Environmental Management Decisions Research Articles

Predicting the Impact of Climate Change on the Distribution of the Key Habitat-Forming Species in the Ne Baltic Sea

Torn, K.; Peterson, A., and Herkul, K., 2020. Predicting the impact of climate change on the distribution of the key habitat-forming species in the NE Baltic Sea. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 177–181. Coconut Creek (Florida), ISSN 0749-0208.Macrophytes provide food, shelter and habitat for a multitude of other species and are therefore considered as important habitat-forming species. Loss or decrease of habitat-forming species severely affects biodiversity and functioning of coastal marine ecosystems. In the brackish Baltic Sea, such special, structuring species are large perennial macroalgae Fucus vesiculosus and Furcellaria lumbricalis on hard seabed and eelgrass Zostera marina and charophytes (Chara spp.) on soft substrates. The Baltic Sea is expected to face severe changes in environmental conditions due to climate change by the end of the 21st century, e.g. decrease in salinity and increase in temperature, wind speed, and storminess. It is essential to forecast changes in the distribution of valuable species in order to provide data for marine environmental protection and management decisions. Boosted regression trees modelling method was used to produce current species distribution models and predict the potential changes based on future climate scenario. Data from over 10 000 benthic sampling sites were used as an input for distribution models. Following the influence of the water depth, the next major drivers of species distribution were substrate type for Fucus, temperature for the charophytes and Furcellaria, and salinity for Zostera. Based on the model predictions, the climate change may cause a significant reduction of the distributional range of Zostera and Furcellaria. Slight decline of Fucus was also detected. Unlike the other habitat-forming species, charophytes are potential winners by probably increasing their distribution in the future. However, charophytes are not able to replace the niche of the other key habitat-forming species due to different substrate, wave exposure and salinity preferences.

Simulation and evaluation of the spatial heterogeneity of shallow-groundwater environmental risk in an urban–rural fringe of megacity: a case study of Shenyang city, northeast China

Shenbei district of Shenyang city, Liaoning province, northeast China is a typical urban–rural (peri-urban) fringe of a megacity that is undergoing rapid urban expansion and the intensification of agricultural management, which threatens the quality of shallow groundwater in the area. This study selects proper indices relevant to vulnerability, stress, function, and adaptability based on the results of groundwater monitoring and the collection and processing of hydrogeological data. Based on the theory of natural disaster risk, an evaluation index system was then established, followed by constructing a groundwater environmental-risk assessment model. Next, the model was used to perform environmental risk grading and spatial pattern assessment on the groundwater based on the statistics and spatial analysis function of ArcGIS. The assessment results were then subjected to a correlation analysis with land use indices and socioeconomic indices, to explore the effects of natural geological conditions, urban expansion and intensive agricultural management on groundwater environment. The results of the study will provide support for land use optimization and water environmental management decision-making during the development and construction of the urban–rural fringe of the area. The results of the study showed that: (1) the spatial distribution of groundwater environmental risk of the Shenbei district is similar to that of groundwater vulnerability. At present, geological conditions are still the main influential factors of groundwater environmental risk in the region; (2) the scale and intensity of urbanization and intensive agricultural management have shown an obvious potential risk to the groundwater environment; (3) the large area paddy fields in the northwest have certain ecological function value, which should be further protected to ensure a win–win situation of agricultural production and environmental safety; (4) it is necessary to put more environmental protection efforts such as ensuring natural vegetation protection, strengthening artificial afforestation, and increasing the number of waste water treatment facilities during the urbanization process, as these measures are significantly effective in maintaining the groundwater quality.

