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Description Tutorial Participants Webinars Tutorial Agenda (pdf)

NIMBioS Virtual Tutorial

Adaptive Management

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Topic: Adaptive Management

Meeting dates: October 26-29, 2020, 5-7 p.m. and 7:30-9:30 p.m.

Due to coronavirus concerns, this tutorial, originally scheduled as an in-person event in April, will be conducted remotely. There will be two webinars each evening for which participants will register. Some selected participants will be invited to attend two breakout hands-on sessions per evening.

Webinar Access and Registration: Click here

Tutorial Schedule (pdf)

Instructors and co-organizers:

  • Iadine Chadés, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
  • Paul L. Fackler, Agricultural & Resource Economics, North Carolina State Univ.
  • David Kling, Applied Economics, Oregon State Univ.
  • Michael Springborn, Environmental Science and Policy, Univ. of California, Davis

Co-organized in cooperation with Suzanne Lenhart (NIMBioS and Mathematics, Univ. of Tennessee, Knoxville) and Greg Wiggins (NIMBioS)

Objectives

Adaptive management seeks to determine sound management strategies in the face of uncertainty concerning the behavior of the system being managed. Specifically it attempts to find strategies for managing dynamic systems while learning the behavior of the system. This tutorial introduces participants to methods for modeling adaptive management, with an emphasis on case studies drawn from environmental and natural resource management. The tutorial reviews the key concept of a Markov Decision Process (MDP) and demonstrates how quantitative adaptive management strategies can be developed using MDPs. Additional conceptual, computational and application aspects will be discussed, including: dynamic programming and Bayesian formalization of learning. Case study applications covered in the tutorial will include management of endangered, invasive, and harvested wild populations. The tutorial features hands-on activities designed to help participants incorporate adaptive management approaches into their own research activities. Some prior knowledge of dynamic optimization, population modeling and/or computer programming is desirable.

The tutorial is appropriate for faculty, post-docs, advanced graduate students, and industry professionals in resource economics, applied ecology, conservation biology, applied mathematics and related fields.

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Adaptive Management WordPress site.   NIMBioS has created a WordPress site to facilitate group communication and information sharing for the tutorial. This is an interactive tool for sharing resources and comments before, during and after the meeting. All participants will receive an official email from WordPress inviting you to join the site. You will be asked to click on the link in the email from WordPress to accept the invitation. Before the meeting, we encourage you to introduce yourself to the rest of the group by writing a post with some details about your background and what you hope to gain from the meeting. Full details on how to post, comment and upload files to the WordPress site are available at the site (http://www.nimbios.org/wordpress-training/adapt/).


A goal of NIMBioS is to enhance the cadre of researchers capable of interdisciplinary efforts across mathematics and biology. As part of this goal, NIMBioS is committed to promoting diversity in all its activities. Diversity is considered in all its aspects, social and scientific, including gender, ethnicity, scientific field, career stage, geography and type of home institution. Questions regarding diversity issues should be directed to diversity@nimbios.org. You can read more about our Diversity Plan on our NIMBioS Policies web page. The NIMBioS building is fully handicapped accessible.


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NIMBioS is supported by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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