# ESA 2013 IPM Workshop: An Introduction to Integral Projection Models

This course is taught as a short module that introduces ecologists to Classical and Bayesian statistical models, maximum entropy models, and simulation models, originally offered in the Spring of 2011. It’s designed as a workshop using R, with a focus on Bayesian modeling and application to species distribution modeling. The original course webpage is here; this page represents an updated version.

PLEASE NOTE THAT THE LINKS BELOW WERE BROKEN WHILE REVAMPING MY WEBSITE AND WILL BE FIXED THE NEXT TIME I TEACH THE COURSE. IF YOU NEED ANYTHING BEFORE THEN, JUST EMAIL.

This module will explore methods for developing predictive models of biodiversity patterns and ecological processes. We will introduce the R statistical programming language and use it to demonstrate exploratory data analysis. We will cover statistical modeling (e.g. Hierarchical Bayes) as well as non-statistical modeling (e.g. Maxent), and simulation modeling (e.g. cellular automaton) approaches and demonstrate how to use these models with datasets provided by EEB faculty members and Intro IPM exercises.r. Participants will be provided with working code to help explore models on their own and under the guidance of instructors.

The goal of this course is to provide students with experience using models that can be applied directly to their own research. As such we will focus on a developing a complete understanding of simpler models, as opposed to a peripheral understanding of more elaborate models. Each session will focus on one or two different modeling strategies or case studies. These meetings will be structured as a workshop with lecture interspersed with guided programming exercises. We will primarily use the statistical programming language R, and will assume that students do not have programming experience. The goal is to balance building confidence with simple models while considering models with sufficient complexity to be useful. We will provide homework assignments where participants make simple modifications to sample code from models discussed in class. Extra help will be available during regularly scheduled office hours for programming questions.

As a prerequisite, students will be provided with a basic introductory packet on the statistical software package R that guides them through sample code. While the course will assume that students are unfamiliar with R, we will expect that students can work through some of the most elementary calculations on their own so that class time can be used efficiently. Students will not be expected to write their own code during the course, but will be asked to read prepared code prepared and make basic modifications.

Slides

Data for exercises. You don't need to download these, R will do it for you.

fynbos site abundance.csv

fynbos sites.csv

- An Ecological Modeler’s Primer on R. A tutorial on basics, entering data, functions and plotting.
- icebreakeR. A clearly written and complete medium-length tutorial.
- Quick-R. A very succinct guide for a handful of standard analyses. Nicely demonstrates how simply some analyses can be coded.
- My R resource page for ecologists.

Slides

Exercises

Data for exercises. You don't need to download these, R will do it for you.

IPANE site data

selected IPANE presence/absence data

In case you're interested, the IPANE website

- Guisan, A., T. C. Edwards, Jr., and T. Hastie. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157:89-100
- Yee., T. and N. Mitchell. 1991. Generalized additive models in plant ecology. Journal of Vegetation Science 2: 587-602.

Download MCMCRobot (scroll down to the bottom of the page). This is a nice little program that Paul Lewis wrote for teaching MCMC concepts. It runs on a PC by clicking on the .exe file. Mac users will have to follow along on this one unless you have experience running pc programs through wine.

Slides

Exercises will be handed out in class

A Bayesian Jargon List to keep track of new terminology

- Clark, J. 2005. Why environmental scientists are becoming bayesian. Ecology Letters 8:2-14
Ellison, A. 2007. Bayesian inference in ecology. Ecology Letters 7: 509–520

Slides

Exercises will be handed out in class

- Clark, J. 2007. Models for Ecological Data. Princeton University Press.
- Wikle, Christopher K. and L. Mark Berliner. 2007. Bayesian tutorial for data assimilation. Physica D: Nonlinear Phenomena 230: 1-16.

Slides

Exercises

Data for exercises. You don't need to download these, R will do it for you.

Live Aloe Data

Dead Aloe Data

New England 5' x '5 Environmental Data

IPANE Occurence Data

Preliminary spGLM run for Celastrus

- Ibáńez, I., Silander, J.A, Jr., Wilson, A., LaFleur, N., Tanaka, N., and Tsuyama, I. 2009 Multi-variate Forecasts of Potential Distribution of Invasive Plant Species Ecological Applications 19(2) 359-375.
- Additional tutorial info for spBayes
- Gelman and Hill. 2008. Data Analysis Using Regression and Multilevel/Hierarchical Models

Slides

Exercises

- Simple WinBUGS models
- WinBUGS Examples Vol. 1, Vol. 2
- Latimer, A.M., S. Banerjee, H. Sang, E. Mosher and J.A. Silander (2009). Hierarchical models facilitate spatial analysis of large data sets: A case study on invasive plant species in the northeastern United States. Ecology Letters 12:144-154.
- Agarwal, D. K., J. A. Silander, Jr., A.E. Gelfand, R.E. Dewar, and J.G. Mickelson, Jr. 2005. Tropical Deforestation in Madagascar: Analyses using hierarchical, spatially explicit, Bayesian regression models. Ecological Modelling 185:105-131
- Hooten, M.B., C.K. Wikle, R.M. Dorazio, and J.A. Royle. 2007. Hierarchical spatio-temporal matrix models for characterizing invasions. Biometrics 63: 558-567

Download Maxent. This is free runs on any operating system. Make sure you've got it up and running before class.

Slides

- Phillips, S., Anderson, R. and R. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190:231-259.
- Phillips, S. and M. Dudík.2008 Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation.Ecography 31: 161-175.

Required pre-class reading: Elith, J.,S. Phillips, T. Hastie, M. Dudík, Y. Chee and C. Yates. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17:43-57, 2011.

Slides

Exercises

- Phillips, S. M. Dudík, J. Elith, C.H. Graham, A. Lehmann, J. Leathwich and S. Ferrier. 2009. Sample Selection Bias and Presence-Only Species Distribution Models: Implications for Background and Pseudo-absence data. Ecological Applications 19:181-197
- Elith, J. et al. Novel methods improve prediction of species' distributions from occurrence data. 2006 Ecography 29: 129-151.
- Elith,J. M. Kearney and S. J. Phillips. 2010.The art of modelling range-shifting species,Methods in Ecology & Evolution 1:330-342

Exercises

Data for exercises. You don't need to download these, R will do it for you.

New England 5' x '5 Environmental Data

IPANE Occurrence Data

- Merow, C., N. LaFleur, J. Silander, A. Wilson & M. Rubega. 2011. Developing dynamic, mechanistic species distribution models: predicting bird-mediated spread of invasive plants across northeastern North America. American Naturalist 178: 30-43. Supplement
- Jongejans, E., O. Skarpaas, and K. Shea. 2008b. Dispersal, demography and spatial population models for conservation and control management. Perspectives in Plant Ecology, Evolution, and Sys- tematics 9:153–170
- Higgins, S. I., and D. M. Richardson. 1996. A review of models of alien plant spread. Ecological Modelling 87:249–265