Phytoplankton Time Series Research Articles

Long-term declines in chlorophyll a and variable phenology revealed by a 60-year estuarine plankton time series

Long-term ecological time series provide a unique perspective on the emergent properties of ecosystems. In aquatic systems, phytoplankton form the base of the food web and their biomass, measured as the concentration of the photosynthetic pigment chlorophyll a (chl a), is an indicator of ecosystem quality. We analyzed temporal trends in chl a from the Long-Term Plankton Time Series in Narragansett Bay, Rhode Island, USA, a temperate estuary experiencing long-term warming and changing anthropogenic nutrient inputs. Dynamic linear models were used to impute and model environmental variables (1959 to 2019) and chl a concentrations (1968 to 2019). A long-term chl a decrease was observed with an average decline in the cumulative annual chl a concentration of 49% and a marked decline of 57% in winter-spring bloom magnitude. The long-term decline in chl a concentration was directly and indirectly associated with multiple environmental factors that are impacted by climate change (e.g., warming temperatures, water column stratification, reduced nutrient concentrations) indicating the importance of accounting for regional climate change effects in ecosystem-based management. Analysis of seasonal phenology revealed that the winter-spring bloom occurred earlier, at a rate of 4.9 ± 2.8 d decade-1. Finally, the high degree of temporal variation in phytoplankton biomass observed in Narragansett Bay appears common among estuaries, coasts, and open oceans. The commonality among these marine ecosystems highlights the need to maintain a robust set of phytoplankton time series in the coming decades to improve signal-to-noise ratios and identify trends in these highly variable environments.

Parallels of quantum superposition in ecological models: from counterintuitive patterns to eco-evolutionary interpretations of cryptic species

BackgroundSuperposition, i.e. the ability of a particle (electron, photon) to occur in different states or positions simultaneously, is a hallmark in the subatomic world of quantum mechanics. Although counterintuitive at first sight, the quantum world has potential to inform macro-systems of people and nature. Using time series and spatial analysis of bird, phytoplankton and benthic invertebrate communities, this paper shows that superposition can occur analogously in redundancy analysis (RDA) frequently used by ecologists.ResultsWe show that within individual ecosystems single species can be associated simultaneously with different orthogonal axes in RDA models, which suggests that they operate in more than one niche spaces. We discuss this counterintuitive result in relation to the statistical and mathematical features of RDA and the recognized limitations with current traditional species concepts based on vegetative morphology.ConclusionWe suggest that such “quantum weirdness” in the models is reconcilable with classical ecosystems logic when the focus of research shifts from morphological species to cryptic species that consist of genetically and ecologically differentiated subpopulations. We support our argument with theoretical discussions of eco-evolutionary interpretations that should become testable once suitable data are available.

An updated end-to-end ecosystem model of the Northern California Current reflecting ecosystem changes due to recent marine heatwaves.

The Northern California Current is a highly productive marine upwelling ecosystem that is economically and ecologically important. It is home to both commercially harvested species and those that are federally listed under the U.S. Endangered Species Act. Recently, there has been a global shift from single-species fisheries management to ecosystem-based fisheries management, which acknowledges that more complex dynamics can reverberate through a food web. Here, we have integrated new research into an end-to-end ecosystem model (i.e., physics to fisheries) using data from long-term ocean surveys, phytoplankton satellite imagery paired with a vertically generalized production model, a recently assembled diet database, fishery catch information, species distribution models, and existing literature. This spatially-explicit model includes 90 living and detrital functional groups ranging from phytoplankton, krill, and forage fish to salmon, seabirds, and marine mammals, and nine fisheries that occur off the coast of Washington, Oregon, and Northern California. This model was updated from previous regional models to account for more recent changes in the Northern California Current (e.g., increases in market squid and some gelatinous zooplankton such as pyrosomes and salps), to expand the previous domain to increase the spatial resolution, to include data from previously unincorporated surveys, and to add improved characterization of endangered species, such as Chinook salmon (Oncorhynchus tshawytscha) and southern resident killer whales (Orcinus orca). Our model is mass-balanced, ecologically plausible, without extinctions, and stable over 150-year simulations. Ammonium and nitrate availability, total primary production rates, and model-derived phytoplankton time series are within realistic ranges. As we move towards holistic ecosystem-based fisheries management, we must continue to openly and collaboratively integrate our disparate datasets and collective knowledge to solve the intricate problems we face. As a tool for future research, we provide the data and code to use our ecosystem model.

