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Impact of ENSO oceanographic variability in the nano- and microphytoplankton dynamics off central coast of Peru – 12° S (2013-2017)
Avy N. Bernales, Sonia Sánchez, Michelle Graco, Elcira Delgado, Jesús Ledesma, Flor Chang, Nelly Jacobo, Diana Alvites, Luc Beaufort and David Correa.
Project: “Integrated study of coastal upwelling off central Peru”
April 24-26, 2018
INTRODUCTION
Messié and Chavez, 2015
Mann & Lazier, 2006
INTRODUCTION
More abundance More diversity
Microphytoplankton (20 - 300 µm) Nanophytoplankton (2 – 20 µm)
Nitrate (µmol /L)
N:P>5 N:P<1
Oceanographic setting
Trade winds weaken, supressing the upwelling
SST ANOMALIES (°C)
STUDY AREA METHODOLOGY
Shelf break
25
1309
Estaciones
80
60
40
20
0
50 40 30 20 10 0nautical miles (nm)
500
400
300
200
Sampling stations
2 7 5
Sampling period: 2013-2017 Sampling frequency: bimonthly Coverage: Up to 50 nm from the
coast
OBJECTIVES
Characterize the nano and microphytoplankton community
structure in a gradient in shore – off shore in front of Callao (12° S) in space and time. Evaluate the influence of environmental parameters in the
composition and distribution of phytoplankton, identifying posible habitat preferences by key species. Identify possible phytoplankton assemblages or key species
of a transition zone.
Sampling with oceanographic rosette at 10 m depth and/or in water column up to 75 m.
Quantitative analysis (Utermöhl) Shannon index diversity Statistical analysis -Olmstead and Tukey diagram - Spatial and temporal classification and ordination (PRIMER 6 and R)
METHODOLOGY
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
0%
20%
40%
60%
80%
100%
Feb
Apr
Jun
Aug
Sep
Oct
Feb
Apr
Jun
Aug
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jul
Aug
Oct
Dec
Feb
Apr
Jun
Aug
Oct
Dec
2013 2014 2015 2016 2017
N°c
ells
. L-1
Station 2 ( 8 nm)
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
0%
20%
40%
60%
80%
100%
Apr
Jun
Sep
Oct
Apr
Jun
Aug
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb
Apr
Jul
Aug
Dec
Feb
Apr
Jun
Aug
Oct
Dec
2013 2014 2015 2016 2017
N°ce
lls. L
-1
Station 5 (30 nm)
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
0%
20%
40%
60%
80%
100%Fe
b
Jun
Sep
Apr
Jun
Aug
Dec
Feb
Apr
Jun
Aug
Oct
Dec
Feb Jul
Aug
Dec
Feb
Apr
Jun
Aug
Oct
Dec
2013 2014 2015 2016 2017
N°ce
lls. L
-1
Station 7 (50 nm)
DIATOMS DINOFLAGELLATES SILICOFLAGELLATES COCCOLITHOPHORIDS PHYTOFLAGELLATES Total Phytoplankton
R E S U L T S
0.00.51.01.52.02.53.03.54.0
1302
1304
1306
1308
1309
1310
1402
1404
1406
1408
1502
1504
1506
1508
1510
1512
1602
1604
1607
1608
1610
1612
1702
1704
1706
1708
1710
1712
H' (
bits
. ind
-1)
Station 2 (8 nm)
0.00.51.01.52.02.53.03.54.0
1304
1306
1309
1310
1404
1406
1408
1502
1504
1506
1508
1510
1512
1602
1604
1607
1608
1612
1702
1704
1706
1708
1710
1712
H' (
bits
. ind
-1)
Station 5 (30 nm)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1302
1306
1309
1404
1406
1408
1502
1504
1506
1508
1510
1512
1602
1607
1608
1612
1702
1704
1706
1708
1710
1712
H' (b
its.in
d-1)
Station 7 (50 nm)
7 5 2
Dinoflagellates7 5 2 7 5 2
80
60
40
20
0
Diatoms7 5 2
Vertical distribution
1702 1704 1702 1704 60 50 40 30 20 10 0 (nm)
80
60
40
