Transcript (31ºN), in the

The structure of planktonic communities under variable
coastal upwelling conditions conditions off cape Ghir
(31ºN), in the Canary Current System (NW Africa)
V. Anabalón, J. Arístegui, C.E. Morales,
I. Andrade, M. Benavides, M.A. Correa-Ramirez, M. Espino, O.
Ettahiri, S. Hormazabal, A. Makaoui, M.F. Montero, A. Orbi
Background
Area of permanent upwelling, narrow shelf, fronts & filaments,
low NO3 concentration compared to other areas and regions.
Pelegri et al. 2005
MOTIVATION
Do changes in upwelling intensity produce significant spatiotemporal variations in the structure of planktonic communities
(coastal and coastal transition zones -CTZ)?
Alonshore
wind
stress
SST
Chl-a
Samplings
APPROACHES AND METHODS

Oceanographic cruises (5): Dec-2008; Feb-, June, Aug,
Oct-2009; transect perpendicular to the coast (7 stations,
coast to app. 150 Km offshore.
- Hydrographic data: CTD with fluorescence sensor. Estimates of water density (as
sigma-t) and stratification intensity (J m-3) according to Bowden (1983).
- Seawater samples at 5 levels (0, 25, maximum fluorescence depth, 90, 150 m depth):
Niskin bottles (5 L); analyses:
* Macro-nutrients (NO2+NO3, PO4, Si)
* Chl-a (total, <20 and <3 µm)
* Micro-organisms: picoplankton (flow-cytometry; only 3 cruises), nanoplankton
(epifluorescence and Utermöhl), and microplankton (Utermöhl).

Satellite time series data: the wider perspective
- Winds (CCMP; ¼° x ¼° resolution);
- SST (AVHRR Pathfinder V5.0 from NOAA;
ftp://data.nodc.noaa.gov/pub/data.nodc/Pathfinder) at 2x2 Km resolution;
- Sea level anomaly (combined processing of TOPEX/JASON at ¼° x ¼°resolution
- ERS altimeter data distributed by AVISO (http://aviso.oceanos.com)  surface
geostrophic flow field;
- Chl-a form HERMES (combined sensors (MODIS, MERIS, SeaWiFS), obtained
from GlobColorWeb (ftp.fr-acri.com).
APPROACHES AND METHODS (2)

Plankton biomass (C):
- Nanoplankton and microplankton: geometric models for cell volume estimates
(Chrzanowski & Simek, 1990; Sun & Liu 2003). C/biovolume conversion factors:
Menden-Deuer & Lessard (2000) for CIL, DIN, and DIAT; Heinbokel (1978) for
Tintinnids; and Borsheim & Bratbak (1987) for FLA.
- Autotrophic picoplankton: 29 fg C/cell - PRO, 100 fg C/cell - SYN (Zubkov et al., 2000),
1.5 pg C/cell - PEUK (Zubkov et al., 1998); HB: 12 fg C/cell (Fukuda et al., 1998).
- Mixotrophy (DIN + CIL), literature recognition of mixotrophy at species/genus level
(40% autotrophy) in the case of microplankton (no autofluorescence data available).

Statistics: multivariate analyses, PRIME software v.6
(Clarke & Warwick, 2001; Clarke & Gorley, 2006)
- MDS (nonmetric multidimensional scaling) for cluster identification; hydrographic and
biological matrices; significance of the clusters – SIMPROF
- ANOSIM for analysis of similarities; SIMPER for groups/species contributions to
similarities and dissimilarities between clusters in the biological matrix.
- BIO-ENV and RELATE to analyze the associations between the biological data and the
environmental variables. The best combinations of variables determined by BIO-ENV
were subjected to further analysis (LINKTREE) to identify the variable(s) which best
represented the separation of the biological components into different groups/cluster.
WEUP
SST: 16-17ºC
SST grad.: <2ºC
Wind: 8-12 m/s NE
Low stability
RELAX
SST: 18ºC
SST grad.: 3.5ºC
Wind: 4-8 m/s NW
MOUP
SST: 19-20ºC
SST grad.: 4ºC
Wind: 4-8 m/s NE
Shoaling of
isopycnals at the
coast &
counterflow
HYDROGRAPHIC CLUSTERS
Variables contribution to cluster separation:
- nutrient concentration: WEUP vs. E1
- nutrient concentration and SST: MOUP vs. E1
- water density, SST and Nº days favourable to upwelling:
WEUP vs. MOUP
Cross-shore variability (E2-E4 vs. CTZ E5-E7 stations)
BIOLOGICAL CLUSTERS

DINOFLAGELLATES (43%)
contributed most to cluster
separation (E1 vs. rest).
DIATOMS (21%) and
CILIATES (21%).

