MAMMAL ASSEMBLAGES of the CUYAHOGA VALLEY NATIONAL PARK: an UPDATE AFTER 30 YEARS a Thesis Submitted to Kent State University I

MAMMAL ASSEMBLAGES OF THE CUYAHOGA VALLEY NATIONAL PARK: AN UPDATE AFTER 30 YEARS

A thesis submitted To Kent State University in partial Fulfillment of the requirements for the Degree of Master of Sciences

Doug J. Marcum

Thesis written by

Douglas J. Marcum

B.S., Kent State University, 2011

M.S., Kent State University, 2017

Approved by

______, Advisor Oscar J. Rocha, Ph.D.

______, Chair, Department of Biological Sciences Laura G. Leff, Ph.D.

______, Dean, College of Arts and Sciences James L. Blank, Ph.D. TABLE OF CONTENTS……………………………………………………………………...... iii LIST OF FIGURES……………………………………………………………………………… v LIST OF TABLES…………………………………………………………………………...... viii ACKNOLEDGEMENTS………………………………………………………………………… x

I. INTRODUCTION TO MAMMALIAN STUDIES IN THE CUYAHOGA VALLEY NATIONAL PARK, OHIO………………………………………...… 1 BACKGROUND………………………………………………………………… 1 STUDY AREA…………………………………………………………………... 2 MAMMALS OF CVNP………………………………………………………….. 5 STUDY DESIGN……………………………………………………………...…. 9 IMPLICATIONS OF RESEARCH…………………………………………...... 11 REFERENCES…………………………………………………………………. 12 II. DISTRIBUTION OF SMALL MAMMAL COMMUNITIES IN CUYAHOGA VALLEY NATIONAL PARK, OHIO AT TWO SPATIAL SCALES……………………………………………………...……... 14 INTRODUCTION…………………………………………………………….... 14 METHODS……………………………………………………………………... 17 STUDY SITES……………………………………………………….… 17 DATA COLLECTION…………………………………………………. 21 RESULTS………………………………………………………………………. 26 DISCUSSION………………………………………………………………..…. 40 REFERENCES…………………………………………………………………. 47 III. COMPARING METHODOLOGIES FOR STUDYING MAMMAL ASSEMBLAGES IN CUYAHOGA VALLEY NATIONAL PARK, OHIO….. 51 INTRODUCTION…………………………………………………………….... 51 METHODS……………………………………………………………………... 55 STUDY AREA…………………………………………………………. 55 DATA COLLECTION…………………………………………………. 56 RESULTS………………………………………………………………………. 66

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DISCUSSION…………………………………………………………………... 85 REFERENCES…………………………………………………………………. 96 VI. CONCLUSIONS, SYNTHESIS, AND FUTURE STUDIES……………….... 100 CONCLUSIONS…………………………………………………………….... 100 FUTURE STUDIES AND MANAGEMENT IMPLICATIONS …………….. 108 REFERENCES………………………………………………………………... 111

APPENDICES A. HISTORICAL SPECIES LISTS FOR MAMMALS IN CUYAHOGA AND SUMMIT COUNTIES, OHIO…………………………………………………….. 114 B. MAMMALS OF CUYAHOGA VALLEY NATIONAL PARK RECORDED BY MAZZER ET AL. 1984………………………………………………………. 116 C. CHAPTER III CONTINGENCY TABLES………………………………………. 117 D. INVERTEBRATE DATA COLLECTED BY JAVIER OJEDA DURING PITFALL TRAPPING EFFORTS……………………………………………….... 118 E. EQUIPMENT SPECIFICATIONS………………………………………………... 120

LIST OF FIGURES Figure 1.1 Map of Cuyahoga Valley National Park……………………………………………... 3

Figure 2.1 Example of vegetation cover maps in CVNP from 1975 and 2013……………….... 18

Figure 2.2 Example of a topographic map indicating the sampling locations for small mammals used by Mazzer et al. (1984) in the CVNP……………………………… 19

Figure 2.3 Satellite image of northeast Ohio encompassing Cuyahoga Valley National Park… 20

Figure 2.4 Schematic representation of the standard trapping grid for small mammals……….. 23

Figure 2.5 Proportions of small mammals captured during Sherman trap efforts (2015) in CVNP………………………………………………………………………………. 27

Figure 2.6 Mean capture rate of small mammals by month……………………………………. 27

Figure 2.7 Average proportion of recaptures for Peromyscus leucopus and Microtus pennsylvanicus during subsequent nights of trapping in the summer season……….. 30

Figure 2.8 Mean canopy openness soil moisture and soil compaction for each successional stage………………………………………………………………………………… 33

Figure 2.9 Mean number of trees in each size class, standing dead trees, and woody debris in the vicinity of each trap for each successional stage…………………………….. 35

Figure 2.10 Mean percent cover for shrubs and herbaceous vegetation in the vicinity of each trap for each successional stage……………………………………………… 36

Figure 2.11 Correlation matrices among small mammal community data and between community data and microhabitat measures………………………………………. 37

Figure 2.12 Canonical Correspondence Analysis relating microhabitat variables to small mammal captures…………………………………………………………………. 38

Figure 3.1 Map of the Cuyahoga Valley watershed and surrounding region of northeast Ohio, United States………………………………………………………………….. 56

Figure 3.2 Photo of a camera trap placement used to inventory mammals in CVNP………….. 58

Figure 3.3 Map of Cuyahoga Valley National Park indicating road transects…………………. 60

Figure 3.4 Schematic representation of pitfall trapping design……………………………….... 62

Figure 3.5 Photos of raptor pellets and an arrangement of identifiable mammal bones……….. 63

Figure 3.6 Abundance and frequency of mammal captures via camera traps………………….. 67

Figure 3.7 Loess regression showing positive relationship between species richness detected and number of camera trap nights…………………………………………. 68

Figure 3.8 Species accumulation curves for 28 camera trap locations in CVNP………………. 70

Figure 3.9 Proportion of three mammal groups represented in three successional stages of vegetation during camera trap surveys…………………………………………...… 71

Figure 3.10 Proportion of three mammal groups (individuals) found in three major forest types……………………………………………………………………………….. 72

Figure 3.11 Total detections for the eight most abundant mammal species during 13 road survey dates in the Cuyahoga Valley……………………………………………… 74

Figure 3.12 Number of individuals captured for each of six small mammals trapped during pitfall trapping across five sites in CVNP…………………………………………. 75

Figure 3.13 Small mammal richness by leaf litter depth across 15 pitfall trap arrays in CVNP……………………………………………………………………………… 77

Figure 3.14 Small mammal abundance by leaf litter depth across 15 pitfall trap arrays in CVNP……………………………………………………………………………… 77

Figure 3.15 Number of individuals identified for the five most common mammal species found in 71 raptor pellets………………………………………………………….. 79

Figure 3.16 Sample-based species rarefaction curve for five mammal inventory methods used in CVNP……………………………………………………………………... 81

Figure 3.17 Raw and corrected richness for Sherman trap, pitfall, and raptor pellet survey methods……………………………………………………………………………. 82

Figure 3.18 Detection biases for three small mammal survey methods by functional mammal groups…………………………………………………………………..... 82

Figure 3.19 Proportion of functional species groups detected by camera traps and road survey methods in CVNP corrected for sampling rate differences………………... 83

Figure 3.20 Principal components analysis comparing small mammal assemblages detected via Sherman trapping, pitfall trapping, and raptor pellet analysis ..………………...... 89

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LIST OF TABLES Table 2.1 Names and descriptions of sites surveyed for small mammals in CVNP………….… 19

Table 2.2 Descriptor and methodology used for the assessment of microhabitat characteristics……………………………………………………………………….... 23

Table 2.3 Descriptor and methodologies used for the assessment of macrohabitat characteristics……………………………………………………………………….... 24

Table 2.4 Analysis of variance for capture rates observed in spring, summer, and fall………... 28

Table 2.5 Comparison of capture rates observed in the first and second trapping session, by sampling site…………………………………………………………………….... 29

Table 2.6 Lincoln-Peterson population density estimates per hectare for Peromyscus leucopus and Microtus pennsylvanicus………………………………………………. 29

Table 2.7 Summary of macrohabitat characteristics and small mammal community data by successional stage……………………………………………………………………. 31

Table 3.1 Functional classification of small mammals detected via Sherman trapping, pitfall trapping, and raptor pellet analysis………………………………………………….. 65

Table 3.2 Functional classification of mammals detected via camera trapping and road surveys……………………………………………………………………………….. 65

Table 3.3 Comparison of effort and success for five methods used to survey mammals in CVNP…………………………………………………………………………….... 66

Table 3.4 Comparison of mammal diversity metrics for three forest types in CVNP………….. 70

Table 3.5 Average detections per sample in forest versus non-forest habitats and wetland versus upland habitats for three functional groups of mammals in CVNP………….. 74

Table 3.6 Summary of pitfall captures and environmental variables measured in the CVNP by array and successional stage……………………………………………… 76

Table 3.7 Detectability rates for all mammal species documented by five survey methodologies in CVNP……………………………………………………………... 80

Table 3.8 Average proportion of species detected by camera traps and road survey methods in the Cuyahoga Valley…………………………………………………….. 84

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Table 4.1 Small mammal species detected among three plant community successional stage categories during two inventories in CVNP………………………………... 101

Table 4.2 Small mammal species detected among three re-sampled sites from Mazzer et al. (1984) in CVNP…………………………………………………………….. 101

Acknowledgements

The completion of this thesis is a monumental achievement for me. Throughout my entire career in school, I’ve been a procrastinator by nature, and I’ve often spent more time and energy in experiencing the world uninhibited by formal projects. I was known as the guy who

“is always out in the woods”. My grandfather and mother would say that I always have “too many irons in the fire”. Knowing this about myself, I have strived in recent years to correct these tendencies by following through with ideas and projects that I’ve started before starting more projects and moving to new ideas. The challenge of completing a Master’s thesis is symbolic of how I’ve been able to find synergy with my own nature and achieve success.

Many people were on the sidelines to coach me and cheer me on during my four-year path in grad school. Towards the end, I felt like my excuse that “I have to stay home and work on my thesis” was getting old with my friends and family. Not that they were annoyed, but I realized that I actually need to finish this project! Friends that inspired me at Kent State included; Adam Geriak, DeShawn Johnson, Theresa Wolanin, Emma Given, and Matt Mackey, among others. I guess I could mention Brendan Morgan even though we didn’t like each other until June of this year when I taught him bird songs. Several people at Kent State compared me to him, and I’ll try to take that as a compliment. Anna Ormiston and Marc Nutter were there to help conceive the idea that I would go to grad school. My initial motivation was that I wanted to teach Local Flora and Ornithology labs, and I had a blast doing that. My first Local Flora class that I taught was especially memorable because I have kept in touch with most of those students who experienced my debut as a teacher in college. I’ve bird-watched with students, eaten wild plants, worked with them at the CVNP, and learned from them as much as I taught them in many

x instances. Some of my proudest moments in grad school came during my time as a teaching assistant.

Several students volunteered to help me with my research, and a couple did projects under me. Lyndsay Tucker politely agreed to the proposition of studying road-killed animals in the park during her REU project in 2015. She was a great student and willing to do anything necessary for the project. Javier Ojeda helped me mostly during the following year with a pitfall trapping survey where he assessed invertebrate communities in concordance with my small mammal data. Javier maintains one of the best attitudes I’ve ever seen, and was always a pleasure to work with. Both of those students are destined for bright futures.

Some of my closest brothers who had my back and always encouraged me even if they had no clue what I was actually doing were important; Brad, Zack, Chris, Matt, Chia, and Nick, especially. My family was always so proud even if they didn’t understand what I was doing either…especially my mom who has always been my number one fan since I was born. I love you, Mom! Additional “family” who were there for me include Kristen who was one of my earliest supporters and believed that I could really do anything. Alex helped me endure the challenge of the first couple years of grad school in every way that she could; those were years that I will never forget. I consider my relationship with her the beginning of my quest to become a better version of myself, and she is largely to thank for that. My girlfriend, Kelsey was very supportive towards the end and she helped me prepare for my defense and convinced me that I would rock it!

I am very thankful to have become close to my best professional role model, Sonia

Bingham over the last seven years since I started working for her in the CVNP. I owe so much to her as she has not only done everything in her power to offer me excellent professional

opportunities, but she is one of my best friends and one of the most light-hearted, kind, and inspiring people that I have ever met. Her mentorship is equally responsible for my professional

growth in the last four years as my experience in grad school. I must also express gratitude for

my other mentors; Dr. Greg Smith took time to help me in the field, offering important

experience with small mammal trapping, and was available to help with my project as much as I

needed. Erik Shaffer also made some field visits with me and shared much of what he has

learned in his extensive experience of being in the woods. Conversations with Erik are always

fun because he is gifted with the ability to really understand the lives of wild animals.

Committee members Dr. Pat Lorch and Dr. Mark Kershner are two of my favorite people at Kent

State. Pat is brilliant and dedicates himself to science and conservation, while still finding time

to attend bluegrass festivals and drink good beer! He is always a pleasure to be around and he

was very prompt and thorough when offering suggestions and guidance for my work. I look

forward to a continued relationship with him. Mark is one of the best teachers at Kent State, and

he has surely changed the lives of countless students. He and I get along great and it is always

fun sharing our latest bird stories with each other. Mark was especially important to me because

I felt like his perspectives as a community ecologist were most similar to my own, and I knew

that I would be able to learn a lot from him.

Lastly, I must thank my advisor, Dr. Oscar Rocha for taking me on as a grad student in the first place! He saw potential in me despite the fact that I may have accidentally fallen asleep in his class during undergrad more than once! Oscar is a miracle-worker both when it comes to providing students with opportunities and by fostering their individual growth as biologists. He is one of the kindest, most genuine people that I have ever met. He patiently supported all of my ideas throughout the development of my thesis project and let me conduct my research how I

xii wanted to do it, only stepping in to give me a kick in the rear when necessary. Oscar taught me everything about being a scientist; experimental design, stats, writing, and presenting. I couldn’t have had a more supportive advisor!

My time at Kent State University is cherished as a place of opportunity and inspiration.

The faculty and staff is composed of many great individuals. Two final mentions are Donna

Warner and Melissa Davis. I had many conversations with Donna throughout my time there.

Not only was she extremely helpful, she was so easy to talk to. She would sometimes threaten me that I better get my work done and graduate! Melissa was a blast to work with as the Local

Flora instructor. She would do anything for me, and always made me feel great about the work that I was doing. I could also talk to her anytime and she encouraged me as much as anyone to achieve my goals at KSU. Local Flora was undoubtedly the most important class for me at Kent

State. Thanks to all who supported me in any capacity, it’s been a great time!

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Chapter I. Introduction to mammalian studies in the Cuyahoga Valley National Park, Ohio

Background

Mammals are one of the most well-loved groups of organisms in the world. This affection is important, because people are strongly driven by emotional connections and will go to lengths to protect something that they care about (Tisdell & Nantha 2007). This can be an essential driver for conservation initiatives, especially for the sake of land protection. Mammals have been a subject of study long before what we call “science” today existed. Primitive humans relied on many species of wild mammals to survive, and understanding their natural history could mean the difference between life and death. Today, most studies of mammals are not this dire but are nonetheless an important endeavor as humans continue to grow and alter the earth.

While most people in developed countries rely on domesticated animals for food and clothing, wild mammals continue to offer sustenance and other ecological services around the world.

Eighty species of mammal are known to have gone extinct since 1500 AD, meanwhile the IUCN currently lists 204 species as critically endangered and another 464 as endangered.

These losses may be a symbol of a changing world, as we are in the midst of what has been called the “sixth great extinction” (Dirzo et al. 2014, Barnosky et al. 2011). Humans have undoubtedly led to the declines and disappearances of many species, but we also have the potential and responsibility to protect them from these influences. To date, science provides a wide-range of technology and methodologies to learn more about our wildlife, their distributions

and abundances, and their environmental needs. Gathering this information allows us to answer

specific questions related to anthropogenic influences on communities, and can help guide management strategies to preserve ecosystems and their sensitive species. Studies can be performed on a range of levels from global populations to individuals; from landscape scales to

the microhabitat characteristics of a square meter.

Protecting species is often achieved through the protection and management of habitats,

and these efforts rely on specific knowledge of habitat characteristics across scales. Conservation of habitats is important beyond protecting endangered species, as local extinctions occur far more rapidly and can have cascading effects in ecosystems (Fortin et al. 2005, Ripple &

Bescheta 2004, Pace et al. 1999). The U.S. National Park Service is one of several federal agencies that protects the country’s natural heritage through public land management. Since we hope that these places will be preserved indefinitely, they make great sites for the study of wildlife conservation. Thankfully, the foresight of conservation-minded people in northeast

Ohio (United States) has left us with such a place where mammals and other species can continue to thrive.

Study Area

This study takes place in the 13,355-ha Cuyahoga Valley National Park (CVNP).

Spanning between Cleveland and Akron in Northeast Ohio, the CVNP is a patchwork of land ownership, land use history, and vegetative cover. Originally designated in 1974 as a national recreation area, and as a national park in 2000, CVNP is one of the most visited national parks in the country, averaging over 2.2 million visits each year (NPS 2017). The park is centered on the

Cuyahoga River, and its floodplains here are flanked by steep valley walls (Figure 1.1).

Figure 1.1 – Map of northeast Ohio, U.S.A. emphasizing the Cuyahoga Valley National Park.

Thirty-five kilometers of the Cuyahoga wind through the national park, although its path is largely confined by a railroad, canal towpath, and roads. The physiographic structure here stems from a long history of ancient seas, rivers, and glaciation (NPS 2017). When the last glaciers retreated about 10,000 years ago, sand, gravel, and fertile soils were deposited throughout the valley. The rich biological diversity in the park owes to these foundations.

The majority of natural habitats in CVNP are forested. Oak-dominated communities are most prevalent, especially on the uplands, with northern red oak (Quercus rubra), eastern white oak (Quercus alba), black oak (Quercus velutina), and shagbark hickory (Carya ovata) as major constituents. Northern hardwood forest is considered a climax community in the region, and can be found on many of the slopes as well as limited uplands. These forests are characterized by

American beech (Fagus grandifolia) and sugar maple (Acer saccharum). Yellow birch (Betula allegheniensis), eastern hemlock (Tsuga canadensis), and tuliptree (Liriodendron tulipifera), also add to the canopy in these systems. Forests on the river floodplain are dominated by American sycamore (Platanus occidentalis), eastern cottonwood (Populus deltoides), and box-elder (Acer negundo), sharing space with black walnut (Juglans nigra), silver maple (Acer saccharinum), and Ohio buckeye (Aesculus glabra). Over 800 hectares of wetlands are mapped within the

CVNP, largely in the form of marshes and swamps along the river corridor. “Perched” wetlands consist of stream headwater drainages, groundwater seeps, and depressional pools. These systems often encourage lush vegetative growth and tend to harbor great biodiversity (Bingham

et al. 2016).

Human impacts play a large role on natural systems in CVNP. Economic ventures in the

valley such as farming, mining, and other industries brought on settlements and transportation

routes such as the Ohio and Erie Canal and a railroad that both span the length of the park and

beyond (NPS 2017). The extensive cultural history of the area has sculpted the landscape to be structurally diverse, with many tracts of land in early plant community successional stages.

European settlement for over two centuries has also introduced numerous exotic invasive plant species to the park. Novel ecosystems have taken hold and are continuing to develop amidst remnants of native habitats. These and previously mentioned factors all contribute to the diversity of resources available to the wildlife of the CVNP.

Mammals of CVNP

Historical data - Mammal communities in the CVNP were first studied officially by

Mazzer et al. (1984) as a part of an inventory of fauna in park lands. They present a summary of previous works in the region on the local mammalian fauna, indicating that between 41-45 species of mammals should exist within the CVNP. A species list generated from these works can be found in Appendix A. Mazzer et al. used a combination of snap traps and tin-can pit traps for detecting small mammals, while larger mammals were documented through direct observation and sign. Mist-netting was also employed for surveying bats. A list of 31 species of mammals found among 10 different plant communities was generated from this work and can be found in Appendix B. Ultimately, they found that “maple-sycamore” and “oak-beech-maple” forests had the highest species richness among habitats described at that time, but a high number of species was also found in “cultivated and suburban” lands. Moreover, the lowest number of species was found in “barren land” and “pine-spruce” forest. This study provides a preliminary understanding of mammals and their “macrohabitat” associations in the CVNP.

