Influence of environmental and dietary exposures on metals accumulation among the residents of a major industrial harbour (Fos-sur-Mer, France).

OBJECTIVES: We sought to determine whether the residents living closer to the core industrial zone (Fos-sur-Mer) had higher trace metals blood and urinary levels than residents who lived further away (Saint-Martin-de-Crau). MATERIALS AND METHODS: As part of The INDEX study, we measured the following trace metals into blood and urine samples of 138 participants (80 in the core industrial zone and 58 in the reference area): Antimony, Arsenic, Cadmium, Chromium, Cobalt, Mercury, Nickel, Lead and Vanadium. Participants were recruited using a stratified random sampling method and had to meet the following inclusion criteria: 30-65 years old, living in the area since at least 3 years, not working in the industrial sector, non-smoker. We used single-pollutant multivariate linear regression models, using substitution when censored data were under 15 % and Tobit models alternatively, adjusting for personal physiological, social, dietary, housing characteristics and leisure activities. We also measured these trace metals in samples of lichens (Xanthoria parietina) and atmospheric particles (PM2.5). RESULTS: We showed higher lichen and air levels of several metals (Cd, Cr, Co, Ni and Pb) in the exposed area. Living close to the core industrial zone was significantly associated with an increase in blood levels of lead (adjusted geometric mean = 17.2 [15.8-18.7] vs 15.1 [13.7-16.7] µg.L-1, p < 0.05). We report significant increase of some metals urinary levels among residents of the industrial port zone, as the result of the use of the environment, itself contaminated by industrial activities: dietary history of self-consumption of vegetables (Cadmium), eggs and poultries (Vanadium). However, Vanadium levels were greater among self-consumers of poultry in the reference area and gardeners had circulatory levels of Lead greater than non-gardeners only in the reference area. Consumption of non-local sea-products increased the level of Cadmium. CONCLUSIONS: These results brought interesting clues, in complement to national programs, regarding the exposure to trace metals of residents living in a major industrial harbor.

Microgeographic local adaptation and ecotype distributions: The role of selective processes on early life-history traits in sympatric, ecologically divergent Symphonia populations.

Trees are characterized by the large number of seeds they produce. Although most of those seeds will never germinate, plenty will. Of those which germinate, many die young, and eventually, only a minute fraction will grow to adult stage and reproduce. Is this just a random process? Do variations in germination and survival at very young stages rely on variations in adaptations to microgeographic heterogeneity? and do these processes matter at all in determining tree species distribution and abundance? We have studied these questions with the Neotropical Symphonia tree species. In the Guiana shield, Symphonia are represented by at least two sympatric taxa or ecotypes, Symphonia globulifera found almost exclusively in bottomlands, and a yet undescribed more generalist taxon/ecotype, Symphonia sp1. A reciprocal transplantation experiment (510 seeds, 16 conditions) was set up and followed over the course of 6 years to evaluate the survival and performance of individuals from different ecotypes and provenances. Germination, survival, growth, and herbivory showed signs of local adaptation, with some combinations of ecotypes and provenances growing faster and surviving better in their own habitat or provenance region. S. globulifera was strongly penalized when planted outside its home habitat but showed the fastest growth rates when planted in its home habitat, suggesting it is a specialist of a high-risk high-gain strategy. Conversely, S. sp1 behaved as a generalist, performing well in a variety of environments. The differential performance of seeds and seedlings in the different habitats matches the known distribution of both ecotypes, indicating that environmental filtering at the very early stages can be a key determinant of tree species distributions, even at the microgeographic level and among very closely related taxa. Furthermore, such differential performance also contributes to explain, in part, the maintenance of the different Symphonia ecotypes living in intimate sympatry despite occasional gene flow.

Hypomethylation of the aquatic invasive plant, Ludwigia grandiflora subsp. hexapetala mimics the adaptive transition into the terrestrial morphotype.

