Geospatial Modeling of Common Raven Activity in Snowy

Home , Raven (wrestler)

GEOSPATIAL MODELING OF COMMON RAVEN ACTIVITY IN SNOWY

PLOVER HABITATS IN COASTAL NORTHERN CALIFORNIA

Matthew Joseph Lau

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. Daniel Barton, Committee Member

Dr. William T. Bean, Committee Member

Dr. Yvonne Everett, Graduate Coordinator

December 2015

ABSTRACT

GEOSPATIAL MODELING OF COMMON RAVEN ACTIVITY IN SNOWY PLOVER HABITATS IN COASTAL NORTHERN CALIFORNIA

Matthew Joseph Lau

The Common Raven (Corvus corax) poses a conservation dilemma because as a native predator it can negatively affect populations of other native species, including the

Western Snowy Plover (Charadrius nivosus nivosus). In Humboldt County, the Common

Raven is one of the primary causes of low reproductive success in Snowy Plovers. To better understand Common Ravens, I investigated their activity and distribution in Snowy

Plover habitats using 11 years of point count data (2004-2014). Furthermore, I analyzed several landscape factors known to influence raven activity at three spatial scales and related them to Common Raven activity using Generalized Additive Models (GAMs).

Common Raven distribution varied appreciably across Snowy Plover habitats and this spatial patterning was consistent across the 11 years. Moreover, Common Raven activity was highest in Snowy Plover habitats that were near more agricultural lands and low- intensity urban areas at all scales (small and large scale). Common Ravens were found to be in high abundance coinciding with areas of high Snowy Plover breeding activity, which warrants prioritizing predator management in these beach habitats.

ACKNOWLEDGMENTS

I would first like to thank my graduate advisor, Dr. Mark Colwell, for the incredible amount of knowledge and inspiration that he has passed on to me, both as his

Master’s student and his undergraduate advisee. He has provided me the stepping stones to achieve all the success I have acquired and has stimulated my love for birds. I would also like to tremendously thank my committee members, Dr. Daniel Barton and Dr.

William T. Bean, for offering the time and energy to assist me in spatial analyses and statistical modeling. I want to acknowledge my fellow graduate students in the Shorebird

Ecology Lab for their companionship, input, and survey effort: Allie Patrick, Dana

Herman, Matt Brinkman, Stephanie Leja, David Orluck, Alexa DeJoannis, and Teresa

King.

I am grateful for the numerous field surveyors that have helped collect data:

Kayla Bonnette, Aaron Gottesman, Chloe Joesten, Garrett Moulton, Elizabeth Feucht,

Derek Harvey, Jasmin Ruvalcaba, Grayson Sandy, and Maryjean Greitl. I would also like to extend my thanks to the following from California State Parks: Amber Transou, Jay

Harris, Carol Wilson, Mark Morrisette, Casey Ryan, and Tony Kurz. Additionally, I am thankful to Jim Watkins of the U.S. Fish and Wildlife Service and Sean McAllister. I would also like to thank Jim Graham and Jeff Dunk for helping me with understanding the complexities of Generalized Additive Models. Anthony Desch and the rest of the

Wildlife faculty were also a crucial part of my success as a graduate student. My research was funded by: Humboldt State University, U.S. Fish and Wildlife Service, Bureau of iii

Land Management, California State Parks, the Asian Pacific Islander Organization, the

Sonoma County Fish and Wildlife Commission, Stockton Sportsmen’s Club, and Marin

Rod and Gun Club. Lastly, but most importantly, I would like to thank my family, for their everlasting support and endurance. I am eternally indebted to all those above and my success is not without them.

TABLE OF CONTENTS

ABSTRACT ...... ii

ACKNOWLEDGMENTS ...... iii

TABLE OF CONTENTS ...... v

LIST OF TABLES ...... vii

LIST OF FIGURES ...... viii

LIST OF APPENDICES ...... ix

INTRODUCTION ...... 1

METHODS ...... 6

Study Area ...... 6

Field Methods ...... 6

Analytical Methods ...... 8

Analyses of Common Raven Distribution and Activity ...... 8

Geospatial Modeling of Common Raven Activity ...... 10

RESULTS ...... 16

Common Raven Distribution and Activity ...... 16

Geospatial Modeling ...... 18

DISCUSSION ...... 25

Common Raven Distribution and Activity ...... 25

Landscape Correlates of Common Raven Activity ...... 27

MANAGEMENT IMPLICATIONS ...... 31

LITERATURE CITED ...... 33

Appendix A ...... 38

Appendix B ...... 39

Appendix C ...... 40

Appendix D ...... 41

LIST OF TABLES

Table 1. Predictor variables, their definitions, and data sources used in geospatial modeling of Common Raven activity using point count data from 2004-2014...... 11

Table 2. Model selection results evaluating relationships between Common Raven activity and anthropogenic variables at three spatial scales: 500 m, 1,450 m, and 3,590 m...... 19

Table 3. Summary of effects of each covariate on Common Raven activity at three focal spatial scales (500 m, 1,450 m, and 3,590 m), based on top models. A (+) indicates a positive effect on Common Raven activity and a (-) indicates a negative relationship. A (+/-) either indicates no significant effect or no clear effect...... 27

vii

LIST OF FIGURES

Figure 1. Map of ocean-fronting beach and gravel bars of the lower Eel River in Humboldt County, California, where observers collected point count data to quantify Common Raven numbers during surveys for Snowy Plovers, 2004 – 2014...... 7

Figure 2. An example of 500 m x 1,500 m (north-south) grids overlaying Clam Beach with associated point count locations from 2004-2014...... 13

Figure 3. Map of average Common Ravens detected per grid cell from Gold Bluffs Beach south to gravel bars of the Eel River, calculated from point count data, 2004-2014...... 17

