Habitat Selection and Response to Restoration by Breeding

HABITAT SELECTION AND RESPONSE TO RESTORATION BY BREEDING

WESTERN SNOWY PLOVERS IN COASTAL NORTHERN CALIFORNIA

Stephanie D. Leja

A Thesis Presented to

The Faculty of Humboldt State University

In Partial Fulfillment of the Requirements for the Degree

Master of Science in Natural Resources: Wildlife

Committee Membership

Dr. Mark A. Colwell, Committee Chair

Dr. Matthew D. Johnson, Committee Member

Dr. William T. Bean, Committee Member

Dr. Alison O'Dowd, Graduate Coordinator

December 2015 ABSTRACT

HABITAT SELECTION AND RESPONSE TO RESTORATION BY BREEDING WESTERN SNOWY PLOVERS IN COASTAL NORTHERN CALIFORNIA

Stephanie D. Leja

Habitat loss and degradation by invasive species is a primary limitation to the recovery of the Western Snowy Plover (Charadrius nivosus nivosus), a federally

threatened shorebird that resides on coastal beaches in Humboldt County, California.

This habitat threat posed by European Beachgrass (Ammophila arenaria) is often

mitigated through dune restoration. Some habitat features altered by restoration (e.g., beach width) may influence plover breeding habitat selection. Further study was needed to determine which physical and social features (e.g., presence of conspecifics) influence plover nesting locations. I evaluated this response of plovers to restoration and identified characteristics that influence nest site selection. In an Information-Theoretic framework, I compared nests (n = 81) and random locations within habitat using logistic regression and

Generalized Linear Mixed Models to produce Resource Selection Function analyses and conduct model selection analyses. Plovers nested on wider, less sloped beaches, with greater coverage of natural debris (e.g., driftwood, shells) and more conspecifics than at random locations. Plovers nested primarily (84%) in restored habitats, although this was influenced by one human-restored site with 33% of nests. These findings can guide coastal dune system managers to generate the features in restoration that improve nesting habitat and facilitate survival and recovery of this threatened Snowy Plover population.

ii ACKNOWLEDGEMENTS

Thank you to Dr. Mark Colwell for his invaluable guidance, advising, and

opportunities for intellectual growth and experience. Thank you to my committee members Dr. Matthew Johnson and Dr. Tim Bean for their guidance and essential thesis reviews. Many thanks to the extensive group of Humboldt State student surveyors and volunteers who collected data as part of the larger Snowy Plover project, and especially to those who helped with my data collection. I thank J. Watkins from the U.S. Fish and

Wildlife Service, J. Irwin of the U.S. Bureau of Land Management, and S. McAllister.

Many thanks to the California State Department of Parks and Recreation: A. Transou, C.

Wilson, M. Morrissette, J. Harris, T. Kurz, and C. Ryan. I am grateful to A. Desch and

the HSU Wildlife Department for providing field equipment. I also extend my gratitude

to the countless others who make this monitoring project happen and thus helped

facilitate my research. This research was funded by the California Department of Fish

and Wildlife, the California Department of Parks and Recreation, Humboldt State

University, the U.S. Bureau of Land Management, and the U.S. Fish and Wildlife

Service, and I thank these entities for their support. I also thank fellow shorebird graduate lab mates A. Patrick, D. Herman, M. Lau, T. King, and the Pink House roommates for their methodological conversations, draft revisions, and fellow graduate student empathy.

I give a huge thank you to my wonderful parents, Mark and Hope Leja, and to Evan Blair for their limitless and essential support and advice throughout this experience.

iii TABLE OF CONTENTS

ABSTRACT ...... ii

ACKNOWLEDGEMENTS ...... iii

LIST OF TABLES ...... vi

LIST OF FIGURES ...... vii

LIST OF APPENDICES ...... viii

INTRODUCTION ...... 1

Plover Nest Site Selection ...... 4

METHODS ...... 7

Study Area ...... 7

Field Methods ...... 10

Geospatial Methods to Obtain Additional Covariates ...... 11

Beach width ...... 13

Three habitat types ...... 13

Conspecific attraction ...... 13

Statistical Analyses ...... 15

Resource Selection Function analyses ...... 16

Habitat type analysis ...... 18

RESULTS ...... 19

Comparison of Restored and Unrestored Habitats ...... 19

Comparison of Nest and Random Site Characteristics ...... 22

DISCUSSION ...... 31

Plovers Selected for Restored Habitats ...... 31

iv Physical Features are Important to Habitat Selection ...... 32

Social Attraction Influences Nest Location Selection ...... 35

Management Recommendations ...... 38

LITERATURE CITED ...... 41

APPENDICES ...... 49

v LIST OF TABLES

Table 1. Definitions of variables used in logistic regression and other statistical analyses to evaluate habitat selection and response to habitat restoration of Snowy Plovers. Bracketed terms represent each of these covariates in the analyses...... 12

Table 2. Summary of the Chi-square goodness-of-fit analysis that evaluated selection between three habitat types by Snowy Plovers along 80 km of ocean-fronting beach in Humboldt County, CA in 2014...... 20

Table 3. A comparison of average (± SD) physical and social characteristics at 81 Snowy Plover nests in three habitat types along ocean-fronting beaches in Humboldt County, CA in 2014...... 21

Table 4. Summary of the comparison of habitat covariate values (mean ± SD) at nest locations and random points within plover nesting habitat in Humboldt County, CA, in 2014, derived from Student’s t-test statistics...... 23

Table 5. A summary of 19 competing models examining relationships between physical and social variables influencing habitat selection of nesting Snowy Plovers in Humboldt County, CA in 2014, showing the top five models with ΔAICc < 6 ranked by Akaike weight (wi), that explain 99% of selection and which represent hypotheses combining nine covariates, included the random effect of site (see Appendix E for full candidate model set)...... 24

Table 6. A summary of 18 competing models after removing one pretending model (which included the width covariate) examining relationships between physical and social variables influencing habitat selection of nesting Snowy Plovers in Humboldt County, CA in 2014, showing the top five models with ΔAICc < 6 ranked by Akaike weight (wi), that explain 99% of selection and which represent hypotheses combining nine covariates, included the random effect of site...... 25

Table 7. A summary of 19 models evaluating relationships between physical and social variables influencing habitat selection of a subset of 22 unique nesting male Snowy Plovers and 22 random points in Humboldt County, CA in 2014, ranked by Akaike weight (wi) and which represent hypotheses combining nine covariates, included the random effect of site...... 30

vi LIST OF FIGURES

Figure 1. Map of study area in Humboldt County, California, identifying areas of human- implemented restoration ( ) and naturally restored ( ) habitat. Focal beaches where breeding plovers occurred in 2014 are labeled. (LRSB = Little River State Beach)...... 8

Figure 2. Section of Clam Beach County Park in Humboldt County, CA, in 2014 showing an example of the fishnet grid clipped to the habitat extent and the placement of three beach width measurements (measured along the gridlines) over example nests and random points...... 14

Figure 3. The index of the probability of habitat use by Snowy Plovers as a function of increasing number of conspecifics in Humboldt County, CA, derived from the full dataset of nests and random locations (n = 162) and the global model of all nine covariates. Nest values are represented at 1.0; random points at 0.0. When there are approximately 2.5 conspecifics present, the index of probability of use is 0.5...... 27

