Immunotoxicity of carbon black nanoparticles to blue mussel hemocytes

The potential for human and ecological toxicity associated with nanomaterials is a growing area of investigation. In mammalian cells, nanoparticles have been shown to induce inflammation and oxidative stress, and changes in cell signalling and gene expression. As the nanotechnology industries increase production, nanoscale products and by products will enter the aquatic environment, posing a possible threat to aquatic organisms. In particular, filter-feeding organisms may represent a unique target group for nanoparticle toxicology. In this work, the effects of commercial nanosized carbon black (NCB) on the immune cells, the hemocytes, of the bivalve mollusc Mytilus, and the possible mechanisms involved were investigated. The results demonstrate that NCB (1, 5, and 10 microg/ml), did not induce significant lysosomal membrane destabilization, as evaluated by the NR retention time assay. A concentration-dependent uptake of NCB by hemocytes was observed and it was associated by a rapid increase in extracellular lysozyme release, extracellular oxyradical production, and nitric oxide (NO) release. Moreover, at the highest concentration tested, NCB induced significant changes in mitochondrial parameters (decrease mitochondrial mass/number and membrane potential), as evaluated by flow cytometry. The effects of NCB were mediated by rapid activation of the stress-activated MAPKs (Mitogen Activated Protein Kinases) p38 and JNKs, that play a key role in immune and inflammatory responses. The results demonstrate that in mussel hemocytes like in mammalian cells NCB exposure can induce inflammatory processes, and indicate that bivalve immunocytes can represent a suitable model for investigating the effects and modes of action of nanoparticles in the cells of aquatic invertebrates.     

Coastal flood protection: What perspective in a changing climate? The THESEUS approach

Coastal areas are vital economic hubs in terms of settlement, industry, agriculture, trade and tourism to mention some key sectors. There are already many coastal problems including erosion, flood risk and long-term habitat deterioration. As economies continue to develop the asset base at risk will grow, while accelerating climate change will increase the likelihood of damaging extreme events, as well as accelerate habitat decline. Existing coastal management and defence approaches are not well tuned to these challenges as they assume a static situation.THESEUS project is developing a systematic approach to delivering both a low-risk coast for human use and healthy habitats for evolving coastal zones subject to multiple change factors. The project examines innovative mitigation and adaptation technologies and integrate the best of these technical measures in a strategic policy context through overarching guidelines. THESEUS activities are carried out within a multidisciplinary framework using 8 study sites across Europe, with specific attention to the most vulnerable coastal environments such as deltas, estuaries and wetlands, where many large cities and industrial areas are located.This paper describes THESEUS approach, and specifically: the Source-Pathway-Receptor-Consquence model for coastal risk assessment; the engineering, social, economic and ecological mitigation measures under analysis; the participatory approach with end users and coastal authorities for the selection and identification of the appropriate defence strategy to be planned in sudy sites.     

Indicators of deforestation in the Southern Brazilian Pre-Amazon

In recent years, Amazonian deforestation has become a question of global concern. Deforestation in Amazon is a complex phenomenon in nature and has been related to traditional agriculture expansion. In this work, land use, socioeconomic and conservation indicators, combined with statistical analysis, were used to understand forces associated with patterns of deforestation. This approach was applied in Southern Brazilian Pre-Amazon in Mato Grosso State, which represents an extensive rain forest-savanna ecotone, located in the south border of Amazon biome. Based on data from the last two agricultural censuses (1995/1996–2006), we compared agricultural expansion in this area and Mato Grosso state. Results have shown that 85 % of state deforestation was concentrated in Southern Pre-Amazon and was closely related to increase in number of cattle and pasture area. PCA results pointed that population (92 %), number of cattle (86.5 %), pastures (84.2 %) and tractors (78.4 %) were variables with highest positive correlation to deforestation. It showed that GDP contributes to an individual axis and has a low correlation to deforestation (37.8 %). Conservation units and indigenous reserves also contribute to a single axis and were negatively correlated to temporary crops area. Results revealed a significant reduction in production and commercialization of extractive products in the region, revealing that the main, almost only conservation policy in Mato Grosso remains the creation of Special Areas. We suggest that further studies are necessary to screen development alternatives to simple cutting trees down. It is important to diversify strategies for deforestation control, and development aspects must be more seriously considered to reach a sustainable deforestation control policy.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

Climate change effects on phytoplankton depend on cell size and food web structure

We investigated the effects of warming on a natural phytoplankton community from the Baltic Sea, based on six mesocosm experiments conducted 2005–2009. We focused on differences in the dynamics of three phytoplankton size groups which are grazed to a variable extent by different zooplankton groups. While small-sized algae were mostly grazer-controlled, light and nutrient availability largely determined the growth of medium- and large-sized algae. Thus, the latter groups dominated at increased light levels. Warming increased mesozooplankton grazing on medium-sized algae, reducing their biomass. The biomass of small-sized algae was not affected by temperature, probably due to an interplay between indirect effects spreading through the food web. Thus, under the higher temperature and lower light levels anticipated for the next decades in the southern Baltic Sea, a higher share of smaller phytoplankton is expected. We conclude that considering the size structure of the phytoplankton community strongly improves the reliability of projections of climate change effects.     

