“Not to be cited without prior reference to the author” ICES C.M. 2012/ R:02 Mapping plankton biomass in the deep-ocean: an ecological provinces approach A. Bode, J.L. Acuña, J. Bueno, M.L. Fernández de Puelles, J.I. González Gordillo, S. HernándezLeón, X. Irigoien, C. Mompeán and M.P. Olivar The distribution of epipelagic plankton biomass by size-fractions was studied during Malaspina-2010 expedition across the Indian, Pacific and Atlantic oceans. The objective was to characterize plankton biomass structure at large spatial scales in poorly explored areas of the ocean. Samples from 95 stations, representative of 3 of major ocean biomes and 12 ecological provinces, were fractionated in 5 size-classes (40 to 5000 µm) and biomass determined as dry weight. Mean plankton biomass was similar for all oceans and major biomes but varied significantly between provinces, being particularly high in the N Pacific Equatorial Countercurrent and in the Caribbean provinces. The differences were mainly due to increases in medium-sized plankton, while for most provinces the biomass was uniformly distributed across logarithmic size-classes. Total and size-class plankton biomass was negatively and non-linearly correlated with the mixing layer depth and with the depth of the chlorophyll maximum across provinces. In contrast only the biomass of the smaller plankton was positively and linearly correlated with sea surface temperature, while the biomass of other size-classes was positively affected by the thermal stratification gradient in the upper layer. The obtained relationships will improve our ability to monitor and model the ocean response to global change. Keywords: plankton, biomass, deep-ocean, size-fractions, ecological provinces, Atlantic, Pacific, Indian Contact author: Antonio Bode. Instituto Español de Oceanografía (IEO). Centro Oceanográfico de A Coruña, Apdo. 130, E15080 A Coruña (Spain). e-mail: antonio.bode@co.ieo.es 50 NASE NPTG NATR 0 PNEC PEQD CARB SPSG ISSG EAFR -50 AUSW SSTC -150 -100 -50 0 TASM 50 100 150 Figure 1. Location of plankton sampling stations (red dots) in the biogeochemical ocean provinces defined in Longhurst (2007). 1 Introduction Despite the importance of the ocean in the response and regulation of global changes (HoeghGuldberg and Bruno, 2010), large regions of the deep ocean remain unexplored. Only the surface is regularly accessed by satellites, thus providing data on primary production and phytoplankton biomass (e.g. Behrenfeld et al., 2006). Information on most of the components of the food web is thus restricted to a few and sparse observations by cruises covering a small part of the unexplored regions, while most repeated cruises concentrate along established navigation routes (Reid et al., 2003; Isla et al., 2004; San Martin et al., 2006). Information on the marine ecosystems for large regions may be provided by models. Ecosystem models allow to estimate biomass and abundance along the food web but for practical reasons the estimations are generally limited to a few components (Shin et al., 2010). Alternatively, models based on macroecological functions and body-size of organisms allow for biomass estimations on practically any organism (Lopez-Urrutia et al., 2006; Jennings et al., 2008). However, the implementation and testing of models require observations acquired in the ocean. One way to overcome the impossibility of having observations on oceanic ecosystem components at all spatial and temporal scales relies in the common properties of ocean biomes and biogeochemical provinces (Longhurst, 2007). These large portions of the ocean are defined over the main geographic and hydrodynamic boundaries and allow for a synthesis of the main biological and biogeochemical processes of production and loss of organic matter and elements in a relatively small number of units. In this way, average values of productivity, biomass and seasonal changes are available for all regions of the ocean, including the deep ocean (Longhurst, 2007). The objectives of this study are to characterize plankton biomass structure in deep ocean provinces and to determine empirical