Long‐term phytoplankton community dynamics in Lake Stechlin (north‐east Germany) under sudden and heavily accelerating eutrophication

Abstract Climate warming and legacy phosphorus (P) in sediments are two main factors counteracting current efforts to prevent eutrophication of temperate freshwater lakes and reservoirs. Climate warming impacts lacustrine thermal regimes, accelerates harmful cyanobacterial growth, and increases the probability of periodic anoxic bottom conditions. Legacy P in sediments becomes influential over eutrophication because of its reactivation under anoxic conditions. Lake Stechlin is a deep glacial lake of the Baltic Lake area, Germany in which artificial external P loading has been effectively blocked for more than 3 decades. The lakes trophic status was oligotrophic, until roughly 2 decades ago when it accelerated toward higher trophic levels. Here we analyse the temporal dynamics of Lake Stechlin phytoplankton biomass and composition in relation to chemical and hydrological covariates, based on monitoring data from 1994 to 2019 at sampling frequency of c. 27 samples per year. Our results show an assemblage change toward cyanobacterial dominance that took place at several temporal scales. Intra‐annual compositional change became more pronounced over the study period, mainly because of changes during the autumn and winter seasons. Results from a structural break point analysis of the phytoplankton time series with covariates, and from a hierarchical cluster analysis show that major changes occurred in 2007–2011. An analysis of early warning indicators did not unambiguously support this period as a critical transition. The change rather occurred as a rapid succession of major events during these years. Results from convergent cross‐mapping causality tests are consistent with the hypothesis that increasing relative water column stability may drive eutrophication, and additionally, that the intra‐annual timing of the biomass change plays an important role in the lakes P loading. Although quantitative data on internal P loading are unavailable, internal P mobilisation from sediments appears to be the most plausible mechanism behind the increased phytoplankton biomass. Lake Stechlin, therefore, is an example of the challenges that legacy P poses to the management of freshwater ecosystems in the context of climate warming, even when external P sources are effectively blocked, and local protective measures are in place.

Phytoplankton communities in a coastal and offshore stations of the northern Adriatic Sea approached by network analysis and different statistical descriptors

The Northern Adriatic Sea is one of the most productive areas of the Mediterranean Sea. Long-term series of phytoplankton are crucial to detect changes in the marine ecosystems, due to its high sensitiveness to environmental conditions.In this study, we compared two long-term phytoplankton time series (1988–2019) related to a coastal and an offshore stations located along the LTER Senigallia-Susak transect (Northern Adriatic Sea), using several statistical descriptors: diversity indices, multivariate statistical analyses (PCA, HCPC, NMDS), IndVal (indicator value analysis) and graph-network analysis. The coastal station was found to be more variable than the offshore one, being directly affected by the Western Adriatic Current and therefore by riverine inputs. The two stations appeared to be more different in winter and autumn, and more similar in summer when riverine waters spread offshore in stratified conditions. Due to its more oligotrophic condition, the offshore phytoplankton community showed a higher biodiversity than the coastal one, where phytoplankton blooms occurred frequently.Graph-network analysis turned out to be a useful tool to study the phytoplankton community through the number of interactions occurring among phytoplankton taxa, that was higher at the offshore station.This study highlighted that any evaluation of the Good Environmental Status (as required by the Marine Strategy Framework Directive) should consider the oceanographic differences between different areas, combining several statistical approaches.

Emergence of Broadband Variability in a Marine Plankton Model Under External Forcing

AbstractTemporal variability in plankton community structure and biomass is often driven by environmental fluctuations: nutrient supplies, light, stratification and temperature. But plankton time series also exhibit variability that is not strongly correlated with key physical variables and is distinctly nonlinear in nature. There is evidence, from both laboratory and modeling studies, that oscillations can arise from ecological interactions alone. In the open ocean, it is challenging to establish the roles and relative importance of environmental versus intrinsic processes in generating the observed ecological variability. To explore this competition, we employ a marine plankton model that supports two mechanisms of intrinsic ecological variability operating at distinct frequencies: predator‐prey interactions between zooplankton and phytoplankton, with timescales of weeks, and resource competition that occurs with multiple nutrients phytoplankton species, with timescales of years. The model is forced by imposing variable nutrient input rates. Representing typical open ocean situations, with periods ranging from subseasonal to multi‐annual. We find that intrinsically‐driven variability generally persists in the presence of extrinsic forcing, and that the interaction between the two can produce variability at frequencies that are not characteristic of either source. The intrinsic frequencies are found to be even more energetic when the extrinsic variability is augmented with stochastic noise. We conclude that interactions between intrinsic and extrinsic sources of variability may contribute to the wide range of observed frequencies in phytoplankton time series, and may explain why it is often difficult to relate planktonic variation to environmental variation alone.

