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Although habitat loss and fragmentation have troubled the Midwest for centuries, landscapes are once again changing profoundly as historically dominant agriculture is replaced by urban development. Urbanization alters both composition and structure of avian communities, yet little is known of the landscape level structure of bird populations. Because urban development may restrict movements of many species, changes in land use should affect connectivity and persistence of local populations that are distributed among remnant habitats. Metapopulations, which are comprised of demographically independent local sub-populations connected via dispersal, have the potential to promote long-term viability of small and isolated urban populations through rescue of sub-populations that are in decline. Such metapopulation dynamics require asynchrony in recruitment and survival rates among sub-populations. To understand the extent to which avian populations in urbanizing landscapes operate as metapopulations, I analyzed the population synchrony (spatial autocorrelation) of densities, rates of local extinction and recolonization for 16 species, and demographic growth rates (!) for 2 species breeding in 14 mature riparian forest sites in central Ohio, USA from 2005-2011. Two scenarios were considered to be consistent with metapopulation structure, cases with (1) high negative spatial autocorrelation and high rates of extinction and recolonization, or (2) low spatial autocorrelation with high rates of extinction and recolonization. Only 3 of

& ##& 16 species (19%) exhibited significant positive spatial autocorrelation across the entire study area. Only Acadian flycatchers in urban sites showed significant positive spatial autocorrelation of demographic growth rates. Furthermore, migrants and residents showed similar degrees of spatial autocorrelation (migrants r = 0.014 ±

0.0585 SE vs. residents r = 0.067 ± 0.0475 SE). Although autocorrelation did not differ significantly between resident and migratory species, patterns of autocorrelation differed between rural and urban landscapes, suggesting that urbanization affected connectivity among remnant patches. Probability of extinction ranged from 0.0 – 0.789 (mean = 0.166 ± 0.0522 SE), whereas probability of recolonization ranged from 0.0 – 1.0 (mean 0.652 ± 0.0815 SE). Low prevalence of positive spatial autocorrelation combined with high rates population turnover provide evidence that spatially subdivided avian populations in my system may function as metapopulations. The possibility that metapopulation dynamics may operate within urbanizing landscapes is particularly important given that demographic analyses indicated that the majority of subpopulations of Acadian flycatchers (Empidonax virescens) and northern cardinals (Cardinalis cardinalis) had negative growth rates and, thus, acted as population sinks. My results suggest that long-term persistence of birds breeding in Midwestern riparian forest fragments may be facilitated by metapopulation dynamics.

& ###& Dedication

To my family and friends, whose influential

love and support has allowed me to

become the person I am today.

& "&

Acknowledgements

I would like to extend a sincere thank you to my advisor Amanda Rodewald. Without her hard work and commitment to excellence none of this would have been possible. I also would like to thank my committee members Robert Gates and Maria Miriti for offering insight and support throughout. There is no way this project would have been completed without scientific, statistical, and most importantly moral support of all of my labmates in the Rodewald Lab. A special thank you to the dozens of field technicians who worked long hours crawling through honeysuckle and multiflora rose and to the current and former graduate students of the Columbus Urban Riparian Project who have collected my data over the years: Jennifer Smith-Castro, Dan Shustack, Ian Ausprey,

Laura Kearns, Desiree Narango, and Linnea Rowse. I am extremely grateful for the support of the National Science Foundation and Ohio Division of Wildlife for funding the field components of this research, and the School of Environment and Natural Resources for supporting me during my graduate program.

& "#&

Vita

May 2010 ………………………….,,B.S. Biology, Gordon College, MA

2010 – present …………………..….Graduate Research/Teaching Associate, School

of Environment and Natural Resources, The

Ohio State University

& "##&

Table of Contents

Abstract …………………………………………………………………………………. iii

Dedication ……………………………………………………………………………..… v

Acknowledgements ……………………………………………………………………... vi

Vita ……………………………………………………………………………………. vii

List of Tables …………………………………………………………………………… x

List of Figures ………………………………………………………………………...… xi

Chapter 1: Metapopulations and Urbanization: A Review of the Literature

1.1 Introduction ………………………………………………………………….1

1.2 Objectives ……………………………………………………….…………...5

1.3 Thesis Format ………………………………………………………………..7

1.4 Metapopulation Dynamics and Urbanization: A Review ……………………7

1.5 Literature Cited ……………………………………………………………..20

Chapter 2: Evidence for metapopulation dynamics in a fragmented urbanizing landscape.

2.1 Abstract ……………………………………………………………………..38

2.2 Introduction ………………………………………………………………....40

& "###& 2.3 Methods ……………………………………………………………………..42

2.4 Results ………………………………………………………………………50

2.5 Discussion …………………………………………………………………..53

2.6 Literature Cited ……………………………………………..………………75

Appendix A: Locations of all riparian forest study sites. Including latitude and longitude,

index of urbanization, and forest width in meters ……………………………….89

Appendix B: Demographic parameters used to calculate growth rates (!) for Acadian

flycatchers (ACFL) and northern cardinals (NOCA) ……………………...……90

Appendix C: An example of the Leslie Matrix structure used to calculate demographic

growth rates. Vales for post fledging survival, juvenile survival, and adult

survival can be found in Table 5.1. Fleds/territory was a unique value for each

Leslie matrix ………………………………………………………………….…91

Appendix D: Leslie Matrix for northern cardinal (NOCA) at the urban site Casto Park in

year 2005. Average number of fledglings per territory was 2.5. Casto 2005

growth rate is; ! = 0.924 ………………………………………………………...92

Appendix E: Lambda (!) values calculated for northern cardinals in all sites and years for

a total of n = 92 lambda calculations ……………………………………………93

Appendix F: Lambda (!) values calculated for Acadian flycatchers in all sites and years

for a total of n = 92 lambda calculations ………………………………………..95

& #Q& Appendix G: Mantel test autocorrelation matrix for Acadian flycatcher lambdas, All

Sites …………………………………………………………………………...…96

Appendix H: 14x14 Mantel test autocorrelation matrix for northern cardinal lambdas, All

Sites …………………………………………………………………………...…98

Appendix I: Number of Acadian flycatcher fledglings per territory used in growth rate

calculations at all sites (n = 14) in all years (2005-2011) …………………….100

Appendix J: Number of Acadian flycatcher fledglings per territory used in growth rate

calculations at all sites (n = 14) in all years (2005-2011) ………………..……101

Appendix K: Matrix of geographic Euclidian distances for all sites (n=14) used in Mantel

tests……………………………………………………………………………..102

Appendix L: Densities for all species (n=16) on each 2 ha study grid in all years 2005-

2011 …………………………………………………………………………….103

Bibliography …………………………………………………………………………….97

& Q& List of Tables

Table 1: Mantel Test autocorrelation coefficients and probabilities of extinction and

recolonization for all species for all species over the entire study area (All Sites; n

= 16). Significance at p < 0.05 is indicated by **, while p < 0.10 is indicated by

*. Acadian flycatcher, Baltimore oriole, blue-gray gnatcatcher, great crested

flycatcher, gray catbird, indigo bunting, red-eyed vireo, and the wood thrush are

all migratory species. American robin, brown-headed cowbird, Carolina

chickadee, Carolina wren, downy woodpecker, northern cardinal, red-bellied

woodpecker, and eastern tufted titmouse are resident species.………………….62

Table 2: Mantel Test autocorrelation coefficients for all species at six rural sites (Three

Creeks, Galena, North Galena, Prairie Oaks, Public Hunting, South Galena).

Significance at p < 0.05 is indicated by **, while p < 0.10 is indicated by *.

Acadian flycatcher, Baltimore oriole, blue-gray gnatcatcher, great crested

flycatcher, gray catbird, indigo bunting, red-eyed vireo, and the wood thrush are

all migratory species. American robin, brown-headed cowbird, Carolina

chickadee, Carolina wren, downy woodpecker, northern cardinal, red-bellied

woodpecker, and eastern tufted titmouse are resident species …………………..64

Table 3: Mantel Test autocorrelation coefficients for all species at eight urban sites

(Casto Park, Cherrybottom, Elk Run, Kenny Park, Lou Berliner Park, Rush Run

Park, Tuttle Park, Woodside Green). Significance at p < 0.05 is indicated by **,

& Q#& while p < 0.10 is indicated by *. Acadian flycatcher, Baltimore oriole, blue-gray

gnatcatcher, great crested flycatcher, gray catbird, indigo bunting, red-eyed vireo,

and the wood thrush are all migratory species. American robin, brown-headed

cowbird, Carolina chickadee, Carolina wren, downy woodpecker, northern

cardinal, red-bellied woodpecker, and eastern tufted titmouse are resident

species ……………………………………………………………………..…….66

& Q##&

List of Figures

Figure 1: .(1#2#+%&)4((&#--,2)4$)#%7&;+P&*$))(4%2&+>&2*$)#$-&$,)+1+44(-$)#+%&$%9&4$)(2&

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& 2)4,1),4( …………………………………………………………………...….. 60