Research & monitoring since 1984 - Much has changed since the last inventory was completed in 1984. The park has acquired more lands while plant communities have aged. The

white-tailed deer (Odocoileus virginicus) population has climbed and the browse pressure from these herds alters forest understories (Laux 2013, Rooney & Waller 2003). Deer populations and their effects on vegetation have been monitored in the park since 1993 via several methods including; fecal pellet counts, spotlighting, and various vegetation monitoring (NPS, unpublished data). The altered structure of habitats due to deer browse may have impacts for other mammalian species living here, especially small mammals residing on the forest floor (Laux

2013, Flowerdew & Ellwood 2001).

The coyote (Canis latrans) colonized the area in the early 1980’s and has since flourished here and in many other regions across the eastern and mid-western United States (Gompper

2012, Gottschang 1981). A larger member of the canid family, this animal has surely exerted competitive pressures on other carnivores (Fedriani et al. 2000) as well as hunting pressure on many smaller mammals (Miller et al. 2012, Henke & Bryant 1999). Local diet analysis on this species has shown the meadow vole (Microtus pennsylvanicus) to be the predominant prey item, with muskrat (Ondatra zibithecus) and eastern cottontail (Sylvilagus floridanus) on the menu as well (Cepek 2004). Local spatial and temporal tracking with radio-collared animals have provided information on habitat use relative to trails and human activity (Wallace 2013).

Beavers (Castor canadensis) have continued to reproduce and expand territory within the

CVNP since their previous decline over the better half of the 20th century (ODNR 2012).

Beavers were absent from Ohio for over 100 years and the state’s population began its rebound in northeastern Ohio in the 1930’s (Gottschang 1981). These keystone species are well-known to alter habitats through their hydrological engineering abilities (Wright et al. 2002, Jones et al.

1994). Beaver populations and territories within the CVNP are tracked by a monitoring program that aims to locate and report on all known lodges on park lands (NPS, unpublished data).

Another mammal group that has received attention recently are the bats (Chiroptera).

Bats have suffered from habitat loss over the decades and from White-nose Syndrome (WNS) more recently, which has led to the listing of two federally endangered species found within

CVNP, the long-eared bat (Myotis septenotrolis) and the Indiana bat (Myotis lucifugus). These listings have created the need for monitoring, and bats have been surveyed in CVNP for more

than a decade (Krynak et al. 2005). Beyond the above mentioned species groups, little attention

has been given to mammal communities within the park as a whole since Mazzer et al. (1984).

Research goals - A primary goal of this project was to produce an updated inventory of

mammals in the CVNP. Groups such as small mammals (rodents, insectivores) and carnivores

have not received as much attention as others and therefore, my study efforts were aimed

towards these groups. The inventory included information about local habitat preferences,

distribution, and relative abundance, as well as an evaluation of survey methods for each species.

Using the preliminary works at hand, I wanted to design a study to capture changes in mammal

communities over time since the designation of the federally-protected land. While this work

does not offer explicit scientific answers to how land protection affected mammal communities,

many insights have been gained as I have attempted to summarize what we know about

mammals within the CVNP.

For my contribution to the knowledge base in this field, I started out with a few guiding research questions; (1) How have plant communities changed in the last 30 years? (2) Have mammal assemblages in each plant community changed in the last 30 years? (3) Are plant communities good predictors of mammal assemblages in the CVNP? (4) Have changes in the distribution of plant communities affected the connectivity of habitat patches? (5) How might

have the arrival and subsequent rapid population growth of coyotes affected mammal assemblages?

In addition to looking at changes in local communities over time, I wanted to weigh in on

a few general discussions related to mammal communities. Often relating to habitat

management, several researchers have described small mammal habitats at two distinct spatial

scales; microhabitat and macrohabitat (e.g. Coppeto et al. 2006, Jorgensen 2004, Williams et al.

2001). The former includes assessments on environmental factors such as canopy openness, soil

compaction, woody debris etc. The latter is largely described by plant communities. I

incorporated assessments at both scales for relating to small mammal communities. Another

common topic that I felt important to my work was that of method bias and detectability of various species. Differences between small mammal methods are well-studied (e.g. Umetsu et

al. 2006, dos Santos-Filho 2006, Torre et al. 2004). I use five methods to survey mammals in

this study and I sought to point out application value for each.

Initial background work for this research involved re-locating study sites used in Mazzer

et al. (1984) in order to set up a re-sampling scheme for small mammals after 30 years. Based on

aerial photography and vegetation mapping, I found that some sites experienced a definite

successional change in plant community, while the majority aged without such distinction. Due

to a limited number of sites with a distinct successional change, differences between survey

methods, and the inability to determine specific levels of effort for the Mazzer et al. study, my

assessment of changes comes from a combined approach using indirect methods. After

establishing my original research methodologies and a period of trial and error I came to a list of

refined questions for this study;

1. How have mammal communities in the CVNP changed since 1984?

2. What scale of habitat characteristics is most important for predicting small

mammal communities? (Chapter 2)

3. Can any changes in small mammal communities be attributed to the abundances

of white-tailed deer or coyote?

4. What local habitat preferences are exhibited by mammal species found within the

CVNP?

5. How does the use of varying survey methodologies and level of effort affect

mammal inventories? (Chapter 3)

Questions 2 and 5 are answered in depth and make up the bulk of the material found in chapters 2 and 3 of this work, respectively. Question 4 is discussed throughout this work, while questions 1 and 3 are considered in chapter 4 in a more speculative fashion.

Study Design

Initial methods for studying small mammal communities included multiple periods of live-trapping with Sherman live-traps (www.shermantraps.com) and pitfall trapping at 13 study sites that represented various plant successional stages and communities. Eight of these sites were taken from the Mazzer et al. inventory. The additional five sites increased replications of successional stages, totaling three meadows, six young forests, and four mature forests. This design was intended to allow for comparisons of mammal communities by site and plant community characteristics from 1984 to now. I wanted to define habitat preferences for small mammals in the park at two scales. “Macrohabitat” characteristics included plant communities, successional stages, patch sizes, and invasive species indices. “Microhabitat” characteristics consisted of 13 measured variables at the trap level. This work is found in Chapter 2.

Medium and large mammals were originally inventoried via motion-activated camera traps. These cameras were distributed across forested sites in the park facing various landscape

features such as game trails, tree cavities, downed logs, and hydrologic features. Cameras

remained in one location for 2-24 weeks. Photos from each placement were analyzed and total

captures per species were tallied along with an estimate of the minimum number of individuals

detected for each species. This number was determined based on unique pelage or other

distinguishing features on animals, as well as by counting multiple animals in one “capture”.

These two counts helped understand the relative abundance and distribution of mammal species

detected. Macrohabitat associations for animals detected this way included preferences for forest

types and successional stages.

Two additional survey methods were utilized as a means to increase the likelihood of

detecting more species, and as a way to cross-reference relative abundances determined from

initial methods. Raptor pellets were collected from 16 sites and mammal remains (largely skulls

and mandibles) were identified to species. This method could not offer habitat associations for

detected mammal species, but did offer another view of relative abundance of small mammals.

A road survey was also conducted in the summer of 2015 in which mammals (dead or alive)

were counted across 182 kilometers of road transect driven each week. This method provided

detections of mammals of all sizes, and could be related to macrohabitat descriptors via analysis

with GIS software.

The end result of five total methodologies being used to detect mammal species in the

CVNP is a more thorough inventory that achieves greater confidence in determining

presence/absence of species within the park. It also allows for comparisons to be made in the

way of efficiency for detecting various mammal groups via each method. All methods and their

findings are discussed in chapter 3.

Implications of research

The mission of the U.S. National Park Service is to preserve the country’s natural and

cultural resources for generations to experience. Many of our greatest treasures in the United

States are protected by this idea. Managing these public lands presents many challenges in trying to decide which resources are priority and how to balance recreational opportunities with

conservation. Natural resource management in CVNP focuses on restoration projects of

degraded systems such as plant communities, streams, and wetlands. Managers must make decisions about how to guide restoration objectives within the natural landscape and these practices affect biological communities. Our stewardship in this park will be reflected by these communities, including mammals. By completing an inventory of the mammalian fauna, we are adding to the timeline of information collected through the years as CVNP continues to develop.

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Chapter II. Distribution of small mammal communities in Cuyahoga Valley National Park, Ohio at two spatial scales.

Introduction

Small mammals play important roles in many ecosystems (Hayward and Phillipson 1979,

Golley et al. 1975,). Not only do they constitute a large portion of predator diets (Mukherjee et al. 2004, Feldhamer et al. 2003, Pearson 1964), they also influence plants through seed dispersal and herbivory (Steele & Smallwood 2002), and can act as ecosystem engineers by tunneling and aerating soils (Feldhamer et al. 2003). Small mammals may serve as indicators of habitat change or integrity (Avenant 2011, Leis et al. 2008, Carey & Harrington 2001). Despite their significance, these animals may often go unnoticed due to their small size, secretive habits, and largely nocturnal nature. Biologists and land managers may gain a greater understanding on local ecosystem processes by studying the small mammal community.

Small mammals exist within a wide range of habitats across their nearly world-wide distribution (Nowak 1999). Understanding exactly what aspects of the landscape determine the occupancy for these animals is a foundational pursuit in ecology. Many researchers have attempted to explain small mammal distributions from multiple habitat scales (eg. Coppetto et al.

2006, Jorgensen 2004, Williams et al. 2002, Kelt et al. 1999). Some have found that

“macrohabitat” classifications such as plant communities (Chupp et al. 2013, Williams et al.

2002, Bowman et al. 2000), geographic regions (Jorgensen and Demarais 1999), or plant successional stages (Morris 1987) are better predictors for explaining small mammal distributions than specific characters within landscapes referred to as “microhabitat” features. In

contrast, others have demonstrated that microhabitat features such as vegetation structure, leaf litter, woody debris, and canopy openness are more important for explaining these distributions

(Williams et al. 2002, Castleberry et al. 2002, Bellows et al. 2001). While some of these differences may be a result of varying methodologies, definitions, and systems studied, each

scale of inquiry provides us with valuable information and allows us to interpret scale-dependent

effects on mammal communities (Coppeto et al. 2006, Williams et al. 2002). Moreover,

understanding the importance of microhabitat characteristics for small mammals may have

management application for rare species which are often specialists (Tallman & Mills 1994).

Application may also extend to better habitat management within forestry operations or

restoration projects (Kalies et al. 2012, Vesk et al. 2008, Converse et al. 2006).

The Cuyahoga Valley National Park (CVNP) comprises an area of approximately 13,400

ha of relatively undeveloped land along 35 km of the Cuyahoga River in northeast Ohio. Tucked

between the cities of Cleveland and Akron, the park is an important green-space and contains a

variety of habitats. In general, the CVNP is characterized by steep, forested ravines formed

along the multiple tributaries to the Cuyahoga River. Upland forests are dominated by oaks

(Quercus spp.) and hickories (Carya spp.), while American beech (Fagus grandifolia) and sugar

maple (Acer saccharum) dominate communities on slopes. The river valley hosts many fertile

habitats, including various wetlands and aquatic systems. Floodplain forests are characterized by

American sycamore (Platanus occidentalis), eastern cottonwood (Populus deltoides), silver

maple (Acer saccharinum), black walnut (Juglans nigra), and box-elder (Acer negundo). The

habitat diversity of CVNP is further accentuated by the land-use history of the region.

Manipulations over previous centuries including the construction of the Ohio and Erie Canal,

various mining operations, and decades of farming, all play roles in what habitats are found here

today.

An inventory of the amphibians, reptiles, birds, and mammals of the Cuyahoga Valley

National Recreation Area was conducted in the early 1980s (Mazzer et al. 1984) and provided the first published list of mammals in the park. The inventory provided documentations of 31 species of mammals inhabiting park lands, along with frequency, macrohabitat preferences, and qualitative abundance information. The goal of this work is to study the distribution and composition of present-day small mammal communities in the CVNP. In particular, the study is designed to examine the role of macro and microhabitat characteristics to explain the distribution of small mammals. This information will contribute to understanding how changes in distribution of habitats over the last three decades have affected small mammals in the CVNP, as well as providing insight for future land management.

Methods

Study Sites

This study was conducted in Cuyahoga Valley National Park in Ohio. In order to achieve

larger goals of this work, small mammals surveys were conducted in some of the same sites used

in the inventory of mammals conducted in 1981-1982 by Mazzer and collaborators (Mazzer et al.

1984). However, the sampling methodology used in this study was different from that of the

previous inventory. Victor snap traps, museum special traps, and tin can pits were used by

Mazzer et al. while Sherman live traps and one-gallon pit traps were used in this study to

minimize impacts to the small mammal community. Since sampling efforts were not described

in the Mazzer paper, no opportunity for direct comparison was possible on the effects of using

different survey methods.

Eight of the thirteen small mammal survey sites selected in this study were re-located

from an earlier inventory (Mazzer et al. 1984) and represented various transitions in plant

communities (Figure 2.1). These sites were located by means of topographic maps (Figure 2.2)

and accompanying written descriptions. Five additional sites were selected to achieve better

representation from the three successional stages (see map, Figure 2.3). Past and present community comparisons will be discussed in Chapter 4 of this thesis. Site descriptions can be

found in Table 2.1. Surveying took place within a distinct and contiguous plant community, of equal age, with at least a 5m-buffer to surrounding habitats in all situations. Typical buffers to adjacent communities were much greater than 5m.

Figure 2.1 – Example of vegetation cover maps in CVNP from 1975 (above) and 2013 (below). Select study sites are indicated by black dots. Vegetation cover categorized by habitat successional stage. Stage values are as follows; 0 = no vegetation, 1 = suburban (mowed) land, 2 = grassland, 3 = shrubland, 4 = young forest, and 5 = mature forest.

Figure 2.2 - Example of a topographic map indicating the sampling locations for small mammals used by Mazzer et al. (1984) in the CVNP.

Table 2.1 - Names and descriptions of sites surveyed for small mammals in the Cuyahoga Valley National Park.

Patch Name Abbrv. Stage Community (ha) Boston Mills - bridges BMB Meadow Grassland Meadow 7.57 Borrow Pit BP Meadow Grassland Meadow 5.34 Coliseum COL Meadow Central Dry Mesic Herbaceous Field 21.41 Quick/A.P. QA Young Forest Mixed Hardwood 1.62 Major - pine MP Young Forest Conifer Plantation 16.15 Beaver Marsh BM Young Forest Bottomland Riparian Hardwood 1.54 Fawn Pond FP Young Forest Bottomland Riparian Hardwood 3.76 Highland HI Young Forest Bottomland Riparian Hardwood 1.74 Chaffe CH Mature Forest Hemlock Hardwood 3.97 Major - mature MM Mature Forest Dry Mesic Oak 22.18 Virginia Kendall - lake VKL Mature Forest Dry Mesic Oak 7.53 Virginia Kendall - octagon VKO Mature Forest Beech Maple Glaciated 51.07

Figure 2.3 - Satellite image of Northeast Ohio encompassing the 13,400 ha Cuyahoga Valley National Park. This map summarizes the ownership types within the park boundary, and emphasizes the surrounding development. Small mammals were surveyed on federally-owned lands.

Composition of successional stages exemplified by all sites were as follows; meadow (n

= 3), young forest (n = 6), and mature forest (n = 4). Successional stage information was

determined for sites in the Mazzer survey as well, which allowed for then and now community

comparisons (Chapter 4). One young forest site was removed from the study after repeated

severe tampering by white-tailed deer (Odocoileus virginicus).

Data collection

Small mammal sampling: Sherman live-traps (model XLK, 7.6 x 9.5 x 30.5 cm, H. B.

Sherman Traps, Inc., Tallahassee, Florida) were deployed across thirteen sites to study

composition, abundance, and distribution of small mammals in the CVNP. At each site, live-

trapping grids consisted of four 90-meter transects separated by 15 meters. In each transect,

traps were placed every 10 meters (4 x 10 array, Figure 4). Four sites deviated from this array in

order to keep consistency within the shape of the plant communities. Two of these sites

contained two 2 x 10 arrays that were separated by a small, fallow grassy path (HI and FP), one had a 2 x 20 array (CH), and the last contained a 3 x 14 array (VKL).

Trapping was conducted from April to October 2015, with each site being sampled at

least twice. Four sites were trapped for a third session. Each trapping session consisted of 40

traps being open for four nights, totaling a potential of 160 trap nights per site each session.

Traps were baited with a mixture of oats and scratch grains. More odorous and tasty baits such as peanut butter or liver were avoided due to high levels of Raccoon (Procyon lotor) tampering in previous studies in the area (M. Plona, G. Smith personal communications). Traps were opened at sunset and checked just after sunrise. Insulation (Polyfil) was added into the traps during nights when the temperature would drop below 50º F. Traps were not opened on nights that were predicted to have heavy rain, although several trapping nights did experience some

rain. Captured animals were identified to species, sexed, and classified as adults or immatures.

Animals were then temporarily marked by means of trimming fur from the rump and then

released. This mark allowed for identification of re-captures. Any incidental mortalities during

the trapping were taken and frozen to be later prepared as voucher specimens for Kent State

University’s mammal collection.

Pitfall traps were also attempted in this study to aid in the detection of subterranean

species such as shrews, moles, and voles. Due to nearly complete failure with this methodology,

pitfall trapping was only pursued at eight of the 13 sites. One-gallon plant pots were placed flush

into the ground three meters (3m) apart from each other in two I-shaped arrays, totaling 16 traps

per site (Figure 2.4). These traps were connected by a length of 4” garden edging which served

as a rigid “drift fence”. The central drift fence was nine meters long, while the two outside

lengths were six meters each. The drift fence was placed into a cleared and scored section of ground to eliminate passage of animals under the fence. Traps were not baited. Traps were covered during the day and opened at night. Traps were kept closed during nights predicted to experience heavy rain. Captured animals were processed the same way as described above with the Sherman traps.

Microhabitat Characteristics: Each Sherman trap station was assessed for a series of

microhabitat characteristics. A 4 x 4 m square grid was centered on each trap location for these

assessments. The measured characteristics were: canopy openness, soil moisture, soil

compaction, tree stems of three size classes, standing dead trees, down woody debris, woody

debris diameter, herbaceous cover and shrub cover, each in two size classes. The descriptions of

each analysis and their corresponding values can be found in Table 2.2.

Figure 2.4 - Schematic representation of the standard trapping grid for small mammals. Red dots indicate Sherman trap locations along each of the 90 m transects and olive squares represent microhabitat assessment area. Black dots indicate the location of pitfall traps in the sampling grid and the black line represents the drift fence connecting them. The blue line represents the invasive species transect.

Table 2.2 - Descriptor and methodology used for the assessment of microhabitat characteristics. Microhabitat assessments were performed in a 4x4m square plot centered on each trap site. For percent cover categories; 0=0% cover, 1=1-24% cover, 2=25-49% cover, 3=51-74% cover, and 4= >75% cover

Microhabitat descriptor Methodology Canopy openness Hemispherical photo taken with fisheye lens. “Visible sky” value calculated with HemiView software. Soil Compaction Soil compaction at 20cm using a soil penetrometer Soil Moisture Soil moisture at 20cm using a soil moisture meter (Aquaterr T-300) Trees 1-5cm All stems within the 4x4m plot (count) Trees 6-20cm All stems within the 4x4m plot (count) Trees >20cm All stems within the 4x4m plot (count) Standing Dead All standing dead trees >15cm within the 4x4m plot (count) DWD All down woody debris >10cm wide and 30cm long (count) DWD dia. Total diameter in centimeters of counted DWD pieces Low Herb Categorical assessment (0-4) of percent cover of herbs <50cm Tall Herb Categorical assessment (0-4) of percent cover of herbs >50cm Low Shrub Categorical assessment (0-4) of percent cover of shrubs <50cm Tall Shrub Categorical assessment (0-4) of percent cover of shrubs >50cm

Macrohabitat characteristics: Each of the thirteen trapping sites were described based on their macrohabitat characteristics. These values were determined via GIS map data (Arc

software version 10.2.2). The measured characteristics were: plant community, successional stage, fragment size, invasive species richness, and invasive species abundance (Table 2.3).

Table 2.3 - Descriptor and methodologies used for the assessment of macrohabitat characteristics. Plant community, successional stage, patch size, and fragment size were calculated with ArcMAP data. Invasive species richness and abundance were assessed in the field. Macrohabitat Methodology descriptor Plant community Community classification based on recent USGS work for the park (Kop et al. 2013). Described by dominant species.

