IMPAQ - IMProvement of AQuaculture high quality fish fry production.
How to increase the reliability of copepods as live prey in Danish fish farms?
Status report
Implementation of ZooImage software in
quantification the development stages of
Acartia tonsa in the intensive cultures
1
Implementation of ZooImage software in quantityfication the development stages of
Acartia tonsa in the intensive cultures
Automatic quantification of developmental stages and biomass of Acartia tonsa in the
intensive cultures by using ZooImage software
Background for this study
The interest in use and cultivation of copepods as live feeds in marine larviculture has been
growing since the 1980s (Schipp, 2006; Støttrup, 2003; Støttrup, et al., 1986). Copepods are
proposed to be perfect supplementary live feeds for traditional ones such as Artemia or
rotifers. Copepods are the main component of food source of marine fish larvae in nature
and the nutritional value of copepods is adequate for fish larvae (Evjemo, et al., 2003;
McKinnon, et al., 2003; Sargent, et al., 1997; Støttrup, 2000). The presence of copepods in
diets, either alone or in combination with rotifers or Artemia, enhances the growth, survival
and/or frequency of normal pigmentation of fish larvae compared to those only fed by
rotifers or Artemia (Evjemo, et al., 2003; McEvoy, et al., 1998; Næss, et al., 1995; Payne
and Rippingale, 2000; Schipp, et al., 1999; Toledo, et al., 1999; Toledo, et al., 2005).
Despite many benefits of copepods, the use of these live feeds in aquaculture is still rare
(Støttrup, 2000) because of challenges in culture techniques and the high cost effectiveness
and the labor intensive requirement in production of copepods (Alver, et al., 2011; Drillet, et
al., 2011; Schipp, 2006; Støttrup, 2000). For the latter issue, the application of automatic
techniques is a key to success in large scale copepod production(Alver, et al., 2011; Drillet,
et al., 2011).
ZooImage is a computer assisted software for analysing plankton images to identify and
classsify different zooplankton groups from preserved samples (Grosjean and Denis, 2007).
ZooImage is an open software which can be modified to meet the different requirements of
users based on a general framework to import images, analyse them, and export results
which is easy to read from any other software such as Microsoft Excel, Matlab, etc
(Grosjean and Denis, 2007). ZooImage has shown a great ability to classify field collected
zooplankton groups at a higher species levels with a high accuracy, ranging from 70 to 86%
2
(Bell and Hopcroft, 2008; Di Mauro, et al., 2011; Gislason and Silva, 2009; Plourde, et al.,
2008). ZooImage was also applied to analyse densities of benthic meiofauna communities at
higher meiofaunal taxa with the accuracy of 82 – 93% (Lindgren, et al., 2013). ZooImage
also shows the ability to differenitate between a group of nauplii of Calanoid copepods and
their coressponding copepodite stages in the field collected samples (Plourde, et al., 2008).
To our knowledge, ZooImage has not been used to analyse stages of a single zooplankton
species yet. In copepod production, quantification of the different developmental stages is
important analysing hatching success, mortality and developmental rate as well as
estimating the densities and biomass of copepod in larval first feeding tank. Plankton
counter which has been used for automatic mornitoring rotifers density in rotifer cultures
and in larval first feeding tanks, has applied to estimate size and developmental stage and
density of Acartia tonsa nauplii within a reasonable margins of error (Alver, et al., 2011).
The calanoid copepod Acartia tonsa used in this experiment was originated from copepods
that was isolated in 1981 and has been cultured in laboratory since then (Støttrup, et al.,
1986). They has a cosmopolitan distribution, wide tolerance to temperature and salinity and
a small body size. They can also produce resting eggs in poor environmental conditions
(Castro-Longoria, 2001) that can be easily collected and stored to use for marine
aquaculture (Drillet, et al., 2006; Støttrup, et al., 1999). Given the high varation in the size
of all copepod stages, the high nutritional value, the easy production of the resting eggs A.
tonsa is indeed one of the most promising copepod species as an appropriate live feed for
marine larviculture. There is no automatic measurement method has been applied in Acartia
tonsa culture in estimating density, developmental stages as well as biomass for both nauplii
and copepod stages.