Figure 2: Mean growth rate (!) values by site for Acadian flycatchers 2005-2011. Dark

black bars indicate mean growth rate, boxes indicate the inter quartile range,

whiskers represents 1.5 x the inter quartile range of the data.………….………..68

Figure 3: Mean growth rate (!) values by site for Acadian flycatchers 2005-2011. Dark

black bars indicate mean growth rate, boxes indicate the inter quartile range,

whiskers represents 1.5 x the inter quartile range of the data……………………69

Figure 4: Mantel correlograms of migrant and resident birds in rural and urban

landscapes respectively (2005-2011). Autocorrelation of migratory guilds

responded differently to landscape type. Autocorrelation of migrants and

& Q###& residents differed significantly from each other in both urban (p = 0.036), and

rural (p = 0.017) sites ……………………………………………………………70

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& $4($&T$--&2#)(2U&*&V&LGLWXYZZZZZZZZZZZZZZZZZZZZZZZZZZZZGG71

Figure 6: Spatial Autocorrelation of demographic growth rates (lambdas) for Acadian

flycatchers and northern cardinals. Strong positive spatial autocorrelation of

urban flycatchers (p = 0.035) coupled with reduced reproductive productivity in

urban sites suggests that urban sites may act as population sinks. Cardinals

showed moderately negative spatial autocorrelation in population growth rates in

all landscape types. ……………………………………………………………...72

Figure 7: Growth rates of Acadian flycatchers and northern cardinals across the urban

index. Flycatchers show a strong decreasing trend in growth rate as urbanization

increases (r=-0.34), while reproductive success of cardinals remains unchanged

with urbanization (r=0.04)……………………………………………………….73

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As elegantly demonstrated by the equilibrium model of island biogeography

(MacArthur and Wilson 1967), area and isolation are strong determinants of species richness on oceanic islands through contemporary processes of extinction (a function of area) and colonization (a function of isolation). Isolated islands have been shown to be colonized by fewer species than islands better connected to the mainland (Stracey and Pimm 2009), and island size is known to strongly affect species richness (Russell et al. 2005). Similarly, in fragmented terrestrial landscapes, area and isolation can profoundly mediate community structure (Laurance 2008, Robinson et al. 1995). As is widely reported in the literature, species richness declines as habitat fragmentation and isolation increases (Andren 1994, Steffan-Dewenter and Tscharntke 1999, Fahrig

2002, Summerville and Crist 2001, Radford et al. 2005, Suzart de Albuquerque and

Rueda 2010). & M& Among the most detrimental consequences of habitat fragmentation are changes in the ability of animals to move among remnant habitat patches, which affects distributional patterns, dispersal, gene flow, and ultimately population size (Keller and

Largiader 2002, Dixon et al. 2007, Sork and Smouse 2006, Figueira 2009, Pavlacky et al.

2009). The ecological literature has shown repeatedly that small populations are vulnerable to extinction because they can easily succumb to a variety of demographic and environmental phenomena such as extreme weather events, inbreeding depression, predation, or disease (Wilcox and Murphy 1985, Newman and Pilson 1997, Gyllenberg et al. 1993, Fahrig 2002). In this way, habitat loss and fragmentation threaten species of nearly every taxa, from invertebrates (Keller and Largiader 2002, Heike et al. 2009,

Foggo et al. 2001), to herptiles (Milan et al. 2009, Toby et al. 2007), mammals (Andren

1994, Bennet 1990, Mortelliti et al. 2010), and birds (Rush and Stutchbury 2008, Opdam

1991, Lampila et al. 2005, Zuckerberg and Porter 2010). Understanding the dynamics and long-term viability of small and isolated populations in fragmented landscapes where land cover changes and habitat loss tend to subdivide historically panmictic populations into small local units is of particular conservation importance (Fahrig 1997, Sala et al.

2000, Sodhi et al. 2004).

Whereas the theory of island biogeography only considers patch area and isolation as determinants of population structure, the composition and structure of the

& K& landscape matrix itself must be considered in fragmented terrestrial landscapes because the matrix can facilitate or impede movements of individuals among patches, altering the impacts of isolation and area (Niemela 1999, Murphy and Lovett-Doust 2004, Parker et al. 2005, Lampila et al. 2005, Hoover et al. 2006, Kennedy et al. 2010, Lloyd and

Marsden 2011). Research has shown that the landscape matrix may facilitate, hinder, or prevent dispersal between sites, acting as a filter for migration (Vandermeer and Carvajal

2001, Jules and Shahani 2003, Kennedy et al. 2010, Tremblay and St Clair 2011).

Theoretical (Donovan et al. 1995, Ovaskainen and Hanski 2002, Grimm et al.

2003, Hanski 2004, Hanski and Ovaskainen 2004) and empirical (Harrison 1991, Hanski

1999, Busch et al. 2000, Sweanor et al. 2000, Hels and Nachman 2002, Lindsay et al.

2005) research has shown that metapopulations – local subpopulations connected through contemporary processes of immigration/emigration – allow for long-term persistence of a species in spite of the deleterious impacts of habitat fragmentation through the recolonization of locally extinct subpopulations. Theoretical ecologist Richard Levins developed the first metapopulation model in the context of an economically significant agricultural pest species (Levins 1969). Since then his metapopulation model, which consists of small dynamic populations in patches that alternate between presence and absence, has been recognized as having important ecological implications (Hanski 1999).

Though not all spatially subdivided habitats operate as metapopulations, organisms living in fragmented habitats nevertheless present an excellent opportunity to investigate metapopulation dynamics which may mitigate some of the deleterious effects of fragmentation (Xu et al. 2005, Wilson 2005, Hanski 1999). Populations of many species living in patchily distributed habitats, such as cougars (Felis concolor) in New Mexico

& W& which are maintained via metapopulation dynamics; without regular movement among sites cougars would likely become extirpated from the region in the region (Sweanor et al. 2000).

In recent years metapopulation structure has been empirically described in a variety of ecosystems including marine ecosystems (Smedbol and Wroblewski 2002,

Grimm et al. 2003), grasslands (Scheiman et al. 2007, Stoll et al. 2009), and forest ecosystems (Donovan et al. 1995, Busch et al. 2000, Sweanor et al. 2000, Foppen et al.

2000, Hels and Nachman 2002, Guiney et al. 2010), and in a wide range of taxa such as fish (Smedbol and Wroblewski 2002, Figueira 2009), invertebrates (Hanksi et al. 1995,

Drechsler et al. 2003, Guiney et al. 2010), herptiles (Hels and Nachman 2002).

Meanwhile, despite the well-known impacts of urbanization on demography, the potential for metapopulation dynamics in urbanizing landscapes remains largely unexamined

(Niemela 1999).

Urban land uses have the potential to affect key elements of metapopulation dynamics, namely local extinction and colonization rates (Hanski and Gilpan 1991,

Opdam 1991, Reed and Levine 2005). Many species experience depressed reproductive success in urban habitat patches, resulting in higher turnover rates, and more frequent local extinction events (Reed and Levine 2005, Huste and Boulinier 2007, Rodewald and

Shustack 2008a), while reduced permeability of the urban matrix can reduce dispersal between sites, thus limiting recolonization events (Bunn et al. 2000, Dey et al. 2006). In order to effectively conserve biodiversity on an urbanizing planet a more thorough understanding of the structure and function of metapopulation dynamics within urban habitat fragments is required.

& [&

Objectives

My research examined population structure and dynamics of riparian forest birds in urbanizing landscapes of Ohio. I used patterns of population synchrony and rates of extinction and recolonization to provide insight into the structure of populations and the extent to which they operate as metapopulations (Figure 1, conceptual diagram of these patterns). There were two scenarios considered to be consistent with metapopulation dynamics, cases with (1) highly negative spatial autocorrelation coupled with high rates of extinction and recolonization, or (2) low spatial autocorrelation with high rates of extinction and recolonization. Within this context I also evaluated the extent to which metapopulation structure was associated with the charactaristics of the landscape matrix and the migratory status of species.

Based on the literature, I expected to find the following relationships: (1)

Population synchrony will be negatively related to distance separating patches due to degrading Moran effects (density-independent environmental factors; Liebhold et al. 2004, Koenig 1998); (2) Population synchrony will be strongly mediated by characteristics of the landscape matrix. Urban land uses reduce landscape permeability, thereby profoundly altering likelihood and success of dispersal events negatively impacting population synchrony (Dunford and Freemark 2005, Kennedy et al. 2010, Delaney et al. 2010); (3) Population synchrony will be related to life history characteristics of focal species. Migratory guild has been linked to dispersal

& \& distances in birds (Paradis et al. 1998, Sutherland et al. 2000, Tittler et al. 2009), with long distance migrants moving on average 7.18 km further than residents in one study (Paradis et al. 1998). Increased vagility could potentially extend the spatial scale of population synchrony in migrants. Migratory status also generally corresponds to habitat and diet specialization, as most Neotropical migrants are insectivorous and fairly specialized in habitat needs. Many resident species, in contrast, regularly use the urban matrix as habitat, which blurs population boundries and increases the likelihood that populations fluctuate synchronously. Thus, movements of migrants might be more impeded by the urban matrix than residents.

Thesis Format

In the present chapter I briefly review the history and literature surrounding the metapopulation concept, relevance for conservation, and the potential implications of urbanization and avian life history attributes on metapopulation structure. Chapter 2 examines my key hypotheses regarding avian metapopulation structure in urbanizing landscapes and is written in manuscript format.

