Year of Award

2017

Document Type

Thesis

Degree Type

Master of Science (MS)

Degree Name

Computer Science

Department or School/College

Computer Science

Committee Chair

Alden Wright

Committee Co-chair

Brian Hand

Commitee Members

Alden Wright, Brian Hand, Jesse Johnson

Keywords

Number of Breeders, Simulation, Population Change, Sensitivity, Specificity

Subject Categories

Numerical Analysis and Scientific Computing

Abstract

Detecting if a population is in decline is an important objective for biologists and conservationists who are monitoring threatened populations. As genetic methods improve effective population size (Ne) and effective number of breeders (Nb) continue to gain popularity as a way to monitor species. Using simulated populations and linkage disequilibrium, we explored detecting population decline through Nb in age structured populations. Through comparisons of sensitivity (1 – false negatives) and specificity (1- false positives) over 1000 replicates, we explored how factors such as starting Nb, number of SNPs, number of individuals sampled, number of breeding cycles monitored, and rate of decline affected the ability to detect changes in the population. Overall, we found Nb can be an effective metric for detecting population declines, if some care is taken during study design to avoid certain conditions. Although specificity did not vary greatly, sensitivity was much more reactive to changes in the factors tested. Under-sampling of the population (< true Nb), insufficient number of breeding cycles monitored (<7 cycles) and low levels of decline (e.g. <7%), are all detrimental to detection of population change.

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© Copyright 2017 Brian Trethewey