Quantifying Seasonal & Historical Shoreline Change on Santa Rosa

Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island, CA

An Environmental Science and Resource Management Capstone Project

by Stephen Bednar

Submitted in partial fulfillment of the requirements for an Environmental Science and Resource Management Bachelors of Science degree from California State University Channel Islands.

May 12, 2015 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar Abstract Eminent sea level rise, and historical management of Santa Rosa Island, CA have likely caused a change in the shoreline position of the islands sandy beaches (Stocker et al. 2013). Using ArcGIS and USGS’s Digital Shoreline Analysis System I analyzed net, and annual rates of change from 1929-2015 using a compilation of historical aerial images, and GPS surveys. Seasonal analysis spanned from November 2014 to March 2015 and indicated 79% of shorelines surveyed accreted at a mean rate of 15.17m ± 8.13. 18% of shorelines eroded at a mean rate of -7.06 ± -5.20 over that period. Historical shoreline positions were drastically different from 1929 to 2015. 29% of shorelines accreted at a mean rate of 30.64 ± 29.67 while 71% of shorelines eroded at a mean rate of -26.05 ± 20.69. Shoreline analysis, incorporating aerial imagery while noisy due to many uncertainties, provides a best case management scenario for resource managers. I. Introduction

Background Sandy beaches play an important role in society and also provide significant zones of ecological biodiversity (McLachlan and Brown 2006a). They also provide important roles in recreation for humans, as well as ecological functions such as nutrient cycling and buffer zones that protect human development and adjacent onshore ecosystems (McLachlan and Brown 2006a). Coastal ecosystems provide economic benefits and recreational benefits for humans, and it is paramount we understand how to manage

Figure 1. A typical sandy beach depicting nearshore, surf, intertidal, backshore, and foredune zones. these manage these assets especially as coastal climates shift, and sea level rise. Given Santa Rosa’s management history, and the imminent threat of sea level rise, shorelines on the Island have likely changed. The following sections will introduce; the values of sandy beaches and challenges facing them, Channel Islands National Park (Santa Rosa Island) and it’s beaches, and Geographical Information Systems (GIS) role in analyzing historical and seasonal changes on the beaches of Santa Rosa Island.

2 1. Sandy Beaches

Sandy beaches are held in high regard by society and are the most heavily used type of shoreline worldwide (Schlacher et al. 2007). Humans have used sandy beach ecosystems for at least 110,000 years as sources of food and other commodities (Nel et al. 2014). The coastlines of the world provide kilometers upon kilometers of sandy beach which play a dynamic role in human recreation and economy because Ecological Functions of Beaches beaches are large tourist draws (Houston 2008). They also play an important role in • Sediment storage and transport ecological functions like nutrient cycling • Wave dissipation and associated buffering and their role as buffers to coastal inundation (McLachlan and Brown against extreme events (storms, tsunamis) 2006a). The key resources that sandy • Dynamic response to sea-level rise (within beaches provide are currently under limits) threat from global climate change (sea • Breakdown of organic materials and level rise), and anthropogenic pressure pollutants facing them is imperative in making • Water filtration and purification informed management decisions. The following section will outline key features • Nutrient mineralization and recycling surrounding sandy beaches that include; • Water storage in dune aquifers and 1. Physical Qualities & Ecology, 2. groundwater discharge through beaches Recreation & Economy, and 3. Threats & • Maintenance of biodiversity and genetic Restoration. resources • Nursery areas for juvenile fish 1.1 Physical Qualities & Ecology The sandy beach ecosystems are • Nesting site for turtles and shorebirds, and defined by a rapidly shifting environment rookeries for pinnipeds shaped by the wind and waves. The • Prey resources for birds and terrestrial physical structure of sandy beach can be wildlife narrow and steep (reflective), to broad • Scenic vistas and recreational opportunities and flat (dissipative) and are characterized by their sand, waves, and • Bait and food organisms tide regimes (Schlacher et al. 2007). The • Functional links between terrestrial and sediment of sandy beaches can be marine environments in the coastal zone composed of many different grain sizes from any sources of sediments from (Defeo et al. 2009) different points in geologic history (McLachlan and Brown 2006c). On most beaches, the grain size ranges from .1 to Figure 2. The many ecological benefits of sandy shores 1.0mm and fall into two mineral categories (quartz and calcium carbonate) and are the product of weathered rocks (McLachlan and Brown 2006b). The sand is a porous substrate that allows for large volumes of seawater to be filtered along beaches (Schlacher et al. 2008). These grains are transported either as bed load or as suspended load that can move longshore, and also on-offshore (McLachlan and Brown 2006b). During storm events beaches tend to

