Multifactorial Uncertainty Assessment for Monitoring Population Abundance using Computer Vision

Computer vision enables in-situ monitoring of animal populations at a lower cost and with less ecosystem disturbance than with human observers. However, computer vision uncertainty may not be fully understood by end-users, and the uncertainty assessments performed by technology experts may not fully address end-user needs. This knowledge gap can yield misinterpretations of computer vision data, and trust issues impeding the transfer of valuable technologies. We bridge this gap with a user-centered analysis of the uncertainty issues. Key uncertainty factors, and their interactions, are identified from the perspective of a core task in ecology research and beyond: counting individuals from different classes. We highlight factors for which uncertainty assessment methods are currently unavailable. The remaining uncertainty assessment methods are not interoperable. Hence it is currently difficult to assess the combined results of multiple uncertainty factors, and their impact on end-user counting tasks. We propose a framework for assessing the multifactorial uncertainty propagation along the data processing pipeline. It integrates methods from both computer vision and ecology domains, and aims at supporting the statistical analysis of abundance trends for population monitoring. Our typology of uncertainty factors and our assessment methods were drawn from interviews with marine ecology and computer vision experts, and from prior work for a fish monitoring application. Our findings contribute to enabling scientific research based on computer vision

Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data

This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and

Navigating the Future V: Marine Science for a Sustainable Future

Navigating the Future is a publication series produced by the European Marine Board providing future perspectives on marine science and technology in Europe. Navigating the Future V (NFV) highlights new knowledge obtained since Navigating the Future IV1 (2013). It is set within the framework of the 2015 Paris Agreement2 and builds on the scientific basis and recommendations of the IPCC reports3. NFV gives recommendations on the science required during the next decade to deliver the ocean we need to support a sustainable future. This will be important for the United Nations Decade of Ocean Science for Sustainable Development4 (2021 – 2030), the implementation of the UN Sustainable Development Goals5 and the European Commission’s next framework programme, Horizon Europe6 (2021 - 2027). There is a growing need to strengthen the links between marine science, society and policy since we cannot properly manage what we do not know. In recent years, the ocean and seas have received new prominence in international agendas. To secure a safe planet a priority is the management of the ocean as a “common good for humanity”, which requires smarter observations to assess of the state of the ocean and predictions about how it may change in the future. The ocean is a three-dimensional space that needs to be managed over time (thus four-dimensional), and there is a need for management and conservation practices that integrate the structure and function of marine ecosystems into these four dimensions (Chapter 2). This includes understanding the dynamic spatial and temporal interplay between ocean physics, chemistry and biology. Multiple stressors including climate change, pollution and over-fishing affect the ocean and we need to better understand and predict their interactions and identify tipping points to decide on management priorities (Chapter 3). This should integrate our understanding of land-ocean-atmosphere processes and approaches to reducing impacts. An improved science base is also needed to help predict and minimize the impact of extreme events such as storm surges, heat waves, dynamic sea-floor processes and tsunamis (Chapter 4). New technologies, data handling and modelling approaches will help us to observe, understand and manage our use of the fourdimensional ocean and the effect of multiple stressors (Chapter 5). Addressing these issues requires a strategic, collective and holistic approach and we need to build a community of sustainability scientists that are able to provide evidence-based support to policy makers within the context of major societal challenges (Chapter 6). We outline new frontiers, knowledge gaps and recommendations needed to manage the ocean as a common good and to develop solutions for a sustainable future (Chapter 7). The governance of sustainability should be at the core of the marine research agenda through co-production and collaboration with stakeholders to identify priorities. There is need for a fully integrated scientific assessment of resilience strategies, associated trade-offs and underlying ethical concepts for the ocean, which should be incorporated into decision support frameworks that involve stakeholders from the outset. To allow the collection, processing and access to all data, a key priority is the development of a business model that ensures the long-term economic sustainability of ocean observations

Empirical and mechanistic approaches to understanding and projecting change in coastal marine communities

This work details the effects of disturbance events on tropical coral reefs and highlights emerging techniques for improved monitoring and assessment of benthic change. The first chapter is in the form of a literature review, which aims to give a broad introduction to reef ecology, the impacts experienced by this system, and the methods used to monitor and assess change. The second chapter highlights a recently developed photogrammetric methodology which can be used to assess change in the marine environment. The methodology is then assessed for accuracy and comparability to standard benthic monitoring techniques. // The proceeding four chapters aim to address a number of ecological and management questions relating to reef community ecology, focussing on physical structure and demonstrating the utility of ‘Structure from Motion’ (SfM) photogrammetry as a monitoring and assessment tool. Chapters three and four more specifically use community managed small-scale Marine Protected Areas (MPAs) in the Philippines as a case study applying SfM, and assess the effectiveness of these MPAs. These chapters further highlight how physical changes can affect the function of the reefs and their associated fisheries. Chapters five and six then investigate how extreme climatic events can affect the structure and growth of reefs in the Indian Ocean, away from the array of confounding anthropogenic factors seen in the Philippines. // The final section looks to bring together these chapters to discuss the benefits of new technology, and the future of reefs under a changing climate

