The pelagic zone is home to a large diversity of organisms such as macrozooplankton and micronekton (MM), connecting the surface productive waters to the mesopelagic layers (200-1000 m) through diel vertical migrations (DVM). Active acoustics complement net sampling observations by detecting sound-scattering layers (SL) of organisms, allowing to monitor the MM dynamics with a high spatio-temporal resolution. Multi-frequency analyses are a pertinent approach to better integrate the rich diversity of organisms composing SLs and their respective dynamics. However, analysing simultaneously emitted acoustic signals with distinct depth ranges and separating spatial from temporal variability is challenging and needs adapted tools to be fully exploited. This study examines the pelagic realm in a transition zone between the Southern Ocean and the subtropical Indian Ocean, crossing the Saint-Paul and Amsterdam islands’ natural reserve. We extended a Multivariate Functional Principal Component Analysis (mfPCA) to analyse the joint vertical variation of five frequencies from two oceanographic cruises (2016 and 2022), allowing the decomposition of the acoustic dataset into orthogonal vertical modes (VM) of variability. We found the first VM to be linked to the temporal variability due to DVM, while the following majorly depict patterns in spatial distribution. Overall, from the subantarctic to the subtropical zones, we observed (i) enrichment of densities in the surface layer (0–100 m), (ii) a decrease in densities in the intermediate layer during the daytime (100–300 m) and (iii) the apparition of an intensive deep scattering layer on the 38 kHz. We explored VMs’ connection with in-situ environmental conditions by clustering our observations into three distinct environmental-acoustic regions. These regions were compared with vertically integrated nautical area scattering coefficient distribution, a proxy for marine organisms’ biomass. Additionally, we analysed species assemblage changes from complementary cruises to further elucidate the observed acoustic distribution. We show that the mfPCA method is promising to better integrate the pelagic horizontal, vertical and temporal dimensions which is a step towards further investigating the control of the environment on the distribution and structuring of pelagic communities.

The location of the Antarctic Polar Front (PF) is mapped in the Southern Indian Ocean by decomposing the shape of temperature and salinity profiles into vertical modes using a functional Principal Component Analysis. We define the PF as the northernmost minimum of temperature at the subsurface and represent it as a linear combination of the first three modes. This method is applied on an ocean reanalysis data set and on in situ observations, revealing a seasonal variability of the PF latitudinal position that is most pronounced between the Conrad Rise and the Kerguelen Plateau. This shift coincides with variations in the transport across the Northern Kerguelen Plateau. We suggest that seasonal changes of the upper stratification may drive the observed variability of the PF, with potentially large implications for the pathways and residence time of water masses over the plateau and the phytoplankton bloom extending southeast of the Kerguelen Islands.

Abstract Structural sensitivity, namely the sensitivity of a model dynamics to slight changes in its mathematical formulation, has already been studied in some models with a small number of state variables. The aim of this study is to investigate the impact of structural sensitivity in a food web model. Especially, the importance of structural sensitivity is compared to that of trophic complexity (number of species, connectance), which is known to strongly influence food web dynamics. Food web structures are built using the niche model. Then food web dynamics are modeled using several type II functional responses parameterized to fit the same predation fluxes. Food web persistence was found to be mostly determined by trophic complexity. At the opposite, even if food web connectance promotes equilibrium dynamics, their occurrence is mainly driven by the choice of the functional response. These conclusions are robust to changes in some parameter values, the fitting method and some model assumptions. In a one-prey/one-predator system, it was shown that the possibility that multiple stable states coexist can be highly structural sensitive. Quantifying this type of uncertainty at the scale of ecosystem models will be both a natural extension to this work and a challenging issue.

Numerous formulations with the same mathematical properties can be relevant to model a biological process. Different formulations can predict different model dynamics like equilibrium vs. oscillations even if they are quantitatively close (structural sensitivity). The question we address in this paper is: does the choice of a formulation affect predictions on the number of stable states? We focus on a predator–prey model with predator competition that exhibits multiple stable states. A bifurcation analysis is realized with respect to prey carrying capacity and species body mass ratio within range of values found in food web models. Bifurcation diagrams built for two type-II functional responses are different in two ways. First, the kind of stable state (equilibrium vs. oscillations) is different for 26.0–49.4% of the parameter values, depending on the parameter space investigated. Using generalized modelling, we highlight the role of functional response slope in this difference. Secondly, the number of stable states is higher with Ivlev's functional response for 0.1–14.3% of the parameter values. These two changes interact to create different model predictions if a parameter value or a state variable is altered. In these two examples of disturbance, Holling's disc equation predicts a higher system resilience. Indeed, Ivlev's functional response predicts that disturbance may trap the system into an alternative stable state that can be escaped from only by a larger alteration (hysteresis phenomena). Two questions arise from this work: (i) how much complex ecological models can be affected by this sensitivity to model formulation? and (ii) how to deal with these uncertainties in model predictions?

