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Apparent survival and morphometrics of two forest bird species at a
landscape scale
by
Brad P. Zitske
B.Sc., University of Wisconsin-Madison, 1998
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
Master of Science in Forestry
In the Graduate Academic Unit of Forestry and Environmental Management
Supervisor: Antony W. Diamond, Ph.D., Department of Biology and Faculty of
Forestry and Environmental Management
Examining Board: Myriam Barbeau, Ph.D., Department of Biology
Marek Krasowski, Ph.D., Faculty of Forestry and Environmental
Management
This thesis is accepted by the
Dean of Graduate Studies
THE UNIVERSITY OF NEW BRUNSWICK
©Brad Zitske
ii
Abstract
Habitat loss and fragmentation frequently have negative consequences for animal
populations. Many studies have shown reduced occurrence of bird species in landscapes
with low amounts of forest cover. One hypothesis to explain this is reduced adult survival
in such landscapes. We tested for the influence of landscape structure on the apparent
annual survival of Blackburnian (Dendroica fusca) and Black-throated Green Warblers
(D. virens) over 7 years in the Greater Fundy Ecosystem, NB, Canada. Minimum annual
survival estimates of both species were influenced by habitat amount at two spatial
extents: local- (100 m radius) and landscape- (2000 m) scales. These results provide
support for the idea that reduced species occurrence in landscapes with low proportions
of habitat is partly due to lower apparent survival in these sites. Younger birds had lower
estimates of annual survival and were in better body condition than older birds.
Condition and local-level habitat affected survival in a separate model set.
iii
Preface
The thesis is presented in articles format and follows the referencing style
required by Conservation Biology, the journal in which I intend on publishing these
papers. I am the principal author and Dr. Antony Diamond and Dr. Matthew Betts are
co-authors on both papers. Chapters 1 and 4 are general introductory and discussion
chapters that do not stand alone in the context of this thesis. The purpose of Chapter 1 is
to provide relevant background information for Chapters 2 and 3. Chapter 2 focuses on
estimating Blackburnian and Black-throated Green Warbler apparent annual and within-
season survival in relation to landscape metrics. Chapter 3 compares age ratios and body
condition of both focal species captured within varying amounts of mature forest. It also
looks at effects of age and body condition on survival using landscape metrics outlined in
Chapter 2. Chapter 4 integrates evidence gathered from the previous chapters into a
synthesis of survival estimates in relation to landscape and morphometric covariates.
Drs. Diamond and Betts were responsible for the development of this project in addition
to providing intellectual and analytical support. This project was funded in part by the
New Brunswick Wildlife Trust Fund, Fundy Model Forest, Fundy National Park, and
ACWERN. Logistical support was generously provided by the New Brunswick
Department of Natural Resources and Energy, Fundy National Park, and ACWERN.
iv
Acknowledgements
This project was a collaborative effort between many people and agencies. I
would particularly like to thank Dr. Antony Diamond for taking a chance on me as a
student and for providing guidance and intellectual support both before and during my
time at UNB. Working with, and learning from Tony has been a joy. My committee
members, Dr. Graham Forbes and Dr. Dan Keppie, were both extremely helpful in
providing indispensable ideas in the development of this project. Many thanks are due to
both of them. I wouldn’t be here today if it weren’t for Dr. Matthew Betts, whose
patience, foresight, and encouragement have been a big part of my life the past seven
years. He has truly been a mentor and friend in every way possible.
I am grateful to the Fundy Model Forest and the people there (Nairn Hay, Jeanne
Moore, Shannon White) for support, maps and a friendly place to stop in Sussex. Renee
Wissink and Edouard Daigle at Fundy National Park provided invaluable resources,
lodging each summer, and logistical support. The FMF, FNP, and the New Brunswick
Wildlife Trust Fund were all critical funding sources, without which this project would
not have been possible. Steve Gordon and Scott Makepeace at the NB Department of
Natural Resources and Energy provided logistical and intellectual support and went
above and beyond our needs.
Landscape-scale research requires not only logistical and financial support, but
also the hard work of many individuals in the field covering hundreds of square
kilometres. I was fortunate to have many dedicated individuals assist me in this task for
three summers: Kevin Dubrow, Jonathan Cormier, Alex Frank, Steve Gullage, Adam
Hadley, Matthew Hadley, Kathleen Pistak, Julia Gustavsen, Dave Hof, Valeria Osorio,
v
Lance Ebel, Stacey Hollis, Andrew Vogels, and of course, Laura Minich. Many thanks
are also due to ‘Zitske’s Angels’: Laura, Ashley Sprague, and Amie Black, for helping
me readjust to academics after a six-year hiatus, and for providing friendship and fun in
the ACWERN lab. I also thank Mathieu Charette, David Drolet, Leeann Haggerty, Matt
Smith and Louise Ritchie for their friendship as well as anyone else I may have forgotten.
Special thanks are due to Andre Breton for his prompt analytical help whenever I needed
it and for helping stimulate ideas for the advancement of this project.
My family also deserves recognition for their support during my time here: AJ,
Bonniejean, George and Irene. And to you Eric, Mom and Dad (I know you’re always
watching over me), thanks for helping make me who I am today. Meeting Laura during
my time here will always make this thesis more special. Thanks to her for
encouragement, discussions and just being ‘you’.
vi
Table of Contents
ABSTRACT ........................................................................................................................... ii
PREFACE ............................................................................................................................ iii
ACKNOWLEDGEMENTS ...................................................................................................... iv
LIST OF TABLES ............................................................................................................... viii
LIST OF FIGURES ............................................................................................................... ix
CHAPTER 1 - GENERAL INTRODUCTION ............................................................................ 1
HABITAT LOSS AND FRAGMENTATION .............................................................................. 1
FOCAL SPECIES ................................................................................................................. 2
IMPORTANCE OF DEMOGRAPHIC PARAMETERS ................................................................. 5
THESIS OBJECTIVES .......................................................................................................... 8
REFERENCES .................................................................................................................. 10
CHAPTER 2 - MINIMUM ESTIMATES OF APPARENT ANNUAL AND SEASONAL SURVIVAL OF
TWO SPECIES OF FOREST BIRDS IN RELATION TO LANDSCAPE METRICS......................... 16
ABSTRACT ..................................................................................................................... 17
INTRODUCTION .............................................................................................................. 17
SPECIFIC OBJECTIVES OF THIS CHAPTER ........................................................................ 21
METHODS ...................................................................................................................... 22
Study Area ................................................................................................................. 22
Capturing, banding, and resighting .......................................................................... 22
Spatial analysis ......................................................................................................... 25
Data analysis ............................................................................................................ 27
Manipulated analysis to correct for breeding dispersal ........................................... 30
RESULTS ........................................................................................................................ 33
Apparent annual survival .......................................................................................... 33
Manipulated analysis to correct for breeding dispersal ........................................... 35
Within-season survival .............................................................................................. 35
Monthly survival rates .............................................................................................. 36
DISCUSSION ................................................................................................................... 37
Apparent annual survival .......................................................................................... 37
Manipulated analysis to correct for breeding dispersal ........................................... 38
Within-season survival .............................................................................................. 40
Monthly survival rates .............................................................................................. 41
General implications ................................................................................................. 42
REFERENCES .................................................................................................................. 44
APPENDIX A. ESTIMATES OF MODEL EFFECT SIZES IN SURVIVAL MODELS FROM CHAPTER
2..................................................................................................................................... 59
CHAPTER 3 – LANDSCAPE-LEVEL AGE RATIOS AND MORPHOMETRICS OF
BLACKBURNIAN (DENDROICA FUSCA) AND BLACK-THROATED GREEN WARBLERS (D.
VIRENS) IN RELATION TO APPARENT ANNUAL SURVIVAL................................................. 63
vii
ABSTRACT ..................................................................................................................... 64
INTRODUCTION .............................................................................................................. 64
SPECIFIC OBJECTIVES OF THIS CHAPTER ......................................................................... 67
METHODS ...................................................................................................................... 67
Study area ................................................................................................................. 67
Study design .............................................................................................................. 68
Field measurements .................................................................................................. 69
Survival analysis ....................................................................................................... 70
Statistical analysis-Age ............................................................................................. 71
Statistical analysis-Condition indices ....................................................................... 72
Statistical analysis-Both age and condition .............................................................. 72
RESULTS ........................................................................................................................ 73
Survival ..................................................................................................................... 73
Age ratios .................................................................................................................. 74
Age and condition ..................................................................................................... 75
DISCUSSION ................................................................................................................... 76
Age ratios and survival ............................................................................................. 76
Condition indices and survival ................................................................................. 78
General implications ................................................................................................. 79
REFERENCES .................................................................................................................. 80
CHAPTER 4 - GENERAL DISCUSSION ................................................................................ 96
SUMMARY OF RESULTS .................................................................................................. 96
POTENTIAL SELECTION MECHANISMS ............................................................................. 99
GENERAL IMPLICATIONS .............................................................................................. 101
REFERENCES ................................................................................................................ 102
APPENDIX B.1. DEFINITIONS OF LANDSCAPE COVARIATES AND OTHER FACTORS
INCORPORATED INTO MODELS FITTED IN PROGRAM MARK. ............................................ 106
APPENDIX B.2. REDUCED M-ARRAY FOR ALL BANDED BIRDS .......................................... 107
APPENDIX B.3. ALL BANDED BIRDS AND RELEVANT ASSOCIATED DATA ......................... 108
viii
List of Tables
TABLE 2.1. NUMBER OF BIRDS BANDED FROM 2000-2006. ................................................ 50
TABLE 2.2. APPARENT ANNUAL SURVIVAL AND RESIGHTING PROBABILITIES OF BIRDS
BANDED FROM 2000-2006 ................................................................................................. 51
TABLE 2.3. APPARENT ANNUAL SURVIVAL AND RESIGHTING PROBABILITIES OF BLBW
BANDED FROM 2000-2006 ................................................................................................. 52
TABLE 2.4. APPARENT ANNUAL SURVIVAL AND RESIGHTING PROBABILITIES OF BTNW
BANDED FROM 2000-2006 ................................................................................................. 52
TABLE 2.5. MANIPULATED DATASET WITH APPARENT ANNUAL SURVIVAL AND RESIGHTING
PROBABILITIES FROM 2004-2006 ....................................................................................... 53
TABLE 2.6. APPARENT WITHIN-SEASON SURVIVAL AND RESIGHTING PROBABILITIES OF
SUBSET OF BLBW AND BTNW FROM 2005 AND 2006 ...................................................... 53
TABLE 2.7. MEAN MODEL-AVERAGED ESTIMATES FROM SURVIVAL MODEL SETS .............. 54
TABLE 2.8. ESTIMATES OF MONTHLY SURVIVAL RATES FOR SURVIVAL MODEL SETS ........ 54
TABLE 3.1. APPARENT ANNUAL SURVIVAL AND RESIGHTING PROBABILITIES AS FUNCTIONS
OF AGE AND LANDSCAPE METRICS OF BLBW AND BTNW BANDED FROM 2000-2006 ...... 84
TABLE 3.2. APPARENT ANNUAL SURVIVAL AND RESIGHTING PROBABILITIES AS FUNCTIONS
OF RESIDUAL FROM BODY CONDITION INDICES AND LANDSCAPE METRICS OF BLBW AND
BTNW BANDED FROM 2003-2006 ..................................................................................... 85
TABLE 3.3. MEAN MODEL-AVERAGED ESTIMATES FROM AGE AND CONDITION MODEL SETS
........................................................................................................................................... 86
TABLE 3.4. ESTIMATES OF MODEL EFFECT SIZES FROM AGE MODEL SET ............................ 86
TABLE 3.5. ESTIMATES OF MODEL EFFECT SIZES FROM CONDITION MODEL SET. ................ 87
TABLE 3.6. MEANS OF CONTINUOUS, LANDSCAPE PREDICTOR VARIABLES AND CONDITION
FOR EACH SPECIES USED TO TEST VARIATION IN CONDITION INDICES FROM 2003-2005. .... 88
TABLE 3.7. RESULTS FROM FACTORIAL ANOVAS FOR DIFFERENCES BETWEEN MEANS OF
SPECIES AND AGE AS CATEGORICAL PREDICTOR VARIABLES OF ALL BLBW AND BTNW
BANDED FROM 2003-2005 ............................................ERROR! BOOKMARK NOT DEFINED.
TABLE 3.8. RESULTS FROM GENERALIZED LINEAR MODELS TESTING THE RESIDUALS FROM
AN ORDINARY LEAST SQUARES REGRESSION OF BODY MASS AGAINST WING LENGTH AS A
FUNCTION OF JULIAN DATE, JULIAN DATE SQUARED, SPECIES, AGE, AND LANDSCAPE
METRICS ............................................................................................................................. 90
TABLE 3.9. ESTIMATES OF MODEL EFFECT SIZES FROM GENERALIZED LINEAR MODELS ..... 91
ix
List of Figures
FIGURE 2.1. FREQUENCY DISTRIBUTIONS OF FOUR LANDSCAPE VARIABLES ASSOCIATED
WITH BANDED MALE BLBW (2000-2005).. ....................................................................... 55
FIGURE 2.2. FREQUENCY DISTRIBUTIONS OF FOUR LANDSCAPE VARIABLES ASSOCIATED
WITH BANDED MALE BTNW (2000-2005).. ....................................................................... 56
FIGURE 2.3. LOCATION OF ALL BLBW BANDED IN MATURE FOREST PATCHES FROM 2000-
2005 IN GREATER FUNDY ECOSYSTEM, NEW BRUNSWICK, CANADA. ............................... 57
FIGURE 2.4. LOCATION OF ALL BTNW BANDED IN MATURE FOREST PATCHES FROM 2000-
2005 IN GREATER FUNDY ECOSYSTEM, NEW BRUNSWICK, CANADA. ............................... 58
FIGURE 3.1. PLOTS OF DIFFERENT AGES OF BLBW AND BTNW BANDED BY WEEK FROM
2000-2005.. ....................................................................................................................... 92
FIGURE 3.2. COMPARISON OF LINEAR REGRESSIONS OF CONDITION INDICES AND MASS/WING
LENGTH RESIDUALS BY TIME (JULIAN DATE) WITH 95% CONFIDENCE INTERVALS. ............ 93
FIGURE 3.3. PLOT OF MEAN CONDITION INDICES OF BLBW AND BTNW BANDED IN
GREATER FUNDY ECOSYSTEM, NB, FROM 2003-2005. ...................................................... 94
FIGURE 3.4. PLOTS OF MASS/WING RESIDUALS ACROSS ALL LANDSCAPE METRICS FOR ALL
BIRDS CAPTURED IN GREATER FUNDY ECOSYSTEM, NB, FROM 2003-2005. ...................... 95
1
Chapter 1 - General Introduction
Habitat loss and fragmentation
A central question in conservation biology and forest management is how to
maintain viable populations of native species over the long term while still harvesting
enough timber to sustain the economy. Habitat fragmentation, often occurring as a result
of forest management, is a landscape-scale process involving breaking apart of habitat
(Forman 1995, Fahrig 2003), while habitat loss is the removal of habitat patches entirely
from the landscape (Robinson et al. 1995, Fahrig 1997) and can take place with or
without fragmentation (Forman 1995). Habitat can be defined as the set of environmental
factors associated with survival and reproduction of an individual species (Block and
Brennan 1993, Morrison 2001). We use the definition to include both vegetation-
structure and all resources within local (territorial) and landscape (home-range) scales.
Recent studies have shown that both habitat loss and fragmentation have
consistently negative effects on forest bird distribution (Wilcove 1985, Andrén 1994,
Hagan et al. 1996, Fahrig 1997, Trzcinski et al. 1999, Boulinier et al. 2001, Schmiegelow
and Mönkkönen 2002, Thompson et al. 2002, Fahrig 2003, Lampila et al. 2005), leading
to potential population subdivision or loss for species requiring certain amounts of habitat
(Wiens 1994, Pimm et al. 1995). McGarigal and McComb (1995) argued that habitat
loss is more important than fragmentation in affecting species distributions. Here we are
not attempting to disentangle the separate effects of the two, only to study the effects of a
reduction of mature forest on a population of two species of forest birds. Many
researchers do not distinguish between loss and fragmentation of habitat because they are
2
often confounded in nature and in study designs (Robinson et al. 1995, Fahrig 1998,
Villard et al. 1999, Fahrig 2003).
Habitat loss can alter the configuration (specific arrangement of spatial elements
such as patches) and composition (proportion of different land cover types) of patches,
resulting in diminished population sizes, increased nest predation and brood parasitism,
and subdivided populations (Martin 1988, McGarigal and McComb 1995, Fahrig 1998,
Villard et al. 1999, Simon et al. 2000, Schmiegelow and Mönkkönen 2002, Thompson et
al. 2002). The best measure of habitat loss is the percentage of habitat amount (here,
forest cover) on the landscape (Fahrig 1997). Trzcinski et al. (1999) and Lee et al. (2002)
argued that the primary focus of managers should be to prevent a decrease in forest cover.
Researchers may be able to better predict bird abundance and test theories about the
effect of habitat loss on populations of forest bird species by observing beyond patch
boundaries and including the proportion of habitat amount at varying landscape scales
(Lee et al. 2002).
Focal species
Species-specific considerations are critical when attempting to quantify potential
outcomes of habitat loss and fragmentation (Schmiegelow and Mönkkönen 2002).
George and Zack (2001) indicated that large-scale factors such as landscape
configuration may make a location undesirable for species even if the vegetation
characteristics and composition are suitable. They stressed the importance of studying
the natural history of a species and its habitat requirements at the proper scale. Birds may
be influenced more by the context of the landscape surrounding a patch than by the
3
content (individual stand characteristics) of a patch (Diamond 1999a, Trzcinski et al.
1999).
Large-scale ecological experiments are necessary to test theories on habitat use of
forest birds (Mazerolle and Villard 1999, Drapeau et al. 2000), particularly in cooperation
with forest managers (Diamond 1999b). Entire communities of birds may be negatively
influenced by landscape-scale alterations in forest cover (Drapeau et al. 2000). And, as
habitat is usually defined by human perception instead of individual species
requirements, this frequently misused term does not take into account how species use
and occur in different habitats in nature (Fischer et al. 2004).
Previous related work (from 2000-2003) explored presence/absence relationships
of forest birds with forest types (Young et al. 2005, Betts et al. 2006a). Blackburnian
Warblers (Dendroica fusca, BLBW) are sensitive to landscape configuration requiring
large amounts of mature mixedwood forest during the breeding season within the Greater
Fundy Ecosystem (GFE) in southeastern New Brunswick (Young et al. 2005, Betts et al.
2006a). Specifically, they require large (> 30 cm dbh) softwood trees for nesting and
large hardwood trees for foraging (Young et al. 2005). The ~1000 km2 Greater Fundy
Ecosystem includes the protected Fundy National Park (206 km2) at its core and extends
from the Big Salmon River to the east and Elgin, NB, to the north (Betts and Forbes
2005).
Mixedwood forest is defined as a stand in which neither deciduous nor coniferous
trees compose more than 75% of the basal area (NBDNRE 1998), but birds may perceive
the forest differently than forest managers. Many species of migratory songbirds inhabit
portions of mixedwood forest within their breeding ranges and it may represent a forest
4
classification in which habitat generalists co-exist with deciduous and coniferous
specialists (Young et al. 2005). Mixedwood forests are diminishing throughout much of
Eastern Canada often due to forest management activities such as timber harvest and
conversions to homogeneous softwood plantations (Betts et al. 2003, Higdon et al. 2006).
Thus, species that require this habitat type are of particular concern to managers. We use
the definition from Young et al. (2005) that classified a mixedwood specialist as ‘one that
specializes on, or more frequently uses forest stands that contain both conifer and
deciduous trees.
Blackburnian Warblers seldom nest in forests without substantial vegetation over
18 meters (Morse 1976), but also exhibit some plasticity within their range provided
certain mature mixedwood components are present, such as large conifers for nesting and
large deciduous trees for foraging (Morse 2004, Young et al. 2005). The New Brunswick
Department of Natural Resources (DNR) adopted this species as an indicator of mature
mixedwood forest (NBDNRE 1998). A management-related indicator species may be
used as an indirect measure of environmental or biological conditions often too difficult,
labour-intensive, and/or expensive to measure directly (Landres et al. 1988). Since
Blackburnian Warblers have been strongly linked with mature mixedwood forest in our
study area and other types of mature forest in other studies (Morse 2004, NBDNRE 2005,
Betts et al. 2006a), we chose to increase our potential sample size of banded individuals
by including all mature forest as the focal habitat for this project. Blackburnian Warblers
were studied to explore the relationship between amounts of mature forest and adult
survival.
5
Throughout most of their range, Black-throated Green Warblers (Dendroica
virens, BTNW) are also associated with mature forest (Morse 2005) and are more
abundant than Blackburnian Warblers in southeastern New Brunswick (Sauer et al. 2005,
Betts et al. 2006a). In some parts of their range, Black-throated Green Warblers do not
show as strong an association with mature mixedwood as do Blackburnian Warblers
(Collins 1983). Robichaud and Villard (1999) described the Black-throated Green
Warbler as a ‘wide ranging habitat generalist.’ Black-throated Green Warblers are
strongly associated with the all types of mature forest in our study area (i.e., mixed,
hardwood, softwood; NBDNRE 2005, Betts et al. 2006a). Given the previous difficulty
of studying Blackburnian Warblers in related studies (Young et al. 2005, Betts et al.
2006a, b) and an overall lack of information on both species, we included the more
abundant Black-throated Green Warblers as a species of comparison.
Importance of demographic parameters
The two focal species are known to exploit different foraging niches (Morse 2004,
Morse 2005) but data are sparse on demographic parameters, specifically survivorship,
for each species. Apparent survival can be defined as the probability that a bird survives
from one year to the next and returns to the same place to breed (Lebreton et al. 1992).
Most species of warblers (including our focal species) are site-faithful to their breeding
grounds (Holmes and Sherry 1992), allowing minimum estimates of survival based on
return rates on breeding grounds. Survival rates are unknown for Blackburnian Warblers
(Morse 2004), and estimates for Black-throated Green Warbler as high as 67% (Morse
2005; see also Roberts 1971, Morse 1989) are based on survival rates of closely related
6
species with comparable reproductive rates and migratory strategies. This rate is likely
overestimated since these studies (Roberts 1971, Morse 1989) occurred before modern
capture-mark-recapture/resight methods that take into account resight probabilities.
A common approach to addressing habitat use questions is to use abundance data,
including presence/absence estimates, which are often gathered from point counts
(Trzcinski et al. 1999, Villard et al. 1999, Lichstein et al. 2002). These techniques have
merit though density alone may not reflect habitat quality (Van Horne 1983; see Bock
and Jones 2004 for review). Lampila et al. (2005) contended that in order to strengthen
inferences made on any habitat fragmentation or habitat loss effects, researchers should
concentrate on basic demographic parameters that may be driving these estimates.
Few studies have tested for fragmentation effects on demographic parameters of
birds, such as survival (Porneluzi and Faaborg 1999, McGarigal and Cushman 2002),
dispersal (movement of birds in relation to natal and breeding sites) (Greenwood and
Harvey 1982), and reproductive success (the probability of successfully raising young
birds that live past a ‘fledging’ period when young leave the nest) (Martin 1988; see
Lampila et al. 2005 for review). Some estimates of reproductive success and dispersal
distances exist for Dendroica warblers (Holmes and Sherry 1992, Cilimburg et al. 2002,
Betts et al. 2006b), but still very little is known about survival rates of these birds.
Accurate survival estimates may have important consequences for how managers
construct population models and this may be particularly critical for species with
declining populations.
There is some discrepancy in the literature as to where most mortality of
migratory songbirds occurs. Dean (1999) banded over 5000 individuals of 58 species in
7
winter in the Bahamas from 1989 to 1994, and found that juveniles had relatively low
over-winter survival rates compared with adults, suggesting that most mortality occurs on
stationary grounds in winter. Sillett and Holmes (2002) concluded that most mortality of
Black-throated Blue Warblers (D. caerulescens) occurred during migration. Jones et al.
(2004) contended that most adult male mortality occurs either during migration or
overwinter. Regardless of the primary causes of mortality or the most critical time
periods in the life of a bird, there is little disagreement about the importance of breeding
grounds to sustaining migratory songbird populations.
Reed (1992) argued that events on the breeding rather than wintering grounds are
likely to cause population decline in Blackburnian Warblers due to diminishing amounts
of mature forest habitat. Higdon et al (2006) suggested that Blackburnian Warblers in
northwestern New Brunswick are in a high risk of extirpation primarily due to a reduction
in mature mixedwood forests. Breeding bird survey (BBS) data in New Brunswick over
the past two decades have documented a decline in Blackburnian Warblers of 4.9% per
year (Sauer et al. 2005), while mature forest has been harvested in southeastern New
Brunswick at a rate greater than replacement during this time (~1.5% / year; Betts et al.
2003). This decline in the population of Blackburnian Warblers may actually be
underestimated due to uneven rates of landscape change in comparison with the BBS data
(Betts et al. 2007). One hypothesis that could explain a more rapid decline in the species
than in its breeding habitat is that survival is reduced in the remaining fragmented forest.
A major flaw of any survival study is the inability to distinguish true mortality
from emigration (Lebreton et al. 1992, Marshall et al. 2000). The ability of birds to move
considerable distances in short periods of time may mean that some birds that are actually
8
alive are missed during resight attempts (Marshall et al. 2000). Betts et al. (2006b)
recorded evidence of two individuals undertaking breeding dispersal when the forest
patches they were originally banded in were harvested. These birds would have been
considered dead, whereas they actually moved out of the study area, resulting in
underestimated survival rates.
Dispersal is difficult to study and incidental observations such as this example are
why the term ‘apparent survival’ is more commonly used. Cilimburg et al. (2002) found
that survival estimates of Yellow Warblers (D. petechia) were increased by 6.5-22.9%
with the inclusion of birds that had dispersed outside of the core study area. We
attempted to approximate estimates closer to true survival by incorporating methods
suggested by Marshall et al. (2004), searching for birds outside the normal resight area to
assess the extent of movement of birds outside their territories.
Many survival studies on habitat loss have been at the local, or patch level: 64 ha
(Sillett and Holmes 2002), 2352 ha (Burke and Nol 2001), 2600 ha (Jones et al. 2004).
Many fewer have looked at broad, landscape-scale effects on forest bird populations
(McGarigal and McComb 1995, Flather and Sauer 1996, Thompson et al. 2002). This
project is the first to estimate survival on a larger, landscape scale (400,000 ha) and will
provide previously lacking survival data for Blackburnian and Black-throated Green
Warblers.
Thesis objectives
The primary objective of this study was to relate apparent annual survival of
Blackburnian and Black-throated Green Warblers to mature forest in the Greater Fundy
9
Ecosystem at a landscape-scale (2000 m). We also used species-specific distribution
models that quantified the probability of occurrence of both species using local-level
predictor variables to define habitat (Betts et al. 2006a, Betts et al. 2007) at the
landscape- and local-scales (100 m). Further descriptions are given in Chapter 2 and in
Appendix B.1. Previous work here has shown that defining landscapes from the
perspective of individual species greatly increases the likelihood of detecting landscape
effects in forest mosaics (Betts et al. 2006a).
Determining whether there is a difference in survival between habitat with a high
degree of loss and more continuous habitat is critical; we know that Blackburnian
Warblers are less abundant in habitats with lower amounts of mature forest cover than in
landscapes with more mature forest but we do not know why (Betts et al. 2006a). We
provide demographic information on two species lacking this information at two different
spatial extents. We report the first survival estimates for both focal species in Chapter 2.
We test hypotheses of age ratios and body condition in relation to landscape
metrics and survival in Chapter 3. Many species of Neotropical migrants are more
abundant in landscapes with extensive forested habitat and larger patches (Robinson et al.
1995, Flather and Sauer 1996, Hobson and Bayne 2000) and there is evidence that birds
in larger woodlots have higher survival rates than birds in landscapes with lower forest
cover (Doherty and Grubb 2002). There is also evidence suggesting that younger birds
are more abundant in suboptimal breeding landscapes with low amounts of mature forest
(Holmes et al. 1996, Bayne and Hobson 2001) and that these individuals have a lower
probability of attracting a mate and reproducing (Porneluzi and Faaborg 1999, Burke and
10
Nol 2000). These individuals may not have sufficient energy reserves thus affecting
fitness parameters such as survival (Schulte-Hostedde et al. 2005).
References
Andrén, H. 1994. Effects of habitat fragmentation on birds and mammals in landscapes
with different proportions of suitable habitat: a review. Oikos 71: 355-366.
Bayne, E.M., and K.A. Hobson. 2001. Effects of habitat fragmentation on pairing success
of Ovenbirds: importance of male age and floater behavior. Auk 118: 380-388.
Betts, M.G., S.E. Franklin, and R.S. Taylor. 2003. Interpretation of landscape pattern and
habitat change for local indicator species using satellite imagery and geographic
information system data in New Brunswick, Canada. Canadian Journal of Forest
Research 33: 1821-1831.
Betts, M.G., and G.J. Forbes (Eds.). 2005. Forest management guidelines to protect
native biodiversity in the Greater Fundy Ecosystem. University of New
Brunswick (UNB) Cooperative Fish and Wildlife Unit, Fredericton, Pp. 110.
Betts, M.G., G.J. Forbes, A.W. Diamond, and P.D. Taylor. 2006a. Independent effects of
habitat amount and fragmentation on songbirds in a forest mosaic: an organism-
based approach. Ecological Applications 16: 1076-1089.
Betts, M.G., B.P. Zitske, A.S. Hadley, and A.W. Diamond. 2006b. Migrant forest
songbirds undertake breeding dispersal following timber harvest. Northeastern
Naturalist 13: 531-536.
Betts, M.G., D. Mitchell, A.W. Diamond, and J. Bêty. 2007. Uneven rates of landscape
change as a source of bias in roadside wildlife surveys. Journal of Wildlife
Management 71: 2266-2273.
Block, W.M., and L.A. Brennan. 1993. The habitat concept in ornithology: theory and
applications. Pages 35-91 in D.M. Power, editor. Current ornithology. Volume 11.
Plenum Press, New York, USA.
Bock, C.E., and Z.F. Jones. 2004. Avian habitat evaluation: should counting birds count?
Frontiers in Ecology and the Environment 2: 403-410.
Boulinier, T., J.D. Nichols, J.E. Hines, J.R. Sauer, C.H. Flather, and K.H. Pollock. 2001.
Forest fragmentation and bird community dynamics: inference at regional scales.
Ecology 82: 1159-1169.
Burke, D., and E. Nol. 2000. Landscape and fragment size effects on reproductive
success of forest breeding birds. Ecological Applications 10: 1749-1761.
11
Burke, D.M., and E. Nol. 2001. Age ratios and return rates of adult male Ovenbirds in
contiguous and fragmented forests. Journal of Field Ornithology 72: 433-438.
Cilimburg, A.B., M.S. Lindberg, J.J. Tewksbury, and S.J. Hejl. 2002. Effects of dispersal
on survival probability of adult Yellow Warblers (Dendroica petechia). The Auk
119: 778-789.
Collins, S.L. 1983. Geographic variation in habitat structure of the Black-throated Green
Warbler (Dendroica virens). Auk 100: 382-389.
Dean, T. 1999. Second-growth habitat use and survival rates of migrant and resident land
birds, North Andros Island, Bahamas. MScF Thesis, University of New
Brunswick.
Diamond, A.W. 1999a. Introduction to biology and conservation of forest birds. Society
of Canadian Ornithologists Special Publication No. 1: 3-8.
Diamond, A.W. 1999b. Concluding remarks: content versus context in forest bird
research. Society of Canadian Ornithologists Special Publication No. 1: 139-143.
Doherty, P., and T. Grubb. 2002. Survivorship of permanent-resident birds in a
fragmented forested landscape. Ecology 83: 844-857.
Drapeau, P., A. Leduc, J.-F. Giroux, J.-P.L. Savard, Y. Bergeron, and W.L. Vickery.
2000. Landscape-scale disturbances and changes in bird communities of boreal
mixed-wood forests. Ecological Monographs 70: 423-444.
Fahrig, L. 1997. Relative effects of habitat loss and fragmentation on population
extinction. Journal of Wildlife Management 61: 603-610.
Fahrig, L. 1998. When does fragmentation of breeding habitat affect population survival?
Ecological Modelling 105: 273-292.
Fahrig, L. 2003. Effects of habitat fragmentation on biodiversity. Annual Review of
Ecology, Evolution, and Systematics 34: 487-515.
Fischer, J., D.B. Lindenmayer, and I. Fazey. 2004. Appreciating ecological complexity:
habitat contours as a conceptual landscape model. Conservation Biology 18:
1245-1253.
Flather, C.S., and J.R. Sauer. 1996. Using landscape ecology to test hypotheses about
large-scale abundance patterns in migratory birds. Ecology 77: 28-35.