& ]&

Metapopulation Dynamics and Urbanization: A Review

In the simplest sense a metapopulation is a system of local populations connected by periodic dispersal of individuals (Hanski and Gilpin 1991, Opdam 1991, Reed and

Levine 2004), though more complex interpretations, applications, and models derived from this concept exist. The most commonly cited metapopulation models are the classic or Levins model, and the source/sink model. These differ in assumptions about patch conditions, numbers, and dispersal ability of the species in question.

The Levins Model

Richard Levins (1969) developed the metapopulation concept to evaluate how to most efficiently eradicate pest insects from agricultural fields. He understood that agricultural fields were dynamic environments and that temporal and spatial variation could be used to control agricultural pests. Levins postulated that the number of habitat patches occupied at a given time was a function of the rate of local extinction for the landscape and the rate of movement or migration among patches. He demonstrated this mathematically with the following equation:

dN / dt = mN (1 – N/T) – eN (1) where N is the number of local populations, m is the migration rate between sites, T is the total number of sites that can support local populations, and e is the extinction probability for a local population (Levins 1969). According to this model, metapopulation size

& ^& (number of occupied patches) stabilizes when rates of extinction and migration among patches are equal.

(2)

Levins showed that in order for a metapopulation to reach equilibrium processes of extinction and migration must be equal. Metapopulation dynamics may become destabilized if frequency of local extinction events becomes too high, or if dispersal between habitat patches are too frequent (Ovaskainen and Hanski 2002). This has been documented mathematically using discrete population models (Hanski 1999). Gyllenberg et al. (1993) showed that high growth rates (low extinction rates) and near absence of migration causes metapopulation dynamics to be extremely chaotic because subpopulations function independently. Chaotic dynamics give way to a more balanced asynchronous metapopulation as migration rates increase (Gyllenberg et al. 1993).

Populations begin to fluctuate in synchrony as a single interbreeding population if migration rates continue to increase beyond a critical threshold (Hanski 1999).

Metapopulation equilibrium as described by Levins can only be reached when population dynamics are sufficiently asynchronous.

Although the Levins model fits metapopulation dynamics for some single species

(Hastings and Wolin 1989, Hanski and Gyllenberg 1993, Hanski and Gilpan 1991), and multispecies systems (Vandermeer 1973, Hanski and Gyllenberg 1993), its assumptions are oversimplified in a number of important respects and hence, are violated in many real world systems (Hanski 1999, Hanski and Ovaskainen 2003, Hanski and Gilpin 1991,

Reed and Levine 20005). The most consistent shortcoming of the classic Levins model is & X& the assumption that habitat quality is uniform across the landscape, resulting in identical extinction and migration rates at all sites. Furthermore, the model assumes all patches contribute equally to the pool of potential migrants, with migration (m) that is not affected by interpatch distance (Levins 1969, Hanski and Gilpin 1991). While the application of the Levins model has advanced our theoretical understanding of metapopulation ecology, other models better accommodate landscape heterogeneity and environmental stochasticity typically encountered in most real world scenarios (Hanski and Gilpan 1991, Harrison 1991). Persistence of a metapopulation depends upon both the characteristics of the landscape and the species in question, both of which are not considered in classic metapopulation models (Ovaskainen and Hanski 2001).

Source – Sink Metapopulations

Recognizing the flawed assumptions regarding patch quality in Levins’ original model, ecologists began to develop metapopulation models with more realistic spatial heterogeneity built in such as the source/sink model (Holt 1985, Pulliam 1988). A source/sink metapopulation is one where migration and survival rates are not equal among sites; rather, some sites are net exporters of dispersers (sources) and others are net importers (sinks) (Pulliam 1988). The dynamics of source and sink sites are determined by the population growth rate (!), death rates of individuals within each site, and the ability of dispersers to move to or from a site (Pulliam 1988). A source is defined as a site where birth rates exceed death rates and the rate of increase, ! > 1, whereas in a sink, deaths exceed births and the growth rate is less than one, ! < 1 (Pulliam 1988, Hanski

1999). A growth rate (!) below 1 indicates that replacement level fertility is not

& _& achieved; the population is declining and will go extinct without immigrants from external sources. Conversely, a source has a rate of increase greater than 1, the population has a net increase of individuals and has a small chance of extinction, although environmental and demographic stochasticities still make extinction possible.

From a conservation perspective, source-sink dynamics not only allow for persistence in habitats that are otherwise unsuitable, but population size in a joint source-sink network often exceeds populations maintained by high quality habitat alone (Pulliam 1988,

Hanski 1999, Pulliam 2002).

Source/sink population dynamics have been empirically established in many species and in many different habitat types (Bergerud 1988, Donovan et al. 1995, Foppen et al. 2000, Hels and Nachman 2002, Caudill 2003, Jolly et al. 2003, Figueira 2009).

Among avian species, source/sink processes are commonly documented among forest dwelling birds living in fragmented woodlots. Migratory songbird populations in the

Midwestern United States, which are often subjected to high rates of nest predation and brood parasitism in small woodlots, have been shown to be population sinks, which must be sustained through immigration from nearby forested landscapes (Robinson et al.

1995). Computer simulations based on densities of reed warblers (Acrocephalus scirpaceus) in fragmented habitat in the Netherlands showed that all sink sites would go extinct in less than 10 years in the absence of immigration from sources (Foppen et al.

2000). While source/sink dynamics have often been documented in agricultural landscapes (Donovan et al. 1995, Robinson et al. 1995, Brawn and Robinson 1996,

Foppen et al. 2000, Hoover et al. 2006), few studies have directly identified urban source/sinks. One such study documented population sinks for key deer (Odocoileus

& ML& virginianus clavium) in areas of increased urbanization. Urban infrastructure limits movements of deer between foraging sites, and roadway collisions are a major source of mortality for urban deer (Harveson et al. 2004).

A substantial literature on avian urban ecology has established that urban development has significant impacts on the ecology avian communities, to the extent that for some species habitat fragments in urban systems may act as population sinks.

Urbanization modifies the reproductive potential of many sensitive species (Rodewald and Bakermans 2006, Rodewald and Shustack 2008a), may increase brood parasitism

(Rodewald and Shustack 2008a, Rodewald 2009), and can increase rates of turnover in site occupancy (Reed and Levine 2004, Huste and Boulinier 2007, Rodewald and

Shustack 2008a).

Avian migratory guilds show differing responses to urbanization; many studies have documented declines in the populations of Nearctic-Neotropical migrants and increases in populations of residents as the extent of urbanization increases (Miller et al.

2003, Dunford and Freemark 2004, Stratford and Robinson 2005, Chace and Walsh 2006,

Rodewald and Bakermans 2006, Minor and Urban 2010).

Conservation Implications of Metapopulation Theory

Habitat degradation and fragmentation due to urban development is considered a serious threat to the functioning of ecological systems (Saunders et al. 1991, Andren

1994, Robinson et al. 1995, Debinski and Holt 2000, Fahrig 2002). Human populations will continue to shift to urban centers in the coming decades, with 70% of the global population expected to live in cities by 2050 (United Nations 2008). As the amount of

& MM& available habitat becomes smaller and more patchily distributed, it is important to consider how a species that evolved in contiguous habitat can survive in a fragmented landscape. Metapopulations can play an important role in the conservation of fragmented systems (Esler 2000), because by definition they link subpopulations that otherwise would succumb to stochastic extinctions or Allee effects (Hanski and Gilpan 1991,

Opdam 1991, Reed and Levine 2004). Several studies have shown that movement of individuals between small isolated subpopulations due to metapopulation dynamics can reduce the risk of local species extinction (Hanski 1999, Busch et al. 2000, Xu et al.

2005).

The Southwestern willow flycatcher (Empidonax traillii extimus) is an excellent example of how metapopulation dynamics can enhance the viability of a threatened population. This federally endangered species breeds only in highly fragmented dense riparian forests, which could potentially result in loss of genetic diversity, and increased heterozygosity among sites. Because flycatcher populations behave as a metapopulation, an inquiry into the genetic health of the population showed only slight differentiation between sites (Busch et al. 2000).

Wildlife managers can also intentionally manipulate metapopulation dynamics in order to stabilize spatially subdivided populations. When habitat loss restricted populations of African wild dogs (Lycaeon pictus) to only one reserve in South Africa, conservation officers began introducing dogs into smaller reserves nearby with the intentions of establishing a metapopulation. Metapopulation dynamics allowed the population to grow from 3 wild dog packs in 1997 to 10 by 2002 (Lindsay et al. 2005).

& MK& Conservation managers have used metapopulation models to predict population viability in fragmented landscapes where metapopulations are often assumed to exist

(Plissner and Haig 2000, Akcakaya et al. 2005, Horne et al. 2011), time to metapopulation extinction (Lahaye et al. 1994, Akcakaya and Atwood 1997, Wilson and

Arcese 2008, Schippers et al. 2011), and dispersal among subpopulations (Lahaye et al.

1994, Plissner and Haig 2000, Alonso et al. 2012).

