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Resolving variability of phytoplankton species composition and blooms in coastal ecosystems
Estuarine, Coastal and Shelf Science 162 (2015) 4e6
Contents lists available at ScienceDirect
Estuarine, Coastal and Shelf Science journal homepage: www.elsevier.com/locate/ecss
Resolving variability of phytoplankton species composition and blooms in coastal ecosystems Riina Klais a, *, James E. Cloern b, Paul J. Harrison c a
Institute of Ecology and Earth Sciences, Tartu University, Lai 40, 51005, Tartu, Estonia United States Geological Survey, MS496, 345 Middlefield Road, Menlo Park, CA, 94025, USA c Dept. Earth & Ocean Sciences, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada b
a r t i c l e i n f o Article history: Available online 14 July 2015
1. Introduction The contributions to this special volume focus on phytoplankton dynamics in coastal ecosystems, where perturbations from terrestrial, atmospheric, oceanic sources and human activities converge to cause changes in phytoplankton communities. Analyses of phytoplankton time series across the range of coastal sites, either as meta-analyses or single site based studies, complete our general understanding of the ecology of coastal phytoplankton dynamics. The role of short-term variability of the phytoplankton community appears to be more important for the annual primary production than previously thought, especially during the high biomass spring bloom period (Gallegos and Neale, 2015). Diel vertical migration of motile species is commonplace even in shallow and presumably well-mixed estuaries (Hall et al., 2015). Comparing phytoplankton patterns in various sites reveals contrasting long-term trends in the last two decades, reflecting the recent history of economic growth in related coastal areas. In Chesapeake Bay Estuary (US east coast) and Thau Lagoon (southern France), oligotrophication has been achieved by different nutrient reduction measures (Gowen et al., 2015; Harding et al., 2015), while in the Patos Lagoon Estuary (Brazil) and SE coast of Arabian Sea, the last two decades showed signs of eutrophication, following the more recent period of economic growth in the area (Haraguchi et al., 2015; Godhe et al., 2015). The global meta-analyses in this volume exposed the great challenges involved when working with this type of data, due to the diversity of idiosyncrasies characteristic to most phytoplankton time series, for example, the taxonomic practices, cell volume calculations (Harrison et al., 2015), volume to carbon conversions
* Corresponding author. http://dx.doi.org/10.1016/j.ecss.2015.07.012 0272-7714/© 2015 Published by Elsevier Ltd.
(Carstensen et al., 2015; Olli et al., 2015). But also the diversity of the patterns themselves makes analyses challenging (Carstensen et al., 2015; Thompson et al., 2015). To begin to move towards more similar practices in plankton monitoring, or at least to describe the sampling procedure in sufficient detail, Zingone et al. (2015) outlined the most critical suggestions for quality control and quality assurance and detailed metadata that would increase the usability and intercomparability of data for other researchers with little or no extra cost. Recurrent intercalibrations between taxonomists and different institutions are critical to harmonize the sample analysis method and ensure the consistency of the information that is being produced (Jakobsen et al., 2015), however, similar workshops could be convened to unify all critical steps of time series collection, i.e. sampling, sample analysis, and the structure and description of data. Despite of the preliminary difficulties in using the existing phytoplankton time series data for cross system comparisons, most studies that have been global to date have used either model outputs or satellite observations, and even the latter do not come close to the level of information that the microscopic analysis of a phytoplankton sample by experienced taxonomist can provide. On the downside, this issue witnesses the continuing bias of the studies towards the northern hemisphere. 2. Short-term variability of phytoplankton production Phytoplankton production depends on algal biomass, light and nutrient availability, and photo-physiological properties of the phytoplankton assemblage (Gallegos and Neale, 2015). Each of these determinants is highly dynamic and hence the resultant rate of phytoplankton production should be similarly variable at scales not captured by regular seasonal patterns. Several contributions to this issue have focused on the causes or consequences of shortterm variability of phytoplankton growth. Gallegos and Neale (2015) analyzed an exceptional 20-year time series consisting of 3443 daily production measurements to determine the role of short-term events in the inter-annual variation of phytoplankton production in Rhode River Estuary. They found that spring events, often related to single species biomass anomalies, coincided with suitable nutrient availability and contributed most of the
R. Klais et al. / Estuarine, Coastal and Shelf Science 162 (2015) 4e6
variability of annual primary production. For the rest of the year, mixed communities of species with different photosynthetic properties responded in various ways to changing light and nutrient availability, which averages out the resulting production variations. Vertical patterns of phytoplankton biomass driven by phytoflagellate migration constitute a natural, short-term process with potentially important impacts on estuarine primary production and community composition. Based on circum-annual records of chlorophyll fluorescence profiles, vertical migration patterns were frequently detected in two shallow estuaries, the Neuse and New River estuaries (Hall et al., 2015). Accounting for the vertical migration of motile species and its effect on the vertical biomass distribution increased the modeled depth-integrated primary production estimates up to 10%, compared to a hypothetical vertically-homogeneous biomass distribution (Hall et al., 2015). Patos Lagoon Estuary (Brazil), a shallow region between the lagoon and the Atlantic Ocean is flushed at time scale of hours to days by wind-driven changes in inflow and outflow regimes, causing salinity fluctuations between 0 and 35 (Haraguchi et al., 2015). In this highly dynamic environment, phytoplankton production and growth is mostly determined by the inflow-outflow regime and resulting retention time of the water, with increasing water retention promoting phytoplankton growth episodes in the basin (Odebrecht et al., 2015).
