Modeling of intra-annual abundance distributions: Constancy and variation in the phenology of marine phytoplankton species over five decades at Helgoland Roads (North Sea)

Defining algal bloom phenology in Lake Erie

Elucidating the impact of global climate change on aquatic ecosystems, particularly through phenological shifts in primary producers, is critical for understanding ecological resilience. Here, we focus on the phenological shifts in chlorophyll as a proxy for algae biomass and primary production in aquatic ecosystems, specifically in Lake Erie as well as concentrations of the toxin microcystin. By tracking temporal changes in each, we identified key phenological phases important to estimate duration, magnitude, and intensity of harmful algal blooms (HABs). Determining which influential biotic and abiotic factors such as temperature, wind speed, nutrient availability, and climate change is most important, is a long-term management need for Lake Erie, which can be explored using our methodology. Our novel statistical framework employing Bayesian generalized additive mixed models described seasonal chlorophyll and particulate microcystin concentration from Lake Erie and our simple geometric method identified the start, peak, and end of algal blooms. This research enhances our understanding of the ecological effects of nutrient pollution on aquatic ecosystems and provides a repeatable method for determining phenological events without the need for user defined cutoffs which aids in the management and mitigation of HABs, safeguarding water quality in regions dependent on lakes for drinking water.

Environmentally induced functional shifts in phytoplankton and their potential consequences for ecosystem functioning

Phytoplanktonic organisms are particularly sensitive to environmental change, and, as they represent a direct link between abiotic and biotic compartments within the marine food web, changes in the functional structure of phytoplankton communities can result in profound impacts on ecosystem functioning. Using a trait-based approach, we examined changes in the functional structure of the southern North Sea phytoplankton over the past five decades in relation to environmental conditions. We identified a shift in functional structure between 1998 and 2004 which coincides with a pronounced increase in diatom and decrease in dinoflagellate abundances, and we provide a mechanistic explanation for this taxonomic change. Early in the 2000s, the phytoplankton functional structure shifted from slow growing, autumn blooming, mixotrophic organisms, towards earlier blooming and faster-growing microalgae. Warming and decreasing dissolved phosphorus concentrations were linked to this rapid reorganization of the functional structure. We identified a potential link between this shift and dissolved nutrient concentrations, and we hypothesise that organisms blooming early and displaying high growth rates efficiently take up nutrients which then are no longer available to late bloomers. Moreover, we identified that the above-mentioned functional change may have bottom-up consequences, through a food quality-driven negative influence on copepod abundances. Overall, our study highlights that, by altering the phytoplankton functional composition, global and regional changes may have profound long-term impacts on coastal ecosystems, impacting both food-web structure and biogeochemical cycles.

Environmental Impact on Harmful Species Pseudo-nitzschia spp. and Phaeocystis globosa Phenology and Niche

Global environmental change modifies the phytoplankton community, which leads to variations in their phenology and potentially causes a temporal mismatch between primary producers and consumers. In parallel, phytoplankton community change can favor the appearance of harmful species, which makes the understanding of the mechanisms involved in structuring phytoplankton ecological niches paramount for preventing future risk. In this study, we aimed to assess for the first time the relationship between environmental conditions, phenology and niche ecology of harmful species Phaeocystis globosa and the complex Pseudo-nitzschia along the French coast of the eastern English Channel. A new method of bloom detection within a time-series was developed, which allowed the characterization of 363 blooms by 22 phenological variables over 11 stations from 1998 to 2019. The pairwise quantification of asymmetric dependencies between the phenological variables revealed the implication of different mechanisms, common and distinct between the taxa studied. A PERMANOVA helped to reveal the importance of seasonal change in the environmental and community variables. The Outlying Mean and the Within Outlying Mean indexes allowed us to position the harmful taxa niche among the rest of community and quantify how their respective phenology impacted the dynamic of their subniches. We also discussed the possible hypothesis involved and the perspective of predictive models.

Evaluation of Machine Learning Predictions of a Highly Resolved Time Series of Chlorophyll-a Concentration

Pelagic chlorophyll-a concentrations are key for evaluation of the environmental status and productivity of marine systems, and data can be provided by in situ measurements, remote sensing and modelling. However, modelling chlorophyll-a is not trivial due to its nonlinear dynamics and complexity. In this study, chlorophyll-a concentrations for the Helgoland Roads time series were modeled using a number of measured water and environmental parameters. We chose three common machine learning algorithms from the literature: the support vector machine regressor, neural networks multi-layer perceptron regressor and random forest regressor. Results showed that the support vector machine regressor slightly outperformed other models. The evaluation with a test dataset and verification with an independent validation dataset for chlorophyll-a concentrations showed a good generalization capacity, evaluated by the root mean squared errors of less than 1 µg L−1. Feature selection and engineering are important and improved the models significantly, as measured in performance, improving the adjusted R2 by a minimum of 48%. We tested SARIMA in comparison and found that the univariate nature of SARIMA does not allow for better results than the machine learning models. Additionally, the computer processing time needed was much higher (prohibitive) for SARIMA.

