habitat suitability model for pacific fisher in a …implementation of a gis-based predictive model...

HABITAT SUITABILITY MODEL FOR PACIFIC FISHER

IN A PORTION OF THE SHASTA-TRINITY NATIONAL FOREST, CALIFORNIA

A THESIS PRESENTED TO

THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCES

IN CANDIDACY FOR THE DEGREE OF

MASTER OF SCIENCE

By

CHARLES D. SHOEMAKER

NORTHWEST MISSOURI STATE UNIVERSITY

MARYVILLE, MISSOURI

DECEMBER, 2014

HABITAT SUITABILITY MODEL

Habitat Suitability Model for Pacific Fisher

in a Portion of the Shasta-Trinity National Forest, California

Charles D. Shoemaker

Northwest Missouri State University

THESIS APPROVED

Thesis Advisor, Dr. Yanfen Le Date

Dr. Patricia Drews Date

Dr. Gregory Haddock Date

Dean of Graduate School Date

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Habitat Suitability Model for Pacific Fisher

in a Portion of the Shasta-Trinity National Forest, California

Abstract

The thesis research presented herein explores the development and

implementation of a GIS-based predictive model for determining potential, suitable

Pacific fisher habitat in a portion of the Shasta-Trinity National Forest, in northern

California. The topic is beneficial because Pacific fisher is a sensitive species, and

various impacts to its existing habitat (e.g., logging, human encroachment,

fragmentation) have made it vital for mankind to protect all available natural resources in

that regard. As an initial step, identifying suitable habitat for the Pacific fisher has played

an important role in such preservation.

The model in this case is based on extensive literature review. From the

literature, relevant model factors, and their associated values and criteria, were

established and then incorporated into an automated GIS-based workflow. Such model

factors include vegetation type, canopy cover, precipitation, elevation, slope, aspect, and

water proximity. In general, for the Pacific fisher, habitat that includes dense, conifer-

dominant forests, with higher degree of moisture, moderate elevation and slope, more

northern-oriented aspect, and close proximity to water sources is ideal. The results of the

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model show predicted, suitable Pacific fisher habitat on differing levels across the

established study area based on these factors and criteria.

The final results of the model are sufficient and as anticipated. Overlaying a data

set of known Pacific fisher sighting locations with the final model results show that a

high level of sightings occur in areas of high habitat suitability, according to the final

model results. The statistical testing further shows that a strong correlation exists

between species occurrence and areas of high habitat suitability as well. The model is

found to be fairly insensitive to minor design changes, also. For example, small changes

in two of the most important model factor weights (i.e., vegetation type and canopy

cover) result in fairly small degrees of change in final outcomes as well. Overall, the

modeling application is a success, per the conditions set forth in the research endeavor.

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TABLE OF CONTENTS

LIST OF FIGURES .......................................................................................................... vii

LIST OF TABLES ........................................................................................................... viii

ACKNOWLEDGMENTS ................................................................................................. ix

LIST OF ACRONYMS .......................................................................................................x

CHAPTER 1. INTRODUCTION ........................................................................................1

1.1. Species Background .................................................................................................2

1.2. Research Objective ..................................................................................................5

1.3. Study Area ...............................................................................................................6

CHAPTER 2. LITERATURE REVIEW .............................................................................8

2.1. Pacific Fisher Home Range and Habitat ..................................................................8

2.1.1. Vegetation Type .............................................................................................12

2.1.2. Canopy Cover ................................................................................................14

2.1.3. Precipitation ..................................................................................................16

2.1.4. Elevation ........................................................................................................16

2.1.5. Slope and Aspect ............................................................................................17

2.1.6. Water Proximity .............................................................................................17

2.2. Habitat Suitability Modeling..................................................................................18

2.2.1. Modeling Background ....................................................................................18

2.2.2. Modeling Approaches ....................................................................................23

2.3. Model Validation ...................................................................................................27

2.4. Sensitivity Analysis ...............................................................................................27

CHAPTER 3. CONCEPTUAL FRAMEWORK AND METHODOLOGY .....................29

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3.1. Description of Data ................................................................................................29

3.2. Model Data Criteria and Development ..................................................................31

3.2.1. Vegetation Type .............................................................................................37

3.2.2. Canopy Cover ................................................................................................42

3.2.3. Precipitation ..................................................................................................43

3.2.4. Elevation ........................................................................................................44

3.2.5. Slope ...............................................................................................................45

3.2.6. Aspect .............................................................................................................46

3.2.7. Water Proximity .............................................................................................47

3.2.8. Final Model Calculation and Results ............................................................49



CHAPTER 4. ANALYSIS RESULTS AND DISCUSSION ............................................57

4.1. Final Model Results ...............................................................................................57



CHAPTER 5. CONCLUSION...........................................................................................65

REFERENCES ..................................................................................................................70

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LIST OF FIGURES

Figure 1. Pacific Fisher ........................................................................................................3

Figure 2. Study Area ............................................................................................................7

Figure 3. Model Flowchart ................................................................................................36

Figure 4. Final Model Results ............................................................................................58

Figure 5. Pacific Fisher Locations and Final Model Results .............................................60

Figure 6. Sensitivity Analysis Extent of Change in Vegetation Type Modification .........63

Figure 7. Sensitivity Analysis Extent of Change in Canopy Cover Modification .............64

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LIST OF TABLES

Table 1. List of Data Sources .............................................................................................31

Table 2. General Model Factor Criteria and Applied Model Weight ................................34

Table 3. Vegetation Type Reclassification ........................................................................39

Table 4. Canopy Cover Reclassification ............................................................................43

Table 5. Precipitation Reclassification ..............................................................................44

Table 6. Elevation Reclassification....................................................................................45

Table 7. Slope Reclassification ..........................................................................................46

Table 8. Aspect Reclassification ........................................................................................47

Table 9. Water Proximity Reclassification ........................................................................49

Table 10. Spearman Rank Correlation Coefficient: Modeled Habitat Suitability and

Pacific Fisher Locations within the Study Area ..........................................................52

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ACKNOWLEDGMENTS

I thank Dr. Yanfen Le and all supporting members of my thesis committee for

their guidance and patience throughout this research endeavor, and for sticking with me

on it over the long haul. Without doubt, I certainly took my “own sweet time” on it. In

addition, I would like to thank the only other person who had a substantial, positive

influence on me in this regard. Without her little nudges of very insightful and thought-

provoking encouragement every now and then, none of this likely would have been

possible. Thank you, Teri.

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LIST OF ACRONYMS

CDFW = California Department of Fish and Wildlife

CNDDB = California Natural Diversity Database

CWHR = California Wildlife Habitat Relationship

DBH = Diameter Breast-Height

DEM = Digital Elevation Model

ESA = Endangered Species Act

Esri = Environmental Systems Research Institute

FIA = Forest Inventory Assessment

GIS = Geographic Information System

HCI = Habitat Compatibility Index

HSI = Habitat Suitability Index

NAD = North American Datum

NED = National Elevation Dataset

NRCS = Natural Resources Conservation Service

PRISM = Parameter-elevation Regressions on Independent Slopes Model

RSL = Remote Sensing Lab

USDA = United States Department of Agriculture

USFS = United States Forest Service

USFWS = United States Fish and Wildlife Service

UTM = Universal Transverse Mercator

WHR = Wildlife Habitat Relationship

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CHAPTER 1. INTRODUCTION

Habitat loss is the single-most, primary threat to the survival of all wildlife in the

United States. Three, major types of habitat loss exist, which include destruction,

fragmentation, and degradation. Habitat destruction implies the deliberate destroying of

forested lands (e.g., bulldozing over everything). Habitat fragmentation refers to

dissecting large swaths of forested lands into smaller, less sustainable “patches,” which

may no longer contain the resources necessary to provide for any given species. An

example of fragmentation could include the building of a new road network in a large,

previously unmaintained or uninterrupted area of forest. Habitat degradation deals with

disruption of ecosystem processes by introducing such factors as pollution or invasive

species. If an ecosystem becomes dramatically changed by human-related encroachment,

like for example increased agriculture, oil or gas exploration, commercial development,

or water diversion-related activities, then it may no longer be able to provide three

necessary components for species survival (i.e., cover, sustenance, and denning or nesting

reserves) (National Wildlife Federation 2014).

As is typically the case with many large, forest-dwelling carnivores, it is no

surprise that the preferred habitat of the Pacific fisher is a critical resource that must be

maintained and managed, if the species is expected to survive and thrive. Possible

prevention of any anticipated disturbance to the species and its habitat due to sustained

human-related encroachment could prove to be a major benefit of the type of research I

have developed. Therefore, a key component in helping to identify areas of concern is

having an accurate representation of potential Pacific fisher habitat within a given study

area, and it is certainly warranted in this case.

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Therefore, this thesis research focuses on determining the area of suitable Pacific

fisher habitat within a defined study area, and how suitable it is based on differing levels

of perceived species preference. It is a perfect scenario for geospatial-based analysis. In

other words, by using appropriate data within a geographic information system (GIS)

environment, various analytical calculations and corresponding results can be developed

to help solve such a complex problem.

1.1. Species Background

The Pacific fisher (Martes pennanti) is a large, stocky, dark brown member of the

weasel family (Mustelidae), and the largest member of the genus Martes (Figure 1) (Self

and Kerns 2001; Hayes and Lewis 2006; Lindstrand 2006). It is a terrestrial, mainly

nocturnal, carnivore species that is found only on the North American continent (Hayes

and Lewis 2006). Unbefitting its name, the Pacific fisher does not eat fish, or even live

by the ocean. Its diet varies greatly and consists of birds, reptiles, insects, other small

mammals, vegetation and fruit (Quinn and Johnson 2008; Center for Biological Diversity

2010). It is the only known animal tough and clever enough to commonly prey on

porcupines, too (Center for Biological Diversity 2010).

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Within California, the Pacific fisher has been historically found in the Sierra

Nevada mountains south to northern Kern County, in the northern Coast Range and in the

Klamath, Trinity, and Cascade Mountains (Self and Kerns 2001). However, the Pacific

fisher population has declined throughout the majority of its range in the Pacific

Northwest, due primarily to habitat loss and fragmentation. It appears to have occupied

less than half of its historic (early 1900s) home range in California alone, just a half-

decade ago (Zielinski et al. 2004). Also, it has been extirpated from Washington state

and the northern Sierra Nevada mountains and remains in an isolated, reintroduced

population in Oregon, northwest California and the southern Sierra Nevada mountains.

Like other carnivore species, Pacific fishers play an important role in the maintenance of

healthy ecosystems. They declined or became extinct in much of their home range due to

habitat loss and heavy trapping in the 18th century (Defenders of Wildlife 2009).

