Exploring How Community Dynamics and Habitat

Niches and Nosey Neighbors: Exploring How Community Dynamics and Habitat

Characteristics Impact Reproductive Success in Forest Interior Bird Communities

A thesis presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Michelle V. Ward

December 2018

Niches and Nosey Neighbors: Exploring How Community Dynamics and Habitat

Characteristics Impact Reproductive Success in Forest Interior Bird Communities

MICHELLE V. WARD

has been approved for

the Department of Biological Sciences

and the College of Arts and Sciences by

Willem M. Roosenburg

Professor of Department of Biological Sciences

Dr. Joseph Sheilds

Dean, College of Arts and Sciences 3 ABSTRACT

WARD, MICHELLE V., M.S., December 2018, Biological Sciences

Niches and Nosey Neighbors: Exploring How Community Dynamics and Habitat

Characteristics Impact Reproductive Success in Forest Interior Bird Communities

Director of Thesis: Willem M. Roosenburg

Forest fragmentation increases edge habitat and reduces core forest habitat. In some cases, forest-interior breeding birds are displaced following fragmentation, leading to increases in bird density in the remaining habitat and reduced reproductive success.

To better understand how similar species occupy that same habitat, we determined how habitat characteristics and arthropod availability affect territory spacing, nest spacing, and reproductive success within a breeding bird community. The focal species included shrub nesting hooded warblers (Setophaga citrina) and wood thrushes (Hylocichla mustelina) and ground nesting ovenbirds (Seiurus aurocapilla) and worm-eating warblers

(Helmitheros vermivorum). The focal species also separate along foraging guilds: ovenbirds and wood thrushes are ground foragers while hooded warblers and worm- eating warblers are foliage gleaning foragers. We compared nest and territory habitat characteristics within and among species. We used a non-metric multi-dimensional scaling of forest habitat variables (tree and vegetation composition) to plot each species in habitat space to determine what nest and territory habitat characteristics were associated with each species, and if focal species overlapped in habitat space. We also determined if habitat and community variables including territory spacing and density, nest spacing and density, and arthropod availability influenced reproductive success. We found that while the focal species overlapped in habitat space, there were significant 4 differences in habitat space among species. Territory (males/ha) and nesting densities

(nests/ha) were positively correlated with arthropod availability, and territory density increased as canopy gaps increased. The reproductive success of all four species collectively decreased as nesting density increased, but foraging or nesting guild densities were not associated with reproductive success. Overall, community dynamics and habitat characteristics play an important role in reproductive success within forest interior communities. Quantifying how community and habitat variables both influence space use and reproductive success can be an informative approach to assessing the ecological niche requirements of forest interior nesting birds.

DEDICATION

This thesis is dedicated to Corvi: the best nest finder, habitat surveyor, and field tech an

ecologist could ask for. Rest in Peace little buddy.

6 ACKNOWLEDGMENTS

I would like to acknowledge Jake Goldman, Brooke Gollaway, Chance Patznick,

Dani Niziolek, Andrew Travers, Brandan Gray, and Paul DeBrosse for their endless entertainment and help in the field. I would also like to thank Dani for her extra help with ArcGIS and other lab duties. I would like to thank Cassie Thompson for her help with processing leaf litter samples and tolerating a Berlese funnel in our living room. I would particularly like to thank Brandan Gray for help with analysis, sample processing, and my fluctuating emotional state throughout this thesis.

7 TABLE OF CONTENTS

Page

Abstract ...... 3 Dedication ...... 5 Acknowledgments...... 6 List of Tables ...... 9 List of Figures ...... 11 Introduction ...... 13 Defining the Niche ...... 14 The Niche in Community Systems ...... 16 Species Account ...... 20 Wood Thrush (Hylocichla Mustelina) ...... 20 Ovenbird (Seiurus Aurocapillus) ...... 21 Hooded Warbler (Setophaga Citrina) ...... 23 Worm-Eating Warbler (Helmitheros Vermivorus) ...... 24 Materials and Methods ...... 25 Site Description ...... 25 Nest Searching ...... 26 Territory Mapping ...... 26 Arthropod Transects...... 27 Leaf Litter Samples ...... 27 Malaise Traps ...... 28 Habitat Surveys ...... 29 Canopy Cover...... 29 Understory Structure and Composition...... 29 Canopy and Mid-Story Structure and Composition...... 29 Geographic Analysis ...... 29 Statistical Analysis ...... 30 Arthropods...... 32 Nest and Territory Spacing...... 32 Reproductive Success...... 32 Results ...... 34 Territory Spacing and the Niche ...... 38 8 Territory Density...... 40 Differences Among Sites - Unlinked Variables...... 43 Differences Among Species - Unlinked Variables ...... 44 Intraspecific Differences Between Nesting Sites and Territory Centers ...... 48 Arthropods ...... 53 Shrub Arthropods...... 53 Leaf Litter Arthropods...... 55 Flying Arthropods...... 57 Habitat and Arthropods ...... 58 Nest Fate and The Niche ...... 60 Nest Fate Within Guilds...... 63 Discussion ...... 65 Habitat Differences Among and Within Species ...... 65 Arthropods and Reproductive Success ...... 66 Nest Spacing and Reproductive Success ...... 67 Conclusion ...... 67 References ...... 69 Appendix ...... 80

9 LIST OF TABLES

Page

Table 1. Variables used to quantify habitat and community niche space for forest- interior nesting passerines ...... 16 Table 2. Nesting and foraging guild classification for each focal species...... 20 Table 3. Abbreviations for the plant species and variables sampled in the habitat plots with common name and scientific name...... 31 Table 4. Sample sizes of nest and territory sample sizes by species and site, with percent successful nests, and percent of nests that failed due to predation. TARC=Tar Hollow control, TART=Tar Hollow thin, ZALT=Zaleski thin...... 36 Table 5...... 37

Table 6. Results of the AICc selected model of territory overlap, with parameter estimates (Estimate), standard error (SE), t, and p-values. The estimates and t-tests for the species (ovenbird, worm-eating warbler, and wood thrush) are in relation to hooded warblers, and the site (TART=Tar Hollow thin and ZALT=Zaleski thin) are in relation to Tar Hollow control...... 39

Table 7. Results of the AICc selected model table of variables for territory density, with parameter estimate (Estimate), standard error (SE), t, and p-values for each variable. The estimates and t-tests of the site (TART=Tar Hollow thin and ZALT=Zaleski thin) are in relation to Tar Hollow control...... 40 Table 8. Tukey post hoc test pairwise comparisons from the PERMANOVA used to determine differences among species centroids in habitat space. HOWA=hooded warbler, OVEN=ovenbird, WEWA=worm-eating warbler, WOTH=wood thrush...... 48

Table 9 Results of the AICc selected model of shrub arthropod biomass, with parameter estimate (Estimate), standard error (SE), t, and p-values. The estimates and t-tests for the species (ovenbird, worm-eating warbler, and wood thrush) are in relation to hooded warblers, and site (TART=Tar Hollow thin and ZALT=Zaleski thin) are in relation to Tar Hollow control...... 53

Table 10. Results of the AICc selected model of leaf litter arthropod biomass, with parameter estimate (Estimate), standard error (SE), t, and p-values for each variable. The estimates and t-tests for the species (ovenbird, worm-eating warbler, and wood thrush) are in relation to hooded warblers, and the site variables (TART=Tar Hollow thin and ZALT=Zaleski thin) are in relation to Tar Hollow control...... 56

Table 11. Results of the AICc selected model of flying insect biomass, with parameter estimate (Estimate), standard error (SE), t, and p-values for each variable. The estimates and t-tests for the species (ovenbird, worm-eating warbler, and wood thrush) are in relation to hooded warblers, and the site variables (TART=Tar Hollow thin and ZALT=Zaleski thin) are in relation to Tar Hollow control...... 57 10 Table 12. Results of Model table examining linear relationships between each arthropod sample type (Variable) and the moisture gradient (NMDS1), with parameter estimate (Estimate), standard error (SE), t, and p-values for each variable...... 59 Table 13. Results of the AICs selected model for nest fate, with corresponding parameter estimate (Estimate), standard error (SE), t, and p-values for each variable. .... 60 Table 14. Results of the AICs selected model for number of fledged young, with corresponding parameter estimate (Estimate), standard error (SE), t, and p-values for each variable...... 62