A multi-plot assessment of vegetation structure using a micro-unmanned aerial system (UAS) in a semi-arid savanna environment

Unmanned Aerial Systems (UAS) represent an important niche platform for measuring vegetation health, structure, and productivity; metrics that directly inform sustainable conservation and development initiatives in rural African savannas. Products derived from UAS imagery have much finer spatial resolutions than traditional satellite or aircraft imagery, allowing the spectral and structural heterogeneity of vegetation to be mapped and monitored with more detail, an advantage especially useful for challenging environments such as dryland savannas. This study uses UAS-captured imagery to assess the efficacy of UAS for monitoring structural characteristics of vegetation in a mixed savanna woodland. The main objective was to compare multiple approaches for extracting woody vegetation structure from UAS imagery and assess correlations between in situ field measurements and UAS estimates. We compare different sensor types to determine whether multispectral data improve estimates of vegetation structure at the expense of spatial resolution. Results indicate that leveraging multispectral reflectance information, particularly in the near-infrared portion of the spectrum, aids in crown delineation, areal estimates, and fractional cover of woody and non-woody vegetation within the study area. We also compare two image segmentation techniques for crown delineation and found that all techniques perform best in grassy savanna sites where trees and shrubs are easily distinguishable. Overall, a region-growing technique consistently exhibits highest levels of agreement with in situ height and crown area measurements, while a simple height threshold is best for determining fractional coverage of structural classes present. Findings from this work contribute to the advancement for applying high spatial resolution, UAS-derived methods in remote sensing analyses with specific consideration towards autonomous crown delineation and resource management initiatives in dryland systems. Lastly, data-informed analyses, as presented here, provide robust scientific evidence that contribute to informing environmental management decisions when considering the use of UAS technology in conservation and wildlife management across Africa.

A system approach to environmental safety management in mining

The list of restrictions, including spatial, temporal, geological, economic, regulatory, etc., within which the mining enterprise operates as a natural-technical system is given. The general scheme of a natural-industrial system and the restrictions which determine the conditions for its functioning is given. It is stated that any natural-industrial system being a sophisticated complex dynamical system is at the same time an inertial system which quality is important for its operation and management decisions. A scheme of functioning of a naturalindustrial system in time has been presented. The process of its functioning is described. With regards to complexity of environmental issues and environmental legal regulations in mining, a risk assessment approach is proposed which is based on taking into account the external limitations and impacts of different origin, related to environmental safety restrictions. Based on the conclusions of different authors the need to consider a mining enterprise as a dynamic “natural - industrial” system which is permanently under impact of changing external factors is demonstrated. An example of changes of environmental legal regulations as one of the external impacts is given.An example of changes in one of the external limitations that determine the risks of environmental management decisions is given. An analysis of changes in the legal requirements for hazardous wastes management over the past 20 years (from 2000 to 2019) is presented. The possible consequences of these changes for mining enterprises are described.. In addition, the risks that arise for mining enterprises in the event of failure to comply with these environmental requirements are described as well. Conclusions are drawn about the possible areas of application of the approach given in the article.

Development of phosphorus sorption capacity-based environmental indices for tile-drained systems.

The persistent environmental relevance of phosphorus (P) and P sorption capacity (PSC) on P loss to surface waters has led to proposals for its inclusion in soil fertility and environmental management programs. As fertility and environmental management decisions are made on a routine basis, the use of laborious P sorption isotherms to quantify PSC is not feasible. Alternatively, pedotransfer functions (pedoTFs) estimate PSC from routinely assessed soil chemical properties. Our objective was to examine the possibility of developing a suitable pedoTF for estimating PSC and to evaluate subsequent PSC-based indices (P saturation ratio [PSR] and soil P storage capacity [SPSC]) using data from an in-field laboratory where tile drain effluent is monitored daily. Phosphorus sorption capacity was well predicted by a pedoTF derived from soil aluminum and organic matter (R²=.60). Segmented-line relationships between PSR and soluble P were observed in both desorption assays (R²=.69) and drainflows (R²=.66) with apparent PSR thresholds in close agreement at 0.21 and 0.24, respectively. Negative SPSC values exhibited linear relationships with increasing soluble P concentrations in both desorption assays and drainflows (R²=.52 and R2 =.53 respectively), whereas positive SPSC values were associated with low SP concentrations. Therefore, PSC-based indices determined using pedoTFs could estimate the potential for subsurface soluble P losses. Also, we determined that both index thresholds coincided with the critical soil-test P level for agronomic P sufficiency (22mg kg-1 Mehlich-3 P) suggesting that the agronomic threshold could serve as an environmental P threshold.

