AN ABSTRACT OF THE DISSERTATION OF
Tracey N. Johnson for the degree of Doctor of Philosophy in Wildlife Science presented on September 14, 2010.
Title: Direct and Indirect Effects of Livestock Grazing Intensity on Processes Regulating Grassland Bird Populations.
Abstract approved:
______Patricia L. Kennedy
In grasslands, grazing by large ungulates can influence vegetation structure,
composition, primary productivity, and ultimately, ecosystem functioning. While grazing
represents a complex disturbance, grazing intensity largely determines the effects of
grazing on vegetation. Structural and compositional changes in the plant community
caused by grazing could have bottom-up effects on species and interactions at higher
trophic levels. Thus, particular management strategies for domestic livestock in
rangeland systems could exert a strong affect on grassland wildlife. Grassland-dependent
songbirds may be particularly susceptible to the effects of domestic grazers because they
depend on grassland vegetation for foraging and nesting. Domestic livestock may
influence grassland-breeding bird populations by affecting settlement decisions, resource
availability, or reproductive success. We investigated the effects of grazing intensity on
grassland vegetation structure and songbird demography in a northwestern bunchgrass
prairie using paddocks with experimentally-manipulated cattle stocking rates. We
compared effects of four stocking rates (0, 14.4, 28.8, and 43.2 animal unit months) on
songbirds using a randomized complete block design with four replicates of each
stocking rate to address hypotheses regarding demography of grassland songbirds.
Overall paddock-level vegetation structure decreased and structural heterogeneity
of vegetation increased with higher stocking rates, and those effects carried over one-year
post-grazing. However, most bird species were able to locate nesting sites with similar
vegetation structure regardless of paddock-level effects of stocking rate. The exceptions
were western meadowlarks and vesper sparrows; nests of these species in paddocks with
higher stocking rates had less vegetative cover. Apparent nest density for grasshopper
sparrows was negatively affected by higher stocking rates. Grazing treatment effects on
songbird population density were restricted to negative effects of higher stocking rates on
savannah sparrows, but this relationship was not observed until the post-treatment year.
Songbird community composition differed between control and heavily-grazed paddocks,
but diversity was not affected by stocking rate.
Nest fates were evaluated to determine whether stocking rate influenced nest
survival or cause-specific nest failure. Other variables such as vegetation structure and
predator community, date, year, and nest age were included to help clarify which
mechanisms might be responsible for differences in nest survival or failure rates among
treatments. For our analysis, we introduce the use of a novel software package,
McNestimate, to estimate the daily probability of nest survival and failure from specific
causes. McNestimate estimates the probability of nest failure from competing causes
when the exact dates of failure are unknown using a Markov Chain framework, and
incorporates a model selection approach which allows the use of covariates to help
identify variables important in explaining variation in the daily probability of nest failure.
Nest predation rates increased with the age of the nest and throughout the breeding
season, but were not affected by stocking rate. The probability of nest failure from
adverse weather declined throughout the season, but the rate of decline depended on year.
Nest failure rates due to trampling were higher in paddocks with higher stocking rates,
but also depended on the number of days cattle were present during the nesting period.
Patterns of overall probability of nest success were driven by predation patterns in the
first year, but in the second year were strongly influenced by the chances of weather-
related nest failure.
Although starvation was not identified as a significant source of nest failure,
grazing-induced changes to vegetation structure and composition could influence food
availability for breeding songbirds, ultimately affecting the composition of nestling diets
and nestling condition. To better understand the relationship between grazing intensity,
nestling diet composition, and subsequent effects on nestling condition, we examined the
invertebrate composition of nestling fecal samples. All species showed strong
preferences for Lepidoptera (moths and butterflies) larvae, and partial preferences for
Coleoptera (beetles) and Araneae (spiders). The proportion of preferred prey items was
not affected by stocking rate. There were effects of bird species on the proportion of
Araneae and Coleoptera and the proportion of Acrididae (short-horned grasshoppers) in
the diet of western meadowlark nestlings decreased with high stocking rates. Growth
rates for western meadowlarks and vesper sparrows were negatively affected by higher
stocking rates. These results suggest that stocking rates can have variable effects on
grassland songbird population and nest density depending on each species’ habitat
requirements. However, negative effects of high stocking rates on nest survival and
nestling condition could have consequences for juvenile survival and recruitment.
Overall, low-to-moderate stocking rates are likely compatible with many grassland bird
species in northwest bunchgrass prairie, and although heavier livestock grazing may help
create suitable vegetation structure for some songbird species, high stocking rates may
influence grassland songbird diet quality, or have negative effects on nestling condition.
We hypothesized that grazing intensity could influence the grassland songbird
community through “bottom-up” effects on vegetation, but effects of grazing at different
intensities did not translate directly through the food web to influence songbird
populations as strongly as lower trophic levels. Processes responsible for changes in
community composition such as immigration or emigration may not have had time to
ensue during our short-term experiment; alternatively, sufficient spatial or temporal
heterogeneity remained in the system, even at the highest grazing intensity, such that
grazing-induced changes in lower trophic levels were irrelevant for most songbird
species. Our results contribute to understanding grassland songbird demographic
responses to different grazing intensities and identify specific mechanisms by which
conservation measures for declining grassland bird populations can be improved.
© Copyright by Tracey N. Johnson September 14, 2010 All Rights Reserved
Direct and Indirect Effects of Livestock Grazing Intensity on Processes Regulating Grassland Bird Populations
by
Tracey N. Johnson
A DISSERTATION
submitted to
Oregon State University
In partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Presented September 14, 2010
Commencement June 2011
Doctor of Philosophy dissertation of Tracey N. Johnson presented on September 14, 2010.
APPROVED:
______Major Professor, representing Wildlife Science
______Head of the Department of Fisheries and Wildlife
______Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my dissertation to any reader upon request.
______Tracey N. Johnson, Author
ACKNOWLEDGEMENTS
Funding and logistical support for this project was provided by the National Research
Initiative of the USDA Cooperative State Research, Education, and Extension Service,
grant 2006-35101-16572, Eastern Oregon Agricultural Research Center – Union Station,
College of Agricultural Sciences at Oregon State University, Department of Fisheries and
Wildlife at Oregon State University, The Nature Conservancy, Prairie Biotic, Inc, Sigma
Xi Grants -in-Aid of Research, Oregon Zoo, The Garden Club of America, and The
Cornell Lab of Ornithology.
Numerous people deserve acknowledgement for contributing time, effort, and
resources to this project. I would first like to thank my advisor, Dr. Patricia Kennedy, for all her help and support during the course of my doctoral studies. Dr. Kennedy provided strong mentorship through her attitude and experience, and always did her best to make sure I had the resources I needed to succeed. I thank my committee members for their time, effort, guidance, and ideas: Drs. Kate Dugger, Richard Miller, David Pyke, and
especially, Dr. Laura Nagy, who made significant contributions to the project overall and
facilitated my collaboration with Dr. Matt Etterson. I would also like to thank the
wonderful people at the Northeast Oregon office of The Nature Conservancy for
providing resources, guidance, and much-needed logistical support: Dr. Rob Taylor, Liza
Jane Nichols, Jeff Fields, and Phil Shepherd. I thank Dr. Sandra DeBano, Dr. Tim
DelCurto, Sam Wyffels, Chiho Kimoto, Dr. Timothy Parker, Dr. Matthew Etterson, Dr.
Greg Brenner, Dr. Joseph Fontaine, Kim Verhelst, and Colette Coiner for their many
contributions. Thanks to the numerous people who assisted with data collection: Andrew
Bennett, Emma Hammond, Joslin Heyward, Brian Kearns, Erin Kennedy, Daniel Kruse,
Kelsey Maloney, Adam Mitchell, Ben Reay, Joseph Smith, Michel Therrien, and Jessica
Torres. I would especially like to acknowledge Andrea Lueders and Eve-line Rodrigues for all their hard work as field crew leaders. They both made invaluable contributions to the project.
Finally, I am most thankful to my husband and colleague, Dr. Karl Kosciuch, who has provided endless support over the last four years. He willingly discussed ideas, gave up his time to provide opportunities for me to work, encouraged me, contributed resources, and kindly cared for my sweet old dog when I was away in the field. I am forever indebted.
CONTRIBUTION OF AUTHORS
Patricia Kennedy assisted with study design and writing of chapters 2-4. Matthew
Etterson assisted with data analysis and writing of chapter 3. Sandra DeBano contributed
invertebrate data and assisted with writing of chapter 4. Greg Brenner conducted forensic
analysis of invertebrate samples for chapter 4.
TABLE OF CONTENTS
Page CHAPTER 1: INTRODUCTION ...... 2
CHAPTER 2: GRASSLAND SONGBIRD COMMUNITY RESPONSES TO EXPERIMENTALLY-MANIPULATED CATTLE STOCKING RATES IN A NORTHWEST BUNCHGRASS PRAIRIE ...... 6
CHAPTER 3: FACTORS INFLUENCING NEST SURVIVAL AND CAUSE-SPECIFIC NEST FAILURE OF GRASSLAND PASSERINES IN GRAZED PRAIRIE ...... 51
CHAPTER 4: THE EFFECTS OF LIVESTOCK GRAZING INTENSITY ON GRASSLAND SONGBIRD PREY SELECTIVITY, NESTLING DIET COMPOSITION, AND GROWTH RATES ...... 95
CHAPTER 5: CONCLUSIONS ...... 131
BIBLIOGRAPHY ...... 137
APPENDICES...... 156
APPENDIX A: MODEL SELECTION RESULTS FOR 30 CANDIDATE MODELS OF DAILY NEST FAILURE RATES FOR SIX SPECIES OF GRASSLAND SONGBIRD BREEDING AT THE ZUMWALT PRAIRIE PRESERVE IN NORTHEASTERN OREGON, USA...... 157 APPENDIX B: VALUES OF PADDOCK-LEVEL COVARIATES USED IN ANALYSIS OF DAILY NEST FAILURE RATES AT THE ZUMWALT PRAIRIE PRESERVE, NORTHEASTERN OREGON, USA...... 159 APPENDIX C: ESTIMATION OF STANDARD ERROR OF DAILY FAILURE RATES IN MCNESTIMATE...... 160
LIST OF FIGURES
Figure Page
2.1. Schematic of three scenarios of how structural heterogeneity, bird diversity, and songbird density may change in response to livestock grazing intensity...... 39
2.2. Experimental blocks and paddocks with breeding bird survey points at the Zumwalt Prairie Preserve, northeastern Oregon, USA...... 40
2.3. Mean paddock-level visual obstruction and mean values of coefficient of variation of paddock-level visual obstruction for four levels of grazing intensity on the Zumwalt Prairie Preserve, northeastern Oregon, USA...... 41
2.4. Mean nest-level visual obstruction for five species of songbird nesting in paddocks exposed to four experimental stocking rates in 2007-08 on the Zumwalt Prairie Preserve, northeastern Oregon, USA...... 43
2.5. Ordination of paddocks (2007-09) in species space using non-metric multidimensional scaling (NMS)...... 45
3.1. Experimental paddocks, grazing treatments, predator sample units, and point transects at the Zumwalt Prairie Preserve, northeastern Oregon, USA...... 83
3.2. Predicted daily nest predation rates for six species of grassland songbird breeding at the Zumwalt Prairie Preserve in northeastern Oregon, USA during 2007 and 2008...... 84
3.3. Predicted daily rates of weather-related nest failures for six species of grassland songbird breeding at the Zumwalt Prairie Preserve in northeastern Oregon, USA during 2007 and 2008...... 85
3.4. Predicted daily rates of nest trampling for six species of grassland birds breeding in paddocks with three levels of experimentally-manipulated stocking rate on the Zumwalt Prairie Preserve in northeastern Oregon, USA during 2007 and 2008...... 86
4.1. Experimental blocks and paddocks with stocking rates, pitfall sampling points, and sweep-net transects at the Zumwalt Prairie Preserve, northeastern Oregon, USA...... 124
4.2. Relativized electivity by invertebrate order within experimental paddocks at the Zumwalt Prairie Preserve, northeast Oregon, USA...... 125
LIST OF FIGURES (Continued)
Figure Page
4.3. Rates of mass gain and tarsus growth (beta values from a regression of age versus mass gain and age versus tarsus length) for songbird nestlings in paddocks grazed at four different stocking rates on the Zumwalt Prairie Preserve, northeastern Oregon, USA...... 126
LIST OF TABLES
Table Page
2.1. Grazing treatments randomly assigned to each 40-ha paddock within each block (n = 4) on the Zumwalt Prairie Preserve, northeastern Oregon, USA...... 46
2.2. Mean population density and mean apparent nest density of ground-nesting songbirds in paddocks with four different cattle stocking rates (n = 4 replicates of each grazing treatment) on the Zumwalt Prairie Preserve, northeastern Oregon, USA...... 47
2.3. Results of a blocked multi-response permutation procedure comparing ground- nesting songbird community composition at the Zumwalt Prairie Preserve, northeastern Oregon, USA...... 48
2.4. Correlations of each variable with axes obtained from a non-metric multidimensional scaling ordination of ground-nesting songbird densities at the Zumwalt Prairie Preserve, northeastern Oregon, USA...... 50
3.1. Evidence used to categorize causes of nest failure for grassland-breeding songbirds at the Zumwalt Prairie Preserve in northeastern Oregon, USA...... 87
3.2. Covariates used to estimate the risk of nest failure from three specific causes for grassland songbirds on the Zumwalt Prairie Preserve, northeast Oregon, USA...... 88
3.3. Number of nests by species, year, grazing treatment, and nest fate that were included in our analysis of nest failure...... 89
3.4. Model selection results for the top ten candidate models of daily nest failure rates for six species of grassland songbird breeding at the Zumwalt Prairie Preserve in northeastern Oregon, USA...... 93
3.5. Overall probability (SE) of nest success for six species of grassland songbird breeding at the Zumwalt Prairie Preserve, northeastern Oregon, USA...... 94
4.1. Number of nests from which nestling fecal samples were collected for each species, year, and grazing treatment at the Zumwalt Prairie Preserve in northeastern Oregon, USA...... 128
4.2. Results from mixed-effects models predicting the proportion of invertebrate taxa detected in nestling diets of five grassland bird species at the Zumwalt Prairie Preserve in northeastern Oregon, USA...... 129
LIST OF TABLES (Continued)
Table Page
4.3. Proportion of each invertebrate taxa in the diet of nestlings reared in paddocks with four different cattle stocking rates at the Zumwalt Prairie Preserve in northeastern Oregon, USA...... 130
Direct and Indirect Effects of Livestock Grazing Intensity on Processes Regulating Grassland Bird Populations
by Tracey N. Johnson
2
CHAPTER 1: INTRODUCTION
Livestock grazing is one of the most widespread causes of habitat modification in the U.S., affecting more than 300 million hectares of land each year (Hobbs 1996,
Lubowski et al. 2006). Thus, large ungulate herbivory plays a substantial role in structuring many types of ecological communities (Huntly 1991, Frank et al. 1998,
Wisdom et al. 2006). In grasslands, herbivores can affect processes at many scales, including: nutrient distribution and cycling, species turnover, and maintenance of spatial heterogeneity (Milchunas et al. 1988, Augustine and McNaughton 1998, Tracy and Frank
1998, Knapp et al. 1999, Adler et al. 2001). Timing and intensity of grazing by herbivores can influence vegetation structure, species composition, primary productivity, and thus, ecosystem functioning (Fleischner 1994, Hobbs 1996, Collins et al. 1998,
Milchunas et al. 1998, Van Auken 2000). Grazing-induced changes in plant communities, the basal trophic level for terrestrial ecosystems, can affect the entire food web (Hunter and Price 1992, Price 2002). For example, removal of biomass by large herbivores can result in less energy available to other consumers, resulting in “bottom- up” effects of grazing (Milchunas et al. 1998). Alternatively, changes in vegetation can influence relationships among species and trophic levels within the food web, ultimately affecting trophic structure and dynamics (Milchunas et al. 1998, Chase et al. 2000).
Alteration of plant communities affects species that use vegetation for foraging, nesting, or escape from predators (Collins 2000, Vickery et al. 2001, Crawford et al. 2004, Bock et al. 2006). For this reason, grassland-nesting birds are a group that could be particularly responsive to the effects of grazing. 3
Many studies have been conducted on the effects of grazing on grassland bird
populations, but results are often disparate among bird species and even among
populations of the same species likely due to the absence of large-scale experiments
designed to evaluate avian responses to grazing management (Fondell and Ball 2004,
Fuhlendorf et al. 2006, Harrison et al. 2010). In managed systems, the type of grazing animal, season of use, grazing regime (e.g., rotational grazing versus season-long), and stocking density can all strongly influence both the direct and indirect effects grazing animals have on breeding birds (Vickery et al. 2001). Grazing intensity, or the “demand” placed upon vegetation by cattle, is a key determinant in predicting the effect livestock have on vegetation (Fuhlendorf and Engle 2001), and, if bottom-up effects are important, upon the entire ecosystem.
Variable responses to grazing by songbirds may also be due to differences among grassland systems and the context in which investigations have taken place. The type of effect grazing has on an ecosystem (e.g., increases, decreases, or negligible changes in system attributes) generally depends on the evolutionary history of grazing within the
ecosystem and site productivity (Milchunas et al. 1988, Frank et al. 1998, Bakker et al.
2006). These two variables are important in determining grassland’s capacity to support
large ungulate grazers and the ability of the plant community to compensate for
herbivory. Thus, breeding bird populations occupying different grassland systems may
respond to grazing in various ways, depending on the effect of grazing upon vegetation.
For example, grasslands in the Great Plains region of North America evolved in the
presence of large herds of ungulate herbivores (Burkhardt 1995) and generally have high 4
productivity relative to arid and semiarid grasslands in the Intermountain West or
American Southwest. Breeding bird populations associated with mesic grasslands may show inconsistent responses to grazing because the effects of livestock herbivory on vegetation structure and composition are short-lived. However, some grassland bird populations associated with arid, less productive grasslands can be more responsive to livestock grazing (Bock et al. 1993), potentially because grazers have a more pronounced effect on the plant community in these systems.
My approach to clarifying the effects of grazing on grassland bird populations is unique for two reasons. First, virtually no studies of grassland-breeding birds investigate the effects of grazing on the entire food web, a context which could help elucidate indirect effects and causal links important in determining the nature and strength of the relationship between livestock grazing and breeding bird populations. Simply evaluating numerical responses, such as the change in breeding bird density or nesting success, without also considering the mechanisms by which population parameters are affected, likely contributes to ambiguity associated with grassland bird responses to grazing. I evaluate the potential mechanisms responsible for changes in grassland bird abundance by quantifying both the direct response of grassland birds (in terms of habitat selection and demography), as well as the responses of other taxa, including populations of potential nest predators and invertebrate prey, which can indirectly influence the response of grassland bird populations to grazing. Secondly, this study is unique because I use an experimental approach. Most studies occurring at a spatial scale relevant to grassland passerines are observational, which limits the ability to control for potentially 5
confounding factors and ultimately restricts inference space. This study contributes to a
better understanding of passerine responses to grazing by isolating one important aspect
of grazing management, controlling for environmental variation, and broadening
inference space.
Using this experimental approach, I aimed to clarify the nature of the relationship
between livestock grazing intensity and grassland songbirds. I used a randomized
complete block design to compare the effects of four stocking rates (representing 0%,
20%, 40%, and 60% utilization of vegetation by domestic livestock) on songbirds.
Chapter 2 explores responses of vegetation structure at two scales (the paddock-level and
nest-level), songbird and nest abundance, and community composition and diversity.
Chapter 3 investigates the effects of stocking rate on nest survival and failure from
specific causes, and explores the importance of additional variables independent of our
grazing treatments. Chapter 4 evaluates the relationship between stocking rate, nestling diet, and nestling condition.
6
CHAPTER 2: GRASSLAND SONGBIRD COMMUNITY RESPONSES TO
EXPERIMENTALLY-MANIPULATED CATTLE STOCKING RATES IN A
NORTHWEST BUNCHGRASS PRAIRIE
ABSTRACT
Livestock grazing can affect critical habitat features for many species of grassland-
breeding songbirds, potentially influencing local population size and community composition. Grazing intensity strongly affects the amount and distribution of vegetative cover available to grassland birds, and may be an important predictor of grassland bird responses to grazing. We compared effects of four different cattle stocking rates
(representing 0%, 20%, 32%, and 46% utilization of vegetation by domestic livestock) on
vegetation height and density (as indexed by visual obstruction), songbird and nest
density, and songbird community composition and diversity in a northwest bunchgrass prairie in Oregon, USA. We used a randomized complete block design with four
replicates of each stocking rate to address hypotheses regarding changes in vegetation
structure and population- and community-level responses of songbirds. Overall paddock-
level visual obstruction decreased and structural heterogeneity increased with increasing
stocking rates, and those effects carried over one year after grazing had ceased.
However, most bird species were able to locate nesting sites with similar visual
obstruction regardless of paddock-level differences in visual obstruction. The exceptions
were western meadowlark and vesper sparrow; for these species, visual obstruction was lower at nests in paddocks with higher stocking rates. Apparent nest density for grasshopper sparrows was negatively affected by higher stocking rates. Grazing 7
treatment effects on songbird density were restricted to negative effects of higher
stocking rates on savannah sparrows, but this relationship was not observed until one year
after grazing had ceased. Songbird community composition differed between control and
heavily-grazed paddocks, and we observed increases in the proportion of horned larks
within heavily-grazed paddocks and a decrease in the proportion of savannah sparrows in
heavily-grazed paddocks from pre-treatment to post-treatment years. However, bird
diversity indices were not affected by grazing treatments. These results support previous research that suggests different species of grassland songbirds can be accommodated by varying livestock grazing management techniques, in this case grazing intensity.
However, we suggest the relationship between livestock grazing intensity and bird diversity may not be easily predictable, and more experimental work should be conducted to clarify patterns of songbird diversity in response to stocking rate.
INTRODUCTION
The composition of bird communities is strongly related to the structural complexity of vegetation (MacArthur and MacArthur 1961, Cody 1968, Reinkensmeyer et al. 2007).
Areas with more heterogeneous structure should support higher diversity (and, thus, differ in community composition) than areas with homogeneous structure due to the presence of different structural features (Bell et al. 1991). Grazing by domestic livestock is a process that can strongly affect habitat structure (Milchunas and Lauenroth 1993,
Townsend and Fuhlendorf 2010). Grazing can cause changes in plant community composition, and altered vegetation structure through trampling and removal of vegetation (West 1993, Cole and Landres 1996). Habitat changes facilitated by grazing 8
can directly and indirectly influence resource availability and habitat selection for associated wildlife, with implications for long-term population viability. In North
America, the loss of suitable grassland habitat from conversion to farmland, as well as rangeland management practices designed only to maximize livestock production, has likely contributed to significant population declines of several species of grassland- breeding birds (Knopf 1994, Vickery et al. 1999, Askins et al. 2007, With et al. 2008).
Large-scale loss of critical grassland habitat highlights the importance of appropriate management and conservation measures for remaining grassland ecosystems, most of which are grazed by domestic livestock (Lubowski et al. 2006, Derner et al. 2009).
Grassland-breeding songbirds may be particularly vulnerable to the effects of livestock grazing because of its influence on vegetation structure. Altered vegetation structure can influence songbird food availability through effects on invertebrate abundance or by affecting the availability of foraging sites (Vickery et al. 2001, O’Neill et al. 2003, DeBano 2006). Additionally, grazing-induced changes in vegetation height, density, and life form can affect the suitability of grazed areas as breeding sites for many species (Vickery et al. 2001). Where grazing occurs after a nesting attempt has been initiated, vegetative cover near the nest may be reduced, altering nest concealment and potentially increasing the likelihood that a nest is depredated (Johnson and Temple 1990).
Alternatively, where grazing occurs before nesting is initiated, vegetation structure may be affected such that the availability of suitable nest sites is altered, influencing settlement decisions and ultimately density of breeding birds (Fondell and Ball 2004). 9
Some researchers have suggested livestock grazing can be used as a management tool to facilitate particular habitat conditions for grassland birds; however, they acknowledge the effects of grazing on bird habitat remain unpredictable (Derner et al.
2009). Much of the effect livestock grazing has on breeding songbirds is likely influenced by the intensity with which the vegetation is grazed. Grazing intensity, defined as the ”frequency of plant defoliation” by herbivores and leading to reduced stubble height and aboveground vegetative biomass (Bransby et al. 1988), directly influences the amount of vegetative cover and forage available to other grassland residents (Milchunas et al. 1998, Fuhlendorf and Engle 2001). Grazing can enhance structural heterogeneity of grassland vegetation unless grazing intensity is high; then, grazing may act to homogenize vegetation structure (McIntyre et al. 2003, Ausden 2007).
If stocking rate, which can be used as a proxy for grazing intensity because it is a function of number of livestock and duration of the grazing period, influences vegetation structure (and thus, number of suitable breeding sites for a species), then density of breeding birds could be affected. Further, if grazing intensity affects heterogeneity of vegetation structure, then stocking rate could also influence the diversity and composition of grassland-breeding bird communities (Toombs et al. 2010).
We used a replicated, experimental approach to evaluate the response of grassland passerines to different cattle stocking rates. Our objectives were to evaluate effects of cattle stocking rate on: 1) vegetation structure, 2) grassland songbird density, and 3) grassland songbird diversity and community composition. Further, we used a multivariate approach to examine the relationship between avian community composition 10
and vegetation structure at multiple spatial scales to enhance our understanding of
grassland songbird responses to grazing-induced changes in vegetation structure. We
hypothesized that vegetation structure would differ among pastures with different
stocking rates, affecting grassland-breeding bird density and community composition.
Specifically, we expected vegetation height and density to decrease linearly with higher
stocking rates, but expected structural heterogeneity of vegetation to increase in areas
grazed at low to moderate stocking rates relative to ungrazed controls, and to decrease or
exhibit a threshold response at high grazing intensity. We expected bird diversity or total
songbird density would concurrently increase in low-to-moderate stocking rates and
decrease at high stocking rates because of a higher number of suitable nesting and
foraging sites for multiple species where structural heterogeneity is highest (Fig. 2.1).
We predicted single-species songbird population density would be a linear function of
stocking rate and could increase or decrease depending on the species’ habitat
preferences. Further, we predicted that breeding bird community composition would differ among areas grazed at different intensities due to the creation of different numbers
of nesting micro-sites suitable for each species in the community.
MATERIALS AND METHODS
Area description
Our study was conducted at The Nature Conservancy’s Zumwalt Prairie Preserve (ZPP)
in northeastern Oregon, USA (Fig. 2.2). The 14,973-ha preserve is part of the largest remaining tract of northwest bunchgrass prairie in North America, and provides breeding habitat for several species of grassland birds including: savannah sparrows (Passerculus 11
sandwichensis), horned larks (Eremophila alpestris), western meadowlarks (Sturnella
neglecta), grasshopper sparrows (Ammodramus savannarum), vesper sparrows (Poocetes
gramineus), and to a lesser extent, Brewer’s sparrows (Spizella breweri). The ZPP is
high elevation grassland (average 1500 m); trees and shrubs occur infrequently and are
limited to drainages. Land use on and surrounding the ZPP has primarily been summer
livestock grazing, first by horses belonging to Native Americans beginning in the early
1700’s, and since the late 1800’s by sheep and cattle when homesteaders introduced non-
native grasses (e.g. Bromus inermis, Phleum pretense) as livestock forage (Kennedy et al.
2009, Bartuszevige et al., in review). The ZPP is presently dominated by native species,
in particular Idaho fescue (Festuca idahoensis) and bluebunch wheatgrass
(Pseudoroegneria spicata). Climate is semi-arid and mean annual temperature is 7.4° C
(Western Regional Climate Center 2009). Mean annual precipitation is 49.0 cm, with an
average of 18.3 cm occurring during the breeding season (April through July).
Experimental design
We used a randomized complete block design with one factor (livestock grazing)
and four grazing treatment levels (stocking rate) to evaluate our predictions. All study
plots were rested from grazing for two years prior to the start of our 4-yr experiment.
Blocking allowed us to control for potential environmental heterogeneity created by variation in historical grazing management. In 2006, fences were erected around four blocks of land 160-ha in size, and within each block, four 40-ha paddocks were partitioned (Fig. 2.2). Pre-treatment data on vegetation structure and songbird population density (see below) were collected in each paddock during 2006. In 2007 and 2008, 12
cattle grazed paddocks from 21 May to 3 July and 29 May to 9 July, respectively. Each
of the four blocks contained one replicate paddock of each randomly assigned stocking
rate (n = 4 replicates of each treatment). Stocking rates corresponding to each grazing
intensity treatment were developed using cattle forage consumption data previously
collected at the ZPP (DelCurto and Williams, unpublished results). The moderate
treatment was based on traditional stocking rates used by ranchers on the surrounding
Zumwalt Prairie (40% forage removal). The three other grazing treatments were
designed to represent light (20%), heavy (60%), or no grazing (control) of vegetation by
cattle. Actual forage consumption values obtained by our stocking rates are presented in
Table 2.1. Timing of grazing coincided with the growing period of dominant grass
species at the site and the height of the breeding season for grassland songbirds. In 2009,
data on post-treatment responses were recorded, but no grazing occurred in our study
paddocks.
Sampling methods
To evaluate vegetation structure in paddocks grazed at different intensities, paddock-level
estimates of visual obstruction (VO) were measured during the passerine breeding season
in each year. Visual obstruction is correlated with aboveground biomass in grasslands
and represents a measure of the vertical height and density of vegetation (Robel et al.
1970, Damiran et al. 2007). We limit our evaluation of changes in vegetation structure to
VO because we expected this parameter to be most affected by stocking rate in the near-
term. Further, vegetation height has been identified as a consistent predictor of habitat
selection by grassland birds (Fisher and Davis 2010), and is easily measured over large 13
areas. We measured VO using a Robel pole held perpendicular to the ground, where an
observer recorded the height of the lowest visible decimeter from 4 m away and 1 m off
the ground (Robel et al. 1970). From 26 June to 24 July in 2006 (pre-treatment year) and
2009 (post-treatment year), two VO measurements were taken 1.5- m away from each of
36 vegetation sampling points systematically placed within each paddock (n = 72
measurements). During the two treatment years (2007-08), paddock-level VO was
measured at 10-m intervals along eight 100-m transects within each paddock (n = 80
measurements) within 24 hours after cattle were removed from paddocks. Transects
were stratified randomly by physiographic characteristics in each paddock to account for
differences in vegetation structure, which included hilltops, swales, rocky outcroppings,
and slopes. Structural heterogeneity was measured as the coefficient of variation of
paddock-level VO.