From a conservation standpoint, synchronous population dynamics among local populations have serious implications for rescue events of declining populations (Koenig

1999, Wilson 2005). If all populations are sinking simultaneously there will be few migrants available to re-colonize local extinctions (Wilson 2005, Harrison 1991).

Asynchronous fluctuations in population dynamics are necessary to balance extinction and migration rates and to reach metapopulation equilibrium (Ovaskainen and Hanski

1991).

Empirical Evidence for Metapopulations Dynamics

In contrast to the strong theoretical underpinnings of metapopulations, empirical identification of metapopulation lags behind, largely due to the challenges of documenting the critical features of metapopulations with field data (Olivier et al 2009).

Spatially subdivided habitats frequently assumed to be metapopulations often are not

(Hanski and Gilpan 1991, Hanski 1999). Hanski (1995) suggested that in order to differentiate a fragmented population from a true metapopulation one should identify the following: (1) that habitat fragments support local breeding populations, (2) that one population is not large enough to ensure long term survival on its own, (3) that habitat

& MW& patches are not too isolated to prevent recolonization or rescue events, (4) that local population dynamics are sufficiently asynchronous. If some or all of these conditions are not met, viable metapopulation dynamics are unlikely to operate in a system

If organisms frequently move unimpeded between habitat fragments they are a single population, while if there is no connectivity between patches they exist independent of each other (Harrison 1991, Hanski and Gilpan 1991). Using models, ecologists have been able to identify dispersal (Reed and Levine 2004, Xu et al. 2005,

Haydon and Steen 1997, Wilson and Arcese 2008), along with the size of subpopulations and habitat fragments (Opdam 1991, Ovaskainen and Hanski 2002, Vogeli et al. 2010), and asynchrony of demographic fluctuations across the landscape as important factors fundamental for long-term metapopulation persistence (Harrison and Quinn 1989, Koenig

1998, Matthews and Gonzalez 2007). Accordingly, empirical evidence of such factors in fragmented systems provides support for the existence of metapopulation dynamics.

Many studies have relied on mark recapture data (Wilson and Arcese 2008, Scheiman et al. 2007, Harrison et al. 1988), the genetic structure of populations (Anderson et al. 2004,

Wang et al. 2003, Busch et al. 2000), or long-term observations of extinction and recolonization events to show movement among habitat fragments (Elser 2000, Hanski and Singer 2001, Hanski 1999). Banding has been used among avian species to identify movements of individuals and subsequently elucidate metapopulation dynamics in many species including bobolinks (Dolichonyx oryzivorus; Scheiman et a. 2007), savanna sparrows (Passerculus sandwichensis; Fajardo et al. 2009), grasshopper sparrows

& M[& (Ammodramus savannarum; Tucker et al. 2010), and song sparrows (Melospiza melodia;

Wilson and Arcese 2008).

The genetic structure of a metapopulation should reflect dispersal between fragments; therefore low heterozygosity between sites is expected in a metapopulation because subpopulations are not genetically isolated. Genetic connectivity among subpopulations has been shown in mammals such as the pika (Ochotona princeps;

Peacock and Smith 2007), and grizzly bear (Ursus arctos horribilus; Proctor et al. 2012), birds such as the southwestern willow flycatcher (Busch et al. 2000), and lesser kestrel

(Falco naumanni; Ortego et al. 2008), fish (Koizumi et al. 2008, Huey et al. 2011), and plants (Fenster et al. 2003).

More recently, ecologists have utilized measures of spatial synchrony of population dynamics in order to identify potential metapopulation structure (Koenig

1998, Koenig 2001, Trenham et al. 2001, Bellamy et al. 2003, Wilon 2005, Olivier et al.

2009). In order for on-going processes of extinction and recolonization to persist in a metapopulation, fluctuations of subpopulations must be asynchronous (spatial autocorrelation negligible or negative) across the landscape (Harrison and Quinn 1989,

Koenig 1998, Matthews and Gonzalez 2007). Therefore, observing spatial asynchrony in the fluctuations of demographic rates indicates that the potential for metapopulation dynamics exists.

Spatial autocorrelation (or synchrony) is a well-known ecological phenomenon in which more proximate populations tend to be more similar than would be expected by random chance (Lichstein et al 2002). While the existence of spatial autocorrelation of ecological phenomenon is well accepted, multiple causes have been proposed. Three

& M\& commonly cited mechanisms hypothesized to cause spatial synchrony are: (a) correlated environmental conditions that drive population dynamics [Moran effects; Moran 1953,

Ranta et al. 1997], (b) dispersal or movement between populations, and (c) interspecific interactions such as predator/prey relationships (Koenig 2002, Liebhold et al. 2004,

Kiviniemi et al. 2011, Rosenstock et al. 2011). Because environmental similarity, dispersal frequency and community similarity all typically decline with distance, any of these mechanisms can produce a pattern of declining synchrony with increasing distance

(Bellamy et al. 2003).

Populations that exist in close spatial proximity will likely be subject to similar environmental pressures, climactic, ecological, or otherwise. Correlated environmental pressures will lead to correlation in demography (Koenig 1998, Koenig 1999, Bjornstad et al 1999, Jones et al 2003, Bellamy et al 2003, Wilson 2005). Bellamy et al. (2003) for example, found that spatial synchrony in populations of several bird species was driven by harsh winter weather. In situations where individuals freely move between habitat patches, populations also exhibit strong spatial autocorrelation, once demographics are no longer independent populations tend to fluctuate in synchrony (Haydon and Steen 1997,

Koenig 1998, Koenig 2002, Lichstein et al. 2002, Liebhold et al. 2004). As the distance among sites increases, dispersal frequency decreases and Moran effects weaken, thus, spatial synchrony decreases, a trend repeatedly seen in the literature (Ranta et al. 1997,

Bellamy et al. 2003, Wilson 2005, Drever 2006, Koenig 2006, Jones et al. 2007). Close- knit interspecific interactions, such as predator/prey relationships, can also cause spatial autocorrelation, when correlated fluctuations in the populations of one species cause correlated fluctuations in another. Spatial autocorrelation in songbird populations in New

& M]& Hampshire for example, were shown to be driven by correlated populations of

Lepidoptera larvae, an important food resource (Jones et al. 2003).

Spatial autocorrelation among avian species has been identified in a variety of different landscapes and species and at spatial scales up to thousands of kilometers

(Koenig 1998, Koenig 2001, Jones et al. 2003, Wilson 2005, Drever et al. 2006, Jones et al. 2007, Reynolds et al. 2011). While spatial autocorrelation is indeed a widespread ecological phenomenon, there is no consensus in the literature concerning the presence or absence of spatial autocorrelation in avian population dynamics (Wilson 2005). Some have found little evidence for spatial synchrony in population dynamics in a variety of landscape types and species (Koenig 1998, Paradis et al. 2000, Wilson 2005, Saether et al. 2011), whereas others report moderate (Cattadori 1999, Jones et al. 2007), or strong levels of synchrony in bird populations (Koenig 2001, Bellamy et al. 2003, Betts et al.

2006). This disparity may be due in part to the fact that quantifying spatial synchrony in a population is typically difficult because of the large datasets required (Reynolds et al.

2011).

Potential mediators of metapopulation dynamics

Avian migratory guild may be an important factor determining the scale of spatial autocorrelation, and metapopulation dynamics (Elser 2000). Migratory guild is known to impact dispersal distance (Paradis et al. 1998, Sutherland et al. 2000, Tittler et al. 2009), as well as reproduction and survival in fragmented habitat (Herkert 1994, Shustack and

Rodewald 2010, Lampila et al. 2005), all of which have important implications for metapopulation persistence (Hanski and Gilpan 1991, Bunn et al. 2000, Reed and Levine

& M^& 2005, Wilson 2005, Dey et al. 2006). Because of differences in dispersal behavior, and responses to habitat, metapopulation dynamics of migrant and resident birds inhabiting the same landscape could potentially be significantly different.

Beyond life history of the species in question, the characteristics of the landscape matrix, which may act as a migration filter, is extremely important in structuring the landscape level dynamics of fragmented populations (Opdam 1991, Vandermeer and

Carvajal 2001, Reed and Levine 2005, Xu et al. 2005). The permeability of the landscape matrix should strongly mediate metapopulation dynamics, especially in urbanizing landscapes which are well known to impact the distribution (Chace and Walsh

2006, Fernandez-Juricic 2000, White et al. 2005), demography (Borgmann and Rodewald

2004, Rodewald and Shustack 2008a, Shustack and Rodewald 2010, Safner et al. 2011), and movements (Niemela 1999, Xu et al. 2005, White et al. 2005, Kennedy et al. 2010) of birds. Species limited by poor dispersal ability will experience even greater isolation when surrounded by an unforgiving urban matrix (Niemela 1999). While birds are generally considered highly vagile organisms, the urban environment still presents a substantial barrier to many species. For example low permeability of urban landscapes reduced rates of interpatch movements in birds and ultimately reduced species richness in

Jamaica (Kennedy et al. 2010). Even wooded streets, which some have hypothesized might act as corridors through urban land use, have been shown to contain significantly fewer species than forest patches (Fernandez-Juricic 2000). Furthermore, avian migratory guilds are known to respond differently to urbanization with Neotropical migrants typically declining while populations of resident birds increasing with

& MX& urbanization (Miller et al. 2003, Dunford and Freemark 2004, Stratford and Robinson

2005, Rodewald and Bakermans 2006, Minor and Urban 2010)

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& W^&

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& WX&

Chapter 2

Evidence for Metapopulation Dynamics In a Fragmented Urbanizing

Landscape.

