AN ANALYSIS OF EASTERN NEARCTIC WOODPECKER DRUMS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of The Ohio State University
By
Robert D. Stark, M.S.
*****
The Ohio State University
2002
Approved by
Dissertation Committee
Professor David Stetson, Advisor Advisor
Professor WM Mitch Masters Evolution, Ecology, and Organismal
Professor Richard Bradley Biology Graduate Program
UMI Number: 3062652
Copyright 2003 by Stark, Robert Douglas
All rights reserved.
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ABSTRACT
The drum of eastern Nearctic woodpecker species was analyzed to test whether this long-
distance signal was species-specific. Woodpecker drums are a series of rapid strikes with the
bird’s bill on a resonant surface, not associated with foraging or cavity excavation, and has
been implicated in a variety of territorial and reproductive behaviors. Previous research on
drums indicated that western Nearctic woodpeckers were syntopically species-specific, with
the cadence of the drum (strikes-sec-1) as the primary variable for species discrimination and
recognition. However, analysis of eastern Nearctic woodpeckers indicated that species
recognition by drum to be more contentious. Analysis of drums for this study included representative signals from all North American woodpecker species, except Lewis’ woodpecker (Melanerpes lewisi), currently recognized by the American Ornithologist’s
Union.
Drums were digitized and analyzed for the following variables: cadence (strikes- sec-1), duration (sec), number of strikes (individual strikes with a bill on a substrate during
one drum), interstrike interval (sec), the duration of a single strike (sec), and the fundamental frequency of the drum (Hz). I used multivariate analysis of variance tests and a discriminant analysis function to reclassify individuals by drum for both syntopic and allotopic species.
Results indicated that drums were distinctive but not species-specific, and all drum variables.
ii Separating syntopic species by biome decreased misclassification when analyzed by a
discriminant function. This was the same pattern as observed in western populations.
Significant misclassifications occurred between Northern flickers and syntopics in all biomes. Next, using discriminant functions analyses and Mantel tests, I tested whether
drums of widely distributed species indicated any geographic variation in drum parameters.
Results indicated no variation in drums correlated or classifiable to region, with the exception
of the black-backed woodpecker which had variation in their cadence, interstrike interval,
and frequency (Hz) attributable to geographic region. Thus, the majority of Nearctic
woodpeckers are acoustically uniform across ranges for drums.
I tested whether phylogeny or morphological phenology (i.e., primarily feather topology) influenced the current structure of woodpecker drums. This tested whether the structure of drums could be attributable to phylogenetic influences or factors that affect species recognition between heterospecifics. I tested whether there were significant correlations between the drums cadence, duration, interstrike interval, and first principal component score versus phenotypic and phylogenic relationships. Results indicated significant correlations in both phenology and phylogeny versus selected drum variables, when all species were analyzed concurrently. Therefore, I deconstructed the dendrograms by sympatric species for six Nearctic biomes, to test whether drum divergence was significantly correlated to either syntopic phylogenic or phenotypic heterospecific influences. Results indicated some significant correlations in phylogeny and phenology when categorized by biome. Further testing indicated that the overall phylogenic significance in woodpeckers versus drum variables were due to differences at the genus, not species, level.
iii Next, I recorded and analyzed the drums of four species of woodpeckers (minimum of 10 drums individual-1) to ascertain whether markers for individual recognition are encoded within the drum of each species. This analysis indicated that markers for individual recognition were encoded in the spacing of strikes within one drum and drum duration, but not within the number of strikes or the cadence within one drum. Further analysis using a discriminant function indicated that the selected drum variables encoded species, but not individual, information for all species.
With the completion of the signal analysis, I used playbacks to test whether the patterns revealed from the signal analysis were recognized and pertinent to the woodpeckers in a complex acoustic environment. I recorded the responses of nine eastern woodpecker species to reciprocal playbacks of conspecifics versus syntopic and allotopic heterospecific woodpecker drums. Results indicated those species with divergent cadences in their drums were able to accurately discriminate heterospecifics versus conspecifics. However, eastern species with similar cadences were equally responsive to heterospecifics versus conspecifics while syntopic, in contrast to western species. Furthermore, allotopic red-naped
(Sphyrapicus nuchalis) and yellow-bellied (S. varius) sapsuckers were found to have similar behavioral responses to reciprocal playbacks. Thus, behavioral responses to playbacks indicated that woodpecker drums were not species-specific.
Nuttall’s (Picoides nuttallii) and white-headed woodpeckers (P. albolarvatus), which are normally allotopic species, are known to have one region of syntopy in the San Gabriel
Mountains, San Bernardino Co., CA. Previous research (Stark et al. 1998) indicated that these species, in allotopy, had drums that were similar in cadence, duration, interstrike
iv interval, and number of strikes. Analysis indicated no significant differences between
species’ drums at this syntopic interface. Results from reciprocal playbacks indicated that neither species could accurately differentiate heterospecific from conspecific drums in
syntopy. Reclassification by logistic regression indicated moderate interspecific
differentiation by drum. Thus, these two species had not differentiated their drums in
syntopy, contrary to theoretical predictions for coexistence of related species. Thus, drums
may not be used for species recognition within this population.
Finally, I tested the variables responsible for species recognition through behavioral
analysis of black-backed woodpeckers (Picoides arcticus) to five experimentally altered
signals versus unmodified conspecific drums. Results indicated that drum cadence and
signal duration were important variables for discrimination of black-backed woodpecker
drums from those of heterospecifics; standard downy woodpecker (P. pubescens) drums
elicited signal differentiation, but artificially doubling drum duration erased clear
discrimination. Modification of conspecific drums had mixed results; further analysis
indicated that a combination of drum parameters may be important for species recognition.
Of the competing hypotheses concerning signal discrimination in birds (i.e. invariant-
features, releaser, sound-environment, alerting-message, additive-redundant, and syntactical),
results indicated support for the additive-redundant hypothesis for drums, similar to that
observed for song in passerines.
v I wish to dedicate this dissertation to my Parents, Sharon and Robert, my brothers Brent
and David, and my sister Alicia. Also, I dedicate this dissertation to my wife Danielle, whose inspiration and support made all of this possible.
vi ACKNOWLEDGMENTS
This research could not have been completed without the cooperation of numerous scientists and resource managers, along with private, local, state and federal
agencies. I thank D. Stetson, WM. Masters, T. Waite, P. Parker, R. Bradley, D. Nelson,
S. Gaunt and the Department of Evolution, Ecology, and Organismal Biology, The Ohio
State University, along with the Marjorie Osborn Fellowship for partial funding and
support of this research. I further thank the Borror Laboratory of Bioacoustics, Museum
of Biological Diversity, The Ohio State University for access to equipment and use of
computer facilities.
For research on recording and playbacks of eastern woodpecker drums,
geographic variation, and phylogeny and phenologies influence on woodpecker drums, I
thank J. Jackson and the Biological Sciences Department, Mississippi State University,
D. Richard and the USFWS staff at Noxubee NWR, J. H. Carter III and K. Brust, along
with M. Nau at the Walthour Moss Foundation, NC, B. Parsons and the North Carolina
Wildlife Resource Commission, J. Walters, Virginia Tech University, T. Engstrom, Tall
Timbers Research Station, E. H. Burtt, Ohio Wesleyan University, and W. Barnard,
Norwich University, along with the biological research station at Algonquin provincial park, and the University of Toronto. Thanks to E. H. Miller for critical evaluation and improvement of this manuscript. Special thanks to D. Strickland and the Ontario
vii provincial parks system for support of research in Algonquin. Also, special thanks to T.
Shisler, Wahkeena Nature Preserve and the Ohio Historical Society for access and
logistic support in research areas. Finally, I thank R. McCarthy and B. McCarthy for
permission to conduct research on their property, and D. Adams and D. Oaks for field
assistance and data collection during the 1997 field season.
For research on Nuttall’s and White-headed woodpeckers, I thank K. Garrett for
information on the location of area of syntopy, P. Parker, J. Jackson, E. Johnson, R.
Conner, M. Morrison, R. Dixon, J. Smallwood, R. Gambs and D. Frey and four
anonymous reviewers for comments and suggestions for improvement of this research,
and the Biological Sciences Department, California Polytechnic State University-San
Luis Obispo for technical support and use of computer facilities. For research on
encoding individual cues in woodpecker drums, I wish to thank to B. Smith and the
Department of Entomology, The Ohio State University for assistance and comments.
Thanks also to Y. Yang and the Statistical Consulting Services, Department of Statistics,
The Ohio State University, and D. Kroodsma and K. Smith for suggestions which
improved this research.
For research on the use of altered drum signals in reciprocal playbacks to Black- backed woodpeckers, I wish to thank D. Strickland, A. Nappi, P. Drapeau, and L. Imbeau and the Groupe de recherche en Écologie forestière interuniversitaire, Département des sciences biologiques, Université du Québec à Montréal, A. DesRoches and the Centre de recherche en biologie forestiere, Faculte de foresterie & geomatique, Universite Laval, and W. Barnard and the Biology Department, Norwich University, along with the parc de
viii conservation des Grands-Jardins for information, access and permission to conduct this research.
ix VITA
Oct. 24, 1969 Born- Orange, California, USA
1989 A.S. Biology, Santa Rosa Junior College
1993 B.S. Biological Sciences, concentration in
Anatomy and Physiology. California Polytechnic
State University, San Luis Obispo
1993-1996 Teaching Associate, Biological Sciences
Department, California Polytechnic State
University, San Luis Obispo
1996 M.S. Biological Sciences, California Polytechnic
State University, San Luis Obispo
1996-present Graduate Teaching Assistant
The Ohio State University
PUBLICATIONS
Research Publication
1. Dodenhoff, D. J., R. D. Stark, and E. V. Johnson. 2001. Do woodpecker drums encode information for species recognition? Condor 103:143-150.
2. Stark, R. D., D. J. Dodenhoff, and E. V. Johnson. 1998. A quantitative analysis of woodpecker drumming. Condor 100:350-356.
x FIELDS OF STUDY
Major Field: Evolution, Ecology, and Organismal Biology
xi TABLE OF CONTENTS
Page
Abstract ii
Dedication vi
Acknowledgements vii
Vita x
List of Tables xv
List of Figures xxii
Chapters:
1. Introduction 1
2. Quantitative analysis of eastern Nearctic woodpecker drums 22
2.1 Methods 28
2.2 Results 30
2.3 Discussion 33
3. Geographic variation in woodpecker drums 64
3.1 Methods 69
3.2 Results 71
3.3 Discussion 75
xii 4. Does phylogeny or phenology influence the structure of woodpecker
drums? 125
4.1 Methods 127
4.2 Results 129
4.3 Discussion 132
5. Encoding individual identity in woodpecker drums 151
5.1 Methods 152
5.2 Results 154
5.3 Discussion 155
6. Behavioral responses to playbacks of heterospecific versus conspecific
drums 161
6.1 Methods 163
6.2 Results 166
6.3 Discussion 168
7. Differentiation at a syntopic interface; Nuttall’s versus White-headed
woodpeckers 185
7.1 Methods 188
7.2 Results 190
7.3 Discussion 192
xiii 8. Behavioral responses to altered drums: Black-backed woodpeckers 201
8.1 Methods 203
8.2 Results 207
8.3 Discussion 210
8.4 Epilogue 224
Appendix A Tables and descriptive statistics 247
Bibliography 302
xiv LIST OF TABLES
Table Page
2.1 Recording locations for this investigation 43
2.2 Recording locations for this investigation 44
2.3 Descriptive statistics of Nearctic woodpecker drums 52
2.4 ANOVA for gender in drums 53
3.1 Kruskal-Wallis regional analysis of woodpecker drums 121
3.2 GLM (MANOVA) regional analysis of woodpecker drums 122
3.3 Mantel results of geographic variation versus drum variables 123
3.4 Mantel results of geographic variation versus drum variables 124
4.1 Mantel results of phenology versus phylogeny 143
4.2 Mantel results of phylogeny versus drum variables
separated by habitat 144
4.3 Mantel results of phylogeny versus drum variables
separated by habitat 145
4.4 Mantel results of phenology versus drum variables
separated by habitat 146
4.5 Mantel results of phenology versus drum variables
separated by habitat 147
4.6 Mantel results of phenology versus phylogeny versus habitat 148
xv 4.7 Mantel results of phylogeny and phenology versus
drum variables for Picoides 149
4.8 Mantel results of phylogeny and phenology versus
drum variables for Melanerpes 150
5.1 Means of selected drum variables for individual recognition 159
5.2 Location of individual markers encoded in woodpecker drums 160
6.1 Playback drums variable descriptive statistics 180
6.2 Descriptive statistics for behavioral responses to playbacks 181
6.3 Descriptive statistics for behavioral responses to playbacks 182
6.4 Playback results 183
7.1 Descriptive statistics of Nuttall’s and white-headed woodpeckers 197
7.2 Coefficient of variation for Nuttall’s and white-headed
woodpecker drum variables of individual versus
population 198
7.3 Behavioral responses to playbacks in Nuttall’s versus
white-headed woodpeckers in Chilao 199
7.4 Table of principal component scores for playbacks in
Nuttall’s versus white-headed woodpeckers 200
8.1 Descriptive statistics of playback drums: modified and
unmodified signals for black-backed woodpeckers 242
8.2 Descriptive statistics of behavioral responses to modified
drums in black-backed woodpeckers 243
xvi 8.3 MANOVA and ANOVA results for the influence of gender
on behavioral responses to drums in black-backed
woodpeckers 244
8.4 Wilcoxon sign-rank test results for each reciprocal
playback (conspecific vs. experimental) conducted
on black-backed woodpeckers for PC1-PC2 245
8.5 Regression statistics of the five behavioral response variables,
PC1 and PC2 for black-backed woodpecker playbacks 246
A.1 Results of the discriminant function analysis for eastern
species run concurrently 248
A.1 Results of the discriminant function analysis for eastern
species run concurrently, continued 249
A.2 Results of the discriminant function analysis for boreal species 250
A.3 Results of the discriminant function analysis for southern
pine forest species 251
A.4 Results of the discriminant function analysis for eastern
deciduous forest species 252
A.5 Results of the discriminant function analysis for southwestern
desert species 253
A.6 Results of the discriminant function analysis for Rocky Mountain
high elevation species 254
xvii A.7 Results of the discriminant function analysis for Texas
chaparral species 255
A.8 Results of the discriminant function analysis for all Nearctic
species, except Lewis’, run concurrently 256
A.8 Results of the discriminant function analysis for all Nearctic
species, except Lewis’, run concurrently, continued 257
A.8 Results of the discriminant function analysis for all Nearctic
species, except Lewis’, run concurrently, continued 258
A.8 Results of the discriminant function analysis for all Nearctic
species, except Lewis’, run concurrently, continued 259
A.9 Regional descriptive statistics for northern flickers 260
A.10 Regional descriptive statistics for acorn woodpeckers 261
A.11 Regional descriptive statistics for black-backed woodpeckers 262
A.12 Regional descriptive statistics for downy woodpeckers 262
A.13 Regional descriptive statistics for golden-fronted woodpeckers 263
A.14 Regional descriptive statistics for gila woodpeckers 263
A.15 Regional descriptive statistics for ladder-backed woodpeckers 264
A.16 Regional descriptive statistics for hairy woodpeckers 265
A.17 Regional descriptive statistics for Nuttall’s woodpeckers 266
A.18 Regional descriptive statistics for pileated woodpeckers 267
A.19 Regional descriptive statistics for red-breasted sapsuckers 267
A.20 Regional descriptive statistics for red-bellied woodpeckers 268
xviii A.21 Regional descriptive statistics for red-headed woodpeckers 268
A.22 Regional descriptive statistics for red-cockaded woodpeckers 269
A.23 Regional descriptive statistics for red-naped sapsuckers 269
A.24 Regional descriptive statistics for three-toed woodpeckers 270
A.25 Regional descriptive statistics for white-headed woodpeckers 270
A.26 Regional descriptive statistics for Williamson’s sapsucker 271
A.27 Regional descriptive statistics for yellow-bellied sapsucker 272
A.28 Summary table for the discriminant function analysis for
region in acorn woodpeckers 273
A.29 Summary table for the discriminant function analysis for
region in black-backed woodpeckers 274
A.30 Summary table for the discriminant function analysis for
region in ladder-backed woodpeckers 275
A.31 Summary table for the discriminant function analysis for
region in red-breasted sapsuckers 276
A.32 Summary table for the discriminant function analysis for
region in red-bellied woodpeckers 277
A.33 Summary table for the discriminant function analysis for
region in red-cockaded woodpeckers 278
A.34 Summary table for the discriminant function analysis for
region in red-headed woodpeckers 279
xix A.35 Summary table for the discriminant function analysis for
region in red-naped sapsuckers 280
A.36 Summary table for the discriminant function analysis for
region in three-toed woodpeckers 281
A.37 Summary table for the discriminant function analysis for
region in white-headed woodpeckers 282
A.38 Summary table for the discriminant function analysis for
region in Williamson’s sapsuckers 283
A.39 Summary table for the discriminant function analysis for
region in yellow-bellied sapsuckers 284
A.40 Summary table for the discriminant function analysis for
region in northern flickers 285
A.40 Summary table for the discriminant function analysis for
region in northern flickers, continued 286
A.40 Summary table for the discriminant function analysis for
region in northern flickers, continued 287
A.40 Summary table for the discriminant function analysis for
region in northern flickers, continued 288
A.41 Summary table for the discriminant function analysis for
region in Nuttall’s woodpeckers 289
A.41 Summary table for the discriminant function analysis for
region in Nuttall’s woodpeckers, continued 290
xx A.41 Summary table for the discriminant function analysis for
region in Nuttall’s woodpeckers, continued 291
A.41 Summary table for the discriminant function analysis for
region in Nuttall’s woodpeckers, continued 292
A.42 Summary table for the discriminant function analysis for
region in pileated woodpeckers 293
A.42 Summary table for the discriminant function analysis for
region in pileated woodpeckers, continued 294
A.43 Summary table for the discriminant function analysis for
region in downy woodpeckers 295
A.43 Summary table for the discriminant function analysis for
region in downy woodpeckers, continued 296
A.44 Summary table for the discriminant function analysis for
region in golden-fronted woodpeckers 297
A.45 Summary table for the discriminant function analysis for
region in hairy woodpeckers 298
A.45 Summary table for the discriminant function analysis for
region in hairy woodpeckers, continued 299
A.45 Summary table for the discriminant function analysis for
region in hairy woodpeckers, continued 300
A.45 Summary table for the discriminant function analysis for
region in hairy woodpeckers, continued 301
xxi LIST OF FIGURES
Table Page
2.1 Recording locations for this investigation 42
2.2 Exemplar sonograms of drums: red-cockaded and red-headed
woodpeckers 45
2.2 Exemplar sonograms of drums: northern and gilded flickers 46
2.3 Exemplar sonograms of drums: Arizona, hairy, and
ladder-backed woodpeckers 47
2.4 Exemplar sonograms of drums: Williamson’s, yellow-bellied,
and red-naped sapsuckers 48
2.5 Exemplar sonograms of drums: black-backed, red-bellied,
and downy woodpeckers 49
2.6 Exemplar sonograms of drums: golden-fronted, and
gila woodpeckers 50
2.6 Exemplar sonograms of drums: pileated,
and three-toed woodpeckers 51
2.7 Graph of the first versus second canonical functions for
eastern Nearctic woodpeckers run concurrently 54
2.8 Graph of the first versus second canonical functions for syntopic
boreal woodpecker species 55
xxii 2.9 Graph of the first versus second canonical functions for syntopic
eastern deciduous forest woodpecker species 56
2.10 Graph of the first versus second canonical functions for syntopic
Rocky Mountain woodpecker species 57
2.11 Graph of the first versus second canonical functions for syntopic
southern pine forest woodpecker species 58
2.12 Graph of the first versus second canonical functions for syntopic
Texas chaparral woodpecker species 59
2.13 Graph of the first versus second canonical functions for syntopic
desert woodpecker species 60
2.14 Graph of the first versus second canonical functions for all Nearctic
woodpecker species, except Lewis’ woodpecker 61
2.15 Sonograms of the “unusual” red-naped sapsucker drum versus
standard population drum for this species 62
2.16 Red-naped sapsucker sonograms of drums 63
3.1 Recording locations for testing geographic variation in
acorn woodpeckers 84
3.2 Recording locations for testing geographic variation in
black-backed woodpeckers 85
3.3 Recording locations for testing geographic variation in
downy woodpeckers 86
xxiii 3.4 Recording locations for testing geographic variation in
gila woodpeckers 87
3.5 Recording locations for testing geographic variation in
golden-fronted woodpeckers 88
3.6 Recording locations for testing geographic variation in
hairy woodpeckers 89
3.7 Recording locations for testing geographic variation in
ladder-backed woodpeckers 90
3.8 Recording locations for testing geographic variation in
northern flickers 91
3.9 Recording locations for testing geographic variation in
pileated woodpeckers 92
3.10 Recording locations for testing geographic variation in
red-bellied woodpeckers 93
3.11 Recording locations for testing geographic variation in
red-cockaded woodpeckers 94
3.12 Recording locations for testing geographic variation in
red-headed woodpeckers 95
3.13 Recording locations for testing geographic variation in
red-naped sapsuckers 96
3.14 Recording locations for testing geographic variation in
three-toed woodpeckers 97
xxiv 3.15 Recording locations for testing geographic variation in
Williamson’s sapsuckers 98
3.16 Recording locations for testing geographic variation in
yellow-bellied sapsuckers 99
3.17 Recording locations for testing geographic variation in
Nuttall’s woodpeckers 100
3.18 Recording locations for testing geographic variation in
white-headed woodpeckers 101
3.19 Recording locations for testing geographic variation in
red-breasted sapsuckers 102
3.20 Graph of the first versus second canonical functions for
reclassifying acorn woodpeckers by habitat 103
3.21 Graph of the first versus second canonical functions for
reclassifying black-backed woodpeckers by habitat 104
3.22 Graph of the first versus second canonical functions for
reclassifying downy woodpeckers by habitat 105
3.23 Graph of the first versus second canonical functions for
reclassifying golden-fronted woodpeckers by habitat 106
3.24 Graph of the first versus second canonical functions for
reclassifying hairy woodpeckers by habitat 107
3.25 Graph of the first versus second canonical functions for
reclassifying ladder-backed woodpeckers by habitat 108
xxv 3.26 Graph of the first versus second canonical functions for
reclassifying northern flickers by habitat 109
3.27 Graph of the first versus second canonical functions for
reclassifying Nuttall’s woodpeckers by habitat 110
3.28 Graph of the first versus second canonical functions for
reclassifying pileated woodpeckers by habitat 111
3.29 Graph of the first versus second canonical functions for
reclassifying red-breasted sapsuckers by habitat 112
3.30 Graph of the first versus second canonical functions for
reclassifying red-bellied woodpeckers by habitat 113
3.31 Graph of the first versus second canonical functions for
reclassifying red-cockaded woodpeckers by habitat 114
3.32 Graph of the first versus second canonical functions for
reclassifying red-headed woodpeckers by habitat 115
3.33 Graph of the first versus second canonical functions for
reclassifying red-naped sapsuckers by habitat 116
3.34 Graph of the first versus second canonical functions for
reclassifying three-toed woodpeckers by habitat 117
3.35 Graph of the first versus second canonical functions for
reclassifying white-headed woodpeckers by habitat 118
3.36 Graph of the first versus second canonical functions for
reclassifying Williamson’s sapsuckers by habitat 119
xxvi 3.37 Graph of the first versus second canonical functions for
reclassifying yellow-bellied sapsucker by habitat 120
4.1 Short’s (1982) Nearctic woodpecker phylogeny 139
4.2 Consensus phylogeny of Nearctic woodpeckers 140
4.3 Male phenogram of Nearctic woodpeckers 141
4.4 Female phenogram of Nearctic woodpeckers 142
6.1 Primary and secondary playback locations 184
8.1 Linear regression of the first principle component
in response to playback stimuli 228
8.2 Linear regression of the second principle component
in response to playback stimuli 229
8.3 Linear regression of the duration spent drumming in
response to playback stimuli 230
8.4 Linear regression of the latency to first response to
playback stimuli 231
8.5 Linear regression of the number of directed flights in
response to playback stimuli 232
8.6 Linear regression of the closest approach to the speaker in
response to playback stimuli 233
8.7 Linear regression of the duration spent within ten meters
of the speaker in response to playback stimuli 234
xxvii 8.8 Regression of the standard residuals for the first principle
component in response to playback stimuli 235
8.9 Regression of the standard residuals for the second principle
component in response to playback stimuli 236
8.10 Regression of the standard residuals for the duration spent
drumming in response to playback stimuli 237
8.11 Regression of the standard residuals for the latency to first
response to playback stimuli 238
8.12 Regression of the standard residuals for the number of
directed flights in response to playback stimuli 239
8.13 Regression of the standard residuals for the closest
approach to the playback speaker in response to
playback stimuli 240
8.14 Regression of the standard residuals for the duration spent
within 10 meters of the playback speaker in response
to playback stimuli 241
xxviii CHAPTER 1
A HISTORICAL REVIEW OF SPECIES-SPECIFICITY AND ACOUSTIC SIGNAL
DISCRIMINATION IN BIRDS
One aspect critical to understanding the evolution and maintenance of vertebrate species and speciation is to understand the factors that promote recognition in systems. In species where mating occurs through non-behavioral mechanisms, such as the release of gametes into an aqueous environment, selection favors conditions that promote homogametic pairings. In this type of recognition system, species signals have evolved in response to intersexual selection (i.e., sperm competition) for gamete recognition, presumably with natural selection against hybrids formed from heterogametic unions. Yet, in species where mate recognition occurs primarily through premating signals, such as song, mate preferences in one sex (i.e., female mate choice) can influence the evolution of the mating signals of the opposite sex, resulting in intersexual selection and promoting divergence in mate recognition signals (Ptacek 2000, Baker 2001).
There has been a significant amount of research dedicated to the influence of sexual selection on the design of signals (Andersson 1994). These signals are complex, and may potentially convey information to receivers concerning species identity, gender, reproductive status, individual identity, and even quality of the sender (Ptacek 2000). In birds, selective
1 pressures maintaining species-specificity may be stronger for some acoustic signals,
including territorial calls and song, while encoding interspecific information in other signals
such as alarm, mobbing, or flight calls (Becker 1982). For example, intraspecific territorial
calls that are broadcast over long distances often have pronounced species distinguishing
characteristics, and have been implicated in sexual selection and speciation.
The traditional view of signal evolution and design suggests that certain features of
the signal are encoded for species recognition. These variables experience stabilizing selection over time, decreasing the variance of these signal parameters and making them reliable indicators (Gerhardt 1991). In intersexual selection, females may use a variety of these signal parameters to make interspecific distinctions, exerting directional selection on signal design (Zuk et al. 1990, Thompson et al. 1997). Sexual selection by females on secondary male sex characteristics, including acoustic signals, may further speciation and the encoding of species-specific signals within populations (Verrell 1988). Several studies have
indicated a preference for these local dialects within populations (Searcy et al. 1997, Searcy
1990, Latruffe et al. 2000). Therefore, this model predicts that species-specificity in
acoustical signals is a byproduct of sexual selection to minimize the risk of hybridization
between heterospecifics, but did not arise specifically as a result of speciation (Paterson
1980, 1981, 1985, Verrell 1988, Price 1998).
Deciphering species signals is a two step process: first, there must be recognition
concerning species identity, and second, a ranking of the signal importance in regards to its required immediacy for generating a response from the receiver. For example, research on mate choice has noted that females gauge male signals relative to one another and then select
2 their mates from the available choices (Price 1998, Littlejohn 1999, Baker 2001). Under
mate choice, it is arguable that the function of sexual selection (in speciation) is to generate
trait differences between populations, including acoustic signals, with sexual imprinting of
these traits as species recognition mechanisms from parents to offspring (Price 1998). Under
these constraints, species recognition could be considered a byproduct of mate selection
(Littlejohn 1999), with divergence of signals being secondary to intraspecific sexual selection
(Gerhardt 1982). If true, then species-specificity in acoustic signals may be an emergent
property of mate choice, allowing for maintenance of genetic integrity between evolutionary
trajectories.
This continuum between sexual selection and species recognition has become the
focus of a number of recent studies (Boake et al. 1997, Verrell 1999), with some authors
arguing that sexual selection and species recognition are essentially the same problem in
animal communication (Ryan and Rand 1993). The classic model of biological speciation
generally assumes that conspecific populations are physically segregated to permit genetic
divergence (i.e., allopatric speciation), and this separation is adequate for reproductive
isolation and maintenance of genetic integrity (i.e., the cessation of gene flow between
populations, Mayr 1942, Futuyama and Mayr 1980). Postmating barriers to interbreeding
arise as byproducts of genetic divergence while populations are in allopatry, while selection fortifies premating barriers to minimize hybridization (Dobzhansky 1951, Miyatake and
Shimizu 1999). This mode of speciation is based on female choice, because it is the females that choose mates based on species-specific traits. Generally, females avoid mating with males of closely-related species having similar but distinct secondary characteristics.
3 According to this ‘reinforcement hypothesis,’ there should be negative fitness consequences from hybridization and a bias towards species assortive mating, as confirmed by recent experiments with flycatchers (Saetre et al. 1997, Alatalo et al. 1994), though conclusive support is generally lacking (Butlin 1987, 1995, Sanderson 1989).
However, in this scenario, sexual selection for specific signal properties are correlated with features that make an individual more attractive to mates. Therefore, mating preferences can influence and promote divergence at both intra- and interspecific levels.
This divergence in mate recognition signals among populations within a species can ultimately lead to reproductive isolation and speciation (Lande 1981, West-Eberhardt 1983).
Unfortunately, tests of whether traits chosen by females are linked to selection for differences between heterospecifics and conspecifics have been mixed. For example, Pieras butterflies used the same variables to select mates as to discriminate heterospecifics from conspecifics
(Weirnasz 1989), but stalk-eyed flies (Drosophila heteroneura) did not show a similar correlation (Boake et al. 1997). Therefore, it is important to understand how senders encode signals with information, and how these signals are interpreted by receivers.
A REVIEW OF ENCODING INFORMATION IN ACOUSTIC SIGNALS
Acoustical signals are often used as a key for the identification and discrimination of closely related species (Mousseau and Howard 1998). There is a role for sexual selection in speciation, based on comparative evidence in that closely related species often vary strikingly in sexually selected traits (West-Eberhard 1983, Ryan and Rand 1993). To allow ample time
4 for sexual selection to act upon these species-specific signals, they must be stable over time,
conspicuous, easily perceived, and highly distinguishable (Lorenz 1935). Functionally, the acoustic repertoire of the bird should include songs and calls for territorial advertisement and defense, formation and maintenance of a pair bond, coordination of reproductive cycles within pairs and populations, individual recognition, and advertisement of motivation
(Hauser 1996, Kroodsma and Miller 1996). These signals may have learned and innate
components (Baptista 1996).
Marler (1957, 1960) predicted that “conflicting requirements of species and
individual identification may lead to encoding information in separate (song) features.”
However, the coding of this information in separate features increased signal complexity, and increasing signal complexity often conflicts with the need for precise transmission of species identity (Marler 1959, 1960, 1961). Marler theorized that other characteristics, which are not necessarily species-specific, may encode other information including individual characteristics, location, motivational state, and fitness.
Most avian communication studies have been restricted to the analysis of repelling conspecific territorial rivals in passerines (Becker 1982, Nelson 1988, Baker 2001).
Important song characters that have been implicated as species-specific variables include
signal length, amplitude pattern, interval between elements, frequency range and tonality,
structure of elements, and the syntax within the song (Becker 1982). Research on each if
these variables have indicated some general trends in acoustical signals:
Signal length: Signal duration has a variable role in species recognition; however,
there is often a minimal length required for species recognition (Kreutzer 1990). Artificially
5 lengthening signals beyond normal durations has little effect on responsivity. Shortening signal length also does not affect response, provided the remaining parts contain all the relevant species information. Obviously, deletion of key variables by decreasing signal duration affects species recognition in birds. However, increased redundancy encoded within the signal may increase signal transmission and detection over long distances (Bremond
1978). Thus, signal length is not considered a species-specific variable except in combination with other parameters.
Amplitude: Amplitude has not been shown to affect species recognition of signals
(Becker 1982). Since acoustical signals are susceptible to disturbances and degradation (via weather, habitat structure, and position and movement of senders and receivers), it is probably not suitable for species recognition. However, signal degradation has been implicated in the ranging of individuals, which is estimating the distance of the receiver from the signaler (Morton 1982, 1986, Naugib 1995, 1996ab, 1997).
Syllable interval duration and frequency: Interval duration between syllables has been shown to be a critical variable for most passerine and non-passerine species recognition
(Becker 1982, Stark et al. 1998). Signal frequency (Hertz) may be important in some species, but not others (Becker 1982). For example, the absolute pitch of signals has been shown to be a reliable indicator of individuals in black-capped chickadees (Poecile atricapillus), and the highly invariant pitch interval (ratio of high to low frequencies) suggest that relative frequencies may encode species information (Weisman et al. 1990). Normally, the signal harmonics and tonality are not important for recognition of avian species; however, the fundamental frequency may be required (Nelson 1988, 1989, Hurley et al. 1991).
6 Finally, it has been noted that body mass co-varies with most song attributes, with smaller
species using higher frequencies and increasing the number of notes in their songs (Badyaev
and Leaf 1997).
Pressure and frequency modulation: Neither sound pressure nor frequency
modulation in acoustical signals has been shown to serve as variables critical for species
identification and recognition. Presumably, this is due to confounding, naturally arising
abiotic disturbances (i.e., wind, physical obstacles, etc.) and signal degradation (i.e.,
reverberation, spherical spreading) as sound travels through the environment (Shiovitz and
Lemon 1980), especially in long distance signaling (Becker 1982). Furthermore, transients
in signals have not been demonstrated to be important in species-specific recognition in
passerines. However, in woodpeckers, the behavioral response to playbacks indicated that
species discrimination to altered cadences within drums was precise, with other spectral and
temporal characteristics possibly being used to encode individual recognition or motivational
state (Dodenhoff et al. 2001). Transients set at the proper cadence were sufficient to elicit species recognition in woodpeckers (Dodenhoff et al. 2001), indicating that the structure of elements may not be critical for signal discrimination in this non-vocal communication system.
Element structure: In the majority of species, the structure of elements in the signal is the decisive species-specific identifier (Becker 1982). For example, cirl buntings
(Emberiza cirlus) song consists of identical repetitions of the same syllable, containing elements, which are strong cues for species recognition (Kreutzer 1990). Differences in syllabic structure between conspecifics were enough for individual identification in song
7 sparrows (Nelson 1987), instead of requiring entire songs which typify different individuals or geographic locales.
Syntax: Syntax is important for recognition in many species (Becker 1982), but not all (Dabelsteen and Pederson 1992, Searcy 1990). Often, variation within species-typical
variables is necessary to elicit a full response (Kreutzer 1990). Furthermore, Kreutzer’s
results complied with the ‘room for variation’ hypothesis of information coding, in that the
species-essential variables are tuned to the natural limits of variation within the species.
Modification of signals beyond these limits decreased or ended the responsivity of target individuals. Other information (i.e., behavioral) may be encoded in the remaining signal variables, including amplitude (Dabelsteen and Pedersen 1992).
DISCRIMINATION OF SIGNALS IN AN ACOUSTICALLY COMPLEX
ENVIRONMENT
Given the evidence of information encoding in song, four strategies have been proposed for effective signal discrimination: 1) increased contrast between sounds of closely related species (Mousseau and Howard 1998), 2) temporal stratification in the timing of daily
or seasonal singing rhythms to minimize acoustical interference within the vocal activities of
syntopic species (Stacier et al. 1996), 3) increased acoustical discrimination by birds (Becker
1982), and 4) the use of multiple parameters for discrimination (Nelson 1988). Increased
contrast is evidenced by divergent vocal signals, termed character displacement, in closely
8 related species (Marler 1959, 1960). Yet, it is unknown whether character displacement
occurs in populations at a higher rate in sympatry over populations in allopatry.
It is now evident that song recognition, rather than the song itself, becomes refined in
areas of sympatry as a result of learning (Lynch and Baker 1991). Experience discriminating
the songs of heterospecifics, through repeated exposure to signals, has shown to increase in
sympatry in some species (Becker 1982), but not others (Park 1995). Research in support of
this observation using common and blue chaffinches (Fringilla coelebs and F. teydea)
indicated that these two species were not interspecifically territorial in areas of sympatry.
This was remarkable given there was no clear difference in song structure in either species
between the observed sympatric and allopatric distributions (Lynch and Baker 1991). The
lack of character displacement, coupled with the species assortive mating, indicated there
was increased vocal discrimination in areas of local sympatry among species. Further, this
research indicated support for the role of action-based learning in associating species-specific songs with distinct morphological phenotypes.
Species with similar signals have learned to differentiate between heterospecifics
through repeated exposure and experience with these signals. This increased interspecific recognition was observed in phenotypically distinct buntings (Sung and Park 1994).
However, the increased vocal discrimination within these two species was not thought to
have arisen from selection for different mate recognition systems, possibly due to phenotypic
and behavioral differences while these species were sympatric (Lynch and Baker 1991). In contrast, when Nuttall’s (Picoides nuttallii) and white-headed woodpeckers (P.
albolarvatus), which are normally allotopic species that have similar drums, coincided on
9 breeding territories, I observed interspecific territoriality. This was demonstrated through
interspecific territorial uniformity in the placement of defended territories along the interface of contact. Neither character displacement in drums nor temporal stratification of the reproductive cycles was observed between these two species (Chapter 7). These results suggest that songs, or acoustic signals in general, are only one component of the total repertoire of mechanisms that provide for genetic isolation between closely related species.
A number of factors influence bird vocalizations, including size, phylogeny,
functionality, and cultural transmission of learned signals (Price 1998, Ptacek 2000, Nelson
2000). It has been noted that song increases in complexity with an increase in latitude (i.e.,
clinal variation), suggesting that sexual selection is stronger (relative to natural selection) in
more northern latitudes (Read and Weary 1992, Price 1998). The mechanism for generating
this observed complexity remains debated. Geographic variation is one confounding variable
in maintaining species-specificity. Signal variables are often highly stereotyped, in spite of
local dialects (Becker 1977), with numerous species having a preference for local song types
(Regelski and Moldenhauser 1996, Searcy et al. 1997, Searcy 1990, Latruffe et al. 2000).
Furthermore, there is some evidence for a sex bias in geographic preference in song, with
either males or females being more selective depending on the species studied (Melman and
Searcy 1999, Baker 2001). Therefore, song variables may differ between populations and
these differences indicate that populational-specific songs may not necessarily be species-
specific signals throughout widely distributed species.
Another confounding variable is the cultural evolution of song, whereby songs (or
other acoustic signals) of offspring resemble the songs of their fathers and grandfathers
10 (Grant and Grant 1996). However, this local transmission of information within a population may help in mate choice and species recognition. The low possibility of misimprinting will
lead to low levels of hybridization and introgression within the population, which may slow postzygotic isolation. This transmission of information between generations may lead to novel genetic and morphological combinations, which could provide a starting point for a new evolutionary trajectory in speciation (Grant and Grant 1996).
Even though many studies have tested whether a single parameter is used for recognition of species, this is often not the case. To minimize ambiguity among species, it has been suggested that individuals use a multitude of variable combinations to specify their songs, although one variable may assume greater significance than others within populations
(Nelson 1988: ‘feature-weighting’ hypothesis). These variables may strengthen the signals releasing effect (categorical coding, Becker 1976, Schubert 1971: ‘releaser model’ of song
recognition) or have additive effects (continuous coding, Shiovitz and Lemon 1980:
‘additive-redundant model’ of song recognition). In the latter case, the strength of behavioral
response may be influenced by the number of cues present in the signal. Within-signal
redundancy may bypass competing noise, allowing easy signal detection by receivers.
Finally, some species use a number of acoustically distinct song types and these
repertoires of redundant song types may be favored by both intrasexual and intersexual
selection (Nelson and Croner 1991, Kroodsma 1982). The role of repertoires remains
unclear. However, it may function in territoriality, decreasing habituation in conspecifics
(Krebs 1976), impersonating multiple singers (Krebs 1977), or be favored by females through sexual selection as a form of honest advertisement (Catchpole 1980). Another
11 possibility is that separate song types are sung in different behavioral contexts, thereby
providing different information to listeners (Nelson 1985), such as motivation or intention
(Lein 1978). Song types are not redundant, but individuals may have song repertoires which number into the hundreds and subject to selective forces that differ from those that operate on the categories of song (Nelson 1988). However, the physical complexity and redundancy of acoustical signals may leave enough traits functionally free from species identification to code behavioral and motivational information.
Given that avian acoustic signals encode detectable information meaningful to receivers, six hypotheses have been proposed concerning how these signals are recognized and discriminated in a complex acoustic environment. These hypothesis differ in the degree to which information is recognized as discrete or additive: ‘invariant-features’, ‘releaser’,
‘sound-environment’, and ‘alerting-message’ hypotheses are discrete, whereas the ‘additive- redundant’ and ‘syntactical’ hypotheses are additive (Date et al. 1991). Therefore, these hypotheses incorporate not only the elements encoded in song, but the features associated with these elements. A summary of the results of the original and following research concerning each of these hypotheses is presented below:
Invariant-features: The ‘invariant-features’ hypothesis (Marler 1960, Emlen 1972) predicts that interspecific stereotyped song features will be used preferentially in song recognition, since these feature would be less likely to overlap with heterospecifics. This hypothesis is based on the concept that discrete stereotyped (invariant) units can effectively convey information of a qualitative nature (Shiovitz and Lemon 1980). For example, signal features may encode species or individual identity (Emlen 1972) or act in a graded manner to
12 convey quantitative information, such as levels of motivation. Further research indicated that this hypothesis does not adequately predict the accuracy of song features in distinguishing conspecific from heterospecific songs, as some invariant features of species overlap (Nelson
1989). This overlap may explain Emlen’s original (1972) observation that not all invariant features were used for species recognition; only those features that contrasted with other species were used for recognition (Nelson 1989). Moreover, variable features of song have been observed to be used in species recognition (Dabelsteen and Pederson 1985). Thus, invariant features encoded in signals may maximize the response of receivers. Yet, this hypothesis may be oversimplified as some features of signals are perceptually more important than others (Nelson 1988: ‘feature-weighting’ hypothesis).
Releaser: The ‘releaser’ hypothesis (Becker 1982) predicts that only certain (usually invariant) acoustical components of song are used for species identification. This hypothesis predicts the concentration of interspecific releasers in signal variables may ensure species- specificity, allowing for the encoding of further information in the remaining characters
(Becker 1982). These acoustical characteristics may act as releasers for a distinct function, which varies between species. However, recent experiments that indicated support for this hypothesis instead produced evidence supporting the ‘additive-redundant’ hypothesis, not the
‘releaser’ hypothesis as the authors contended (Park et al. 1995). No study has definitively indicated that the ‘releaser’ hypothesis was preferred over the ‘additive-redundant’ hypothesis for species recognition. Hence, this hypothesis has rallied little support in recent investigations.
13 Syntactical: Even though the role of syntax is variable in species recognition (Becker
1982), its role in recognition may be underestimated (Ratcliffe and Weisman 1987). The
‘syntactical’ hypothesis recognized that several parts of the song contribute together to evoke
a response, but that a maximal response is only given if song elements are sequentially
correctly arranged (Ratcliffe and Weisman 1987). The basis for this hypothesis lies in estradiol experiments with female brown-headed cowbirds (Molothrus ater), which indicated
that the order of signals was a key factor in affecting song potency. The first and second
components of a male song were critical in eliciting a female’s solicitation response,
indicating that females had an internal representation of the phase order of conspecific song
(Ratcliffe and Weisman 1987). Theoretically, sexual selection would favor males whose
song had the proper sequential order in their song. Nevertheless, this ‘additive’ effect of syntax could be interpreted as support for the ‘additive-redundant’ hypothesis of species recognition, with syntax as an important variable for signal recognition.
Sound-environment: This hypothesis predicts that the relationship between a species song and the songs of sympatric species within an area will indicate which signal features provide accurate song-discrimination cues (Dabelsteen and Pederson 1985, Marler 1960). In a complex environment, signals should diverge to allow for increased discrimination between species, maximizing the information transmitted between conspecifics while minimizing error between non-competitors. Further, it predicts that features which are distinctive, relative to other species in the local population, will be used as preferred recognition cues in that system and that variability is only one component of species distinctiveness. The second component of this hypothesis is the relationship between different species in an acoustic
14 space. This allows for variable features to be useful song recognition cues if they increase
heterospecific discrimination (Dabelsteen and Pederson 1985, Nelson 1988). Syntopic
species often differ markedly in their vocalizations, which suggest that female selection can
influence signal variables and may serve in species isolation. Therefore, it is possible that
female mate-choice, to minimize error expressed through heterogametic unions, has driven
signal design rather than the influence of sympatric heterospecifics. Though the sound
environment hypothesis is plausible, I have not found any research or results that have been
conducted to either further support or reject this hypothesis.
Alerting-message: The ‘alerting-message’ hypothesis (Richards 1981) predicts that
some song components or variables identify the species, while other sections function to gain
the listeners attention. This hypothesis predicts that the tailoring of the structure of song
should facilitate the detection and recognition of a song over long distances (Richards 1980).
Thus, signals are separated into ‘alerting’ and ‘message’ components to minimize the time
spent in territorial vigilance. Theoretically, the highly detectable alerting mechanism
preceding the message is stereotyped temporally, minimizing equivocation and increasing
signal detection and information transmitted (Richards 1980). Yet, one study (Khanna et al.
1997) did not find the partitioning of song into these components using the same species as
the Richards (1980). Instead, Khanna’s research (1997) indicated that both the first and
second components of towhee song (Piplio erythrophthalamus) encoded species information
(i.e., all ‘message’). Potentially, towhee song acts either through summation or correct
syntax to elicit a maximal behavioral response in receivers. This would provide evidence for either the syntactical or additive redundant hypotheses, instead of the alerting-message
15 hypothesis. To date, there are no published studies that support the alerting-message
hypothesis over alternate explanations.
Additive-redundant: The ‘additive-redundant’ hypothesis (Emlen 1971, Shiovitz and
Lemon 1980, King and West 1983) predicts that different parts of the song act together in
summation to maximize the behavioral response of receivers, and the response given by
receivers is dependent on the number of cues discriminated within the encoded signal. The basis of the ‘additive-redundant’ hypothesis comes from research on indigo buntings (Emlen
1971, Shiovitz and Lemon 1980). The indigo bunting vocalizations are stereotyped in frequency, duration, and amplitude of the syllables. Information could be encoded in the syllables two ways: as learned cues or as innate feature detectors. Results indicated that multiple variables were used to encode species specificity in this signal. The amount of
‘signal variation tolerance’ (i.e., the deviation from species typical songs), indicated an additive process in recognition: songs that contained more correct variables elicited proportionally stronger behavioral responses to presented stimuli. In Emlen’s (1971) study, the redundancy encoded in the song was required for maximizing the target individual’s response to playback stimuli.
Significant evidence has been amassed supporting the ‘additive-redundant’
hypothesis over competing hypotheses (Nelson 1987, 1988, 1989ab, Date et al. 1991, Park
1995, Sung et al. 1995, Baker 2001). Results of playbacks on song and swamp sparrows
indicated that only species-specific attributes were discriminated between these species, and responses were proportional to the number of cues present in the signal (Nelson 1988).
