MODELING THE SPATIAL DISTRIBUTION OF INVASIVE PLANT SPECIES ON SOUTHEAST FARALLON ISLAND

A Thesis submitted to the faculty of /\5> San Francisco State University In partial fulfillment of the requirements for o?0 4 the Degree

( a E Q & *

. (^3'? Master of Arts

In

Geography

by

Quentin James Clark

San Francisco, California

August 2017 Copyright by Quentin Janies Clark 2017 CERTIFICATION OF APPROVAL

I certify that I have read Modeling the Spatial Distribution of Invasive Plant Species on

Southeast Farallon Island by Quentin James Clark, and that in my opinion this work meets the criteria for approving a thesis submitted in partial fulfillment of the requirement for the degree Master of Arts in Geography at San Francisco State University.

Barbara A. Holzman, Pl£D. Professor Emerita

Professor MODELING THE SPATIAL DISTRIBUTION OF INVASIVE PLANT SPECIES ON SOUTHEAST FARALLON ISLAND

Quentin James Clark San Francisco, California 2017

This project analyzes the spatial distribution of seven invasive plant species on Southeast Farallon Island, California using a generalized additive model (GAM). Species selected for modeling are Chenopodium murale (nettle-leaved goosefoot), Ehrharta erecta (panic veldt grass), Plantago coronopus (cut-leaf plantain), Tetragonia tetragonioides (New Zealand spinach), an annual grasses group, a Malva spp. (mallow) group, and Sonchus spp. (sowthistle) group. Models identify patterns and causal factors in the distribution of targeted species, as well as detect locations within the study area that are vulnerable to future colonization. Results reveal that elevation, surface slope, surface aspect, topographic position, human activity, and proximity to pinniped haul-out locations have the greatest influence on the predicted distributions of targeted species. Furthermore, distribution maps identify multiple locations on the island that are vulnerable to invasions by one or more target species.

I certify that the AbstractAbstract is a correct representation of the content of this thesis.thesis.

Date ACKNOWLEDGEMENTS

Foremost, I would like to thank Dr. Barbara A. Holzman, my committee chair, for her willingness to work with me on this project and her unwavering support throughout the entire process. Thank you to Dr. Ellen Hines for her guidance, patience, and endless positivity. Many thanks to Gerard McChesney and Jonathan Shore of the U.S. Fish and Wildlife Service; Russell Bradley, Pete Warzybok, and Ryan Berger of Point Blue Conservation Science; and Richard Chasey, Kerstin Kalchmayr, Lauren Scheinberg, Jeff Blumenthal, Sara Fiori, Rob Thoms, Aiko Weverka, Darren Blackburn, and Anna Studwell of San Francisco State University. Funding for this work was partly provided by a grant from USFWS (#F15PX02325), the College of Science and Engineering, and the Department of Geography & Environment. Lastly, I would like to thank my partner, Francesca MacCormack, who is my inspiration and guiding light, and my parents, Dan and Gretchen, who instilled in me my love of geography and exploration at a young age.

v TABLE OF CONTENTS

List of Tables...... vii

List of Figures...... viii

List of Appendices...... ix

Introduction...... 1

Project Objective...... 6

Methods...... 8

Study Area...... 8

Field Data...... 10

Environmental Predictor Variables...... 18

Analysis and Modeling...... 25

Results...... 29

Discussion...... 38

Environmental Predictor Variables and Predicted Distributions...... 39

Overall Model Performance...... 48

Management Implications...... 51

Limitations of Data...... 53

Conclusion...... 55

References 57 LIST OF TABLES

Table Page

1. Species selected for modeling...... 7 2. Environmental predictor variables...... 19 3. Presence cells for each targeted species...... 25 4. Predicted presence cells for each targeted species...... 30 5. Variables selected for model training...... 30 6. Variable influence on predicted distributions...... 31 7. Assessment of model performance...... 31

vii LIST OF FIGURES

Figures Page

1. Map of Southeast Farallon Island...... 71 2. Results from 2016 invasive plant inventory...... 72 3. Distribution of Chenopodium murale on SEFI in 2016...... 73 4. Distribution of Ehrharta erecta on SEFI in 2016...... 74 5. Distribution of Plantago coronopus on SEFI in 2016...... 75 6. Distribution of Tetragonia tetragonioides on SEFI in 2016...... 76 7. Distribution of annual grasses on SEFI in 2016...... 77 8. Distribution of Malva spp. on SEFI in 2016...... 78 9. Distribution of Sonchus spp. on SEFI in 2016...... 79 10. 10A-D Maps of elevation, surface slope, and transformed aspect...... 80 11. 11A-D Maps of solar radiation, TPI, TCI, and soil depth...... 81 12. 12A-C Maps of distance to trails & structures, seabirds, and pinnipeds 82 13. Predicted distribution of target species combined...... 83 14. Predicted distribution of Chenopodium murale...... 84 15. Predicted distribution of Ehrharta erecta...... 85 16. Predicted distribution of Plantago coronopus...... 86 17. Predicted distribution of Tetragonia tetragonioides...... 87 18. Predicted distribution of annual grasses...... 88 19. Predicted distribution of Malva spp...... 89 20. Predicted distribution of Sonchus spp...... 90 /

LIST OF APPENDICES

Appendix Page

A. ANOVA Test Results...... 91 B. Descriptive Statistics for Predicted Presence Cells ..94

ix 1

Introduction

The dispersion of plant species beyond the boundaries of their evolutionary origins is a global phenomenon with significant ecological consequences. On oceanic islands, the effects of invasive non-native plant species are particularly acute. Invasive plants can significantly alter the processes and functionality of island ecosystems, as well as have a negative influence on the distribution and abundance of native flora and fauna (Meyer &

Lavergne 2004). Impacts are magnified on islands due to vulnerabilities such as geographic isolation, limited terrestrial size, high levels of endemism, and low biodiversity

(Castro et al. 2007). The introduction of invasive plant species is considered one of the greatest threats to the continued survival of native, often endemic, flora and fauna on oceanic islands around the world (Vitousek 2002).

On Southeast Farallon Island (SEFI), located 48 kilometers west of San Francisco,

California (Figure 1), invasive plant species have an impact on the ecological makeup of the island. The presence of invasive plant species is a concern for the U.S. Fish and

Wildlife Service (USFWS), which manages the island, due to the influence these species have on the Island’s native species (USFWS 2009). Invasive plant species on SEFI displace native vegetation and eliminate viable habitat for native plant species (USFWS

2009). Furthermore, invasive species form dense patches of vegetation that are thought to impact the breeding habitats of multiple seabird species present on the Island (USFWS

2009). 2

Invasive plants are defined as plant species that are able to establish new populations outside their native habitat due to anthropogenic disturbance and/or activity

(Sax 2001). According to Richardson et al. (2000), the process for a non-native plant species to become an invasive species is comprised of three stages: introduction, naturalization, and invasion. During the introduction stage, a plant species overcomes a major geographical barrier in order to arrive in a new environment as a result of both intentional, and unintentional, human activity or agency. The majority of non-native flora survive in new habitats as “casuals,” meaning they can reproduce sexually or vegetatively, but do not have the ability to maintain or expand their populations over long periods of time. During naturalization, non-native plants overcome environmental and/or physiological barriers that had previously prevented the species from expanding beyond the initial site(s) of introduction. The final phase, invasion, consists of the widespread distribution of a non-native plant species into multiple habitats that are removed from sites of initial introduction, including the colonization of unaltered, naturally occurring environments.

Invasive plant species compete directly with native plants for resources such as of sunlight, freshwater, nutrients, and growing space (Dark 2004). Competitive interactions between introduced and native species on oceanic islands routinely lead to localized extirpations and reductions in native plant species richness (Gritti et al. 2006).

Furthermore, invasive plant species can alter ecosystem wide processes such as hydrologic drainage patterns, fire regimes, soil chemistry, soil erodibility, and viable foraging and/or 3

nesting habitat for native fauna (Dark 2004; Lindenmayer & McCarthy 2001; Meyer &

Lavergne 2004).

A consensus does not exist regarding the prominence of invasive plant species as agents of extinction (Castro et al. 2007; Vila et al. 2011). Studies reveal, however, that a persistent trend of decreasing overall plant species richness exists on islands where invasive plants are actively known to grow (Kueffer et al. 2010; Powell et al. 2013; Vila et al. 2015). Decreases in species richness due to the presence of invasive plant species have been observed on islands throughout the Hawaiian Archipelago (Meyer & Lavergne

2004; Wester 1992), the Channel Islands of California (Beatty 1991; Beatty & Licari 1992;

Halvorson 1992; Klinger 1998), across the southern Indian and Pacific Oceans (Kingsford et al. 2009; Meyer & Lavergne 2004), off the coasts of east Asia and western South

America ( Dimbock et al. 2003; Castro et al. 2007; Wu et al. 2004), and within the

Mediterranean Basin (Vila et al. 2006).

The development of invasive plant inventories serves as one of the most common forms of research in the field of invasive plant studies, as well as one of the most important

(Flint & Rehkemper 2002; Hejda et al. 2009; Mack et al. 2000; Mack & Lonsdale 2002;

Richardson 2004). Inventory programs are a crucial first step in the development of effective invasive plant management/eradication programs (Flint & Rehkemper 2002).

Furthermore, invasive plant inventories produce the raw data necessary for identifying spatial trends in the movement and distribution of invasive plants using geographic information systems (GIS) and/or species distribution modeling (SDM) (Richardson 2004). 4

SDM is a vital tool in the study of invasive plant ecology and biogeography. SDM is used to identify patterns influencing the distribution of invasive plants and to identify habitat types that may be susceptible to future invasions (Guisan & Thuiller 2005; Meyer

& Lavergne 2004; Pearson et al. 2006). SDM utilizes empirical models to relate the known occurrences of a selected species with a suite of environmental variables in order to predict the presence and/or absence of a species within a given location (Guisan & Zimmerman

2000). Models used in SDM establish statistically derived response surfaces that define the abiotic conditions in which populations can be sustained (Guisan & Thuiller 2005;

Guisan & Zimmermann 2000; Pearson et al. 2006). Response surfaces either interpolate the distribution of a species based on a sample dataset, predict the potential distribution of a species for a given area, or further the understanding of factors that control the distribution of a species (Franklin & Miller 2009; Higgins etal. 1999).

The majority of SDM platforms utilize occurrence records that are in a presence- absence format (Franklin & Miller 2009). Presence-absence datasets contain location records of both species detection and non-detection, and are commonly collected via inventories, field sampling techniques, or opportunistic observations (Elith et al. 2006;

Graham et al. 2004). The environmental variables, also known as environmental predictor variables, reflect the primary variables influencing the distribution of a species: limiting factors and disturbances (Higgins & Richardson 1996; Guisan & Zimmerman 2000).

Limiting factors, or regulators, are variables that control the physiology of a species (e.g. air temperature, water availability, soil depth, and soil composition). Disturbance factors 5

are defined as alterations to environmental systems that occur either naturally or by human activity.

SDM has been used to model and analyze the factors influencing the distribution of invasive plant species on islands across the globe. In the Hawaiian archipelago, Vorsino et al. (2014) used SDM to predict the distribution of multiple invasive plant species under varying climate change scenarios. In the Mediterranean Basin, distribution models have been used to assess the impact of invasive plants on biodiversity and soil structure (Vila et al. 2006), analyze the physiological traits that have led to successful invasions by invasive plants (Lloret et al. 2005), and identify habitat types vulnerable to invasion (Affre et al.

2010). On the Channel Islands of Southern California, SDM has been used to analyze and predict the distribution of invasive fennel (Foeniculum vulgare) (Brenton & Klinger 1994), as well as to assess the relationship between invasive plant species diversity and the physical and/or environmental characteristics of the islands (Moody 2000).

The Generalized Additive Model (GAM) is a commonly used SDM platform utilized for modeling the distribution of invasive plant species (see Bradley et al. 2015;

Broennimann et al. 2007; Mainali et al. 2015; Peknicova & Berchova-Bimova 2016;

Vivian etal. 2014). Developed by Hastie & Tibrishani (1986), GAMs are a non-parametric extension of the Generalized Linear Model (GLM) that provide a flexible approach to identifying and defining non-linear relationships between dependent and independent variables (Yee & Mitchell 1991). GAMs use smoothing functions to assess the non-linear relationship between predictors and responses (Franklin & Miller 2009), and allow the data to determine the shape of the response curves, rather than being limited by the shapes 6

available in a parametric class (Yee & Mitchell 1991). GAMs are useful when the relationship between variables is not easily fitted by linear models (Thuiller et al. 2003).

On oceanic islands, GAMs have been used to assess the impact of invasive plants on native and/or endemic plant species richness on the island of Crete (Trigas et al. 2013).

In Hawai’i, the factors influencing the dispersal of invasive grass species were identified using the GAM framework (Oliveira-Xavier & D’Antonio 2016). While Tweiten et al.

(2014) used a GAM to analyze the resiliency of Hawaiian forests to non-native plant invasions. Lastly, on the island of New Caledonia response surfaces predicting the spread of invasive grasses and herbs were developed using the GAM framework (Ibanez et al.

2013).