Figure 1. Pacific Fisher (Sierra Forest Legacy 2010)

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Although Pacific fishers have rebounded in some areas, reintroduction efforts are critical

in re-establishing the species to former portions of its home range (Defenders of Wildlife

2009).

Pacific fishers depend on mature, old growth forests for adequate habitat, and they

use large areas of primarily coniferous forests with fairly dense canopies, large trees,

snags, and downed logs. They are often thought of as being among the most habitat-

specialized mammals on the North American continent (Zielinski et al. 2004). However,

much of the species’ habitat has been impacted by decades of logging and road building

(Defenders of Wildlife 2007, 2009; Center for Biological Diversity 2010). So, the above-

mentioned physical and biological characteristics put the Pacific fisher in direct conflict

with human modification of the landscape. Yet even in areas with relative Pacific fisher

abundance, the species is overly secretive and is rarely seen by people. This also

compounds problems with species population monitoring and predictive models

established to determine how to best save the sensitive species (Defenders of Wildlife

2009).

Moreover, the Pacific fisher is a very interesting and important biological species,

and it has been of concern for a long time. Protection for the species began when various

conservation groups petitioned the United States Fish and Wildlife Service (USFWS) in

2000 to protect it under the Endangered Species Act (ESA) (Center for Biological

Diversity 2010). During April 2004, the USFWS determined that the Pacific fisher is a

critically imperiled species and warrants protection under the ESA, but this protection

was precluded by other actions to protect it. Instead, the agency placed the Pacific fisher

on the growing list of species that are considered to be candidates for eventual protection

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under the ESA. Unhappy with the neglect the USFWS had shown for the Pacific fisher,

the Center for Biological Diversity (2010), along with key allies, filed a class-action law

suit against the USFWS based on its prior actions concerning the Pacific fisher.

Furthermore, the State of California has recently decided to neglect the Pacific fisher,

also. Once considered a species of special concern, due to a 2008 petition to make the

species a candidate for protection, the State of California stated in early 2010 that it

would not place the imperiled mammal on its own endangered species list after all. As of

November 2010, the State of California is being sued over its actions as well because it is

believed the species currently needs the utmost protection on all levels (Self and Kerns

2001; Center for Biological Diversity 2010). The Pacific fisher has also been identified

as a sensitive species by the United States Forest Service (USFS) (Self and Kerns 2001).

However, on the contrary, at least one timber industry has filed lawsuit seeking to remove

the Pacific fisher from the list of species that are candidates for protection; even though it

is well known that timber harvesting has contributed heavily to the decline of the species.

So, given the apparent high stakes of fisher habitat, some form of conflict is likely to

persist and intensify regarding the Pacific fisher well into the future (Defenders of

Wildlife 2007, 2009).

1.2. Research Objective

The objective of this research is to build a model that predicts suitable Pacific

fisher habitat within a defined study area, using both raster- and vector-based data sets

inside a GIS-based application. The model is predictive by nature, and stems from both

descriptive and inductive components. In addition, the model is composed of multiple

processes, which use various data sets to arrive at a final, predicted model result.

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Therefore, the possibility of providing yet another piece of useful information, in support

of the struggle between “humans versus beast,” is anticipated. Ultimately, the research

attempts to answer the specific question of, “What is the area of suitable Pacific fisher

habitat within a defined study area, and how suitable is it based on differing levels of

perceived species preference?”

1.3. Study Area

The study area for this research covers portions of three counties in northern

California, which include Shasta, Siskiyou, and Trinity Counties (Figure 2). The entire

study area falls within the Shasta-Trinity National Forest, and is defined by the extents of

the McCloud, Mount Shasta, and Shasta Lake Ranger Districts. The combined area of

land covers approximately 2.47 million acres. Generally speaking, the study area is north

of Redding, north and west of Burney, south of Yreka, and east of Weaverville, which

encompasses the greater area of land north of Shasta Lake (prominent reservoir) and

south of Mount Shasta (prominent mountain). The area is extremely rugged over much

of its coverage, consisting of dense evergreen forests and steep, mountainous terrain. It is

located in a portion of the southern end of the Cascade mountain range, which is known

for its extensive volcanoes (e.g., Mount Shasta). The smaller mountain chains to the west

of the study area include the Klamath Mountains, as well as the Trinity Alps. The study

area is sparsely inhabited by humans. The largest nearby city is Redding, which contains

approximately 100,000 people, and is located just outside the southern boundary (City of

Redding 2010). There are other smaller towns (e.g., Mt. Shasta, McCloud, Burney)

scattered throughout the study area, but they contain far lower human population totals.

7

Figure 2. Study Area

8

CHAPTER 2. LITERATURE REVIEW

2.1. Pacific Fisher Home Range and Habitat

In general, habitat can be thought of as a place where a particular animal lives, or

more specifically the place that characterizes the landscape in a meaningful manner with

respect to living and non-living species use. Scientifically speaking, the definition of

species habitat is usually taken one step further, and it is described as what an animal

needs to survive and reproduce – in this case, the Pacific fisher (Corridor Design 2010).

In the United States, prior research has been performed regarding Pacific fisher

habitat suitability, and done so by what appears to be a small group of individuals.

However, Pacific fisher has received little study with respect to habitat characteristics

specifically in the western United States (Zielinski et al. 2004). Most of the research

specifically involves habitat suitability assessment and/or population estimation and

associated dynamics. After all, analyses of the potential advantages and disadvantages of

various forest management policies are often facilitated by the use of habitat suitability

models (McComb et al. 2007).

In the western United States, California is unique in the fact that Pacific fishers

have been present there since before European settlement, and reintroduction attempts of

the species from other locations has never occurred. Also, it is thought that the Pacific

fisher population in northwestern California is probably the largest in the western United

States, but it is also quite isolated from any other Pacific fisher population (Zielinski et

al. 2004).

Pacific fishers have very large home ranges, with males covering approximately

9,900 acres and females covering approximately 3,700 – 6,200 acres (McComb et al.

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2007). Documented Pacific fisher home range size in the Shasta-Trinity National Forest

averages approximately 5,800 acres (Yeager 2005). Pacific fisher are widely distributed

across a variety of different habitat types in the Shasta-Trinity National Forest, according

to various sighting reports, monitoring results, and study findings. Furthermore, over 550

observations of Pacific fisher have been recorded in the Shasta-Trinity National Forest

from 1941 to 2005. These observations are the result of monitoring efforts (via both

tracking plates and baited camera stations), trapping, incidental sightings, and research

report results (Quinn and Johnson 2008).

Due to the species’ very large home ranges, Pacific fisher suitable habitat has

been determined to consist mainly of uninhabited public land, with a small portion of

private land that is adjacent to large areas of public land, which is exactly the type of

scenario being investigated in this research. Small areas of private land, thought to be

used by Pacific fisher, are usually for foraging purposes only; denning largely occurs in

remote areas of public (e.g., federal) lands (McComb et al. 2007).

Pacific fisher habitat selection in California involves selection of suitable sites

based on stand type, vegetation, and topographic characteristics. The species prefers

standing trees that are quite large (e.g., average DBH >100 cm), and dense tree canopies

(Zielinski et al. 2004). Zielinski et al. (2004) note that trees used for resting by the

Pacific fisher are generally much larger than the average available tree in their study

areas, which implies that Pacific fishers may seek out the largest trees and snags available

for their shelter. However, they also recorded the species inhabiting smaller tree-based

structures, which suggests that while not ideal, they too can provide adequate cover.

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Pacific fisher are apparently most selective about natal denning and resting sites

as far as habitat suitability is concerned, and least selective about foraging locations

(Zielinski et al. 2004). However, even though Pacific fisher typically avoid non-forested

and open areas as suitable habitat, they will forage in a wide variety of habitat types

(seral forest stages) based on their very diverse prey base (Quinn and Johnson 2008).

Based on published sources and expert opinion, Pacific fishers use a wide variety of

habitat types for their many life-sustaining requirements, but the primary constituent

elements of Pacific fisher habitat are found in large areas of contiguous or interconnected

forests with mid- to late-successional aspects, including old-growth trees with high

density and larger trunk sizes (Defenders of Wildlife 2007; Lindstrand 2006; McComb et

al. 2007; Quinn and Johnson 2008; USFWS 2004; Zielinski et al. 2004). Zielinski et al.

(2004) and Lindstrand (2006) further state that mature or late-successional conifer forests

are the preferred habitat for Pacific fisher in the western United States. McComb et al.

(2007) agree and add that a hardwood component, to some degree, is also quite

important. Hardwoods add a significant component of species diversity to conifer-

dominant forest stands, based on lichens and invertebrates, and various ecological

processes (e.g., nitrogen fixation) (McComb et al. 2007).

Pacific fisher have been found in areas that are generally not considered to be

suitable habitat in northern California, too, including open second-growth conifer,

hardwood conifer, and hardwood habitats, which also have substantial chaparral

coverage. Species detections have also been recorded near both residential and industrial

development locations, as well as on barren land (Lindstrand 2006).

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According to camera-bait station surveys conducted from 2003 – 2005,

Lindstrand (2006) experienced Pacific fisher detections surrounding the entire region of

Shasta Lake, except for a large area of the north-central portion of the lake, which mostly

is comprised of the McCloud River arm. The majority of Lindstrand’s (2006) Pacific

fisher detections are found in remote, uninhabited locations though. A few of his

detections are found within less than +/- 2.5 kilometers from residential areas, as well as

a boat marina.

While Pacific fishers have been found in second-growth forests and areas with

sparse canopy cover, it is not fully understood if these types of habitat conditions are the

result of transient activity by the species, or if they are truly characteristic of the species’

typical home range. Furthermore, it is not likely that early- to mid-successional forests

would provide the necessary resources for the Pacific fisher to make those types of

habitat more preferential to the species over more mature forest stands (Defenders of

Wildlife 2007).

Various processes, such as forest growth, disturbance, and resource extraction,

have a profound effect on resting structure availability within suitable habitat for the

Pacific fisher. Also, it is no surprise that the forests of the western United States have

undergone significant changes in the past few decades. For example, logging and natural

fire damages have resulted in substantial amounts of late-successional forest decline in

the Pacific Northwest in the recent past. With Pacific fisher being known to select for

areas of large trees, snags, and dense cover, which late-successional stands provide, it is

no wonder that Pacific fishers are ultimately affected by such practices and events that

serve to change and shape overall forest ecosystems (Zielinski et al. 2004).

