Station L4 Research Articles

The Western Channel Observatory: a century of physical, chemical and biological data compiled from pelagic and benthic habitats in the western English Channel

Abstract. The Western Channel Observatory (WCO) comprises a series of pelagic, benthic and atmospheric sampling sites within 40 km of Plymouth, UK, that have been sampled by the Plymouth institutes on a regular basis since 1903. This longevity of recording and the high frequency of observations provide a unique combination of data; for example temperature data were first collected in 1903, and the reference station L4, where nearly 400 planktonic taxa have been enumerated, has been sampled on a weekly basis since 1988. While the component datasets have been archived, here we provide the first summary database bringing together a wide suite of the observations. This provides monthly average values of some of the key pelagic and benthic measurements for the inshore site L4 (50∘15.00′ N, 4∘13.02′ W; approx. depth 55 m), the offshore site E1 (50∘02.00′ N, 4∘22.00′ W; approx. depth 75 m) and the intermediate L5 site (50∘10.80′ N, 4∘18.00′ W; approx. depth 58 m). In brief, these data include the following: water temperature (from 1903); macronutrients (from 1934); dissolved inorganic carbon and total alkalinity (from 2008); methane and nitrous oxide (from 2011); chlorophyll a (from 1992); high-performance liquid chromatography (HPLC)-derived pigments (from 1999); <20 µm plankton by flow cytometry, including bacteria (8 functional groups from 2007); phytoplankton by microscopy (6 functional groups from 1992); microplankton and mesozooplankton from FlowCam (6 groups from 2012); Noctiluca sp. dinoflagellate (from 1997); mesozooplankton by microscopy (8 groups from 1988); Calanus helgolandicus egg production rates (from 1992); fish larvae from the Young Fish Trawl survey (4 groups from 1924); benthic macrofauna (4 groups from 2008); demersal fish (19 families from 2008); blue shark, Prionace glauca (from 1958); and 16S alpha diversity for sediment and water column (from 2012). These data have varying coverage with respect to time and depth resolution. The metadata tables describe each dataset and provide pointers to the source data and other related Western Channel Observatory datasets and outputs not compiled here. We provide summaries of the main trends in seasonality and some major climate-related shifts that have been revealed over the last century. The data are available from the Data Archive for Seabed Species and Habitats (DASSH): https://doi.org/10.17031/645110fb81749 (McEvoy and Atkinson, 2023). Making these data fully accessible and including units of both abundance and biomass will stimulate a variety of uptakes. These may include uses as an educational resource for projects, for models and budgets, for the analysis of seasonality and long-term change in a coupled benthic–pelagic system, or for supporting UK and north-eastern Atlantic policy and management.

Composition and Patterns of Taxa Assemblages in the Western Channel Assessed by 18S Sequencing, Microscopy and Flow Cytometry

Plankton monitoring by microscopy offers a long-term ecological perspective of plankton communities, but detection approaches are uniquely biased. Genetic identification of marine plankton has become standard but is still not used in routine monitoring. This study assesses the value that genetic methods bring to microscopic and flow cytometry monitoring methods in the Western (English) Channel. An 18S high throughput sequencing (HTS) diversity survey of plankton taxa was performed on samples collected from an automated Water and Microplankton Sampler (WaMS) deployed on the Continuous Plankton Recorder platform (CPR) from 2011–2012. This survey detected contrasting but complementary taxa assemblages to that of microscopic surveys, mostly composed of smaller or naked or thin-walled plankton taxa, with most phytoplankton being under 10 µm infrequently recorded by other surveys. Most genetically-detected taxa in the survey were mixotrophic or heterotrophic. In comparison with microscopic phytoplankton counts from the CPR survey and Western Channel Observatory station L4, only 8–12 taxonomic families were common to all three surveys, most of them dinoflagellates, with a bias towards larger diatoms and dinoflagellate taxa in microscopy surveys. Additional quantitative real-time PCR detection of two potentially harmful taxa, the pelagophyte, Aureococcus anophagefferens and four Pseudo-nitzschia from 2011–2013. This revealed that growth of A. anophagefferens was elevated in the summer of 2011, whilst the appearance of Pseudo-nitzschia delicatissima in February of that year contrasted with it’s timing at a coastal station. Smaller phytoplankton measured by flow cytometry had distinct seasonality and abundance in the mid-Atlantic compared to coastal regions reflecting distinct conditions.