Forman, R.T.T. 1995. Land mosaics: The ecology of landscapes and regions.
Cambridge University Press, Cambridge, United Kingdom.
12
George, T.L., and S. Zack. 2001. Spatial and temporal considerations in restoring habitat
for wildlife. Restoration Ecology 9: 272-279.
Greenwood, P.J., and P.H. Harvey. 1982. The natal and breeding dispersal of birds.
Annual Review of Ecology, Evolution, and Systematics 13: 1-21.
Hagan, J.M., W.M. Vander Haegen, and P.S. McKinley. 1996. The early development of
forest fragmentation effects on birds. Conservation Biology 10: 188-202.
Higdon, J.W., D.A. MacLean, J.M. Hagan, and J.M. Reed. 2006. Risk of extirpation of
vertebrate species on an industrial forest in New Brunswick, Canada: 1945, 2002,
and 2027. Canadian Journal of Forest Research 36: 467-481.
Hobson, K.A., and E.M. Bayne. 2000. Effects of forest fragmentation by agriculture on
avian communities in the southern boreal mixedwoods of western Canada. Wilson
Bulletin 112: 373-387.
Holmes, R.T., and T.W. Sherry. 1992. Site-fidelity of migratory warblers in temperate
breeding and Neotropical wintering areas: implications for population dynamics,
habitat selection, and conservation. Pages 563-575 in J. M. Hagan and D. W.
Johnston, editors. Ecology and conservation of Neotropical migrant landbirds.
Smithsonian Institution Press, Washington, D.C.
Holmes, R.T., P.P. Marra, and T.W. Sherry. 1996. Habitat-specific demography of Black-
throated Blue Warblers (Dendroica caerulescens): implications for population
dynamics. Journal of Animal Ecology 65: 183-195.
Jones, J., J.J. Barg, T.S. Sillett, M.L. Veit, and R.J. Robertson. 2004. Minimum estimates
of survival and population growth for Cerulean Warblers (Dendroica cerulea)
breeding in Ontario, Canada. The Auk 121: 15-22.
Lampila, P., M. Mönkkönen, and A. Desrochers. 2005. Demographic responses by birds
to forest fragmentation. Conservation Biology 19: 1537-1546.
Landres, P.B., J. Verner, and J.W. Thomas. 1988. Ecological uses of vertebrate indicator
species: a critique. Conservation Biology 2: 316-328.
Lebreton, J.-D., K.P. Burnham, J. Clobert, and D.R. Anderson. 1992. Modeling survival
and testing biological hypotheses using marked animals: A unified approach with
case studies. Ecological Monographs 62: 67-118.
Lee, M., L. Fahrig, K. Freemark, and D.J. Currie. 2002. Importance of patch scale vs.
landscape scale on selected forest birds. Oikos 96: 110-118.
Lichstein, J.W., T.R. Simons, and K.E. Franzreb. 2002. Landscape effects on breeding
songbird abundance in managed forests. Ecological Applications 12: 836-857.
13
Marshall, M.R., R.R. Wilson, and R.J. Cooper. 2000. Estimating survival of Neotropical-
Nearctic migratory birds: are they dead or just dispersed? U.S. Forest Service
General Technical Report RMRS-P-16.
Marshall, M.R., D.R. Diefenbach, L.A. Wood, and R.J. Cooper. 2004. Annual survival
estimation of migratory songbirds confounded by incomplete breeding site-
fidelity: study designs that may help. Animal Biodiversity and Conservation 27:
59-72.
Martin, T.E. 1988. Habitat and area effects on forest bird assemblages: Is nest predation
an influence? Ecology 69: 74-84.
Mazerolle, M.J., and M.-A. Villard. 1999. Patch characteristics and landscape context as
predictors of species presence and abundance: a review. Ecoscience 6: 117-124.
McGarigal, K., and W.C. McComb. 1995. Relationships between landscape structure and
breeding birds in the Oregon Coast Range. Ecological Monographs 65: 235-259.
McGarigal, K., and S.A. Cushman. 2002. Comparative evaluation of experimental
approaches to the study of habitat fragmentation effects. Ecological Applications
12: 335-345.
Morrison, M.L. 2001. A proposed research emphasis to overcome the limits of wildlife-
habitat relationship studies. Journal of Wildlife Management 65: 613-623.
Morse, D.H. 1976. Variables affecting the density and territory size of breeding spruce-
woods warblers. Ecology 57: 290-301.
Morse, D.H. 1989. American warblers: an ecological and behavioral perspective. Harvard
Univ. Press, Cambridge, MA.
Morse, D.H. 2004. Blackburnian Warbler (Dendroica fusca). The Birds of North
America Online. (A. Poole, Ed.). Ithaca: Cornell Laboratory of Ornithology;
Retrieved from The Birds of North American Online database:
http://bna.birds.cornell.edu/BNA/account/Blackburnian_Warbler/.
Morse, D.H. 2005. Black-throated Green Warbler (Dendroica virens). The Birds of North
America Online (A. Poole, Ed.). Ithaca: Cornell Laboratory of Ornithology;
Retrieved from The Birds of North American Online database:
http://bna.birds.cornell.edu/BNA/account/Black-throated_Green_Warbler/.
New Brunswick Department of Natural Resources and Energy. 1998. Ecological land
classification for New Brunswick: Ecoregion, Ecodistrict, and Ecosite Level. New
Brunswick, Canada.
New Brunswick Department of Natural Resources and Energy. 2005. Habitat definitions
for old-forest vertebrates in New Brunswick. New Brunswick, Canada.
14
Pimm, S.L., G.L. Russell, J.L. Gittleman, and T.M. Brooks. 1995. The future of
biodiversity. Science 269: 347-350.
Porneluzi, P.A., and J. Faaborg. 1999. Season-long fecundity, survival, and viability of
Ovenbirds in fragmented and unfragmented landscapes. Conservation Biology 13:
1151-1161.
Reed, J.M. 1992. A system for ranking conservation priorities for Neotropical migrant
birds based on relative susceptibility to extinction. Pp. 524–536 in Ecology and
conservation of Neotropical migrant landbirds (J.M. Hagan, III and D.W.
Johnston, eds.) Smithson. Inst. Press, Washington, D.C.
Roberts, J.O.L. 1971. Survival among some North American wood warblers. Bird
Banding 42: 165-184.
Robichaud, I., and M.-A. Villard. 1999. Do Black-throated Green Warblers prefer
conifers? Meso- and microhabitat use in a mixedwood forest. Condor 101: 262-
271.
Robinson, S.K., F.R. Thompson III, T.M. Donovan, D.R. Whitehead, and J. Faaborg.
1995. Regional forest fragmentation and the nesting success of migratory birds.
Science 267: 1987-1989.
Sauer, J.R., J.E. Hines, and J. Fallon. 2005. The North American Breeding Bird Survey,
Results and Analysis 1966-2004. Version 2005.2. USGS Patuxent Wildlife
Research Center, Laurel, MD.
Schmiegelow, F.K.A., and M. Mönkkönen. 2002. Habitat loss and fragmentation in
dynamic landscapes: avian perspectives from the boreal forest. Ecological
Applications 12: 375-389.
Schulte-Hostedde, A.I., B. Zinner, J.S. Millar, and G.J. Hickling. 2005. Restitution of
mass-size residuals: validating body condition indices. Ecology 86: 155-163.
Sillett, T.S., and R.T. Holmes. 2002. Variation in survivorship of a migratory songbird
throughout its annual cycle. Journal of Animal Ecology 71: 296-308.
Simon, N.P.P., F.E. Schwab, and A.W. Diamond. 2000. Patterns of breeding bird
abundance in relation to logging in western Labrador. Canadian Journal of Forest
Research 30: 257-263.
Thompson, F.R., T.M. Donovan, R.M. DeGraaf, J. Faaborg, and S.K. Robinson. 2002. A
multi-scale perspective of the effects of forest fragmentation on birds in eastern
forests. Pages 8-19 in T.L. George, and D.S. Dobkin, editors. Effects of habitat
fragmentation on birds in western landscapes: contrasts with paradigms from the
eastern U.S. Studies in Avian Biology No. 25. Cooper Ornithological Society,
Ornithological Societies of North America, Lawrence, Kansas.
15
Trzcinski, M.K., L. Fahrig, and G. Merriam. 1999. Independent effects of forest cover
and fragmentation on the distribution of forest breeding birds. Ecological
Applications 9: 586-593.
Van Horne, B. 1983. Density as a misleading indicator of habitat quality. Journal of
Wildlife Management 47: 893-901.
Villard, M.-A., M.K. Trzcinski, and G. Merriam. 1999. Fragmentation effects on forest
birds: relative influence of woodland cover and configuration on landscape
occupancy. Conservation Biology 13: 774-783.
Wiens, J.A. 1994. Habitat fragmentation: island vs. landscape perspectives on bird
conservation. Ibis 137: S97-S104.
Wilcove, D.S. 1985. Nest predation in forest tracts and the decline of migratory
songbirds. Ecology 66: 1211-1214.
Young, L., M.G. Betts, and A.W. Diamond. 2005. Do Blackburnian Warblers select
mixed forest? The importance of spatial resolution in defining habitat. Forest
Ecology and Management 214: 358-372.
16
Chapter 2 - Minimum estimates of apparent annual and seasonal survival of two
species of forest birds in relation to landscape metrics
Brad P. Zitske1, Matthew G. Betts
2, and Antony W. Diamond
3
B.P. ZITSKE1, Faculty of Forestry and Environmental Management, University of New
Brunswick, Bag Service #45111, Fredericton, New Brunswick, E3B 6E1, Canada.
M.G. BETTS2, Department of Forest Science, 216 Richardson Hall, Oregon State
University, Corvallis, Oregon, 97331, USA.
A.W. DIAMOND3, Atlantic Cooperative Wildlife Ecology Research Network,
Department of Biology, University of New Brunswick, Bag Service #45111, Fredericton,
New Brunswick, E3B 6E1, Canada.
1 Corresponding author email: [email protected]. Brad Zitske collected and analyzed
survival data, interpreted results, and wrote manuscript. 2 Matthew Betts provided analytical support and habitat models and edited manuscript.
3 Antony Diamond supervised Master’s thesis and edited manuscript
* This manuscript is in preparation for submission to Conservation Biology.
17
Abstract
Blackburnian Warblers (Dendroica fusca) were shown to be less abundant in
landscapes with lower amounts of mature forest. One hypothesis explaining this result is
reduced adult survival in such landscapes. We tested for the influence of landscape
structure on the apparent annual and within-season survival of Blackburnian and Black-
throated Green Warblers (D. virens) over 7 years (annual) and 2 years (seasonal) in the
Greater Fundy Ecosystem, NB, Canada. Annual survival estimates of both species were
not influenced by amount of mature forest, but rather amount of predicted habitat at the
local- (100 m radius) and landscape-scales (2000 m). Within-season survival
probabilities were influenced by species, the amount of landscape-scale habitat and year
they were monitored, suggesting an inter-annual effect. These results provide some
support for the hypothesis that reduced species occurrence in landscapes with low
proportions of habitat is partly due to lower apparent survival in these sites.
Introduction
It is becoming increasingly apparent that forest fragmentation and habitat loss
have detrimental effects on native species (Pimm et al. 1995, Robinson et al. 1995, Hagan
et al. 1996, Trzcinski et al. 1999). Both habitat loss and fragmentation may occur as a
result of forest management activities (Forman 1995), but are confounded in nature
(Fahrig 1998, 2003, Villard et al. 1999). As such, it is difficult to test which factor has
the greater impact on avian populations. Possible negative impacts of habitat
fragmentation and loss are lower recolonization rates (Wiens 1994), increased mortality
rates of individuals dispersing between patches (Fahrig and Merriam 1994), decreased
18
reproductive success (Wilcove 1985, Robinson et al. 1995), and increased local
extinction rates (Pimm et al. 1995).
The proportion of forest cover, or habitat amount (both proxies of available
habitat), is the best measure of habitat loss and there is some evidence that as this metric
increases, so does species persistence (McGarigal and McComb 1995, Fahrig 1997). For
the purposes of this study we did not attempt to differentiate between effects of habitat
fragmentation and loss, but rather focused on effects of a reduction of forest cover from
the landscape.
Many researchers have studied the influences of fragmented landscapes on bird
populations (McGarigal and McComb 1995, Villard et al. 1999, Betts et al. 2006a).
However, most studies were aimed at the local or patch scale (Burke and Nol 2001,
Sillett and Holmes 2002, Jones et al. 2004) rather than the landscape scale (McGarigal
and McComb 1995, Norton et al. 2000). Recent evidence indicates that landscape scale
habitat degradation can have negative impacts on bird populations (Drapeau et al. 2000),
and suggests that designs incorporating extents beyond the typical patch scale may allow
researchers to better understand the importance of landscapes on avian populations.
Though Van Horne’s seminal paper (1983) warned of the potential dangers
associated with using density data as an indicator of habitat quality, abundance data,
including presence/absence estimates, are still useful for addressing basic habitat use
questions (Trzcinski et al. 1999, Villard et al. 1999, Lichstein et al. 2002, Betts et al.
2006a). However, weak inferences are often made from these measures of avian
abundance about the factors responsible for fluctuations in population size. And
importantly, these methods also ignore the basic demographic parameters, including adult
19
and juvenile survival, that are directly responsible for changes in population abundances
(Lampila et al. 2005).
The few existing studies on avian survival in fragmented landscapes have been
conducted in agricultural landscapes where the distinction between patches and matrix is
unambiguous (Porneluzi and Faaborg 1999, Doherty and Grubb 2002, but see Bayne and
Hobson 2002). Whether fragmentation caused by timber harvesting in a forest mosaic
affects animal survival is relatively unknown. Without such data, studies on population
viability of native species in relation to varying degrees of timber harvest (e.g., Larson et
al. 2004) have little basis. Any differences in survival among landscapes may have
important consequences for species with declining numbers.
Previous work in New Brunswick (NB; Canada) identified Blackburnian Warbler
(Dendroica fusca, BLBW) as strongly associated with mature mixedwood forest (Young
et al. 2005, Betts et al. 2006a), though they require certain structural components of all
types of mature forest (> 60 year old; NBDNRE 2005) within their range (Morse 1976,
2004). Specifically, they require both hardwood and softwood trees over 30 cm dbh
(NBDNRE 2005, Young et al. 2005). Mixedwood forests are declining in southeastern
NB at a rate greater than replacement (~1.5% loss/year), primarily as a result of timber
harvest (Betts et al. 2003) and are thus of conservation concern (Betts and Forbes 2005).
Meanwhile, breeding bird survey (BBS) data over the past two decades have documented
a decline of Blackburnian Warblers in NB of approximately 4.9% per year (Sauer et al.
2005). This population decline may have been underestimated due to uneven rates of
landscape change compared to the BBS data (Betts et al. 2007). If in fact the species
population is rapidly declining, a key contributor to this decline may be reduced survival
20
due to a reduction of mature mixedwood forest. Results from Betts et al. (2006b)
suggested that Blackburnian Warblers may be sensitive to landscape configuration,
occurring less frequently in landscapes with low proportions of mature forest. We
hypothesized that this reduced occurrence in landscapes with lower amounts of mature
forest is due to lower adult survival in these landscapes.
Black-throated Green Warblers (D. virens, BTNW) are also associated with
mature forest in NB (NBDNRE 2005, Betts et al. 2006b), however, this species exhibits
greater breeding habitat plasticity (Collins 1983, Morse 2005) and is more abundant in
the region compared to Blackburnian Warblers (Sauer et al. 2005). To maximize our
probability of capturing our focal species and for purposes of comparison, we broadened
the habitat scope of our study from mature mixedwood to all mature forest in the study
area. Though both species are relatively common throughout their ranges, rates of adult
apparent survival remain unknown.
The primary objective of this study was to explore whether apparent annual
survival of adult Blackburnian and Black-throated Green Warblers is related to the
amount of mature forest in the Greater Fundy Ecosystem, NB, Canada. Resources are
generally scarcer in landscapes with low forest cover (Root 1973) and individuals
inhabiting these landscapes will have a more difficult time persisting.
As with any study examining survival, permanent emigration and true mortality
are confounded as researchers rely on birds being site-faithful to historic breeding or
territory locations (Brownie and Robson 1983). These birds are missed during resighting
occasions and are assumed dead. We accounted for the ability of birds to travel
substantial distances in short periods by searching outside the core resight area for a
21
subset of known banded individuals. We used the number resighted during this
component to correct our survival estimates to a level which is comparable to related
species. We report the first apparent survival estimates for both species using modern
capture-mark-recapture methods and provide the first analysis evaluating the effects of
landscape pattern on the apparent survival of songbirds in a forest mosaic.
A secondary objective of this study was to track a marked subset of both
populations to estimate within-season survival probabilities. This approach allows us to
determine the effect of survival directly on the breeding grounds. Insights can be gained
by tracking banded birds throughout the breeding season and looking at resight
probabilities independent of survival. As few studies have incorporated a within-season
component there are few benchmarks with which to compare (but see Sillett and Holmes
2002, Jones et al. 2004).
The specific objectives of this chapter are:
(1) To determine if there is a correlation between a reduction of mature forest at a
large spatial extent (landscape-scale) and apparent annual survival of two
forest bird species, one with narrow habitat use (BLBW) and a congener with
wider habitat use (BTNW).
(2) To determine the influence of incomplete breeding site-fidelity on the survival
estimates.
(3) To determine the within-season survival of the two focal species.
22
Methods
Study Area
The study area encompassed ~4000 km2 (400,000 ha) within the Greater Fundy
Ecosystem (GFE), New Brunswick (NB), Canada (66.08°-64.96°W, 46.08°-45.47°N),
including sections of the Fundy Model Forest (FMF). This region is Acadian forest and
is characterized by 89% forest cover and rolling topography (NBDNRE 1998). Forest
cover is mostly yellow birch (Betula alleghaniensis), sugar maple (Acer saccharum),
American beech (Fagus grandifolia), balsam fir (Abies balsamea), and red spruce (Picea
rubens), with black spruce (P. mariana) in some low-lying areas. Intensive forest
management activities (i.e., clearcutting, planting of spruce and pine, and thinning) since
the 1970s have reduced mature forest on the landscape to approximately 12-50%,
resulting in a heterogeneous landscape (NBDNRE 1998). Fundy National Park is a
relatively small protected area (206km2/20,600 ha) within the study area with greater than
80% contiguous mature forest (> 60 year old).
Capturing, banding, and resighting
This project commenced in the summer of 2004 and continued for two successive
summers (2005 and 2006). The most reliable time to capture territorial individuals was
between 25 May and 30 July each year. We used banded birds of both focal species from
a related study from 2000-2003 (Table 2.1) to obtain 7 consecutive years of banding and
resight data. We placed an emphasis on capturing BLBW in landscapes with low
amounts of mature forest (< 30% within 2000 m) in 2004 and 2005, since they were less
abundant in these landscapes. Blackburnian Warblers are socially subordinate to Black-
23
throated Green Warblers as part of an inter-specific dominance hierarchy (Morse 2004),
and were therefore prioritized for capture due to the difficulty of monitoring (capture,
mark, and resight) and to assure that we had enough data to model survival for this
species.
We captured individual birds by using a combination of audio playback,
conspecific decoys, and mist-netting (with 30 mm mesh mist-nets). We captured birds
opportunistically; that is, if an individual Blackburnian Warbler was encountered in a
mature forest patch and responded aggressively to playback, a net was set up to attempt
capture.
Upon capture, we fitted each adult bird with a unique combination of two
coloured, plastic leg bands and one Canadian Wildlife Service aluminum band. We used
plumage characteristics (Pyle 1997) to determine age and sex of each bird. We took
photographs in the field for identification purposes and verified each bird in the fall for
independent ageing. Our capture method is strongly male-biased; of 572 individuals of
both species (Table 2.1), we captured only 11 females (BLBW, n=8; BTNW, n=3), and
excluded all from our analysis. We immediately released all birds after processing.
At each original capture location, we attempted to resight banded birds in
subsequent years using audio playback (same recording used to capture birds) a minimum
of two times each year. We first played a recording of Black-capped Chickadee (Poecile
atricapillus, BCCH) mobbing calls for 5 minutes to search for banded individuals,
because many species of forest birds respond aggressively to BCCH sounds (Gunn et al.
2000, Betts et al. 2005). If we did not resight the bird during the mobbing tape, we then
played a species-specific tape of territorial male song for 5 minutes at the banding site
24
and repeated at 50 m radii in each cardinal (N, E, S, W) direction for 5 minutes. We spent
a minimum of 30 minutes and a maximum of 60 minutes attempting to resight each bird
on each visit for a minimum of 2 visits per year. We recorded only complete confirmation
of a band combination. In situations of partial band combinations (i.e., one leg not
observed, or one color not confirmed), we increased effort until we confirmed the
complete combination. Observers were not provided with band combinations prior to
resighting effort and whenever possible, no observer was assigned to resight the same
bird twice.
Because we observed high variation in response to audio playback of specific
songs we were concerned about the influence of capture bias by capturing only the most
aggressive Blackburnian Warblers. We tested for the potential of this by ranking
aggressive behaviour to audio playback for all individual Blackburnian Warblers that
were encountered but not captured. Black-throated Green Warblers are much more
aggressive to playback than Blackburnian Warblers, so we were not concerned about
capturing only the most aggressive individuals of that species. We assessed individuals a
‘1’ if they showed aggressive behaviour (wing flicking, and/or flying near playback
equipment) and a ‘0’ if they were not aggressive (and consequently no net was set up).
We also quantified the time spent attempting to capture each individual regardless of
successful capture. If capture bias influenced our ability to detect landscape effects we
expected to see an influence of landscape composition (% mature forest, % habitat
amount) on both bird aggression and capture effort.
25
Spatial analysis
Given that Blackburnian Warblers and Black-throated Green Warblers are
dependent on mature forest during the breeding period (May to August) to different
extents, we used all mature forest in our study area to maximize our probability of
encountering, and subsequently capturing as many individuals as possible. Patches that
were searched were not selected randomly among all possible mature forest patches, but
were chosen to represent a range of amount of mature forest cover at a 2000 m
(landscape) scale. The 2000 m scale represents the maximum distance of natal dispersal
proposed for migratory warblers (Bowman 2003) as well as the distance birds may travel
in the breeding season to seek out extra-pair copulations (Norris and Stutchbury 2001).
As true randomization is impossible to achieve in large-scale studies, we used a stratified
randomized design consisting of 10 km by 10 km blocks within the study area. We
assigned random blocks daily to researchers to capture focal species in any mature forest
patches. Patches within blocks were not sampled in a truly random fashion due to
logistical constraints.
We tested for differences in apparent survival of both species in relation to the
amount of mature forest (> 60 years) within 2000 m radius of the capture location of each
bird (‘Mature’). Additionally, we used a species-centered approach with local-level
predictor variables as definitions of habitat for each species at the local- (‘Hab100’) and
landscape-level (‘Hab2000’; Betts et al. 2006b). These previously derived and validated
species distribution models quantified the probability of occurrence of both species from
Geographic Information System models (ArcGIS, ESRI Software) (Betts et al. 2006a,
2007).
26
These spatially explicit habitat models were defined in Betts et al. (2006a) as:
BLBW = 1/(exp (3.58 + 15.63(R) + 1.63(S) + 0.82(Y) - 0.62(M) - 1.42(O) -
0.61(CC) - 0.17(Slope)) + 1)
BTNW = 1/(exp (1.46 + 0.65(R) + 0.22(S) + 0.07(Y) - 0.18(M) - 0.18(O) + 0.14
(HW) + 1.01 (SW) + 0.03 (IMW) - 0.19 (TMW) - 0.56 (SP2)) + 1)
Here, BLBW is the probability of Blackburnian Warbler occurrence, R, S, Y, M, and O
are age classes representing regenerating, sapling, young, mature, and overmature
respectively (NBDNRE 2005); CC = crown closure; Slope = slope of ground in degrees;
BTNW is the probability of Black-throated Green Warbler occurrence, with the same age
class variables as Blackburnian Warblers; and HW, SW, IMW, and TMW are cover type
variables representing hardwood, softwood, shade-intolerant mixedwood, and shade-
tolerant mixedwood, respectively; SP2 = secondary species group of HW or SW. All
GIS land cover data is from the New Brunswick Forest Inventory (NBDNRE 1998) and
is based on interpreted aerial photos taken in 1993 and updated in 2000 with satellite
images (30 m2 resolution) (Betts et al. 2003).
Previous work has shown that defining landscapes from the perspective of
individual species greatly increases the likelihood of detecting landscape effects in forest
mosaics (Betts et al. 2006b). We summed the amount of mature forest (‘Mature’) at 2000
m and the amount of habitat, weighted by estimated probability of occurrence for each
focal species based on Betts et al. (2006a) ( p̂ ) at both 100 m (‘Hab100’) and 2000 m
27
(‘Hab2000’) spatial extents. The 100 m scale represents the territory size, or local scale
of a typical individual of both focal species (Morse 2004, 2005). We also summed the
amount of poor-quality matrix at 2000 m. In some species non-habitat gaps may be a
limiting factor or inhospitable for movement. We defined poor-quality matrix as areas
with very low values of p̂ (< 95 percentile, p̂ = 0.05). Descriptions of all covariates are
given in Appendix B.1. To summarize, our four landscape covariates were mature forest
(2000 m), species-specific habitat (100 m and 2000 m extents), and non-habitat matrix
(2000 m). We chose to represent all of the landscape covariates as continuous variables
rather than categorizing them due to the non-normal distribution of samples of both focal
species (Fig. 2.1 and 2.2). In addition to the four landscape covariates, we constrained
survival to test for continuous linear changes (increasing or decreasing over time) in
survival among years of the study (‘Trend’) (Cooch and White 2002).
Data Analysis
We separately estimated annual and within-season survival probabilities using
program MARK (White and Burnham 1999; hereafter ‘MARK’) and the open
population, Cormack-Jolly-Seber (CJS), model type (Cormack 1964, Jolly 1965, Seber
1965); ‘open population’ refers to the allowance for births, deaths, immigration, and
emigration during the sampling process (within year for this project). However, it is
assumed that all emigration is permanent since it cannot be separated from mortality
(Pollock et al. 1990). We applied a combination of the analytical strategies suggested by
Lebreton et al. (1992) and Burnham and Anderson (2002).
Encounter histories (EHs) used to estimate annual survival included seven May-
July occasions (periods of time), one from each year, 2000-2006, with intervening
28
August-April intervals over which we estimated survival probabilities. An example of an
annual EH is: 0011000, where this individual was banded in 2002, resighted in 2003,
then not resighted in 2004 to 2006. Within-season encounter histories included four
occasions, representing 1-3 day resighting periods from 2005 and 2006 separated by 10-
14 day intervals, over which we estimated within-season survival. An example of a
seasonal EH is: 1101, where this individual was banded on, e.g. June 1, resighted again
10-14 days later (June 11-15), not resighted 10-14 days after the second occasion, and
positively resighted 10-14 days later.
At least three occasions (one period of marking and two subsequent periods of
resighting) are necessary to produce a reliable survival estimate using capture-mark-
recapture/resight (CMR) methods (Anders and Marshall 2005). We grouped years and
species to increase the sample size. If there was a species-effect as we predicted, then
this would receive strong support in our models. Given their relative strength in the
annual survival models, we included both local- and landscape-scale predictor variables
to test any influences on seasonal survival.
Independently for each species, we began by fitting a global model consisting of
separate apparent survival (denoted by Φ) and resight (p) parameters with time-
dependence ((Φ (t), p (t); Tables 2.2 and 2.3). Due to sparse data, the datasets testing
annual survival for each species independently did not converge. This means that this
fully time-dependent model did not fit our data well, resulting in most parameters to be
poorly estimated (Burnham and Anderson 2002). This forced us to apply a reduced
model as our starting or global model for these datasets ((Φ (t), p (t reduced)); Tables 2.4 and
2.5). We achieved this by constraining 2001-2004 resighting parameters (p); the last two
29
resighting probabilities remained as time-dependent (p in the first year of the study is not
estimable, Pollock et al. 1990). A fully time-dependent global model (Φ (t), p (t))
converged properly when fitted to all other datasets (Tables 2.2, 2.3, and 2.6). In
summary, for the annual and within-season datasets (both species), models (Φ (t), p (t
reduced)) and (Φ (t), p (t)) were used as starting or global (most parameterized in the model
set) models, respectively.
We used an information-theoretic approach (Burnham and Anderson 2002) to
determine support for competing models, which is advantageous because it measures and
reflects model selection uncertainty. We ranked models in each candidate set best to
worst by Akaike’s Information Criterion (AIC) adjusted for small sample size (AICc)
(Akaike 1973). To accommodate for a potential lack of fit in the data, we need some
measure of the magnitude of extra binomial variation (overdispersion). We estimated
this using the variance inflation factor (ĉ) of our global model with the parametric
bootstrap option in program MARK (White and Burnham 1999) to determine if our data
were overdispersed. For all datasets, ĉ was < 1, so we made no overdispersion
adjustments.
AICc is the AICc difference between the top ranked model (smallest AICc value;
AICc = 0) and a competing model. Burnham and Anderson (2002) suggest the
following ‘rough-rules-of-thumb’ for comparing support for competing models: a AICc
of 0-4 demonstrates essentially equal support for the competing and top models; Δ AICc
of 4-7 shows considerable support for the top model; and AICc > 10 shows essentially
exclusive support for the top model. Along with AICc, we also refer to model
likelihoods, AICc weights (wi), and evidence ratios (ER) when comparing models. AIC
30
weights sum to 1 across the model set; thus, these describe relative support for each
model. ERs provide a ratio of evidence in support of model i relative to model j using the
estimator AICc weight of model i divided by the AICc weight of model j.
We formulated models to test hypotheses in three categories: (1) Landscape
structure hypotheses: these models included all landscape metrics described above and
predicted that some landscape variables (‘Mature’ and ‘Habitat’) would influence
survival more than constant survival; (2) Time-dependent hypotheses: these models
tested whether annual survival varied across years or whether seasonal survival varied as
a function of time of breeding season; (3) Species-dependent hypotheses: these models
tested for differences between species, predicting that BLBW survival would be
influenced more by mature forest.
Manipulated analysis to correct for breeding dispersal
To estimate the extent that permanent emigration might have confounded our
survival estimates, we searched outside the bounds of our resight radii (50 m) at four
locations in 2006 using methods suggested by Marshall et al. (2004). This approach
allowed us to estimate the extent that we underestimated survival by including a number
of birds that were missed during our standard resight attempts (twice per year for each
individual). The validity of survival estimates from capture-mark-recapture models
hinges on the assumption that there is minimal permanent emigration from the study area
(Williams et al. 2002). Though the species we examined are thought to be site faithful
(Morse 2004, 2005) and several previous studies have assumed no substantial among-
year movement (Sillett and Holmes 2002, Jones et al. 2004), recent evidence showing
31
breeding dispersal in passerines suggests that this established standard in the field may
not be correct (Cilimburg et al. 2002, Betts et al. 2006c).
The four sites were chosen to include two ‘low cover’ sites with less than 30%
mature forest and two ‘high cover’ sites with greater than 70% mature forest at the 2000
m scale. Each site had ≥ 5 banded individuals and consisted of mature forest within 2000
m that was roughly similar in area for each site. Search areas in low cover landscapes
had approximately 427 and 465 hectares (mean 446 ha) compared with high cover
landscapes of 410 and 485 hectares (mean 452 ha), respectively. Simultaneous observers
were spaced 100 m apart and were in contact by two-way radios to ensure birds were not
counted twice. Observers moved in the same direction using compasses and played a
species-specific tape each time either focal species was encountered. When a focal
species was not encountered, observers would stop every 100 m and play a Black-capped
Chickadee mobbing tape followed by a species-specific tape for 5 minutes to elicit a
response. Birds detected with bands were identified prior to reinitiating search. We
recorded spatial coordinates of marked individuals with GPS and compared these to the
original capture locations. We then estimated distances between these two points using
ArcView 3.3.