Lessons Learnt from the Long-Term Management of a Large (Re)constructed Wetland, the Kis-Balaton Protection System (Hungary)

Environmental management decisions should be made based on solid scientific evidence that relies on monitoring and modeling. In practice, changing economic, societal, and political boundary conditions often interfere with management during large, long, and complex projects. The result may be a sub-optimal development path that may finally diverge from the original intentions and be economically or technically ineffective. Nevertheless, unforeseen benefits may be created in the end. The Kis-Balaton wetland system is a typical illustration of such a case. Despite tremendous investments and huge efforts put in monitoring and modeling, the sequence of decisions during implementation can hardly be considered optimal. We use a catchment model and a basic water quality model to coherently review the impacts of management decisions during the 30-year history. Due to the complexity of the system, science mostly excelled in finding explanations for observed changes after the event instead of predicting the impacts of management measures a priori. In parallel, the political setting and sectoral authorities experienced rearrangements during system implementation. Despite being expensive as a water quality management investment originally targeting nutrient removal, the Kis-Balaton wetland system created a huge ecological asset, and thereby became worth the price.

Modeling of environmental aspects related to reverse osmosis desalination supply chain

Background: This study aimed to model optimization of strategic environmental management decisions in the operation of reverse osmosis desalination, emphasizing the costs required for the environmental protection during the production of freshwater using reverse osmosis technology. Methods: This analytical research was conducted in five cities of Hormozgan province in Iran for 18 months from February 2018 to September 2019. The research includes eight phases of defining the research problem, data collection, preliminary data analysis and decision criteria, mathematical modeling, model validation, information preparation, analysis and finally discussion, conclusions and suggestions. The main environmental issues were the carbon dioxide (CO2 ) release rate due to power demand and rejected brine water (RBW) were entered the mathematical model. Results: The desalination plants of Abu Musa, Bandar Abbas, Qeshm, Sirik, and Hormoz with water production flow rate of 2100, 89 000, 5300, 3300 and 1500 m3 /d can generate 2360.82, 100053.80, 5958.260, 3709.86 and 1686.30 tons/year of CO2 emissions respectively. This output requires 1.35, 57.47, 3.42, 2.13 and 0.97 million USD for controlling the process, respectively. For reduction of the negative effect of RBW 0.75, 22.79, 1.78, 1.15 and 0.55 million USD respectively, is needed. Conclusion: Recommendations for environmental impacts protection of RBW, for desalination capacity up to 50 000 m3 /d, are; (a) for desalination capacity up to 50 000 m3 /d; dilution the RBW using raw water before entering into the sea, (b) for capacity of 50 000-100 000 m3 /d; dispersing RBW in sea using diffuser, and (c) for capacity more than 100 000 m3 /d; hybrid water desalination plants and power plant. Application of power plant cooling water to dilute RBW may reduce cost.

Source and health risk apportionment for PM2.5 collected in Sha-Lu area, Taiwan

Many investigations have found that the effects of chemical composition of PM2.5 plays an important role on human health. This study develops an air pollution source reduction strategy that apportions health risk among different emission sources by combining the receptor model and human health risk assessment technique. The goal is to identify emission source of PM2.5 that have the greatest impact on human health. We establish a PM2.5 sampling station in Sha-Lu, Taiwan. Five PM2.5 pollution sources were identified in this study. They are steel industry, residual oil combustion, road dust and traffic, coal-fired power plant, and glass production industry. The results of health risk assessment indicated that the metal contributes the most cancer risk is arsenic, followed by hexavalent chromium. The emissions of these two metals are closely related to coal-fired power plant. We suggested that the power plants should use coal with a low As and Cr content of coal in order to reduce the health risk associated with exposure to PM2.5. The limitation of this study is that only a small fraction of compounds was measured in the samples. However, this study demonstrates that an analysis of the degree to which emission sources contribute to health risks is a useful technique. Most importantly, it provides valuable information for making environmental management decision.

