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Indicator species (IS) are used to monitor environmental changes, assess the efficacy of management, and provide warning signals for impending ecological shifts. Though widely adopted in recent years by ecologists, conservation biologists, and environmental practitioners, the use of IS has been criticized for several reasons, notably the lack of justification behind the choice of any given indicator. In this review, we assess how ecologists have selected, used, and evaluated the performance of the indicator species. We reviewed all articles published in Ecological Indicators (EI) between January 2001 and December 2014, focusing on the number of indicators used (one or more); common taxa employed; terminology, application, and rationale behind selection criteria; and performance assessment methods. Over the last 14 years, 1914 scientific papers were published in EI, describing studies conducted in 53 countries on six continents; of these, 817 (43%) used biological organisms as indicators. Terms used to describe organisms in IS research included “ecological index”, “environmental index”, “indicator species”, “bioindicator”, and “biomonitor,” but these and other terms often were not clearly defined. Twenty percent of IS publications used only a single species as an indicator; the remainder used groups of species as indicators. Nearly 50% of the taxa used as indicators were animals, 70% of which were invertebrates. The most common applications behind the use of IS were to: monitor ecosystem or environmental health and integrity (42%); assess habitat restoration (18%); and assess effects of pollution and contamination (18%). Indicators were chosen most frequently based on previously cited research (40%), local abundance (5%), ecological significance and/or conservation status (13%), or a combination of two or more of these reasons (25%). Surprisingly, 17% of the reviewed papers cited no clear justification for their choice of indicator. The vast majority (99%) of publications used statistical methods to assess the performance of the selected indicators. This review not only improves our understanding of the current uses and applications of IS, but will also inform practitioners about how to better select and evaluate ecological indicators when conducting future IS research. [ABSTRACT FROM AUTHOR]

Highlights • A Super-SBM with undesirable output model is applied. • The environmental efficiency of 283 Chinese cities during 2003–2016 is analyzed. • The overall environmental efficiency performance across the Chinese cities is low. • China has achieved significant improvement in environmental efficiency across survey period. Abstract In the past decades, unprecedented dramatic economic development in the Chinese cities has also brought serious environmental problems. In line with this, the Chinese government has been investing great efforts and resources in addressing environmental problems. However, little previous studies have been conducted in examining whether this development is efficient from a win-win perspective balance between economic growth and environmental protection. This paper presents a comprehensive analysis on the environmental efficiency (EE) in the process of dramatic economic development in the Chinese cities. The Super-slack-based measure (Super-SBM) model is adopted to analyze the EE performance. Data used for analysis are collected from 283 sample cities in China. The research results indicate that the overall EE performance across the Chinese cities is relatively low, and the differences in EE performance among the 283 cities are significant. The top three EE performers are Shenzhen, Sanya and Erdos, and Baiyin, Xinzhou and Liupanshui are the bottom three performers. The results also suggest that the EE performance in the Eastern area, where most first-tier cities are located in, is significantly higher than that in the Central and Western areas (aggregated with fourth and fifth tier-cities). From the dynamic perspective, the results show that China has achieved significant improvement in environmental efficiency across the surveyed period of 2003–2016. The research findings provide valuable references not only for the Chinese policy-makers and practitioners to adopt measures for promoting a win-win balance between environmental protection and economic development in the Chinese context, but also for further studies on environmental efficiency in the international context. [ABSTRACT FROM AUTHOR]

Size spectra are important indicators of biomass distribution, for fundamental understanding as well as for application in management. While many size spectra models have been developed, it is unclear how response to stress varies across models and in comparison to data. In the present study, we therefor collected papers on empirical size spectra in stressed aquatic ecosystems and compared the results to theoretical models’ predictions from literature. A general decrease of the slope, as suggested by most of the studied models, is supported by the data found. The investigated empirical studies revealed a strong trend for the slope: 40 out of 57 studies reported a decrease with stress. Deviations from the linear model increased in 10 out of 20 cases and the intercept became larger in 25 out of 43 cases. The data concerning specific impact drivers is generally consistent and supportive of current expert opinions. Our results support that an integration of deviation from the linear model and intercept in the size spectra models could provide better coupling between modeling and field surveys for some stressors. [ABSTRACT FROM AUTHOR]