11 LIST OF FIGURES

Page

Figure 1. Location of study sites identified by triangles. Tar Hollow control (TARC)=red, Tar Hollow thin (TART)=blue, and Zaleski thin (ZALT)=yellow...... 25 Figure 2. All territories and nest locations of focal species in study sites. In territory maps A-C: Red polygons = hooded warbler territories, green polygons = worm-eating warbler territories, blue polygons =wood thrush territories, dark orange polygons = ovenbird territories. In nest location maps D-F: Red circles = hooded warbler nests, orange circles = ovenbird nests, blue circles = wood thrush nests, green circles = worm-eating warbler nests. A and D = Tar Hollow control, B and E = Tar Hollow thin, C and F = Zaleski thin...... 35 Figure 3. Least-squares mean (circle) and 95% confidence intervals (vertical bars) of overall territory overlap (m2) among study sites. TARC = Tar Hollow control, TART = Tar Hollow thin, ZALT = Zaleski thin ...... 39 Figure 4. Predicted values of overall territory density is negatively correlated with NMDS2, suggesting territory density decreased as canopy cover increased, and that high territory densities were associated with canopy gaps and canopy white oaks. TARC=Tar Hollow control (red), TART=Tar Hollow thin (green), and ZALT= Zaleski thin (blue)...... 41 Figure 5. Predicted values of overall territory density increased with increased leaf litter biomass. TARC=Tar Hollow control (red), TART=Tar Hollow thin (green), and ZALT=Zaleski thin (blue)...... 42 Figure 6. Least-squares mean (circle) and 95% confidence intervals (vertical bars) of overall territory density (males/ha) among study sites. TARC = Tar Hollow control, TART = Tar Hollow thin, ZALT = Zaleski thin...... 43 Figure 7. The position of each study site in habitat space. Convex hulls are drawn around habitat space in each forest and the centroids of habitat space (95% confidence ellipses based on the standard deviation of the weighted distance from the group centroid) for TARC = red oval, TART = blue oval, ZALT = yellow oval). Each point (square, circle, or triangle) represents the location of male territory center or female nest location in habitat space...... 44 Figure 8. Position of each focal species in habitat space (NMDS1 vs NMDS2) based on nesting and territory habitat characteristics. Hooded warbler (red), worm-eating warbler (yellow), ovenbird (black) and wood thrush (blue) convex hulls are drawn around each species’ habitat space and 95% confidence ellipses based on the standard deviation of the weighted distance from the group centroid are indicated...... 46 Figure 9. Position of each focal species in habitat space (NMDS1 vs NMDS3) based on nesting and territory habitat characteristics. Hooded warbler (red), worm-eating warbler (yellow), ovenbird (black) and wood thrush (blue) convex hulls are drawn around each species’ habitat space and 95% confidence ellipses based on the standard deviation of the weighted distance from the group centroid are indicated...... 47 12 Figure 10. Habitat space of hooded warbler nests and territory centers. A. NMDS1 versus NMDS2. Hooded warbler nest locations (blue) have slightly more canopy cover than territory centers (red). B) NMDS 1 versus NMDS 3. Nest locations were associated with more mid-story maples than territory centers...... 49 Figure 11. Habitat space of wood thrush (Hylocichla mustelina) nests (blue) and territory centers (red)...... 50 Figure 12. Habitat space of ovenbird (Seiurus aurocapilla) nests (blue) and territory centers (red)...... 51 Figure 13. Habitat space of worm-eating warbler (Helmitheros vermivorum) nests (blue) and territory centers (red)...... 52 Figure 14. Arthropod availability among focal species’ territories (HOWA = hooded warbler, OVEN = ovenbird, WEWA = worm-eating warbler, and WOTH = wood thrush), by sample type: A. Shrub arthropod biomass (transects); B. Flying arthropod biomass (malaise traps); C. Leaf litter arthropod biomass (leaf litter samples). The box and whisker plot indicate the median (middle horizontal line), upper and lower quartile of data (quartile 2 and 3, each represent 25% of data), and quartile 1 and 4 (each 25% of data, vertical lines)...... 54 Figure 15. Least-squares mean (circle) and 95% confidence intervals (vertical bars) for shrub arthropod biomass among study sites. TARC = Tar Hollow control, TART = Tar Hollow thin, ZALT = Zaleski thin...... 55 Figure 16. Least-squares mean (circle) and 95% confidence intervals (vertical bars) for leaf litter arthropod biomass among study sites. TARC = Tar Hollow control, TART = Tar Hollow thin, ZALT = Zaleski thin...... 56 Figure 17. Least-squares mean (circle) and 95% confidence intervals (vertical bars) for flying arthropod biomass among study sites. TARC = Tar Hollow control, TART = Tar Hollow thin, ZALT = Zaleski thin...... 58 Figure 18. Raw data representing biomass changes across the moisture gradient (NMDS1) based on sample type. Circles (leaf litter), triangles (flying), and squares (shrub) represent each sample. red=flying arthropod biomass (g), yellow=leaf litter arthropod biomass (g)...... 59 Figure 19. Predicted probability of nest survival increases as flying arthropod biomass (g) increases...... 61 Figure 20. Predicted probability of nest survival decreases as overall nest density (nests/ha) increases ...... 62 Figure 21. Number of fledged young (predicted values) decreases as overall nest density (nests/ha) increases...... 63

13 INTRODUCTION

Forest interior nesting neotropical migrants face many challenges, including forest fragmentation, habitat loss, and brood parasitism (Brittingam & Temple 1982; Rich et al.

1994; Dowell et al. 2000; Taylor & Stutchbury 2015, Gardner et al. 2017). Forest fragmentation increases edge habitat and reduces contiguous stands of core forest habitat.

Breeding birds may be displaced following habitat fragmentation and loss resulting in higher breeding densities in the remaining habitat, leading to reduced recruitment due to depleted resources, increased predation, and increased brood parasitism rates (Hagan et al. 1996; Barber & Martin, 1997; Martin 1995). In addition, 64% of bird species in North

America are identified as climate threatened or endangered based on current climate and range change projections (Langham et al, 2015).

Neotropical migrants are currently experiencing population declines throughout their ranges (Robbins et al. 1989; Sherry & Holmes 1996; Sanderson et al. 2006; Jones &

Cresswell 2010, Sauer et al. 2014). In Ohio, there is a call for research to investigate nesting success and general breeding ecology for climate threatened species including the wood thrush (Hylocichla mustelina), worm-eating warbler (Helmitheros vermivorum), ovenbird (Seiurus aurocapilla) and hooded warbler (Setophaga citrina) to increase our knowledge of their reproductive success in forested habitats (Rodewald et al. 2016). In this paper, we will investigate the habitat and community characteristics associated with territory spacing, nest spacing, and reproductive success of focal species using concepts pertaining to ecological niche. 14 Defining the Niche

Collectively, an ecological niche is comprised of a set of ecological variables that the focal species or population needs to survive and persist (Peterson et al. 2011). The combination of variables that make up a species’ ecological niche varies from species to species (Peterson et al. 2011). Many definitions and parameters for ecological niche exist, and a large degree of confusion and discrepancy over niche definitions still divide ecologists (Peterson et al. 2011; Chase & Leibold 2003). Joseph Grinnell was one of the first ecologists to use niche in an ecological context (Grinnell 1917; Chase & Leibold

2003). Grinnell defined the now termed Grinnellian niche, which is comprised of abiotic conditions, habitat structure and habitat species composition required for a species to exist in a given space (Grinnell 1917). Grinnell’s definition of niche emphasized the requirements that explain a species’ coarse-grained geographic distribution across a landscape. Grinnell’s definition of niche was followed by the work of Charles Elton

(1927), who defined species’ niche in terms of zero-growth isoclines, impact factors, and supply points, i.e. the dynamic role a species plays in its community. Some variables that comprise the Eltonian niche include food availability, social and demographic interactions, and predator and parasite interactions (Soberon, 2007). Grinnell defined niche as properties of the environment, while Elton defined niche as a property of the species (Peterson et al. 2011).

Finally, George Hutchinson (1957) approached niche more holistically, and broadly defined niche as an "n-dimensional hypervolume", where the dimensions are environmental conditions and resources that allow a population to persist. Hutchinson also made an important distinction between linked and unlinked dimensions of the niche, 15 where linked dimensions are dynamically linked to the population levels of the species

(Chase & Leibold 2003, Peterson et al. 2011) while unlinked dimensions are not. An example of a linked dimension for a forest-dwelling insectivorous passerine is arthropod availability: the arthropods available have a direct influence on the bird population through food availability, and the bird population directly impacts the arthropod community through predation (Karr, 1976; Patten & Burger, 1998; Askenmo et al. 1977).

An example of an unlinked dimension is nesting habitat: the presence of ideal nesting habitat will directly impact the population of birds, but the nesting birds have no direct effect on the nesting sites (for focal species). Peterson et al. (2011) maintains that the linked dimensions Hutchinson described are analogous to the Eltonian niche (the role of the species), while the unlinked dimensions are analogous to the Grinnellian niche (the requirements of the species).

In this paper, we will use the definitions of niche proposed by Peterson et al.

(2011), which are a combination of popular ideals stemmed from classic work done by

Grinnell (1917), Elton (1927), and Hutchinson (1957). We collected both unlinked and linked variables as defined by Peterson et al. (2011) for focal species. The unlinked variables we recorded are comprised of habitat vegetation structure characteristics and soil moisture characteristics (Table 1), which we refer to as habitat variables. We refer to the linked variables we collected (Table 1) as community variables, and refer to linked and unlinked variables collectively as the ecological niche.

Table 1.

Variables used to quantify habitat and community niche space for forest-interior nesting passerines Habitat Variables Community Variables (Unlinked Dimensions) (Linked Dimensions) Tree Species and size class Percent Territory Overlap

IMI (Integrated Moisture Index) Arthropod Biomass (for ground, shrub,

and flying Arthropod communities)

Percent Canopy Cover Nest Density (nests/ha)

Number of tree (mid-story/canopy) stems Nearest Neighbor Distance (m)

Number of understory stems Territory Density (territories/ha)

Nearest Neighbor species

The Niche in Community Systems

Intra- and interspecific interactions influence the reproductive success of nesting birds, and individuals may maximize their reproductive success by choosing structural, biotic and abiotic conditions that minimize deleterious influences on reproductive success

(Martin 1988a,b; Martin 1987; Martin & Martin 2001). In other words, sites that have optimal unlinked and linked conditions for a particular species are predicted to support more individuals (Martinez-Meyer et al. 2013; James et al. 1984; Martin 1988b). For example, the abundance and density of five thrush (Turdidae) and one warbler species

(Seiurus aurocapilla) were related to the abiotic and structural conditions that the species needs within their range (James et al. 1984). Wood thrush and ovenbird densities were 17 positively correlated with structural components of the habitat including canopy height, canopy cover and forest structure (James et al. 1984).

Interspecific interactions also can influence habitat space use, causing individuals to inhabit suboptimal habitat. For example, wood thrushes generalize their habitat space use when they exist in the same space as guild members, but become more habitat specific in the absence of other species (James et al. 1984). Thus, to determine if a habitat is high quality, researchers must evaluate population densities, reproductive success, and linked and unlinked habitat dimensions for that species within their home range and across the landscape.

Another important resource dynamic for breeding birds is food availability.