Analysis using the ‘just meaningful differences’ (i.e., modifications that are perceptible and
16 measurably alter the response of target individuals) in creation of the stimulus relative to the control indicated that field sparrows are sensitive to changes in phrase structure, trill-note duration, trill-note shape, song frequency, and inter-note interval. Changes in approach responses decreased significantly when the variation in song (except trill-note shape) approached 2-3 standard deviations relative to the control, noting that the statistical variation approximated the natural variation within the populations (Nelson 1988). Furthermore, multiple features were integrated in song recognition and were differentially weighted in song recognition; in this study, song frequency (invariant) was more important in recognition than other invariant and variant features. This indicated that the weighting of the importance of features within a song to have additive effects on the response of receivers may be widespread among species (Nelson 1988).
Only one study has attempted to distinguish the importance of each of these hypotheses on a single species, the american redstart (Setophaga ruticilla, Date et al. 1991).
Unfortunately, the ‘sound-environment’ hypothesis was not tested by this study. American
Redstarts showed no support for the ‘alerting-message’ hypothesis, since equally distorted portions of the song produced a minimal response, opposite to that observed by Richards
(1981) with towhees. Species identification in redstarts was not achieved through sequential portions of the song, and therefore did not support the concept of a template for song recognition (Marler 1976), though it may be important during developmental learning.
Finally, there was no evidence for the ‘syntactical’ hypothesis in this species (Date et al.
1991). Analysis of redstart songs originally supported the ‘releaser’ hypothesis; however, repeated presentations indicated that a combination of variables was necessary to evoke a
17 maximal response. Therefore, the playbacks indicated equal responsivity to differing
portions of the song, supporting the ‘additive-redundant’ hypothesis over the ‘releaser’
hypothesis.
Relative to males, little is known concerning females reaction to song (Searcy 1990), which is remarkable in that a second function of song is relevant in sexual selection through
mate attraction, and formation and maintenance of the pair bond (Searcy and Yasukawa
1996, Pfenning 1998, Ptacek 2000). In many songbird species, females select males based
on the quality of their song (Sundburg et al. 2001), and that female prefer specific song types in males (O’Loghlen and Beecher 1997). Research on red-winged blackbirds (Agelaius phoeniceus) treated with estradiol indicated that females reacted more strongly to full songs with all of the introductory notes removed (Searcy 1990). This may indicate that females react to different song variables than males, or use differential cues to evoke a behavioral response.
In another study, female song in testosterone implanted european starlings (Sturnus vulgaris) had a structure close to male song, but most of the species-specific elements were missing. The vocal repertoire was also smaller than the male’s repertoire; however, whistle types matched their mate and other females within the population (Hausberger and Black
1991). Therefore, females may remove signal variables which cue male aggression, or use songs which invoke an intermediate response from males. A preliminary trend emerging from female response to playbacks suggests that females may be better than males in discriminating song features correlated to genetic quality or resource-holding potential
(Birkhead and Möller 1993). One trend observed in regards to sexual selection, is that
18 females apparently prefer more complex songs (Searcy 1992, Hasselquist et al. 1996) even in
species with simple songs (Searcy 1992), possibly using an increase in complexity as an
honest indicator of the heritable true fitness of males.
INVESTIGATION INTO EASTERN NEARCTIC WOODPECKER DRUMS
Woodpeckers are unable to generate songs similar to those given by passerines
due to structural differences in their syrinx. Instead, woodpeckers employ two different signals for acoustic communication; a vocal series of calls and a non-vocal drum. These investigations centered on the drum of woodpeckers. Woodpecker drums are a rapid,
repetitive series of strikes with the bill on a substrate, not associated with foraging or
cavity excavation (Bent 1939, Pynnönen 1939, Short 1974). Functions attributed to
drums have ranged from individual localization to their correlation with territorial and
reproductive behaviors (Kilham 1959, Lawrence 1967, Short 1982, Eberhardt 1997).
Though the specific functions of drums are debated, and may vary between species, there
is a general consensus that this signal is a form of long-distance communication (Crockett
1975, Trombino 1998, Dodenhoff et al. 2001). Results pertaining to species-specificity
noted that the cadence of drums (strikes-sec-1) encodes information detectable and
meaningful to receivers (Dodenhoff et al. 2001, Trombino 1998, Crusoe 1980).
Until recently, woodpecker drums had been considered species-specific, with each
species having its own unique signal (Welty and Baptista 1988). Behaviorally, woodpeckers
were considered to react indiscriminately to heterospecific drums (Winker et al. 1995),
19 though recent experiments have indicated that may not be the case (Trombino 1998,
Dodenhoff et al. 2001). There are relatively few parameters that woodpeckers can encode
species information in drums. Recent investigations indicated that western Nearctic species were syntopically, but not allotopically, species-specific in their drums. Species accurately
differentiated between heterospecific drums, using the cadence of drums (strikes-sec-1) to delimit species identity.
Acoustic theory predicts that closely related species must have divergent signals for coexistence of heterospecifics, but analysis of eastern Nearctic woodpecker drums indicated that this fundamental principle in animal communication may not apply (Chapter 2). Thus, there were two primary lines of investigation in this study: a statistical analysis of the drum of eastern Nearctic woodpecker species, and a behavioral analysis of individuals to playbacks of conspecific versus heterospecific and modified woodpecker drums. To investigate this, I used a series of analyses to investigate whether eastern Nearctic woodpecker drums conformed to the predictions of acoustic theory.
The following chapters summarize the results of these investigations into the communication system of eastern Nearctic woodpeckers. I start this investigation with an analysis of the drum of all eastern Nearctic woodpecker species (Chapter 2), using multivariate statistics to test whether there was a significant demarcation in heterospecific signal parameters. In Chapter 3, I continued the analysis of woodpecker drums for geographic variation, using Mantel tests to uncover trends in drumming in eastern species.
Previous research I conducted noted that phenotypically similar species often had divergent drums, and vice verse. Thus, I was interested in whether species that were either
20 phenotypically or phylogenically similar had signals that correlated to either of these
relationships (Chapter 4). In Chapter 5, I completed the statistical analysis of drums by testing whether individual markers were encoded in the drums of four woodpecker species.
Once the statistical analysis was completed, I tested whether target individuals could differentiate heterospecific from conspecific, syntopic and allotopic drums using behavioral responses to playbacks as the assay (Chapter 6). Given the observed lack of differentiation between allotopic species, I tested whether normally allotopic species changed their drums, or their ability to discriminate species, in syntopy (Chapter 7). Finally, I modified drums of black-backed and downy woodpeckers to test how individual black-backed woodpeckers responded to artificially modified signals (Chapter 8). This analysis allowed investigation into the way in which woodpeckers interpret signals, and compare the response of this species to previous research conducted on passerines.
21 CHAPTER 2
A QUANTITATIVE ANALYSIS OF EASTERN NEARCTIC WOODPECKER
DRUMS
The study of vertebrate communication is a rich area of biological research.
Communication can be achieved through a variety of channels, ranging from chemical diffusion of molecules to elaborate visual displays between conspecifics. It is through these interactions that individuals can manipulate their environment, gather information, signal intent or need, and generally associate with their surroundings. Numerous types of organisms have been documented to communicate (Ptacek 2000), each with the objective of changing their environment by notifying a receiver with information pertinent to the sender.
Birds use both visual and auditory signal channels for communication, though most avian species live in habitats that favor acoustic signals due to limited visual distances between conspecifics. Species that live in windy, open habitats have developed communication systems that are primarily visual (Lank and Dale 2001), but habitats that restrict visual cues between neighbors, such as forests, are conducive to communication by sound. Thus, for a wide variety of birds, communication occurs by use of either songs or calls. Woodpeckers lack the ability to generate songs similar to those used by
22 passerines (Brackenbury 1982), due to structural differences in the syrinx. Instead, in
addition to their vocal repertoire of calls, woodpeckers employ a long distance, non-vocal
acoustical signal aptly referred to as a drum. A woodpecker drum is a rapid, repetitive series of strikes with the bill on a substrate, not associated with foraging or cavity excavation (Bent 1939, Pynnönen 1939, Short 1974, 1982, Winkler and Short 1978,
Kilham 1983). Drumming in woodpeckers is unusual in that a separate instrument is required in coalition with the bird’s bill to produce the signal (Skutch 1985). Other species often produce non-vocal signals either through the use of modified anatomical structures, such as adapted feathers, or through behavioral use of morphological structures, such as the wing drum of ruffed grouse (Bonasa umbellus) or snapping of the bill (ex. Empidonax flycatchers).
As an instrumental signal, a drum has been noted to be a form of long distance communication that may or may not elicit heterospecific or conspecific responses
(Crockett 1975, Winkler and Short 1978, Dodenhoff et al. 2001). General functions attributed to drumming include mate attraction, pair bond formation, synchronization of breeding cycles, and establishment and maintenance of territories (Kilham 1959,
Lawrence 1967, Winkler and Short 1978, McGregor 1993, Eberhardt 1997, Trombino
1998). Secondary functions of drums, which are attributable to most acoustic signals, include localization and ranging of individuals. Other functions for drums have been proposed, including the encoding of information concerning suitable breeding locations
(Short 1982), though such evidence is largely anecdotal (Eberhardt 1997). Given that the functions of drumming generally correspond to bird song, researchers have concluded
23 that drums should have characteristics similar to vocal signals. Some researchers have extrapolated this argument and postulated that woodpecker drums, though non-vocal, are the evolutionary counterpart to passerine song (Pynnönen 1939, Lawrence 1967), though this viewpoint has been debated (Winkler and Short 1978, Short 1982).
Surprisingly, very little information has previously been published concerning mechanical acoustical signals in avian species (Prum 1998, Mikich 1996). As a signal that is primarily used for long-distance communication, results pertaining to species- specificity in woodpecker drumming have been mixed. Drumming may be species- specific (Lawrence 1967, Perrins and Middleton 1989), diagnostic in some species (Short
1982), or too ambiguous for species recognition (Short 1971, Winkler et al. 1995).
Winkler and Short (1978) attribute variation observed in drums to numerous sources, including motivational context and geographic variation. One study indicated that drums are syntopically, but not allotopically species-specific, with the drum cadence (in strikes- sec-1) as the primary variable used to delimit species (Stark et al. 1998).
Furthermore, there is little consensus as to whether drums encode species-specific information detectable by receivers. Lawrence (1967) attributed observed heterospecific responses to chance following Pynnönen's (1939) assertion that drums were not exchanged interspecifically. Comparisons of drums of three-toed, black-backed, and hairy woodpeckers indicated that differences and changes in cadence within the drum were distinctive (Short 1974). However, despite these differences, Short observed interspecific reactivity to drumming which lead him to conclude that drums were not
24 species-specific. Winkler and Short (1978) further cited numerous examples of interspecific responses to drums that supported Short’s previous observations.
Dodenhoff et al. (2001) demonstrated that western Nearctic woodpecker drums
encode species-specific information, and that normally, signals are not exchanged
interspecifically. However, if an individual’s signal contains the species variables of another syntopic species, it may elicit a heterospecific response comparable to that given by conspecifics. Furthermore, her analysis indicated that the cadence of drums was the primary variable for signal recognition in those study species. However, her investigation was restricted by the limited range and number of species investigated.
Despite Winkler’s and Short’s conclusions, avian reference books refer to the
drum as species-specific and denote each having its own unique drum (Perrins and
Middleton 1985, Welty and Baptista 1988). If the resolving power of woodpecker
hearing is similar to other non-passerines (Dooling 1982), then there is a theoretical upper
limit on the maximum resolvable unmodulated cadence of a drum of approximately 43
strikes-sec-1. This limitation in avian hearing dictates that the number of possible
combinations of unmodulated woodpecker drums is finite. Energetic considerations
would dictate that stereotypes in one or more drum variables should minimize this
signal's equivocation, maximizing both the efficiency and information transmitted, since
ambiguity at the receiver's end should be the same (Wilson 1975). Therefore, signal
theory predicts that woodpecker drums are species-specific.
Problems arise with this argument when drums are considered over a broad
geographic scale. There are currently 214 species of woodpeckers recognized world-
25 wide, classified into 27 genera within three subfamilies (Winkler et al. 1995) most of which are known to drum. It is unlikely that the limited number of variables available within a drum could describe the wide variety of patterns documented for each species to have its own unique signal. However, according to the acoustic competition hypothesis
(Bremond 1978, Naugler and Ratcliff 1994), only syntopic species must compete with one another for signaling space and differentiation within their environment. For woodpeckers, selection should favor those species whose drums minimize the signal’s equivocation, as their drums would elicit responses only from direct competitors.
An extension of the acoustic competition hypothesis dictates that it is reasonable to expect allotopic species have similar, if not identical, acoustic signals given that selective pressures between similar species do not exist. In fact, according to the acoustic adaptation hypothesis, signals are modified to maximize efficient transmission under the selective pressures of local environments (Wiley and Richards 1978, Wiley and Richards
1982). Signal convergence would be predicted to occur under different habitats with similar external pressures, such as two coniferous forests on different continents. Thus, the drums of allotopic woodpeckers living in different geographic regions may be indistinguishable from one another.
Currently, there are three hypotheses regarding the role of woodpecker drums as signals used for identifying species. First, woodpecker drums are species-specific
(Pynnönen 1939, Lawrence 1967, Perrins and Middleton 1985, Welty and Baptista 1988).
Second, drums are not distinctive (i.e., classifiable) because other signals, perhaps series calls, are used for species specificity (Short 1982). Third, drums are syntopically but not
26 allotopically species-specific, with cadence of the drum used as the predominant variable
for encoding species identification (Stark et al. 1998). This study will test whether
eastern woodpeckers differentiate the signal of conspecifics from syntopic heterospecifics
in a method similar to their western counterparts. This investigation will also test
whether cadence of drums is the primary variable for recognition of species or are other
variable combinations required for correct classification.
I tested the competing hypotheses concerning Nearctic woodpecker drums to
discover whether the identity of species is encoded within this non-vocal signal. I recorded the drums of all central and eastern Nearctic woodpeckers (except Lewis’ woodpecker [Melanerpes lewis]) to analyze for signal distinction in heterospecific signal variables. If drums are species-specific, drums should encode enough information to allow for partitioning of signals by species. However, eastern woodpecker species may be similar to their western counterparts in that drums of allotopic species may have comparable drums, while the drums of syntopic species remain acoustically distinct.
Finally, it is possible that the drums of syntopics may overlap (i.e., no heterospecific influence), as long as other factors for species recognition are maintained within populations.
27 METHODS
SPECIES AND STUDY AREAS
Recordings of woodpeckers were made across North America during the 1997-
2001 breeding seasons (Fig. 2.1, Table 2.1 and 2.2). Species investigated include downy
(Picoides pubescens), hairy (P. villosus), red-bellied (Melanerpes carolinensis), red-
headed (M. erythrocephalis), and pileated (Dryocopus pileatus) woodpeckers, and the
northern flicker [yellow-shafted race, Colaptes auratus] which occur in eastern deciduous
forests. Furthermore, drums of the red-cockaded woodpecker (P. borealis), endemic to
southern pine forests, were also recorded. Species endemic to northeastern coniferous
forests included the black-backed (P. arcticus) and three-toed woodpeckers (P.
tridactylus) and yellow-bellied sapsucker (Sphyrapicus varius). Given the paucity of
quantified data concerning woodpecker drumming, I also recorded Arizona (P. arizonae), gila (M. uropygialis), and ladder-backed woodpeckers (P. scalaris) from the southwestern deserts, acorn (M. formicivorus), Nuttall’s (P. nuttallii), and golden-fronted woodpecker (M. aurifrons) in the western woodlands and Texas chaparral, and the white- headed woodpecker (P. albolarvatus) along with the red-breasted (S. ruber),
Williamson’s (S. thyroideus), and red-naped (S. nuchalis) sapsucker complex endemic to the northern and western coniferous forests.
28 EQUIPMENT AND QUANTITATIVE ANALYSIS
Recordings were made using a Marantz PMD 222 professional cassette recorder,
or Sony TCD5 ProII, with an Audio-Technica 815R microphone connected to a Deneke
PS1 phantom power source. Microphones were covered with a zeppelin to minimize
external influences on the recording (i.e., wind). Drums were digitized using RTSD (real
time spectrograph display) and analyzed using SIGNAL 3.1 for the following variables:
cadence (strikes-sec-1), duration (sec), number of strikes (individual strikes with a bill on
a substrate during one drum), interstrike interval between strikes (sec), the duration of a
single strike (sec), and the fundamental frequency of the drum (Hz).
The overall temporal pattern in unmodulated drumming was not included in this analysis, due to the difficulty in incorporating this continuous variable into a discriminant function and its lack of statistical independence. Previous tests indicated this parameter is not important for species recognition (Dodenhoff et al. 2001) or reclassification (Stark et al. 1998). Most woodpeckers either speed up or slow down their drum from beginning to end; only Northern flickers had a drum that remained relatively constant throughout
(Stark et al. 1998).
I employed either a multivariate analysis of variance (MANOVA, N > 3) or a
Kruskal-Wallis non-parametric ANOVA (N < 3), depending on sample size, to test for
variation between species, regions, and gender. If differences were found within or
between species using the MANOVA, I employed one-tailed ANOVA’s using
Bonferroni’s-corrected P-values to identify the values that differed. The MANOVA
29 could indicate further the presence of regional variation within species. This will be
more fully explored in chapter 3.
To test whether drums were species-specific in eastern Nearctic woodpecker
species, I used a discriminant function analysis to reclassify individuals based on the
selected variables of drums (James and McCulloch 1990). Given that I hypothesized that
the drums of allotopic eastern woodpeckers are not species-specific (Stark et al. 1998), I
separated species into sympatric and allopatric distributions to test whether biome
division decreased the error rates in reclassification of individuals into species. I used the
mean of each variable for each individual for each species for this analysis. Furthermore,
the discriminant function analysis identified the variable(s) required for species
separation and reclassification under the various divisions of the data set.
RESULTS
I recorded and analyzed 3548 drums recorded from 604 individuals, representing
17 Nearctic woodpecker species (Figures 2.2-2.8). Descriptive statistics (Mean + S.E.) were calculated for the selected drum variables for all woodpecker species recorded over the course of this investigation (Table 2.3). Analysis of drums using a MANOVA
(GLM) indicated significant differences between species for all signal variables (P <
0.001). Thus, woodpecker drums had significant variation to allow reclassification to species by a discriminant function. Analysis for differences within variables of drums
30 between genders noted some significant differences: Black-backed woodpeckers differed in the cadence of their drums (Table 2.4), but post-hoc Tukey tests indicated these differences did not exist between males and females (P = 0.276), but between females and individuals of unidentified gender (P = 0.011). The difference between genders in
Yellow-bellied sapsucker’s interstrike interval followed a similar pattern: males differed from individuals of unidentified gender (P = 0.01), but not females from males (P =
0.283). All other variables of drums were non-significant between genders for all remaining species (P > 0.05, Table 2.4). Thus, the drums of males and females are, for all intents and purposes, identical within Nearctic species with little influence of gender on signal design.
In general, eastern woodpeckers had similar temporal patterns to their western counterparts within each drum. Downy, ladder-backed, and gila woodpeckers slowed their drum cadence within one drum (strikes spaced farther apart), while red-cockaded, red-bellied, golden-fronted, black-backed, three-toed, and pileated woodpeckers increased their cadence within one drum. Hairy woodpeckers were similar to their western counterparts in that drums either slowed or sped up in cadence, a pattern also observed in the red-headed woodpeckers. Arizona woodpeckers, with the small sample sizes collected, could not be analyzed with any certainty for patterns of strikes within drums. Both the northern and gilded flickers had signals that remained constant in the spacing of strikes throughout one drum.
A discriminant function analysis was used to reclassify individual woodpeckers to species using the selected drum variables; both original classification and cross-validation
31 tests were conducted. In cross validation, each case is classified by the functions derived
from all cases other than that case, giving a better representation of the reclassification
matrix for the sample. Reclassification of eastern Nearctic species by drum indicated
significant errors by the discriminant function. Only 45% of the original groups were
correctly classified by the discriminant function based on the selected drum variables,
with a decrease to 42.5% using cross-validation (Table A.1 [Appendix A], Figure 2.9).
Though there were numerous errors throughout the reclassification matrix, those species
with similar cadences within their drums often had the highest levels of reciprocal
misclassification. For example, northern flickers were misclassified as Arizona (12.5%)
and red-headed woodpeckers (19.1%), and were only correctly classified 46.4% of the
time (42% in cross-validation, Table A.1).
Given this high level of misclassification, I divided the data into syntopic species,
as only these species interact acoustically within their environment (Figures 2.10 - 2.15).
In all cases, reclassification rates improved (i.e., error rates and misclassification
decreased) for the division by biome versus all species tested concurrently. Boreal
species were identified correctly 79.4% of the time (77.6% cross-validated, Table A.2),
species endemic to southern pine forests increased to 64.8% (63.3% cross-validated,
Table A.3), species endemic to eastern deciduous forests climbed to 62.9% (61.2% cross-
validated, Table A.4), species endemic to deserts increased to 57% (48.9% cross- validated, Table A.5), species endemic to Rocky Mountain high elevations increased to
75.6% (71.8% cross-validated, Table A.6), and species endemic to the Texas chaparral
rose to an astounding 91.3% of original grouped cases correctly classified (88.0% cross-
32 validated, Table A.7). In all cases, all drum variables were required for correct species reclassification.
Finally, I compared the drums of individuals recorded during this investigation with data collected from a previous study (Stark et al. 1998). These individuals, collected in California from 1993-1996, incorporated additional species into the discriminant function analysis to retest whether drumming is species-specific in woodpeckers. All
Nearctic species, except Lewis’ woodpecker (Melanerpes lewis), were included in this analysis. Results indicated only 41.1% of the individuals were correctly reclassified into their original species when analyzed concurrently (38.9% cross-validated, Table A.8, Fig.
2.16). Thus, the addition of species increased misclassification, with additional errors occurring between eastern and western Nearctic species. Clearly, drums are not species- specific. However, partitioning species into their respective biome increased specificity in woodpecker drums.
DISCUSSION
Conspecific analysis indicated that the drums of male and female woodpeckers were indistinguishable from one another across an array of woodpecker species endemic to eastern North America. This parallels the results of previous studies that indicated no structural differences in drums attributable to sex (Stark et al. 1998, Short 1982). Values for the variables of drums obtained in this study are comparable to those from previous
33 reports (Winkler and Short 1978), suggesting that drums are relatively uniform across geographic regions. As with previous analyses, there was no evidence for structural changes in the drum throughout the breeding cycle, though the pattern in the use of drums has been noted to change throughout the breeding season (Miller and Bock 1972).
Descriptive statistics of woodpecker drums suggested that there is significant overlap in a number of variables across species, though there is sufficient variation to allow for reclassification by a discriminant function. Overlap in the cadence of drums, noted as being of primary importance in separating western picids by drum, was found between numerous eastern picids. Arizona and hairy woodpeckers, black-backed and downy woodpeckers, red-headed and red-cockaded woodpeckers, and gilded and northern flickers are all examples of dyads that showed significant overlap in drum cadence. Yet differences in other drum variables, such as duration, allowed for partitioning between species with a similar cadence.
Furthermore, golden-fronted, gila, pileated, and three-toed woodpeckers are all species that have overlapping cadences in their drum. I noted that many species with similar drums were phenotypically divergent, while others were similar (i.e., gilded and northern flickers). Results from this investigation, along with previous research that indicated phenotypically similar species have divergent drums (i.e., hairy and downy woodpeckers, Nuttall’s and ladder-backed woodpeckers), led me to analyze drums for correlations with woodpecker phylogeny and phenology (chapter 4).
Analysis of all eastern picids reclassified simultaneously by the discriminant function resulted in poor separation of species based on selected drum variables, with an
34 overall error rate of 55%. Clearly, drumming in Nearctic woodpeckers is not species-
specific. The discriminant analysis required all drum variables for separating species when run concurrently and when species were partitioned by biome. Addition of western woodpecker species to the reclassification matrix increased the error rate to 58.9% when all species were run concurrently. Thus, the addition of species increased misclassifications, indicating that there was significant overlap in the drums of allotopic eastern and western woodpecker species.
Among woodpeckers that were the target of this investigation, notable misclassifications (> 10%) occurred between Arizona woodpeckers with hairy, red- cockaded, ladder-backed, red-headed woodpeckers and northern flickers. Downy, gila, and red-bellied woodpeckers were classified as golden-fronted woodpeckers. Yellow- bellied and red-naped sapsuckers were reciprocally misclassified over 20% of the time.
Eastern northern flickers were regularly misclassified as a variety of heterospecifics, continuing the trend observed in their western counterparts. For example, northern flickers were classified as red-headed (19.6%) or Arizona (12.5%) woodpeckers; they were correctly classified as conspecifics only 43% of the time.
Reanalysis of the drums by syntopic species decreased the error rate, resulting in better separation and classification of individuals into species. In boreal and Rocky
Mountain species, northern flickers and hairy woodpeckers resulted in the majority of reciprocal misclassifications. Some error resulted between three-toed and black-backed woodpeckers, and curiously, between three-toed and yellow-bellied sapsuckers in boreal biomes (three-toed and red-naped sapsuckers in the Rockies). Other species were fairly
35 specific within these acoustic environments. Misclassifications between northern flickers
and both red-headed and hairy woodpeckers significantly increased the error rate in
eastern deciduous forests, as did errors between downy and red-bellied woodpeckers.
These errors further increased in the analysis of southern pine forests, with the addition of
red-cockaded woodpeckers; red-cockaded woodpeckers were misclassified as red-headed
and hairy woodpeckers, and northern flickers. Among the Texas chaparral species, errors
continued in the northern flickers, this time with ladder-backed woodpeckers; golden-
fronted woodpeckers remained specific in their acoustic environment.
Partitioning by biome decreased error rates relative to analyzing all species
concurrently. However, the error rate for each biome division ranged from a high of
51.1% for desert species to a low of 12% for Texas specialties. The average error rate across biomes was 31.5% (68.5% accuracy). Thus, the extremely low error rates observed in western biomes (Stark et al. 1998) were not observed to such an acute degree
in the east. This may account for a number of reports from previous researchers that
drums of woodpeckers are too ambiguous for species identification and recognition
(Lawrence 1967, Kilham 1983, Winker and Short 1978, Winkler et al. 1995). All of
these investigations were located east of the continental divide. Nevertheless, this study
indicated that species could be discriminated accurately for a majority of woodpecker
combinations; most errors arose from ambiguity in signals between certain dyads. As
predicted (Stark et al. 1998), drums of downy and hairy woodpeckers were never
misclassified as one another in eastern populations contrary to previous reports of signal ambiguity (Short 1982).
36 Unlike most picids, sapsucker possess drums that are modulated, having an
introductory portion followed by doublets (or triplets) of strikes spaced erratically over
the remaining drum (Fig. 2.4). Thus, the overall cadence and spacing of strikes is highly
variable in sapsuckers. Yet, these drums are distinctive within the environment. Only
yellow-bellied sapsuckers are found east of the Rocky Mountains, and red-breasted
sapsuckers are confined to the mountain ranges of the far west (ex. Sierra, Cascade).
Red-naped sapsuckers are indigenous to the Rocky Mountains; Williamson’s sapsuckers
are found west of the Rocky Mountains, and their range overlaps with those of red-naped
and red-breasted sapsuckers. Williamson’s sapsucker drums are distinctive from all these
species; only red-naped and yellow-bellied sapsucker drums were reciprocally
misclassified on a regular basis.
Though yellow-bellied and red-naped sapsuckers were given species status based
on genetic data (Johnson and Johnson 1985, Johnson and Zink 1984, Cicero and Johnson
1995), significant overlap in drumming and recognition remains (chapter 6). However, I
recorded two distinctly different types of red-naped sapsucker drums (Figures 2.17 and
2.18), raising the possibility of the beginning of drum divergence between these species.
Both types of drums were recorded near Estes Park, Colorado, from a single population
of red-naped sapsuckers. This individual’s “unusual” drum, for lack of a better term, was
recorded from a male drumming during the dawn chorus in response to neighboring conspecifics. This drum (Fig. 2.17) is clearly different from drums recorded from other neighboring red-naped sapsuckers (Fig. 2.18). This drum contains significantly more introductory strikes at a much higher cadence than other conspecifics; where there are
37 doublets in normal red-naped sapsucker drums, these unusual drums contain multiple
strikes in groupings of 2-7 individual strikes. Thus, the structure of this drum varies
significantly from both conspecifics and heterospecifics (i.e., Williamson’s sapsucker,
Fig. 2.5, A). The significance of this unusual drum is unknown.
As in the west, eastern northern flickers had the highest misclassification rates with heterospecifics. Thus, drums from northern flickers should elicit heterospecific and conspecific responses. Yet, northern flickers rarely drum, and then only during a very short time during their breeding cycle relative to other woodpeckers. Given this species’ limited use of drums, it is likely that its drums are not used for both territorial and reproductive behaviors. Instead, flickers employ a “long call” for territorial announcement; drumming is restricted to intersexual reproductive encounters during a short period of the breeding season (Moore 1995). The exact function of drumming in northern flickers has not been studied. By minimizing their use of drums, northern flickers can maintain specificity within their signal by stratifying their breeding cycle relative to heterospecifics. In short, by drumming earlier (or later) in the breeding season versus heterospecifics, specificity can be maintained in northern flickers between heterospecifics whose drums overlap. Superficially, this appears to be the strategy employed by northern flickers, though this hypothesis remains untested. The previous report that northern flickers “long call” more in the east than the west (Stark et al. 1998:
Johnson pers. observ.) may not be valid. My observations from 1997-2001 indicate that eastern populations signal at a rate nearly equivalent to their western counterparts, and
38 drums are equally rare in both locations. In both areas, the rate (i.e., drums hour-1) at which signals are given appears to be density dependent.
It should be noted that not all drums appear equivalent across species, especially concerning their function. Species differ in many ways in their use of drums, but one extreme example (relative to other species) is the low amplitude drum of red-cockaded woodpeckers. This drum is given within the nesting colony early in the morning, often just after the birds emerge from their cavity. This signal is difficult to record: its low amplitude rarely carries more that a few hundred feet from the drummer, is easily lost in ambient noise, and is likely used only under specific social exchanges. Its function remains unclear, especially in regards to the cooperative breeding strategy employed by this species, but it is not used to establish territorial boundaries with conspecifics
(Jackson 1994). However, I observed drums being given directly after encounters with competing conspecific groups, perhaps to rally kin or signal dominance within the flock.
It should be noted that not all drums of red-cockaded woodpeckers are of low amplitude:
I have recordings of one female in North Carolina (Sandhills Game Lands) whose drum was equivalent in amplitude to that given by any species of similar size. Instead, it is likely that individuals are selecting the resonance of the drumming substrate that is appropriate to the behavioral situation.
In summary, this analysis tested a number of predictions made by previous woodpecker research. Clearly, woodpecker drums are not species specific, but signals are less distinctive in eastern populations versus those in the west. Yet, many woodpecker drums are classifiable to species; only a few species have significant overlap
39 with heterospecifics, notably the northern flicker, and these species often limit their
drums through restricted use during the breeding season. Other species have similar
drums, such as the downy and red-bellied woodpeckers, yet these species are often
phenotypically or phylogenetically divergent. Reciprocally, morphologically similar
species often have divergent drums. This hypothesis, that phenotypically distinct species
may have convergent signals, will be analyzed in chapter 4. Thus, I conclude that
woodpecker drums are distinctive, but not specific, in North America.
Categorizing species by biome, and secondarily into only syntopic species,
increased the rate of correct classification which correlated to previous research that
indicated syntopic but not allotopic specificity in woodpecker drums. However, the
distinct acoustic divisions observed in western populations were less discernable in the
eastern Nearctic. Error rates calculated by the discriminant function indicated good
conspecific reclassification. Previous research on passerines indicated that the presence
or absence of heterospecifics may influence the song attributes (Naugler and Ratcliff
1994, Doutrelant and Lambrechts 1999), however the influence of heterospecifics on the design of woodpecker drums remains contentious given the significant misclassifications among syntopic species.
Under this “acoustic competition hypothesis,” whereby signal variability within species is inversely correlated with the amount of acoustic competition from other species, signals are discernable between closely related heterospecifics which has the effect of minimizing the risk of hybridization. In woodpeckers, the high error rate between some species indicated that drum design does not appear to be influenced by the
40 presence of syntopic species. This was the same pattern as observed in snow buntings
(Epsmark 1999), which showed contradictory evidence concerning heterospecific influence on the complexity of signals. Still, this analysis indicated that signal discrimination could occur statistically, but whether the birds could distinguish one another by drum in a complex acoustical environment was unknown. This will be the focus of chapters 6, 7, and 8, where I examined signal detection and recognition of drums in Nearctic woodpecker populations.
41
Figure 2.1. Location of woodpeckers recorded during the course of this study. Red
circles indicate locations recorded from 1997-2001, while yellow circles are areas
previously surveyed from 1993-1996 (Stark et al. 1998). Circle sizes are not
representative of sample sizes, but cover the recording locations within areas.
42
State or GPS Coordinates Location Region North West Adirondack Mountains NY 44o 00’ 01” 07o 430’ 01” Algonquin Provincial Park ON, Canada 45o 47’ 02” 07o 852’ 43” Angelina National Forest TX 31o 29’ 41” 09o 446’ 02” Apalachicola National Forest FL 30o 17’ 30” 08o 455’ 02” Apartment, Columbus OH 39o 57’ 40” 08o 259’ 56” Ausable River MI 43o 39’ 08” 08o 407’ 47” Battle-Darby Regional Park OH 39o 53’ 27” 08o 313’ 19” Bentsen-Rio Grande State Park TX 26o 10’ 22” 09o 022’ 56” Bishops Nob WV 38o 13’ 29” 08o 032’ 00” Blacklick Woods OH 39o 57’ 40” 08o 259’ 56” Blackshear Drive GA 30o 42’ 45” 08o 408’ 15” Blacktail Plateau, Yellowstone WY 46o 53’ 54” 10o 501’ 08” Cave Creek AZ 31o 51’ 20” 10o 903’ 00” Chubb River Crossing, Lake Placid NY 44o 16’ 01” 07o 357’ 30” Coral Head FL 41o 39’ 40” 09o 131’ 48” Cranberry Glades WV 38o 13’ 29” 08o 032’ 00” Delaware State Wildlife Area OH 40o 17’ 55” 08o 304’ 05” Devil’s Tower National Monument WY 44o 35’ 27” 10o 442’ 54” Ferd’s Bog NY 43o 46’ 10” 07o 449’ 02” Garner State Park TX 29o 34’ 58” 09o 944’ 21” Gleason’s Landing MI 43o 52’ 16” 08o 555’ 13” Grand Jardine Provincial Park QC, Canada 47o 21’ 19” 07o 127’ 30” Grand Teton National Park WY 43o 50’ 00” 11o 042’ 00” Heritage Camp NC 35o 15’ 22” 07o 916’ 57” Highway 14, Burgess Junction WY 44o 46’ 12” 10o 731’ 09” Hocking Hills OH 39o 42’ 49” 08o 235’ 58” Inspiration Point, Grand Teton WY 43o 50’ 00” 11o 042’ 00” Johnson Canyon TX 34o 36’ 17” 10o 107’ 49” Joshua Tree National Park CA 33o 55’ 00” 11o 555’ 00” Kerrville TX 30o 16’ 30” 09o 852’ 18” Killdeer Plains State Wildlife Area OH 40o 44’ 21” 08o 315’ 14” Kino Springs AZ 31o 21’ 47” 11o 048’ 34” Kraus Woods, Delaware OH 40o 17’ 55” 08o 304’ 05” Laguna Atascosa National Wildlife Area TX 26o 17’ 00” 09o 723’ 00” Lake Hope OH 39o 16’ 56” 08o 223’ 42” Logan OH 39o 32’ 24” 08o 224’ 26” Loon Lake NY 44o 33’ 06” 07o 403’ 26” Los Angeles National Forest CA 34o 11’ 23” 11o 807’ 49” Lost Maples State Park TX 29o 44’ 42” 09o 933’ 17”
Table 2.1. Recording locations used during the course of this investigation.
43
State or GPS Coordinates Location Region North West Mack Lake FL 30o 17’ 30” 08o 455’ 02” McGee Marsh OH 41o 37’ 16” 08o 309’ 40” Mentor Township MI 43o 39’ 08” 08o 407’ 47” Mio MI 43o 39’ 08” 08o 407’ 47” Monongahela National Forest WV 38o 13’ 29” 08o 032’ 00” Moose Lake NH 45o 06’ 15” 07o 061’ 30” Natchez Trace Parkway TN 34o 39’ 10” 08o 806’ 35” Noxubee National Wildlife Refuge MS 33o 15’ 05” 08o 846’ 60” Oclockonee State Park FL 30o 05’ 00” 08o 427’ 30” Optima National Wildlife Refuge OK 36o 36’ 59” 10o 111’ 25” Oscoda county MI 43o 39’ 08” 08o 407’ 47” Patagonia FL 31o 32’ 22” 11o 045’ 20” Pickaway county OH 39o 32’ 10” 08o 255’ 20” Piedmont National Wildlife Refuge GA 33o 07’ 31” 08o 344’ 59” Point Pelee National Park ON, Canada 42o 19’ 53” 08o 242’ 29” Rocky Mountain National Park CO 40o 26’ 15” 10o 533’ 45” Rockbridge State Wildlife Area OH 39o 32’ 24” 08o 224’ 26” Sam Houston National Forest TX 30o 32’ 00” 09o 521’ 00” San Luis Obispo county CA 35o 16’ 58” 12o 039’ 31” San Pedro National Riparian Area AZ 32o 59’ 04” 11o 046’ 56” Sandhills Nature Preserve NC 35o 10’ 26” 07o 923’ 33” Santa Ana National Wildlife Refuge TX 26o 04’ 11” 09o 808’ 42” Sequoia National Forest CA 36o 08’ 34” 11o 836’ 30” Stages Pond OH 39o 36’ 02” 08o 256’ 46” Stubblefield Lake TX 29o 01’ 49” 09o 519’ 45” Tall Timbers Biological Research Station FL 30o 33’ 49” 08o 402’ 44” Tar Hollow WV 39o 21’ 23” 08o 249’ 34” Three Lakes National Wildlife Refuge FL 27o 56’ 15” 08o 107’ 28” Uncas Road, Adirondack mountains NY 43o 46’ 10” 07o 449’ 02” Victory Bog VT 44o 34’ 30” 07o 147’ 05” Wade Tract Nature Preserve GA 31o 00’ 40” 08o 351’ 59” Wahkeena Nature Preserve OH 39o 42’ 49” 08o 235’ 58” Walthour Moss Foundation NC 35o 10’ 26” 07o 923’ 33” Waterton Glacier National Park MT 48o 51’ 37” 11o 326’ 09” Weymouth Woods NC 35o 10’ 26” 07o 923’ 33” Wheatley Provincial Park ON, Canada 42o 19’ 53” 08o 242’ 29” White Mountains NH 43o 59’ 42” 07o 120’ 26” Wilson Lake KS 38o 57’ 29” 09o 829’ 40” Wright Lake FL 30o 50’ 00” 08o 457’ 30” Yosemite National Park CA 37o 51’ 00” 11o 934’ 00”
Table 2.2. Recording locations used during the course of this investigation.
44
A)
B)
Figure 2.2. Exemplar sonograms of drums. A) Red-cockaded
woodpecker B) Red-headed woodpecker.
45
A)
B)
Figure 2.3. Exemplar sonograms of drums. A) Northern flicker
B) Gilded flicker.
46
A)
B)
C)
Figure 2.4. Exemplar sonograms of drums. A) Arizona woodpecker
B) Hairy woodpecker C) Ladder-backed woodpecker.
47
A)
B)
C)
Figure 2.5. Exemplar sonograms of drums. A) Williamson’s sapsucker
B) Red-naped sapsucker C) Yellow-bellied sapsucker.
48
A)
B)
C)
Figure 2.6. Exemplar sonograms of drums. A) Black-backed woodpecker
B) Red-bellied woodpecker C) Downy woodpecker.
49
A)
B)
Figure 2.7. Exemplar sonograms of drums. A) Golden-fronted
woodpecker B) Gila woodpecker
50
A)
B)
Figure 2.8. Exemplar sonograms of drums. A) Pileated woodpecker
B) Three-toed woodpecker.
51
Strike Interstrike Species N No. Strikes Cadence1 Duration 2 Duration 2 Interval2 Frequency3 Arizona woodpecker 2 14.25 + 3.37 25.27 + 1.39 565.89 + 222.3 39.83 + 9.72 26.19 + 58.02 1088.3 + 292.1 Black-backed woodpecker 47 25.52 + 0.70 17.54 + 0.29 1461.4 + 45.85 62.23 + 2.01 249.7 + 11.97 943.49 + 60.26 Downy woodpecker 91 13.98 + 0.50 16.64 + 0.21 846.94 + 32.95 60.96 + 1.44 61.56 + 8.60 976.36 + 43.30 Golden-fronted woodpecker 16 10.64 + 1.19 15.86 + 0.49 683.97 + 78.58 64.98 + 3.44 47.42 + 20.51 827.83 + 103.3 Gilded flicker 2 13.00 + 3.37 22.24 + 1.39 568.49 + 222.3 45.12 + 9.72 46.69 + 58.02 753.38 + 292.1 Gila woodpecker 9 11.76 + 1.59 14.99 + 0.66 794.81 + 104.8 69.23 + 4.58 53.70 + 27.35 895.35 + 137.7 Hairy woodpecker 45 20.37 + 0.71 25.24 + 0.29 814.49 + 46.85 42.11 + 2.05 130.7 + 12.23 857.37 + 61.58 Ladder-backed woodpecker 20 24.21 + 1.07 28.02 + 0.44 859.13 + 70.28 39.80 + 3.07 241.9 + 18.35 1264.3 + 92.37 Northern flicker 56 21.18 + 0.64 23.35 + 0.26 914.11 + 42.00 49.43 + 1.84 134.7 + 10.96 904.02 + 55.20 Pileated woodpecker 41 22.23 + 0.75 14.78 + 0.31 1524.8 + 49.09 69.87 + 2.15 132.1 + 12.81 615.98 + 64.51 Red-bellied woodpecker 46 13.13 + 0.70 18.14 + 0.29 722.16 + 46.34 56.96 + 2.03 40.40 + 12.10 876.65 + 60.91 Red-cockaded woodpecker 78 11.26 + 0.54 21.47 + 0.22 529.95 + 35.59 50.96 + 1.56 39.16 + 9.29 1040.9 + 46.77 Red-headed woodpecker 77 15.95 + 0.54 21.30 + 0.22 752.45 + 35.82 50.20 + 1.57 51.23 + 9.35 707.50 + 47.08 Red-naped sapsucker 37 15.69 + 0.78 12.00 + 0.32 1359.5 + 51.67 97.99 + 2.26 104.5 + 13.49 1212.6 + 67.91 Three-toed woodpecker 9 16.62 + 1.59 13.86 + 0.66 1208.8 + 104.8 74.65 + 4.58 80.82 + 27.35 973.37 + 137.7 Williamson’s sapsucker 3 12.08 + 2.76 12.03 + 1.14 1123.8 + 181.5 89.60 + 7.94 82.34 + 47.37 772.55 + 238.5 Yellow-bellied sapsucker 25 22.05 + 0.95 9.16 + 0.39 2534.9 + 62.86 118.1 + 2.75 195.1 + 16.41 699.43 + 82.62
1 strikes sec-1 2 milliseconds 3 hertz
Table 2.3. Mean (+/- SE) for the selected drum variables for eastern Nearctic woodpecker drums recorded during the course of this investigation.
Species
Drum variables BBWO DOWO HAWO LBWO NOFL PIWO RBWO RCWO RNSA TTWO YBSA
Number of strikes 0.257 0.558 0.577 0.950 0.261 0.203 0.934 0.190 0.707 0.143 0.081
Cadence1 0.012 0.829 0.981 0.780 0.610 0.581 0.308 0.309 0.922 0.164 0.784
Duration2 0.074 0.538 0.797 0.932 0.162 0.248 0.994 0.583 0.856 0.919 0.099
Average strike duration2 0.658 0.862 0.182 0.160 0.764 0.507 0.273 0.480 0.762 0.079 0.877
Interstrike interval duration2 0.724 0.668 0.382 0.161 0.231 0.200 0.730 0.703 0.285 0.693 0.000
Frequency3 0.436 0.287 0.346 0.331 0.613 0.686 0.445 0.887 0.050 0.496 0.295
1 strikes sec-1
2 sec
3 hertz
Table 2.4. Results of the analysis of variance for differences in drum variables due to gender for eastern woodpecker species.
Significance is at the α = 0.05 level.
Figure 2.9. Graph of the first versus second canonical function used in the discriminant function analysis to reclassify individual woodpeckers from eastern Nearctic species using the selected drum variables. Points are individuals, colors represent different species. Pink, downy; grey, hairy; aqua, red-bellied; brown, red-headed; blue, pileated; purple, northern flicker; orange, red-cockaded, black, black-backed; olive, three-toed; green, yellow-bellied sapsucker; yellow, Arizona; white, gila; brick, ladder-backed; dark green, acorn; dark grey, Nuttall’s; red, red-naped (S. nuchalis) sapsucker.
54
Figure 2.10. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual woodpeckers
endemic to boreal biomes using the selected drum variables.
55
Figure 2.11. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual woodpeckers
endemic to eastern deciduous forests using the selected drum variables.
56
Figure 2.12. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual woodpeckers
endemic to the Rocky Mountains using the selected drum variables.
57
Figure 2.13. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual woodpeckers
endemic to southern pine forests using the selected drum variables.
58
Figure 2.14. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual woodpeckers
endemic to the Texas chaparral using the selected drum variables.
59
Figure 2.15. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual woodpeckers
endemic to southwestern Nearctic deserts using the selected drum variables.
60
Figure 2.16. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual woodpeckers
from all Nearctic species (except Lewis’ woodpecker) using the selected
drum variables. Shapes are individuals, colors are different species. Note
the significant overlap in drums (i.e., not specific), but that groups are
distinctive when tested concurrently.
61
A)
B)
C)
Figure 2.17. Exemplar sonograms of red-naped sapsucker drums. A) Unusual drum B) Second unusual replicate
C) Standard drum.
62
A)
B)
C)
Figure 2.18. Exemplar sonograms (A-C) of standard red-naped sapsucker drums from several different individuals.
Note the different structure from the unusual conspecific drums.
63 CHAPTER 3
GEOGRAPHIC VARIATION IN WOODPECKER DRUMS: A SEARCH FOR
REGIONAL VARIATION IN WIDELY DISTRIBUTED SPECIES
Variation is the precondition for selection, as it provides the material for the
mechanism by which phenotypic traits evolve. Investigating the resulting patterns may
reveal important information regarding the factors of selection that account for trait
evolution. Oscine acoustic signals have served as a paradigm for these types of studies,
since they exhibit both cultural and genetic evolution (Catchpole and Slater 1995). The
acoustic signals of non-passerines have been largely overlooked by these investigations,
as non-passerines are predicted to have innate vocalizations (Payne 1982, Martens 1996).
Yet, I believe there is value in reexamining a non-passerine species for regional
differences, as there are two theories that predict a pattern of geographic variation:
cultural evolution (Krebs and Kroodsma 1980), and the acoustic adaptation hypothesis
(Wiley and Richards 1978).