This paper presents the R package BioFTF, which is a tool for statistical biodiversity assessment in the functional data analysis framework. Diversity is a key topic in many research fields; however, in the literature, it is demonstrated that the existing indices do not capture the different aspects of this concept. Thus, a main drawback is that different indicators may lead to different orderings among communities according to their biodiversity. A possible method to evaluate biodiversity consists in using diversity profiles that are curves depending on a specific parameter. In this setting, it is possible to adopt some functional instruments proposed in the literature, such as the first and second derivatives, the curvature, the radius of curvature and the arc length. Specifically, the derivatives and the curvature (or the radius of curvature) highlight any peculiar behaviour of the profiles, whereas the arc length helps in ranking curves, given the richness. Because these instruments do not solve the issue of ranking communities with different numbers of species, we propose an important methodological contribution that introduces the surface area. Indeed, this tool is a scalar measure that reflects the information provided by the biodiversity profile and allows for ordering communities with different richness. However, this approach requires mathematical skills that the average user may not have; thus, our idea is to provide a user-friendly tool for both non-statistician and statistician practitioners to measure biodiversity in a functional context. [ABSTRACT FROM AUTHOR]

The ecological price of economic growth is a heavily debated issue, where ideologies often neglect factual information. In this paper, through the relationship of the ecological footprint and GDP, we examine the tendencies of eco-efficiency in the first decade of the 21st century. We conclude that the average ecological footprint intensity of countries have improved significantly in the given period. In 2009, 50 percent less area was needed to produce a unit of GDP. Many countries could reach the so-called strong decoupling − these countries could increase GDP while decreasing the ecological footprint in absolute terms. We also repeated the analysis of a scientific article published in 2004. We managed to update data and identify ecologically positive tendencies. In ten years, the average of the world’s ecological footprint intensity has significantly improved, it halved all in all. We found that 90 percent of the countries started to move to the direction of sustainable development. Among the studied 131 countries, 40 experienced strong decoupling (absolute decrease of resource use), in 77 countries weak decoupling occurred (relative decrease of resource use), and there were only 14 countries, where no decoupling could be observed (relative increase of resource use). [ABSTRACT FROM AUTHOR]

[Display omitted] • The vegetation cover status of 9 provinces in the Yellow River basin was analyzed. • The water poverty status of 9 provinces in the Yellow River basin is analyzed. • There is a U-shaped relationship between vegetation cover and water poverty. • There were differences in the effects of vegetation cover on WPI components. Vegetation plays an important role in improving the water resource environment. Faced with the serious water poverty problem in various regions of China, whether vegetation restoration can further alleviate water poverty needs to be studied urgently. Taking 9 provinces in the Yellow River basin in China as an example, this paper first uses the framework of the existing water poverty index (WPI) to calculate the WPI of each province over the years from 1999 to 2019 and evaluate the water poverty status of each province. Later, the normalized difference vegetation index (NDVI) was used to analyze the vegetation restoration status of each area. Finally, using provincial panel data for the study area from 1999 to 2019, a comprehensive feasible generalized least squares (FGLS) model was used to analyze the impact of vegetation cover on water poverty. It was found that there was no pure linear relationship between vegetation cover and water poverty; instead, there was a significant U-shaped relationship. Moreover, the influence of vegetation cover on each component of the WPI has certain differences. This research advances knowledge on the relationship between vegetation cover and water poverty and provides ideas for effectively alleviating regional water poverty. [ABSTRACT FROM AUTHOR]