Project Objective

In 2004, the USFWS developed a weed management plan to control the spread of invasive plant species on SEFI (USFWS 2009). In order to assess the effectiveness of control efforts, the management plan requires that the Island’s invasive plant community be inventoried and mapped every three years (USFWS 2009). The first of these inventories was completed in 2016 (see Holzman et al. 2017). Results reveal an extensive distribution of invasive plants on the island, with approximately 24% of SEFI inhabited by invasive, non-native flora. The distribution of invasives is concentrated on the western and southern portions of the island, and throughout locations where current and historic anthropogenic activity is most prevalent (Holzman et al. 2017) (Figure 2). 7

This project analyzes the spatial distribution of seven invasive plant species of SEFI using a generalized additive model (GAM). All occurrence records are from the Flolzman et al. (2017) inventory. Species selected for modeling are: Chenopodium murale (nettle­ leaved goosefoot), Ehrharta erecta (panic veldt grass), Plantago coronopus (cut-leaf plantain), Tetragonia tetragonioides (New Zealand spinach), an annual grasses group,

Malva spp. (mallow), and Sonchus spp. (sowthistle) (Table 1). An additional model is developed using the combined distribution of all seven selected species/species groups.

Table 1. Species selected for modeling. Scientific Name [Family + aceae]3 Common Name Chenopodium murale L. [Chenopodi-] Nettle leaf goosefoot Ehrharta erecta Lam. [Po-] Panic veldt grass Plantago coronopus L. [Plantagin] Cut-leaf plantain Tetragonia tetragonioides (Pall.) New Zealand spinach Species Groups Annual grasses Avena fatua L. [Po-] W ild oat Avena barbata Link [Po-] Slender wild oat Bromus diandrus Roth. [Po-] Ripgut brome Festuca bromoides L. [Po-] Brome fescue Hordeum murinum L. [Po-] Wall barley Malva spp. Malva neglecta Wallr. [Malva-] Common mallow Malva parviflora L. [Malva-] Cheeseweed Malva pseudolavatera Webb &Berthel. [Malva-] Cretan mallow Sonchus spp. Sonchus asper (L.) subsp. asper Hill [Aster-] Prickly sow thistle, Sonchus oleraceus L. [Aster-] Common sowthistle

“Scientific nomenclature follows the Jepson Flora Project (eds.) [2017] Jepson eFlora, available at: http://ucjeps.berkeley.edu/IJM.html

Model results are mapped and analyzed in order to identify patterns and causal factors in the distribution of modeled species. Additionally, model results are used to identify locations within the study area vulnerable to future colonization by invasive plant 8

species. It is hypothesized that results will reveal that the distribution of targeted plant

species is most heavily influenced by human disturbance, proximity to seabird colonies,

and surface aspect.

Methods

Study Area

Southeast Farallon Island (SEFI) is located in the eastern Pacific Ocean,

approximately 48 kilometers (30 miles) west of San Francisco, California and 32 kilometers (20 miles) southwest of Point Reyes, California (see Figure 1). The island is within the administrative boundaries of the Farallon National Wildlife Refuge, which is comprised of four island groups: Noonday Rock, the North Farallon Islands, Middle

Farallon Island, and the South Farallon Islands. SEFI (37°42’ N, 123°0’ W), which is a part of the South Farallon Islands, is the largest (29 hectares) and only inhabited island within the entire Farallon Islands system. SEFI is characterized by a rocky shoreline, a low-lying terrace that encompasses the western half of the island (the Marine Terrace), rocky outcroppings, and talus cliffs. The geology is predominantly salt-and-pepper granite

with coarse quartz diorite (Hannah 1951; Schoenherr et al. 1999). Soil layering is minimal, however, in some low-lying areas, such as the Marine Terrace, layering exceeding 20

centimeters thick occurs (Vennum et al. 1994). Soils are comprised of guano, granitic sand, vegetation, bone fragments, and other decomposing detritus (Vennum et al. 1994).

At approximately 105m (343 feet) above sea-level, the summit of Lighthouse Hill is the highest point of elevation on SEFI. 9

Air temperatures on SEFI are consistent throughout the year, rarely falling below

7°C (45°F) or exceeding 18°C (65°F) (USFWS 2009). Precipitation accumulation on the island averages 25 centimeters (10 inches) annually, with the greatest amount of rainfall occurring during winter months (USFWS 2009). During the summer, SEFI is routinely blanketed by fog, however, moisture from summer fog is not taken into annual precipitation figures (USFWS 2009). No standing or running fresh water is present on the islands

(USFWS 2009).

The dominant plant species on SEFI is the native annual Lasthenia maritima

(maritime goldfields). L. maritima is endemic to offshore islands from central California to Vancouver Island, British Columbia (Vasey 1985). Vegetation on SEFI is entirely herbaceous, save for two tree species, Hesperocyparis macrocarpa (Monterey cypress) and

Pinus radiata (Monterey pine), and two shrub species, Malva arborea (tree mallow) and

Coprosma repens (creeping mirrorplant).

Beginning in the late 1700’s, increases in anthropogenic activity and rapid changes in land use brought about accidental and intentional introduction of many non-native floral and faunal species on the islands (White 1995). A feral European rabbit population, introduced by island residents, significantly reduced the ground cover of native vegetation across SEFI (Pinney 1965). Reductions in native vegetation is thought to have allowed non-native vegetation to establish viable populations on SEFI once the rabbits were eradicated in 1975 (White 1995). 10

Field Data

Field data from the 2016 SEFI invasive plant inventory conducted by Holzman et al. (2017) are used for the development of distribution models (Figure 2). These data contain the occurrence records of the seven selected plant species/species groups on SEFI.

Species selection are based on the total number of separate occurrences mapped by

Holzman et al. (2017). All species selected for modeling, except for P. coronopus (n =

48), have greater than 90 separate occurrences. Total occurrences are used as the determining factor for species selection because sample size has been found to have a direct impact on SDM performance (Franklin & Miller 2009). Numerous studies cited by

Franklin & Miller (2009) conclude that the minimum number of occurrences observations needed to achieve high model performance is between 5 0 - 100 samples. The occurrence data of targeted species serve as the dependent variable for each model.

Chenopodium mu rale

Chenopodium murale is a fast-growing, herbaceous, winter annual native to Europe and northern Africa (Batish et al. 2007; El-Khatib et al. 2004). It is a highly fecund species, with individual plants capable of producing ~24,000 seeds (Holm et al. 1997). The species exhibits allelopathic tendencies; reducing growth rates and seed viability of competing plant species (Batish et al. 2007; El-Khatib et al. 2004; Holm et al. 1997).

C. murale favors highly disturbed areas, especially along roadsides, waste places, and agricultural sites (Gupta & Narayan 2012). The species prefers moist soils with high light exposure and minimal competition (Elkarmi et al. 2009). C. murale is capable of establishing itself across a wide variety of substrate types, and is tolerant of nutrient poor 11

soils (Huxley 1992). C. murale exhibits a high degree of phenotypic plasticity, allowing it to tolerate a diverse array of environmental conditions outside of its native range (Gupta &

Narayan 2012; Holm et al. 1997).

There were 90 separate observations of C. murale (1,497 presence cells) recorded on SEFI (Holzman et al. 2017) in 2016. Observations of C. murale were located predominantly within the Marine Terrace and at the base of Subrick Point (Figure 3).

Ehrharta erecta

Ehrharta erecta is a perennial grass species capable of growing to 200 cm in height

(Hickman 1993). Reproduction is carried out vegetatively, or via seed (Hickman 1993).

Seeds are dispersed primarily by wind, but long distance transportation of seeds is common via contact with the fur of animals, or the clothing of people (Sigg 1996). There is some anecdotal evidence of E. erecta seeds being transported by bird species (Ogle, 1988).

E. erecta is native to southern Africa, where it extends from the fertile southwestern

Cape region of South Africa to Mozambique (Acock 1953; McIntyre & Ladiges 1985).

The species is classified as invasive in the United States, New Zealand, Australia, southern

Europe, and China (Herbst & Clayton 1998; McIntyre & Ladiges 1985; Ricciardi &

Anzalone 1999; Stebbins 1949). E. erecta generally occurs in shaded areas with above average moisture (Goldblatt & Manning 2000). The species is commonly found within coastal habitats (especially stabilized dunes), along riparian corridors, and throughout urban areas (Pickart 2000). Species presence is strongly correlated with available moisture and well-drained sandy, or sandy-clay, soils (Sigg 1996). Once naturalized, the species is 12

capable of expanding into drier, more exposed habitats, including undisturbed wildlands

(Pickart 2000).

On SEFI, 102 separate observations (878 presence cells) of E. erecta were made by

Holzman et al. (2017) in 2016. Species observations were found throughout the higher elevations of Lighthouse Hill, on the south facing slopes of Little Lighthouse Hill, and on

Cormorant Blind Hill (Figure 4).

Plantago coronopus

Plantago coronopus is a highly adaptable herbaceous species native to Eurasia. It is capable of behaving as an annual, perennial, or short-lived biennial depending on environmental and/or climatic conditions (Dodds 1953; Waite 1984; Waite & Hutchings

1982). Individuals within a given population of P. coronopus have even been found to exhibit differing life cycle strategies depending on varying micro-climate conditions

(Waite 1984).

The species is wind-pollinated, and reproduces via seed only (Koelewijn 1998).

Distribution outside its native range is wide-spread, with the species found on all continents except Antarctica (Koyro 2006). P. coronopus is halophytic, capable of growing in substrates with salt concentrations as high as 30% (Koyro 2006). Because of its tolerance of salinity, the species is most commonly found along coastal margins, or within salt marshes or estuarine systems (Bueno et al. 2017; Koyro 2006; Waite 1984). In addition to high salt concentrations, P. coronopus prefers locations with high levels of disturbance

(along roadsides or walking paths), compacted sandy soils, high concentrations of nitrogen, 13

and minimal competition from other plant species (Bueno et al. 2017; Waite 1984; Waite

& Hutchings 1982).

There were 48 separate observations (5,765 presence cells) of P. coronopus made by Holzman et al. (2017) in 2016. The majority of species observations were made in close proximity to the trails and manmade structures located in the Marine Terrace. Additional observations of P. coronopus were made on Lighthouse Hill (Figure 5).

Tetragonia tetragonioides

Tetragonia tetragonioides is a trailing vine with dark green and succulent leaves that is capable of behaving as either an annual or a perennial species (Chasey 2016). T. tetragonioides is native to New Zealand, where it is considered a minor food crop and is cultivated as such (Azevedo-Meleiro & Rodriguez-Amaya 2005). Given its agricultural value, the species is grown in many areas outside of its native range, e.g. Australia, Japan, south-eastern China, Hawaii, Chile, Uruguay, Brazil, and the United States (Gray 1997;

Wilson et al. 1999; Yamaguchi 1983). T. tetragonioides routinely escapes from fields of cultivation, and has become naturalized in many locations where it was originally planted for agricultural purposes (Cal-IPC 2017).

T. tetragonioides reproduces via seed only, with dispersal carried out primarily by water (Gray 1997). Seeds can remain viable for up to one month in salt water (Taylor

1994). T. tetragonioides is a halophyte (Yousif et al. 2010), and grows predominantly in high saline environments along coastal margins or within estuarine habitats (Gray 1997; 14

Taylor 1994). Additionally, the species is drought-tolerant, and prefers warm locations with well-drained sandy or rocky soils (Taylor 1994).

On SEFI, there were 293 separate observations (10,760 presence cells) of T. tetragonioides made by Holzman et al. (2017) in 2016 (Figure 6). T. tetragonioides was found throughout the study area, with large patches located on Lighthouse Hill, and on the north side of the island near a seabird (common murre and Brandt’s cormorant) colony

(Figure 6). Additional observations were made in the Marine Terrace, on Cormorant Blind

Hill, across Little Lighthouse Hill, and on Shubrick Point. T. tetragonioides is the most widely distributed and abundant invasive plant species present on SEFI.

Annual grasses

The annual grasses group is comprised of five species: Avena barbata (slender oat),

Avena fatua (wild oat), Bromus diandrus (ripgut brome), Festuca bromoides (brome fescue), and Hordeum murinum (mouse barley). All five species are native to Europe.

Both species of the Avena genus (A. barbata & A. fatua) are tufted annuals that produce flowering stems that can grow upwards of 150 cm tall (Sharma & Vanden Bom

1977). Reproduction is carried out via seed, with seeds dropping in the near vicinity of parent plants (Sharma & Vanden Bom 1977). Both species prefer temperate climates with nitrogen rich clay, or clay-loam, soils (Carson & Hill 1986; Sharma & Vanden Bom 1977).

Disturbance factors play a prominent role in the distribution of A. barbata and A. fatua, as both species are commonly found in areas altered by human activity, agricultural production, or livestock grazing (Marshall & Jain 1966; Sharma & Vanden Bom 1977).

Both species exhibit a wide-range of phenotypic plasticity, the ability to change phenotypic 15

expression in response to differing environmental conditions, which has facilitated their distribution and naturalization across a variety of locations and habitats (Marshall & Jain

1966).