The four grid searches occurred where there were a total of 26 Blackburnian
Warblers and 47 Black-throated Green Warblers banded within a 2000 m radius of each
search area. We resighted two individual Blackburnian Warblers (out of a possible 26
banded within 2000 m 2/26 = 7.7%) and five individual Black-throated Green Warblers
(out of a possible 47 banded within 2000 m 5/47 = 10.6%) that were previously not
resighted. These individuals moved a range of 65-650 m (mean = 266.4 m) from their
32
original banding locations and were missed in previous resight attempts. In the
manipulated analyses, we increased the number of birds resighted during normal searches
(50 m radii) by the proportion resighted during the extended searches (7.7% of 141 total
birds = 11 new BLBW and 10.6% of 220 total birds = 23 new BTNW). We achieved this
by manipulating existing real EHs for all birds banded in 2004 and 2005 (the core of this
study). The manipulated EHs account for an unknown proportion of the number of
individuals that may have dispersed from the study area. Thus, an individual with the EH
of 110 would have an additional resight added to the fourth occasion (111). All ‘new’
resights were added randomly to encounter histories in the manipulated dataset. And as a
result, estimates of resighting and (more importantly) apparent survival probabilities
from an analysis of these data will be higher and more realistic than our analysis of
the unmanipulated dataset because it accounts for individuals that were previously
presumed dead. We analyzed this manipulation in MARK to test for group and time
effects and increased our survival estimates by over 13%. We achieved this by mean
model-averaging the manipulated results with additional resighted birds during this time
interval and comparing with mean model-averaged estimates from the larger dataset
(2000-2006). While this approach allowed us to increase our survival estimates
experimentally, it is based on small sample sizes and caution should be taken in the
interpretation.
33
Results
Apparent annual survival
Overall, we banded 205 male BLBW and 356 male BTNW over the 7 years of the
study (Table 2.1, Figures 2.3 and 2.4) in landscapes with 7.7-98.1% (mean = 39.7 1.5%
(1 standard error (SE)) and 5.8-96.4% mature forest (mean = 45.7 1.9%) for
Blackburnian Warblers and Black-throated Green Warblers, respectively. We resighted
54 BLBW and 105 BTNW at least once (Appendix B.2). We tested survival for both
species grouped and each species independently.
Pooling both species in all sites, we tested apparent survival in relation to mature
forest and matrix (Table 2.2). We treated species effects as ‘group’ effects. Thus, if a
top model (Δ AICc 4) showed a group effect in survival, one of the species would have
different survival than the other. Mean model-averaged estimates were 0.339 ± 0.05 for
BLBW Φ and 0.337 ± 0.342 for BTNW Φ in the grouped model set. Both species
appeared to respond to landscape structure in similar ways with models including
interactions between species and landscape covariates receiving similarly low support (<
0.02 AICc weights (wi); see Appendix B.1 for description of landscape covariates). Both
scales of predicted occurrence (‘Hab2000’ and ‘Hab100’) received stronger support than
mature forest (Table 2.2; Δ AICc 4; Models C and D vs. Model F) suggesting that these
covariates have more influence on survival estimates. As no species-specific models
testing for group effects (species) received strong support we can infer that both species
have essentially similar survival rates and also similar plasticities in habitat requirements
though the standard error was high for BTNW Φ. We held p constant for all models with
covariates as our initial questions revolved around survival.
34
Pooling all BLBW banded in mature forest throughout the study area, we added
landscape-scale covariates and modelled these metrics individually (Table 2.3). Six
models showed strong support (Δ AICc 4). All of the landscape covariates (Models C,
D, E, and F) showed strong support but the top model (A) showed constant (no time-
dependence) Φ and p. The survival estimates are model-averaged, a technique used when
there are multiple models showing strong support (Burnham and Anderson 2002). Mean
model-averaged annual estimates for all BLBW Φ models yielded = 0.361 ± 0.055 and p
= 0.690 ± 0.112 (Table 2.7).
For BTNW, we excluded the year 2000 from our model building because we
resighted 8 out of 10 birds the following year. This anomalous event skewed estimates
and model building when it was incorporated. With this year included in our initial
models, fit was poor and standard errors were large. Excluding this year improved model
fit and decreased standard errors, thus justifying its exclusion. All BTNW banded in
mature forest were pooled and yielded a similar result to BLBW with six models showing
strong support (Table 2.4). Again, all of the landscape covariates received Δ AICc values
of 4. Model B had an evidence ratio of 1.02, very nearly equal to the top-ranked model
A that had both Φ and p constant. Mean model-averaged annual estimates for all BTNW
models yielded Φ = 0.341 ± 0.035 and for p = 0.775 ± 0.074 (Table 2.8).
Assessment of our aggression index showed that Blackburnian Warblers that
were not captured were more aggressive, but not significantly so, in landscapes with less
mature forest than non-aggressive birds (Welch two-sample t-test = 1.67, p = 0.104;
mean % mature forest of aggressive birds = 0.413 ± 0.018; mean % mature forest of non-
aggressive birds = 0.513 ± 0.057). Uncaptured BLBW were less aggressive in landscapes
35
with less local- (n = 160, t = 0.125, p = 0.902; mean % ‘Hab100’ aggressive birds = 0.132
± 0.004; n = 29, mean % ‘Hab100’ non-aggressive birds = 0.133 ± 0.011) and landscape-
level habitat (t = 0.625, p = 0.536; mean % ‘Hab2000’ aggressive birds = 0.451 ± 0.013;
mean %‘Hab2000’ non-aggressive birds = 0.478 ± 0.040). None of the above tests were
statistically significant.
However, captured BLBW occurred in landscapes with more mature forest than
uncaptured BLBW, but not significantly so (Welch two-sample t-test = -1.025, p = 0.306;
mean amount of mature forest of captured birds = 0.455 ± 0.02; mean amount of mature
forest of birds not previously captured = 0.425 ± 0.022). Captured BLBW occurred in
landscapes with significantly less habitat at 2000 m (t = 6.92, p < 0.001, mean captured =
0.320 ± 0.01; mean uncaptured = 0.446 ± 0.015) and significantly less habitat at 100 m (t
= 17.36, p < 0.001, mean captured = 0.137 ± 0.005; mean uncaptured = 0.459 ± 0.018).
Manipulated analysis to correct for breeding dispersal
The intensive grid searches resulted in resighting seven birds that had previously
unknown fates or were presumed dead. After including these individuals of both species
grouped in a corrected dataset, the model-averaged survival estimate was higher than the
uncorrected estimate (Table 2.5, Φ = 0.475 ± 0.092 vs. Φ = 0.343 ± 0.031 [uncorrected]).
Within-season survival
We tracked a subset of 44 Blackburnian and 99 Black-throated Green Warblers
(Table 2.1) to estimate within-season survival. We grouped years and species to increase
the sample size over four occasions and included both local- and landscape-scale species-
specific predictor variables. Landscape-scale habitat (‘Hab2000’; Appendix B.1) was
36
present in all of the top six models (Table 2.4). The weight of evidence for landscape
habitat using summed AICc weights for all models > 0.01 was 84% versus only 7.7% for
local-scale habitat, indicating that it was a more useful variable for explaining seasonal
survival. The top-ranked model suggested that the effects of species and year were
additive. Model-averaged within-season survival estimates were 0.976 ± 0.077 for
Blackburnian Warblers in 2005 and 2006 combined, and 0.928 ± 0.120 for Black-
throated Green Warblers in 2005 and 2006 combined. The survival estimate for all birds
was 0.952 ± 0.098 while the resight estimate was 0.531 ± 0.085. Interestingly, models
testing differences between species and year within-season resight probabilities received
little or no support. All top-ranked models suggested decreasing resighting probabilities
over the three encounter occasions (model-averaged estimates during Time 1 (June 15-
June 28) = 0.787 ± 0.048, Time 2 (June 29-July 12) = 0.492 ± 0.049, Time 3 (July 13-
July 26) = 0.313 ± 0.156) suggesting the birds were more difficult to detect as the season
progressed.
Monthly survival rates
We converted both apparent annual (AA) and within-season (WS) survival rates
to monthly survival rates (Table 2.8). These monthly survival probabilities cannot be
compared formally as the within-season estimates are nested within apparent annual
estimates. We converted apparent annual survival estimates by raising the annual
survival rate to the 12th
root (12 month duration in annual analysis for August to May
interval), e.g. from Table 2.3, BLBW AA Φ = 120.340 = 0.914 monthly survival
estimate. We converted within-season survival estimates to monthly rates by raising to
37
the 2nd
root (2 month duration in within-season analysis for June to July interval), e.g.
from Table 2.6, BLBW WS Φ = 20.976 = 0.988 monthly survival estimate.
Discussion
Apparent annual survival
We predicted that Blackburnian Warbler survival would be highest in landscapes
with high amounts of mature forest cover. Survival was not influenced by the amount of
mature forest as all models including this variable ranked low. When we grouped
species, this model was not highly ranked. This suggests that our mature forest indicator
species (Blackburnian Warbler) was no more sensitive to landscape than Black-throated
Green Warblers, the species with wider habitat tolerance. Models including local-level
predictors of habitat were supported more than models with mature forest, suggesting that
these variables influence the annual survival of our focal species more than mature forest.
The top model grouping species showed time-dependence in both survival and resight
probabilities. This may have been an artefact of small sample sizes early during the 7-
year study. Eight out of ten BTNW banded in 2000 were resighted in 2001, resulting in
poor model fit when modelling this species independently. This may have influenced the
group model because this result is not consistent with other studies (e.g. Bayne and
Hobson 2002, Jones et al. 2004).
There appeared to be a capture bias with uncaptured birds responding less
aggressively to playback with increasing amounts of mature forest. However, there was
no statistical significance between the mean amounts of mature forest for uncaptured and
captured birds. Thus, it is possible that we captured only the most aggressive individuals
38
which are likely to out-compete more subordinate individuals for more optimal habitat, if
indeed more mature forest is equivalent to optimal habitat.
Few other studies have examined the influence of landscape on survival of forest
songbirds and the results are mixed. Porneluzi and Faaborg (1999) studied fragmentation
effects on demographic parameters of Ovenbirds (Seiurus aurocapillus) and found that
survival did not differ between landscapes. Ovenbirds are a migratory species of warbler
that is also strongly associated with mature forest (Van Horn and Donovan 1994).
Apparent annual survival of successful, territorial male breeders was 0.62 in landscapes
fragmented by forestry and 0.61 in unfragmented landscapes. Bayne and Hobson (2002)
also studied apparent annual survival of Ovenbirds but included patches fragmented by
agriculture in addition to patches fragmented by forestry; they found survival to be lowest
(0.34) in forested fragments in the agricultural landscape. Survival in forestry-caused
fragments was 0.56 while it was the highest in contiguous forest (0.62). Doherty and
Grubb (2002) studied apparent annual survival of permanent residents in a landscape
fragmented by agriculture. They found annual survival of three species of forest birds
(Carolina Chickadee [Poecile carolinensis], White-breasted Nuthatch [Sitta carolinensis],
and Downy Woodpecker [Picoides pubescens]) to increase with fragment area.
Manipulated analysis to correct for breeding dispersal
We correctly predicted that we would detect some individuals outside our resight
area when we searched beyond the ‘normal’ resight area in 2006. This was not
unexpected as our resight radii were small (50 m) due to logistical constraints. We
observed 7 individuals that were resighted more than 50 m from the locations where they
were originally banded (4 in the low cover landscapes and 3 in high cover). None of
39
these individuals had been resighted previously in any years after initial banding.
Expectedly, this is evidence that we are underestimating survival rates. We suggest that
individuals are moving much more than we previously thought, i.e. individuals appear to
be ‘off territory’ fairly often. Whether this is an artefact of within-season movement or
incomplete breeding site fidelity is of future interest.
By using these known, banded birds and extrapolating this example over our
banded population, we increased our survival estimates within a range of congener
survival similar in other studies. This value (Φ = 0.475 0.092) is similar to those in
other studies of Dendroica warblers and is likely more representative of biologically
accurate annual survival. Sillett and Holmes (2002) estimated Black-throated Blue
Warbler (D. caerulescens) apparent annual survival to be 0.51 in a 64-ha plot within the
Hubbard Brook Experimental Forest in New Hampshire, USA, but this site was studied
intensively throughout the breeding season and included resight probabilities of 0.93. In
a study based entirely on band recovery data that acknowledged the likelihood of
underestimated survival probabilities, Stewart (1988) found that Yellow-rumped Warbler
(D. coronata) annual survival was 0.45. Jones et al. (2004) reported survival of 0.54 in
Cerulean Warblers (D. cerulea) in Ontario. Roberts (1971) estimated Yellow Warbler
(D. petechia) survival to be 0.53. Cilimburg et al. (2002) estimated survival of this
species to be 0.42 for males in a core area within their study location in Montana. They
accounted for underestimated survival probabilities caused by dispersal and searched
outside of original core banding locations, thereby increasing survival estimates by
between 0.065 and 0.229.
40
This technique to increase our estimates was based on a small subset of the
population, but we suggest that this may be a useful method of improving survival
estimates in other projects. Large, spatial-scale studies are inherently difficult due to
logistic and time constraints but searching intensively outside normal resight range can
provide a method to detect individuals that might otherwise be missed. Smaller, spatial-
scale studies can search intensively over the entire study area to account for this possible
bias, but landscape-scale inference is not justified in these studies. White and Burnham
(1999) urged that the best way to increase the accuracy of survival estimates is for
researchers to maximize resight probabilities.
Within-season survival
By following a subset of birds to test within-season survival we were able to test
the prediction that birds have high survival rates over the breeding season. For within-
season survival, these are the first rates for both of our focal species reported to our
knowledge and as such, there is no reference point for comparison (but see Monthly
survival below).
Model-averaged within-season survival estimates were 0.976 ± 0.077 for
Blackburnian Warblers in 2005 and 2006 combined, and 0.928 ± 0.120 for Black-
throated Green Warblers in 2005 and 2006 combined. Survival estimates for all birds
were 0.952 ± 0.098 while resight estimates were 0.531 ± 0.085. Interestingly, models
examining differences between species and year in within-season resight probabilities
received little or no support. The top-ranked models showed decreasing resight
probability over each resighting interval. The first interval refers to the first two weeks of
the breeding season and each successive interval is 10-14 days after the previous.
41
Detection of Blackburnian Warblers, which forage in the uppermost section of the
canopy (Morse 2004), is difficult even under ideal circumstances. By resighting known
banded individuals at least three times (encounter occasions) throughout the breeding
season, we expected that we would observe them more frequently because they were
known to be alive. However, decreasing resight probabilities within the season suggest
that birds are likely moving outside their territorial boundaries more than expected.
Moreover, these differences are likely due to birds becoming less likely to respond to
playback as they tend to nestlings and fledglings as the breeding season progresses.
While high survival rates within the breeding season were not surprising, the
comparably low within-season resight probabilities suggest a mechanism that may be of
interest for further study. If birds are indeed seeking out extra-pair copulations, as has
been suggested in the literature (Norris and Stutchbury 2001), many survival estimates
relying on CMR techniques will be underestimated by confounding true mortality with
permanent emigration. Small standard errors suggest that our estimates are reliable, but
the evidence of incomplete breeding site-fidelity and low resight rates within the season
provides possible direction for future research. We recommend that apparent annual
survival studies that rely on site fidelity of birds incorporate components into their studies
to take into account individual movement regardless of the mechanism involved (e.g.,
extra-pair copulations, breeding dispersal, etc.).
Monthly survival rates
Monthly survival estimates derived from annual survival probabilities for both
species grouped were 0.914, 0.914 for all BLBW, and 0.913 for all BTNW. The
simulated annual survival estimate (0.475) corrected with individuals resighted during the
42
intensive grid searches is likely closer to a biologically accurate level based on estimates
from other Dendroica warblers. Monthly survival calculated from this estimate is 0.940.
Converting within-season survival estimates to monthly estimates yielded 0.988
for BLBW and 0.963 for BTNW. Jones et al. (2004) found survival of Cerulean
Warblers to be lower from August to May (0.93) than June to July (0.98), indicating that
most mortality occurred either on migration or on wintering grounds in South America.
Estimating survival over the winter months is difficult and has not been documented
thoroughly in the literature. Dean (1999) reported survival probabilities to be lowest
overwinter in the Bahamas, while Sillett and Holmes (2002) recorded high monthly
survival estimates for Black-throated Blue Warblers during the stationary periods: 1.0
from May to August in New Hampshire and 0.99 from October to March in Jamaica.
They reported monthly survival estimates to be lowest during migratory periods (0.77-
0.81 0.02). We could not test this for our sample as we marked individuals only on
breeding grounds. It is likely that our focal species suffer similar fates to Black-throated
Blue Warblers during these periods. Blackburnian Warblers migrate farther than either
Black-throated Blue or Black-throated Green Warblers (Morse 2004, 2005) and may have
higher mortality on their migratory passage than either of these species though this
prediction is untested.
General implications
Our project is the first to our knowledge to test theories of avian survival at such a
large spatial scale with substantial sample sizes. We also report the first estimates of
monthly, seasonal, and annual survival for either of our focal species. Our corrected
survival rates are comparable to those of closely related species in other studies. These
43
models contribute to our overall understanding of the basic biology of two species of
forest songbirds while presenting more questions for further study. A reduction of
mature forest did not strongly affect survival in our focal species. Our focal species were
affected more by landscape structure than by composition. This highlights the
importance of species-specific habitat models. These survival estimates will be useful to
biologists and forest managers who will be able to use these rates to construct population
viability models to assess any possible risks of declining populations associated with the
focal species and mature forest.
44
References
Akaike, H. 1973. Information theory as an extension of the maximum likelihood
principle. Pages 267-281 in B.N. Petrov, and F. Csaki, (eds.) Second International
Symposium on Information Theory. Akademiai Kiado, Budapest.
Anders, A.D., and M.R. Marshall. 2005. Increasing the accuracy of productivity and
survival estimates in assessing landbird population status. Conservation Biology
19: 66-74.
Bayne, E.M., and K.A. Hobson. 2002. Apparent survival of male ovenbirds in
fragmented and forested boreal landscapes. Ecology 83: 1307-1316.
Betts, M.G., S.E. Franklin, and R.S. Taylor. 2003. Interpretation of landscape pattern and
habitat change for local indicator species using satellite imagery and geographic
information system data in New Brunswick, Canada. Canadian Journal of Forest
Research 33: 1821-1831.
Betts, M.G., and G.J. Forbes (Eds.). 2005. Forest management guidelines to protect
native biodiversity in the Greater Fundy Ecosystem. University of New
Brunswick (UNB) Cooperative Fish and Wildlife Unit, Fredericton, Pp. 110.
Betts, M.G., A.S. Hadley, and P.J. Doran. 2005. Avian mobbing response is restricted by
territory boundaries: experimental evidence from two species of forest warblers.
Ethology 111: 821-835.
Betts, M.G., G.J. Forbes, A.W. Diamond, and P.D. Taylor. 2006a. Independent effects of
habitat amount and fragmentation on songbirds in a forest mosaic: an organism-
based approach. Ecological Applications 16: 1076-1089.
Betts, M.G., B.P. Zitske, A.S. Hadley, and A.W. Diamond. 2006b. Migrant forest
songbirds undertake breeding dispersal following timber harvest. Northeastern
Naturalist 13: 531-536.
Betts, M.G., D. Mitchell, A.W. Diamond, and J. Bêty. 2007. Uneven rates of landscape
change as a source of bias in roadside wildlife surveys. Journal of Wildlife
Management 71: 2266-2273.
Bowman, J.C. 2003. Is dispersal distance of birds proportional to territory size? Canadian
Journal of Zoology 81: 195-202.
Brownie, C., and D.S. Robson. 1983. Estimation of time-specific survival rates from tag-
resighting samples: a generalization of the Jolly-Seber Model. Biometrics 39:
437-453.
Burke, D.M., and E. Nol. 2001. Age ratios and return rates of adult male Ovenbirds in
contiguous and fragmented forests. Journal of Field Ornithology 72: 433-438.
45
Burnham, K.P., and D.R. Anderson. 2002. Model Selection and Inference: A practical
information-theoretic approach, 2nd ed. Springer, New York.
Cilimburg, A.B., M.S. Lindberg, J.J. Tewksbury, and S.J. Hejl. 2002. Effects of dispersal
on survival probability of adult Yellow Warblers (Dendroica petechia). Auk 119:
778-789.
Collins, S.L. 1983. Geographic variation in habitat structure of the Black-throated Green
Warbler (Dendroica virens). Auk 100: 382-389.
Cooch, E. and White, G.C. 2002. Program MARK: A Gentle Introduction. Available at:
http://www.phidot.org/software/mark/docs/book/.
Cormack, R.M. 1964. Estimates of survival from the sighting of marked animals.
Biometrika 51: 429-438.
Dean, T. 1999. Second-growth habitat use and survival rates of migrant and resident land
birds, North Andros Island, Bahamas. MScF Thesis, University of New
Brunswick.
DeGraaf, R.M., J.B. Hestbeck, and M. Yamasaki. 1998. Associations between breeding
bird abundance and stand structure in the White Mountains, New Hampshire and
Maine, USA. Forest Ecology and Management 103: 217-233.
Doepker, R.V., R.D. Earle, and J.J. Ozoga. 1992. Characteristics of Blackburnian
Warbler, Dendroica fusca, breeding habitat in upper Michigan. Canadian Field-
Naturalist 106: 366-371.
Doherty, P., and T. Grubb. 2002. Survivorship of permanent-resident birds in a
fragmented forested landscape. Ecology 83: 844-857.
Donovan, T.M., and F.R. Thompson III. 2001. Modeling the ecological trap hypothesis: a
habitat and demographic analysis for migrant songbirds. Ecological Applications
11: 871-882.
Drapeau, P., A. Leduc, J.-F. Giroux, J.-P.L. Savard, Y. Bergeron, and W.L. Vickery.
2000. Landscape-scale disturbances and changes in bird communities of boreal
mixed-wood forests. Ecological Monographs 70: 423-444.
Fahrig, L. 1997. Relative effects of habitat loss and fragmentation on population
extinction. Journal of Wildlife Management 61: 603-610.
Fahrig, L. 1998. When does fragmentation of breeding habitat affect population survival?
Ecological Modelling 105: 273-292.
46
Fahrig, L. 2003. Effects of habitat fragmentation on biodiversity. Annual Review of
Ecology, Evolution, and Systematics 34: 487-515.
Fahrig, L., and G. Merriam. 1994. Conservation of fragmented populations. Conservation
Biology 8: 50-59.
Forman, R.T.T. 1995. Land mosaics: The ecology of landscapes and regions.
Cambridge University Press, Cambridge, United Kingdom.
Girard, C., M. Darveau, J.-P.L. Savard, and J. Huot. 2004. Are temperate mixed wood
forests perceived by birds as a distinct forest type? Canadian Journal of Forest
Research 34: 1895-1907.
Gunn, J.S., A. Desrochers, M.-A. Villard, and J. Ibarzabal. 2000. Playbacks of mobbing
calls of black-capped chickadees as a method to estimate reproductive activity of
forest birds. Journal of Field Ornithology 71: 472-483.
Hagan, J.M., W.M. Vander Haegen, and P.S. McKinley. 1996. The early development of
forest fragmentation effects on birds. Conservation Biology 10: 188-202.
Jolly, G.M. 1965. Explicit estimates from capture-recapture data with both death and
immigration-stochastic model. Biometrika 52: 225-247.
Jones, J., J.J. Barg, T.S. Sillett, M.L. Veit, and R.J. Robertson. 2004. Minimum estimates
of survival and population growth for Cerulean Warblers (Dendroica cerulea)
breeding in Ontario, Canada. Auk 121: 15-22.
Lampila, P., M. Mönkkönen, and A. Desrochers. 2005. Demographic responses by birds
to forest fragmentation. Conservation Biology 19: 1537-1546.
Larson, M.A., F.R. Thompson, J.J. Millspaugh, W.D. Dijak, and S.R. Shifley. 2004.
Linking population viability, habitat suitability, and landscape simulation models
for conservation planning. Ecological Modelling 180: 103-118.
Lebreton, J.-D., K.P. Burnham, J. Clobert, and D.R. Anderson. 1992. Modeling survival
and testing biological hypotheses using marked animals: A unified approach with
case studies. Ecological Monographs 62: 67-118.
Lichstein, J.W., T.R. Simons, and K.E. Franzreb. 2002. Landscape effects on breeding
songbird abundance in managed forests. Ecological Applications 12: 836-857.
Marshall, M.R., D.R. Diefenbach, L.A. Wood, and R.J. Cooper. 2004. Annual survival
estimation of migratory songbirds confounded by incomplete breeding site-
fidelity: study designs that may help. Animal Biodiversity and Conservation 27:
59-72.
47
McGarigal, K., and W.C. McComb. 1995. Relationships between landscape structure and
breeding birds in the Oregon Coast Range. Ecological Monographs 65: 235-259.
Morse, D.H. 1976. Variables affecting the density and territory size of breeding spruce-
woods warblers. Ecology 57: 290-301.
Morse, D.H. 2004. Blackburnian Warbler (Dendroica fusca). The Birds of North
America Online. (A. Poole, Ed.) Ithaca: Cornell Laboratory of Ornithology;
Retrieved from The Birds of North American Online database:
http://bna.birds.cornell.edu/BNA/account/Blackburnian_Warbler/.
Morse, D.H. 2005. Black-throated Green Warbler (Dendroica virens). The Birds of North
America Online (A. Poole, Ed.). Ithaca: Cornell Laboratory of Ornithology;
Retrieved from The Birds of North American Online database:
http://bna.birds.cornell.edu/BNA/account/Black-throated_Green_Warbler/.
New Brunswick Department of Natural Resources and Energy. 1998. New Brunswick
Forest Inventory. Fredericton, N.B., Canada.
New Brunswick Department of Natural Resources and Energy. 2005. Habitat definitions
for old-forest vertebrates in New Brunswick. New Brunswick, Canada.
Norris, D.R., and B.J.M. Stutchbury. 2001. Extraterritorial movements of a forest
songbird in a fragmented landscape. Conservation Biology 15: 729-736.
Norton, M.R., S.J. Hannon, and F.K.A. Schmiegelow. 2000. Fragments are not islands:
patch vs. landscape perspectives on songbird presence and abundance in a
harvested boreal forest. Ecography 23: 209-223.
Pimm, S.L., G.L. Russell, J.L. Gittleman, and T.M. Brooks. 1995. The future of
biodiversity. Science 269: 347-350.
Pollock, K.H., J.D. Nichols, C. Brownie, and J.E. Hines. 1990. Statistical inference for
capture-recapture experiments. Wildlife Monographs 107: 1-97.
Porneluzi, P.A., and J. Faaborg. 1999. Season-long fecundity, survival, and viability of
Ovenbirds in fragmented and unfragmented landscapes. Conservation Biology 13:
1151-1161.
Pyle, P. 1997. Identification guide to North American birds. Part I: Columbidae to
Ploceidae. Slate Creek Press, Bolinas, CA.
Roberts, J.O.L. 1971. Survival among some North American wood warblers. Bird
Banding 42: 165-184.
Robinson, S.K., F.R. Thompson III, T.M. Donovan, D.R. Whitehead, and J. Faaborg.
1995. Regional forest fragmentation and the nesting success of migratory birds.
Science 267: 1987-1989.
48
Root, R.B. 1973. Organization of a plant-arthropod association in simple and diverse
habitats: the fauna of collards. Ecological Monographs 45: 95-120.
Sauer, J.R., J.E. Hines, and J. Fallon. 2005. The North American Breeding Bird Survey,
Results and Analysis 1966-2004. Version 2005.2. USGS Patuxent Wildlife
Research Center, Laurel, MD.
Seber, G.A.F. 1965. A note on the multiple recapture census. Biometrika 52: 249-259.
Sillett, T.S., and R.T. Holmes. 2002. Variation in survivorship of a migratory songbird
throughout its annual cycle. Journal of Animal Ecology 71: 296-308.
Stewart, P.A. 1988. Annual survival rate of Yellow-rumped Warbler. North American
Bird Bander 13: 106.
Trzcinski, M.K., L. Fahrig, and G. Merriam. 1999. Independent effects of forest cover
and fragmentation on the distribution of forest breeding birds. Ecological
Applications 9: 586-593.
Van Horn, M., and T.M. Donovan. 1994. Ovenbird (Seiurus aurocapillus). In The Birds
of North America, No. 88 (A. Poole and F. Gill, Eds.). Philadelphia: The
Academy of Natural Sciences; Washington, D.C.: The American Ornithologists’
Union.
Van Horne, B. 1983. Density as a misleading indicator of habitat quality. Journal of
Wildlife Management 47: 893-901.
Villard, M.-A., M.K. Trzcinski, and G. Merriam. 1999. Fragmentation effects on forest
birds: relative influence of woodland cover and configuration on landscape
occupancy. Conservation Biology 13: 774-783.
White, G.C., and K.P. Burnham. 1999. Program MARK: survival estimation from
populations of marked animals. Bird Study 46 Supplement: 120-138.
Wiens, J.A. 1994. Habitat fragmentation: island v. landscape perspectives on bird
conservation. Ibis 137: S97-S104.
Wilcove, D.S. 1985. Nest predation in forest tracts and the decline of migratory
songbirds. Ecology 66: 1211 1214.
Williams, B.K., J.D. Nichols, and M.J. Conroy. 2002. Analysis and management of
animal populations: modeling, estimation, and decision-making. Academic Press,
Boston, Massachusetts.
49
Young, L., M.G. Betts, and A.W. Diamond. 2005. Do Blackburnian Warblers select
mixed forest? The importance of spatial resolution in defining habitat. Forest
Ecology and Management 214: 358-372.
50
TABLES
Table 2.1. Number of Blackburnian (BLBW) and Black-throated Green Warblers
(BTNW) banded from 2000-2006 included in the apparent annual survival and within-
season survival analyses.
Apparent Annual Within-season
Species 2000 2001 2002 2003 2004 2005 Total 2005 2006 Total
BLBW 15 16 16 17 96 45 205 34 10 44
BTNW 10 62 45 19 146 74 356 72 27 99
Total 25 78 61 36 242 119 561 106 37 143
51
Table 2.2. Models fitted to the annual Blackburnian (n = 205) and Black-throated Green
Warblers (n= 356) dataset grouped by species (2000-2006) to assess variation in apparent
survival and resighting probabilities including model selection criteria ranked by
ascending AICc. See Appendix B.1 for definitions of landscape variables (‘Mature’,
‘Matrix’, ‘Hab2000’, and ‘Hab100’).
Model AICc AICc wi ER ML K Deviance
A {Φ (t) p (t)} 1191.16 0.00 0.313 1.00 1.000 10 1170.92
B {Φ (.) p (.)} 1193.59 2.42 0.093 3.36 0.298 2 1189.57
C {Φ (hab100) p (.)} 1194.25 3.09 0.067 4.68 0.214 3 1188.22
D {Φ (hab2000) p (.)} 1194.41 3.25 0.062 5.07 0.197 3 1188.38
E {Φ (.) p (species)} 1194.93 3.77 0.048 6.58 0.152 3 1188.91
F {Φ (mature) p (.)} 1194.98 3.82 0.046 6.76 0.148 3 1188.96
G {Φ (t) p (.)} 1195.18 4.01 0.042 7.44 0.134 7 1181.06
H {Φ (species) p (.)} 1195.30 4.13 0.040 7.89 0.127 3 1189.27
I {Φ (matrix) p (.)} 1195.59 4.43 0.034 9.14 0.109 3 1189.56
J {Φ (.) p (t)} 1195.86 4.70 0.030 10.48 0.096 7 1181.74
K {Φ (hab100+hab2000) p (.)} 1196.18 5.02 0.025 12.30 0.081 4 1188.14
L {Φ (species + hab100) p (.)} 1196.27 5.10 0.024 12.83 0.078 4 1188.22
M {Φ (species + hab2000) p (.)} 1196.38 5.21 0.023 13.57 0.074 4 1188.34
N {Φ (t) p (species)} 1196.52 5.36 0.021 14.55 0.069 8 1180.36
O {Φ (species) p (species)} 1196.95 5.79 0.017 18.05 0.055 4 1188.91
P {Φ (species + mature) p (.)} 1196.99 5.82 0.017 18.38 0.054 4 1188.94
Q {Φ (species * mature) p (.)} 1196.99 5.82 0.017 18.38 0.054 4 1188.94
R {Φ (species + matrix) p (.)} 1197.29 6.13 0.015 21.39 0.047 4 1189.25
S {Φ (species) p (t)} 1197.52 6.36 0.013 24.04 0.042 8 1181.37
T {Φ (species * hab100) p (.)} 1198.25 7.08 0.009 34.52 0.029 5 1188.18
U {Φ (species * hab2000) p (.)} 1198.40 7.24 0.008 37.27 0.027 5 1188.33
V {Φ (species * matrix) p (.)} 1199.30 8.14 0.005 58.56 0.017 5 1189.24
Parameter definitions: Φ = survival, p = resight probability, (.) parameter constant, (t)
parameter as a function of year, (species) parameter as a function of group (species), wi =
Model weight, ER = Evidence ratio, ML = Model likelihood, K = number of parameters.
52
Table 2.3. Models fitted to the annual BLBW (n = 205) dataset (2000-2006) to assess
variation in apparent survival and resighting probabilities including model selection
criteria ranked ascending by AICc.
