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The long-term patterns of malaria in the East African highlands typically involve not only a general upward trend in cases but also a dramatic increase in the size of epidemic outbreaks. The role of climate variability in driving epidemic cycles at interannual time scales remains controversial, in part because it has been seen as conflicting with the alternative explanation of purely endogenous cycles exclusively generated by the nonlinear dynamics of the disease. We analyse a long temporal record of monthly cases from 1970 to 2003 in a highland of western Kenya with both a time-series epidemiological model (time-series susceptible-infected-recovered) and a statistical approach specifically developed for non-stationary patterns. Results show that multiyear cycles of malaria outbreaks appear in the 1980s, concomitant with the timing of a regime shift in the dynamics of cases; the cycles become more pronounced in the 1990s, when the coupling between disease and rainfall is also stronger as the variance of rainfall increased at the frequencies of coupling. Disease dynamics and climate forcing play complementary and interacting roles at different temporal scales. Thus, these mechanisms should not be viewed as alternative and their interaction needs to be integrated in the development of future predictive models.

The patterns of variations in fisheries time series are known to result from a complex combination of species and fisheries dynamics all coupled with environmental forcing (including climate, trophic interactions, etc.). Disentangling the relative effects of these factors has been a major goal of fisheries science for both conceptual and management reasons. By examining the variability of 169 tuna and billfish time series of catch and catch per unit effort (CPUE) throughout the Atlantic as well as their linkage to the North Atlantic Oscillation (NAO), we find that the importance of these factors differed according to the spatial scale. At the scale of the entire Atlantic the patterns of variations are primarily spatially structured, whereas at a more regional scale the patterns of variations were primarily related to the fishing gear. Furthermore, the NAO appeared to also structure the patterns of variations of tuna time series, especially over the North Atlantic. We conclude that the patterns of variations in fisheries time series of tuna and billfish only poorly reflect the underlying dynamics of these fish populations; they appear to be shaped by several successive embedded processes, each interacting with each other. Our results emphasize the necessity for scientific data when investigating the population dynamics of large pelagic fishes, because CPUE fluctuations are not directly attributable to change in species' abundance.

Population biologists interested in the possible causes of population cycles and synchronized population fluctuations have shown in studies that spatial population synchrony is a general phenomenon. Cazelles and Boudjema discuss explanations for spatial synchrony in population fluctuations other than dispersal, such as the Moran effect and phase synchronization.

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.

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. 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. 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.

Determining the factors underlying the long-range spatial spread of infectious diseases is a key issue regarding their control. Dengue is the most important arboviral disease worldwide and a major public health problem in tropical areas. However the determinants shaping its dynamics at a national scale remain poorly understood. Here we describe the spatial-temporal pattern of propagation of annual epidemics in Cambodia and discuss the role that human movements play in the observed pattern. We used wavelet phase analysis to analyse time-series data of 105,598 hospitalized cases reported between 2002 and 2008 in the 135 (/180) most populous districts in Cambodia. We reveal spatial heterogeneity in the propagation of the annual epidemic. Each year, epidemics are highly synchronous over a large geographic area along the busiest national road of the country whereas travelling waves emanate from a few rural areas and move slowly along the Mekong River at a speed of ~11 km per week (95% confidence interval 3-18 km per week) towards the capital, Phnom Penh. We suggest human movements - using roads as a surrogate - play a major role in the spread of dengue fever at a national scale. These findings constitute a new starting point in the understanding of the processes driving dengue spread.

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/ƒ βmodels 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/ƒ βmodels 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.

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 Adélie, 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 (mean±SD: 0.92±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 (0.57±0.22) and breeding success (0.70±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.

Ecosystems and populations are known to be influenced not only by long-term climatic trends, but also by other short-term climatic modes, such as interannual and decadal-scale variabilities. Because interactions between climatic forcing, biotic and abiotic components of ecosystems are subtle and complex, analysis of long-term series of both biological and physical factors is essential to understanding these interactions. Here, we apply a wavelet analysis simultaneously to long-term datasets on the environment and on the populations and breeding success of three Antarctic seabirds (southern fulmar, snow petrel, emperor penguin) breeding in Terre Adélie, to study the effects of climate fluctuations on Antarctic marine ecosystems. We show that over the past 40 years, populations and demographic parameters of the three species fluctuate with a periodicity of 3-5 years that was also detected in sea-ice extent and the Southern Oscillation Index. Although the major periodicity of these interannual fluctuations is not common to different species and environmental variables, their cyclic characteristics reveal a significant change since 1980. Moreover, sliding-correlation analysis highlighted the relationships between environmental variables and the demography of the three species, with important change of correlation occurring between the end of the 1970s and the beginning of the 1980s. These results suggest that a regime shift has probably occurred during this period, significantly affecting the Antarctic ecosystem, but with contrasted effects on the three species.

1. We propose the method of 'phase analysis' for studying the spatio-temporal fluctuations of animal populations, and use this method for helping identify remarkable synchronized population fluctuations that may sometimes be found over very large spatial domains. 2. The method requires decomposing the observed time series of population fluctuations into two components - one that quantifies the changing phase of the signal, and the other that quantifies the changing amplitude. 3. Two populations are considered to be 'phase synchronized' if there is locking or synchrony between their phase components, while their associated amplitudes may nevertheless remain largely uncorrelated. 4. Since environmental noise often masks population synchrony, a null hypothesis approach is used to detect whether the phase variables are locked more than would be expected by chance alone. 5. The technique is thus particularly appropriate for ecological analyses where it is often important to study evidence of weak interactions in irregular non-stationary and noisy time series. Because climatic patterns (and predicted climate changes) almost certainly influence population dynamics, the approach appears particularly relevant for analysing the potential links between climatic fluctuations and population abundance.