The Role of Fog in the Hydrological Functioning of Tropical Island Ecosystems

THE ROLE OF FOG IN THE HYDROLOGICAL FUNCTIONING OF TROPICAL ISLAND ECOSYSTEMS

Sarah Rose Schmitt

A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Geography (Ecohydrology) in the College of Arts & Sciences.

Chapel Hill 2018

Approved by:

Diego Riveros-Iregui

Jia Hu

Conghe Song

Aaron Moody

Lawrence Band

ABSTRACT

Sarah Rose Schmitt: The Role of Fog in the Hydrological Functioning of Tropical Island Ecosystems (Under the direction of Diego Riveros-Iregui)

Fog is a critical water source in many tropical ecosystems, especially those that are semi- arid, or seasonally dry. Patterns of fog water input to these ecosystems are poorly understood, and currently limited by a lack of in-situ data spanning both space and time. Large gaps exist in our understanding of the spatiotemporal variability and mechanisms driving fog water deposition, and how fog travels through tropical systems.

Given the significance of fog to semi-arid ecosystems across the globe, I use stable isotopes, remote sensing and plant physiological analyses to examine the role of fog in the semi- arid ecosystems of San Cristóbal Island, Galápagos and Ascension Island, UK by utilizing data from four field campaigns. I first create a ground-based optical fog detection scheme to trace fine-temporal scale mechanisms driving fog formation, evolution and dissipation across varying hydroclimatic zones. I then establish the isotopic signature of fog and other environmental waters to assess the overall contribution of fog to different microclimatic zones and under different hydroclimatic regimes. And finally, I trace fog through the Galápagos ecosystem, specifically examining how native versus invasive flora utilize fog under varying hydroclimatic conditions.

In this research, I create a simple model to predict degree-of-fogginess with commonly measured meteorological variables, and I show how different fog formation mechanisms can be taking place in tandem over a concentrated spatial scale. I also demonstrate that fog is a common phenomenon on San Cristóbal Island, especially during the dry season, and that fog is

iii consistently enriched compared to co-collected rainfall. Finally, I utilize the isotopic signature of fog and other environmental waters to trace water sources through the Galápagos ecosystem, suggesting that invasive guava’s water use strategy (including fog water utilization) plays a key role in its competitive capacity versus co-occurring native plants.

Through this island lens, I address the critical disconnect between the hydrological and ecological role that fog plays in tropical island ecosystems. Taken together, these findings will be critical in projecting future water resource availability across many seasonally arid ecosystems, especially those that may become drier under future regimes of climate change.

ACKNOWLEDGEMENTS

I am forever indebted to a number of people who have helped me in a number of ways.

From making sure that I had a drink in hand at TRU when I needed to take a step back from my research to encouraging me to pursue research that I once thought was beyond my comprehension, you have all been incredible.

I gratefully acknowledge the mentorship of my advisor, Diego. Your guidance has had an incredible impact on me, and you have challenged me to become a better scientist. I am so appreciative of the time you have invested in helping me develop my ideas into invigorating,

“sexy” projects, and you have taught me that I am far more intelligent and capable than I ever thought possible. We have truly grown together over the past five years, and I hope that I have taught you at least a fraction of what you have taught me.

I am also immensely grateful to my committee members: Jia, Conghe, Aaron and Larry.

You are all incredible scientists. And I am beyond grateful that you all have provided amazing insights throughout this whole dissertation process that have played a pivotal role in my research and writing. I have genuinely enjoyed spending time with and getting to know each of you.

I also gratefully acknowledge my colleagues and collaborators outside of my committee.

Dr. Touchette, thank you for being an incredible mentor and fostering my passion for research.

Coertney, Pepe, Giulianna, Pablo, Erin, Rebecca and Madelyn, thank you all for your support in the field. Geovanny and Angel, thank you for being such amazing collaborators on San

Cristóbal. Fellow members of the Carbonshed Lab, thank you all for your feedback on my project, from its infancy to completion. Tong, Nuvan and Varun, thank you for tolerating my

v endless questions about coding and statistics. John and Manny, thank you for being my work wives and reminding me that you can work hard and play hard. And thank you to my countless friends and supporters too numerous to name.

I’d also like to thank the organizations and institutions that have provided me with funding and logistical support throughout my research. Funding for this research was provided by: the NSF Graduate Research Fellowship, NSF SAVI: Crossing the Boundaries of Critical

Zone Science with a Virtual Institute, the UNC-USFQ Consortium, the Geological Society of

America Graduate Student Research Grant, the National Geographic Young Explorer’s Grant, the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI)

Pathfinder Fellowship, the National Center for Atmospheric Research (NCAR) Visiting

Researcher Award, the UNC Department of Geography, the UNC Institute for the Study of the

Americas, the American Geophysical Union, the Thomas S. Kenan III Graduate Fellowship, and the Thomas S. and Helen Borda Royster & Snowden and Elspeth Merck Henry Dissertation

Fellowship. Logistical support for this research was provided by the Galápagos Science Center, the Galápagos National Park, the Ascension Island Government Conservation Department, and the United States Air Force on Ascension Island.

Lastly, I am especially grateful to my loved ones who have always had faith in me even when I didn’t have faith in myself. Mom, Dad, Marybeth, Emily and Sammy – your love and support for me has never wavered. Mom and Dad, your countless sacrifices and unconditional love have afforded me more opportunities than I ever thought possible. Krista and Alex, your empathy and humor were pivotal in helping me face the challenges of graduate school. And Sam, your love, kindness and support have been invaluable. Without your encouragement, this dissertation certainly wouldn’t be possible.

TABLE OF CONTENTS

LIST OF TABLES………………………………………………………………………………..xi

LIST OF FIGURES……………..……………………………………………………………….xii

LIST OF ABBREVIATIONS AND SYMBOLS………………………………………………..xv

CHAPTER ONE: INTRODUCTION………………………………………………..……………1

CHAPTER TWO: CHARACTERIZING FOG INUNDATION ALONG A TOPOGRAPHIC GRADIENT WITH GROUND-BASED CAMERAS………………………....5

Introduction………………………………………………………...……………………...5

Methods………………………………………………………………………..…………..9

Study site……………………………………………………….………………….9

In-situ, camera-based data collection ……………………………………………11

Image preprocessing……………………………………………………………..12

Normalized Difference Fog Index (NDFI) & Degree-of- Fogginess Model (DOFmodel) …………………………………………………....12

Validation of degree-of-fogginess model (DOFmodel) …………………………...13

Predicting degree-of-fogginess via a random forest model……………………...14

Assessing the utility of DOFmodel relative to meteorological measurements…….15

Understanding spatiotemporal patterns of degree-of-fogginess…………………16

Results…………………………………………………………………………..………..16

DOFmodel as a proxy for degree-of-fogginess…………………………………….16

Validating DOFmodel as a model for degree-of-fogginess………………………..17

Understanding fog cover (DOFmodel) through random forest…………………….17

vii Utility of DOFmodel relative to meteorological measurements…………………...18

Diurnal- and elevation-based assessment of fog cover: relevant meteorological parameters in predicting fog persistence in near-real time……...18

Discussion……………………………………………………………………..…………20

Is DOFmodel a suitable proxy for degree-of-fogginess?...... 20

Do fog formation mechanisms vary by elevation?...... 21

Conclusions………………………………………………………………………………23

Tables and Figures………………………………………………………….……………25

CHAPTER THREE: THE ROLE OF FOG, OROGRAPHY AND SEASONALITY ON PRECIPITATION IN A SEMI-ARID, TROPICAL ISLAND…………………………………..35

Introduction………………………………………………………...…………………….35

Materials and Methods………………………….……………………………..…………39

Characterizing the Galápagos hydroclimate……………………….…………….39

Fog collector design……………………………………………………………...41

Precipitation collection…………………………………………………………..41

Climate variables………………………………………………………………...42

Isotope analysis and isotope theory……………………………………...………42

Data processing…………………………………………………………………..45

Results…………………………………………………………………………..………..46

Seasonality in Galápagos………………………………………………………...46

Comparing seasonal isotopic values of fog, rain, and throughfall……………….46

Variability of isotopic composition of water sources (δ2H, δ18O, D-excess)……47

Discussion……………………………………………………………………..…………48

Does fog isotopic composition vary on event and seasonal timescales?...... 49

viii What drives the seasonal variability of throughfall isotopic composition?...... 51

Does the amount effect control rainfall isotopic composition?...... 52

Does moisture source region control rainfall isotopic composition?...... 53

What are the main drivers of local moisture recycling?...... 54

Conclusions………………………………………………………………………………56

Tables and Figures………………………………………………………….……………58

CHAPTER FOUR: GUAVA, EXPERT INVADER.…………………………………………....65

Introduction………………………………………………………...…………………….65

Methods………………………………………………………………………..………...68

Study sites………………………………………………………………….…….68

Field sampling………………………………………………….……….….…….71

Stable isotope analysis…………………………………….…….……………….73

Data processing…………………………………….…………………………….75

Results…………………………………………………………………………..………..75

Galápagos water supply and plant water demand dynamics…………………….75

Examining water use by PSGU and co-located native plants………………..…..76

Comparing PSGU across field sites and seasons…………………….…………..77

Quantifying plant water stress……………………………………..…………….78

Discussion……………………………………………………………………..…………79

Galápagos plant water availability……………………………………...………..79

Interspecific species competition for water sources……………………………..80

PSGU water use across hydroclimatic gradients……………….………………..83

Plant water stress: site and species variability……………………………….…..84

ix Conclusions………………………………………………………………………………85

Tables and Figures………………………………………………………….……………87

CHAPTER FIVE: SUMMARY AND CONCLUSIONS………………….…………………….95

SUPPLEMENTARY FIGURES…………………...………………………………………….100

Supplementary Figure 1………………………………………………………………100

Supplementary Figure 2………………………………………………………………...101

REFERENCES…………………………………………………………………………………103

x LIST OF TABLES

Table I. Cameras used to detect cloud immersion along Breakneck Valley Path, Ascension Island………………………………………………….……………….……….25

Table II. Parameter estimates and 95% confidence interval on the log odds ratio scale for the logistic regression model estimating fog immersion as a function of DOFmodel and camera site.………………………………….……………….……….26

Table III. Pearson’s R values of DOFmodel at three different elevations along Breakneck Valley Path in relation to meteorological parameters for time frames encompassing all daylight hours (7:00 – 18:30), morning/evening hours (7:00 – 9:00 & 17:00 – 18:30) and midday hours (9:30 – 16:30). Insignificant Pearson’s R values (-0.1 to 0.1) are denoted with an asterisk. Observation period took place from Aug-Dec, 2016 (dry, foggy season).………………………………………...…….……….27

Table IV. Conditions in Galápagos during water input studies. We designated 'season' following Trueman and D'Ozouville (2010) and 'extreme conditions' via SST deviations in the Niño 3.4 region following NOAA (2017) and Null (2017). Precipitation is expressed as the seasonal daily mean, and temperature/ wind speed in daily mean range….…………….…………………………………………...……58

Table V. Linear relationship between δ18O and δ2H (see Figure 12 for accompanying graphics)…………………….……..…………………...... …….59

Table VI. Conditions in Galápagos during this study. Precipitation is expressed as seasonal daily mean and temperature/wind speed in seasonal daily mean range. Adapted from Schmitt et al. 2018.……………….……………………………………….87

Table VII. Plant monitoring transects on San Cristóbal Island, Galápagos.….………………….88

xi LIST OF FIGURES

Figure 1. Ascension Island location in the South Atlantic Ocean……………………………….28

Figure 2. Daily noontime low cloud base height during the dry season in Ascension Island, 2015 (METAR, 2015). The dashed line represents the Height of the peak of Green Mountain, the elevation below which fog can form………..…..…29

Figure 3. Conceptual representation of NDFI, where green reflectance is lower in cloudier conditions (created with SketchUp software)………………………...………30

Figure 4. Correlation between DOFmodel and visually classified degree-of-fogginess….……….31

Figure 5. OOB-predicted DOFmodel versus actual DOFmodel, to assess the efficacy of the random forest model…………………………………………….………………………..32

Figure 6. Marginal effects plot for logistic regression validation model………………………..33

Figure 7. a) Diurnal pattern of DOFmodel with elevation. The diurnal pattern of DOFmodel was calculated as the average DOFmodel for a given camera over one foggy season (Aug-Dec 2016) for each half hour at each site. Values were omitted for a given camera/time when <70% of the DOFmodel data was available due to low lighting conditions (see Methods for details) b) Diurnal pattern of dewpoint depression with elevation, calculated as average dewpoint depression over one foggy season (Aug-Dec 2016) for each half hour.…………………………………….34

Figure 8. Hydroclimatic variation of daily average temperature, RH, and wind speed and monthly total precipitation during our study period in San Cristóbal Island, Galápagos. Shaded boxes represent FS during which data was collected. Note that precipitation data is missing prior to ~day 20 of FS1. …………….…………………60

Figure 9. Field site adjacent to El Junco Lake, San Cristóbal Island. The two photos at El Junco demonstrate differential fog inundation conditions between FS1 (top) and FS3 (bottom), both dry seasons. This demonstrates the high interannual variability of fog occurrence in Galápagos………………………………….……...61

Figure 10. Fog versus rain oxygen isotopic ratios. Each point represents co-collected fog/rain samples………….………………………………………………………..62

Figure 11. Precipitation and D-excess values of precipitation samples at the El Junco field site, San Cristóbal Island, Galápagos. Each box represents a FS of study (from L to R: FS1, FS2, FS3). The solid line indicates the global average rainfall D-excess and the dashed line indicates the seasonal average fog D-excess. Note that precipitation data is missing prior to ~day 20 of FS1……...…………………………63

xii Figure 12. Linear relationship between δ18O and δD of precipitation samples collected on San Cristóbal Island, Galápagos. Isotopic graphs are separated by FS1 (L), FS2 (middle), FS3 (right), and an overall representation of water isotopic data from all seasons. On each graph, the LMWL is displayed in orange, the GMWL is displayed in black and the fog line is displayed in a dashed line. Each line represented has an accompanying equation in Table V……………...……………………..64

Figure 13. Daily total precipitation (blue bars) and potential evapotranspiration (PET, pink lines) for all three field sites (located at 320, 520, and 670 m, respectively). Panels A, B and C represent field seasons one, two and three, respectively. This graphic visually represents plant water supply (blue bars) versus plant water demand (pink lines). Note that meteorological data is missing from MIR/CA for FS2, so no graphics could be rendered……………..……...………..89

Figure 14. FS1 plant xylem and precipitation isotope values. Panels A, B, and C represent sequential sampling rounds ~2 weeks apart throughout FS1. Invasive plant xylem values (PSGU in every graph) are shown in red and native plant values (BUGR for MIR, ZAFA for CA and MIRO for EJ) are shown in green. Precipitation values are shown in blue: circles indicate fog samples, diamonds indicate rainfall samples and inverted triangles indicate throughfall samples. Soil samples were not collected for FS1. Dashed lines extending from the plant xylem samples are for visualization purposes only...... ……90

Figure 15. FS2 soil, plant xylem and precipitation isotope values. Panels A and B represent sequential sampling rounds ~1 week apart in FS2. Invasive plant xylem values (PSGU in every graph) are shown in red and native plant values (BUGR for MIR, ZAFA for CA and MIRO for EJ) are shown in green. Precipitation values are shown in blue: circles indicate fog samples, diamonds indicate rainfall samples and inverted triangles indicate throughfall samples. Soil samples at increasing depths are displayed in black. Dashed lines extending from the plant xylem samples are for visualization purposes only………………...... ………….91

Figure 16. FS2 soil, plant xylem and precipitation isotope values. Panels A, B and C represent sequential sampling rounds ~2 weeks apart in FS3. Invasive plant xylem values (PSGU in every graph) are shown in red and native plant values (MIRO) are shown in green; note that only samples from one field site (EJ) were collected. Precipitation values are shown in blue: circles indicate fog samples, diamonds indicate rainfall samples, inverted triangles indicate throughfall samples, triangles indicate surface water samples (from EJ Lake) and squares represent groundwater samples. Soil samples at increasing depths are displayed in black. Dashed lines extending from the plant xylem samples are for visualization purposes only…………………………………………………………………..92

xiii Figure 17. Plant xylem dual-isotope values for all three FS of study. Panels A, B and C represent samples from EJ, CA and MIR, respectively. Hues of pink indicate invasive plants (PSGU at all sites), and hues of green indicate native plants (from top to bottom: MIRO, ZAFA and BUGR). Precipitation values from each FS are shown as blue points and the black dashed line represents the GMWL……………………………………………………………………………93

Figure 18. Leaf water potential (Ψ) of native (BUGR, ZAFA and MIRO) and invasive (PSGU) plant stands at MIR (A), CA (B) and EJ (C) during FS3. Sampling rounds took place on 6/12/16, 6/21/16 and 6/24/16, respectively. Red asterisks denote significant differences (p ≤ 0.05) between species at a given site/round (interspecies assessment) and black asterisks denote significant differences (p ≤ 0.05) within species at a given site/round (intraspecies assessment)…………..94

Supplementary Figure 1. Map of camera trap locations and approximate orientations along the BV and Dew Pond Paths ascending Green Mountain. Basemap from Quickbird © [2006] DigitalGlobe, Inc. Imagery acquired by Ascension Island Government (AIG). See Table I for details on each camera………………………………….100

Supplementary Figure 2. Vegetation features and ROIs for each camera (from top to bottom: BV3, BV6, BV8)…………………………………………………………………...101

xiv LIST OF ABBREVIATIONS AND SYMBOLS

δ2H Deuterium (Hydrogen-2)

δ18O Oxygen-18

° Degrees (Celsius)

Ψ Leaf water potential

‰ Permil

AIG Ascension Island Government

AMF-1 Ascension Island Site

ANCOVA Analysis of covariance

ARM Atmospheric Radiation Measurement Program

AVHRR Advanced Very High Resolution Radiometer

B/W Black and white

BUGR Bursera graveolens (ironwood)

BV Breakneck Valley

BV3 Breakneck Valley three (lowest elevation camera)

BV6 Breakneck Valley six (mid-elevation camera)

BV8 Breakneck Valley eight (highest elevation camera)

