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Until recently, the sustainable development indicators published by Eurostat were divided into 10 thematic areas. Currently apart of this division, the new indicators system divided into 17 objectives of the Sustainable Development Strategy according The 2030 Agenda for Sustainable Development has been published. The analyses presented in this paper are concentrated on the previous system of division of sustainable development indicators. The indicators in this database are divided into 3 levels reflecting their hierarchy: headline, operational and explanatory level of indicators. The purpose of the paper is a comparative analysis of relationship between the results achieved by individual EU Member States based on hierarchical structure of indicators published by Eurostat. Such a way of analysing sustainable development indicators has not been yet practiced in the literature in this field. The value added of the paper to the current state of knowledge in this field is the opportunity not only to determine, for example, the position occupied by individual Member States within individual areas of sustainable development monitored by Eurostat, but also to analyse interrelationships occurring within particular levels of monitoring of the Sustainable Development Strategy. According to the results of the research, differences in the ranking of EU Member States on the consecutive levels of monitoring the sustainable development strategy for different areas can be observed. In line with the hierarchical method of presenting the sustainable development indicators adopted by Eurostat, state rankings were prepared for each selected indicators aggregation level. For this purpose, a taxonomic measure of development based on Weber median vector was used. The results obtained clearly present the relatively low level of correlation between the results obtained by individual EU Member States on the subsequent levels of monitoring the implementation of EU strategy. This way of hierarchical analysis can be also used in the analysis based on indicators elaborated for The 2030 Agenda for Sustainable Development. [ABSTRACT FROM AUTHOR]

• The study area is typical, and has instructive and significant. • Compared with RSEI, the useful data of IRSEI for analysis increased by 3.26%. • We use GDP sub-industry spatialization model to represent the urbanization process. • Our study is important to know how urbanization affects the environment. As the fourth pole of China's economic growth, the Chengdu-Chongqing urban agglomeration plays a significant role in reinforcing the ecological barrier in the upper reaches of the Yangtze River, and is crucial for ecological and environmental protection strategies. In this paper, Google Earth Engine (GEE) and the MODIS images from 2000, 2005, 2010, 2015, and 2022 were utilized to construct the Improved Remote Sensing Ecological Index (IRSEI) to characterize the ecological quality more accurately than RSEI. Additionally, combined with nighttime light remote sensing data, land use data and socio-economic data, the GDP sub-industry spatialization model was utilized to analyze the urbanization process in depth. To dynamically monitor and evaluate the interaction between urbanization and ecological environment quality, a coupling coordination model incorporating the above methods was developed. The results show that (1) The effective information of the IRSEI in ecological quality analysis increased by 3.26% compared to RSEI, and the correlation with each ecological index was higher; (2) The ecological environment quality of the Chengdu-Chongqing urban agglomeration peaked in 2005 and has been declining since then, with the rate of decline gradually slowing down from 2005 to 2022; (3) The GDP sub-industry spatialization model is more suitable for characterizing the scattered villages, and can effectively exhibit the urbanization process. From 2000 to 2022, urbanization was rapidly developed, and the level of core cities such as Chengdu and Chongqing far exceeded those of neighboring cities; (4) The coupling coordination level between IRSEI and GDP spatialization model generally increased, indicating ongoing improvements in the synergy between ecological environment and urbanization in the Chengdu-Chongqing urban agglomeration. This study developed a method for quickly monitoring and assessing the relationship between ecological environment quality and urbanization by using IRSEI and GDP sub-industry spatialization model, and provides scientific analytical methods and data support for monitoring and governing the ecological environment in emerging urban agglomerations. [ABSTRACT FROM AUTHOR]

• SDG15 indicators of forest extent are complemented with those of forest quality. • Indicators include naturalness, forest structure, fragmentation, and connectivity. • High quality forests were lost faster than forest extent during 2002–2021. • Indicators of forest quality improve SDG15 reporting. The UN 2030 Agenda for Sustainable Development Goal 15, termed Life on Land, is monitored by indicators and sub-indicators that largely deal with forest extent. In countries with structurally complex and species-rich forests, indicators and sub-indicators of forest quality are also needed to effectively monitor and sustain ecological integrity. The goal of the paper is to demonstrate the use of complementary sub-indicators of forest quality for SDG15 reporting and conservation planning. Our objective is to apply these sub-indicators within Colombia, Ecuador, and Peru and evaluate spatial patterns and trends over time as a basis for revealing how the results complement the official indicators of forest extent and forest extent in protected areas in informing conservation. The sub-indicators of forest quality quantify naturalness, riparian forest, forest structure and integrity, forest fragmentation, and forest connectivity. We quantified change during 2000–2021 in these metrics and highlighted insights gained from the complementary sub-indicators of forest quality relative to the official sub-indicators based on forest extent, Forests covered about 60–70% of the forested ecoregions in each country in 2000 and this proportion declined in all three countries by approximately 4% by 2021. Only a subset of the forested area was of high forest quality. Natural forests represented about 40% of forests in Colombian and Ecuador in 2000 and 50% in Peru. Those proportions declined: by 6.3% in Colombia, 6.5% in Ecuador, and 3.4% in Peru. Even less of the forested area was Core Forest in 2013; less than 28% among countries. During 2013–2021, the proportion of forest that was Core decreased by 2.3% in Colombia, 4.5% in Ecuador, and 6.7% in Peru. Connected Forests were about 17–22% of forests among the countries in 2013 and declined 10.4% in Colombia, 1.6% in Ecuador, and 3.8% in Peru by 2021. Forests high in forest structure were 10–18% of forests in 2012 among the countries and increased by 1.1–2% by 2021. Forests of high integrity were 7–13% of forests in 2012 and increased by1.4–2% by 2021. Riparian forests represented less than about 7–9% among the countries and declined by 0.6–1.3% by 2021. Thus, the area of highly quality forest across the countries was substantially less than full forest extent and high-quality forest declined at a higher rate than forest extent during 2000–2021. Forest structure and integrity did increase slightly over this time period. Our results for trends in forest naturalness, riparian association, within stand structure, fragmentation, and connectivity demonstrate how consideration of forest quality provides a much stronger basis for evaluating success in meeting SDG15 targets than consideration of forest extent alone. [ABSTRACT FROM AUTHOR]

• An ecological risk assessment model of Jiaozhou bay in China is established. • The model is developed based on multi-source satellite and in-situ sample datasets. • The effects of multiple risk sources threatening the ecology are considered. • The risk sources for multiple years are detected using remote sensing images. • The ecological risk situation of Jiaozhou Bay for multiple years is assessed. Healthy coastal area have high ecological service value. However, because of the uncontrolled utilization of marine resources and the discharge of pollutants, the ecological environments of many coastal areas are faced with ecological risks, such as oil spills and Enteromorpha blooms. Therefore, it is of great significance to monitor and assess the ecological risks in coastal areas. The remote sensing technique can be used to obtain frequent and wide-coverage images of the areas of interest, and has become an effective tool to monitor the ecological risks occurring in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative ecological risk assessment framework was established using multi-source remote sensing images and in-situ sample data, which fully considers the potential harm of oil spills and Enteromorpha blooms on the ecology of the bay. Specifically, the oil spill risk for Jiaozhou Bay from 2017 to 2019 was identified by a deep learning based method from synthetic aperture radar (SAR) images, and the Enteromorpha bloom risk for the corresponding years was identified by a floating algae index (FAI) threshold method from multi-source optical images. Then, using habitat information and ground observation data about the marine species in the bay, environmental vulnerability maps were generated. Finally, a disaster-causing factors and disaster-bearing factors weighted risk assessment model was developed. The risk source monitoring results show that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, while the outbreak intensity of Enteromorpha blooms generally became higher. The ecological risk assessment results reveal that the high risk areas of Jiaozhou Bay are mainly around the transportation regions and the ports, with a higher risk degree in the eastern and southern parts. To support our work, the well-trained oil spill detection network model and some information about the oil spill events that have occurred in Jiaozhou Bay are provided in this paper. [ABSTRACT FROM AUTHOR]

Sustainability indicators (SIs) are not just traditional performance metrics but are value laden pathways to supporting urban development. This paper presents a functional classification for SIs. The following six classes are used to illustrate the various functions of SIs: (F1) Political and Operational ; (F2) Problem Recognition and Awareness ; (F3) Justificatory ; (F4) Monitoring Control and Reporting ; (F5) Normative Guidance ; (F6) Communication and Opinion Forming . The Houston Sustainability Indicators (HSI) program was used as a heuristic case study of how the functional classification could be applied. F1 was illustrated by carefully choosing geographic boundaries for the study. F2 was highlighted by careful review of the socio-economics of persons in the Food Desert. F3 was demonstrated by a look at issues of calculating population growth totals and also setting standards for access to parks. F4 was illustrated by a look at Employment figures. F5 was highlighted by a look at affordability in Houston. Lastly F6 was explained by a look at income inequality. This paper is intended to strengthen the importance of sustainability in development planning, through the illustration of key functions for SIs. [ABSTRACT FROM AUTHOR]