Correction: Martinez et al. Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean. Remote Sens. 2020, 12, 4156

The authors wish to make the following corrections to the paper [...]

A phytoplankton time series in the Northwestern Adriatic Sea: Structure and dynamics of the assemblages in a coastal ecosystem

Phytoplankton assemblage structure was analyzed based on time series data (2008–2019) together with environmental variables in two coastal sites in the Northwestern Adriatic Sea. The main component of phytoplankton assemblage was the diatom group. Distinct seasonal and inter annual variations in the presence of taxa were observed within the phytoplankton assemblage over the study period. Mainly four taxa showed a non-random pattern in the binary time series, such as three diatoms Skeletonema marinoi, Thalassionema nitzschioides and Dactyliosolen fragilissimus, and undetermined Dinophyceae. S. marinoi was more frequent in winter and early spring, whereas T. nitzschioides showed an opposite pattern, being more frequent in late spring and summer. In both cases, deviation from randomness was caused by a clear and almost stationary annual cycle. On the contrary, deviation from randomness depended on the long-term trend of D. fragilissimus time series, as this diatom showed an increasing frequency of occurrence since 2016. A clear phytoplankton assemblage structure affected by seasonal and environmental factors was observed. This was very evident for representative specie (i.e., S. marinoi associated with colder and rich nutrient waters or Heterocapsa niei and T. nitzschioides associated with high temperature and low nutrient conditions) and other species associated with various environmental parameters. Furthermore, the analysis of the association among phytoplankton taxa showed a partial and varied pattern. Our results highlight that phytoplankton time series showed changes in assemblage structure exhibiting a regular 12-month period affected by environmental factors. The long time series observations are important to understand the phytoplankton assemblage structure in relation to environmental changes and human pressures, crucial to achieving the Good Environmental Status in compliance to environmental directives.

Predictable shifts in diversity and ecosystem function in phytoplankton communities along coastal salinity continua

AbstractSalinity is a major environmental predictor of phytoplankton species richness and composition. We hypothesize that the variation in phytoplankton richness along coastal salinity gradients is reflected in essential ecosystem functions like resource use efficiency (RUE)—the proportion of limiting resource that is converted into biomass. To test the hypothesis, we analyzed time series of phytoplankton and environmental variables from the Chesapeake Bay and the Baltic Sea. We analyzed the relationship among salinity, diversity, and RUE in bivariate and interaction association, and in structural equation model (SEM)—a form of path analysis to resolve multivariate relationships among interrelated variables. We concluded that the intrusion of marine water will lead to rapid change in species diversity on the fresh side. Despite diversity drop, individual community functions, like RUE, remain relatively resilient, reflecting functional redundancy. We propose that the salinity gradient also reflects trophic complexity, allowing stable resource use at reduced diversity.

Ba/Ca profiles in shells of Pecten maximus – A proxy for specific primary producers rather than bulk phytoplankton

Molar barium-to‑calcium ratios in bivalve shells (Ba/Cashell) have been proposed in a variety of studies to serve as a potential proxy for the reconstruction of phytoplankton dynamics. However, the link between phytoplankton and Ba/Cashell profiles remains unclear and needs to be deciphered more accurately. In this study, we analyzed the relationship between Ba/Cashell peaks and specific phytoplankton species, and assessed the applicability of Ba/Cashell ratios as a species-specific phytoplankton proxy. The timing of peaks in highly resolved Ba/Cashell time-series in Pecten maximus shells (Bay of Brest, France) from two years (2011, 2012) were compared to the chlorophyll a concentration and the occurrence of individual diatom and dinoflagellate species. In addition, Monte Carlo simulations were used to approximate a species-specific contribution to the measured Ba/Cashell peaks. The results clearly demonstrated that the Ba/Cashell profiles cannot be explained either by the chlorophyll a concentration nor by the total diatom or dinoflagellate abundance. Instead, time-series of specific phytoplankton, especially diatoms, revealed a high degree of synchronicity with Ba/Cashell peaks when temporally lagged by 8 to 13 days (depending on species). The Monte Carlo simulations suggested that the Ba/Cashell peak heights cannot be fully explained by the observed phytoplankton cell concentration, but rather by individually weighted phytoplankton time-series, most likely caused by inter-species differences such as cell size and chemical ability to adsorb Ba. Moreover, the approximated species-specific weighting factors agreed well between the studied years. According to our findings, Ba/Cashell peaks are likely associated with blooms of specific phytoplankton taxa, with a time lag of ca. one to two weeks, and the amount of cell-associated Ba varies between phytoplankton species. These conclusions provide further insights into the formation of Ba enrichments in bivalve shells and improves the applicability of Ba/Cashell profiles as a species-specific proxy of past phytoplankton dynamics.