CA Cerro Alto

CL Confidence level

cm Centimeters

CSICs Cloud-sensitive image characteristics

D-excess Deuterium excess

DEM Digital elevation model

xv DNs Digital numbers

DOE Department of Energy

DOFmodel Degree-of-fog model

EJ El Junco

ENSO El Niño-Southern Oscillation

EXIF Exchangeable Image Files

FOV Field-of-view

FS1 Field Season One

FS2 Field Season Two

FS3 Field Season Three

GMW Global Meteoric Water Line

GNIP Global Network of Isotopes in Precipitation

GOES Geostationary Operational Environmental Satellite system

GOES-R ABI “” Advanced Baseline Imager

ITCZ Intertropical Convergence Zone

KAZRARSCL KA-band ARM Zenith Radars Active Remote Sensing of Clouds Product

LMWL Local meteoric water line

LWP Leaf water potential

m/s Meters per second

MAP Mean annual precipitation

MASL Meters above sea level (elevation)

MIR Mirador

MIRO Miconia Robinsoniana (miconia)

xvi mm/hr Millimeters per hour

mL Milliliters

MODIS Moderate Resolution Imaging Spectroradiometer

MPL Micropulse LiDAR

MSG SEVIRI Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager

Myr Million years

NDFI Normalized Difference Fog Index

OOB Out-of-bag

P Total annual precipitation

p-value Statistical significance value

PET Potential evapotranspiration

PSGU Psidium guajava (guava)

R2 Linear correlation coefficient

RGB Red-green-blue (color image)

RH Relative humidity (%)

ROI Region of Interest

SE Southeast

SST Sea Surface Temperature

std dev Standard deviation

TWI Trade wind inversion

VSMOW Vienna Standard Mean Ocean Water

ZAFA Zanthoxylum fagara (cat’s claw)

xvii

CHAPTER ONE: INTRODUCTION

In the past 50 years global freshwater use has more than tripled (UNESCO, 2012), and human-driven water demand is only expected to increase in coming decades due to population growth and climate change (Hoekstra, 2006). In fact, humans have already utilized more than

50% of available freshwater resources on the planet (Postel et al., 1996). Safe and accessible water supplies are not available to much of the human population – more than 780 million lack access to fresh drinking water, 2.5 billion lack adequate water sanitation, and 2-5 million (mostly children) die at the hands of preventable water-related disease each year (Gleick, 2002; UN,

2009; WHO/UNICEF, 2012). These growing issues of global freshwater supply and quality are wide-reaching, and impact much of the world. Water scarcity, a principal global water challenge that takes place when water demand nears or exceeds available freshwater supply, impacts every continent in the world (Cooley et al., 2014). Nearly 20% of the global population (1.2 billion people) live in regions impacted by water scarcity imposed by water withdrawals for agricultural, industrial and domestic use, and an additional 1.6 billion people live in regions of economic water scarcity, a phenomena that occurs when water is available but financial limitations prohibit access (IWMI, 2007). Water quality, an issue exacerbated by population growth, expansion of the agricultural and industrial sectors and climate change, has also been deemed a global water challenge (Cooley et al., 2014). Increasingly poor water quality is a threat to both the health of humans and ecosystems around the world (Palaniappan et al., 2010). Further, climate change will increase the variability of freshwater resource availability (Bates et al., 2008; Donat et al.,

2016) and intensify the global water cycle, making dry regions drier and wet regions wetter

1 (Held and Soden, 2006; Chadwick et al., 2015). Together, these three issues of water supply, water quality and mounting climate change comprise some of the most predominant global-scale water issues.

Freshwater availability is of particular concern in the tropics, as they are home to some of the most quickly urbanizing regions on the planet (Newman et al., 2006). Despite housing over half of the world’s renewable freshwater resources, almost half of the population is already vulnerable to water stress (James Cook University, 2014). The tropics are dominated by ‘humid tropical’ and ‘arid/semi-arid’ climates, including some of the driest regions in the world (Chen and Chen, 2013). Water-limited environments (where annual precipitation is lower than annual plant water demand) such as these arid/semi-arid regions in the tropics are characterized by low and extremely variable precipitation, sensitivity to environmental change and potential for catastrophic change (Breshears, 2005). Although variable in their geology and vegetation, these regions are particularly susceptible to climate change-related impacts, especially those related to increased freshwater stress (Parry et al., 2008). Examples of hydrologically-related climate change impacts include decreased fog water input (particularly in the dry season) (Johnstone and

Dawson, 2010), substantial shifts in regional rainfall patterns (Chadwick et al., 2015), and invasive woody plant encroachment (Caldeira et al., 2015; van Kleunen et al., 2015). When such impacts manifest in tandem, understanding how they interact to determine future freshwater availability is of critical importance.

Such issues related to freshwater availability are particularly pressing in tropical islands.

Over half of inhabited tropical islands in one particular study (43 islands total) are already facing some degree of freshwater stress, and this issue is predicted to worsen if climate change continues along its current course (Holding et al., 2016). This stress is particularly prevalent

2 because most freshwater in small islands is held in groundwater reserves, whose resource availability is partially determined by the interactions between island geology, topography, and transport dynamics in the critical zone, or the multi-layered interface between groundwater and the atmosphere. Changes in precipitation seasonality, mean annual precipitation (MAP), evapotranspiration (plant water demand) and runoff can easily lead to the depletion of these groundwater stores (Ault, 2016).

Fog, or stratus cloud that resides at the earth’s surface, is a key precipitation input in many island ecosystems (Croft, 2003; Bruijnzeel et al., 2005; Gultepe et al., 2007; Scholl et al.,

2011; Koracin et al., 2014; Scholl and Murphy, 2014), and has proven to provide much-needed moisture to otherwise dry or seasonally dry ecosystems (Bruijnzeel et al., 2005; Scholl et al.,

2011). Current climate change predictions suggest that higher air temperature will lead to an increase in cloud base height, which will move fog cover upward in elevation; this phenomenon has been termed cloud lifting (Pounds et al., 1999). This implies that many ecosystems worldwide that rely on fog as a water input (e.g., montane cloud forests, coastal regions, islands) are threatened by the impending decrease in fog cover, cloud water interception and thus total precipitation, and may experience a sudden loss of an important source of critical zone water

(Pounds et al., 1999; Still et al., 1999). Unfortunately, decline in fog cover over recent decades has been demonstrated in certain regions such as coastal California, Eurasia, South America and

China (Warren et al., 2007; Johnstone and Dawson, 2010; Williams et al., 2015; Klemm and

Lin, 2016). The effects of such decline have already manifested; for example, a 46% decrease in the number of winter fog days in the Central Valley of California since 1981 has had a negative economic impact on fruit and nut production in the most fertile valley in the world (Baldocchi and Waller, 2014; Zenovich, 2017). Such studies demonstrating the devastating effects of fog

3 decline clearly underline the necessity to better understand fog dynamics as a whole.

In this dissertation, I present my investigations of fog water dynamics across different hydroclimatic conditions and spatiotemporal scales. The general objectives of this study were: 1) to trace fine-temporal scale mechanisms driving fog formation, evolution and dissipation 2) to assess the overall contribution of fog to different microclimatic zones and under different hydroclimatic regimes and 3) to disentangle how fog travels through the ecosystem, and specifically how native versus invasive flora utilize fog water under different regimes of water availability. This information is critical in projecting water resource availability in seasonally water-stressed ecosystems under potentially drier conditions imposed by future regimes of climate change.

CHAPTER TWO: CHARACTERIZING FOG INUNDATION ALONG A TOPOGRAPHIC GRADIENT ON A TROPICAL ISLAND WITH GROUND-BASED CAMERAS

Introduction

Fog deposition is a significant source of water in the overall water budgets of many seasonally dry tropical ecosystems (Cavelier et al., 1996; Rhodes et al., 2006; Liu et al., 2007;

Scholl et al., 2007). Further, many plants in these tropical systems and particularly in islands where freshwater is particularly limited, are known to rely on fog as a water source (Goldsmith et al., 2012; Gotsch et al., 2014a; Fu et al., 2015). One study in Hawaii, for example, demonstrated the high capacity of shrubs in foggy, windward-exposed sites to intercept cloud water via stemflow (Takahashi et al., 2011). Other studies have suggested that direct uptake of cloud water from the leaf surface or a potential mixture of stemflow and foliar uptake contribute to a plant’s capacity to intercept cloud water (Pryet et al., 2012; Gotsch et al., 2014b). Despite the clear significance of fog in tropical ecosystems, it is often one of the most poorly quantified components of the water balance (Takahashi et al., 2011).

Fog is a boundary layer phenomenon, as its formation, evolution and dissipation are largely dictated by surface processes such as: radiative cooling, humidity, wind speed, turbulent mixing, heat and moisture at the surface, surface cover (vegetation, soil, etc.), vertical and horizontal wind, among other factors (Bruijnzeel et al., 2005). Many of these surface factors are governed by seasonal mesoscale and synoptic-scale climate patterns, and the mechanisms resulting in fog formation vary widely, even across small spatial scales (Kaseke et al., 2017a,

2017b). Three principal fog types in island ecosystems, distinguished via their formation

5 mechanisms, are advection fog, orographic fog and radiation fog. Advection fog occurs when clouds generated over the ocean are transported from the wind to the coast (Bruijnzeel et al.,

2005). Orographic fog, a more local phenomenon, occurs when warm, moist air mass is forced upwards by topography, undergoes adiabatic cooling and condenses when cooling is sufficient for the air mass to reach its dew point (Petersen et al., 2015). Radiation fog, the most local-scale of the three fog types, occurs principally via radiative cooling of stratus cloud tops, which can destabilize the boundary layer via turbulent mixing and bring moister air from above to form fog via fluxes of heat and moisture at the earth’s surface (Bruijnzeel et al., 2005; Gultepe et al.,

2007). Radiation fog in particular is known to form less frequently over smooth (soil, concrete) than rough terrain (Gultepe et al., 2007), as the fluxes of water from the canopy play a large role in creating fog in many situations (Azevedo and Morgan, 1974; Dawson, 1998).

Satellite remote sensing is one of the principal methods used to understand spatiotemporal dynamics of fog inundation in a given region (Gultepe et al., 2007; Koracin et al.,

2014). Differences in particle size, particle density, height, and texture allow fog to be distinguishable from other clouds in satellite imagery (Cermak and Bendix, 2008; Wen et al.,

2009; Liu and Lu, 2011). Sun-synchronous satellites such as AVHRR and MODIS have been used to detect fog (Hunt, 1973; Eyre et al., 1984; Bendix et al., 2005; Wen et al., 2009; Schulz et al., 2016), but are limited by low temporal resolution and the potential for obstructed views of fog by higher clouds. Geosynchronous satellites such as MSG SEVIRI and GOES have also been used to study fog and boast higher temporal resolution than their sun-synchronous counterparts

(Ellrod, 1995; Cermak and Bendix, 2011; Iacobellis and Cayan, 2013), but are still limited in their inherent inability to directly detect fog base height and spatial resolution that often cannot capture site-specific fog dynamics (Schulz et al., 2012).

6 Tropical islands are one of the places on earth where identifying diurnal and topographic variability in fog water deposition is crucial. Many tropical islands face particularly acute freshwater stress because of seasonally-variable rainfall patterns that will likely persist or be exacerbated under future climate change (Sobel et al., 2011; Chadwick et al., 2015), extreme precipitation regimes imposed by events such as ENSO (Holding et al., 2016; Permana et al.,

2016; Martin et al., 2018) and high moisture demand by plants (Ault, 2016; Karnauskas et al.,

2016). It is therefore unsurprising that many tropical island ecosystems are heavily reliant on fog as a water input, with fog contributing up to 75% of the overall water budget in certain tropical regions (See Review by Bruijnzeel et al., 2011).

Examining fog dynamics in smaller regions of interest such as tropical islands requires data with higher spatial and temporal resolution than is possible via most satellite-based fog detection schemes. Thus, alternative methods such as those utilizing ground-based optical cameras, have been developed to track fog (Bendix et al., 2008; Bradley et al., 2010; Schulz et al., 2014, 2016; Wen et al., 2014; Bassiouni and Scholl, 2015). In one study, a black and white

(B/W) webcam was placed above the cloud layer in Southern Ecuador and fog was identified via manually defined brightness thresholds in the images (Bendix et al., 2008). A digital elevation model (DEM) was then projected to the view of the camera to identify the contact points between the terrain and cloud surface (cloud top height). This analysis was novel in that it was one of the first to utilize a simple ground-based webcam system to track fog, but it was limited in that it required a lot of manual classification on the part of the user. Another ground-based camera study also used B/W imagery to determine three levels of fogginess based on empirically-derived grey histogram thresholds (Liu and Lu, 2011). While this analysis allowed for more than one level of fogginess to be derived, this study was limited in that B/W imagery is

7 not suitable for variable lighting conditions/viewing geometries and required many calculations to account for these differences during the study period. Following the advent of B/W ground- camera methods, the use of RGB (color) cameras for fog detection became more prevalent.

Many RGB, ground-based camera methods for fog detection are premised on Koschmeider’s

Law (Middleton, 1952), a theory on the apparent luminance of objects observed against background sky. While studies utilizing this theory have been novel in that they can detect fog immersion with a high degree of accuracy, the methods associated with its inclusion in fog detection algorithms are quite complex (Hautiere et al., 2006; Choi et al., 2014).

One particularly novel ground-based fog detection method was implemented in Puerto

Rico and, more simply than those studies integrating Koschmieder’s Law, was based on the concept of light propagation through fog against a vegetated background at a close range

(Bassiouni et al., 2017). In the presence of fog, the reflected visible light from vegetation must be propagated through the fog. Diffusion and adsorption occur in this medium, modifying the spectral signature of vegetation seen from the camera and thus providing signals to detect the presence and abundance of fog (Hautiere et al., 2006). This study utilized four cloud-sensitive image characteristics (CSICs) for small image regions: contrast, coefficient of variation for each pixel, entropy of luminance of each pixel, and image colorfulness. From this, nighttime and daytime classification of foggy vs. not foggy conditions (binary outcome) can be distinguished with 80-94% accuracy for both daytime and nighttime images. This method was superior to previous methods because moving vegetation and lighting changes are accounted for without a reference clear image, threshold value determination, or human calibration and the method shows promise for generalizable application across similar remote sites. However, the method is

8 complex, not completely automated, and doesn’t account for degree-of-fogginess (e.g. light fog versus heavy fog, which is significant in determining ecosystem freshwater availability).

Here, we propose a simpler ground-based fog detection scheme that builds directly upon that of Bassiouni and colleagues ( 2017) to assess patterns of fog in the island ecosystem of

Ascension Island, UK. The objectives of this study were 1) to develop a simple and generalizable model to predict degree-of-fogginess at a given elevation 2) to assess how meteorological drivers of fog vary by topographic position; and 3) to evaluate how fog cover varies diurnally during the foggy/dry season. This type of spatiotemporal characterization of fog is necessary to better understand the role of fog across various tropical island ecosystems.

Methods

Study site

This study was conducted on Ascension Island, an isolated peak on the tropical mid-

Atlantic ridge, almost equidistant between South America and Africa (-7.94º S, -14.37º W)

(Figure 1). Ascension exhibits a steep microclimate zonation over a dense spatial scale (Duffey and Duffey, 1964). Mean annual precipitation (MAP) varies along steep topographic gradients, ranging from < 200 mm in the arid lowlands to ~700 mm in the humid highlands (Ascension,

2015a). Ascension exhibits distinct interannual variability in total annual precipitation, and demonstrates great spatial variability of rainfall brought on by very localized precipitation events. Interestingly, Ascension also boasts the only completely manmade cloud forest in the world (Ashmole and Ashmole, 2000; Wilkinson, 2004).

Ascension also has distinct seasons -- the dry/foggy season spans from mid-August to

December (MacFall, 2005), but fog persists in the Green Mountain highland region throughout much of the year (Catling and Stroud, 2013). Fog occurs along Breakneck Valley along the

9 windward side of the island and up to the highest elevations (~860 MASL) much of the year, but the most dense fog occurs in the dry/foggy season (August – December) (Duffey and Duffey,

1964).

Several local and mesoscale processes lead to the formation of fog on Ascension. The

Ascension region in the SE Atlantic is characterized by a consistent deck of stratocumulus clouds, capped by a low-level inversion generated via widespread subsidence (Krishnamurti et al., 1993; Garstang et al., 1996; Moxim and Levy, 2000; Painemal et al., 2015; Brown, 2016).

Ascension experiences near-constant SE trade winds below the trade wind inversion (TWI)

(Stein et al., 2015; Rolph, 2016) that exhibits little diurnal variation (Brownlow et al., 2016).

The strong trade wind inversion prohibits vertical cloud development above the boundary layer and thus ensures generally low rainfall (Rowlands, 2001). These clouds below the boundary layer are maintained via turbulent mixing and longwave cooling at the cloud top, and are largely maintained via moisture supply from the ocean and atmospheric entrainment of dry air

(Bennartz, 2007).

The SE trade winds supply Green Mountain with moisture to sustain the only completely man-made cloud forest in the world (Wilkinson, 2004). This low cloud cover on Ascension plays a significant role in the local hydrologic cycle, and annually averages ~49% daytime cover

(Hahn et al., 2003). Historical data suggests that mean low cloud base height is below the peak of Green Mountain (859 MASL) during much of the dry season, indicating that fog water contribution potentially provides an important source of water in the foggy/dry season (Figure 2).

On Ascension, even high-spatial and temporal resolution satellite-based fog detection schemes do not indicate clear historic cloud cover patterns.

10 Seasonal or interannual patterns of cloud cover at an island-wide scale have never been sufficiently established on Ascension Island (Wilson and Jetz, 2016), suggesting high spatial heterogeneity of cloud cover on the island. This uncertainty highlights the necessity to develop a finer temporal method to monitor cloud cover on Ascension under future regimes of climate change where cloud base height could increase (Pounds et al., 1999; Still et al., 1999) and a potential primary source of water for Ascension cloud forest plants could disappear (Ascension,

2015b). Such analysis, while spatially concentrated, could serve to further the knowledge of the role of fog/low clouds in the tropics and subtropics, regions that currently exhibit disagreements within and between climate models, satellite observations and in-situ observations (Trenberth and Fasullo, 2009; Dim et al., 2011; Eastman and Warren, 2013; Dolinar et al., 2015; Norris et al., 2016).