Figure 4. Probability curves for the index of probability of use of plover nesting habitat in Humboldt County, CA, as a function of the habitat characteristics width, slope, Ammophila arenaria, native vegetation, open sand, and other cover (e.g., driftwood, shells, stones), derived from the global model of a resource selection function analysis. Nest values are represented at 1.0; random points at 0.0...... 28

vii LIST OF APPENDICES

Appendix A. Summary of pertinent information on the history of coastal dune native habitat restoration projects conducted in Snowy Plover habitat within Recovery Unit 2 over the last 22 years (1992-2014). BLM = Bureau of Land Management, CSP = California State Parks, CMA = Cooperative Management Area, EPPA = Endangered Plant Protection Area, SB = State Beach, SP = State Park, SRA = State Recreation Area. Note that area (total = 206 ha) includes foredune and wrack west of the restored site, measured from my delineations approximating human-restored habitat at that site; thus this value may differ slightly from the reported areas in the literature. Count of nests are those that fall within human- restored habitat only (n = 38)...... 49

Appendix B. Map of Little River State Beach and Clam Beach County Park, CA, depicting polygons representing three types of habitat (human-implemented restoration, natural restoration, and unrestored) as identified in 2014 and delineated by the extent of available plover habitat. These three habitat types were assigned throughout approximately 80 km of beach habitat in the study area. .... 50

Appendix C. Section of Clam Beach County Park in Humboldt County, CA, in 2014 showing an example of the delineated extent of plover nesting habitat as evident by the black line tracing the high tide wrack line and the edge of A. arenaria (the green and brown to the east of the line) too thick for a plover to permeate...... 51

Appendix D. Visual summary depicting (a) a typical cross section of the beach profile showing the measurement method for slope across nests in the foredune and backdune of plover beach nesting habitat; and (b) a typical plan view for 100 m diameter relevé plots showing where beach width was obtained at all nests and random points throughout the study area in 2014...... 52

Appendix E. Complete candidate model set of 19 a priori models representing hypotheses combining nine covariates from a resource selection function analysis of 81 nests and 81 random points. Models are ranked by greatest Akaike weight (wi) indicating the top explanatory model...... 53

viii 1 INTRODUCTION

Habitat is defined as the resources and conditions of an area that an organism requires to survive and reproduce (Hall et al. 1997). Loss and degradation of habitat,

especially when followed by invasion of exotic species, is the most significant threat to

imperiled wild birds (Reed 1995, Johnson 2007). Degradation limits the resources

essential for survival and reproduction and affects the behaviors of habitat selection.

Habitat loss can be mitigated by restoration, which is the process of returning degraded

land to a natural state with desirable functions via manipulation of the biological and

physical characteristics of the site (NRC 1992, Urbanska et al. 1997). The goal of habitat

restoration for wildlife is to recreate habitat elements that both attract and enhance the

survival and reproduction of target species (Morrison 2013). These elements include

vegetation type, ground cover, topography, presence of conspecifics, prey and predator

presence, and other physical and social components (Wiens 1985, Krausman 1999).

These landscape features may serve as proximate factors (cues used to determine site suitability) in habitat selection, and they can be used by conservationists to evaluate the effect of critical habitat restoration efforts on focal species (Krausman 1999, Krebs

2001), especially threatened and endangered taxa. Hence, habitat is a critical component in the conservation of threatened and endangered species (USFWS 2012).

In the context of the U.S. Endangered Species Act, critical habitat is “the occupied or unoccupied geographic areas that provide the physical and biological features essential to the conservation and recovery of a species” (Camaclang et al. 2014,

2 USFWS 2011a). Restoration of these critical areas can be human-implemented or occur through natural disturbance processes and events. Effective human-implemented habitat restoration requires detailed and accurate information regarding habitat selection (gauged as the choice by individuals to disproportionately use a particular habitat given alternative available habitat; Alldredge and Griswold 2006, Johnson 2007). For breeding birds, understanding nesting habitat selection is essential to conservation efforts (such as restoration) that improve population recovery.

Threatened ground-nesting birds have benefited from habitat restoration. Studies of the Piping Plover (Charadrius melodus) on the Missouri River illustrated that knowledge about proximate factors such as open, sandy areas with sparse vegetation can be used to design and implement restoration projects capable of increasing plover reproductive success (Catlin et al. 2011). Powell and Collier (2000) found that in newly created Least Tern (Sternula antillarum browni) and Western Snowy Plover (Charadrius nivosus nivosus) breeding habitat, restoration generated less-vegetated areas that attracted plovers and increased reproductive success. These findings demonstrate that it is crucial for land managers conducting restoration to identify habitat features that attract threatened species. Along the Pacific coast of California, restoration has been implemented to mitigate loss and degradation of Western Snowy Plover breeding habitat by introduced European beachgrass (Ammophila arenaria), but Snowy Plover habitat selection and response to restoration has not been thoroughly evaluated.

The Pacific coast population of the Western Snowy Plover (hereafter, plover) was listed as threatened in 1993 under the United States Endangered Species Act (USFWS

3 1993). The recovery plan for the plover (USFWS 2007) identified three primary factors

that are thought to limit this population (i.e., compromise productivity and survival),

including: 1) predation of chicks and eggs; 2) human disturbance; and 3) habitat loss and

degradation owing to invasive species, especially A. arenaria. The ecosystem impact of

the invasion of A. arenaria has been addressed by habitat restoration (Pickart 2008, East

2008, Pickart 2013, Bureau of Land Management 2014, California State Parks 2014).

However, the response of plovers to this commonly implemented management technique

to remove A. arenaria has been largely unstudied.

Ammophila arenaria threatens the native biota along much of the California coast

(Pickart 1997). Where A. arenaria has invaded, there is reduced open area, narrower

beach width, steeper slopes, and half the diversity of plant species as in dunes dominated

by the native dunegrass Leymus mollis (Barbour et al. 1976). Ammophila arenaria creates

an impenetrable ridge at the crest of the foredune closest to the ocean and encroaches into

native habitat and overlapping plover breeding areas. Consequently, much of the

remaining available habitat has been altered (Buell et al. 1995, Pickart 2008). In

response, management agencies (multiple public and private agencies and nonprofit

organizations) have implemented ecosystem-level (Grumbine 1994) habitat restoration in

coastal dunes over the last several decades (see Powell and Collier 2000, Pickart 2008,

California State Parks 2013). The primary focus of this restoration is to facilitate

reestablishment of native dune vegetation and topography by removing highly invasive A.

arenaria and non-native iceplant (Carpobrotus spp.; Wiedemann and Pickart 1996,

Pickart 2008, Pickart 2013, Maltez-Mouro 2010, California State Parks 2014). This

4 management assists native plant and wildlife species by regenerating open dune

ecosystem habitat and making it available for recolonization. Thus, it can also benefit

ground-nesting shorebirds such as the plover, which utilize these coastal habitats year-

round, and particularly for breeding (Brindock and Colwell 2011, Colwell et al. 2014a).

Plover Nest Site Selection

Plover breeding habitat along the Pacific coast is broadly defined as open, sparsely vegetated habitat limited at its extent by rocky intertidal zones, steep bluffs and dunes, thick vegetation, rivers, and dense stands of invading A. arenaria that occur along open-ocean fronting beaches; these unsuitable areas decrease available habitat for nesting

plovers (Page and Stenzel 1981, USFWS 2007). Snowy Plovers appear to use a variety of

cues in the process of selecting a breeding site within this critical habitat. At the coarsest

spatial scale, Page and Stenzel (1981) noted that the greatest concentrations of breeding

plovers occurred at coastal beaches near the mouths of rivers and creeks, ponds, and

lagoons. At finer spatial scales, plovers have been shown to court and nest along wide, sparsely vegetated beaches (Muir and Colwell 2010). Patrick and Colwell (2014) found that plovers nested on wider beaches than at random locations. Individuals appear to select low-height foredunes with scattered driftwood and low-growing dune mat vegetation comprised of several native species including L. mollis, as well as areas of expansive, flat sand that extend into the backdunes of beaches, all of which are characteristics generated by habitat restoration (Page and Stenzel 1981, Pickart 2008,

Muir and Colwell 2010). These findings indicate that the aforementioned characteristics

5 of habitat, along with additional unrecognized features, influence nesting habitat

selection. Plovers may select nesting sites that are naturally disturbed by seasonal

flooding and scouring events that subsequently restore habitat features and cause

favorable changes in them (e.g., more width and open sand, and removal of A. arenaria).

