The Response of Bark-Gleaning Birds and Their Prey

THE RESPONSE OF BARK-GLEANING BIRDS AND THEIR PREY

TO THINNING AND PRESCRIBED FIRE IN EASTSIDE PINE FORESTS IN

NORTHERN CALIFORNIA

Christopher James Rall

A thesis

Presented to

The Faculty of Humboldt State University

In Partial Fulfillment

of the Requirements for the Degree

Master of Science

December 2006

ACKNOWLEDGEMENTS

I would like to thank my advisor, Steve Zack, for encouraging me to take on and

complete this project, and for helping to bring the manuscript back to the big story when I

was at risk of getting lost in the details. While initially somewhat bewildering, some of his more esoteric metaphors, such as a reference to “Maoist self-flagellation,” kept me entertained while trying to address his suggestions for improving the manuscript.

I thank my committee members and two others for their expert assistance with

various aspects of the project. Michael Camann helped me with arthropod sampling trap

design and identification. Matt Johnson helped me grapple with food availability issues, and Luke George provided help with study design and lots of general guidance. Two

non-committee members stepped up the plate, Sylvia Mori and Howard Stauffer. They

helped with the challenges of analyzing a large and unwieldy dataset.

I owe special thanks to Kerry Farris for passing the figurative baton to me. She laid much of the ground work and justification for this study with her work on foraging

behavior of woodpeckers, and she was also extremely helpful in sharing her experiences

and advice on designing and conducting foraging studies.

The interdisciplinary team associated with experimental forest treatments at both

Blacks Mountain Experimental Forest, and Goosenest Adaptive Management Area

provided both a physical and intellectual context for my research. They provided large stands with well chosen, randomized treatments and conducted excellent research that I

hope my work will complement. Bill Laudenslayer provided inspiration and insight.

Nick Vaughn and Brian Wing were extremely helpful in providing related data collected by Martin Ritchie that I was able to incorporate into some models.

I thank my lab mates, Greg Brown, Mike Palladini and Jinelle Hutchins for advice and company during the long hours of counting bugs and slogging through data. Mike deserves special mention for being a great friend both in the lab and out at the field housing.

Thanks to folks in the field housing at Grass Lake and Mt. Hebron for great fun company, especially those that volunteered to help with the “back-pack” arthropod traps.

Special thanks to Elizabeth Donadio for being a great field assistant.

Thanks to my family, especially my parents for raising me to value education and excellence. I especially want to thank my late grandfather, Chick, for his generous financial support and hopefully correct belief that I could do something worthwhile with that support. My dog, Casa Lago’s Lord Nelson KD, performed splendidly at the task of helping me laugh and relax. Lastly, and most importantly, I thank my wife, Becky, for her advice, companionship, patience and love through a big endeavor.

iii

ABSTRACT

I studied foraging responses of White-headed Woodpeckers (Picoides

albolarvatus), Brown Creepers (Certhia americana) and White-breasted Nuthatches

(Sitta carolinensis) to restoration efforts in eastside pine forest using mechanical thinning

and prescribed fire in order to understand trophic relationships in these forests. I focused

on the role of food availability and how changes in food availability might be caused by

either increased solar radiation in more open thinned stands, or the reintroduction of

disturbance, including fire, blowdown, and mechanical thinning. I also investigated the

relationship between tree size, foraging behavior and prey availability.

I conducted the study from May to July 2003 and 2004 in the Goosenest Adaptive

Management Area in northern California. Five 40-ha replicates each of thinned, thinned / burned, and fire-suppressed dense forest plots were established at the study site. I used arthropod sampling and foraging observations to answer my research questions. Analysis involved generating models to describe food availability, important prey taxa abundance, distribution of each bird species on plots, and the selection and use of foraging trees. The best models describing these phenomena, along with some Chi-Square contingency tests of independence of foraging microsite selection, were examined together to assess the support for each hypothesized relationship.

My results indicate that forest restoration can affect food availability, and that food availability can affect distribution of birds at certain scales. Food availability varied by forestry treatment for White-headed Woodpecker, as did the most important prey

iv taxon for Brown Creeper, although there were fewer prey on the treated plots for Brown

Creeper. White-headed Woodpeckers foraged in areas that were likely to have more food, both at the plot and tree scales. This was only true at the tree scale for Brown

Creepers.

The results also suggest that both solar radiation and disturbance play roles in this process. Brown Creepers responded to top-killed trees and beetle outbreak, although their prey responded to blowdown at the plot scale and to trees with fir snags within 20 m. White-headed Woodpeckers, on the other hand responded most strongly to thinning and beetle-attack. There was strong evidence that solar radiation in thinned stands plays an important role in White-headed Woodpecker’s response to eastside pine restoration.

The woodpeckers and their primary food, ants, responded positively to thinning. Shade and canopy cover were important for woodpecker foraging and for the ants they eat.

Those results together suggest a system in which sunnier, more open stands force ants to aggregate more tightly on the shadier trees making them more available as prey to White- headed Woodpeckers. Due to low sample sizes, it is difficult to draw definitive

conclusions about White-breasted Nuthatch except that they respond positively to

thinning and prescribed fire.

All three bird species selected and spent more time foraging on larger trees. This

occurred despite the lack of evidence that larger trees have a higher density of prey than smaller trees per bark surface area.

Food availability may play a larger role than previously thought in influencing local abundance of bark gleaning birds. Managers should consider the effects of actions

v on both nest-site availability and food availability when managing for White-headed

Woodpecker or Brown Creeper. Bark-gleaning birds select large trees for foraging, but

this is not always because of greater food density on these trees. While more study is

needed to understand this phenomenon, land managers should continue to recognize the

importance of large trees as foraging habitat for bark-gleaning birds as well as a source of

appropriate snags for nesting.

TABLE OF CONTENTS

ACKNOWLEDGEMENTS...... ii

ABSTRACT...... iv

TABLE OF CONTENTS...... vii

LIST OF TABLES...... ix

LIST OF FIGURES ...... xi

LIST OF APPENDICES...... xiv

INTRODUCTION ...... 1

STUDY AREA ...... 9

METHODS ...... 12

Arthropod Sampling...... 12

Foraging Observations...... 14

Vegetation Measurements...... 16

Diet…...... 23

Food Availability Estimates...... 25

Analysis...... 26

RESULTS ...... 32

Patterns of use by White-headed Woodpecker ...... 32

Patterns of use by Brown Creeper ...... 42

Patterns of use by White-breasted Nuthatch...... 52

DISCUSSION...... 67

White-headed Woodpecker...... 76

vii

Brown Creeper...... 82

White-breasted Nuthatch ...... 85

Use of Large Trees...... 87

Conclusions...... 89

LITERATURE CITED ...... 91

APPENDICES ...... 98

viii

LIST OF TABLES

Table Page

1 Microhabitat categories for microsite foraging observations...... 17

2 Description of explanatory variables including levels at which they are measured, groups into which they are clustered, variable name, type of variable description of the variable, and the model selection procedures for which it was used...... 18

3 Taxonomic levels to which arthropods were identified, and the proportion of each taxon in the diet of White-headed Woodpecker and Brown Creeper sensu Otvos and Stark (1985)...... 24

4 Best modelsa describing White-headed Woodpecker food and foraging, and confidence intervals of coefficients for those models (Goosenest Adaptive Management Area 2003 and 2004)...... 35

5 Best modelsa describing Brown Creeper food and foraging including the type of model, variables included, and estimates and confidence intervals of coefficients for these models (Goosenest Adaptive Management Area 2003 and 2004)...... 47

6 Best modelsa describing White-breasted Nuthatch food and foraging including the type of model, variables included, and estimates and confidence intervals of coefficients for these models (Goosenest Adaptive Management Area 2003 and 2004)...... 60

7 Predictions for the hypothesis that food varies across treatments for bark- gleaning birds and whether those predictions held true (Goosenest Adaptive Management Area 2004)...... 68

8 Predictions for the hypothesis that food-availability drives the distribution of White-headed Woodpecker, Brown Creeper and White-breasted Nuthatch, and whether those predictions held true (Goosenest Adaptive Management Area 2003 and 2004)...... 69

9 Summary of evidence supporting or refuting the hypothesis that disturbance plays a role in the distribution of bark-gleaning birds in response to prey-availability (Goosenest Adaptive Management Area 2003 and 2004)...... 70

10 Summary of evidence supporting (or refuting) the hypothesis that solar exposure plays a role in the distribution of bark-gleaning birds in response to prey-availability (Goosenest Adaptive Management Area 2003 and 2004)...... 73

11 Summary of results related to the hypothesis that large trees are important foraging habitat for bark-gleaning birds (Goosenest Adaptive Management Area 2003 and 2004)...... 77

LIST OF FIGURES

Figure Page

1 The location of study site (Goosenest Adaptive Management Area) in northern California...... 10

2 Belt trap used for arthropod sampling...... 13

3 White-headed Woodpecker use of tree species and horizontal strata according to microsite data. Only data from the use of live trees is displayed (Goosenest Adaptive Management Area 2003 and 2004)...... 33

4 White-headed Woodpecker use of tree species and vertical strata according to microsite data. Only data from the use of live trees is displayed (Goosenest Adaptive Management Area 2003 and 2004)...... 34

5 Predicted detection probability of White-headed Woodpecker by nest-site availability and treatment according to detection logistic regression model, which includes the effects of treatment, nest-site availability and period (Table 4). Appropriate snags were defined as ponderosa pine snags with at least some bark missing (Goosenest Adaptive Management Area 2003 and 2004)...... 39

6 White-headed Woodpecker utilization of the top of used trees on trees with or without top damage. The bird was considered to have utilized the top of the tree if it arrived or departed high on the tree (on the upper third) ( 2 = 7.046, df = 1, P = 0.008) (Goosenest Adaptive Management Area 2003 and 2004)...... 41

7 White-headed Woodpecker use of different aspects of trees during foraging observations, based on microsite data ( 2 = 7.636, df = 3, P = 0.0542) (Goosenest Adaptive Management Area 2003 and 2004)...... 43

8 White-headed Woodpecker foraging bouts in the shade compared with expected use of shade based on the mean ocular estimate of available shade on used trees, based on microsite data ( 2 = 1.077, df = 1, P = 0.299) (Goosenest Adaptive Management Area 2003 and 2004)...... 44

9 Brown Creeper use of tree species and horizontal strata of tree according to microsite data. Data displayed is limited to live trees used (Goosenest Adaptive Management Area 2003 and 2004)...... 45

10 Brown Creeper use of tree species and vertical strata of tree according to microsite data. Data displayed is limited to live trees used (Goosenest Adaptive Management Area 2003 and 2004)...... 46

11 Predicted number of bark-boring beetles (Scolytidae) on the side of each tree, based on the Scolytidae model. Effects of volume of downed pine on each plot and treatment are included in the graph, although other variables from the model (Table 5) contribute to the predicted value (Goosenest Adaptive Management Area 2003 and 2004)...... 50

12 Brown Creeper utilization of the top of used trees on trees with or without top damage (2 = 4.525, df = 1, P = 0.033). The bird was considered to have utilized the top of the tree if it arrived or departed high on the tree (on the upper third) (Goosenest Adaptive Management Area 2003 and 2004)...... 53

13 Brown Creeper utilization of the bottom of used trees with or without bottom damage (2 = 4.942, df = 1, P = 0.026). The bird was considered to have utilized the bottom of the tree if it arrived or departed from the bottom third (Goosenest Adaptive Management Area 2003 and 2004)...... 54

14 Brown Creeper use of different aspects of trees during foraging observations, based on microsite data ( 2 = 12.516, df = 3, P = 0.006) (Goosenest Adaptive Management Area 2003 and 2004)...... 55

15 Brown Creeper foraging bouts in the shade compared with expected use of shade based on the mean ocular estimate of available shade on used trees, based on microsite data ( 2 = 8.067, df = 1, P = 0.005) (Goosenest Adaptive Management Area 2003 and 2004)...... 56

16 White-breasted Nuthatch use of tree species and horizontal strata of tree according to microsite data (Goosenest Adaptive Management Area 2003 and 2004)...... 57

17 White-breasted Nuthatch use of tree species and vertical strata of tree according to microsite data (Goosenest Adaptive Management Area 2003 and 2004)...... 58

18 White-breasted Nuthatch utilization of the top of used trees on trees with or without top damage (2 = 0.585, df = 1, P = 0.444). The bird was considered to have utilized the top of the tree if it arrived or departed high on the tree (on the upper third) (Goosenest Adaptive Management Area 2003 and 2004)...... 63

xii

19 White-breasted Nuthatch utilization of the bottom of used trees with or without bottom damage ( 2 = 0.723, df = 1, P = 0.395). The bird was considered to have utilized the bottom of the tree if it arrived or departed from the bottom third (Goosenest Adaptive Management Area 2003 and 2004)...... 64

20 White-breasted Nuthatch use of different aspects of trees during foraging observations, based on microsite data ( 2 = 3.952, df = 3, P = 0.267) (Goosenest Adaptive Management Area 2003 and 2004)...... 65

21 White-breasted Nuthatch foraging bouts in the shade compared with expected use of shade based on the mean ocular estimate of available shade on used trees, based on microsite data ( 2 = 0.073, df = 1, P = 0.785) (Goosenest Adaptive Management Area 2003 and 2004)...... 66

xiii

LIST OF APPENDICES

Appendix Page

A Model selection procedure for the White-headed Woodpecker food availability linear regression model including relative AIC scores (∆AIC) and AIC weights (wAIC) for each model (Goosenest Adaptive Management Area 2004)...... 98

B Model selection procedure for the Formicidae negative binomial model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004)...... 101

C Model selection procedure for the White-headed Woodpecker detection logistic regression model including the relative AICc scores (AICc ) and the AICc weight (WAICc) for each model (Goosenest Adaptive Management Area 2004)...... 105

D Model selection procedure for the White-headed Woodpecker tree- selection paired logistic regression model including relative AIC scores (∆AIC) and AIC weights (WAIC) for each model (Goosenest Adaptive Management Area 2003 and 2004)...... 107

E Model selection procedure for the White-headed Woodpecker tree-time linear regression model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Gooenest Adaptive Management Area 2003 and 2004)...... 109

F Model selection procedure for the Brown Creeper food-availability linear regression model including relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004)...... 112

G Model selection procedure for the Scolytidae negative binomial model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004)...... 115

H Model selection procedure for the Brown Creeper detection rate linear regressioin model including relative AICc scores (AICc ) and the AICc weight (WAICc) for each model (Goosenest Adaptive Management Area 2004)...... 1199

xiv

I Model selection procedure for the Brown Creeper tree-selection paired logistic regression model including relative AICc scores (∆AIC) and AIC weights (wAIC) for each model (Goosenest Adaptive Management Area 2003 and 2004)...... 120

J Model selection procedure for the Brown Creeper tree-time linear regression model including relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2003 and 2004)...... 122

K Model selection procedure for the Curculionidae poisson model including relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004)...... 125

L Model selection procedure for the White-breasted Nuthatch detection logistic regression model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004)...... 128

M Model selection procedure for the White-breasted Nuthatch tree-selection paired logistic regression model including the relative AIC scores (∆AIC) and AIC weights (WAIC) for each model (Goosenest Adaptive Management Area 2003 and 2004)...... 129

N Model selection procedure for the White-breasted Nuthatch tree-time linear regression model including relative AICc scores (∆AICc) and AICc weights (WAICc) of variables in each group. Variables in bold were used in the model selection procedure (Goosenest Adaptive Management Area 2003 and 2004)...... 130

INTRODUCTION

Fire suppression and logging over the past century have changed forest structure

and ecology in eastside ponderosa pine (Pinus ponderosa) forests of Northern California.

The suppression of fire and widespread logging of large trees has resulted in forests with

a higher proportion of smaller trees, a denser understory, and an increase in proportion of

less fire tolerant tree species like white fir (Abies concolor) (Agee 1994, Covington and

Moore 1994, Camp et al. 1995, Brown, R. T. et al. 2004). The historic pattern of frequent, low intensity fires that maintained open stands dominated by ponderosa pine

(Agee 1994) has been disrupted by fire suppression. The result of such management is, paradoxically, that the risk of catastrophic high-intensity fires has increased

(Weatherspoon 1996). Warming of the climate in this century will lengthen fire seasons, increase the area that burns each year (McKenzie et al. 2004), and further increase fire severity (Brown, T. J. et al. 2004) thus further increasing the risk of catastrophic fire.

Fire return intervals in eastside pine forest historically ranged from seven to 38 years depending on moisture levels at the site (Bork 1985). Fires were low intensity

(Weaver 1959, Agee 1994), and helped to maintain pine-dominated open park-like stands

(Agee 1994). While ponderosa pine has many adaptations enabling it to survive and reproduce in environments with frequent fire (Saveland and Bunting 1988), white fir seedlings and saplings are highly vulnerable to mortality from fire (Laacke 1990, Oliver and Ryker 1990). White fir’s shade tolerance may contribute to its vulnerability since shady areas typically have more continuous fuels from needle deposition than open areas

1 2 (Agee 1994). Fire suppression, preventing fires caused by lightning and native-

Americans from occurring or spreading on the landscape, became common after 1900

(Agee 1994). This caused many eastside pine stands to convert to mixed conifer or white

fir (Agee 1994). Logging of large ponderosa pines over the last century helped shade

tolerant species that were already invading gain dominance more quickly (Agee 1994).

Logging of large pines also removed the source of large pine snags required for nesting by many cavity nesting birds (Zack et al. 2002)

Changes in forest structure and disturbance regimes have likely changed wildlife populations using the forest. Historically, low intensity fire maintained a park-like structure in most ponderosa pine forest (Agee 1994). More recently, many of these forests have become choked with dense stands of small trees. For bark and foliage- gleaning birds, these structural differences probably affect the quality and quantity of substrate available for foraging. For example, Raphael et al. (1987) noted moderate decreases in bark-foraging birds and increases in foliage gleaners over the course of 25 years post-fire (with subsequent fire suppression) in ponderosa pine forest in the Sierra

Nevada. Conversely, experimental forest manipulations in eastside pine forest that thin smaller trees and woody understory, while leaving larger trees, and that apply prescribed fire seem to result in a sudden increase of some bark-foraging birds and a decrease in foliage-gleaners (George, T. L. and S. Zack, Department of Wildlife, Humboldt State

University, 1 Harpst Street, Arcata, CA 95521, unpublished data 2002). These observations suggest that bark-foraging birds were a more common component of the avian community in eastside pine’s historic condition of a century ago.