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It is a widely accepted practice to retain adequate densities of large trees and

snags across a forested landscape. However, for various reasons, these types of structures

have declined in abundance over time. For example, it is very common for large trees to

be the first targeted during a timber harvest effort, and snags are commonly removed

from public lands. Furthermore, snags have been historically reduced from national

forest lands because of even-age management, fuelwood cutting, and elimination as a

potential source of disease and insects. Safety, funding, and inconsistent standards and

guidelines within snag retention programs on national forests have caused problematic

situations as well (Bate et al. 1999).

2.1.1. Vegetation Type

Perhaps the most important model factor in any species-based habitat suitability

model is land cover, and it is the most important factor in this research for Pacific fisher,

also. Land cover is very important because it reflects the fact that land cover is largely

related to food sources, hiding cover, thermal cover, and human encroachment, to some

degree. In addition, “vegetation type” is sometimes used for this model factor because

the majority of land cover classes are coincidental to common names of vegetation

communities. Vegetation data are typically classified categorically. However, they may

contain continuous attributes, too. An example of a continuous metric would be tree-

canopy closure (measured in percent closure). Also, vegetation data may be available in

a GIS layer with a couple of dozen coarse classes or upwards of a hundred classes.

However, it can be useful to lump many classes into smaller classes, simply for the

reason of not trying to distinguish among very closely related species types (Corridor

Design 2010).

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With respect to vegetation, key suitable habitat elements of the Pacific fisher

include large snags and downed woody debris (e.g., logs) for denning purposes and dense

canopy closure for protection from the sun (Quinn and Johnson 2008). They usually

select forest stands with trees that are either hollow or contain many snags (McComb et

al. 2007). Zielinski et al. (2004) note that preferable resting habitat during various

seasons consists of large, live trees containing snags, hollows, stumps or logs, witches

brooms, and other species nests, as well as brush or rock piles, and holes at ground level.

As a result, resting and denning site selection may be the most limiting habitat suitability

factor across the Pacific fisher’s home range. Multi-staged canopy layers, hardwoods

with large diameter breast-height (DBH) measurements, and steep slopes in close

proximity to water sources are important factors as well (Lindstrand 2006; Quinn and

Johnson 2008). Quinn and Johnson (2008) state that tree cavities play a huge role in

Fisher habitat selection, too. The trees need to be old enough to bear the type of stresses

that eventually spur cavity formation that will be useful to Pacific fishers (e.g., decay and

woodpecker activity). Also, known tree species that decay to form substantial cavities

have a higher influence on suitable Pacific fisher habitat than those tree species that do

not.

Lindstrand’s (2006) 2003 – 2005 Pacific fisher surveys found that, based on

Mayer and Laudenslayer’s (1988) habitat classification system, the dominate habitat type

found at detection locations is montane hardwood-conifer stands intermixed with

ponderosa pine, montane hardwood, blue oak – foothill pine, and mixed chaparral.

Open- to moderate-canopied hardwood-conifer stands, dominated by California black

oak, canyon live oak, ponderosa pine, and occasionally Douglas-fir, are the typical type

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of tree habitat found at all of Lindstrand’s (2006) detection sites. He notes that hardwood

and chaparral habitats mainly consist of the same types of tree species, also including

brewer oak, and there are small inclusions of blue oak, interior live oak, and foothill pine

as part of the blue oak – foothill pine habitats. Zielinski et al. (2004) state that tree

species common to their northwestern California study area are California black oak,

interior live oak, canyon live oak, madrone, chinquapin, tanoak, sugar pine, ponderosa

pine, Jeffrey pine, red and white fir, and incense cedar. Douglas fir dominated Pacific

fisher site locations, with hardwoods, specifically California black oak, are a close

second. In addition, black oaks tend to produce cavities, which Pacific fisher favor.

Lindstrand (2006) mentions that his 2003 – 2005 survey study area consists of not

only scattered patches of dense-canopy conifer and mixed-conifer stands, but interspersed

large trees, downed logs, and snags are somewhat common as well. Lindstrand (2006)

states that the general habitat types where Pacific fisher are found around Shasta Lake are

not typical of the conifer-dominated habitats they are known to use in California.

2.1.2. Canopy Cover

It is a known fact that Pacific fisher prefer forest stands with very dense canopy

cover. Likewise, they avoid areas with very low canopy cover. In addition, Zielinski

(1999) states that percent canopy density is one of the most important factors when

speaking in terms of utmost suitable habitat conditions for Pacific fisher (Defenders of

Wildlife 2007). In general, pacific fisher favor dense tree canopies, consisting of at least

60% closure (Zielinski et al. 2004). In their 2004 study, Zielinski et al. state that they

had an average canopy closure of 93.4% for Pacific fisher resting site locations, which is

deemed to be excellent. According to Quinn and Johnson (2008), their Gemmill Thin

15

Project has a remaining canopy closure, after tree thinning, of approximately 60%, and

that value is still well within the parameters considered to be suitable habitat for Pacific

fisher. Self and Kerns (in Quinn and Johnson, 2008) report an average canopy closure of

71% as being the mark between suitable versus less-suitable Pacific fisher habitat. So, it

appears as though a threshold of 60 – 70% average canopy closure is agreed upon to be

the lower limit of what is considered to be “prime” Pacific fisher habitat, and it decreases

in suitability from there.

Zielinski (1999) states that average tree size is equally important when speaking

in terms of utmost suitable habitat conditions for Pacific fisher, also (Defenders of

Wildlife 2007). In general, pacific fisher favor tree size classes 4 and 5 (28 – 61 cm DBH

and >61 cm DBH) (Zielinski et al. 2004). Defenders of Wildlife (2007) narrow the range

further by stating that forest stands with trees averaging 94 – 120 cm DBH are

preferential for highly suitable Pacific fisher habitat. In their study, Zielinski et al.

(2004) note the importance of tree DBH measurements within their study areas

description as well. Trees averaging >30 cm DBH covered about half of the areas, and

trees averaging >61 cm DBH covered about 1/10th

of the areas. In addition, they

established minimum ideal DBH sizes for trees in highly suitable Pacific fisher habitat.

Minimum DBH size for conifers is >80 cm, for hardwoods it is >56 cm, and for logs it is

>62 cm. Their ideal mean variable values for Pacific fisher resting site locations are as

follows: Average hardwood DBH (cm) = 69.0, average conifer-live DBH = 117.2, and

average conifer-snag DBH = 119.8. Their overall average DBH is 62.9 cm. Dead and

downed woody debris >15 cm are of interest and for anything >30 cm, length, maximum

and minimum diameter, and rate of decay are measured, too. Ground cover percentage is

16

also measured (Zielinski et al. 2004). Quinn and Johnson (2008) state that snags and

downed, woody debris greater than 49 – 61 cm DBH are preferable for suitable Pacific

fisher habitat. McComb et al. (2007) express interest in snags and trees with >100 cm

DBH in their study.

2.1.3. Precipitation

Previous research has attempted to largely describe Pacific fisher habitat by using

predictive model variables, even though abiotic variables (e.g., elevation and

precipitation) have been significant at predicting Pacific fisher presence, too. Therefore,

Pacific fisher species abundance may be better described by predictive model variables

that describe net primary productivity of the landscape, such as mean annual

precipitation, mean annual temperature, annual potential evapotranspiration, and even

solar radiation. Very specific predictive model variables of net primary productivity

(e.g., mean annual rainfall) have been significant predictors of Pacific fisher presence in

California in the past (Self et al. 2008).

2.1.4. Elevation

Elevation, which is a determinant of land cover and vegetation, can affect the

thermal environment of a species, and the amount and form of precipitation in a given

study area. Elevation is a factor typically used when there is literature stating that the

species occurs within a certain range of elevation. Depending on interpretation of the

literature, we often recognize three classes (e.g., below, within, and above the elevation

limits) for elevation. Digital elevation models (DEM) are used in the modeling efforts,

and they are also the basis for several derivable variables, including aspect, slope, and

topographic position. Topographic position may be correlated with moisture, heat, cover,

17

and vegetation factors. For example, some species are reported, in the scientific

literature, to be associated with features like canyon bottoms, steep slopes, or other

topographic locations. Estimating topographic position can be very easy and is usually

done by classifying raster pixels into any number of classes, such as steep slope, ridgetop,

or valley bottom (Corridor Design 2010). Zielinski et al. (2004) note this same type of

approach to determining topographic position in their studies, too. In Lindstrand (2006),

most Pacific fisher locations are found around the greater Shasta Lake area, which has an

average elevation range of around 1,000 – 2,000 feet.

2.1.5. Slope and Aspect

Also derived from DEM data, slope may be correlated with protection from

predators and human encroachment. An ideal example is the close association between

bighorn sheep and steep terrain they require to escape predators. Aspect may be a

determinant of solar radiation, and consequently temperature, soil moisture, and

vegetation factors (Corridor Design 2010). Zielinski et al. (2004) display interest in both

aspect and percent slope at Pacific fisher locations in their study, as well as general

elevation. They state that ideal mean percent slope for Pacific fisher habitat is

approximately 40%. In general, north-oriented aspects are more preferable, also.

2.1.6. Water Proximity

According to Corridor Design (2010), another important model factor for some

species is distance to water, which may be correlated with water, movement, and food

sources. Scientific literature occasionally admits that a certain species is usually found

within a specified distance of water. This is a true statement about Pacific fisher as well.

18

Pacific fisher prefer habitat that is cool, and moist or damp, so proximity to water

is an important variable in consideration of their ideal habitat locations (Zielinski et al.

2004). Closer is certainly better, within a few hundred meters being most ideal. Quinn

and Johnson (2008) state that moderately steep slopes in close proximity to water are

considered to be prime components of suitable Pacific fisher habitat.

2.2. Habitat Suitability Modeling

2.2.1. Modeling Background

Technically speaking, the only way for scientists to determine what an animal

needs, habitat-wise, to survive and reproduce is to conduct detailed experiments to try to

gain solid, factual knowledge on the subject (e.g., habitat modeling). In addition, five

major components are typically examined when determining suitable habitat for a

particular species, and they are dependent on what the species is doing in a certain area or

with respect to a certain variable of the landscape. The components are food, water,

hiding or ambush cover (depending on whether the species of interest is considered to be

prey or predator), thermal cover, and special needs sites (e.g., reproduction, resting,

denning). Species survival and successful reproduction hinge on adequate and

sustainable combinations of these factors over time (Corridor Design 2010).

With that said, habitat models are great “tools” that allow one to effectively assess

the quality of habitat for a species within a specific study area. More specifically, within

a GIS, habitat suitability models can commonly determine species suitability derived

from data such as land use/cover, elevation, topography, human

encroachment/disturbance (e.g., distance from roads, road density), or other important

factors available as GIS layers. The various layers are referred to as factors within the

19

GIS model. Also, there can be multiple, defined groups in each factor. For example, a

factor for vegetation may include groups such as oak woodland, annual grassland, and

urban/barren (Corridor Design 2010).