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]

In 2003, the European Union (EU) launched the Action Plan for Forest Law Enforcement, Governance and Trade (FLEGT). The Action Plan aims to combat illegalities in the forest sector, using trade as the leverage to involve producers, traders and consumers in its implementation. A key instrument emerging from the Action Plan is Voluntary Partnership Agreements (VPAs). These are bilateral trade agreements between the EU and wood exporting countries, to ensure that all wood exports were produced and traded in accordance with the laws of the originating country. Six such agreements were negotiated so far and with their implementation nearing, it is important to develop methodology for monitoring key impacts of a VPA. The VPAs and the Action Plan specifically outline the requirement to monitor their impacts on the livelihoods of forest dependent communities. For this an effective VPA impact monitoring system has to be built that fits with the context and capacity of the country. This paper aims to contribute to these developments by proposing an indicator framework for a country-level VPA impact monitoring. The framework comprises of 46 indicators designed to capture changes across ten key impact areas of VPAs. The paper also discusses requirements and possible challenges of VPA impact monitoring as well as its implications to good forest governance in the VPA countries and offers principles for cost-effective VPA impact monitoring. [ABSTRACT FROM AUTHOR]

• Entropy and quantile-based measures were used to evaluate the interpolation models. • New statistics provide a valuable piece of information on the pattern of the errors under investigation. • New tools can be used profitably to effectively assess the forecast quality. • Aggregating information across multiple summary statistics is recommended. Spatial interpolation has been applied for mapping various variables in a wide range of environmental disciplines. This study aims to develop novel tools for examining the relative performance of different interpolation methods. We shall quantify and compare the quality of interpolation models by applying, among others, some inequality indices of error distributions. Such indices can generally be classified as non-dimensional and global. The performance measures explored here provide a valuable supplement to the conventional accuracy assessment, and have so far received only scant attention in the relevant literature. Given a wide range of potential applications for the methods discussed here, the main focus of the paper will be on empirical research concerning variability of soil properties. The eight interpolation methods, i.e., Inverse Distance Weighting (IDW), Modified Shepard's Method (MS), Radial Basis Function (RBF), Natural Neighbour (NaN), Nearest Neighbour (NeN), Triangulation with Linear Interpolation (TIN), Local Polynomial (LP) and Ordinary Kriging (OK) were applied to estimate spatial distribution of soil pH, nitrogen, potassium and phosphorus content. Biplot methods were applied to visually examine the numerical results on the assessment of prediction quality. The ordinary kriging showed superior performance compared to the competing methods in majority of the cases. Significantly, predictions by kriging approaches revealed substantial improvement by considering data transformations. As concerns the other tested methods, the IDW and the LP algorithms tend to share similar characteristics. In turn, the NeN, RBF and MS algorithms scored relatively small inequality indices, when compared to the other methods. The use of new proposed measures will enable practitioners to gain more insightful and comprehensive evaluations of spatial interpolation techniques. [ABSTRACT FROM AUTHOR]

• Ecological resiliency framework; multi-factors superposition; dynamic analysis. As a result of years of monitoring the ecological resiliency of natural areas and cities, it has become clear that it is both important and often feasible to implement ecological and environmental restoration in conjunction with ongoing processes of landscape change development and urbanization. Ecological resiliency and spatiotemporal evolution studies can objectively reveal the resiliency of ecosystems to external disturbances. Ecological monitoring and assessment can also help planners understand regional ecological spatial differentiation patterns and provided data support for planning. In this paper we have analyzes quantitatively the interrelationships of ecological factors in Dianchi Lake Basin (DLB) over the past 30 years and explored the spatial and temporal dynamics of ecological resiliency. Based on remote sensing images and primary data in 1995, 2005, 2010, 2015, 2018, and 2022, we used the center of gravity migration and kernel density analysis to explore the spatial and temporal changes of ecological resiliency. We built the overall resiliency evaluation system using entropy weight in the TOPSIS model, and finally simulate the future changes based on CA-Markov (CA-MC) model. The results show that from 1995 to 2022, the ecological resiliency of land use and vegetation cover in DLB decreased substantially. An important finding was that the ecological resiliency of riparian buffer zone and landscape pattern were generally increasing. The distribution of barycenter movement and kernel density of different levels of ecological resiliency differed significantly and showed fluctuating changes. The extreme low resiliency and extremely resilient areas shift to the northeast, the mildly resilient areas shift to the northwest, and the highly resilient areas shift to the southeast. The overall resiliency level of DLB is predicted to slowly increase from 2022 to 2030 by deduction of the CA-MC model. Our analysis suggests that the study of the evolution of regional ecological resiliency can provide a timely understanding of regional ecological evolution patterns and propose ecological protection strategies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Machine learning solution provided enhancement to expert system lake assessment. • Heatmap representation of variables aids intuitive data comprehension. • Additional measure was introduced to support expert assessment method. • Prioritization of ecological actions aided with early outlier detection. The results of ecological condition assessments of ecosystems are related to key decisions taken for the purpose of remedial measures or maintaining their current state. In the assessment process, experts come across extensive datasets, the quality, and completeness of which do not always allow for a reliable evaluation, especially if a single empirical approach is used. In this paper, results of machine learning algorithms are presented, with a focus on Self-Organizing Maps. In this context, measurements of component parameters for the assessment of the ecological state of lake ecosystems were subjected to the process of unsupervised machine learning with the aim to create an alternative assessment approach based on the capabilities of neural networks. Results are mapped and compared with expert evaluations, allowing to extend knowledge about sub-clusters present in the data. The primary target of this paper is the ecological assessment expert. At an early stage, information was obtained about the presence of ecological outliers that may be subject to separate monitoring or verification of environmental activities and objectives. In the back-mapping process, the presented technique of map construction and clustering with various versions of the division was referenced to a set of expert classification findings, revealing the underlying structure of the results when addressed with an unsupervised data comprehension. The approach introduced here does not intend to interfere with the format of an original assessment methodology. Rather it aims at obtaining useful additional information which may help in making better decisions. [ABSTRACT FROM AUTHOR]

Abstract: Studies on fate and toxicity are important aspects in environmental impact assessments of chemicals. In order to elucidate “what is worst” a ranking of the chemicals under investigation is of significant interest, which obviously constitutes a multi-indicator system (MIS). Partial order methodologies turn out to be advantageous tools for analyses of such systems. In the present paper we focus on QSAR generated fate and toxicity indicators of 30 chemicals, comprising of a variety of chemical structures. In many risk assessment studies the level of a multi-indicator system is abandoned and the data are aggregated in order to obtain a composite index. However, such aggregation techniques are far from being trivial. The present paper describes an alternative strategy to handle MIS, i.e., applying partial order methodology leading to useful information. The paper demonstrate how partial order techniques lead to a weak order of the 30 chemicals as well as to the relative importance of the single indicators included in the study. Specifically, the paper addresses the topics on the “relations between the indicators” and “do chemical structures constitute a valid basis for ranking chemicals according to their environmental impact?”. [Copyright &y& Elsevier]

Abstract: The main objective of this paper is to present an assessment approach for ecosystem services in an urban context covering the local and the regional scale. It was applied to different European cities. A set of indicators representing important urban ecosystem goods and services – local climate regulation, air cooling and recreation – was tested using spatial data along an urban–rural gradient. The results show that there is neither a typical rural–urban gradient in terms of urban ecosystem service provisioning nor a uniform urban spatial pattern of service provisioning that can serve as a generic model for cities. The results demonstrate that (1) core cities do not necessarily provide fewer ecosystem services compared to their regions and (2) there were no patches found within the four case study cities where all of the indicators report very high performance values. The analysis further shows that a high degree of imperviousness does not necessarily entail low ecosystem service provisioning if an urban structure contains a considerable amount of mature trees which support carbon storage and biodiversity. The results of the present paper provide insights into potentials and trade-offs between different urban ecosystem services that should be considered during urban planning when setting targets and establishing thresholds to protect environmental resources, ecosystem services and biodiversity for residents. [Copyright &y& Elsevier]