3. Long-term changes After removing the effect of short-period inflow-outflow regimes on the phytoplankton growth, the long-term increasing trends of cyanobacteria, dinoflagellates and the nitrogen/phosphorus ratio could be detected in the Patos Lagoon Estuary (Haraguchi et al., 2015), indicating ongoing eutrophication. Similarly, the eutrophication signal was apparent in coastal waters of the SE Arabian Sea near Mangalore during 1990e2010, where increasing nitrogen concentrations were accompanied by increasing abundances of diatoms, especially the large-celled genera Coscinodiscus, Odontella and Ditylum, simultaneously with a decline in the N2-fixing marine cyanobacteria Trichodesmium sp. during the warm, calm and stratified pre-monsoon period (Godhe et al., 2015). These results contrast with those in Chesapeake Bay and Thau Lagoon where studies of phytoplankton composition revealed signs of oligotrophication. Thau Lagoon (southern France) is a weakly flushed shallow lagoon and analysis using two new tools based on phytoplankton life forms showed a reduction of dinoflagellates, which reflected oligotrophication in response to reduced inorganic phosphorus inputs. Long-term trends in Chesapeake Bay were analyzed by Harding et al. (2015) using abundances of major taxonomic groups from algal photopigments (1995e2004) and cell counts (1985e2007). These independent time-series were sufficiently long to encompass the droughteflood cycle and document climate effects on hydrology that lead to inter-annual variability of freshwater and nutrient inputs. Diatoms were the predominant taxonomic group, with significant contributions by dinoflagellates, cryptophytes, and cyanobacteria, depending on the salinity zone and season. The authors reported high diatom abundance during wet years compared to dry years and the long-term average, and distinguished climate effects from secular trends using flowadjusted diatom abundances. A significant downward trend in diatoms occurred late in the time series, suggesting that continuing reductions in nutrient loading may lead to lower diatom abundance in the future.
5
4. Global meta-analyses Efforts to develop theories of ecosystem or community organization must be grounded in studies to discover patterns that can be quantified within systems and compared across systems (Levin, 1992). This was the ultimate aim of the extensive SCOR WG 137 data compilation e cross-system comparisons of phytoplankton variability at seasonal and longer time scales. An important recurrent pattern was the occurrence of seasonal or episodic blooms, defined as substantial deviations above background phytoplankton biomass. Carstensen et al. (2015) analyzed data from 86 coastal sites (representing nearly 30,000 microscopically analyzed samples) to determine the common characteristics of phytoplankton blooms e their drivers, timing, and species composition. They found that significant biomass deviations in ~25% of the samples were most frequent during spring (February to May, depending on the latitude), but occurred at any time of year. Blooms in these 86 estuarine-coastal sites were most often dominated by diatoms, especially the centric chain-forming types (Skeletonema costatum s.l., Dactyliosolen fragilissimus, and Cerataulina pelagica). Thompson et al. (2015) analyzed effects of precipitation on algal composition, comparing wet and dry regions of the globe, and inferred that increasing precipitation in summer months was most commonly linked to increases in chlorophyll a concentration and abundance of chlorophytes. Olli et al. (2015) compared the taxonomic composition of phytoplankton communities from seven geographic regions of the world coastal ocean, revealing commonness of diatoms, dinoflagellates and recurring unidentified taxa. Although the taxonomic resolution of datasets differed, a common biomassediversity relationship was observed in all systems with a biomass plateau at intermediate diversity. 