How Rock Climbing Impacts Bryophyte and Lichen

Rock Climbing or Lichen Climbing? How Rock Climbing Impacts Bryophyte and Lichen

Communities Within the Red River Gorge

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in

the Graduate School of The Ohio State University

Jordan Reding

Graduate Program in Environment and Natural Resources

The Ohio State University

2019

Thesis Committee

Dr. G. Matt Davies, Advisor

Dr. Robert Klips

Dr. Kaiguang Zhao

Dr. Stephen Matthews

Copyrighted by

Jordan Reding

2019

Abstract

Outdoor recreation has consistently been one of America’s greatest pastimes. Recently, the development of rock climbing areas has increased, becoming a $3.8 million industry in the Red River Gorge, Kentucky. This development has been shown to have differing levels of effects on the biota on cliff faces, with difficulty isolating natural effects to anthropogenic ones. The goals of this study are: 1) to determine the best practices of different abundance acquisition methods, determine if they can sense a disturbance gradient, and compare them to in-situ visual estimation, and 2) determine how rock climbing impacts cryptogamic abundance, species richness, and community composition while controlling for environmental factors. Accurately estimating vegetative abundance is a cornerstone of many ecological studies and a variety of methods to collect such data have been developed. In certain situations, for instance determining cryptogam abundance on rock surfaces, study sites can be difficult to access. Determining the best method to use when estimating abundance is an important part of collecting accurate data, gaining data in an efficient way, and limiting exposure to hazardous terrain.

Because of this, it is important to understand the different types of methods available and how they compare to one another. Due to the increase in the popularity of rock climbing, understanding the impact of rock climbing is increasingly important, which ii requires abundance, species richness, and community composition data of cryptogamic communities on rock surfaces. Abundance was estimated using four different approaches: 1) visual cover estimation (total life-form and species-specific cover); 2) visual estimation of total cover from quadrat photographs; 3) unsupervised classification of quadrat photographs using the Interface Definition Language program Environment for Visualizing Images (ENVI); and 4) chlorophyll florescence using a Hansatech pocket

PEA. The climbing use intensity, heat load index, and microtopographic variation were estimated for each quadrat. When comparing cover estimation methods, the ENVI method was the most strongly predictive of field visual estimation (R2 = 0.67), closely followed by visual photographic methods (R2 = 0.60). Chlorophyll florescence was not predictive of field visual methods (R2 = 0.09) but provided important insight to the presence of photosynthetic material not visible to the human eye. In-situ visual abundance, photographic visual abundance, and ENVI methods all detected significant differences in abundance across the climbing disturbance gradient. Community composition was significantly impacted (P < 0.01) by rock-climbing, the vertical resource gradient, climbing use intensity, microtopographic variation and heat load. Climbing impact interacted with the vertical resource gradient and the largest impacts on cryptogam abundance were seen at higher elevations on the routes. To minimize the impact of rock climbing, route establishers should avoid bolting to the top of the cliff face, climbers should avoid “topping out”, and climbers should climb as close the line of bolts (or guidebook depicted center of route) as possible.

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Acknowledgments

I would like to thank Dr. G. Matt Davies for working closely with me and acting as my academic advisor. Without him, this project would not have been possible. Brooke Ely, for all her gracious help in the field, collecting data and samples while suspended in the air by a rope for hours at a time. I would like to thank Dr. Robert Klips and Megan Osika at the Ohio State Museum of Biological Diversity for helping identify lichen and bryophyte samples. I would also like to thank Dr. Kaiguang Zhou for giving me guidance on proper ENVI techniques and helping me work through the specifics of the program. I would like to thank Dr. Robert Klips, Dr. Kaiguang Zhou and Dr. Steven Matthews for serving on my master’s committee. I would like to thank the Friends of Muir Valley and

The Red River Gorge Climbers’ Coalition for allowing me to conduct research on their land. Lastly, I would like to thank the Kentucky Native Plant Society, The Ohio State

University’s GradRoots organization, and the OARDC for making this research possible through their generous funding.

Vita

2012 ...... Graduated Kenston High School

2013 – 2016 ...... Rock Climbing Instructor at Outdoor Adventure Center,

The Ohio State University

2016 ...... B.S. Environmental Science with Ecosystem Restoration

Specialization, The Ohio State University

2016 ...... Intern at Colorado Outward Bound School

2017 ...... Assistant Instructor at North Carolina Outward Bound

School

Fields of Study

Major Field: Environment and Natural Resources

Table of Contents

Abstract ...... ii Acknowledgments...... iv Vita ...... v List of Figures ...... viii List of Tables ...... x Comparison of Methods to Estimate Cryptogam Abundance on Rock Surfaces within the Red River Gorge, Kentucky...... 1 Introduction ...... 1 Methods...... 5 Study Area ...... 5 Site Selection ...... 6 Plot Establishment ...... 7 Visual Cover Estimation ...... 9 Photographic Cover Estimation ...... 9 Unsupervised Image Classification to Assess Cover ...... 11 Chlorophyll Florescence Abundance Estimates ...... 14 Method Comparison...... 17 Results ...... 17 Determining Recording Best Practices ...... 17 Methods’ Ability to Characterize Stress Gradients ...... 23 Comparison of All Methods...... 31 Discussion ...... 32 Determining Best Practices ...... 32 Methods’ Ability to Characterize Stress Gradients ...... 35 Comparison of All Methods...... 38

Conclusion ...... 40 The Effects of Rock Climbing on Bryophyte and Lichen Community Composition in the Red River Gorge, Kentucky...... 42 Introduction ...... 42 Methods...... 46 Study Area ...... 46 Site Selection ...... 47 Plot Establishment ...... 48 Field Measurements ...... 50 Data Analysis ...... 51 Results ...... 54 Diversity and Plant Functional Type Abundance ...... 54 Variation in Species Composition ...... 59 Variation in Function Type Composition ...... 64 Discussion ...... 69 Diversity and Plant Functional Type Abundance ...... 69 Variation in Species Composition ...... 71 Variation in Functional Type Composition ...... 74 Conclusion ...... 77 Bibliography ...... 80 Appendix A: Chapter 1 Tables ...... 89 Appendix B: Chapter 2 Tables ...... 96

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List of Figures

Figure 1. Linked Screens within an ENVI analysis...... 12 Figure 2. Leaf clips fixed to points on a quadrat using Metolious climbing tape...... 16 Figure 3. Visual field abundance measured against photographic abundance of both unadjusted and adjusted photographs...... 18 Figure 4. The effects of changing the number of iterations and the class range size on the estimated abundance using the ENVI method...... 19 Figure 5. Abundance found using different methods on ENVI compared to visual abundance of photographs...... 21 Figure 6. Abundance found using different methods on ENVI compared to on-site visual abundance...... 22 Figure 7. Chiseled rock, unchiseled bare rock, and lichen substrates were tested using the chlorophyll fluorometer...... 23 Figure 8. Total abundance, lichen abundance, and bryophyte abundance estimates found per quadrat position for the in-situ visual estimation method...... 25 Figure 9. Total abundance, lichen abundance, and bryophyte abundance estimates found per quadrat position for photographic visual estimation method...... 26 Figure 10. Total abundance, lichen abundance, and bryophyte abundance estimates found per quadrat position for the ENVI method...... 28 Figure 11. Lichen abundance, bryophyte abundance, and rock abundance estimates found per quadrat position for the chlorophyll florescence (weighted) method...... 29 Figure 12. Mean lichen, bryophyte, and rock chlorophyll florescence found per quadrat position...... 30 Figure 13. The photographic, ENVI and chlorophyll florescence abundance estimates compared to the field visual method...... 31 Figure 14. Total abundance of quadrat positions...... 56 Figure 15. The Abundance of each functional type of each quadrat position...... 58 Figure 16. NMDS of the species based community composition with each quadrat relative to horizontal position...... 60 Figure 17. NMDS of the species based community composition of each quadrat relative to vertical position...... 61 Figure 18. NMDS ordination of community composition with a surface overlay depicting climbing use intensity (CUI), microtopographic variation and heat load.. 1...... 63 Figure 19. NMDS of the functional type based community composition of each quadrat relative to horizontal position...... 65

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Figure 20. NMDS of the functional type based community composition of each quadrat relative to vertical position...... 66 Figure 21. NMDS ordination of functional type composition with a surface overlay depicting climbing use intensity (CUI), microtopographic variation and heat load...... 68

List of Tables

Table A. 1. Linear model results for comparing photographic method abundance estimates to field visual abundance measurements...... 89 Table A. 2. Linear model results of iteration and class range combinations using the majority technique...... 89 Table A. 3. Linear model on how adjusting and estimate technique compare on 3 iteration 3-10 class range analysis and 3 iteration 3-12 class range analysis...... 90 Table A. 4.Tukey multiple comparisons of means of the chlorophyll florescence of the lichen, chiseled rock, and unchiseled rock substrates...... 90 Table A. 5. Linear mixed effects model of total abundance in relation to quadrat position for visual field abundance...... 91 Table A. 6. Linear mixed effects model of lichen abundance in relation to quadrat position for visual field abundance...... 91 Table A. 7. Linear mixed effects model of total abundance in relation to quadrat position for photographic field abundance...... 92 Table A. 8. Linear mixed effects model of lichen abundance in relation to quadrat position for photographic abundance...... 92 Table A. 9. Linear mixed effects model of total abundance in relation to quadrat position for ENVI abundance...... 93 Table A. 10. Linear mixed effects model of lichen abundance in relation to quadrat position for ENVI abundance...... 93 Table A. 11. Linear mixed effects model of lichen and photosynthetic endolithic microorganism abundance in relation to quadrat position for chlorophyll florescence abundance...... 94 Table A. 12. Linear mixed effects model of mean lichen chlorophyll florescence readings and mean photosynthetic endolithic microorganism chlorophyll florescence readings in relation to quadrat position...... 94 Table A. 13. Linear models and RMSE analysis of the total abundance found using photographic, ENVI, and chlorophyll florescence methods as related to the field visual method...... 95 Table B. 1. . List of species name, corresponding ID, and the functional type of that species...... 96 Table B. 2. Average of linear mixed effects models results for total abundance...... 97 Table B. 3. Average of linear mixed effects models results for species richness...... 97 Table B. 4. Average of linear mixed effects models results for Shannon species diversity...... 98 x

Table B. 5. PERMANOVA of community species composition...... 98 Table B. 6. Testing for significant differences in dispersion between horizontal position and vertical position of community species composition...... 99 Table B. 7. PERMANOVA of functional type composition...... 99 Table B. 8. Testing for significant differences in dispersion between horizontal position and vertical position of functional type composition...... 99

Comparison of Methods to Estimate Cryptogam Abundance on Rock

Surfaces within the Red River Gorge, Kentucky.

Introduction

In many ecological studies, vegetation abundance is used to determine ecosystem health (Rhodes et. al. 2007), land-use changes (Tasser and Tappeiner 2002), and human impacts (Clark and Hessl 2015). Many field-based studies do this by visually assessing cover (e.g. Kuntz and Larson 2006a), by taking photographs and visually assessing the resulting images (e.g. Nuzzo 1996, Farris 1998), or by analyzing photographs using computer spatial analysis programs to determine indicators of vegetation cover such as NDVI (e.g. Gould 2000). Determining which methods are appropriate to use could depend on the potential constraints of the locations of the vegetation communities being studied and the resources available for the monitoring.

Determining the differences between, and the potential constraints of, vegetative sampling methods is important because ecological monitoring programs frequently lack the power to reject a false null hypothesis (Legg and Nagy 2006). The best method of gathering data may depend on the objectives of the research, the habitat type, and the need to minimize impact to the study site (Legg and Nagy 2006).

However, many methods of determining abundance may be prone to detection mistakes (Elphick 2008), so comparing these methods is vital to understand which method is best for a given research project.

Cliff faces hold cryptogamic communities that can be technically difficult and time consuming to access, often requiring rope systems skills or evening technical rock climbing. Identifying species within the community in-situ can also be extremely time consuming and often requires expert level knowledge of lichen and bryophytes. When considering this type of environment, visual assessment of abundance could be disadvantageous due to the prolonged presence in dangerous terrain, and cost of the safety system to implement. Research on rock-face lichen and bryophyte communities is limited, but the majority of studies have estimated cryptogam abundance using in-situ visual cover estimation to understand the impact of rock climbing on cryptogam communities (Kuntz and Larson 2006a, Clark and Hessl 2015, Garibotti et. al. 2011,

Adams and Zaniewski 2012). Many such studies have found rock climbing as a negative impact on abundance (Clark and Hessl 2015, Rusterholz et. al. 2004, Nuzzo 1995, Camp and Knight 1998). A few studies have used visual assessments of photographs taken in the field to determine abundance of cryptogam communities (Nuzzo 1996, Farris 1998).

There is a small number of studies using NDVI (Miloš et. al. 2018, Casanovas et. al.

