Multi-parametric study of behavioural modulation in demersal decapods at the VENUS cabled observatory in Saanich Inlet, British Columbia, Canada

Matabos, Marjolaine; Aguzzi, Jacopo; Robert, Katleen; Costa, Corrado; Menesatti, Paolo; Juniper, S. Kim

doi:10.1016/j.jembe.2011.02.041

“…The recent implementation of cabled video observatories may provide a new technological solution for studying the activity rhythms of fishes in the field at the required high frequency and duration (from minutes to months) (Glover et al 2010, Aguzzi et al 2010, Matabos et al 2011. The monitoring capability of these platforms has recently been evaluated for fishes in coastal areas, but only relative to the detection of seasonal abundance variations (Barans et al 2005, Condal et al 2012).…”

Section: Resale or Republication Not Permitted Without Written Consenmentioning

confidence: 99%

Daily activity rhythms in temperate coastal fishes: insights from cabled observatory video monitoring

Aguzzi¹,

Sbragaglia²,

Santamaría³

et al. 2013

Mar. Ecol. Prog. Ser.

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The rhythmic behavior of marine species generates uncertainties in population and biodiversity assessments if the frequency of sampling is too low and irregular over time. Few attempts have been made to link community changes to the rhythmic behavior of individuals within populations. Cabled video observatories can be used to explore community changes over different temporal windows as a result of the activity rhythms of individuals within populations. In this study, we used, for the first time, a coastal cabled observatory (OBSEA) to video monitor activity rhythms of different fish species within an artificial reef area at a high frequency. During 1 mo, 30 min daily count patterns were continuously measured and compared with the corresponding solar irradiance. A significant (p < 0.05) day-night patterning was observed in the majority of recognized taxa by Chi-Square periodogram analysis. Three types of rhythms were identified in waveform plotting: (1) diurnal (Chromis chromis, Coris julis, Diplodus annularis, D. cervinus, D. sargus, D. vulgaris, Serranus cabrilla, Dentex dentex, Symphodus sp.); (2) nocturnal (Atherina sp. and Scorpaena sp.); and (3) crepuscular (Apogon imberbis, Oblada melanura, and Spicara maena). Diurnal species clustered around maximum averaged irradiance (computed from a cosinor analysis). The results were discussed evaluating whether visual count time series represent a reliable proxy for the swimming activity rhythms of individuals and whether the complex habitat use of coastal fishes would require the use of spatial networks of cameras. KEY WORDS:OBSEA · Cabled video observatories · Swimming rhythms · Western Mediterranean · Coastal areas · Temperate fishes · Irradiance · Daily activity Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 486: [223][224][225][226][227][228][229][230][231][232][233][234][235][236] 2013 seasons in coastal areas (Condal et al. 2012). The consideration of these aspects of activity and perceived community structure is critical in monitoring the effectiveness of marine protected areas, in improving survey designs for fisheries assessment, and in evaluating habitat use (Willis et al. 2000). To date, few attempts have been made to link temporal changes in communities to the behavioral rhythms of individuals within populations (Aguzzi et al. 2011a). Fish swimming/resting activity rhythms have traditionally been studied through laboratory experiments (re viewed by Reebs 2002, López-Olmeda & Sánchez-Vázquez 2010. Over the past few decades, however, the activity rhythms of a variety of species have also been the focus of increased study in the field based on a variety of sampling techniques subject to technical constraints on the repetition and duration of sampling. These techniques include the use of beach seines (Harmelin-Vivien & Francour 1992, Letourneur et al. 2001, Dul

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“…For a theoretically elaborated approach to the multiple change-point case see e.g. Hawkins, 2001. Although the application to time-series data on bacterial mat coverage was intended to illustrate the extension of this method from discrete to normally distributed variables, it also agrees with certain observations of Matabos et al, 2011a. In fact, when applying cross-correlation analysis, the bacterial mat coverage showed significant correlation to oxygen concentration in the water.…”

Section: Resultsmentioning

confidence: 74%

“…In any case, the question remains open: which one is the real (or the dominant) cause of the observed phenomenon? The results of our change-point analysis, to some extent, also contributes to the study of this problem: Before the observed major oxygen intrusion, our method also provided two change-points (each of them follows a local maximum of the oxygen concentration, see Figure 2 of Matabos et al, 2011a). This separated normal distributions and at each change-point the mean value changed to a smaller one, giving an insight to the effect of minor peaks in oxygen concentration.…”

