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Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods. [ABSTRACT FROM AUTHOR]

Dengue is the world's most important vector-borne viral disease. The dengue mosquito and virus are sensitive to climate variability and change. Temperature, humidity and precipitation influence mosquito biology, abundance and habitat, and the virus replication speed. In this study, we develop a modelling procedure to quantify the added value of including climate information in a dengue model for the 76 provinces of Thailand, from 1982-2013. We first developed a seasonal-spatial model, to account for dependency structures from 1 month to the next and between provinces. We then tested precipitation and temperature variables at varying time lags, using linear and nonlinear functional forms, to determine an optimum combination of time lags to describe dengue relative risk. Model parameters were estimated using integrated nested Laplace approximation. This approach provides a novel opportunity to perform model selection in a Bayesian framework, while accounting for underlying spatial and temporal dependency structures and linear or nonlinear functional forms. We quantified the additional variation explained by interannual climate variations, above that provided by the seasonal-spatial model. Overall, an additional 8 % of the variance in dengue relative risk can be explained by accounting for interannual variations in precipitation and temperature in the previous month. The inclusion of nonlinear functions of climate in the model framework improved the model for 79 % of the provinces. Therefore, climate forecast information could significantly contribute to a national dengue early warning system in Thailand. [ABSTRACT FROM AUTHOR]

Estimating the degree of individual specialisation is likely to be sensitive to the methods used, as they record individuals' resource use over different time-periods. We combined animal-borne video cameras, GPS/TDR loggers and stable isotope values of plasma, red cells and sub-sampled whiskers to investigate individual foraging specialisation in female Australian fur seals ( Arctocephalus pusillus doriferus) over various timescales. Combining these methods enabled us to (1) provide quantitative information on individuals' diet, allowing the identification of prey, (2) infer the temporal consistency of individual specialisation, and (3) assess how different methods and timescales affect our estimation of the degree of specialisation. Short-term inter-individual variation in diet was observed in the video data (mean pairwise overlap = 0.60), with the sampled population being composed of both generalist and specialist individuals (nested network). However, the brevity of the temporal window is likely to artificially increase the level of specialisation by not recording the entire diet of seals. Indeed, the correlation in isotopic values was tighter between the red cells and whiskers (mid- to long-term foraging ecology) than between plasma and red cells (short- to mid-term) ( R = 0.93-0.73 vs. 0.55-0.41). δC and δN values of whiskers confirmed the temporal consistency of individual specialisation. Variation in isotopic niche was consistent across seasons and years, indicating long-term habitat (WIC/TNW = 0.28) and dietary (WIC/TNW = 0.39) specialisation. The results also highlight time-averaging issues (under-estimation of the degree of specialisation) when calculating individual specialisation indices over long time-periods, so that no single timescale may provide a complete and accurate picture, emphasising the benefits of using complementary methods. [ABSTRACT FROM AUTHOR]

Recent collapses of population cycles in several species highlight the mutable nature of population behavior as well as the potential role of human-induced environmental change in causing population dynamics to shift. We investigate changes in the cyclicity of gypsy moth ( Lymantria dispar) outbreaks by applying wavelet analysis to an 86-year time series of forest defoliation in the northeastern United States. Gypsy moth population dynamics shifted on at least four occasions during the study period (1924-2009); strongly cyclical outbreaks were observed between ca. 1943-1965 and ca. 1978-1996, with noncyclical dynamics in the intervening years. During intervals of cyclical dynamics, harmonic oscillations at cycle lengths of 4-5 and 8-10 years co-occurred. Cross-correlation analyses indicated that the intensity of suppression efforts (area treated by insecticide application) did not significantly reduce the total area of defoliation across the region in subsequent years, and no relationship was found between insecticide use and the cyclicity of outbreaks. A gypsy moth population model incorporating empirically based trophic interactions produced shifting population dynamics similar to that observed in the defoliation data. Gypsy moth cycles were the result of a high-density limit cycle driven by a specialist pathogen. Though a generalist predator did not produce an alternative stable equilibrium, cyclical fluctuations in predator density did generate extended intervals of noncyclical behavior in the gypsy moth population. These results suggest that changes in gypsy moth population behavior are driven by trophic interactions, rather than by changes in climatic conditions frequently implicated in other systems. [ABSTRACT FROM AUTHOR]