Climate change has a strong impact on marine ecosystems, including temperate species. Analysing the diversity of thermotolerance levels within species along with their genetic structure enables a better understanding of their potential response to climate change. We performed this integrative study on the Mediterranean octocoral Eunicella cavolini, with samples from different depths and by means of a common garden experiment. This species does not host photosynthetic Symbiodinium, enabling us to focus on the cnidarian response. We compared the thermotolerance of individuals from 20 m and 40 m depths from the same site and with replicates from the same colony. On the basis of an innovative statistical analysis of necrosis kinetics and risk, we demonstrated the occurrence of a very different response between depths at this local scale, with lower thermotolerance of deep individuals. Strongly thermotolerant individuals were observed at 20 m with necrosis appearing at higher temperatures than observed in situ. On the basis of nine microsatellite loci, we showed that these marked thermotolerance differences occur within a single population. This suggests the importance of acclimatization processes in adaptation to these different depths. In addition, differences between replicates demonstrated the occurrence of a variability of response between fragments from the same colony with the possibility of an interaction with a tank effect. Our results provide a basis for studying adaptation and acclimatization in Mediterranean octocorals in a heterogeneous environment.

We consider the statistical analysis of a 1.7-year high-frequency sampled time series, between 2009 and 2010, recorded at the ANTARES observatory in the deep NW Mediterranean Sea (2475 m depth). The objective was to estimate relationships between bioluminescence and environmental time series (temperature, salinity and current speed). As this entire dataset is characterized by non-linearity and non-stationarity, two time-frequency decomposition methods (wavelet and Hilbert-Huang) were used. These mathematical methods are dedicated to the analysis of a signal at various time and frequencies scales. This work propose some statistical tools dedicated to the study of relationships between two time series. Our study highlights three events of high bioluminescence activity in March 2009, December 2009 and March 2010. We demonstrate that the two events occurring in March 2009 and 2010 are correlated to the arrival of newly formed deep water masses at frequencies of approximately 4.8 x 10(-7) (period of 24.1 days). In contrast, the event in December 2009 is only correlated with current speed at frequencies of approximately 1.9 x 10(-6) (period of 6.0 days). The use of both wavelet and Hilbert-Huang transformations has proven to be successful for the analysis of multivariate time series. These methods are well-suited in a context of the increasing number of long time series recorded in oceanography. (C) 2014 Elsevier Ltd. All rights reserved.

Over-parametrization in modelling is a well-known issue that makes it hard to identify which part of a model is responsible for a given behaviour. In line with that ascertainment, this work presents the outline of an empirical method to simplify models by decreasing the number of parameters. By using regression trees to classify outputs according to related input parameters, the method provides the modeller with an objective tool to reduce the range of the used parameters and, under certain conditions, to establish relations between them. Thereby, the complexity of the model is reduced on the basis of mathematical arguments. As an example, a dynamic energy budget-based model of a mesopelagic bacterial ecosystem is simplified using the presented method. The main benefits of such a method are thus highlighted: (i) more robust parameter estimations; (ii) less complex formulations; and (iii) fewer modelling assumptions. To conclude, the difficulties encountered are discussed, and several solutions are proposed to deal with them.

In this study, we use data from the ANTARES neutrino telescope, located 40 km off the French Mediterranean coast at 2475 m depth. In natural environments, non-stationary and non-linearly datasets are commonly recorded but only two methods are existing to explore these particular data. The wavelet (Torrence and Compo, 1998) and the Hilbert-Huang (Huang et al., 1998) methods are edicated to analyse fluctuations at various scales of time and frequencies. Both methods decompose time series into bases of functions dedicated to specific frequencies. This study provides an analysis of time series using these two decomposition methods. Several long time series between the end of 2007 and the middle of 2010 have been analysed providing informations on links between the biological variable bioluminescence and oceanographic variables. These relations are characterized using common frequencies excited in the signals and time where they are excited. Then, in a final aim bioluminescence sensors would be a new way to provide informations of ecological global changes taking place in the deep ecosystems.