While the standing crop of arthropod biomass may far exceed the needs of a breeding population (Rotenberry 1980), the acquisition of food may be limited due to cryptic

(Boardman et al. 1974; Kettlewell & Conn 1977) and unpalatable prey (Rodreguez &

Levin 1976). Search and handling time and energy can also create energy constraints for foraging birds (Holmes et al. 1979). Thus, forest interior insectivorous birds can be food- limited during the breeding season (Martin 1986; Duguay et al. 2000). Nonetheless, locally high arthropod biomass is associated with higher reproductive success. For example, invertebrate biomass was positively correlated with daily nest survival in wood thrushes (Duguay et al. 2000) and reproductive success in ovenbirds (Seagle & Sturtevant

2005). Ovenbirds also have been found to establish territories in areas with higher invertebrate biomass (Burke & Nol 1998). Thus, arthropod availability can affect both nest success and bird distributions across the landscape. 18 While linked and unlinked dimensions in the ecological niche of breeding birds have been studied individually, both niche concepts are rarely quantified together

(Soberon 2010). When both unlinked and linked variables are not assessed together, however, authors may be making incorrect links between abundance and/or density and reproductive success by assuming areas with many individuals of the same species is high quality habitat for that species (Soberon 2007, 2010; Feria & Peterson, 2002).

Understanding how linked and unlinked niche components affect reproductive success will allow researchers to better understand the requirements of a successful population or community, and to better predict community responses to environmental changes.

In this paper, we combine unlinked and linked dimensions with guild and community reproductive success in an effort to understand the relationships between a species’ distribution within a landscape, community interactions, and reproductive success. Understanding and quantifying niche for a focal species requires a multidimensional approach that incorporates the unlinked and linked conditions where a species is observed, and the conditions needed for positive or stable population growth.

We predicted that birds in similar foraging and nesting guilds may overlap in resource use, and therefore experience decreased reproductive success when nesting in higher densities. In this project, we will explore the following questions:

1) Where do the focal species fall in nesting and territory habitat space, and are there

differences among species in habitat space?

2) Does food availability differ among species’ territories and study sites?

3) What habitat and community variables are related to territory spacing among

species? 19 4) What habitat and community variables are related to nest spacing among species?

5) What habitat and community variables are related to nest survival and number of

fledged young? 20 SPECIES ACCOUNT

The focal species were chosen because they represent two foraging and two nesting guilds (Table 2). Members of the ground nesting guild (ovenbird and worm- eating warbler) construct their nests in the leaf litter, while members of the shrub nesting guild (hooded warbler and wood thrush) construct their nests in understory shrubs and in dense understory vegetation. Additionally, members of the ground foraging guild

(ovenbird and wood thrush) forage for prey by sifting through the leaf litter, while members of the vegetation foraging guild (worm-eating warbler and hooded warbler) hunt for prey by gleaning arthropods from leaves and stems in the understory, mid-story, and sometimes canopy.

Table 2.

Nesting and foraging guild classification for each focal species. Guild Ground Foragers Vegetation Foragers

Ground Nesting Ovenbird Worm-eating Warbler

Shrub Nesting Wood Thrush Hooded Warbler

Wood Thrush (Hylocichla mustelina)

Wood thrushes breed in wooded habitats throughout Eastern - Midwestern United

States and Southeastern Canada during the breeding season (May-July). Wood thrushes occupy mesic forests with well-developed understories, but will also use closed-canopy forests with sparse understory (Peterjohn 2001). In Ohio, the wood thrush breeds at five times greater density in the heavily forested areas of the Ohio Hills (southeast quadrant of 21 the state) compared to moderately forested areas like the Allegheny Plateau (Rodewald et al. 2016). Wood thrushes typically forage on the ground in the leaflitter (Holmes and

Robinson 1988), and construct a nest of leaves, mud, and grass in shrubs or trees 1-6m off the ground (Evans et al. 2011). In the counties where we conducted research (Vinton and Ross counties), the Second Breeding Bird Atlas of Ohio estimated wood thrush territory density at more than 0.12 singing males/hectare (ha) in forested areas. Nesting densities have been reported at 0.02 to 0.54 nests/ha in large forest stands (>100 ha,

Hoover et al. 1995; Gram et al. 2003).

The estimated population in Ohio is 760,000 singing males, and state abundances have remained relatively unchanged over the past 45 years. In contrast, the North

American Breeding Bird Survey (BBS) results indicate that wood thrush populations have declined 2% per year since the 1960’s across their range, with the most dramatic losses (5.5% per year) occurring at the northernmost extent of their range (Sauer et al.

2014). The wood thrush is considered climate threatened (Langham et al. 2015), and a species of Continental Importance by Partners in Flight (Rich 2004). The wood thrush is listed at the highest conservation priority level and a National Conservation Priority by the Ohio Bird Conservation Initiative (Rodewald et al. 2016). Approximately 5.4% of the global wood thrush population nests in Ohio (PIFSC 2013), which emphasizes the importance of conservation research for the species within the state.

Ovenbird (Seiurus aurocapillus)

Ovenbirds breed in hardwood forests from the Mid-Atlantic states to Northeastern

British Columbia. The ovenbird abundance in Ohio was estimated at 250,000 singing males, with the highest densities in the Ohio Hills (Rodewald et al. 2016). In Vinton and 22 Ross counties, territory density was estimated between 0.004 to greater than 0.08 singing males/ha (Rodewald et al. 2016), whereas other studies have found 0.83 singing males/ha in large forest stands (Burke 1998). Nesting densities for ovenbirds have ranged from

0.02-0.04 nests/ha (Gram et al. 2003).

The ovenbird constructs its nest on the ground in the leaf litter, and typically raise one brood per breeding season (Porneluzi et al. 2011). Ovenbirds are ground-foragers, but will forage on understory vegetation if leaflitter arthropods are sparse (Streby et al.

2011). The ovenbird is area-sensitive and requires large tracts of forest to breed (Robbins et al. 1989). Ovenbird area sensitivity may explain the 21% decline in block occupancy from the First Ohio Breeding Bird Atlas (1982-87) to the Second (2006-2011): ovenbirds disappeared from blocks where agricultural practice increased (Rodewald et al. 2016).

The BBS however, has indicated a long-term increase of 1.8% per year since 1966 in ovenbird populations in Ohio (Sauer et al. 2014). Population increases in highly forested regions may be offsetting losses in highly agricultural areas in Ohio, resulting in an overall net population increase (Rodewald et al. 2016).

In general, ovenbird populations are declining on the southern edges of the ovenbird’s range, while populations in northeastern and north-central states are increasing (Sauer et al. 2014). The population trends have been attributed to availability of breeding habitat, because ovenbird success was closely attributed to forest floor ecology, specifically forest soil fertility (Pabian & Brittingham 2011). The spread of non-native and invasive earthworms resulting in reduced leaf litter was associated with declines in ovenbird density and nesting success (Loss et al. 2012). The ovenbird was 23 described as climate threatened by Langham et al. (2015), and a priority species by the

Ohio Bird Conservation Initiative (Rodewald et al. 2016).

Hooded Warbler (Setophaga citrina)

Hooded warblers breed in mesic deciduous forests from southern Florida to New

York. Hooded warblers nest in dense understory vegetation, typically on forest edges or in canopy gaps (Ogden & Stutchbury, 1994). Hooded warblers arrive on their breeding areas at the end of April in Ohio and breed until late July. Male and female hooded warblers are known to segregate foraging areas in their wintering grounds, with females foraging predominantly in the understory and mid-story, and males foraging in the mid- story and canopy for arthropods (Lynch et al. 1985). The Second Breeding Bird Atlas of

Ohio estimated 175,000 singing males in Ohio, with territory densities estimated at 0 to greater than 0.06 singing males/ha in Vinton and Ross counties. Tarof et al. (1997) reported territory densities up to 0.1 singing males/ha in contiguous hardwood deciduous forests in northwestern Pennsylvania. Nesting densities in hardwood deciduous forests in

Pennsylvania and bottomland forests in South Carolina have been reported at 0.1 nests/ha

(Moorman et al. 2002; Tarof et al. 1998).

Hooded warblers have increased throughout most of their breeding range, most notably increasing 400% in Ontario since 2001 (Cadman et al. 2007). Conversely, hooded warblers are projected to decrease in abundance along the Appalachians and along southeastern coasts due to changes in seasonality (Matthews et al. 2004). Hooded warblers were identified as climate threatened by Langham et al. (2015), and as a high priority species by the Ohio Bird Conservation Initiative (Rodewald et al, 2016). 24 Worm-Eating Warbler (Helmitheros vermivorus)

The worm-eating warbler breeds in large tracts of deciduous forest from northern

Louisiana to southern New York. While they nest on the ground in the leaf litter, they almost never forage on the ground: they use their bill to manipulate dead leaf clumps on shrub layer vegetation and glean arthropods from bark, stems and leaves (Greenberg

1987). Nests are typically located on forested slopes and ravines (Bent 1953, Stewart &

Robbins 1958), and consist of dry leaves and grasses (Vitz et al. 2013). Worm-eating warbler nesting densities have been reported between 0.02 to 0.025 nests/ha (Gram et al.

2003). The Second Breeding Bird Atlas estimated the Ohio worm-eating warbler population at about 35,000 singing males, with territory densities estimated at 0 to 0.025 singing males/ha in Vinton and Ross counties (Rodewald et al. 2016). However, Gram et al. (2003) reported territory densities up to 0.2 singing males/ha in large forest stands in

Missouri.

BBS data indicate that the worm-eating warblers’ Ohio population increased 5% per year between 1982 and 2011 (Rodewald et al. 2016). The highest densities of worm- eating warblers occur in the Ohio Hills region, which includes Zaleski and Tar Hollow

State Forest. Worm-eating warblers are area-sensitive and require large forest tracts for breeding (Robbins et al. 1989). The spread of non-native invasive earthworms and the reduction of forest leaf litter may also have a negative impact on breeding success of the worm-eating warbler (Loss et al. 2012), but there is little known about worm-eating warbler nesting biology within Ohio. Worm-eating warblers were identified as climate threatened by Langham et al. (2015), and as a highest priority species by the Ohio Bird

Conservation Initiative (Rodewald et al. 2016). 25 MATERIALS AND METHODS

Site Description

Our study was conducted in three ~25-30 ha plots: two in Tar Hollow State Forest

(control: TARC and thinned: TART) and one in Zaleski State Forest (thinned: ZALT).

