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Invasive Coqui Frogs Serve as Novel Prey for Birds in Hawaii, and Not as Competitors

Robyn L. Smith Utah State University

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This Thesis is brought to you for free and open access by the Graduate Studies at DigitalCommons@USU. It has been accepted for inclusion in All Graduate Theses and Dissertations by an authorized administrator of DigitalCommons@USU. For more information, please contact [email protected]. INVASIVE COQUI FROGS SERVE AS NOVEL PREY FOR BIRDS IN HAWAII,

AND NOT AS COMPETITORS

by

Robyn L. Smith

A thesis submitted in partial fulfillment of the requirements for the degree

of

MASTER OF SCIENCE

in

Ecology

Approved:

______Karen H. Beard David N. Koons Major Professor Committee Member

______Aaron B. Shiels Mark R. McLellan Committee Member Vice President for Research and Dean of the School of Graduate Studies

UTAH STATE UNIVERSITY Logan, Utah

2016

ii

Copyright © Robyn L. Smith 2016

All Rights Reserved

iii ABSTRACT

Invasive Coqui Frogs Serve as Novel Prey for Birds in Hawaii, and Not as Competitors

by

Robyn L. Smith, Master of Science

Utah State University, 2016

Major Professor: Karen H. Beard Department: Wildland Resources

The Puerto Rican coqui frog (Eleutherodactylus coqui) has been hypothesized to affect bird communities in Hawaii by 1) competing with insectivorous birds for prey, 2) providing prey for predatory birds, and 3) bolstering populations of non-native mammals by serving as prey. No previous studies have collected empirical data on these potential impacts. We investigated the impact of coquis on birds at two scales.

For our first research question, we used stable isotope analyses to address whether three species of insectivorous bird, one native and two non-native, and coquis could compete for invertebrate prey. We found that the coquis overlapped in isotopic niche space with all three bird species, which suggests these species occupy a similar place in the food web. However, our Bayesian diet analysis suggests that coquis mostly feed on

Acari, Amphipoda, and Blattodea (>90%), and only consume about 2% Araneae, the only diet source they share with birds. This result suggests that coquis do not heavily compete with these bird species for prey. iv For our second research question, we conducted avian point counts in coqui and

non-coqui plots across 15 sites on the island of Hawaii. We modeled whether coqui

presence or density explained patterns of insectivorous, vertebrate-preying, and native bird abundance. We estimated abundances of birds in coqui and non-coqui plots using hierarchical Bayesian N-mixture models with random effects. We tested whether habitat variables differed across coqui and non-coqui plots and whether coqui density was correlated with any habitat variable to more confidently attribute changes in bird abundance across coqui and non-coqui plots to the frogs. We found that coquis were

associated with greater abundances of vertebrate-preying, generalist insectivorous, and

non-native birds in Hawaii. Vertebrate-preying birds showed the strongest association,

with a 0.97 probability of abundance being at least 50% higher in coqui plots. Native

birds did not show differences in abundance across coqui and non-coqui plots. Because

insectivorous bird and native bird abundance did not differ across coqui invasion fronts,

our results suggest that coquis primarily affect Hawaiian birds by serving as a food

resource for predatory birds, and not as competitors for invertebrate prey.

(128 pages)

v PUBLIC ABSTRACT

Invasive Coqui Frogs Serve as Novel Prey for Birds in Hawaii, and Not as Competitors

Robyn L. Smith

The Puerto Rican coqui frog was introduced the Hawaiian Islands in the late

1980s. Because of the frog’s loud call and high abundance, the State of Hawaii has spent

millions of dollars on its eradication. Conservationists are also concerned that the frog

could negatively impact Hawaii’s endemic birds, which are already threatened by a host

of other invasive species, either by competing with them for insects or by increasing bird

predators. The purpose of this research was to investigate the impacts of coquis on

Hawaiian birds. First, we examined overlap in prey resources between coquis and birds, and second, we investigated whether coquis change bird abundance across Hawaii Island.

We used naturally occurring stable isotopes to quantify the position of coquis and

birds in the food chain. We found that coquis and insectivorous birds do occupy a similar

position in the food web. However, using more detailed analyses of their diets, we found

that the birds and coquis did not share many prey resources. Coquis mostly feed on

insects on the forest floor, while insectivorous birds feed on insects in the forest canopy.

This suggests that coquis and birds do not strongly compete.

We measured bird populations in coqui and non-coqui areas across 15 sites on the

island of Hawaii. We found that bird abundances were never lower in coqui areas,

suggesting that coquis do not negatively impact birds in Hawaii. We found that coquis

co-exist with four native species. Non-native predatory birds increased in coqui areas,

suggesting that the primary way coquis affect birds is by serving as a food resource. vi ACKNOWLEDGMENTS

I would like to thank my major professor Dr. Karen Beard for always being there to help me talk through my ideas, being so responsive with whatever I needed, and for helping me so much with the writing process. I would also like to thank my committee members Dr. Dave Koons, for patiently helping me with coding and analysis questions; and Dr. Aaron Shiels for his help while I was in Hawaii, as well as with my isotope work.

I would like to thank the USDA APHIS NWRC and the USU Ecology Center for providing the funding for this research. Thank you also to all of the folks at the NWRC

Hilo, Hawaii field station for the use of their field vehicles, equipment, and storage space.

Thank you to Hawaii DOFAW, NARS, and State Parks for help obtaining research access permits. I am so grateful to my field technicians for helping me in often difficult situations: Katie Dunbar, Jessy Johnson, and particularly Arthur Wallis, who gave 110% effort every single day. I am also very grateful to Bret Mossman for help with insect identification in the lab.

Thank you to my fellow Beard lab mates: Lindsay Carlson, Ryan Choi, Leandro

Do Nascimiento, Rodrigo Ferreira, Shane Hill, and Martin Holdrege for your friendship, support, and thoughtful input on my project. Also, thank you to all my fellow graduate students in the USU community. I am so grateful for the loving support of my parents

Ron and Diane Smith and my brother Justin. Lastly, I would like to thank Andrew

Crusoe. Words cannot describe how thankful I am for everything that you do—I will be forever indebted to this project for giving me the opportunity to meet you.

Robyn L. Smith vii CONTENTS

Page

ABSTRACT ...... iii

PUBLIC ABSTRACT ...... v

ACKNOWLEDGMENTS ...... vi

LIST OF TABLES ...... ix

LIST OF FIGURES ...... xi

CHAPTER

1. INTRODUCTION ...... 1

Literature Review...... 3 Research Objectives ...... 10 References ...... 11

2. DIFFERENT PREY RESOURCES SUGGEST LITTLE COMPETITION BETWEEN NON-NATIVE FROGS AND INSECTIVOROUS BIRDS DESPITE ISOTOPIC NICHE OVERLAP ...... 21

Abstract ...... 21 Introduction ...... 22 Methods...... 24 Results ...... 32 Discussion ...... 36 References ...... 43

3. INVASIVE COQUI FROGS ARE ASSOCIATED WITH GREATER ABUNDANCES OF NON-NATIVE BIRDS IN HAWAII ...... 53

Abstract ...... 53 Introduction ...... 54 Methods...... 56 Results ...... 67 Discussion ...... 77 Literature Cited ...... 84 viii 4. SUMMARY AND CONCLUSIONS ...... 95

References ...... 98

APPENDICES ...... 102

Appendix A ...... 103 Appendix B ...... 108

ix LIST OF TABLES

Table Page

2-1 Known diet sources of study species ...... 28

2-2 Isotope niche metrics for coqui and bird species. The location of the centroid (LOC) indicates where the niche is centered in isotopic space. The mean Euclidean distance to the centroid (CD) and mean Euclidean nearest-neighbor distance (MNND) are estimates of trophic diversity within a species. The core isotopic niche width is represented by the median Bayesian standard ellipse area (SEAB) and the 95% Bayesian credible intervals in parenthesis ...... 34

3-1 Individual species included in foraging group classifications. Native species were assigned to foraging groups based on designations in Banko and Banko (2009). Non-native species were assigned to foraging groups based on designations in del Hoyo et al. (2008). Some birds were classified in multiple groups. MLA= Maximum-likelihood analysis in “unmarked”; BA= Bayesian analysis in JAGS ...... 61

3-2 One-way ANOVA of environmental differences across coqui and non-coqui sites. * indicates significant differences...... 69

3-3 Maximum-likelihood models from the package “unmarked” in R. λ is abundance and p is detection probability. Native bird species are marked with * ...... 71

3-4 Bayesian model output of coqui (Eleutherodactylus coqui) effects on bird abundance by functional group, origin or species. Effect size is the 95% Bayesian confidence intervals for the direction and magnitude of effect size of either coqui presence (P) or coqui density (D), based on maximum- likelihood model results (see Table 3-3). Only percent increases are presented because no bird had a negative relationship with the coqui ...... 75

A1 Comparison of Bayesian niche widths and 95% credible intervals of three random samples of coqui and female, male, and subadult coquis to all frogs. Niche widths were considered significantly different if there was no overlap in credible intervals ...... 105

A2 Mean δ13C and δ15N values of invertebrate, vertebrate, and plant groups of interest in Manuka Natural Area Reserve. SE added in parenthesis for groups with greater than one observation ...... 106

x B1 Variance inflation factors (VIF) between coqui density and seven environmental variables and graphs of linear relationships between coqui density and environmental variables. A conservative estimate of collinearity is if VIF > 3 (Zuur et al. 2010) ...... 110

B2 Estimates of coqui density calculated from line-transect distance sampling surveys for all sites and plots ...... 114

B3 Mean values of plot-level environmental covariates included in bird abundance models in each of the fifteen study sites ...... 116

xi LIST OF FIGURES

Figure Page

1-1 Web of possible interactions between the coqui and Hawaiian bird communities. Citations marked with a (†) refer to empirical studies on the ecological effects of coqui. Citations marked with a (*) refer to hypothesized effects of coqui ...... 6

2-1 Discrimination-corrected mean isotopic signatures of the coqui (n=30), Hawaii amakihi (n=10), Japanese white-eye (n=10), red-billed leiothrix (n=10), and Hawaiian hoary bat (n=3). Bars indicate standard errors ...... 33

2-2 Discrimination-corrected core isotopic niches of Coqui (n=30), Hawaii Amakihi (n=10), Japanese White-eye (n=10), and Red-billed Leiothrix (n=10), represented by standard ellipse area ...... 34

2-3 Diet proportions of a) coqui, b) Hawaii amakihi, c) Japanese white-eye, and d) red-billed leiothrix dietary sources. Darkest gray boxes indicate 50% credible interval, lighter gray indicate 75% credible interval, and lightest gray indicate 95% credible interval...... 35

2-4 Mean isotope values (+- SE bars) for discrimination-corrected coqui, Hawaii amakihi, Japanese white-eye, red-billed leiothrix, and Hawaiian hoary bats plotted with invertebrates (gray) and plants (black) ...... 37

3-1 Fifteen sites on the island of Hawaii. Site abbreviations are Eden Roc (ER), Fern Forest (FF), Hamakua FR (HM), Kaupukuea Homestead (KH), Kaloko (KL), Kalopa (KP), Kulani (KU), Kaiwiki (KW), Manuka A (MA), Manuka B (MB), Stainback (SB), Saddle Road (SR), Volcano A (VA), Volcano B (VB), Waipio (WP) ...... 57

3-2 Histograms of the posterior estimates of vertebrate-preying bird abundance in coqui and non-coqui plots. The dashed line indicates the median value of vertebrate-preying bird abundance in coqui plots. The solid line indicates the median value of vertebrate-preying bird abundance in non-coqui plots. The intermediate shaded area shows the amount of overlap between the histograms ...... 71

A1 Core isotopic Bayesian ellipses for three random samples of 10 coqui plotted with the three bird species (a,b,c), and compared to the core isotopic Bayesian ellipses for all 30 coqui samples (d; see Figure 2-2 in chapter 2) ...104

xii B1 Coqui abundance plotted against canopy height ...... 111

B2 Coqui abundance plotted against percent native canopy ...... 111

B3 Coqui abundance plotted against percent canopy cover ...... 112

B4 Coqui abundance plotted against understory density ...... 112

B5 Coqui abundance plotted against understory height ...... 113

B6 Coqui abundance plotted against native understory...... 113

B7 Coqui abundance plotted against elevation ...... 114

CHAPTER 1

INTRODUCTION

Rising global trade has increased biotic invasions worldwide (di Castri 1989), with few native ecosystems unaffected. While not all introduced species establish populations in their new range (Lockwood et al. 2005), those species that do establish and spread can cause enormous damage environmentally, economically, and socially (Mack et al. 2000; Pimentel et al. 2005). Furthermore, non-native species have been implicated in the decline of global biodiversity (Lowe et al. 2004; Berglund et al. 2013). It has been proposed that 54% of well-documented extinctions can be attributed, at least in part, to the spread of non-native species (Clavero and Garcia-Berthou 2005). However, many

studies on the impact of non-natives have been criticized for lack of a strong causal mechanism between the invasion and native species decline (Gurevitch and Padilla

2004), particularly because species introductions are often correlated with other

anthropogenic pressures that may have a stronger effect on native species (MacDougall

and Turkington 2005; Berglund et al. 2013). Thus, understanding the exact role of non- native species in the decline of native species is a primary concern for conservation

researchers, managers, and policy makers.

Insular island ecosystems seem particularly vulnerable to negative impacts from

non-native species (Mack et al. 2000; Courchamp et al. 2003; Sax and Gaines 2008). The

effects of non-natives on insular island endemics remains controversial, with some

studies showing negligible effects (Case 1996; Berglund et al. 2013), and others showing

that non-natives have an important effect on native species decline, both directly 2 (Courchamp et al. 2003), and in synergy with other anthropogenic forces (Clavero et al.

2009). Studies of animal extinctions on islands directly caused by non-natives show that predation is the main cause of extirpation (Sax and Gaines 2008), such as the case of

brown tree snakes on Guam (Fritts and Rodda 1998), or mammalian predators on oceanic

islands worldwide (Blackburn et al. 2004). Other interactions implicated in native species

decline, such as competition, show less evidence in studies over large spatial scales

because disentangling competition from other landscape changes can be difficult (Case

1996). However, over smaller spatial scales, non-native competitors can result in the

reduction of native species abundance (Beggs and Wilson 1991; Hansen and Muller

2009; Cole and Harris 2011).

A well-known example of a highly invaded island system is the Hawaiian Island

chain. Hawaii is highly impacted by non-native species, both in terms of the number and

biomass of successful invasions as well as the number of endangered species threatened

by these invaders (Loope and Mueller-Dombois 1989). The costs of regulation, eradication, and management of non-native species in Hawaii is extensive, with large amounts of federal and state funding directed towards control (Loope and Kraus 2009), and these costs are expected to increase in the future. Furthermore, because non-native species often prevent the recovery of endangered endemic fauna, at least in part, the government must bear the expense of conservation-reliant species (Reed et al. 2012). Of particular concern is the endemic avifauna of Hawaii, of which many species are already vulnerable to extinction because of small population sizes and limited geographic ranges

(Camp et al. 2009).