Abstract: Although habitat loss and fragmentation have impacted Midwestern ecosystems for centuries, landscapes are once again profoundly changing as historically dominant agriculture is replaced by urban development. Because urban development may restrict movements of many animal species, changes in land use should affect connectivity and persistence of local populations that are distributed among remnant habitats. Metapopulations, which are comprised of demographically independent local sub-populations connected via dispersal, have the potential to increase long-term viability of small and isolated urban populations through rescue of patches that are in decline.

Such dynamics require asynchrony in recruitment and survival rates among sub- populations as well as a pattern of extinction and recolonization events. To understand the extent to which avian populations in urbanizing landscapes operate as a metapopulation, I analyzed the synchrony (spatial autocorrelation) of densities for 16 avian species, extinction and recolonization rates, and demographic growth rates (!) for 2 species that breed in 14 mature riparian forest sites in central Ohio, USA during 2005-

2011. Two scenarios were considered to be consistent with metapopulation structure: (1) & W_& high negative spatial autocorrelation and high rates of extinction and recolonization, or

(2) low spatial autocorrelation with high rates of extinction and recolonization. I detected consistently low or negative spatial autocorrelation and relatively high rates of population turnover, thereby providing some evidence that bird populations in my system may function as metapopulations. Although autocorrelation did not differ significantly between resident and migratory species, patterns of autocorrelation differed between rural and urban landscapes, suggesting that urbanization may affect connectivity among remnant patches. The possibility that metapopulation dynamics may operate within urbanizing landscapes is particularly important given that demographic analyses indicated that most subpopulations of two focal species had negative growth rates and, thus, acted as population sinks. My results suggest that long-term persistence of birds breeding in Midwestern riparian forest fragments may be facilitated by metapopulation dynamics

& [L&

Introduction

Small populations in fragmented habitats are vulnerable to extinction (Wilcox and

Murphy 1985, Newman and Pilson 1997, Gyllenberg et al. 1993, Fahrig 2002, Hanski and Ovaskainen 2003). However, if structured as metapopulations, where local subpopulations are linked through extinction and recolonization, long-term viability can be much greater than for a single small population or completely isolated subpopulations.

Indeed metapopulation structure is known to reduce risk of extinction in fragmented landscapes across a wide range of taxa including plants (Kivniemi and Lofgren 2009), invertebrates (Hanksi et al. 1995, Drechsler et al. 2003, Guiney et al. 2010, Harrison et al.

1988), fish (Smedbol and Wroblewski 2002, Figueira 2009), herptiles (Marsh and

Trenham 2001, Hels and Nachman 2002, Templeton et al. 2011), mammals (Sweanor et al. 2000, Magle et al. 2010), and birds (Busch et al. 2000, Cattadori et al. 1999, Scheiman et al. 2007, Ringsby et al. 2002). Yet not all spatially subdivided populations function as metapopulations, and presumptions based on spatial structure alone can overestimate persistence and viability of populations. Demographics of local subpopulation must be independent, with a unique probability of extinction and a non-zero rate of recolonization to be considered a metapopulation (Hanski and Gilpin 1991, Koenig 1998). Complete lack of dispersal or movement among subpopulations precludes any possibility of recolonization (Xu et al. 2005). On the other hand if sites are strongly connected by

& [M& movement and dispersal they will function as a single population. Likewise, if population dynamics among subpopulations are highly synchronous, it is less likely that one subpopulation is increasing while another decreases (Hanski and Gilpin 1991, Wilson

2005, Munkemuller and Johst 2006). Consequently, asynchronous population dynamics are considered both prerequisites for and indicators of potential metapopulation dynamics

(Driscoll 2007).

Spatial autocorrelation or synchrony is the tendency for ecological variables at nearby points in space and time to have more similar values than would be expected by chance. Spatial autocorrelation has been documented throughout nature in species ranging from large carnivorous mammals to fungal plant pathogens (Lichtenshtien et al.

2002, Liebhold et al. 2004), and at spatial scales from approximately 1 m in protists, to thousands of kilometers in large free ranging species (Liebhold et al. 2004). Despite the ubiquity of autocorrelation in ecological systems, the underlying mechanisms remain unclear. Three commonly cited mechanisms hypothesized to cause spatial synchrony are:

(a) correlated environmental conditions that drive population dynamics [Moran effects;

Moran 1953, Ranta et al. 1997], (b) dispersal or movement between populations, and (c) interspecific interactions such as predator/prey relationships (Koenig 2002, Liebhold et al. 2004, Kiviniemi et al. 2011, Rosenstock et al. 2011). Any of these mechanisms can produce a pattern of declining synchrony with increasing distance (Bellamy et al. 2003).

Unfortunately, evaluating spatial synchrony is often difficult as it requires data sets spanning large spatial and temporal scales (Koenig 1999), such as long-term national or regional surveys (Bock and Lepthian 1976, Koenig 1998, Koenig 2001, Paradis et al.

2000, Trenham et al. 2003, Koenig 2006, Jones et al. 2007, Saether et al. 2011)

& [K& Although the literature on spatial synchrony within fragmented landscapes is somewhat equivocal (Bellamy et al. 2003, Drever 2006, Tait et al. 2005, Huitu et al.

2008), landscape alteration has the potential to disrupt or modify patterns of spatial autocorrelation in population dynamics by influencing local climatic conditions (Ewers and Didham 2006), dispersal (Keller and Largiader 2003, Dixon et al. 2007, Pavlacky et al. 2009), and predator/prey communities and interactions (Ewers and Didham 2006).

Metapopulations within urbanizing landscapes remain largely unexamined (Niemela

1999), despite the fact that reproductive rates, population turnover, and landscape permeability are strongly affected by urban land uses (Hanski and Gilpin 1991, Opdam

1991, Soule et al. 1988, Reed and Levine 2004). In his review of urban bird research

Marzluff et al. (2001) asserted that the majority of contemporary urban bird research, which focuses on community composition and structure provides little insight into potential metapopulation structures that may exist.

I used patterns of population synchrony and rates of extinction and recolonization to provide insight into the structure of populations and the extent to which they operate as a metapopulation (Figure 1). In this way, I used patterns of synchrony and recolonization/extinction to provide evidence of metapopulations. There were two scenarios considered to be consistent with metapopulation structure, cases with (1) high negative spatial autocorrelation and high rates of extinction and recolonization, or (2) low spatial autocorrelation coupled with high rates of extinction and recolonization. Within this context, I also evaluated the extent to which metapopulation structure was associated with characteristics of the landscape matrix and the migratory status of species.

& [W& Methods

Study System

I conducted my research in 14 mature riparian forest fragments located within the greater Columbus metropolitan areas of Franklin and Delaware Counties, Ohio, USA.

Fragments were separated by >2km and consisted of mature forests that were of comparable size, shape, and configuration across a rural-urban gradient (Rodewald and

Shustack 2008a). Common trees and woody understory plants included sycamore

(Platanus occidentalis), boxelder (Acer negundo), sugar maple (Acer saccharum), black walnut (Juglans nigra), ash (Fraxinus spp.), American hackberry (Celtis occidentalis),

Amur honeysuckle (Lonicera maackii), tall paw-paw (Asimina triloba), and Ohio buckeye (Aesculus glabra).

Landscape composition within a 1 km radius of each site was quantified using digital orthophotos (2002-2004), and building data from Franklin and Delaware Counties.

A 1km scale of landscape composition was used because other studies have shown it to be strongly associated with bird communities (Rodewald and Yahner 2001, Sundell-

Turner and Rodewald 2008). A principle components analysis was performed on landscape metrics (e.g., number of buildings, percent cover by different land uses) for each site, the first principle component, hereafter referred to as ‘urban index’, accounted for 80% of the variation within the data set (Rodewald and Shustack 2008a). Increasing values of the urban index indicate increasingly urban landscapes. Importantly, matrix composition was not confounded with habitat availability, as forest width was not correlated with the urban index (r = -0.12) (Rodewald and Shustack 2008a). Sites with an urban index >0 were classified as urban (Casto, Cherry Bottom, Elk Run, Kenny, Lou, & [[& Rush Run, Tuttle Park, and Woodside Green, mean urban index=0.806), while those with an urban index <0 were classified as rural (Three Creeks, Galena, North Galena, Prairie

Oaks, Public Hunting, South Galena, mean urban index= - 0.93).

Study Organisms

I studied common bird species breeding on my study sites, including 8 migrant species (Acadian flycatcher – Empidonax virescens, Baltimore oriole – Icterus galbula, blue-gray gnatcatcher – Polioptila caerulea, great-crested flycatcher – Myiarchus crinitus, gray catbird – Dumetella carolinensis, indigo bunting – Passerina cyanea, red- eyed vireo – Vireo olivaceus, and wood thrush – Hylocichla mustelina), and 8 resident species (American robin – Turdus migratorius, brown-headed cowbird – Molothrus ater,

Carolina chickadee – Poecile carolinensis, Carolina wren – Thryothorus ludovicianus, downy woodpecker – Picoides pubescens, northern cardinal – Cardinalis cardinalis, red- bellied woodpecker – Melanerpes carolinus, and tufted titmouse – Baeolophus bicolor).