Section: Resultsmentioning

confidence: 81%

“…In the present paper we illustrate the efficiency of our method in applying it to detect change-points in the "ready-made" time-series data on the bacterial mat coverage of a seafloor area. The data we will use have been collected by the authors of Matabos et al, 2011a, and made available at Dryad Digital Repository: http://dx.doi.org/ 10.5061/dryad.db2gd, see Matabos et al, 2011b. Since we use these data for illustrating our methodology, we only shortly summarize the circumstances of data collection below.…”

Section: Experimental Datamentioning

confidence: 99%

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Change-Point Method Applied to the Detection of Temporal Variations in Seafloor Bacterial Mat Coverage

López¹,

Rodríguez²,

Gámez³

et al. 2017

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ABSTRACT. The paper is aimed at a methodological development of change-point detection, applicable in identifying abrupt changes in temporal or spatial data sequences. In earlier papers we developed a method for detecting a change in the parameters of a discrete distribution, with the simultaneous estimation of the (deterministic but unknown) distribution parameters before and after the change. In this paper we not only extend this method to the case of normal distributions, but also provide a new algorithm for the iterative refining of the estimation of the change-point, based on a "cleaning" of mixed-up parts of the samples. The appropriate size of reduced part of the sample is analytically calculated for the case of normal distributions. This "cleaning" is combined with our original change-point detection method. Our new algorithm is not only validated on artificial data, but also applied to a real environmental data set collected and analysed by other authors in a seafloor observatory. Our results detecting abrupt changes of bacterial mat coverage of a seafloor area are in harmony with the biological fluctuations and changes in the abiotic environment, analysed recently by other authors using a different method. We also provide a comparison with other existing change-point detection methods: a one-dimensional version of the gradient method widely used for edge detection, and a maximum type statistical method well-known in environmental studies. Although normality conditions of our method are rather restrictive, its application potential for environmental data sets is also demonstrated.

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“…This new algorithm is validated and applied to the detection of change-points in the time-series data on the bacterial mat coverage of a seafloor area, described in Matabos et al, 2011a, and deposited in repository Matabos et al, 2011b. Although the theory of change-point analysis is mathematiccally rather involved, we emphasize that our method uses only sophomore statistics.…”

Section: Introductionmentioning

confidence: 99%

Change-Point Method Applied to the Detection of Temporal Variations in Seafloor Bacterial Mat Coverage

López¹

2015

J ENV INFORM

1

0

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ABSTRACT. The paper is aimed at a methodological development of change-point detection, applicable in identifying abrupt changes in temporal or spatial data sequences. In earlier papers we developed a method for detecting a change in the parameters of a discrete distribution, with the simultaneous estimation of the (deterministic but unknown) distribution parameters before and after the change. In this paper we not only extend this method to the case of normal distributions, but also provide a new algorithm for the iterative refining of the estimation of the change-point, based on a "cleaning" of mixed-up parts of the samples. The appropriate size of reduced part of the sample is analytically calculated for the case of normal distributions. This "cleaning" is combined with our original change-point detection method. Our new algorithm is not only validated on artificial data, but also applied to a real environmental data set collected and analysed by other authors in a seafloor observatory. Our results detecting abrupt changes of bacterial mat coverage of a seafloor area are in harmony with the biological fluctuations and changes in the abiotic environment, analysed recently by other authors using a different method. We also provide a comparison with other existing change-point detection methods: a one-dimensional version of the gradient method widely used for edge detection, and a maximum type statistical method well-known in environmental studies. Although normality conditions of our method are rather restrictive, its application potential for environmental data sets is also demonstrated.

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Multi-parametric study of behavioural modulation in demersal decapods at the VENUS cabled observatory in Saanich Inlet, British Columbia, Canada

Cited by 37 publications

References 47 publications

Daily activity rhythms in temperate coastal fishes: insights from cabled observatory video monitoring

Daily activity rhythms in temperate coastal fishes: insights from cabled observatory video monitoring

Change-Point Method Applied to the Detection of Temporal Variations in Seafloor Bacterial Mat Coverage

Change-Point Method Applied to the Detection of Temporal Variations in Seafloor Bacterial Mat Coverage

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