Variability in the phytoplankton community of Kavaratti reef ecosystem (northern Indian Ocean) during peak and waning periods of El Niño 2016

El Niño, an interannual climate event characterized by elevated oceanic temperature, is a prime threat for coral reef ecosystems worldwide, owing to their thermal threshold sensitivity. Phytoplankton plays a crucial role in the sustenance of reef trophodynamics. The cell size of the phytoplankton forms the “master morphological trait” with implications for growth, resource acquisition, and adaptability to nutrients. In the context of a strong El Niño prediction for 2015–2016, the present study was undertaken to evaluate the variations in the size-structured phytoplankton of Kavaratti reef waters, a major coral atoll along the southeast coast of India. The present study witnessed a remarkable change in the physicochemical environment of the reef water and massive coral bleaching with the progression of El Niño 2015–2016 from its peak to waning phase. The fluctuations observed in sea surface temperature, pH, and nutrient concentration of the reef water with the El Niño progression resulted in a remarkable shift in phytoplankton size structure, abundance, and community composition of the reef waters. Though low nutrient concentration of the waning phase resulted in lower phytoplankton biomass and abundance, the diazotroph Trichodesmium erythraeum predominated the reef waters, owing to its capability of the atmospheric nitrogen fixation and dissolved organic phosphate utilization.     

Probabilistic assessment of phenophase-wise agricultural drought risk under different sowing windows: a case study with rainfed soybean

A new methodology for crop-growth stage-specific assessment of agricultural drought risk under a variable sowing window is proposed for the soybean crop. It encompasses three drought indices, which include Crop-Specific Drought Index (CSDI), Vegetation Condition Index (VCI), and Standardized Precipitation Evapotranspiration Index (SPEI). The unique features of crop-growth stage-specific nature and spatial and multi-scalar coverage provide a comprehensive assessment of agricultural drought risk. This study was conducted in 10 major soybean-growing districts of Madhya Pradesh state of India. These areas contribute about 60% of the total soybean production for the country. The phenophase most vulnerable to agricultural drought was identified (germination and flowering in our case) for each district across four sowing windows. The agricultural drought risk was quantified at various severity levels (moderate, severe, and very severe) for each growth stage and sowing window. Validation of the proposed new methodology also yielded results with a high correlation coefficient between percent probability of agricultural drought risk and yield risk (r?=?0.92). Assessment by proximity matrix yielded a similar statistic. Expectations for the proposed methodology are better mitigation-oriented management and improved crop contingency plans for planners and decision makers.     

A Novel Formulation for Designing a Monitoring Strategy: Application to the Design of a River Quality Monitoring System

In this work, we propose a technique to automatically optimize the monitoring of any distributed indicator (concentration of a substance along a river, blood pressure of a patient over time, etc.) for which a reliable estimate is previously available. From a mathematical point of view, the problem is based on obtaining a reliable estimate of the chosen indicator (e.g., by numerical simulation), and then solving a multi-objective optimization problem (with mixed real and integer variables) whose solution must provide an efficient and satisfactory monitoring strategy. As an illustrative case, we show the steps to follow in order to implement that strategy when designing a system for monitoring water quality in a river. Finally, we present and analyze the results when applying the proposed technique to study a real case in the Neuse River (North Carolina, USA).     

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.     

Seasonal Differences in the Levels of Gaseous Air Pollutants in the Vicinity of a Waste Dump

Hydrogen sulphide, ammonia, nitrogen dioxide, mercaptans and sulphur dioxide (H₂S, NH₃, NO₂, R-SH, SO₂) concentrations were measured at the location in the vicinity of the waste dump to determine the air pollution level of these pollutants prior to the operation of the Mobile Thermal Treatment Plant. Samples were collected over one year period. Seasonal differences, and the influence of meteorological parameters (temperature, relative humidity, pressure and wind direction) on the air pollution levels were studied. Results show relatively low concentrations of H₂S, NO₂, R-SH and SO₂, while NH₃ levels were higher compared to the guideline values. Good weather conditions (high air pressure and low relative humidity) are connected to long range transport of NO₂, while higher temperatures result in elevated NH₃ and R-SH concentrations. Because of the predominant northeast wind direction (the same as the waste dump direction), the contribution of air pollution from the direction of the waste dump at the measuring site is significant, but that does not necessarily mean that the pollutants originated from that source.     