3 Literature Review

Threats to Hawaiian birds from invasive species

Of the 100 bird species listed on the US Endangered Species List, 33% of them

are only found in the Hawaiian Islands (USFWS 2014). Most populations of these birds

have not recovered at least in part because of non-native species (Reed et al. 2012).The

three main threats that non-native vertebrates pose to Hawaiian birds are 1) the spread of

avian diseases, 2) habitat destruction or modification, and 3) predation.

Most Hawaiian birds have no resilience to the diseases avian malaria and pox

virus, which were brought to the islands through the release of non-native cage birds

(Ahumada et al. 2009; LaPointe et al. 2009).These diseases have forced a contraction of

many species’ native ranges to areas where temperatures are too cold to enable mosquito

reproduction (van Riper et al. 1986, Ahumada et al. 2009), and are unlikely to be

eliminated without the removal of introduced birds, which can act as reservoirs for the

disease (Foster 2009). Feral pigs can further exacerbate the spread of avian diseases, because the rooting and upturning of the soil creates water-filled wallows that facilitate mosquito breeding (Ahumada et al. 2009).

In most lowland areas of the Hawaiian Islands, the forested landscape has been completely converted to monoculture stands of non-native plants, which native birds do not extensively utilize (Woodward et al. 1990). Non-native ungulate browsing activity further degrades and fragments native forests, can eliminate native understory plants

(Pratt and Jacobi 2009; Flaspohler et al. 2010), and facilitates non-native plant establishment (Nogueira-Filho et al. 2009). Populations of birds that specialize on native 4 plants, such as the Palila (Loxioides bailleui), are significantly higher in areas where

ungulates are controlled (Banko et al. 2013).

Finally, introduced vertebrate mammals, such as feral cats (Felis catus), rats

(Rattus spp.), and small Indian mongooses (Herpestes javanicus), have devastated native bird populations that evolved in the absence of such predators. For example, the

Hawaiian Goose (Branta sandvicensis) requires captive breeding to sustain wild populations; the only successful breeding population exists on the island of Kauai, where there are no mongooses (Jarvi et al. 2009). Tree-nesting and shrub-nesting birds have not escaped predation either, and the removal of feral cats and rats in reserve areas has resulted in increases in nest production for several species of forest bird (VanderWerf and

Smith 2002; Hess et al. 2004).

Though not as extensively studied in the literature, another potential threat to native birds is competition with non-native species, which has been implicated in the historic decline of some Hawaiian bird species (Scott et al. 1986; Banko and Banko 2009;

Lindsey et al. 2009). In native forests, abundant non-native avian competitors, such as the

Japanese White-eye (Zosterops japonicus), have been linked to declines in reproductive success of certain native birds, though further investigation is warranted (Freed and Cann

2009; Kingsford 2010). In addition to other non-native threats, the unintentional introduction and spread of the terrestrial Puerto Rican coqui frog (Eleutherodactylus coqui) on the island of Hawaii since the 1980s had caused concerns that coquis could negatively affect the island’s native bird communities. Coquis could also interact with non-native bird species, which are extremely abundant in a variety of forest habitats in

Hawaii (Scott et al. 1986; Foster and Robinson 2007). 5 History of coqui invasion in Hawaii and potential effects on birds

The coqui was introduced to the Hawaiian Islands via the horticulture trade prior

to 1988 (Kraus et al. 1999), and populations have further spread as a result of accidental

and intentional introductions (Kraus and Campbell 2002). In Puerto Rico, the coqui is

one of the most abundant vertebrates in its native range, and it occupies a broad range of

habitats (Stewart and Woolbright 1996). On the island of Hawaii, coquis have established

in most humid, lowland forest habitats (Beard et al. 2009), and they have the potential to

spread over 49% of the island (Bisrat et al. 2012). Distributional models show the coqui could reach elevations up to 2000 m (Bisrat et al. 2012), well within the current ranges of most endangered bird species (Ahumada et al. 2009). In Hawaii, coqui populations regularly reach higher densities than what is typically found in their native range (Beard et al. 2008), which could be caused by the larger number of retreat sites in recent volcanic substrates (Woolbright et al. 2006), or by enemy release from a lack of predators in

Hawaii (Beard and Pitt 2005). These extremely high densities of frogs, up to 91,000 frogs/ha (Beard et al. 2008), may influence native (or non-native) bird abundance in three

main ways: 1) limit the amount of arthropod prey available to birds through resource

exploitative competition, 2) increase the abundance of bird predators by providing a new

abundant prey resource, or 3) serve as a new abundant prey resource for predatory birds

(Fig. 1-1)

6

Fig. 1-1 Web of possible interactions between the coqui and Hawaiian bird communities. Citations marked with a (†) refer to empirical studies on the ecological effects of coqui. Citations marked with a (*) refer to hypothesized effects of coqui.

High densities of coquis in native forest habitats could reduce arthropod prey for native insectivorous birds, as well as the endangered native, insectivorous bat Lasiurus cinereus semotus (Bernard and Mautz 2016). It has been estimated that coquis can consume up to 690,000 prey items/ha/night (Beard et al. 2008). Coquis are generalist predators (Stewart and Woolbright 1996), and will consume a wide variety of invertebrate prey in Hawaii (Beard 2007). Coquis have been shown to decrease total arthropod abundance and to change arthropod community composition in consistent ways across invasion fronts on the island of Hawaii (Choi and Beard 2012). Because coquis 7 consume mainly leaf litter and foliage insects (Stewart and Woolbright 1996; Choi and

Beard 2012), native or non-native insectivorous birds that feed on the ground or glean

from foliage may be most vulnerable to coqui invasions. Coquis could also act as food resources for predators of native birds, such as rats and mongooses, or facilitate establishment of other avian predators such as the brown tree snake if they were to

establish in Hawaii (Kraus et al. 1999; Beard and Pitt 2005). Coquis have been found in

the digestive tracts of mongooses in Hawaii, but future research is needed to determine

whether coqui invasions could increase the densities of these predators (Beard and Pitt

2006). Alternately, coquis could act as a food resource for non-native or native birds

themselves, particularly some of the larger non-native birds, but also the native raptors.

These hypotheses, although they have been discussed well in the literature, have not been

supported with research.

While investigating these potential relationships, the fact that coquis and some

bird species could merely be associated with the same or different habitat types, without a

causal mechanism, should be kept in mind. This association may be particularly true if

non-native vegetation provides greater insect resources for coquis as well as bird species

(Tuttle et al. 2009). Other studies have shown general associations between native and

non-native species abundances and richness that are more likely a result of abiotic, or

location-dependent factors (McKinney 2006; White and Houlahan 2007) and therefore, similar distributional correlations may exist between coquis and birds. Many species of birds occur within the same elevational range of the coqui, but could be unaffected by them because of dissimilar diets and because coquis are nocturnal while most birds are diurnal. Furthermore, while consistent changes in insectivorous bird abundance across 8 coqui invasion fronts would be highly suggestive that they are competing, secondary

measures of overlap, such as diet, could help determine if the interaction is truly

competitive. Because coquis have complex effects on invertebrate communities, assessing the amount of dietary overlap between coquis and insectivorous birds would be necessary to determine if they do, in fact, compete for prey, and would help interpret any

changes in insectivorous bird abundance with coqui on the landscape level. Comparing

bird diets before and after experimental removals of frogs or comparing of bird diets

across coqui invasion fronts would be particularly instructive.

Using stable isotope analyses (SIA) to assess competition

Resource-exploitative competition can be difficult to measure in natural

ecosystems, because without any direct interactions between the species, it is difficult to

attribute any changes in the abundance of a species to competition with another species.

A researcher must demonstrate that 1) the two species consume a large proportion of a shared resource, 2) that the resource is limiting, and 3) that population changes in one or both species result from this limitation in prey resources (Schoener 1983). To date, there

are no studies that investigate whether coquis and Hawaiian birds share the same prey

resources, beyond the most basic designation of these species as insectivores.

Though there are several methods to compare dietary overlap, stable isotopes are a particularly useful tool for diet studies, because of the relatively inexpensive cost of analysis, the minimum invasiveness of the procedure to the study organisms, and because it provides an index for niche width that can be compared across many different kinds of organisms (Bearhop et al. 2004). Diet studies using stable isotope analysis take advantage 9 of the natural isotope processes of mixing and fractionation, which lead to different ratios

of isotopes in predators and their prey (Fry 2006). As long as one can collect and analyze the isotopes of both a predator and its prey base, mixing-models can be used to infer the contribution of diet sources into the predator’s tissue (Phillips 2012). Species that have similar isotopic signatures are more likely to feed on the same prey items, and thus, may be more likely to compete with one another for prey (Shiels et al. 2013; Gavrilchuk et al.

2014).

Using δ isotope notation of the sample relative to a laboratory standard, the δ isotope values between samples can be compared with a high level of precision (Fry

2006). δ15N and δ13C are the most commonly utilized isotopes, because fractionation of

15N increases through each level in the food chain (Minagawa and Wada 1984), and different photosynthetic processes lead to differing levels of 13C among C3, C4, and

CAM plants (Montalvo et al. 2013). These processes can be used to indicate both the relative trophic level of a predator and what prey is consumed, particularly if prey is specialized on certain plants.

Furthermore, depending on the tissue sampled, stable isotope analysis can provide

information on diet over several temporal scales. Tissues such as blood that have a high

turnover rate are useful for short-term diet designations. Tissues that have slow turnover such as bone, muscle, and feathers are more appropriate for studies of long-term integration of prey resources into the diet (Inger and Bearhop 2008).

Because of the properties listed above, stable isotope analysis is an appropriate method to compare dietary niche width between dissimilar taxonomic groups, assuming that the study organisms all inhabit the same geographic space (Bearhop et al. 2004; 10 Newsome et al. 2007). The isotope signatures can then be compared between potential

competitors to determine niche overlap; this method has been used successfully for many vertebrate taxa (Stewart et al. 2003; Araujo et al. 2007; Lara et al. 2012). Similar stable isotope studies have been done in Hawaiian forests comparing the diets of non-native rodent species in native forests (Shiels et al. 2013). Furthermore, several studies have utilized landscape-level distributional patterns and dietary studies to address questions of competition and the impact of an invasive species on native fauna (Roemer et al. 2002;

Mitchell and Banks 2005; Cole and Harris 2011), so these methods in combination can be used successfully to address whether coquis and insectivorous birds compete in Hawaii.

Research Objectives

The purpose of this research is to determine whether or not coqui invasions impact bird communities on Hawaii. The first part of this research uses stable isotope

analysis as an index for trophic niche to determine whether or not coquis and insectivorous birds utilize the same arthropod resources. This will be the focus of Chapter

2, which is written as a manuscript for the journal Biological Invasions and is co-

authored by Karen H. Beard and Aaron B. Shiels. The second part of this research uses a

landscape-level approach to address which native or non-native bird communities change

in abundance across coqui invasion fronts on the island of Hawaii. This will be the focus

of Chapter 3, which is written as a manuscript for the journal The Condor: Ornithological

Applications and is co-authored by Karen H. Beard and David N. Koons.

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21 CHAPTER 2

DIFFERENT PREY RESOURCES SUGGEST LITTLE COMPETITION BETWEEN

NON-NATIVE FROGS AND INSECTIVOROUS BIRDS DESPITE

ISOTOPIC NICHE OVERLAP1

Abstract

Non-native amphibians often compete with native amphibians in their introduced

range, but their competitive effects on other vertebrates are less well known. The Puerto

Rican coqui frog (Eleutherodactylus coqui) has colonized the island of Hawaii, and has

been hypothesized to compete with insectivorous birds and bats. To address if the coqui

could compete with these vertebrates, we used stable isotope analyses to compare the

trophic position and isotopic niche overlap between the coqui, three insectivorous bird

species, and the Hawaiian hoary bat. Coquis shared similar trophic position to Hawaii

amakihi, Japanese white-eye, and red-billed leiothrix. Coquis were about 3‰ less

enriched in δ15N than the Hawaiian hoary bat, suggesting the bats feed at a higher trophic level than coquis. Analyses of potential diet sources between coquis and each of the three bird species indicate that there was more dietary overlap between bird species than any of the birds and the coqui. Results suggest that Acari, Amphipoda, and Blattodea made up

>90% of coqui diet, while Araneae made up only 2% of coqui diet, but approximately

25% of amakihi and white-eye diet. The three bird species shared similar proportions of

Lepidoptera larvae, which were ~25% of their diet. Results suggest that coquis share few food resources with insectivorous birds, but occupy a similar trophic position, which

1 This chapter was coauthored with Karen H. Beard and Aaron B. Shiels and submitted as a manuscript to the journal Biological Invasions. 22 could indicate weak competition. However, resource competition may not be the only way coquis impact insectivorous birds, and future research should examine whether coqui invasions are associated with changes in bird abundance.

Introduction

Although most amphibian species are threatened worldwide (Stuart et al. 2004), some species are spreading globally and are significant threats to native wildlife (Kraus

2015). Because they can spread rapidly after introduction (Phillips et al. 2007) and attain high densities (Greenlees et al. 2006), amphibians can have strong ecological impacts in their new range. Non-native amphibians have been shown to change invertebrate communities (Choi and Beard 2012), and through resource competition, reduce native amphibian populations and change amphibian community composition (Kupferberg

1997; Smith 2005; Richter-Boix et al. 2012) . However, few studies have examined whether non-native amphibians compete with other native vertebrate taxa (e.g., Boland

2004). Amphibian invasions are of particular concern on remote oceanic islands, because these islands rarely have native amphibian assemblages (Kraus 2015), and thus, endemic taxa often evolve without amphibian competitors. One such invasion is the Puerto Rican coqui frog (Eleutherodactylus coqui) to the Hawaiian Islands in the late 1980s (Kraus et al. 1999).

Coquis are now widespread on the island of Hawaii and have colonized many moist habitats, while they have been controlled or restricted on the other Hawaiian

Islands (Beard et al. 2009; Bisrat et al. 2012; Olson et al. 2012). They reproduce through direct development (Stewart and Woolbright 1996), and are terrestrial throughout all life 23 stages. At night, coquis climb onto understory vegetation from diurnal retreat sites to

forage on invertebrates, and can change invertebrate community structure and reduce

invertebrate numbers where they invade (Choi and Beard 2012). Because their

populations can attain extremely high densities, up to 90,000 frogs/ha (Woolbright et al.

2006; Beard et al. 2008), they could reduce prey resources for Hawaii’s native

vertebrates. Kraus et al. (1999) first proposed that the coqui could compete with native

insectivorous birds for invertebrate prey on the Hawaiian Islands. Coquis may also

compete with non-native insectivorous birds, which are abundant in lowland forest

habitats (Scott et al. 1986) and where most coqui populations are found (Olson et al.

2012). Beard and Pitt (2005) proposed that coquis could compete with the insectivorous

native Hawaiian hoary bat (Lasiurus cinereus semotus) because they both feed

nocturnally, and bats move into the lowlands during critical breeding periods (Menard

2001). To assess whether coquis compete with birds and bats for invertebrate prey, overlap between their trophic positions and food resources should be compared.

Methods for comparing the trophic position and food resources among different

vertebrate taxa present some challenges. Stomach contents and fecal material may not be

easily compared between all vertebrate taxa because of differing digestive systems

(Bearhop et al. 2004), and stomach contents generally require lethal capture of target

organisms, which is undesirable for species of conservation concern. Stable isotope

analyses provide a reasonable alternative to traditional stomach content and fecal

analysis. For one, the trophic position and the diet of different taxa can be compared on

standardized isotope axes (Bearhop et al. 2004), as long as one obtains estimates of the trophic base. Furthermore, stable isotopes reflect the assimilation of prey into the diet 24 over time, in contrast to stomach contents, which do not persist long in the digestive tract.