3
Aims
The general objective of this study is to adapt the ZooImage analysis system into our
continuous A. tonsa culture to apply it to the aquaculture industry. Specifically, a training
set of profiles of copepod developmental stages was created to evaluate the efficiecy of the
system in counting eggs, analyze profiles of various copepod stages, estimate both density
and biomass (size) of copepod cultures.
Future perspectives
The successful application of ZooImage software in indentification of eggs and different
developmental stages of Acartia tonsa in the culture will enable the ability to automatically
monitor parameters such as hatching success, mortality and developmental rate in A. tonsa
production not only for the laboratory scale but also for comercial purpose in mornitoring
the density of copepods as feed of finfish larval cultures. Moreover, the set up of ZooImage
system for indentification of A. tonsa stages is designed with an inexpensive scanner and
computer, and easy to train for new users which features are to ensure the application of this
technique in hatcheries.
4
Materials and methods
Copepod origin and culture
The experimental copepod Acartia tonsa was originally isolated from Øresund, Denmark in
1981 (Støttrup, et al., 1986) and has been continuously maintained at a constant salinity (32
ppt), temperature (17°C) in the dark in laboratory since then. Copepods were fed with
monoalgal diet Rhodomonas baltica in excess of 950 µg C/L (Berggreen, et al., 1988;
Kiørboe, et al., 1985) and eggs were harvested, cleaned and stored in 0.2 filtered seawater in
closed test tubes at 2 – 3°C without the presence ofoxygen (zero oxygen condition) (Drillet,
et al., 2006).
Egg preparation for reference library of ZooImage program
760 ± 112 (n = 5) of eggs were collected from the culture and incubated in each of five 600mL-bottles (acid washed polycarbonate Nalgene®). All eggs were at 2–hour old age to
ensure the homogenized stages of copepod in the samples for creating reference library of
ZooImage programe. In each bottle ca. 20000 cell/mL (equivalent to 950 µg C/L) of R.
baltica was added daily to ensure excessive food for copepods (Kiørboe et al., 1985;
Berggreen et al., 1988). The bottles were fully filled to avoid air bubbles of inside, corvered
with a plastic film together with a lid and fixed to a 75 cm diameter plankton wheel (1 rpm)
in a 17°C climate room under constant photoperiod of 24:0 light:dark. Afterward 10 mL
samples were taken from each bottle every 24 hours by 10 mL sampler (Witeg, W-company
Preciso 10 mL, NS 29.2/32) and followed the sample preparation procedure for ZooImage
analysis (see below).
Cold stored eggs were diluted to different densities. After that two 10 mL sub-sample of
each density were used for counting by ZooImage and manual, respectively.
Collection of multiple-stage samples
To determine the accuracy of ZooImage program with manual method in identification
developmental stages stages of A. tonsa, samples of copepods from the culture were
collected by a 1 L beaker and then concentrated to different densities by filtering through 52
µm mesh sieve. To have all stages of A. tonsa in the samples for analyzing, copepods were
5
daily sampled during the developmental period (14 days). Samples were fixed in ethanol
(70%). After all copepod stages were sampled, they were mixed together and
diluted/concentrated to different densities, following the sample preparation procedure for
ZooImage analysis (described in the next section). The copepod densities in the petri-dish
were the final densities for statistical analyses.
Automated sample analysis
In this study, ZooImage program was used without any modification as we would expect
this to be the typical default for most of potentially new users. Samples were scanned by an
Epson Perfection V500 Photo colour (6400 dpi, 16 bit gray scale in the positive film mode).