5. Increasing the comparability and usability of diverse phytoplankton datasets Prior to the preparation of the manuscripts of this special issue, a major effort was undertaken to harmonize and initiate a quality control check of the many globally distributed phytoplankton time series compiled by members of SCOR Working Group 137. This process revealed the challenges of comparing microscopically analyzed phytoplankton samples, largely due to the significant variability among laboratories in the methods used to count, identify and measure the volume of phytoplankton cells (Harrison et al., 2015), or the taxonomic nomenclature used and resolution reported (Olli et al., 2015; Jakobsen et al., 2015; Zingone et al., 2015). In addition, there are also analytical and sampling errors inherent to microscopic examination of water samples, which may yield up to 2-fold differences in estimated cell abundances even between replicates of a sample by one analyst (Jakobsen et al., 2015). Despite these problems, microscopical analysis remains the standard method for estimating the species-specific carbon biomass of phytoplankton in natural samples (Hillebrand et al., 1999; Harrison et al., 2015). The error involved in carbon biomass estimates derived from microscopy can be reduced by periodic updates of cell volume measurements and monthly volume measurements for the ecologically important diatoms to capture the large seasonal cycle in their cell volume (Jakobsen et al., 2015; Harrison et al., 2015). Regional quantitative species-level phytoplankton time series are exceedingly valuable for determining distribution and diversity patterns (Olli et al., 2015). However, the accurate estimates of diversity patterns depend critically on the taxonomic skills of the microscopist and on the counting effort (Olli et al., 2015). The taxonomy of phytoplankton is under constant revision, and there is an unavoidable time lag before the changes in
6
R. Klais et al. / Estuarine, Coastal and Shelf Science 162 (2015) 4e6
taxonomy are applied to the routine analysis in many time series. Consequently, species are often reported by different, older or synonymous names in various time series. These nomenclatural differences/errors can be corrected using web-based search engines, e.g. World Register of Marine Species (www.marinespecies. org) or Algaebase (www.algaebase.org) (Olli et al., 2015; Zingone et al., 2015).
Committee on Oceanic Research (SCOR), from grant OCE-1243377 from the U.S. National Science Foundation, and from national SCOR committees. We thank Prof. Mike Elliott, for his help and assistance as Editor-In-Chief. During the editorial work of this special issue, Riina Klais was supported by Estonian Research Council (Personal Research Grant 39).
6. Perspectives Long-term monitoring of phytoplankton communities requires commitments of substantial resources and is increasingly difficult to sustain, despite the fact that the cost of collecting that data is low compared to the value of the information obtained. Active use of the data that long term monitoring programs produce demonstrates the value of the time series, not only as a tracker of the changes in the ecosystem, but also as a source of data for scientific studies, including cross system meta-analyses (McQuatters-Gollop et al., 2015). Despite the great efforts given to data collection, some of the existing data still remain under-utilized for a variety of reasons, including insufficient time allocated for analysis or hesitancy of principal investigators to make their data widely available. Time series contain valuable information and their value grows as they lengthen. The best opportunities of extracting information from phytoplankton time series will come if the data are made available after carefully ensuring their quality, thoroughly documenting them with detailed metadata (Zingone et al., 2015). Members of SCOR WG 137 took the first steps toward data harmonization by compiling the data sets, creating consistent data storage protocols, and documenting and mapping the existing monitoring sites (see the http://wg137.net). Some of these datasets already had a wellestablished data management system and hence provided data with consistent quality. One of the best examples is the Chesapeake Bay monitoring program for its user-friendly metadata and organization. In the long run, it really is in the data providers' ultimate interest that the dataset is used, gains positive feedback through publications and attracts more research