The Acadian flycatcher and northern cardinal were used as focal species for a more intensive demographic examination.

The Acadian flycatcher is a Nearctic-Neotropical migratory songbird that breeds primarily in mature deciduous forests of eastern North America. This species is considered sensitive to forest patch size and habitat fragmentation, and numbers have steadily declined over much of its range (Whitehead and Taylor 2002). In many regions the Acadian flycatcher has been listed as a priority or threatened species because of its sensitivity to perturbations such as habitat fragmentation, urbanization, and nest parasitism (Nott 2008, Environment Canada 2012, Kluberantz 2012). Abundance and

& [\& reproductive success of flycatchers decline with increasing urbanization surrounding forest fragments in my study system (Rodewald and Bakermans 2006, Shustack and

Rodewald 2008a).

The northern cardinal is a synanthropic temperate resident bird species that is widely distributed across the eastern United States. Cardinal populations respond positively to urbanization, and urban forests are able to support 2-4 times the density of cardinals as more rural forests (Leston and Rodewald 2006). However, productivity and survival of cardinals are not consistently related to urbanization (Rodewald and Shustack

2008b).

Field Methods

Density and composition of the avian community at all sites were estimated using spot map surveys conducted 10 times each year within a 2-ha grid located at each site

(Bibby et al. 2000). From May through early July 2005 through 2011 each grid was systematically walked in an approximately 1-hour survey in which all singing/calling individuals were recorded on a map of the site grid. Counter-singing as well as defensive behavior were also noted. All surveys were conducted between approximately 0.5-hrs before sunrise and 1030-hrs when strong winds and rain were not present. End of season territory delineations were based on clustering of recorded observations with special consideration given to counter-singing and other territorial behaviors. The result for each species is an estimated density of territorial breeding males on each 2-ha study grid.

Nest searching and territory monitoring for our two focal species (Acadian flycatchers and northern cardinals) occurred annually during April-August. Flycatchers

& []& and cardinals were captured with mist nests and each bird was tagged with one silver

USGS leg band (steel for cardinals, aluminum for flycatchers) along with a unique combination of three color bands. Nests were located by observing parental behavior and secondarily by searching appropriate nesting substrates. The contents of each nest were closely monitored every 2-4 days until the fate of the nest (successfully fledged/failed) was determined. Successful nesting territories were monitored further to confirm the number of fledged young. In the case of nests that were too high to visually check for eggs or nestlings, the nest was watched for " 30 minutes for behavioral cues as to nest stage. Territories of banded individuals on our 2-ha study grid were closely monitored with special care given to locating all nests and all fledged young over the course of the season in order to accurately estimate reproductive productivity.

Data Analysis

Tests for spatial autocorrelation

I quantified the degree of ecological similarity among spatially distinct sites using

Mantel’s tests for spatial autocorrelation (Mantel 1967), which tests for similarity between (1) a matrix of geographic distances between locations, and (2) a matrix of ecological “distances” (i.e. differences) between environmental parameters. The null hypothesis of the Mantel test is that values of the environmental parameters are distributed randomly throughout space, in which case statistical significance indicates that locations in space differ significantly from random, or, that environmental values are not randomly distributed in space (Mantel 1967). The test results in what is known as a

& [^& normalized Mantel statistic that is simply the correlation of all pairwise elements of matrices X and Y, which like any product-moment coefficient ranges from 1 to -1.

Mantel tests were run with PASSaGE – Pattern Analysis Spatial Statistics and

Geographic Exegesis, a program written specifically for spatial analysis in natural sciences (Rosenberg and Anderson 2011). One of the original matrices is permuted prior to regression with the other matrix, this process is repeated over 1000 trials with each trial yielding a unique normalized Mantel statistic. If the spatial and ecological distributions were not associated, one would expect permutated values of the Mantel statistic to be normally distributed around the original observed normalized Mantel statistic (Fortin and Gurevitch 2001). Therefore, statistical significance is determined by the number of trials with a Mantel statistic greater than or less than the original observed

Mantel statistic prior to permutation (e.g., if 990 of the 1000 trials yield a Mantel statistic less than the original observed value, the p-value = 0.01). I also ran a modified form of the Mantel test known as the Mantel correlogram which calculates spatial autocorrelation within distinct distance categories. The correlogram shows the strength of correlation between the two matrices across a range of five distance categories (Cushman et al.

2005).

For each species I created three unique geographic distance matrices, one for all sites (n=14), one for rural sites (n=6), and one for urban sites (n=8). Each is a symmetrical, square n x n matrix of Euclidian straight-line distance. Each i,j component of the matrix was calculated as:

! ! !!" ! !!! ! !!! ! !!! ! !!!

& [X& where xiyi and xjyj are the geospatial coordinates (latitude and longitude) of sites i and j.

For each species I used PASSaGE to create a correlation matrix of demographic fluctuations from log transformed [ln(x+1)] changes in species density from year to year.

Each i,j component of the matrix was calculated as:

The result is a matrix of correlations in year-by-year territory density fluctuations for each i,j site comparison. As with the geographic matrices I created three autocorrelation matrices for each species, one for all sites (n=14), one for rural sites (n=6), and one for urban sites (n=8).

Leslie Matrix Population Modeling

I also tested for spatial autocorrelation of the population growth rates (lambdas) of

Acadian flycatchers and northern cardinals. Lambda values were calculated using a

Leslie matrix procedure (Caswell 2001). The Leslie matrix is an age-classified population-modeling matrix in which each column represents an individual age class of the population. I created a unique Leslie matrix for each of my focal species at each site in every year, resulting in a total of 98 Leslie matrices for each species. As commonly applied to bird populations, I used a three age-class structure that included post-fledging, juvenile, and adult. I did not include egg or nestling survival (prior to fledging) because rates of nest predation and brood parasitism in my system are relatively high, as a result

& [_& the number of successfully fledged young is a far better indicator of territory specific fecundity than number of eggs laid. My Leslie matrices were structured as follows:

"#$%&#!'()*&+! ,! ! ,! !"

#" ,! ,! ,! $" %"

! ! !

F represents the territory specific fertility of the adult reproductive age class. P represents the period between offspring leaving the nest and natal dispersal, or post- fledging survival (Ausprey and Rodewald 2011). J represents juvenile survival, from natal dispersal through adulthood. Finally, A represents adult survival. The average number of young fledged per territory in every year for each site was used to calculate fecundity of the final age class (F).

Post fledging survival (P) estimates were obtained from a previous radio- telemetry study of fledged birds in our system. Cumulative survivorship during the post- fledging period was 0.44 ± 0.077 for cardinals and 0.72 ± 0.077 for flycatchers (Ausprey

2010, Ausprey and Rodewald 2011). Because post-fledging survival did not vary with urbanization, I used the same estimates for both urban and rural sites. Juvenile survivorship (J), between post fledging and the age of reproduction, was estimated as post fledging survival*0.5, 0.36 for flycatchers, and 0.22 for cardinals, as is commonly used in the literature (Noon and Sauer 2001).

& \L& Estimates of adult survival were based on mark recapture (resight) data of banded birds within the study system. Survivorship estimates were calculated using program

MARK. Similar to post-fledging survival, adult survival was not associated with urbanization. Cardinal and flycatcher survivorship was 0.64±0.039 and 0.53±0.056 respectively, and did not differ between urban and rural landscapes (Rodewald and

Shustack 2008b, Rodewald and Shustack 2008a). Although it is typical for demographers to use females when modeling population structure (Caswell 2001), I used male survivorship estimates in my Leslie matrices given that density estimates were based on territories, which were indicated almost exclusively by male song and defensive behavior. Because fecundity estimates varied by site and year, an individual Leslie matrix was constructed for each site in each year. Lambda was then calculated as the dominant eigenvalue of the Leslie matrix using the program MATLAB. Autocorrelation matrices of year-by-year changes in flycatcher and cardinal lambdas were computed using the same methods described for spot map data.

Probabilities of Extinction and Recolonization

The classic Levins metapopulation model indicates that metapopulation equilibrium is reached when rates of local extinction and recolonization are equal and opposite. Using annual spot map data I estimated extinction and recolonization rates for all 16 study species over the 7 year study period. An extinction event was said to have occurred when a patch was occupied in year x and unoccupied in year x+1. Likewise, a recolonization event was declared when a patch went from unoccupied in year x to occupied in year x+1. Numbers of extinction (or recolonization) possibilities were tallied

& \M& as the total number of occupied (or unoccupied) sites where the density of the subsequent year was also estimated. Finally, the probability of extinction or recolonization was calculated as the number of extinction/recolonization events divided by the number of possible extinction/recolonization events.

AICc Model Selection

I compared the weight of evidence for multiple models using Akaike’s

Information Criteria (AICc) corrected for small sample sizes. The model with the lowest

AICc value was considered to have the strongest explanatory power, while models within

<2 #AICc of the top model were considered equally plausible. Akaike weights of evidence (AICw) indicated the degree of support for a given model and represented the likelihood that a model was the best model given the data.