Because not all birds acquire a mate and initiate a breeding attempt, and grazing
could have different effects on settlement and productivity, we measured responses of
both songbird population density (adult birds exhibiting territorial behaviors) and nest
density. To estimate breeding bird density, we conducted 5-minute fixed radius point
counts (Ralph et al. 1995) using distance-sampling from mid-May to early July from
2006-09. Each paddock contained 16 point transects systematically placed at 150-m
intervals, but excluded a 16-m buffer around the inner edge of each paddock to minimize
potential fence line effects on bird surveys (Fig. 2.2). Each survey point was visited
twice in 2006, and three times in 2007-09. Point counts were conducted by five different
observers over the four years of the study. Surveys were conducted from 06:30 to 10:30 14
(PST) in all years, and surveys were never conducted during periods of precipitation, fog,
or winds exceeding 5.3 m/s. All birds seen or heard within 75 m were recorded, and
individuals flushed within the count circle as the observer approached a survey point
were included in counts for that point. Individuals thought to have moved from one
count circle to the next were recorded only once to minimize the potential for over-
counting individuals. Distance from the observer to the spot each bird was first detected
was estimated to the nearest meter using a laser range finder.
Grassland songbird nests were located and counted by dragging a 25-m rope
between two observers across vegetation and then intensively searching areas from which
adult birds flushed (Winter et al. 2003). Each paddock was systematically searched in its
entirety three times in 2007 and twice in 2008 from 1 May to 25 July. We also located
nests opportunistically during vegetation surveys. Once nests were discovered, they were
marked with a handheld GPS and a survey flag placed 10- and 30-m from the nest using a
random compass bearing. To ensure we could relocate nests if cattle disturbed survey
flags, we spray-painted the ground where the flag was placed. To evaluate nest-site
selection, we measured VO at nests once a nesting attempt was complete (n = 157 nests
in 2007, n = 115 in 2008; no nest data are available for 2006 and 2009). We recorded
nest-level VO by inserting a Robel pole into the nest cup and recording VO once from
each cardinal direction. We then averaged the four VO values to obtain one value for
each nest.
15
Data analysis
We used mixed models to evaluate the trend of response variables relative to stocking
rates. Response variables included paddock-level VO, structural heterogeneity of
vegetation (represented as the coefficient of variation of paddock-level VO), total
songbird population density, and avian species diversity. Nest-level VO, population
density and apparent nest density were additional response variables modeled separately
for each species. Data were modeled using PROC MIXED in SAS System V. 9.3.1 (SAS
Institute Inc. 2002-03). We included the number of cattle as a continuous predictor and
tested for linear, quadratic, and cubic effects on response variables. Because initial
songbird density within a paddock could influence changes in density related to grazing
intensity, we used pre-treatment density from data collected in 2006 as a covariate when
estimating trends in songbird population density. To address the question of whether
structural heterogeneity influenced diversity or the density of songbirds in a paddock, we
used the coefficient of variation of paddock-level VO as a covariate when evaluating
effects on diversity and total songbird density. To evaluate the relationship between nest
density and vegetation structure, we used mean paddock-level VO as a covariate when
modeling effects on nest density.
We assigned number of livestock and year as fixed effects, and block, block x
treatment and block x year as random effects. Year was a fixed effect because treatments
could have cumulative effects over time since livestock remove vegetation every season,
and the amount and distribution of remaining vegetation in year t + 1 depends on the
amount of vegetation removed in year t. A random effect of block allows us to make 16
inferences beyond the blocks used in our study; a random effect of block x treatment and
block x year identifies each paddock in each year as the experimental unit. We estimated
denominator degrees of freedom in each analysis using the Satterthwaite approximation method (Litell et al. 2006). Prior to testing models, we evaluated correlations among all predictor variables to ensure that highly correlated variables (/r/ > 0.6) were not included in the same model. An alpha ≤ 0.05 was considered significant. Data were log- transformed where needed to satisfy assumptions of normality and homogeneity of variance. Finally, to identify the most parsimonious model for each response variable, we used backward elimination of fixed-effect predictor variables based on the smallest F-
value to identify final models.
Breeding bird densities were estimated using Program Distance Ver. 5 Release 5
(Thomas et al. 2005) to account for the influence of potential differences in detection
probabilities among species and habitats on density estimates. Preliminary analyses
included all observations and suggested no differences in detection probabilities among
observers based on comparisons of point estimates and 95% confidence intervals
(Ramsey and Schafer 2002). Detection functions were fitted separately for each paddock
to ensure that paddock-level density estimates did not co-vary and comparisons of
songbird density could be made among treatments. Because this resulted in several
species with a low number of detections, we followed the approach outlined in Alldredge
et al. (2007). We created two groups of species from the community of grassland
passerines that breed on the ZPP. We assumed a common detection function among
species within a group, and pooled observations of those species to increase precision of 17
density estimates. The first group included savannah sparrows, grasshopper sparrows,
and vesper sparrows, and the second group included horned larks and western
meadowlarks. Species assignments to a group were chosen based on similarity in
behavior, plumage crypticity, and song characteristics. We omitted Brewer’s sparrows
(as well as other uncommon species) from all analyses because they are rare at our study
site and exhibit highly variable, irruptive patterns of occurrence that would potentially
obscure important patterns in breeding bird density. Candidate models included uniform
key function with cosine and simple polynomial adjustments, and half-normal key
function with cosine, simple polynomial, or hermite polynomial adjustments. Best
models were selected using goodness-of-fit tests and Akaike’s Information Criterion
corrected for small sample size (AICc; Buckland et al. 2001, Burnham and Anderson
2002).
To describe relationships among songbird community composition, paddock-level
vegetative structure, and vegetation preferences unique to each species (hereafter, species
traits), we used data collected from our point counts and analyzed them using a multivariate approach. The species matrix (16 experimental paddocks X 5 species)
contained songbird population density estimates within each paddock for each species
and year. The environmental matrix (16 experimental paddocks X 5 variables) contained
experimental design variables (block: A, B, C, or D; treatment: control, low, moderate, or
high grazing intensity; and year: 2006-09), mean paddock-level VO values, and the
coefficient of variation (CV) of paddock-level VO values. The species trait matrix (2
trait variables X 5 species) contained measurements of mean nest-level VO and the CV of 18
nest-level VO for each species. Coefficient of variation for total density for each species
was moderate (96%; Table 9.2 in McCune and Grace 2002) so density data were log- transformed to account for this variation (McCune and Grace 2002). Subsequently, the
CV for species totals decreased to <40%. Transformed density data were evaluated for
skewness and extreme values by comparing the standard deviation of mean Euclidean
distances for each paddock to all other paddocks (McCune and Grace 2002). One
paddock was identified as an outlier in 2007; however, it was a weak outlier (2.7 standard
deviations) and was therefore retained for analysis.
To determine whether there were differences in breeding bird community
composition among treatments within each year, we conducted a Blocked Multi-
Response Permutation Procedure (MRBP; Mielke 1984) on the transformed data. MRBP
is a non-parametric procedure used for testing the hypothesis of no difference among
groups in a randomized block experiment (McCune and Grace 2002). We used median
alignment within blocks, which focuses the analysis on differences among treatments
within a given experimental block and accounts for the blocking in our experimental
design (McCune and Grace 2002). Stocking rate treatments were used as a priori groups
for comparison of community composition. Distances were calculated using the
Euclidian measure and groups were defined by treatments (each group included four
paddocks). We used non-metric multidimensional scaling (NMS) to elucidate the MRBP
results and evaluate the relationship between grassland-breeding songbird community
composition and grazing treatment, year, and VO values (Kruskal 1964, Mather 1976).
Data from all years were included in the MRBP analysis; however, only data from 2007- 19
09 were included in the ordination because including pre-treatment data from 2006 would
have obscured any treatment-related patterns.
Euclidean distance measurement was used for the ordination. Final
dimensionality of data was assessed by evaluating final stress (where stress is a measure
of departure from monotonicity between distance in original species space and distance in
reduced ordination space) versus the number of dimensions and by performing a
randomization test (250 runs; McCune and Grace 2002). To address the potential
biological factors influencing patterns in breeding bird density and VO, we overlaid
values of paddock- and nest-level VO onto the final ordination. Correlations of vectors
from the environmental and species trait matrices with axes from the ordination represent
the direction and strength of relationships. All multivariate analyses were conducted in
PC-ORD V 6.9 (McCune and Mefford 2009).
RESULTS
Vegetation structure
We detected a significant year-by-treatment interaction effect for mean paddock-level
VO (F3,33 = 6.70, P < 0.01). There was no pre-existing trend in paddock-level VO the year before cattle grazed (2006: β = -0.004, t = -1.12, df = 42.8, P = 0.27; Fig. 2.3A).
After cattle were allowed to graze for six weeks at different intensities, we observed
decreased VO corresponding to increased grazing intensity at the paddock level (2007: β
= -0.02, t = -6.28, df = 42.8, P < 0.0001; 2008: β = -0.02, t = -5.20, df = 42.8, P <
0.0001). Post-treatment data indicated the difference in paddock-level VO among
treatments remained one year after grazing took place (2009: β = -0.008, t = -2.08, df = 20
42.8, P = 0.04). We detected significant quadratic (F1,10 = 5.03, P = 0.05) and linear
year-by-treatment interaction effects (F3,33 = 3.95, P = 0.02) for structural heterogeneity.
No pre-existing differences in structural heterogeneity were detected before cattle were
allowed to graze (2006: β = 0.74, t = 1.97, df = 15.3, P = 0.07; Fig. 2.3B). Heterogeneity
increased with increasing grazing intensity in the first year of grazing (β = 1.56, t = 4.13,
df = 15.3, P < 0.001) and the second year of grazing (β = 1.56, t = 4.15, df = 15.3, P <
0.001). Post-treatment data indicated a carryover effect of grazing and we observed
higher structural heterogeneity in paddocks that had been grazed at a higher stocking rate
one year after grazing occurred (2009: β = 1.30, t = 3.34, df = 15.3, P < 0.01). Over all
years, however, the final model suggested a quadratic effect of stocking rate on structural
heterogeneity where heterogeneity increased from controls to moderate stocking rates,
but decreased at the highest stocking rate.
We found significant effects of year (F1,3 = 18.13, P = 0.02) and stocking rate
(F1,53 = 18.37, P < 0.0001) on nest-level VO for vesper sparrows (Fig. 2.4). Nest-level
VO for vesper sparrows was lower in 2007 ( x = 1.17 dm ± 0.07 dm, SE) than in 2008
( x = 1.41 dm ± 0.06 dm, t = -3.36, df = 3, P = 0.04), but decreased with higher stocking rates similarly in both years (β = -0.01, t = -4.29, df = 53, P < 0.0001). There was a
significant effect of stocking rate on nest-level VO for western meadowlarks (F1,23 =
7.36, P = 0.01); VO at the nest (over both years because there was no effect of year)
decreased with increased stocking rate (β = -0.01, t = -2.71, df = 23, P = 0.01). We
detected no significant effects of any predictor variable on nest-level VO for savannah
sparrows, horned larks, and grasshopper sparrows. 21
Population and nest density
Estimated songbird density within each paddock ranged from 0 – 1.93 individuals per ha
for each species (Table 2.2). We detected significant year-by-treatment interaction
effects (F2,21.6 = 3.92, P = 0.04) and pre-treatment density effects (F1, 8.79 = 9.27, P =
0.01) on savannah sparrow density, which was negatively correlated with higher stocking rates only after grazing had ceased (2009: β = -0.03, t = -2.24, df = 14.6, P = 0.04). Pre-
treatment density explained 20.1% of the variation in savannah sparrow density. Vesper
sparrow and horned lark density was affected by pre-treatment density (vesper sparrow:
F1,12 = 8.69, P = 0.01; horned lark: F1,13.7 = 7.87, P = 0.01), but grazing treatment was not included in the final model for either species. Pre-treatment density explained 18.7% of
the variation in vesper sparrow density and 38.8% of the variation in horned lark density.
We observed no significant effects of any predictor variable on grasshopper sparrow or
western meadowlark density. Total songbird density (all species combined) was affected
by year (F3,9 = 12.2, P < 0.01) but not stocking rate. Total songbird density was higher in
2006 than any other year of the study (2006-2007: mean difference = 0.47 individuals per
ha, t = 4.04, df = 9, P < 0.01; 2006-2008: mean difference = 0.69, t = 5.92, df = 9, P <
0.001; 2006-2009: mean difference = 0.41 individuals, t = 3.50, df = 9, P < 0.01). All
other years were similar in total density.
We located 275 nests over two years (158 in 2007 and 117 in 2008). Savannah
sparrow nests were most common (n = 92), followed by vesper sparrow (n = 76), western
meadowlark (n = 45), horned lark (n = 44), and grasshopper sparrow (n = 18; Table 2.2).
Grasshopper sparrow nest density was affected by grazing treatment (F1,11 = 6.97, P = 22
0.02); nest density decreased with increased stocking rate (β = -0.02, t = -2.39, P = 0.04) and we found no grasshopper sparrow nests in the high stocking rate treatment (Table
2.2). The effect of stocking rate on western meadowlark nest density depended on year
(F1,11 = 4.97, P = 0.05). In 2007, there was a positive effect of stocking rate on
meadowlark nest density (β = 0.02, t = 2.28, P = 0.03), but in 2008 there was no effect (β
= -0.01, t = -0.56, P = 0.58). We found no effects of any predictor variable on savannah
sparrow, vesper sparrow, or horned lark nest density.
Diversity and community composition
The MRBP showed differences in patterns of breeding bird community composition among treatments during the second treatment year and one year post-treatment (Table
2.3). Pairwise comparisons of all grazing treatments by year revealed the relationship
between heavily grazed paddocks (high) and ungrazed paddocks (control) dominated
patterns in differences in community composition among treatments. This difference was
driven by a decrease in the prevalence of savannah sparrows (from 56% of the bird
community in 2006 to 36% in 2009) and an increase in the prevalence of horned larks
(from 19% in 2006 to 38% in 2009) in heavily-grazed (high) paddocks from the pre-
treatment to post-treatment year (Table 2.2). There was also evidence that patterns of
community composition differed between controls and lightly grazed (low) treatments
one year after grazing ceased, but there were no clear directional changes for any one
species across years that is responsible for this pattern. Homogeneity within treatment
groups that differed in composition was fairly high (A = 0.19 – 0.38). There were no pre-
treatment differences in community composition among paddocks. Songbird diversity 23
ranged from 0.91 – 1.60 (Shannon’s Diversity Index), and we identified no significant
effects on diversity.
Ordination of the paddock-level dataset of transformed total density yielded a 3-
dimensional solution (final stress = 7.5, instability <0.00001) and total R2 = 0.96 (axis 1:
R2 = 0.15; axis 2: R2 = 0.65; axis 3: R2 = 0.16). The R2 value represents the variance in
the original distance matrix represented in ordination space. Paddock-level VO and the
variation in paddock-level VO are at opposite ends of axis 1, suggesting that axis 1
represents a gradient of paddock-level vegetation structure where higher VO is correlated
with less variability in VO (Figure 2.5; Table 2.3). Axis 2 separates mean nest-level VO
and the variability in nest-level VO and represents a gradient of local-scale vegetation
structure where higher values of mean nest-level VO are correlated with less variability in vegetation structure at the nest site (Figure 2.5; Table 2.4). Treatments did not show a
strong association with any axis, although paddocks grazed with the highest stocking
rates were concentrated at the upper end of axis 2 (Figure 2.5; Table 2.4). Vesper
sparrows (r = 0.90) and horned larks (r = 0.87) had strong associations with axis 2.
Savannah sparrows (r = 0.66) and grasshopper sparrows (r = 0.69) had strong
associations with axis 1 (Table 2.4). Western meadowlarks (r = -0.61) and grasshopper
sparrows (r = 0.68) had strong associations with axis 3. However, because axis 3 did not
capture any environmental or species trait variables included in the NMS, we excluded
the third axis from the graphic presentation for ease of interpretation.
24
DISCUSSION
Effects of grazing intensity on vegetation structure
Vegetation height and density (as indexed by visual obstruction; Robel et al. 1970) at the paddock-level decreased with higher stocking rates, which is consistent with results reported from similar studies of grazing intensity (Salo 2003, Townsend and Fuhlendorf
2010). We observed a similar magnitude of effect of stocking rate on paddock-level visual obstruction in both treatment years although growing seasons in these years varied
climatically, influencing aboveground primary production. Spring 2007 was
characterized by relatively dry conditions (total precipitation in May and June was > 2.5
cm below the 36-year average), and 2008 was characterized by a late, wet spring with
cool temperatures (R. Taylor, The Nature Conservancy, unpublished results) and
markedly lower visual obstruction even in control paddocks (Fig. 2.3A). These results
suggest the effect of cattle stocking rate on visual obstruction is a general response even
under varying climatic conditions and primary productivity. The effect of stocking rate
on paddock-level visual obstruction carried over into the post-treatment year, a
noteworthy result considering visual obstruction values overall were much higher in
2009, likely reflecting the increase in standing aboveground biomass (S. Wyffels,
unpublished results); however, differences among paddocks were not as pronounced as in
treatment years.
Grazing by cattle resulted in more structural heterogeneity compared to paddocks
that were not grazed by domestic livestock. Significant quadratic effects of stocking rate
over all years supports the hypothesis that structural heterogeneity is highest at moderate 25
stocking rates and lower at heavily-grazed and ungrazed paddocks; however, the
relationship depended on year, and within-year responses were linear and positive.
Average forage utilization under high stocking rates in this study was approximately 46%
(Wyffels 2009), which did not result in more homogeneous vegetation structure than
moderate stocking rates as predicted under a quadratic response (Fig. 2.1). However, higher levels of forage utilization by domestic livestock than were achieved in this study
could result in more homogeneous structure. Our results suggest that with increased
stocking rates, forage consumption by cattle within a paddock is not uniform across the
paddock, which is consistent with the idea that grazers often “patch graze” by
preferentially grazing some areas repeatedly while others are left ungrazed until forage
availability is low (Willms et al. 1988, Coghenour 1991). Forage availability likely
remained high enough even under the highest stocking rate that cattle did not have to
utilize previously ungrazed patches during the six-week grazing period, which would
likely have homogenized vegetation structure to some degree. Similar studies of the
effects of grazing intensity on spatial heterogeneity of vegetation have produced mixed
results, some resulting in increases and others resulting in decreases in heterogeneity, and
results are likely affected by the vegetation utilization level, response variable, and spatial
scale evaluated (reviewed in Townsend and Fuhlendorf 2010).
Nest-level visual obstruction for three of the five songbird species studied was not
affected by stocking rate, suggesting many birds are able to locate and choose nesting
sites with similar structure regardless of differences in overall paddock-level vegetation
structure. Western meadowlarks and vesper sparrows were the exception; nest-level 26
visual obstruction for these species decreased with higher stocking rates. These results
may be explained by temporal or spatial overlap between nest sites and grazing by cattle.
When we investigated temporal patterns of nest activity, we found that vesper sparrows consistently had mean clutch initiation dates in early June (2007: 9 June; 2008: 5 June;
Johnson, unpubl. data). A typical 21-d nesting cycle for this species resulted in nesting attempts which were completed (on average) near the end of June and during the latter half of the grazing period, when treatment effects on vegetation structure would have been most evident. This pattern was not seen for any other species, all of which initiated
7-10 days earlier or later. Thus, vesper sparrow nesting chronology tracked most closely with the cattle grazing period in our study and resulted in the detection of stocking rate effects on nest-level visual obstruction.
When we investigated temporal patterns of nest activity for western meadowlarks, we found less support for the hypothesis that there was temporal overlap of nesting activity for western meadowlarks with the latter half of the cattle grazing period. For western meadowlarks, spatial rather than temporal overlap between nest sites and grazed patches may explain the observed pattern. Western meadowlarks prefer nest sites in mesic areas with moderate litter cover, relatively high vegetative cover (compared to other species present at our study site), and show no avoidance of vegetation communities dominated by non-native forage grasses (Dechant et al. 2003c; Kennedy et
al. 2009). Livestock in our study showed preferences for vegetation patches that included
non-native forage grasses over patches that were exclusively native bunchgrasses, and
patches of non-native forage grasses were often restricted to swales where water 27
availability and aboveground biomass is likely to be higher (Wyffels 2009; S. Wyffels,
personal communication). If patches dominated by non-native grasses offer the
vegetative structure required by meadowlarks, overlap between cattle diet and
meadowlark site choice could explain the effect of stocking rate on visual obstruction at
meadowlark nests.
Effects of grazing intensity on grassland songbirds
Within-species similarity of nest-site visual obstruction among stocking rates should lead
to differences in songbird and/or nest density through the creation or elimination of
suitable nest patches. Western meadowlark nest density was not influenced by decreased
visual obstruction at the nest, although we detected a change from a positive effect of stocking rate on meadowlark nest density in the first treatment year to no effect in the
second treatment year. Additional years of livestock grazing at our experimental
stocking rates could clarify whether the change in meadowlark nest density is the
beginning of a trend towards decreased nest density under higher stocking rates.
Effects of stocking rate on absolute bird population density were restricted to
negative effects of higher stocking rates on savannah sparrows, and this relationship was
not observed until the post-treatment year. This could represent a lag effect of grazing
intensity, especially if savannah sparrows make settlement decisions based on the
previous year’s conditions. Alternatively, a lag effect of grazing intensity could be
observed if habitat conditions unsuitable for savannah sparrows take more than one
treatment year to be created by grazing (i.e., if there is some threshold to changes in
visual obstruction that is not surpassed until after two complete seasons of grazing at 28
higher intensities). However, we detected differences in avian community composition
between control and heavily-grazed paddocks during the second treatment year, which
were partially driven by decreases in the relative density of savannah sparrows in
paddocks with high stocking rates, suggesting the lag effect may not take two complete
seasons of grazing. A negative effect of high grazing pressure on savannah sparrow
density has been reported from previous observational studies, although the effects of
light-to-moderate grazing have been variable (Anstey et al. 1995, Bélanger and Picard
1999).
Horned lark and vesper sparrow density were affected by pre-treatment density,
and the NMS revealed densities of these two species were positively correlated with
variability in nest-level visual obstruction (Table 2.4). Both species prefer relatively
short, sparse vegetation for nesting (Dechant et al. 2003a, Dinkins et al. 2003, this study).
However, lower visual obstruction created by higher grazing intensity did not result in
significant increases in the absolute density of either species. We did not evaluate
differences in horizontal vegetation structure, which influences the extent of bare ground.
If these species also choose breeding sites based on the availability of bare ground, it could influence the association with variability in nest-level visual obstruction. Further, horned larks at our study site began breeding earlier than other species, and many nests were initiated before cattle were introduced to paddocks. Horned larks may have been limited by the availability of patches with the sparse vegetative cover they prefer before grazing began, thus limiting their response to grazing treatments. 29
Results from the NMS suggest that grasshopper sparrow density is positively
associated with increasing vegetation structure and negatively associated with variability
in vegetation structure, and we observed a negative effect of high grazing intensity on grasshopper sparrow nest density. A similar negative effect of grazing intensity and variability in vegetation structure on grasshopper sparrow density has been described in other studies (reviewed in Dechant et al. 2003b). Although we cannot exclude the
possibility that we simply had lower detection rate of grasshopper sparrow nests in
heavily-grazed paddocks, we believe this explanation is unlikely because we: 1)
standardized nest-searching effort among all paddocks, and 2) likely had higher nest
detection rates in more heavily-grazed paddocks due to lower visual obstruction overall.
Most detections of grasshopper sparrows in heavily-grazed paddocks were of males
(97%), whereas in low- and moderately- grazed paddocks males made up slightly fewer
detections (90% and 87%, respectively), suggesting there could be differences in mate-
pairing success among grazing treatments. Low pairing success can be indicative of low
resource availability (Probst and Hayes 1987, Zanette 2001), and because grasshopper
sparrows showed an affinity for relatively high visual obstruction and low structural
heterogeneity, heavily-grazed paddocks may represent areas of limited resources for this
species.
We found no support for the hypothesis that more structural heterogeneity
translated into higher bird diversity. Our results are consistent with a study by Wiens
(1974) where no relationships between structural heterogeneity and bird diversity were
observed in multiple types of grasslands. The intrinsically low diversity of grassland 30
songbird communities may limit the range of diversity values possible in grasslands, and
may make detection of significant relationships between structural heterogeneity and
diversity difficult (Wiens 1974). Alternatively, our results could be an artifact of the
structural variable we measured. We focused on a single variable (visual obstruction),
but the structural complexity of grassland vegetation also includes other parameters
important to grassland songbird habitat selection, like ground cover, plant functional
groups, and litter depth (Fuhlendorf et al. 2006, Fisher and Davis 2010). The absence of
a significant relationship between bird diversity and structural heterogeneity as measured
by a gradient of visual obstruction alone does not preclude a significant relationship
between bird diversity and a multivariate structural gradient (Tews et al. 2004). Because
there is a low limit to the amount of vertical structural heterogeneity that can exist in
grasslands, ground-nesting birds likely use additional cues when making settlement decisions and visual obstruction may not be the most critical feature of structural
heterogeneity (Roth 1976).
We observed a unique composition of grassland songbird communities in control
paddocks compared to heavily-grazed paddocks, but we did not observe the expected
changes in bird diversity. Researchers have previously suggested that increased
structural heterogeneity induced by grazing at different intensities will contribute to
greater bird diversity (Chapman et al. 2004, Toombs et al. 2010). We suggest that the
relationship between livestock grazing intensity and grassland bird diversity may not be
predicted easily, and that more experimental work should be conducted to help identify
structural gradients and stocking rates that might contribute to increased bird diversity. 31
An experimental framework in which vegetation utilization rates required by livestock to
achieve particular vegetation attributes will help clarify response patterns among
songbird species and increase the ability to create or modify management practices that contribute to grassland bird conservation.
These results contribute to a developing body of literature that suggests the effects of livestock grazing on grassland songbirds is variable and, depends upon the species and response variable evaluated. The patterns we observed also support the notion that variability in livestock management may be critical to providing suitable habitat for a greater suite of grassland songbird species. Ungrazed areas and areas with low-to- moderate stocking rates may provide suitable habitat for species preferring more vegetative cover, while high stocking rates may provide suitable habitat for species preferring less cover. Management strategies that include long-term rest from grazing may therefore contribute to the loss of suitable habitat for species on the lower end of this continuum of vegetative cover. However, an understanding of the effects of stocking rate on songbird demographic rates as well as overall rangeland health is needed before large- scale implementation of grazing management scenarios incorporate a wide range of grazing intensities for the sake of grassland songbird conservation.
32
LITERATURE CITED
Alldredge, M. W., K. H. Pollock, T. R. Simons, and S. A. Shriner. 2007. Multiple- species analysis of point count data: a more parsimonious modeling framework. Journal of Applied Ecology 44: 281-290.
Anstey, D. A., S. K. Davis, D. C. Duncan, and M. Skeel. 1995. Distribution and habitat requirements of eight grassland songbird species in southern Saskatchewan. Saskatchewan Wetland Conservation Corporation, Regina, Saskatchewan. 11 pages.
Askins, R. A., F. Chávez-Ramírez, B. C. Dale, C. A. Haas, J. R. Herkert, F. L. Knopf, and P. D. Vickery. 2007. Conservation of grassland birds in North America: understanding ecological processes in different regions. Ornithological Monographs 64: 1-46.
Ausden, M. 2007. Habitat Management for Conservation: A Handbook of Techniques. Oxford University Press, New York.
Bélanger, L., and M. Picard. 1999. Cattle grazing and avian communities of the St. Lawrence River Islands. Journal of Range Management 52: 332-338.
Bell, S. S., E. D. McCoy, and H. R. Mushinsky. 1991. Habitat Structure: The Physical Arrangement of Objects in Space. Chapman & Hall, London, UK.
Bransby, D. I., B. E. Conrad, H. M. Dick, and J. W. Drane. 1988. Justification for grazing intensity experiments: analyzing and interpreting grazing data. Journal of Range Management 41: 274-279.
Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas. 2001. Introduction to Distance Sampling: Estimating Abundance of Biological Populations. Oxford University Press, New York.
Burnham, K. P., and D. R. Anderson. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, second ed. Springer, New York.
Chapman, R. N., D. M. Engle, R. E. Masters, and D. M. Leslie, Jr. 2004. Grassland vegetation and bird communities in the southern Great Plains of North America. Agriculture, Ecosystems and Environment 104: 577-585.
Cody, M. L. 1968. On the methods of resource division in grassland bird communities. American Naturalist 102: 107–147. 33
Coghenour, M. B. 1991. Spatial components of plant-herbivore interactions in pastoral, ranching, and native ungulate ecosystems. Journal of Range Management 44: 530-542.
Cole, D. N., and P. B. Landres. 1996. Threats to wilderness ecosystems: impacts and research needs. Ecological Applications 6: 168-184.
Damiran, D., T. DelCurto, E. Darambazar, A. A. Clark, P. L. Kennedy, R. V. Taylor. 2007. Visual obstruction: weight technique for estimating production on northwestern bunchgrass prairie rangelands. Proceedings of Western Section, American Society of Animal Sciences 58: 225-228.
DeBano, S. 2006. Effects of livestock grazing on aboveground insect communities in semi-arid grasslands of southeastern Arizona. Biodiversity and Conservation 15: 2547-2564.
Dechant, J. A., M. F. Dinkins, D. H. Johnson, L. D. Igl, C. M. Goldade, and B. R. Euliss. 2003a. Effects of management practices on grassland birds: Vesper Sparrow. Northern Prairie Wildlife Research Center, Jamestown, ND. Northern Prairie Wildlife Research Center Online. http://www.npwrc.usgs.gov/resource/ literatr/grasbird/vesp/vesp.htm (Accessed June 2, 2010).
Dechant, J. A., M. L. Sondreal, D. H. Johnson, L. D. Igl, C. M. Goldade, M. P. Nenneman, and B. R. Euliss. 2003b. Effects of management practices on grassland birds: Grasshopper Sparrow. Northern Prairie Wildlife Research Center, Jamestown, ND. Northern Prairie Wildlife Research Center Online. http://www.npwrc. usgs.gov/ resource/literatr/grasbird/ grsp/grsp.htm (Accessed June 2, 2010).
Dechant, J. A., M. L. Sondreal, D. H. Johnson, L. D. Igl, C. M. Goldade, A. L. Zimmerman, and B. R. Euliss. 2003c. Effects of management practices on grassland birds: Western Meadowlark. Northern Prairie Wildlife Research Center, Jamestown, ND. Northern Prairie Wildlife Research Center Online. http://www.npwrc.usgs. gov/resource/ literatr/grasbird/weme/weme.htm (Accessed June 2, 1010).