3 If more abundant bark-foraging birds are indicative of historic eastside pine

characteristics, several mechanisms could create this association. The response of bark-

foraging birds to thinning and prescribed fire seems likely to be a direct result of changes

in nest site availability and (or) food availability caused by the change in forest structure

and reintroduction of fire (Brawn and Balda 1988, Zack et al. 2002).

Researchers and managers have focused on snag and nest site availability for

cavity nesting birds (McClellan and Frissell 1975, Scott 1979, Thomas et al. 1979,

Raphael and White 1984, Zarnowitz and Manuwal 1985, McComb et al. 1986, Morrison

et al. 1986, Brawn et al. 1987, Brawn and Balda 1988, Horton and Mannan 1988,

Schreiber and deCalesta 1992, Ohmann et al. 1994, Bull et al. 1997, Ross and Niwa

1997, Ganey 1999, Martin and Eadie 1999, Zack et al. 2002) but many bird populations

may be limited by foraging sites or food availability rather than nest sites. For example,

Zack et al. (2002) found differences in large snag availability between two sites in northeast California, but found that even in the “snag-poor” site (where this study was subsequently conducted) only about 20% of snags had cavities, and only 2% of those were active during a single season. Because it takes several years for snags created in a disturbance to reach a level of decay making them useful for primary cavity nesters

(Hughes 2000), management actions that recruit new snags will not increase nest-site availability for several years. Furthermore, Brawn and Balda (1988) concluded that nest-site limitation for cavity-nesting insect gleaners was contingent on availability of foraging substrate. While many woodpeckers forage heavily on newly recruited snags

(Dixon and Saab 2000, Hughes 2000), bark-foragers such as White-headed Woodpecker

4 (Picoides albolarvatus), Brown Creeper (Certhia americana) and White-breasted

Nuthatch (Sitta carolinensis) forage mostly on live trees, and thus rely on snags primarily for nesting (Hejl et al. 2002, Garrett et al. 1996, Pravosudov and Grubb 1993).

Changes in food availability may be caused by changes in forest structure and disturbance regime (Zack et al. 2002, Brawn and Balda 1988). Fire can cause outbreaks of insects beneficial to bark-foraging birds. For example, Black-backed Woodpecker

(Picoides arcticus) and Hairy Woodpecker (P. villosus) respond to bark-boring

(Scolytidae) and wood-boring (Buprestidae and Cerambycidae) beetle outbreaks following high-severity forest fires (Dixon and Saab 2000, Saab and Dudley 1998, Hutto

1995). However, some bark-foraging birds associated with old-growth pine stands

respond more strongly to habitat characteristics other than those directly caused by fire.

For example, White-headed Woodpecker, Brown Creeper and White-breasted Nuthatch select large live conifers for foraging (Hejl et al. 2002, Hughes 2000, Garret et al. 1996,

Stallcup 1968). Old-growth pines historically occurred in open stands created and maintained by fire (Agee 1994). Therefore, it is likely that bark-gleaning birds would exploit increasing arthropod food abundance caused by frequent, low-severity fires in their preferred habitat.

Alternatively, open forest structure may result in a change in abundance or aggregation of bark-dwelling arthropods, making them more available to bark-foraging birds. Nicolai (1986) found sun exposure to have profound effects on temperatures at different locations on boles of European tree species. Furthermore, he found certain arthropod taxa aggregating at different aspects and microsites within the bark. These

5 microsites coincided with different radiation and temperature exposures. Experimental efforts to “open-up” ponderosa pine stands certainly increased the amount of solar radiation reaching the trunks of trees. If this pattern applies to bark taxa of eastside pine forests, forest thinning, and the resultant sun exposure could affect aggregation of bark arthropods, and their availability as prey to bark-gleaners.

There have been many studies comparing foraging behavior of various bark- foraging birds. For example, Hughes (2000) found snags at different stages of decay to be selected by different woodpeckers for foraging in east side pine. Large diameter trees are selected over smaller trees by Hairy Woodpecker (Hughes 2000, Weikel and Hayes

1999), White-headed Woodpecker (Hughes 2000, Dixon 1995, Morrison et al. 1987) and

Brown Creeper (Weikel and Hayes 1999). Jackson (1979) suggested larger trees have deeper bark furrows providing for larger numbers and a higher diversity of bark fauna.

Few studies have examined food availability for bark-foraging birds directly. Mariani and Manuwal (1990) found a series of correlations between Brown Creeper abundance,

“medium-sized (6-11mm)” spiders (Araneae), bark-furrow depth and tree diameter in forests dominated by Douglas-fir (Psuedotsuga menziesii) and western hemlock (Tsuga heterophylla). No studies have examined the effect of forest thinning and fire on prey availability on the bark of live trees, where many bark-gleaners generally forage.

White-headed Woodpecker, Brown Creeper and White-breasted Nuthatch are the common live-tree bark-foragers (Pravosudov and Grubb 1993, Garrett et al. 1996, Hejl et al. 2002) at the Goosenest Adaptive Management Area where forest treatments of thinning and prescribed fire have been applied for ecological study (Ritchie and Harcksen

6 1999). White-headed Woodpecker and White-Breasted Nuthatch have shown increases

in response to thinning and prescribed fire (George, T. L. and S. Zack, Department of

Wildlife, Humboldt State University, 1 Harpst Street, Arcata, CA 95521, unpublished data 2002). Brown Creeper numbers were not significantly affected by restoration treatments (George, T. L. and S. Zack, Department of Wildlife, Humboldt State

University, 1 Harpst Street, Arcata, CA 95521, unpublished data 2002), but they select strongly for large live trees for foraging (Hejl et al. 2002), making them a potential indicator of late seral forest characteristics.

Dixon (1995) suggested that White-headed Woodpecker is an important indicator of old-growth eastside pine, because it reaches its highest density in the Pacific

Northwest in this habitat type. Because of its indicator species status as well as regional range retraction and population decline, White-headed Woodpecker has been listed as a

Sensitive Species for the intermountain and northern regions by the U. S. Forest Service and a Critical Sensitive Species by the Oregon Department of Fish and Wildlife (Dixon

1995, Garrett et al. 1996). In my study area, White-headed Woodpeckers were not detected in point count surveys conducted prior to application of thinning and prescribed fire. Their numbers were about three times greater on the burned treatment than the unburned/thinned forest in the year following the application of prescribed fire, and were seldom detected in fire-suppressed dense forest (George, T. L. and S. Zack, Department of Wildlife, Humboldt State University, 1 Harpst Street, Arcata, CA 95521, unpublished data 2002). As a woodpecker that rarely drills deeply when foraging, (Hughes 2000,

Garrett et al. 1996) this bird fits into the guild of bark-gleaners for the purposes of my

7 study. Diet of the northern subspecies of White-headed Woodpecker includes ants

(Formicidae), metallic wood-boring beetles (Buprestidae) and scale insects (Coccidae),

amongst other arthropod prey (Otvos and Stark 1985). In autumn they often feed on pine

seeds (Garrett et al. 1996).

The Brown Creeper is a common bark gleaner that, like the White-headed

Woodpecker, selects large diameter live trees for foraging (Hejl et al. 2002). It nests

under loose bark on snags, and prefers foraging habitat with large trees and a high basal

area (Hejl et al. 2002). It forages on the surface of bark and in crevices for spiders,

pseudoscorpions (Pseudoscorpiones) and bark-boring beetles (Scolytidae) (Hejl et al.

2002). Typically, the bird climbs a tree while foraging, and flies down to the base of the

next tree (Franzreb 1985). While common on all treatments, Brown Creepers were slightly more numerous in burned and unburned thinned forest than in the fire suppressed

“control” at the study site, although the difference was not statistically significant

(George, T. L. and S. Zack, Department of Wildlife, Humboldt State University, 1 Harpst

Street, Arcata, CA 95521, unpublished data 2002). Increases were detected on thinned

stands at Blacks Mountain Experimental Forest, an additional eastside pine site with

similar experimental forest treatments (George, T. L. and S. Zack, Department of

Wildlife, Humboldt State University, 1 Harpst Street, Arcata, CA 95521, unpublished

data 2000).

At the study site, White-breasted Nuthatches were detected only where treatments

had thinned the forest (George, T. L. and S. Zack, Department of Wildlife, Humboldt

State University, 1 Harpst Street, Arcata, CA 95521, unpublished data 2002). White-

8 breasted Nuthatches also showed increases on similar forest treatments at Blacks

Mountain Experimental Forest (George, T. L. and S. Zack, Department of Wildlife,

Humboldt State University, 1 Harpst Street, Arcata, CA 95521, unpublished data 2000).

These birds are generally associated with hardwoods but can occupy coniferous forest if

large ponderosa pines are present (Pravosudov and Grubb 1993). Their breeding season

diet in eastside pine forest consists of weevils (Curculionidae), leaf beetles

(Chrysomelidae) and earwigs (Dermaptera) as well as some ants.

My objectives were to (1) investigate differences in prey availability for White-

headed Woodpecker, Brown Creeper, and White-breasted Nuthatch in an experimental

forest setting with recently burned and unburned eastside pine forest of similar open

structure, as well as nearby “control” plots of fire-suppressed dense forest; (2) test the

hypothesis that food plays a role in the distribution of each bird species on these silvicultural treatments; (3) investigate the roles that disturbance and solar exposure play on prey abundance and distribution, and whether they play any part in prey availability to the three focal species; and (4) examine the relationship between tree size, foraging behavior and prey availability. These objectives are necessary in order to understand the relationship of bark-gleaning birds to the historical condition of eastside pine.

STUDY AREA

Goosenest Adaptive Management Area (41°33’N, 121°55’W) (Figure 1) was established in 1996 to test three different management approaches to restoration of interior ponderosa pine forest (Ritchie and Harcksen 1999). The goal was to accelerate development of late successional forest characteristics, and contrast those treatments with

controls. Twenty 40-ha plots were laid out with a 100m grid of monuments. Each of

four treatments was applied to five plots chosen at random. Those treatments were “big tree”, “pine emphasis” (pine), “pine emphasis burned” (burn) and “control”. The big tree

treatment involved thinning the stand to leave the largest trees, many of which are white

fir that was not historically dominant. The pine treatment involved thinning the stand with an emphasis on leaving mostly large diameter pines. Burned plots received the same thinning regimen as the pine emphasis units and were subsequently prescribed burned. Control plots had no thinning or prescribed fire. Thinning treatments were applied 1998-2000 and prescribed burns took place autumn of 2001. Surveyors installed grid markers at 100-m intervals on all plots to facilitate spatial reference. For more on

the application of forestry treatments at Goosenest Adaptive Management Area, see

Ritchie and Harcksen (1999).

In this study I used only the pine, burn and control treatments. The pine and burn

treatments offer the opportunity to compare effects of prescribed fire on stands that have

been thinned in a similar fashion. The term “thinned” in this paper refers to both the pine

and burn treatments. The control treatment allowed me to distinguish the effects of

9 10

Figure 1. The location of study site (Goosenest Adaptive Management Area) in northern California.

11 thinning from the effects of prescribed fire. Thinning and prescribed fire are the two

primary tools used by land managers to restore fire-suppressed forest to a historic park-

like condition (Brown, R. T. et al. 2004). While frequent fire historically helped maintain

a park-like structure (Agee 1994), comparing the pine and burn treatments here allows land managers to not only examine implications of management choices (Ritchie and

Harksen 1999), but to speculate about the specific role of fire versus the role of structure in wildlife habitat relationships.

Thinned plots, both unburned and burned, are dominated by a combination of

ponderosa pine and white fir, while control plots are dominated more by white fir.

Incense cedar (Calocedrus decurrens) and sugar pine (P. lambertiana) are present in

smaller numbers on most plots with some red fir (Abies magnifica) and lodgepole pine

(Pinus contorta) on a few plots as well (Ritchie and Harcksen 1999).

On 14 December and 20 December 2002, two incidents of high winds resulted in large amounts of blowdown on the plots. The first event involved wind speeds of 97-113 km per hour with 10-13 cm of precipitation in the form of rain and snow. The second involved lower wind speeds of 40-56 km per hour and heavy snowfall. The resultant blowdown was variable between plots and within plots, with the most extensive windfall on thinned plots (Brian Wing, US Forest Service, Pacific Southwest Research Station

3644 Avtech Parkway, Redding, CA 96002, personal communication).

METHODS

To assess differences in prey availability to bark gleaners, I used a combination of approaches described by Wolda (1990), and Poulin and Lebvre (1997). Wolda (1990) defined prey availability as “abundance of potential prey items in microhabitats used by an insectivore when searching for food.” Using this approach, I took several steps to

measure the relevant arthropod abundance “available” to bark-gleaning birds. I sampled arthropods. I conducted foraging observations to investigate where birds preferentially foraged and to ensure traps were placed in the appropriate microhabitat. I collected vegetation data in conjunction with arthropod sampling and foraging observations to investigate the roles of treatments, nest site availability, disturbance, solar exposure and tree size in explaining differences in bird abundance and prey availability. I collected diet information from the literature to determine which arthropods captured were available to bark-gleaning birds. I adapted a mathematical formula provided by Poulin and Lebvre (1997) to remove the bias of a given arthropod trap towards certain taxa, and weigh each taxon by the proportion in the diet of the bird.

Arthropod Sampling

I designed “belt” sticky traps (Figure 2) adapted from Collins et al. (2002). The

trap forces arthropods traveling up or down the tree to cross a sticky surface, where they

are trapped. Traps were placed at chest height (~1.4 m above the ground). I wrapped each tree with a 5 cm wide strip of foam mattress, taping it in place. Next, I wrapped over the foam with white duct tape. A second layer of duct tape was then wrapped over

Figure 2. Belt trap used for arthropod sampling.

14 the top of that. On this second layer of duct tape, I marked the cardinal directions and the

trap identification number with a black sharpie marker. I applied caulk on top and underneath the trap, wherever fissures in the bark might enable arthropods to find a tunnel under the trap. Finally, I applied a 3-cm wide coating of Tangle-Trap (Tanglefoot

Co., Grand Rapids, MI) to the surface of the top layer of tape all the way around the tree.

When the trapping period was over, I placed plastic wrap over the sticky surface of the trap and removed the top layer of tape with the captured arthropods.

I stratified my sampling between ponderosa pine and white fir, and between larger

trees (45-55cm dbh) and smaller trees (25-35cm dbh). On each plot, I sampled 6 larger trees and 2 smaller trees. White fir and ponderosa pine were sampled equally (three large and one small of each species on each plot). I selected trees by randomly selecting each grid-point using hundredths of a second on a digital stop watch as a random numbers and selecting the closest tree that fit the parameters of tree species and size.

All traps were deployed 12-14 May 2004, replaced 16-18 June 2004, and taken down 21-23 July 2004. This provided two 5-week trapping periods corresponding to the first and second half of the nesting season for all three bird species (Hejl et al. 2002,

Garrett et al. 1996, Pravosudov and Grubb 1993).

Foraging Observations

I utilized foraging observations to determine if placement of arthropod traps mimicked foraging locations, to examine foraging preferences of the birds, and to

establish the distribution of birds. Foraging observation methods were approved by the

Humboldt State University Institutional Care and Use Committee (IACUC no.

15 02/03.W.105.E). In 2003 and 2004, foraging observations were performed in two 4.5-

week sessions from 15 May to 15 June and from 19 June to 20 July, beginning at sunrise each day. These periods correspond to the two arthropod trapping periods. Each

observer visited 2 plots per day. Plots were visited in the same order throughout the

season, so that number of days between visits to a given plot was maximized, and the plot

was visited at a different time of day (early morning vs. late morning) on each successive

visit. In 2004 when there were two observers, the observers switched plots after two visits.

When visiting a plot, the observer systematically visited every 100-m gridpoint.

The observer listened for the 3 focal species and attempted to locate one visually if it was

heard. To avoid sampling the same individual or its mate twice on the same day, I

refrained from taking an observation on a Brown Creeper within 200m of a previous

observation, and limited observations on White-headed Woodpecker and White-breasted

Nuthatches to one per plot visit (encounter rates of the latter two species were much lower than Brown Creeper). These measures provided for a temporal spacing of at least 3 days between consecutive observations on the same individual bird. This should have

been adequate to ensure independence between observations (Porter et al. 1985, Hejl et al. 1990). I allowed no more than 3 observations (of all species combined) on a plot visit so observations would be more evenly distributed among the plots. The amount of time spent searching the plot for birds was recorded for each visit.

I conducted observations in a systematic and nested fashion. That is, each foraging observation included one microsite observation, data on up to 5 trees used by the

16 bird, and data on an equal number of unused trees. Once a bird was sighted and the tape-

recorder started, I waited 10 seconds to reduce bias toward conspicuous behavior (Hejl et

al. 1990, Recher and Gebski 1990), and then recorded information on the location of the

next foraging maneuver. This information constituted the microsite observation. To

avoid dependence between observations, I collected only one data-point of microhabitat use per observation of an individual on a given day. Microhabitat categories (Table 1) were loosely based on Weikel and Hayes (1999).

I continued to follow the bird for up to 5 trees. I announced when the bird arrived or departed from a tree so I could calculate the time spent on each tree. Relative height

(low, middle or high) was recorded upon both arrival and departure, so for most trees it was known whether a bird visited the top and/or bottom of the tree. When the observation was over, I selected a random tree for each used tree in the following way: the observer spun a compass several times without looking at it to obtain the quadrant

(NE, SE, SW, NW) in which to find a random tree. Within that quadrant, the observer selected the closest tree 10 to 50 m away that was the same status (live or dead) and that was not used by the bird. If no appropriate tree could be found in that direction, the observer would randomly select a new quadrant until an appropriate tree could be found.

I recorded data on each used tree and each paired random tree (Table 2).