The study sites are part of the United States Fire and Fire Surrogacy (FFS) Project

(Yaussy et al. 2003; Yaussy 2001). TART and ZALT were selectively harvested in 2001, and TARC is an unaltered forest stand. Each stand has an established grid of marked trees, each 50 meters apart throughout the study sites.

Figure 1. Location of study sites identified by triangles. Tar Hollow control (TARC)=red, Tar Hollow thin (TART)=blue, and Zaleski thin (ZALT)=yellow.

Tar Hollow and Zaleski state forests are mature (~100-year-old), second growth, temperate deciduous forests in Southeastern Ohio dominated primarily by white oak

(Quercus alba), chestnut oak (Q. prinus), hickories (Carya spp.), and black oak (Q. 26 velutina). The primary canopy species include Quercus alba (white oak), Quercus prinus

(chestnut oak), Carya spp. (hickories), Quercus rubus, and Quercus velutina (red and black oak). Understory vegetation includes Smilax spp. (greenbrier), Viburnum acerifolium (maple-leaf viburnum), Rubus spp., Lindera benzoin (spicebush) and many small-diameter saplings (i.e., <10 cm DBH [diameter at breast height]) (Williams &

Miles 2016; Albrecht & McCarthy 2006).

Nest Searching

From May through July, we searched for and monitored nests of focal shrub and ground nesting birds following the BBIRD protocol (Martin et al. 1997) within our study sites. We systematically searched each grid cell for nests, while simultaneously listening and watching for ground and shrub nesting species. When a nest was found, we georeferenced the location. We recorded the spatial and structural characteristics of the nest location, which included nest height (±0.1m), the plant species the nest was in, and the species of vegetation surrounding the nest. We observed nests every 2-4 days and recorded nest stage (lay, incubation, or nestling), number of eggs and nestlings, and nest status (nest stage: build, line, lay, incubate, nestling, fledge/fail). Exposure days (number of days the nest was observed) were calculated using the Mayfield (1975) method. As nestlings grew closer to fledging age (8-12 days old), we monitored nests more frequently

(every 1-2 days) to determine nest fate. If nests were found empty before nestling day 8, it was assumed that the nest had failed/been depredated.

Territory Mapping

We mapped male territories for hooded warblers, wood thrushes, ovenbirds, and worm-eating warblers following Bibby et al. (1992). One assumption in territory 27 mapping non-colonial passerines is that members of the same species live in fixed, discrete, non-overlapping ranges (Bibby et al. 1992). Males of all four focal species are known to maintain territories in their breeding ranges (Bertin 1977; Neudorf et al. 1997;

Stenger & Falls 1959; Gale et al. 1997). We completed territory mapping in each stand at least once every 10 days. All mapping took place between sunrise and 12:00 hr. We georeferenced male locations on a grid and recorded movements and interactions using territory mapping techniques described by Bibby (1992) to determine territory size and density. We defined a male territory using a minimum of three detections recorded at least 10 days apart. Detections were transposed into ArcGIS, and convex hulls drawn around each male’s detections to delineate each territory.

Arthropod Transects

We quantified shrub arthropod abundance and availability within 50m of each nest and male territory center using a 10m transect (1m wide by 2m high). Two arthropod sampling periods (June 1st-30th and July 1st-31st) were used to quantify arthropods for each nest and territory for each focal species. Arthropod samples were taken a minimum of two weeks apart for each nest or territory. Distance from territory center/nest was determined using a random number generator. At 1m points along the transect, we counted all arthropods on 50 leaves and associated stems up to 2m high, and measured arthropod size (length in mm). Shrub arthropod biomass for each territory and nest site was calculated by averaging biomass samples collected at each location.

Leaf Litter Samples

Within 50m of each territory center and nest, we collected one leaf litter sample per sample period (described above). Distance from territory center/nest was determined 28 using a random number generator. Leaf litter was collected from a 0.25m2 area for each sample (per Levings & Windsor, 1984), and placed in a sealed plastic bag for the remainder of the day. All samples were stored in a cool, dark area until they were processed. The leaf litter samples were placed into Berlese Funnels (MacFadyen 1961) within 48 hours of collection and remained there for 48 hours. Biomass was not affected

2 by time until processing (F1, 154 = 1.12, R adj = 0.001, p = 0.29). A container with ethanol

(70%) under the Berlese Funnel caught the arthropods. Samples were stored in 70% ethanol for later weighing. Samples were removed from their original containers and blotted dry with a Kim© wipe and weighed by zeroing a vial with ethanol on an analytical balance (Mettler Toledo AE200), and immediately adding arthropods to the vial to determine wet mass (± 0.00001g). Leaf litter arthropod biomass for each territory and nest site was calculated by averaging biomass samples collected at each location.

Malaise Traps

Malaise traps were erected on each male hooded warbler territory and at ten random locations in each study site due to logistical constraints. Flying arthropod biomass for each territory was assigned by averaging two malaise samples (one per sample period, described above) taken within 100m of each territory and nest. Malaise traps sampled for 24 hours, but were left up longer to compensate for the time temperatures dropped below 7˚C and/or hours of heavy rainfall when flying invertebrates are inactive. Arthropods were stored in 70% ethanol, and later weighed to determine wet mass (described above). Flying arthropod biomass for each territory and nest site was calculated by averaging biomass samples collected at each location. 29 Habitat Surveys

We estimated habitat composition and structure using 0.04 ha (11.3m radius circles) plots at territory centers (n = 99) and nest locations (n = 76), using the BBIRD protocol (Martin et al. 1997), but modified the protocol (described below) to meet the goals of this study. We conducted habitat surveys from mid-June to early September and collected the following data for each habitat plot.

Canopy Cover

We estimated canopy cover from spherical densitometer readings taken at each of the four cardinal directions at the center of each plot. Canopy cover data were collected between June 1st and August 31st.

Understory Structure and Composition

We quantified understory structure and composition within a 1m radius circle centered in each plot. Because of the high stem densities in our study sites, we modified the BBIRD protocol 5m radius circle to a 1m radius circle. The number of woody (shrubs and saplings) vertical stems at 10cm above the ground was recorded by species and size class (< 3 cm, 3-6 cm, and 6.1-10cm).

Canopy and Mid-story Structure and Composition

We quantified canopy and mid-story structure and composition within an 11.3m radius circle. All trees were identified to species and size class (10-23 cm, 23-38cm,

>38cm DBH) following Williams & Miles (2016).

Geographic Analysis

We used ArcGIS to analyze spatial relationships within and among bird species.

Territory area (m2), nearest neighbor distances (meters, for territories and nests), and 30 percent territory overlap for each individual was calculated in ArcGIS. Nest (nests/ha) and territory (singing males/ha) densities were calculated using the Kernel Density tool in the Spatial Analyst toolbox. We used the integrated moisture index (IMI) layer provided by the USDA Fire and Fire Surrogacy Project to determine the IMI value (1-100, low values = drier conditions, high values = wetter conditions) at each territory and nest site.

Landscape features such as slope-aspect shading index, cumulative flow of water downslope, curvature of the landscape, and water-holding capacity of the soil were used to create the IMI layer (Iverson et al. 2003).

Statistical Analysis

All statistical analyses were completed in R (version 3.4.2, R Core Team, 2017).

We used the habitat variables at the nest and territory centers (understory vegetation and tree species and size class, Table 2) to create a Jaccard-based distance matrix with function metaMDS in package vegan (Okansen et al. 2013) where Jaccard index is 푑푗푘 =

2퐵 , and B is Bray-Curtis dissimilarity. Vegetation species and size classes with low (1+퐵) sample sizes (< 20 occurrences in the dataset) were removed prior to analysis to reduce the stress of the final axis solution. We applied non-metric multidimensional scaling

(NMDS) for visual interpretation and examined correlations of each habitat variable with the NMDS axes to determine what variables were associated with the habitat space. We used Pearson’s product moment correlation to determine the relationship between IMI and NMDS1, and to determine the relationship between percent canopy cover and

NMDS2.

31 Table 3.

Abbreviations for the plant species and variables sampled in the habitat plots with common name and scientific name. Plant Code Common Name Scientific name FAGR American Beech Fagus grandifolia Carya.spp Hickory spp. Carya spp. LITU Tulip Poplar Liriodendron tulipifera NYSY Black Gum Nyssa sylvatica QUAL White Oak Quercus alba QUPR Chestnut Oak Quercus prinus QURU Red Oak/Black Oak Quercus rubra SAAL Sassafras Sassafras albidum SNAG Snag, dead standing tree Not Applicable ACRU Red Maple Acer rubrum CACA Musclewood Carpinus caroliniana COFL Flowering Dogwood Corus florida ASH Ash spp. Fraxinus spp. LIBE Spicebush Lindera benzoin OXAR Sourwood Oxydendrum arboreum PAQU Virginia creeper Parthenocissus quinquefolia QUVE Black Oak Quercus velutina Rubus Blackberry, Raspberry, or Wineberry Rubus spp. ULspp Elm spp. Ulmus spp. VACC Blueberry Vacciunium spp. Smilax Greenbriar spp, Smilax spp. TORA Poison Ivy Toxicodendron radicans LIBE Spicebush Lindera benzoin

To assist in visualization and interpretation of species relationships in habitat space, we plotted both (1) the 95% confidence ellipses based on the standard deviation of the weighted averages from the centroid (function ordiellipse; Oksanen et al. 2013), and

(2) the convex polygons (function ordihull) of each species to circumscribe total habitat space. We used a Permutational Multivariate Analysis of Variance (PERMANOVA; function adonis in vegan; Oksanen et al. 2013) to determine if the centroids for each species and site differed in habitat space (testing the hypothesis of no difference among centroids within multivariate habitat space). For this analysis, we ran 5000 permutations 32 with bird species, site, and the interaction of bird species and site as predictor variables.