There are two main ways to develop species-specific habitat suitability models: 1)

Literature review (or expert opinion-based) habitat suitability models and 2) empirical

and statistical techniques for estimating habitat suitability. With the literature review

approach, valuable information pertaining to the construction and structure of a model is

gained through past experiences, and gleaned from existing literature on the subject.

With the empirical and statistical techniques approach, existing data pertaining to actual

species locations are used to predict future locations and suitable habitat conditions

(Corridor Design 2010).

The most common approach to habitat suitability modeling is based on literature

review (or expert opinion), which follows ideas founded by the USFWS. Literature-

based models can promote uncertainty and error in results when trying to translate habitat

study information to habitat suitability factor criteria. This can also cause model

validation to become a challenging task. However, they are relatively easy to create, do

not require new collection of detailed field data, are easily modifiable, and can be applied

to multiple study areas, allowing for rapid analyses for not only the species in question

but many different species of interest (Corridor Design 2010).

In their research study, McComb et al. (2007) show the model validation task to

be both informative and frustrating. This is mainly due to one portion of their study area

having conflicting model results. Also, the authors are unsure why the anomalies took

place. They suggest that any reader of their specific research in this regard to use caution

20

when interpreting the results, and to view the results in a relative rather than an absolute

sense.

With respect to forest policies and their effects on specific species (e.g., Pacific

fisher), McComb et al. (2007) address some limitations they experienced while

conducting their research. First, their models are largely developed using existing

foundational literature and expert opinion, rather than using substantial empirical data.

Again, this makes model validation a tedious process. Second, in general, empirical-

based models assume that a change in value corresponds to a relative change in habitat

suitability; this is known as habitat “fitness.” However, in their case, sufficient empirical

data are not available to adequately test for habitat fitness for their species of focus.

Third, as with any location-specific modeling attempt, conditions in other locations may

vary to certain degrees, and the fit of their specific models to other areas may or may not

be relevant based on various conditions. Fourth, indicators used in their study are

dependent on underlying models that predict variable values that contain errors and

constraining assumptions. These types of models cannot be tested successfully in a

typical scientific experiment. Nevertheless, the authors go on to state that even though

their work and models have apparent limitations, they still represent useful “thought-

experiments,” which can provide valuable insight into best practices for forest

management policies.

The other approach, empirical and statistical techniques, to habitat suitability

modeling is based on species occurrence. This type of approach may be more accurate

than literature review-based models, but it also requires extensive field observation data

and considerable time to implement. However, if accurate presence-absence data are

21

available for the species of interest, then empirical and statistical models can be

developed by relating species occurrence data to habitat-based model factors. Various

statistical techniques (e.g., generalized linear or additive models) can then be used to

create a result of species probability of occurrence at any raster pixel. Data is typically

extracted from GIS-based layers, integrated into occurrence matrices, analyzed with the

chosen statistics, and imported back into the GIS to create a map depicting probability of

occurrence (Corridor Design 2010).

Habitat suitability modeling typically requires a scientist to assign a weight to

each model factor and a habitat suitability rank to each class within a model factor.

Habitat suitability ranks for all of the model factors are then combined (mathematically

added) to form a single, species habitat suitability map with a suitability rank for each

raster pixel. Model factors such as land cover or vegetation, topography, and human

encroachment tend to dominate habitat suitability models typically because these types of

data are the only relevant ones widely available as GIS layers (Corridor Design 2010).

Corridor Design (2010) suggests that scaling for habitat factors can be categorical

(e.g., landcover types or topographic classes) or numerical (e.g., percent slope or distance

from a type of cover) in design. Furthermore, if there is a choice between the two types

of scaling, categorical is almost always preferred. Also, when using a categorical

variable, it is best to limit the number of classes based on scientific understanding. For

example, distance-to-roads can be an important factor in a habitat suitability model for a

snake species. Oftentimes, snakes meet their demise on roadways. In this example, we

will say the average daily movement of the snake species has a distance of 100 m.

Therefore, snakes up to 100 m away could be affected by increased mortality rates due to

http://www.unine.ch/cscf/grasp/

22

the roadways. Snake species can also “hear” through their jaw structures, and a prior

study has proven that they can sense vibrations from passing cars up to 50 m away.

Likewise, the vibrations could confuse the snakes, and force them to avoid areas within

50 m of a roadway. With this type of information, three habitat model classes (e.g., 0-50

m, 50-100 m, and >100 m from any roadway) are all that are needed to adequately serve

the model. On the other hand, more than three classes could be created, but how would

habitat suitability for each of them be determined? In other words, a more complex

model would be no better than a simple one, in reality.

Habitat use for any given species is driven by the availability of food, nest sites,

and other resources, safety from predators and other hazards, presence of competitors or

facilitating species, and many other factors. However, GIS models that predict habitat

suitability are usually more simplified. They are typically based on one to five factors,

for example, which may include land cover or vegetation, up to a couple of factors

related to human encroachment, and up to a couple of topographic-related factors. GIS

models are built on these basic factors for one simple reason: they are really the only

relevant and substantiated factors for which georeferenced spatial data are available for

an entire study area (Corridor Design 2010).

Problematic, too, is the fact that each of the GIS layers is related to some aspect of

food, cover, and other important components of species habitat, but the GIS layers do not

correspond well with realistic habitat factors. Therefore, what can be done about the

incompleteness of GIS-based models? Corridor Design (2010) proposes three simple

guidelines: 1) There may not be much of a choice but to build GIS models based on

factors for which data are readily available, even if the factors are not entirely

23

comprehensive with respect to all known species habitat criteria, but credibility will be

strengthened by acknowledging the potential issue, nonetheless. 2) Sensitivity analysis

can be used to see how much predicted suitable habitat changes when different

assumptions about the model inputs or structure of the model vary. 3) Develop a good

GIS-based foundation of incidental data known to affect habitat use by the species of

interest. With reliable GIS data of such features readily available, many existing models

could be drastically improved upon quickly and efficiently.

2.2.2. Modeling Approaches

Early habitat modeling methods include habitat suitability index (HSI) models,

pattern recognition models, and statistical relationships (e.g., regression models).

Another type of habitat modeling approach, the wildlife habitat relationship (WHR)

model, has been used widely to specifically assist in management decisions across

managed forests. WHR models have been confirmed to be a useful tool in the successful

prediction of species occurrence and abundance across a wide range of habitat types

(McComb et al. 2007). For example, Self and Kerns (2001) found significant nonrandom

use of California Wildlife Habitat Relationship (CWHR) habitat types by Pacific fishers

within their study area. They also found that Pacific fishers selectively used CWHR

types based on specific tree density and size classes. These types mainly consist of

Klamath mixed conifer and montane hardwood conifer, although other types are certainly

used as well. Distance to water plays a substantial role, also, with closer proximity to

water being preferential.

Zielinski et al. (2006b) use Forest Inventory Assessment (FIA) data to assess and

model Pacific fisher habitat in their particular study, which is a different type of approach

24

to examining suitable habitat characteristics based on other historical studies of the

species. FIA data use probability-based data samples to estimate forest-related

characteristics. Their study compares vegetation and topographic data together. They

also found that while the model is constrained to the use of model variables only

available in the FIA data, they did not find it to be a shortcoming. In fact, the strategy

makes it quite possible to compare average Pacific fisher habitat suitability before and

after prescribed forest management treatments, among administrative units, across

regions, and over time, which obviously can be a more flexible and feasible approach.

McComb et al. (2007) base their habitat modeling research on the traditional HSI

protocol. However, they add a new twist to their approach, which involves looking at

species-specific multiple spatial scales. The approach basically extends the HSI protocol

to include a spatially explicit assessment of habitat quality. They dub their approach a

habitat compatibility index (HCI). Nevertheless, their modified approach still assesses

habitat quality based on a scale range, considering various life requisites of a particular

species, just like traditional HSI models do. For example, their HCI model approach

includes indices that are associated with foraging and reproductive characteristics of a

given species. Their indices are also ranked on a scale from what is considered to be

optimum habitat to null habitat. Index values are then determined for any given location

based on model factors for the particular species (e.g., vegetation characteristics).

Selection of particular variables, or model factors, and their relationship to capability

indices is supported with a culmination of information, based on existing literature

review. Furthermore, their models are designed to operate with GIS-based raster data.

Overlay results provide the prediction, via habitat capability indices, of suitable species

25

habitat based on both reproduction and foraging requirements. Selection of variables

(e.g., vegetative and physical) for their HCI models depends on four basic criteria. First,

variables are chosen based on strong supporting empirical evidence from published

literature and expert opinion. Second, relevant, existing data and information are limited

to GIS layers that are largely the result of satellite imagery, environmental data, or field-

collected data. Third, variables are selected based on stand-level forest data. Fourth,

variables that had only noticeable influence on modeled indices values are selected as a

result of model sensitivity analysis. Furthermore, they note that a major assumption of

this type of modeling is that optimum values for the various life requisite variables are

known. However, optimum values are rarely ever known, mainly due to a lack of

sufficient empirical data to support the specifications. Therefore, optimum values are

estimated based on deriving variable averages for the given factors for habitat types used

by the species being studied. Model validation for this research is assessed by using

georeferenced animal abundance data (e.g., species location data). Finally, threshold HCI

scores are used to categorize species habitat suitability into rankings of low, medium, and

high. In order to accomplish this, frequency distributions of HCI scores are examined,

and breaks are applied at the lower and upper thirds marks of the range to define three

distinct classes of data. Afterward, it is decided that both medium and high habitat would

be considered suitable habitat conditions for the species of interest. Low habitat is not

considered to be suitable. For model validation purposes, their study compares species

occurrence data with only habitat shown to be high suitability. This fact is due to the

assumption that species viability is largely associated with areas of high habitat

suitability.

26

Davis et al. (2007) state that mapping – more specifically predictive modeling –

of species distributions is used widely in environmental and conservation planning efforts

to help protect and recover rare and endangered species. Spatially explicit statistical

models of species-environment associations can aid in the identification of critical habitat

areas for species protection or reintroduction and to better project distribution changes

under various effects (e.g., climate change). Species habitat modeling assumes that the

observed distribution of a species represents its true suitable habitat choice as well.

However, this assumption could be untenable for rare and endangered species, such as

the Pacific fisher, whose distributions have been reduced by forest overharvesting or

suitable habitat degradation. Further, if the species has not reoccupied habitat that is

presumed to be suitable due to social, demographic, or dispersal influences, it may be

difficult or impossible to distinguish unsuitable habitat from unoccupied habitat

conditions.