Ongoing global changes affect ecosystems and open up new opportunities for biological invasion. The ability of invasive species to rapidly adapt to new environments represents a relevant model for studying short-term adaptation mechanisms. The aquatic invasive plant, Ludwigia grandiflora subsp. hexapetala, is classified as harmful in European rivers. In French wet meadows, this species has shown a rapid transition from aquatic to terrestrial environments with emergence of two distinct morphotypes in 5 years. To understand the heritable mechanisms involved in adjustment to such a new environment, we investigate both genetic and epigenetic as possible sources of flexibility involved in this fast terrestrial transition. We found a low overall genetic differentiation between the two morphotypes arguing against the possibility that terrestrial morphotype emerged from a new adaptive genetic capacity. Artificial hypomethylation was induced on both morphotypes to assess the epigenetic hypothesis. We analyzed global DNA methylation, morphological changes, phytohormones and metabolite profiles of both morphotype responses in both aquatic and terrestrial conditions in shoot and root tissues. Hypomethylation significantly affected morphological variables, phytohormone levels and the amount of some metabolites. The effects of hypomethylation depended on morphotypes, conditions and plant tissues, which highlighted differences among the morphotypes and their plasticity. Using a correlative integrative approach, we showed that hypomethylation of the aquatic morphotype mimicked the characteristics of the terrestrial morphotype. Our data suggest that DNA methylation rather than a new adaptive genetic capacity is playing a key role in L. grandiflora subsp. hexapetala plasticity during its rapid aquatic to terrestrial transition.

Identifying stationary phases in multivariate time series for highlighting behavioural modes and home range settlements.

Recent advances in biologging open promising perspectives in the study of animal movements at numerous scales. It is now possible to record time series of animal locations and ancillary data (e.g. activity level derived from on-board accelerometers) over extended areas and long durations with a high spatial and temporal resolution. Such time series are often piecewise stationary, as the animal may alternate between different stationary phases (i.e. characterized by a specific mean and variance of some key parameter for limited periods). Identifying when these phases start and end is a critical first step to understand the dynamics of the underlying movement processes. We introduce a new segmentation-clustering method we called segclust2d (available as a r package at cran.r-project.org/package=segclust2d). It can segment bivariate (or more generally multivariate) time series and possibly cluster the various segments obtained, corresponding to different phases assumed to be stationary. This method is easy to use, as it only requires specifying a minimum segment length (to prevent over-segmentation), based on biological rather than statistical considerations. This method can be applied to bivariate piecewise time series of any nature. We focus here on two types of time series related to animal movement, corresponding to (a) at large scale, series of bivariate coordinates of relocations, to highlight temporary home ranges, and (b) at smaller scale, bivariate series derived from relocations data, such as speed and turning angle, to highlight different behavioural modes such as transit, feeding and resting. Using computer simulations, we show that segclust2d can rival and even outperform previous, more complex methods, which were specifically developed to highlight changes of movement modes or home range shifts (based on hidden Markov and Ornstein-Uhlenbeck modelling), which, contrary to our method, usually require the user to provide relevant initial guesses to be efficient. Furthermore, we demonstrate it on actual examples involving a zebra's small-scale movements and an elephant's large-scale movements, to illustrate how various movement modes and home range shifts, respectively, can be identified.

A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data.

Omic data are characterized by the presence of strong dependence structures that result either from data acquisition or from some underlying biological processes. Applying statistical procedures that do not adjust the variable selection step to the dependence pattern may result in a loss of power and the selection of spurious variables. The goal of this paper is to propose a variable selection procedure within the multivariate linear model framework that accounts for the dependence between the multiple responses. We shall focus on a specific type of dependence which consists in assuming that the responses of a given individual can be modelled as a time series. We propose a novel Lasso-based approach within the framework of the multivariate linear model taking into account the dependence structure by using different types of stationary processes covariance structures for the random error matrix. Our numerical experiments show that including the estimation of the covariance matrix of the random error matrix in the Lasso criterion dramatically improves the variable selection performance. Our approach is successfully applied to an untargeted LC-MS (Liquid Chromatography-Mass Spectrometry) data set made of African copals samples. Our methodology is implemented in the R package MultiVarSel which is available from the Comprehensive R Archive Network (CRAN).

Metrics for describing dyadic movement: a review.