When significant differences among centroids were found, we used pairwise permutational multivariate analysis of variance (pairwise PERMANOVA) post hoc tests

(p method = fdr) to compare pairwise differences in species and sites with 999 permutations using the function pairwise.per.manova in the RVAideMemoir package

(Herve 2018) .

Arthropods

We used linear models (lm, stats package) to determine differences in arthropod biomass by site, species, sample type and habitat. The best model for each comparison was selected using the function model.sel (package MuMIn), with AICc (sample size corrected Akaike information criterion) to compare models (see Appendix Tables 1-3 for full models). The AICc model selection criteria was used on all candidate sets of general and generalized models.

Nest and Territory spacing

We used linear models to determine unlinked and linked variables that best predicted territory density, territory overlap, and nest density (see Appendix Tables 4-6 for full models).

Reproductive Success

We used generalized linear models (stats package, R Core Team 2017) to determine what unlinked and linked variables best predicted nest survival (fledge/fail, binomial distribution) and number of fledged young (Poisson distribution, see Appendix

Tables 7 and 8 for full models). We also used generalized linear models to examine if 33 overall nesting density influenced nest fate, and if nesting density of guild members

(foraging and nesting guild) influenced nest fate.

We used generalized linear models (R Core Team 2017) to determine how nesting density, territory density, and arthropod biomass interacted with nest success (fledge/fail, binomial distribution) and number of fledged young (Poisson distribution). We also used generalized linear models to determine if nesting density of guild members influenced nest success (fledge/fail, binomial distribution). We used a general linear model to test the hypothesis that focal species differed in arthropod availability within their territories.

We used linear models (lm, stats package) to determine how NMDS1 and

NMDS2 for nests and territory centers interacted with nesting and territory density, territory overlap, and arthropod biomass. We also used linear models to determine the relationship between territory density and arthropod availability. 34 RESULTS

We located 95 nests and identified 149 territories among the four focal species

(Fig 2, Table 4). The habitat space determined from the habitat characteristics collected at nest sites and territory centers for all four focal species identified three primary axes

(NMDS convergent solution with stress = 24.08%). Correlations of the habitat variables with each NMDS axis (Table 5) identified NMDS1 as a moisture gradient, NMDS2 as a canopy closure gradient, and NMDS3 as a mid-story complexity gradient. The moisture gradient (NMDS1) ranged from xeric habitats with chestnut oak (Quercus prinus) in the canopy and mid-story, and chestnut oaks and sassafras (Sassafras albidum) saplings in the understory (-) to mesic forest with vegetation structure comprised of understory muslewood (Carpinus caroliniana), mid-story American beech (Fagus grandifolia) and canopy tulip tree (Liriodendron tulipifera) (-) (Table 5). Furthermore, the integrated moisture index (IMI) was positively correlated with NMDS1 (Pearson’s r = 0.19, t =

2.60, df = 173 p = 0.01).

Figure 2. All territories and nest locations of focal species in study sites. In territory maps A-C: Red polygons = hooded warbler territories, green polygons = worm-eating warbler territories, blue polygons =wood thrush territories, dark orange polygons = ovenbird territories. In nest location maps D-F: Red circles = hooded warbler nests, orange circles = ovenbird nests, blue circles = wood thrush nests, green circles = worm- eating warbler nests. A and D = Tar Hollow control, B and E = Tar Hollow thin, C and F = Zaleski thin.

36 Table 4.

Sample sizes of nest and territory sample sizes by species and site, with percent successful nests, and percent of nests that failed due to predation. TARC=Tar Hollow control, TART=Tar Hollow thin, ZALT=Zaleski thin. Species Site Nests Territories % Successful Nests % Depredated Nests Hooded Warbler TARC 32 24 40.63 31.25 TART 10 12 80.00 20.00 ZALT 14 12 78.57 14.29 Wood Thrush TARC 10 9 70.00 10.00 TART 1 7 100.00 0.00 ZALT 7 8 28.57 14.29 Worm-eating Warbler TARC 4 8 25.00 50.00 TART 5 8 100.00 0.00 ZALT 1 5 0.00 100.00 Ovenbird TARC 5 21 60.00 20.00 TART 3 22 100.00 0.00 ZALT 5 13 20.00 40.00

37 Table 5.

NMDS variable correlations with woody plant species and size class. Correlations of the habitat characteristics with NMDS axes were used to interpret the habitat space. Correlations larger than the absolute value of 0.3 are highlighted in bold. Stem size classes: V1 < 3 cm, T1 = 10–23 cm, T2 = 23–38 cm, T3 > 38 cm. Plant Codes and Size Class NMDS1 NMDS2 NMDS3 ACRU.T1 -0.13 0.05 -0.36 ACRU.T2 0.16 0.41 -0.44 ACRU.T3 0.22 0.34 -0.08 FAGR.T1 0.23 0.09 0.09 Carya.T1 0.27 0.07 0.37 Carya.T2 0.24 0.15 0.19 LITU.T1 0.38 -0.07 0.11 LITU.T2 0.20 0.07 -0.06 LITU.T3 0.31 0.12 0.14 NYSY.T1 -0.06 0.39 0.16 NYSY.T2 0.16 0.07 0.23 QUAL.T1 0.06 -0.19 0.30 QUAL.T2 -0.17 -0.28 0.30 QUAL.T3 -0.06 -0.50 -0.16 QUPR.T1 -0.33 0.21 0.07 QUPR.T2 -0.46 0.40 0.08 QUPR.T3 -0.49 0.36 0.08 QURU.T2 -0.12 0.04 0.21 QURU.T3 -0.10 -0.27 0.19 snag.T1 0.04 0.28 0.23 SNAG.T3 0.38 -0.15 0.17 ACRU.V1 0.02 -0.11 0.30 CACA.V1 0.24 0.11 0.26 Carya.V1 0.22 0.10 0.14 LIBE.V1 0.19 0.08 -0.34 NYSY.V1 0.02 0.21 0.33 OXAR.V1 -0.15 0.13 -0.08 PAQU.V1 0.12 -0.11 -0.23 QUAL.V1 -0.09 -0.15 -0.15 QUPR.V1 -0.34 0.19 0.18 QURU.V1 -0.25 -0.20 0.12 QUVE.V1 -0.12 0.00 0.15 Rubus.V1 0.23 -0.18 0.13 SAAL.V1 -0.35 -0.12 -0.07 Smilax.V1 -0.13 0.03 0.13 TORA.V1 0.26 -0.08 -0.13 ULspp.V1 0.14 -0.10 0.02 VACC.V1 -0.26 -0.10 0.04 38

The canopy closure gradient (NMDS2) ranged from canopy gaps with canopy white oaks on one end (-), to mid-story maples, canopy chestnut oaks, and closed canopy on the other (+). Additionally, NMDS2 was positively correlated with percent canopy cover (Pearson’s r = 0.24, t = 3.31, df = 173, p < 0.01). The mid-story complexity gradient (NMDS3) extended from a dense mid-story composed of maples on one end (-), to mid-story hickories and white oaks with understory black gum on the other (+).

Territory Spacing and the Niche

We mapped the territories of male hooded warblers (n = 48), wood thrushes (n =

24), ovenbirds (n = 56) and worm-eating warblers (n = 21). The average territory sizes for hooded warblers, wood thrushes, ovenbirds, and worm-eating warblers were

2302.93±173.22 m2; 2259.64±178.20 m2; 2130.05±164.08 m2; and 2262.86±183.36 m2, respectively. Territory size did not predict the amount of territory overlap (F1, 147 = 1.69,

2 R adj = 0.005, p = 0.20). The variables that best predicted overall territory overlap were

2 NMDS1, bird species, and site (F6, 92 = 3.13, R adj = 0.12, p < 0.01; Table 6, Appendix

Table 5). The selected model for amount of territory overlap suggested each species differed in the amount of territory overlap experienced (t = 2.13, p < 0.05) however

Tukey post hoc test indicated no difference in territory overlap among species (p > 0.05).

The amount of territory overlap in TART (lsmean = 61.73 ± 5.83 m2) was significantly higher than ZALT (lsmean = 34.46 ± 5.84 m2; Tukey post hoc test p = 0.002; Fig 3).

Table 6.

Results of the AICc selected model of territory overlap, with parameter estimates (Estimate), standard error (SE), t, and p-values. The estimates and t-tests for the species (ovenbird, worm-eating warbler, and wood thrush) are in relation to hooded warblers, and the site (TART=Tar Hollow thin and ZALT=Zaleski thin) are in relation to Tar Hollow control. Variable Estimate SE t p NMDS1 5.36 9.65 0.56 0.58 TART 14.26 7.47 1.91 0.06 ZALT -13.01 7.74 -1.68 0.10 Ovenbird 16.65 7.81 2.13 0.04 Worm-eating Warbler 10.41 9.19 1.13 0.26 Wood Thrush 16.92 8.62 1.96 0.05

Figure 3. Least-squares mean (circle) and 95% confidence intervals (vertical bars) of overall territory overlap (m2) among study sites. TARC = Tar Hollow control, TART = Tar Hollow thin, ZALT = Zaleski thin

40 Territory density

Territory density was best predicted by NMDS2, leaf litter biomass, and site (F4,

2 94 = 7.41, R adj = 0.21, p < 0.0001; Table 7, Appendix Table 4). Territory density

(singing males/ha) decreased as canopy complexity (NMDS2) increased (t = -2.20, p =

0.03; Fig 4), and increased as leaf litter arthropod biomass increased (t = 2.16, p = 0.03;

Fig 5). Territory density significantly differed by site (t = -2.86, p < 0.01; Fig 6), and

Tukey post hoc tests indicated that ZALT (lsmean = 2.90 ± 0.18 singing males/ha) had significantly lower territory density than TARC (lsmean = 3.6 ± 0.17 singing males/ha, p

= 0.01) and TART (lsmean = 3.87 ± 0.19 singing males/ha, p < 0.001).