Abstract: The commercial cultivation of genetically modified (GM) crops in the European Union (EU) necessitates, according to EU legislation, the setting up of a General Surveillance (GS) system that should be able to detect unanticipated effects of GM crops on the environment. Although the applicant is responsible for setting up GS as well as for reporting the results, EU Member States may implement additional supporting surveillance programmes. Devising a GS system to detect unanticipated effects is not straightforward and requires clearly defined protection goals, suitable indicators that are linked to measurable parameters and an objective system for assessing the data. This paper describes a number of recommendations for the development of a General Surveillance system of the soil ecosystem specifically focussed on the situation in the Netherlands. The overarching protection goal of General Surveillance is ‘soil quality’, which is translated into more practical terms of ecosystem services that are relevant for soil quality, and that can be used to select measurable parameters and thus make a link with actual measurements. Ultimately, if and when effects on ecosystem services are detected, decision makers will have to decide whether these effects are acceptable or not. As a support for these decision-making processes, this paper discusses the modalities for the development of a stakeholder participation model. The model involves three groups of persons: the land users, the soil scientists and the decision makers. For reasons of cost effectiveness, a GS system of the soil ecosystem will have to make use of existing networks. The Dutch Soil Quality Network (DSQN) offers an existing infrastructure for soil sampling for GS. Finally, the GS system may be extended to contain data from the Dutch Ecological Monitoring Network, earth observation systems as well as other data resources such as farmers questionnaires or reports form organisations involved in nature conservation. Ideally these data are compiled by a Central Reporting Office (CRO) and maintained in a Geographic Information System (GIS) based database. [Copyright &y& Elsevier]

Abstract: In the context of new tools suitable for making local authorities more pro-active in terms of effectiveness in environmental management, this paper analyzes the case of the application of EMAS (Eco-Management and Audit Scheme) to local authorities in Italy. EMAS is a European tool that was revised in 2009 with Regulation (EC) N. 1221/2009, named EMAS III. This paper has two goals. The first goal is to analyze how the local authorities, with an environmental management system registered under EMAS, evaluate the environmental aspects related to their activities. The second goal is to propose a set of specific indicators to be monitored, which would fulfill the EMAS III compulsory core-set of indicators. To do this all the EMAS registered public authorities in Italy have been contacted to obtain a copy of their most recent validated environmental statements. Then, for each environmental aspect considered, a matrix has been processed, reporting on related activities, environmental significance, indicators, and environmental targets to improve the performance of the EMS. The analysed environmental statements (75% response rate) deal with a high number of environmental aspects and refer to a wide range of activities carried out by the local authorities. The set of indicators demonstrates that registered local authorities have already enough information for the purpose of complying with the new EMAS III core indicators. On the contrary, EMAS III does not properly cover the several environmental issues and impacts faced by local authorities, so that a different, wider and more specific framework is needed for their environmental performance evaluation. As a result of this study, a set of 13 indicators has been proposed, based on those already in use by local authorities and on those used by the European Environment Agency for environmental assessments. Therefore, this paper can represent a first step towards the definition of sectorial reference guidelines that will be developed at the European level by the European Commission. [Copyright &y& Elsevier]

Graphical abstract Highlights • This study introduced a modified total storage deficit index to monitor drought in basins with high water consumption. • This study assessed the potential of GRACE observation for developing a drought monitoring system. • Two drought events were found in 2008–09 and 2010–11 over Markazi basin in Iran. • This study analyzed the relationship between climatic teleconnection signals and the extreme events over the study area. Abstract Although total storage deficit index (TSDI) is a well-known GRACE-based drought index, it is not efficient for the basins with high consumption because water harvesting in these areas affect GRACE signal. To overcome this limitation, the modified total storage deficit index (MTSDI) is introduced in this paper. To develop MTSDI, residuals of the signal were used instead of total signal. The proposed approach was applied to monitor drought in the Markazi Basin, Iran, during 2002–2016. Based on the obtained results, TSDI detected a moderate drought event in 2012 and a long-term severe drought from 2013 to 2016, which is not compatible with the recorded results of standardized precipitation index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI). Meanwhile MTSDI identified two drought events in 2008–09 and 2010–11 that coincided with droughts detected using SPI and SPEI. Results revealed that MTSDI is highly correlated with SPI and SPEI at a 12 month-scale, showing a correlation coefficient of 0.75 and 0.62, respectively. On the other hand, the coefficient of correlation between TSDI and SPI12 was 0.42 and between TSDI and SPEI12 was 0.26. A pixel-by-pixel analysis showed MTSDI and SPI/SPEI had a significant correlation in most areas of the basin. Further, the investigation of droughts in Markazi Basin revealed that dry years coincide with the occurrence of strong La Niña events. Overall, the results indicated a good potential of GRACE observations for developing a drought monitoring system, which its performance can be enhanced with respect to large-scale climate signals. [ABSTRACT FROM AUTHOR]

Highlights • A structured approach for determining the relevancy of management objectives. • Central objectives to evaluate the success of Swiss river widenings were identified. • The approach is applicable to a wide range of management domains. • Relevancy analysis supports problem structuring in Multi-Criteria Decision Analysis. Abstract Environmental monitoring covers many different management domains. They range from biodiversity conservation to water protection, natural hazard prevention, impact assessment, resource use, or environmental restoration. The need for clear objectives has long been emphasized in the management literature, but has often received only little attention in monitoring design. This is partly due to the lack of systematic approaches for setting objectives. In this paper, we present a formal approach based on Multi-Criteria Decision Analysis (MCDA), namely relevancy analysis, to prioritize management objectives. We demonstrate its use for a river restoration monitoring program in Switzerland comprising 35 physical and biological objectives. The relevancy of an objective is based on two main components, the general importance of the objective, and the problem-specific impact range. The general importance of an objective is used to identify the most important management objectives from a hierarchy within the national program (i.e. objectives to be monitored). The impact range is used to understand which management objectives are more important to monitor for different river restoration measures (i.e. sub-selection of the objectives specifically for the restoration measure). The general importance of a management objective was determined based on the frequency an objective is mentioned in selected policy documents, and the legislative power and temporal persistency of each document. The impact ranges were identified from a questionnaire involving 15 Swiss river restoration professionals. Hereby, we assumed that the impact range increases as the anticipated impact of the restoration measure on a specific management objective increases, and as the uncertainty to anticipate this impact increases. For simplicity, we applied the approach only to one restoration measure, namely river widening. Six management objectives scored high in relevancy in terms of both impact range and general importance – channel structure and diversity, structure and diversity of the banks and floodplains, sediment dynamics, longitudinal connectivity, lateral connectivity, and vertical connectivity. Hence, these objectives are both important national objectives for river monitoring (according to the legislation) and are objectives that are potentially highly impacted by a river widening project. We performed sensitivity analyses related to the calculation of the general importance, the impact range, and the relevancy of the objectives. The practice-oriented approach can be transferred to a broad diversity of decision situations where there is a need for systematic evaluation of the importance of objectives. It could be a useful tool for social learning and finding group consensus about the priority of the objectives. [ABSTRACT FROM AUTHOR]

Abstract: The goal of this paper is to introduce a statistical concept to derive ecological classifications of terrestrial and marine environments. Such ecological regionalisations reflect spatial combinations of biotic and abiotic characteristics and therefore may serve for environmental planning and monitoring issues. Referring to two case studies the paper presents how to calculate and map ecological defined regions from geodata by use of decision tree models and GIS-techniques. The first study deals with marine environments, exemplified by benthic habitats in the North Sea. The second study is on an ecological land classification of Europe which was computed using surface data on potential natural vegetation, elevation, soil texture and several climatic elements. Both the marine and the terrestrial ecoregions maps were combined with exposure data provided by environmental monitoring activities. The ecological land classification of Europe was intersected with measurement data on the metal loads in mosses taken from the German part of UN ECE moss surveys. In this way, the temporal development of the metal bioaccumulation within ecoregions since 1990 was assessed. The benthic habitat map was used to regionalise the temporal trend of the temperature conditions near the sea floor for the months of July and January from 1995 to 2000 by analysing these measurements according to the spatial categories of the habitat map. In the future, both ecological regionalisations should be used as a spatial framework for the analysis of up-to-date meteorological and phenological data in order to disclose climate change induced impacts. [Copyright &y& Elsevier]

Population monitoring of large carnivores, particularly in remote montane landscapes, represents a considerable conservation challenge. Occupancy modeling using repeated detection/non-detection surveys offers a practical and robust tool for assessments of this type. Sign surveys or photographic detections have been the two primary survey methods to inform occupancy models. However, these approaches are expensive to implement and resource-intensive. Thus, their applicability for assessing the distribution of rare large carnivores residing in inaccessible landscapes is limited, particularly when large scale species monitoring is desired. Here, our intent was to predict the occupancy of the endangered Persian leopard ( Panthera pardus saxicolor ) inhabiting the rugged mountains of northeastern Iran. Using a Bayesian occupancy modeling framework, we compared patterns of leopard occupancy derived from standardized monitoring data implemented using spatially-replicated sign surveys to those informed by citizen scientist observations. We found that leopard occupancy probability was comparable between the two survey methods (sign survey = 0.92, 95% CI 0.85–1 and citizen science = 0.94, 95% CI 0.88–1) though detection probability varied (sign survey = 0.52, 95% CI 0.46–0.58 and citizen science = 0.25, 95% CI 0.18–0.32). The magnitude of effect among the environmental covariates that predicted leopard occupancy probability was also similar for the two methods. Thus, while yielding comparable predictions, the citizen science approaches were half the cost of sign surveys. The implementation of the effective citizen scientist data enabled us to expand by two-fold the monitored area while halving the costs in comparison to the area investigated via sign surveys. Our paper demonstrates that citizen science surveys represent a cost-effective, reliable, and surprisingly overlooked means to efficiently assess occupancy, particularly for rare large carnivores inhabiting mountainous landscapes. [ABSTRACT FROM AUTHOR]