Ecological and economic management of innovation activity of enterprises

Purpose. To analyze the current state of innovation and environmental projects, to develop management strategies and algorithms for project environmental and economic management in subsoil use, to propose a mathematical model of the system of perception and support of solutions for effective environmental and economic management. Methodology. Different methods of cognition were used for scientific research both general and special. Analytical study on the state and realities of innovation in Ukraine in general and in particular in the mining industry, identification of trends in world practice were conducted based on using the methods of content analysis, quantitative and qualitative comparison. Methods of scientific abstraction and systematization were used to develop strategies and basic algorithm of project environmental and economic management in subsoil use. Mathematical methods were used to develop a mathematical model and coordinate it with the developed algorithm of project environmental and economic management for subsequent use in the system of perception and support of decisions. Findings. A systematic approach to the definition of management strategies and an algorithm of project environmental and economic management for the implementation of effective economic and environmental management of mining enterprises is proposed. A mathematical model designed for use in the system of perception and support of decisions for effective environmental and economic management has been developed. An analytical study on the state of innovation in Ukraine in general and in particular in the extractive industry has been conducted. This provided an opportunity to identify trends in the dynamics of change regarding the introduction of innovations and identify the causes of existing trends. Originality. The study identified threatening trends in the practice of introducing innovation. To solve production and scientific problems in the presence of significant challenges, new tools for finding optimal solutions in the environmental and economic management of subsoil use were proposed. Using a systematic approach, a mechanism for selecting strategies, an algorithm of project environmental and economic management and an original mathematical model have been proposed. Practical value. The results of the study can be used both to create new tools for information support of environmental and economic management, and by scientists and practitioners to develop effective solutions for the management and implementation of innovative projects in subsoil use.

Создание базы геоданных мелиоративной системы Калининградской области https://doi.org/10.35595/2414-9179-2020-3-26-184-198

The article describes the experience of creating a geodatabase of the melioration system of the Kaliningrad region for integration into an agriculture management automated system. The uniqueness of this melioration system is the scale of drainage facilities created during East Prussia time and Soviet period. The characteristic of the current condition of melioration system facilities is given. Actual problems and potential risks in the context of climate change are highlighted. The relevance of digitalization in melioration sector of public administration and in in the context of transboundary cooperation is explained. The primary data model, the structure of cartographic layers, and the composition of attribute information are considered. The features of the initial data and the problems of their preparation are described. The technology of inputting poorly formalized data has been developed. Authors used own service programs for geometry control, topology, and automation of operations, which allows increasing the productivity and accuracy of data input in comparison with standard means of basic geographic information systems. Thematic maps, examples of which are given, are the information basis for monitoring of drained lands of the Kaliningrad region to make environmental and economic management decisions. Promising areas of application of the geodatabase are proposed: geoportal project based on server data storage with using satellite information; project for hydrological modelling of hazardous and catastrophic occurrence in various melioration subsystems. The created geodatabase allows increasing the efficiency of processing and analysis of information about melioration systems on a local and regional scale enables the geographic visualization, helps in melioration management making-decisions.

Использование лазерного сканирования для оценки динамики абразионно-оползневых процессов в прибрежной зоне Севастополя

The article presents the results of a long-term study of hazardous environmental processes on the Sevastopol coast. Laser scanning allows estimating with high accuracy the natural and anthropogenic dynamics of Sevastopol coast that differ in the concentration of various exogenous processes in relatively small areas and pose a threat to the economic activities of the population. Using laser scanning (lidar shooting), key coastal sections were surveyed in the area of Nemetskaya gully and Uchkuevka beach, where landslide and abrasion processes are displaying. Ground laser survey was performed using a three-dimensional laser scanning system. It was carried out in July 2016, July 2017, July and October 2018. The study showed that the average rate of cliff destruction in the area of the Uchkuevka beach was 0.8 m/year. The maximum cliff destruction rate reached 4.6 m, which was observed in the local ten-meter section of the block slide. Because of terracing, an anthropogenic “cutout” about 150 m long, about 300 m wide and 2.5 to 10 m deep was formed in the area of the Nemetskaya gully. The calculation showed that the Vume of removed soil was 393,649.14 m3, and the Vume of dumped soil on the beach was 28,669.62 m3. The results can be used to monitor hazardous exogenous processes, model and predict coastal development, and make decisions for environmental management in the coastal zone.