We use (cross) wavelet analysis to decompose the time-frequency effects of oil price changes on the macroeconomy. We argue that the relation between oil prices and industrial production is not clear-cut. There are periods and frequencies where the causality runs from one variable to the other and vice-versa, justifying some instability in the empirical evidence about the macroeconomic effects of oil price shocks. We also show that the volatility of both the inflation rate and the industrial output growth rate started to decrease in the decades of 1950 and 1960. [ABSTRACT FROM AUTHOR]

Dengue fever is the most prevalent mosquito-borne viral disease of humans in tropical lands. As an efficient vaccine is not yet available, the only means to prevent epidemics is to control mosquito populations. These are influenced by human behavior and climatic conditions and thus, need constant effort and are very expansive. Examples of succeeded prevention are rare because of the continuous reintroduction of virus or vector from outside, or growing resistance of mosquito populations to insecticides. Climate variability and global warming are other factors which may favour epidemics of dengue. During a pilot study in Claris EC project, a model for the transmission of dengue was built, to serve as a tool for estimating the risk of epidemic transmission and eventually forecasting the risk under climatic change scenarios. An ultimate objective would be to use the model as an early warning system with meteorological forecasts as input, thus allowing better decision making and prevention. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Time series measured from real-world systems are generally noisy, complex and display statistical properties that evolve continuously over time. Here, we present a method that combines wavelet analysis and non-stationary surrogates to detect short-lived spatial coherent patterns from multivariate time-series. In contrast with standard methods, the surrogate data proposed here are realisations of a non-stationary stochastic process, preserving both the amplitude and time-frequency distributions of original data. We evaluate this framework on synthetic and real-world time series, and we show that it can provide useful insights into the time-resolved structure of spatially extended systems. [ABSTRACT FROM AUTHOR]

Zika virus (ZIKV) is a mosquito-borne flavivirus that predominantly circulates between humans and Aedes mosquitoes. Clinical studies have shown that Zika viruria in patients persists for an extended period, and results in infectious virions being excreted. Here, we demonstrate that Aedes mosquitoes are permissive to ZIKV infection when breeding in urine or sewage containing low concentrations of ZIKV. Mosquito larvae and pupae, including from field Aedes aegypti can acquire ZIKV from contaminated aquatic systems, resulting in ZIKV infection of adult females. Adult mosquitoes can transmit infectious virions to susceptible type I/II interferon receptor-deficient (ifnagr-/-) C57BL/6 (AG6) mice. Furthermore, ZIKV viruria from infected AG6 mice can causes mosquito infection during the aquatic life stages. Our studies suggest that infectious urine could be a natural ZIKV source, which is potentially transmissible to mosquitoes when breeding in an aquatic environment. Here the authors show that Aedes mosquitoes can acquire ZIKV by breeding in contaminated aquatic systems, and that these infected mosquitoes can transmit ZIKV to susceptible mice. This suggests that human urine containing aquatic environments could contribute to ZIKV transmission. [ABSTRACT FROM AUTHOR]

In the last decades, the notion that cities are in a state of equilibrium with a centralised organisation has given place to the viewpoint of cities in disequilibrium and organised from bottom to up. In this perspective, cities are evolving systems that exhibit emergent phenomena built from local decisions. While urban evolution promotes the emergence of positive social phenomena such as the formation of innovation hubs and the increase in cultural diversity, it also yields negative phenomena such as increases in criminal activity. Yet, we are still far from understanding the driving mechanisms of these phenomena. In particular, approaches to analyse urban phenomena are limited in scope by neglecting both temporal non-stationarity and spatial heterogeneity. In the case of criminal activity, we know for more than one century that crime peaks during specific times of the year, but the literature still fails to characterise the mobility of crime. Here we develop an approach to describe the spatial, temporal, and periodic variations in urban quantities. With crime data from 12 cities, we characterise how the periodicity of crime varies spatially across the city over time. We confirm one-year criminal cycles and show that this periodicity occurs unevenly across the city. These ‘waves of crime’ keep travelling across the city: while cities have a stable number of regions with a circannual period, the regions exhibit non-stationary series. Our findings support the concept of cities in a constant change, influencing urban phenomena—in agreement with the notion of cities not in equilibrium. [ABSTRACT FROM AUTHOR]

People who the author would like to thank for their assistance in the creation of the publication "EcoHealth" are mentioned.