Seasonality and interannual variability of copepods in the Western English Channel, Celtic Sea, Bay of Biscay, and Cantabrian Sea with a special emphasis to Calanus helgolandicus and Acartia clausi

Abstract A total of five mesozooplankton time series data sets were assembled to compare the seasonal and interannual patterns of abundance of calanoid copepods in the Western English Channel (Station L4), Celtic Sea, Bay of Biscay (Continuous Plankton Recorder), and the Cantabrian Sea (RADIALES time series, Santander, St-4 and St-6) from January 1992 to December 1999. A strong seasonal component in taxonomic composition was detected at the locations considered. There was also a strong latitudinal effect on diversity at each location, southernmost locations being more diverse. The seasonal dynamics and year-to-year variability of two copepod species: Calanus helgolandicus and Acartia clausi were studied in detail. A latitudinal pattern in the seasonal cycles of both copepod species was observed. The peaks of both occur earlier in spring in the warmer southern region and move northwards, consistent with the temperature regimes at each location, supporting the broad concept that species occupy a thermal niche in time as well as in space. There was a strong degree of interannual variability between sites and between species. No clear trends, but some coherent events among data sets, reveal a regional response to environmental forcing factors. Correlations suggest possible connections with environmental indices like the North Atlantic Oscillation and the Gulf Stream North Wall index. There was a positive correlation between the NAO and the abundance of C. helgolandicus at station L4 off Plymouth; however, the relationship in the Celtic Sea and Bay of Biscay was opposite to that expected based on previous results. Despite the differences in the sampling techniques used within each dataset, the results are comparable and coherent in terms of taxonomic composition and the seasonal and interannual patterns detected.

On the Seasonal Dynamics of Phytoplankton Chlorophyll-a Concentration in Nearshore and Offshore Waters of Plymouth, in the English Channel: Enlisting the Help of a Surfer

The role of phytoplankton as ocean primary producers and their influence on global biogeochemical cycles makes them arguably the most important living organisms in the sea. Like plants on land, phytoplankton exhibit seasonal cycles that are controlled by physical, chemical, and biological processes. Nearshore coastal waters often contain the highest levels of phytoplankton biomass. Yet, owing to difficulties in sampling this dynamic region, less is known about the seasonality of phytoplankton in the nearshore (e.g., surf zone) compared to offshore coastal, shelf and open ocean waters. Here, we analyse an annual dataset of chlorophyll-a concentration—a proxy of phytoplankton biomass—and sea surface temperature (SST) collected by a surfer at Bovisand Beach in Plymouth, UK on a near weekly basis between September 2017 and September 2018. By comparing this dataset with a complementary in-situ dataset collected 7 km offshore from the coastline (11 km from Bovisand Beach) at Station L4 of the Western Channel Observatory, and guided by satellite observations of light availability, we investigated differences in phytoplankton seasonal cycles between nearshore and offshore coastal waters. Whereas similarities in phytoplankton biomass were observed in autumn, winter and spring, we observed significant differences between sites during the summer months of July and August. Offshore (Station L4) chlorophyll-a concentrations dropped dramatically, whereas chlorophyll-a concentrations in the nearshore (Bovsiand Beach) remained high. We found chlorophyll-a in the nearshore to be significantly positively correlated with SST and PAR over the seasonal cycle, but no significant correlations were observed at the offshore location. However, offshore correlation coefficients were found to be more consistent with those observed in the nearshore when summer data (June–August 2018) were removed. Analysis of physical (temperature and density) and chemical variables (nutrients) suggest that the offshore site (Station L4) becomes stratified and nutrient limited at the surface during the summer, in contrast to the nearshore. However, we acknowledge that additional experiments are needed to verify this hypothesis. Considering predicted changes in ocean stratification, our findings may help understand how the spatial distribution of phytoplankton phenology within temperate coastal seas could be impacted by climate change. Additionally, this study emphasises the potential for using marine citizen science as a platform for acquiring environmental data in otherwise challenging regions of the ocean, for understanding ecological indicators such as phytoplankton abundance and phenology. We discuss the limitations of our study and future work needed to explore nearshore phytoplankton dynamics.

