Ecological Niche Models

Cameron Aldridge, Sunil Kumar

Description:

This is a broad-ranging seminar course designed to expose students to the variety of quantitative and statistical techniques available to assess species-habitat relationships. Ecological niche models are known by a variety of names including species distribution models, habitat relationship models, bioclimatic envelopes, and others. The concepts and application of these models are not new, although there are many new statistical algorithms and machine learning methods to estimate niche relationships. They all rely on the concept of the niche�¯�¿�½the set of environmental conditions in which a species can survive and persist. They are particularly topical today because of concerns over the effects of climate change and invasive species on the distribution and abundance of native species and ecosystems. Our goal is to expose students to a suite of different analytical approaches and their applications so as to give them a new tool set for future design of experiments and analyses of data. The course structure is one 50 minute period per week. The first portion of the class will consist of a brief introduction to a topic (~15 minutes). This is intended to cover some of the theoretical and conceptual foundations of a given statistical algorithm used for niche modeling, and provide an overview for a key paper we will read on the specific topic. The remainder of the class will be a discussion on the topic and the key paper lead by that week�¯�¿�½s presenters (instructor, students, or visiting speaker). Students will lead one class presentation and discussion. Topics discussed in this course could include resource selection functions (RSFs), resource utilization functions (RUFs), occupancy models, habitat suitability index (HSI) models, count-based models (Poisson and negative binomial regression), survival models, generalized linear models (GLMs) and generalized additive models (GAMs), classification and regression trees (CART), random forest, boosted regression trees, maximum entropy modeling (or Maxent), Ecological Niche Factor Analyses (ENFA), genetic algorithm for rule-set production (GARP), quantile regression, Bayesian belief networks, model assessment and evaluation, and more.

Section: 3
Credits: 1
Restrictions: none
First Meeting: 1/18/2011
Meeting Times: Tuesday 13:00 �¯�¿�½ 13:50 (we will adjust at 1st meeting if needed to meet schedules)
Classroom: NESB B215
CRN: 10365
Cross Listed: none
Enrollment Limit: 0
Background: none
Course Text: none
Instructor Contact Info:
      Cameron Aldridge aldridge@nrel.colostate.edu 226-9433
      Sunil Kumar sunil@nrel.colostate.edu 491-7056