users and new ideas, thereby already justifying the resources required to sustain the monitoring. Zingone et al. (2015) compiled a list of the most critical quality assurance guidelines that would greatly increase the value of phytoplankton time series and facilitate more cross ecosystem comparisons, including the consistent documentation of sampling and sample analysis methods. As a next step, compilation of a global database for phytoplankton functional traits will open up new avenues for exploring the ecological meaning of phytoplankton community changes and identifying their underlying causes. Acknowledgments Partial support for this activity was provided by the Scientific
References Carstensen, J., Klais, R., Cloern, J.E., 2015. Phytoplankton blooms in estuarine and coastal waters: seasonal patterns and key species. Estuar. Coast. Shelf Sci. Gallegos, C.L., Neale, P.J., 2015. Long-term variations in primary production in a eutrophic sub-estuary: contribution of short-term events. Estuar. Coast. Shelf Sci. http://dx.doi.org/10.1016/j.ecss.2015.01.015. Godhe, A., Narayanaswamy, C., Klais, R., Moorthy, K.S.V., Ramesh, R., Rai, A., Reddy, H.R.V., 2015. Long-term patterns of net phytoplankton and hydrography in coastal SE Arabian sea: what can be inferred from genus level data? Estuar. Coast. Shelf Sci. http://dx.doi.org/10.1016/j.ecss.2015.03.006. Gowen, R.J., Collos, Y., Tett, P., Scherer, C., Bec, B., Abadie, E., Allen, M., O'Brien, T., 2015. Response of Diatom and Dinoflagellate Lifeforms to Reduced Phosphorus Loading: a Case Study in the Thau Lagoon. http://dx.doi.org/10.1016/j.ecss.2015. 03.033. Hall, N., Whipple, A.C., Paerl, H.W., 2015. Vertical Spatio-temporal Patterns of Phytoplankton Due to Migration Behaviors in Two Shallow, Microtidal Estuaries: Influence on Phytoplankton Function and Structure. http://dx.doi.org/10. 1016/j.ecss.2015.03.032. Haraguchi, L., Carstensen, J., Abreu, P.C., Odebrecht, C., 2015. Long-term Changes of the Phytoplankton Community and Biomass in the Subtropical Shallow Patos Lagoon Estuary, Brazil. http://dx.doi.org/10.1016/j.ecss.2015.03.007. Harding Jr., L.W., Adolf, J.E., Mallonee, M.E., Miller, W.D., Gallegos, C.L., Perry, E.S., Johnson, J.M., Sellner, K.G., Paerl, H.W., 2015. Climate Effects on Phytoplankton Floral Composition in Chesapeake Bay. http://dx.doi.org/10.1016/j.ecss.2014.12. 030. Harrison, P.J., Zingone, A., Mickelson, M.J., Lehtinen, S., Ramaiah, N., Kraberg, A., Sun, J., McQuatters-Gollop, A., Jakobsen, H.H., 2015. Cell biovolumes of marine phytoplankton from globally distributed data sets. Estuar. Coast. Shelf Sci. Hillebrand, H., Dürselen, C.-D., Kirschtel, D., Pollingher, U., Zohary, T., 1999. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403e424. Jakobsen, H.H., Carstensen, J., Harrison, P.J., Zingone, A., 2015. Estimating time series phytoplankton carbon biomass: inter-lab comparison of species identification and comparison of volume-to-carbon scaling ratios. Estuar. Coast. Shelf Sci. Levin, S.A., 1992. The problem of pattern and scale in ecology. Ecology 73, 1943e1967. €t, P., Johns, D.G., Owens, N.J.P., McQuatters-Gollop, A., Edwards, M., Helaoue Raitsos, D.E., Schroeder, D., Skinner, J., Stern, R.F., 2015. The continuous plankton recorder survey: how can long-term phytoplankton datasets contribute to the assessment of good environmental status? Estuar. Coast. Shelf Sci. Odebrecht, C., Abreu, P.C., Carstensen, J., 2015. Retention Time Generates Short-term Phytoplankton Blooms in a Shallow Microtidal Subtropical Estuary. http://dx. doi.org/10.1016/j.ecss.2015.03.004. Olli, K., Paerl, H.W., Klais, R., 2015. Diversity of Coastal Phytoplankton Assemblages e Cross Ecosystem Comparison. http://dx.doi.org/10.1016/j.ecss.2015.03.015. Thompson, P.A., O'Brien, T.D., Paerls, H.W., Peierls, B.L., Harrison, P.J., Robb, M., 2015. Precipitation as a Driver of Phytoplankton Ecology in Coastal Waters: a Climatic Perspective. http://dx.doi.org/10.1016/j.ecss.2015.04.004. Zingone, A., Harrison, P.J., Kraberg, A., Lehtinen, S., McQuatters-Gollop, A., O'Brien, T., Sun, J., Jakobsen, H.H., 2015. Increasing the quality, comparability and accessibility of phytoplankton species composition time-series data. Estuar. Coast. Shelf Sci.