However, there are some limitations to using isotopes in diet analysis. Diet models can

show high sensitivity depending on the trophic discrimination factors chosen (Bond and

Diamond 2011); stable isotope diet analyses are less precise than stable isotope analyses

in identifying prey items to species; and, when assessing competition between species,

isotope analysis can have difficulty separating groups when the food web base has similar

δ13C signatures (Post 2002). However, for the purposes of comparing the trophic position

and general overlap in prey resources, isotopes can help address the likelihood of

competition between co-existing species (Beaulieu and Sockman 2012; Shiels et al.

2013).

Here we use stable isotope analyses to address three primary questions: 1) What is

the relative trophic position, measured using δ15N and δ13C, of coquis and their potential

vertebrate competitors, 2) What is the degree of isotopic niche similarity between coquis and potential vertebrate competitors, and 3) What are the potential food sources and contribution of these sources to diet among coquis and potential vertebrate competitors?

We use the results to address whether introduced coquis are likely to compete with insectivorous birds and bats in Hawaii.

Methods

Study site description

We conducted our research in a 30-ha area of lowland mesic forest in Manuka

Natural Area Reserve (hereafter Manuka) on the island of Hawaii, USA (19° 07' N, 155°

49' W; elevation: 540 m). Mean annual temperature is 18°C and mean annual 25 precipitation is 838 mm, with a maximum mean monthly precipitation and temperature difference of 20 mm and 4°C, respectively (Giambelluca et al. 2013). The four dominant

tree species in the reserve include two natives: Metrosideros polymorpha and Psychotria

hawaiiensis, and two non-natives Schinus terebinthifolius and Aleurites moluccana.

Dominant shrubs and additional trees in the understory include the native Psydrax

odorata and non-natives Psidium cattleianum and Ochna serrulata.

We chose Manuka for this study because it has the highest density of coquis on

record (Beard et al. 2008). Manuka also has a high abundance (>10 individuals/ha) of

native birds, such as the apapane (Himatione sanguinea), Hawaii amakihi

(Chlorodrepanis virens), and Hawaii elepaio (Chasiempis sandwichensis), and a high abundance of non-native birds such as the kalij pheasant (Lophura leucomelanos), house finch (Haemorhous mexicanus), northern cardinal (Cardinalis cardinalis), red-billed leothrix (Leiothrix lutea), and Japanese white-eye (Zosterops japonicus) (R.L. Smith and

K.H. Beard unpubl. Data). Hoary bats have been observed foraging at Manuka (Jacobs

1994), and were observed during the course of our study.

We compared the isotopic composition of Hawaii amakihi, Japanese white-eye, and red-billed leiothrix to the coquis because they were the most abundant insectivorous birds in the study area (R. Smith and K.H. Beard, unpubl. data) and because they represent both native and non-native species. Hawaii amakihi, Japanese white-eye, and red-billed leiothrix are generalist insectivores, but they also consume nectar and fruit (del

Hoyo et al. 2008; Banko and Banko 2009).

26 Sample collection

We collected all samples between 22 July and 19 August 2014. Five mist-nests were set up 200 m apart to collect independent bird samples in different areas of the reserve. All frogs, insects, and plant material were collected within 50 m of each of the five mist-net locations. With 50-m buffers around the mist-net locations; this made up a total study area of about 30-ha.

To capture the full range of prey that coquis consume, we targeted coqui of different ages and sex classes because they have been shown to have slightly different diets (Beard 2007). We hand-captured a total of 30 frogs [10 males, 10 females, and 10 pre-adults defined as <25 mm snout-vent-length (Woolbright 2005)] between 1930 and

0000 h. To euthanize frogs, we cooled and then froze them in an ice bath for 24 h (Shine et al. 2015), which ensured that decomposition would not change their isotope ratios before drying (Krab et al. 2012). We sampled frog thigh muscle tissue because its tissue turnover rate is most similar to bird feathers and bat wing membranes (Caut et al. 2009).

We captured birds between 0600 to 1100 h and 1400 to 1800 h using an array of four 12 m x 3 m mist-nets, for a total of 336 net-hours. We checked nets a maximum of

20 min apart to minimize stress on captured birds. We removed tail feathers from the first

10 individuals captured of Hawaii amakihi, Japanese white-eye, and red-billed leiothrix.

If we captured individuals of non-target species or beyond 10 individuals of the target species, these birds were released. We chose feathers as an isotope source because sampling feathers is a non-lethal, non-invasive way to collect tissue (Bearhop et al.

2004). Previous studies using feathers in stable isotope analyses have shown that 10 individuals is a reasonable number to obtain good isotope estimates (Jackson et al. 2011). 27 We took secondary feathers from a few Hawaii amakihi for which all their tail feathers

were in pin, because it is unsafe to remove these feathers at this time (Spotswood et al.

2012). Minute differences in isotope signatures between feather tracts typically do not

change interpretations of trophic position (Jaeger et al. 2009), unless species are highly

migratory and molt over long periods of time (Zelanko et al. 2011), which are not

characteristics of our study species.

We also obtained three individual Hawaiian hoary bats collected from various

locations on the island of Hawaii. The US Department of Agriculture, Wildlife Services

in Hawaii confirmed the species and sampled hoary bat wing membrane tissue. We could

only compare their relative trophic position to that of the coqui and birds because the

hoary bat is much more migratory in nature than the birds studied (del Hoyo et al. 2008)

or the coqui. Therefore, regardless of where they were collected, bats would be less likely

to reflect specific prey base signatures at a given site (Post 2002).

Frog muscle tissue turns over roughly 60 to 80 days (Cloyed et al. 2015),

Hawaiian bird feather molt takes about 90 to 120 days (Freed and Cann 2012), and bat

wing membrane tissue turnover is about 50 days (Roswag et al. 2015). Because the turnover rates for all these tissues are within 2 to 6 months, we felt that all samples collected for isotope analyses reflected the resource base for that year and should be comparable.

To obtain isotope signatures from a diverse potential prey base, we targeted invertebrate groups that our vertebrates likely consume. We extracted leaf litter invertebrates from leaf litter using 12 Burlese-Tullgren funnels three times over the collection period. We collected flying invertebrates every two days during the course of 28 the study from four Malaise traps placed near four of the five mist-net locations. To capture non-flying canopy invertebrates, we placed a bag over branches of dominant plant species at heights of 0-2 m, vigorously shook, and vacuumed invertebrates out of the bag with an aspirator. We opportunistically hand-collected certain invertebrate groups, like large Araneae, Blattodea, and Coleoptera. We used a blacklight trap to capture nocturnal flying invertebrates between 1930 and 2300 h on four nights. We also hand-collected leaves, litter, fruit, and flower samples from the dominant canopy and understory plants. We included plant samples in our collections as an isotopic base for which to compare our invertebrate and vertebrate samples, and as potential food items for our birds (Table 2-1).

Table 2-1 Known diet sources of study species

Species Scientific Name Diet Sources Citations Coqui frog Eleutherodactylus Acari, Amphipoda, (Beard 2007) coqui Araneae, Blattodea, (Wallis et al. 2016) Hymenoptera- Formicidae, Isopoda Hawaii Chlorodrepanis virens Araneae, Homoptera, (Baldwin 1953; Amakihi Lepidoptera Larvae, Banko and Banko Neuroptera, Ohia Nectar 2009; Banko et al. 2015) Japanese Zosterops japonicus Araneae, Homoptera, (Banko and Banko White-eye Lepidoptera Larvae, 2009; Banko et al. Neuroptera, Orthoptera, 2015; del Hoyo et Ohia Nectar, Fruit al. 2008; Scott et al. 1986) Red-billed Leiothrix lutea Diptera, Hymenoptera- (Banko et al. 2015; Leiothrix Wasps, Lepidoptera del Hoyo et al. Adult, Lepidoptera 2008; Scott et al. Larvae, Fruit 1986)

29 The samples were then stored dry in glass vials or paper bags before sorting, which ensured that preservatives did not change the isotopic signatures (Krab et al.

2012). All samples, except bird feathers, were thoroughly rinsed with water to eliminate

any contaminants before drying. We rinsed feathers with acetone to remove oils and then

rinsed them thoroughly with water to remove the acetone before drying (Bontempo et al.

2014). Once rinsed, we placed samples in a drying oven at 60°C for 48 h. We ground

each sample into a very fine, evenly-sized powder using a mortar and pestle, but in the

case of the feather samples, we cut feathers into very small (<1 mm in width and length)

pieces with scissors (Bontempo et al. 2014). Samples were analyzed for δ15N and δ13C using a PDZ Europa ANCA-GSL elemental analyzer interfaced to a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK) at the University of

California Davis Stable Isotope Facility. Bat samples were analyzed with a Thermo-

Finnegan Delta V IRMS Isotope Ratio Mass Spectrometer at the University of Hawaii,

Hilo. Both machines were calibrated using peach tree leaves (NIST 1547), and values

were standardized to the international standards of Vienna PeeDee Belemnite for δ13C

and Air for δ15N.

Statistical methods: isotope discrimination correction

Prior to statistical analysis, we corrected the raw isotope values of the vertebrates

using trophic discrimination factors as is typical for these analyses (see Parnell et al.

2010; Jackson et al. 2011). These corrections are needed because differences in raw isotope values between species could falsely be attributed to separate diets, yet could result from different isotopic discrimination rates on the same diet. Ideally, one would 30 use a discrimination factor empirically determined via a controlled feeding study in the

laboratory. However, these studies take considerable time and resources to conduct,

particularly for species that are hard to rear in the laboratory, and may not ultimately

reflect diet discrimination in natural systems. Because we did not determine trophic

discrimination values ourselves, we used taxon- and tissue- specific values reported in the

literature, as has been done in other studies (Gavrilchuk et al. 2014; Paez-Rosas et al.

2014). We corrected bird feathers by 2.18 ∆13C and 3.84 ∆15N (Caut et al. 2009), frog muscle tissues by 1.6 ∆13C and 3.1 ∆15N (Cloyed et al. 2015), and bat wing membrane

tissues by 4.0 ∆13C and 3.7 ∆15N (Roswag et al. 2015).

Statistical methods: interspecific isotopic niche variation

We used t-tests with an alpha value of 0.05 to test for significant differences in

δ13C or δ15N between all pairwise comparisons of coquis, birds, and bats. We considered

trophic position significantly different if the isotope differences were >2-3‰. (DeNiro

and Epstein 1981). Bats were limited to this analysis because we had a small sample size

and the bats were not collected from the specific study area.

We calculated stable isotope standard ellipses to compare both overlap and niche

width among coquis and the three bird species, and calculated Layman metrics (Layman

et al. 2007) to compare the degree of their dietary specialization. We plotted maximum

likelihood standard ellipses and visually compared them for overlap in core isotopic niche

among species (Jackson et al. 2011). We estimated niche width for the coquis and three bird species using a Bayesian standard ellipses approach (Jackson et al. 2011), which is useful to calculate uncertainty in estimates based on differences in sample size (30 for 31 frogs and 10 for each bird species). It should be noted that we tested whether the

difference in sample size influenced the final results with randomly selected frog samples

of 10 and using male, female, and sub-adult frogs separately; qualitative differences in

results were not detected (see Fig. A1 and Table A1 in Appendix A). We simulated

Bayesian ellipses 105 times to derive 95% Bayesian credible intervals for niche width

sizes. We considered niche width sizes to be different if there was no overlap between

credible intervals.

To compare dietary specialization among species, we calculated the Layman

metrics of mean Euclidean distance to the centroid and mean nearest-neighbor Euclidean distance (Layman et al. 2007), which quantify the difference between individual isotope

points within a population. We generated null distributions from residual permutation

procedures to test for differences in these two metrics among species, and we considered

them significantly different if the difference did not overlap zero (Turner et al. 2010).

Statistical methods: Diet variation

To determine the relative proportions of diet sources contributing to coqui and bird diet, we used Bayesian mixing models in the package siar in R (Parnell and Jackson

2013). This approach allows the incorporation of more dietary sources (recommended no

more than five) into the models than n+1 sources in traditional mixture models (Parnell et

al. 2010). We used a literature search to determine a priori the most likely invertebrate

and plant groups to contribute to coqui and bird diet, and specific diet sources included in

the model differed among species (see Table 2-1). We tested sources for significant

differences in isotopic signatures using Hotelling’s t-tests and an alpha value of 0.05, and 32 sources that were not different were combined into a single group in the diet analysis

(Gavrilchuk et al. 2014). Diet sources that we combined were Acari, Amphipoda, and

Blattodea for the coquis; Homoptera and Neuroptera for amakihi and white-eye; and

Diptera and Hymenoptera-wasps for leiothrix. Concentrations of C and N in these diet

sources were incorporated into the siar model to determine more accurately the

contribution of each source (Phillips and Koch 2002), particularly because plant and

animal tissues can have very different concentrations.

We ran model simulations a total of 108 times to derive credible intervals for diet

proportions. We then compared the mean proportion of shared sources in the diets of coquis and the three bird species to assess the amount of overlap in diet. We considered proportional contributions of sources in the diets within and among species to be different if there was no overlap in the Bayesian credible intervals.

Results

Relative trophic position of coquis, birds, and bats

Bat tissue was the most enriched in δ15N relative to the other vertebrate samples

(two sample t-test, all pairwise comparisons: p < 0.001) (Fig. 2-1). Bats were about 2-3‰

higher in δ15N than the other vertebrates. Japanese white-eye and red-billed leiothrix were more enriched in δ15N than Hawaii amakihi and coquis (two sample t-test, p <

0.05), but were not different from one another (two sample t-test, t = 0.065, df = 13.63, p

= 0.47). Coquis and amakihi also did not differ from one another in δ15N (two sample t-

test, t = 0.74, df = 10.33, p = 0.76). Pairwise comparisons of coqui, bird, and bat δ13C

signatures revealed no differences among species, except Hawaii amakihi, which were 33 more enriched than coquis (two sample t-test, t = 1.79, df = 20.15, p-value = 0.044) (Fig.

2-1).

Fig. 2-1 Discrimination-corrected mean isotopic signatures of the coqui (n=30), Hawaii amakihi (n=10), Japanese white-eye (n=10), red-billed leiothrix (n=10), and Hawaiian hoary bat (n=3). Bars indicate standard errors

Interspecific isotopic niche variation

Coquis overlapped the most in core isotopic niche space with Hawaii amakihi and

Japanese white-eye, and showed less overlap in isotopic niche space with red-billed leiothrix (Fig. 2-2), but had some overlap with all three species. Core red-billed leiothrix niche space overlapped almost entirely with the Japanese white-eye, and both non-native birds had more overlap with one another than with the Hawaii amakihi.

Japanese white-eyes had larger niche widths than coquis, but niche widths comparisons of all other species were not different (Table 2-2). There was no difference in distance to 34 the centroid and mean nearest neighbor distance among any of the bird species or between the birds and the frogs (Table 2-2).

