Deep Reinforcement Learning for Conservation Decisions

Lapeyrolerie, Marcus; Chapman, Melissa; Norman, Kari; Boettiger, Carl

doi:10.48550/arxiv.2106.08272

Cited by 2 publications

(2 citation statements)

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“…This learning technique may give the much-needed empirical support to mechanistic frameworks which often face challenges in obtaining the required ecosystem state knowledge under the effect of potential management actions (Polasky et al, 2011). Furthermore, RL can support iterative and near real-time forecasting of management outcomes, thereby supporting agile decision-making for effective and quick management applications (Lapeyrolerie et al, 2021). This technology highlights the ability of AI to produce immediate, actionable solutions without introducing long-term threats to environmental systems (Nishant et al, 2020).…”

Section: Predictive Models and Adaptabilitymentioning

confidence: 99%

Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective

et al. 2022

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Conservation of marine ecosystems has been highlighted as a priority to ensure a sustainable future. Effective management requires data collection over large spatio-temporal scales, readily accessible and integrated information from monitoring, and tools to support decision-making. However, there are many roadblocks to achieving adequate and timely information on both the effectiveness, and long-term success of conservation efforts, including limited funding, inadequate sampling, and data processing bottlenecks. These factors can result in ineffective, or even detrimental, management decisions in already impacted ecosystems. An automated approach facilitated by artificial intelligence (AI) provides conservation managers with a toolkit that can help alleviate a number of these issues by reducing the monitoring bottlenecks and long-term costs of monitoring. Automating the collection, transfer, and processing of data provides managers access to greater information, thereby facilitating timely and effective management. Incorporating automation and big data availability into a decision support system with a user-friendly interface also enables effective adaptive management. We summarise the current state of artificial intelligence and automation techniques used in marine science and use examples in other disciplines to identify existing and potentially transferable methods that can enable automated monitoring and improve predictive modelling capabilities to support decision making. We also discuss emerging technologies that are likely to be useful as research in computer science and associated technologies continues to develop and become more accessible. Our perspective highlights the potential of AI and big data analytics for supporting decision-making, but also points to important knowledge gaps in multiple areas of the automation processes. These current challenges should be prioritised in conservation research to move toward implementing AI and automation in conservation management for a more informed understanding of impacted ecosystems to result in successful outcomes for conservation managers. We conclude that the current research and emphasis on automated and AI assisted tools in several scientific disciplines may mean the future of monitoring and management in marine science is facilitated and improved by the implementation of automation.

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Section: Predictive Models and Adaptabilitymentioning

confidence: 99%

Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective

et al. 2022

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“…To tackle these issues, we have recently adapted an approach based on reinforcement learning, a branch of artificial intelligence that has been used in gaming and finances for several years, but had not yet been applied in conservation, even though similar goals have been pursued under various methodologies (Bone & Dragi cevi c, 2010;Chadès et al, 2008Chadès et al, , 2014Lapeyrolerie et al, 2021;Marescot et al, 2013). We call our approach CAPTAIN: Conservation Area Prioritisation Through Artificial INtelligence (Silvestro et al, 2022).…”

Section: Challenge 1: Prioritising Area Conservationmentioning

confidence: 99%

Integrating machine learning, remote sensing and citizen science to create an early warning system for biodiversity

Antonelli

Dhanjal‐Adams

Silvestro

2022

Plants People Planet

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Societal Impact Statement The development and implementation of conservation actions that address urgent threats to biodiversity across the globe require large amounts of historic and current data. Machine learning approaches offer the tools to process, analyse and interpret large data sets, giving insights into trends and guiding evidence‐based allocation of limited resources to maximise positive biodiversity outcomes. Here we describe how data acquired from remote sensing, citizen science and other monitoring approaches could feed in near‐real time into an early warning system for biodiversity that integrates automated red‐listing of species with the identification of priority areas for conservation. Summary Application of machine learning approaches is aiding biodiversity conservation and research at a time of rapid global change. Two emerging topics and their data requirements are presented. First, to identify areas of priority protection for preventing biodiversity loss, reinforcement learning is used by training models that take into account human disturbance and climate change under recurrent monitoring schemes. Second, neural networks are used to approximate classification of species into Red List categories of the International Union for Conservation of Nature, offering the possibility of real‐time re‐classification after events such as widespread fires and deforestation. We discuss how the identification of areas and species most at risk could be integrated into an ‘early warning system’ based on climatic monitoring, remotely sensed land‐use changes and near‐real time biological and threat data from citizen science initiatives. Such system would help guide actions to prevent biodiversity loss at the speed required for effective conservation.

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Deep Reinforcement Learning for Conservation Decisions

Cited by 2 publications

References 46 publications

Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective

Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective

Integrating machine learning, remote sensing and citizen science to create an early warning system for biodiversity

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