Transcript Slide 1

Black sea OUTLOOK Conference, Odessa, 01-04 October , 2011
Snejana Moncheva1, Maria Pantazi2, Larisa Pautova3, Laura
Boichenko4, Dan Vassiliu4,Luydmila Mantzosh5
1Institute of Oceanology-BAS, Parvi Mai str., No 40, P.O.Box 152, Bulgaria
2HCMR P.O. BOX 712, Anavissos 19013, Greece
3P.P Shirshov IO-RAS, 36, Nahimovski prospect, Moscow, Russia, 117997
4NIMRD “Grigore Antipa”, Mamaia bul., No 300, Constanta 3 , RO-900581
5IBSS,2 Nakhimov Ave. Sevastopol, 99011 Ukraine
The quality of biological data has gained a recognition as an essential
part of international monitoring programmes, in response to the demand
for strategic environmental evaluations such as the EU WFD, the MSFD
etc. Informed decisions for environmental sound management can be
made only on the basis of reliable data, and therefore certain level of
data quality should be achieved to assure accuracy and precision of all
measurement systems.
The structural characteristics of phytoplankton communities bear
valuable information about the evolution of microalgal communities and
the trajectories of shifts under multiple environmental factors, including
anthropogenic impacts. Details of phytoplankton analytical procedures
are essential to compare data produced by different analysts either
during a long-term monitoring programs in one area or between different
areas in order to evaluate statistically significant long-term trends or
spatial differences.
OBJECTIVES
“Quality control of phytoplankton counting and biovolume
estimates—an easy task or a Gordian knot?”
E.Rott et all., 2007
I - present results from the intercalibration excercise

assess the degree of comparability/differences in phytoplankton and
chlorophyll a data analysis among 4 Black sea laboratories

and where possible to make recommendations for further
improvement and harmonization of research methodology in the Black Sea .
II present actions taken towards solutions
The expectation is that the results will assist regional assessments based on
combined data sets
MATERIALS AND METHODS
Samples from two stations (coastal and open
sea) were collected and distributed for
laboratory analysis in 3 replicates for each
partner (SESAME intercalibation cruiseApri’2009)
Phytoplankton attributes:
Map of sampling stations
Phytoplankton Total abundance [cells/l]
Phytoplankton Total biomass (wet weight) –
[mg/m3]
Phytoplankton common Taxonomic classes
(Bacillariophyceae, Dinophyceae,
Prymnesiophyceae, Cryptophyceae and
Small flagellates ).
Chlorophyll a measurements
Statistical analysis
Participants
IBSS-Sebastopol - UkR
NIMRD – RO
IO-BAS – BG
IO-RAS - RU
Robust statistical treatment (ANOVA test,
Tukey test and a Lavene statistic) was
applied in order to check the homogeneity
of the variance between the groups.
Stock plots were designed based on
averages and standard deviation and the
the Bray Curtis similarity index
Common
statistics
employed
during
phytoplankton ring tests (average ±1 stdev
and CV < 20 %).
Inventory of in-house lab methods
Phytoplankton counts
Participant
Sample
concentration
Counting
chamber
IO-BAS
NIMRD-
Decantation Utermol
Decantation Utermol
IO-RUS
Decantation/inverse
filtration
IBSS
Decantation
Segwick Rafter
Utermol
Utermol
Nogott’s up to 0.1
ml
Nauman’s
chamber 1 or 5
ml
Nauman’s
chamber
1 ml
0.05 ml
No cells
Volume of counted per
Type of microscope subsample sample
Nicon inverted+
immage analysis
Inverted
1 ml
0.1 ml
Light compound
1 ml
Light compound
0.1 ml
400 cells
Chlorophyll a measurements
Partner
IO-BAS
NIMRD
IO-RAS
Extraction
Sample preparation
24 hours extraction
90% acetone rpm cuvette 1cm L
24 hours extraction
90% acetone rpm cuvette 1cm L
90% acetone
Instrument
7000
Spectrophotometer
4000
Spectrophotometer
Fluorimeter
Equations
reference
Jeffrey and
Humphrey
(1975)
SCOR
UNESCO (1968)
RESULTS
Phytoplankton abundance
Analysis of
variance:
Source
D
F
Sum of
squares
Mean
squares
Model
2
0,028
0,014
Error
Corrected
Total
4
0,002
0,000
6
0,030
F
31,233
Pr > F
0,004
Countries / Tukey (HSD) / Analysis of the differences
between the categories with a confidence interval of 95%:
Pr
Differ
Standardize Critical
>
Signifi
Contrast
ence
d difference
value
Diff
cant
UKR vs.
0,0
Yes
RUS
0,151
7,748
3,564
03
UKR vs.
0,0
Yes
BLG
0,118
5,548
3,564
11
BLG vs.