Fig. 2-2 Discrimination-corrected core isotopic niches of Coqui (n=30), Hawaii Amakihi (n=10), Japanese White-eye (n=10), and Red-billed Leiothrix (n=10), represented by standard ellipse area

Table 2-2 Isotope niche metrics for coqui and bird species. The location of the centroid (LOC) indicates where the niche is centered in isotopic space. The mean Euclidean distance to the centroid (CD) and mean Euclidean nearest-neighbor distance (MNND) are estimates of trophic diversity within a species. The core isotopic niche width is represented by the median Bayesian standard ellipse area (SEAB) and the 95% Bayesian credible intervals in parenthesis

13 15 Species N LOC (δ C and δ N ) CD MNND SEAB Coqui 30 -25.5, -1.25 0.61 1.21 1.31 (0.94, 1.91) Hawaii amakihi 10 -25.2, -1.62 0.61 1.22 2.82 (1.64, 5.48) Japanese white-eye 10 -24.9, 0.12 0.55 1.10 4.60 (2.67, 8.93) Red-billed leiothrix 10 -25.4, 0.08 0.45 0.91 1.76 (1.02, 3.42)

35 Bird and coqui diet inference

The mean proportion of each potential dietary source varied among the coqui and bird species (Fig. 2-3). Acari + Amphipoda + Blattodea contributed the most to coqui

diet (>90%), and Araneae (~2%), Isopoda (~3%), and Formicidae (~4%) were less

important (Fig. 2-3 a). In contrast, Araneae contributed about 25% to both amakihi and

Fig. 2-3 Diet proportions of a) coqui, b) Hawaii amakihi, c) Japanese white-eye, and d) red-billed leiothrix dietary sources. Darkest gray boxes indicate 50% credible interval, lighter gray indicate 75% credible interval, and lightest gray indicate 95% credible interval

36 white-eye diets (Fig. 2-3 b,c). While the percentage of Homoptera + Neuroptera in

amakihi diet was about two times higher than that of white-eyes (43% for amakihi,

22%for white-eye), there was wide overlap in credible intervals, and therefore no

statistical difference. All bird species shared similar mean proportions of Lepidoptera

larvae (19% for amakihi, 18% for white-eye, 17% for leiothrix). Diptera + Wasps accounted for 70% of leiothrix diet, and were a higher mean proportion than either fruit or adult Lepidoptera, though not Lepidoptera larvae (Fig. 2-3 d).

Discussion

The similarity of δ15N signatures, niche width size, and the overlap in isotopic

niche space among coquis and the bird species in our study suggests that coquis occupy a

similar trophic level to generalist insectivorous birds in Hawaii. These results were

unexpected because all the birds we analyzed consume nectar and fruit as well as

invertebrates (Table 2-1) while coquis feed only on invertebrates; thus, we expected that

coquis would have more enriched δ15N values than the birds, and that coquis would show

greater dietary specialization. Coquis and amakihi did show a difference in δ13C, which could indicate that they feed on invertebrates from a slightly different plant base. The isotopic overlap between coquis and birds could indicate a shared trophic position and food resources, but the similar isotopic signatures could also be generated from divergent foraging strategies (Bearhop et al. 2004), or a C3 C base supporting multiple food webs

(Fry 2006). Therefore, we cannot assess whether coquis and birds compete based on

overlap in isotopic niche space alone. We did observe that the bats occupy a higher 37 higher trophic level than either the birds or coqui. Other diet studies have shown that

Hawaiian hoary bats feed predominantly on flying insects, such as Coleoptera and adult

Lepidoptera (Jacobs 1999; Bernard and Mautz 2016), which had more enriched δ15N than most other invertebrate groups we sampled (Fig. 2-4). However, the bat samples were collected during different times and on different parts of the island, and therefore the

Fig. 2-4 Mean isotope values (+- SE bars) for discrimination-corrected coqui, Hawaii amakihi, Japanese white-eye, red-billed leiothrix, and Hawaiian hoary bats plotted with invertebrates (gray) and plants (black)

isotopic values of the invertebrates in our study may not reflect the total range these bats consume. 38 Our more detailed diet source analyses suggest that there is little overlap in food

resources between coquis and amakihi (Fig. 2-3 a,b) and between coquis and white-eye

(Fig. 2-3 a,c), and essentially no shared food resources between coquis and leiothrix (Fig.

2-3 a,d). This result is interesting because leiothrix primarily forage in the lower canopy and understory, where coquis likely obtain some prey. Amakihi and white-eye can forage in these zones, but mostly forage in the mid to upper canopy, where the coqui is thought less likely to forage (Banko and Banko 2009; Wallis et al. 2016). The credible intervals do overlap for the proportion of the only shared diet source, Araneae, between coqui, amakihi, and white-eyes; although the mean proportion is only 2% of the diet for coqui and it is ~25% for amakihi and white-eye diet. Abundance of Araneae and other predatory insects in canopy foliage has been shown to increase with bird exclusion, which suggests that top-down control can limit their populations (Gruner 2004). Even though coquis can attain extremely high densities, foliage-collected Araneae have not been shown to differ across the invasion fronts on Hawaii (i.e., Araneae are not reduced in the areas where coquis have invaded compared to neighboring areas where they have not; Choi and Beard 2012).

The three bird species showed substantial overlap in isotopic niche space (Fig.

2-2), and there was more overlap in diet sources between the bird species than with the

coqui, suggesting that there could be more interspecific competition among birds.

Japanese white-eyes have similar proportions of invertebrate prey groups (Araneae,

Homoptera, and Lepidoptera) and ohia flowers in their diet as Hawaii amakihi (Fig. 2-3

b,c), which supports the conclusion of other studies in Hawaii that white-eyes could

compete with amakihi and other native honeycreepers for food (Mountainspring and 39 Scott 1985; Freed and Cann 2009). Alternately, similar proportions of prey resources

between these generalist insectivore species could reflect the high relative abundance of

these invertebrates in the environment (Banko et al. 2014; Banko et al. 2015). Of the

birds, Japanese white-eyes had the widest mean isotopic niche space (Table 2-2), likely

reflecting their high adaptability and generalized diet (Mountainspring and Scott 1985;

Scott et al. 1986). Our results show that the three bird species shared similar proportions

of Lepidoptera larvae (Fig. 2-3 b,c,d), but at another sites on the island of Hawaii

(Hakalau) amakihi have been found to consume twice as many Lepidoptera larvae as

white-eye and leiothrix (Banko et al. 2015). Lepidoptera larvae are thought to be a

limiting prey resource for native Hawaiian birds (Banko and Banko 2009), particularly

during reproductive periods. Extremely high densities of Japanese white-eye and red-

billed leiothrix could have negative consequences for native insectivorous birds if they

reduce Lepidoptera populations.

The proportion of diet sources for coquis from our isotopic analyses are similar to

the sources previously found in stomach content analyses conducted at Manuka (Beard

2007; Choi and Beard 2012). Both types of analyses suggest that the majority of the prey

in their diet is from the leaf litter, in this study, identified as Acari, Blattodea, and

Amphipoda. The only difference between these analyses is the notable exception that the

mean proportion of Formicidae in the diet inferred from our analysis (4%) is less than

the frequency of Formicidae found from stomach content analysis conducted at this site:

8% in Beard (2007) and 28% in Wallis et al. (2016). There are at least two possible explanations for this pattern. The first is that the frequency of prey items may not be as good an approximation of dietary assimilation as prey volume. In Wallis et al. (2016), 40 Formicidae only constitute 1.4% of the prey volume, while Amphipoda (25.8%) and

Blattodea (42.5%) make up a greater proportion, a combined volume more similar to our

isotope diet predictions (Fig. 2-3 a). The second potential explanation is that specific items in the diet can assimilate at different rates into tissues (Bearhop et al. 2002), and

there may be differences in biochemical digestibility between Formicidae and other prey

groups that would result in less incorporation into coqui muscle tissue (Cardwell 1996).

Though we provide evidence that coquis largely do not share food resources with

insectivorous birds in Hawaii, our results are limited. First, we only sampled one location

within one time period, which may not reflect the full range of isotopic dynamics across

years and seasons (Post 2002). Coquis have been in Manuka for over a decade (Beard

2007), and the diet of the bird species could have changed over the course of the coqui

invasion. Because we do not have samples from before the invasion, we cannot address

this. Secondly, by only sampling one location, we cannot eliminate the possibility that

birds and coquis might compete for resources elsewhere on the island. Coqui diet can

vary greatly across sites (Beard 2007; Choi and Beard 2012), and in some sites they

consume a greater proportion of insect groups such as Hemiptera and Lepidoptera larvae

(Wallis et al. 2016), which both amakihi and white-eyes in Hawaii consume (Banko et al.

2014; Banko et al. 2015). Finally, the bird diet sources from the literature that we used in

this study were not collected from this site, but from other sites across Hawaii. We felt

these sources were likely representative of what they consume at Manuka because the

main diet sources for these birds are consistent across sites (Baldwin 1953; Banko et al.

2015) and present at this site. 41 Although coquis and insectivorous birds had substantial overlap in isotopic niche

space, which could suggest competition, we did not find evidence that they share similar

proportions of prey resources in our more detailed diet source analyses. Our diet results

support previous findings that coquis forage mostly on leaf litter insects in Hawaii (Beard

2007), while amakihi, white-eye, and leiothrix primarily forage in foliage and on tree trunks (Banko and Banko 2009). Thus, birds and coquis likely forage on prey in different microhabitats. Furthermore, there is a general lack of larger scale geographic overlap between coquis and many native birds. Manuka is one of the few mid-elevation areas where native birds are still abundant on the island of Hawaii. In many cases, native

Hawaiian birds are restricted to elevations above 1500 m (Camp et al. 2009), where the coqui has not yet invaded or may be unable to invade because of colder temperatures

(Bisrat et al. 2012; Olson et al. 2012).

It is important to note that while this study focused on whether birds and coquis compete, there are other ways that the coqui frog invasion may influence Hawaiian birds.

First, they may provide a novel prey resource for predatory birds, which is typically the strongest trophic effect of invasive species (Sax and Gaines 2008). Our choice of bird species did not investigate this potential interaction. Second, coqui invasions could alter invertebrate communities in other ways that influence birds. For example, coquis have been shown to increase flying Diptera where they invade (Choi and Beard 2012), which could positively affect bird species that feed on these groups. Finally, coquis have been shown to increase leaf litter decomposition rates, rates of nutrient cycling, and non-native plant growth, but not native plant growth (Sin et al. 2008). An increase in non-native plant growth could result in increased food resources for non-native birds or alternatively 42 decreased food resources for species dependent on native plants. To more fully understand the impact of the coqui on Hawaiian birds, future research should determine if bird population sizes change in response to coqui invasions.

Furthermore, other introduced vertebrate species on the island of Hawaii may be more important competitors of birds and coquis. Jackson’s chameleon, as well as 19 other species of lizard (Kraus 2009), have been introduced to Hawaii, and may be more important competitors of birds because they are diurnal and feed in the lower canopy, and take some of the same prey groups (Kraus et al. 2012). Of the introduced rodents, house mice are the most insectivorous, and Lepidoptera larvae constitute a large proportion of their diet (Shiels et al. 2013). Of other introduced amphibians, greenhouse frogs are more likely competitors of the coquis because they forage in the leaf-litter (Olson and Beard

2012).

At this point in time, the coqui has not successfully invaded Pacific Islands outside the Hawaiian Islands. They were introduced to Guam, but did not establish

(Christy et al. 2007). White-eyes, on the other hand, are widespread throughout the

Pacific (van Riper 2000), and may be a concern for sympatric birds on other Pacific

Islands because of their generalist insectivorous habits and ability to exploit a variety of niches. However, competition with non-natives on islands often does not produce measurable population change, compared to predation and disease (Sax and Gaines

2008); therefore, such competition, if it exists, may be difficult to detect. Perhaps the most important way that white-eyes affect native birds on Pacific islands is as a reservoir for avian diseases to which natives have little to no immunity (Foster 2009; LaPointe et al. 2009). Whereas the most important way that coquis may affect vertebrate 43 communities is as novel prey (Beard and Pitt 2005; Beard and Pitt 2006) or as a reservoir

for disease (Beard and O'Neill 2005), and not as competitors.

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53 CHAPTER 3

INVASIVE COQUI FROGS ARE ASSOCIATED WITH GREATER ABUNDANCES

OF NON-NATIVE PREDATORY BIRDS IN HAWAII2

ABSTRACT

Few, if any, studies have collected empirical data on the effects of non-native amphibians on birds. The Puerto Rican coqui frog (Eleutherodactylus coqui), introduced to the Hawaiian Islands in the late 1980s, is an appropriate species to study the effects of a non-native amphibian on bird communities because it is fully terrestrial and achieves high population densities. Coquis have been hypothesized to compete with native birds for invertebrate prey and provide food resources for predatory birds. To test whether coquis measurably affect bird abundance, we conducted point counts of birds in coqui invaded and adjacent uninvaded plots across 15 sites on the island of Hawaii. We used hierarchical Bayesian N-mixture models to model the effect of coqui presence and density on abundance of birds, and calculated a Bayesian probability of increased or decreased bird abundance in coqui and non-coqui plots. We tested whether habitat variables differed between coqui and non-coqui plots, and found no differences. We found that coqui presence or density was associated with higher abundances of vertebrate-preying birds, generalist insectivorous birds, and non-native birds on the island of Hawaii. In fact, there was a 0.97 probability that vertebrate-preying bird abundance was at least 50% higher in coqui than non-coqui plots. We suggest that generalist insectivores increase with coquis because many can consume small, pre-adult frogs.

2 This chapter was coauthored with Karen H. Beard and David N. Koons and submitted as a manuscript for the journal The Condor: Ornithological Applications. 54 While four native bird species were found to co-occur with coquis, native bird abundance

(20% of our total observations), did not show differences across coqui and non-coqui

plots. Though many researchers have hypothesized that coquis may be strong competitors

of birds in Hawaii, our results suggest, like many other studies of non-native vertebrates

on islands, that coquis affect birds most strongly through predatory interactions; in this

case, primarily serving as novel prey for non-native predatory birds.

INTRODUCTION

Although predation is the most common way that non-native vertebrates affect

invaded systems (Roemer et al. 2002, Blackburn et al. 2004, Sax and Gaines 2008),

competition is often cited as another potential driver of community change (Courchamp

et al. 2000, Mack et al. 2000). Furthermore, native species are often the species of most

concern following an invasion, but non-native species now dominate many invaded

systems (Zavaleta et al. 2001, Hobbs et al. 2009) and can interact with new species in

complex and unpredictable ways (Simberloff and Van Holle 1999, Green et al. 2011).

Complex interactions between invaded vertebrate communities and non-native mammals, birds, and reptiles have been well documented (Fritts and Rodda 1998, Courchamp et al.

2003, Martin-Albarracin et al. 2015), but the effects of terrestrial non-native amphibians on invaded communities are less well known, with the possible exception of cane toads

(Shine 2010, Kraus 2015).

