Browsing by Author "Kenneth H. Pollock, Committee Chair"

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Catch Curve and Capture Recapture Models: A Bayesian Combined Approach
(2009-03-19) Griffith, Emily Hohmeister; Dennis Boos, Committee Member; Kenneth H. Pollock, Committee Chair; Sujit K. Ghosh, Committee Co-Chair; Kevin Gross, Committee Member
When studying animal populations, one demographic parameter of interest is the annual rate of survival. Methods for estimating survival rates of animal populations fall into two general categories: methods based on marked or non-marked animals. Catch curve analysis falls into the latter category of non-marked animal methods, and is based on strong assumptions about population dynamics. Capture-recapture methods, on the other hand, use marked animals and require assumptions about homogeneous individual capture and survival probabilities. We focus specifically on Chapman and RobsonÃ¢â‚¬â„¢s catch curve analysis, the Cormack-Jolly-Seber (CJS) open population model, and Udevtiz and BallacheyÃ¢â‚¬â„¢s augmentation of catch curve data with ages-at-death data, which are a random sample from the natural deaths that occur in a population between two time periods. In Chapter 1, we develop the Bayesian approach to catch curve analysis, beginning with the simple situation of a single catch curve. After extending our method to multiple years, we relax the model assumptions to include random effects for survival across years. The proposed model is validated using predictive distributions and compared with the traditional methods. We conclude that many benefits can be obtained from the Bayesian approach to the analysis of a single or multiple year catch curve. In Chapter 2, we augment catch curve data with capture-recapture data in a hierarchical Bayesian framework. We estimate the fidelity rate and the population growth rate. We illustrate these models with a data set and simulation study. In Chapter 3, we develop a Bayesian method for analyzing catch curve and ages-at-death data together, based on the likelihoods developed in Udevitz and Ballachey. We utilize the Bayesian framework and relax both the assumption of a stable age-distribution and that of a known population growth rate.
Probabilistic Allele Calling to Improve Population Size Estimates from Non-Invasive Genetic Mark-Recapture Analysis
(2009-08-10) Supple, Megan Ann; W. Owen McMillan, Committee Member; Kenneth H. Pollock, Committee Chair; Kevin Gross, Committee Member
Accurate estimates of population sizes are often necessary to help researchers better understand how wildlife populations are changing over time. Researchers often use traditional mark-recapture methods to estimate wildlife population sizes. A variety of models, with varying assumptions, are available to analyze traditional mark-recapture data. The utility of traditional mark-recapture methods is limited when sampling rare or elusive species. Capture probabilities may not be high enough due to the difficulties and cost of capturing the animals. In addition, physical capture can be stressful, even deadly, to the animals. The limitations of traditional mark-recapture methods can sometimes be addressed by utilizing non-invasive genetic mark-recapture methods. Using the non-invasive genetic method, individuals are not physically captured and tagged. Instead, non-invasive genetic samples, such as hair or scat, are collected and genotyped at multiple microsatellite markers. An individual's genotype serves as a DNA tag, uniquely identifying that individual. DNA is extracted from each sample and the extracted DNA is PCR amplified multiple times at several microsatellite loci. The results of each PCR amplification are visualized using capillary electrophoresis, resulting in an electropherogram. Alleles are called by interpreting the peak heights and/or peak areas on the electropherogram. While non-invasive genetic methods solve some of the problems of traditional mark-recapture, they also introduce some new problems. One major problem introduced by non-invasive genetic methods is the misidentification of individuals. The DNA from non-invasive samples is often low in quality and/or low in quantity, which increases the probability of genotyping errors. In addition, poor marker selection can result in individuals sharing a genotype. Traditional mark-recapture methods are not robust to violations of the assumption that individuals are correctly identified. Genotyping errors cause overestimation of population size; markers that lack the power to distinguish between individuals cause underestimation of population size. To achieve better population size estimates, I propose a new probabilistic allele calling method. In the traditional method, definitive allele calls are made independently for each PCR replicate of a sample. Then, the definitive allele calls are examined to determine the sample's genotype. The new method assigns probabilities to allele calls, rather than determining a definitive allele call. Probabilities are assigned to possible allele calls based on electropherogram peak heights. For cases of possible allelic drop out, a portion of the probability distribution for the PCR replicate is assigned to a heterozygous allele call with one undesignated allele. For each sample, the allele call probabilities at each locus, including allele calls with undesignated alleles, are averaged from the PCR replicates. Then, possible allele calls with undesignated alleles are assigned based on the allele frequencies in the averaged probabilities. The genotype with the highest probability is assigned as the sample's genotype. Using the probabilistic method, uncertainty remains in the allele calls until all the PCR replicates of a sample are examined. This allows more information from the electropherograms to be utilized when determining genotypes. To examine the proposed probabilistic allele calling method, I compared it to a traditional method by running computer simulations that examine a variety of scenarios. For each simulation scenario, a population was generated and sampled using non-invasive genetic mark-recapture methods. Each sample, which contained DNA of low quality and quantity, was genotyped at multiple microsatellite loci, with multiple PCR replicates for each locus. Genotypes were determined for samples using a traditional allele calling method and the new probabilistic allele calling method. The resulting genotypes were matched and the data was analyzed using four traditional closed mark-recapture models. The probabilistic method performed better than the traditional method in almost all cases. When more than two PCR replicates were examined, the estimates from the probabilistic method were less biased and more precise than estimates from the traditional method. Using the probabilistic method, good estimates can be achieved using fewer PCR replicates. This new method of analyzing non-invasive genetic mark-recapture data has the potential to allow wildlife population sizes to be accurately estimated using non-invasive methods in less time and at lower cost than current methods.