relationships predicting plankton biomass in the deep ocean. Materials and methods Plankton samples were collected during Malaspina-2010 expedition (Fig. 1). In this study only samples from 95 stations visited during legs 4 (February 2010) to 7 (July 2010) were employed. Samples were collected by vertical hauls of a bongo-type net (30 cm diameter, 40 µm mesh size) between 200 m depth and the surface Table 1. Biogeochemical ocean provinces and biomes, as during early morning hours. Plankton defined in Longhurst (2007), sampled in this study. was size-fractionated using sieves of 200, 500, 1000, 2000 and 5000 µm, Code Province Biome collected on pre-weighted glass-fiber EAFR E. Africa Coastal Province Coastal filters and oven dried (60°C, 24 h) on ISSG Indian S. Subtropical Gyre Province Trades board. Biomass was later determined AUSW Australia‐Indonesia Coastal Province Coastal in the laboratory as dry weight (±0.01 SSTC S. Subtropical Convergence Province Westerlies Westerlies mg). Hydrographic information was TASM Tasman Sea Province SPSG S. Pacific Subtropical Gyre Province Trades obtained from CTD-rosette casts at PEQD Pacific Equatorial Divergence Province Trades the same stations and chlorophyll-a PNEC N. Pacific Equatorial Countercurrent Province Trades was determined from acetonic NPTG N. Pacific Tropical Gyre Province Trades extracts of phytoplankton collected at CARB Caribbean Province Trades Trades up to 8 discrete depths in the photic NATR N. Atlantic Tropical Gyral Province Westerlies layer (>0.1% of surface NASE N. Atlantic Subtropical Gyral Province (East) 2 photosynthetically active irradiance). Details of the sampling and analytical methods employed can be found in Moreno-Ostos (2012). The difference in temperature between the surface and 50 m depth was used as an index of stratification of the upper ocean. In addition to in situ measurements, mean values of primary production, surface chlorophyll, mixing layer and euphotic depth, and stratification for each sampled biogeochemical province were obtained from Longhurst (2007). Results and discussion Mean biomass distribution DW (mg m-3) Our study covered 12 Pacific Atlantic provinces in 3 ocean 35 basins and were DW * 30 distributed over all Indian biomes, except the polar 25 biome (Table 1). A 20 * * preliminary analysis 15 * indicated no significant 10 differences between mean 5 values of total plankton 18 5 3 9 2 6 9 5 15 2 6 15 0 biomass (i.e. the sum of biomass in all sizefractions) between biomes or oceans (ANOVA, Figure 2. Mean (±se) plankton biomass (mg DW m-3) in the P>0.05). Mean plankton biogeochemical ocean provinces defined in Longhurst (2007) and biomass was also very major oceans. The numbers at each bar indicate the number of similar among provinces (Fig. 2) but in this case some differences appeared due to the values found in the North Pacific Equatorial Countercurrent Province (PNEC, Dunnett-C test, P<0.05). Although temporal variability (e.g. seasonality) may explain some of these differences, the similarity in mean biomass of plankton in the upper ocean, particularly in the central regions, is consistent with an steady state of the ecosystems, with primary production inputs balanced by consumer and export losses (Platt and Denman, 1977; Rodriguez and Mullin, 1986). Biomass by size classes There was an even distribution of biomass by the chosen size classes in most provinces (Fig. 3). This implies an approximately equal amount of biomass in logarithmic individual body carbon classes, as predicted by pelagic size spectrum theory (Platt and Denman, 1977; Kerr and Dickie, 2001), and reported for most of the oligotrophic deep ocean (e.g. Rodriguez and Mullin, 1986; Piontkovski et al., 2003; San Martín et al., 2005). Only in some provinces there are an excess of biomass in some classes, as in the Caribbean that showed maximum biomass in large mesozooplankton (500-1000 µm), suggesting temporal deviations from the steady state. The use of a single net to capture a relatively large range of plankton sizes in this study may have negatively affected the collection of macrozooplankton (>1000 