The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

Implementing result-based agri-environmental payments by means of modelling

From a theoretical point of view, result-based agri-environmental payments are clearly preferable to action-based payments. However, they suffer from two major practical disadvantages: costs of measuring the results and payment uncertainty for the participating farmers. In this paper, we propose an alternative design to overcome these two disadvantages by means of modelling (instead of measuring) the results. We describe the concept of model-informed result-based agri-environmental payments (MIRBAP), including a hypothetical example of payments for the protection and enhancement of soil functions. We offer a comprehensive discussion of the relative advantages and disadvantages of MIRBAP, showing that it not only unites most of the advantages of result-based and action-based schemes, but also adds two new advantages: the potential to address trade-offs among multiple policy objectives and management for long-term environmental effects. We argue that MIRBAP would be a valuable addition to the agri-environmental policy toolbox and a reflection of recent advancements in agri-environmental modelling

Access beyond geographic accessibility: understanding opportunities to human needs in a physical-virtual world

Access to basic human needs, such as food and healthcare, is conceptually understood to be comprised of multiple spatial and aspatial dimensions. However, research in this area has traditionally been explored with spatial accessibility measures that almost exclusively focus on just two dimensions. Namely, the availability of resources, services, and facilities, and the accessibility or ease to which locations of these opportunities can be reached with existing land-use and transport systems under temporal constraints and considering individual characteristics of people. These calculated measures are insufficient in holistically capturing available opportunities as they ignore other components, such as the emergence of virtual space to carry out activities and interactions enabled by modern information and communication technologies (ICT). Human dynamics today exist in a hybrid physical-virtual space, and recent research has highlighted the importance of understanding ICT, individual behavior, local context, social relations, and human perceptions in identifying opportunities available to people. However, there lacks a holistic approach that relates these different aspects to access research. This dissertation addresses this gap by proposing a new conceptual framework for the geography of access for various kinds of human needs, using food access as a case study to illustrate how the proposed framework can be applied to address critical societal issues. An interactive multispace geographic information system (GIS) web application is developed to better understand and visualize individual potential food access based on the conceptual framework. This dissertation contributes to the body of research with a proposed conceptual framework of access in a hybrid physical-virtual world, integration of various big and small data sources to reveal information relating to the access of people, and novel development of a multi-space GIS to analyze and visualize access to opportunities

Non-convex resource allocation in communication networks

The continuously growing number of applications competing for resources in current communication networks highlights the necessity for efficient resource allocation mechanisms to maximize user satisfaction. Optimization Theory can provide the necessary tools to develop such mechanisms that will allocate network resources optimally and fairly among users. However, the resource allocation problem in current networks has characteristics that turn the respective optimization problem into a non-convex one. First, current networks very often consist of a number of wireless links, whose capacity is not constant but follows Shannon capacity formula, which is a non-convex function. Second, the majority of the traffic in current networks is generated by multimedia applications, which are non-concave functions of rate. Third, current resource allocation methods follow the (bandwidth) proportional fairness policy, which when applied to networks shared by both concave and non-concave utilities leads to unfair resource allocations. These characteristics make current convex optimization frameworks inefficient in several aspects. This work aims to develop a non-convex optimization framework that will be able to allocate resources efficiently for non-convex resource allocation formulations. Towards this goal, a necessary and sufficient condition for the convergence of any primal-dual optimization algorithm to the optimal solution is proven. The wide applicability of this condition makes this a fundamental contribution for Optimization Theory in general. A number of optimization formulations are proposed, cases where this condition is not met are analysed and efficient alternative heuristics are provided to handle these cases. Furthermore, a novel multi-sigmoidal utility shape is proposed to model user satisfaction for multi-tiered multimedia applications more accurately. The advantages of such non-convex utilities and their effect in the optimization process are thoroughly examined. Alternative allocation policies are also investigated with respect to their ability to allocate resources fairly and deal with the non-convexity of the resource allocation problem. Specifically, the advantages of using Utility Proportional Fairness as an allocation policy are examined with respect to the development of distributed algorithms, their convergence to the optimal solution and their ability to adapt to the Quality of Service requirements of each application

Monitoring biodiversity change through effective global coordination

The ability to monitor changes in biodiversity, and their societal impact, is critical to conserving species and managing ecosystems. While emerging technologies increase the breadth and reach of data acquisition, monitoring efforts are still spatially and temporally fragmented, and taxonomically biased. Appropriate long-term information remains therefore limited. The Group on Earth Observations Biodiversity Observation Network (GEO BON) aims to provide a general framework for biodiversity monitoring to support decision makers. Here, we discuss the coordinated observing system adopted by GEO BON, and review challenges and advances in its implementation, focusing on two interconnected core components — the Essential Biodiversity Variables as a standard framework for biodiversity monitoring, and the Biodiversity Observation Networks that support harmonized observation systems — while highlighting their societal relevance

A data-driven model for Fennoscandian wildfire danger

Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. Data-driven models are suitable for identification of dominant factors of complex and partly unknown processes and can both help improve process-based models and work as independent models. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly (2001-2019) satellite-based fire occurrence dataset at a 0.25° spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian Forest Fire Weather Index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This demonstrates the potential of developing reliable data-driven models for regions with a high-quality fire occurrence record and the limitation of using satellite-based fire occurrence data in regions subject to small fires not identified by satellites. We conclude that data-driven fire danger probability models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and the selected predictors can further be used to assess potential changes in fire danger probability under different (future) climate scenarios