Successional Stage Categories derived from USGS veg. map (open, shrub, young forest, mature forest, see Figure 3)

Fragment size Size of tract of land which is bounded by roads, railroads, rivers etc. Invasive sp. Number of invasive plant species encountered along the total length of Richness transects in the plot.

Invasive sp. Total number of invasive plant stems encountered along the total length Abundance of transects in the plot.

Data analysis - Population densities were estimated for Peromyscus and Microtus at the

site level using the Lincoln-Peterson index. Species richness (R), Shannon’s diversity index (J’),

and Evenness (H’) were calculated collectively for each successional stage. Microhabitat values

were used to analyze within-site associations for each species by comparing trap stations with

and without captures of a particular species. Microhabitat values were also averaged at the site level and related to individual abundances and species richness. Analysis of variance was used to describe differences in microhabitat averages by successional stage. Canonical correspondence analysis (CCA) was performed to understand the community variation explained

24 by microhabitat assessments. Forward selection was set at p≤.05 to reduce the model to the most important variables. Analysis of variance was used to relate macrohabitat data to individual abundances and species richness.

Results

Sherman Trap effort - A total of 342 individuals representing seven small mammal

species were recorded in 703 total captures over 4,136 trap nights. Of the seven species

captured, white-footed mouse (Peromyscus leucopus) was the most frequently encountered (256

individuals), accounting for 74.85% of all captures (Figure 2.5). Meadow vole (Microtus pennsylvanicus) and Short-tailed Shrew (Blarina brevicauda) made up the majority of the rest of

the captures (64 and 17 individuals, accounting for 18.71% and 4.97% of all captures; respectively). Peromyscus was found at 11 of the 13 sites, while M. pennsylvanicus and B. brevicauda were found at seven sites and five sites, respectively. Meadow jumping mouse

(Zapus hudsonicus) was captured once each at two different sites, and the three remaining

species were only captured once throughout the entire survey.

Capture Rates - Mean capture rate (individuals captured/trap nights) increased

continuously as the field season progressed from April to October across all sites (Figure 2.6).

Analysis of variance showed that there were significant differences in mean capture rates

between seasons, where capture rate increased from spring to summer to fall (Table 2.4). Simple

linear regression showed that capture rate was significantly influenced by month (F6,22= 3.6287,

p= 0.0118, R2= 0.4974). Capture rates increased from 0.028 ± 0.035 and 0.042 ± 0.028 in April

and May, respectively, to 0.377 ± 0.212 in September/October. This increase indicated that the

capture rate was more than ten times greater in the last two months than in the early months of

the study. Overall, capture rates in the early sampling months (April and May) were lower than

in the later sampling months (July-October), while rates in Sep/Oct were greater than June as

well (Tukey HSD < 0.01).

Figure 2.5 – Proportions of small mammals captured during Sherman trap efforts (2015) in CVNP. Four species included in the “other” category are meadow jumping mouse (Zapus hudsonicus), eastern chipmunk (Tamias striatus), masked shrew (Sorex cinereus), and long- tailed weasel (Mustela frenata).

Sherman Trap Success 60%

45% 0.377

30% 0.253 0.167

Capture rate 0.145 15% 0.028 0.042 0% April May June July August Sep/Oct Month

Figure 2.6 – Mean capture rate by month; individuals per trap night from April to October of 2015, across all sites. Number of sites sampled each month: April (n = 4), May (n = 5), June (n = 7), July (n = 3), August (n = 7), Sep/Oct (n = 3).

Table 2.4 –Analysis of variance for capture rates observed in spring, summer, and fall. Analysis of variance indicates a significant effect of season. Number of sites sampled in spring (April, May; n=9), summer (June, July, August; n=17), fall (September, October; n=3). Seasons not connected by same letter are significantly different. SE indicates Standard Error.

Source DF F p-value Season Mean Capture Rate SE

Model 2 13.6022 <0.0001 Spring 3.5% A 3.5% Error 26 Summer 19.3% B 2.5% Fall 37.7% C 6.0%

A paired Student’s t-test was conducted to compare capture rates between the first and

the second trapping session in each location. This analysis indicated that, in general, there was a

significant increase captures from the first to the second visit on most sites (t=4.83, p=0.0005)

(Table 2.5). Lower capture rates in the second visit with respect to the first visit were only

observed in two sites (COL and FP). Percent increase of capture rates in the rest of the sites ranged from 93% to 2058%.

Population demographics - Examination of P. leucopus individuals sexed in this study revealed that 1.10 males were captured for each captured female (n=160). Meanwhile, 0.92 males were captured for each female (n=25) in M. pennsylvanicus across all sites. In addition,

12.5% of the P. leucopus individuals captured were immature, while only 8% of the M. pennsylvanicus individuals were immature.

Population density estimates - Mark and re-capture methods allowed for estimation of population densities for two rodent species. Since animals were not individually marked, I used the Lincoln-Peterson Index for estimating population densities. Densities for P. leucopus were

highest in mature forests and lowest in meadows, on average (F2,9=4.6251, p=0.0415), although

Table 2.5 – Comparison of capture rates observed in the first and second trapping session, by sampling site. Only two sites experienced a decrease in capture rate, while there was a significant increase in capture success overall.

Capture Rate Percent Mean Site Stage 1st visit 2nd visit Change Rate BMB Meadow 0.019 0.049 153% 0.034 BP Meadow 0.007 0.144 2058% 0.075 QAP Young Forest 0.058 0.135 134% 0.097 MP Young Forest 0.007 0.069 886% 0.038 VKL Mature Forest 0.000 0.075 1093% 0.040 BM Young Forest 0.044 0.103 134% 0.074 MM Mature Forest 0.035 0.110 211% 0.073 COL Meadow 0.028 0.025 -9% 0.026 VKO Mature Forest 0.036 0.119 235% 0.078 HI Young Forest 0.053 0.184 246% 0.118 CH Mature Forest 0.072 0.140 93% 0.106 FP Young Forest 0.085 0.064 -25% 0.075

Table 2.6 – Lincoln-Peterson population density estimates per hectare for Peromyscus leucopus (PL) and Microtus pennsylvanicus (MP). Numbers in bold indicate the highest density for each species at a particular successional stage.

Site Stage PL MP BMB Meadow 0 8.4 ± 2.8 BP Meadow 12.2 ± 2.4 4.9 ± 0.7 COL Meadow 0 3.5 ± 0.7 QA Young Forest 33.8 ± 8.0 0 MP Young Forest 22.4 ± 3.4 1.4 ± 0.0 BM Young Forest 15.7 ± 3.1 0 FP Young Forest 3.2 ± 0.6 8.6 ± 2.1 HI Young Forest 26.0 ± 5.2 3.2 ± 0.6 CH Mature Forest 26.7 ± 9.0 0 MM Mature Forest 24.5 ± 4.9 0 VKL Mature Forest 18.9 ± 3.9 0 VKO Mature Forest 21.0 ± 4.2 0

the highest single density estimate for this species came from a young forest site (QA) with an

estimate of 33.8 individuals/ha (Table 2.6). For M. pennsylvanicus, densities were greatest in meadows and lowest (absent) in mature forests, but these differences were not significant

(F2,9=3.7887, p=0.0640). The highest single estimate for M. pennsylvanicus was a density of 8.6

individuals/ha on a young forest site (FP). Not enough data was available to calculate density

estimates for other species.

After arcsine transformation, analysis of variance revealed that proportional percentage

of re-captured individuals increased throughout subsequent trap nights for P. leucopus

(F2,27=5.4787, p = 0.0101; Figure 2.7). Tukey HSD revealed a significant difference between

nights two and four. Subsequent trap nights for M. pennsylvanicus followed a similar trend

(F2,13=2.4042, p= 0.1293), but there was not enough data to find a significant difference between nights (Figure 2.7).

P. leucopus re-captures M. pennsylvanicus re-captures 95%

100% 100% 78% 82% caps caps 80% 80% - - 64% 66% 60% 53% 60% 40% 40% 20% 20% Proportion of Proportion re Proportion of Proportion re 0% 0% N2 N3 N4 N2 N3 N4 Sequential nights Sequential nights

Figure 2.7 – Average proportion of recaptures for Peromyscus leucopus (left) and Microtus pennsylvanicus (right) during subsequent nights of trapping in the summer season. More captures of P. leucopus at more sites lead to significant differences between nights two (N2) and four (N4; Tukey HSD), while no differences were significant for M. pennsylvanicus.

Macrohabitat characteristics – A summary of the characteristics of each of the 12 trapping sites according to successional stages is given in Table 2.7. Of the three classified successional stages

(meadow, young forest, and mature forest), trapping sites located in mature forests (n = 4) had the largest average plant community patch sizes, followed by those of meadow (n = 3) and young forest (n = 5). On average, young forest communities contained the highest invasive species richness and abundance. Meanwhile, trapping sites in meadows had the lowest invasive species

richness but had higher invasive species abundance than mature forests.

Table 2.7 - Summary of macrohabitat characteristics and small mammal community data by successional stage. Community data is averaged among sites from the second (summer) visit. Meadow (n = 3), Young Forest (n = 5), Mature Forest (n = 4). Values in bold indicate the highest mean value for each measure.

Successional Stage Meadow Young Forest Mature Forest Mean Patch size (ha) 11.44+4.10 4.96+2.53 21.18+9.28 Invasive Species Richness 1.00+.47 5.80+1.25 3.00+.94 Invasive Species abundance 53.33+42.73 228.8+30.55 26.00+10.61 Small Mammal Richness 2.0+0.0 2.6+.2 1.5+.3 Abundance Small Mammals 11.0+8.7 17.4+8.4 18.3+5.1 Peromyscus leucopus 4.3+7.5 13.8+9.0 17.8+4.6 Microtus pennsylvanicus 5.7+2.1 2.0+2.9 0 Blarina brevicauda 0.7+1.2 1.2+2.9 0.3+0.5

Captures - Mature forest sites averaged the highest number of total individuals captured,

followed by young forest sites, then meadow sites (Table 2.7). In total, young forest sites had

the greatest overall species richness (R=5.8072), followed by meadows (R=3.7684), and mature forest (R=2.7767). When sites were pooled by successional stage, meadows had both the greatest

diversity (Shannon Index; H’=0.9486) and evenness (J’=0.6843), followed by young forest

(H’=0.6049, J’=0.3376) and mature forest (H’=0.1242, J’=0.1131). These findings revealed that

the changes in the Shannon Diversity Index among successional stages results from both the

31 differences in the number of species trapped and their abundances. When diversity metrics were calculated by site, successional stage had a significant effect (F2,9=7.0885, p=0.0142). Tukey

HSD revealed that young forests and meadows had higher diversity scores than mature forests.

On average, Peromyscus leucopus was found in greatest abundance in mature forests, followed by young forests and meadows (Table 2.7). Concordantly, this species exhibited a strong positive correlation with later successional stages in analysis (r=0.7419, p=0.0029). Microtus pennsylvanicus was a stark contrast to P. leucopus, being most abundant in meadows, followed by young forests. This species was not detected in mature forests at all and showed a negative correlation to later successional stages (r=-0.6283, p=0.0144). Blarina brevicauda was detected most frequently in young forests, followed by meadows, then mature forests. Overall species diversity was positively correlated with invasive species abundance (r=0.6940, p=0.0061), and invasive species richness (r=0.5544, p=0.0307).

Microhabitat characteristics – Analysis of variance was performed on all microhabitat variables to analyze differences related to successional stage. There were significant differences in the three physical factors examined: canopy openness, soil moisture and soil compaction.

Naturally, canopy openness decreased as successional stage increased, (F2,9=1,823.95, p<0.0001). Post-hoc testing (Tukey HSD) showed that all three stages were different with respect to canopy openness (Figure 2.8 A). Soil compaction was greatest in meadow sites and lowest in young forests, but mature forests were not significantly different from the other two stages, (F2,9=9.3248, p=0.0064; Figure 2.8 B). Soil moisture was significantly lower in mature forests than both meadows and young forests, (F2,9=28.9164, p=0.0001; Figure 2.8 C). Overall, mature forests had less canopy openness and soil moisture than meadows and young forests.

However, mature forest did not differ from meadows and young forest with respect to soil compaction.

(A) Canopy Openness 100.0%

100.0% 75.0% a

50.0% b 25.0% c 27.8% Percent visible sky Percent 15.6% 0.0% Meadow Young Forest Mature Forest

(B) Soil Compaction 3.75 a 3. ab 2.25 2.449 6) b -

(0 1.5 1.8087 0.75 1.3073

Compaction category Compaction category 0. Meadow Young Forest Mature Forest

45% 54% 52% b 30% 31% 15% 0% Percent moisture at 30 cm 30 at moisture Percent Meadow Young Forest Mature Forest

Stage

Figure 2.8 - Mean canopy openness (A), soil moisture (B) and soil compaction (C) for each successional stage. Means represented by the same lower case letter are not significantly different based on Tukey’s post-hoc test. Error bars indicate one Standard error.

My results also showed that, on average, there were differences in the composition of

trees in three different size classes among successional stages (Figure 2.9). In general, meadows by definition lack trees, but no significant differences in the abundance of small, medium, or

large trees were found between young and mature forests (F2,9=21.071, p<0.0001, F2,9=50.2613, p<0.0001 and F2,9=28.3322, p<0.0001; respectively) (Figure 2.9 A, B, and C). However, there

were more standing dead trees in the young forest sites (Figure 2.9 D, F2,9=7.7629, p=0.0005)

and more down woody debris accumulated in mature forest sites (Figure 2.9 E, F2,9=11.4387, p=0.0034).

Sampling sites also varied by measure of shrub and herbaceous plants cover (Figure

2.10). Shrubs of both size classes (smaller and larger than 0.50 m tall) were most prevalent in young forests, followed by mature forests, then meadows (F2,9=8.5351, p=0.0083 and

F2,9=13.4381, p=0.0020, for each shrub size class respectively), as a whole, young forests had

more low shrubs than meadows and more tall shrubs than both meadows and mature forests

(Figure 2.10 A and B). In addition, both low and tall herbaceous cover decreased as successional stage increased, but this was only significant with regard to low herbs (F2,9=22.4044, p=0.0003,

Figure 2.10 A). Tall herbaceous cover was lower in mature forests (Figure 2.10 B).

Species associations among sites – My data suggests several significant associations

between species abundances and habitat characteristics (Figure 2.11). Abundance of P. leucopus

was positively correlated with the abundance of medium (r=0.5892, p=0.0219) and large trees

(r=0.6471, p=0.0115), and had the strongest positive correlation with the amount of downed

woody debris (r=0.6715, p=0.0084). This species was also negatively correlated with canopy

openness (r=-0.7346, p=0.0033), soil moisture (r=-0.6533, p=0.0106), low (r=-0.6379, p=0.0128)

and tall herbaceous cover (r=-0.5029, p=0.0478), with canopy openness showing the greatest

(A) Trees 1-5 cm DBH (B) Trees 5-20 cm DBH 3.75 2.25 b b b

3. 1.8 b 2.25 1.35 1.5041 1.3478 1.5 0.9 1.5041

Stem count a Stem count a 0.75 1.3478 0.45 0.0465 0.0465 0. 0. Meadow Young Mature Meadow Young Mature Forest Forest Forest Forest

b 0.4

0.6 0.3 0.4995 0.4 0.458 Count 0.2 b a b 0.1796

Stem count 0.2 0.1 [VALUE]0 0.0728 [VALUE]0 0. 0. Meadow Young Mature Meadow Young Mature Forest Forest Forest Forest

(E) Woody Debris c 1.8

1.35 b 0.9 1.2346

Count a 0.45 0.7299 [VALUE]0 0. Meadow Young Mature Forest Forest

Figures 2.9 – Mean number of trees in each size classes (A, B, C), standing dead trees (D) and woody debris (E) in the vicinity of each trap for each successional stage. Assessment was conducted in 4 x 4 m plots centered on each trap location. Values represented by the same lower case letter are not significantly different based on Tukey’s post-hoc test. Error bars indicate one standard error.

(A) Shrub Cover (<50 cm) (B) Shrub Cover (> 50 cm) 2.25 a 2. a

4) 4) - - 1.5 1.5 b 1.4729 1. 1.276 0.75 0.9894 c b c 0.5 Categorical (0 Categorical

Categorical (0 Categorical 0. 0.214 0.0816 0. 0. Meadow Young Mature Meadow Young Mature Forest Forest Forest Forest

(D) Herbaceous Cover (<50 cm) (C) Herbaceous Cover (>50 cm)

5 3. a 4) 4) b - - 4 b 2.25 b 3 3.7196 c 1.5 2 2.3004 1.1333 a 1 0.75 1.1039 Categorical (0 Categorical Categorical (0 Categorical 0.8612 0.075 0 0. Meadow Young Mature Meadow Young Mature Forest Forest Forest Forest

Figure 2.10 - Mean percent cover (categorical) for shrubs (A, B) and herbaceous vegetation (C, D) in the vicinity of each trap for each successional stage. Assessment was conducted separately for short (< 50 cm) and tall (> 50 cm) vegetation in 4 x 4 m plots centered on each trap location. Means represented by the same lower case letter are not significantly different based on Tukey’s post-hoc test. Error bars indicate one standard error. negative correlation. In contrast, M. pennsylvanicus abundance was positively correlated with canopy openness (r=0.5480,p=0.0325), soil moisture (r=0.6763, p=0.0079), and low herbaceous

cover (r=0.7231, p=0.0040), with low herbs being the strongest positive correlation. This

species also showed a negative correlations to large trees (r=-0.5315, p=0.0377). Meanwhile, B.

brevicauda showed only a positive correlation with tall herbaceous cover (r=0.5622, p=0.0286).

Canonical correspondence analysis revealed that 42% of the variation in small mammal

community abundances could be explained by all microhabitat variables; with 38% of the

Figure 2.11 - Correlation matrices among small mammal community data (top left), and between community data and microhabitat measures (bottom). Dark blue relates to strong negative correlations while dark red relates to strong positive correlations (see scale at bottom). Produced with “matcor” function in R, package “CCA”. variation being explained by the first axis. Analysis of variance with 999 permutations resulted in significance (F13,476 = 15.563, p<0.001). Forward selection was used to refine the model down to six important environmental variables (F6,483 = 33.368, p<0.001, Figure 2.12). These remaining variables, in order of importance were: canopy openness, tall herbaceous cover, low herbaceous cover, low shrub cover, soil moisture, and tall shrub cover.

Figure 2.12 – Canonical Correspondence Analysis relating microhabitat variables to small mammal captures. Forward selection reduced the microhabitat variables to the six most important. Peromyscus lies near the center of the plot due to the fact that it was found on 10 of the 12 sites. Microtus is positively correlated with microhabitat features in meadows (canopy openness, moisture, herbaceous cover), while Peromyscus is not. Blarina is weakly correlated with herbaceous cover. Species associations within sites – During this study, P. leucopus and M. pennsylvanicus were found to be sympatric on four of the 13 sites. Three of these sites were young forests and the last was a meadow (BP). Capture data from these four sites were analyzed individually (site by site) to understand differences in microhabitat associations between the two species. At the meadow site, P. leucopus associated with low shrubs more than M. pennsylvanicus (T92=2.505,

p=0.0140). Among the three young forest sites, M. pennsylvanicus captures were associated

with more canopy openness (T34=2.009, p=0.0261 and T76=2.976, p=0.0020) and tall shrubs

(T76=1.948, p=0.0275 and T34=3.47, p=0.0014) than captures of P. leucopus on two sites.

Additionally, P. leucopus associated more with small trees (T34=2.663, p=0.0059) and low

shrubs (T34=4.0345, p=0.0001) than M. pennsylvanicus in one of the three young forests.

Microtus pennsylvanicus associated with low herbs (T27=4.090, p=0.0002) and greater soil moisture (T27=2.811, p=0.0045) than P. leucopus on one of the three sites.

Within each site, analysis of variance was used to compare microhabitat values between

stations with and without captures for a particular animal. Of the 10 sites where P. leucopus was

captured, various microhabitat associations were significant within six sites. Captures were

fewer in areas with greater down woody debris diameter on three sites (young forest,

F1,57=11.6510, p=0.0012, mature forests, F1,60=15.9832, p=0.0002, and F1,65=4.2042, p=0.0444).

