Are Landscape Configuration Metrics Worth Including When Predicting Specialist and Generalist Bird Species Density? A Case of the Generalised Additive Model Approach

Due to the complexity of factors that influence species density on a large geographical scale, the effectiveness of the species distribution model (SDM) is still debatable. That is why the buffer zone (the area within 100 m from the outside edge of the patch), the core, i.e. (patches excluding the 100 m buffer zone from the patch’s edge) and patch shape are explored in this study to see how they affect the density of habitat specialist and generalist bird species. Two sets of generalised additive models were generated separately for each of the four bird species: One set of models contained landscape configuration metrics as an additional predictor variable, and the other did not. The results showed that models including the core, the buffer zone and the shape of patches turned out to be definitely better than models without them. Specialist species, the Corn bunting and the Wood nuthatch, are more likely to occur in the core of the preferred patches, and they choose those of a simple shape; while generalist species, the Whinchat and the Tree pipit, are more probable to be present in the buffer zone of a more complicated shape. Thus, the results clearly show that specific landscape configuration models can improve the predictive power of SDMs and can be used as an effective tool for predicting species density and functional bird diversity (specialist and generalist). Furthermore, from the applied ecology perspective, detailed landscape configuration metrics can be considered as a surrogate of elusive habitat conditions.     

Assessing the regional impacts of increased energy maize cultivation on farmland birds

The increasing cultivation of energy crops in Germany substantially affects the habitat function of agricultural landscapes. Precise ex ante evaluations regarding the impacts of this cultivation on farmland bird populations are rare. The objective of this paper was to implement a methodology to assess the regional impacts of increasing energy maize cultivation on the habitat quality of agricultural lands for farmland birds. We selected five farmland bird indicator species with varying habitat demands. Using a crop suitability modelling approach, we analysed the availability of potential habitat areas according to different land use scenarios for a real landscape in Northeast Germany. The model was based on crop architecture, cultivation period, and landscape preconditions. Our results showed that the habitat suitability of different crops varied between bird species, and scenario calculations revealed an increase and a decrease in the size of the potential breeding and feeding habitats, respectively. The effects observed in scenario 1 (increased energy maize by 15 %) were not reproduced in all cases in scenario 2 (increased energy maize by 30 %). Spatial aggregation of energy maize resulted in a negative effect for some species. Changes in the composition of the farmland bird communities, the negative effects on farmland bird species limited in distribution and spread and the relevance of the type of agricultural land use being replaced by energy crops are also discussed. In conclusion, we suggest a trade-off between biodiversity and energy targets by identifying biodiversity-friendly energy cropping systems.     

Comparative DFT study of inner-sphere As(III) complexes on hydrated α-Fe2O3(0001) surface models

The long-recognized risk to human health arising from arsenic-contaminated waters is known to be linked to partitioning reactions between arsenic and natural solid phases. Currently, the ability to predict As surface complexation is limited by the lack of molecular-level understanding of As-solid interactions. In the present study, we use density functional theory (DFT) to model mono-, bi-, and tri-dentate As(III) surface complexes on different (previously proposed) structural models for hydrated hematite, modeled as α-Fe(2)O(3)(0001)-water interfaces. One of the modeled hematite-water interfaces is terminated entirely by hydroxyl surface functional groups, comprised of hematite lattice oxygen atoms. The other hematite-water interface is an iron-terminated model in which the outermost oxygen functional groups are water (and water dissociation product) ligands. We report the DFT trends in adsorption energies in terms of As-hematite coordination, hematite surface geometry/stoichiometry, and oxygen functional group identity. The DFT energetics predict that a monodentate As(III) surface complex is preferred on both hematite-water structures, suggesting that the two structural models here employed do not sufficiently represent the true surface structure to reproduce the experimental observation of As(III) bidentate coordination. However, the results do elucidate fundamental concepts of interface reactivity: A key result, supported by electronic structure analysis, is that ligand oxygen functional groups cannot be treated on equal ground with true surface oxygen functional groups. For the systems modeled here the distinction between surface and ligand functional groups supersedes the differences in oxygen coordination with surface Fe. We discuss the impact of this finding on the application of bond-valence-based predictions of mineral-water reactivity, and use the results of this study to pose questions and directions for ongoing modeling efforts aimed at linking macroscopic reactivity with molecular-level understanding.     