One amphibian invasion that could have community-level repercussions in terrestrial systems is that of the Puerto Rican coqui frog (Eleutherodactylus coqui), which was accidentally introduced to the Hawaiian Islands in the late 1980s via the nursery 55 trade (Kraus et al. 1999). The Hawaiian Islands have no native terrestrial reptile or amphibian species. After its introduction, the coqui spread rapidly on the island of

Hawaii (Kraus and Campbell 2002), where it is now widespread despite control efforts

(Sin and Radford 2007, Olson et al. 2012). The coqui is a terrestrial frog that breeds via direct development in the leaf litter (Townsend and Stewart 1994). Coquis hatch from eggs at 7 mm, and adults range in size from 25 to 47 mm (Woolbright 2005, Beard 2007).

During the day, coquis use diurnal retreat sites, often in the forest floor, and emerge at night to find mates while foraging on invertebrates in the understory foliage and in the leaf litter (Stewart and Woolbright 1996, Wallis et al. 2016). The invasion is of ecological concern because coquis in Hawaii can reach extremely high population densities, up to 91,000 frogs/ha in some locations (Beard et al. 2008).

The abundance and insectivorous nature of coquis makes them a concern for

Hawaiian birds. Coquis have been shown to reduce total invertebrates while decreasing and increasing particular invertebrate taxa (Choi and Beard 2012). Such effects on shared prey could have complex effects on bird communities. Kraus et al. (1999) first proposed that the frog could compete with native birds, many of which are specialist insectivores

(Banko and Banko 2009) and are already threatened by a host of introduced organisms

(Reed et al. 2012). Coquis could also compete with non-native insectivorous birds, whose distributions overlap extensively with that of the coqui (Scott et al. 1986, Olson et al.

2012). Alternately, coquis could provide an abundant, year-round food resource for some native vertebrate-preying birds, such as Hawaiian raptors (Beard and Pitt 2005), or non- native vertebrate-preying birds. Finally, coquis could provide an abundant food resource for non-native mammal avian-predators, such as rats and mongooses (Kraus et al. 1999, 56 Beard and Pitt 2006). If coquis bolster populations of avian predators in forested areas,

native bird populations could decline through apparent competition.

Because the bird community in Hawaii has so many rare and endemic species,

understanding the effect of this invasion on birds is important for guiding management

decisions. The overall objective of our study was to investigate at the landscape scale

whether there are measurable changes in bird communities in areas where the coqui has

invaded. Our approach was to measure bird communities across the island in areas where

the coqui has invaded, and in neighboring areas with similar vegetation, where the coqui

has not yet invaded, hereafter referred to as invasion fronts (as in Choi and Beard 2012).

We took this approach because we wanted any differences we detected to be a result of

the frog and not habitat or other environmental differences. The three main questions we

address in this paper are 1) Are coquis associated with lower insectivorous bird

abundance, overall or for individual species? 2) Are coquis associated with higher

abundance of vertebrate-preying birds, overall or for individual species? and 3) Are coquis associated with overall differences in native or non-native bird abundance?

METHODS

Study Design and Site Selection

Research was conducted at 15 sites on the island of Hawaii with coqui invasion fronts large enough for our study design (Figure 3-1). We used 10 of 15 sites that had previously been used to investigate invertebrate community change across invasion fronts 57 (Choi and Beard 2012). Five sites did not meet the size requirements for this study. We drove around the island and talked to local experts to find five additional sites. We believe we surveyed all possible fronts on the island that met our size requirements.

FIGURE 3-1. Fifteen sites on the island of Hawaii. Site abbreviations are Eden Roc (ER), Fern Forest (FF), Hamakua FR (HM), Kaupukuea Homestead (KH), Kaloko (KL), Kalopa (KP), Kulani (KU), Kaiwiki (KW), Manuka A (MA), Manuka B (MB), Stainback (SB), Saddle Road (SR), Volcano A (VA), Volcano B (VB), Waipio (WP)

We determined coqui presence or absence on each side of the front by listening for 20 minutes between 1900 and 0200 h, peak hours of calling (Woolbright 1985), for the loud (70 dB at 0.5 m) two-note mating call on three separate nights over a three-week period in December 2013 and January 2014. Designations were also confirmed during subsequent sampling. Twelve sites were large enough to establish three replicate coqui 58 plots and three replicate non-coqui plots on each side of the front, while three sites (KH,

VA, and VB; Figure 3-1) could only accommodate two coqui and non-coqui plots.

To assume independence of bird observations, each plot was placed a minimum of 150 m apart (Camp et al. 2009). Plots on the same side of an invasion front had a mean distance of 570 m between neighboring plots (range: 150-1634m), and plots on either side of the invasion front had a mean distance of 935 m (range: 294-2121m). The mean elevation difference between non-coqui and coqui plots was 20 m (range: -97—160 m).

Because coqui populations are often distributed near roads (Olson et al. 2012), we placed both coqui and non-coqui plots the same distance (>50 m) from roads, trails, buildings, agricultural fields, or other such habitat edges to avoid biasing bird observations.

Bird Abundance Surveys

From February to June 2014, bird surveys were conducted at all sites using a variable-circular plot design (Camp et al. 2009). Observations were made during hours of peak bird activity, between 0600 and 1000 h (Camp et al. 2009). The observer stood in the center of a plot and settled for two minutes to allow the birds to adjust to observer presence. During this acclimation period, the observer recorded the date and the weather conditions, including temperature, precipitation, cloud cover, and wind speed. These variables were modeled as detection covariates in our abundance models (described below). Observations were not made in heavy precipitation or wind above 25 kph, as these variables strongly affect detection probabilities for Hawaiian birds (Scott et al.

1986). 59 Over a subsequent period of 10 minutes, each individual bird seen or heard was

identified to species, and distance from the observer was recorded to the nearest meter.

Alternately colored flags were placed at 5-m intervals to help the observer estimate distances. Counts were repeated five times at each site throughout the sampling period.

Observations of birds to 30 m were used in the analyses to estimate avian abundance within a standardized plot area that closely corresponded to frog density measurements, and also because the effective detection distances for many Hawaiian forest birds drop

off after ~30 m (Scott et al. 1986, Camp et al. 2009).

Coqui Density Surveys

Because we predicted that changes in bird communities may be greater where

coqui densities are higher, coqui density was estimated in each of the invaded plots.

Coqui density was measured using line-transect distance-sampling surveys (Buckland et

al. 2001), which have been used to estimate population densities of coquis in other

studies (Choi and Beard 2012, Kalnicky et al. 2013). We used methods similar to those

used in Choi and Beard (2012).

In each coqui plot from June to July 2014, starting at 1930 h, two observers

surveyed frogs using headlamps on one of six adjoining 5-m wide, 30-m long parallel

transects, walking slowly and searching for frogs for 30 min. When a frog was either seen

or heard, the perpendicular distance from the observer was recorded. At the end of each

transect, researchers moved to the next adjoining transect, until the entire plot and total of

six transects were surveyed, for a total time of 180 observation minutes per plot.

60 Environmental Variables

Habitat variables and elevation (hereafter “environmental variables” when described together) were collected in all plots for three reasons: 1) to test whether there were habitat or elevation differences between plots on either side of coqui invasion fronts, 2) to see which, if any, of these variables are correlated with coqui density, and 3) to see which, if any, of these variables, in addition to coqui density and presence, explain bird abundances among species in a community. Habitat variables included canopy height, canopy cover, percent native canopy, understory height, understory density, and percent native understory (see Appendix B S1 for detailed methods). These variables were included because they have been shown to affect Hawaiian birds at local scales

(Scott et al. 1986).

Statistical Analyses

Testing for plot differences

To test whether each environmental variable differed across coqui and non-coqui plots within each site, we conducted a one-way ANOVA test for each variable with site as a block (using a conservative α = 0.05 without a Bonferroni correction). To further test whether coqui density was strongly linked to environmental variables, we examined multicollinearity using variance inflation factors (VIF), and considered variables significantly correlated to coqui density if VIF >3 (Zuur et al. 2010). Coqui densities were estimated using the distance-sampling functions in the package “unmarked” in R

(Fiske and Chandler 2011, see Table B1).

61 Modeling avian response to coqui

We modeled whether coqui density or coqui presence were related to abundances of five avian foraging groups (generalist insectivores, obligate insectivores, vertebrate predators, frugivores, nectarivores), total native, and total non-native birds. We determined foraging group designations for native bird species from Banko and Banko

(2009). We determined foraging group designations for non-native bird species from del

Hoyo et al. (2008) and from The Birds of North America Online (The Birds of North

America: https://birdsna.org. 2015). Some bird species were classified into multiple foraging groups (see Table 3-1).

TABLE 3-1. Individual species included in foraging group classifications. Native species were assigned to foraging groups based on designations in Banko and Banko (2009). Non-native species were assigned to foraging groups based on designations in del Hoyo et al. (2008). Some birds were classified in multiple groups. MLA= Maximum-likelihood analysis in “unmarked”; BA= Bayesian analysis in JAGS

Foraging Species Species Origin Modeled Modeled % of group Included Included with with BA Observ classification (Common (Scientific MLA ations Name) Name) in Foragi ng Group Vertebrate- Common Acridothere Non- Yes No 40.0% preying Myna s tristis native Birds Chinese Garrulax Non- Yes Yes 59.0% Hwamei or canorus native Melodious Laughing- Thrush Io or Buteo Native No No 1.0% Hawaiian solitarius Hawk* Generalist Japanese Horornis Non- Yes No 4.6% Insectivorou Bush- diphone native s Birds Warbler TABLE 3-1 Continued 62 Foraging Species Species Origin Modeled Modeled % of group Included Included with with BA Observ classification (Common (Scientific MLA ations Name) Name) in Foragi ng Group Japanese Zosterops Non- Yes Yes 71.4% White-eye japonicus native Red-billed Leiothrix Non- Yes No 5.6% Leiothrix lutea native Chinese Garrulax Non- Yes Yes 3.5% Hwamei or canorus native Melodious Laughing- Thrush Common Acridothere Non- Yes No 2.4% Myna s tristis native Saffron Sicalis Non- No No 0.1% Finch flaveola native Hawaii Chlorodrep Native Yes No 10.6% Amakihi* anis virens Hawaii Chasiempis Native Yes No 1.9% Elepaio* sandwichen sis Obligate Japanese Horornis Non- Yes No 70.6% Insectivores Bush- diphone native Warbler Hawaii Chasiempis Native Yes No 29.4% Elepaio* sandwichen sis Frugivores/ Kalij Lophura Non- No No 0.3% Granivores Pheasant leucomelan native os Rock Dove Columba Non- No No 0.1% livia native Spotted Spilopelia Non- Yes No 1.1% Dove chinensis native Zebra Dove Geopelia Non- Yes No 0.7% striata native Japanese Zosterops Non- Yes Yes 68.7% White-eye japonicus native Red-billed Leiothrix Non- Yes No 5.4% Leiothrix lutea native TABLE 3-1 Continued 63 Foraging Species Species Origin Modeled Modeled % of group Included Included with with BA Observ classification (Common (Scientific MLA ations Name) Name) in Foragi ng Group Chinese Garrulax Non- Yes Yes 3.4% Hwamei or canorus native Melodious Laughing- Thrush House Finch Haemorhou Non- Yes Yes 7.3% s mexicanus native Yellow- Non- Yes No 0.9% fronted native Canary Nutmeg Lonchura Non- No No 0.4% Mannikin or punctulata native Scaly- breasted Munia Northern Cardinalis Non- Yes Yes 11.5% Cardinal cardinalis native Saffron Sicalis Non- No No 0.1% Finch flaveola native Yellow- Paroaria Non- No No 0.1% billed capitata native Cardinal Omao Myadestes Native No No 0.1% obscurus Nectarivoro Japanese Zosterops Non- Yes Yes 74.4% us White-eye japonicus native Hawaii Chlorodrep Native Yes No 11.0% Amakihi* anis virens Apapane* Himatione Native Yes No 14.6% sanguinea * Species marked with a star were included in native models.

We used hierarchical Bayesian N-mixture models to model bird abundance (Kéry and Schaub 2012). N-mixture models utilize repeated counts at a sampling plot to estimate abundance while accounting for imperfect detection probabilities (Royle 2004, 64 Kéry et al. 2005). Because hierarchical modeling frameworks can easily estimate abundance for individual or groups of locations of interest (Royle and Dorazio 2006), we felt this approach was appropriate to test for differences in bird abundances across coqui invasion fronts. We used N-mixture models rather than distance-sampling because N- mixture models 1) are easier to use to obtain posterior estimates of abundance from sample units (i.e., plots) of a known area (Denes et al. 2015), 2) control for random variation among plots and sites, 3) are better with fewer sampling units, and 4) because it is less sensitive than distance-sampling to violations of modeling assumptions (e.g., non- monotonic detection functions, Kéry et al. 2005). Observations from our first sampling period were not used to control for observer inexperience, and to better meet the N- mixture assumption of population closure across the period of repeated counts (Royle

2004). We obtained posterior estimates of abundance for only the most numerous individual species (Chinese Hwamei, House Finch, Northern Cardinal, and Japanese

White-eye) using Bayesian analysis, because N-mixture models have been shown to yield biased abundance estimates for rare species (which is common among available methods for abundance estimation, Warren et al. 2013).

Models with alternative combinations of associated abundance and detection environmental covariates for all focal avian groups and species, but without random effects, were first compared using maximum likelihood estimation and AIC model selection criteria in the “unmarked” package in R (Royle 2004, Fiske and Chandler

2011). Additive and interactive combinations of environmental covariates were chosen a priori based on previous studies of smaller-scale habitat associations of Hawaiian birds

(Scott et al. 1986, Camp et al. 2009). Coqui density and coqui presence covariates were 65 always compared in separate models. To achieve more accurate estimates of abundance

in coqui and non-coqui plots, top models with corresponding covariates were then

modeled with site and plot-within-site random effects using the hierarchical Bayesian N-

mixture model described below (as recommended in Kéry and Schaub 2012). Nine

individual species of the 13 investigated were only modeled using maximum-likelihood

methods, because they had insufficient observations to justify use of Bayesian analysis

with random effects (see Table 3-1).

Bayesian N-mixture Model to Estimate Abundance:

Observed count at site h, plot i, and survey j

yhij|Nhi ~ Binomial(Nhi, phij)

Generalized linear model for detection

logit(phij) = βp + βenv*envhij

where phij is the probability of detection for site h, plot i, and observation period j; βp is

the intercept for detection probability; βenv is one of k coefficients for the estimated effect of an environmental or observation-level variable on detection; and envhij is a matrix of

an environmental covariate value at site h, plot i, and on the jth observation period.

Random effects were not modeled in the detection function because there was only one

observer and observation conditions were strictly controlled in the study design.

Poisson model of realized abundance (N) at site h and plot i

Nhi ~ Poisson(λhi) 66 This simple Poisson model was used for all models except for obligate insectivores,

native birds, and the Chinese Hwamei. We used a zero-inflated Poisson (ZIP) model to

estimate “site suitability” for these birds because they were not observed at every site,

which resulted in more zeros than expected with a Poisson distribution.

ZIP model of realized abundance (N) at site h and plot i

zh ~ Bernoulli(Ω)

Nhi|zh ~ Poisson(zhλhi)

where zh is the latent suitability state of site h. We used Poisson and ZIP models because negative-binomial N-mixture models can produce ecologically unrealistic parameter

estimates in situations similar to ours (Joseph et al. 2009).