Vegetation Measurements

Measurements were taken on and around each tree supplied with an arthropod

trap, on each tree used by birds in foraging observations and on random unused trees

paired with the foraging trees. All vegetation measurements are explained in Table 2,

17 Table 1. Microhabitat categories for microsite foraging observations.

Strata Site Description

Vertical Low Bottom third of tree Middle Middle third of tree High Top third of tree

Horizontal Bole Trunk of tree Branches Branches

Table 2. Description of explanatory variables including levels at which they are measured, groups into which they are clustered, variable name, type of variable, description of the variable, and the model selection procedures for which it was used.

Modelsb Level Group Variable Typea Description fa ta dl dr ts tt

Plot treatment g Thinned, burned or control (burned plots are also thinned) x x x x x period g First or second half of season x x x x food availability c mean of food availability across all traps on a plot x x nest-site avail. c index of number of appropriately decayed snags surveyed on plot x x PIPO_basalc c estimate of the basal area of ponderosa pine per acre xc

plot-level blowdown FirVolume c Volume of fir blown down on plot x x x PineVolume c Volume of pine blown down on plot x x x TotalVolume c Volume of trees blown down on plot x x x

Visit obs g observer x x wind c average estimated windspeed over the course of the visit to a plot x time_elapsed c time spent searching the plot for birds x temp c average temperature over the course of the visit to a plot x date c Julian date of visit x

Observation time c Time of observation x sex g Sex of bird: male, female or unknown x

Modelsb Level Group Variable Typea Description fa ta dl dr ts tt

Tree treesp g Tree species x x x x fire_effects g whether tree shows signs of being burned or not x x x x size g big (45-55 cm dbh) or small (25-35 cm dbh) x x dbh c diameter at breast height x x coned g whether the White-headed Woodpecker foraged on cones xd

solar exposure shade c Ocular estimate of percentage of shade on bole (50-100%) x x canopy_south c densiometer reading facing south with back to tree (0-96) x x

Beetle_Evidence beetle_entry g signs of beetle entry such as pitch tubes or frass x x x x beetle_exit g presence of beetle exit holes on bark x x x x beetle g any beetle evidence x x x x

Damage top_dam g damage at the top of the tree such as a split top, dead top or broken top x x x x bottom_dam g damage at the bottom of the tree such as scarred bark or cambium damage x x x x gen_dam g Any damage on the tree x x x x

Modelsb Level Group Variable Typea Description fa ta dl dr ts tt

Aspect aspect g East, south, west or north side of tree x x canopy c densiometer reading with back to tree facing a cardinal direction, 0-96. x x

dead trees all_10m c These are all measures of dead trees near the trap tree. "snag" connotes snags only, x x down means only down trees are counted, all means both snags and blowdown are all_20m c x x counted, fir means only firs are counted and pine means only pines are counted. The x x all_30m c number is the maximum number of meters away from the tree that the blowdown is all_40m c counted. The variable is the total basal area represented. These variables are used as x x snag_10m c aspect level variables x x snag_20m c x x snag_30m c x x snag_40m c x x down_10m c x x down_20m c x x down_30m c x x down_40m c x x firall_10m c x x firall_20m c x x firall_30m c x x firall_40m c x x firsnag_10m c x x firsnag_20m c x x firsnag_30m c x x firsnag_40m c x x firdown_10m c x x firdown_20m c x x

Modelsb Level Group Variable Typea Description fa ta dl dr ts tt

Aspect dead trees firdown_30m c see above x x contd contd firdown_40m c x x pineall_10m c x x pineall_20m c x x pineall_30m c x x pineall_40m c x x pinesnag_10m c x x pinesnag_20m c x x pinesnag_30m c x x pinesnag_40m c x x pinedown_10m c x x pinedown_20m c x x pinedown_30m c x x pinedown_40m c x x

a "g" is for categorical or grouping variable, "c" is for continuous variable b models are food avialablility (fa), taxon abundance (ta), detection logistic (dl), detection rate (dr), tree selection (ts) and tree time (tt) c PIPO_basal used only in Brown Creeper analysis. d cone variable only used with White-headed Woodpecker analysis.

22 except the plot-level blowdown variables, nest-site availability and the aspect-level dead tree variables.

The volume of blowdown on each plot was estimated by scientists from Pacific

Southwest Research Station of the United States Forest Service. All plots were censused with the exception of four (two pine, one burn and one control). On the censused plots, every recently fallen tree was counted, and its dbh was measured. Four plots were only surveyed due to the extremely large amounts of blowdown. In the survey procedure, a crew went to established grid points and re-inventoried previously recorded trees to determine which trees had fallen. These trees had been previously measured via a circular plot method. This sample data was then extrapolated over the entire plot to create the estimates of volume.

To generate the nest-site availability variable and aspect-level dead tree group of variables, I censused all dead trees over 20cm dbh within 40 m of each arthropod trap tree. For each dead tree I recorded the species, size class, bark and foliage retention, distance from the trap tree and status (whether standing or blown down). These data were used to generate variables describing the number of dead trees around each trap

(Table 2), and an index of nest-site availability on each plot. The nest-site availability variable was calculated as the mean number of snags of the appropriate species, with the appropriate level of bark retention to represent an available nest snag for the bird species in question. Appropriate snags were based on nest site data from the study site over five years of bird study (George, T. L. and S. Zack, Humboldt State University, Department of Wildlife, 1 Harpst Street, Arcata, CA 95521, unpublished data 2001-2005). For

23 White-headed Woodpeckers, all ponderosa pine snags over 20 cm dbh with at least some

bark missing were considered appropriate. For Brown Creepers, ponderosa pine snags

over 20 cm dbh with some, but not all bark missing were included. Ponderosa pine snags and Sugar pine snags larger than 50cm dbh were considered appropriate for White-

breasted Nuthatch.

Diet

In order to determine the importance of different arthropods to the food availability of the three focal bird species, I needed information on the diet of each bird.

I used results from Otvos and Stark (1985) on the proportion of each taxon in the diet for

White-headed Woodpecker and Brown Creeper (Table 3). Their study was conducted in mixed conifer forest in the Sierra Nevada, an area which is generally similar to my study site. Taxa that represented less than one percent of the diet were not included. Anderson

(1976) reported on dietary analysis of White-breasted Nuthatch in eastside pine forest west of Sisters, Oregon, but his results are only available in figures, rather than actual reported proportions. He found Forficulidae (earwigs, Dermaptera), Chrysomelidae (leaf beetles, Coleoptera) and Curculionidae (weevils, Coleoptera) to be important food sources for White-breasted Nuthatches in eastside pine forest in the breeding season. I selected Curculionidae for modeling food availability for White-breasted Nuthatch because it was the most important prey-type I captured in adequate numbers to model.

Table 3. Taxonomic levels to which arthropods were identified, and the proportion of each taxon in the diet of White-headed Woodpecker and Brown Creeper sensu Otvos and Stark (1985).

Percent volume in diet Class Order Family Age Class Common Name White-headed Brown Number Captured Woodpecker Creeper

Arachnida Pseudoscorpiones pseudoscorpions 9.70 10

Araneae spiders 5.50 5.70 991

Insecta Isoptera termites 2.50 74

Hemiptera true bugs 3.30 7.50 128

Homoptera Aphidae aphids 2.85 0 Coccidae scale insects 17.15 0

Lepidoptera larva caterpillars 1.45 13

Coleoptera Cerambycidae long-horned beetles 0.10 1.80 74 Curculionidae weevils 0.50 9.70 162 Elateridae click-beetles 3.65 4.20 503 Trogossitidae bark-gnawing beetles 1.95 9.60 8 Scolytidae bark-boring beetles 1.45 28.10 425 Tenebrionidae darkling beetles 4.95 0.60 7 Staphylinidae rove beetles 1.10

Hymenoptera Formicidae ants 12.70 1084

Food Availability Estimates

Each trap was divided into 8 segments marked on the plastic wrap which covered the captured arthropods. By marking the half-way point between each cardinal direction, arthropods could be grouped and sorted by cardinal direction. Arthropods at least 3 mm in length were identified to order or family depending on importance in the diet of the birds and ease of identification (Table 3). They were measured to the nearest millimeter, and lengths were converted to biomass estimates mathematically using Rogers et al.’s

(1976) conversion formula:

mass = 0.0305 * (length)2.62

Next, I used an adaptation of Poulin and LeFebvres’ (1997) method to calculate food

availability on each trap:

d m food availability = t t ƒt mtotal

where:

dt = % volume of taxon in diet

mt = mass of taxon in trap

mtotal = total mass of taxon captured

This formula eliminated the trapping bias toward certain taxa because each taxon was reduced to a proportion. The weighting of each taxon by the percentage of that taxon in the diet of the bird addressed the issue of some taxa being more important than others, and also provided different measures of food availability for different bird species from the same set of traps. Even if the traps tended to capture more of a certain taxon, that

26 taxon did not contribute more to the food availability value unless it represented a higher percentage of the diet of the bird. If fewer than 15 individuals were captured, the taxon was eliminated from the food availability calculation because a small number of captures can over-inflate food availability values on a few traps (Poulin and Lefebvre 1997). To account for the bias of longer traps capturing more arthropods, I divided the food availability value by the trap length. For White-breasted Nuthatch, I was unable to generate a food availability estimate, because diet proportions were unavailable

(Anderson 1976).

Analysis

For each species, I examined proportions of microsite observations on different

parts of the tree, generated a series of models describing food availability and foraging preferences, and conducted chi square tests of foraging microsite selection. I tested each

hypothesis by looking at which variables were included (or not included) in the series of

best models together with the results of the chi square tests.

To verify that the traps were placed in appropriate habitats for each species, I examined proportions of microsite observations by whether the tree was live or dead, tree species, vertical strata and horizontal strata. From these descriptive statistics I determined what percentage of foraging microsites were low on the bole of a live tree, the location of my traps.

For each bird species I ran model selection procedures for up to five response

variables: food availability (White-headed Woodpecker and Brown Creepers only), number of arthropods (for each important arthropod taxon in the diet of the bird),

27 detection (detection rate for Brown Creeper), used vs. random tree (to test for tree selection), and time on tree (to test for tree use). Because of the exploratory nature of this study, I was unable to strictly adhere to a pure a priori approach to model selection as described by Burnham and Anderson (2002). Instead, I attempted to reduce the risk of spurious results by using a systematic approach to model selection. This worked by selecting a best model at the most coarse scale (usually the plot scale) using AIC scores, and gradually moving to finer and finer scales (e.g. tree and aspect scale) to find the best model to explain a response variable. The variables included in the best model at each scale were then used in the selection procedure at the next finer scale. Because my goal was to develop a model that includes variables at multiple scales, I considered the finest scale to be the scale of the experimental unit, i.e. the tree or aspect scale depending on the model. Incorporating multiple random effects (at both the plot and tree level) would have clouded the exploratory process of selecting variables at multiple scales (Burnham and

Anderson 2002).

I examined variable distributions using the NCSS (Hintze 2004) scatter plots module. In order to meet assumptions of normality, all three plot-level blowdown variables, the explanatory plot-level food availability variable, and all 36 aspect-level dead tree variables (Table 2) were log transformed. These transformations, and subsequent model selection procedures were run in SAS® software (SAS 2004), and AIC tables were generated in Excel. When the SAS procedure used did not provide an AIC score, I calculated the AIC score in Excel using the mean square error (MSE) value from

28 the SAS output using the least squares case formula (Burnham and Anderson 2002,

Stauffer in press).

In the food availability models, the response variable was food availability / length of the trap to deal with the bias of longer traps (traps on larger diameter trees) capturing more arthropods. I started with 52 explanatory variables (Table 2). Each model was run with SAS software. Initially, groups of variables that were likely to be highly collinear (beetle evidence, tree-damage and plot level blowdown) were paired down to one variable using PROC REG with the SELECTION=BEST option. I ranked the variables in each group by AICc score. Because the aspect-level dead-tree groups contained 36 variables, I used SELECTION=FORWARD and used the first variable selected in the subsequent analysis. Next, all likely models were run using PROC GLM at each scale starting with the plot level, and moving down through tree level and aspect level. Variables from the best model at a given level were carried on to the analysis at the next finer level.

For Brown Creeper and White-headed Woodpecker, I chose taxa that both represent a large portion of the diet, and vary enough in the environment to affect a bird’s choices of where to forage. To do this I ran linear regressions with food availability as the response variable, and numbers of each taxon as an independent variable. Any taxon that scored an r2 of 0.2 or higher was considered important. With White-breasted

Nuthatches, where this was not possible, I chose to model Curculionidae because it was highly prevalent in the diet (Anderson 1976) and captured in adequate numbers.

29 Models used to describe distribution of individual arthropod taxa were run as either Poisson models or negative binomial models depending on presence of over- dispersion (McCullagh and Nelder 1989, Dobson 2001). Poisson models are usually appropriate for animals that are randomly dispersed, while the negative binomial model is more accurate for animals that are aggregated in their distribution (Stauffer in press). If the variance of the response variable was more than twice the mean, I used the negative binomial model. Otherwise I used the Poisson model. All models were run in SAS using

PROC GENMOD with DIST=POISSON or DIST=NB depending on the distribution chosen. Trap length was used as an offset variable to account for the bias of longer traps.

To select variables (Table 2), I first chose the best univariate variables from the groups: beetle evidence, damage, blowdown and dead trees. Next I proceeded from the plot level scale down to the tree level, and then the aspect level, adding the variables from the best model at each level.

I modeled the distribution of birds amongst the plots. For White-headed

Woodpeckers and White-breasted Nuthatches, I allowed only one detection per visit in my foraging observation protocol. Therefore, I used logistic regression to model the probability of detection on a given visit. I did this in two steps, selecting the best model among the variables treatment, session, year, nest site availability and food availability, and determining if any visit scale variables should be added, such as wind speed, observer or time spent searching the plot. I ran best models in NCSS (Hintze 2004) to generate standard model evaluation metrics (Fielding and Bell 1997). For Brown

Creeper I had differing numbers of detections on a given visit to a plot. So I generated

30 the continuous variable “detection rate” as the number of detections on a plot in a given session, divided by the amount of time spent searching. I modeled creeper detection rate

by treatment, session, year, nest site availability and food availability using multiple linear regression, selecting the best model based on AICc score. I used only foraging data from 2004 in the detection models, because food availability was only measured in that year.

For the tree selection model I used paired logistic regression (Hosmer and

Lemeshow 2000) to group the used and random trees from each foraging observation.

This enabled me to determine on which trees a bird tended to forage and which it tended to avoid, given a specified group of trees. Here, only tree-level variables were relevant, so the selection of the best model involved only two steps. Decisions about variables from groups had to be made (Table 2). To do this I ran each variable in a group as a univariate model with the response, and chose the best from each group based on AICc

scores. The variables were run in all sensible combinations. I used PROC PHREG in the

SAS software as per Hosmer and Lemeshow (2000) to run these models. For White- headed Woodpeckers and Brown Creepers, I limited this analysis to trees on which the bird spent a minimum of 10 seconds to reduce the effect of trees that the birds alighted on for a few moments. I did not do this with the White-breasted Nuthatches because the sample size was small. I limited the analysis to live trees only, because all three species have been shown to forage primarily on live trees (Pravosudov and Grubb 1993, Garrett et al. 1996, Hejl et al. 2002).

31 The tree-time model is another way to look at which trees are important foraging locations for the birds. For this model, the response variable was the amount of time the bird spent on each tree. See Table 2 for explanatory variables. For this procedure I again selected the best variable from each group and then ran through each level, starting with the plot level, then visit, observation, and tree level using PROC GLM in the SAS software. Again, analysis was limited to live trees.

I examined microsite use using 2 contingency tests of independence. All 2 values were calculated in Excel. I tested selection for damaged portions of the tree, aspect and shade. To determine if birds selected the top portion of the tree when it was damaged, I decided whether the bird visited the top of the tree based on whether it arrived or departed from the top third of the tree. This way I was able to include all trees from all observations where I had the arrival and departure heights recorded. I did a similar test for selection of damage at the base of the tree. For the aspect selection, I tested whether the birds used all four cardinal aspects equally, based on the microsite observations. To test for the selection of shade, I compared the frequency of microsite foraging locations in the shade with the expected frequency based on the average ocular estimate of shade on the bole.

RESULTS

One hundred twenty one traps were placed for two sessions including an extra trap placed due to a field error. Due to damage from bears or other animals, 115 traps were collected in the first trapping session, and 121 were collected in the second. Several taxonomic groups (Psuedoscorpiones, Aphidae, Coccidae, Lepidopteran larvae,

Trogossitidae and Tenebrionidae (Table 3)) were not included in food availability calculations because fewer than 15 of each of these taxa were captured.

Patterns of use by White-headed Woodpecker

I made 34 woodpecker observations in 76 plot visits in 2003 and 65 observations

in 165 visits in 2004. Due to gaps in the data, I had a sample size of 98 for microsite

description, 64 for shade selection, 88 for aspect selection, 67 for the tree selection model

(166 trees), and 69 for the tree time model (170 trees).

Of all White-headed Woodpecker microsite observations, 73% were on live trees.

Observations on live trees were split between white fir (58%) and ponderosa pine (40%),

with one observation on an incense cedar. Most observations were on the bole (Figure

3), and most were low (Figure 4). In total 25.5% of microsite observations were low on

the bole of a live tree.

The food availability selection procedure (Appendix A) resulted in a best model

with the effects of treatment and “beetle exit” included (Table 4). The model indicates

that there was more food on thinned plots and more food on trees with beetle exit holes.

There is also an indication in the model that there was more food on burned plots in

32 33

50 45 incense cedar

s 40

n white fir o i t 35 a ponderosa pine v r

e 30 s b O

25 f o

r 20 e b

m 15 u

N 10 5 0 Bole Branches Horizontal Strata

Figure 3. White-headed Woodpecker use of tree species and horizontal strata according to microsite data. Only data from the use of live trees is displayed (Goosenest Adaptive Management Area 2003 and 2004).

High ponderosa pine

a white fir t a r

t incense cedar S

a Middle c i t r e V

Low

0 10 20 30 Number of Observations

Figure 4. White-headed Woodpecker use of tree species and vertical strata according to microsite data. Only data from the use of live trees is displayed (Goosenest Adaptive Management Area 2003 and 2004).