Bromus diandrus is a winter annual that can grow as high as 120 cm (Saarela &

Peterson 2017). Stands of B. diandrus are often dense, preventing seeds from other species from germinating (Gordon & Rice 1993). Plants reproduce via seed only, with the distribution of seeds concentrated around parent plants (Kon & Blacklow 1989). Seed production is high, with dense stands capable of producing ~1000 seeds per square meter

(Kon & Blacklow 1989). B. diandrus is found across a variety of habitats from coastal sand dunes to open grasslands (Holloran et al. 2004; Kon & Blacklow 1989). The species prefers areas with heavy disturbance and nitrogen rich soils (Best 2008). In the Gulf Islands of southwestern British Columbia, dense stands of B. diandrus have been found in proximity to locations disturbed by roosting bird species (Best 2008). The species is an efficient water user and is capable of surviving in arid environments (Bicak & Sternberg

1993).

Festuca bromoides is a tufted winter annual that can produce flowering stems reaching 50 cm in height (Smith Jr. & Aiken 2017). Reproduction is carried out via seed only. F. bromoides prefers warm, low elevation locations that are disturbed and have nitrogen rich soils (Frenot et al. 2001). As with B. diandrus, F. bromoides has been found in abundance at locations disturbed by roosting bird species in the Gulf Islands (Best 2008).

Hordeum murinum is a tufted winter annual that can reach 60 cm in height.

Reproduction is carried out via seed, with seed production and seed viability strongly 16

correlated with precipitation (Johnstone et al. 2009). In its native range of Europe and northern Africa, H. murinum is considered a ruderal species given its preference for highly disturbed, nutrient rich soils (Davison 1970). In locations where the species is considered invasive, i.e. North and South America, Australia, and New Zealand, H. murinum occupies both ruderal habitats and grassland environments (Davison 1971). The species prefers low elevation sites, with minimal competition, high sun exposure, and soils that are compacted and rich in both nitrogen and phosphorus (Davison 1971).

There were 142 separate observations (10,106 presence cells) of the annual grasses group recorded on SEFI in 2016 (Holzman et al. 2017). Species distribution was wide­ spread across the island, with the highest concentrations found on the Marine Terrace and on the south facing slopes of Lighthouse Hill (Figure 7).

Malva spp.

The Malva spp. group is comprised of three species from the Malva genus: Malva neglecta (common mallow), Malva parviflora (cheeseweed), and Malva pseudolavatera

(Cretan mallow). All three species are native to Eurasia, but are commonly found in

Australia, New Zealand, and the United States, where they are considered invasive (Greer

& Thorpe 2009). They are erect, herbaceous annuals that reproduce via seed only

(Chauhan et al. 2006).

M. neglecta, M. parviflora, and M. pseudolavatera prefer disturbed habitats, and are generally found in agricultural fields, or along roadsides (Chauhan et al. 2006; Hill

2017). All three species prefer slightly acidic soils, but are tolerant of moderate saline conditions as well (Chauhan et al. 2006). All species within the Malva spp. group are 17

capable of solar-tracking, with leaves rotating from an easterly orientation in the morning to a westerly orientation by the afternoon (Fisher et al. 1989; Greer & Thorpe 2009). Doing so allows M. neglecta, M. parviflora, and M. pseudolavatera to keep leaf blades facing the sun throughout the course of the day, thus maximizing photosynthetic activity (Ehleringer

& Forseth 1980). As a result of their ability to track the movement of the sun, all three species have a preference for areas with high levels of light exposure (Fisher et al. 1989;

Greer & Thorpe 2009).

There were 145 separate observations (2,511 presence cells) of Malva spp. recorded on SEFI in 2016 (Holzman et al. 2017). Observations of Malva spp. were found on the

Marine Terrace and the eastern facing slopes of Lighthouse Hill (Figure 8).

Sonchus spp.

The Sonchus spp. group is made up of two species of the genus Sonchus: Sonchus asper (spiny sowthistle) and Sonchus oleraceus (common sowthistle). Both species are native to Europe and western Asia, but are now widely distributed across the globe

(Hutchison et al. 1984). S. asper and S. oleraceus are herbaceous winter annuals that produce a hollow, flowering stem that can grow up to 120 cm in height (Holm et al. 1997;

Hutchison et al. 1984). Reproduction is carried out via seed only. S. asper and -S. oleraceus have a strong preference for disturbed areas with minimal competition, especially along roadsides, sidewalks, or walking paths (Hutchison et al. 1984). Both species prefer well- drained, slightly acidic soils, but are tolerant of moderately saline conditions as well (Lewin

1948). 18

Holzman et al. (2017) recorded 111 separate observations (819 presence cells) of

Sonchus spp. on the Island in 2016. These occurrences were found primarily within close proximity to the walking trails and manmade structures located in the Marine Terrace, on

Lighthouse Hill, and at the base of Shubrick Point (Figure 9).

Environmental Predictor Variables

Eleven environmental predictor variables were used for developing the distribution models of the selected species/species groups. The predictor variables serve as the model’s independent variables. All variables were generated using the ArcGIS (version 10.4) software suite (ESRI 2016). All variables were produced in raster format with a cell resolution of two meters (4m2). Cell resolution was set to match the minimum mapping unit for the Holzman et al. (2017) inventory.

Variables selected for modeling were: elevation, surface slope, transformed aspect

(northness and eastness), solar radiation accumulation, Topographic Convergence Index

(TCI) (Wolock & McCabe 1995), Topographic Position Index (TPI) (Weiss 2001), soil depth, distance to walking trails and structures, distance to seabird colonies, and distance to pinniped haul-out sites (Table 2). The majority of predictor variables selected were used in previous efforts to model invasive plant species and have been shown to facilitate their distribution (Dobrowski et al. 2008; Evangelista et al. 2008; Kumar et al. 2006; Lambrinos

2002; Stohlgren et al. 2010). 19

Table 2. Environmental variables developed and used for modelling. Data Variable Description Units Type Resolution Source Elevation Terrestrial elevation of Continuous Meters 2m CSMP 2010 study area. Slope angle of study Surface Slope Continuous Degrees 2m CSMP 2010 area's surface.

Trigonometric transformation of Northness Continuous NA 2m CSMP 2010 surface aspect; northness = cos(aspect). Trigonometric transformation of Eastness Continuous NA 2m CSMP 2010 surface aspect; eastness = sin( aspect). Accumulation of solar Solar radiation across study Radiation Continuous Wh m-2 2m CSMP 2010 area for 2016 calendar Accumulation year. Topographic Accumulated Potential soil moisture CSMP 2010; Convergence Continuous weight of 2m on study area. Dilits 2010 Index upslope cells Topographic Landform types and CSMP 2010; Position slope positions within Continuous NA 2m Dilits 2010 Index study area. Hawk 2015; Point Blue Depth of soil across Soil Depth Continuous Centimeters 2m Conservation study area. Science, pers. comm. Euclidean distance to Distance to nearest trail/structure CSMP 2010; Trails and Continuous Meters 2m from any location Hawk 2015 Structures within study area. CSMP 2010; Euclidean distance to Distance to Point Blue nearest seabird colony Seabird Continuous Meters 2m Conservation from any location Colonies Science, pers. within study area. comm. CSMP 2010; Distance to Euclidean distance to Point Blue Marine nearest pinniped haul- Continuous Meters 2m Conservation Mammal out site from any Science, pers. Haul-Outs location on SEFI comm. 20

Elevation

A digital elevation model (DEM) was created that represents the terrestrial elevation, above mean sea level, of SEFI (Figure 10A). Elevation data are continuous and are in meters above sea level. Data for this surface model were collected by the California

Seafloor Mapping Program (CSMP) in 2010 using a laser scanner at a resolution of two meters (see CSMP 2010 for further documentation).

Surface Slope

A surface model was created to represent the surface slope of the study area (Figure

10B). Surface slope represents the slope angle of a surface ranging from flat (0°) to vertical

(90°). Data are continuous and were derived from the SEFI DEM (CSMP 2010) using the slope tool in ArcGIS (ESRI 2016).

Transformed Aspect (Northness & Eastness)

Two digital terrain models were created to represent the surface aspect of SEFI.

Surface aspect is a representation of a surface’s physical orientation with respect to direction Aspect values are given in degrees ranging from 0 to 360. Data are continuous and were derived from the SEFI DEM (CSMP 2010) using the aspect tool in ArcGIS (ESRI

2016).

In order to eliminate errors commonly associated with the circular nature of surface aspect data, trigonometric transformations were applied to the dataset. The transformations are known as northness and eastness (Evangelista et al. 2008; Guisan et al. 1999; Gutierrez et al. 2005; Kumar et al. 2006). Northness was calculated using the following equation: northness = cos(aspect). Values from the northness equation range from 1 to -1. Northness 21

takes values close to 1 if the aspect is northerly, close to -1 if the aspect is southerly, and

close to 0 if aspect is east/west facing (Figure IOC).

Eastness was calculated using the following equation: eastness = sin(aspect).

Values from the eastness equation range from 1 to -1. Eastness takes values close to 1 if the aspect is eastward, close to -1 if the aspect is westward, and close to 0 if aspect is northerly or southerly (Figure 10D). Transformed aspect data are continuous and were generated by using the map algebra function in ArcGIS (ESRI 2016).

Solar Radiation Accumulation

A surface model was created to represent the amount of solar radiation accumulated on SEFI during the primary growing season on the Island (1 November 2015 - 1 June

2016) (Figure 11 A). Solar radiation surface models take into consideration aspect, latitude, date, and meteorological conditions. Solar radiation data are used to model the distributions of invasive plants in studies by Dimbock et al. (2003), Evangelista et al.

(2008), Giljohann et al. (2011), Gray (2005), and Stohlgren et al. (2008). Solar radiation data are continuous and represent kilowatt-hours per square meter (kWh m'2). Data were

derived from the SEFI DEM (CSMP 2010) using the area solar radiation tool in ArcGIS

(ESRI 2016).

Topographic Convergence Index (Wolock & McCabe 1995)

A surface model was developed to represent the Topographic Convergence Index

(TCI) of SEFI (Figure 1 IB). TCI represents potential surface moisture, and is a function of both topographic position and surface flow direction (Wolock & McCabe 1995). TCI was calculated according to the amount of water flowing into a raster cell in relation to the 22

amount of water flowing out of a cell (Kuppinger et al. 2010). TCI is used to model invasive plant distributions in studies by Dobrowski et al. (2008), Evangelista et al. (2008)

Kuppinger et al. (2010), McDonald et al. (2008), and Peterson et al. (2003). The data are continuous, and were derived from the SEFI DEM (CSMP 2010) using the Topographic

Convergence Index tool (Dilits 2010) in ArcGIS (ESRI 2016). TCI values represent the accumulated weight of all upslope cells flowing into a given cell (Dilits 2010).

Topographic Position Index (Weiss 2001)

A surface model was created to represent the Topographic Position Index (TPI) for

SEFI (Figure 11C). TPI is an index used to classify topographical features into landform types and/or slope positions (Weiss 2001). Furthermore, TPI is used to assess the influence topography has on the ecological characteristics of an area (Weiss 2001). TPI uses an algorithm to calculate the difference in elevation between a given cell and the mean elevation of a neighborhood of cells within a given search radius (De Reu et al. 2013;

Guisan et al. 1999; Weiss 2001). TPI surface models are used in studies of invasive plants byChong etal. (2006), Dimbock et al. (2003), Giljohann etal. (2011), Giorgis etal. (2011) and Stohlgren et al. (1998). TPI data are continuous and were derived from SEFI DEM

(CSMP 2010) using the Topographic Position Index tool (Dilits 2010) in ArcGIS (ESRI

2016). For visualization purposes only, TPI scores were classified according to the parameters established by Weiss (2001).

Soil Depth

A digital surface model was created to represent soil depth of SEFI (Figure 1 ID).

Soil depth values were interpolated from measurements taken across SEFI. Depth 23

measurements are courtesy of Hawk (2015), and research personnel with Point Blue

Conservation Science of Petaluma, CA. Studies by Belcher et al. (1995), Dombush &

Wilsey (2010), Lundholm & Larson (2003), McColley & Hodgkinson (1970), and Sheley

& Larson (1997) indicate that soil depth plays a role in the germination rates, physiological processes, and distributional patterns of plant species. Soil depth values are continuous and in centimeters below the surface. Surface interpolation was carried out using the inverse distance weighted (IDW) interpolation tool in ArcGIS (ESRI 2016). IDW was selected to ensure that interpolated figures are within the range of the minimum and maximum observed soil depth measurements.

Distance to Trails and Structures

A digital surface model was developed to represent the Euclidean distance to the nearest walking trail and/or manmade structure from any location on SEFI (Figure 12A).

Walking trails and structures are mapped by Hawk (2015). The calculation of distance to both trails and structures allows for current human disturbance (near walking trails and manmade structures that are currently in use) and historic human disturbance (proximal to manmade structures no longer in use) to be incorporated into models. Measurements of distance to structures, trails, or roads are used by Arevalo et al. (2010) Hansen & Clevenger

(2005), Giorgis et al. (2011), Mortensen et al. (2009), and Mount & Pickering (2009) to model the distribution of invasive plants. Euclidean distance data are continuous, with distance values in meters (data are classified for mapping and visualization purposes only).