Remote sensing with time series data offers considerable potential in the trajectory of post forest fire dynamics beyond the current monitoring of structural attributes that are displayed in the post-fire area. Many studies have addressed this topic by using time series remote sensing indices; however, this approach has sometimes been demonstrated as an unrealistic and biased representation of the post-fire forest patterns due to the saturation issues of vegetation indices. These saturation issues then lead to an underestimation of the forest successional stages and an overestimation of the forest recovery rate. This paper aims to develop a framework for trajectory of the post-fire forest patterns in the Siberian boreal larch forest ( Larix sibirica ) with the synergistic use of different remote sensing based vegetation-cover indicators derived from the Landsat time series and the WorldView-2 images. A time-series of the forest recovery index (FRI) and fractional vegetation cover (FVC) has been analyzed to estimate the rates of forest regeneration and vegetation recovery across different burn severity levels in the Siberian larch forest. The results showed that the FRI method can be used to observe the regrowth of the larch forest from the tenth year after the fire overlapping with the period of significant increase in the sapling stem volume. The post-fire larch forest canopy can fully recover to the pre-fire condition with respect to the magnitude of the FRI values after 30–47 years where the highest regeneration rate was observed in the moderate burn severity areas followed by the low and high burn severity. On the other hand, the FVC method was positively correlated with burn severity and more sensitive for evaluating the early stages of the forest succession in which the FVC dramatically increases after 5–6 years after the fire. The significant growth of FVC was accentuated by the maximum emergence of the sapling density as well as the rapid growth of herbaceous plants, grasses, shrubs, and shade-intolerant trees immediately after the fire, which could not be evaluated using the FRI. Both time series of the FRI and the FVC are valuable tools for determining the dominant stages of the post-fire larch forest succession in order to understand the relationships between fire disturbance and natural cycles of the boreal larch forest. [ABSTRACT FROM AUTHOR]

Predicting stability from current ecosystem performance is theoretically difficult, but early statistical warning signals (EWS) may enable the anticipation of regime shifts. However, little is known regarding the behavior of EWS in shifts with cyclic dynamics. In this study, we use indicators to assess the stability of a three-species system in a competitive loop similar to a rock–paper–scissors (RPS) hierarchy. In two scenarios, the RPS is simulated using a 3-D automaton whose input matrix combines probabilities of pair-wise dominance with differential reaction frequencies. The first scenario uses the data of a microbial experiment in which the RPS hierarchy is characterized by incomplete dominance within species pairs and differences in reaction frequency between species pairs. The input of the second model was chosen to generate a contrasting scenario: a symmetric RPS interaction gradually subjected to a stressor. The reaction frequency of one species pair was modeled to decay linearly over time. The relative species abundances are monitored spatiotemporally. In the first scenario, abundances oscillate stably despite initial large swings, whereas in the second scenario, one species gradually dominates, eventually resulting in transitivity. In both scenarios, species cluster spatially in patches of single species. In scenario 1, the average patch size remains constant throughout the iterations and possibly contributes to the overall stability; however, in the second scenario, a further homogenization takes place. In the first scenario, EWS reflected the system's stability with species abundances settling into a stable basin. In scenario 1 only one of the EWS indicators detected consistently the loss of resilience. Sensitivity analysis revealed excessive variability in dominance resulted in immediate loss of the RPS hierarchy. [ABSTRACT FROM AUTHOR]

The foundational concept for our research, which is largely shared by statisticians and ecologists, is that biodiversity is one of the most important indicators for environmental assessment. Because this indicator decreases in relation to ecosystem stressors, its measurement is essential for predicting future biological impacts of environmental damages. Although many indices have been proposed, no universally accepted measure for biodiversity has yet been established. In this context, the use of diversity profiles allows the analyst to display a family of indices in a single graph. However, this approach presents two critical limitations: first, a community composition is not always interpretable; second, the diversity profiles could lead to ranking issues when the curves intersect each other. The aim of this paper is to resolve these limitations by introducing functional biodiversity tools. In particular, three functional measures are proposed: the derivatives, the radius of curvature and the curve length. The analysis of derivatives and of the radius of curvature addresses the first limitation and highlights the characteristics, the differences and the similarities among communities. Arc length addresses the second limitation, providing a scalar measure that leads to a unique communities ranking for a given pattern of richness even if profiles intersect. The proposed functional models are applied to a real data set involving lichen biodiversity in the province of Genoa, Italy. Our approach allowed us to analyze the characteristics of lichen communities and to identify the biodiversity ranking. The combined use of these tools provides a useful method for identifying areas of high environmental risk, with the potential to address the monitoring of environmental policies. [ABSTRACT FROM AUTHOR]