2014), or Photoshop and Idrisi GIS (McCarthy and Zaniewski 2018) on cryptogam communities. Miloš et. al. (2018) used NDVI on lichen communities in Antarctica, but this study was primarily concerned with characterizing differences in the spectral

2 reflectance of lichens than abundance measurements per se. There have been limited studies involving the use of chlorophyll florescence. Chlorophyll florescence is mainly used to quantify sample stress or health. Most of these studies used chlorophyll florescence to gain information on the status of lichen soil crusts (Wu et. al. 2012) and the lichens directly (Miloš et. al. 2018). However, like spatial methods, chlorophyll florescence has been used primarily to determine spectral differences in samples or sample health rather than to estimate the abundance of cryptogams

Each technique of gathering abundance information has advantages and disadvantages. Visual estimation, the most frequently-used method, takes place at the field site so data is not lost through translation to other forms of medium, but visual estimation relies on human expertise, is subject to bias and clarity of eyesight, is often has different results when observers are compared (Morrison and Young 2016). It is also time consuming in the field, which could impact quantity of data collected during a season. Visual estimation of abundance from photographs of quadrats is considerably less time consuming in the field since photographs can be analyzed in a laboratory setting. However, this method is subject to both human error (Casanovas et. al. 2013), and information loss (Jokiel et. al. 2015) depending on the quality of camera. In the case of lichens specifically, this method may underestimate crustose lichens, the majority of lichen found on a rock face, due to their cryptic nature (Kuntz and Larson 2006a). Spatial analysis programs classify different colors seen throughout an image into different color range classes. They can do this through supervised classification or unsupervised

3 classification. Supervised classification asks the user to define which parts of the image are categorically different, and then the program separates the image based on that information (Hasmadi et. al. 2009). Unsupervised classification analyzes the image and separates the different pixels into color classes before the user interprets the image

(Hasmadi et. al. 2009). Spatial analysis can reduce human error and bias, but similar to photograph visual assessment, they may cause a loss of data due to imperfections of technology used, and potential classification errors within program. Chlorophyll florescence measurements have yet to be used as a method of determining cryptogam abundance. To date, there has been no study comparing methods of estimating cryptogam abundance on rock surface environments.

The overall goal of this study is to provide information to improve studies assessing abundance of vegetation on rock surfaces by determining the best practices of lesser used methods, as well as comparing the advantages, disadvantages, and results shown by each method. The objectives of this study are to: (1) determine the best practices to utilize photographic visual estimation, spatial analysis programs, and chlorophyll florescence to determine cryptogam abundance; (2) assess the ability of each method to capture differences in cryptogam abundance across a disturbance gradient; and (3) compare the three novel methods to assess abundance to in-situ visual estimates.

Methods

Study Area

This study was conducted at the Red River Gorge (RRG) in eastern Kentucky

(37.784° N, 83.682° W). The RRG generally refers to the 26,000-acre area, between 145 m and 435 m of elevation, where wind and water have eroded the western section of the Cumberland Plateau (Studlar and Snider 1989). Corbin sandstone-conglomerate makes up the majority of the rock surfaces at the RRG (Studlar and Snider 1989), which is the type of rock that is specifically sought after for climbing purposes (Ellington 2012), while other rock types include Newman limestone, Borden calcareous shale, and siltstones (Studlar and Snider 1989). The RRG is generally humid through the summer months with 114cm of rainfall per year (Studlar and Snider 1989), maximum average temperatures no higher than 34°C, and minimum average temperatures between -1 and

4°C (Studlar and Snider 1989). This has allowed development of a diverse vegetation community including magnolias and hemlocks, with oak-pine forests that are often found on the upper parts of the ridges (Higgins 1970).

The RRG is known for its spelunking, rappelling, kayaking, hiking, and rock climbing opportunities (Ellington 2013), and rock climbing alone generates $3.8 million to the surrounding area’s economy (Maples et. al. 2018). With over 2,500 climbing routes within the Gorge (Adventure Projects Inc., 2019), there is a multitude of bare rock surfaces with lichen and bryophyte communities, many of which may be significantly impacted by climbing use. 5

Site Selection

The online database containing rock climbing route information, mountainproject.com (Adventure Projects Inc., 2019), was used to define an initial long- list of potential routes. This led to 753 routes selected based on a search criterion of routes graded between 5.7 YDS (easy) to 5.11c YDS (difficult intermediate). Routes were selected this way to ensure ease of access (climbability) so that each route could be climbed, and abundance assessed. From these 753 routes, each route was individually assessed based on its slope, homogeneity of rock macrostructure, and micro and macro topography to determine the best study sites. Slopes less than 90 degrees were preferable because access was easier on lower angled rock climbs than vertical or overhung surfaces. Homogeneity of rock macrostructure and lower topographic surface variation was preferable because large inconsistencies in the rock, cervices, or large features may decrease accessibility. Overall, 42 routes met these criteria.

The Red River Gorge Climber’s Coalition (RRGCC) and Friends of Muir Valley, property managers of rock climbing areas within the RRG, were contacted and access to their land was approved. This allowed vegetative surveying on rock surfaces to be conducted on 26 routes in the Pendergrass-Murray Recreation Preserve, Miller Fork

Recreation Preserve, and Muir Valley. Routes were surveyed in a randomly selected order and a total of 19 routes were surveyed by the end of the field study which took place between June 6, 2018 and August 6, 2018.

Plot Establishment

Rock climb location and identity were confirmed using the “Red River Gorge

South” guidebook (Ellington 2012). Once identified, the route was climbed, and multiple rappel lines were constructed from the two-bolt rappel ring anchors found at the top of most climbs within the RRG. A single quad anchor was attached to each rappel ring with a locking carabiner, and one locking carabiner at each masterpoint on the quad was used as the connection point for a fixed line. The fixed lines could then be used for safely ascending and descending the route at will.

Once the safety system was in place, quadrats on the vertical rock face were established at various heights and horizontal positions. Quadrats (25 cm x 50 cm) were chalked onto the rock with a 2 x 5 tick mark grid along the perimeter of each quadrat, creating unclosed squares that each held 10% of the total quadrat area. This was done to ease abundance estimates. Quadrats were established at three different heights

(vertical positions) and three horizontal positions. Variation in horizontal position was used to create a disturbance gradient of “unclimbed” to “climbed.” Since areas off-route are rarely (if ever) climbed, and quadrats within the climbing route are more heavily traversed, a disturbance gradient can be established by creating quadrats in the center of a route, off the route, and between the points on and off the route. The variation in vertical positions was used to create an environmental stress or resource gradient.

Conditions at the bottom of the climb tend to be shadier while sections on the upper

7 surface receive more sun. While this is the case, vertical position may also create a lesser climbing disturbance gradient since not all climbers access the top of the route.

Across all routes, vertical positions were roughly 1.5m from the base of the rock climb (lower quadrat), 50% of the total height (middle quadrat), and 75% of the total height (upper quadrat). The total height of each route was found within the guidebook

(Ellington 2012), and the height of each route was estimated in the field using a combination of visual estimation, guidebook description, and the placement of bolts

(protection found throughout the duration of the rock climb). For example, if there was a 30 meter rock climb with seven bolts and a set of anchors marking the top of the 30 meters, and each bolt is roughly evenly placed, then the fourth bolt would mark the middle of the route, and thus the location of the middle set of quadrats. In terms of horizontal position, quadrats were placed in the center of the route, off the route where there was no climbing, and an area between the two (fringe).

Quadrat horizontal positions were determined by evaluating the placement of bolts (a rock climber must be within arm’s reach of a bolt in order to properly protect themselves), the natural path, or path of least resistance, of the climb, and the description of the climb found within the guidebook (Ellington 2012). Each horizontal position was placed at each vertical position, creating a total of nine quadrats per route in a 3 x 3 grid. The center of the route quadrat was placed either exactly in-line with the bolts that indicated the route, or at the center of climbed surface depicted by the path of least resistance or guidebook description. The center of the off-route quadrat was

8 placed 1.5 m to the right or left of the center of the route quadrat in order to simulate a location on the climb where a climber would be unable to access protective bolts. The center of the fringe quadrat was placed 0.75 m to the left or right of the center of the route quadrat. This was done to account for climbers with shorter arms that may have difficulty accessing the bolt from the fringe location, as well as creating a distance from the bolt that some climbers may utilize, while others may not.

Visual Cover Estimation

Quadrats were misted with a fine spray (8-10 dispersals of water per quadrat) prior to monitoring. This activated the photobiont of lichens and saturated the bryophytes, making each lifeform easier to distinguish from underlying rock. Abundance of each quadrat was estimated visually after thoroughly investigating the different lichens and bryophytes with the bare eye and a hand lens. Total abundance, total lichen abundance, and total bryophyte abundance were recorded.

Photographic Cover Estimation

After the quadrats were sprayed, a Canon Rebel T7 DSLR camera was used to take a photograph of each quadrat. An 18 mm – 55 mm lens was used to take each photograph. Each photograph was taken using the camera’s “Large” file sizing option, which encompassing 24 megapixels, to ensure the most clear and detailed image of each quadrat. The manual setting was used, and the appropriate focus (around 30 mm generally) was used to capture the entirety of each quadrat. The ISO was set to 6400, and the F-stop and shutter speed adjusted such as to minimize handshake and

9 movement associated with being on rappel, and to center the light meter so each photograph had similar exposure regardless of the environmental lighting. The F-stop

(aperture) was generally set to F/22 through in 9 of the 172 photographs, a F-stop of

F/18 was used. The shutter speed varied between 1/4 seconds and 1/4000 seconds .

Edges of each photograph contained the chalked border of the quadrat, so the camera was a similar distance from the rock (approximately 0.5m) when each photograph was taken. This was done in order to make sure each photo was of comparable quality.

Photographs were imported onto a computer in the laboratory. Each photograph was cropped in order to remove chalk lines. This was done to both remove the error that chalk may provide in an on-screen viewing and remove the chalk for ENVI analysis (see below). Microsoft Photo Editor was used to create color adjusted images (referred to as

“adjusted” images) with increased brightness and/or saturation that made differences in color more apparent. This was done in order to make lichen and bryophytes easier to visually identify on screen. Total cover, lichen cover, bryophyte cover, and algal cover were estimated visually on the computer screen twice, once using the unadjusted photograph and once using the adjusted photograph.

Thirteen pairs of unadjusted and adjusted photographs were randomly selected and cryptogam abundance visually estimated. These 13 images were compared to the field estimated abundance for each of the quadrats using linear regression (function: lm,

R v3.4.4; R Core Team 2018). The estimate type (unadjusted or adjusted) that was most highly correlated with field visual abundance estimations was then used as the ultimate

10 method of determining abundance in photographs of all quadrats. This selected method was used to determine the photo-based abundance estimates for all quadrats. These were later used to determine abundance changes over a disturbance gradient and compared to other methods in this study.

Unsupervised Image Classification to Assess Cover

IDL software's ENVI version 4.3 program (Exelis Visual Information Solutions,

2006) was used to classify quadrat photographs into color classes via ISOdata unsupervised classification. Unsupervised classification was specifically used in order to reduce initial human error introduced within supervised classification. This method has also shown more promising results than supervised classification when estimating the cover of other cryptic vegetation types (Duffy et. al. 2018). Initial analysis focused on determining the sensitivity of key classification conditions on the resulting color area estimates of 13 quadrats with a range of 3-95% vegetative cover on unadjusted photographs. This subsample of 15

Figure 1. Linked Screens within an ENVI analysis. ENVI is capable of showing two separate screens that allow users to view both the camera photograph and the classified image for comparison.

quadrats was determined by randomly selecting quadrats from the full dataset. ENVI analysis of the subsample was completed on both adjusted and unadjusted photographs. Class and iteration values were determined by testing different numbers of permitted of color classes (1-6, 5-10, 3-10, 3-12), and iteration values of 1 and 3.

These analyses were completed on cropped, and both adjusted and unadjusted, 12 photographs. Based on this analysis default settings were used for the unsupervised classification, except the number of color classes was set to 3-12, and the iteration number was set to 3.

To relate color class abundance to cryptogam abundance, 10 pixels were randomly selected in each color class within the ENVI classified image output. Each pixel was categorized as being covered by one of four substrates: lichen, bryophyte, algae, or rock based on a visual assessment of each pixel in the counterpart image (Figure 1).

Percent cover of each substrate was then determined by both a "majority" or a

"proportional" estimate type.

Within the majority estimate type, the substrate of each color class was defined as the single substrate which made up the majority of the 10 randomly selected pixels.

For example, if 6 of the 10 pixels within one color class were visually determined to be lichen, the entire color class was counted as lichen. In the event of a tie, an 11th pixel was randomly selected within the color class as the “tiebreaker.” ENVI was used to determine the percent cover of each color class in each image, which made it possible to calculate the total percent cover of each corresponding substrate in each image. In the proportional estimate type, each color class could represent multiple substrates, defined by the percentages of different substrates found in the 10 randomly selected pixels per color class. This proportion was then multiplied by the overall percentage of pixels in a certain color class within the image. For example, if 6 out of 10 pixels randomly selected in the color class "orange" were denoted as "lichen" and the color

13 class "orange" was calculated by ENVI to contain 10% of the pixels found in the image, lichen would be considered 60% of "orange," and therefore, 6% of the total image

(assuming lichen was not found in other color classes).