These last decades, the Berre lagoon (in southeastern France) has been deeply affected since the 1930s by strong inputs of contaminants associated with industrial development and since 1966 by huge inputs of freshwater and silts due to the installation of a hydroelectric power plant. Surveys of the surface sediment contamination have been sparsely performed since 1964 for management and research purposes. These surveys were performed by various laboratories that investigated different chemicals and sampling areas using different analysis protocols. Therefore, the available data are disconnected in time and space and differ in quality. In order to reconstruct coherent time series of sediment contamination from this heterogeneous datasets and to discuss the influences of industrial and hydroelectric discharges we used a statistical approach. This approach is based on Principal Component Analysis (PCA) and Fuzzy clustering analysis on data from one extensive survey realized on surface sediments in 1976. The PCA allowed identifying two geochemical indexes describing the main surface sediment geochemical characteristics. The fuzzy clustering analysis on these indexes allowed identifying sub-areas under the specific influence of industrial or hydroelectric discharges. This allowed us to reconstruct, for each sub-area, a coherent and interpretable long-term time series of sediment contamination from the available database. Reconstructed temporal trends allowed us to estimate: (i) the overall decrease of sediment contamination since the mid-1970 attributed to industrial discharge regulations enacted at this period and (ii) the dilution of the concentrations of sediment bound contaminants induced by the hydroelectric power plant and its associated particulate matter inputs.

Laboratory microcosm experiments were performed to evaluate petroleum hydrocarbon biodegradation and phospholipid fatty acid (PLFA) composition in either non-contaminated (NC) or contaminated (C) sedimentary microcosms with a crude oil. An analytical procedure was developed to extract both hydrocarbons and PLFAs from the same culture. PLFAs were analyzed over time during an aerobic microcosm experiment (0–21 days) to obtain a better understanding of the structural changes in bacterial sedimentary communities. We observed an increase in both hydrocarbon degraders and heterotrophic bacteria as reflected in PLFA concentrations. Inferred total biomass ranged from 4 to 11.3×109 and 22.1 to 199.2×109 bacterial cellskg−1 dry sediment for NC and C sediments, respectively. Total biomass was greater in C than NC sediment, increasing by about twice, 3×, 13× and 50× for 0, 2, 7 and 21days, respectively. These modifications coincided with removal of petroleum hydrocarbons that reached 35% of total hydrocarbon and 64% of total n-alkanes after 21days. Microbial community structure changes over time in the C and NC microcosms were shown with principal components analysis and a study of some characteristic PLFAs as biomarkers. Some modifications occurred after several days of petroleum exposure, but crude oil addition did not produce an obvious increase in the biomass of petroleum degraders.

Spatial and temporal heterogeneity are often described as important factors having a strong impact on biodiversity. The effect of heterogeneity is in most cases analyzed by the response of biotic interactions such as competition of predation. It may also modify intrinsic population properties such as growth rate. Most of the studies are theoretic since it is often difficult to manipulate spatial heterogeneity in practice. Despite the large number of studies dealing with this topics, it is still difficult to understand how the heterogeneity affects populations dynamics. On the basis of a very simple model, this paper aims to explicitly provide a simple mechanism which can explain why spatial heterogeneity may be a favorable factor for production. We consider a two patch model and a logistic growth is assumed on each patch. A general condition on the migration rates and the local subpopulation growth rates is provided under which the total carrying capacity is higher than the sum of the local carrying capacities, which is not intuitive. As we illustrate, this result is robust under stochastic perturbations.

Situated close to the French Mediterranean coast, the Berre lagoon is a brackish water lagoon highly disturbed by freshwater inputs coming from an hydroelectric power station. Since 1996, in the framework of an ecological program, a self sampling station hourly samples physicochemical variables such as temperature salinity and dissolved oxygen rates. This paper presents a statistical method that allows to summarize the dynamics of the water column with a sequence of daily typical states. This method takes into account the heterogeneity of the variables and keeps their temporal structure. Thus, the typical states are described by the hourly evolution of the distributions of each variable. Most of the time (74 % of the observations), the water column is in a stratified state. The water column rocks to a mixed state under the effects of wind (19 % of the observations). Transient days between these two states can be isolated in 7 % of cases. Results show that the long-term system dynamics is not correlated with the daily environmental variations. (C) 2001 Academie des sciences / Editions scientifiques et medicales Elsevier SAS.

This article presents results on daily forecasts of dissolved oxygen rates in a lagoon, the ‘Etang de Berre’, situated close to the French Mediterranean sea coast and submitted to particular meteorological conditions and freshwater inputs. To provide estimates of the response variable y (dissolved oxygen), a set of jointed decision rules is constructed by successive binary splitting of the predictor space composed of wind speed and freshwater flow data. The prediction model is displayed in the shape of a binary decision tree. We propose a transfer procedure in order to improve the prediction error of the tree model. Results obtained on the ‘Etang de Berre’ data set allow to describe and precise the effects of the environmental variables on the dissolved oxygen dynamics. The transfer procedure applied after the tree building process gives gains of prediction accuracy of about 17%.