This paper investigates the causal relationships between per capita CO 2 emissions, gross domestic product (GDP), renewable and non-renewable energy consumption, and international trade for a panel of 25 OECD countries over the period 1980–2010. Short-run Granger causality tests show the existence of bidirectional causality between: renewable energy consumption and imports, renewable and non-renewable energy consumption, non-renewable energy and trade (exports or imports); and unidirectional causality running from: exports to renewable energy, trade to CO 2 emissions, output to renewable energy. There are also long-run bidirectional causalities between all our considered variables. Our long-run fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) estimates show that the inverted U-shaped environmental Kuznets curve (EKC) hypothesis is verified for this sample of OECD countries. They also show that increasing non-renewable energy increases CO 2 emissions. Interestingly, increasing trade or renewable energy reduces CO 2 emissions. According to these results, more trade and more use of renewable energy are efficient strategies to combat global warming in these countries. [ABSTRACT FROM AUTHOR]

Increased awareness about the uncertainty of ecological water quality (EWQ) assessment tools in river management has led to the identification of the underlying uncertainty sources and the quantification of their effect on assessment. More specifically, with respect to macroinvertebrate-based EWQ assessment, use of erroneous abundance data has been identified as a (possible) source of uncertainty. In this paper, the effect of erroneous abundance data on the uncertainty of an EWQ assessment index was investigated. A model simulation based method, the virtual ecologist approach, was used to estimate the impact of abundance data errors on the uncertainty of the Multimetric Macroinvertebrate Index Flanders (MMIF). The results of this study show that the effects of relative small errors on the MMIF and assessment are limited. Additionally, it is observed that uncertainties due to abundance errors increase with decreasing EWQ (i.e. lower MMIF). This is important, since decision-makers typically formulate management actions for rivers with a low EWQ. In short, the innovative virtual ecologist approach proved to be very successful to research the index uncertainty and present a unique insight in the functioning of the assessment index. [ABSTRACT FROM AUTHOR]

Incidental catch is the major threat to the survival of loggerhead turtles ( Caretta caretta ) in the Mediterranean and the main reason for their decline. More than 100,000 turtles are estimated to be caught annually due to fishing practices; 10–50% die. Bottom trawls are among the fishing gears exerting the worst impact on sea turtle populations, especially in the coastal waters of Tunisia and Libya, northern Adriatic Sea, the Mediterranean coastal areas of Turkey and Egypt, where the continental shelf is large and turtles in the demersal stage are commonly found also in winter. Seven adult-sized loggerhead turtles captured incidentally by bottom trawls in the central-northern Adriatic Sea and treated in rescue centres were tagged with satellite transmitters before release. In this paper Italian bottom trawl track data obtained from vessel monitoring systems (VMSs) were analysed by VMSbase R suite to identify the areas of maximum fishing effort in the Adriatic. These data were combined with satellite information on turtle distribution to provide an interaction index enabling prediction of potential trawl–turtle interaction hotspots and periods. The present pilot study can be considered as a risk-analysis approach directed at identifying the areas and times of possible trawling–turtle interactions in a Mediterranean trawl fishery. By identifying the areas and seasons at highest risk of turtle bycatch, the index here developed has the potential to provide key information to design and implement mitigation strategies. Vessel monitoring system is actually in force in several countries, therefore the approach here studied might have a worldwide application. [ABSTRACT FROM AUTHOR]

The present study investigates the dynamic relationship between energy intensity and CO 2 emissions by incorporating economic growth in environment CO 2 emissions function using data of Sub Saharan African countries. For this purpose, we applied panel cointegration to examine the long run relationship between the series. We employed the VECM Granger causality to test the direction of causality amid the variables. At panel level, our results validate the existence of cointegration among the series. The long run panel results show that energy intensity has positive and statistically significant impact on CO 2 emissions. There is also positive and negative link of non-linear and linear terms of real GDP per capita with CO 2 emissions supporting the presence of environmental Kuznets curve (EKC). The causality analysis reveals the bidirectional causality between economic growth and CO 2 emissions while energy intensity Granger causes economic growth and hence CO 2 emissions, while across the individual countries, the results differ. This paper opens up new insights for policy makers to design comprehensive economic, energy and environmental policy for sustainable long run economic growth. [ABSTRACT FROM AUTHOR]

