Habitat complexity reduces the growth of aggressive and dominant brown trout (<Emphasis Type="Italic">Salmo trutta</Emphasis>) relative to subordinates

Animals often prefer areas containing physical structure, and population density often increases with structural complexity, presumably because physical complexity in habitats may offer protection from predators and aggressive competitors. Consequently, increased habitat complexity often results in reduced territory size, lower aggression levels and reduced resource monopolisation by dominants. If behavioural plasticity is limited at early life stages, increased habitat complexity may reduce the relative fitness of aggressive, dominant strategies. Here we tested this hypothesis in an experiment on newly emerged brown trout (Salmo trutta) fry. We show, for the first time, that increased habitat complexity reduces the fitness (i.e. growth rate) of aggressive dominant individuals in relation to subordinates, and that this relation is reversed in simple habitats. Variation in environmental complexity may thus induce fluctuating selective pressures, maintaining behavioural variation in natural populations and allowing subordinate and dominant strategies to coexist.     

Tracking land cover change along the western edge of the U.S. Corn Belt from 1984 through 2016 using satellite sensor data: observed trends and contributing factors

The Cropland Data Layers (CDL) are high-resolution geo-referenced data products made available by the U.S. Department of Agriculture. However, the CDL lacks in its ability to be employed as a tool to identify the impact of the gradually evolving drivers of land use change, e.g., climate change, due to its limited historical depth. We implement a robust, phenology-based satellite image classification algorithm to identify historical cropland allocation in eastern South Dakota and North Dakota predating the initial CDL by 13 and 22 years, respectively. Five major land covers, i.e., corn, soybeans, wheat, alfalfa and grass (including native grass, hay and pasture) are identified using archived Landsat-5 surface reflectance data, while achieving CDL-like accuracy. The long-term rate of grassland loss during 1985–2011 is found to be significantly lower (26,781 hectares or 1.5% annually) relative to the near-term rate of grassland loss during 2006-’11 (84,545 hectares or 5.2% annually). We find similar discrepancy in regional corn expansion rates. Our value-added raster data provide opportunities for improved identification of land use drivers, whereas relying solely on the CDL’s restricted historical extent may lead to biased land use change estimates and misguide policy.     

Model of coral population response to accelerated bleaching and mass mortality in a changing climate

We model coral community response to bleaching and mass mortality events which are predicted to increase in frequency with climate change. The model was parameterized for the Arabian/Persian Gulf, but is generally applicable. We assume three species groups (Acropora, faviids, and Porites) in two life-stages each where the juveniles are in competition but the adults can enter a size-refuge in which they cannot be competitively displaced. An aggressive group (Acropora species) dominates at equilibrium, which is not reached due to mass mortality events that primarily disadvantage this group (compensatory mortality, >90% versus 25% in faviids and Porites) roughly every 15 years. Population parameters (N individuals, carrying capacity) were calculated from satellite imagery and in situ transects, vital rates (fecundity, mortality, and survival) were derived from the model, field observations, and literature. It is shown that populations and unaltered community structure can persist despite repeated 90% mortality, given sufficiently high fecundity of the remaining population or import from connected populations. The frequency of disturbance determines the dominant group—in low frequency Acropora, in high frequency Porites. This is congruent with field observations. The model of an isolated population was more sensitive to parameter changes than that of connected populations. Highest sensitivity was to mortality rate and recruitment rate. Community composition was sensitive to spacing of disturbances and level of catastrophic mortality. Decreased mortality led to Acropora dominance, increased mortality led to Acropora extinction. In nature, closely spaced disturbances have severely disadvantaged Acropora populations over the last decade. Unless a longer (>10 years) disturbance-free interval can be maintained, a permanent shift away from Acropora dominance will be observed. A mortality rate of 99% in Acropora, as observed in 1996, is not sustainable if repetitive and neither is a disturbance frequency <15 years—each leading to population collapse. This shows that the severity and/or the spacing of the 1996–1998–2002 disturbances were unusual in frequency and duration.     

Use and abuse of Pb-isotope fingerprinting technique and GIS mapping data to assess lead in environmental studies

The danger to health, especially of young children, from lead-bearing particulates in the surface environment is still a valid concern. Given the multifaceted uses of lead today as well as historically, the sources of lead must be investigated to distinguish the etiology of lead contamination on the environment. The lead isotope finger printing technique based on isotopic ratio analyses (IRA) is one of the methods used commonly for provenancing lead sources in environmental studies throughout the world. This technique, however, has some inherited shortcomings. Therefore, caution must be used in the interpretation of the results, as this technique can lead to overestimations as well as underestimations of the true source etiology/apportionment of lead in specific environments. This paper illustrates this with some examples. Data on geographical information systems (GIS) mapping in urban regions are also presented in this paper.     

Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning

● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste. ● Feature selection methods were used to improve models’ prediction accuracy. ● The best model predicted C, H, and N contents with accuracy R ² ≥ 0.93, 0.87, 0.97. ● Some suitable models showed insensitivity to spectral noise. ● Under moisture interference, the models still had good prediction performance. Elemental composition is a key parameter in solid waste treatment and disposal. This study has proposed a method based on infrared spectroscopy and machine learning algorithms that can rapidly predict the elemental composition (C, H, N, S) of solid waste. Both noise and moisture spectral interference that may occur in practical application are investigated. By comparing two feature selection methods and five machine learning algorithms, the most suitable models are selected. Moreover, the impacts of noise and moisture on the models are discussed, with paper, plastic, textiles, wood, and leather as examples of recyclable waste components. The results show that the combination of the feature selection and K-nearest neighbor (KNN) approaches exhibits the best prediction performance and generalization ability. Particularly, the coefficient of determination (R²) of the validation set, cross validation and test set are higher than 0.93, 0.89, and 0.97 for predicting the C, H, and N contents, respectively. Further, KNN is less sensitive to noise. Under moisture interference, the combination of feature selection and support vector regression or partial least-squares regression shows satisfactory results. Therefore, the elemental compositions of solid waste are quickly and accurately predicted under noise and moisture disturbances using infrared spectroscopy and machine learning algorithms.     

Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city

An image-recognition-based diagnosis system of pipe defect types was established. 1043 practical pipe images were gathered by CCTV robot in a southern Chinese city. The overall accuracy of the system is 84% and the highest accuracy is 99.3%. The accuracy shows positive correlation to the number of training samples. Closed circuit television (CCTV) systems are widely used to inspect sewer pipe conditions. During the diagnosis process, the manual diagnosis of defects is time consuming, labor intensive and error prone. To assist inspectors in diagnosing sewer pipe defects on CCTV inspection images, this paper presents an image recognition algorithm that applies features extraction and machine learning approaches. An algorithm of image recognition techniques, including Hu invariant moment, texture features, lateral Fourier transform and Daubechies (DBn) wavelet transform, was used to describe the features of defects, and support vector machines were used to classify sewer pipe defects. According to the inspection results, seven defects were defined; the diagnostic system was applied to a sewer pipe system in a southern city of China, and 28,760 m of sewer pipes were inspected. The results revealed that the classification accuracies of the different defects ranged from 51.6% to 99.3%. The overall accuracy reached 84.1%. The diagnosing accuracy depended on the number of the training samples, and four fitting curves were applied to fit the data. According to this paper, the logarithmic fitting curve presents the highest coefficient of determination of 0.882, and more than 200 images need to be used for training samples to guarantee the accuracy higher than 85%.     

Predicting suitable habitat for the European lobster (Homarus gammarus), on the Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis

Predicting species distribution and habitat suitability (HS) modelling, across broad spatial scales, is now a major challenge in marine ecology. The resulting knowledge is of considerable use in supporting the implementation of environmental legislation, integrated coastal zone management and ecosystem-based fisheries management. This contribution considers the identification of seafloor morphological characteristics, together with wave energy conditions, that determine the presence of European lobster (Homarus gammarus); and it predicts suitable habitats over the Basque continental shelf (Bay of Biscay), in summer. The results obtained, by applying Ecological-Niche Factor Analysis (ENFA), indicate that lobster habitat differs considerably from the mean environmental condition over the study area; likewise, that it is restrictive in terms of the range of conditions in which they dwell. The best of the environmental predictors found to be: distance to the rock substrate; Benthic Position Index; wave flux over the seafloor; and the underlying bathymetry. A habitat suitability map was produced, with a high model quality (Boyce index: 0.98 ± 0.06). The most suitable habitat for European lobster are locations at the boundary between sedimentary- and rocky-bottoms, coincident with seafloor depressions with a steep slope, with medium to high wave energy conditions, and located within a range of water depths of 35–40 m. This approach demonstrates the applicability of the method in case studies where only presence data are available, together with the inclusion of environmental variables obtained from different sources.     

Evaluation of suitable tiger habitat in Chandoli National Park,India, using spatial modelling of environmental variables

Application of remote sensing and Geographic Information System (GIS) as a tool has assumed an immense significance in habitat suitability modelling for various wildlife species and now a days these are widely used in conservation biology and wildlife management.     

Mapping of the benthic communities in the Taranto seas using side-scan sonar and an underwater video camera

Side-scan sonar and underwater video camera records as well as dredging samples were used to map the bottom morphology and biocoenoses in the Taranto seas. A 1:20,000 scale chart has been produced with all the data recorded. Most of the study area consists of biocoenoses affected by the anthropogenic activities of the town of Taranto. Some native broadly tolerant species seem to benefit from these activities and become increasingly dominant. Environmental modifications also seem to favour the settlement of exotic species.     

Photocatalytic mineralization of a water pollutant,Sunset Yellow dye by an advanced oxidation process using a modified catalyst

ZnS-loaded TiO₂ (ZnS–TiO₂) was synthesized by a sol–gel method. The catalyst was characterized by using different techniques (XRD, HR-SEM, EDS, DRS, PL, XPS, and BET methods). The photocatalytic activity of ZnS–TiO₂ was investigated for the degradation of Sunset Yellow FCF (SY) dye in an aqueous solution using ultraviolet light. ZnS–TiO₂ is found to be more efficient than prepared TiO₂, TiO₂–P25, TiO₂ (Merck), and ZnS at pH 7 for the mineralization of SY. The effects of operational parameters such as the amount of photocatalyst, dye concentration, and initial pH on photo mineralization of SY have been analyzed. The mineralization of SY has been confirmed by chemical oxygen demand measurements. The catalyst is found to be reusable.     

Simulation of NO and N2O emissions from a spruce forest during a freeze/thaw event using an N-flux submodel from the PnET-N-DNDC model integrated to CoupModel

The amount of nitrogen gases (N₂O, NO and N₂) emitted from forest soils depends on interactions between soil properties, climatic factors and soil management. To increase the understanding of nitrogen processes in soil ecosystems, two dynamic models, CoupModel (coupled heat and mass transfer model for soil–plant–atmosphere systems) and the denitrification–decomposition (DNDC) model were selected. Both are dynamic models with different submodels for soil, vegetation, hydrology and climate system. CoupModel has a higher degree of detail on soil physical and abiotic components, whereas the DNDC model contains details of microbiological processes involved in production of nitrogen gases. To improve the previous simple submodel of nitrogen emission in CoupModel, we included a submodel corresponding to the forest version of DNDC containing photosynthesis/evapotranspiration-nitrogen (PnET-N-DNDC model).