Peromyscus leucopus was more likely to be captured near small shrubs on two sites (young forest, F1,68=4.2576, p=0.0429, mature forest, F1,57=21.5265, p<0.0001). This species was

negatively associated with tall herb cover on two young forest sites (F1,57=6.7327, p=0.0120 and

F1,56=16.9407, p=0.0001). Small trees (young forests; F1,56=4.4808, p=0.0387 and F1,57=4.9404,

p=0.0302), soil moisture (young forest; F1,56=11.0085, p=0.0016 and mature forest;

F1,60=4.4027, p=0.0401), and low herb cover (young forests; F1,76=5.6441, p=0.0200 and

F1,57=19.7985, p<0.0001) were each important on two sites, but averages went in different

directions.

Significant microhabitat associations were found for M. pennsylvanicus at four of six sites. Two young forest sites provided evidence that this species prefers greater low herbaceous cover (F1,42=18.9097, p<0.0001 and F1,41=4.1479, p=0.0482), while a meadow site showed the

preference for tall herbaceous cover (F1,42=29.9726, p<0.0001). The same meadow showed a

negative relationship with low herb cover (F1,42=42.5767, p<0.0001). Canopy openness

(F1,42=12.8871, p=0.0009), soil moisture (F1,42=6.2131, p=0.0167) , and down woody debris

(F1,57=8.6122, p=0.0050) were important microhabitat features to predict M. pennsylvanicus

within young forest sites; each showing positive trends, while medium-sized trees (F1,51=6.6791,

p=0.0127) was a negative association at this stage. Soil compaction was important on two sites,

but in different directions (young forest; F1,42=4.3734, p=0.0426 and meadow; F1,41=4.1479,

p=0.0482). No significant microhabitat associations were found for B. brevicauda.

Discussion

Original goals and challenges - One of the main goals of this study was to determine

changes in the distribution and composition of small mammal assemblages after the establishment of the Cuyahoga Valley National Park. In particular, I examined the relationship

between small mammal assemblages and the characteristics of plant communities. However, trapping results yielded far fewer species than I anticipated for this study. Pitfall traps were employed but captured only a single mammal over the course of six weeks at eight sites in 343 trap nights. Without the pitfalls performing as expected, Sherman traps provided the only data on small mammal communities for this portion of the study. Sherman traps are known to be limited with respect to what animals they can reliably capture, and this is why pitfall trapping is important as it is usually the most efficient live-capture method to survey the insectivore community (Stephens and Anderson 2014, Umetsu et al 2006, Williams and Braun 1983). It is likely that some typically uncommon species were missed in this study due to the level and type of trapping effort. Despite the lack of data on insectivore communities, Sherman traps performed to the level that could be expected and provided ample data for the three commonest

species of small mammals to be analyzed. Understanding the relationships between these three

species and their habitats gives us insight towards changes in assemblages over time.

Capture success and re-captures - In general, capture success in Sherman traps increased

during subsequent site visits in this study. Rodents are notoriously prolific breeders, where

white-footed mice (Peromyscus leucopus) can raise up to six litters in a year between March and

November, and meadow voles (Microtus pennsylvanicus) capable of producing 10 litters in a

year (Gottschang 1981). For this reason, population growth can be expected throughout the year, and numbers diminish over the winter when most of these animals stop reproducing (and continue to be preyed upon or perish by other ways). Percentage of re-captures across all sites increased each night for P. leucopus and M. pennsylvanicus. These numbers give confidence that the populations were well-sampled as 82% of P. leucopus captures and 95% of M. pennsylvanicus captures were re-caps by night four. A duration of four trap nights is commonplace for small mammal inventories, and has been shown to be sufficient for assessing species richness (Conrad et al. 2008).

Mammal assemblages - White-footed mice accounted for three quarters of the total captures and were found at all but two study sites (both meadows). This species is known to be

an abundant generalist (Laux 2013, Mitchell et al 1997, Adler and Wilson 1987, Gottschang

1981), and my data reflects that. Based on habitat analyses, it seems that the primary indicator of

this animal’s presence within the CVNP is woody vegetation. Ample research has described P.

leucopus as an associate of scrubby and forested habitats (Gottschang 1981, Wegner and

Merriam 1979, Baker 1968), and it is documented in few studies using grasslands (Adler and

Tamarin 1984, Stickel 1968). Some researchers have shown that P. leucopus avoids grasslands

and similar open habitats (Yahner 1983, Wegner and Merriam 1979). In a study of habitat use

within a fragmented system of forests, hedgerows, and fields, Wegner and Merriam (1979) found

that P. leucopus used forests and hedgerows with similar frequencies and avoided using fields,

even for travel. Furthermore, of the captures that were made in fields, over half of them were

within 5 meters of the forest edge. In my study, individual counts of P. leucopus increased with

habitat succession, reaching the highest counts in mature forests. Lincoln-Peterson population

density estimates in mature forests ranged from 15-35.7 mice per hectare. One population at a

young forest site was estimated at 33.8 ± 8.0 mice per hectare. These numbers are towards the

high end of density estimations for P. leucopus according to previous small mammal studies in

Ohio (Gottschang 1981), indicating that populations may have grown in recent decades. Long-

term population density studies for this species in eastern young forests estimate densities

between 1.2-57.5 mice/ha in a successional shrubland community (western Pennsylvania, USA)

and between 0.3-22.9 mice/ha in a young forest (eastern Virginia, USA). Both studies showed

strong annual fluctuations with peak densities in fall (Kenzer and Linzey 1997, Terman 1993).

Some evidence has been provided that population cycles of this animal may be related to the

mast production of important winter foods such as red oak (Quercus rubra; Wang et al. 2009).

While P. leucopus was captured in abundance in one particular meadow, the meadow

vole proved to be the dominant species in these systems. This animal was the second most

abundant species captured overall and prefers an open canopy with herbaceous ground cover,

based on habitat analyses. Microtus pennsylvanicus was not captured at any mature forest sites,

but it was detected at three of five young forest sites. Conversely to P. leucopus, individual

counts of M. pennsylvanicus decreased with habitat succession, and were greatest in meadows.

Population density estimates for this species in meadows ranged from 2.8-11.2 voles per hectare.

These numbers pale in comparison to some previous density estimates made in Ohio and surrounding lands, where numbers in excess of 100 voles per hectare have been estimated

(Gottschang 1981). The same review has shown populations of M. pennsylvanicus to experience cyclic fluctuations and my numbers could reflect a low within these cycles. It is also reasonable to suspect that hunting pressure from the coyote population keeps meadow vole densities at bay

(Miller et al. 2012, Cepek 2004). Most M. pennsylvanicus captures on night four (95%) were re-

captured individuals, suggesting that the sites were adequately surveyed for this species, and that

density estimates are well-founded. Short-tailed shrews (Blarina brevicauda) were captured in limited quantities by Sherman trap efforts, but some habitat trends were still gathered. This animal was found most frequently in young forests, and had an affinity for tall herbaceous cover.

With only 17 individuals captured at five sites, it would be inappropriate to attempt to draw any further conclusions on the habitat trends for B. brevicauda.

Successional stage summaries - My results indicate that successional stage is the most important macrohabitat variable to predict small mammal assemblages. Few macrohabitat variables were recorded for each site, in addition to a collection of microhabitat values.

Microhabitat values were averaged for each site, and these averages provide a description of the structure of each successional stage. Mature forests averaged the largest patch sizes, and are the dominant successional stage in the park. Microhabitat averages detail that these forests have the least canopy openness, soil moisture, and herbaceous cover. They have the most downed woody debris. Small mammal species richness and diversity was lowest in these systems, but overall small mammal abundance was greatest here. This was because the most common small mammal detected in this study (P. leucopus) preferred mature forests. As previously mentioned, species

richness and diversity as determined by Sherman trapping alone does not accurately represent the entire small mammal community. Additional species can be found in these mature forests and some are described in Chapter 3. The young forests in this study were the smallest patches and averaged the most invasive plant species richness and abundance in addition to the most standing dead trees. As evidenced by the earlier successional condition, these lands have typically experienced more recent disturbance which allowed easier colonization by the exotic plants; many of which are shrubs. Young forests also contained the greatest small mammal species richness. So while these sites may be degraded in the sense of native botanical communities,

they are still very productive as small mammal habitats. MacArthur and MacArthur (1961) were one of the first to demonstrate that vertebrate diversity related to the structural diversity of vegetation, rather than its species composition. In a popular review by Tews et al. (2004), small mammal diversity was positively correlated to habitat heterogeneity in five of eight studies examined. Young forests were the most numerous (n = 5) successional stage studied here, and

also showed the greatest variation among many microhabitat values (Figures 2.8, 2.9. 2.10). The

heterogeneity across and within these systems likely lead to the greater species richness found.

Meadows are the extreme opposite of mature forests, being characterized with complete canopy openness, high soil moisture, and dense herbaceous cover. This stage scored the highest

Shannon diversity value for small mammals overall, since all but one species detected in the

study were found in these systems.

Microhabitat results - Microhabitat values were related to small mammal communities in

two ways. First, the averages per site were compared to the abundance of each mammal

captured there. This helped articulate exactly what sort of habitat characteristics each animal was associated with. At a coarse scale, Peromyscus leucopus prefers mature forests. Based on

microhabitat averages, these sites are characterized by having abundant woody debris, closed

canopies, larger trees, and shrub cover. The second way in which microhabitat values were used

was at the individual trap scale that looked to describe specific features within a habitat that

small mammals associated with. Within forests for example, P. leucopus was found more

frequently in areas with greater shrub cover, and less in areas with tall herbaceous cover. This

animal was found away from areas with large woody debris. While on one scale, P. leucopus is

abundant in systems that contain more woody debris (mature forests), but within these systems

they locate away from them. Based on the results from looking at these microhabitat

associations on two scales, it seems that woody vegetation is the single most important factor for

the presence of P. leucopus. Similarly, the single most important factor for M. pennsylvanicus

was herbaceous cover. They located near areas of greater herb cover within meadows and young

forests, and preferred tall herbs over low herbs in one meadow. Previous studies on habitat

preferences for this species have produced similar results (Stephens and Anderson 2014, Bellows

et al. 2001, Gottschang 1981). Some works also detail that M. pennsylvanicus prefers wetter

meadows over dry meadows (Stephens and Anderson 2014), a trend slightly detected in this

study as well.

Meadow vole and white-footed mouse sympatry - The two most abundant rodent species

captured in this study complement each other in regards to habitat preferences; i.e., they are

found in greatest abundances in opposite habitat types. Both species were found in young

forests, where habitat heterogeneity offered microhabitat features that favor both species.

Understanding the gradient between P. leucopus and M. pennsylvanicus habitats is of particular

interest here, and my results yielded some insight towards this. Where sympatry between these

two species occurred, P. leucopus associated with more trees and shrubs, and M. pennsylvanicus

associated with more canopy openness. Some support was also found that M. pennsylvanicus associated more with low herb cover and soil moisture. These two features are correlated with

canopy openness. Structurally, M. pennsylvanicus requires a decent amount of low, thick herbaceous cover for protection, especially for nesting (Gottschang 1981, M’Closkey and

Fieldwick 1975). Within this strata, the voles construct a labyrinth of runways to navigate their territories. These runways provide some cover from the long list of predators that feed on them.

Similarly, P. leucopus utilizes trees and shrubs for cover and often for nesting sites as this animal is a capable climber (Gottschang 1981). Ultimately, when these two species co-exist within a

young forest site, they are depending on the habitat heterogeneity to offer preferred conditions, and they seem to segregate on a local level. Another instance of sympatry occurred with these species both being captured at a native meadow site. This unique circumstance warrants further

study to determine the use of meadows by P. leucopus in CVNP.

Importance of scale - Many of the microhabitat qualities measured here and in other

studies tend to be correlated. Most studies show that small mammals are better predicted on a

macrohabitat scale than by microhabitat features within a site (Coppeto et al 2006, Orrock et al

2000, Jorgensen and Demarius 1999). When relating small mammal abundance to microhabitat averages by site, spatial autocorrelation is a concern. Acknowledging this, my first

“microhabitat” analysis becomes another way of measuring species abundance and composition related to structural features that is somewhere between a micro and macro habitat analysis. The true microhabitat analysis was performed when capture number per trap per species was assessed within a site. Once occupancy was determined, these microhabitat affinities found for small mammals represent a finer scale of habitat preference. Microhabitat analysis was not necessary to predict occupancy for the mammals captured in this study, but these details did explain how two common rodent species segregated within young forests. Macrohabitat descriptors were sufficient for predicting occupancy of these animals. Therefore, microhabitat characteristics should be observed as criteria to explain small mammal density rather than occupancy, since these characteristics may be indicative of foraging, cover, and nesting resources.

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Chapter III. Comparing methodologies for studying mammal assemblages in Cuyahoga Valley National Park, Ohio.

Introduction A wide range of methodologies have been described for inventorying mammal

communities (e.g. Ericsson & Wallin 1999, Burnham et al. 1980, Croon et al. 1968, Alcorn

1946). Depending on the life history of a target species group, certain methods have proven more efficient than others (Tobler et al. 2008, Torre et al. 2004, Silveira et al. 2003). Patterns of locomotion, body mass, disposition towards traps and/or landscape features may all affect the detectability for particular animals (Harmsen et al. 2010, Barea-Azcón et al. 2007). For example, arboreal mammals spend most of their time above the ground, and therefore are not often detected by ground-based trapping methodologies (Torre et al. 2004). Camera traps often prove useful for detecting secretive species such as carnivores, but show differences in detectability rates based on body size and wariness to survey equipment (Gomper et al. 2006).

Small mammals encompass several taxonomic orders and due to ecological differences require various trapping methods to adequately inventory as a group (Sedivec & Whidden 2007, Umetsu

et al. 2006, Bonvicino et al. 2003).

While some form of physical trapping is generally required to survey small mammal

communities, larger mammals can be detected via more indirect methods. Traditional indirect

methods used to survey larger mammals include line transects for counting animals or their sign

such as droppings, tracks, or dwellings (Burnham et al. 1980). With the development of roads

and increase in automobile traffic over the last few decades, road-killed animals can be used to

gather information on species distributions, relative abundance, habitat associations, and activity

periods (Santos et al. 2011, Kanda et al. 2006, Casper 1978). Some vocal animals, such as

canids, can be surveyed through solicited calls when recordings of sirens or howls are broadcasted (Okoniewski & Chambers 1984, Alcorn 1946). Large game animals such as deer can be counted via aerial infrared surveillance (Wiggers & Beckerman 1993, Croon et al. 1968).

Information about the presence of mammals in a given area may be gathered opportunistically through hunter and trapper surveys (Ericsson & Wallin 1999), in addition to solicitation of other outdoorsmen, landowners, etc.

More recently, camera-trapping has become a popular and effective way to survey many terrestrial animals. This method is of particular appeal due to the minimal amount of field time required to gather large amounts of data. Although somewhat costly, this methodology is considered a very effective way to survey the mammal community (Srbek-Araujo & Chiarello

2005, Silveira et al. 2003). Without a thorough, guided study design, many such inventory methods simply produce presence/absence data (Burton et al. 2015). Relative abundances can be estimated based on detections per sample unit, and species ranges and frequencies interpreted as well (Caughley 1977). Caution must be exercised with the interpretation of these data since probabilities of detection vary by species and camera placement, etc. (Sollmann et al. 2013,

Williams et al. 2002). Even with a lack of a strict methodology and great replication, data from

camera trapping can produce inferences about biodiversity, conservation, and natural resource management (Waldon et al. 2011).

Cuyahoga Valley National Park (CVNP) is a roughly 13,400-hectare national park situated among suburban land between the cities of Cleveland and Akron, Ohio, USA. It provides habitat for many mammal species. Historical surveys stated that at least 37 species of

mammals resided in meadows, old fields, shrub and young forests, mature forests, and various

wetland or aquatic habitats (Mazzer et al. 1984, NPS 2015). Earlier works in the region

suggested that 45-50 species of mammal could range into the lands of CVNP, but several of

these species are yet to be confirmed (Gottschang 1981). While much is known about the

conspicuous mammals inhabiting the park, other groups such as small carnivores and small

mammals remain somewhat more of a mystery. The first inventory of mammalian fauna within

park lands in was completed in 1984 (Mazzer et al. 1984); a work that included distributions of

31 mammal species. In their inventory, Mazzer et al. used multiple methods including; snap-

trapping and pitfall trapping for small mammals, mist-netting for bats, identifying tracks and

sign, collecting deceased animals, and direct observations. Here, I describe an updated inventory

of the mammalian fauna that also utilizes a combination of methods. Although the methods used

and level of effort are different between this work and the previously mentioned work, broad

comparisons of the communities of then and now can be made (discussed in Chapter 4). The

Mazzer et al. report alluded to future changes for mammals in CVNP over time and set a

foundation for inventory and monitoring research. Comparing mammal communities through

time builds a deeper understanding of their ecology in the face of an ever-changing environment.

The following research goals are addressed in this study:

1. Provide an updated inventory of mammalian species inhabiting the CVNP, with

estimates of relative abundance and local ranges.

2. Describe habitat affinities and other ecological relationships for mammalian

species within the CVNP whenever possible.

3. Describe the utility of various methods for surveying mammal assemblages and

compare methods to cross-reference relative abundance estimates and determine

inherent biases for each method.

Methods

Study Area

This work was conducted in the Cuyahoga Valley, Ohio, USA and largely within the

Cuyahoga Valley National Park. This fertile landscape between the cities of Cleveland and

Akron (Figure 3.1) has been long known as an area of biological importance (Manner & Corbett

1990). The valley was occupied by humans for centuries prior to European colonization of the

area, and has experienced different levels of disturbance over time. Likely the greatest

disturbance came with the intensive clearing and farming that occurred here and throughout most

of the eastern and mid-western United States by the first half of the 20th century.

Upon the establishment of the CVNP, much of the land returned to natural succession

after many decades of anthropogenic land uses. Some habitats in the park have remained intact

for many years and are considered to be mature forests today, but most posit that there is no old-

growth forest remaining in the valley. From a successional point of view, these habitats can be classified as meadows, shrublands, young forests, and mature forests. Plant communities in the park are largely described by their dominant species. Approximately 74% of park land is forested, predominantly by oak hardwood, northern hardwood, and mixed successional hardwood communities (Hop et al. 2013). Over 800 hectares of the park are identified as

55 wetlands and these areas add layers of habitat structure and diversity through unique hydrology, biogeochemistry, and plant communities. The natural diversity of the region as well as the

Figure 3.1 – Map of the Cuyahoga Valley watershed and surrounding region of northeast Ohio, United States. Courtesy of Wikipedia. diversity of land-use history combines to offer a great variety of habitats for mammals within this mosaic.

Data Collection

Mammalian surveys - Four different sampling methodologies were used to survey the composition, abundance and distribution of mammal communities in the CVNP; camera

trapping, road survey, pitfall trapping, and raptor pellet analysis. These methodologies inherently

differ in their ability to detect mammals of different sizes and behaviors, therefore it is valuable

to determine how they could be combined to provide the best description of mammal communities across different successional stages of forest regeneration, different vegetation types, and other land cover characteristics.

Camera Trapping – Camera “traps” also known as “trail cameras” or “game cameras” were employed for the purposes of documenting medium and large mammal species. Up to 11 cameras (four models; Appendix E) were deployed at a time in various habitats throughout the park. Cameras were left to capture images of passing animals for one to four months at a time.

Typical camera placement was not usually over 50cm from the ground on a small tree (20-50 cm

DBH) aimed towards a particular feature (Figure 3.2). Features included trails, dens, tree cavities, or water features. Accommodations were made as needed to allow for the best field of view from the sensor and the camera. Placement was low enough as to not exclude smaller mammals, and cameras never faced south as a precaution against direct sunlight that could ruin exposures. Camera mode was a burst of three photos with no delay between triggers. Photos were downloaded periodically and analyzed.

For each camera location, total captures of each identifiable species were counted. A

burst of three photos was considered one capture. The burst allows for easier detection and

identification of an animal captured in the photos. Further analysis included the estimation of

minimum number of individuals per site which was determined by capturing multiple animals in

one photo series or recognizing variation in sex, pelage, etc. Relative abundance was calculated

for each species as the number of captures per trap night. A trap night was considered one 24-

hour period. Frequency was described as the proportion of camera locations at which each

57 species was detected. Species richness for each camera location was plotted against camera trap nights to understand the effect of effort on species detections. Loess regression was used to

Figure 3.2 - Photo of a camera trap placement used to inventory mammals in Cuyahoga Valley National Park. A cable lock was used to discourage theft, and sticks could be wedged between the camera and the tree to achieve the desired angle of view. produce a smoothed line that allows for easier interpretation of complex relationships without assumptions of normality (Cleveland & Devlin 1988). Location data accompanying each capture included plant community and successional stage. Capture data by site was corrected for uneven sampling by means of rarefaction for comparing species richness (n=100). Additionally, data on individuals by site were rarified and set to 10 individuals for a similar analysis that attempted to

control for differences in detectability between species (Gotelli & Colwell 2001). These

calculations were performed with the Vegan package in R Studio.