3 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar be eroded and flattened as sediment is moved offshore (erosion) and during calmer periods sand is moved back onshore (accretion) (McLachlan and Brown 2006b). The movement of sand is a result of many complex interactions between the slope of the beach, waves, and tides that occur in six microtidal (wave-dominated) sandy beach types (McLachlan and Brown 2006b). The two extremes are reflective and dissipative. Reflective beaches sediment is stored in the intertidal zone and the backshore, while dissipative beaches are eroded, and the sediment is stored in sandbanks offshore in the broad surf zone ( Figure 1) (McLachlan and Brown 2006b). There are also intermediate forms that fall in between these two extreme forms of sandy beaches. The processes that form a beach’s makeup are highly complicated and are a result of many complex morphological mechanisms working together. The physical qualities of sandy beaches lend themselves to critical ecological function ( Figure 2). Beaches to most are not thought of as hot spots for diversity. However, these area house a plethora of species beneath the sand. The sediment of the intertidal zone is home to small organism (bacteria, protozoans and small metazoans)m as well as larger invertebrates (polychaete worms, clams, whelks, and crustaceans). These organisms have shown adaptions in mobility, burrowing ability, and a flexibility in coping with conditions that change rapidly (Schlacher et al. 2008). Beaches also provide nursery grounds for fish species that are important for local commercial fisheries and supporting fish stocks (Schlacher et al. 2007). The beach also plays an important role in marine ecosystems and land-based ecosystems. The sandy beach ecosystem relies on marine sources such as drift algae, plants, and carrion that supply food to crustaceans, insects, and higher invertebrates like birds (Schlacher et al. 2008). Beaches have a direct link with other surrounding ecosystems like that of the surf zone, and coastal dunes that are affected physically as well as biologically (Schlacher et al. 2008). The productivity of beaches seem miniscule in relationship to other ecosystems, but in reality they play a vital role between in the connection between land and sea. 1.2 Recreation & Economy Sandy beaches do more than provide ecological value; they are also hubs of society that provide esthetic values as well as recreational. Understanding that 75% of the world’s population will live within 60km of the coast in the near future helps to realize beaches immense value (Amyot and Grant 2014). In a study by (Ghermandi and Nunes 2013), sandy beaches were valued the highest and visited the most frequently worldwide. As travel and tourism become dominant forces in economies throughout the world beaches are increasingly becoming the target tourist destinations (Houston 2008). Travel and tourism are the United States as well as the world’s leading employers, and beaches are a heavily targeted destination (Houston 2008). Beaches in California alone provide 322 billion dollars annually to the economy (Houston 2008). Understanding that these resources provide important sources of ecological diversity, as well as their importance economically help to realize how important these sandy beach communities are to society.

1.3 Threats & Management

4 The threats to sandy beach shorelines range from localized effects (source pollution), to global impacts (climate change), which impact every beach on every shoreline across the globe (Defeo et al. 2009). Understanding the anthropogenic pressures shorelines face is paramount in managing future changes to sandy beach ecosystems. Since beaches are playgrounds for society, management efforts have been geared towards elevating the recreational experience. These efforts include use ecologically harmful practices like beach grooming, beach nourishment, and coastal armoring (Defeo et al. 2009), however these management strategies are not used on Santa Rosa Island. Climate change is, however, an anthropogenic pressure that is affecting its shorelines. According to (Stocker et al. 2013), it is certain that over the past century sea level has risen at rates of ~1.7mm yr-1 (1900-2010) and between 1993-2010 those rates have risen to ~3.2mm yr-1. Although sea level is known to be rising, the effects are not well understood in beach ecosystems. It is known, however, that sea level rise threatens beaches (especially dissipative) as the high-water mark moves inland, which could cause entire habitats to erode away (Defeo et al. 2009). Climate change and sea level rise will have an unquestionable impact on islands, and conservation may be needed to keep these ecosystems afloat. Understanding the long-term threats of climate change will help conservationist prioritize threats that are inherent products of sea level rise and other climate shifts (Courchamp et al. 2014). The National Park Service is afforded the management control of Santa Rosa Island, which will dictate management decisions pertaining to climate change in the future.

2. Channel Islands National Park Island Management Agency San Miguel National Park Service/ Department of Defense Santa Rosa National Park Service Santa Cruz National Park Service/ The Nature Conservancy Anacapa National Park Service Santa Barbara National Park Service San Nicolas Department of Defense Santa Catalina Private/ Catalina Island Conservancy San Clemente Department of Defense

Table 1. The eight Channel Islands and the agencies whom manage them.

The California Channel Islands are a result of millions of years of tectonics. The Islands are transverse which means they trend east-west unlike many ranges in California which trend north-south (Roberts 2014). The islands have not always looked like they did today. As far as about ten thousand years ago during the last ice-age, the four northern islands were one island named ‘Santa Rosae’, which was only 5 miles from the mainland shore (Roberts 2014). Today there are eight Channel Islands which fall under the management of several different agencies ( Table 1). The National Park Service has become the predominant land managers of the islands in the past quarter century. The following section will focus on resources and management history of Santa Rosa Island, which is the second largest of the Channel Island, and contains the majority of sandy beach ecosystem within Channel Islands National Park.

5 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar 2.1 Santa Rosa Island Santa Rosa Island ( Figure 3) is the second largest island in the Channel Islands at 53.000 acres and is located 40 nautical miles of the coast of Ventura California. The Island has an intriguing, long history of human presence and resource management, as well as a unique ecology. In the 1959 bones were discovered on Santa Island in Arlington Canyon that date back to 13,000 years ago (National Park Service n.d., Orr 1962). Chumash Indians inhabited the islands since the late Holocene and thrived on seabirds, sea mammals, as well as fish and shellfish (National Park Service n.d.). The Chumash thrived on the islands using it natural resources sustainable until the Spanish removed them during the mission era (National Park Service n.d.). After the Chumash were removed from the island it remained empty for the next 20 years, although the island was exploited by otter hunters during the late 1700’s and early 1800’s (National Park Service n.d.). The most influential changes to the island came during the ranching era. In 1821, Mexico gained independence from Spain and received possession of the Channel Islands. Santa Rosa was eventually given to the Carillo brother as part of a land grant to prevent foreign interests from claiming the land (National Park Service n.d.). The Carillo brothers sold the island shortly after that to Alpheus Thompson and John C. Jones. The two would build the first houses Figure 3. Santa Rosa Island shown with Santa Cruz Island, and corrals on the island, Anacapa Island, and mainland California to the East, and San and brought livestock to the Miguel Island to the West. island in 1844; 270 head of cattle, 51 ewes, two rams, and nine horses (National Park Service n.d.). The island was further developed over the following decade. It was eventually sold to T. Wallace More and brother A.P More acquired the entirety of the island in 1870 and operated a sheep ranching effort with 80.000 sheep grazing the island by 1888 (National Park Service n.d.). A.P More eventually held all of the shares of the Island but passed away in 1893 when it was sold in entirety to Vail & Vickers Co. (National Park Service n.d.). Vail and Vickers operated a cattle ranch on Santa Rosa Island, which supported 3.000 to 7,000 heads of at any time. During the period of introduction of cattle, sheep were removed from the Island in their entirety. The cattle were young and sold to buyers on the mainland, and the operation continued on the island until 1998 when the National Park Service took full possession of the island (National Park Service n.d.). Channel