0,3
No
RUS
0,032
1,671
3,564
21
Stock plot of the total phytoplankton abundance [cells/l average and stdev] by partners
Tukey's d critical value:
5,04
Hierarchical clustering showed similarity between
Bulgarian and Russian data > 85% while between
Ukrainian - Bulgarian and Ukrainian - Russian was >
75% )
The statistical treatment of total abundance data
show significant differences between the
Ukrainian-Bulgarian results and between the
Ukrainian - Russian results, while the difference
between Bulgarian and Russian data were not
significant
Similarity cluster matrix of total phytoplankton abundance
[cells/l - square root transformation] by labs
Phytoplankton abundance
Countries / Tukey (HSD) / Analysis of the differences between the
categories with a confidence interval of 95%:
Differen
Standardized
Critical
Pr > Signific
Contrast
ce
difference
value
Diff
ant
BLG vs.
0,01
Yes
UKR
1,417
5,530
3,564
1
BLG vs.
0,69
No
RUS
0,199
0,852
3,564
5
RUS vs.
0,01
Yes
UKR
1,217
5,206
3,564
4
Tukey's d critical value:
5,04
Countries / Tukey (HSD) / Analysis of the differences between the
categories with a confidence interval of 95%:
Differe
Standardized
Critical
Pr > Signific
Contrast
nce
difference
value
Diff
ant
BLG vs.
0,0
Yes
UKR
1,417
5,530
3,564
11
BLG vs.
0,6
No
RUS
0,199
0,852
3,564
95
RUS vs.
0,0
Yes
UKR
1,217
5,206
3,564
14
Tukey's d critical value:
5,04
Countries / Tukey (HSD) / Analysis of the differences between the
categories with a confidence interval of 95%:
Differ
Standardized
Critical
Pr > Signifi
Contrast
ence
difference
value
Diff
cant
UKR vs.
0,00
Yes
RUS
0,762
7,987
3,564
3
UKR vs.
0,00
Yes
BLG
0,639
6,113
3,564
8
BLG vs.
0,47
No
RUS
0,123
1,291
3,564
1
Tukey's d critical
value:
5,04
Phytoplankton abundance
Average abundance [cells/l] stdev and CV [%] by partners
Average
N [cells/l]
Lab
stdev
CV%
BLG
3520319
167824.6
4.8
RUS
3268008
178086.8
5.4
UKR
4620032
160602.3
3.5
RO
3253500
all
3665465
648061.9
17.7
BG/RO
3386909
188669.4
5.6
BG/RO/RUS
3347276
150035.3
4.5
Based on testing the reproducibility of the in-house
analysis (replicates) and employing the CV < 20%
assumption for the total numerical abundance the
results reveal a good reproducibility of the in-house
replicates and very close results between the different
labs, with the exception of
Ukraine, where the
difference was between 25-30%
LAB Dinophyce stdev
RU
BG
RO
UK2
All
Average
stdev
CV%
BAC
[cells/l]
2318659 261722
11.3
2942261
26535
7.0
3253500
3787862 294579
7.8
3075570 613651
20.0
ae average
{cells/l]
RU
BG
RO
UK
All
7803
9913
20600
54495
23203
CV
%
1173
2342
15.0
23.6
9180
21601
16.8
93.1
Prymnesio
phyceae
Average
[cells/l]
46913
53026
67350
118422
71428
stdev
CV%
10611
5840
22.6
11.0
18608
32479
15.7
45.5
Small
stdev
CV
flagellates
%
Average
[cells/l]
753600 421620 55.9
347713 142143 40.9
600
65790
55825 84.9
291926 342668 117.4
The comparison of phytoplankton abundance results by taxonomic classes reveal compliance to the
20% CV only for Bacillariophyceae, while for the other classes the differences among the
participating labs are substantial, especially critical for the small flagellates, where even the in-house
results show inconsistencies for all partners
Phytoplankton biomass
Countries / Tukey (HSD) / Analysis of the differences between the
categories with a confidence interval of 95%:
Contrast
UKR vs.
RUS
UKR vs.
BLG
BLG vs.