Data were generated using the Euclidean distance tool in ArcGIS (ESRI 2016). 24

Distance to Seabird Colonies

A raster surface was created representing the Euclidean distance to the nearest high density seabird colony/nesting site from any location on SEFI (Figure 12B). High density seabird colonies/nesting sites were identified according to relative measures of density made by biologists and resource managers working on SEFI (Point Blue Conservation

Science; USFWS, pers. comm). The location and extent of identified colonies/nesting sites were established through consultation with biologists from Point Blue Conservation

Science. Seabirds congregating in large colonies cause significant changes both physically and chemically to the environment where colonies are located (Mulder & Keal 2001; Ellis

2005). Such alterations are found to be positively correlated with a rise in non-native plant species found in proximity to seabird colonies (Ellis 2005; McMaster 2001; Vidal et al.

2000). Distance data are continuous and are in meters. Data were generated using the

Euclidean distance tool in ArcGIS (ESRI 2016).

Distance to Pinniped Haul-Out Sites

A raster dataset was developed representing the Euclidean distance to the nearest pinniped haul-out site from any location on SEFI (Figure 12C). Pinniped haul-out sites were established for a five-year period beginning in 2012 and ending in 2016 (Point Blue

Conservation Science, pers. comm.). Pinnipeds are known to cause significant reductions in the ground cover of plant communities, both native and non-native, located near major haul-out sites (Favero-Longo et al. 2010). Studies by Hausmann et al. (2013) and Ryan et al. (2003) suggest that disturbance to ground cover caused by resting pinniped species can 25

foster the establishment of invasive plant species. Distance data are continuous and in

meters. Data were developed using the Euclidean distance tool in ArcGIS (ESRI 2016).

Analysis and Modeling

Distribution models were developed for each species/species group, and for the

combined distribution of combined targeted species, using a GAM modeling framework.

Models were generated for SEFI only. Models were developed using the ‘gam’ package in the R statistical software platform (R Core Team 2017). Model testing and the production of prediction surfaces was completed using the Marine Geospatial Ecology

Toolset (MGET) (Roberts et al. 2010) in ArcGIS (ESRI 2016).

Occurrence records from Holzman et al. (2017) were converted from polygons to a series of presence cells, with each cell representing 4m2. This was done to create a one- for-one match between presence cells and the cells of each predictor variable surface model. Each model used a dataset that contains 71,808 observations (Table 3).

Table 3. Presence cells for each targeted species.

Species Name Presence Absence Presence % All species 20,725 51,083 28.86 C. murale 1,497 70,311 2.08 E. erecta 878 70,930 1.22 P. coronopus 5,765 66,043 8.03 T. tetragonioides 10,760 61,048 14.98 Annual grasses 10,106 61,702 14.07 Malva spp. 2,511 69,297 3.50 Sonchus spp. 819 70,989 1.14

Observations were given a binary classification, with species presence coded as 1 and species absence coded as 0. Values from environmental predictor variables were 26

applied to each observation using the extract values by point tool in ArcGIS (ESRI 2016).

Cells containing species presence/absence and environmental predictor data were used as the input values for each model.

The observations of each selected species were divided into training and testing datasets (Fielding & Bell 1997). The training set was comprised of 75% of the total presence/absence observations for each species, and the testing set was made-up of 25% of the total presence/absence observations. Observations were randomly selected for both the training and testing datasets. According to Fielding & Bell (1997) and Franklin & Miller

(2009), a 75/25 split of occurrence data is considered best practice for training and testing distribution models that use 10 or more predictor variables.

Model calibration was executed using the R statistical software platform (R Core

Team 2017) using the training datasets for each selected species. All environmental predictor variables were used during the initial phases of model training. However, in order to achieve optimum model performance, an automated stepwise backwards procedure was used to remove collinear and/or insignificant environment variables from the model. All variables selected for the final model were statistically significant at p < 0.01 threshold, or the 99% confidence level.

A one-way analysis of variance (ANOVA) test was carried out by the “gam” package upon the completion of the stepwise backwards selection procedure. The ANOVA test allowed for the significant predictor variables for each model to be identified according to the P-value assigned to each variable. In the event that multiple predictor variables had the same equally low P-value, the F-value was used to determine the most influential 27

predictor variables. The F-value, or F-ratio, assesses the degree of variance exhibited by the mean of each predictor variable relative to the means of all other predictor variables selected by the model. The greater the F-value of a given variable, the greater the variance of that variable, and the higher the likelihood that the difference between the mean value of that variable relative to all other variables is due to something other than chance. The variable with the highest F-value is therefore the most influential of all other variables selected by the model (Moore et al. 2009; Stockburger 2013). The results table for each

ANOVA test can be found in Appendix A.

The test dataset was used to evaluate the predictive performance of the final models produced during model training. Using the corresponding test dataset of each model, a response surface that predicts the probability of species presence on SEFI was generated.

The higher the probability value for a given location, the greater the likelihood that the modeled species would be present within that location (Yee & Mitchell 1991; Stohlgren et al. 2010). A probability threshold (i.e. the probability value above which a species is predicted to be present for a given location) was established for each model. The establishment of a presence threshold allowed response surfaces to be given a binary classification of predicted species presence and predicted species absence. Probability thresholds were set to equal the mean of predicted probability for each model (Cramer

2003; Fielding & Haworth 1995; Liu et al. 2005). Presence thresholds were used to assess model performance.

Assessments of model performance were carried out via a determination of the area under the curve (AUC) of the receiver operating characteristic (ROC). Additional 28

performance assessments were made through a calculation of each model’s Sensitivity,

False Positive Rate (FPR), Specificity, and False Negative Rate (FNR).

The AUC statistic is a metric used to measure the predictive performance of a model (Elith et al. 2006; Evangelista et al. 2008; Kumar & Stohlgren. 2009). When used within the context of SDM, AUC measures the ability of a model to accurately differentiate between sites of species presence and species absence (Hanley & McNeil 1982). AUC values range from 0 to 1; the greater the AUC value the higher the accuracy, or performance, of the model. AUC values greater than 0.9 indicate high model performance

(i.e. prediction accuracy), values between 0.89 - 0.7 indicate average model performance, values ranging from 0.69 - 0.5 indicate poor model performance, and values less than 0.5 indicate predictions that are no better than random (Elith et al. 2006; Stohlgren et al. 2010;

Swets 1988). Any model with an AUC score below 0.8 is considered unacceptable.

Sensitivity is a measure of the proportion of true species presence that is accurately predicted by the model. Sensitivity is calculated using the following equation: Sensitivity

- TP / (TP+ FN) where TP equals true positives and FN equals false negatives (Franklin

& Miller 2009). Specificity is a measure of the proportion of true species absences accurately predicted by the model. Specificity is calculated according to the following equation: Specificity = TN / (TN + FP) where TN equals true negatives and FP equals false positives (Franklin & Miller 2009).

The False Positive Rate (FPR) is a measure of the rate at which species presence is predicted for locations that are observed as species absence. The FPR is calculated using the following equation: FPR = 1 - Specificity. The False Negative Rate (FNR) is a measure 29

of the rate at which species absence is predicted in locations observed as species presence.

The FNR is calculated using the following equation: FNR = 1 - Sensitivity.

Lastly, predictions of potential distribution were generated from the response surfaces produced by each model. Surfaces were initially produced in vector format, but were converted to raster format for visualization and mapping.

Results

Results are presented in eight sections, one section for each of the models developed. Each section reports on the total number of presence/absence predictions made by each model, the environmental predictor variables selected for model training, the influence of each selected variable on modeled outcomes, and the performance results from model testing. Additionally, each section reports on the predicted distribution of targeted species according to the response surface generated by each model.

Combined Target Species Model

The combined target species model predicted 32,237 cells classified as species presence and 39,571 cells classified as species absence (Table 4), which results in a 44.89% presence when combining all target species on SEFI. The 44.89% predicted presence is an increase of 16.03 percent from the observed presence recorded by Holzman et al. (2017)

(Table 4). 30

Table 4. Predicted presence cells for each targeted species Difference Species Name* Presence Absence Presence % from Observed Presence % All target species 32,237 39,571 44.89 + 16.03 C. murale 26,141 45,667 36.40 + 34.32 E. erecta 6,175 65,633 8.60 + 7.38 P. coronopus 17,616 54,192 24.53 + 16.50 T. tetragonioides 21,280 50,528 29.63 + 14.65 Annual grasses 28,407 43,401 39.56 + 25.49 Malva spp. 24,676 47,132 34.36 + 30.87 Sonchus spp. 20,828 50,980 29.01 + 27.86 * A total of 71,808 predictions were made by each model.

The model utilized all eleven of the environmental predictor variables available

(Table 5). The most influential variables on the combined target species model were northness and distance to trails and structures (Table 6).

Table 5. Variables selected for model training All c E. p. T. tetragoni­ Annual Malva Sonchus Variable* species murale erecta coronopus oides grasses spp. spp. DEM XX XXX X X

Slope XXXXX XX - Northness X - X XX X XX

Eastness XX - X - XX - Solar Radiation XXX - XX X - TCI XXXXX X XX TPI X XXX X X - X Soil Depth XX - X XXX X Trails & Structures XXXX - XX X Seabirds XXXX X X X X Pinnipeds X -- X X X - X *A11 selected variables are statistically significant at p < 0.01. 31

Table 6. The top two significant variables for each model

Model Variable All Species Northness Distance to trails and structures C. murale Distance to seabird nesting sites TPI E. erecta Elevation Northness P. coronopus Distance to trails and structures Slope T. tetragonioides Northness Elevation Annual grasses Distance to trails and structures Slope Malva spp. Distance to trails and structures Slope Sonchus spp. Distance to trails and structures Northness

The presence threshold for the combined target species model is set at 0.251, and

the AUC score is 0.837 (Table 7). All individual species models performed at a higher

level than the combined target species model. The Sensitivity and FPR results reveal that the combined model correctly predicted locations of species presence at 80.3%, and

incorrectly predicted presence locations at 29.6%. The Specificity and FNR values signify that the combined model correctly predicted locations of species absence at 70.4%, and

incorrectly predicted locations of species absence at 19.7% (Table 7).

Table 7. Assessment of model performance

All C E. P T. tetrago­ Annual Malva Sonchus Statistic species murale erecta coronopus nioides grasses spp. spp. Presence 0.251 0.074 0.015 0.090 0.163 0.100 0.035 0.014 Threshold AUC 0.837 0.852 0.986 0.921 0.862 0.870 0.841 0.845 Sensitivity 0.803 0.587 0.991 0.947 0.778 0.948 0.880 0.882 FPR 0.296 0.066 0.065 0.189 0.208 0.308 0.325 0.289 Specificity 0.704 0.934 0.935 0.811 0.792 0.692 0.675 0.711 FNR 0.197 0.413 0.009 0.053 0.222 0.052 0.120 0.118 32

The response surface of the combined target species model predicts that these invasive species have the potential be found across large portions of the study area. This model predicts that these species may be present throughout the Marine Terrace of SEFI, on all slopes of both Lighthouse Hill and Little Lighthouse Hill, upslope from the common murre colony the north side of the island, on the south side of Shubrick Point, and on

Cormorant Blind Hill (Figure 13).

Chenopodium murale

The Chenopodium murale model predicted 26,141 cells on SEFI as species presence and 45,667 cells as species absence (Table 4). The model predicted a 36.40% presence for C. murale on SEFI. The number of occurrences predicted by the model results in an increase of 34.32 percent from the observed presence recorded by Holzman et al.

(2017).

The model used nine of the eleven environmental predictor variables provided. The variables selected for modeling were: elevation, surface slope, eastness, solar radiation accumulation, TCI, TPI, soil depth, distance to structures and trails, and distance to seabird nesting sites (Table 5). The top two significant variables on the predicted distribution of

C. murale were distance to seabird nesting sites and TPI. (Table 6).

The presence threshold for the C. murale model is set at 0.074, and the AUC value for the model is 0.852 (Table 7). The Sensitivity and FPR values reveal that the model correctly predicted locations of species presence at 58.7%, and that 6.6% of predicted locations of species presence are in locations observed as species absence. The Specificity 33

and FNR scores indicate that the model correctly predicted locations of species absence at

93.4%, and incorrectly predicted locations of absence at 41.3% (Table 7).

The C. murale model predicts that the species has the potential to be found across

large portions of the study area. Areas predicted to be suitable habitat for C. murale include

the Marine Terrace, the ridgelines of Lighthouse Hill and Little Lighthouse Hill, on the

south side of Shubrick Point, and at North Landing (Figure 14).

Ehrharta erecta

The Ehrharta erecta model predicted 6,175 presence cells and 65,633 cells as

species absence (Table 4). The model predicted a presence percentage of 8.60% for E.

erecta on SEFI. The predicted presence is an increase of 7.38 percent from the observed

presence percentage made by Holzman et al. (2017).

The model incorporated eight out of the eleven predictor variables initially

provided. The variables selected for modeling were: elevation, surface slope, northness,

solar radiation accumulation, TCI, TPI, distance to trails and structures, and distance to

seabird nesting sites (Table 5). Elevation and northness were the most influential predictor

variables in the E. erecta model (Table 6).