In-situ, camera-based data collection

Daytime images were recorded at three different elevations using Bushnell Trophy

Cam™ trail cameras, from the period between September 13th to December 5th 2016. The cameras were installed every ~100 meters in elevation along the windward-exposed Breakneck

Valley path (Table 1 & Supplementary Figure 1). Raw RGB images were captured and archived at 30-minute intervals. Exposure time and white point were adjusted for each camera following

(Bassiouni et al., 2017) to get maximum contrast for each image. Each camera’s viewing direction was determined using high-spatial resolution QuickBird imagery to ascertain a field-of- view (FOV) representative of fog cover (facing into the valley) at each elevation (Appendix 1).

The viewing axis of each camera was pointed directly at nearby vegetation (<10 m away), so that cloud immersion in the camera FOV is directly representative of fog cover at that height. The altitude of one camera facing vegetation >10 m away (BV6) was adjusted using a DEM to reflect

11 the actual altitude of fog immersion. While each camera could be slightly rotated when the SD card is replaced every month, we trace fog only over specifically delineated vegetation features in each camera’s respective FOV that remain constant over time (Supplementary Figure 2).

Image preprocessing

To prepare images for analysis, a few steps were taken to pre-process each batch of images. All image processing was performed in R statistical software (RStudio v. 1.1.383). First, the date/time stamps were extracted from each image from EXIF (Exchangable Image File) metadata using the exifr package (Dunnington and Harvey, 2017). Sunrise and sunset times were then computed for each day in the study period using the StreamMetabolism package (Sefick Jr.,

2016); all images taken before sunrise and after sunset on each day of the study were discarded and excluded from analysis so as to only include daytime images. Then, all images that were triggered and not set on the timed interval (every 30 minutes) were discarded. Any daytime images that were taken in greyscale mode due to low-lighting conditions were identified via equal DNs in the R, G and B bands and discarded.

The Normalized Difference Fog Index (NDFI) & Degree-of-Fogginess Model (DOFmodel)

To derive the degree-of-fogginess from the images, we developed a spectral measure based on the optical color images. The biophysical basis of the measure is the spectral signature of green vegetation in the visible spectrum, i.e. higher reflectance in the green wavelength and lower reflectance in the red wavelength, as red light is strongly absorbed by green vegetation. In the presence of fog, the contrast in reflectance between green and red light diminishes (Figure 3).

Therefore, we propose a spectral index based on the digital values recorded by the camera in the green and red light, the Normalized Difference Fog Index (NDFI) (Equation 1) where:

�� = (1) ()

12 To quantify the degree-of-fogginess via the NDFI, we followed a number of image processing steps using the Phenopix package (Filippa et al., 2017). First, we took images from each camera and drew a region of interest (ROI) on a specific vegetation feature to exclude areas in the image with less distinctive spectral signature (Supplementary Figure 2). In designating the

ROI for each camera, parts of the vegetation that were subject to move with heavy winds or rain and expose background sky, which could lead to an over-estimation of fog immersion, were excluded. We then applied NDFI to each pixel of each ROI in each image using the Phenopix package. For each image, we discarded negative NFDI values (~2% of values that were faulty, as healthy vegetation should never appear more red than green). From this subset of NDFI values, the NDFI median and NDFI standard deviation were then computed for the ROI in each image.

We then developed a simple model to relate the degree-of-fogginess (DOFmodel) in each image to the NDFI median and NDFI standard deviation within the ROI of each image as (Equation 2):

DOFmodel = NDFImedian + NDFIstd dev (2) where NDFImedian of each ROI was deemed a robust measure of central tendency, as it is resistant to outlier pixels. Standard deviation was included in the model to account for variation of degree- of-fogginess. DOFmodel is highest under clear conditions and lowest under dense fog conditions.

Validation of degree-of-fogginess model (DOFmodel)

DOFmodel was first validated via a visual evaluation of a random subset of images from

BV8. DOFmodel was further validated via the Active Remote Sensing of Clouds Product Using

KA-band ARM Zenith Radars (KAZRARSCL) from the Department of Energy (DOE)

Atmospheric Radiation Measurement Program (ARM) Ascension Island (AMF1) site, located below the base of the Breakneck Valley path at ~341 meters above sea level (MASL) (Johnson et al., 2016). This value-added product merges KA-band radar data with a cloud mask generated

13 via micropulse lidar (MPL), cloud base from the ceilometer and information from radiosonde soundings, rain gauge and microwave radiometer measurements to provide a best estimate of the lowest cloud base height (Clothiaux et al., 2000). Observations of lowest cloud base height from this datastream were extracted on the half hour and compared to DOFmodel observations at all three elevations to validate DOFmodel. Cloud-base heights retrieved from KAZRARSCL were used as the standard estimate of the lowest cloud base height. For each of the three cameras in the Breakneck Valley (BV3, BV6, and BV8), a fog immersion binary outcome was created with value 1 if the detected cloud base was less than or equal to the altitude of the camera (576m,

720m, and 806m respectively). Otherwise, fog immersion was set to 0. First, mean DOFmodel values were calculated for both fog-immersed and non-fog immersed conditions to evaluate the efficacy of DOFmodel. Second, a logistic regression model was run and an odds ratio scale utilized to determine if DOFmodel is a suitable method to determine degree-of-fogginess. The predictors in the logistic regression model were DOFmodel, camera location treated categorically, and the interaction of DOFmodel and camera location. Taken together, these three validation exercises allowed us to determine whether or not it was appropriate to utilize DOFmodel as a direct proxy for degree-of-fogginess in the random forest model.

Predicting degree-of-fogginess via a random forest model

We extracted in-situ weather data from the DOE ARM AMF1 site, located below the base of the Breakneck Valley path at ~341 MASL (Cook and Sullivan, 2016; Kyrouac and

Springston, 2016). Descriptions of the instrumentation deployed at the AMF1 site are described in other studies (Miller and Slingo, 2007; Mather and Voyles, 2013); only a subset of instrumentation was used in the current study. Each data stream was averaged over thirty-minute intervals corresponding to the time when each photo was taken so that the weather and

14 topography data could be used as predictors of fog occurrence (Kyrouac and Springston, 2016).

The weather variables used included temperature, relative humidity, precipitation, wind speed, atmospheric pressure, dew point, surface soil heat flux, dew point depression, radiative fluxes

(upwelling/downwelling shortwave/longwave radiation and net radiation), leaf surface wetness and an overall calculated surface energy balance term (Wang and Rossow, 1995; Wang et al.,

1999; Guidard and Tzanos, 2007; Obregon et al., 2011; Bassiouni et al., 2017). Dew point depression was calculated following Bassiouni et al. (2017). Other predictive variables included in our random forest model were elevation, time of day and day of year.

While different thresholds of weather variables conducive to fog formation have been established, they often do not translate beyond a single site. For example, the minimum relative humidity threshold used in other models varies across different regions (~87% to 93%) (Wang and Rossow, 1995; Wang et al., 1999; Guidard and Tzanos, 2007). To create a model that predicts degree-of-fog at a given camera location from elevation, time, and weather variables, we utilized a random forest model in the R package randomForestSRC (Ishwaran and Kogalur,

2017). This oft-used supervised machine learning approach flexibly models nonlinear associations and interactions via merging multiple decision trees for an accurate and stable prediction while avoiding overfitting (Fernández-Delgado et al., 2014). Random forest models also boast the capacity to measure the relative importance of each feature in the prediction.

Assessing the utility of DOFmodel relative to meteorological measurements

To evaluate the utility of DOFmodel as an index of degree-of-fogginess, a logistic regression model that uses DOFmodel to predict the binary fog immersion outcome was compared to a model using and local meteorological variables. For this comparison, a random forest classification model was selected due the flexibility and outstanding performance of random

15 forests (Fernández-Delgado et al., 2014). Models were compared using the C-statistic, also known as the area under receiver operating characteristic (Harrell et al., 1982). The C-statistic ranges from 0.5 if the model does no better than chance to 1 if the model has perfect classification performance. The Efron-Gong optimism bootstrap was used to obtain unbiased estimates of out-of-sample classification for the logistic regression model (Efron and Gong,

1983). Out-of-bag (OOB) prediction was used to obtain analogous unbiased estimates of out-of- sample classification performance for the random forest classification model.

Understanding spatiotemporal patterns of degree-of-fogginess

In addition to the random forest model, we utilized Pearson’s R to compare DOFmodel and each meteorological variable to characterize elevation-dependent differences in fog formation mechanisms on Ascension. Seasonally averaged diurnal cycles of fog cover on Ascension were assessed graphically.

Results

DOFmodel as a proxy for degree-of-fogginess

We evaluated the relationship between degree-of-fogginess and DOFmodel with 33 randomly selected color photographs. We calculated DOFmodel for the ROI in each photograph and visually estimated the degree-of-fogginess in each ROI. Figure 4 shows the relationship

2 between visually-derived degree-of-fogginess and DOFmodel, which yielded an R value of 0.68 and a p-value of 4.6E-9. The strong linear statistical relationship between degree-of-fogginess and DOFmodel support the use of DOFmodel as a proxy for degree-of-fogginess in color photographs (Figure 4). This strong linear relationship between degree-of-fogginess and

DOFmodel therefore allows us to use DOFmodel as a proxy for degree-of-fogginess in understanding

16 the spatiotemporal variation of degree-of-fogginess with other commonly meteorological variables. Thus, we use DOFmodel as the independent variable in our random forest model.

Validating DOFmodel as a model for degree-of-fogginess

Across all cameras, mean DOFmodel value for fog-immersed conditions was lower than that for clear conditions (x = 0.06 ± 0.03 for fog-immersed and x = 0.09 ± 0.04 for clear conditions). However, the standard deviation was large between photographs within the same binary “fog-immersed” or “clear” categories.

Then, logistic regression was used to validate DOFmodel as a proxy for degree-of- fogginess. After controlling for site, DOFmodel was found to have a statistically significant negative association with fog immersion, with lower values of DOFmodel being associated higher probability of fog immersion (Table 2). All three sites show this pattern of negative association between DOFmodel and fog immersion (Figure 6). Given the strong correlation between DOFmodel and degree-of-fogginess, we justify the use of DOFmodel as a proxy for degree-of-fogginess in our random forest model.

Understanding fog cover (DOFmodel) through random forest

Our random forest model, a simple and automated algorithm for predicting fog cover using local meteorological variables, elevation, time of day and time of year explained 54.2% of the variance in all cameras combined with an out-of-bag error rate of 9E-4. OOB error rate is representative of prediction error in a random forest model, as it is ascertained using the 1/3 of the data not used to train the random forest model. In comparing OOB predicted DOFmodel with

2 actual DOFmodel, we found an R value of 0.54 and p-value of <2.2E-16 (Figure 5).

During all daytime hours across all cameras considered together, the most important variables in predicting degree-of fog (DOFmodel) were: dewpoint (R = -0.504), downwelling

17 longwave irradiance (R = -0.306) and upwelling shortwave irradiance (R = 0.171). These numbers suggest that dewpoint is a strong metric in determining fog occurrence, as has been found in other regions other regions (Obregon et al., 2011; Bassiouni et al., 2017). The strong negative correlation between downwelling longwave irradiance and DOFmodel suggests that the presence of fog (lower DOFmodel) increases downwelling longwave radiation. Other variables were input into the model, but played a lesser role in predicting DOFmodel at a given time point when all cameras were considered together (see Discussion for assessment of meteorological variables by camera).

Utility of DOFmodel relative to meteorological measurements

The random forest model achieved a C-statistic of 0.97, indicating near perfect classification. However, this model had 22 local meteorological variables as predictor variables.

In contrast, the logistic regression model only included DOFmodel, camera location treated categorically (i.e. dummy variables for BV6 and BV8), and the interaction of DOFmodel and camera location. This logistic regression model attained a C-statistic of 0.73. While the logistic regression model did not perform as well as the random forest model, it used a much smaller set of predictors, generated easily interpreted regression coefficients on the odds ratio with acceptable performance.

Diurnal- and elevation-based assessment of fog cover: relevant meteorological parameters in predicting fog persistence in near-real time

DOFmodel was applied to three sites ranging from 576-806 MASL along the Breakneck

Valley path in Ascension Island from September to December of 2016. Daytime temporal and elevation patterns were assessed across each of the three cameras. Across all cameras, there is a clear diurnal pattern of fog immersion, but said pattern differs by camera/elevation (Figure 7a).

18 At BV3 (576 m, Supplementary Figure 2), fog cover peaked after sunrise into the late morning, dissipated throughout the afternoon and increased again through the early evening

(Figure 7a). BV3 fog cover exhibited a strong negative correlation with dewpoint depression and especially so during midday hours (Table 3). There seemed to be little seasonal variation at BV3, irrespective of time of day (Table 3). During morning/evening hours, there was a strong positive correlation between fog cover and net radiation/downwelling longwave radiation in particular

(Table 3). During midday hours, all radiative fluxes, surface soil heat flux, time of day and wetness were all significantly correlated with fog cover (Table 3).

At BV6 (720 m, Supplementary Figure 2), average fog cover was fairly consistent throughout daytime hours but was lowest in early morning and early evening (Figure 7a). BV6

DOFmodel exhibited a very strong negative correlation between dewpoint depression and fog occurrence across all times of day (Table 3). In the mornings/evenings, DOFmodel was strongly positively correlated with downwelling longwave radiation, and in the middle of the day downwelling longwave and shortwave radiation, net radiation, surface soil heat flux and wetness were all significant (Table 3).

At BV8 (806 m, Supplementary Figure 2), degree-of-fogginess was highest in the early morning and evening hours and lowest in the mid to late morning hours (Figure 7a). In both the mornings/evenings and midday hours, time of day was significantly correlated with fog cover.

Day of year was also significantly correlated with fog cover, but most so in the mornings/evenings (Table 3). In the mornings/evenings, the most significantly correlated variables with fog cover were (in order of significance): day of year, time of day, downwelling longwave radiation, wetness, and surface soil heat flux (Table 3). In the midday hours, the most significantly correlated variables with DOFmodel were (in order of significance): downwelling

19 longwave radiation, downwelling shortwave radiation, net radiation, time of day, and wetness/surface soil heat flux (Table 3).

Discussion

Is DOFmodel a suitable proxy for degree-of-fogginess?

The robust correlation between DOFmodel and visually-derived degree-of-fogginess

(Figure 4) serves as a proof-of-concept that DOFmodel derived from the color photographs is a suitable proxy for degree-of-fogginess. This relationship was further validated via logistic regression, in which there was a strong negative association between DOFmodel and degree-of- fogginess across all cameras, which is what we would expect given that we designed DOFmodel to increase as degree-of-fogginess decreases. In tandem, this data suggests that DOFmodel is a simple and generalizable model suitable for predicting degree-of-fogginess at a given elevation. Thus, we use DOFmodel as a proxy for degree-of-fogginess in our random forest model. We demonstrate that DOFmodel presents an attractive alternative to prior methods to detect degree-of-fogginess in regions where meteorological measurements are costly or difficult to obtain.

Our study also demonstrates the efficacy of using a simple random forest model in predicting degree-of-fogginess. First, the OOB error rate was low and the R2 value of the random forest model was significant (Figure 5). The value of using a random forest model to predict degree-of-fogginess was further validated via the mean DOFmodel values of cloudy versus non- cloudy images (determined as such via ground-based instrumentation that measured cloud base height). The significant difference between the means of the cloudy and non-cloudy groups further supported the utility of the random forest model. Finally, the random forest model achieved a near-perfect C-statistic, demonstrating its high classification capacity. Considered together, our data suggests that simple models such as random forest and logistic regression can

20 be used in tandem with our simple and generalizable index (DOFmodel) to predict degree-of- fogginess.

Do fog formation mechanisms vary by elevation?

Across all cameras, dewpoint depression was the single strongest predictor of DOFmodel, which is to be expected given that temperature and relative humidity conditions are known to play a significant role in fog formation (Wang and Rossow, 1995; Wang et al., 1999; Guidard and Tzanos, 2007; Obregon et al., 2011; Bassiouni et al., 2017). However, the other variables predicting degree-of-fogginess in our model varied between cameras (Table 3). This suggests that different meteorological conditions favor fog formation at different elevations.

Over all cameras, the diurnal course of degree-of-fogginess was clearly related to the diurnal course of dewpoint depression, i.e. the difference between air temperature and dewpoint

(Figure 6b). It is clear that generally, a higher dewpoint depression during daytime is correlated with reduced fog cover in the dry study season. However, we note that our meteorological data from a single site located below the base of the Breakneck Valley path at ~341 m, so any lags between maximum dewpoint depression and minimum fog cover are likely a direct result of the difference in elevation between sites. Further, we acknowledge that microclimates likely differ between sites of varying elevation and this meteorological data was intended to be used as a general characterization of site conditions. Future studies could investigate using our method on sites with more local meteorological data.

Fog cover at BV3 was negatively correlated with net radiation during sunrise/sunset hours in particular (Table 3), suggesting that an increase in radiation led to the perpetuation of fog during the morning/evening hours. Such a pattern suggests the presence of radiation fog via longwave radiative cooling (Pilie et al., 1975; Bruijnzeel et al., 2005). Other studies suggest that

21 radiation fog forms again before sunset (Whiteman and McKee, 1982), and fog reached a local maximum at BV3 around 17:00 (Figure 7a), supporting the hypothesis that radiation fog was taking place there. Further evidence for radiation fog at this site is that it is located near the base of a valley on Green Mountain – radiation fog is known to form in steep valleys such as this one in particular (Pilie et al., 1975; Bruijnzeel et al., 2005; Liu et al., 2006). In summation, the diurnal pattern exhibited by BV3 suggests that radiation fog was likely a principal mechanism generating/clearing fog at these elevations, as high morning fog cover followed by rapid clearance with increased solar radiation during the daytime and higher cover in the early evening is characteristic of radiation fog (Obregon et al., 2011).

Fog cover at BV6, located atop a ridge above the valley on the windward side of Green

Mountain, exhibited a different diurnal pattern than BV3. At BV6, fog was consistently most dense in the midday hours (Figure 7a). This diurnal pattern of consistent fog cover even in the times of day in which net radiation is particularly high suggests that a possible combination of orographic and advection fog events took place at this elevation (Cereceda et al., 2002; Westbeld et al., 2009; Lehnert et al., 2018). Further evidence for advection fog formation is the low sea surface temperature in the general region SE of Ascension Island relative to the land surface temperature. Further evidence for orographic fog at this location is the topographic location of the camera on the windward-exposed side of the island. Together, this information preliminarily suggests that a combination of advection and orographic fog was likely the dominant mechanism generating fog at this location.