Table 4. Best modelsa describing White-headed Woodpecker food and foraging, and confidence intervals of coefficients for those models (Goosenest Adaptive Management Area 2003 and 2004).

Explanatory 95%C.I. Bounds Response Variable Model Type Variables Estimate (lower,upper)

Food availability linear, response intercept -7.332 (-7.509,-7.154) log transformed trt Burn 0.037 (-0.185,0.260) Control -0.284 (-0.505,-0.063) Pine 0

b_exit 0 -0.295 (-0.483,-0.106) 1 0

Formicidae negative binomial Intercept -4.102 (-4.848,-3.356)

trt Burn 0.216 (-0.068,0.499) Control -0.849 (-1.177,-0.521) Pine 0

top_dam 0 0.926 (0.409,1.442) 1 0

size big -0.384 (-0.657,-0.112) small 0.000

b_exit 0 -0.290 (-0.537,-0.043) 1 0

canopy 0.013 (0.007,0.020)

firall_30m 0.025 (0.012,0.037)

Dispersion 2.459 (2.107,2.812)

36 Table 4. Best modelsa describing White-headed Woodpecker food and foraging, and confidence intervals of coefficients from those models (Goosenest Adaptive Management Area 2003 and 2004) (continued).

Explanatory 95%C.I. Bounds Response Variable Model Type Variables Estimate (lower,upper)

Detection logistic regression Intercept -1.590 (-2.718,-0.582)

trt Burn 1.378 (0.515,2.305) Control -5.567 (-8.717,-3.486) Pine 0

nest-site avail. 0.801 (0.290,1.351)

period 1 0.750 (-0.062,1.593) period 2 0

Tree-selection paired logisitic dbh 0.129 (0.058,0.201)

dbhsq -0.001 (-0.001,0.000)

beetle_exit 0.496 (-0.093,1.084)

shade 0.033 (0.010,0.055)

Tree-time linear intercept 20.350 (13.235,27.466) square root transformed date -0.076 (-0.113,-0.040)

dbh 0.049 (0.013,0.084)

can_south 0.026 (0.004,0.049)

cone 0 -2.958 (-4.525,-1.391) 1 0

aSee Appendices A-E for model selection procedure results for these 5 best models.

37 addition to the effect of thinning, but the burn effect was not statistically significant. The

food availability model was significant, but had a low r2 value (r2 = 0.014, df = 941, P =

0.002).

No single taxon had a relationship with food availability with an r2 > 0.2, but

Formicidae did come close (r2 = 0.190, df = 942, P < 0.001) so a model for Formicidae

was examined. The selection procedure for the Formicidae model (Appendix B) resulted

in a best model with many coefficients (Table 4). In this model, the 95% confidence

interval for dispersion was above two, indicating that use of the negative binomial model

was appropriate. None of the 95% confidence intervals of the coefficients intersected

zero except the coefficient for the burned treatment (Table 4). The Formicidae model

indicates that there were more ants on thinned plots, on small trees with healthy tops and

with beetle exit holes, and on sides of the tree with more canopy cover and more dead fir

trees within 30 m (Table 4).

The detection model selection procedure (Appendix C) resulted in a best model

that included significant coefficients for treatment, nest-site availability and period (Table

4). All 95% confidence intervals failed to intersect zero except period (Table 4). This

model fit the data well (Sensitivity = 76.0%, Specificity = 81.5%, Percentage Correctly

Classified = 78.2%, Area Under the ROC Curve = 0.863). The detection model indicates

that woodpeckers were most likely to be found foraging on burn plots, least likely on

controls and more likely on plots with more appropriate snags for nesting (Table 4).

They were also more likely to be detected in the first half of the breeding season, but not

significantly so (Table 4). While nest-site availability was in the best detection model

38 (Table 4), it was secondary to treatment in importance. In fact, had treatment not been

included in the model, nest-site availability would have the sign of its coefficient

reversed, so that more nest sites would mean fewer woodpeckers (Figure 5).

The tree-selection model selection procedure (Appendix D) resulted in a best model with a quadratic effect of dbh, and linear effects of beetle exit and shade (Table 4).

This best model showed excellent goodness of fit (Wald 2 = 46.078, df = 4, P < 0.001)

but the confidence interval for the beetle exit coefficient intersected zero and the confidence interval for the quadratic component of the dbh effect, dbhsq, touched zero

(Table 4). The tree selection model indicates that White-headed Woodpeckers tend to

select larger trees, with more shade on the bole for foraging, and possibly are more likely

to select trees with beetle exit holes (Table 4).

The selection procedure for the tree-time model (Appendix E) resulted in a best model including the effects of date, dbh, canopy to the south and cone foraging behavior

(Table 4). This model fit the data relatively well given the r2 value (r2 = 0.222, df = 165,

P < 0.001). No confidence intervals intersected zero (Table 4). The tree time model indicates that woodpeckers spent more time foraging on trees earlier in the breeding season, on larger trees with more canopy cover to the south, and tended to spend more time if they were foraging on cones (Table 4). Residual analysis indicated that both linear regression models, food availability and tree time, met the assumptions of normality of residuals and homoscedasticity.

The collection of best models concerning White-headed Woodpecker foraging and food availability (Table 4) addressed the major aims of this study. Food availability

1 0.9 Burn n o i t 0.8 Control c e

t 0.7 e Pine d 0.6 f o 0.5 y t i l

i 0.4 b

a 0.3 b o r 0.2 P 0.1 0 0 1 2 3 4 5 Nest-site availability (average number of appropriate snags per 80 m diameter plot)

Figure 5. Predicted detection probability of White-headed Woodpecker by nest-site availability and treatment according to detection logistic regression model, which includes the effects of treatment, nest-site availability and period (Table 4). Appropriate snags were defined as ponderosa pine snags with at least some bark missing (Goosenest Adaptive Management Area 2004).

40 was not included in the best detection model. With the exception of the tree time model,

every top model for White-headed Woodpeckers that could include coefficients for

treatment did include them. In every instance, the treatment effect followed the same

pattern of highest abundance, food availability and detection probability on burned plots

and lowest on controls. While nest site availability had a positive effect on detection

probability (Table 4), the effect was only detectable once the overwhelming effect of treatment was accounted for (Figure 4). Variables supportive of the disturbance hypothesis were present in the Formicidae (top dam, beetle exit, firall 30m) and tree- selection (beetle exit) top models, although the top dam coefficient estimate in the

Formicidae model suggested more ants on trees without top damage. Variables supportive of the solar radiation hypothesis were present in the Formicidae (canopy), tree-selection (shade) and tree-time (canopy to south) best models. In terms of tree size, the coefficient for large trees in the Formicidae model was negative, while coefficients for dbh in both the tree selection and tree time models were positive. The effect on tree selection due to dbh included a negative second order coefficient suggesting decreasing effect of dbh with increasing size. The tree-time model included effects of date and cone

(Table 4).

Based on arrival and departure heights on all used trees, White-headed

Woodpeckers did not utilize the lower portion of trees significantly more often when the bottom of the tree was damaged (2 = 0.371, df = 1, P = 0.543). However, they were

more likely to utilize the top of a tree if the top was damaged (Figure 6).

80 High 70

s Not high e

e 60 r t

o 50

r e

b 40 m

u 30 N 20 10 0 Top damage No top damage

Figure 6. White-headed Woodpecker utilization of the top of used trees on trees with or without top damage. The bird was considered to have utilized the top of the tree if it arrived or departed high on the tree (on the upper third) ( 2 = 7.046, df = 1, P = 0.008) (Goosenest Adaptive Management Area 2003 and 2004).

42 Microsite data indicate that White-headed Woodpeckers tended to favor the west

side of trees and avoid the east side, although it was not quite statistically significant

(Figure 7). There was also weak evidence that White-headed Woodpeckers used shady

locations out of proportion to their availability, although this was not statistically

significant (Figure 8).

Patterns of use by Brown Creeper

I collected 70 creeper observations in 2003 and 173 in 2004. Due to gaps in the data, I had a sample size of 238 for microsite description, 221 for shade selection, 215 for aspect selection, 148 observations for the tree selection model (398 trees), and 155 observations for the tree time model (434 trees).

Of all creeper microsite observations, 84% were on live trees. On live trees, twice as many observations were on ponderosa pine as were on white fir. Six observations were on sugar pine. Most observations were on the bole (Figure 9), and most were middle (43%) or low (41%) (Figure 10). In total, 28.6% of microsite observations were low on the bole of a live tree.

The food availability selection procedure (Appendix F) resulted in a model with total volume, period and beetle included in the best model (Table 5). This model was significant, but had a poor r2 value (r2 = 0.061, df = 940, P < 0.001). It indicates that food

availability for Brown Creeper was greater on plots with more blowdown, in the first half

of the breeding season, and on trees with evidence of beetles.

Scolytidae was the only taxon whose relationship with food availability had an r2

value exceeding 0.2 (r2 = 0.263, df = 942, P < 0.001).The selection procedure for the

s 30 n o i t

a 25 v r e s 20 b O

o 15

r e b 10 m u

N 5 0 East North West South Aspect

Figure 7. White-headed Woodpecker use of different aspects of trees during foraging observations, based on microsite data ( 2 = 7.636, df = 3, P = 0.0542) (Goosenest Adaptive Management Area 2003 and 2004).

use

s 50 n o

i expected t

a 40 v r e s b O

30 f o

r e

b 20 m u N 10

0 shade sun

Figure 8. White-headed Woodpecker foraging bouts in the shade compared with expected use of shade based on the mean ocular estimate of available shade on used trees, based on microsite data ( 2 = 1.077, df = 1, P = 0.299) (Goosenest Adaptive Management Area 2003 and 2004).

180 sugar pine 160 white fir s

n 140 ponderosa pine o i t

a 120 v r e s

b 100 O

f 80 o

r e

b 60 m u 40 N 20 0 Bole Branches

Figure 9. Brown Creeper use of tree species and horizontal strata of tree according to microsite data. Data displayed is limited to live trees used (Goosenest Adaptive Management Area 2003 and 2004).

ponderosa pine High white fir sugar pine a t a r t S

a Middle c i t r e V

Low

0 20 40 60 80 Number of Observations

Figure 10. Brown Creeper use of tree species and vertical strata of tree according to microsite data. Data displayed is limited to live trees used (Goosenest Adaptive Management Area 2003 and 2004).

Table 5. Best modelsa describing Brown Creeper food and foraging including the type of model, variables included, and estimates and confidence intervals of coefficients for these models (Goosenest Adaptive Management Area 2003 and 2004).

Explanatory 95%C.I. Bounds Response Variable Model Type Variables Estimate (lower,upper)

Food availability linear, response intercept -11.489 (-12.733,-10.245) log transformed TotalVolume 0.390 (0.266,0.514)

Period 1 0.356 (0.122,0.590) 2 0

Beetle 0 -0.504 (-0.746,-0.262) 1 0

Scolytidae negative binomial Intercept -8.299 (-9.774,-6.825)

trt Burn -0.136 (-0.440,0.168) Control 0.507 (0.120,0.895) Pine 0

period 1 0.728 (0.482,0.975) 2 0

PineVolume 0.515 (0.379,0.652)

treesp ABCO 0.265 (-0.004,0.534) PIPO 0

b_exit 0 -0.261 (-0.559,0.037) 1 0

canopy -0.008 (-0.014,-0.001)

firsnag_20m 0.081 (0.059,0.103)

Dispersion 1.392 (1.028,1.756)

48 Table 5. Best modelsa describing Brown Creeper food and foraging including the type of model, variables included, and estimates and confidence intervals of coefficients for these models (Goosenest Adaptive Management Area 2003 and 2004) (continued).

Explanatory 95%C.I. Bounds Response Variable Model Type Variables Estimate (lower,upper)

Detection rate linear Intercept 0.010 (0.008,0.012)

Tree-selection paired logisitic dbh 0.113 (0.074,0.152)

dbhsq -0.001 (-0.001,0.000)

beetle 0.692 (0.292,1.091)

top_dam 0.684 (0.096,1.272)

can_south -0.012 (-0.023,-0.001)

Tree-time linear Intercept 3.294 (2.234,4.355) square root transformed dbh 0.078 (0.059,0.098)

aSee Appendices F-J for model selection procedures that resulted in these top models.

49 Scolytidae model (Appendix G) resulted in a best model with many coefficients (Table

5). In this model, the 95% confidence interval for dispersion was greater than one and did not intersect one, indicating that a negative binomial model was appropriate as opposed to a Poisson model. Some of the 95% confidence intervals of the coefficients intersected zero including the coefficient for the burned treatment, tree species and beetle exit (Table 5). The Scolytidae model indicates there were more bark beetles on control plots, more in the first half of the breeding season, more on plots with more pine blowdown, more on white fir as opposed to ponderosa pine (not significantly more), more on trees with beetle exit holes (not significantly more), less on aspects of trees with more canopy cover, and more on sides of trees where there were more fir snags within 20 m. The effect of having more beetles on controls was overwhelmed by the effect of downed pine on each plot in the Scolytidae model (Table 5, Figure 11).

The detection rate model selection procedure (Appendix H) resulted in the null model being the best model (Table 5). This indicates that Brown Creepers were evenly distributed amongst the plots.

The tree-selection model selection procedure (Appendix I) resulted in a best model with a quadratic effect of dbh, and linear effects of beetle, top damage and canopy to the south of the tree (Table 5). This best model showed excellent goodness of fit

(Wald 2 = 98.670, df = 4, P < 0.001) and no confidence intervals intersected zero, although the quadratic part of the dbh effect touches zero (Table 5). This model indicates that Brown Creepers tended to select larger trees with evidence of beetle attack, damage at their tops, and less canopy cover to the south.

8 e

a Burn d

i 7 t

y Control l

o 6 c Pine S

o 5

r e

b 4 m u n

3 d e t

c 2 i d e

r 1 P 0 0 2 4 6 8 10 12 Log of pine volume (cu. ft. per plot)

Figure 11. Predicted number of bark-boring beetles (Scolytidae) on the side of each tree, based on the Scolytidae model. Effects of volume of downed pine on each plot and treatment are included in the graph, although other variables from the model (Table 5) contribute to the predicted value (Goosenest Adaptive Management Area 2004).

51 The selection procedure for the tree-time model (Appendix J) resulted in a best

model including only the effects of dbh (Table 5). This model did not fit the data well

given the r2 value (r2 = 0.123, df = 165, P < 0.001), but the confidence intervals did not intersect zero (Table 5). The tree-time model indicates that Brown Creepers spent more time foraging on larger trees. Residual analysis indicated that the two linear regression models, food availability and tree time, met the assumptions of normality of residuals and homoscedasticity. Since the best detection rate model was the null model, residual analysis was not relevant in this case.

The collection of best models describing Brown Creeper food and foraging (Table

5) addressed the major aims of this study. The first aim was to determine if food availability varied across treatments. Treatment was not included in the best food availability model, but the Scolytidae model showed a negative effect of thinning.

Blowdown, which is more prevalent on the thinned plots, had a positive effect in both the food availability and Scolytidae models (Figure 11). The second aim of the study was to determine if birds distribute themselves according to food availability. The best detection rate model included no covariates. The food availability and tree-selection models both included the effect of beetle evidence, while the Scolytidae model contained the effect of beetle exits. A negative effect of canopy cover was common to both the Scolytidae and tree-selection models. There were no other variables in common between food models and foraging models. The third aim of the study was to investigate the roles of disturbance and solar radiation on food availability. Both food models included effects of plot-level blow-down, and the Scolytidae model included an effect of fir snags within

52 20m of the side of the tree. Beetle or beetle exit was an effect in the food availability,

Scolytidae and tree selection models. The effect of top damage was only included in the

tree selection model. Canopy, a variable related to solar exposure, was negative in the

Scolytidae model and the tree selection model. Meanwhile the effect of the shady

“control” treatment in the Scolytidae model was positive. The effect of dbh was strong in

the tree-selection and tree-time models, but not present in either best food model.

Based on arrival and departure heights on all used trees, creeepers utilized the top

portion of trees significantly more often when the tree had top damage (Figure 12). They

were also more likely to utilize the bottom of a tree if the bottom of the tree was damaged

(Figure 13).

Microsite data indicate that Brown Creepers tended to favor the west side of trees and avoid the east side (Figure 14). There was also evidence of the Brown Creepers using the shade out of proportion to its availability (Figure 15).

Patterns of use by White-breasted Nuthatch

I collected 5 nuthatch observations in 2003 and 19 in 2004. I was able to use all

the observations for the description of micro-site selection, but due to gaps in the data, I

had a sample size of 21 for shade and aspect selection, 20 for the tree-selection model (65

trees), and 19 for the tree time model (55 trees).

Of all nuthatch microsite observations, 92% were on live trees including

ponderosa pine (59%), white fir (36%) and one sugar pine (5%). Most observations

(91%) were on the bole (Figure 16), and most (80%) were low (Figure 17). In total

66.7% of microsite observations were low on the bole of a live tree.

300 High 250 Not high s

e 200 e r t

f o

r 150 e b m u

N 100

0 Top damage No top damage

Figure 12. Brown Creeper utilization of the top of used trees on trees with or without top damage (2 = 4.525, df = 1, P = 0.033). The bird was considered to have utilized the top of the tree if it arrived or departed high on the tree (on the upper third) (Goosenest Adaptive Management Area 2003 and 2004).

180

160 Low 140 Not low s

e 120 e r t

o 100

r e

b 80 m u

N 60 40 20 0 Bottom damage No bottom damage

Figure 13. Brown Creeper utilization of the bottom of used trees with or without bottom damage (2 = 4.942, df = 1, P = 0.026). The bird was considered to have utilized the bottom of the tree if it arrived or departed from the bottom third (Goosenest Adaptive Management Area 2003 and 2004).

s 70 n o i

t 60 a v r

e 50 s b

O 40

f o

r 30 e b

m 20 u N 10 0 East North West South Aspect

Figure 14. Brown Creeper use of different aspects of trees during foraging observations, based on microsite data ( 2 = 12.516, df = 3, P = 0.006) (Goosenest Adaptive Management Area 2003 and 2004).