Model AICc AICc wi ER ML K Deviance
A {Φ (.) p (.)} 304.58 0.00 0.338 -- 1.000 2 300.53
B {Φ (trend) p (.)} 306.42 1.84 0.135 2.51 0.399 3 300.31
C {Φ (mature) p (.)} 306.44 1.86 0.133 2.53 0.395 3 300.34
D {Φ (hab100) p (.)} 306.49 1.91 0.130 2.60 0.385 3 300.38
E {Φ (matrix) p (.)} 306.63 2.05 0.121 2.78 0.359 3 300.52
F {Φ (hab2000) p (.)} 306.63 2.05 0.121 2.78 0.359 3 300.53
G {Φ (t) p (.)} 310.88 6.29 0.015 23.26 0.043 7 296.37
H {Φ (.) p (t)} 313.58 8.99 0.004 89.84 0.011 7 299.07
I {Φ (t) p (t reduced)} 313.87 9.29 0.003 104.26 0.010 9 295.06
Parameter definitions: Φ = survival, p = resight probability, (.) parameter constant, (t)
parameter as a function of year, trend = continuous changes over time, wi = Model
weight, ER = Evidence ratio, ML = Model likelihood, K = number of parameters.
Table 2.4. Models fitted to the annual BTNW (n = 356) dataset (2001-2006) to assess
variation in apparent survival and resighting probabilities including model selection
criteria ranked ascending by AICc.
Model AICc AICc wi ER ML K Deviance
A {Φ (.) p (.)} 537.46 0.00 0.252 -- 1 2 533.43
B {Φ (hab100) p (.)} 537.51 0.05 0.246 1.02 0.976 3 531.45
C {Φ (trend) p (.)} 538.74 1.28 0.133 1.90 0.527 3 532.68
D {Φ (hab2000) p (.)} 539.14 1.68 0.109 2.32 0.432 3 533.08
E {Φ (matrix) p (.)} 539.37 1.92 0.097 2.61 0.384 3 533.31
F {Φ (mature) p (.)} 539.49 2.03 0.091 2.76 0.362 3 533.43
G {Φ (.) p (t)} 541.45 3.99 0.034 7.36 0.136 6 529.24
H {Φ (t) p (.)} 542.22 4.77 0.023 10.84 0.092 6 530.01
I {Φ (t) p (t reduced)} 543.34 5.88 0.013 18.94 0.053 7 529.06
Parameter definitions: Φ = survival, p = resight probability, (.) parameter constant, (t)
parameter as a function of year, trend = continuous changes over time, wi = Model
weight, ER = Evidence ratio, ML = Model likelihood, K = number of parameters.
53
Table 2.5. Models fitted to the annual Blackburnian Warbler (n = 146) and Black-
throated Green Warbler (n = 230) dataset grouped by species (2004-2006) to assess
variation in apparent survival and resighting probabilities including model selection
criteria ranked by ascending AICc. Data fitted to these models consist of manipulated
encounter histories (based on actual encounters) and is corrected by birds observed
during intensive grid searches (see text).
Model AICc AICc wi ER ML K Deviance
A {Φ (.) p (year)} 640.92 0.00 0.316 -- 1.000 3 8.68
B {Φ (year) p (.)} 640.92 0.00 0.316 -- 1.000 3 8.68
C {Φ (year) p (species)} 641.78 0.86 0.205 1.54 0.650 4 7.50
D {Φ (species) p (year)} 642.27 1.35 0.161 1.97 0.508 4 7.99
E {Φ (.) p (.)} 652.44 11.52 0.001 315.86 0.003 2 22.22
F {Φ (.) p (species)} 653.42 12.50 0.001 521.85 0.002 3 21.17
G {Φ (species) p (.)} 653.85 12.92 0.000 648.78 0.002 3 21.61
H {Φ (species) p
(species)} 655.45 14.53 0.000 1412.06 0.001 4 21.17
Parameter definitions: Φ = survival, p = resight probability, (.) parameter constant, (year)
parameter as a function of year, (species) parameter as a function of group (species), wi =
Model weight, ER = Evidence ratio, ML = Model likelihood, K = number of parameters.
Table 2.6. Models fitted to the within-season Blackburnian Warbler (N = 44) and Black-
throated Green Warbler (N = 99) dataset grouped (2005, 2006) by species and by year to
assess variation in apparent survival (Φ) and resighting probabilities (p) as functions of
age and landscape metrics (see Appendix B.1) including model selection criteria ranked
ascending by AICc. Only models with weights > 0.01 are shown.
Model AICc AICc wi ER ML K Deviance
A {Φ (species year + hab2000) p (t)} 466.21 0.00 0.472 1.00 1.000 7 451.79
B {Φ (species year * hab2000) p (t)} 468.96 2.75 0.119 3.96 0.252 11 445.95
C {Φ (year + hab2000) p (t)} 469.77 3.56 0.079 5.94 0.169 6 457.45
D {Φ (species + hab2000) p (t)} 469.81 3.60 0.078 6.06 0.165 6 457.50
E {Φ (year * hab2000) p (t)} 470.33 4.12 0.060 7.84 0.128 7 455.90
F {Φ (species * hab2000) p (t)} 471.55 5.34 0.033 14.43 0.069 7 457.12
G {Φ (year + hab100) p (t)} 471.70 5.50 0.030 15.62 0.064 6 459.39
H {Φ (year) p (t)} 471.74 5.53 0.030 15.91 0.063 5 461.52
I {Φ (species year) p (t)} 471.97 5.76 0.026 17.85 0.056 6 459.66
J {Φ (year * hab100) p (t)} 471.98 5.77 0.026 17.91 0.056 7 457.56
K {Φ (species year + hab100) p (t)} 473.63 7.42 0.012 40.90 0.025 7 459.21
Parameter definitions: Φ = survival, p = resight probability, (.) parameter constant, (t)
parameter as a function of time, (species year) parameter as a function of species and year
(year), wi = Model weight, ER = Evidence ratio, ML = Model likelihood, K = number of
parameters.
54
Table 2.7. Mean model-averaged survival (Φ) and resight (p) probabilities from model
sets (AA - Apparent annual, WS - Within-season, BLBW - Blackburnian Warbler, and
BTNW - Black-throated Green Warbler) and tables with associated standard errors (SE)
and 95% confidence intervals.
Model set Parameter Estimate SE 95 % CI
Lower Upper
Table 2.2; AA Φ
Φ: BLBW 0.3396 0.0573 0.2387 0.4595
Φ: BTNW 0.3373 0.3422 0.0248 0.9132
p: BLBW 0.7681 0.1086 0.4793 0.9065
p: BTNW 0.7617 0.1130 0.4648 0.9052
Table 2.3; BLBW AA Φ Φ 0.3610 0.0554 0.2608 0.4750
p 0.6907 0.1112 0.4463 0.8604
Table 2.4; BTNW AA Φ Φ 0.3410 0.0350 0.2757 0.4122
p 0.7753 0.0744 0.5992 0.8866
Table 2.5; Manipulated AA
Φ
Φ 0.4750 0.0919 0.3115 0.6533
p 0.6279 0.1241 0.3847 0.8285
Table 2.6; WS Φ
Φ: BLBW 0.9758 0.0770 0.6025 1
Φ: BTNW 0.9281 0.1198 0.3582 0.9952
p: t1 0.7868 0.0482 0.6775 0.8663
p: t2 0.4923 0.0493 0.3972 0.5880
p: t3 0.3133 0.1562 0.0991 0.6545
Parameter definitions: Φ = survival, p = resighting probability
Table 2.8. Estimates of monthly survival (Φ) rates for Blackburnian (BLBW) and Black-
throated Green Warblers (BTNW) computed by raising apparent annual (AA) survival
estimates to the 10th
root and within-season (WS) survival estimates to the 2nd
root.
Model AA Φ
estimate
WS Φ
estimate
Monthly Φ
estimate
BLBW AA Φ 0.340 -- 0.914
BTNW AA Φ 0.337 -- 0.913
Grouped AA Φ 0.339 -- 0.914
Manipulated AA Φ 0.475 -- 0.940
BLBW WS Φ -- 0.976 0.988
BTNW WS Φ -- 0.928 0.963
55
Figure 2.1. Frequency distributions of four landscape variables (x-axes of all plots are
percentages of: A - mature forest at 2000 m; B - matrix at 2000 m; C - habitat at 2000 m;
D - habitat at 100 m) associated with banded male BLBW from 2000-2005.
56
Figure 2.2. Frequency distributions of four landscape variables (x-axes of all plots are
percentages of: A - mature forest at 2000 m; B - matrix at 2000 m; C - habitat at 2000 m;
D - habitat at 100 m) associated with banded male BTNW (2000-2005).
57
Figure 2.3. Location of all BLBW banded in mature forest patches from 2000-2005 in Greater Fundy Ecosystem, New
Brunswick, Canada. Each block is 10 km by 10 km.
58
Figure 2.4. Location of all BTNW banded in mature forest patches from 2000-2005 in Greater Fundy Ecosystem, New
Brunswick, Canada. Each block is 10 km by 10 km.
59
Appendix A. Estimates of model effect sizes in survival models from Chapter 2.
Appendix A.1. Estimates of model effect sizes (i) with SE and 95% confidence limits
for effects from the best model (Δ AICc 2) of the annual BLBW and BTNW dataset
grouped (N = 561; 2000-2006) to assess variation in apparent survival (Φ) and resighting
probabilities (p) from Table 2.2. Note high estimates and errors for Φ in 2006 and p in
2001, 2004, and 2006. This occurs when real parameter estimates approach ‘1’ and
cannot be computed properly in Program MARK. The parameters in 2006 are
inestimable.
Model Label i SE 95 % Confidence Limit
Lower Upper
A {Φ (t) p (t)}
Φ: 2001 -0.619 0.331 -1.269 0.031
Φ: 2002 -0.113 0.420 -0.936 0.709 Φ: 2003 -1.397 0.296 -1.978 -0.817 Φ: 2004 -1.026 0.267 -1.550 -0.502 Φ: 2005 -0.479 0.195 -0.860 -0.097 Φ: 2006 0.008 23.976 -46.986 47.002 p: 2001 14.912 763.955 -1482.439 1512.263
p: 2002 0.629 0.639 -0.622 1.881 p: 2003 1.712 1.042 -0.331 3.755 p: 2004 15.754 1619.388 -3158.246 3189.753 p: 2005 0.499 0.317 -0.123 1.122
p: 2006 0.008 23.976 -46.986 47.002
60
Appendix A.2. Estimates of model effect sizes (i) with SE and 95% confidence limits
for effects from the six best models (Δ AICc 2) of the annual BLBW dataset (N = 205; 2000-2006) to assess variation in apparent survival (Φ) and resighting probabilities (p)
from Table 2.3.
Model Label i SE 95 % Confidence Limit
Lower Upper
A {Φ (.) p (.)} Φ -0.286 0.101 -0.483 -0.088
p 0.390 0.226 -0.053 0.832
B {Φ (trend) p (.)} Φ -0.051 0.110 -0.267 0.164
p 0.818 0.489 -0.139 1.776
Int -0.350 0.534 -1.397 0.696
C {Φ (mature) p (.)} Φ -0.272 0.618 -1.482 0.939
p 0.799 0.488 -0.158 1.756
Int -0.450 0.361 -1.157 0.257
D {Φ (hab100) p (.)} Φ 0.297 0.780 -1.232 1.825
p 0.798 0.489 -0.160 1.756
Int -0.717 0.419 -1.538 0.104
E {Φ (matrix) p (.)} Φ 0.098 1.258 -2.367 2.563
p 0.799 0.488 -0.159 1.756
Int -0.598 0.323 -1.231 0.035
F {Φ (hab2000) p (.)} Φ -0.044 1.159 -2.316 2.228
p 0.799 0.488 -0.157 1.756
Int -0.565 0.436 -1.418 0.289
61
Appendix A.3. Estimates of model effect sizes (i) with SE and 95% confidence limits
for effects from the six best models (Δ AICc 2) of the annual BTNW dataset (N = 356; 2001-2006) to assess variation in apparent survival (Φ) and resighting probabilities (p)
from Table 2.4.
Model Label i SE 95 % Confidence Limit
Lower Upper
A {Φ (.) p (.)} Φ -0.316 0.066 -0.444 -0.187
p 0.579 0.168 0.249 0.909
B {Φ (hab100) p (.)} Φ 1.722 1.286 -0.799 4.242
p 1.234 0.402 0.446 2.021
Int -2.149 1.137 -4.379 0.080
C {Φ (trend) p (.)} Φ 0.071 0.083 -0.091 0.233
p 1.214 0.404 0.423 2.005
Int -0.883 0.313 -1.496 -0.270
D {Φ (hab2000) p (.)} Φ 0.740 1.256 -1.721 3.202
p 1.231 0.402 0.442 2.020
Int -1.165 0.898 -2.925 0.595
E {Φ (matrix) p (.)} Φ -0.650 1.937 -4.447 3.147
p 1.234 0.403 0.444 2.023
Int -0.592 0.202 -0.988 -0.196
F {Φ (mature) p (.)} Φ 0.002 0.491 -0.961 0.965
p 1.228 0.402 0.439 2.017
Int -0.643 0.239 -1.111 -0.174
62
Appendix A.4. Estimates of model effect sizes (i) with SE and 95% confidence limits
for effects from the five best models (Δ AICc 2) of the manipulated annual BLBW (N = 146) and BTNW (N = 230) dataset grouped (2004-2006) to assess variation in apparent
survival (Φ) and resighting probabilities (p) from Table 2.5.
Model Label i SE 95% Confidence Limit
Lower Upper
A {Φ (.) p (t)} Φ 0.068 0.121 -0.169 0.304
p (2005) 0.411 0.170 0.077 0.745
p (2006) -0.182 0.165 -0.506 0.142
B {Φ (t) p (.)} Φ (2005) 0.068 0.121 -0.169 0.304
Φ (2006) -0.385 0.093 -0.567 -0.202
p 0.411 0.170 0.077 0.745
C {Φ (t) p (t)} Φ (2005) 0.068 0.121 -0.169 0.304
Φ (2006) -0.065 0.000 -0.065 -0.065
p (2005) 0.411 0.170 0.077 0.745
p (2006) -0.065 0.000 -0.065 -0.065
D {Φ (t) p (g)} Φ (2005) 0.071 0.120 -0.164 0.306
Φ (2006) -0.384 0.093 -0.566 -0.201
p (BLBW) 0.271 0.198 -0.118 0.660
p (BTNW) 0.497 0.196 0.113 0.882
E {Φ (g) p (t)} Φ (BLBW) 0.001 0.141 -0.275 0.276
Φ (BTNW) 0.110 0.134 -0.153 0.372
p (2005) 0.412 0.170 0.078 0.746
p (2006) -0.182 0.165 -0.506 0.141
Appendix A.5. Estimates of model effect sizes (i) with SE and 95% confidence limits
for effects from the best model (Δ AICc 2) of the within-season BLBW (N = 44) and
BTNW (N = 99) dataset grouped (2005 and 2006) by species and by year to assess
variation in apparent survival (Φ) and resighting probabilities (p) as functions of age and
landscape metrics from Table 2.6. Resight probabilities refer to time intervals of 10-14
days throughout the breeding season. Note high estimates and errors for all Φ. This
occurs when real parameter estimates approach ‘1’ and cannot be computed properly in
Program MARK.
Model Label i SE 95 % Confidence Limit
Lower Upper
A {Φ (species year
+ hab2000) p (t)}
BLBW 2005 10.750 3.225 4.429 17.071 BLBW 2006 -3.972 1.741 -7.383 -0.560 BTNW 2005 -8.656 3.162 -14.854 -2.458 BTNW 2006 8.385 1790.446 -3500.889 3517.659
hab2000 4.676 1.479 1.778 7.574 p: t1 1.257 0.264 0.739 1.776 p: t2 -0.030 0.188 -0.398 0.339
p: t3 -0.792 0.206 -1.195 -0.388
63
Chapter 3 – Age ratios and morphometrics of Blackburnian (Dendroica fusca) and
Black-throated Green Warblers (D. virens) in relation to apparent annual survival
and landscape covariates
Brad P. Zitske1, Matthew G. Betts
2, and Antony W. Diamond
3
B.P. ZITSKE2, Faculty of Forestry and Environmental Management, University of New
Brunswick, Bag Service #45111, Fredericton, New Brunswick, E3B 6E1, Canada.
M.G. BETTS2, Department of Forest Science, 216 Richardson Hall, Oregon State
University, Corvallis, Oregon, 97331, USA.
A.W. DIAMOND3, Atlantic Cooperative Wildlife Ecology Research Network,
Department of Biology, University of New Brunswick, Bag Service #45111, Fredericton,
New Brunswick, E3B 6E1, Canada.
1 Corresponding author email: [email protected]. Brad Zitske collected and analyzed
survival and morphometric data, interpreted results, and wrote manuscript. 2 Matthew Betts provided analytical support and habitat models and edited manuscript.
3 Antony Diamond supervised Master’s thesis and edited manuscript
* This manuscript is in preparation for submission to Conservation Biology.
64
Abstract
The distribution of birds among varying habitat amounts can have important
consequences on their apparent survival rates. Many studies have shown that
inexperienced breeders predominate in less productive habitats. These animals may have
lower body condition, in turn affecting survival probabilities as most mortality occurs
either on migration or on the wintering grounds. We tested these hypotheses on
Blackburnian and Black-throated Green Warblers in New Brunswick, Canada from 2000
to 2007 using landscape covariates. Annual survival estimates of both species were
influenced by habitat amount at the local scale (100 m radius), but only for inexperienced
breeders. Annual survival of both species was influenced by the additive effects of body
condition and amount of species-specific habitat at the local-scale. Younger birds were
in better condition than older birds, while body condition was influenced more by time of
year captured and by species than by any of the landscape metrics.
Introduction
The geographical distribution of organisms among their respective habitats and
demographic parameters influencing these decisions can have important consequences
for population dynamics (Bowers 1994, Holmes et al. 1996). Previous experience of
breeding birds influences reproductive success (Nol and Smith 1987) and likely has an
effect on other demographic parameters such as survivorship (Clobert et al. 1988,
Doherty and Grubb 2002). It is commonly thought that younger, more inexperienced
birds occupy less productive areas during the breeding season (Burke and Nol 2001) and
may suffer reduced pairing success (Villard et al. 1993, Burke and Nol 1998), lower
65
reproductive success (Robinson et al. 1995, Porneluzi and Faaborg 1999), and lower
survival in fragmented patches (Bayne and Hobson 2002, Doherty and Grubb 2002).
Possible causes of these factors may be higher predation rates (Wilcove 1985), lower
food supplies (Martin 1987), and despotic behaviour by more experienced birds (Graves
1997, Rohwer 2004).
Food resources are less readily available and at a lower density in landscapes with
low forest cover (Root 1973, Burke and Nol 1998) and as a result, any individuals
predominating in these sites will suffer trade-offs between fecundity and survival
(Rohwer 2004). There is recent evidence that poor quality wintering habitat produces
differences in individual body condition and that these effects can carry over to other
periods of the annual cycle of migratory birds (Norris 2005, Studds and Marra 2005).
However, there is a paucity of information on how poor-quality habitat on
breeding grounds may affect individuals in lower body condition (but see Sillett and
Holmes 2002). For the purposes of this study, we assume that lower amounts of mature
forest are representative of lower quality habitats. Body condition refers to the relative
size of energy stores (body mass) compared with structural components (e.g., wing
length) between individuals (Jakob et al. 1996). Individuals with substantial energy
reserves are more likely to achieve reproductive success (Møller et al. 1998) and those in
prime body condition will likely have higher survival rates than others in poor condition
(Chastel et al. 1995, Schulte-Hostedde et al. 2005).
Individuals in poor body condition may be more likely to occur in lower quality
habitats (Burke and Nol 2001). There has been much debate over the appropriate
approach to measuring body condition. A common technique has been to record linear
66
measurements and masses of individuals to compute a ratio index (e.g., body mass/wing
length) (Chastel et al. 1995, Jakob et al. 1996, Burke and Nol 2001). Other studies have
vindicated the use of a residual index, which uses the residuals from a regression of body
mass on body size where a positive residual represents an individual in better condition
than one with a negative residual (Green 2001, Schulte-Hostedde et al. 2005). Of late,
the residual index has garnered more support as the most useful index because it does not
vary with body size (Jakob et al. 1996, Schulte-Hostedde et al. 2005). Our intent in this
project is not to expound on this debate, but rather use appropriate methods to investigate
our questions.
We predicted survival rates to be lower for young birds and for birds in poor
condition. We also predicted that younger birds and those in poor condition would
predominate in landscapes with low amounts of mature forest. Testing these predictions
are particularly critical for species of conservation concern. Breeding Bird Survey (BBS)
data over the past two decades have documented a decline of Blackburnian Warblers
(Dendroica fusca, BLBW) in NB of ~4.9% per year since the 1970s (Sauer et al. 2005).
Blackburnian and Black-throated Green Warblers (D. virens, BTNW) are two species
associated with mature mixedwood forests in New Brunswick (NB), Canada (Young et
al. 2005, Betts et al. 2006b). This forest type is declining at a rate greater than
replacement (~1.5%/ year), primarily as a result of timber harvest (Betts et al. 2003) and
is thus of conservation concern (Betts and Forbes 2005). To increase our potential of
capturing as many individuals as possible and given that both focal species are associated
with all types of mature forest in our study area (Betts et al. 2006a), we broadened our
scope to include all mature forest (> 60 year old) using Geographical Information
67
Systems (GIS) data originating from the New Brunswick Forest Inventory (updated in
2000).
Our objectives for this project were to test for landscape effects on age ratios and
body condition of Blackburnian and Black-throated Green Warblers in NB using
previously defined landscape metrics (Chapter 2, Appendix B.1, see Methods - Study
Design). We also examined apparent annual survival of these species as functions of age
at time of capture, body condition indices, and landscape metrics of the two focal species.
The specific objectives of this chapter are:
(1) To determine the influence of age and body condition on apparent annual
survival estimates in relation to landscape metrics.
(2) To determine how age and body condition are affected by amount of mature
forest, predicted habitat amount at local- and landscape-scales, and the amount
of non-habitat matrix.
(3) To determine if there are differences between species banded among the
landscape metrics and to compare ages and condition indices between species.
Methods
Study Area
Research was conducted within the Greater Fundy Ecosystem (GFE), New
Brunswick (NB), Canada (66.08°-64.96°W, 46.08°-45.47°N), including sections of the
Fundy Model Forest (FMF), Fundy National Park (FNP), and the Southern Uplands
Ecoregion (4000 km2/400,000 ha). Acadian forest dominates the area and the main tree
species are yellow birch (Betula alleghaniensis), sugar maple (Acer saccharum),
68
American beech (Fagus grandifolia), balsam fir (Abies balsamea), and red spruce (Picea
rubens), with black spruce (P. mariana) in some low-lying areas (NBDNRE 1993).
Intensive forestry activities (i.e., clearcutting, plantations, and thinning) are common in
all areas of the FMF outside of FNP.
Study design
Species were selected based on their association with mature mixedwood forest.
Blackburnian Warblers are strongly associated with this forest type (Morse 2004, Young
et al. 2005) while Black-throated Green Warblers exhibit greater plasticity (Collins 1983,
Morse 2005) and are more abundant in the region compared to Blackburnian Warblers
(Betts et al. 2006a). Both species are associated with all types of mature forest in our
study area (Morse 2004, 2005, Betts et al. 2006a) and birds were banded along a range of
mature forest within a 2000 m radius in the GFE. We prioritzed capture of Blackburnian
Warblers in landscapes with low amounts of mature forest based on previous difficulties
of capture in these landscapes (B.P. Zitske pers obs). Patches were not randomly
selected but were chosen to represent a range of mature forest according to a randomized
stratified design. The 2000 m scale constitutes the proposed maximum distance of natal
dispersal for Neotropical migrants (Bowman 2003) and the distance birds may travel
within the breeding season to search for extra-pair copulations (Norris and Stutchbury
2001).
We summed area of all mature forest (‘Mature’, > 60 years old, NBDNRE 2005)
around each captured bird at a 2000 m radius using GIS land cover data in ArcView 3.3.
We also summed the amount of predicted habitat at local- (100 m, ‘Hab100’) and
landscape-scales (2000 m, ‘Hab2000’), and non-habitat matrix at 2000 m (‘Matrix’). The
69
above metrics were obtained with local-level vegetation predictor variables and point
count data to predict occurrence of both species in a related study (Betts et al. 2006a, b).
Habitat models were summed at two extents: 2000 m and 100 m, representing the size of
a typical territory for our focal species (Morse 2004, 2005). Inhospitable matrix occurred
where the probability of occurrence of each species was less than 5% (p ≤ 0.05).
Field measurements
We used banded birds of both focal species from a related study from 2000-2003
and prioritized capturing as many BLBW as possible in 2004 and 2005 (the core of this
study) since they were less abundant in these landscapes. We captured territorial males
between 25 May and 30 July (the most reliable time to capture territorial individuals) of
each year using a combination of audio playback, conspecific decoys, and mist-netting
(with 30 mm mesh mist-nets). We assumed that we captured only territorial males based
on their aggressive response to audio playback.
We fitted each adult bird with a unique combination of two coloured plastic leg
bands and one Canadian Wildlife Service aluminum band. We determined age and sex of
each bird using plumage characteristics (Pyle 1997) and measured the natural chord wing
length with a standard wing ruler. We took digital photographs of each individual in the
field and determined ages of all birds using these pictures in the autumn without knowing
ages determined in the field. We then compared both assessments of age to verify
precision. We determined ages of birds in one of the following categories: after hatch
year (‘AHY’; unknown age with confounding plumage characteristics), second year
(‘SY’; first-year breeder in first alternate plumage), or after second year (‘ASY’; at least
70
second-year breeder in definitive alternate plumage). Due to the unknown age of AHY
birds, only SY and ASY birds were included in age analyses (n = 512).
We measured body mass of captured individuals (n = 155 BLBW and n = 230
BTNW) with a 30-gram (g) spring scale to the nearest 0.25 g. Because we resighted
birds as opposed to recapturing them, we used only morphometric and age data obtained
at time of initial capture. We resighted birds in subsequent years from the original
capture location using audio playback 50 m at each cardinal direction (N, E, S, W) a
minimum of two attempts per season.
Survival Analysis
We estimated the effects of age and body condition on survival using program
MARK (White and Burnham 1999; hereafter ‘MARK’; see Chapter 2 for details). We
imported data for each species into MARK to estimate annual survival (hereafter,
‘survival’) as individual encounter histories (EHs). Age and condition analyses had
different datasets (different EHs for each banded individual) as the age dataset included
512 SY and ASY birds. The condition dataset consisted of a different amount of birds (n
= 385) because some birds were inadvertently released before recording relevant data.
Annual EHs to test for body condition were four occasions long, with each occasion
representing a different year from 2003-2006. An example EH for a bird analyzed in the
condition dataset is: 1100, where this individual was banded in 2003, resighted in 2004,
and not resighted in 2005 or 2006. Morphometrics measurements were not taken prior to
2003, so birds banded from 2000-2002 were not used in this analysis. Annual EHs to test
for age effects were seven occasions long, with each occasion representing a different
year from 2000-2006. An example EH for a bird analyzed in the age dataset is: 1110100,
71
where this individual was banded in 2000, resighted in 2001 and 2002, not resighted in
2003, resighted again in 2004, and not resighted in 2005 or 2006. Independently for each
dataset, we began by fitting a global model consisting of separate apparent survival
(denoted by Φ) and resight (p) parameters with time-dependence (Φ (t), p (t)). We
estimated the variance inflation factor (ĉ) from our global model using the parametric
bootstrap option in program MARK (White and Burnham 1999) to determine if our data
were overdispersed (a source of underestimated sampling variances).
We used an information-theoretic approach (Burnham and Anderson 2002) to
determine support for competing models. We ranked models in each candidate set by
Akaike’s Information Criterion (AIC; Akaike 1973) adjusted for small sample size
(AICc), ranked best to worst (lowest AIC to highest AIC). QAICc identifies that AIC has
been adjusted for overdispersed data and small sample size (c; Burnham and Anderson
2002). For the model testing the influence of body condition, ĉ was < 1, so we made no
overdispersion adjustments. The age model fitted the data poorly so an adjustment of ĉ
= 1.31 was necessary to improve fit. Given our small sample sizes, we applied the small-
sample correction (AICc) to all models. If more than one model receives strong support,
estimates of survival and resight probabilities are frequently model-averaged based on the
AIC weights (Burnham and Anderson 2002).
Statistical analysis-Age
We formulated separate models to assess our hypothesis regarding annual
variation in survival due to ages and condition indices in MARK. We used Pearson’s
chi-square tests with Yates’ continuity correction (which reduces the overall 2 and
72
minimizes error due to bias, Zar 1999) to test if age ratios of captured birds varied by
species and landscape.
Statistical analysis-Condition indices
We calculated a ratio index of condition by dividing body mass by wing length to
compare both species on the same scale. We used the residuals from linear regressions of
body mass to wing length and verified that assumptions of regression (i.e. linearity,
independence, normality, homogeneity of variance) were satisfied. We used the ratio
index to do coarse, exploratory plots over time but this approach has been shown to
control inadequately for variations in body size, while the residual index provides a
straightforward interpretation biologically and does not correlate with body size (Jakob et
al. 1996). Thus, we applied the residuals to test hypotheses about condition differences
in our survival models and generalized linear models (GLMs).
Statistical analysis-both age and condition
We used factorial ANOVAs to test for differences between species and ages as
categorical predictor variables (2 levels for each) and mean values of all continuous,
landscape metrics and condition indices. Assumptions of homogeneity of variance were
checked using Cochran’s test. Assumptions of normality were met for all predictor
variables except for ‘Matrix’ and ‘Hab100’. The ‘Matrix’ variable was square root-
transformed and the ‘Hab100’ metric was rank-transformed because assumptions were
not met with other transformations.
Additionally, we used GLMs with a normal distribution in the data and normal
(identity link) function to test for differences in age and residual condition indices of all
73
banded birds as a function of the percentage of each of the four landscape covariates
(‘Mature’, ‘Matrix’, ‘Hab100’ and ‘Hab2000’). All models were fitted in R 2.5.1 (R
Development Core Team 2007). We predicted that younger birds would predominate in
landscapes with lower amounts of mature forest and habitat at both scales and that they
would have lower survival rates than older, more experienced birds. We predicted that
individuals in lower (poor) body condition would be found more often and have lower
survival rates in landscapes with lower percentages of mature forest and habitat at both
scales.
Results
Survival
Older birds had higher annual survival rates than younger birds (model-averaged
survival estimates for ASY birds = 0.367 ± 0.035 and for SY = 0.224 ± 0.041 for SY;
Table 3.1). Estimates are percentages between 0.00 and 1.00. Survival of inexperienced
breeders is related to predicted habitat amount at the local scale (100 m) (Tables 3.1, 3.3
and 3.4; Fig. 3.1). The weight of evidence is often used to assess relative support for
different models and is derived from the strength of each model relative to other models
(Burnham and Anderson 2002). Relative support for different landscape metrics using
summed AICc weights for both age groups in the age model set was: ‘Mature’ = 6.1%,
‘Matrix’ = 7.8% ‘Hab2000’ = 21.7%, ‘Hab100’ = 55.5%. Thus, the more support for a
variable, the more confident we are that this variable explains variation in survival.
The influence of body condition on survival showed clear influence of local-level
predicted habitat (‘Hab100’), as it was present in three of the top four models, all of
74
which received strong support ( AICc ≤ 4; Table 3.2). Relative support using summed
AICc weights of different landscape metrics on condition residuals was: ‘Mature’ = 6.2%,
‘Matrix’ = 7.5% ‘Hab2000’ = 11.9%, ‘Hab100’ = 44.1%. Again, predicted local-level
habitat was the best landscape covariate that explained variation in survival probabilities.
Age ratios
From 2000 to 2005, we caught the following numbers of species/age class
categories: total n = 512; SY, BLBW n = 61; BTNW n = 133; ASY, BLBW n = 135;
BTNW n = 183 (Figure 3.1). We captured a significantly higher proportion of SY
BTNW than SY BLBW (73% and 45%, respectively) (2 =5.72, df = 1, p = 0.017). We
captured a significantly higher proportion of ASY BLBW in higher percentages of local-
level landscape (‘Hab100’) than SY BLBW (mean % of Hab100 of BLBW ASY 0.498 ±
0.020, mean % of Hab100 of BLBW SY 0.412 ± 0.026, F = 3.90, p = 0.049). All
landscape covariates were significant predictors of species distribution (Tables 3.6 and
3.7) with BLBW captured more frequently in sites with higher percentages of mature
forest (mean % mature forest of BLBW 0.457 ± 0.019, mean % mature forest of BTNW
0.397 ± 0.015, F = 4.51, p = 0.034) and matrix (mean % of matrix of BLBW 0.208 ±
0.010, mean % matrix of BTNW 0.085 ± 0.004, F = 153.02, p < 0.001), and BTNW
captured in higher percentages of ‘Hab2000’ (mean % of Hab2000 of BTNW 0.703 ±
0.006, mean % of Hab2000 of BLBW 0.322 ± 0.010, F = 1026.39, p < 0.001) and
Hab100 (mean % of Hab100 of BTNW 0.865 ± 0.007, mean % of Hab100 of BLBW
0.471 ± 0.016, F = 528.29, p < 0.001).