Abstract: This paper explores the conceptual common ground between visual and ecological landscape indicators. Indicators can be used to summarise complex information about landscape functions and the ability to extract a common set of indicators for analysing different landscape functions may provide valuable support for multiple-use planning. The development of landscape ecological indicators has been a very active research field that has resulted in a wide range of landscape metrics and composite indices with a strong conceptual base in landscape ecological principles. For the visual aspects of landscapes, however, this conceptual base is often missing and thus hindering progress in the development of indicators. This paper presents the results of an analysis of the correspondence between ecological indicators and visual indicators in order to explore whether there is common ground in both concept and operation. The conceptual level is presented for both ecological and visual indicators, as it is the concepts that define their aim and scope. We feel that transparency over this issue is crucial to the development and use of environmental indicators. The approach identified a candidate set of indicators that capture important aspects of both ecological and visual quality. The strength of the approach is that it forces us to focus on the identification of what we wish to indicate by means of landscape theory and assessment that are relevant to a specific landscape context. We believe that the approach presented here forms the basis for development of new methods for understanding the impact of landscape change in a management and planning context. [Copyright &y& Elsevier]

• Methods using macroalgal assemblages in monitoring programs were reviewed. • A total of 215 papers were selected. • A large variability among geographical areas was highlighted. • Most studies focused on mapping the distribution of kelps or Fucales. • In Europe many ecological quality indices were developed. Macroalgae have been utilized as biological indicators of ecosystem health in many monitoring programs worldwide. These programs have utilized various methods to quantify macroalgal community structures. The aim of this study was to provide an overview of current progress by reviewing techniques and methods in both monitoring programs and impact evaluation studies that use macroalgal assemblage data. A total of 215 papers were selected and divided into four categories: macroalgal assemblage monitoring, macroalgal mapping, developing and employing ecological indices based on macroalgae, and developing and employing generic ecological indices including macroalgae. The number and goals of macroalgal monitoring programs are very different among geographical areas. In Europe, the recent European Union Directives led to the development of indices as tool to monitor the ecological quality of coastal systems. In other geographic regions, most studies focused on mapping the distribution of kelps or Fucales. This demonstrates the necessity to harmonize marine macroalgal monitoring, identifying common metrics and approaches in sampling design, field measurements, taxonomic resolution and data management, in order to develop standardized procedures which may allow data obtained to be compared. [ABSTRACT FROM AUTHOR]

Spatial interpolation has been applied for mapping various variables in a wide range of environmental disciplines. This study aims to develop novel tools for examining the relative performance of different interpolation methods. We shall quantify and compare the quality of interpolation models by applying, among others, some inequality indices of error distributions. Such indices can generally be classified as non-dimensional and global. The performance measures explored here provide a valuable supplement to the conventional accuracy assessment, and have so far received only scant attention in the relevant literature. Given a wide range of potential applications for the methods discussed here, the main focus of the paper will be on empirical research concerning variability of soil properties. The eight interpolation methods, i.e., Inverse Distance Weighting (IDW), Modified Shepard’s Method (MS), Radial Basis Function (RBF), Natural Neighbour (NaN), Nearest Neighbour (NeN), Triangulation with Linear Interpolation (TIN), Local Polynomial (LP) and Ordinary Kriging (OK) were applied to estimate spatial distribution of soil pH, nitrogen, potassium and phosphorus content. Biplot methods were applied to visually examine the numerical results on the assessment of prediction quality. The ordinary kriging showed superior performance compared to the competing methods in majority of the cases. Significantly, predictions by kriging approaches revealed substantial improvement by considering data transformations. As concerns the other tested methods, the IDW and the LP algorithms tend to share similar characteristics. In turn, the NeN, RBF and MS algorithms scored relatively small inequality indices, when compared to the other methods. The use of new proposed measures will enable practitioners to gain more insightful and comprehensive evaluations of spatial interpolation techniques.

This paper reviews and discusses the methods and metrics used for the assessment of the ecological status of marine angiosperms comparing the European with the South African situation. In Europe salt marsh and seagrasses are an important biological element for establishing the ecological quality status of transitional waters and in South Africa changes over time in the salt marsh and submerged macrophyte habitats (species richness, abundance and community composition) is used nationally to assess the health of estuaries. In Europe several studies have developed metrics that include salt marsh species composition and community structure to assess the ecological quality status. Deviation of taxonomic composition and abundance from a reference situation is investigated. Multi-metric approaches have been shown to provide a more holistic view of the ecological status of the ecosystem. Many indices are highly dependent on historical data to assess the deviation from reference conditions. Within the WFD spirit one widely used approach for salt marsh assessment, the Best’s method, the baseline can be determined based on the first sampling effort, by the largest previously recorded size of the salt marsh or using the “maximum potential size” of the salt marsh from habitat prediction models. In South Africa all habitat below the 5 m contour line is considered estuary habitat and any land occupied here by agricultural or other developments is considered as a loss of habitat from the reference condition. For seagrasses European metrics are based on attributes from the community (e.g., taxonomic composition, epiphytes), the population (e.g., bed extent, shoots density), but also quantified at individual species (e.g., leaves length) or physiological levels (e.g., stable isotopic signatures). Seagrass habitats in South African estuaries are highly dynamic in response to floods and an understanding of this is needed before present ecological status can be assessed. [ABSTRACT FROM AUTHOR]

Over the past decade, dramatic declines in frog populations have been noticed worldwide. To examine this decline, monitoring frogs is becoming increasingly important. Compared to traditional field survey methods, recent advances in acoustic sensor technology have greatly extended spatial and temporal scales for monitoring animal populations. In this paper, we examine the problem of monitoring frog populations by analysing acoustic sensor data, where the population is reflected by community calling activity and species richness. Specifically, a novel acoustic event detection (AED) algorithm is first proposed to filter out those recordings without frog calls. Then, multi-label learning is used to classify each individual recording with six acoustic features: linear predictive coding coefficients, Mel-frequency cepstral coefficients, linear-frequency cepstral coefficients, acoustic complexity index, acoustic diversity index, and acoustic evenness index. Next, frog community calling activity and species richness are estimated by accumulating the results of AED and multi-label learning, respectively. Finally, ordinary least squares regression (OLS) is conducted to reveal the relationship between frog populations (frog calling activity and species richness) and weather variables (maximum temperature and rainfall). Experimental results demonstrate that our proposed intelligent system can significantly facilitate the effort to estimate frog community calling activity and species richness with comparable accuracies. The statistical results of OLS indicate that rainfall pattern has a lagged impact on frog community calling activity (significant in the first day after rainy day) and species richness (significant in the fourth day after rainy day). Temperature is shown to affect species richness but is less likely to change calling activity. [ABSTRACT FROM AUTHOR]

[Display omitted] • Landscape of blue carbon research from the perspective of bibliometrics. • Blue carbon ecosystem was the main research area, but macroalgae deserve attention. • Artificial intervention and methods are potential hotspots in the future research. • Statistics, co-occurrence analysis and thematic evolution are main used methods. Blue carbon sequestration is a nature-based solution to environmental problems caused by climate change and has a significant positive impact on the planet and human well-being. This paper bibliometrically and systematically analyzed 2,613 studies found in the Web of Science Core Collection (WoSCC) from 2003 to 2021 to explore the hotspots and trends of blue carbon research using the Bibliometrix package in R studio and VOSviewer software. The results based on network co-occurrence analysis indicated that mangroves, seagrasses, and salt marshes were the major blue carbon ecosystems (BCEs), and mangrove carbon sinks were the most popular central research area. Furthermore, phytoplankton and microalgae have essential roles in the marine carbon cycle and bioenergy utilization, as revealed by the high proportion of published literature, while the carbon transport mechanisms of seagrass and macroalgae was understudied. Thematic evolution analysis revealed that remote sensing was the most commonly used technology as an emerging research field for ecological monitoring and ecological investigation in blue carbon research in recent years. In addition, increasing carbon sinks by artificial approaches, fishery carbon sinks, and carbon sequestration by macroalgae have been potential hotspots for blue carbon research development. The findings from this study shed light on the trends of blue carbon in the past eighteen years, current research points in the field, and areas that need more research investment going forward. [ABSTRACT FROM AUTHOR]