Geoecological frame of the megalopolis – landscape-zonal standard of sustainable development of geosystems

The natural-man-made complex is an interconnected paragenetic geosystem that functions: “natural skeleto – technogenic structures – heat capacity – wind flow – moisture – biological effects – chemical reactions (corrosion) – a cartographic image”. The ghosting characteristics of a natural framework, as a cartographic image of space, serve as indicators and guide marks for making decisions in environmental management in order to protect objects and prevent damage from damaging factors, such as corrosion. Sustainable functioning of the geosystem ensures environmental safety and reliability of any territory and structure. The methodology for assessing the geoecological framework of the territory is substantiated and the analysis of the influence of anthropogenically transformed geosystems is performed. Diagnostics of the ecological state of an object or territory is aimed at establishing a deviation from the “norm”, a typical state of the structure and organization of geosystems. The basis of any diagnosis is the availability of a database of etalons, norms or standards.

Predicting and comparing chronic water quality criteria from physicochemical properties of transition metals

Criteria continuous concentrations (CCCs) are the scientific basis for making sound environmental management decisions and assessing surface water quality. Transition metals are a group of elements, similar in physicochemical properties perpendicularly and horizontally across the Periodic Table, some of which are widely distributed in aquatic environments and can cause adverse effects on aquatic life and human health. Currently internationally recommended CCCs are available for only seven transition metals. It is challenging to derive CCCs for transition metals based on scarce empirical information. The present study found significant relationships between the suggested CCCs of transition metals and their nine physicochemical properties and an empirical model was developed to satisfactorily predict the CCCs of 56 transition metals by use of the most relevant parameter, r, for protecting aquatic ecosystems. Predicted values were largely consistent with the CCCs and CMCs predicted previously and over 80% predicted CMCs of transition metals are greater than or equal to their CCCs. The present study shows methodological advantages in obtaining CCCs, and provides reliable reference values for setting chronic WQC and assessing risk of transition metals.

Development of the method of forecasting the atmospheric air pollution parameters based on error correction by neural-like structures of the model of successive geometric transformations

The article describes the importance of improving existing and exploring new algorithms for predicting environmental parameters to improve the quality of environmental monitoring. Because the organization and management of production require the development of new approaches to the problem of control and management of industrial sources of harmful substances based on new information technologies. One of the most problematic places in industrial air quality control and management systems is the development of advanced prospective air pollution forecasting algorithms. These algorithms must take into account t situational changes in data distribution and do not require retraining of atmospheric air pollution parameters. With the advent of neural-like structures, there is a need for their study, including the task of predicting the parameters of air pollution. The object of research is the neural-like structures of the Model of Successive Geometric Transformations. A method for predicting the parameters of atmospheric air pollution based on error correction with the help of a committee of different types of neural-like structures is proposed. In the course of the study, three methods for predicting the parameters of atmospheric air pollution were analyzed: a Generalized Regression Neural Network, a Radial Basis Function, and a neural-like structure of Sequential Geometric Transformations Model. A combination of these methods was performed and the results of the three methods were compared. It is experimentally determined that the prediction of atmospheric air pollution parameters based on the error correction using the committee of neural-like structures of the Sequential Geometric Transformations Model provides a prediction error reduction by 7 % of the General Regression Neural Network and by 2.6 % of the Radial Basis Function with extended General Regression Network. The obtained results increase the reliability and speed of forecasting of atmospheric air parameters to improve the quality of monitoring of emissions of harmful impurities in production and to make environmental management decisions.