Abstract: The Human Sustainable Development Index (HSDI) has been proposed as a way to amend the United Nations’ Human Development Index (HDI) by adding an environmental dimension. Despite some attention in the media, the HSDI remained largely ignored by the scientific community. This paper aims at overcoming this issue by presenting an updated version of the index, based on recently available UN data, including a complete description of the procedure leading to its calculation and a critical assessment of its relation with some established environmental indicators. We found that, while the HSDI represents a step ahead from the HDI, it remains insufficient in its representation of environmental sustainability. A better equilibrium between social, economic and environmental goals is needed to reach a true index of sustainable development. [Copyright &y& Elsevier]

Abstract: The first section of the paper discusses the background to the use of habitats as indicators for biodiversity including a discussion of the range of definitions that have been used. Habitats can now be recorded consistently across Europe at different time intervals in order to estimate stock and change as an indicator of biodiversity that is efficient and relatively easy to record. Habitats are considered to be the third level in a hierarchy with biomes and landscapes as higher categories and vegetation, species and genetic diversity as lower levels. An advantage of using habitats is that many other taxa are associated with them and examples are given from the literature. Examples are also given of the association between habitats and species in European Environmental Zones using expert judgement. Statistical analysis using a range of procedures can also be used to assess the association between species and habitats. Reliable data on the extent, status and changes in European habitats is essential for policy makers across the European Union and would also be important for promoting species conservation. [Copyright &y& Elsevier]

Abstract: Under the European Union Thematic Strategy for Soil Protection, the European Commission Directorate-General for the Environment (DG Environment) and the European Environmental Agency (EEA) have identified decline in soil organic matter and soil losses by erosion as priorities for the collection of policy-relevant soil data at European scale. To support EU policies related to soil conditions the data collected on corresponding indicators requires establishing agreed datasets for the EU Member States on these themes. In 2010, the Joint Research Centre of the European Commission (JRC), which is charged with the collection of soil data at European scale and hosts the European Soil Data Centre (ESDAC), conducted a project to collect data on soil organic carbon and soil erosion in Europe using the European Environment Information and Observation Network for soil (EIONET-SOIL). The data submitted by participating countries are their best estimate and represent an official point of view. The technical approach taken allows a country to easily update the records when new data become available. This paper presents the first results obtained from analyzing the soil organic carbon data submitted to EIONET-SOIL. The collected data were compared with the modelled data of the European topsoil organic carbon content developed at the JRC. The modelled data follow the general pattern of the geographic distribution of collected data, but show higher values compared to the EIONET-SOIL data. The important role of soil organic carbon (SOC) as an indicator of soil quality underlines the need for using common methods of sampling, analysing and reporting soil organic carbon to provide a standard product, such as EIONET-SOIL. [Copyright &y& Elsevier]

Abstract: Water quality of groundwater, rivers, and seas is still negatively influenced by high diffuse nitrogen and phosphorus discharge. Increasing importance is attached to cost-effectiveness to indicate sufficient measures to reduce nutrient input of land use. The aim of this paper is to find cost-effective solutions for diffuse nutrient load reductions for the German Baltic Sea region (by formulating scenarios for a 25% and 50% reduction of nitrogen and phosphorus). I combined nutrient emission and land use modelling (literature analyses, MONERIS-data) with economic land use analysis (e.g. opportunity costs, costs of the production methods) in a cost-effectiveness analysis. The results show that total reduction costs need not be as expensive as often anticipated (e.g. 25% simultaneous reduction of nitrogen and phosphorus costs about 8–20€(hay)−1). Advisory service turned out as the most cost-effective measure and the most important indicator for the cost-saving potential. The measure included guidelines to the farmers on land use and water quality optimization. For example, if a 100% advisory service is successful, total costs for a 25% simultaneous reduction of nitrogen and phosphorus could drop by 65%, corresponding to 23million €y−1 compared to a 50% advisory service. Due to a lack of data, I could not consider the additional nutrient retention capacity of wetlands and buffer strips in the analysis. The policy recommendations derived from this study deliver insights for the strategic orientation of water protection policy and useful indicators to measure the performance. The results are of interest not only in the Baltic Sea context, but also for supporting and facilitating the application of the cost-effectiveness analysis postulated in the Water Framework Directive. Future research should concentrate on improving data availability (e.g. livestock husbandry) and models and on implementing further transaction costs and additional indicators like nutrient retention of wetlands. [Copyright &y& Elsevier]

