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"Ecosystem analysis"
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Spatial analysis of coastal environments
\"At the convergence of the land and sea, coastal environments are some of the most dynamic and populated places on Earth. This book explains how the many varied forms of spatial analysis, including mapping, monitoring and modelling, can be applied to a range of coastal environments such as estuaries, mangroves, seagrass beds and coral reefs. Presenting empirical geographical approaches to modelling, which draw on recent developments in remote sensing technology, geographical information science and spatial statistics, it provides the analytical tools to map, monitor and explain or predict coastal features. With detailed case studies and accompanying online practical exercises, it is an ideal resource for undergraduate courses in spatial science. Taking a broad view of spatial analysis and covering basic and advanced analytical areas such as spatial data and geostatistics, it is also a useful reference for ecologists, geomorphologists, geographers and modellers interested in understanding coastal environments\"-- Provided by publisher.
Book
The digital transformation of the healthcare industry: exploring the rise of emerging platform ecosystems and their influence on the role of patients
While traditional organizations create value within the boundaries of their firm or supply chain, digital platforms leverage and orchestrate a platform-mediated ecosystem to create and co-create value with a much wider array of partners and actors. Although the change to two-sided markets and their generalization to platform ecosystems have been adopted among various industries, both academic research and industry adoption have lagged behind in the healthcare industry. To the best of our knowledge current Information Systems research has not yet incorporated an interorganizational perspective of the digital transformation of healthcare. This neglects a wide range of emerging changes, including changing segmentation of industry market participants, changing patient segments, changing patient roles as decision makers, and their interaction in patient care. This study therefore investigates the digital transformation of the healthcare industry by analyzing 1830 healthcare organizations found on Crunchbase. We derived a generic value ecosystem of the digital healthcare industry and validated our findings with industry experts from the traditional and the start-up healthcare domains. The results indicate 8 new roles within healthcare, namely: information platforms, data collection technology, market intermediaries, services for remote and on-demand healthcare, augmented and virtual reality provider, blockchain-based PHR, cloud service provider, and intelligent data analysis for healthcare provider. Our results further illustrate how these roles transform value proposition, value capture, and value delivery in the healthcare industry. We discuss competition between new entrants and incumbents and elaborate how digital health innovations contribute to the changing role of patients.
Journal Article
Core, intertwined, and ecosystem-specific clusters in platform ecosystems: analyzing similarities in the digital transformation of the automotive, blockchain, financial, insurance and IIoT industry
Digital transformation is continuously changing ecosystems, which also forces established companies to re-evaluate their value proposition. However, only transformations of single ecosystems have been studied. Therefore, this work targets to examine the similarities of digital transformation in five platform ecosystems: automotive, blockchain, financial, insurance, and IIoT. For our analysis, we combine the strengths of conceptual modeling using e3 value with a cluster analysis based on text mining to identify similarities in the respective ecosystems. As a result, we identified 15 clusters. Cluster 01 is the core cluster, containing the roles of organizations from all five ecosystems. Cluster 02–05 are intertwined, as they include roles from at least two ecosystems. Clusters 06–15 are ecosystem-specific that only include roles found in one ecosystem. Scholars and practitioners can use these clusters when analyzing or building a new platform ecosystem, or transforming a traditional ecosystem towards a platform ecosystem.
Journal Article
Increased adoption of best practices in ecological forecasting enables comparisons of forecastability
Near-term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross-ecosystem analysis of near-term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near-term (≤10-yr forecast horizon) ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near-term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near-term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.