Therefore, it is easy to envision predictive variable mapping and modeling for

sustained Pacific fisher habitat suitability being a paramount key in the existence of the

species. Along with that is the fact that key variables must be identified and measured if

various habitat suitability modeling attempts are to become fruitful. Many variables may

be taken into account, too, including level of forest canopy cover and vegetation type,

precipitation, elevation, and physical geographic location, all of which affect model

results for any given location.

Nevertheless, regional modeling of habitat suitability is a key tool for

conservation and restoration of wide-ranging species like the Pacific fisher. Pacific fisher

population dynamics and viability at any particular site will be strongly influenced by the

27

regional distribution of suitable habitat due to the species occupying fairly large home

ranges and dispersing over long distances over time (Carroll 2005). Therefore, trends

toward landscape management across large land ownerships (e.g., national forests) may

help reduce suitable habitat fragmentation and degradation, and increase forest structure

characteristics in future forests, thus improving the value of the lands for Pacific fishers

(Hayes and Lewis 2006).

2.3. Model Validation

Statistically speaking, Spearman’s Rank Correlation Coefficient (rs) is the most

widely used measure of the strength of association between two variables in geographic

problems with data in ranked form (McGrew and Monroe 2000). Therefore, for the

purpose of validating a model, a Spearman Rank Correlation Coefficient statistical test

would be appropriate to assess the correlation of known Pacific fisher sightings in

relation to their frequency within modeled habitat suitability values in a defined study

area. The test can determine whether or not higher frequencies of Pacific fisher

occurrences would be evident in areas with higher modeled habitat suitability values.

2.4. Sensitivity Analysis

Sensitivity analysis is the study of how the uncertainty in the output of a model

(numerical or otherwise) can be apportioned to different sources of uncertainty in the

model input. Sensitivity analysis is hence considered by some as a prerequisite for model

building in any setting and in any field where models are used (Saltelli et al. 2002).

Model development usually consists of several logical steps, one of which should be the

determination of model input parameters which most influence model output. So, a

sensitivity analysis of model input parameters can serve as a guide to any further

28

application of the model (Ascough et al. 2005). Therefore, sensitivity analysis can play

an important role in model validation throughout the course of model development and

refinement (Frey et al. 2004).

Technically, the only way to address uncertainty in overlay modeling applications

is to perform sensitivity analyses, where the overall variability in the possible output can

be examined (O’Sullivan and Unwin 2003). Goldmeier (2012) states that a simple, but

powerful, method of sensitivity analysis includes varying the weights in a weighted sum

model. The procedure is known as One-Way Sensitivity Analysis.

29

CHAPTER 3. CONCEPTUAL FRAMEWORK AND METHODOLOGY

3.1. Description of Data

The main model factors include vegetation type, canopy cover, precipitation,

elevation, slope, aspect, and water proximity (e.g., streams and other water bodies).

Other supporting data include the actual study area boundary, and background layers

such as Environmental Systems Research Institute (Esri) Street Map data, etc.

One of the most important data sets used in the model – at least from foundational

and operational perspectives, nonetheless, and regardless of thematic importance – is the

elevation data (and subsequent aspect and slope data). In this case, I have chosen to use

National Elevation Dataset (NED) tiles. The NED data were acquired online via the

United States Department of Agriculture (USDA) Natural Resources Conservation

Service (NRCS) Data Gateway web interface in August 2012. The NED data also serve

to define the model’s resolution and spatial reference. All other corresponding data are

based on the same spatial reference as well. Per the NED data, the model’s resolution is

10-meter cell size. All data are projected to Universal Transverse Mercator (UTM) Zone

10N, North American Datum (NAD) 1983, Meters. The original NED data exist in the

form of 1:24,000, 7.5-minute, orthocorrected quadrangles. Sixty-three of them are

needed to adequately cover the study area. The NED tiles are merged into a single raster,

and clipped to the extent of the study area. All corresponding data are clipped to the

study area boundary as well.

The vegetation type and canopy cover layers come from one parent layer. The

parent layer contains a wide array of vegetation-based attribute information, and was

acquired from the USFS Region 5 Remote Sensing Lab (RSL). The data are based on

30

USFS FIA information, via a survey time span of 2001 - 2007. Both layers contain

polygon features that represent differing levels of vegetation types and percent canopy

cover. Like the NED data, vegetation type and canopy cover are very important layers

for the model, and the most important in terms of habitat suitability level for the species.

Precipitation data comes from Parameter-elevation Regressions on Independent

Slopes Model (PRISM) information, which were downloaded from the NRCS Data

Gateway. This is a data set that shows annual precipitation for the state of California,

from 1971 – 2000. Streams and water body layers, from the USFS and Esri, respectively,

are used for the water proximity model factor. The water-related data are from 2010.

The study area boundary layer, as well as the basic background layers, is used to prepare

necessary maps in support of this final thesis document. A layer of existing Pacific fisher

locations within the study area is used to assist in model validation. The layer is derived

from the California Department of Fish and Wildlife’s (CDFW) California Natural

Diversity Database (CNDDB) system, and was acquired in August 2012.

Finally, most of the data sets that are used in my research are vector-based, or are

at least in that particular format in their original form. These data are converted to raster-

based format, if needed, before actual inclusion into the model; an exception here is some

of the data that are used for validation, as well as supporting “background” layers. Table

1 outlines all of the data sources used in this research.

31

Table 1. List of Data Sources

Data Source

Vegetation Type USFS Region 5 RSL

Canopy Cover USFS Region 5 RSL

Precipitation USDA-NRCS PRISM

Elevation (and Slope and Aspect) NED

Water Proximity Esri and USFS Shasta-Trinity NF

Pacific Fisher Locations CDFW CNDDB

Study Area User-Defined

Supporting Background Data Esri

3.2. Model Data Criteria and Development

For the purposes of this thesis research, a literature review-based model approach

is chosen. Per the data sets described in the previous subsection, the model is governed

by specific model factor criteria that are associated with them. Strictly speaking, the

model considers factor criteria that are deemed to be more ideal than other criteria, which

are concerned with the same types of themes. For example, vegetation types, which are

of the highest suitability for Pacific fisher habitat, include evergreens (conifers) and some

dense hardwoods. Other vegetation types decrease in suitability based on vegetation

species (e.g., brushes and grasslands). Therefore, with respect to vegetation types and

how they ultimately affect Pacific fisher habitat suitability, some types are more preferred

than others and are therefore ranked and classified accordingly per information stated in

32

the foundational literature (Self and Kerns 2001; Carroll 2005; Hayes and Lewis 2006;

Zielinski et al. 2006a, 2006b; Davis et al. 2007; Self et al. 2008). All data layers used in

the model follow the same type of process.

In addition, the chosen model weights for this research are selected by giving

priority to those factors where the foundational literature suggests more importance for

some themes versus other themes. For example, it has already been stated that the

vegetation-based themes are the most important themes in the model, based on habitat

specificity. Water proximity is chosen as next highest priority. Precipitation and all of

the elevation-derived themes are classed with lesser importance and given the same

model weights across the board.

Basically, there are eight different factors that work within the model, including

both water bodies (lakes) and streams to represent water features. If all factors are set to

equal regarding their model weights, every factor will have a weight of 1/8. In decimal

form, each factor will be set to a model weight of 0.125. However, we have already

established that the two vegetation-based themes are more important than all of the other

factors, and so their model weights should be a value greater than 0.125. Water

proximity should be less than the two vegetation-based themes. All of the other themes

should be even less than that.

For this research scenario, I set the model weights for vegetation type and canopy

cover to 0.175. For water proximity, I chose a model weight of 0.125. For all other

themes, I chose model weights of 0.10. The chosen hierarchy is a snapshot in time. It

provides a final model result that flows with what is stated in the literature, where

vegetation-based themes are more important than water, and water is more important than

33

any of the other chosen themes. Of course, the values that are chosen here are not set in

stone. They could be adjusted to produce different outcomes. However, pinning down

exact, finite model weight values for each of the eight themes is not the focus of this

research. A realistic representation of a working model is the focus, based on guidelines

set forth in a literature-based habitat modeling scenario. The chosen scenario

accomplishes that task as well.

Table 2 provides a brief overview of the general model factors, their criteria, and

applied model weights for this research. This information is explained in more detail

farther below, in individual model factor subsections.

34

Table 2. General Model Factor Criteria and Applied Model Weight

Factor Criteria (Suitability Level) Applied Model Weight

Vegetation Type Barren/water/grasslands = not

good

Chaparral/manzanita/willow =

okay

Alder/aspen = better

Hardwoods (oaks) = more ideal

Evergreens = best

0.175

Canopy Cover 10 – 19% = not good

20 – 39% = okay

40 – 69% = more ideal

70 – 100% = best

0.175

Precipitation 0 – 40 inches = not good

40 – 60 inches = okay

60 – 80 inches = better

80 – 100 inches = more ideal

>100 inches = best

0.10

Elevation >3,000 meters = not good

2,000 – 3,000 meters = okay

1,500 – 2,000 meters = better

0 – 1,000 meters = more ideal

1,000 – 1,500 meters = best

0.10

Slope 60 – 90% = not good

40 – 60% = okay

0 – 10% = more ideal

10 – 40% = best

0.10

Aspect Southern aspects = okay

Eastern/western aspects = better

Northern aspects = best

0.10

Water Proximity

(Water Bodies)

>1.0 mile = not good

0.5 – 1 mile = okay

0.25 – 0.5 mile = more ideal

<0.25 mile = best

0.125

35

Table 2. General Model Factor Criteria and Applied Model Weight (cont.)

Water Proximity

(Streams)

>1.0 mile = not good

0.5 – 1 mile = okay

0.25 – 0.5 mile = more ideal

<0.25 mile = best

0.125

All of the data used in the model are subjected to a conversion process, if needed.

These processes are established to handle vector-to-raster data conversion, since the

model is designed to work with only raster data sets in the end. Next, the new raster data

sets are reclassified, in order to make them more suitable for ranking via raster calculator-

based functions. Once the individual rasters are reclassified, the model overlays the

various data sets and generates a single raster result that shows predicted habitat

suitability based on a ranked numerical system, with higher rankings being associated

with more suitable habitat than lower rankings.

The model in this research is a product of Esri’s ModelBuilder – a specialized

application of the ArcGIS software platform (Environmental Systems Research Institute

2010). Figure 3 provides a flowchart view of the entire model. The model exists as a

tool within ArcGIS, and relies on that software platform to operate effectively and

efficiently.