Figure 5. Response curve outputs for each of the covariates from the top Generalized Additive Model (GAM) of the 1,450 m scale ( RAVENS ~ AGR + URBL + URBH + WATER + FOR). The curves show the modeled effects as solid lines, with 95% Bayesian credible intervals in shaded grey. The y-axis is on that of the linear predictor, but due to identifiability constraints, they are presented in a mean-centered fashion. The labels indicate the predictor variable with its associated effective degrees of freedom (Wood 2006)...... 22

Figure 6. Response curve outputs for each of the covariates from one of two top Generalized Additive Model (GAM) of the 3,590 m scale (RAVENS ~ HUM + ROAD + AGR + URBL + WATER + FOR). The curves show the modeled effects as solid lines, with 95% Bayesian credible intervals in shaded grey. The y-axis is on that of the linear predictor, but due to identifiability constraints, they are resented in a mean-centered fashion. The labels indicate the predictor variable with its associated effective degrees of freedom (Wood 2006)...... 23

viii

LIST OF APPENDICES

Appendix A: 18 candidate models based on seven predictor variables, used for the three spatial scales: 500 m, 1,450 m, and 3,590 m...... 38

Appendix B: Average (±SD) number and average (±SD) incidence of Common Ravens at each ocean-fronting beach and Eel River gravel bar site, averaged across 11 years (2004- 2014), listed from north to south and then west to east, respectively...... 39

Appendix C: Hot spot map for all years combined resulting from the Hot Spot Analysis (Getis-Ord Gi*) Tool in ArcGIS v.10.1, using Common Raven point count data from 2004 -2014. Hot spots (red dots) indicate spatial clustering of high counts of Common Ravens, whereas cold spots (blue dots) indicate spatial clustering of low counts...... 40

Appendix D: Results from Analysis of Variance (ANOVA) tests for significant effects of covariates in top models for each spatial scale. See Wood (2006) for further Generalized Additive Model (GAM) theory...... 41

INTRODUCTION

Habitat destruction and fragmentation caused by landscape alterations are the leading cause in the decline of global biodiversity (Coté and Sutherland 1997; Schneider

2001; Withey and Marzluff 2009). Urbanization and other anthropogenic modifications of natural landscapes often negatively affect the abundance, distribution, and behavior of a variety of wildlife species, especially taxa with specialized niches (Coté and Sutherland

1997; Marzluff and Neatherlin 2006; MacDonald and Bolton 2008). By contrast, opportunistic generalists may benefit from these habitat changes (Marzluff and Ewing

2001).

Populations of synanthropic, generalist predators, including many members of the family Corvidae (e.g., jays, crows, and magpies), have increased in abundance and distribution globally (Boarman 1993; Kelly et al. 2002; Marzluff and Neatherlin 2006;

Sorace and Gustin 2009; Peery and Henry 2010). The Common Raven (Corvus corax; hereafter raven), in particular, is commensal with humans across much of North America

(Boarman and Heinrich 1999; Marzluff and Neatherlin 2006) and populations have increased greatly in many areas (Marzluff et al. 1994). Numerous studies have shown a positive correlation between abundance and productivity of these corvids with human activity and altered landscapes (Kristan and Boarman 2003; Marzluff and Neatherlin

2006; Sorace and Gustin 2009; Withey et al. 2009).

The raven poses a conservation dilemma because as predators they can negatively affect threatened and sensitive species (Boarman 1993; Luginbuhl et al. 2001; Sorace and

Gustin 2009). Ravens often prey on eggs or vulnerable individuals (e.g., young, diseased, or small size; Marzluff and Neatherlin 2006). Anthropogenic food subsidies and habitat alterations sustain large populations that “spillover” to forage in surrounding habitats

(Schneider 2001; Peery and Henry 2010). In North America, ravens negatively affect several threatened and endangered species including the Desert Tortoise (Gopherus agassizii; Camp et al. 1993), California Least Tern (Sternula antillarum browni; USFWS

1985), California Condor (Gymnogyps californianus; Snyder and Snyder 2000), San

Clemente Island Loggerhead Shrike (Lanius ludovicianus mearnsi; Scott and Morrison

1990), Greater Sandhill Crane (Grus canadensis; Littlefield 1995), Marbled Murrelet

(Brachyramphus marmoratus; Singer et al. 1991), Greater Sage-Grouse (Centrocercus urophasianus; Coates et al. 2008), and Snowy Plover (Charadrius nivosus; USFWS

2007).

In 1993, the United States Fish and Wildlife Service (USFWS) listed the Pacific

Coast population of the Snowy Plover (hereafter plover) as a threatened species under the

Endangered Species Act based on evidence of a declining population and loss of breeding habitat (Page et al. 1991; USFWS 1993). The species’ recovery plan identified three factors limiting population recovery: 1) loss and degradation of breeding habitats (e.g., from invasion of European beach grass [Ammophila arenaria]; Muir and Colwell 2010),

2) disturbance from human activity (e.g., dogs and vehicles; Lafferty et al. 2006), and 3) predation of eggs and chicks by mammalian and avian predators (i.e., red fox [Vulpes vulpes], grey fox [Urocyon cinereoargenteus], mustelids, raptors, gulls [Larus spp.],

American Crow [Corvus brachyrhynchos] and ravens; Neuman et al. 2004; USFWS

2007; Burrell and Colwell 2012).

The recovery plan designated six geographically-based recovery units. In coastal northern California, Recovery Unit 2 (RU2) includes Mendocino, Humboldt, and Del

Norte counties (USFWS 2007). Plovers breeding in RU2 exhibit chronically low reproductive success (Colwell et al. 2010; Burrell and Colwell 2012) such that the population is considered an ecological “sink” (Eberhart-Phillips and Colwell 2014), which is maintained by immigration from elsewhere along the Pacific coast (Mullin et al.