Abstract: To assess numerical information on the aquatic vegetation of running water bodies, the ‘Kohler method’, a five-level descriptor scale estimating the relative abundance of species in river sections as survey units has been frequently used. Although this scale is ordinal without any information on differences between the states, data are often evaluated by arithmetic operations as if they were on the ratio scale. Metrizing ordinal information may facilitate correct data evaluation; however, the outcome highly depends on mathematical procedures applied to the data set. This paper demonstrates that if ordinal data are analyzed by ordination methods suited to this scale type, then results primarily reflect presence/absences. To the contrary, additional information was added to Kohler''s scale when the applied analyses used values on ratio scales; therefore they provided inappropriate data evaluation. Four different conversion methods (the use of 1–5 values, the third power, the mean values of Braun-Blanquet cover classes and the replacement of ordinal states by ranks) were applied to reveal how the conversion procedures determine the intervals between the substituting metric values and the level of importance given to species frequency and abundance. When ordinal scale was substituted by numbers 1–5 and ranks, frequency of species was taken greatly into account. After cubic conversion the large values became considerably overemphasized, therefore species occurring in a few (or very few) survey units with high abundance values were considered more dominant than less abundant but frequently present ones. The mean values of Braun-Blanquet''s cover classes gave large ordinal scores and the frequent species to have substantive representation. The results demonstrate that conscious choice of intervals between the scores is inevitable; otherwise one generates unknown or misleading differences in species abundance. Although every researcher assessing aquatic macrophytes by Kohler''s method surely has an idea on the differences between the applied scores, the use of identical, widely accepted scale substitution is prerequisite for reliable comparison. [Copyright &y& Elsevier]

Abstract: The European Union (EU) Water Framework Directive depends, for effective implementation, on Member States (MSs) agreeing to a concept of the unimpacted “reference” state, which will then provide the “expected” value in Ecological Quality Ratio (EQR) calculations. Reference assemblages of organism groups will, in turn, vary, due to geological, hydrological, climatic, physicochemical and biological factors. Member States tackle this by establishing “types” which share common characteristics. However, for the purposes of ensuring consistent application, broad transboundary types were also established within five Geographical Intercalibration Groups (GIGs, referred to here as “regions”) as part of the EU''s intercalibration exercise. In this paper, we evaluate these types using river diatom assemblages and also provide reference threshold values for two common metrics used in pan-European diatom assessments. A database was assembled, representing 14 EU Member States from Ireland and Portugal in the West, to Estonia and Cyprus in the East, in order to explore biogeographical patterns in assemblages unaffected by anthropogenic pressures. Multivariate analyses were used to examine this pattern and its relationship with geographic, typological and abiotic parameters. After taxonomic harmonisation, NMDS ordination of samples indicated weak differences in assemblage composition among regions. ANOSIM analyses, in turn, indicated that MS was the best factor to group similar samples whereas alkalinity, recognised as the primary environmental variable structuring diatom communities, although significant, explained less variability in the dataset. This, we believe, reflects the importance of methodological factors other than taxonomy (e.g. counting protocol, sample season) that may be constant within a MS but which vary between MSs. When two diatom metrics, the TI and IPS, were applied to the data, differences in the distribution of the metric scores between MS were generally not statistically significant even though some differences between regions were apparent. A trend of increasing values of TI (decreasing values of IPS) was observed in the sequence: Nordic