The presence threshold for the model is set at 0.015, and the AUC score is 0.986

(Table 7). The Sensitivity and FPR values indicate that the model correctly predicted

locations of species presence at 99.1%, and that 6.5% of predicted presence observations

are in locations observed as absence. The Specificity and FNR values signify that the model correctly predicted locations of species absence at 93.5%, and incorrectly predicted

species absence at 0.9% (Table 7). 34

The model predicts that E. erecta is found across the higher elevation slopes and

ridgelines of Lighthouse Hill, along all ridgelines of Little Lighthouse Hill, and on the

southwestern face of Cormorant Blind Hill. Additionally, the model predicts that the

species could be present on the south facing slopes of Shubrick Point, and on the ridge

above Sea Lion Cove (Figure 15).

Plantago coronopus

The Plantago coronopus model predicted 17,616 presence cells and 54,192 absence

cells (Table 4). The model predicted a 24.53% presence for P. coronopus. The predicted

presence for this species is an increase of 16.5 percent from the observations made by

Holzman et al. (2017).

The model used ten predictor variables: elevation, surface slope, northness,

eastnesss, TCI, TPI, soil depth, distance to trails and structures, distance to seabird nesting

sites, and distance to pinniped haul-out sites (Table 5). Distance to trails and structures

and slope were the most influential variables in the P. coronopus model (Table 6).

The presence threshold for the P. coronopus model is set at 0.090, and the model

has an AUC score of 0.921 (Table 7). The Sensitivity and FPR values indicate that the

model correctly predicted locations of species presence at 94.7%, and incorrectly predicted

presence locations at of 18.9%. The Specificity and FNR scores signify that the model

correctly predicted locations of species absence at an 81.1 %, and incorrectly predicted

absence locations at a 5.3% (Table 7).

The P. coronopus model predicts that the species has the potential to occur throughout the Marine Terrace. Furthermore, the surface indicates that the species can be 35

present along the trails leading to Shubrick Point and North Landing, and at the top of

Cormorant Blind Hill (Figure 16).

Tetragonia tetragonioides

The Tetragonia tetragonioides model predicted 21,280 cells as species presence and 50,528 cells as species absence (Table 4). The model produced a 29.63% presence for the species on SEFI. The predicted presence is an increase of 14.65 percent from the presence observed by Holzman et al. (2017).

The final T. tetragonioides model used nine predictor variables: elevation, surface slope, northness, solar radiation accumulation, TCI, TPI, soil depth, distance to seabird nesting sites, and distance to pinniped haul-out sites (Table 5). The most influential variables in the T. tetragonioides model were northness and elevation (Table 6).

The presence threshold for the T. tetragonioides model is set at 0.163, and the model has an AUC score of 0.862 (Table 7). The Sensitivity and FPR scores indicate that the model correctly predicted species presence at 77.8% and incorrectly predicted species presence at a 20.8%. The Specificity and FNR values signify that the model correctly predicted species absence 79.2% of the time and incorrectly predicted absence at a 22.2%

(Table 7).

The model predicts that T. tetragonioides has the potential to be present on all slopes of Lighthouse Hill and Little Lighthouse Hill. Additionally, the model predicts that the species can be found throughout the western portion of the Marine Terrace, on

Cormorant Blind Hill, east of North Landing, in close proximity to the seabird colony on the north side of the Island, and on the southern slopes of Shubrick Point (Figure 17). 36

Annual grasses

The annual grasses model classified 28,407 presence cells and 43,401 cells as species absence (Table 4). The model generated a 39.56% presence for the annual grasses group. The predicted presence is an increase of 25.49 percent from the observations recorded by Holzman et al. (2017).

The annual grasses model used all eleven of the available predictor variables (Table

5). The top variables in the annual grasses model were distance to trails and structures and slope (Table 6).

The presence threshold for the annual grasses model is set at 0.1, and the model has an AUC score of 0.870 (Table 7). The Sensitivity value indicates that the model correctly predicted locations of species presence at 94.8%, and the FPR score indicates that the model incorrectly predicted presence locations 30.8% of the time. The Specificity and

FNR scores signify that the model correctly predicted locations of species absence at

69.2%, and incorrectly predicted absence locations at a 5.2% (Table 7).

The annual grasses model predicts that species occurrence can potentially be found throughout the Marine Terrace and across the south and southwestern facing slopes of

Lighthouse Hill. Furthermore, the model predicts that annual grasses can occur on Little

Lighthouse Hill, along the trails leading to North Landing and Shubrick Point, and at the base of Shubrick Point (Figure 18).

Malva spp.

The Malva spp. model predicted 24,676 presence cells and 47,132 cells classified as species absence (Table 4). The model generated a 34.36% presence for the genus on 37

SEFI. The predicted presence is an increase of 30.87 percent from the observations recorded by Holzman et al. (2017).

The Malva spp. model utilized eight predictor variables: surface slope, northness, eastness, solar radiation accumulation, TCI, soil depth, distance to trails and structures, and distance to seabird nesting sites (Table 5). The most influential variables were distance to trails and structures and slope (Table 6).

The presence threshold of the Malva spp. model is set at 0.035, and the AUC score is 0.841 (Table 7). The Sensitivity and FPR scores signify that the model correctly predicted species presence at an 88% and incorrectly predicted presence at 32.5%. The

Specificity and FNR values reveal that the model correctly predicted absence 67.5% of the time and incorrectly predicts absence at 12% (Table 7).

The Malva spp. model predicts that the species can be found on the eastern slope of Lighthouse Hill, throughout the majority of the Marine Terrace, and in close proximity to all walking trails on the island. Additionally, the model predicts that the species has the potential to occur at North Landing, at the base of Shubrick Point, and on the summit of

Lighthouse Hill (Figure 19).

Sonchus spp.

The Sonchus spp. model generated 20,828 presence cells and 50,980 cells classified as species absence (Table 4). The model produced a 29.01% presence for Sonchus spp. on

SEFI. The presence predicted by the model is an increase o f27.86 percent from the number of observations recorded by Holzman et al. (2017). 38

The Sonchus spp. model used eight predictor variables: elevation, northness, TCI,

TPI, soil depth, distance to structures and trails, distance to seabird nesting sites, and

distance to pinniped haul-out sites (Table 5). The most influential predictor variables in

the Sonchus spp. model were distance to trails and structures and northness (Table 6).

The presence threshold of the Sonchus spp. model is set at 0.014, and the model has

an AUC score of 0.845 (Table 7). The Sensitivity and FPR values signify that the model

accurately predicted species presence at 88.2% and incorrectly predicted species presence

at 28.9%. The Specificity score indicates that the model correctly predicted locations of

species absence at 71.1%, while the FNR signifies that the model incorrectly predicted

species absence at 11.8% (Table 7).

The model predicts that Sonchus can be found in close proximity to all walking trails on the Island. Additionally, the surface predicts that Sonchus spp. has the potential

to be present on the northern and southern slopes of Little Lighthouse Hill, on Cormorant

Blind Hill, and on Shubrick Point (Figure 20).

Discussion

The discussion is presented in four sections. Section one examines the influence of

environmental predictor variables on predicted distributions and whether the influence of

those variables reflects each species’ autecological growth preferences. Additionally,

section one compares the predicted distribution of each species with the observed

distribution from the Holzman et al. (2017). Section two is a discussion of the overall modeling performance, paying particular attention to the predictive capabilities of the 39

models and the influence of environmental predictor variables. Section three investigates

the implications modeling results may have on resource management on SEFI. Lastly,

section four is a disclosure of the limitations of the data presented in this project.

Environmental Predictor Variables and Predicted Distributions

Combined Target Species Model

For the combined target species model, the most influential variables were northness and distance to trails and structures (Table 6). The effect of these variables is represented in the model’s response surface. The response surface predicts that the majority of targeted plants can be found on the south facing slopes of all hillsides on SEFI and throughout the Marine Terrace, where the bulk of the walking trails and manmade structures on the Island are present. The average northness value of predicted presence locations for all targeted species is -0.5 (Appendix B, Table Bl). This indicates that, on average, presence predictions have a southerly aspect. The average distance to walking trails or structures for presence predictions is 20.1 meter (Appendix B, Table Bl), indicating that the influence of the variable is on predictions of presence and not predictions

of absence. The role these variables play in the model’s final output reflects the predominant autecological growth preferences shared by all of the species targeted in this project, prolonged sun exposure, soil disturbance and dispersal avenues.

The combined target species distribution map (Figure 13) represents a prediction of species presence on SEFI that is largely similar to the distribution of invasive plant species currently seen on the Island. The model predicts that target invasive plants are 40

found throughout the Marine Terrace, on the southern and southwestern slopes of

Lighthouse Hill, and upslope from the common murre colony located on the north side of the Island. All of these locations are currently occupied by invasive plant species. The map also indicates the potential for invasive plants to expand into new habitat within the study area, especially around the path leading to Shubrick Point and east of Sea Lion Cove

(Figure 13).

Chenopodium murale

The most influential variables on the predicted distribution of Chenopodium murale are distance to seabird nesting sites and TPI (Table 6). The effect of these variables is only partially apparent in the response surface generated by the model. The surface predicts that C. murale is found predominantly in the low-lying flat areas of SEFI, especially the

Marine Terrace, which is a reflection of the influence of the TPI variable. However, the response surface does not predict occurrences of C. murale in close proximity to high density seabird nesting sites. For example, the response surface predicts that the species will be largely absent from the western end of the Marine Terrace (Figure 14), despite that part of the Island being home to a seabird colony and the highest concentration of C. murale occurrence observations from the Holzman et al. (2017) inventory. The average distance to seabird nesting sites for predicted C. murale presence cells is 85.4 meters (Appendix B,

Table B2). While the distance to seabird nesting sites variable does reflect C. murale’s preference for nitrogen rich, disturbed soils, it would appear that the variable is exerting more of an influence on predictions of absence rather than presence. This result could explain why the model made absence predictions in locations where the species was 41

observed in 2016, and why the overall performance of the model is average (see AUC score

in Table 7).

The C. murale model predicts a distribution for the species that reflects its present and potential distribution on SEFI (Figure 14). The predictions of species presence throughout the Marine Terrace and on the island’s north side are both locations where the species is currently known to grow. The predictions of species presence on Cormorant

Blind Hill, the south facing slopes of Little Lighthouse Hill, on North Landing, and on the western and southern faces of Shubrick Point represent habitat that may be suitable for new invasions of C. murale (Figure 14).

Ehrharta erecta

For the Ehrharta erecta model, elevation and northness were the most influential variables on the predicted distribution of the species (Table 6). The effect of these variables is well represented in the model’s response surface. The model predicts that E. erecta can be found predominantly at higher elevations, along both the north and south facing ridgelines and upper slopes (i.e. talus cliffs) of SEFI. The average elevation of predicted

presence cells of E. erecta is 59.7 meters, and the average northness value is -0.3 (Appendix

B, Table B3). The average elevation value indicates that the influence of the variable is on predictions of presence not absence, while the northness value indicates that, on average, E. erecta presence predictions have a southerly aspect. From an autecological perspective, this prediction reflects E. erecta’s preference for habitat that is shaded and consistently moist and cool. Due to the terrain variability of the Island’s higher elevation locations, cool and moist habitat is found throughout the ridgelines and talus cliffs of SEFI. 42

The E. erecta model predicts a distribution that mirrors its current distribution.

Predictions of species presence on the upper portions of Lighthouse Hill, atop the ridgelines

of Little Lighthouse Hill, and on the southwestern face of Cormorant Blind Hill are all

locations where the species is currently found (Figure 15). Additionally, the model predicts the species’ potential spread into areas including the lower elevation ridges of Lighthouse

Hill and Little Lighthouse Hill, the south facing slopes of Shubrick Point, and the ridgeline above Sea Lion Cove in the northwestern part of the Island. Given the species ability to expand rapidly into new habitat, locations predicted to be vulnerable to incursion by E. erecta should be monitored carefully. Any new occurrences of the species should be removed to prevent E. erecta from further expanding its population on SEFI (Figure 15).

Plantago coronopus

Distance to trails and structures and surface slope were the most influential variables in the Plantago coronopus model. (Table 6). The effect of these variables is represented in the response surface generated by the model. The surface predicts that P. coronopus can be found throughout the Marine Terrace, in close proximity to the walking

trails and manmade structures located there. Furthermore, the response surface predicts

that the P. coronopus will only be present in locations where surface slope is minimal. The influence of these variables reflects the species’ preference for highly disturbed soils

(Waite 1984; Waite & Hutchings 1982), as the majority of human activity on SEFI occurs on the trails and around the structures located in the relatively flat Marine Terrace. Human use of trails can also serve as dispersal agents for the species. The average distance to walking trails and structures for predicted presence locations of P. coronopus is 14 meters 43

(Appendix B, Table B4). This distance indicates that the proximity to walking trails and structures is influential on predictions of species presence and not predictions of absence.

The average surface slope of presence predictions is 5.1 degrees (Appendix B, Table B4), which is the lowest average slope of any targeted species.