Fog cover at BV8, the highest camera on the windward side of Green Mountain, exhibits the highest level of fog cover in the early morning and evening hours. This diurnal pattern suggests that radiation fog likely contributed to the deposition of fog in this area. However, its

22 topographic position, evidence of wind-driven fog in the field, and high volume of fog water input (Schmitt, unpublished data) suggest that orographic and advection fog likely contributed as well. Therefore, we suggest that advection, orographic and radiation fog all contributed to overall fog water input at BV8. Future work should focus on further disentangling fog formation mechanisms by elevation.

Conclusions

In this study, we create a simple and generalizable model (DOFmodel) to predict degree-of- fogginess from ground-based optical cameras. We suggest that DOFmodel derived from color photographs is a suitable proxy for degree-of-fogginess, and in doing so develop the first ever non-binary fog classification system using in-situ cameras. We suggest that DOFmodel presents an attractive alternative to prior methods to detect degree-of-fogginess in regions where meteorological measurements are costly or difficult to obtain. Our data also suggests that simple models such as random forest and logistic regression can be used to predict degree-of-fogginess with commonly measured meteorological variables. Finally, we suggest that our random forest model that predicts degree-of-fogginess from ground-based cameras and weather variables can be translated to predict degree-of-fogginess in other remote ecosystems.

We also found that dewpoint depression was the single strongest predictor of fog cover, but the correlation between fog cover and other meteorological variables vary by elevation. This suggests that fog formation mechanisms likely vary by elevation even along a relatively short transect. In assessing these variables with the diurnal patterns of fog cover at different elevations, we suggest that in the lower valley, radiation fog was the dominant fog type and in the higher elevations, advection/orographic fog were likely the dominant fog types. This analysis of fog formation mechanisms on Ascension Island warrants further investigation via next-generation

23 high-resolution satellites that have been launched since the conclusion of this study (i.e. GOES-R

ABI), as different types of fog could become less prevalent under future climate change.

24 Tables and Figures

Table I. Cameras used to detect cloud immersion along Breakneck Valley Path, Ascension Island. Camera/Label (in Appendix 1) Location Description Altitude (m) BV3 In dense pines 576 BV6 Facing valley just below catchment 720 BV8 Just above fog collectors 806

25 Table II. Parameter estimates and 95% confidence interval on the log odds ratio scale for the logistic regression model estimating fog immersion as a function of DOFmodel and camera site. Estimate Lower CL Upper CL p-value Intercept 1.57 1.34 1.81 <2.00e-16 DOFmodel -9.71 -12.37 -7.14 3.06e-13 BV6 2.07 1.65 2.50 <2.00e-16 BV8 1.80 1.40 2.21 <2.00e-16 DOFmodel * BV6 -11.77 -16.04 -7.54 5.53e-08 DOFmodel * BV8 -1.83 -5.54 1.91 0.33 Nagelkerke R2 = 0.168, N=5474

26 Table III. Pearson’s R values of DOFmodel at three different elevations along Breakneck Valley Path in relation to meteorological parameters for time frames encompassing all daylight hours (7:00 – 18:30), morning/evening hours (7:00 – 9:00 & 17:00 – 18:30) and midday hours (9:30 – 16:30). Insignificant Pearson’s R values (-0.1 to 0.1) are denoted with an asterisk. Observation period took place from Aug-Dec 2016 (dry, foggy season).

DOFmodel BV3 BV6 BV8 versus: All Morning/ Midday All Morning/ Midday All Morning/ Midday daytime evening (9:30 – daytime evening (9:30 – daytime evening (9:30 – (7:00-9:00 & 16:30) (7:00-9:00 & 16:30) (7:00-9:00 & 16:30) 17:00-18:30) 17:00-18:30) 17:00-18:30) Dewpoint 0.23 0.28 0.32 0.62 0.56 0.75 0.56 0.43 0.61 depression Downwelling -0.15 -0.17 -0.12 -0.43 -0.38 -0.47 -0.30 -0.22 -0.36 longwave radiation Downwelling * * 0.18 0.21 * 0.41 0.25 * 0.29 shortwave radiation Net radiation * -0.20 0.17 * * 0.39 0.22 -0.12 0.28 Surface soil * * -0.27 * * -0.31 * 0.18 -0.14 heat flux Day of year * * * * -0.12 * 0.13 0.27 * Time of day * * 0.26 * -0.15 * -0.128 -0.23 -0.16 Wetness 0.11 * 0.25 0.14 * 0.28 * -0.21 0.14

Table 3. Pearson’s correlation coefficients of fog cover (NDFI) at canopy level at three different elevations along Breakneck Valley Path in relation to meteorological parameters for time frames encompassing all daylight hours (7:00 – 18:30), morning/ evening hours (7:00-9:00 & 17:00-18:30) and midday hours (9:30 – 16:30). Insignificant Pearson’s R values (-0.1 to 0.1) are denoted with an asterisk. Observation period took place from August to December 2016 (dry, foggy season).

Figure 1. Ascension Island location in the South Atlantic Ocean.

Figure 2. Daily noontime low cloud base height during the dry season in Ascension Island, 2015 (METAR, 2015). The dashed line represents the height of the peak of Green Mountain, the elevation below which fog can form.

Figure 3. Conceptual representation of NDFI, where green reflectance is lower in cloudier conditions (created with SketchUp software).

Figure 4. Correlation between DOFmodel and visually classified degree-of-fogginess.

Figure 5. OOB-predicted DOFmodel versus actual DOFmodel, to assess the efficacy of the random forest model.

Figure 6. Marginal effects plot for logistic regression validation model.

33 BV3 (576 m)

BV6 (720 m)

BV8 (806 m) model DOF ) ° Dew Point Depression (C

Time of Day

Figure 7. a) Diurnal pattern of DOFmodel with elevation. The diurnal pattern of DOFmodel was calculated as the average DOFmodel for a given camera over one foggy season (Aug- Dec 2016) for each half hour at each site. Values were omitted for a given camera/time when <70% of the DOFmodel data was available due to low lighting conditions (see Methods for details) b) Diurnal pattern of dewpoint depression with elevation, calculated as average dewpoint depression over one foggy season (Aug-Dec 2016) for each half hour.

CHAPTER THREE: THE ROLE OF FOG, OROGRAPHY AND SEASONALITY ON PRECIPITATION IN A SEMI-ARID, TROPICAL ISLAND1

Introduction

The tropics are concurrently one of the fastest urbanizing regions on the planet and home to the most highly concentrated human populations experiencing water scarcity (Newman et al.,

2006; Mekonnen and Hoekstra, 2016). Although variable in their geology and vegetation, water- limited environments in the tropics are characterized by low and extremely variable precipitation, sensitivity to environmental change and vulnerability to catastrophic change

(Breshears, 2005; Parry et al., 2008; Chadwick et al., 2015). This variability and unpredictable nature of precipitation can lead to issues related to freshwater availability. For example, Holding et al. (2016) found that over half of inhabited tropical islands are already facing some degree of freshwater stress, and this issue is predicted to worsen if climate change continues along its current course. In fact, many tropical islands are more susceptible to the effects of depleted drinking water stores than they are to sea level rise (Karnauskas et al., 2016). The Galápagos

Islands are no exception to the looming threat of water resource depletion; warming of the equatorial Pacific Ocean over the last 150 years (Cobb et al., 2003; Conroy et al., 2009) warrants an increased focus on understanding water resource variability over both event and seasonal timescales.

While rain is a major form of precipitation in the tropics, fog (stratus cloud that resides at

1 This chapter previously appeared in the journal Hydrological Processes. The original citation is as follows: Schmitt SR, Riveros-Iregui DA, Hu J. 2018. The role of fog, orography, and seasonality on precipitation in a semiarid, tropical island. Hydrological Processes 32, 2792-2805, https://doi.org/10.1002/hyp.13228 Special Issue: Stable Isotopes in Hydrological Studies

35 the earth’s surface) is another key precipitation input in many island ecosystems (Croft, 2003;

Bruijnzeel et al., 2005; Gultepe et al., 2007; Scholl et al., 2011; Koracin et al., 2014; Scholl and

Murphy, 2014) and can provide much-needed moisture to otherwise dry or seasonally dry ecosystems (Bruijnzeel et al., 2005; Scholl et al., 2011). Current climate change predictions suggest that higher air temperatures will lead to an increase in cloud base height, which will move fog cover upward in elevation; this phenomenon has been termed ‘cloud lifting’ (Pounds et al., 1999). This implies that many ecosystems worldwide that rely on fog as a water input are threatened by the impending decrease in fog cover and may experience a sudden loss of a critical source of water (Pounds et al., 1999; Still et al., 1999; Hu and Riveros-Iregui, 2016).

Unfortunately, decline in fog cover over the past century has been observed in certain regions, such as coastal California, Eurasia, South America and China (Warren et al., 2007; Johnstone and Dawson, 2010; Williams et al., 2015; Klemm and Lin, 2016); however, absent from these studies are tropical regions, particularly tropical islands. Thus, there remains a need to better understand rain and fog dynamics in these vulnerable ecosystems.

In tropical islands like Galápagos, there are three principal mechanisms that lead to fog formation, and the role of each mechanism in generating fog cover likely varies on both seasonal and event-based timescales. Advection fog, formed by moist air moving over a cooler surface and cooling air below its dewpoint (American Meteorological Society, 2012) has been found in other regions with oceanic upwelling such as the California Channel Islands (Scholl et al., 2011).

It is the most synoptically generated of the three fog types, and its formation is correlated with higher air temperature/sea surface temperature (SST) of the fog source region (Johnstone and

Dawson, 2010). Orographic fog, formed locally and lifted by the trade winds along the windward slope of an island (American Meteorological Society, 2012), has been found in other tropical

36 islands such as Hawaii and Puerto Rico (Scholl et al., 2011). It is most often formed under stronger trade winds and accompanying low-lying atmospheric inversions (Roe, 2005). Radiation fog, on the other hand, is formed when radiational cooling at nighttime reduces the air temperature to or below its dewpoint (American Meteorological Society, 2012b) and has been found in other tropical regions with surface water bodies (Bruijnzeel et al., 2005). In the tropics, it often takes place in the cool season under low wind conditions (Bruijnzeel et al., 2005).

Understanding that these processes can occur individually or simultaneously, and understanding the degree to which each respective fog formation mechanism contributes to the overall water balance of a given island is crucial in understanding freshwater inputs in a given system (Kaseke et al., 2017).

Given the importance of fog as a water source in certain tropical regions, recent studies have focused on quantifying the contribution of this ‘occult’ (hidden) water source. One commonly used method for partitioning precipitation input is stable isotope analysis of oxygen and hydrogen (δ18O and δ2H); because rain and fog are formed under different equilibrium and fractionation processes, the isotopic signature of rain and fog are often different (Scholl et al.,

2011). Isotope theory demonstrates a clear pattern of fog water enrichment relative to rainwater

(Dawson, 1998; Gat, 2005). Under these scenarios, studies have used stable isotope analysis to track different moisture inputs in order to understand the relative contribution of rain versus fog to streamflow and groundwater (Scholl et al., 1996, 2015; Takahashi et al., 2011). Other studies have demonstrated that the utilization of fog drip by plants in subtropical settings was highly variable over space and time (Fischer et al., 2016). However, it can be difficult to quantify fog’s overall contribution to the annual water budget using stable isotopes in trade wind-exposed sites in the tropics, when dry-season orographic rainfall is isotopically similar to orographically-

37 generated fog (Feild and Dawson, 1998; Scholl et al., 2007). Thus, pivotal to this approach is the ability to separate fog and rain isotopically; in other words, these two moisture inputs must differ sufficiently for any type of mixing model to be used.

In recent decades, climate change has already caused increased seasonality and increased

SST in the Galápagos archipelago (Conroy et al., 2009; Wolff, 2010; Liu et al., 2015). Regional models suggest that there is >90% chance of increased rainfall in the region of Galápagos over the 21st Century (IPCC, 2013), but changes in Galápagos may not necessarily reflect regional patterns and critical downscaling is required to better assess potential island-specific changes

(Sachs and Ladd, 2010; Liu et al., 2013). Other global models suggest increased precipitation in the tropics, but also posit that the Intertropical Convergence Zone (ITCZ) could migrate northwards and lead to lower precipitation in the eastern equatorial Pacific (EEP) (Putnam and

Broecker, 2017). Thus, future hydrometeorological conditions in Galápagos remain unclear.

In addition to the seasonality imposed by varying degrees of fog water deposition, the

Galápagos Islands are also subject to extreme hydrometeorological events such as El Niño due to their distinct location along the equatorial Pacific (Atwood and Sachs, 2014). During canonical

El Niño events, the SE trade winds weaken, the thermocline deepens, upwelling declines and the

Galápagos experiences anomalously warm SST (Zhang et al., 2014). Higher SSTs lead to deterioration of the atmospheric inversion layer, triggering convection and precipitation rates 4-7 times higher than average (Trueman and D’Ozouville, 2010; Atwood and Sachs, 2014). La Niña events, on the other hand, lead to anomalously low SST, strengthening of the SE trade winds, and increased subsidence, leading to drastically low precipitation (Trueman and D’Ozouville,

2010). El Niño strength and intensity have doubled in the past three decades (Lee and

McPhaden, 2010), and is expected to continue to do so in the face of global climate change (Cai

38 et al., 2014). This will likely result in both extreme floods (El Niño) and extreme droughts (La

Niña) in the Galápagos region (Power et al., 2013).

To date, there is only one study that has examined the role of seasonal patterns and extreme events the isotopic composition of precipitation in the Galápagos archipelago (Martin et al., 2018). This study found that on one island in the Galápagos archipelago, Santa Cruz Island, precipitation isotopic composition in the highlands was closely linked to precipitation amount on a monthly timescale, but that this effect was much stronger during the wet season. Further, it found that the process of atmospheric convection was a primary driver of precipitation isotopic variability. Here, we examine whether a similar combination of local and regional processes play a role in the isotopic composition of precipitation on the oldest and easternmost island of San

Cristóbal, and how the role of different mechanisms vary on event- and seasonally-based scales throughout the progression of the 2016 El Niño. The overall objective of this study was to characterize the hydrometeorological conditions in the highland region of San Cristóbal Island,

Galápagos across three distinct seasons: dry season (FS1), wet El Niño season (FS2) (one of the strongest El Niño events on record), and extremely dry season (FS3). Specifically, we addressed the following questions: 1) Does fog isotopic composition vary on event and seasonal timescales? 2) What drives the seasonal variability of throughfall isotopic composition? 3) Is the amount effect or moisture source region a more dominant control rainfall isotopic composition? and 4) What are the main drivers of local moisture recycling?

Materials and Methods

Characterizing the Galápagos hydroclimate

The Galápagos Islands are made up of 13 main islands and over 100 smaller islets, and are located ~1,000 km off of mainland Ecuador in the tropical Pacific. In Galápagos, rainfall

39 occurs in two discrete seasons, as tropical Pacific precipitation is largely dictated by variability with both the ENSO and the annual north–south migration of the Intertropical Convergence Zone

(ITCZ) (Figure 8) (Nelson and Sachs, 2016). In Galápagos, the hot/wet season takes place from

January to May when the ITCZ is at its southernmost position. It is characterized by very localized and irregular convective rain events, and rainfall amount is positively correlated with

SST (Trueman and D’Ozouville, 2010). The interplay between the warm climate and the occasional heavy rain events results in high evapotranspiration rates during this season. The cool/dry season takes place from June to December, when the ITCZ is further north. During much of this season, an inversion layer forms, preventing the further rise of moisture-laden air and leading to the formation of frequent fog above approximately 400 meters above sea level

(MASL) (Sachs and Ladd, 2010; Trueman and D’Ozouville, 2010).

A typical cool/dry season is characterized by less rain and more fog input into the ecosystem (Pryet et al., 2012; Percy et al., 2016). In Santa Cruz, it has been demonstrated that fog constitutes 26 ± 16% of the incident rainfall at 650 MASL during the dry season (Pryet et al.,

2012). To date, this is the only study that has attempted to quantify and understand the contribution of fog to the water budget in the Galápagos archipelago. However, this study was limited in that it estimated cloud water interception indirectly via a wet canopy water budget method. To overcome the limitation that cloud water interception varies both spatially and by varying canopy architecture, we build on this prior work by collecting fog outside of the canopy and use stable isotopic methods to distinguish fog from rainfall.

Of the 13 main islands, five are inhabited and are all facing critical challenges related to water supply and quality. This study was conducted on San Cristóbal Island, the most easterly and oldest island in the archipelago (2.5 MY) (Violette et al., 2014). San Cristóbal is a basaltic

40 island that encompasses ~558 km2. El Junco Lake, our primary study site, lies at ~670 MASL atop a caldera of an extant volcano in the highlands of San Cristóbal. Interestingly, it is the only permanent freshwater body in the archipelago (Zhang et al., 2014). The small catchment surrounding the lake (0.13 km2) contributes rainwater to this body of water largely in the wet season of each year (Colinvaux, 1972).

Fog collector design

We modified a Juvik-type fog gauge (Juvik and Ekern, 1978) in light of its prior performance in study sites with similar variable wind shifts to those that take place in Galápagos

(Fischer and Still, 2007; Giambelluca et al., 2011; Holwerda et al., 2011). This gauge is often deployed with an adjustable rain ‘hat,’ which reduces contamination of fog samples by rain. We further modified this design by creating a triple-cylinder mesh collection system in lieu of the commonly used double-cylinder mesh, to increase the surface area of the collection system

(Fischer and Still, 2007). After extensive testing, we concluded that the triple-cylinder mesh was useful in collecting fog-only samples, as it provided the maximum amount of surface area for fog collection while preventing the apparatus from collecting rain (as this increase in surface area allowed us to lower the rain hat). While our rain hat was deemed effective, we acknowledge that contamination of fog samples by rainfall during high-wind rainy periods may have taken place, but this potential issue was unavoidable.