Dissimilarity between:
WEUP - RELAX:
DINOFLAGELLATES (38%)
and CILIATES (32%).
WEUP - MOUP:
DINOFLAGELLATES (37%)
and DIATOMS (34%).
RELAX - MOUP: DIATOMS
(34%); CILIATES (24%) and
DINOFLAGELLATES (22%).
•
Inclusion of the picoplankton
fraction (only 3 samplings):
minimal influence in terms of
biomass.
BIOMASS: micro+nanoplankton




Dominance of microplankton
(>53%): DINOFLAGELLATES
+ CILIATES
Nanoplankton:
DINOFLAGELLATES +
FLAGELLATES
Autotrophic-C: DIATOMS
exceptions
Dec-08: APP, AFL, ADIN
Aug-09: ADIN + DIAT
As Chl-a: nanoautotrophs.
Heterotrophic-C: DINOFLAG.
RELEVANCE OF MIXOTROPHY
Mean H:A biomass ratios (pico-to micro): 3 samplings
- No correction mixotrophy: >1 (inverted pyramid)
- Correction for mixotrophy: <1 (normal pyramid)
BIOMASS-C
STRUCTURE IN CAPE GHIR:
BIOMASS DIFFERENCES (Linktree)
micro+nanoplankton
CONCLUSIONS
RESTRICTIONS: SNAPSHOT OF THE SYSTEM
Two main upwelling phases  weak (no gradients
acrosshore) and moderate (strong crosshore gradients).
Separation of the most coastal station (depth effect). Cluster
formation influenced by nutrient concentration (spatial), SST
and upwelling constancy (temporal).
Hydrographic clusters were representative of the spatiotemporal variability in planktonic assemblages  changes in
the upwelling intensity do influence community structure.
The dominant functional groups (C-biomass) were mixed
assemblages of DIN and CIL (>51%); DIAT contributions
were moderate to low (<35%). Total Chl-a was dominated by
the nanoplankton and mixotrophy is important in H:A
evaluation for this system.
QUESTIONS UNSOLVED
Is upwelling intensity in the region (NW Africa) increasing or
decreasing ???
Is the presence of mixed autotrophic assemblages a
consequence of recent changes in upwelling intensity in this
region ???
How well can be represent mixotrophs in primary production
(PP) estimates and in ecosystem models??? How well can
be represent other types of PP or nutrient requirements ???
Heterotrophic:autotrophic ratios – how well can be estimate
the biomass of the diverse components ??? (basic!)
Biomass estimates: Chl-a versus Carbon; relevance in
remote sensing estimates of primary production (changing
C:Chl-a ratios)
Sampling
Dec-08 C1
Feb-09 C2
A/ Chl-a total
Am/ Chl-a total
MAT/ Chl-a micro
NAT/Chl-a nano
APP/ Chl-a pico
50
60
30
74
42
148
50
60
100
140
20
162
310
390
31
330
450
75
90
160
14
Jun-09 C3
Aug-09 C4
MAT/Chl-a micro
64
80
Oct-09 C5
27
REGRESSIONS
All samplings
A/Chl-a total
y = 34.8x +926
R² = 0.49
p= 0.001
Am/ Chl-a total
y = 44.0x+1122
R² = 0.58
p= 0.0007
MAT/ Chl-a Micro
y = 61.3x + 528
R² = 0.35
p = 0.1
MATm/ Chl-a Micro
y = 92.5x + 1127
R² = 0.51
p = 0.05
NAT/ Chl-a Nano
y = 44.9x -172
R² = 0.60 p=0.0001
APP/ Chl-a Pico
y = 89.9x + 311
R² = 0.59
p = 0.0001