Derner, J. D., W. K. Laurenroth, P. Stapp, and D. J. Augustine. 2009. Livestock as ecosystem engineers for grassland bird habitat in the western Great Plains of North America. Rangeland Ecology and Management 62: 111-118.
34
Dinkins, M. F., A. L. Zimmerman, J. A. Dechant, B. D. Parkin, D. H. Johnson, L. D. Igl, C. M. Goldade, and B. R. Euliss. 2003. Effects of management practices on grassland birds: Horned Lark. Northern Prairie Wildlife Research Center, Jamestown, ND. Northern Prairie Wildlife Research Center Online. http://www.npwrc.usgs.gov/resource/ literatr/grasbird/hola/hola.htm (Accessed June 2, 2010).
Evans, D. M., S. M. Redpath, S. A. Evans, D. A. Elston, and P. Dennis. 2005. Livestock grazing affects the egg size of an insectivorous passerine. Biology Letters 1: 322- 325.
Fisher, R. J., and S. K. Davis. 2010. From Wiens to Robel: A review of grassland-bird habitat selection. Journal of Wildlife Management 74: 265-273.
Fondell, T. F., and I. J. Ball. 2004. Density and success of bird nests relative to grazing on western Montana grasslands. Biological Conservation 117: 203-213.
Fontaine, A. L., P. L. Kennedy, and D. H. Johnson. 2004. Effects of distance from cattle water developments on grassland birds. Journal of Range Management 57: 238- 242.
Fuhlendorf, S. D., and D. M. Engle. 2001. Restoring heterogeneity on rangelands: ecosystem management based on evolutionary grazing patterns. BioScience 51: 625-632.
Fuhlendorf, S. D., W. C. Harrell, D. M. Engle, R. G. Hamilton, C. A. Davis, and D. M. Leslie, Jr. 2006. Should heterogeneity be the basis for conservation? Grassland bird response to fire and grazing. Ecological Applications 16: 1706-1716.
Johnson, R. G., and S. A. Temple. 1990. Nest predation and brood parasitism of tallgrass prairie birds. Journal of Wildlife Management 54: 106-111.
Kennedy, P. L., S. J. DeBano, A. M. Bartuszevige, and A. S. Lueders. 2009. Effects of native and non-native grassland plant communities on breeding passerine birds: implications for restoration of northwest bunchgrass prairie. Restoration Ecology 17: 515-525.
Knopf, F. L. 1994. Avian assemblages on altered grasslands. Studies in Avian Biology 15: 247-257.
Knopf, F. L. 1996. Prairie legacies—birds, in: Samson, F. B., Knopf, F. L. (Eds.), Prairie Conservation: Preserving North America’s Most Endangered Ecosystem. Island Press, Washington, D.C., pp. 135–148. 35
Kruskal, J. B. 1964. Nonmetric multidimensional scaling: a numerical method. Psycometrika 29: 115-129.
Litell, R. C., G. A. Milliken, W. W. Stroup, R. D. Wolfinger, and O. Schabenberger. 2006. SAS for Mixed Models, second ed. SAS Institute Inc., North Carolina.
Lubowski, R. N., M. Vesterby, S. Bucholtz, A. Baez, and M. J. Roberts. 2006. Major uses of land in the United States, 2002. U. S. Department of Agriculture, Economic Research Service, Economic Information Bulletin No. EIB-14.
MacArthur, R. H., and J. W. MacArthur. 1961. On bird species diversity. Ecology 42: 594–598.
Mather, P. M. 1976. Computational Methods of Multivariate Analysis in Physical Geography. John Wiley & Sons, London.
McCune, B., and J. B. Grace. 2002. Analysis of Ecological Communities. MjM Software, Glenedon Beach, Oregon, USA.
McCune, B., and M. J. Mefford. 2009. PC-ORD. In: Multivariate Analysis of Ecological Data. Version 6.91 beta, MjM Software, Glenedon Beach, Oregon, USA.
McIntyre, S., K. M. Heard, and T. G. Martin. 2003. The relative importance of cattle grazing in subtropical grasslands: does it reduce or enhance biodiversity? Journal of Applied Ecology 40: 445-457.
McNaughton, S. J. 1984. Grazing lawns: animals in herds, plant form, and coevolution. American Naturalist 124: 863-886.
Mielke, P. W., Jr. 1984. Meteorological applications of permutation techniques based on distance functions, in: Krishnaiah, P.R., Sen, P.K., (Eds.), Handbook of Statistics, Vol. 4. Elsevier Science Publishers, The Hague, The Netherlands, pp. 813-830.
Milchunas, D. G., and W. K. Lauenroth. 1993. Quantitative effects of grazing on vegetation and soils over a global range of environments. Ecological Monographs 63: 327-366.
Milchunas, D. G., W. K. Laurenroth, and I. C. Burke. 1998. Livestock grazing: animal and plant biodiversity of shortgrass steppe and the relationship to ecosystem function. Oikos 83: 65-74.
36
O’Neill, K. M., B. E. Olson, M. G. Rolston, R. Wallander, D. P. Larson, and C. E. Seibert. 2003. Effects of livestock grazing on rangeland grasshopper (Orthoptera: Acrididae) abundance. Agriculture, Ecosystems, and Environment 97: 51-64.
Probst, J. R., and J. P. Hayes. 1987. Pairing success of Kirtland’s warblers in marginal versus suitable habitat. Auk 104: 234-241.
Ralph, C. J., J. R. Sauer, and S. Droege. 1995. Monitoring bird populations by point counts. United States Department of Agriculture, Forest Service, Pacific Southwest Research Station Gen. Tech. Rep. PSW-GTR-149. 187 p.
Ramsey, F. L., and D. W. Schafer. 2002. The Statistical Sleuth, second ed. Duxbury, Pacific Grove, California.
Reinkensmeyer, D. P., R. F. Miller, R. G. Anthony, and V. E. Marr. 2007. Avian community structure along a Mountain Big Sagebrush successional gradient. Journal of Wildlife Management 71: 1057-1066.
Robel, R. J., J. N. Briggs, A. D. Dayton, and L. C. Hulbert. 1970. Relationships between visual obstruction measurements and weight of grassland vegetation. Journal of Range Management 23: 295-297.
Roth, R. R. 1976. Spatial heterogeneity and bird species diversity. Ecology 57: 773- 782.
Salo, E. D. 2003. Effects of grazing intensity and temporal application of grazing treatments on nongame birds in North Dakota mixed-grass prairie. MS Thesis, South Dakota State University. 87 pp.
Sayre, N. F. 2009. Bad abstractions: response to Sullivan. Conservation Biology 23: 1050-1052.
Tews, J., U. Brose, V. Grimm, K. Tielbörger, M. C. Wichmann, M. Schwager, and F. Jeltsch. 2004. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. Journal of Biogeography 31: 79-92.
Thomas, L., J. L. Laake, S. Strindberg, F. F. C. Marques, S. T. Buckland, D. L. Borchers, D. R. Anderson, K. P. Burnham, S. L. Hedley, J. H. Pollard, J. R. B. Bishop, and T. A. Marques. 2005. Distance 5.0. Release 5. Research Unit for Wildlife Population Assessment, University of St. Andrews, UK. http://www.ruwpa.st- and.ac.uk/distance/
37
Toombs, T. P., J. D. Derner, D. J. Augustine, B. Krueger, and S. Gallagher. 2010. Managing for biodiversity and livestock: a scale-dependent approach for promoting vegetation heterogeneity in western Great Plains grasslands. Rangelands 32: 10-15.
Townsend, D. E., and S. D. Fuhlendorf. 2010. Evaluating relationships between spatial heterogeneity and the biotic and abiotic environments. American Midland Naturalist 163: 351-365.
Vickery, J. A., J. R. Tallowin, R. E. Feber, E. J. Asteraki, P. W. Atkinson, R. J. Fuller, and V. K. Brown. 2001. The management of lowland neutral grasslands in Britain: effects of agricultural practices on birds and their food resources. Journal of Applied Ecology 38: 647-664.
Vickery, P. D., P. L. Tubaro, J. M. Cardosado Da Silva, B. G. Peterjohn, J. R. Herkert, and R. B. Cavalcanti. 1999. Conservation of grassland birds in the western hemisphere. Studies in Avian Biology 19: 2-26.
West, N. E. 1993. Biodiversity of rangelands. Journal of Range Management 46: 2-13.
Western Regional Climate Center. 2009. Enterprise 20 NNE, Oregon. Western Regional Climate Center, Oregon Climate Summaries website http://www.wrcc.dri.edu/ cgi-bin/cliMAIN.pl?oren20 (Accessed August 04, 2009).
Wiens, J. A. 1974. Habitat heterogeneity and avian community structure in North American grasslands. American Midland Naturalist 91: 195-213.
Willms, W. D., J. F. Dormaar, and G. B. Schaalje. 1988. Stability of grazed patches on rough fescue grasslands. Journal of Range Management 41: 503-508.
Winter, M., S. E. Hawks, J. A. Shaffer, and D. H. Johnson. 2003. Guidelines for finding nests of passerine birds in tallgrass prairie. Prairie Naturalist 35: 197-211.
With, K. A., A. W. King, and W. E. Jensen. 2008. Remaining large grasslands may not be sufficient to prevent grassland bird declines. Biological Conservation 141: 3152-3167.
Wyffels, S. 2009. Influence of stocking density on grazing beef cattle performance, diet composition, foraging efficiency, and diet quality on a late-spring early-summer native bunchgrass prairie. MS Thesis, Oregon State University. 124 pp.
38
Zanette, L. 2001. Indicators of habitat quality and the reproductive output of a forest songbird in small and large fragments. Journal of Avian Biology 32: 38-46.
39
Fig. 2.1. Schematic of three scenarios of how structural heterogeneity, bird diversity, and songbird density may change in response to livestock grazing intensity. Solid line: linear response (may be negative or positive); dashed line: threshold response; dotted line: quadratic response.
variable Response
None Low Moderate High
Grazing Intensity
40
Fig. 2.2. Experimental blocks and paddocks with breeding bird survey points at the Zumwalt Prairie Preserve, northeastern Oregon, USA.
41
Fig. 2.3. A) Mean paddock-level visual obstruction and, B) structural heterogeneity (mean coefficient of variation of paddock-level visual obstruction) for four stocking rates (n = 4 replicates of each stocking rate) on the Zumwalt Prairie Preserve, northeastern Oregon, USA. Pre-treatment data were collected in 2006, paddocks were grazed in 2007 and 2008, and post-treatment data were collected in 2009. Significant trends from a mixed model are indicated with regression lines. The continuous predictor variable used in mixed models (number of animal units; AU) corresponding to each grazing treatment is presented on the X-axis. One AU is defined as a mature cow and her calf.
A)
Pre-treatment year Treatment year 1 2.5
2.0
1.5
1.0
0.5 Visual obstruction (decimeters) 0.0 Treatment year 2 Post-treatment year 2.5
2.0
1.5
1.0
0.5 Visual obstruction (decimeters) 0.0 control low moderate high control low moderate high (0 AU) (10 AU) (20 AU) (30 AU) (0 AU) (10 AU) (20 AU) (30 AU) Stocking rate Stocking rate
42
B)
Pre-treatment year Treatment year 1 1.0
0.8
0.6
0.4
Visual obstruction Visual obstruction 0.2
0.0 coefficient (decimeters) of variation Treatment year 2 Post-treatment year 1.0
0.8
0.6
0.4
Visual obstruction Visual obstruction 0.2
0.0 coefficient of variation (decimeters) variation of coefficient control low moderate high control low moderate high (0 AU) (10 AU) (20 AU) (30 AU) (0 AU) (10 AU) (20 AU) (30 AU) Stocking rate Stocking rate
43
Fig. 2.4. Mean nest-level visual obstruction for five species of songbird nesting in paddocks exposed to four experimental stocking rates in 2007-08 on the Zumwalt Prairie Preserve, northeastern Oregon, USA. Significant trends from a mixed model are indicated with regression lines. Vesper sparrows showed significant year and treatment effects; western meadowlarks showed significant treatment effects. The continuous predictor variable used in mixed models (number of animal units; AU) corresponding to each grazing treatment is presented on the X-axis. One AU is defined as a mature cow and her calf.
44
Savannah sparrow Vesper sparrow 3.0 2007 2.5 2008
2.0
1.5
1.0 Visual obstruction obstruction Visual
(decimeter;CI) ± 95% 0.5
0.0 Western meadowlark Horned lark 3.0
2.5
2.0
1.5
1.0
Visual obstruction Visual 0.5 CI) ± 95% (decimeter;
0.0 Grasshopper sparrow Control Low Moderate High 3.0 (0 AU) (10 AU) (20 AU) (30 AU) Stocking rate 2.5
2.0
1.5
1.0 0.5
Visual obstruction obstruction Visual (decimeter; ± 95% CI) 0.0
-0.5 Control Low Moderate High (0 AU) (10 AU) (20 AU) (30 AU) Stocking rate
45
Figure 2.5. Ordination of paddocks (2007-09) in species space using non-metric multidimensional scaling (NMS). Four different stocking rate treatments did not produce four distinct bird communities after two years of grazing, although paddocks grazed with high stocking rates are concentrated at the upper end of axis 2. Arrows along axes represent relationships of environmental (paddock-level visual obstruction; VO) and species trait (nest-level VO) variables with each axis: VO (decimeters). Species codes: VESP (vesper sparrow); HOLA (horned lark); GRSP (grasshopper sparrow); WEME (western meadowlark); SAVS (savannah sparrow).
46
Table 2.1. Grazing treatments randomly assigned to each 40-ha paddock within each block (n = 4) on the Zumwalt Prairie Preserve, northeastern Oregon, USA.
Animal unit Mean Percent Forage Treatment monthsb Consumptionc (SD)
Controla 0.00 9.52 (3.05)
Low 14.4 20.18 (4.08)
Moderate 28.8 31.66 (5.72)
High 43.2 46.09 (11.68)
a Control treatments represented no use by domestic livestock. However, native herbivores (e.g., ungulates, insects) were present in control paddocks. We present mean forage consumption for control paddocks to account for background levels of native herbivory at the study site.
b One animal unit is defined as a mature cow and calf. We assume
each animal unit consumes 20 kg/day and a grazing period of 42 days.
c Forage consumption was averaged over the two treatment years (2007-08). Methods for determining forage consumption are presented in Wyffels (2009).
Table 2.2. Mean population density (individuals per ha; estimated using Program Distance) and mean apparent nest density of ground-nesting songbirds in paddocks with four different cattle stocking rates (n = 4 replicates of each grazing treatment) on the Zumwalt Prairie Preserve, northeastern Oregon, USA.
Mean apparent nest density Mean population density (95% CI)a (95% CI) Species Grazing intensity 2006 2007 2008 2009 2007 2008 Savannah control 1.26 (0.93,1.59) 0.86 (0.74,0.97) 0.62 (0.44,0.79) 1.04 (0.57,1.51) 0.10(0.07,0.13) 0.06(0.01,0.11) sparrow low 0.93 (0.70,1.16) 0.62 (0.28,0.96) 0.36 (0.24,0.48) 0.54 (0.30,0.80) 0.04(0.01,0.06) 0.06(0.02,0.11) moderate 0.86 (0.59,1.13) 1.06 (0.26,1.86) 0.60 (0.20,1.00) 0.76 (0.26,1.27) 0.11(0.03,0.19) 0.08(0.02,0.13) high 1.23 (0.52,1.93) 0.68 (0.40,0.97) 0.30 (0.21,0.39) 0.46 (0.15,0.76) 0.07(0.01,0.13) 0.06(0.04,0.09) Vesper control 0.09 (0.00,0.20) 0.04 (0.01,0.07) 0.07 (0.03,0.11) 0.05 (0.00,0.10) 0.03(0.00,0.05) 0.02(0.01,0.03) sparrow low 0.14 (0.02,0.26) 0.09 (0.01,0.17) 0.13 (0.00,0.26) 0.09 (0.03,0.16) 0.10(0.02,0.18) 0.09(0.02,0.17) moderate 0.09 (0.00,0.23) 0.07 (0.00,0.14) 0.07 (0.02,0.12) 0.08 (0.01,0.15) 0.07(0.01,0.12) 0.04(0.01,0.08) high 0.16 (0.00,0.34) 0.04 (0.01,0.08) 0.09 (0.05,0.14) 0.09 (0.04,0.14) 0.04(0.01,0.07) 0.09(0.01,0.17)
Western control 0.26 (0.12,0.39) 0.18 (0.06,0.29) 0.15 (0.10,0.20) 0.10 (0.06,0.14) 0.03(0.00,0.05) 0.03(0.00,0.05) meadowlark low 0.14 (0.11,0.18) 0.15 (0.01,0.29) 0.19 (0.05,0.32) 0.11 (0.08,0.14) 0.04(0.01,0.07) 0.02(0.01,0.03)
moderate 0.13 (0.03,0.22) 0.06 (0.03,0.08) 0.11 (0.05,0.16) 0.07 (0.03,0.11) 0.06(0.02,0.10) 0.03(0.02,0.04) high 0.16 (0.11,0.21) 0.19 (0.01,0.36) 0.13 (0.05,0.20) 0.07 (0.02,0.12) 0.07(0.03,0.11) 0.01(0.00,0.03) Horned lark control 0.31 (0.06,0.55) 0.26 (0.07,0.44) 0.23 (0.03,0.42) 0.20 (0.08,0.32) 0.05(0.03,0.07) 0.01(0.00,0.03) low 0.40 (0.07,0.73) 0.36 (0.12,0.61) 0.41 (0.14,0.67) 0.50 (0.18,0.82) 0.07(0.02,0.12) 0.03(0.00,0.05) moderate 0.42 (0.00,0.92) 0.19 (0.04,0.35) 0.36 (0.11,0.60) 0.41 (0.16,0.66) 0.04(0.02,0.07) 0.01(0.00,0.03) high 0.38 (0.00,0.75) 0.36 (0.01,0.72) 0.32 (0.21,0.43) 0.44 (0.27,0.60) 0.02(0.00,0.04) 0.04(0.01,0.08) Grasshopper control 0.13 (0.05,0.21) 0.08 (0.01,0.15) 0.13 (0.08,0.17) 0.26 (0.17,0.34) 0.03(0.00,0.05) 0.02(0.01,0.03) sparrow low 0.11 (0.03,0.19) 0.08 (0.04,0.12) 0.14 (0.02,0.27) 0.16 (0.12,0.21) 0.03(0.00,0.06) 0.01(0.00,0.02) moderate 0.13 (0.00,0.26) 0.09 (0.02,0.15) 0.16 (0.03,0.28) 0.22 (0.15,0.29) 0.02(0.00,0.04) 0.02(0.01,0.03) high 0.14 (0.05,0.24) 0.09 (0.04,0.14) 0.07 (0.00,0.14) 0.14 (0.09,0.18) 0.00 0.00 a Confidence limits extending below zero were truncated at zero. 47
48
Table 2.3. Results of a blocked multi-response permutation procedure comparing ground-nesting songbird community composition at the Zumwalt Prairie Preserve, northeastern Oregon, USA. Pairwise treatment comparisons are among four different cattle stocking rates (n = 4 replicates of each stocking rate). Asterisks (*) denote statistically significant differences in group composition.
Treatment comparison Year Ta Ab P-value
Control vs. Low 2006 -0.65 0.04 0.27 2007 0.40 -0.04 0.66 2008 -0.33 0.03 0.37 2009 -1.55 0.23 0.07 Control vs. Moderate 2006 0.56 -0.07 0.67 2007 0.67 -0.05 0.73 2008 0.61 -0.04 0.71 2009 -0.46 0.06 0.28 Control vs. High 2006 0.57 -0.05 0.69 2007 0.54 -0.06 0.70 2008 -1.93 0.19 *0.04 2009 -2.33 0.38 *0.03 Low vs. Moderate 2006 0.87 -0.15 0.82 2007 0.91 -0.09 0.82 2008 0.16 -0.02 0.50 2009 1.11 -0.13 0.87 Low vs. High 2006 1.76 -0.15 0.97 2007 1.06 -0.13 0.86 2008 0.37 -0.05 0.57 2009 0.98 -0.10 0.84 Moderate vs. High 2006 1.07 -0.13 0.87 2007 -0.46 0.02 0.31 49
Table 2.3 continued 2008 -0.56 0.05 0.26 2009 -0.53 0.04 0.23
a T = (δ – m δ) / s δ , where δ = the weighted mean within-group distance, m δ = mean of δ under the null hypothesis, and s δ = standard deviation of δ under the null hypothesis. The T statistic describes the degree of separation between groups, and the more negative the value, the larger the difference between groups (McCune and Grace 2002). b Chance-corrected within-group agreement, describing within-group similarity (1 = perfect similarity; McCune and Grace 2002).
50
Table 2.4. Correlations of each variable with axes obtained from a non-metric multidimensional scaling ordination of ground-nesting songbird densities at the Zumwalt Prairie Preserve, northeastern Oregon, USA. Year = 2007 and 2008.
Correlation Coefficient
Variable Axis 1 Axis 2
Year 0.21 0.21
Stocking rate -0.22 0.23
Species
Savannah sparrow 0.66 -0.63
Vesper sparrow -0.05 0.90
Western meadowlark 0.29 -0.26
Horned lark 0.26 0.87
Grasshopper sparrow 0.69 0.16
Visual obstruction (VO)
Paddock-level VO 0.39 -0.27
Paddock-level VO coefficient of variation -0.39 0.42
Nest-level VO 0.05 -0.59
Nest-level VO coefficient of variation -0.57 0.97
51
CHAPTER 3: FACTORS INFLUENCING NEST SURVIVAL AND CAUSE-
SPECIFIC NEST FAILURE OF GRASSLAND PASSERINES IN GRAZED
PRAIRIE
ABSTRACT
1. Nest failure negatively affects seasonal fecundity of many songbird populations.
Nesting attempts can fail for various reasons, and determining the risk associated with
failure from specific causes may provide more effective ways to manage threatened bird
populations.
2. Land use activities, such as domestic livestock grazing, may influence nest failure for
grassland-nesting songbirds indirectly through effects on vegetation structure or
numerical or functional responses of the predator community, or directly by trampling
nests. We hypothesized that stocking rate may be an important determinant in the effect
of grazing on grassland songbird nest fate because stocking rate largely influences the
amount and distribution of remaining vegetation, as well as the number of large
herbivores to which nests are exposed.
3. In 2007 and 2008, nest fates of six species of grassland-nesting birds were evaluated
under four different stocking rates in experimental paddocks in northeastern Oregon,
U.S.A. to determine whether stocking rate influenced nest fate. Other variables such as
vegetation structure, predator community, date, year, and nest age were evaluated to help
clarify which mechanisms might be responsible for differences in nest survival or cause-
specific failure rates among treatments.
52
4. We introduce the use of a software package, McNestimate, to estimate daily
probability of nest survival and failure from specific causes. McNestimate estimates
probability of nest failure from competing causes even when exact failure dates are
unknown using a Markov Chain framework developed by Etterson et al. (2007a,b). It
also incorporates a model selection approach and allows the use of covariates to help
determine which variables are important in explaining variation in the probability of daily
nest failure.
5. In a previous study (T. Johnson Chapter 2) we observed a linear decrease in the
amount of remaining vegetation as stocking rate increased. However, the only nest failures related to stocking rate were failures due to trampling. The probability of nest failure due to trampling was higher in paddocks with higher stocking rates, but also depended on the number of days cattle were present during the nesting period. The probability of nest predation increased with the age of the nest and throughout the breeding season. The probability of nest failure from adverse weather declined throughout the season, but the rate of decline depended on year.
6. Our results help clarify mechanisms by which grassland bird reproductive success may be influenced by cattle grazing. We discuss potential management implications and
suggest our framework be used in future studies of nest failure.
INTRODUCTION
Nest failure negatively affects seasonal fecundity, a parameter important to
population growth rate, and can be a selective agent in the evolution of avian life history
traits (Martin 1995; Stahl & Oli 2006; Clark & Martin 2007). Nesting attempts may fail
53
for various reasons including: adverse weather, brood parasitism, nestling starvation,
abandonment, competition, egg failure, and most commonly, nest predation (Ricklefs
1969). Determining the risk of nest failure from specific causes can provide insights into
effective ways to increase bird populations, assuming land managers can target sources of
failure (e.g., predators or brood parasites; Etterson, Nagy & Robinson 2007).
Land management practices and other anthropogenic activities may contribute to songbird population trajectories if they influence the probability of nest failure. One such land use is livestock grazing. Globally, domestic livestock grazing is the most extensive land use, affecting 33 million km2 (or 25% of the earth’s land surface; Asner et al. 2004).
In the U.S., livestock grazing occurs on > 3 million km2 annually (Lubowski et al. 2006).
Grazing influences vegetation characteristics and other habitat features in important ways
that can affect breeding birds. Grazing by livestock affects vegetation structure and
composition, which can influence the risk of nest predation for birds if nest concealment
is reduced, or if it creates habitat structure with fewer potential nest sites predators must search before encountering a nest (Johnson & Temple 1990; Chalfoun & Martin 2009).
Livestock grazing may also affect nest predator abundance or activity by creating habitat characteristics preferred by predators (e.g., habitat edges; Uresk, MacCracken & Bjugstad
1982; Phillips et al. 2003; Sutter & Ritchison 2005). If grazing occurs near active nests, local microclimate could be affected, potentially altering the ability of a nest site to buffer against severe weather (Nelson & Martin 1999; Suedkamp Wells & Fuhlendorf 2005).
Further, livestock can directly cause nest failure by trampling or depredating nests (Paine et al. 1996; Nack & Ribic 2005).
54
Many grassland bird species have declined in abundance: 17 North American
species have declined between 1966 and 2002 (Askins et al. 2007), and some grassland
bird species in other regions including western Europe (Vickery et al. 2001), South
America (Vickery et al. 1999), central Asia (Sánchez-Zapata et al. 2003), and eastern
Africa (Muchai et al. 2002) have shown similar trends. One of the most important factors correlated with these declines appears to be loss of suitable habitat (Vickery et al. 2001;
Askins et al. 2007). Therefore, managing remaining grasslands in ways that contribute to the reproductive success of grassland birds will be critical to their conservation.
However, grazing is a complex disturbance in both time and space, and factors such as length of grazing period, season of use, number of animals, and history of grazing at a site can influence the effects of grazing on other grassland animals (Sayre 2009).
Stocking rate, defined as the number of livestock on a given area for a specified time, could be an important factor influencing nest failure rates because it largely determines the amount and distribution of vegetation available for use by grassland birds (Fuhlendorf
& Engle 2001), as well as the length of time nests are exposed to a particular density of livestock.
Attributing the risk of nest failure from specific causes to particular grazing management scenarios will provide management-specific demographic parameters critical to developing appropriate conservation strategies for grassland birds. Cause- specific nest failure is rarely estimated because few analytical methods are available that account for discovery bias (the bias associated with discovering nests at different stages in the nesting cycle; Mayfield 1961). Of those methods that do adjust for discovery bias,
55
many require exclusion of information about nests that failed due to causes other than the
cause of interest (e.g., mark-recapture models used in Program MARK; White &
Burnham 1999), affecting sample size, eliminating important information about survival
rates, and biasing remaining survival and failure rates. Other methods require detailed
(and often unavailable) information on timing of nest success or failure, or number of
young fledged (Etterson, Nagy & Robinson 2007). Heisy and Fuller (1985) developed a software program (MICROMORT) that allows estimation of survival and cause-specific mortality using radio telemetry data. This approach was later applied to nest failure data
(Ohlendorf, Hothem & Welsh 1989; Reidy, Stake & Thompson 2008), but their
framework did not incorporate model selection or allow the use of covariates. Etterson,
Nagy & Robinson (2007) and Etterson, Olsen & Greenberg (2007) demonstrated how estimation using a Markov chain can incorporate multiple, competing causes of failure even when exact failure dates are unknown. We use that framework here, and for the first time, have incorporated the use of covariates, to estimate nest failure probabilities
from specific causes for grassland songbirds breeding in a grazed prairie.
Our objective was to evaluate whether cattle stocking rate during the active plant
growing season influences the probability of nest failure from specific causes for
grassland-nesting songbirds. To better understand the mechanisms involved with cause-
specific nest failure in grazed systems, we evaluated (i) grazing-induced changes in
vegetation structure at both the paddock- and nest-level, and (ii) numerical and functional
responses of the predator community in relation to four experimentally-manipulated
stocking rates.
56
METHODS
We conducted our study at The Nature Conservancy’s Zumwalt Prairie Preserve (ZPP) in northeastern Oregon, U.S.A. The ZPP is approximately 15,000 ha and part of the larger
Zumwalt Prairie ecosystem (approximately 65,000 ha; Kennedy et al. 2009). The site is
dominated by Idaho fescue (Festuca idahoensis [Elmer]) and bluebunch wheatgrass
(Pseudoroegneria spicata [Pursh] A. Löve) prairie on a high-elevation plateau
(approximately 1500 m), and partially-wooded canyon lands that descend toward the
Imnaha River along the plateau’s edge. Growing season aboveground primary
production at our study site is less than 225 g/m2 on average (DelCurto & Taylor,
unpublished data). The ZPP has been grazed by domestic livestock during the summer
months since the early 1700’s, first by Native American horses and since the 1800’s by
cattle and sheep (Bartuszevige et al., unpublished data). Grassland birds breeding in this system are savannah sparrow (Passerculus sandwishensis), horned lark (Eremophila alpestris), western meadowlark (Sturnella neglecta), grasshopper sparrow (Ammodramus
savannarum), vesper sparrow (Poocetes gramineus), and less commonly, Brewer’s
sparrow (Spizella breweri).
Experimental Design
We established 16 study paddocks in a randomized complete block design with one factor (livestock grazing) and four grazing treatment levels (stocking rates). The blocking factor was historical grazing management. In 2006, The Nature Conservancy erected fences around four blocks of land 160 ha in size, and within each block, four contiguous paddocks were partitioned, each 40 ha in size (Fig. 3.1). Each of the four
57
blocks contained one replicate paddock of each assigned stocking rate (n = 4 replicates of each stocking rate). We randomly assigned stocking rates to paddocks within each block.
