Using Machine Learning to Observe
Abundance Patterns of the Dino
flagellate Noctiluca scintillans in
the Western English Channel
Noctiluca scintillans is a Harmful Algal Bloom (HAB) species with a wide
geographic distribution. It frequently
blooms and causes negative impacts on
marine ecosystems [1], although this
species is not thought to be a toxin producer. As a result of their high biomass
bloom-forming abilities, N. scintillans
can potentially cause anoxic conditions
and the generation of high levels of ammonia leading to localised die-offs of
higher trophic levels [2].
We aim to develop a rapid detection
of this species from flow cell-sorted
images of plankton off the south-west
coast of the UK. To that end, we have
trained a Random Forest (RF) algorithm
to i) rapidly and accurately quantify the
abundance of Noctiluca in seawater
samples of mixed plankton communities, ii) aid the examination of temporal
patterns in Noctiluca abundance in the
long-term, and iii) create a monitoring
tool to show when cell counts approach
bloom levels.
Seawater samples taken between
March and December 2018 at the L4 autonomous buoy in the Western English
Channel were processed using dynamic
imaging particle analysis (DIPA) performed using FlowCam (Fluid Imaging
Technologies, USA). All particles ranging in size from 380 to 1500 μm were
automatically imaged and enumerated
by VisualSpreadsheet (the software accompanying FlowCam).
A RF algorithm was then trained
and tested on 32 particle features (for
example, length, width, area, transparency etc. as measured by VisualSpreadsheet) in a mixed plankton community
of 246,000 total particles; 167,000 of
these particles had been manually identified as Noctiluca cells to give a True
Count against which to test the model.
One of the challenges of training the RF
model was to encompass the ontogenetic changes seen in Noctiluca cells
over their development and so training
sets were compiled from cell samples
taken on random dates throughout the
year. Fig. 1 shows examples of some of
the morphological and textural variety
of Noctiluca that our RF model is tasked
to classify.
The resulting model was then deployed on the 2018 dataset to estimate
Noctiluca abundance. RF model abundance and True Count were compared
using linear regression with a significance level = 0.05. A strong positive
relationship was found between the RF
abundance and True Count, with the RF
model almost exactly matching the True
Count (p < 0.001, R2 = 1.000). It must be
noted that the model is limited by FlowCams ability to identify individual particles in a sample which is likely not as
accurate as manual counting with microscopy [3]
A total of 33 plankton samples were
collected between March and December
2018 comprising 1,008,084 particles in
total, 540,499 of which were manually
identified as Noctiluca from the FlowCam images. To observe the temporal
pattern of Noctiluca abundance, the RF
model was deployed on the 2018 datasets and compared with total particle
counts (Fig. 2).
Fig. 1. Examples of morphological and textural variety of Noctiluca scintillans
14
HARMFUL ALGAE NEWS NO. 64 / 2020
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