6 Islands National Park was created in 1980, and Santa Rosa Island was bought from Vail and Vickers in December of 1986. A special land use permit allowed the Vail’s to retain a twenty-five-year reservation of use occupancy. During this time, there was a continuation of cattle ranching, as well as a continuation of deer and elk hunting on the island until 2009 (National Park Service n.d.). The island is currently in a state of recovery that is being managed by the National Park Service. There are six endemic plants that occur only on Santa Rosa Island. Santttaaa RRRosa and is home to three Channel Island endemic terrestrial mammals; island fox, island spotted skunk and the island deer mouse (National Park Service n.d.). To help these ecosystems recover, the National Park Service has removed all of the non-native ungulates from the island that will help riparian zones, and coastal zones to return to their previous states. "As on the other islands, the National Park Service has made great efforts to preserve and protect these island resources, including enforcement of marine protected areas, stabilization of cultural sites, rehabilitation of historic buildings, removal of non-native plants and animals, recovery of island foxes, and reestablishment of bald eagles” ("Santa Rosa Island - Channel Islands National Park (U.S. National Park Service)” n.d.). The National Park Service has recently collaborated with California State University Channel Island (CSUCI). The goal of the relationship is to facilitate research on the island, and will lead to many research opportunities on the island.

3. GIS Analysis The sandy beaches of Santa Rosa Island support dynamic population of shorebirds like the western snowy plover, pinniped rookeries, coastal dune ecosystems, and estuarine ecosystems. Although the beaches of Santa Rosa Island do not face any direct anthropogenic pressures today, the threats of sea level rise associated with global climate change threaten these ecosystems. It is important for management implications that changes in these sandy beach communities be monitored for historical as well as seasonal changes. Development in Geographic Information Systems now makes analyzing seasonal and historical of shorelines relatively simple, and repeatable. Past management regimes and sea level rise have likely altered these important, protected coastal which also calls for a monitoring need. Beaches on Santa Rosa Island are likely changing seasonally, and historically.

II. Methodology

Background No similar research has been completed which quantifies shoreline change on the sandy beaches of Santa Rosa Island. However, there are many ongoing projects globally which aim to monitor shoreline change using Geographic Information Systems (GIS). The development of GIS has allowed for the analysis of spatial datasets, and the development of Global Positioning Systems (GPS) with sub-meter accuracy allows for analysis of seasonal shifts in shorelines as well as historical shifts(Psuty et al. 2010). Use of GIS software for historic shoreline positioning and analysis is the overwhelming choice of most members of the scientific community. GIS makes it

7 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar possible to georectify historical aerial imagery and extract an accurate shoreline position. Santa Rosa Island’s stretches of sandy beach have been cataloged in air photos dating from 1929 to 2012, making for a well-documented sandy beach shoreline and a well-suited candidate for analysis using GIS.

1. Site Selection Six sites were selected for repeated seasonal shoreline analysis and historical analysis. The sites were chosen based on their ability to be repeatedly analyzed over a four- month period. Travel restrictions and distance from California State University Channel Islands Field Research Station made these sites the most reliable for repeated seasonal

Figure 4. Sites surveyed for historical, and long term seasonal analysis. Skunk Pt (Site A) is the most northerly site, and East Pt (Site F) is the most southerly. With the exception of portions of Skunk Pt. and East Pt. the majority of Sites are east facing. surveys. Each sandy beach site varies in length, aspect, slope, as well as marked differences in adjacent ecosystem types which include dune scrub, estuaries, and cliff edges. The beaches are also habitat for sea lions, northern elephant seals, and western snowy plovers. See Figure 4for a map of site locations.

2. Historic Shoreline Analysis Analyzing historic shoreline change on SRI required acquiring historic aerial imagery from the University of California Santa Barbara (UCSB) Aerial Image Research Service (AIRS). In conjunction with Channel Islands National Park Service images from 1929, 1940, 1954, 1964, 1977, 1987, 1998, and 2012 were compiled ( Figure 5). Aerial imagery from 2012 was georectified at the time the images were taken so further processing of these images were not needed, and a standard five meters of uncertainty was attributed

8 to the shoreline. The remaining images purchased from AIRS were not georeferenced at the time of purchase, so further image processing was needed. The images were georectified into the Nad83 2011 UTM Zone 11 using the georectification tool available in ArcGIS. A minimum of 3 and maximum of 6 control points Figure 5. Two aerial images used fo r shorelines analysis were used for each image from on Santa Rosa Island from 1929 (left) and 2012 (right). the most recent basemap available and included stationary objects like rocks, or in worst case scenarios, evident river meanders. An image was not used for analysis unless a Root Mean Square (RMS) Error of 4 or below was achieved. The georectification process produced an error that had to be accounted for and will be covered in the Error Analysis section below. Georectified aerial photography is archived with the Environmental Science & Resource Management Department at CSUCI. After all of the images were georectified; polylines were then digitized on the shorelines of each site. The High Water Line (HWL) was determined to be the most identifiable and repeatable proxy of shoreline position available in the photos that range from scales of 1:18000 to 1:40000. The HWL is a result of the last highest tide that leaves a line of wet dark colored sand contrasting with a lightly colored dry sand. The line is identifiable in both aerial photos as well as in the field. A polyline of the shoreline was digitized for each image by tracing the HWL after each shoreline was digitized it was imported into historic shoreline geodatabase for analysis. Digitizing the shorelines produced another source of error that had to be accounted for and will be covered in the Error Analysis section below.