In order to examine the relationship between site characteristics and the abundance and

distribution of mammals, captured animals were classified into three functional groups. These

groups consisted of: Artiodactylia (white-tailed deer), Carnivora (carnivores & Virginia opossum), and Rodentia (rodents & eastern cottontail). Trapping locations were grouped

according to successional stage and plant community type. Three successional stages were

compared; shrublands (n=4), young forests (n=12), and mature forests (n=12). Six plant community types were identified among the trapping locations, including northern hardwood

(beech-maple), oak hardwood, riparian hardwood, successional mixed hardwood, successional conifer, and successional shrub field. Of these, three major forest types that were sampled in multiple locations and exceeded 100 captured animals were used for community analysis;

namely, northern hardwood (n=4 sites), oak hardwood (n=9 sites), and successional mixed

hardwood (n=7 sites). Analyses for trends by functional group consisted of analysis of variance

(ANOVA) and Chi-square tests for homogeneity in the distribution among forest types.

Road survey – A road survey was conducted during June and July of 2015. Effectively,

three driving transects were created (see map - Figure 3.3). Surveys were conducted twice each

week, with one primary transect being surveyed each time (Riverview/AP) and the other two

(VV/Brandywine and Quick/303/Wheatley) were alternated and surveyed once per week. A

total of 113 miles (182 km) were covered each week. Surveying was conducted on

Monday/Thursday or Tuesday/Friday each week to maximize time between sampling. During

the survey, two spotters drove no faster than 25-30 mph (40-48 km/h) along the roads searching

for road-killed animals, as well as live animals within 10m of the road on either side. When an

animal was found, it was identified to species and moved off the road (if dead). Few exceptions were only able to be classified to higher taxonomic units. GPS coordinates were taken which

Figure 3.3 – Map of Cuyahoga Valley National Park indicating road transects used to survey road-killed mammals, as well as live animals within 10m of the road on either side. Mammals detected dead or alive are depicted along these transects. The base layer indicates forest cover and wetlands within the park.

allowed for later analysis of macrohabitat associations for each animal via ArcGIS. This was done by creating a 60-meter buffer (radius) around the GPS location and relating the plant community data to each detection. In order to examine the relationship between the abundance of mammal functional groups (Artiodactylia, Carnivora, and Rodentia) and vegetation types along each transect, plant communities in the vicinity of the encounters were classified using two basic dichotomies: 1) forested or non-forested, and 2) wetland or upland. Summaries of detections were generated for each unique date and vegetation type (n=110), and analysis of variance (ANOVA) was used to determine associations between mammal functional groups and the above-mentioned basic land cover classifications. Statistical significance was determined after Bonferroni corrections were made for multiple tests.

Pitfall trapping – A new design was implemented in the summer of 2016 to make up for the lack of success in the first season of pitfall trapping (summer 2015, Chapter 2). Surveys were completed at five of the original sites; Boston Mills – bridges (BMB), Quick/AP (QA),

Virginia Kendall – octagon (VKO), Major – mature (MM), and Borrow Pit (BP). Survey design followed a traditional ‘Y’- shaped drift fence array with a larger two-gallon bucket in the center and a one-gallon bucket at the end of each arm. Aluminum flashing (18”) was buried 3” into the soil to serve as drift fence between pits. Each fence was three meters long. Three arrays were installed at each location and were surveyed for three weeks (Figure 3.4). Traps were left open continuously and checked just before sunset and just after sunrise each day. Captured animals were identified to species, aged and sexed (when possible), and released. Incidental mortalities were collected and frozen to be later added to the Kent State vertebrate zoology collection.

Occurrence of precipitation (rain or no rain) was recorded during each trap night. One trap night was considered one pit trap open from dusk until dawn.

Additional environmental variables recorded for each array included leaf litter depth,

invertebrate richness, and invertebrate abundance. Litter depth was recorded at each array as an

average from three measurements. Invertebrates were sampled twice by Javier Ojeda during

each mammal trapping period. Traps were emptied 24 hours before each sampling. Nearly all

individuals were identified to genus (with a few exceptions identified to family). Invertebrate

taxa richness and abundance were related to captures of small mammals through linear

regression and analysis of variance (ANOVA).

Figure 3.4 – Schematic representation of pitfall trapping design at each site. Pitfall array (left) with two-gallon bucket in the middle and one-gallon buckets on the sides. Three meters of aluminum flashing served as drift fence between the pits. An example of array placement within a study site is shown to the right. Raptor pellet survey – As an additional method to assess the relative abundances of small mammals in the CVNP, raptor pellets were collected and analyzed for prey remains. Strategic searches in appropriate habitats turned up 16 roosting sites for three owl species. These sites were visited between one and three times from September 2015 to July 2016. Any pellets or bones found were collected and brought back to the lab for sorting and analysis (Figures 3.5a and

3.5b). With a few exceptions, all mammal remains were identified by skulls or mandibles.

Minimum number of individuals was counted through organization of the bones. Relative abundance for each species was calculated by number of individuals per sample event. Habitat

Figures 3.5a (top) and 3.5b (bottom) – Photos of pellets collected from a great-horned owl (Bubo virginianus) roost near the Coliseum (COL) grassland and an arrangement of identifiable bones from a pellet sample.

data was not associated with these findings since the location where each prey item was captured

could not be verified.

Method comparisons – The four methods presented in this chapter as well as Sherman

trapping (chapter 2) were assessed for their utility in studying targeted mammal groups. In

particular, I looked at success rates (captures per unit effort), richness, and detectability among

various functional groups of mammals. An alternative measure of effort was calculated as

detections per researcher hour. Researcher hours for each method accounted for all of the time

spent in the field and in the lab collecting data via a particular method by all participants.

Small mammals were studied via three methods (Sherman trapping, pitfall trapping, and raptor pellet analysis) and were grouped functionally as arboreal rodents, woodland rodents, grassland rodents, and insectivores after Torre et al. 2004. Specific classifications for this study can be viewed in Table 3.1. Average detection for each functional group was calculated as the proportion of detections over all samples for the three methods. Detection bias for each functional group was then determined by looking at the number of standard deviations each method was from the total average (all methods) with respect to each functional group. Species richness was compared between the three methods at a sample size of 45 (rarefaction, R 3.2.3 vegan package; Gotelli & Colwell 2001). Principle components analysis (PCA) was performed in R 3.2.3 using the vegan package to visualize differences in assemblages via each method.

Data for this analysis was Hellinger transformed.

Road survey and camera trapping were compared for their detection of functional

mammal groups as well. Mammals detected via these methods were categorized as deer, small

mammals, large rodents (including eastern cottontail), small carnivores (including Virginia

opossum), and large carnivores (Table 3.2). Rarefaction was set to 164 individuals to examine

65 species richness detected by each method. Proportional functional group composition as well as species composition by method was assessed by analyzing detections from 13 road surveys and

42 camera placements. Percent contributions by each functional group or species were arcsine transformed and analysis of variance (ANOVA) was performed.

Table 3.2 – Functional classification of small mammals detected via Sherman trapping, pitfall trapping, and raptor pellet analysis. Groups adapted from Torre et al. 2004.

Arboreal rodents Insectivores eastern gray squirrel (Sciurus carolinensis) masked shrew (Sorex cinereus) red squirrel (Tamiascurius hudsonicus) short-tailed shrew (Blarina brevicauda) southern flying squirrel (Glaucomys volans) hairy-tailed mole (Parascalops brewerii)

Woodland rodents Grassland rodents eastern chipmunk (Tamias striatus) meadow vole (Microtus pennsylvanicus) white-footed mouse (Peromyscus leucopus) meadow jumping mouse (Zapus hudsonicus) woodland vole (Microtus pinetorum)

Table 3.3 – Functional classification of mammals detected via camera trapping and road surveys. (*) indicates that Virginia opossum and eastern cottontail are grouped with functionally similar mammals, and do not belong to these groups taxonomically.

Small mammals Small carnivores hairy-tailed mole (Parascalops brewerii) Virginia opossum (Didelphis virginiana)* white-footed mouse (Peromyscus leucopus) raccoon (Procyon lotor) meadow vole (Microtus pennsylvanicus) least weasel (Mustela nivalis) southern flying squirrel (Glaucomys volans) long-tailed weasel (Mustela frenata) striped skunk (Mephitis mephitis) domestic cat (Felis domesticus)

Large rodents Large carnivores eastern cottontail (Sylvilagus floridanus)* coyote (Canis latrans) groundhog (Marmota monax) red fox (Vulpes vulpes) gray squirrel (Sciurus carolinensis) fox squirrel (Sciurus niger) red squirrel (Tamiascurius hudsonicus) beaver (Castor canadensis)

Results Mammalian surveys – Total survey effort for mammals during this study (including

Sherman trapping, Chapter 2) produced records for 25 species. Table 3.3 gives an overview of

each method with regard to target species, total species detected, sample size, effort, and capture rates. Results from each method will be discussed individually before comparing all methods at the end of this section.

Table 3.3 – Comparison of effort and success for five methods used to survey mammals in Cuyahoga Valley National Park. n = total samples per method, hours indicate person-hours spent completing survey methods. Traditional capture rate (success rate) is shown for trapping methods where trap nights indicate unit effort. A secondary capture rate was calculated to express the amount of captures per effort for all methods (captures per researcher hours).

Capture Capture Species rate rate 2 Targets detected n Effort Hours (cap/TN) (cap/hour) Sherman* sm. mammals 7 703 4,136 TN** 464 0.17 1.52

Pitfall sm. mammals 6 44 1,254 TN** 280 0.04 0.16

sm. mammals, Pellet med. mammals 11 225 71 pellets 88 - 2.56

med. mammals, Camera lg. mammals 18 8,468 2,886 TN** 179 2.93 47.31

med. mammals, Road lg. mammals 16 164 13 runs 39 - 4.21 *data from chapter 2 in thesis, ** TN = Trap nights

Camera trapping - A total of 2,886 camera trap nights provided 8,468 “captures” among

42 camera placements in the CVNP between June 2014 and May 2016. Eighteen species of mammal were identified representing ten families including; Didelphidae (one species),

Leporidae (one species), Castoridae (one species), Sciuridae (six species), Cricetidae (one species), Canidae (two species), Procyonidae (one species), Mustelidae (three species),

Mephitidae (one species), and Cervidae (one species). White-tailed deer (Odocoileus virginicus)

accounted for roughly half (52.5%) of all identified captures (Figure 3.5). Gray squirrel

(Sciurius carolinensis) and eastern chipmunk (Tamias striatus) were the next most frequent captured animals comprising 16% and 11.8% of identifiable captures, respectively. Raccoon

(Procyon lotor) was the most detected carnivore (6.4% of identifiable captures), followed by coyote (Canis latrans) at 3.4%. White-tailed deer was the most frequent species, being captured at 95% of sites (40 of 42 locations), while raccoon, coyote, and fox squirrel (Sciurius niger) were captured at 79%, 69%, and 62% of sites (33, 29, and 26 locations), respectively (Figure 3.6).

Abundance and Frequency of Mammal Captures 100% Captures Sites 75%

50%

25% Proportion of Proportion Total

Species

Figure 3.6 - Percent contribution of each species of mammal to the total number of captures and percent of sites where each species was captured by camera traps across 42 trap locations. Mammals are labeled by the first three letters of the genus and species. ODOVIR = white-tailed deer, SCICAR = gray squirrel, TAMSTR = eastern chipmunk, SCINIG = eastern fox squirrel, PROLOT = raccoon, CANLAT = coyote, PERLEU = white-footed mouse, TAMHUD = red squirrel, DIDVIR = Virginia opossum.

Trapping effort, defined as the number of trap nights, varied by site. Trap nights ranged from 20 to 164 among sites, while captures fell between five and 624. Number of captures,

individuals, and species all increased with more trap nights (F1,40=17.1048, p=0.0002,

F1,40=13.7577, p=0.0006, and F1,40=5.6505, p=0.0223; respectively). Richness by site was

2 positively correlated with number of captures (R =0.35, F1,40=21.5377, p<0.0001). Loess regression showed that average species accumulation for all sites leveled out at about 60 trap

nights (Figure 3.7).

Figure 3.7 – Loess regression showing positive relationship between species richness detected and number of camera trap nights in Cuyahoga Valley National Park. Species accumulation levels off at around 60 trap nights on average, before rising again after 100 trap nights. Variations in camera placement, model, season, and habitat are not controlled for in this assessment. Twenty-eight camera sites produced >100 captures and were considered for analysis. At

a sample size of 100, rarefaction estimated that 88.3% of species were detected, on average, from

these 28 sites. At 200 samples it is estimated that 96.1% of species are detected, on average

(Figure 3.8). Camera trapping sites represented seven major plant community types; northern

hardwood (beech-maple), oak hardwood, riparian hardwood, successional mixed hardwood,

successional conifer, shrub wetland, and successional shrub field. Among these, three major

forest types were considered for community analysis; northern hardwood (n=4 sites), oak

hardwood (n=9 sites), and successional mixed hardwood (n=7 sites). Fourteen species were

found in northern hardwood sites, while thirteen and nine were found in oak hardwood and

mixed successional hardwood, respectively. After correcting for sample size (rarefaction,

n=100), northern hardwood forests had the greatest species richness, averaging 6.8 species per

site, while oak hardwood and successional mixed hardwood averaged 5.0 and 3.8 species per

site, respectively (F2,17=5.6919, p=0.0128, Table 3.4). Tukey HSD revealed that northern

hardwood forests had significantly more species than successional mixed hardwood forests.

Average Shannon diversity and evenness scores differed between the three vegetation types, but

these results were not significant.

I also examined species richness among 28 camera sites in relation to three successional

stages; mature forest (n=12), young forest (n=12), and shrubland (n=4). I found that mature

forests contained more species than young forests (F2,25=3.8667, p=0.0344, Tukey HSD), while

shrublands were not significantly different in terms of species richness from either. Further

analysis using broad functional groups, showed that mammal composition varied by successional

2 stage (χ 4=419.0843, p<0.00001, Appendix C). Young forests contained a larger proportion of

deer (Artiodactylia) than mature forests (F2,25=3.9599, p=0.0321), while Rodentia comprised a larger proportion of captures in mature forests than young forests (F2,25=3.9491, p=0.0323,

Figure 3.9).

Figure 3.8 – Species accumulation curves for 28 camera trap locations in Cuyahoga Valley National Park. Generated in R version 3.2.3, vegan package. On average, 88.3% of species were detected after 100 samples, and 96.1% were detected after 200 samples. Number of trap nights needed to collect a desired sample size may vary due high local detectability rates in certain mammals.

Table 3.4 – Comparison of mammal diversity metrics for three forest types in CVNP. Based on rarefied data, sample=100, “n” represents number of camera locations. Average diversity represents Shannon diversity values. Average richness values represented by different letters are significantly different.

Average Average Average diversity evenness Community n richness (H’) (J’)

Northern hardwood 4 6.8 ± 0.5A 1.32 0.45

Oak hardwood 9 5.0 ± 1.4AB 0.86 0.29

Successional mixed hardwood 7 3.8 ± 1.2B 0.67 0.24

Figure 3.9 – Proportion of three mammal groups represented in three successional stages of vegetation during camera trap surveys. For functional comparison, eastern cottontail (Sylvilagus floridanus) was included with Rodentia and Virginia opossum (Didelphis virginiana) was included with Carnivora. At least 10 individual animals were documented at 20 camera locations. Composition of

these individuals by functional groups was different among three major forest types

2 (χ 4=320.9719, p<0.000001, Appendix C). Figure 3.10 shows that successional mixed

hardwoods (n = 5) contained the greatest proportion of Artiodactylia and northern hardwoods (n

= 3) the least (F2,12=8.6087, p=0.0048), while northern hardwood forests contained a greater

proportion of carnivores than both oak hardwood and successional mixed hardwood forests

(F2,12=9.7900, p=0.0030). No significant differences were found in the proportion of rodents between forest types.

Figure 3.10 – Proportion of three mammal groups (individuals) found in three major forest types. For functional comparison, eastern cottontail (Sylvilagus floridanus) was included with Rodentia and Virginia opossum (Didelphis virginiana) was included with Carnivora. When estimated individuals were analyzed by successional stage, the results affirmed those of the previous dataset. Number of deer (Artiodactylia) were greatest in young forests and lowest in mature forests (F2,12=8.8592, p=0.0023), while individuals of Rodentia were most numerous in mature forests and least in young forests (F2,12=4.1297, p=0.0345).

Road survey - Seven hundred forty-nine total miles were driven over 13 survey dates between June and July 2015. Sixteen mammal species were detected (dead or alive), totaling

164 individuals. Species documented represented eleven families including Didelphidae (one

species), Talpidae (one species), Leporidae (one species), Sciuridae (five species), Cricetidae

(one species), Canidae (one species), Mustelidae (two species), Mephitidae (one species),

Procyonidae (one species), Felidae (one species), and Cerviidae (one species). Eastern chipmunk

(Tamias striatus) was the most abundant species found, representing 28.7% of the total individuals (Figure 3.11). Red squirrel (Tamiascurius hudsonicus) and white-tailed deer

(Odocoileus virginicus) followed, representing 16.5% each. Raccoon was the most abundant carnivore found, comprising 4.9% of all detections.

Mammals inside the park boundary were found within 60 meters of 17 vegetation types.

Detections were analyzed from 110 unique date and vegetation type combinations. These vegetation types broadly represented forested (n = 53) and non-forested (n = 57) habitats.

Abundance of deer and total mammals were greater near forests (F1,108=7.5666, p=0.0070 and

F1,108=8.4976, p=0.0043; respectively). Vegetation types also represented wetland (n=49) and

upland habitats (n=61). Total mammal abundance was greater near uplands (F1,108=7.5346,

p=0.0071; Table 3.5).

Road Survey 50

25 Individuals 13

Species

Figure 3.11 – Total detections for the eight most abundant mammal species during 13 road survey dates in the Cuyahoga Valley. Mammals are labeled by the first three letters of their genus and species. TAMSTR = eastern chipmunk, ODOVIR = white-tailed deer, TAMHUD = red squirrel, SCINIG = eastern fox squirrel, SYLFLO = eastern cottontail, PROLOT = raccoon, SCICAR = gray squirrel, DIDVIR = Virginia opossum. “Other” comprises all eight remaining species; groundhog, striped skunk, meadow vole, feral cat, long-tailed weasel, hairy-tailed mole, red fox, and mink.

Table 3.5 – Average detection per sample during road surveys in forest versus non-forest habitats and wetland versus upland habitats for three functional groups of mammals in CVNP. Each unique combination of vegetation type and date was considered a sample. Values in bold indicate a significant preference after bonferroni correction.

forest non-forest wetland upland Artiodactylia 0.72±0.14 0.19±0.13 0.22±0.14 0.62±0.13 Carnivora 0.26±0.07 0.25±0.07 0.20±0.08 0.30±0.07 Rodentia 2.42±0.27 1.54±0.26 1.49±0.28 2.34±0.25 Total 3.40±0.35 1.98±0.34 1.92±0.36 3.26±0.33

Pitfall trapping - A total of 1,254 trap nights across five study sites produced 44 captures

of six small mammal species. Short-tailed shrew (Blarina brevicauda) comprised 50% of all

captures. Masked shrew (Sorex cinereus) and woodland vole (Microtus pinetorum) were the next most frequently captured species at 25% and 9%, respectively (Figure 3.12). Additional species detected were meadow vole (Microtus pennsylvanicus), white-footed mouse

(Peromyscus leucopus), and hairy-tailed mole (Parascalops brewerii). Blarina brevicauda was the most widespread species in this study, being captured at four of five sites. Sorex cinereus,

Microtus pinetorum, Micrtous pennsylvanicus, and Peromyscus leucopus were captured at two sites each, while Parascalops brewerii was captured once.

Pitfall Captures

Woodland Vole Meadow Vole White-footed Mouse Hairy-tailed Mole Short-tailed Shrew Masked Shrew

0 5 10 15 20 25 Individuals

Figure 3.12 – Number of individuals captured for each of six small mammals trapped during pitfall trapping across five sites in CVNP. A summary of mammal captures and environmental variables is given in Table 3.6.