75
Age and condition
For comparison, we plotted mean condition indices and mass/wing length
residuals of banded individuals of both species in Figure 3.2. A plot of mean condition
indices over time revealed a polynomial distribution (Fig. 3.3). For subsequent models
including Julian date, we squared date and used this as another predictor variable to
explain variation in condition. Plots of residuals across all landscape metrics are given in
Fig. 3.4. We used all birds in our sample with body mass and wing length measurements
to compute condition indices (Table 3.6; n = 385; mean = 0.149 ± 0.0004; range = 0.129-
0.180). SY BLBW had higher condition indices than ASY BLBW (mean CI of SY
BLBW 0.150 ± 0.001, mean CI of ASY BLBW 0.147 ± 0.001, F = 7.1, p < 0.001). All
SY birds of both species grouped had higher condition indices than ASY birds grouped
(mean CI of SY 0.150 ± 0.001, mean CI of ASY 0.148 ± 0.001, F = 7.1, p < 0.001), but
SY BTNW did not have significantly higher condition indices than ASY BTNW (mean
CI of SY BTNW 0.150 ± 0.001, mean CI of ASY BTNW 0.150 ± 0.001). BTNW had
higher condition indices than BLBW, but not significantly (mean CI of BTNW 0.150 ±
0.001, mean CI of BLBW 0.148 ± 0.001, F = 3.1, p = 0.08). Condition residuals were
influenced more by the inclusion of the polynomial Julian date (‘Jdate2’) in GLMs (Table
3.8) than by Julian date alone, suggesting temporal variation. Birds with lower residuals,
and therefore in poorer condition, were captured earlier and later in the breeding season
with a peak of higher condition birds from June 15 to July 5 (Fig. 3.3). Condition also
showed temporal variation with species, age and all of the landscape covariates of all
individuals captured.
76
Discussion
Age ratios and survival
Our primary objective was to relate annual survival of the two focal species as
functions of age and landscape. Older individuals (ASY) in our study had higher survival
estimates than younger birds, while survival of inexperienced (SY) birds appeared to be
more dependent on the amount of predicted habitat at the local scale (100 m). SY birds
are more likely to show breeding dispersal, particularly if they are pushed into lower
quality habitat during the breeding season and if they are unsuccessful breeders
(Porneluzi and Faaborg 1999, Burke and Nol 2001). If nests fail or if fecundity is lower
in landscapes with lower amounts of forest cover than in higher cover landscapes (Paton
1994, Donovan et al. 1997), birds may move to another patch of suitable habitat in
subsequent years and may be missed on future resight attempts. In this scenario dispersal
will be confounded with true mortality and high breeding dispersal will result in
underestimated survival probabilities (Rohwer 2004).
Graves (1997) suggested that there might be a maximum number of yearling birds
allowed into high-quality breeding habitat by experienced birds. Similarly, younger birds
are often forced into lower quality habitats on the wintering grounds (Marra et al. 1998)
and survival rates consequently will be lower on migration to the breeding grounds than
on the stationary winter grounds (Sillett and Holmes 2002). SY birds may be particularly
sensitive to amount of forest cover on the breeding grounds. Densities of both focal
species in sub-optimal habitat, i.e. young forest, were smaller (0.4-0.6 BLBW pairs/ha
and 1.2 BTNW pairs/ha) than in mature forest (0.7-1 BLBW pairs/ha and 1.8-2 BTNW
pairs/ha; Morse 2004, 2005) in Maine, USA. If ASY birds are in better habitat, they are
77
more likely to survive between years and less likely to disperse (Greenwood and Harvey
1982) assuming that they are successful at reproducing.
While the survival rates were lower for SY birds as expected, the only landscape
metric that affected survival significantly between ages of either species was predicted
local-scale habitat (‘Hab100’) for Blackburnian Warblers. Our prediction was that
younger individuals would be more prevalent with lower amounts of mature forest. This
was not well supported. The lack of a difference between occurrence of banded ASY and
SY among the four landscape covariates, except BLBW ASY and SY, may mean that
territorial males do not distinguish between forest types. Captured Blackburnian
Warblers differed significantly among all landscape metrics from captured Black-
throated Green Warblers.
Few existing studies on avian survival with a landscape context have examined
age ratios (but see Burke and Nol 2001, Doherty and Grubb 2002). Our results
suggesting that adult birds have higher survival probabilities were consistent with only
Doherty and Grubb (2002). They studied permanent resident species and found that
younger individuals had lower survival rates than older individuals in Black-capped
Chickadee (0.31 vs. 0.43), White-breasted Nuthatch (Sitta carolinensis) (0.21 vs. 0.26),
and Downy Woodpecker (Picoides pubescens) (0.21 vs. 0.26). Burke and Nol (2001)
used return rates, as opposed to more comprehensive survival analyses that take into
account resight probabilities, and found that 0.40 SY Ovenbirds (Seiurus aurocapillus)
returned, compared with only 0.346 ASYs. This study (Burke and Nol 2001) was based
on comparatively small samples (ASY n = 35 and SY n = 26). In an age-related survival
study in contiguous forest, Sillett and Holmes (2002) recorded similar survival in SY and
78
ASY Black-throated Blue Warblers (D. caerulescens) breeding in New Hampshire, USA
(0.514 and 0.512, respectively). As such, there is much variation in survival probabilities
and comparisons are often difficult.
Condition indices and survival
Survival models including residuals from condition indices showed strong support
for local-scale habitat of all birds grouped by species and age classes. SY Blackburnian
Warblers were in better condition than ASY Blackburnian Warblers. We observed the
same result in both species grouped, but not in Black-throated Green Warblers.
However, Marra et al. (1998) found that older American Redstarts (Setophaga ruticilla)
arrived on the breeding grounds first and are generally in better condition than later-
arriving birds. Our results may be due to the energy expenditure required defending
territories. Both our focal species become territorial shortly after arrival on the breeding
grounds with older birds arriving first and subsequently warding off intruding younger
males (Morse 2004, 2005). Territorial disputes are known to increase hormonal levels
and decrease fat stores (Romero et al. 1997) and, therefore, body condition. Black-
throated Green Warblers had higher condition indices than Blackburnian Warblers, which
is interesting in that this species is one of the most dominant wood warblers (Morse
2005) and one might expect them to expend more energy than Blackburnian Warblers in
inter-specific disputes. Perhaps high condition indices in Black-throated Green Warblers
allow them to behave more dominantly to other species of wood warblers.
The inclusion of the squared Julian date variable to represent the polynomial
relationship with time reduced much of the variation in the residuals of condition index.
Models including the squared Julian date performed much better than Julian date alone.
79
The temporal variation can be explained by the substantial energy expenditure of
migration, which depletes energy reserves and causes birds to arrive in poor condition
(Marra and Holberton 1998). In a study on Common Redpolls (Carduelis flammea)
breeding in Alaska, Romero et al. (1997) found body weight to increase gradually as
males began feeding young. We hypothesize that in our focal species body condition
increases while females incubate, peaks while tending nests, then decreases until young
fledge and leave the nest. Performing a mensurative experiment to test this is a direction
of possible future research.
General implications
Mature forest did not influence survival of either species in the age and condition
data sets. Older birds of both species had higher survival probabilities than younger birds
while predicted local-level habitat influenced survival of younger birds more than older
birds. Predicted local-level habitat also influenced survival of birds in better condition.
This, however, is less intuitive; in our study, younger birds were in better condition than
older birds and there was no obvious correlation with any of our landscape predictor
variables except for Blackburnian Warblers in local-level habitat. Our study is the only
project to our knowledge to incorporate the effects of age and body condition on survival
of birds in relation to a reduction of mature forest and other landscape covariates. As
such, it cannot be compared with other studies. This is perhaps the most interesting result
as it accents the importance of species-specific habitat models.
80
References
Akaike, H. 1973. Information theory as an extension of the maximum likelihood
principle. Pages 267-281 in B.N. Petrov, and F. Csaki, (eds.) Second International
Symposium on Information Theory. Akademiai Kiado, Budapest.
Bayne, E.M., and K.A. Hobson. 2002. Apparent survival of male ovenbirds in
fragmented and forested boreal landscapes. Ecology 83: 1307-1316.
Betts, M.G., S.E. Franklin, and R.S. Taylor. 2003. Interpretation of landscape pattern and
habitat change for local indicator species using satellite imagery and geographic
information system data in New Brunswick, Canada. Canadian Journal of Forest
Research 33: 1821-1831.
Betts, M.G., and G.J. Forbes (Eds.). 2005. Forest management guidelines to protect
native biodiversity in the Greater Fundy Ecosystem. University of New
Brunswick (UNB) Cooperative Fish and Wildlife Unit, Fredericton, Pp. 110.
Betts, M.G., A.W. Diamond, G.J. Forbes, M.-A. Villard, and J. Gunn. 2006a. The
importance of spatial autocorrelation, extent and resolution in predicting forest
bird occurrence. Ecological Modelling 191: 197-224.
Betts, M.G., G.J. Forbes, A.W. Diamond, and P.D. Taylor. 2006b. Independent effects of
habitat amount and fragmentation on songbirds in a forest mosaic: an organism-
based approach. Ecological Applications 16: 1076-1089.
Betts, M.G., D. Mitchell, A.W. Diamond, and J. Bêty. 2007. Uneven rates of landscape
change as a source of bias in roadside wildlife surveys. Journal of Wildlife
Management 71: 2266-2273.
Bowers, M.A. 1994. Dynamics of age- and habitat-structured populations. Oikos 69: 327-
333.
Bowman, J.C. 2003. Is dispersal distance of birds proportional to territory size? Canadian
Journal of Zoology 81: 195-202.
Burke, D.M., and E. Nol. 1998. Influence of food abundance, nest-site habitat, and forest
fragmentation on breeding ovenbirds. Auk 115: 96-104.
Burke, D.M., and E. Nol. 2001. Age ratios and return rates of adult male Ovenbirds in
contiguous and fragmented forests. Journal of Field Ornithology 72: 433-438.
Burnham, K.P., and D.R. Anderson. 2002. Model Selection and Inference: A practical
information-theoretic approach, 2nd ed. Springer, New York.
Chastel, O., H. Weimerskirch, and P. Jouventin. 1995. Body condition and seabird
reproductive performance: a study of three petrel species. Ecology 76: 2240-2246.
81
Clobert, J., C.M. Perrins, R.H. McCleery, and A.G. Gosler. 1988. Survival rate in the
great tit Parus major in relation to sex, age, and immigration status. Journal of
Animal Ecology 57: 287-306.
Collins, S.L. 1983. Geographic variation in habitat structure of the Black-throated Green
Warbler (Dendroica virens). Auk 100: 382-389.
Doherty, P., and T. Grubb. 2002. Survivorship of permanent-resident birds in a
fragmented forested landscape. Ecology 83: 844-857.
Donovan, T.M., P.W. Jones, E.M. Annand, and F.R. Thompson III. 1997. Variation in
local-scale edge effects: mechanisms and landscape context. Ecology 78: 2064-
2075.
Graves, G.R. 1997. Geographic clines of age ratios of black-throated blue warblers
(Dendroica caerulescens). Ecology 78: 2524-2531.
Green, A.J. 2001. Mass/length residuals: measures of body condition or generators of
spurious results? Ecology 82: 1473-1483.
Greenwood, P.J., and P.H. Harvey. 1982. The natal and breeding dispersal of birds.
Annual Review of Ecology, Evolution, and Systematics 13: 1-21.
Holmes, R.T., P.P. Marra, and T.W. Sherry. 1996 Habitat-specific demography of
breeding black-throated blue warblers (Dendroica caerulescens): implications for
population dynamics. Journal of Animal Ecology 65: 183-195.
Jakob, E.M., S.D. Marshall, and G.W. Uetz. 1996. Estimating fitness: a comparison of
body condition indices. Oikos 77: 61-67.
Marra, P.P., and R.I. Holberton. 1998. Corticosterone levels as indicators of habitat
quality: effects of habitat segregation in a migratory bird during the non-breeding
season. Oecologia 116: 1432-1439.
Marra, P.P., K.A. Hobson, and R.T. Holmes. 1998. Linking winter and summer events in
a migratory bird by using stable-carbon isotopes. Science 282: 1884-1886.
Martin, T.E. 1987. Food as a limit on breeding birds: a life-history perspective. Annual
Review of Ecology, Evolution, and Systematics 18: 453-487.
Møller, A.P., P. Christe, J. Erritzøe, and J. Mavarez. 1998. Condition, disease, and
immune defence. Oikos 83: 301-306.
Morse, D.H. 2004. Blackburnian Warbler (Dendroica fusca). The Birds of North
America Online. (A. Poole, Ed.) Ithaca: Cornell Laboratory of Ornithology;
82
Retrieved from The Birds of North American Online database:
http://bna.birds.cornell.edu/BNA/account/Blackburnian_Warbler/.
Morse, D.H. 2005. Black-throated Green Warbler (Dendroica virens). The Birds of North
America Online (A. Poole, Ed.). Ithaca: Cornell Laboratory of Ornithology;
Retrieved from The Birds of North American Online database:
http://bna.birds.cornell.edu/BNA/account/Black-throated_Green_Warbler/.
New Brunswick Department of Natural Resources and Energy. 1998. New Brunswick
Forest Inventory. Fredericton, N.B., Canada.
New Brunswick Department of Natural Resources and Energy. 2005. Habitat definitions
for old-forest vertebrates in New Brunswick. New Brunswick, Canada.
Nol, E., and J.N.M. Smith. 1987. Effects of age and breeding experience on seasonal
reproductive success in the song sparrow. Journal of Animal Ecology 56: 301-
313.
Norris, D.R. 2005. Carry-over effects and habitat quality in migratory populations. Oikos
109: 178-186.
Norris, D.R., and B.J.M. Stutchbury. 2001. Extraterritorial movements of a forest
songbird in a fragmented landscape. Conservation Biology 15: 729-736.
Paton, P.W.C. 1994. The effect of edge on avian nest success: how strong is the
evidence? Conservation Biology 8: 17-26.
Porneluzi, P.A., and J. Faaborg. 1999. Season-long fecundity, survival, and viability of
Ovenbirds in fragmented and unfragmented landscapes. Conservation Biology 13:
1151-1161.
Pradel, R., J.E. Hines, J.-D. Lebreton, and J.D. Nichols. 1997. Accounting for transients
in capture-recapture experiments and estimating their abundance. Biometrics 53:
60-72.
Pyle, P. 1997. Identification guide to North American birds. Part I: Columbidae to
Ploceidae. Slate Creek Press, Bolinas, CA.
R Development Core Team. 2007. R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved
from: http://www.R-project.org.
Robinson, S.K., F.R. Thompson III, T.M. Donovan, D.R. Whitehead, and J. Faaborg.
1995. Regional forest fragmentation and the nesting success of migratory birds.
Science 267: 1987-1989.
Rohwer, S. 2004. Using age ratios to infer survival and despotic breeding dispersal in
hybridizing warblers. Ecology 85: 423-431.
83
Romero, L.M., K.K. Soma, K.M. O’Reilly, R. Suydam, J.C. Wingfield. 1997. Territorial
behavior, hormonal changes, and body condition in an arctic-breeding bird, the
redpoll (Carduelis flammea). Behavior 134: 727-747.
Root, R.B. 1973. Organization of a plant-arthropod association in simple and diverse
habitats: the fauna of collards. Ecological Monographs 45: 95-120.
Sauer, J.R., J.E. Hines, and J. Fallon. 2005. The North American Breeding Bird Survey,
Results and Analysis 1966-2004. Version 2005.2. USGS Patuxent Wildlife
Research Center, Laurel, MD.
Schulte-Hostedde, A.I., B. Zinner, J.S. Millar, and G.J. Hickling. 2005. Restitution of
mass-size residuals: validating body condition indices. Ecology 86: 155-163.
Sillett, T.S., and R.T. Holmes. 2002. Variation in survivorship of a migratory songbird
throughout its annual cycle. Journal of Animal Ecology 71: 296-308.
Studds, C.E., and P.P. Marra. 2005. Nonbreeding habitat occupancy and population
processes: an upgrade experiment with a migratory bird. Ecology 86: 2380-2385.
Villard, M.-A., P.R. Martin, and C.G. Drummond. 1993. Habitat fragmentation and
pairing success in the Ovenbird (Seiurus aurocapillus). Auk 110: 759-768.
White, G.C., and K.P. Burnham. 1999. Program MARK: survival estimation from
populations of marked animals. Bird Study 46 Supplement: 120-138.
Wilcove, D.S. 1985. Nest predation in forest tracts and the decline of migratory
songbirds. Ecology 66: 1211 1214.
Young, L., M.G. Betts, and A.W. Diamond. 2005. Do Blackburnian Warblers select
mixed forest? The importance of spatial resolution in defining habitat. Forest
Ecology and Management 214: 358-372.
Zar, J.H. 1999. Biostatistical Analysis. 4th
Ed. Prentice Hall. Upper Saddle River, New
Jersey.
84
TABLES
Table 3.1. Models fitted to the annual BLBW (N = 196) and BTNW (N = 316) dataset
grouped (2000-2006) by species to assess variation in apparent survival and resighting
probabilities as functions of age and landscape metrics including model selection criteria
ranked by ascending QAICc, with variance inflation factor (ĉ) adjusted to 1.31. See
Appendix B.1 for descriptions of landscape covariates.
Model QAICc QAIC
c wi ER ML K QDev
A {Φ (age * hab100) p (.)} 606.09 0.00 0.233 1.00 1.000 5 596.00 B {Φ (age + hab100) p (.)} 606.65 0.56 0.176 1.32 0.756 4 598.59 C {Φ (age + hab2000) p (.)} 608.23 2.14 0.080 2.91 0.344 4 600.16 D {Φ (species age) p (.)} 608.62 2.53 0.066 3.54 0.282 5 598.53 E {Φ (species age + hab100) p (.)} 608.67 2.58 0.064 3.63 0.276 6 596.53 F {Φ (age) p (.)} 609.05 2.96 0.053 4.39 0.228 3 603.01 G {Φ (age * hab2000) p (.)} 609.12 3.03 0.051 4.54 0.220 5 599.02 H {Φ (age * hab2000 + hab100) p (.)} 609.51 3.42 0.042 5.54 0.181 6 597.38 I {Φ (age + matrix) p (.)} 610.00 3.91 0.033 7.06 0.142 4 601.94 J {Φ (species age + hab2000) p (.)} 610.25 4.16 0.029 7.99 0.125 6 598.11 K {Φ (species age + mature) p (.)} 610.56 4.47 0.025 9.35 0.107 6 598.43 L {Φ (species age + matrix) p (.)} 610.62 4.53 0.024 9.63 0.104 6 598.49 M {Φ (age + mature) p (.)} 611.06 4.97 0.019 11.99 0.083 4 603.00 N {Φ (hab100) p (.)} 611.21 5.12 0.018 12.95 0.077 3 605.18 O {Φ (age * matrix) p (.)} 611.98 5.89 0.012 18.99 0.053 5 601.88 P {Φ (species + hab100) p (.)} 612.79 6.70 0.008 28.55 0.035 4 604.73 Q {Φ (.) p (.)} 612.92 6.83 0.008 30.45 0.033 2 608.90 R {Φ (hab2000) p (.)} 613.02 6.92 0.007 31.87 0.031 3 606.98 S {Φ (species age * hab100) p (.)} 613.02 6.93 0.007 32.00 0.031 9 594.73 T {Φ (age * mature) p (.)} 613.09 7.00 0.007 33.14 0.030 5 603.00 U {Φ (species) p (.)} 613.46 7.36 0.006 39.70 0.025 3 607.42 V {Φ (species * hab100) p (.)} 613.67 7.58 0.005 44.23 0.023 5 603.57 W {Φ (matrix) p (.)} 614.11 8.02 0.004 55.26 0.018 3 608.08 X {Φ (species age * mature) p (.)} 614.46 8.37 0.004 65.72 0.015 9 596.17 Y {Φ (mature) p (.)} 614.93 8.84 0.003 82.79 0.012 3 608.89 Z {Φ (species + hab2000) p (.)} 615.04 8.95 0.003 87.79 0.011 4 606.98 AA {Φ (species + mature) p (.)} 615.40 9.31 0.002 104.80 0.010 4 607.33 AB {Φ (species + matrix) p (.)} 615.42 9.33 0.002 106.23 0.009 4 607.36 AC {Φ (species age * hab2000) p (.)} 615.74 9.65 0.002 124.41 0.008 9 597.45 AD {Φ (species age * hab2000 +
hab100) p (.)} 615.79 9.69 0.002 127.13 0.008 10 595.43
AE {Φ (species age * matrix) p (.)} 616.06 9.97 0.002 146.32 0.007 9 597.77 AF {Φ (species * hab2000) p (.)} 616.88 10.78 0.001 219.48 0.005 5 606.78 AG {Φ (species * mature) p (.)} 617.19 11.10 0.001 255.66 0.004 5 607.09 AH {Φ (species * matrix) p (.)} 617.41 11.32 0.001 287.22 0.004 5 607.31 AI {Φ (t) p (.)} 618.88 12.79 0.000 596.54 0.002 7 604.70
85
AJ {Φ (t) p (t)} 623.96 17.87 0.000 7755.00 0.000 11 601.53
Table 3.2. Models fitted to the annual BLBW (N = 155) and BTNW (N = 230) dataset
grouped (2003-2006) by species to assess variation in apparent survival and resighting
probabilities as functions of residual from body condition indices (‘resid’) and landscape
metrics including model selection criteria ranked by ascending AICc. See Appendix B.1
for descriptions of landscape covariates.
Model AICc
AIC
c wi ER ML K Dev
A {Φ (resid + hab100) p
(.)} 552.02 0.00 0.240 1.00 1.000 4 543.92
B {Φ (resid * hab100) p
(.)} 553.58 1.56 0.110 2.19 0.458 5 543.44
C {Φ (.) p (.)} 553.65 1.63 0.106 2.26 0.442 2 549.62
D {Φ (species + resid +
hab100) p (.)} 553.96 1.94 0.091 2.64 0.379 5 543.81
E {Φ (species + resid) p
(.)} 554.37 2.35 0.074 3.24 0.309 4 546.28
F {Φ (resid + hab2000) p
(.)} 554.60 2.58 0.066 3.64 0.275 4 546.50
G {Φ (resid) p (.)} 554.76 2.74 0.061 3.94 0.254 3 548.70
H {Φ (resid + matrix) p
(.)} 555.86 3.84 0.035 6.82 0.147 4 547.76
I {Φ (species + resid +
mature) p (.)} 556.31 4.29 0.028 8.53 0.117 5 546.16
J {Φ (species + resid +
hab2000) p (.)} 556.35 4.33 0.028 8.70 0.115 5 546.20
K {Φ (species + resid +
matrix) p (.)} 556.40 4.38 0.027 8.94 0.112 5 546.26
L {Φ (species * resid) p
(.)} 556.42 4.40 0.027 9.03 0.111 5 546.27
M {Φ (resid + mature) p
(.)} 556.51 4.49 0.025 9.46 0.106 4 548.42
N {Φ (resid * hab2000) p
(.)} 556.53 4.52 0.025 9.57 0.105 5 546.39
O {Φ (t) p (t)} 557.08 5.06 0.019 12.53 0.080 5 546.93
P {Φ (t) p (.)} 557.26 5.24 0.017 13.77 0.073 4 549.17
Q {Φ (resid * matrix) p (.)} 557.90 5.88 0.013 18.92 0.053 5 547.75
R {Φ (resid * mature) p
(.)} 558.55 6.53 0.009 26.26 0.038 5 548.41
Parameter definitions: Φ = survival, p = resight probability, (.) parameter constant, (t)
parameter as a function of time, wi = Model weight, ER = Evidence ratio, ML = Model
likelihood, K = number of parameters, Dev = deviance.
86
Table 3.3. Model-averaged estimates from model sets (Age; Age model, CI; Condition
index (body mass/wing length), BLBW; Blackburnian Warbler, and BTNW; Black-
throated Green Warbler) and tables with associated standard errors (SE) and 95%
confidence intervals from Tables 3.1 and 3.2.
Model Parameter Estimate SE 95% Confidence Limit
Lower Upper
Table 1; Age Φ
Φ: ASY 0.3667 0.0346 0.3019 0.4367 Φ: SY 0.2237 0.0411 0.1534 0.3142
p 0.7706 0.0679 0.6128 0.8770
Table 2; CI Φ Φ 0.3614 0.0461 0.2767 0.4557
p 0.7263 0.0823 0.5411 0.8566
Parameter definitions: Φ = survival, p = resight probability
Table 3.4. Estimates of model effect sizes (i) with SE and 95% confidence limits for
effects from the two best models (Δ AICc 2) of the annual BLBW and BTNW dataset
grouped (N = 512; 2000-2006) to assess variation in apparent survival (Φ) and resighting
probabilities (p) as functions of age (‘ASY’, after second year and ‘SY’, second year) and
landscape metrics from Table 3.1. Model intercepts denoted by ‘int’.
Model Label i SE 95 % Confidence Limit
Lower Upper
A {Φ (age * hab100) p (.)}
Φ: ASY int -0.546 0.149 -0.838 -0.255
Φ: SY int -0.698 0.255 -1.198 -0.198
ASY * hab100 0.129 0.129 -0.122 0.382
SY * hab100 0.445 0.290 -0.124 1.014
p 1.212 0.384 0.459 1.964
B {Φ (age + hab100) p (.)}
Φ: ASY int -0.549 0.150 -0.843 -0.257
Φ: SY int -0.587 0.233 -1.044 -0.130
hab100 0.232 0.113 0.012 0.453
p 1.205 0.384 0.451 1.958
87
Table 3.5. Estimates of model effect sizes (i) with SE and 95% confidence limits for
effects from the two best models (Δ AICc 2) of the annual BLBW and BTNW dataset grouped (N = 355; 2000-2006) to assess variation in apparent survival (Φ) and resighting
probabilities (p) as functions of residuals from mass/wing length regressions and
landscape metrics from Table 3.2. Model intercepts denoted by ‘int’.
Model Label i SE
95% Confidence
Limit
Lower Upper
A {Φ (resid + hab100)
p (.)}
int -0.5738 0.1669 -0.9009 -0.2466
resid 0.1428 0.1136 -0.0798 0.3654
hab100 0.2594 0.1210 0.0222 0.4966
p 0.9828 0.4143 0.1708 1.7948
B {Φ (resid * hab100)
p (.)}
int -0.5878 0.1679 -0.9169 -0.2587
resid 0.1403 0.1139 -0.0828 0.3635
hab100 0.2670 0.1224 0.0271 0.5070
resid*hab100 -0.0760 0.1094 -0.2903 0.1384
p 0.9860 0.4138 0.1750 1.7971
C {Φ (.) p (.)} Phi -0.5524 0.1662 -0.8782 -0.2265
p 0.9622 0.4129 0.1528 1.7716
D {Φ (species + resid +
hab100) p (.)}
int -0.4985 0.2826 -1.0523 0.0553
species -0.1311 0.3960 -0.9072 0.6450
resid 0.1404 0.1138 -0.0827 0.3634
hab100 0.3126 0.2016 -0.0826 0.7077
p 0.9855 0.4147 0.1728 1.7982
88
Table 3.6. Means of continuous, landscape predictor variables for each species (‘BLBW’, Blackburnian Warbler; ‘BTNW’,
Black-throated Green Warbler) with one standard error used to test variation in condition indices (‘CI’, computed as body
mass/wing length). For descriptions of landscape metrics, see Appendix B.1. All birds were aged: after hatch year (‘AHY’),
after second year (‘ASY’) and second year (‘SY’) at time of capture in the Greater Fundy Ecosystem, New Brunswick, Canada
from 2003-2005.
Species N Age CI Mature Matrix Hab2000 Hab100
BLBW 8 AHY 0.147 ± 0.002 0.533 ± 0.103 0.183 ± 0.046 0.365 ± 0.040 0.504 ± 0.081
98 ASY 0.147 ± 0.001 0.460 ± 0.023 0.205 ± 0.011 0.333 ± 0.013 0.498 ± 0.020
49 SY 0.150 ± 0.001 0.437 ± 0.035 0.218 ± 0.019 0.293 ± 0.016 0.412 ± 0.026
All BLBW 155 0.148 ± 0.001 0.457 ± 0.019 0.208 ± 0.010 0.322 ± 0.010 0.471 ± 0.016
BTNW 21 AHY 0.153 ± 0.002 0.422 ± 0.053 0.085 ± 0.012 0.716 ± 0.020 0.818 ± 0.034
112 ASY 0.150 ± 0.001 0.400 ± 0.022 0.083 ± 0.006 0.698 ± 0.010 0.875 ± 0.008
97 SY 0.150 ± 0.001 0.388 ± 0.022 0.088 ± 0.007 0.705 ± 0.009 0.863 ± 0.011
All BTNW 230 0.150 ± 0.001 0.397 ± 0.015 0.085 ± 0.004 0.703 ± 0.006 0.865 ± 0.007
Both
Grouped
29 AHY 0.151 ± 0.002 0.453 ± 0.048 0.112 ± 0.017 0.619 ± 0.035 0.732 ± 0.042
210 ASY 0.148 ± 0.001 0.428 ± 0.016 0.140 ± 0.007 0.528 ± 0.015 0.699 ± 0.017
146 SY 0.150 ± 0.001 0.404 ± 0.019 0.132 ± 0.009 0.567 ± 0.018 0.712 ± 0.021
All Birds 385 0.149 ± 0.000 0.421 ± 0.012 0.135 ± 0.006 0.549 ± 0.011 0.706 ± 0.012
89
Table 3.7. Factorial ANOVAs testing for differences between means of species and age as categorical predictor variables (2
levels for each) and response variables: ‘CI’ - Condition indices (mass/wing length), ‘Mature’ (amount of mature forest at
2000 m), ‘Square-root transformed Matrix’ (amount of non-habitat matrix at 2000 m square-root transformed to meet
assumptions of normality), ‘Hab2000’ (amount of predicted habitat at 2000 m), and ‘Rank-transformed Hab100’ (amount of
predicted habitat at 100 m to meet assumptions of normality). All associated degrees of freedom (‘df’), mean sum of squares
(‘MS’), F-value, and p-values (significance denoted by ‘*’ at p ≤ 0.05) are given.
Effect CI Mature Square-root transformed Matrix
df MS F p MS F p MS F p
Species 1 0.000 3.1 0.080 0.238 4.51 0.034* 2.132 153.02 < 0.001*
Age 1 0.000 7.1 < 0.001* 0.026 0.49 0.483 0.010 0.73 0.393
Species *
Age 1 0.000 2.8 0.093 0.003 0.05 0.827 0.000 0.01 0.915
Residual 352 0.000 0.053 0.014
Hab2000 Rank-transformed Hab100
df MS F p MS F p
Species 1 12.103 1026.39 < 0.001* 2208922.387 528.29 < 0.001*
Age 1 0.021 1.79 0.182 16306.603 3.90 0.049*
Species *
Age 1 0.044 3.75 0.054 7272.135 1.74 0.188
Residual 352 0.012 4181.245
90
Table 3.8. Results from generalized linear models (GLM) testing the residuals from an
ordinary least squares regression of body mass against wing length (body condition
index) as a function of Julian date (‘Jdate’), Julian date squared (‘Jdate2’ for polynomial
distribution), species, age, and landscape metrics (defined in text). See Appendix B.1 for
descriptions of landscape covariates.