3. Historic Shoreline Uncertainty and Error Analysis Analysis of shoreline change could not begin until shoreline uncertainties were addressed. Uncertainty associated with measurement, as well as seasonal trends, were considered for each shoreline. Digitizing error Ed was assessed by digitizing a single shoreline twice and was computed by calculating the Standard Deviation of the differences of shoreline digitization attempts (Fletcher et al. 2012). To assess map deformations caused during the georectification process a rectification Er error is assessed for each image and is equal to the RMS value (Fletcher et al. 2012). Another source of error comes from the pixel size of the image, if a pixel represents 1 meter then the shoreline cannot resolve more accurately than within 1 meter. Pixel error Ep is equal to the pixel size of the image. Seasonal and tidal uncertainties present a different challenge in uncertainty analysis. Due to insufficient tidal data in relationship with the shoreline position tidal uncertainties had to be omitted from error analysis. Seasonal shoreline collected over the course of the past year was used to determine seasonal variability in shorelines because the images were taken throughout the year. Seasonal error Es was calculated

9 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar by determining the Standard Deviation of the differences in the summer and winter shorelines. Because the errors associated with historic shoreline mapping are random and do not correlate they can be placed in a quadrature (Fletcher et al. 2012). Each shorelines uncertainty was calculated using the following formula:

U= V(ES2+Ep2 + Er2+Ed2 + Et2)

After uncertainties had been calculated, they were then attributed to each respective shoreline year. Subsequently, the shorelines were analyzed in Digital Shoreline Analysis System (DSAS) an extension for ArcMap. Total uncertainty can be found in Table 2. A list of all uncertainty values can be found in the appendices.

Shoreline Date Source Uncertainty (m) Notes 06-30-1929 UCSB Map Library ±12.69 Exact Date Unknown 06-24-1940 UCSB Map Library ±12.62 Exact Date Unknown 06-06-1954 UCSB Map Library ±12.66 Exact Date Unknown 02-14-1964 UCSB Map Library ±12.69 Exact Date Unknown 01-11-1977 UCSB Map Library ±13.14 Exact Date Unknown 01-01-1990 UCSB Map Library ±13.05 Exact Date Unknown 01-01-2003 UCSB Map Library ±13.14 Exact Date Unknown 01-01-2012 National Park Service ±05.00 Exact Date Unknown

Table 2. Shoreline uncertainties were calculated for each georectified aerial image for WLR.

4. Seasonal Shoreline Analysis The survey techniques used are based the Northeast Coastal and Barrier Network Geomorphological Monitoring Protocol (Psuty et al. 2010). Each shoreline survey was taken using a Trimble 2008 GeoXT GNSS unit with a Tempest external antenna with sub-meter accuracy. Shorelines were logged in Nad83 2011 UTM Zone 11. In order to effectively repeat the shoreline survey the conditions were as similar each pass. Each survey was taken on or a close as possible to neap tide conditions when there is least difference between low and high tides ( Figure 6). The lapping of waves in neap tide conditions creates a relatively defined swash line on most Figure 6. Seasonal shoreline beaches. Swash lines can be made of material such analysis, completed on periods of as kelp wrack, sea shells, and other ocean debris and neap tide, were completed with a a recognizable and repeatable feature to follow on a sub-meter GPS device. shoreline survey. Each site on Santa Rosa Island has beaches that are defined by rocky beach ends allowing for a defined start and end point. Beaches were surveyed by starting at either end of the beach and walking the neap tide swash line as closely as possible to the end of the beach when the survey should end. This was repeated on each beach over the course of four months in 2014

10 and 2015. The data from each shoreline survey was transferred from the Trimble 2008 GeoXT to PC using Trimble’s Pathfinder Office. Before being exported as shapefiles, shoreline files were differentially corrected to the Nad83 CORS Datum to increase the data accuracy, metadata for differential correction is provided in the appendix. Files were added to a seasonal shoreline database for data analysis using the Digital Shoreline Analysis System. A standard uncertainty of five meters was attributed to each shoreline collected in the field due to wave height and run up ( Table 3). The data collection sheet for seasonal shoreline surveys is available in the appendices.

Shoreline Date Source Uncertainty (m) Notes 11/09/2014 GPS Survey ±05.00 Winter Neap Tide Shoreline 01/15/2015 GPS Survey ±05.00 High High Tide 01/18/2015 GPS Survey ±05.00 Low Low Tide 03/19/2015 GPS Survey ±05.00 Spring Neap Tide Shoreline

Table 3. Four GPS shoreline surveys were used to analyze seasonal, and tidal change on Santa Rosa’s shoreline

5. Data Analysis: Digital Shoreline Analysis System

Figure 7. Digital Shoreline analysis employs the single transect method for shoreline analysis. A virtual baseline is used to measure change over time on a series of evenly spaced transects (20m).

The United States Geologic Survey’s freeware analysis program DSAS (Thieler et al. 2009). It is a program that works in conjunction with ArcGIS to compute the rate of change statistics of shoreline positions using the single transect method ( Figure 7). The analysis system uses an arbitrary geospatial baseline that is used to measure the rate of change in a shoreline. Transects are broadcasted from the baseline every 20m and

11 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar can be set to broadcast at any interval and length that allows for a general beach profile. Each time a transect intercepts various historic shorelines, a point is recorded allowing for linear regression analysis of the shorelines movement over time (Figure 8). DSAS boast’s useful statistical analysis packages which include; Shoreline Change Envelope(SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), Linear Figure 8. The single transect method provides annual rate of change Regression (LRR), and statistics like Weighted Linear Regression with a 99.9% Weighted Weighted Linear Regression Confidence Interval (± value) (WLR). SCE calculates the distance from the furthest recorded shoreline from the baseline to the closest recorded shoreline and is not related to time. NSM calculates the distance between the oldest recorded shoreline and the most recently recorded shoreline and is affected by date. EPR Calculates average movement of the shoreline per year based on the amount of shoreline movement divided by the time between the shorelines. LRR uses every point on each transect to determine a least squares regression line that more accurately describes shoreline movement per year. WLR adds one more layer of accuracy by weighting each shoreline with its uncertainty. The WLR also produces a Weighted Confidence Interval (WCI) which is this case provides a ± value for shoreline movement (WLR) with 99.9% confidence.