Invertebrates collected at these sites represented 56 different families (Appendix D). Arrays in

mature forests (n=6) had lower invertebrate abundance than both meadows (n=6) and young

forests (n=3) (F1,13 = 17.4723, p<0.0001, Tukey HSD). Arrays in the young forest contained

greater richness than both meadows and mature forests (F1,13 = 43.072, p<0.0001, Tukey HSD).

Litter depth was not significantly greater at mature forest arrays than young forests, but both

forests were greater than meadows (F1,13 = 7.7781, p=0.0068, Tukey HSD). Mammal richness and abundance increased in areas of greater litter depth (F1,13=5.5634, p=0.0347, and

F1,13=15.2388, p=0.0018; respectively. Figures 3.13 and 3.14). Sorex cinereus reached its

highest abundance in young forests (F1,13 = 14.3676, p=0.0007) and was found in areas with

greater leaf litter depth F1,13=9.8549, p=0.0078) and greater invertebrate species richness

F1,13=19.7807 , p=0.0007).

Table 3.6 – Summary of pitfall captures and environmental variables measured in the CVNP by array and successional stage. Rain nights for each array are given and totals represent rain nights x arrays open for each successional stage. YF = young forest, MF = mature forest.

Rain Litter depth Mammal Mammal Invert. Invert. Site Stage nights (cm) abundance richness abundance Richness BMB1 Meadow 5 0 3 2 103 7 BMB2 Meadow 5 0 2 2 106 7 BMB3 Meadow 5 0 0 0 178 9 BP1 Meadow 4 0 0 0 191 10 BP2 Meadow 4 0 1 1 163 8 BP3 Meadow 4 0 0 0 90 11 QA1 YF 3 4.1 5 2 172 22 QA2 YF 3 3.2 10 4 153 17 QA3 YF 3 0 2 2 145 15 VKO1 MF 9 3.21 9 4 27 4 VKO2 MF 9 3.22 5 2 30 5 VKO3 MF 9 5.1 6 2 14 7 MM1 MF 1 2.21 1 1 10 5 MM2 MF 1 1.13 0 0 12 3 MM3 MF 1 1.59 1 1 3 3 All Meadow 27 0 6 2 831 29 All Young Forest 9 2.43 17 5 470 31 All Mature Forest 30 2.74 22 5 96 21 totals 45 6 1397 56

Pitfall Traps 5 y = 0.3985x + 0.9021 R² = 0.2997

4 p=0.0347

Mammal richness 1

0 0 1 2 3 4 5 6 Litter depth (cm)

Figure 3.13 – Small mammal richness by leaf litter depth across 15 pitfall trap arrays in Cuyahoga Valley National Park. R2 = 0.30, p=0.0347.

Pitfall Traps 12 y = 1.3533x + 0.8563

R² = 0.5396 10 p=0.0018 8 6 4 2 Mammal abundance 0 0 1 2 3 4 5 6 Litter depth (cm)

Figure 3.14 – Small mammal abundance by leaf litter depth across 15 pitfall trap arrays in Cuyahoga Valley National Park. R2 = 0.54, p=0.0018.

Capture success for small mammals increased dramatically during rainy nights (F1,110 =

35.2911, p<0.0001). Overall, 19% of 112 survey nights experienced rain, and 71% of captures

were made on these nights across all sites. No small mammals were captured in pitfalls during

the day. Trap mortality occurred for three of six species surveyed. Sorex cinereus experienced

the highest mortality rate at 45%, followed by Microtus pinetorum and Blarina brevicauda at

40% and 27%, respectively. Overall mortality observed during pitfall trapping was 29%. At

least two of the thirteen total mortalities occurred when two animals were trapped together and

one killed the other. Blarina brevicauda was the killer each time this occurred. There were also

two instances when two animals survived in a trap together, once with two Blarina and once

with Blarina and Microtus pinetorum.

Raptor pellet analysis - Seventy-one pellets as well as numerous pellet fragments and

individual bones were collected over 18 sample dates. Remains of 225 individual animals were

identified representing 12 species from seven families including; Didelphidae (one species),

Talpidae (one species), Soricidae (two species), Leporidae (one species), Sciuridae (three

species), Cricetidae (three species) and Mustelidae (one species). White-footed mouse

(Peromyscus leucopus) was the most abundant mammal, representing 41.8% of identified individuals. Short-tailed shrew (Blarina brevicauda) and meadow vole (Microtus pennsylvanicus) represented 20% and 19.1% of individuals, respectively (Figure 3.15). These three small mammals were found in 67, 72, and 56 percent of samples, respectively.

Raptor Pellet Analysis

Other Southern Flying Squirrel Hairy-tailed Mole Meadow Vole Short-tailed Shrew White-footed Mouse

0 20 40 60 80 100 Individuals

Figure 3.15 – Number of individuals identified for the five most common mammal species found in 71 raptor pellets. “Other” species detected included; Didelphis virginiana, Sorex cinereus, Sylvilagus floridanus, Microtus pinetorum, Tamias striatus, Sciurus carolinensis, and Mustela frenata. Method comparison - Twenty-five species of mammal were detected in and around the

CVNP as a result of five survey methodologies. Average detection rate (detections/researcher

hours) for all species was greatest for camera trapping, and lowest for pitfall trapping (Table

3.7). On average, each species was detected by 2.4 methods. Four species were detected by only

one method; Zapus hudsonicus (meadow jumping mouse), Castor canadensis (beaver), Canis

latrans, and Mustela nivalis (least weasel). Three species were detected by four of five methods;

Peromyscus leucopus, Microtus pennsylvanicus, and Mustela frenata (long-tailed weasel).

Overall survey effort is shown in Table 3.3. Range of effort and capture success varied greatly

between methods. Camera trapping yielded the highest success for both measures of capture rate

(trap nights and researcher hours), and was the only method that reached a clear asymptote in its

rarefaction curve (Figure 3.16).

Table 3.7 – Detectability rates for all mammal species documented by five survey methodologies in Cuyahoga Valley National Park. Rate is expressed as detections per hour of researcher effort. Values in bold indicate best detection method for each species.

Species Common Name Camera Road Sherman Pitfall Pellet D. virginiana Opossum 0.173 0.128 - - 0.011 S. cinereus Masked Shrew - - 0.002 0.039 0.011 B. brevicauda Short-tailed Shrew - - 0.050 0.079 0.511 P. brewerii Hairy-tailed Mole - 0.026 - 0.004 0.250 S. floridanus Eastern cottontail 0.179 0.256 - - 0.045 T. striatus Chipmunk 5.559 1.205 0.002 - - M. monax Groundhog 0.101 0.103 - - - S. carolinensis Gray Squirrel 7.587 0.179 - - 0.011 S. niger Fox Squirrel 3.676 0.359 - - - T. hudsonicus Red Squirrel 0.592 0.692 - - 0.034 G. volans Flying Squirrel 0.106 - - - 0.102 C. canadensis Beaver 0.056 - - - - P. leucopus White-footed Mouse 0.631 - 1.170 0.011 1.057 M. pennsylvanicus Meadow Vole - 0.077 0.284 0.011 0.477 M. pinetorum Woodland Vole - - - 0.014 0.011 Z. hudsonicus Meadow Jumping Mouse - - 0.004 - - C. latrans Coyote 1.620 - - - - V. vulpes Red Fox 0.084 0.026 - - - P. lotor Raccoon 3.034 0.205 - - - M. nivalis Least Weasel 0.022 - - - - M. frenata Long-tailed Weasel 0.084 0.051 0.002 - 0.011 M. vison Mink 0.011 0.026 - - - M. mephitus Striped Skunk 0.073 0.103 - - - F. catus Feral Cat 0.011 0.077 - - - O. virginianus White-tailed Deer 24.855 0.692 - - -

Figure 3.16 – Sample-based species rarefaction curve for five mammal inventory methods used in Cuyahoga Valley National Park; Camera trapping (1), Road survey (2), Sherman trapping (3), Pellet analysis (4), and Pitfall trapping (5). Camera trapping effort reached a clear asymptote, while the rest of the methods did not. Including Sherman trap sampling (Chapter 2), three methods were used to survey the small mammal community. After rarefaction (sample = 44), pitfalls detected the highest overall richness of target species, followed by raptor pellets (Figure 3.17). Considering all species detected, raptor pellets were the only method of the three to detect arboreal rodents (squirrels).

Raptor pellets were biased towards detecting grassland rodents (M. pennsylvanicus) over woodland rodents (P. leucopus, T. striatus, M. pinetorum) and insectivores (B. brevicauda, P. brewerii, S. cinereus). Sherman trapping was biased towards capturing rodents (especially woodland rodents), and was deficient at capturing insectivores. Conversely, pitfall traps largely captured insectivores and under-sampled all rodents (Figure 3.18).

Small mammal richness R adj. R 14

Species detected Species 3

0 Sherman Pellet Pitfall

Figure 3.17 – Raw and corrected richness for Sherman trap, pitfall, and raptor pellet survey methods. Direct comparison was limited by varying sample sizes. Blue bars include all species detected while orange bars indicate corrected (rarefied to 44 samples) species richness for targeted species only (insectivores and non-sciurid rodents).

Figure 3.18 – Detection biases for three small mammal survey methods by functional mammal groups. Proportions of detections were calculated for each method to produce an average for all methods. Standard deviations from the average proportion of captures are shown. Arboreal rodents (n=3), Woodland rodents (n=3), Grassland rodents (n=2), Insectivores (n=3).

Unadjusted results show that camera trapping yielded the most species and detections per

hour effort (Table 3.7). Comparatively, the road survey achieved only a small fraction of the sample size (2%) and found the second richest species list at the second highest detection rate.

After correcting for the difference in sample size, camera trapping richness was reduced to 10.3

species (as compared to 16 for the road survey). Proportions of mammal functional groups were

calculated for 42 camera placements and 13 road surveys. Percent contributions for each sample

were arcsine transformed for analysis. Cameras detected a greater proportion of deer and large

carnivores (coyote, red fox) than the road survey (F1,53=17.2238, p<0.0001 and F1,53=4.5712,

p=0.0371, respectively, Figure 3.19). The road survey detected a greater proportion

of large rodents (red squirrels and larger), and small mammals (largely chipmunks), than

cameras (F1,53=6.8933, p=0.0113 and F1,53=8.2772, p=0.0058; respectively).

Proportion of Detections 60.0% Camera Road 45.0%

30.0%

15.0%

0.0% Deer Large Small Small Large rodents mammals carnivores carnivores

Figure 3.19 – Proportion of functional species groups detected by camera traps and road survey methods in Cuyahoga Valley National Park corrected for sampling rate differences. Large rodents = Tamiasciurus husonicus, Sciurus carolinensis, Sciurus niger, Marmota monax, and Sylvilagus floridanus. Small mammals = Tamias striatus, Parascalops brewerii, and Microtus pennsylvanicus. Small carnivores = Mustela frenata, Mustela vison, Mustela nivalis, Mephitis mephitis, Procyon lotor, and Didelphis virginiana. Large carnivores = Canis latrans and Vulpes vulpes.

Proportion of detections were also analyzed at the species level. White-tailed deer were removed from this analysis since they comprised over half of all camera detections. In addition to five species not detected by the road survey, camera traps detected a greater proportion of raccoons (F1,52=4.3346, p=0.0424, Table 3.8). Eastern cottontail, eastern chipmunk, groundhog, red squirrel, long-tailed weasel, and striped skunk were all detected in greater proportions on the road survey (F1,52=7.8917, p=0.0070, F1,52=9.1784, p=0.0038, F1,52=11.2715, p=0.0015,

F1,52=24.6595, p<0.0001, F1,52=4.7278, p=0.0343, and F1,52=13.7067, p=0.0005; respectively).

Three additional species were detected on the road survey and not detected by camera traps.

Table 3.8 – Average proportion of species detected by camera traps (n=42) and road survey (n=13) methods in the Cuyahoga Valley. White-tailed deer comprised over 50% of camera detections and were excluded from this analysis to better understand trends with other species. Some road detections were made outside the national park boundary. Numbers in bold are significantly higher.

Species Common Name Camera Road Didelphis virginiana Virginia Opossum 1.2±0.9% 3.4±1.5% Parascalops brewerii Hairy-tailed Mole - 1.0±0.5% Sylvilagus floridanus Eastern Cottontail 1.5±1.3% 9.1±2.3% Tamias striatus Eastern Chipmunk 7.9±3.2% 30.0±5.7% Marmota monax Groundhog 0.2±0.3% 2.4±0.6% Sciurius carolinensis Eastern Gray Squirrel 17.0±3.6% 6.3±6.4% Sciurius niger Eastern Fox Squirrel 15.1±2.9% 10.3±5.1% Tamiasciurus hudsonicus Red Squirrel 2.7±1.8% 20.6±3.1% Glaucomys volans Southern Flying Squirrel 0.1±0.0% - Castor canadensis Beaver 1.6±1.3% - Peromyscus leucopus White-footed Mouse 1.2±0.8% - Microtus pennsylvanicus Meadow Vole - 2.5±0.7% Canis latrans Coyote 27.0±5.0% - Vulpes vulpes Red Fox 2.1±1.8% 0.5±3.1% Procyon lotor Raccoon 22.5±3.5% 5.8±6.2% Mustela nivalis Least Weasel 0.0±0.0% - Mustela frenata Long-tailed Weasel 0.2±0.3% 1.4±0.5% Mustela vison Mink 0.0±0.3% 1.3±0.6% Mephitis mephitus Striped Skunk 0.1±0.4% 3.3±0.8% Felis catus Domestic Cat - 2.0±0.5%

Discussion

Twenty-five mammal species were detected through the combined efforts of all methods

utilized in this inventory. Camera trapping was the primary method selected to survey medium

and large mammals, particularly to document secretive animals such as carnivores. The road survey augmented this inventory as an additional method to document this group of mammals.

Both methods were very successful, resulting in the highest overall richness counts, and both

were capable of detecting animals of all sizes. Pitfall traps and raptor pellets were critical for detecting small mammals, and greatly assisted the data collected by Sherman traps (Chapter 2).

Pitfalls largely captured semi-fossorial animals that were not well-represented by Sherman traps while raptor pellets detected species captured by both Sherman traps and pitfall methods, serving as a reference for relative abundance inferences.

Sample sizes, success rates, and effort - Sample size varied greatly between all methods with pitfall trapping (n = 44) and camera trapping (n = 8,673) being the extremes of the range.

This was partially due to the great difference in capture success rates between methods. Pitfall trapping achieved a 4% success rate (captures/trap night), while camera trapping experienced a

293% success rate. Furthermore, over twice the number of trap nights occurred for camera traps than pitfall traps, as the latter was a very labor-intensive method. Pitfall trapping produced only

0.16 captures per hour effort, while camera trapping collected 47.31 captures per hour (Table

3.7). Both were primary methods enlisted for this study, but pitfall trapping failed completely during the first field season (see chapter 2). An improved pitfall trapping effort was implemented the following field season that experienced more success. Additionally, an important realization was made during this effort in 2016; rain significantly affected capture success for small mammals. Other studies have experienced this phenomenon (Umetsu et al

2006, Bury and Corn 1987). Therefore it is reasonable to believe that pitfall trapping in 2015

would have been more successful if traps weren’t closed during predicted rain events. The road

survey and raptor pellet analysis were added as secondary methods to augment the inventory and

did not reach great sample sizes, although these methods collected 4.21 and 2.56 mammals per

hour, respectively (Table 3.7).

Species rarefaction curves (Figure 3.17) help visualize the completeness of an effort by

re-sampling and averaging species richness from a dataset, randomly without replacement, for

each possible sample size (Gotelli & Colwell 2001). In my study, camera trapping results

showed a clear asymptote with a great sample size, while the curve for the Sherman trapping

effort didn’t quite level out. This was likely due to the fact that several species were rare or incidental captures in this effort. Based on species expectations and findings from other methods

the Sherman trapping effort likely was near its asymptote. Due to limited sample sizes, the road survey and pellet analysis curves were still rising and were probably capable of detecting more species with greater effort. Similarly, pitfall trapping would require a much greater effort to

achieve a thorough inventory. When using rarefaction to correct for differing sample sizes between methods, care must be taken to understand inherent limitations. For example, a small

sample size may be representative in a homogenous habitat with low species diversity, but may

not be capable of accurately representing the community in a more heterogeneous environment.

Future studies used to compare mammal communities via different methods should take

detection rates and effort needed to acquire adequate samples into account.

In my results, I introduce an alternative measure of sampling effort; researcher hours.

Others have used similar alternative measures of measuring capture success by resource effort

(Garden et al. 2007, Catlin et al. 1997). Not all methods used in this study can be easily

compared by trap nights or sampling periods due to the nature of each survey. Researcher hours

is a practical unit of effort because one can determine what methods are viable, given time and

personnel resources. This measure of effort emphasizes that pitfall trapping is a very labor-

intensive method, requiring a very large amount of time to be fully successful in surveying small

mammals. Others have found pitfall trapping to be the least successful method for surveying mammals per cost as well (Garden et al. 2007). Conversely, it is apparent that camera trapping requires very little time and maintenance by researchers to gather large samples, with financial

input being the limiting factor (Espartosa et al. 2012, Tobler et al. 2008).

Small mammal methods - Sherman trapping was the primary method for studying small mammals in this inventory, while pitfall trapping and raptor pellet analysis assisted in this pursuit. It is well known that each method is proficient in detecting a certain suite of species, while lacking much capability to detect others (Sedivec & Whidden 2007, Umetsu et al. 2006,

Torre et al. 2004, Sealander & James 1958). For this reason, using multiple methods helps establish a more detailed and comprehensive inventory. In this study, raptor pellets exhibited the highest detection rate by researcher hours and found the greatest overall species richness. These data were limited though, by the lack of habitat information accompanying the samples (as capture sites were unknown). Direct captures of small mammals via Sherman traps and pitfalls allowed for specific habitat analyses.

Sherman traps only reliably captured rodents, and captures of insectivores likely did not

reflect the true density of these animals at each site. Pitfall trapping was a very complimentary

method, proving critical for detecting the animals in the insectivore community. Prior to the

pitfall sampling in 2016, only one detection had been made of a Sorex shrew (Chapter 2). These tiny insectivores are represented in northeast Ohio by two species; S. cinereus and S. fumeus.

The latter has not been detected within CVNP in recent studies, while the former is known to

persist (Laux 2013). The improved pitfall trapping effort provided data on this inconspicuous

animal that describes relative abundance and habitat associations. Another species of interest,

the woodland vole (Microtus pinetorum) seemed to be reliably captured in pitfalls as well, but

densities of this species are apparently far lower than that of the white-footed mouse

(Peromyscus leucopus). As a result, pitfall trapping under-sampled woodland rodents (since P. leucopus can easily escape the shallow pits), but this method was critical for detecting an

uncommon species in this group. Woodland voles have been documented as difficult to capture

at times, suggesting that traps may need to be placed at burrow openings or along well-worn runways (Barbour & Davis 1974).

Habitat analysis through pitfall trapping also showed that mammal richness was positively correlated with leaf litter depth, and that S. cinereus was also correlated with invertebrate diversity in addition to leaf litter depth. These findings are expected as studies have shown that insectivores and other semi-fossorial mammals (targeted species) rely on litter

structure on the surface for moving about as well as maintaining homeostasis (Laux 2013,

Churchfield 1990, Gottschang 1981). The young forest site had the greatest invertebrate richness among successional stages and greater abundance than mature forests. These findings should be interpreted with caution though, as replications were few. The fact that sites of the same stage were much closer to each other in most instances than sites of differing stage poses the problem of spatial autocorrelation. A more thorough study design with greater replication and representation throughout the park would be needed to fully assess habitat requirements for mammals captured in pitfalls.

Raptor pellets were the only method of the three to detect arboreal rodents (squirrels).

Live traps were not placed in trees since all members but the southern flying squirrel (Glaucomys

volans) are very conspicuous and live capture was not necessary to study these animals for

inventory purposes. Pellets revealed a fair proportion of all mammal functional groups, showing

a slight bias towards grassland rodents. This method provided records for nearly all small

mammals that were documented by Sherman and pitfall traps (absent Zapus hudsonicus).

Despite the lack of habitat data, this method proved very useful as a reference to understanding small mammal distribution and abundance, and the species composition fortifies relative abundance findings from the other two methods. The complimentary nature of these three methods is illustrated in Figure 3.20.

Figure 3.20 – Principal components analysis comparing small mammal assemblages detected via Sherman trapping, pitfall trapping, and raptor pellet analysis. Sherman traps and pitfall traps were complimentary to each other in assessing various functional groups while raptor pellets were capable of detecting mammals from all functional groups. Produced with vegan package in R 3.2.3; data Hellinger transformed.