We determined a moderate stocking rate of 28.8 animal unit months (AUMs; the amount of forage a mature cow and her calf use in a month) based on average stocking rates for the entire Zumwalt Prairie, and based on pilot data on vegetation utilization by cattle collected at the ZPP (DelCurto & Williams, unpublished data). We expected 40% of available vegetation to be used in moderately-stocked paddocks. Three other stocking rates (low: 14.4 AUMs, high: 43.2 AUMs, and control: 0 AUMs) were to represent 20%,
60%, or no use of vegetation by livestock, respectively. Cattle grazed paddocks from 21
May to 3 July and 29 May to 9 July in 2007 and 2008, respectively. Actual utilization of vegetation averaged over our two treatment years were as follows: low (20.2% ± 4.1%,
SD); moderate (31.7% ± 5.7%); high (46.1% ± 11.7%; Wyffels 2009). Utilization on control paddocks was 9.5% ± 3.1%, and represented the level of utilization by native herbivores (ungulates and invertebrates).
Nest searches and fate determination
We conducted systematic searches for grassland bird nests from 1 May to 25 July
three times in 2007 and twice in 2008. Each paddock was searched in its entirety using
the rope-dragging method to flush incubating birds (Winter et al. 2003). We also found
nests opportunistically during nest monitoring visits and vegetation surveys. Although
nest-searching effort was standardized among paddocks, we were not able to calculate
nest detection rates for each treatment. Once a nest was discovered, its location was
marked with a GPS, and survey flags and spray paint were placed 10- and 30-m from the
58
nest in a random direction to reduce the chances that nest predators learned our marking
system. When a nest contained eggs, nest age was determined by flotation (Dinsmore,
White & Knopf 2002); when nestlings were present, age was determined using detailed
descriptions based on voucher photographs from previous research at our study site
(Kennedy et al. unpublished data). Nest age was accurately determined to within two
days for most nests by using information gathered during visits to successful nests. Nests
were typically monitored every one to four days until the nesting attempt was complete.
Observers used care to minimize the potential influence nest monitoring might have on
the risk of nest failure. All nest visits were preceded by brief surveys during which no
nest predators were detected; all equipment and observer’s boots were treated with
human scent killer; time spent at each nest was kept to a minimum; disturbed vegetation
was replaced; nest contents were processed at least 10-m from a nest; and whenever
possible, nests were approached from different directions or nest checks were conducted
from a distance with binoculars.
Nest fates were determined based on available evidence at the nest following
procedures similar to those described by Manolis, Andersen & Cuthbert (2000). If a nest
was found empty within two days of the age at which fledging should occur based on
species accounts in the Birds of North America (BNA) series (Beason 1995; Vickery
1996; Rotenberry, Patten & Preston 1999; Jones & Cornely 2002; Davis & Lanyon 2008;
Wheelright & Rising 2008), and there was physical evidence of fledging present at the
nest (e.g., observation of fledglings in the nest vicinity, or fecal sacs on the edge of the
nest cup or around the nest, and adults alarm-calling near the nest), the nest was
59
considered successful. For all nests that were sufficiently developed to have fledged
during the final nest check interval, we assumed the nest was successful and discarded information collected after the estimated date of fledging. Stanley (2004) presented
justification for this method and reasoned that it does not result in biased estimates of
nest survival. We followed Stanley’s (2004) criteria stringently, which forced us to
exclude data on nest failures that occurred near the estimated fledging date (within 2
days) because including data on nests where evidence of failure was clear but excluding
data on nests where evidence of failure was not clear results in biased estimates of nest
survival and failure (Stanley 2004). Causes of failure in our system included: predation,
trampling by ungulates, weather, abandonment, egg failure, and failures due to unknown
causes. For our analysis, these five causes of failure were combined into four: predation,
trampled, weather, and other based on evidence presented in Table 3.1.
Interpretation of evidence of nest fates has implications for estimating cause-
specific failure probabilities, and the potential to misclassify nests exists. For example, a
nest may have initially failed due to abandonment but was scavenged before the next visit
by an observer, resulting in misclassification as a predated nest. However, nests that
failed > two days before fledging were classified as predated when we found no evidence
to indicate otherwise, which results in no classification error for predated nests which
were actually predated. However, predation probability estimates may be positively
biased and remaining failure probabilities may be negatively biased because nests with
fates other than predation may have been included in the predation category (Etterson,
Nagy & Robinson 2007).
60
Cause-specific nest failure
Once primary causes of nest failure and potential covariates were identified we generated
a priori models specific to each cause of nest failure. Our set of candidate models
included covariates that were independent of grazing treatment and predator abundance that we expected could influence variation in nest failure rates, such as nest age (Ricklefs
1969), date (Benson, Brown & Bednarz 2010), and year (Greenwood et al. 1995; Table
3.2). Because temporal factors (date and year) and those intrinsic to the bird’s biology
(nest age) likely influence the effect of habitat-related variables, we first identified independent covariates that were important in explaining patterns of nest failure. We subsequently retained them in the analysis and included additional covariates related to grazing treatment, vegetation structure, and the predator community to address our hypotheses (Benson, Brown & Bednarz 2010). We expected age of nest to influence predation rates: predation risk could increase from incubation to the nestling stage because activity at the nest should be higher when adult birds deliver food items and nestlings actively beg (Martin, Scott & Menge 2000). We anticipated date-specific variation in failure rates, but made no assumption about the nature of the relationship between date and nest failure (Natarajan & McCullough 1999; Grant et al. 2005). We expected nest predation to increase at less concealed nest sites, in paddocks with more predator activity, and in paddocks with patchier vegetation because nest sites could potentially be more obvious to predators (Benson, Brown & Bednarz 2010). We predicted that weather-related nest failures could vary within season or between years, and would increase at nest sites with less vegetative cover. Finally, we expected nest
61
trampling would increase with livestock density, or with the length of time cattle were
present. Interactions between variables were included only if we expected the effect of
one covariate to depend on the level of the other (e.g., we expected the effect of livestock
density on trampling rates might depend on the length of time cattle were present). Nests
that failed due to abandonment, egg failure, or unknown causes were not of interest but
were included in our analysis in the category labeled “other” (Etterson, Nagy & Robinson
2007; Etterson, Olsen & Greenberg 2007); we assumed a constant rate of failure for these
nests in all analyses and do not make inferences regarding these causes of failure.
Covariates were evaluated for collinearity before estimating nest-failure probabilities; no
strong relationships (r > /0.70/) were identified. Comparisons among variables
potentially related to grazing treatment (e.g., vegetation structure, predator community)
would be investigated after we identified which covariates were important in explaining
variation in nest failure.
We estimated daily nest survival and failure and tested hypotheses about causes of
variation in daily failure rates using the program McNestimate. McNestimate uses
maximum likelihood estimation formulated as a Markov chain to estimate daily
probabilities of cause-specific nest failures and survival (Etterson, Nagy & Robinson
2007, Etterson, Olsen & Greenberg 2007). Preliminary analysis indicated sample sizes of
nests were inadequate to estimate nest failure probabilities separately for each species;
thus, data from nests of all species were pooled. However, we did not expect differences
in rates of cause-specific nest failure among species because all species share similar
nesting substrate, and there is little synchronization of clutch initiation within species at
62
our study site (T. Johnson, unpublished data). We evaluated a set of 30 candidate models
(Appendix A), and chose the best model using Akaike’s Information Criterion (AIC;
Burnham & Anderson 2002).
Estimating covariates
Vegetation structure
We described vegetation structure using visual obstruction measured at the paddock- and nest-level. Visual obstruction is a measure of the height and vertical
density of vegetation, and is correlated with standing aboveground biomass in grasslands
(Robel et al. 1970). Paddock-level visual obstruction (VO) was measured after six weeks
of grazing (< 24 hrs after cattle were removed). We used a Robel pole held perpendicular
to the ground, where an observer recorded the height of the lowest visible decimeter from
4-m away and 1-m above ground level (Robel et al. 1970). VO was measured at 10-m
intervals along eight 100-m transects within each paddock. VO transect locations were
stratified randomly by physiography to account for variation in vegetation structure (e.g.,
by hilltop, swale, rocky outcroppings, and slopes). We present the coefficient of
variation of paddock-level VO as an index of habitat patchiness in each paddock.
Nest-level VO was measured at the completion of each nesting attempt. A Robel
pole was placed directly into the nest cup, and VO was recorded from each cardinal
direction. We use the average of these four VO measurements as a score for each nest.
Nest predators
63
Potential nest predators in this system include: coyote (Canis latrans), mesopredators
(including badger [Taxidea taxus], porcupine [Erethizon dorsatum], striped skunk
[Mephitis mephitis], and weasel [Mustela spp.]), snakes (including common garter snake
[Thamnophis sirtalis], racer [Coluber constrictor], northern pacific rattlesnake [Crotalus
viridus oreganus], and rubber boa [Charina bottae]), avian predators (including common
raven [Corvus corax], black-billed magpie [Pica hudsonia], American kestrel [Falco sparverius], ferruginous hawk [Buteo regalis], golden eagle [Aquila chrysaetos], northern
harrier [Circus cyaneus], merlin [Falco columbarius], prairie falcon [Falco mexicanus],
red-tailed hawk [Buteo jamaicensis], short-eared owl [Asio flammeus], Swainson’s hawk
[Buteo swainsoni]), and rodents (including yellow-bellied marmot [Marmota flaviventris], Belding’s ground squirrel [Spermophilus beldingi], Ord’s kangaroo rat
[Dipodomys ordii], and western harvest mouse [Reithrodontomys megalotis]). Because
the suite of potential nest predators was diverse, we used three different methods to
quantify the predator community: a modified track survey technique for mesopredators
and coyotes (after Kuehl & Clark 2002), opportunistic encounters of snake and avian
predators, and point transect surveys for Belding’s ground squirrels. Belding’s ground
squirrels (the only species of ground squirrel present at our study site) are extremely
abundant at our study site, and ground squirrels have been reported as important songbird
nest predators in other systems (Cottrell 1981; Morton, Sockman & Peterson 1993;
Renfrew & Ribic 2003). Although we did not directly observe ground squirrels
depredating nests, we suspected they could be a significant source of nest failure in our
64
study because of their high abundance and concentrated exclusively on estimating ground
squirrel abundance rather than surveying the entire rodent community.
Index of mesopredator and coyote activity
Track surveys provided an index of activity for small mammals, mesopredators, and
coyotes, and accounted for differences in abundance or visitation rates in paddocks
(Kuehl & Clark 2002). We established eight predator sample units along the fence just outside each paddock (within 3-m of the fence; Fig. 3.1). Predator sample units were placed outside paddocks to eliminate the risk of disturbance by cattle (C. Ribic 2006,
USGS, personal communication). Each predator sample unit consisted of two 1-m2 predator track stations spaced 1-m apart and parallel to the paddock edge. Predator sample units were ≥ 20 m from each other along the fence (or fences) that represented the outside edge of a paddock. Predator sample units for adjacent paddocks were at least 250 m away from each other to help ensure independence between sample units associated with different stocking rates. Each tracking station was composed of fine-grained sand raked over until smooth, and an 8.9 cm white disk placed in the center. The contrast in color between the disk and sand attracts passing predators to the center of the track station so that they leave a more discernable track in the raked sand (Animal and Plant
Health Inspection Service 1998). Track surveys took place between 2 June to 17 July twice in 2007, and once in 2008 because of weather delays. The timing of track surveys coincided with the height of the songbird nesting season. All tracking stations were checked within a six-hr period in the morning after two nights of exposure. Tracks and
65
other sign were identified to the lowest possible taxa using Verts and Carraway (1998)
and Elbroch (2003) as references.
We limited our analysis of track survey data to detections of mesopredator species
and coyotes. Activity was calculated as the proportion of sample units with a
mesopredator or coyote track present for each paddock. Because there were differences
in sampling effort between years, we averaged detections for the two track surveys in
2007.
Index of snake and avian predator abundance
Opportunistic encounters with potential nest predators were recorded during incidental field activities. We followed methods in Patten and Bolger (2003), and recorded all snakes and avian predators (including raptors) observed during nest searches, nest monitoring visits, and point transect surveys.
We transformed snake and avian predator detections into encounter probabilities to standardize for differences in sampling effort. Snake encounter probabilities were calculated as the number of individuals per 24-hrs of surveys. Avian predator encounter probabilities were calculated as the number of encounters with any avian predator divided by the total number of surveys (Patten & Bolger 2003).
Belding’s ground squirrel density
To estimate density of Belding’s ground squirrels, we used distance-sampling at point transects (n = 16 per paddock; Fig. 3.1). All ground squirrels encountered within 75 m
over a five minute period were recorded, and the distance from the observer to the
squirrel’s initial location was estimated using a laser range finder. Points were
66
systematically placed 150 m from each other throughout each paddock, and the outer
boundary of count circles adjacent to fences excluded a 16-m buffer to account for
potential edge effects on detections. We surveyed each point three times from 16 May to
25 June in 2007-08. Surveys took place from 0630 – 1000 PST to account for
temperature-influenced constraints on daily ground squirrel activity (E. Muecke 2006
personal communication, Váczi, Koósz & Altbäcker 2006).
Ground squirrel density was estimated using Program Distance Ver. 5 Release 5
(Thomas et al. 2010). Because seven different observers conducted ground squirrel counts over two years, we first tested for observer differences in detection probability.
We considered all detections pooled across paddocks, post-stratified by observer, and compared overlap of 95% confidence intervals of estimated detection probability for each observer (Ramsey & Schafer 2002). We found differences in detection probabilities among some observers. Consequently, we modeled all density estimates using observer as a covariate. Because detections in most paddocks were sparse (owing to patchy distributions of ground squirrel colonies), we estimated the detection function by pooling data from all paddocks but used the fitted model to estimate a separate average probability density function in each paddock. This allows for separate detection probabilities among paddocks and results in density estimates that are less biased than using a global detection function alone (Thomas et al. 2010). Estimated Belding’s ground squirrel density was used as the covariate value for nest failure analysis.
Paddock-level visual obstruction and data from track surveys, opportunistic encounters, and point transect surveys for ground squirrels were calculated for each
67
paddock, then used as a paddock-level covariate in our nest failure analysis. Nest-level
visual obstruction was specific to each nest.
RESULTS
There were significant effects of cattle stocking rate on vegetation. We observed a linear
increase in the amount of vegetation removed as stocking rate increased (S. Wyffels,
2009). We observed a complementary linear decrease in the amount of vegetation
remaining (as indexed by visual obstruction) as stocking rate increased (T. Johnson,
Chapter 2).
We located and monitored 291 nests in 2007-08. Sixteen nests were excluded due
to insufficient data and four failed due to observer-related causes, resulting in 271 nests
included in our nest failure analysis (Table 3.3). The mean length between nest visits
was 2.3 days, and all intervals between nest visits were < 7 days long.
We identified no model that clearly fit the data best (Table 3.4). Although the
first six models competed for best-fit based on ΔAIC values < 2, the top three models
together garnered a substantial amount of support relative to competing models (summed
weights = 0.60). All models that garnered > 10% of support included age or date effects
on the daily probability of predation, date or year effects on the daily probability of
weather-caused failures, and grazing treatment effects on the probability of trampling
(Table 3.4). The model that garnered the most support included age and date effects on
the daily probability of predation, interactive effects of date and year on failures related
to adverse weather, and interactive effects of livestock density and the number of days
livestock were present before clutch initiation on the probability of trampling (Akaike
68
weight = 0.36; Table 3.4). We report estimates of nest survival and failure from this
model. Daily probability of predation increased with age of the nest (β = 0.04, coefficient of variation = 1.26%), and increased over the course of the nesting season (β =
0.02, coefficient of variation = 0.30% ; Fig. 3.2). Daily probability of weather-related nest failure decreased throughout the nesting season, but rate of decrease depended on year (β = -0.01, coefficient of variation = 10.53%; Fig. 3.3). Daily probability that a nest was trampled was initially higher in paddocks with high livestock density, but decreased with number of days a nest was exposed to cattle (β = -0.004, coefficient of variation =
0.34%; Fig. 3.4). Daily probability of success at the mean value of each covariate was
0.96 (95% CL = 0.87-0.99). Daily probabilities of failure (± 95% CL) at the mean value for each covariate were as follows: predation = 0.026 (0.021-0.033); weather = 0.003
(0.001-0.006); trampled = 0.003 (0.001-0.006).
Overall probabilities of nest success for each species reflecting effects identified as important by model selection results are given in Table 3.5. The overall probability that a nest was successful ranged from 0.16 for an early-season nest in 2008 in a paddock with the highest stocking rate to 0.64 for an early-season nest in 2007 in a control paddock (Table 3.5). Early-season nests in 2008 had higher risk of failure from adverse weather, and in both years exhibit an effect of grazing treatment because of the effect of
number of days cattle were present before clutch initiation on the risk of trampling.
Based on summed Akaike weights for all models that included each covariate
(regardless of the cause of failure the covariate was associated with), number of cattle and year were the most important covariates related to nest failure (relative importance of
69
each variable = 0.96), followed by date (0.87), the number of days cattle were present
before a nest was initiated (0.83), age of the nest (0.72), Belding’s ground squirrel density
(0.08), nest-level visual obstruction (0.06), snake encounter rate (0.05), avian predator
encounter rate (0.05), mesopredator activity (0.05), and habitat patchiness (0.02).
Three competing models suggested that vegetation characteristics, Belding’s
ground squirrel density, or grazing treatment could be related to nest predation, but had
weak support (Table 3.4). Visual obstruction at the nest (NLVO) was the only
vegetation-related variable associated with nest predation, but its effects depended on the
age of the nest (β = -0.05, coefficient of variation = 5.65%; Table 3.4). As nest-level
visual obstruction increased, the daily probability of nest predation decreased, but only
during the latter half of the nesting cycle. Belding’s ground squirrel (BGS) density was
the only predator covariate associated with nest predation (Table 3.4). BGS density had a
weak, inverse relationship with the probability of predation (β = -0.003, coefficient of
variation = 0.17%). Number of cattle (CN) had a weak, inverse relationship with the
daily probability of nest predation (β = -0.004, coefficient of variation = 3.14%).
DISCUSSION
Nest predation accounted for most of the failed nests in our study (73% of unsuccessful
nests), with trampling, adverse weather, and other causes accounting for a much smaller
proportion of nest failures (Table 3.2). Grassland-nesting birds typically experience high
levels of predation, and the rates we observed are comparable to those reported in other
studies of grassland songbirds (Martin 1995; Winter 1999; Davis 2003). Predation risk in
our study was a function of nest age and time of season, but we identified no variables
70
related to grazing treatment or the predator community that were strongly associated with
the risk of predation.
Previous investigations of avian nest survival have demonstrated that survival probabilities can be influenced by nest age. Dinsmore, White & Knopf (2002) found that daily survival of mountain plover nests increased with the age of the nest, while Grant et al. (2005) observed cubic effects of decreasing daily survival from incubation to shortly after hatching, then increasing survival through the brood-rearing stage for two species of grassland songbirds. We have demonstrated empirically that differences in nest survival with nest age are mostly attributable to changes in predation rate over the nesting cycle
for grassland songbirds in our system. We were not able to test for cubic effects on nest predation due to sample size limitations, and preliminary analyses suggested quadratic effects of nest age were not a good fit to the data. The linear increase in daily probability of nest predation with nest age that we observed supports patterns of higher nest survival during the incubation stage than in the nestling stage seen in other studies of grassland
songbird nest success (Patterson & Best 1996, Davis 2003). Increased activity of adults
and nestlings at the nest may result in nests that are more obvious to predators, and higher
predation rates, during the latter part of the nesting cycle for altricial species (Briskie,
Martin & Martin 1999; Martin, Scott & Menge 2000). Alternatively, increasing
predation risk over the nesting cycle could reflect a pattern of cumulative risk in which
nests that are active for a longer period face a greater risk of partial losses to any cause
(e.g. weather, egg failure, partial predation), but ultimately resulting in total nest failure from predation (Grant et al. 2005).
71
Rates of daily nest predation also increased throughout the breeding season.
Although time-specific patterns of nest survival have been widely documented, patterns
of nest predation are rarely reported (Grant et al. 2005; Gentry, Swanson & Carlisle 2006;
Goddard & Dawson 2009; but see Zimmerman 1984; Burhans et al. 2002). A similar
pattern of increasing nest predation throughout the breeding season was reported in Grant
et al. (2005) for clay-colored and vesper sparrows, and the authors speculated it may have
been related to changes in nest predator activity or abundance throughout the season.
Other researchers found a strong relationship between seasonal variation in nest survival and seasonal variation in predator activity (Sperry et al. 2008). Although we attempted to
relate aspects of the predator community and grazing treatment to predation rates, we did
not detect any strong associations between our predator-related covariates and nest
predation. However, the density of Belding’s ground squirrels was weakly related to daily predation rates. Unexpectedly, the daily probability of predation was negatively
correlated with ground squirrel density. This relationship may be explained if a negative
correlation exists between date and ground squirrel density. We were not able to evaluate
seasonal trends in ground squirrel density based on our distance-sampling methods, but a
seasonal decline in ground squirrel density is plausible. Ground squirrel activity is strongly influenced by ambient temperature, and higher temperatures suppress
aboveground activity (Váczi, Koósz & Altbäcker 2006). Temperature increases over the
breeding season at our study site, which could decrease the time ground squirrels spend
aboveground later in the season (R. Taylor, unpublished data). Additionally, Belding’s
ground squirrels enter dormancy belowground after maximum body mass is attained
72
(Morton 1975). Although the time at which this occurs is variable among and within
populations, maximum body mass is likely attained by the time vegetation senesces in
early-to-mid-July, which coincides with the time of season when ground squirrels were
no longer observed at our study site (Verts & Carraway 1998, T. Johnson, personal
observation). Alternatively, nest predator access to other diet items may influence
seasonal patterns of predation. If abundance of alternative prey decreases over the course
of the bird breeding season, then nest predators may switch to bird nests later in the
season. The probable seasonal decline in ground squirrel density lends support to this
hypothesis if ground squirrels and songbirds share common predators.
The seasonal declines in weather-related nest failure we observed are not
surprising given weather patterns at our high-elevation study site. Most precipitation
falls during the winter months, but there is a secondary peak in May-June, which
coincides with the early part of the breeding season (Western Regional Climate Center
2009). Often, precipitation during this time falls in the form of snow or hail, contributing
to nest failures in the early part of the season. This pattern was observed in both years, but 2008 was a particularly cold, wet, late spring which explains the difference in rates of seasonal decline of daily nest failure risk due to severe weather between years (R. Taylor, unpublished data).
The probability that a nest was trampled was higher in paddocks with higher stocking rates, but also depended on the amount of time the nest was exposed to livestock. Nests that were initiated near the date cattle were introduced to paddocks had higher trampling rates when livestock density was high, but there was no difference in
73
trampling rates among grazing treatments if a nest was initiated within ~ 20 days of the
date cattle were removed from paddocks. The importance of the length of grazing period
on trampling rates has been documented previously in other studies of cattle-caused nest failure (Jensen, Rollins & Gillen 1990; Paine et al. 1996). Because native ungulates are present at our study site (mule deer [Odocoileus hemionus] and elk [Cervus canadensis]), it is possible that not all trampled nests were a result of domestic livestock activity. One nest in our sample was trampled before cattle were introduced to paddocks. However, the native ungulates observed in our study paddocks spent much less time there and occurred in lower densities than cattle (T.Johnson, personal observation), and treatment differences in the probability of trampling suggests that a large proportion of trampled nests are very likely attributable to cattle activity.
Although a relatively small proportion of nest failure was attributed to trampling in this study, higher stocking rates could lead to higher trampling rates. The highest stocking rate in our study was 1.08 animal units per ha; however, in more mesic, productive grassland systems (e.g., tallgrass prairie) stocking densities can be at least as high as 2.13 animal units per ha in early, intensive stocking grazing systems (Owensby,
Cochran & Smith 1988). In systems with higher stocking densities, trampling could potentially be a significant source of nest failure for grassland birds (Jensen, Rollins &
Gillen 1990).
Overall probabilities of nest success were low (< 65%), and reflect the cumulative risk of failure from all causes. The lowest chances of nest success were observed during the early part of the 2008 breeding season, and during this year, mid-season nests in
74
general had the highest overall chances of success. However, in 2007 when the risk of
failure due to weather was lower, early-season nests had the highest chances of success,
suggesting the risk of predation dictates patterns of nest success throughout the season in
years with less severe weather.
These results have implications for livestock grazing management. Overall, we
did not observe strong effects of our grazing treatments on the daily risk of nest failure
for grassland songbirds. The daily risk of nest failure due to trampling was much lower
than the daily risk of nest predation, which was not affected by our grazing treatments.
Our results suggest that grasslands managed for livestock grazing may be compatible
with grassland songbird conservation, at least within the context of the grazing treatments
examined here. Low to moderate stocking rates may have negligible effects on the risk
of nest failure, and higher stocking rates may contribute less to nest loss from trampling if
the degree of temporal overlap between livestock and songbird nesting activity is low.
Further, timing and duration of grazing during the nesting season should be considered
when evaluating the overall chance of nest success. In years when widespread failure
due to weather is likely, delaying the introduction of livestock or reducing livestock
density until the risk from severe weather resides may help mitigate negative effects on
nest success. However, our results suggest the most effective strategies for decreasing
nest failure for grassland songbirds will likely involve decreasing the risk of nest
predation. Once important species of nest predator are identified, grassland managers
should evaluate management strategies to create habitats that reduce the risk of nest
75
predation, and the benefits of predator control versus management strategies that
minimize refugia for common nest predators should be examined.
Studies of avian reproductive success often report probabilities of nest survival,
but when failure rates are reported, they typically represent the proportion of nests that
failed from particular causes and are not estimated probabilities (e.g., Frederick 1987;
Mudge & Talbot 1993; Bowman & Woolfenden 2001; Bader & Bednarz 2009).
Proportions do not include measures of variability and are extremely sensitive to sample
size, whereas probabilities include variance estimates and more easily lend themselves to
statistical comparison. The framework we used produces probability estimates for cause-
specific failure and incorporates the use of covariates and model selection which allowed
us to identify variables important in explaining variation associated with each cause of
failure. The ability to directly estimate the probability of failure from specific causes
allows researchers improved ways of directly testing hypotheses regarding nest failure.
For instance, we were able to address the hypothesis that livestock grazing can decrease
vegetation structure near the nest and influence the risk of nest predation. Further,
information on the risk of failure from particular causes allows conservation efforts to be
focused on the most significant causes of reproductive failure (here, nest predation), or alternatively, to be focused on causes of nest failure more easily affected by changes in
management (trampling). Evaluating relationships among environmental variables,
management strategies, and the variation in nest failure from specific causes will be
valuable in developing more effective conservation strategies. We suggest the
framework presented here be used in future studies of nest failure.
76
LITERATURE CITED
Animal and Plant Health Inspection Service. 1998. Sensory and behavioral methods for managing predation on livestock. National Wildlife Research Center Fiscal Year Highlights Report. Miscellaneous Publication 1552. U.S. Department of Agriculture, National Wildlife Research Center, Fort Collins, Colorado, USA.
Askins, R. A., F. Chávez-Ramírez, B. C. Dale, C. A. Haas, J. R. Herkert, F. L. Knopf, and P. D. Vickery. 2007. Conservation of grassland birds in North America: understanding ecological processes in different regions. Ornithological Monograph 64: 1-46.
Asner, G. P., A. J. Elmore, L. P. Olander, R. E. Martin, and A. T. Harris. 2004. Grazing systems, ecosystem responses, and global change. Annual Review of Environmental Resources 29: 261-299.
Bader, T. C. and J. C. Bednarz. 2009. Reproductive success and causes of nest failures for Mississippi Kites: a sink population in eastern Arkansas? Wetlands 29: 598- 606.
Beason, R. C. 1995. Horned Lark (Eremophila alpestris), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu. proxy.library.oregonstate.edu/bna/species/195.
Benson, T. J., J. D. Brown, and J. C. Bednarz. 2010. Identifying predators clarifies predictors of nest success in a temperate passerine. Journal of Animal Ecology 79: 225-234.
Briskie, J. V., P. R. Martin, and T. E. Martin. 1999. Nest predation and the evolution of nestling begging calls. Proceedings of the Royal Society of London Series B 2666: 2153-2159.
Bowman, R., and G. E. Woolfenden. 2001. Nest success and the timing of nest failure of Florida Scrub-Jays in suburban and wildland habitats. p. 383-402, In Avian Conservation and Ecology in an Urbanizing World, Marzluff, J.M., R. Bowman, and R.E. Donnelly (eds.). Kluwer Academic Publ., New York, NY.
Burhans, D. E., D. Dearborn, F. R. Thompson III, and J. Faaborg. 2002. Factors affecting predation at songbird nests in old fields. Journal of Wildlife Management 66: 240-249.
77
Burnham, K. P., and D. R. Anderson. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. Springer, New York, USA.
Chalfoun, A. D., and T. E. Martin. 2009. Habitat structure mediates predation risk for sedentary prey: experimental tests of alternative hypotheses. Journal of Animal Ecology 78: 497-503.
Clark, M. E., and T. E. Martin. 2007. Modeling tradeoffs in avian life history traits and consequences for population growth. Ecological Modelling 209: 110-120.
Cottrell, M. J. 1981. Resource partitioning and reproductive success of hawks (Buteo spp.) in an Oregon prairie. M.S. Thesis. Oregon State University, Corvallis, Oregon.
Davis, S. K. 2003. Nesting ecology of mixed-grass prairie songbirds in southern Saskatchewan. Wilson Bulletin 115: 119-130.
Davis, S. K., and W. E. Lanyon. 2008. Western Meadowlark (Sturnella neglecta), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu.proxy.library.oregonstate.edu/bna/species/104.
Dinsmore, S. J., G. C. White, and F. L. Knopf. 2002. Advanced techniques for modeling avian nest survival. Ecology 83: 3476-3488.
Elbroch, M. 2003. Mammal Tracks & Sign: A Guide to North American Species. Stackpole Books, Mechanicsburg, Pennsylvania, USA.
Etterson, M. A., L. R. Nagy, and T. R. Robinson. 2007. Partitioning risk among different causes of nest failure. Auk 124: 432-443.
Etterson, M. A., B. Olsen, and R. Greenberg. 2007. The analysis of covariates in multi- fate Markov chain nest-failure models. Studies in Avian Biology 34: 55-64.
Frederick, P. 1987. Chronic tidally-induced nest failure in a colony of white ibises. Condor 89: 413-419.
Fuhlendorf, S. D., and D. M. Engle. 2001. Restoring heterogeneity on rangelands: ecosystem management based on evolutionary grazing patterns. BioScience 51: 625-632.