The intensification of agricultural land use and urbanisation has increased nutrient loads in aquatic ecosystems. Nitrogen loads can alter ecosystem structure and functioning, resulting in increased algal productivity, algal blooms and eutrophication. The principal aim of the present paper is to extend the use of epilithic δ 15 N signatures to a lake ecosystem in order to evaluate the potential impact of anthropogenic nitrogen discharges (organic and inorganic) that can also reach coastal waters. Epilithic associations were collected from volcanic rocks in different seasons in shallow water along the entire perimeter of Lake Bracciano and analysed for their nitrogen stable isotope signatures. Furthermore, some stones were moved from an unpolluted site to a polluted one in order to verify the effect on the nitrogen signature of the epilithic association. The epilithon’s δ 15 N signatures provided strong evidence of the space-time variability of N inputs. The differing quality of nitrogen loads was reflected in high isotopic variation within the lake, especially at the beginning of summer (1.7‰ ≤ δ 15 N ≤ 13.3‰), while in winter, when anthropogenic pressure was lowest, the δ 15 N signature variation was less accentuated (3.1‰ ≤ δ 15 N ≤ 7.6‰). At all sampling times, spatial variability was found to be related to the various human activities along the lake shore (especially tourism and agriculture), while seasonal variation at all sampling sites was related to the intensity of anthropogenic pressures (higher in summer and lower in winter). Our results showed that epilithic algal associations and the physicochemical properties of the water did not influence the δ 15 N signature, which in contrast was strongly related to the site-specific effect of human activities around the lake. Thus, the distribution of δ 15 N across space and time can be used to direct nutrient reduction strategies in the region and can assist in monitoring the effectiveness of environmental protection measures. [ABSTRACT FROM AUTHOR]

In this paper, we present concentrations of an array of major and trace elements (Ag, Al, As, Ba, Bi, Cd, Co, Cr, Cu, Fe, Mn, Mo, Rb, Sb, Sc, Sr, Th, Tl, U, V, Zn) in living Sphagnum mosses from four southern German bogs and compare them with moss monitoring data of the respective regions. To do this, Sphagnum mosses were collected in Upper Bavaria (Oberbayern, OB) and the Northern Black Forest (Nordschwarzwald, NBF). Surfaces of Sphagnum carpets were marked with plastic mesh and, one year later, the annual moss production was harvested. Up to 12 samples (40 cm × 40 cm) were collected per site, and 6–10 sites investigated per bog. The concentrations of these elements were then determined in acid digests using sector field ICP-MS. Variations within a given sampling site were in the range of 2 to 3-fold for all major and trace element concentrations except for Mn (12-fold) and Tl (38-fold). For most of the elements, concentrations between bogs of a given region were significantly different and atmospheric deposition of particles seems to be considerably affected by local circumstances such as tree canopy interception and microtopography. Comparing trace element concentrations measured in Sphagnum mosses for 2007 with published moss monitoring data for 2005 resulted in a very good agreement for most elements. Clearly, Sphagnum mosses from bogs are useful biomonitors for estimating atmospheric contamination by metals. This supports the use of Sphagnum in atmospheric deposition monitoring especially in cases where Sphagnum is abundant (e.g., boreal forests). In regions with neither bogs nor forests, living Sphagnum moss bags could be used to the same effect. [ABSTRACT FROM AUTHOR]

Increased global demand for groundwater has resulted in the need to measure and monitor this resource. Rather than monitoring groundwater simply though water chemistry and levels, which provides a ‘snapshot’ of the conditions at any given time, a more holistic approach to managing groundwater resources and their changes over time is needed. Korbel & Hose (2011) introduced the first structured framework for measuring groundwater health – the Groundwater Health Index (GHI). This multimetric, two-tiered framework uses biotic and abiotic components of the groundwater ecosystem to measure and identify ecosystem health. The framework can be used to classify impacted from non-impacted groundwaters, however it has certain limitations. With increased research and associated knowledge of groundwater ecosystems in recent times, it is now timely to attempt to build on the GHI framework. This paper refines and improves the GHI by incorporating a weighting system to account for natural factors contributing to variations in biotic distribution and is tested on data within four geologically similar alluvial aquifers in intensively irrigated agricultural areas of New South Wales and Queensland, Australia. Using a combination of microbial, stygofaunal, water chemistry and environmental indicators, the ‘weighted GHI’ framework was able to discriminate three distinct ecosystem health classifications; that of ‘similar to reference’(displaying reference-like condition) ‘mild deviation from reference’ (sites failing to meet between 2 and 3 benchmarks) and ‘major deviation from reference’ (sites with more than four benchmarks exceeded). The framework indicated that groundwater health deviated from reference condition in all four catchments studied, with irrigated sites consistently displaying deviations from reference ecosystem health. Tier 2 benchmarks were set using results from the Gwydir River, and were tested on the adjacent Namoi River catchment, the Condamine and Lower Macquarie catchments. Results indicated that ecosystem health benchmarks may be associate with aquifer typology, rather than being applicable only for local areas. [ABSTRACT FROM AUTHOR]

The rainfall regime is one of the main abiotic components that can cause modifications in the breeding activity of animal species. It has a direct effect on the environmental conditions, and acts as a modifier of the landscape and soundscape. Variations in water quality and acidity, flooding, erosion, and sound distortion are usually related with the presence of rain. Thereby, ecological studies in populations and communities would benefit from improvements in the estimation of rainfall patterns throughout space and time. In this paper, a method for automatic detection of rainfall in forests by using acoustic recordings is proposed. This approach is based on the estimation of the mean value and signal to noise ratio of the power spectral density in the frequency band in which the sound of the raindrops falling over the vegetation layers of the forest is more prominent (i.e. 600–1200 Hz). The results of this method were compared with human auditory identification and data provided by a pluviometer. We achieved a correlation of 95.23% between the data provided by the pluviometer and the predictions of a regression model. Furthermore, we attained a general accuracy between 92.90% and 99.98% when identifying different intensity levels of rainfall on recordings. Nowadays, passive monitoring recorders have been extensively used to study of acoustic-based breeding processes of several animal species. Our method uses the signals acquired by these recorders in order to identify and quantify rainfall events in short and long time spans. The proposed approach will automatically provide information about the rainfall patterns experienced by target species based on audio recordings. [ABSTRACT FROM AUTHOR]

Impacts of human civilization on ecosystems threaten global biodiversity. In a changing environment, traditional in situ approaches to biodiversity monitoring have made significant steps forward to quantify and evaluate BD at many scales but still, these methods are limited to comparatively small areas. Earth observation (EO) techniques may provide a solution to overcome this shortcoming by measuring entities of interest at different spatial and temporal scales. This paper provides a comprehensive overview of the role of EO to detect, describe, explain, predict and assess biodiversity. Here, we focus on three main aspects related to biodiversity − taxonomic diversity, functional diversity and structural diversity, which integrate different levels of organization − molecular, genetic, individual, species, populations, communities, biomes, ecosystems and landscapes. In particular, we discuss the recording of taxonomic elements of biodiversity through the identification of animal and plant species. We highlight the importance of the spectral traits (ST) and spectral trait variations (STV) concept for EO-based biodiversity research. Furthermore we provide examples of spectral traits/spectral trait variations used in EO applications for quantifying taxonomic diversity, functional diversity and structural diversity. We discuss the use of EO to monitor biodiversity and habitat quality using different remote-sensing techniques. Finally, we suggest specifically important steps for a better integration of EO in biodiversity research. EO methods represent an affordable, repeatable and comparable method for measuring, describing, explaining and modelling taxonomic, functional and structural diversity. Upcoming sensor developments will provide opportunities to quantify spectral traits, currently not detectable with EO, and will surely help to describe biodiversity in more detail. Therefore, new concepts are needed to tightly integrate EO sensor networks with the identification of biodiversity. This will mean taking completely new directions in the future to link complex, large data, different approaches and models. [ABSTRACT FROM AUTHOR]