Contents lists available at ScienceDirect
Estuarine, Coastal and Shelf Science journal homepage: www.elsevier.com/locate/ecss
Resolving variability of phytoplankton species composition and blooms in coastal ecosystems Riina Klais a, *, James E. Cloern b, Paul J. Harrison c a
Institute of Ecology and Earth Sciences, Tartu University, Lai 40, 51005, Tartu, Estonia United States Geological Survey, MS496, 345 Middlefield Road, Menlo Park, CA, 94025, USA c Dept. Earth & Ocean Sciences, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada b
a r t i c l e i n f o Article history: Available online 14 July 2015
1. Introduction The contributions to this special volume focus on phytoplankton dynamics in coastal ecosystems, where perturbations from terrestrial, atmospheric, oceanic sources and human activities converge to cause changes in phytoplankton communities. Analyses of phytoplankton time series across the range of coastal sites, either as meta-analyses or single site based studies, complete our general understanding of the ecology of coastal phytoplankton dynamics. The role of short-term variability of the phytoplankton community appears to be more important for the annual primary production than previously thought, especially during the high biomass spring bloom period (Gallegos and Neale, 2015). Diel vertical migration of motile species is commonplace even in shallow and presumably well-mixed estuaries (Hall et al., 2015). Comparing phytoplankton patterns in various sites reveals contrasting long-term trends in the last two decades, reflecting the recent history of economic growth in related coastal areas. In Chesapeake Bay Estuary (US east coast) and Thau Lagoon (southern France), oligotrophication has been achieved by different nutrient reduction measures (Gowen et al., 2015; Harding et al., 2015), while in the Patos Lagoon Estuary (Brazil) and SE coast of Arabian Sea, the last two decades showed signs of eutrophication, following the more recent period of economic growth in the area (Haraguchi et al., 2015; Godhe et al., 2015). The global meta-analyses in this volume exposed the great challenges involved when working with this type of data, due to the diversity of idiosyncrasies characteristic to most phytoplankton time series, for example, the taxonomic practices, cell volume calculations (Harrison et al., 2015), volume to carbon conversions
* Corresponding author. http://dx.doi.org/10.1016/j.ecss.2015.07.012 0272-7714/© 2015 Published by Elsevier Ltd.
(Carstensen et al., 2015; Olli et al., 2015). But also the diversity of the patterns themselves makes analyses challenging (Carstensen et al., 2015; Thompson et al., 2015). To begin to move towards more similar practices in plankton monitoring, or at least to describe the sampling procedure in sufficient detail, Zingone et al. (2015) outlined the most critical suggestions for quality control and quality assurance and detailed metadata that would increase the usability and intercomparability of data for other researchers with little or no extra cost. Recurrent intercalibrations between taxonomists and different institutions are critical to harmonize the sample analysis method and ensure the consistency of the information that is being produced (Jakobsen et al., 2015), however, similar workshops could be convened to unify all critical steps of time series collection, i.e. sampling, sample analysis, and the structure and description of data. Despite of the preliminary difficulties in using the existing phytoplankton time series data for cross system comparisons, most studies that have been global to date have used either model outputs or satellite observations, and even the latter do not come close to the level of information that the microscopic analysis of a phytoplankton sample by experienced taxonomist can provide. On the downside, this issue witnesses the continuing bias of the studies towards the northern hemisphere. 2. Short-term variability of phytoplankton production Phytoplankton production depends on algal biomass, light and nutrient availability, and photo-physiological properties of the phytoplankton assemblage (Gallegos and Neale, 2015). Each of these determinants is highly dynamic and hence the resultant rate of phytoplankton production should be similarly variable at scales not captured by regular seasonal patterns. Several contributions to this issue have focused on the causes or consequences of shortterm variability of phytoplankton growth. Gallegos and Neale (2015) analyzed an exceptional 20-year time series consisting of 3443 daily production measurements to determine the role of short-term events in the inter-annual variation of phytoplankton production in Rhode River Estuary. They found that spring events, often related to single species biomass anomalies, coincided with suitable nutrient availability and contributed most of the
R. Klais et al. / Estuarine, Coastal and Shelf Science 162 (2015) 4e6
variability of annual primary production. For the rest of the year, mixed communities of species with different photosynthetic properties responded in various ways to changing light and nutrient availability, which averages out the resulting production variations. Vertical patterns of phytoplankton biomass driven by phytoflagellate migration constitute a natural, short-term process with potentially important impacts on estuarine primary production and community composition. Based on circum-annual records of chlorophyll fluorescence profiles, vertical migration patterns were frequently detected in two shallow estuaries, the Neuse and New River estuaries (Hall et al., 2015). Accounting for the vertical migration of motile species and its effect on the vertical biomass distribution increased the modeled depth-integrated primary production estimates up to 10%, compared to a hypothetical vertically-homogeneous biomass distribution (Hall et al., 2015). Patos Lagoon Estuary (Brazil), a shallow region between the lagoon and the Atlantic Ocean is flushed at time scale of hours to days by wind-driven changes in inflow and outflow regimes, causing salinity fluctuations between 0 and 35 (Haraguchi et al., 2015). In this highly dynamic environment, phytoplankton production and growth is mostly determined by the inflow-outflow regime and resulting retention time of the water, with increasing water retention promoting phytoplankton growth episodes in the basin (Odebrecht et al., 2015).