36

Figure 3. Model Flowchart

37

The following subsections examine each of the model factors in more detail. The

idea here is to provide you with descriptive explanations of how each factor is designed

and constructed.

3.2.1. Vegetation Type

The vegetation type model factor is derived from the USFS vegetation data. The

vegetation data contains an attribute field for type of vegetation, based on CWHR

descriptions, with values for each polygon. For the study area in question, the vegetation

type values cover a large array of vegetation types. The types range from barren and/or

urban landscapes (no real vegetation present) to dense conifer and/or hardwood forests.

The individual vegetation types are grouped into basic categories and reclassified for

purposes of the model. Basically, any vegetation type that has little to no vegetation

suitable for the Pacific fisher (e.g., barren, urban, water, agricultural and grasslands) is

placed into the least desirable category. Scrublands and “squatted” vegetation types

come next (e.g., sagebrush, manzanita, and chaparral). Vegetation types consisting of

mainly less desirable hardwoods follow, such as alders and maples. Any type of oak-

based vegetation type falls into the fourth category. Any type of conifer is placed into the

fifth, or most desirable, category.

These value groups are arbitrary and only represent a snapshot in time. Specific

values are not as important in the overall operation of the model, compared to the general

fact that the value groups follow the notion that substantial, older-growth vegetation

types provide a better habitat condition than do other vegetation types. For the Pacific

fisher, older-growth forest stands with high concentration of predominantly conifers and

38

some mixture of hardwoods (specifically oaks) is key; hence the conifers and oaks

received the most desirable reclassification ratings.

With the new vegetation type reclassification scheme, reclassified values of 5

meet the highest habitat suitability, per the model criteria established for this theme or

factor. Habitat suitability then tiers downward, in descending order, from 4 to 1 with

decreasing desirability. Table 3 shows the nature of the reclassification scheme.

39

Table 3. Vegetation Type Reclassification

Vegetation Type Reclassified Value

Agriculture (General) 1

Alpine Grasses and Forbs 1

Alpine Mixed Scrub 2

Annual Grasses and Forbs 1

Barren 1

Basin Sagebrush 2

Bigleaf Maple 3

Bitterbrush 2

Bitterbrush – Sagebrush 2

Black Oak 4

Blue Oak 4

Brewer Oak 4

Canyon Live Oak 4

Cottonwood – Alder 3

Curlleaf Mtn Mahogany 2

Douglas Fir – Pine 5

Douglas Fir – White Fir 5

Eastside Pine 5

Gray Pine 5

Great Basin – Chaparral 2

40

Table 3. Vegetation Type Reclassification (cont.)

Greenleaf Manzanita 2

Huckleberry Oak 4

Incense Cedar 5

Int Mixed Hardwood 4

Int Lake/Pond 1

Jeffrey Pine 5

Knobcone Pine 5

Lodgepole Pine 5

Low Sagebrush 2

Lower Mixed Chaparral 2

Mixed Conifer – Fir 5

Mixed Conifer – Pine 5

Montane Mixed Hardwood 5

Mountain Alder 3

Mountain Hemlock 5

Mountain Whitethorn 2

No Data 1

Non-Native/Orn Grass 1

Oregon White Oak 4

Pacific Douglas Fir 5

Per Grasses and Forbs 1

Per Lake/Pond 1

41


Pinemat Manzanita 2

Ponderosa Pine 5

Ponderosa Pine – White Fir 5

Port Orford Cedar 5

Quaking Aspen 3

Rabbitbrush 2

Red Fir 5

Reservoir 1

Riparian Mixed Hardwood 4

River/Stream/Canal 1

Scrub Oak 4

Snow/Ice 1

Subalpine Conifers 5

Tule – Cattail 2

Ultramafic Mixed Conifer 5

Up Mont Mixed Chaparral 2

Up Mont Mixed Shrub 2

Urban – Bare Soil 1

Urban/Indust Impoundment 1

Urban/Dev (General) 1

Valley Oak 4

Wedgeleaf Ceanothus 2

42


Western Juniper 5

Wet Meadows 1

White Fir 5

Whitebark Pine 5

Whiteleaf Manzanita 2

Willow 2

Willow – Alder 2

Willow – Shrub 2

3.2.2. Canopy Cover

The canopy cover model factor is derived from the USFS vegetation data. The

vegetation data contains an attribute field for percent canopy cover, with values for each

polygon. For the study area in question, the percent canopy cover values range from 10 –

100%. The percent canopy cover values are grouped into desirability ranges, and are

reclassified for purposes of the model. Percent canopy cover value groups chosen for this

research include 10 – 19, 20 – 39, 40 – 69, and 70 – 100 percent. There is a category to

handle areas with unknown or no-data as well. These value groups are arbitrary and only

represent a snapshot in time. Specific values are not as important in the overall operation

of the model, compared to the general fact that the value groups follow the notion that

denser canopy cover is a far better habitat condition for the Pacific fisher than is sparsely

covered land areas.

43

With the new percent canopy cover reclassification scheme, reclassified values of

5 meet the highest habitat suitability, per the model criteria established for this theme or


decreasing desirability. Table 4 shows the nature of the reclassification scheme.

Table 4. Canopy Cover Reclassification

Canopy Cover (%) Reclassified Value

No Data or Unknown 1

10 – 19 2

20 – 39 3

40 – 69 4

70 – 100 5

3.2.3. Precipitation

The precipitation model factor is derived from the PRISM data. The PRISM

values are in inches and increment by a factor of two through a series of odd numbers.

For the study area in question, the PRISM values range from 23 – 109 inches. The

PRISM values are grouped into desirability ranges and are reclassified for purposes of the

model. PRISM value groups that are chosen for this research include 23 – 40, 40 – 60,

60 – 80, 80 – 100, and 100 – 109 inches. These value groups are arbitrary and only

represent a snapshot in time. Specific values are not as important in the overall operation

of the model, compared to the general fact that the value groups follow the notion that

more moisture is a better habitat condition in this case. Basically, more moisture equates

44

to more potential sources of water and thriving, older-growth vegetation for the Pacific

fisher, which is what it prefers.

With the new precipitation reclassification scheme, reclassified values of 5 meet

the highest habitat suitability, per the model criteria established for this theme or factor.

Habitat suitability then tiers downward, in descending order, from 4 to 1 with decreasing

desirability. Table 5 shows the nature of the reclassification scheme.

Table 5. Precipitation Reclassification

Precipitation (inches) Reclassified Value

23 – 40 1

40 – 60 2

60 – 80 3

80 – 100 4

100 – 109 5

3.2.4. Elevation

The elevation model factor is derived from the NED-based DEM data. The

elevation values for the study area range from 174 – 4,315 meters. The individual

elevation values are grouped into desirability ranges and reclassified for purposes of the

model. Elevation value groups that are chosen for this research include 174 – 1,000;

1,000 – 1,500; 1,500 – 2,000; 2,000 – 3,000; and 3,000 – 4,315 meters. These value

groups are arbitrary and only represent a snapshot in time. Specific values are not as

important in the overall operation of the model, compared to the general fact that the

value groups follow the notion that lower elevations provide a better habitat condition,

45

than do higher elevations, mainly because denser vegetation does not thrive at higher

elevations.

With the new elevation reclassification scheme, reclassified values of 5 meet the

highest habitat suitability, per the model criteria established for this theme or factor.



Table 6. Elevation Reclassification

Elevation (meters) Reclassified Value

174 – 1,000 4

1,000 – 1,500 5

1,500 – 2,000 3

2,000 – 3,000 2

3,000 – 4,315 1

3.2.5. Slope

The slope model factor is derived from the same NED-based DEM data. The

slope values for the study area range from 0 – 90 degrees. The individual slope values

are grouped into desirability ranges and reclassified for purposes of the model. Slope

value groups that are chosen for this research include 0 – 10, 10 – 40, 40 – 60, and 60 –

90 degrees. These value groups are arbitrary, and only represent a snapshot in time.

Specific values are not as important in the overall operation of the model, compared to

the general fact that the value groups follow the notion that more moderate slopes provide

a better habitat condition than do steeper slopes.

46

With the new slope reclassification scheme, reclassified values of 5 meet the




Table 7. Slope Reclassification

Slope (%) Reclassified Value

0 – 10 4

10 – 40 5

40 – 60 3

60 – 90 2

3.2.6. Aspect

The aspect model factor is derived from the NED-based DEM data. The aspect

range for the study area is -1 – 360 degrees. The individual aspect values are grouped

into basic categories and reclassified for purposes of the model. Basically, any aspect

value with a southern-facing direction is placed into the least desirable category.

Southeastern- and southwestern-facing aspect values come next. Aspect values that are

more or less east or west in direction follow. Aspect values facing northeast or northwest

fall into the fourth category. Northern-facing aspect values are placed into the fifth, or

most desirable, category.

These value groups are arbitrary and only represent a snapshot in time. Specific

values are not as important in the overall operation of the model, compared to the general

fact that the value groups follow the notion that more northern-facing aspects provide a

47

better habitat condition than do more southern-facing directions. In general, northern-

facing aspects are cooler, moister, and contain a richer array of thriving vegetation types,

all of which are more conducive to ideal Pacific fisher habitat.

With the new aspect reclassification scheme, reclassified values of 5 meet the




Table 8. Aspect Reclassification

Aspect (degrees) Reclassified Value

-1 1

0 – 22.5 5

22.5 – 67.5 4

67.5 – 112.5 3

112.5 – 157.5 2

157.5 – 202.5 1

202.5 – 247.5 2

247.5 – 292.5 3

292.5 – 337.5 4

337.5 – 360 5

3.2.7. Water Proximity

The water proximity model factor is made up of two different data layers.

Polyline features are streams from the USFS hydrology-based data set, and polygon

48

features are water bodies (lakes) from the Esri data. In addition, the streams layer

contains only major stream features (i.e., rivers) that are considered to be perennial, or

having substantial water year-round. For purposes of the model, all features are

considered as water features though, for the purpose of providing water for the species.

As noted earlier, the two, basic types of water features in this study are left

separated and not combined for purposes of the modeling analysis. From a biological

perspective, Lindstrand (2014) agrees that this practice is justifiable for a specific reason.

Pacific fisher is a very elusive and secretive species with an utmost importance in

maintaining safety via cover. While the species may certainly seek water in areas that

tend to be more open (e.g., edges of large water bodies), the species is far more likely to

seek out water under the safety of substantial cover instead (e.g., streams or rivers within

heavily forested areas). Therefore, technically-speaking, stream features can play a more

substantial role in sources of water for Pacific fisher rather than larger bodies of water.