Relative abundance inferences for small mammals - Considering all three methods, it

appears that P. leucopus is an abundant rodent in all woodland systems, while M. pinetorum is

uncommon to rare. Microtus pennsylvanicus is common in open habitats and can be found in

certain wooded areas with a dense herb layer (Chapter 2), while Z. hudsonicus is uncommon to

rare but may tolerate a wider range of macrohabitats than M. pennsylvanicus (based on combined incidental observations). As for insectivores, B. brevicauda is abundant and widespread among

all habitats, while S. cinereus may be locally common in appropriate woodland habitats and rare

or absent from open habitats. Hairy-tailed moles (Parascalops brewerii) appear to be common

throughout wooded areas in the park based on incidental observations and raptor pellet analysis.

Detections via trapping and even raptor pellets likely under sample moles since they are highly

fossorial animals. Special effort is usually required to capture moles, and effective methods

often involve kill-trapping along main tunnels (Barbour & Davis 1974). Evidence of their

tunneling can be seen throughout most woodlands in the park, especially under logs or when they

cross trails.

Medium and large mammal methods - Camera trapping is a widely used methodology for

studying mammals; especially large carnivores (Burton et al. 2015, O’Connell et al. 2010, Tobler

et al. 2008). I used cameras to determine the frequency and abundance of medium and large-

sized mammals in various woodlands of CVNP. The road survey was an additional method used

to detect similar species. Each method is biased in its own way. Cameras are usually placed

along trails or interesting features in the landscape, and these placements can be biased towards

more conspicuous animals who would use these features (Burton et al. 2015, Sollmann et al.

2013, O’Connell et al. 2006). Road surveys are biased due to the variability of tolerance an

animal has for roads and disturbed areas, as well as the variation between species’ ability to

safely and secretly cross roads (Forman & Alexander 1998). Coyotes, for example have been

shown to shy away from roads and trails when possible, in addition to shifting their activity

periods largely to night in areas with high human activity (Wallace 2013, Gehrt et al. 2009).

Other animals such as deer, rabbits, groundhogs, and skunks can be seen frequently utilizing

roads and the disturbed areas along them for foraging. These dispositions toward roads affect the

likelihood that each mammal species would be detected. Furthermore, cameras may be more likely to detect nocturnal species than the road survey since less traffic at night reduces the chance of these species being run over or being seen during daytime surveys.

In general, mammals that have adapted well to human-altered landscapes were detected more frequently via the road survey. Groundhogs and cottontails prefer to forage on edges

(Smith et al. 2000, Gottschang 1981, Barbour & Davis 1974) so roadsides offer ample habitat.

Red squirrels were found frequently in residential areas. This animal prefers coniferous forests throughout much of its native range, but has adapted well to the ornamental landscapes of suburbia (Gottschang 1981). I have also observed this squirrel thriving nearly anywhere in the

park where mature black walnut (Juglans nigra) trees can be found. Gray squirrels can be found

throughout the park, but are known to prefer interior woodlands, and thus were detected with

greater frequency via camera traps (Gottschang 1981). Effectively, camera traps sampled within

woodland habitat patches and the road survey sampled between woodland (and other) habitat

patches. Due to the level of fragmentation in the CVNP, these two methods still experience a

high level of overlap in species compositions despite differences in habitat sampled.

Macrohabitat associations for medium and large mammals – Camera trap data revealed

that northern hardwood forests contained the greatest species richness, the highest proportion of

individual carnivores, and the lowest proportion of individual deer (Table 3.4, Figure 3.10).

Northern hardwoods are typically climax communities in the region, and may have received relatively less disturbance or had longer recovery periods than other communities in the area, in turn offering safer habitat for predators who tend to be secretive and weary of humans. Another explanation could be that northern hardwood forests offer more food and/or structural resources for wildlife. American beech (Fagus grandifolia) is a major component in these systems and is documented as a great asset for wildlife for its cavity-forming tendencies and its highly- nutritious nuts (Tubbs & Houston 1990, Leopold 1987). Denning and foraging opportunities for prey species in these systems could consequently draw more carnivores in as well. Oak hardwoods had the greatest proportion of individual rodents. Members of the Sciuridae

(squirrel) family made-up the majority of these captures, and the multitude of nut-producing trees in oak hardwood forests explains this trend since this is a favorite food source (Gottschang

1981, Korschgen 1981). The highest proportions of deer were found in successional mixed hardwoods and young forests in general (Figures 3.8, 3.9). White-tailed deer are known to prefer edges (Alverson et al. 1988, Gottschang 1981, Leopold 1987), and young forest areas likely contain more edge and increased growth rates in vegetation. These highly-productive systems likely offer an abundance of foraging opportunities for deer, as well as bedding opportunities in areas of thick cover (Gottschang 1981).

Relative abundance inferences for medium and large mammals - It has been apparent for

the last couple decades that coyotes are an abundant carnivore in the region, especially within the

CVNP (ODNR 2016, Wallace 2013). This animal was detected at 69% of all camera placements

while comparatively, the red fox (Vulpes vulpes) was only detected at 5.3% of locations.

Another canid, the gray fox (Urocyon cinereoargenteus) has not been officially documented in the CVNP since a road-killed specimen was found in 2004 (NPS unpublished data). My survey

methods support the idea that coyotes are very widespread and that they are the dominant large carnivore in the park and they greatly outnumber any other functionally similar carnivores such

as foxes or bobcat (Felis rufus). These results should be interpreted with some caution though

due to the non-random selection of camera placements. Red foxes existing beside abundant

coyote territories in the CVNP may be pushed closer to suburban edges. Future camera work

could attempt to assess carnivore assemblages along a gradient of development in and around the

CVNP.

Small carnivores were a group of particular interest for camera and road methods.

Raccoons were by far the most abundant and widespread animal in this group. Although this

species can be fairly large, they were grouped with small carnivores due to their trophic ecology.

Raccoons can be found anywhere near woodlands and suburban areas in the park, and are known

to prefer streams in natural settings (Gottschang 1981). Partially due to its integration with suburbia, The CVNP offers an abundance of resources for this animal. Raccoons have been shown to reach greater population densities in urban and suburban areas (Prange et al. 2003). A

functionally similar species, the Virginia opossum, appears to be less abundant. For mustelids,

long-tailed weasels and minks appear to be common and fairly widespread. Minks have a known affinity for streams and wetlands (Gottschang 1981), while long-tailed weasels appear to be generalists in wooded habitats. A single detection of a least weasel (Mustela nivalis) hints that it may be a localized and/or secretive member of the CVNP fauna. Previous records have indicated presence of the species as well (Laux 2013, Mazzer et al. 1984, Gottschang 1981).

Squirrels (Sciuridae) were not targeted by any method, but were well-surveyed by cameras and from roads. The fox squirrel appears to be the most widespread species in the park, being found within many habitats. The other squirrels were detected less frequently and showed

some sort of affinity for a particular habitat. Gray squirrels were partial to interior woodlands,

red squirrels and chipmunks to suburban areas, and southern flying squirrel likely showed imperfect detection due to their arboreal nature. Captures of this species on camera traps should

be viewed as incidental species since they don’t spend much time on or near the ground where cameras we aimed (Gottschang 1981). Groundhogs were not detected frequently on camera traps, likely since camera locations were away from preferred edge habitat (Swihart 1992,

Gottschang 1981, Barbour & Davis 1974). Additionally, this species does not typically venture very far from their dens as many larger mammal species do (Swihart 1992). Incidental observations of dens combined with several road detections posit that groundhogs are common in the CVNP.

Conclusions - Data gathered from these various inventory techniques provides a formal assessment of mammal assemblages in the CVNP. The utility of enlisting multiple methods for surveying species groups provides greater opportunity for detecting more species, and helps overcome the biases inherent with any single method (Jones et al. 1996). I have shown that

Sherman trapping and pitfall trapping detect complimentary groups of species while raptor pellet detections are encompassing of both assemblages. Similarly, camera trapping and road surveys function as complimentary to each other by means of sampling edge versus interior of forested tracts. This work is considered a foundational effort for understanding current mammal assemblages and how they are best surveyed, and will remain as a snapshot in time of mammalian communities in the CVNP.

Monitoring recommendations - Any of the methods described in this chapter could be performed in greater depth with more effort if a specific study question wished to be addressed.

Greater coverage of park lands and multiple years of sampling for each method would be greatly

beneficial to obtain confidence in conclusions of species abundance and distribution. Since

pitfall trapping is a very labor-intensive method, preliminary investigations of small mammal

assemblages could be gathered through extensive raptor pellet analysis throughout the park.

Once a species of interest is detected, a trapping scheme could be designed using Sherman traps, pitfalls, and snap traps to gather small mammal assemblage data with high confidence. Raptor pellet analyses have been used successfully as preliminary monitoring methods in other areas

(i.e. Avenant 2005, Bonvicino & Bezerra 2003).

Data on road-killed animals is freely available and can be gathered incidentally at any time. This data can be helpful for assessing species distributions throughout the park. Future camera trapping surveys should incorporate more camera placements and could use a random grid array across the park to estimate species densities. A local cooperating organization, the

Cleveland Metroparks has a camera monitoring program in place currently (2017) and these methods could be extended into the lands of CVNP.

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Gehrt, S. D., Anchor, C., & White, L. A. (2009). Home range and landscape use of coyotes in a metropolitan landscape: conflict or coexistence? Journal of Mammalogy, 90(5), 1045-1057. Gompper, M. E., Kays, R. W., Ray, J. C., Lapoint, S. D., Bogan, D. A., & Cryan, J. R. (2006). A comparison of noninvasive techniques to survey carnivore communities in northeastern North America. Wildlife Society Bulletin, 34(4), 1142-1151. Gotelli, N. J., & Colwell, R. K. (2001). Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology letters, 4(4), 379-391.

Gottschang, J. L. (1981). A guide to the mammals of Ohio. Ohio State University Press.

Harmsen, B. J., Foster, R. J., Silver, S., Ostro, L., & Doncaster, C. P. (2010). Differential use of trails by forest mammals and the implications for camera‐trap studies: a case study from Belize. Biotropica, 42(1), 126-133.

Hop K., J. Drake, A. Strassman, E. Hoy, J. Jakusz, S. Menard, and J. Dieck. 2013. Vegetation Information for the Cuyahoga Valley National Park Vegetation Inventory Project. Natural Resource Technical Report. NPS/HTLN/NRTR—2013/792. National Park Service. Fort Collins, Colorado

Jones, C., McShea, W. J., Conroy, M. J., & Kunz, T. H. (1996). Capturing mammals. Measuring and monitoring biological diversity: standard methods for mammals. Smithsonian Institution Press, Washington, DC, 115-155.

Kanda, L. L., Fuller, T. K., & Sievert, P. R. (2006). Landscape associations of road-killed Virginia opossums (Didelphis virginiana) in central Massachusetts. The American midland naturalist, 156(1), 128-134.

Korschgen, L. J. (1981). Foods of fox and gray squirrels in Missouri. The Journal of Wildlife Management, 45(1), 260-266.

Laux, S. A. (2013). A multi-taxonomic approach to assess the impact of overabundant white- tailed deer (Odocoileus virginianus) in forest ecosystems across northeast Ohio. Cleveland State University.

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Manner, Barbara M. and Robert G. Corbett. (1990). Environmental Atlas of the Cuyahoga Valley National Recreation Area. Surprise Valley Publications, Monroeville, PA.

Mazzer, Sam J., Lowell P. Orr, David W. Waller. Wildlife Survey of the Cuyahoga Valley National Recreation Area. November 1984.

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O'Connell, A. F., Nichols, J. D., & Karanth, K. U. (Eds.). (2010). Camera traps in animal ecology: methods and analyses. Springer Science & Business Media.

O'Connell Jr, A. F., Talancy, N. W., Bailey, L. L., Sauer, J. R., Cook, R., & Gilbert, A. T. (2006). Estimating site occupancy and detection probability parameters for meso-and large mammals in a coastal ecosystem. Journal of Wildlife Management, 70(6), 1625-1633.

Ohio Department of Natural Resources (ODNR). 2016. Furbearers: bowhunter survey. Retrieved from http://wildlife.ohiodnr.gov/species-and-habitats/fish-and-wildlife-research

Okoniewski, J. C., & Chambers, R. E. (1984). Coyote vocal response to an electronic siren and human howling. The Journal of wildlife management, 48(1), 217-222.

Prange, S., Gehrt, S. D., & Wiggers, E. P. (2003). Demographic factors contributing to high raccoon densities in urban landscapes. The Journal of Wildlife Management, 324-333.

Santos, S. M., Carvalho, F., & Mira, A. (2011). How long do the dead survive on the road? Carcass persistence probability and implications for road-kill monitoring surveys. PLoS One, 6(9), e25383.

Sealander, J. A., & James, D. (1958). Relative efficiency of different small mammal traps. Journal of Mammalogy, 39(2), 215-223.

Sedivec, S. A., & Whidden, H. P. (2007). Importance of trap type for the detection and conservation of small mammals. Park Science, 24(2), 67-71. Si, X., Kays, R., & Ding, P. (2014). How long is enough to detect terrestrial animals? Estimating the minimum trapping effort on camera traps. PeerJ, 2, e374. Silveira, L., Jácomo, A. T., & Diniz-Filho, J. A. F. (2003). Camera trap, line transect census and track surveys: a comparative evaluation. Biological Conservation, 114(3), 351-355. Smith, D. F., & Litvaitis, J. A. (2000). Foraging strategies of sympatric lagomorphs: implications for differential success in fragmented landscapes. Canadian Journal of Zoology, 78(12), 2134- 2141. Sollmann, R., Mohamed, A., Samejima, H., & Wilting, A. (2013). Risky business or simple solution–Relative abundance indices from camera-trapping. Biological Conservation, 159, 405- 412. Srbek-Araujo, A. C., & Chiarello, A. G. (2005). Is camera-trapping an efficient method for surveying mammals in Neotropical forests? A case study in south-eastern Brazil. Journal of Tropical Ecology, 21(01), 121-125.

Tobler, M. W., Carrillo‐Percastegui, S. E., Leite Pitman, R., Mares, R., & Powell, G. (2008). An evaluation of camera traps for inventorying large‐and medium‐sized terrestrial rainforest mammals. Animal Conservation, 11(3), 169-178.

Torre, I., Arrizabalaga, A., & Flaquer, C. (2004). Three methods for assessing richness and composition of small mammal communities. Journal of Mammalogy, 85(3), 524-530. Tubbs, C. H., & Houston, D. R. (1990). Fagus grandifolia Ehrh. American beech. Silvics of North America, 2(654), 325. Umetsu, F., Naxara, L., & Pardini, R. (2006). Evaluating the efficiency of pitfall traps for sampling small mammals in the Neotropics. Journal of Mammalogy, 87(4), 757-765. Waldon, J., Miller, B. W., & Miller, C. M. (2011). A model biodiversity monitoring protocol for REDD projects. Tropical Conservation Science, 4(3), 254-260. Wallace, B. F. (2013). Coyote spatial and temporal use of recreational parklands as a function of human activity within the Cuyahoga Valley, Ohio. The University of Akron. Wiggers, E. P., & Beckerman, S. F. (1993). Use of thermal infrared sensing to survey white- tailed deer populations. Wildlife Society Bulletin (1973-2006), 21(3), 263-268. Williams, B. K., Nichols, J. D., & Conroy, M. J. (2002). Analysis and management of animal populations. Academic Press.

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Chaper IV. Conclusions, Synthesis, and Future Studies

Conclusions

This updated inventory of the mammalian fauna in Cuyahoga Valley National Park

(CVNP) brings new information and sheds light on topics for future research. In addition to the

inventory, the original goals of this project were to understand how mammal communities have

changed since the last complete inventory by Mazzer et al. (1984), and how changes in mammal

communities could be related to habitat protection, plant community succession, and keystone

species. Although a replicated study from Mazzer et al. (1984) could not be completed, these

questions can be addressed broadly through indirect methods of this research.

Vegetation mapping from 1975 in combination with aerial photographs from 1981

allowed me to describe the macrohabitats of sites surveyed by Mazzer et al. (1984). Current

vegetation mapping (Hop et al. 2013) and ground-truthing allowed me to do the same for sites that I surveyed 34 years later. A chart of small mammals captured during both inventories

provides species affinities for successional stages past and present (Figure 4.1). Three study sites

re-sampled from Mazzer et al. (1984) experienced effective pitfall trapping (2016) in addition to

Sherman trapping and were compared to previous findings (Figure 4.2). A few notable differences are apparent through written species accounts and occupancy comparisons.

Mazzer et al. (1984) had better representation of insectivores while I had better representation of rodents. Hairy-tailed moles (Parascalops brewerii) were widely apparent to

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Mazzer et al. (1984) through “tunnels […] in uplands”, and I found the same to be true; the

tunnels seem to be very common. This species was captured in a field previously (Mazzer et al.

Table 4.1 – Small mammal species detected among three plant community successional stage categories during two inventories in the Cuyahoga Valley National Park. Historical data from Mazzer et al. (1984). YF = young forest, MF = mature forest. *Rattus norvegicus was detected outside of natural habitats by Mazzer et al. (1984). *Condylura cristata was documented incidentally (dead specimen) during the course of my work.

Meadow Scrub/YF MF Total Species 1984 2017 1984 2017 1984 2017 1984 2017 P. brewerii x x x x S. aquaticus x x *C. cristata x x x x S. cinereus x x x x x S. fumeus x x B. brevicauda x x x x x x x x P. leucopus x x x x x x x M. pinetorum x x x M. pennsylvanicus x x x x x x x *R. norvegicus x M. musculus x x Z. hudsonicus x x x 5 4 6 7 4 5 10 8

Table 4.2 - Small mammal species detected among three re-sampled sites from Mazzer et al. (1984) in the Cuyahoga Valley National Park. 2017 methods included two sessions of Sherman trapping and a three-week period of pitfall trapping. 1984 methods included snap trapping and pitfall trapping. YF = young forest, MF = mature forest.

BMB (meadow) QA (Scrub/YF) VKO (MF) Species 1984 2017 1984 2017 1984 2017 P. brewerii x x S. cinereus x x x B. brevicauda x x x x x x P. leucopus x x x x M. pinetorum x x M. pennsylvanicus x x M. musculus x 2 3 3 5 2 5

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1984) and in a young forest in my study. Based on local evidence, moles are not accurately

represented without targeted trapping, and I was unable to detect either of the other two native

mole species in my survey. Special efforts would be required in order to accurately assess mole

populations in the park. Mazzer et al. (1984) were able to locate a few star-nosed moles

(Condylura cristata) during their survey but they don’t indicate whether they were trapped or found dead. This animal is known to be highly aquatic and they are known to persist within the park’s wetlands (C.Davis, Cuyahoga Valley National Park, personal communication 2016). The eastern mole (Scalopus aquaticus) captured by Mazzer et al. (1984) seems notable since this is typically a prairie species near the edge of its range in northeast Ohio (Gottschang 1981).

I was able to locate two of three shrew species that were documented by Mazzer et al.

(1984), while missing the smoky shrew (Sorex fumeus). This species was reported by Mazzer et

al. (1984) as “uncommon” and it was found at three scrubby sites, two of which I surveyed in

2015 (BM and MP, site descriptions in Chapter 2). Unfortunately, I did not get an effective

session of pitfall trapping at these sites to check for presence of S. fumeus. This species is

described by Gottschang (1981) as being a boreal species preferring shady, damp, cool woods

while shying away from open fields. Bole and Moulthrop (1942) describe it as a frequent

associate of hemlock-beech forests. Based on these accounts, I would predict that S. fumeus

persists within the CVNP. A similar long-tailed relative, the masked shrew (Sorex cinereus) was

fairly well-represented in my study. I found this species in both young and mature forests while

Mazzer et al. (1984) documented them in old fields and scrubby areas. More extensive pitfall

trapping would be necessary to determine if there has been a local habitat shift in the species.

The short-tailed shrew (Blarina brevicauda) remains an abundant resident of the CVNP, being

captured in all successional stages across years.

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For rodents, white-footed mice (Peromyscus leucopus) and meadow voles (Microtus

pennsylvanicus) remain as common, well-represented species throughout the CVNP. One surprising capture during my study was an adult male M. pennsylvanicus in the interior of a mature forest (VKO), likely during a dispersal event. Woodland voles (Microtus pinetorum)

were not detected by Mazzer et al. (1984), but found in my survey at two of three forested pitfall

sites (QA and VKO, 2016). I also found a single skull of this animal from a barred owl (Strix

varia) roost near the Virginia Kendall ledges. This species is apparently uncommon in the

CVNP, but Gottschang (1981) describes the species as “seldom seen and not well-known” in

addition to having “cyclic [populations] and [being] concentrated in certain restricted areas”.