Journal Article
Remote Sensing of Ecosystem Water Use Efficiency: A Review of Direct and Indirect Estimation Methods
Water use efficiency (WUE) is a key index for understanding the ecosystem of carbon–water coupling. The undistinguishable carbon–water coupling mechanism and uncertainties of indirect methods by remote sensing products and process models render challenges for WUE remote sensing. In this paper, current progress in direct and indirect methods of WUE estimation by remote sensing is reviewed. Indirect methods based on gross primary production (GPP)/evapotranspiration (ET) from ground observation, processed models and remote sensing are the main ways to estimate WUE in which carbon and water cycles are independent processes. Various empirical models based on meteorological variables and remote sensed vegetation indices to estimate WUE proved the ability of remotely sensed data for WUE estimating. The analytical model provides a mechanistic opportunity for WUE estimation on an ecosystem scale, while the hypothesis has yet to be validated and applied for the shorter time scales. An optimized response of canopy conductance to atmospheric vapor pressure deficit (VPD) in an analytical model inverted from the conductance model has been also challenged. Partitioning transpiration (T) and evaporation (E) is a more complex phenomenon than that stated in the analytic model and needs a more precise remote sensing retrieval algorithm as well as ground validation, which is an opportunity for remote sensing to extrapolate WUE estimation from sites to a regional scale. Although studies on controlling the mechanism of environmental factors have provided an opportunity to improve WUE remote sensing, the mismatch in the spatial and temporal resolution of meteorological products and remote sensing data, as well as the uncertainty of meteorological reanalysis data, add further challenges. Therefore, improving the remote sensing-based methods of GPP and ET, developing high-quality meteorological forcing datasets and building mechanistic remote sensing models directly acting on carbon–water cycle coupling are possible ways to improve WUE remote sensing. Improvement in direct WUE remote sensing methods or remote sensing-driven ecosystem analysis methods can promote a better understanding of the global ecosystem carbon–water coupling mechanisms and vegetation functions–climate feedbacks to serve for the future global carbon neutrality.
Journal Article
Assessing ecosystem health changes in UlanSuhai Lake using hierarchical analysis and entropy method
【Objective】UlanSuhai Lake, a critical surface water body in the Hetao Irrigation District of Inner Mongolia, plays a vital role in supporting various sectors, including agriculture, industry and local livelihoods. Its ecological health has been influenced by diverse biotic and abiotic factors that have shown significant spatial and temporal variations. This paper is to analyze its ecological health and the underling determinants over recent years. 【Method】Ecological health of the lake was analyzed using data measured from 2015 to 2019. Key indicators considered include hydrological dynamics, physical, chemical, biological traits of the lake water, as well as social and ecological services the lake provides. The evaluation was calculated using the hierarchical analysis-entropy weight comprehensive health index.【Result】Ecosystem health of the lake showed a notable improvement over the study period. From 2015 to 2016, ecosystem health of the lake was classified as poor, transitioning to moderate from 2017 to 2018, and showing a marked improvement by 2019. Furthermore, the water quality of the lake has consistently improved over the last decade, now reaching Grade V of the national water quality standards. 【Conclusion】The hierarchical analysis-entropy weight comprehensive health index method is reliable and practical for evaluating ecological health of lakes, effectively identifying temporal changes and the underlying influencing factors. These findings provide a valuable baseline for guiding protection, management, and sustainable development of UlanSuhai Lake.
Journal Article
Deep Soil Horizons: Contribution and Importance to Soil Carbon Pools and in Assessing Whole-Ecosystem Response to Management and Global Change
Most of the C in terrestrial ecosystems is found in the soil. Although C calculations indicate that soils are more important than plants as reservoirs of C, soil rarely receives the attention given aboveground ecosystem components when C budgets are calculated. When soil pools are quantified they are typically sampled to relatively shallow depths. Shallow soil sampling in research includes studies that estimate C and nutrient pools and studies assessing the response of terrestrial ecosystems (i.e., forests, grasslands, and agricultural fields) to management treatments. Although many soils have sola that are substantially deeper than 20 cm and C accumulates well below these depths in many soils, the majority of studies of soil C sample to depths of 20 cm or less, generally because of the difficulty and cost of sampling the soil profile deeper. Shallow soil sampling is often justified by assuming that deeper soil horizons are stable and will not change over time, although some medium- and long-term studies do not support this assumption. Shallow soil sampling can result in both a major underestimate of soil C present in the soil profile and an inability to adequately measure the impacts of both treatments for specific goals (i.e., tillage, fertilization, and vegetation management) or other changes (i.e., global change and atmospheric inputs) over time in whole-ecosystem studies. We assessed the potential of shallow soil sampling to underestimate C in the soil profile as well as to change the conclusions of studies of management treatments on soil C. Results showed that where soils were sampled to at least 80 cm or more depth 27-77% of mineral soil C was found >20 cm in depth. In addition, analysis of results for 105 different studies of N fertilization in forests and N fertilization or conversion to switchgrass in agricultural studies shows that deeper sampling can actually change the conclusions of results of some research studies of net C accumulation or loss. Researchers wishing to either quantify soil C pools or measure changes of soil C over time are cautioned to sample soil profiles as deeply as possible and not assume that deeper soil horizons are not a critical part of adequate ecosystem analysis.