Generalized linear mixed model of Abundance

log(λhi) = αλ + αcoq*coqhi + αenv*envhi + Eh + εhi

where λhi is the measure of abundance for site h and plot i; αλ is the intercept for

abundance; αcoq is the coefficient of coqui effect on abundance; coqhi is the coqui

presence/absence or coqui density for site h and plot i; αenv is one of k coefficients for the

effect of an environmental variable on abundance; envhi is an environmental covariate

value for site h and plot i; Eh is a random effect for site; and εhi is a plot-within-site

2 random effect. Random site effects were specified as Eh ~ Normal(0, σ h). Random plot-

2 within site effects were specified as εhi ~ Normal(0, σ hi), and the variance parameter for

both random effects was truncated between -3 and 3 for all models except the native birds

2 (for computational efficiency), for which σ hi was truncated between -10 and 10.

Moderately informative priors were specified for all parameters in the Bayesian analysis 67 by using the mean and twice the standard deviation of parameter estimates for each of αλ,

αcoq, αenv, βp, and βenv from the maximum likelihood analysis.

Models were run using Markov Chain Monte Carlo (MCMC) algorithm using

JAGS 3.3.0 (Plummer 2012), run from the R2jags package (Su and Yajima 2015) in R.

For each model, we ran three chains for 106 iterations and discarded 20000 as burn-in.

We then thinned the samples by keeping every 1000th sample. We used Gelman’s (1996)

statistic to monitor chain convergence.

𝑅𝑅� Mean abundance per plot in the coqui and non-coqui plots were then estimated as

derived parameters, and the Bayesian probability of abundance being higher or lower in

coqui plots was calculated from the posterior distributions to determine the positive or

negative effect of the coqui:

n pper = ∑ ((coqui abundancen/total coqui plots) > per*(non-coqui plot abundancen/total non- coqui plots))

n total simulations

where n is the number of simulations, and per is the percent change of interest (0%, 5%,

10%, etc). A pper of 0.5 represents a roughly equal chance of increase or decrease in

abundance. The probability of increased abundance in coqui plots was considered

significant if the probability was greater than 0.90, and the probability of decreased

abundance in coqui plots was considered significant if the probability was lower than

0.10.

RESULTS

We recorded 5707 birds representing 21 species, of which 16 were non-native

(80% of total observations) and five were native (20% of total observations). Non-native 68 species included Common Myna (Acridotheres tristis), House Finch (Haemorhous mexicanus), Hwamei (Garrulax canorus), Japanese-Bush Warbler (Cettia diphone),

Japanese White-eye (Zosterops japonicus), Java Sparrow (Lonchura oryzivora), Kalij

Pheasant (Lophura leucomelanos), Northern Cardinal (Cardinalis cardinalis), Scaly- breasted Munia (Lonchura punctulata), Rock Pigeon (Columba livia), Red-billed

Leiothrix (Leiothrix lutea), Saffron Finch (Sicalis flaveola), Spotted Dove (Spilopelia chinensis), Yellow-billed Cardinal (Paroaria capitata), Yellow-fronted Canary (Serinus mozambicus), and Zebra Dove (Geopelia striata). The native species observed included:

Apapane (Himatione sanguinea), Hawaii Amakihi (Chlorodrepanis virens), Hawaii

Elepaio (Chasiempis sandwichensis), Hawaiian Hawk (Buteo solitarius), and Omao

(Myadestes obscurus). The Japanese White-eye was the most abundant non-native bird, observed at all 15 sites with 2534 total observations. The Apapane was the most abundant native bird, observed at 6 sites with 496 total observations. Four of the five native birds were observed in both coqui and non-coqui sites. The Omao was only observed in one non-coqui plots. For the non-native birds, 14 species were observed in both coqui and non-coqui plots, the Yellow-billed Cardinal was observed in two coqui plots, and the

Java Sparrow was observed in one non-coqui plot.

Plot-level Environmental Differences

No habitat variable we measured differed across coqui and non-coqui plots (Table

3-2). However, elevation was significantly different; mean elevation in coqui plots was lower than in non-coqui plots (mean difference: 20 m; DF=1, F-statistic=6.63, p=0.01). 69 Additionally, no habitat variable we measured was significantly correlated with coqui

density (see Appendix S2).

Foraging Groups

Our top maximum-likelihood models for bird abundance indicated a positive

association between overall insectivorous bird abundance and coqui density (Table 3-3),

and the subsequent Bayesian analysis (that controlled for site and plot-within-site random effects) indicated a 0.96 probability of generalist insectivores being >1% higher in

TABLE 3-2. One-way ANOVA of environmental differences across coqui and non- coqui sites. * indicates significant differences.

Environmental Coqui Sum of Degrees F statistic P-value Variable treatment squares of effect and Freedom Site block effect Canopy Cover Coqui 4 1 0.0334 0.8555 Site 49133 14 26.1343 2.2e-16* Canopy Height Coqui 47.7 1 1.8320 0.1804 Site 4593.0 14 12.6016 7.455e-14* % Native Coqui 94 1 0.140 0.7095 Canopy Site 127122 14 13.163 2.722e-14* Understory Coqui 0.0095 1 0.7458 0.3909 Density Site 2.2023 14 12.3153 1.26e-13* Understory Coqui 0.148 1 0.0990 0.754015 Height Site 59.884 14 2.8652 0.001958* % Native Coqui 107 1 0.1807 0.672117 Understory Site 61128 14 7.3782 4.731e-9* Elevation Coqui 7254 1 6.6287 0.01222* Site 1669924 14 109.0018 2e-16*

70 abundance in sites with coquis than without coquis (Table 3-4). Obligate insectivorous

bird abundance did not show an association with coqui density or presence in our

maximum-likelihood models (Table 3-3), and did not differ in abundance between coqui

and non-coqui plots in Bayesian models (Table 3-4). Vertebrate-preying birds showed a strong association with coqui presence in top maximum-likelihood models (Table 3-3), and the Bayesian analysis indicated a 0.97 probability of being > 50% higher in abundance in coqui sites than non-coqui sites (Figure 3-2). Top maximum-likelihood models for frugivorous bird abundances showed a positive association with coqui density

(Table 3-3), but the Bayesian analysis did not show higher or lower abundance in coqui

sites (Table 3-4). Nectarivorous birds showed no association with coqui density or presence in top maximum-likelihood models and did not show higher or lower abundance

in coqui sites in Bayesian models (Table 3-4).

Native vs. Non-native Birds

Native birds were positively and strongly associated with percent native canopy,

but were not associated with coqui density or coqui presence (Table 3-3). In contrast,

coqui density was a top predictor of non-native bird abundance in our top maximum- likelihood models (Table 3-3), and the Bayesian analysis indicated that non-natives had a

0.97 probability of being >5% higher in abundance in coqui sites than non-coqui sites

(Table 3-4).

71

FIGURE 3-2. Histograms of the posterior estimates of vertebrate-preying bird abundance in coqui and non-coqui plots. The dashed line indicates the median value of vertebrate-preying bird abundance in coqui plots. The solid line indicates the median value of vertebrate-preying bird abundance in non-coqui plots. The intermediate shaded area shows the amount of overlap between the histograms.

TABLE 3-3. Maximum-likelihood models from the package “unmarked” in R. λ is abundance and p is detection probability. Native bird species are marked with *.

Group or Top Model AIC Abundance Abundance Species (ΔAIC = 0) weight Covariate Effect Covariate Size Estimates Standard Errors Functional Group Vertebrate- λ(coqui + 0.54 coqui = 0.305 coqui=0.125 preying Birds elevation)p(date elevation = -0.318 elevation=0.142 + canopy cover)

Generalist λ(% native 0.44 % native understory % native Insectivorous understory + = 0.154 understory = Birds coqui coqui density = 0.0324 density)p(wind 0.126 Coqui density= speed + time) 0.0292

TABLE 3-3 Continued 72 Group or Top Model AIC Abundance Abundance Species (ΔAIC = 0) weight Covariate Effect Covariate Size Estimates Standard Errors Frugivorous λ(% native 0.58 % native % native Birds understory + understory= 0.0952 understory= coqui coqui density = 0.029 density)p(wind 0.0863 coqui density = speed + cloud 0.0271 cover)

Nectarivorous λ(% native 0.53 % native understory % native Birds understory + % = 0.128 understory = native % native canopy = 0.0584 canopy)p(time*c 0.183 % native loud cover) canopy = 0.061

Obligate λ(% native 0.64 % native canopy = % native Insectivorous canopy*canopy 0.636 canopy = 0.193 Birds height)p(canopy canopy height = - canopy height = height*canopy 0.588 0.219 cover)

Native or Non-native Group All Natives λ(% native 0.32 % native canopy = % native canopy)p(cloud 2.03 canopy = 0.418 cover + canopy height)

All Non- λ(% native 0.26 % native % native natives understory + understory=0.1066 understory=0.03 coqui coqui density = 23 density)p(date + 0.0844 coqui density = cloud cover) 0.0314

Individual Species Apapane* λ(% native 0.35 % native canopy = % native (Himatione canopy)p(canopy 2.11 canopy = 0.431 sanguinea) cover)

Chinese λ(understory 0.46 understory height = understory Hwamei height)p(canopy -0.473 height = 0.163 TABLE 3-3 Continued 73 Group or Top Model AIC Abundance Abundance Species (ΔAIC = 0) weight Covariate Effect Covariate Size Estimates Standard Errors (Garrulax height*understor canorus) y density)

Common λ(coqui*elevatio 0.34 coqui = 0.701 coqui = 0.284 Myna n*understory elevation = -0.282 elevation = (Acridotheres density)p(unders understory density 0.277 tristis) tory density*date = -0.241 understory + canopy height) density = 0.263

Hawaii λ(canopy 0.95 Canopy cover = Canopy cover = Amakihi* cover)p(canopy 1.55 0.453 (Chlorodrepa cover*understor nis virens) y density)

Hawaii λ(canopy height* 0.46 Canopy height = Canopy height Elepaio* % native 0.969 = 0.333 (Chasiempis canopy)p(wind) % native canopy = % native sandwichensis 0.562 canopy = 0.359 )

House Finch λ(coqui density* 0.57 coqui density = coqui density = (Haemorhous % native 0.490 0.144 mexicanus) understory)p(dat % native understory % native e + precipitation) = 0.498 understory = 0.135

Japanese λ(canopy 0.99 canopy height = - canopy height = Bush-Warbler height*understor 1.97 0.469 (Horornis y understory density understory diphone) density)p(unders = 2.12 density = 0.467 tory density)

Japanese λ(elevation* % 0.89 elevation = 0.0796 Elevation = White-eye native % native understory 0.0323 (Zosterops understory)p(tim = 0.0575 % native japonicus) e*cloud cover) understory = 0.0317

Northern λ(elevation* 0.64 elevation = -0.2059 elevation = Cardinal canopy cover * 0.1222 TABLE 3-3 Continued 74 Group or Top Model AIC Abundance Abundance Species (ΔAIC = 0) weight Covariate Effect Covariate Size Estimates Standard Errors (Cardinalis canopy height) canopy cover = - canopy cover = cardinalis) p(temperature) 0.1724 0.1287 canopy height = canopy height = 0.2073 0.0839

Red-billed λ(canopy 0.29 canopy cover =- canopy cover = Leiothrix cover*canopy 0.661 0.576 (Leiothrix height + coqui canopy height = canopy height = lutea) density) 0.931 0.428 p(canopy coqui density = coqui density = cover*canopy 0.186 0.158 height)

Spotted Dove λ(understory 0.51 understory density understory (Spilopelia density + canopy = 1.082 density = 0.404 chinensis) height) p(time + canopy height = - canopy height = cloud) 0.768 0.444

Yellow- λ(% native 0.86 % native canopy = % native fronted canopy) p(date + 1.05 canopy = 0.361 Canary temp) (Serinus mozambicus)

Zebra Dove λ(elevation + 0.18 elevation = -0.884 elevation = (Geopelia canopy height) canopy height = - 0.380 striata) p(wind + 0.460 canopy height = time*date) 0.306

75 TABLE 3-4. Bayesian model output of coqui (Eleutherodactylus coqui) effects on bird abundance by functional group, origin or species. Effect size is the 95% Bayesian confidence intervals for the direction and magnitude of effect size of either coqui presence (P) or coqui density (D), based on maximum-likelihood model results (see Table 3-3). Only percent increases are presented because no bird had a negative relationship with the coqui.

Group or Coqui Percent increases of interest Species Effect Size >1 >5% >10 >15 >25% >50 >75 >100 % % % % % %

Probability that group or species shows increase of interest (pper) (>0.90 is considered significant) Functional Group Vertebrate- P: 1.0 1.00 1.00* 1.00* 1.00* 0.97 0.84 0.57 preying 0.342 (- * * * Birds 0.275, 0.955)

Generalist D: 0.06 0.9 0.72 0.19 0.02 0.00 0.00 0.00 0.00 Insectivoro (0.003, 6* us Birds 0.120)

Frugivorou D: 0.5 0.11 0.00 0.00 0.00 0.00 0.00 0.00 s Birds 0.024 (- 2 0.03, 0.08)

Nectarivoro P: 0.3 0.09 0.00 0.00 0.00 0.00 0.00 0.00 us Birds 0.007 (- 7 1.06, 1.22)

Obligate P: - 0.2 0.14 0.07 0.04 0.01 0.00 0.00 0.00 Insectivoro 0.072 (- 1 us Birds 0.613, 0.478)

TABLE 3-4 Continued 76 Native or Non-native Group All Natives P: 0.1 0.04 0.01 0.00 0.00 0.00 0.00 0.00 0.167 (- 3 0.17, 0.551)

All Non- D: 1.0 0.97 0.63 0.12 0.00 0.00 0.00 0.00 natives 0.055 (- * * 0.002, 0.114)

Individual Species

Chinese P: 0.7 0.68 0.59 0.50 0.34 0.08 0.01 0.002 Hwamei 0.034 (- 4 (Garrulax 0.597, canorus) 0.641)

House D: 0.9 0.91 0.81 0.69 0.39 0.03 0.00 0.00 Finch 0.0148 5* * (Haemorho (-1.828, us 2.600) mexicanus)

Japanese P: 0.7 0.34 0.07 0.00 0.0 0.00 0.00 0.00 White-eye 0.034 (- 0 (Zosterops 0.094, japonicus) 0.155)

TABLE 3-4 Continued

Northern P: 0.6 0.53 0.35 0.22 0.05 0.00 0.00 0.00 Cardinal 0.054 (- 7 (Cardinalis 0.221, cardinalis) 0.346)

* Significant increases.

77

Individual Species

For the 13 species with enough observations to develop maximum-likelihood N- mixture models, no native or non-native species showed a negative response to the coqui.

Abundances of three species (Common Myna, House Finch, and Red-billed Leiothrix) were positively associated with coqui presence or density (Table 3-3). Of the four species

(Hwamei, House Finch, Japanese White-eye, and Northern Cardinal) with sufficient

observations to conduct a Bayesian analysis, the House Finch had a 0.91 probability of

being > 5% higher in abundance in coqui sites than non-coqui sites (Table 3-4). The

Japanese White-eye (the most abundant insectivorous bird), Hwamei, and Northern

Cardinal did not show higher or lower abundance in coqui plots (Table 3-4).