All image acquisition and processing were performed on an Intel (R) Core (TM) i5-2500
CPU @ 3.30GHz 3.29 GHz, 2.97 GB of RAM Physical Address Extension, Windows XP,
Professional Version 2002 computer.
Sample preparation for scanning
A known volume of copepod samples taken from the culturing systems was filtered through
a 52 µm mesh-size sieve and cleaned with 0.2 µm filtered seawater. Samples were then
fixed immediately with ethanol 70% on the sieve and followed by a fine rinse with either
ethanol 70% or deionized water to remove the leftover of seawater in the samples which can
reduce the contrast of scanned images. Copepod samples were transferred to 60 mm petridish for scanning. Clean petri-dishes free-scratches were employed for scanning because the
presence of dusts and scratches in the scanned images can be mis-identified as copepods by
ZooImagea thereby reducing the accuracy of the quantification (Gislason and Silva, 2009).
When scanning, the volume of liquid in the petri-dish should be minimized (ca. 5 -10 mL)
to eliminate the movement of copepods that could make the picture blurrily.
After scaning for ZooImage analysis, the same sample of copepods in petri-dish was
manually classified under a stereo microscope. The description of developmental stages of
A. tonsa were described by Murphy and Cohen (1978), Cohen and Doyle (1984) and
Sabatini (1990). The data of density/abundance of each stage recorded by ZooImage and
manual identification was compared by linear regression analysis.
6
Creating the training set (Reference library)
The accuracy of ZooImage in identification of zooplankton relies on the training set or
reference library which learns the program how to classify zooplankton. During the
processing of a scanned image in ZooImage, individual object is extracted (creation of
vignettes). There are total 26 features of each vignettes were measured but the most
importance are equivalent circular diameter (ECD), area, mean, major, minor, and they were
saved in a metadata file generated for each sample. The reference library is a training set in
which vignettes are manually classified in different taxonomical groups. These vignettes are
representative of the variability of analyzed samples. Once the training set was created,
classifiers was built based on six machine learning algorithms which are attached to
ZooImage program (Grosjean and Denis, 2007). Each classifier was evaluated using a 10fold cross validation confusion matrix to compare errors between manual and automatic
recognition. The principle of cross validation method is randomly divided the training set
into 10 equal fractions. The learning phase is made on 9/10 of these training set then it
predicts the last tenth part, called test set. This process was repeated 10 times, each time
using a different part for evaluation the remaining 9 parts are put together to create the
classifier. The confusion matrix is a square contingency table that compares all groups of
manual classification and all groups of the automatic recognition (Table 1).
In this study, a referency library/training set was established by manually selecting and
sorting individual vignettes of items (including copepod and non-copepod) following the
procedure of Grosjean and Denis (2007) and categorized into 14 groups. At least 20
vignettes is needed for each group in the training set. The experimental organism Acartia
tonsa develops from egg through six nauplii and six copepodite stages, the last copepodite
stage is adult (Cohen and Doyle, 1984; Murphy and Cohen, 1978; Sabatini, 1990). The
developmental stages of A. tonsa, based on the similarity in the body size and the
morphologies among the different stages decribed by Murphy and Cohen (1978) and
Sabatini (1990) were categorized into 6 groups including: single egg, double eggs, nauplii IIII, nauplii IV-VI, copepodite stages I-III, copepodite stages IV-V-male and female
7
copepods (Fig. 1). Note that a double eggs group was two eggs sticking together that have
observed in a high frequency in the scanned images of egg samples (Double eggs vignettes
were 13.8% compared to number of single egg vignettes in the egg sample for creating
training set). Non-copepod objects, mainly bubbles, debris, petri-dish-edges which were
also extracted from ZooImage processing, were also added into the training set in order to
minimize the misidentification during picture analysis (Table 1).