III. Results

1.1 Historical Shorelines Two tests were run to analyze multidecadal shoreline change; NSM and WLR at a 99.9 percent confidence rate. NSM analyzing only 1929 and 2015 shorelines revealed that 71% of shorelines surveyed have eroded at mean rates of -26.05 20.69m. 29% have accreted at mean rates of 30.63 ± 29.67m over that period. Minimum and maximum rates of change were -117m of erosion and 108m of accretion on Site A. Sites B, C, D, and E’s rates of erosion fell between -39m and 20.The majority of shorelines surveyed displayed negative rates of change, except Site F, which was 22% erosive and 78% accreting, rates of accretion for Site F was 14.12 ± 3.7. A full list of NSM statistics for every Site can be found in Table 4A, and associated graphs can be found in Figure 9. WLR, which produced an annual rate of change statistic and WCI produced similar results when analyzing change from multiple shorelines from 1929 to 2015. 76% of the shorelines studied exhibited annual trends of erosion of rates of -.2976 ± .2086 m/y over that period. Minimum and maximum yearly change on Santa Rosa’s shorelines fell between -1.03 m/y and to 1.1 m/y. Again the highest rates of erosion as well as

12 accretion fell on Site A. A full list of WLR and WCI statistics for every Site can be found in Table 4B and associated graphs can be found in Figure 10. Aerial imagery can be found with the ESRM department at CSUCI.

1.2 Seasonal Shorelines NSM was analyzed for each site using GPS survey data on 11/9/2014 and 3/9/15. Seasonal NSM on Santa Rosa Island exhibited seasonal trends of accretion on 79% of beaches at mean rates of 15.18 ± 8.137m. 21% of beach exhibited erosive trends at mean rates of -7.068 ± 5.196. Minimum and maximum rates of change ranged from - 24.1m to 36m. Site F and E were both exhibited erosive rates of change at mean rates of between -6.7m and 14m, 33% of Site A also showed rates of erosion. A full list of seasonal NSM statics can be found in Table 4C, and associated graphs can be found in Figure 11. Table 4A. Site # of transects (20 m % Eroding Mean NSM % Accreting Mean NSM of Range (m) intervals) Of Eroding (m) Accreting (m) Site A 131 67 -31.76 ± 21.48 33 41.34 ± 30.24 -117 to 108 Site B 26 100 -13.52 ± 5.060 0 N/A -24.6 to -1.6 Site C 19 63 -13.79 ± 8.470 37 3.30 ± 2.57 -25.8 to 6.7 Site D 14 71 -3.720 ± 6.720 29 5.29 ± 4.52 -22.6 to 9.9 Site E 21 90 -13.92 ± 7.700 10 1.94 ± .74 -39.3 to 2.4 Site F 9 22 -32.14 ± 9.030 78 14.12 ± 3.7 -38.5 to 20 Total 220 71% -26.05 ± 20.69 29% 30.63 ± 29.67 -117 to 108

Table 4B. Site # of transects (20 m % Eroding Mean WLR & % Accreting Mean WLR & WCI Range intervals) WCI of of Accreting (m/y) of shoreline eroding (m/y) rates (m/y) Site A 129 65 -.4148 ± 1.190 35 .4368 ± 1.684 -1.03 to 1.1 Site B 25 100 -.1612 ± .6475 0 N/A -.23 to -.04 Site C 18 67 -.1791 ± .7558 33 .0466 ± .5942 -.33 to .12 Site D 26 100 -.0933 ± .4201 0 N/A -.03 to -.16 Site E 20 100 -.2222 ± .8206 0 N/A -.36 to -.06 Site F 6 100 -.2283 ± .6443 0 N/A -.36 to -.18 Total 216 76 -.2976 ± .2086 24 .4 ± .3457 -1.03 to 1.1

Table 4C. Site # of transects (20 m % Eroding Mean NSM % Accreting Mean NSM of Range (m) intervals) Of Eroding (m) Accreting (m) Site A 131 15 -7.820 ± 5.700 85 18.03 ± .7360 -24.1 to 36 Site B 24 25 -6.455 ± 2.586 75 10.49 ± 6.170 -9.78 to 23 Site C 18 0 N/A 100 18.02 ± 3.746 3.97 to 18.1 Site D 8 13 1.680 87 8.344 ± 5.552 -.68 to 15.8 Site E 17 59 -10.27 ± 5.365 35 8.952 ±4.290 -14.7 to 14 Site F 6 100 -3.131 ± 2.3547 0 N/A -5.81 to -.67 Total 210 18 -7.068 ± 5.196 79 15.18 ± 8.137 -24 to 36.6

Tables 4A. - 4C. Table 4A.Shoreline movement between 2015 and 1929 only, mean NSM & Standard Deviation are reported for both accreting and eroding shorelines. Table 4B. Multidecadal shoreline movement between 1929 and 2015, WLR with WCI is reported for both accreting and eroding shorelines. Table 4C. Shoreline movement between November 2014 & March 2015, mean NSM & Standard Deviation are reported for both accreting and eroding shorelines.

13 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar

Net Shoreline Movement 1929-2015

Figure 9.NSM between 1929 and 2015, reported with mean and standard deviation.

14 Weighted Linear Regression 1929-2015

15 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar

Net Shoreline Movement 11/9/14 - 3/9/15

Figure 11. Neap tide shorelines recorded on 11/92014 and 3/9/2015 were analyzed for change between that period. NSM & Standard deviation is reported.