DISCUSSION

We expected that insectivorous bird abundance would show a negative

relationship with the coqui if coquis compete with these birds for prey. Contrary to our

expectations, generalist insectivorous birds showed slightly higher abundance in coqui

plots, and obligate insectivorous birds had no relationship with them at all. This result

suggests that strong competition, resulting in reductions in bird abundance, between these

birds and the coqui is unlikely. Although coquis have been shown to reduce total

invertebrates in Hawaii, they primarily reduce leaf litter insects (Choi and Beard 2012), and not foliage or flying insects, which may be more important to Hawaiian insectivorous birds (Banko and Banko 2009, Banko et al. 2015). Our results suggest it is more likely

that coquis affect the invertebrate community in a way that favors generalist

insectivorous birds. Coquis have been shown to increase certain flying insects such as 78 Diptera (Choi and Beard 2012), which could favor species such as the Red-billed

Leiothrix that feed on these groups. Coquis are thought to increase some invertebrate groups by increasing the amount of decomposing biomass (i.e., frog bodies) and excrement where they invade (Tuttle et al. 2009). Alternately, juvenile coquis, which emerge from eggs at ~7 mm, could provide a novel food resource for insectivorous birds that feed on larger insects and forage in the understory, such as the Hwamei or the Red- billed Leiothrix. We have observed these two species attempting to consume live coquis

(S. Hill and K. Beard, personal observation). Furthermore, these birds have similar foraging habits as the Puerto Rican Tanager (Nesospingus speculiferus) and the Red- legged Thrush (Turdus plumbeus), which are predators of the coqui in their native Puerto

Rican habitats (Stewart and Woolbright 1996). Considering this other evidence, direct predation or scavenging may be a better explanation for the positive association between coquis and generalist insectivorous birds than changes in the invertebrate community.

As further support for this potential explanation, we also found that coqui presence was associated with higher vertebrate-preying bird abundance. In fact, vertebrate-preying birds were the only foraging birds where results strongly predicted

(probability of 0.97) that their abundance was much higher (>50%) in coqui sites than non-coqui sites. More specifically, Common Mynas, which accounted for about 40% of

the vertebrate-preying bird observations, had a positive relationship with coqui presence

in the top maximum-likelihood model. Common Mynas are known predators and

scavengers of vertebrates on other islands (Foster 2009, Burns et al. 2013), and can

quickly adapt their foraging behavior to take advantage of novel prey resources (Sol et al.

2011). Mynas are likely large enough to consume adult (25-47 mm) as well as juvenile 79 frogs. Unlike other significant results for foraging groups, species origins (native or non- native) and individual species, coqui presence and not density was associated with higher vertebrate-preying bird abundance. One reason these birds may respond to coqui presence and not coqui density is because predatory birds have larger home ranges than other forest birds in Hawaii (Scott et al. 1986). Both the Hawaiian Hawk and Mynas are extremely territorial (Blanvillain et al. 2003, Klavitter 2009), and because the area of our plots are smaller than their territory sizes (Klavitter 2009), coqui presence could be a better predictor of their abundance than local coqui density.

We predicted that frugivorous and nectarivorous birds would be the least likely of the foraging groups to show a relationship with coquis. Frugivorous birds showed a positive association with coqui density in our top maximum-likelihood models; but, in our hierarchical Bayesian models, the effect of coquis was negligible. This result highlights the importance of accounting for random site and plot-within-site variation, which cannot currently be controlled for in maximum-likelihood models in the package

“unmarked” (Fiske and Chandler 2011). House Finches, on the other hand, which are frugivorous, had higher abundance in coqui plots in both the maximum-likelihood and the Bayesian analysis with random effects. This result is more difficult to interpret, but could be explained by indirect effects of coquis on nutrient cycling. Coquis have been shown to increase nutrient deposition and leaf litter decomposition rates (Beard et al.

2002, Beard et al. 2003, Sin et al. 2008), as well as increase non-native plant growth, in particular that of strawberry guava (Psidium cattleianum), a common non-native plant with an abundance of year-round fruit (Sin et al. 2008). If an increase in non-native plant growth leads to more fruit availability, House Finches (and perhaps other non-native 80 frugivorous birds) could increase in abundance where coquis are present. The most

available nectar resource, the native ohia (Metrosideros polymorpha), does not show increased growth in sites with coquis (Sin et al. 2008), which is expected because ohia

also does not increase growth in sites with increased nutrient availability (Vitousek et al.

1987, Loh and Daehler 2008), and this may explain why nectarivorous birds do not show a similar increase in abundance with increased coqui presence or density. Because we did not measure fruit availability in plots, we cannot determine if coquis are associated with increased non-native fruit production.

We found that the overall abundance of native birds showed no difference across coqui invasion fronts. The abundance of the two native insectivores tested, Hawaii

Amakihi and Hawaii Elepaio, did not show any relationship to coquis in top maximum-

likelihood models, which suggests that coquis are not reducing native insectivorous birds

in Hawaii, as has been previously hypothesized (Kraus et al. 1999, Beard and Pitt 2005).

Additionally, it has been suggested that native birds could decline if coquis increase

predatory non-native mammals (Kraus et al. 1999, Beard and Pitt 2005). Because we did

not find negative associations between coquis and any native bird, our results suggest that

apparent competition is not having a significant effect on native bird abundance, at least

not for the species we observed in the sites studied. Future research should address

whether coquis can increase the abundance of non-native mammals, such as rats and mongoose, to further investigate their impacts on vertebrate communities in Hawaii.

Across sites, native bird abundance increased with percent native canopy, supporting previous research that canopy food resources may increase native bird abundance (Hart et al. 2011). Interestingly, native birds did not respond strongly to 81 elevation in our models, which has been shown to limit forest bird distributions because of the interaction between elevation and mosquito-borne diseases (Ahumada et al. 2009,

Atkinson and LaPointe 2009).The species we observed in these mostly lowland invasion fronts are the most resistant to avian malaria and pox virus (Reynolds et al. 2003, Foster et al. 2007). These native bird species will likely be able to coexist with coquis, and at least one species, the predatory Hawaiian Hawk may consume them. The Red-tailed

Hawk (Buteo jamaicensis), a similar species in the same genus as the Hawaiian Hawk, feeds extensively on coquis in Puerto Rico (Stewart and Woolbright 1996). Another native species that could potentially consume coquis is the Hawaiian Crow or Alala

(Corvus hawaiiensis), a critically endangered corvid that was classified as extinct in the wild, but which will be reintroduced to the island of Hawaii over the next five years.

The fact that coquis show up in top maximum likelihood models and as a significant variable in our Bayesian analyses with positive effects on some groups and species suggests one of two things, either: 1) coquis themselves influence the abundance of bird species, or 2) coquis are associated with other factors in these plots that influence the abundances of bird species (MacDougall and Turkington 2005, Berglund et al. 2013).

We measured other variables in these plots in an attempt to address this second explanation. We purposely chose areas where plots on either side of the front were as similar as possible in habitat. We did this so we could more confidently attribute differences detected across the fronts to coquis and not environmental variables. No environmental variables that we measured, except elevation, were different across the fronts. The elevation difference likely occurred because coqui were introduced and have established primarily in lowland areas and invasions tend to move upslope on roads and 82 major trails (Bisrat et al. 2012, Olson et al. 2012); however, it should be noted that the

difference in elevation between these plots was small (mean difference 20 m). No birds

showed a positive association with both coquis and elevation (Table 3-3), so we are fairly confident that the models were able to disentangle these two variables. We acknowledge that our design cannot completely rule out the possibility that coquis are associated with some habitat variables that bird species are responding to that we did not measure, and that it is difficult to determine this without estimates of bird abundances prior to the coqui invasion or experimental evidence (Gurevitch and Padilla 2004), but we made every attempt to address this possibility during site selection and in our analyses. Furthermore, because predation is the most direct potential interaction between birds and the coqui, and vertebrate-preying birds showed the largest changes in abundance, we feel that these birds are most likely responding to coquis and not some other variable.

The fact that coquis are associated with increased abundance of generalist insectivores, vertebrate predators, total non-native birds, and some species, suggests that they likely play a role in influencing the abundance of at least some birds. Interestingly, coquis were always associated with higher bird abundances, or had no relationship, but were never associated with lower bird abundances, as would be expected if coquis were strong competitors with birds. Perhaps this is unsurprising because coquis forage mostly on leaf litter insects in Hawaii, while most Hawaiian birds forage on insects from the canopy and understory (Smith et al. in review). We believe that our strong results regarding vertebrate-preying birds in particular, but also generalist insectivores, suggest that the main effect of coquis on Hawaii’s bird communities is as a novel prey resource

(Beard and Pitt 2005). Our results support multiple studies that have found non-native 83 species mainly affect invaded island ecosystems through predatory interactions and not

through competition (Mack et al. 2000, Courchamp et al. 2003, Gurevitch and Padilla

2004, Sax and Gaines 2008, Shine 2010). The next step in this research would be

collecting data, either using camera trapping, observations, or diet analyses, to confirm

that vertebrate predators and generalist insectivores actually consume coquis in numbers

that might influence bird populations.

Our results suggest that coquis do not affect native bird abundances, at least for

the set of species with which we observed them to overlap, but that they are associated

with higher abundances of non-native birds, in particular vertebrate-preying birds.

Because controlling coqui populations is not always practical or possible (Tuttle et al.

2008), managers might consider preventing future establishment of coqui on islands

where they do not yet exist (i.e., other Pacific Islands) and other Hawaiian Islands, like

Kauai and Oahu, where they have been eradicated, because increases in non-native birds

may be undesirable for several reasons. First, non-native birds are a host of non-native diseases, like avian malaria and avian pox virus (Ahumada et al. 2009). These diseases are the single greatest cause of native bird extinction and decline in the Hawaiian Islands

(LaPointe et al. 2009, Reed et al. 2012). Increased abundance of non-native hosts could increase the transmission rate of these diseases to native birds (Ahumada et al. 2009).

Second, increases in non-native vertebrate-preying birds, particularly the Common Myna, may pose a risk to native birds because they can be nest predators or territorial aggressors

(Blanvillain et al. 2003, Burns et al. 2013), and have been associated with historic declines of native birds in Hawaii (Banko and Banko 2009). Mynas are also considered a nuisance to humans in their non-native ranges (Yap et al. 2002, Saavedra et al. 2015). 84 Third, because we observed higher abundances of vertebrate-preying birds in coqui plots,

it is not unreasonable to think that small mammals could also increase where coquis

invade (Kraus et al. 1999, Beard and Pitt 2005, Beard and Pitt 2006). Managers in the

Hakalau Forest National Wildlife Refuge, where most populations of endangered

Hawaiian birds are restricted, have spent up to 7000 USD per km2 on small mammal control (Nelson et al. 2002), and could spend more if the expansion of coquis into more native bird habitat increases non-native mammals in these areas. We recommend that managers continue to monitor native and non-native bird abundance in coqui invasion fronts on the island of Hawaii, and prevent establishment of coqui populations in new areas.

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95 CHAPTER 4

SUMMARY AND CONCLUSIONS

In the first part of this research, we investigated whether invasive coqui frogs and

Hawaiian birds overlap in isotopic niche and share prey resources. We found that coquis occupied a lower trophic level than the Hawaiian hoary bat, but showed extensive isotopic overlap with three species of generalist insectivorous birds, one native and two

non-natives. This was surprising because coquis only eat invertebrates (Beard 2007), whereas all the three species of birds that we analyzed take plant material as well as invertebrate prey (Banko and Banko 2009). This isotopic overlap could indicate competition for resources, but there are several mechanisms that could drive similar isotopic signatures in a food web (Kelly 2000; Post 2002; Caut et al. 2008). To further

investigate whether coquis and birds could share resources, we used the isotopic

signatures of invertebrate prey in mixing models to assess the proportion of insect groups

taken by these species. We found little overlap in diet sources among the coquis and

birds, but extensive overlap between the bird species, particularly in the proportion of

Lepidoptera larvae. Our results suggest that coquis and birds likely do not share prey

resources, and thus are not likely to compete for prey. Our results also support previous

research showing that coquis mostly feed in the leaf litter (Beard 2007; Choi and Beard

2012; Wallis et al. 2016), and that these three bird species primarily feed on invertebrates

in the understory foliage and canopy (Banko et al. 2015).

However, we must acknowledge that there are several limitations with this

approach. By only using one study site, we may not have characterized the full range of 96 isotopic niche of coquis and birds across a variety of habitats in Hawaii. Although the

coqui diet at this site has been well characterized (Wallis et al. 2016), without gathering

the stomach contents of birds directly, we were limited to using diet sources known a

priori from the literature in Hawaii, which may not accurately reflect their use of prey

resources at this site. Stable isotope analyses are more powerful when combined with

stomach content or fecal analysis (Vinson and Budy 2011). Because coquis consume

invertebrate groups relative to their availability (Beard 2007), their diet can vary greatly

across sites, and there may be other sites where coquis consume greater proportions of

invertebrates preferred by birds. To more completely address whether coquis and birds

share diet resources, we recommend that future research use both stable isotope and

stomach content analysis to characterize the diets of coquis and birds, and that this study

is replicated across more sites on the island Hawaii. Furthermore, characterizing bird diet

across coqui invasion fronts, in a similar design to Chapter 3, would provide stronger

evidence for or against competition, depending on whether any changes were seen.

In the second part of this research, we investigated whether bird abundance

differed across 15 coqui invasion fronts on the island of Hawaii. We tested for

differences in habitat variables between plots because we wanted to attribute any

observed changes in bird abundance to the coqui and not to any differences in habitat. To

account for imperfect detection probability while simultaneously estimating abundance,

we used N-mixture models (Royle 2004). Because of site variability, we used a hierarchical Bayesian model to account for site and plot-within-site random effects (Kéry and Schaub 2012), and obtained posterior estimates of abundance in coqui and non-coqui sites. We were particularly interested in which foraging groups responded, whether native 97 or non-native birds responded, and if any individual species responded to coqui presence

or density. There was a small elevation difference between coqui and non-coqui plots, but

no other habitat variable differed, and coqui density was not significantly correlated with

any habitat variable. We found that no foraging group, native or non-native birds, or

individual species showed a negative response to the coqui. Predatory birds, generalist

insectivorous birds, and total non-natives showed higher abundance in coqui than non-

coqui plots. Predatory birds had the strongest response to coqui presence, with a 97%

probability of >50% higher abundance in coqui sites. Because many of the generalist

insectivorous birds that showed a response to the coqui are similar to predators of the

coqui in their native Puerto Rico (Stewart and Woolbright 1996), we suggest that these

birds could be preying on juvenile coquis. Native bird abundance did not differ across the

invasion fronts. These results suggests that coquis mostly affect bird abundance through providing a novel source of prey for predatory birds (Kraus et al. 1999; Beard and Pitt

2005), particularly non-native species, and suggests that competition between coquis and

birds, if present, is not strong enough to induce changes in bird abundance.

While we tested for differences in habitat variables in this study, because we saw

only positive relationships between coqui presence and density and bird abundance, we

acknowledge that it is possible coquis and birds could be responding to similar habitat

features that we did not measure. Non-native species distributions are often found to be

positively correlated, without any mechanism of interaction between them (MacDougall

and Turkington 2005; Berglund et al. 2013). Without experimental evidence (Gurevitch and Padilla 2004), or bird abundance data prior to the invasion, we cannot completely say that coquis change bird abundance where they invade, though the fact that predatory birds 98 showed the strongest response to coquis suggests that coquis could serve as an abundant prey resource for birds. Future research should use camera trapping, foraging observations, and stomach content analyses to confirm that predatory and generalist insectivorous birds forage on coquis in quantities that could affect their abundances.