Individual biomass estimation
From scanned image of samples, ZooImage extracts vignettes and measures individual
surface area of the organism from the number of pixels contained in its two-dimensional
images. The individual area is defined by the silhouette of the organism after changes in its
grey-level threshold, and is then automatically transformed into an ellipse of equivalent area
with its major and minor axes scaled to the general shape an organism (Alcaraz, et al.,
2003). By this way, the ECD is created which represents the most accurate estimated
measurement in size of individual organism that can be obtained automatically.
ZooImage was design to calculate biomasses of organisms in a non destructive way by
using a relationship found in the literature between size of organisms and their biomass.
ZooImage using allometric relation between biomass and ECD as in following equation for
biomass calculation:
𝐵 = (𝑃1 × 𝐸𝐶𝐷 + 𝑃2 )𝑃3 (1)
where B is the biomass (e.g., carbon content, dry mass), and P1, P2 and P3 are the allometric
parameters. This general equation is thus limited to the relationship between the biomass
and the ECD. Therefore, the relationship between ECD and other manual measurement
provided in literature (e.g., length, body areas) needs to be found to find out these allometric
parameters (P1, P2 and P3) for biomass calculation. The default allometric parameters of P1,
P2 and P3 in equation (1) are 1, 0 and 1 respectively.
In this study, individual biomass was calculated by estimating bio-volume that was
described by Di Mauro et al. (2011) with some adaptions for Acartia tonsa. The volume of
each organism (V) is then estimated by the calculation of the corresponding volume of
8
revolution ellipsoids by replacing the value of allometric parameters with those taken from
the volume equation of a sphere. To do that we consider first:
4
V = π3 (2)
3
where the radius (r) of the equivalent circle can be estimated using:
ECD = 2r
and then the ECD can be replaced in the volume Eq. (1) as follows:
4
V = π(
ECD 3
3
2
) (3)
Now, in Eq. (4), the volume is referred to the object with a particular ECD value, and when
solving this equation, the allometric parameters can be replaced in Eq. (1) by:
3 𝜋
𝑃1 = √ = 0.806
6
P2 = 0
P3 = 3
As a result, the individual biovolume of all copepod vignettes used in the training set were
collected. Body wet weight can be derived from measurements of body biovolume by
applying a factor of 1.025 for specific gravity (Chojnacki, 1983). Then, dry weight is
generally obtained by multiplying the wet weight by 0.20 (Chojnacki, 1983; Cushing, et al.,
1958) and the carbon content accounts for 45% of the dry weight (Ara, 2001).
In comparison with ZooImage biomass calculation, the length of (total length of nauplli and
prosome length of copepodite) of all copepod vignettes in the training set was manual
measured using ImagesJ software. Manual individual biomass estimation was calculated by
above measured length using regression equation between length and body weight from
Berggreen et al. (1988) as:
Nauplii: 𝑊 = 3.18 × 10−6 𝐿3.31 (r2 = 0.91)
Copepodite: 𝑊 = 1.11 × 10−5 𝐿2.92 (r2 = 0.98)
9
The whole set of manual biomass results were compared with ZooImage biomass
calculation by linear regression analysis, once for all of naupplii stages and once for all of
copepod stages.
10
Preliminary results
Training set
From 45 to 297 vignettes were added to each group of the training set. The overall accuracy
for ZooImage training set was 90.7 %. The accuracy of identification of developmental stages
of A. tonsa, excluding non copepod groups such as bubbles or debris, increased to 94.7% with
a the cross-validation (Table 1), implying that the misidentification between different groups
of copepod stages rarely occurred. More impressively the accuracy of the single egg
identification achieved 100%. Among different copepod stages, the misclassification occured
more often between copepodite stages IV-V-Male and female groups but the overall accuracy
in classification of these two groups were still above 90%. The presence of debris in samples
was shown to cause 1.2 – 6.0% misclassification with 5/7 groups of copepods in the training
set (Table 1).