Oscines learn their songs, so passerines provide a confounding model for geographic analysis, since their signals may result from either or both processes. In passerines, songs evolve culturally as a result of learning, with transmission of
64 information between generations (Payne 1996). In addition, other components of song are genetic with constraints limiting variation due to anatomical and neurological organization (Arnold 1982). As a consequence of learning, oscines exhibit geographic variation in the form of regional dialects. In many passerines, females have been documented to prefer local or specific song types in males and select those males based on the quality of their song (O’Loghlen and Beecher 1997, Sundburg et al. 2001). This cultural evolution of song is important in the maintenance of regional variation for mate choice and species recognition (Grant and Grant 1996, Melman and Searcy 1999), and may lead to novel genetic and morphological combinations. This could provide a starting point for a new evolutionary trajectory for speciation in birds (Grant and Grant 1996).
Geographic variation may also result from predictions of the acoustic adaptation hypothesis, where signals are modified for efficient transmission in local environments
(Wiley and Richards 1978). Differing selection pressures in separate environments may result in geographic variation across widely distributed species. This may result in signal divergence or convergence, depending on the selection pressures within each environment. For example, passerines living in open habitats have been documented to modify their songs with more trills than conspecifics living in dense habitats, increasing signal detection in areas with low reverberation (Krebs and Kroodsma 1980). Therefore, the origin of macrogeographic variation in song is controversial because signal variation may arise by either (or both) of these methods.
As previously noted, the acoustic signals of non-passerines have largely been overlooked by previous investigations, as species are predicted to have innate
65 vocalizations (Martens 1996). Since most of these species are not documented to learn
their songs, signals are assumed to probably be stereotyped across regions. However, the
songs of flycatchers (Megarynchus pitangua and Myiozetetes similis) appear to have
regional variation in their vocalizations, though these suboscines are not known to learn their songs (Kroosdma et al. 1996). However, invariant features are also found in passerine song. It is likely that the largely innate nature of the vocals of most non- passerines reduces the influence of learning (i.e., cultural evolution), it does not reduce the potential of sexual selection. Both passerines and non-passerine signals have sexual selection pressures for species specificity that will result in an invariant pattern. Thus,
the encoding of species recognition cues must be taken into account when making
comparisons concerning geographic variation for both groups.
Acoustic signals have been encoded with characteristics that may reflect the
effects of differing selective pressures on that signal. Passerine song has both invariant
and variant characteristics and often these invariant characteristics encode species
identity. This structure leaves the remaining characteristics pliable for encoding regional
variation. For example, white-crowned sparrows (Zonotrichia leucophrys) with regional dialects still encode species-typical characteristics in their song (Nelson and Marler 1993,
Nelson 2000). Song variables may differ between populations, and these differences indicate that populational-specific songs may not necessarily be species-specific signals throughout widely distributed species. However, signal variables are often highly stereotyped in spite of local dialects (Becker 1977), with numerous species having a preference for local song types (Regelski and Moldenhauser 1996, Searcy et al. 1997,
66 Searcy 1990, Latruffe et al. 2000). Woodpecker drums have been documented to be
stereotyped across a limited geographic range (Stark et al. 1998), with no significant
modification of the signal in response to varying habitats. There are no studies that suggest woodpeckers learn their vocal or non-vocal signals. Thus, woodpecker drums can be used as a model to test for the influence of signal adaptation to local environments on a macrogeographic scale in the absence of cultural transmission of signal design between generations.
Woodpecker drums are rapid repetitive strikes of the bird’s bill on a resonant substrate, not associated with foraging or cavity excavation. Functions attributed to drums include their importance in a number of territorial and reproductive behaviors ranging from mate selection and synchronization of breeding cycles to repelling conspecifics and status signaling (Kilham 1983, Eberhardt 1997, Trombino 1998).
Though their functions may vary among species, or even populations, authors generally agree that this signal is a form of long-distance communication for the majority of woodpecker species (Bent 1939, Lawrence 1967, Short 1982, Jackson 1994). Given that this signal has been implicated in a number of intersexual conspecific behaviors, it is likely under sexual selection for signal-specificity within each locale. Thus, modification
of this signal may have occurred across species distributions to facilitate recognition.
Variables such as the drums cadence (strikes sec-1) are subject to scattering,
reverberation, and degradation from naturally occurring obstacles within the
environment. Thus, a number of drum variables may be modified to maximize signal transmission within the environment: cadence may be increased or decreased to
67 minimize reverberation, drum frequency (hertz) may change to exploit signal channels within the habitat, or duration (sec) of drums may be altered to increase signal
detectability or minimize energy expenditures of senders.
Geographic variation in the acoustic signals of woodpeckers has never been
investigated, partly due to the difficulty in obtaining samples within species across
regions. Nevertheless, woodpecker drums have been considered to “undoubtedly vary
geographically” (Winkler et al. 1995), likely due to predictions from passerine research
whereby songs are commonly modified locally under sexual selection pressures within populations. However, no evidence was presented to support the hypothesis that woodpecker signals vary geographically. This study tested whether regional patterns of variation in woodpecker drums correlated to geographic distance. In short, are the drums of woodpeckers more similar to one another locally, with an increase in variation noted in proportion to the distance between sampled populations. One previous analysis indicated that the cadence of drums may increase with latitude (this manuscript, Chapter 7), which would correlate with an increase in relative body sizes within species (J.A. Jackson, pers. comm.). Contrarily, there may be stereotypy in species-specific variables throughout regions (i.e., no significant regional variation). It is also possible that drums are highly stereotyped in core populations and increase in variability at the periphery of their distribution.
68 METHODS
Nineteen species were analyzed for geographic variation in each of their selected
drum variables. Species included acorn (Melanerpes formicivorus), Arizona (Picoides
arizonae), black-backed (P. arcticus), downy (P. pubescens), gila (M. uropygialis),
golden-fronted (M. aurifrons), hairy (P. villosus), ladder-backed (P. scalaris), Nuttall’s
(P. nuttallii), pileated (Dryocopus pileatus), red-bellied (M. carolinensis), red-cockaded,
red-headed (M. erythrocephalis), three-toed (P. tridactylus ), and white-headed
woodpeckers (P. albolarvatus), northern flickers (Colaptes auratus), and red-naped
(Sphyrapicus nuchalis), Williamson’s (S. thyroideus), and yellow-bellied sapsuckers (S.
varius), Figures 3.1 – 3.19). Those North American species not studied either had
inadequate sample sizes for an analysis or all the individuals were recorded from a single
location. Areas with individuals recorded from multiple locations (ex., Central Ohio)
were pooled together to form a single region (Figures 3.1-3.19). Preliminary tests
included either a MANOVA (N > 3) or Kruskal-Wallis (N < 3) test for regional differences, followed by a discriminant function analysis to test for reclassification of individuals by drum versus location. Finally, I used Mantel distance matrices to correlate drum variables versus geographic distance within each species (N > 3).
Drum variables included cadence (strikes-sec-1), duration (sec), number of strikes-
drum-1, interstrike interval (sec), strike duration (sec), and the fundamental frequency of drums (Hz). Furthermore, drums from this study were compared to a database of drums previously described, but never analyzed, for geographic differences (Stark et al. 1998)
69 maximizing the coverage of North America. Mantel distance matrices were created by
subtracting the differences in geographic distances between sampled populations and
comparing them to the subtracted differences in the means for each variable for each
population within species (i.e., distance matrices). Thus, there were separate matrices to
be tested for each drum variable versus geographic distance (Warren 2002).
It is necessary to distinguish between continuous regional variation and dialects.
Dialects must form categories wherein the majority of acoustic variation is confined to
exclusive regions, and furthermore, the acoustical differences must be discrete rather than
continuous within signals. I did not test for regional dialects, which have been described
for three avian orders (Warren 2002) and a few mammalian species (Ford 1991).
However, regional variation in drums, which may be continuous in variation across a
latitudinal or longitudinal axis, may be found in woodpecker drums.
Mantel tests were conducted on black-backed, downy, hairy, pileated, red-bellied,
red-cockaded, and red-headed woodpeckers, northern flickers, and yellow-bellied sapsuckers. Other species did not have a sufficient number of recording locations for
analysis using Mantel tests. As previously stated, those species with insufficient sample
sizes for correlative analysis were analyzed using only Kruskal-Wallis tests for
differences in drum variables versus location.
70 RESULTS
Descriptive statistics (Mean + S.E.) for the differences in species drums for each
location are listed in Appendix A (Tables A.9 – A.27 [Appendix A]). These tables include regional descriptions of drums from individuals recorded during the course of this investigation. I also included detailed information from individuals from California that were recorded from 1993-1996 to maximize geographic distance and coverage of North
America. Those locations within California were from an earlier study, as this investigation did not include any recordings from this region. Unreported values in each table were variables that were not calculated. Since species recorded in California were not a primary focus of this investigation, only those variables which matched their eastern
counterparts were compared for geographic variation.
Those species with few regional locations (ex., ladder-backed or gila
woodpeckers, N < 3) were analyzed using a Kruskal-Wallis for each variable (Table 3.1).
There were no significant differences in drum variables within most species across
locations (all P > 0.05). The exception was found in yellow-bellied sapsuckers, where
regional differences were found in all selected variables (P < 0.05) except the average
number of strikes per drum (P = 0.173, Table 3.1). Given the highly variable nature of modulated sapsucker drums, and the low sample sizes involved in some of these locations
(Table A.27, Appendix A), these differences in drums are tenuous and may be biologically meaningless. Therefore, a behavioral analysis for signal recognition was conducted to test this difference (see chapter 6).
71 Species with a larger number of recording locations, generally those that are broadly distributed across North America, were preliminarily analyzed using a
MANOVA. Hairy, pileated, red-cockaded, three-toed, and white-headed woodpeckers had no significant difference in their drum across locations (all P > 0.05). Other species all had significant differences (P < 0.05, Table 3.2): univariate ANOVA’s indicated that black-backed woodpeckers and northern flickers differed regionally due to drum frequency (Hz, all P < 0.001) but other variables were non-significant (Table 3.2). Red- bellied woodpeckers, red-headed woodpeckers, and red-naped sapsuckers further differed in cadence and strike duration. In contrast, downy woodpeckers did not differ in any of these variables, but differed in drum duration and number of strikes per drum (variables that were intercorrelated).
The multivariate analysis of variance test is robust to intercorrelation of input variables, as the independence of drum variables preferred for this test is likely violated.
Unfortunately, this limits the MANOVA in detecting minor changes within the variables.
In contrast, the Mantel test correlates distance matrices between geographic distance and differences between signal variables for each region. Results indicated significant correlations in black-backed woodpeckers in the drums cadence, duration, and strike duration (all P < 0.05, Table 3.3). Frequency was correlated with distance in downy and hairy woodpeckers (P < 0.03), but no other variables were correlated in these species (all
P > 0.05). No significant correlations were found for any drum variable versus geographic distance in northern flickers, yellow-bellied sapsuckers, pileated, red-bellied, red-cockaded, or red-headed woodpeckers (all P > 0.05, Table 3.3, 3.4). This is
72 especially notable for red-headed and red-cockaded woodpeckers, which were recorded near the historical center of their ranges, and along the current edge of their distribution
(Table 3.4).
Reclassification by a discriminant function analysis for region indicated poor discrimination between individuals (Tables A.28 – A.45 [Appendix A], Figures 3.20 –
3.37). Acorn woodpeckers were originally classified at 87.5% accuracy, but dropped significantly to 12.5% in cross-validation (Table A.28). Black-backed woodpeckers also had poor reclassification by region; only 39.3% were correctly classified (28.6% cross- validated, Table A.29). Ladder-backed woodpeckers originally showed good reclassification (76.9%), but cross-validation by the discriminant function indicated no correct reclassification by drum for region (0.0% accuracy, Table A.30). Red-breasted sapsuckers fell from 60% to 30% upon cross-validation for region (Table A.31), while red-bellied woodpeckers fell from 56.5% to 41.3% (Table A.32). Red-cockaded and red- headed woodpeckers performed similarly, respectively falling from 44.9% and 45.5% to
29.5% and 32.5% upon cross-validation (Tables A.33 – A.34).
Red-naped and Williamson’s sapsuckers, and three-toed woodpeckers originally appeared to be classified correctly by region (100% accuracy), but cross-validation indicated a 0.0% correct reclassification, likely due to small sample sizes (Tables A.35,
A.36, and A.38). White-headed woodpeckers were classified 51.2% of the time correct
(39% cross-validated, Table A.37) by region. In yellow-bellied sapsuckers, 70.2% of original grouped cases correctly classified to region by the discriminant function. This
73 decreased to 68.4% through cross-validation, a similar pattern observed throughout this analysis (Table A.39).
Species with a larger number of recording locations did not fare any better when analyzed for regional distinctiveness. Northern flickers were classified at a mere 32.9% accuracy for region, and dropped to a paltry 12.3% after cross-validation (Table A.40).
Nuttall’s woodpeckers, indigenous to far western North America, were analyzed across this restricted range and shown to be classified accurately only 30.6% (15.3% cross- validated) of the time by the selected drum variables (Table A.41). Golden-fronted woodpeckers, whose range is restricted primarily to Texas and Northern Mexico, classified to region at a promising 87.5%, which also significantly dropped to 37.5% upon cross-validation (Table A.44). Furthermore, pileated woodpeckers, which have a broad range across the Nearctic, had a similar reclassification matrix: 33.3% accuracy
(7.1% cross-validated) to region by drum (Table A.42). Downy woodpeckers, the most common picid in North America, classified at 31.5% accuracy (18.9% cross-validated) to region (Table A.43). Finally, the hairy woodpecker, another broadly distributed species in the Nearctic, fared no better than the others once reclassified: 23.8%, with a decrease to 10.0% once cross-validated by the discriminant function (Table A.45).
This lack of regional differentiation within woodpecker drums is easiest to visualize graphically (Figures 3.20 -3.37), whereby the overlapping nature of individuals indicated minimal demarcation within species across regions in North America. There was no apparent latitudinal or longitudinal separation in drums within species, with the notable exception of the black-backed woodpecker. Thus, the evidence compiled through
74 the inferential statistics, Mantel tests for regional variation, and reclassification by the
discriminant function analysis, all indicate that there is no significant evidence to support regional variation in drums within the majority of Nearctic woodpecker species.
DISCUSSION
Woodpeckers are unusual in that they employ two discrete signal types for
territorial and reproductive interactions. Unique to woodpeckers is the drum; this non-
vocal signal was reported to likely vary geographically in a manner similar to that of
passerine song (Winker and Short 1978), contrary to the predictions that learning is
required to establish local signal variants (Martens 1996). My results indicate little
support for geographic variation in woodpecker drums, with few correlations found
between geographic distances versus selected drum variables. Furthermore, the present
investigation found few instances of drum modification to account for different
environmental selective pressures. For most woodpecker species, there were no
correlations found between populations recorded in a variety of locations versus
geographic distance.
Results from this investigation of the Kruskal-Wallis tests indicated that only the
yellow-bellied sapsucker had possible regional distinctiveness; all other species with few
recording locations had no differences within their drum variables versus location. In
species with a larger number of recording locations, including white-headed, three-toed,
75 pileated, red-cockaded, and hairy woodpeckers, there were no regional markers found in
their drum variables. Black-backed, downy, red-bellied, red-headed woodpeckers, red-
naped sapsuckers, and northern flickers all had differences in drums in regards to
location, often in drum basal frequency. Yet, the analysis for regional distinctiveness
using a discriminant function indicated poor separation and high error rates for
reclassification in all woodpecker species. Given this contradiction, I used Mantel
correlations to test whether woodpecker species had drums that correlated to geographic
distance.
Results from this investigation indicated that black-backed woodpeckers had correlations in the drums cadence, duration, and strike duration versus geographic distance. Individual black-backed woodpeckers had faster drums in the east (cadence, in strikes sec-1), and their drums are shorter in duration than their western counterparts.
Thus, black-backed woodpeckers have regionally distinctive drums. Of those species
investigated, black-backed woodpeckers were one of the likely candidates for regional
distinctiveness in drums (see below). Unfortunately, this investigation did not test
whether the drum of either population is being modified to local environments or whether
there are other selective pressures driving the differences between populations. The
single individual recorded in Wyoming had a drum slower than either population, with a
much longer duration. Therefore, it is possible that recordings from an intermediate
location would erase the difference between eastern and western populations.
Alternately, drums may be contained along a continuum, with individuals recorded
between either regional extreme falling between these two averages.
76 Still, eastern and western populations of black-backed woodpeckers had predictable differences between sampled populations. Forest fragmentation has left these two populations relatively isolated from one another, given the large distances between breeding populations (Dixon and Saab 2000). Furthermore, the amount of gene flow within black-backed woodpeckers across their range is unknown. Thus, if these two populations are isolated, then it is reasonable to assume that signals will diverge over
time as selection pressures will vary between populations, as predicted by the acoustic adaptation hypothesis. Perhaps, this investigation shows the beginning of this divergence, with an increase in signal differentiation occurring over future generations.
If there was an interruption in gene flow, coupled with a change in mate attraction signals, it is possible this species may eventually split into two distinct species, even in the absence of differences in other aspects of their morphology. There is previous evidence for this type of speciation event, as shown by the Palearctic chiffchaffs
Phylloscopus collybita and Ph. brehmii (Martens 1996).
One confounding variable of the black-backed woodpecker comparison is the difference in latitude and the effect of clinal variation within woodpecker morphology.
Species increase in body size at more northern latitudes versus southern conspecifics.
This increase relates to both size and mass of the bird, both variables that would affect drumming in woodpeckers. Thus, the variation observed within black-backed woodpeckers may be a result of this incremental difference in size. It would be revealing to test populations located between these two regions, and sample western populations at
77 the same latitude as those sampled in the east to control for possible clinal variation in drums.
I suggest a study be conducted to investigate a single broadly distributed species across this latitudinal gradient to test the effect of size versus drum. I further suggest the hairy woodpecker, whose range spans from Central America to Alaska with a near doubling in body mass and length across this range (J. Jackson, pers. comm., Short 1982).
Though this investigation noted that hairy woodpeckers vary drums only in frequency
(Hz) versus distance, it would be instructive to test clinal variation across a latitudinal
transect. Unfortunately, this confounding variable can only be answered through further
investigation.
Downy and hairy woodpeckers were found to have correlations between the
frequency (Hz) of their drums and geographic distance, but not between any other drum
variable versus location. This is likely and ecological effect on drums; the resonant
properties of the drum posts are similar in comparable environments, but will differ
across diverse habitats. For example, downy drums are similar in frequency at higher
latitudes, having a lower frequency in their drums than those at more southern locations.
Hairy woodpeckers showed a similar, but opposite, pattern: those at higher latitudes (or higher elevations) had higher drum frequencies than those in the lowlands or southern locations. All other tested species indicated no correlation between geographic distance and drum variables, indicating no regional variation in woodpecker drums for those parameters.
78 Woodpecker species that are restricted to specific ecotypes and those that are generalists in their habitat preference were represented in this investigation. Habitat specialists, such as the red-cockaded woodpecker, did not have any significant modifications to their drums across regions. Thus, when habitats were uniform, drums did not significantly vary between populations. This would be predicted by the acoustic
adaptation hypothesis for signal modification in response to local selective pressures.
Habitat generalists, such as the downy and hairy woodpeckers, had a different pattern:
these species had variations in the fundamental frequency of the drum that correlated to
distance, and thereby secondarily to habitat differences. Thus, as the types of available
substrate changed, the drum frequency likely co-varied with the properties of the
substrate. However, of the remaining drum variables, none of which were dependant on
external influences for phenotypic expression, all were stereotyped across regions
irrespective of habitats. This is in opposition to the predictions of acoustic adaptation,
which would predict signal modification to the differing selective pressures in the various
regions. Thus, there is little evidence that the adaptation to local environments results in geographic variation in woodpeckers, as was predicted by the acoustic adaptation hypothesis.
Research has documented numerous avian species that encode regional variation in their signals (Thielcke 1969, Searcy et al. 1997, Baker 2001). For most species, these regional differences have been documented to have a significant influence on breeding biology, especially concerning female choice in sexual selection. Numerous studies on passerines have indicated female preferences for local dialects (Martins and Kessler
79 2000, Bradbury et al. 2001, Searcy et al. 2002). Surprisingly, studies have rarely documented conservation of signals across large geographic regions (Martens 1996). The willow warbler (Phylloscopus trochilus) has differences in their song across populations, but playback experiments showed that no “regiolects” occurred across populations
(Martens 1996). Thus, this species is acoustically uniform across its range. Another example is the coal tit (Parus ater), whose song is acoustically uniform across the
Palearctic, even though its plumage is highly variable within the species (Thielcke
1969b).
Alternately, species may be consistent over a large portion of their range, with variation at specific regions along the edges of the distribution. The common chiffchaff
(Phylloscopus collybita) and willow tit (Parus major) are two passerine species with this pattern, where large areas are acoustically consistent and small areas of regional demarcation in signals occur (Martens 1996, Martens and Nazarenko 1993). This study can not dismiss this signal pattern for Nearctic woodpeckers in regards to drums. These large areas of consistency could have subpopulations of local distinctiveness within species, especially in areas of contact with syntopics or in areas with differing environmental selective pressures on signal design.
Variation in passerine song is often found between individuals, neighborhoods, and locations, but for drums in the majority of woodpecker species this was not found.
Granted, minor variation in drums occurs between, and even within individuals (chapter
5). Certainly, there is variation between species (chapter 2). However, for the most part, there is little difference in the average drum from an individual within a species across
80 regions. This leaves the question: Are there any benefits to signal conservation across geographic regions? Species recognition to minimize hybridizations with syntopics is normally cited as the primary factor for signal specificity. However, regional dialect formation does not necessarily remove species-specific parameters, as senders encode information in different signal parameters (Marler 1957, 1960). Drums have little variation relative to songs or calls, which could minimize the probability of encoding regional information. Second, other signals, possibly vocal series calls or contact calls, could encode regional information more efficiently. Finally, regional variants of signals have only been reported in species that learn their songs (Martens 1996), and there have been no studies to test whether woodpeckers learn their vocal or non-vocal signals.
Payne (1996) hypothesized that migratory populations would be more likely to have regional dialects versus sedentary populations, yet the majority of woodpeckers are residential. Even species that are considered eruptive, such as black-backed woodpeckers that move into new burns, become short-term year-round residents at these locations
(Dixon and Saab 2000). Few woodpecker species are seasonal short distance migrants
(ex., northern flicker) or migratory (ex., yellow-bellied sapsucker). However, neither of these two species indicated any correlations for drum variables versus geographic distance, which was the same pattern observed in most sedentary species. Only the black-backed woodpecker indicated any populational significant differences across North
America, with the caveats previously discussed.
The lack of variation within woodpecker drums is notable; stereotypy in signal design is rarely demonstrated across such large landscapes. Yet, drumming is
81 constrained by the underlying musculature of each individual, and this musculature is not likely to significantly differ regionally across individuals within species. Given that woodpeckers use these muscles for a variety of other functions, including foraging and cavity excavation, there may not be selection for musculature variation across locations.
Selection for other aspects of woodpecker survival associated with the bird’s morphology may override signal variation and improvisation in drumming, as those individuals may
be ill equipped for these other behaviors necessary for survival. In short, drum
consistency may be a result of constraints by the bird’s anatomy, which is relatively
consistent across its range.
Alternately, there may be strong intersexual selection pressures for signal
specificity, given the limited number of signal variables. Drums may be coupled to an
intersexual sensory bias within species, which restricts variation within signals to a
limited range within woodpeckers. Individual’s that drum at specific set parameters are
more likely to attract receptive mates, increasing their number of copulations and in turn,
their reproductive output and inclusive fitness. Individuals that signal outside this
sensory range have a lower probability of mating success, leaving fewer offspring. If
drumming is constrained by the bird’s musculature, this coupling of selection for specific
drum variables with those individuals with proper musculature would reinforce this
system, as resultant offspring would have the correct morphology of the successful
parent. Further investigation is warranted.
In summary, the hypothesis that woodpecker drums regionally vary was not
supported by this analysis. Neither was their any evidence to support differences within
82 drums at the edges of species ranges, examined through patterns in red-headed and red-
cockaded woodpeckers, versus core populations. Instead, these results indicated
uniformity in most species drums across the Nearctic. Woodpeckers vary in size within species, becoming larger at higher latitudes, but this did not transfer into differences in drums for the majority of woodpecker species. This consistency within mating signals is unusual, and has been noted for only a few avian species worldwide. The importance of signal uniformity across geographic regions remains poorly understood, but has been implicated as necessary for species recognition, sexual selection, and maintaining genetic integrity and gene flow within widely distributed species. Numerous questions remain that concern drum conservation and recognition in woodpeckers, especially concerning the breeding biology and impact on speciation within this avian group. Clearly, a significant amount of research remains to be completed in regards to woodpecker drums.
83
Figure 3.1. Recording locations of acorn woodpeckers used for the analysis
of geographic variation in drumming.
84
Figure 3.2. Recording locations of black-backed woodpeckers used for the
analysis of geographic variation in drumming.
85
Figure 3.3. Recording locations of downy woodpeckers used for the analysis
of geographic variation in drumming.
86
Figure 3.4. Recording locations of gila woodpeckers used for the analysis of
geographic variation in drumming.
87
Figure 3.5. Recording locations of golden-fronted woodpeckers used for the
analysis of geographic variation in drumming.
88
Figure 3.6. Recording locations of hairy woodpeckers used for the analysis of
geographic variation in drumming.
89
Figure 3.7. Recording locations of ladder-backed woodpeckers used for
the analysis of geographic variation in drumming.
90
Figure 3.8. Recording locations of northern flickers used for the analysis
of geographic variation in drumming.
91
Figure 3.9. Recording locations of pileated woodpeckers used for the analysis
of geographic variation in drumming.
92
Figure 3.10. Recording locations of red-bellied woodpeckers used for the
analysis of geographic variation in drumming.
93
Figure 3.11. Recording locations of red-cockaded woodpeckers used for the
analysis of geographic variation in drumming.
94
Figure 3.12. Recording locations of red-headed woodpeckers used for the
analysis of geographic variation in drumming.
95
Figure 3.13. Recording locations of red-naped sapsuckers used for the
analysis of geographic variation in drumming.
96
Figure 3.14. Recording locations of three-toed woodpeckers used for the
analysis of geographic variation in drumming.
97
Figure 3.15. Recording locations of Williamson's sapsuckers used for the
analysis of geographic variation in drumming.
98
Figure 3.16. Recording locations of yellow-bellied sapsuckers used for the
analysis of geographic variation in drumming.
99
Figure 3.17. Recording locations of Nuttall's woodpeckers used for the analysis
of geographic variation in drumming.
100
Figure 3.18. Recording locations of white-headed woodpeckers used for the
analysis of geographic variation in drumming.
101
Figure 3.19. Recording locations of red-breasted sapsuckers used for the
analysis of geographic variation in drumming.
102
Figure 3.20. Graph of the first versus second canonical function used
in the discriminant function analysis to reclassify individual acorn
woodpeckers by region.
103
Figure 3.21. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual black-backed
woodpeckers by region.
104
Figure 3.22. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual downy
woodpeckers by region.
105
Figure 3.23. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual golden-fronted
woodpeckers by region.
106
Figure 3.24. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual hairy woodpeckers
by region. Figure legend: Triangles are group centroids, squares are
individuals. Green, Sequoia national forest; brown, southern Sequoia national
forest; blue, San Luis Obispo county; pea, Colorado; light green, West Virginia;
brick, New Hampshire; grey, Texas; black, Oklahoma; yellow, North Carolina;
aqua, Mississippi; pink, Michigan; light blue, Ohio; lime, Wyoming; red, New
York.
107
Figure 3.25. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual ladder-backed
woodpeckers by region.
108
Figure 3.26. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual northern flickers
by region. Figure legend: Triangles are group centroids, squares are individuals.
Lime, Los Padres national forest; blue, San Luis Obispo; brick, Georgia; dark blue,
Kansas; pea, Colorado; green, Texas; light brick, Florida; grey, Oklahoma; light
gey, North Carolina; yellow, Mississippi; aqua, Michigan; pink, Ohio; blue,
Wyoming; lime, Ontario; red, Arizona.
109
Figure 3.27. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual Nuttall’s
woodpeckers by region. Figure legend: Triangles are group centroids,
squares are individuals. Light blue, Woodland, CA; brick, Salinas river valley;
blue, Cougar camp, Lopez lake; pea, Cerro alto; green, Stenner creek; brick,
Bidwell park; grey, High mountain road; light grey, Las Pilitas road; yellow,
Sweet springs nature preserve; aqua, Cottonwood road, Cayucos; pink, Reservoir
canyon; blue, Morro bay; lime, Poly canyon, CPSU; red, River access road, Pozo.
110
Figure 3.28. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual pileated
woodpeckers by region.
111
Figure 3.29. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual red-breasted
sapsuckers by region.
112
Figure 3.30. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual red-bellied
woodpeckers by region.
113
Figure 3.31. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual red-cockaded
woodpeckers by region.
114
Figure 3.32. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual red-headed
woodpeckers by region.
115
Figure 3.33. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual red-naped
sapsuckers by region.
116
Figure 3.34. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual three-toed
woodpeckers by region.
117
Figure 3.35. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual white-headed
woodpeckers by region.
118
Figure 3.36. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual Williamson’s
sapsuckers by region.
119
Figure 3.37. Graph of the first versus second canonical function used in
the discriminant function analysis to reclassify individual yellow-bellied
sapsuckers by region.
120
Drum variables ACWO GIWO GFWO LBWO NUWO RBSA WISA YBSA
Number of strikes 0.247 0.120 0.260 0.033 0.501 0.555 0.284 0.173
Cadence1 0.325 0.699 0.095 0.445 0.067 0.733 0.532 0.001
Duration2 0.721 0.121 0.263 0.048 0.222 0.727 0.284 0.002
Average strike duration2 0.325 0.121 0.323 0.488 0.276 0.700 0.532 0.003
Interstrike interval duration2 - 0.121 0.717 0.194 - - 0.221 -
Frequency3 - 0.121 0.428 0.425 - - 1.000 -
1 strikes sec-1
2 sec
3 hertz
Table 3.1. Results (P-values) of Kruskal-Wallis tests for differences in drum variables versus regions for Nearctic woodpecker species.
121
Drum variables BBWO DOWO HAWO NOFL PIWO RBWO RCWO RHWO RNSA TTWO WHWO
Wilks’ Lambda 0.001 0.028 0.061 0.000 0.582 0.000 0.177 0.000 0.032 0.596 0.108
Number of strikes 0.235 0.035 - 0.131 - 0.220 - 0.560 0.216 - -
Cadence1 0.143 0.164 - 0.270 - 0.034 - 0.000 0.035 - -
Duration2 0.044 0.002 - 0.062 - 0.074 - 0.079 0.170 - -
Average strike duration2 0.742 0.070 - 0.104 - 0.011 - 0.017 0.024 - -
Interstrike interval duration2 0.917 0.120 - 0.687 - 0.600 - 0.777 0.573 - -
Frequency3 0.000 0.123 - 0.000 - 0.007 - 0.000 0.023 - -
1 strikes sec-1 2 sec 3 hertz
Table 3.2. Results if General Linear Model (GLM) tests for differences in drum variables versus regions for
Nearctic woodpecker species. Significant Wilks’ Lambda tests (P < 0.05) further include the univariate values for the corrected model of tests of between-subject effects.
# Trials that Exceeded MCV1 MCV P-value Black-backed woodpecker
Number of strikes 0.0024 4987 0.9974 Cadence2 0.5718 189 0.0378 Duration3 0.7547 20 0.0040 Frequency4 0.0570 4659 0.9318 Interstrike interval3 0.8757 305 0.0610 Strike duration3 0.7353 11 0.0022
Downy woodpecker
Number of strikes 0.2249 463 0.0926 Cadence -0.0660 3229 0.6458 Duration -0.0916 2536 0.5072 Frequency 0.3946 150 0.0300 Interstrike interval 0.0644 3735 0.7470 Strike duration -0.1599 1314 0.2628
Hairy woodpecker
Number of strikes -0.1471 853 0.1706 Cadence -0.0048 4829 0.9658 Duration 0.0192 4288 0.8576 Frequency 0.7343 0 0.0002 Interstrike interval 0.2664 232 0.1464 Strike duration 0.1228 1022 0.2044
Northern flicker
Number of strikes -0.1057 995 0.1190 Cadence -0.0183 4108 0.8216 Duration -0.0129 4355 0.8710 Frequency -0.0751 2562 0.5124 Interstrike interval -0.0107 4623 0.9246 Strike duration -0.0560 2444 0.4888
Pileated woodpecker
Number of strikes -0.1081 2472 0.4944 Cadence -0.0302 4262 0.8524 Duration -0.0705 3206 0.6412 Frequency -0.0217 4489 0.8978 Interstrike interval 0.0332 4234 0.8468 Strike duration 0.2320 683 0.1366 1 matrix correlation value 2 strikes sec-1 3 sec 4 hertz
Table 3.3. Results of the Mantel tests (5000 iterations) for correlating geographic regions versus the selected drum variables in woodpeckers.
123
# Trials that Exceeded MCV1 MCV P-value Red-bellied woodpecker
Number of strikes -0.0062 4903 0.9806 Cadence 0.2145 2365 0.4730 Duration 0.2661 1430 0.2860 Frequency 0.0710 4177 0.8354 Interstrike interval 0.3437 907 0.1814 Strike duration 0.3567 600 0.1200
Red-cockaded woodpecker
Number of strikes 0.0958 2229 0.4458 Cadence 0.2076 388 0.0776 Duration -0.0834 2434 0.4868 Frequency 0.4488 0 0.1170 Interstrike interval -0.1713 825 0.1650 Strike duration 0.2332 247 0.0494
Red-headed woodpecker
Number of strikes -0.0644 3905 0.7810 Cadence -0.1136 2864 0.5728 Duration 0.0356 4359 0.8718 Frequency -0.0949 3320 0.6640 Interstrike interval -0.0644 3916 0.7832 Strike duration -0.1501 2542 0.5084
Yellow-bellied sapsucker
Number of strikes 0.8066 632 0.1264 Cadence -0.1780 5000 1.0000 Duration 0.3552 2076 0.4152 Strike duration 0.1276 4126 0.8252
1 matrix correlation value 2 strikes sec-1 3 sec 4 hertz
Table 3.4. Results of the Mantel tests (5000 iterations) for correlating geographic regions versus the selected drum variables in woodpeckers.
124 CHAPTER 4
HAS PHYLOGENY OR PHENOLOGY INFLUENCED THE STRUCTURE OF
WOODPECKER DRUMS?
There has been little research conducted on woodpecker drumming, even less concerning possible phylogenic or phenotypic influences on the development of this signal across species (Winkler and Short 1978). In woodpeckers, drumming is a series of rapid strikes with their bill on a resonant surface, not associated with foraging or cavity excavation. As a long-distance communication signal, drumming has been implicated in territoriality, ranging, individual and species recognition, and a number of reproductive behaviors (Eberhardt 1997, Winkler et al. 1995, Wilkins and Ritchison 1999, Dodenhoff et al. 2001). Previous research noted that phenotypically similar species had divergent drums in sympatry (Stark et al. 1998), and reciprocally, phenotypically divergent species had convergent drums. However, it was unknown whether this pattern applied across a large array of species.
Drumming has been implicated as important in a number of territorial and reproductive behaviors (i.e., pair bond maintenance, synchronization of breeding cycles).
Therefore, I would expect (based on acoustic theory) that selection for signal specificity
125 between sympatric heterospecifics should have occurred (Podos 2001). Previous research noted that the associated pattern of strikes within one drum was correlated to the primary foraging method of each woodpecker species (Kaiser 1990). Thus, drums are likely constrained by the underlying structural morphology of each species. It is reasonable to predict that these differences may be correlated to divergences within the woodpecker phylogeny. In contrast, if mates are attracted to individuals based on their drum, then phenotypically similar species should have divergent signals, minimizing the risk of interspecific hybridization. Under this scenario, one or more drum variables should be correlated to the morphology of each species, with divergence occurring between phenotypically similar woodpecker species.
This investigation tested for correlations in drum variables from Nearctic woodpecker species versus both their phylogenic relationships and phenotypic similarities/differences. Given that heterospecific divergence in signals has been implicated as necessary for coexistence of closely related species (Becker 1982, Mousseau and Howard 1998), I hypothesized that 1) drums may vary between species due to differences in phylogenetic relationships, having either divergent or similar drums, 2) phenotypically similar species would have more divergent drums, 3) both phylogenic and phenotypic influences have driven heterospecific drum divergence, producing either divergent or similar drums, and finally 4) syntopic species would best illustrate divergence, which would correlate to previous observations (Stark et al. 1998).
126 METHODS
Given the lack of a resolved phylogeny for Nearctic woodpeckers, I reconstructed
Short’s (1982) unresolved woodpecker phylogeny (Fig. 4.1) and ranked the nodes of
divergence within the dendrogram between species. For example, the highly related red-
breasted (Sphyrapicus ruber) and red-naped (S. nuchalis) sapsuckers were ranked as having
a divergence of 1, as only a single node separated these two species. I repeated this for all
possible combinations of the 22 extant Nearctic species, creating a distance matrix.
Species included the red-breasted, red-naped, yellow-bellied (S. varius) and Williamson’s
sapsuckers (S. thyroideus), northern (Colaptes auratus) and gilded (C. chrysoides) flickers, along with pileated (Dryocopus pileatus), red-bellied (Melanerpes carolinensis), red- headed (M. erythrocephalis), acorn (M. formicivorus), golden-fronted (M. aurifrons),
Lewis’s (M. lewis), gila (M. uropygialis), white-headed (Picoides albolarvatus), ladder- backed (P. scalaris), red-cockaded (P. borealis), Nuttall’s (P. nuttallii), Arizona (P.
arizonae), downy (P. pubescens), hairy (P. villosus), three-toed (P. tridactylus), and black- backed (P. arcticus) woodpeckers. Given criticisms of Short’s (1982) woodpecker phylogeny (Tennant 1991, DeFilippis 1995), I constructed a second consensus phylogeny using current American Ornithologist’s Union (AOU) classifications (Fig. 4.2), along with a corresponding distance matrix.
To investigate whether phenotypic divergence in external morphology was correlated to divergence in woodpecker drums, I constructed a binary phenogram using 24 external characteristics for both males and females for all 22 nominant Nearctic species
127 (including red-shafted and yellow-shafted northern flickers, due to their high phenotypic divergence, Appendix 4.1). Two matrices were created, one for males and one for females, given that woodpeckers are sexually dimorphic. I used a hierarchical cluster analysis
(Squared Euclidean distances, z-scored) to construct a divergence dendrogram for both sexes for phenology (Figs. 4.3 and 4.4), and calculated the corresponding matrices between species using the same method for creating the phylogeny matrix. Finally, I divided the woodpecker species by their sympatric occurrence in habitats, based on their distributions during the breeding season, and tested whether there were divergence’s due to sympatric heterospecific influences.
For this analysis, woodpeckers were categorized as sympatric in the following habitats: Western coniferous forest- Red-naped, Williamson’s, and red-breasted sapsuckers, three-toed, black-backed, hairy, white-headed, and pileated woodpeckers, and the northern flicker. Western oak woodlands- Downy, hairy, Nuttall’s, Lewis’, and acorn woodpeckers, and the northern flicker. Desert southwest- Golden-fronted, ladder-backed, acorn, gila, and Arizona woodpeckers, along with northern and gilded flickers. Eastern deciduous forests- Yellow-bellied sapsucker, red-bellied, red-headed, downy, hairy, and pileated woodpeckers, and the northern flicker. Boreal- Yellow-bellied sapsucker, three- toed, black-backed, hairy, and pileated woodpeckers, and the northern flicker. Southern pine forests- Red-bellied, red-headed, red-cockaded, downy, hairy, and pileated woodpeckers, and the northern flicker.
I tested the phylogenic and phenotypic matrices versus the means for each variable, for each species drums, using the variables of cadence (strikes-sec-1), duration (sec),
128 interstrike interval (sec), and the first principal component score (PC1, eigenvalue > 1.0)
for each species. Values for each drum variable were taken from previously published
species accounts, along with unpublished data (Stark et al. 1998, Winkler and Short 1978,
Wilkins and Ritchison 1999, Duncan 1990, Stark unpubl. data [Chapter 2]). These variables have been noted as important for species discrimination and recognition in woodpeckers (Duncan 1990, Stark et al 1998, Tennant 1998, Dodenhoff et al. 2001). I used Mantel tests (Monte Carlo design, 500 iterations) to test for significant correlation’s between drum variables (i.e., cadence, duration, interstrike interval, PC1) versus phylogeny
(Short’s and consensus) and phenology (male and female) using the created matrices.
RESULTS
Significant correlations were found when all species were analyzed concurrently for both phylogenies versus selected drum variables (Table 4.1). Phylogenetically divergent species were found to have divergent drums, and reciprocally, similar drums were found in phylogenetically similar species. Analysis of phenology (i.e., external phenotype) versus the selected drum variables indicated significant correlations for the drums cadence and
PC1 for both males and females. Hence, phenotypically similar species had divergent drums, and these drums varied in their overall structure (PC1) and cadence (Table 4.1).
Separation by habitat tested whether any trends could be found within sympatric species, which may have been masked when all species were analyzed concurrently. In relation to
129 phylogeny and phenology, significant correlation’s were found when species were categorized by biome: in relation to phylogeny (Tables 4.2, 4.3), correlation’s were found in the drum’s interstrike interval for western coniferous forests for both phylogenies, PC1 for Short’s phylogeny and in drum duration for Southern pine forests for both phylogenies
(all P < 0.05). All other iterations were non-significant for phylogeny versus the selected drum variables (P > 0.05).
In relation to phenology (Tables 4.4, 4.5), species endemic to western oak woodlands and southern pine forests were non-significant for either sex in relation to all drum variables (all P > 0.05). Species endemic to boreal forests had a significant correlation in female drum duration (P = 0.03), but not in any other combination (all P >
0.05). Species endemic to western coniferous forests were correlated significantly in PC1 and interstrike interval for both males and females (all P < 0.05), while cadence was correlated significantly in male and female desert species (P < 0.01). Other significant correlation’s in the species endemic to deserts included interstrike interval for females (P =
0.03) and duration for males (P < 0.01). All other iterations were non-significant (P >
0.05).
Given the lack of significant correlation’s or trends uncovered using Mantel tests, I tested phylogeny versus phenology while simultaneously controlling for habitat (Table
4.6). As expected, there were numerous significant correlations: non-significant correlations were found in species endemic to eastern deciduous forests and deserts for phylogeny versus female phenology (all P > 0.05). All other iterations were significant for males and females for all habitats versus both phylogenies (all P < 0.05). Thus, in general,
130 species that were more phylogenetically divergent were also more phenotypically divergent
within each habitat.
Given the lack of correlations within the subgroup comparisons suggested that the
observed overall significance may have been driven by one or more divisions among species. Therefore, I tested the Melanerpes (N = 6) and Picoides (N = 9) for correlative trends between species within genera; other genera were excluded, due to inadequate sample sizes. Significant correlations were found in comparisons of Short’s phylogeny versus both male and female phenograms (both P = 0.02) and the consensus phylogeny versus female phenology for Picoides (P = 0.05, Table 4.7). All other iterations comparing drum variables versus both phylogenies and both phenologies were non-significant for
Picoides (all P > 0.05). Results indicated that there were no significant correlations for
Melanerpes versus all iterations of phylogeny and phenology versus all the selected drum variables (all P > 0.05, Table 4.8). Neither of these genera indicated any significant trends in the selected drum variables between species. However, both genera indicated that the more phenotypically divergent species were also more phylogenetically divergent. Thus, divergence observed in the concurrent comparisons indicated that drums were more similar within genera that between genera. Hence, results indicated that overall phylogenic and phenotypic significant divergence in drumming was due to differences at the genus, not species, level.
131 DISCUSSION
Analyzing of the influences of natural and sexual selection on acoustic signal
development is arduous, given that it is impossible to know the precursor of a current
signal. Thus, correlative studies that test patterns underlying the divergence of signals have the potential to uncover whether selection has acted on the signal due to environmental or
reproductive pressures. Signals that converge to maximize environmental channels, or have similar structure to maximize detectability, often show a similar pattern across a range of syntopic species. Signals that have been selected to increase reproductive output (i.e., mate attraction or localization) do not necessarily have to diverge, but are required to encode species identity. If no such identity was encoded within the signal, then upon meeting, there should be adequate alternate cues for receivers to quickly identify and differentiate ambiguous signalers.
Early research on woodpeckers noted that few drums were identifiable to species by observers (Bent 1939, Kilham 1983). Of North American woodpeckers, the drums of pileated woodpeckers and sapsuckers were considered diagnostic; however, classifying other species by drum was considered contentious (Short 1982, Winkler et al 1995).
Additional research indicated that western species, within habitats, had diagnostic signals that were detectable and recognizable by receivers (Stark et al. 1998, Trombino 1998,
Dodenhoff et al. 2001). Thus, woodpeckers could encode species identity within their drums, satisfying one requirement of signal design being influenced by sexual selection pressures of conspecifics. However, sexual selection is likely to be only one pressure that
132 has driven the design of woodpecker drums. There is a superficial trend of an inverse
relationship between acoustic and visual signals; woodpecker species with divergent drums
are often phenotypically similar (i.e., ladder-backed and Nuttall’s woodpeckers; hairy and
downy woodpeckers), and vice verse. However, not all combinations of woodpecker
species showed this trend: northern and gilded flickers, red-naped and yellow-bellied
sapsuckers, and black-backed and three-toed woodpeckers are all phenotypically similar
species with similar drums.
Research also indicated that these latter combinations of species were highly related phylogenetically (Tennant 1991, Cicero and Johnson 1995). However, none of these species are noted to be syntopic during the breeding season. For example, red-naped and yellow-bellied sapsuckers are broadly allotopic, with only a limited range of syntopy in western North America. This same pattern has been observed in northern flickers; only along the flicker’s southern range do these species have an interface and have been documented to hybridize with gilded flickers (Short 1972, Short 1982), the same as the
studies of syntopic sapsuckers (Trombino 1998, Johnson and Johnson 1985). Black-backed
and three-toed woodpeckers are sympatric; however, these species are found in different
habitat niches across their range (Hansen 2001). Thus, observations that phenotypically
similar species with similar drums are often allopatric are probably not coincidental, given that maintaining specificity during the breeding season is a priority among individuals.
Though it is likely that the underlying morphology of species may limit the amount of variation that can occur within drums from different woodpecker genera, results from this analysis indicated that there were few correlation’s within Nearctic woodpecker drums
133 versus species. Categorization into sympatric species indicated few correlations that could
be attributed to heterospecific influences. Drums and their corresponding variables were found to be more similar within than between genera. Therefore, drums were not found to have diverged significantly within genera across the entire array of North American woodpeckers. Since there were no observed general trends uncovered for drum variables, it is unlikely that the presence of syntopic heterospecifics has driven signal design.
