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

MOTIVATIONDo changes in upwelling intensity produce significant spatio-temporal variations in the structure of planktonic communities (coastal and coastal transition zones -CTZ)?

Samplings

SST

Chl-a

Alonshore wind stress

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).

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.

APPROACHES AND METHODS (2)

WEUPSST: 16-17ºCSST grad.: <2ºCWind: 8-12 m/s NELow stability

RELAXSST: 18ºCSST grad.: 3.5ºCWind: 4-8 m/s NW

MOUPSST: 19-20ºCSST grad.: 4ºC Wind: 4-8 m/s NEShoaling of isopycnals at the coast & counterflow

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. MOUPCross-shore variability (E2-E4 vs. CTZ E5-E7 stations)

HYDROGRAPHIC CLUSTERS

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.

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.

BIOMASS: micro+nanoplankton

Mean H:A biomass ratios (pico-to micro): 3 samplings- No correction mixotrophy: >1 (inverted pyramid) - Correction for mixotrophy: <1 (normal pyramid)

RELEVANCE OF MIXOTROPHY

BIOMASS DIFFERENCES (Linktree)BIOMASS-C STRUCTURE IN CAPE GHIR:

micro+nanoplankton

CONCLUSIONSRESTRICTIONS: 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 spatio- temporal 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 UNSOLVEDIs 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 A/ Chl-a total Am/ Chl-a total MAT/Chl-a micro MAT/ Chl-a micro NAT/Chl-a nano APP/ Chl-a pico

Dec-08 C1 50 60 30 74 42 148

Feb-09 C2 50 60 100 140 20 162

Jun-09 C3     310 390 31  

Aug-09 C4 64 80 330 450 75 27

Oct-09 C5     90 160 14  

             

REGRESSIONS

All samplings A/Chl-a total Am/ Chl-a total MAT/ Chl-a Micro MATm/ Chl-a Micro NAT/ Chl-a Nano APP/ Chl-a Pico

 

y = 34.8x +926R² = 0.49 p= 0.001

y = 44.0x+1122R² = 0.58 p= 0.0007

 y = 61.3x + 528

R² = 0.35p = 0.1

y = 92.5x + 1127R² = 0.51p = 0.05

y = 44.9x -172R² = 0.60 p=0.0001

y = 89.9x + 311R² = 0.59 p = 0.0001

  H/ Chl-a total Hm/ Chl-a total MHT/ Chl-a total MHTm/ Chl-a total HB/NHT HB/ Chl-a total

 

y = 34.8x+753R² = 0.51P=0.05

y =40.4x+1143R² = 0.51P=0.05

y = 46.2x + 1012R² = 0.57p=0.003

y = 34.7x + 742R² = 0.51p=0.003

y = 0.2x + 11R² = 0.47p=0.003

y = 9.4x + 109R² = 0.61 p=0.0002