3. Long-term changes After removing the effect of short-period inflow-outflow regimes on the phytoplankton growth, the long-term increasing trends of cyanobacteria, dinoflagellates and the nitrogen/phosphorus ratio could be detected in the Patos Lagoon Estuary (Haraguchi et al., 2015), indicating ongoing eutrophication. Similarly, the eutrophication signal was apparent in coastal waters of the SE Arabian Sea near Mangalore during 1990e2010, where increasing nitrogen concentrations were accompanied by increasing abundances of diatoms, especially the large-celled genera Coscinodiscus, Odontella and Ditylum, simultaneously with a decline in the N2-fixing marine cyanobacteria Trichodesmium sp. during the warm, calm and stratified pre-monsoon period (Godhe et al., 2015). These results contrast with those in Chesapeake Bay and Thau Lagoon where studies of phytoplankton composition revealed signs of oligotrophication. Thau Lagoon (southern France) is a weakly flushed shallow lagoon and analysis using two new tools based on phytoplankton life forms showed a reduction of dinoflagellates, which reflected oligotrophication in response to reduced inorganic phosphorus inputs. Long-term trends in Chesapeake Bay were analyzed by Harding et al. (2015) using abundances of major taxonomic groups from algal photopigments (1995e2004) and cell counts (1985e2007). These independent time-series were sufficiently long to encompass the droughteflood cycle and document climate effects on hydrology that lead to inter-annual variability of freshwater and nutrient inputs. Diatoms were the predominant taxonomic group, with significant contributions by dinoflagellates, cryptophytes, and cyanobacteria, depending on the salinity zone and season. The authors reported high diatom abundance during wet years compared to dry years and the long-term average, and distinguished climate effects from secular trends using flowadjusted diatom abundances. A significant downward trend in diatoms occurred late in the time series, suggesting that continuing reductions in nutrient loading may lead to lower diatom abundance in the future.
5
4. Global meta-analyses Efforts to develop theories of ecosystem or community organization must be grounded in studies to discover patterns that can be quantified within systems and compared across systems (Levin, 1992). This was the ultimate aim of the extensive SCOR WG 137 data compilation e cross-system comparisons of phytoplankton variability at seasonal and longer time scales. An important recurrent pattern was the occurrence of seasonal or episodic blooms, defined as substantial deviations above background phytoplankton biomass. Carstensen et al. (2015) analyzed data from 86 coastal sites (representing nearly 30,000 microscopically analyzed samples) to determine the common characteristics of phytoplankton blooms e their drivers, timing, and species composition. They found that significant biomass deviations in ~25% of the samples were most frequent during spring (February to May, depending on the latitude), but occurred at any time of year. Blooms in these 86 estuarine-coastal sites were most often dominated by diatoms, especially the centric chain-forming types (Skeletonema costatum s.l., Dactyliosolen fragilissimus, and Cerataulina pelagica). Thompson et al. (2015) analyzed effects of precipitation on algal composition, comparing wet and dry regions of the globe, and inferred that increasing precipitation in summer months was most commonly linked to increases in chlorophyll a concentration and abundance of chlorophytes. Olli et al. (2015) compared the taxonomic composition of phytoplankton communities from seven geographic regions of the world coastal ocean, revealing commonness of diatoms, dinoflagellates and recurring unidentified taxa. Although the taxonomic resolution of datasets differed, a common biomassediversity relationship was observed in all systems with a biomass plateau at intermediate diversity. 