Given that drumming has been implicated in reproductive behaviors and has been shown to encode species identity, it is likely that this signal is under sexual selection with the resultant signal design possibly driven through female choice. Thus, the heterospecific signal convergence observed in woodpeckers may persist over time given adequate alternative barriers to gene flow, such as physical isolation. Results from this analysis indicated that morphology (phenology) alone was an inadequate explanation for signal convergence; phenotypically similar species may or may not have similar drums. Results further indicated that physical isolation alone was inadequate as an explanation for signal divergence at the gross level of habitat division used in this analysis. However, within a given habitat, the interaction of divergent morphology and phylogeny correlated to drum divergence. Thus, as a general trend, phenotypically and phylogenetically similar species with similar drums do not co-occur within a given habitat. When this occurs, species are divided into different niches within habitats within the local environment, thereby minimizing interspecific ambiguity.
In species that have been documented to hybridize, drums may be one cue that allows individuals to selectively assort between potential mates. If drums act as a
134 prezygotic isolating mechanism between woodpecker species, then divergence within drums should parallel the rates of interspecific hybridization, with hybrids occurring in phenotypically similar or phylogenetically related species. Various rates of hybridization have been documented for woodpeckers; in Sphyrapicus, hybrids have been noted to occur between red-naped and Williamson’s sapsuckers (Dobbs et al. 1997). However, assortive mating has been documented between red-naped and red-breasted sapsuckers when syntopic (Trombino 1998). Reproductive character displacement (Brown and Wilson
1956), which concerns the evolution and divergence of advertisement signals, was implicated as important for species discrimination and recognition in sapsuckers (Trombino
1998). Sapsucker species were observed to shift drum variables in response to the presence of syntopic heterospecifics. This shift occurred within, but not between, species and did not influence conspecific recognition within populations.
Within Melanerpes, hybrids have been noted between gila and golden-fronted woodpeckers, and also between golden-fronted and red-bellied woodpeckers (Husak and
Maxwell 1998). Hybrids have not been observed between gila and red-bellied woodpeckers, probably due to their allopatric distributions. Hybridization also occurs between species within the Nearctic Colaptes genus (Moore 1995). Within Picoides,
Nuttall’s woodpeckers have been documented to hybridize with both downy and ladder- backed woodpeckers in syntopy (Lowther 2000). Previous research indicated that Nuttall’s woodpeckers could differentiate between both downy and ladder-backed woodpeckers versus conspecifics (Dodenhoff et al. 2001). However, research also indicated that there was some overlap in drums between downy and Nuttall’s woodpeckers (Stark et al. 1998),
135 and that this overlap was sufficient to initiate a heterospecific response (in Nuttall’s woodpecker) equivalent to that given to a conspecific. However, for the majority of drum
playbacks, downy and Nuttall’s woodpeckers could differentiate between species. In
relation to ladder-backed woodpeckers, their drums have diverged significantly from those given by Nuttall’s woodpeckers. Yet, their high phenotypic similarity may have led to the low rates of documented hybridization, even though drums were discernible between species (Short 1982, Stark et al 1998). However, these species are broadly allotopic, with only limited areas of syntopy throughout western and southwestern North America (Short
1971).
Normally allotopic sapsuckers have been documented to maintain their specificity along syntopic interfaces through changes in, among other things, the structure of their
drums (Trombino 1996). This was hypothesized to occur to minimize hybrids between
closely related species, which had a detrimental affect on the reproductive output of parents
(Trombino 1996). This character shift observed in syntopic Sphyrapicus has not been
found in either Melanerpes or Picoides. To date, tests for shifts from an allotopic to
syntopic state within each species for either Melanerpes or Picoides have not adequately
been studied. Thus, the character displacement hypothesis may explain the patterns
observed in drumming for these selected drum variables within species, but may not
adequately explain the patterns of drumming between species. To my knowledge, there has
not been any research published on the relation of drumming in heterospecific versus
conspecific discrimination in any of the remaining species that have been noted to
hybridize. However, that the vast majorities of these species were allotopic or separated by
136 habitat if classified as broadly sympatric is probably not coincidental. Finally, this analysis was dependent on the accuracy of the unresolved phylogeny for woodpeckers; both the results and interpretation of this analysis likely would be altered if future research revealed a resolved phylogeny that varied significantly from either phylogeny used in this analysis.
Though the phenological data is relatively robust versus the phylogenic data, incorporation of further phenotypic variables (ex., bill mass or the ratio of neck musculature to body weight) theoretically could alter the observed patterns. Clearly, considerable research remains to be completed on this aspect of woodpecker drumming.
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APPENDIX 4.1.
External morphological characteristics used to construct the binary phenogram for male and female Nearctic woodpeckers (terminology from Pyle 1997): Overall external feather color (black, brown), back feather pattern (solid, stripped, barred), nape feather color (black, brown, red, white, yellow, gray), overall head feather color (black, brown, red, white, gray), crown feather color (black, brown, red, white, yellow, gray), crested (yes, no), subauricular stripe (mustache) feather color (black, brown, red, white, red), auricular feather color (black, brown, red, white, gray), breast feather color (black, brown, red, white, buff, gray), breast
feather pattern (none, spotted), lateral breast feather pattern (none, spotted, barred, striped), abdominal feather
color (black, brown, red, white, yellow, gray, buff), abdominal feather pattern (none, spotted, barred), throat
feather color (black, brown, red, white, yellow, gray), wing (coverts, primaries and secondaries) feather color
(black, red, yellow, brown), wing (coverts, primaries and secondaries) feather pattern (none, barred, patch),
retrices color (black, brown, red, yellow), retrices pattern (none, spotted), outer retrices (r6) color (black,
white, brown), outer retrices (r6) feather pattern (none, spotted), rump patch (yes, no), rump patch feather
color (white, black, brown), number of toes (three, four), size (6” - 13”+, by 1” increments).
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Yellow-bellied sapsucker Red-naped sapsucker Red-breasted sapsucker Williamson's sapsucker Acorn woodpecker Red-headed woodpecker Lewis' woodpecker Gila woodpecker Golden-fronted woodpecker Red-bellied woodpecker Hairy woodpecker Arizona woodpecker White-headed woodpecker Three-toed woodpecker Black-backed woodpecker Downy woodpecker Nuttall's woodpecker Ladder-backed woodpecker Red-cockaded woodpecker Northern flicker Gilded flicker Pileated woodpecker
Figure 4.1. Unresolved woodpecker phylogeny of the 22 extant species which occur in North
America, derived from anatomical and behavioral characteristics (Short 1982). Phylogenetic distances indicated are arbitrary.
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Yellow-bellied sapsucker Red-naped sapsucker 1 Red-breasted sapsucker 2 Williamson's sapsucker Lewis' woodpecker 3 Red-headed woodpecker 5 Gila woodpecker 4 Golden-fronted woodpecker Red-bellied woodpecker Acorn woodpecker Hairy woodpecker White-headed woodpecker 6 Arizona woodpecker Three-toed woodpecker Black-backed woodpecker Downy woodpecker Nuttall's woodpecker 7 Ladder-backed woodpecker Red-cockaded woodpecker Northern flicker 8 Gilded flicker Pileated woodpecker
Figure 4.2. A consensus unresolved woodpecker phylogeny of the 22 extant species which occur in North America, originally derived from anatomical and behavioral characteristics
(Short 1982) and modified by various molecular phylogenies and further research on each species. Phylogenic distances are arbitrary. References: 1 = Johnson and Zink 1984, 2 =
Tobalske 1997, 3 = Tobalske 1997, 4 = Husak and Maxwell 1998, 5 = Koenig et al. 1995,
6 = Garrett et al. 1996, 7 = Jackson 1994, 8 = Bull and Jackson 1995, Moore 1995.
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Distance
0 5 10 15 20 25 Species +------+------+------+------+------+
YBSA ______RNSA _| |_____ WISA ______| |___ RBSA ______| |_____ RBWO ______| | | RHWO ______| |___| | GFWO ______| | TTWO ______| BBWO ___| |___| DOWO ______| | HAWO _____| |______| |_____ LBWO ______| | | NUWO ______| |_| | | RCWO ______| | |_____ WHWO ______| | | PIWO ______| |___| | |_____ ACWO ______| | | | GIWO ______| | |___ LEWO ______| | | NOFL(rs)1 ______| | GIFL ______| | ______| | NOFL(ys)2 ______| | AZWO ______|
Figure 4.3. Hierarchical cluster analysis for male morphological characteristics
(phenogram) for all 21 Nearctic woodpecker species. 1 rs = red-shafted, 2 ys = yellow- shafted phenotype of C. auratus.
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Distance 0 5 10 15 20 25 Species +------+------+------+------+------+
YBSA ______RNSA _| |_____ RBSA ______| |_____ RBWO ______| |_ RHWO ______| | | GIWO ______| | WISA ______| | TTWO ______| BBWO ___| |______| DOWO ______| | HAWO _____ |_____| | RCWO _____|___ | |______NUWO _____| |___| | | LBWO ______| | | GFWO ______| |___ WHWO ______| | | PIWO ______| |_____| | | ACWO ______| | |_____ LEWO ______| | | NOFL (ys)1 ______| | GIFL ______| |______| | NOFL (rs)2 ______| | STWO ______|
Figure 4.4. Hierarchical cluster analysis for female morphological characteristics
(phenogram) for all 21 Nearctic woodpecker species. 1 ys = yellow-shafted, 2 rs = red-
shafted phenotype of C. auratus.
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Of 500, Matrix # trials Correlation exceeding Value Nearctic woodpeckers (21) P-value MCV (MCV)
PC1 X Short 0.57 286 -0.0378 Cadence X Short 0.012 6 0.1753 Duration X Short 0.002 0 0.2369 Interstrike interval X Short 0.02 10 0.1628
PC1 X Consensus 0.876 438 0.0112 Cadence X Consensus 0.034 17 0.1442 Duration X Consensus 0.02 10 0.1534 Interstrike interval X Consensus 0.22 110 0.0809
PC1 X Female phenogram 0.002 0 -0.2468 Cadence X Female phenogram 0.016 8 0.1654 Duration X Female phenogram 0.73 365 -0.0221 Interstrike interval X Fem. phenogram 0.038 19 0.1422
PC1 X Male phenogram 0.002 0 -0.3589 Cadence X Male phenogram 0.016 8 0.1579 Duration X Male phenogram 0.842 421 -0.0126 Interstrike interval X Male phenogram 0.192 96 0.0945
Table 4.1. Mantel results of woodpecker phenogram versus phylogeny for all 21 Nearctic species using distance matrices. Tests were run with 500 iterations.
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Of 500, Matrix # trials Correlation exceeding Value Boreal forest (6) P-value MCV (MCV)
Short X PC1 0.154 77 -0.3969 Short X Cadence 0.862 431 0.0515 Short X Duration 0.186 93 -0.3742 Short X Interstrike interval 0.786 393 0.0772 Consensus X PC1 0.158 79 -0.3969 Consensus X Cadence 0.852 426 0.0515 Consensus X Duration 0.18 90 -0.3742 Consensus X Interstrike interval 0.82 410 0.0772
Western coniferous forest (9) Short X PC1 0.012 6 -0.3627 Short X Cadence 0.24 120 0.2001 Short X Duration 0.374 187 0.1530 Short X Interstrike interval 0.05 25 0.3229 Consensus X PC1 0.026 13 -0.3551 Consensus X Cadence 0.212 106 0.2178 Consensus X Duration 0.196 98 0.2195 Consen. X Interstrike interval 0.038 19 0.3569
Eastern deciduous forest (7) Short X PC1 0.56 280 -0.1254 Short X Cadence 0.852 426 -0.0585 Short X Duration 0.102 51 0.3956 Short X Interstrike interval 0.032 19 0.3569 Consensus X PC1 0.60 304 -0.1072 Consensus X Cadence 0.96 480 -0.0120 Consensus X Duration 0.132 66 0.3548 Consensus X Interstrike interval 0.354 159 -0.2155
Table 4.2. Mantel results of woodpecker phylogeny versus drum variables, divided by habitat. Matrices are divergence matrices
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Of 500, Matrix # trials Correlation exceeding Value Desert southwest and Texas (7) P-value MCV (MCV)
Short X PC1 0.08 40 0.4005 Short X Cadence 0.372 186 0.2009 Short X Duration 0.29 145 0.2604 Short X Interstrike interval 0.838 419 0.0463 Consensus X PC1 0.154 77 0.3287 Consensus X Cadence 0.378 189 0.2059 Consensus X Duration 0.304 152 0.2812 Consensus X Interstrike interval 0.862 431 0.0419
Western oak woodlands (6) Short X PC1 0.394 197 0.2369 Short X Cadence 0.68 340 -0.1047 Short X Duration 0.50 250 -0.1704 Short X Interstrike interval 0.278 139 -0.2518 Consensus X PC1 0.402 201 0.2383 Consensus X Cadence 0.58 290 -0.1640 Consensus X Duration 0.626 313 -0.1178 Consensus X Interstrike interval 0.328 164 -0.2323
Southern pine forests (7) Short X PC1 0.176 88 0.3642 Short X Cadence 0.544 272 0.1236 Short X Duration 0.01 5 0.6566 Short X Interstrike interval 0.484 242 -0.1163 Consensus X PC1 0.19 95 0.3642 Consensus X Cadence 0.498 249 0.1236 Consensus X Duration 0.006 3 0.6566 Consensus X Interstrike interval 0.464 232 -0.1163
Table 4.3. Mantel results of woodpecker phylogeny versus drum variables, divided by habitat. Matrices are divergence matrices
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Of 500, Matrix # trials Correlation exceeding Value Boreal forest (6) P-value MCV (MCV) Male X PC1 0.066 33 -0.5380 Male X Cadence 0.86 430 0.0463 Male X Duration 0.058 29 -0.5465 Male X Interstrike interval 0.94 471 0.0217 Female X PC1 0.07 35 -0.5380 Female X Cadence 0.858 429 0.0463 Female X Duration 0.032 16 -0.5465 Female X Interstrike interval 0.948 474 0.0217 Western coniferous forest (9) Male X PC1 0.014 7 -0.4638 Male X Cadence 0.076 38 0.3256 Male X Duration 0.882 441 -0.0352 Male X Interstrike interval 0.002 0 0.5167 Female X PC1 0.006 3 -0.4865 Female X Cadence 0.156 78 0.2716 Female X Duration 0.476 238 -0.1321 Female X Interstrike interval 0.002 0 0.5088 Eastern deciduous forest (7) Male X PC1 0.524 262 -0.1445 Male X Cadence 0.810 405 -0.0591 Male X Duration 0.204 102 0.2964 Male X Interstrike interval 0.934 467 -0.0166 Female X PC1 0.542 271 -0.1365 Female X Cadence 0.482 241 -0.1725 Female X Duration 0.564 282 -0.1366 Female X Interstrike interval 0.836 418 -0.0483
Table 4.4. Mantel results of woodpecker phenogram versus drum variables, divided by habitat. Matrices are divergence matrices.
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Of 500, Matrix # trials Correlation exceeding Value Desert southwest and Texas (7) P-value MCV (MCV) Male X PC1 0.61 305 -0.1168 Male X Cadence 0.02 10 0.5237 Male X Duration 0.006 3 0.5866 Male X Interstrike interval 0.09 45 0.3780 Female X PC1 0.554 277 -0.1466 Female X Cadence 0.002 0 0.6956 Female X Duration 0.304 152 0.2749 Female X Interstrike interval 0.026 13 0.4905 Western oak woodlands (6) Male X PC1 0.968 484 -0.0131 Male X Cadence 0.122 61 -0.4346 Male X Duration 0.996 498 0.0138 Male X Interstrike interval 0.108 54 -0.3905 Female X PC1 0.846 423 0.0398 Female X Cadence 0.172 86 -0.3715 Female X Duration 0.862 431 0.0553 Female X Interstrike interval 0.514 257 -0.1853 Southern pine forests (7) Male X PC1 0.68 340 0.1101 Male X Cadence 0.502 251 -0.1302 Male X Duration 0.322 161 0.3235 Male X Interstrike interval 0.286 143 -0.2024 Female X PC1 0.918 459 0.0226 Female X Cadence 0.232 116 -0.2525 Female X Duration 0.954 477 0.0258 Female X Interstrike interval 0.19 95 -0.2409
Table 4.5. Mantel results of woodpecker phenogram versus drum variables, divided by habitat. Matrices are divergence matrices.
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Of 500, Matrix # trials Correlation exceeding Value Boreal forest (6) P-value MCV (MCV) Short X Male 0.002 1 0.8535 Short X Female 0.002 0 0.8535 Consensus X Male 0.002 0 0.8535 Consensus X Female 0.002 1 0.8535 Western coniferous forest (9) Short X Male 0.002 0 0.6542 Short X Female 0.002 0 0.5649 Consensus X Male 0.002 0 0.6556 Consensus X Female 0.002 0 0.5526 Eastern deciduous forest (7) Short X Male 0.002 0 0.7470 Short X Female 0.1620 81 0.3434 Consensus X Male 0.002 0 0.7226 Consensus X Female 0.17 85 0.3322 Desert southwest and Texas (7) Short X Male 0.02 10 0.5178 Short X Female 0.17 85 0.3109 Consensus X Male 0.022 11 0.5695 Consensus X Female 0.088 44 0.3891 Western oak woodlands (6) Short X Male 0.01 5 0.6204 Short X Female 0.026 13 0.6204 Consensus X Male 0.002 0 0.7366 Consensus X Female 0.006 3 0.7366 Southern pine forests (7) Short X Male 0.006 3 0.7951 Short X Female 0.016 8 0.6146 Consensus X Male 0.002 1 0.7951 Consensus X Female 0.016 8 0.6146
Table 4.6. Mantel results of woodpecker phenogram versus phylogeny versus habitat. Matrices are divergence matrices.
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Of 500, Matrix # trials Correlation exceeding Value Picoides species P-value MCV (MCV) Short X Female phenogram 0.02 10 0.3956 Short X Male phenogram 0.026 13 0.3767 Short X PC1 0.496 248 -0.1131 Short X Cadence 0.774 387 0.0580 Short X Duration 0.96 480 -0.0111 Short X Interstrike interval 0.726 363 0.0596
Consensus X Female phenogram 0.048 24 0.3329 Consensus X Male phenogram 0.062 31 0.3171 Consensus X PC1 0.64 310 -0.0814 Consensus X Cadence 0.804 402 0.0363 Consensus X Duration 0.936 468 0.0118 Consensus X Interstrike interval 0.748 374 0.0568
Female phenogram X PC1 0.632 316 -0.0904 Female phenogram X Cadence 0.696 348 0.0643 Female phenogram X Duration 0.558 279 -0.1114 Female phenogram X Interstrike interval 0.82 410 0.0370
Male phenogram X PC1 0.718 359 -0.0647 Male phenogram X Cadence 0.922 461 0.0182 Male phenogram X Duration 0.762 381 -0.0673 Male phenogram X Interstrike interval 0.968 484 -0.0065
Table 4.7. Mantel results of woodpecker phenology and phylogeny versus drum variables for only Picoides. Matrices are divergence matrices.
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Of 500, Matrix # trials Correlation exceeding Value Melanerpes species P-value MCV (MCV) Short X Female phenogram 0.916 458 0.0410 Short X Male phenogram 0.406 203 0.2255 Short X PC1 0.702 351 0.0943 Short X Cadence 0.094 47 0.4896 Short X Duration 0.95 475 -0.0203 Short X Interstrike interval 0.664 332 -0.1190
Consensus X Female phenogram 0.51 255 0.175 Consensus X Male phenogram 0.068 34 0.4811 Consensus X PC1 0.504 252 -0.1748 Consensus X Cadence 0.596 298 0.1596 Consensus X Duration 0.312 156 -0.3209 Consensus X Interstrike interval 0.318 159 -0.2909
Female phenogram X PC1 0.274 137 -0.2937 Female phenogram X Cadence 0.75 375 -0.1137 Female phenogram X Duration 0.958 479 0.0142 Female phenogram X Interstrike interval 0.326 163 -0.2564
Male phenogram X PC1 0.31 155 -0.2774 Male phenogram X Cadence 0.696 348 -0.0995 Male phenogram X Duration 0.28 140 -0.2833 Male phenogram X Interstrike interval 0.232 116 -0.3130
Table 4.8. Mantel results of woodpecker phenology and phylogeny versus drum variables for only Melanerpes. Matrices are divergence matrices.
150 CHAPTER 5
ARE CUES FOR INDIVIDUAL RECOGNITION ENCODED IN WOODPECKER
DRUMS? A STATISTICAL ANALYSIS.
Although recognition of individuals over long distances may occur by several methods, most avian species live in habitats that favor auditory communication. These acoustical signals have been noted to encode numerous types of information including species and individual identification (Stoddard et al. 1991, Mcgregor and Byle 1992), mating and social status (Hanski and Laurila 1993, Hoi-Leitner et al. 1993, Zahavi 1993,
Levin 1996 a, b), and may also be a measure of an individual’s fitness (Keterson et al.
1992, Saino et al. 1997, Hoikkala et al. 1998). Individual identity is considered a prerequisite for establishment of a stable social system (Trivers 1971). Woodpeckers employ a number of acoustical signals, including a vocal and non-vocal repertoire of calls and drums (Short 1978, Winkler et al. 1995). However, the type, location, and amount of information encoded in these signals remains relatively unknown.
Vocal signals in woodpeckers are often distinctive as to species (Short 1982,
Winkler et al. 1995) but non-vocal signals have been shown not to be species-specific
(Stark et al. 1998, Dodenhoff et al. 2001). These non-vocal acoustical communication signals in woodpeckers have been classified into three categories: drumming, mutual
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tapping, and incidental tapping while foraging (Short 1982, Wilkins and Ritchison 1999).
Of these categories, only drumming has been shown to be used in long-distance
communication, while the other two types of non-vocal signals are primarily used over short distances (Short 1982, Lawrence 1967).
Drumming in woodpeckers is a rapid repetitive series of strikes with their bill on a
resonant surface, not associated with foraging or cavity excavation. Previous analysis
indicated that the drum is syntopically species-specific, with the cadence of drums being
the primary variable for species recognition and demarcation (Stark et al. 1998).
Furthermore, these differences in drums between species are perceived and differentiated within the acoustic space (Dodenhoff et al. 2001). Functions attributed to drumming
include localization of individuals, mate attraction, and territoriality (Short 1982, Kilham
1983). Yet, whether cues for individual information are encoded within drums remains unknown. Using four woodpecker species, this investigation tested whether drums encode individual specific cues, and simultaneously identified which drum variables were stereotyped within individuals. Variables that are stereotyped within, but not between, individuals are suitable for encoding individual identification.
METHODS
Nuttall’s (Picoides nuttallii, N = 20, 527 drums), white-headed (P. albolarvatus,
N = 16, 325 drums), hairy (P. villosus, N = 20, 682 drums), and downy (P. pubescens,
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N = 15, 672 drums) woodpeckers were recorded during each species breeding season
(Feb.-July) from 1993-1996 in the Los Padres, Inyo, and Sierra National Forests, CA.
Sample individuals were unmated, and recorded during one drumming bout in the pre-
nesting phase of the breeding cycle (February - April). Previous analysis detected no
geographic variation for these samples within each species (Stark et al. 1998). Drums
were digitized and analyzed for the variables of cadence (strikes-sec-1), number of strikes, duration (sec), and interstrike interval within one drum (sec) using Soundedit 2.0.2
(1990).
Analysis of the test for the homogeneity of variance indicated that the data for
each variable for all four species was not normally distributed (all P < 0.05), except for the spacing of strikes in hairy woodpeckers (P = 1.0). Instead, the data were distributed continuously for the variables within each species. Thus, for each species I calculated the coefficient of variation (CV) for each drum variable (mean + S.E.), using ten randomly
selected drums from each individual, given that equal sample sizes were required for the analysis (Sokal and Rohlf 1982). In addition, I calculated the ratio of the CV (ratio =
variation between individuals/variation within individuals) for each drum variable, which
allowed an estimation of individual markers encoded within the drum from different
species. A high CV ratio identified variables that potentially encode individual identity
(Falls 1982). However, analysis of the CV does not allow for testing the significance of
potential overlap for each variable, termed the relative variability (Lewontin 1966). To analyze any overlap, I calculated the significance of the CV through use of the ‘c’
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2 2 statistic (Dawkins and Dawkins 1974), where c = (CV1 - CV2)/sqrt(SCV1 + SCV2 ). This statistic was compared to a ‘t’ distribution for significance (Fischer and Yates 1963).
If individual variation in drum variables was found to be stereotyped relative to the populational variation in drumming, I tested whether this variation allowed for reclassification of drums into both the correct species and correct individual using a discriminant function analysis. For this test, I used all the drums recorded from each individual (N > 10 drums individual-1) for all four species.
RESULTS
Descriptive statistics for each drum variable for each species were calculated
(Table 5.1, [data are a subset from Stark et al. 1998, where each individual had a
minimum of 10 drums]). Results indicated that the variation within drum cadence and number of strikes were not significantly different within versus between individuals for any species (all P > 0.05, d. f. = 1, c-critical value = 6.314 at α = 0.05, Table 5.2).
However, markers for individual recognition were encoded within the drum duration and the interstrike interval within each drum for all four species. Variation within individuals was significantly less than between individuals (all P < 0.05) for the drum variables selected in all four species (Table 5.2).
Significant misclassifications occurred between Nuttall’s and white-headed woodpeckers (30% Nuttall’s, 26% white-headed reciprocally misclassified) using a
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discriminant function, while both Nuttall’s and white-headed woodpeckers were misclassified as downy woodpeckers at lower rates (8% and 10%). Hairy woodpeckers
were misclassified rarely as white-headed woodpeckers (7%). All other misclassifications were < 2% between species. Overall reclassification by the discriminant function indicated that individuals could be classified correctly into the appropriate species (overall accuracy: 82%). However, individuals could not be classified correctly by a discriminant function within each species using the four selected drum variables (31.1-38.7% correct). Therefore, these results indicated that species, but not individual, information was encoded in the drums of all four woodpecker species.
DISCUSSION
Marler (1957) proposed that the complex structure of bird song allowed for encoding of different types of information in separate signal variables. Results from this study on woodpecker non-vocal signals are consistent with this hypothesis; statistically, drums are encoded with information for species and individual information in separate signal features. While species identification and recognition have been noted to occur in the cadence of woodpecker drums (Stark et al. 1998, Dodenhoff et al. 2001), this analysis indicated that individual recognition cues may be encoded within the drums interstrike interval and duration. Furthermore, the ‘interstrike interval’ variable may correlate to individual identification cues found in the temporal fine structure of passerines (Schottler
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and Henning 1993). Finally, the discriminant analysis indicated that drums may be classified to a species level, but not to specific individuals within each species using these variables.
There remain a number of intrinsic and extrinsic factors associated with individual-specific encoding of information not accounted for by this statistical analysis.
For example, this analysis did not account for behaviors associated with drumming (ex. inter-drum interval or signaler location), or for the spectral properties of the drum.
However, research on the spectral properties of drumming indicated that drum sites are selected for generation of longer wavelengths (Eberhardt 1997). Furthermore, attenuation of frequencies as the signal passes through the environment may limit the significance of spectral variables for recognition (Becker 1982, Wiley and Richards
1982). This does not limit the use of spectral components for other aspects associated with acoustical communication, including ranging of individuals (Naugib 1995, Nelson
2000). Nevertheless, both spectral properties and behaviors associated with drumming
(e.g., interdrum interval) should be tested before discounting their contribution to encoding individual information.
The most significant aspect that could not be accounted for by this analysis concerns the “neighborhood effect” (Randall 1989, 1997). In essence, individuals that do not normally encounter one another may have overlapping signal variables, and still retain individual identity within their “neighborhood,” as observed in banner-tailed kangaroo rats (Dipodomys spectabolis). This requires that these two individuals do not share a common border with a third individual (i.e., N > 2 territories apart), who
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potentially could confuse the signal overlap between the two individuals. Thus, individuality may be encoded within local populations even if individual markers are not encoded across the geographic distribution of each species. However, this was observed in species that learn their acoustic signals, not within woodpeckers that are assumed to have innate invariant signals.
Finally, this analysis did not account for possible temporal variation in drums within an individual over a breeding season. Theoretically, the structure of woodpecker drums may structurally change within an individual during differing phases of the breeding cycle; this sample may not account for the true amount of variability encoded in the drum by an individual over time. However, this analysis focused primarily on whether individual markers, not individual variability, were encoded within woodpecker drums. Although temporal variation may occur, research on passerines indicated that changes in recognition by receivers are often behaviorally based, rather than due to physical changes in the signal (Stoddard 1996). Passerines have been shown to switch song types at differing phases of the breeding season (Stacier 1996), and during agonistic encounters (Kramer et al. 1985). Also, research has indicated that the presence of song repertoires does not hinder individual recognition; identity is retained throughout differing signals from an individual (Weary et al. 1992). There have been no published studies which have demonstrated that behavioral changes (i.e., singing rate) or reproductive status of signalers remove individual markers (or selectively encode markers) within signals during specific stages of the breeding season.
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I have conducted research on known individuals to analyze the behavioral data
associated with drumming, as well as whether encoded variation is perceived within the
acoustical environment (unpublished data). However, I suggest that research be conducted on the perception of non-vocal signals by woodpeckers: specifically that data be collected on continuous versus categorical classification of signals by woodpeckers, as has been done on passerines (Dooling 1982, Searcy et al. 1999). Although this is the first study to analyze the drum variables which may encode individual recognition, it did not test whether individuals use these variables for recognition, or whether these variables are perceptually significant to these species. Also, it is unknown whether other woodpecker genera or other Picoides species follow the pattern observed in this analysis. Clearly, the results of this study posed more questions than it answered, leaving significant areas for further research.
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N N Variable Interstrike
Species Indiv. Drums Cadence2 N Strikes Duration3 Interval3
Nuttall’s 20 527 20.6 + 0.8 20.5 + 3.8 1.0 + 0.3 0.051 + 0.002
White-headed 16 325 19.6 + 0.9 20.2 + 5.2 1.0 + 0.3 0.052 + 0.003
Hairy 20 682 25.8 + 0.7 25.5 + 3.6 1.0 + 0.2 0.040 + 0.001
Downy 15 672 16.8 + 0.7 13.7 + 2.7 0.8 + 0.2 0.064 + 0.003
1 subset of data from Stark et al. 1998
2 strikes-sec-1
3 sec
Table 5.1. Means (+ S.D.) for the selected drum variables for each species in this study1. Each individual had a minimum of ten drums.
Variable N Interstrike Species N CV Cadence1 Strikes Duration2 Interval2 Nuttall’s 20 Between 7.73 + 1.03 30.21 + 1.40 28.57 + 1.36 8.99 + 1.04 Within 4.05 + 1.05 18.53 + 1.89 19.49 + 1.99 3.93 + 1.05 Ratio 1.91 1.63 1.47 2.29 c statistic3 2.066 1.588 25.9 1011 95% CI Between 5.68 - 9.79 27.41 - 33.00 25.86 - 31.28 6.92 - 11.06 Within 1.96 - 6.14 14.76 - 22.30 15.50 - 23.47 1.82 - 6.03 White-headed 16 Between 6.22 + 1.02 33.01 + 1.61 33.31 + 1.62 8.03 + 1.04 Within 4.33 + 1.05 26.80 + 3.19 27.72 + 3.31 5.26 + 1.11 Ratio 1.44 1.23 1.20 1.53 c statistic 1.281 0.754 25.95 118.3 95% CI Between 4.18 - 8.26 29.80 - 36.22 30.08 - 36.54 5.96 - 10.10 Within 2.22 - 6.43 20.42 - 33.19 21.10 - 34.35 3.04 - 7.48 Hairy 20 Between 4.74 + 1.01 17.73 + 1.12 17.51 + 1.12 4.80 + 1.01 Within 2.83 + 1.02 14.04 + 1.51 14.62 + 1.55 2.76 + 1.02 Ratio 1.67 1.26 1.20 1.74 c statistic 1.367 0.472 12.86 570.1 95% CI Between 2.72 - 6.76 15.49 - 19.97 15.28 - 19.75 2.78 - 6.82 Within 0.79 - 4.87 11.02 - 17.07 11.52 - 17.72 0.72 - 4.80 Downy 15 Between 7.96 + 1.02 22.62 + 1.20 25.26 + 1.25 7.95 + 1.02 Within 4.10 + 1.05 19.46 + 2.09 20.39 + 2.19 4.07 + 1.05 Ratio 1.94 1.16 1.24 1.95 c statistic 2.487 0.672 19.79 763.1 95% CI Between 5.91 - 10.0 20.22 - 25.01 22.67 - 27.75 5.90 - 10.0 Within 2.00 - 6.19 15.28 - 23.63 16.01 - 24.77 1.97 - 6.16
1 strikes-sec-1 2 sec 2 2 3 Significance of the coefficient of variation is calculated by c = (CV1 - CV2)/sqrt(SCV1 + SCV2 ). Critical value = 6.314 at α = 0.05. (Dawkins and Dawkins 1974).
Table 5.2. Location of variation encoded in the drum variables of four species. The coefficient of variation (CV) is presented as a percentage of the mean (+ S.E.), with the CV ratio calculated by dividing the variation between individuals by the variation within individuals. The 95% confidence interval (CI) for the drum variables within and between individuals for each species is reported. CHAPTER 6
ARE EASTERN NEARCTIC WOODPECKER DRUMS SPECIES-SPECIFIC? A
BEHAVIORAL ANALYSIS OF RECIPROCAL PLAYBACKS TO CONSPECIFIC
VERSUS HETEROSPECIFIC DRUMS.
Relative to research on passerine song, there is a paucity of information concerning behavioral responses to non-vocal acoustic signals in birds (Prum 1998). Avian acoustic communication normally occurs through either songs or calls, but woodpeckers lack the ability to generate songs similar to those used by passerines (Baker 2001, Brackenbury
1982). In addition to a vocal repertoire of calls, woodpeckers employ a non-vocal acoustic communication signal termed a drum. Drumming in woodpeckers is a rapid repetitive series of strikes with their bill on a resonant substrate, not associated with foraging or cavity excavation. This signal has been noted to be a form of long-distance communication which may or may not elicit heterospecific responses (Crockett 1975, Dodenhoff et al. 2001,
Winkler and Short 1978). Drumming has been implicated in territoriality, mate attraction, pair-bond maintenance, localization of individuals (Duncan 1990, Wilkins and Ritchison
1999), and even as an indicator of territorial quality (Short 1982), though this has been questioned (Eberhardt 1997).
161 Avian reference books generally refer to woodpecker drums as species-specific, with
each species having its own unique drum (Welty and Baptista 1988). However, as a long-
distance communication signal, results pertaining to species-specificity and interspecific
response to drums have been mixed (Dodenhoff et al. 2001, Kilham 1983, Winkler and Short
1978). Drumming may be species-specific (Bent 1939, Lawrence 1967), diagnostic in some
species (Short 1982), or too ambiguous for species recognition (Winkler and Short 1978,
Winkler et al. 1995). Winkler and Short (1978) attributed ambiguity in drums to numerous
sources, such as motivational context, while others have attributed heterospecific responses
to random chance (Lawrence 1967).
Previous research indicated that drums in western Nearctic woodpeckers were
syntopically but not allotopically species-specific, with the drums cadence (strikes-sec-1)
used by individuals as the primary variable for species discrimination and recognition
(Dodenhoff et al. 2001, Stark et al. 1998). Syntopic species were defined as closely related
species that co-occur, and often occupy different niches within a habitat. Reciprocally,
allotopic species usually occupy the same ecological niche, but do not normally co-occur
within a habitat. These terms are in contrast to the more generalized terms of sympatry and
allopatry, which refer to species overlap over broad geographic regions, not within habitats.
Currently, there are five hypotheses regarding the role of woodpecker drumming in species recognition. First, drums are species-specific and not normally given interspecifically (Lawrence 1967). Second, drums are not distinctive because vocal signals
(i.e., series calls) are used for species differentiation (Short 1982). Third, drums are fairly
species-specific, even to the human ear, but woodpeckers may react indiscriminately
162 (Winkler et al. 1995). Fourth, drums are syntopically, but not allotopically, species-specific with the cadence of drums used as the predominant variable for species identification (Stark et al. 1998). Finally, the drums cadence encodes species identity; however, sympatric heterospecific woodpecker drums are not frequently given interspecifically due to divergence within this signal variable. Interspecific responses comparable to those given to conspecifics may result if heterospecific individuals have similar cadences within their drum (Dodenhoff et al. 2001). This study tested whether eastern Nearctic woodpeckers, similar to their western counterparts, differentiated the drums of conspecifics from those of syntopic and allotopic heterospecifics.
METHODS
I recorded the behavioral responses of nine eastern Nearctic woodpecker species to playbacks of syntopic and allotopic heterospecific versus conspecific woodpecker drums.
Playbacks were conducted during February - June, 1997-2001. The use of playbacks on woodpeckers has not been well documented in the literature, with only a few authors successfully using this technique (Dodenhoff et al. 2001, Crusoe 1980). Primary playback locations were situated throughout Central Ohio, with secondary locations ranging across
North America (Figure 6.1). Study species included downy (Picoides pubescens), hairy (P. villosus), pileated (Dryocopus pileatus), red-bellied (Melanerpes carolinensis), red-headed
(M. erythrocephalus), and black-backed (P. arcticus) woodpeckers, the northern flicker
163 (Colaptes auratus), along with yellow-bellied and red-naped sapsuckers. Playback stimuli included these species, as well as the three-toed woodpecker (P. tridactylus), whose drum is similar in cadence and duration to the downy woodpecker, but only half the duration of the black-backed woodpecker (Table 6.1).
Given that only sympatric species were found to have divergent signals in western populations (Stark et al. 1998), species were categorized as being either syntopic or allotopic based on each species’ breeding range within sampled areas. Using the above species, I categorized downy, hairy, pileated, red-bellied, and red-headed woodpeckers and northern flickers as syntopic in eastern deciduous and southern pine forests. Species endemic to northern boreal forest included the downy, hairy, pileated, and black-backed woodpeckers, northern flicker, and yellow-bellied sapsuckers. Red-naped sapsuckers were studied in
Wyoming and Colorado when I observed that, to my ears, their drum was similar to that of eastern yellow-bellied sapsuckers.
Subject birds were observed for a minimum of five minutes prior to presentation of stimuli. This interval was to assure that the target individual was not currently engaged in territorial or reproductive behaviors with another conspecific. Only individuals who were foraging, preening, or excavating cavities, and within acoustical range of the playback speaker (~100 m) were used in this analysis; birds unresponsive to both playback stimuli were excluded. Playbacks were conducted in a balanced randomized pairwise design, with a minimum of 30 minutes between presentations of stimuli and compared behavioral responses to conspecific versus heterospecific drums. This interval was selected based on log-
164 survivorship curves for the duration that woodpeckers remain responsive to presentation of previous stimuli (Dodenhoff 1996).
Playback tapes were made from high quality field recordings and commercially available audio recordings of each species (Cornell 1992 a, b). Playback stimuli were composed of a minimum of ten different drums, recorded from different individuals, for each species. Drums are digitized and edited together using Signal 3.1 (Engineering design 1999) at a standardized 4 second interval to imitate a drumming woodpecker. Standardization of the interval between drums minimized the probability of accidentally encoding any individual or behavioral cues, such as motivational state, which may have been encoded within this drum variable. Playback tapes were standardized for decibel level. Playback stimuli covered the species-typical range for each drum variable (Stark et al. 1998, Wilkins and Ritchison 1999, Crusoe 1980, Duncan 1990, Stark unpubl. data, Table 6.1).
I measured the following behavioral responses during playbacks: latency to first response (sec), number of flights over the speaker, duration spent within 10m of the speaker
(sec), closest approach to the speaker (m), and the total duration spent drumming in response to stimuli (sec, Tables 6.2 and 6.3). A principal-components analysis (PCA) was used to reduce the five intercorrelated variables into a first and second (PC1, PC2) principal component score (eigenvalue > 1.0). These scores were compared using two-tailed
Wilcoxon’s signed-rank tests (Sokal and Rohlf 1981). Only species with non-significant results in both PC1 and PC2 were classified as being unable to differentiate between conspecific versus heterospecific drums. The probability that a single individual was used
165 more than once over the course of these playback experiments was minimized by the use of
numerous playback sites, and the exclusion of further sampling at that location.
RESULTS
Analysis of woodpecker behavioral responses to playbacks of heterospecific versus
conspecific drums indicated that species with divergent cadences within drums were able to
accurately discriminate between signals, assessed through divergent behavioral responses to presented stimuli (Table 6.4). However, syntopic eastern species with similar cadences
indicated that those species did not elicit differential behavioral responses from
heterospecific signals relative to drums of conspecifics. I interpreted these results as species
being unable to differentiate between signals (Table 6.4). For example, there were no
significant differences in behavioral responses in black-backed woodpeckers to playback
drums of conspecifics versus those given to the three-toed woodpecker stimuli (Table 6.4).
Eastern hairy woodpeckers could not differentiate northern flicker drums from
conspecifics, the same pattern as observed in their western counterparts. Downy
woodpeckers also could not differentiate drums of pileated woodpeckers from those of
conspecifics, though this could be due to the marginal sample size collected. Finally, red- bellied woodpeckers could not differentiate conspecific drums from those of downy woodpeckers (Table 6.4). Thus, I did not observe the divergence in behavioral responses to
166 syntopic drum signals in eastern populations of woodpeckers that I previously noted for western Nearctic species over the course of this investigation.
Other non-significant results were found between downy, black-backed, and three- toed woodpeckers (Table 6.4), which were allotopic species with drums of similar cadence.
Behavioral responses of downy woodpeckers indicated that they were unable to discriminate
black-backed woodpecker drums from those of conspecifics, similar to their western
counterparts. However, the reciprocal playback was not equivalent; black-backed
woodpeckers could differentiate between stimuli, possibly due to the increased duration of
black-backed woodpecker drums versus those of downy woodpeckers. Yet, black-backed
woodpeckers were apparently unable to differentiate conspecific drums from those of three-
toed woodpeckers, which are similar in duration to downy woodpeckers (Table 6.4). These
results indicated that drum cadence was an important variable for species recognition within
eastern populations, and that drum duration remains contentious as a species identifier.
Finally, there were no significant differences in behavioral responses to drums observed in
red-naped versus yellow-bellied sapsuckers to reciprocal playbacks among allotopic
populations. Thus, behavioral responses to playback stimuli indicated that drums were not
recognized as species-specific in eastern Nearctic populations.
Species with similar, but not identical drum cadences elicited mixed results; red-
headed woodpeckers and northern flickers, as well as downy to red-bellied woodpeckers,
were not significantly different in response to heterospecific versus conspecific drums in
PC1, but significantly different in PC2. This was the same pattern observed in northern
flickers to drums of hairy woodpeckers. In contrast, numerous species indicated differential
167 responses in PC1, but not PC2 (Table 6.4). These mixed results may account for the
conclusions of previous researchers that woodpecker drums were too ambiguous to
accurately encode species identity. Nevertheless, the significant difference in either PC1 or
PC2 indicated that some interspecific discrimination between signals probably occurred
within these species, which in turn elicited a differential behavioral response in reciprocal
playbacks.
DISCUSSION
The majority of studies on woodpecker drums have been conducted on eastern
Nearctic species, with researchers concluding that there was significant interspecific
ambiguity by receivers in response in to drums (Crusoe 1980, Winkler and Short 1978,
Kilham 1983, Duncan 1990, Winkler et al. 1995). Eastern woodpecker species were able to
discriminate drums in syntopy, responding in a predictable manner to presented signals
(Stark et al. 1998, Dodenhoff et al. 2001). However, the results from this study indicated
through behavioral responses to playbacks that woodpecker drums were not species-specific,
refuting Bent (1939) and Lawrence (1967), but were predictable based upon the similarity or
dissimilarity of heterospecific drum cadence. Furthermore, woodpeckers did not react indiscriminately to playback stimuli, refuting Winkler et al. (1995); eastern species with
similar drums elicited equivalent behavioral responses to heterospecific versus conspecific
drums while syntopic.
168 There are two alternate interpretations of these results. First, woodpeckers responded
similarly to drums of conspecifics and those heterospecifics that have similar drums. For
example, downy woodpeckers did not exhibit a differential behavioral response to playbacks
of black-backed woodpecker drums from those given to a conspecific playback. Thus,
woodpeckers may respond in a “matched-filter” manner; those drums that approximate an
“internal template” of a signal may elicit an appropriate response (Becker 1982). Signals that
do not match conspecific parameters are ignored by the individual. Alternately, woodpeckers
may be able to correctly categorize the drums of all syntopic species, responding only to
those signals that are categorized as conspecific or ambiguous to species. In this model, a
woodpecker would respond appropriately to conspecific signals and signals interpreted as
species ambiguous, but not respond to signals of non-competing syntopic heterospecifics.
For example, a downy woodpecker would not respond to a drum of a hairy or red-headed woodpecker in syntopy, as these species exploit different resources to the downy within the environment. Both of these models would explain the pattern of results observed in this investigation.
Woodpeckers did respond similarly to drums of syntopic and allotopic species that had similar drum variables. Previous research has indicated that the drums’ cadence is the primary factor for species recognition in western Nearctic populations (Dodenhoff et al.
2001). In chapter 5 of this manuscript, cadence was indicated as an important variable for encoding species identity in woodpecker drums. Finally, the results from these playback experiments noted only species with overlapping drum cadences (chapter 2) gave similar behavioral responses to drums. In all cases, species with divergent drum cadences gave a
169 differential behavioral response to playbacks, indicating that drums had been categorized by
the woodpeckers as being different. Overall, these results were consistent with the
predictions of Dodenhoff et al. (2001), as drums were not normally exchanged interspecifically due to divergence within the cadence of drums. These results also refute one prediction of Stark et al. (1998) that divergence in woodpecker drums occurs within syntopic woodpecker populations. However, not all reports of interspecific ambiguity in eastern populations of woodpeckers, especially those between downy and hairy woodpeckers
(Lawrence 1967, Kilham 1983), were supported by this study.
I noted that the most phenotypically divergent species had the most convergent drum cadences: downy, pileated, red-bellied and three-toed woodpeckers all have similar drum cadences and are all morphologically different. However, this trend may not be universal; black-backed and three-toed woodpeckers, along with red-naped and yellow-bellied sapsuckers, are all phenotypically similar and have similar drums. Furthermore, results indicated that these species had difficulty discriminating between these playback pairings.
Thus, these species should have difficulty differentiating these heterospecific signals in syntopy. Given that drumming has been implicated as important in reproductive behaviors, the fact that several of these species were separated by habitat and/or geography may not be coincidental.
Results indicated that drums were poor indicators for species identity in some dyads of sympatric eastern Nearctic woodpecker populations, contrary to western populations.
Thus, drums are not a reliable prezygotic isolating mechanism between these woodpecker species. Yet, most comparisons indicated that syntopic species could discriminate
170 heterospecific from conspecific drums. Furthermore, given similar cadences, drums did elicit
species-specific behavioral responses to playback drums between allopatric species, which
was the same pattern as observed in western Nearctic species (Stark et al. 1998). However, a
lack of differential behavioral responses does not requisitely indicate that there was a lack of
discrimination by individuals between signals. Indeed, species may be able to differentiate
drums of heterospecifics from conspecifics, but exhibit identical responses based on the
receiver’s perception of the drum. This perception potentially has significant ramifications
concerning the drums’ role within woodpecker communication.
Individuals may respond equivalently to similar drums to minimize the possibility of
mistakes, given an appropriately high cost associated with error (i.e., loss of mate or
territory). For example, in this study downy woodpeckers apparently could not differentiate drums of pileated woodpeckers in syntopy, even though the drums of pileated woodpeckers are often considered diagnostic to humans (Winkler and Short 1978). However, the individual strikes within a pileated woodpeckers drum are not consistent throughout one drum; strikes are spaced at greater intervals at the beginning of the drum relative to strikes at the end of the drum (i.e., the cadence increases, Stark et al. 1998). The latter half of the pileated’s drum has a cadence near to that of downy woodpeckers (~17 strikes-sec-1), and given in a duration similar to that of the average Downy woodpecker drum (Table 6.1).