The modeled distribution map for P. coronopus identifies locations where the species is known to occur (Figure 16). The model predicts that the species can be found throughout the Marine Terrace, where it is currently found in great abundance. However, the model also indicates that there is potential for the species to spread beyond the Marine

Terrace and into new locations on SEFI. Locations vulnerable to colonization by P. coronopus include areas along the walking trails leading to Shubrick Point and North

Landing, on Cormorant Blind Hill, and at the top of Shubrick Point (Figure 16).

Tetragonia tetragonioides

The most influential environmental variables on the Tetragonia tetragonioides model were northness and elevation (Table 6). The influence of these variables is reflected in the response surface generated by the model. The surface predicts that T. tetragonioides can be found on almost all hillsides of SEFI, and will be absent across the majority of the

Island’s low-lying, flat terrain. Predictions of species presence carry an average northness value of -0.4 and an average elevation of 33.1 meters (Appendix B, Table B5). These values indicate that, on average, predictions of T. tetragonioides have a southerly aspect and are made at the higher elevations on SEFI. The influence of these variables is reflective of the species’ preference for warm, well-drained, rocky soils (Taylor 1994). The hillsides 44

of SEFI would provide T. tetragonioides with the greatest exposure to the sun and the rockiest soils present on the Island.

The model’s response surface represents a distribution for T. tetragonioides that is similar to the species’ current distribution. The model predicts that T. tetragonioides can be found on all slopes of Lighthouse Hill and Little Lighthouse Hill, and on the islands north side in close proximity to the common murre colony (Figure 17). All of these locations are areas where the species is known to exist. Additionally, the model predicts that the species has the potential to expand to include habitat on the southern slopes of

Shubrick Point, the western half of the Marine Terrace, and on the slopes east of Sea Lion

Cove (Figure 17).

Annual grasses

The environmental predictor variables with the greatest effect on the annual grasses model were distance to trails and structures and surface slope (Table 6). The model predicts that species in the annual grasses group occur in close in proximity to all walking trails and manmade structures of SEFI. Furthermore, the model predicts that annual grasses will occur in areas where surface slope angels range from moderate (Lighthouse Hill) to relatively flat (Marine Terrace). The influence of these variables is reflective of the predominant growth preferences of the annual grasses group, high levels of soil disturbance and dispersal agents (humans) and prolonged exposure to the sun (Best 2008; Bicak &

Sternberg 1993; Davison 1970; Davison 1971; Frenot et al. 2001; Marshall & Jain 1966;

Sharma & Vanden Bom 1977). 45

The majority of human activity on SEFI occurs on the walking trails, or around the manmade structures present on the Island. In addition to being a vector for human activity, the trails and structures of the Island are located in areas with prolonged exposure to the sun, especially within the Marine Terrace and on the south face of Lighthouse Hill. The average distance to walking trails and structures for predicted presence locations of species in the annual grasses group is 15.9 meters (Appendix B, Table B6). The average surface slope for presence locations of the annual grasses group is 12.9 degrees (Appendix B, Table

B6). The average distance value indicates that proximity to walking trails and structures is influential on predictions of species presence and not predictions of absence. The average slope value indicates that presence predictions occur on both flat and moderately steep surfaces present in the study area.

The annual grasses model predicts occurrences of annual grasses throughout the

Marine Terrace, especially the southern section of the Terrace, and across the south and southwestern facing slopes of Lighthouse Hill (Figure 18). These are locations where annual grasses are known to exist. The model also predicts these species in new locations, in particular on Little Lighthouse Hill, North Landing, and at the base of Shubrick Point

(Figure 18).

Malva spp.

Distance to walking trails and structures and surface slope were the most influential variables on the predicted distribution of species in the Malva spp. group (Table 6). The influence of these variables is apparent in the response surface generated by the model.

The surface predicts that Malva spp. occur within close proximity of all walking trails and 46

manmade structures on the Island, and in locations with moderate to minimal surface slope angles such as the Marine Terrace, or the south side of Lighthouse Hill. These variables reflect the predominant growth preferences of the Malva spp. group, i.e. disturbed soils and prolonged sun exposure. The majority of human activity on SEFI occurs around the trails and structures, or in the low-lying flat areas. These locations also happen to have some of the greatest exposure to the sun available on the Island. Uninterrupted exposure to the sun is necessary for species in the Malva spp. group to maximize the solar tracking capabilities of individual plants (Chauhan et al. 2006; Fisher et al. 1989; Greer & Thorpe 2009; Hill

2017). The average distance to walking trails and structures for predicted presence locations of species in the Malva spp. group is 10.7 meters, and the average slope angle was 11.2 degrees (Appendix B, Table B7). This indicates that proximity to walking trails and structures, as well as flatter slope angles, are influential on predictions of species presence.

The Malva spp. model predicts that these species are found in many locations where it is known to occur, including on the east slopes of Lighthouse Hill, throughout the

Marine Terrace, and in close proximity to the walking trails and structures on the east side of the island (Figure 19). In addition, the model predicts that the species has the potential to expand to areas on both sides of walking trails to Lighthouse Hill, North Landing, and

Shubrick Point, throughout the North Landing, along the base of Shubrick Point, and at the summit of Lighthouse Hill (Figure 19). 47

Sonchus spp.

The most influential environmental variables in the Sonchus spp. model were distance to walking trails and structures and northness (Table 6). The model predicts that species in the Sonchus spp. group are found in proximity to all walking trails and structures and on the majority south facing slopes on SEFI. The influence of the distance to trails and structures and northness variables reflects the predominant growth preference of species within this genus, i.e. highly disturbed locations that are commonly near roadsides and walking paths and heavy exposure to the sun (Hutchison et al. 1984). The average distance to walking trails and structures for predicted presence locations of species in the annual grasses group is 13.6 meters, and the average northness value is -0.4 (Appendix B,

Table B8). The average distance indicates that proximity to walking trails and structures is influential on predictions of species presence and not predictions of absence. The average northness value indicates that predictions of Sonchus spp. have a predominantly southern aspect.

According to the response surface developed by the model, the species is predicted to occur throughout the Marine Terrace, and in close proximity to all trails and structures on the Island (Figure 20). These trails may function as dispersal corridors for the species. All of these locations are areas where Sonchus spp. currently grow. The map also indicates the potential for the species to be found in locations where it is not currently observed, especially on the southern and western slopes Little Lighthouse Hill, east of Sea

Lion Cove, and on the North Landing (Figure 20). 48

Overall Model Performance

All eight models meet the minimum predictive performance threshold. Six of the eight models (combined target species, C. murale, T. tetragonioides, annual grasses, Malva spp., and Sonchus spp.) have AUC scores that indicate average predictive performance

(Elith et al. 2006; Stohlgren et al. 2010; Swets 1988). The highest performing models are the E. erecta and P. coronopus models, and the lowest performing models are the combined target species and Malva spp.

Model performance appears to be driven largely by the geographic variability exhibited by the observations of species presence used for model training. The observation records used by the E. erecta and P. coronopus models are more geographically homogeneous in nature, while the occurrence observations for all other models are more diverse in their geographic distribution. For example, the vast majority E. erecta presence records come from the south facing slopes and ridgelines of Lighthouse Hill. Conversely, the observations for the Sonchus spp. group come from many different locations on SEFI, ranging from the low-lying Marine Terrace and North Landing, to the steep slopes of

Lighthouse Hill, Cormorant Blind Hill, and Shubrick Point.

The homogeneity of occurrence observations for both E. erecta and P. coronopus caused the predicted distributions for those two species to be confined, for the most part, to the locations where the species are known to be present. This increases the likelihood of the model making correct predictions of species presence and/or absence, driving up overall model performance. On the other hand, the variability of the occurrence observations for all other modeled species caused the predicted distributions to not be 49

limited to the locations where those species are currently observed. This increases the likelihood of the model making over predictions of species presence, which consequently suppresses model performance.

The models over-predicted species presence on SEFI (Table 4). While this tendency influences the predictive performance, such errors of commission do not harm the overall viability of the models. Errors of commission are to be expected when using

GAMs to predict the distribution of invasive plant species (Franklin & Miller 2009). As was the case with the data gathered by Holzman et al (2017), it is common for invasive plant observation records to contain a greater number of absence observations than presence observations (Franklin & Miller 2009). Subsequently, predictions of species presence are expected to occur in locations that have been observed as absence, as these locations represent suitable habitat that has yet to be realized by a target species. The tendency for models to predict species presence in areas observed as absence is desirable, as these locations represent the habitat that is vulnerable to future colonization.

Only two predictor variables were selected by all eight models following the completion of the stepwise backwards selection procedure: TCI and distance to nearest seabird nesting sites (Table 5). However, there are six predictor variables that were selected by all but one distribution model. These variables are elevation, surface slope, northness, TPI, soil depth, and distance to trails and structures (Table 5). The consistent selection of these variables represents, at least in some capacity, autecological growth preferences that are shared by all of the species targeted for this project (i.e. highly disturbed areas with nitrogen rich soils and prolonged exposure to the sun). 50

The variables representing eastness and distance to pinniped haul-out sites were dropped by three models following the completion of the stepwise backwards selection process. The consistent exclusion of these variables is logical for the eastness variable but not for the variable representing distance to pinniped haul-outs. The division of species presence and absence is more pronounced along the lines of north versus south facing slopes than it is between east and west facing slopes, so it makes sense that the northness variable was selected at a higher rate than the eastness variable. However, the dropping of the distance to pinniped haul-outs variable is interesting. The dataset represents both a major disturbance factor on SEFI and a primary source of nitrogen for the Island’s soil system. For example, both soil disturbance and nitrogen rich soils are growth preferences for C. murale, yet the model dropped the pinnipeds variable. The exclusion of this variable is likely due to the geographical positions of the occurrence observations used for model training. The occurrence observations for all species that excluded the pinnipeds variable

(C. murale, E. erecta, and Malva spp.) are far removed from pinniped haul-out locations, especially for E. erecta.

Two predictor variables were consistently significant on modeled outcomes, distance to trails and structures and northness. The distance to trails and structures was the most influential variable for four models, and the northness variable was the most influential variable for two models (Table 6). Furthermore, the northness variable was the second or third most influential variable for three other models (Appendix A, Figures A3,

A6, A8). These two variables reflect the predominant growth preferences shared by all 51

species targeted in this project, heavily disturbed locations with significant levels of sun exposure. Trails may also represent opportunities for seed dispersal.

Management Implications

The management of invasive plant species on SEFI is carried out by the USFWS.

The eight distribution models developed for this project shed light on the factors influencing the distribution of invasive plants on the island. Furthermore, the prediction surfaces generated by each model highlight both the realized and potential distribution of each target species/species group. The insights gained from this project can be directly incorporated into the weed management program developed specifically for controlling the spread of invasive plant species on SEFI.

The maps of predicted distribution identify multiple locations within the study area that are currently free of invasive plant species, or have very few occurrences, but are vulnerable to future invasions. These locations include the south side of Shubrick Point, the southern and western facing slopes of Cormorant Blind Hill, all sides of Little

Lighthouse Hill, and the North Landing. USFWS personnel should be aware of these locations, and resources should be allocated to ensure that these areas remain free of invasive plants during weed control campaigns. Additionally, vulnerable sites should be monitored throughout the year for any new occurrences of target species, or other non­ native species. Any occurrences of invasive plant species found in these vulnerable locations should be removed immediately. As noted by Mack et al. (2000) and Mack &

Lonsdale (2002), the complete eradication of invasive plant species from a given location is most likely when occurrences are removed during the earliest stages of colonization. 52

Once a species has become naturalized within a location, its complete removal can become exceedingly difficult (Mack et al. 2000; Mack & Lonsdale 2002).

While the response surfaces generated by each of the models represent viable predictions of species presence, the Ehrharta erecta model should be considered the most beneficial and/or useful from a management perspective. The model’s predictive capability is considerably greater than all other models (see AUC score in Table 7), so all presence predictions should be considered highly suitable habitat for occurrences of the species. Furthermore, the predicted distribution for E. erecta is confined to the ridgelines and upper slopes of the Island, which differs greatly from the more universal predictions generated by all of the other models. The more localized prediction of E. erecta presence can allow USFWS to focus management efforts on specific locations within the study area and increase the likelihood of effectively eradicating the species from the Island. Given the species ability to expand rabidly in wildland habitat, the incorporation of the results from the E. erecta model into invasive plant management efforts on SEFI is recommended.

Each of the vulnerable locations within the study area share at least one commonality, their proximity to either walking trails, manmade structures, or both. While there are other environmental variables and disturbance factors that play a role in the distribution of invasive plant species on SEFI, only human activity can be managed. While practices are in place to reduce the impact human activity on the distribution of invasive plant species, more can and should be done.

Although human activity should be confined to the walking trails of the Island, it is recognized that research and/or management tasks undertaken by Refuge personnel 53

demand that human activity sometimes occurs off trail. To minimize the impact of off-

trail activity, more boot brushes should be installed, used, and maintained in more locations

on SEFI to minimize the dispersal invasive plant seeds. Furthermore, all research

personnel should carry hand-held brushes to use on their shoes before and after completing

off-trail fieldwork. Access to the areas identified as vulnerable to future colonization

should be reduced, as well. Finally, all research personnel should be educated on the

invasive species present on the island. Personnel should be aware of what these species

look like in the field, where these species are currently growing, and the locations that are

vulnerable to future invasions.