RUS
Differen
ce
Standardized
difference
Critical
value
0,192
3,319
3,564
0,115
1,809
3,564
0,078
1,338
3,564
Tukey's d critical value:
Pr >
Diff
0,06
2
0,27
8
0,45
0
Signif
icant
No
No
No
5,04
Stock plot of the total phytoplankton biomass [mg/m3
- average and stdev] by partners
Phytoplankton total biomass
3500
B[mg/m3]
3000
2500
2000
1500
1000
500
0
RU
BG
RO
UK
Similar to the total abundance the hierarchical clustering of total biomass showed similarity between
Bulgarian and Russian data > 85% while between Ukrainian - Bulgarian and Ukrainian - Russian was >
75% )
Phytoplankton biomass
Average B [mg/m3] stdev
1935.736
2284.548
2861.63
2974.873
2514.197
RU
BG
RO
UK
All
LAB
CV%
372
103
19.2
4.5
4
489
0.16
19.5
RO
Bacillari stdev CV% Dinostdev CV% Prymne stdev CV% Small stdev CV%
ophycea
phyceae
sio
flagell
e Av B
Av B
phyceae
ates
[mg/m3]
[mg/m3
Av B
Av B
[mg/m3
[mg/
m3]
1620.711
420
25.9 111.800
5
4.5 9.098
3
28.0 58.87
22 37.1
2
2007.946 150
7.5 88.630
12
13.9 11.775
1
8.5 93.11
38 40.9
8
2607.660
228.330
9.770
0.190
UKR
2614.258
86
3.3 297.024
95
32.0 52.264
15
27.8 4.263
All
2212.644
486
22.0 181.446
98
54.2 20.727
21
101.6 39.11
1
RU
BG
4
84.7
45 114.7
Similar to the phytoplankton abundance at the level of taxonomic classes the differences among the
participating labs are substantial, especially critical for Prymnesiophyceae and small flagellates,
where even the in-house results show inconsistencies for all partners. Albeit the good agreement
between the data among some of the labs this is not systematic for all the taxonomic groups
Bacillariophyceae
Bacillariophyceae
Biovolume
[mkm3]
60000
50000
304-RU
40000
304-BG
30000
304-RO
20000
304-UK
10000
0
0
2
4
6
8
10
species
Dinophyceae
Dinophyceae
Biovolume
[mkm3]
120000
100000
304-RU
80000
304-BG
60000
304-RO
40000
304-UK
20000
0
0
2
4
species
1.Cerataulina pelagica
2.Chaetoceros socialis
3.Chaetoceros curvisetus
4. Nitzschia tenuirostris
5. Proboscia alata
6. Pseudo-nitzschia p-delicatissima
7. Skeletonema costatum
8.Thalassionema nitzschioides
6
8
10
Ceratium fusus -1
Gyrodinium fusius -2
Heterocapsa triquetra -3
Prorocentrum compressum -4
Prorocentrum micans -5
Protoperidinium bipes -6
Protoperidinium granii -7
Scrippsiella trochoidea -8
Prymnesiophyceae
Emiliania huxleyi
Small flagellates
For the common species biovolume comparative analysis
reveal the differences are substantial, for the most abundant
species such as Pseudo-nitzschia delicatissima and Emiliania
huxley the biovolume varies more than twice (202-409 mkm3
and 145- 268 mkm3 respectively ) for some species the
differences exceeding 3 fold
As expected the analyzed sub-sample volume is
important for the species diversity (no of species)
recorded in the samples
Volume of
subsample
RUS
BG
RO
UKR
1
1
0.1
0.1
No species
57
59
39
31
Chlorophyll a [mg/m3]
Chlorophyll a - st. 301 - RO, RU, BG
12
chl. a [mkg/l]
10
BG
8
Ro
6
Rus
4
2
0
0
1
2
3
4
5
chl. a [mkg/l]
Chlorophyll a - st.304
8.00
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
BG
Ro
Rus
0
1
2
3
4
5
Station
S-BG01-05 (M301)
S-BG01-05 (M301)
S-BG01-05 (M301)
average
stdev
CV%
S-BG01-13
S-BG01-13
S-BG01-13
average
stdev
CV%
S-BG01-08 (M304)
S-BG01-08 (M304)
S-BG01-08 (M304)
average
stdev
CV%
BG
RO
7.53
7.13
7.97
7.54
0.42
5.6
0.61
0.61
0.61
0.61
0.00
0.00
6.33
6.32
6.86
6.50
0.31
4.75
8.08
7.85
10.19
8.70
1.29
14.8
0.68
0.72
0.68
0.69
0.02
3.06
6.24
6.29
6.84
6.46
0.33
5.16
RUS
7.53
6.11
7.92
7.19
0.95
13.3
0.46
0.59
0.38
0.48
0.15
30.41
4.18
3.82
4.71
4.24
0.45
10.50
The results of chlorophyll a measurements reveal good in-house reproducibility for BG and RO
and higher than 10% difference for RU lab - Table 18. The difference between the BG and RO
data is within the (range average ±s 1stdev)
CONCLUSSIONS and RECCOMENDATIONS
•
For the total phytoplankton abundance the results between Bulgaria,
Russia and Romania are comparable (insignificant differences) while the
difference with Ukrainian lab is between 25-30%
•
For the total phytoplankton biomass there is a good agreement
between Romania and Ukraine, about 20% (acceptable) difference between
Bulgaria and all other labs and a 30% difference between Russia, Romania and
Ukraine.
•
Both for the phytoplankton abundance and biomass at the level of
taxonomic classes the
differences are substantial especially for
Prymnesiophyceae and Small flagellates. If taxonomic classes data would be
used as one data set they should be treated with caution.