An increase or decrease in cadence has not been shown to be an important variable for species recognition (Dodenhoff et al. 2001). However, this variable may affect species recognition if the change within a drum is sufficient to overlap a divergent heterospecifics cadence. Alternately, individuals may respond categorically to drums, being tolerant towards
171 minor structural modifications to the signal, as noted in passerines (Matheson and Aubin
2001). Hypothetically, individuals that categorized signals would respond the same to
signals with minor differences in their structure. Potentially, this would be advantageous to
species living in a forest environment, as this would minimize error created by environmental
conditions, including reverberation and degradation of the signal as it passes through the
environment. Thus, woodpecker drums have species-essential parameters that appear to be
“tuned” to the natural limits of variation of signal variation in that species discrimination
ceases when signals are changed beyond these limits. Therefore, these results fit the “room
for variation” hypothesis for the principles of coding information in acoustic signals
(Dabelsteen and Pederson 1992).
Species with convergent drums may maintain preferences for these signals with divergent morphology; high phenotypic divergence should allow for genetic isolation to be maintained without alteration of this acoustic display. For example, red-bellied woodpeckers apparently could not differentiate a conspecific drum from that of a downy woodpecker.
Reciprocally, downy woodpeckers had a mixed response: the significant response in PC2 in the downy versus red-bellied woodpecker playback was ascribed to an increased number of flights and duration spent within 10m of the speaker at longer levels to red-bellied drums versus those given to conspecifics. This increased vigilance observed in downy woodpeckers may be due to red-bellied woodpeckers being perceived as representing a greater threat than a conspecific, given that red-bellied woodpeckers have been noted to usurp or destroy nest cavities of downy woodpeckers (Kilham 1983). Thus, the observed behavioral responses to drums may be indicative of competitive interactions between species, not as a lack of
172 discrimination between signals. In regards to recognition, the perception of drums within and between species remains to be adequately studied.
Red-naped and yellow-bellied sapsuckers indicated no significant difference in behavioral responses to reciprocal playback stimuli of drums within both of these species while allotopic; red-naped sapsucker populations from Colorado and Wyoming were unable to differentiate yellow-bellied sapsucker drums recorded in New York and Ontario from conspecifics, and vice versa. Preliminary playbacks using Williamson’s sapsuckers (S. varius) were conducted on both species as a control. Unfortunately, sample sizes were insufficient for statistical analysis. However, the preliminary trend indicated that both yellow-bellied and red-naped sapsuckers should have been able to differentiate the
Williamson’s sapsucker drum from conspecifics. Red-naped sapsuckers have been shown to differentiate drums of red-breasted (S. ruber) sapsuckers in syntopy (Trombino 1998), and these two species have been noted to assort selectively when mating in sympatry (Johnson and Johnson 1985), which has led to their classification as biological species (Johnson and
Zink 1984, Cicero and Johnson 1995). Thus, it is reasonable to deduce that sapsucker drums encode species identity meaningful to birds as well as observers.
Given the lack of apparent heterospecific discrimination in allotopy, it would be revealing to study red-naped and yellow-bellied sapsuckers at a syntopic interface in the western United States to uncover whether discrimination or signal differentiation has occurred, or whether they would maintain interspecific territories. Research on the closely related red-breasted versus red-naped sapsuckers at a syntopic interface indicated that drums differed from syntopic versus allotopic populations (Trombino 1998). Drums differed in
173 their duration (containing more doublets) and in the interval of doublets contained within drums, which was interpreted as support for the reproductive character displacement hypothesis (Trombino 1998). This was due to the observed shift of drum variables within woodpecker drums from an allotopic to syntopic state within a species. Whether this pattern would apply to the yellow-bellied versus red-naped sapsucker comparison in syntopy is unknown.
Kaiser (1990) noted that the overall pattern (i.e., an increase or decrease in the interstrike interval) within one drum was correlated to the foraging strategy of each species.
Thus, drumming is likely dependent and constrained by the underlying morphology of each species. Therefore, modification of the drum’s cadence may not occur readily within differing populations of a given species. However, modification of the drum’s duration potentially could be used to identify specific populations or individuals within groups, given duration has been noted to be more variable than cadence (Stark et al. 1998). Geographic variation within woodpecker drums was the focus of Chapter 3. I propose that further research on phylogeny, phenology, and biogeography of woodpeckers versus their influence on the development of drums are required to answer whether observed trends are applicable across an array of species.
IMPLICATIONS FOR THE EVOLUTION OF ADVERTISEMENT SIGNALS
In a broader context, the results of this study have implications concerning the divergence of advertisement signals in the evolution of communication systems. Despite the
174 variety of hypotheses concerning advertisement signals, they can be categorized into two themes: recognition (i.e., reproductive character displacement or specific mate recognition) or competition models (i.e., honest advertisement or aesthetic traits). Recognition models assert signals that prevent hybridization between closely related species should be favored, resulting in species using diverse signals (Podos 2001). In contrast, competition models assert mechanisms that develop secondary sexual characteristics, often as a means to increase reproductive output, are responsible for the diversity of signals used for mate advertisement
(Butlin 1995).
In reproductive character displacement, species with similar characters in allopatry were hypothesized to diverge from their original state in sympatry (Brown and Wilson 1956).
Thus, signals are functionally a prezygotic isolator to minimize hybridization between species, and may be required for coexistence of syntopics (Becker 1982). Resultant species should exhibit shifts in signal characters due to the presence of a related species in sympatry.
Results from this study indicated that this has not occurred widely within woodpecker drums between species. Character displacement has been noted to occur within Sphyrapicus woodpecker species, not between species (Trombino 1998) and has not been observed either in Melanerpes or Picoides genera, though research is lacking. Character displacement predicts that the direction of shift may be either convergent or divergent: convergent shifts were expected in response to optimal environmental conditions, whereas divergent shifts would be the result of pressures to avoid hybridization between similar species. Thus, divergence observed within sapsucker drums was likely due to sexual selection pressures to avoid interspecific hybridization (Trombino 1998).
175 Given that woodpecker drum cadence encodes species information (Dodenhoff et al.
2001, Duncan 1990), the convergence in eastern Nearctic woodpecker drums were probably
not shifted to maximize transmission within their environment, since a wide range of drum
cadences have been noted among eastern species (Table 6.1). For example, downy, red-
bellied, and pileated woodpeckers all drum at similar cadences, but the faster drum cadence
of syntopic hairy woodpeckers and northern flickers may indicate that the signal structure
convergence is not in response to maximizing signal transmission. Furthermore, the red-
headed woodpecker’s drum cadence fell between these two groups in syntopy. However, this observation does not preclude the existence of two or more different signal channels
being utilized by species within eastern forests.
It is worth recalling that the vocal repertoires are divergent between eastern woodpecker species with convergent drums. Thus, there may simply be a lack of selection for drums to diverge in syntopy given divergence in other acoustic signals. However, a lack of character shift in drums between species may still allow for signal specificity, if the signals are shifted temporally. Yet, I have not observed a dramatic shift in the timing of when drums were given (either daily or seasonally) between syntopic species throughout the breeding season, though some minor stratification may have occurred (Lawrence 1967). For example, northern flickers drummed for only a short time during the breeding season relative to hairy woodpeckers, and hairy woodpeckers have difficulty differentiating drums of northern flickers versus conspecifics. This is probably not coincidental.
In contrast, the specific mate recognition hypothesis (Paterson 1978) predicts that the divergence of signal characters was in response to optimal transmission between conspecifics
176 within a local environment, not as a result of character displacement. Instead, signal diversity was the result of adaptations to new environments in allopatry, with the co- evolution of mutual recognition between signaler and receiver (Paterson 1978, Verrell 1988).
In this system, mates prefer specific signals, with different groups forming divergent preferences for independent reasons without selection against hybridization. Therefore, signal diversity was due to selection favoring mate recognition within, rather than between populations. Consequently, this system would generate signals that are significantly divergent when species become sympatric. However, this hypothesis does not explain why woodpecker drums were not divergent between sympatric species, given that signals evolved in allopatry. One possible explanation may be the phenotypic divergence in allotopy led to a lack of selection pressure for signal divergence in sympatry. For example, downy and red- bellied woodpeckers apparently prefer the same drum signal, but are phenotypically and phylogenically divergent.
Competition models, such as sexual selection of honest advertisement signals, assert that the diversity among male advertisement signals was dependent on female choice; females select males based on characters that increase fecundity, given those characters are
correlated to male fitness (Hamilton and Zuk 1982, West-Eberhardt 1983). Theoretically,
this would allow for rapid divergence between subgroups, as a consequence of variation in
female choice and the resultant reproductive output. Although one aspect of this selection would result in specific signals, it allowed for variation to be maintained within species.
Signals would contain species characters, as well as variable characters, to indicate male quality. Honest advertisement does not assume that syntopic signals must differ, just that
177 they have an indicative pattern that mates prefer. This may explain the drums of downy and red-bellied woodpeckers, which have overlapping variables in their drums, as long as there
are reproductive barriers to minimize hybridization.
However, if drums are used as indicators of male quality and this indicator is
common among species, then I would expect mates from numerous species to use this
indicator. I observed that the interdrum interval (number of drums-time-1) decreased during
aggressive interactions, or when individuals competed for mates. This may allow individuals
to assess the quality (i.e., condition of health) of the signaler or adjust their response depending on the perceived level of threat (Langemann et al. 2000). Yet, it is unknown whether drums could be classified as an honest signal, or whether they convey any information concerning mate quality. To my knowledge, this hypothesis has not been tested, so I can not eliminate whether woodpecker drums are a form of honest advertisement.
Finally, the aesthetic traits hypothesis predicts no correlation between the character of choice and mate quality. Female preferences are arbitrary, but become genetically linked to the male trait through successive generations. Assortive mating over time resulted in divergence between groups of females differing in trait preferences, from a genetic correlation between the male trait and female preference (Fisher 1958). However, females do not acquire immediate benefits in fecundity or reproductive output. Since traits were not linked to mate quality, there was no expectation of similar preferences between species.
Thus, advertisement signals would be specific for each species’ mating system. However, in regards to drumming, I would have expected greater divergence between syntopic signals than was observed. Given the similarity within drums of closely related species it is
178 unknown whether the aesthetic traits hypothesis may apply to the evolution of signal
divergence, as it relates to woodpecker drums.
Of the competing hypotheses concerning signal evolution between species, the
present results conform to the predictions of the specific mate recognition hypothesis for the
structure of drums, though further research will likely modify these preliminary results.
However, research on sapsuckers at syntopic interfaces has indicated that those species which hybridize with syntopics may undergo character displacement to maintain specificity within species at different populations (Trombino 1998). Though results from this investigation indicated that woodpecker drums were not species-specific, target individuals
responded only to a limited range of signal variables (characteristics) within playback drums.
Thus, drums encode species information detectable by receivers. This could indicate support for the sexual selection models of honest advertisement, as conspecifics have been noted to
respond only to specific signals. Since it is unknown why females choose this signal, or even if drumming is critical for mate selection, I can not discount the honest advertisement hypothesis as an explanation for the observed patterns. Finally, these theories may not be mutually exclusive, which could compound problems of interpretation; a coevolved system
(i.e., specific mate recognition hypothesis) between signaler and receiver could be wrapped in competitive models, which predicts signal divergence due to sexual selection pressures.
179
Number of Interstrike
Species Cadence1 Duration2 Strikes Interval2
Pileated woodpecker 14.2 + 0.4 1.7 + 0.1 23.7 + 4.3 0.074 + 0.005
Black-backed woodpecker 16.5 + 0.6 1.9 + 0.4 30.9 + 6.0 0.064 + 0.003
Red-bellied woodpecker 16.5 + 1.2 0.8 + 0.2 12.9 + 1.1 0.065 + 0.002
Downy woodpecker 17.1 + 1.4 0.8 + 0.2 12.7 + 1.3 0.064 + 0.005
Three-toed woodpecker 17.1 + 1.1 0.8 + 0.2 13.1 + 1.1 0.064 + 0.004
Red-headed woodpecker 20.7 + 1.1 0.7 + 0.2 15.1 + 0.8 0.050 + 0.002
Northern flicker 22.3 + 1.3 1.1 + 0.3 25.2 + 4.5 0.047 + 0.004
Hairy woodpecker 26.1 + 1.1 1.0 + 0.2 24.9 + 3.9 0.040 + 0.002
Yellow-bellied sapsucker3 10.5 + 2.5 2.7 + 1.4 25.6 + 11.3 0.084 + 0.018
Red-naped sapsucker3 14.2 + 1.0 1.4 + 0.4 19.6 + 3.3 0.078 + 0.015
1 strikes-sec-1
2 sec
3 Discontinuous drum
Table 6.1. Playback stimuli drum variables (Mean + S.D.) for the playback stimuli used
during the course of this study.
180
Drum Latency to Number of Closest Duration Species Playback N duration first response flights over approach within 10m (sec) (sec) the speaker (m) sec) Black-backed Black-backed 15 47.5 + 15.3 8.5 + 1.3 0.5 + 0.2 17.7 + 2.7 11.7 + 9.4 Downy 15 6.2 + 3.6 9.9 + 2.1 0.3 + 0.2 14.1 + 1.5 30.0 + 17.5 Black-backed Black-backed 10 32.8 + 16.6 12.3 + 4.1 0.3 + 0.2 12.5 + 2.1 30.0 + 15.5 Three-toed 10 20.1 + 17.0 13.5 + 4.5 0.4 + 0.2 11.5 + 1.8 44.0 + 19.2 Black-backed Black-backed 7 66.7 + 35.3 10.6 + 2.6 1.1 + 0.4 12.1 + 2.1 8.6 + 7.1 Hairy 7 2.1 + 2.1 5.7 + 3.9 0.1 + 0.1 15.0 + 5.5 0.0 + 0.0 Downy Downy 14 18.1 + 9.6 11.2 + 2.0 0.6 + 0.1 12.2 + 1.4 30.7 + 13.0 Black-backed 14 39.1 + 24.4 6.2 + 1.6 0.6 + 0.1 8.3 + 1.8 52.6 + 24.7 Downy Downy 20 26.0 + 9.8 13.4 + 3.0 0.4 + 0.1 14.4 + 1.6 17.3 + 6.7 Hairy 20 0.0 + 0.0 0.0 + 0.0 0.1 + 0.1 0.0 + 0.0 0.0 + 0.0 Downy Downy 13 28.1 + 14.3 12.7 + 2.4 0.5 + 0.1 15.4 + 1.1 18.8 + 14.5 N. flicker 13 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 2.3 + 2.3 Downy Downy 7 2.9 + 1.8 14.3 + 5.7 0.6 + 0.2 13.9 + 3.4 21.4 + 8.6 Pileated 7 1.9 + 1.4 4.3 + 1.9 0.6 + 0.4 13.6 + 4.5 8.6 + 5.5 Downy Downy 46 70.8 + 17.6 21.7 + 3.7 0.4 + 0.1 13.1 + 0.7 88.7 + 18.0 Red-bellied 46 88.8 + 27.0 19.0 + 3.1 0.7 + 0.1 12.2 + 0.7 122 + 27.3 Downy Downy 13 20.2 + 8.6 7.7 + 1.0 0.1 + 0.1 15.0 + 1.0 17.7 + 9.1 Red-headed 13 0.8 + 0.9 1.6 + 1.6 0.0 + 0.0 1.5 + 1.5 0.0 + 0.0 Hairy Hairy 6 34.5 + 21.0 9.2 + 2.0 0.7 + 0.2 11.8 + 3.9 57.5 + 20.3 Black-backed 6 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 Hairy Hairy 25 67.8 + 23.5 9.2 + 2.1 0.9 + 0.1 11.3 + 1.3 67.5 + 22.3 Downy 25 0.72 + 0.5 2.1 + 1.1 0.1 + 0.1 2.6 + 1.1 4.5 + 4.1 Hairy Hairy 11 14.1 + 8.1 9.7 + 2.6 0.8 + 0.2 13.7 + 2.8 55.9 + 28.1 N. flicker 11 3.2 + 2.8 6.6 + 2.6 0.1 + 0.1 9.1 + 3.7 10.6 + 5.9 Hairy Hairy 12 38.1 + 20.0 13.9 + 3.1 0.5 + 0.2 13.8 + 1.5 56.8 + 22.9 Pileated 12 0.3 + 0.3 3.3 + 2.6 0.0 + 0.0 3.3 + 2.3 0.0 + 0.0
Table 6.2. Behavioral responses (Mean + S.E.) for each species for each reciprocal playback conducted during this study.
Drum Latency to Number of Closest Duration Species Playback N duration first response flights over approach within 10m (sec) (sec) the speaker (m) (sec) Hairy Hairy 10 9.9 + 6.8 12.0 + 4.3 1.0 + 0.2 12.6 + 2.6 28.4 + 14.3 Red-bellied 10 0.0 + 0.0 1.2 + 1.0 0.0 + 0.0 1.5 + 1.5 0.0 + 0.0 Hairy Hairy 12 29.0 + 12.7 7.1 + 1.3 0.6 + 0.2 13.9 + 1.6 35.0 + 13.3 Yellow-bellied s. 12 0.0 + 0.0 1.3 + 1.3 0.1 + 0.1 1.7 + 1.7 0.0 + 0.0 Northern flicker N. flicker 10 47.0 + 17.8 12.2 + 2.5 0.4 + 0.2 13.5 + 1.3 24.0 + 12.3 Black-backed 10 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 Northern flicker N. flicker 9 54.0 + 18.0 11.1 + 1.1 0.2 + 0.1 13.9 + 1.4 20.0 + 11.9 Downy 9 0.0 + 0.0 1.1 + 1.1 0.0 + 0.0 1.1 + 1.1 0.0 + 0.0 Northern flicker N. flicker 23 9.1 + 5.5 9.1 + 2.1 0.6 + 0.1 16.3 + 2.2 16.6 + 7.9 Hairy 23 17.7 + 11.8 8.1 + 2.9 0.2 + 0.1 10.0 + 3.2 0.4 + 0.4 Pileated Pileated 10 65.3 + 44.3 22.0 + 3.5 0.7 + 0.2 20.0 + 4.4 47.0 + 45.9 Hairy 10 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 Red-bellied Red-bellied 21 17.7 + 7.4 16.3 + 3.0 0.3 + 0.1 20.5 + 4.2 35.5 + 16.0 Downy 21 26.4 + 9.7 13.7 + 2.4 0.2 + 0.1 20.5 + 4.2 28.1 + 15.3 Red-bellied Red-bellied 13 19.8 + 9.7 27.0 + 8.6 0.3 + 0.1 15.8 + 2.3 30.2 + 10.4 Hairy 13 2.3 + 1.6 2.6 + 1.9 0.0 + 0.0 7.3 + 3.3 0.0 + 0.0 Red-headed Red-headed 13 57.2 + 18.6 10.0 + 2.1 0.2 + 0.1 14.6 + 1.2 35.0 + 16.9 Hairy 13 0.0 + 0.0 0.9 + 0.9 0.1 + 0.1 1.9 + 1.5 0.0 + 0.0 Red-headed Red-headed 10 34.8 + 18.1 8.9 + 3.1 0.3 + 0.2 15.5 + 1.6 23.0 + 14.9 N. flicker 10 2.6 + 2.3 5.2 + 3.2 0.3 + 0.2 8.0 + 3.3 0.0 + 0.0 Red-headed Red-headed 13 48.3 + 14.9 11.9 + 2.4 0.4 + 0.1 14.2 + 1.7 39.0 + 16.3 Red-bellied 13 4.2 + 4.2 0.8 + 0.8 0.0 + 0.0 0.0 + 0.0 0.0 + 0.0 Red-naped s. Red-naped s. 17 13.5 + 5.6 12.0 + 2.8 0.4 + 0.1 16.8 + 1.4 15.9 + 5.5 Yellow-bellied s. 17 27.9 + 14.1 17.3 + 2.8 0.4 + 0.1 17.4 + 1.3 15.1 + 6.3 Yellow-bellied s. Yellow-bellied s. 12 17.7 + 10.1 9.3 + 1.6 0.4 + 0.2 14.2 + 1.8 16.3 + 10.1 Hairy 12 0.0 + 0.0 2.5 + 1.8 0.0 + 0.0 2.5 + 1.7 0.0 + 0.0 Yellow-bellied s. Yellow-bellied s. 11 4.1 + 2.6 9.2 + 2.3 0.0 + 0.0 26.4 + 8.2 8.5 + 6.1 Red-naped s. 11 6.4 + 4.2 7.1 + 1.2 0.3 + 0.1 20.5 + 3.2 9.4 + 5.4
Table 6.3. Behavioral responses (Mean + S.E.) for each species for each reciprocal playback conducted during this study.
Playback stimulus
Species DOWO HAWO NOFL PIWO RBWO RHWO BBWO YBSA RNSA TTWO10
DOWO1 PC1 0.01 (20) 0.01 (13) 0.59 (7) 0.10 (46) 0.01 (13) 0.20 (14) - - -
PC2 0.01 (20) 0.01 (13) 0.28 (7) 0.02 (46) 0.05 (13) 0.67 (14) - - -
HAWO2 PC1 0.01 (25) 0.12 (11) 0.01 (12) 0.01 (10) - 0.04 (6) 0.01 (12) - -
PC2 0.03 (25) 0.26 (11) 0.22 (12) 0.15 (10) - 0.52 (6) 0.26 (12) - -
NOFL3 PC1 0.01 (9) 0.12 (23) - - - 0.01 (10) - - -
PC2 0.03 (9) 0.01 (23) - - - 0.76 (10) - - -
PIWO4 PC1 - 0.01 (10) ------
PC2 - 0.05 (10) ------
RBWO5 PC1 0.57 (21) 0.01 (13) ------
PC2 0.16 (21) 0.04 (13) ------
RHWO6 PC1 - 0.01 (13) 1.00 (10) - 0.01 (13) - - - -
PC2 - 0.73 (13) 0.02 (10) - 0.18 (13) - - - -
BBWO7 PC1 0.02 (15) 0.03 (7) ------0.54 (10)
PC2 0.41 (15) 0.55 (7) ------0.48 (10)
YBSA8 PC1 - 0.01 (12) - - - - - 0.42 (11) -
PC2 - 0.97 (12) - - - - - 0.26 (11) -
RNSA9 PC1 ------0.41 (17) -
PC2 ------0.23 (17) -
1 = Downy woodpecker, 2 = Hairy woodpecker, 3= Northern flicker, 4 = Pileated woodpecker, 5 = Red-
bellied woodpecker, 6 = Red-headed woodpecker, 7 = Black-backed woodpecker, 8 = Yellow-bellied
sapsucker, 9 = Red-naped sapsucker, 10 = Three-toed woodpecker.
Table 6.4. Results of playbacks of conspecific versus heterospecific drums for eastern
Nearctic woodpeckers. Results are tabulated as P-value (N) for the first and second
principal components (PC1, PC2).
183
Figure 6.1. Primary playback locations for this study included 1) Franklin (N 39o 59’ W 82o 59’, Delaware (N
40o 11’ W 33o 04’), Fairfield (N 39o 41’ W 82o 34’), Hocking (N 39o 26’ W 82o 16’) and Marion counties (N
40o 35’ W 83o 04’), central Ohio. Secondary playback locations included 2) Apalachicola national forest,
Florida (N 30o 27’ W 84o 59’), 3) Grady county, Georgia (N 33o 07’ W 83o 42’), 4) Noxubee national wildlife
refuge, Noxubee county, Mississippi (N 33o 55’, W 88o 53’), 5) Moore and Richmond counties, North Carolina
(N 35o 15’ W 79o 31’), 6) Monongahela national forest, West Virginia (N 38o 16’ W 81o 15’), 7) Roosevelt
national forest, Colorado (N 40o 15’ W 106o 02’), 8) Russell county, Kansas (N 30o 03’ W 98o 01’), 9) Cloud peak wilderness, Wyoming (N 44o 22’ W 107o 25’), 10) Huron national forest, Oscoda county, Michigan
(N 44o 36’ W 84o 44’), 11) Adirondack mountains, New York (N 44o 11’ W 74o 26’), 12) Victory bog,
Vermont (N 44o 52’ W 72o 22’), 13) White mountain national forest, New Hampshire (N 44o 04’ W 71o 24’),
and 14) Algonquin provincial park, Ontario, Canada (N 45o 28’ W 78o 07’).
184 CHAPTER 7
STRUCTURE AND DISCRIMINATION OF DRUMS IN SYNTOPIC NUTTALL’S
AND WHITE-HEADED WOODPECKERS.
Drumming in woodpeckers is a rapid repetitive series of strikes with the bill on a
resonant substrate, not associated with foraging or cavity excavation. This behavior has
been implicated in territoriality, mate attraction, pair-bond maintenance, and localization
of individuals (Lawrence 1967; Short 1982; Welty and Baptista 1988). As a long- distance communication signal, results pertaining to species-specificity and interspecific response to drums have been mixed (Lawrence 1967; Crockett 1975; Winkler and Short
1978; Kilham 1983; Dodenhoff et al. 2001). Past research indicated that drumming in western Nearctic woodpecker species were syntopically but not allotopically species- specific, with the cadence of the drum (strikes sec-1) as the primary variable for
separation and recognition of species (Stark et al. 1998, Dodenhoff et al. 2001).
Syntopic species are defined as closely related species that co-occur but often
occupy different niches within a habitat, while allotopic species do not co-occur in a
habitat but may occupy the same ecological niche. These are in contrast to the more
generalized terms of sympatry and allopatry, which refer to species overlap over broad
geographic regions, not within habitats. As Nuttall’s (Picoides nuttallii) and white-
185 headed woodpeckers (P. albolarvatus) vary slightly in size and habitat preference, and
exploit slightly different niches within the environment, they conform to the definition of
syntopic species.
Nuttall’s and white-headed woodpeckers have similar drums in allotopy (Stark et
al. 1998), and both species respond similarly to conspecific and heterospecific drums
(Dodenhoff et al. 2001). I hypothesized that this reflected a lack of selective pressure for
signal divergence in allotopy (Stark et al. 1998). There is a significant body of literature
concerning heterospecific acoustic signals in a complex environment (Baker 2001), and
indicating that species have the ability to discriminate overlapping heterospecific signals
from those given by conspecifics (Hulse et al. 1997, Benney and Braaten 2000).
Given that signal divergence among highly related species has been implicated as
necessary for coexistence of species (Becker 1982, Mousseau and Howard 1998), in
syntopy I expected to observe one of the following: 1) signal divergence between species
to maintain species-specificity (Becker 1982, Doutrelant et al. 2000), especially given the
implication that drumming primarily functions in mate attraction, and hence, premating
isolation to minimize the probability of hybridization. In drums, this could occur through a change in element structure, such as a shift in frequencies attributes (Hz), or through a divergent change in signal structure in one or both species (Miller 1982). 2) Individuals may avoid ambiguity through minimizing or avoiding signal overlap in the timing, either daily or seasonally, of the drum signal (Staicer et al. 1996, Lawrence 1967). 3) Finally,
Nuttall’s and white-headed woodpeckers may have similar drums in syntopy, remaining
186 interspecifically ambiguous in syntopic as well as allotopic populations. If this is the case, then I may observe interspecific territoriality along this interface of contact.
STUDY AREA
Recordings of drumming Nuttall’s and white-headed woodpeckers were made on
May 10-14, 1996 at the Chilao campground and Charlton Flats picnic area in the San
Gabriel Mountains, Angeles National Forest, San Bernardino County, CA (N 340 22’, W
1180 07’, elevation: ~2000 m, area: ~120 ha) at the interface of contact between these two
species. Both species are resident at these locations (Garrett et al. 1996, Lowther 2000).
Heterospecific territories were intermixed along this interface and only individuals whose
territory bordered a heterospecifics were used in this analysis. This area was a mosaic of
interdigitating codominant mixed coniferous forest, oak woodland (Quercus spp.), and
montane chaparral. Higher elevations contained primarily white-headed woodpeckers,
while lower elevations contained primarily Nuttall’s woodpeckers. Recordings and
playbacks were not conducted at these differing elevations, as research on allotopic
populations had already been completed (Stark et al. 1998, Dodenhoff et al. 2001). Other
woodpecker species present at this interface included the northern flicker (Colaptes
auratus) and acorn woodpecker (Melanerpes formicivorus). Hairy woodpeckers (P.
villosus), which were known to occur at a low abundance throughout the San Gabriel
Mountains (Garrett, pers. comm), were not observed during the course of this study.
187 METHODS
Drums were recorded, digitized (using Canary 1.2.1, Cornell 1996), and analyzed
for the following variables: cadence, duration (sec), number of strikes per drum,
interstrike interval (sec), introductory interstrike interval (defined as the mean of the first
4 strikes, in sec), terminal interstrike interval (mean of the terminal 4 strikes, in sec), and
the frequency at maximal and secondary amplitude (Hz) within each drum. Previous
analysis of drumming in these species indicated no significant difference between drums
of males or females, or those initiated by playbacks or spontaneously given (Stark et al.
1998). I tested whether the drums given by individuals had greater variation than
population averages by calculating the coefficient of variation for both groups.
To satisfy the independence of samples, I averaged variables within individuals
for drums of both species; use of unpooled data would have violated the assumption of
independence for the statistical tests, pseudoreplicating the data (Kroodsma 1989). I used
t-tests with Bonferroni-corrected P-values to test for species differences in drum variables (Sokal and Rohlf 1981). Logistic regression, rather than discriminant analysis, was used to reclassify individuals based on the variables selected for the drum of each
species, as logistic regression does not assume a normal distribution within each drum
variable. Finally, through comparison to data previously collected on each species (Stark
et al. 1998), drums were tested for regional differences within California for both species
using an analysis of variance.
188 Playback experiments were conducted on both Nuttall’s (N = 6) and white-headed woodpeckers (N = 9). Playback tapes were made from species-typical drums (N = 12 signals, based on results from Stark et al. 1998). Playback experiments (Nuttall’s vs. white-headed) were conducted in a balanced randomized pairwise design for the order of stimuli for each individual, with a minimum of 45 minutes between presentations of stimuli. Behavioral responses to conspecific and heterospecific drums were measured by the following variables: time to first drum (latency, in sec), total time spent drumming
(sec), time to first response (latency, in sec), number of flights over the speaker, closest approach to the speaker (m), and time spent within five meters of the speaker. A principal-components analysis (PCA) was used to reduce the six intercorrelated variables into first and second principal component scores (eigenvalue > 1.0), and compared using a two-tailed Wilcoxon’s signed-rank test for differential responses to conspecific versus heterospecific signals (Sokal and Rohlf 1981).
Finally, global positioning satellite (GPS) coordinates were recorded for eleven neighboring individuals. Readings were taken at drumming posts to estimate the distance between breeding pairs. Drumming, especially during nestling feeding when our observations were made (Garrett et al. 1996), normally occurs only within a territory
(Lawrence 1967, Short 1982, Eberhardt 1997). This allowed me to calculate a general estimation of distance between territorial neighbors. Given this interface had interdigitated conifers and oaks, territorial holders of both species had a mix of both vegetative communities within their territory. Therefore, I expected the distribution of individuals at this location to be either 1) random (i.e., no territoriality), 2) uniform
189 within but not between species (i.e., intraspecific territoriality, species separated by community type), 3) uniform between but not within species, or 4) uniform both within and between species (i.e., interspecific territoriality or territorial exclusion).
RESULTS
Twelve Nuttall’s (N = 139 total drums) and 10 white-headed woodpeckers (N =
115 total drums) were recorded between 06:30-13:00 and 16:00-18:00 PDT during the
nesting stage of the breeding season along their interface of contact. I did not observe
any temporal separation between species in drumming; individuals of both species
signaled at the same time and remained responsive throughout this study (Mann-Whitney
U, P = 0.69). Analysis using t -tests indicated significant differences in the interstrike
interval (introductory and terminal) and number of strikes within one drum between
species (Table 7.1). A power analysis (β analysis) of each t-test indicated a range of
0.05-0.64 (Table 7.1), thus the results of the t-tests should be interpreted cautiously.
However, correcting for the number of t -tests conducted, there were no experimentwise
significant differences between species for any of the drum variables (t -test P > 0.016,
Bonferroni-corrected α = 0.006).
The coefficients of variation for this study population noted that the drum cadence
in Nuttall’s woodpeckers were found to have less variation within individuals than
between individuals; all other variables fell within individuals versus population variation
190 (Table 7.2). This indicated that pooling the data for the logistic regression would be representative of the variation within the population. Reclassification using logistic regression resulted in incomplete separation between species. Ten Nuttall’s were correctly classified, and two were misclassified as white-headed Woodpeckers (83.3% accuracy). Reciprocally, white-headed woodpeckers were correctly classified seven times; however, three were misclassified as Nuttall’s (70.0% accuracy) resulting in an overall accuracy of 77.3%. The introductory interstrike interval in drumming was identified as the primary variable used for separation and reclassification of species; all other variables were poor indicators of species identity.
Behavioral responses to playbacks were recorded for each variable (Table 7.3), and reduced using a principal component analysis (PCA). The first and second principal component (PC) explained 48.3% and 21.6% of the variation in behavioral responses to conspecific and heterospecific drums (Table 7.4). Both components were used to compare responses to drums. The data were pooled and compared for response to experimental (i.e., heterospecific) versus control (i.e., conspecific) drums for both species. PC1 incorporated responses attributable to territorial and/or mate defense, while
PC2 featured primarily response and approach variables. Analysis using Wilcoxon’s signed-rank tests indicated no significant differences between the responses of individuals to playback drums (N = 15, PC1 P = 0.32, PC2 P = 0.29). Division of the data set by species indicated that neither Nuttall’s (N = 6, PC1 P = 1.0, PC2 P = 0.85) nor white-headed woodpeckers (N = 9, PC1 P = 0.57, PC2 P = 0.83) exhibited a differential
191 behavioral response to playbacks of heterospecific drums from those given to playbacks
of conspecific drums.
GPS readings between nearest neighbors which shared a border indicated an
average of 0.44 + 0.16 km between Nuttall’s and white-headed woodpeckers (N = 4),
which was similar to Nuttall’s:Nuttall’s (0.28 + 0.15 Km, N = 3) or white-headed:white-
headed neighbors (0.44 + 0.20 Km, N = 4). Small sample sizes precluded statistical
analysis.
Finally, tests for regional differences in drums indicated differences in cadence (P
= 0.002) and interstrike interval for both species (P < 0.001). Nuttall’s woodpeckers had
a slower drum cadence in the San Gabriel Mountains that in San Luis Obispo County (N
35o16’, W 120o39’), CA (19.6 + 1.5 vs. 20.6 + 1.0, P = 0.008). White-headed woodpeckers had a slower drum cadence in the San Gabriel Mountains than in the Sierra
(N 37o45’, W 119o37’) and Sequoia (N 36o08’, W 118o29’) National Forests (19.7 + 1.3
vs. 18.7 + 0.9, P < 0.05), but were not significantly different from individuals recorded at
Mt. Abel-Mt. Pinos area, Los Padres National Forest (N 34o49’, W 119o08’), CA (P =
0.29).
DISCUSSION
The acoustic competition hypothesis predicts that signal divergence would occur
to maximize information transmission in local environments, including species
192 recognition (Becker 1982). Initially, differences were detected between these species at this location in the introductory and terminal spacing of strikes, along with the number of strikes within one drum. However, controlling for multiple comparisons and the small sample sizes, there were no significant differences between drums of these species overall. Thus, drums have not significantly diverged between these two species in syntopy at this location. Furthermore, there were no significant differences in the timing of the drum signals, or in behavioral responses of individuals to playback stimuli.
Territorial sizes are similar in both these species (Garrett et al 1996, Lowther 2000).
However, the distances between species was similar to distances observed within species, which indicated interspecific territorial uniformity along this interface. Yet, it is unknown whether the distance observed between individuals was the minimal distance required for successful nesting, based on competitive exclusion to resources located within territories.
Dodenhoff et al. (2001) predicted that drums given by an individual that overlapped a heterospecifics should elicit the appropriate conspecific response. Overall results reported here are consistent with this hypothesis; differences between these species’ drums did not elicit differential behavioral responses at this location. Nelson
(1988) argued that a multitude of (invariant) variable combinations may be used for signal-specificity in passerines, though one variable may assume greater perceptual significance than others. Moreover, the relationship between species in acoustic space may allow alterable (i.e., non-overlapping) signal features to be accurate recognition cues
193 for between-species discrimination (Dabelsteen and Pederson 1985; Nelson 1988; Nelson
1989 a, b).
The results of the playback experiments show that the divergent parameters
encoded within drums are not currently employed by individuals in this location for identification, but this does not preclude their future use as a species identifier at this location. Yet, some parameters (i.e., interstrike interval) would be subject to degradation and reverberation in the environment, which could minimize their biological significance
(Naugib 1995). Furthermore, it is likely that woodpeckers respond to signal elements categorically, as noted in passerines (Searcy et al. 1999); minor variation between signals may not be perceived or categorized as different signals. To date, there have been no studies conducted on this aspect of woodpecker communication.
Numerous researchers have reported interspecific reactions to drumming (Short
1982; Kilham 1983), though explanations of their responses have varied from interspecific ambiguity (Winkler et al. 1995) to random chance (Lawrence 1967).
Behavioral and statistical analysis of these two species’ drums indicated that they have not differentiated their signals significantly in syntopy. This was the same pattern as seen in previous analyses of allotopic populations (Stark et al. 1998, Dodenhoff et al. 2001).
Thus, drums may attract hetero- or conspecific individuals between these species, and
therefore can not be considered a reliable isolating mechanism at this location.
Furthermore, there is no evidence that the presence of heterospecifics has caused signals to diverge in sympatry, contrary to the predictions of the acoustic competition hypothesis.
194 The vocal repertoire of Nuttall’s and white-headed woodpeckers are distinct and
may be used in conjunction with, or instead of, the drum to specify identity (Short 1982;
Winkler et al. 1995; Garrett et al. 1996). On four separate occasions during drum
playback experiments, both Nuttall’s and white-headed woodpeckers were
simultaneously attracted into the speaker location. However, neither species reacted in a
territorial manner; I did not observe any interactions that indicated either species recognized the other as the possible signaler. Given that drumming has the function of mate attraction and pair bond maintenance (Short 1982, Wilkins and Ritchison 1999), I suggest that signal divergence may not have occurred because species use a suite of visual, acoustical, and behavioral cues to maintain genetic isolation. Thus, selection pressure for drum divergence may not be paramount at this location.
Both the drums of Nuttall’s and white-headed woodpeckers were found to be regionally distinctive. Both Nuttall’s and white-headed woodpeckers drummed at slightly slower cadences versus more northern conspecifics. White-headed woodpeckers from this location drummed similar to those located in the Los Padres National Forest, but not the Sierra National Forest. The Angeles and Los Padres National Forest are contiguous, but separated from the Sierra Mountains by the San Joaquin Valley. Thus, there is a physical barrier to prevent gene flow between these populations of white- headed woodpeckers. Furthermore, this apparent “dialect” correlates to the biogeography of P. a. albolarvatus and P. a. gravirostris subspecies (Garrett et al. 1996), with the drum cadence slightly slower in the P. a. gravirostris subspecies. However, white-headed woodpeckers located in the Mt. Abel-Mt. Pinos region are considered part of the P. a.
195 albolarvatus, not the P. a. gravirostris subspecies, though intermediate in form (Garrett
et al. 1996, Grinnell 1905).
There were no such correlations within the Nuttall’s woodpecker, as there are no
ascribed subspecies (Short 1971, Lowther 2000). Also, there are no significant physical
barriers that separate Nuttall’s from this study location from either more northern or
southern conspecifics (Lowther 2000). It is unknown whether this is an actual drum
“dialect” within white-headed woodpeckers or whether there is a trend of increased cadence with increased latitude (i.e., clinal variation) as a general phenomenon among woodpecker drums. Analysis of geographic variation was the focus of Chapter 4, where further analysis indicated no significant variation in drums attributable to geographic distance or clinal variation.
This is the first study to test whether normally allotopic woodpeckers with similar drums diverge or differentiate this signals in when they come together at an interface.
Results indicated that these phenotypically divergent species were acoustically indistinguishable by drum in syntopy, in contrast to predictions of the acoustical competition hypothesis for syntopic signal divergence. Therefore, I feel that an analysis of drumming across species may be informative in testing whether this is a general phenomenon in woodpecker communication.
196
Drum variables Nuttall’s White-headed P β
Cadence1 19.6 + 1.5 18.7 + 0.9 0.14 0.26
Duration2 1.07 + 0.17 0.89 + 0.24 0.09 0.28
Average number of strikes per drum 20.8 + 2.7 16.7 + 4.5 0.02 0.57
Interstrike interval2 -0.002 + 0.002 -0.001 + 0.002 0.17 0.15
Introductory interstrike interval2 0.053 + 0.005 0.058 + 0.004 0.02 0.61
Terminal interstrike interval2 0.055 + 0.004 0.059 + 0.004 0.02 0.59
Frequency at maximum amplitude3 825 + 87 916 + 117 0.06 0.37
Secondary power frequency peak3 1237 + 118 1202 + 82 0.37 0.05
1 strikes-sec-1
2 sec
3 Hz [cycles-s-1]
Table 7.1. Means (+ S.D.) for the drum variables of Nuttall’s and white-headed woodpeckers recorded in the San Gabriel Mountains. T-test nominal P-values and
β for α = 0.05 between variables are reported. Bonferroni-corrected P-values for the experimentwise error rate is α = 0.006.
197
Drum variable Level Nuttall’s White-headed
Cadence1 Individual 2.11 + 0.64 4.22 + 2.23
Population 7.51 4.70
Duration2 Individual 21.2 + 8.40 26.6 + 9.74
Population 16.0 26.9
Average number of Individual 20.3 + 8.87 26.6 + 9.42
strikes per drum Population 13.0 27.1
Interstrike interval2 Individual -45.0 + 92.9 -112 + 15.2
Population -72.6 -132
Intro. interstrike Individual 4.42 + 4.79 12.4 + 16.6
interval2 Population 9.64 7.23
Terminal interstrike Individual 4.19 + 4.13 12.0 + 14.2
interval2 Population 7.70 6.17
Freq. at maximum Individual 8.20 + 4.71 3.97 + 5.43
amplitude3 Population 10.5 12.8
Secondary power Individual 7.11 + 3.89 4.11 + 5.04
frequency peak3 Population 9.57 6.84
1 strikes-sec-1 2 sec 3 Hz [cycles-sec-1]
Table 7.2. Coefficients of variation for each drum variable for both the individuals (+ S.E.) and population of Nuttall’s and white-headed woodpeckers recorded in the San Gabriel Mountains.
198
Variable Signal Nuttall’s White-headed
Total time drumming1 Nuttall’s 311.8 + 151.8 435.2 + 244.2
White-headed 153.0 + 214.9 608.1 + 262.4
Time spent within 5 m Nuttall’s 412.3 + 203.7 483.9 + 245.8 of speaker1 White-headed 238.0 + 182.4 676.8 + 245.5
Time to first drum1 Nuttall’s 71.1 + 45.30 159.0 + 167.7
White-headed 86.3 + 84.00 145.0 + 149.6
Time to first response1 Nuttall’s 48.5 + 35.10 141.5 + 122.5
White-headed 82.5 + 86.10 152.6 + 161.5
Number of flights over Nuttall’s 0.5 + 0.55 1.89 + 2.47 speaker White-headed 0.83 + 0.98 0.89 + 0.78
Closest approach to speaker2 Nuttall’s 6.0 + 3.22 8.22 + 8.61
White-headed 3.33 + 0.82 5.78 + 3.27
1 sec
2 m
Table 7.3. Means (+ S.D.) for the behavioral responses to playbacks for Nuttall’s
(N = 6) and white-headed (N = 9) woodpeckers in the San Gabriel Mountains.
199
Interspecific Nuttall’s White-headed
Response measures PC1 PC2 PC1 PC2 PC1 PC2
Duration spent drumming1 0.890 -0.280 -0.417 0.427 0.548 0.196
Time spent within 5m of speaker1 0.836 -0.276 -0.306 0.581 0.526 0.168
Time to first drum1 0.767 0.404 -0.477 -0.390 0.432 -0.409
Time to first response1 0.671 0.567 -0.404 -0.484 0.347 -0.569
Number of flights over speaker 0.574 -0.564 -0.499 -0.121 0.337 0.468
Closest approach2 0.195 0.538 -0.304 0.282 -0.045 -0.474
1 sec
2 m
Table 7.4. Component matrix of the PCA for behavioral responses.
200 CHAPTER 8
AN ANALYSIS OF BLACK-BACKED WOODPECKER BEHAVIORAL
RESPONSES TO PLAYBACKS OF CONSPECIFIC, HETEROSPECIFIC, AND
COMPUTER MODIFIED DRUMS.
Woodpeckers lack the ability to generate songs similar to those used by passerines
(Brackenbury 1982). To compensate it has been suggested that woodpeckers evolved a non-vocal acoustical signal, referred to as a drum, derived from a ritualization process of their foraging movements (McFarland 1985). A woodpecker drum is a rapid, repetitive series of strikes with the bill on a substrate, not associated with foraging or cavity excavation (Bent 1939, Pynnönen 1939, Short 1974). Functions attributed to drums have ranged from individual localization to their correlation with territorial and reproductive
behaviors (Kilham 1959, Lawrence 1967, Short 1982, Eberhardt 1997). Though the
specific functions of drums are debated, and may vary between species, there is a general
consensus that this instrumental signal is a form of long distance communication
(Crockett 1975, Trombino 1998, Dodenhoff et al. 2001). Results pertaining to species-
specificity noted that the cadence of woodpecker drums (strikes-sec-1) encoded
information detectable and meaningful to receivers (Dodenhoff et al. 2001, Trombino
1998, Crusoe 1980).
201 Limited research has been conducted concerning mechanical acoustical signals in
avian species (Prum 1998, Mikich 1996, Trombino 1998). However, avian reference
books refer to the drum as species-specific and indicate that each species has its own
unique drum (Perrins and Middleton 1985, Welty and Baptista 1988), though research
has cast doubt on that claim (Stark et al. 1998). Dodenhoff et al. (2001) established that woodpecker drums encode species-specific information, and that signals normally are not exchanged interspecifically. The behavioral responses to heterospecific signals significantly differed from responses given to conspecific playbacks of woodpecker drums. However, a previous investigation indicated that if the cadence of an individual’s drums overlapped another syntopic species, that individual may elicit a heterospecific response comparable to that given to a conspecific (Chapter 6, Dodenhoff et al. 2001).
Furthermore, artificially-created drums with cadences set at the specific cadence of a target species elicited a response from an individual equivalent to those given to conspecifics (Dodenhoff et al. 2001). Thus, an invariant element structure (in that study, a transient spike produced by the strike of the bill on a substrate, and also a pure tone of
400 Hz) was not required for species recognition in woodpeckers given that other parameters remained unaltered (Dodenhoff et al. 2001). However, other drum parameters, such as duration or pattern of strikes within drums, which may affect species recognition, have not been tested.