However, from a GIS-based modeling perspective, separating the water features as I have

elected to do can cause issues in how the model is intended to function. I explain these

potential issues in Chapter 5, as a form of model limitation.

The water features are used to reclassify land surrounding them based on distance

or proximity. Distance values that are chosen for this research include quarter-mile

(1,320 feet), half-mile (2,640 feet), one-mile (5,280 feet), and > one-mile (158,400 feet)

intervals. The 158,400-foot value handles anything greater than a mile, and ensures

adequate areal coverage to every extent of the study area. These value groups are

arbitrary and only represent a snapshot in time. Specific values are not as important in

the overall operation of the model, compared to the general fact that the value groups

49

follow the notion that closer distance to a water source provides a better habitat condition

for the Pacific fisher.

With the new water proximity reclassification scheme, reclassified values of 5

meet the highest habitat suitability, per the model criteria established for this theme or


decreasing desirability. Basically, a suitability value of 5 corresponds to a distance

within a quarter-mile, 4 to within a half-mile, 3 to within one mile, and 2 for anything

greater than a mile of any given water feature. Table 9 shows the nature of the

reclassification scheme.

Table 9. Water Proximity Reclassification

Water Proximity (feet) Reclassified Value

1,320 5

2,640 4

5,280 3

158,400 2

3.2.8. Final Model Calculation and Results

The final model results are calculated through a Single Map Algebra Output

expression. The calculation for the expression is as follows:

Final Model Results = Int(("Canopy2" * 0.175 + "Vegetation2" * 0.175 +

"Precipitation2" * 0.10 + "Streams2" * 0.125 + "Water Bodies2" * 0.125 + "Aspect2" *

0.10 + "Elevation2" * 0.10 + "Slope2" * 0.10) * 8)

50

Basically, the expression above adds all of the final, “level 2” themes together,

with also weighting them as defined earlier in this document, and produces a final raster-

based result for modeled Pacific fisher habitat suitability. The habitat suitability values in

the final model results range from 12 – 37, with 37 being the highest suitability and 12

being the lowest.


Geospatial data layers used in this analysis include existing Pacific fisher location

data from the CNDDB and the final model analysis results raster. The Pacific fisher

locations are limited to only those lands owned by the USFS, since the highest quality

data used in this research is the data that fall within that specific land ownership. The

analysis is performed manually using calculation formulae specific to the Spearman Rank

Correlation Coefficient, as listed in both McGrew and Monroe (2000) and Siegel (1956).

Two variables are directly associated with this test: Pacific fisher locations (dependent

variable) and modeled habitat suitability values (independent variable). Pacific fisher

locations are point features, where one point represents one Pacific fisher location and its

relation to where it lies on any given habitat suitability value.

Both variables are ranked based on specific criteria, and there are ten paired ranks

in the analysis since ten different and unique modeled habitat suitability values are

affected. The rank values for habitat suitability range from 1 to 10 and are assigned

based on the affected suitability values derived from the final model results. For

example, the suitability value considered to be the least desirable is assigned the lowest

suitability rank number (1). The remaining suitability rank numbers are assigned in

ascending order based upon increasing suitability value.

51

With respect to the Pacific fisher locations, the rank values are assigned by

averaging paired ranks. Siegel (1956) states that when tied ranks occur, each of them

receives the average of the ranks that would have been assigned had no ties happened,

which is a common procedure in this specific statistical test. For example, habitat

suitability values 24 and 28 have habitat suitability ranks of 2 and 5, respectively.

Likewise, habitat suitability ranks 2 and 5 have Pacific fisher location values of 2. In

other words, the Pacific fisher location values for each of those habitat suitability ranks

are paired or the same. Habitat suitability ranks 2 and 5 would have corresponding

Pacific fisher location ranks of 2 and 3, respectively, if the Pacific fisher location rank

values were not averaged for paired Pacific fisher location values. However, through the

averaging mechanism, new Pacific fisher location rank values for habitat suitability ranks

2 and 5 become 2.5 instead (2 + 3 = 5 / 2 = 2.5). The same type of averaging calculation

occurs for habitat suitability values that have Pacific fisher location values of 3. Table 10

shows foundational data associated with this approach.

52

Table 10. Spearman Rank Correlation Coefficient: Modeled Habitat Suitability and

Pacific Fisher Locations within the Study Area

Pacific

Fisher

Locations

Pacific

Fisher

Locations

Rank

Habitat

Suitability

(Final

Result)

Habitat

Suitability

Rank

Difference

(d) (of

Ranks)

d2

1 1 22 1 0 0

2 2.5 24 2 0.5 0.25

2 2.5 28 5 -2.5 6.25

3 6 26 3 3 9

3 6 27 4 2 4

3 6 29 6 0 0

3 6 31 8 -2 4

3 6 32 9 -3 9

4 9 33 10 -1 1

7 10 30 7 3 9

42.5

53

The hypotheses surrounding this particular analysis are:

H0: rs = 0 (No correlation between modeled habitat suitability value and

location.)

HA: rs > 0 (Measurable positive correlation between modeled habitat

suitability value and location.)

Descriptive statistics for data used in this analysis are:

N (number of pairs/samples) = 10

Siegel (1956) states that with a large proportion of tied ranks in either variable, a

specific correction factor must be applied into a modified computation formula for rs.

The correction factor is T, where:

T = t3 – t / 12

t = The number of observations tied at a given rank.

The modified computation formula for rs includes calculations for the sums of

squares for both variables, with corrected ties applied (Siegel 1956). The formulae for

computing the sums of squares are as follows:

Σx2 = ((N

3 – N) / 12) – ΣTx

Σy2 = ((N

3 – N) / 12) – ΣTy

Siegel (1956) recommends a modified rs formula of:

rs = (Σx2 + Σy

2 - Σd

2) / 2√(Σx

2)(Σy

2)

The corresponding test computations for this particular analysis are:

Σx2 = ((10

3 – 10) / 12) – ((2

3 – 2 / 12) + (5

3 – 5 / 12)) = 82.5 – 10.5 = 72

Σy2 = ((10

3 – 10) / 12) – 0 = 82.5

rs = (72 + 82.5 – 42.5) / 2√(72)(82.5) = 0.727

54

Overall, the computation results above support the alternative hypothesis. A more

detailed discussion of these results follows below, in section 4.2.


Since this research is based on a weighted-sum model, I have chosen a one-way

sensitivity analysis approach to test how sensitive the model might be, which fits with

Goldmeier’s (2012) studies. Basically, I sought to determine what the final model results

would look like if I were to make small adjustments to two of the model factor weights

independently, and how much change would occur in each of the results due to that type

of scenario. In other words, how “sensitive” would the model become from change if I

were to make a small adjustment in each of the weights?

The foundational literature clearly shows that of all factors to possibly consider

for highly suitable Pacific fisher habitat, none are more important than the two, basic

vegetation-based criteria explained earlier in this document (i.e., vegetation type and

canopy cover) (Self and Kerns 2001; Carroll 2005; Hayes and Lewis 2006; Zielinski et

al. 2006a, 2006b; Davis et al. 2007; Self et al. 2008). Therefore, I decided to run the

model two more times, but with a different final model results calculation formula for

each of the two runs.

For the first, additional model run, I increased the vegetation type model factor

weight and left all other model factor weights the same. The calculation for the new

single map algebra output expression is as follows:



0.10 + "Elevation2" * 0.10 + "Slope2" * 0.10) * 8)

55

Basically, the new expression above adds all of the final, “level 2” themes

together, just like in the original model, but this time the vegetation type model factor

weight is increased by 0.005. The habitat suitability values in the new final model results

raster range from 12 – 37, with 37 being the highest suitability and 12 being the lowest,

just like the original final model results.

Finally, as a form of “change detection,” a simple map algebra equation is used to

calculate the difference between the original final model results and the sensitivity-based

final model results regarding the vegetation type value increase. This step produces a

new raster with values: 0 and -1. Value 0 indicates there is no change between the two

final model results. Value -1 indicates there is change between the two final model

results.

For the second, additional model run, I increased the canopy cover model factor

weight and left all other model factor weights the same; including changing vegetation

type back to its original value of 0.175. The calculation for the new single map algebra

output expression is as follows:



0.10 + "Elevation2" * 0.10 + "Slope2" * 0.10) * 8)

Again, the new expression above adds all of the final, “level 2” themes together,

just like in the original model, but this time the canopy cover model factor weight is

increased by 0.005. The habitat suitability values in the new final model results raster

range from 12 – 37 also, with 37 being the highest suitability and 12 being the lowest.

56

For change detection, the same simple map algebra equation is used to calculate

the difference between the original final model results and the sensitivity-based final

model results regarding the canopy cover value increase. This step produces a new raster

with values: 0 and -1. As was the case in the vegetation type model run, value 0 indicates

there is no change between the two final model results. Value -1 indicates there is

change between the two final model results.

57

CHAPTER 4. ANALYSIS RESULTS AND DISCUSSION

4.1. Final Model Results

The habitat suitability values in the final model results range from 12 – 37, with

37 being the highest suitability and 12 being the lowest. Figure 4 contains a visual

representation of the project study area showing the final model results. The data have

been classified into five, basic tiers of suitability. In general, darkest shades of red are

most suitable habitat, medium shades of red are “middle-of-the-road,” and lightest shades

of red are not very suitable.

When looking at Figure 4, it is easy to see that there is not much prime suitable

habitat for Pacific fisher throughout the study area. Most of it (the darkest shades of red)

is located along or near the few major rivers north of Shasta Lake, which flow north-to-

south and empty into Shasta Lake. The medium shades of red are dominant in the central

area of the study area. The precipitation data likely has a strong influence here, since it is

across that particular region that high levels of rain and snowfall occur; it is commonly

referred to as a “banana belt.” The lighter shades of red are found more northward and

into the dryer areas around Mount Shasta and to the east of it. In those areas, there is less

moisture, and the forests tend to be more open and “scrubby,” without a lot of dense,

older-growth vegetation. An exception to that revelation is the extensive amount of

lighter red in the southern half of the study area. The reason for all of the lighter red in

those areas is largely due to non-existent vegetation data on private lands within the

national forest. Basically, given the final results of this analysis, one would expect to

find most Pacific fisher activity in the southern half of the study area, where there is

58

higher moisture content, close proximity to water, and extensive denser and older

vegetation.

Figure 4. Final Model Results

59


Figure 5 illustrates the distribution of Pacific fisher locations overlaid with the

final model results raster. The Pacific fisher locations shown are from the CNDDB data,

where known Pacific fisher sightings have been found and recorded. As you can see,

Pacific fisher have been sighted throughout most of the study area; even in the direct

vicinity of Mount Shasta itself.