Soils provide many ecosystem services that are ultimately dependent on the local diversity and belowground abundance of organisms. Soil biodiversity is affected negatively by many threats and there is a perceived policy requirement for the effective biological monitoring of soils at the European level. The aim of this study was to evaluate and recommend policy relevant, cost-effective soil biological indicators for biodiversity and ecosystem function across Europe. A total of 18 potential indicators were selected using a logical-sieve based approach. This paper considers the use of indicators from the ‘top down’ (i.e. concerned with the process of indicator selection), rather than from the ‘bottom up’ detail of how individual indicators perform at specific sites and with specific treatments. The indicators assessed a range of microbial, faunal and functional attributes, newer nucleic acids based techniques, morphological approaches and process based measurements. They were tested at 6 European experimental sites already in operation and chosen according to land-use, climatic zone and differences in land management intensity. These were 4 arable sites, one each in Atlantic, Continental, Mediterranean and Pannonian climate zones, and 2 grassland sites, one each in Atlantic and Continental zones. At each site we sampled three replicated plots of contrasting management intensity and, while the treatments varied from site to site, their disturbance effects were quantified in terms of land use intensity. The field sampling and laboratory analysis were standardised through a combination of ISO protocols, or standard operating procedures if the former were not available. Sites were sampled twice, in autumn 2012 and spring or autumn 2013, with relative costs of the different indicators being determined each time. A breakdown of the cost effectiveness of the indicators showed the expected trade-off between effort required in the field and effort required in the laboratory. All the indicators were able to differentiate between the sites but, as no single indicator was sensitive to all the differences in land use intensity, we suggest that an indicator programme should be based upon a suite of different indicators. For monitoring under the European climatic zones and land uses of this study, indicators for ecosystem functions related to the services of water regulation, C-sequestration and nutrient provision would include a minimum suite of: earthworms; functional genes; and bait lamina. For effective monitoring of biodiversity all taxonomic groups would need to be addressed. [ABSTRACT FROM AUTHOR]

• We investigate the transmission of AQI fluctuation by complex network theory. • AQI networks of various time periods display small-world effect. • It is observed that some extreme short-term AQI fluctuation can be controlled. • Complex network analysis contributes to the prediction of AQI fluctuation. • Policy recommendations for environment management are proposed. Although many works have focused on the issues related to Air Quality Index (AQI) from the perspective of statistics or economics, the dynamic and temporal characteristics of AQI time series remain unclear. The main reason is that AQI time series has both non-linearity and complexity, which cannot be characterized by conventional ways. The inherent transmission laws of AQI volatility are of great importance for environment monitoring and management system. Thus, this paper has endeavored to address the issue from the interdisciplinary perspective of complex network theory. First, we collect the annual and the total AQI data of three cities (Shanghai, Wuhan and Guangzhou) in China from January 1, 2015 to December 31, 2017. Then the coarse graining method is employed in combination with the theory of phase space reconstruction to convert AQI time series into complex network. The coarse graining method is used to transform yearly and total AQI time series into the corresponding symbol sequences. In accordance with the theory of phase space reconstruction, we adopt C C method and false nearest neighbor algorithm to estimate the optimal time delay and embedding dimension of AQI time series, respectively. The optimal time delay is integrated with the embedding dimension to map symbol sequences into AQI complex network. Based on complex network theory, we investigate the transmission of AQI fluctuation by the temporal characteristics and dynamics of yearly AQI networks (YAN) and total AQI networks (TAN), which include node strength, betweenness centrality and community structure. The empirical results show: (1) the AQI networks of various periods display small-world effect; (2) network evolution tends to be more complex and the modality in YAN has less alternative transformation paths than that in TAN; (3) the node strength and betweenness centrality in measuring the importance of nodes achieve a relative consistency; (4) the three cities have the same dominant fluctuation modalities both in YAN and in TAN; (5) the uneven distribution of transmission ability between clusters implies a preferential transmission whether in YAN or TAN. These findings not only help us to interpret the frequent volatility of AQI and control some extreme short-term AQI fluctuation, but also contribute to the reliability assessment of AQI and the prediction of AQI fluctuation. In summary, this paper not only reveals the temporal dynamic properties of AQI time series, but also provides some policy recommendations for environmental monitoring and management. [ABSTRACT FROM AUTHOR]

• Simulation the river habitat quality in Luanhe based on the eco-hydrodynamic model. • Constructing a two-dimensional (2D) eco-hydrodynamic model. • The habitat suitability indices (HSIs) of various target species were determined. • The spatial distribution and dynamic change process were obtained. Fishes are at the top of the aquatic food chain, are the best indicators of the health status of aquatic ecosystems and are the organisms most sensitive to changes in the aquatic ecological environment. Given the shortcomings of the commonly used river physical habitat models that only consider hydraulic factors, a two-dimensional (2D) eco-hydrodynamic model, containing eco-hydrological elements, was constructed using the identified driving forces of the ecological evolution of rivers. The main hydrological elements are water depth and flow velocity, while the main ecological elements are NH3-N, chemical oxygen demand, and sediments. Based on field survey data, relevant studies, and a comprehensive analysis of the natural geographical characteristics of the Luanhe River Basin, the dominant fish and plant species in the study area (the river section from the Daheiting-Taolinkou Reservoir to Luanxian Hydrological Station) were determined to be Cypriniformes and reed, respectively. The habitat suitability indices (HSIs) of various target species were determined using the model simulation method and the observational statistical method, and the river habitat quality model was constructed based on the 2D eco-hydrodynamic model. The spatial distribution and dynamic change process of the habitat quality of carp in the Qianxi section downstream of the Daheiting Reservoir and reed in the Qianxi section of the Luanhe River were obtained by calculation. Comparison with existing studies and ecological monitoring data shows that the model proposed in this paper is reliable and effective, which will help with the development of ecological protection and management efforts for rivers with not enough ecological monitoring data. [ABSTRACT FROM AUTHOR]

Growing evidence demonstrates that fluctuating metal stressors can have profound impact on the ecophysiological responses in aquatic species. However, how environmental stochasticity affects the complex damage-recovery dynamics in organisms remains difficult to predict. The objective of this paper was to investigate the stochastic behavior in the damage-recovery dynamics in tilapia in response to pulse waterborne copper (Cu). We developed a mathematical framework that allows discrimination between damage and recovery processes in tilapia exposed to designed pulse Cu scenarios. We built deterministic nonlinear models for the damage-recovery dynamics that produce response surfaces describing killing/recovery rate–Cu-pulse interval interactions. Here we showed that the stochastic switching behavior arose from competition among killing, recovery rates, and Cu pulse frequency. This competition resulted in an ultrasensitivity appeared in whole body, gills, muscle, liver, and kidney with Hill coefficients of ≥7, 4, 7, 5, and 5, respectively, at Cu 3 mg L −1 , dilution rate 0.05 h −1 , and pulse interval 72 h, indicating that a stochastic switch-like response was generated. We argue that the role of gill-associated Hill coefficient as a direct signal of the stochastic switch-like response in the damage-recovery dynamics in response to pulse metal stressor can serve as a sensitive indicator for risk detection in fluctuating environments. Our approach constitutes a general method to identify the stochastic switch-like response for aquatic species exposed to fluctuating metal stressors, which may help to predict and, eventually, expand our understanding of the damage-recovery dynamics. Finally, we implicate that Hill coefficient-based switch-like signal and its damage with hazard response can be linked in an information theoretic framework to handle environmental stochasticity. [ABSTRACT FROM AUTHOR]

Different Life Cycle Impact Assessment (LCIA) methods have been developed over the past twenty years. While the use of a single score indicator may, on the one hand, ease the decision making it may also induce a loss of information. On the other hand, the use of a large set of environmental indicators may increase the difficulty of decision making due to the high number of parameters. In this paper, a statistical methodology is used to identify a simplified set of environmental indicators. This methodology applies Principal Component Analysis (PCA) to five LCIA methods (CML’01, Eco-indicator99, IMPACT 2002+, ecological scarcity, EPD) using a building products database. For each method, the results show that only 4–6 dimensions are sufficient to explain at least 90–95% of the variance for each set of indicators. They refer to the following environmental themes: fossil-fuel consumption, ecotoxicity, ionising radiation, land use and mineral resources. The use of a selected set of five LCI flows is found to be as relevant as current LCIA methods from a statistical point of view. Furthermore, PCA applied to a building LCA case study shows similar trends as for the database results. Further studies are now needed to confirm these findings for going towards simplified structural relationships among LCIA indicators for building materials. [ABSTRACT FROM AUTHOR]

Spatial stratified heterogeneity, referring to the within-strata variance less than the between strata-variance, is ubiquitous in ecological phenomena, such as ecological zones and many ecological variables. Spatial stratified heterogeneity reflects the essence of nature, implies potential distinct mechanisms by strata, suggests possible determinants of the observed process, allows the representativeness of observations of the earth, and enforces the applicability of statistical inferences. In this paper, we propose a q -statistic method to measure the degree of spatial stratified heterogeneity and to test its significance. The q value is within [0,1] (0 if a spatial stratification of heterogeneity is not significant, and 1 if there is a perfect spatial stratification of heterogeneity). The exact probability density function is derived. The q -statistic is illustrated by two examples, wherein we assess the spatial stratified heterogeneities of a hand map and the distribution of the annual NDVI in China. [ABSTRACT FROM AUTHOR]