16 IV. Discussion

1. How & why might shorelines be changing on Santa Rosa Island? Overall trends on Santa Rosa Island suggest that the majority of the island's beaches have been eroding since the first aerial photograph in 1929. Seasonally between November and March the majority of shorelines that were west facing accreted. Eroding shorelines were limited to Site F, which faced south eroded, along with northwest facing portions of Site A, and SW facing shoreline on Site E ( Figures 12 &13). It is likely that the opposite sediment transport will occur during the summer months although monitoring needs to continue to know for certain Figure 12. Site E on Santa Rosa Island in a state (McLachlan and Brown 2006c). of seasonal shoreline erosion in January 2015. Historical shoreline change may be Northern elephant seals can be seen in the attributed to climate change driven background during their annual haul out on Santa functions such as sea level rise and an Rosa’s Beaches. increase in wave stress. Historical shoreline change may also be driven by changes in land-use practices over the past 150 years. Seasonal shoreline accretion is most likely primarily driven by environmental factors such as landward sediment deposition, and swell direction & storminess. Understanding the rates and causes of shoreline movement, will help develop sound management strategies in the coastal zone of Santa Rosa Island.

1.1 Climate Change Climate change has become an ever increasing field of study in the last three decades and will continue you to be a topic of debate in the future. Climate changes effects on ecosystems and biodiversity, however, is not (Bellard et al. 2014). 180,000 islands exist in the world today and contain 20% of the world’s biodiversity (Bellard et al. 2014). Santa Rosa Island is not a low- lying island and is not at risk of total submersion. However under current rates of sea level rise has been calculated by the IPCC at Figure 13. Another view of seasonal erosion taking place at Site E on Santa Rosa Island during January 2015.

17 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar global rates of 3.2mm a year, and have been predicted to top 1 m by the end of the century. These rates of change will likely alter coastal dune and estuarine systems on Santa Rosa. A study in Wales on the implications of sea level rise for coastal dune habitat conservation (Saye and Pye 2007) indicated that net dune habitat loss in the 15 sites studied substantially outweighed habitat gain. Sea level rise may have the same effect on coastal habitats on Santa Rosa Island. However, no studies have on the topic have been researched. In increase in climate change driven increase or decrease in wave stress may also play a role in shoreline change on Santa Rosa Island (Mori et al. 2010). Climate change may be playing the most dominant role in erosive trends on all sites surveyed on Santa Rosa, and may threaten biodiversity on the island.

1.2 Sea Level Rise & Increased Wave Stress It is important to note that large-scale spatial patterns of sea level change have only been known to a high precision since 1993. This is when satellite altimetry became available, and during that period rates increased to 3.6mm y compared to rate of 1.8mm y between 1990 and 210 (Rhein et al. 2013). It is virtually certain that global sea level is rising, and large-scale winds and ocean circulation can cause geographically diverse effects on individual locations (Rhein et al. 2013). Increased wave stress from wind can increase isolated effects such as coastal flooding, storm surge, and higher water events (Mori et al. 2010). These factors increase the certainty that coastlines will change, and sandy beaches on Santa Rosa Island will face important management decisions.

1.3 Management History Qualitative evidence of new vegetation can be seen in Figure 5. The denuded Skunk Point (Site A), is likely the product of uncontrolled sheep grazing in the late 19th century to the early 20th century (National Park Service n.d.). Since grazing of all kind has been discontinued since 2009, it is likely that vegetation has been allowed to regrow. Northerly prevailing winds blow across the island most days and sediment was likely deposited on the adjacent beach. Since the vegetation has occurred this action has probably stopped (Miller et al. 2010), however, sedimentation rates are unknown. The adjacent shoreline is eroding at WLR and WCI rates of -.67 m/y ± 1.01 and -.27m/y ± 1.23. This stretch of shoreline that is eroding most regularly and can be seen in Figure 13. Figure 14. Net shoreline movement on Santa Rosa Island (1929-2015). Shoreline positions were variable between Another source of decades, which increased data noise. shoreline change may be from

18 landward sedimentation rates. Riparian zones are beginning to heal, and sedimentation rates may be lowering from historic values. Research by (Cole and Liu 1994) analyzed sediment cores on a Santa Rosa Island estuary that revealed that until large animals were introduced sedimentation rates averaged .7 mm/yr. After the island had been settled, sedimentation increased to 13 mm/y and peaked during a period of heavy sheep grazing at 23 mm/yr between 1874 and 1920 (Cole and Liu 1994).

2.1 Challenges of Historical Analysis Historical analysis of aerial imagery posed many challenges including, georectification, digitization of shorelines, and uncertainty analysis of shorelines. The High Water Line was chosen as the proxy of choice for shoreline digitization as it was the most visible in all images. However exact dates of almost of historic shorelines were not provided, so tidal and seasonal data was unknown, providing or much uncertainty ( Figure 14). Another shortfall of using the High Water Line as a proxy and aerial imagery analysis in general, is that it only provides horizontal data for shoreline movement, and not a true depiction of sediment movement as a whole (Ruggiero and List 2009). Another aspect not taken into account when predicting rates of change shore shoreline position is slope, and wave force data that can help predict more accurate rates of change (Ruggiero and List 2009). Limited resources and the scope of this project did not allow for historical imagery analysis for all beaches on Santa Rosa, so only a piece of the shoreline movement story can be seen.

2.2 Challenges of Seasonal Analysis Seasonal analysis was largely limited due to the time scope of this project, as well as time constraints, limited time on the island and limited transportation on the island. Ideally if travel permitted all accessible coastal sites would have been surveyed over the period of a full year. For seasonal analysis of shorelines a mid-winter and a mid summer shoreline should be used(Psuty et al. 2010). In the case of this study, a late fall and late winter shoreline was used for analysis. Restricted time on the island also didn’t always allow for seasonal shorelines to be surveyed on neap tide that would have provided a more repeatable shoreline (Psuty et al. 2010). Another proxy for shoreline position can be cliff edges and vegetation lines. Another aspect not taken into account when predicting rates of change shore shoreline position is slope, and wave force data that can help predict more accurate rates of change.