Physico-Chemical Quality of Drinking Water in Rohtas District of Bihar

Water Quality Survey of Rohtas district of Bihar was conducted. Samples were collected from differentsources and analysed. 209 samples were collectedfrom 196 villages. Results of water quality surveyidentified the problem areas in respect of high iron,manganese, fluoride, nitrate and brackishness of water in the district.     

Landscape Correlates of Breeding Bird Richness Across the United States Mid-Atlantic Region

Using a new set of landscape indicator data generated by the U.S.EPA, and a comprehensive breeding bird database from the National Breeding Bird Survey, we evaluated associations between breeding bird richness and landscape characteristics across the entire mid-Atlantic region of the United States. We evaluated how these relationships varied among different groupings (guilds) of birds based on functional, structural, and compositional aspects of individual species demographics. Forest edge was by far the most important landscape attribute affecting the richness of the lumped specialist and generalist guilds; specialist species richness was negatively associated with forest edge and generalist richness was positively associated with forest edge. Landscape variables (indicators) explained a greater proportion of specialist species richness than the generalist guild (46% and 31%, respectively). The lower value in generalists may reflect finer-scale distributions of open habitat that go undetected by the Landsat satellite, open habitats created by roads (the areas from which breeding bird data are obtained), and the lumping of a wide variety of species into the generalist category. A further breakdown of species into 16 guilds showed considerable variation in the response of breeding birds to landscape conditions; forest obligate species had the strongest association with landscape indicators measured in this study (55% of the total variation explained) and forest generalists and open ground nesters the lowest (17% of the total variation explained). The variable response of guild species richness to landscape pattern suggests that one must consider species' demographics when assessing the consequences of landscape change on breeding birds.     

Responses of Physical, Chemical, and Biological Indicators of Water Quality to a Gradient of Agricultural Land Use in the Yakima River Basin, Washington

The condition of 25 stream sites in the Yakima River Basin, Washington, were assessed by the U.S. Geological Survey's National Water-Quality Assessment Program. Multimetric condition indices were developed and used to rank sites on the basis of physical, chemical, and biological characteristics. These indices showed that sites in the Cascades and Eastern Cascades ecoregions were largely unimpaired. In contrast, all but two sites in the Columbia Basin ecoregion were impaired, some severely. Agriculture (nutrients and pesticides) was the primary factor associated with impairment and all impaired sites were characterized by multiple indicators of impairment. All indices of biological condition (fish, invertebrates, and algae) declined as agricultural intensity increased. The response exhibited by invertebrates and algae suggested a threshold response with conditions declining precipitously at relatively low levels of agricultural intensity and little response at moderate to high levels of agricultural intensity. This pattern of response suggests that the success of mitigation will vary depending upon where on the response curve the mitigation is undertaken. Because the form of the community condition response is critical to effective water-quality management, the National Water-Quality Assessment Program is conducting studies to examine the response of biota to gradients of land-use intensity and the relevance of these responses to water-quality management. These land-use gradient pilot studies will be conducted in several urban areas starting in 1999.     

Accumulation of Heavy Metals in Epiphytic Lichens Near a Municipal Solid Waste Incinerator (Central Italy)

The epiphytic lichen Parmelia caperata was usedas biomonitor in the area of a municipal solid wasteincinerator (Poggibonsi, central Italy) to investigatethe levels and the spatial distribution of the heavymetals Al, Cd, Cr, Cu, Fe, Hg, Pb and Zn. Levels ofAl, Cu and Hg were similar to those in unpollutedareas, whereas high values were found for Cr, Zn andespecially Cd. The distribution pattern of the lastthree metals and the exponential relationship of theirconcentrations with distance from the incinerator,showed that the disposal plant is a local source ofatmospheric pollution due to Cd, Cr and Zn. For thesemetals, long-term hazard should be seriously taken into account.