Plovers nest in wide, open sandy areas that are sparsely vegetated (Page et al.

2009, Muir and Colwell 2010). Muir and Colwell (2010) determined that plovers selected

sites for courtship and nesting that had lower percent cover than random locations. This

open habitat provides unobstructed sightlines for these ground-dwelling birds to observe

and respond to predators at a distance, which is hypothesized to reduce predation risk to

adults, chicks, and eggs (Hardy and Colwell 2012). Plovers typically lay three-egg clutches in a shallowly scraped depression on the ground. This leaves the nest vulnerable, although the eggs are well camouflaged by cryptically patterned, speckled coloration

(Page et al. 2009). Incubating adult plovers can also observe potential predators at a

distance through open, flat habitat and use scattered debris and heterogeneous cover to

sneak away from their nest to hide; this adaptation to these habitat features, combined

with egg crypsis, improves nest survival (Page et al. 1985, Colwell et al. 2011). Plovers

nest in a semi-colonial distribution (Patrick 2013), indicating that additional factors, such

as conspecific social cues, may also influence selection of nesting habitat.

It is widely recognized that birds use social cues (e.g., the presence of

conspecifics) to select breeding sites in high quality nesting habitat (Wiens 1985, Ward

and Schlossberg 2004, Ahlering and Faaborg 2006). Location of conspecifics in the

landscape may provide information on the suitability and quality of the habitat (Betts et

6 al. 2008), especially to naïve and inexperienced individuals (i.e., those searching for their

first breeding site). In plovers, this social attraction has been shown to influence the

location at which naïve individuals first nest (Nelson 2007), which likely affects the

semi-colonial nature of the species (Patrick 2013). These behaviors in plovers indicate

that conspecific attraction (i.e., the tendency for individuals of a species to settle near

other members of the same species; Ward and Schlossberg 2004), may be an important

facet of habitat selection that should be considered in management. However, the social

effect of conspecifics as a feature of landscape-level habitat and as an influence on

breeding habitat selection has not been fully addressed in plovers.

Remarkably, no study of the Western Snowy Plover has collectively evaluated plover nesting habitat selection in terms of conspecific influence and the physical cues used by plovers to select breeding habitat. Additionally, no study has assessed the response of plovers to habitat restoration in this context. To address these components of plover ecology, I built on a long-term study of an intensively monitored population of plovers in coastal northern California to 1) quantify response of breeding plovers to human-implemented and natural habitat restoration, 2) assess the role of both physical and social habitat characteristics in the selection of nesting locations by plovers, and 3) make management recommendations based on these findings.

7 METHODS

Study Area

In 2014, I studied a color-marked population of 52 plovers (Colwell et al. 2014a) that bred along approximately 80 km of ocean-fronting beaches in Humboldt County,

California (Figure 1). Beaches were composed of open, sandy substrate separated by rocky intertidal areas and backed by dunes, wetlands, lagoons, rivers, and bluffs (Page and Stenzel 1981, Muir and Colwell 2010, USFWS 2012). Portions of the beaches consisted of wide debris fields and sand spits. Beach width at nest sites in three years prior to 2014 (2005, 2009, 2010), on average (± SD), was 225 ± 112 m (range 33–478 m;

Patrick and Colwell 2014).

Much of this habitat included varying amounts of woody debris and dead vegetation, marine algal and shell debris, and human trash. It was also sparsely vegetated with native and nonnative vegetation. Common native coastal strand plants in this area included American dunegrass (Leymus mollis), yellow and pink sand verbena (Abronia spp.), Glehnia (Glehnia littoralis), beach bursage (Ambrosia chamissonis), American searocket (Cakile edentula), European searocket (C. maritima; non-native, but limited threat), beach morning glory (Calystegia soldanella), and beach strawberry (Fragaria chiloensis). Most beaches were dominated by dense stands of non-native, invasive

European beachgrass and patches of non-native iceplant (Pickart 2008).

9 Over the past two decades, the extent of A. arenaria has varied across the study

area in association with three types of habitat, two of which are generated by restoration.

One type of habitat is human-implemented restoration (i.e., “human-restored”). The

second type is restoration from natural processes such as seasonal flooding and beach

scouring with no human-implemented restoration, especially at river mouths (i.e.,

“naturally restored”). All other areas have no history of human-restoration or evidence of

extensive natural restoration; as a result, A. arenaria is widespread (i.e., “unrestored” habitat; Appendix A and B).

Among human-restored sites, the years since initial treatment and stage of

restoration varied; some were newly treated in November 2013, while other sites had

been restored up to 22 years prior and thus have a functional native ecosystem with only a “maintenance” level of management (Appendix B). Areas of natural restoration included wide, open areas of sand that extended eastward into the backdunes of the habitat; these areas were created by high-energy wave action that over-washed and eroded the foredunes. Naturally restored areas also occurred where rivers enter the

Pacific Ocean. Those sand spits were occasionally inundated by extreme high-tide waves or flood waters associated with winter storm events; this clears the sand spits of vegetation (including A. arenaria), and often accretes and erodes large amounts of woody debris and sand. Long sand spits present along coastal lagoons (e.g., in Humboldt

Lagoons State Park) also provide plover habitat along their length and are occasionally over-washed and scoured at breach points by large waves during storm events.

10 Field Methods

As part of a regular, long-term (14 year) and multi-agency plover population monitoring project (Colwell et al. 2014a), observers surveyed the study area for plovers from mid-March through September 2014. Surveyors conducted regular (i.e., weekly at

LRSB, Clam Beach, Mad River, Eel River Wildlife Area, and Centerville; twice a week at South Spit; and twice a month at Gold Bluffs Beach, Stone Lagoon, Big Lagoon, Dry

Lagoon, and North Spit) surveys of ocean-fronting beaches. During surveys, observers searched for color-banded plovers and nests while documenting additional disturbance and predator threats (Colwell et al. 2010, Burrell and Colwell 2012). Observers recorded the position of plovers and nests using a handheld Personal Digital Assistant (Dell Axim

X50; PDA) equipped with a global positioning system (GPS) and ESRI Mobile GIS software (ArcPad). Observers conducted research under permits (USFWS Federal recovery # TE-73361A-0, California Department of Fish and Wildlife scientific collecting # SC-000496, Humboldt State University IACUC # 14/15.W.07-A, California

Department of Parks and Recreation scientific investigation # 14-635-036, and USFWS banding # 10457 and # 22971) following the methods of Colwell et al. (2010, 2013).

I delineated the extent of available plover nesting habitat by walking the boundaries of the study area and using a Garmin GPS unit and the “tracks” function to record this limit. I defined the western boundary of this habitat as the last high tide wrack line. The eastern edge was where either A. arenaria grew in dense stands or where other

11 features would inhibit nesting to the east (e.g., river edge, rocky cliff, wetland, other

dense vegetation; see Appendix C for an example).