Model AIC ∆ AIC wi ER K
Jdate2 + Species 472.12 0 0.189 1.00 4 Jdate2 473.10 0.98 0.116 1.63 3 Jdate2 + Species + Age 473.63 1.51 0.089 2.13 5 Jdate2 + Species + Mature 474.02 1.90 0.073 2.59 5 Jdate2 + Species + Hab100 474.06 1.94 0.072 2.64 5 Jdate2 + Species + Hab2000 474.11 1.99 0.070 2.70 5 Jdate2 + Species + Matrix 474.11 1.99 0.070 2.70 5 Jdate2 + Age + Hab100 474.55 2.43 0.056 3.37 5 Jdate2 + Age + Hab2000 474.61 2.49 0.054 3.47 5 Jdate2 + Age 474.92 2.80 0.047 4.06 4 Jdate2 + Species + Age + Mature 475.51 3.39 0.035 5.45 6 Jdate2 * Matrix + Age 475.58 3.46 0.033 5.64 7 Jdate2 + Species + Age + Hab100 475.61 3.49 0.033 5.73 6 Jdate2 + Species + Age + Hab2000 475.62 3.50 0.033 5.75 6 Jdate2 + Species + Age + Matrix 475.63 3.51 0.033 5.78 6 Jdate2 + Age + Matrix 475.91 3.79 0.028 6.65 5 Jdate2 * Hab2000 + Age 475.92 3.80 0.028 6.69 7 Jdate2 + Age + Mature 476.62 4.50 0.020 9.49 5 Jdate2 * Species + Age 477.21 5.09 0.015 12.74 7 Jdate2 * Age 477.34 5.22 0.014 13.60 4 Jdate2 * Hab100 + Age 478.41 6.29 0.008 23.22 7 Jdate2 * Mature + Age 479.04 6.92 0.006 31.82 7 Species 490.08 17.96 0.000 > 7942.63 2 Species + Age 490.62 18.50 0.000 > 7942.63 3 Species * Age 490.67 18.55 0.000 > 7942.63 4 Species + Mature 491.07 18.95 0.000 > 7942.63 3 Hab100 491.4 19.28 0.000 > 7942.63 2 Species + Hab100 491.96 19.84 0.000 > 7942.63 3 Species + Hab2000 491.98 19.86 0.000 > 7942.63 3 Species + Matrix 492.04 19.92 0.000 > 7942.63 3 Hab2000 492.26 20.14 0.000 > 7942.63 2 Hab100 + Age 492.53 20.41 0.000 > 7942.63 3 Hab2000 + Age 493.16 21.04 0.000 > 7942.63 3 Mature 494.48 22.36 0.000 > 7942.63 2 Matrix 494.7 22.58 0.000 > 7942.63 2 Jdate 494.78 22.66 0.000 > 7942.63 2 Mature + Age 495.61 23.49 0.000 > 7942.63 3 Matrix + Age 495.87 23.75 0.000 > 7942.63 3 Jdate + Age 496.05 23.93 0.000 > 7942.63 3
91
Table 3.9. Estimates of model effect sizes (i) with SE, 95% lower (LCI) and upper
(UCI) confidence intervals, t values, and p values (*denotes significance at 0.05 level)
from the seven best models (Δ AICc 2) of the annual BLBW and BTNW dataset grouped (N = 355; 2000-2006) to test the residuals from an ordinary least squares
regression of body mass against wing length (body condition index) as a function of
Julian date (‘Jdate’), Julian date squared (‘Jdate2’, for polynomial distribution), species,
age, and landscape metrics (defined in text) from Table 3.8.
Model Parameter i SE LCI UCI t value p value
Jdate2 +
Species
Intercept 0.052 0.039 -0.024 0.128 1.332 0.184
Julian Date 0.424 0.473 -0.503 1.350 0.897 0.371
Julian Date2 -2.193 0.470 -3.114 -1.272 -4.666 4.36e-06*
Species -0.088 0.051 -0.189 0.012 -1.721 0.086
Jdate2
Intercept 0.000 0.025 -0.048 0.048 0.000 1.000
Julian Date 0.563 0.467 -0.352 1.478 1.206 0.229
Julian Date2 -2.301 0.467 -3.216 -1.386 -4.928 1.28e-06 *
Jdate2 +
Species + Age
Intercept 0.041 0.042 -0.042 0.123 0.959 0.338
Julian Date 0.412 0.473 -0.515 1.340 0.872 0.384
Julian Date2 2.156 0.473 1.228 3.084 -4.556 7.21e-06 *
Species 0.094 0.052 -0.008 0.195 -1.804 0.072
Age 0.035 0.051 -0.065 0.135 0.692 0.490
Jdate2 +
Species +
Mature
Intercept 0.037 0.063 -0.087 0.161 0.580 0.563
Julian Date 0.439 0.476 -0.494 1.372 0.922 0.357
Julian Date2 -2.167 0.478 -3.104 -1.230 -4.535 7.93e-06 *
Species -0.086 0.052 -0.188 0.015 -1.671 0.096
Mature 0.034 0.110 -0.182 0.250 0.306 0.760
Jdate2 +
Species +
Hab100
Intercept 0.070 0.089 -0.104 0.243 0.790 0.430
Julian Date 0.434 0.475 -0.498 1.365 0.913 0.362
Julian Date2 -2.188 0.471 -3.111 -1.265 -4.646 4.79e-06 *
Species -0.073 0.086 -0.241 0.095 -0.850 0.396
Hab100 -0.039 0.170 -0.372 0.295 -0.227 0.820
Jdate2 +
Species +
Hab2000
Intercept 0.049 0.083 -0.113 0.211 0.590 0.555
Julian Date 0.425 0.474 -0.504 1.353 0.896 0.371
Julian Date2 -2.191 0.472 -3.116 -1.267 -4.646 4.78e-06 *
Species -0.092 0.101 -0.290 0.106 -0.909 0.364
Hab2000 0.010 0.228 -0.438 0.457 0.042 0.967
Jdate2 +
Species +
Matrix
Intercept 0.049 0.069 -0.087 0.185 0.711 0.478
Julian Date 0.422 0.474 -0.507 1.352 0.891 0.374
Julian Date2 -2.194 0.472 -3.119 -1.270 -4.653 4.65e-06 *
Species -0.087 0.062 -0.207 0.034 -1.410 0.159
Matrix 0.012 0.275 -0.526 0.550 0.045 0.964
92
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9
Week
Nu
mb
er
of
Ba
nd
ed
BL
BW
ASY
SY
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10
Week
Nu
mb
er
of
Ba
nd
ed
BT
NW
ASY
SY
Figure 3.1. Plots of different ages (‘ASY’, after second year; and ‘SY’, second year) of
Blackburnian (‘BLBW’, top) and Black-throated Green Warblers (‘BTNW’, bottom)
banded by week in the Greater Fundy Ecosystem from 2000-2005. Week definitions are:
(1) May 25-31, (2) June 1-7, (3) June 8-14, (4) June 15-21, (5) June 22-28, (6) June 29-
July 5, (7) July 6-12, (8) July 13-19, (9) July 20-26, (10) July 27-31.
93
Figure 3.2. Comparison of linear regressions of condition indices and mass/wing length
residuals by time (Julian date) with 95% confidence intervals.
94
Figure 3.3. Plot of mean condition indices (‘CI’, body mass/wing length) of Blackburnian
(‘BLBW’) and Black-throated Green Warblers (‘BTNW’) banded in Greater Fundy
Ecosystem, NB, from 2003-2005. Week definitions are given in Fig. 1.
95
Figure 3.4. Plots of mass/wing residuals across all landscape variables (y-axes of all plots
are percentages of: A - mature forest at 2000 m; B - matrix at 2000 m; C -predicted
habitat at 2000 m; D - predicted habitat at 100 m) for all birds captured in Greater Fundy
Ecosystem, NB, from 2003-2005.
96
Chapter 4 - General Discussion
Summary of results
In this thesis I have provided basic demographic information previously lacking
for two species of forest songbirds. Additionally, I analyzed these survival estimates in
relation to landscape metrics, age ratios, and body condition. I predicted that
Blackburnian Warblers would have lower survival estimates than Black-throated Green
Warblers in landscapes with less mature forest, based on lower probabilities of
occurrence in these landscapes (Betts et al. 2006b). In addition to the amount of mature
forest at 2000 m, I used landscape metrics from models predicting species occurrence
based on local-level predictor variables developed by Betts et al. (2006a). These
variables were: predicted species-specific habitat at local (100 m) and landscape scales
(2000 m), and non-habitat matrix (2000 m). Mature forest did not affect survival
estimates for either species when grouped in the same model set. The landscape
covariates with the most influence on survival of both species grouped were species-
specific habitat models at both local and landscape scale.
Estimating survival rates accurately depends on the supposition that if a bird
survives from year to year and returns within the bounds of the study area, then it should
be observed again in the same location. Many projects rely on traditional capture-mark-
resight/recapture (CMR) techniques to study migratory songbirds that are faithful to
breeding areas. Cormack-Jolly-Seber (CJS) models have been widely used to correct raw
return rates to estimate resight probability. A major assumption of all CMR models is
that all marked individuals have an equal probability of being resighted (Lebreton et al.
1992). But if a bird is merely moving through the study area when banded it likely may
97
not be observed again; and if it is less than perfectly site-faithful, it will also not be seen
again. My resight radii were limited to 50 m and there is evidence that birds move
outside these bounds due to incomplete site-fidelity (Betts et al. 2006c). Thus, I
acknowledge that we underestimated survival of both of our focal species.
Large-scale ecological experiments are inherently difficult to perform due to
logistical and time constraints. Studies on smaller scales have the advantage of searching
intensively for banded individuals within the study area, but landscape-scale inferences
are limited in these studies. The best way to increase survival estimates is to increase
resight probabilities (White and Burnham 1999). Sillett and Holmes (2002) estimated
survival of Black-throated Blue Warblers (Dendroica caerulescens) on a small 64-hectare
plot at 0.51, while Jones et al. (2004) estimated Cerulean Warbler (D. cerulea) survival at
0.54 on a 2600 ha plot. Both of these estimates for congeners of my study species were
substantially larger than any of our estimates. Thus, these survival estimates for both
species should be considered minimum estimates.
I attempted to approximate the degree to which I underestimated survival
probabilities by searching outside the bounds of the standard resight radii in 2006. I used
these data to manipulate encounter histories to correct for breeding dispersal and analysed
these data in a new model set. This resulted in increased survival estimates over 13%
from models that did not take into account movement (Φ = 0.475 ± 0.092 [corrected] vs.
Φ = 0.343 ± 0.031 [uncorrected]). I believe this estimate to be more biologically accurate
because it is closer to estimates for related species in other studies (Stewart 1988,
Cilimburg et al. 2002, Sillett and Holmes 2002, Jones et al. 2004).
98
By tracking a subset of the banded population throughout the breeding season in
2005 and 2006, I was able to examine whether habitat loss affects survival directly on the
breeding grounds. I predicted that birds would have high survival probabilities within the
breeding season. Within-season survival was influenced by landscape-level habitat
(weight of evidence 84% vs. 7.7% for local-level habitat) and was high (0.95 ± 0.10) as
expected.
I predicted that younger birds would predominate in landscapes with lower
proportions of mature forest and species-specific habitat. The only landscape metric that
significantly influenced the distribution of different ages of birds was predicted local-
level habitat for BLBW. The lack of a difference between age ratios and landscape
variables suggests that birds do not necessarily perceive landscapes with lower amounts
of mature forest as being of low quality (Porneluzi and Faaborg 1999). They are more
likely to require certain structural components within a patch. Young et al. (2005)
suggested that Blackburnian Warblers require a range of mature forest tree species
provided there are large (> 30 cm DBH) deciduous trees for foraging and large conifers
for nesting. This is consistent with other studies that affirm that local-scale factors
explain more variation in abundance than landscape variables (Norton et al. 2000, Hagan
and Meehan 2002).
Older birds had higher survival probabilities than younger birds while local-level
habitat influenced survival of younger birds more than older birds. The additive effects
of body condition and local-level habitat influenced survival in the condition model set.
All younger birds were in significantly better body condition than older birds. This was
also true for Blackburnian Warblers. Body condition varied temporally, showing a
99
polynomial distribution. Birds arrive on the breeding grounds in poor condition and
increase fat stores as the breeding season progresses (Romero et al. 1997) until condition
reaches a peak around late June. Body condition was influenced by date and species in
top models. Age, and the amounts of mature forest, and local- and landscape-level
habitat also influenced body condition suggesting that there are other mechanisms at
work.
Potential selection mechanisms
Researchers have frequently studied bird abundance in relation to a gradient in
habitat loss and fragmentation (McGarigal and McComb 1995, Hagan et al. 1996, Villard
et al. 1999, Drapeau et al. 2000, Norton et al. 2000, Lichstein et al. 2002). The results of
these studies are mixed; some show a relatively strong influence of landscape structure
on bird occurrence (Villard et al. 1999, Betts et al. 2007), while others show weak
landscape influences in comparison to local-scale variables (Hagan and Meehan 2002).
However, in both instances, little is known about the mechanisms driving observed
patterns in abundance. It has long been recognized that in certain instances abundance
can be a poor measure of habitat quality (Van Horne 1983). Animals may be drawn to
sinks (Pulliam and Danielson 1991) or ecological traps (Schlaepfer et al. 2002) where
reproduction and/ or survival are low. Thus, detecting only small effects of landscape
structure on species abundance does not necessarily indicate the absence of underlying
demographic effects. We found evidence that survival was tied strongly to models
predicting occurrence across a gradient of habitat loss. These results provide some
100
support for the hypothesis that reduced species occurrence in landscapes with low
proportions of habitat is due partly to lower apparent survival on these sites.
Lower survival rates in landscapes with reduced local-scale habitat (100 m) may
be due to a number of mechanisms. We found no evidence that the amount of non-
habitat matrix (2000 m) affects survival though other studies have demonstrated that
isolation effects may be due to the presence of conspecifics (Mönkkönen et al. 1999,
Danchin et al. 2004) or limited dispersal capabilities (Goodwin and Fahrig 2002, Betts et
al. 2006a). Thus, birds are more likely to settle and less likely to disperse to lower
quality habitat if in the neighbourhood of conspecifics. However, they may be more
likely to disperse and thus missed on subsequent resight attempts if low quality habitat
holds insufficient resources and if a more permeable matrix aids movement. This may be
the case in a landscape fragmented by forestry where the delineation between forest and
matrix is unclear. Blackburnian Warblers have a broad foraging niche (Morse 2005,
Young et al. 2005) and may be able to move through a variety of forest types
unrestrictedly.
Younger Blackburnian Warblers were captured more often in areas with less
local-scale habitat than older Blackburnian Warblers. Also, younger birds of both species
had lower survival rates. These results are congruent with the hypothesis that older
individuals often force young birds into lower quality habitat. However the lack of an
obvious age bias in lower proportions of habitat suggests that older birds may have a
mechanism, such as larger territory size or the use of multiple patches, to cope with
reduced resources in fragmented landscapes.
101
Body condition results showed that survival of both species was influenced by the
amount of local-level habitat, but in all captured individuals in our study, younger birds
were in better condition than older birds. We suggest this may be due to the high
territoriality of older males and the energy required to defend territories from intruders.
Body condition was influenced more by the polynomial relationship with time of year
banded than by any landscape covariates. This adds credence to the interpretation that
birds adjust to reduced resources in fragmented landscapes.
General implications
Species-specific habitat definitions are of critical importance but these approaches
to quantifying landscape characteristics are uncommon (but see Reunanen et al. 2002,
Betts et al. 2006b) and managing for individual species is not realistic. The landscape
metrics we used, except mature forest, were based on abundance models from previous
related work (Betts et al. 2006a, 2006b). Those results suggested that Blackburnian
Warblers were susceptible to a population decline if timber harvesting creates high
amounts of non-quality matrix habitat and recommended enhancing the connectivity of
the landscape. They also suggested that manipulating the spatial configuration of
Blackburnian Warbler habitat is unlikely to have positive results but rather retention of
the amount of habitat on the landscape is crucial.
To manage these species, we need to understand how these species respond to
relative effects of breeding, migratory, and overwinter periods. It may be necessary to
amalgamate species into groups with similar habitat requirements. It is clear that a
reduction of breeding habitat will have continued negative impacts on the populations of
102
these species. What is less clear is how a reduction of high-quality wintering habitat
affects the population dynamics of these species. The availability of wintering habitat is
unknown and more research is needed to answer this question. Given the decline in
Blackburnian Warblers in New Brunswick over the past two decades and the decline of
mature forest in New Brunswick, developing conservation plans depends on gathering
accurate survival estimates. We have confidence that we have provided such information
in this study though other demographic parameters, such as fecundity and dispersal, and
other factors, such as the influence of extra-pair copulations, are of further interest.
To add strength to our survival estimates, we could have done more to increase
resight probabilities by searching a still larger area outside of the bounds of the original
core search areas to account for movement. Radio-tracking a subset of individuals would
gain further insight into their intra- and inter-seasonal movements and how these species
perceive landscape variables. Linking our survival estimates with other demographic
parameters and habitat use is a direction of future interest.
References
Betts, M.G., A.W. Diamond, G.J. Forbes, M.-A. Villard, and J. Gunn. 2006a. The
importance of spatial autocorrelation, extent and resolution in predicting forest
bird occurrence. Ecological Modelling 191: 197-224.
Betts, M.G., G.J. Forbes, A.W. Diamond, and P.D. Taylor. 2006b. Independent effects of
habitat amount and fragmentation on songbirds in a forest mosaic: an organism-
based approach. Ecological Applications 16: 1076-1089.
Betts, M.G., B.P. Zitske, A.S. Hadley, and A.W. Diamond. 2006c. Migrant forest
songbirds undertake breeding dispersal following timber harvest. Northeastern
Naturalist 13: 531-536.
103
Betts, M.G., D. Mitchell, A.W. Diamond, and J. Bêty. 2007. Uneven rates of landscape
change as a source of bias in roadside wildlife surveys. Journal of Wildlife
Management 71: 2266-2273.
Cilimburg, A.B., M.S. Lindberg, J.J. Tewksbury, and S.J. Hejl. 2002. Effects of dispersal
on survival probability of adult Yellow Warblers (Dendroica petechia). The Auk
119: 778-789.
Danchin, E., L.-A. Giraldeau, T.J. Valone, and R.H. Wagner. 2004. Public information:
from nosy neighbors to cultural evolution. Science 305: 487-491.
Dean, T. 1999. Second-growth habitat use and survival rates of migrant and resident land
birds, North Andros Island, Bahamas. MScF Thesis, University of New
Brunswick.
Drapeau, P., A. Leduc, J.-F. Giroux, J.-P.L. Savard, Y. Bergeron, and W.L. Vickery.
2000. Landscape-scale disturbances and changes in bird communities of boreal
mixed-wood forests. Ecological Monographs 70: 423-444.
Goodwin, B.J., and L. Fahrig. 2002. How does landscape structure influence landscape
connectivity? Oikos 99: 552-570.
Hagan, J.M., W.M. Vander Haegen, and P.S. McKinley. 1996. The early development of
forest fragmentation effects on birds. Conservation Biology 10: 188-202.
Hagan, J.M., and A.L. Meehan. 2002. The effectiveness of stand-level variables for
explaining occurrence in an industrial forest. Forest Science 48: 231-242.
Jones, J., J.J. Barg, T.S. Sillett, M.L. Veit, and R.J. Robertson. 2004. Minimum estimates
of survival and population growth for Cerulean Warblers (Dendroica cerulea)
breeding in Ontario, Canada. Auk 121: 15-22.
Lebreton, J.-D., K.P. Burnham, J. Clobert, and D.R. Anderson. 1992. Modeling survival
and testing biological hypotheses using marked animals: A unified approach with
case studies. Ecological Monographs 62: 67-118.
Lichstein, J.W., T.R. Simons, and K.E. Franzreb. 2002. Landscape effects on breeding
songbird abundance in managed forests. Ecological Applications 12: 836-857.
McGarigal, K., and W.C. McComb. 1995. Relationships between landscape structure and
breeding birds in the Oregon Coast Range. Ecological Monographs 65: 235-259.
Mönkkönen, M., P. Helle, G.J. Niemi, and K. Montgomery. 1999. Evolution of
heterospecific attraction: using other species as cues in habitat selection.
Evolutionary Ecology 13: 91-104.
104
Morse, D.H. 2004. Blackburnian Warbler (Dendroica fusca). The Birds of North
America Online. (A. Poole, Ed.) Ithaca: Cornell Laboratory of Ornithology;
Retrieved from The Birds of North American Online database:
http://bna.birds.cornell.edu/BNA/account/Blackburnian_Warbler/.
Morse, D.H. 2005. Black-throated Green Warbler (Dendroica virens). The Birds of North
America Online (A. Poole, Ed.). Ithaca: Cornell Laboratory of Ornithology;
Retrieved from The Birds of North American Online database:
http://bna.birds.cornell.edu/BNA/account/Black-throated_Green_Warbler/.
Norris, D.R. 2005. Carry-over effects and habitat quality in migratory populations. Oikos
109: 178-186.
Norton, M.R., S.J. Hannon, and F.K.A. Schmiegelow. 2000. Fragments are not islands:
patch vs. landscape perspectives on songbird presence and abundance in a
harvested boreal forest. Ecography 23: 209-223.
Porneluzi, P.A., and J. Faaborg. 1999. Season-long fecundity, survival, and viability of
Ovenbirds in fragmented and unfragmented landscapes. Conservation Biology 13:
1151-1161.
Pulliam, H.R., and B.J. Danielson. 1991. Sources, sinks, and habitat selection: a
landscape perspective on population dynamics. American Naturalist 137: S50-
S66.
Reunanen, P., A. Nikula, M. Mönkkönen, E. Hurme, and V. Nivala. 2002. Predicting
occupancy for the Siberian flying squirrel in old-growth forest patches. Ecological
Applications 12:1188-1198.
Romero, L.M., K.K. Soma, K.M. O’Reilly, R. Suydam, J.C. Wingfield. 1997. Territorial
behavior, hormonal changes, and body condition in an arctic-breeding bird, the
redpoll (Carduelis flammea). Behavior 134: 727-747.
Schlaepfer, M.A., M.C. Runge, and P.W. Sherman. 2002. Ecological and evolutionary
traps. Trends in Ecology and Evolution 17: 474-480.
Sillett, T.S., and R.T. Holmes. 2002. Variation in survivorship of a migratory songbird
throughout its annual cycle. Journal of Animal Ecology 71: 296-308.
Stewart, P.A. 1988. Annual survival rate of Yellow-rumped Warbler. North American
Bird Bander 13: 106.
Studds, C.E., and P.P. Marra. 2005. Nonbreeding habitat occupancy and population
processes: an upgrade experiment with a migratory bird. Ecology 86: 2380-2385.
Van Horne, B. 1983. Density as a misleading indicator of habitat quality. Journal of
Wildlife Management 47: 893-901.
105
Villard, M.-A., M.K. Trzcinski, and G. Merriam. 1999. Fragmentation effects on forest
birds: relative influence of woodland cover and configuration on landscape
occupancy. Conservation Biology 13: 774-783.
White, G.C., and K.P. Burnham. 1999. Program MARK: survival estimation from
populations of marked animals. Bird Study 46: S120-S138.
Young, L., M.G. Betts, and A.W. Diamond. 2005. Do Blackburnian Warblers select
mixed forest? The importance of spatial resolution in defining habitat. Forest
Ecology and Management 214: 358-372.
106
Appendix B.1. Covariates and other factors incorporated into models fitted in program
MARK to assess their importance as drivers in the annual and within-season survival
processes for Blackburnian and Black-throated Green Warblers monitored in the Greater
Fundy Ecosystem, New Brunswick, Canada from 2000-2006.
Covariate Definition Mean Range SE
Hab100‡ Amount of habitat within a 100
m radius 0.7164 0.0101-0.9978 0.0105
Hab2000‡ Amount of habitat within a
2000 m radius 0.5605 0.0947-0.8668 0.0091
Matrix‡ Amount of inhospitable matrix
within a 2000 m radius 0.1269 0.0059-0.4925 0.0046
Mature◊ Amount of mature forest within
a 2000 m radius 0.4133 0.0586-0.9809 0.0099
Trend
Survival constrained to test for
continuous linear changes in
survival over time
NA NA NA
(.) Survival or resight probabilities
constant NA NA NA
(t) Survival or resight probabilities
vary as a function of time NA NA NA
(t reduced)§
Time-dependence as above
‘reduced’ to constrain years
2001-2004 as constant
NA NA NA
‡Derived from Betts et al. 2006b models. Units were summed as estimated probabilities
of occurrence for both species (p) at 100 or 2000 m radii with ArcView 3.3.
◊Amounts of mature forest for all 30 m2 pixels summed within 2000 m radii.
§Constraint necessary to estimate all parameters in the model; more complex model
failed to converge.
107
Appendix B.2. Reduced m-array for male Blackburnian and Black-throated Green
Warblers for this study including the number of marked and resighted birds occurring in
the Greater Fundy Ecosystem, New Brunswick, Canada. Numbers are pooled among all
banding sites from 2000-2006. Ri is the number of all individuals marked or resighted in
year i, including newly marked and previously marked individuals. Annual values
indicate the number of individuals from a given release cohort that were resighted for the
first time in that year; ri indicates the total number from a release cohort that were
resighted at least once; and mj is the total number of individuals resighted in a given year.
Species Year Ri 2001 2002 2003 2004 2005 2006 ri
BLBW 2000 15 4 0 0 0 0 0 4
2001 20 7 2 0 0 0 9
2002 23 3 0 0 0 3
2003 22 6 0 0 6
2004 103 26 4 30
2005 69 15 15
mj 4 7 5 6 26 19
BTNW 2000 10 8 0 0 0 0 0 10
2001 70 18 1 0 0 0 19
2002 59 12 1 0 0 13
2003 31 9 0 0 9
2004 151 37 5 42
2005 103 32 32
mj 8 18 13 10 37 37
108
Appendix B.3. All banded birds from 2000-2005 ordered according to species (BLBW and BTNW; Blackburnian Warbler and
Black-throated Green Warbler) with coordinates of banding location (UTM; Universal Transverse Mercator, Zone 20T,
NAD83 database). ‘Resighted once’ column refers to a bird being resighted a minimum of one occasion during all years of
study. Age is indicated by AHY (‘After Hatch Year’), ASY (‘After Second Year’), and SY (‘Second Year’). Condition index
is computed by body mass (g) / wing length (mm) and is incomplete if either of these two metrics were not recorded at time of
capture. Landscape covariates are the next four columns and are percentages. Definitions of these are given in Appendix B.1.
Effort is quantified in minutes and was not taken prior to 2004.