Estimation of the Human Impact on Nutrient Loads Carried by the Elbe River

The reunification of Germany led to dramatically reduced emissions of nitrogen (N) and phosphorus (P) to the environment. The aim of the present study was to examine how these exceptional decreases influenced the amounts of nutrients carried by the Elbe River to the North Sea. In particular, we attempted to extract anthropogenic signals from time series of riverine loads of nitrogen and phosphorus by developing a normalization technique that enabled removal of natural fluctuations caused by several weather-dependent variables. This analysis revealed several notable downward trends. The normalized loads of total-N and NO3-N exhibited an almost linear trend, even though the nitrogen surplus in agriculture dropped dramatically in 1990 and then slowly increased. Furthermore, the decrease in total-P loads was found to be considerably smaller close to the mouth of the river than further upstream. Studying the predictive ability of different normalization models showed the following: (i) nutrient loads were influenced primarily by water discharge; (ii) models taking into account water temperature, load of suspended particulate matter, and salinity were superior for some combinations of sampling sites and nutrient species; semiparametric normalization models were almost invariably better than ordinary regression models.     

Water quality status and trends in agriculture-dominated headwaters; a national monitoring network for assessing the effectiveness of national and European manure legislation in The Netherlands

Large nutrient losses to groundwater and surface waters are a major drawback of the highly productive agricultural sector in The Netherlands. The resulting high nutrient concentrations in water resources threaten their ecological, industrial, and recreational functions. To mitigate eutrophication problems, legislation on nutrient application in agriculture was enforced in 1986 in The Netherlands. The objective of this study was to evaluate this manure policy by assessing the water quality status and trends in agriculture-dominated headwaters. We used datasets from 5 agricultural test catchments and from 167 existing monitoring locations in agricultural headwaters. Trend analysis for these locations showed a fast reduction of nutrient concentrations after the enforcement of the manure legislation (median slopes of ?0.55 mg/l per decade for total nitrogen (N-tot) and ?0.020 mg/l per decade for total phosphorus (P-tot)). Still, up to 76 % of the selected locations currently do not comply with either the environmental quality standards (EQSs) for nitrogen (N-tot) or phosphorus (P-tot). This indicates that further improvement of agricultural water quality is needed. We observed that weather-related variations in nutrient concentrations strongly influence the compliance testing results, both for individual locations and for the aggregated results at the national scale. Another important finding is that testing compliance for nutrients based on summer average concentrations may underestimate the agricultural impact on ecosystem health. The focus on summer concentrations does not account for the environmental impact of high winter loads from agricultural headwaters towards downstream water bodies.     

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.     

Changes in bird species composition after a wind farm installation: A case study in South America

Facing the growing demand for renewable energy sources, the use of wind energy has been significantly increasing worldwide. Wind farms are known to present low environmental impact and their impact on bird fauna has been the most studied and discussed. In this study, we evaluated the composition of bird communities and changes in land use during three phases of wind farm development: pre-construction, construction and operation. Secondary data was obtained on bird communities, provided by the wildlife monitoring report submitted to the state environment agency of Rio Grande do Sul, Brazil. The total number of species recorded for all phases of wind farm construction was 163 species. One hundred species were present during all phases, and 20 were recorded during at least two phases, 32 species were exclusive to the pre-construction phase, four species were recorded only during the construction phase and seven recorded during the operation phase. The evaluation of bird community structure revealed that the pre-construction phase differed from the 4 years of operation, indicating that an impact on bird species' composition is evident, but more detailed and longer surveys are needed to confirm this trend. The bird community responded to landscape changes, mainly due to the reduction of native and exotic forest cover, in areas affected by wind farms installation and operation, in both, species' composition and environmental guilds. Analysis of secondary data allows us to evaluate which changes may have resulted from the implantation of the wind energy industry to the regional bird fauna, and our findings demonstrates tha the changes associated with the construction and operation of this wind farm have negatively affected the bird community.     