Uncovering the intricacies of microbial community dynamics at Helgoland Roads at the end of a spring bloom using automated sampling and 18S meta-barcoding

In May 2016, the remote-controlled Automated Filtration System for Marine Microbes (AUTOFIM) was implemented in parallel to the Long Term Ecological Research (LTER) observatory Helgoland Roads in the German Bight. We collected samples for characterization of dynamics within the eukaryotic microbial communities at the end of a phytoplankton bloom via 18S meta-barcoding. Understanding consequences of environmental change for key marine ecosystem processes, such as phytoplankton bloom dynamics requires information on biodiversity and species occurrences with adequate temporal and taxonomic resolution via time series observations. Sampling automation and molecular high throughput methods can serve these needs by improving the resolution of current conventional marine time series observations. A technical evaluation based on an investigation of eukaryotic microbes using the partial 18S rRNA gene suggests that automated filtration with the AUTOFIM device and preservation of the plankton samples leads to highly similar 18S community profiles, compared to manual filtration and snap freezing. The molecular data were correlated with conventional microscopic counts. Overall, we observed substantial change in the eukaryotic microbial community structure during the observation period. A simultaneous decline of diatom and ciliate sequences succeeded a peak of Miracula helgolandica, suggesting a potential impact of these oomycete parasites on diatom bloom dynamics and phenology in the North Sea. As oomycetes are not routinely counted at Helgoland Roads LTER, our findings illustrate the benefits of combining automated filtration with metabarcodingto augment classical time series observations, particularly for taxa currently neglected due to methodological constraints.

Rapid succession drives spring community dynamics of small protists at Helgoland Roads, North Sea

The dynamics of diatoms and dinoflagellates have been monitored for many decades at the Helgoland Roads Long-Term Ecological Research site and are relatively well understood. In contrast, small-sized eukaryotic microbes and their community changes are still much more elusive, mainly due to their small size and uniform morphology, which makes them difficult to identify microscopically. By using next-generation sequencing, we wanted to shed light on the Helgoland planktonic community dynamics, including nano- and picoplankton, during a spring bloom. We took samples from March to May 2016 and sequenced the V4 region of the 18S rDNA. Our results showed that mixotrophic and heterotrophic taxa were more abundant than autotrophic diatoms. Dinoflagellates dominated the sequence assemblage, and several small-sized eukaryotic microbes like Haptophyta, Choanoflagellata, Marine Stramenopiles and Syndiniales were identified. A diverse background community including taxa from all size classes was present during the whole sampling period. Five phases with several communities were distinguished. The fastest changes in community composition took place in phase 3, while the communities from phases 1 to 5 were more similar to each other despite contrasting environmental conditions. Synergy effects of next-generation sequencing and traditional methods may be exploited in future long-term observations.

Short-term changes in polysaccharide utilization mechanisms of marine bacterioplankton during a spring phytoplankton bloom

Spring phytoplankton blooms in temperate environments contribute disproportionately to global marine productivity. Bloom-derived organic matter, much of it occurring as polysaccharides, fuels biogeochemical cycles driven by interacting autotrophic and heterotrophic communities. We tracked changes in the mode of polysaccharide utilization by heterotrophic bacteria during the course of a diatom-dominated bloom in the German Bight, North Sea. Polysaccharides can be taken up in a 'selfish' mode, where initial hydrolysis is coupled to transport into the periplasm, such that little to no low-molecular weight (LMW) products are externally released to the environment. Alternatively, polysaccharides hydrolyzed by cell-surface attached or free extracellular enzymes (external hydrolysis) yield LMW products available to the wider bacterioplankton community. In the early bloom phase, selfish activity was accompanied by low extracellular hydrolysis rates of a few polysaccharides. As the bloom progressed, selfish uptake increased markedly, and external hydrolysis rates increased, but only for a limited range of substrates. The late bloom phase was characterized by high external hydrolysis rates of a broad range of polysaccharides and reduced selfish uptake of polysaccharides, except for laminarin. Substrate utilization mode is related both to substrate structural complexity and to the bloom-stage dependent composition of the heterotrophic bacterial community.

Environmental variables driving species and genus level changes in annual plankton biomass

Abiotic variables subject to global change are known to affect plankton biomasses, and these effects can be species-specific. Here, we investigate the environmental drivers of annual biomass using plankton data from the Gulf of Finland in the northern Baltic Sea, spanning years 1993-2016. We estimated annual biomass time-series of 31 nanoplankton and microplankton species and genera from day-level data, accounting for the average phenology and wind. We found wind effects on day-level biomass in 16 taxa. We subsequently used state-space models to connect the annual biomass changes with potential environmental drivers (temperature, salinity, stratification, ice cover and inorganic nutrients), simultaneously accounting for temporal trends. We found clear environmental effects influencing the annual biomasses of Dinobryon faculiferum, Eutreptiella spp., Protoperidinium bipes, Pseudopedinella spp., Snowella spp. and Thalassiosira baltica and indicative effects in 10 additional taxa. These effects mostly concerned temperature, salinity or stratification. Together, these 16 taxa represent two-thirds of the summer biomass in the sampled community. The inter-annual variability observed in salinity and temperature is relatively low compared to scenarios of predicted change in these variables. Therefore, the potential impacts of the presented effects on plankton biomasses are considerable.