Limitations of Data

The results of this project provide unique insights into the spatial distribution of the invasive plant community of SEFI. There are, however, limitations to both the data and the models presented here that must be acknowledged. The observation data used to train the models are from a single year (2016), and may not be representative of a species’

complete distribution throughout the study area. Highly variable factors such as

germination requirements, current climatic conditions, and the footprint of eradication

efforts undertaken by resource managers can either limit, or enhance, the number of

occurrences of a species during a given year. Fluctuations in the location, total number,

and density of species occurrences can have an effect on model outcomes, especially if presence observations were to be made in locations that differed from the observations made for 2016. Occurrence records from any future inventory efforts on SEFI should be 54

incorporated into the models to either confirm currently predicted distributions, or develop

an even more comprehensive understanding of species distributions.

Furthermore, a temporal constraint, or invasion rate, is not associated with the

predictions of species presence. This is due to occurrence observations being from a single

growing season (2016), and because growth rates specific to SEFI are not known for

targeted species. This limitation calls for the results of future inventory efforts to be

incorporated into each model, and for further research into the growth rates of invasive plant species on SEFI.

Finally, the effect of competitive interactions between plant species, both native

and non-native, on the predicted distribution of targeted species is not taken into consideration. This was due to the time constraints of the project, as well as the GAM platform’s inability to model interaction terms such as the competition for resources between different species (Franklin & Miller 2009). While species such as T. tetragonioides are known to outcompete SEFI natives like L. maritima (USFWS 2009), it

is entirely possible that the presence of other species may be a limiting factor for other

target species, especially those that prefer habitat with minimal competition (i.e. C. murale,

P. coronopus, and Sonchus spp.). If future inventory and modeling efforts want to include

species interactions, then modeling platforms that are capable of incorporating species interactions such as decision trees or multivariate adaptive regression splines (MARS) should be used (Franklin & Miller 2009).

This project should be considered a viable first step in the analysis of SEFI’s invasive plant community, and a legitimate reference for future resource management 55

efforts. However, it is imperative that inventory and monitoring efforts continue on the island so that these models can be updated over time. Furthermore, it would be advisable for research into the growth rates of invasive plant species to be conducted so that time constraints could be applied to predictions of species presence. Finally, additional models should be developed so that competitive interactions between plant can be included in future predictions of distribution.

Conclusion

This project analyzed the spatial distribution of seven invasive plant species/species groups currently growing on SEFI. The analysis was carried out via the generalized additive model (GAM) platform using occurrence records that were collected by the

Holzman et al. (2017) inventory. Modelling results were analyzed and mapped in order to identify the factors with the greatest influence on predicted distributions, as well as to locate areas vulnerable to future invasions by selected species.

It was hypothesized that human activity, proximity to seabird colonies, and surface aspect would play the most prominent role in predicted distributions. Results revealed that distance to walking trails and manmade structures and northness had the greatest influence on the predicted distributions of target species. Furthermore, distribution maps identify multiple locations on the island that would be vulnerable to invasions by one or more target species. Those locations include Shubrick Point, Cormorant Blind Hill, Little Lighthouse, and the North Landing. As a result, the hypothesis is generally accepted. 56

Recommendations for how modeling results should be incorporated into the island’s weed management plan were also made. Recommendations focus on prioritizing monitoring and eradication efforts on the areas identified as vulnerable to future invasions and on one species in specific, Ehrharta erecta. Additional management suggestions were made that includes reducing human activity in and around vulnerable locations, increasing the number of boot brushes available to personnel working on the island, and providing more educational resources for personnel stationed on SEFI.

Lastly, the limitations of the data and models developed for this project were disclosed. Limitations include the narrow timeframe for which observation records were gathered and the GAM platforms inability to incorporate terms of interaction between species. Solutions to mitigate the impacts of these shortcomings were presented in the form of future research opportunities. 57

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Figure 1 - Map o f Southeast Farallon Island and its location relative to the San Francisco Bay Area. 72

Invasive Plant Species

Southeast Farallon Island

I I Annual Grasses \ Lmmmj Annual Grasses i-*-3 & Plantago coronopus I Chenopodium murale | Ehrharta erecta Feature Type | Malva spp. O Patch Feature i Malva spp. ■ & T. tetragonioides < = > Linear Feature I Plantago coronopus Polygon Feature I Sonchus spp. "TT~ri~Trr' Walking Trails 0 25 50 100 Meters | Tetragonia tetragonioides Contours at 10 Meters ---1 1--1-- 1---1 Map by Q.). Clark Data Sources - Holzman et al. 2017 / CSU Monterey Bay / Hawk 2015 Figure 2 - Distribution o f targeted species on Southeast Farallon Island from Holzman et al (2017). 73

Chenopodium murale

Southeast Farallon Island

Shubrick Point

C. murale Feature Type ® Patch Feature Linear Feature ff^gi Polygon Feature Patch Radius Walking Trails 10 Meters 0 25 50 100 Meters Contours at 10 Meters ^ 5 Meters -----1 1---1----1----1 Map by Q. J. Clark Data Sources - Holzman et al. 2017 / CSU Monterey Bay / Hawk 2015 Figure 3 - Distribution o f Chenopodium murale on Southeast Farallon Island in 2016. 74

Figure 4 - Distribution o f Ehrharta erecta on Southeast Farallon Island in 2016. 75

Plantago coronopus

Southeast Farallon Island

P. coronopus Feature Type © Patch Feature Linear Feature Polygon Feature Patch Radius — Walking Trails 10 Meters 0 25 50 100 Meters Contours at 10 Meters 5 Meters ---1 1--1--1---1 Map by Q. J. Clark Data Sources - Holzman et al. 2017 / CSU Monterey Bay / Hawk 2015 Figure 5 - Distribution o f Plantago coronopus on Southeast Farallon Island in 2016. 76

Tetragonia tetragonioides

Southeast Farallon Island

Common murre colony

Shubrick Point

T. tetragonioides Feature Type • Patch Feature Linear Feature Polygon Feature ===== Walking Trails 0 25 50 100 Meters Contours at 10 Meters 5 Meters -----1 1---1----1----1 Map by Q. J. Clark Data Sources - Holzman et al. 2017 / CSU Monterey Bay / Hawk 2015 Figure 6 - Distribution o f Tetragonia tetragonioides on Southeast Farallon Island in 2016. 77

Annual Grasses

Southeast Farallon Island

Annual Grasses Feature Type © Patch Feature <====» Linear Feature Polygon Feature =^=^= Walking Trails 0 25 50 100 Meters Contours at 10 Meters ---1 1--1-- 1-- 1 Map by Q.J. Clark Data Sources - Holzman et al. 2017 / CSU Monterey Bay / Hawk 2015 Figure 7 - Distribution o f the annual grasses group on Southeast Farallon Island in 2016. 78

Malva spp.

Southeast Farallon Island

Malva spp. Feature Type © Patch Feature g = > Linear Feature igp Polygon Feature — Walking Trails 0 25 50 100 Meters Contours at 10 Meters -----1 1---1----1----1 Map by Q. J. Clark Data Sources - Holzman et al. 2017 / CSU Monterey Bay / Hawk 2015 Figure 8 - Distribution o f the Malva spp. group on So utheast Farallon Island in 2016. 79

Sonchus spp.

Southeast Farallon Island

Shubrick Point

Marine Terrace Sonchusspp. Feature Type • Patch Feature «■■■ Linear Feature Patch Radius Walking Trails 10 Meters 0 25 50 100 Meters Contours at 10 Meters ^ 5 Meters -----1 1---1--- 1----1 Data Sources - Holzman et al. 2017 / CSU Monterey Bay / Hawk 2015

Figure 9 - Distribution o f the Sonchus spp. group on Southeast Farallon Island in 2016. 80

E levation Surface Slope I I 6 Southeast Farallon Island Southeast Farallon Island

Slope Angle Decree* • vtii

Figure 10 A Terrain model representing elevation on Southeast Farallon Island rigure 10 a - terrain model representing surface slope on Southeast Farallon Island Northness I Eastness Southeast Farallon Island Southeast Farallon Island

Figure 10 C Terrain model representing north ness on Southeast Farallon Island. Figure 10 D Terrain model representing easiness on Southeast Famllon Island. 81

Solar Radiation Topographic Convergence ► ' 1 November 2015 - 1 lune 2016 Index (TCI) o I Southeast Farallon Island Southeast Farallon Island

Potential Surface Moisture > of Contributing OIU

Figure 11 A Surface model representing solar radiation accumulation on Southeast Figure II B Surf ace model representing the Topographic Convergence Index of Farallon Island from November /" th m u g h J u n e I* o f 2016. Southeast Farallon Island . - Topographic Position Soil Depth Index (TPI) I Southeast Farallon Island Southeast Farallon Island

Land Form* & Slope Position ■ I *&*** Hilltop Soil Depth

Figure II C Surface model representing the Topographic Position Index of Southeast F igu re III) Surface model representing soil depth on Southeast Farallon Island. Farallon Island 82

Distance to Trails & Structures Distance to High Density I Seabird Nesting Sites Southeast Farallon Island Southeast Farallon Island

Seabird Species

I 1 Western Cwll

Figure 12 A Surface mode/ representing Eudklean distance to trails and structures Figure 12 B Surface model representing Euclidean distance to high density■ seabird on Southeast Farallon Island nesting sites on Southeast Farallon Island. Distance to Pinniped Haul-Out Locations I Southeast Farallon Island

Meter* OKI 10 30 20-30 30 « 1 1|«0.» '|> » I . I 1 1

Figure 12 C Surface model representing Euclidean distance to pinniped haul-outs on Southeast Farallon Island. 83

All Target Species I Southeast Farallon Island

Common murre

Sea Lion Cove

Shubrick Point Cormorant Blind Hill

M arine

Predicted Species Presence I Observed Species Presence Walking Trails 0 25 50 100 Meters Contours at 10 Meters -----1 1---1----1----1 Map by : Q. J. Clark Data Sources - Holzman et al. 2017 / CSU Monterey Bay / Hawk 2015 Figure 13 - Predicted distribution o f all target invasive plant species on Southeast Farallon Island. 84

Chenopodium murale Southeast Farallon Island

Shubrick Point Cormorant Blind Hill

Marine Terrace

Predicted Species Presence Observed Species Presence Walking Trails 0 25 50 100 Meters Contours at 10 Meters -----1 1---1----1----1 Map by : Q. J. Clark Data Sources - Holzman et a!. 2017 / CSU Monterey Bay i Hawk 2015 Figure 14 - Predicted distribution distribution o f Cheonpodium murale on Southeast Farallon Island. 85

Figure 15 - Predicted distribution ofEhrharta erecta on Southeast Farallon Island. 86

Plantago coronopus Southeast Farallon Island

Cormorant Shubrick Point Blind Hill

Marine Terrace

M Predicted Species Presence 1 1 Observed Species Presence *“***» Walking Trails 0 25 50 100 Meters Contours at 10 Meters -1 1-1-1-1 Map bv : Q. J. Clark Data Sources - Holztnan et al. 2017 / CSU Monterey Bay / Hawk 2015 Figure 16 - Predicted distribution o f Plantago coronopus on Southeast Farallon Island. 87

Figure 17 - Predicted distribution o f Tetragonia tetragonioides on Southeast Farallon Island. 88

Figure 18 - Predicted distribution o f the annual grasses group on Southeast Farallon Island. 89

Figure 19 - Predicted distribution o f the Malva spp. group on Southeast Farallon Island. 90

Figure 20 - Predicted distribution o f the Sonchus spp. group on Southeast Farallon Island. 91

Appendix A - ANOVA Test Results

Anova for Parametric Effects - All Targeted Species

Df Sum Sq Mean Sq F value P r(>F) THESIS DEM FINAL 1 1394 1394.22 1543.893 < 2.2e-16 *** THESIS SLOPE 1 1025 1024.61 1134.609 < 2.2e-16 *** THESIS NORTHNESS FINAL 1 2704 2703.66 :2993.915 < 2.2e-16 *** THESIS EASTNESS FINAL 1 17 17.24 19.094 1.246e-05 *** THESIS SOLAR RADIATION FINAL 1 199 199.06 220.428 < 2.2e-16 *** THESIS TCI RESAMPLED 1 560 560.08 620.206 < 2.2e-16 *** THESIS TOPOGRAPHIC POSITION INDEX 1 1007 1006.76 1114.844 < 2.2e-16 *** THESIS SOIL FINAL 1 1275 1275.42 :1412.343 < 2.2e-16 *** THESIS DISTANCE STRUCTURES TRAILS 1 1659 1659.32 :1837.460 < 2.2e-16 *** THESIS DISTANCE SEABIRDS 1 239 239.06 264.721 < 2.2e-16 *** THESIS_DISTANCE_PINNIPEDS 1 756 755.99 837.148 < 2.2e-16 *** Residuals 53632 48432 0.90

Signif. codes: 0 '***' 0.001 '**' 0.01 '*• 0.05 \ • 0.:1 . , 1 Figure A10 - Results table from ANOVA test for final version of All Targeted Species model.