Bayesian two-part modeling of phytoplankton biomass and occurrence

Phytoplankton biomass data often involve zero outcomes preventing a description by continuous distributions with positive support such as the lognormal distribution commonly used to describe ecological data. Two usual solutions: ignoring the zeroes and adding a small positive number to all outcomes, induce bias and reduce predictive power. To address these shortcomings, we design a Bayesian two-part model with a binary component for presence or absence and a continuous component involving a lognormal model for non-zero biomass. We specify two equations relating species-specific occurrence probabilities and expected log-biomasses when present to potential covariates, with spike-and-slab priors imposed on linear effects to selectively discard the irrelevant predictors. We analyze the biomass data of 74 phytoplankton (57 diatoms and 17 dinoflagellates) recorded weekly at Station L4 (Western English Channel, UK) between April 2003 and December 2009, along with measurements of abiotic covariates. Our results disclose different combinations of environmental predictors for the occurrence and the biomass of individual species. Overall, the occurrence of dinoflagellates is associated with higher temperature and irradiance levels compared to diatoms, with virtually no dependence on nutrient concentrations. Irradiance emerges as the key predictor of biomass when species are present. Optimum temperatures for biomass accumulation and temperature sensitivities vary widely among and within functional types. Compared to one-stage models based on usual zero-handling approaches, our two-part model stands out with higher prediction accuracy. The two-part modeling approach provides a valuable framework for decoupling the predictors of species occurrence and abundance from observational data.

Intraseasonal predictability of natural phytoplankton population dynamics.

It is difficult to make skillful predictions about the future dynamics of marine phytoplankton populations. Here, we use a 22‐year time series of monthly average abundances for 198 phytoplankton taxa from Station L4 in the Western English Channel (1992–2014) to test whether and how aggregating phytoplankton into multi‐species assemblages can improve predictability of their temporal dynamics. Using a non‐parametric framework to assess predictability, we demonstrate that the prediction skill is significantly affected by how species data are grouped into assemblages, the presence of noise, and stochastic behavior within species. Overall, we find that predictability one month into the future increases when species are aggregated together into assemblages with more species, compared with the predictability of individual taxa. However, predictability within dinoflagellates and larger phytoplankton (>12 μm cell radius) is low overall and does not increase by aggregating similar species together. High variability in the data, due to observational error (noise) or stochasticity in population growth rates, reduces the predictability of individual species more than the predictability of assemblages. These findings show that there is greater potential for univariate prediction of species assemblages or whole‐community metrics, such as total chlorophyll or biomass, than for the individual dynamics of phytoplankton species.

Optimising sampling frequency for monitoring heterotrophic protists in a marine ecosystem

Abstract Heterotrophic protists are essential components of the marine ecosystem, yet they are often excluded from monitoring programmes. With limited resources, monitoring strategies need to be optimised considering both scientific knowledge and available resources. In doing so, it is crucial to understand how sampling frequency affects the value of the data. We analysed 11 years of weekly heterotrophic protist time-series data from Station L4 in the Western English Channel to explore how different sampling intervals impact data quality. In the L4 dataset, comprising 55 protist taxa, the reduction of sampling frequency from weekly to four times a year at specific seasons decreased the number of taxa encountered by 38% for ciliates and 29% for heterotrophic dinoflagellates while the mean annual biomass or its mean variation were not affected. Furthermore, when samples were taken only four times a year, biomass peaks of the ten most important taxa were often missed. The primary motivator for this study was furthering the development of the heterotrophic protist monitoring in temperate and subarctic marine areas, e.g. the Baltic Sea. Based on our findings, we give recommendations on sampling frequency to optimise the value of heterotrophic protist monitoring.