In conclusion, the combined results of this research suggest that coquis mostly affect bird communities through predatory interactions, in this case as a novel source of prey, and not as competitors. We did not find that coquis shared a significant proportion of prey resources with any insectivorous bird, and we did not see any differences in native bird abundance across coqui invasion fronts. These results suggest that coquis do not negatively affect native insectivorous birds in Hawaii, as has previously been hypothesized (Kraus et al. 1999; Beard and Pitt 2005). However, we found that non- native bird abundance was higher in coqui sites, most likely because coquis provide a prey resource for non-native predatory birds. Because we found that coquis are associated with higher predatory bird abundance, it is reasonable to predict that non-native predatory mammals may increase in coqui sites (Kraus et al. 1999; Beard and Pitt 2006). Because non-natives predators are a significant challenge for native species conservation in

Hawaii (Lindsey et al. 2009; Reed et al. 2012), we recommend that managers continue to monitor the coqui invasion on the island of Hawaii, and prevent the establishment or re- establishment of coqui on other Hawaiian islands and throughout the Pacific.

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102

APPENDICES 103

APPENDIX A

Appendix to Chapter 2

104

Fig. A1: Core isotopic Bayesian ellipses for three random samples of 10 coqui plotted with the three bird species (a,b,c), and compared to the core isotopic Bayesian ellipses for all 30 coqui samples (d; see Figure 2-2 in chapter 2).

105 Table A1: Comparison of Bayesian niche widths and 95% credible intervals of three random samples of coqui and female, male, and subadult coquis to all frogs. Niche widths were considered significantly different if there was no overlap in credible intervals.

Sample Median niche width 95% credible intervals

Random Sample 1 (n=10) 1.51 (0.87, 2.93) Random Sample 2 (n=10) 1.29 (0.75, 2.51) Random Sample 3 (n=10) 1.16 (0.67, 2.26) Females (n=10) 1.83 (1.07, 3.96) Males (n=10) 1.33 (0.77, 2.50) Subadults (n=10) 1.32 (0.78, 2.41) All frogs (n=30) 1.31 (0.94, 1.91)

106 Table A2 Mean δ13C and δ15N values of invertebrate, vertebrate, and plant groups of interest in Manuka Natural Area Reserve. SE added in parenthesis for groups with greater than one observation

Group Order or Spp N δ13C (‰) δ15N (‰) Invertebrate Acari 3 -25.82 (0.88) -1.35 (1.07) Amphipoda* 5 -25.06 (0.20) -0.71 (0.14) Araneae 5 -25.52 (0.44) 2.76 (0.55) (>10mm) Araneae 5 -25.88 (0.23) 1.70 (0.55) (<10mm) Blattodea* 5 -25.77 (0.62) -0.98 (0.35) Chilopoda 1 -24.27 2.05 Coleoptera 5 -24.16 (1.32) 0.35 (1.59) Collembola 1 -26.53 -2.97 Diplopoda 1 -21.6 -0.98 Diptera 13 -25.21 (0.51) 2.27 (1.14) Gastropoda 4 -21.26 (1.52) 1.22 (0.78) Homoptera 9 -26.83 (0.50) -1 (0.63) Hymenoptera: 6 -26.69 (0.19) 0.34 (0.56) Formicidae* Hymenoptera: 3 -25.22 (1.23) -0.38 (1.57) Wasps

Isopoda* 5 -24.15 (0.53) 1.31 (0.38) Isoptera 1 -26.76 -0.5 Lepidoptera: 10 -27.77 (1.08) 1.81 (0.55) Adult Lepidoptera: 4 -27.16 (0.43) 0.26 (0.27) Larvae Neuroptera 2 -26.96 (1.05) -1.81 (0.74) Oligochaeta* 1 -24.33 0.31 Orthoptera 5 -26.73 (0.11) 0.54 (0.31)

Vertebrate Amphibia Eleutherodactylus 30 -23.85 (0.09) 1.85 (0.13) coqui * Aves Hemignathus 10 -23.02 (0.12) 2.22 (0.48) virens Leiothrix lutea* 10 -23.20 (0.16) 3.92 (0.25) Zosterops 10 -22.78 (0.31) 3.96 (0.48) japonicus * Table A2 Continued 107 Mammalia Lasiurus cinereus 3 -21.19 (0.83) 7.48 (0.29) semotus (wing membrane) Plant Leaves Ageratina 1 -30.94 0.43 riparia* Diospyros 1 -29.22 -2.26 sandwichensis Metrosideros 6 -30.08 (0.31) -2.72 (0.35) polymorpha Ochna serrulata* 5 -31.44 (0.21) -1.26 (0.4) Pipturus albidus 1 -29.81 0.22 Psidium 5 -30.32 (0.32) -0.29 (0.89) cattleianum* Psidium guajava* 1 -29.6 -1.66 Schinus 3 -30.15 (0.70) -0.90 (0.18) terebinthifolius Flowers Metrosideros 5 -28.47 (0.64) -2.74 (0.14) polymorpha Schinus 1 -29.85 -3.67 terebinthifolius* Fruit Ochna serrulata* 5 -28.06 (0.52) -2.19 (0.95) Psidium 5 -28.82 (0.65) -1.86 (0.50) cattleianum* Psidium guajava* 1 -32.64 -1.12 Litter Metrosideros 5 -28.76 (0.45) -2.08 (0.27) polymorpha Ochna serrulata* 5 -30.46 (0.44) -2.48 (0.24) Pipturus albidus 1 -29.32 0.91 Psidium 5 -29.09 (0.44) -2.13 (0.083) cattleianum* Wood Metrosideros 3 -27.94 (0.67) -1.99 (0.53) polymorpha Schinus 1 -27.65 -0.45 terebinthifolius*

* Indicates all non-native taxa in Manuka Natural Area Reserve.

108

APPENDIX B

Appendix to Chapter 3

109 S1: Detailed methods for habitat variable collection

Vegetation Survey Methods

1. Canopy measurements (canopy cover, canopy height, percent native canopy)

a. Canopy cover- Canopy cover was calculated using a canopy densiometer. We

took canopy cover at the central point, and at points located 7.5 m and 15 m in

each cardinal direction (N, E, S, W).

b. Canopy height- In each of the 4, 5 m x 5 m plots at a distance of 7.5 m from

the central point we estimated the height of the 2 individual trees closest to the

N and S point of the plot that were >10 cm diameter at breast height (DBH).

Height was estimated to the nearest 5 m using the stick method/rangefinder.

c. We identified these 8 individual trees to species level. The percentage of

native canopy species was be taken by dividing the number of native trees by

8.

2. Understory measurements (understory density, understory height, percent native

understory)

a. Understory density- Understory density was measured at 4 points in the plot,

at a distance of 7.5 m from the central point. Using a Nudds checkerboard

(100 checkered squares on a 0.5 m x 2 m board), we took 4 photos of the

understory at each of the 4 points, in each cardinal direction at 5m, for a total

of 16 photographs at each plot. Photos were taken from a standard height of

1.5 m off the ground. Understory density was measured by counting the 110 number of squares obscured by vegetation and divided by the total number of

squares.

b. Understory height- Understory height was measured at 4, 5 m x 5 m plots at a

distance of 7.5 m in each cardinal direction. We defined an understory plant as

any free standing stem (not a vine) <10 cm DBH. The height of the two

understory plants closest to the N and S point of each plot was estimated

visually to the nearest m, for a total of 8 plants per study plot.

c. These 8 individuals at each were identified to species. The percentage of

native understory species was taken by dividing the total number of

understory plants by 8.

TABLE B1: Variance inflation factors (VIF) between coqui density and seven environmental variables and graphs of linear relationships between coqui density and environmental variables. A conservative estimate of collinearity is if VIF > 3 (Zuur et al. 2010).

Canopy Canopy % Understory Understory % Native Elevation Cover Height Native Density Height Understory Canopy 1.98 1.90 2.18 1.36 1.97 1.90 1.45

111 35 y = 0.0019x + 12.341 R² = 0.0908 30

25

20

15

10 Canopy (m) Height Canopy 5

0 0 1000 2000 3000 4000 5000 6000 7000 Coqui abundance (frogs/ha)

FIGURE B1: Coqui abundance plotted against canopy height.

120 y = 0.0033x + 55.728 R² = 0.009 100

80

60

40 Native Canopy (%) Canopy Native 20

0 0 1000 2000 3000 4000 5000 6000 7000 Coqui abundance (frogs/ha)

FIGURE B2: Coqui abundance plotted against percent native canopy.

112

100 y = 0.0007x + 69.873 90 R² = 0.0011 80 70 60 50 40 30 Canopy (%) Cover Canopy 20 10 0 0 1000 2000 3000 4000 5000 6000 7000 Coqui abundance (frogs/ha)

FIGURE B3: Coqui abundance plotted against percent canopy cover.

1.2 y = -5E-05x + 0.6344 R² = 0.1042 1

0.8

0.6

0.4 Understory Density Understory 0.2

0 0 1000 2000 3000 4000 5000 6000 7000 Coqui abundance (frogs/ha)

FIGURE B4: Coqui abundance plotted against understory density.

113 9 y = -0.0003x + 2.6123 R² = 0.045 8 7 6 5 4 3 2 Understory (m) height Understory 1 0 0 1000 2000 3000 4000 5000 6000 7000 Coqui abundance (frogs/ha)

FIGURE B5: Coqui abundance plotted against understory height.

120 y = 0.0025x + 37.142 R² = 0.0088 100

80

60

40

Native Understory (%) Understory Native 20

0 0 1000 2000 3000 4000 5000 6000 7000 Coqui abundance (frogs/ha)

FIGURE B6: Coqui abundance plotted against native understory

114 1200 y = -0.0146x + 647.05 R² = 0.0199 1000

800

600

Elevation (m) Elevation 400

200

0 0 1000 2000 3000 4000 5000 6000 7000 Coqui abundance (frogs/ha)

FIGURE B7: Coqui abundance plotted against elevation.

TABLE B2: Estimates of coqui density calculated from line-transect distance sampling surveys for all sites and plots.

Site Point* P/A Density*** Site Point* P/A** Density*** ** Eden Roc C1 1 Kaiwiki C1 1 590 121 (ER) (KW) C2 1 1122 C2 1 200 C3 1 511 C3 1 123 N1 0 0 N1 0 0 N2 0 0 N2 0 0 N3 0 0 N3 0 0 Fern Forest C1 1 Manuka C1 1 631 2170 (FF) A (MA) C2 1 807 C2 1 2872 C3 1 530 C3 1 5948 N1 0 0 N1 0 0 N2 0 0 N2 0 0 N3 0 0 N3 0 0 Hamakua C1 1 Manuka C1 1 502 2730 B (MB) C2 1 474 C2 1 2998 C3 1 401 C3 1 4328 N1 0 0 N1 0 0 N2 0 0 N2 0 0 N3 0 0 N3 0 0 115 Kaupukuea C1 1 Saddle C1 1 HomesteadTABLE B2 Continued 691 Road 968 (KH) (SR) C2 1 290 C2 1 79 N1 0 0 C3 1 463 N2 0 0 N1 0 0 Kaloko C1 1 N2 0 565 0 (KO) C2 1 101 N3 0 0 C3 0 Stainback C1 1 0 607 (SB) N1 0 0 C2 1 769 N2 0 0 C3 1 293 N3 0 0 N1 0 0 Kalopa C1 1 N2 0 2407 0 (KP) C2 1 436 N3 0 0 C3 1 Volcano C1 1 417 78 A (VA) N1 0 0 C2 1 50 N2 0 0 N1 0 0 N3 0 0 N2 0 0 Kulani C1 1 Volcano C1 0 0 (KU) 419 B (VB) C2 1 389 C2 1 211 C3 1 469 N1 0 0 N1 0 0 N2 0 0 N2 0 Waipio C1 1 227 0 (WP) N3 0 0 C2 0 0 C3 1 390 N1 0 0 N2 0 0 N3 0 0 Abbreviations are as follows: * C=coqui, N=noncoqui, ** P/A represents Presence (1) or Absence (0), ***Density is expressed as frogs/ha.

TABLE B3: Mean values of plot-level environmental covariates included in bird abundance models in each of the fifteen study sites.

Site Annual Annual Plot Coqui Canopy Canopy Percent Understory Understor Percent Elevation rainfall temperature type* Density cover height native Density** y height native (m) (mm) (°C) (frogs/ha) (%) (m) canopy (m) understory Eden Roc 4702 18.9 Coq 741 37 5.2 33 0.71 2.2 29 537 (ER) Non 40 4.5 67 0.87 2.4 25 507 Fern 4915 18.1 Coq 656 35 7.9 100 0.67 1.4 75 685 Forest (FF) Non 27 8.1 100 0.58 1.8 75 636 Hamakua 2432 18.9 Coq 459 85 22.1 5 0.51 3.0 8 666 (HM) Non 90 18.6 17 0.36 3.2 4 654 Kaupaku 4250 19.0 Coq 491 88 28.8 0 0.56 2.1 0 466 ea Homestea d (KH) Non 84 23.9 0 0.56 2.9 0 466 Kaloko 1251 17.7 Coq 333 89 15.3 100 0.49 2.9 88 878 (KL) Non 84 19.0 100 0.62 2.4 78 902 Kalopa 2640 19.1 Coq 1087 90 9.7 90 0.30 1.8 92 650 (KP) Non 93 15.7 68 0.34 2.5 42 685 Kulani 5248 18.9 Coq 426 89 13.0 56 0.78 5.6 8 509 (KU) Non 92 25.7 0 0.70 2.1 4 516 Kaiwiki 4373 18.2 Coq 148 72 10.6 67 0.84 1.9 38 565 (KW) Non 82 10.2 40 0.81 3.3 29 648 Manuka 838 19.7 Coq 3663 68 19.0 69 0.42 1.2 18 572 A (MA) Non 73 18.8 100 0.46 2.5 95 599 116

TABLE B3 Continued Manuka 838 19.8 Coq 3352 79 21.6 72 0.55 2.3 58 604 B (MB) Non 63 20.9 85 0.45 1.2 75 652 Stainback 5759 17.8 Coq 556 86 20.9 8 0.65 2.4 58 689 (SB) Non 91 24.3 0 0.62 2.4 17 694 Saddle 4815 17.3 Coq 503 20 5.3 100 0.45 1.0 54 739 Road (SR) Non 6 1.9 100 0.37 1.1 42 844 Volcano 5483 17.2 Coq 64 82 3.7 100 0.81 3.7 13 823 A (VA) Non 86 8.4 90 0.81 5.5 31 810 Volcano 4075 17.0 Coq 211 68 10.0 100 0.74 2.6 25 969 B (VB) Non 74 6.4 100 0.72 2.3 92 929 Waipio 2264 20.9 Coq 309 90 21.3 0 0.45 1.4 0 372 (WP) Non 88 27.9 0 0.36 1.8 9 420 *Coq= coquis present, Non = coquis absent. ** Calculated as the proportion of a Nudds checkerboard that is obscured by

vegetation at a distance of 5m.

117