Book of Abstracts

ICES Annual Science Conference, 19 – 23 September 2011, Gdańsk Music and Congress Center, Gdańsk, Poland. IMR contributors: Benjamin Planque, Torild Johansen, Tuula Skarstein, Jon‐Ivar Westgaard, Halvor Knutsen, Kristin Helle, Michael Pennington, Marek Ostrowski, Nils Olav Handegard, Mette Skern‐Mauritzen, Edda Johannesen, Ulf Lindstrøm, Harald Gjøsæter, Ken Drinkwater, Trond Kristiansen, Geir Ottersen, Esben Moland Olse

Data Informed Health Simulation Modeling

Combining reliable data with dynamic models can enhance the understanding of health-related phenomena. Smartphone sensor data characterizing discrete states is often suitable for analysis with machine learning classifiers. For dynamic models with continuous states, high-velocity data also serves an important role in model parameterization and calibration. Particle filtering (PF), combined with dynamic models, can support accurate recurrent estimation of continuous system state. This thesis explored these and related ideas with several case studies. The first employed multivariate Hidden Markov models (HMMs) to identify smoking intervals, using time-series of smartphone-based sensor data. Findings demonstrated that multivariate HMMs can achieve notable accuracy in classifying smoking state, with performance being strongly elevated by appropriate data conditioning. Reflecting the advantages of dynamic simulation models, this thesis has contributed two applications of articulated dynamic models: An agent-based model (ABM) of smoking and E-Cigarette use and a hybrid multi-scale model of diabetes in pregnancy (DIP). The ABM of smoking and E-Cigarette use, informed by cross-sectional data, supports investigations of smoking behavior change in light of the influence of social networks and E-Cigarette use. The DIP model was evidenced by both longitudinal and cross-sectional data, and is notable for its use of interwoven ABM, system dynamics (SD), and discrete event simulation elements to explore the interaction of risk factors, coupled dynamics of glycemia regulation, and intervention tradeoffs to address the growing incidence of DIP in the Australia Capital Territory. The final study applied PF with an SD model of mosquito development to estimate the underlying Culex mosquito population using various direct observations, including time series of weather-related factors and mosquito trap counts. The results demonstrate the effectiveness of PF in regrounding the states and evolving model parameters based on incoming observations. Using PF in the context of automated model calibration allows optimization of the values of parameters to markedly reduce model discrepancy. Collectively, the thesis demonstrates how characteristics and availability of data can influence model structure and scope, how dynamic model structure directly affects the ways that data can be used, and how advanced analysis methods for calibration and filtering can enhance model accuracy and versatility

Recent developments on precision beekeeping: A systematic literature review

The aim of this systematic review was to point out the current state of precision beekeeping and to draw implications for future studies. Precision beekeeping is defined as an apiary management strategy based on monitoring individual bee colonies to minimize resource consumption and maximize bee productivity. This subject that has met with a growing interest from researchers in recent years because of its environmental implications. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was selected to conduct this review. The literature search was carried out in the Scopus database for articles published between 2015 and 2023, being a very recent issue. After two rounds screening and examination, 201 studies were considered to be analysed. They were classified based on the internal parameters of the hive, in turn divided by weight, internal temperature, relative humidity, flight activity, sounds and vibrations, gases, and external parameters, in turn divided by wind speed, rainfall and ambient temperature. The study also considered possible undesirable effects of the use of sensors on bees, economic aspects and applications of Geographic Information System technologies in beekeeping. Based on the review and analysis, some conclusions and further directions were put forward

Extended Methods to Handle Classification Biases

Classifiers can provide counts of items per class, but systematic classification errors yield biases (e.g., if a class is often misclassified as another, its size may be under-estimated). To handle classification biases, the statistics and epidemiology domains devised methods for estimating unbiased class sizes (or class probabilities) without identifying which individual items are misclassified. These bias correction methods are applicable to machine learning classifiers, but in some cases yield high result variance and increased biases. We present the applicability and drawbacks of existing methods and extend them with three novel methods. Our Sample-to-Sample method provides accurate confidence intervals for the bias correction results. Our Maximum Determinant method predicts which classifier yields the least result variance. Our Ratio-to-TP method details the error decomposition in classifier outputs (i.e., how many items classified as class Cy truly belong to Cx, for all possible classes) and has properties of interest for applying the Maximum Determinant method. Our methods are demonstrated empirically, and we discuss the need for establishing theory and guidelines for choosing the methods and classifier to apply