The response of temperate aquatic ecosystems to global warming: novel insights from a multidisciplinary project

This article serves as an introduction to this special issue of Marine Biology, but also as a review of the key findings of the AQUASHIFT research program which is the source of the articles published in this issue. AQUASHIFT is an interdisciplinary research program targeted to analyze the response of temperate zone aquatic ecosystems (both marine and freshwater) to global warming. The main conclusions of AQUASHIFT relate to (a) shifts in geographic distribution, (b) shifts in seasonality, (c) temporal mismatch in food chains, (d) biomass responses to warming, (e) responses of body size, (f) harmful bloom intensity, (f), changes of biodiversity, and (g) the dependence of shifts to temperature changes during critical seasonal windows.     

Species-specific effect of macrobenthic assemblages on meiobenthos and nematode community structure in shallow sandy sediments

Three functionally different macrofaunal species (the filter- and/or surface deposit-feeding polychaete Hediste diversicolor, and the suspension-feeding bivalves Mya arenaria and Cerastoderma glaucum) were introduced as single- and two-species treatments into microcosms containing sandy sediment with a natural meiofaunal community. H. diversicolor is a burrowing species building a system of galleries, C. glaucum lives actively near the sediment surface acting as a biodiffuser and M. arenaria buries deeply and leads a sessile lifestyle. It is shown that H. diversicolor extended the vertical distribution of meiofauna into deeper sediment layers compared to the control and non-Hediste treatments. The response of the nematode community varied significantly among treatments and was dependant on the macrobenthic species composition but not on the species number. Nematode assemblages in all treatments with the polychaete, both in monoculture and with either bivalve, differed significantly from those recorded in other treatments and were more similar than replicates within any other single treatment. H. diversicolor also appeared to have stimulated nematode species diversity. The present study demonstrated that the impact of macrobenthic assemblages on meiofauna is not a simple summation of individual species effects but is species specific.     

Predicting arsenic concentrations in groundwater of San Luis Valley,Colorado: implications for individual-level lifetime exposure assessment

Consumption of inorganic arsenic in drinking water at high levels has been associated with chronic diseases. Risk is less clear at lower levels of arsenic, in part due to difficulties in estimating exposure. Herein we characterize spatial and temporal variability of arsenic concentrations and develop models for predicting aquifer arsenic concentrations in the San Luis Valley, Colorado, an area of moderately elevated arsenic in groundwater. This study included historical water samples with total arsenic concentrations from 595 unique well locations. A longitudinal analysis established temporal stability in arsenic levels in individual wells. The mean arsenic levels for a random sample of 535 wells were incorporated into five kriging models to predict groundwater arsenic concentrations at any point in time. A separate validation dataset (n = 60 wells) was used to identify the model with strongest predictability. Findings indicate that arsenic concentrations are temporally stable (r = 0.88; 95 % CI 0.83–0.92 for samples collected from the same well 15–25 years apart) and the spatial model created using ordinary kriging best predicted arsenic concentrations (ρ = 0.72 between predicted and observed validation data). These findings illustrate the value of geostatistical modeling of arsenic and suggest the San Luis Valley is a good region for conducting epidemiologic studies of groundwater metals because of the ability to accurately predict variation in groundwater arsenic concentrations.     

Using expert knowledge to support Endangered Species Act decision-making for data-deficient species

Many questions relevant to conservation decision-making are characterized by extreme uncertainty due to lack of empirical data and complexity of the underlying ecologic processes, leading to a rapid increase in the use of structured protocols to elicit expert knowledge. Published ecologic applications often employ a modified Delphi method, where experts provide judgments anonymously and mathematical aggregation techniques are used to combine judgments. The Sheffield elicitation framework (SHELF) differs in its behavioral approach to synthesizing individual judgments into a fully specified probability distribution for an unknown quantity. We used the SHELF protocol remotely to assess extinction risk of three subterranean aquatic species that are being considered for listing under the U.S. Endangered Species Act. We provided experts an empirical threat assessment for each known locality over a video conference and recorded judgments on the probability of population persistence over four generations with online submission forms and R-shiny apps available through the SHELF package. Despite large uncertainty for all populations, there were key differences between species’ risk of extirpation based on spatial variation in dominant threats, local land use and management practices, and species’ microhabitat. The resulting probability distributions provided decision makers with a full picture of uncertainty that was consistent with the probabilistic nature of risk assessments. Discussion among experts during SHELF's behavioral aggregation stage clearly documented dominant threats (e.g., development, timber harvest, animal agriculture, and cave visitation) and their interactions with local cave geology and species’ habitat. Our virtual implementation of the SHELF protocol demonstrated the flexibility of the approach for conservation applications operating on budgets and time lines that can limit in-person meetings of geographically dispersed experts.