5. Increasing the comparability and usability of diverse phytoplankton datasets Prior to the preparation of the manuscripts of this special issue, a major effort was undertaken to harmonize and initiate a quality control check of the many globally distributed phytoplankton time series compiled by members of SCOR Working Group 137. This process revealed the challenges of comparing microscopically analyzed phytoplankton samples, largely due to the significant variability among laboratories in the methods used to count, identify and measure the volume of phytoplankton cells (Harrison et al., 2015), or the taxonomic nomenclature used and resolution reported (Olli et al., 2015; Jakobsen et al., 2015; Zingone et al., 2015). In addition, there are also analytical and sampling errors inherent to microscopic examination of water samples, which may yield up to 2-fold differences in estimated cell abundances even between replicates of a sample by one analyst (Jakobsen et al., 2015). Despite these problems, microscopical analysis remains the standard method for estimating the species-specific carbon biomass of phytoplankton in natural samples (Hillebrand et al., 1999; Harrison et al., 2015). The error involved in carbon biomass estimates derived from microscopy can be reduced by periodic updates of cell volume measurements and monthly volume measurements for the ecologically important diatoms to capture the large seasonal cycle in their cell volume (Jakobsen et al., 2015; Harrison et al., 2015). Regional quantitative species-level phytoplankton time series are exceedingly valuable for determining distribution and diversity patterns (Olli et al., 2015). However, the accurate estimates of diversity patterns depend critically on the taxonomic skills of the microscopist and on the counting effort (Olli et al., 2015). The taxonomy of phytoplankton is under constant revision, and there is an unavoidable time lag before the changes in
6
R. Klais et al. / Estuarine, Coastal and Shelf Science 162 (2015) 4e6
taxonomy are applied to the routine analysis in many time series. Consequently, species are often reported by different, older or synonymous names in various time series. These nomenclatural differences/errors can be corrected using web-based search engines, e.g. World Register of Marine Species (www.marinespecies. org) or Algaebase (www.algaebase.org) (Olli et al., 2015; Zingone et al., 2015).
Committee on Oceanic Research (SCOR), from grant OCE-1243377 from the U.S. National Science Foundation, and from national SCOR committees. We thank Prof. Mike Elliott, for his help and assistance as Editor-In-Chief. During the editorial work of this special issue, Riina Klais was supported by Estonian Research Council (Personal Research Grant 39).
6. Perspectives Long-term monitoring of phytoplankton communities requires commitments of substantial resources and is increasingly difficult to sustain, despite the fact that the cost of collecting that data is low compared to the value of the information obtained. Active use of the data that long term monitoring programs produce demonstrates the value of the time series, not only as a tracker of the changes in the ecosystem, but also as a source of data for scientific studies, including cross system meta-analyses (McQuatters-Gollop et al., 2015). Despite the great efforts given to data collection, some of the existing data still remain under-utilized for a variety of reasons, including insufficient time allocated for analysis or hesitancy of principal investigators to make their data widely available. Time series contain valuable information and their value grows as they lengthen. The best opportunities of extracting information from phytoplankton time series will come if the data are made available after carefully ensuring their quality, thoroughly documenting them with detailed metadata (Zingone et al., 2015). Members of SCOR WG 137 took the first steps toward data harmonization by compiling the data sets, creating consistent data storage protocols, and documenting and mapping the existing monitoring sites (see the http://wg137.net). Some of these datasets already had a wellestablished data management system and hence provided data with consistent quality. One of the best examples is the Chesapeake Bay monitoring program for its user-friendly metadata and organization. In the long run, it really is in the data providers' ultimate interest that the dataset is used, gains positive feedback through publications and attracts more research users and new ideas, thereby already justifying the resources required to sustain the monitoring. Zingone et al. (2015) compiled a list of the most critical quality assurance guidelines that would greatly increase the value of phytoplankton time series and facilitate more cross ecosystem comparisons, including the consistent documentation of sampling and sample analysis methods. As a next step, compilation of a global database for phytoplankton functional traits will open up new avenues for exploring the ecological meaning of phytoplankton community changes and identifying their underlying causes. Acknowledgments Partial support for this activity was provided by the Scientific
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