In movement ecology, the few works that have taken collective behaviour into account are data-driven and rely on simplistic theoretical assumptions, relying in metrics that may or may not be measuring what is intended. In the present paper, we focus on pairwise joint-movement behaviour, where individuals move together during at least a segment of their path. We investigate the adequacy of twelve metrics introduced in previous works for assessing joint movement by analysing their theoretical properties and confronting them with contrasting case scenarios. Two criteria are taken into account for review of those metrics: 1) practical use, and 2) dependence on parameters and underlying assumptions. When analysing the similarities between the metrics as defined, we show how some of them can be expressed using general mathematical forms. In addition, we evaluate the ability of each metric to assess specific aspects of joint-movement behaviour: proximity (closeness in space-time) and coordination (synchrony) in direction and speed. We found that some metrics are better suited to assess proximity and others are more sensitive to coordination. To help readers choose metrics, we elaborate a graphical representation of the metrics in the coordination and proximity space based on our results, and give a few examples of proximity and coordination focus in different movement studies.

Biological traits, rather than environment, shape detection curves of large vertebrates in neotropical rainforests.

Line transect surveys are widely used in Neotropical rainforests to estimate the population abundance of medium- and large-sized vertebrates. The use of indices such as encounter rate has been criticized because the probability of animal detection may fluctuate due to the heterogeneity of environmental conditions among sites. In addition, the morphological and behavioral characteristics (biological traits) of species affect their detectability. In this study, we compared the extent to which environmental conditions and species' biological traits bias abundance estimates in terra firme rainforests in French Guiana. The selected environmental conditions included both physical conditions and forest structure covariates, while the selected biological traits included the morphological and behavioral characteristics of species. We used the distance sampling method to model the detection probability as an explicit function of environmental conditions and biological traits and implemented a model selection process to determine the relative importance of each group of covariates. Biological traits contributed to the variability of animal detectability more than environmental conditions, which had only a marginal effect. Detectability was best for large animals with uniform or disruptive markings that live in groups in the canopy top. Detectability was worst for small, solitary, terrestrial animals with mottled markings. In the terra firme rainforests that represent ~80% of the Amazonia and Guianas regions, our findings support the use of relative indices such as the encounter rate to compare population abundance between sites in species-specific studies. Even though terra firme rainforests may appear similar between regions of Amazonia and the Guianas, comparability must be ensured, especially in forests disturbed by human activity. The detection probability can be used as an indicator of species' vulnerability to hunting and, thus, to the risk of local extinction. Only a few biological trait covariates are required to correctly estimate the detectability of the majority of medium- and large-sized vertebrates. Thus, a biological trait model could be useful in predicting the detection probabilities of rare, uncommon, or localized species for which few data are available to fit the detection function.

Statistical ecology comes of age.

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Effects of stochasticity in early life history on steepness and population growth rate estimates: an illustration on Atlantic bluefin tuna.

The intrinsic population growth rate (r) of the surplus production function used in the biomass dynamic model and the steepness (h) of the stock-recruitment relationship used in age-structured population dynamics models are two key parameters in fish stock assessment. There is generally insufficient information in the data to estimate these parameters that thus have to be constrained. We developed methods to directly estimate the probability distributions of r and h for the Atlantic bluefin tuna (Thunnus thynnus, Scombridae), using all available biological and ecological information. We examined the existing literature to define appropriate probability distributions of key life history parameters associated with intrinsic growth rate and steepness, paying particular attention to the natural mortality for early life history stages. The estimated probability distribution of the population intrinsic growth rate was weakly informative, with an estimated mean r = 0.77 (±0.53) and an interquartile range of (0.34, 1.12). The estimated distribution of h was more informative, but also strongly asymmetric with an estimated mean h = 0.89 (±0.20) and a median of 0.99. We note that these two key demographic parameters strongly depend on the distribution of early life history mortality rate (M(0)), which is known to exhibit high year-to-year variations. This variability results in a widely spread distribution of M(0) that affects the distribution of the intrinsic population growth rate and further makes the spawning stock biomass an inadequate proxy to predict recruitment levels.