ESM 211: Applied Population Ecology

Fall 2013

TR 1:00-2:15 Bren Hall 1510

Instructor: Bruce Kendall
4514 Bren Hall
x7539

NOTE: Bruce has a half-time appointment at the Graduate Division. He expects to generally be available at the Bren School on MWF mornings and TR afternoons.

Office hours MF 9-10AM. Once we start doing computer exercises I will plan to spend some of that time in the GIS lab.

Course objectives

The overall goals of the course are to teach you the scientific underpinnings or endangered species management and introduce you to the tools of quantitative risk assessment. I expect you to develop an understanding of

  • the population processes that are important for rare/endangered species, invasive species, and harvested species
  • how to design population models from qualitative biological information
  • how to parameterize population models
  • how to use the population model to project the fate of the population under various management scenarios
  • how to cope with variability and uncertainty in all of the above
  • how to use ecological information and population models to inform management decision-making
  • population monitoring programs: study design, data collection, data analysis
  • how to assess information about species declines
  • the policy schemes for evaluating and classifying species endangerment.

Textbook

There is no textbook; readings will be posted on the course website.

You may find the following of interest (we are reading several chapters): Morris and Doak (2002)Quantitative Conservation Biology(Sinauer).There may be a copy of this text in the Bren Library.

Grading

Homework assignments 70%

Class participation 30%

  • Assignments will be due one week after the lab in which the material is introduced
  • Late assignments will have a reduced grade unless approved in advance.
  • Class participation means engaging in classroom activities and doing the background preparation for planned discussions.
  • We will sometimes have class in the GIS Lab, where we will work through some of the analyses that we have learned in class.This will be a chance for you to get hands on experience doing these analyses with me there to answer both conceptual questions and help with the fiddly technical difficulties.

Computing

Many scientists use Matlab to perform population modeling, and the examples in the textbook are given in Matlab.However, Matlab is expensive outside the academic environment. It uses a scripting language that is conceptually similar to R (though it doesn't have all the statistical features); but the language differs in almost every detail of vocabulary and syntax.Instead we will be using R, which MESM students will have already encountered. It does modeling in addition to statistics, and is fast replacing Matlab as the standard platform for ecological modeling. If you don't already have R on your personal computer, you can download it from

There are two dedicated PVA software packages out there: VORTEX ( and RAMAS ( is free, and while it is actively supported, it is a bit tedious to use.More importantly, it is not very flexible: it only works with age structured models (not size or stage), and is primarily designed to work with species that have relatively low per-capita reproduction.It is mostly used by practitioners doing PVAs on mammals or birds.It is installed on the lab computers, and if there is interest I can run an extra class on it.

RAMAS is very expensive ($600 - $1600 for nonprofit organizations!) and is very user friendly (it has the best help system of any software I’ve ever seen).It does demographic PVAs with any kind of structure, and the more expensive version provides some linkage to GIS to aid in developing spatial PVAs. You may have used a simple version of it in ESM 201. Because of its price, it is not commonly used.

If you are not a Bren student you will need to fill out paperwork to obtain a computer account so that you can use the computer resources here.

About the instructor

Bruce Kendall is a quantitative population ecologist. With a background in physics and math, he originally encountered ecology through his interest in chaos theory. His current scientific objective is to improve our ability to conserve biological diversity, using models, ecological theory, and advanced statistics. This includes:

  • Improving predictions of extinction risk in small populations
  • Expanding our understanding of how connectivity affects the dynamics of spatially structured populations
  • Integrating monitoring data with other ecological information to understand the patterns and causes of species declines

He has worked on projects that apply to birds, mammals, plants, and nearshore marine species. He is a member of the IUCN Equid Species Survival Group.

Course topics

We will be covering the following topics; the exact schedule is not yet final as I am waiting on finalizing some guest lectures.

Introduction to applied population ecology

  • Have an overview of how population models can inform species management (1)
  • Know each other's backgrounds
  • Understand the logistics of the course

The causes and measures of decline and extinction

  • Understand the population processes that lead to endangerment (1)
  • Understand how to describe the risk of decline and extinction (6)

Official classifications of endangerment - the ESA and the IUCN redlist (8)

  • Understand the two most widespread threat classification schemes
  • Evaluate the evidence used for listing and recovery under the ESA
  • Likely guest lecture: Kirstina Barry (MESM 2012), USFWS

Simple count-based PVA

  • Understand stochastic exponential growth (2, 3)
  • Understand how to use stochastic models to project extinction risk (4, 5)

Lab: Simple count-based PVA

  • Understand stochastic exponential growth (SEG) (2, 3)
  • Understand how to use stochastic models to project extinction risk (4, 5)

Lab: Incorporating uncertainty into simple count-based PVA

  • Understand how finite datasets lead to parameter uncertainty (5)
  • Use bootstrapping to evaluate effects of uncertainty (5)

Population monitoring

  • Understand the challenges associated with monitoring abundance (7)
  • Understand the importance of spatial design (7)
  • Become familiar with the most common monitoring techniques (7)

Accounting for measurement error in count-based PVA

  • Understand how measurement error biases PVA (5)
  • Understand why accounting for measurement error makes the SEG model more robust (3)
  • Know the various techniques for incorporating measurement error in the SEG model (2, 3)

Lab: Estimating measurement error and incorporating it in count-based PVA

  • Quantify the measurement error in abundance estimates (7)
  • Be able to incorporate measurement error in the stochastic exponential growth model (4)

Introduction to demographic models (2 classes)

  • Recognize the importance of age, size, and stage (2)
  • Understand the representations of structure in models (life cycle graphs, systems of equations, matrices) (2)
  • Recognize the differences between age-structured, stage-structured, and size-structured models (2)
  • Understand how to construct and use pre-breeding and post-breeding census models (3)

Lab: Demographic PVA with Vortex

  • Learn to construct a Vortex model (3)
  • Understand how to analyze the extinction risk using Vortex (4)

Analyzing demographic models; sensitivity analysis

  • Understand transient and asymptotic dynamics of deterministic models (4)
  • Understand asymptotic dynamics of stochastic models (4)

Case studies in demographic modeling (2 classes)

  • Understand how demographic models can be used to inform management (6)

California Island fox: endangered species success story

  • Possible guest lecture by Tim Coonen (6, 7)

Biological invasion

  • Understand the ecological impacts of invasive species

Decision theory for population management

  • Understand the issues associated with decision making under uncertainty

Dispersal and Metapopulations

  • Understand how dispersal and spatial proximity interact to spread risks in spatially extended species
  • Understand how patch models can be used to predict the effects of habitat loss in metapopulations

Last modified: Monday, September 30, 2013, 11:29 AM