Table 7.

Results of the AICc selected model table of variables for territory density, with parameter estimate (Estimate), standard error (SE), t, and p-values for each variable. The estimates and t-tests of the site (TART=Tar Hollow thin and ZALT=Zaleski thin) are in relation to Tar Hollow control. Variable Estimate SE t p NMDS2 -0.80 0.36 -2.20 0.03 Leaf Litter Biomass 2.55 1.18 2.16 0.03 TART 0.30 0.26 1.15 0.26 ZALT -0.74 0.26 -2.86 0.01

Figure 4. Predicted values of overall territory density is negatively correlated with NMDS2, suggesting territory density decreased as canopy cover increased, and that high territory densities were associated with canopy gaps and canopy white oaks. TARC=Tar Hollow control (red), TART=Tar Hollow thin (green), and ZALT=Zaleski thin (blue).

Figure 5. Predicted values of overall territory density increased with increased leaf litter biomass. TARC=Tar Hollow control (red), TART=Tar Hollow thin (green), and ZALT=Zaleski thin (blue).

Figure 6. Least-squares mean (circle) and 95% confidence intervals (vertical bars) of overall territory density (males/ha) among study sites. TARC = Tar Hollow control, TART = Tar Hollow thin, ZALT = Zaleski thin.

Differences Among Sites - Unlinked Variables

We found differences in the centroids of habitat space by site (PERMANOVA F3,

2 174 = 2.37, R = 0.04, p < 0.001; Fig 7). Zaleski thin (ZALT) was associated with the most xeric habitat and canopy gaps. Tar Hollow thin (TART) was also associated with canopy gaps, but was more mesic than ZALT. As predicted, Tar Hollow control (TARC) was associated with more canopy closure, because TARC was not harvested. Both thinned sites were associated with more canopy white oaks, while TARC was associated with more canopy chestnut oaks. 44

Figure 7. The position of each study site in habitat space. Convex hulls are drawn around habitat space in each forest and the centroids of habitat space (95% confidence ellipses based on the standard deviation of the weighted distance from the group centroid) for TARC = red oval, TART = blue oval, ZALT = yellow oval). Each point (square, circle, or triangle) represents the location of male territory center or female nest location in habitat space.

Differences Among Species - Unlinked Variables

We found differences in the centroids of habitat space by species (PERMANOVA

2 F3, 174 = 2.09, R = 0.03, p < 0.001; Fig 8 and 9) but not the interaction between species

2 and site (PERMANOVA F3, 174 = 0.96, R = 0.03, p = 0.53). Tukey post hoc tests 45 indicated differences among species centroids in habitat space (Table 8). Wood thrushes were associated with more canopy gaps than the other three focal species, and worm- eating warblers were associated with more canopy closure (Fig 8). Hooded warblers and ovenbirds inhabited an intermediate space between worm-eating warblers and wood thrushes. More separation between worm-eating warblers and the other focal species can be seen when NMDS1 is compared with NMDS3 (Fig 9). Here, worm-eating warblers are associated with more shaded understory (more mid-story maples), as well as more xeric habitat than the other three focal species.

Figure 8. Position of each focal species in habitat space (NMDS1 vs NMDS2) based on nesting and territory habitat characteristics. Hooded warbler (red), worm-eating warbler (yellow), ovenbird (black) and wood thrush (blue) convex hulls are drawn around each species’ habitat space and 95% confidence ellipses based on the standard deviation of the weighted distance from the group centroid are indicated.

Figure 9. Position of each focal species in habitat space (NMDS1 vs NMDS3) based on nesting and territory habitat characteristics. Hooded warbler (red), worm-eating warbler (yellow), ovenbird (black) and wood thrush (blue) convex hulls are drawn around each species’ habitat space and 95% confidence ellipses based on the standard deviation of the weighted distance from the group centroid are indicated.

Table 8.

Tukey post hoc test pairwise comparisons from the PERMANOVA used to determine differences among species centroids in habitat space. HOWA=hooded warbler, OVEN=ovenbird, WEWA=worm-eating warbler, WOTH=wood thrush. Comparison p

HOWA-OVEN 0.003

HOWA-WEWA 0.003

HOWA-WOTH 0.022

OVEN-WEWA 0.035

OVEN-WOTH 0.546

WEWA-WOTH 0.026

Intraspecific Differences between Nesting Sites and Territory Centers

We predicted that nesting and male territory habitat would not differ within species. In hooded warblers, female nesting habitat had slightly more canopy closure

(Fig 10A) with more mid-story maples (more shaded understory, Fig 10B) compared to

2 male territory habitat (PERMANOVA F3, 174 = 2.09, R = 0.03, p < 0.001, Tukey post hoc test, p = 0.01). Wood thrush nest locations were associated with more mesic habitat than male territory centers (Tukey post hoc test, p = 0.02, Fig. 11). There were no intraspecific differences in habitat space observed within ovenbirds (Tukey post hoc test, p = 0.20, Fig 12) or within worm-eating warblers (Tukey post hoc test, p = 0.82, Fig 13), however this may be due to low sample size. 49

Figure 10. Habitat space of hooded warbler nests and territory centers. A. NMDS1 versus NMDS2. Hooded warbler nest locations (blue) have slightly more canopy cover than territory centers (red). B) NMDS 1 versus NMDS 3. Nest locations were associated with more mid-story maples than territory centers. 50

Figure 11. Habitat space of wood thrush (Hylocichla mustelina) nests (blue) and territory centers (red).

Figure 12. Habitat space of ovenbird (Seiurus aurocapilla) nests (blue) and territory centers (red).

Figure 13. Habitat space of worm-eating warbler (Helmitheros vermivorum) nests (blue) and territory centers (red).

53 Arthropods

Shrub arthropods

Shrub arthropods were comprised predominantly of spiders, aphids, and

Lepidopteran larvae. Bird species and site were included in the AICc selected model of

2 shrub arthropod biomass (F5, 143 = 3.31, R adj = 0.07, p = 0.01; Table 9, Appendix Table

1), however there were no differences in shrub arthropod biomass among bird species’ territories (Tukey post hoc test, p > 0.6; Fig 14). There were significant differences by site. TARC (lsmean = 0.32 ± 0.01 mm/leaf) had significantly greater shrub arthropod biomass than TART (lsmean = 0.27 ± 0.01 mm/leaf; Tukey post hoc test, p = 0.04) and

ZALT (lsmean = 0.24 ± 0.02 mm/leaf; p < 0.001; Fig 15).

Table 9

Results of the AICc selected model of shrub arthropod biomass, with parameter estimate (Estimate), standard error (SE), t, and p-values. The estimates and t-tests for the species (ovenbird, worm-eating warbler, and wood thrush) are in relation to hooded warblers, and site (TART=Tar Hollow thin and ZALT=Zaleski thin) are in relation to Tar Hollow control. Variable Estimate SE t p Ovenbird -0.001 0.02 -0.03 0.98 Worm-eating Warbler 0.002 0.03 0.07 0.95 Wood Thrush -0.01 0.02 -0.39 0.69 TART -0.05 0.02 -2.47 0.01 ZALT -0.08 0.02 -3.88 >0.01

Figure 14. Arthropod availability among focal species’ territories (HOWA = hooded warbler, OVEN = ovenbird, WEWA = worm-eating warbler, and WOTH = wood thrush), by sample type: A. Shrub arthropod biomass (transects); B. Flying arthropod biomass (malaise traps); C. Leaf litter arthropod biomass (leaf litter samples). The box and whisker plot indicate the median (middle horizontal line), upper and lower quartile of data (quartile 2 and 3, each represent 25% of data), and quartile 1 and 4 (each 25% of data, vertical lines).

Figure 15. Least-squares mean (circle) and 95% confidence intervals (vertical bars) for shrub arthropod biomass among study sites. TARC = Tar Hollow control, TART = Tar Hollow thin, ZALT = Zaleski thin.

Leaf litter arthropods

Leaf litter arthropods were comprised predominantly of spiders, Collembolans, and Myriapods. Leaf litter arthropod biomass was best predicted by bird species and site

2 (F5, 143 = 1.67, R adj = 0.02, p = 0.14; Table 10, Appendix Table 2). There were no differences in leaf litter arthropod biomass among bird species’ territories (Tukey post hoc test, p > 0.1, Fig 14) or site (Tukey post hoc test, p > 0.05; Fig 16).

56 Table 10.

Results of the AICc selected model of leaf litter arthropod biomass, with parameter estimate (Estimate), standard error (SE), t, and p-values for each variable. The estimates and t-tests for the species (ovenbird, worm-eating warbler, and wood thrush) are in relation to hooded warblers, and the site variables (TART=Tar Hollow thin and ZALT=Zaleski thin) are in relation to Tar Hollow control. Variable Estimate SE t p Ovenbird 0.02 0.02 1.02 0.31 Worm-eating Warbler 0.03 0.02 1.57 0.12 Wood Thrush 0.02 0.02 1.01 0.32 TART 0.003 0.02 0.22 0.83 ZALT -0.03 0.02 -2.03 0.04

Figure 16. Least-squares mean (circle) and 95% confidence intervals (vertical bars) for leaf litter arthropod biomass among study sites. TARC = Tar Hollow control, TART = Tar Hollow thin, ZALT = Zaleski thin.

57 Flying arthropods

Flying arthropods were comprised predominantly of Dipterans and

Hymenopterans. Flying arthropod biomass was best predicted by bird species and site

2 (F5, 143 = 6.42, R adj = 0.16, p < 0.0001; Table 11, Appendix Table 3). There were no differences in flying arthropod biomass among bird species’ territories (Tukey post hoc test, p > 0.1, Fig 14), but there were significant differences by site (Fig 17). ZALT

(lsmean = 0.05 ± 0.004g) had significantly more flying arthropods than TARC (lsmean =

0.02 ± 0.003g; Tukey post hoc test, p < 0.001), and TART (lsmean = 0.03 ± 0.003g; p <

0.01).