20
0
Dep
th (m
)
7 5 2 Coccolithophorids
50 40 30 20 10 0 (nm)
7 5 2 7 5 2
60 50 40 30 20 10 0 (nm)
Phytoflagellates
50 40 30 20 10 0 (nm)
7 5 2
RESULTS Obtaining phytoplankton species hierarchy
s1
s2
s3 s4
s5s6
s7s8
s9s10
s11
s12s13
s14
s15
s16
s17
s18s19
s20
s21
s22
s23
s24
s25
s26s27
s28s29s30
s31
s32
s33
s34
s35
s36s37
s38
s39
s40
s41
s42s43
s44
s45
s46
s47
s48
s49
s50
s51
s52
s53
s54
s55s56
s57s58
s59
s60
s61
s62
s63
s64
s65
s66
s67s68
s69
s70
s71
s72
s73s74
s75
s76
s77
s78s79
s80s81 s82
s83
s84
s85s86s87s88
s89
s90
s91 s92
s93
s94s95s96
s97
s98
s99
s100
s101
s102
s103
s104
s105
s106
s107
s108
s109
s110s111s112
s113s114
s115s116
s117s118s119
s120s121s122
s123s124
s125s126
s127
s128
s129s130
s131
s132
s133
s134s135s136
s137
s138
s139s140
s141
s142
s143
s144
s145s146s147
s148s149
s150
s151
s152s153
s154s155
s156s157
s158s159
s160s161
s162
s163s164s165s166
s167s168
s169s170s171
s172s173
s174
s175
s176 s177s178
s179s180
s181
s182
s183
s184s185
s186s187
s188
s189
0
2
4
6
8
10
12
14
16
18
0 20 40 60 80 100 120
Ln (∑
abso
lute
den
sity)
Frequency %
Dominants
Ocasionals
Rares
Constants
Olmstead y Tukey diagram of micro and nanophytoplankton species off Callao (2013-2017)
• 189 species were obtained by Utermöhl method.
• They were classified in
diagram to perform multivariate analysis then.
• 61 dominant species were determined
C: Coastal (8 nm) I: Intermediate (30 nm) O: Oceanic (50 nm)
Spatial Clustering and Canonical correspondence analysis (CCA) showing the distribution of dominant phytoplankton species in relation to environmental variables
RESULTS
(14
%)
(25 %)
D pum Nitz sp.
O. hid
L und P mic
(11 %)
(9 %
)
Temporal Clustering and Canonical correspondence analysis (CCA) showing the ocurrence of dominant phytoplankton species in relation to environmental
variables
RESULTS
4 main phytoplankton assemblages were obtained
RESULTS
Improving coccolithophorids sampling !
Estacionesst1 st2 st3 st4 st5 st6 st7
stock
total
(x10
3 cell/L
)
0
20
40
60
80
100
120
140
160
180mean abundance
X -43.80 x103 cel/L
Coastal area offshore
Alvites D. 2016. MsC Thesis (Graco M. L. Beaufort). Frontiers El Niño 2018
Coccolithophorids
ADVANCES Upwelling Low pH/high CO2
Composition and abundance of coccolithophorids
Ophiaster spp.
Helicosphaera carteri Calcidiscus leptoporus
Florisphaera profunda Gephyrocapsa oceanica
Emiliania huxleyi
Otros: Acanthoica quatrospina Anoplosolenia brasiliensis Calciosolenia sp. Calciopappus rigidus Gaardelia corolla Michaelsarsia adriaticus Pappomonas sp. Type 3 Polycrater sp. Reticulofenestra parvula Syracosphaera anthos Syracosphaera prolongata Syracosphaera molischii
CONCLUSIONS
There was a gradient in shore- off shore in all years, except in 2015 due warmer EN event conditions.
There is a variable transition zone at 30 nm that is favourable for dinoflagellates and coccolithophorids development.
Seasonal and interannual oceanographic influence (annual cycle of phytoplankton and ENSO signal) is reflected in the high variability of abundances and contributions of nano and microphytoplankton.
Ecological preferences were determined for D. pumila, Nitzschia sp, O. hidroideus, L. undulatum and P.micans.
Thank you for the financial support to :