Time series analysis of marine data: A key knowledge at the crossroads of marine sciences

Puillat, Ingrid; Prevosto, Marc; Mercier, Herlé; Thomas, Sindhura Rose

doi:10.1016/j.jmarsys.2013.11.010

Cited by 6 publications

(4 citation statements)

References 26 publications

(16 reference statements)

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“…Spectral techniques are fundamental tools used by a multitude of disciplines (e.g., climatology, Ghil et al () to provide comprehensible and interpretable information in the frequency domain, which is especially efficient for identify underlying mechanisms (Fulcher et al ). They are particularly relevant for multiscale non‐stationary, nonlinear and noisy signals, such as those recorded in tidal systems (Puillat et al ), by distinguishing between deterministic and stochastic processes. In coastal science, the four most commonly used spectral methods are Power Spectrum Analysis (PSA), Continuous Wavelet Transform (CWT), the Singular Spectrum Analysis (SSA) and Empirical Mode Decomposition (EMD) (Table ).…”

Section: Overview Of the Main Published Work Using Spectral Methods mentioning

confidence: 99%

Evaluation of spectral methods for high-frequency multiannual time series in coastal transitional waters: advantages of combined analyses

Jalón‐Rojas

Schmidt

Sottolichio

2016

Limnol. Oceanogr. Methods

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High-frequency monitoring is currently a major component in the management and research of the coastal system responses to ongoing global changes. This monitoring is essential in tidal systems to address the multiscale variability of physico-chemical parameters. The analysis of the resulting multiscale, nonlinear, non-stationary and noisy time series requires adequate techniques; however, to date, there are no standardized methods. Spectral methods might be useful tools to reveal the main variability time scales, and thus their associated forcings. The most widely used methods in coastal systems are Lomb-Scargle Periodogram (LSP), Singular Spectral Analysis (SSA), Continuous Wavelet Transform (CWT), and Empirical Mode Decomposition (EMD), but their relevance for high-frequency, long-term records is still largely unexplored. In this article, these spectral methods are tested and compared using a high-frequency 10-yr turbidity dataset in the Gironde estuary. Advantages and limitations of each method are evaluated on the basis of five criteria: (1) efficiency for incomplete time series, (2) appropriateness for time-varying analysis, (3) ability to recognize processes without complementary environmental variables, (4) capacity to calculate the relative importance of forcings, and (5) capacity to identify long-term trends. SSA is the only analysis method to satisfy all the criteria, even with 70% missing data. Combining methods is also a promising strategy; i.e., SSA 1 LSP for better recognition of processes; CWT 1 SSA and EMD 1 CWT for short-term (seasonal) and long-term (>1 yr) analysis, respectively. The purpose of this methodological framework is to serve as a reference for future postprocessing of data from monitoring programs in coastal waters.

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Section: Overview Of the Main Published Work Using Spectral Methods mentioning

confidence: 99%

Evaluation of spectral methods for high-frequency multiannual time series in coastal transitional waters: advantages of combined analyses

Jalón‐Rojas

Schmidt

Sottolichio

2016

Limnol. Oceanogr. Methods

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“…This monitoring development is being based on the combined use of optoacoustic and molecular biological sensors which are being implemented in the framework of cabled observatories. The capability to acquire a temporally related time series of multiparametric habitat and biological data allows researchers to envision aspects such as benthic primary production via chemosynthesis, deep-sea species ecological niches, and food web structure. − These data sets can be used to feed new numerical-based ecology approaches centered on multivariate statistics, time series analysis and ecosystem modeling (e.g., see refs and − ), in order to estimate the level of significance for putative cause–effect relationships (i.e., environmental control versus species and communities response) and provide an immediate vision of complex ecological processes at a local scale (e.g., species tolerance to the variation of key habitat drivers). This approach allows a transition from a still too descriptive deep-water and deep-sea ecology into a more quantitative one, as occurs in more directly accessible coastal areas and land.…”

Section: Roadmap For the Monitoring Of Ecosystem Indicatorsmentioning

confidence: 99%

New High-Tech Flexible Networks for the Monitoring of Deep-Sea Ecosystems

Aguzzi

Chatzievangelou

Marini

et al. 2019

Environ. Sci. Technol.

108

146

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“…These interactive windows could allow us to visualize complex biological and environmental information in the form of synthetic graphic outputs, highlighting significant global change trends. Putative causes (i.e., the environmental control) and effect (ecological indicator variation) relationships could be analyzed via the choice of different multivariate statistics and time series analysis approaches [34,35,[76][77][78] that are selected based on data quality.…”

Section: Cyber-infrastructure Developmentmentioning

confidence: 99%

The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms

Aguzzi

Chatzievangelou

Francescangeli

et al. 2020

Sensors

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Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.

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Time series analysis of marine data: A key knowledge at the crossroads of marine sciences

Abstract: International audienc

Cited by 6 publications

References 26 publications

Evaluation of spectral methods for high-frequency multiannual time series in coastal transitional waters: advantages of combined analyses

Evaluation of spectral methods for high-frequency multiannual time series in coastal transitional waters: advantages of combined analyses

New High-Tech Flexible Networks for the Monitoring of Deep-Sea Ecosystems

The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms

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