At each nest (n = 81) and an equivalent number of random points, I measured a suite of variables to characterize the physical features of habitat. From the edges of the delineated extent of available habitat, I determined the slope using a clinometer

(Appendix D). I collected percent cover measurements of biotic and abiotic habitat features (e.g., A. arenaria, bare sand; see Table 1) in a 100 m diameter circular plot

(Appendix D). I used a modified relevé vegetation survey method and a rangefinder to delineate these 100 m plots, identify cover types (see Table 1), and quantify the percent coverage of each cover type using ocular estimates (Ralph et al. 1993, Wood et al. 2010).

Geospatial Methods to Obtain Additional Covariates

I determined several additional variables from data collected in the field by conducting geospatial analyses using high-resolution imagery from the National

Agriculture Imagery Program (NAIP; date of image 23 June 2014) and ArcGIS 10.1 software (ERSI 2011). I selected all covariates (Table 1) based on the need (identified in literature review) to assess them for potential influence on habitat selection. I generated random points (n = 81) for my analyses from within the available nesting habitat that I traced in the field using the “create random points” tool in ArcGIS. I also constrained placement of random points such that no two points occurred within a 20 m buffer of other points, as this is the minimum distance between conspecific nests observed over 13 years (Patrick 2013).

12 Table 1. Definitions of variables used in logistic regression and other statistical analyses to evaluate habitat selection and response to habitat restoration of Snowy Plovers. Bracketed terms represent each of these covariates in the analyses.

1. Response Variables: Range of Values Description

Real nest and random 1 (Nest) or Binary response of used (i.e., plover nest) or locations 0 (Random) available (i.e., a randomly generated location). [used] 2. Predictor Variables: Range of Values Description

Beach width 0–500 meters Distance between the western and eastern [width] extent of the available habitat, centered on a focal nest or random point

Beach slope 0.0–10.0 % Percent slope between high tide line and crest [slope] of the foredune closest to the ocean, centered over a focal point. Alternatively, if point is in backdunes, from crest of foredune to next backdune. See Appendix D

A. arenaria vegetation cover 0–100% Percent of Ammophila arenaria in [ammoph] 100 m relevé plot. See Appendix D

Native vegetation cover 0–100% Percent of native vegetation (verbena, [native] searocket, etc) in 100 m plot

Openness of habitat 0–100% Percent of 100 m plot that is bare sand and [open] open habitat

Other ground cover 0–100% Percent of other ground cover aside from [other] native or invasive grasses (woody debris, shell carapaces, stones, algae, dead vegetation)

Conspecific social factor 0–45 Count of uniquely marked plovers (excluding [birds] nest owners) present within 500 m of a real or random nest site within seven days prior to and including nest initiation (date that first egg of three-egg clutch is laid)

Habitat restoration 1,2, or 3 Categories representing habitat types: [type] 1 = Human-restored, 2 = naturally restored, and 3 = unrestored

Study site CN,BL,MR,DL, Two letter codes representing eleven focal [site] SL,GB,NS,CS, sites; included in model to account for random ES,CV,SS effect of site

13 Beach width

Using ArcGIS and a digitized polygon layer of the delineation of nesting habitat that I traced in the field, I measured beach width at nests and random points as the distance (m) between the eastern and western edges of suitable habitat (Table 1;

Appendix C). I followed the methods of Patrick and Colwell (2014) and used the “create

fishnet” tool to generate a grid along the primary axis of each beach site with 10 m cells

wide enough to completely cover the habitat polygon (Figure 2). I used the clip tool to

cut each grid to fit the extent of the habitat polygon, and then measured beach width as

the length of the gridline that intersected or was nearest (within 5 m) to each focal nest or

random point. I report average (± SD) width, for ease of comparison with literature.

Three habitat types

From the polygon of plover nesting habitat overlaid on NAIP imagery, I digitized

three habitat types (human-restored, naturally restored, and unrestored) throughout the entire study area (Appendix B). I determined where each type occurred within the study

area from a review of restoration reports from the region over the past 20 years

(Appendix A). I measured the total area in hectares of each habitat type in the study area to obtain a measure of habitat availability (Neu et al. 1974).

Conspecific attraction

Since plovers are semi-colonial (Patrick 2013) and evidence suggests that

conspecifics influence nest site selection (Nelson 2007), I quantified the number of

conspecifics within 500 m of each nest and random point in the seven days prior to and

including the date of clutch initiation. I used a 500 m radius because average plover home

15 ranges (1.6 ± 0.2 km) and mean nearest neighbor distances (to conspecific nest; = 551

m, median = 180 m) exceed this 500 m radius (Pearson 2011, Patrick 2013), and𝑥𝑥 thus̅

may overlap. Conspecifics within this radius are more likely to interact and, therefore, influence nest site selection (Nelson 2007). To decrease spatial autocorrelation between birds and nests, I did not analyze these two social attraction variables together, and used

the more constrained, restoration-scale 500 m buffer.

I used a seven day window to minimize any confounding effects of plovers re-

nesting in the same area after clutch loss; seven is the average number of days plovers

have been observed to begin a new clutch (Warriner et al. 1986, Page et al. 2009). To

define this one-week window, I determined the initiation date (day that the first egg was

laid) of each nest and included the six days prior. For uncertain initiation dates, I back-

calculated to determine the date following the methods of Mayfield (1975). For random

points, I used random number and date generators to obtain initiation dates that fell

within the breeding season and then assigned them to the random points.

Statistical Analyses

To evaluate nest site selection, I conducted statistical analyses following Thomas

and Taylor (2006) and Alldredge and Griswold (2006). In this framework, I compared

resource units (e.g., beach width, slope, number of conspecifics) in terms of use (i.e., nest

locations) and availability (i.e., random points) of habitat throughout the study area. I

conducted analyses using RStudio v.0.98 (RStudio 2014) and Program R v.3.2 (R Core

Team 2014).

16 Resource Selection Function analyses

I used logistic regression to conduct a Resource Selection Function analysis (RSF;

McLoughlin et al. 2010) based on the full dataset (n = 162). I represented the binary response variable for my analysis as nests = 1 and random points = 0. This RSF generated a relative index of the probability of use (i.e., selection) by plovers of available habitat in relation to nine habitat features (e.g., width, percent cover, types, sites; Table 1). To assess the relative importance of these predictor variables, I used an information-theoretic approach (Burnham and Anderson 2002) to rank a candidate set of 19 a priori weighted models, which represented hypotheses of how these nine biologically important habitat characteristics relate to nest site selection. I used model selection and the second-order

Akaike’s Information Criterion corrected for small sample sizes (AICc; Hurvich and Tsai

1989, Burnham and Anderson 2002) to determine the most parsimonious model out of the candidate model set (Appendix E) and to generate beta coefficients to evaluate selection.

I inferred the best-fitting model based on the Akaike weight (wi), a probability calculated from the normalized likelihood of each candidate model used to rank each model by its relative measure of support. To build these models, I used a binomial generalized linear mixed-effect model code from the {lme4} package in Program R with a logit link function. I used this logistic modeling method to account for the random effect of “site” on the linear combination of the fixed effect predictor variables in the model (Jaeger 2008, McLoughlin et al. 2010). I coded habitat type (i.e., human-restored, naturally restored, unrestored) as a categorical variable with three levels (McDonald et al.

2005). Type was not included in every model in order to evaluate its effect explicitly.

17 I used evidence ratios to evaluate the best models in the candidate set for all

models with ΔAICc values less than 6 (Anderson 2008). There was no strong top

explanatory model (i.e., one with an AICc weight > 0.9) in this 19 model set, indicating

model selection uncertainty; thus, I tested the top model for goodness of fit by calculating

the percent deviance explained by the top model (Zuur et al. 2009). Prior to modeling, I

screened the data to evaluate normality, overdispersion, and collinearity (Burnham and

Anderson 2002). The global candidate model was not overdispersed (ĉ < 1.0). The covariates representing nests and birds were strongly correlated (Pearson’s correlation >

0.7) in my original dataset, so I used only birds in this modeling analysis to represent the measure of conspecific social attraction, although both were highly significant covariates

(P < 0.001). I did not include interaction terms in order to minimize spurious findings associated with correlated variables (Johnson and Omland 2004).