250 use

s availability

n 200 o i t a v r

e 150 s b O

f o

r 100 e b m u

N 50

0 shade sun

Figure 15. Brown Creeper foraging bouts in the shade compared with expected use of shade based on the mean ocular estimate of available shade on used trees, based on microsite data ( 2 = 8.067, df = 1, P = 0.005) (Goosenest Adaptive Management Area 2003 and 2004).

25 sugar pine s

n 20 o

i white fir t a v

r ponderosa pine e

s 15 b O

f o 10 r e b m

u 5 N

0 Bole Branch Horizontal Strata

Figure 16. White-breasted Nuthatch use of tree species and horizontal strata of tree according to microsite data (Goosenest Adaptive Management Area 2003 and 2004).

ponderosa pine High white fir a

t sugar pine a r t S

a Middle c i t r e V

Low

0 5 10 15 20 Number of observations

Figure 17. White-breasted Nuthatch use of tree species and vertical strata of tree according to microsite data (Goosenest Adaptive Management Area 2003 and 2004).

59 No food availability model was tested because no numeric description of White-

breasted Nuthatch diet in ponderosa pine habitat was available. I chose to model

Curculionidae as the most important prey for White-breasted Nuthatch. Anderson (1976) also names Forficulidae (earwigs, Dermaptera) and Chrysomelidae (leaf beetles,

Coleoptera) as major breeding season prey-types, but I only captured adequate numbers of Curculionidae for analysis. The selection procedure for the Curculionidae model

(Appendix K) resulted in a best model with significant effects of tree-damage, tree-size and canopy cover (Table 6). The model suggests there were more weevils on small damaged trees with lower canopy cover (Table 6). The deviance divided by the degrees of freedom was 0.6713. This indicated no over-dispersion, so the Poisson model was the correct model to use. None of the 95% confidence intervals of the coefficients intersected zero (Table 6) indicating a good model fit to the data.

The detection model selection procedure (Appendix L) resulted in a best model including the effects of treatment and period (Table 6). I was unable to generate confidence intervals due to quasi separation, but the coefficients show higher likelihood of detection on burned plots, much lower on controls, and higher likelihood of detection in the second period (Table 6). Furthermore, running the model in NCSS to generate goodness of fit statistics resulted in a different coefficient for treatment effect of control than what is reported in Table 6. When the cut-point was adjusted to 0.15, the model fit the data well (Sensitivity = 68.4%, Specificity = 70.5%, Correctly Classified = 70.3%,

Area Under ROC Curve = 0.778). As there were no detections on control plots, there

60 Table 6. Best modelsa describing White-breasted Nuthatch food and foraging including the type of model, variables included, and estimates and confidence intervals of coefficients for these models (Goosenest Adaptive Management Area 2003 and 2004).

Explanatory 95%C.I. Bounds Response Variable Model Type Variables Estimate (lower,upper)

Curculionidae poisson Intercept -4.058 (-4.567,-3.548)

gen_dam 0 -0.355 (-0.679,-0.031) 1 0

size big -0.698 (-1.040,-0.357) small 0

canopy -0.007 (-0.014,-0.001)

Detection logistic regression Intercept -1.526

trt Burn 0.591 Control -13.341 Pine 0

period 1 -0.867 2 0

Tree selection paired logisitic dbh 0.027 (-0.002,0.055) regression beetle_exit 0.930 (-0.112,1.971)

can_south 0.018 (-0.005,0.041)

Tree time linear Intercept 0.225 (-2.023,2.474) response log transformed dbh 0.140 (0.052,0.228)

dbhsq -0.001 (-0.002,0.000) aSee Appendices K-N for model selection procedures that resulted in these top models.

61 would not be a precise low point for the control coefficient as long as it was low enough

that the model would not predict nuthatch presence on a control plot.

The tree-selection model selection procedure (Appendix M) resulted in a best

model with a linear effect of dbh, as well effects of beetle exit and canopy to south (Table

6). This best model showed fair goodness of fit (Wald 2 = 10.011, df = 3, P < 0.019),

but confidence intervals for all three coefficients intersected zero (Table 6). While none of the effects are statistically significant the tree selection model indicated that foraging

White-breasted Nuthatches tended to select larger trees with beetle exit holes and more canopy cover to the south (Table 6).

The selection procedure for the tree-time model (Appendix N) resulted in a best model with a quadratic effect of dbh (Table 6). This model fit the data fairly well given the r2 value (r2 = 0.196, df = 52, P = 0.003), and only the confidence interval of the

intercept intersected zero, although the confidence interval of the quadratic component

touches zero (Table 6). The tree time model indicates that White-breasted Nuthatches

tended to spend more time foraging on larger trees. Residual analysis indicated that the

tree-time model met the assumptions of normality of residuals and homoscedasticity.

The collection of best models describing White-breasted Nuthatch food and

foraging (Table 6) addressed the aims of the study. The first aim was to determine if food availability varied across treatments. The Curculionidae model, which was my only food model for White-breasted Nuthatches, did not include a treatment effect (Table 6).

In terms of asking whether birds distribute themselves according to food availability, the

best detection model for White-breasted Nuthatches included the effects of treatment and

62 period (Table 6). There were no commonalities between food models and foraging

models at the tree scale. The effect of tree damage in the Curculionidae model was not

present in any of the foraging models, and the effects of canopy and tree size in the

Curculionidae model were the opposite of related effects in the foraging models (Table

6). The effect of canopy in the Curculionidae model was negative, but it was positive in

the tree-selection model (Table 6). The Curculionidae model shows fewer weevils on

large trees while the foraging models show White-breasted Nuthatches selecting larger

trees and foraging on larger trees for longer periods of time (Table 6).

Based on arrival and departure heights on all used trees, White-breasted

Nuthatches tended to utilize the top portion of trees more often when the top of the tree

was damaged (Figure 18) and tended to use the bottom of tree more often if it was

damaged, but the association was weak enough that the sample size (n=55) was too small for statistically significant results (Figure 19).

Microsite data indicate that White-breasted Nuthatches tend to favor the west and north sides of trees and avoid the east and south sides although it was not statistically significant (Figure 20). White-breasted Nuthatches did not seem to use shade out of proportion to its availability (Figure 21).

50 45 High 40 Not high

s 35 e e r t

30 f o

r 25 e b

m 20 u

N 15 10 5 0 Top damage No top damage

Figure 18. White-breasted Nuthatch utilization of the top of used trees on trees with or without top damage (2 = 0.585, df = 1, P = 0.444). The bird was considered to have utilized the top of the tree if it arrived or departed high on the tree (on the upper third) (Goosenest Adaptive Management Area 2003 and 2004).

40 Low 35 Not low 30 s e e

r 25 t

f o

r 20 e b m

u 15 N 10 5

0 Bottom damage No bottom damage

Figure 19. White-breasted Nuthatch utilization of the bottom of used trees with or without bottom damage ( 2 = 0.723, df = 1, P = 0.395). The bird was considered to have utilized the bottom of the tree if it arrived or departed from the bottom third (Goosenest Adaptive Management Area 2003 and 2004).

9 8 s n

o 7 i t a

v 6 r e s

b 5 O

f 4 o

e 3 b m

u 2 N 1 0 East North West South Aspect

Figure 20. White-breasted Nuthatch use of different aspects of trees during foraging observations, based on microsite data ( 2 = 3.952, df = 3, P = 0.267) (Goosenest Adaptive Management Area 2003 and 2004).

18 16 used

s expected n 14 o i t a 12 v r e s

b 10 O

f 8 o

r e

b 6 m u 4 N 2 0 shade sun

Figure 21. White-breasted Nuthatch foraging bouts in the shade compared with expected use of shade based on the mean ocular estimate of available shade on used trees, based on microsite data ( 2 = 0.073, df = 1, P = 0.785) (Goosenest Adaptive Management Area 2003 and 2004).

DISCUSSION

My results suggest that forest structure restoration can affect food availability, and that food availability can affect distribution of birds at a variety of scales. Results also suggest that both solar radiation and disturbance play roles in this process. Food availability varied across treatments for White-headed Woodpecker, and at least for one prey taxon for Brown Creeper, although there was less food on treated plots for Brown

Creeper (Table 7). White-headed Woodpeckers seemed to forage in areas that were likely to have more food, both at the plot and tree scales, although this was only true at the tree scale for Brown Creepers (Table 8) suggesting that food availability has some effect on the distribution of birds.

Some results suggest that disturbance plays a role in bark-gleaning bird response to variation in food availability. Brown Creepers and their food seemed to respond to blowdown, nearby snags, and beetle outbreak, although they showed some response to tree damage at the tree and microsite scales (Table 9). On the other hand, White-headed

Woodpeckers and the ants they prey on responded most strongly to thinning and on trees which had been infested by beetles (Table 9).

There was strong evidence that solar radiation in these more open thinned stands increased food availability, driving White-headed Woodpecker’s response to eastside pine restoration (Table 10). The woodpeckers and their primary food’s response to thinning, along with evidence that shade and canopy cover were important for foraging and the ants they forage on supports this (Table 10). Due to low sample sizes, it is hard

67 68 Table 7. Predictions for the hypothesis that food varies across treatments for bark- gleaning birds and whether those predictions held true (Goosenest Adaptive Management Area 2004).

Predictions

Species Food availability model Important arthropod food model includes treatment effect includes treatment effect

White-headed Woodpecker yes yes

Brown Creeper no (second best model does) yes, but highest on controls

White-breasted Nuthatch n/a no

Table 8. Predictions for the hypothesis that food-availability drives the distribution of White-headed Woodpecker, Brown Creeper and White-breasted Nuthatch, and whether those predictions held true (Goosenest Adaptive Management Area 2003 and 2004).

A positive food-availability coefficient Effects in food models should be similar Prediction should be included in best detection to effects in foraging models (rate) model

Species Effects at plot scale Effects at tree scale

effect of treatment follows same positive effect of beetle-exit in both White-headed Woodpecker no pattern for food, Formicidae and Formicidae and tree-selection models detection models,

beetle evidence effect present for food, no variation in Scolytidae and tree-selection models. Brown Creeper no detection rate by plot. Scolytidae and tree-selection models both have negative effect of canopy.

none: effect of tree size and canopy White-breasted Nuthatch no none opposite between food and foraging models

70 Table 9. Summary of evidence supporting or refuting the hypothesis that disturbance plays a role in the distribution of bark-gleaning birds in response to prey- availability (Goosenest Adaptive Management Area 2003 and 2004).

Species Scale Model / Test Variable / Result Effect

White- plot food-availability treatment thin-sig., burn-insig. headed Formicidae treatment thin-sig., burn-insig. Woodpecker detection treatment thin-sig., burn-sig. tree-time treatment none

food-availability plot-level blowdown none Formicidae plot-level blowdown none detection plot-level blowdown none tree-time plot-level blowdown none

tree food-availability tree damage none Formicidae top damage negative tree-selection tree damage none tree-time tree damage none

food beetle exit positive Formicidae beetle exit positive tree-selection beetle exit positive tree-time beetle evidence none

food dead trees none Formicidae dead trees none tree-selection dead trees none tree-time dead trees none

microsite top-damage selection for tree-tops significant bottom-damage none no

Table 9. Summary of evidence supporting or refuting the hypothesis that disturbance plays a role in the distribution of bark-gleaning birds in response to prey- availability (Goosenest Adaptive Management Area 2003 and 2004) (continued).

Species Scale Model / Test Variable / Result Effect

Brown plot food-availability treatment none Creeper Scolytidae treatment more on controls detection treatment none tree-time treatment none

food-availability total blowdown positive Scolytidae pine blowdown positive detection plot-level blowdown none tree-time plot-level blowdown none

tree food-availability tree damage none Scolytidae tree damage none tree-selection top damage positive tree-time tree damage none

food beetle evidence positive Scolytidae beetle exit positive tree-selection beetle evidence positive tree-time beetle evidence none

food dead trees none Scolytidae fir snags within 20 m positive tree-selection dead trees none tree-time dead trees none

microsite top-damage selection for tree-tops significant bottom-damage selection for tree-bottoms significant

Species Scale Model / Test Variable / Result Effect

White- plot Curculionidae treatment none breasted detection treatment more on burn, none on control Nuthatch tree-time treatment none

Curculionidae plot-level blowdown none detection plot-level blowdown none tree-time plot-level blowdown none

tree Curculionidae general damage positive tree-selection tree damage none tree-time tree damage none

Curculionidae beetle evidence none tree-selection beetle exit positive tree-time beetle evidence none

Curculionidae dead trees none tree-selection dead trees none tree-time dead trees none

microsite top-damage n/a non-significant bottom-damage n/a non-significant

Table 10. Summary of evidence supporting (or refuting) the hypothesis that solar exposure plays a role in the distribution of bark-gleaning birds in response to prey-availability (Goosenest Adaptive Management Area 2003 and 2004).

Species Scale Model / Test Variable / Result Effect

White-headed plot food-availability Treatment(thinning) positive Woodpecker Formicidae Treatment(thinning) positive detection Treatment(thinning) positive tree-time Treatment(thinning) none

tree tree-time canopy-south positive tree-selection shade positive

microsite food-availability canopy none Formicidae canopy positive aspect selection for west, avoiding east almost significant shade selection for shade non-significant

Species Scale Model / Test Variable / Result Effect

Brown plot food-availability Treatment(thinning) none Creeper Scolytidae Treatment(thinning) negative detection Treatment(thinning) none tree-time Treatment(thinning) none

tree tree-selection canopy-south negative tree-selection or tree-time shade none

microsite food-availability canopy none Scolytidae canopy negative aspect selection for west, avoiding east significant shade selection for shade significant

Species Scale Model / Test Variable / Result Effect

White-breasted plot Curculionidae Treatment(thinning) none Nuthatch detection Treatment(thinning) positive tree-time Treatment(thinning) none

tree tree-selection canopy-south positive, non-significant tree-selection or tree-time shade none

microsite Curculionidae canopy negative aspect selection for north and west non-significant shade selection for shade non-significant

76 to draw definitive conclusions about White-breasted nuthatch response to thinning and

prescribed fire (Tables 7-10), except that they do indeed respond to it (see “Detection”

Table 6). All three bird species selected and spent more time foraging on larger trees, even though there was no evidence that larger trees have a higher density of prey than smaller trees (Table 11).

There were some methodological weaknesses with this study that were unavoidable. Foraging observations were assumed to be independent. However, it is likely that that multiple observations were taken on some birds. This is a problem in most observational studies. The diet information, upon which the food availability estimates rely, is from other sites which may have had different arthropod communities.

There may be differing opinions on what the experimental unit should have been for many of the statistical tests. The study was also limited to the breeding season, and it is unknown whether any of the results here have any effect on the fitness, and resultant population dynamics of these three bird species. However, as animals that neither reproduce extremely rapidly nor migrate long distances, these birds do not meet the typical criteria of populations in which habitat quality can become decoupled from density (Van Horne 1983). Despite these weaknesses, this study should be useful toward understanding the mechanisms for bark-gleaning bird response to thinning, prescribed fire and other disturbances.

White-headed Woodpecker

The first aim of the study was to investigate differences in prey availability across

treatments. Treatment was included as a variable in the best model for both food

77 Table 11. Summary of results related to the hypothesis that large trees are important foraging habitat for bark-gleaning birds (Goosenest Adaptive Management Area 2003 and 2004).

food foraging Species models variable effect models variable effect

White-headed food size none tree-selection dbh pos. Woodpecker Formicidae size neg. tree-time dbh pos.

Brown food size none tree-selection dbh pos. Creeper Scolytidae size none tree-time dbh pos.

White-breasted Curculionidae size neg. tree-selection dbh pos. Nuthatch tree-time dbh pos.

78 availability (although the model was weak) and the most important food item, Formicidae

(Table 7). Food availability was higher on thinned plots for White-headed Woodpeckers, and burning on top of that possibly increased food availability even more.

The second aim was to test the hypothesis that food availability affects distribution of birds on the plots. If this hypothesis was true, we would expect food availability to be included in the best detection model, and for factors and covariates in the foraging models to be similar to those in the food availability and taxon models.

Food availability itself was not included in the detection model (Table 8). However, the bird response according to the detection model followed the same pattern on the treatments as that for both measures of food availability. I found the highest food availability and detection probability on burned plots, and lowest on controls (Table 8).

This is evidence for the importance of food to the distribution of White-headed

Woodpeckers with regard to thinning and prescribed fire.

Nest-site availability has been the focus of management of western forests for woodpeckers and other cavity nesters (McClellan and Frissell 1975, Scott 1979, Thomas et al. 1979, Raphael and White 1984, Zarnowitz and Manuwal 1985, McComb et al.

1986, Morrison et al. 1986, Brawn et al. 1987, Brawn and Balda 1988, Horton and

Mannan 1988, Schreiber and deCalesta 1992, Ohmann et al. 1994, Bull et al. 1997, Ross and Niwa 1997, Ganey 1999, Martin and Eadie 1999, Zack et al. 2002). While nest-site availability was in the best detection model, it was secondary to treatment in importance.

In fact, without treatment included in the model, nest-site availability would be negatively related to White-headed Woodpecker abundance (Figure 4). This means that

79 while nest-site availability is important, it does not explain the rapid response of White-

headed Woodpecker to thinning and prescribed fire. Thus it seems more likely that

increased food availability brought on by treatments intended to restore old growth

characteristics drives the response of White-headed Woodpeckers. Some researchers

have suggested that food availability plays a role independent of nest-site availability for

cavity nesters (Brawn and Balda1988, Zack et al. 2002). This does not contradict

previous studies emphasizing the importance of managing stands with snags for nest-site availability (Raphael et al. 1984, Milne and Hejl 1989, Dixon 1995,) since nest-site

availability still influences to White-headed Woodpecker abundance (Dixon 1995,

Garrett et al. 1996).

The timing of White-headed Woodpecker response to treatments was instructive.