The majority and proportional abundance estimates were tested for correlation with photographic visual estimation via linear modeling (function: lm, R v3.4.4). The best performing estimate type was then applied to the entire image set (i.e. all quadrats) as the ultimate method of determining abundance via ENVI, and further used in method comparison.

Chlorophyll Florescence Abundance Estimates

A Hansatech Pocket PEA was used to determine chlorophyll florescence. A chlorophyll florescence measurement can determine the presence of chlorophyll within a sample, and a higher chlorophyll florescence measurement generally corresponds to a less stressed organism (Unal et. al. 2009). Hansatech leaf clips were separated by removing the lower section including the foam pad that ensures darkening of a sample of a leaf. This removal was necessary in order to fix the leaf clip to a rock surface. Before collecting data, leaf clips were placed on a variety of substrates, including fern leaves, tree leaves, bare rock surfaces, lichen, and bryophytes to ensure the PEA was calibrated and functional. The PEA was also tested within the RRG by assessing fluorescence of on rock surfaces visually characterized as being “bare”, saxicolous lichens, and a rock surface that had been manually chiseled away to reveal unweathered, uncolonized pristine rock. Ten measurements of chlorophyll florescence were taken on lichen, bare

14 rock, and chiseled rock. Linear models (function: lm, R v3.4.4) and Tukey multiple comparisons of means (function: TukeyHSD) were used to determine if the PEA could detect differences in chlorophyll florescence between the different substrates.

To determine abundance based on chlorophyll fluorescence, once mid-elevation quadrats were established, 10 leaf clips were placed using a stratified randomized placement method. Only mid-elevation quadrats were tested using chlorophyll florescence, but all three horizontal positions were tested, creating a total of three quadrats per route. Stratified random placement was used in order to ensure enough photosynthetic organisms were accounted for during the sampling, instead of bare rock surfaces that often accounted for the majority of quadrats. Leaf clips were placed on the substrate using Metolious Climbing Tape to adhere the entire edge of the leaf clip to the rock surface without inhibiting chlorophyll fluorometer contact with the leaf clip (Figure

2). Once a leaf clip was placed on a substrate, a mist of water was sprayed into the opening of the leaf clip before the darkening mechanism was shut. The sample was then darkened for at least 30 minutes, per the recommendations given by Hansatech. Once thoroughly darkened, the chlorophyll florescence measurement (Fv/Fm) was recorded.

Figure 2. Leaf clips fixed to random points on a quadrat using Metolious climbing tape.

To quantify lifeform abundance, mean chlorophyll florescence per substrate within a quadrat was calculated. This measurement was then multiplied by the field visual abundance of the substrate to gain a weighted measurement of abundance using both leaf clip readings and visual abundance. Additionally, mean chlorophyll florescence was used to measure the stress level of each lifeform. Differences in lifeform mean fluorescence and quadrat weighted fluorescence across the climbing-induced stress gradient were assessed using linear mixed effects models (function: lmer, R package: lme4, Bates et. al. 2015). Horizontal position was used as a fixed effect, while route was defined as a random effect. 16

Method Comparison

Once the best practice for each measurement method was identified, the R2 value was determined using three separate linear models (function: lm, R v3.4.4;) of each method compared to in-situ visual estimation. The p-value, slope, and root mean square error (RMSE) were also calculated (function: rmse, R package: Metric (Hamner and Frasco (2018)).

Using linear mixed effects modeling (function: lmer, R package: lme4 (Bates et. al. 2015)), each method was tested to see if it could determine a difference in abundance between quadrat positions using horizontal position and vertical position as interacting fixed effects and route identity as a random effect.

Results

Determining Recording Best Practices

Visual cover estimates from photographs: After analyzing the adjusted and unadjusted photographs of the sample quadrats, the linear model showed an R2 value of

0.59 between unadjusted photograph abundance estimates and field abundance estimates, and a 0.50 R2 value between adjusted photograph abundance estimates and field abundance estimates (Figure 3).

Figure 3. Visual field abundance measured against photographic abundance of both unadjusted (top) and adjusted (bottom) photographs. The black line represents a 1:1 line, the colored line represents the regression relationship between each method.

The linear model also showed that field abundance estimates were significantly higher than both unadjusted and adjusted photograph abundance estimates (Appendix

A, Table A. 1). Therefore, unadjusted photos were used when finding differences throughout a disturbance gradient and when comparing this method to other methods.

Figure 4. The effects of changing the number of iterations (left) and the class range size (right) on the estimated abundance using the ENVI method.

Unsupervised image classification: Linear models showed differences in abundance between estimates depending on the iteration and class range settings used in ENVI (Appendix A, Table A. 2). In the linear mixed effects model comparing 1 iteration to 3 iterations, and a 1-6 class range to 5-10, 3-10, and 3-12 class ranges, only the 3 iteration and 3-10 class range was significantly different. The 3 iteration 3-12 class range was found as near-significant. The 3 iteration 3-12 class range method was also significantly different when compared to the 3 iteration 3-10 class range method with a linear model (Appendix A, Table A. 3). When examining the ranges of the iteration and class ranges graphically (Figure 4), the 3 iteration setting and the 3-12 class range had a much higher range of abundance estimates than the others. The 3-12 class range and 3 iteration settings were considered the best estimation types since the abundance range 19 found using these estimation techniques were more representative of the photographic abundance analysis.

Within the ENVI methods testing, the sample quadrats assessing both photograph correcting techniques (unadjusted vs. adjusted) and abundance acquisition techniques (majority vs. proportional) were correlated to photographic methods and field methods. Linear modeling found that the unadjusted proportional method was most highly correlated with the photographic method (R2 = 0.87), while the unadjusted majority (R2 = 0.84), adjusted proportional (R2 = 0.77), and adjusted majority (R2 = 0.60) methods were not as highly correlated (Figure 5). In contrast to the photographic method comparison, the adjusted majority ENVI method was found to be the highest correlated abundance estimate to field estimation (R2 = 0.66), while the unadjusted majority (R2 = 0.52), unadjusted proportional (R2 = 0.63), and adjusted majority (R2 =

0.60) methods were not as highly correlated (Figure 6). Linear model results, however, showed that differences in adjusting and abundance acquisition techniques were insignificant when compared to each other. Therefore, the best ENVI method was determined as using a 3-12 class range, 3 iteration runs, unadjusted photos, and a proportional abundance estimation technique.

ENVI AbundanceENVI(proportional)

Photo Abundance (proportional)

Figure 5. Abundance found using different methods on ENVI compared to visual abundance of photographs. ENVI settings were set to 3 iterations and a 3-12 class range for each comparison: (A) the unadjusted photo using the majority technique. (B) The unadjusted photo using the proportional technique. (C) The adjusted photo using the majority technique. (D) The adjusted photo using the proportional technique.

ENVI AbundanceENVI(proportional)

Field Abundance (proportional)

Figure 6. Abundance found using different methods on ENVI compared to on-site visual abundance. ENVI settings were set to 3 iterations and a 3-12 class range for each comparison: (A) the unadjusted photo using the majority technique. (B) The unadjusted photo using the proportional technique. (C) The adjusted photo using the majority technique. (D) The adjusted photo using the proportional technique.

Chlorophyll florescence: In the process of determining capability of the PEA, chlorophyll florescence of unchiseled rock, chiseled rock, and lichen samples were compared (Figure 7). The chiseled rock surface was found to have significantly less florescence than the unchiseled rock surface and lichen. The unchiseled rock and the

22 lichen chlorophyll florescence readings, however, were not significantly different from one another (Appendix A, Table A. 4).

Figure 7. Chiseled rock, unchiseled bare rock, and lichen substrates were tested using the chlorophyll fluorometer for 10 samples used within each substrate category in the RRG.

Methods’ Ability to Characterize Stress Gradients

Field visual estimates found significant differences in total abundance and lichen abundance between quadrat positions, along both horizontal and vertical gradients

(Figure 8). Total abundance (Appendix A, Table A. 5) and lichen abundance (Appendix A, 23

Table A. 6) of off-route quadrats were significantly higher than both route and fringe quadrats. Upper quadrats also had significantly higher total abundances than lower and middle quadrats. Upper quadrats also had significantly greater lichen abundance than lower quadrats and middle quadrats. There were, however, significant interactions between horizontal positions and vertical positions for both total and lichen abundances. Larger differences in abundance across vertical positions were seen in off- route quadrats than route or fringe quadrats (Figure 8). Differences in bryophyte abundance could not be modeled with confidence due to highly zero-inflated data, but graphical trends appeared to show higher bryophyte abundances as quadrats declined in elevation and moved off-route.

Figure 8. Total abundance (A), lichen abundance (B), and bryophyte abundance (C) estimates found per quadrat position for the in-situ visual estimation method. Quadrat positions are depicted on the X-axis as 2 letters: the first being the abbreviation for the vertical position (L = lower, M = middle, U = upper) and the second being an abbreviation for the horizontal position (R = route, F = fringe, O = off-route).

In terms of visual estimates from photographs (Figure 9), Off-route quadrats had significantly greater total abundance than both route and fringe quadrats, and lower quadrats, and upper quadrats had significantly greater total abundance than middle quadrats (Appendix A, Table A. 7). When looking at lichen abundance (Appendix A, Table

A. 8), off-route quadrats also had a significantly greater abundance than both route and fringe quadrats, and upper quadrats had significantly greater abundance than lower quadrats and middle quadrats. Interactions between horizontal and vertical positions of 25 quadrats were also significant. Similar to the field abundance method, larger differences in abundance in vertical positions were seen in off-route quadrats for both total abundance and lichen abundance. Once again, bryophyte abundance differences could not be modeled because of very highly zero-inflated data, but graphical evidence shows bryophytes were only recorded as being present in lower off-route and middle off-route quadrats.

Figure 9. Total abundance (A), lichen abundance (B), and bryophyte abundance (C) estimates found per quadrat position for photographic visual estimation method. Quadrat positions are depicted on the X-axis as 2 letters: the first being the abbreviation for the vertical position (L = lower, M = middle, U = upper) and the

second being an abbreviation for the horizontal position (R = route, F = fringe, O = off-route).

The linear mixed effects model of ENVI analysis revealed that ENVI could detect a significant impact in horizontal and vertical stress gradients in total and lichen abundances (Figure 10). Off-route quadrats had significantly higher abundance than route and fringe quadrats for both total and lichen abundances. For total abundance, however, lower quadrats were significantly lower, but middle quadrats were not significantly different from upper quadrats (Appendix A, Table A. 9). Similar results were found with regards to lichen abundance with lower quadrats being significantly lower and middle quadrats not being significantly different from upper quadrats (Appendix A,

Table A. 10). Bryophyte abundances could not be compared in this way due to zero- inflated data, but just like photographic analysis, bryophytes are only present on lower off-route quadrats and middle off- route quadrats.

Figure 10. Total abundance (A), lichen abundance (B), and bryophyte abundance (C) estimates found per quadrat position for the ENVI method. Quadrat positions are depicted on the X-axis as 2 letters: the first being the abbreviation for the vertical position (L = lower, M = middle, U = upper) and the second being an abbreviation for the horizontal position (R = route, F = fringe, O = off-route).

Weighted chlorophyll florescence was used as a mechanism of determining abundance across a horizontal gradient (Figure 11). Lichen abundance was found to be significantly different over the horizontal stress gradient with off route quadrats

(Appendix A, Table A. 11) having significantly higher abundance than route quadrats and fringe quadrats being near-significantly different. Bryophyte abundance could not be compared statistically due to very highly zero inflated data, but higher bryophyte

28 abundances were found more frequently on off-route quadrats than route and fringe quadrats. Route and fringe quadrats seemed to have similar bryophyte abundances.

Chlorophyll Florescence (Fv/Fm) Florescence Chlorophyll

Quadrat Horizontal Position

Figure 11. Lichen abundance (A), bryophyte abundance (B), and rock abundance (C) estimates found per quadrat position for the chlorophyll florescence (weighted) method. Quadrat horizontal positions are depicted on the X-axis (R = route, F = fringe, O = off-route).

Mean chlorophyll florescence, a measure of cryptogam health, was not significantly different across all locations for lichen substrates (Figure 12). Mean chlorophyll florescence was significantly different in both route quadrats and fringe

29 quadrats when compared to off-route quadrats for rock substrates (Appendix A, Table

A. 12). Bryophytes could not be modelled due to zero-inflated data, but bryophyte chlorophyll florescence values were slightly higher than lichen chlorophyll florescence values in off-route quadrats (Figure 12).

Chlorophyll Florescence (Fv/Fm) Florescence Chlorophyll

Quadrat Horizontal Position

Figure 12. Mean lichen (A), bryophyte (B), and rock (C) chlorophyll florescence found per quadrat position. Quadrat horizontal positions are depicted on the X- axis (R = route, F = fringe, O = off-route).

Comparison of All Methods

The linear models between cover estimates produced by the visual field, visual photographic, and ENVI methods (Appendix A, Table A. 13) indicated that both the visual photographic and ENVI methods were well related with field methods of estimating abundance (Figure 13). The ENVI method was the most highly related (R2 =

0.66) with a RMSE of 0.13, while the photographic method was slightly less related (R2 =

0.60) with a RMSE of 0.16.

Method(proportion)Abundance

Field Abundance (proportion)

Figure 13. The photographic (A), ENVI (B) and chlorophyll florescence (C) abundance estimates compared to the field visual method. The black line

represents a 1:1 line, the colored line represents the regression relationship between each method.

The linear model between field and the weighted chlorophyll florescence method (Appendix A, Table A. 14) showed that chlorophyll florescence was not very related to the field abundance with a R2 value of 0.09 and a RMSE of 0.27.

Discussion

Determining Best Practices

Unadjusted photograph estimates were more strongly correlated with field estimates than adjusted photographs. In addition to these correlation values, previous studies also used unadjusted photographs to determine cryptogam abundance on rock surfaces (Farris 1998, Nuzzo 1996), although only because these studies were not using digital cameras. McCarthy and Zaniewski (2018) did use adjusted images, but more for the purpose of isolating lichen colonies than changing saturation and lighting of the photograph. adjusting the photograph made certain colors on the rock more apparent as well as lichen. A green or blue tint often appeared on the rock surface on the image when adjusting the saturation, while increasing the lighting could make lichen and rock less discernable through creating too much white space within the image. Because of this, unadjusted photographs were considered the best practice when using photographs to determine cryptogam abundance on rock surfaces.

Within the ENVI settings, the 3 iteration setting was considered the best option because it allowed ENVI to reassess the classification after it had classified an image the first time. Within the 1 iteration setting, ENVI would classify the image without comparing it to other versions of the classified image, so ENVI was using the first result it could calculate as the final result. Using 3 iterations, however, ENVI was capable of classifying the image 3 separate times and compare each classification to create the output. A 3-12 class range was most likely optimal because it allowed ENVI to most freely choose how many color classes an image should have. This allowed ENVI to choose the most appropriate number of classes an image should have without restrictions from the user. The 3-12 class range also allowed flexibility within the ENVI program to choose a different number of color classes from one image to another based on the analysis of each image separately. This approach allowed the analysis to be suited towards contents of each individual image.

The proportional technique was most likely considered the better technique because it allowed more precise measurements on substrate abundances than the majority technique. The proportional technique was capable of splitting color class abundances based on individual pixels surveyed, where the majority technique relied on

6 or more pixels to fully dedicate a color class to a substrate abundance. The majority method also failed to consider color classes that may have contained more than one substrate. The unadjusted photographs analyzed in ENVI were most likely the most highly correlated with photographic visual methods because, as previously mentioned,

33 blue-green tints or white spots often appears on the image when adjusting the saturation or lighting. While adjusted proportional techniques did perform better when comparing ENVI to field methods, the differences in correlations between the unadjusted and adjusted photographs were less than the differences in correlation between photographic methods and ENVI. With this included, ENVI methods were considered more closely aligned with photographic methods since ENVI was based on the photographs taken from the field, and not the visual estimation of the plots within the field. Therefore, the best ENVI methods were found to be importing unadjusted photographs into ENVI, setting the iteration number to 3 and the class range to 3-12, and using the proportional method to determine the abundance of cryptogams.

When testing the chlorophyll fluorometer on various substrates, there were no significant differences when comparing unchiseled rock and lichen using the Tukey multiple comparisons analysis. This implies that there a biofilm of photosynthetically active material on the surface of the rock face. When comparing chiseled rock to both unchiseled rock and lichen substrates, the chiseled rock showed significantly less florescence. This confirms that there is some sort of photosynthetic material covering the rock that could not be seen with the naked eye or using a hand lens in the field.

Considering the moist climate of the Red River Gorge, a biofilm on top of the rock surface could potentially be supported. An algal layer developing on sandstone cliff faces has been found in neighboring sandstone cliffs in Ohio (Casamatta et. al. 2002).

Casamatta et. al. (2002) observed 140 different species of algal flora existing on

34 sandstone cliffs, with the majority of the taxa existent within mesic areas. Endolithic cyanobacteria and algae were also found on limestone cliffs within the Niagara

Escarpment, Canada (Matthes et. al. 2000), and even within historic stone buildings constructed from limestone, sandstone, granite, basalt, and soapstone (Gaylarde et. al.

2012).

Methods’ Ability to Characterize Stress Gradients

Analysis of field and photographic methods both found significant interactions between the horizontal and vertical position of quadrats. This indicates that changes in abundance with elevation are dependent on the horizontal position considered. Where significant interactions occur, abundance differences with horizontal position were greater in upper than lower vertical positions. This may be due to the differing environmental conditions present on lower elevations than upper positions, and the amount of control climbing has on cryptogam populations. At lower elevations, the shady conditions may be less optimal for lichens than the sunny conditions present on the upper quadrats, which would allow more lichens to establish on upper quadrats as a whole. This is congruent with the findings of Palmqvist and Sundberg (2000), who found lichen growth strongly correlated with irradiance. Rock climbing may control cryptogam populations, but only to a certain minimum population, since cryptogams may exist in cracks or pockets that climbers generally cannot access. On upper quadrats, the larger cryptogam populations are diminished to the minimum abundance allowed by climbing while on lower quadrats, the lower cryptogam populations are also diminished to this

35 minimum, but since there is less abundance to begin with, the greater difference is seen in upper quadrats.

Field, photographic and ENVI methods all found significant differences in total and lichen abundances through vertical and horizontal stress gradients. Field and photographic methods showed significant increases in abundances as quadrats went from route to off-route, and lower to upper positions. Trends in field and photographic methods were also found to be similar in studies by Vanha-Majamaa et. al. (2000) and

Neeser et. al. (2000). This indicates that the stress gradient created by rock climbing

(Clark and Hessl 2015, Rusterholz et. al. 2004, Nuzzo 1995, Camp and Knight 1998) was identifiable, and that the rock climbing did have a negative influence on abundance.

ENVI could also detect these changes in abundance, except ENVI did not determine a significant difference in abundance between middle elevation quadrats and upper quadrats for both total and lichen cover. This indicates that field and photographic methods may be more capable of sensing differences in abundance between study areas than ENVI-based approaches. This may show that the use of human eyes can better distinguish differences in quadrat abundance than ENVI can, even if only slightly.

This may only be the case given that the same observer is used for every quadrat due to between-observer bias, however (Morrison and Young 2016).

Weighted chlorophyll florescence also found a significant difference along the horizontal stress gradient, but the magnitude of the detected differences was not as great as the field, photographic, or ENVI methods. The chlorophyll florescence method

36 found a difference between the abundance of rock between horizontal positions as well, but this is most likely a conflicting result of lichen and bryophyte abundance. That is, to say that as lichen and bryophyte abundances increase, the “abundance” of rock, and the detectable endolithic algal flora, naturally decreases.

Mean chlorophyll abundance showed no significant difference between lichen and rock substrates. Since there was not sufficient data to analyze bryophytes effectively, a future study may be interested in a larger sample size, or specifically investigating bryophytes. Differences in the strip and violin plots (Figure 12) at different horizontal positions seem to indicate higher chlorophyll fluorescence readings in off- route quadrats than route or fringe quadrats for both rock and lichen. This may show that rock climbing creates a more stressful environment for the lichen and photosynthetic material, like photosynthetic cyanobacteria or algae (Casamatta et. al.

2002, Matthes et. al. 2000), present on the rock surface. This could also be an effect of the fact that different lichen species have different cortex thicknesses, which could cause differences in chlorophyll florescence measurements. While this is not statistically significant, it may indicate a potential trend with a larger sample size. Future studies may be interested in categorizing organisms into functional type or species before taking chlorophyll florescence measurements in order to mitigate error through differing cortex thicknesses of species. This may be the case when evaluating photosynthetic capabilities of vascular plant species as well. Perhaps creating an index

37 of the chlorophyll florescence of healthy vs. stressed species could be advantageous for future analyses.

Comparison of All Methods

Visually assessing the abundance of a quadrat can be time consuming in the field, especially with saxicolous lichens that may only be discovered with a hand lens, but it is currently the standard approach in most ecological research. Because of this, photographic, ENVI, and chlorophyll florescence measurements were compared to visual abundance estimates in an effort to understand their advantages, disadvantages, and accuracies.

Photographic methods take less time, perhaps thirty seconds per quadrat in the field once safety system has been established, and a few minutes of estimation on a laboratory computer, to determine the level of cryptogam abundance. Since a hand lens in unavailable through photographic methods, it may be impossible to detect certain cryptic crustose lichens without being directly in the field (Kuntz and Larson 2006a). The camera itself introduces another potential source of error since the images are introduced to the potential sources of bias or inefficiencies produced by putting the raw abundance information through another medium (the image, instead of direct viewing in the field) before interpretation. Photographic methods correlated highly with field methods indicating that this form of measuring abundance received similar results to the field methods. This method can also be costly, since a camera must be purchased if

38 not owned. But perhaps the largest drawback is that species of cryptogams are often hard to identify without investigating them in person, and misidentification is a concern.

ENVI analysis also requires less time in the field, since photographs of each quadrat are simply taken and the rest is done in a lab setting (roughly 30 seconds of field time once quadrat safety system has been established). ENVI, however, takes a considerable time in a lab setting through the proportional method of determining abundance (about 15-20 minutes per quadrat). Furthermore, ENVI removes a level of human inaccuracy, but introduces potential inaccuracy of the ENVI program, since ENVI may have difficulty distinguishing lichen apart from other parts of the photograph from color alone. ENVI methods showed a slightly higher correlation to field methods than photographic methods did, indicating that removing some visual bias in a lab setting may create a result similar to a field setting. ENVI may also identify color changes on a pixel level that a human eye cannot detect when viewing a photograph of a quadrat on a computer screen but may see when viewing the quadrat in the field.

Using photography as a means of assessing abundance may also circumvent the safety issues associates with rock climbing and rough terrain. If using a drone to access sites and take a picture while remaining on the ground away from the immediate study site. Duffy et. al. (2018) used high resolution cameras that could determine seagrass cover from a drone, without having to directly access their study site. The use of a more accurate camera may also increase the ability to detect the presence of, or even

39 identify, lichen from image data. Van Der Veen and Csatho (2005) showed that the use of multispectral imagery could allow saxicolous lichen to be identified.

Chlorophyll florescence abundance methods consumed the most time in the field since fixing leaf clips on rock surface, waiting for sample darkening, and taking the chlorophyll florescence measurement takes about 1 hour per route (9 quadrats). This method also relies on having a field visual estimate of abundance to calculate weighted chlorophyll florescence measurement. Compared to photographic and ENVI methods, chlorophyll florescence was the least correlated to field methods. While not the best measure of abundance comparatively, chlorophyll florescence is able to provide valuable insight on the presence of photosynthetic material unobservable by visually assessing a quadrat with the naked eye or hand lens. This method is also subject to error when measuring florescence of lichen due to the thallus differences between lichen species and amount of solar radiation each lichen receives (Gauslaa et. al. 2001). Since the cortex surrounds the algal layer, which would be the fluorescing component, the varying thicknesses of cortex may impede the light pulses or readings recorded by the chlorophyll fluorometer.

Conclusion

Overall, the field, photographic, and ENVI methods appear to be reliable methods to estimate cryptogam abundance on rock surfaces since they all detected a change in abundance over a stress gradient and correlated rather strongly despite the potential bias each one introduced. Each method has advantages and disadvantages 40 depending on the time constraint, level of accuracy necessary, cost, or the amount of bias allowed for the study or research being conducted. Using chlorophyll florescence as a method for estimating abundance is not advised because of its time constraints, cost

(chlorophyll fluorometers are expensive), and limited ability to assess abundance compared to other methods assessed. While not the best method for estimating abundance, chlorophyll florescence did gather information on photosynthetic material not observable by any of the other methods.

While these methods have been compared and assessed, there are still important aspects of this type of research to consider. There are many other methods of assessing abundance besides the ones discussed in this study, like infrared cameras

(Schoenecker et. al. 2018) or different image classification software (Luscier et. al.

2006). There are many different types of communities besides rock-dwelling cryptogam communities where this comparison of methodologies could be relevant as well. Future study on these methods within different study sites such as rock surfaces hundreds of feet off the ground, alpine zones, photosynthetic microorganism communities, or more generally, deciduous or pine forests could provide more insight on how different ways of collecting abundance data compare to one another.

The Effects of Rock Climbing on Bryophyte and Lichen Community

Composition in the Red River Gorge, Kentucky.

Introduction

National Parks, National Forests, and other natural areas are important in providing ecosystem services that benefit society and the natural world. They provide economic goods like lumber and minerals, environmental regulatory processes including carbon cycling and wastewater treatment, recreational value (Guerry et. al. 2015), and wildlife habitat (Ezebilo and Mattsson 2009). Outdoor recreation has become a significant industry, worth over 400 billion dollars (U.S. Department of Commerce, 2016) in the United States, but the potential exists for humans to have detrimental effects on the natural resources that make it so appealing in the first place (Giuliano 1994). For example, backpacking and hiking create human-cut trails in pristine landscapes (Giuliano

1994) by compacting soil and potentially creating fractured landscapes. Mountain biking increases also impacts landscapes by increasing erosion potential (Pickering et. al. 2010).

The recent increase in the number of rock climbers (Krajick 1999) also creates increased human pressure on the ecosystems present on cliff-faces.

Cliff-face habitats have unique conditions that allow organisms with specific habitat requirements and disturbance requirements to exist, contributing to the biodiversity of the landscape (Kunz and Larson 2006a). The autotrophic community comprises mostly of lichens, bryophytes, and non-woody vascular plants, which are affected by many factors. The type of rock or substrate present has a great influence on the lichen species able to colonize (Brodo et. al 2001). Variation in microtopography influences the surface area of the rock, creating more habitat space and potentially crevices that holds moisture more readily than bare rock surface (Kuntz and Larson

2006b). A change in aspect can impact the amount of sunlight a certain area of the cliff face receives, which can also affect moisture levels and impact the community present on the surface (Kuntz and Larson 2006b). Slope of the rock surface can affect factors associated with vegetation colonization through a variety of factors including seed rain

(Holzschuh 2016) and ease of establishment. Most recently, direct human activity has shown to have an impact on these autotrophic communities (Camp and Knight 1998,

Holzschuh 2016, Clark and Hessl 2015).

The effect of rock climbing on plant species abundance and composition in vertical rock environments has received limited previous research attention. Studies include small cliff bands in Minnesota (Farris 1998) and dolomitic cliffs in Illinois (Nuzzo

1996), to limestone cliffs in Spain (Lorite et. al. 2017) and the San Jura Mountain Range in Switzerland (Rusterholz et. al. 2003). Studies have shown that rock climbing has a significant negative impact on both species richness and species abundance (Tessler and

Clark 2016) of bryophytes. In contrast, some studies have shown there is no effect on bryophyte species richness or abundance (Kuntz and Larson 2006b, Baur et. al. 2007,

Clark and Hessl 2015). Kuntz and Larson (2006a) specifically noted that species richness dropped by 33% on climbed areas, but this was not statistically significant. Bryophytes may be more affected by microtopographic changes, like crevice size and frequency

(Kuntz and Larson 2006a). Climbers may also avoid vegetated areas due to topographic features or the presence of the vegetation itself (Farris 1998).

Like bryophytes, there are mixed conclusions on the effects of rock climbing on lichen communities. Lichen species richness has ranged from being, although insignificant, 51% lower in climbed surfaces than unclimbed surfaces (McMillan and

Larson 2002), to having no significant difference (Kuntz and Larson 2006a), to have a significant negative difference (Adams and Zaniewski 2012). Similarly, the effects of rock climbing on lichen abundance ranged from being insignificant (McMillan and Larson

2002), to very significant (Adams and Zaniewski 2012), to even having a positive significant difference (Kuntz and Larson 2006a).

Many of the studies completed specifically on rock climbing’s effect on vegetation either minimally account for microtopographic variation, or do not account for it at all (Holzschuh 2016), which could account for some of the variation in these studies (Kuntz and Larson 2006b). However, microtopography is only one source of potential variation. Other important factors include rock type, aspect, slope, and

44 potential for seed rain, which are minimally covered in most of the current studies

(Holzschuh 2016).

To date most studies of climbing’s effects have been based on relatively simplistic comparisons between climbed routes and unclimbed rock (Holzschuh 2016).

This type of analysis may provide a good general measure of the effects of climbing but does not allow inferences about how the effects of climbing vary between and within sites due to factors like microtopography, aspect and, critically, level of use.

Furthermore, there has been no previous research on this topic at the Red River Gorge in Kentucky, which rock climbing development has increased very dramatically in recent years (Maples et. al. 2018).

Spelunkers and rappel enthusiasts climbed and established the first routes within the Red River Gorge (RRG) (Ellington 2010). In the 1980s, the number of routes doubled, and climbers started coming to the area in greater numbers (Maples et. al.

2018). In 2004, the Red River Gorge Climber Coalition (RRGCC) purchased a 750-acre plot of land named the Pendergrass-Murray Recreation Preserve (PMRP) and in that same year, Rick and Liz Weber purchased Muir Valley. In 2013, the RRGCC purchased

Miller Fork Recreation Preserve (MFRP) that housed 309 acres, and again in 2017, the

RRGCC purchased Bald Rock Recreation Preserve (BRRP) at 102 acres. Today, PMRP is home to over 400 routes, Muir Valley is home to over 400 routes, MFRP has over 300 routes, and BRRP has almost 150 (Adventure Projects Inc., 2019). Today, the Red River

Gorge has over 2500 routes (Maples et. al. 2018), and in 2015 alone, there were almost

7500 climbers estimated to visit the RRG (Maples et. al. 2018).

Due to the variation found in the results from previous research, and the lack of information on impacts in heavily exploited areas such as the Red River Gorge, it is imperative to understand how rock climbing is affecting cliff ecosystems. Based on the pitfalls of similar studies completed in the past that were discussed by Holzschuh (2016) and Kuntz and Larson (2006b), the objectives of this project are: (1) to quantify species abundance, richness and composition of unclimbed and climbed surfaces while accounting for variation in intensity in use within and between routes, and (2) to control for non-climbing related factors that affect species diversity and richness, including microtopographic variation between rock slab study sites.

Methods

Study Area

The Red River Gorge (RRG) is an internationally-renown rock climbing destination located within a mix of private and forest service land (Ellington 2012) in eastern Kentucky (37.784, -83.682). The Gorge exists between 145 m and 435 m of elevation, and the general area contains about 26,000 acres and was created by wind and water erosion (Studlar and Snider 1989). The Gorge consists of many different types of rock including Newman limestone, Borden calcareous shale, and siltstones (Studlar and Snider 1989), but the rock it is most famous for, and the type in which climbers

46 enjoy the most, is its Corbin sandstone-conglomerate (Ellington 2012, Ellington 2013,

Studlar and Snider 1989).

Within this large area exists hundreds of rock climbing routes (Adventure

Projects Inc., 2019) that climbers utilize throughout the summer and fall. Climbers often hike anywhere from 5 to 35 minutes (Ellington 2012, Ellington 2013, Weber 2014) to access these routes through a vegetation community comprising of magnolias and hemlocks, and often oak-pine forests on the higher elevation locations (Higgins 1970).

The overhanging sandstone can make It easier for climbers to find refuge from the rain

(Ellington 2012, Ellington 2013), which averages to 114 cm of rainfall per year (Studlar and Snider 1989), creating a humid, temperate rainforest environment. Maximum average temperatures are around 34°C, and minimums between -1 and 4°C (Studlar and

Snider 1989).

Site Selection

Within the Red River Gorge, there are over 2,500 rock climbing routes

(Adventure Projects Inc., 2019). Using mountainproject.com (Adventure Projects Inc.,

2019), a world database of rock climbing routes, I used a search criterion that filtered rock climbs within the RRG to those between a difficulty grade of 5.7 (easy) and 5.11c

(difficult intermediate), resulting in 753 routes. Each route was individually assessed based on online photographs of the rock climb (Adventure Projects Inc., 2019), a guidebook description (Ellington 2012), and comments climbers left on mountain project route description page (Adventure Projects Inc., 2019). Each route was assessed

47 based on level of homogeneity of micro and macro topography, ease of access to quadrats, and a desired height of > 9 m. Overall, 42 routes met these criteria.

The land managers of Red River Gorge climbing areas were contacted. The Red

River Gorge Climber’s Coalition (RRGCC), and The Friends of Muir Valley (FMV) both allowed vegetation surveying to occur on their land. This allowed all routes within

Pendergrass-Murray Recreation Preserve, Miller Fork Recreation Preserve, and Muir

Valley to be considered as possibilities for research. Research did not include USDA

Forest Service land, Motherlode region, or the Chocolate Factory. This created a final list of 26 routes. Routes were randomized on a random number generator and surveyed in the resulting respective order.

Plot Establishment

Each rock climb was identified using the “Red River Gorge South” guidebook

(Ellington 2012). Once at the base, the route was climbed, and a safety system was implemented at the top consisting of a quad anchor with two locking carabiners on the rappel rings, one locking carabiner on each master point with a fixed rope attached to each for ascension and descension.

Nine total quadrats were then established on each route. Quadrats were drawn in chalk on the rock face using a 25 cm by 50 cm frame and were then marked into a 2 x

5 tic marked grid, splitting each quadrat into outlined squares that each contained 10% of the total area. Quadrats were marked at three horizontal positions and three vertical positions. Horizontal positions were 1.5 m off the ground, 50% of the height of the rock

48 climb, and 75% of the height of the climb. Height of the rock climb was found in the guidebook (Ellington 2012). Height of each quadrat was estimated via a combination of visual assessment, guidebook description, and placement of bolts. For example, if there was a 30 m climb with 5 bolts and a set of anchors, the third bolt would generally mark the 50% height mark.

Three horizontal positions were established for every vertical position. Quadrats were placed in the center of the route, on a section of rock located off-route, and a section between the two denoted as the “fringe.” The quadrat located in the center of the route was determined by using a combination of bolt placement (a climber must be able to access a bolt for protection while climbing the route), the flow of the climb as determined by the climber, and the description located in the guidebook (Ellington

2012). The center of the fringe quadrat was placed 0.75 m to the left or the right of center of the route quadrat. This distance metric was used in order to account for shorter climbers that may or may not be able to reach the bolt from that distance, as well as creating a reasonable distance from the center of the climb that some climbers may or may not use, thus creating an “intermediate use” zone. The center of the off- route quadrat was created 1.5 m to the left or right of the route quadrat. This was to ensure climbers could not reach protective bolts while in this zone, thus creating a quadrat in an area where climbers would not be able to safely climb.

Field Measurements

The aspect of each quadrat was recorded using a compass. If the quadrat was on an arete or dihedral feature, the aspect was taken from each side of the quadrat. When analyzing aspect, a measure of “folded aspect” was used (McCune and Keon, 2002) as a method of creating a noncircular dataset from aspect. A clinometer was used to measure the slope at the center of each quadrat.

Microtopographic variation was recorded using a malleable string on the rock surface and by running it from the upper left corner to the lower right corner of each quadrat. The string was pushed into the uneven surface of the rock for the entire diagonal length of the quadrat, so inconsistencies in the rock’s surface would create extra length in the string. Therefore, the longer the string distance, the greater the microtopographic variation present within the quadrat.

Vegetation cover and species abundance were visually estimated. Quadrats were first sprayed with 8-10 mist sprays of water to activate the photobiont in lichen, and the tissue of bryophytes so they were easier to see. Vegetation cover was taken by visually estimating each individual species present and then adding up each cover percentage into the “total cover.” In the field, individual species were differentiated by functional type and phenotypic differences and collected to later be identified in a laboratory setting. Bryophytes and fruticose lichen were collected by hand, umbilicate, squamulose, granulose, and foliose lichens were collected by scraping them off with a chisel edge, and crustose lichens were collected by taking a hammer and chisel directly

50 to the rock. Crustose lichens were only collected from unclimbed quadrats in order to preserve the integrity of the rock climb.

Samples were identified using information from Brodo et. al. (2001), Brodo et. al.

(2016), CNALH (2019), Lendemer (2018). If species could not be identified, they were labelled by functional type.

Data Analysis

All data analysis was completed in R (v3.4.4). Linear mixed effects modeling

(function: lmer, R package: lme4 (Bates et. al. 2015)) was used to determine environmental effects on total abundance, species richness and Shannon species diversity. Horizontal position and vertical position were defined as fixed effects; climbing use intensity, string distance, and heat load index were included as covariates; route identity was fitted as a random effect. For each model, all possible combinations of the variables were examined using the dredge function (R package: MuMIn (Barton

2018)). Scree plots of the change in AIC across all models would be averaged (function: model.avg, R package: MuMIn (Barton 2018)). In all cases, all averaged models were within 2.15 AIC points of the optimal model. Functional type-specific abundance was analyzed visually by observing trends in strip and violin plots. Initially, linear mixed effects modeling (function: lmer, R package: lme4 (Bates et. al. 2015)) was attempted, but since most functional types were not seen on the majority of quadrats, the data was highly “zero-inflated” making it impossible to detect or model meaningful trends in abundance.

Rare species can add significant noise to ordinations whilst providing little interpretable ecological information (Gauch 1982). I defined rare species as those that occurred in < 4% of quadrats. These were combined into categories based on their functional type. Functional types of lichens were denoted as crustose, foliose, fruticose, granulose, squamulose, and umbilicate. Bryophytes remained labeled as bryophytes and were not separated into separate functional types. Cladonian lichens, however, were not identified to the species level and are denoted as “cladonia spp” because they are extremely difficult to identify when not fruiting. Community data was then analyzed in two ways, i) by species (with rare species defined as combined functional types); and ii) according to the total abundance of the plant functional types. In addition to species, functional types were used in order to generate trends and results that may be compared to other research regarding cryptogam communities on rock faces, even though this generalization may sacrifice some species specific attributes. Shannon diversity was also calculated for each quadrat (function: diversity, R package: vegan

(Oksanen et. al. 2019)) based on the raw, uncombined abundances of each species.

Nonmetric multidimensional scaling (NMDS) ordination (function: metaMDS, R package: vegan (Oksanen et. al. 2019)) was run on both functional type and species composition data. NMDS was run on species data and functional type data separately, with data transformation (combining rare species and removing quadrats with no species) completed beforehand. A maximum of 3000 tries was set on species data

NMDS and 1000 maximum tries was set on functional type data NMDS, and each was

NMDS was set to three dimensions. Three dimensions were used because models would otherwise not converge. Convex hulls (function: ordihull, R package: vegan (Oksanen et. al. 2019)) were used to visually represent trends in composition in relation to the vertical and horizontal position of the quadrats. These hulls create a figure around all data points within each horizontal or vertical position class (the points of the figure each connecting to an outermost point within each quadrat position), which allowed viewers to discern changes in community composition over quadrat positional changes.

Centroids of the community structures for each quadrat position were also used to observe community compositional changes.

A Permutational multivariate analysis of variance (PERMANOVA) was used in order to determine the effect of horizontal position on species composition and functional type composition after vertical position was accounted for, and vice versa

(function: adonis, R package: vegan (Oksanen et. al. 2019)). Species and functional type data were correlated with horizontal position and vertical position twice, the first time looking at horizontal position once vertical position was accounted for, the second with vertical position once horizontal position as accounted for. Each PERMANOVA was conducted with 999 permutation using the “bray” distance matrix method. A dissimilarity index was also calculated for each Permanova (function: vegdist, R package: vegan (Oksanen et. al. 2019)), and then tested for homogeneity (function: betadisper, R package: vegan (Oksanen et. al. 2019)) along each quadrat position using median and

53 centroid analyses. This was used to determine if the PERMANOVA were influenced by the spread of the data instead of the influences of the means of the variables.

In order to determine how environmental gradients influence functional type composition and species composition, surface overlays of climbing use intensity, heat load, and microtopographic variation were created over the NMDS (function: ordisurf, R package: vegan (Oksanen et. al. 2019)). Surface overlays allowed the viewer to visually observe how species and functional type averages exist along each environmental gradient, showing the quantitative level of the gradient that each species or functional type is associated with. Climbing use intensity was estimated for each route using Clark and Hessl’s (2015) climbing use intensity formula, the difficulty (Adventure Projects Inc.,

2019), hiking time to access (Ellington 2012, Weber 2016), and thrill rating (Adventure

Projects Inc., 2019). Slope, aspect, and a latitude found were used to estimate McCune and Keon’s (2002) heat load index.

Results

Diversity and Plant Functional Type Abundance

Lichen functional type groups found at the Red River Gorge included crustose, foliose, granulose, squamulose, fruticose, and umbilicates. Acrocarpous and pleurocarpous mosses, and liverworts were found throughout the Gorge as well

(Appendix B, Table B. 1). Overall, 42 different species were identified throughout all routes. 14 crustose, 6 foliose, 6 granulose, 3 squamulose, 2 umbilicate and 1 fruticose lichens were found and 10 bryophyte species were found (Appendix B, Table B. 1). 54

Quadrat-level total abundance ranged from 0 % to 96 % (Figure 14), with route quadrats generally having the least amount of cover, fringe quadrats having an intermediate amount of cover, and off-route quadrats having the most cover. There was also a significant interaction between horizontal position and vertical position changes

(Appendix B, Table B. 2). This indicates that the changes in abundance based on vertical position are dependent on the horizontal positions examined. The linear mixed effects model of total species abundance showed a significant (P < 0.001) effect of off route quadrats as compared to the route quadrats (Appendix B, Table B. 2).

Figure 14. Total abundance of quadrat positions. Each point represents the total abundance of one quadrat. Quadrat positions are depicted on the X-axis as 2 letters: the first being the abbreviation for the vertical position (L = lower, M = middle, U = upper) and the second being an abbreviation for the horizontal position (R = route, F = fringe, O = off-route).

Crustose and granulose lichens were the most abundant functional type, and were both rather consistent across all quadrats, except for granulose lichens increasing slightly across horizontal positional changes (Figure 15). Foliose, squamulose,

56 umbilicate, and fruticose lichen all showed large increases in abundance as quadrats moved from route to off-route.

These functional types also showed an increase in abundance as elevation increased, but not to the same degree as horizontal positional change. Like lichen abundance, the bryophyte abundance increased as quadrats moved away from climbed areas. But unlike lichens, bryophyte abundance increased as elevation decreased. For all functional type abundances, horizontal positional change seems slightly more impactful than vertical positional change.

The linear mixed effects models run on species richness (Appendix B, Table B. 3) showed that off-route horizontal position was significant (P > 0.001) when compared to the route position, showing a positive correlation with species richness. Heat load was also found to be a significant (P = 0.034) positive effect on species richness. All other variables were not significant. When investigating Shannon species diversity, there was a significant negative difference between the route and the off-route quadrats

(Appendix B, Table B. 4), but all other factors were nonsignificant.

Cover (proportional)Cover

Visually EstimatedVisually

Quadrat Position

Figure 15. The Abundance of each functional type of each quadrat position. Quadrat positions are depicted on the X-axis as 2 letters: the first being the abbreviation for the vertical position (L = lower, M = middle, U = upper) and the second being an abbreviation for the horizontal position (R = route, F = fringe, O = off-route). Variation in Species Composition

The NMDS of species composition converged after 765 tries with a stress level of

0.13. Route, fringe, and off-route quadrats are generally located in the same central cloud within the NMDS, with a small number of fringe and off-route positioned as outliers away from the central cloud (Figure 16). Most lichen and bryophyte species were associated with all three horizontal positions. Species that are more associated with fringe and off-route quadrats include Leproloma membranaceum, Cladonia spp.,

Calypogeia neesiana, Lepraria sp. and Umbilicaria mammulata. Species only associated with off-route quadrats are rare bryophyte species, and Leucobryum glaucum. No species were more affiliated with route quadrats than off-route or fringe quadrats.

Figure 16. NMDS of the species based community composition with each quadrat relative to horizontal position. Each species code (D) can be found on Appendix B, Table B. 1. A) Community structuring of route quadrats. B) Community structuring of fringe quadrats. C) Community structuring of off-route quadrats. D) Community structuring of species compared to the extent of each quadrat position.

Examining patterns relating to vertical position within the NMDS (Figure 17) revealed that most species are found within the overlap of all three vertical position hulls. Key differences included Lepraria spp. being more associated with middle and upper level quadrats, Leproloma membranaceum being associated with upper and

60 lower quadrats, and rare bryophyte species and Leucobryum glaucum being more associated with lower quadrats.

Figure 17. NMDS of the species based community composition of each quadrat relative to vertical position. Each species code (D) can be found on Appendix B, table B. 1. A) Community structuring of lower quadrats. B) Community structuring of middle quadrats. C) Community structuring of upper quadrats. D) Community structuring of species compared to the extent of each quadrat position.

After accounting for vertical position, horizontal position was significant in the

PERMANOVA analysis (Appendix B, Table B. 5). Similarly, after accounting for horizontal position, the vertical position was also considered significant (Appendix B, Table B. 5).

The results from the multivariate homogeneity of groups dispersions tests were found to be nonsignificant for both PERMANOVAs, suggesting that the differences in mean composition between quadrat positions were analyzed by the PERMANOVA instead of differences in variability in composition within groups (Appendix B, Table B. 6).

General Additive Models were used to visualize the effects of heat load, climbing use intensity, and microtopographic variation on community composition (Figure 18).

Most of the quadrats and species are located at the upper end of the heat load spectrum (between 0.45-0.55 MJ cm–2 yr–1) found on routes, with the exception of a few outliers. The heat load index effect was significant (P < 0.001) and explained 15.4% of the deviance in community composition. Most quadrats tended to be located on routes that had an intermediate level of use (CUI = 1.38-1.42). Some species outliers like

Leucobryum glaucum, Lepraria membranaceum, Lepraria spp., Calypogeia neesiana, rare umbilicates, Cladonians, and rare bryophytes were more strongly associated with routes having higher CUI (1.44-1.48), whereas Physcia subtilis preferred lower used areas (CUI approx. 1.36). Climbing use intensity was a significant (P = 0.008) indicator of community composition and explained 9.24% of the deviance. Most quadrats were associated with lower microtopographic variation (MV= 60-63 cm). Microtopographic variation also was a significant effect (P > 0.001) and explained 18.6% of the deviance.

Figure 18. NMDS ordination of community composition with a surface overlay depicting (A) climbing use intensity (CUI), (B) microtopographic variation and (C) heat load. Species codes and be found in Appendix B, table B. 1. Variation in Function Type Composition

Convergence of the NMDS of functional type composition occurred after 40 tries with a stress level of 0.10. Functional types likely to be more resistant to climbing, for example crustose and foliose, appeared in the lower left corner of the NMDS and the X- axis shows a gradient of climbing intensity. The Y-axis, however, did not appear to follow any easily interpretable trend. When this NMDS was related to quadrat horizontal position (Figure 19), it revealed that most different lichen functional types and bryophytes are found within quadrats of all three horizontal positions. Granulose lichens appeared to be associated with fringe and off-route quadrats, and very weakly associated with route quadrats. Fruticose lichens were more associated with off-route quadrats, but still appeared outside of any of the hulls. All other functional types are equally associated with all horizontal positions. All lichen functional types and bryophytes were associated with all three vertical positions except for fruticose lichens, which were more associated with upper quadrats, but located outside of every vertical position hull (Figure 20).

Figure 19. NMDS of the functional type based community composition of each quadrat relative to horizontal position. (A) Community structuring of route quadrats. (B) Community structuring of fringe quadrats. (C) Community structuring of off-route quadrats. (D) Community structuring of functional types compared to the extent of each quadrat position.

PERMANOVA results showed that horizontal position (Appendix B, Table B. 7) had a significant effect on functional type composition after vertical position had been accounted for (P = 0.001), and that vertical position also had a significant effect on functional type composition after horizontal position was considered (P = 0.002). Both

65 multivariate homogeneity of groups dispersions tests were non-significant, indicating each PERMANOVA detected differences in mean community between quadrats instead of the variability within the community composition of each quadrat (Appendix B, Table

B. 8).

Figure 20. NMDS of the functional type based community composition of each quadrat relative to vertical position. (A) Community structuring of lower quadrats. (B) Community structuring of middle quadrats. (C) Community structuring of upper

quadrats. (D) Community structuring of functional types compared to the extent of each quadrat position.

The surface overlays of climbing use intensity (Figure 21) showed that squamulose lichen and bryophytes were associated with more concentrate climbing, umbilicates and granulose lichen generally were found on quadrats with an average level of climbing, and foliose, crustose, and fruticose lichen were associated with lower

CUI. As a variable, CUI significantly impacted functional type composition (P = 0.014) and explained 8.43% of the deviance. Microtopographic variation (Figure 21) was also a significant factor (p < 0.001) that explained 20.5% of deviance in function type community structure. Foliose, umbilicate, and crustose lichen were found on the lower end of the surface overly, while granulose and fruticose were on the upper end and squamulose and bryophytes were in a mid-range. Functional types existed on a wide spread of heat load (Figure 21), with foliose and crustose lichen on the upper end (0.60

MJ cm–2 yr–1) and fruticose and granulose lichen more associated with routes that had less heat (0.25 MJ cm–2 yr–1). Heat load explained 19.9% of the deviance of functional type community variation and was a significant factor (P < 0.001).

Figure 21. NMDS ordination of functional type composition with a surface overlay depicting (A) climbing use intensity (CUI), (B) microtopographic variation and (C) heat load. Discussion

Diversity and Plant Functional Type Abundance

Crustose lichens were found to be the most abundant functional type, similar to studies by Kuntz and Larson (2006a) and Farris (1998). This could potentially be explained by the resilience to disturbance crustose lichen have due to their integration into the crystal structure of the rock (Brodo et. al. 2001). This integration also potentially gives the crustose lichen a refuge from disturbance in the structure of the rock, which protects it from environmental conditions. Fruticose lichens were the least frequent, which can be explained by their morphology. Fruticose lichens are extremely susceptible to scraping or abrasion disturbance and are easily plucked off the rock by hand. Other lichen functional types fall somewhere between the crustose-fruticose spectrum. Bryophytes were generally rare, and were mostly found in lower, shaded quadrats, pockets or crevices that both protected them from sun exposure and helped them retain moisture. Clark and Hessl (2015) also found bryophytes in shadier and wetter conditions cliff faces within the New River Gorge, West Virginia.

As quadrats trended from the center of the route to off route positions, total abundance increased. Since climbing also decreased from the center of the route outward, this indicates that climbing has a negative effect on lichen and bryophyte abundances. This trend was universal across routes and was statistically significant. In agreement with these results, climbing was found to have a negative affect by multiple previous studies (Rusterholtz et. al. 2003, Adams and Zaniewski 2012, Nuzzo 1996). 69

According to Kuntz and Larson (2006a), however, rock climbing positivity influenced lichen communities, which may be due to crustose lichens having more resistance to climbing stress and being more capable of establishing on climbing disturbed surfaces than other vegetation types (Adams and Zaniewski 2012). Clark and Hessl (2015) found no effect on species abundance and richness of bryophytes, potentially due to climbers’ avoidance of such species (Nuzzo 1996).

The significant interactions between horizontal position and vertical position indicate that differences in abundance due to vertical position changes is dependent on the horizontal position. Environmental effect differences between lower and upper quadrats combined with the frequency of climbing may explain this. Environmental conditions may allow higher total abundances on upper quadrats due to sunnier conditions (Adams and Zaniewski 2012, and Clark and Hessl 2015, Palmqvist and

Sundberg 2000), than lower quadrats, but quadrats located on the climbing area are subjected to climbing while those off-route are not. Climbing may control cryptogam populations, but to a certain fixed amount instead of complete extirpation from quadrats due to microtopographic variation as refuge, increased resilience from lichen over time, or inability to control lichen forms once they reach a certain size minimum.

On both upper and lower quadrats with a higher potential vegetative cover, rock climbing brings these population to the fixed amount within the route, but off the route, populations are free from the control climbing exerts. So greater differences in abundance may be seen in vertical positional change off-route than on route.

Lichen species were generally more abundant in upper quadrats as opposed to lower quadrats, which might be explained by three different reasons. Lichens may grow faster on dryer, sunnier habitats (Palmqvist and Sundberg 2000), and do better when those conditions, which are met on upper quadrats more than lower quadrats (Adams and Zaniewski 2012, Clark and Hessl 2015). It may also be difficult for lichens to establish on lower quadrats due to the increased climbing intensity they experience since some climbers may not make it to the top of the route. Another potential reason for this is the competition lichen may have with bryophytes, which tend to have higher success rates in more mesic and less sunny conditions (Berg et. al. 2002) present at the bottom of the route. Clark and Hessl (2015) found bryophytes are also more abundant at lower parts of the cliff face. Another important factor to consider is the bryophyte presence itself. Climbers may simply avoid putting their hands and shoes on bryophytes

(Nuzzo 1996) since their slippery surface may cause climbers to fall. So even if climbing use intensity may be higher at the bottom of the climb generally, climbing intensity may be lower for areas dominated by bryophytes specifically.

Variation in Species Composition

The linear mixed effects model on Shannon species richness indicated that as quadrats were moved from the route position to the off route position, species richness decreased, which was also found on sandstone cliff faces in Pennsylvania (Adams and

Zaniewski 2012). Whilst greater heat load had a significant positive impact, similar to sandstone cliffs at the New River Gorge, West Virginia and Pennsylvania (Adams and

Zaniewski 2012, Clark and Hessl 2015). This indicates climbing does have a negative impact on species richness, and confirms lichens’ preference for dryer, sunnier conditions found by Palmqvist and Sundberg (2000). Furthermore, species diversity was negatively impacted by the change in horizontal position, indicating a negative effect from rock climbing.

Since rare bryophytes, umbilicates, and other common bryophyte species appear on the lower left part of the NMDS, and granulose species generally appeared on the upper right part, it appeared as though both the X and Y-axes indicated gradients of increasing climbing use originating from more climbing resistant species like Phlyctis petraea and Lecanora oreinoides in the upper right portion of the NMDS.

While richness, diversity and community composition changed, most species identified were associated with all three horizontal positions. Some species, however, namely certain bryophytes, squamulose lichens, and granulose lichens more associated in fringe and off-route quadrats, most likely due to their lack of resilience to climbing

(Adams and Zaniewski 2012), while species occurring in the middle of the ordination were potentially generalists. Outlier points found in this NMDS were quadrats that contained 60% or more bryophytes, or quadrats that contained Lepraria sp., an unidentified granulose species. Similar to the NMDS results showing horizontal hulls, the vertical hulls of the showed the majority of species present were found within all three vertical positions. Rare bryophytes and Leucobryum glaucum were more associated with lower quadrats, which makes sense due to the more mesic conditions. Lepraria spp. was

72 more associated with middle and upper quadrats, potentially due to its inability to compete with species accustomed to mesic settings, or perhaps it is more resilient to brighter, drier environments.

When mean effects of horizontal positional change were removed to determine the effects of vertical position change, vertical position remained significant. The horizontal position remained significant when removing the mean effects of vertical position as well. This indicates that regardless of the interaction between horizontal and vertical position, both were still significant indicators of community change.

Most species existed within the upper end of the heat load found on routes (0.50

MJ cm–2 yr–1), which shows that most lichen preferred dryer, sunnier habitat. Similar results were discovered by Adams and Zaniewski (2012) and Clark and Hessl (2015).

Rare bryophytes and Leucobryum glaucum were more associated with a heat load of

0.55 MJ cm–2 yr–1, but since this heat load index is focused on a route level and not a quadrat level, this measurement may be misconstrued. While this effect was significant, there is potential noise in the amount of solar radiation each quadrat would receive due to the lack of quantification of shading effects. Future studies want to quantify shading effects to gain a more complete picture of heat load index effects.

The mean climbing use intensity found within the routes was 1.41 ± 0.139, which is where most species were found within the ordination (see Figure 18. Exceptions here include rare bryophytes and Leucobryum glaucum and Calypogeia neesiana which existed towards the upper end of CUI (CUI = 146-148). But since climbers may try to

73 avoid bryophyte areas (Nuzzo 1996), CUI might not be a good indicator of bryophyte population. Rare umbilicates and cladonians existed on areas high CUI areas as well, but since they weren’t located on route quadrats, they wouldn’t experience very high use intensity.

The mean and standard deviation of microtopographic variation found within the routes was 62.62 cm ± 6.04 cm. Most species preferred 61-62cm of microtopographic variation, potentially proving that heterogeneity of habitat (the intermediate of microtopography) created the best spread of community structure.

Lepraria spp. existed in areas with higher microtopographic variation, which may be because of the refuge from disturbance microtopography offers their dusty and fragile structure. Bryophyte species existed in more variable microtopographic areas as well, this is most likely due to increased moisture content in crevices and potentially more protection from climbers. Crustose and foliose species were more associated with lower variations in microtopography. This is most likely because they are more resistant to climbing disturbance and could potentially outcompete species that are less resistant to climbing disturbance.

Variation in Functional Type Composition

The functional type NMDS showed similar results to the species NMDS regarding the X-axis; it appeared to relate to a gradient consisting of the presence of climbing. The

Y-axis, however, did not appear to follow any readily discernable trend.

All functional types were associated with all quadrat horizontal positions, except fruticose lichens. Fruticose lichens were outside the hull of all horizontal positions

(Figure 19) but were most closely associated with off-route quadrats, most likely due to those areas having the least amount of climbing use. Since PERMANOVA showed horizontal and vertical positional effects were both significant, and the dispersal was not considered significant, the results shows that even though there are small differences in functional type composition, that difference is still significant, similarly to the

PERMANOVA results found in the species section.

Function types existed on a spectrum of heat load. Crustose and foliose lichen existed on the upper end, umbilicates, bryophytes, and squamulose in the midrange, and fruticose and granulose on the lower end. Crustose and foliose lichen were more likely to be tolerant of hotter, dryer environments since they were seen at higher heat loads. While bryophytes were in the midrange, they generally exist on lower quadrats, so they may experience shading or protection due to microtopographic variation.

Similar to bryophytes, squamulose lichen may utilize rock features, but umbilicate lichens were often seen on upper quadrats, suggesting they may be sun tolerant, unable to compete with other functional types in shadier environments, or highly sensitive to disturbance. Granulose and fruticose lichen appeared in upper quadrats and exist on the lowest end of heat load, this suggests they are not very sun tolerant, or utilize microtopography for shade.

The analysis of climbing use intensity (CUI) had somewhat expected results.

Crustose, foliose and fruticose lichen were more associated with routes on the low end of the CUI spectrum, most likely for different reasons. Since crustose and foliose lichen were more prevalent on route quadrats across all routes. their existence on lower CUI routes would mean that while they were resilient enough to climbing to exist there, they were more abundant in lower intensity areas. Fruticose lichen may be extremely impacted by climbing due to their resilience, so associated with lower CUI makes sense as well, but since they were strongly associated with off-route quadrats, perhaps CUI is not a good indicator of fruticose lichen. Bryophytes, granulose lichen, umbilicate lichen, and squamulose lichen tended to higher CUI. These groups also tended to exist on fringe and off-route quadrats, and may not be as impacted by climbing, or perhaps they could have been protected by microtopographic features. In the case of bryophytes, they could be more protected due to climbers avoiding bryophytes (Nuzzo 1996). CUI was, however, significant and explained 8.48% of the deviance observed between quadrats, so it seems like this is still a relevant measure of functional type differences between the quadrats.

Foliose and crustose lichens appeared to be associated with lower microtopographic variation, most likely due to their ability to establish directly on vertical rock faces, and perhaps due to their higher resilience toward climbing disturbance. Umbilicates and bryophytes on average occurred in similar levels of microtopographic variation as crustose and foliose lichens. Since climbers may avoid these areas, or since these

76 functional types tend to exist on off-route quadrats, they did not need the protection from climbing disturbance. Granulose and fruticose lichen tended to be associated with higher microtopographic variation, most likely due to the protection microtopographic variation offers. Lastly, squamulose lichen appeared to be in an intermediate area of microtopographic variation. Squamulose lichen would be less resilient than foliose, but more resilient than umbilicates and fruticose, so a middle ground may be appropriate for them in order to maximize disturbance protection on route without competition.

The variation in microtopography was very significant and explained 20.6% of the deviation between functional types found in each quadrat community, making it an important factor in community differences.

Conclusion

Rock climbing is becoming an increasingly popular sport, especially within the Red

River Gorge, Kentucky. Currently, rock climbing is a $3.8 million industry in the Red River

Gorge alone, with new climbing areas constantly being developed. With this in mind, determining the effect rock climbing has on cliff face communities is becoming increasingly important. Studies that attempted to quantify the effects of rock climbing in the past may have done so without taking other contributing factors into account, therefore, the goals of this study were to: (1) to quantify species abundance, richness and composition of unclimbed and climbed surfaces while accounting for variation in intensity in use within and between routes, and (2) to control for non-climbing related

77 factors that affect species diversity and richness including microtopographic variation between rock slab study sites.

Cryptogam abundance was found to be significantly influenced by the change in horizontal position, the main variable determining climbing disturbance. Abundance also increased as vertical position increased in height, potentially due to increased heat load or decreased climb use, but this was not found to be significant. Species richness and diversity also were negatively impacted as horizontal position shifted from off-route areas to route areas, indicating climbing created less rich and less diverse communities.

Horizontal position, vertical position, climbing use intensity, microtopographic variation, and heat load all proved to be significant indicators of both functional type community composition and species community composition. While abundance decreased with increasing climbing disturbance, the potential still existed for most species or functional types to exist on any given quadrat.

In order to decrease the impact of rock climbing on these communities, climbers should avoid climbing to the top of the route whenever possible. This would avoid damage to the upper sections of the cliff that have lower environmental stress. Climbers should also consider staying on route as much as possible while climbing to avoid impacting fringe or off-route areas.

While these findings are relevant and significant, there are several more important pieces of information necessary to understand the total impact of rock climbing within the RRG. one important question to consider would be to find out how

78 much exposed rock areas within the RRG are used by rock climbers, and how many more routes are being developed each year? While the Red River Gorge is a large climbing area, more research should be done on other climbing locations around the world as well. Lastly, while there is still much to learn about the subject, it is also important to consider the economic and social benefits rock climbing offers as well as the environmental impact while making management decisions. So, while rock climbing does have a negative impact on cliff face cryptogam abundance and it does change community structure, there is still more to learn on the subject, and more factors to consider when making large management decisions.

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Appendix A: Chapter 1 Tables

Table A. 1. Linear model results for comparing photographic method abundance estimates to field visual abundance measurements (1 = unadjusted photographs, 2 = adjusted photographs). Models created using baseR function “lm” included field total abundance as a fixed effect.

1) Variable Std. error DF t value P Field Abundance 0.239 1 3.957 0.002 2) Variable Std. error DF t value P Field Abundance 0.237 1 3.328 0.007

Table A. 2. Linear model results of iteration and class range combinations using the majority technique. This linear model also tested if adjusting had effects. Model created using baseR function “lm” included adjusting, iterations and classes as fixed effects.

Variable Std. error DF t value P Editing effects 0.057 1 -0.355 0.724 1 iteration, 5-10 0.130 5 -0.365 0.717 classes 3 iterations, 1-6 0.130 5 -0.871 0.389 classes 3 iterations, 5-10 0.130 5 -0.778 0.441 classes 3 iterations, 3-10 0.105 5 -3.189 0.003 classes 3 iterations, 3-12 0.096 5 -2.001 0.052 classes

Table A. 3. Linear model on how adjusting and estimate technique compare on 3 iteration 3-10 class range analysis and 3 iteration 3-12 class range analysis. Model created using baseR function “lm” included technique, adjusted photographs nested within technique, and iterations and classes nested in technique.

Variable Std. error DF t value P Proportion technique 0.084 1 0.486 0.628 Majority technique and edited 0.056 2 -0.363 0.718

Proportional technique and Edited 0.056 2 0.553 0.582

Majority technique and 3 iterations, 3-12 classes 0.062 2 2.292 0.025

Proportional technique and 3 iterations, 3-12 classes 0.062 2 2.333 0.023

Table A. 4.Tukey multiple comparisons of means of the chlorophyll florescence of the lichen, chiseled rock, and unchiseled rock substrates. Model created using baseR function “lm” and “TukeyHSD” included substrate as a fixed effect.

Variable Lower Upper P Lichen vs. 0.552 0.878 < 0.001 Chiseled Unchiseled 0.406 0.732 < 0.001 vs. Chiseled Unchiseled -0.309 0.0171 0.0858 vs. Lichen

Table A. 5. Linear mixed effects model of total abundance in relation to quadrat position for visual field abundance. Model created using package “lme4” function “lmer” included horizontal position and vertical position as fixed effects and route as a random effect.

Variable Std. error DF t value P Horizontal position = route 0.053 145 -6.605 < 0.001 Horizontal position = fringe 0.053 145 -5.901 < 0.001 Vertical position = lower 0.053 145 -3.865 < 0.001 Vertical position = middle 0.053 145 -2.465 0.015 Route : lower 0.076 145 2.133 0.035 Fringe : lower 0.075 145 2.732 0.007 Route : middle 0.076 145 1.861 0.065 Fringe : middle 0.075 145 2.627 0.010

Table A. 6. Linear mixed effects model of lichen abundance in relation to quadrat position for visual field abundance. Model created using package “lme4” function “lmer” included horizontal position and vertical position as fixed effects and route as a random effect.

Variable Std. error DF t value P Horizontal position = route 0.050 145 -6.638 < 0.001 Horizontal position = fringe 0.050 145 -6.097 < 0.001 Vertical position = lower 0.050 145 -5.455 < 0.001 Vertical position = middle 0.050 145 -3.078 0.002 Route : lower 0.071 145 3.102 0.002 Fringe : lower 0.071 145 3.302 0.001 Route : middle 0.071 145 2.243 0.026 Fringe : middle 0.071 145 3.183 0.002

Table A. 7. Linear mixed effects model of total abundance in relation to quadrat position for photographic field abundance. Model created using package “lme4” function “lmer” included horizontal position and vertical position as fixed effects and route as a random effect.

Variable Std. error DF t value P Horizontal position = route 0.057 153 -7.239 < 0.001 Horizontal position = fringe 0.056 153 -5.540 < 0.001 Vertical position = lower 0.057 153 -3.893 < 0.001 Vertical position = middle 0.057 153 -2.397 0.018 Route : lower 0.080 153 2.622 0.010 Fringe : lower 0.080 153 2.262 0.025 Route : middle 0.080 153 1.544 0.125 Fringe : middle 0.080 153 1.521 0.130

Table A. 8. Linear mixed effects model of lichen abundance in relation to quadrat position for photographic abundance. Model created using package “lme4” function “lmer” included horizontal position and vertical position as fixed effects and route as a random effect.

Variable Std. error DF t value P Horizontal position = route 0.052 153 -7.624 < 0.001 Horizontal position = fringe 0.052 153 -5.996 < 0.001 Vertical position = lower 0.052 153 -6.041 < 0.001 Vertical position = middle 0.052 153 -3.328 0.001 Route : lower 0.074 153 3.601 < 0.001 Fringe : lower 0.073 153 3.139 0.002 Route : middle 0.074 153 2.261 0.025 Fringe : middle 0.073 153 2.207 0.029

Table A. 9. Linear mixed effects model of total abundance in relation to quadrat position for ENVI abundance. Model created using package “lme4” function “lmer” included horizontal position and vertical position as fixed effects and route as a random effect.

Variable Std. error DF t value P Horizontal position = route 0.051 153 -5.212 < 0.001 Horizontal position = fringe 0.050 153 -4.131 < 0.001 Vertical position = lower 0.051 153 -2.044 0.045 Vertical position = middle 0.051 153 -1.699 0.091 Route : lower 0.072 153 0.928 0.356 Fringe : lower 0.071 153 0.968 0.334 Route : middle 0.072 153 0.952 0.342 Fringe : middle 0.071 153 1.198 0.233

Table A. 10. Linear mixed effects model of lichen abundance in relation to quadrat position for ENVI abundance. Model created using package “lme4” function “lmer” included horizontal position and vertical position as fixed effects and route as a random effect.

Variable Std. error DF t value P Horizontal position = route 0.045 152 -5.231 < 0.001 Horizontal position = fringe 0.045 152 -3.970 < 0.001 Vertical position = lower 0.045 152 -3.516 < 0.001 Vertical position = middle 0.046 152 -1.460 0.146 Route : lower 0.064 152 1.560 0.121 Fringe : lower 0.064 152 1.008 0.315 Route : middle 0.065 152 0.812 0.418 Fringe : middle 0.064 152 0.866 0.388

Table A. 11. Linear mixed effects model of lichen (1) and photosynthetic endolithic microorganism abundance (2) in relation to quadrat position for chlorophyll florescence abundance. Models created using package “lme4” function “lmer” included horizontal position as a fixed effect and route as a random effect.

1) Variable Std. error DF t value P Horizontal position = route 0.031 36 -3.803 < 0.001 Horizontal position = fringe 0.031 36 -1.82 0.077 2) Variable Std. error DF t value P Horizontal position = route 0.033 36 2.097 0.043 Horizontal position = fringe 0.033 36 0.488 0.628

Table A. 12. Linear mixed effects model of mean lichen chlorophyll florescence readings (1) and mean photosynthetic endolithic microorganism chlorophyll florescence readings (2) in relation to quadrat position. Models created using package “lme4” function “lmer” included horizontal position as a fixed effect and route as a random effect.

1) Variable Std. error DF t value P Horizontal position = route 0.015 35 -0.339 0.737 Horizontal position = fringe 0.014 35 -0.910 0.369 2) Variable Std. error DF t value P Horizontal position = route 0.037 32 -2.036 0.050 Horizontal position = fringe 0.038 33 -2.311 0.027

Table A. 13. Linear models and RMSE analysis of the total abundance found using photographic, ENVI, and chlorophyll florescence methods as related to the field visual method. Model created using BaseR function “lm” and Metrics package function “rmse”.

R2 p slope RMSE Photograph 0.604 < 0.001 0.706 0.158 ENVI 0.665 < 0.001 0.903 0.130 Chlorophyll 0.094 0.012 0.527 0.268 Florescence

Appendix B: Chapter 2 Tables

Table B. 1. . List of species name, corresponding ID, and the functional type of that species.

Species Species ID Functional Type Unknown cladonia species CLADA squamulose Amandinea punctata AMpu crustose Buellia vernicoma BUve crustose Calypogeia neesiana CAne bryophyte Cephaloziellarubella CEru bryophyte Cresponea premnea CRpr crustose Dicranumfulvum DIfu bryophyte Dirina massiliensis f. soriediata DIma crustose Fusidea recensa FUre crustose Hypotrachnya imbricatula HYim foliose Lasallia papulosa LApa umbilicate Lecanora cenisia LEce crustose Lecanora oreinoides LEor crustose Lecanora pseudistera LEps crustose Lecidella stigmatea LEst crustose Lepraria caesiella cf LEca granulose Lepraria finkii LEfi granulose Lepraria neglecta LEne granulose Unknown lepraria species LEsp granulose Lepraria vouauxii LEvo granulose Leproloma membranaceum cf LEme granulose Leucobryumglaucum LEgl bryophyte Myelochroa obsessa MYob foliose Parmatrema reticulatum PAre foliose Pertusaria amara cf PEam crustose Phlyctis petraea PHpe crustose Physcia subtilis PHsu foliose Plagiothecium laetum PLla bryophyte Polytrichum piliferum POpi bryophyte Porpidia albocaerulescens POal crustose Porpidia crustulata POcr crustose Pylaisiadelpha tenuirostris PYte bryophyte Racomitrium heterostichum RAhe bryophyte Rhizoplaca subdiscrepens RHsu crustose Rhynchostegium serrulatum RHse bryophyte Sematophyllum demissum SEde bryophyte Umbilicaria mammulata UMma umbilicate Usnea amblyoclada USam fruticose Xanthoparmelia conspersa XAco foliose Xanthoparmelia tasmanica XAta foliose 96

Table B. 2. Average (function: dredge, package: MuMIn) of linear mixed effects models (function: lmer, package lme4) results for total abundance. Horizontal position, vertical position, heat load, climbing use intensity, and microtopographic variation were fixed effects, and route was a random effect.

Variable Z value P Horizontal position = route 4.81 > 0.001 Horizontal position = fringe 3.32 > 0.001 Vertical position = lower 2.25 0.024 Vertical position = middle 1.32 0.186 Route : lower 1.94 0.052 Fringe : lower 2.81 0.005 Route : middle 1.65 0.099 Fringe : middle 2.56 0.011 Heat load 1.10 0.270 Microtopographic variation 0.48 0.628

Table B. 3. Average (function: dredge, package: MuMIn) of linear mixed effects models (function: lmer, package lme4) results for species richness. Horizontal position, vertical position, heat load, climbing use intensity, and microtopographic variation were fixed effects, and route was a random effect.

Variable Z value P Horizontal position = route 4.01 > 0.001 Horizontal position = fringe 2.41 0.016 Vertical position = lower 0.24 0.812 Vertical position = middle 0.17 0.868 Microtopographic variation 0.31 0.760 Heat load 2.12 0.034

Table B. 4. Average (function: dredge, package: MuMIn) of linear mixed effects models (function: lmer, package lme4) results for Shannon species diversity. Horizontal position, vertical position, heat load, climbing use intensity, and microtopographic variation were fixed effects, and route was a random effect.

Variable Z value P Horizontal position = route 2.40 0.016 Horizontal position = fringe 1.54 0.125 Vertical position = lower 0.53 0.599 Vertical position = middle 0.13 0.899 Microtopographic variation 1.30 0.192 Heat load 0.99 0.325 Climbing use intensity 0.67 0.505

Table B. 5. PERMANOVA (function: ADONIS, package: vegan) of community species composition. The effect of horizontal position after vertical position was accounted for (1) and the effect of vertical position once horizontal position was accounted for (2).

1) Variable DF Pseudo-F R² P Horizontal position 2 2.38 0.028 0.001 Horizontal : vertical 4 0.76 0.018 0.889 2) Variable DF Pseudo-F R² P Vertical position 2 2.29 0.027 0.002 Horizontal : vertical 4 0.76 0.018 0.898

Table B. 6. Testing for significant differences in dispersion between horizontal position and vertical position (function: betadisper, package: vegan) of community species composition.

Variable DF F value P HP 2 0.768 0.466 VP 2 2.242 0.111

Table B. 7. PERMANOVA (function: ADONIS, package: vegan) of functional type composition. The effect of horizontal position after vertical position was accounted for (1) and the effect of vertical position once horizontal position was accounted for (2).

1) Variable DF Pseudo-F R² P Horizontal position 2 8.34 0.090 0.001 Horizontal : vertical 4 0.83 0.018 0.656 2) Variable DF Pseudo-F R² P Vertical position 2 3.20 0.035 0.002 Horizontal : vertical 4 0.83 0.018 0.646

Table B. 8. Testing for significant differences in dispersion between horizontal position and vertical position (function: betadisper, package: vegan) of functional type composition.

Variable DF F value P HP 2 0.647 0.525 VP 2 2.242 0.111