Responses of Gulf of Alaska plankton communities to a marine heat wave

Time series of phytoplankton and zooplankton collected from the shelf and oceanic northern Gulf of Alaska from 2000 to 2018 are examined to describe changes in abundance and composition that occurred during the 2014–2016 marine heat wave (MHW). Zooplankton abundances were very high on the shelf during the MHW, particularly copepods and pteropods, while large diatoms were very low. Community Temperature Indices (CTI) were derived and showed significant, positive correlations with temperature for both trophic levels on the shelf and in the deep ocean. While no common taxa disappeared from the communities, there were changes in relative abundance that contributed to the increase in CTI. Additionally, some rarer taxa were not found during or after the MHW, and fewer new taxa appeared with its onset. There is thus evidence for a change in ecosystem functioning in the lower trophic levels with the northeast Pacific MHW bringing; lower plankton taxonomic richness, a bias towards species that prefer warm conditions, increased effects down the food chain, likely exerted by changes in forage fish, and uncertainty in data from 2017 to 2018 as to whether plankton metrics had, or would, return to pre-MHW values.

Phytoplankton Time-Series in a LTER Site of the Adriatic Sea: Methodological Approach to Decipher Community Structure and Indicative Taxa

In the shallow and landlocked northeast Adriatic Sea, environmental factors have changed in recent decades. Their influence on seasonal and inter-annual variability of phytoplankton has been documented in the recent literature. Here, we decipher the long-term variability of phytoplankton phenology at a Long-Term Ecological Research site (Gulf of Trieste, Slovenia). Structural changes in the phytoplankton community (period 2005–2017) were analysed using a multivariate protocol based on Bayesian clustering. The protocol was modified from the literature to fit the needs of the study, using correspondence analysis and k-means clustering. A novel index for ordination and selection of taxa based on frequency and evenness was developed. The Total Inertia analysis showed that this index better preserved the available information. Typical seasonal assemblages were highlighted by applying the Indicative Value index in conjunction with likelihood ratio values. We obtained a rough picture of the seasonal separation of the diatom-dominated community from the mixed community and a refined picture of the phenology of the assemblages and bloom events. The spring diatom peak proved to be inconstant and short-lived, while the autumn bloom was generally long and diverse. As expected for nearshore environments, the average life span of the assemblages was found to be short-periodic (2–4 months). The second part of the year and the last part of the series were more prone to changes in terms of typical assemblages.

Phytoplankton responses to meteorological and hydrological forcing at decadal to seasonal time scales

One of the challenges for predicting global change effects on aquatic ecosystems is the vague understanding of the mechanisms of multiple controlling factors affecting phytoplankton dynamics at different time scales. Here we distinguish between hydrometeorological forcing of phytoplankton dynamics at time scales from days to decades based on a 54-year monthly phytoplankton time series from a large shallow Lake Võrtsjärv (58°16′N, 26°02′E) in Estonia, combined with daily data on forcing factors—thermal-, wind-, light- and water-level regimes. By using variance partitioning with linear mixed effect modelling (LME), we found a continuum from the large dominant K-selected filamentous cyanobacteria with strongest decadal scale variation (8–30%) to r-selected phytoflagellates with large stochastic variability (80–96%). External forcing revealed strong seasonal variation (up to 80%), while specifically water level and wind speed had a robust decadal variation (8% and 20%, respectively). The effect of external variables was proportionally manifested in the time scales of phytoplankton variation. Temperature, with a clear seasonal variation, had no impact on the dominant cold tolerant filamentous cyanobacteria in Lake Võrtsjärv. We found the LME as a reliable method for resolving the temporal cross-scale problem. It yielded quantitative results that matched our intuitive understanding of the dynamics of different variables.

