ESM 211 - Bren School of Environmental Science & Management

ESM 211: Applied Population Ecology
Fall 2013
TR 1:00-2:15 Bren Hall 1510
Instructor: Bruce Kendall
4514 Bren Hall
[email protected]
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
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.
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
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.
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
There are two dedicated PVA software packages out there: VORTEX
( and RAMAS ( VORTEX 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
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
Know the various techniques for incorporating measurement error in the SEG model (2,
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
Last modified: Monday, September 30, 2013, 11:29 AM