Abstract: Regionally mandated budgets often ignore important sub-regional differences. To help identify hot-spots, where environmental pressures and agricultural activities combine and heighten the need to optimise farming strategies, we recommend using detailed spatial target analysis. In this paper, we propose a methodology for identifying different agro-environments, test that method in a case-study territory in the western Po River plain (the largest and most intensive agricultural area in Italy), and then calculate the nutrient budget indicators of these defined agro-environments as a means to assess environmental sustainability. We identified five Macro Land Units (MLUs) representing five different agro-environments from official datasets and territorial surveys, detected and quantified land use, crop productivity, and fertilisation management in these MLUs, and calculated nutrient budgets according to the IRENA European methodology. As expected, the highest nutrient surpluses (103, 39, and 95kgha−1 for N, P, and K, respectively) were detected in the most intensely managed area. N surpluses were attributed to excess mineral inputs and P surpluses to excess organic inputs. At the territorial scale, the manure N load was far below the 170kgha−1 threshold; at the crop scale, maize showed the least-optimised fertilisation management. This work suggests that GIS-based analysis of environmental pressures of agricultural activities at a sub-regional level is useful for identifying areas and crops for which fertilization must be well managed. The proposed methodology depends on accurate collection and collation of farm data into GIS databases; public authorities should promote investment in planning and managing data collection in agriculture. [ABSTRACT FROM AUTHOR]

Abstract: The quasi-experimental approach of before–after control–impact (BACI) sampling can help decide when changes are due to human activities rather than natural variability. Detailed arguments for and against BACI designs and analytic methods are widespread in the literature, but far less attention has been paid to the mechanics of analyzing a BACI experiment. This paper demonstrates randomized intervention analysis with user-friendly software, where observations are paired in time before and after intervention. We provide examples using dragonfly count data in vegetation removal experiments. [ABSTRACT FROM AUTHOR]

Abstract: Coastal environments contain some of the marine world''s most important ecosystems and represent significant resources for human industry and recreation. Water quality in the coastal environment is extremely important for a number of reasons from the protection of marine organisms and the well being of marine ecosystems to the health of people in the region and the safety of industries such as aquaculture. As a result it is essential that environmental health in coastal environments is monitored. Traditional monitoring methods include assessment of biological indices or direct measurements of water quality, which are based on in situ data collection and hence are often spatially or temporally limited. Remote sensing imagery is increasingly used as a rich source of spatial information, providing more detailed coverage then other methods. But the complexity of information in the imagery requires new analysis techniques that allow us to identify the components and possible causes of spatial and temporal variability. This paper presents a review of methods to analyse spatial and temporal variations in remote sensing data of coastal water quality and discusses and compares these methods and the outcomes they achieve. Selected techniques are illustrated by using a sample dataset of MODIS chlorophyll-a imagery. We consider classification methods (cluster analysis, discriminant analysis) that may be used in exploratory, confirmatory and predictive ways, methods that summarize and identify patterns within complex datasets (factor analysis, principal components analysis, self-organizing maps), and techniques that explicitly analyse spatial relationships (the semivariogram and geographically weighted regression). Each technique has a different purpose and addresses different questions. This review identifies how these methods can be utilized to address water quality variability in order to foster a wider application of such techniques for coastal water quality assessment and monitoring. [Copyright &y& Elsevier]

Abstract: The main objective of this work is to measure water quality using a stochastic index built with tools of Probability Theory. Its great advantage is that it accounts for the underlying uncertainty in quality classification that results from variations in the data for individual physical and chemical characteristics, considering them as random variables. We compare the results obtained by measuring water quality with this index (the probabilistic index, PI) and a classical deterministic index (the general quality index, GQI). Also we do a comparison with other usual indices in order to validate the PI index. To demonstrate the application of PI and GQI indices, we used information from the Confederación Hidrográfica del Júcar (Spain) for the period between 1990 and 2005. Our analyses showed that GQI was positively correlated with PI throughout the study period. Because of its high potential, the PI index can be used to analyze possible temporal changes in water quality, as well as to determine the individual influence of each variable in water quality. The results suggested an improvement in water quality over time and that omitting individually, one at a time, five of the nine water quality variables from the PI calculation significantly improved the calculated water quality. Novelty of this paper is the design of an index that takes into account the uncertainty inherent to data, and its application for measuring water quality from a general perspective. [Copyright &y& Elsevier]