Habitats in the Wadden Sea, a world heritage area, are affected by land subsidence resulting from natural gas extraction and by sea level rise. Here we describe a method to monitor changes in habitat types by producing sequential maps based on point information followed by mapping using a multinomial logit regression model with abiotic variables of which maps are available as predictors. In a 70 ha study area a total of 904 vegetation samples has been collected in seven sampling rounds with an interval of 2–3 years. Half of the vegetation plots was permanent, violating the assumption of independent data in multinomial logistic regression. This paper shows how this dependency can be accounted for by adding a random effect to the multinomial logit (MLN) model, thus becoming a mixed multinomial logit (MMNL) model. In principle all regression coefficients can be taken as random, but in this study only the intercepts are treated as location-specific random variables (random intercepts model). With six habitat types we have five intercepts, so that the number of extra model parameters becomes 15, 5 variances and 10 covariances. The likelihood ratio test showed that the MMNL model fitted significantly better than the MNL model with the same fixed effects. McFadden- R 2 for the MMNL model was 0.467, versus 0.395 for the MNL model. The estimated coefficients of the MMNL and MNL model were comparable; those of altitude, the most important predictor, differed most. The MMNL model accounts for pseudo-replication at the permanent plots, which explains the larger standard errors of the MMNL coefficients. The habitat type at a given location-year combination was predicted by the habitat type with the largest predicted probability. The series of maps shows local trends in habitat types most likely driven by sea-level rise, soil subsidence, and a restoration project. We conclude that in environmental modeling of categorical variables using panel data, dependency of repeated observations at permanent plots should be accounted for. This will affect the estimated probabilities of the categories, and even stronger the standard errors of the regression coefficients. [ABSTRACT FROM AUTHOR]

Ecosystem research benefits enormously from the fact that comprehensive data sets of high quality, and covering long time periods are now increasingly more available. However, facing apparently complex interdependencies between numerous ecosystem components, there is urgent need rethinking our approaches in ecosystem research and applying new tools of data analysis. The concept presented in this paper is based on two pillars. Firstly, it postulates that ecosystems are multiple feedback systems and thus are highly constrained. Consequently, the effective dimensionality of multivariate ecosystem data sets is expected to be rather low compared to the number of observables. Secondly, it assumes that ecosystems are characterized by continuity in time and space as well as between entities which are often treated as distinct units. Implementing this concept in ecosystem research requires new tools for analysing large multivariate data sets. This study presents some of them, which were applied to a comprehensive water quality data set from a long-term monitoring program in Northeast Germany in the Uckermark region, one of the LTER-D (Long Term Ecological Research network, Germany) sites. The effective dimensionality was assessed by the Correlation Dimension approach as well as by a Principal Component Analysis and was in fact substantially lower than the number of observables. Continuity in time, space and between different types of water bodies was studied by combining Self-Organizing Maps with Sammon's Mapping. Groundwater, kettle hole and stream water samples exhibited some overlap, confirming continuity between different types of water bodies. Clear long-term shifts were found at the stream sampling sites. There was strong evidence that the intensity of single processes had changed at these sites rather than that new processes developed. Thus the more recent data did not occupy new subregions of the phase space of observations. Short-term variability of the kettle hole water samples differed substantially from that of the stream water samples, suggesting different processes generating the dynamics in these two types of water bodies. However, again, this seemed to be due to differing intensities of single processes rather than to completely different processes. We feel that research aiming at elucidating apparently complex interactions in ecosystems could make much more efficient use from now available large monitoring data sets by implementing the suggested concept and using corresponding innovative tools of system analysis. [ABSTRACT FROM AUTHOR]

Remote sensing can provide real-time and dynamic information for terrestrial ecosystems, facilitating effective drought monitoring. A recently proposed remotely sensed Drought Severity Index (DSI), integrating both vegetation condition and evapotranspiration information, shows considerable potential for drought monitoring at the global scale. However, there has been little research on regional DSI applications, especially concerning agricultural drought. As the most important winter wheat producing region in China, North China has suffered from frequent droughts in recent years, demonstrating high demand for efficient agricultural drought monitoring and drought impact analyses. In this paper, the capability of the MODIS DSI for agricultural drought monitoring was evaluated and the drought impacts on winter wheat yield were assessed for 5 provinces in North China. First, the MODIS DSI was compared with precipitation and soil moisture at the province level to examine its capability for characterizing moisture status. Then specifically for agricultural drought monitoring, the MODIS DSI was evaluated against agricultural drought severity at the province level. The impacts of agricultural drought on winter wheat yield during the main growing season were also explored using 8-day MODIS DSI data. Overall, the MODIS DSI is generally effective for characterizing moisture conditions at the province level, with varying ability during the main winter wheat growing season and the best relationship observed in April during the jointing and booting stages. The MODIS DSI agrees well with agricultural drought severity at the province level, with better performance in rainfed-dominated than irrigation-dominated regions. Drought shows varying impacts on winter wheat yield at different stages of the main growing season, with the most significant impacts found during the heading and grain-filling stages, which could be used as the key alert period for effective agricultural drought monitoring. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate whether countries tend to relocate their ecological footprint as they grow richer. The analysis is carried out for a panel of 116 countries by employing the production and import components of the ecological footprint data of the Global Footprint Network for the period 2004–2008. With few exceptions, the existing Environmental Kuznets Curve (EKC) literature concentrates only on the income-environmental degradation nexus in the home country and neglects the negative consequences of home consumption spilled out. Controlling for the effects of openness to trade, biological capacity, population density, industry share and energy per capita as well as stringency of environmental regulation and environmental regulation enforcement, we detect an EKC-type relationship only between per capita income and footprint of domestic production. Within the income range, import footprint is found to be monotonically increasing with income. Moreover, we find that domestic environmental regulations do not influence country decisions to import environmentally harmful products from abroad; but they do affect domestic production characteristics. Hence, our findings indicate the importance of environmental regulations and provide support for the “Pollution Haven” and “Race-to-the-Bottom” hypotheses. [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]

Tree leaves and needles are commonly used in biomonitoring as reliable indicators of environmental pollution to monitor a wide range of mainly airborne contaminants such as heavy metals and persistent organic pollutants (POPs). The most severe challenges are the reproducibility and comparability of results. This is the only basis upon which correct assessment can be ensured. Standardizing the collection and preservation of leaf samples can overcome such problems and improve the quality of the results. On this account, efforts are being made to promote standardization on national (Association of German Engineers, VDI) and international (European Committee for Standardization, CEN) level. The objective of this review is to investigate the current state of quality assurance in passive biomonitoring by analyzing 86 peer-reviewed papers on tree leaf-sampling and use. We show that little attention has been given to standardizing how leaves are collected in the field and stored as well as to the documentation of selection criteria and sampling conditions. Important information is often absent, including crucial aspects of sample collection and preservation, for example, the selection of sampling sites, exposure, sampling period, age and condition of target compartments. Potential standards are suggested and discussed as a foundation for the development of quality assurance standards to conceptualize and perform sampling. For some aspects of sampling the current literature yields little information to derive and establish standards. In the first place, this concerns the selection of sample species. Besides of this, it is important to document and report data concerning sampling conditions in a publication. Overall, further research is urgently required to define scientifically valid standards, which will ensure a high level of quality and comparability of the samples collected in the field. [ABSTRACT FROM AUTHOR]

The present paper concerns the study of atmospheric deposition of heavy metals, using a carpet-forming-moss species ( Hypnum cupressiforme ) as a bioindicator. It provides a complementary method to evaluate elemental deposition from the atmosphere to terrestrial systems. Compared with conventional precipitation analysis, it is an easier and cheaper method that ensures a high sampling density over the monitored area. The moss samples were collected over the whole territory of the country by following, more or less, a systematic sampling scheme, which is often used in environmental studies because it is convenient to implement in field campaigns, often providing good precision and complete coverage of the target population compared with random sampling. The 2010/2011 ICP vegetation moss survey data were used in this study. The unwashed, dried samples were digested completely by the microwave digestion method. The concentration ( C ) of selected trace metals (As, Cr, Cu, Ni, V and Zn) and conservative metals (Al, Li and Fe) were determined by ICP-AES and AAS (Cd and As) methods. To characterize the natural and the anthropogenic pattern of heavy metal deposition throughout the whole territory, the normalization process using lithium as a normalizing element was carried out on the C data for 11 elements of 44 moss samples. The obtained data set was used to compensate the natural variability of trace metals, so that the anthropogenic metal contributors could be detected and quantified. Descriptive statistics and multivariate analyses were used for the statistical treatment of the normalized concentration ( NC ) data using the MINITAB 17 software package. The statistical parameters of the NC data are discussed. The level of contamination was evaluated by calculating the enrichment factors, whereas the most probable local anthropogenic emitter sources were identified. The statistical analysis of NC data demonstrated that the normalization process is useful for evaluating the relative contributions of anthropogenic and natural sources of elemental deposition from the atmosphere to terrestrial systems, when the main pollution sources are from fine dust particles. [ABSTRACT FROM AUTHOR]