2010). From 2004-2014, reproductive success was below the level necessary to maintain the population (Colwell et al. 2014). Between 2001-2014, the RU2 breeding population has fluctuated from a high of 63 in 2002 to a low of 19 in 2009 (Colwell et al. 2011).

Ravens have been identified as the primary cause of reproductive failure in northern

California, particularly in Humboldt County where most plovers breed (Burrell and

Colwell 2012; Hardy and Colwell 2012).

Evidence suggests ravens are an important predator of plover eggs, including: 1) a positive correlation between corvids and nest predation rate (Burrell and Colwell 2012),

2) anecdotal observations by surveyors, and 3) video camera footage (Burrell and

Colwell 2012). In 2008 and 2009, a nest monitoring study using cameras showed that ravens preyed upon 70% of failed video-monitored nests (i.e., hatched no chicks) at Clam

Beach where most plovers bred (Burrell and Colwell 2012); humans caused the remainder of nest failures. Furthermore, there were many “unknown” causes of nest failure where eggs disappeared and the presumed cause was predation. Video camera

4 evidence showed that ravens preyed upon eggs at 77% of those that observers recorded as failed due to “unknown” reasons, which suggested that researchers underestimate the importance of raven predation (Burrell and Colwell 2012).

Given the evidence that ravens negatively affect reproductive success of plovers in Humboldt County, it is important to understand the landscape and habitat features influencing variation in raven activity, especially to inform where predator management is needed most. There are few quantitative studies in coastal northern California that have focused on ravens (Hardy and Colwell 2012). As a result, very little is known about the local population of this abundant native species. Studies on factors that influence raven distribution and activity are plentiful in many areas outside of Humboldt County.

Across their range in North America, raven activity correlates positively with various indices of human activity such as road density (Austin 1971; Knight and

Kawashima 1993), campgrounds (Marzluff and Neatherlin 2006), urban development

(Kelly et al. 2002; Sorace and Gustin 2009), and agriculture (Boarman 1993; Kelly et al.

2002). In the Mojave Desert, ravens were more abundant along highways due to vehicle- created carrion, which provides a rich source of easily-accessible food (Austin 1971;

Knight and Kawashima 1993). In Washington, raven abundance was highest near human settlements and campgrounds (Marzluff and Neatherlin 2006).

Agricultural areas also subsidize raven populations with an abundance of food

(Boarman 1993; Kelly et al. 2002). In a study of raven populations in western North

America using Christmas Bird Count and Breeding Bird Survey data, farmland was found to be the most important variable affecting an increase in raven numbers (Marzluff

5 et al. 1994). Raven abundance decreases with increasing high density urban environments

(Marzluff et al. 1994), although this trend is not as pronounced in coastal habitats (Kelly et al. 2002). Furthermore, low-intensity urban areas can positively affect raven abundance

(Marzluff and Neatherlin 2006).

It is probable that the above anthropogenic factors are positively affecting distribution and activity of ravens in Humboldt County. Based on these past studies, I examined relationships between several landscape features and raven activity in plover habitats (Burrell and Colwell 2012; Hardy and Colwell 2012). Specifically, my research had three objectives: 1) to summarize raven activity and distribution along gravel bars and ocean-fronting beaches where plovers breed; 2) to create and test a geospatial model relating activity to anthropogenic factors; and 3) to investigate what factors influence raven abundance and activity.

METHODS

Study Area

I studied ravens along approximately 80 km of ocean-fronting beaches and 15 km of the lower Eel River in Humboldt County, California (Figure 1), using point count data collected from 2004 - 2014. Coastal beaches in Humboldt County are characterized by comparatively homogenous sandy substrates with patchy dense stands of European beach grass and an assortment of drift wood, native plants, shell carapaces, and stones (Colwell et al. 2010; Burrell and Colwell 2012). Riverine habitats are characterized by a heterogeneous mix of sand and coarse substrates with sparse vegetation (willows [Salix sp.] and White Sweet-clover [Melilotus alba]). The amount of human activity within each habitat type varies greatly, with beach areas having nearly ten times more human activity than gravel bars (Colwell et al. 2010).

Field Methods

Each year, from 2004 to 2014, observers surveyed for plovers and collected data on raven activity at 7-10 day intervals, between 0600-1400, from March until September

(Colwell et al. 2010; Hardy and Colwell 2012). Observers walked the length of each site, surveying principally for breeding plovers, while recording data on ravens.

Observers collected data on raven activity using a modified point count method

(see Colwell et al. 2010) that followed a systematic interval between successive

Gold Bluffs Beach

Stone Lagoon

Big Lagoon

Pacific Ocean Clam Beach

Mad River Beach

North Spit

Eureka South Spit

Eel River Wildlife Area Eel River Gravel Bars

Centerville Beach

0800, 0820, 0840), signaled by a preset alarm to conduct a point count. The 20 minute interval allowed sufficient time between data points to maintain an accurate depiction of habitat characteristics while reducing spatial autocorrelation (Legendre 1993; Elith and

Leathwick 2009; Colwell et al. 2010). The instantaneous point count included recording the number of humans, corvids (ravens and American Crows), dogs, vehicles, and horses detected within a 500 m radius. Observers recorded point count data using a Dell Axim

X50 Personal Digital Assistant (PDA) programmed with ArcPad 6 (ESRI 2011) and outfitted with an auxiliary GPS unit (GPS Ultra Holux CR-271). Observers conducted research under federal, state, and university permits (USFWS permit #10457; California

Department of Fish and Wildlife collecting permit #0946; State Parks collecting permit

14-635-008; Humboldt State University IACUC 14/15.W.07.A).