11
(a)
(b)
(c)
(e)
(d)
(g)
(h)
(f)
Fig. 1. Examples of scanned image and vignettes of different stages of Acartia tonsa extracted from digitalized samples using
ZooImage and used in the reference library: a. scanned image, b. single egg, c. double eggs, d. nauplii I-III, e. nauplii IV-VI, f.
copepodite stages I-III, g. copepodite stages IV-V-male, h. female copepod
12
Table 1. Confusion matrix for the 14 categories of the training set by the random forest algorithm.
User classification in training set
Class (n =14)
Single egg (01)
Double eggs (02)
Nauplli I – III (03)
Nauplli IV – VI (04)
Copepodite I – III (05)
01
155
1
02
50
03
04
2
130
6 176
ZooImage classification prediction
05 06 07 08 09 10
11
12
13
14
3
4
6
1
1
164
Copepodite IV – V – Male (06)
275 19
Female (07)
18 169
Fecale pellet (08)
96
3
Bubble (09)
2
1 135
3
Debris (10)
4
35
1
5
Big debris (11)
2
4
10
3
2
8
1 138
Shadow (12)
1
5
2
38
3
Twigs (13)
1
1
81 14
Petridishedge (14)
4
12 203
Total from ZooImage pre.
158 52 142 186 168 296 188 102 143 44 159 47 93 220
Under-/overestimation
1.02 0.93 1.06 0.98 1.02 1.01 1.01 1.03 1.01 0.98 0.95 0.96 0.96 1.00
Total from
manual cla.
P (%)
155
56
134
189
165
100.0
294
187
99
141
45
168
49
97
219
93.5
90.4
97.0
95.7
77.8
82.1
77.6
83.5
92.7
89.3
97.0
93.1
99.4
Gen. Acc.
(%)
94.7
90.7
85.6
Note: Rows are taxonomic (user) classificaton and columns are ZI (automatic) identification prediction of the same classifications. The
diagonal represents the correct recognition.The numbers in the cells illustrate the number of the vignettes of each assigned group while the
colors indicate the percentage of the total number of vignettes in each class: yellow >0 – 10%, orange 10 – 20%, red 20 – 100%. The
number in the cells outside the diagonal represents the misclassifed vignettes. The total man. is the total number of vignettes of each class
used in the training set and P (%) is the percentage of correctly identificated vignettes. Gen. Acc. is the general accuracy of training set for
all of copepod stages groups, all of non copepod groups and for the whole training set. The total from ZI pre. (row) is the number of
vignettes classified by ZI as a particular class and the under-/overestimation row represents the amount a class is being under/overestimated by ZI programme.
13
Efficiency of ZooImage in counting Acartia tonsa eggs
ZooImage identified the abundance of copepod eggs about 97% similarly compared to
manual method. ZooImage tended to be under-estimated about 10% of copepod eggs
abundance compared to manual method when only the data of single egg group in the
training set was computed (Fig. 2A). In contrast, ZooImage over-estimated ca. 10%
compared to manual methods when egg abundances accounted both single and double eggs
data (Fig. 2B).
140000
140000
A
B
120000
ZooImage abundance (eggs/L)
ZooImage abundance (eggs/L)
120000
100000
80000
60000
40000
y = 0.9028 x + 2509
R2 = 0.9712
n = 21
20000
100000
80000
60000
40000
y = 1.105 x - 2081
R2 = 0.9795
n = 21
20000
0
0
0
20000
40000
60000
0
80000 100000 120000 140000
20000
40000
60000
80000 100000 120000 140000
Manual abundance (eggs/L)
Manual abundance (eggs/L)
Fig. 2. Linear regression of density of Acartia tonsa eggs estimates base on manual
quantification and automated once using ZooImage software. The straight lines represent
best fits of the linear regression. Only single vignettes of egg from ZooImage results was
taken into account in Fig. 1A, while the data in Fig. 1B was the total number of eggs from
vignettes of single and double eggs.
14
Efficiency of ZooImage in identification abundance of different stages of A.tonsa
ZooImage identified above 85% for all of different stages of A. tonsa, especially reaching
97.7% at the copepodite IV – V – Male similarly compared to manual count (Fig. 3).