Thinning was accomplished 1998-2000 and prescribed burns took place in autumn of

2001. White-headed Woodpeckers appeared in large numbers in spring of 2002, and their abundance was three times more dense on burned plots than on the pine and big tree treatment plots (T. L. George and S. Zack, Humboldt State University, Department of

Wildlife, 1 Harpst Street, Arcata, CA 95521, unpublished data 2002). Evidence of

White-headed Woodpecker response to fire was weaker in the second and third year postfire when I collected my observations. High densities on burn plots in earlier data suggest fire as a possible cue that attracted a large number of birds to the area. The fact that treatment, rather than food availability, was a direct driver of detection probability, supports the idea that the woodpeckers used fire as a cue to find plots with more food.

Alternately, the relatively minor difference in density between burned and unburned plots

80 in this study compared with earlier data at the same site suggests that a radical increase in

food availability due to fire may be short-lived, lasting only one year (Hutchins 2005).

There was some support for the hypothesis that disturbance explains changes in

food availability on the treatments (Table 9). In both food abundance models, burned

plots had the most food of any treatment, yet not significantly more than on thinned plots.

Control plots had significantly less food. Several models contained coefficients for

variables suggestive of the importance of disturbance. Beetle exit was included in the

Formicidae and the tree-selection models, suggesting that trees with beetle exits tended to

have more ants, and to be selected by foraging White-headed Woodpeckers. The

Formicidae model also suggests that there were more ants on a tree when there were more dead firs within 30 m. Formicidae model included a negative effect of top damage

suggesting fewer ants on trees with damaged tops. This is the opposite effect from what would be predicted for the disturbance hypothesis. However, White-headed

Woodpeckers forage on the top of top-damaged trees more often than they forage in the top of trees without top damage. These seemingly contradictory results are probably

related to the fact that White-headed Woodpeckers have a varied diet. Foraging in

damaged tree tops may be related to prey other than ants, or to a species of ant that I

tended not to capture on my traps at the bases of trees. I frequently observed White-

headed Woodpeckers foraging on dead tops of white firs. These dead tops are likely the

result of an outbreak of fir engraver beetle (Scolytus ventralis) (Owen 2003, Overhulser

2005) and likely contain successive groups of fauna that typically follow the initial beetle

81 attack on the tree (Koenigs et al. 2002). In all, results suggest that beetle outbreaks may

drive food availability for White-headed Woodpeckers in the study area.

Support for the solar hypothesis was more consistent (Table 10). The Formicidae

model, while having coefficients indicating significantly fewer ants on the shadier

unthinned control plots, showed there were more ants on sides of trees with more canopy

cover. And even though White-headed Woodpeckers were more common on the more

open treated plots, they tended to forage more often on the apparently shadier west side

of trees, and on trees with more shade. They also tended to spend more time on a tree if

the tree had more canopy cover to the south. This suggests that ants may aggregate in

shady areas on thinned plots, making them more available to White-headed

Woodpeckers. My experience actively searching for arthropods on bark brought up

another explanation for birds’ tendency to forage in the shade. The glare of the sun on

the surface of bark makes seeing into the dark fissures more difficult, so finding a shady

area may help the searcher with visibility. While this may explain birds selecting shade

in part, the aggregation concept of the solar radiation hypothesis better explains the foraging and prey availability results together.

Large trees have been cited as important elements of White-headed Woodpecker foraging habitat (Morrison et al. 1987, Dixon 1995, Hughes 2000). My results do little to shed light on why this pattern seems prevalent (Table 11). The second order function of dbh describing tree selection suggests White-headed Woodpeckers selected larger trees but the effect leveled off as the difference in size increased. They also spent more time foraging on a tree if it had a larger diameter. But since the tree-time relationship with

82 dbh is linear, time spent may just be proportional to the surface area of the tree. That is,

White-headed Woodpeckers may appear to select larger trees because they have more

surface area. The Formicidae model indicated that ant density was lower on larger trees.

So, while White-headed Woodpeckers tended to use larger trees, and to use trees longer if

they were bigger, it is unclear why.

White-headed Woodpeckers spent more time per tree earlier in the season and

when they were foraging on cones as opposed to bark. The effect of date is likely related

to the demands of nesting. Parents must forage more rapidly later in the season to feed

growing nestlings. Foraging on cones was uncommon. About 13% of trees visited (9% of observations) involved cone foraging. It occurred exclusively on white fir, since no other tree species had cone crops during the study. Cones were clustered in the tops of the trees. I can only speculate that foraging on this resource dictated the strategy of methodically pecking away at most or all the cones on each tree, and thus it took more time per tree than it would to check a portion of the trunk of a tree for arthropods.

Brown Creeper

My results do not support the hypothesis that treatments increased food availability for Brown Creeper (Table 7). Treatment was not a factor in the food availability model. In the Scolytidae model there was evidence of more food on the controls, but this effect was overwhelmed by the effect of downed pine on each plot in the same model. With more bark-boring beetles on plots with more downed pine, and more downed pine on thinned plots, the treatment effects on Scolytidae may have been masked by a lack of blowdown on control plots and the influence of other prey taxa. The

83 negative coefficient for canopy cover in the Scolytidae model would also counter the

effect of the control treatment, since there tends to be more canopy cover on the control

plots.

There was no evidence that Brown Creepers’ distribution was driven by food availability at the plot scale, but at the tree scale there was (Table 8). There were no effects in the best detection model, indicating that Brown Creepers are randomly distributed at the plot scale with respect to the variables I tested. However, Brown

Creepers foraged in locations more likely to have more food at the tree scale. They selected trees with beetle evidence and lower canopy cover. My food models indicate that trees with beetle evidence tended to have more food, and trees with both beetle evidence and lower canopy cover tended to have more bark-boring beetles, a key food resource. The tree-selection model indicates that Brown Creepers chose large trees with evidence of beetle activity. The tree-time model indicates that they spent more time on larger diameter trees. The food models do not suggest a higher density of food on larger trees. So, while Brown Creepers seem to select trees that are likely to have beetles, their selection of large trees may be for some reason other than food availability.

There is much evidence that disturbance plays a role in food availability for

Brown Creepers (Table 9). Food was more abundant on plots with more blowdown. The most important food item was more abundant on plots with more pine blowdown and on trees with more snags within 20 m. So it seems clear that trees killed by disturbance contribute to increased food on live trees for Brown Creepers. Since Scolytidae was the most important food taxon, if one views beetle outbreaks as a form of disturbance in-and-

84 of themselves, Brown Creepers by definition respond to disturbance when they respond

to food. Brown Creepers selected trees with beetle evidence. Brown Creepers also responded to tree damage. They selected trees with top damage, and once on damaged trees, they were more likely to forage on the damaged portion if there was one.

There was no evidence for the solar radiation hypothesis with Brown Creepers

(Table 10). In fact, the Scolytidae model showed there was more of the most important

food taxon on unthinned controls. Scolytidae were more abundant on sides of the tree

with less canopy cover, and Brown Creepers selected trees with less canopy cover, but

they selected for shady microsites. Brown Creepers may avoid sunny foraging locations,

despite there possibly being more food there, due to visibility into furrows being impaired

by glare on the surface of the bark.

The size of trees was important in Brown Creeper foraging in this study as well as

others (Franzreb 1985, Mariani and Manuwal 1990, Weikel and Hayes 1999). However,

tree size did not seem to directly affect food availability. Neither the food-availability

model nor the Scolytidae model included the effect of tree size, but both tree-level

foraging models did include the effect of tree diameter (Table 11). Like White-headed

Woodpecker, it would seem that Brown Creepers selected large trees for a reason other than food availability. My results concerning food availability on large trees contradicts a study by Mariani and Manuwal (1990). They found that Brown Creepers in Douglas-fir forest in Washington State were most abundant where there were more spiders, and that spiders occurred on trees with greater furrow depth, which was in turn associated with larger trees. Spiders were predominantly cited as a food source for Brown Creepers in

85 Douglas-fir dominated forests (Hejl et al. 2002), but no instances of spiders as a major

food have been found in pine dominated habitat.

White-breasted Nuthatch

There were several challenges with the nuthatch portion of this study. Lack of lack of quantification of the diet prevented me from exploring the contributions of different taxa to the food availability for this species. The detection model was problematic in that there were inconsistencies in the way different computer programs maximized the likelihood function, and confidence intervals for the coefficients could not be generated. Low foraging observation sample sizes made analysis of that data problematic. The clear response of this bird to the treatments, and its unique association with pine forest make studying mechanisms for its response worthwhile despite the challenges.

There is no evidence that nuthatch food availability varied according to treatment

(Table 7). I was unable to generate a food model, and the Curculionidae model did not include any effects from treatment. Instead, weevils were most abundant on small damaged trees with low canopy cover.

White-breasted Nuthatches clearly responded to treatments based on the detection model, but the mechanism for that response was not apparent. Like the White-headed

Woodpecker, White-breasted Nuthatches were most abundant on burned plots and least abundant on controls. Statistical goodness of fit of the detection model was suspect, but the model seems to fit with what was heuristically observed. No White-breasted

Nuthatches were ever detected on control plots. There are no commonalities between

86 food and foraging models (Table 8). Thus, there is no evidence that White-breasted

Nuthatches forage in areas where there is likely to be more food.

There was evidence that disturbance plays a role in food availability, but the evidence did not extend to foraging behavior (Table 9). Disturbance in the form of the effect of treatments was present in the detection model, where thinned treatments had a higher detection probability and burned plots had the highest probability of detection.

The Curculionidae model indicated that there were more weevils on trees with damage.

However, White-breasted Nuthatches did not select trees with damage or forage longer on trees with damage. There was an emerging pattern of White-breasted Nuthatches foraging on the damaged vertical stratum of a tree if it was present, but the low sample size made a significant result unlikely.

The presence of a substantial effect of control in the best detection model, plus the fact that White-breasted Nuthatches were never detected on controls, indicated that nuthatch distribution is probably driven partially by thinning at the study site. But, evidence that solar radiation was driving changes in food availability for White-breasted

Nuthatches was very weak (Table 10). The two solar exposure related variables in any food or foraging best model were contradictory. The statistically non-significant positive effect of canopy cover on the south side of the tree in the tree-selection model contradicted the negative effect of canopy in the Curculionidae model. White-breasted

Nuthatches could be choosing to forage on trees with more canopy cover for increased availability of some food item I did not model, for thermoregulation purposes, for reduced glare when searching bark crevices, or for some combination of these.

87 Like White-headed Woodpeckers and Brown Creepers, White-breasted

Nuthatches used large trees for foraging despite the lack of evidence that there is

proportionately more food on large trees (Table 11). The best Curculionidae model

indicated more weevils on smaller trees. The effect of tree diameter was also strong in

both tree level foraging models indicating that White-breasted Nuthatches select and

spend more time foraging on larger trees. If there is more nuthatch food on larger trees, it

must be due to increased numbers of some other food item besides Curculionidae, such as

Araneae or Buprestidae.

Use of Large Trees

Bark-gleaning birds consistently selected larger trees and spent more time foraging on larger trees in this study and others (Morrison et al. 1987, Dixon 1995,

Weikel and Hayes 1999, Hughes 2000, Woolf 2003). However, I found no evidence that food availability or any important food taxon was denser on larger trees (Table 11).

Mariani and Manuwal (1990) did find higher abundance of spiders on large trees in

Douglas fir forest, but I found no evidence that spiders are an important food taxon for

Brown Creeper. There are several non-mutually-exclusive explanations why birds select larger trees for foraging: experimental error, diversity, surface area, and energetics of flight.

It could be that I was measuring the wrong food, and that there are important food taxa that are more abundant on larger trees. This seems unlikely given that the diet information I used is from similar habitat (Otvos and Stark 1985), and my selection of

88 important food items took into account both proportion in the diet as well as variability of

each taxon in the environment.

There may be a higher diversity of bark fauna on larger trees. This could be due to the effect of tree size according to the theory of island biogeography (MacArthur and

Wilson 1967) or large trees’ tendency to have more deeply furrowed bark providing more varied microhabitats (Jackson 1979). If there is a higher diversity of arthropods on larger trees, it raises the probability that some appropriate prey species will be available on a larger tree at any time even if there is not higher density of any one particularly important prey taxon on larger trees.

Another possibility is that the effect of tree diameter on foraging use is an artifact of surface area. Surface area of a tree increases exponentially with an increase in diameter (Jackson 1979). If one assumes that the height of a tree increases in proportion to diameter, doubling the diameter of a tree will quadruple the bark surface area on the bole. Therefore, if bark-gleaners spend time on trees proportional to their surface area, we would expect to see them spend more time on larger trees, and we would be more likely to detect them on large trees because they spend more time there (Franzreb 1985).

Mariani and Manuwal (1990) did not correct for size of trees they were sampling for arthropods in their initial correlations, so they may have found more spiders on larger trees simply because there are an equal number of spiders per unit surface area on each tree. To correct for surface area, I assumed each trap sampled an equal height of each tree above and below the trap and used length of each trap, which is proportional to tree diameter, as an offset. If the traps were successful at sampling the whole tree, this

89 method would over-estimate arthropod density on larger trees, since they tend to be taller.

I did not detect higher densities on larger trees, so I conclude that densities of prey

modeled are the same or lower on large trees compared with small trees.

Finally, the energy demands of flight between trees may make it more efficient to

forage on few large trees rather than many small trees (Franzreb 1985). Because the

energetic demands of flight are high (Gill 2000), and tree climbing can be very efficient

(Norberg 1986), foraging on larger trees makes sense. There would be proportionally

more time spent climbing instead of flying when foraging on fewer large trees. In

addition, since large trees tend to be taller, there is more possibility of gliding down to the

base of the next foraging tree from a greater height, especially in the case of Brown

Creeper which follows this foraging strategy consistently (Franzreb 1985).

Conclusions

The suppression of fire and change to a dense forest structure in many ponderosa pine dominated forests has likely changed wildlife populations in these forests. The bark- gleaning guild of birds has been responsive to applications of thinning and prescribed fire designed to redirect the trajectory of degraded forest towards an open park-like structure dominated by large pines, a structure that was typical a century ago. Understanding this response helps us to understand both the relationship between bark-gleaning birds and eastside pine’s historical condition as well as the consequences of forest restoration efforts on this indicative group of birds.

Thinning and prescribed fire have different effects on different bark-gleaning birds. Changes in food availability brought on by different silviculture treatments,

90 prominently thinning and prescribed fire, probably play a role in some of these birds’

responses. This is most clear with White-headed Woodpeckers, which responded to

treatments that increased food availability much more strongly than they responded to nest-site availability. Managers should consider the effects of actions on both nest-site

availability and food availability when managing for White-headed Woodpecker or

Brown Creeper.

There is evidence that both increased solar radiation brought on by thinning, and disturbance processes, whether associated with or independent of deliberate forest

treatments, contribute to increases in food availability. In particular, White-headed

Woodpeckers seem to benefit from the aggregation of ants in more scarce shade on

thinned stands. Brown Creepers likely benefit from beetle outbreaks brought on by

disturbances such as blowdown.

Bark-gleaning birds select large trees for foraging, but this is not always because

of greater food density on these trees. While more study is needed to understand this

phenomenon, land managers should continue to recognize the importance of large trees

as foraging habitat for bark-gleaning birds as well as a source of appropriate snags for nesting.

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APPENDICES

Appendix A. Model selection procedure for the White-headed Woodpecker food availability linear regression model including relative AIC scores (∆AIC) and AIC weights (wAIC) for each model (Goosenest Adaptive Management Area 2004).

Level Variables AIC wAIC

Variable groupsa

Beetle b_exitb 0.000 0.955 beetle 6.180 0.043 b_entry 12.779 0.002

Damage top_damb 0.000 0.409 bottom_dam 0.585 0.306 gen_dam 0.725 0.285

Plot-level Blowdown FirVolumeb 0.000 0.525 TotalVolume 1.517 0.246 PineVolume 1.660 0.229

Plot trtb 0.000 0.307 trt, period 1.220 0.167 null model 1.554 0.141 trt, FirVolume 2.017 0.112 FirVolume 2.455 0.090 period 2.850 0.074 trt, FirVolume, period 3.241 0.061 FirVolume, period 3.735 0.047

98 99 Appendix A. Model selection procedure for the White-headed Woodpecker food availability linear regression model including relative AIC scores (∆AIC) and AIC weights (wAIC) for each model (Goosenest Adaptive Management Area 2004) (continued).

Level Variables AIC wAIC

Tree trt, b_exitb 0.000 0.152 b_exitb 0.036 0.149 trt, b_exit, size 0.985 0.093 b_exit, size 1.067 0.089 b_exit, top_dam 1.674 0.066 trt, b_exit, top_dam 1.745 0.064 trt, b_exit, tree_species 1.998 0.056 b_exit, tree_species 2.046 0.055 trt 2.079 0.054 trt, size 2.339 0.047 trt, tree_species 3.339 0.029 trt, size, tree_species 3.588 0.025 size 3.834 0.022 trt, top_dam 3.909 0.022 trt, top_dam, size 4.077 0.020 tree_species 4.870 0.013 size, tree_species 5.061 0.012 trt, top_dam, tree_species 5.119 0.012 top_dam 5.319 0.011 top_dam, size 5.400 0.010 top_dam, tree_species 6.491 0.006

100 Appendix A. Model selection procedure for the White-headed Woodpecker food availability linear regression model including relative AIC scores (∆AIC) and AIC weights (wAIC) for each model (Goosenest Adaptive Management Area 2004) (continued).

Level Variables AIC wAIC

Aspect trt, b_exitc 0.000 0.167 b_exit 0.036 0.164 trt, b_exit, canopy 0.881 0.108 trt, b_exit, pinedown_10m 0.995 0.102 b_exit, pinedown_10m 1.075 0.098 b_exit, canopy 2.009 0.061 trt, b_exit, canopy, pinedown_10m 2.094 0.059 b_exit, canopy, pinedown_10m 3.004 0.037 trt, b_exit, aspect 3.042 0.037 b_exit, aspect 3.082 0.036 trt, b_exit, aspect, canopy 3.742 0.026 trt, b_exit, aspect, pinedown_10m 4.099 0.022 b_exit, aspect, pinedown_10m 4.182 0.021 pinedown_10m 4.546 0.017 b_exit, aspect, canopy 5.090 0.013 canopy 5.176 0.013 canopy, pinedown_10m 5.952 0.009 aspect 6.706 0.006 aspect, pinedown_10m 7.683 0.004 aspect, canopy 8.343 0.003 aThe aspect-level dead tree variable pinedown_10m was the first to be selected in a forward stepwise procedure (R2 = 0.003, df = 942, P = 0.113). bVariables from these models were used at subsequent levels. cBest model. See Table 4 for coefficients. .

