Foliar nutrient and metal levels of crops in the Mount Cameroon area—reference values for plant nutrition and environmental monitoring

The growing population number and traffic loads, increasing environmental pressures, agricultural intensification, and the establishment of Mount Cameroon National Park demand farsighted environmental management in the region and the definition of a favorable ecological status. Since plants grow in the interface between soils and the atmosphere they can be used as passive biomonitors for the environmental quality. At the same time, the accumulation of nutrients and pollutants in crops is linked to human health, so that foliar elemental levels can be used as an integrative measure for environmental pollution and impact assessment. In the present study, we collected leaf samples of plantain, cassava, cocoyam, and maize on 28 sites at the southern flanks of Mt. Cameroon and determined 20 chemical elements. Air pollution in the study area comes from biomass and waste burning mainly, but emissions from traffic and a large refinery were believed to also play a significant role. However, spatial patterns in foliar elemental concentrations reflected the geochemistry rather than specific sources of pollution. Significant differences in foliar metal and nutrient levels were observed between the four species, indicating a different demand and uptake of specific elements. The results were compared to published data on nutrient concentrations in the tested species and the so-called reference plant. The data can be used as a baseline for future studies in plant nutrition and the environmental monitoring in inner tropical regions where these crops are grown.     

Metal concentrations in American oyster <Emphasis Type="Italic">Crassotrea virginica</Emphasis> and adjacent sediments from harvestable and non-harvestable sites in the Southeastern USA

Human population growth in coastal areas continues to threaten estuarine ecosystems and resources. Populations of Crassostrea virginica have declined across the USA due to water quality degradation, disease pressure, alteration of habitat, and other changes related to anthropogenic impacts. Metals that may be present in estuarine habitats can bioaccumulate in oysters, with potential consequences to the health of oysters and humans consumers. This study (1) evaluated the occurrence and relationships of metal concentrations in oyster tissue versus estuarine sediments, (2) examined oyster tissue concentrations in relation to state water quality designations, and (3) evaluated the potential risk for humans from oyster consumption related to metal concentrations from harvestable waters. Results indicated metal concentrations in sediments and oysters along coastal South Carolina remain low compared to other areas and that concentrations in oyster tissue and adjacent sediments were not highly correlated with each other. However, high concentrations of some metals occurred in oysters sampled from areas designated as Approved for Harvesting. This is important because most harvest area designation systems rely on regular bacterial monitoring when evaluating the safety of consumption. Others safety measurements may be necessary as part of routine monitoring.     

Can we document if regulation and Best Available Techniques (BAT) have any positive impact on the marine environment? A case based on a steel mill in Greece

In order to document the impact of Best Available Techniques (BAT) and implementation of regulation on the improvement of the coastal marine environment state, we examined the case of a representative steel mill located at the Gulf of Elefsis (Greece). The evaluation of metal pollution was based on the analysis of major and trace elements, organic carbon, magnetic properties, and sediment accumulation rates, in sediment cores obtained from the vicinity of the plant. The analytical data are discussed in relation to steel production, changes of production routes, and adoption of BAT introduced in order to fulfill EU and national legislation. The results show that the input of pollutants to sediments and the degree of contamination were reduced by approximately 40–70% in the decade 2003–2015 in comparison to the periods of high discharges (1963–2002), whereas the toxicity risks from “high-to-extremely high” were reduced to “medium-to-high.”     

A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling

Classification and regression problems are a central issue in geosciences. In this paper, we present Classification and Regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance; then, it selects the optimal configuration of explanatory variables; finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling.     

A moving target—incorporating knowledge of the spatial ecology of fish into the assessment and management of freshwater fish populations

Freshwater fish move vertically and horizontally through the aquatic landscape for a variety of reasons, such as to find and exploit patchy resources or to locate essential habitats (e.g., for spawning). Inherent challenges exist with the assessment of fish populations because they are moving targets. We submit that quantifying and describing the spatial ecology of fish and their habitat is an important component of freshwater fishery assessment and management. With a growing number of tools available for studying the spatial ecology of fishes (e.g., telemetry, population genetics, hydroacoustics, otolith microchemistry, stable isotope analysis), new knowledge can now be generated and incorporated into biological assessment and fishery management. For example, knowing when, where, and how to deploy assessment gears is essential to inform, refine, or calibrate assessment protocols. Such information is also useful for quantifying or avoiding bycatch of imperiled species. Knowledge of habitat connectivity and usage can identify critically important migration corridors and habitats and can be used to improve our understanding of variables that influence spatial structuring of fish populations. Similarly, demographic processes are partly driven by the behavior of fish and mediated by environmental drivers. Information on these processes is critical to the development and application of realistic population dynamics models. Collectively, biological assessment, when informed by knowledge of spatial ecology, can provide managers with the ability to understand how and when fish and their habitats may be exposed to different threats. Naturally, this knowledge helps to better evaluate or develop strategies to protect the long-term viability of fishery production. Failure to understand the spatial ecology of fishes and to incorporate spatiotemporal data can bias population assessments and forecasts and potentially lead to ineffective or counterproductive management actions.