COMING SOON! PQDT Open is getting a new home!

ProQuest Open Access Dissertations & Theses will remain freely available as part of a new and enhanced search experience at

Questions? Please refer to this FAQ.

Dissertation/Thesis Abstract

Estimating Clapper Rail (Rallus crepitans) Survivorship and Implementation of Estimates into Individual-Based Population Models
by Feura, Jared Michael, M.S., Mississippi State University, 2018, 92; 10980144
Abstract (Summary)

Sea-level rise is a concern for the future of coastal marsh obligate species such as the Clapper Rail (Rallus crepitans). Clapper Rails possess the potential to indicate changes to marsh ecological state due to population variation related to habitat features exhibited in previous study. Estimates for Clapper Rail survival are among the key missing parameters to create predictive models for Clapper Rail populations. I estimated Clapper Rail survival using data collected from six automated telemetry towers located in two Mississippi marshes. Thirty adult rails were harnessed with radio transmitters around telemetry towers to provide evidence of a rail’s status, alive or dead. Using survival estimates in conjunction with existing empirical data, I created an individual-based model that incorporated existing Sea-level Affecting Marsh Models, which predict changing land cover. These models showed that Clapper Rails will likely persist, though at decreased populations, through changes in habitat due to sea-level rise.

Indexing (document details)
Advisor: Rush, Scott A.
Commitee: Evans, Kristine O., Woodrey, Mark S.
School: Mississippi State University
Department: Wildlife and Fisheries
School Location: United States -- Mississippi
Source: MAI 58/03M(E), Masters Abstracts International
Subjects: Wildlife Conservation, Wildlife Management, Ecology
Keywords: Automated telemetry, Individual-based model, Nanotag, Sea-level rise
Publication Number: 10980144
ISBN: 978-0-438-75947-3
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy