Finding Niebla Homalea: Predicting the Distribution of a Fog Lichen

FINDING NIEBLA HOMALEA: PREDICTING THE DISTRIBUTION OF A

FOG LICHEN

A Thesis

Presented to the faculty of the Department of Biological Sciences

California State University, Sacramento

Submitted in partial satisfaction of the requirements for the degree of

MASTER OF SCIENCE

Biological Science

(Ecology, Evolution, and Conservation)

Jamie Marie LeFevre

SUMMER 2019

Jamie Marie LeFevre

FINDING NIEBLA HOMALEA: PREDICTING THE DISTRIBUTION OF A

FOG LICHEN

A Thesis

Jamie Marie LeFevre

Approved by:

______, Committee Chair Ronald M. Coleman, Ph.D.

______, Second Reader Jamie M. Kneitel, Ph.D.

______, Third Reader Benjamin H. Becker, Ph.D.

______Date

iii

Student: Jamie Marie LeFevre

I certify that this student has met the requirements for format contained in the University format manual, and that this thesis is suitable for shelving in the Library and credits to be awarded for the thesis.

______, Graduate Coordinator ______Jim Baxter Ph.D. Date

Department of Biological Sciences

Abstract

FINDING NIEBLA HOMALEA: PREDICTING THE DISTRIBUTION OF

FOG LICHEN

Jamie LeFevre

The fundamental niche is the set of conditions within which a species would be successful in the absence of biotic factors that might limit success. When a species or taxa is not well studied, as in the case with many lichens, understanding the environmental conditions that form its fundamental niche is a useful starting to point to predict species distribution. In recent years, ecological niche models have been used to successfully estimate fundamental niches and to predict species distribution.

Ecological niche models are based on species presence and absence data and combinations of environmental variables. Once the environmental requirements for a species have been identified, predictive models can be used to extrapolate where the species would be expected to occur elsewhere in the landscape, allowing the fundamental niche to be mapped. The fundamental niche is reflected on a map based on the areas where abiotic conditions are right for a given species to occur. This lays the foundation to

explore larger questions such as the role of biotic interactions, predicting species responses to climate change, or developing conservation strategies.

In this study, I have applied ecological niche modeling to Niebla homalea, a fog lichen endemic to the coastal zones of California and Baja California. The distribution and habitat requirements of N. homalea are largely unknown and the goal of this study is to expand our understanding of these attributes. Presence/absence data of N. homalea were collected at Bodega Bay, Point Reyes National Seashore, Half Moon Bay/San

Francisco, and Monterey; in California, United States of America. Sampling sites were restricted to public lands with rock outcrops and stratified by average summer day-time fog density. Data on nine independent variables were also collected.

Non-parametric multiplicative regression (NPMR) was used to find which independent variables are the best predictor variables for the presence of N. homalea.

NPMR uses a local multiplicative smoothing function with leave-one-out cross-validation to estimate the response variable. The best predictor variables were found to be precipitation, habitat type, temperature, and fog density.

The model was then used to extrapolate the probability that N. homalea exists in similar environments to those where the data were collected, based on independent variable characteristics. A distribution map representing the relative likelihood (high to

low) of suitable habitat, based on the inter-relationships of the predictor variables was created using ArcGIS. The mapping allowed a comparison between the predicted habitat suitability map and the known distribution of N. homalea. A comparison of predicted habitat suitability and known locations of N. homalea found that N. homalea tended to be in high probability of occurrence areas on the habitat suitability map.

______, Committee Chair Ronald M. Coleman, Ph.D.

______Date

vii

ACKNOWLEDGEMENTS

I would like to thank Pam Kirkbride for introducing me to the wonderful world of lichens and Shelly Benson for introducing me to fog lichens, and sharing her knowledge and goals. I hope the information from this study can benefit your ongoing research on fog lichens. I would like to thank Susie Bennett from the National Park Service for letting me collect data at Rancho Corral de Tierra on Montara Mountain and Montara State

Beach and for your enthusiasm for my research. I would also like to thank Bruce McCune for answering my questions when I got stuck using HyperNiche. I would like to thank my husband, Joe Griffin, and my daughter, Alyssa Egbert, for their support and going with me on the lichen hunts. Without their constant support and encouragement, I would not have been able to complete my research. Finally, I would like to thank my committee members Jamie Kneitel, and Benjamin Becker for their guidance during my research and manuscript preparation. I would like to express my sincere gratitude to my advisor, Ron

Coleman, for allowing me freedom to explore my own ideas and at same time giving guidance to complete my research and “get it done”.

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TABLE OF CONTENTS Page

Acknowledgements ...... viii

List of Tables ...... xii

List of Figures ...... xiii

Chapter

1. INTRODUCTION ...... 1

Predictive Habitat Modeling ...... 3

Lichen Distribution ...... 6

Fog Lichens ...... 8

Hypothesis...... 10

2. MATERIALS AND METHODS ...... 12

Study Sites ...... 12

Data Collection ...... 17

Response variables ...... 17

Predictor variables ...... 21

Analysis...... 23

Model Strategy and Evaluation ...... 29

3. RESULTS…...... 33

Data Collection ...... 33

NPMR Model ...... 33

Sensitivity of Individual Predictors ...... 41

Predictive Sensitivity of Occurrence ...... 45

4. DISCUSSION ...... 49

Predictive Sensitivity of Occurrence ...... 52

Ecological Niches and Conservation ...... 59

Model Implications ...... 61

Model Limitations ...... 63

Suggestions for Future Research ...... 65

5. CONCLUSION ...... 67

Appendix A. Field data collected at Bodega Bay: sampling site location, rock

group number and location, species recorded at each rock group

and percent cover of each lichen species on each rock group ...... 69

Appendix B. Representitive photographs of lichens observed at Bodega Bay ...... 86

Appendix C. Field data collected at Point Reyes National Seashore: sampling site

location, rock group number and location, species recorded at each

rock group and percent cover of each lichen species on each rock

group ...... 91

Appendix D. Representitive photographs of lichens observed at Point Reyes

National Seashore ...... 102

Appendix E. Field data collected at Half Moon Bay and San Francisco: sampling

site location, rock group number and location, species recorded at

each rock group and percent cover of each lichen species on each

rock group ...... 108

Appendix F. Representitive photographs of lichens observed at Half Moon Bay

and San Francisco ...... 118

Appendix G. Presence and absence of N. homalea at sampling locations ...... 122

Literature Cited ...... 126

LIST OF TABLES

Tables Page

1. Results of a stepwise free search in HyperNiche software that identified

the best models specifying the top one, two, three, and four predictor

variables...... 38

2. Relative strength of the models listed in Table 1, in comparison to the naïve

model. The quality of each model is expressed by three evaluation

statistics: log likelihood ratios (logB), aveB, and the area under the curve

statistic (AUC)...... 39

3. Mean Response Values of N. homalea Presence based on habitat types

within the sampling sites ...... 46

xii

LIST OF FIGURES

Figures Page

1. Photo of N. homalea on granite substrate taken at Bodega Bay ...... 9

2. Location of data collection sites grouped into general sampling area for

comparative purposes...... 18

3. Weights of observations relative to distance to the target point. How quickly

weights diminish from the distance target point is controlled by the standard

deviation of the Gaussian weight function (McCune 2011)...... 25

4. Presence/absence of N. homalea observed at the Bodega Bay sample

location ...... 34

5. Presence/absence of N. homalea observed at the Point Reyes National

Seashore data sample location ...... 35

6. Presence/absence of N. homalea observed at the Half Moon Bay/ San

Francisco data sample location ...... 36

7. Presence/absence of N. homalea observed at the Monterey data sample

location ...... 37

8. 3D Contour Response Curve of Precipitation and Fog Density. The graph

is showing the different probability of N homalea with in the predictor

space given the two variables: precipitation and fog density. Red is higher

probability and black is lower probability...... 42

xiii

9. 3D Contour Response Curve of Temperature and Fog Density. The graph is

showing the different probability of N homalea with in the predictor space

given the two variables: temperature and fog density. Red is higher

probability and black is lower probability...... 43

10. 3D Contour Response Curve of Precipitation and Temperature. The graph

is showing the different probability of N homalea with in the predictor

space given the two variables: precipitation and temperature. Red is higher

probability and black is lower probability...... 44

11. The predicted sensitivity of occurrence of N. homalea mapped over the

study area based on precipitation, temperature, and fog density ...... 48

12. Observations recorded in the iNaturalist databases of N. homalea, overlaid

on the predicted sensitivity map ...... 56

13. A grid of points spaced at 0.1 degrees overlaid on the map showing

predicted sensitivity of occurrence...... 57

14. The top histogram shows the frequency of N. homalea iNaturalist

observations within the predicted sensitivity ranges. The bottom histogram

shows the frequency of evenly spaced grid points within the predicted

sensitivity ranges. Comparison of the two histograms shows the frequency

of sensitivity values associated with the N. homalea observations are within

areas of high probability but the frequency of sensitivity values associated

with the evenly spaced grid points follows no obvious pattern...... 58

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INTRODUCTION

The niche concept is an important topic in ecology and has been defined in several ways. The niche concept was first described by Grinnell in 1917. The Grinnellian niche concept is based on the idea that a species’ niche is determined by its habitat requirements and the behaviors that allow it to persist (Grinnell 1917). A different concept of the niche was developed by Elton (1927) who suggested that a niche is focused on the functional role a species plays and the impact it has on the community.

Elton’s niche definition gave way to further explanations of the niche concept, most notably the Hutchinson (1957) niche definition (Wiens et al. 2009).

Hutchinson (1957) described species as having “fundamental” and “realized” niches. A fundamental niche is the range of environmental conditions under which a species can successfully grow, develop, and reproduce (Hutchinson 1957). Abiotic conditions such as climate, nutrients, and altitude impose limits on the ability of a species to persist and shape the distribution of a species within the fundamental niche (Soberón and Peterson 2005). However, favorable locations within the fundamental niche may remain unoccupied by the species due to biotic factors, including competition, disease, and predation, which can limit or enhance populations (Soberón and Peterson 2005). A realized niche is a restricted region within the niche, where a species actually occurs. The difference between the realized and fundamental niches is defined by those areas where a species is excluded from parts of its fundamental niche due to interactions with other species (Kearney and Porter 2004). Hutchinson’s niche definition is frequently used in ecology but work continues to refine the concept.

Soberón refined the niche concept by proposing a simplified description based on three factors: abiotic conditions, biotic factors, and accessibility (Soberón and Peterson

2005, Soberón 2007, Soberón and Nakamura 2009). Soberón argued that a species will not always be present when the abiotic environmental conditions that form the fundamental niche are suitable unless the right combinations of species interactions and biotic factors also exist. These factors, that comprise Hutchinson’s realized niche, include the presence of hosts, food, pollinations, etc. and the absence of competitors and disease.

Soberón also considers access to habitat through dispersal or colonization as part of the realized niche.

The niche a species occupies, and the environmental conditions that form the niche, correspond directly to the area through which a species could potentially be distributed. Ecological niche models are a common tool used to better understand species distributions (Guisan and Zimmermann 2000). The models only consider abiotic factors and do not include the influence of biotic effects (Pearson and Dawson 2003), therefore the output of ecological niche models can be described as the fundamental niche for a species. Ecological niche models can be used as a starting point for understanding habitat requirements of a species but they can also be used to understand how a species will respond to climatic changes.

Over the last fifteen years, numerous studies have examined niche properties and species distributions in relation to changing climatic conditions. Studies project future species distributions based on the known environmental requirements of a given species using a range of future climate conditions (Thuiller et al. 2005). Species-climate relationships and potential changes in species distribution have been documented in

3 vegetation (Hamann and Wang 2006, Rehfeldt et al. 2006, Iverson et al. 2007, Thuiller et al. 2008), lichens (Ellis et al. 2007), and faunal changes (Lawer et al. 2009, Kearney and

Porter, 2004).

Ecological niche models can reconstruct niches by associating species occurrence with combinations of environmental variables. Once the environmental requirements for a species have been identified, predictive models can be used to extrapolate where the species could occur elsewhere in the landscape. Calculating and mapping the fundamental niche allows for the determination of an area where abiotic conditions are right for a given species to occur. By comparing the differences between the observed range of a species and the theoretical range implied by the fundamental niche, researchers are in a position to develop an understanding of biotic or other factors that may be causing the discrepancy, and to develop conservation measures that specifically target these factors.

Predictive Habitat Modeling

If the environmental conditions that govern a species’ niche can be quantified by field work or ecological niche models, then predictions can be made regarding species distribution and abundance using statistical models (Kearney and Porter 2004). Predictive habitat models are useful tools for identifying probable locations of species and the important factors conditioning their distribution (McCune 2006). They are also used to gain an initial understanding of poorly studied species.

Species performance, as it relates to environmental variables, can be measured in a variety of ways, including presence/absence and abundance (McCune 2011).

Knowledge of the environmental factors that are important to a species is necessary to understand distribution patterns. This knowledgealso provides background information for predictive habitat models (Tremlova and Münzbergová 2007). Predictive habitat models are commonly used to understand and predict distributions in both terrestrial and aquatic ecosystems. Numerous studies that model species distribution have been developed for mammals, birds, and plants, but there is a growing interest in developing models for lesser studied taxa, such as lichens.

A number of distribution models for lichens have been developed during the last decade. Waser et al. (2004) developed a model using multiple regression to predict species richness of lichens using remote sensing data. The model accurately predicts species richness for lichen species that grow on trees but predicting species richness for lichens on rocks and soils needs refinement. Using ecological niche factor analysis,

Martinez et al. (2006) modeled the distribution of threatened lichen species in Spain and develops habitat suitability maps within the country. Bergamini et al. (2007) used multiple linear regression to model species richness of microlichens by evaluating different variables, including species richness, climatic, substrate, and abiotic variables.

Radies et al. (2009) predicted lichen diversity of a British Columbia inland temperate rainforest using linear regression and spatial niche modeling. These studies used predictive modeling to determine species responses in a wide range of habitats.

The response of a species to multiple environmental variables is complex and can be hard to predict. McCune (2006) argued that problems arise when applying linear regression models to species responses, because linear regression does not capture interactions with other factors or nonlinear responses to independent variables. Although

5 linear functions can be fitted with polynomial terms, the model is still constrained to a particular functional form. McCune developed a niche-based habitat modeling program

(HyperNiche) using Non-Parametric Multiplicative Regression (NPMR). NPMR analysis allows flexibility and can account for hump-shaped responses. It can predict how a species will respond to environmental variables without making any assumptions about the shape of the response curve and therefore does not require advanced knowledge of the shape of the species’ response curves to various environmental gradients (McCune 2011).

NPMR attempts to explain how a species responds to a range of multiple environmental variables, and how strongly those environmental variables influence its distribution. This information can be used to gain a better understanding of the species and it allows for the development of future hypothesis. Further, HyperNiche can be applied to a Geographic

Information System (GIS) to develop maps of probable species occurrence.

Numerous researchers have applied NPMR to lichen studies. Berryman and

McCune (2006) used NPMR to model the biomass of lichens in the Cascade Mountains based on topography, stand structure, and lichen community composition. They found that lichen biomass changes with elevation and with age of trees. Ellis et al. (2007) used

NPMR to predict the responses of common lichens in the United Kingdom to three different climate scenarios (present day, low greenhouse gas emissions level, and high greenhouse gas emissions level). Results indicated that there is potential for a significant change in the distribution of lichens under the high greenhouse gas emissions level scenario. Also, using NPMR, Cristofolini et al. (2008) examine lichen diversity in a pre- alpine area of Italy and the response of the lichen to air pollution compared against other ecological variables. They found that distance to the sources of pollution was the best

6 predictor for lichen diversity, but tree stand and substrate-related factors were also strong indicators. Shrestha (2010) used NPMR and logistic regression to predict the distribution of two lichen species in response to air pollution and evaluated the strengths and limitations of the two models. Shrestha generated distribution maps of both lichen species using each model type then verified the predicted distributions in the field. These field surveys show that the NPMR model produced more accurate results than the Logistic

Regression model. NPMR has been successfully used to predict lichen distribution patterns and in developing species occurrence probability maps that can be used to develop predictive maps for the species locations across larger areas.

Lichen Distribution

When species or taxa are not well studied and incompletely understood, as is the case with many lichens, distribution is a particularly useful starting point when seeking to understand the environmental requirements that underpin the distribution of a species in geographic space. Distribution patterns have become a topic of interest in lichen biology over the last decade. Lichens occur worldwide and have distinctive distributional patterns

(Sales et al. 2016). Many species of lichens have large geographical ranges and extremely wide ecological niches (Printzen et al. 2013). The ranges of many common lichens extend from temperate zones into polar regions (Printzen et al. 2008). This is especially true for crustose lichens (lichens that form a crust-like structure on the substrate). Other lichens are restricted to very specific habitats.

Distributions of lichen species and communities are influenced by many environmental factors. The factors which determine where a particular species of lichen

7 occurs depends on the climate (including temperature and rainfall), substrate chemistry

(whether it is alkaline or acidic), physical factors (surface texture of the substrate, light exposure), surrounding vegetation, and pollution levels (Printzen et al. 2013). Lichens can occur on nearly all surfaces of any substrate. They commonly occur on tree trunks or branches, on other plants, and on rocks and soil crusts (Nash 2008). A few species occur in freshwater streams or in the marine intertidal zones (Hawksworth 2000). Lichens are also found on manmade structures, including concrete and metal structures such as buildings, sidewalks, old cars and tires, and road signs. Overall, lichens are very successful and are found in almost all terrestrial habitats.

The symbiotic nature of lichens adds complexity to determining their distribution.

Lichens are a composite organism composed of a fungus (the mycobiont) and one or more photosynthetic partners (the photobiont), usually a green alga or cyanobacteria

(Sanders 2001). The symbiotic relationship of a lichen is complex, but in general, the mycobiont provides a physical structure that captures minerals and protects the photobiont, while the photobiont produces food through photosynthesis, which in turn benefits the mycobiont (Hawksworth 2000). Lichens allow the mycobiont and photobiont to thrive under ecological conditions that they might not be able to tolerate individually.

The symbiotic relationship of lichens enables the mycobiont and photobiont to tolerate a wide range of environmental stressors including extreme ﬂuctuations of temperatures, desiccation, ultraviolet radiation, and salinity (Nash 2008). The distributional range of a lichen may be limited by its ecological restrictions, but also by dispersal limitations of the mycobiont or the lack of a suitable photobiont in the environment (Printzen et al. 2013). Moreover, lichens are known to incorporate different

8 photobionts in different habitats (Blaha et al. 2006, Yahr et al. 2006). The mycobiont can switch its photobiont partner to a more suitable photobiont to better adapt to its environment (Piercey-Normore and DePriest 2001), although the time over which a mycobiont can switch its photobiont partner is unknown (Printzen et al. 2013).

Fog Lichens

Little is known about the distribution and environmental requirements of the genus Niebla (fog lichens). Fog lichens occur along the coast of California and Baja

California and are restricted to the coastal fog zones (Sharnoff 2014). Fog lichens are pale green fruticose lichens which occur almost exclusively on rocky substrate. There are nine species of Niebla known to occur in California but only two species, N. homalea and

N. combeoides, are found on rock outcrops in the San Francisco Bay area. The range of both species overlap; collecticely they occur along the coastline from Marin County to

Baja California. N. homalea is the most common species and occurs farther inland than other Niebla species.

Although N. homalea and N. combeoides share similar distributions, their growth forms are different. N. homalea forms in clumps and has multiple flat branching blades.

The blades come to a point or have apothecia (reproductive structures) at the tip (Figure

1). The branches exhibit ridges that look like plates and small depressions, but N. homalea can be highly variable in its form (Sharnoff 2014). N. combeoides also forms in clumps but has unbranched blades. Apothecia are abundant at the tips of the blades and there can be as many as three per tip (Sharnoff 2014).

Figure 1. Photo of N. homalea on granite substrate taken at Bodega Bay

This study uses field data analyzed in HyperNiche modeling software to identify the environmental variables that comprise the abiotic factors forming the fundamental niche of the lichen N. homalea. The model results are used to map the predicted distribution of the lichen in northern California based on current climatic and environmental conditions in the region as reflected in GIS layers. These results are immediately applicable to modeling the effects of climate change on the lichen as projected changes in the data reflected in the spatial datasets could be applied to predict different distributions under different climatic circumstances. In addition to this application, the study is intended to lay the foundation for future studies that may answer more specific questions, such as the role of biotic interactions in the performance of N. homalea, the responses of this and other lichens to climate change, and ultimately for developing conservation strategies.

Hypothesis

My objective was to create a model to predict the distribution of the common coastal fog lichen, N. homalea, by identifying locations where the lichen does and does not occur and identifying the environmental factors that may influence the occurrence of the lichen. N. homalea has not been studied sufficiently to meaningfully understand the factors that influence its distribution; however, other studies have shown rainfall, temperature, vegetation, and solar exposure are important factors to lichen distribution

(Ellis et al. 2007, Ellis and Coppins 2006, Radies et al. 2009, and Bolliger et al. 2007). I predict that elevation, distance to coast line, aspect, geology, precipitation, temperature,

11 fog density, habitat type, and cover type are major factors that influence N. homalea.

Using NPMR, I tested my hypothesis that the presence of N. homalea is dependent on elevation, distance to coast line, aspect, geology, precipitation, temperature, fog density, habitat type, and cover type.

I tested this hypothesis by collecting presence/absence data of N. homalea in areas with suitable substrate and environmental variables that could be important to the success of N. homalea. HyperNiche was used to develop models based on the habitat variables that best predict the presence of N. homalea. The predictive ability of each model was validated using HyperNiche, which statistically evaluates the model against a naive model.

A sensitivity analysis was conducted which evaluated the importance of the predictor variables within the model. The model was then used to extrapolate the probability that N. homalea exists in similar environments to those where the data were collected. A distribution map representing the predicted sensitivity of occurrence, based on the relationships of the predictor variables, was created using ArcGIS.

MATERIALS AND METHODS

The methodology used in this study included collecting presence/ absence data of

N. homalea in areas with apparently suitable habitat (representing species response), collecting data on the variables to be evaluated for their utility as predictors of species occurrence (e.g., elevation, temperature, fog density, etc.), model building and validation, and then mapping the results. I built a model to assess which variable or combination of variables predicted the presence of N. homalea most effectively. The model results were exported into a GIS to make predictions about the presence or absence of the lichen in the northernmost extent of its known range.

Study Sites

Data were collected at four locations along the northern coast of California. All four locations (Bodega Bay, Point Reyes National Seashore, Half Moon Bay/ San

Francisco, and Monterey) were located in the coastal fog zone, which I expected to support N. homalea populations based on known distributions. The coast in all four locations was comprised of a series of beaches, separated by rocky bluffs and headlands.

Data collection focused on the northern extent of N. homalea’s range so that I could sample areas potentially outside its range. It was feasible to travel to this portion of N. homalea’s range during weekend trips from Sacramento, California.

Bodega Bay. Bodega Bay is located in Sonoma County, approximately 88.5 kilometers north of San Francisco, California. Elevation ranged from sea level to 323 meters at the highest peak. Average temperature was 12 degrees Celsius and average annual rainfall was 1,016 millimeters per year (usclimatedata.com). The average

13 temperature varied by approximately 6.6 degrees Celsius throughout the year. Data were collected at rock outcrops located between 50 meters to 12,900 meters from the coast.

The geology of the area is complex because it sits on two different tectonic plates, the

North American Plate and the Pacific Plate, separated by the San Andreas fault. The rock formation on the Pacific Plate is mainly Salinian block, itself comprised of granitic rocks

(Blake et al. 2002). At Bodega Bay, the rocks are mostly Cretaceous granite with overlying sand and gravel. The formation on the North American Plate is the Franciscan

Complex, a collection of various sedimentary rock types that are mostly oceanic in nature. Rock types in the Franciscan Complex include sandstone, chert, shale, and argillite (Johnson et al. 2015).

Habitat types where data were collected included coastal dunes, coastal scrub, annual grasslands, and Douglas fir forests (California State Parks 2007). Coastal dune and coastal shrub habitats support short hardy plant species that can tolerate high winds, salt spray, poor soils, and little rain. Plant species found in the coastal dune and coastal shrub habitats included iceplant (Aizoaceae spp.), coyote bush (Baccharis pilularis), sagebrush (Artemisia tridentate), buckwheat (Eriogonum spp.), poison oak (Quercus stellate), lupine (Lupinus spp.), and various wild flowers. Annual grasslands were found in areas that had been disturbed and on terraces undergoing succession. Plant species included non-native grasses, purple needlegrass (Nassella pulchra), blue-eyed grass

(Sisyrinchium spp.), seaside daisy (Erigeron glaucus), lupines, and California poppy

(Eschscholzia californica). Douglas fir forest occured mostly on upper slopes and ridge tops. This plant community was dominated by Douglas firs (Pseudotsuga menziesii) with

California bay (Umbellularia californica), coastal live oak (Quercus agrifolia), and

14 madrones (Arbutus menziesii). Understory species included coyote bush, poison oak, and blackberry (Rubus) (California State Parks 2007).

Point Reyes National Seashore. Point Reyes National Seashore is located in

Marin County, approximately 48 kilometers north of San Francisco, California. Elevation ranged from sea level to 366 meters at the highest peak. Average temperate was 8 degrees Celsius and average annual rainfall was 838 millimeters per year

(areavibes.com). The average temperature varied by approximately 10 degrees Celsius throughout the year. Data were collected from rock outcrops located between 30 meters and 2,900 meters from the coast. The geology of the area is also divided by the San

Andreas fault, but Point Reyes National Seashore is mostly on the Pacific Plate. Rocks are mostly Cretaceous granite, sandstone, and metasandstone (Clark and Brabb 1997).

Point Reyes National Seashore had a variety of habitat communities, and data were collected in coastal dune, coastal shrub, Bishop pine forest, and Douglas fir forest communities (Point Reyes National Seashore Association 2014). Bishop pine forest and

Douglas fir forest habitats occured at higher elevations. Bishop pines tended to grow within sight of the ocean and Douglas fir occured in areas further inland. The understories of the two forest types were comprised of similar species including coffeeberry (Frangula californica), manzanita (Arctostaphylos spp.), Ceanothus spp., poison oak, and huckleberry (Vaccinium spp.) (Point Reyes National Seashore

Association 2014).

Half Moon Bay/ San Francisco. Half Moon Bay is located in San Mateo County, approximately 30.5 kilometers south of San Francisco, California. Elevation ranged from sea level to 548 meters at the highest peak. Average temperate was 6 degrees Celsius and

15 average annual rainfall was 736 millimeters per year (usclimatedata.com). The average temperature varied by approximately 9.1 degrees Celsius throughout the year. Data were collected at rock outcrops found between 80 meters and 4,600 meters from the coast. The geology of the area is primarily Montara Mountain Granitic Rock formation which is an ancient, medium-to-coarsely crystalline, foliated granite that is deeply fractured and weathered (Peninsula Open Space Trust 2001). Rock outcrops are common at the upper elevations.

Habitat types where data were collected included coastal dunes, coastal scrub, annual grasslands and prairie, and coastal chaparral habitat (Peninsula Open Space Trust

2001). Grassland and prairie habitats were found in the lower foothill areas and on exposed ridges. Plant species included annual grasses, lupines, and wildflowers. Coastal chaparral habitat was the predominant habitat type in the mountainous areas. Plants included coyote brush, poison oak, sagebrush, manzanita, coffeeberry, wild lilac

(Ceanothus spp.), lupine, and stonecrops (Sedum spp.).

All the Half Moon Bay sites were located in high and medium fog density zones, so data were also collected at Mount Davidson and Glen Canyon Park in San Francisco, which were the nearest locations where data could be collected in lower density fog zones. Elevation ranged from 73 meters to 280 meters at the peak of Mount Davidson.

Average temperate was 14 degrees Celsius and annual average rainfall was 600 millimeters per year (usclimatedata.com). The average temperature varied by approximately 7.3 degrees Celsius throughout the year. Data were collected at rock outcrops located between 6,200 meters and 7,632 meters from the coast. The San

Francisco Peninsula is divided by the San Andreas fault and sits between the Salinian

16 granitic rocks and the Franciscan Complex. Data were collected at sites comprised of

Franciscan rocks. Rock types were mainly chert and sandstone. Habitat types where data were collected included annual grasslands and mixed forest. Mixed forest predominately consisted of introduced eucalyptus (Eucalyptus globulus) and Monterey pine (Pinus radiata), with an understory of broom (Cytisus scoparius), blackberry, ferns, and coyote bush.

Monterey. Monterey is located in Monterey County, approximately 140 kilometers south of San Francisco, California. Elevation ranged from sea level to 641 meters at the highest peak. Average temperate was 13 degrees Celsius and annual average annual rain fall was 537 millimeters per year (usclimatedata.com). The average temperature varied by approximately 8.5 degrees Celsius throughout the year. Data were collected at rock outcrops located between 80 meters and 19,733 meters from the coast.

The geology around Monterey is very complex, due to the number of seismic faults passing through the area. Uplifting and tilting from seismic activity have produced several sedimentary units, while the San Andreas fault has offset major elements (U.S.

Geology Service 1977). Monterey sits on the Pacific Plate which is mostly Cretaceous granite with overlying sand and marine terrace sediments (Clark et al. 1997).

Habitat types where data were collected included coastal dunes, coastal scrub,

Monterey Cypress stands, and Monterey Pine forests (Pebble Beach Company 2002).

Monterey Pine forests were located at the inland sites. The understory plant species included manzanita, ceanothus, monkeyflower (Mimulus), and coffeeberry. Tree cover was dense, and herbaceous species were either absent due to shade, or occurred at gaps in the canopy.

Data Collection

Response variables

Data were collected on public lands with rock outcrops and stratified by average summer daytime fog density, lumped into three categories: high density (from 10.5 to 14 hr/day), medium density (from 7.5 to 10.5 hr/day), and low density (4 to 7.5 hr/day) zones). Data were collected between 2017 and 2018 at 23 sites: four at Bodega Bay, six at Point Reyes, seven at Half Moon Bay/San Francisco, and six at Monterey (Figure 2).

Due to the unpredictable distribution of rocks, I used subjective judgement to focus my sampling work on rocks that I could easily access. Generally, the sampling of lichens for this study was done within a few hundred meters of the nearest road (access was often limited by fences) or hiking trails (access was limited by steep slopes or dense vegetation) to allow time to sample as many sites as possible. This method allowed me to quickly move through an area in an efficient manner, often skipping large areas that had no rocks.

I ended up using this sampling method after exploring various alternatives as described below. I had originally attempted to sample rocks systematically using a grid plot system. I established a grid over aerial imagery (via Google Earth) of the area I proposed to collect data from and randomly selected plots to sample. However, this was unsuccessful because most of the randomly selected plots either had no rocks to sample, were located in areas where access was restricted (i.e., fences and steep topography), or

Figure 2. Location of data collection sites grouped into general sampling area for comparative purposes

19 were located on private lands. After removing the inaccessible plots from the sampling plan, only a small number of plots remained, which would not provide sufficient information for my analysis.

At the scale of individual rocks and outcrops, I attempted to set up line transects across the rocks and collect data using various sized quadrats (1 meter by 1 meter square,

10 centimeter (cm) by 40 cm, and 20 cm by 20 cm quadrats). I laid out a tape measure across a rock and collected data by placing the quadrats along the tape measure in even intervals, ranging from a 10% to 50% sample of each rock. Given the nature of how N. homalea grows, individual organisms are not easily counted without damaging the lichen.

In order to quantify density, two methods were attempted, visual estimation using a guide

(Anderson 1986), and counting presence/absence in sub-quadrats. The latter method was employed using 20 cm by 20 cm and 10 cm by 40 cm rectangles constructed of polyvinyl chloride pipe, with a 10 cm square grid inside made of mason’s twine. Presence or absence in each of the 25 sub-quadrats was tallied and the percent cover estimated by the average of these counts. Unfortunately, in practice the strings were found to be too potentially damaging to the lichens and this method was abandoned.

While initially promising, the transect sampling method proved infeasible, partly because of the risk of damaging the lichens, and the uneven topography and size of rocks made it hard to sample evenly. Many rocks were small (less than one meter in length) while others were several meters long. By visual estimation, N. homalea was often present on one percent or less of the rock surface, which meant that if a small rock was sampled, it was often wholly contained within the sample quadrat and any lichen located on the rock would be counted. In the case of larger rocks, a smaller sample might miss

20 the lichen entirely, thus artificially suggesting that N. homalea did not exist, or was considerably rarer in areas that simply had more viable substrate available. Additionally, the logistical challenge of placing a transect evenly across the diverse range of rock sizes and shapes and to use the same size quadrat was frequently impractical. Ultimately, visual approximation of percent cover proved to be the most practical method.

Though HyperNiche works with presence/absence data, the information collected about percent cover was useful to address the question of potential bias introduced by the varied size of sampled rocks: are larger rocks more likely to host N. homalea than smaller rocks? Rock size data were not collected at every sampling location, but I have a data set of 23 individual rocks where this was recorded, ranging in size from 0.24 to 6 square meters. I estimated the area of N. homalea coverage on these rocks by multiplying the estimated percent cover by the surface area of the rocks themselves. Using a simple linear regression, I looked for a relationship between the overall size of a rock and the area covered by N. homalea. If larger rocks were biasing the sample by increasing the likelihood of N. homalea being present, then I would expect that the average area covered by N. homalea would correlate positively with rock size. The regression indicated that virtually no relationship exists between these variables, with a coefficient of determination of 0.00006. This indicates that in this small sample, only 0.06% of the variability in the coverage of N. homalea is predicted by rock size, therefore it does not seem that my sample was biased by not controlling for rock size.

Based on the results of the preliminary sampling approaches, I decided that areas would be sampled based on opportunistic access to rock and that percent cover would be estimated. My final sampling design met the following criteria: (1) sampling occurred

21 across a fog density gradient, ranging from high to low density, and (2) sampling occurred in a range of habitat types. Satellite imagery, fog density maps, landcover maps, and public lands maps were used to identify accessible areas meeting my criteria.

Percent cover of all lichens on the rocks were recorded. Macrolichens (fruticose and foliose growth forms) present on the rocks were identified to either genus or species. The identification of specimens was primarily based on A Field Guide to California Lichen

(Sharnoff 2014). Microlichens (crustose growth form) were identified to genus to the extent possible using identification keys and chemical tests in the field. Chemical tests

(potassium hydroxide and bleach) were used in the field to detect color changes.

Components of certain lichens react with the test chemicals to give color reactions which help with identification of a species. If I was not able to identify the microlichen, it was recorded as “unknown species”. All lichens were numbered and photographed. The location of each site was photographed, and its location recorded using Universal

Transverse Mercator coordinates in zone 10, relative to North American Datum 1983.

Predictor variables

Predictor variables (the independent variables) in this study were GIS derived using the Environmental Systems Research Institute (ESRI) ArcGIS 10.5.1 software package. A total of nine predictor variables including elevation, distance to coast line, aspect, geology, average monthly rainfall, mean temperature, fog density, habitat type, and cover type were prepared for the data collection sites.

Elevation. Elevation data at the resolution of 1 arc second (approximately 30 meters) was acquired from the National Elevation Dataset (NED), United States

Geological Survey (USGS). The elevation of each data collection site was determined by using the ‘Extract values to points’ tool from the Spatial Analysist toolbox in ArcGIS.

Elevation at the data collection sites ranged from 0 to 549 meters.

Distance from coastline. The proximity of sample points was measured to the coastline shapefile using the ‘Near’ tool from the ‘Proximity Analysis’ toolbox in

ArcGIS. The coastline shapefile was made manually with data from the ESRI World

Imagery layer. Distance to coastline was used as a proxy indicator for unknown variables that could influence fog lichen distribution (e.g., salt spray).

Aspect. Aspect was generated using the Spatial Analysis toolbox and topographic contours in ArcGIS. Aspect was generated using GIS, rather than collected in the field, for consistency.

Geology. California Geological Survey, Geologic Data Map No. 2, was obtained from ArcGIS online. The geology/ rock type at each data collection site was determined by using the ‘Extract values to points’ tool from the Spatial Analysist toolbox.

Precipitation. Average annual precipitation (1950-2000) from Global Climate

Data, International Center for Tropical Agriculture (CIAT), was obtained from ArcGIS.

The average annual precipitation at each data collection site was determined by using the

‘Extract values to points’ tool from the Spatial Analysist toolbox.

Temperature. Mean annual temperature (1950-2000) from Global Climate Data,

CIAT was obtained from ArcGIS. Spatial resolution was 1km. The mean temperature at each data collection site was determined by using the ‘Extract values to points’ tool from the Spatial Analysist toolbox.

Fog Inundation Zone. Average hours of daytime fog and low cloud cover recorded from a decade of summers (1999 -2009) for north and central California was obtained from the USGS. Raster data (4km grid cell) reflects an average calculated for

June, July, August and September over the 10-year period. The average hours of fog and low cloud cover per day uses a 7 a.m. to 6 p.m. day. Values ranged from one to 14 hours per day.

Habitat Type and Cover Type. A vegetation classification and mapping layer was obtained from the Forest Service website:https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=calveg. The habitat type and cover at each data collection site were determined using the ‘Extract values to points’ tool from the Spatial Analysist toolbox.

Analysis

Non-parametric multiplicative regression

Non-Parametric Multiplicative Regression (NPMR) is a method that seeks relationships between a response variable and predictor variables without estimating any coefficients in a fixed mathematical form. Instead, NPMR finds an optimal fit of the data to a local model (via local mean estimation or local linear regression). The outcome identifies which predictors have the largest and most important effect on species presence. A local model seeks a relationship that fits to each data point, as opposed to a global model that applies the same relationship throughout the sample space (McCune

2011). Each data point in the local model is weighed via a kernel function (e.g., Gaussian kernel) according to its distance from the target point. The target point is a point in the

24 environmental space for which predictions are being made. The environmental space is a multidimensional space where the number of predictor variables is defined by the number of environmental variables. The model interpolates the response surface to give the fitted value (estimate) for that spot in the landscape. A Gaussian kernel gives full weight (1.0) to an observation in the same environment as the target point and reduces the weight of the observation (to near zero) as the distance from the target point increases (McCune

2011).

Tolerance is defined as the standard deviation of the Gaussian weight function.

Specifically, it defines how information is borrowed from nearby areas when estimating the values of a target point. A small standard deviation is comparable to having a small window and weight is given only to observations that are close to the target point. On the other hand, a large standard deviation is comparable to having a larger window and weight is given to observations over a broader area (McCune 2006). Tolerance (which acts as a measure of niche breadth) is proportional to the range of each predictor. Figure 3 shows the how the weight of an observation changes relative to the distance of the target point (figure modified from McCune 2011).

The weights given to individual predictors are combined multiplicatively rather than additively, which accounts for interactions between individual predictors (McCune

2006). The combined weight of the predictors can be zero if a single predictor or combination of predictors is found to be unacceptable. The model output identifies which predictors most effectively predict the presence of a species. Ultimately, the model allows

Figure 3. Weights of observations relative to distance to the target point. How quickly weights diminish from the distance target point is controlled by the standard deviation of the Gaussian weight function. Figure adapted from McCune 2011.

26 the user to identify the relationship between predictor values and the probability of the occurrence of a species at a given target point.

NPMR estimates the response of y (the response variable) from its relationship with the predictors (m) in the matrix (X). A matrix is a table of response variables and predictor variables. The following equation is used to estimate a response (y) at target point v:

푛 푚 ∑ 푦ᵢ (∏ 푤∗푖푗) 푖=1, 푖≠푣 푗=1 ŷv = 푛 푚 ∑ (∏ 푤∗푖푗) 푖=1, 푖≠푣 푗=1

Notation (adapted from McCune 2011):

i = array index for n sample units (sites)

j = array index for m predictor variables

m = number of predictor variables in the model.

n = number of sample units (sites).

sj = standard deviation of the Gaussian weighting function for predictor variable j,

applied to a given predictor, such that the full range of observed values for that

variable falls over six standard deviations. This is also known as the smoothing

parameter or bandwidth (tolerance).

X = matrix of predictors (habitat or environmental variables) with i = 1 to n rows

(sample units) and j = 1 to m columns (variables).

v = vector specifying the habitat at the target point, this vector being row vector

of j = 1 to m columns

(variables).

w*ij = weight applied to point i for predictor j. The asterisk indicates that it is a

univariate weight, as opposed to a weight from the matrix W.

W = n x n diagonal matrix with each diagonal element being a product of weights

from each predictor variable.

y = vector of observed presence-absence (response variable). This is a column

vector of i = 1 to n rows (sample units).

ŷv = fitted value or estimated probability of occurrence of species at target point v.

= sum of 1 to n

= multiply from 1 to m

The value of ŷv is estimated by combining weights of the predictors which are multiplied by the combined weight of the observed value of y (at point i), and divided by the sum of the combined weights. This gives a weighted average as an estimate of the probability of occurrence. The weight applied at a particular point is defined as:

The w* indicates that it is a univariate weight (the weight for a single predictor j at sample unit i) (McCune 2011).

NPMR uses a leave-one-out cross-validation to search for the model with the best predictive ability. The notation i ≠ v indicates that “if the target point v is one of the calibration data points, then it is excluded from the basis for the estimate of yv” (McCune

2006). Using leave-one-out cross-validation reduces overfitting and produces a more realistic error estimate.

NPMR uses neighborhood size to provide flexibility and also controls for overfitting (McCune 2011). The neighborhood size is the amount of data used to estimate the predicted response. It is defined by the tolerance range around the target point. The value of the neighborhood size decreases with an increase in number of variables (Yost

2008). The minimum neighborhood size for an estimate therefore controls how broadly a model is extrapolated in the predictor space and avoids estimating a response in the predictor space where there is insufficient data (Gies et al. 2015). McCune defines a reasonable minimum average neighborhood size as 5 percent of the sample size (N* ≥

0.05[n]) (McCune 2011).

Sensitivity Analysis of the Individual Quantitative Predictors

The individual importance of a quantitative predictor within the NPMR model can be assessed using the sensitivity analysis built into HyperNiche by analyzing the sensitivity to changes in that predictor. The sensitivity analysis involves nudging the observed values of a predictor up and down, and then measuring the change in the estimated response for each data point. HyperNiche nudges each predictor one at a time

29 by + or - 5% its total range of variability. The greater the sensitivity, the more influence that variable has in the model. Sensitivity is calculated by:

Sensitivity = mean difference in response / range in response difference in predictor / range in predictor

A sensitivity of 0 means that there is no change in the response, whereas, a sensitivity of 1.0 means that “a 10% change in the predictor would, on average, produce a

10% change in the response” (McCune 2011). The quantitative predictors that were evaluated are elevation, distance to coast line, precipitation, temperature, and fog density.

Analysis of the Individual Categorical Predictors

Categorical predictors (aspect, rock type, habitat type, and cover type) were evaluated in HyperNiche using the “Response to Categoricals” function, which produces a table showing the average value for the response variable within each category.

Model Strategy and Evaluation

Non-parametric multiplicative regression

NPMR models were developed using the HyperNiche v2 software developed by

McCune & Mefford (2004). I used a local mean model with a Gaussian weighting function using the default NPMR settings (i.e., improvement criterion = 5%, step size =

5, maximum allowable missing estimates = 10%, data predictor ratio = 10, and minimum neighborhood size of 5 % of the number of sample points), to calculate probability of occurrence. This is a stepwise free search which seeks a range of models using different combinations of the predictors.

To select the best models, I selected “delete all but best for N predictors” to determine the top one predictor, top two predictors, top three predictors and top four predictors. HyperNiche uses a leave-one-out cross-validation to search for the best models and the selected predictors are based on the results of the cross-validation. The best models are selected based on the logB values. HyperNiche filters the model list by keeping the single best model for each number of predictors for each response variable.

I evaluated the quality (i.e., accuracy) of the one predictor, top two predictors, top three predictors, and top four predictors models using three evaluation statistics, namely log likelihood ratios (logB), aveB, and the area under the curve statistic (AUC).

LogB evaluates the predictive ability of the model compared to the naïve model, expressed in powers of 10. The naïve model assumes that the probability that the response valiable occurrs at any given site is the average overall frequency of occurrence in the study area. The values of logB are based on a leave-one-out cross-validation to help guard against overfitting the model. LogB is a descriptive statistic and its value will increase as the strength of the model increases. A negative logB value indicates that the model is worse than the naïve model. The same rational can be used to compare the relative strength of models against each other by calculating the difference between logB, or “change in logB”, by subtracting the logB of the model to the next lower dimensional model.

LogB is unbounded and can be large when there is a strong relationship in the model with a very large data set, whereas, in a small data set, the logB value will be smaller. The model with the highest logB value is considered the best model (Yost 2008).

According to Kass and Raftery (1995), logB values higher than 2.0 provide decisive

31 evidence against the naïve model. A value between 1/2 and 1 is considered substantial and a value between 0 and 1/2 is negligible.

The aveB also reflects whether or not the NPMR models are improvements over the native model. The aveB is the average contribution of a sample unit to the logB. The values for aveB can be compared across data sets and with different sample sizes. An aveB value of 1 indicates that the model is no better than the naïve model and an aveB value of less than one means the model is worse than the naïve model. A model with an aveB of more than one is better than the naïve model.

AUC represents the chance that a randomly selected site with species presence will have a predicted probability higher than a randomly selected site with species absence. Therefore, AUC assesses the discrimination ability of the models and identifies a threshold value for projected presence/absence. Weak models have an AUC of 0.5 and strong models have an AUC of 1. Following Swets (1988), and Araujo and Guisan

(2006), I interpreted AUC values of 0.9 as excellent, 0.8–0.9 as very good, 0.7–0.8 as satisfactory and below 0.7 as poor.

The optimal NPMR models were determined based on the logB value, aveB, and the AUC. The models were further evaluated using a randomization (Monte Carlo) test.

The Monte Carlo simulation tests the null hypothesis that the fit of the selected model is no better than could be obtained by chance alone. To perform the randomization test,

HyperNiche shuffles the response variable, destroying the observed relationship with the predictors, then attempts to fit the best model possible, using a free search. The procedure of randomization followed by free search for a model was repeated 100 times. The proportion of randomization runs that result in an equal or better fit is used as the p value

32 for the test. The optimum models were then graphed in HyperNiche using the 3D response curve.

Mapping

HyperNiche can be used with GIS data to extrapolate presence across a geographic space using the predictor variables. To do this, HyperNiche requires spatial data prepared in American Standard Code for Information Interchange (ASCII) grid files.

The GIS layers for the predictor variables exist in either polygon or raster files types, and so had to be converted to ASCII format.

To convert the GIS layers, a fishnet file and a label file covering the study area were created. The fishnet file can be imagined as a patchwork of square polygon shapes and the label file is a grid of points centered in each square of the fishnet. The predictor variables (e.g., fog density) that were presented in raster format were converted to contours. The contour files and the predictor variables that were in the polygon files were joined to the label file using the Spatial Join function in ArcGIS. The Spatial Join function was used to associate the label file data with the fishnet file. The fishnet file was exported to a raster file which was then converted to an ASCII file. This produces ASCII files of identical size, shape, and pixel size (resolution), as required by HyperNiche.

The ASCII files of the predictor variables were inputted into HyperNiche which produced a new ASCII file reflecting the estimated frequency of occurrence of N. homalea across the study area at the same resolution as the original fishnet data. This

ASCII file was inputted into ArcGIS to produce a probability of occurrence map of N. homalea over the area of analysis.

RESULTS

Data Collection

A total of 154 rocks were sampled. During data collection, 21 species and 13 genera of lichens were identified. Five species of microlichens were noted but not identified. There were 28 lichens recorded at Bodega Bay, 23 lichens recorded at Point

Reyes National Seashore, 22 lichens recorded at Half Moon Bay/San Francisco and 17 lichens recorded at Monterey. Field data collected, and representative photographs of the lichens observed at each study site, are presented in Appendix A - F. Of the 154 rocks that were sampled, N. homalea was present on 76 rocks and absent from 78 rocks

(Appendix G). Presence and absence of N. homalea at each study site is shown in Figures

4-7.

NPMR Model

NPMR, using a local mean, Gaussian weights, and a minimum neighborhood size of 7.70, generated 647 competing models in a stepwise free search. The results were narrowed to the four best models based on one, two, three, or four predictor variables using the built-in tools in HyperNiche (Table 1). Each of the four models was further evaluated using the statistical tools built into the program. The optimal model was determined based on the logB value, aveB, and the AUC (Table 2). The four-predictor variable model had the highest logB value of 12.46, an aveB of 1.19 which represents a

19 percent improvement over the naïve model, and an AUC value of 0.752 which is

Figure 4. Presence/absence of N. homalea observed at the Bodega Bay sample location

Figure 5. Presence/absence of N. homalea observed at the Point Reyes National Seashore data sample location

Figure 6. Presence/absence of N. homalea observed at the Half Moon Bay/ San Francisco data sample location

Figure 7. Presence/absence of N. homalea observed at the Monterey data sample location

Table 1. Results of a stepwise free search in HyperNiche software that identified the best models specifying the top one, two, three, and four predictor variables. Model Log Var. 1 Tol. Var. 2 Tol.* Var. 3 Tol. Var. 4 Tol. No. B 98 4.30 Precip. 52.41 415 11.53 Precip. 104.82 Habitat 0.00 572 12.20 Precip. 104.82 Habitat 0.00 Temp. 0.78 1058 12.46 Precip. 104.82 Habitat 0.00 Temp. 0.78 Fog 350.0 Density “Var.” is the variable and “Tol.” is the tolerance value Precip = average annual precipitation; Temp = mean temperature. * Habitat is a categorical variable. Categorical variables always receive a tolerance = 0.

Table 2. Relative strength of the models listed in Table 1, in comparison to the naïve model. The quality of each model is expressed by three evaluation statistics: log likelihood ratios (logB), aveB, and the area under the curve statistic (AUC). Model No. of LogB LogB aveB AUC No. variables change 98 1 4.30 4.30 1.01 0.595 415 2 11.53 7.23 1.15 0.666 572 3 12.20 0.67 1.20 0.760 1058 4 12.46 0.26 1.19 0.752

40 satisfactory according to the Swets scoring system. The change in logB between the four- variable model and three-variable model was 0.26 which means that although the fourth predictor variable contributes to the predictive ability of the model, it does not contribute much. The top predictor variables were precipitation, habitat, temperature, and fog density.

Despite a slightly lower logB value of 12.20, the three-variable model had a slightly higher aveB value of 1.20 than the four-variable model, this represents a 20 percent improvement over the naïve model. It had an AUC value of 0.76, which is satisfactory according to the Swets scoring system. Although the aveB and AUC values of the three-variable model are slightly higher than the four-predictor model, the difference is negligible.

The three and four-variable models were further evaluated using a randomization

(Monte Carlo) test with 100 randomized runs. The resulting p value was less than 0.01 in both the three and four-variable models, which is statistically significant. This means that the model is a significant improvement over the naïve model. The four-variable model was included for further evaluation because all sample sites existed in areas with fog, and

I anticipate fog density to be under evaluated by the model, but is an important variable for discriminating against less foggy island areas.

HyperNiche is able to create 3D (i.e., x, y, z variable) response curves. The program was used to create a response map where z values are represented by contour lines. The 3D response curve only allows two predictors (x or y) and one response variable (z) and cannot be used for categorical variables. Precipitation, temperature, and fog density were used as the predictor variables and the response variable is presence of

N. homalea. The resulting 3D contour response curves show the probability of species occurrence in relationship to the predictors (Figures 8-10).

Niebla homalea is most likely to occur in areas that receive between 400 and

1,200 millimeters of precipitation and where fog density values are between 400 and

1,400 (4 to 14 hours per day of fog on average). N. homalea is most prevalent in areas with high precipitation and low fog density, but is also prevalent in areas with low precipitation and high fog density (Figure 8). Areas that are between 11.5 and 14 degrees

Celsius with fog density values between 400 and 1400 are where N. homalea is likely to be present (Figure 9). N. homalea is mostly found in areas with a mean annual temperature between 12 and 13 degrees Celsius. Areas with low precipitation and low temperature support the occurance of N. homalea as well as areas with higher precipitation and higher temperatures (Figure 10). These results indicate that, taken independently or in pairs, these variables are not complete predictors of the presence of

N. homalea. Absent additional environment information, it is not clear why these divergent conditions are both potentially conducive to the presence of the lichen.

Sensitivity of Individual Predictors

The four-predictor model identified the combination of precipitation, habitat type, temperature, and fog density as the most important predictors to N. homalea, but the model does not rank the importance of the individual predictors. To evaluate the relative importance of individual quantitative predictors, a sensitivity analysis was conducted.

The greater the sensitivity of the predictor variable, the larger influence it has in the

Precipitation(mm/yr)

Figure 8. 3D Contour Response Curve of Precipitation and Fog Density. The graph shows the different probability of N homalea with in the predictor space given the two variables: precipitation and fog density. Response (presence) is represented by contour lines. Red indicates higher probability of occurrence and black indicates low probability of occurrence. The response to precipitation differs with changes in fog density.

Tem

pera

tureC)

(C)o Temperature(

Figure 9. 3D Contour Response Curve of Temperature and Fog Density. The graph sows the different probability of N homalea with in the predictor space given the two variables: temperature and fog density. Response (presence) is represented by contour lines. Red indicates higher probability of occurrence and black indicates low probability of occurrence. The response to temperature differs with changes in fog density.

Prec ipita

tion Precipitation(mm/yr)

Figure 10. 3D Contour Response Curve of Precipitation and Temperature. The graph shows the different probability of N homalea with in the predictor space given the two variables: precipitation and temperature. Response (presence) is represented by contour lines. Red indicates higher probability of occurrence and black indicates low probability of occurrence. The response to precipitation differs with changes in temperature.

45 model. The sensitivity analysis calculated that precipitation had a sensitivity of 0.36, temperature had a sensitivity of 0.15 and fog density was 0.05. This means that precipitation had the greatest influence in the model, individually producing a 3% change in the response.

Because habitat type is a categorical variable, the response data is a simple descriptive statistic based on the average occurrence of N. homalea in the different habitat types. Fifteen different habitat types have been mapped in the study area. Based on the presence/ absence data recorded, N. homalea was present 66% of the time on rocks in annual grass and forbs habitat. It was present 33% of the time on rocks in coyote bush habitat, as well as California sagebrush habitat. Too few observations were taken in the other habitat types to generate meaningful results. The average value for the presence of

N. homalea within each habitat type is shown in Table 3.

Predictive Sensitivity of Occurrence

The estimated frequency of N. homalea occurrence over the study area was generated using precipitation, temperature, and fog density. Habitat type could not be included in the caluculations because it is a categorical value and therefore not numerically quantifiable. A series of points were generated in the GIS and overlaid in a grid pattern over the study area; each point was associated with the central point in a square-shaped fishnet pattern. These points are then associated with the environmental variables at each location and loaded into the HyperNiche software. HyperNiche calculates the probability that N. homalea will occur at each of these locations and associates that value with each point. The point data are then re-combined with the

Table 3. Mean Response Values of N. homalea Presence based on habitat types within the sampling sites Habitat Type Number of Sites with Presence the Habitat Type Annual Grass and Forbs 75 0.67 Interior Mixed Hardwood 2 0.00 Redwood Doug Fir 1 0.00 Pacific Doug Fir 8 0.00 Coyote Brush 27 0.33 Barren 2 0.00 Bishop Pine 2 0.00 California Sagebrush 18 0.33 Urban 1 1.00 Blue Oak 3 1.00 Monterey Cypress 5 0.60 Monterey Pine 2 0.50 Coastal Bluff Scrub 1 1.00 Coast Live Oak 6 0.17 Ice Plant 1 1.00

47 fishnet grid to create a patchwork representation of probability across the study area. A map of predicted sensitivity of occurrence for N. homalea is shown in Figure 11.

Figure 11. The predicted sensitivity of occurrence of N. homalea mapped over the study area based on precipitation, temperature, and fog density

DISCUSSION

I predicted nine variables would influence the presence of N. homalea; however, the results of the predictive habitat model developed using NPMR concluded that the best predictor variables were precipitation, habitat type, and temperature, and fog density.

These results can be used to develop further hypotheses and better understand N. homalea. Consider precipitation for example. The model identified precipitation as the most important factor conditioning the presence of N. homalea. Average annual precipitation at the data collection sites ranged from 392 mm to 1,440 mm. N. homalea was not documented in any area with more than 1,073 mm of precipitation. Although N. homalea can tolerate a range of precipitation, the results indicate it may have an upper limit and cannot tolerate very wet conditions. Field data found N. homalea present in areas with high precipitation but low fog density or in areas with low precipitation and high fog density areas.

If wet conditions are a limiting factor for the distribution of N. homalea, this could explain why N. homalea has not been documented north of Sonoma County.

Average annual precipitation in Mendocino, Humboldt, and Del Norte Counties ranges from 1,000 mm to 3,000 mm (Fick and Hijmans 2017). In addition, fog travels further inland in these counties creating very wet conditions year-round. In contrast, the average annual precipitation along the coast from San Francisco County to the southern border of

California (nearly the full extent of N. homalea’s observed range) ranges from 100 mm to

1,000 mm (Fick and Hijmans 2017) and the high-density fog does not travel as far inland.

Mean annual temperature was another important factor in the presence of N. homalea. The mean annual temperature at the data collection sites ranged from 11 to 15 degrees Celsius. This research did not document N. homalea in any areas with mean annual temperatures above 14 degrees Celsius. The fieldwork found N. homalea present in areas with low precipitation and low temperature and in areas with higher precipitation with higher temperatures. It may be that these variables mitigate one another, that N. homalea can tolerate an increase in one variable, only if it is offset by an increase in the other. Additional data in the mid-ranges would be necessary to determine if the trends hold true and if there is a bell-shaped response.

Fog density also contributed to the predictive ability of the model. Field data was collected in different fog density areas and the species was present in all these areas, but was primarily found in medium to high fog density zones. Though the presence of at least occasional daily fog does seem to play an important role in determining presence of N. homalea, the fog density may not be as important. Data were not collected in areas of very low fog density, such as is observed farther inland. Future studies along these lines should sample as broad a range of environemental variation as possible to determine if fog density is a stronger predictor.

Habitat types were also found to be an important factor in predicting the presence of N. homalea. The predominant habitat type was annual grassland and forbs followed by

California sagebrush and coyote brush. These habitat types have low growing vegetation with little to no canopy cover, which may contribute to the success of N. homalea in these habitats. Field surveys documented N. homalea in open areas but it was always absent in forested areas that were shaded by tree canopy. This result is not definitive however, as

51 the number of data samples collected in forested areas was very low. If more data were collected in forested areas, additional data points could determine if habitat type is a key predictor to the species. Another variable considered in model development was cover type, which is similar to habitat type. Although cover type was determined to not be a key predictor to species presence, N. homalea was predominantly found in open areas with little to no canopy. This suggests that sun exposure may be important environmental factor to N. homalea. Future research should consider solar radiation modeling in order to address this question.

The model did not show that elevation, distance to coast, aspect, or rock type were key predictors of the presence of N. homalea. Field data were collected from 0 meters to 550 meters in elevation. N. homalea was found at 0 meters but was not documented at elevations above 250 meters, even though approximately one third of the data sites (53 sites) were collected at those higher elevations. The distance-to-coast-line measurement was intended to capture unknown variables that could influence distribution, such as salt spray or ocean winds which contribute to harsh growing conditions. Data were collected in locations ranging from 18 meters to 15,000 meters from the coast, and N. homalea was found between 18 and 5,000 meters. N. homalea was distributed relatively evenly up to 5,000 meters from the coast, it appears salt spray or ocean winds may not be toxic to the species. I noted in the field that most rock out crops adjacent to the ocean had high levels of foot traffic. Disturbance from walking on, sitting, and climbing on the rock outcrops could hinder the establishment of N. homalea, but any effects of this disturabnce were not detected by the model.

The data show that neither aspect nor rock type are important factors for the presence of N. homalea. N. homalea was found in equal frequencies on topography facing in all directions and was found on all rock types within the surveyed area. Granite and chert were the predominant rock types in the area and despite significant differences in the chemical composition and texture of these rock types, N. homalea was found on both. N. homalea was also found on mélange and sandstone which were less predominant in the study area.

My results are consistent with other studies that determined which environmental variables explain the distribution of other species of lichens. Several other studies have also identified rainfall, temperature, and vegetation as important factors in the distribution of lichens and species richness (Ellis et al. 2007, Ellis and Coppins 2006, and

Dietrich and Scheidegger 1997). Additionally, other predictive modeling studies found climatic variables and altitude are important factors in predicting lichen species distribution patterns (Shrestha 2010, Radies et al. 2009 and Bolliger et al. 2007).

Predictive Sensitivity of Occurrence

HyperNiche can use the model to predict species presence across a geographic space using GIS data sets for quantitative independent variable data. Predicting species presence based on the most important predictor variables allows for a means of understanding the species range. The predictive map can be used as an external means of validating the model and can also be used to formulate new hypotheses or target areas to survey to improve the model. The results of the predictive sensitivity of occurrence map were intuitively satisfying. They showed a high probability that N. homalea will occur

53 along the coastline and in the areas surrounding the San Francisco Bay, and tapers to lower probability inland.

Observations of N. homalea made by avocational naturalists, and recorded on the iNaturalist program, were overlaid on the predictive sensitivity of occurrence map as an informal test of the efficacy of the model predictions. The iNaturalist program

(http://inaturalist.org) is a website that was established in 2008 and acquired by the

California Academy of Sciences in 2014. The program is a web-based citizen science project where species observations are recorded, photographed, and shared via the iNaturalist website and cellular phone application. As of April 2019, over 354,500 users have collected over 13.2 million observations of more than 84,000 species worldwide. To date, there are no scientific studies analyzing the validity of the data found in iNaturalist, but nonetheless it appears to be a useful monitor of the likely presence of an organism in an area.

Observations recorded with iNaturalist are considered open data that can be used in scientific research projects. Species are reported by the individual posting the observations on the website and then verified by a minimum of two other iNaturalist users. Although misidentifications may occur, the verification process reduces the frequency of errors. A total of 342 observations of N. homalea were recorded by over forty iNaturalist users in my study area (iNaturalist 2018). The observations of N. homalea include eight observations I recorded during my data collection for this study.

My observations were retained because there was no easy way to remove my sites from the iNaturalist data output. However, my observations comprise only a small portion of

54 the total observations, so the effect of those points should be minimal. Figure 12 shows the iNaturalist observations of N. homalea overlaid on the predictive sensitivity of occurrence map.

Using GIS, the predicted sensitivity values for the locations of the iNaturalist observations (n=342) were compiled and then plotted in a histogram. The histogram represents which sensitivity zone each observation falls into (Figure 12). The histogram shows that virtually every one of the N. homalea observations were reported in areas that the model predicts a high probability of occurrence of the lichen. The only exception is a group of six observations that were reported in an area where the predicted probability of occurrence is low (category 4). Interestingly, the iNaturlaist photographs of the lichens observed at these sites show a morphology that is distinct from most of the observed specimens. In these cases, the branches of N. homalea appear thicker and wider than is typical for the species.

Broadly speaking, the genus Niebla is not thoroughly described and the precise number and definition of species has not been fully established (Brodo et al. 2001: 457).

These observations suggest two interesting possibilities. N. homalea can be highly variable in its form and the observations may have captured a localized adaptation to sub- optimal conditions, or it is possible that they reflect a unique sub-species that is adapted to slightly different conditions.

In order to determine whether the iNaturalist observations of N. homalea are in the high predicted sensitivity areas as a result of random chance or a real correlation between the observed locations and the model’s predictions, a histogram was made to reflect the overall frequency of sensitivity values across the study area. A grid of points

55 spaced at 0.1 degrees (n=251) was generated to evenly sample sensitivity values across the study area using GIS. The grid file was overlaid on the map showing predicted sensitivity of occurrence (Figure 13) and each point was associated with the sensitivity value predicted at its location. The frequency of each sensitivity value sampled was plotted in a histogram (Figure 14). The histogram shows that the frequency of sensitivity values associated with the evenly spaced sample points follows no readily discernable pattern, and contrasts sharply with the frequency of sensitivity values associated with the locations of N. homalea observations. Therefore, the strong trend of N. homalea being present in areas of high predicted sensitivity is not the result of random chance.

It is possible that the model may overestimate the likelihood that N. homalea will occur in inland areas. All the iNaturalist observations of N. homalea were recorded close to the coastline but the model predicted N. homalea’s fundamental niche extending considerably further inland along the coast and around the San Francisco Bay area. This could mean that either no iNaturalist user has been to locations further inland to report an observation, there is no rock substrate for N. homalea to become established in these areas, or that other predictor variables, such as solar radiation, that were not captured in this study may play an important role in the presence of N. homalea. This could also be an artifact of missing areas of low fog density, such as is found further inland.

Figure 12. Observations recorded in the iNaturalist databases of N. homalea, overlaid on the predicted sensitivity map

Figure 13. A grid of points spaced at 0.1 degrees overlaid on the map showing predicted sensitivity of occurrence.

120

100

40 CountofObservations 20

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 Predicted Sensitivity of Occurance

20 CountofGrid Points 10

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 Predicted Sensitivity of Occurance

Figure 14. The top histogram shows the frequency of N. homalea iNaturalist observations within the predicted sensitivity ranges. The bottom histogram shows the frequency of evenly spaced grid points within the predicted sensitivity ranges. Comparison of the two histograms shows that the frequency of sensitivity values associated with the N. homalea observations are within areas of high probability but the frequency of sensitivity values associated with the evenly spaced grid points follows no obvious pattern.

Ecological Niches and Conservation

Understanding the distribution of species is a fundamental concept in ecology.

When determining the distribution of a species that is not well studied, as is the case with

N. homalea, it is necessary to understand the fundamental niche of the species and to identify the basic environmental requirements the organism requires for survival and reproduction. Ecological niche models are successful in identifying the environmental requirements of a species (Kearney and Porter 2004) and the information gained from niche models can be used when planning conservation efforts.

In the last fifteen years, there have been several attempts to apply niche studies to conservation. One example is the work of Alleman and Hester (2011) who determined constraints on the fundamental niche of black mangrove (Avicennia germinans) to refine their understanding of that species’ niche along the southern Louisiana coast. Alleman and Hester (2011) reported that the resistance to salinity, sand burial, and water level in black mangrove seedlings varies with age, suggesting that, in effect, the fundamental niche of the species changes throughout its life cycle. This allowed for the improvement of restoration plans for the mangrove by targeting the planting of seedlings at the optimum age for resilience. The Alleman and Hester (2011) study, like the present study, identified specific and measurable aspects of a species’ niche that were associated with the presence of the species in question. Although N. homalea has not been well studied, methods similar to those used by Alleman and Hester (2011) could be applied to conservation efforts of N. homalea if the population were to decline in the future.

Applying niche models to conservation efforts can be difficult because modeling requires presence data of the species to be collected, and certain species traits pose sampling challenges. For example, short lived organisms, mobile organisms, nocturnal organisms, and organisms that are seasonally present can be hard to sample. Lichens, however, are an ideal organism to use to when studying niches because they are long lived, are present is nearly all ecosystems, and are always visible. Most lichen species live for decades to hundreds of years (Nash 2008). One benefit of the essential symbiotic relationship of an alga and a fungus is increased resilience, and as such lichens can be found in almost all terrestrial habitats ranging from the tropics to the arctic (Nash 2008).

Lichens also show distinctive distribution patterns on the micro and macro levels (Nash

2008) and therefore can be used to study niches at different scales. In addition, lichens are easy to find because they are sessile, grow on a variety of substrates (soils, trees, rocks, and metal), and are always present, unlike vascular plants which can be deciduous or most easily identified when flowering. For these reasons, numerous other studies have also used lichens to study niches (e.g., Martenez et al. 2006, Shrestha 2010, Ellis et al.,

2007, Galvich et al. 2005).

Niche studies are not the only ways that the study of lichens can inform conservation efforts. The biology of lichens also makes them effective environmental indicators for detecting changes to habitats. Lichens have widespread distributions and as such they are affected by environmental disturbances on a local, regional, and global levels (Nash 2008). Lichen communities are often species-rich but natural disasters and human influences such as deforestation and agricultural practices can result in a loss of lichen diversity (Nash 2008). Lichens are particularly sensitive to atmospheric pollutants

(Nash 2008) and have been used to understand changes in pollution levels when a new pollutant source (e.g., a copper smelter) is introduced (Purvis et al. 2003). They have also been used to measure the recovery of an area after a pollutant source was removed (Loppi et al. 2004).

Model Implications

Interest in ecological niche concepts has grown with the development of GIS technology and improved modeling techniques (Kearney and Porter 2004). Modeling how a species might respond to environmental changes is becoming increasingly important as the climate changes (Wiens et al. 2009). The Intergovernmental Panel on

Climate Change (IPCC 2007) described changes to the climate which may affect the distribution and abundance of species.

Species generally respond to changes in climate in four ways: shifting distribution, adaptation in place, remaining in place in smaller refugia, or extirpation and extinction (Wiens et al. 2009). The extent to which a species can shift its distribution is constrained by the mobility and dispersal limitations of the organism. Adaptation to change may occur over a lengthy period of time. Annual plants, such as grasses, may adapt more quickly, but adaptation occurs more slowly in long lived species. If a species is unable to change its location or adapt to new conditions, it may remain in areas of refugia (pockets of unchanged environments) or it may become extinct.

Anticipating how a given species will respond to climate change poses a challenge to resource managers who must consider future environmental conditions in their planning and land management. Resource managers have several options in this

62 regard. They can continue with their current practices using “business-as-usual” methods, intuitively guess as to what the future could be based on experience, or use quantitative models to anticipate future conditions (Wiens et al. 2009). The so-called business-as-usual and guessing methods are unreliable ways to manage environmental changes. Quantitative models, though imperfect, can provide resource managers with baseline information to develop consistent and defensible approaches to manage for future environmental conditions.

Models are being used to understand how species distributions may change under different climate scenarios (Ellis et al. 2007). The model presented in this study uses environmental variables to define the current niche occupied by N. homalea. This model and others like it can be used in conjunction with climate-change models to project how a species niche might change under different climatic scenarios. These projections could highlight areas that are particularly vulnerable to species loss and areas that might experience an increase in species richness as ranges shift with changing conditions.

Consider what the present model tells us about the ranges of temperature and precipitation that favor the presence of N. homalea and how those data could be used with a current model of projected climatic chances to anticipate how N. homalea will react to a changing climate. The Cal-Adapt website, (www.cal-adapt.org) provides geographically based predicitions of temperature and precipitation based on different climate change proejctions. For example, the Cal-Adapt model predicts that over the next 50-80 years, mean annual temperature at Tomales Point will see a temperature increase in the range of 2 to 4 degrees C, and precipitation will increase in the range of

140 to 240 mm annually. If the climate shifts to what Cal-Adapt model predicts Tomales

Point would still be in the range of N. homalea based on my model. However, in areas where temperatures are higher and precipitation is lower, and the model suggests that the presence of N. homalea is less likely, the predicted climate change could increase its distribution into these areas. Future conservation efforts are not likely to be needed to preserve this species, but its distribution may be a useful indicator of environmental changes.

Model Limitations

The model does seem to capture the general range where N. homalea could occur but in its current state of development, the model has several limitations. For example, the actual distribution of suitable substrate (i.e., rock outcrops) is unknown which causes the model to somewhat overestimate N. homalea’s range in areas where suitable substrate may not exist. A GIS layer identifying rock outcrops was not available for the study area and is probably not feasible to produce. It is hypothetically possible that a model could be developed by to predict the locations of rock outcrops based on bedrock geology and weathering factors; however, no such models are known to exist, and developing such a model was beyond the scope of this study. Rock outcrops cannot realistically be identified directly from high resolution satellite imagery. Manually digitizing the rock outcrops would be time intensive and would not capture rocks that are hidden by vegetation or tree canopies. Small rocks might not be visible. Currently, commercially available satellite imagery provides ground resolution between 0.46 meters and 1.56 meters. At this resolution, many rock outcrops would not be discernable.

The model predicts the sensitivity of occurrence of N. homalea on a broad geographic scale and does not take into account fine-scale microclimates. Microclimates are known to influence local and regional distributions of species (Suggitt et al. 2011). If the distribution of N. homalea is being affected at the landscape scale by microclimatic influences, such as the effects of wind speed, vegetation structure, or thermal variation, these interactions are likely not detected by the model. Similarly, the model cannot consider the influence of site-specific biotic factors, such as competition, that could influence the density of N. homalea. Competition over space, for example, could occur between individuals of N. homalea or with other species. Smaller-scale observational studies could access these more site-specific variables in the future, informed by the general parameters conditioning the presence of N. homalea set forth here.

Ultimately, this model is not structured to capture species density. The response variable for the model I built is binary (presence/ absence). This was done to allow the model to predict the probability that N. homalea will occur at a landscape scale. Density is a quantitative measurement, and is an incompatible response output of the model.

Other analytical methods such as multivariate analysis or analysis of variance that can consider microclimatic and biotic variables would be more appropriate to access these questions.

Despite the limitations, NPMR identified the environmental variables that best predict the presence of N. homalea. These predictor variables form the abiotic environmental underpinnings of the fundamental niche. These key abiotic factors are among the many environmental variables that are directly affected by climate change. As additional study improves our understanding of how these factors condition the success

65 of N. homalea and other species, we will be able to forecast the impacts of our changing climate more precisely. This knowledge could also be used to improve current methods of environmental management.

Suggestions for Future Research

Mapping of Substrate

If the locations of rock outcrops within the study area could be determined accurately, perhaps by using a color-based search of very high-resolution aerial photography (not satellite imagery), the rock outcrop locations could be overlaid on the predicted sensitivity map. The resulting map would show which sensitivity zone each rock outcrop falls into. Such a dataset would resolve the methodological challenges surrounding the collection of a truly random sample and a random sample survey for N. homalea could then be conducted. The results would be useful to further verify the validity of the model, to refine the predictive sensitivity map, and to potentially formulate new hypotheses.

Microclimate variables

Other studies have shown that microclimatic variables, such as evaporation rate, exposure to rainwater, and crevice depth, influence the presence and absence of lichens

(Ranius et al. 2008). Though these kinds of variables may be too situationally variable to be meaningfully incorporated into a large-scale GIS based model like the one developed here, the outlines of a fundamental niche implied by its results could be refined in the future by a different methodology that considered these other variables.

Species Density and Lichen Diversity

The model developed in this study identified the environmental variables that best predict the presence of N. homalea and began to explain the species distribution. The results provide a solid foundation from which to determine the driving factors explaining species density or lichen diversity. Species density and lichen diversity are known to be influenced by land management procedures (Moning et al. 2009, Kalwij et al. 2005), substrate texture (Ranius et al. 2008), and species dispersal limits and establishment periods (Nathan and Muller-Landau 2000, Werth et al. 2006). Future studies could test which of these factors are the major driving influences for species density or lichen diversity.

Species Interactions

Like the microclimate variables discussed above, species interactions at the scale of individual rock faces are not easily modelled using GIS. A study focussed on species interactions on the rock outcrops would certainly produce interesting results. Space is limited on the rocks and many sampling sites had multiple species of lichens present within that limited space. Observations during data collection did not find lichens growing on top of each other, instead each maintained a shared edge when two lichens touched. It is possible that the presence of a crustose lichen could prohibit the establishment of N. homalea.

CONCLUSION

I tested the hypothesis that the presence of N. homalea is conditioned by elevation, distance to coast line, aspect, geology, precipitation, temperature, fog density, habitat type, and cover type. In order to test this, I collected data at 154 sites in four locations along the northern coast of California. The field data were analyzed using

NPMR to determine the best predictor variables. These analyses showed that precipitation, temperature, habitat type, and fog density appear to be the strongest predictors of the presence of N. homalea and are the important environmental factors that condition its fundamental niche.

My results aligned with other studies (Ellis et al. 2007, Ellis and Coppins 2006, and Dietrich and Scheidegger 1997) who identified rainfall, temperature, and vegetation as important factors in the distribution of other lichens. Similarly, studies conducted by

Shrestha (2010), Radies et al. (2009) and Bolliger et al. (2007) documented that climatic variables (temperature, soil moisture, solar exposure) and altitude are important for predicting species distribution patterns of lichens.

The HyperNiche model provides valuable information about the distribution of N. homalea. The overall goal of this research was to develop a predictive distribution map of

N. homalea in northern California. The predictive distribution map indicates there are high probability areas further inland than where the species has been documented.

Further investigations should be conducted in these areas to gain a better understanding of the full range of N. homalea. Mapping the locations of rock outcrops within the study area and then spatially comparing the data to the predicted sensitivity of occurrence of N.

68 homalea, as mapped here, would further define where suitable habitat may exist. In addition, when developing models for predicting the distribution of lichens, sampling sites should be stratified across all predictor variables in order to fully analyze their effects. Smaller scale studies looking at microclimatic data could further refine our understanding of suitable habitat for N. homalea.

APPENDIX A

Field Data collected at Bodega Bay: Sampling site location, rock group number and location, species recorded at each rock group and percent cover of each lichen species on

each rock group

Bodega Bay Data Data collected along the Ocean Song at Bodega Bay Rock group 1 0497904; 4250090 Lichen 1 – Rhizocarpon macrosporum bright yellow/ lime green, black apothecia, think black line around lichen body. Lichen 2 – Xanthoria candelaria bright orange, no apothecia, fine toothed Lichen 3 – Umbilicaria phaea dark brown/ black, smooth surface, lobed, black apothecia, bottom surface black and smooth Lichen 4 – Xanthoparmelia (?) yellow/green lobed, brown at tips, apothecia brown, underside brown with dark spots Lichen 5 - Physcia (?) white, small, branched, black apothecia, with/ gray underside Lichen 6 - crust, Pale gray/ light green, black apothecia Lichen 7 – crust charcoal gray, bumpy, no apothecia Lichen 8 – crust, medium gray Lichen 9 – crust, medium brown, black apothecia Rock group 2 0497628; 4250284 Lichen 1 Parmotrema perlatum broad lobes, white/ pale gray, black underside, cila on margins, soredia at margins, on rock, shaded below a bay tree Lichen 2 – green/ yellow lobed, brown around tips, dark underside, top of thallus looks like it has cracks, Xanthoparmelia (?)

Lichen 3 – Parmelia pale gray/ white, crackel/ lines, square lobes, black on bottom Lichen 4 – crust, white with black apothecia Lichen 5 – crust, all white, black line buffering around lichen body Lichen 6 - crust. Pale gray/ light green, black apothecia Lichen 7 – crust charcoal gray, bumpy, no apothecia Lichen 8 – crust, medium gray Rock group 3 0497855; 4250270 Lichen 1 – crust, white/ gray, black apothecia Lichen 2 – Flavoparmelia caperata, pale yellow/ green, lobes, brown tips/ black underneath, smooth, in shade under a tree Lichen 3 – crust brown/ green, black apothecia Lichen 4 - Punctelia stictica (confirmed via i-nat) white thallus, lobes overlapping, dark brown at tips, white spots, black underside Lichen 5 – Umblicaria phaea Lichen 6 – Rhizocarpon macrosporum Lichen 7 - Physcia (?) white, small, branched, black apothecia, with/ gray underside Lichen 8 - crust, all white, white heavy black line around lichen body Lichen 9 - Xanthoparmelia (?) yellow/green lobed, brown at tips, apothecia brown, underside brown with dark spots Lichen 10 – Caloplaca crust, bright orange, fine dots

Data collected along the Island in the sky trail at Bodega Bay Rock group 1 0496083; 4255423 Percent Cover Large rocks covered in moss. Under canopy 0 and heavily shaded by Douglas firs, pine, and Bay trees. No lichen. Rock group 2 0496302; 4255204 Percent Cover Large rocks covered in moss, sticks, and other 0 debris from trees. Under canopy and heavily shaded. No lichen. Rock group 3 0496254; 4254518 Percent Cover On ridge, in full sun. Rock size: 8 feet x 18 feet. Lichen 1 – Lecanora pingus light green, 5 crustos, bubble gum Lichen 2 – Xanthoria candelaria bright <1 orange, no apothecia, fine toothed Lichen 3 – unknown crust crust, pale gray, 40 black apothecia, bumpy Lichen 4 – unknown crust crust, all black, flat, 15 black solid line around edge, black spots in center (Protoparmeliopsis ?) Lichen 5 – Buellia halonia crust, light green, <1 black apothecia (dots), black outline around outer edge Lichen 6 – Parmelia pale gray/ white, crackel/ <1 lines, square lobes, black on bottom Lichen 7 – Xanthoparmelia green/ yellow 15 lobed, brown around tips, dark underside Lichen 8 - – Umblicaria phaea brown with <1 dark apothecia. Lichen 9 – Punctelia stictica folious, green/ yellow lobed, white dots, brown edges Lichen 10 - Physcia all white, lobed, white <1 underside Lichen 11 – Lecanora muralis folious, green <1 lobed, brown/ golden apothecia Lichen 12 – Cladonia – pixy cups <1 Others – mosses and such Rock group 4 0496295; 42544408 Percent Cover Full sun, Rock size: 15 feet x 30 feet Lichen 1 – Xanthoparmelia green/ yellow 5 lobed, dark underside Lichen 2 – Parmelia pale gray/ white, white <1 lines, lobes, black on bottom

Lichen 3 – Crustos, gray, bumpy, black 50 apothecia, C-, K=+ yellow Lichen 4 – Xanthoparmelia folious, lobed, <1 green, brown apothecia Lichen 5 – Xanthoria candelaria bright <1 orange, no apothecia, fine toothed Lichen 6 – Physcia (?) all white, lobed, white <1 underside, no apothecia, K+ yellow, C- Lichen 7 – Cladonia pixy cups <1 Lichen 8 – unknown crust 1 crustos, all white, <1 K+ yellow Lichen 9 – Unknown crust 2 Crustos, dark <1 gray/ black, K-, C- (no photo) Lichen 10 – Caloplaca fire dots, bright orange <1 apothecia Lichen 11- Unknown crust 3 crustos, brownish <1 gray, black apothecia, K-, C-, got darker after chemicals but no color change. Catillaria lenticularis (?) Other – mosses 2 Rock group 5 0496291; 4254221 Percent Cover Full sun, 9 feet x 6 feet Lichen 1 – Lecanora pinguis light green, <1 crustos, bubble gum Lichen 2 - Physcia (?) all white, lobed, white 30 underside, black apothecia Lichen 3 - Xanthoria candelaria bright 2 orange, no apothecia, fine toothed Lichen 4 – Lecanora phryganitis green, brown <1 coral like, brown/ beige apothecia Lichen 5 – Xanthoparmelia folious, green, 2 brown apothecia, dark brown underside Lichen 6 – Rhizocarpon macrosporum crustos, <1 bright yellow/ lime green, black apothecia Lichen 7 - Umblicaria phaea brown with dark 5 apothecia Lichen 8 – Flavoparmelia caperata foilious, <1 green, black underside, texture and under Lichen 9 - Punctelia stictica white thallus, 8 lobes overlapping, dark brown at tips, white spots, black underside Lichen 10 – crustos, white/ gray thallus, <1 bumpy, K+ yellow, C- Rock group 6 0496311; 4254174 Percent Cover

Full sun, flat and wide spread. Chert, old chipping sites 9 feet x 6 feet Lichen 1 - Xanthoparmelia folious, green, 60 brown apothecia, dark brown underside Lichen 2 – Unknown crust 5 crustos, gray 15 Lichen 3 - Rhizocarpon macrosporum bright <1 yellow/ lime green, black apothecia, think black line around lichen body Lichen 4 – Unknown crust 2 crustos, black <1 Lichen 5 - Umblicaria phaea brown with dark <1 apothecia Lichen 6 - Physcia (?) all white, lobed, white 2 underside, black apothecia Rock group 7 0496425; 4254962 Percent Cover Full sun, 12 feet x 6 feet Lichen 1 - Umblicaria phaea brown with dark <1 apothecia Lichen 2 – Xanthoparmelia green/ yellow 10 lobed, dark underside, black/ brown apothecia Lichen 3 - Rhizocarpon macrosporum bright <1 yellow/ lime green, black apothecia, think black line around lichen body Lichen 4 – Thelomma mammosum crustos, 10 gray Lichen 5 – crustos, black 2 Lichen 6 - Xanthoria candelaria bright <1 orange, no apothecia, fine toothed Lichen 7 - Physcia (?) all white, lobed, white 5 underside, black apothecia Lichen 8 - foilious, green, sorida, white dots on 2 thallus, black underside Lichen 9 - Lecanora pingus light green, <1 crustos, bubble gum Lichen 10 - Lecanora phryganitis green, brown <1 coral like, brown/ beige apothecia Lichen 11 – Punctelia stictica folious, white 2 thallus with white dots, brown edges at tips Lichen 12- Cladonia <1 Lichen 13 – Punctelia folious, green thallus, <1 white dots

Data collected at Bodega Head in Bodega Bay Rock group 1 0495097; 4239441 Percent Cover Lichen 1 – Xanthoria candelaria bright 1 orange, no apothecia, fine toothed, K+ red Lichen 2 – Lecanora pingus light green, <1 crustos, bubble gum, C+ yellow Lichen 3 – Crustos, light gray, bumpy, beige to 25 black apothecia, C-, K=+ yellow Lichen 4 – crustos, white, beige apothecia, K+ <1 yellow, C- Lichen 5 – Cladonia – pixy cups <1 Rock group 2 0495134; 4239413 Percent Cover Full sun, poison oak Lichen 1 – Lecanora pingus light green, <1 crustos, bubble gum Lichen 2 – Crust, pale gray, black apothecia, 40 bumpy Lichen 3 – Xanthoparmelia (?) small lobed, <1 green, dark brown/ black underside, no apothecia, has soridia Lichen 4 – Parmotrema Foliose, white thallus, <1 black underside, black rhizines underside, Lichen 5 – Xanthoria candelaria bright <1 orange, no apothecia, fine toothed Rock group 3 0495134; 4239413 Percent Cover Lichen 1 – Lecanora pingus light green, 3 crustos, bubble gum Lichen 2 – Niebla homalea 1 Lichen 3 – Xanthoria candelaria bright <1 orange, no apothecia, fine toothed Lichen 4 – Parmotrema folious, white thallus, <1 black underside with black rhizines, soridia on margins, Lichen 5 – Buellia halonia crust, green with black Lichen 6 - Xanthoparmelia (?) small lobed, <1 green, dark brown/ black underside, no apothecia, has soredia Lichen 7 - Physconia(?) all white, lobed, <1 black underside, black rhizines, no apothecia K-, C- Lichen 8 – Lecanora californica Crust, pale 10 gray, with apothecia,

Rock group 4 0494944; 4232914 Percent Cover Lichen 1 – Lecanora pingus light green, <1 crustos, bubble gum Lichen 2 – Xanthoria bright orange, no <1 apothecia Lichen 3 – Buellia halonia crust, green thllus, <1 black apothecia, black around edges, K-, C+ light yellow Button lichen (?), Buellia halonia/ Porpidia crustulantces (?) Lichen 4 – Lecanora californica gray crust 10 Rock group 5 0494954; 4239239 Percent Cover Lichen 1 - Lecanora pingus light green, 15 crustos, bubble gum Lichen 2 – Niebla homalea <1 Lichen 3 – Xanthoria bright orange, no <1 apothecia Lichen 4 – Buellia halonia crust, green thllus, <1 black apothecia, black around edges, K-, C+ light yellow Button lichen (?), Buellia halonia/ Porpidia crustulantces (?) Lichen 5 – crust, dark gray K-, C- <1 Lichen 6 – Lecanora californica crust, gray 20 Lichen 7 – crust medium gray 15 Rock Group 6 494977; 4239300 Percent Cover Lichen 1 - Lecanora pingus light green, 5 crustos, bubble gum Lichen 2 – Xanthoria bright orange, no <1 apothecia Lichen 3 - Niebla homalea <1 Lichen 4 - Buellia halonia crust, green thllus, 2 black apothecia, black around edges, K-, C+ light yellow Button lichen (?), Buellia halonia/ Porpidia crustulantces (?) Lichen 5 – crust, dark gray/ charcoal, black 10 squiggly lines Lichen 6 – crust, brown/ beige C-, K+ red 15 Lichen 7 – crust, light gray, C-, K+ yellow 20 Rock Group 7 495019; 4239284 Percent Cover Lichen 1 - Lecanora pingus light green, 5 crustos, bubble gum

Lichen 2 – Xanthoria bright orange, no 1 apothecia Lichen 3 - Niebla homalea <1 Lichen 4 – Crust, brown/ beige, bumpy 15 Lichen 5 – crust, dark gray/ charcoal, black 5 with white squiggly lines Lichen 6 - crust, gray, black dots 20 Lichen 7 - Buellia halonia crust, green thllus, <1 black apothecia, black around edges, K-, C+ light yellow Button lichen (?), Buellia halonia/ Porpidia crustulantces (?) Rock Group 8 – 0494997; 4239258 Percent Cover Lichen 1 - Lecanora pingus light green, 2 crustos, bubble gum Lichen 2 – Xanthoria bright orange, no <1 apothecia Lichen 3 - Buellia halonia crust, green thllus, 5 black apothecia, black around edges, K-, C+ light yellow Button lichen (?), Buellia halonia/ Porpidia crustulantces (?) Lichen 4 – crust, gray, apothecia small, beige/ 25 dark brown Lichen 5 – crust, dark gray/ charcoal, squiggles 10 Lichen 6 – crust, brown/ beige <1 Rock group 9 – 0494982; 4239318 Percent Cover Lichen 1 - Lecanora pingus light green, <1 crustos, bubble gum Lichen 2 – Xanthoria bright orange, no 5 apothecia Lichen 3 - Niebla homalea <1 Lichen 4 - Buellia halonia crust, green thllus, <1 black apothecia, black around edges, K-, C+ light yellow Button lichen (?), Lichen 5 – crust, brown/ beige <1 Lichen 6 – crust, light gray 25 Lichen 7 – crust, dark gray/ charcoal <1 Lichen 8 – folious, white, lobes, cracked lines, <1 black underside with black rhizines, C- K+ yellow

Rock Group 10 – 0494942; 4239337 Percent Cover Lichen 1 - Lecanora pingus light green, 5 crustos, bubble gum Lichen 2 – Xanthoria bright orange, no <1 apothecia Lichen 3 - Niebla homalea <1 Lichen 4 – crust, brown/ beige 60 Lichen 5 - Buellia halonia crust, green thllus, 2 black apothecia, black around edges, K-, C+ light yellow Button lichen (?), Buellia halonia/ Porpidia crustulantces (?) Lichen 6 – crust, light gray 25 Lichen 7 – crust, dark gray/ charcoal <1

Data collected near Shell Beach at Bodega Bay Rock group 1 0491127; 4252378 Percent Cover Lichen 1 – Niebla homalea, K+ yellow <1 Lichen 2 – Xanthoparmelia folious, green, no <1 apthocia, brown underside Lichen 3 – Caloplaca Crust, orange dots <1 Lichen 4 – Physcia folious, white, light 2 underside, small flat lobes, very lobed K+ yellow, C- Lichen 5 – Dimelaena radiata? crust, gray, <1 black line around edge, black apothecia, flat, K+ yellow to orange to red, C- Lichen 6 – unknown crust crust, dark gray to 2 almost black, bumps, black apothecia Lichen 7 – Parmotrema folious, white, larger 2 body, less lobes, soridea on edges, black underside, C-, K+ yellow (lots on shade side) Lichen 8 – Xanthoparmelia folious, green, <1 yellow towards center, soridia, underside black, smooth, K+ yellow, C- Lichen 9 – Diploschistes scruposus crust, <1 small, white, black apothecia, black line around edge Lichen 10 – Thelomma mammosum crust, <1 white, bumpy, black apothecia, raised, K+ yellow to orange, C- Lichen 11 – Buellia halonia crust, green, black <1 apothecia K, C – (Kinda yellow) Lichen 12 - Ramalina folious, green, straggly <1 (ramalina ?) Lichen 13 – Pertusaria california potato 5 lichen (on shaded areas) Lichen 14 - Lecanora pingus light green, <1 crustos, bubble gum Rock group 2 0491195; 4252691 Percent Cover Full sun, open Lichen 1 – Lecanora pingus light green, 1 crustos, bubble gum Lichen 2 – Flavoparmelia caperata folious, <1 green (shield), soridia , black underneight, brown at tip, smooth looking, black bumps underside Lichen 3 – Xanthoria bright orange, no <1 apothecia

Lichen 4 – Niebla homalea 1 Lichen 5 – Physcia folious, white, fine lobed, <1 white underside, white to tan Lichen 6 – Parmotrema folious white, back <1 gray, larger lobed, black underside, back hairs Lichen 7 – unknown crust crust, black, white 1 apothecia Lichen 8 – unknown crust crust, white 5 Lichen 9 – Buellia halonia crust, green, black 1 apothecia Lichen 10 – Parmotrema folious, white, large <1 lobes, cilia on margins Lichen 11- Pertusaria california potato lichen <1 Lichen 12 – unknown crust crust, dark gray/ 7 charcoal Lichen 13 – Dimelaena crust, white, black <1 apothecia, cups Lichen 14 - Xanthoparmelia folious, green, <1 flat, small lobes, black underside Rock group 3 0491638; 4252898 Percent Cover Lichen 1 – Niebla homalea 1 Lichen 2 – Xanthoparmelia folious, green, 5 small lobed, over lapping, black underside Lichen 3 – folious, green, flat and fat, crinkley <1 white lines, black underside Lichen 4 - Lecanora pingus light green, <1 crustos, bubble gum Lichen 5 – unknown crust crust, white, black 80 apothecia Lichen 6 – Caloplaca orange dots <1 Lichen 7 - Xanthoria bright orange <1 Lichen 8 – unknown crust crust, dark gray 10 Lichen 9 – folious, green, soridea, black x underside Rock Group 4 0491653; 4252902 Percent Cover Lichen 1 – Niebla homalea 1 Lichen 2 – folious, green, large, slightly lobed, 1 soridia, crackly surface, white dots, black underside Lichen 3 – Caloplaca orange dots <1 Lichen 4 – Xnthoparmelia folious, pale green, 1 small lobed, overlapping, brown apothecia, brown underside

Lichen 5 – Buellia halonia crust, green with <1 black apothecia Lichen 6 – unknown crust crust, white, black 40 apothecia Lichen 7 – unknown crust crust, gray charcoal 40 Lichen 8 – Thelomma mammosum crust <1 white, raised apothecia Rock Group 5 0491663; 4252902 Percent Cover Lichen 1 – Niebla homalea 5 Lichen 2 – Xanthoparmelia folious, pale green, 1 fine lobed, brown apothecia, brown underside Lichen 3 – unknown crust crust, white, black <1 apothecia Lichen 4 – Physcia folious, white, white 1 underside Lichen 5 – Acarospora socialis crust, lime <1 yellow Lichen 6 – crust, gray with pink/orange <1 (potato?) Lichen 7 – Caloplaca orange dots <1 Lichen 8 – Buellia halonia crust, green, black X apothecia Lichen 9 – Lecanora pingus light green, <1 crustos, bubble gum Lichen 10 – crust, white, small bumpy 40 Lichen 11 – crust, gray 40 Rock Group 6 0491737; 4252931 Percent Cover Lichen 1 – Niebla homalea 2 Lichen 2 – Lecanora pingus light green, <1 crustos, bubble gum Lichen 3 – Xanthoparmelia folious, pale green, 5 small lobed, black underside Lichen 4 – unknown crust crust, gray 70 Lichen 5 – Rhizocarpon crust, lime green, <1 black apothecia Lichen 6 - Xanthoria bright orange <1 Lichen 7 – Parmotrema folious, white, black <1 underside, soridia, cilia on margins Lichen 8 – Pertusaria california potato lichen 1 Lichen 9 – Flavoparmelia caperata folious, <1 green, fat lobes, brown apothecia Lichen 10 – unknown crust crust, white, beige <1 apothecia

Rock Group 7 0491729; 4252879 Percent Cover Lichen 1 – Niebla homalea <1 Lichen 2 – Lecanora pingus light green, 2 crustos, bubble gum Lichen 3 - Xanthoparmelia folious, green, 10 brown on tips, brown apothecia, fine lobes, overlapping, brown underside Lichen 4 – Folious, green flat fat lobes, brown 1 underside, soridia Lichen 5 – Physcia folious, white, small <1 branched lobes, black/ brown apothecia Lichen 6 – Parmotrema folious, white, fat <1 lobes, brown underside, cilia on margins Lichen 7 – Buellia halonia crust, green black <1 apothecia Lichen 8 – Xanthoria bright orange <1 Lichen 9 – unknown crust crust, gray 60 Rock Group 8 0491163; 4252508 Percent Cover Lichen 1 – Niebla homalea 2 Lichen 2 – unknown crust crust, white, beige 1 apothecia, K+ yellow, C- Lichen 3 – Pertusaria california potato lichen 5 Lichen 4 – unknown crust crust, brown with <1 black apothecia Lichen 5 – unknown crust crust, white, black 15 apothecia, K+ yellow, C- Lichen 6 – crust, gray, black apothecia X Lichen 7 – Xanthoparmelia folious, green, 34 small lobed Lichen 8 – Flavoparmelia caperata folious, 2 green, flat lobed, Lichen 9 – Xanthoria bright orange <1 Lichen 10 – Physcia folious, white, small lobes <1 Lichen 11 – Lecanora pingus light green, <1 crustos, bubble gum Rock Group 9 0491167; 4252475 Percent Cover Lichen 1 – Lecanora pingus light green, 2 crustos, bubble gum Lichen 2 – Niebla homalea 5 Lichen 3 – Xanthoria bright orange <1 Lichen 4 – Pertusaria california potato lichen 30 Lichen 5 – crust, white, black apothecia 2 Lichen 6 – Physcia folious, white, small lobes, <1 white underside

Lichen 7 – folious, darker green, crackles on 5 top, brown underside, soridia Lichen 8 – Flavoparmelia caperat folious 1 green, fat flat lobes Lichen 9 - Buellia halonia crust, green with <1 black apothecia Lichen 10 – crust, charcoal gray 10 Rock Group 10 0491164; 4252483 Percent Cover Lichen 1 – Niebla homalea 5 Lichen 2 – Xanthoparmelia cumberlandia <1 folious, green, small lobed, brown apothecia Lichen 3 – Xanthoria bright orange <1 Lichen 4 – crust, white, black apothecia 15 Lichen 5 – Buellia halonia crust, green, black 1 apothecia Lichen 6 – Lecanora pingus light green, <1 crustos, bubble gum Lichen 7 – folious, green, fat flat lobes, soredia <1 Lichen 8 – folious, green, soridea on edges, <1 Lichen 9 – folious, white, lines on top, small <1 lobes, black underside Lichen 10 – Pertusaria california potato lichen 1 Lichen 11 - Thelomma mammosum crust, 30 lighter gray Lichen 12 – crust, dark gray 5

Data collected along Highway 1 at Bodega Bay Rock group 1 0491305; 4251669 Percent Cover Lichen 1 – Niebla homalea, K+ yellow 10 Lichen 2 – crust, black gray, back apothecia 20 Lichen 3 – crust, white 2 Lichen 4 – crust, medium gray 5 Lichen 5 – Xanthoparmelia folious, green, 2 black rhizines, brown apothecia, brown underside Lichen 6 – Xanthoria bright orange, no <1 apothecia Lichen 7 – Physcia folious, white, small lobed, <1 black Lichen 8 - Pertusaria california potato lichen 8 Lichen 9 – folious, green, black underside, fat <1 broad lobed, black rhizines near edge, K+ yellow Rock Group 2 0491327; 4251337 Percent Cover Lichen 1 – Niebla homalea, K+ yellow 2 Lichen 2 – Xanthoria bright orange, no 2 apothecia Lichen 3 – crust, black/ charcoal gray 15 Lichen 4 - Pertusaria california potato lichen <1 Lichen 5 – crust, white <1 Lichen 6 – crust, black with white edges <1 Lichen 7 – Xanthoparmelia folious, green, 2 dark at edges, small fine lobes, dark underside, brown apthocia Lichen 8 – Xanthoria folious/ crust orange, <1 fine lobes Lichen 9 – folious, white, brown underside, <1 rhizines Lichen 10 – crust, medium gray 5 Rock group 3 0491334; 4251334 Percent Cover Lichen 1 – Lecanora pingus light green, 5 crustos, bubble gum Lichen 2 – Caloplaca crust, orange 2 Lichen 3 – Xanthoparmelia folious, green, 1 brown apothecia Lichen 4 – folious, white, fine lobes, tan to 2 black underside, Lichen 5 – Lecanora phryganitis (coral like) <1 Lichen 6 - Xanthoria bright orange, no <1 apothecia

Lichen 7 – Pertusaria california potato lichen <1 Lichen 8 – crust, black with white around Lichen 9 – crust, white, black aptohecia Rock Group 4 0491375; 4251206 Percent Cover Lichen 1 – folious, green, fine lobed, dark 5 underside Lichen 2 – crust, beige/ brown, black apothecia 20 Lichen 3 – crust, black with white edges, black 4 apothecia Lichen 4 – crust, brown, black apothecia <1 Lichen 5 – Lecanora phryganitis (coral like) <1 Lichen 6 – crust, medium gray 5 Lichen 7 – Caloplaca crust, orange <1 Rock Group 5 0491527; 4251086 Percent Cover Lichen 1- crust, brown, black apothecia <1 Lichen 2 – crust, gray, black at edge, black 5 apothecia Lichen 3 – crust, white, black apothocia <1 Lichen 4 – crust, whitish gray, black at edge X Lichen 5 – crust, beige to white 10 Lichen 6 – Caloplaca crust, orange <1 Lichen 7 – crust, yellow <1 Rock Group 6 0491540; 4250194 Percent Cover Lichen 1 – folious, green, brown to black 1 apothecia, brown underside Lichen 2 – Buellia halonia crust, green, black 1 dots Lichen 3 – crust, black/ charcoal gray black <1 apothecia Lichen 4 – crust, white 2 Lichen 5 - Xanthoria bright orange <1 Lichen 6 – Caloplaca crust, orange <1 Lichen 7 – curst, brown, black apothecia 15 Lichen 8 – crust, medium gray 50 Lichen 9 – Lecanora pingus light green, <1 crustos, bubble gum Rock Group 7 0491546; 4251108 Percent Cover Lichen 1 – Lecanora pingus light green, <1 crustos, bubble gum Lichen 2 – crust, black/ charcoal gray 5 Lichen 3 – Buellia halonia crust, green, black 1 apothecia Lichen 4 – Caloplaca crust, orange <1 Lichen 5 – crust, black apothecia, no real body 1

Lichen 6 – crust, brown, small back apothecia <1 Lichen 7 – crust, gray/ white, black apothecia 10 Lichen 8 – Flavoparmelia caperata folious, <1 green large flat lobes Lichen 9 – Lecanora phryganitis (coral like) <1 Lichen 10 – Niebla homalea <1 Rock Group 8 0491548; 4251121 Percent Cover Lichen 1 – Lecanora pingus light green, <1 crustos, bubble gum Lichen 2 – Niebla homalea <1 Lichen 3 – folious, green, small lobed, brown <1 underside Lichen 4 – Buellia halonia crust, green, black 10 dots Lichen 5 – crust, white, black aothecia 10 Lichen 6 – crust, gray, medium, black 25 apothecia Lichen 7 – Caloplaca crust, orange <1 Lichen 8 – crust, charcoal gray 5 Lichen 9 – folious, white, black underside <1 Lichen 10 - Niebla homalea (?), thick and <1 stubby Rock Group 9 0491715; 4250298 Percent Cover Lichen 1 – Niebla homalea <1 Lichen 2 – folious, green, small lobed, brown 15 underside Lichen 3 – crust, white, black apothecia, black 25 out around edges Lichen 4 – Caloplaca crust, orange <1 Lichen 5 – crust, dark charcoal gray 1 Lichen 6 – crust, medium gray/ beige 15 Lichen 7 – Buellia halonia crust, green, black <1 apothecia Lichen 8 – crust, brown, black apothecia 1

APPENDIX B

Representitive photographs of lichens observed at Bodega Bay

Photographs of Lichens at Bodega Bay

Xanthoparmelia

Niebla homalea

Umblicaria phaea

Cladonia

Rhizocarpon macrosporum

Punctelia stictica

Pertusaria california (potato lichen)

Parmotrema

Lecanora pingus

Physcia

Lecanora phryganitis

Buellia halonia

Unknown species

Unknown species (white crustos)

Xanthoria (bright orange). Unknown species (gray crustos)

APPENDIX C

Field Data collected at Point Reyes National Seashore: Sampling site location, rock group

number and location, species recorded at each rock group and percent cover of each

lichen species on each rock group

Point Reyes National Seashore Data Data collected near Coast Camp at Point Reyes National Seashore Rock group 1 0154300; 4207644 Percent Cover Lichen 1 – Parmotrema folious, white, black 40 underside, cilia on edge, sorida on margins Lichen 2 – Flavoparmelia caperata folious, 20 green, black underside, mostly smooth, some soridia on surface, large lobed Lichen 3 – crust, gray <1 Rock group 2 0514299; 4207642 Percent Cover Lichen 1 – Parmotrema perlatum folious, 70 white, ruffle lichen, white on top, black underside, soridia on margins, small cilia on margins Lichen 2 – Flavoparmelia caperata folious, 15 green, large lobes, green top, black underside, soridia Lichen 3 – Cladonia, candle stick <1 Lichen 4 – Usnea <1 Lichen 5 – Lepraria dust lichen, green <1 Rock group 3 0514297; 4207633 Percent Cover Lichen 1 – Flavoparmelia caperata folious, 1 greenshield, large lobed Lichen 2 – Usnea, highly branched 1 Rock group 4 0514273; 4207607 Percent Cover Lichen 1 – Lepraria dust lichen, green <1 Rock group 5 0513926; 4207466 Percent Cover Lichen 1 – Xanthoparmelia folious, green, 15 small lobed, dark underside, large brown apothecia Lichen 2 – Parmotrema folious, white, small 2 lobed, branchy, dark underside Lichen 3 – Calioplaca, fire dots <1 Lichen 4 – Buellia crust, green, black <1 apothecia, button lichen

Lichen 5 – crust, white, cracked and bumpy 20 Lichen 6 – crust, dark gray <1 Lichen 7 – crust, white/ beige 30 Lichen 8 – crust, white, smooth 20 Rock group 6 0513926; 4207462 Percent Cover Lichen 1 – Flavoparmelia caperata folious, 1 green, large lobes, black underside, Lichen 2 – folious, green, small lobed 10 Lichen 3 – Calioplaca, fire dots <1 Lichen 4 – folious, white, black underside, 1 rhizines Lichen 5 – Rinodina bolanderi crust, white 5 with beige, black apothecia Lichen 6 – crust, black <1 Lichen 7 – Acarospora crust, lime green <1 Lichen 8 – Pertusaria californica potato lichen 30 Lichen 9 – crust, white, flat <1

Data collected along the Pebble Beach Trail at Point Reyes National Seashore Rock group 1 0509625; 4220482 Percent Cover Lichen 1 – Cladonia 2 Lichen 2 – Parmotrema folious, pale green, <1 brown tip, dark underside, large lobes, rhizines under, cilia on margins Rock group 2 0509326; 4220513 Percent Cover Lichen 1 – Parmotrema folious, green, brown 2 at tips, small lobes, pale tips/ dark underside Lichen 2 – Physcia (?), small lobes, white <1 folious, white edges, brown underside Lichen 3 – curst, whitish x

Data collected on the road to Tomalas Point at Point Reyes National Seashore Rock group 1 0507917; 4219167 Percent Cover Lichen 1 – Niebla homalea 15 Lichen 2 – Xanthoparmelia folious, green, 20 small lobed, brown apothecia, brown underside Lichen 3 – Physcia folious, white, small lobes, 70 light underside Lichen 4 – Pertusaria california potato lichen <1 Lichen 5 – crust, black, speckled white <1 Rock group 2 0507917; 4219158 Percent Cover Lichen 1 – Xanthoparmelia folious, green, 40 black apothecia Lichen 2 – crust, black 1 Rock group 3 0507917; 4219168 Percent Cover Lichen 1 – Niebla homalea 2 Lichen 2 – Xanthoparmelia folious, green, 40 borwn underside, brown apothecia, small lobed Lichen 3 – crust, black 1 Lichen 4 – Physcia folious, white, small lobed, <1 light underside Lichen 5 – Buellia crust, green, black <1 apothecia, button lichen Rock group 4 0507936; 4219177 Percent Cover Lichen 1 – folious, green, small lobed, brown apothecia, brown underside Lichen 2 – crust, black 3 Lichen 3 – Physcia foilious, white, small <1 lobed, white underside Lichen 4 – Flavoparmelia caperata folious, <1 green, large lobed Lichen 5 – Niebla homalea <1 Rock group 5 0507921; 4219182 Percent Cover Lichen 1 – Niebla homalea 5 Lichen 2 – folious, green (gray green), small 65 lobed, brown apothecia, brown underside Lichen 3 – folious, yellow green, large lobed, 1 dark underside, soridia on margins, cracked on top Lichen 4 – crust, black 3 Lichen 5 – crust, whitish 1 Lichen 6 – folious, white, dark underside, <1 white lobed Lichen 7 – Buellia button lichen <1

Rock group 6 0513926; 4207462 Percent Cover Lichen 1 – Niebla homalea <1 Lichen 2 – folious, white, dark underside, 15 larger lobed, soridia on top, rhizines on bottom Lichen 3 – folious, yellow green, large lobed, 10 dark underside, soridia on margins, cracked on top Lichen 4 – folious, green, small lobed 1 Lichen 5 – crust, black 15 Lichen 6 – Xanthoria, orange fuzzy <1 Lichen 7 – crust, white bumpy <1 Rock group 7 0507889; 4219246 Percent Cover Lichen 1 – Niebla homalea <1 Lichen 2 – Xanthoparmelia folious, green, 60 small lobed, apohecia, brown underside Lichen 3 – Flavoparmelia caperata folious, 10 yellow green, large lobed, soridia Lichen 4 – Pertusaria california potato lichen 2 Lichen 5 – crust, black <1 Lichen 6 – Xanthoria <1

Data collected at Tomalas Point at Point Reyes National Seashore Rock group 1 0502129; 54229828 Percent Cover Lichen 1 – Lecanora pinguas 25 Lichen 2 – Xanthoria 5 Lichen 3 – folious, white, black underside, 1 smooth, cilia on edge Lichen 4 – crust, gray, brown apothecia, 30 Lichen 5 – folious, yellow green, large lobed, <1 soridia Lichen 6 – crust, dark green 2 Lichen 7 – folious, white, light underside, <1 brown raised apothecia Rock group 2 0502203; 4229861 Percent Cover Lichen 1 – Niebla homalea <1 Lichen 2 – Usnea, very branchy 1 Lichen 3 – Xanthoria, fuzzy orange <1 Lichen 4 – Flavoparmelia caperata green 10 shield, folious, yellow green Lichen 5 – Parmotrema ruffle lichen, soridia 2 on edge, cilia Lichen 6 – crust, light gray/ white, raised 70 apothecia Rock group 3 0502206; 4229862 Percent Cover Lichen 1 – Usnea 3 Lichen 2 – Xanthoria 2 Lichen 3 – Flavoparmelia caperata green 2 shield, folious, yellow green Lichen 4 – Parmotrema ruffle lichen 2 Lichen 5 – crust, light gray/ white, black 60 apothecia Rock group 4 0502182; 4229602 Percent Cover Lichen 1 – Pertusaria california potato lichen 80 Lichen 2 – Flavoparmelia caperata green 1 shield, folious, yellow green Lichen 3 – Parmotrema ruffle lichen 1 Lichen 4 – Lecanora pinguas <1 Lichen 5 – Usnea <1 Lichen 6 - Niebla homalea <1 Lichen 7 – crust, brown apothecia 2 Lichen 8 – crust, black 1 Rock group 5 0502254; 4229647 Percent Cover Lichen 1 – Niebla homalea 5 Lichen 2 – Lecanora pinguas <1 Lichen 3 – Pertusaria california potato lichen 15

Lichen 4 – Xanthoria <1 Lichen 5 – crust, black 1 Lichen 6 – crust, whitish 2 Lichen 7 – Flavoparmelia caperata green <1 shield, folious, yellow green Lichen 8 – Buellia button lichen <1 Rock group 6 0502269; 4229645 Percent Cover Lichen 1 – Lecanora pinguas 25 Lichen 2 – Pertusaria california potato lichen 60 Lichen 3 – Niebla homalea <1 Lichen 4 – folious, white, small lobed, dark 1 underside, rhizines present, crackled on top Lichen 5 – crust, black and white <1 Lichen 6 – crust, apothecia 2 Rock group 7 0502277; 4229517 Percent Cover Lichen 1 – Niebla homalea <1 Lichen 2 – Lecanora pinguas 5 Lichen 3 – Caloplaca 1 Lichen 4 – Pertusaria california potato lichen 70 Lichen 5 – crust, black 5 Lichen 6 – curst, white/ gray, apothecia 1 Rock group 8 0502265; 4229506 Percent Cover Lichen 1 – Niebla homalea 5 Lichen 2 – Lecanora pinguas 5 Lichen 3 – crust, black 15 Lichen 4 – Buellia button lichen <1 Lichen 5 – folious, white, ruffle <1 Lichen 6 – crust, gray 10 Lichen 7 – Pertusaria california potato lichen 2 Lichen 8 – crust, apothecia x Rock group 9 0502321; 4229318 Percent Cover Lichen 1 – Niebla homalea 1 Lichen 2 – Lecanora pinguas 60 Lichen 3 – Caloplaca crust, orange 2 Lichen 4 – crust, black 5 Lichen 5 – Parmotrema ruffle lichen 1 Rock group 10 0502392; 4229338 Percent Cover Lichen 1 – Lecanora pinguas 50 Lichen 2 – Niebla homalea <1 Lichen 3 – crust, black 10 Lichen 4 – Buellia button lichen <1 Lichen 5 – crust, gray, apothecia 3 Lichen 6 – Caloplaca crust, orange <1

Lichen 7 – Flavoparmelia caperata green <1 shield, folious, yellow green Lichen 8 – Physcia (?) <1

Data collected near the light house at Point Reyes National Seashore Rock group 1 0499226; 4205481 Percent Cover Lichen 1 – Niebla homalea 16 Lichen 2 – Buellia dispersa crust, brown, 60 black apothecia Lichen 3 – Xanthoria crust, orange 3 Lichen 4 – crust, black, and white 5 Lichen 5 – Buellia button lichen 1 Lichen 6 – Ochrolechia androgyna crust, gray 5 with lots of bumps Lichen 7 – crust, brown 2 Rock group 2 0499219; 4205487 Percent Cover Lichen 1 – Niebla homalea 60 Lichen 2 – crust, white rim, black apothecia 5 Lichen 3 – crust, black and white 1 Lichen 4 – Buellia button lichen <1 Lichen 5 – Parmotrema ruffle lichen 1 Lichen 6 – Ochrolechia androgyna crust, <1 white, bumpy Rock group 3 0499064; 4205524 Percent Cover Lichen 1 – Niebla homalea 5 Lichen 2 – Pertusaria californica potato lichen 15 Lichen 3 – Lecanora pinguas 5 Lichen 4 – Lecanora phryganitis (coral) 10 Lichen 5 – Xanthoria <1 Lichen 6 – crust, black and white 1 Lichen 7 – Parmotrema ruffle lichen <1 Lichen 8 – crust, white rim, black apothecia 1 Rock group 4 0499070; 4205503 Percent Cover Lichen 1 – Niebla homalea 25 Lichen 2 – Pertusaria californica potato lichen 10 Lichen 3 – crust, white rim, black apothecia <1 Lichen 4 – Parmotrema ruffle lichen <1 Lichen 5 – Lecanora phryganitis (coral) <1 Lichen 6 - crust, black and white 8 Lichen 7 – Physcia adscendens - Rock group 5 0499055; 4205502 Percent Cover Lichen 1 – Niebla homalea 5 Lichen 2 – Lecanora pinguas 1 Lichen 3 – Pertusaria californica potato lichen 50 Lichen 4 – Xanthoria crust, orange, 1 Lichen 5 – crust, white rim, black apothecia 1 Lichen 6 – Flavoparmelia caperata green <1 shield, folious, yellow green

100

Lichen 7 – Lecanora phryganitis (coral) 1 Lichen 8 – Buellia button lichen <1 Rock group 6 0498990; 4205512 Percent Cover Lichen 1 – Pertusaria californica potato lichen 60 Lichen 2 – Usnea, very branchy 5 Lichen 3 – crust, white, bumpy 5 Lichen 4 – crust, black and white 10 Lichen 5 – Parmotrema ruffle lichen <1 Lichen 6 – Flavopunctelia green shield, <1 folious, yellow green Rock group 7 0498982; 4205495 Percent Cover Lichen 1 – Niebla homalea <1 Lichen 2 – Xanthoria 1 Lichen 3 – Unsea <1 Lichen 4 – crust, white rim, black apothecia 15 Lichen 5 – Buellia button lichen 1 Lichen 6 – Flavopunctelia green shield, <1 folious, yellow green Rock group 8 0498933; 4205536 Percent Cover Lichen 1 – Niebla homalea 5 Lichen 2 – crust, white, bumpy 5 Lichen 3 – Lecanora phryganitis (coral) 1 Lichen 4 – Buellia button lichen, light brown/ 15 black apothecia Lichen 5 – crust, white rim, black apothecia 1 Lichen 6 – Xanthoria <1 Lichen 7 – Candelariella crust, lemon yellow <1 Lichen 8 – crust, egg yellow, bumpy 2 Lichen 9 – Caloplaca ignea crust, orange <1 Lichen 10 – Pertusaria californica potato 1 lichen Rock group 9 049831; 4205535 Percent Cover Lichen 1 – Niebla homalea 1 Lichen 2 – Xanthoria 60 Lichen 3 – Lecanora phryganitis (coral) 2 Lichen 4 – Pertusaria californica potato lichen 5 Lichen 5 – crust, white rim, black apothecia 1 Lichen 6 – crust, black and white Lichen 7 – Parmotrema ruffle lichen Lichen 8 – Xanthoparmelia folious, green brown apothecia Rock group 10 0498917; 4205523 Percent Cover Lichen 1 – Niebla homalea 5 Lichen 2 – Xanthoria 1

101

Lichen 3 – Buellia button lichen 1 Lichen 4 – folious, green small lobed <1 Lichen 5 – crust, white rim, brown apotehcia 10 Lichen 6 – Buellia button lichen, brown <1 Rock group 11 049885; 4205528 Lichen 1 – Niebla homalea 2 Lichen 2 – Lecanora phryganitis (coral) <1 Lichen 3 – Buellia button lichen <1 Lichen 4 – Pertusaria californica potato lichen 10 Lichen 5 – crust, white rim, black apothecia 2 Lichen 6 – Xanthoria 2 Lichen 7 – crust, gray <1 Lichen 8 – crust, black/ white <1 Lichen 9 – Flavopunctelia green shield <1

102

APPENDIX D

Representitive photographs of lichens observed at Point Reyes National Seashore

Point Reyes National Seashore Lichens

Xanthoparmelia

Niebla homalea

103

Unsea

Cladonia

Lecanora pinguis

104

Lecanora phryganitis

Pertusaria california

Physcia adscendens

105

Physcia

Parmotrema

Xanthoria

106

Caloplaca

Candelariella

Unknown species

107

Unknown species

108

APPENDIX E

Field Data collected at Half Moon Bay and San Francisco: Sampling site location, rock

group number and location, species recorded at each rock group and percent cover of

each lichen species on each rock group

Half Moon Bay/ San Francisco Data Data collected at Pedro Point at Half Moon Bay Rock group 1 0543544; 4160002 Percent Cover Sandstone 1m x 60 cm Lichen 1 – Parmotrema ruffle lichen <1 Lichen 2 – folious, yellow green (green 1 shield), brown apothecia, brown underside Lichen 3 – crust, white, black apothecia <1 Lichen 4 – Cladonia – pixy cups <1 Rock group 2 0542950; 4160212 Percent Cover Sandstone 60cm x 40cm Lichen 1 – Niebla homalea <1 Lichen 2 – Parmotrema ruffle lichen <1 Lichen 3 – crust, white, black apothecia 15 Lichen 4 – Pertusaria californica potato lichen 1 Lichen 5 – Buellia button lichen <1 Lichen 6 – Caloplaca <1 Lichen 7 – Buellia crust, brown button lichen 5 Rock group 3 0542956; 4160204 Percent Cover 60m x 3m Lichen 1 – folious, green, small lobed, brown 15 apotehcia, brown underside Lichen 2 – Parmotrema ruffle lichen, sorida on 1 margin Lichen 3 – Physcia folious, white, small lobed, 2 white underside Lichen 4 – Buellia button lichen <1 Lichen 5 – crust, white 1 Lichen 6 – Caloplaca <1 Lichen 7 - folious, small lobed, black top and <1 underside Lichen 8 – Crust, lime green and black 20 Lichen 9 – crust, light gray 2 Rock group 4 0542885; 4160615 Percent Cover 1m x 40cm

109

Lichen 1 – Crust, lime yellow <1 Lichen 2 – crust, white, black apothecia 5 Lichen 3 – Buellia button lichen, brown 1 Lichen 4 – Pertusaria californica potato lichen <1 Lichen 5 – crust, charcoal gray 10 Rock group 5 0542808; 4160753 Percent Cover 2m x 1.75m Lichen 1 – Niebla homalea <1 Lichen 2 – Pertusaria californica potato lichen 2 Lichen 3 – Parmotrema ruffle lichen <1 Lichen 4 – crust, white 30 Lichen 5 – Graphis scripta script lichen <1 Lichen 6 – Buellia button lichen, brown <1 Lichen 7 – Caloplaca <1 Lichen 8 – crust, charcoal gray <1 Rock group 6 0542805; 4160769 Percent Cover 50cm x 70cm Lichen 1 – Pertusaria californica potato lichen 15 Lichen 2 – crust, white 70 Lichen 3 – Parmotrema ruffle lichen 5 Lichen 4 – Hypogymnia 1 Rock group 7 0542805; 4160777 Percent Cover Need to measure rock size Lichen 1 – Niebla homalea 5 Lichen 2 – Pertusaria californica potato 15 Lichen 3 – Parmotrema ruffle lichen 1 Lichen 4 – Flavoparmelia caperata green X shield, black underside Lichen 5 – Lecanora pinguis 2 Lichen 6 – Lecanora (coral) 2 Lichen 7 – Buellia button lichen 1 Lichen 8 – crust, white, black apothecia 20 Lichen 9 – crust, yellow <1 Lichen 10 – crust, gray <1 Lichen 11 – crust, white <1 Lichen 12 - Ramalina <1 Rock group 8 0542816; 4160807 Percent Cover 1.5m x 1m Lichen 1 – Niebla homalea <1 Lichen 2 – Pertusaria californica potato lichen 10 Lichen 3 – crust, white, black apothecia 2 Lichen 4 – Parmotrema ruffle lichen <1 Lichen 5 – crust, beige/ white, beige apothecia 2

110

Rock group 9 0542815; 4160811 Percent Cover 2m x 2m Lichen 1 – Niebla homalea 2 Lichen 2 – Pertusaria californica potato lichen 20 Lichen 3 – crust, gray, black apothecia 2 Lichen 4 – crust, charcoal gray, 8 Lichen 5 – crust, white, black apothecia 15 Lichen 6 - Lecanora pinguis 1 Lichen 7 – crust, brown, black apothecia 1 Lichen 8 - foilious, green, small lobes, light <1 underside Lichen 9 - foilious, green, large lobes, black <1 underside Lichen 10 – Buellia button lichen, brown 1 Rock group 10 0542819; 4160815 Percent Cover 2m x 1.5m Lichen 1 – Niebla homalea 5 Lichen 2 – Pertusaria californica potato lichen 2 Lichen 3 – Xanthoria 1 Lichen 4 – folious, gray/ green, small lobed, <1 light underside Lichen 5 – crust, white, black apothecia 15 Lichen 6 – Lecanora pinguis 1 Lichen 7 – Buellia button lichen <1 Lichen 8 – crust, white, beige apothecia 15

111

Data collected at Rancho Corral de Tierra at Half Moon Bay Rock group 1 0545278; 4157132 Percent Cover 3m x 2m Lichen 1 – folious, green, small lobes, brown 5 apotehcia, dark underside Lichen 2 – folious, gray/ green, phedocilia <1 (white dots), dark underside Lichen 3 – crust, white, black apotehcia 2 Lichen 4 – crust, dark gray <1 Lichen 5 – Xanthoria <1 Rock group 2 0542950; 4160212 Percent Cover Sandstone 60cm x 40cm Lichen 1 – Niebla homalea <1 Lichen 2 – Parmotrema ruffle lichen <1 Lichen 3 – crust, white, black apothecia 15 Lichen 4 – Pertusaria californica potato lichen 1 Lichen 5 – Buellia button lichen <1 Lichen 6 – Caloplaca <1 Lichen 7 – crust, brown button lichen 5 Rock group 3 0542956; 4160204 Percent Cover 60m x 3m Lichen 1 – folious, green, small lobed, brown 15 apotehcia, brown underside Lichen 2 – Parmotrema ruffle lichen, sorida on 1 margin Lichen 3 – folious, white, small lobed, white 2 underside Lichen 4 – Buellia button lichen <1 Lichen 5 – crust, white 1 Lichen 6 – Caloplaca <1 Lichen 7 - folious, small lobed, black top and <1 underside Lichen 8 – Crust, lime green and black 20 Lichen 9 – crust, light gray 2 Rock group 4 0542885; 4160615 Percent Cover 1m x 40cm Lichen 1 – Crust, lime yellow <1 Lichen 2 – crust, white, black apothecia 5 Lichen 3 – Buellia button lichen, brown 1 Lichen 4 – Pertusaria californica potato lichen <1 Lichen 5 – crust, charcoal gray 10 Rock group 5 0542808; 4160753 Percent Cover 2m x 1.75m Lichen 1 – Niebla homalea <1

112

Lichen 2 – Pertusaria californica potato lichen 2 Lichen 3 – Parmotrema ruffle lichen <1 Lichen 4 – crust, white 30 Lichen 5 – Graphis scripta script lichen <1 Lichen 6 – Buellia button lichen, brown <1 Lichen 7 – Caloplaca <1 Lichen 8 – crust, charcoal gray <1 Rock group 6 0542805; 4160769 Percent Cover 50cm x 70cm Lichen 1 – Pertusaria californica potato lichen 15 Lichen 2 – crust, white 70 Lichen 3 – Parmotrema ruffle lichen 5 Lichen 4 – Hypogymnia 1 Rock group 7 0542805; 4160777 Percent Cover Need to measure rock size Lichen 1 – Niebla homalea 5 Lichen 2 – Pertusaria californica potato 15 Lichen 3 – Parmotrema ruffle lichen 1 Lichen 4 – Flavoparmelia caperata green X shield, black underside Lichen 5 – Lecanora pinguis 2 Lichen 6 – Lecanora (coral) 2 Lichen 7 – Buellia button lichen 1 Lichen 8 – crust, white, black apothecia 20 Lichen 9 – crust, yellow <1 Lichen 10 – crust, gray <1 Lichen 11 – crust, white <1 Lichen 12 - Ramalina <1 Rock group 8 0542816; 4160807 Percent Cover 1.5m x 1m Lichen 1 – Niebla homalea <1 Lichen 2 – Pertusaria californica potato lichen 10 Lichen 3 – crust, white, black apothecia 2 Lichen 4 – Parmotrema ruffle lichen <1 Lichen 5 – crust, beige/ white, beige apothecia 2 Rock group 9 0542815; 4160811 Percent Cover 2m x 2m Lichen 1 – Niebla homalea 2 Lichen 2 – Pertusaria californica potato lichen 20 Lichen 3 – crust, gray, black apothecia 2 Lichen 4 – crust, charcoal gray, 8 Lichen 5 – crust, white, black apothecia 15 Lichen 6 - Lecanora pinguis 1 Lichen 7 – crust, brown, black apothecia 1

113

Lichen 8 - foilious, green, small lobes, light <1 underside Lichen 9 - foilious, green, large lobes, black <1 underside Lichen 10 – Buellia button lichen, brown 1 Rock group 10 0542819; 4160815 Percent Cover Lichen 1 – folious, green, small lobes, brown 5 apotehcia, dark underside Lichen 2 – folious, gray/ green, phedocilia <1 (white dots), dark underside Lichen 3 – crust, white, black apotehcia 2 Lichen 4 – crust, dark gray <1 Lichen 5 – Xanthoria <1

114

Data collected at Mt Davidson San Francisco Rock group 1 0548303; 4177059 Percent Cover 4m x 1.5m Lichen 1 – Niebla homalea <1 Lichen 2 – Flavoparmelia caperata folious, <1 green shield, large lobed, soridea in middle Lichen 3 – Lecanora pingus <1 Lichen 4 – Buellia button lichen <1 Lichen 5 – Xanthoria <1 Lichen 6 – crust, bright yellow 1 Lichen 7 – Crust, white, black apothecia 50 Lichen 8 – Crust, charcoal gray 15 Rock group 2 0548284; 4177013 Percent Cover 2m x 1.5m Lichen 1 – Niebla homalea 2 Lichen 2 – Flavoparmelia caperata folious, 2 green shield, large lobed, soridia on top, brown underside, black rhizines Lichen 3 –folious, gray, small lobed, brown <1 underside Lichen 4 – Xanthoria <1 Lichen 5 – crust, white, black apothecia 1 Lichen 6 – Crust, salt and pepper, black 20 apothecia, black line around edge Lichen 7 – Xanthoparmelia folious, green, 1 small lobed, brown apothecia Lichen 8 – Crust, charcoal gray 15 Rock group 3 0548284; 4177013 Percent Cover 2m x 1.5m Lichen 1 – Niebla homalea 1 Lichen 2 – Flavoparmelia caperata folious, <1 green shield Lichen 3 – Physcia folious, white, small lobed, <1 brown underside Lichen 4 – follious, small lobed, green, light 5 underside Lichen 5 – crust, bright yellow <1 Lichen 6 – crust, white <1 Lichen 7 - Crust, salt and pepper, black 15 apothecia, black line around edge Rock group 4 0548259; 4176961 Percent Cover 2m x 3m Lichen 1 – Flavoparmelia caperata <1

115

Lichen 2 – follious, green, small lobed, 5 apothecia Lichen 3 – crust, brown, white at tips 1 Lichen 4 – crust, salt and pepper 10 Lichen 5 – Buellia, crust, brown <1 Rock group 5 0548275; 4176935 Percent Cover Chert 3m x 1.5m Lichen 1 – Flavoparmelia caperata green 5 shield Lichen 2 – follious, green, small lobed, brown 5 bottom Lichen 3 – Niebla homalea <1 Lichen 4 – Xanthoria <1 Lichen 5 – crust, gray 20 Lichen 6 – folious, white, small lobes, white <1 underside Lichen 7 – Parmotrema ruffle lichen <1 Rock group 6 0548249; 4176923 Percent Cover 2m x 3m Lichen 1 – Niebla homalea <1 Lichen 2 – Flavoparmelia caperata green 10 shield Lichen 3 – crust, gray 70 Lichen 4 – crust, white <1 Lichen 5 – Xanthoria <1 Lichen 6 – Punctelia folious, green, white dots, <1 dark underside Rock group 7 0548208; 4176938 Percent Cover Chert 2m x 5m Lichen 1 – folious, small lobed, green, brown 5 apothecia Lichen 2 – crust, light gray 1 Lichen 3 – crust, brown, fuzzy black apothecia <1 Lichen 4 – Xanthoria <1 Lichen 5 – crust charcoal gray <1 Lichen 6 – crust, tan brown 90

116

Data collected at Glen Canyon San Francisco Rock group 1 0549332; 4177145 Percent Cover 2m x 3m Lichen 1 – Niebla homalea <1 Lichen 2 – Flavoparmelia caperata folious, 5 green shield, brown apothecia, brown underside Lichen 3 – folious, green, small lobes <1 Lichen 4 – crust, gray, black apothecia 30 Lichen 5 – folious, white, dark underside 2 Lichen 6 – Punctelia stictica <1 folious, green, large lobes, white dots Lichen 7 - Xanthoria <1 Lichen 8 – Crust, white 1 Rock group 2 0549316; 4177158 Percent Cover 2m x 3m Lichen 1 – Flavoparmelia caperata folious, 30 green shield Lichen 2 – crust, gray, black apothecia 10 Lichen 3 – crust, white <1 Lichen 4 – Crust, brown, black apothecia 5 Lichen 5 – Niebla homalea <1 Rock group 3 0549295; 41777163 Percent Cover 2m x 2m Lichen 1 – Flavoparmelia caperata folious, 5 green shield Lichen 2 – folious, white, small lobed, dark 1 underside Lichen 3 – crust, gray, black apothecia 25 Lichen 4 – Parmotrema ruffle lchen, soridia on <1 margin, cilia on edge Lichen 5 – Punctelia folious green, white <1 dots, soridia on margins Lichen 6 – crust, yellow 1 Rock group 4 0549294; 4177164 Percent Cover 3m x 1.5m Lichen 1 – Niebla homalea 1 Lichen 2 – Flavoparmelia caperata 5 Lichen 3 – crust, gray, black apothecia 20 Lichen 4 – crust, yellow <1 Lichen 5 – Usnea <1 Lichen 6 – crust, white <1 Lichen 7 - Punctelia stictica 20 folious, green, white dots

117

Lichen 8 – Crust, brown Rock group 5 0549263; 4177189 Percent Cover Chert 2m x 1.75m Lichen 1 – Xanthoparmelia folious green, 5 small lobed, brown underside Lichen 2 – crust, yellow <1 Lichen 3 – crust, brown, black edges 2 Lichen 4 – crust, white 1 Lichen 5 – crust, brown 1 Lichen 6 – Punctelia folious, green, large 1 lobes, white dots Rock group 6 0549229; 4177217 Percent Cover Chert 3m x 1.5m Lichen 1 – Flavoparmelia caperata folious, 10 green shield Lichen 2 – crust, gray 15 Lichen 3 – Parmotrema ruffle lichen, cilia on <1 edge Lichen 4 – Punctelia folious, green, large <1 lobes, white dots, dark underside Lichen 5 – Xanthoparmelia folious, green, 2 small lobed, dark underside Lichen 6 – Niebla homalea <1 Lichen 7 – crust, yellow <1 Lichen 8 – crust, brown 5 Rock group 7 0542805; 4160777 Percent Cover Chert 3m x 5m Lichen 1 – crust, white, black apothecia, black 1 on edges Lichen 2 – crust, yellow <1 Lichen 3 – Xanthoria <1 Lichen 4 – Xanthoparmelia folious, green, 1 small lobes, brown apothecia, dark underside Lichen 5 – Flavoparmelia caperata folious, <1 green shield Lichen 6 – Physcia, foliuos, white, small <1 lobed, white underside Lichen 7 – crust, gray speckled 10

118

APPENDIX F

Representitive photographs of lichens observed at Half Moon Bay and San Francisco

Half Moon Bay/ San Francisco Lichens

Flavoparmelia caperata

Niebla homalea

Punctelia stictica

119

Physcia

Xanthoria

Buellia

120

Parmotrema

Rhizocarpon

Unknown species

121

Unknown species

122

APPENDIX G

Presence and absence of N. homalea at sampling locations

Bodega Bay Data Rock Group Site Number Easting Northing Presence Ocean Song OS1 497904 4250090 N OS2 497628 4250284 N OS3 497855 4250270 N Island in the Sky SKY1 496083 4255423 N SKY2 496302 4255204 N SKY3 496254 4254518 N SKY4 496295 4254408 N SKY5 496291 4254221 N SKY6 496311 4254174 N SKY7 496425 4653962 N Bodega Head BH1 495097 4239441 N BH2 495134 4239413 N BH3 495099 4239426 Y BH4 494944 4232914 Y BH5 494954 4239239 N BH6 494977 4239300 Y BH7 495019 4139284 Y BH8 494997 4239258 N BH9 494982 4239318 Y BH10 494942 4239337 Y Shell Beach SB1 491127 4252378 Y SB2 491195 4252691 Y SB3 491638 4252898 Y SB4 491653 4252902 Y SB5 491663 4252902 Y SB6 491737 4252931 Y

123

SB7 491729 4252879 Y SB8 491163 4252508 Y SB9 49167 4252475 Y SB10 491164 4252583 Y Hwy 1 HWY1 491035 4251669 Y HWY2 491327 4251337 Y HWY3 491334 4251334 N HWY4 491375 4251206 N HWY5 491527 4251086 N HWY6 491540 4250194 N HWY7 491546 4251108 Y HWY8 491548 4251121 Y HWY9 491715 4250298 Y

Point Reyes National Seashore Data Rock Group Site Number Easting Northing Presence Coast Camp Trail CC1 154300 4207644 N CC2 514299 4207642 N CC3 514297 4207633 N CC4 514273 4207607 N CC5 513926 4207466 N CC6 513926 4207462 N Pebble Beach Trail BT1 509625 4220482 N BT2 590326 4220513 N Road to Tomalas Point RTP1 507917 421967 Y RTP2 507917 4219158 N RTP3 507917 4219168 Y RTP4 507936 4219177 Y RTP5 507921 4219182 Y RTP6 507880 4219198 Y RTP7 507889 4219246 Y Tomalas Point Trail TPT1 502129 54229828 N TPT2 502203 4229861 N TPT3 502206 4229862 N TPT4 502182 4229602 Y TPT5 502254 4229647 Y TPT6 502269 4229645 Y TPT7 502277 4229517 Y

124

TPT8 502265 4229506 Y TPT9 502321 4229318 Y TPT10 502392 4229338 Y Light House LH1 499226 4205481 Y LH2 499219 4205487 Y LH3 499064 4205524 Y LH4 499070 4205503 Y LH5 499055 4205502 Y LH6 498990 4205512 N LH7 498982 4205495 Y LH8 498933 4205536 Y LH9 498931 4205535 Y LH10 498917 4205523 Y LH11 498885 4205528 Y

Half Moon Bay/ San Francisco Data Rock Group Site Number Easting Northing Presence Pedro Point PP1 543544 4160002 N PP2 542950 4160212 Y PP3 542956 4160204 N PP4 542885 4160615 N PP5 542808 4160753 Y PP6 542805 4160769 N PP7 542805 4160777 Y PP8 542816 4160807 Y PP9 542815 4160811 Y PP10 542819 4160815 Y Rancho Corral de Terra RCT1 545278 4157132 N RCT2 545296 4157156 N RCT3 545304 4157199 N RCT4 545469 4157242 N RCT5 545540 4157284 N RCT6 545524 4157444 N RCT7 545468 4157487 N RCT8 545454 4157513 N RCT9 545367 4157541 N RCT10 545341 4157555 N Mount Davidson MD1 548303 4177059 Y MD2 548284 4177013 Y MD3 548272 4177007 Y

125

MD4 548259 4176961 N MD5 548275 4176935 Y MD6 548249 4176923 Y MD7 548208 4176938 N Glen Canyon Park GCP1 549332 4177145 Y GCP2 549316 4177158 Y GCP3 549298 4177163 N GCP4 549294 4177164 Y GCP5 549263 4177189 N GCP6 549229 4177217 Y GCP7 549261 4177142 N

Monterey Data Site Rock Group Number Easting Northing Presence Monterey Bay Coast MBC1 593861 4042381 Y MBC2 593844 4042420 N MBC3 593679 4042365 Y MBC4 593662 4042380 N MBC5 593665 4042370 Y Monterey Bay North Trail MBNT1 594417 404234 Y MBNT2 594458 4042371 N Monterey Bay MM trail MBMMT1 594427 404142 Y Monterey Bay Toro Regional Park MBTRP1 617051 4051806 N Monterey Bay Garland Ranch Park MBGRP1 610582 4040333 N MBGRP2 610496 4040239 N MBGRP3 610466 4040286 N MBGRP4 610615 4040319 N Monterey Bay Point Pinos MBPP1 593528 4046812 Y MBPP2 592948 4051336 N MBPP3 593095 4051685 Y MBPP4 595776 4055024 Y MBPP5 596133 4054802 N

126

LITERATURE CITED

Alleman L. K., and M.W. Hester. 2011. Reﬁnement of the fundamental niche of black mangrove (Avicennia germinans) seedlings in Louisiana: Applications for restoration. Wetlands Ecology and Management 19:47-60

Anderson, E. W. 1986. A Guide for Estimating Cover. Rangeland, 8:236-238

Araujo, M.B. and A. Guisan. 2006. Five (or so) challenges for species distribution modelling. Journal of Biogeography 33:1677-1688

AreaVibes. Point Reyes Station weather, available at https://www.areavibes.com/point+reyes+station-ca/weather/

Blaha, J., E. Baloch, M. Grube. 2006. High photobiont diversity in symbioses of the euryoecious lichen Lecanora rupicola (Lecanoraceae, Ascomycota). Biological Journal of the Linnean Society, 88:283-293

Blake, M.C., Jr. Graymer, R.W. and R.E. Stamski. 2002. Geologic map and map database of western Sonoma, northernmost Marin, and southernmost Mendocino counties, California: U.S. Geological Survey Miscellaneous Field Studies Map 2402, scale 1:100,000, available at http://pubs.usgs.gov/mf/2002/2402/

Bergamini, A., S. Stofer, J. Bolliger, and C. Scheidegger. 2007. Evaluating macrolichens and environmental variables as predictors of the diversity of epiphytic microlichens. The Lichenologist, 39:475-489

Bolliger, J., A. Bergamini, S. Stofer, F. Kienast, and C. Scheidegger. 2007. Predicting the potential spatial distributions of epiphytic lichen species at the landscape scale. The Lichenologist, 39:27

Cal Adapt website. http://cal-adapt.org. Accessed on May 2, 2019

California State Parks. 2007. Sonoma Coast State Park Final General Plan and Environmental Impact Report

127

Clark, J.C., and E.E. Brabb. 1997. Geology of the Point Reyes National Seashore and Vicinity, Marin County, California: A Digital Database: U.S. Geological Survey Open-File Report 97-456; available at https://pubs.usgs.gov/of/1997/of97-456/pr- map.pdf

Clark, J.C., W.R. Dupre, and L.I. Rosenberg. 1997. Geologic Map of the Monterey and Seaside 7.5-minute Quadrangles, Monterey County, California: A Digital Database: U.S. Geological Survey Open-File Report 97-030; available at https://pubs.usgs.gov/of/1997/of97-030/mo-se.pdf

Cristofolini, F., P. Giordani, E.Gottardini, and P. Modenesi. 2008. The response of epiphytic lichens to air pollution and subsets of ecological predictors: A case study from the Italian Prealps. Environmental Pollution, 151:308-317

Dietrich, M., and C. Scheidegger.1997. Frequency, diversity and ecological strategies of epiphytic lichens in the Swiss Central Pleateau and the Pre-Alps. The Lichenologist, 29:237-258

Ellis, C. J., and B. J. Coppins. 2006. Contrasting functional traits maintain lichen epiphyte diversity in response to climate and autogenic succession. Journal of Biogeography, 33:1643-1656

Ellis, C. J., B.J. Coppins, T.P. Dawson. 2007. Seaward Response of British lichens to climate change scenarios: Trends and uncertainties in the projected impact for contrasting biogeographic groups. Biological Conservation, 140:217-235

Elton, C. 1927. Animal Ecology. Segwick and Jackson. London

Fick, S.E. and R.J. Hijmans. 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37:4302- 4315

Galvich, D.A., L.H. Geiser, and A.G. Mikulin. 2005. Rare Epiphytic Coastal Lichen Habitats, Modeling, and Management in the Pacific Northwest. The Bryologist, 108:377-390

128

Gies, M., M. Sondermann, D. Hering, C.K. Feld. 2015. A comparison of modelled and actual distributions of eleven benthic macroinvertebrate species in a Central European mountain catchment. Hydrobiologia, 758:123-140

Grinnell, J. 1917. The niche-relationships of the California Thrasher.

The Auk 34:427-433

Guisan, A. and N.E. Zimmermann. 2000. Predictive habitat distribution models in

ecology. Ecological Modelling 135:147-186

Hamann, A., and T. Wang. 2006. Potential Effects of Climate Change on Ecosystem and

Tree Species Distribution in British Columbia. Ecology, 87:2773-2786

Hawksworth, D.L. 2000. Freshwater and marine lichen-forming fungi In: Hyde KD, Ho

WH, Pointing SB (eds) Aquatic mycology across the millennium, vol 5. Fungal

Diversity Press, Hong Kong, pp 1-7

Hutchinson G.E., 1957. Concluding Remarks. Cold Spring Harbor Symposia on

Quantitative Biology, 22:415-422 iNaturalist website https://www.inaturalist.org/observations?place_id=any&taxon_id=147221 accessed November 4, 2018

Iverson, L.R., A.M. Prasad, S.N. Matthews, M. Peters. 2007. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management, 254:390-406

Johnson, S.Y, S.R. Hartwell, and M.W. Manson. 2015. Offshore and Onshore Geology and Geomorphology, Offshore of Bodega Head Map Area, California Map, scale 1:24,000, available at https://pubs.usgs.gov/of/2015/1140/ofr20151140_sheet10.pdf

129

Kass. R.E., A.E. Raftery. 1995. Bayes Factor. Journal of the American Statistical Association, 90:773-795

Kearney, M. and W.P. Porter. 2004. Mapping the Fundamental Niche: Physiology,

Climate, and the Distribution of a Nocturnal Lizard. Ecology, 85:3119-3131

Lawler, J.J., S.L. Shafer, D. White, P. Kareiva, E.P. Maurer, A.R. Blaustein and P.J. Bartlei. 2009. Projected Climate-Induced Faunal Change in the Western Hemisphere. Ecology, 90:588-597

Loppi S., L. Frati, L. Paoli, V. Bigagli, C. Rossetti, C. Bruscoli, A. Corsin. 2004. Biodiversity of epiphytic lichens and heavy metal contents of Flavoparmelia caperata thalli as indicators of temporal variations of air pollution in the town of Montecatini Terme (central Italy). Science of the Total Environment, 326:113– 122

Martinez, I., F. Carreno, A. Escudero, and A. Rubio. 2006. Are threatened lichen species well protected in Spain? Effectiveness of a protected area network. Biological Conservation, 133:500-511

McCune, B. 2006. Non-Parametric Habitat Models with Automatic Interactions. Journal of Vegetation Science, 17:819-830

McCune, B. 2011. Nonparametric multiplicative regression for habitat modeling. http://www.pcord.com/NPMRintro.pdf

McCune B., U. Arup, O. Breuss, E. Di Meglio, J. Di Meglio, T.L. Esslinger, N. Magain, J. Miadlikowska, A.E. Miller, L. Muggia, P.R. Nelson, R. Rosentreter, M. Schultz, J.W. Sheard, T. Tønsberg and J. Walton. 2018. Biodiversity and ecology of lichens of Katmai and Lake Clark National Parks and Preserves, Alaska. Mycosphere 9: 859–93

Nash, T. H. 2008. Lichen Biology. 2nd edition. Cambridge. New York: Cambridge

University Press

130

Pearson, R.G. and T.P. Dawson. 2003. Predicting the impacts of climate change on the

distribution of species: are bioclimate envelope models useful? Global Ecology &

Biogeography 12:361-371

Pebble Beach Company. 2002. Del Monte Forest Preservation and Development Plan, available at http://www.co.monterey.ca.us/planning/docs/eirs/pbc/feir/pdfs- text/feir_appf/f-1/4-mrmp_appa4_mpf_02_zander.pdf

Peninsula Open Space Trust. 2001. Rancho Corral de Tierra Palomares Biological Report and Study Compilation

Peterson, A.T., J. Soberson, R. Peasron, R.P. Anderson, E. Martines-Meyer, M. Nakamura, and M.B. Araujo. Ecological Niches and Geographic Distributions. 1st edition. New Jersey: Princeton University Press

Piercey-Normore, M.D., and P.T. DePriest. 2001. Algal Switching among Lichen

Symbioses, American Journal of Botany, 88:1490-1498

Point Reyes National Seashore Association. 2014. Plant Communities, available at http://www.ptreyes.org/activities/plant-communities

Printzen, C. S. Domaschke, F. Fernández-Mendoza, and S. Pérez-Ortega. 2013.

Biogeography and ecology of Centraria aculeate, a widely distributed lichen with

bipolar distribution. MycoKeys 6:33-53

Purvis, O.W., P.J. Chimonides, G.C. Jones, I.N. Mikhailova, B. Spiro, D.J. Weiss and

B.J. Williamson. 2003. Lichen biomonitoring near Karabash Smelter Town, Ural

Mountains, Russia, One of the Most Polluted Areas in the World. The Royal

Society, 271:221–226

131

Radies, D., D. Coxson, C. Johnson, and K. Konwicki. 2009. Predicting canopy macrolichen diversity and abundance within old-growth inland temperate rainforests. Forest Ecology and Management, 259:86-97

Ranius, T., P. Johansson, N. Berg, M. Niklasson. 2008. The influence of tree age and microhabitat quality on the occurrence of crustose lichens associated with old oaks. Journal of Vegetation Science, 19: 653-662

Rehfeldt, G.E., N.L. Crookston, M.V. Warwell and J.S. Evans. 2006. Empirical Analyses of Plant‐Climate Relationships for the Western United States. International Journal of Plant Science, 167:1123-1150

Sales, K., L. Kerr, and J. Gardner. 2016. Factors influencing epiphytic moss and lichen

distribution within Killarney National Park. Bioscience Horizons, 9:1-12

Sanders, W. 2001. Lichens: The Interface between Mycology and Plant Morphology:

Whereas most other fungi live as an absorptive mycelium inside their food

substrate, the lichen fungi construct a plant-like body within which photosynthetic

algal symbionts are cultivated BioScience, 51:1025-1035

Sharnoff S. 2014. A Field Guide to California Lichens. Yale University Press. 179-184

Shrestha, G. 2010. Predicting the Distribution of Air Pollution Sensitive Lichens. All theses and Dissertations. Paper 2595

Soberón, J. 2007. Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters, 10:1-9

Soberón, J. and A. T., Peterson. 2005. Interpretation of Models of Fundamental

Ecological Niches and Species’ Distribution Areas. Biodiversity Informatics, 2:1-

132

Soberón, J. and M. Nakamura. 2009. Niches and distributional areas: Concepts, methods,

and assumptions. PNAS, 106:19644-19650

Swets, J.A. 1988. Measuring the Accuracy of Diagnostic Systems. Science, 240:1285- 1293

Thuiller, W., S. Lavorel, and M.B. Araúj. 2005. Niche Properties and Geographical

Extent as Predictors of Species Sensitivity to Climate Change. Global Ecology

and Biogeography, 14:347-357

Thuiller, W., C. Albert, M.B. Araujo, P.M. Berry, M. Cabeza, A. Guisan, T. Hickler, G.F.

Midgley, J. Paterson, F.M. Schurr, M.T. Sykes, N.E. Zimmermann. 2008.

Predicting global change impacts on plant species’ distributions: future

challenges. Perspectives in Plant Ecology, Evolution and Systematics, 9:137– 152

Tremlova K., Z. Munzbergova. 2007. Importance of species traits for species distribution in fragmented landscapes. Ecology, 88: 965-977

US Climate Data: Bodega Bay climate available at https://www.usclimatedata.com/climate/bodega- bay/california/united-states/usca1860

Half Moon Bay climate available at https://www.usclimatedata.com/climate/half- moon-bay/california/united-states/usca0459

San Francisco climate available at https://www.usclimatedata.com/climate/san- francisco/california/united-states/usca0987

Monterey climate available at https://www.usclimatedata.com/climate/san- francisco/california/united-states/usca0987

U.S. Geology Service (USGS). 1977. Geology of the Monterey Bay region, California available at https://pubs.er.usgs.gov/publication/ofr77718

133

Yahr, R., R. Vilgalys, P.T. DePriest. 2006. Geographic variation in algal partners of

Cladonia subtenuis (Cladoniaceae) highlights the dynamic nature of a lichen

symbiosis. New Phytologist, 171:847-860

Yost, A. 2008. Probabilistic modeling and mapping of plant indicator species in a Northeast Oregon industrial forest, USA. Ecological Indicators, 8:46-56

Waser, L. T., S. Stofer, M. Schwarz, M. Kuchler, E. Ivits, and C. Scheidegger. 2004.

Prediction of biodiversity - regression of lichen species richness on remote sensing data. Community Ecology, 5:121-133

Werth, S., H. Tommervik, and A. Elvebakk. 2005. Epiphytic macrolichen communities along regional gradients in northern Norway. Journal of Vegetation Science, 16:199-208

Wiens, J.A., D. Stralberg, D. Jongsomjit, C. Howell, and M. Snyder. 2009. Niches, models, and climate change: Assessing the assumptions and uncertainties. PNAS, 106:19729-19736