Spatial Modeling of Detection and Abundance from Count Surveys of Animal Populations
(2006-12-27) Webster, Raymond Anthony; Kenneth H. Pollock, Committee Chair; Sujit K. Ghosh, Committee Member; Cavell Brownie, Committee Member; Kevin Gross, Committee Member
When analyzing data from surveys of animal populations, it has been common in the past to ignore important factors such as variation in animal detection probabilities across space, and spatial dependence in animal density. We present a unified framework for modeling animal survey data collected at spatially replicated survey sites in the form of repeated counts, "removal" counts, or "capture" history counts, that simultaneously models spatial variation in density and variation in detection probabilities due to changes in covariates across the landscape. The models have a complex hierarchical structure that makes them suited to Bayesian analysis using Markov chain Monte Carlo (MCMC) algorithms. To ensure that these algorithms are computationally efficient, we use conditional autogressive (CAR) models for modeling spatial dependence. We apply our models to two examples of animal survey data. In the first, an intensive repeated count survey of juvenile Coho Salmon in McGarvey Creek, Northern California, we detected moderate spatial dependence in density, and models which account for spatial dependence produced more precise predictions at unsurveyed habitat units, and thus more precise estimates of total stream abundance, than models which assumed spatial independence. Through a small simulation study, we show that ignoring heterogeneity in detection probabilities can lead to significant underestimation of total abundance. However, inclusion of heterogeneity using a random effect in the detection component of the model can lead to problems in Bayesian MCMC modeling for typical survey designs, and for this reason we stress the importance of accounting for heterogeneity by incorporating covariates in modeling detection probability. In our second example, we consider a large survey of birds in the Great Smoky Mountains National Park. We fit models to the three types of survey data, repeated counts, "removal" counts, and "capture" history counts. Our methods lead to maps of predicted relative density which are an improvement over those that would follow from ignoring spatial dependence. Modeling shows that variation in detection probability can also affect inference, particularly when a species is relatively difficult to detect. Our work also highlights the importance of good survey design for bird species modeling. We point out that these types of bird survey data, particularly removal and capture-recapture counts (which require individual birds to be identified), are prone to errors in bird identification. Although we obtain similar results for all three types of survey data, which implies that the effect of identification errors may be small, the consequences of such errors in the data requires further investigation. Finally, we present parametric models for combined distance and capture-recapture survey data from both line and point transect surveys that allow for two types of animal movement: permanent avoidance or attraction to a transect, or temporary displacement of animals in the vicinity of a transect. The models have a simple form, with parameters that quantify the impact transects and observers have on local density. We combine these density models with logistic-linear models for detection probability using the likelihood framework of Borchers et al. (1998) for combined distance and capture-recapture data. This allows us to separately estimate the parameters of both the density and detection components of the model, which is not possible using the standard methods of distance sampling. Through a simulation study, we show that, provided sufficient animals are detected, the model parameters have little bias, and lead to improved estimates of density over a simple uniform density model, particularly for line transect surveys. Model selection by AIC generally chooses the correct density model. We apply our models to the Great Smoky Mountains bird survey data, and find some evidence of observer effects on local bird density.
Super Population Capture-Recapture Model Augmented with Genetic Data
(2009-11-20) Wen, Zhi; Kenneth H. Pollock, Committee Chair; Charlie Smith, Committee Co-Chair; Sujit Ghosh, Committee Member; James Nichols, Committee Member; Thomas Reiland, Committee Member
Ecologists applying capture-recapture models to animal populations sometimes have access to addition information about individuals' populations of origin. For example, tests that assign an individual's genotype to its most likely source population are increas- ingly used. Here we show how to augment a super population capture-recapture model with such information. We consider a single super population model without age structure, and split the entry probability into separate components due to births in situ and immigration. We show that it is possible to estimate these two probabilities separately. We first consider the case of perfect information about population of origin, where we can distinguish individ- uals born in situ from immigrants with certainty. Then we consider the more realistic case of imperfect information, where we use genetic or other information to assign probabilities to each individual's origin in situ or outside the population. We use a resampling approach to impute the perfect origination assignment data based on the imperfect assignment tests. The integration of data on population of origin with capture-recapture data allows us to determine the contributions of immigration and in situ reproeuction to the growth of the population, an issue of importance to ecologists. Further, the augmentation of capture- recapture data with origination data should improve the preciesion of parameter estimates. We illustrate our new models with capture-recapture and genetic assignment test data from a population of banner-tailed kangaroo rats Dipodomys spectabilis in Arizona. In chapter 4, we evaluate the value of marine reserves for fisheries using tag-return, tag-recapture and telemetry models. We estimate the patch-specific fishing mortality, natu- ral mortality, and movement rates. We first focus on tag-return models for a two-site model with one area a marine reserve and one area a fishing area. We consider tag-return, tag- recapture and telemetry models in various combinations for two site models where one area is a marine reserve and one is subject to regular fishing. Then we illustrate our methods with a comprehensive simulation study.
Use of Natural Tags in Closed Population Capture-Recapture Studies: Modeling Misidentification
(2007-08-14) Yoshizaki, Jun; Nicholas M. Haddad, Committee Co-Chair; Cavell Brownie, Committee Member; Joseph E. Hightower, Committee Member; James D. Nichols, Committee Member; Kenneth H. Pollock, Committee Chair