Table 11.

Results of the AICc selected model of flying insect biomass, with parameter estimate (Estimate), standard error (SE), t, and p-values for each variable. The estimates and t- tests for the species (ovenbird, worm-eating warbler, and wood thrush) are in relation to hooded warblers, and the site variables (TART=Tar Hollow thin and ZALT=Zaleski thin) are in relation to Tar Hollow control. Variable Estimate SE t p Ovenbird 0.001 0.005 0.20 0.85 Worm-eating Warbler 0.01 0.01 1.42 0.16 Wood Thrush 0.01 0.01 1.30 0.19 TART 0.01 0.005 1.84 0.07 ZALT 0.03 0.01 5.23 >0.0001

Figure 17. Least-squares mean (circle) and 95% confidence intervals (vertical bars) for flying arthropod biomass among study sites. TARC = Tar Hollow control, TART = Tar Hollow thin, ZALT = Zaleski thin.

Habitat and Arthropods

We determined that ground, shrub, and flying arthropod biomasses were related to

2 the moisture gradient (NMDS1) (F3, 95 = 6.41, R adj = 0.14, p < 0.001; Fig 18, Table 12).

Specifically, flying arthropod biomass was greater in xeric habitat (t = -2.43, p = 0.02), and leaf litter arthropod biomass was greater in more mesic habitat (t = 3.36, p < 0.01).

Both NMDS2 and NMDS3 were not associated with leaf litter, shrub, and flying arthropod biomass (p > 0.05).

Table 12.

Results of Model table examining linear relationships between each arthropod sample type (Variable) and the moisture gradient (NMDS1), with parameter estimate (Estimate), standard error (SE), t, and p-values for each variable. Variable Estimate SE t p Flying Arthropod Biomass (g) -2.83 1.16 -2.43 0.02 Leaf Litter Biomass (g) 1.24 0.37 3.36 0.001 Shrub Arthropod Biomass (mm/leaf) 0.03 0.34 0.10 0.92

Figure 18. Raw data representing biomass changes across the moisture gradient (NMDS1) based on sample type. Circles (leaf litter), triangles (flying), and squares (shrub) represent each sample. red=flying arthropod biomass (g), yellow=leaf litter arthropod biomass (g).

Nest Fate and the Niche

The AICc selected models for nest success included flying arthropod biomass,

NMDS2, and exposure days (Tables 13, Appendix Tables 7). Nest success increased by

3% per mg of flying insect biomass (Odds Ratio (OR) = 1.03 [95% CI = 0.99 – 1.06] z =

1.94, p = 0.05; Fig 19). In a separate model, we determined if nest success was associated with nest density and found that nesting success decreased 44% with each additional nest/ha (OR = 0.56 [95% CI = 0.31 – 0.99], z = -2.44, p = 0.01; Fig 20).

Table 13.

Results of the AICs selected model for nest fate, with corresponding parameter estimate (Estimate), standard error (SE), t, and p-values for each variable. Variable Estimate SE z p NMDS2 -1.46 0.96 -1.52 0.13 Flying Insect Biomass 31.71 16.39 1.94 0.05 Exposure Days 0.17 0.05 3.67 >0.001

Figure 19. Predicted probability of nest survival increases as flying arthropod biomass (g) increases.

Figure 20. Predicted probability of nest survival decreases as overall nest density (nests/ha) increases

The AICc selected model for number of fledged young included flying arthropod biomass, NMDS2, and exposure days (Table 14, Appendix Table 8). There was a trend for number of fledged young increasing as flying insect biomass increased, however it was insignificant (z = 1.81, p = 0.07). Number of fledged young significantly decreased as overall nesting density increased (z = -2.03, p = 0.04; Fig 21).

Table 14.

Results of the AICs selected model for number of fledged young, with corresponding parameter estimate (Estimate), standard error (SE), t, and p-values for each variable. Variable Estimate SE z p NMDS2 -0.13 0.26 -0.48 0.63 Flying Insect Biomass 7.66 4.22 1.81 0.07 Exposure Days 0.05 0.01 5.03 >0.0001 63

Figure 21. Number of fledged young (predicted values) decreases as overall nest density (nests/ha) increases.

Nest Fate Within Guilds

Within the shrub nesting guild, wood thrush nesting density had no influence on the probability of hooded warbler nest success (OR = 1.08 [95% CI = 0.51 – 2.31], z =

0.20, p = 0.84) and vice versa (OR = 1.75 [95% CI = 0.09 – 32.34], z = 0.38, p = 0.71).

Within the ground nesting guild, ovenbird nesting density had no influence on the probability of worm-eating warbler nest success (OR = 1.33 [95% CI = 0.01– 329], z =

0.10, p = 0.92) and vice versa (OR = 3.62 [95% CI = 0.02 – 823.75], z = 0.46, p = 0.64).

Within foraging guilds, there was no influence of worm-eating warbler nest density on 64 the probability of hooded warbler nest success (OR = 0.56 [95% CI = 0.07 – 4.70], z = -

0.53, p = 0.59) and vice versa (OR = 0.52 [95% CI = 0.02 – 9.83], z = -0.43, p = 0.66), and no influence of ovenbird nesting density on the probability of wood thrush nest success (OR = 9.0 [95% 0 - ∞], z = -0.003, p = 1.0) and vice versa (OR = 1.98 [95% CI =

0.02 – 182.60], z = 0.30, p = 0.77).

65 DISCUSSION

Habitat Differences Among and Within Species

The focal species we observed do overlap in habitat space use but the centroids within habitat space differed among species, suggesting the focal species are partitioning their habitat occupancy based on their territory and nesting needs. Further, we found that female nesting and male territory habitat differ within hooded warblers and wood thrushes. Lynch et al (1985) found that male and female hooded warblers use structurally different parts of the forest to forage in their wintering grounds, where females typically spend their time close to the ground foraging in dense vegetation while males forage in the mid-story and canopy. Experiments involving hand-reared hooded warblers also found that naive males chose taller, vertically oriented substrate than females (Morton

1990). Habitat segregation between sexes has not previously been observed in wood thrushes. However, wood thrushes forage in the leaf litter but nest in understory vegetation, suggesting optimal foraging environment differs from optimal nesting environment. Additionally, sex specific differences in habitat occupancy may be explained by males choosing areas with high arthropod availability to attract females to forage in their territories, and thus increase the likelihood of mating success. Males on food-abundant territories were more likely to sire extra-pair young during their social mate’s incubation stage than those on food-poor territories (Kaiser et al, 2017). Worm- eating warblers and ovenbirds did not exhibit sex-specific habitat occupancy, but this may be due to the small nest sample size for the ground-nesting species. It is likely that we did not find all ground nests in our study sites, which may have affected our results.

Overall, sex specific differences in hooded warbler and wood thrush habitat 66 characteristics demonstrate the need for heterogeneous forest stands, and the need to better understand the requirements of females in addition to singing males. It is important to understand how female habitat needs differ from males, and which female ecological niche variables are associated with reproductive success, because if females do not find the required linked and unlinked conditions for breeding in a habitat there will be reduced reproduction.

Arthropods and Reproductive Success

We found that flying arthropod biomass was positively correlated with reproductive success. This is consistent with findings by Martin (1986) and Duguay

(2000) who both contest that bird communities are food limited in their breeding ranges.

Specifically, Martin (1986, 1987) argues that while prey may be abundant, birds are limited in their time allocation to find and process prey while also allocating time for parental care and self-maintenance. When birds nest in areas with more abundant prey, they can allocate less time to searching for prey (reducing foraging time) and more time to nest defense, self-maintenance, and parental care, thus improving reproductive success

(Martin 1987). Furthermore, the reproductive success of all bird species whose nests were found in three different sylvicultural treatments had higher daily nest survival in areas of high invertebrate biomass (Duguay et al. 2000). Duguay et al. (2000) also found that wood thrush nests located in areas with high invertebrate biomass had faster growth rates, and improved likelihood of fledging.

We found that territory density was positively correlated with leaf litter arthropod biomass, which suggests that bird densities are dependent on food availability. Similarly, arthropod availability was positively associated with the abundance and distribution of 67 ovenbirds in Ontario (Burke & Nol, 1998), six species of warbler wintering in Jamaica

(Johnson & Sherry 2001), and grassland birds in Kansas (Hickman et al. 2006).

While this study showed that greater flying arthropod biomass was associated with increased reproductive success, it is important to note that our results may be driven by the larger sample size of hooded warbler nests. Hooded warblers nest in areas with canopy gaps, and flying insect abundance increased in forests after canopy gaps were created (Taki et al. 2009). Caution should be applied in making inferences about each species separately, because all four species were included in the model.

Nest Spacing and Reproductive Success

Areas of high nesting density had reduced overall nest survival, which is consistent with previous work in this field (Hagan et al, 1996; Barber & Martin, 1997;

Martin, 1995) but remains an important concept in avian conservation biology today.

Birds that nest in high densities are more susceptible to predation and brood parasitism

(Barber & Martin 1997; Martin 1988a). Nest predation is the leading cause of failure for breeding birds (Gates & Gysel 1978; Bollinger & Linder 1994; Filliater et al. 1994; Nagy

& Holmes 2004). This study did not quantify the predator distribution across the sites, but nest predation was the leading cause of failure in all three sites. Year-to-year variation in predation rates and nest success exists among the study sites (Williams, unpublished data), so long term data are needed to account for temporal variation in predation pressures.

Conclusion

In this paper, we have demonstrated that critical linked dimensions for forest interior ground and shrub nesting birds include arthropod biomass and nesting density. 68 Critical unlinked dimensions include soil moisture, habitat structure, and habitat composition. We found that the soil moisture gradient influences arthropod abundance, and in turn arthropod abundance is positively related to nest success and territory density.

