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Coastal ecosystems are increasingly experiencing anthropogenic pressures such as climate warming, CO 2 increase, metal and organic pollution, overfishing, and resource extraction. Some resulting stressors are more direct like pollution and fisheries, and others more indirect like ocean acidification, yet they jointly affect marine biota, communities, and entire ecosystems. While single-stressor effects have been widely investigated, the interactive effects of multiple stressors on ecosystems are less researched. In this study, we review the literature on multiple stressors and their interactive effects in coastal environments across organisms. We classify the interactions into three categories: synergistic, additive, and antagonistic. We found phytoplankton and bivalves to be the most studied taxonomic groups. Climate warming is identified as the most dominant stressor which, in combination, with other stressors such as ocean acidification, eutrophication, and metal pollution exacerbate adverse effects on physiological traits such as growth rate, fitness, basal respiration, and size. Phytoplankton appears to be most sensitive to interactions between warming, metal and nutrient pollution. In warm and nutrient-enriched environments, the presence of metals considerably affects the uptake of nutrients, and increases respiration costs and toxin production in phytoplankton. For bivalves, warming and low pH are the most lethal stressors. The combined effect of heat stress and ocean acidification leads to decreased growth rate, shell size, and acid-base regulation capacity in bivalves. However, for a holistic understanding of how coastal food webs will evolve with ongoing changes, we suggest more research on ecosystem-level responses. This can be achieved by combining in-situ observations from controlled environments (e.g. mesocosm experiments) with modelling approaches.

The effect of ocean acidification on growth and calcification of the marine algae Emiliania huxleyi was investigated in a series of mesocosm experiments where enclosed water volumes that comprised a natural plankton community were exposed to different carbon dioxide (CO2) concentrations. Calcification rates observed during those experiments were found to be highly variable, even among replicate mesocosms that were subject to similar CO2 perturbations. Here, data from an ocean acidification mesocosm experiment are reanalysed with an optimality-based dynamical plankton model. According to our model approach, cellular calcite formation is sensitive to variations in CO2 at the organism level. We investigate the temporal changes and variability in observations, with a focus on resolving observed differences in total alkalinity and particulate inorganic carbon (PIC). We explore how much of the variability in the data can be explained by variations of the initial conditions and by the level of CO2 perturbation. Nine mesocosms of one experiment were sorted into three groups of high, medium, and low calcification rates and analysed separately. The spread of the three optimised ensemble model solutions captures most of the observed variability. Our results show that small variations in initial abundance of coccolithophores and the prevailing physiological acclimation states generate differences in calcification that are larger than those induced by ocean acidification. Accordingly, large deviations between optimal mass flux estimates of carbon and of nitrogen are identified even between mesocosms that were subject to similar ocean acidification conditions. With our model-based data analysis we document how an ocean acidification response signal in calcification can be disentangled from the observed variability in PIC.

The effect of ocean acidification on growth and calcification of the marine algae Emiliania huxleyi was investigated in a series of mesocosm experiments where enclosed water volumes that comprised a natural plankton community were exposed to different carbon dioxide (CO.sub.2) concentrations. Calcification rates observed during those experiments were found to be highly variable, even among replicate mesocosms that were subject to similar CO.sub.2 perturbations. Here, data from an ocean acidification mesocosm experiment are reanalysed with an optimality-based dynamical plankton model. According to our model approach, cellular calcite formation is sensitive to variations in CO.sub.2 at the organism level. We investigate the temporal changes and variability in observations, with a focus on resolving observed differences in total alkalinity and particulate inorganic carbon (PIC). We explore how much of the variability in the data can be explained by variations of the initial conditions and by the level of CO.sub.2 perturbation. Nine mesocosms of one experiment were sorted into three groups of high, medium, and low calcification rates and analysed separately. The spread of the three optimised ensemble model solutions captures most of the observed variability. Our results show that small variations in initial abundance of coccolithophores and the prevailing physiological acclimation states generate differences in calcification that are larger than those induced by ocean acidification. Accordingly, large deviations between optimal mass flux estimates of carbon and of nitrogen are identified even between mesocosms that were subject to similar ocean acidification conditions. With our model-based data analysis we document how an ocean acidification response signal in calcification can be disentangled from the observed variability in PIC.