78
Gentry, D. J., D. L. Swanson, and J. D. Carlisle. 2006. Species richness and nesting success of migrant forest birds in natural river corridors and anthropogenic woodlands in southeastern South Dakota. Condor 108: 140-153.
Goddard, A. D., and R. D. Dawson. 2009. Seasonal changes in habitat features influencing nest-survival of Sharp-tailed grouse in northeastern British Columbia, Canada. Ecoscience 16: 476-482.
Grant, T. A., T. L. Shaffer, E. M. Madden, and P. J. Pietz. 2005. Time-specific variation in passerine nest survival: new insights for old questions. Auk 122: 661-672.
Greenwood, R. J., A. B. Sargent, D. H. Johnson, L. M. Cowardin, and T. L. Shaffer. 1995. Factors associated with duck nest success in the prairie pothole region of Canada. Wildlife Monographs 128: 1–57.
IPCC. 2001. Climate change 2001: impacts, adaptation, and vulnerability. Technical Summary. Contribution to the third assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.
Jensen, H. P., D. Rollins, and R. L. Gillen. 1990. Effects of cattle stock density on trampling loss of simulated ground nests. Wildlife Society Bulletin 18: 71-74.
Johnson, R. G., and S. A. Temple. 1990. Nest predation and brood parasitism of tallgrass prairie birds. Journal of Wildlife Management 54: 106-111.
Jones, S. L., and J. E. Cornely. 2002. Vesper Sparrow (Pooecetes gramineus), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu .proxy.library.oregonstate.edu/bna/species/624.
Heisey, D. M., and T. K. Fuller. 1985. Evaluation of survival and cause specific mortality rates using telemetry data. Journal of Wildlife Management 49: 668– 74.
Kennedy, P. L., S. J. DeBano, A. M. Bartuszevige, and A. S. Lueders. 2009. Effects of native and non-native grassland plant communities on breeding passerine birds: implications for restoration of northwest bunchgrass prairie. Restoration Ecology, 17: 515-525.
Kuehl, A. K., and W. R. Clark. 2002. Predator activity related to landscape features in northern Iowa. Journal of Wildlife Management 66: 1213-1223.
79
Lubowski, R.N., M. Vesterby, S. Bucholtz, A. Baez, and M. J. Roberts. 2006. Major uses of land in the United States, 2002. Economic Information Bulletin No. EIB- 14. U.S. Department of Agriculture, Economic Research Service. Washington D.C.
Manolis, J. C., D. E. Andersen, and F. J. Cuthbert. 2000. Uncertain nest fates in songbird studies and variation in Mayfield estimation. Auk 117: 615-626.
Martin, T. E. 1995. Avian life history evolution in relation to nest sites, nest predation, and food. Ecological Monographs 65: 101-127.
Martin, T. E., J. Scott, and C. Menge. 2000. Nest predation increases with parental activity: separating nest site and parental activity effects. Proceedings of the Royal Society of London B, 267: 22887-2293.
Mayfield, H. F. 1961. Nesting success calculated from exposure. Wilson Bulletin 73: 255-261.
Morton, M. L. 1975. Seasonal cycles of body weights and lipids in Belding ground squirrels. Bulletin of the Southern California Academy of Sciences 74: 128-143.
Morton, M. L., K. W. Sockman, and L. E. Peterson. 1993. Nest predation in the mountain white-crowned sparrow. Condor 95: 72-82.
Muchai, M., L. Bennun, L. Lens, M. Rayment, and G. Pisano. 2002. Land-use and the conservation of Sharpe’s Longclaw Macronyx sharpie in central Kenya. Bird Conservation International 12: 107-121.
Mudge, G. P., and T. R. Talbot. 1993. The breeding biology and causes of nest failure of Scottish Black-throated Divers. Ibis 135: 113-120.
Nack, J. L., and C. A. Ribic. 2005. Apparent predation by cattle at grassland bird nests. Wilson Bulletin 117: 56-62.
Natarajan, N. R., and C. E. McCullough. 1999. Modeling heterogeneity in nest survival data. Biometrics 55: 553-559.
Nelson, K. J., and K. Martin. 1999. Thermal aspects of nest-site location for vesper sparrows and horned larks in British Columbia. Studies in Avian Biology 19: 137-143.
80
Ohlendorf, H. M., R. L. Hothem, and D. Welsh. 1989. Nest success, cause-specific nest failure, and hatchability of aquatic birds at selenium-contaminated Kesterson Reservoir and a reference site. Condor 91: 787-796.
Owensby, C. E., R. Cochran, and E. F. Smith. 1988. Stocking rate effects on intensive- early stocked Flint Hills bluestem range. Journal of Range Management 41: 483- 487.
Paine, L., D. J. Undersander, D. W. Sample, G. A. Bartelt, and T. A. Schatteman. 1996. Cattle trampling of simulated ground nests in rotationally grazed pastures. Journal of Range Management 49: 294-300.
Patten, M.A., and D. T. Bolger. 2003. Variation in top-down control of avian reproductive success across a fragmentation gradient. Oikos 101: 479-488.
Patterson, M. P., and L. B. Best. 1996. Bird abundance and nesting success in Iowa CRP fields: the importance of vegetation structure and composition. American Midland Naturalist 135: 153–167.
Phillips, M. L., W. R. Clark, M. A. Sovada, D. J. Horn, R. R. Koford, and R. J. Greenwood. 2003. Predator selection of prairie landscape features and its relation to duck nest success. Journal of Wildlife Management 67: 104-114.
Ramsey, F.L., and D. W. Schafer. 2002. The Statistical Sleuth, 2nd ed. Duxbury, Pacific Grove, California, USA.
Reidy, J. L., M. M. Stake, and F. R. Thompson, III. 2008. Golden-cheeked warbler nest mortality and predators in urban and rural landscapes. Condor 110: 458-466.
Renfrew, R. B., and C. A. Ribic. 2003. Grassland passerine nest predators near pasture edges identified on videotape. Auk 120: 371-383.
Ricklefs, R. E. 1969. An analysis of nesting mortality in birds. Smithsonian Contributions in Zoology 9: 1–48.
Robel, R. J., J. N. Briggs, A. D. Dayton, and L. C. Hulbert. 1970. Relationships between visual obstruction measurements and weight of grassland vegetation. Journal of Range Management 23: 295-297.
Rotenberry, J. T., M. A. Patten, and K. L. Preston. 1999. Brewer's Sparrow (Spizella breweri), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu.proxy.library.oregonstate.edu/bna/species/390.
81
Sánchez-Zapata, J. A., M. Carrete, A. Gravilov, S. Sklyarenko, O. Ceballos, J. A. Donázar, and F. Hiraldo. 2003. Land use changes and raptor conservation in steppe habitats of Eastern Kazakhstan. Biological Conservation 111: 71-77.
Sayre, N. F. 2009. Bad abstractions: response to Sullivan. Conservation Biology 23: 1050-1052.
Sperry, J. H., R. G. Peak, D. A. Simprich, and P. J. Weatherhead. 2008. Snake activity affects seasonal variation in nest predation risk for birds. Journal of Avian Biology 39: 379-383.
Stahl, J. T., and M. K. Oli. 2006. Relative importance of avian life history variables to population growth rate. Ecological Modelling 198: 23-39.
Stanley, T. R. 2004. When should Mayfield model data be discarded? Wilson Bulletin 116: 267-269.
Suedkamp Wells, K. M., and S. D. Fuhlendorf. 2005. Comparison of microclimate at grassland bird nests with different substrates. Prairie Naturalist 37: 21-28.
Sutter, B., and G. Ritchison. 2005. Effects of grazing on vegetation structure, prey availability, and reproductive success of Grasshopper Sparrows. Journal of Field Ornithology 76: 345-351.
Thomas, L., S. T. Buckland, E. A. Rexstad, J. L. Laake, S. Strindberg, S. L. Hedley, J. R. B. Bishop, T. A. Marques, and K. P. Burnham. 2010. Distance software: design and analysis of distance sampling surveys for estimating population size. Journal of Applied Ecology 47: 5-14.
Townsend, D. E., II, and S. D. Fuhlendorf. 2010. Evaluating relationships between spatial heterogeneity and the biotic and abiotic environments. American Midland Naturalist 163: 351-365.
Uresk D. W., J. G. MacCracken, and A. F. Bjugstad. 1982. Prairie dog density and cattle grazing relationships. In: Timm RM and Johnson RJ (Eds). Proceedings of the Fifth Great Plains Wildlife Damage Control Workshop. Institute of Agriculture and Natural Resources, University of Nebraska, Lincoln, NE.
Váczi, O., B. Koósz, and V. Altbäcker. 2006. Modified ambient temperature perception affects daily activity patterns in the European Ground Squirrel (Spermophilus citellus). Journal of Mammalogy 87: 54-59.
82
Verts, B. J., and L. N. Carraway. 1998. Land Mammals of Oregon. University of California Press, Berkeley and Los Angeles, California, USA.
Vickery, J. A., J. R. Tallowin, R. E. Feber, E. J. Asteraki, P. W. Atkinson, R. J. Fuller, and V. K. Brown. 2001. The management of lowland neutral grasslands in Britain: effects of agricultural practices on birds and their food resources. Journal of Applied Ecology 38: 647-664.
Vickery, P. D. 1996. Grasshopper Sparrow (Ammodramus savannarum), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu. proxy.library.oregonstate.edu/bna/species/239.
Vickery, P. D., P. L. Tubaro, J. M. Cardoso Da Silva, B. G. Peterjohn, J. R. Herkert, and R. B. Cavalcanti. 1999. Conservation of grassland birds in the Western Hemisphere. Studies in Avian Biology 19: 2-26.
Western Regional Climate Center. 2009. Enterprise 20 NNE, Oregon. Western Regional Climate Center, Oregon Climate Summaries website http://www.wrcc.dri.edu/ cgi-bin/cliMAIN.pl?oren20 (Accessed August 04, 2009).
Wheelwright, N. T., and J. D. Rising. 2008. Savannah Sparrow (Passerculus sandwichensis), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu.proxy.library.oregonstate.edu/bna/species/045.
White, G. C., and K. P. Burnham. 1999. Program MARK: Survival estimation from populations of marked animals. Bird Study 46 (Supplement): 120-138.
Winter, M. 1999. Nesting biology of Dickcissels and Henslow’s Sparrows in southwestern Missouri prairie fragments. Wilson Bulletin 111: 515-527.
Winter, M., S. E. Hawks, J. A. Shaffer, and D. H. Johnson. 2003. Guidelines for finding nests of passerine birds in tallgrass prairie. Prairie Naturalist 35: 197-211.
Wyffels, S. 2009. Influence of stocking density on grazing beef cattle performance, diet composition, foraging efficiency, and diet quality on a late-spring early-summer native bunchgrass prairie. M.S. thesis, Oregon State University, Corvallis, OR.
Zimmerman, J. L. 1984. Nest predation and its relationship to habitat and nest density in Dickcissels. Condor 86: 68-72.
83
Figure 3.1. Experimental paddocks, grazing treatments, predator sample units, and point transects at the Zumwalt Prairie Preserve, northeastern Oregon, U.S.A.
84
Fig. 3.2. Predicted daily nest predation rates for six species of grassland songbird breeding at the Zumwalt Prairie Preserve in northeastern Oregon, U.S.A. during 2007 and 2008. Parameter values are from the model with the highest Akaike weight (see Table 3.4). The three nests presented show effects of nest age and day of season on the probability of nest predation for an early-season nest (May 1-June 1), a mid-season nest (May 30 -June 30), and a late-season nest (June 28-July 29). Day 1 of the season = May 1 and day 90 = July 29. We present a 32-day nesting period (including the laying period), representing the species at our study site with the longest nesting cycle (western meadowlarks). All other species have nesting periods spanning 23-28 days. For clarity, only probabilities from every other day in the nesting period are shown. Standard errors were calculated using the delta method (Appendix C).
Probability of nest failure due to predation 0.20 Early-season nest Mid-season nest Late-season nest 0.15
0.10
0.05
Daily probability (± 95% CI) 0.00
0 20406080
Day of season
85
Fig. 3.3. Predicted daily rates of weather-related nest failures for six species of grassland songbird breeding at the Zumwalt Prairie Preserve in northeastern Oregon, U.S.A during 2007 and 2008. Parameter values are from the model with the highest Akaike weight (see Table 3.4). Day 1 of the season = May 1 and day 90 = July 29. We observed a seasonal decline in the daily probability of weather-related nest failure, and rates of decline were different between years. For clarity, only probabilities from every other day are shown. Standard errors were calculated using the delta method (Appendix C).
Probability of nest failure due to weather 0.16
0.14 2007
2008 0.12
0.10
0.08
0.06
0.04
0.02
Daily probability (± 95% CI) 0.00
0 0 0 0 0 10 2 3 40 50 60 7 8 90 Day of season
86
Fig. 3.4. Predicted daily rates of nest trampling for six species of grassland birds breeding in paddocks with three levels of experimentally-manipulated stocking rate on the Zumwalt Prairie Preserve in northeastern Oregon, U.S.A. during 2007 and 2008. Parameter values are from the model with the highest Akaike weight (see Table 3.4). The x-axis represents a 44-day grazing period. Initially, there is a higher probability of nest trampling in paddocks with higher livestock densities; however, the difference among livestock densities is negated if clutches are initiated within ~20 days of livestock removal from paddocks. For clarity, only probabilities for every other day are shown. Standard errors were calculated using the delta method (Appendix C).
Probability of nest failure due to trampling Stocking rate: Low 0.06 Moderate High
0.04
0.02
Daily probability (± 95% CI) (± 95% probability Daily 0.00 0 10203040 No. of days cattle present before nest initiation
87
Table 3.1. Evidence used to categorize causes of nest failure for grassland-breeding songbirds at the Zumwalt Prairie Preserve in northeastern Oregon, U.S.A.
Cause of failure Evidence Predation All eggs or young absent from a nest prematurely, or; Egg or nestling remains present and showing evidence of predation, or; Nest cup ripped apart or removed from the ground but no evidence of contents, or; Predation event observed
Trampled Eggs or nestlings present in or near nest but were crushed, or; Nest was no longer intact and contents were spilled
Weather Nests found to be inactive within 24 hrs after a severe weather event, or; If eggs showed evidence of hail damage, or; If nestlings were dead or dying after a storm
Other Adult not observed for more than two consecutive visits to a nest, and; (abandoned*, Nest contents were obviously not attended (e.g., cold or wet eggs, unknown failure, dead or dying nestlings, ants present in nest), or; egg failure) Incubation extending beyond average length of incubation with no evidence of egg development (per floatation), or; Failure, but cause unclear
* Nests that were abandoned within 24-hrs after a storm were labeled as weather-related failures.
Table 3.2. Covariates used to estimate the risk of nest failure from three specific causes for grassland songbirds on the Zumwalt Prairie Preserve, northeast Oregon, USA.
Predated Trampled Weather age of nest (AGE) stocking rate (CN) day of season (DATE) day of season (DATE) number of days cattle present year (YEAR) year (YEAR) before clutch initiation (NGD) visual obstruction at the nest (NLVO) stocking rate (CN) Interactions: Interactions: visual obstruction at the nest (NLVO) CN*NGD DATE*YEAR patchiness of vegetation (PLVOCV) encounter rate of avian predators (AVIAN) Belding's ground squirrel density (BGS) activity index for mesopredators (MESO) encounter rate of snakes (SNAKES) Interactions: AGE*BGS AGE*CN AGE*NLVO AGE*MESO AGE*PLVOCV 88 Table 3.3. Number of nests by species, year, grazing treatment, and nest fate that were included in our analysis of nest failure. All nests were located during 2007 and 2008 at the Zumwalt Prairie Preserve in northeastern Oregon, U.S.A.
Nest fate successful predated weather trampled other total Savannah sparrow 2007 Control 15 1 0 0 0 16 Low 6 0 0 0 0 6 Moderate 12 4 0 0 1 17 High 8 3 0 0 0 11 2008 Control 5 4 0 0 1 10 Low 6 4 0 0 0 10 Moderate 5 3 1 1 1 11 High 7 1 0 0 1 9 Vesper sparrow 2007 Control 2 2 0 0 0 4 Low 4 9 0 0 1 14 Moderate 3 5 0 0 2 10 High 3 1 0 1 1 6
89
Table 3.3 continued 2008 Control 3 0 0 0 0 3 Low 8 5 1 0 1 15 Moderate 2 4 1 0 0 7 High 7 3 2 1 0 13 Horned lark 2007 Control 5 2 0 0 0 7 Low 7 2 0 0 0 9 Moderate 3 2 1 0 1 7 High 2 1 0 0 0 3 2008 Control 1 1 0 0 0 2 Low 3 1 0 0 0 4 Moderate 1 1 0 0 0 2 High 4 2 0 1 0 7 Western meadowlark 2007 Control 2 1 0 0 0 3 Low 3 3 0 0 0 6 Moderate 3 5 1 1 0 10 High 5 3 0 1 1 10
90
Table 3.3 continued 2008 Control 2 1 0 0 0 3 Low 1 0 1 0 1 3 Moderate 1 2 0 1 0 4 High 0 0 0 1 0 1 Grasshopper sparrow 2007 Control 2 1 0 0 1 4 Low 3 1 0 0 0 4 Moderate 2 0 0 0 0 2 High 0 0 0 0 0 0 2008 Control 2 1 0 0 0 3 Low 1 0 0 0 0 1 Moderate 2 0 0 1 0 3 High 0 0 0 0 0 0 Brewer's sparrow 2007 Control 2 0 0 0 0 2 Low 1 1 0 1 0 3 Moderate 4 2 0 0 0 6 High 0 0 0 0 0 0
91
Table 3.3 continued 2008 Control 0 0 0 0 0 0 Low 0 0 0 0 0 0 Moderate 0 0 0 0 0 0 High 0 0 0 0 0 0 Total (% of total): 158 (58%) 82(30%) 8(3%) 10(4%) 13(5%) 271
92 Table 3.4. Model selection results for the top ten candidate models of daily nest failure rates for six species of grassland songbird breeding at the Zumwalt Prairie Preserve in northeastern Oregon, U.S.A. Model ranks are based on Akaike’s Information Criterion (AIC) values, differences in AIC values (Δ AIC), and Akaike weights (Wi). AIC values were computed using the minimized negative log-likelihood (NLL) function and the number of parameters in the model (K).
Model structurea Predated Weather Trampled NLL AIC Δ AIC Weight K age + date date*year CN*NGD 452.68 929.57 0.00 0.36 12 date year CN 458.26 930.60 1.02 0.13 7 date year CN*NGD 456.33 930.78 1.20 0.11 9 age + date + BGS date*year CN*NGD 452.41 931.08 1.50 0.08 13 age*NLVO year CN*NGD 453.55 931.32 1.75 0.06 12 age + date + CN date*year CN*NGD 452.62 931.49 1.91 0.05 13 age + date + AVIAN date*year CN*NGD 452.68 931.61 2.04 0.05 13 age + date + MESO date*year CN*NGD 452.68 931.61 2.04 0.05 13 age + date + SNAKES date*year CN*NGD 452.68 931.61 2.04 0.05 13 age*PLVOCV year CN*NGD 454.03 932.27 2.70 0.02 12 ... a Model structure for the three parameters shown included: age = age of nest, AVIAN = encounter rate for avian predators, BGS = Belding's ground squirrel density, CN = number of livestock, date = day of season, MESO = mesopredator activity index, NGD = number of days cattle were present before a nest was initiated, PLVOCV = coefficient of variation of paddock-level visual obstruction, SNAKES = snake encounter rate, year = 2007 or 2008.
93 Table 3.5. Overall probability (SE) of nest success for six species of grassland songbird breeding at the Zumwalt Prairie Preserve, northeastern Oregon, U.S.A. Estimates reported are from the top model in Table 3.4, and are separated by species (reflecting differences in the length of the nesting cycle), time of season (reflecting date effects on predation and weather-related failures and effects of the number of days cattle were present before clutch initiation on failures due to trampling), year (reflecting year effects on weather-related failures), and stocking rate (reflecting grazing treatment effects on failures due to trampling).
Mid-season nest Late-season nest Early-season nest Species (days in nesting cycle)a Control Low Moderate High Control Low Moderate High Control Low Moderate High Savannah sparrow (24) 2007 0.52(0.08) 0.51(0.08) 0.46(0.08) 0.32(0.07) 0.46(0.05) 0.45(0.05) 0.42(0.05) 0.36(0.05) 0.34(0.12) 0.34(0.10) 0.34(0.10) 0.34(0.10) 2008 0.30(0.06) 0.30(0.07) 0.27(0.06) 0.19(0.05) 0.40(0.07) 0.39(0.06) 0.36(0.06) 0.27(0.05) 0.33(0.10) 0.33(0.09) 0.32(0.09) 0.32(0.10) Vesper sparrow (21) 2007 0.58(0.08) 0.56(0.08) 0.52(0.08) 0.38(0.08) 0.53(0.03) 0.51(0.04) 0.49(0.04) 0.42(0.05) 0.41(0.09) 0.41(0.07) 0.41(0.06) 0.41(0.07) 2008 0.35(0.05) 0.35(0.05) 0.32(0.05) 0.23(0.05) 0.46(0.05) 0.45(0.05) 0.42(0.05) 0.32(0.05) 0.40(0.07) 0.40(0.07) 0.40(0.06) 0.39(0.07) Horned lark (20) 2007 0.60(0.08) 0.58(0.08) 0.54(0.08) 0.40(0.08) 0.55(0.03) 0.54(0.03) 0.51(0.03) 0.45(0.05) 0.44(0.08) 0.44(0.06) 0.44(0.05) 0.44(0.06) 2008 0.37(0.04) 0.36(0.04) 0.33(0.05) 0.25(0.05) 0.49(0.05) 0.48(0.05) 0.44(0.05) 0.35(0.05) 0.43(0.07) 0.43(0.06) 0.42(0.06) 0.42(0.06) Western meadowlark (26) 2007 0.48(0.08) 0.47(0.08) 0.42(0.08) 0.29(0.07) 0.41(0.06) 0.40(0.06) 0.38(0.05) 0.32(0.06) 0.29(0.13) 0.29(0.12) 0.29(0.12) 0.29(0.12) 2008 0.27(0.08) 0.27(0.08) 0.24(0.07) 0.16(0.04) 0.36(0.06) 0.35(0.06) 0.32(0.06) 0.23(0.05) 0.28(0.12) 0.28(0.12) 0.28(0.11) 0.27(0.12) Grasshopper sparrow (20) 2007 0.60(0.08) 0.58(0.08) 0.54(0.08) 0.40(0.08) 0.55(0.03) 0.54(0.03) 0.51(0.03) 0.45(0.05) 0.44(0.08) 0.44(0.06) 0.44(0.05) 0.44(0.06) 2008 0.37(0.04) 0.36(0.04) 0.33(0.05) 0.25(0.05) 0.49(0.05) 0.48(0.05) 0.44(0.05) 0.35(0.05) 0.43(0.07) 0.43(0.06) 0.42(0.06) 0.42(0.06) Brewer's sparrow (18) 2007 0.64(0.08) 0.62(0.08) 0.58(0.08) 0.44(0.08) 0.59(0.03) 0.58(0.03) 0.56(0.03) 0.49(0.05) 0.49(0.07) 0.49(0.05) 0.49(0.04) 0.49(0.04) 2008 0.41(0.04) 0.40(0.04) 0.37(0.05) 0.29(0.06) 0.53(0.04) 0.52(0.05) 0.49(0.05) 0.39(0.05) 0.48(0.05) 0.48(0.05) 0.48(0.04) 0.47(0.05)
a Length of nesting cycle for each species is the sum of incubation and nestling periods obtained from species accounts in The Birds of North America Online (Cornell University 2004). Daily probabilities were exponentiated over the length of the nesting cycle to estimate overall probabilities. 94 95
CHAPTER 4: THE EFFECTS OF LIVESTOCK GRAZING INTENSITY ON
GRASSLAND SONGBIRD PREY SELECTIVITY, NESTLING DIET
COMPOSITION, AND GROWTH RATES
ABSTRACT
Grazing by domestic livestock can affect the structure and composition of grassland vegetation, which could in turn influence the availability of invertebrate prey for grassland songbirds. Changes in prey availability caused by grazing may alter songbird diet composition or quality, and potentially influence nestling quality by affecting growth rates. While grazing represents a complex disturbance, grazing intensity largely determines effects on vegetation, and potentially, on other grassland consumers. To better understand the relationship between grazing intensity and songbird diet composition, and subsequent effects on nestling growth, we examined the composition of fecal samples from nestlings of grassland songbirds breeding in paddocks with experimentally-manipulated stocking rates in a Northwest Bunchgrass Prairie. All songbird species showed strong preferences for Lepidoptera (moths and butterflies) larvae, and partial preferences for Coleoptera (beetles) and Araneae (spiders). The proportion of Araneae and Coleoptera in nestling diets differed among some songbird species, and the proportion of Acrididae (short-horned grasshoppers) in the diet of
Western Meadowlark nestlings was negatively affected by high stocking rates. Growth rates for Western Meadowlarks and Vesper Sparrows were negatively affected by higher stocking rates, suggesting the decrease in Acrididae was detrimental to Western
Meadowlarks. Cause of slower growth rates for Vesper Sparrows could be because most
96
Vesper Sparrow nestlings were reared during the latter half of our grazing treatments when effects on invertebrate populations would have been strongest. Negative effects of increased livestock grazing intensity on nestling condition could have consequences for juvenile survival and recruitment. We suggest that future studies of effects of different grazing management practices on grassland songbirds include measures of food
availability or utilization, along with subsequent effects on nestling quality or survival.
These approaches may help identify mechanisms responsible for grassland songbird
population declines.
INTRODUCTION
The abundance of food determines future population size for songbirds through direct
effects on body condition and survival of young (Rodenhouse and Holmes 1992). The
availability and distribution of food for insectivorous passerines is often related to
vegetation characteristics, and is a function of the abundance, accessibility, and quality of
appropriate foraging patches (Robinson and Holmes 1982). Vegetation structure, and the
alteration thereof, can influence the availability of foraging or breeding sites for
invertebrate prey, and floristic composition can affect invertebrate community
composition through food web relationships (Koricheva et al. 2000, O’Neill et al. 2003).
For example, Zalik and Strong (2008) found declines in biomass of Savannah Sparrow
(Passerculus sandwichensis) invertebrate prey after hayfields were harvested compared
to season-long increases in fields that were not harvested. Hickman et al. (2006) found
lower food availability for grassland songbirds in pastures dominated by the introduced
forage grass old world bluestem (Bothriochloa ischaemum) compared to native prairie,
97
and concluded that it was directly attributable to a lack of forbs in old world bluestem
plant communities. Thus, any process affecting structure or composition of vegetation
can potentially alter food availability for passerines, which may have subsequent effects
on reproductive success and population growth.
Domestic livestock grazing is a process that has direct effects on the
characteristics of vegetation. Grazing can influence plant species composition, plant
spacing, extent of bare ground, litter depth, vertical density and height of vegetation, and
other structural features of plants (Schulz and Leininger 1990, Jones 2000, Sutter and
Ritchison 2005). Grazing-induced structural and compositional changes to vegetation
can have bottom-up effects on invertebrate abundance and diversity (e.g., Kruess and
Tscharntke 2002, O’Neill et al. 2003, Joern 2004, DeBano 2006), and therefore birds.
Grazing intensity (represented by stocking rate in a domestic livestock grazing system)
during the active growing season is a critical determinant of the effect of grazing on
vegetation features because it dictates the amount of vegetation removed by herbivores,
and the amount and distribution of vegetation remaining (Milchunas et al. 1998,
Fuhlendorf and Engle 2001). Because most temperate-breeding songbirds rely on invertebrate prey to feed young, it follows that grazing intensity could have important
effects on songbird prey availability mediated through its influence on vegetation.
Changes in the availability of invertebrate prey as a result of grazing could affect
songbird diet composition, which could in turn alter reproductive success because diets
that differ in composition may differ in quality as well (Boag 1987, Kaspari and Joern
1993). Diet quality is an important factor in reproductive success because it can directly
98
affect nestling growth rate and body condition (Boag 1987, Johnston 1993). Nestling
growth rate influences reproductive success by affecting the amount of time nestlings are
restricted to the nest and vulnerable to nest predation, and higher body mass at fledging is
directly related to increased post-fledging survival (Lack 1968, Naef-Daenzer et al. 2001,
Monrós et al. 2002, Yackel Adams et al. 2006). If differences in nestling diet
composition affect nestling growth rate or mass gain, there could be consequences for
juvenile recruitment and ultimately, population growth.
Grassland songbirds may be particularly susceptible to grazing-induced changes
in invertebrate populations because grassland ecosystems are used primarily for livestock
production. Each year, ~300 million ha of grassland pasture and range are grazed in the
U.S. (Lubowski et al. 2006) while many songbird species depend exclusively or partly on
grassland vegetation for nesting and foraging. Grazing management practices focused
solely on maximizing livestock production may be at odds with habitat requirements of
some grassland-breeding birds (e.g., With et al. 2008). In Britain, researchers have
attributed grassland bird declines to decreased invertebrate abundance and diversity
caused from agricultural intensification, increased densities of livestock, and overall
reductions in the structural complexity of grassland vegetation (Vickery et al. 2001).
However, effects of livestock grazing on grassland songbird diet have not been
investigated for North American prairie ecosystems. Given recent declines of several
North American grassland bird species (Sauer et al. 2007), understanding the relationship
between livestock grazing management, songbird prey requirements, and effects on
99
reproductive success may provide much-needed information for conservation strategies
aimed at these declining bird populations.
To explore relationships between livestock grazing management, songbird prey requirements, and subsequent effects on nestling condition for North American grassland songbirds, our objectives were to: i) identify invertebrate groups important in the diet of grassland songbird nestlings by describing selectivity preferences for invertebrate taxa, ii) determine whether diet composition or nestling growth rate is affected by grazing intensity, and iii) investigate whether diet composition influenced nestling growth rate by examining the relationship between abundance of preferred invertebrate groups in the diet and nestling growth rate.