Examining how unlinked variables interact with linked variables, and how unlinked and linked variables influence reproductive success identifies the ecological niche variables that influence reproductive success in the ground and shrub nesting forest interior bird community. This is the first study to our knowledge to quantify unlinked and linked components of a breeding bird community and that measures the effects of unlinked and linked variables on community reproductive success. Our results suggest that quantifying both unlinked and linked dimensions of the niche in tandem with reproductive success may be an important component of developing prescriptive management conditions for the focal species.

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80 APPENDIX

Table A1: Model selection table for determining differences in shrub arthropod biomass by species. df = degrees of freedom, logLik = log likelihood, AICc = sample size corrected Akaike information criteria, Δ = delta AICc, w = AICc weight.

Model df logLik AICc Δ w

Species + Site 7 140.27 -265.7 0.0 0.995

Species 5 132.37 -254.3 11.42 0.003

Species+Site+Species*Site 13 140.74 -252.8 12.96 0.002

Table A2: Model selection table for determining differences in leaf litter arthropod biomass by species. df = degrees of freedom, logLik = log likelihood, AICc = sample size corrected Akaike information criteria, Δ = delta AICc, w = AICc weight.

Model df logLik AICc Δ w

Species + Site 7 164.33 -313.9 0.0 0.63

Species 5 161.53 -312.7 1.21 0.35

Species+Site+Species*Site 13 167.70 -306.7 7.15 0.02

Table A3: Model selection table for determining differences in flying arthropod biomass by species. df = degrees of freedom, logLik = log likelihood, AICc = sample size corrected Akaike information criteria, Δ = delta AICc, w = AICc weight.

Model df logLik AICc Δ w

Species + Site 7 343.60 -672.4 0.0 0.63

Species+Site+Species*Site 13 350.02 -671.3 1.06 0.37

Species 5 330.53 -650.6 21.76 0.00

81 Table A4: Model selection table for determining what variables best predict territory density among focal species. df = degrees of freedom, logLik = log likelihood, AICc = sample size corrected Akaike information criteria, Δ = delta AICc, w = AICc weight, LB = leaf litter biomass, FB = flying insect biomass, SB = shrub arthropod biomass.

Model df logLik AICc Δ w

LB + NMDS2 + Site 6 -139.43 291.8 0.00 0.52

LB + NMDS2 + FB + Site 7 -139.26 293.8 2.00 0.19

LB + NMDS2 + FB + SB + Site 8 -138.22 294.0 2.28 0.17

LB + NMDS2 + FB + SB + Site + LB*FB + 12 -133.77 295.2 3.41 0.01 LB*SB + FB*SB + LB*FB*SB

LB + NMDS2 + FB + SB + Site + LB*FB + 13 -133.77 297.8 6.06 0.03 LB*SB + FB*SB + LB*FB*SB + NMDS1

LB + NMDS2 + FB + SB + Site + LB*FB + 16 -133.37 305.4 13.6 0.001 LB*SB + FB*SB + LB*FB*SB + NMDS1 + Species

LB + Site 5 -223.56 457.5 165.77 0.00

LB + FB + Site + LB*FB 7 -223.07 460.9 169.17 0.00

82 Table A5: Model selection table for determining what variables best predict territory overlap among focal species. df = degrees of freedom, logLik = log likelihood, AICc = sample size corrected Akaike information criteria, Δ = delta AICc, w = AICc weight, LB = leaf litter biomass, FB = flying insect biomass, SB = shrub arthropod biomass.

Model df logLik AICc Δ w

NMDS1 + Site + Species 8 -474.67 966.9 0.0 0.325

Site + NMDS2 + Species 8 -474.69 967.0 0.04 0.320

SB + Site + NMDS2 + Species 9 -473.76 967.5 0.60 0.241

FB + SB + Site + NMDS2 + Species 10 -473.68 969.9 2.92 0.076

LB + FB + SB + Site + NMDS2 + 11 -473.59 972.2 5.27 0.023 Species

LB + NMDS1 + FB +SB + Site + 12 -473.53 974.7 7.75 0.007 NMDS2 + Species

LB + NMDS1 + FB +SB + Site + 12 -473.72 975.1 8.12 0.006 LB*FB + LB*SB + FB*SB + LB*FB*SB

LB + NMDS1 + FB + SB + Site + 13 -473.33 976.9 10.01 0.002 LB*FB + LB*SB + FB*SB + LB*FB*SB + NMDS2

LB + NMDS1 + FB + SB + Site + 16 -471.10 980.8 13.90 0.00 LB*FB + LB*SB + FB*SB + LB*FB*SB + NMDS2 + Species

SB + Site + Species 8 -721.43 1459.9 492.96 0.00

LB + FB + SB + Site + Species 10 -721.43 1464.5 497.52 0.00

83 Table A6: Model selection table for determining what variables best predict nest density among focal species. df = degrees of freedom, logLik = log likelihood, AICc = sample size corrected Akaike information criteria, Δ = delta AICc, w = AICc weight, LB = leaf litter biomass, FB = flying insect biomass, SB = shrub arthropod biomass.

Model df logLik AICc Δ w

NMDS`+ FB + Site 6 -77.995 169.2 0.00 0.498

NMDS1 + FB + Site + SB 7 -77.091 169.8 0.62 0.364

NMDS1 + NMDS2 + FB + Site + SB 8 -77.079 172.3 3.10 0.106

NMDS1 + NMDS2 + LB + FB + Site + 9 -77.050 174.8 5.62 0.030 SB

NMDS1 + Species + NMDS2 + LB + 12 -76.469 181.9 12.68 0.001 FB + Site + SB

Species + NMDS2 6 -84.476 182.2 12.96 0.001

NMDS1 + Species + NMDS2 7 -83.571 182.8 13.58 0.001

NMDS1 3 -88.509 183.4 14.14 0.000

NMDS1 + Species 6 -87.907 189.0 19.82 0.000

NMDS1 + Species + NMDS2 + LB + 16 -76.117 193.5 24.25 0.000 FB + Site + SB + LB*FB + LB*SB + FB*SB + LB*FB*SB

FB + Site + SB 6 -103.889 220.7 51.53 0.000

FB + Site 5 -105.381 221.4 52.23 0.000

84 Table A7: Model selection table for determining what variables best predict nest fate among focal species. df = degrees of freedom, logLik = log likelihood, AICc = sample size corrected Akaike information criteria, Δ = delta AICc, w = AICc weight, LB = leaf litter biomass, FB = flying insect biomass, SB = shrub arthropod biomass.

Model df logLik AICc Δ w

ExpDays + NMDS2 + FB 4 -37.96 84.5 0.00 0.482

ExpDays + NMDS2 + FB + NestDens 5 -37.75 86.3 1.87 0.189

LB + ExpDays + NMDS2 + FB 5 -37.95 86.8 2.28 0.154

LB + ExpDays + NMDS2 + FB + Site 7 -35.85 87.3 2.87 0.115

ExpDays + NMDS2 + FB + NestDens + Site 9 -34.65 90.0 5.55 0.030 + NestDens*Site

LB + ExpDays + NMDS2 + FB + NestDens + 10 -34.61 92.6 8.13 0.008 Site + NestDens*Site

LB + ExpDays + FB + NestDens + Site + 10 -34.61 92.6 8.13 0.008 NestDens*Site

LB + ExpDays + NMDS2 + FB + NestDens + 10 -34.61 92.6 8.13 0.008 Site + NestDens*Site

LB + ExpDays + NMDS1 + NMDS2 + FB + 11 -34.46 95.0 10.57 0.002 NestDens + Site + NestDens*Site

LB + Species + ExpDays + NMDS1 + 14 -30.41 95.7 11.23 0.002 NMDS2 + FB + NestDens + Site + NestDens*Site

LB + Species + ExpDays + NMDS1 + 15 -30.40 98.8 14.33 0.000 NMDS2 + FB + NestDens + Site + SB + NestDens*Site

NMDS2 + FB 3 -47.55 101.4 16.95 0.000

NMDS2 2 -49.53 103.2 18.75 0.000

NMDS2 + NestDens 3 -48.72 103.8 19.29 0.000

FB + NestDens 3 -57.89 122.0 37.57 0.000

NestDens 2 -60.31 124.8 40.28 0.000

FB 2 -61.44 127.0 42.53 0.000

85 Table A8: Model selection table for determining what variables best predict number of fledged young among focal species. df = degrees of freedom, logLik = log likelihood, AICc = sample size corrected Akaike information criteria, Δ = delta AICc, w = AICc weight, LB = leaf litter biomass, FB = flying insect biomass, SB = shrub arthropod biomass.

Model df logLik AICc Δ w

ExpDays + NMDS2 + FB 4 -130.75 270.1 0.00 0.215

LB + ExpDays + NMDS2 + FB + Site 7 -127.69 271.2 1.07 0.126

ExpDays + NMDS2 + FB + NestDens 5 -130.22 271.4 1.26 0.114

ExpDays + NMDS2 + FB + NestDens + Site + 9 -125.29 271.5 1.42 0.105 NestDens*Site

LB + Species + ExpDays + NMDS1 + 14 -118.08 271.7 1.56 0.099 NMDS2 + FB + NestDens + Site + NestDens*Site

LB + ExpDays + NMDS2 + FB + NestDens + 10 -124.26 272.2 2.09 0.076 Site + NestDens*Site

LB + ExpDays + NMDS2 + FB 5 -130.68 272.3 2.19 0.072

LB + Species + ExpDays + NMDS1 + 15 -117.89 274.5 4.40 0.024 NMDS2 + FB + NestDens + Site + SB + NestDens*Site

LB + ExpDays + NMDS1 + NMDS2 + FB + 11 -124.25 275.0 4.87 0.019 NestDens + Site + NestDens*Site

NMDS2 + FB 3 -143.65 293.6 23.55 0.000

NMDS2 + NestDens 3 -145.65 297.7 27.56 0.000

NMDS2 2 -146.91 298.0 27.89 0.000

FB + NestDens 3 -173.32 352.9 82.82 0.000

NestDens 2 -176.21 356.6 86.46 0.000

FB 2 -177.55 359.2 89.13 0.000

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