Population connectivity: combining methods for estimating avian dispersal and migratory linkages

 
 
 
 

Population connectivity: combining methods for estimating avian dispersal and migratory linkages

Show simple item record


dc.contributor.advisor Waite, Thomas A en_US
dc.contributor.author Ibarguen, Siri B en_US
dc.date.accessioned 2008-07-07T18:51:10Z
dc.date.available 2008-07-07T18:51:10Z
dc.date.created 2004 en_US
dc.date.issued 2008-07-07T18:51:10Z
dc.identifier.uri http://rave.ohiolink.edu/etdc/view?acc_num=osu1079979416 en_US
dc.identifier.uri http://hdl.handle.net/2374.OX/5070
dc.description We use a variety of methods to study population connectivity. In Chapter 1, we use stable isotope ratios in feathers to make Bayesian inferences about the migratory connectivity between breeding and wintering grounds of Henslow’s sparrows. We use hydrogen and carbon stable isotope ratios (deltaH and deltaC). We compare the deltaH and deltaC of feathers from wintering sparrows to five breeding region deltaH and deltaC to estimate the probability that each individual wintering sparrow originated from each of the five regions. Breeding bird abundances are used as prior probabilities of breeding region origin. We conclude that there are no clear linkages between specific breeding regions and wintering sites. In Chapter 2, we use three methods to estimate dispersal in Henslow’s sparrows. 1)deltaH in feathers are used to determine whether an individual breeding bird has a deltaH signature characteristic of the breeding site. 2) Song structure is used as the signature of an individual’s previous breeding-ground origin. 3) Genetic markers are used to evaluate population structure. Genetic structure is evaluated using three estimates. Fst estimates and private alleles are used to calculate the number of migrants per generation (Nm) between sites. Private alleles are evaluated to determine if they are truly private. A Bayesian clustering method is used to infer the number of populations. All methods revealed high rates of dispersal. In Chapter 3, three methods for estimating dispersal are compared: deltaH in feathers, genetic population structure, and spatial autocorrelation (SAC). We compare the dispersal estimates of five migratory species. With the SAC analysis, we find no clear evidence for dispersal as a major synchronizing agent. However, new statistical methods may allow for the parsing out the effect of dispersal. One species had historically high dispersal (limited genetic structure) but currently low dispersal (high deltaH correlations). Another species had a deltaH correlation value indicating low current dispersal. Three other species are all found to have high dispersal, both historically and currently. Comparing dispersal estimates may allow researchers to evaluate how dispersal rates have changed over time, as well as how well estimation methods agree. en_US
dc.format application/pdf en_US
dc.format xvii, 143 p.: ill en_US
dc.rights unrestricted en_US
dc.rights Copyright and permissions information available at the source archive en_US
dc.subject Population connectivity en_US
dc.subject Migratory linkages en_US
dc.subject Dispersal en_US
dc.subject Henslow's sparrow en_US
dc.subject Ammodramus henslowii en_US
dc.subject Spatial autocorrelation en_US
dc.subject Gene flow en_US
dc.subject Meme flow en_US
dc.subject Geographic variation in song en_US
dc.subject Private alleles en_US
dc.subject Bayesian analysis en_US
dc.subject Stable isotope ratios en_US
dc.title Population connectivity: combining methods for estimating avian dispersal and migratory linkages en_US
dc.type Electronic Thesis or Dissertation en_US
dc.degree.name PhD en_US
dc.degree.level doctoral en_US
dc.degree.discipline Evolution, Ecology, and Organismal Biology en_US
dc.degree.grantor Ohio State University en_US
dc.contributor.publisher Ohio State University / OhioLINK en_US

Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record