I repeated this model selection analysis with a smaller subset of 22 nests of uniquely-marked male plovers and 22 random points distributed throughout the study area to reduce pseudo-replication from a male potentially repeatedly (by re-nesting) selecting the same features of habitat and location. This also addressed possible oversampling of one human-restored site (i.e., Little River State Beach) where plovers bred in high density. The full model for this analysis was overdispersed (ĉ > 1.0), so I used quasi-AICc in my model selection process (QAICc; Burnham and Anderson 2002) to

adjust for any lack of fit of the global model. I conducted further comparisons of

selection preferences between nests and random points using Student’s t-test statistics.

18 Habitat type analysis

To determine if plovers selected between habitat types (human-restored, naturally restored, and unrestored), I conducted a Chi-square goodness-of-fit test, which compared proportional use (number of nests in each type) to the proportional availability (number of hectares of each type; Neu et al. 1974, McLean et al. 1998, Boyce et al. 2002). Next, I

evaluated selection ratios of proportional use and availability. Ratios greater than 1.0 indicated selection for a particular type; a ratio less than 1.0 indicated selection against it

(McDonald et al. 2005). I standardized these ratios to compare among types (Manly et al.

2002, Desbiez et al. 2009). Following this, I calculated the 95% Bonferroni simultaneous confidence interval for each habitat type to obtain the measure of selection for or against each habitat while accounting for the relationships between types (Neu et al. 1974, Byers et al.1984). This test built on the RSF analysis that described the influence of habitat type on selection by identifying which habitat types were selected. I also used analyses of variance to compare values of the covariates between the three types of habitat.

19 RESULTS

In 2014, Snowy Plovers initiated 81 nests along 80 km of ocean-fronting beaches

in Humboldt County, California. The extent of available nesting area within this habitat

was 629 hectares. Of this total, 32.8% (206 ha) was restored by humans, 21.6% (136 ha)

was naturally restored habitat, and 45.6% (287 ha) was unrestored habitat (Appendix A).

Comparison of Restored and Unrestored Habitats

Plovers nested disproportionately (χ² = 35.18, df = 2, P < 0.001) in restored habitats (Table 2). Plovers selected for human-restored and naturally restored habitats, as

indicated by selection ratios > 1.0 and Bonferroni-adjusted 95% confidence intervals that did not overlap 1.0; they avoided unrestored beaches. When ratios were standardized

against each other, it became evident that nests were placed proportionally most often

(51%) in naturally restored habitat, and that unrestored areas were used less than

available (Table 2). There was no difference in the distribution of random points amongst

the three habitat types.

Habitat features at nests differed among the three habitat types (Table 3). Both

types of restored habitats were wider, had more gentle slopes, and contained less A.

arenaria cover than unrestored beaches. Human-restored areas had more conspecifics and greater native vegetation cover than naturally restored and unrestored habitat. In naturally restored areas, nests occurred more often where more woody debris, stones, and shells covered the ground, but these covariates were not correlated with habitat type.

20 Table 2. Summary of the Chi-square goodness-of-fit analysis that evaluated selection between three habitat types by Snowy Plovers along 80 km of ocean-fronting beach in Humboldt County, CA in 2014.

Lower 95% Upper 95% Proportional Proportional Habitat type Confidence Confidence Selection Usea Availabilityb Ratio Interval Interval Human 0.469 0.328 0.433 0.505 1.43

Natural 0.395 0.216 0.360 0.431 1.83

Unrestored 0.136 0.456 0.111 0.161 0.30

a Proportion of nests that were within the habitat type. b Proportion of total available area that was the habitat type.

21 Table 3. A comparison of average (± SD) physical and social characteristics at 81 Snowy Plover nests in three habitat types along ocean-fronting beaches in Humboldt County, CA in 2014.

Human-restored Naturally Unrestored Covariate Habitat Restored Habitat Habitat F P-value (n = 38) (n = 32) (n = 11) Birds 14.8 ± 11.5 2.7 ± 3.8 4.4 ±7.2 18.5 < 0.001

Nests 2.6 ± 1.7 0.6 ± 1.1 0.3 ± 0.5 22.6 < 0.001

Width, m 284 ± 86 180 ± 70 117 ± 35 29.2 < 0.001

Slope, % 2.4 ± 1.2 1.9 ± 1.4 3.6 ± 1.6 6.2 < 0.001

Percent cover:

Native 32.5 ± 21.1 13.7 ± 11.4 17.5 ± 12.2 11.6 < 0.001

Ammoph 9.3 ± 16.2 9.8 ± 12.0 24.1 ± 8.0 5.3 < 0.001

Open 42.1 ± 26.2 44.8 ± 22.9 45.5 ± 22.7 0.2 0.860

Other 15.6 ± 14.1 30.7 ± 23.8 12.8 ± 11.5 7.2 < 0.01

22 Comparison of Nest and Random Site Characteristics

Plovers nested on wider, less-sloped beaches, nearer to more conspecifics, and where greater amounts of ground cover occurred (e.g., woody debris, shells, and stones) compared with random locations (Table 4). Plovers established nests at locations with less A. arenaria, although this result was marginally significant (P = 0.052).

The top model in the RSF analysis that compared 19 models from the full dataset

(n = 162) included conspecifics, beach slope, and amounts of native vegetation, open habitat, and natural debris. This model held 54% of the AICc weight (Table 5), with

47.1% deviance explained when compared with the null model. The second-ranked

model (with 28% of the probability weight) included these same variables and added

beach width; however, width was not a significant predictor (β = 0.003, 95% CI = −0.003

– 0.009) and there was no change in deviance (deviance explained = 47.2%). This

indicated that the second model was a pretending model (i.e., one with an uninformative

parameter that did not change the deviance when included; Anderson 2008). In addition,

ΔAICc and the evidence ratio for this model were < 2 (Table 5), showing that the model was explanatory but that the added beach width covariate was not (Burnham and

Anderson 2002). Thus, I removed the pretending model and conducted the RSF analysis again with 18 models. The new top model explained 75% of the weight (Table 6). Beach width was a significant predictor (β = 0.007, 95% CI = 0.002 – 0.012) in the new second- ranked model, which held 11% of the weight (Table 6). This was the most parsimonious

model (with two covariates, birds and width), which explained 43.2% of the deviance.

23 Table 4. Summary of the comparison of habitat covariate values (mean ± SD) at nest locations and random points within plover nesting habitat in Humboldt County, CA, in 2014, derived from Student’s t-test statistics.

Nest Random Point Predictor variable P-value (n = 81) (n = 81) Beach width [width] 220 ± 98 m 121 ± 78 m < 0.001

Conspecific social factor [birds] 8.6 ± 10.4 0.69 ± 2.0 < 0.001

Nests (alternate social factor) 1.5 ± 1.7 0.04 ± 0.19 < 0.001

% Beach slope [slope] 2.4 ± 1.4 3.9 ± 2.5 < 0.001

% A. arenaria cover [ammoph] 11.5 ± 14.5 16.1 ± 15.1 0.052

% Native cover [native] 23.0 ± 18.9 27.5 ± 19.2 0.137

% Openness of habitat [open] 43.6 ± 24.2 39.5 ± 23.8 0.277

% Other ground cover [other] 21.2 ± 19.7 12.9 ± 14.1 0.002

24 Table 5. A summary of 19 competing models examining relationships between physical and social variables influencing habitat selection of nesting Snowy Plovers in Humboldt County, CA in 2014, showing the top five models with ΔAICc < 6 ranked by Akaike weight (wi), that explain 99% of selection and which represent hypotheses combining nine covariates, included the random effect of site (see Appendix E for full candidate model set).

