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Evaluating American Marten Habitat Q...

Joyce, Michael James.

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Evaluating American Marten Habitat Quality Using Airborne Light Detection and Ranging (LiDAR) Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evaluating American Marten Habitat Quality Using Airborne Light Detection and Ranging (LiDAR) Data./
Author:	Joyce, Michael James.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	199 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-05, Section: B.
Contained By:	Dissertations Abstracts International80-05B.
Subject:	Wildlife Conservation. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10973885
ISBN:	9780438607262

Evaluating American Marten Habitat Quality Using Airborne Light Detection and Ranging (LiDAR) Data.
Joyce, Michael James.

Evaluating American Marten Habitat Quality Using Airborne Light Detection and Ranging (LiDAR) Data. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 199 p.

Source: Dissertations Abstracts International, Volume: 80-05, Section: B.

Thesis (Ph.D.)--University of Minnesota, 2018.

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

Understanding the factors that influence animal distribution and density is a central theme in animal ecology. For imperiled species, understanding the resources and conditions that allow animals to occupy the landscape is critical for development of effective conservation strategies. Not surprisingly, habitat selection is a common focus of wildlife research. This dissertation project focused on addressing two main challenges that limit the application of a fitness-based approach to understanding habitat selection: 1) data on fine-scale habitat resources and conditions required for the development and testing of resource- and fitness-based definitions of habitat are generally not available across entire study areas, and 2) indirect measurements of fitness (e.g., survival or reproductive success) are often not considered when assessing habitat selection patterns, in part, because of the difficulty of measuring fitness correlates for free-ranging animals with long life-spans and large home ranges. My first two chapters address the first challenge by using airborne light detection and ranging (LiDAR) data to measure fine-scale characteristics known to be selected by my focal species, American martens (Martes americana ). In Chapter 1, I demonstrate that LiDAR data can be used to detect individual pieces of coarse woody debris, an important habitat component that provides martens with foraging habitat and access to the subnivean layer. In Chapter 2, I created statistical models to estimate 5 response variables relating to tree size and density and evaluated how well models will perform when imputed across the entire study area. I found that the models I created performed well when applied to new data, but that performance depended on the response variable being modeled. My last two chapters address the second challenge by evaluating how landscape and forest structure influence mortality risk for martens. In Chapter 3, I evaluated factors influencing harvest mortality risk. I found that martens living close to roads have higher harvest risk because trappers use roads to set and check traps efficiently. Consequently, distribution of roads can have a profound impact on habitat quality, which has important implications for gene flow and population structure. In Chapter 4, I used LiDAR data and classified satellite imagery to examine the role of forest structure in mediating interactions between martens and predators. I found that sites where martens were killed by predators were associated with non-forested areas including wetlands, shrublands, and young and regenerating forests. Although martens generally avoid non-forested areas that are associated with higher predation risk, martens must move near or through risky areas while moving across heterogeneous, managed landscapes.

ISBN: 9780438607262Subjects--Topical Terms:

3433384
Wildlife Conservation.

Evaluating American Marten Habitat Quality Using Airborne Light Detection and Ranging (LiDAR) Data.
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300 $a 199 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-05, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Moen, Ron A.
502 $a Thesis (Ph.D.)--University of Minnesota, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a Understanding the factors that influence animal distribution and density is a central theme in animal ecology. For imperiled species, understanding the resources and conditions that allow animals to occupy the landscape is critical for development of effective conservation strategies. Not surprisingly, habitat selection is a common focus of wildlife research. This dissertation project focused on addressing two main challenges that limit the application of a fitness-based approach to understanding habitat selection: 1) data on fine-scale habitat resources and conditions required for the development and testing of resource- and fitness-based definitions of habitat are generally not available across entire study areas, and 2) indirect measurements of fitness (e.g., survival or reproductive success) are often not considered when assessing habitat selection patterns, in part, because of the difficulty of measuring fitness correlates for free-ranging animals with long life-spans and large home ranges. My first two chapters address the first challenge by using airborne light detection and ranging (LiDAR) data to measure fine-scale characteristics known to be selected by my focal species, American martens (Martes americana ). In Chapter 1, I demonstrate that LiDAR data can be used to detect individual pieces of coarse woody debris, an important habitat component that provides martens with foraging habitat and access to the subnivean layer. In Chapter 2, I created statistical models to estimate 5 response variables relating to tree size and density and evaluated how well models will perform when imputed across the entire study area. I found that the models I created performed well when applied to new data, but that performance depended on the response variable being modeled. My last two chapters address the second challenge by evaluating how landscape and forest structure influence mortality risk for martens. In Chapter 3, I evaluated factors influencing harvest mortality risk. I found that martens living close to roads have higher harvest risk because trappers use roads to set and check traps efficiently. Consequently, distribution of roads can have a profound impact on habitat quality, which has important implications for gene flow and population structure. In Chapter 4, I used LiDAR data and classified satellite imagery to examine the role of forest structure in mediating interactions between martens and predators. I found that sites where martens were killed by predators were associated with non-forested areas including wetlands, shrublands, and young and regenerating forests. Although martens generally avoid non-forested areas that are associated with higher predation risk, martens must move near or through risky areas while moving across heterogeneous, managed landscapes.
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