Integration of temporal environmental variation by the marine plankton community

Theory and observations suggest that low frequency variation in marine plankton populations, or red noise, may arise through cumulative integration of white noise atmospheric forcing by the ocean and its amplification within food webs. Here, we revisit evidence for the integration of stochastic atmospheric variations by comparing the power spectra of time series of atmospheric and oceanographic conditions to the population dynamics of 150 plankton taxa at Station L4 in the Western English Channel. The power spectra of oceanographic conditions (sea surface temperature, surface nitrate) are redder than those of atmospheric forcing (surface wind stress, net heat fluxes) at Station L4. However, plankton populations have power spectral slopes across trophic levels and body sizes that are redder than atmospheric forcing but whiter than oceanographic conditions. While zooplankton have redder spectral slopes than phytoplankton, there is no significant relationship between power spectral slope and body size or generation length. Using a predator-prey model, we show that the whitening of plankton time series relative to oceanographic conditions arises from noisy plankton bloom dynamics in this strongly seasonal system. The model indicates that, for typical predator-prey interactions, where the predator is on average 10 times longer than the prey, grazing leads to a modest reddening of phytoplankton variability by their larger and longer lived zooplankton consumers. Our findings suggest that, beyond extrinsic forcing by the environment, predator-prey interactions play a role in influencing the power spectra of time series of plankton populations.

A Trait-Based Clustering for Phytoplankton Biomass Modeling and Prediction

When designing models for predicting phytoplankton biomass or characterizing traits, it is useful to aggregate the myriad of species into a few biologically meaningful groups and focus on group-level attributes, the common practice being to combine phytoplankton species by functional types. However, biogeochemists and plankton ecologists debate the most applicable grouping for describing phytoplankton biomass patterns and predicting future community structure. Although trait-based approaches are increasingly being advocated, methods are missing for the generation of trait-based taxa as alternatives to functional types. Here we introduce such a method and demonstrate the usefulness of the resulting clustering with field data. We parameterize a Bayesian model of biomass dynamics and analyze long-term phytoplankton data collected at Station L4 in the Western English Channel between April 2003 and December 2009. We examine the tradeoffs encountered regarding trait characterization and biomass prediction when aggregating biomass by (1) functional types, (2) the trait-based clusters generated by our method, and (3) total biomass. The model conveniently extracted trait values under the trait-based clustering, but required well-constrained priors under the functional type categorization. It also more accurately predicted total biomass under the trait-based clustering and the total biomass aggregation with comparable root mean squared prediction errors, which were roughly five-fold lower than under the functional type grouping. Although the total biomass grouping ignores taxonomic differences in phytoplankton traits, it predicts total biomass change as well as the trait-based clustering. Our results corroborate the value of trait-based approaches in investigating the mechanisms underlying phytoplankton biomass dynamics and predicting the community response to environmental changes.

Sensitivity of Modeled CO2 Air–Sea Flux in a Coastal Environment to Surface Temperature Gradients, Surfactants, and Satellite Data Assimilation

This work evaluates the sensitivity of CO2 air–sea gas exchange in a coastal site to four different model system configurations of the 1D coupled hydrodynamic–ecosystem model GOTM–ERSEM, towards identifying critical dynamics of relevance when specifically addressing quantification of air–sea CO2 exchange. The European Sea Regional Ecosystem Model (ERSEM) is a biomass and functional group-based biogeochemical model that includes a comprehensive carbonate system and explicitly simulates the production of dissolved organic carbon, dissolved inorganic carbon and organic matter. The model was implemented at the coastal station L4 (4 nm south of Plymouth, 50°15.00’N, 4°13.02’W, depth of 51 m). The model performance was evaluated using more than 1500 hydrological and biochemical observations routinely collected at L4 through the Western Coastal Observatory activities of 2008–2009. In addition to a reference simulation (A), we ran three distinct experiments to investigate the sensitivity of the carbonate system and modeled air–sea fluxes to (B) the sea-surface temperature (SST) diurnal cycle and thus also the near-surface vertical gradients, (C) biological suppression of gas exchange and (D) data assimilation using satellite Earth observation data. The reference simulation captures well the physical environment (simulated SST has a correlation with observations equal to 0.94 with a p > 0.95). Overall, the model captures the seasonal signal in most biogeochemical variables including the air–sea flux of CO2 and primary production and can capture some of the intra-seasonal variability and short-lived blooms. The model correctly reproduces the seasonality of nutrients (correlation > 0.80 for silicate, nitrate and phosphate), surface chlorophyll-a (correlation > 0.43) and total biomass (correlation > 0.7) in a two year run for 2008–2009. The model simulates well the concentration of DIC, pH and in-water partial pressure of CO2 (pCO2) with correlations between 0.4–0.5. The model result suggest that L4 is a weak net source of CO2 (0.3–1.8 molCm−2 year−1). The results of the three sensitivity experiments indicate that both resolving the temperature profile near the surface and assimilation of surface chlorophyll-a significantly impact the skill of simulating the biogeochemistry at L4 and all of the carbonate chemistry related variables. These results indicate that our forecasting ability of CO2 air–sea flux in shelf seas environments and their impact in climate modeling should consider both model refinements as means of reducing uncertainties and errors in any future climate projections.

Resilience of the Copepod Oithona similis to Climatic Variability: Egg Production, Mortality, and Vertical Habitat Partitioning

There has been an overall decline in copepod populations across the North Atlantic over the past few decades. Reasons for these declines are unclear, and several major species, including the cyclopoid copepod Oithona similis, have maintained stable populations at station L4 in the western English Channel. To identify the factors contributing to this stability, we conducted a one-year intensive study of O. similis at L4 over 2017-2018, a period of high climatic variability. For context, dominant frequency state analysis was applied to the 30-year L4 time series to derive the baseline dynamics of the Oithona spp. population. The Oithona spp. baseline demonstrated stable densities and a bimodal annual cycle. These dynamics, as well as those of reproductive output and phenological timings, were upheld in 2017-2018, indicating resilience to climatic variability. During 2017-2018, all life stages of O. similis were relatively scarce in the top 2 m of the water column, despite the presence of abundant food. Naupliar stages occurred predominantly around 10 m depth, with subsequent life stages progressively deeper. We suggest this vertical structuring may represent different trade-offs between feeding and mortality risk between ontogenetic stages. To determine the traits that contribute to population stability, we compare O. similis with the large, biomass-dominant copepod, Calanus helgolandicus. Despite having contrasting functional traits, both species have exhibited strong population stability over the time series. Our results provide evidence that mortality plays a major role in maintaining population dynamics.

Collaborative Deep Learning Models to Handle Class Imbalance in FlowCam Plankton Imagery

Using automated imaging technologies, it is now possible to generate previously unprecedented volumes of plankton image data which can be used to study the composition of plankton assemblages. However, the current need to manually classify individual images introduces a bottleneck into processing chains. Although Machine Learning techniques have been used to try and address this issue, past efforts have suffered from accuracy limitations, especially in minority classes. Here we use state-of-the-art methods in Deep Learning to investigate suitable architectures for training an automated plankton classification system which achieves high efficacy for both abundant and rare taxa. We collected live plankton from Station L4 in the Western English Channel and imaged 11,371 particles covering 104 taxonomic groups using the automated plankton imaging system FlowCam. The image set contained a severe class imbalance, with some taxa represented by > 600 images while other, rarer taxa were represented by just 14. We demonstrate that by allowing multiple Deep Learning models to collaborate in a single classification system, classification accuracy improves for minority classes when compared with the best individual model. The top collaborative model achieved a 6 % improvement in F1 accuracy over the best individual model, while overall accuracy improved by 3.2 %. This resulted in a 97.4 % overall accuracy score and a 96.2 % F1 macro score on a separate holdout test set containing 104 taxonomic groups. Based on a survey of similar studies in the literature, we believe collaborative deep learning models can significantly improve the accuracy of existing automated plankton classification systems.