Anthropogenic influences on the water quality of the Baiyangdian Lake in North China over the last decade

Baiyangdian Lake, the largest shallow lake in the North China Plain, is essential for maintaining ecosystem functioning in this highly populated region. To explore the influences of human activities on the lake’s water quality, an improved Water Quality Index (WQI) method and multivariate statistical techniques were adopted to assess the temporal and spatial variations of the lake’s water quality and explore the dominant factors of these variations. Datasets for 11 water quality parameters from six monitoring stations were used to evaluate the period spanning from 2006 to 2016. Assessment of the annual WQI showed that the water quality of the lake has generally improved over the past decade. Cluster analysis divided 12 months into the dry and wet periods and the six monitoring stations into those located in the western and eastern parts of the lake. Discriminant analysis demonstrated that with only two parameters (water temperature and fluoride) and six parameters (dissolved oxygen, ammonia nitrogen, total nitrogen, total phosphorus, anionic surfactant, and fecal coliform), 96.0% and 93.8% of the water quality data can be classified into the correct spatial and temporal clusters, respectively. For the principal component analysis and factor analysis, the varifactors detected for the two temporal clusters were similar, and varifactors related to pollution explained more variance in the water quality variation than the ones representing natural factors. For the two spatial clusters, the varifactors were different, indicating they are influenced by different types of anthropogenic activities. Correlation analysis between lake water level and water quality indicated that environmental water allocation to the lake generally improve water quality. These findings provide a more thorough understanding of driving mechanism of water quality and may be helpful for making environmental management decisions in Baiyangdian Lake and other large, shallow lakes in highly populated dryland regions.

Integrating uncertainty of preferences and predictions in decision models: An application to regional wastewater planning

Decision-making in environmental management requires eliciting preferences of stakeholders and predicting outcomes of decision alternatives. Usually, preferences and predictions are both uncertain. Uncertainty of predictions can be tackled by multi-attribute utility theory, but the uncertainty of preferences remains a challenge. We demonstrate an approach for including both uncertainties in a multi-criteria decision analysis (MCDA), using utility theory and the concept of expected expected utility. For a decision regarding a regional merger of wastewater infrastructure in Switzerland, we constructed preference models for four stakeholders. These models also allowed for non-additive interactions between objectives. We evaluated the performance of eleven decision alternatives for which we predicted potential outcomes. Even though uncertainties were high, we could draw conclusions based on the expected expected utility of alternatives. Building a pipeline to discharge treated wastewater to a larger river emerged as a potential consensus alternative to mitigate the problem of micropollutants in a small stream. We investigated the robustness of the findings with sensitivity analysis regarding the preference parameters and the included objectives. In their actual decision, the stakeholders partly preferred other alternatives than those proposed by the model. Their choices could be explained by reduced decision models in which only few objectives were included. This may indicate the use of simplified choice heuristics by the stakeholders. The presented approach is feasible for supporting other difficult environmental or engineering decisions in practice, for which we give a number of recommendations.

Unpacking the Black Box: Demystifying Ecological Models Through Interactive Workshops and Hands-On Learning

Environmental management decisions increasingly rely on ecological models to forecast outcomes of management actions. Models are becoming increasingly complex through the integration of processes from multiple disciplines (e.g., linking engineering and ecological models). These models are often viewed as mysterious, baffling black boxes, which can lead to mistrust, misinterpretation and/or misapplication of model results. Numerical models have historically been developed without decision makers, coordinating partners, or stakeholders playing active roles in model development, which further complicates communication. Ultimately, mistrust of models and associated outputs can lead to poor decision-making, increase the risk of ineffective decisions, and lead to litigation over decisions. Improved ecological model development practices are needed to increase transparency, include stakeholders and decision makers throughout the entire modeling process from conceptualization through application, and overcome common communication barriers. Building from participatory modeling and prototyping methods, we have developed a workshop approach for applied ecosystem modeling problems that cultivates a foundational understanding of ecological models through hands-on, interactive model development. In this workshop environment, interdisciplinary and interagency working groups co-develop models in real-time which demystifies technical issues and educates participants on the modeling process. The purpose of this paper is to synthesize a repeatable mediated modeling workshop and identify its utility in overcoming major communication challenges of integrated modeling for complex environmental problems. The workshop approach informs modeling teams of the complexity facing decision-makers, creates a sense of model ownership by participants, builds trust among partners, and ultimately increases “buy-in” of the eventual decision.

Local Residents' Perceptions for Ecosystem Services: A Case Study of Fenghe River Watershed.