3. Implications for Future Research Monitoring shoreline change effectively entails understanding historic changes, as well as gaining an understanding of beach morphology, a littoral understanding of sediment transport off the coast of the island. It would also be of great importance to understand landward sediment transport in the form of wind and water erosion. Seasonal data should also continue to be collected on a regular basis to gain an understanding of shoreline change on different temporal scales as riparian zones heal, and vegetation cover increases island wide. Research by (Romine et al. 2009) employed a polynomial model to calculate rates of shoreline change on Oahu, Hawaii. The single transect method fails to capture alongshore sediment movement, and polynomial models can include alongshore

19 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar variation in their models (Romine et al. 2009). This may provide a more telling story of historic change in shoreline position, especially on Skunk Point (Site A) which was extremely variable in shoreline position. In order to fully understand how shorelines are changing, all of the preceding considerations must be taken into account. In addition, another proxy of shoreline position such as vegetation should be used as a proxy for shoreline change using the single transect method, and results should be compared with shorelines digitized at the high water line. Similar results would support shoreline movement on Santa Rosa Island.

V. Conclusion As considerable evidence suggests that sea levels are rising, and historical management of Santa Rosa Island likely increased sedimentation rates island wide, it is likely that these factors have changed sandy beaches on Santa Rosa Island. The islands beaches are an important haven of human recreation, and ecology found nowhere else, and it is necessary for management decisions that we understand changes in these environments. Seasonal and Historical (multidecadal aerial imagery) analysis provided a glimpse into two different temporal periods. Seasonal shorelines exhibited cyclical changes, however to understand seasonal changes on Santa Rosa Islands beaches more sites need to be frequently monitored island wide. Historical trends in shoreline change are mostly erosive, at mean rates of -.2976 ± .2086, which suggest the action of sea level rise. However, a complete understanding of seasonal changes, morphological changes, and effects on ecosystems must be understood to inform future management decisions. Future research should aim to fine tune shoreline analysis on Santa Rosa Island to identify true hot spots of shoreline change. Shoreline change statistics suggest high variability in shoreline positions of Sana Rosa Island, but provides insight into overall trends in shoreline movement and are an important management asset. Well managed shorelines on Santa Rosa Island can help to protect biodiversity and natural resources, as well as cultural resources within Channel Islands National Park.

VI. Acknowledgements I could not have completed this project without the help of my advisors, Sean Anderson, Cause Hanna, and Rocky Rudolph. They provided me with so much motivation, knowledge, insight and guidance along the way, and I could not finished a project of this scope without them. I can’t express enough gratitude towards CSUCI and the NPS service for facilitating such an amazing research opportunity in such a wild place. Cause, Tracy, and Solstice, thanks for welcoming me to the island so many times through the year. Rocky supported me through the entire process of my project, and without his effort to obtain historical imagery of the Channel Islands, this project would not have been possible. Trips to Santa Rosa would not have been possible without Island Packers and the NPS thank you so much for getting us there and back. Financial support from the Havasi Foundation and Scott Westcott made these trips possible, thank you. I could not have completed my 20 mile round trip beach GPS surveys without the company of my awesome colleagues; Kira West, Sean Clark, Taylor Lane, Mike McGurk, Brittany Lucero, Blake Swendrowski, Alexis Wallengren, and Nathan Hilpert. Thank you all again so much!

20 References Amyot, J., and J. Grant. 2014. Environmental Function Analysis: A decision support tool for integrated sandy beach planning. Ocean & Coastal Management 102, Part A:317-327. Bellard, C., C. Leclerc, and F. Courchamp. 2014. Impact of sea level rise on the 10 insular biodiversity hotspots. Global Ecology & Biogeography 23:203-212. Cole, K. L., and G.-W. Liu. 1994. Holocene Paleoecology of an Estuary on Santa Rosa Island, California. Quaternary Research 41:326-335. Courchamp, F., B. D. Hoffmann, J. C. Russell, C. Leclerc, and C. Bellard. 2014. Climate change, sea-level rise, and conservation: keeping island biodiversity afloat. Trends in Ecology & Evolution 29:127-130. Defeo, O., A. McLachlan, D. S. Schoeman, T. A. Schlacher, J. Dugan, A. Jones, M. Lastra, and F. Scapini. 2009. Threats to sandy beach ecosystems: A review. Estuarine, Coastal and Shelf Science 81:1-12. Ghermandi, A., and P. A. L. D. Nunes. 2013. A global map of coastal recreation values: Results from a spatially explicit meta-analysis. Ecological Economics 86:1-15. Houston, J. R. 2008. The economic value of beaches: a 2008 update. Shore and Beach 76:22-26. McLachlan, A., and A. C. Brown. 2006a. 1 - Introduction. Pages 1-3 in A. M. C. Brown, editor. The Ecology of Sandy Shores (Second Edition). Academic Press, Burlington. McLachlan, A., and A. C. Brown. 2006b. 2 - The Physical Environment. Pages 5-30 in A. M. C. Brown, editor. The Ecology of Sandy Shores (Second Edition). Academic Press, Burlington. McLachlan, A., and A. C. Brown. 2006c. 3 - The Interstitial Environment. Pages 31-54 in A. M. C. Brown, editor. The Ecology of Sandy Shores (Second Edition). Academic Press, Burlington. Miller, T. E., E. S. Gornish, and H. L. Buckley. 2010. Climate and Coastal Dune Vegetation: Disturbance, Recovery, and Succession. Plant Ecology 206:97-104. Mori, N., T. Yasuda, H. Mase, T. Tom, and Y. Oku. 2010. Projection of Extreme Wave Climate Change under Global Warming. Hydrological Research Letters 4:15-19. National Park Service. (n.d.). Section 3: Santa Rosa Island. Pages 135-396 Channel Islands National Park - Historic Resource Study. Nel, R., E. E. Campbell, L. Harris, L. Hauser, D. S. Schoeman, A. McLachlan, D. R. du Preez, K. Bezuidenhout, and T. A. Schlacher. 2014. The status of sandy beach science: Past trends, progress, and possible futures. Estuarine, Coastal and Shelf Science 150, Part A:1-10. Orr, P. C. 1962. Arlington Springs Man. Science 135:219. Psuty, N., T. Silveira, M. Duffy, J. Pace, and D. Skidds. 2010, March. Northeast Coastal and Barrier Network Geomorphological Monitoring Protocol: The National Park Service, Natural Resource Program Center. Rhein, M., S. Rintoul, S. Aoki, E. Campos, D. Chambers, R. Feely, S. Gulev, G. Johnson, S. Josey, and A. Kostianoy. 2013. Observations: ocean. Roberts, G. 2014. Geologic Formations - Channel Islands National Park (U.S. National Park Service). http://www.nps.gov/chis/learn/nature/geologicformations.htm .