Bayesian inference to partition determinants of community dynamics from observational time series

Ecological communities are shaped by a complex interplay between abiotic forcing, biotic regulation and demographic stochasticity. However, community dynamics modelers tend to focus on abiotic forcing overlooking biotic interactions, due to notorious challenges involved in modeling and quantifying inter-specific interactions, particularly for species-rich systems such as planktonic assemblages. Nevertheless, inclusive models with regard to the full range of plausible drivers are essential to characterizing and predicting community response to environmental changes. Here we develop a Bayesian model for identifying, from in-situ time series, the biotic, abiotic and stochastic factors underlying the dynamics of species-rich communities, focusing on the joint biomass dynamics of biologically meaningful groups. We parameterize a multivariate model of population co-variation with an explicit account for demographic stochasticity, density-dependent feedbacks, pairwise interactions, and abiotic stress mediated by changing environmental conditions and resource availability, and work out explicit formulae for partitioning the temporal variance of each group in its biotic, abiotic and stochastic components. We illustrate the methodology by analyzing the joint biomass dynamics of four major phytoplankton functional types namely, diatoms, dinoflagellates, coccolithophores and phytoflagellates at Station L4 in the Western English Channel using weekly biomass records and coincident measurements of environmental covariates describing water conditions and potentially limiting resources. Abiotic and biotic factors explain comparable amounts of temporal variance in log-biomass growth across functional types. Our results demonstrate that effective modelling of resource limitation and inter-specific interactions is critical for quantifying the relative importance of abiotic and biotic factors.

Uncovering the environmental drivers of short-term temporal dynamics in an epibenthic community from the Western English Channel

AbstractBenthic communities, critical to the health and function of marine ecosystems, are under increasing pressure from anthropogenic impacts such as pollution, eutrophication and climate change. In order to refine predictions of likely future changes in benthic communities resulting from these impacts, we must first better constrain their responses to natural seasonality in environmental conditions. Epibenthic time series data (July 2008–May 2014) have been collected from Station L4, situated 7.25 nautical miles south of Plymouth in the Western English Channel. These data were analysed to establish patterns in community abundance, wet biomass and composition, and to link any observed patterns to environmental variables. A clear response to the input of organic material from phytoplankton blooms was detected, with sediment surface living deposit feeders showing an immediate increase in abundance, while predators and scavengers responded later, with an increase in biomass. We suggest that this response is a result of two factors. The low organic content of the L4 sediment results in food limitation of the community, and the mild winter/early spring bottom water temperatures allow the benthos to take immediate advantage of bloom sedimentation. An inter-annual change in community composition was also detected, as the community shifted from one dominated by the anomuran Anapagurus laevis to one dominated by the gastropod Turitella communis. This appeared to be related to a period of high larval recruitment for T. communis in 2013/2014, suggesting that changes in the recruitment success of one species can affect the structure of an entire community.

A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations

Abstract. The dynamics of biogeochemical models are determined by the mathematical equations used to describe the main biological processes. Earlier studies have shown that small changes in the model formulation may lead to major changes in system dynamics, a property known as structural sensitivity. We assessed the impact of structural sensitivity in a biogeochemical model of intermediate complexity by modelling the chlorophyll and dissolved inorganic nitrogen (DIN) concentrations. The model is run at five different oceanographic stations spanning three different regimes: oligotrophic, coastal, and the abyssal plain, over a 10-year timescale to observe the effect in different regions. A 1-D Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration, and Acidification (MEDUSA) ensemble was used with each ensemble member having a combination of tuned function parameterizations that describe some of the key biogeochemical processes, namely nutrient uptake, zooplankton grazing, and plankton mortalities. The impact is quantified using phytoplankton phenology (initiation, bloom time, peak height, duration, and termination of phytoplankton blooms) and statistical measures such as RMSE (root-mean-squared error), mean, and range for chlorophyll and nutrients. The spread of the ensemble as a measure of uncertainty is assessed against observations using the normalized RMSE ratio (NRR). We found that even small perturbations in model structure can produce large ensemble spreads. The range of 10-year mean surface chlorophyll concentration in the ensemble is between 0.14 and 3.69 mg m−3 at coastal stations, 0.43 and 1.11 mg m−3 on the abyssal plain, and 0.004 and 0.16 mg m−3 at the oligotrophic stations. Changing both phytoplankton and zooplankton mortalities and the grazing functions has the largest impact on chlorophyll concentrations. The in situ measurements of bloom timings, duration, and terminations lie mostly within the ensemble range. The RMSEs between in situ observations and the ensemble mean and median are mostly reduced compared to the default model output. The NRRs for monthly variability suggest that the ensemble spread is generally narrow (NRR 1.21–1.39 for DIN and 1.19–1.39 for chlorophyll profiles, 1.07–1.40 for surface chlorophyll, and 1.01–1.40 for depth-integrated chlorophyll). Among the five stations, the most reliable ensembles are obtained for the oligotrophic station ALOHA (for the surface and integrated chlorophyll and bloom peak height), for coastal station L4 (for inter-annual mean), and for the abyssal plain station PAP (for bloom peak height). Overall our study provides a novel way to generate a realistic ensemble of a biogeochemical model by perturbing the model equations and parameterizations, which will be helpful for the probabilistic predictions.