Abstract: A large number of indicators have been developed, in order to assess agricultural sustainability. However, there is no unified theoretical basis for the creation of a scientifically substantiated system of indicators and especially for data collection, analysis, scale and final goal. This paper proposes a methodological approach to assess and to compare the sustainability level of agricultural plant production systems on regional scale combining the three pillars of sustainability environment, economy and society. The combination of 21 individual indicators expressed a unique indicator was realized using the Multiattribute Value Theory (MAVT). The proposed methodology was testified on two geographical regions in Greece, through an empirical study, utilizing questionnaires completed during interviews with farm managers. The questionnaire was designed to gather data on current agricultural practices applied in each particular region and was separated into three broad groups of questions concerning (a) crop management practices, (b) economic performance and (c) social characteristics of each farm. The results of our study demonstrate the overall status of the studied regions, regarding their level of agricultural sustainability. Finally, findings related to the acceptability of the proposed methodological framework were discussed. [Copyright &y& Elsevier]

[Display omitted] • The relation between urbanization and water environment in Hanjiang Basin was studied. • Coupling coordination degree model between UAWE of Hanjiang Basin was established. • The positive interaction effect of UAWE is mainly focused on the middle and lower reaches of the Hanjiang River. • Put forward feasible measures to realize the sustainable development of UAWE. The urbanization process of the Hanjiang Basin is speeding up and facing many challenges. It is necessary to take active actions to realize the balance between urbanization development and water environment protection. This paper aims to establish a comprehensive evaluation model of urbanization and water environment (UAWE) and explore the development of UAWE in the Hanjiang Basin in China from 2010 to 2018. In terms of methodology, these evaluation models are combined with the Coupling coordination degree model (CCDM), Exploratory Spatial Data Analysis (ESDA), and Geographically and Temporally Weighted Regression (GTWR) to measure and analyze the coupling degree (CD) and spatiotemporal heterogeneity of UAWE. Our findings include the following: (1) From 2010 to 2018, in all regions of the Hanjiang Basin, urbanization showed a gradual upward trend. Among Them, the comprehensive urbanization index (CUI) of Xiangyang increased by 25.37%. (2) The comprehensive water environment index (CWEI) presents a fluctuating growth, with the rate of increase in Jingmen's reaching 17.44%. (3) There is heterogeneity in time and space between cities in the Hanjiang Basin, and the positive interaction effect of UAWE is mainly focused on the middle and lower reaches of the Hanjiang River. (4) Coupling coordination degree (CCD) in the basin shows an increasing trend, and the areas with high CCD are mainly concentrated in Xiangyang and Xiantao. (5) ESDA shows that the CCD among cities in the basin has spatial correlation and heterogeneity, and the regions with similar CCD tend to be distributed centrally in space. [ABSTRACT FROM AUTHOR]

• We focus on how beta diversity partitioning can support inference about communities. • We suggest a new framework for partitioning beta diversity (the SET framework). • We examine the performance of the previous (BAS, POD) and new (SET) frameworks. • The replacement component of the BAS framework cannot be recommended. • For correct inference, we suggest the application of the POD and SET frameworks. Extracting meaningful information from community data is among the most challenging tasks of community ecology. Whereas presence-absence data matrices are commonly used in different analytical frameworks, we argue that the conceptual distinction among community patterns (on which inference is made), pairwise pattern components (PPC s, which reflect the unique response of communities for pairs of sites), and measures (which quantify a relevant property of PPC s), liberates our field from the possible misinterpretation of results derived from existing approaches. The novel concept suggested here is the PPC , which can be efficiently used to identify response types of communities, thereby shortening the list of potential explanatory processes. Consequently, the introduction of PPC s supports the analysis of real data sets and links patterns to ecological hypotheses. Based on PPC s, we proposed a new partitioning of beta diversity (SET framework) into intersection (I) of nestedness and beta diversity and the relative complement (RC) of nestedness in beta diversity. We performed an algebraic assessment of three existing partitioning frameworks of beta diversity: the BAS (Baselga, 2010, Global Ecology and Biogeography), POD (Podani and Schmera, 2011 Oikos) and SET (present paper). We found that when a community pattern is anti-nested, which is characterized by the presence of both Replacement and Richness difference PPC s, the BAS framework falsely indicates a 100% share of replacement from beta diversity. In contrast, the POD and SET partitioning procedures detect the presence and the proper size of PPC s for all types of community patterns. In conclusion, we argue that breaking down community patterns into PPC s and then quantifying the importance of these PPC s form a straightforward strategy to extract information from community data under a broad range of circumstances. [ABSTRACT FROM AUTHOR]