Welcome to the interactive web schedule for the 2019 Midwest Fish & Wildlife Conference! Please note, this event has passed. To return to the main Conference website, go to: www.midwestfw.org.
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CONFERENCE SCHEDULE UPDATES & CHANGES: As a result of the prolonged government shutdown, we experienced a number of cancellations and changes to the schedule. Cancellations and changes are listed here (as of January 26, 2019).
The workshop aims at providing a hands-on experience for attendees to learn and use multilevel (or mixed effect) models using R, including, (1) a general overview of the linear multilevel modeling approach, (2) its applications, (3) its implementation in R (using R package lme4), (4) its implications in ecological applications, and (5) its expansion to modeling nonlinear and non-normal problems. The workshop will be largely based on Chapter 10 of Qian (2016), including examples from modeling stream ecosystem responses to watershed urbanization, statistical issues related to Toledo water crisis, detecting illegal poaching of endangered plant species in National Parks, modeling changes in Lake Erie nearshore fish community in response to shoreline changes, and setting nutrient criteria for lakes and streams. Attendees are encouraged to submit case studies ahead of the time to be included in the workshop. The workshop will discuss some important conceptual issues that are commonly encountered in applications, through the development of empirical Bayes methods and the Bayesian hierarchical models, as well as Stein's paradox in classical statistics. The discussion will help attendees to better recognize when multilevel modeling is appropriate.
Intended Audience: For graduate students and professionals with knowledge at the level of a graduate-level biostatistics course, including statistical inference using hypothesis testing and estimation, statistical modeling (linear and nonlinear regression), and a working knowledge of statistical programming language R.
Presenters: Dr. Song Qian, The University of Toledo