Cum. Evidence Model LogL K AICc ΔAICc wi wi ratios birds + slope + native + open + other −69.82 7 154.4 0.00 0.541 0.54 —

birds + width + slope + native + open + other −69.35 8 155.7 1.29 0.283 0.82 1.908

birds + width −74.94 4 158.1 3.78 0.082 0.91 6.610

birds + width + ammoph −74.30 5 159.0 4.63 0.054 0.96 10.11

birds + type −74.98 5 160.3 5.98 0.027 0.99 19.88

LogL: Log Likelihood of the model. K: the number of model parameters.

AICc: Akaike’s Information Criterion corrected for small sample size.

ΔAICc: change in AICc value between the top model and each additional model.

wi: the proportion of the total Akaike weight held by each candidate model.

Cum. wi: cumulative weight of all models.

25 Table 6. A summary of 18 competing models after removing one pretending model (which included the width covariate) examining relationships between physical and social variables influencing habitat selection of nesting Snowy Plovers in Humboldt County, CA in 2014, showing the top five models with ΔAICc < 6 ranked by Akaike weight (wi), that explain 99% of selection and which represent hypotheses combining nine covariates, included the random effect of site.

Cum. Evidence Model LogL K AICc ΔAICc wi wi ratios birds +slope + native + open + other -69.82 7 154.4 0.00 0.755 0.75 —

birds + width -74.94 4 158.1 3.78 0.114 0.87 6.607

birds + width + ammoph -74.30 5 159.0 4.63 0.075 0.94 10.10

birds + type -74.98 5 160.3 5.98 0.038 0.98 19.86

birds -78.25 3 162.7 8.30 0.012 0.99 63.40

LogL: Log Likelihood of the model. K: the number of model parameters.

AICc: Akaike’s Information Criterion corrected for small sample size.

ΔAICc: change in AICc value between the top model and each additional model.

wi: the proportion of the total Akaike weight held by each candidate model.

Cum. wi: cumulative weight of all models.

26 The top model from both of these full dataset analyses yielded similar explanatory

results for selection; both top models in each candidate set included the same covariates.

Plovers were more likely to establish nests where there were wider beaches, more

conspecifics (β = 0.198, 95% CI = 0.055–0.342), less sloping beaches (β = −0.365, 95%

CI = −0.626 – −0.103), and greater amounts of other cover including driftwood, shells,

and stones (β = 0.047, 95% CI = 0.011–0.083). The 95% confidence interval for the beta

coefficient of each variable did not overlap zero, indicating that these covariates were

significant predictors of nest placement. The relationships to plover selection for native

vegetation (β = 0.021, 95% CI = −0.014–0.056) and openness of habitat (β = 0.031, 95%

CI = −0.002–0.064) in the top model were not as strongly supported. No univariate model

in either of these full dataset analyses explained > 1% of habitat use by plovers (Table 6).

Resource selection function regression curves for each of nine predictor

covariates in the global model showed positive and negative selection relationships.

Plover selection was strongly influenced by increased presence of conspecifics (Figure

3). Increasing beach widths and amounts of other natural debris cover were also

positively related to the index of the probability of plover use of the location; plovers

selected nesting areas with higher values for these variables (Figure 4). Increasing openness of habitat, accounted for by bare sand, had a weaker positive selection relationship (Figure 4). As values for beach slope, amount of A. arenaria, and native

vegetation cover each increased, selection decreased; this showed negative relationships

between the index of the probability of use and these habitat variables (Figure 4).

29 Plover selection of human-restored sites was concentrated at only a few locations throughout the study area; 33% of nests were placed on one restored area at Little River

State Beach alone. Therefore, I addressed this possible oversampling bias (influenced by multiple nests of individuals) by analyzing a subset of 22 nests (one from each male) and

22 random points. The top model held 43% of the explanatory weight, with slope as the only covariate (Table 7). Plovers nested on more gently sloping beaches (β = −0.488,

95% CI = −0.906 – −0.070) than expected. The univariate model containing other natural debris (β = 0.045, 95% CI = 0.001 – 0.089) held an additional 17% of the explanatory weight (Table 7) and further explained selection. Univariate models with beach width (β

= 0.008, 95% CI = −0.001 – 0.016) and conspecific birds (β = 0.156, 95% CI = −0.055 –

0.366) were weaker explanations for selection.

30 Table 7. A summary of 19 models evaluating relationships between physical and social variables influencing habitat selection of a subset of 22 unique nesting male Snowy Plovers and 22 random points in Humboldt County, CA in 2014, ranked by Akaike weight (wi) and which represent hypotheses combining nine covariates, included the random effect of site.

Cum. Evidence Model LogL K QAICc ΔQAICc wi wi ratios slope −25.05 3 56.69 0.00 0.433 0.43 —

other −25.98 3 58.56 1.87 0.170 0.60 2.55

width −26.88 3 60.36 3.67 0.069 0.67 6.28

birds −27.11 3 60.82 4.13 0.055 0.73 7.88

birds + width −25.94 4 60.90 4.21 0.053 0.78 8.21

birds +slope + native + open + other −22.27 7 61.65 4.96 0.036 0.81 11.95

ammoph −27.81 3 62.22 5.53 0.027 0.84 15.90

type −26.76 4 62.56 5.86 0.023 0.87 18.80

LogL: Log Likelihood of the model. K: the number of model parameters.

AICc: Akaike’s Information Criterion corrected for small sample size.

ΔAICc: change in AICc value between the top model and each additional model.

wi: the proportion of the total Akaike weight held by each candidate model.

Cum. wi: cumulative weight of all models.

31 DISCUSSION

Several important findings emerge from this study, which is the first to evaluate

nest site selection of Pacific coast Western Snowy Plovers in the context of habitat

restoration and social attraction. First, plovers nested disproportionately more frequently

in restored habitats, especially those that were naturally disturbed and then regenerated.

Second, key physical features of the habitat that are normally altered during restoration

activities (i.e., slope, beach width, debris cover) appear to distinguish sites where plovers

nest. Third, plovers preferentially nested near conspecifics, which was the most

influential social variable on nest location selection. Each of these observations has

important value for restoration and management methods applied to improve the

attractiveness and quality of habitat to breeding Snowy Plovers.

Plovers Selected for Restored Habitats

In 2014, Snowy Plovers in northern California established more nests within

restored habitats than in unrestored habitats, as shown by selection ratios. This was

especially the case for naturally restored habitats, which tended to be at the mouths of

rivers and had wider beaches, gentler slopes, less A. arenaria, and more driftwood, shell,

and stone debris. This result corroborates earlier observations that plovers appeared to preferentially breed at river mouths that had the aforementioned features (Page and

Stenzel 1981). The natural disturbance that occurs at these locations (occasional over-

wash during storm events, tidal action, river flooding, or lagoon breaching) restores these

32 breeding areas and is important to Snowy Plovers and other species that exist in dynamic

coastal habitats (Pardini et al. 2015). These scouring events deplete both native and

invasive vegetation, as well as accrete and shift sand and driftwood routinely to open up

and alter habitat. I found that similar results are produced by the mechanisms of human-

implemented restoration, and that plovers also respond positively to this process. Powell

and Collier (2000) and Catlin et al. (2011) also reported that plovers nested in human-

created habitats that were sparsely vegetated, albeit at a finer nest site scale. In restoration

conducted by humans, a generally one-time erosive treatment (e.g., mechanical removal

of A. arenaria via bull-dozing) of a location was followed by occasional hand-pulling,

disking, mowing, and other less habitat-disturbing re-treatments (see Appendix A). This restorative process replicates the seasonal changes that alter and restore natural habitats to a state attractive to plovers.