•
The difference of chl. a results between BG and RO is about 10% and
the data sets could be comparable. The BG and RO measurements are between
1.3-1.5 higher than the RUS results.
The results give ground for the following recommendations:
A phytoplankton check-list with unified geometric shapes for the different
species is essential to avoid differences in the species biovolume estimation that
reflect the final biomass results
Analysis of at least 1 ml counting chamber is highly recommended to better
detect species diversity
In order to avoid taxonomic miss-match WoRMS taxonomy is mandatory
Phytoplankton Workshop, Istanbul 21-23 June 2010
Country
Bulgria
Organisation
IO-BAS-BG
Expert Name
Contact details
Assoc. Prof. Snejana Moncheva [email protected]
Romania
NIMRD – ,RO
Dr. Laura Boichenko
[email protected]
Russia
IO P.P.Shirshov, RAS,
Dr. Alexander Mikaelyan
[email protected]
Turkey
,inop,Turkey
Mr. Fatih Sahin
[email protected]
Ukraine
IBSS, Sebastopol
Mrs. Oleksandra Sergeeva
[email protected]
Ukraine
IBSS, Sebastopol
Dr. Vladimir Vladimirov
[email protected]
Ukraine
IBSS, Sebastopol
Dr. Yuliya Bryantseva
[email protected]
Ukraine
IBSS, Sebastopol
Mr. Denis Slipetskyy
[email protected]
Ukraine
Botanical Institute, Kiev
Dr. Alexander Krahmalnii
[email protected]
Ukraine
Odessa University, Odessa Mrs. Natalia Dereziuk
[email protected]
Black Sea Commission Prof. Ahmet Kideys
Permanent Secretariat
Black Sea Commission Dr. Violeta Velikova
Permanent Secretariat
[email protected]
[email protected]
Black Sea Commission Mr. Vladimir Myroshnychenko volodymyr.myroshnychenko@bl
Permanent Secretariat
acksea-commission.org
Black Sea Commission Ms. Nilufer Akpinar
Permanent Secretariat
UP-GRADE BS SCENE PROJECT
+
[email protected]
BLACK SEA COMMISSION
Phytoplankton expert group
CHECK LIST OF BLACK SEA PHYTOPLANKTON, REFERENCE
BIOVOLUMES and DATA BASE
Software (Oleksandra Sergeyeva, Kseniia Skuratova IBSS, Sebastopol,
Ukraine)
The special software for creation of online marine species checklists was developed.
This software is based on wiki engine and has special developed functions which
make it easy to add, delete, move species and add any type of structured information
in the form of patterns, which can be easily added by the checklist administrator on
request of users.
Each species has the corresponding page, where all information is placed either in
form of predefined patterns or in the form of text, images, tables etc.
on-line: http://phyto.bss.ibss.org.ua
Biovolume, shapes etc.(Bryantsteva Y., IBSS, Sebastopol, Ukraine)
Efforts have been made to create one reference list of biovolumes for Black Sea
microalgae. Thus for each species in the checklist the appropriate suggested figure
to calculate biovolume was attached. For detailed research of morphometric
characteristics of the community the more precise figure is also suggested where
possible.
on-line: http://phyto.bss.ibss.org.ua
UPGRADE BLACK SEA SCENE
GA 226592, FP7, EC
1
INTRODUCTION
4
2
THE QUALITY SYSTEM FOR BIOLOGICAL DATA - QA/QC
4
3
QA/QC FOR FIELD SAMPLING - SAMPLE PRESERVATION AND STORAGE
6
Equipment
Sampling Protocol
Sample Preservation
Sub-Sampling – Validation of Homogenization
4
GUIDELINES FOR QUALITY
CONTROL OF BIOLOGICAL
DATA- PHYTOPLANKTON
QUALITY ASSESSMENT FOR LABORATORY ANALYSIS
6
Taxonomy
Cell Counts
Biovolume/Biomass Estimation
Re-Analysis
Repeatability and Reproducibility
Uncertainty
Control Charts for Biological Measurements
Training and Inter-Laboratory Comparability Testing
5
QUALITY ASSURANCE OF DATA REPORTING
12
Documentation
Data management
MetaData Reporting Form
Snejana Moncheva
November 2010
6.
DATA FLAGGING SYSTEM
14
7.
CONCLUSIONS
15
8.
REFERENCES
16
9.
LIST OF PARTICIPANTS
17
PHYTOPLANKTON MANUAL - updated
Conduct
ring
tests
site operational
!!!!
Phytoplankton expert group
Maintain the BS Commission web
Apply the QC/QA guidelines to real
data sets
Finalise the data-base format
Finalise the automated system
Finalise the check-list
Participants in theSESAME intercalibration cruise
THANK YOU FOR THE ATTENTION