Analytical Methods

Analyses of Common Raven Distribution and Activity

I collated point count data from 2004-2014 and removed observations (N = 240) where Universal Transverse Mercator (UTM) coordinates indicated obvious errors (i.e., points that were spatially inaccurate due to GPS error or observer error). To investigate raven distribution and activity, I summarized data using the Hot Spot Analysis (Getis-Ord

Gi*) tool in ArcGIS v.10.1 (ESRI 2011) to calculate a Getis-Ord Gi* spatial statistic for each point count data point:

∑푛 푤 푥 − 푥̅ ∑푛 푤 ∗ 푗=1 푖,푗 푗 푗=1 푖,푗 퐺푖 = 2 ∑푛 2 ∑푛 √(푛 푗=1 푤푖,푗 − ( 푗=1 푤푖,푗) ) 푆 푛 − 1 where 푥푗 is the attribute value for feature 푗 (e.g., a data point with an associated continuous measurement), 푤푖,푗 is the spatial weight between i and j, n is equal to the total

∑푛 푥 ∑푛 푥 2 number of features and 푥̅ = 푗=1 푗 and 푆 = √ 푗=1 푗 − (푥̅)2 (ESRI 2011). This method 푛 푛 identifies statistically significant spatial clustering of high counts (i.e., hot spots) and low counts (i.e., cold spots) of ravens. The tool examines the number of ravens at a point in relation to points within a 500 m radius and calculates statistical significance using a z- score and P-value, point by point. I used point counts from all 11 years, which resulted in a map output with blue and red points—indicating cold and hot spots, respectively— portraying the distribution of ravens in plover habitats.

At a coarser spatial scale, I examined the consistency with which the ranked average number of ravens detected at sites remained constant across years using the

Kendall’s Coefficient of Concordance (Siegel 1956; Legendre 2005). The Kendall’s

Coefficient (W) can range from zero to one, where zero indicates no consistency in the rankings of sites from year to year and a value of one indicates perfect correlation in site rankings from year to year (Siegel 1956). For this analysis, I only used data from 2007-

2014, where point count data were available for six of the main ocean-fronting beach sites, so that they can be ranked relative to each other (Clam North, Clam South, Mad

River Beach, South Spit, Eel River Wildlife Area, and Centerville Beach). I utilized all

10 point count data from the Eel River gravel bar sites for the same years, 2007 – 2014. I separated sites by habitat types because of the differing variation in the abundance of ravens (Colwell et al. 2014). I conducted analysis using the {irr} library in program R

(Gamer et al. 2012; R Development Core Team 2014).

Geospatial Modeling of Common Raven Activity

I used the same point count data to investigate factors influencing raven activity in plover habitats, by relating several anthropogenic predictor variables to raven activity using Generalized Additive Models (GAMs). I utilized point count data from 2004-2012 as the training dataset (n = 14,809) for statistical modeling. To test the accuracy and predictive power of the top models, I used the remaining point count data from 2013-

2014 as the validation data (n = 3,946). Furthermore, in the breeding season of 2014, I organized survey effort to include four additional sites where observers surveyed bi- monthly (Gold Bluffs Beach, Stone Lagoon, Big Lagoon, and North Spit).

I modeled raven activity using seven covariates that I hypothesized would influence variation in raven activity (Table 1). I selected these covariates based on a literature review and field observations. Since different variables may influence raven activity differently across spatial scales, I modeled raven activity at three scales: a localized scale (500 m), an intermediate scale (1,450 m), and a larger landscape scale

(3,590 m); these corresponded to radii based on the smallest to largest home range estimates of ravens (Boarman and Heinrich 1999; Coates et al. 2014).

Table 1. Predictor variables, their definitions, and data sources used in geospatial modeling of Common Raven activity using point count data from 2004-2014. Explanatory Variable Abbreviation Definition Data Source Human index HUM Average number of humans, dogs, horses, and vehicles per point count. Point count Averaged for each grid cell. data

Distance to roads ROAD Average distance from a point count to the nearest road, using the Near Census Bureau tool in ArcGIS v.10.1. TIGER/Lines1

Agricultural land AGR Average amount of agricultural area (ha) within a grid; defined as areas NLCD 20112 for production of annual cultivated crops and plants for livestock grazing.

Low-intensity urban area URBL Average amount of low-intensity urban area (ha) within a grid; defined NLCD 20112 as areas considered open space with a mixture of constructed materials and vegetation. Impervious surface account for 0-49% of total cover.

High-intensity urban URBH Average amount of high-intensity urban area (ha) within a grid; defined NLCD 20112 area as areas with highly developed areas where people reside/work in high numbers. Impervious surfaces account for 50-100% of total cover. Averaged for each grid cell.

Area of open water WATER Average amount of open water (ha), both fresh and salt water, within a NLCD 20112 grid.

Area of forested habitats FOR Average amount of forested habitats (ha) within a grid; areas including NLCD 20112 deciduous, evergreen, and mixed forests.

1Topologically Integrated Geographic Encoding and Referencing, a format used by the U.S. Census Bureau to describe spatial attributes. 2National Land Cover Dataset 2011, developed by the U.S. Department of Interior and U.S. Geological Survey. I measured amount of land cover area in Geospatial Modeling Environment (GME) using the “isectpolyrst” tool (GME 2014).

Because survey effort varied greatly among sites and across years, I utilized a grid system to summarize both the response variable and predictor variables. I used a geographic information system (GIS) to create a grid (500 m north to south and 1,500 m east to west) starting at the northern extent of the study area (Gold Bluffs Beach), extending south to include beaches, as well as Eel River gravel bar sites (Figure 2).