To make environmental management decisions more executive and targeted, it is essential for decision-making to include local residents’ perceptions and preferences for ecosystem services (ES) and biodiversity (BD). This study conducted a questionnaire survey with 386 local residents to explore social perceptions for ES and BD in the Fenghe River watershed. ES contain food from agriculture (AGR), food from livestock (LIV), fresh water (FW), air purification (AP), water purification (WP), water retention (WR), soil retention (SR), aesthetics (AES), recreation (RE), and spirit (SP) in this study. Ranking and Likert scales are combined to identify residents’ preferences for ES and BD. The hypothetical market method is used to identify the willingness to pay for BD and ES. Independent sample T-test, one-way ANOVA, and Spearman correlation are used to analyze preference heterogeneity. The results show that: (1) residents prefer WP, AP, AGR, and FW; (2) 51.3% of respondents are willing to pay a fee for improving ES while 48.7% of respondents are unwilling to pay; (3) residents’ personal and residential factors affect preference heterogeneity. Last, we put forward three management suggestions on controlling environmental pollution, improving residents’ awareness of ES, and establishing a multi-channel fund based on government financial resources for improving ES in the Fenghe River watershed. Integrating stakeholders’ perceptions for ES into decision-making can promote the sustainable development in Fenghe River watershed.

Esdm: A tool for creating and exploring ensembles of predictions from species distribution and abundance models

Abstract Species distribution models (SDMs) are a valuable statistical approach for both understanding species distributions and identifying potential impacts of environmental changes or management decisions to species, but multiple SDMs for the same species in a region can create confusion in decision‐making processes. One solution is to create ensembles (i.e. combinations) of predictions from existing SDMs. However, creating ensembles can be challenging if the predictions were made at different spatial resolutions, using different data sources, or with different prediction value types (e.g. abundance and probability of occurrence). We present esdm, an r package that allows users to create an ensemble of SDM predictions overlaid onto a single base geometry. These predictions can be evaluated (e.g. through among‐model uncertainty or AUC, TSS and RMSE metrics), mapped, and exported. esdm includes a built‐in GUI created using the r package shiny, which makes the package accessible to non‐r users. We provide an overview of esdm functionality and use esdm to create an ensemble of predictions from three blue whale Balaenoptera musculus SDMs for the California Current Ecosystem.

Comparison of GLUE and DREAM for the estimation of cultivar parameters in the APSIM-maize model

Process-based crop models are popular scientific tools to study the impacts of environmental conditions and management decisions on crop growth. Some cultivar parameters in crop models cannot be measured directly and need to be estimated. In this research, two most popular Bayesian methods, namely generalized likelihood uncertainty estimation (GLUE) and Differential Evolution Adaptive Metropolis (DREAM), were used for the first time to estimate parameters of the maize module of the Agricultural Productions Systems sIMulator model (APSIM-maize). Both theoretical and real-world evaluations were conducted to compare the performances of these two methods. The maize yields from 2003 to 2006 were used for model calibration, and the yields from 2007 to 2013 were used for model validation. Both GLUE and DREAM performed well in the theoretical and real-world evaluation. During the validation period (2007–2013), when the heteroscedastic model error assumption was adopted in DREAM, on average approximate 90% of observed yield values were captured in the 95% confidence band of DREAM (P-factor = 90.47%), which was larger than that using GLUE (P-factor = 80.93%). Meanwhile the uncertainty bands of DREAM (R-factor = 4.42) were wider than those of GLUE (R-factor = 2.32). If only one parameter set was allowed to be used in the simulation, the weighted mean parameter values according to the likelihood of each parameter set performed better than the parameter set with maximum likelihoods for GLUE while the opposite is true for DREAM. But considering future analysis in the real-world evaluation, the moderate performance of these two methods suggests that a single parameter set obtained by neither GLUE nor DREAM is satisfactory and ensemble simulation is needed. Overall, GLUE and DREAM had similar performance, but GLUE is more convenient and simpler to use than DREAM. So we think GLUE is a better choice than DREAM for estimating cultivar parameters of APSIM-maize.