ZooImage over-estimated abundances of the smallest groups of copepod stages, eggs and
nauplii, especially for eggs with more than 2 times higher compared to manual count (Table
2). ZooImage under-estimated about 30% when accounting the density of copepod early
coepodite stages of I – III groups (Table 2). The difference between two methods in
abundance of biggest copepod stages, Copepodite IV – V – Male and Female, was less than
10% (Table 2).
Table 2. Results from linear regression analysis between abundance estimates using manual
(independent variable) and automated by ZooImage (dependent variable) of the same
samples.
Coefficient
b
n
F
R2
P-value
Eggs
2.039
569.1
26
157.4
0.8677
< 0.0001
Nauplii I-III
1.012
-213.7
19
84.1
0.8318
< 0.0001
Nauplii IV-VI
1.088
-70.5
15
93.1
0.8775
< 0.0001
Copepodite I-III
0.738
57.5
19
252
0.9368
< 0.0001
Copepodite IV-V-Male
0.958
0.5
29
1402
0.9811
< 0.0001
Female
0.9799
-64.8
29
752.6
0.9654
< 0.0001
Group
15
Nauplii stages I-III
Eggs
A
ZooImage abundance (ind./L)
ZooImage abundance (eggs/L)
B
8000
3000
2000
1000
0
6000
4000
2000
0
0
1000
2000
3000
0
2000
4000
6000
8000
Manual abundance (eggs/L)
Manual abundance (ind./L)
Nauplii stages IV-VI
Copepodite stages I-III
D
C
ZooImage abundance (ind./L)
ZooImage abundance (ind./L)
6000
4000
2000
0
3000
2000
1000
0
0
2000
4000
6000
0
1000
2000
3000
Manual abundance (ind./L)
Manual abundance (ind./L)
Copepodite stages IV-V-Male
Adult Female
E
F
6000
ZooImage abundance (ind./L)
ZooImage abundance (ind./L)
8000
6000
4000
4000
2000
2000
0
0
0
2000
4000
6000
0
8000
Manual abundance (ind./L)
2000
4000
6000
Manual abundance (ind./L)
Fig. 3. Scatterplots of abundance estimates base on manual quantification and automated
onces using ZooImage software for six groups of Acartia tonsa stages in multiple stages
samples. The straight lines represent best fits of the linear regression.
16
Individual biomass calculation
Individual biomass shows a significant correlation between ZooImage and manual
estimation. ZooImage estimated individual biomass from extracted biovolume from 1.2 and
2.3 times higher in nauplii copepod stages respectively compared to individual manual
estimation based on the length of organism from the picture.
Nauplii stages
Copepodite stages
15
ZooImage Biomass (µg C/ind.)
ZooImage Biomass ( µg C/ind.)
1.0
0.8
0.6
0.4
y = 1.227 x + 0.076
R2 = 0.9458
n = 323
0.2
0.0
0.0
10
5
y = 2.276 x + 0.228
R2 = 0.9468
n = 646
0
0.2
0.4
0.6
0
Manual Biomass ( µg C/ind.)
2
4
Manual Biomass (µg C/ind.)
Fig. 4. Comparison between Individual biomass estimated by ZooImage program and
manual methodsof nauplii and copepodite of Acartia tonsa from all of vignettes of
reference library
17
6
Primary conclusion
In this study, a created highly accuracy training set of different groups of copepod stages
which enable to identify different stages of copepod A. tonsa in the intensive culture.
Ongoing task
- Testing the ability of using ZooImage program for culture of A. tonsa in in the
recirculating aquaculture system (RAS) at RUC by taking samples of copepods from RAS
and analyse them by both ZooImage and manual methods. Some adjustment for the training
set may be needed.
- Estimating biomass of the whole sample of copepods analysed by ZooImage from
individual biomass of copepods from reference library.
18
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