Coastal phytoplankton bloom dynamics in the Tyrrhenian Sea: Advantage of integrating in situ observations, large-scale analysis and forecast systems

Coastal systems represent the most dynamic natural systems on Earth, making their study particularly challenging. A holistic approach that integrates a set of monitoring tools for data collection (i.e., satellite imagery, numerical models and in situ observations) may provide different information about coastal ecosystems at different spatial and temporal scales. Of course, none of these tools are perfect, being that each is characterized by intrinsic errors and therefore specific uncertainties, which is also an important subject of investigation. Long-term high-resolution in situ observations of the phytoplankton biomass at a coastal site (Civitavecchia, Tyrrhenian Sea), provided by the Civitavecchia Coastal Environment Monitoring System (C-CEMS) observational platform, are presented, discussed and integrated with data from the Copernicus Marine Environment Monitoring Services (CMEMS) for the Mediterranean Sea, which are generated by the MedBFM model system, and satellite observations from the CMEMS Ocean Colour database. The analysis of the time series of phytoplankton provided by in situ, satellite and model data show the typical dynamics of coastal temperate systems, which are characterized by spring and autumn blooms and significant interannual variability. The empirical orthogonal function (EOF) analysis highlights the consistency among the multiplatform datasets, whereas integrating the local in situ time series with a spatial analysis from model and satellite data provides information about the extent of coastal bloom phenomena and the relevance of the observation location with respect to surroundings. Our study of the dynamics of coastal blooms in the Civitavecchia coastal system allows us to propose a best practice framework that may be of general interest, and potentially applied to any multiplatform monitoring system (MPMS). The MPMS approach allows us to investigate the interannual variability over different horizontal and vertical scales, reflecting the variability in natural drivers (i.e., atmospheric forcings, coastal currents, upwelling, and land inputs), as typically expected in coastal areas.

Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean

Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific behaviors. Here, we show that this approach can also be applied on satellite observations and can even be further improved by testing performances of different machine learning algorithms, the SVR and a neural network with dense layers (a multi-layer perceptron, MLP). The MLP, thanks to its ability to capture complex non-linear relationships, outperforms the SVR to capture satellite Chl spatial patterns (correlation of 0.75 vs. 0.65 on a global scale, respectively) along with its interannual variability and trend, despite an underestimated amplitude. Among deep learning algorithms, neural network such as MLP models appear to be promising tools to investigate phytoplankton long-term time-series.

Phytoplankton Diversity Effect on Ecosystem Functioning in a Coastal Upwelling System

Species composition plays a key role in ecosystem functioning. Theoretical, experimental and field studies show positive effects of biodiversity on ecosystem processes. However, this link can differ between taxonomic and functional diversity components and also across trophic levels. These relationships have been hardly studied in planktonic communities of coastal upwelling systems. Using a 28-year time series of phytoplankton and zooplankton assemblages, we examined the effects of phytoplankton diversity on resource use efficiency (RUE, ratio of biomass to limiting resource) at the two trophic levels in the Galician upwelling system (NW Iberian peninsula). By fitting generalized least square models, we show that phytoplankton diversity was the best predictor for RUE across planktonic trophic levels. This link varied depending on the biodiversity component considered: while the effect of phytoplankton richness on RUE was positive for phytoplankton RUE and negative for zooplankton RUE, phytoplankton evenness effect was negative for phytoplankton RUE and positive for zooplankton RUE. Overall, taxonomic diversity had higher explanatory power than functional diversity, and variability in phytoplankton and zooplankton RUE decreased with increasing phytoplankton taxonomic diversity. Phytoplankton used resources more efficiently in warmer waters and at greater upwelling intensity, although these effects were not as strong as those for biodiversity. These results suggest that phytoplankton species numbers in highly dynamic upwelling systems are important for maintaining the planktonic biomass production leading us to hypothesize the relevance of complementarity effects. However, we further postulate that a selection effect may operate also because assemblages with low evenness were dominated by diatoms with specific functional traits increasing their ability to exploit resources more efficiently.

Inhomogeneity detection in phytoplankton time series using multivariate analyses

Phytoplankton communities have long been used as water quality indicators within environmental policies. This has fostered the development of national and international phytoplankton monitoring programs, but these networks are subject to sources of uncertainty due to laboratory issues. Nevertheless, studies regarding the interference associated with these aspects are not well-documented. Hence, a long time series (2003–2015) from the Basque continental shelf (southeastern Bay of Biscay) was analyzed to evaluate the uncertainty given by laboratory strategies when studying phytoplankton variability. Variability in phytoplankton communities was explained not only by environmental conditions but also by changes in fixatives (glutaraldehyde and acidic Lugol's solution) and laboratory staff. Based on Bray-Curtis distances, phytoplankton assemblages were found to be significantly dissimilar according to the effect of changes in the specialist handling the sample and the employed fixative. The pair-wise permutational multivariate analysis of variance (PERMANOVA) showed significant differences between the two fixatives utilized and also between the three taxonomists involved. Thus, laboratory-related effects should be considered in the study of phytoplankton time series.