Investigating the Interdependence Between Non-Hydroelectric Renewable Energy,Agricultural Value Added,and Arable Land Use in Argentina

We examine the dynamic relationships between per capita carbon dioxide emissions, real gross domestic product (GDP), non-hydroelectric renewable energy (NHRE) consumption, agricultural value added (AVA), and agricultural land (AGRL) use for the case of Argentina over the period 1980–2013 by employing the autoregressive distributed lag bound approach to cointegration and Granger causality tests. The Fisher statistics of the Wald test are examined, and the existence of a long-run cointegration between variables is proved. There are long-run bidirectional causalities between all considered variables. The short-run Granger causality suggests bidirectional causality between AVA and agricultural land use, unidirectional causalities running from AGRL to NHRE and from NHRE to AVA. Long-run elasticity estimates suggest that increasing AGRL reduces carbon emissions; increasing AVA increases GDP and reduces pollution, AGRL, and NHRE; and increasing NHRE reduces AVA and AGRL. Thus, it seems that agriculture and renewable energy are substitute activities and compete for land use. We recommend that Argentina should continue to encourage agricultural production. The substitutability between agricultural and non-hydroelectric renewable energy productions, and their competition for agricultural land use, should be at least reduced or even stopped by encouraging research and development in second-generation (or even in third-generation) biofuel production and in new technologies for renewable energy and for agriculture more efficient in land use.

     

The effects of landscape-level disturbance on the composition of Minnesota caddisfly (Insecta: Trichoptera) trophic functional groups: evidence for ecosystem homogenization

Over 300,000 caddisfly specimens representing 249 species were collected from nearly 250 sites throughout Minnesota during 2000 and 2001 to determine the effects of human disturbance on the composition of caddisfly trophic functional groups at the landscape level. Canonical correspondence analysis determined that stream width was the most important variable influencing functional group composition in regions of the state with relatively low disturbance, and that differences in the caddisfly fauna between sizes of streams generally followed trends predicted by the river continuum concept. In regions of the state with moderate disturbance, both stream width and the percentage of disturbed habitat upstream of a site were important variables influencing functional group composition. In highly disturbed regions, no variables corresponded to changes in the composition of caddisfly functional groups. Instead, ecosystems were homogeneous: fine-particle filtering collectors dominated in all sizes of streams. The observed aquatic ecosystem homogenization is attributed mostly to input of fine-particle organic and inorganic sediment from extensive agriculture.     

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Economic development, variation in weather patterns and natural disasters focus attention on the management of water resources. This paper reviews the literature on the development of mathematical programming models for water resource management under uncertainty between 2010 and 2017. A systematic search of the academic literature identified 448 journal articles on water resource management for examination. Bibliometric analysis is employed to investigate the methods that researchers are currently using to address this problem and to identify recent trends in research in the area. The research reveals that stochastic dynamic programming and multistage stochastic programming are the methods most commonly applied. Water resource allocation, climate change, water quality and agricultural irrigation are amongst the most frequently discussed topics in the literature. A more detailed examination of the literature on each of these topics is included. The findings suggest that there is a need for mathematical programming models of large-scale water systems that deal with uncertainty and multiobjectives in an effective and computationally efficient way.     

Criteria to Assess and Select Sites for Long-Term Avian Monitoring in an Urbanizing Landscape

A methodology was developed to prioritize the suitability of sites for long-term monitoring of avian populations, including vulnerable species, both to enhance assessment of changes in ecological resources and to facilitate land-use planning at the regional scale. This paper argues that a successful monitoring program begins with a site prioritization procedure that integrates scores based on spatial controls with ecological and socio-economic indicators, particularly those dependent on community involvement. The evaluation strategy in this study combines 1) spatial controls such as land ownership and accessibility, with 2) biological and habitat indicators such as vulnerable species and habitat connectivity, and 3) community and agency variables such as volunteer commitment and agency priorities. In total, a set of ten indicators was identified. This strategy was applied to predominantly agricultural landscapes, which are experiencing increasing human pressures, in three sub-watersheds of the Credit River, Southern Ontario. Specifically, bird populations were recorded during the breeding seasons of 2000-2002 in nine land units or habitat types including marsh, deciduous forest, and grasslands as mapped by Credit Valley Conservation (CVC) following Ecological Land Classification (ELC) guidelines. CVC selected sites for long-term monitoring in 2002 and the relationships between the scored (or ranked) sites and the selected long-term monitoring sites are discussed.     