Journal Article
Variational quantum enhanced deep transfer learning for small underwater aqua species image classification
Precise underwater classification of small aquaculture species is essential for sustainable fisheries management, biodiversity monitoring, and automated marine ecosystem analysis. But it is still a challenging task owing to underwater image distortions from poor visibility, lighting changes, occlusions, and the high computational complexity of traditional deep learning models. To address these issues, we propose a Lightweight Variational Quantum Enhanced Deep Transfer Learning framework. This hybrid deep transfer learning model integrates pretrained classical convolutional neural networks with variational quantum circuits to improve feature representation and classification efficiency. The framework is designed to reduce computational complexity while enhancing accuracy by leveraging quantum feature extraction techniques. Experimental evaluations on curated small aquafarming species dataset demonstrate that the proposed approach achieves high classification accuracy (up to 99.25%) with significantly fewer parameters and floating-point operations, indicating its potential for resource-constrained applications. Ablation studies further validate the impact of quantum layers on model performance. These results suggest that quantum deep transfer learning models can offer a promising direction for robust and efficient underwater species classification.
Journal Article
The Influence of Ecoenvironment Factors on the Development of Skiing
Skiing depends on the external environment, in which material, energy and information are frequently exchanged, and this external environment greatly affects the quality of skiing’s existence. In this article, an ecosystem assessment algorithm based on ANN (Artificial Neural Network) is put forward. Based on this, a skiing development model under the influence of ecosystem factors is constructed to explore the influence of ecosystem factors on skiing development. The simulation results show that after many iterations, the error of this method is better than that of the comparison algorithm in ecosystem analysis, with the error reduced by 28.17 % and the recall rate reaching 94.65 %, which is improved by 16.88 % compared with the comparison algorithm. Therefore, this model can provide theoretical support for studying the influence of ecological and environmental factors on the growth of skiing. Based on the analysis of the characteristics of the regional landscape ecosystem, the eco-environmental impact assessment of the ski resort project predicts the impact of project construction on the regional landscape ecosystem. It explores ways and means to maintain the ecological integrity of the natural system to carry out project construction on the premise of protecting the local ecosystem and building a natural and artificial composite landscape ecosystem with reasonable structure and high efficiency.
Journal Article
Mapping health evidence ecosystem in Brazil: a mixed-methods study protocol for developing a framework
Background
Evidence ecosystems play a vital role in informing and shaping policy; however, little is known about how these systems function, particularly in diverse national contexts. This article presents a mixed-methods study designed to be the first to systematically map and characterize Brazil’s health evidence ecosystem, focusing on key institutions, actors, and practices across the country. We aim to map, describe and analyse the ecosystem of production, use and dissemination of policy evidence in Brazil’s five regions.
Methods
We will adopt a five-step mixed-method approach to understand Brazil’s health evidence ecosystem comprehensively. Five steps will be undertaken: (a) cultural adaptation of two published situation analysis tools, (b) digital ethnography to identify and categorize organizations that are evidence producers, users, or intermediaries as well the types of evidence demanded, (c) autoethnography workshops with selected organizations, (d) interviews to apply an evidence institutionalization and (e) focus groups to produce a situation analysis of institutional practices, governance and the overall environment for evidence use in policy in each of the five Brazilian regions.
Discussion
This study combines diverse tools to understand how evidence is produced, disseminated and utilized in Brazil. The study of regional differences and institutional practices can help to identify barriers and facilitators to the effective use of evidence in policy. Furthermore, it may inform the development of strategies to strengthen the evidence ecosystem in Brazil and serve as a roadmap for other countries that aiming to conduct a comprehensive evidence ecosystem analysis.
Journal Article