Black-backed woodpeckers have historically been classified as burn specialists, immigrating to fire stands within two years reportedly in response to beetle infestations of the newly exposed surfaces (Dixon and Saab 2000). Individuals have been noted to
202 remain for several years before emigrating to new burns. Acoustically, black-backed
woodpecker drums are specific to their burned environment; other woodpecker species
that commonly utilize burns for foraging in eastern Nearctic northern forests include the hairy (P. villosus), and less commonly the pileated (Dryocopus pileatus) woodpeckers, northern flickers (Colaptes auratus), and yellow-bellied sapsuckers (Sphyrapicus varius).
All of these species’ drums significantly differ from those of black-backed woodpeckers
(Chapter 2). Previous research on black-backed woodpeckers (Picoides arcticus)
indicated that this species has difficulty differentiating conspecific drums from those of the generally allopatric downy woodpecker (P. pubescens) probably due to similar drum cadences (Stark et al. 1998, Dodenhoff et al. 2001). This investigation tested whether modification of one or more drum parameters affected species recognition, using the assay of black-backed woodpecker’s behavioral response to playbacks of conspecific versus both heterospecific and experimentally manipulated drums.
METHODS
Playbacks were conducted in April 1997 in Victory Bog, Vermont (N 44o 52’ W
72o 22’), April 1997 and June 2000-01 in the Adirondack Mountains, New York (N 44o
11’ W 74o 26’), and in June 1998-2000 in Algonquin Provincial Park, Ontario, Canada
(N 45o 28’ W 78o 07’). Finally, playbacks were conducted in June 2001 in Grand Jardine
Provincial Park, Quebec, Canada (N 47 o 43’ W 70 o 45’). Only one downy woodpecker
203 was observed occupying a territory near a black-backed woodpecker territory (Bat Lake,
Algonquin Provincial Park, Ontario); all other black-backed playback and recording
locations did not appear to contain active downy territories during the course of this
study. In Vermont, New York, and Algonquin, black-backed woodpeckers were
encountered in mature black spruce (Picea mariana) forests, while those in Grand Jardine were nesting in a large (5200 Ha) coniferous forest burn that occurred in 1999.
Black-backed woodpeckers were an ideal subject species for this investigation.
The drum of black-backed woodpeckers are relatively long, easily modified, have few parameters (relative to passerine song, Baker 2001), and are documented to overlap other species in their drums cadence (Stark et al. 1998, Chapter 2). Drums are given by both
sexes throughout the year in response to conspecific intrusion and playback stimuli
(Dixon and Saab 2000). Playback drums, which were derived from high quality field
recordings made during the course of this investigation, conformed to previously reported
values for this species (Stark et al. 1998, Table 8.1). Drums were digitized using
SIGNAL 3.1 (Engineering design 1999), standardized for decibel level, and edited
together at a set interval (5 sec) to imitate a drumming woodpecker. Standardization of
the interdrum interval minimized the probability that individual or behavioral cues, such
as motivational state of the sender, were accidentally encoded within that signal variable.
Five experimental signals were tested versus conspecific black-backed woodpecker
drums.
Experimental playback signals included modified black-backed woodpecker
drums along with standard and modified downy woodpecker drums. Specifically, there
204 were three modified black-backed woodpecker drums: a reversed signal, an introductory
drum portion only, and a terminal drum portion only (Table 8.1). The reversed signal
was an inversion of the conspecific playback drums, effectively reversing the interstrike-
interval pattern of strikes in a drum (interval pattern increased in duration, rather than the
original decrease in interstrike-interval) and strike (element) structure. The introductory
drum signal contained only the first half of the drum (halving duration), while the
terminal drum contained only the second half of the drum (again, half the standard drum
duration); both retained the proper interstrike-interval pattern.
Next, I modified standard downy woodpecker drums by editing two drums
together. This signal effectively doubled the duration of the downy woodpecker drum,
while simultaneously modifying in the interstrike interval pattern from that of the
standard downy woodpecker drum. Both the standard and double duration signal were used for playbacks. Thus, playback signals varied the duration, interstrike interval pattern, and elemental structure of experimental drums versus unmodified black-backed
woodpecker drums.
Playbacks were conducted under the following guidelines: Target birds were
observed for a minimum of five minutes prior to presentation of stimuli, to assure that the
target individual was not currently engaged in territorial or reproductive behaviors with
another conspecific. Only birds that were foraging, preening, or excavating cavities, and
within acoustical range of the playback speaker were used in this analysis; birds unresponsive to any stimuli were excluded from further analysis. Playbacks were conducted in a balanced randomized design, with a minimum of 30 minutes between
205 presentations of stimuli. This interval was selected based on log-survivorship curves for the duration that woodpeckers remain responsive to presentation of previous stimuli
(Dodenhoff 1996).
Behavioral responses to conspecific versus heterospecific drums were compared.
For this analysis, I measured the following behavioral responses to playbacks: latency to first response (sec), number of flights over the speaker, duration spent within 10m of the speaker (sec), closest approach to the speaker (m), and the total duration spent drumming in response to stimuli (sec, Table 8.2). These variables have previously been shown to be reliable indicators for the strength of response to playbacks of drums (Dodenhoff 1996).
For each response variable, I calculated the descriptive statistics and used a multivariate analysis of variance (MANOVA) to test for differences in overall or intersexual behavioral responses to playback stimuli. As both sexes have been documented to engage in territorial defense to conspecific intrusion (Dixon and Saab 2000), I did not expect to find a difference in response to drum due to sex. A principal-components analysis (PCA) was then used to reduce the five intercorrelated variables into a first and second (PC1, PC2) principal component score (eigenvalue > 1.0). These scores were then compared using two-tailed Wilcoxon’s signed-rank tests to examine differences in paired behavioral responses to playback stimuli (Sokal and Rohlf 1981).
However, this type of analysis does not detect whether there was any underlying pattern to the behavioral responses to varying playback stimuli. Given that drums were closely modified versions of the original drum or from a species that black-backed woodpeckers have difficulty discriminating in allotopy, I tested whether there were any
206 trends in behavioral responses to the differing playback signals using a linear regression
(after Nelson 1987). I hypothesized that black-backed woodpeckers would decrease their
response to stimuli in proportion to the increasing amount of modification of the original
signal’s temporal components. Previous research indicated that the acoustical structure
of the strike (i.e., transient) is not an important species identifier (Dodenhoff et al. 2001).
Thus, I expected to find a decrease in response in the following order: black-backed unmodified, double length downy (1 difference), terminal black-backed woodpecker (3 differences), downy woodpecker (3 differences), introductory black-backed (3 differences), and reversed black-backed playback drum (4 differences).
RESULTS
I recorded and analyzed the behavioral responses of black-backed woodpeckers for each paired playback of experimental versus conspecific drums (Table 8.2). Results
indicated no overall significant differences in response to playbacks of differing signals
(Wilk’s Lambda, MANOVA P = 0.21), though there was one significant difference in the pairwise comparisons for closest approach to the speaker (ANOVA, P = 0.05). All other behavioral response variables were not indicative of differences between signals (P =
0.32 - 0.86, NS). Tests for intersexual differences in response to playback stimuli indicated no significant differences in behavioral responses for all signals (Table 8.3,
207 MANOVA, P = 0.11-0.77, NS). Thus, I pooled both sexes to increase sample sizes in further analyses.
Results of the Wilcoxon sign-rank tests indicated significant differences in behavioral responses to drums of downy woodpeckers (N = 22, P = 0.03), reversed black- backed woodpecker (N = 23, P = 0.02), and the introductory black-backed woodpecker drum (N = 17, P = 0.01) versus conspecific drums for the first principal component score
(PC1, Table 8.4). PC1 was weighted higher for behavioral response variables correlated to approach and time spent in response to playbacks (i.e., drum duration, closest approach, duration spent within 10m). There were no significant differences in the second principal component scores for these signals (PC2, P = 0.14 - 0.78, NS). PC2 was weighted higher for variables correlated to rapid response, including the number of directed flights over the speaker and the latency to first response. Playbacks of the double duration downy woodpecker drum (N = 16) and the terminal black-backed woodpecker drum (N = 12) did not elicit differential behavioral responses versus conspecific drums for both principal component scores (P = 0.15 - 0.91, NS).
Results from the linear regression analysis indicated that there were several significant differences in behavioral responses, along with the first and second principle component scores, in black-backed woodpeckers (Table 8.5). Only the number of directed flights toward the playback speaker and the duration of time spent within 10 meters of the speaker were non-significant (Table 8.5); all other regressions were significant (Table 8.5, Figs. 8.1 - 8.7). However, the percent of variation in behavioral responses accounted for by each regression was negligible, ranging from 0.4% to 13%
208 (Table 8.5). Therefore, I consider only the first principle component (11.6%), duration
spent drumming in response to stimuli (8.3%), and the closest approach to the speaker
(13.1%) relevant for further discussion. All other linear regressions, whether significant
or not, could not adequately explain the variation within the data and are not discussed
further.
Regressions of the standardized predicted values for each variable mirrored those
of the direct variable measurement (R2 = 0.4 - 13.1%), while the regression of the residuals indicated that outliers were not driving the regression output (R2 = 88.4 -
99.6%, Figs. 8.8 - 8.12). Thus, analysis indicated that black-backed woodpeckers
responded behaviorally stronger overall (PC1) to their own species playback, with a
decreased response to signals in proportion to the extent they were modified. Species
spent a longer duration drumming in response to a perceived intruder (Fig. 8.2), and did
not approach as closely to the speaker (Fig. 8.3). Instead, individuals flew directly to their drumming post or nest cavity to respond acoustically to the intruder. As the amount of signal modification increased, woodpeckers spent more time searching for the sound source (or apparent signaler), but significantly less time in other territorial behaviors.
Finally, these results indicated that individual black-backed woodpeckers reacted in a graded response to playback stimuli, not in the all-or-nothing manner that had been
inferred by past research on woodpeckers.
209 DISCUSSION
Previous research on woodpeckers indicated that drums ought to have a cadence that is similar to that of conspecifics, or there will be little to no behavioral response given to the playback (Dodenhoff et al. 2001, Chapter 6). However, the extent to which variables other than cadence influenced the behavioral responses of individuals was unknown. In this study, black-backed woodpeckers responded predictably to the modified playback signals; as expected, the strongest behavioral response was given to conspecific playbacks, with a decrease in the intensity of response to signals that proportionally differed from that of conspecifics. Significant differences in behavioral response to drums were found in signals that varied by more than a single co-variable, with further modifications predictably decreasing the response of receivers to presented stimuli.
Drums were modified in their duration, spacing, number of strikes, and the structure of the element. Artificially increasing duration of the drum of downy woodpeckers from a signal that is normally half the length of that of black-backed woodpeckers resulted in the signal eliciting a response equal to that given to conspecific drums. Thus, drum duration is a perceptually significant variable used for discriminating similar drums of heterospecifics from conspecifics in an acoustically complex environment. Returning the downy woodpecker drum to its original length decreased the response of black-backed woodpeckers proportionally, culminating in a difference detectable through paired playbacks. Furthermore, modifying the black-backed
210 woodpecker drum to half its original length decreased responsivity in receivers; the
response to introductory and terminal sections of the black-backed woodpecker drums
were similar to one another, but differed in their spacing patterns.
There were differences in response of black-backed woodpeckers between playbacks of introductory and terminal portions of drums. The terminal black-backed woodpecker drum elicited behavioral responses similar to that given to conspecifics, whereas the introductory portion elicited a response similar to that given to downy
woodpecker drums. These two signals differ in their spacing pattern of individual strikes; the terminal drum has a much more rapid decrease of duration between individual strikes
(i.e., interstrike interval) relative to the introductory drum. This pattern may allow black- backed woodpeckers to recognize this drum as that given by a conspecific. It is theoretically possible that black-backed woodpeckers differentiate their drum into two portions, similar to that predicted by the alerting-message hypothesis (below), consisting of a general and specific portion of the drum. Thus, the terminal section of the drum may encode information specific to black-backed woodpeckers, while the introductory portion
of the drum may simply act as an attractant to gain the signalers attention. However, this
does not exclude other possible explanations for the observed pattern.
Further modifying the signal significantly changed the response of receivers. The
reversed black-backed woodpecker drum altered the element of the strike, along with the
pattern of strikes given. Previous study indicated that a transient strike, or even a pure tone,
artificially set at the proper cadence and spacing pattern elicited a response similar to that
given to conspecific drums across Picoides (Dodenhoff et al. 2001). Yet, altering this
211 element structure did influence the response of receivers, decreasing recognition and subsequent behaviors. Thus, the results of the playbacks suggest that each negative alteration of the drum (i.e., farther away from conspecific) decreases response, and each positive alteration of drums (i.e., more similar to conspecific) increases response in an additive fashion.
Furthermore, alterations of some of the drum parameters were perceptually more important than others: alteration of drum cadence may eliminate species recognition altogether (Chapter 6, Dodenhoff et al. 2001), whereas altering drum duration or the number of strikes had less of an effect on signal recognition. Thus, woodpecker drums have species- essential parameters that appear to be “tuned” to the natural limits of signal variation in that species discrimination ceases when signals are changed beyond these limits. Therefore, these results fit the “room for variation” hypothesis for the principles of coding information in acoustic signals (Dabelsteen and Pederson 1992).
IMPLICATIONS FOR THE RECOGNITION OF ACOUSTIC SIGNALS IN A
COMPLEX ENVIRONMENT
Acoustical signals are often used as a key for the identification and discrimination of closely related species (Mousseau and Howard 1998, Podos 2001). These differences in signals are correctly perceived, recognized, and categorized by an array of species.
Often, these acoustic differences are used to maintain isolation and the resulting genetic
212 integrity of populations. Six hypotheses have been proposed concerning how acoustical
signals are recognized and discriminated in a complex acoustical environment. These six hypotheses differ in the degree to which information is recognized as either discrete or additive (Date et al. 1991); invariant-features, releaser, sound-environment, and alerting- message hypotheses are discrete, whereas the additive-redundant and syntactical hypotheses are additive.
These hypotheses incorporate both the elements encoded in signals and the features associated with these elements. Though this study did not directly address these hypotheses, nor compare them experimentally, it may be informative to compare the results obtained in this study to the predictions of each competing hypothesis. It should be noted that much of the following information was previously explained in Chapter 1.
However, this section incorporates the findings of this study in relation to the competing
hypotheses, and may uncover whether woodpeckers respond in a manner previously observed in other avian species. First, I will summarize the results of these investigations
(chapters 2-8) as they relate to the evolution of recognition systems. Then, I will present the hypotheses in detail, and indicate how evidence from this study pertains to each hypothesis.
Woodpecker drums are serially repeated invariant transient spikes modified temporally. Results from this series of investigations indicate that an alteration of differing portions of the drum modifies responsivity to the signal, with varying results relative to the portion of the drum modified. This may help to explain the numerous reports of heterospecific responses observed in woodpeckers to drums (Winker et al.
213 1995, Kilham 1983); overlap in some parameters may trigger a partial response from neighboring heterospecifics but a maximal response is only given when specific parameters are matched to species-typical averages (chapters 6, 8). Artificially altering signal parameters decreases response, but altering some parameters is perceptually more important (chapter 8). For example, modification of the drums cadence, leaving other parameters unaltered, can result in a complete elimination of response (Dodenhoff et al.
2001). However, alteration of one or more secondary parameters may decrease responsivity in proportion to the number of modifications made (chapter 8), or not affect recognition at all (Dodenhoff et al. 2001).
Woodpecker drums lack the requisite encoded signal variation for hypotheses generated to explain bird song; there are few signal parameters and elements are unchanged throughout the signal (i.e., transients). Temporal differences prevalent in drums are not generally considered diagnostic, though they are distinctive to species
(chapter 2). Furthermore, results from these investigations indicated there is little evidence that the presence of heterospecifics influence signal design (chapter 4, 5, and 7).
Given theses findings, whether the characteristics of drums follow any predictions of the six competing hypotheses concerning the evolution of recognition systems can be discussed.
Given the lack of variation within the signal elements, two hypotheses are not applicable to drums: invariant-features and alerting-message hypotheses both rely on different portions of the signal encoding features in different song variables (ex., trill versus warble). Thus, as elements within drums are relatively invariant, these hypotheses
214 are difficult to apply. The sound-environment hypothesis, whereby signals are modified
for environmental specificity or transmission, is difficult to apply; signals are not
modified due to the presence of heterospecifics, however there may be convergence by different species towards specific signal channels. Thus, this hypothesis can not be
discounted for woodpecker drums. This leaves three hypotheses to consider: releaser,
additive-redundant and syntactical. Woodpeckers do not respond in an all-or-nothing
manner (as predicted by the releaser hypothesis), but grade their response to the number
of cues present in the signal. Furthermore, the syntax of drums is not a critical factor for
recognition, but correct syntax elicits a greater response from target individuals during
playbacks. Thus, results obtained from these studies do fit the predictions of the releaser and syntactical hypotheses, but only partially. However, results from the predictions of
the releaser and syntactical hypotheses also can be interpreted as support for the additive-
redundant hypothesis, which is more inclusive and better explains all the results collected
in these investigations. Thus, drums are consistent with the predictions of the additive
redundant hypothesis for signal recognition.
The invariant-features hypothesis (Marler 1960, Emlen 1972) predicts that song
features which are interspecifically stereotyped are preferentially used in song
recognition, since these features would be less likely to overlap with heterospecifics. This
hypothesis is based on the concept that discrete stereotyped (invariant) units can convey
information of a qualitative nature effectively (Shiovitz and Lemon 1980), such as
species or individual identity (Emlen 1972), or can act in a graded manner to convey
quantitative information, such as levels of motivation. Since Emlen’s (1972) original
215 study, further research has indicated that this hypothesis could not adequately predict the
accuracy of song features in distinguishing conspecific from heterospecific songs, as
some of the invariant features of species overlap (Nelson 1989).
This overlap may explain Emlen’s original observation that not all invariant
features are used for species recognition; only those features which contrasted with other
species may be used for recognition (Nelson 1989). However, research has also indicated
that variable features of song may be used in species recognition (Dabelsteen and
Pederson 1985). Thus, invariant features encoded in signals may maximize the response
of receivers. Yet, this hypothesis may be oversimplified, as some features may be perceptually more important than others (Nelson 1988: ‘feature-weighting’ hypothesis).
For example, in passerines, element structure is considered critical for species recognition
whereas signal duration has a variable effect on recognition. Woodpecker drums are
simply serially repeated invariant transient spikes, unlike the variable elements of song,
which makes interpretation of results in relation to this hypothesis dubious.
When woodpecker drums were artificially created by selecting a single strike
from a heterospecific and repeating this strike at a cadence similar to a conspecific (while
maintaining all other drum parameters) did not influence the behavioral response of
receivers in regards to species recognition (Dodenhoff et al. 2001). Using a pure tone
(400 Hz) also had little impact on Nuttall’s woodpeckers (Picoides nuttallii) behavioral response. Yet, reversal of the original strike (allowing the normal element reverberation to precede the transient) in this study did influence recognition, but this parameter was co-varied with other variables; no independent test of the influence of reversal only of
216 strikes was conducted. Thus, modifications in element structure (i.e., the transient strike) may influence the behavioral response of woodpeckers, however, its physical structure likely to be of little perceptual importance in relation to other encoded information, supporting previous research in passerines (Nelson 1987). Recently, reverberation of
elements has been shown to influence the behavioral response of conspecifics, increasing
response during playbacks relative to altered signals (Slabbekoorn et al. 2002). My
results preliminarily support the importance of reverberation in eliciting appropriate
behavioral responses, though further research is required.
In contrast, the releaser hypothesis (Becker 1982) predicts that only certain
(invariant) acoustical components of song are used for species identification. This hypothesis predicts the concentration of interspecific releasers in signal variables may ensure species-specificity, allowing for the encoding of further information in the remaining characters (Becker 1982). These acoustical characteristics may act as releasers
for a distinct function, which may vary between species. Again, given that drums are relatively invariant in elemental structure makes it unlikely that I can apply the releaser hypothesis to explain woodpecker responses to drums playbacks. This hypothesis requires that elements within signals change, but that some of the encoded elements remain stereotyped both within and between individuals. Woodpecker drums lack this
requisite variation, and the temporal differences prevalent in drums are not generally
considered diagnostic in the context of this hypothesis. Finally, support for this
hypothesis even among passerines has been limited: one experiment that originally
indicated support for the ‘releaser’ hypothesis in bush warblers (Cettia diphone) noted
217 that invariant features of the song elicited a predictable behavioral response; altering song
elements decreased the response (Park et al. 1995). However, I believe the authors
misinterpreted their results which indicated that the behavioral responses increased and
decreased in a predictable manner in proportion to the number of correct cues encoded
within each signal. Thus, their results indicated support for the ‘additive-redundant’
hypothesis, not the ‘releaser’ hypothesis as the authors contended (Park et al. 1995). To
date, no investigation has definitively indicated that the ‘releaser’ hypothesis was a better
model to explain the results of a given study versus the ‘additive-redundant’ hypothesis
for species recognition.
The sound-environment hypothesis predicts that the relationship between a
species song and the songs of sympatric species in acoustic space will indicate which
features provide accurate song-discrimination cues (Dabelsteen and Pederson 1985,
Marler 1960). In a complex environment, signals should diverge for increased discrimination between species, maximizing the information transmitted between conspecifics. It predicts further that features which are distinctive relative to other species in the local population will be used as preferred recognition cues in that system,
and that variability is only one component of species distinctiveness. The second
component of the sound-environment hypothesis is the relationship between different
species in an acoustic space, which allows for variable features to be useful song
recognition cues if they increase species discrimination (Dabelsteen and Pederson 1985,
Nelson 1988).
218 Syntopic species often differ markedly in their vocalizations, which suggests that
female selection influences signal variables and may serve in species isolation (Podos
2001). Though logically appealing, there currently is little empirical evidence to support
this hypothesis. Most research that may indicate support for this hypothesis can be
derived from research on preferences for local population song (Stoddard 1996), or along
zones of hybridization (Johnson and Johnson 1985, Trombino 1998). In woodpeckers,
the generally allotopic red-naped (S. nuchalis) and yellow-bellied sapsuckers were shown
to have character shifted aspects of their drums in syntopy (Trombino 1998).
Furthermore, these variables were shown to be important for species recognition within
this zone of sympatry. Yet, I did not find the pattern observed in Sphyrapicus in Picoides
(Chapter 7), and there is little evidence for the influence of heterospecifics on the influence of signal design.
Ryan and Rand (1993) review of the literature on signal evolution summarizes a large number of published papers that indicate female preference for different or novel signals may drive signal evolution in a complex acoustical environment, and that these preferences can vary between individuals, populations, and species (see further reviews by Searcy and Yasukawa 1996, Boake et al. 1997, Pfenning 1998). It is likely that the evolution of drums has been influenced by female choice, intrasexual competition, and constrained by morphology, practicality, and applicability in a dynamic environment.
Thus, woodpecker drums do not appear to conform to the sound-environment hypothesis, as several species use a similar drum for signaling. Given this lack of divergence, I suggest that the sound-environment hypothesis is not applicable to this acoustic system.
219 In contrast, the alerting-message hypothesis (Richards 1981) predicts that some
song components or variables identify the species, while other sections function to gain the listener’s attention. This hypothesis predicts that the tailoring of song structure should facilitate the detection and recognition of a song over long distances (Richards 1980).
Thus, signals are separated into “alerting” and “message” components to minimize the time spent in territorial vigilance. Theoretically, the highly detectable alerting mechanism preceding the message is stereotyped temporally, minimizing equivocation and increasing signal detection and information transmitted (Richards 1980). This might explain the results of the black-backed woodpeckers as well; individuals responded more strongly to drums which contained only the terminal portion of the signal relative to the introductory portion.
In theory, this terminal portion of the drums may encode species information to a greater extent (i.e., perceptually) than the introductory portion. This would correlate to research on emberizids, where regional and species distinctiveness are often encoded in the terminal portion of the song (Bradley 1977, Bradley 1994). However, neither the introductory or terminal portion of the black-backed woodpecker drum initiated a response equivalent to the conspecific signal, both eliciting a decreased response relative
to the standard drum. Furthermore, one study (Khanna et al. 1997) did not find the
partitioning of song into these components using the same species as Richards (1980).
Instead, Khanna’s (1997) research indicated that both the first and second components of
eastern towhee song (Piplio erythrophthalamus) encoded species information (i.e., all
“message”). Potentially, this song acts through summation, or through correct syntax, to
220 elicit a maximal behavioral response in receivers. This would provide support for either the syntactical or additive-redundant hypotheses instead of the alerting-message. To date, there are no published studies that support the alerting-message hypothesis over alternative explanations.
Even though the role of syntax is variable in species recognition (Becker 1982), its role has been underestimated (Ratcliffe and Weisman 1987). The syntactical hypothesis (Ratcliffe and Weisman 1987) recognized that several parts of the song contribute together to evoke a response, but that a maximal response is only given if song elements are arranged correctly (Genter and Hulse 2000). The basis for this hypothesis lies in estradiol experiments in female brown-headed cowbirds (Molothrus ater), which indicated that the phase order was a key factor in affecting song potency. The first and second components of a male song were critical in eliciting a female’s solicitation response, indicating that females have an internal representation of the phase order of conspecific song (Ratcliffe and Weisman 1987). Sexual selection would therefore favor males whose song has the proper sequential order in their song. Unfortunately, this
‘additive’ affect of syntax could be interpreted as support for the ‘additive-redundant’ hypothesis of species recognition.
This contradictory evidence or contrasting interpretations of results for the various hypotheses was largely resolved with the formation of the “additive-redundant” hypotheses for signal discrimination. This hypothesis (Emlen 1971, Shiovitz and Lemon
1980, King and West 1983) predicts that different parts of the song act together in summation, and the response given by receivers is dependent on the number of cues
221 discriminated within the encoded signal. The original basis of the additive-redundant
hypothesis comes from research on the indigo bunting (Passerina cyanea, Emlen 1971,
Shiovitz and Lemon 1980), whose vocalizations are stereotyped in frequency, duration,
and amplitude of the syllables. This allowed for encoding information in the syllables
three ways: as learned cues, innate invariant cues, or as innate feature detectors. Results
of the research indicated that multiple variables were used for species specificity in this
species. The amount of signal variation tolerance (i.e., deviation from species typical
songs) indicated an additive process in recognition. This redundancy encoded within
song may be required for maximizing the individual’s response to stimuli.
Since these original investigations, significant evidence has been amassed for
supporting the ‘additive-redundant’ hypothesis over competing hypotheses (Park 1995,
Sung et al. 1995, Nelson 1987, 1988, 1989 a,b, Date et al. 1991, Genter and Hulse 2000,
Holland et al. 2000, Vicario et al. 2001, Baker 2001). In a direct test of competing
hypotheses, the results indicated support primarily for the additive-redundant hypothesis
(Date et al. 1991): Using American redstarts (Setophaga ruticilla), results indicated no
support for the ‘alerting-message’ hypothesis, since equally distorted portions of the song
produced a minimal response, opposite to that achieved by Richards (1981) with towhees
(but supporting Khanna 1997). The species identification in Redstarts was not achieved
through sequential portions of the song, and therefore did not support the concept of a template for song recognition (Marler 1976), though this template may be important during developmental learning (plastic phase) of song. Analysis of redstart songs originally supported the ‘releaser’ hypothesis; however repeated presentations indicated
222 that a combination of variables was necessary to evoke a maximal response. Therefore, the playbacks indicated equal responsivity to differing portions of the song, supporting the ‘additive-redundant’ hypothesis over the ‘releaser’ hypothesis. There was no
evidence for the ‘syntactical’ hypothesis in this species (Date et al. 1991), and the ‘sound- environment’ hypothesis was not tested with this study.
Results of playbacks on song and swamp sparrows indicated that only species- specific attributes were discriminated between these species, and responses were proportional to the number of cues present in the signal (Nelson 1988). Analysis using the ‘just meaningful differences’ in creation of the stimulus relative to the control indicated that field sparrows are sensitive to changes in phrase structure, trill-note duration, trill-note shape, song frequency, and internote interval. Changes in approach responses decreased significantly with variation in song (except trill-note shape) approached 2-3 standard deviations relative to the control song. Therefore, Nelson
(1988) concluded that the statistical variation approximated the natural variation within the populations. Furthermore, multiple features were integrated in song recognition and were differentially weighted in song recognition; song frequency (invariant) was more important in recognition than other invariant and variant features (Nelson 1988). This indicated that weighting of the importance of features within a song to have additive effects on the response of receivers, which Nelson (1988) hypothesized may be widespread among species.
It is not surprising that woodpeckers are showing the same trends in recognition of drums as observed in passerine song. Results from this investigation clearly indicate
223 that an alteration of differing portions of the drum modifies responsivity to the signal, with varying results relative to the portion modified. This may help to explain numerous previous observations of heterospecific responses observed in woodpeckers to drums
(Winker et al. 1995, Kilham 1983); overlap in some parameters may trigger a partial response from neighboring heterospecifics but a maximal response is only given when specific parameters are matched to species-typical averages. Artificially altering signal parameters decreases response, but altering some parameters is perceptually more important. For example, modification of the drums cadence, leaving other parameters unaltered, can result in a complete elimination of response (Dodenhoff et al. 2001).
Conversely, decreasing drum duration or the pattern of strikes within a drum, may decrease receiver response but not to a level that receivers no longer respond.
Furthermore, alteration of one or more parameters decreased responsivity in proportion to the number of modifications made. Thus, at least black-backed woodpeckers appear to use a system similar to passerines, and this system is best explained by the additive- redundant hypothesis of signal recognition.
EPILOGUE
The paucity of data concerning woodpecker drums was the inspiration for these investigations. Few of these species had ever been documented or analyzed for species- specificity, geographic variation, or recognition of this signal in an acoustically complex
224 environment. Given the length and detail of this study, it may be beneficial to quickly review these findings, and note unique features to this investigation.
Chapter 2
1) There is no difference in the drums of male and female woodpeckers in all
species.
2) Drums are distinctive to species, but not species-specific.
3) Reclassification to biome increased signal specificity versus concurrent
analysis.
4) Eastern syntopic species may have similar drums, in contrast to their western
counterparts. Allotopic species may have similar drums.
5) First documented recordings of red-cockaded woodpeckers.
6) First documented recordings of two different drums in red-naped sapsuckers.
Chapter 3
7) There was no geographic variation found in the drums of most Nearctic
woodpecker species, with the exception of black-backed woodpeckers.
8) Black-backed woodpeckers had regionally distinctive drums between eastern
and western populations using Mantel tests.
9) Woodpeckers are acoustically uniform within species across the Nearctic in
regard to drums.
225 Chapter 4
10) Woodpecker drums are not influenced by phenotype (i.e., feather topology)
in that similar species may or may not have similar drums.
11) Species more related phylogenetically are more likely to have similar drums.
12) First calculated phenograms of male and female woodpeckers.
13) First assembly of unresolved woodpecker phylogeny through 2002.
14) Heterospecifics do not appear to have affected the structure of drums within
species.
Chapter 5
15) Markers for individual recognition are encoded in drum duration and
interstrike interval for all four woodpecker species investigated.
16) Markers are not useful for reclassifying individuals by drum.
17) Drums are not individually identifiable.
Chapter 6
18) Playbacks indicated drums not species-specific, but distinctive.
19) Syntopic species with similar drums gave similar responses to stimuli.
20) Species with divergent drums gave divergent behavioral response to stimuli.
21) Red-naped and yellow-bellied sapsuckers could not differentiate drums in
reciprocal playbacks, a first documentation.
226 Chapter 7
22) Syntopy does not influence the drums of normally allotopic woodpecker
species.
23) Heterospecific woodpecker with similar drums were spaced uniformly across
this interface, possibly showing acoustic heterospecific territoriality.
Chapter 8
24) Experimentally modified drums decreased response of black-backed
woodpeckers in proportion to the number of changes made to the signal.
25) Drum cadence and duration are important variables for species recognition in
black-backed woodpeckers.
26) A combination of drum parameters is necessary to elicit an appropriate
response from target woodpeckers.
27) Black-backed woodpeckers conform to the predictions of the additive-
redundant hypothesis for the recognition of signals in an acoustically
complex environment.
Clearly, this series of investigations gave rise to far more questions than it answered, leaving significant avenues for future research.
227
4
2
0 PC1
-2
-4
-6 Rsq = 0.1161 10 2 3 4 5 6 7
Figure 8.1. Linear regression of the first principle component
in response to playback stimuli.
228
2
1
0
-1 PC2 -2
-3
-4
-5 Rsq = 0.0302 0 1 2 3 4 5 6 7
Figure 8.2. Linear regression of the second principle component
in response to playback stimuli.
229
260
240
220
200
180
160
140
120
100
80
60
40 20
0 Duration spent drummingDuration spent stimuli to response in (sec) -20 Rsq = 0.0833 0 1 2 3 4 5 6 7
Figure 8.3. Linear regression of the duration spent drumming
in response to playbacks of various stimuli.
230
60
55
50
45
40
35
30
25
20
15
Latency to firstre sponse(sec)10
5
0 -5 Rsq = 0.0414 0 1 2 3 4 5 6 7
Figure 8.4. Linear regression of the latency to first response
to playbacks of various stimuli.
231
3.5
3.0
2.5
2.0
1.5
1.0
.5
Number of directed flights over the speaker the over directedNumber of flights 0.0
-.5 Rsq = 0.0036 0 1 2 3 4 5 6 7
Figure 8.5. Linear regression of the number of directed flights in
response to various playback stimuli.
232
60
50
40
30
20
10 Closest approach to the speaker (m) speaker the to approach Closest 0
-10 Rsq = 0.1312 0 1 2 43 5 6 7
Figure 8.6. Linear regression of the closest approach to the speaker
in response to various playback stimuli.
233
260
240
220
200
180
160
140
120
100
80
60
40 20
Duration spent within Durationm spent 10 of the speaker (sec) 0 -20 Rsq = 0.0046 0 1 2 3 4 5 6 7
Figure 8.7. Linear regression of the duration spent within
ten meters of the speaker in response to playback stimuli.
234
3
2
1
0 PC1
-1
-2
-3 Rsq = 0.8839 0 1 2 3 4 5 6 7
Figure 8.8. Regression of the standard residuals for the
first principle component in response to playback stimuli.
235
2.0
1.5
1.0
.5 PC2 0.0
-.5
-1.0
-1.5 Rsq = 0.9698 0 1 2 3 4 5 6 7
Figure 8.9. Regression of the standard residuals for the
second principle component in response to playback stimuli.
236
2.0
1.5
1.0
.5
0.0
-.5
-1.0 Duration drummi ng in response to stimuli (sec)
-1.5 Rsq = 0.9167 0 1 2 3 4 5 6 7
Figure 8.10. Regression of the standard residuals for the
duration spent drumming in response to playback stimuli.
237
3
2
1
0 Latency to firstre sponse(sec) -1
-2 Rsq = 0.9586 0 1 2 3 4 5 6 7
Figure 8.12. Regression of the standard residuals for the
latency to first response to playback stimuli.
238
2.0
1.5
1.0
.5
0.0
-.5
Number of directed flights toward speaker toward directedNumber of flights -1.0
-1.5 Rsq = 0.9964 0 1 2 3 4 5 6 7
Figure 8.12. Regression of the standard residuals for the
number of directed flights in response to playback stimuli.
239
2
1
0
-1 Closest approach to the speaker (m) speaker the to approach Closest
-2 Rsq = 0.8688 0 1 2 3 4 5 6 7
Figure 8.13. Regression of the standard residuals for the
closest approach in response to playback stimuli.
240
2.0
1.5
1.0
.5
0.0
-.5
-1.0 Duration spent within Durationm spent 10 of speaker (sec)
-1.5 Rsq = 0.9954 0 1 2 3 4 5 6 7
Figure 8.14. Regression of the standard residuals for the
duration spent within 10 meters of the playback speaker in
response to stimuli.
241
Mean Mean Overall
introductory terminal Mean
interstrike interstrike interstrike Interdrum
Variable Cadence1 Duration2 interval2 interval2 interval 2 interval2
Black-backed woodpecker 16.30 + 0.60 1.90 + 0.40 0.074 + 0.003 0.060 + 0.002 0.064 + 0.002 5.0 + 0.0
Reversed Black-backed 15.69 + 0.01 2.04 + 0.01 0.062 + 0.002 0.074 + 0.003 0.065 + 0.002 5.0 + 0.0
Introductory Black-backed 15.32 + 0.01 1.11 + 0.01 0.074 + 0.003 0.066 + 0.002 0.068 + 0.002 5.0 + 0.0
Terminal Black-backed 17.07 + 0.01 0.99 + 0.01 0.064 + 0.003 0.060 + 0.002 0.061 + 0.002 5.0 + 0.0
Downy woodpecker 17.10 + 1.40 0.80 + 0.20 0.064 + 0.003 0.065 + 0.002 0.064 + 0.002 5.0 + 0.0
Downy: double duration 16.00 + 0.01 1.58 + 0.01 0.066 + 0.003 0.068 + 0.002 0.067 + 0.002 5.0 + 0.0
1 strikes-sec-1
2 sec
Table 8.1. Descriptive statistics for the playback tape variables (Mean + S.D.) for black-backed woodpeckers.
Duration
Total duration Latency to N flights Closest spent
Playback Signal N drumming1 1 st response1 over speaker approach2 within 10m
Reversed Black-backed Conspecific 23 24.9 + 42.3 9.7 + 8.0 0.17 + 0.4 15.7 + 3.5 21.7 + 55.9
Experimental 23 17.3 + 60.7 5.7 + 8.4 0.17 + 0.4 9.8 + 7.3 26.0 + 61.3
Introductory Black-backed Conspecific 17 20.8 + 38.8 8.8 + 8.7 0.12 + 0.3 16.2 + 3.3 21.2 + 59.8
Experimental 17 3.8 + 10.6 4.4 + 5.4 0.12 + 0.3 10.1 + 8.3 6.4 + 14.9
Terminal Black-backed Conspecific 12 18.4 + 36.0 7.6 + 6.1 0.08 + 0.3 15.4 + 4.0 2.5 + 8.7
Experimental 12 13.5 + 26.9 6.3 + 7.9 0.08 + 0.3 9.6 + 7.5 5.0 + 12.4
Downy woodpecker Conspecific 22 36.2 + 52.0 8.3 + 5.1 0.36 + 0.7 16.9 + 8.9 24.3 + 58.7
Heterospecific 22 8.2 + 15.8 9.2 + 7.4 0.27 + 0.5 14.4 + 5.3 35.0 + 69.7
Downy: double duration Conspecific 16 7.9 + 15.1 7.5 + 5.7 0.06 + 0.3 15.6 + 4.4 20.7 + 52.3
Experimental 16 24.6 + 60.9 9.4 + 12.5 0.38 + 0.5 15.6 + 4.0 14.1 + 32.6
1 sec 2 m
Table 8.2. Descriptive statistics for the behavioral response variables measured (Mean + S.D.) in black-backed woodpeckers for each paired playback conducted during the course of this study.
Total duration Latency to No. flights Closest Duration spent
Playback d.f. MANOVA drumming1 1st response1 over speaker approach2 within 10m
Reversed Black-backed 5, 40 0.11 0.27 0.44 0.43 0.36 0.03
Introductory Black-backed 5, 28 0.77 0.95 0.32 0.84 0.62 0.55
Terminal Black-backed 5, 18 0.27 0.21 0.84 0.23 0.76 0.15
Downy woodpecker 5, 38 0.49 0.18 0.84 0.16 0.31 0.22
Downy: double duration 5, 26 0.38 0.14 0.76 0.96 0.17 0.13
1 sec
2 m
Table 8.3. MANOVA (GLM) and the corresponding ANOVA’s for the influence of gender (male vs. female) for the behavioral responses to the
experimental playback drums in black-backed woodpeckers.
Experimental signal N PC1 PC2
Reversed Black-backed 23 0.02 0.14
Introductory Black-backed 17 0.01 0.78
Terminal Black-backed 12 0.15 0.91
Downy woodpecker 22 0.03 0.58
Downy: double duration 16 0.67 0.24
Table 8.4. Wilcoxon sign-rank test results for each reciprocal playback (conspecific vs. experimental) conducted on black- backed woodpeckers for the first and second principle component scores.
Slope Intercept %R2 F P-value
First principle component 0.1971 0.6361 11.6 16.68 0.000
Second principle component 0.0057 0.3743 3.0 3.95 0.049
Duration spent drumming1 -3.709 30.077 8.3 11.54 0.001
Latency to first response1 -0.8018 10.332 4.1 5.48 0.021
Number of directed flights -0.0167 0.2677 0.4 0.46 0.500
Closest approach to the speaker2 -1.371 17.846 13.1 19.17 0.000
Duration spent within 10 meters1 1.592 13.34 0.5 0.59 0.443
1 sec
2 m
Table 8.5. Regression statistics of the five behavioral response variables and the first and second
principal components for black-backed woodpecker playbacks.
APPENDIX A
SUMMARY TABLES OF WOODPECKER RECLASSIFICATION BY SELECTED
DRUM VARIABLES FOR ALL NEARCTIC SPECIES, EASTERN SPECIES ONLY,
AND SYNTOPIC SPECIES DIVIDED BY BIOME.
Predicted Group Membership Species ACWO AZWO BBWO DOWO GFWO GIFL GIWO HAWO LAWO NOFL PIWO RBWO RCWO RHWO RNSA TTWO WISA YBSA Total Original ACWO 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Count AZWO 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 BBWO 0.0 0.0 37.0 3.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 3.0 0.0 0.0 1.0 1.0 0.0 0.0 47 DOWO 0.0 0.0 4.0 33.0 19.0 0.0 8.0 0.0 0.0 1.0 3.0 14.0 2.0 2.0 0.0 5.0 0.0 0.0 91 GFWO 0.0 0.0 0.0 1.0 7.0 0.0 5.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 1.0 0.0 0.0 16 GIFL 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 2 GIWO 0.0 0.0 0.0 1.0 2.0 0.0 3.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 9 HAWO 0.0 11.0 0.0 0.0 0.0 4.0 0.0 9.0 7.0 10.0 0.0 0.0 1.0 3.0 0.0 0.0 0.0 0.0 45 LBWO 0.0 4.0 0.0 0.0 0.0 0.0 0.0 4.0 12.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 NOFL 0.0 7.0 1.0 0.0 0.0 3.0 0.0 4.0 3.0 26.0 0.0 1.0 0.0 11.0 0.0 0.0 0.0 0.0 56 PIWO 0.0 0.0 2.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 27.0 0.0 0.0 0.0 0.0 8.0 0.0 1.0 41 RBWO 0.0 0.0 0.0 6.0 9.0 0.0 1.0 0.0 0.0 0.0 0.0 20.0 3.0 7.0 0.0 0.0 0.0 0.0 46 RCWO 0.0 14.0 0.0 1.0 3.0 10.0 0.0 2.0 0.0 2.0 0.0 6.0 30.0 8.0 0.0 1.0 1.0 0.0 78 RHWO 0.0 9.0 1.0 1.0 0.0 6.0 0.0 3.0 0.0 9.0 1.0 12.0 7.0 28.0 0.0 0.0 0.0 0.0 77 RNSA 0.0 0.0 0.0 0.0 1.0 0.0 3.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 14.0 7.0 8.0 2.0 37 TTWO 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 5.0 1.0 0.0 9 WISA 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 3 YBSA 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 5.0 0.0 1.0 17.0 25
% ACWO 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 AZWO 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 BBWO 0.0 0.0 78.7 6.4 0.0 0.0 0.0 0.0 0.0 2.1 2.1 6.4 0.0 0.0 2.1 2.1 0.0 0.0 100 DOWO 0.0 0.0 4.4 36.3 20.9 0.0 8.8 0.0 0.0 1.1 3.3 15.4 2.2 2.2 0.0 5.5 0.0 0.0 100 GFWO 0.0 0.0 0.0 6.3 43.8 0.0 31.3 0.0 0.0 0.0 0.0 12.5 0.0 0.0 0.0 6.3 0.0 0.0 100 GIFL 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 100 GIWO 0.0 0.0 0.0 11.1 22.2 0.0 33.3 0.0 0.0 0.0 0.0 11.1 0.0 0.0 11.1 11.1 0.0 0.0 100 HAWO 0.0 24.4 0.0 0.0 0.0 8.9 0.0 20.0 15.6 22.2 0.0 0.0 2.2 6.7 0.0 0.0 0.0 0.0 100 LBWO 0.0 20.0 0.0 0.0 0.0 0.0 0.0 20.0 60.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 NOFL 0.0 12.5 1.8 0.0 0.0 5.4 0.0 7.1 5.4 46.4 0.0 1.8 0.0 19.6 0.0 0.0 0.0 0.0 100 PIWO 0.0 0.0 4.9 4.9 0.0 0.0 2.4 0.0 0.0 0.0 65.9 0.0 0.0 0.0 0.0 19.5 0.0 2.4 100 RBWO 0.0 0.0 0.0 13.0 19.6 0.0 2.2 0.0 0.0 0.0 0.0 43.5 6.5 15.2 0.0 0.0 0.0 0.0 100 RCWO 0.0 17.9 0.0 1.3 3.8 12.8 0.0 2.6 0.0 2.6 0.0 7.7 38.5 10.3 0.0 1.3 1.3 0.0 100 RHWO 0.0 11.7 1.3 1.3 0.0 7.8 0.0 3.9 0.0 11.7 1.3 15.6 9.1 36.4 0.0 0.0 0.0 0.0 100 RNSA 0.0 0.0 0.0 0.0 2.7 0.0 8.1 0.0 0.0 0.0 5.4 0.0 0.0 0.0 37.8 18.9 21.6 5.4 100 TTWO 0.0 0.0 0.0 0.0 0.0 0.0 11.1 0.0 0.0 0.0 0.0 11.1 0.0 0.0 11.1 55.6 11.1 0.0 100 WISA 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0 33.3 0.0 100 YBSA 0.0 0.0 0.0 0.0 0.0 0.0 4.0 0.0 0.0 0.0 4.0 0.0 0.0 0.0 20.0 0.0 4.0 68.0 100
Table A.1. Discriminant function analysis for reclassification of eastern Nearctic woodpeckers when run concurrently, using the selected drum variables.