Precipitation collection

Water samples (fog, rain and throughfall) were collected at El Junco Lake (~670 MASL), located in the very humid zone of San Cristóbal Island, Galápagos (Figure 9). Rain and throughfall samples were collected from plastic rain collectors (2706 Jumbo Manual Rain

Gauge, Taylor Precision), co-located with fog collectors around the vicinity of El Junco Lake.

41 These samples were collected over three field seasons (FS) with variable hydroclimatic conditions between June 2015 and June 2016 (Figure 8). Samples were collected every 3-8 days, based on precipitation amount and frequency. Mineral oil was placed in all collectors to prevent evaporative enrichment of the oxygen and hydrogen isotopes. All water samples were collected in airtight 15 mL amber bottles, sealed using parafilm, and refrigerated prior to analysis to prevent evaporation and water loss.

Climate variables

Local climate information was collected from a weather station installed adjacent to El

Junco and logged at 15-minute intervals. Weather station variables used in this analysis include: air temperature/relative humidity (RH) (model CS215, Campbell Scientific Inc., Logan, Utah), precipitation (tipping-bucket rain gauge, Texas Electronics model TE525WS, Campbell

Scientific Inc., Logan, Utah), incoming solar radiation (Apogee Instruments, model CS300

Campbell Scientific Inc., Logan, Utah), soil moisture (model CS616, Campbell Scientific Inc.,

Logan, Utah), and wind speed/wind direction (R.M. Young, model 03002, Campbell Scientific

Inc., Logan, Utah). Local SST was collected on neighboring Santa Cruz Island by the Charles

Darwin Foundation (Foundation, 2017). Regional ENSO indices computed via the optimum interpolation (OI) SST v2 analysis (Reynolds et al., 2002) were also utilized.

Isotope analysis and isotope theory

At the end of each field campaign, all samples were analyzed for 18O/16O (δ18O) and

2H/1H (δ2H) using laser spectrometry (Picarro L2130-I, Picarro Inc., Santa Clara, CA, USA).

Relative abundance of the rarer heavy isotope over the more common lighter isotope was measured in per mil (‰) deviations from Vienna Standard Mean Ocean Water (VSMOW). This

42 relative abundance measure is referred to as delta notation, defined in Equation 3 (and it applies to hydrogen in the same manner):

δ O = − 1 ∗ 1000‰ (3)

The isotopic composition of precipitation in semi-arid tropical regions can vary due to differences in precipitation amount and precipitation source, which change on both seasonal- and event-based timescales. The amount effect, attributable to a combination of regional and local atmospheric processes, has long been recognized as one of the most dominant mechanisms of monthly, seasonal and annual variability in tropical precipitation (Dansgard, 1964; Rozanski et al., 1993; Araguas-Araguas et al., 2000). The amount effect is the progressive rainout of the heaviest molecules first throughout the progression of a storm (Craig, 1961). This Rayleigh distillation process gradually decreases the remaining water vapor 18O and 2H as a storm cloud progressively “rains out”, so that more depleted δ18O and δ2H values are often found during seasons with heavy rainfall (Dansgard, 1964). The seasonal effect, controlled by numerous local and non-local moisture source conditions along with temperature-dependent evaporative fractionation processes, often leads to more enriched δ18O and δ2H values in the summer and more depleted values in the winter on a global scale (Dansgard, 1964; Rozanski et al., 1993;

Feng et al., 2009). This effect has been recognized as a key mechanism dictating seasonal differences in precipitation isotopic composition in the tropics in particular, and has been attributed to thermodynamic processes such as precipitation condensation temperature, moisture origins, transport pathways and raindrop evaporation (Rhodes et al., 2006; Kurita et al., 2009;

Scholl et al., 2009; Permana et al., 2016).

43 To frame our isotope results within a global perspective, we also used the Global

Network of Isotopes in Precipitation (GNIP) network, which has provided monthly values of precipitation isotopic composition (δ2H and δ18O) from stations around the globe since 1961, and has greatly improved our understanding of environmental variables impacting the isotopic composition of global precipitation (Dansgard, 1964). Assessments of global GNIP data led to the creation of the global meteoric water line (GMWL) (Equation 4) (Craig, 1961), defined as:

δH ‰ = 8δO + 10 (4)

Changes in climate and humidity between different regions cause locally-generated meteoric water lines to deviate from the global average (Rozanski et al., 1993). The value of the intercept of the GMWL is controlled by evaporation processes in the major source regions of the vapor (largely controlled by environmental conditions such as: SST, humidity, and wind speed)

(Dansgard, 1964) and the value of the slope is determined by the ratio of equilibrium enrichments (kinetic isotope effects) for δ2H and δ18O, respectively (Rozanski et al., 1993). In this study, we computed an overall Local Meteoric Water (LMWL) with all rainfall values, then subdivided it by season to understand seasonally-variable processes that influence the isotopic composition of precipitation. Fog lines were also computed for each season using only fog values.

The concept of deuterium excess (D-excess) (Dansgard, 1964) better conceptualizes the vertical offset (along the δ2H axis) of a given water sample from the GMWL (Equation 5).

Mathematically, this relationship is defined as:

D (‰) = δ2H – 8 * δ18O (5)

D-excess of precipitation inputs varies due to differences in environmental variables such as ocean temperature/humidity in precipitation source regions, local climate and land cover

44 (Scholl et al., 2007; Pfahl and Sodemann, 2014). In tropical islands, D-excess is tightly coupled with physical conditions of the oceanic source area of precipitation, the prevailing conditions during the evolution/interaction of mixing air masses on the way to the site of precipitation, local climate and local land cover (Merlivat and Jouzel, 1979; Fröhlich, 2001). In tropical regions, higher D-excess has been associated with more locally-derived, trade-wind delivered orographic precipitation (in which water vapor is formed below the atmospheric boundary layer in warmer conditions) (Scholl et al., 2015) and with precipitation from water vapor that originated from re- evaporated rainfall (canopy interception, lakes and falling rain) (Scholl et al., 2007). We calculated D-excess of each sample to understand the role of locally recycled water and fractionation processes that had the potential to isotopically alter our samples (Hoefs, 2009).

Data processing

A combination of chi-squared tests, histograms and residual analyses were used to discard samples that were likely fractionated during the collection/analysis process. Of the 113 samples that were processed, 109 were utilized in this analysis after excluding potentially faulty values. This data was not precipitation-weighted as it is in many studies such as (Wu et al.,

2015), as exact sample interval was not available for all sites/seasons. Linear models and

Pearson’s correlation coefficients were calculated to determine relationships between local and regional climate variables and isotopic values. All water samples were compared within and between seasons to evaluate the relative contribution of fog to the water balance of each island.

Local meteoric water lines were generated for each site and over each field season to determine the source of hydrologic inputs (trade wind-generated fog versus rain) and ANCOVA tests were used to compare the similarity of fog and rain slopes within field seasons. D-excess was also computed for each sample to understand the role of locally recycled water and fractionation

45 processes that had the potential to isotopically alter our samples. All statistical analyses were performed with RStudio 1.0.143 (RStudio, 2015).

Results

Seasonality in Galápagos

In the thirteen months comprising our three field seasons (July 2015 – July 2016), total annual precipitation (P) at El Junco was 2840 mm, of which 29% fell during the wet season (Jun-

Dec) (Table IV). During FS1, the site received more precipitation than a typical dry season, likely due to the progression of the very strong El Niño event. During FS2, there was heavy precipitation that far exceeded precipitaiton in FS1/FS3. The precipitation rate during larger events was considerably high, reaching up to 38.61 mm/hr. In FS3, the site received very little precipitation (Table IV). Light rainfall events (single 15-minute intervals at a rate of ≤1.016 mm/hr) contributed to 35% of the total moisture input in FS3, and provided a consistent (albeit very light) source of water throughout this season; 59% of the days in FS3 received water only from these very light events. The precipitation rate during FS3 never exceeded 3.81 mm/hr.

Wind speed and temperature also ranged between seasons (Figure 8).

Comparing seasonal isotopic values of fog, rain, and throughfall

Studies have found both significant differences in the isotopic values between fog and rain as well as marginal or no differences between fog and rain in tropical regions (Scholl et al.,

2011). Studies have also found differences between fog and rain isotopic composition that varies seasonally, especially in regions that rely on orographic rainfall for some or part of the year

(Scholl and Murphy, 2014). To assess if differences existed at our site and whether this relationship varied seasonally, we compared paired fog/rain isotope values across the three seasons. When plotted in dual δ2H-δ18O space, paired fog/rain events consistently plotted above

46 the 1:1 line (Figure 10). This suggests that across all field seasons and for almost all paired fog: rain samples, fog was typically more enriched compared to rain samples.

Because of the interplay of within-canopy processes such as evaporation, mixing with pre-event water and stemflow contributions, the isotopic composition of throughfall is often more enriched than is gross precipitation collected during the same event (Scholl et al., 2011;

Muñoz-Villers and McDonnell, 2012; Evaristo et al., 2016; Sprenger et al., 2016). Throughfall isotope values in this study often fell close to or above rainfall collected at the same time (Figure

11, Figure 12). In FS2 when lots of high-intensity precipitation took place, throughfall isotopic composition was close to that of rainfall (Figure 11, Figure 12). Throughfall isotopic composition also varied seasonally, and was consistently more enriched in FS3 than in FS2

(Figure 12).

Variability of isotopic composition of water sources (δ2H, δ18O, D-excess)

Precipitation isotopic composition in the tropics often varies seasonally or in response to extreme events (Scholl and Murphy, 2014; Sánchez-Murillo et al., 2016b). To understand intra- and interseasonal variability of precipitation isotopic composition at our site, we assessed seasonal changes in the isotopic composition of precipitation. Rainfall during FS1 was represented by a mixture of sporadic, high-intensity events and consistent, lower-intensity events, with δ18O values ranging between -3.4 and -1.2‰. During the wet season, FS2, rainfall was represented by consistent precipitation events of varying intensity, with δ18O values ranging between -2.8 and -0.5‰. During very dry FS3, rainfall was characterized by sporadic precipitation events that were more isotopically enriched than the other two field seasons, with

δ18O values ranging between -2.4 and +0.89‰. Fog isotopic composition varied intra- and inter- seasonally. Fog samples were most depleted during FS1 and most enriched during FS2 (Figure

47 12). Across all three field-seasons, D-excess values ranged from 3.2 to 21.2‰; D-excess values ranged from 11.9 to 15.9‰ for the moderately dry season (FS1), 3.2 to 12.7‰ for the wet season

(FS2), and 10.9 to 21.2‰ for the very dry season (FS3) (Figure 11). Seasonal differences in the isotopic composition of precipitation are further demonstrated in our seasonal local meteoric water lines (Figure 12, Table V). Between field seasons, there was a clear decline in slope (8.70

– 6.45 – 5.06) and a notable decline in intercept during FS2 (15.40 – 7.33 – 11.26).

To assess the influence of the amount effect on our precipitation values, we examined the correlation between precipitation amount and rainfall δ18O; we found no relationship between rainfall δ18O and precipitation amount on a monthly (R2 = 0.56, p = 0.14) or weekly timestep (R2

= 0.14, p = 0.26) over all field seasons combined. However, we found a strongly positive relationship between rainfall δ18O and precipitation amount on a weekly timestep during the dry season (R2 = 0.83, p = 0.004), but no relationship between δ18O and precipitation amount during the wet season (R2 = 0.03, p = 0.84). To assess the influence of the seasonal effect on our precipitation values, we examined the correlation between local air temperature and δ18O; we found a strong positive correlation between air temperature and δ18O on a weekly timescale (R2 =

0.43, p = 0.03). This suggests a potential manifestation of this temperature/seasonal effect that has been observed in other tropical regions (Scholl et al., 2009).

Discussion

We observed a wide range of hydrometeorological conditions during the three field seasons of study (Figure 8). These seasonal differences were largely imposed by the combined effects of the typical intra-annual migration pattern of the ITCZ and the very strong El Niño event (Table IV). The distinct differences in the hydroclimatic regimes between the two dry seasons (bookending the wet F2, El Niño event) suggests that the wet/dry seasonality typically

48 imposed by the migration of the ITCZ was shifted by the progression of one the strongest El

Niño events on record (NOAA, 2017; Sánchez-Murillo et al., 2016).

Does fog isotopic composition vary on event and seasonal timescales?

Fog is a common phenomenon on San Cristóbal Island, especially during the dry season

(Figure 9). The relative enrichment of fog versus rain found in this study is consistent with what has been observed across other tropical regions such as Hawaii (Scholl et al., 2011) and Puerto

Rico (Eugster et al., 2006). Fog isotope values measured worldwide have a relatively wide range, from -10.4 to +2.7 ‰ in δ18O and -71 to +13‰ in δ2H, and largely depend on the vapor sources and temperature in the study region. In tropical regions subject to trade-wind orographic conditions, the range for fog water isotopic values is -5 to -1.2‰ in δ18O and -17 to -1.3‰ in δ2H

(Scholl et al., 2011). Our observations ranged from -3.4 to +0.89 ‰ in δ18O and -14 to +15.7‰ in δ2H. Because fog is a first-stage precipitate controlled by equilibrium fractionation processes

(Gonfiantini and Longinelli, 1962; Stewart, 1975), our wide range of fog isotopic values appear to reflect differences in fog source region (Kaseke et al., 2016, 2017b, 2017a).

Fog isotopic values in this study were highly variable between events and seasons (Figure

12). One possible explanation for these intra- and interseasonal variations of fog isotopic values is differences in the source region of fog. According to Kaseke et al. (2017), fog derived from oceanic vapor (known as advection fog) in Namibia consistently plots along the GMWL, whereas fog formed from local meteoric water (known as radiation fog) falls along the LMWL and water inputs formed from a combination of two air masses plot between the two meteoric water lines. However, applying this methodology to our study site is difficult because our site is oceanic and not continental; thus, these mechanisms are more spatially concentrated and more difficult to distinguish by looking at fog samples in comparison to LMWLs and GMWLs

49 (Bruijnzeel et al., 2005). Therefore, additional information on conditions under which each mechanism takes place must be considered when classifying fog type.

Of the fog formation mechanisms taking place in Galápagos, the most local mechanism driving fog formation was radiation fog formed on-site (from El Junco Lake or from the canopy), closely followed by orographic fog lifted from the nearby ocean, and finally advection fog formed over the ocean further from the island. These results are in line with other studies that have found radiation fog to often occur during the cool/dry season in the tropics (Liu et al.,

2006) and during lower-wind conditions (Bruijnzeel et al., 2005) (lower wind speeds in

FS1/FS3), further suggesting that radiation fog was likely prevalent during these seasons.

More evidence for radiation fog lies in the similarity of the slope of the fog line during all field seasons. Following (Kaseke et al., 2017a), we reasoned that if the slope of rain and fog values were similar, radiation fog is likely present. We found that during all field seasons, fog and rain lines were not significantly different (ANCOVA p-values FS1: 0.06, FS2: 0.13, FS3:

0.70), suggesting that locally-formed radiation fog likely contributed to overall fog during all seasons but most so during FS3, the coolest and driest season (Figure 8).

The fact that orographic fog formation is tightly coupled with stronger trade winds and accompanying low-lying atmospheric inversions during the dry season suggests that orographic fog was also likely contributing to overall fog deposition in FS1/FS3 (Roe, 2005).

During the wet season of FS2, however, the isotopic data suggest that a combination of local and non-local waters likely contributed to fog formation (i.e., likely a combination of radiation, advection, and orographic mechanisms). The knowledge that advection is associated with high winds in coastal settings (Goodman, 1977; Cereceda and Schemenauer, 1991) further suggests that advection fog was also taking place in FS2, as the winds were highest at this time. In short,

50 our findings suggest that the differences in fog formation mechanisms vary seasonally, and that the relative contribution from orographic, advective and radiative processes contribute to the observed seasonal differences in fog isotopic composition. Nevertheless, future studies seeking to further disentangle these mechanisms via methods such as satellites and ground-based cameras are certainly warranted to better understand how these processes take place in Galápagos

(Cereceda et al., 2002; Bassiouni et al., 2017), as fog formation mechanisms are clearly sensitive to fluctuations in climate.

What drives the seasonal variability of throughfall isotopic composition?

Our results revealed two distinct patterns in the isotopic composition of throughfall.

During the dry seasons (FS1 and FS3), the isotopic composition of throughfall is often similar to or enriched compared to rainfall collected at the same time (Figure 11), whereas during the wet season (FS2), when high-intensity precipitation took place, the isotopic composition of throughfall was closer to that of rainfall. It has been previously shown that the isotopic composition of throughfall samples is determined by the interaction of many complex processes in the canopy (Allen et al., 2017). One such process is the movement of precipitation through the canopy, otherwise known as the selection process. The selection process occurs when a lower proportion of enriched precipitation from the end of a storm (as a result of rainout) remains in the canopy and does not contribute to throughfall (DeWalle, D.R., Swistock, 1994; Brodersen et al.,

2000; Kato, H., Onda, Y., Nanko, K., Gomi, T., Yamanaka, T., Kawaguchi, 2013). Other processes that may occur in the canopy are evaporative fractionation (Allen et al., 2014) and isotopic exchange (Holwerda et al., 2011), imposing a relationship between isotopic spatial variability and throughfall amount. Our results suggest that during the dry season when rainfall is low and fog drip is high, throughfall is more isotopically distinct from rainfall, as observed in

51 previous studies (Allen et al., 2014). However, during the wet season when rainfall is high, throughfall and rainfall are more isotopically similar, consistent with studies in similar ecosystems (Zhang et al., 2010).

Does the amount effect control rainfall isotopic composition?

Our results showed a strongly positive relationship between the δ18O of rainfall and precipitation amount on weekly timescale in the dry season, which is opposite to what would be expected on the basis of the amount effect (Dansgard, 1964; Rozanski et al., 1993; Araguas-

Araguas et al., 2000). No strong negative relationship was found at monthly timescales or across wet seasons. However, at the event scale, the most depleted rainfall δ18O samples in our entire study were following the two days in FS2 with anomalously high precipitation (> 80 mm), suggesting an amount effect during very heavy rainfall events (Figure 11). Thus, the amount effect in its traditional definition is not consistently expressed in Galápagos precipitation, congruent with studies in other tropical regions (Risi et al., 2008; Kurita, 2013; Moerman et al.,

2013), but is biased towards very heavy rainfall, as reported for Galápagos at monthly time scales during the 1997-1998 Niño (Conroy et al., 2016). Our results suggest that, at least for San

Cristóbal, this effect is only manifested at the event time scale and only following high rainfall.