Determinism versus randomness in plankton dynamics: The analysis of noisy time series based on the recurrence plots

Abstract The quantitative analysis of recurrence plots while applied to mathematical models was shown to be an effective tool in recognizing a frontier between deterministic chaos and random processes. In nature, however, unlike mathematical models, deterministic processes are closely intertwined with random influences. As a result, the non-structural distributions of points on the recurrence plots, which are typical of random processes, are inevitably superimposed on the aperiodic structures characteristic of chaos. Taking into account that the stochastic impacts are an inherent feature of the dynamics of populations in the wild, we present here the results of the analysis of recurrence plots in order to reveal the extent to which irregular phytoplankton oscillations in the Naroch Lakes, Belarus, are susceptible to stochastic impacts. We demonstrate that numerical assessments of the horizon of predictability Tpr of the dynamics under study and the average number Pd of the points that belong to the diagonal segments on the recurrence plots can furnish insights into the extent to which the dynamics of both model and phytoplankton populations are affected by random components. Specifically, a comparative analysis of the values of Tpr and Pd for the time series of phytoplankton and the time series of random processes allows us to conclude that random components of the phytoplankton dynamics in the Naroch Lakes do not prevent recognition of chaotic nature of these dynamics.

Contribution of benthic microalgae to the temporal variation in phytoplankton assemblages in a macrotidal system.

Suspended marine benthic microalgae in the water column reflect the close relationship between the benthic and pelagic components of coastal ecosystems. In this study, a 12-year phytoplankton time-series was used to investigate the contribution of benthic microalgae to the pelagic system at a site along the French-Atlantic coast. Furthermore, all taxa identified were allocated into different growth forms in order to study their seasonal patterns. The highest contribution of benthic microalgae was observed during the winter period, reaching up to 60% of the carbon biomass in the water column. The haptobenthic growth form showed the highest contribution in terms of biomass, dominant in the fall-winter period when the turbidity and the river flow were high. The epipelic growth form did not follow any seasonal pattern. The epiphytic diatom Licmophora was most commonly found during summer. As benthic microalgae were found in the water column throughout the year, the temporal variation detected in the structure of pelagic assemblages in a macrotidal ecosystem was partly derived from the differentiated contribution of several benthic growth forms.

Evaluation of phytoplankton quality and toxicity risk based on a long-term time series previous to the implementation of a bivalve farm (Basque coast as a case study)

In the last decades there has been a great development in aquaculture worldwide and, on the Basque coast (southeastern Bay of Biscay) in particular, there is a recent interest in implementing bivalve aquaculture in open marine waters. In this context, the study of phytoplankton is essential given that it is the main source of energy for bivalves and, at the same time, a main potential toxicity risk. Bivalves, as filter-feeding organisms, can accumulate phycotoxins and these can be transferred through the food-chain, posing a threat to humans. All this, together with a recently installed pilot-scale bivalve farming, motivated a study of the phytoplankton community. Here, 11-year phytoplankton time series from 16 nearshore and 3 offshore stations off the Basque Country are analyzed, as a preliminary step for evaluating the potential of this region for aquaculture development. Special attention was given to bloom events and potentially toxic taxa. A total of 32 bloom-forming taxa were detected, mostly diatoms. In regard to harmful species, all stations presented many potentially toxic taxa, mostly dinoflagellates. The diatom genus Pseudo-nitzschia was the one blooming in more stations. Pseudo-nitzschia spp. as well as the dinoflagellates Dinophysis spp. and Alexandrium spp., which might be causative of Amnesic, Diarrheic and Paralytic Shellfish Poisoning, respectively, exceeded the abundance limits that would imply toxicity risk in several occasions, mostly during spring and summer. However, it occurred at a low frequency (in average, <15% for Pseudo-nitzschia spp. and <10% for the dinoflagellates). Overall, phytoplankton community composition and abundance, together with the low frequencies for the exceeded alert limits by the three main phycotoxin producing genera, suggest that the area presents appropriate conditions for bivalve aquaculture.