There were a total of 99 grids for the training dataset and 98 additional grids when I added four additional sites in the breeding season of 2014 for the validation dataset (total

= 197 grids).

Within each grid cell, I collated all the point count data and calculated the average number of ravens and averaged covariates from all the data points within that grid cell.

Thus, I treated each grid cell as the sample unit for statistical modeling. I completed exploratory analyses by: 1) investigating any correlations between the covariates using pair plots; 2) examining the distribution of the response variable; and 3) estimating the amount of spatial autocorrelation by calculating Moran’s I statistics for each year in

ArcGIS v.10.1.

I formulated 18 candidate models using a combination of the seven predictor variables based on my hypotheses on what affected raven activity in my study area

(Appendix A). For each spatial scale, I utilized GAMs with cubic regression splines and a

Gaussian distribution with a log link function to relate raven activity to the seven predictor variables using the {mgcv} modeling library (Wood 2006; Zuur et al. 2009).

Clam Beach

Figure 2. An example of 500 m x 1,500 m (north-south) grids overlaying Clam Beach with associated point count locations from 2004-2014. 14

For the cubic regression splines, I improved model fit of the data using cross- validation methods, as defined by Wood (2006). I conducted all statistical analyses in

RStudio v.0.98 (RStudio 2012) and program R (R Development Core Team 2012).

GAMs are modified Generalized Linear Models (GLMs) that involve a series of smoothing functions for each explanatory variable (X1, X2, etc.; Wood 2006):

2 Yi = α + f1 (X1) + f2 (X2) + …+ fk (Xk) + εi where εi ~ N (0, σ )

GAMs allow more flexibility in describing the dependence of the response variable on multiple explanatory variables by using multiple penalized regression splines. This type of GAM controls the number of parameters of each regression line by penalizing models that underfit or overfit the data by increasing their AICc values (Wood 2006). Because there are multiple linear coefficients for the spline functions of each covariate, there are no calculated β coefficients for each variable (Wood 2006). Furthermore, GAMs output easily interpretable response curves for each explanatory variable.

I conducted model selection using an information-theoretic approach (Burnham and Anderson 2002). I evaluated the strength of support for each model using Akaike’s

Information Criterion adjusted for small sample sizes (AICc) and Akaike weights (wi).

Akaike weights estimate the relative frequency that a model would be best supported out of alternatives if the statistical test was calculated repeatedly (Burnham and Anderson

2002). I considered a model as the best supported model when it had a wi > 0.9 (Kristan and Boarman 2003). If no model resulted in a wi > 0.9, then I considered those models that were within seven AICc units of the model with the highest wi the most plausible explanations of the data (Burnham et al. 2011).

When I selected a model as the top model, it characterized the best representation of the variation in the data relative to the other models, and did not necessarily state that it fits the data precisely (Burnham et al. 2011). For this reason, once I selected top models for each spatial scale, I conducted model validation and diagnostics to test model fit and accuracy. I assessed model deviance values, adjusted R2 values, and residual plots.

Finally, I used the top models to predict raven activity using validation data (point count data from 2013-2014) to compare predictions with observed data. I assessed the residuals as a measure of model accuracy for each of the spatial scales.

RESULTS

Common Raven Distribution and Activity

Observers collected 18,755 point count data points over the 11 years (14,809 from

2004-2012 and 3,946 from 2013-2014). Ravens occurred on 33.1% of all point counts with a mean (± SD) number of ravens detected of 0.99 ± 2.75. The mean (± SD) number of ravens detected per point count at each survey site ranged from 0.23 ± 0.13 to 4.36 ±

3.12 (Appendix B; compare with Colwell et al. 2010). Ravens were significantly more abundant (t8.65 = 2.58, P = 0.03) on gravel bars (1.62 ± 0.70) compared with beaches

(0.94 ± 0.24). The ranked order of raven abundances was consistent across years for both ocean-fronting beaches (W = 0.88, χ2 = 39.4, P < 0.001) and gravel bars (W = 0.55, χ2 =

35.4, P < 0.001).

The hot spot analysis produced one map with statistically significant hot and cold spots distributed throughout plover habitats (Appendix C). Based on this hot spot map, there is conspicuous and consistent spatial patterning in raven distribution across sites where plovers breed. A similar pattern is seen at a coarser spatial scale in a map of average raven abundance per grid cell (Figure 3).

Clam Beach

Mad River Beach

Gold Bluffs Beach

North Spit

South Spit Stone Lagoon

Eel River Wildlife Area

Big Lagoon Eel River Gravel Bars

Centerville Beach

Figure 3. Map of average Common Ravens detected per grid cell from Gold Bluffs Beach south to gravel bars of the Eel River, calculated from point count data, 2004-2014. 18

Geospatial Modeling

I evaluated 18 models for each of the three spatial scales using 99 grid cells as the sample unit (Table 2). There was an uncontested top model at each of the spatial scales; all univariate models had no support. At all spatial scales, low-intensity urban areas, agricultural lands, and amount of open water appeared in the top models.

At the fine spatial scale (500 m), the top model had 97.9% of model weight

(Table 2), 91.2% of the deviance explained, and an adjusted R2 of 0.855. All covariates included in the model had significant effects, based on an analysis of variance (ANOVA) of the smooth terms (Appendix D; Wood 2006). The response curves (Figure 4) indicated that ravens were more abundant at sites with more human activity and greater area of low-intensity urban habitat; whereas distance to roads, agricultural lands and amount of surrounding water had unclear relationships with raven activity (i.e., response curves were indecipherable of any trend). Residual plots showed constant variance in the residuals (homoscedasticity) and a normal distribution, indicating adequate model fit

(Wood 2006; Zuur et al. 2009). Furthermore, the observed values of average raven abundance from the data and corresponding values predicted by the top model had a nearly linear relationship (adjusted R2 = 0.92).