Further records of a new diatom species in the English Channel and North Sea: the importance of image-referenced data

BackgroundIn September 2015, an at the time undescribed, autotrophic taxon was discovered in the western English Channel (station L4) and also in the eastern English Channel and Celtic Sea during the Polarstern Cruise PS95 a month later. Subsequent investigations revealed further extensive records (going back to 1992) at stations in the English Channel and the southern North Sea (e.g. Helgoland Roads and Sylt Roads time series stations). Stations in the Northern North Sea have not recorded this distinct taxon. With the available records and crucially, the accompanying image metadata, we are able to chart a clear distribution record with occurrences being restricted to the southern North Sea and English Channel.MethodsThe biological data shown are from Lugol-fixed Utermöhl counts and investigations of live and Formalin-fixed net hauls (20 μm mesh size). All image material shown is available in the online repository Planktonnet (http://planktonnet.awi.de).ResultsWe report the distribution, based on geo-referenced image records of an easily recognisable, yet taxonomically uncertain, autotrophic organism.ConclusionsDistribution patterns of the unidentified autotrophic taxon suggests entry of this taxon into/out of the North Sea via the English Channel. Further investigations providing image-documented information over several years is clearly necessary to clarify its dynamics and ecological characteristics.

Active bacterioplankton community response to dissolved 'free' deoxyribonucleic acid (dDNA) in surface coastal marine waters.

Seawater contains dissolved 'free' DNA (dDNA) that is part of a larger <0.2 µm pool of DNA (D-DNA) including viruses and uncharacterised bound DNA. Previous studies have shown that bacterioplankton readily degrade dDNA, and culture-based approaches have identified several potential dDNA-utilising taxa. This study characterised the seasonal variation in D-DNA concentrations at Station L4, a coastal marine observatory in the Western English Channel, and linked changes in concentration to cognate physicochemical and biological factors. The impact of dDNA addition on active bacterioplankton communities at Station L4 was then determined using 16S rRNA high-throughput sequencing and RNA Stable Isotope Probing (RNA SIP) with 13C-labelled diatom-derived dDNA. Compared to other major bacterioplankton orders, the Rhodobacterales actively responded to dDNA additions in amended microcosms and RNA SIP identified two Rhodobacterales populations most closely associated with the genera Halocynthiibacter and Sulfitobacter that assimilated the 13C-labelled dDNA. Here we demonstrate that dDNA is a source of dissolved organic carbon for some members of the major bacterioplankton group the Marine Roseobacter Clade. This study enhances our understanding of roles of specific bacterioplankton taxa in dissolved organic matter cycling in coastal waters with potential implications for nitrogen and phosphorus regeneration processes.

Mortality of Calanus helgolandicus: Sources, differences between the sexes and consumptive and nonconsumptive processes

