Complex interplays among population dynamics, environmental forcing, and exploitation in fisheries.

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.

Shifting patterns: malaria dynamics and rainfall variability in an African highland.

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.

Early-life sexual segregation: ontogeny of isotopic niche differentiation in the Antarctic fur seal.

Investigating the ontogeny of niche differentiation enables to determine at which life-stages sexual segregation arises, providing insights into the main factors driving resource partitioning. We investigated the ontogeny of foraging ecology in Antarctic fur seals (Arctocephalus gazella), a highly dimorphic species with contrasting breeding strategies between sexes. Sequential δ(13)C and δ(15)N values of whiskers provided a longitudinal proxy of the foraging niche throughout the whole life of seals, from weaning, when size dimorphism is minimal to the age of 5. Females exhibited an early-life ontogenetic shift, from a total segregation during their first year at-sea, to a similar isotopic niche as breeding females as early as age 2. In contrast, males showed a progressive change in isotopic niche throughout their development such that 5-year-old males did not share the same niche as territorial bulls. Interestingly, males and females segregated straight after weaning with males appearing to feed in more southerly habitats than females. This spatial segregation was of similar amplitude as observed in breeding adults and was maintained throughout development. Such early-life niche differentiation is an unusual pattern and indicates size dimorphism and breeding constraints do not directly drive sexual segregation contrary to what has been assumed in otariid seals.

Unexpected coherence and conservation.

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.

Estimation of CHD risk in a French working population using a modified Framingham model. The PCV-METRA Group.

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.

Blowout bifurcation with non-normal parameters in population dynamics.

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.

Using the Kalman filter and dynamic models to assess the changing HIV/AIDS epidemic.

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.

Does homologous reinfection drive multiple-wave influenza outbreaks? Accounting for immunodynamics in epidemiological models.

Epidemiological models of influenza transmission usually assume that recovered individuals instantly develop a fully protective immunity against the infecting strain. However, recent studies have highlighted host heterogeneity in the development of this immune response, characterized by delay and even absence of protection, that could lead to homologous reinfection (HR). Here, we investigate how these immunological mechanisms at the individual level shape the epidemiological dynamics at the population level. In particular, because HR was observed during the successive waves of past pandemics, we assess its role in driving multiple-wave influenza outbreaks. We develop a novel mechanistic model accounting for host heterogeneity in the immune response. Immunological parameters are inferred by fitting our dynamical model to a two-wave influenza epidemic that occurred on the remote island of Tristan da Cunha (TdC) in 1971, and during which HR occurred in 92 of 284 islanders. We then explore the dynamics predicted by our model for various population settings. We find that our model can explain HR over both short (e.g. week) and long (e.g. month) time-scales, as reported during past pandemics. In particular, our results reveal that the HR wave on TdC was a natural consequence of the exceptional contact configuration and high susceptibility of this small and isolated community. By contrast, in larger, less mixed and partially protected populations, HR alone cannot generate multiple-wave outbreaks. However, in the latter case, we find that a significant proportion of infected hosts would remain unprotected at the end of the pandemic season and should therefore benefit from vaccination. Crucially, we show that failing to account for these unprotected individuals can lead to large underestimation of the magnitude of the first post-pandemic season. These results are relevant in the context of the 2009 A/H1N1 influenza post-pandemic era.