METHODS
Study area and experimental design
Our study took place at The Nature Conservancy’s (TNC) Zumwalt Prairie Preserve
(ZPP) in northeastern Oregon. The ~ 15,000 ha ZPP is part of the largest remaining tract
of Northwest Bunchgrass Prairie in North America, and is managed to restore and protect
the biological diversity of this ecosystem. The site is dominated by Idaho fescue
(Festuca idahoensis) and bluebunch wheatgrass (Pseudoroegneria spicata). Very little
of the preserve was ever cultivated (< 8% since the 1930’s; Bartuszevige et al., in
review). However, it has been grazed by livestock at varying intensities during the
summer months, first by horses belonging to Native Americans (since the 1700’s), and
later by sheep and cattle belonging to Euro-American settlers (since the late 1800’s;
Bartuszevige et al., in review). Greater than 90% of the breeding grassland songbird
100
community is comprised of: Savannah Sparrow (Passerculus sandwichensis), Horned
Lark (Eremophila alpestris), Western Meadowlark (Sturnella neglecta), Grasshopper
Sparrow (Ammodramus savannarum), Vesper Sparrow (Poocetes gramineus), and
Brewer’s Sparrow (Spizella breweri).
We used a randomized complete block design with one factor (livestock grazing)
and four grazing treatment levels (stocking rate). Blocking by location allowed us to
control for potential heterogeneity created by differences in historic grazing management.
In 2006, TNC erected fences to create four experimental blocks, each 160 ha in size, and
within each block, four 40-ha paddocks were partitioned (Fig. 4.1). Each of the four
blocks contained one replicate paddock of each assigned stocking rate (n = 4 replicates of each stocking rate). Stocking rate treatments were low (14.4 animal unit months;
AUMs), moderate (28.8 AUMs), high (43.2 AUMs), and controls (0 AUMs). One AUM is the amount of forage used by a mature cow and her calf in one month. The four stocking rates were designed to represent light (20% vegetation removal), moderate (40% removal), heavy (60% removal), or no grazing (control) of vegetation by domestic livestock. The moderate treatment was based on traditional stocking rates used by ranchers on the surrounding Zumwalt Prairie and data on vegetation utilization by cattle
previously collected at the ZPP (DelCurto and Williams, unpubl. data). Stocking rate treatments were assigned randomly to paddocks within each block. Cattle grazed
paddocks from 21 May to 3 July and 29 May to 9 July in 2007 and 2008, respectively.
All paddocks were rested for three years prior to data collection. Actual utilization of
vegetation by cattle averaged over 2007 and 2008 were as follows: low (20.2% ± 4.1%,
101
SD); moderate (31.7% ± 5.7%); high (46.1% ± 11.7%; Wyffels 2009). Control paddocks
also experienced vegetation removal (9.5% ± 3.1%), but represented the level of forage
consumption by native herbivores (ungulates and invertebrates).
Invertebrate sampling
We used pitfall traps and sweep-net sampling to obtain information on invertebrate
abundance as a function of stocking rate in our experimental paddocks. Sampling with
two different methods minimizes bias associated with any one method and enabled us to
collect invertebrates from the same substrates from which grassland birds forage (Disney
1986). Pitfall traps were used for sampling ground-dwelling invertebrates and sweep-
netting was used for sampling invertebrates found in vegetation (Martin 1977, New
1998). Sampling with pitfall traps occurred twice per summer: from 2-11 June and 9-16
July 2007, and 4-13 June and 10-18 July 2008. Traps were placed every 90 m in each
paddock (n = 16 traps per paddock; Fig. 4.1). Pitfall traps were 550-ml plastic cups
buried with the top flush with ground level and covered for ≥ 24 hrs, then opened, filled
with soapy water, and collected one week later. Samples were stored in 70% ethanol
until specimens were sorted and identified in the laboratory. Data were summarized as
the average number of taxon per trap.
Sweep-net samples were collected once per paddock from 13-21 July 2007 and
from 2-16 June and 10-18 July 2008. Samples were collected along four 25-m transects
spaced 100 m apart in the center of each paddock. To standardize effort, one observer
swept a 38-cm canvas net through vegetation at 1-m intervals while slowly walking a
102
distance of ~ 25 m along each transect. Sweep-net samples were placed in plastic bags and frozen until they were processed in the laboratory.
Diet sampling and nestling growth rates
We searched for grassland bird nests using the rope-dragging method (Winter et al.
2003). Paddocks were systematically searched in their entirety from early May to late
July three times in 2007 and twice in 2008. Once a nest was discovered, a flag was placed and the ground was spray-painted in a random direction 10- and 30-m away from the nest, and the nest location was recorded with a GPS unit.
When nestlings were present, we individually marked and measured each nestling and opportunistically collected fecal sacs during handling. We collected fecal sacs from nestlings that were age 2-10 days. Fecal sacs were stored in 95% ethanol until laboratory analysis was conducted. Use of fecal samples to examine diet composition may not detect all invertebrates present in the diet due to differences in digestibility among prey items of different species and size (Kaspari and Joern 1993, Carlisle and Holberton
2006). However, all techniques used in evaluating avian diet have limitations, and fecal analysis is commonly used because it is non-invasive (Durst et al. 2008). Fecal analysis was preferable in our study because we measured additional response variables (nest survival, productivity, and nestling growth rate) that could have been negatively affected by more invasive diet sampling methods.
In the laboratory, invertebrate fragments were separated from fecal sacs using a dissecting microscope and soaked in 70% ethanol for at least five minutes to remove debris from fragments (analysis conducted by Pacific Analytics). Fragments were placed
103
in a watch glass and fragments that provided identifiable characteristics of various taxa
were sorted and removed for photography and further identification. Fragments were
identified to the lowest taxonomic unit possible by comparison to reference collections of invertebrates collected in pitfall traps and sweep-net samples at the ZPP, the Oregon State
University Systematic Entomology Collection, and to drawings in references by Barnes
(1968), Borror et al. (1989), Bullough (1958), Dillon and Dillon (1972), Matsuda (1965),
Peterson (1962), Ralph et al. (1985), Smith and Capinera (2005), Snodgrass (1935, 1951),
Stehr (1987), and White (1983). The total number of prey items was estimated for each sample by counting the number of each fragment type from different prey items. For
example, caterpillar mandibles were matched (one right and one left mandible of similar
size and morphology) and the minimum number of individuals of each taxonomic group
was estimated. A similar procedure was used with spider fangs, beetle legs, grasshopper mandibles, and other quantifiable fragments. Where heads were typically intact (e.g.,
plant hoppers), the number of heads present was used as an estimate of abundance for that taxa. Many invertebrate parts were highly fragmented and difficult to identify; thus, we generally limited our analysis to the order level with the exception of short-horned grasshoppers (Acrididae). We present data on nestling diet as the proportion of each invertebrate taxon detected in the diet of each songbird species.
We measured tarsus length to the nearest 0.01 mm using digital calipers and nestling mass to the nearest 0.25 g using a vertical spring scale. Nests were visited every
1-4 days and measurements were collected from nestlings during each visit. For nests found after nestlings had hatched, nestling age was estimated using voucher descriptions
104
of nestlings of known age from previous research at our study site (Kennedy et al.
unpublished data). Measurements were not collected from nestlings that were < 2 days
from fledging to avoid prematurely forcing young from the nest.
Statistical analyses
To describe diet preferences of grassland-breeding birds for each stocking rate, we used
Vanderploeg and Scavia’s (1979a) relativized electivity index (E*):
= − + nWnWE ])1([)]/1([* ii i
where n = the number of kind of prey items and Wi is Vanderploeg and Scavia’s (1979b),
selectivity coefficient calculated as:
n = iii ∑ prprW ii )()( i=1 ,
where ri = the proportion of prey item i in the diet, and pi = the proportion of prey item i
in the environment. E* reflects a predator’s selection of a particular prey item both as a
function of that prey’s availability and the availability of alternative prey (Lechowicz
1982). The average of pitfall and sweep-net sample proportions was used to calculate pi.
We did not conduct formal statistical tests of electivity indices because they are markedly
nonlinear and thus, formal parametric tests are inappropriate. Although we could use
nonparametric tests, direct comparisons of electivity indices require all samples to
contain the same prey types (Lechowicz 1982). Because we cannot be certain that birds
had access to the same prey types among stocking rates, we chose to limit our statistical
comparisons to type of prey utilized in the diet rather than diet preferences. Therefore,
105
we address the question of whether nestling diet composition was affected by stocking rate. To do this, we evaluated the relationship between stocking rate and proportion of each invertebrate taxon in nestling diet (below).
To address the question of whether nestling growth rate was affected by stocking rate, we first estimated growth rates for each species. We visually examined graphs of growth measurements (age versus mass and age versus tarsus length) for all nestlings of each species. Relationships between nestling mass and age and tarsus length and age were linear for all species. Thus, we used the slope of a linear regression line as an estimate of rate of nestling growth (following Dearborn et al. 1998). We assumed that nestlings within the same nest would not have rates of growth independent of each other, so we pooled measurements among nestlings within the same nest and calculated an average growth rate per nest from a mean of 3.0 measurements per nestling (range: 2-4).
Average growth rate is a biologically important parameter because higher growth rates
result in shorter nestling periods (which could influence the risk of predation; Stodola et
al. 2010) or may influence mass of nestlings at fledging (Naef-Daenzer and Keller 1999).
Mass at fledging may have been a more direct measurement of nestling condition and
potential for post-fledging survival; however, we did not measure nestlings that were < 2
days from fledging to avoid prematurely forcing nestlings from a nest. Further,
restricting our evaluation of nestling growth to condition at fledging would have severely
restricted our sample sizes due to high nest predation rates at our study site (Chapter 3).
We used mixed models to evaluate whether stocking rate affected nestling diet
composition or growth rates. The data were modeled using PROC MIXED in SAS
106
System V. 9.3.1 (SAS Institute Inc. 2002-03). To address whether diet composition was affected by stocking rate, we included stocking rate and bird species as fixed predictor variables and tested for effects on the proportion of each invertebrate taxon present in the diet. Stocking rate was a continuous predictor variable representing the number of animal units (one animal unit = a mature cow and her calf) within each stocking rate (0,
10, 20, and 30). We tested for effects of bird species to account for potential differences in prey preference or foraging methods. We also tested for interactions between stocking rate and bird species because stocking rates could influence the availability of species- specific foraging sites or prey items differently, potentially resulting in differential effects on diet composition among songbird species. We included nestling age and date of fecal sample collection as random effects because we collected samples throughout the breeding season, and wanted to account for differences in diet composition throughout the nestling period as well as differences in phenology among invertebrate prey items
(Meunier and Bédard 1984, Peterson and Best 1986). We also included block and block x treatment as random effects. A random effect of block allowed us to make broader inferences from our data than to just the blocks in our data set, and a random effect of block x treatment allows for a random effect of paddock, effectively identifying the paddock as our experimental unit. However, the nest was our biological unit of interest and we assume independence among nests. Thus, the residual variation in our model
(variation among nests in a paddock) is analogous to subsampling variation. Data were log-transformed where needed to satisfy assumptions of normality and homogeneity of variance.
107
We tested for an effect of stocking rate on nestling growth using rate of mass gain
and rate of tarsus growth as the response variable in two separate analyses. We assumed differences in growth rate among species and conducted separate analyses for each species. We assigned stocking rate as a fixed effect, and block, block x treatment as random effects. We pooled data on diet composition and nestling growth over the two years of our study because in several cases, we were able to collect data from only one nest in a treatment, and consequently, had no replication; in other cases, we had no samples from a particular treatment for one year, but we did for the other year. Important differences in effects on diet composition or nestling growth could exist between years due to variation in invertebrate populations or time lag or additive effects of grazing over two years, but we did not have data to address these hypotheses. To identify the most parsimonious model for each response variable, we used backward elimination of predictor variables based on the smallest F-value to identify final models (Hocking
1976).
To examine the amount of variation in nestling growth rate explained by the
proportion of important prey items in the diet, we used only nests from which we
collected fecal samples and growth measurements. Once preferred prey were identified
from electivity analysis (electivity values > 0), we used that taxon as a predictor variable in two separate regression analyses (one that included rate of mass gain as a response, and a separate analysis that included rate of tarsus growth as a response). We concluded significance when α ≤ 0.05 for all analyses. Values reported are untransformed means ±
SE.
108
RESULTS
Nestling diet
We collected fecal samples from 106 nests in 2007 (n = 61) and 2008 (n = 45), from
which 721 prey items were identified. Samples were collected from nests of Savannah
Sparrows (n = 31), Horned Larks (n = 27), Vesper Sparrows (n = 20), Western
Meadowlarks (n = 18), and Grasshopper Sparrows (n = 12; Table 4.1). The mean
number of prey items identified per nest for all fecal samples collected was 6.76 ± 1.60.
Across all samples, proportion of prey items in nestling diets included: Lepidoptera
(butterflies and moths; 0.31), Acrididae (short-horned grasshoppers; 0.28), Araneae
(spiders; 0.20), Coleoptera (beetles; 0.14), Homoptera (leafhoppers, planthoppers, aphids,
etc.; 0.06), Hemiptera (true bugs; 0.009), Diptera (flies; 0.007), and Hymenoptera (wasps,
bees, and ants; 0.004). All Lepidoptera were larvae, and all but one Coleoptera were
adults.
Relativized electivity indices for each invertebrate taxon are presented for 2007
and 2008 in Fig. 4.2. The number of Lepidoptera larvae found in fecal samples was
higher than expected under opportunistic feeding in almost all paddocks (except two
paddocks in the high treatment in 2007), indicating a strong preference for Lepidoptera
(Fig. 4.2). Patterns for Coleoptera and Araneae were less clear; paddocks were dispersed across the preference gradient for these two taxa. However, in 2007 we detected a clear preference for Araneae in heavily-grazed paddocks, whereas Araneae were consumed at random or less than expected under random feeding in all other treatments that year. The same pattern was not apparent in 2008 (Fig. 4.2). Homoptera and Acrididae were
109
generally consumed as or less than expected under random feeding, and Hemiptera,
Hymenoptera, and Diptera were rarely consumed.
There was a treatment-by-species interaction effect on the proportion of Acrididae
present in nestling diets (Table 4.2). The proportion of Acrididae in nestling diets was
not affected by stocking rates for any species except Western Meadowlarks, for which it
decreased with higher stocking rates (β = - 0.02, t = - 3.15, P < 0.01; Table 4.3). There
were species effects on the proportion of Araneae and Coleoptera in nestling diets, but no
effects of stocking rate (Tables 4.2, 4.3). Araneae made up 36% (± 7%) of Western
Meadowlark diets; 24% (± 5%) of Vesper Sparrow diets; 21% (± 2%) of Horned Lark
diets; 19% (± 4%) of Savannah Sparrow diets; 16% (± 2%) of Grasshopper Sparrow diets
(Table 4.3). Coleoptera made up 17% (±2%) of Western Meadowlark diets; 17% (±3%)
of Vesper Sparrow diets; 18% (±6%) of Savannah Sparrow diets; 18% (±4%) of Horned
Lark diets; and only 4% (±0.5%) of Grasshopper Sparrow diets. We detected no significant effects of any predictor variable on the proportion of Lepidoptera, Homoptera,
Hemiptera, Hymenoptera, or Diptera in nestling diets.
Nestling growth
We measured growth rates on nestlings from 150 nests; 30 (20% of total) nests in control
paddocks, 42 (28%) in lightly-grazed paddocks, 41(27%) in moderately-grazed paddocks,
and 37 (25%) in heavily-grazed paddocks. Rate of mass gain for Vesper Sparrow
nestlings was affected by stocking rate (F1, 21 = 13.37, P < 0.01), and we observed slower
rates of mass gain (β = - 0.02, t = - 3.66, P < 0.01) with higher stocking rates (Fig. 4.3).
A similar pattern was detected for tarsus growth for Vesper Sparrow nestlings (F1, 21 =
110
38.02, P < 0.0001). Tarsus growth was slower in paddocks with higher stocking rates (β
= - 0.02, t = - 6.17, P < 0.0001; Fig. 4.4). Western Meadowlark growth was also
negatively affected by higher stocking rates (F1,9 = 17.43, P < 0.01). Rate of mass gain
for Western Meadowlark nestlings was slower in paddocks with higher stocking rates (β
= - 0.13, t = - 4.18, P < 0.01), but we detected no effects of any predictor variable on the
rate of tarsus growth. We detected no significant effects on rate of mass gain or tarsus growth for Savannah Sparrows or Horned Larks (Fig. 4.3). We did not obtain growth measurements from a sufficient number of Grasshopper Sparrow nests to model effects on nestling growth.
There were 78 nests from which we obtained a fecal sample and nestling growth measurements. The proportion of Lepidoptera present in nestling diets was not significantly related to growth rate, and explained very little variation in rates of mass
gain and tarsus growth (R-squared range = 0.00 - 0.23 for all species).
DISCUSSION
Diet composition
Few studies have attempted to quantify the composition of grassland songbird diets, and
none have examined the effect of stocking rate on grassland songbird diet using an
experimental framework. Of those studies that examined diet, many concluded grassland songbirds are largely dietary opportunists (Fretwell 1972, Wiens and Rotenberry 1979,
Rotenberry 1980, Wiens 1989a, 1989b). We found evidence to the contrary; birds in our study showed strong preferences for Lepidoptera (butterflies and moths), and partial preferences for Coleoptera (beetles) and Araneae (spiders). Preferences for specific taxa
111
in nestling diets, including Lepidoptera larvae, were observed in another study of grassland songbird diets (Kaspari and Joern 1993), and, although preferences were not quantified in his study, Maher (1979) found Lepidoptera to be a common component of grassland songbird nestling diets. Lepidoptera have been identified as important diet items in other systems as well, including forests (Hagar et al. 2007) and sage steppe habitats (Peterson and Best 1986).
Lepidoptera larvae may be preferred for several reasons. Caterpillars are an important source of lutein, a carotenoid critical in the development of yellow feathers
(Slagsvold and Lifjeld 1985). Carotenoids also provide antioxidants and immuno- stimulants, which are important in mitigating oxidative damage incurred during growth or infection (McGraw and Ardia 2003, Isaksson and Andersson 2007). Further, caterpillars have relatively less chitin than some other invertebrate taxa (e.g., grasshoppers; Magrath et al. 2004), and are likely of higher energetic value to nestlings because a greater portion of individual caterpillar prey items is digestible (Karasov 1990). Thus, we assume that preferences we observed for Lepidoptera are likely a reflection of their nutritional quality and importance, and birds with larger proportions of Lepidoptera in their diet have higher-quality diets. We note that items which are more digestible (e.g., caterpillars) may be under-represented in our samples, suggesting Lepidoptera larvae could be an even more important component of nestling diets than we have demonstrated here.
The proportion of Lepidoptera in the diet of songbird nestlings was not affected by stocking rate. Although we lack estimates of exact species composition of
Lepidoptera in nestling diets, the Lepidopteran prey items that were identified to family
112
were Noctuidae and Pyralidae, which are both families of moths. DeBano (unpublished data) found a lower abundance of moths in grazed versus ungrazed paddocks in this study, meaning adult songbirds potentially had to increase the number or length of foraging trips to compensate for reduced availability of some preferred prey items for nestlings in grazed paddocks.
Partial preferences for beetles and spiders in nestling diets could be driven by different preferences among bird species; indeed, we observed that spiders were more common in Western Meadowlark diets than all other bird species, and beetles were relatively common for all species except Grasshopper Sparrows (Table 4.3). However, there may be a trend among grazing treatments that could also explain partial preferences.
There were slightly higher electivity values for beetles in moderate and heavily-grazed paddocks, and there were higher electivity values for spiders in heavily-grazed paddocks during 2007. Although we observed a decrease in the abundance of spiders with increased grazing intensity in paddocks (S. DeBano, unpublished data), increasing preference for these two groups in more heavily-grazed paddocks could reflect an increase in accessibility through changes in vegetation structure. If most of the beetles and spiders in nestling diets are ground-dwelling species, it could explain patterns we observed; however, we were not able to investigate this hypothesis because we were limited by the taxonomic resolution of the fecal analysis. In a previous study of nestling diet at our study site, the proportion of beetles in nestling fecal samples was negatively correlated with litter cover (Kennedy et al. 2009, S. DeBano, unpublished data). In this study, we observed higher vegetation consumption by cattle with higher stocking rates
113
which resulted in decreased vertical density of vegetation (Chapter 2), and is typically
associated with less litter cover and more bare ground (Naeth et al. 1991). Lower stature
of vegetation along with increased amounts of bare ground could make detection of
ground-dwelling invertebrates easier, resulting in an increase in the proportion
represented in the diet even though paddock-level abundance decreased.
The proportion of Acrididae in nestling diets was influenced by stocking rate, but
only for Western Meadowlarks. The reason for this is unclear: abundance of Acrididae in
paddocks was similar among grazing treatments (S. DeBano, unpublished results), but
differences in the proportion of Acrididae in diets among nestlings of different species
may reflect differences in foraging strategies or preferences in prey size for nestlings.
Western Meadowlarks apparently switched from grasshoppers to spiders in more heavily-
grazed paddocks, which may be related to a change in foraging methods. Little detailed
information is available on time spent employing specific foraging tactics for the species
in our study, but if Western Meadowlarks have a broader foraging niche than other
species, it could explain the variability we observed in the proportion of Acrididae
present in nestling diets.
Nestling growth
Vesper Sparrow and Western Meadowlark growth were negatively affected by
higher stocking rates. However, we did not detect significant decreases in the proportion
of preferred items in nestling diets for these species, nor was there a strong relationship between the proportion of Lepidoptera in the diet and nestling growth for most species.
The highest R2 value observed was for Western Meadowlarks (0.23). Although not
114
statistically significant, the explanation of almost one-fourth of the variation in nestling mass gain by the proportion of one taxon in the diet may be biologically significant. If our fecal analysis resulted in low detection rates for other important prey items, it could
explain the lack of a strong relationship between growth rates and proportion of preferred
prey items in nestling diet. Further, we only assessed the composition of diet and did not evaluate effects of stocking rate on invertebrate prey size or biomass, which may also
affect growth rate. The relative proportion of particular taxa in nestling diets would not
necessarily decrease for biomass to be negatively affected because smaller prey items of
the same taxa could be fed to nestlings. Monitoring prey delivery rate and prey size may
clarify the relationship between nestling growth and stocking rates for species where diet
composition is unaffected but nestling growth rate decreased.
The negative effect of higher stocking rates for growth rate of Western
Meadowlark nestlings could be related to smaller proportions of Acrididae in Western
Meadowlark diets in paddocks with higher stocking rates. The presence of certain prey
items in nestling diets can influence growth rates (Golet et al. 2000). Although there was
no clear preference for Acrididae, suggesting grasshoppers may not be high-quality prey
items for Western Meadowlark nestlings, they could represent “bulk” items that offer
important energetic resources. The difference in diet composition from relatively fewer
grasshoppers to relatively more spiders in heavily-grazed paddocks may not have
compensated nestlings’ energetic requirements, and resulted in lower rates of mass gain.
The reason for effects of stocking rate on the growth rate of only two of four
species evaluated is not clear. Western Meadowlarks are the largest species we studied
115
(89-115 g; Davis and Lanyon 2008), and had the largest potential brood size (laying up to
six eggs; T. Johnson, unpublished data). Given the documented relationship between
body mass and energy requirements in birds (Nagy 1987), greater body mass and
potentially greater brood size suggest Western Meadowlarks likely have greater prey
requirements than other species of grassland songbird. Thus, we would predict that if
stocking rates had negative effects on food availability or quality, effects on growth rate
might first be detected for this species. Vesper Sparrows are similar in size to other
species included in our study, so greater prey requirements do not explain the negative
effect of higher stocking rate on nestling growth. However, timing of clutch initiation for
Vesper Sparrows tracked more closely with timing of our grazing treatments than any
other species. Mean clutch initiation for Vesper Sparrows was in early June (2007: 9
June; 2008: 5 June), and a typical 21-d nesting cycle resulted in nestling periods that
coincided with the latter half of the grazing period, when treatment effects on vegetation
structure (and potentially, on important prey populations) would have been strongest.
Number of fledglings per nest is a common metric evaluated for grassland birds under different land management scenarios, but nestling condition is not frequently measured (e.g., Sandercock et al. 2008, With et al. 2008, Rahmig et al. 2008). Although an indirect measure of reproductive success, there are important effects of nestling condition on recruitment, and recruitment is ultimately a function of fecundity and juvenile survival (Pulliam 1988, Hochachka and Smith 1991). Nestlings in poorer
condition may have decreased survival, which means population growth rates would be
116
negatively affected even when productivity is similar among grazing management scenarios.
Although livestock grazing may be an effective management tool to create
particular vegetation conditions for grassland-breeding songbirds (Chapter 2), our results
suggest that higher cattle stocking rates may influence grassland songbird diet
composition and quality, and may have negative effects on nestling condition. Thus, high
stocking rates may not be an effective management tool for some grassland songbird
species. Clarifying mechanisms responsible for changes in grassland songbird
demography will result in the most effective conservation strategies for declining
populations; we suggest that further examination of effects of grazing management
practices on invertebrate prey abundance and quality, along with subsequent effects on
nestling quality and juvenile survival, be made a priority for grassland songbirds.
117
LITERATURE CITED
Barnes, R. D. 1968. Invertebrate zoology. Second Edition. W. B. Saunders Company, Philadelphia.
Boag, P. T. 1987. Effects of nestling diet on growth and adult size of zebra finches (Poephila guttata). Auk 104: 155-166.
Borror, D. J., C. A. Triplehorn, and N. F. Johnson. 1989. An introduction to the study of insects. Sixth Edition. Saunders College Publishing, Philadelphia.
Bullough, W. S. 1958. Practical invertebrate anatomy. Second Edition. MacMillan and Company, LTD, New York.
Carlisle, J. D., and R. L. Holberton. 2006. Relative efficiency of fecal versus regurgitated samples for assessing diet and the deleterious effects of a tartar emetic on migratory birds. Journal of Field Ornithology 77: 126-135.
Davis, Stephen K. and Wesley E. Lanyon. 2008. Western Meadowlark (Sturnella neglecta), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu/bna/species/104doi:10.2173/bna.104.
Dearborn, D. C., A. D. Anders, F. R. Thompson III, and J. Faaborg. 1998. Effects of cowbird parasitism on parental provisioning and nestling food acquisition and growth. Condor 100: 326-334.
DeBano, S. 2006. Effects of livestock grazing on aboveground insect communities in semi-arid grasslands of southeastern Arizona. Biodiversity and Conservation 15: 2547-2564.
Dillon, E. S., and L. S. Dillon. 1972. A manual of common beetles of eastern North America. Dover Publications, Inc., New York.
Disney, R. H. L. 1986. Assessment using invertebrates, posing the problem, p. 271-293. In M. B. Usher [ed.], Wildlife conservation evaluation. Chapman and Hall, London.
Durst, S. L., T. C. Theimer, E. H. Paxton, and M. K. Sogge. 2008. Age, habitat, and yearly variation in the diet of a generalist insectivore, the Southwestern Willow Flycatcher. Condor 110: 514-525.
118
Fretwell, S. D. 1972. Populations in a seasonal environment. Princeton University Press, Princeton, NJ.
Fuhlendorf, S. D., and D. M. Engle. 2001. Restoring heterogeneity on rangelands: ecosystem management based on evolutionary grazing patterns. BioScience 51: 625-632.
Golet, G. H., K. J. Kuletz, D. D. Roby, and D. B. Irons. 2000. Adult prey choice affects chick growth and reproductive success in pigeon guillemots. Auk 117: 82-91.
Hagar, J. C., K. M. Dugger, and E. E. Starkey. 2007. Arthropod prey of Wilson’s Warblers in the understory of Douglas-Fir forests. Wilson Journal of Ornithology 119: 533-546.
Hickman, K. R., G. H. Farley, R. Channell, and J. E. Steier. 2006. Effects of Old World Bluestem (Bothriochloa ischaemum) on food availability and avian community composition within the mixed-grass prairie. Southwestern Naturalist 51: 524-530.
Hochachka, W., and J. N. M. Smith. 1991. Determinants and consequences of nestling condition in song sparrows. Journal of Animal Ecology 60: 995-1008.
Hocking, R. R. 1976. The analysis and selection of variables in linear regression. Biometrics 32: 1-49.
Isaksson, C., and S. Andersson. 2007. Carotenoid diet and nestling provisioning in urban and rural great tits Parus major. Journal of Avian Biology 38: 564-572.
Joern, A. 2004. Variation in grasshopper (Acrididae) densities in response to fire frequency and bison grazing in tallgrass prairie. Community and Ecosystem Ecology 33: 1617-1625.
Johnston, R. D. 1993. Effects of diet quality on the nestling growth of a wild insectivorous passerine, the house martin Delichon urbica. Functional Ecology 7: 255-266.
Jones, A. 2000. Effects of cattle grazing on North American arid ecosystems: a quantitative review. Western North American Naturalist 60: 155-164.
Karasov, W. H. 1990. Digestion in birds; chemical and physiological determinants and ecological implications. Studies in Avian Biology 13: 391-415.
Kaspari, M., and A. Joern. 1993. Prey choice by three insectivorous grassland birds: reevaluating opportunism. Oikos 68: 414-430.
119
Kennedy, P. L., S. J. DeBano, A. M. Bartuszevige, and A. S. Lueders. 2009. Effects of native and non-native grassland plant communities on breeding passerine birds: implications for restoration of northwest bunchgrass prairie. Restoration Ecology 17: 515-525.
Kleintjes, P. K., and D. L. Dahlsten. 1992. A comparison of three techniques for analyzing the arthropod diet of plain titmouse and chestnut-backed chickadee nestlings. Journal of Field Ornithology 63: 276-285.
Koricheva, J., C. P. H. Mulder, B. Schmid, J. Joshi, and K. Huss-Danell. 2000. Numerical responses of different trophic groups of invertebrates to manipulations of plant diversity in grasslands. Oecologia 125: 271-282.
Kruess, A., and T. Tscharntke. 2002. Grazing intensity and the diversity of grasshoppers, butterflies, and trap-nesting bees and wasps. Conservation Biology 16: 1570-1580.
Lack, D. 1968. Ecological adaptations for breeding in birds. Methuen, London.
Lechowicz, M. J. 1982. The sampling characteristics of electivity indices. Oecologia 52: 22-30.
Lubowski, R. N., M. Vesterby, S. Bucholtz, A. Baez, and M. J. Roberts. 2006. Major uses of land in the United States, 2002. USDA Economic Information Bulletin No. EIB-14.
Maher, W. J. 1979. Nestling diets of prairie passerine birds at Matador, Saskatchewan, Canada. Ibis 121: 437-452.
Martin, J. E. H. 1977. The insects and arachnids of Canada, part 1: collecting, preparing, and preserving insects, mites, and spiders. Biosystematics Research Institute, Research Branch, Canada Department of Agriculture, Ottawa, Ontario.
Martin, T. E. 1987. Food as a limit on breeding birds: a life-history perspective. Annual Review of Ecology and Systematics 18: 453–487.