However, the comparatively high D-excess in Galápagos in the drier seasons (Figure 11) and the lower LMWL slopes (Figure 12, Table V) suggest that sub-cloud evaporation is a dominant control on the isotopic composition of rainfall in Galápagos in the dry season

(Yamanaka et al., 2007; Risi et al., 2008; Pang et al., 2011; Kurita, 2013; Conroy et al., 2016).

In semi-arid regions, sub-cloud evaporative processes and isotopic equilibration with enriched vapor below the cloud base when rainfall is lighter is a principal mechanism driving the amount effect. In Namibia and China for example, it was found that low rainfall and short-lived events

52 create conditions that are favorable for isotopic enrichment via sub-cloud evaporative processes

(Pang et al., 2011; Kaseke et al., 2016). Thus, the amount effect appears to take place in

Galápagos, but the dominance of this mechanism in controlling the isotopic variability of rainfall does not necessarily manifest in its traditional definition of rainout.

Does moisture source region control rainfall isotopic composition?

While tropical rainfall may appear to be largely convective, studies in the tropical

Atlantic, northern Australia and the western equatorial Pacific have shown that almost all convective events occur in association with stratiform rainfall (Houze, 1997a, 1997b; Aggarwal et al., 2016), where stratiform rainfall (large-scale, regional systems with low-intensity precipitation) is more isotopically depleted than convective rainfall (small-scale, local systems with high-intensity precipitation) (Aggarwal et al., 2016; Conroy et al., 2016; Sánchez-Murillo et al., 2016b). Enriched rainfall samples from FS2 (Figure 12) suggest that warm El Niño conditions favored intensified convective precipitation regimes, which is further supported by the high rainfall intensity in FS2 that is often associated with convective rainfall. However, during

FS2, rainfall δ18O values exhibited a bimodal distribution, indicating the presence of significant amounts of precipitation from two distinct climate mechanisms – thus, stratiform precipitation also likely contributed to overall precipitation FS2. In FS3, rainfall δ18O values exhibit a unimodal distribution, indicating the presence of significant amounts of precipitation from one distinct mechanism, likely stratiform in origin. In tropical rainfall, there is typically a greater ratio of stratiform rainfall (locally-generated, lower intensity) versus convective (synoptically- generated, higher-intensity), and rainfall should produce a higher LMWL slope, as δ2H values of stratiform rainfall are lower than that of convective rainfall (Kurita, 2013; Conroy et al., 2016).

This could explain the higher LMWL slope in FS1 (Table V), where lower-intensity (likely

53 stratiform) rainfall was a dominant rainfall mechanism. Thus, our results suggest a seasonal shift in precipitation source, where locally-generated stratiform precipitation plays a larger role in

Galápagos rainfall in the dry season, while synoptically-generated convective rainfall plays a larger role in Galápagos rainfall in the wet season. Rainfall source region, therefore, appears to be a dominant control on the isotopic composition of rainfall in Galápagos.

Intra-seasonal variation indicated that precipitation source in a given season can vary on very short timescales. A study conducted in the tropics through the evolution of the most recent

El Niño event found a decrease in D-excess prior to and during the evolution of the El Niño associated with differences in regional moisture transport mechanisms (Sánchez-Murillo et al.,

2016b). Our analysis points to a similar pattern of D-excess decline (Figure 11), suggesting that

Galápagos experienced a similar change in regional transport mechanisms. Similar shifts to lower D-excess with heavier precipitation have been found during monsoonal rains in SW China

(Liu et al., 2005), synoptically-generated storms in Hawaii (Scholl et al., 2002, 2015), and numerous tropical island stations due to rainout in the wet season/with the shift of the ITCZ

(Rozanski et al., 1993). Thus, in Galápagos it is likely that seasonal changes in moisture source region were caused by a combination of the El Niño event and the interannual migration of the

ITCZ. Understanding the different mechanisms driving rainfall formation and how the relative importance of each mechanism varies temporally is significant in understanding water availability under the increased seasonality that is already being observed in Galápagos as a direct result of human-mediated climate change (Wolff, 2010).

What are the main drivers of local moisture recycling?

Precipitation D-excess values can be influenced by both synoptic scale weather across the different seasons, as well as by local evaporative processes (Dee et al., 2011). The position of the

54 ITCZ, for example, is known to drive wet/dry seasonality and this shift can have large influences in the isotopic composition of precipitation and corresponding variability in d-excess (Sánchez-

Murillo et al., 2016a). However, it has also been well documented that high D-excess values are indicative of locally recycled water, or the return of water from the land surface as precipitation

(Pfahl and Sodemann, 2014). Some of the highest D-excess values observed in this study occurred during FS3 (rain D-excess = 17.4‰ ± 2.24; fog D-excess = 16.3‰ ± 2.43), suggesting that locally recycled water was a larger component of precipitation in this very dry season than during wetter periods (FS1 and FS2) (Figure 11, Table 1). The low slope of the LMWL in FS3 also suggests evaporative enrichment of local waters (indicative of recycling), which is characteristic of arid environments (Kaseke et al., 2017b).

Higher D-excess of precipitation is also associated with water vapor that originates from re-evaporated rainfall (canopy interception, lakes and falling rain) (Fröhlich, 2001; Scholl et al.,

2007). Our study site is subject to fog inundation, especially in the dry seasons (FS1 and FS3)

(Pryet et al., 2012), and dense canopy coverage that likely leads to high canopy interception, which then provides the moisture source for evaporation. Our fog isotopic values are consistent with those found in Costa Rica, another TMCF subject to variable trade wind-driven precipitation during the dry season (up to 20‰) posed by ITCZ-driven seasonality (Rhodes et al., 2006). Similar to our findings, the study from Costa Rica found that although there were high fluctuations in event-to-event isotopic signatures, the seasonal signals indicated that evaporated water was more significant during transitional/dry seasons. Furthermore, our D-excess values were also similar to values measured from the windward side of the Hawaiian islands (13-21‰)

(Scholl et al., 2007).

55 D-excess of monthly-averaged rainfall at Bellavista, in the highlands of neighboring

Santa Cruz Island, Galápagos, averaged 10.30‰ ± 1.61‰ (1σ) and ranged from 6.34‰ to

15.5‰ (Martin et al., 2018). These values indicate a lower maximum and a smaller seasonal range than at our site, a site subject to similar regional processes. Thus, we propose that the higher D-excess at our site reflects a combination of denser/higher canopy cover (greater interception/re-evaporation) and evaporation from El Junco Lake. Unlike our El Junco site on

San Cristóbal Island, the Bellavista site on Santa Cruz lacks a large, adjacent freshwater body.

The likelihood of surface water at El Junco contributing to higher D-excess can be seen in the isotopic values of surface water at El Junco during the two dry seasons, where the slopes and intercept become lower than that of the GMWL.

Conclusions

This study presents an assessment of precipitation isotopic variability in San Cristóbal

Island, Galápagos. We present the stark differences in hydroclimatic conditions between three field seasons, and suggest that seasonal differences in local climatic variables such as precipitation amount/intensity were imposed by the combined effects of the typical intra-annual migration pattern of the ITCZ and the very strong El Niño event that manifested in varying degrees of intensity in Galápagos during our study.

We examined the variability in isotopic composition of different precipitation inputs under varying climatic conditions and sought to understand the mechanisms driving seasonal and event-based variability in the isotopic composition of precipitation in Galápagos. Fog is a common phenomenon on San Cristóbal Island, especially during the dry season, and our results showed that fog is consistently enriched compared to co-collected rainfall. This understanding will be useful in tracing different water sources and their fate through the Galápagos ecosystem

56 in future work. Our results also suggested that the relative contribution of fog formed via different mechanisms (orographic, advective, radiation) varies seasonally. This understanding is critical in projecting future freshwater availability in ecosystems that may experience less fog formed via particular mechanisms as a direct result of climate change (Pounds et al., 1999; Still et al., 1999).

Further, we found that rainfall isotopic composition varied intra- and inter-seasonally as a result of combined effects. The amount effect appears to control the isotopic composition of precipitation on the event scale only with very heavy rainfall; however, sub-cloud evaporative processes (the non-traditional manifestation of the amount effect) appeared to be a dominant control on the isotopic composition of rainfall in the Galápagos in the dry season. In addition, we found that the source region is likely the most dominant control of the isotopic composition of rainfall in the Galápagos at both the seasonal and event scales. However, it is important to note that these processes, among others, take place in tandem and are not mutually exclusive, as has been suggested previously for Galápagos (Martin et al., 2018). Future work on the isotopic composition of rainfall via HYSPLIT back-trajectories could suggest specific source regions of precipitation across different seasons and events.

While previous studies have investigated groundwater recharge dynamics (Warrier et al.,

2012), this study provides the first-ever characterization of precipitation inputs on San Cristóbal

Island. While future work can focus on tracing these sources of water through the system, this study disentangles seasonally-variable water-generating mechanisms that are significant in understanding water resource availability on the island and in similar island ecosystems.

57 Tables and Figures

58 Table V. Linear relationship between δ18O and δ2H (see Figure 12 for accompanying graphics).

Figure 10. Fog versus rain oxygen isotopic ratios. Each point represents co-collected fog/rain samples.

Figure 11. Precipitation and D-excess values of precipitation samples at El Junco field site, San Cristóbal Island, Galápagos. Each box represents a FS of study (from L to R: FS1, FS2, FS3). The solid line indicates the global average rainfall D-excess and the dashed line indicates the seasonal average fog D-excess. Note that precipitation data is missing prior to ~day 20 of FS1.

Figure 12. Linear relationship between δ18O and δD of precipitation samples collected on San Cristóbal Island, Galápagos. Isotopic graphs are separated by FS1 (L), FS2 (middle), FS3 (right), and an overall representation of water isotopic data from all seasons. On each graph, the LMWL is displayed in orange, the GMWL is displayed in black and the fog line is displayed in a dashed line. Each line represented has an accompanying equation in Table V.

CHAPTER FOUR: GUAVA, EXPERT INVADER

Introduction

Invasive species have the capacity to profoundly alter the structure and function of the ecosystems they invade, and often ultimately result in the loss of native species and ecosystem diversity. The effects of such alien species have been described as “immense, insidious and usually irreversible” (IUCN, 2000), and annual global economic losses due to species invasions are more than an order of magnitude higher than all natural disasters combined (estimated to be

~1.4 trillion/year) (Ricciardi et al., 2011). The effects of plant invasions are especially concerning in their potential impact on the terrestrial water balance. Numerous studies suggest that invasive species use more water per unit ground area and exploit groundwater stores more so than do native species, demonstrating the potentially detrimental effects of plant invasion on the movement and availability of water in the critical zone (Scott et al., 2006; Cavaleri and Sack,

2010).

Plant invasion is a more pervasive issue in tropical Pacific islands than it is in any other region on earth (van Kleunen et al., 2015). Unlike continental systems, islands lack biotic diversity and are therefore especially vulnerable to naturalization and invasion (Mack, 1996).

This vulnerability is particularly clear in the Galápagos Islands, where introduced plant species outnumber native plant species almost 2:1 (or 800:447) (Wiggins et al., 1971; Tye, 2006).

During this time, 71% of total agricultural land in San Cristóbal had been invaded by alien plant species, largely due to agricultural abandonment (Villa and Segarra, 2011). A recent meta- analysis on invasion in oceanic islands suggested that invasion rates are much higher in regions

65 that exhibit high population density and economic activity than do less developed regions

(Kueffer et al., 2010), further demonstrating the vulnerability of the Galápagos to plant invasion.

Of the invasive plants in Galápagos, one of the most highly invasive is Psidium guajava

(PSGU, or guava) (Loope et al., 1988; Itow, 2003; Lowe et al., 2004). Preliminary evidence suggests that it is drought-tolerant, shade-tolerant, tolerant of temporary water-logging and grows well in a wide range of soil pH (Binggeli et al., 1998). It has both deep and shallow rooting systems, likely in response to its original geographic range in the tropical Americas

(semi-arid) (Purohit and Mukherjee, 1974; Cronk and Fuller, 1995). It is evergreen (Paull and

Duarte, 2012), and other similarly broadleaved species in its genus have exhibited the capacity to intercept fog water via stemflow (Takahashi et al., 2011). These characteristics preliminarily suggest how PSGU is an invader so effective that it has been deemed a “transformer species” in

Galápagos (Tye, 2007).

In addition to the aforementioned characteristics of PSGU that likely contribute to its role as an expert invader, both the high rainfall associated with El Niño and fires associated with La

Niña have further facilitated the spread of PSGU following dieback of native plants in Galápagos

(Tye, 2001). Following the 1982-1983 El Niño, PSGU was found to be favored by higher rainfall

(Luong and Toro, 1985), and following the 1997-1998 El Niño, its cover increased sixfold in the highlands of Santa Cruz (Jäger et al., 2009). PSGU’s aptitude for survival under ENSO conditions that will likely become more frequent/extreme under future climate change (Power et al., 2013; Cai et al., 2014) suggest that PSGU may propagate more quickly than ever before under future climate regimes. Given PSGU’s ability to grow under extreme hydrometeorological conditions that will likely become more common under future climate change (Allen and Ingram,

66 2002; Mora et al., 2013; Chadwick et al., 2015; Donat et al., 2016), examining the water use dynamics of guava across a hydroclimatic/elevational gradient is particularly timely and relevant.

While a growing body of work recognizes the effects of invasive species in affecting ecosystem water budgets (Scott et al., 2006; Cavaleri and Sack, 2010; Funk, 2013; Dzikiti et al.,

2016), quantifying these effects at the scale of particular invasive species requires a comprehensive assessment of how a given species utilizes water under different conditions. In

Galápagos, our previous work has characterized different sources of water and how the contribution of each respective water source varies seasonally and with extreme events (Chapter

Three). However, the role of different water sources in the competitive dynamics posed by invasion of non-native species in the region is still poorly understood.

The motivation for this study is threefold. First, plant invasion is an incredibly pervasive issue in Galápagos. Second, there is preliminary evidence suggesting that PSGU exhibits high water use plasticity in other regions (Purohit and Mukherjee, 1974; Takahashi et al., 2011). And third, guava is one of the top 100 invasive plants in the world, but it is unclear what exactly makes it such an adept invader (Lowe et al., 2004). Therefore, we seek to better understand how

PSGU utilizes different water sources and how it can potentially impact the ecohydrology of the

Galápagos Islands and other ecosystems around the globe. We hypothesize that its use of water may play a role in its invasiveness, and therefore pose the following questions:

1. Do PSGU plants use water differently than do co-occurring native plant species?

2. Does PSGU water use vary across hydroclimatic gradients?

67 Methods

Study sites

The Galápagos Islands are located ~1,000 km off of mainland Ecuador in the tropical

Pacific. San Cristóbal Island, much like other islands in the archipelago, exhibits a distinct, sharp microclimatic zonation imposed by varying precipitation inputs with elevation. The climate in

Galápagos is arid, hot desert (type BWh in Köppen climate classification) (Peel et al., 2007), with mean annual precipitation (MAP) that varies along steep topographic/dominant vegetation gradients, ranging from <400 mm/yr in the arid lowlands to >1500 mm/yr in very humid highlands (>400 meters above sea level (MASL)) (Pryet et al., 2012). Data from the Global

Network of Isotopes in Precipitation (GNIP) indicate intra-annual variability and seasonality of water inputs to the system (Feng et al., 2009). The El Niño Southern Oscillation (ENSO) leads to extreme patterns of rainfall on the archipelago, often resulting in heavy rains during El Niño and droughts during La Niña (Snell and Rea, 1999; Trueman and D’Ozouville, 2010).

In the region surrounding Galápagos, trade winds dominate but vary in magnitude due to the annual migration of the Intertropical Convergence Zone (ITCZ) (Wyrtki and Meyers, 1976;

Trueman and D’Ozouville, 2010). The trade winds are stronger during the cool, garúa (fog) season (June-Dec) and weaker during the warm, wet (convective rainfall) season and during El

Niño events (Itow, 2003; Trueman and D’Ozouville, 2010). A belt of fog persists via a stable trade wind inversion (TWI) layer at ~250 - 300 MASL during this cooler season (Pryet et al.,

2012; Warrier et al., 2012), imparting seasonality on island hydrology (Trueman and

D’Ozouville, 2010). This fog is particularly significant because it supplies the highland cloud forest region with a consistent source of water in this drier season and leaves the lowland region comparatively very dry (Trueman and D’Ozouville, 2010; Stoops, 2014). Preliminary research

68 suggests that fog may contribute up to 25% of the annual water input to the archipelago (Pryet et al., 2012).

In this study, samples were collected during three field seasons (FS) between June 2015 and June 2016, a period during which a very strong ENSO event was taking place (Table VI).

This research took place at three study sites in distinct microclimatic zones across the island bracketing the fog belt, from the arid lowlands to the humid highlands (Table VII). The furthest distance between sites was 6.70 km.

At each field site, a weather station was installed and instrumented to measure numerous local climate variables at thirty-minute intervals. Each weather station logged environmental variables critical to contextualizing this assessment of plant-water interactions, such as: air temperature/relative humidity (RH) (model CS215, Campbell Scientific Inc., Logan, Utah), precipitation (tipping-bucket rain gauge, Texas Electronics model TE525WS, Campbell

Scientific Inc., Logan, Utah), incoming solar radiation (Apogee Instruments, model CS300

Campbell Scientific Inc., Logan, Utah), soil moisture (model CS616, Campbell Scientific Inc.,

Logan, Utah), and wind speed/wind direction (R.M. Young, model 03002, Campbell Scientific

Inc., Logan, Utah).

The lowest elevation site, Mirador (MIR), lies below the fog belt at ~320 MASL. Thus, fog doesn’t occur at this site. This leeward-oriented rocky outcrop is located on a private farm in which the surrounding area has historically been used and is still being used for cultivating seedlings and cattle grazing. The soils at MIR are ultisol (USDA and NRCS, 1999) and the texture of soils is mainly sandy clay loam, sandy loam and clay (Percy, Unpublished data).