µm) that may have avoided the net. This may be the case of samples collected in the Atlantic where biomass decreased with increasing sizes (Fig. 3). However, most samples from the other basins did not show such decrease, suggesting that there were increases of small plankton in the Atlantic instead. Similarly there may have been losses of large zooplankton 3 because the sampling was performed during daylight when some macrozooplankton is expected to be in deep water layers (Gallienne et al., 2001), but other detailed vertical studies in the deep ocean have found a remarkably constant and low biomass of large zooplankton in the upper 400 m (Koppelmann, and Weikert, 1992). Pacific 10 Indian TASM DW (mg m-3) 8 10 EAFR 2 6 0 40-200 4 10 CARB 200-500 500-1000 1000-2000 >2000 ISSG 8 8 DW (mg m-3) 40-200 DW (mg m-3) 8 10 6 4 2 6 4 0 40-200 4 6 2 0 200-500 500-1000 1000-2000 10 >2000 40-200 200-500 500-1000 1000-2000 >2000 10 PEQD NATR 2 0 40-200 200-500 500-1000 1000-2000 >2000 10 AUSW DW (mg m-3) 8 8 8 DW (mg m-3) DW (mg m-3) >2000 SPSG 0 DW (mg m-3) 200-500 500-1000 1000-2000 10 2 6 4 2 6 200-500 500-1000 1000-2000 >2000 40-200 200-500 500-1000 1000-2000 >2000 SSTC DW (mg m-3) 40-200 PNEC NASE 4 2 6 0 4 10 2 8 >2000 8 6 8 200-500 500-1000 1000-2000 10 8 10 4 0 40-200 10 0 6 2 0 4 2 6 4 2 0 40-200 200-500 500-1000 1000-2000 >2000 40-200 200-500 500-1000 1000-2000 >2000 NPTG 0 40-200 200-500 500-1000 1000-2000 >2000 DW (mg m-3) DW (mg m-3) 4 DW (mg m-3) DW (mg m-3) 8 Atlantic 6 6 4 2 0 40-200 0.1 200-500 500-1000 1000-2000 1.0 5.9 27.9 >2000 167.9 µg C Figure 3. Mean (±se) plankton biomass (mg DW m-3) by size-classes (µm) in the biogeochemical ocean provinces defined in Longhurst (2007) and major oceans. The median value of individual carbon biomass for each class is indicated in the yellow box. 4 Empirical relationships between biomass and oceanographic variables Notwithstanding they were mostly located in deep waters, the sampled provinces were characterized by a large range of vertical oceanographic structure and consequently primary production and biomass (Table 2). These values allowed for exploring their relationships with plankton biomass and producing empirical predictive functions. Table 2. Mean values of oceanographic variables characterising the biogeochemical provinces, as defined in Longhurst (2007) and those measured in this study. PP: primary production (mg C m-2 d-1), Chla0: surface chlorophyll-a from satellite (mg Chla m-2), Chlai: photic depth integrated chlorophyll-a from in situ samples (mg Chla m-2), ze: photic zone depth (m), MLD: mixing layer depth (m), DCM: depth of chlorophyll maximum (m), SST: sea surface temperature (⁰C), T50: temperature at 50 m (⁰C), T0_200: mean temperature of the layer 0-200 m (⁰C), T0-50: temperature difference between 0 and 50 m (⁰C). Province EAFR ISSG AUSW SSTC TASM SPSG PEQD PNEC NPTG CARB NATR NASE PP 0.52 0.19 0.55 0.37 0.45 0.24 0.31 0.29 0.16 0.52 0.29 0.33 Chla0 Mean values (Longhurst, 2007) ze MLD SST 2.92 2.08 5.42 8.67 9.92 3.21 6.13 4.29 1.38 6.58 3.63 3.88 49.5 63.3 46.8 50.0 40.0 62.9 55.8 53.3 68.8 44.2 55.2 55.0 34.7 41.7 43.0 87.9 67.9 49.2 32.9 22.5 43.8 23.0 36.2 61.3 24.04 23.03 23.45 13.96 17.82 22.83 26.01 27.56 23.71 26.94 25.56 19.62 T50 22.78 22.10 22.63 13.34 17.09 22.10 24.37 24.25 22.72 25.84 24.56 18.45 T0‐50 1.26 0.93 0.81 0.62 0.73 0.72 1.63 3.31 0.99 1.10 1.00 1.17 Chlai 32.19 13.24 15.72 16.72 25.63 17.06 18.35 23.33 21.38 17.89 15.77 12.96 Mean values in situ DCM SST 121.00 71.33 24.80 169.59 111.78 23.63 145.20 85.20 22.31 133.00 58.50 16.96 129.00 65.00 21.45 156.25 92.00 27.22 134.75 59.60 27.96 133.93 41.67 28.03 171.83 119.00 23.72 135.00 78.00 28.96 186.25 131.89 27.15 162.00 120.50 22.59 ze T0_200 18.80 18.83 18.16 13.90 18.35 24.69 25.24 17.98 20.58 25.40 23.89 20.37 T0‐50 1.96 1.69 1.48 0.07 0.19 0.12 0.07 5.89 0.35 0.71 1.38 0.86 Mean values of biomass by province were not significantly correlated with either mean reported primary production or surface chlorophyll (Table 3). However there was a positive correlation between biomass in the 40-200 µm size class and temperature which