At the intermediate spatial scale (1,450 m), the top model had 98.0% of model weight, explained 94.4% of the deviance, and had an adjusted R2 of 0.911. This model contained five covariates, of which four had significant effects: agriculture, low-intensity urban area, high-intensity urban area, and amount of open water (Table 2). Similar to the

Table 2. Model selection results evaluating relationships between Common Raven activity and anthropogenic variables at three spatial scales: 500 m, 1,450 m, and 3,590 m.

Model logL AICc Δ AICc wi

Fine Scale (500 m)

HUM + ROAD + AGR + URBL + WATER -43.85 250.89 0 0.979

URBL + AGR -104.52 258.93 8.03 0.018

AGR + URBL + URBH -108.36 263.48 12.59 0.018

Intermediate Scale (1,450 m)

AGR + URBL + URBH + WATER + FOR -21.35 171.35 0 0.98

URBL + URBH + WATER + FOR -53.93 180.72 9.37 0.009

HUM + ROAD + AGR + URBL + WATER -42.30 181.62 10.27 0.006

Landscape Scale (3,590 m)

HUM + ROAD + AGR + URBL + WATER 11.00 90.29 0 0.94 + FOR

URBL + URBH + WATER + FOR -6.80 96.14 5.85 0.05

AGR + URBL + URBH + WATER + FOR 19.16 99.48 9.18 0.009

Figure 4. Response curve outputs for each of the covariates from the top Generalized Additive Model (GAM) of the 500 m scale (RAVENS ~ HUM + ROAD + AGR + URBL + WATER). The curves show the modeled effects as solid lines, with 95% Bayesian credible intervals in shaded grey. The y-axis is on that of the linear predictor, but due to identifiability constraints, they are presented in a mean-centered fashion. The labels indicate the predictor variable with its associated effective degrees of freedom (Wood 2006). 21 fine scale, raven activity increased in association with low-intensity urban area, in addition to agricultural lands (Figure 5). The residual plots for this model were very similar to the local scale model, portraying constant variance and a normal distribution for the residuals, indicating good model fit to the data. The predictions matched the observed average raven abundances in the training dataset (R2 = 0.94), but over-predicted for some observations that had a smaller average number of ravens.

At the landscape scale, the top model explained 97.3% of the deviance and had an adjusted R2 of 0.96 (Table 2). Agricultural lands and low-intensity urban areas had a positive relationship with raven activity. Additionally, the amount of open water and forested lands showed significant increases in raven activity (Figure 6). Predicted values matched the observed average raven abundances well (R2 = 0.974).

I used the validation data (point count data from 2013-2014) to examine the accuracy and generality of the top models at each scale. Model validation showed that the predictive accuracy for the top models at all three spatial scales was low; R2 values from linear models comparing predicted values with observed values ranged from 0.001 to

0.009. Therefore, there were large discrepancies between observed and predicted average raven abundance, which indicates that the models may be overfitting the data (Elith and

Leathwick 2009; Zuur et al. 2009).

Overfitting may have resulted from the addition of new sites in 2014 to the validation data. For this reason, I removed data from sites that were surveyed in 2014 that were not previously surveyed between 2004 - 2012 (training data), and tested the top models again. This vastly improved the accuracy of predictions compared to the observed

24 average abundances at the intermediate (R2 = 0.53), and landscape scales (R2 = 0.53). The finest spatial scale still performed poorly (R2 = 0.0001). GAMs are susceptible to overfitting datasets, making it difficult to generalize the results outside of the original study area (Wood 2006; Zuur et al. 2009).

DISCUSSION

The Common Raven is widely known to be an important predator of a variety of sensitive and threatened species (Singer et al. 1991; Camp et al. 1993; Littlefield 1995;

Snyder and Snyder 2000; USFWS 2007; Coates et al. 2008). For the Snowy Plover, ravens are thought to be a significant predator of eggs and chicks, especially in Humboldt

County (Colwell et al. 2010; Burrell and Colwell 2012). For this reason, quantifying raven distribution throughout plover habitats and understanding landscape features that influence raven activity is vital for effective predator management. In this study, I found that: 1) raven activity varied greatly across gravel bar and ocean-fronting beaches; 2) patterns in raven activity were consistent across 11 years; and 3) variation in raven activity across habitats where plovers have bred is influenced by a number of habitat features associated with anthropogenic activity, which likely provide food for ravens.

Common Raven Distribution and Activity

Raven activity was conspicuously variable across 80 km of ocean-fronting beach and 15 km of gravel bar habitats (Appendix C). Ravens were significantly more abundant on riverine gravel bars than ocean-fronting beaches, despite a substantial amount of variation within habitat types (Appendix B). Landscape level trends in raven abundances were relatively consistent across the 11 years. These results extend and confirm the findings of Colwell et al. (2010) for the same study sites in the first five years of plover monitoring efforts.

There are noticeably few studies of the distribution of raven activity in coastal

California (Kelly et al. 2002; Clucas et al. in press). Raven abundance was variable within Arcata, California, a town within several kilometers of plover habitat, with more ravens near agriculture (Clucas et al. in press). Similarly, Kelly et al. (2002) found patterns of high and low raven abundances across coastal Marin County, California.

There was a gradient from low to high numbers of ravens from inland habitats to the coast, with the highest number of ravens occurring in agricultural areas, indicating that landscape features affect the distribution of ravens. The patchy distribution of raven activity within plover habitat (as seen in the hot spot analysis), is also probably due to the spatial distribution of various anthropogenic habitats across the landscape.