Existing wireless coal mine environmental monitoring system has large transmissions of redundancy data. Monitoring and treatment of multiple environmental parameters are mutually independent, with low instantaneity and reliability. To solve the above problems, this paper designs the coal mine environmental monitoring system based on multi-agent, taking advantages of the harmony and intelligence of the multi-agent technology. This system designs the environmental monitoring agent from both hardware and software aspects which can cooperate mutually and work harmoniously between each other. Thus, the quantity of transmission of data can be reduced, which optimizes the structure of the coal mine environmental monitoring system. Results of simulation experiments prove that the multi-agent-based coal mine environmental monitoring system can reduce the transmission delay and packet loss probability of data and enhance the instantaneity and reliability of data transmission in the system. Therefore, the new system is more applicable and advanced in the field of coal mine environmental monitoring. [ABSTRACT FROM AUTHOR]

As environmental problems become more and more serious all over the world, environmental efficiency evaluation has drawn increasing interests of many scholars and governments’ decision makers. Data envelopment analysis (DEA) as a non-parametric approach for evaluating the relative efficiency of a group of decision making units (DMUs) has been widely extended and applied in many areas. Among various DEA models, enhanced Russell measure (ERM) model can measure the inefficiencies from input orientation and output orientation simultaneously. In this paper, we proposed a new non-orientation DEA approach based on enhanced Russell measure for measuring the environmental efficiency of a DMU and meanwhile, provided the closest target for the evaluated DMU to efficient with less effort. At last, our approach was applied to a practical example about thermal power enterprises. The results show that our model can provide a much easier way for the inefficient enterprises to improve their efficiencies than the enhanced Russell measure method. Moreover, the provided benchmarks for enterprises through our model can be used to further rank the efficient ones. [ABSTRACT FROM AUTHOR]

Abstract: Recently, the science and policy agenda on biodiversity moved to include ecosystem services assessments and it is recognised that for determining the effectiveness and progress of policy frameworks monitoring is crucial. Within European monitoring schemes, data is collected following different sampling protocols for a range of biodiversity or context related aspects; from EU-wide general land cover mapping to red list species within Annex I habitats. In this paper, we analysed field instructions of seven monitoring schemes on the extent to which they can provide data on the provision of ecosystem services and what additional information may be needed for future monitoring of ecosystem services. We compared seven monitoring schemes (i.e. CORINE Land Cover, Land Use Cover Area Survey (LUCAS), European Biodiversity Observation Network (EBONE), biodiversity monitoring on organic and low-input farming systems (BioBio), National Inventory of the Landscape of Sweden (NILS) and Pan-European Common Birds Monitoring (PECBM) and UK Butterfly monitoring (UK-BM)) by scoring the quality of recorded parameters and the adequacy of sampling protocols for ecosystem service monitoring. All the examined schemes were able to provide some parameters on ecosystem services, but the quality of the parameters on average did not exceed the level of qualitative data. Additionally, the divergence between the sampling designs of the schemes and the spatial characteristics of ecosystem services reduced the potential monitoring value of all schemes. Monitoring schemes including a range of sampling methods, scales and included the recording of data on habitats, such as EBONE, BioBio and NILS, provided the best data on the provision of ecosystem services. We conclude that improvement of the monitoring of ecosystem services is hindered by several knowledge gaps: (1) a robust definition and conceptual framework of ecosystem services; (2) the linkage between biodiversity and ecosystem services; and (3) the interpretation of monitoring data. In addition to ecosystem service monitoring, biodiversity monitoring unremittingly remains very important, at least to identify trade-offs between the management for services and the resulting biodiversity status. [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: Marine waters in general and coastal areas in particular suffer globally from stressing events. These events can be largely attributed to higher levels of human-induced eutrophication, contamination by pollutant, and siltation. The European Union and other countries have adopted legislation to guide monitoring efforts and set goals for good quality coastal environments. Few biological quality elements were identified by legislators for their ability to detect and identify sources of ecosystem perturbations. Research programs have developed indices calculation methods for benthic invertebrate fauna, phytoplankton, macroalgae and angiosperms. Most of these indices are multimetric, based on the richness, abundance and biomass of the community. Such indices determine the quality at one site after observational comparison with a reference situation. Functional indices, however, are based on species ecological preferenda and autoecology. Since species are generally response-specific, functional index allows the discernment of the stressing factor involved in the ecosystem perturbation. The final index value provides a direct appreciation of the ecological status of the study site. At the present time, there are few functional indices that can be used on a routine basis for monitoring coastal waters. Because biological quality elements cited below are not available in all areas, the range of monitoring is restricted to particular environments. This review paper suggests that marine benthic diatoms (Bacillariophyceae) may result in a functional index. Arguments for the use of these organisms as bio-indicators include knowledge from the past and present use of diatoms in freshwater environmental analysis. Marine benthic diatoms are abundant and ubiquitous in terms of ecosystems and water chemistry and are sensitive to nutrient variation, as revealed by two studies conducted in mesotrophic and oligotrophic areas. This review is focused on benthic diatoms and reveals that no study has yet been conducted on the use of these organisms as bio-indicators. Aspects of practical sampling and sample treatment for marine diatoms are considered. As the bio-indication role of diatoms is little explored in marine environments, the dataset on ecological indicator values of species is scarce. Future work that relates to taxonomic determination and physico-chemical description of sites are needed to better define species ecological preferenda, prior to the development of a marine diatom biotic index. [Copyright &y& Elsevier]

Abstract: In this paper, we ask if the use of a voluntary standard assessment system for environmental reporting could help mitigate the damage caused by oil and gas companies to developing nations. What level of data reporting by these companies is needed to allow the assessment of environmental practices? To answer these questions, we evaluate the environmental practices of eight oil and gas companies against the Sustainability Reporting Guidelines issued in 2006 by the Global Reporting Initiative (GRI). Content analysis of their environmental reports 2009 indicates that they made reasonable efforts to disclose their environmental performance in accordance with the GRI Sustainability Reporting Guidelines. These guidelines appear to provide a robust and readily available tool for reporting comprehensive progress concerning all aspects of environmental activities. The voluntary adoption of the guidelines by a vast majority of the oil and gas companies increases transparency, credibility and comparability in sustainability reporting. In addition, five of the eight oil and gas companies disclosed that they had obtained third-party assurance of their sustainability reports. [Copyright &y& Elsevier]

Abstract: This paper focuses on the application of various biomonitoring techniques in China. We report a study in the Pearl River Basin (Guangzhou) based on the application of diatom indices as well as a study on the waterways in Wuhan based on evaluation of toxicity (using phytotoxicity, Daphnia magna and Microtox™ tests) and the Extended Biotic Index (EBI). Regarding the diatom indices, acceptable results were obtained based on comparison of the chemical water quality level and the European and Japanese indices, despite a lack of taxonomic information. The toxicity tests applied to the Wuhan waterways (Yangtze and Han Rivers) produced interesting results and can be considered to represent a useful tool for water pollution control in this area. Application of the EBI in Wuhan produced results that were contradictory to the toxicological analyses, as there were no indications of toxicity, whereas EBI indicated poor water quality. It can be concluded that in principle, certain European biological indicators can be considered to represent feasible tools to be applied in China. However, further studies will have to be carried out to develop bioindices based on Chinese data sets. The use of bioindices based on macroinvertebrates is limited to less polluted and smaller rivers with a lithic river substratum, whereas diatom indices are also applicable under extreme conditions (e.g., under high pollution loads or in large river streams with sandy riverbed sediments through installing artificial substrates). [Copyright &y& Elsevier]

Abstract: The development of multimetric indices (MMIs) as a means of providing integrative measures of ecosystem condition is becoming widespread. An increasingly recognized problem for the interpretability of MMIs is controlling for the potentially confounding influences of environmental covariates. Most common approaches to handling covariates are based on simple notions of statistical control, leaving the causal implications of covariates and their adjustment unstated. In this paper, we use graphical models to examine some of the potential impacts of environmental covariates on the observed signals between human disturbance and potential response metrics. Using simulations based on various causal networks, we show how environmental covariates can both obscure and exaggerate the effects of human disturbance on individual metrics. We then examine from a causal interpretation standpoint the common practice of adjusting ecological metrics for environmental influences using only the set of sites deemed to be in reference condition. We present and examine the performance of an alternative approach to metric adjustment that uses the whole set of sites and models both environmental and human disturbance effects simultaneously. The findings from our analyses indicate that failing to model and adjust metrics can result in a systematic bias towards those metrics in which environmental covariates function to artificially strengthen the metric–disturbance relationship resulting in MMIs that do not accurately measure impacts of human disturbance. We also find that a “whole-set modeling approach” requires fewer assumptions and is more efficient with the given information than the more commonly applied “reference-set” approach. [Copyright &y& Elsevier]