Prediction of lichen diversity in an UNESCO biosphere reserve – correlation of high resolution remote sensing data with field samples

The present study focuses on developing models to predict lichen species richness in a UNESCO Biosphere Reserve of the Swiss Pre-Alps following a gradient of land-use intensity combining remote sensing data and regression models. The predictive power of the models and the obtained r ranging from 0.5 for lichens on soil to 0.8 for lichens on trees can be regarded as satisfactory to good, respectively. The study revealed that a combination of airborne and spaceborne remote sensing data produced a variety of ecological meaningful variables.     

When speed matters: The importance of flight speed in an avian collision risk model

Renewable energy continues to grow globally, and the number of offshore wind farms is set to increase. Whilst wind energy developments provide energy security and reduced carbon budgets, they may impact bird populations through collision mortality, habitat modification and avoidance. To date, avian collision mortality has received the most attention and collision risk models have been developed to estimate the potential mortality caused by wind turbines. The utility of these models relies not only on their underlying assumptions but also on the data available to ensure the predictions are informative. Using a stochastic collision risk model (sCRM; based on the Band collision risk model) as an example, we explore the importance of bird flight speed and consider how the assumptions of the model influence the sensitivity to flight speed. Furthermore we explore the consequences of using site-specific GPS-derived flight speed rather than a standard generic value, with Lesser Black-backed Gulls Larus fuscus as an example, and consider how this generic value is currently used. We found that the model was most sensitive to the parameters of bird density, non-avoidance rate and percentage of birds at collision risk height, as well as bird flight speed. Using site-specific flight speed data derived from GPS tags rather than a standard value reduced the predicted number of collisions. We highlight that within the model, both the estimation of the probability of collision (PColl) and the flux of birds are sensitive to the bird flight speed; this sensitivity acts in opposite directions but the two do not necessarily balance out. Therefore, when the sCRM is used as generally done, there is little difference in collision estimates if airspeeds (bird flight speed relative to air through which it is moving) are used rather than groundspeeds (bird flight speed relative to ground). Estimates of seabird collision rates in relation to offshore wind farms are impacting future offshore wind development. By using site specific flight speed estimates and, accounting for different speeds in relation to wind direction, we demonstrate that cumulative collision estimates can be affected, highlighting the need for more representative flight speed data and where possible site-specific data.     

A Comparison Between Regression Models and Genetic Programming for Predictions of Chlorophyll-a Concentrations in Northern Lakes

Chlorophyll-a (chl-a) concentrations are often used as a proxy for water quality problems as well as phytoplankton blooms. Available chl-a models range from simple phosphorus loading models to complex regression and dynamic models. A comparison of multiple regression models was made with genetic programming (GP) techniques to predict chl-a concentrations over a large range of 104 Swedish lakes. Independent variables used were lake area, mean depth, iron, latitude, ammonium, nitrogen + nitrate, pH, phosphate, secchi depth, silicon, temperature, total phosphorus, total nitrogen and total organic carbon. GP is a method based on the Darwinian evolution theory. This implies that a program will be able to test different mathematical equations, iterating and improving each equation using fundamental ideas from evolution theory to increase the predictive power. A good correspondence was found between the multiple regression and the GP modelling approach. No significant improvement of the predictive power was found using GP, and it is therefore recommended that multiple regression methods should be preferred when predicting chl-a concentrations as these models tend to be less complex and the modelling approach is easier to use. Results from GP were in some cases more accurate compared to multiple regressions; however, the best model was created by multiple regressions which used concentrations of total phosphorus, total nitrogen and latitude as independent variables. These findings will be an important note for limnologists and modelling managers when developing future models of chl-a concentrations in lakes.     