On beaches, the hot spot analysis revealed that high numbers of ravens coincided with some areas where plovers have bred, with negative consequences for plover productivity (Burrell and Colwell 2012; Colwell et al. 2014). In other words, plover nest survival was lowest in areas with higher numbers of ravens (Burrell and Colwell 2012).

However, on certain gravel bars, there are areas of high raven abundances where plovers have historically had markedly higher nest success in comparison to beaches (Colwell et al. 2011). This supports previous findings that nest crypsis afforded by the gravel substrate negates the risk of nest predation in some gravel bar habitats with raven hot spots (Colwell et al. 2011; Herman and Colwell 2015).

Landscape Correlates of Common Raven Activity

Analysis of habitat correlates of raven activity resulted in similar patterns across spatial scales (Table 3). At the finest spatial scale, ravens were more abundant where more humans were present during point counts. At the two larger spatial scales, ravens were more abundant in plover habitats where there were greater amounts of low-intensity urban areas and agricultural habitats. I interpret these results as evidence of an indirect positive relationship between a synanthropic omnivore and landscape features associated with greater availability of food resources.

Covariate 500 m scale 1,450 m scale 3,590 m scale

Human index + +/-

Distance to roads - +/-

Agriculture +/- + +

Low-intensity + + + urban area

High-intensity + + urban area

Open water +/- +

Forest habitats +/- +

Across North America, ravens are more abundant in human-altered landscapes

(Knight and Kawashima 1993; Knight et al. 1993; Kristan and Boarman 2003; Bui et al.

2010; Webb et al. 2011). Ravens had smaller home ranges and higher reproductive success in areas closer to human settlements and urban areas, likely due to associated, reliable food sources (Marzluff and Neatherlin 2006). In other ecosystems, raven activity is also correlated with urban areas (Kristan et al. 2004; Balensperger et al. 2013). In

Alaska, ravens concentrated in urban Fairbanks compared to the outskirts, relying on anthropogenic food subsidies found near markets and parking lots throughout the winter

(Baltensperger et al. 2013). Moreover, Kristan et al. (2004) found ravens had higher reproductive success in areas closer to urban areas in the Mojave Desert due to more diverse food resources.

As opportunistic generalists, ravens in plover habitats are expected to exploit a variety of food sources near urban areas adjacent to ocean-fronting beach sites, including parking lots. Furthermore, the variable distribution of raven activity across the landscape in my study area may be due to the presence of juvenile and non-breeding ravens, since young birds often concentrate in areas of reliable anthropogenic food resources (Marzluff et al. 1994; Marzluff and Neatherlin 2006). The relatively large number of ravens found at the Clam and Mad River Beach sites may be a reflection of the number of these roaming groups of ravens that are not able to establish territories and concentrate in plover habitats that have large amounts of surrounding urban area that provide food and other resources (Marzluff and Heinrich 1991).

Agricultural lands provide a variety of food sources for ravens, including grains, invertebrates, and cattle carcasses (Knight et al. 1993; Kelly et al. 2002; Clucas et al. in press). Elsewhere in coastal northern California, raven activity was greater in areas dominated by agriculture probably because of enhanced foraging opportunities at garbage dumps, livestock carcasses, and feedlots, as well as picnic areas. Agricultural areas in other ecosystems positively affect raven activity (Knight et al. 1993; Manzer and Hannon

2005). In sagebrush habitat, landscapes with more cropland and sparse grasslands contained higher abundances of ravens (Manzer and Hannon 2005). In the Mojave

Desert, Knight et al. (1993) found more ravens in agricultural fields than in rangeland and desert controls. In Humboldt County, landscapes around plover habitat consist of a mosaic of agriculture, extensive road networks, and picnic areas, which likely creates similar conditions that favor high raven abundance.

This study found no significant effect of roads on raven activity in plover habitats, likely due to the ubiquitous food resources available in other forms in low-intensity urban areas and agricultural fields (e.g., trash, picnic areas, and invertebrates). Proximity to roads was an important variable that influenced raven activity in several studies, particularly in sagebrush and desert ecosystems (Knight and Kawashima 1993; Knight et al. 1993; Bui et al. 2010; Coates et al. 2014). Linear stretches of road provided ample supplies of point subsidies (i.e., roadkill), providing reliable and frequent food sources for ravens. In my study, roads were considered as part of the low-intensity cover class in the

National Landcover Database and may have been accounted for in that particular variable.

I did not include a variable that considered temporally unstable point subsidies

(e.g., carcasses washed up on beach habitats or road kills), which are an important aspect of raven biology (Boarman and Heinrich 1999; Webb et al. 2011). These data were difficult to quantify and did not exist for the past 15 years coinciding with my point count data. However, ravens are often observed feeding on washed up carcasses on the wave slope of beach habitats (personal observation). It is possible that the distance to road or low-intensity urban area variables were a proxy for road kills, however. This is another aspect that may be influencing raven abundance in plover habitats.

It is important to note that there may be additional variables at a finer scale than the variables I examined in my analysis that influences raven abundance, such as the presence of competitors (e.g., American Crows), locations of breeding pair home ranges, or available food resources in plover habitats. Additionally, it is certainly possible that finer scale conditions, rather than landscape factors, have a larger influence on raven activity in plover habitats.

MANAGEMENT IMPLICATIONS

The study of the distribution and activity of Common Ravens in relation to habitat features is a vital step in addressing impacts on Snowy Plovers. In Humboldt County, ravens are abundant in many habitats where Snowy Plovers breed. Currently, plovers breed only on ocean-fronting beaches, where there are noticeable raven hot spots. Hence, predator management and other conservation efforts on some ocean-fronting beach sites should be a high priority.