AbstractWhile losses from mortality are as important as gains from reproduction in zooplankton population dynamics, the former are more challenging to quantify. We used two approaches to provide complementary insights into the mortality of a biomass‐dominant copepod, Calanus helgolandicus, at Station L4 in the English Channel. Using a neutral‐red staining method, we found that dead carcasses represented a mean of 9% of the C. helgolandicus copepodites sampled. The resulting nonconsumptive mortality rates are the first that have been derived for C. helgolandicus; and estimates suggest a contribution of 0–54% (median of 4.4%) to the total mortality rate. Consumptive mortality (i.e., that due to removal by predation), dominated for most of the year and contributed a mean of 89% to total mortality. Nonconsumptive mortality increased during summer and winter, and was positively related to maximum wind speed during the preceding 72 h, indicating that extreme weather events may lead to increased mortality. Using the Vertical Life Table approach, mortality rates across the CV‐adult male stage pair were on average ∼ 2.5 times greater than those of CV‐adult females. Adult male consumptive mortality rates were ∼ 6 times greater than those for females; adult male nonconsumptive rates were twice those of females, suggesting that predation is of greater significance to male loss rates. Summer CV‐adult mortality rates were positively correlated to temperature, and to the abundance of predatory chaetognaths and siphonophores, suggesting that the gelatinous predator assemblage is the dominant agent for population control of late stage copepodites of C. helgolandicus at L4.

Seasonality of Oithona similis and Calanus helgolandicus reproduction and abundance: contrasting responses to environmental variation at a shelf site

The pelagic copepods Oithona similis and Calanus helgolandicus have overlapping geographic ranges, yet contrast in feeding mode, reproductive strategy, and body size. We investigate how these contrasting traits influence the seasonality of copepod abundance and reproductive output under environmental variation, using time series data collected over 25 years at the Western Channel Observatory station L4. The proportional change in Egg Production Rate (EPR, eggs female-1 d-1) over the annual cycle was ~10-fold and similar for both species, although EPR of O. similis was only ~ 11% that of C. helgolandicus. The timing of EPR maxima for O. similis coincided with increased Sea Surface Temperature (SST) in summer, likely due to a temperature-dependent brooding period. Conversely, EPR of broadcast spawning C. helgolandicus was more strongly related to Net Heat Flux (NHF) and diatom biomass, both parameters associated with the spring phytoplankton bloom. In both species, female body mass negatively correlated with SST, with a 7.5% reduction in body mass per °C in C. helgolandicus compared to just 2.3% in O. similis. Finally, seasonality of EPR and adult and copepodite abundance was strongly decoupled in both species, suggesting that optimum conditions for reproduction and abundance occur at different times of the year.

Phytoplankton traits from long-term oceanographic time-series

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 576:11-25 (2017) - DOI: https://doi.org/10.3354/meps12220 Phytoplankton traits from long-term oceanographic time-series Crispin M. Mutshinda1, Zoe V. Finkel2, Claire E. Widdicombe3, Andrew J. Irwin1,* 1Mathematics & Computer Science, Mount Allison University, Sackville, NB E4L 1E6, Canada 2Environmental Science, Mount Allison University, Sackville, NB E4L 1A7, Canada 3Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK *Corresponding author: airwin@mta.ca ABSTRACT: Trait values are usually extracted from laboratory studies of single phytoplankton species, which presents challenges for understanding the immense diversity of phytoplankton species and the wide range of dynamic ocean environments. Here we use a Bayesian approach and a trait-based model to extract trait values for 4 functional types and 10 diatom species from field data collected at Station L4 in the Western Channel Observatory, English Channel. We find differences in maximum net growth rate, temperature optimum and sensitivity, half-saturation constants for light and nitrogen, and density-dependent loss terms across the functional types. We find evidence of very high linear loss rates, suggesting that grazing may be even more important than commonly assumed and differences in density-dependent loss rates across functional types, indicating the presence of strong niche differentiation among functional types. Low half-saturation constants for nitrogen at the functional type level may indicate widespread mixotrophy. At the species level, we find a wide range of density-dependent effects, which may be a signal of diversity in grazing susceptibility or biotic interactions. This approach may be a way to obtain more realistic and better-constrained trait values for functional types to be used in ecosystem modeling. KEY WORDS: Phytoplankton · Time series · Traits · Growth rate · Grazing rate · English Channel Full text in pdf format Supplementary material PreviousNextCite this article as: Mutshinda CM, Finkel ZV, Widdicombe CE, Irwin AJ (2017) Phytoplankton traits from long-term oceanographic time-series. Mar Ecol Prog Ser 576:11-25. https://doi.org/10.3354/meps12220 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 576. Online publication date: August 03, 2017 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2017 Inter-Research.