Species Date
Banded UTM UTM Resighted
once Age Condition
Index Mature Matrix Hab00 Hab100 Effort
BLBW 5/7/02 327755 5059947 0 ASY 0.2414 0.3738 0.1513 0.3558 BLBW 1/6/02 326380 5069477 0 ASY 0.4278 0.3301 0.2788 0.1054 BLBW 29/06/03 330827 5069871 1 AHY 0.1402 0.2746 0.2372 0.3236 0.0812 BLBW 3/6/05 338291 5053938 1 ASY 0.1493 0.4915 0.0954 0.3147 0.5831 1 BLBW 11/6/04 337720 5054891 0 SY 0.1629 0.4839 0.1018 0.2905 0.6861 60 BLBW 13/7/05 348897 5056698 0 ASY 0.1393 0.5839 0.1009 0.3437 0.5042 30 BLBW 13/7/05 348897 5056698 1 SY 0.1449 0.5839 0.1009 0.3437 0.5042 30 BLBW 11/7/05 349009 5056784 1 ASY 0.1449 0.5664 0.1063 0.3397 0.3928 7 BLBW 11/7/05 349234 5057264 0 ASY 0.1413 0.4700 0.1261 0.3012 0.4983 6 BLBW 3/6/02 345848 5066771 0 ASY 0.3056 0.2407 0.2021 0.2025 BLBW 18/7/05 346968 5070210 1 ASY 0.1393 0.7218 0.1409 0.3625 0.6526 7 BLBW 22/6/04 348943 5069300 0 SY 0.1486 0.7749 0.0932 0.3480 0.2137 10 BLBW 22/6/04 348943 5069300 0 SY 0.1613 0.7749 0.0932 0.3480 0.2137 30 BLBW 22/6/04 346968 5070210 1 ASY 0.1536 0.7218 0.1409 0.3625 0.6526 40 BLBW 22/6/04 347575 5068906 0 SY 0.1515 0.5155 0.1874 0.2604 0.1711 42 BLBW 22/6/04 347255 5069987 0 ASY 0.1558 0.6716 0.1665 0.3333 0.4441 30 BLBW 22/6/04 347317 5069788 0 SY 0.1553 0.6566 0.1725 0.3190 0.4378 30 BLBW 5/7/05 339789 5054056 0 ASY 0.1680 0.5787 0.0967 0.3163 0.3083 5 BLBW 25/6/05 339845 5054363 0 ASY 0.1418 0.5363 0.1173 0.2957 0.2022 2 BLBW 19/07/00 343290 5046398 1 ASY 0.6258 0.1877 0.4974 0.7245 BLBW 9/7/00 343353 5046224 0 SY 0.6195 0.2012 0.4952 0.6030
109
BLBW 29/6/04 346342 5049356 0 ASY 0.1464 0.5138 0.2378 0.4680 0.7458 60 BLBW 1/6/04 343481 5046386 0 SY 0.1402 0.6016 0.2007 0.4952 0.5779 6 BLBW 25/7/05 347004 5050089 1 ASY 0.1536 0.5429 0.3054 0.4253 0.6697 10 BLBW 28/6/04 347290 5050242 1 ASY 0.1536 0.5371 0.3407 0.4137 0.6285 1 BLBW 28/6/04 347320 5050323 1 ASY 0.1507 0.5392 0.3437 0.4111 0.7057 28 BLBW 17/7/04 340926 5045035 0 ASY 0.1514 0.6452 0.1036 0.5169 0.8984 10 BLBW 17/7/04 340926 5045035 0 SY 0.1486 0.6452 0.1036 0.5169 0.8984 10 BLBW 17/7/04 341174 5045099 1 ASY 0.1449 0.6289 0.1144 0.5148 0.8062 22 BLBW 20/7/05 316053 5044769 0 SY 0.1338 0.1663 0.3700 0.2059 0.6788 6 BLBW 13/6/04 317714 5047527 0 ASY 0.1413 0.1361 0.4163 0.1625 0.8586 20 BLBW 6/7/05 317966 5047235 0 SY 0.1493 0.1500 0.3654 0.1803 0.2744 10 BLBW 25/6/04 316066 5044775 0 ASY 0.1536 0.1651 0.3700 0.2059 0.6788 11 BLBW 20/07/03 345932 5050017 0 ASY 0.1377 0.4874 0.2270 0.4390 0.3254 BLBW 20/06/03 345945 5049854 0 ASY 0.1357 0.4924 0.2211 0.4507 0.3415 BLBW 22/7/04 345945 5049854 1 ASY 0.1393 0.4924 0.2211 0.4507 0.3415 8 BLBW 29/6/04 345991 5048843 0 ASY 0.1558 0.4697 0.2301 0.4680 0.8422 12 BLBW 5/6/04 335230 5056505 0 SY 0.1424 0.7423 0.0833 0.3115 0.3212 20 BLBW 5/7/05 340708 5062164 0 ASY 0.1522 0.3167 0.3575 0.2090 0.6453 2 BLBW 5/7/05 341330 5061225 0 ASY 0.1462 0.4034 0.2812 0.2523 0.3167 5 BLBW 5/7/05 341606 5061302 0 ASY 0.1514 0.3874 0.3020 0.2441 0.4777 15 BLBW 5/7/05 341713 5061459 0 ASY 0.1429 0.3656 0.3100 0.2391 0.4703 10 BLBW 10/7/05 341980 5061710 0 SY 0.1493 0.3656 0.3100 0.2391 0.4777 15 BLBW 1/7/04 341888 5045833 0 ASY 0.1464 0.6743 0.1093 0.5131 0.7497 30 BLBW 1/7/04 342042 5045958 0 ASY 0.1618 0.6720 0.1128 0.5099 0.7821 12 BLBW 2/7/03 342308 5046103 1 ASY 0.1418 0.6600 0.1169 0.5108 0.7245 BLBW 7/7/04 342457 5078919 0 ASY 0.1493 0.4786 0.2416 0.3005 0.3307 3 BLBW 6/7/05 343860 5057839 0 SY 0.8555 0.0206 0.5246 0.4082 20 BLBW 30/6/04 344138 5057826 0 ASY 0.1507 0.8349 0.0234 0.5339 0.3862 20 BLBW 30/6/04 344348 5057685 1 ASY 0.1558 0.8404 0.0217 0.5530 0.6093 50 BLBW 4/6/04 342827 5047959 0 ASY 0.1515 0.6821 0.0827 0.5486 0.6348 45 BLBW 7/7/04 337486 5071995 1 ASY 0.1429 0.4402 0.2044 0.4180 0.8596 55 BLBW 30/6/05 329392 5048894 0 SY 0.1567 0.2018 0.4668 0.1526 0.2374 1 BLBW 30/6/05 329437 5048707 0 ASY 0.1408 0.2242 0.4606 0.1633 0.2287 10
110
BLBW 13/06/01 332803 5048657 0 ASY 0.2242 0.3002 0.2434 0.6121 BLBW 24/6/04 334014 5048928 0 ASY 0.1630 0.2469 0.2960 0.2645 0.4351 2 BLBW 1/7/05 334026 5046814 1 SY 0.1581 0.2666 0.4253 0.2805 0.4260 28 BLBW 28/6/04 323041 5051010 0 SY 0.1493 0.2685 0.1708 0.2121 0.4200 9 BLBW 28/6/04 323352 5051387 0 SY 0.1464 0.2562 0.1795 0.2031 0.1718 8 BLBW 2/6/05 328049 5050498 1 SY 0.1642 0.0770 0.3036 0.1154 0.1830 1 BLBW 12/6/04 328680 5049380 0 ASY 0.1471 0.1552 0.4251 0.1266 0.2112 10 BLBW 12/6/04 328741 5049416 1 ASY 0.1642 0.1532 0.4246 0.1266 0.2200 15
BLBW 16/7/02 328999 5058621 0 ASY 0.1206 0.4137 0.1236 0.3579
BLBW 16/7/02 328999 5058621 0 ASY 0.1206 0.4137 0.1236 0.3579
BLBW 21/7/01 329365 5058832 1 SY 0.1035 0.4289 0.1179 0.3781
BLBW 23/7/01 351755 5062208 0 ASY 0.2926 0.1643 0.1945 0.3610
BLBW 12/6/04 344553 5049788 0 ASY 0.1449 0.4512 0.1370 0.4263 0.5768 1
BLBW 13/6/04 344288 5052420 0 ASY 0.1389 0.4424 0.1816 0.3395 0.4791 9
BLBW 13/6/04 344580 5052472 1 ASY 0.1413 0.4902 0.1673 0.3767 0.7242 8
BLBW 5/7/05 344898 5052505 0 SY 0.1455 0.5316 0.1577 0.4094 0.5332 3
BLBW 29/06/03 320557 5058081 0 AHY 0.1439 0.4755 0.2472 0.3785 0.1718
BLBW 20/6/05 342967 5058436 0 AHY 0.1606 0.7584 0.0603 0.4581 0.1524 30
BLBW 22/6/04 342997 5058429 0 SY 0.1538 0.7581 0.0602 0.4593 0.5398 30
BLBW 22/6/04 343162 5058352 0 SY 0.1357 0.7744 0.0554 0.4701 0.5007 30
BLBW 22/6/04 343207 5058580 0 SY 0.1439 0.7333 0.0674 0.4607 0.5402 30
BLBW 11/7/02 341248 5061333 0 SY 0.3842 0.2843 0.2470 0.3457
BLBW 18/6/04 341435 5056633 0 SY 0.1418 0.5925 0.0848 0.3383 0.2699 46
BLBW 29/5/02 341435 5058132 0 ASY 0.5925 0.0829 0.3638 0.6096
BLBW 22/6/04 341628 5055673 0 SY 0.1523 0.7055 0.0787 0.3506 0.3191 3
BLBW 5/7/00 341692 5057915 1 ASY 0.6473 0.0738 0.3818 0.4445
BLBW 11/6/04 341693 5054549 0 ASY 0.1304 0.7652 0.0654 0.3518 0.2793 30
BLBW 11/6/04 341693 5054549 1 ASY 0.1536 0.7652 0.0654 0.3518 0.2793 30
BLBW 18/6/04 341767 5058013 1 ASY 0.1429 0.6658 0.0690 0.3892 0.4487 60
BLBW 18/6/04 341814 5054686 0 ASY 0.1429 0.7838 0.0660 0.3578 0.2577 2
BLBW 18/6/04 342021 5054889 0 SY 0.1507 0.8137 0.0622 0.3763 0.3942 4
BLBW 28/5/02 342285 5058640 0 ASY 0.6993 0.0736 0.4100 0.5594
BLBW 22/06/03 342294 5058638 1 ASY 0.1500 0.6993 0.0733 0.4115 0.5946
111
BLBW 23/06/03 342463 5057845 1 ASY 0.1690 0.7562 0.0542 0.4388 0.4354
BLBW 23/06/03 342489 5057950 0 ASY 0.1434 0.7573 0.0521 0.4401 0.3415
BLBW 22/6/04 342490 5057999 0 SY 0.1493 0.7557 0.0524 0.4390 0.2839 15
BLBW 25/7/02 342737 5058560 0 ASY 0.7400 0.0665 0.4399 0.3956
BLBW 19/07/00 342737 5058560 1 AHY 0.7400 0.0665 0.4399 0.1133
BLBW 17/7/02 342890 5057902 0 ASY 0.8169 0.0442 0.4708 0.2008
BLBW 29/5/02 342890 5057902 0 SY 0.8169 0.0442 0.4708 0.2008
BLBW 26/6/05 342999 5057859 0 SY 0.1567 0.8328 0.0413 0.4781 0.2441 15
BLBW 19/6/04 331027 5057131 1 ASY 0.1434 0.3158 0.3524 0.1786 0.3394 4
BLBW 4/6/05 331285 5056031 0 ASY 0.1348 0.2590 0.3815 0.1594 0.4092 20
BLBW 17/07/03 331884 5056430 0 ASY 0.1377 0.3664 0.2968 0.1999 0.3991
BLBW 4/7/05 332054 5055453 0 AHY 0.1418 0.2920 0.3805 0.1727 0.1036 5
BLBW 4/7/05 332081 5055393 0 SY 0.1444 0.2909 0.3801 0.1727 0.3314 20
BLBW 22/7/01 332354 5062159 0 ASY 0.4264 0.2345 0.2368 0.3087
BLBW 26/06/01 333218 5058805 0 ASY 0.3807 0.2407 0.2272 0.0391
BLBW 25/6/01 342192 5057481 0 ASY 0.7188 0.0598 0.4182 0.3673
BLBW 12/7/01 342250 5057258 0 ASY 0.7231 0.0577 0.4145 0.5147
BLBW 13/7/05 320921 5047945 0 ASY 0.2608 0.2341 0.2289 0.3293 30
BLBW 25/6/01 323098 5048333 0 ASY 0.1174 0.3274 0.1413 0.1459
BLBW 12/7/01 348123 5054828 0 ASY 0.6762 0.0692 0.4812 0.4839
BLBW 16/7/04 352778 5057932 1 SY 0.1486 0.2173 0.1050 0.3604 0.6180 40
BLBW 16/7/04 352934 5057872 0 ASY 0.1479 0.2076 0.1064 0.3585 0.5869 90
BLBW 17/7/04 353080 5059689 1 ASY 0.1341 0.2475 0.0913 0.3818 0.4127 10
BLBW 17/7/04 353155 5059614 0 ASY 0.1486 0.2426 0.0899 0.3884 0.2943 15
BLBW 16/6/04 353527 5065435 0 SY 0.1567 0.2125 0.3203 0.1754 0.4036 60
BLBW 12/7/04 354499 5057500 1 ASY 0.1522 0.1459 0.2104 0.3160 0.3998 30
BLBW 12/7/04 354675 5057422 0 SY 0.1455 0.1326 0.2335 0.2951 0.6529 30
BLBW 1/7/04 354979 5057166 0 ASY 0.1429 0.1152 0.2885 0.2519 0.5758 40
BLBW 1/7/04 355140 5057110 1 AHY 0.1464 0.1157 0.3057 0.2389 0.1803 9
BLBW 1/7/04 355317 5057031 0 SY 0.1536 0.1106 0.3309 0.2199 0.4916 5
BLBW 13/7/04 355425 5056434 0 SY 0.1567 0.0994 0.4078 0.1956 0.6781 9
BLBW 1/7/04 355458 5056859 0 ASY 0.1321 0.1040 0.3683 0.2042 0.4445 30
BLBW 8/7/03 357230 5057399 0 SY 0.1500 0.0994 0.4925 0.1506 0.0101
112
BLBW 3/7/01 332766 5057653 0 ASY 0.4844 0.2492 0.2410 0.7594
BLBW 21/06/01 332825 5058135 0 ASY 0.4227 0.2447 0.2253 0.7493
BLBW 21/06/01 332860 5057878 1 ASY 0.4585 0.2449 0.2330 0.6480
BLBW 24/6/04 332948 5058064 0 SY 0.1500 0.4309 0.2419 0.2294 0.6072 6
BLBW 7/7/00 333047 5057934 1 ASY 0.4558 0.2357 0.2377 0.4623
BLBW 8/6/04 308611 5054792 0 SY 0.1871 0.2968 0.2947 0.6327 30
BLBW 8/6/04 308725 5054807 0 ASY 0.1500 0.1797 0.3045 0.2894 0.7989 15
BLBW 8/6/05 308889 5054996 0 ASY 0.1408 0.1794 0.3006 0.2942 0.8492 35
BLBW 4/7/05 317022 5054641 0 ASY 0.1429 0.2268 0.2932 0.2166 0.6054 10
BLBW 15/6/04 317078 5055625 1 ASY 0.1486 0.1943 0.3086 0.1963 0.5056 20
BLBW 4/7/05 317215 5054673 0 SY 0.1581 0.2306 0.2928 0.2188 0.5531 30
BLBW 15/6/04 316856 5055769 0 SY 0.1471 0.1874 0.3081 0.1941 0.3359 9
BLBW 8/6/01 329790 5065883 0 ASY 0.1171 0.3015 0.1648 0.1903
BLBW 9/7/00 344740 5056994 1 ASY 0.9037 0.0129 0.5987 0.5517
BLBW 11/7/00 345111 5057048 0 SY 0.8606 0.0130 0.6011 0.6295
BLBW 7/12/00 345250 5056921 0 SY 0.8661 0.0121 0.6039 0.7277
BLBW 9/7/04 323213 5061186 1 ASY 0.1654 0.3848 0.3116 0.2986 0.4019 10
BLBW 3/7/05 326834 5064120 1 ASY 0.1549 0.2320 0.2983 0.1900 0.3862 1
BLBW 21/6/04 326999 5064194 1 ASY 0.1357 0.2222 0.3078 0.1855 0.5157 45
BLBW 12/6/02 327451 5061634 0 SY 0.2964 0.3246 0.1748 0.1833
BLBW 17/6/04 328513 5063098 0 ASY 0.1377 0.1590 0.3742 0.1333 0.2933 2
BLBW 8/7/02 329774 5059815 1 ASY 0.1215 0.4509 0.1118 0.2325
BLBW 30/5/02 329869 5060847 0 ASY 0.0787 0.4848 0.0947 0.1571
BLBW 14/6/05 328513 5063098 0 SY 0.1577 0.1590 0.3742 0.1333 0.2933 10
BLBW 21/7/05 314797 5037497 0 ASY 0.1400 0.3123 0.2533 0.2443 0.5353 10
BLBW 17/7/04 316315 5040362 0 ASY 0.1429 0.4596 0.2829 0.3087 0.9260 15
BLBW 12/7/04 316438 5040213 0 SY 0.1413 0.4566 0.2757 0.3014 0.7088 5
BLBW 14/7/04 316587 5043031 0 SY 0.1397 0.3189 0.3337 0.2618 0.5841 45
BLBW 4/7/02 317782 5040829 0 ASY 0.4174 0.2616 0.2500 0.3181
BLBW 6/7/05 321368 5067315 0 ASY 0.1397 0.6626 0.1453 0.5356 0.4902 5
BLBW 6/7/05 321460 5067273 0 ASY 0.1357 0.6486 0.1521 0.5281 0.4396 5
BLBW 6/7/05 321596 5067320 1 ASY 0.1429 0.6338 0.1540 0.5164 0.5618 10
BLBW 6/7/05 321891 5067440 0 ASY 0.1455 0.6062 0.1583 0.5026 0.9640 10
113
BLBW 8/7/04 321891 5067440 1 AHY 0.1486 0.6062 0.1583 0.5026 0.2761 5
BLBW 8/7/04 321948 5067532 0 ASY 0.1536 0.6046 0.1586 0.5016 0.8436 10
BLBW 8/7/04 322035 5067259 0 ASY 0.1449 0.5992 0.1632 0.4990 0.7078 5
BLBW 8/7/04 322055 5067422 0 SY 0.1532 0.5992 0.1594 0.4994 0.4061 15
BLBW 14/6/04 341395 5059475 0 SY 0.1532 0.5448 0.1181 0.3335 0.2182 10
BLBW 14/6/04 341990 5059447 0 ASY 0.1558 0.5828 0.1175 0.3592 0.3984 10
BLBW 27/6/04 340077 5067992 0 ASY 0.1530 0.5058 0.2196 0.3610 0.6512 16
BLBW 11/7/01 323413 5048795 1 ASY 0.1122 0.3311 0.1322 0.1334
BLBW 24/06/03 323881 5049678 0 ASY 0.1418 0.1529 0.2656 0.1507 0.1788
BLBW 10/6/04 324327 5050315 1 ASY 0.1408 0.1373 0.2640 0.1375 0.2458 44
BLBW 18/7/04 325581 5049906 1 ASY 0.1500 0.1171 0.3321 0.1204 0.4330 30
BLBW 18/7/04 325603 5050129 0 ASY 0.1553 0.1137 0.3248 0.1173 0.4452 4
BLBW 10/6/04 325866 5050618 1 ASY 0.1419 0.1113 0.3261 0.1121 0.2064 60
BLBW 14/6/04 354204 5067210 0 SY 0.1360 0.2219 0.3395 0.1473 0.2832 4
BLBW 26/6/05 354204 5067210 0 SY 0.1500 0.2219 0.3395 0.1473 0.2832 10
BLBW 18/7/04 355102 5066126 1 ASY 0.1449 0.2019 0.2951 0.1794 0.2322 15
BLBW 18/7/04 355175 5066097 0 ASY 0.1536 0.2074 0.2977 0.1826 0.2119 20
BLBW 6/7/03 356779 5065268 0 ASY 0.1471 0.3307 0.1915 0.2640 0.6620
BLBW 25/6/05 356814 5064994 0 ASY 0.1530 0.3359 0.1662 0.2823 0.4169 65
BLBW 6/7/03 356814 5064994 0 ASY 0.1544 0.3359 0.1662 0.2823 0.4169
BLBW 1/7/05 342096 5054374 1 SY 0.1515 0.8038 0.0598 0.3719 0.3104 3
BLBW 14/6/04 342391 5054616 1 AHY 0.1507 0.8317 0.0530 0.3901 0.0973 4
BLBW 6/6/05 342578 5054911 0 ASY 0.1429 0.8687 0.0456 0.4138 0.4162 7
BLBW 1/2/05 342919 5055321 1 AHY 0.1434 0.9137 0.0225 0.4587 0.0926 10
BLBW 19/07/00 343184 5055214 1 ASY 0.9153 0.0187 0.4716 0.3408
BLBW 21/07/01 343231 5055325 0 ASY 0.9257 0.0162 0.4816 0.3596
BLBW 14/6/04 343650 5055416 0 ASY 0.1522 0.9508 0.0099 0.5217 0.4106 30
BLBW 6/6/05 344131 5055763 0 ASY 0.1500 0.9809 0.0065 0.5656 0.4777 2
BLBW 16/6/04 344864 5061500 0 ASY 0.1413 0.5570 0.1463 0.3076 0.5422 20
BLBW 16/6/04 344888 5061840 0 ASY 0.1464 0.5128 0.1662 0.2853 0.5346 2
BLBW 11/7/00 344921 5062267 0 SY 0.5209 0.1605 0.2876 0.4312
BLBW 12/7/05 344966 5061952 0 ASY 0.1449 0.5174 0.1505 0.2892 0.5608 30
BLBW 9/7/00 345036 5061451 0 SY 0.5832 0.1162 0.3175 0.4766
114
BLBW 16/6/04 345157 5061437 0 ASY 0.1507 0.6004 0.1162 0.3175 0.4766 14
BLBW 16/6/04 345165 5061388 0 SY 0.1653 0.6047 0.1139 0.3193 0.5367 15
BLBW 29/6/04 310563 5047601 0 SY 0.1538 0.0914 0.4763 0.1214 0.4801 16
BLBW 19/7/00 330602 5058550 0 ASY 0.1982 0.3902 0.1418 0.2294
BLBW 9/6/04 345212 5049685 0 SY 0.1553 0.4423 0.1813 0.4356 0.4571 24
BLBW 8/7/03 345307 5050008 0 ASY 0.1475 0.4527 0.2071 0.4241 0.5080
BLBW 2/7/03 345387 5056641 0 ASY 0.1471 0.4561 0.0092 0.6006 0.7573
BLBW 10/7/03 330943 5061163 1 ASY 0.1304 0.2304 0.3584 0.1558 0.3872
BLBW 3/7/00 331702 5060150 0 ASY 0.1767 0.3884 0.1383 0.1547
BLBW 18/7/00 331880 5059713 1 ASY 0.1897 0.3698 0.1498 0.4186
BLBW 31/5/02 332475 5059397 0 ASY 0.2513 0.3284 0.1768 0.1735
BLBW 12/7/00 332500 5059231 0 ASY 0.2736 0.3126 0.1836 0.4172
BLBW 10/7/01 332604 5061622 0 SY 0.3749 0.2633 0.2217 0.2605
BLBW 7/7/03 333973 5058580 0 SY 0.1538 0.4393 0.2130 0.2432 0.2018
BLBW 16/6/04 353553 5064834 0 ASY 0.1321 0.2433 0.3050 0.2051 0.4888 5
BLBW 6/6/04 333849 5058531 0 SY 0.1507 0.4393 0.2035 0.2452 0.2130 2
BLBW 5/7/04 334028 5057750 1 ASY 0.1429 0.5896 0.1614 0.2798 0.1589 1
BLBW 5/7/04 334051 5057738 1 ASY 0.1464 0.5918 0.1614 0.2798 0.1589 20
BLBW 5/7/04 334102 5057924 0 ASY 0.1397 0.5609 0.1655 0.2737 0.1519 30
BTNW 20/7/01 326338 5057929 0 AHY 0.3356 0.0569 0.7051 0.8432
BTNW 20/7/01 326562 5058029 1 ASY 0.3325 0.0537 0.7024 0.8149
BTNW 4/7/01 326566 5059550 0 ASY 0.3282 0.0398 0.6805 0.9045
BTNW 4/7/01 326566 5059550 0 SY 0.3282 0.0398 0.6805 0.9045
BTNW 20/7/01 327009 5059488 0 ASY 0.2510 0.0477 0.6578 0.9474
BTNW 6/7/01 327213 5059757 0 AHY 0.2380 0.0459 0.6483 0.9474
BTNW 6/7/01 327213 5059757 0 ASY 0.2380 0.0459 0.6483 0.9474
BTNW 12/6/01 327237 5059239 0 ASY 0.2438 0.0427 0.6524 0.8610
BTNW 20/7/01 327628 5058035 0 ASY 0.2568 0.0543 0.6685 0.9053
BTNW 8/7/01 327750 5057969 1 SY 0.2358 0.0582 0.6595 0.9082
BTNW 5/7/00 328016 5060127 1 AHY 0.2253 0.0558 0.6234 0.8944
BTNW 5/7/00 328016 5060127 1 ASY 0.2253 0.0558 0.6234 0.8944
BTNW 18/7/02 328129 5057093 0 ASY 0.1722 0.0666 0.6511 0.8998
BTNW 8/7/02 326233 5069537 1 SY 0.4197 0.0609 0.6653 0.8131
115
BTNW 19/7/05 330787 5069948 0 SY 0.1371 0.2819 0.1618 0.6854 0.7111 30
BTNW 3/6/05 338110 5054170 0 ASY 0.1468 0.4902 0.0866 0.7796 0.8004 16
BTNW 3/6/05 338119 5053766 0 ASY 0.1406 0.5208 0.0798 0.7874 0.9074 2
BTNW 3/6/05 338119 5054058 1 ASY 0.1475 0.4974 0.0844 0.7824 0.8893 5
BTNW 3/6/05 338291 5053938 0 ASY 0.1406 0.4915 0.0839 0.7830 0.9474 5
BTNW 3/6/05 338291 5053938 0 ASY 0.1429 0.4915 0.0839 0.7830 0.9474 5
BTNW 13/7/05 338458 5054651 1 SY 0.1627 0.4201 0.1001 0.7593 0.9093 20
BTNW 11/6/04 337720 5054891 0 SY 0.1389 0.4839 0.0826 0.7736 0.9031 15
BTNW 11/6/04 337774 5054753 0 SY 0.1492 0.4872 0.0839 0.7742 0.8065 5
BTNW 11/7/05 348713 5056566 0 SY 0.1573 0.5945 0.0341 0.8152 0.8947 35
BTNW 13/7/05 348897 5056698 1 SY 0.1439 0.5839 0.0363 0.8088 0.9731 20
BTNW 13/7/05 348897 5056698 1 SY 0.1462 0.5839 0.0363 0.8088 0.9731 10
BTNW 13/7/05 348897 5056698 0 SY 0.1492 0.5839 0.0363 0.8088 0.9731 20
BTNW 11/7/05 349009 5056794 1 SY 0.1542 0.5664 0.0374 0.8051 0.9510 10
BTNW 11/7/05 349234 5057264 0 ASY 0.1532 0.4700 0.0426 0.7959 0.8969 4
BTNW 22/6/04 343317 5069788 0 ASY 0.1484 0.4247 0.2390 0.6380 0.9227 5
BTNW 7/6/04 345855 5066717 0 SY 0.1445 0.3039 0.0463 0.7348 0.6915 20
BTNW 22/6/04 347062 5070120 0 ASY 0.1484 0.6977 0.0315 0.7673 0.9056 30
BTNW 22/6/04 347062 5070120 1 ASY 0.1563 0.6977 0.0315 0.7673 0.9056 30
BTNW 22/6/04 347255 5069987 0 SY 0.1627 0.6716 0.0329 0.7537 0.9056 10
BTNW 22/6/04 347575 5068906 0 AHY 0.1508 0.5155 0.0501 0.7404 0.7445 4
BTNW 22/6/04 348943 5069300 0 ASY 0.1532 0.7615 0.0368 0.7892 0.6345 12
BTNW 5/7/05 339789 5054056 1 ASY 0.1500 0.5787 0.0889 0.7727 0.9201 4
BTNW 5/7/05 339789 5054056 1 ASY 0.1516 0.5787 0.0889 0.7727 0.9201 3
BTNW 5/7/05 339789 5054056 1 SY 0.1613 0.5787 0.0889 0.7727 0.9201 3
BTNW 25/6/05 339795 5054584 0 ASY 0.1563 0.4988 0.1159 0.7452 0.7721 1
BTNW 25/6/05 339845 5054363 0 ASY 0.1406 0.5363 0.1088 0.7536 0.9474 2
BTNW 27/5/02 343425 5046321 0 ASY 0.3916 0.1965 0.4280 0.8686
BTNW 1/6/04 343481 5046386 0 SY 0.1475 0.6016 0.2004 0.4368 0.8446 8
BTNW 3/7/04 343743 5046474 0 ASY 0.1563 0.3657 0.2110 0.4148 0.9347 11
BTNW 29/6/04 346186 5049083 0 ASY 0.1423 0.3770 0.2746 0.4879 0.9053 7
BTNW 29/6/04 346280 5049690 1 SY 0.1492 0.4174 0.2862 0.5421 0.8555 3
BTNW 29/6/04 346342 5049356 0 ASY 0.1462 0.5138 0.2830 0.4906 0.8926 9
116
BTNW 25/7/05 347037 5050183 0 ASY 0.1548 0.3803 0.3432 0.4146 0.8595 1
BTNW 25/7/05 347004 5050089 0 SY 0.1613 0.5429 0.3376 0.4137 0.8773 2
BTNW 17/7/04 340926 5045035 1 ASY 0.1349 0.6452 0.1555 0.4760 0.8947 4
BTNW 17/7/04 340926 5045035 1 ASY 0.1429 0.6452 0.1555 0.4760 0.8947 4
BTNW 17/7/04 341174 5045099 0 AHY 0.1371 0.6289 0.1561 0.4597 0.8947 2
BTNW 10/6/05 341753 5045294 0 SY 0.3728 0.1496 0.4438 0.8947 3
BTNW 3/7/05 341969 5045462 0 ASY 0.1445 0.3872 0.1462 0.4566 0.8621 7
BTNW 29/6/04 342329 5045935 0 ASY 0.1532 0.4505 0.1491 0.5131 0.7742 1
BTNW 25/6/04 315969 5044953 0 ASY 0.1639 0.1624 0.0528 0.6813 0.8479 10
BTNW 25/6/04 315969 5044953 0 SY 0.1500 0.1624 0.0528 0.6813 0.8479 5
BTNW 20/7/05 316053 5044769 0 ASY 0.1434 0.1663 0.0488 0.6864 0.9474 5
BTNW 25/6/04 316066 5044775 0 ASY 0.1577 0.1651 0.0488 0.6864 0.9474 3
BTNW 25/6/04 316066 5044775 1 ASY 0.1587 0.1651 0.0488 0.6864 0.9474 20
BTNW 13/6/04 316425 5044228 0 SY 0.1598 0.1734 0.0690 0.6865 0.9049 5
BTNW 13/6/04 316525 5044085 1 SY 0.1598 0.1795 0.0713 0.6881 0.7920 1
BTNW 13/6/04 317714 5047527 0 SY 0.1532 0.1361 0.1978 0.6219 0.9053 3
BTNW 20/06/03 345945 5049854 0 AHY 0.4924 0.2690 0.6141 0.8947
BTNW 29/6/04 345991 5048843 1 SY 0.1423 0.3538 0.2722 0.4920 0.8947 3
BTNW 26/5/04 345995 5049772 0 AHY 0.1508 0.4460 0.2723 0.5992 0.8882 4
BTNW 2/6/04 346074 5049890 0 ASY 0.1500 0.4425 0.2828 0.5918 0.8947 5
BTNW 5/6/05 335147 5056178 1 ASY 0.1639 0.7364 0.0665 0.7944 0.8599 5
BTNW 5/6/04 335230 5056505 1 SY 0.1500 0.7423 0.0743 0.7909 0.8893 5
BTNW 9/6/04 335252 5056330 0 SY 0.1587 0.7319 0.0704 0.7939 0.8947 1
BTNW 5/6/05 335286 5056427 0 ASY 0.1587 0.7292 0.0712 0.7933 0.8947 2
BTNW 28/6/05 334628 5058437 0 SY 0.1613 0.4551 0.0975 0.7197 0.7699 15
BTNW 17/7/02 335638 5058971 0 SY 0.3459 0.0607 0.6973 0.8806
BTNW 17/7/02 335643 5058925 0 ASY 0.3526 0.0627 0.6969 0.8062
BTNW 21/7/01 340215 5061350 1 ASY 0.3513 0.0502 0.6854 0.9474
BTNW 21/7/01 340267 5061234 1 ASY 0.3689 0.0497 0.6946 0.7829
BTNW 5/7/05 341606 5061302 1 ASY 0.1587 0.3874 0.0546 0.7101 0.8926 15
BTNW 5/7/02 350002 5065574 0 SY 0.3919 0.0407 0.7499 0.9310
BTNW 2/7/03 342308 5046103 0 SY 0.6600 0.1477 0.5549 0.8316
BTNW 7/7/04 342885 5078887 0 ASY 0.1468 0.4496 0.2044 0.6187 0.9053 1
117
BTNW 6/7/05 343860 5057839 0 AHY 0.8555 0.0179 0.8505 0.9401 10
BTNW 30/6/04 344348 5057688 0 AHY 0.1500 0.8404 0.0169 0.8527 0.8947 30
BTNW 30/6/04 344348 5057688 1 ASY 0.1477 0.8404 0.0169 0.8527 0.8947 7
BTNW 30/6/04 344348 5057688 0 SY 0.1613 0.8404 0.0169 0.8527 0.8947 3
BTNW 4/6/04 342699 5048103 0 AHY 0.1532 0.6871 0.0698 0.7898 0.8748 5
BTNW 4/6/04 342827 5047959 0 ASY 0.1423 0.6821 0.0802 0.7792 0.8947 5
BTNW 4/6/04 343065 5047908 0 SY 0.1389 0.6421 0.1046 0.7573 0.8338 5
BTNW 27/5/02 343398 5047688 0 ASY 0.5741 0.1655 0.6963 0.9100
BTNW 7/7/04 337486 5071995 1 AHY 0.1475 0.4402 0.1236 0.7263 0.8719 15
BTNW 30/6/05 329359 5048552 1 ASY 0.1587 0.2374 0.0869 0.5727 0.8918 5
BTNW 30/6/05 329392 5048894 0 ASY 0.1515 0.2018 0.0871 0.5630 0.8316 15
BTNW 30/6/05 329392 5048894 0 ASY 0.1548 0.2018 0.0871 0.5630 0.8316 15
BTNW 30/6/05 329392 5048894 0 ASY 0.1563 0.2018 0.0871 0.5630 0.8316 5
BTNW 30/6/05 329437 5048707 0 ASY 0.1508 0.2242 0.0854 0.5695 0.6690 15
BTNW 30/6/05 329437 5048707 0 SY 0.1548 0.2242 0.0854 0.5695 0.6690 10
BTNW 24/6/04 331236 5049438 1 SY 0.1563 0.1820 0.0497 0.6084 0.9456 5
BTNW 24/6/04 331373 5049802 1 ASY 0.1613 0.1493 0.0508 0.6199 0.9292 2
BTNW 4/7/02 332803 5048657 0 SY 0.2242 0.0349 0.6627 0.8639
BTNW 24/6/04 334014 5048928 0 SY 0.1602 0.2469 0.0258 0.6588 0.8305 3
BTNW 1/7/05 334026 5046814 1 SY 0.1492 0.2666 0.0234 0.5740 0.8316 5
BTNW 24/6/04 334169 5048780 0 ASY 0.1613 0.2554 0.0243 0.6584 0.8316 2
BTNW 14/7/04 334183 5048704 0 ASY 0.1557 0.2580 0.0240 0.6553 0.8338 4
BTNW 6/7/03 322029 5052703 1 ASY 0.1532 0.3045 0.0378 0.7427 0.8973
BTNW 28/6/04 323041 5051010 0 SY 0.1532 0.2685 0.0370 0.7250 0.9445 5
BTNW 12/6/04 327919 5050745 0 SY 0.1434 0.0668 0.0861 0.6675 0.8740 10
BTNW 18/06/03 328046 5050499 1 AHY 0.1587 0.0757 0.0925 0.6674 0.8817
BTNW 26/7/02 328048 5050489 0 ASY 0.0757 0.0925 0.6674 0.8817
BTNW 2/6/05 328049 5050498 0 ASY 0.1492 0.0770 0.0925 0.6674 0.8817 10
BTNW 12/6/04 328680 5049380 0 SY 0.1411 0.1552 0.1008 0.6041 0.7713 10
BTNW 12/6/04 328680 5049380 0 SY 0.1468 0.1552 0.1008 0.6041 0.7713 10
BTNW 21/6/01 329365 5058832 0 SY 0.1035 0.0682 0.6238 0.9053
BTNW 17/7/02 351755 5062208 1 ASY 0.2926 0.0522 0.7713 0.8947
BTNW 25/7/02 353068 5063339 0 SY 0.3476 0.0422 0.7764 0.9274
118
BTNW 21/7/04 353079 5059636 0 AHY 0.1573 0.2422 0.0099 0.8032 0.8904 16
BTNW 29/5/05 344016 5051804 0 SY 0.1371 0.3329 0.1351 0.7491 0.7466 20
BTNW 12/6/04 344553 5049788 0 AHY 0.1516 0.4512 0.1123 0.7552 0.7096 1
BTNW 12/6/04 344553 5049788 1 ASY 0.1573 0.4512 0.1123 0.7552 0.7096 10
BTNW 12/6/04 344846 5049645 0 AHY 0.1462 0.4475 0.1215 0.7470 0.2777 2
BTNW 20/6/01 319219 5058565 0 ASY 0.4134 0.0891 0.7008 0.9053
BTNW 6/7/01 319679 5058296 1 SY 0.4598 0.0249 0.7382 0.6799
BTNW 12/6/01 330478 5061791 0 ASY 0.1802 0.0623 0.6680 0.6984
