Bridging adaptive management and reinforcement learning for more robust decisions

Chapman, Melissa; Xu, Lily; Lapeyrolerie, Marcus; Boettiger, Carl

doi:10.1098/rstb.2022.0195

Cited by 8 publications

(2 citation statements)

References 84 publications

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“…In addition to the advancement of detection and attribution frameworks in ecological systems, machine learning and artificial intelligence are essential to realizing a sustainable future [ 142 , 143 ]. These tools have become indispensable in combining remote sensing and ground data, conducting spatio-temporal extrapolations, and projecting plausible futures.…”

Section: Sharing Monitoring Data Tools and Facilities: Opportunities ...mentioning

confidence: 99%

Perspective: sustainability challenges, opportunities and solutions for long-term ecosystem observations

Mori,

Suzuki,

Hori

et al. 2023

Phil. Trans. R. Soc. B

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As interest in natural capital grows and society increasingly recognizes the value of biodiversity, we must discuss how ecosystem observations to detect changes in biodiversity can be sustained through collaboration across regions and sectors. However, there are many barriers to establishing and sustaining large-scale, fine-resolution ecosystem observations. First, comprehensive monitoring data on both biodiversity and possible anthropogenic factors are lacking. Second, some in situ ecosystem observations cannot be systematically established and maintained across locations. Third, equitable solutions across sectors and countries are needed to build a global network. Here, by examining individual cases and emerging frameworks, mainly from (but not limited to) Japan, we illustrate how ecological science relies on long-term data and how neglecting basic monitoring of our home planet further reduces our chances of overcoming the environmental crisis. We also discuss emerging techniques and opportunities, such as environmental DNA and citizen science as well as using the existing and forgotten sites of monitoring, that can help overcome some of the difficulties in establishing and sustaining ecosystem observations at a large scale with fine resolution. Overall, this paper presents a call to action for joint monitoring of biodiversity and anthropogenic factors, the systematic establishment and maintenance of in situ observations, and equitable solutions across sectors and countries to build a global network, beyond cultures, languages, and economic status. We hope that our proposed framework and the examples from Japan can serve as a starting point for further discussions and collaborations among stakeholders across multiple sectors of society. It is time to take the next step in detecting changes in socio-ecological systems, and if monitoring and observation can be made more equitable and feasible, they will play an even more important role in ensuring global sustainability for future generations. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’.

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Section: Sharing Monitoring Data Tools and Facilities: Opportunities ...mentioning

confidence: 99%

Perspective: sustainability challenges, opportunities and solutions for long-term ecosystem observations

Mori,

Suzuki,

Hori

et al. 2023

Phil. Trans. R. Soc. B

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“…Chapman et al . [ 56 ] suggest one way forward is harnessing artificial intelligence through reinforcement learning from multiple observations and models to guide management action when we cannot infer underlying social–ecological processes. Still, the reinforcement learning process relies on a comprehensive portfolio of empirically motivated, process-based scenario models.…”

Section: Contributions Summarymentioning

confidence: 99%

Detecting and attributing the causes of biodiversity change: needs, gaps and solutions

Tekwa

Gonzalez

Zurell

et al. 2023

Phil. Trans. R. Soc. B

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This issue addresses the multifaceted problems of understanding biodiversity change to meet emerging international development and conservation goals, national economic accounting and diverse community needs. Recent international agreements highlight the need to establish monitoring and assessment programmes at national and regional levels. We identify an opportunity for the research community to develop the methods for robust detection and attribution of biodiversity change that will contribute to national assessments and guide conservation action. The 16 contributions of this issue address six major aspects of biodiversity assessment: connecting policy to science, establishing observation, improving statistical estimation, detecting change, attributing causes and projecting the future. These studies are led by experts in Indigenous studies, economics, ecology, conservation, statistics, and computer science, with representations from Asia, Africa, South America, North America and Europe. The results place biodiversity science in the context of policy needs and provide an updated roadmap for how to observe biodiversity change in a way that supports conservation action via robust detection and attribution science. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’

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Data Science for Social Climate Change Modelling: Emerging Technologies Review

Ustyianovych

2024

Lecture Notes on Data Engineering and Communications Technologies

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Bridging adaptive management and reinforcement learning for more robust decisions

Cited by 8 publications

References 84 publications

Perspective: sustainability challenges, opportunities and solutions for long-term ecosystem observations

Perspective: sustainability challenges, opportunities and solutions for long-term ecosystem observations

Detecting and attributing the causes of biodiversity change: needs, gaps and solutions

Data Science for Social Climate Change Modelling: Emerging Technologies Review

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