How do organisms update their view of the ‘state of the world’?

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Bayesian Updating: How do organisms integrate current and prior information to assess the state of their environment?

Overview — Organisms often modify traits to match their environment, but information on the current state of the environment can come from many sources. Organisms can directly assess current conditions and respond through phenotypic plasticity, but most environments exhibit some degree of temporal autocorrelation where previous conditions provide clues about what current or future conditions are likely to be.  There is actually a remarkably rich array of pre-existing information available to organisms about the “state of the world” that can be used to produce appropriate phenotypes.  For example, genes inherited by offspring provide information on the environments experienced by ancestors.  Similarly, epigenetic variation, maternal effects, cultural transmission, oral history and memory of prior environments also provide sources of information on past environments that can be used to predict what current environments are likely to be. So how do organisms integrate current and pre-existing information on the state of their environment?

Bayesian Updating, is a framework for understanding how organisms might incorporate prior information into their responses to current conditions.  Bayesian Updating is based on Bayes' Theorem (a statistical principle), but we don’t expect that organisms are performing any of the associated calculations. This framework simply leverages the essence of Bayes' Theorem – that our current expectation about the state of the world is based on some prior expectation that is updated as new information becomes available.  Importantly, this framework acknowledges that how an organism responds to a particular environment is likely to be affected by its pre-existing assessment of the state of the world.  More specifically, how organisms respond to an experimental manipulation that you invoke might depend on their previous experiences prior to the experiment.

This discussion-based course would introduce and explore Bayesian Updating and its application to a diverse range of topics in biology.  It is my hope that this topic will attract a wide range of students with diverse perspectives, who will lead our discussions into exciting new areas.  Some of these might include:

·      How do organisms use various forms of information on predation risk to appropriately adjust foraging behaviour?

·      To what degree is mate choice affected by prior assessment of potential mate quality?

·      When should we expect to see the predominance of Mendelian inheritance as opposed to epigenetic inheritance or maternal effects?

·      How should plasticity/canalization change with age?

·      What is the role of genetics, maternal effects and early-life experiences in the glucocorticoid response to a stressor?

·      How ‘adaptive’ should adaptive management be?

·      How long do memories last and how long should they last?

·      How much is Traditional Ecological Knowledge affected by personal experience versus oral history?

Schedule (day and time): Fridays 1:00 - 2:30 pm

First Class: January 31, 2020

Location: Norlin Commons Blue Room (E109) for now

Course Structure: Weekly discussions on a paper or papers from the primary literature. McAdam will lead the first two weeks to provide an overview of the topic. Students will select a sub-topic of particular interest to them, select a paper (or papers) in that area and lead the discussion that week. Discussions would typically begin with a brief presentation or primer by the person(s) leading the discussion.

In our first class we discussed Tips for Successful Seminar Discussions. I have transcribed these ideas into a Google Doc that can be found here.

Course Schedule: I have created a Google Sheet here. Please sign up for the week(s) when you would like to lead the discussion. Please also identify the topic and paper for discussion on this sheet. Please circulate a copy of the paper one week in advance of the discussion rather than expecting the class to fetch the paper from the Google Sheet.

For more information contact: Andrew McAdam andrew.mcadam@colorado.edu