invaders in fluctuating communities. Although the dynamics of intermittent rarity are highly irregular, the distribution of time spent in phases of rarity (`rarity times') involves strong regularity. Specifically, intermittent rarity is governed by a well-defined power law. The scaling exponent (-3/2) is a universal feature of intermittent rarity: it does not depend on species demographic parameters; it is insensitive to environmental stochasticity; and the same exponent is found in very different models of nonstructured populations. The distribution of rarity times implies that the dynamics of rarity have no characteristic timescale. Yet in practice the universal scaling law offers a general form of prediction in which one can calculate the frequency of occurrence of rarity phases of any given duration. Data on marine fish communities support the prediction of a -3/2 power law underlying the dynamics of intermittently rare species. The scale-free dynamics reported here place intermittent rarity in the same class as the critical states of other nonlinear dynamical systems in the physical sciences. At a critical state, general laws govern the systems' dynamics irrespective to the specific details of the interactions between constituents.

Background Dengue fever is a mosquito-borne disease that affects between 50 and 100 million people each year. Increasing our understanding of the heterogeneous transmission patterns of dengue at different spatial scales could have considerable public health value by guiding intervention strategies. Methods Based on the weekly number of dengue cases in Perú by province, we investigated the association between dengue incidence during the period 1994-2008 and demographic and climate factors across geographic regions of the country. Results Our findings support the presence of significant differences in the timing of dengue epidemics between jungle and coastal regions, with differences significantly associated with the timing of the seasonal cycle of mean temperature. Conclusions Dengue is highly persistent in jungle areas of Perú where epidemics peak most frequently around March when rainfall is abundant. Differences in the timing of dengue epidemics in jungle and coastal regions are significantly associated with the seasonal temperature cycle. Our results suggest that dengue is frequently imported into coastal regions through infective sparks from endemic jungle areas and/or cities of other neighboring endemic countries, where propitious environmental conditions promote year-round mosquito breeding sites. If jungle endemic areas are responsible for multiple dengue introductions into coastal areas, our findings suggest that curtailing the transmission of dengue in these most persistent areas could lead to significant reductions in dengue incidence in coastal areas where dengue incidence typically reaches low levels during the dry season.

By making use of a parametric adaptive control, it is possible to synchronize, desynchronize or resynchronize a network of globally coupled chaotic oscillators in the presence of large noise. The method, base on the Kalman filter, is applied to logistic and Henon map lattices. This method can be regarded as an adaptive mechanism with potential relevance to biological systems. This mechanism may allow complex self-regulated biological networks to modify their clustering behavior in so as to fit environmental changes, thus optimizing their performances.

We explored the effects of regime shifts (drastic changes usually observed in marine ecosystems, corresponding to climatic variability) in a terrestrial system focusing on a key event that occurred in 1976-77. We used data on the gray-sided vole Clethrionomys rufocanus (Sundevall, 1846) from 89 time series covering 31 yr (1962–1992), recorded in Asahikawa, Hokkaido, Japan, where both cyclic and non-cyclic populations occur. Wavelet analyses demonstrated a clear shift of dynamic patterns in the mid-1970s, presumably resulting from the Aleutian Low Pressure (as measured by the Aleutian Low Pressure Index). The vole populations exhibited erratic fluctuations until the mid- 1970s, and then changed their pattern to cyclic fluctuations at a 4 yr interval. The structure of density dependence changed during the regime shift. Although the strength of direct density dependence was similar, delayed density dependence became stronger after the shift. Altogether these findings suggest that changing climate may affect the ecological interactions among voles, predators and resources.

Understanding the spatio-temporal dynamics of endemic infections is of critical importance for a deeper understanding of pathogen transmission, and for the design of more efficient public health strategies. However, very few studies in this domain have focused on emerging infections, generating a gap of knowledge that hampers epidemiological response planning. Here, we analyze the case of a Chikungunya outbreak that occurred in Martinique in 2014. Using time series estimates from a network of sentinel practitioners covering the entire island, we first analyze the spatio-temporal dynamics and show that the largest city has served as the epicenter of this epidemic. We further show that the epidemic spread from there through two different propagation waves moving northwards and southwards, probably by individuals moving along the road network. We then develop a mathematical model to explore the drivers of the temporal dynamics of this mosquito-borne virus. Finally, we show that human behavior, inferred by a textual analysis of messages published on the social network Twitter, is required to explain the epidemiological dynamics over time. Overall, our results suggest that human behavior has been a key component of the outbreak propagation, and we argue that such results can lead to more efficient public health strategies specifically targeting the propagation process.

Complex dynamic relationships between humans, pathogens, and the environment lead to the emergence of new diseases and the re-emergence of old ones. Due to concern about the impact of increasing global climate variability and change, many recent studies have focused on relationships between infectious disease and climate [1–5].