EST
523 First Exam Topic List
Explain the following statistical terminology:
bias, precision, accuracy, consistency, parameter and statistic
Be able to select a simple random sample,
stratified random sample, systematic random sample and estimate
the population mean or total.
State the advantages a stratified design
has over a SRS,
State the limitations of unreplicated
systematic sample and how variance estimation is usually performed.
Describe how confidence intervals are
constructed, the assumptions underlying them, etc.
Describe and distinguish between measures
of frequency, density, percent cover and biomass (Higgins et al.
in Bookhout 1994 reading)
Describe the different measurement
methods for the above variables.
Emphasis will be placed on qualities such as bias and precision,
sample size, characteristics that must be considered in choosing
a measurement method (e.g., quadrat
shape and size, plant distribution). (Krebs 64-71)
Use Wiegert’s
method to select optimal quadrat size,
understand its components. (Krebs 68,70)
Compute Goodness of Fit tests for spatial
distribution using quadrat counts. (Krebs)
Explain the connection between spatial
distribution of plants and statistical distributions used to examine
the physical distribution. (Krebs 72-81)
Compute and interpret index of dispersion
and use it in formal hypothesis testing. (Krebs 76)
Describe the advantages, disadvantages
and assumptions underlying plotless
methods. (Bonham 148-165)
Compute an index and test for spatial
pattern using closest individual and nearest neighbor data. (Krebs
136-140)
Explain how and why Diggle’s
estimator(s) are more robust to nonrandom spatial patterns (Krebs
140-143)
Summarize data from PCQ method and
estimate variables of interest including relative cover, relative
frequency and relative density. (Handout)
Ssummarize data from
line-intercept method and estimate variables of interest (Handout,
Mueller-Dumbois and Ellenburg
reading)
Summarize data from point-intercept
method and estimate variables of interest (Handout, Mueller-Dumbois
and Ellenburg reading)
Be a critical thinker.
EST
523 Second Exam Topic List
Indices
Explain what population indices are,
how they are typically used, and why they are used
Describe the key assumption underlying
their (index) use.
Distinguish between direct and indirect
classes of indices and give examples of each.
Describe the factors that affect the
proportionality of population indices to population size (e.g.,
Anderson 2001,
Engeman 2003, Anderson 2003 articles).
Describe how the collection of population
estimation methods (capture recapture, distance methods, removal
methods, etc.) can be seen as generalizations of index methods.
Distance
Sampling (Buckland et al. 1993)
Explain how Distance methods are generalizations
of strip transect methods.
State the main assumptions underlying
distance methodology.
Contrast the advantages and disadvantages
of lines versus point transects.
Describe the design considerations
involved in properly executing distance sampling.
Explain the purpose of the detectability
function and the procedure for developing it.
State the advantages of defining a
probability density function from the detectability
function.
Describe the principle of maximum likelihood
estimation.
Describe the methods of model selection
that are commonly used in distance modeling.
Describe the methods for estimating
variance and confidence interval construction of estimated density.
EST
523 Third Exam Topic List
Capture-recapture
methods
Describe
how the Petersen estimator adjusts an index for detectability.
Describe
the assumptions underlying the Petersen estimator.
Explain
the advantages of Chapman’s version of Petersen’s estimator.
Describe
how the assumptions are likely to be violated and what effect
that has on the estimator.
Consider
how you would design a survey to reduce the possibility of assumption
violations.
Closed
population models with
multiple marking periods.
Recognize
the parameterization of the each of the models Mo, Mt, Mb, i.e.,
what parameters are estimated.
What
estimation method is used for the above set of models?
Explain
the basis for the jackknife estimator for model Mh.
i.e., what estimate is modified for bias?
Explain
how model Mb eliminates the effect of trap response in its estimation
process.
Given
a set of data, could you use a least squares regression approach
for estimating N?
Model
Mbh, explain how it removes the effect
of heterogeneity and trap response.
Model
selection
Describe
the techniques and considerations that are available to assist
in selecting an appropriate model.
Explain
the parsimony principle.
Open
population model
Jolly-Seber model estimators of parameters, explain
what they are computing.
State
the assumptions underlying the use of this model
What
are the effects of assumption violations?
Explain
how the marked population size is estimated (e.g., what groups
of animals are followed and what is measured?)
Robust
Design
Describe
the structure of the data for this design.
What
are its advantages over a strictly open design approach (e.g.,
Jolly-Seber model).
Removal
Method
State
the assumptions underlying the method, compute an estimate of
N, and recognize when the estimator fails.
Explain
the connection between the removal method and model Mb and the
differences in their estimation methods.
Explain how the generalized removal
estimator handles heterogeneity.