Human activity positively correlated with raven activity, suggesting that humans provide attractants (i.e., food) for ravens, although this requires further study. I recommend that agencies and land managers work to understand and reduce the facets of recreation (e.g., garbage) that have this effect on raven activity.

Additionally, education can be an effective management tool to spread knowledge on how humans’ positive influence on ravens can have a negative impact on plovers in the form of supplemental food. A campaign of signage that explains the role that humans may play in providing supplemental food that attracts ravens—with negative consequences for plovers—can be useful to attract attention to this problem.

The positive association between various indices of human activity (i.e., agriculture and urban areas) with raven abundance suggest that human-altered landscapes provide attractants for ravens. It may be a much more difficult task to manage such attractants in these areas. I suggest further research that examines specifically how ravens utilize these habitats. Furthermore, additional research can investigate what resources

32 ravens are utilizing on ocean-fronting beach sites. To aid conservation of Snowy Plovers, it is further warranted to investigate the relationship between raven activity, aspects of

Snowy Plover breeding biology (i.e., per capita reproductive success), and additional limiting factors suppressing population recovery of the Snowy Plover in northern

California.

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APPENDIX A

Appendix A: 18 candidate models based on seven predictor variables, used for the three spatial scales: 500 m, 1,450 m, and 3,590 m.

1. HUM 2. HUM + ROAD 3. AGR 4. ROAD + AGR 5. HUM + ROAD + AGR 6. URBL 7. ROAD + URBL 8. URBL + AGR 9. ROAD + AGR+ URBL 10. HUM + ROAD + AGR + URBL 11. HUM + ROAD + AGR + URBL + WATER 12. HUM + ROAD + AGR + URBL + WATER + FOR 13. URBL + URBH 14. AGR + URBL + URBH 15. AGR + WATER + FOR 16. URBL + WATER + FOR 17. URBL + URBH + WATER + FOR 18. AGR + URBL + URBH + WATER + FOR

APPENDIX B

Appendix B: Average (±SD) number and average (±SD) incidence of Common Ravens at each ocean-fronting beach and Eel River gravel bar site, averaged across 11 years (2004-2014), listed from north to south and then west to east, respectively.

Average Average Years Site Numbera Incidenceb N Surveyed Gold Bluff Beach 0.39 ± 1.03 0.17 ± 1.03 130 1 Stone Lagoon 0.56 ± 1.54 0.17 ± 1.54 92 4 Big Lagoon 0.78 ± 1.93 0.27 ± 1.93 211 5 Clam Beach (North) 1.43 ± 0.51 0.39 ± 0.09 5,162 11 Clam Beach (South) 1.05 ± 0.37 0.38 ± 0.09 3,201 11 Mad River Beach 1.83 ± 0.58 0.50 ± 0.17 1,435 9 North Spit 0.35 ± 0.89 0.17 ± 0.89 182 1 South Spit 0.23 ± 0.13 0.10 ± 0.05 1,878 8 Eel River Wildlife Area 0.44 ± 0.23 0.17 ± 0.07 1,481 10 Centerville Beach 0.43 ± 0.19 0.19 ± 0.08 1,115 10 Sandy Prairie gravel bar 1.61 ± 1.28 0.37 ± 0.19 468 11 Drake gravel bar 0.50 ± 1.53 0.19 ± 0.10 152 11 Worswick gravel bar 0.48 ± 0.20 0.21 ± 0.08 1,300 11 Mercer-Fraser gravel bar 0.63 ± 0.46 0.20 ± 0.11 144 6 Fernbridge gravel bar 0.71 ± 0.47 0.30 ± 0.13 392 10 Singley gravel bar 2.66 ± 0.93 0.75 ± 0.20 228 10 Loleta gravel bar 1.40 ± 0.49 0.49 ± 0.18 785 11 Ropers gravel bar 2.89 ± 1.04 0.65 ± 0.18 288 11 Fulmar gravel bar 4.36 ± 3.12 0.76 ± 0.20 256 9 Cock-Robin Island gravel bar 1.57 ± 0.85 0.54 ± 0.18 321 11 a Number of individual birds detected instantaneously within 500 m of observer. b Proportion of point counts with at least one Common Raven detected; averaged across 11 (2004-14) years of data collection at each site.

APPENDIX C

Appendix C: Hot spot map for all years combined resulting from the Hot Spot Analysis (Getis- Ord Gi*) Tool in ArcGIS v.10.1, using Common Raven point count data from 2004 -2014. Hot spots (red dots) indicate spatial clustering of high counts of Common Ravens, whereas cold spots (blue dots) indicate spatial clustering of low counts. 41

APPENDIX D

Model and Covariates edf1 res.df2 F p-value Fine Scale (500 m)

HUM 8.07 8.57 3.62 0.001 ROADS 7.73 8.36 3.29 0.003 AGR 8.76 8.93 5.26 2.55e-05 URBL 9.00 9.00 5.06 3.88e-05 WATER 8.71 8.94 5.51 1.39e-05 Intermediate Scale (1,450 m)

AGR 8.74 8.96 5.74 5.98e-06 URBL 5.44 6.14 5.22 0.0002 URBH 9.00 9.00 13.69 1.07e-14 WATER 8.99 8.99 4.86 4.97e-05 FOR 4.44 5.07 2.07 0.08 Landscape Scale (3,590 m)

HUM 1.00 1.00 0.18 0.67 ROADS 4.32 5.01 5.65 0.0002 AGR 9.00 9.00 9.43 5.10e-10 URBL 9.00 9.00 11.0 9.34e-12 WATER 4.58 5.27 19.28 7.78e-14 FOR 7.27 7.69 5.70 1.87e-05

1 Estimated degrees of freedom – altered degrees of freedom based on penalization of model terms. 2 Residual degrees of freedom.