Cross-validated Predicted group membership Species ACWO AZWO BBWO DOWO GFWO GIFL GIWO HAWO LAWO NOFL PIWO RBWO RCWO RHWO RNSA TTWO WISA YBSA Total Count ACWO 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 AZWO 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 BBWO 0.0 0.0 36.0 3.0 0.0 0.0 0.0 0.0 0.0 1.0 2.0 3.0 0.0 0.0 1.0 1.0 0.0 0.0 47 DOWO 0.0 0.0 4.0 33.0 19.0 0.0 8.0 0.0 0.0 1.0 3.0 14.0 2.0 2.0 0.0 5.0 0.0 0.0 91 GFWO 0.0 0.0 0.0 1.0 6.0 0.0 6.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 1.0 0.0 0.0 16 GIFL 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 2 GIWO 0.0 0.0 0.0 2.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 9 HAWO 0.0 11.0 0.0 0.0 0.0 4.0 0.0 8.0 7.0 11.0 0.0 0.0 1.0 3.0 0.0 0.0 0.0 0.0 45 LBWO 0.0 4.0 0.0 0.0 0.0 0.0 0.0 6.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20 NOFL 0.0 7.0 1.0 0.0 0.0 3.0 0.0 4.0 5.0 24.0 0.0 1.0 0.0 11.0 0.0 0.0 0.0 0.0 56 PIWO 0.0 0.0 2.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 27.0 0.0 0.0 0.0 0.0 8.0 0.0 1.0 41 RBWO 0.0 0.0 0.0 6.0 9.0 0.0 1.0 0.0 0.0 0.0 0.0 20.0 3.0 7.0 0.0 0.0 0.0 0.0 46 RCWO 0.0 14.0 0.0 1.0 3.0 11.0 0.0 2.0 0.0 2.0 0.0 6.0 29.0 8.0 0.0 1.0 1.0 0.0 78 RHWO 0.0 9.0 1.0 1.0 0.0 7.0 0.0 3.0 0.0 9.0 1.0 13.0 7.0 26.0 0.0 0.0 0.0 0.0 77 RNSA 0.0 0.0 0.0 0.0 1.0 0.0 3.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 12.0 7.0 10.0 2.0 37 TTWO 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 5.0 1.0 0.0 9 WISA 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 3 YBSA 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 5.0 0.0 1.0 17.0 25
% ACWO 0.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 AZWO 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 BBWO 0.0 0.0 76.6 6.4 0.0 0.0 0.0 0.0 0.0 2.1 4.3 6.4 0.0 0.0 2.1 2.1 0.0 0.0 100 DOWO 0.0 0.0 4.4 36.3 20.9 0.0 8.8 0.0 0.0 1.1 3.3 15.4 2.2 2.2 0.0 5.5 0.0 0.0 100 GFWO 0.0 0.0 0.0 6.3 37.5 0.0 37.5 0.0 0.0 0.0 0.0 12.5 0.0 0.0 0.0 6.3 0.0 0.0 100 GIFL 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 100 GIWO 0.0 0.0 0.0 22.2 22.2 0.0 22.2 0.0 0.0 0.0 0.0 11.1 0.0 0.0 11.1 11.1 0.0 0.0 100 HAWO 0.0 24.4 0.0 0.0 0.0 8.9 0.0 17.8 15.6 24.4 0.0 0.0 2.2 6.7 0.0 0.0 0.0 0.0 100 LBWO 0.0 20.0 0.0 0.0 0.0 0.0 0.0 30.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 NOFL 0.0 12.5 1.8 0.0 0.0 5.4 0.0 7.1 8.9 42.9 0.0 1.8 0.0 19.6 0.0 0.0 0.0 0.0 100 PIWO 0.0 0.0 4.9 4.9 0.0 0.0 2.4 0.0 0.0 0.0 65.9 0.0 0.0 0.0 0.0 19.5 0.0 2.4 100 RBWO 0.0 0.0 0.0 13.0 19.6 0.0 2.2 0.0 0.0 0.0 0.0 43.5 6.5 15.2 0.0 0.0 0.0 0.0 100 RCWO 0.0 17.9 0.0 1.3 3.8 14.1 0.0 2.6 0.0 2.6 0.0 7.7 37.2 10.3 0.0 1.3 1.3 0.0 100 RHWO 0.0 11.7 1.3 1.3 0.0 9.1 0.0 3.9 0.0 11.7 1.3 16.9 9.1 33.8 0.0 0.0 0.0 0.0 100 RNSA 0.0 0.0 0.0 0.0 2.7 0.0 8.1 0.0 0.0 0.0 5.4 0.0 0.0 0.0 32.4 18.9 27.0 5.4 100 TTWO 0.0 0.0 0.0 0.0 0.0 0.0 11.1 0.0 0.0 0.0 0.0 11.1 0.0 0.0 11.1 55.6 11.1 0.0 100 WISA 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 66.7 0.0 0.0 0.0 100 YBSA 0.0 0.0 0.0 0.0 0.0 0.0 4.0 0.0 0.0 0.0 4.0 0.0 0.0 0.0 20.0 0.0 4.0 68.0 100 45.0% of original grouped cases correctly classified. 42.5% of cross-validated grouped cases correctly classified.
Table A.1. Discriminant function analysis for reclassification of eastern Nearctic woodpeckers when run concurrently, using the selected drum variables, continued.
Predicted Group Membership Species BBWO HAWO NOFL PIWO TTWO YBSA Total Original BBWO 42.0 0.0 1.0 2.0 2.0 0.0 47 Count HAWO 0.0 28.0 17.0 0.0 0.0 0.0 45 NOFL 0.0 12.0 44.0 0.0 0.0 0.0 56 PIWO 0.0 0.0 0.0 35.0 5.0 1.0 41 TTWO 1.0 0.0 0.0 1.0 7.0 0.0 9 YBSA 0.0 0.0 0.0 1.0 3.0 21.0 25 % BBWO 89.4 0.0 2.1 4.3 4.3 0.0 100 HAWO 0.0 62.2 37.8 0.0 0.0 0.0 100 NOFL 0.0 21.4 78.6 0.0 0.0 0.0 100 PIWO 0.0 0.0 0.0 85.4 12.2 2.4 100 TTWO 11.1 0.0 0.0 11.1 77.8 0.0 100 YBSA 0.0 0.0 0.0 4.0 12.0 84.0 100 Cross-validated Count BBWO 42.0 0.0 1.0 2.0 2.0 0.0 47 HAWO 0.0 28.0 17.0 0.0 0.0 0.0 45 NOFL 1.0 12.0 43.0 0.0 0.0 0.0 56 PIWO 0.0 0.0 0.0 35.0 5.0 1.0 41 TTWO 1.0 0.0 0.0 1.0 7.0 0.0 9 YBSA 0.0 0.0 0.0 2.0 5.0 18.0 25 % BBWO 89.4 0.0 2.1 4.3 4.3 0.0 100 HAWO 0.0 62.2 37.8 0.0 0.0 0.0 100 NOFL 1.8 21.4 76.8 0.0 0.0 0.0 100 PIWO 0.0 0.0 0.0 85.4 12.2 2.4 100 TTWO 11.1 0.0 0.0 11.1 77.8 0.0 100 YBSA 0.0 0.0 0.0 8.0 20.0 72.0 100
79.4% of original grouped cases correctly classified. 77.6% of cross-validated grouped cases correctly classified.
Table A.2. Discriminant function analysis for reclassification of boreal woodpeckers when
separated by biome, using the selected drum variables.
Predicted Group Membership Species DOWO HAWO NOFL PIWO RBWO RHWO YBSA Total Original DOWO 61.0 0.0 0.0 8.0 19.0 3.0 0.0 91 Count HAWO 0.0 27.0 9.0 0.0 0.0 9.0 0.0 45 NOFL 0.0 11.0 30.0 0.0 1.0 14.0 0.0 56 PIWO 2.0 0.0 0.0 38.0 0.0 0.0 1.0 41 RBWO 11.0 0.0 0.0 0.0 27.0 8.0 0.0 46 RHWO 2.0 11.0 8.0 1.0 11.0 44.0 0.0 77 YBSA 1.0 0.0 0.0 4.0 0.0 0.0 20.0 25
% DOWO 67.0 0.0 0.0 8.8 20.9 3.3 0.0 100 HAWO 0.0 60.0 20.0 0.0 0.0 20.0 0.0 100 NOFL 0.0 19.6 53.6 0.0 1.8 25.0 0.0 100 PIWO 4.9 0.0 0.0 92.7 0.0 0.0 2.4 100 RBWO 23.9 0.0 0.0 0.0 58.7 17.4 0.0 100 RHWO 2.6 14.3 10.4 1.3 14.3 57.1 0.0 100 YBSA 4.0 0.0 0.0 16.0 0.0 0.0 80.0 100 Cross-validated Count DOWO 61.0 0.0 0.0 8.0 19.0 3.0 0.0 91 HAWO 0.0 25.0 11.0 0.0 0.0 9.0 0.0 45 NOFL 0.0 11.0 30.0 0.0 1.0 14.0 0.0 56 PIWO 3.0 0.0 0.0 37.0 0.0 0.0 1.0 41 RBWO 12.0 0.0 0.0 0.0 26.0 8.0 0.0 46 RHWO 2.0 11.0 8.0 1.0 12.0 43.0 0.0 77 YBSA 1.0 0.0 0.0 5.0 0.0 0.0 19.0 25
% DOWO 67.0 0.0 0.0 8.8 20.9 3.3 0.0 100 HAWO 0.0 55.6 24.4 0.0 0.0 20.0 0.0 100 NOFL 0.0 19.6 53.6 0.0 1.8 25.0 0.0 100 PIWO 7.3 0.0 0.0 90.2 0.0 0.0 2.4 100 RBWO 26.1 0.0 0.0 0.0 56.5 17.4 0.0 100 RHWO 2.6 14.3 10.4 1.3 15.6 55.8 0.0 100 YBSA 4.0 0.0 0.0 20.0 0.0 0.0 76.0 100
64.8% of original grouped cases correctly classified. 63.3% of cross-validated grouped cases correctly classified.
Table A.3. Discriminant function analysis for reclassification of southern pine forest woodpeckers when separated by biome, using the selected drum variables.
Predicted Group Membership Species DOWO HAWO NOFL PIWO RBWO RHWO Total Original DOWO 60 0 0 7 21 3 91 Count HAWO 0 27 9 0 0 9 45 NOFL 0 11 31 0 1 13 56 PIWO 8 0 0 33 0 0 41 RBWO 10 0 0 0 29 7 46 RHWO 3 10 9 0 11 44 77
% DOWO 65.9 .0 .0 7.7 23.1 3.3 100.0 HAWO .0 60.0 20.0 .0 .0 20.0 100.0 NOFL .0 19.6 55.4 .0 1.8 23.2 100.0 PIWO 19.5 .0 .0 80.5 .0 .0 100.0 RBWO 21.7 .0 .0 .0 63.0 15.2 100.0 RHWO 3.9 13.0 11.7 .0 14.3 57.1 100.0 Cross-validated Count DOWO 60 0 0 7 21 3 91 HAWO 0 27 9 0 0 9 45 NOFL 1 12 28 0 1 14 56 PIWO 9 0 0 32 0 0 41 RBWO 10 0 0 0 28 8 46 RHWO 3 10 9 0 12 43 77
% DOWO 65.9 .0 .0 7.7 23.1 3.3 100.0 HAWO .0 60.0 20.0 .0 .0 20.0 100.0 NOFL 1.8 21.4 50.0 .0 1.8 25.0 100.0 PIWO 22.0 .0 .0 78.0 .0 .0 100.0 RBWO 21.7 .0 .0 .0 60.9 17.4 100.0 RHWO 3.9 13.0 11.7 .0 15.6 55.8 100.0
62.9% of original grouped cases correctly classified. 61.2% of cross-validated grouped cases correctly classified.
Table A.4. Discriminant function analysis for reclassification of eastern deciduous forest woodpeckers when separated by biome, using the selected drum variables.
Predicted Group Membership Species ACWO AZWO GIFL GIWO HAWO LAWO NOFL Total Original Count ACWO 1 0 0 0 0 0 0 1 AZWO 0 2 0 0 0 0 0 2 GIFL 0 0 1 0 0 0 1 2 GIWO 0 0 0 9 0 0 0 9 HAWO 0 11 3 0 14 6 11 45 LAWO 0 4 0 0 3 13 0 20 NOFL 0 4 7 0 6 2 37 56
% ACWO 100.0 .0 .0 .0 .0 .0 .0 100.0 AZWO .0 100.0 .0 .0 .0 .0 .0 100.0 GIFL .0 .0 50.0 .0 .0 .0 50.0 100.0 GIWO .0 .0 .0 100.0 .0 .0 .0 100.0 HAWO .0 24.4 6.7 .0 31.1 13.3 24.4 100.0 LAWO .0 20.0 .0 .0 15.0 65.0 .0 100.0 NOFL .0 7.1 12.5 .0 10.7 3.6 66.1 100.0
Cross-validated Count ACWO 0 0 1 0 0 0 0 1 AZWO 0 1 0 0 1 0 0 2 GIFL 1 0 0 0 0 0 1 2 GIWO 1 0 1 7 0 0 0 9 HAWO 0 11 3 0 12 6 13 45 LAWO 0 4 0 0 4 12 0 20 NOFL 0 4 8 0 8 2 34 56
% ACWO .0 .0 100.0 .0 .0 .0 .0 100.0 AZWO .0 50.0 .0 .0 50.0 .0 .0 100.0 GIFL 50.0 .0 .0 .0 .0 .0 50.0 100.0 GIWO 11.1 .0 11.1 77.8 .0 .0 .0 100.0 HAWO .0 24.4 6.7 .0 26.7 13.3 28.9 100.0 LAWO .0 20.0 .0 .0 20.0 60.0 .0 100.0 NOFL .0 7.1 14.3 .0 14.3 3.6 60.7 100.0
57.0% of original grouped cases correctly classified. 48.9% of cross-validated grouped cases correctly classified.
Table A.5. Discriminant function analysis for reclassification of western desert woodpecker species when separated by biome, using the selected drum variables.
Predicted Group Membership Species BBWO HAWO NOFL PIWO RNSA TTWO WISA Total Original Count BBWO 42 0 0 2 0 3 0 47 HAWO 0 29 16 0 0 0 0 45 NOFL 0 12 44 0 0 0 0 56 PIWO 0 0 0 35 1 5 0 41 RNSA 0 0 0 0 23 9 5 37 TTWO 1 0 0 1 2 5 0 9 WISA 0 0 0 0 1 0 2 3
% BBWO 89.4 .0 .0 4.3 .0 6.4 .0 100.0 HAWO .0 64.4 35.6 .0 .0 .0 .0 100.0 NOFL .0 21.4 78.6 .0 .0 .0 .0 100.0 PIWO .0 .0 .0 85.4 2.4 12.2 .0 100.0 RNSA .0 .0 .0 .0 62.2 24.3 13.5 100.0 TTWO 11.1 .0 .0 11.1 22.2 55.6 .0 100.0 WISA .0 .0 .0 .0 33.3 .0 66.7 100.0
Cross-validated Count BBWO 39 0 1 4 0 3 0 47 HAWO 0 29 16 0 0 0 0 45 NOFL 1 12 42 0 1 0 0 56 PIWO 0 0 0 35 1 5 0 41 RNSA 0 0 0 0 21 9 7 37 TTWO 1 0 0 1 2 4 1 9 WISA 0 0 0 0 1 1 1 3
% BBWO 83.0 .0 2.1 8.5 .0 6.4 .0 100.0 HAWO .0 64.4 35.6 .0 .0 .0 .0 100.0 NOFL 1.8 21.4 75.0 .0 1.8 .0 .0 100.0 PIWO .0 .0 .0 85.4 2.4 12.2 .0 100.0 RNSA .0 .0 .0 .0 56.8 24.3 18.9 100.0 TTWO 11.1 .0 .0 11.1 22.2 44.4 11.1 100.0 WISA .0 .0 .0 .0 33.3 33.3 33.3 100.0
75.6% of original grouped cases correctly classified. 71.8% of cross-validated grouped cases correctly classified.
Table A.6. Discriminant function analysis for reclassification of western Rocky Mountain woodpecker species when separated by biome, using the selected drum variables.
Predicted Group Membership Species GFWO LAWO NOFL Total Original Count GFWO 16 0 0 16 LAWO 0 19 1 20 NOFL 0 7 49 56
% GFWO 100.0 .0 .0 100.0 LAWO .0 95.0 5.0 100.0 NOFL .0 12.5 87.5 100.0
Cross-validated Count GFWO 16 0 0 16 LAWO 0 17 3 20 NOFL 1 7 48 56
% GFWO 100.0 .0 .0 100.0 LAWO .0 85.0 15.0 100.0 NOFL 1.8 12.5 85.7 100.0
91.3% of original grouped cases correctly classified. 88.0% of cross-validated grouped cases correctly classified.
Table A.7. Discriminant function analysis for reclassification of Texas woodpecker species when separated by biome, using the selected drum variables.
Predicted Group Membership Species ACWO AZWO BBWO DOWO GFWO GIFL GIWO HAWO LAWO NOFL PIWO RBWO RCWO RHWO RNSA TTWO WISA YBSA NUWO WHWO RBSA Total Original ACWO 4 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 8 Count AZWO 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 BBWO 1 0 42 2 0 0 0 0 0 1 3 1 0 0 1 1 0 0 0 3 1 56 DOWO 7 0 3 42 17 0 15 0 0 0 4 15 2 1 0 4 0 0 2 1 0 113 GFWO 0 0 0 2 6 0 6 0 0 0 0 1 0 0 0 1 0 0 0 0 0 16 GIFL 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 2 GIWO 1 0 0 0 3 0 3 0 0 0 0 0 0 0 1 1 0 0 0 0 0 9 HAWO 0 12 0 0 0 6 0 28 18 13 0 0 1 1 0 0 0 0 1 0 0 80 LAWO 0 3 0 0 0 0 0 8 14 1 0 0 0 0 0 0 0 0 0 0 0 26 NOFL 0 8 2 0 0 5 0 4 2 36 0 0 0 6 0 0 0 0 8 2 0 73 PIWO 0 0 2 4 0 0 1 0 0 0 28 0 0 0 0 8 0 1 0 0 1 45 RBWO 7 0 0 7 7 0 1 0 0 0 0 14 1 3 0 0 0 0 1 5 0 46 RCWO 13 14 0 1 3 12 1 1 0 1 0 2 22 7 1 0 0 0 0 0 0 78 RHWO 4 9 1 2 0 10 0 2 0 8 0 7 6 12 0 0 0 0 10 6 0 77 RNSA 0 0 0 0 0 0 4 0 0 0 1 0 0 0 10 7 6 2 0 0 7 37 TTWO 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 5 2 0 0 1 0 9 WISA 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 5 YBSA 0 0 0 0 0 0 1 0 0 0 0 0 0 0 2 0 1 17 0 0 4 25 NUWO 0 0 2 0 0 1 0 0 0 4 0 1 1 16 0 0 0 0 33 14 0 72 WHWO 5 0 3 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 10 19 0 41 RBSA 1 0 0 0 0 0 1 0 0 0 3 0 0 0 2 0 0 0 0 0 3 10
Table A.8. Discriminant function analysis for reclassification of all Nearctic woodpeckers (except Lewis’ woodpecker) when run concurrently, using the selected drum variables.
Predicted Group Membership Species ACWO AZWO BBWO DOWO GFWO GIFL GIWO HAWO LAWO NOFL PIWO RBWO RCWO RHWO RNSA TTWO WISA YBSA NUWO WHWO RBSA Total % ACWO 50.0 .0 .0 12.5 .0 12.5 12.5 .0 .0 .0 .0 .0 12.5 .0 .0 .0 .0 .0 .0 .0 .0 100.0 AZWO .0 100 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 100.0 BBWO 1.8 .0 75.0 3.6 .0 .0 .0 .0 .0 1.8 5.4 1.8 .0 .0 1.8 1.8 .0 .0 .0 5.4 1.8 100.0 DOWO 6.2 .0 2.7 37.2 15.0 .0 13.3 .0 .0 .0 3.5 13.3 1.8 .9 .0 3.5 .0 .0 1.8 .9 .0 100.0 GFWO .0 .0 .0 12.5 37.5 .0 37.5 .0 .0 .0 .0 6.3 .0 .0 .0 6.3 .0 .0 .0 .0 .0 100.0 GIFL .0 .0 .0 .0 .0 .0 .0 .0 .0 50.0 .0 .0 50.0 .0 .0 .0 .0 .0 .0 .0 .0 100.0 GIWO 11.1 .0 .0 .0 33.3 .0 33.3 .0 .0 .0 .0 .0 .0 .0 11.1 11.1 .0 .0 .0 .0 .0 100.0 HAWO .0 15.0 .0 .0 .0 7.5 .0 35.0 22.5 16.3 .0 .0 1.3 1.3 .0 .0 .0 .0 1.3 .0 .0 100.0 LAWO .0 11.5 .0 .0 .0 .0 .0 30.8 53.8 3.8 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 100.0 NOFL .0 11.0 2.7 .0 .0 6.8 .0 5.5 2.7 49.3 .0 .0 .0 8.2 .0 .0 .0 .0 11.0 2.7 .0 100.0 PIWO .0 .0 4.4 8.9 .0 .0 2.2 .0 .0 .0 62.2 .0 .0 .0 .0 17.8 .0 2.2 .0 .0 2.2 100.0 RBWO 15.2 .0 .0 15.2 15.2 .0 2.2 .0 .0 .0 .0 30.4 2.2 6.5 .0 .0 .0 .0 2.2 10.9 .0 100.0 RCWO 16.7 17.9 .0 1.3 3.8 15.4 1.3 1.3 .0 1.3 .0 2.6 28.2 9.0 1.3 .0 .0 .0 .0 .0 .0 100.0 RHWO 5.2 11.7 1.3 2.6 .0 13.0 .0 2.6 .0 10.4 .0 9.1 7.8 15.6 .0 .0 .0 .0 13.0 7.8 .0 100.0 RNSA .0 .0 .0 .0 .0 .0 10.8 .0 .0 .0 2.7 .0 .0 .0 27.0 18.9 16.2 5.4 .0 .0 18.9 100.0 TTWO .0 .0 .0 .0 .0 .0 11.1 .0 .0 .0 .0 .0 .0 .0 .0 55.6 22.2 .0 .0 11.1 .0 100.0 WISA .0 .0 .0 .0 .0 .0 20.0 .0 .0 .0 .0 .0 .0 .0 20.0 20.0 20.0 .0 .0 .0 20.0 100.0 YBSA .0 .0 .0 .0 .0 .0 4.0 .0 .0 .0 .0 .0 .0 .0 8.0 .0 4.0 68.0 .0 .0 16.0 100.0 NUWO .0 .0 2.8 .0 .0 1.4 .0 .0 .0 5.6 .0 1.4 1.4 22.2 .0 .0 .0 .0 45.8 19.4 .0 100.0 WHWO 12.2 .0 7.3 2.4 .0 .0 2.4 .0 .0 .0 .0 .0 2.4 2.4 .0 .0 .0 .0 24.4 46.3 .0 100.0 RBSA 10.0 .0 .0 .0 .0 .0 10.0 .0 .0 .0 30.0 .0 .0 .0 20.0 .0 .0 .0 .0 .0 30.0 100.0
Table A.8. Discriminant function analysis for reclassification of all Nearctic woodpeckers (except Lewis’ woodpecker) when run concurrently, using the selected drum variables, continued.
Cross-validated Predicted Group Membership Species ACWO AZWO BBWO DOWO GFWO GIFL GIWO HAWO LAWO NOFL PIWO RBWO RCWO RHWO RNSA TTWO WISA YBSA NUWO WHWO RBSA Total Count ACWO 3 0 0 1 0 1 1 0 0 0 0 0 2 0 0 0 0 0 0 0 0 8 AZWO 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 BBWO 1 0 42 2 0 0 0 0 0 1 3 1 0 0 1 1 0 0 0 3 1 56 DOWO 7 0 3 42 17 0 15 0 0 0 4 15 2 1 0 4 0 0 2 1 0 113 GFWO 2 0 0 2 5 0 5 0 0 0 0 1 0 0 0 1 0 0 0 0 0 16 GIFL 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 2 GIWO 1 0 0 0 3 0 3 0 0 0 0 0 0 0 1 1 0 0 0 0 0 9 HAWO 0 12 0 0 0 6 0 28 18 13 0 0 1 1 0 0 0 0 1 0 0 80 LAWO 0 3 0 0 0 0 0 8 14 1 0 0 0 0 0 0 0 0 0 0 0 26 NOFL 0 8 2 0 0 5 0 5 3 33 0 0 1 6 0 0 0 0 8 2 0 73 PIWO 0 0 5 4 0 0 1 0 0 0 25 0 0 0 0 8 0 1 0 0 1 45 RBWO 8 0 0 7 7 0 1 0 0 0 0 13 1 3 0 0 0 0 1 5 0 46 RCWO 13 14 0 1 3 13 1 1 0 2 0 2 20 7 1 0 0 0 0 0 0 78 RHWO 4 9 1 2 0 10 0 2 0 8 0 7 6 12 0 0 0 0 10 6 0 77 RNSA 0 0 0 0 0 0 4 0 0 0 1 0 0 0 8 7 7 2 0 0 8 37 TTWO 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 5 2 0 0 1 0 9 WISA 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 2 5 YBSA 0 0 0 0 0 0 1 0 0 0 0 0 0 0 2 0 1 17 0 0 4 25 NUWO 0 0 3 0 0 1 0 0 0 4 0 1 1 17 0 0 0 0 31 14 0 72 WHWO 5 0 3 1 0 0 1 0 0 0 0 1 1 1 0 0 0 0 11 17 0 41 RBSA 1 0 0 0 0 0 1 0 0 0 3 0 0 0 2 0 0 0 0 0 3 10
Table A.8. Discriminant function analysis for reclassification of all Nearctic woodpeckers (except Lewis’ woodpecker) when run concurrently, using the selected drum variables, continued.
Predicted Group Membership Species ACWO AZWO BBWO DOWO GFWO GIFL GIWO HAWO LAWO NOFL PIWO RBWO RCWO RHWO RNSA TTWO WISA YBSA NUWO WHWO RBSA Total % ACWO 37.5 .0 .0 12.5 .0 12.5 12.5 .0 .0 .0 .0 .0 25.0 .0 .0 .0 .0 .0 .0 .0 .0 100.0 AZWO .0 100 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 100.0 BBWO 1.8 .0 75.0 3.6 .0 .0 .0 .0 .0 1.8 5.4 1.8 .0 .0 1.8 1.8 .0 .0 .0 5.4 1.8 100.0 DOWO 6.2 .0 2.7 37.2 15.0 .0 13.3 .0 .0 .0 3.5 13.3 1.8 .9 .0 3.5 .0 .0 1.8 .9 .0 100.0 GFWO 12.5 .0 .0 12.5 31.3 .0 31.3 .0 .0 .0 .0 6.3 .0 .0 .0 6.3 .0 .0 .0 .0 .0 100.0 GIFL .0 .0 .0 .0 .0 .0 .0 .0 .0 50.0 .0 .0 50.0 .0 .0 .0 .0 .0 .0 .0 .0 100.0 GIWO 11.1 .0 .0 .0 33.3 .0 33.3 .0 .0 .0 .0 .0 .0 .0 11.1 11.1 .0 .0 .0 .0 .0 100.0 HAWO .0 15.0 .0 .0 .0 7.5 .0 35.0 22.5 16.3 .0 .0 1.3 1.3 .0 .0 .0 .0 1.3 .0 .0 100.0 LAWO .0 11.5 .0 .0 .0 .0 .0 30.8 53.8 3.8 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .0 100.0 NOFL .0 11.0 2.7 .0 .0 6.8 .0 6.8 4.1 45.2 .0 .0 1.4 8.2 .0 .0 .0 .0 11.0 2.7 .0 100.0 PIWO .0 .0 11.1 8.9 .0 .0 2.2 .0 .0 .0 55.6 .0 .0 .0 .0 17.8 .0 2.2 .0 .0 2.2 100.0 RBWO 17.4 .0 .0 15.2 15.2 .0 2.2 .0 .0 .0 .0 28.3 2.2 6.5 .0 .0 .0 .0 2.2 10.9 .0 100.0 RCWO 16.7 17.9 .0 1.3 3.8 16.7 1.3 1.3 .0 2.6 .0 2.6 25.6 9.0 1.3 .0 .0 .0 .0 .0 .0 100.0 RHWO 5.2 11.7 1.3 2.6 .0 13.0 .0 2.6 .0 10.4 .0 9.1 7.8 15.6 .0 .0 .0 .0 13.0 7.8 .0 100.0 RNSA .0 .0 .0 .0 .0 .0 10.8 .0 .0 .0 2.7 .0 .0 .0 21.6 18.9 18.9 5.4 .0 .0 21.6 100.0 TTWO .0 .0 .0 .0 .0 .0 11.1 .0 .0 .0 .0 .0 .0 .0 .0 55.6 22.2 .0 .0 11.1 .0 100.0 WISA .0 .0 .0 .0 .0 .0 20.0 .0 .0 .0 .0 .0 .0 .0 20.0 20.0 .0 .0 .0 .0 40.0 100.0 YBSA .0 .0 .0 .0 .0 .0 4.0 .0 .0 .0 .0 .0 .0 .0 8.0 .0 4.0 68.0 .0 .0 16.0 100.0 NUWO .0 .0 4.2 .0 .0 1.4 .0 .0 .0 5.6 .0 1.4 1.4 23.6 .0 .0 .0 .0 43.1 19.4 .0 100.0 WHWO 12.2 .0 7.3 2.4 .0 .0 2.4 .0 .0 .0 .0 2.4 2.4 2.4 .0 .0 .0 .0 26.8 41.5 .0 100.0 RBSA 10.0 .0 .0 .0 .0 .0 10.0 .0 .0 .0 30.0 .0 .0 .0 20.0 .0 .0 .0 .0 .0 30.0 100.0
41.1% of original grouped cases correctly classified. 38.9% of cross-validated grouped cases correctly classified.
Table A.8. Discriminant function analysis for reclassification of all Nearctic woodpeckers (except Lewis’ woodpecker) when run concurrently, using the selected drum variables, continued. DESCRIPTIVE STATISTICS (MEAN + S.E.) AND THE DISCRIMINANT FUNCTION RECLASSIFICATION SUMMARY TABLES FOR REGIONAL VARIATION IN WOODPECKER DRUMS ACROSS HABITATS.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2 Mean Frequency3 Arizona 4 19.03 + 1.49 22.60 + 0.73 851.06 + 48.02 50.25 + 2.33 76.41 + 29.30 912.69 + 65.32 Ontario 3 18.99 + 1.74 23.35 + 0.30 816.66 + 63.15 43.41 + 0.87 72.16 + 24.56 642.71 + 36.74 Wyoming 1 36.00 + 0.00 24.00 + 0.00 1642.3 + 0.000 69.74 + 0.00 193.9 + 0.000 821.41 + 0.000 Ohio 6 20.87 + 2.59 22.90 + 0.51 920.63 + 115.4 71.81 + 16.8 117.5 + 22.79 637.20 + 73.55 Michigan 7 21.57 + 2.06 22.18 + 0.58 986.62 + 89.96 51.11 + 4.19 107.7 + 35.29 732.89 + 46.97 Mississippi 1 17.00 + 0.00 21.01 + 0.00 809.10 + 0.000 47.59 + 0.00 32.56 + 0.000 1174.8 + 0.000 North Carolina 5 24.17 + 2.28 23.06 + 0.93 1044.9 + 116.5 45.98 + 2.89 185.6 + 52.57 745.64 + 96.83 Oklahoma 2 19.99 + 0.56 23.03 + 0.08 869.98 + 16.36 43.54 + 0.20 179.4 + 8.590 1100.5 + 150.5 Florida 1 25.18 + 0.00 22.49 + 0.00 1119.3 + 0.000 44.51 + 0.00 306.5 + 0.000 1228.7 + 0.000 Texas 1 18.00 + 0.00 25.70 + 0.00 700.00 + 0.000 66.50 + 0.00 48.64 + 0.000 4583.2 + 0.000 Colorado 19 20.41 + 1.58 23.63 + 0.49 861.32 + 58.48 46.10 + 1.94 134.1 + 36.29 844.11 + 62.39 Kansas 4 16.98 + 3.07 25.29 + 0.75 688.99 + 144.3 39.72 + 1.21 137.9 + 102.6 1230.6 + 132.6 Georgia 2 32.00 + 1.00 25.04 + 0.26 1275.0 + 25.00 34.02 + 0.85 295.6 + 34.59 697.33 + 0.850 San Luis Obispo county, CA 2 21.97 + 1.03 19.35 + 0.63 1136.3 + 17.00 53.73 + 3.07 . . . . Los Angeles county, CA 4 24.36 + 2.63 21.41 + 0.30 1141.7 + 127.2 48.63 + 0.57 . . . . Sequoia National Forest, CA 1 24.70 + 2.23 22.76 + 0.20 1095.0 + 120.0 45.90 + 0.10 . . . . Sierra National Forest, CA 1 13.00 + 0.00 24.10 + 0.00 540.00 + 0.000 45.00 + 0.00 . . . . Northern California 1 32.33 + 0.00 21.30 + 0.00 1518.3 + 0.000 49.00 + 0.00 . . . . Los Angeles county, CA 6 28.13 + 2.00 22.98 + 0.44 1224.8 + 85.63 45.17 + 0.95 . . . . Ohio 1 25.00 + 0.00 24.78 + 0.00 1011.7 + 0.000 41.06 + 0.00 . . . .
1 strikes sec-1 2 milliseconds 3 hertz
Table A.9. Descriptive statistics (Mean + S.E.) of northern flicker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Interstrike interval2 Arizona 1 12.00 + 0.00 21.51 + 0.00 557.94 + 0.00 50.10 + 0.00 San Luis Obispo county, CA 2 10.46 + 1.39 17.49 + 0.37 611.18 + 95.46 64.32 + 1.32 Los Angeles county, CA 5 8.97 + 0.86 18.16 + 1.22 516.68 + 83.72 64.58 + 4.32
1 strikes sec-1 2 milliseconds
Table A.10. Descriptive statistics (Mean + S.E.) of acorn woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2 Mean Frequency3 New York 4 24.20 + 2.72 18.89 + 0.31 1280.9 + 137.5 53.06 + 0.86 219.34 + 37.40 769.58 + 76.27 Vermont 1 24.36 + 0.00 18.61 + 0.00 1298.9 + 0.000 53.87 + 0.00 274.70 + 0.000 886.63 + 0.000 Ontario 27 26.29 + 1.16 17.47 + 0.24 1507.9 + 64.76 64.39 + 3.96 251.07 + 26.49 805.49 + 52.46 Wyoming 1 36.35 + 0.00 15.81 + 0.00 2297.2 + 0.000 63.55 + 0.00 363.11 + 0.000 686.25 + 0.000 Quebec 14 23.73 + 1.32 17.33 + 0.40 1375.0 + 66.47 61.19 + 1.99 245.81 + 38.55 1281.8 + 59.52 Sierra National Forest, CA 9 30.90 + 1.98 16.26 + 0.21 1905.4 + 125.7 64.21 + 0.94 . . . .
1 strikes sec-1 2 milliseconds 3 hertz
Table A.11. Descriptive statistics (Mean + S.E.) of black-backed woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2 Mean Frequency3 Ontario 5 19.34 + 2.03 15.31 + 0.44 1258.7 + 102.3 66.04 + 2.06 116.2 + 38.8 780.67 + 30.76 Wyoming 2 18.25 + 2.25 14.80 + 0.70 1266.5 + 233.6 69.78 + 5.22 77.13 + 34.2 801.24 + 82.55 Ohio 45 13.75 + 0.60 16.77 + 0.22 823.02 + 37.81 60.02 + 0.79 59.60 + 5.77 920.94 + 46.55 Michigan 4 11.59 + 0.66 15.85 + 0.36 737.27 + 42.54 64.29 + 1.81 48.79 + 1.67 915.04 + 207.5 Mississippi 7 12.15 + 1.06 16.22 + 0.35 762.68 + 73.74 62.29 + 1.55 46.02 + 2.26 1016.9 + 125.6 North Carolina 6 15.59 + 1.15 17.22 + 0.30 909.12 + 63.76 58.37 + 1.04 59.29 + 9.08 889.03 + 98.11 Oklahoma 3 15.33 + 1.07 16.57 + 0.36 922.59 + 46.13 60.50 + 1.31 45.10 + 4.06 1062.1 + 59.56 Florida 18 13.03 + 1.06 16.99 + 0.44 771.27 + 56.19 60.85 + 1.65 63.06 + 7.50 1186.5 + 91.96 Texas 1 14.45 + 0.00 17.69 + 0.00 816.45 + 0.00 56.63 + 0.00 41.56 + 0.00 1244.2 + 0.000 San Luis Obispo county, CA 18 12.50 + 0.68 16.90 + 0.28 748.11 + 44.63 64.33 + 1.21 . . . . Northern California 2 14.13 + 2.88 16.08 + 0.33 891.25 + 188.8 68.13 + 2.13 . . . .
1 strikes sec-1 2 milliseconds 3 hertz
Table A.12. Descriptive statistics (Mean + S.E.) of downy woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2 Mean Frequency3 Santa Ana NWR 13 10.42 + 0.60 16.21 + 0.34 651.32 + 42.99 63.81 + 3.54 46.96 + 2.90 818.48 + 67.46 Benson State Park 1 16.00 + 0.00 13.78 + 0.00 1161.5 + 0.00 72.59 + 0.00 44.74 + 0.00 585.94 + 0.000 Kerrville-Schreiner State Park 2 9.42 + 2.92 14.62 + 0.55 657.42 + 222.7 68.77 + 2.37 51.76 + 2.99 1009.6 + 213.2 Total 16 10.64 + 0.66 15.86 + 0.34 683.97 + 51.27 64.98 + 2.94 47.42 + 2.40 827.83 + 62.14
1 strikes sec-1 2 milliseconds 3 hertz
Table A.13. Descriptive statistics (Mean + S.E.) of golden-fronted woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2 Mean Frequency3 Patagonia-Sonoita Creek Preserve 8 12.48 + 0.61 14.99 + 0.55 844.16 + 64.30 72.74 + 4.46 56.90 + 8.34 966.76 + 132.67 San Pedro Nat. Riparian Corridor 1 6.00 + 0.00 15.00 + 0.00 400.00 + 0.000 41.17 + 0.00 28.10 + 0.00 324.03 + 0.000
1 strikes sec-1 2 milliseconds 3 hertz
Table A.14. Descriptive statistics (Mean + S.E.) of gila woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2 Mean Frequency3 Arizona 5 17.95 + 3.20 26.89 + 0.41 651.98 + 117.8 46.20 + 6.08 72.89 + 27.89 1362.7 + 302.2 Texas 15 26.30 + 1.34 28.40 + 0.66 928.18 + 38.19 37.67 + 1.28 298.3 + 48.92 1231.4 + 50.82 Joshua Tree National Park, CA 6 26.55 + 1.82 28.25 + 0.81 905.84 + 55.42 35.91 + 1.03 . . . .
1 strikes sec-1 2 milliseconds 3 hertz
Table A.15. Descriptive statistics (Mean + S.E.) of ladder-backed woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2 Mean Frequency3 New York 1 16.67 + 0.00 23.55 + 0.00 739.93 + 0.000 43.33 + 0.00 69.04 + 0.000 568.36 + 0.000 Wyoming 1 23.00 + 0.00 24.03 + 0.00 914.41 + 0.000 55.33 + 0.00 367.90 + 0.000 1300.5 + 0.000 Ohio 17 19.76 + 1.40 24.50 + 0.29 826.63 + 63.80 44.81 + 1.56 108.88 + 26.76 859.93 + 74.00 Michigan 6 22.09 + 1.67 25.89 + 0.84 850.62 + 55.93 39.28 + 1.45 182.14 + 60.08 909.49 + 103.7 Mississippi 3 18.75 + 3.24 24.67 + 2.07 756.07 + 60.03 46.51 + 8.13 112.35 + 79.53 874.37 + 158.4 North Carolina 2 19.50 + 0.50 24.90 + 1.56 784.37 + 66.25 40.38 + 2.58 77.62 + 52.15 708.40 + 47.93 Oklahoma 2 19.06 + 2.06 26.69 + 1.25 715.88 + 107.6 37.73 + 1.95 110.05 + 84.62 1148.9 + 40.30 Texas 1 22.00 + 0.00 27.50 + 0.00 800.00 + 0.000 32.01 + 0.00 121.51 + 0.000 737.97 + 0.000 New Hampshire 4 20.10 + 4.27 26.32 + 1.44 738.74 + 127.8 38.76 + 2.24 131.23 + 46.46 626.55 + 62.82 West Virginia 5 23.98 + 1.51 24.31 + 0.97 981.93 + 69.19 41.59 + 1.74 189.46 + 38.45 739.25 + 97.80 Colorado 3 17.67 + 0.33 28.27 + 2.35 635.93 + 42.97 35.96 + 2.98 64.82 + 42.96 1119.7 + 76.08 San Luis Obispo county, CA 19 25.79 + 0.66 26.13 + 0.24 988.61 + 23.06 39.69 + 0.45 . . . . Sequoia National Forest, CA 8 23.60 + 1.59 26.52 + 0.74 900.17 + 75.87 38.62 + 1.29 . . . . Sierra National Forest, CA 7 23.87 + 1.99 25.42 + 0.53 942.22 + 78.86 40.64 + 0.88 . . . . Northern California 1 21.70 + 0.00 26.50 + 0.00 820.00 + 0.000 40.40 + 0.00 . . . .
1 strikes sec-1 2 milliseconds 3 hertz
Table A.16. Descriptive statistics (Mean + S.E.) of hairy woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Interstrike interval2 River access road, Pozo 14 19.17 + 0.77 20.40 + 0.24 945.08 + 43.47 50.92 + 0.52 Poly Canyon, CPSU, SLO 3 17.10 + 1.37 20.41 + 0.51 837.86 + 56.08 51.02 + 1.14 Morro Bay campground 4 20.00 + 3.82 21.97 + 0.42 926.33 + 189.3 48.97 + 0.46 Resevoir canyon, SLO 6 22.70 + 2.88 20.29 + 0.62 1106.8 + 114.2 50.83 + 1.99 Cottonwood Road, Cayucos 2 16.17 + 0.63 21.88 + 0.20 740.27 + 20.73 49.01 + 0.71 Sweet springs 3 19.74 + 1.91 19.78 + 0.21 1006.5 + 107.8 50.61 + 0.81 Las Pilitas road 4 19.47 + 1.62 20.72 + 0.78 954.10 + 97.08 50.20 + 2.17 Santa Margarita, Highway 58 2 21.83 + 1.94 19.59 + 0.48 1117.4 + 128.5 51.66 + 0.66 Bidwell Park 4 19.99 + 1.19 20.67 + 0.38 970.74 + 46.42 50.09 + 0.70 Stenner Creek, CPSU, SLO 2 17.51 + 1.09 20.71 + 0.31 843.12 + 34.88 50.36 + 0.76 Cerro Alto 4 18.18 + 1.31 20.82 + 0.43 873.54 + 49.78 50.37 + 0.63 Cougar camp, Lopez Lake, SLO 1 17.69 + 0.00 21.73 + 0.00 817.31 + 0.000 48.50 + 0.00 Salinas River Valley, High Mountain Rd. 10 20.02 + 0.78 20.64 + 0.28 979.07 + 45.42 50.34 + 0.47 Woodland, CA 1 25.00 + 0.00 22.00 + 0.00 1142.7 + 0.000 48.80 + 0.00 Chilao and Charleton Flat 12 20.70 + 0.64 19.66 + 0.43 1059.0 + 40.03 53.98 + 1.34
1 strikes sec-1 2 milliseconds
Table A.17. Descriptive statistics (Mean + S.E.) of Nuttall’s woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2 Mean Frequency3 New York 1 23.00 + 0.00 13.05 + 0.00 1755.4 + 0.0000 77.21 + 0.00 151.72 + 0.000 567.11 + 0.000 Ontario 4 22.00 + 2.38 13.59 + 0.26 1613.8 + 156.53 76.13 + 1.65 148.03 + 37.23 604.14 + 53.84 Ohio 14 22.24 + 1.61 14.54 + 0.23 1527.1 + 113.79 69.54 + 2.16 133.55 + 24.88 662.24 + 64.96 Michigan 4 23.02 + 1.83 15.11 + 0.36 1524.6 + 126.37 67.22 + 1.71 119.42 + 21.74 437.98 + 20.27 Mississippi 4 22.75 + 4.29 14.37 + 0.36 1772.5 + 243.77 82.36 + 11.1 117.46 + 24.07 643.30 + 175.4 North Carolina 3 22.32 + 1.26 15.95 + 0.45 1405.1 + 116.15 64.21 + 1.71 157.51 + 29.78 728.44 + 71.34 Florida 5 22.10 + 1.58 15.80 + 0.29 1403.9 + 110.51 63.45 + 1.22 127.93 + 41.43 656.47 + 84.53 Texas 2 21.00 + 2.00 15.42 + 0.98 1357.4 + 42.59 63.07 + 8.41 113.26 + 2.820 594.74 + 22.12 New Hampshire 1 15.20 + 0.00 14.43 + 0.00 1034.4 + 0.0000 71.24 + 0.00 92.53 + 0.000 553.15 + 0.000 Sierra National Forest, CA 4 24.77 + 2.01 14.05 + 0.09 1759.9 + 142.94 73.21 + 1.73 . . . .