Per the Spearman Rank Correlation Coefficient analysis, the total number of

Pacific fisher locations used in this analysis is 31. The locations fall within a range of 22

– 33 as far as habitat suitability final result values are concerned. Twenty-five of the

Pacific fisher locations fall on a habitat suitability value of 27 or greater (the two darkest

shades of red, indicating higher suitability, in Figure 5). Therefore, approximately 81%

of the known Pacific fisher locations used in this analysis fall into what is deemed to be

higher suitability for the species, in the given study area. The remaining 6, or 19%,

Pacific fisher locations fall on the medium shades of red, or middle-of-the-road,

suitability tier, which is deemed to be decent suitability for Pacific fisher. None of the

Pacific fisher locations fall on the worst habitat suitability tiers of the lightest shades of

red.

60

Furthermore, and again with respect to the Spearman Rank Correlation

Coefficient analysis, the calculated rs value of 0.727 is close to 1. An rs value close to 1

corresponds to a strong positive correlation between the variables being tested. In this

Figure 5. Pacific Fisher Locations and Final Model Results

61

case, the calculated rs value clearly shows that Pacific fisher locations are positively

correlated to modeled habitat suitability values within the study area, which supports the

alternative hypothesis. Moreover, the University of the West of England (2014) states

that with a significance level of 0.05, the corresponding critical value for this analysis is

0.564. Since the calculated rs value is greater than the critical value at the chosen

significance level, the alternative hypothesis is again supported. Therefore, the null

hypothesis is safely rejected for this model and its corresponding results.


With respect to the sensitivity analysis performed in this research, Figure 6 shows

the final outcome after the vegetation type model factor weight was increased, and Figure

7 shows the final outcome after the canopy cover model factor weight was increased.

Right away, it is quite apparent that little change occurred as a result of increasing the

model factor weights for both vegetation type and canopy cover. Red, in each figure,

indicates areas of change, and grey indicates no change. To determine just how much

change occurred, I performed a simple ratio calculation based on raster cells in the final

change detection rasters for each of the two results. In each of the final change detection

rasters, there are a total of 50,002,002 raster cells. The total number of raster cells that

are coded as “change” in the vegetation type run is 5,507,475. The ratio of

5,507,475/50,002,002 produces a percentage value of approximately 11.0%. In other

words, approximately 11.0% of the total raster cells that make up a raster that covers the

entire study area changed by one habitat result value, after re-running the model with the

modified vegetation type model factor weight. The total number of raster cells that are

coded as “change” in the canopy cover run is 1,426,417. The ratio of

62

1,426,417/50,002,002 produces a percentage value of approximately 2.9%. In other

words, approximately 2.9% of the total raster cells that make up a raster that covers the

entire study area changed by one habitat result value, after re-running the model with the

modified canopy cover model factor weight.

Both 11.0% and 2.9% of change is rather small, and in my professional opinion it

certainly indicates that the model is not overly sensitive to slight changes in model factor

weight in a weighted sum model; especially considering two factors that are deemed to be

most important in this case. Changing the model factor weights for vegetation type and

canopy cover do not cause great change, when speaking in terms of individual model

factor weights as a ratio of a whole, either. For example, vegetation type and canopy

cover have the highest applied weights in the original model calculation. Both have

weights of 0.175. Streams and water bodies have the next highest amount of weight, at

0.125 each. Precipitation, elevation, aspect, and slope are all set to 0.10 in weight, in the

original model. In the sensitivity analysis approach, all weights remain the same except

for vegetation type and canopy cover during each of their independent runs. As a result,

subtle change in model factor weight, in this case, produces small change in the final

model results. Furthermore, in Figures 6 and 7, it can be seen that the existing Pacific

fisher locations used in this study are more or less unaffected by the results of the

sensitivity analysis as well.

63

Figure 6. Sensitivity Analysis Extent of Change in Vegetation Type Modification

64

Figure 7. Sensitivity Analysis Extent of Change in Canopy Cover Modification

65

CHAPTER 5. CONCLUSION

As stated early on, the objective of this research is to build a predictive model that

reasonably identifies potential, suitable Pacific fisher habitat within a defined study area,

using a GIS-based approach. Also, the chosen approach includes the traditional method

of identifying relevant model factors through extensive literature review, and then

implementing them into the model to ultimately produce accurate results for the species

in question. Again, the reason why this type of research is important is because it may

help to better protect the species and its natural environment or habitat, especially in light

of the effect of human impact on each.

With that said, the model appears to be an overall success. It certainly serves its

purpose, which is to effectively identify suitable Pacific fisher habitat within the defined

study area, based upon a number of different physical and biological factors and

associated criteria. The model is proven as well. It is validated, and deemed to operate

correctly. Also, through the use of existing, supporting data and powerful statistical-

based testing, the model is shown to indeed be intact with respect to it performing as

intended. The model results shows that a large percentage (81%) of the species test

location sites fall within predicted high suitability habitat, which supports the notion that

the model functions correctly.

In addition, the Spearman Rank Correlation Coefficient analysis results show that

the variables of known species location and predicted high suitability habitat are strongly

correlated; again, this is sufficient evidence that the model performs as intended. The

model is proven to be fairly insensitive to subtle change. The sensitivity analysis shows

that with little change in the vegetation type and canopy cover model factor weights,

66

when run independently, fairly small differences occur in the final model output results.

However, the need to establish accurate model factor criteria and weights at the

beginning of the process is still important, if the main goal is to specifically define such

information.

The overall model design and implementation process surrounding this particular

research has proven to be a smooth endeavor, more or less. However, the model itself is

certainly not perfect. As is commonly the case with any GIS-based endeavor, the

outcome and final results of any given scenario are strongly dependent on the data that

are initially integrated. In other words, a model and its results are only as good as the

data that go into it. A limiting factor in this case is the fact that much of the USFS data

are limited to USFS lands. That means that vegetation type and canopy cover do not

exist for private lands within large portions of the national forest. Therefore, habitat

suitability results are obviously skewed, and not as accurate as they could be in some

portions of the study area. This fact does not affect validation efforts though, since

location data used are located only on public lands, where the best chance of predicted

habitat suitability level will occur.

Another limiting factor of the model, which I mentioned in section 3.2.7 Water

Proximity, is the separation of the two, basic types of water features. From a GIS-based

modeling perspective, this choice causes a situation where: 1) The proximity to water

model factor may inadvertently hold more weight than is intended, and 2) water

proximity raster cell scores may end up being less for cells that are actually closer to a

water feature, as opposed to cells with higher scores and are farther away from a water

feature. So, in effect, a reverse-process scenario can occur with respect to water features

67

and their calculated cell scores. Obviously, this effect can skew or bias final model

results.

Several possible solutions exist for potentially mitigating the water proximity

problem. As one solution, it can be assumed that “water is water” and that all water

features can be combined into one, final water proximity layer, before inclusion in the

final model calculation. As another solution, it can be assumed that large bodies of water

may be insignificant enough to just exclude them from the model and only include

streams instead. As a final solution, and more likely the best and most feasible option,

independent model factor weights or reclassification schemes for each water feature type

can be adjusted so that a more realistic scenario is achieved between weighting streams

versus water bodies in the model, rather than making everything equal for both types as I

have done.

Perhaps the most challenging portion of the research design is implementing the

approach to the sensitivity analysis section of the project, but only because I had never

been exposed to sensitivity analysis before. In the beginning, I really did not know what

to expect, what I should do in that regard, or what type of process should take place.

However, the one-way sensitivity analysis approach that I chose for this effort, which I

learned about through various literature readings, is rather simple to understand and easy

to perform.

The potential contribution of this research to the various professional arenas is

multifaceted. First, it is yet another example of using a literature-based approach to

model development and implementation and can serve as a supporting guideline for

others who may wish to explore the same type of analysis. Second, it provides a perfect

68

example of sophisticated use of GIS in the area of applied natural resources, in relation to

posing a question or concern and then constructing a viable solution to help provide

reliable answers. In other words, it shows one of the many ways GIS can be used to help

better understand spatial phenomena. Third, it provides a direct benefit to the national

forest that is chosen for this research and many others like it. For the Shasta-Trinity

National Forest, it provides a reliable, firsthand look at potential, suitable habitat

locations for Pacific fisher in portions of the forest. Of course, the model components

could be adjusted accordingly, if need be, or for use on other, similar forests, and updated

with relevant literature information for those areas. So, the model can certainly be made

more applicable, if need be, given some level of refinement.

The type of research that was performed here can definitely be expanded upon.

The first thing that comes to mind in this regard is the use of a more comprehensive data

set. Unfortunately, based on my knowledge of the topic and general location, one does

not exist, however. As is usually the case in most situations, GIS data tend to be more

extensive and prevalent on public lands rather than private lands. Nevertheless, this

model and the type of research it represents could be applied to other areas that are made

up of entirely public lands, and chances are a better outcome would result.

In addition, the research presented here is based solely on the more popular

literature review approach for habitat suitability modeling. Of course, an avenue of

further expansion could be to run the same type of analyses using the alternative

approach of presence-absence species occurrences. This type of alternative approach was

explained earlier (Corridor Design 2010), and at least one group of researchers that is

interested in Pacific fisher studies has already taken this level of approach in the past

69

(Carroll et al. 1999). Corridor Design (2010) states that presence-absence analyses tend

to provide more accurate modeling results, but the processes involved are almost always

more complicated and time-consuming. Nevertheless, it would be interesting to examine

the similarities and contrasts between the two avenues, but presence-absence analyses

were beyond the scope of this immediate endeavor.

Lastly, another avenue that could stem from this type of research is to not only

investigate potential habitat suitability for the species, but to study “habitat fitness,” also.

Habitat fitness addresses quality of habitat and how well it is suited to species population

persistence, based on a number of different biological and physical factors. In fact,

Aldridge and Boyce (2007) suggest that detailed, empirical models that predict both

species occurrence and habitat fitness across a landscape are vital to better understand

various processes that are related to species persistence and survival.

70

REFERENCES

Aldridge, C.L., and M.S. Boyce. 2007. Linking occurrence and fitness to persistence: a

habitat-based approach for endangered Greater Sage-Grouse. Ecological

Applications 17(2): 508-526.

Ascough II, J.C, T.R. Green, L. Ma, and L.R. Ahjua. 2005. Key Criteria and Selection of

Sensitivity Analysis Methods Applied to Natural Resource Models. USDA-ARS,

Great Plains Systems Research Unit, Fort Collins, Colorado. 7 p.

Bate, L.J., E.O. Garton, and M.J. Wisdom. 1999. Estimating Snag and Large Tree

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