BTNW 12/6/01 330478 5061791 1 ASY 0.1802 0.0623 0.6680 0.6984
BTNW 19/07/00 330482 5061384 1 AHY 0.1600 0.0589 0.6651 0.9474
BTNW 11/6/01 330794 5061407 0 ASY 0.2222 0.0511 0.6980 0.9223
BTNW 12/6/01 330794 5061407 1 ASY 0.2222 0.0511 0.6980 0.9223
BTNW 13/6/01 330805 5062749 0 ASY 0.2856 0.0880 0.6815 0.9437
BTNW 27/5/04 330806 5061240 0 ASY 0.1385 0.2109 0.0514 0.6966 0.9474 10
BTNW 27/5/04 331027 5061318 0 ASY 0.1500 0.2487 0.0491 0.7137 0.8563 10
BTNW 13/6/04 344288 5052420 1 SY 0.1331 0.4424 0.1244 0.7600 0.9637 1
BTNW 13/6/04 344400 5052435 0 SY 0.1308 0.4532 0.1210 0.7631 0.9819 2
BTNW 13/6/04 344580 5052472 0 ASY 0.1523 0.4902 0.1178 0.7669 0.9848 10
BTNW 23/6/05 344723 5052599 0 SY 0.1508 0.5222 0.1158 0.7686 0.9789 10
BTNW 23/6/05 344723 5052599 1 SY 0.1653 0.5222 0.1158 0.7686 0.9789 10
BTNW 28/6/04 344841 5052177 1 SY 0.1557 0.4515 0.1197 0.7633 0.7132 6
BTNW 13/6/04 344856 5052190 1 SY 0.1508 0.4605 0.1197 0.7633 0.7132 1
BTNW 5/7/05 344898 5052505 1 ASY 0.1484 0.5316 0.1162 0.7674 0.9789 1
BTNW 20/6/05 342967 5058436 1 SY 0.1708 0.7584 0.0482 0.8183 0.9739 10
BTNW 22/6/04 343162 5058352 0 ASY 0.1445 0.7744 0.0473 0.8200 0.8102 20
BTNW 9/7/00 341248 5061333 1 AHY 0.3842 0.0464 0.7132 0.9789
BTNW 9/6/05 341564 5055677 0 ASY 0.1516 0.6814 0.0687 0.7857 0.8802 2
BTNW 9/6/05 341636 5055686 0 AHY 0.1523 0.6933 0.0668 0.7881 0.7985 9
BTNW 11/6/04 341791 5054445 0 SY 0.1500 0.7709 0.0488 0.8111 0.9292 20
BTNW 18/6/04 341814 5054686 0 ASY 0.1406 0.7838 0.0537 0.8057 0.8167 5
BTNW 18/6/04 342021 5054889 1 SY 0.1429 0.8137 0.0511 0.8091 0.8947 20
BTNW 22/06/03 342221 5058635 1 AHY 0.1803 0.6827 0.0551 0.8078 0.9034
BTNW 7/7/00 342253 5057872 1 ASY 0.7163 0.0528 0.8137 0.9456
119
BTNW 23/06/03 342451 5057895 0 SY 0.1500 0.7423 0.0508 0.8180 0.9419
BTNW 23/06/03 342489 5057950 0 ASY 0.1587 0.7573 0.0492 0.8195 0.7826
BTNW 24/7/01 342509 5058554 1 ASY 0.7085 0.0505 0.8142 0.9053
BTNW 24/7/01 343066 5058163 1 SY 0.7903 0.0428 0.8260 0.9579
BTNW 19/6/04 331027 5057131 0 SY 0.1641 0.3158 0.0758 0.6879 0.9445 17
BTNW 4/6/05 331285 5056031 0 SY 0.1500 0.2590 0.0561 0.6837 0.8955 5
BTNW 17/07/03 331884 5056430 1 ASY 0.1429 0.3664 0.0793 0.6890 0.9328
BTNW 4/7/05 332054 5055453 0 SY 0.1452 0.2920 0.0720 0.6541 0.9474 20
BTNW 4/7/05 332081 5055393 0 SY 0.1417 0.2909 0.0708 0.6530 0.9474 5
BTNW 17/07/03 333587 5054560 0 ASY 0.4342 0.0450 0.6996 0.9445
BTNW 26/6/01 342188 5057098 0 SY 0.7028 0.0541 0.8110 0.8947
BTNW 29/5/02 342189 5057456 0 ASY 0.7055 0.0505 0.8152 0.8751
BTNW 29/5/02 342192 5057481 0 ASY 0.7188 0.0508 0.8150 0.8708
BTNW 25/6/01 342196 5057268 1 ASY 0.7002 0.0525 0.8133 0.8947
BTNW 19/7/01 342199 5057405 1 ASY 0.7045 0.0505 0.8152 0.8817
BTNW 26/5/02 342249 5057873 0 AHY 0.7163 0.0528 0.8137 0.9456
BTNW 29/5/02 342250 5057258 0 ASY 0.7231 0.0504 0.8162 0.8947
BTNW 30/6/04 342281 5057856 0 SY 0.1468 0.7202 0.0529 0.8140 0.9474 20
BTNW 13/7/05 320754 5048010 1 SY 0.1406 0.2592 0.0451 0.6794 0.9020 15
BTNW 13/7/05 320794 5047875 0 SY 0.1457 0.2604 0.0447 0.6726 0.9241 8
BTNW 29/7/04 323098 5048333 0 ASY 0.1508 0.1174 0.0726 0.5336 0.7590 10
BTNW 11/7/02 323098 5048333 0 ASY 0.1174 0.0726 0.5336 0.7590
BTNW 20/7/01 323209 5048201 1 ASY 0.0968 0.0785 0.5184 0.7034
BTNW 25/7/02 348123 5054828 1 AHY 0.6762 0.0196 0.8294 0.8316
BTNW 11/6/04 348138 5054769 0 ASY 0.1475 0.6641 0.0208 0.8269 0.8316 2
BTNW 16/7/04 352778 5057932 1 ASY 0.1418 0.2173 0.0167 0.7764 0.9474 2
BTNW 16/7/04 352836 5057872 1 ASY 0.1548 0.2087 0.0173 0.7762 0.9474 5
BTNW 17/7/04 352905 5059976 0 ASY 0.1452 0.2527 0.0152 0.8058 0.8907 4
BTNW 16/7/04 352934 5057872 1 ASY 0.1523 0.2076 0.0180 0.7755 0.9474 3
BTNW 17/7/04 353080 5059689 0 SY 0.1429 0.2475 0.0112 0.8043 0.8730 10
BTNW 17/7/04 353080 5059689 0 SY 0.1458 0.2475 0.0112 0.8043 0.8730 11
BTNW 24/6/05 353144 5059569 0 SY 0.1639 0.2378 0.0082 0.8045 0.8947 30
BTNW 17/7/04 353155 5059614 0 SY 0.2426 0.0083 0.8053 0.8904 2
120
BTNW 16/6/04 353527 5065435 0 SY 0.1429 0.2125 0.0489 0.6720 0.8973 5
BTNW 16/6/04 353553 5064834 0 AHY 0.1587 0.2433 0.0498 0.6766 0.8795 2
BTNW 12/7/04 354499 5057500 0 SY 0.1445 0.1459 0.0379 0.7102 0.6450 10
BTNW 12/7/04 354499 5057500 0 SY 0.1492 0.1459 0.0379 0.7102 0.6450 10
BTNW 12/7/04 354675 5057422 0 SY 0.1523 0.1326 0.0423 0.6938 0.7633 5
BTNW 1/7/04 354979 5057166 0 ASY 0.1468 0.1152 0.0505 0.6665 0.9089 2
BTNW 1/7/04 355140 5057110 1 SY 0.1468 0.1157 0.0557 0.6542 0.8152 2
BTNW 1/7/04 355317 5057031 0 AHY 0.1602 0.1106 0.0697 0.6383 0.8298 4
BTNW 13/7/04 355425 5056434 0 SY 0.1557 0.0994 0.1147 0.6044 0.9038 10
BTNW 13/7/04 355425 5056434 0 SY 0.1598 0.0994 0.1147 0.6044 0.9038 5
BTNW 1/7/04 355458 5056859 0 SY 0.1532 0.1040 0.0782 0.6270 0.9220 10
BTNW 15/6/05 355460 5057003 0 SY 0.1532 0.1066 0.0740 0.6343 0.8189 10
BTNW 15/6/05 355460 5057003 0 SY 0.1639 0.1066 0.0740 0.6343 0.8189 10
BTNW 13/7/04 355507 5056522 1 ASY 0.1587 0.0987 0.1034 0.6080 0.9093 31
BTNW 8/7/03 357029 5057543 0 ASY 0.1452 0.1078 0.1442 0.5752 0.8316
BTNW 31/5/04 322473 5048510 1 ASY 0.1429 0.1589 0.0673 0.5966 0.8015 15
BTNW 31/5/04 323100 5048338 0 ASY 0.1468 0.1154 0.0726 0.5336 0.7590 1
BTNW 17/7/02 326879 5044044 1 ASY 0.4545 0.0395 0.6876 0.8817
BTNW 17/7/02 326879 5044044 1 ASY 0.4545 0.0395 0.6876 0.8817
BTNW 24/6/04 332724 5058191 0 ASY 0.1385 0.4115 0.1055 0.7097 0.9978 2
BTNW 24/6/04 332724 5058191 0 ASY 0.1445 0.4115 0.1055 0.7097 0.9978 1
BTNW 30/5/02 332749 5057739 0 SY 0.4611 0.1133 0.7012 0.9474
BTNW 1/6/02 332751 5057620 0 SY 0.4777 0.1133 0.6997 0.9474
BTNW 1/6/02 332825 5058135 1 ASY 0.4227 0.1072 0.7079 0.9713
BTNW 1/6/02 332863 5058229 0 SY 0.4110 0.1075 0.7098 0.9902
BTNW 10/6/03 332907 5058105 0 SY 0.1557 0.4192 0.1091 0.7080 0.9691
BTNW 23/7/01 332911 5058282 1 ASY 0.4086 0.1084 0.7107 0.9742
BTNW 24/5/04 332958 5058106 1 SY 0.1333 0.4202 0.1093 0.7085 0.9702 5
BTNW 12/6/02 332982 5057979 0 SY 0.4366 0.1099 0.7089 0.9474
BTNW 20/7/01 332984 5057960 0 SY 0.4406 0.1099 0.7089 0.9474
BTNW 23/7/01 333030 5058233 0 ASY 0.4145 0.1081 0.7137 0.9169
BTNW 26/6/01 333047 5057947 0 SY 0.4558 0.1088 0.7116 0.8853
BTNW 12/6/02 333098 5058162 0 SY 0.4254 0.1090 0.7135 0.5619
121
BTNW 23/7/02 307297 5054204 0 ASY 0.1787 0.1796 0.6684 0.9474
BTNW 8/6/04 308401 5054558 0 SY 0.1708 0.1865 0.2164 0.6374 0.8040 2
BTNW 8/6/05 308725 5054807 1 ASY 0.1452 0.1797 0.2019 0.6401 0.9350 5
BTNW 8/6/05 308725 5054807 0 SY 0.1462 0.1797 0.2019 0.6401 0.9350 2
BTNW 8/6/04 308858 5054680 1 SY 0.1573 0.1621 0.2070 0.6295 0.8595 1
BTNW 8/7/04 310468 5054007 1 ASY 0.1548 0.1213 0.2186 0.6230 0.8592 2
BTNW 15/6/04 316856 5055769 0 ASY 0.1484 0.1874 0.0521 0.7026 0.7677 8
BTNW 15/6/04 316856 5055769 0 ASY 0.1523 0.1874 0.0521 0.7026 0.7677 5
BTNW 4/7/05 317022 5054641 0 ASY 0.1457 0.2268 0.0495 0.7116 0.8966 4
BTNW 4/7/05 317215 5054673 0 ASY 0.1484 0.2306 0.0512 0.7147 0.9009 30
BTNW 22/6/01 329085 5065270 0 ASY 0.0903 0.1557 0.5972 0.9437
BTNW 8/6/02 329736 5065896 0 SY 0.1157 0.1234 0.6633 0.8087
BTNW 7/6/02 329741 5065933 0 SY 0.1178 0.1189 0.6677 0.8926
BTNW 10/7/01 330675 5064440 0 ASY 0.2456 0.1474 0.6649 0.7967
BTNW 12/7/01 345107 5056922 0 AHY 0.8662 0.0093 0.8649 0.9267
BTNW 18/6/02 321783 5064070 0 ASY 0.5034 0.0199 0.7022 0.8065
BTNW 29/06/03 323144 5061107 0 SY 0.1371 0.3883 0.0340 0.6347 0.9350
BTNW 24/06/03 323235 5061224 1 ASY 0.1429 0.3749 0.0345 0.6302 0.9147
BTNW 9/6/02 323235 5061224 1 ASY 0.3749 0.0345 0.6302 0.9147
BTNW 21/5/02 324248 5065000 0 AHY 0.5102 0.0225 0.7174 0.9209
BTNW 3/7/05 326834 5064120 0 SY 0.1573 0.2320 0.0296 0.6837 0.9111 1
BTNW 10/7/01 326836 5055178 0 ASY 0.3129 0.0392 0.7462 0.8966
BTNW 10/6/01 326910 5065222 0 SY 0.2058 0.0396 0.6430 0.5111
BTNW 10/7/01 326925 5065028 0 SY 0.1898 0.0402 0.6497 0.2421
BTNW 11/7/01 326954 5065808 0 ASY 0.2661 0.0386 0.6372 0.6149
BTNW 11/7/01 327199 5065264 0 ASY 0.1897 0.0410 0.6463 0.8570
BTNW 11/7/01 327199 5065264 0 ASY 0.1897 0.0410 0.6463 0.8570
BTNW 11/7/01 327065 5061895 0 ASY 0.3002 0.0461 0.6874 0.9474
BTNW 11/7/01 327451 5061634 0 ASY 0.2964 0.0505 0.6708 0.7347
BTNW 9/7/01 328001 5062912 0 SY 0.2095 0.1018 0.6165 0.8820
BTNW 17/6/04 328513 5063098 1 ASY 0.1462 0.1590 0.1078 0.6097 0.8711 4
BTNW 14/6/05 328513 5063098 0 ASY 0.1500 0.1590 0.1078 0.6097 0.8711 1
BTNW 7/7/01 329165 5058476 1 ASY 0.1195 0.0628 0.6235 0.9474
122
BTNW 25/6/02 329334 5059577 0 ASY 0.1024 0.0676 0.6323 0.8044
BTNW 2/6/04 329515 5058780 0 ASY 0.1371 0.0948 0.0736 0.6231 0.8857 2
BTNW 28/06/00 329657 5059046 1 SY 0.0832 0.0722 0.6328 0.9201
BTNW 22/6/01 329741 5060713 0 AHY 0.0849 0.0706 0.6213 0.7721
BTNW 23/6/01 329770 5060805 0 AHY 0.0807 0.0690 0.6211 0.9100
BTNW 23/6/01 329770 5060805 0 SY 0.0807 0.0690 0.6211 0.9100
BTNW 26/5/04 329796 5061029 0 ASY 0.1406 0.0756 0.0721 0.6188 0.8944 8
BTNW 26/5/04 329969 5061011 0 ASY 0.1411 0.0783 0.0677 0.6275 0.8820 10
BTNW 10/7/01 329988 5060903 0 AHY 0.0775 0.0672 0.6272 0.7677
BTNW 19/06/01 330157 5061135 0 ASY 0.0936 0.0652 0.6329 0.9463
BTNW 26/5/04 330204 5061071 0 SY 0.1500 0.0971 0.0644 0.6340 0.9474 3
BTNW 5/6/04 330260 5061156 1 ASY 0.1468 0.1081 0.0632 0.6387 0.9452 2
BTNW 31/5/04 330405 5058580 1 AHY 0.1349 0.1558 0.0775 0.6435 0.6457 10
BTNW 18/7/02 314813 5037483 1 ASY 0.3069 0.0769 0.6880 0.7517
BTNW 12/7/04 316438 5040213 0 SY 0.1583 0.4566 0.0877 0.6715 0.8512 5
BTNW 14/7/04 316587 5043031 0 SY 0.1402 0.3189 0.0772 0.6925 0.7525 2
BTNW 16/7/02 317782 5040829 0 ASY 0.4174 0.0865 0.6914 0.8915
BTNW 6/7/05 321368 5067315 0 SY 0.1406 0.6626 0.1030 0.7705 0.7924 5
BTNW 6/7/05 321368 5067315 0 SY 0.1492 0.6626 0.1030 0.7705 0.7924 5
BTNW 8/7/04 321891 5067440 1 SY 0.1452 0.6062 0.1236 0.7489 0.9935 2
BTNW 8/7/04 321948 5067532 0 ASY 0.1508 0.6046 0.1261 0.7470 0.9641 5
BTNW 8/7/04 322035 5067259 0 AHY 0.1587 0.5992 0.1203 0.7458 0.9568 1
BTNW 14/6/04 341387 5060288 0 SY 0.1492 0.4917 0.0662 0.7537 0.7441 10
BTNW 27/6/04 340077 5067992 1 ASY 0.1613 0.5058 0.1154 0.7179 0.7477 5
BTNW 27/6/04 340267 5067796 0 ASY 0.1516 0.4871 0.1039 0.7200 0.7746 5
BTNW 16/7/02 323413 5048795 0 AHY 0.1122 0.0692 0.5355 0.9169
BTNW 10/6/04 324180 5050446 0 SY 0.1468 0.1406 0.0386 0.6182 0.9169 7
BTNW 10/6/04 324327 5050315 1 ASY 0.1508 0.1373 0.0435 0.6072 0.9474 3
BTNW 12/6/03 325046 5050589 0 AHY 0.1106 0.0452 0.6332 0.9789
BTNW 18/7/04 325581 5049906 0 AHY 0.1171 0.0673 0.6112 0.8947 6
BTNW 18/7/04 325603 5050129 1 SY 0.1508 0.1137 0.0641 0.6188 0.8951 8
BTNW 10/6/04 325780 5050423 0 AHY 0.1548 0.1081 0.0607 0.6331 0.8820 1
BTNW 10/6/04 325866 5050618 0 SY 0.1310 0.1113 0.0579 0.6383 0.8813 3
123
BTNW 10/6/04 325866 5050618 0 SY 0.1475 0.1113 0.0579 0.6383 0.8813 3
BTNW 14/6/04 354204 5067210 0 ASY 0.1587 0.2219 0.0762 0.6402 0.8998 10
BTNW 26/6/05 354204 5067210 0 SY 0.1452 0.2219 0.0762 0.6402 0.8998 10
BTNW 24/7/02 354427 5068636 0 ASY 0.2359 0.0740 0.6596 0.9318
BTNW 18/7/04 355175 5066097 1 ASY 0.1508 0.2074 0.0604 0.6661 0.7862 10
BTNW 6/7/03 356779 5065268 0 SY 0.1290 0.3307 0.0147 0.7512 0.8947
BTNW 10/7/02 356783 5065263 0 SY 0.3249 0.0147 0.7512 0.8947
BTNW 25/6/05 356814 5064994 1 ASY 0.1484 0.3359 0.0144 0.7598 0.8966 10
BTNW 6/7/03 356814 5064995 1 ASY 0.1406 0.3359 0.0144 0.7598 0.8966
BTNW 1/7/05 342096 5054374 1 ASY 0.1508 0.8038 0.0456 0.8162 0.7557 10
BTNW 1/7/05 342096 5054374 0 SY 0.1520 0.8038 0.0456 0.8162 0.7557 5
BTNW 14/6/04 342391 5054616 0 SY 0.1695 0.8317 0.0426 0.8218 0.8926 5
BTNW 6/6/05 342538 5054651 0 ASY 0.1468 0.8249 0.0403 0.8249 0.8570 2
BTNW 1/7/05 342564 5054883 1 SY 0.1473 0.8520 0.0379 0.8269 0.8708 5
BTNW 6/6/05 342578 5054911 0 ASY 0.1508 0.8687 0.0376 0.8272 0.8795 17
BTNW 14/6/04 342763 5055255 0 SY 0.1568 0.8864 0.0274 0.8381 0.8475 11
BTNW 6/6/05 342773 5055256 1 ASY 0.1523 0.8864 0.0274 0.8381 0.8475 10
BTNW 1/7/05 342919 5055321 0 AHY 0.1615 0.8979 0.0214 0.8449 0.9474 2
BTNW 14/6/04 343034 5055287 0 ASY 0.1429 0.9002 0.0199 0.8474 0.9474 20
BTNW 14/6/04 343034 5055287 0 ASY 0.1445 0.9002 0.0199 0.8474 0.9474 20
BTNW 27/6/01 343231 5055325 1 SY 0.9257 0.0157 0.8530 0.9256
BTNW 14/6/04 343272 5055309 0 ASY 0.1475 0.9106 0.0155 0.8534 0.9274 3
BTNW 26/6/01 343358 5055316 0 ASY 0.9144 0.0145 0.8549 0.9274
BTNW 28/5/02 343382 5055299 0 ASY 0.9131 0.0144 0.8553 0.9147
BTNW 5/6/02 343475 5055254 0 ASY 0.9154 0.0138 0.8566 0.9067
BTNW 4/7/04 343505 5055200 0 ASY 0.1653 0.9115 0.0135 0.8572 0.9005 35
BTNW 13/6/02 343508 5055182 1 ASY 0.9087 0.0135 0.8572 0.9005
BTNW 13/6/02 343564 5055178 0 SY 0.9123 0.0126 0.8585 0.9187
BTNW 13/6/02 343647 5055233 0 SY 0.9217 0.0095 0.8615 0.9401
BTNW 6/6/05 344131 5055763 1 ASY 0.1587 0.9809 0.0059 0.8668 0.9459 5
BTNW 21/6/04 331811 5060081 0 AHY 0.1468 0.1774 0.0592 0.7043 0.9002 30
BTNW 16/6/04 344864 5061500 0 SY 0.1516 0.5570 0.0384 0.7847 0.8969 3
BTNW 16/6/04 344888 5061840 0 SY 0.1529 0.5128 0.0312 0.7835 0.9053 9
124
BTNW 23/7/01 344921 5062267 0 ASY 0.5209 0.0223 0.7927 0.9249
BTNW 23/7/01 344921 5062267 0 ASY 0.5209 0.0223 0.7927 0.9249
BTNW 16/6/04 344966 5061952 1 ASY 0.1462 0.5174 0.0261 0.7919 0.9027 8
BTNW 16/6/04 344966 5061952 0 SY 0.5174 0.0261 0.7919 0.9027 5
BTNW 23/07/01 345014 5061617 0 SY 0.5517 0.0358 0.7881 0.8955
BTNW 16/6/04 345157 5061437 0 ASY 0.1516 0.6004 0.0371 0.7952 0.8984 2
BTNW 23/7/01 345636 506118 0 ASY 0.5517 0.0223 0.7927 0.9249
BTNW 23/7/02 316175 5059344 0 ASY 0.3426 0.2584 0.5997 0.9111
BTNW 20/7/04 330651 5053131 1 ASY 0.1523 0.0586 0.0426 0.6792 0.8878 15
BTNW 27/06/00 330301 5058285 0 AHY 0.1584 0.0804 0.6515 0.8926
BTNW 6/7/01 330439 5058538 0 SY 0.1646 0.0787 0.6468 0.7147
BTNW 27/06/00 330602 5058550 1 AHY 0.1982 0.0723 0.6594 0.8991
BTNW 1/6/05 331700 5058507 1 ASY 0.1468 0.3144 0.0981 0.6883 0.9056 10
BTNW 12/6/01 331701 5058569 1 ASY 0.3041 0.0964 0.6888 0.9053
BTNW 8/7/03 345301 5049956 1 ASY 0.1508 0.4435 0.1829 0.6945 0.8436
BTNW 8/7/03 345301 5049956 0 SY 0.1500 0.4435 0.1829 0.6945 0.8436
BTNW 8/7/03 345307 5050008 1 SY 0.1452 0.4527 0.1890 0.6894 0.8465
BTNW 25/7/02 345606 5050424 0 ASY 0.4586 0.2342 0.6509 0.8998
BTNW 12/6/04 345706 5049229 0 SY 0.1563 0.4033 0.2648 0.5928 0.8664 2
BTNW 12/6/04 345797 5048997 1 SY 0.1523 0.3806 0.2685 0.5500 0.8947 8
BTNW 4/6/05 330932 5057181 0 AHY 0.1532 0.2966 0.0781 0.6830 0.9212 2
BTNW 4/6/05 330946 5057067 0 ASY 0.1563 0.2955 0.0780 0.6839 0.9459 5
BTNW 13/6/01 331702 5060150 0 ASY 0.1767 0.0557 0.7033 0.8980
BTNW 13/6/01 331726 5059480 0 ASY 0.1888 0.0758 0.6924 0.9474
BTNW 4/6/04 331781 5060116 0 ASY 0.1500 0.1766 0.0575 0.7044 0.8976 1
BTNW 17/6/04 331901 5059687 0 ASY 0.1563 0.1882 0.0780 0.7019 0.9387 3
BTNW 13/6/01 331925 5059856 0 ASY 0.1867 0.0722 0.7034 0.9111
BTNW 18/07/00 332500 5059231 1 ASY 0.2736 0.0938 0.7057 0.9053
BTNW 22/5/04 332522 5059416 0 ASY 0.2451 0.0937 0.7050 0.8875 10
BTNW 8/7/02 332530 5059409 1 ASY 0.2490 0.0938 0.7051 0.9122
BTNW 12/7/00 332604 5061622 0 SY 0.3749 0.0382 0.7611 0.9456
BTNW 23/5/04 333084 5058704 0 ASY 0.1371 0.3760 0.0971 0.7248 0.3016 1
BTNW 8/7/04 333124 5058673 0 SY 0.1639 0.3857 0.0977 0.7250 0.1514 15
125
BTNW 24/5/04 333161 5058534 0 AHY 0.1525 0.4069 0.0995 0.7251 0.5880 10
BTNW 2/6/05 333709 5058722 0 ASY 0.1563 0.4074 0.0906 0.7334 0.8846 3
BTNW 6/6/04 333849 5058531 1 ASY 0.1508 0.4393 0.0906 0.7369 0.7775 1
BTNW 4/6/04 333865 5062059 1 ASY 0.1423 0.4121 0.0274 0.7084 0.8987 7
BTNW 2/6/05 333886 5058754 0 ASY 0.1418 0.3980 0.0889 0.7271 0.9002 2
BTNW 26/5/04 333960 5058274 0 ASY 0.1445 0.4796 0.0968 0.7428 0.8947 7
BTNW 7/7/03 333973 5058580 0 ASY 0.1452 0.4393 0.0898 0.7342 0.8958
BTNW 26/7/04 334029 5057715 0 SY 0.1467 0.5846 0.0941 0.7579 0.9401 1
BTNW 20/7/01 334080 5062139 0 ASY 0.4001 0.0273 0.6947 0.9053
BTNW 20/7/01 334080 5062139 1 ASY 0.4001 0.0273 0.6947 0.9053
BTNW 10/6/04 334147 5057924 0 SY 0.1492 0.5557 0.0938 0.7543 0.8367 2
BTNW 1/6/04 334992 5061158 0 ASY 0.1492 0.2653 0.0320 0.6615 0.9394 3
Curriculum Vitae
BRAD P. ZITSKE
302-C Saunders St., Fredericton, NB, Canada E3B 1N8
(H) 506.454.4404, E-mail: [email protected]
EDUCATION
B.S. Natural Resources (Wildlife Ecology), University of Wisconsin-Madison 1993-1998
M.Sc. Forestry, University of New Brunswick (UNB) 2004-Present
Courses Instructed Research Foundations in Ecology Field Course–Bird Section (BIO3383), UNB
Summer 2006
Courses Providing Teaching Assistance
Field Methods in Ecology (BIO2105), UNB Winter 2005
Research Foundations in Ecology Field Course (BIO3383), UNB Summer 2005
Ornithology (BIO4723), UNB Fall 2005
Ornithology (BIO4723), UNB Fall 2006
Forest Management Practicum (FOR5020), UNB Fall and winter terms 2005-2006
RESEARCH EXPERIENCE
Migratory Bird Consultant
Curry and Kerlinger, LLC, Altoona, Pennsylvania, USA March-April 2007
Conducted observations of migratory birds, focusing on Golden Eagles, throughout
spring migratory period at proposed wind power sites
Prepared data collection for report submission to state and local authorities
Consulted with independent landowners about access rights for wind towers Field Research Assistant
University of Southern Mississippi, Wiggins, Mississippi, USA March-May 2004
Conducted point counts of all avian species along permanent sample transects in four
quadrats throughout southern six counties of Mississippi
Collected and identified invertebrate samples along each transect twice weekly Field Research Assistant/ Crew Leader
Greater Fundy Ecosystem Research Group, Fundy National Park area, New
Brunswick, Canada May-Aug 2001-2003
Established ~ 400 sample points via orienteering, GIS, map-reading, and GPS skills
Organized daily logistics, point count, mist netting and resighting schedule for crew of seven field assistants
Organized 4 years of resight and banding data into database; trained and taught
identification of Eastern birds by sight and sound to inexperienced assistants
Conducted point counts within a 4000 km2 sample area; collected observations of
evidence of avian reproductive success using audio playback of mobbing
Target-banded focal species using mist-nets in dense woods; resighted previously color-banded birds within sample area; provided banding assistance at two Fundy
National Park Mapping Avian Productivity and Survivorship (MAPS) stations
Sampled vegetation within sample area using forest mensurative techniques
Assisted in small mammal trap set-up on a flying squirrel research project
Big Sur Ornithology Lab Banding Intern
Ventana Wilderness Society, Big Sur, California, USA Oct-Dec 2002
Assisted in operating a constant effort banding station as well as remote sites Field Observer
Audubon Society of New Hampshire, Bartlett, New Hampshire, USA May-July 2000
Conducted point counts along perma-plot transects within the White Mountain National Forest as part of 12-year study on species abundance
Field Research Technician
Tishomingo National Wildlife Refuge (NWR), Tishomingo, Oklahoma, USA Aug-
Nov 1999
Conducted counts of migrating waterbirds (waterfowl, shorebirds, and non-game colonial waterfowl) for an ongoing study of species composition at the refuge
Intern
Massachusetts Audubon Society Coastal Waterbird Program, Cape Cod,
Massachusetts, USA May-Aug 1998
Conducted behavioral studies on waterbirds, including federally threatened species;
searched for shorebird nests; recorded and organized data; educated public
VOLUNTEER ACTIVITIES
Maritime Breeding Bird Atlas Sackville, New Brunswick, Canada May-July 2006
Conducted observations of breeding for any bird species encountered within census blocks
Ferry Bluff Eagle Council Sauk City, Wisconsin, USA Dec-Jan 2001-2002
Radio-tracked Bald Eagles around Sauk City area using radio telemetry Long Point Bird Observatory Port Rowan, Ontario, Canada Mar-April 2000
Learned banding and extracting skills using mist nets and other traps, conducted
daily censuses to determine bird species composition at remote field sites and
headquarters, educated public on conservation merits of banding, performed
banding in front of school groups and other visitors
Tishomingo NWR Tishomingo, Oklahoma, USA Aug-Nov 1999
Assisted with the restructuring of the refuge bird list, helped with weekly deer population censuses, aided refuge personnel with bird identification skills,
conducted general maintenance of refuge
PUBLISHED ABSTRACTS 2007 Greater Fundy Ecosystem Research Group/Fundy National Park Science
Meeting, Alma, NB, Canada, May 2007. Minimum estimates of survival of a
mature forest bird indicator species in relation to a reduction of mature forest at a
landscape-scale. Zitske, B.P., A.W. Diamond, & M.G. Betts.
11th
Annual ACWERN Conference. St. John’s, NL, Canada, October 2006. Apparent
and within-season survival of a forest bird indicator species in relation to
landscape-scale forest management. Zitske, B.P., A.W. Diamond, & M.G. Betts.
4th
North American Ornithological Conference. Veracruz, Mexico, Poster session.
October 2006. Apparent annual and within-season survival of a forest bird indicator
species in relation to landscape-scale forestry. Zitske, B.P., A.W. Diamond, &
M.G. Betts.
10th
Annual ACWERN Conference. Kouchibouguac, NB, November 2005. Apparent
survival of a forest bird indicator species in relation to landscape-scale forest
management. Zitske, B.P., A.W. Diamond, & M.G. Betts.
2005 Society of Canadian Ornithologists Annual Meeting, Halifax, NS, Poster session.
October 2005. Apparent survival and morphometrics of a forest bird indicator
species in relation to landscape-scale forest management. Zitske, B.P., A.W.
Diamond, & M.G. Betts.
9th
Annual Atlantic Cooperative Wildlife Ecology Research Network (ACWERN)
Conference. UNB, November 2004. Survival of a forest bird indicator species in
relation to landscape-scale forest management. Zitske, B.P., A.W. Diamond, &
M.G. Betts.
PEER-REVIEWED PUBLICATIONS
Betts, M.G., Zitske, B.P., Hadley, A.S., and Diamond, A.W. 2006. Migrant forest
songbirds undertake breeding dispersal post harvest. Northeastern Naturalist 13(4):
531-536.
Zitske, B.P., M.G. Betts, A.W. Diamond. In preparation. Apparent annual and
seasonal survival of Blackburnian (Dendroica fusca) and Black-throated Green
Warblers (D. virens) in relation to landscape structure.
MEMBERSHIPS
American Ornithologist’s Union 1996-Present
Madison (WIS) Audubon Society and National Audubon Society 1998-Present
American Birding Association 1999-Present
Society of Canadian Ornithologists 2005-Present
Distribution of BLBW banded by Habitat 2000
Percentage of at Habitat 2000-m
0.0 0.2 0.4 0.6 0.8 1.0
Fre
qu
en
cy
0.00
0.05
0.10
0.15
0.20
0.25
0.30