Climate change and reduction in nutrient loads have significant effects on primary production and phytoplankton growth dynamics. Since in the last few decades in many regions, nutrients in lakes were reduced simultaneously as the climate changed. Yet, it remains unclear which of the two has impacted primary production the most. In this study, we couple the General Ocean Turbulence Model with the Ecological Regional Ocean Model to disentangle the effects of climate change and reoligotrophication on primary production (PP) in Lake Geneva, Switzerland–France. We apply a data assimilation method to calibrate the model with the observations from the past (1981–1990) and validate it against the in situ data from the present decade (2011–2019). Both decades represent different climate conditions and trophic states of the lake. We show that the model is skilful to reproduce assimilated and unassimilated observations from both periods. According to our results, the effect of reoligotrophication on PP is marginally higher than that of warming, leading to a net decrease in primary production by 10% from the past to the present. The areal phosphorus supply in Lake Geneva, in spite of a decrease by ∼70%, is still characteristic of a meso-to-eutrophic ecosystem. This points towards an incomplete reoligotrophication of the lake. The effects of future climate change on winter mixing and PP dynamics have also been studied. Although there would be a significant reduction in deep mixing, the autotrophic production in Lake Geneva is expected to increase by ∼20% by the end of twenty-first century, largely due to stimulation in biomass build-up of temperature-dependent algae (e.g. dinoflagellates and cyanobacteria). Considering our results to represent other large temperate lakes with similar trophic status and water residence time as Lake Geneva, future climate scenarios are expected to bring back symptoms of eutrophication.

To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling. We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time- and space-dependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.

The effect of ocean acidification on growth and calcification of the marine algae Emiliania huxleyi was investigated in a series of mesocosm experiments where enclosed water volumes that comprised a natural plankton community were exposed to different carbon dioxide (CO2) concentrations. Calcification rates observed during those experiments were found to be highly variable, even among replicate mesocosms that were subject to similar CO2 perturbations. Here, data from an ocean acidification mesocosm experiment are reanalysed with an optimality-based dynamical plankton model. According to our model approach, cellular calcite formation is sensitive to variations in CO2 at the organism level. We investigate the temporal changes and variability in observations, with a focus on resolving observed differences in total alkalinity and particulate inorganic carbon (PIC). We explore how much of the variability in the data can be explained by variations of the initial conditions and by the level of CO2 perturbation. Nine mesocosms of one experiment were sorted into three groups of high, medium, and low calcification rates and analysed separately. The spread of the three optimised ensemble model solutions captures most of the observed variability. Our results show that small variations in initial abundance of coccolithophores and the prevailing physiological acclimation states generate differences in calcification that are larger than those induced by ocean acidification. Accordingly, large deviations between optimal mass flux estimates of carbon and of nitrogen are identified even between mesocosms that were subject to similar ocean acidification conditions. With our model-based data analysis we document how an ocean acidification response signal in calcification can be disentangled from the observed variability in PIC.

Neuroblastoma exhibits significant intratumoral heterogeneity and resistance to differentiation therapy. We identify a regulatory axis between the protein-coding gene BASP1 and its antisense lncRNA BASP1-AS1 as a molecular switch between proliferation and neuronal differentiation in SH-SY5Y neuroblastoma cells. BASP1 maintains a proliferative, undifferentiated state by upregulating Wnt3a signaling and stemness-associated markers. Knockdown of BASP1 inhibits both proliferation and neuronal gene expression, implicating it as a context-specific oncogenic driver. In contrast, BASP1-AS1 is transiently induced by retinoic acid (RA) and initiates early neuronal differentiation via DCX and MAP2 induction. BASP1-AS1 represses Wnt3a and activates Notch1, redirecting the signaling balance toward a differentiation-permissive state. A reciprocal suppression between BASP1 and BASP1-AS1 underlies a transition from Wnt3a to Wnt2 activity as differentiation progresses. LiCl-mediated Wnt3a activation suppresses BASP1-AS1 and reinduces Sox2, highlighting Wnt3a’s role in maintaining stemness and therapy resistance. Post-RA BDNF treatment reinforces terminal differentiation, defined by high BASP1-AS1, DCX, and MAP2, and loss of proliferative signatures. Together, these findings identify the BASP1/BASP1-AS1 axis as a central node integrating Wnt and Notch pathways to regulate plasticity and lineage progression in neuroblastoma. This axis represents a potential target for overcoming differentiation blockade and therapeutic resistance.

