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Data Integration in Population and Community Ecology Using Hierarchical Modeling.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data Integration in Population and Community Ecology Using Hierarchical Modeling./
Author:	Farr, Matthew T.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	136 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
Subject:	Ecology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28771712
ISBN:	9798471109773

Data Integration in Population and Community Ecology Using Hierarchical Modeling.
Farr, Matthew T.

Data Integration in Population and Community Ecology Using Hierarchical Modeling. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 136 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Michigan State University, 2021.

This item must not be sold to any third party vendors.

In this dissertation, I develop and apply methods for data integration using hierarchical modeling to estimate the status, trends, and demography of wildlife populations and communities. I use multi-level statistical and mathematical models to explicitly link observed data to latent ecological processes. By separately modeling observational and ecological processes, I can integrate multiple disparate data sources into a unified framework to estimate ecologically relevant population and community parameters, often in the context of wildlife conservation. In Chapter One, I apply a multispecies hierarchical distance sampling model to assess the effect of management actions on a carnivore community in the Masai Mara National Reserve, Kenya. I assess variation in species-level responses to passive management, resulting in human disturbance and apex predator declines. In Chapter Two, I develop an integrated distribution model that uses distance sampling and presence-only data to jointly estimate species abundance. I apply this model to a case study on black-backed jackals (Canis mesomelas) to evaluate the effects of anthropogenic disturbance on the distribution of jackals across the Masai Mara National Reserve. In Chapter Three, I evaluate status and trends of species in a forest dwelling duiker community using detection-nondetection data. I develop a multispecies dynamic N-occupancy model to estimate species-level abundance, demographic parameters, and quasi-extinction probabilities. In Chapter Four, I create a spatiotemporal integrated model to estimate the effects of weather conditions on monarch butterflies (Danaus plexippus) during spring migration. Each chapter illustrates a unique application of data integration in wildlife ecology, either by combining data on multiple species to estimate population and community-level parameters or by combining disparate data sources on a single species to estimate demography and other population-level parameters. Data integration is a powerful framework that leverages all available information to address pressing conservation challenges.

ISBN: 9798471109773Subjects--Topical Terms:

516476
Ecology.
Subjects--Index Terms:

Data integration

Data Integration in Population and Community Ecology Using Hierarchical Modeling.
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100 1 $a Farr, Matthew T. $0 (orcid)0000-0003-1011-6851 $3 3685598
245 1 0 $a Data Integration in Population and Community Ecology Using Hierarchical Modeling.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 136 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
500 $a Advisor: Zipkin, Elise.
502 $a Thesis (Ph.D.)--Michigan State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a In this dissertation, I develop and apply methods for data integration using hierarchical modeling to estimate the status, trends, and demography of wildlife populations and communities. I use multi-level statistical and mathematical models to explicitly link observed data to latent ecological processes. By separately modeling observational and ecological processes, I can integrate multiple disparate data sources into a unified framework to estimate ecologically relevant population and community parameters, often in the context of wildlife conservation. In Chapter One, I apply a multispecies hierarchical distance sampling model to assess the effect of management actions on a carnivore community in the Masai Mara National Reserve, Kenya. I assess variation in species-level responses to passive management, resulting in human disturbance and apex predator declines. In Chapter Two, I develop an integrated distribution model that uses distance sampling and presence-only data to jointly estimate species abundance. I apply this model to a case study on black-backed jackals (Canis mesomelas) to evaluate the effects of anthropogenic disturbance on the distribution of jackals across the Masai Mara National Reserve. In Chapter Three, I evaluate status and trends of species in a forest dwelling duiker community using detection-nondetection data. I develop a multispecies dynamic N-occupancy model to estimate species-level abundance, demographic parameters, and quasi-extinction probabilities. In Chapter Four, I create a spatiotemporal integrated model to estimate the effects of weather conditions on monarch butterflies (Danaus plexippus) during spring migration. Each chapter illustrates a unique application of data integration in wildlife ecology, either by combining data on multiple species to estimate population and community-level parameters or by combining disparate data sources on a single species to estimate demography and other population-level parameters. Data integration is a powerful framework that leverages all available information to address pressing conservation challenges.
590 $a School code: 0128.
650 4 $a Ecology. $3 516476
650 4 $a Zoology. $3 518878
653 $a Data integration
653 $a Population
653 $a Community ecology
690 $a 0329
690 $a 0472
710 2 $a Michigan State University. $b Integrative Biology - Doctor of Philosophy. $3 3346727
773 0 $t Dissertations Abstracts International $g 83-05B.
790 $a 0128
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28771712