Aggregates and their distributions determined from LOPC observations made using an autonomous profiling float

Petrik, Colleen M.; Jackson, George A.; Checkley, David M.

doi:10.1016/j.dsr.2012.12.009

Cited by 26 publications

(42 citation statements)

References 53 publications

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“…Using an independent method (Petrik et al, 2013) to estimate the size distribution of zooplankton tends to validate the present conclusions. The Petrik et al (2013) method focuses on estimating the size distribution of non-living particles and the total volume they represent.…”

Section: Correction Of the Size Spectramentioning

confidence: 50%

“…However, the discrimination between living and nonliving particles with these instruments is not possible, although using derived parameters, such as transparency, allows some discrimination (e.g., particles > 2 mm measured by the LOPC; Checkley et al, 2008). Recently, Petrik et al (2013) proposed a way of discriminating living and nonliving particles smaller than 2 mm counted by the LOPC by fitting a log-normal curve to the biovolume spectrum; residuals are considered to be living particles. Another way of measuring the zooplankton size structure is through traditional net samples followed by manual or automated counting and sizing (Bachiller et al, 2012;Vandromme et al, 2012).…”

Section: P Vandromme Et Al: Biscay Zooplankton Size Spectramentioning

confidence: 99%

“…The overestimation of the intercept by the LOPC was also reduced with the PLS regression (no significant difference in means observed, Student t test p = 0.39). The Petrik et al (2013) slopes and intercepts, respectively. The overestimation of intercepts was also reduced with no significant difference between the means (Student t test p = 0.31).…”

Section: Accuracy Of Estimated Zooplankton Size Spectramentioning

confidence: 99%

“…Steep size spectra are mostly observed along the coasts, especially in the Gironde plume area and off Galicia, whereas the flattest size , and after the PLS (partial least square) regression was performed on the LOPC (Ŷ , black plain square). In addition, the result of estimating NBSS parameters of LOPC data corrected using the method of Petrik et al (2013) are added (grey stars). The x = y line is added to each plot.…”

Section: Spatial and Interannual Distribution Of Zooplanktonmentioning

confidence: 99%

“…The methodology of Petrik et al (2013) was also implemented in the analysis. The LOPC size spectra were converted into volumes, and a log-normal distribution representing the non-living particles was estimated.…”

Section: Zooplankton Size Distribution Analysismentioning

confidence: 99%

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Springtime zooplankton size structure over the continental shelf of the Bay of Biscay

Vandromme

Nogueira²,

Huret

et al. 2014

Ocean Sci.

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Abstract. Linking lower and higher trophic levels requires special focus on the essential role played by mid-trophic levels, i.e., the zooplankton. One of the most relevant pieces of information regarding zooplankton in terms of flux of energy lies in its size structure. In this study, an extensive data set of size measurements is presented, covering parts of the western European continental shelf and slope, from the Galician coast to the Ushant front, during the springs from 2005 to 2012. Zooplankton size spectra were estimated using measurements carried out in situ with the Laser Optical Plankton Counter (LOPC) and with an image analysis of WP2 net samples (200 µm mesh size) performed following the ZooScan methodology. The LOPC counts and sizes particles within 100-2000 µm of spherical equivalent diameter (ESD), whereas the WP2/ZooScan allows for counting, sizing and identification of zooplankton from ∼ 400 µm ESD. The difference between the LOPC (all particles) and the WP2/ZooScan (zooplankton only) was assumed to provide the size distribution of non-living particles, whose descriptors were related to a set of explanatory variables (including physical, biological and geographic descriptors). A statistical correction based on these explanatory variables was further applied to the LOPC size distribution in order to remove the non-living particles part, and therefore estimate the size distribution of zooplankton. This extensive data set provides relevant information about the zooplankton size distribution variability, productivity and trophic transfer efficiency in the pelagic ecosystem of the Bay of Biscay at a regional and interannual scale.