This study aimed to analyze the role of Dickopff1 [DKK1] as a potential differentiating agent for the neuroblastoma cell line SHSY5Y and neurospheres derived from it. SHSY5Y neurospheres were formed from undifferentiated adherent cultures. The cellular properties and gene expression were used to study the effect of DKK1 on SHSY5Y-formed neurospheres. Its effect on SHSY5Y neuronal differentiation was also studied. SHSY5Y adherent undifferentiated cells were grown as neurospheres. Treatment of neurospheres resulted in their fragmentation. It also resulted in reduced mRNA expression of markers of cancer stem cells, pluripotency, and proliferation [p≤0.05]. DKK1 treatment also resulted in reduced mRNA expression of β-catenin and TCF genes. There was significantly higher expression of neuronal differentiation genes in SHSY5Y adherent cells grown in complete DMEM media containing DKK1 as compared to cells grown without DKK1. We also found DKK1 synergized with retinoic acid-induced differentiation of neuroblastoma cells. DKK1 was able to convincingly abrogate neurosphere formation and promote neuronal differentiation of SHSY5Y cells, including synergy with retinoic acid. This was accompanied by corresponding changes in mRNA markers of cancer stem cells, pluripotency and proliferation. These results may have implications for neuroblastoma stemness and differentiation.

The SNS has recently become a European hub for installations of offshore wind farms (OWF), while extensive areas have been designated as marine protected areas (MPAs). Together with the already noticeable effects of climate warming, the region transforms from an area dominated by free ranging fisheries and shipping into an industrial landscape dominated by stationary activities. To inform decision making processes around the spatial allocation of fisheries, conservation measures and licence areas for offshore renewables in the southern North Sea, we modelled the spatial distribution of 179 epibenthic invertebrate and fish species. We identify both current and future hotspots of epibenthic and demersal fish diversity and their overlap with OWFs and MPAs. Hotspots of epibenthic and fish diversity are mostly found along the English Coast, located within an extensive network of MPA and OWF sites, which may provide opportunities for conservation and fisheries alike. In the central SNS, the MPA network could be complemented by OWF, where co-use regulations with other human uses are excluded, to protect sensitive species as well as epibenthic and demersal fish communities. Scenarios for three different time periods (until 2040, 2070 & 2100) revealed that global warming might cause an increase in the probability of occurrence for more than half of the analysed demersal fish species (n = 63). The applications in our study demonstrate the relevance of comprehensive knowledge about current, near and far future distribution patterns of species and species communities to enhance the effectiveness of marine spatial planning including spatial conservation measures.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://zenodo.org/records/11261320

Coastal ecosystems are increasingly experiencing anthropogenic pressures such as climate heating, CO2 increase, metal and organic pollution, overfishing and resource extraction. Some resulting stressors are more direct like fisheries, others more indirect like ocean acidification, yet they jointly affect marine biota, communities and entire ecosystems. While single-stressor effects have been widely investigated, the interactive effects of multiple stressors on ecosystems are less researched. In this study, we review the literature on multiple stressors and their interactive effects in coastal environments across organisms. We classify the interactions into three categories: synergistic, additive, and antagonistic. We found phytoplankton and mollusks to be the most studied taxonomic groups. The stressor combinations of climate warming, ocean acidification, eutrophication, and metal pollution are the most critical for coastal ecosystems as they exacerbate adverse effects on physiological traits such as growth rate, basal respiration, and size. Phytoplankton appears to be most sensitive to interactions between metal and nutrient pollution. In nutrient-enriched environments, the presence of metals considerably affects the uptake of nutrients, and increases respiration costs and toxin production in phytoplankton. For mollusks, warming and low pH are the most lethal stressors. The combined effect of heat stress and ocean acidification leads to decreased growth rate, shell size, and acid-base regulation capacity in mollusks. However, for a holistic understanding of how coastal food webs will evolve with ongoing changes, we suggest more research on ecosystem-level responses. This can be achieved by combining in-situ observations from controlled environments (e.g. mesocosm experiments) with modelling approaches.