Between July 2015 and June 2016 (missing data 12/5 - 1/18), CA received 865 mm of precipitation, which peaked in November (196 mm) and was lowest in May (0 mm). Of the 315

69 days instrumented, 137 rain days were recorded (43%). Average air temperature was 22.2° C, with the highest in March (24.9° C) and lowest in August (19.6° C). Average RH was 94% for the year, and average wind speed was 3.11 m/s.

The mid-elevation site, Cerro Alto (CA), lies on the lower edge of the fog belt at ~520

MASL. Thus, fog sometimes occurs at this site. This leeward-oriented site is located on the same private farm as MIR, as the surrounding area has historically been used and is still being used for growing crops and cattle grazing. The soils at CA are ultisol (USDA and NRCS, 1999) and the texture of soils is mainly loamy sand and clay (Percy, Unpublished data). Between July 2015 and

June 2016 (missing data 12/22 - 1/20), CA received 1165 mm of precipitation, which peaked in

December (363 mm) and was lowest in May (0 mm). Of the 328 days instrumented, 159 rain days were recorded (48%). Average air temperature was 21.5° C, with the highest in March

(24.4° C) and lowest in August (18.9° C). Average RH was 90% for the year, and average wind speed was 4.15 m/s.

The high-elevation site, El Junco (EJ), lies below the upper edge of the fog belt at ~670

MASL. Fog takes place here often, and more so in the dry season. This windward-oriented site sits alongside a freshwater lake atop an extant caldera is the highest point on the island, and is located within the Galápagos National Park. EJ is a popular place for tourists to visit, and in the past twenty years serious guava eradication efforts have taken place at this site. The soils at EJ are oxisol (USDA and NRCS, 1999) and the texture of soils is mainly sandy loam (Percy,

Unpublished data). Between July 2015 and June 2016, EJ received 2840 mm of precipitation, which peaked in December (821 mm) and was lowest in April (10 mm). Of the 360 days instrumented, 279 rain days were recorded (78%). Average air temperature was 20.0° C, with the

70 highest in March (22.9° C) and lowest in August (17.8° C). Average RH was 99% for the year, and average wind speed was 3.12 m/s.

Field sampling

Water, plant xylem and soil samples were collected from the three field sites (Table VII) over three field seasons (FS) with variable hydroclimatic conditions between June 2015 and June

2016 (Figure 13).

Environmental waters were collected to assess sources of plant water uptake.

Groundwater was sampled from the outflow points of a network of contact springs ~2km below the EJ site. Grab samples of surface water were taken at points along EJ lake and from a perennial surface stream between MIR and CA. Fog was collected in Juvik-type fog gauges surrounding EJ lake and adjacent to the CA weather station (Juvik and Ekern, 1978). Rain and throughfall samples were collected at each site from plastic rain collectors (2706 Jumbo Manual

Rain Gauge, Taylor Precision), co-located with fog collectors (except at fogless MIR).

Throughfall was always collected under a woody individual. Samples were collected every 3-8 days, based on precipitation amount and frequency. Mineral oil was placed in all collectors to prevent evaporative enrichment of the oxygen and hydrogen isotopes. Water samples were collected in airtight 15 mL amber bottles and sealed with parafilm before being transported to the lab. A complete assessment of these environmental waters can be found in (Schmitt et al., 2018).

Plant xylem samples were collected every 7-16 days (eight sample collection dates total) from five individuals/site of invasive P. guajava (PSGU) and five individuals/site of the most predominant, co-occurring native species. At low-elevation MIR, B. graveolens (BUGR) was selected as the co-occurring native species. BUGR has a shallow, lateral rooting system

(Wiggins et al., 1971) and is deciduous in nature (Hamann, 2001). At mid-elevation CA, Z.

71 fagara (ZAFA) was selected. ZAFA has shallow roots that often stay in the upper 40 cm of soil

(Watts, 1993; Midwood et al., 1998), has small leaves, low water demand and is semi-deciduous

(Wiggins et al., 1971). At high-elevation EJ, M. robinsoniana (MIRO) was selected. MIRO is deeply rooted (Jackson et al., 1999; McMullen, 1999), has large leaves and is an evergreen shrub

(Mueller-Dombois and Fosberg, 1998). Other species in the same genus are known to be very efficient at intercepting fog due in part to its large leaves (Takahashi et al., 2011), and it has been posited that MIRO on neighboring Santa Cruz Island has the capacity to intercept fog (Pryet et al., 2012). Xylem samples were cut with plant clippers, phloem tissue was removed to avoid contamination by isotopically enriched water (Querejeta et al., 2007), stem samples were collected in airtight 15 mL amber bottles and sealed with parafilm before being transported to the lab.

Soil samples were also collected at each site each time xylem samples were collected. A soil sampling pit in a central location of each site (at least 2 m from the nearest woody plant) was erected, and soil samples were collected via a hand trowel at six depths (at 0cm, 5cm, 10cm,

20cm, 30cm, 40cm). Soils below 40cm were not collected due to the underlying rock or impenetrable clay below 40cm at certain sampling sites. Soil samples were collected in airtight

15 mL amber bottles and sealed with parafilm before being transported to the lab. These samples were collected to better understand both the depth from which plants extract soil water and to understand shallow sub-surface flowpaths. Following the collection of each sample in the field, all water, plant xylem and soil samples were refrigerated prior to analysis to prevent evaporation and water loss.

Midday leaf water potential (LWP or Ψ) measurements were taken on three different dates in FS3 from each individual from which xylem was being taken. Midday LWP is known to

72 represent maximum daily drought stress (Lambers et al., 1998; Bhaskar and Ackerly, 2006).

Twigs were cut cleanly with plant clippers at ~1m height to control for variation in gravimetric water potential (Baguskas et al., 2016). LWP measurements were taken with the PMS Model

670 Pressure Chamber Instrument following the methods of (Jarvis, 1976) within two minutes of twig collection to better understand spatiotemporal patterns of plant water stress.

Stable isotope analysis

Stable isotopes provide a powerful lens for understanding the movement of water through watersheds and ecosystems (Kendall and McDonnell, 1998). Using stable isotopes allows one to distinguish the dominant water sources of different plant species in different ecosystems

(Dawson, 1991, 1998; Ehleringer et al., 1991; Jackson et al., 1999; Eggemeyer et al., 2008). The premise of using stable isotopes to track plant water use lies in the fact that plant-water uptake is generally a non-fractionating process, so xylem isotope water is an accurate reflection of plant source water (Zimmermann et al., 1966; Ehleringer and Dawson, 1992). Deeper soil water use was isotopically distinguished from shallower soil water use via more depleted isotopic signatures (Dawson, 1993) and were assessed graphically following (Goldsmith et al., 2012).

Here, we use xylem water stable isotopes to explore changes and variations in rooting depth and source water by site and season. In semi-arid environments, water isotopes in the upper soil profile experience evaporative fractionation and thus exhibits a more enriched water isotope signature (Emery and Sites, 2013). Thus, we refer to the xylem samples with this more enriched signature as “surface moisture”, as it is indistinguishable from similarly enriched precipitation inputs.

Following each field season, plant and soil water were extracted via cryogenic vacuum distillation (West et al., 2006). The extraction time of soil and xylem samples ranged from 1-3

73 hours, depending on the amount of water in each respective sample. Samples were then analyzed for 18O/16O (δ18O) and 2H/1H (δ2H) using laser spectrometry (Picarro L2130-I, Picarro Inc., Santa

Clara, CA, USA) in The Hu Lab at Montana State University. For all samples, the relative abundance of the rarer heavy isotope over the more common lighter isotope was measured in per mil delta notation (‰). Calibration standards were utilized to adjust the deviations from Vienna

Standard Mean Ocean Water (VSMOW). This relative abundance measure is referred to as delta notation, defined in Equation 6 (and it applies to hydrogen in the same manner):

δ O = − 1 ∗ 1000‰ (6)

No weighting of δ2H or δ18O was applied to precipitation samples. To frame our isotope results within a global perspective, we also used the Global Network of Isotopes in Precipitation

(GNIP) network, which has provided monthly values of precipitation isotopic composition (δ2H and δ18O) from stations around the globe since 1961, and has greatly improved our understanding of environmental variables impacting the isotopic composition of global precipitation (Dansgard, 1964). Assessments of global GNIP data led to the creation of the global meteoric water line (GMWL) (Equation 7) (Craig, 1961), defined as:

δH ‰ = 8δO + 10 (7)

(Dansgard, 1964) and the value of the slope is determined by the ratio of equilibrium enrichments (kinetic isotope effects) for δ2H and δ18O, respectively (Rozanski et al., 1993).

74 Data processing

A combination of chi-squared tests, histograms and residual analyses were used to discard plant xylem samples that were likely fractionated during the collection/analysis process.

Of the 180 plant xylem and soil samples that were processed, 175 were utilized in this analysis after excluding potentially faulty values. Potential evapotranspiration was calculated on a daily time step following the FAO-56 calculation method to evaluate the relative plant water supply to plant water demand (Zotarelli and Dukes, 2010). Nonparametric Pairwise Wilcoxon rank sum tests were used to compare the similarity of plant water use within and between species, field sites and field seasons, as F-tests revealed that differences in variances between groups didn’t allow for parametric comparisons. Nonparametric Pairwise Wilcoxon rank sum tests were also used to compare the similarity of plant water stress (LWP) between species, field sites and sample dates. For both sets of Pairwise Wilcoxon rank sum tests, a BH correction (Benjamini and Hochberg, 1995) was used to control for false discovery rate. All statistical analyses were performed with RStudio 1.0.143 (RStudio, 2015).

Results

Galápagos water supply and plant water demand dynamics

Plant water supply (precipitation) and demand (potential evapotranspiration, or PET) exhibited high variability between our three field seasons and three field sites (Figure 13). In FS1

(Figure 13A), all sites received consistent, light precipitation events that wetted the canopy at least once daily. All three sites, but particularly EJ, also received flashy, heavier precipitation events every ~3-5 days during FS1. Due to consistent precipitation inputs across all sites, plant water demand on the seasonal scale rarely exceeded plant water supply in FS1. In FS2 (Figure

13B), EJ received very intense, high precipitation across the whole field season, and plant water

75 demand never approached plant water supply. While data was not available for MIR and CA due to faulty equipment during FS2, heavy precipitation was observed at these sites and almost certainly exceeded plant water demand. In FS3 (Figure 13C), EJ received anomalously sparse and light precipitation, but more frequently than did MIR and CA. However, plant water demand exceeded plant water supply for almost the entire field season in FS3. MIR and CA only received precipitation once in the 44 days of data collection, and with the exception of this single precipitation event, plant water demand continuously exceeded plant water supply in FS3. The

CA field site was so dry that that a fire occurred nearby, although none of the plants in this study were burned as a result.

Examining water use by PSGU and co-located native plants

At MIR, PSGU and BUGR exhibited comparable isotopic composition during FS1, whereas PSGU was consistently more depleted than BUGR during FS2. This suggests that

PSGU and BUGR likely utilized similar water sources in FS1 (Figure 14), whereas PSGU likely utilized deeper soil water than BUGR in FS2 (Figure 15). In FS2, PSGU appeared to use water from soil deeper than 30 cm and BUGR utilized much shallower, enriched surface moisture

(Wilcox p = 1.4 E-4) (Figure 15). Across FS1 and FS2, which were two distinctly different dry versus wet seasons, PSGU appeared to utilize a very narrow range of water sources (Wilcox p =

0.05), whereas BUGR appeared to utilize a wider range of water sources (Wilcox p = 1.4 E-4).

At CA, PSGU and ZAFA exhibited a comparable isotopic composition during FS1 and

FS2 (Figure 14, Figure 15). However, PSGU in FS2 appeared to utilize a greater range of water sources than did ZAFA (Figure 15), and appeared to shift to deeper soil water stores between

FS1 and FS2 (at or below 10 cm). In contrast, ZAFA appeared to use more enriched surface moisture in FS2 (Figure 15).

76 At EJ, PSGU and MIRO exhibited a comparable isotopic composition during all field seasons. This suggests that the two species utilized similar water sources, which were enriched and appeared to be comprised of surface moisture (Figure 14, Figure 15, Figure 16). In FS2, both species utilized more depleted water sources, likely indicative of a shift to deeper water sources

(Figure 15). In FS3, PSGU and MIRO exhibited a comparable enriched isotopic composition, suggesting that they were likely using surface moisture at this time (Figure 16). However, significant differences were noted within species (intraspecific differences) between field seasons: MIRO in FS1 versus FS3 (Wilcox p = 1.9 E-4), MIRO in FS2 versus FS3 (Wilcox p =

1.9E-3), and PSGU in FS1 versus FS3 (Wilcox p = 0.001).

Comparable results can be observed in Figure 17. In comparing FS1 and FS2, we found that all plants (native and non-native) tended to use more enriched water, likely indicative of surface water, during FS1. During FS2, however, all plants tended to use more depleted water sources relative the meteoric waters that fell below the GMWL (Figure 17). This figure also demonstrates that BUGR in FS2 in particular used fractionated water, further suggesting that this species likely used evaporated, fractionated surface moisture.

Comparing PSGU across field sites and seasons

Across all sites, PSGU utilized more depleted water in FS2 (wetter) than FS1 (drier)

(Figure 15, Figure 14). Compared to PSGU at CA and MIR, PSGU at wetter EJ used the most enriched water (surface moisture) in FS1 (Figure 14). At CA, PSGU only appears to use surface moisture on one sampling date of the two field seasons during which it was sampled (Figure

14a). Wilcoxon rank sum tests revealed that when comparing PSGU across sites but keeping seasons constant, no two groups were significantly different with the exception of PSGU in FS1 versus PSGU in FS3 at EJ (Wilcox p = 0.001).

77 Quantifying plant water stress

In FS3 (very dry season), both native and invasive plant stands at the drier sites (MIR,

CA) experienced higher degrees of water stress than did the plant stands at EJ (Figure 18).

However, the degree of plant water stress experienced by each species varied significantly by site and sampling round.

At MIR, PSGU and ZAFA fared similarly by sampling round and BUGR experienced less water stress than did either species (Figure 18). PSGU was significantly more water stressed than BUGR across sampling rounds one and three. In rounds two and three, ZAFA was significantly more water stressed than BUGR. While PSGU and ZAFA didn’t exhibit significant differences in the degree of plant water stress across the twelve-day study period, BUGR experienced varying degrees of water stress at that time.

At CA, PSGU and ZAFA fared similarly by sampling round with the exception of round one (Figure 18). PSGU experienced significantly less water stress than did ZAFA in round one, but the two species fared similarly in rounds two and three. While PSGU didn’t exhibit significant differences in the degree of water stress it experienced between rounds, the water stress ZAFA experienced in round one was significantly greater than it was in rounds two and three.

At EJ, degree of plant water stress varied more by sampling round than it did by species

(Figure 18). Both MIRO and PSGU experienced significant differences in water stress between all sampling rounds, but fared similarly in each sampling round except round three. Thus, these species appeared to experience plant water stress very similarly in this very dry season at the highest elevation site.

78 Discussion

Galápagos plant water availability

All three field sites reflect the precipitation patterns of the El Niño as it typically manifests in the equatorial Pacific: anomalously high precipitation during an El Niño that increases as the El Niño gains strength, and anomalously low precipitation after the El Niño event concludes and when the La Niña commences (Atwood and Sachs, 2014; Nelson and Sachs,

2016; Sánchez-Murillo et al., 2016b; Schmitt et al., 2018). Further, water availability at all three sites reflected the general seasonality imposed by the intra-annual migration of the ITCZ

(Atwood and Sachs, 2014). Following suit, water supply appeared to meet and potentially exceed plant water demand in FS1, far exceed plant water demand in FS2, and consistently fail to meet plant water demand in FS3. The stark contrast between plant water supply and demand dynamics between seasons and sites provided a unique lens through which we examined variable patterns of plant water use in this study, as cloud forest plants are particularly susceptible to extreme drought (Gotsch et al., 2017).

Further, data from FS3 (Figure 16) preliminarily suggest the existence of two water worlds at EJ. Two water worlds, or the existence of two separate soil water pools of more mobile precipitation that contributes to groundwater recharge/streamflow and a less mobile pool of water that contributes to plant fluxes (Brooks et al., 2010; Goldsmith et al., 2012; Hervé-

Fernández et al., 2016), warrants further investigation at this site when the remainder of our soil water data is processed. This future investigation could provide a better understanding of both plant-available freshwater and groundwater recharge in Galápagos, as most ecohydrological models are premised on the assumption that translatory flow (a single well-mixed soil water pool) is taking place (Hewlett and Hibbert, 1967).

79 Interspecific species competition for water sources

Interspecific competition dynamics between PSGU and co-occurring native species differed by hydrometeorological conditions that were variable by both site and season.

At MIR, PSGU and BUGR competed for similar water sources in FS1 and PSGU shifted to deeper soil water use in FS2. This shift in FS2 likely reflects PSGU’s deeper rooting capacity

(up to 200 cm in Galápagos) and its attempt to avoid competition for water with native BUGR, whose roots are far shallower (no tap roots) and are instead lateral in their distribution (Wiggins et al., 1971; Huttel, 1986). Generally, plants give priority to using stable and continuous water sources, although they can vary their water uptake strategy under special conditions (Zhao and

Wang, 2018). In this case, plants that diversified their water acquisition strategy over the field seasons were from this dry site (MIR), where competition for water led both native and non- native plants to use water from a range of soil depths. Similar to the finding by (Zhang et al.,

2017), the shrubs from our study that appear to change strategy were highly dependent on precipitation-derived water. A study in another semi-arid region also found the highest seasonal differences in surface moisture use at intermediate levels of site moisture availability (Guo et al.,

2018), consistent with our findings. Co-existing woody species in other semi-arid environments have also demonstrated the capacity to exploit different water resources (Xu et al., 2011;

Bertrand et al., 2014; Wu et al., 2016; Grossiord et al., 2017; Gao et al., 2018). This niche complementarity (differentiation) in water sources among coexisting species is a mechanism that is known to foster more diverse communities with both high productivity and high resilience to drought (Loreau et al., 2001).