Appendix B. Model selection procedure for the Formicidae negative binomial model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004).

Level Variables AIC wAIC

Variable groups

Beetle b_exita 0.000 0.959 beetle 6.648 0.035 b_entry 10.105 0.006

Damage top_dama 0.000 0.955 gen_dam 6.240 0.042 bottom_dam 11.988 0.002

Plot-level blowdown FirVolumea 0.000 0.992 TotalVolume 10.740 0.005 PineVolume 11.377 0.003

Dead trees firall_30ma 0.000 0.534 firdown_30m 2.004 0.196 firall_40m 2.670 0.140 firdown_40m 4.102 0.069 firdown_20m 6.883 0.017 pinedown_40m 7.955 0.010 firall_20m 9.023 0.006 pinesnag_10m 10.088 0.003 firdown_10m 10.170 0.003 pinesnag_20m 10.241 0.003 snag_20m 10.522 0.003 pinedown_30m 10.839 0.002 firall_10m 11.100 0.002 snag_10m 12.652 0.001 down_10m 12.765 0.001 all_30m 13.037 0.001 all_40m 13.178 0.001 pineall_40m 13.200 0.001 pineall_20m 13.782 0.001 pineall_10m 13.862 0.001 pinesnag_40m 13.944 0.001 firsnag_30m 14.061 0.000

101 102 Appendix B. Model selection procedure for the Formicidae negative binomial model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004) (continued).

Level Variables AICc wAICc

Variable groups (continued)

Dead trees (continued) pineall_30m 14.118 0.000 all_10m 14.181 0.000 down_30m 14.571 0.000 pinedown_20m 14.621 0.000 down_40m 14.625 0.000 pinesnag_30m* 14.645 0.000 firsnag_20m 14.660 0.000 pinedown_10m 14.862 0.000 snag_30m 14.891 0.000 all_20m 14.928 0.000 snag_40m 14.978 0.000 down_20m 15.010 0.000 firsnag_10m 15.026 0.000 firsnag_40m 15.034 0.000

Plot trta 0.000 0.508 trt, FirVolume 1.680 0.219 trt, period 1.960 0.190 trt, period, FirVolume 3.624 0.083 FirVolume 35.752 0.000 period, FirVolume 37.056 0.000 null model 45.237 0.000 period 46.872 0.000

103 Appendix B. Model selection procedure for the Formicidae negative binomial model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004) (continued).

Level Variables AICc wAICc

Tree trt, top_dam, sizea 0.000 0.832 trt, size, b_exit 4.514 0.087 trt, size 7.571 0.019 trt, size, tree_species 6.347 0.035 trt, top_dam 10.526 0.004 trt, top_dam, b_exit 8.503 0.012 trt, top_dam, tree_species 8.914 0.010 trt, tree_species, b_exit 14.533 0.001 trt 19.187 0.000 trt, b_exit 17.185 0.000 trt, tree_species 17.996 0.000 size, b_exit 36.850 0.000 top_dam, size 39.689 0.000 top_dam, b_exit 42.583 0.000 size 50.434 0.000 size, tree_species 50.374 0.000 b_exit 54.285 0.000 tree_species, b_exit 52.930 0.000 top_dam 55.463 0.000 top_dam, tree_species 55.343 0.000 tree_species 68.419 0.000

a posteriori trt, top_dam, size, b_exita -2.964

104 Appendix B. Model selection procedure for the Formicidae negative binomial model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004) (continued).

Level Variables AICc wAICc

Aspect trt, top_dam, size, b_exit, canopy, firall_30mb 0.000 0.827 trt, top_dam, size, canopy, firall_30m 3.169 0.169 trt, top_dam, size, b_exit, canopy 13.213 0.001 trt, top_dam, size, b_exit, aspect, canopy 14.107 0.001 trt, top_dam, size, b_exit, firall_30m 14.575 0.001 trt, top_dam, size, b_exit, aspect, firall_30m 15.314 0.000 trt, top_dam, size, canopy 15.368 0.000 trt, top_dam, size, firall_30m 16.090 0.000 trt, top_dam, size, aspect, canopy 16.388 0.000 trt, top_dam, size, aspect, firall_30m 16.895 0.000 trt, top_dam, size, b_exit, aspect 23.012 0.000 trt, top_dam, size, b_exit 23.027 0.000 trt, top_dam, size, aspect 24.075 0.000 aspect, firall_30m 70.268 0.000 firall_30m 71.316 0.000 canopy, firall_30m 71.566 0.000 aspect 81.357 0.000 aspect, canopy 83.300 0.000 canopy 86.124 0.000 aVariables from these models were used at subsequent levels. bBest model. See Table 4 for coefficients.

Appendix C. Model selection procedure for the White-headed Woodpecker detection logistic regression model follows including the relative AICc scores (AICc ) and the AICc weight (WAICc) for each model (Goosenest Adaptive Management Area 2004).

Level Variables AICc wAICc

Plot / period trt, nest, perioda 0.000 0.451 trt, nest 1.146 0.254 trt, food, nest, period 2.063 0.161 trt, food, nest 2.965 0.102 trt, period 7.543 0.010 trt, food, period 7.911 0.009 trt, food 8.351 0.007 trt 8.535 0.006 nest, period 60.021 0.000 nest 60.503 0.000 food, nest, period 62.081 0.000 food, nest 62.575 0.000 food, period 73.120 0.000 period 73.205 0.000 null model 73.580 0.000 food 74.026 0.000

105 106

Level Variables AICc wAICc

Visit trt, nest, periodb 0.000 0.311 trt, nest, period, time_elapsed 1.472 0.149 trt, nest, period obs 1.884 0.121 trt, nest, period wind 2.116 0.108 trt, nest, wind 2.814 0.076 trt, nest, time_elapsed 3.011 0.069 trt, nest, obs 3.041 0.068 trt, nest, obs wind 4.361 0.035 trt, nest, obs, time_elapsed 4.425 0.034 trt, nest, wind, time_elapsed 4.792 0.028 time_elapsed 74.197 0.000 obs, time_elapsed 74.547 0.000 obs 75.270 0.000 wind 75.568 0.000 wind, time_elapsed 76.184 0.000 obs, wind, time_elapsed 76.644 0.000 obs, wind 77.336 0.000 aVariables from these models were used at subsequent levels. bBest model. See Table 4 for coefficients.

Appendix D. Model selection procedure for the White-headed Woodpecker tree- selection paired logistic regression model including relative AIC scores (∆AIC) and AIC weights (WAIC) for each model (Goosenest Adaptive Management Area 2003 and 2004).

Level Variables AIC wAIC

Variable groups

Beetle beetle_exita 0.000 0.889 beetle 4.181 0.110 beetle_entry 13.183 0.001

Damage top_dama 0.000 0.713 bottom_dam 2.882 0.169 gen_dam 3.594 0.118

Solar exposure shadea 0.000 0.769 can_south 2.409 0.231

dbh dbh dbhsqa 0.000 0.600 dbha 0.813 0.400

107 108

Level Variables AIC wAIC

Tree dbh, dbhsq, beetle_exit, shadeb 0.000 0.192 dbh, dbhsq, top_dam, shade 0.541 0.147 dbh, dbhsq, shade 0.786 0.130 dbh, top_dam, shade 1.104 0.111 dbh, beetle_exit, shade 1.435 0.094 dbh, shade 2.033 0.070 dbh, shade, tree_species 2.212 0.064 dbh, dbhsq, shade, fire_effects 2.529 0.054 dbh, shade, fire_effects 3.788 0.029 dbh, dbhsq, beetle_exit, top_dam 4.424 0.021 dbh, beetle_exit, top_dam 4.635 0.019 dbh, dbhsq, top_dam 6.011 0.010 dbh, top_dam 6.059 0.009 dbh, dbhsq, beetle_exit 6.243 0.008 dbh, top_dam, tree_species 6.767 0.007 dbh, beetle_exit 7.163 0.005 dbh, beetle_exit, tree_species 7.179 0.005 dbh, top_dam, fire_effects 7.788 0.004 dbh, dbhsq, top_dam, fire_effects 7.796 0.004 dbh, dbhsq 7.827 0.004 dbh, dbhsq, beetle_exit, fire_effects 8.231 0.003 dbh 8.640 0.003 dbh, dbhsq, tree_species 8.695 0.002 dbh, beetle_exit, fire_effects 9.145 0.002 dbh, tree_species 9.331 0.002 dbh, dbhsq, fire_effects 9.764 0.001 dbh, fire_effects 10.566 0.001 dbh, tree-species, fire_effects 11.224 0.001 tree_species 54.167 0.000 tree_species, fire_effects 54.388 0.000 fire_effects 58.357 0.000 aVariables from these models were used at subsequent levels. bBest model. See Table 4 for coefficients. .

Appendix E. Model selection procedure for the White-headed Woodpecker tree-time linear regression model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Gooenest Adaptive Management Area 2003 and 2004).

Level Variables AICc wAICc

Variable groups

Beetle beetle_exita 0.000 0.499 beetle 0.812 0.332 beetle_entry 2.167 0.169

Damage bottom_dama 0.000 0.370 gen_dam 0.320 0.315 top_dam 0.322 0.315

Solar exposure can_southa 0.000 0.817 shade 2.996 0.183

dbh dbha 0.000 0.679 dbh, dbhsq 1.502 0.321

Plot-level blowdown PineVolumea 0.000 0.375 FirVolume 0.070 0.362 TotalVolume 0.706 0.263

Plot nulla 0.000 0.570 PineVolume 1.344 0.291 trt 3.735 0.088 trt, PineVolume 4.852 0.050

Visit datea 0.000 0.416 obs, date 1.277 0.220 temp, date 1.441 0.202 temp, obs, date 2.924 0.096 obs 5.674 0.024 null model 5.695 0.024 temp, obs 7.705 0.009 temp 7.735 0.009

109 110 Appendix E. Model selection procedure for the White-headed Woodpecker tree-time linear regression model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Gooenest Adaptive Management Area 2003 and 2004) (continued).

Level Variables AICc wAICc

Observation datea 0.000 0.473 date, time 1.315 0.245 date, sex 2.765 0.119 date, time, sex 4.040 0.063 time 5.492 0.030 null 5.695 0.027 time, sex 6.208 0.021 sex 6.254 0.021

Tree date, dbh, cone 0.000 0.213 date, can_south, cone 0.842 0.140 date, cone 0.847 0.140 date, beetle_exit, cone 1.765 0.088 date, bottom_dam, cone 2.633 0.057 date, fire_effects, cone 2.713 0.055 date, dbh, can_south 3.628 0.035 date, can_south 3.978 0.029 date, dbh 4.100 0.027 date 4.250 0.025 date, can_south, beetle_exit 4.901 0.018 date, beetle_exit 5.063 0.017 date, dbh, fire_effects 5.497 0.014 date, can_south, bottom_dam 5.529 0.013 date, dbh, beetle_exit 5.618 0.013 date, fire_effects, can_south 6.013 0.011 date, bottom_dam 6.085 0.010 date, fire_effects 6.120 0.010 date, dbh, bottom_dam 6.128 0.010 dbh, cone 6.453 0.008 can_south, cone 6.462 0.008 date, fire_effects, beetle_exit 6.535 0.008 date, beetle_exit, bottom_dam 7.034 0.006 beetle_exit, cone 7.578 0.005 cone 7.677 0.005 dbh, can_south 7.748 0.004

111 Appendix E. Model selection procedure for the White-headed Woodpecker tree-time linear regression model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Gooenest Adaptive Management Area 2003 and 2004) (continued).

Level Variables AICc wAICc

date, fire_effects, bottom_dam 7.947 0.004 can_south 8.519 0.003 date, treesp 8.610 0.003 can_south, beetle_exit 8.617 0.003 bottom_dam, cone 9.338 0.002 dbh 9.401 0.002 fire_effects, cone 9.751 0.002 beetle_exit 9.796 0.002 can_south, bottom_dam 9.838 0.002 dbh, beetle_exit 10.270 0.001 fire_effects, can_south 10.566 0.001 dbh, fire_effects 11.279 0.001 dbh, bottom_dam 11.365 0.001 fire_effects, beetle_exit 11.635 0.001 bottom_dam 11.664 0.001 beetle_exit, bottom_dam 11.713 0.001 fire_effects 11.994 0.001 treesp, cone 12.020 0.001 treesp, can_south 12.843 0.000 fire_effects, bottom_dam 13.733 0.000 dbh, treesp 14.188 0.000 treesp, beetle_exit 14.533 0.000 treesp 14.828 0.000 treesp, bottom_dam 16.637 0.000 treesp, fire_effects 16.921 0.000

a posteriori models date, dbh, can_south, coneb -0.212 date, dbh, can_south, cone year 1.844 aVariables from these models were used at subsequent levels. bBest model. See Table 4 for coefficients.

Appendix F. Model selection procedure for the Brown Creeper food-availability linear regression model including relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004).

Level Variables AICc wAICc

Variable groupsa

beetle beetleb 0.000 0.670 b_exit 1.425 0.329 b_entry 12.336 0.001

damage bottom_damb 0.000 0.490 gen_dam 1.046 0.291 top_dam 1.608 0.219

plot-level blowdown TotalVolumeb 0.000 0.535 PineVolume 0.283 0.465 FirVolume 20.386 0.000

Plot / period TotalVolume, periodb 0.000 0.552 TotalVolume 1.771 0.228 trt, TotalVolume, period 2.553 0.154 trt, TotalVolume 4.342 0.063 trt, period 12.836 0.001 period 13.020 0.001 trt 14.475 0.000 null model 14.763 0.000

112 113 Appendix F. Model selection procedure for the Brown Creeper food-availability linear regression model including relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004) (continued).

Level Variables AICc wAICc

Tree TotalVolume, period, beetleb 0.000 0.367 TotalVolume, period, size, beetle 0.966 0.227 TotalVolume, period, tree_species, beetle 1.910 0.141 TotalVolume, period, bottom_dam, beetle 1.995 0.136 TotalVolume, period 5.192 0.027 TotalVolume, period, tree_species 5.371 0.025 TotalVolume, period, size 5.450 0.024 TotalVolume, period, size, tree_species 5.607 0.022 TotalVolume, period, bottom_dam, tree_species 7.120 0.010 TotalVolume, period, bottom_dam 7.157 0.010 TotalVolume, period, bottom_dam, size 7.381 0.009 beetle 16.041 0.000 size, beetle 16.992 0.000 bottom_dam, beetle 17.698 0.000 tree_species, beetle 17.877 0.000 tree_species 20.179 0.000 size 20.268 0.000 size, tree_species 20.472 0.000 bottom_dam, tree_species 20.716 0.000 bottom_dam 21.125 0.000 bottom_dam, size 21.305 0.000

114 Appendix F. Model selection procedure for the Brown Creeper food-availability linear regression model including relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004) (continued).

Level Variables AICc wAICc

Aspect TotalVolume, period, beetlec 0.000 0.466 TotalVolume, period, beetle, down_40m 1.507 0.219 TotalVolume, period, beetle, canopy 1.911 0.179 TotalVolume, period, beetle, canopy, down_40m 3.413 0.085 TotalVolume, period, beetle, aspect 5.876 0.025 TotalVolume, period, beetle, aspect, down_40m 7.389 0.012 TotalVolume, period, beetle, aspect, canopy 7.780 0.010 TotalVolume, period, beetle, aspect, down_40m, canopy 9.285 0.004 canopy, down_40m 15.497 0.000 down_40m 15.949 0.000 canopy 17.574 0.000 aspect, down_40m, canopy 21.409 0.000 aspect, down_40m 21.745 0.000 aspect, canopy 23.510 0.000 aspect 25.804 0.000 aThe aspect-level dead tree variable down_40m was the first variable selected in the forward stepwise procedure (R2 = 0.015, df = 942, P < 0.001 ) bVariables from these models were used at subsequent levels. cBest model. See Table 5 for coefficients. .

Appendix G. Model selection procedure for the Scolytidae negative binomial model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004).