Assessment of trace metals in four bird species from Korea

In birds, heavy metal concentrations are influenced by diet intake, migratory pattern, and residence time. In the present study, heavy metal concentrations (in microgram per gram dry weight) were measured in livers of four bird species from Korea. Iron concentrations were greater in Eurasian Woodcocks (Scolopax rusticola) than in Grey Herons (Ardea cinerea), Little Egrets (Egretta garzetta), and Schrenck’s Bitterns (Ixobrychus eurhythmus). Copper concentrations in Grey Herons were significantly higher than in other species. Lead concentrations were greater in Schrenck’s Bitterns and Eurasian Woodcocks than in Grey Herons and Little Egrets. Eurasian Woodcocks had higher cadmium concentrations than in other species. Zinc and manganese concentrations did not differ among species. Iron, zinc, manganese, and copper concentrations from this study were within the range of other Korean bird studies, and these concentrations were far below toxic levels. Cadmium and lead accumulation trends in each species were different, and the results might be associated with their migration pattern and residence time in Korea. Grey Herons, Little Egrets, and Schrenck’s Bitterns are usually summer visitors, and Eurasian Woodcocks are passage migrants. But herons and egrets were collected in spring, autumn, and winter, but not during breeding season. They might be residents, so they could more reflect Korean cadmium and lead contaminations than Schrenck’s Bitterns. However, Eurasian Woodcocks could more reflect habitats outside Korea because of their short staying time in Korea.     

The effects of meteorological parameters in ambient noise modelling studies in Delhi

The acoustic environment in urban areas has gained prominence in recent times due to rapid industrial and commercial development in metropolitan cities. Various attempts have been made to predict and model the trends in urban ambient noise levels using different statistical and dynamic models. The present study makes an attempt to examine the role of meteorological parameters affecting the ambient noise levels in Delhi. The results show significant improvement in overall noise scenario of Delhi since the introduction of compressed natural gas vehicles in public transport of Delhi. The noise level is significantly reduced by high vegetation cover as well as by low relative humidity over Delhi. The regression models developed for the present study clearly show the significant contribution of meteorological parameters in governing the ambient noise levels in Delhi.     

Modelling Wetland Bird Response to Water Level Changes in the Lake Ontario – St. Lawrence River Hydrosystem

Lake Ontario and St. Lawrence River (LOSL) wetland bird abundance and diversity are greatly influenced by lake and river hydrology. Our study used an interdisciplinary ecosystem approach, blending avian and plant ecology, ecohydraulic, statistical ecology and modelling to evaluate potential impacts of water level fluctuations on indicator species representative of the wetland breeding bird assemblages in the entire LOSL freshwater system. Multi-year (2000–2003) bird surveys captured bird distribution and density in wetland habitats under varying degrees of water inandation, depth and fluctuation. Analyses revealed strong associations between estimated breeding pair densities and plant communities, water depth, and degree of water level fluctuation during the breeding season for a suite of wetland bird species using marsh, wet meadow, shrub swamp and treed swamp habitats. These quantitative associations were used to develop wetland bird performance indicators for use in a LOSL water regulation review study. Several bird species also nest at or near the water surface and are thus vulnerable to nest flooding or stranding. Changes to the seasonal hydrology of Lake Ontario and St. Lawrence River that result in an increased frequency or magnitude of these nest failure events may have a significant impact on regional population sustainability. Long term nest record databases were analyzed to create nesting flooding and stranding probability equations based on water level increases and decreases during the breeding season. These species-specific nesting relationships were incorporated into a reproduction index.Many breeding bird species were strongly associated with specific wetland plant communities. Predicted habitat suitability, as measured by estimated breeding pair density, can also change significantly within a specific wetland plant community based solely on changes in water depth during the breeding season. Three indicator species, Black Tern, Least Bittern and Virginia Rail were selected as key environmental performance indicators for alternate regulation plan comparisons.Water regulation criteria should be such that the long term diversity and abundance of wetland plant communities and frequency of spring flooding in marsh habitats during breeding are not reduced. Magnitude and frequency of water level change during the nesting season (May–July) can also adversely impact reproductive success of many wetland bird species. As such, regulation criteria that increase the seasonal magnitude and frequency of water level change may be detrimental to the long term viability of certain regional breeding bird populations.     