Similar to the water use dynamics at MIR, PSGU and ZAFA at CA competed for similar water resources in FS1, and PSGU shifted to deeper soil water stores in FS2. This switch from

80 surface moisture to deeper soil water likely reflects PSGU’s deeper rooting capacity (up to 200 cm in Galápagos) compared to more shallow-rooted ZAFA, whose roots often remain in the upper 40 cm of soil (Huttel, 1986; Watts, 1993; Midwood et al., 1998). ZAFA in other tropical regions have been known to benefit from hydraulically lifted water (Prieto et al., 2012), but there is no evidence to suggest that hydraulic lift by PSGU occurs. However, if hydraulic lift does occur in Galápagos, then ZAFA could greatly benefit from this extra moisture source. Also consistent with other studies, we found that water source differences between coexisting shrub species was more apparent in more arid locations (MIR, CA) than more wet locations (EJ)

(Zhang et al., 2017).

At EJ, PSGU and MIRO competed for similar water sources across all field seasons

(Figure 16), suggesting that competition for water was negligible at EJ as compared to other sites, consistent with another study also found that plants exhibit higher overlap of water sources when water is not a limiting resource (Guo et al., 2018). However, PSGU drew water from a slightly wider range of water sources within and between the three sampling dates, reflecting its greater water use plasticity. The plasticity of both PSGU and MIRO water use at EJ, the wettest site, reflect high variability of water acquisition strategies in both species at this site. Higher water use plasticity by plants in wet sites has been documented in other tropical regions

(Kulmatiski and Beard, 2013; Archer et al., 2017). Both species shifted to the most depleted water sources in FS2, reflecting deeper soil water use and not simply depleted atmospheric water inputs in FS2 compared to other field seasons (Figure 17); this was likely a result of both species increasing their rooting depth in response to high intensity precipitation events that recharged deeper soil water stores. Such a response has been found in shrubs and invasive plants in other semi-arid ecosystems (Cheng et al., 2006; Kulmatiski and Beard, 2013; Archer et al., 2017).

81 Also, the shift to more enriched, shallow soil water in FS3 took place concurrently with the gradual defoliation of MIRO plants at EJ. The literature suggests that reduced-canopy photosynthesis via defoliation alters patterns of root activity to favor water from shallower soils, so this process may have played a role in the most enriched waters used by MIRO in FS3

(Snyder and Williams, 2003). However, variation in plant water use dynamics at the intra-site and intra-species level could be due to the interplay of other factors such as fine-scale heterogeneity of landscape position, soil type, stand age and stand density.

Taken together, data from the different sites suggest that guava’s resilience to extreme events may foster its invasive capacity. Guava uses water differently than do native plants in semi-arid sites, and guava has been documented to survive extreme ENSO events in these more arid sites whereas native plants, such as BUGR, have been documented to experience massive dieoffs following ENSO events (Luong and Toro, 1985; Tye and Aldaz, 1999).While water use similarities from isotopic data suggest similarities in plant water use between guava and MIRO at

EJ, MIRO plants at EJ were experiencing mass mortality following the very strong El Niño event

(FS3).

At all sites, all plants utilized the most depleted soil water that fell below the GMWL during FS2 (Figure 17). This finding is consistent with that found it other tropical catchments during wet periods, as plants were accessing deeper water stores at that time (Evaristo et al.,

2016). Further evidence for deeper soil water use lies in the precipitation isotopes in FS2, that were not significantly different than precipitation isotopes in FS1 (Schmitt et al., 2018), suggesting that seasonal changes in precipitation isotopic composition at all sites cannot account for the use of more depleted water at this time. Woody species in other regions that also received

82 anomalously high precipitation during El Niño exhibited similar patterns of xylem isotopic depletion during wet El Niño events and enrichment during dry La Niña events (Pendall, 2000).

In FS1 and FS3, all plants used more enriched water than they did in FS2. This finding is consistent with water use by cloud forest plants in Costa Rica, where even at the peak of the dry season, stable isotopes in all species were consistent in their use of shallower soil water (20-60 cm) (Goldsmith et al., 2012).

PSGU water use across hydroclimatic gradients

The consistent use of more enriched water by PSGU at EJ in FS1 suggests that it is utilizing fog water, which is known to be enriched compared to co-collected rainfall at many sites (Dawson, 1998; Gat, 2001), including Galápagos (Schmitt et al., 2018). While PSGU at CA only appears to use fog on one sampling round, fog seems to contribute more to overall PSGU water use at EJ than it does at CA. The use of more depleted water by PSGU in wet FS2 again suggested that hydrological niche partitioning, which improves plant adaptability in water- limited ecosystems (Moreno-Gutiérrez et al., 2012), was taking place: PSGU demonstrated the ability to increase its rooting depth in response to high intensity precipitation events that recharged deeper soil water stores. Such a response has been found in other shrubs and invasive plants in other ecosystems (Kulmatiski and Beard, 2013; Archer et al., 2017). This high propensity for exploiting deeper soil water stores under higher deep soil recharge conditions likely played a role in the sixfold increase in PSGU cover in the highlands of Santa Cruz Island,

Galápagos after the 1997-1998 El Niño event and the increase in cover after the 1982-1983 El

Niño event in Galápagos (Luong and Toro, 1985; Jäger et al., 2009). Similar plant water use plasticity under varying hydrometeorological conditions been exhibited by other competitive

83 shrub species in semi-arid environments (Duan et al., 2008; Zhou et al., 2015; Grossiord et al.,

2017; Liu et al., 2018).

Plant water stress: site and species variability

Plant water stress (measured as leaf water potential, or LWP) varied significantly under different hydrometeorological conditions (Figure 18). We observed significantly lower water stress in plants at EJ as compared to the drier sites during FS3. We suggest that this may be a combination of slightly more frequent (albeit incredibly light) precipitation events at EJ during this very dry season and the utilization of fog at EJ in FS3, a time during which fog frequently blanketed the EJ field site (Schmitt et al., 2018). We suggest that fog may have played a role in

EJ plant water stress (LWP) dynamics because woody plants in semi-arid regions are known to cope with drought by intercepting water delivered by fog, particularly in the dry season

(Holwerda et al., 2006; Johnstone and Dawson, 2010; Fischer et al., 2016). In coastal California, for example, fog water contributes 13-45% of annual transpiration, and likely more so during particularly dry periods (Dawson, 1998). Fog water use by plants is also known to reduce LWP across various TMCF regions (Eller et al., 2013; Goldsmith and Matzke, 2013; Gotsch et al.,

2014b). Fog-improved water potentials in cloud forest plants have been attributed to both foliar uptake (direct uptake of fog by leaves), reduced transpiration due to fog water on leaf surfaces

(Berry et al., 2014) and low vapor pressure deficit (VPD) (Dawson, 1998).

One study in Hawaii demonstrated the fog interception capacities of close relatives of our

EJ study species; it found that Miconia (MIRO relative) plant stands were more efficient than

Psidium (PSGU relative) plant stands at intercepting cloud water via stemflow (Takahashi et al.,

2011). If this holds true in Galápagos, this could offer a competitive advantage for the native

MIRO. Further, both species at EJ have large leaves and high water demand, suggesting that fog

84 utilization during dry conditions may be an important survival mechanism for both species during periods of low water availability.

Our findings also suggest that PSGU at CA experienced less stress than did co-occurring

ZAFA in FS3 due to its capacity to intercept fog. Midday water potential of -1.5 MPa is the permanent wilting point for many crop plants (Lambers et al., 1998), so ZAFA in FS3 certainly exhibited extreme stress, particularly in R1. ZAFA has smaller leaves, lower water demand and is semi-deciduous (Wiggins et al., 1971); these characteristics suggest that it may not have the capacity to intercept fog as does PSGU (Fu et al., 2015). At MIR, native BUGR was significantly less water stressed than invasive PSGU and native ZAFA across the three sample dates. We suggest that this may be a result of its lower water demand due to its documented deciduousness before soil water drops below a critical threshold (Wiggins et al., 1971; Mueller-

Dombois and Fosberg, 1998; Morgan, 2009).

Conclusions

Guava is one of the most insidious, invasive plants in the world, but it was previously unclear what exactly makes it such an adept invader. Here, we present three lines of evidence that PSGU is an adept invader via its expert use of water that enables it to invade, and in some instances, outcompete, its co-occurring native counterparts. First, PSGU exhibited higher water use plasticity than its native counterparts in the semi-arid sites (MIR/CA), as it demonstrated its capacity to partition water from both shallow and deep water stores that some of the co-occurring native species likely couldn’t access. Second, PSGU appears to be able to intercept fog water, particularly under drier conditions (EJ). And third, PSGU exhibited a more consistent degree of water stress than did its native counterparts under very dry conditions, suggesting consistency of water acquisition and potentially higher capacity to survive under drought conditions. These

85 findings have significant implications for conservation managers moving forward, as PSGU may propagate more quickly and efficiently than ever before under future hydroclimatic regimes imposed by a changing climate.

86 Tables and Figures

Table VI. Conditions in Galápagos during this study. Precipitation is expressed as seasonal daily mean and temperature/wind speed in seasonal daily mean range. Adapted from Schmitt et al. 2018.

87 Table VII. Plant monitoring transects on San Cristóbal Island, Galápagos. Site Name Microclimatic Landscape Species (common Abbrev. Invasive Zone (MASL) Orientation name) status Mirador Edge of arid- Leeward P. guajava (guava) PSGU Invasive (MIR) transition zone B. graveolens BUGR Native (320) (ironwood) Cerro Alto Edge of Leeward P. guajava (guava) PSGU Invasive (CA) transition-humid Z. fagara (cat’s ZAFA Native zone (520) claw) El Junco Very humid zone Windward P. guajava (guava) PSGU Invasive (EJ) (670) M. robinsoniana MIRO Native (miconia)

Figure 16. FS3 soil, plant xylem and precipitation isotope values. Panels A, B and C represent sequential sampling rounds ~2 weeks apart in FS3. Invasive plant xylem values (PSGU in every graph) are shown in red and native plant values (MIRO) are shown in green; note that only samples from one field site (EJ) were collected. Precipitation values are shown in blue: circles indicate fog samples, diamonds indicate rainfall samples, inverted triangles indicate throughfall samples, triangles indicate surface water samples (from EJ Lake) and squares represent groundwater samples. Soil samples at increasing depths are displayed in black. Dashed lines extending from the plant xylem samples are for visualization purposes only.

Figure 17. Plant xylem dual-isotope values for all three FS of study. Panels A, B and C represent samples from EJ, CA and MIR, respectively. Hues of pink indicate invasive plants (PSGU at all sites), and hues of green indicate native plants (from top to bottom: MIRO, ZAFA and BUGR). Precipitation values from each FS are shown as blue points and the black dashed line represents the GMWL.

CHAPTER FIVE: SUMMARY AND CONCLUSIONS

Given the significance of fog to semi-arid ecosystems across the globe, I use stable isotopes, remote sensing and plant physiological analyses to examine the role of fog in the semi- arid ecosystems of San Cristóbal Island, Galápagos and Ascension Island, UK by utilizing data from four field campaigns. In Chapter Two, I create a ground-based optical fog detection scheme to trace fine-temporal scale mechanisms driving fog formation, evolution and dissipation across varying hydroclimatic zones. In Chapter Three, I establish the isotopic signature of fog and other environmental waters to assess the overall contribution of fog to different microclimatic zones and under different hydroclimatic regimes. In Chapter Four, I trace fog through the Galápagos ecosystem, specifically examining how native versus invasive flora utilize fog under varying hydroclimatic conditions.

The findings from this research regarding fog in semi-arid ecosystems make three primary contributions within the field of ecohydrology:

(1) Creating a simple and generalizable model (DOFmodel) to predict degree-of- fogginess from ground-based optical cameras. Fog is a boundary layer phenomenon, as its formation, evolution and dissipation are largely dictated by surface processes such as: radiative

95 cooling, humidity, wind speed, turbulent mixing, heat and moisture at the surface, surface cover

(vegetation, soil, etc.), vertical and horizontal wind, among other factors (Bruijnzeel et al.,

2005). Many of these surface factors are governed by seasonal mesoscale and synoptic-scale climate patterns, and the mechanisms resulting in fog formation vary widely, even across small spatial scales (Kaseke et al., 2017a, 2017b). Three principal fog types in island ecosystems, distinguished via their formation mechanisms, are advective fog, orographic fog and radiation fog. Satellite remote sensing is one of the principal methods used to understand spatiotemporal dynamics of fog inundation in a given region (Gultepe et al., 2007; Koracin et al., 2014).

Examining fog dynamics in smaller regions of interest such as tropical islands requires data with higher spatial and temporal resolution than is possible via most satellite-based fog detection schemes. However, no ground-based fog detection scheme to date has allowed for the discrimination of different fog types across seasonal and diurnal scales. In Chapter Two of this dissertation, I create a ground-based optical fog detection scheme to trace fine-temporal scale mechanisms driving fog formation, evolution and dissipation across varying hydroclimatic zones in Ascension Island, UK, the only manmade cloud forest in the world (Ashmole and Ashmole,

2000; Wilkinson, 2004). In this chapter, I showed that DOFmodel derived from color photographs is a suitable proxy for degree-of-fogginess, and in doing so I develop the first ever non-binary fog classification system using in-situ cameras. I suggest that DOFmodel presents an attractive alternative to prior methods to detect degree-of-fogginess in regions where meteorological measurements are costly or difficult to obtain. This research further suggests that simple models such as random forest and logistic regression can be used to predict degree-of-fogginess with commonly measured meteorological variables. Finally, we suggest that our simple model can be applied to other ecosystems worldwide.

96 (2) Characterizing fog water deposition in an island ecosystem under different hydrometeorological extremes. Fog deposition is a significant source of water in the overall water budgets of many seasonally dry tropical ecosystems (Cavelier et al., 1996; Rhodes et al.,

2006; Liu et al., 2007; Scholl et al., 2007). Despite the clear significance of fog in tropical ecosystems, it is often one of the most poorly quantified components of the water balance

(Takahashi et al., 2011). In Chapter Three of this dissertation, I establish the isotopic signature of fog and other environmental waters to assess the overall contribution of fog to different microclimatic zones and under different hydroclimatic regimes. Here, I present the first-ever assessment of precipitation isotopic variability in San Cristóbal Island, Galápagos. I present the stark differences in hydroclimatic conditions between three field seasons, and show that climate variability was imposed by the combined effects of the typical intra-annual migration pattern of the ITCZ and a very strong El Niño event. I establish that fog is a common phenomenon on San

Cristóbal, especially during the dry season, and I found that fog is consistently enriched compared to co-collected rainfall. I further suggest that the relative contribution of fog formed via different mechanisms (orographic, advective, radiation) varied seasonally. This is significant because fog formed via certain mechanisms may become more sparse than other fog types under future climate change. I also found that the source region is the most dominant control of the isotopic composition of rainfall in the Galápagos at both the seasonal and event scales, but sub- cloud evaporative processes (the non-traditional manifestation of the amount effect) became a dominant control on the isotopic composition of rainfall during the dry season. Overall, my findings suggest that understanding seasonally-variable water-generating mechanisms is paramount to effective water resource management on San Cristóbal Island and other semi-arid island ecosystems under current and future regimes of climate change. This understanding will

97 be critical in a future that may experience less fog as a direct result of climate change (Pounds et al., 1999; Still et al., 1999).

(3) Understanding the ecohydrological impacts of alien guava invasion under future regimes of climate change. Many plants in semi-arid tropical systems, and particularly in islands where freshwater is particularly limited, are known to rely on fog as a water source

(Goldsmith et al., 2012; Gotsch et al., 2014a; Fu et al., 2015). One study in Hawaii, for example, demonstrated the high capacity of shrubs in foggy, windward-exposed sites to intercept cloud water via stemflow (Takahashi et al., 2011). Other studies have suggested that direct uptake of cloud water from the leaf surface or a potential mixture of stemflow and foliar uptake contribute to a plant’s capacity to intercept cloud water (Pryet et al., 2012; Gotsch et al., 2014b). It is therefore unsurprising that many tropical island ecosystems are heavily reliant on fog as a water input, with fog contributing up to 75% of the overall water budget in certain tropical regions (See

Review by Bruijnzeel et al., 2011). Guava, a plant anecdotally suggested to utilize fog and other environmental waters, is one of the most insidious, invasive plants in the world. Preliminary evidence suggests that guava exhibits high water use plasticity in other regions (Purohit and

Mukherjee, 1974; Takahashi et al., 2011), but it was previously unclear what exactly makes it such an adept invader (Lowe et al., 2004). Therefore, in Chapter Four of this dissertation, I seek to help the scientific community better understand how guava utilizes different water sources and how it can potentially impact the ecohydrology of the Galápagos Islands and other ecosystems around the globe. In this chapter, I present three lines of evidence that guava is an adept invader via its expert use of water that enables it to invade, and in some instances, outcompete, its co- occurring native counterparts. First, guava exhibited high water use plasticity, demonstrating its capacity to partition water from both shallow and deep water stores that some of the co-occurring

98 native species likely couldn’t access. Second, guava appears to be able to intercept fog water, particularly under drier conditions. And third, guava exhibited a more consistent degree of water stress than did its native counterparts under very dry conditions, suggesting consistency of water acquisition and potentially higher capacity to survive under drought conditions. These findings have significant implications for conservation managers moving forward, as guava may propagate more quickly and efficiently than ever before under future hydroclimatic regimes imposed by a changing climate.

In summary, this dissertation research examines connects between the hydrosphere and the ecosphere in two tropical island ecosystems. I create a simple model to predict degree-of- fogginess with commonly measured meteorological variables, and I show how different fog formation mechanisms can be taking place in tandem over a concentrated spatial scale. I also demonstrate that fog is a common phenomenon on San Cristóbal Island, especially during the dry season, and that fog is consistently enriched compared to co-collected rainfall. Finally, I utilize the isotopic signature of fog and other environmental waters to trace water sources through the Galápagos ecosystem, suggesting that invasive guava’s water use strategy (including fog water utilization) plays a key role in its competitive capacity versus co-occurring native plants. Through this island lens, I address the critical disconnect between the hydrological and ecological role that fog plays in tropical island ecosystems. Taken together, these findings address critical gaps in fog research, especially in remote regions with sparse data coverage.

Further, they will be critical in projecting future water resource availability across many seasonally arid ecosystems, especially those that may become drier under future regimes of climate change.

99 SUPPLEMENTARY FIGURES

100

101

Supplementary Figure 2. Vegetation features and ROIs for each camera (from top to bottom: BV3, BV6, BV8).

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