My results show that human-restored areas provided usable habitat and similar features to these naturally restored sites, and that plovers often utilized them both. Powell and Collier (2000) and MacDonald et al. (2010) suggested that management by human-

implemented restoration throughout available habitat provides suitable nesting areas for

plovers; my findings show that the spatially and temporally dynamic nesting habitats of

the Pacific coast sand dune system could also support that practice.

Physical Features are Important to Habitat Selection

My study found that certain key physical characteristics were present in restored

sites that plovers nested within, one of which was beach slope. Plovers showed strong

33 selection of nest sites on gentler slopes that ranged from 0–4%; this was particularly true for the subset of 22 unique plovers. Similarly, Maslo et al. (2011) found that Piping

Plovers nested in gently sloping dune habitat. European Golden-Plovers (Pluvialis apricaria) nesting on flatter habitat had higher nest survival (Whittingham et al. 2002); this suggests that the gentle slopes Snowy Plovers select for may contribute to greater nesting success for them as well. The flatter habitat generated during restoration likely increases plover ability to observe predators approaching at a distance (Hardy and

Colwell 2012), which may contribute to increased survival of adults, chicks, and nests.

A second physical feature, beach width, was also important to selection of restored sites. I found that plovers nested on wider beaches; on average (± SD), beach width was 220 ± 98 m (range 50–383 m) at nests when compared with the average (±

SD) at random locations. This extends and refines the research of Patrick and Colwell

(2014), which showed that nests within the same study area occurred on similarly wider beaches, with an average width of 225 ± 112 m (range 33–478 m) for three years (i.e.,

2005, 2009, 2010). Maslo et al. (2011) found that Piping Plovers also nested on beaches that were often greater than 150 m in width. In 2014, plovers selected similarly wide human-restored and naturally restored areas over narrower, unrestored portions of beach.

Plovers selected nest sites within the restored areas that had more driftwood, stones, and shell debris compared to random locations, which corroborates earlier findings by Wilson-Jacobs and Meslow (1984) and Valle and Scarton (1999). Increased, sparse debris and native vegetation provides camouflage and refugia from predators

(Hardy and Colwell 2012, Webber et al. 2013), and can increase survival when combined

34 with the plover’s adaptation to utilizing this cover (Page et al. 1985, Colwell et al. 2011).

I found that plovers selected for greater debris at multiple spatial scales (i.e., by landscape-level restored habitat type and in 50 m radii around nests). This extends the

findings of studies conducted at a much finer scale, in which the presence of debris was

also a positive influence on plover nest site selection (Grover and Knopf 1982, Powell

2001, Maslo et al. 2011). Powell and Collier (2000) also found that Snowy Plovers nested

nearer to an object and selected for sparse, but greater, cover than at random within a

smaller spatial scale of analysis. Muir and Colwell (2010) showed that the nests and

courtship scrapes of plovers occurred in open habitats where the cover of A. arenaria was

low. My finding that more native vegetation and less A. arenaria was evident around

nests at the landscape scale of restoration supports and expands this earlier work. In 2014,

plovers used key physical features at multiple spatial scales (Johnson 1980, Mayor et al.

2009), including the landscape scale at which restoration is conducted and the local patch

scale around nests. Shoreline characteristics appear to drive landscape-scale selection of

Snowy Plover breeding locations, a selection behavior pattern which is also observed in

other coastal-nesting species (Tessler et al. 2007).

Notably, there was a bias in the full dataset analysis of nests because site selection was concentrated at only a few human-restored locations; 33% of these nests occurred in one restored area. Addressing this, my analysis of a subset of 22 male plovers confirmed that individual plovers selected for shallower slope, followed by driftwood and shell debris, and then width. This ranking contrasted slightly when compared with the full dataset, but nevertheless identifies the same important physical features.

35 Social Attraction Influences Nest Location Selection

In addition to identifying important physical features used by plovers to select

nesting habitat, I showed that plovers also use the presence of conspecifics to select nest sites. An increasing number of conspecifics present at the nest site was the primary covariate that distinguished nests from random sites in my modeling. This is supported by the semi-colonial aggregations of breeding plovers (Patrick and Colwell 2014), the social nature of plovers during the non-breeding season (Brindock and Colwell 2011), and prior analyses showing that inexperienced plovers nested nearer to experienced birds who had previously nested at a location (Nelson 2007). Piping plovers have also been observed to nest where conspecifics had previously bred (Rioux et al. 2011). It is common throughout avian biology for bird species to use the cue of conspecifics as indication of suitable resources and nesting habitat (Wiens 1985, Danchin et al. 1998, Ward and Schlossberg

2004, Ahlering and Faaborg 2006). My findings provide evidence that social attraction may increase selection and nesting density in locations where other Snowy Plovers are already present. The lack of strong territoriality in Snowy Plovers (Page et al. 2009) may allow for increased influence of conspecific attraction on nesting habitat selection, whereas in other species such as Piping Plovers, greater territoriality may negatively influence density and spatial nesting patterns (Cairns 1982, Colwell 2000).

My research found that conspecific attraction is an important facet of nesting habitat selection in Snowy Plovers; however, the simultaneous influences of both social and physical features of habitat create potentially confounding effects on selection. To

36 parse out these coincident influences, I further documented social attraction in the context of restoration. In 2014, plovers nested nearer to conspecifics in human-restored habitats compared to nests in naturally restored and unrestored areas. This human-implemented restoration appeared especially effective because it was often applied to sites where plovers were already present and had nested and wintered in prior years (Colwell et al.

2014a). Conspecific presence during the breeding season or previous winter may attract and retain plovers to these sites post-restoration (M.A. Colwell, personal communication). My study showed that plovers will utilize restored habitat if conspecifics are near the restored site; however, the proximity of plovers that already winter in the area may have created a stronger appearance of restored habitat selection.

Powell and Collier (2000) found that restored habitats were also used by wintering plovers, indicating year-round importance of these created habitats.

Additional factors such as prior site knowledge, naïvety to nesting, or an effect of temporal scale from seasonal changes in plover habitat selection may also influence nesting habitat selection (Mayor et al. 2009). My findings come primarily from areas restored by humans over the last 20 years, with varying degrees of subsequent re- treatment and completeness of restoration (Appendix A). Positive selection in the short term for newly human-restored habitats has been documented (BLM 2012, CSP 2014,

Appendix A), and expanding this research to multiple years would be informative.

It is also noteworthy that there is a bias in where habitat restoration is implemented by humans, which is limited by available property, land management entity, purpose of restoration, and funding (see Appendix A). Consequently, restoration has been

37 conducted in several locations where plovers were already breeding, further complicating

the ability to determine correlation and causation between restoration and plover use of a

location. Differences in plover use of human-implemented restoration areas was

exemplified by two sites, one of which (Little River State Beach) had the highest activity

of breeding and wintering plovers, whereas the other (South Spit) had very infrequent

detections of individuals in 2014 and in several prior years (Colwell et al. 2014a, Colwell

et al. 2014b). The year-around presence of conspecifics at Little River State Beach may

attract and retain more plovers in that restored habitat compared to others such as South

Spit. Paired with high nest failure rates (Colwell et al. 2014a) that occasionally prompted

re-nesting attempts, this effect may have biased my findings, although I did account for it in the analysis of a subset of 22 unique plovers.