Level Variables AICc wAICc

Variable groups

Beetle b_exita 0.000 0.491 beetle 0.239 0.436 b_entry 3.790 0.074

Damage top_dama 0.000 0.395 bottom_dam 0.514 0.305 gen_dam 0.550 0.300

Plot-level blowdown PineVolumea 0.000 0.784 TotalVolume 2.572 0.216 FirVolume 46.396 0.000

Aspect-level dead trees firsnag_20ma 0.000 0.649 all_20m 1.602 0.291 firsnag_30m 5.390 0.044 firall_20m 7.688 0.014 snag_20m 13.952 0.001 down_40m 13.974 0.001 down_30m 14.096 0.001 firdown_40m 15.172 0.000 down_20m 16.536 0.000 firdown_30m 16.956 0.000 firdown_20m 19.427 0.000 all_30m 22.095 0.000 firall_30m 23.000 0.000 firsnag_40m 23.631 0.000 all_40m 24.787 0.000 firall_40m 25.689 0.000 snag_30m 35.111 0.000 pineall_40m 40.558 0.000 pinesnag_20m 43.386 0.000 snag_40m 43.680 0.000 pineall_20m 49.636 0.000 pineall_30m 50.131 0.000 115 116

Level Variables AICc wAICc

Variable groups (continued)

Aspect-level dead trees firall_10m 51.169 0.000 (continued) pinesnag_40m 51.231 0.000 all_10m 53.110 0.000 firsnag_10m 54.221 0.000 snag_10m 56.013 0.000 pinesnag_30m 57.895 0.000 firdown_10m 58.814 0.000 down_10m 58.944 0.000 pinedown_30m 59.880 0.000 pinedown_40m 60.372 0.000 pinedown_20m 60.946 0.000 pineall_10m 61.537 0.000 pinedown_10m 61.671 0.000 pinesnag_10m 61.853 0.000

Plot trt, period, PineVolumea 0.000 0.925 period, PineVolume 5.012 0.075 PineVolume 27.597 0.000 trt, PineVolume 23.792 0.000 trt, period 47.406 0.000 period 62.595 0.000 trt 71.582 0.000 null model 87.908 0.000

Tree trt, period, PineVolume, tree_species, b_exita 0.000 0.994 trt, period, PineVolume, tree_species 12.622 0.002 trt, period, PineVolume, bottom_dam, tree_species 12.083 0.002 trt, period, PineVolume, size, tree_species 12.622 0.002 trt, period, PineVolume, b_exit 17.828 0.000 trt, period, PineVolume, bottom_dam, b_exit 17.548 0.000 trt, period, PineVolume, size, b_exit 17.824 0.000

Level Variables AICc wAICc

Tree trt, period, PineVolume 23.463 0.000 (continued) trt, period, PineVolume, bottom_dam 23.354 0.000 trt, period, PineVolume, size 23.386 0.000 trt, period, PineVolume, bottom_dam, size 23.293 0.000 tree_species, b_exit 94.544 0.000 tree_species 102.570 0.000 size, tree_species 102.499 0.000 bottom_dam, tree_species 102.567 0.000 b_exit 106.929 0.000 size, b_exit 106.865 0.000 bottom_dam, b_exit 106.929 0.000 size 111.064 0.000 bottom_dam 111.305 0.000 bottom_dam, size 110.964 0.000

Level Variables AICc wAICc

Aspect trt, period, PineVolume, tree_species, b_exit, canopy, firsnag_20mb 0.000 0.699 trt, period, PineVolume, tree_species, b_exit, firsnag_20m 2.504 0.200 trt, period, PineVolume, tree_species, b_exit, aspect, firsnag_20m, canopy 4.473 0.075 trt, period, PineVolume, tree_species, b_exit, aspect, firsnag_20m 6.513 0.027 trt, period, PineVolume, tree_species, b_exit, canopy 50.183 0.000 trt, period, PineVolume, tree_species, b_exit, aspect, canopy 52.762 0.000 trt, period, PineVolume, tree_species, b_exit 54.256 0.000 trt, period, PineVolume, tree_species, b_exit, aspect 59.752 0.000 canopy, firsnag_20m 75.337 0.000 aspect, firsnag_20m, canopy 79.723 0.000 firsnag_20m 93.309 0.000 aspect, firsnag_20m 96.784 0.000 canopy 123.590 0.000 aspect, canopy 126.451 0.000 aspect 153.081 0.000 aVariables from these models were used at subsequent levels. bBest model. See Table 5 for coefficients.

Appendix H. Model selection procedure for the Brown Creeper detection rate linear regression model including relative AICc scores (AICc ) and the AICc weight (WAICc) for each model (Goosenest Adaptive Management Area 2004).

Model AICc wAICc

null modela 0.00 0.330 food 1.64 0.146 PIPO_Basal 1.95 0.124 nest 2.21 0.109 food, PIPO_Basal 3.92 0.047 nest, trt 3.95 0.046 food, nest 4.03 0.044 trt 4.08 0.043 nest, PIPO_Basal 4.43 0.036 trt, PIPO_Basal 5.93 0.017 food, trt 6.25 0.015 nest, trt, PIPO_Basal 6.37 0.014 food, nest, PIPO_Basal 6.59 0.012 food, nest, trt 6.83 0.011 food, trt, PIPO_Basal 8.61 0.004 food, nest, trt, PIPO_Basal 9.55 0.003 aBest model. See Table 5 for coefficients.

119

Appendix I. Model selection procedure for the Brown Creeper tree-selection paired logistic regression model including relative AICc scores (∆AIC) and AIC weights (wAIC) for each model (Goosenest Adaptive Management Area 2003 and 2004).

Level Variables AIC wAIC

Variable groups

Beetle beetlea 1.000 0.953 beetle_exit 0.041 0.039 beetle_entry 0.008 0.008

Damage top_dama 1.000 0.415 bottom_dam 0.706 0.293 gen_dam 0.704 0.292

Solar exposure can_southa 1.000 0.950 shade 0.053 0.050

dbh dbh, dbhsqa 1.000 0.986 dbh 0.015 0.014

120 121

Level Variables AIC wAIC

Tree dbh, dbhsq, beetle, top_dam 0.000 0.422 dbh, dbhsq, beetle, can_south 0.473 0.333 dbh, dbhsq, beetle, fire_effects 2.799 0.104 dbh, dbhsq, beetle 2.917 0.098 dbh, dbhsq, tree_species, beetle 6.369 0.017 dbh, dbhsq, top_dam, can_south 6.993 0.013 dbh, dbhsq, top_dam, fire_effects 9.331 0.004 dbh, dbhsq, can_south, fire_effects 10.172 0.003 dbh, dbhsq, top_dam 10.422 0.002 dbh, dbhsq, can_south 10.814 0.002 dbh, dbhsq, fire_effects 12.898 0.001 dbh, dbhsq 13.779 0.000 dbh, dbhsq, tree_species, can_south 14.711 0.000 dbh, dbhsq, tree_species, fire_effects 16.762 0.000 dbh, dbhsq, tree_species 17.571 0.000 fire_effects 127.151 0.000 tree_species, fire_effects 128.600 0.000 tree_species 130.980 0.000

A Posteriori dbh, dbhsq, beetle, top_dam, can_southb -2.860 aVariables from these models were used at subsequent levels. bBest model. See Table 5 for coefficients.

Appendix J. Model selection procedure for the Brown Creeper tree-time linear regression model including relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2003 and 2004).

Level Variables AICc wAICc

Variable groups

Beetle beetlea 0.000 0.507 beetle_exit 0.460 0.403 beetle_entry 3.443 0.091

Damage top_dama 0.000 0.499 gen_dam 1.294 0.261 bottom_dam 1.462 0.240

Solar exposure shadea 0.000 0.656 can_south 1.293 0.344

dbha 0.000 0.713 dbh, dbhsq 1.822 0.287

Plot-level blowdown TotalVolume 0.000 0.353 PineVolume 0.165 0.325 FirVolumeab 0.187 0.322

Plot trta 0.000 0.541 trt, FirVolume 0.820 0.359 null 4.016 0.073 FirVolume 6.019 0.027

122 123

Level Variables AICc wAICc

Visit trta 0.000 0.296 trt, obs 0.928 0.186 trt, date 1.936 0.113 trt, temp 2.013 0.108 trt, obs, date 2.850 0.071 trt, temp, obs 2.939 0.068 trt, temp, date 3.926 0.042 obs 4.709 0.028 trt, temp, obs, date 4.828 0.027 date 5.968 0.015 temp 5.984 0.015 obs, date 6.645 0.011 temp, obs 6.669 0.011 temp, date 7.911 0.006 temp, obs, date 8.567 0.004

Observation trta 0.000 0.568 trt, time 1.545 0.262 trt, sex 3.825 0.084 trt, time, sex 5.341 0.039 time 5.730 0.032 sex 8.005 0.010 time, sex 9.714 0.004

Tree dbhc 0.000 0.175 dbh, beetle 0.255 0.154 dbh, top_dam 0.397 0.143 trt, dbh 0.920 0.110 dbh, fire_effects 1.419 0.086 trt, dbh, top_dam 1.721 0.074 dbh, shade 1.924 0.067 trt, dbh, fire_effects 2.301 0.055

124

Level Variables AICc wAICc

Tree (continued) trt, dbh, beetle 2.310 0.055 trt, dbh, shade 2.898 0.041 dbh, treesp 2.974 0.040 trt 18.794 0.000 trt, beetle 19.260 0.000 trt, top_dam 19.805 0.000 trt, beetle, top_dam 20.093 0.000 beetle 20.157 0.000 trt, fire_effects 20.258 0.000 beetle, top_dam 20.395 0.000 trt, fire_effects, beetle 20.489 0.000 trt, shade 20.830 0.000 fire_effects, beetle 21.211 0.000 trt, shade, beetle 21.225 0.000 trt, fire_effects, top_dam 21.351 0.000 trt, treesp 21.655 0.000 trt, shade, top_dam 21.840 0.000 shade, beetle 22.081 0.000 trt, fire_effects, shade 22.306 0.000 fire_effects 22.488 0.000 treesp, beetle 22.832 0.000 fire_effects, top_dam 22.899 0.000 top_dam 23.248 0.000 shade 23.959 0.000 fire_effects, shade 24.176 0.000 shade, top_dam 24.599 0.000 treesp, fire_effects 25.926 0.000 treesp 26.276 0.000 treesp, top_dam 26.743 0.000 treesp, shade 27.289 0.000 aVariables from these models were used at subsequent levels. bFir volume was used in subsequent sleelction procedures instead of the top scoring variable in the plot-level blowdown group because it had the only positive coefficient. cBest model. See Table 5 for coefficients.

Appendix K. Model selection procedure for the Curculionidae poisson model including relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004).

Level Variables AICc wAICc

Variable groups

beetle b_exita 0.000 0.525 beetle 0.562 0.397 b_entry 3.812 0.078

damage gen_dama 0.000 0.666 bottom_dam 1.520 0.312 top_dam 6.799 0.022

plot-level blowdown PineVolumea 0.000 0.664 TotalVolume 1.777 0.273 FirVolume 4.723 0.063

Aspect-level dead trees firdown_10ma 0.000 0.136 all_20m 0.475 0.107 down_20m 0.792 0.091 firsnag_40m 1.669 0.059 snag_40m 1.883 0.053 pinedown_40m 2.331 0.042 firdown_20m 2.497 0.039 firall_10m 2.665 0.036 pineall_20m 3.003 0.030 pineall_40m 3.021 0.030 pinesnag_10m 3.377 0.025 firsnag_30m 3.420 0.025 all_10m 3.444 0.024 down_40m 3.533 0.023 pinedown_30m 3.743 0.021 down_10m 4.008 0.018 pinedown_20m 4.068 0.018 firall_20m 4.073 0.018 pineall_30m 4.177 0.017 all_40m 4.354 0.015 pinesnag_40m 4.431 0.015 down_30m 4.628 0.013

125 126 Appendix K. Model selection procedure for the Curculionidae poisson model including relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004) (continued).

Level Variables AICc wAICc

Variable groups (continued)

Aspect-level dead trees snag_30m 4.646 0.013 (continued) snag_10m 4.884 0.012 firall_40m 4.925 0.012 firsnag_10m 4.986 0.011 pinesnag_20m 5.021 0.011 all_30m 5.066 0.011 firall_30m 5.131 0.010 snag_20m 5.245 0.010 pinedown_10m 5.292 0.010 pineall_10m 5.365 0.009 pinesnag_30m 5.368 0.009 firdown_30m 5.456 0.009 firsnag_20m 5.493 0.009 firdown_40m 5.493 0.009

Plot PineVolumea 0.000 0.226 period, PineVolume 0.323 0.193 trt 0.745 0.156 trt, period 0.974 0.139 trt, PineVolume 1.307 0.118 trt, period, PineVolume 1.550 0.104 null model 3.751 0.035 period 4.107 0.029

Tree gen_dam, sizea 0.000 0.519 size, b_exit 1.322 0.268 PineVolume, size, b_exit 4.169 0.064 size, tree_species 4.795 0.047 PineVolume, gen_dam, size 4.806 0.047 size 5.401 0.035 PineVolume, size, tree_species 9.157 0.005

127 Appendix K. Model selection procedure for the Curculionidae poisson model including relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004) (continued).

Level Variables AICc wAICc

Tree (continued) PineVolume, size 9.706 0.004 gen_dam, b_exit 10.282 0.003 tree_species, b_exit 10.940 0.002 gen_dam 11.005 0.002 gen_dam, tree_species 11.534 0.002 PineVolume, tree_species, b_exit 14.025 0.000 PineVolume, gen_dam, b_exit 14.138 0.000 b_exit 14.700 0.000 PineVolume, gen_dam 15.616 0.000 PineVolume, gen_dam, tree_species 16.105 0.000 tree_species 16.122 0.000 PineVolume, b_exit 17.804 0.000 PineVolume, tree_species 20.520 0.000 PineVolume 20.891 0.000

Aspect gen_dam, size, canopyb 0.000 0.287 gen_dam, size, canopy, firdown_10m 0.290 0.248 gen_dam, size, firdown_10m 1.143 0.162 gen_dam, size 2.490 0.083 gen_dam, size, aspect, canopy 2.613 0.078 gen_dam, size, aspect, firdown_10m, canopy 2.782 0.071 gen_dam, size, aspect, firdown_10m 3.556 0.048 gen_dam, size, aspect 5.116 0.022 canopy, firdown_10m 14.936 0.000 firdown_10m 15.568 0.000 canopy 16.004 0.000 aspect, firdown_10m, canopy 17.204 0.000 aspect, firdown_10m 17.797 0.000 aspect, canopy 18.533 0.000 aspect 21.705 0.000 aVariables from these models were used at subsequent levels. bBest model. See Table 6 for coefficients.

Appendix L. Model selection procedure for the White-breasted Nuthatch detection logistic regression model including the relative AICc scores (∆AICc) and AICc weights (WAICc) for each model (Goosenest Adaptive Management Area 2004).

Level Variables AICc wAICc

Plot trt, perioda 0.000 0.290 trt, nest, period 0.006 0.289 trt 0.636 0.211 trt, nest 0.664 0.208 period 14.511 0.000 null model 14.805 0.000 nest, period 16.532 0.000 nest 16.808 0.000

Visit trt, periodb 0.000 0.315 trt, period, wind 1.810 0.128 trt, period, obs 2.077 0.112 trt, period, time_elapsed 2.116 0.109 trt, time_elapsed 2.630 0.085 trt, obs 2.688 0.082 trt, wind 2.704 0.082 trt, wind, time_elapsed 4.742 0.029 trt, obs, time_elapsed 4.754 0.029 trt, obs, wind 4.802 0.029 time_elapsed 16.609 0.000 wind 16.812 0.000 obs 16.852 0.000 wind, time_elapsed 18.632 0.000 obs, time_elapsed 18.641 0.000 obs, wind 18.877 0.000 obs, wind, time_elapsed 20.710 0.000 aVariables from these models were used at subsequent levels. bBest model. See Table 6 for coefficients.

128

Appendix M. Model selection procedure for the White-breasted Nuthatch tree-selection paired logistic regression model including the relative AIC scores (∆AIC) and AIC weights (WAIC) for each model (Goosenest Adaptive Management Area 2003 and 2004).

Level Variables AIC wAIC

Variable groups

Beetle beetle_exita 0.000 0.579 beetle 0.974 0.356 beetle_entry 4.378 0.065

Damage bottom_dama 0.000 0.367 top_dam 0.292 0.317 gen_dam 0.292 0.317

Solar exposure can_southa 0.000 0.878 shade 3.951 0.122

dbh dbha 0.000 0.721 dbh, dbhsq 1.899 0.279

Tree dbh, beetle_exit, can_southb 0.000 0.244 dbh, beetle_exit 0.352 0.204 dbh, can_south 1.336 0.125 dbh 1.625 0.108 dbh, beetle_exit, bottom_dam 2.330 0.076 dbh, beetle_exit, tree_species 2.361 0.075 dbh, bottom_dam, can_south 3.294 0.047 dbh, bottom_dam 3.620 0.040 dbh, can_south, tree_species 3.960 0.034 dbh, tree_species 4.516 0.025 null model 6.007 0.012 dbh, bottom_dam, tree_species 6.513 0.009 tree_species 9.145 0.003 aVariables from these models were used at subsequent levels. bBest model. See Table 6 for coefficients.

129

Appendix N. Model selection procedure for the White-breasted Nuthatch tree-time linear regression model including relative AICc scores (∆AICc) and AICc weights (WAICc) of variables in each group. Variables in bold were used in the model selection procedure (Goosenest Adaptive Management Area 2003 and 2004).

Level Variables AICc wAICc

Beetle beetle_entrya 0.000 0.410 beetle_exit 0.576 0.307 beetle 0.745 0.283

Damage top_dama 0.000 0.351 bottom_dam 0.093 0.335 gen_dam 0.225 0.314

Solar exposure shadea 0.000 0.524 can_south 0.193 0.476

dbh dbh dbhsqa 0.000 0.647 dbh 1.210 0.353

Plot-level blowdown PineVolumea 0.000 0.393 TotalVolume 0.448 0.314 FirVolume 0.593 0.292

Plot null modela 0.000 0.490 trt (Burn and Pine only) 1.339 0.251 PineVolume 1.570 0.224 trt, PineVolume 5.244 0.036

Visit nulla 0.000 0.348 temp 1.270 0.184 obs 1.880 0.136 date 2.101 0.122 temp, date 3.037 0.076 temp, obs 3.392 0.064 obs, date 4.062 0.046 temp, obs, date 5.282 0.025

130 131

Level Variables AICc wAICc

Observation null modela 0.000 0.554 time 1.003 0.335 sex 4.012 0.074 time, sex 5.443 0.036

Tree dbh, dbhsqb 0.000 0.138 dbh, dbhsq, fire_effects 0.177 0.126 dbh, dbhsq, beetle_entry 0.189 0.125 dbh, dbhsq, top_dam 0.445 0.110 dbh, dbhsq, shade 0.463 0.109 null model 0.803 0.092 fire_effects 2.142 0.047 beetle_entry 2.217 0.046 shade 2.622 0.037 top_dam 2.740 0.035 fire_effects, beetle_entry 4.053 0.018 fire_effects, shade 4.134 0.017 dbh, dbhsq, treesp 4.139 0.017 fire_effects, top_dam 4.170 0.017 beetle_entry, top_dam 4.234 0.017 shade, beetle_entry 4.262 0.016 shade, top_dam 4.723 0.013 treesp 5.914 0.007 treesp, fire_effects 7.727 0.003 treesp, beetle_entry 7.962 0.003 treesp, top_dam 8.078 0.002 treesp, shade 8.259 0.002 aVariables from these models were used at subsequent levels. bBest model. See Table 6 for coefficients.