Effects of the Conservation Reserve Program on northern bobwhite and grassland birds

The Conservation Reserve Program (CRP) has converted just over 36 million acres of cropland into potential wildlife habitat, primarily grassland. Thus, the CRP should benefit grassland songbirds, a group of species that is declining across the United States and is of conservation concern. Additionally, the CRP is an important part of multi-agency, regional efforts to restore northern bobwhite populations. However, comprehensive assessments of the wildlife benefits of CRP at regional scales are lacking. We used Breeding Bird Survey and National Resources Inventory data to assess the potential for the CRP to benefit northern bobwhite and other grassland birds with overlapping ranges and similar habitat associations. We built regression models for 15 species in seven different ecological regions. Forty-nine of 108 total models contained significant CRP effects (P < 0.05), and 48 of the 49 contained positive effects. Responses to CRP varied across ecological regions. Only eastern meadowlark was positively-related to CRP in all the ecological regions, and western meadowlark was the only species never related to CRP. CRP was a strong predictor of bird abundance compared to other land cover types. The potential for CRP habitat as a regional conservation tool to benefit declining grassland bird populations should continue to be assessed at a variety of spatial scales. We caution that bird-CRP relations varied from region to region and among species. Because the NRI provides relatively coarse resolution information on CRP, more detailed information about CRP habitats (spatial arrangement, age of the habitat (time since planting), specific conservation practices used) should be included in future assessments to fully understand where and to what extent CRP can benefit grassland birds.     

Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques

Acid mine drainage (AMD) is a global problem that may have serious human health and environmental implications. Laboratory and field tests are commonly used for predicting AMD, however, this is challenging since its formation varies from site-to-site for a number of reasons. Furthermore, these tests are often conducted at small-scale over a short period of time. Subsequently, extrapolation of these results into large-scale setting of mine sites introduce huge uncertainties for decision-makers. This study presents machine learning techniques to develop models to predict AMD quality using historical monitoring data of a mine site. The machine learning techniques explored in this study include artificial neural networks (ANN), support vector machine with polynomial (SVM-Poly) and radial base function (SVM-RBF) kernels, model tree (M5P), and K-nearest neighbors (K-NN). Input variables (physico-chemical parameters) that influence drainage dynamics are identified and used to develop models to predict copper concentrations. For these selected techniques, the predictive accuracy and uncertainty were evaluated based on different statistical measures. The results showed that SVM-Poly performed best, followed by the SVM-RBF, ANN, M5P, and KNN techniques. Overall, this study demonstrates that the machine learning techniques are promising tools for predicting AMD quality.     

Advanced energy consumption system for smart farm based on reactive energy utilization technologies

Over the decades, both agriculture and energy grids have encountered significant challenges, such as the lack of power supply to agricultural farms and the difficulties of renewable energy use in the electricity sector. To overcome and address these issues, this paper presents an idea of merging smart farm and renewable energy, using surplus renewable energy for the agriculture sector using the smart farm for renewable energy supplies. First, demonstrate the framework to connect a smart farm energy system and analyze its feasibility and advantages. In this paper, Reactive Energy Utilization Technology (REUT) has been used as an efficient energy consumption system for the smart farm. It is used to predict the optimum wind power scenario in the smart farm by monitoring continuous control variables, with the lowest voltage reached in each scenario, and minimized actual total power losses. A combined system consisting of smart farming production, as well as renewable energy consumption, has been evaluated in experimental results. Experimental results and discussion shows that the proposed method has better renewable energy consumption, accuracy(96.7%), and efficiency(95.6%) for smart farming.