Throughout the study area in 2014 there were several instances of restored area with minimal plover activity. This suggested that physical features can be attractive, but plovers may either nest only in low densities or do not breed at all in otherwise-suitable

habitat without conspecifics present (Colwell et al. 2014a). Other studies have

documented instances with Piping Plovers where habitat was restored by humans to

include suitable characteristics, but then remained unoccupied or only marginally used by

plovers (Powell and Collier 2000, Catlin et al. 2011); this may be explained by the lack of

conspecifics at the restoration location.

38 Management Recommendations

Habitat is often protected and managed for the benefit of the Snowy Plover

(USFWS 2007), and my research provides new evidence that plovers select physical and social nesting habitat features at a large scale comparable to that at which land managers restore coastal habitat. This is informative to land managers working to conserve the threatened Snowy Plover and improve the quality of breeding habitat.

I found that human-implemented restoration generated physical characteristics similar to features generated by natural processes in naturally restored areas, and plovers nested more often in both of these restored habitats. This suggests that current management approaches to restoring coastal dune habitats effectively replicate these natural processes and supplement naturally regenerated habitat that plovers select. This restoration acts as an additional plover management strategy where other methods have not worked or are otherwise prohibitive to implement (Liebezeit and George 2002,

Peterson and Colwell 2014). Thus, I recommend that both human-restored and naturally restored habitats be managed and conserved.

For human-implemented restoration, I recommend removal of non-native

vegetation while allowing for native vegetation to repopulate. Current practice of this

showed that, in 2014, human-restored habitat had higher native vegetation cover than the

other two habitat types; and that in both restored habitats types invasive A. arenaria was

decreased by more than half. I also recommend conserving the wider beaches and gentle

slopes that plovers utilize. Including these features effectively meets the primary goal of

39 restoration (to restore coastal dune mat vegetation and topography) while generating

beneficial suitable plover habitat (Pickart 2008).

I also recommend supplementing debris cover in restored areas with additional

driftwood, shells, and stones at the appropriate scale. Notably, my results showed double

the amount of driftwood debris in naturally restored habitats than human-restored and

unrestored habitat, indicating the importance of this debris to plovers. Accordingly, the

amounts and size of natural debris that plovers use as cover should be quantified and

incorporated into restoration projects. My findings support similar and commonly implemented practices of distributing oyster shell substrate and woody debris to enhance coastal sandy beach habitat (BLM 2012, Donehower et al. 2013). Herman and Colwell

(2015) showed the importance of substrate type (i.e., gravel and sand) to lifetime reproductive success and survival; my findings may also support that cover density and type may influence survival and productivity of nests, chicks, and adults; however, further analysis is needed.

Conducting research to establish thresholds of slope, beach width, vegetation

cover, debris cover, and conspecific presence is a recommended next step to establish

more specific parameters for human-implemented restoration (Maslo et al. 2011). Based on my findings, using confidence levels such that most nests fell within the following values (mean ± SD), I recommend general guidelines for restoring plover breeding habitat such as: slope not to exceed < 4%; beach width greater than 123 meters; percent cover of A. arenaria less than 26%; percent cover of native vegetation less than 42%; and percent cover of woody, shell, and stone debris less than 41%. These suitable nesting

40 habitat characteristics can be generated or conserved, but the restored habitat may still not attract and establish plovers without the presence of conspecifics. I recommend considering the presence of conspecifics and the role of social attraction carefully in determining future candidate locations for restoration and conservation.

My findings provide public and private land managers with essential information for tailoring restoration methods to provide attractive and potentially beneficial habitat for the Snowy Plover. Understanding how physical and social characteristics impact plover nesting habitat selection enhances understanding of plover biology and informs

conservation efforts such as habitat restoration, which is imperative to the management

and recovery of the Snowy Plover. This research provides the foundation for

understanding what habitat features can influence predation risk, the survival of nests and

chicks, and the reproductive success of plovers, which ultimately can lead to the recovery

of this federally-threatened Western Snowy Plover population throughout its range.

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49 APPENDICES

Project Location Year Project Publications and Restoration # nests Enacted Reports Area, ha in 2014

Gold Bluffs Beach 2004-2008, 2013 CSP 2014 62 1

Humboldt Lagoons SP 2002-2004 CSP 2014 38 3

Little River SB 2004, 2009-2013 CSP 2014, Forys 2011 20 27

Clam Beach County Park 2008 East 2008 17 6

Lanphere Dunes 1992 Pickart 1997, 2008, 2013 8 0

Ma-l’el Dunes 1994, 2004, 2008, USFWS 2011b, BLM 21 1 2011 2014 Town of Manila / 2000-2014 BLM 2014 2 0 Samoa Dunes EPPA

South Spit CMA 2002-2014 BLM 2010, 2012, 2014 38 0

50 Appendix B. Map of Little River State Beach and Clam Beach County Park, CA, depicting polygons representing three types of habitat (human-implemented restoration, natural restoration, and unrestored) as identified in 2014 and delineated by the extent of available plover habitat. These three habitat types were assigned throughout approximately 80 km of beach habitat in the study area.

51 Appendix C. Section of Clam Beach County Park in Humboldt County, CA, in 2014 showing an example of the delineated extent of plover nesting habitat as evident by the black line tracing the high tide wrack line and the edge of A. arenaria (the green and brown to the east of the line) too thick for a plover to permeate.

52 Appendix D. Visual summary depicting (a) a typical cross section of the beach profile showing the measurement method for slope across nests in the foredune and backdune of plover beach nesting habitat; and (b) a typical plan view for 100 m diameter relevé plots showing where beach width was obtained at all nests and random points throughout the study area in 2014.

53 Appendix E. Complete candidate model set of 19 a priori models representing hypotheses combining nine covariates from a resource selection function analysis of 81 nests and 81 random points. Models are ranked by greatest Akaike weight (wi) indicating the top explanatory model.

Model LogL K AICc ΔAICc wi Cum. wi birds +slope + native + open + other −69.82 7 154.4 0.00 0.541 0.54 birds + width + slope + native + open + other −69.35 8 155.7 1.29 0.283 0.82 birds + width −74.94 4 158.1 3.78 0.082 0.91 birds + width + ammoph −74.30 5 159.0 4.63 0.054 0.96 birds + type −74.98 5 160.3 5.98 0.027 0.99 birds −78.25 3 162.7 8.30 0.009 1.00 width + native + open + other −76.98 6 166.5 12.15 0.001 1.00 width −80.27 3 166.7 12.33 0.001 1.00 width + ammoph + open + other −77.32 6 167.2 12.83 0.001 1.00 width + slope + open + other + type −75.70 8 168.3 13.98 0.006 1.00 width + slope + type −78.15 6 168.8 14.47 0.000 1.00 slope + ammoph + native + open +other + type −75.30 9 169.8 15.43 0.000 1.00 slope −81.85 3 169.9 15.49 0.000 1.00 other −82.32 3 170.8 16.44 0.000 1.00 ammoph + native + open + other −80.35 6 173.2 18.88 0.000 1.00 type −82.79 4 173.8 19.47 0.000 1.00 ammoph −85.23 3 176.6 22.25 0.000 1.00 open −86.50 3 179.2 24.79 0.000 1.00 native −86.80 3 179.8 25.39 0.000 1.00

LogL: Log Likelihood of the model K: the number of model parameters

AICc: Akaike’s Information Criterion corrected for small sample size

∆ AICc: change in AICc value between the top model and each additional model wi: the proportion of the total Akaike weight held by each candidate model Cum. wi: cumulative weight of all models