21 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar Romine, B. M., C. H. Fletcher, L. N. Frazer, A. S. Genz, M. M. Barbee, and S.-C. Lim. 2009. Historical Shoreline Change, Southeast Oahu, Hawaii; Applying Polynomial Models to Calculate Shoreline Change Rates. Journal of Coastal Research 25:1236-1253. Ruggiero, P., and J. H. List. 2009. Improving Accuracy and Statistical Reliability of Shoreline Position and Change Rate Estimates. Journal of Coastal Research 25:1069-1081. Santa Rosa Island - Channel Islands National Park (U.S. National Park Service). (n.d.). . http://www.nps.gov/chis/planyourvisit/santa-rosa-island.htm . Saye, S. E., and K. Pye. 2007. Implications of sea level rise for coastal dune habitat conservation in Wales, UK. Journal of Coastal Conservation 11:31-52. Schlacher, T. A., J. Dugan, D. S. Schoeman, M. Lastra, A. Jones, F. Scapini, A. McLachlan, and O. Defeo. 2007. Sandy beaches at the brink: Sandy beach conservation crisis. Diversity and Distributions 13:556-560. Schlacher, T. A., D. S. Schoeman, J. Dugan, M. Lastra, A. Jones, F. Scapini, and A. McLachlan. 2008. Sandy beach ecosystems: key features, sampling issues, management challenges and climate change impacts. Marine Ecology 29. Stocker, T. F., D. Qin, G. K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex, and B. M. Midgley. 2013. IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Thieler, E. R., E. A. Himmelstoss, J. L. Zichichi, and Ergul, and Ayhan. 2009. Digital Shoreline Analysis System (DSAS) version 4.0— An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008 1278. Support.

Appendix

1. Seasonal Shoreline Data Collection Sheet

Santa Rosa Island - Seasonal Shoreline Survey

Survey Date: Start Time: End Time: Observer: Last High Tide: Closest Neap Tide Date: Closest Storm Event Date

22 Survey Notes:

GPS Device

Make: Model: Data Download / Correction:

Software Used for Download Real-Time Correction & Base Station

Post-Correction & Base Station

Other Data Edits:

2. Historical Shoreline Uncertainty Data

Shoreline Year: 1929 Frame Id: d-14, c-14 Pixel Error: 1.17 Rectification Error: 1.022582 Seasonal: 8.326588 Tidal: 9.329889 Digitizing Error: 1.43456 Total Uncertainty 12.69 Scale: 1:18,000

313-39, 319 Shoreline Year: 1940 Frame Id: 104 Pixel Error: 0.95 Rectification Error: 0.0582421 Seasonal: 8.326588 Tidal: 9.329889

23 Quantifying Seasonal & Historical Shoreline Change on Santa Rosa Island Bednar

Digitizing Error: 1.43456 Total Uncertainty 12.62 Scale: 1:20,000

Shoreline Year: 1954 Frame Id: 8k-96, 10k-61 Pixel Error: 0.87 Rectification Error: 1.12 Seasonal: 8.326588 Tidal: 9.329889 Digitizing Error: 1.43456 Total Uncertainty 12.66 Scale: 1:20,000

Shoreline Year: 1964 Frame Id ha-wc 114 Pixel Error: 1.6332 Rectification Error: 0.292296 Seasonal: 8.326588 Tidal: 9.329889 Digitizing Error: 1.43456 Total Uncertainty 12.69 Scale:

Frame Shoreline Year: 1977 Id: 77-006-021456-225 Pixel Error: 1.9 Rectification Error: 3.28443 Seasonal: 8.326588 Tidal: 9.329889 Digitizing Error: 1.43456 Total Uncertainty 13.14 Scale: 1:32,500

Frame Shoreline Year: 1990 Id: napp-1893-86 Pixel Error: 1.89 Rectification Error: 2.88974 Seasonal: 8.326588

24 Tidal: 9.329889 Digitizing Error: 1.43456 Total Uncertainty 13.05 Scale: 1:40,000

Frame Shoreline Year: 2000 Id: napp-3c 12466-121 Pixel Error: 1.8 Rectification Error: 3.32388 Seasonal: 8.326588 Tidal: 9.329889 Digitizing Error: 1.43456 Total Uncertainty 13.14 Scale: 1:40,000

3. Differential Correction Used for GPS Surveys

GPS Hardware: Trimble GeoXT 2008 with Tempest Antenna GPS Software: Trimble Terrasync 5.60 Data Management: Trimble GPS Pathfinder Office software 5.40

In Pathfinder Office (PFO), differential correction was performed on data each time it was collected from the field.

Base Station used to differentially correct: CORS, SRS1_SCGN_CS2000 (SRS1), CALIFORNIA (ITRF00 (1997)-Derived from IGS08 (NEW)) Used reference position from basefiles setting in PFO.

After differential correction, shapefiles were exported using this projection: NAD 1983 (2011) UTM Zone 10N.prj