can be described by a linear function (Fig. 4) implying enhanced biomass of small plankton at high temperatures. This relationship is consistent with the prediction of smaller body sizes with warming by macroecological theories (Lopez-Urrutia et al., 2006). The strongest negative correlations were found between mean biomass in several size classes and the Table 3. Correlations (Pearson’s r) between mean values of plankton biomass depth of the (mg DW m-3) by size classes and oceanographic variables characterising the mixing layer biogeochemical provinces. province: mean values from Longhurst (2007), in (Table 3). In the situ: mean values measured in this study. Yellow shading indicate signficant case of total values (P<0.05). Variable names as in Table 2. biomass this DW40‐200 DW200‐500 DW500‐1000 DW1000‐2000 DW2000‐5000 DWtotal PP 0.337 0.267 0.406 0.283 0.180 0.359 relationship can province 0.175 0.238 0.224 0.226 0.005 0.213 Chla0 be described by a ze ‐0.328 ‐0.308 ‐0.378 ‐0.244 ‐0.144 ‐0.343 negative power MLD ‐0.655 ‐0.584 ‐0.517 ‐0.588 ‐0.329 ‐0.607 SST 0.623 0.562 0.469 0.563 0.268 0.562 function 0.581 0.490 0.459 0.533 0.162 0.509 T50 indicating a rapid T0‐50 0.515 0.633 0.280 0.425 0.659 0.538 decrease of in situ SST 0.664 0.518 0.480 0.544 0.088 0.522 DCM ‐0.483 ‐0.587 ‐0.366 ‐0.580 ‐0.729 ‐0.595 biomass as the 0.442 0.217 0.323 0.368 ‐0.432 0.227 T0_200 mixing layer 0.302 0.440 0.190 0.171 0.722 0.388 T0‐50 Int. Chla 0.134 0.083 0.033 0.114 0.442 0.160 deepens but a ze ‐0.439 ‐0.446 ‐0.373 ‐0.503 ‐0.582 ‐0.518 stabilization of 5 this decrease at depths larger than 50 m (Fig. 4). Other studies have also reported the inverse relationship between mesozooplankton biomass and the depth of the thermocline (Isla et al., 2004; San Martín et al., 2006). In addition, positive correlations were found between thermal stratification in the upper 50 m and biomass of some classes, particularly of macrozooplankton. However, this relationship is caused by the large biomass and stratification found at PNEC, while there was no significant correlation when values for this province were removed (Fig. 4). Similar correlations and relationships were found when using mean values of environmental variables measured in situ during the Malaspina cruises (Table 3). 30 6 DW40-200 (mg m-3) DWtotal (mg m-3) 5 20 10 y = 98.98x-0.52 R² = 0.533 4 3 2 y = 0.154x - 0.656 R² = 0.441 1 0 0 0 20 40 60 80 100 10 20 MLD (m) PNEC 4 3 2 y = 0.869x + 2.098 R² = 0.400 1 40 6 PNEC 5 DW2000-5000 (mg m-3) DW200-500 (mg m-3) 6 30 SST (⁰C) 5 4 3 y = 0.783x + 1.673 R² = 0.434 2 1 0 0 0 1 2 3 4 T0-50 (⁰C) 0 1 2 3 4 A larger number of significant correlations were found when using concurrent measurements of environmental variables and plankton biomass (Table 4). The detailed analysis of these relationships, however, revealed that generally non linear functions provided the best fits and that samples obtained at PNEC provided most of the variability (Fig. 5). These results suggest that T0-50 (⁰C) the sampling of PNEC included a larger range of ecosystem states than for the other provinces, as it included stations located at both geographic limits of the province, thus producing relationships between biomass and environmental variables similar to those observed at scales of the whole ocean including all provinces. For instance, the increase of biomass with temperature and thermal stratification or the negative relationship between biomass and the depth of the euphotic zone (Fig. 5) mimic those found using mean Table 4. Correlations (Pearson’s r) between values of plankton provincial values (Fig. 4). In biomass (mg DW m-3) by size classes and oceanographic any case, the correlation variables measured concurrently. Yellow shading indicate coefficients were generally signficant values (P<0.05). Variable names as in Table 2. larger when using mean values Figure 4. Example of significant relationships between mean values of plankton biomass and oceanographic variables averaged by biogeochemical province. The red lines and the equations indicate a significant regression function (P<0.05). R2: determination coefficient. Blue