1 strikes sec-1 2 milliseconds 3 hertz
Table A.18. Descriptive statistics (Mean + S.E.) of pileated woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Los Angeles county, CA 3 15.88 + 2.95 12.74 + 2.40 1423.9 + 427.9 110.6 + 25.1 Sequoia National Forest, CA 2 17.30 + 5.45 13.60 + 0.80 1302.7 + 479.8 79.96 + 2.54 Sierra National Forest, CA 5 18.99 + 1.81 12.19 + 0.93 1570.6 + 80.66 88.64 + 6.82
1 strikes sec-1 2 milliseconds
Table A.19. Descriptive statistics (Mean + S.E.) of red-breasted sapsucker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2Mean Frequency3 Ohio 17 12.46 + 0.66 17.88 + 0.32 695.34 + 29.14 56.81 + 1.21 42.08 + 1.79 781.01 + 78.97 Michigan 3 10.00 + 3.46 16.85 + 1.33 570.02 + 180.3 62.63 + 4.98 43.90 + 3.87 652.47 + 85.86 Mississippi 12 13.92 + 0.92 17.49 + 0.47 803.19 + 47.61 61.35 + 2.21 40.99 + 2.76 775.35 + 93.86 Florida 11 13.15 + 1.18 19.42 + 0.47 667.25 + 51.52 53.84 + 2.36 37.58 + 2.68 1204.7 + 94.16 Georgia 2.00 16.75 + 2.25 18.29 + 1.02 925.93 + 174.1 43.69 + 8.14 33.38 + 11.0 1022.1 + 43.95 Tennessee 1 17.00 + 0.00 19.80 + 0.00 858.64 + 0.000 50.51 + 0.00 39.34 + 0.00 491.15 + 0.000
1 strikes sec-1 2 milliseconds 3 hertz
Table A.20. Descriptive statistics (Mean + S.E.) of red-bellied woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2Mean Frequency3 Wyoming 5 16.82 + 1.21 20.79 + 0.70 825.56 + 83.01 49.34 + 2.29 60.18 + 15.97 761.59 + 92.58 Ohio 12 17.55 + 1.22 20.93 + 0.52 848.17 + 63.30 51.14 + 1.65 59.83 + 16.43 601.36 + 58.31 Mississippi 34 15.48 + 0.75 20.73 + 0.31 735.47 + 32.30 52.25 + 1.73 50.03 + 5.670 636.99 + 21.71 Florida 8 15.47 + 1.41 19.22 + 0.37 811.23 + 79.21 54.39 + 2.67 59.64 + 21.00 873.79 + 103.1 Kansas 4 14.00 + 0.72 26.38 + 0.32 532.36 + 25.73 38.01 + 0.51 25.19 + 0.670 1168.8 + 42.08 Georgia 13 16.60 + 1.06 22.95 + 0.47 728.49 + 42.29 45.89 + 1.98 47.99 + 10.83 720.72 + 23.78 Iowa 1 12.00 + 0.00 22.36 + 0.00 536.72 + 0.000 44.73 + 0.00 23.43 + 0.000 760.90 + 0.000
1 strikes sec-1 2 milliseconds 3 hertz
Table A.21. Descriptive statistics (Mean + S.E.) of red-headed woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2 Mean Frequency3 Noxubee National Wildlife Refuge 23 10.45 + 0.81 20.87 + 0.43 496.94 + 32.66 51.41 + 1.94 34.69 + 1.77 811.64 + 66.89 Sandhills State Wildlife Area, NC 7 11.65 + 0.54 21.11 + 0.65 560.63 + 35.64 53.00 + 7.26 36.62 + 6.71 849.61 + 121.2 Three Lakes, Florida 7 13.43 + 1.01 22.35 + 1.52 632.64 + 80.07 46.51 + 3.19 43.82 + 8.19 961.83 + 121.3 Oclockonee State Park, Florida 1 14.50 + 0.0 18.39 + 0.00 799.89 + 0.000 54.51 + 0.00 33.15 + 0.00 822.93 + 320.8 Sam Houston National Forest, TX 2 8.000 + 2.00 17.00 + 1.21 471.23 + 91.07 59.34 + 4.02 43.26 + 6.08 852.50 + 226.8 Appachicola National Forest, FL 7 11.78 + 1.11 21.40 + 1.27 551.46 + 47.29 52.62 + 3.13 57.87 + 16.2 1003.8 + 121.2 Angelina National Forest, Texas 6 10.48 + 1.59 23.43 + 0.62 454.05 + 76.64 43.10 + 1.19 33.14 + 1.68 1299.6 + 130.9 Walthour Moss Foundation Lands 2 9.000 + 0.00 21.88 + 0.72 432.70 + 2.820 61.57 + 13.4 41.98 + 9.18 670.15 + 226.8 Weymouth Woods, North Carolina 3 12.59 + 2.30 23.43 + 1.19 531.45 + 75.99 43.36 + 2.05 41.30 + 12.1 938.73 + 185.2 Wright Lake, Florida 8 12.48 + 1.40 20.73 + 1.14 618.15 + 80.14 54.52 + 5.05 40.23 + 5.52 1037.5 + 113.4 Wade Tract, Georgia 9 11.30 + 0.90 21.98 + 1.32 511.13 + 19.65 52.85 + 7.44 38.29 + 6.86 643.33 + 106.9 Piedmont National Wildlife Refuge 3 9.710 + 2.19 23.33 + 0.82 407.28 + 87.91 43.67 + 1.60 31.89 + 1.16 780.34 + 185.2
1 strikes sec-1 2 milliseconds 3 hertz
Table A.22. Descriptive statistics (Mean + S.E.) of red-cockaded woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2Mean Frequency3 Wyoming 2 14.95 + 3.55 8.98 + 0.52 1765.7 + 374.9 143.0 + 21.7 123.40 + 25.92 929.82 + 4.900 Colorado 37 16.59 + 0.81 12.14 + 0.28 1425.2 + 91.53 96.01 + 4.04 105.91 + 11.05 1182.6 + 43.23 Alberta 2 10.25 + 2.25 11.75 + 0.85 861.44 + 127.5 85.57 + 6.18 62.370 + 0.130 959.19 + 61.62
1 strikes sec-1 2 milliseconds 3 hertz
Table A.23. Descriptive statistics (Mean + S.E.) of red-naped sapsucker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2Mean Frequency3 Wyoming 1 17.14 + 0.00 12.85 + 0.00 1324.4 + 0.00 79.29 + 0.00 101.2 + 0.000 674.31 + 0.000 Quebec 2 15.81 + 0.50 11.82 + 0.38 1339.0 + 1.81 88.58 + 3.32 100.5 + 10.75 1084.4 + 34.54 Colorado 6 16.81 + 1.30 14.71 + 0.90 1146.2 + 73.3 69.24 + 3.52 70.85 + 15.82 986.21 + 104.8
1 strikes sec-1 2 milliseconds 3 hertz
Table A.24. Descriptive statistics (Mean + S.E.) of three-toed woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Interstrike interval2 Los Angeles county, CA 18 16.93 + 1.15 18.82 + 0.29 903.17 + 60.82 58.05 + 1.52 Sequoia National Forest, CA 11 18.25 + 1.69 19.58 + 0.27 1001.9 + 61.17 50.24 + 0.71 Sierra National Forest, CA 12 19.01 + 1.98 19.83 + 0.53 945.97 + 79.47 52.00 + 1.27
1 strikes sec-1 2 milliseconds 3 hertz
Table A.25. Descriptive statistics (Mean + S.E.) of white-headed woodpecker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2 Mean Frequency3 Wyoming 2 9.13 + 0.13 12.32 + 1.65 786.62 + 142.7 85.19 + 13.64 67.33 + 16.48 744.37 + 88.42 Montana 1 18.00 + 0.00 11.44 + 0.00 1798.0 + 0.00 98.43 + 0.000 112.4 + 0.000 828.93 + 0.000 Sierra National Forest, CA 2 16.67 + 4.67 10.42 + 2.48 1421.7 + 491.7 77.50 + 11.50 . . . .
1 strikes sec-1 2 milliseconds 3 hertz
Table A.26. Descriptive statistics (Mean + S.E.) of Williamson’s sapsucker drums in different Nearctic locations.
N No. Strikes Cadence1 Duration2 Strike Duration2 Interval Duration2 Mean Frequency3 Chubb River, Lake Placid, NY 4 26.83 + 0.65 9.18 + 0.66 2990.2 + 250.55 114.48 + 0.000 634.74 + 0.000 1296.5 + 307.9 Algonquin Provincial Park, Ont. 10 22.55 + 1.97 13.01 + 0.73 1819.2 + 195.47 . . . . 1139.6 + 48.37 Old Forge, Adirondack Mtn 16 27.97 + 2.38 10.55 + 0.37 2805.4 + 330.00 95.84 + 6.210 148.20 + 21.98 1049.3 + 64.56 Kraus Wilderness Preserve, OH 1 18.33 + 0.00 14.37 + 0.00 1300.0 + 0.0000 . . . . 1599.3 + 0.000 Gleason's Landing, New York 4 15.50 + 2.60 24.06 + 9.69 1006.3 + 363.77 . . . . 1212.1 + 205.8 North Adirondack Mountains 10 19.77 + 1.81 8.78 + 0.65 2319.6 + 218.78 121.98 + 10.16 164.03 + 22.30 653.58 + 40.66 Moose Lake, New York 12 22.97 + 2.89 9.19 + 0.74 2692.3 + 319.37 118.96 + 9.880 192.27 + 32.54 618.19 + 35.01
1 strikes sec-1 2 milliseconds 3 hertz
Table A.27. Descriptive statistics (Mean + S.E.) of yellow-bellied sapsucker drums in different Nearctic locations.
Predicted Group Membership Location AZ SLO LA Total Original Count Arizona 1 0 0 1 San Luis Obispo county, CA 0 2 0 2 Los Angeles county, CA 0 1 4 5
% Arizona 100 0 0 100 San Luis Obispo county, CA 0 100 0 100 Los Angeles county, CA 0 20 80 100 Cross-validation Count Arizona 0 1 0 1 San Luis Obispo county, CA 2 0 0 2 Los Angeles county, CA 4 0 1 5
% Arizona 0 100 0 100 San Luis Obispo county, CA 100 0 0 100 Los Angeles county, CA 80 0 20 100
87.5% of original grouped cases correctly classified. 12.5% of cross-validated grouped cases correctly classified.
Table A.28. Discriminant function analysis for the variable region in acorn woodpeckers.
Predicted Group Membership Location NY VT Ontario WY Quebec Yos. Total Original Count New York 1.0 3.0 0.0 0.0 0.0 0.0 4 Vermont 0.0 1.0 0.0 0.0 0.0 0.0 1 Ontario 4.0 5.0 9.0 1.0 3.0 5.0 27 Wyoming 0.0 0.0 0.0 1.0 0.0 0.0 1 Quebec 2.0 2.0 3.0 0.0 5.0 2.0 14 Yosemite, CA 0.0 0.0 1.0 2.0 1.0 5.0 9
% New York 25.0 75.0 0.0 0.0 0.0 0.0 100 Vermont 0.0 100.0 0.0 0.0 0.0 0.0 100 Ontario 14.8 18.5 33.3 3.7 11.1 18.5 100 Wyoming 0.0 0.0 0.0 100.0 0.0 0.0 100 Quebec 14.3 14.3 21.4 0.0 35.7 14.3 100 Yosemite, CA 0.0 0.0 11.1 22.2 11.1 55.6 100 Cross-validated Count New York 0.0 4.0 0.0 0.0 0.0 0.0 4 Vermont 1.0 0.0 0.0 0.0 0.0 0.0 1 Ontario 4.0 5.0 8.0 1.0 4.0 5.0 27 Wyoming 0.0 0.0 0.0 0.0 0.0 1.0 1 Quebec 2.0 2.0 3.0 0.0 5.0 2.0 14 Yosemite, CA 0.0 0.0 2.0 3.0 1.0 3.0 9
% New York 0.0 100.0 0.0 0.0 0.0 0.0 100 Vermont 100.0 0.0 0.0 0.0 0.0 0.0 100 Ontario 14.8 18.5 29.6 3.7 14.8 18.5 100 Wyoming 0.0 0.0 0.0 0.0 0.0 100.0 100 Quebec 14.3 14.3 21.4 0.0 35.7 14.3 100 Yosemite, CA 0.0 0.0 22.2 33.3 11.1 33.3 100
39.3% of original grouped cases correctly classified. 28.6% of cross-validated grouped cases correctly classified.
Table A.29. Discriminant function analysis for the variable region for black-backed woodpeckers.
Predicted Group Membership Location AZ TX JTNP Total Original Count Arizona 4.0 1.0 0.0 5 Texas 0.0 14.0 1.0 15 Joshua Tree National Park, CA 0.0 4.0 2.0 6
% Arizona 80.0 20.0 0.0 100 Texas 0.0 93.3 6.7 100 Joshua Tree National Park, CA 0.0 66.7 33.3 100 Cross-validated Count Arizona 0.0 2.0 3.0 5 Texas 15.0 0.0 0.0 15 Joshua Tree National Park, CA 6.0 0.0 0.0 6
% Arizona 0.0 40.0 60.0 100 Texas 100.0 0.0 0.0 100 Joshua Tree National Park, CA 100.0 0.0 0.0 100
76.9% of original grouped cases correctly classified. 0% of cross-validated grouped cases correctly classified.
Table A.30. Discriminant function analysis for the variable region in ladder-backed woodpeckers.
Predicted Group Membership Location LA Seq. NF Yos. Total Original Los Angeles county, CA 3.0 0.0 0.0 3 Count Sequoia National Forest, CA 0.0 1.0 1.0 2 Yosemite, CA 0.0 3.0 2.0 5
% Los Angeles county, CA 100.0 0.0 0.0 100 Sequoia National Forest, CA 0.0 50.0 50.0 100 Yosemite, CA 0.0 60.0 40.0 100
Cross-validated Count Los Angeles county, CA 2.0 0.0 1.0 3 Sequoia National Forest, CA 0.0 0.0 2.0 2 Yosemite, CA 1.0 3.0 1.0 5
% Los Angeles county, CA 66.7 0.0 33.3 100 Sequoia National Forest, CA 0.0 0.0 100.0 100 Yosemite, CA 20.0 60.0 20.0 100
60.0% of original grouped cases correctly classified. 30.0% of cross-validated grouped cases correctly classified.
Table A.31. Discriminant function analysis for the variable region in red-breasted sapsuckers
Predicted Group Membership Location OH MI MS FL GA TN Total Original Count Ohio 8.0 1.0 3.0 3.0 0.0 2.0 17 Michigan 1.0 1.0 1.0 0.0 0.0 0.0 3 Mississippi 4.0 0.0 7.0 0.0 0.0 1.0 12 Florida 2.0 0.0 1.0 8.0 0.0 0.0 11 Georgia 1.0 0.0 0.0 0.0 1.0 0.0 2 Tennessee 0.0 0.0 0.0 0.0 0.0 1.0 1
% Ohio 47.1 5.9 17.6 17.6 0.0 11.8 100 Michigan 33.3 33.3 33.3 0.0 0.0 0.0 100 Mississippi 33.3 0.0 58.3 0.0 0.0 8.3 100 Florida 18.2 0.0 9.1 72.7 0.0 0.0 100 Georgia 50.0 0.0 0.0 0.0 50.0 0.0 100 Tennessee 0.0 0.0 0.0 0.0 0.0 100.0 100 Cross-validated Count Ohio 6.0 2.0 3.0 4.0 0.0 2.0 17 Michigan 2.0 0.0 1.0 0.0 0.0 0.0 3 Mississippi 4.0 0.0 6.0 1.0 0.0 1.0 12 Florida 2.0 1.0 1.0 7.0 0.0 0.0 11 Georgia 1.0 0.0 1.0 0.0 0.0 0.0 2 Tennessee 1.0 0.0 0.0 0.0 0.0 0.0 1
% Ohio 35.3 11.8 17.6 23.5 0.0 11.8 100 Michigan 66.7 0.0 33.3 0.0 0.0 0.0 100 Mississippi 33.3 0.0 50.0 8.3 0.0 8.3 100 Florida 18.2 9.1 9.1 63.6 0.0 0.0 100 Georgia 50.0 0.0 50.0 0.0 0.0 0.0 100 Tennessee 100.0 0.0 0.0 0.0 0.0 0.0 100
56.5% of original grouped cases correctly classified. 41.3% of cross-validated grouped cases correctly classified.
Table A.32. Discriminant function analysis for the variable region in red-bellied woodpeckers.
Predicted Group Membership Location MS NC FL TX GA Total Original Count Mississippi 14.0 2.0 2.0 2.0 3.0 23 North Carolina 2.0 3.0 1.0 2.0 4.0 12 Florida 3.0 3.0 7.0 6.0 4.0 23 Texas 2.0 0.0 1.0 4.0 1.0 8 Georgia 2.0 1.0 1.0 1.0 7.0 12
% Mississippi 60.9 8.7 8.7 8.7 13.0 100 North Carolina 16.7 25.0 8.3 16.7 33.3 100 Florida 13.0 13.0 30.4 26.1 17.4 100 Texas 25.0 0.0 12.5 50.0 12.5 100 Georgia 16.7 8.3 8.3 8.3 58.3 100 Cross-validated Count Mississippi 9.0 4.0 3.0 3.0 4.0 23 North Carolina 2.0 0.0 1.0 2.0 7.0 12 Florida 4.0 3.0 6.0 6.0 4.0 23 Texas 2.0 0.0 1.0 4.0 1.0 8 Georgia 4.0 2.0 1.0 1.0 4.0 12
% Mississippi 39.1 17.4 13.0 13.0 17.4 100 North Carolina 16.7 0.0 8.3 16.7 58.3 100 Florida 17.4 13.0 26.1 26.1 17.4 100 Texas 25.0 0.0 12.5 50.0 12.5 100 Georgia 33.3 16.7 8.3 8.3 33.3 100
44.9% of original grouped cases correctly classified. 29.5% of cross-validated grouped cases correctly classified.
Table A.33. Discriminant function analysis for the variable region in red-cockaded woodpeckers.
Predicted Group Membership Location WY OH MS FL KS GA IA Total Original Count Wyoming 1.0 1.0 2.0 1.0 0.0 0.0 0.0 5 Ohio 1.0 5.0 2.0 2.0 0.0 1.0 1.0 12 Mississippi 4.0 8.0 10.0 4.0 0.0 2.0 6.0 34 Florida 0.0 0.0 2.0 6.0 0.0 0.0 0.0 8 Kansas 0.0 0.0 0.0 0.0 4.0 0.0 0.0 4 Georgia 1.0 1.0 1.0 0.0 0.0 8.0 2.0 13 Iowa 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1
% Wyoming 20.0 20.0 40.0 20.0 0.0 0.0 0.0 100 Ohio 8.3 41.7 16.7 16.7 0.0 8.3 8.3 100 Mississippi 11.8 23.5 29.4 11.8 0.0 5.9 17.6 100 Florida 0.0 0.0 25.0 75.0 0.0 0.0 0.0 100 Kansas 0.0 0.0 0.0 0.0 100.0 0.0 0.0 100 Georgia 7.7 7.7 7.7 0.0 0.0 61.5 15.4 100 Iowa 0.0 0.0 0.0 0.0 0.0 0.0 100.0 100 Cross-validated Count Wyoming 0.0 1.0 2.0 1.0 0.0 1.0 0.0 5 Ohio 1.0 5.0 2.0 2.0 0.0 1.0 1.0 12 Mississippi 4.0 10.0 7.0 5.0 0.0 2.0 6.0 34 Florida 1.0 1.0 2.0 4.0 0.0 0.0 0.0 8 Kansas 0.0 0.0 0.0 0.0 4.0 0.0 0.0 4 Georgia 1.0 2.0 1.0 0.0 1.0 5.0 3.0 13 Iowa 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1
% Wyoming 0.0 20.0 40.0 20.0 0.0 20.0 0.0 100 Ohio 8.3 41.7 16.7 16.7 0.0 8.3 8.3 100 Mississippi 11.8 29.4 20.6 14.7 0.0 5.9 17.6 100 Florida 12.5 12.5 25.0 50.0 0.0 0.0 0.0 100 Kansas 0.0 0.0 0.0 0.0 100.0 0.0 0.0 100 Georgia 7.7 15.4 7.7 0.0 7.7 38.5 23.1 100 Iowa 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100
45.5% of original grouped cases correctly classified. 32.5% of cross-validated grouped cases correctly classified.
Table A.34. Discriminant function analysis for the variable region in red-headed woodpeckers.
Predicted Group Membership Location WY CO Alberta Total Original Count Wyoming 2.0 0.0 0.0 2 Colorado 1.0 27.0 5.0 33 Alberta 0.0 0.0 2.0 2
% Wyoming 100.0 0.0 0.0 100 Colorado 3.0 81.8 15.2 100 Alberta 0.0 0.0 100.0 10 Cross-validated Count Wyoming 1.0 0.0 1.0 2 Colorado 1.0 24.0 8.0 33 Alberta 0.0 0.0 2.0 2
% Wyoming 50.0 0.0 50.0 100 Colorado 3.0 72.7 24.2 100 Alberta 0.0 0.0 100.0 100
83.8% of original grouped cases correctly classified. 73.0% of cross-validated grouped cases correctly classified.
Table A.35. Discriminant function analysis for the variable region in red-naped sapsuckers.
Predicted Group Membership Location WY Quebec CO Total Original Count Wyoming 1 0 0 1 Quebec 0 2 0 2 Colorado 0 0 6 6
% Wyoming 100 0 0 100 Quebec 0 100 0 100 Colorado 0 0 100 100 Cross-validated Count Wyoming 0 1 0 1 Quebec 2 0 0 2 Colorado 4 2 0 6
% Wyoming 0 100 0 100 Quebec 100 0 0 100 Colorado 66.7 33.3 0 100
100.0% of original grouped cases correctly classified. 0% of cross-validated grouped cases correctly classified.
Table A.36. Discriminant function analysis for the variable region in three-toed woodpeckers.
Predicted Group Membership Location LA Seq. S. Seq. Yos. N. Seq. Total Original Count Los Angeles county, CA 11.0 1.0 0.0 5.0 1.0 18 Sequoia National Forest, CA 0.0 3.0 2.0 0.0 2.0 7 S. Sequoia National Forest, CA 0.0 0.0 1.0 1.0 1.0 3 Yosemite, CA 1.0 1.0 1.0 5.0 4.0 12 N. Sequoia National Forest, CA 0.0 0.0 0.0 0.0 1.0 1
% Los Angeles county, CA 61.1 5.6 0.0 27.8 5.6 100 Sequoia National Forest, CA 0.0 42.9 28.6 0.0 28.6 100 Sequoia National Forest, CA 0.0 0.0 33.3 33.3 33.3 100 Yosemite, CA 8.3 8.3 8.3 41.7 33.3 100 Sequoia National Forest, CA 0.0 0.0 0.0 0.0 100.0 100 Cross-validated Count Los Angeles county, CA 10.0 1.0 0.0 6.0 1.0 18 Sequoia National Forest, CA 0.0 1.0 3.0 1.0 2.0 7 Sequoia National Forest, CA 0.0 1.0 0.0 1.0 1.0 3 Yosemite, CA 1.0 1.0 1.0 5.0 4.0 12 Sequoia National Forest, CA 0.0 0.0 0.0 1.0 0.0 1
% Los Angeles county, CA 55.6 5.6 0.0 33.3 5.6 100 Sequoia National Forest, CA 0.0 14.3 42.9 14.3 28.6 100 Sequoia National Forest, CA 0.0 33.3 0.0 33.3 33.3 100 Yosemite, CA 8.3 8.3 8.3 41.7 33.3 100 Sequoia National Forest, CA 0.0 0.0 0.0 100.0 0.0 100
51.2% of original grouped cases correctly classified. 39.0% of cross-validated grouped cases correctly classified.
Table A.37. Discriminant function analysis for the variable region in white-headed woodpeckers.
Predicted Group Membership Location WY MT Yose. Total Original Count Wyoming 2 0 0 2 Montana 0 1 0 1 Yosemite, CA 0 0 2 2
% Wyoming 100 0 0 100 Montana 0 100 0 100 Yosemite, CA 0 0 100 100 Cross-validated Count Wyoming 0 2 0 2 Montana 1 0 0 1 Yosemite, CA 1 1 0 2
% Wyoming 0 100 0 100 Montana 100 0 0 100 Yosemite, CA 50 50 0 100
100.0% of original grouped cases correctly classified. 0% of cross-validated grouped cases correctly classified.
Table A.38. Discriminant function analysis for the variable region in Williamson’s sapsuckers.
Predicted Group Membership Location NY Ontario OH MI Total Original Count New York 32.0 6.0 4.0 0.0 42 Ontario 1.0 6.0 2.0 0.0 9 Ohio 0.0 1.0 1.0 0.0 2 Michigan 0.0 2.0 1.0 1.0 4
% New York 76.2 14.3 9.5 0.0 100 Ontario 11.1 66.7 22.2 0.0 100 Ohio 0.0 50.0 50.0 0.0 100 Michigan 0.0 50.0 25.0 25.0 100 Cross-validated Count New York 32.0 6.0 4.0 0.0 42 Ontario 1.0 5.0 3.0 0.0 9 Ohio 0.0 1.0 1.0 0.0 2 Michigan 0.0 2.0 1.0 1.0 4
% New York 76.2 14.3 9.5 0.0 100 Ontario 11.1 55.6 33.3 0.0 100 Ohio 0.0 50.0 50.0 0.0 100 Michigan 0.0 50.0 25.0 25.0 100
70.2% of original grouped cases correctly classified. 68.4% of cross-validated grouped cases correctly classified.
Table A.39. Discriminant function analysis for the variable region in yellow- bellied sapsuckers.
Predicted Group Membership Location AZ ON WY OH MI MS NC OK FL TX CO KS GA SLO LA Seq SoSeq Yos N.CA LA OH Total Original Arizona 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 Count Ontario 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 Wyoming 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Ohio 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 7 Michigan 0.0 1.0 0.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 7 Mississippi 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 N. Carolina \0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 5 Oklahoma 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 Florida 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Texas 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Colorado 2.0 3.0 0.0 1.0 0.0 2.0 1.0 0.0 0.0 0.0 0.0 2.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 3.0 19 Kansas 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 Georgia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 SLO county, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 LA county, CA 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 4 Sequoia NF, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1 So. Seq. NF, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1 Yosemite, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1 N. California 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1 S.LA county, CA 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 6 Ohio 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1
Table A.40. Discriminant function analysis for the variable region in northern flickers.
Predicted group membership Location AZ ON WY OH MI MS NC OK FL TX CO KS GA SLO LA Seq SoSeq Yos N.CA LA OH Total % Arizona 0.0 0.0 0.0 0.0 0.0 25.0 0.0 25.0 0.0 0.0 25.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Ontario 0.0 66.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Wyoming 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Ohio 0.0 0.0 0.0 28.6 0.0 0.0 0.0 0.0 14.3 0.0 0.0 0.0 14.3 0.0 0.0 14.3 14.3 14.3 0.0 0.0 0.0 100 Michigan 0.0 14.3 0.0 0.0 14.3 14.3 0.0 14.3 0.0 0.0 0.0 0.0 0.0 0.0 14.3 0.0 0.0 0.0 14.3 0.0 14.3 100 Mississippi 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 North Carolina 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.0 20.0 0.0 0.0 20.0 0.0 20.0 0.0 0.0 0.0 0.0 20.0 0.0 100 Oklahoma 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Florida 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Texas 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Colorado 10.5 15.8 0.0 5.3 0.0 10.5 5.3 0.0 0.0 0.0 0.0 10.5 5.3 0.0 0.0 5.3 5.3 5.3 0.0 5.3 15.8 100 Kansas 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 0.0 75.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Georgia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 SLO county, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 LA county, CA 25.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 25.0 0.0 0.0 100 Sequoia NF, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 100 S. Seq. NF, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 100 Yosemite, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 100 N. California 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 100 S. LA co., CA 16.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16.7 0.0 0.0 0.0 16.7 0.0 0.0 0.0 0.0 0.0 16.7 16.7 16.7 100 Ohio 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 100
Table A.40. Discriminant function analysis for the variable region in northern flickers, continued.
Cross-validated Predicted group membership Location AZ ON WY OH MI MS NC OK FL TX CO KS GA SLO LA Seq SoSeq Yos N.CA LA OH Total Count Arizona 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 Ontario 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 Wyoming 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1 Ohio 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 2.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 7 Michigan 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 1.0 0.0 1.0 7 Mississippi 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 North Carolina 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 5 Oklahoma 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 Florida 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 Texas 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Colorado 2.0 2.0 0.0 1.0 0.0 3.0 1.0 0.0 0.0 0.0 0.0 2.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 3.0 19 Kansas 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 Georgia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1 SLO county, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 LA county, CA 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 4 Sequoia NF, CA 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 S. Seq. NF, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1 Yosemite, CA 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 N. California 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1 S. LA co., CA 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 6 Ohio 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1
Table A.40. Discriminant function analysis for the variable region in northern flickers, continued.
Predicted group membership Location AZ ON WY OH MI MS NC OK FL TX CO KS GA SLO LA Seq SoSeq Yos N.CA LA OH Total % Arizona 0.0 0.0 0.0 0.0 0.0 25.0 0.0 25.0 0.0 0.0 25.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Ontario \0.0 33.3 0.0 0.0 0.0 0.0 0.0 33.3 0.0 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Wyoming 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 100 Ohio 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14.3 28.6 0.0 0.0 14.3 0.0 0.0 14.3 14.3 14.3 0.0 0.0 0.0 100 Michigan 0.0 14.3 0.0 0.0 0.0 14.3 0.0 14.3 0.0 0.0 0.0 0.0 0.0 0.0 28.6 0.0 0.0 0.0 14.3 0.0 14.3 100 Mississippi 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 North Carolina 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.0 20.0 0.0 0.0 20.0 0.0 20.0 0.0 0.0 0.0 0.0 20.0 0.0 100 Oklahoma 0.0 50.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Florida 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 100 Texas 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Colorado 10.5 10.5 0.0 5.3 0.0 15.8 5.3 0.0 0.0 0.0 0.0 10.5 5.3 0.0 0.0 5.3 5.3 5.3 0.0 5.3 15.8 100 Kansas 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 0.0 75.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Georgia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 100 SLO county, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 LA county, CA 25.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 25.0 0.0 0.0 100 Sequoia NF, CA 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 S. Seq. NF, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 100 Yosemite, CA 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 N. California 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 100 S. LA co., CA 16.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16.7 0.0 0.0 0.0 16.7 0.0 0.0 0.0 0.0 0.0 16.7 16.7 16.7 100 Ohio 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100
32.9% of original grouped cases correctly classified. 12.3% of cross-validated grouped cases correctly classified.
Table A.40. Discriminant function analysis for the variable region in northern flickers, continued.
Predicted Group Membership Location RiRd. P.CnynMB Res. Cott. SSpr. LPil. H58 Bid Stenn Cerro Coug Salin Wood Chilao Total Original River access road, Pozo 1.0 3.0 0.0 1.0 1.0 1.0 0.0 0.0 2.0 0.0 3.0 0.0 1.0 0.0 1.0 14 Count Poly Canyon, CPSU, SLO 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 3 Morro Bay campground 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 4 Reservoir canyon, SLO 0.0 1.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 6 Cottonwood Rd, Cayucos 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 Sweet springs, Los Osos 0.0 1.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 Las Pilitas road 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 1.0 0.0 1.0 4 Santa Margarita, Hwy 58 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 Bidwell Park 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 4 Stenner Creek, CPSU 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 2 Cerro Alto 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 4 Cougar camp, Lopez Lake, SLO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1 Salinas River Valley 0.0 0.0 0.0 0.0 1.0 1.0 1.0 2.0 1.0 0.0 1.0 1.0 1.0 0.0 1.0 10 Woodland, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1 Chilao and Charleton Flat, LA, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 3.0 0.0 1.0 0.0 6.0 12
Table A.41. Discriminant function analysis for the variable region in Nuttall’s woodpeckers.
Predicted Group Membership Location RiRd. P.CnynMB Res. Cott. SSpr. LPil. H58 Bid Stenn Cerro Coug Salin Wood Chilao Total % River access road, Pozo 7.1 21.4 0.0 7.1 7.1 7.1 0.0 0.0 14.3 0.0 21.4 0.0 7.1 0.0 7.1 100 Poly Canyon, CPSU, SLO 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0 33.3 0.0 0.0 0.0 0.0 100 Morro Bay campground 0.0 0.0 25.0 25.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 100 Reservoir canyon, SLO 0.0 16.7 0.0 33.3 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16.7 0.0 100 Cottonwood Road, Cayucos 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Sweet springs, Los Osos 0.0 33.3 0.0 0.0 0.0 66.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Las Pilitas road 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 25.0 0.0 25.0 100 Santa Margarita, Highway 58 50.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Bidwell Park 0.0 25.0 0.0 0.0 0.0 0.0 25.0 0.0 25.0 0.0 25.0 0.0 0.0 0.0 0.0 100 Stenner Creek, CPSU, SLO 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 100 Cerro Alto 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 0.0 25.0 0.0 100 Cougar camp, Lopez Lake, SLO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 100 Salinas River Valley, High Mtn.Rd. 0.0 0.0 0.0 0.0 10.0 10.0 10.0 20.0 10.0 0.0 10.0 10.0 10.0 0.0 10.0 100 Woodland, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 100 Chilao and Charleton Flat, LA, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.3 8.3 0.0 25.0 0.0 8.3 0.0 50.0 100
Table A.41. Discriminant function analysis for the variable region in Nuttall’s woodpeckers, continued.
Cross-validated Predicted Group Membership Location RiRd. P.CnynMB Res. Cott. SSpr. LPil. H58 Bid Stenn Cerro Coug Salin Wood Chilao Total
Count River access road, Pozo 1.0 3.0 0.0 1.0 1.0 1.0 0.0 0.0 2.0 0.0 3.0 0.0 1.0 0.0 1.0 14 Poly Canyon, CPSU, SLO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 3 Morro Bay campground 0.0 0.0 0.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 4 Reservoir canyon, SLO 0.0 1.0 0.0 1.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 6 Cottonwood Road, Cayucos 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 Sweet springs, Los Osos 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 Las Pilitas road 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 1.0 0.0 1.0 4 Santa Margarita, Highway 58 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 Bidwell Park 0.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 4 Stenner Creek, CPSU, SLO 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 2 Cerro Alto 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 4 Cougar camp, Lopez Lake, SLO 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Salinas River Valley, High Mtn.Rd. 0.0 0.0 0.0 0.0 1.0 1.0 1.0 2.0 2.0 0.0 1.0 1.0 0.0 0.0 1.0 10 Woodland, CA 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Chilao and Charleton Flat, LA, CA 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 2.0 0.0 3.0 0.0 0.0 0.0 5.0 12
Table A.41. Discriminant function analysis for the variable region in Nuttall’s woodpeckers, continued.
Predicted Group Membership Location RiRd. P.CnynMB Res. Cott. SSpr. LPil. H58 Bid Stenn Cerro Coug Salin Wood Chilao Total % River access road, Pozo 7.1 21.4 0.0 7.1 7.1 7.1 0.0 0.0 14.3 0.0 21.4 0.0 7.1 0.0 7.1 100 Poly Canyon, CPSU, SLO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33.3 33.3 33.3 0.0 0.0 0.0 0.0 100 Morro Bay campground 0.0 0.0 0.0 25.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 100 Reservoir canyon, SLO 0.0 16.7 0.0 16.7 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0 100 Cottonwood Road, Cayucos 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Sweet springs, Los Osos 0.0 33.3 0.0 0.0 0.0 33.3 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Las Pilitas road 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 25.0 0.0 25.0 100 Santa Margarita, Highway 58 50.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Bidwell Park 0.0 25.0 0.0 25.0 0.0 0.0 25.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 0.0 100 Stenner Creek, CPSU, SLO 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 100 Cerro Alto 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 0.0 0.0 25.0 0.0 100 Cougar camp, Lopez Lake, SLO 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Salinas River Valley, High Mtn Rd. 0.0 0.0 0.0 0.0 10.0 10.0 10.0 20.0 20.0 0.0 10.0 10.0 0.0 0.0 10.0 100 Woodland, CA 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Chilao and Charleton Flat, LA, CA 0.0 0.0 8.3 0.0 0.0 0.0 0.0 8.3 16.7 0.0 25.0 0.0 0.0 0.0 41.7 100 30.6% of original grouped cases correctly classified. 15.3% of cross-validated grouped cases correctly classified.
Table A.41. Discriminant function analysis for the variable region in Nuttall’s woodpeckers, continued.
Predicted Group Membership Location NY Ontario OH MI MS NC FL TX NH Yos. Total Original Count New York 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Ontario 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 4 Ohio 0.0 3.0 2.0 1.0 0.0 1.0 0.0 1.0 3.0 3.0 14 Michigan 0.0 0.0 2.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 4 Mississippi 0.0 0.0 1.0 0.0 2.0 0.0 0.0 0.0 0.0 1.0 4 North Carolina 0.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 0.0 0.0 3 Florida 0.0 0.0 1.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 5 Texas 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 2 New Hampshire 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1 Yosemite, CA 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 4
% New York 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Ontario 25.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 100 Ohio 0.0 21.4 14.3 7.1 0.0 7.1 0.0 7.1 21.4 21.4 100 Michigan 0.0 0.0 50.0 0.0 0.0 25.0 0.0 25.0 0.0 0.0 100 Mississippi 0.0 0.0 25.0 0.0 50.0 0.0 0.0 0.0 0.0 25.0 100 North Carolina 0.0 0.0 0.0 33.3 0.0 33.3 33.3 0.0 0.0 0.0 100 Florida 0.0 0.0 20.0 0.0 0.0 40.0 40.0 0.0 0.0 0.0 100 Texas 0.0 0.0 50.0 0.0 0.0 0.0 50.0 0.0 0.0 0.0 100 New Hampshire 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 100 Yosemite, CA 0.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 75.0 100
Table A.42. Discriminant function analysis for the variable region in pileated woodpeckers.
Predicted Group Membership Cross-validated Location NY Ontario OH MI MS NC FL TX NH Yos. Total Count New York 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Ontario 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 4 Ohio 0.0 3.0 1.0 2.0 0.0 1.0 0.0 1.0 3.0 3.0 14 Michigan 0.0 0.0 2.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 4 Mississippi 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 4 North Carolina 0.0 0.0 0.0 1.0 0.0 0.0 2.0 0.0 0.0 0.0 3 Florida 0.0 0.0 1.0 0.0 0.0 4.0 0.0 0.0 0.0 0.0 5 Texas 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 2 New Hampshire 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Yosemite, CA 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 4
% New York 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Ontario 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 100 Ohio 0.0 21.4 7.1 14.3 0.0 7.1 0.0 7.1 21.4 21.4 100 Michigan 0.0 0.0 50.0 0.0 0.0 25.0 0.0 25.0 0.0 0.0 100 Mississippi 25.0 0.0 25.0 0.0 0.0 0.0 0.0 0.0 25.0 25.0 100 North Carolina 0.0 0.0 0.0 33.3 0.0 0.0 66.7 0.0 0.0 0.0 100 Florida 0.0 0.0 20.0 0.0 0.0 80.0 0.0 0.0 0.0 0.0 100 Texas 0.0 0.0 50.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 100 New Hampshire 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Yosemite, CA 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 100
33.3% of original grouped cases correctly classified. 7.1% of cross-validated grouped cases correctly classified.
Table A.42. Discriminant function analysis for the variable region in pileated woodpeckers, continued.
Predicted Group Membership Location Ontario WY OH MI MS NC OK FL TX SLO N. CA Total Original Count Ontario 4.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 Wyoming 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 2 Ohio 4.0 1.0 6.0 8.0 5.0 4.0 7.0 1.0 8.0 1.0 0.0 45 Michigan 0.0 0.0 0.0 3.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 4 Mississippi 0.0 0.0 0.0 1.0 4.0 0.0 2.0 0.0 0.0 0.0 0.0 7 North Carolina 0.0 0.0 1.0 0.0 0.0 3.0 1.0 0.0 1.0 0.0 0.0 6 Oklahoma 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 3 Florida 0.0 0.0 0.0 4.0 2.0 3.0 3.0 0.0 3.0 1.0 2.0 18 Texas 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1 SLO county, CA 0.0 0.0 0.0 0.0 1.0 0.0 0.0 2.0 0.0 11.0 4.0 18 N. California 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 2
% Ontario 80.0 20.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Wyoming 0.0 50.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 100 Ohio 8.9 2.2 13.3 17.8 11.1 8.9 15.6 2.2 17.8 2.2 0.0 100 Michigan 0.0 0.0 0.0 75.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Mississippi 0.0 0.0 0.0 14.3 57.1 0.0 28.6 0.0 0.0 0.0 0.0 100 North Carolina 0.0 0.0 16.7 0.0 0.0 50.0 16.7 0.0 16.7 0.0 0.0 100 Oklahoma 0.0 0.0 0.0 0.0 33.3 33.3 33.3 0.0 0.0 0.0 0.0 100 Florida 0.0 0.0 0.0 22.2 11.1 16.7 16.7 0.0 16.7 5.6 11.1 100 Texas 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 100 SLO county, CA 0.0 0.0 0.0 0.0 5.6 0.0 0.0 11.1 0.0 61.1 22.2 100 N. California 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 100
Table A.43. Discriminant function analysis for the variable region for downy woodpeckers.
Predicted Group Membership Cross-validated Location Ontario WY OH MI MS NC OK FL TX SLO N. CA Total Count Ontario 3.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 Wyoming 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 2 Ohio 4.0 1.0 5.0 8.0 5.0 4.0 7.0 1.0 9.0 1.0 0.0 45 Michigan 0.0 0.0 0.0 2.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 4 Mississippi 0.0 0.0 0.0 4.0 0.0 0.0 2.0 0.0 1.0 0.0 0.0 7 North Carolina 0.0 0.0 1.0 0.0 0.0 2.0 1.0 0.0 2.0 0.0 0.0 6 Oklahoma 0.0 0.0 0.0 0.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 3 Florida 0.0 0.0 0.0 4.0 2.0 3.0 3.0 0.0 3.0 1.0 2.0 18 Texas 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1 SLO county, CA 0.0 0.0 0.0 0.0 1.0 0.0 0.0 2.0 0.0 9.0 6.0 18 N. California 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 2
% Ontario 60.0 40.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Wyoming 50.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 100 Ohio 8.9 2.2 11.1 17.8 11.1 8.9 15.6 2.2 20.0 2.2 0.0 100 Michigan 0.0 0.0 0.0 50.0 25.0 0.0 0.0 0.0 0.0 0.0 25.0 100 Mississippi 0.0 0.0 0.0 57.1 0.0 0.0 28.6 0.0 14.3 0.0 0.0 100 North Carolina 0.0 0.0 16.7 0.0 0.0 33.3 16.7 0.0 33.3 0.0 0.0 100 Oklahoma 0.0 0.0 0.0 0.0 33.3 66.7 0.0 0.0 0.0 0.0 0.0 100 Florida 0.0 0.0 0.0 22.2 11.1 16.7 16.7 0.0 16.7 5.6 11.1 100 Texas 0.0 0.0 0.0 0.0 0.0 100.0 0.0 0.0 0.0 0.0 0.0 100 SLO county, CA 0.0 0.0 0.0 0.0 5.6 0.0 0.0 11.1 0.0 50.0 33.3 100 N. California 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 100
31.5% of original grouped cases correctly classified. 18.9% of cross-validated grouped cases correctly classified.
Table A.43. Discriminant function analysis for the variable region for downy woodpeckers.
Predicted Group Membership Location` SANWR Benson SP KSSP Total Original Count Santa Ana National Wildlife Refuge 11.0 1.0 1.0 13 Benson State Park 0.0 1.0 0.0 1 Kerrville-Schreiner State Park 0.0 0.0 2.0 2
% Santa Ana National Wildlife Refuge 84.6 7.7 7.7 100 Benson State Park 0.0 100.0 0.0 100 Kerrville-Schreiner State Park 0.0 0.0 100.0 100 Cross-validated Count Santa Ana National Wildlife Refuge 6.0 2.0 5.0 13 Benson State Park 1.0 0.0 0.0 1 Kerrville-Schreiner State Park 2.0 0.0 0.0 2
% Santa Ana National Wildlife Refuge 46.2 15.4 38.5 100 Benson State Park 100.0 0.0 0.0 100 Kerrville-Schreiner State Park 100.0 0.0 0.0 100
87.5% of original grouped cases correctly classified. 37.5% of cross-validated grouped cases correctly classified.
Table A.44. Discriminant function analysis for the variable region in golden-fronted woodpeckers.
Predicted Group Membership Location NY WY OH MI MS NC OK TX NH WV CO SLO Seq SoS. Yos. N CATotal Original New York 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Count Wyoming 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Ohio 1.0 1.0 2.0 0.0 2.0 6.0 1.0 0.0 0.0 3.0 0.0 0.0 0.0 1.0 0.0 0.0 17 Michigan 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 2.0 0.0 0.0 0.0 1.0 1.0 0.0 6 Mississippi 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 3 North Carolina 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 2 Oklahoma 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 2 Texas 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 New Hampshire 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 4 West Virginia 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 1.0 0.0 5 Colorado 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 3 San Luis Obispo county, CA 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 3.0 0.0 8.0 4.0 1.0 19 Sequoia National Forest, CA 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 3 Southern Sequoia NF, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 2.0 0.0 1.0 5 Yosemite, CA 0.0 0.0 0.0 0.0 0.0 1.0 2.0 0.0 0.0 3.0 0.0 0.0 0.0 1.0 0.0 0.0 7 Northern California 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1
Table A.45. Discriminant function analysis for the variable region in hairy woodpeckers.
Predicted Group Membership Location NY WY OH MI MS NC OK TX NH WV CO SLO Seq SoS. Yos. N CATotal % New York 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Wyoming 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Ohio 5.9 5.9 11.8 0.0 11.8 35.3 5.9 0.0 0.0 17.6 0.0 0.0 0.0 5.9 0.0 0.0 100 Michigan 0.0 0.0 0.0 16.7 0.0 0.0 16.7 0.0 0.0 33.3 0.0 0.0 0.0 16.7 16.7 0.0 100 Mississippi 0.0 33.3 0.0 0.0 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0 0.0 100 North Carolina 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Oklahoma 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 100 Texas 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 New Hampshire 0.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 100 West Virginia 0.0 0.0 0.0 0.0 0.0 0.0 20.0 0.0 0.0 60.0 0.0 0.0 0.0 0.0 20.0 0.0 100 Colorado 0.0 0.0 0.0 0.0 0.0 33.3 0.0 0.0 0.0 0.0 66.7 0.0 0.0 0.0 0.0 0.0 100 San Luis Obispo county, CA 0.0 0.0 0.0 5.3 0.0 0.0 0.0 0.0 0.0 10.5 0.0 15.8 0.0 42.1 21.1 5.3 100 Sequoia National Forest, CA 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0 0.0 0.0 0.0 33.3 33.3 0.0 0.0 0.0 100 Sequoia National Forest, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.0 0.0 0.0 20.0 40.0 0.0 20.0 100 Yosemite, CA 0.0 0.0 0.0 0.0 0.0 14.3 28.6 0.0 0.0 42.9 0.0 0.0 0.0 14.3 0.0 0.0 100 Northern California 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 100
Table A.45. Discriminant function analysis for the variable region in hairy woodpeckers, continued.
Cross-validated Predicted group membership Location NY WY OH MI MS NC OK TX NH WV CO SLO Seq So. Seq. Yos.NCa Total Count New York 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Wyoming 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 Ohio 2.0 1.0 1.0 0.0 2.0 6.0 1.0 0.0 0.0 3.0 0.0 0.0 0.0 1.0 0.0 0.0 17 Michigan 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 2.0 0.0 0.0 0.0 1.0 1.0 0.0 6 Mississippi 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 3 North Carolina 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 2 Oklahoma 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 2 Texas 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 New Hampshire 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 1.0 4 West Virginia 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 1.0 0.0 5 Colorado 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 3 San Luis Obispo county, CA 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 3.0 0.0 8.0 4.0 1.0 19 Sequoia National Forest, CA 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 3 Sequoia National Forest, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 2.0 1.0 0.0 0.0 1.0 5 Yosemite, CA 0.0 0.0 0.0 0.0 0.0 1.0 2.0 0.0 0.0 3.0 0.0 0.0 0.0 1.0 0.0 0.0 7 Northern California 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1
Table A.45. Discriminant function analysis for the variable region in hairy woodpeckers, continued.
Cross-validated Predicted group membership Location NY WY OH MI MS NC OK TX NH WV CO SLO Seq So. Seq. Yos.NCa Total % New York 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Wyoming 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Ohio 11.8 5.9 5.9 0.0 11.8 35.3 5.9 0.0 0.0 17.6 0.0 0.0 0.0 5.9 0.0 0.0 100 Michigan 0.0 0.0 0.0 16.7 0.0 0.0 16.7 0.0 0.0 33.3 0.0 0.0 0.0 16.7 16.7 0.0 100 Mississippi 0.0 33.3 0.0 0.0 0.0 33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 33.3 0.0 0.0 100 North Carolina 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 100 Oklahoma 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 100 Texas 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 New Hampshire 0.0 0.0 0.0 0.0 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 25.0 100 West Virginia 0.0 0.0 20.0 0.0 0.0 0.0 20.0 0.0 0.0 40.0 0.0 0.0 0.0 0.0 20.0 0.0 100 Colorado 0.0 0.0 0.0 0.0 0.0 33.3 33.3 0.0 0.0 0.0 33.3 0.0 0.0 0.0 0.0 0.0 100 San Luis Obispo county, CA 0.0 0.0 0.0 5.3 0.0 0.0 0.0 0.0 0.0 10.5 0.0 15.8 0.0 42.1 21.1 5.3 100 Sequoia National Forest, CA 0.0 0.0 0.0 33.3 0.0 0.0 33.3 0.0 0.0 0.0 0.0 33.3 0.0 0.0 0.0 0.0 100 Sequoia National Forest, CA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.0 0.0 40.0 20.0 0.0 0.0 20.0 100 Yosemite, CA 0.0 0.0 0.0 0.0 0.0 14.3 28.6 0.0 0.0 42.9 0.0 0.0 0.0 14.3 0.0 0.0 100 Northern California 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100 0.0 0.0 0.0 100 23.8% of original grouped cases correctly classified. 10.0% of cross-validated grouped cases correctly classified.
Table A.45. Discriminant function analysis for the variable region in hairy woodpeckers, continued.
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