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Section: Correction Of the Size Spectramentioning

confidence: 50%

Section: P Vandromme Et Al: Biscay Zooplankton Size Spectramentioning

confidence: 99%

Section: Accuracy Of Estimated Zooplankton Size Spectramentioning

confidence: 99%

Section: Spatial and Interannual Distribution Of Zooplanktonmentioning

confidence: 99%

Section: Zooplankton Size Distribution Analysismentioning

confidence: 99%

See 3 more Smart Citations

Springtime zooplankton size structure over the continental shelf of the Bay of Biscay

Vandromme

Nogueira²,

Huret

et al. 2014

Ocean Sci.

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Size distribution of aggregates across different aquatic systems around Japan shows that stronger aggregates are formed under turbulence

Takeuchi,

Giering,

Yamazaki

2024

Limnology & Oceanography

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Marine aggregates, composed of various particles, play a crucial role in ocean carbon storage. The overall size distribution of the aggregates (number size spectra) is controlled by the balance between aggregation and disaggregation processes. Turbulence has been proposed to facilitate both aggregation and disaggregation by increasing the collision rate of aggregates or sometimes directly tearing them apart. Predominant processes driven by turbulence typically depend on the level of turbulence—relatively weak turbulence is associated with aggregation while stronger turbulence promotes disaggregation. Aggregate strength also plays a key role, as strongly bonded aggregates can withstand turbulence better, leading to lower disaggregation rates. While the relationship between turbulence and aggregate strength has been studied numerically and experimentally, field measurements remain limited. Here, we compare our number size spectra to turbulence intensity from the field measurements across different environmental settings around Japan to determine the effect of turbulence on aggregate strength. We combined measurements from 10 sites with different environmental settings and observed the flatter slopes (higher net aggregation rate) and shifts in the intersection lengths with an increase of turbulence, while strong turbulence is typically linked with disaggregation. Our findings suggested that stronger aggregates are formed under stronger turbulence and the overall population of strong aggregates also increases with an increase of turbulence intensity. We also compared our number size spectra with three other confounding factors (fluorescence, salinity, and aggregate compositions) to confirm the effects of turbulence are dominant in our aggregate dynamics.

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Automated plankton image analysis using convolutional neural networks

Luo

Irisson

Graham

et al. 2018

Limnology & Ocean Methods

121

123

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The rise of in situ plankton imaging systems, particularly high-volume imagers such as the In Situ Ichthyoplankton Imaging System, has increased the need for fast processing and accurate classification tools that can identify a high diversity of organisms and nonliving particles of biological origin. Previous methods for automated classification have yielded moderate results that either can resolve few groups at high accuracy or many groups at relatively low accuracy. However, with the advent of new deep learning tools such as convolutional neural networks (CNNs), the automated identification of plankton images can be vastly improved. Here, we describe an image processing procedure that includes preprocessing, segmentation, classification, and postprocessing for the accurate identification of 108 classes of plankton using spatially sparse CNNs. Following a filtering process to remove images with low classification scores, a fully random evaluation of the classification showed that average precision was 84% and recall was 40% for all groups. Reliably classifying rare biological classes was difficult, so after excluding the 12 rarest taxa, classification accuracy for the remaining biological groups became > 90%. This method provides proof of concept for the effectiveness of an automated classification scheme using deep-learning methods, which can be applied to a range of plankton or biological imaging systems, with the eventual application in a variety of ecological monitoring and fisheries management contexts.

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Aggregates and their distributions determined from LOPC observations made using an autonomous profiling float

Cited by 26 publications

References 53 publications

Springtime zooplankton size structure over the continental shelf of the Bay of Biscay

Springtime zooplankton size structure over the continental shelf of the Bay of Biscay

Size distribution of aggregates across different aquatic systems around Japan shows that stronger aggregates are formed under turbulence

Automated plankton image analysis using convolutional neural networks

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