I selected a number of a priori models based on likely drivers of spatial autocorrelation in my system: distance between sites because spatial autocorrelation is known to decline with distance, migratory status and landscape type (rural vs. urban) because both may alter dispersal rates and dispersal distance in birds, probability of extinction and recolonization because high rates of turnover should be indicative of asynchronous population dynamics, as well as interaction of distance with extinction and landscape type.

Results

Species densities

& \K& Only 3 of 16 species (18.8%) exhibited significant (P < 0.10) positive spatial autocorrelation across the entire study area (all 14 sites), whereas seven species (43.7%) showed negative spatial autocorrelation. Though migrants and residents showed similar degrees of autocorrelation as a whole (migrants r = 0.014 ± 0.0585 vs. residents r = 0.067

± 0.0475; p=0.494), five of eight migrant species showed negative spatial autocorrelation compared to only two of eight resident species (Table 1). The mean scale of positive spatial synchrony was 25.5 km for the entire study area and did not differ between migratory guilds.

Spatial autocorrelation in rural sites ranged from r = -0.371 (gray catbird) to r =

0.391 (blue-gray gnatcatcher; Table 2), with only the gnatcatcher (P = 0.08) showing marginal significance. Autocorrelation in urban sites ranged from r = -0.206 (Baltimore oriole) to r = 0.471 (Acadian flycatcher; Table 3), with Acadian flycatchers (P = 0.03) and Carolina wrens (P = 0.07) showing positive significant autocorrelation. Mean autocorrelation of migrant and resident birds did not differ from each other in rural sites

(migrants r = -0.094 ± 0.099 vs residents r = 0.060 ± 0.0611; p=0.211) or urban sites

(migrants r = 0.053 ± 0.0792 vs. residents r = -0.0238 ± 0.051; p = 0.429).

Guild densities

Migrants: Density of migrants was negatively autocorrelated for the study area (r

= -0.117; left-tailed P = 0.287) and rural sites (r = -0.443; left-tailed P = 0.027), but showed positive spatial autocorrelation in urban sites (r = 0.129; P = 0.154). Mantel correlograms showed that the degree of autocorrelation varied among distance classes.

Autocorrelation differed between rural and urban sites at the smallest spatial scale (rural

& \W& = -0.341 vs. urban =0.084: P = 0.023), but not at larger scales (rural = 0.0124 vs. urban =

0.0162; P = 0.935).

Residents: Residents showed weakly negative autocorrelation in both rural (r = -

0.095; P = 0.549) and urban sites (r = -0.05131; P = 0.0569), while the entire landscape yielded positive and significant spatial autocorrelation (r = 0.312; P = 0.038). Like migrants, autocorrelation of residents differed between rural and urban landscapes at the smallest spatial scale (p = 0.007). Autocorrelation also differed between migrant and resident birds at the smallest distance category in both rural (p = 0.017) and urban (p =

0.036) landscapes (Fig. 3).

Drivers of autocorrelation

No variables (migratory status, landscape type, distance, probabilities of extinction or recolonization) explained variation in autocorrelation among sites as evidenced by the null model appearing in the top model set (#AICc= 1.26).

Extinction and recolonization probability

Over seven years of spot mapping territories (2005-2011) 109 local extinction events and 123 colonization events were observed. Only one species (northern cardinal) lacked any extinction or colonization events over the course of the study, whereas the

Carolina wren had the greatest number of extinction (22) and colonization (19) events of any species. The Baltimore oriole had the highest probability of extinction over the course of the study (0.789), and was also the only species with a probability of extinction higher than probability of recolonization (0.373).

& \[& Probability of extinction ranged from 0.0 – 0.789 with mean 0.166 ± 0.0522; probability of recolonization ranged from 0.0 – 1.0 with mean 0.652 ± 0.0815.

Probability of extinction (migrants = 0.2283; residents = 0.1027, p=0.2457) and recolonization (migrants = 0.5747; residents = 0.7293, p=0.3613) was similar among migratory guilds.

Demography

Mean population growth rate of flycatchers in the different landscapes was; All =

0.788±0.027; Rural = 0.853±0.038; Urban = 0.7387±0.036. Mean population growth rates for cardinals were; All = 0.868±0.011; Rural = 0.866±0.021; Urban = 0.871±0.012.

Spatial autocorrelation for the Acadian flycatcher was landscape-dependent, with subpopulations being negatively autocorrelated in rural landscapes (r = -0.212; p =

0.862), but significantly positively autocorrelated in urban landscapes (r = 0.511, p =

0.035). Autocorrelation across the entire landscape approached zero (r = 0.08102; p =

0.25).

Spatial autocorrelation of northern cardinals was similarly negative across the study area as a whole (r = -0.148) and within rural (r = -0.21776) and urban sites (r = -

0.237). Based on an AIC adjusted for small sample sizes on lambda autocorrelation data, no variables (species, distance, landscape type, probabilities of extinction/recolonization) explained variation of Mantel correlation for lambdas as the NULL model had the highest explanatory power (AICc weight = 0.2074).

Discussion

& \\& Although many landscapes of the urbanizing Midwest are characterized by fragmented and spatially subdivided habitat remnants, the appropriateness of applying a metapopulation perspective to bird conservation efforts has not been previously evaluated. Weak synchrony (i.e., positive spatial autocorrelation) of population densities and demographic rates of flycatchers and cardinals coupled with high rates of population turnover in my study provides some of the first evidence that birds breeding in

Midwestern riparian forests may function as metapopulations.

Metapopulations have been suspected in many avian species including shorebirds, seabirds, raptors, and songbirds (Lahaye et al. 1994, Spendelow et al. 1995, Akcakaya and Atwood 1997, Foppen et al. 2000, Plissner and Haig 2000, Ringsby et al. 2002,

Ainley et al. 2003, Scheiman et al. 2007, Ortego et al. 2008), and have been used to make informed conservation decisions by modeling the impact demographic, environmental, or human induced perturbations will have on metapopulation survival (Lahaye et al. 1994,

Donovan et al. 1995a, Akcakaya and Atwood 1997, Cattadori et al. 1999, Plissner and

Haig 2000, Ringsby et al. 2002, Ainley et al. 2003, Acakaya et al. 2005, Tucker et al.

2010, Schippers et al. 2011, Alonso et al. 2004). However, many of these studies have assumed the presence of metapopulation dynamics on the basis of patchily distributed habitat or breeding populations, without other supporting data on population structure

(Lahaye et al. 1994, Akcakaya and Atwood 1997, Cattadori et al. 1999, Plissner and Haig

2000, Ainley et al. 2003 Acakaya et al. 2005, Alonso et al. 2004). My study contributes to a small but growing literature that uses population synchrony to elucidate metapopulation dynamics (Liebhold et al. 2004). An important distinction of my work is

& \]& the fact that I also used population turnover rates as complementary evidence for metapopulations.

Among the most striking patterns in my system were high rates of local extinction and recolonization, which are foundational elements of the metapopulation concept

(Hanski and Gilpin 1991, Gyllenberg et al. 1993, Hanski 1999, Xu et al. 2005). With the sole exception of northern cardinals, every species examined experienced at least one extinction (mean = 7.3 extinctions) and colonization event (mean = 8.2 colonizations).

Moreover, each subpopulation had a 16.6% chance of extinction, with nearly 70% of extirpated sites being recolonized during the course of my research. Extinction rates for bird populations in fragmented systems are quite variable and have been measured as low as 3% to > 60%, depending on habitat patch size and quality, and population size within the patch (Bellamy et al. 1996, Boulinier et al. 2001, Doherty et al. 2003, Huste and

Boulinier 2007). Rates of local extinction in fragmented or disturbed landscapes have been shown to be as much as two times greater than rates of colonization (Crooks et al.

2000), making rescue events much more unlikely. My data, however, demonstrate the strong recolonization potential of species in my system.

Responses to urbanization also seem to differ between migratory guilds, which is consistent with reports of contrasting population responses of many migratory and resident birds to urban development (Miller et al. 2003, Dunford and Freemark 2005,

Stratford and Robinson 2005, Rodewald and Bakermans 2006, Minor and Urban 2010).

Populations of Nearctic-neotropical migrant birds in my system were twice as likely to experience local extinction as were resident species perhaps stemming from their sensitivity to urbanization, Huste and Boulinier (2007) described a similar pattern of

& \^& extinction for migrant and resident birds in Paris, France, showing that rates of extinction for migratory birds were significantly higher (residents extinction rate = 0.03±0.01 vs. migrants = 0.20±0.03) than those of resident birds.

Among migratory species, low levels of positive spatial autocorrelation, combined with high rates of local extinction and recolonization are consistent with the assumptions of metapopulation theory (Harrison and Quinn 1989, Hanski and Gilpin

1991). For example, although the two most distant sites in my study system, North

Galena and Public Hunting, are only 61 km apart, well within the maximum range of dispersal for most bird species (Hosner and Winkler 2007), the heavily disturbed urban matrix may functionally isolate sites for birds that are sensitive to urbanization (Kennedy et al. 2010, Tremblay and St Clair 2011). More moderate levels of spatial autocorrelation among populations of resident bird species may reflect their readiness to move through and breed within the urban/suburban landscape matrix in my system (Malpass and

Rodewald unpublished data).