Matsuda, R. 1965. Morphology and evolution of the insect head. The American Entomological Institute, Ann Arbor, MI.
McGraw, K. J., and D. R. Ardia. 2003. Carotenoids, immunocompetence, and the information content of sexual colors: an experimental test. American Naturalist 162: 704-712.
120
Meunier, M., and J. Bédard. 1984. Nestling foods of the Savannah Sparrow. Canadian Journal of Zoology 62: 23-40.
Milchunas, D. G., W. K. Laurenroth, and I. C. Burke. 1998. Livestock grazing: animal and plant biodiversity of shortgrass steppe and the relationship to ecosystem function. Oikos 83: 65-74.
Monrós, J. S., E. J. Belda, and E. Barba. 2002. Post-fledging survival of individual great tits: the effect of hatching date and fledging mass. Oikos 99: 481-488.
Naef-Daenzer, B., and L. F. Keller. 1999. The foraging performance of Great and Blue Tits (Parus major and P. caeruleus) in relation to caterpillar development, and its consequences for nestling growth and fledging weight. Journal of Animal Ecology 68: 708-718.
Naef-Daenzer, B., F. Widmer, and M. Nuber. 2001. Differential post-fledging survival of great and coal tits in relation to their condition and fledging date. Journal of Animal Ecology 70: 730-738.
Naeth, M. A., A. W. Bailey, D. J. Pluth, D. S. Chanasyk, and R. T. Hardin. 1991. Grazing impacts on litter and soil organic matter in mixed prairie and fescue grassland ecosystems of Alberta. Journal of Range Management 44: 7-12.
Nagy, K. A. 1987. Field metabolic rate and food requirement scaling in mammals and birds. Ecological Monographs 57: 111-128.
Nagy, L. R., and R. T. Holmes. 2005. Food limits annual fecundity of a migratory songbird: an experimental study. Ecology 86: 675-681.
New, T. R. 1998. Invertebrate surveys for conservation. Oxford University Press, Oxford.
Ober, H. K., and J. P. Hayes. 2010. Determinants of nocturnal Lepidopteran diversity and community structure in a conifer-dominated forest. Biodiversity and Conservation 19: 761-774.
O’Neill, K. M., B. E. Olson, M. G. Rolston, R. Wallander, D. P. Larson, and C. E. Seibert. 2003. Effects of livestock grazing on rangeland grasshopper (Orthoptera: Acrididae) abundance. Agriculture, Ecosystems and Environment 97: 51-64.
Peterson, A. 1962. Larvae of insects: an introduction to nearctic species, I and II. Ohio State University, Columbus, OH.
121
Peterson, K. L., and L. B. Best. 1986. Diets of nestling Sage Sparrows and Brewer’s Sparrows in an Idaho sagebrush community. Journal of Field Ornithology 57: 283-294.
Pulliam, H. R. 1988. Sources, sinks, and population regulation. American Naturalist 132: 652-661.
Rahmig, C. J., W. E. Jensen, and K. A. With. 2008. Grassland bird responses to land management in the largest remaining tallgrass prairie. Conservation Biology 23: 420-432.
Ralph, C. P., S. E. Nagata, and C. J. Ralph. 1985. Analysis of droppings to describe diets of small birds. Journal of Field Ornithology 56:165-174.
Robinson, S. K., and R. T. Holmes. 1982. Foraging behavior of forest birds: the relationships among search tactics, diet, and habitat structure. Ecology 63:1918– 1931.
Rodenhouse, N. L., and R. T. Holmes. 1992. Results of experimental and natural food reductions for breeding Black-throated Blue Warblers. Ecology 73:357–372.
Rotenberry, J. T. 1980. Dietary relationships among shrubsteppe passerine birds: competition or opportunism in a variable environment. Ecological Monographs 50: 93-110.
Sandercock, B. K., E. L. Hewitt, and K. L. Kosciuch. 2008. Effects of experimental cowbird removals on brood parasitism and nest predation in a grassland songbird. Auk 125: 820-830.
Sauer, J. R., J. E. Hines, and J. Fallon. 2007. The North American breeding bird survey, results and analysis 1966–2006. Version 10-13-2007. Laurel, MD, USA: USGS Patuxent Wildlife Research Center.
Schulz, T. T., and W. C. Leininger. 1990. Differences in riparian vegetation structure between grazed areas and exclosures. Journal of Range Management 43: 295- 299.
Slagsvold, T., and J. T. Lifjeld. 1985. Variation in plumage colour of the Great Tit Parus major in relation to habitat, season and food. Journal of Zoology 206: 321- 328.
Smith, T. R., and J. L. Capinera. 2005. Mandibular morphology of some Floridian grasshoppers (Orthoptera: Acrididae). Florida Entomologist 88: 204-207.
122
Snodgrass, R. E. 1935. Principles of insect morphology. McGraw-Hill Book Company, Inc., New York, N.Y.
Snodgrass, R. E. 1951. Comparative studies on the head of mandibulate arthropods. Comstock Publishing Company, Inc., Ithaca, N.Y.
Stehr, F. 1987. Immature insects, I and II. Kendall/Hunt Publishing Company, Dubuque, IA.
Stodola, K. W., D. A. Buehler, D. H. Kim, K. E. Franzreb, and E. T. Linder. 2010. Biotic and abiotic factors governing nestling-period length in the Ovenbird (Seiurus aurocapilla). Auk 127: 204-211.
Sutter, B., and G. Ritchison. 2005. Effects of grazing on vegetation structure, prey availability, and reproductive success of Grasshopper Sparrows. Journal of Field Ornithology 76: 345-351.
Vanderploeg, H. A., and D. Scavia. 1979a. Two electivity indices for feeding with special reference to zooplankton grazing. Journal of the Fisheries Research Board of Canada 36: 362-365.
Vanderploeg, H. A., and D. Scavia. 1979b. Calculation and use of selectivity coefficients of feeding: zooplankton grazing. Ecological Modelling 7: 135-149.
Vickery, J. A., J. R. Tallowin, R. E. Feber, E. J. Asteraki, P. W. Atkinson, R. J. Fuller, and V. K. Brown. 2001. The management of lowland neutral grasslands in Britain: effects of agricultural practices on birds and their food resources. Journal of Applied Ecology 38: 647-664.
White, R. E. 1983. Peterson field guides: beetles. Houghton Mifflin Company, New York.
Wiens, J. A., and J. T. Rotenberry. 1979. Diet niche relationships among North American grassland and shrubsteppe birds. Oecologia 42: 253-292.
Wiens, J. A. 1989a. The ecology of bird communities I: foundations and patterns. Cambridge University Press, Cambridge.
Wiens, J. A. 1989b. The ecology of bird communities II: processes and variation. Cambridge University Press, Cambridge.
Winter, M., S. E. Hawks, J. A Shaffer, and D. H. Johnson. 2003. Guidelines for finding nests of passerine birds in tallgrass prairie. Prairie Naturalist 35: 197-211.
123
With, K. A., A. W. King, and W. E. Jensen. 2008. Remaining large grasslands may not be sufficient to prevent grassland bird declines. Biological Conservation 141: 3152-3167.
Wyffels, S. 2009. Influence of stocking density on grazing beef cattle performance, diet composition, foraging efficiency, and diet quality on a late-spring early-summer native bunchgrass prairie. M.S. thesis, Oregon State University, Corvallis, OR.
Yackel Adams, A. A., S. K. Skagen, and J. A. Savidge. 2006. Modeling post-fledging survival of lark buntings in response to ecological and biological factors. Ecology 87: 178-188.
Zalik, N. J., and A. M. Strong. 2008. Effects of hay cropping on invertebrate biomass and the breeding ecology of Savannah Sparrows (Passerculus sandwichensis). Auk 125: 700-710.
124
Figure 4.1. Experimental blocks and paddocks with stocking rates, pitfall sampling points, and sweep-net transects at the Zumwalt Prairie Preserve, northeastern Oregon, USA.
125
Fig. 4.2. Relativized electivity by invertebrate order within experimental paddocks at the Zumwalt Prairie Preserve, northeast Oregon, USA. Each symbol represents the preference value for each invertebrate order in each paddock in each year. Where there are fewer than eight symbols for each stocking rate, there is overlap of electivity values. Each of 16 paddocks was treated with one of four stocking rate treatments (circle = control; triangle = low; square = moderate; diamond = high) for two years (filled symbols = 2007; empty symbols = 2008). Electivity values > 0 indicate a preference by songbirds for that invertebrate taxon; values < 0 indicate avoidance.
1.0
0.5
Preferred 0.0 Avoided
-0.5
Relativized Electivity (E*) Electivity Relativized -1.0
a a a e a a a r r ae a er er er e e e d er t t t t i t t p p p p d p p an i i i o o o o r r d D e i c l A m em en o o A H ep m C H L y
H
126
Fig. 4.3. Rates of mass gain and tarsus growth (beta values from a regression of age versus mass gain and age versus tarsus length) for songbird nestlings in paddocks grazed at four different stocking rates on the Zumwalt Prairie Preserve, northeastern Oregon, USA. Significant trends from a mixed model are indicated with regression lines: solid line: mass gain; broken line: tarsus length.
Savannah Sparrow
4 Mass gain Tarsus length
3
2
Rate of growth 1
0 Vesper Sparrow
4
3
2
Rate of growth 1
0 Control Low Moderate High (0 AU) (10 AU) (20 AU) (30 AU) Stocking rate
127
Horned Lark
4 Mass gain Tarsus length
3
2
Rate of growth 1
0 Western Meadowlark 10
8
6
4 Rate of growth
2
0 Control Low Moderate High (0 AU) (10 AU) (20 AU) (30 AU) Stocking rate
128
Table 4.1. Number of nests from which nestling fecal samples were collected for each species, year, and grazing treatment at the Zumwalt Prairie Preserve in northeastern Oregon, USA.
Stocking rate Species Year Control Low Moderate High Savannah Sparrow 2007 5 2 7 5 2008 2 2 3 5 Horned Lark 2007 4 9 3 2 2008 1 3 1 4 Vesper Sparrow 2007 1 4 2 1 2008 3 3 0 6 Western Meadowlark 2007 3 4 3 2 2008 2 2 1 1 Grasshopper Sparrow 2007 2 3 1 *0 2008 3 0 3 *0 * We detected no Grasshopper Sparrow nests in paddocks with high stocking rates.
129
Table 4.2. Results from mixed-effects models predicting the proportion of invertebrate taxa detected in nestling diets of five grassland bird species at the Zumwalt Prairie Preserve in northeastern Oregon, USA. Species effects indicate a difference in the proportion of that invertebrate taxa among songbird species.
Invertebrate Taxaa Effects d.f. F-value P-value Acrididae treatment x species 4, 80 3.50 0.01 treatment 1, 80 11.33 < 0.01 species 4, 80 2.50 0.05 Coleoptera species 4, 84 13.43 < 0.01 Araneae species 4, 84 6.39 < 0.01 a We detected no significant effects of any predictor variable on the proportion of Lepidoptera, Homoptera, Hemiptera, Hymenoptera, or Diptera in nestling diets.
Stocking Invertebrate Taxon Table 4.3. Proportionrate of each invertebrate taxa in the diet of nestlings reared in paddocks with four different cattle stocking rates at the Zumwalt Prairie Preserve in northeastern Oregon, USA. Values represent the mean of each taxon detected in fecal samples collected during 2007 and 2008.
Species Acrididae Araneae Coleoptera Diptera Hemiptera Homoptera Hymenoptera Lepidoptera Savannah Sparrow control 0.22 0.21 0.24 0.01 0.02 0.00 0.00 0.30 low 0.04 0.04 0.32 0.00 0.01 0.25 0.00 0.34 moderate 0.31 0.23 0.12 0.08 0.02 0.06 0.00 0.18 high 0.24 0.22 0.16 0.00 0.01 0.08 0.00 0.29 Vesper Sparrow control 0.19 0.29 0.14 0.00 0.00 0.00 0.00 0.38 low 0.19 0.26 0.20 0.04 0.01 0.10 0.00 0.21 moderate 0.39 0.15 0.14 0.00 0.00 0.00 0.00 0.31 high 0.20 0.22 0.17 0.00 0.00 0.07 0.01 0.33 Horned Lark control 0.24 0.12 0.12 0.00 0.00 0.07 0.00 0.45 low 0.34 0.28 0.20 0.00 0.00 0.00 0.00 0.18 moderate 0.34 0.07 0.36 0.00 0.00 0.01 0.00 0.22 high 0.37 0.25 0.09 0.00 0.00 0.08 0.00 0.21 Western Meadowlark controla 0.26 0.23 0.16 0.00 0.00 0.00 0.01 0.34 low control 0.220.29 0.30 0.21 0.21 0.03 0.00 0.00 0.00 0.04 0.00 0.03 0.03 0.00 0.24 0.40 moderatelow 0.130.58 0.27 0.05 0.23 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.37 0.33 moderate 0.42 0.17 0.06 0.00 0.00 0.03 0.00 0.33 a We located no Grasshopperhigh Sparrow nests in paddocks0.00 with high 0.83 stocking rates. 0.00 0.00 0.00 0.00 0.00 0.17 Grasshopper Sparrow 130 131
CHAPTER 5: CONCLUSIONS
Livestock grazing is a complex disturbance in both space and time, and its effect on
grassland songbirds is highly variable and often difficult to predict. This difficulty stems
from a lack of experimental approaches in which one aspect of grazing management is
isolated and its effects examined. The goal of this study was to evaluate demographic responses of several species of grassland songbirds to stocking rate, a characteristic of livestock grazing management that we hypothesized would largely influence the nature of the relationship between grassland songbirds and grazing due to its expected effects on
vegetation.
Previous research on effects of livestock grazing has demonstrated that grazing
can affect local population density of grassland songbirds by altering grassland
vegetation structure. We examined the effect of four different cattle stocking rates on
vegetation structure as well as the density of several species of grassland songbird, and
our results were only partially congruent with our predictions. We found that after a six- week grazing period, the amount of vegetative cover at the paddock-level (40 ha) decreased and structural heterogeneity of vegetative cover increased in a linear fashion with increasing stocking rates. Most bird species were able to locate nesting sites with similar vegetative cover regardless of paddock-level differences in cover, but cover at western meadowlark and vesper sparrow nests was lower in paddocks with higher stocking rates. We expected changes in songbird population or nest density would result from effects of stocking rate on vegetative cover because the number of nesting or foraging sites suitable for any particular songbird species should be affected by paddock-
132
level changes in cover. However, effects of stocking rate on songbird density were not as
clear as effects on vegetation structure. Savannah sparrow population density decreased
as expected in paddocks with higher stocking rates, but only in the year after grazing
ceased, and grasshopper sparrow nest density decreased with higher stocking rates.
Composition of grassland songbird communities was influenced by stocking rate, and
there was an increase in the proportion of species preferring less vegetative cover for
nesting and a decrease in the proportion of species preferring more vegetative cover for
nesting from ungrazed to heavily-grazed paddocks over the course of the study.
The responses of songbird density that we observed support the notion that
grazing management can be used to manipulate habitat for grassland-breeding songbirds,
and variability in livestock management may be critical to providing suitable habitat for a greater suite of grassland songbird species. Ungrazed areas and areas with low-to- moderate stocking rates may provide suitable habitat for species preferring more
vegetative cover (e.g., savannah sparrow), while high stocking rates may provide suitable
habitat for species preferring less cover (e.g., horned lark). Management strategies that
include long-term rest from grazing may therefore contribute to the loss of suitable
habitat for species on the lower end of this continuum of vegetative cover (e.g., horned
lark). However, after two years of grazing at different intensities, we did not observe
strong treatment effects on population density for all species. Additional years of grazing
treatments should be evaluated to determine whether the expected changes in songbird
density (based on documented habitat preferences for each species) occur, or if larger
disparities in forage utilization are required.
133
An understanding of the effects of stocking rate on songbird productivity is
critical before management recommendations aimed at grassland bird conservation can
be made. To that end, we examined effects of cattle stocking rate on nest survival and
cause-specific failure. Because food can limit reproductive success for breeding birds
and ultimately influence juvenile recruitment through effects on nestling condition, we
also evaluated the relationship among cattle stocking rates, nestling diet composition, and
nestling condition.
Grassland songbird nests in our study failed mostly due to predation, and less
commonly due to trampling by ungulates and adverse weather. The risk of predation
increased with the age of the nest and throughout the breeding season, but was not
affected by cattle stocking rate, suggesting that conservation efforts for breeding
grassland songbirds might be most effective if focused on managing for reductions in
nest predation. Risk of nest failure from trampling was higher in paddocks with higher stocking rates, but only for the first half of the grazing period. These results suggest that timing of cattle grazing should be an important consideration in grassland systems being
managed for livestock and ground-nesting songbirds because the greater the temporal overlap between active nests and large grazers, the greater the risk of trampling.
However, this pattern was evident only for high stocking rates. If grazing is to coincide temporally with the nesting season, low-to-moderate stocking rates minimize risk of nest trampling. Risk of weather-related nest failure was higher in the early part of the nesting season and decreased throughout the first 20-30 days, but the rate of decrease was influenced by year. Although risk of weather-related failure was low compared to risk of
134
nest predation, overall estimates of nest success reflecting risk of failure from all causes suggested that weather-related failures drove patterns of nest success in 2008 (a year characterized by adverse weather early in the nesting season) rather than predation. In years when widespread nest failure due to weather is likely, delaying introduction of livestock or reducing livestock density until risk from severe weather resides may help mitigate negative effects on nest success.
Although starvation was not identified as a significant source of nest failure, grazing-induced changes to vegetation structure and composition may influence food availability for breeding songbirds, ultimately affecting the composition of nestling diets and nestling condition. Adult grassland songbirds in our system preferred to feed nestlings caterpillars, spiders and beetles, and to a lesser degree, short-horned grasshoppers. Stocking rate did not strongly influence composition of nestling diets, the exception being that the proportion of short-horned grasshoppers in nestling western meadowlark diets was negatively affected by higher stocking rates. Higher stocking rates decreased nestling growth rates for western meadowlarks and vesper sparrows, suggesting the reduction of grasshoppers in nestling diets was detrimental to western meadowlarks. Slower growth rates of vesper sparrow nestlings may be related to timing of livestock grazing: most vesper sparrow nestlings were reared during the latter half of the grazing period when effects of stocking rate on vegetation (and potentially, on invertebrate populations) were most evident. Negative effects of increased livestock grazing intensity on nestling condition could have consequences for juvenile survival and recruitment, and suggest that although livestock grazing may help create suitable habitat
135
conditions for some songbird species, high stocking rates may influence grassland
songbird diet quality, or have negative effects on nestling condition.
We hypothesized that grazing intensity could influence the grassland songbird
community through “bottom-up” effects on vegetation. We observed clear treatment
differences on vegetation structure, and some treatment effects on invertebrate
populations (S. DeBano, unpublished results), but effects of grazing at different
intensities did not translate directly through the food web to influence songbird
populations as strongly as it influenced vegetation and invertebrates. Similar responses
have been observed in other food web experiments, and may be because processes
responsible for changes in community composition and trophic relationships such as
immigration and emigration are mismatched with the temporal scale of short-term “press experiments” such as ours (Leibold et al. 1997). Additional years of grazing treatments may help clarify whether there is a time lag for predicted changes in population density of songbirds. Alternatively, the dampening of grazing effects from vegetation to songbirds may be due to sufficient spatial or temporal heterogeneity remaining in the system, even at the highest grazing intensity, such that grazing-induced changes in lower trophic levels were irrelevant for most songbird species. For western meadowlarks and vesper sparrows, however, we observed negative effects of heavier grazing (cover at the nest and nestling growth rates were affected for both species), suggesting not all species were able to compensate for changes to the system at lower trophic levels. These results also highlight the importance of examining more than one demographic response when investigating response patterns of grassland passerines to livestock grazing, because
136
although there were not strong treatment effects on songbird density, there were treatment effects related to productivity.
Our results provide new insight into responses of grassland passerines to livestock grazing. Understanding patterns of songbird demographic responses to specific grazing management scenarios is useful for land managers given the ubiquity of livestock grazing in grasslands. Additionally, attributing changes in demographic rates to specific causal mechanisms will allow for creation of more effective conservation measures for declining grassland bird populations.
137
BIBLIOGRAPHY
Adler, P. B., D. A. Raff, and W. K. Lauenroth. 2001. The effect of grazing on the spatial heterogeneity of vegetation. Oecologia 128: 465-479.
Alldredge, M. W., K. H. Pollock, T. R. Simons, and S. A. Shriner. 2007. Multiple- species analysis of point count data: a more parsimonious modeling framework. Journal of Applied Ecology 44: 281-290.
Animal and Plant Health Inspection Service. 1998. Sensory and behavioral methods for managing predation on livestock. National Wildlife Research Center Fiscal Year Highlights Report. Miscellaneous Publication 1552. U.S. Department of Agriculture, National Wildlife Research Center, Fort Collins, Colorado, USA.
Anstey, D. A., S. K. Davis, D. C. Duncan, and M. Skeel. 1995. Distribution and habitat requirements of eight grassland songbird species in southern Saskatchewan. Saskatchewan Wetland Conservation Corporation, Regina, Saskatchewan. 11 pages.
Askins, R. A., F. Chávez-Ramírez, B. C. Dale, C. A. Haas, J. R. Herkert, F. L. Knopf, and P. D. Vickery. 2007. Conservation of grassland birds in North America: understanding ecological processes in different regions. Ornithological Monographs 64: 1-46.
Asner, G. P., A. J. Elmore, L. P. Olander, R. E. Martin, and A. T. Harris. 2004. Grazing systems, ecosystem responses, and global change. Annual Review of Environmental Resources 29: 261-299.
Augustine, D. J. and S. J. McNaughton. 1998. Ungulate effects on the functional species composition of plant communities: herbivore selectivity and plant tolerance. Journal of Wildlife Management 62: 1165-1183.
Ausden, M. 2007. Habitat Management for Conservation: A Handbook of Techniques. Oxford University Press, New York.
Bader, T. C. and J. C. Bednarz. 2009. Reproductive success and causes of nest failures for Mississippi Kites: a sink population in eastern Arkansas? Wetlands 29: 598- 606.
Bakker, E. S., M. E. Ritchie, H. Olff, D. G. Milchunas, and J. M. H. Knops. 2006. Herbivore impact on grassland plant diversity depends on habitat productivity and herbivore size. Ecology Letters 9: 780-788.
138
Barnes, R. D. 1968. Invertebrate zoology. Second Edition. W. B. Saunders Company, Philadelphia.
Beason, R. C. 1995. Horned Lark (Eremophila alpestris), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu. proxy.library.oregonstate.edu/ bna/species/195.
Bélanger, L., and M. Picard. 1999. Cattle grazing and avian communities of the St. Lawrence River Islands. Journal of Range Management 52: 332-338.
Bell, S. S., E. D. McCoy, and H. R. Mushinsky. 1991. Habitat Structure: The Physical Arrangement of Objects in Space. Chapman & Hall, London, UK.
Benson, T. J., J. D. Brown, and J. C. Bednarz. 2010. Identifying predators clarifies predictors of nest success in a temperate passerine. Journal of Animal Ecology 79: 225-234.
Boag, P. T. 1987. Effects of nestling diet on growth and adult size of zebra finches (Poephila guttata). Auk 104: 155-166.
Bock, C. E., V. A. Saab, T. D. Rich, and D. S. Dobkin. 1993. Effects of livestock grazing on neotropical migratory landbirds in western North America In D.M. Finch and P.W. Stangel (eds.) Status and management of neotropical migratory birds. Gen. Tech. Rep. RM-229. Rocky Mountain Forest and Range Experiment Station, USDA, Forest Service.
Bock, C. E., Z. F. Jones, and J. H. Bock. 2006. Rodent communities in an exurbanizing southwestern landscape (U.S.A.). Conservation Biology 20: 1242-1250.
Borror, D. J., C. A. Triplehorn, and N. F. Johnson. 1989. An introduction to the study of insects. Sixth Edition. Saunders College Publishing, Philadelphia.
Bowman, R. and G. E. Woolfenden. 2001. Nest success and the timing of nest failure of Florida Scrub-Jays in suburban and wildland habitats. p. 383-402, In Avian Conservation and Ecology in an Urbanizing World, Marzluff, J. M., R. Bowman, and R. E. Donnelly (eds.). Kluwer Academic Publ., New York, NY.
Briskie, J. V., P. R. Martin, and T. E. Martin. 1999. Nest predation and the evolution of nestling begging calls. Proceedings of the Royal Society of London Series B 2666: 2153-2159.
139
Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas. 2001. Introduction to Distance Sampling: Estimating Abundance of Biological Populations. Oxford University Press, New York.
Bullough, W. S. 1958. Practical invertebrate anatomy. Second Edition. MacMillan and Company, LTD, New York.
Burhans, D. E., D. Dearborn, F. R. Thompson III, and J. Faaborg. 2002. Factors affecting predation at songbird nests in old fields. Journal of Wildlife Management 66: 240-249.
Burkhardt, J. W. 1995. Herbivory in the Intermountain West: an overview of evolutionary history, historical cultural impacts and lessons from the past. University of Idaho Station Bulletin 58.
Burnham, K. P., and D. R. Anderson. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, second ed. Springer, New York.
Carlisle, J. D., and R. L. Holberton. 2006. Relative efficiency of fecal versus regurgitated samples for assessing diet and the deleterious effects of a tartar emetic on migratory birds. Journal of Field Ornithology 77: 126-135.
Chalfoun, A. D., and T. E. Martin. 2009. Habitat structure mediates predation risk for sedentary prey: experimental tests of alternative hypotheses. Journal of Animal Ecology 78: 497-503.
Chapman, R. N., D. M. Engle, R. E. Masters, and D. M. Leslie Jr. 2004. Grassland vegetation and bird communities in the southern Great Plains of North America. Agriculture, Ecosystems and Environment 104: 577-585.
Chase, J. M., M. A. Leibold, A. L. Downing, and J. B. Shurin. 2000. The effects of productivity, herbivory, and plant species turnover in grassland food webs. Ecology 81: 2485-24987.
Clark, M. E., and T. E. Martin. 2007. Modeling tradeoffs in avian life history traits and consequences for population growth. Ecological Modelling 209: 110-120.
Cody, M. L. 1968. On the methods of resource division in grassland bird communities. American Naturalist 102: 107–147.
Coghenour, M. B. 1991. Spatial components of plant-herbivore interactions in pastoral, ranching, and native ungulate ecosystems. Journal of Range Management 44: 530-542.
140
Cole, D. N., and P. B. Landres. 1996. Threats to wilderness ecosystems: impacts and research needs. Ecological Applications 6: 168-184.
Collins, S. L., A. K. Knapp, J. M. Briggs, J. M. Blair, and E. M. Steinauer. 1998. Modulation of diversity by grazing and mowing in native tallgrass prairie. Science 280: 745-747.
Collins, S. L. 2000. Disturbance frequency and community stability in native tallgrass prairie. American Naturalist 155: 311-325.
Cottrell, M. J. 1981. Resource partitioning and reproductive success of hawks (Buteo spp.) in an Oregon prairie. M.S. Thesis. Oregon State University, Corvallis, Oregon.
Crawford, J. A., R. A. Olson, N. E. West, J. C. Mosley, M. A. Schroeder, T. D. Whitson, R. F. Miller, M. A. Gregg, and C. S. Boyd. 2004. Ecology and management of sage-grouse and sage-grouse habitat. Journal of Range Management 57: 2-19.
Damiran, D., T. DelCurto, E. Darambazar, A. A. Clark, P. L. Kennedy, and R. V. Taylor. 2007. Visual obstruction: weight technique for estimating production on northwestern bunchgrass prairie rangelands. Proceedings of Western Section, American Society of Animal Sciences 58: 225-228.
Davis, S. K. 2003. Nesting ecology of mixed-grass prairie songbirds in southern Saskatchewan. Wilson Bulletin 115: 119-130.
Davis, S. K. and W. E. Lanyon. 2008. Western Meadowlark (Sturnella neglecta), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu.proxy.library. oregonstate.edu/bna/species/104.
Dearborn, D. C., A. D. Anders, F. R. Thompson III, and J. Faaborg. 1998. Effects of cowbird parasitism on parental provisioning and nestling food acquisition and growth. Condor 100: 326-334.
DeBano, S. 2006. Effects of livestock grazing on aboveground insect communities in semi-arid grasslands of southeastern Arizona. Biodiversity and Conservation 15: 2547-2564.
Dechant, J. A., M. F. Dinkins, D. H. Johnson, L. D. Igl, C. M. Goldade, and B. R. Euliss. 2003. Effects of management practices on grassland birds: Vesper Sparrow. Northern Prairie Wildlife Research Center, Jamestown, ND. Northern Prairie
141
Wildlife Research Center Online. http://www.npwrc.usgs.gov/resource/ literatr/grasbird/vesp/vesp.htm (Accessed June 2, 2010).
Dechant, J. A., M. L. Sondreal, D. H. Johnson, L. D. Igl, C. M. Goldade, M. P. Nenneman, and B. R. Euliss. 2003. Effects of management practices on grassland birds: Grasshopper Sparrow. Northern Prairie Wildlife Research Center, Jamestown, ND. Northern Prairie Wildlife Research Center Online. http://www.npwrc.usgs.gov/resource/literatr/grasbird/ grsp/grsp.htm (Accessed June 2, 2010).
Dechant, J. A., M. L. Sondreal, D. H. Johnson, L. D. Igl, C. M. Goldade, A. L. Zimmerman, and B. R. Euliss. 2003. Effects of management practices on grassland birds: Western Meadowlark. Northern Prairie Wildlife Research Center, Jamestown, ND. Northern Prairie Wildlife Research Center Online. http://www.npwrc.usgs.gov/resource/ literatr/grasbird/weme/weme.htm (Accessed June 2, 1010).
Derner, J. D., W. K. Laurenroth, P. Stapp, and D. J. Augustine. 2009. Livestock as ecosystem engineers for grassland bird habitat in the western Great Plains of North America. Rangeland Ecology and Management 62: 111-118.
Dillon, E. S., and L. S. Dillon. 1972. A manual of common beetles of eastern North America. Dover Publications, Inc., New York.
Dinkins, M. F., A. L. Zimmerman, J. A. Dechant, B. D. Parkin, D. H. Johnson, L. D. Igl, C. M. Goldade, and B. R. Euliss. 2003. Effects of management practices on grassland birds: Horned Lark. Northern Prairie Wildlife Research Center, Jamestown, ND. Northern Prairie Wildlife Research Center Online. http://www.npwrc.usgs.gov/resource/ literatr/grasbird/hola/hola.htm (Accessed June 2, 2010).