circles indicate the value for PNEC province. Variable names as in Table 2. than when using in situ values at each station, despite the lower number of data points in the former. While the decrease in plankton biomass with the deepening of SST T50 T0‐50 DCM T0_200 Int. Chla ze DW40‐200 DW200‐500 DW500‐1000 DW1000‐2000 DW2000‐5000 DWtotal 0.327 0.255 0.289 0.246 0.113 0.277 0.140 ‐0.001 ‐0.059 0.086 ‐0.321 ‐0.085 0.196 0.283 0.412 0.175 0.542 0.426 ‐0.471 ‐0.505 ‐0.491 ‐0.535 ‐0.495 ‐0.608 0.099 ‐0.082 ‐0.081 0.007 ‐0.286 ‐0.124 0.217 0.238 0.170 0.226 0.181 0.247 ‐0.311 ‐0.341 ‐0.327 ‐0.354 ‐0.317 ‐0.399 6 the mixing layer is consistent with the decrease in primary production in regions with low supply of nutrients from deep layers, the increase in plankton biomass with temperature and thermal stratification is contrary to the expected decrease in plankton with global warming (e.g. Behrenfeld et al., 2006). These findings imply a low dependence between trophic transfer along the size spectrum and primary production, suggesting that oligotrophic ecosystems of the tropical and subtropical ocean are very efficient in the transfer of biomass up the food web despite their low primary production values (San Martin et al., 2005). This interpretation is supported by the equivalence of biomass values along the size classes (implying flat slopes of the biomass size spectrum) in most provinces and the absence of significant correlations with primary production or chlorophyll values. 50 50 PNEC 40 DWtotal (mg m-3) DWtotal (mg m-3) 40 30 20 10 PNEC y = 0.895x + 12.55 R² = 0.181 30 20 10 R² = 0.161 0 0 10 20 30 40 0 5 SST (⁰C) 15 50 50 PNEC 30 20 PNEC 40 DWtotal (mg m-3) 40 DWtotal (mg m-3) 10 T0-50 (⁰C) 30 y = -0.098x + 29.45 R² = 0.161 20 10 10 R² = 0.130 0 0 0 10 20 30 integrated Chla (mg m-2) 40 0 100 200 300 ze (m) Figure 5. Example of significant relationships between values of plankton biomass and oceanographic variables measured concurrently. The red lines and the equations indicate a significant regression function (P<0.05). R2: determination coefficient. Blue circles and orange dots indicate samples from PNEC province. Variable names as in Table 2. Conclusions Low plankton biomass (10-15 mg DW m-3) was found in most deep ocean subtropical and tropical provinces (except PNEC). This biomass was almost constant along logarithmic classes of individual body size in most provinces, suggesting steady state in the plankton. Mean plankton biomass by province was not directly correlated with mean values of primary production or surface chlorophyll. Instead, a negative correlation was found between biomass and the depth of the upper mixing layer or the depth of the chlorophyll maximum, which resulted the best predictors of biomass. Also at province level, small plankton biomass increased with SST and biomass of other classes increased with stratification. 7 Local plankton biomass was correlated with integrated chlorophyll for some classes but all classes were negatively correlated with the depth of the chlorophyll maximum or the depth of the euphotic zone, best predictors of biomass. At local scale the biomass increases with SST and stratification were due to the variability observed at PNEC. These results contribute to the global database of plankton biomass and will allow for the estimation of biomass in unexplored regions Acknowledgements We acknowledge the collaboration of the Commander Chief and crew of R/V Hesperides, to the Chief Scientists and to the technicians of UTM-CSIC during the 7 legs of the cruise. Sampling of plankton was achieved thanks to the coordinated participation of all scientists and technicians of Workpackage 7 (Zooplankton). CTD and chlorophyll data were provided by Workpackages 1 (Physical oceanography) and 4 (Ocean optics). This research was funded by project Malaspina-2010 (CSD200800077) funded by program CONSOLIDER-INGENIO 2010 of the Ministerio de Ciencia e Innovación (Spain), and by funds of the Instituto Español de Oceanografia (IEO). C.M. and J.B. were supported by PFPI fellowships of IEO. 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