Understanding the park’s population of this vole would likely require a multiple-year study with

several survey methods. I was not able to capture any in Sherman traps, but had decent success

in pitfall traps (chapter 3). Some researchers suggest that including snap-trapping is usually the

most comprehensive way to survey the small mammal community (T. Mattson, Cleveland

Museum of Natural History, personal communication 2015, Sedivec & Whidden 2007), and may

be warranted for such a project.

Another rodent that I was able to find that Mazzer et al. (1984) did not was the meadow

jumping mouse (Zapus hudsonicus). In addition to single Sherman trap captures at two study

sites (COL and BM), I captured this species during a demonstration (Howe meadow), along a

stream (Haskell Run near ledges), and after a prescribed burn (Terra Vista). While the species is

apparently fairly widespread throughout several habitat types in the park, trapping may require

special effort (Barbour & Davis 1974). Lastly, two introduced rodents that have been previously

documented in the park (Mazzer et al. 1984) were not re-located during my surveys. Both the

house mouse (Mus musculus) and Norway rat (Rattus norvegicus) are likely just constituents

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around human dwellings and structures, with isolated populations in the CVNP. Since many old farm buildings have been demolished or restored, there may be less habitat for these species than there was three decades ago. Many occupants of old park homestead sites trap mice in their buildings, but every one of these animals that I have investigated has been P. leucopus. A survey

along the railroad corridor and at old farmsteads could turn up these species. Mazzer et al.

(1984) captured M. musculus in a meadow that I re-sampled (BMB), but it seems that this population has since contracted.

Changes in mammal assemblages over the last few decades in CVNP may be attributed to a few factors. Plant communities have been aging since the protection of the park in 1974.

Some sites in the park may have shown a distinct habitat successional shift since the work of

Mazzer et al. (1984) while other sites (such as many forests) may have simply aged. The trend of maturing habitats in the CVNP surely has an impact on the biota. Many species, such as M. pennsylvanicus are early succession specialists. I have shown in my work that as a forest canopy reduces the amount of low herbaceous vegetation, the site becomes less productive for M. pennsylvanicus and more suited for P. leucopus. There are disturbed sites within CVNP that

have remained as meadows for decades and will likely continue to do so, offering habitats for the

early-successional species.

The more “ephemeral” stages in habitat succession, namely the scrub/shrub and young forest stages tend to foster vigorous plant growth thereby providing food and cover resources to many animals (Swanson et al. 2011, Litvaitis 2001). Edge habitats and disturbed areas that have an open canopy (such as large tree falls) can produce similar conditions (DeGraaf & Yamasaki

2003, Alverson et al. 1988, Leopold 1987). I saw the greatest diversity and abundance of small

mammal species in young forest sites during my study (Chapter 2). Table 4.1 showed that both

105 studies experienced the highest diversity in mid-successional habitats as well. The importance of mid-successional habitats for wildlife is well-known (e.g. King & Schlossberg 2014, Holmes &

Sherry 2001, Litvaitis 1993, Peterjohn & Zimmerman 1989). The biological diversity of the

CVNP owes partially to the heterogeneity of habitats and land use histories that created them.

Two keystone species; the white-tailed deer (Odocoileus virginicus) and coyote (Canis latrans) have surely affected mammal communities over the decades through direct and indirect ecological processes. Both species have been extremely successful in the region and have increased their populations in the park since the last inventory (ODNR 2015, Plona 1999, Mazzer et al. 1984). Deer are considered to be very overpopulated in the CVNP and their foraging behaviors may have effects on small mammal communities (Laux 2013). Deer compete with rodents for important foods such as acorns and other nuts (Flowerdew & Ellwood 2001,

Chapman & Feldhamer 1982). Deer browse can significantly alter forests through selective foraging activities (Rooney & Waller 2003, Russell et al. 2001), which in turn affects small mammals. Laux (2013) showed that sites with greater deer density in the Cuyahoga Valley had lower abundance and diversity of insectivores. The mechanism responsible being that reduced vegetation cover leads to reduced moisture and litter on the forest floor. Insectivores require moisture for physiological requirements and litter provides habitat structure for these subterranean creatures (Churchfield 1981, Getz 1961). Mazzer et al. (1984) noted that B. brevicauda was likely the most abundant small mammal in the CVNP, and also found two Sorex shrew species. Blarina was well-represented in my study, but it seems that P. leucopus is the most abundant small mammal, based on my efforts. Additionally, I detected a single Sorex species and this animal (S. cinereus) appears to be uncommon. It is possible that the abundance

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and diversity of shrew species has decreased since Mazzer et al. (1984), owing to the habitat

changes induced by the overpopulation of white-tailed deer.

Another major player, the coyote, has become an important part of the park’s fauna since

Mazzer et al. (1984). Coyotes were just entering the scene in the early 1980s and the park’s

population has likely been increasing ever since (ODNR 2015). Coyotes affect other mammals

through predation and competition (Gosselink et al. 2007, Chapman & Feldhamer 1982).

Bowhunter surveys in Ohio have shown a downward trend in sightings of red fox (Vulpes

vulpes) and gray fox (Urocyon cinereoargenteus) throughout the state since 1990 (ODNR 2015).

Potential territory for these smaller canids is reduced by the increased presence of coyotes.

Coyotes will kill foxes (Gosselink et al. 2007, Sargeant et al. 1989), and foxes show avoidance of areas used often by coyotes (Major & Sherburne 1987, Sargeant et al. 1987, Voigt & Earle

1983). Foxes also compete with coyotes for food (Gese et al. 1996, Cypher 1993). Mazzer et al.

(1984) documented the red fox as being common and the gray fox as infrequent (despite being recorded from six locations). In my experience, red foxes are still around but they seem to locate along the edge of suburbia while coyotes occupy the majority of natural habitats. I was not able

to confirm presence of a gray fox in the CVNP between the years of 2014-2017. Fox

populations likely declined as a direct result of coyotes.

Coyote predation may also have impacts on small mammal assemblages (Salo et al. 2010,

Sundell 2006, Henke & Bryant 1999). Through opportunistic hunting behavior, coyotes may

function to perform population checks on the most abundant species such as meadow voles and

white-footed mice. By preying on the most abundant species, coyotes could promote greater

diversity by reducing competitive exclusion as was shown in an example from Texas (Henke &

Bryant 1999). Meadow voles were the dominant food item in a diet analysis of coyotes in the

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CVNP (Cepek 2004). Grassland rodents such as deer mouse (Peromyscus maniculatus) or meadow jumping mouse could benefit from this hunting pressure on a competitor. The former species has yet to be documented in CVNP but exists in surrounding counties (Gottschang 1981).

Similarly in wooded habitats, woodland voles and woodland jumping mice (Neozapus insignis) are two species that could benefit from increased hunting pressure on the abundant white-footed mouse. The latter species is a boreal specialist and has been documented in the vicinity of

CVNP, but not yet within (Gottschang 1981). Strategic trapping in cooler microclimate woodlands in the park may produce a record for this secretive animal. Exclosures that function to keep coyotes out of various habitat patches could be studied in comparison to control sites to assess impacts of coyote predation on small mammal communities. Care would need to be taken when interpreting such results as the removal of deer would have its own effects. An alternative to exclosures could be a removal or hazing program that deterred coyotes from certain lands in the park.

Local coyote diet analysis found that white-tailed deer, eastern cottontail (Sylvilagus

floridanus), raccoon (Procyon lotor), and muskrat (Ondatra zibethecus) were also incorporated

(Cepek 2004). The author determined that the majority of deer and raccoon eaten were likely

scavenged road-killed animals, but coyotes do prey on live deer, especially fawns (Kilgo et al.

2012, Stout 1982, Cook et al. 1971). The extent at which coyotes affect deer populations has

been a topic of debate for some time, and is not easily studied (Cepek 2004).

The CVNP remains a refuge for biodiversity amidst two sprawling metropolitan areas.

While available habitat outside of CVNP may have decreased since the park’s designation in

1974, protected lands within have recovered and matured, offering valuable habitat to many of

Ohio’s mammal species. In this thesis, I have attempted to produce an updated inventory of the

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terrestrial mammals of CVNP, complete with details regarding species habitat preferences and

change in occurrence since Mazzer et al. 1984. Some rare and secretive species could have gone

undetected, and future investigations should put forth special effort towards locating these

animals.

Future studies and management implications

Thorough, targeted surveys should be implemented to determine the presence of several

small mammal species. Conservative use of snap traps should be considered during the next

serious inventory as a supplement to live-trapping with Sherman traps and pitfalls. I have shown and discussed that each method has proficiencies and deficiencies for detecting various members of the community. Pitfall trapping should incorporate larger buckets (at least 2-gallon), and arrays should be left open for several weeks. Bait could be considered, but tampering from raccoon and other non-target animals could affect success, and special effort may be required to keep them from sabotaging traps. Species to be targeted tend to be habitat specialists and should be looked for accordingly. I would encourage the continuation of owl pellet analyses as a simple method to assess relative abundances in the small mammal community. Owls may use the same roosting areas for many years and these sources could be monitored over time. This method may provide preliminary evidence that a species exists within or near the CVNP, and trapping efforts could follow. An example from Ireland described new records of a shrew species found via barn

owl (Tyto alba) pellets (Tosh et al. 2008).

In general, the abundance and diversity of small mammal communities relies on the

quality of habitats available for reproduction. Small mammal diversity was highest in

successional habitats in both this study and from Mazzer et al. 1984. Ample food and nesting

109 cover resources in addition to the heterogeneity of habitats are likely the causes for this trend.

Managing wildlife habitat currently tends to be outside of the scope of CVNP, but there is plenty of habitat restoration work being done that attempts to promote native plant communities. The work being done in these highly invaded areas may be important to wildlife and consideration should be taken to provide habitat structure throughout the management process.

Management of the park’s white-tailed deer population began in 2016, and should indirectly benefit insectivores in forest systems through the increased litter production and plants on the forest floor. Other small mammals such as M. pinetorum may also benefit (Laux 2013).

Prescribed burns in the park began in 2012 and occur on two units to date, with several others in the scope. Maintenance of grasslands will promote early successional specialists, and provide opportunity for fire effects monitoring. Studies have observed rapid changes in small mammal communities following fire and similar disturbances (Fox 1982, Ford et al. 1999). Kaufmann et al (1990) produced an excellent review on fire response in small mammal species. In summary of numerous studies, P. leucopus is a “fire-positive” species while M. pennsylvanicus is a “fire- negative” species. Data from one of my meadow sites (BP, Chapter 2) corroborates this finding in that summer trapping (6 weeks post-burn) showed a 2:1 ratio of P. leucopus individuals to M. pennsylvanicus individuals (n=21), while fall trapping (12 weeks post-burn) showed the inverse ratio of 2:1 in favor of Microtus (n=21). Furthermore, B. brevicauda (another fire-negative species) appeared during fall trapping (n=2) and was absent in summer. More recent trapping at this site (summer 2016, 14 months post-fire) has shown a 2:1 ratio of Microtus to Peromyscus

(n=6). It appears that the larger and more aggressive M. pennsylvanicus may displace P. leucopus from early successional habitats through interference competition. Wirtz and Pearson

(1960) demonstrated aggressive behavior and dominance of M. pennsylvanicus over P. leucopus

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through forced encounters in captivity. Further study on the occurrence of P. leucopus in

meadows should look at patch size, distance to edge, plant community, and time since

disturbance (fire).

Lastly, the CVNP is composed of numerous parcels and suffers greatly from habitat

fragmentation. Following the recommendation of Mazzer et al. (1984), care should be taken to

minimize fragmentation through strategic visitor-use planning. Despite that the park already sanctions over 200 kilometers in trails, new trails are still in the works and may have a negative impact on some wildlife. By keeping the majority of visitors to designated regions of the park, we could leave a few other regions relatively un-developed for the purpose of nature. Many animals would surely appreciate the peace.

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References

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Appendix A: Historical species lists for mammals in Cuyahoga and Summit counties, Ohio Table A.1 – Mammals listed as occurring in Cuyahoga and Summit counties, Ohio, U.S.A. from four historical works; Mammals of Ohio (Gottschang 1981), Mammals of the Eastern United States (Hamilton and Whittaker 1979), Mammals of the Great Lakes Region (Burt 1979), and The recent mammal collection in the Cleveland Museum of Natural History (Bole and Moulthrop 1942). Species documented are indicated by an ‘X’.

Bole & Gottschang Hamilton & Moulthrop Order Common Name Species '81 Whitaker '79 Burt '57 '42 Marsupiala Virginia opossum Didelphi virginiana X X X X Insectivora Masked shrew Sorex cinereus X X X X Smoky shrew Sorex fumeus X X - X Short-tailed shrew Blarina brevicauda X X X X Pygmy shrew Microsorex hoyi - X - - Least shrew Cryptotis parva X X X X Hairy-taile mole Parascalops breweri X X X X Eastern mole Scalopus aquaticus X X - - Star-nosed mole Condylura cristata X X X X Chiroptera Little brown bat Myotis lucifugus X X X X Indiana bat Myotis sodalis X X X - Long-eared bat Myotis septentrionalis - X X X Small-footed myotis Myotis leibii - - X - Silver-haired bat Lasionycteris noctivagans X X X X Eastern pipistrelle Pipistrellus subflavus X X X X Big brown bat Eptesicus fuscus X X X X Red bat Lasurius borealis X X X X Hoary bat Lasurius cinereus X X X X Evening bat Nycticeius humeralis X - X - Lagomorpha Eastern cottontail Sylvilagus floridanus X X X X Rodentia Eastern chipmunk Tamias striatus X X X X Woodchuck Marmota monax X X X X

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Bole & Gottschang Hamilton & Moulthrop Order Common Name Species '81 Whitaker '79 Burt '57 '42 Rodentia Gray squirrel Sciurus allegheniensis X X X X (cont’d) Fox squirrel Sciurus niger X X X X Red squirrel Tamiasciurus hudsonicus X X X X Southern flying squirrel Glaucomys volans X X X X Beaver Castor canadensis X X X X Deer mouse Peromyscus maniculatus X - X X White-footed mouse Peromyscus leucopus X X X X Meadow vole Microtus pennsylvanicus X X X X Woodland vole Microtus pinetorum X X X X Muskrat Ondatra zibethicus X X X X Southern bog lemming Synaptomys cooperi X X X X Norway rat Rattus norvegicus X X X X House mouse Mus musculus X X X X Meadow jumping mouse Zapus hudsonius X X X X Woodland jumping mouse Napaeozapus insignis X - X X Carnivora Coyote Canis latrans X - X - Red fox Vulpes vulpes X X X X Gray fox Urocyon cinereoargenteus X X X X Racoon Procyon lotor X X X X Short-tailed weasel Mustela erminea X - - X Least weasel Mustela nivalis X X X X Long-tailed weasel Mustela frenata X X X X Mink Mustela vison X X X - Badger Taxidea taxus X - - X Striped skunk Mephitus mephitus X X X X River otter Lutra canadensis - X - - Artiodactyla White-tailed deer Odocoileus virginicus X X X X Total 45 42 43 41

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Appendix B. Mammals of Cuyahoga Valley National Park recorded by Mazzer et al. (1984)

Figure A.2 – Original scan from “Wildlife Survey of the Cuyahoga Valley National Recreation Area” by Samuel J. Mazzer, Lowell P. Orr, and David W. Waller (1984).

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Appendix C. Contingency tables from chapter III Table A.2 – Contingency table showing representation of three major taxonomic groupings of mammals captured on camera traps among three wooded successional stages. Data from each camera location (n = 28) was rarefied and scaled to 100 captures. Eastern cottontail was grouped with Rodentia and Virginia opossum was grouped with Carnivora due to functional similarities.

Stage Artiodactylia Carnivora Rodentia total Shrubland 75 72 253 400 Young Forest 750 87 363 1200 Mature Forest 872 141 187 1200

Table A.3 - Contingency table showing representation of three major taxonomic groupings of mammals captured on camera traps among three forest communities. Data from each camera location (n = 13) was rarefied and scaled to 10 individuals. Eastern cottontail was grouped with Rodentia and Virginia opossum was grouped with Carnivora due to functional similarities.

Community Artiodactylia Carnivora Rodentia total Northern Hardwood 6 15 9 30 Oak Hardwood 17 12 21 50 Successional Mixed Hardwood 31 9 10 50

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Appendix D. Invertebrate data collected by Javier Ojeda during pitfall trapping efforts Table A.4 – Summary of invertebrate data collected by Javier Ojeda during pitfall trapping efforts in Cuyahoga Valley National Park. Numbers with site abbreviations correspond to each array. Each site was sampled twice during the summer of 2016.

Taxa BMB-1 BMB-2 BMB-3 BPT-1 BPT-2 BPT-3 QA-1 QA-2 QA-3 VKO-1 VKO-2 VKO-3 MM-1 MM-2 MM-3 total Acanthocephala 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Acritus 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 3 Agelenopsis 13 12 21 6 6 5 10 2 5 0 0 0 0 0 0 80 Amaurobius 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 3 Apheloria 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Armadillidium 6 13 29 99 63 34 31 4 10 0 0 0 4 5 0 298 Blatta 0 0 0 0 6 1 0 0 0 0 0 0 0 0 0 7 Calasoma / Agonum 2 5 17 23 4 1 22 32 42 0 3 3 1 0 0 155 Camponotus 0 1 0 2 0 0 2 1 0 0 0 2 0 0 0 8 Carabus 0 0 0 5 0 0 0 0 0 0 2 0 2 0 1 10 Ceuthophilus 0 0 0 1 0 0 0 0 0 4 0 0 0 0 0 5 Cicindela 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Copris 0 0 0 0 0 0 2 6 0 0 0 0 0 0 0 8 Curculio 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Dermacentor 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 2 Dolomedes 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 Dysdera 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 Formica 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 4 Galerita 3 0 1 0 0 0 2 4 11 7 0 0 0 0 0 28 Gryllus 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 Hadrobunus 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 Halyomorpha 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 3 Herpyllus 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 2 Hogna 0 0 1 5 0 0 2 1 1 0 0 0 0 0 0 10 Leucauge 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 3

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Taxa BMB-1 BMB-2 BMB-3 BPT-1 BPT-2 BPT-3 QA-1 QA-2 QA-3 VKO-1 VKO-2 VKO-3 MM-1 MM-2 MM-3 total Limax 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 Lumbricus 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 Magicicada 0 0 1 0 0 0 37 40 26 0 1 3 0 0 0 108 Malacosoma 0 0 0 0 0 0 1 10 2 0 0 0 0 0 0 13 Melanotus 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 3 Meloe 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 2 Meracantha 0 0 0 0 0 2 0 0 0 0 1 0 0 0 0 3 Misumenoides 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 4 Monomorium 71 60 97 5 21 0 0 0 0 0 0 0 0 0 0 254 Nabidae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Narceus 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 Necrophilia 0 0 0 10 32 4 4 0 6 5 8 0 0 0 0 69 Nicrophorus 0 0 0 0 0 0 6 8 4 6 1 0 0 0 0 25 Odontotaenius 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 Oniscus 2 0 3 14 25 25 25 9 8 0 0 0 1 6 1 119 Operophtera 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 Phidippus 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 2 Philonthus 0 0 0 7 0 5 4 4 5 0 0 1 0 0 0 26 Pholcus 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2 Pisaurina 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Pseudopolydesmus 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 2 Pterostichus 4 13 2 5 0 2 11 15 20 1 6 1 2 0 1 83 Scarites 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 3 Scudderia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Sehirus 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 2 Staphylinus 0 0 0 7 5 4 0 0 0 3 7 0 0 0 0 26 Trichordestra 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 3 Trombidium 1 0 2 0 0 0 0 2 1 0 0 0 0 0 0 6 Uloma 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 2 Total 103 106 178 191 163 90 172 153 145 27 30 14 10 12 3 1397

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Appendix E. Equipment used during camera trap surveys Table A.5 – Trail cameras used for inventorying mammals in the Cuyahoga Valley National Park. Cameras were rotated between 42 total locations in the park between June 2014 and May 2016.

Trail cameras used Brand Model Qty Reconyx RC60 3 Bushnell Trophy Cam 2 Moultrie M880 4 Skypoint Eclypse 2

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