Anova for Parametric Effects - Chenopodium murale Df Sum Sq Mean Sq F value Pr(>F) THESIS DEM FINAL 1 45 45.03 24.876 6.133e-07 *** THESIS SLOPE 1 94 93.80 51.822 6.157e-13 *** THESIS EASTNESS FINAL 1 57 56.79 31.371 2.142e-08 *** THESIS SOLAR RADIATION FINAL 1 39 38.59 21.321 3.894e-06 *** THESIS TCI RESAMPLED 1 40 40.12 22.163 2.511e-06 *** THESIS TOPOGRAPHIC POSITION INDEX 1 379 378.55 209.131 < 2.2e-16 *** THESIS SOIL FINAL 1 104 104.04 57.475 3.477e-14 THESIS DISTANCE STRUCTURES TRAILS 1 29 29.35 16.212 5.671e-05 *** THESIS J)ISTANCE__SEABIRDS 1 610 610.24 337.127 < 2.2e-16 *** Residuals 53628 97073 1.81 Signif. codes: 0 '***' 0.001 '**' 0.01 0.05 ' . ' 0. 1 1 ' 1 Figure A2 - Results table from ANOVA test for final version of Chenopodium murale model.

Anova for Parametric Effects - Ehrharta erecta

Df Sum Sq Mean Sq F value Pr(>F) THESIS DEM FINAL 1 540.5 540.53 3967.39 < 2.2e-16 THESIS SLOPE 1 32.3 32.26 236.77 < 2.2e-16 THESIS NORTHNESS FINAL 1 267.3 267.30 1961.89 < 2.2e-16 THESIS SOLAR RADIATION FINAL 1 25.2 25.22 185.09 < 2.2e-16 THESIS TCI RESAMPLED 1 54.1 54.10 397.09 < 2.2e-16 *** THESIS TOPOGRAPHIC POSITION INDEX 1 115.7 115.68 849.08 < 2.2e-16 THESIS DISTANCE STRUCTURES TRAILS 1 26.0 26.03 191.07 < 2.2e-16 *** THESISJ)ISTANCE_SEABIRDS 1 82.4 82.38 604.66 < 2.2e-16 *** Residuals 536157304.7 0.14

Signif. codes: 0 '***' 0.001 '** ' 0.01 '*' 0. 05 '.' 0 .1 ' ‘ 1

Figure A3 - Results table from ANOVA test for final version of Ehrharta erecta model. 92

Anova for Parametric Effects - Plantago coronopus

Df Sum Sq Mean Sq F value Pr(>F) THESIS DEM FINAL 1 140 139.57 69.274 < 2.2e-16 *** THESIS SLOPE 1 1232 1232.49 611.754 < 2.2e-16 *** THESIS NORTHNESS FINAL 1 172 172.29 85.518 < 2.2e-16 *** THESIS EASTNESS FINAL 1 62 62.09 30.819 2.846e-08 *** THESIS TCI RESAMPLED 1 411 410.52 203.762 < 2.2e-16 *** THESIS TOPOGRAPHIC POSITION INDEX 1 140 139.77 69.376 < 2.2e-16 *** THESIS SOIL FINAL 1 230 230.24 114.279 < 2.2e-16 *** THESIS DISTANCE STRUCTURES TRAILS 1 1537 1536.55 762.674 < 2.2e-16 *** THESIS DISTANCE SEABIRDS 1 35 35.08 17.412 3 .014e-05 *** THESIS_DISTANCE__PINNIPEDS 1 65 65.36 32.444 1 .233e-08 *** Residuals 53645 108078 2.01

Signif. codes: 0 '***' 0.001 '** 1 0.01 '*• 0.05 '.' 0 .1 ' 1 1 Figure A4 - Results table from ANOVA test for final version of Plantago coronopus model.

Anova for Parametric Effects - Tetragonia tetragonioides

Df Sum Sq Mean Sq F value Pr(>F) THESIS DEM FINAL 1 2088 2087.55 :1663.314 < 2.2e-16 *** THESIS SLOPE 1 187 187.00 148.997 < 2.2e-16 *** THESIS NORTHNESS FINAL 1 2229 2229.41 :1776.340 < 2.2e-16 *** THESIS SOLAR RADIATION FINAL 1 145 145.06 115.578 < 2.2e-16 THESIS TCI RESAMPLED 1 38 38.11 30.365 3. 595e-08 *** THESIS TOPOGRAPHIC POSITION INDEX 1 1648 1648.20 :1313.245 < 2.2e-16 *** THESIS SOIL FINAL 1 733 733.20 584.194 < 2.2e-16 *** THESIS DISTANCE SEABIRDS 1 1082 1082.34 862.382 < 2.2e-16 *** THESIS_DISTANCE_PINNIPEDS 1 255 254.98 203.160 < 2.2e-16 *** Residuals 53636 67316 1.26

Signif. codes: 0 ' * * * ' 0.001 '**' 0.01 '*' 0.05 ' .' 0.:1 , . 1

Figure A 5 - Results table from ANOVA test for final version of Tetragonia tetragonioides model.

Anova for Parametric Effects - Annual Grasses

Df Sum Sq Mean Sq F value Pr(>F) THESIS DEM FINAL 1 37 37.11 57.9992 2 .665e-14 THESIS SLOPE 1 1014 1014.38 1585.5743 < 2.2e-16 THESIS NORTHNESS FINAL 1 847 846.59 1323.3085 < 2.2e-16 *** THESIS EASTNESS FINAL 1 4 4.06 6.3446 0.01178 * THESIS SOLAR RADIATION FINAL 1 438 438.40 685.2631 < 2.2e-16 THESIS TCI RESAMPLED 1 556 556.30 869.5552 < 2.2e-16 THESIS TOPOGRAPHIC POSITION INDEX 1 495 494.89 773.5647 < 2.2e-16 *** THESIS SOIL FINAL 1 196 195.50 305.5942 < 2.2e-16 *** THESIS DISTANCE STRUCTURES TRAILS 1 1575 1574.56 2461.2025 < 2.2e-16 *** THESIS DISTANCE SEABIRDS 1 4 4.02 6.2820 0.01220 * THESIS_DISTANCE_PINNIPEDS 1 810 809.96 1266.0595 < 2.2e-16 *** Residuals !53635 34313 0.64

Signif. codes: 0 '***' 0.001 '**' 0.01 '** 0.05 ' 0.1 1 ' 1

Figure A 6 - Results table from ANOVA test for final version of Annual Grasses model. 93

Anova for Parametric Effects - Malva spp •

Df Sum Sq Mean Sq F value Pr(>F) THESIS SLOPE 1 336 336.11 477.581 < 2.2e-16 *** THESIS NORTHNESS FINAL 1 38 37.82 53.736 2.325e-13 *** THESIS EASTNESS FINAL 1 128 128.12 182.054 < 2.2e-16 *** THESIS SOLAR RADIATION FINAL 1 21 21.24 30.174 3.969e-08 *** THESIS TCI RESAMPLED 1 70 69.88 99.289 < 2.2e-16 *** THESIS SOIL FINAL 1 13 12.64 17.966 2.252e-05 *** THESIS DISTANCE STRUCTURES TRAILS 1 782 782.15 1111.370 < 2.2e-16 *** THESISJ)ISTANCE_SEABIRDS 1 45 44.73 63.559 1.587e-15 *** Residuals 53615 37733 0.70

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 ' 0.1 ' ' 1

Figure A7 - Results table from ANOVA test for final version o f Malva spp. model.

Anova for Parametric Effects - Sonchus spp.

Df Sum Sq Mean Sq F value Pr(>F) THESIS DEM FINAL 1 14 14.224 10.153 0.001441 THESIS NORTHNESS FINAL 1 62 61.539 43.928 3.439e-ll *** THESIS TCI RESAMPLED 1 10 10.400 7.424 0.006438 ** THESIS TOPOGRAPHIC POSITION INDEX 1 56 56.173 40.097 2.435e-10 *** THESIS SOIL FINAL 1 37 37.409 26.703 2.381e-07 *** THESIS DISTANCE STRUCTURES TRAILS 1 240 239.617 171.042 < 2.2e-16 *** THESIS DISTANCE SEABIRDS 1 28 28.334 20.225 6.898e-06 *** THESIS__DISTANCE_PINNIPEDS 1 30 29.767 21.248 4.045e-06 *** Residuals 53622 75120 1.401

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 ' . ' 0.1 ' ' 1

Figure A8 - Results table from ANOVA test for final version of Sonchus spp. model. Appendix B - Descriptive Statistics for Predicted Presence Points

Table Bl. All Target Species Predictor Variable* Mean Range Elevation (msl) 25.7 0.0-105.5 Surface Slope (°) 18.8 0.0 - 79.2 Northness -0.5 -1.0- 1.0 Eastness -0.1 -1.0- 1.0 Solar Radiation (kWh) 332.1 104.2-383.4 TCI (# of cells) 41.8 0.0 - 556.0 TPI 0.2 -14.5-21.2 Soil Depth (cm) 8.3 0.0 - 33.7 Trails & Structures (m) 20.1 0.0-100.2 Seabirds (m) 35.7 0.0-201.7 Pinnipeds (m) 78.7 0.0-164.7 *Only variables selected by final model are shown

Table B2. Cheno£odium murale Predictor Variable* Mean Range Elevation (msl) 13.2 0.0-105.1 Surface Slope (°) 9.3 0.0 - 75.7 Eastness -0.1 -1.0- 1.0 Solar Radiation (kWh) 343.4 123.2-383.4 TCI (# of cells) 51.0 0.0 - 508.9 TPI 0.7 -7 - 22.5 Soil Depth (cm) 8.2 0.0-33.6 Trails & Structures (m) 25.0 0.0 - 144.3 Seabirds (m) 85.4 0.0-257.1 *Only variables selected by final model are shown Table B3. Ehrharta erecta Predictor Variable* Mean Range Elevation (msl) 59.5 7.7-105.5 Surface Slope (°) 41.7 1.4-71.2 Northness -0.3 -1.0-1.0 Solar Radiation (kWh) 301.3 117.9-383.4 TCI (# of cells) 3.0 0.0 - 9.9 TPI 4.4 -6.1 -22.0 Trails & Structures (m) 24.8 0.0 - 84.9 Seabirds (m) 14.9 0.0 - 66.5 *Only variables selected by final model are shown

Table B4. Plantago coronopus Predictor Variable* Mean Range Elevation (msl) 12.3 5.9-104.7 Surface Slope (°) 5.1 0.0-51.8 Northness -0.6 -1.0- 1.0 Eastness -0.1 -1.0-1.0 TCI (# of cells) 70.5 0.0 - 556.0 TPI 0.0 -4.0 - 22.5 Soil Depth (cm) 10.4 0.0 - 33.7 Trails & Structures (m) 14.0 0.0 - 69.7 Seabirds (m) 63.0 0.0 - 206.5 Pinnipeds (m) 56.5 0.0-145.1 *Only variables selected by final model are shown Table B5. Tetragonia tetragonioides Predictor Variable* Mean Range Elevation (msl) 33.3 0.0-105.1 Surface Slope (°) 29.8 0.0 - 79.2 Northness -0.4 -1.0- 1.0 Solar Radiation (kWh) 313.9 31.2 -383.4 TCI (# of cells) 15.9 0.0-259.3 TPI -0.2 -15.9-19.9 Soil Depth (cm) 6.6 0.0-33.7 Trails & Structures (m) 13.6 0.0 - 86.2 Seabirds (m) 88.3 0.0-164.7 Pinnipeds (m) 33.3 0.0-105.1 *Only variables selected by final model are shown

Table B6. Annual Grasses Predictor Variable* Mean Range Elevation (msl) 20.3 0.0-105.5 Surface Slope (°) 12.9 0.0 - 63.2 Northness -0.6 -1.0-1.0 Eastness -0.1 -1.0- 1.0 Solar Radiation (kWh) 347.7 207.8 - 383.4 TCI (# of cells) 50.2 0.0 - 556.0 TPI 0.0 -12.5-21.3 Soil Depth (cm) 8.6 0.0-28.5 Trails & Structures (m) 15.9 0.0-73.8 Seabirds (m) 48.5 0.0-211.7 Pinnipeds (m) 73.9 0.0-164.7 *Only variables selected by final model are shown Table B7. Malva spp. Predictor Variable* Mean Range Surface Slope (°) 11.2 0.0 - 62.5 Northness -0.5 -1.0- 1.0 Eastness 0.0 -1.0-1.0 Solar Radiation (kWh) 343.4 181.5-381.9 TCI (# of cells) 50.8 0.0 - 556.0 Soil Depth (cm) 7.5 0.0-27.1 Trails & Structures (m) 10.7 0.0 - 46.9 Seabirds (m) 51.7 0.0 - 205.2 *Only variables selected by final model

Table B8. Sonchus spp. Predictor Variable* Mean Range Elevation (msl) 33.3 0.0-105.1 Northness -0.4 -1.0- 1.0 TCI (# of cells) 15.9 0.0 - 259.3 TPI -0.2 -15.9-19.9 Soil Depth (cm) 6.6 0.0-33.7 Trails & Structures (m) 13.6 0.0 - 86.2 Seabirds (m) 88.3 0.0-164.7 Pinnipeds (m) 33.3 0.0-105.1 *Only variables selected by final model are shown