Demographic data for cardinals and flycatchers highlight the importance of considering habitat type and life history characteristics in source-sink metapopulation dynamics. Although a couple of sites showed positive growth for flycatchers, most populations of both species appeared to be in decline (!<1). Nevertheless, patterns of spatial autocorrelation suggest that the potential for rescue events differed between the two species. Autocorrelation of lambdas in urban sites was significantly positive for urban-avoiding flycatchers, with all sites regularly showing low growth rates, possibly due in part to high rates of nest parasitism in urban forests (Rodewald and Shustack

2008a, Rodewald 2009). Rural sites showed marginally higher reproductive growth rates

& \X& than urban sites in flycatchers (P = 0.097). Source-sink dynamics have been documented in woodlots fragmented by an agricultural matrix (Donovan et al. 1995b, Robinson et al.

1995, Brawn and Robinson 1996, Foppen et al. 2000, Hoover et al. 2006), but fewer studies have examined source-sink dynamics in urban systems. My research shows consistently lower reproductive success and higher population turnover among urban flycatchers suggesting that urban sites are acting as population sinks in my system.

Although high density of individuals might suggest that urban sites improve reproductive output of cardinals, mean lambda was below zero (negative growth rate) for every site, with only 8/112 calculated lambdas (all site/years) being >1 (positive growth rate). Theoretical and experimental work has shown that populations comprised entirely of long-term sinks may persist given temporal and spatial variation (asynchrony) in habitat quality and reproductive success (Matthews and Gonzalez 2007). Growth rates of cardinals were negatively autocorrelated in all landscape types, and therefore may persist in spite of regionally poor reproductive success. Balogh et al. (2011) found a similar structure of shifting population sources in gray catbirds breeding in the suburban matrix.

As one of the few studies to directly compare metapopulation structure of migrant and resident bird species using both spatial synchrony and population turnover rates, my research is among the first to provide evidence for metapopulation dynamics in bird communities in an urbanizing landscape. Furthermore, the frequency of local extinctions and subsequent rescue events (recolonizations) in my system suggests that that metapopulation dynamics might enhance population persistence in my system. Snep et al. (2005) demonstrated through ecological modeling that peripheral (rural) sources in a rural/urban metapopulation, could boost urban populations by as much as 56 percent.

& \_& Urban sites appear to be acting as habitat sinks for Acadian flycatchers in my system, and therefore must be supported by source sites, likely those along the periphery of urban development. That said, important caveats of my study include being limited by field data collection at relatively small spatial scales, inability to census all habitats within the landscape, and lack of direct dispersal observation (movements were inferred from data analyses). My results suggest that the conservation value of urban habitats will be best ensured if nearby source habitats are protected as well. Furthermore, urban green spaces provide more than wildlife habitat, they also act as an important interface for human/environmental interaction. For many city dwellers, urban forest remnants provide the only opportunity to experience and interact with the natural world, and can help to develop and foster strong conservation attitudes among the burgeoning urban population

(Nisbet et al. 2009).

& ]L&

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Table 1. Mantel Test autocorrelation coefficients and probabilities of extinction and recolonization for all species for all species over

the entire study area (All Sites; n = 16). Significance at p < 0.05 is indicated by **, while p < 0.10 is indicated by *. Acadian

"A flycatcher, Baltimore oriole, blue-gray gnatcatcher, great crested flycatcher, gray catbird, indigo bunting, red-eyed vireo, and the wood !

thrush are all migratory species. American robin, brown-headed cowbird, Carolina chickadee, Carolina wren, downy woodpecker,

northern cardinal, red-bellied woodpecker, and eastern tufted titmouse are resident species.

! "B!

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! "A! !

Table 2. Mantel Test autocorrelation coefficients for all species at six rural sites (Three Creeks, Galena, North Galena, Prairie Oaks,

Public Hunting, South Galena). Significance at p < 0.05 is indicated by **, while p < 0.10 is indicated by *. Acadian flycatcher,

"C Baltimore oriole, blue-gray gnatcatcher, great crested flycatcher, gray catbird, indigo bunting, red-eyed vireo, and the wood thrush are !

all migratory species. American robin, brown-headed cowbird, Carolina chickadee, Carolina wren, downy woodpecker, northern

cardinal, red-bellied woodpecker, and Eastern tufted titmouse are resident species.

! ""!

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! "C!

Table 3. Mantel Test autocorrelation coefficients for all species at eight urban sites (Casto Park, Cherrybottom, Elk Run, Kenny Park,

Lou Berliner Park, Rush Run Park, Tuttle Park, Woodside Green). Significance at p < 0.05 is indicated by **, while p < 0.10 is

indicated by *. Acadian flycatcher, Baltimore oriole, blue-gray gnatcatcher, great crested flycatcher, gray catbird, indigo bunting, ! "D

! red-eyed vireo, and the wood thrush are all migratory species. American robin, brown-headed cowbird, Carolina chickadee, Carolina

wren, downy woodpecker, northern cardinal, red-bellied woodpecker, and eastern tufted titmouse are resident species.

! "E!

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"E H$.>)&'$!J.-'!/09,:&39&,1.*51%&+$/$('1.0! 235A:98! 232:;@! 23A59! ! K>F'*!J>>%L-#I-.!/7$/&$%-.*#16-./-'.0! 232449;! 23888! 23929!

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

Table 3.

! "D! "F !

Figure 2. Mean growth rate (!) values by site for Acadian flycatchers 2005-2011. Dark black bars indicate mean growth rate, boxes

indicate the inter quartile range, whiskers represents 1.5 x the inter quartile range of the data.

! "F! DG !

Figure 3: Mean growth rate (!) values by site for Acadian flycatchers 2005-2011. Dark black bars indicate mean growth rate, boxes indicate the inter quartile range, whiskers represents 1.5 x the inter quartile range of the data.

! DG! 0.1 ox o x o ox x ox x x

0.0 o ox x o o

-0.1 o -0.2

Autocorrelation Residents Rural Residents Urban Migrants Rural -0.3 x Migrants Urban

1 2 3 4 5 Distance Classes ! ! Figure 4. Mantel correlograms of migrant and resident birds in rural and urban landscapes respectively (2005-2011). Autocorrelation of migratory guilds responded differently to landscape type. Autocorrelation of migrants and residents differed significantly from each other in both urban (p = 0.036), and rural (p = 0.017) sites.

!

! "#!

All Sites Rural Sites Urban Sites

0.39518 0.4

0.16405 0.2

0.07469 0.06250

0.02024 0.0

Autocorrelation -0.08976 -0.09255 -0.2

-0.38143 -0.4

-0.43848

All.Species Migrant.Species Resident.Species

%&'()*!+,!-./0&/1!/(0232))*1/0&24!25!6.*3&*6!78!9&')/02)8!'(&1:;!!<&'=!1*>*16!25!6./0&/1!

/(0232))*1/0&24!&4:&3/0*!0=/0!'*2')/.=&3/118!.)2?&9/0*!6&0*6!6=2@!92)*!6&9&1/)!

:*92')/.=&3!51(30(/0&246!0=/4!@2(1:!7*!*?.*30*:!78!3=/43*;!!A*6&:*40!6.*3&*6!

6=2@*:!60)24'!.26&0&>*!6./0&/1!/(0232))*1/0&24!&4!0=*!60(:8!/)*/!B/11!6&0*6C!.!D!

E;EFGH;!!!!

! "$!

0.6

0.5011 All Sites Rural Sites Urban Sites 0.4 0.2

0.08102 Autocorrelation -0.21186 -0.14796 -0.23740 -0.21776 0.0 -0.2

Acadian.Flycatchers Northern.Cardinals

Figure 6: Spatial Autocorrelation of demographic growth rates (lambdas) for Acadian flycatchers and northern cardinals. Strong positive spatial autocorrelation of urban flycatchers (p = 0.035) coupled with reduced reproductive productivity in urban sites suggests that urban sites may act as population sinks. Cardinals showed moderately negative spatial autocorrelation in population growth rates in all landscape types. ! "F!

Figure 7: Growth rates of Acadian flycatchers and northern cardinals across the urban index. A more positive urban index indicates a more urban site, while negative urban index values indicate more rural sites. Flycatchers show a strong decreasing trend in growth rate as urbanization increases (r = -0.34), while reproductive success of cardinals remains unchanged with urbanization (r = 0.04).

! "I!

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Adult survival estimates for both species were calculated from mark/recapture data

(Rodewald and Shustack 2008 a,b).

!

! "#!

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Appendix C: An example of the Leslie Matrix structure used to calculate demographic growth rates. Vales for post fledging survival, juvenile survival, and adult survival can be found in Appendix B. Number of fledglings per territory was a unique value for each

Leslie matrix.

! "-!

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7% 7% ;8?% 78:;% 7% 7% 7% 78=>% 78?=% % !@@-1.0A%F% % %

Appendix D: Leslie Matrix for northern cardinal (NOCA) at the urban site Casto Park in year 2005. Average number of fledglings per territory was 2.5. Casto 2005 growth rate is; ! = 0.924.

! ".! !

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