Dinsmore, S. J., G. C. White, and F. L. Knopf. 2002. Advanced techniques for modeling avian nest survival. Ecology 83: 3476-3488.
Disney, R. H. L. 1986. Assessment using invertebrates, posing the problem, p. 271-293. In M. B. Usher [ed.], Wildlife conservation evaluation. Chapman and Hall, London.
Durst, S. L., T. C. Theimer, E. H. Paxton, and M. K. Sogge. 2008. Age, habitat, and yearly variation in the diet of a generalist insectivore, the Southwestern Willow Flycatcher. Condor 110: 514-525.
142
Elbroch, M. 2003. Mammal Tracks & Sign: A Guide to North American Species. Stackpole Books, Mechanicsburg, Pennsylvania, USA.
Etterson, M. A., L. R. Nagy, and T. R. Robinson. 2007. Partitioning risk among different causes of nest failure. Auk 124: 432-443.
Etterson, M. A., B. Olsen, and R. Greenberg. 2007. The analysis of covariates in multi- fate Markov chain nest-failure models. Studies in Avian Biology 34: 55-64.
Evans, D. M., S. M. Redpath, S. A. Evans, D. A. Elston, and P. Dennis. 2005. Livestock grazing affects the egg size of an insectivorous passerine. Biology Letters 1: 322- 325.
Fisher, R. J., and S. K. Davis. 2010. From Wiens to Robel: A review of grassland-bird habitat selection. Journal of Wildlife Management 74: 265-273.
Fleischner, T. L. 1994. Ecological costs of livestock grazing in western North America. Conservation Biology 8: 629-644.
Fondell, T. F., and I. J. Ball. 2004. Density and success of bird nests relative to grazing on western Montana grasslands. Biological Conservation 117: 203-213.
Frank, D. A., S. J. McNaughton, and B. F. Tracy. 1998. The ecology of the Earth’s grazing ecosystems. Bioscience 48:513-521.
Frederick, P. 1987. Chronic tidally-induced nest failure in a colony of white ibises. Condor 89: 413-419.
Fretwell, S. D. 1972. Populations in a seasonal environment. Princeton University Press, Princeton, NJ.
Fuhlendorf, S. D., and D. M. Engle. 2001. Restoring heterogeneity on rangelands: ecosystem management based on evolutionary grazing patterns. BioScience 51: 625-632.
Fuhlendorf, S. D., W. C. Harrell, D. M. Engle, R. G. Hamilton, C. A. Davis, , and D. M. Leslie Jr. 2006. Should heterogeneity be the basis for conservation? Grassland bird response to fire and grazing. Ecological Applications 16: 1706-1716.
Gentry, D. J., D. L. Swanson, and J. D. Carlisle. 2006. Species richness and nesting success of migrant forest birds in natural river corridors and anthropogenic woodlands in southeastern South Dakota. Condor 108: 140-153.
143
Goddard, A. D., and R. D. Dawson. 2009. Seasonal changes in habitat features influencing nest-survival of Sharp-tailed grouse in northeastern British Columbia, Canada. Ecoscience 16: 476-482.
Golet, G. H., K. J. Kuletz, D. D. Roby, and D. B. Irons. 2000. Adult prey choice affects chick growth and reproductive success in pigeon guillemots. Auk 117: 82-91.
Grant, T. A., T. L. Shaffer, E. M. Madden, and P. J. Pietz. 2005. Time-specific variation in passerine nest survival: new insights for old questions. Auk 122: 661-672.
Greenwood, R. J., A. B. Sargent, D. H. Johnson, L. M. Cowardin, and T. L. Shaffer. 1995. Factors associated with duck nest success in the prairie pothole region of Canada. Wildlife Monographs 128: 1–57.
Hagar, J. C., K. M. Dugger, and E. E. Starkey. 2007. Arthropod prey of Wilson’s Warblers in the understory of Douglas-Fir forests. Wilson Journal of Ornithology 119: 533-546.
Harrison, M. L., N. A. Mahoney, P. Robinson, A. Newbury, and D. J. Green. 2010. Vesper sparrows and western meadowlarks show a mixed response to cattle grazing in the intermountain region of British Columbia. Avian Conservation and Ecology 5: 1 [online] URL: http://www.ace-eco.org/vol5/iss1/art1/.
Heisey, D. M., and T. K. Fuller. 1985. Evaluation of survival and cause specific mortality rates using telemetry data. Journal of Wildlife Management 49: 668– 74.
Hickman, K. R., G. H. Farley, R. Channell, and J. E. Steier. 2006. Effects of Old World Bluestem (Bothriochloa ischaemum) on food availability and avian community composition within the mixed-grass prairie. Southwestern Naturalist 51: 524-530.
Hobbs, N.T. 1996. Modification of ecosystems by ungulates. Journal of Wildlife Management 60: 695-713.
Hochachka, W., and J. N. M. Smith. 1991. Determinants and consequences of nestling condition in song sparrows. Journal of Animal Ecology 60: 995-1008.
Hocking, R. R. 1976. The analysis and selection of variables in linear regression. Biometrics 32: 1-49.
Hunter, M. D. and P. W. Price. 1992. Playing chutes and ladders: heterogeneity and the relative roles of bottom-up and top-down forces in natural communities. Ecology 73: 724-732.
144
Huntly, N. 1991. Herbivores and the dynamics of communities and ecosystems. Annual Review of Ecology and Systematics 22: 477-503.
IPCC. 2001. Climate change 2001: impacts, adaptation, and vulnerability. Technical Summary. Contribution to the third assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.
Isaksson, C., and S. Andersson. 2007. Carotenoid diet and nestling provisioning in urban and rural great tits Parus major. Journal of Avian Biology 38: 564-572.
Jensen, H. P., D. Rollins, and R. L. Gillen. 1990. Effects of cattle stock density on trampling loss of simulated ground nests. Wildlife Society Bulletin 18: 71-74.
Joern, A. 2004. Variation in grasshopper (Acrididae) densities in response to fire frequency and bison grazing in tallgrass prairie. Community and Ecosystem Ecology 33: 1617-1625.
Johnson, R. G., and S. A Temple. 1990. Nest predation and brood parasitism of tallgrass prairie birds. Journal of Wildlife Management 54: 106-111.
Johnston, R. D. 1993. Effects of diet quality on the nestling growth of a wild insectivorous passerine, the house martin Delichon urbica. Functional Ecology 7: 255-266.
Jones, A. 2000. Effects of cattle grazing on North American arid ecosystems: a quantitative review. Western North American Naturalist 60: 155-164.
Jones, S. L. and J. E. Cornely. 2002. Vesper Sparrow (Pooecetes gramineus), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell. edu.proxy.library.oregonstate.edu/bna/species/624.
Karasov, W. H. 1990. Digestion in birds; chemical and physiological determinants and ecological implications. Studies in Avian Biology 13: 391-415.
Kaspari, M., and A. Joern. 1993. Prey choice by three insectivorous grassland birds: reevaluating opportunism. Oikos 68: 414-430.
Kennedy, P. L., S. J. DeBano, A. M. Bartuszevige, and A. S. Lueders. 2009. Effects of native and non-native grassland plant communities on breeding passerine birds: implications for restoration of northwest bunchgrass prairie. Restoration Ecology 17: 515-525.
145
Kleintjes, P. K., and D. L. Dahlsten. 1992. A comparison of three techniques for analyzing the arthropod diet of plain titmouse and chestnut-backed chickadee nestlings. Journal of Field Ornithology 63: 276-285.
Knapp, A. K., J. M. Blair, J. M. Briggs, S. L. Collins, D. C. Hartnett, L. C. Johnson, and E. G. Towne. 1999. The keystone role of bison in North American tallgrass prairie. BioScience 49: 39-50.
Knopf, F. L. 1994. Avian assemblages on altered grasslands. Studies in Avian Biology 15: 247-257.
Knopf, F. L. 1996. Prairie legacies—birds, in: Samson, F. B., Knopf, F. L. (Eds.), Prairie Conservation: Preserving North America’s Most Endangered Ecosystem. Island Press, Washington, D.C., pp. 135–148.
Koricheva, J., C. P. H. Mulder, B. Schmid, J. Joshi, and K. Huss-Danell. 2000. Numerical responses of different trophic groups of invertebrates to manipulations of plant diversity in grasslands. Oecologia 125: 271-282.
Kruess, A., and T. Tscharntke. 2002. Grazing intensity and the diversity of grasshoppers, butterflies, and trap-nesting bees and wasps. Conservation Biology 16: 1570-1580.
Kruskal, J. B. 1964. Nonmetric multidimensional scaling: a numerical method. Psycometrika 29: 115-129.
Kuehl, A. K. and W. R. Clark. 2002. Predator activity related to landscape features in northern Iowa. Journal of Wildlife Management 66: 1213-1223.
Lack, D. 1968. Ecological adaptations for breeding in birds. Methuen, London.
Lechowicz, M. J. 1982. The sampling characteristics of electivity indices. Oecologia 52: 22-30.
Leibold, M. A., J. M. Chase, J. B. Shurin, and A. L. Downing. 1997. Species turnover and the regulation of trophic structure. Annual Review of Ecology, Evolution, and Systematics 28: 467-494.
Litell, R. C., G. A. Milliken, W. W. Stroup, R. D. Wolfinger, and O. Schabenberger. 2006. SAS for Mixed Models, second ed. SAS Institute Inc., North Carolina.
146
Lubowski, R. N., M. Vesterby, S. Bucholtz, , A. Baez, and M. J. Roberts. 2006. Major uses of land in the United States, 2002. U.S. Department of Agriculture, Economic Research Service, Economic Information Bulletin No. EIB-14.
MacArthur, R. H., and J. W. MacArthur. 1961. On bird species diversity. Ecology 42: 594–598.
Maher, W. J. 1979. Nestling diets of prairie passerine birds at Matador, Saskatchewan, Canada. Ibis 121: 437-452.
Manolis, J. C., D. E. Anderson, and F. J. Cuthbert. 2000. Uncertain nest fates in songbird studies and variation in Mayfield estimation. Auk 117: 615-626.
Martin, J. E. H. 1977. The insects and arachnids of Canada, part 1: collecting, preparing, and preserving insects, mites, and spiders. Biosystematics Research Institute, Research Branch, Canada Department of Agriculture, Ottawa, Ontario.
Martin, T. E. 1987. Food as a limit on breeding birds: a life-history perspective. Annual Review of Ecology and Systematics 18: 453–487.
Martin, T. E. 1995. Avian life history evolution in relation to nest sites, nest predation, and food. Ecological Monographs 65: 101-127.
Martin, T. E., J. Scott, and C. Menge. 2000. Nest predation increases with parental activity: separating nest site and parental activity effects. Proceedings of the Royal Society of London B 267: 2287-2293.
Mather, P. M. 1976. Computational Methods of Multivariate Analysis in Physical Geography. John Wiley & Sons, London.
Matsuda, R. 1965. Morphology and evolution of the insect head. The American Entomological Institute, Ann Arbor, MI.
Mayfield, H. F. 1961. Nesting success calculated from exposure. Wilson Bulletin 73: 255-261.
McCune, B., and J. B. Grace. 2002. Analysis of Ecological Communities. MjM Software, Glenedon Beach, Oregon, USA.
McCune, B., and M. J. Mefford. 2009. PC-ORD. In: Multivariate Analysis of Ecological Data. Version 6.91 beta, MjM Software, Glenedon Beach, Oregon, USA.
147
McGraw, K. J., and D. R. Ardia. 2003. Carotenoids, immunocompetence, and the information content of sexual colors: an experimental test. American Naturalist 162: 704-712.
McIntyre, S., K. M. Heard, and T. G. Martin. 2003. The relative importance of cattle grazing in subtropical grasslands: does it reduce or enhance biodiversity? Journal of Applied Ecology 40: 445-457.
McNaughton, S. J. 1984. Grazing lawns: animals in herds, plant form, and coevolution. American Naturalist 124: 863-886.
Meunier, M., and J. Bédard. 1984. Nestling foods of the Savannah Sparrow. Canadian Journal of Zoology 62: 23-40.
Mielke, P. W., Jr. 1984. Meteorological applications of permutation techniques based on distance functions, in: Krishnaiah, P.R., Sen, P.K., (Eds.), Handbook of Statistics, Vol. 4. Elsevier Science Publishers, The Hague, The Netherlands, pp. 813-830.
Milchunas, D. G., O. E. Sala, and W. K. Lauenroth. 1988. A generalized model of the effects of grazing by large herbivores on grassland community structure. American Naturalist 132: 87-106.
Milchunas, D. G., and W. K. Lauenroth. 1993. Quantitative effects of grazing on vegetation and soils over a global range of environments. Ecological Monographs 63: 327-366.
Milchunas, D. G., W. K. Laurenroth, and I. C. Burke. 1998. Livestock grazing: animal and plant biodiversity of shortgrass steppe and the relationship to ecosystem function. Oikos 83: 65-74.
Monrós, J. S., E. J. Belda, and E. Barba. 2002. Post-fledging survival of individual great tits: the effect of hatching date and fledging mass. Oikos 99: 481-488.
Morton, M. L. 1975. Seasonal cycles of body weights and lipids in Belding ground squirrels. Bulletin of the Southern California Academy of Sciences 74: 128-143.
Morton, M. L., K. W. Sockman, and L. E. Peterson. 1993. Nest predation in the mountain white-crowned sparrow. Condor 95: 72-82.
Muchai, M., L. Bennun, L. Lens, M. Rayment, and G. Pisano. 2002. Land-use and the conservation of Sharpe’s Longclaw Macronyx sharpie in central Kenya. Bird Conservation International 12: 107-121.
148
Mudge, G. P., and T. R. Talbot. 1993. The breeding biology and causes of nest failure of Scottish Black-throated Divers. Ibis 135: 113-120.
Nack, J. L., and C. A. Ribic. 2005. Apparent predation by cattle at grassland bird nests. Wilson Bulletin 117: 56-62.
Naef-Daenzer, B., and L. F. Keller. 1999. The foraging performance of Great and Blue Tits (Parus major and P. caeruleus) in relation to caterpillar development, and its consequences for nestling growth and fledging weight. Journal of Animal Ecology 68: 708-718.
Naef-Daenzer, B., F. Widmer, and M. Nuber. 2001. Differential post-fledging survival of great and coal tits in relation to their condition and fledging date. Journal of Animal Ecology 70: 730-738.
Naeth, M. A., A. W. Bailey, D. J. Pluth, D. S. Chanasyk, and R. T. Hardin. 1991. Grazing impacts on litter and soil organic matter in mixed prairie and fescue grassland ecosystems of Alberta. Journal of Range Management 44: 7-12.
Nagy, K. A. 1987. Field metabolic rate and food requirement scaling in mammals and birds. Ecological Monographs 57: 111-128.
Nagy, L. R., and R. T. Holmes. 2005. Food limits annual fecundity of a migratory songbird: an experimental study. Ecology 86: 675-681.
Natarajan, N. R., and C. E. McCullough. 1999. Modeling heterogeneity in nest survival data. Biometrics 55: 553-559.
Nelson, K. J., and K. Martin. 1999. Thermal aspects of nest-site location for vesper sparrows and horned larks in British Columbia. Studies in Avian Biology 19: 137-143.
New, T. R. 1998. Invertebrate surveys for conservation. Oxford University Press, Oxford.
Ober, H. K., and J. P. Hayes. 2010. Determinants of nocturnal Lepidopteran diversity and community structure in a conifer-dominated forest. Biodiversity and Conservation 19: 761-774.
Ohlendorf, H. M., R. L. Hothem, and D. Welsh. 1989. Nest success, cause-specific nest failure, and hatchability of aquatic birds at selenium-contaminated Kesterson Reservoir and a reference site. Condor 91: 787-796.
149
O’Neill, K. M., B. E. Olson, M. G. Rolston, R. Wallander, D. P. Larson, and C. E. Seibert. 2003. Effects of livestock grazing on rangeland grasshopper (Orthoptera: Acrididae) abundance. Agriculture, Ecosystems and Environment 97: 51-64.
Owensby, C. E., R. Cochran, and E. F. Smith. 1988. Stocking rate effects on intensive- early stocked Flint Hills bluestem range. Journal of Range Management 41: 483- 487.
Paine, L., D. J. Undersander, D. W. Sample, G. A. Bartelt, and T. A. Schatteman. 1996. Cattle trampling of simulated ground nests in rotationally grazed pastures. Journal of Range Management 49: 294-300.
Patten, M.A., and D. T. Bolger. 2003. Variation in top-down control of avian reproductive success across a fragmentation gradient. Oikos 101: 479-488.
Patterson, M. P., and L. B. Best. 1996. Bird abundance and nesting success in Iowa CRP fields: the importance of vegetation structure and composition. American Midland Naturalist 135: 153–167.
Peterson, A. 1962. Larvae of insects: an introduction to nearctic species, I and II. Ohio State University, Columbus, OH.
Peterson, K. L., and L. B. Best. 1986. Diets of nestling Sage Sparrows and Brewer’s Sparrows in an Idaho sagebrush community. Journal of Field Ornithology 57: 283-294.
Phillips, M. L., W. R. Clark, M. A. Sovada, D. J. Horn, R. R. Koford, and R. J. Greenwood. 2003. Predator selection of prairie landscape features and its relation to duck nest success. Journal of Wildlife Management 67: 104-114.
Price, P. 2002. Resource-driven terrestrial interaction webs. Ecological Research 17: 241-247.
Probst, J. R., and J. P. Hayes. 1987. Pairing success of Kirtland’s warblers in marginal versus suitable habitat. Auk 104: 234-241.
Pulliam, H. R. 1988. Sources, sinks, and population regulation. American Naturalist 132: 652-661.
Rahmig, C. J., W. E. Jensen, and K. A. With. 2008. Grassland bird responses to land management in the largest remaining tallgrass prairie. Conservation Biology 23: 420-432.
150
Ralph, C. J., J. R. Sauer, and S. Droege. 1995. Monitoring bird populations by point counts. United States Department of Agriculture, Forest Service, Pacific Southwest Research Station Gen. Tech. Rep. PSW-GTR-149. 187 p.
Ralph, C. P., S. E. Nagata, and C. J. Ralph. 1985. Analysis of droppings to describe diets of small birds. Journal of Field Ornithology 56: 165-174.
Ramsey, F.L., and D. W. Schafer. 2002. The Statistical Sleuth, Second ed. Duxbury, Pacific Grove, California, USA.
Reidy, J. L., M. M. Stake, and F. R. Thompson III. 2008. Golden-cheeked warbler nest mortality and predators in urban and rural landscapes. Condor 110: 458-466.
Renfrew, R. B., and C. A. Ribic. 2003. Grassland passerine nest predators near pasture edges identified on videotape. Auk 120: 371-383.
Ricklefs, R. E. 1969. An analysis of nesting mortality in birds. Smithsonian Contributions in Zoology 9: 1–48.
Robel, R. J., J. N. Briggs, A. D. Dayton, and L. C. Hulbert. 1970. Relationships between visual obstruction measurements and weight of grassland vegetation. Journal of Range Management 23: 295-297.
Robinson, S. K., and R. T. Holmes. 1982. Foraging behavior of forest birds: the relationships among search tactics, diet, and habitat structure. Ecology 63: 1918– 1931.
Rodenhouse, N. L., and R. T. Holmes. 1992. Results of experimental and natural food reductions for breeding Black-throated Blue Warblers. Ecology 73: 357–372.
Rotenberry, J. T. 1980. Dietary relationships among shrubsteppe passerine birds: competition or opportunism in a variable environment. Ecological Monographs 50: 93-110.
Rotenberry, J. T., M. A. Patten, and K. L. Preston. 1999. Brewer's Sparrow (Spizella breweri), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu. proxy.library.oregonstate.edu/bna/species/390.
Roth, R. R. 1976. Spatial heterogeneity and bird species diversity. Ecology 57: 773- 782.
151
Salo, E. D. 2003. Effects of grazing intensity and temporal application of grazing treatments on nongame birds in North Dakota mixed-grass prairie. M.S. Thesis, South Dakota State University. 87 pp.
Sánchez-Zapata, J. A., M. Carrete, A. Gravilov, S. Sklyarenko, O. Ceballos, J. A. Donázar, and F. Hiraldo. 2003. Land use changes and raptor conservation in steppe habitats of Eastern Kazakhstan. Biological Conservation 111: 71-77.
Sandercock, B. K., E. L. Hewitt, and K. L. Kosciuch. 2008. Effects of experimental cowbird removals on brood parasitism and nest predation in a grassland songbird. Auk 125: 820-830.
Sauer, J. R., J. E. Hines, and J. Fallon. 2007. The North American breeding bird survey, results and analysis 1966–2006. Version 10-13-2007. Laurel, MD, USA: USGS Patuxent Wildlife Research Center.
Sayre, N. F. 2009. Bad abstractions: response to Sullivan. Conservation Biology 23: 1050-1052.
Schulz, T. T., and W. C. Leininger. 1990. Differences in riparian vegetation structure between grazed areas and exclosures. Journal of Range Management 43: 295- 299.
Slagsvold, T., and J. T. Lifjeld. 1985. Variation in plumage colour of the Great Tit Parus major in relation to habitat, season and food. Journal of Zoology 206: 321- 328.
Smith, T. R., and J. L. Capinera. 2005. Mandibular morphology of some Floridian grasshoppers (Orthoptera: Acrididae). Florida Entomologist 88: 204-207.
Snodgrass, R. E. 1935. Principles of insect morphology. McGraw-Hill Book Company, Inc., New York, N.Y.
Snodgrass, R. E. 1951. Comparative studies on the head of mandibulate arthropods. Comstock Publishing Company, Inc., Ithaca, N.Y.
Sperry, J. H., R. G. Peak, D. A. Simprich, and P. J. Weatherhead. 2008. Snake activity affects seasonal variation in nest predation risk for birds. Journal of Avian Biology 39: 379-383.
Stahl, J. T., and M. K. Oli. 2006. Relative importance of avian life history variables to population growth rate. Ecological Modelling 198: 23-39.
152
Stanley, T. R. 2004. When should Mayfield model data be discarded? Wilson Bulletin 116: 267-269.
Stehr, F. 1987. Immature insects, I and II. Kendall/Hunt Publishing Company, Dubuque, IA.
Stodola, K. W., D. A. Buehler, D. H. Kim, K. E. Franzreb, and E. T. Linder. 2010. Biotic and abiotic factors governing nestling-period length in the Ovenbird (Seiurus aurocapilla). Auk 127: 204-211.
Suedkamp Wells, K. M., and S. D. Fuhlendorf. 2005. Comparison of microclimate at grassland bird nests with different substrates. Prairie Naturalist 37: 21-28.
Sutter, B., and G. Ritchison. 2005. Effects of grazing on vegetation structure, prey availability, and reproductive success of Grasshopper Sparrows. Journal of Field Ornithology 76: 345-351.
Tews, J., U. Brose, V. Grimm, K. Tielbörger, M. C. Wichmann, M. Schwager, and F. Jeltsch. 2004. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. Journal of Biogeography 31: 79-92.
Thomas, L., J. L. Laake, S. Strindberg, F. F. C. Marques, S. T. Buckland, D. L. Borchers, D. R. Anderson, K. P. Burnham, S. L. Hedley, J. H. Pollard, J. R. B. Bishop, and T. A. Marques. 2005. Distance 5.0. Release 5. Research Unit for Wildlife Population Assessment, University of St. Andrews, UK. http://www.ruwpa.st- and.ac.uk/distance/.
Thomas, L., S. T. Buckland, E.A. Rexstad, J. L. Laake, S. Strindberg, S. L. Hedley, J. R. B. Bishop, T. A. Marques, and K. P. Burnham. 2010. Distance software: design and analysis of distance sampling surveys for estimating population size. Journal of Applied Ecology 47: 5-14.
Toombs, T. P., J. D. Derner, D. J. Augustine, B. Krueger, and S. Gallagher. 2010. Managing for biodiversity and livestock: a scale-dependent approach for promoting vegetation heterogeneity in western Great Plains grasslands. Rangelands 32: 10-15.
Townsend, D. E., and S. D. Fuhlendorf. 2010. Evaluating relationships between spatial heterogeneity and the biotic and abiotic environments. American Midland Naturalist 163: 351-365.
153
Tracy, B. F. and D. A. Frank. 1998. Herbivore influence on soil microbial biomass and nitrogen mineralization in a northern grassland ecosystem: Yellowstone National Park. Oecologia 114: 556-562.
Uresk D. W., J. G. MacCracken, and A. F. Bjugstad. 1982. Prairie dog density and cattle grazing relationships. In: Timm RM and Johnson RJ (Eds). Proceedings of the Fifth Great Plains Wildlife Damage Control Workshop. Institute of Agriculture and Natural Resources, University of Nebraska, Lincoln, NE.
Váczi, O., B. Koósz, and V. Altbäcker. 2006. Modified ambient temperature perception affects daily activity patterns in the European Ground Squirrel (Spermophilus citellus). Journal of Mammalogy 87: 54-59.
Van Auken, O. W. 2000. Shrub invasions of North American semiarid grasslands. Annual Review of Ecology and Systematics 31: 197-215.
Vanderploeg, H. A., and D. Scavia. 1979. Two electivity indices for feeding with special reference to zooplankton grazing. Journal of the Fisheries Research Board of Canada 36: 362-365.
Vanderploeg, H. A., and D. Scavia. 1979. Calculation and use of selectivity coefficients of feeding: zooplankton grazing. Ecological Modelling 7: 135-149.
Verts, B. J., and L. N. Carraway. 1998. Land Mammals of Oregon. University of California Press, Berkeley and Los Angeles, California, USA.
Vickery, J. A., J. R. Tallowin, R. E. Feber, E. J. Asteraki, P. W. Atkinson, R. J. Fuller, and V. K. Brown. 2001. The management of lowland neutral grasslands in Britain: effects of agricultural practices on birds and their food resources. Journal of Applied Ecology 38: 647-664.
Vickery, P. D. 1996. Grasshopper Sparrow (Ammodramus savannarum), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu. proxy.library. oregonstate.edu/bna/species/239.
Vickery, P. D., P. L. Tubaro, J. M. Cardosado Da Silva, B. G. Peterjohn, J. R. Herkert, and R. B. Cavalcanti. 1999. Conservation of grassland birds in the western hemisphere. Studies in Avian Biology 19: 2-26.
West, N. E. 1993. Biodiversity of rangelands. Journal of Range Management 46: 2-13.
154
Western Regional Climate Center. 2009. Enterprise 20 NNE, Oregon. Western Regional Climate Center, Oregon Climate Summaries website http://www.wrcc.dri.edu/ cgi-bin/cliMAIN.pl?oren20 (Accessed August 04, 2009).
Wheelwright, N. T., and J. D. Rising. 2008. Savannah Sparrow (Passerculus sandwichensis), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America Online: http://bna.birds.cornell.edu.proxy.library.oregonstate.edu/bna/species/045.
White, G. C., and K. P. Burnham. 1999. Program MARK: Survival estimation from populations of marked animals. Bird Study 46 (Supplement): 120-138.
White, R. E. 1983. Peterson field guides: beetles. Houghton Mifflin Company, New York.
Wiens, J. A. 1974. Habitat heterogeneity and avian community structure in North American grasslands. American Midland Naturalist 91: 195-213.
Wiens, J. A., and J. T. Rotenberry. 1979. Diet niche relationships among North American grassland and shrubsteppe birds. Oecologia 42: 253-292.
Wiens, J. A. 1989. The ecology of bird communities I: foundations and patterns. Cambridge University Press, Cambridge.
Wiens, J. A. 1989. The ecology of bird communities II: processes and variation. Cambridge University Press, Cambridge.
Willms, W. D., J. F. Dormaar, and G. B. Schaalje. 1988. Stability of grazed patches on rough fescue grasslands. Journal of Range Management 41: 503-508.
Winter, M. 1999. Nesting biology of Dickcissels and Henslow’s Sparrows in southwestern Missouri prairie fragments. Wilson Bulletin 111: 515-527.
Winter, M., S. E. Hawks, J. A Shaffer, and D. H. Johnson. 2003. Guidelines for finding nests of passerine birds in tallgrass prairie. Prairie Naturalist 35: 197-211.
Wisdom, M. J., M. Vavra, J. M. Boyd, M. A. Hemstrom, A. A. Ager, and B. K. Johnson. 2006. Understanding ungulate herbivory-episodic disturbance effects on vegetation dynamics: Knowledge gaps and management needs. Wildlife Society Bulletin 34: 283-292.
155
With, K. A., A. W. King, and W. E. Jensen. 2008. Remaining large grasslands may not be sufficient to prevent grassland bird declines. Biological Conservation 141: 3152-3167.
Wyffels, S. 2009. Influence of stocking density on grazing beef cattle performance, diet composition, foraging efficiency, and diet quality on a late-spring early-summer native bunchgrass prairie. M.S. thesis, Oregon State University, Corvallis, OR.
Yackel Adams, A. A., S. K. Skagen, and J. A. Savidge. 2006. Modeling post-fledging survival of lark buntings in response to ecological and biological factors. Ecology 87: 178-188.
Zalik, N. J., and A. M. Strong. 2008. Effects of hay cropping on invertebrate biomass and the breeding ecology of Savannah Sparrows (Passerculus sandwichensis). Auk 125: 700-710.
Zanette, L. 2001. Indicators of habitat quality and the reproductive output of a forest songbird in small and large fragments. Journal of Avian Biology 32: 38-46.
Zimmerman, J. L. 1984. Nest predation and its relationship to habitat and nest density in Dickcissels. Condor 86: 68-72.
156
APPENDICES
Appendix A. Model selection results for 30 candidate models of daily nest failure rates for six species of grassland songbird breeding at the Zumwalt Prairie Preserve in northeastern Oregon, U.S.A. Model ranks are based on Akaike’s Information Criterion (AIC) values, differences in AIC values (Δ AIC), and Akaike weights (Wi). AIC values were computed using the minimized negative log-likelihood (NLL) function and the number of parameters in the model (K).
Model structurea Depredated Weather Trampled NLLAIC Δ AIC Weight K age + date date*year CN*NGD 452.68 929.57 0.00 0.36 12 date year CN 458.26930.60 1.02 0.13 7 date year CN*NGD456.33 930.78 1.20 0.11 9 age + date + BGS date*year CN*NGD 452.41 931.08 1.50 0.08 13 age*NLVO year CN*NGD453.55 931.32 1.75 0.06 12 age + date + CN date*year CN*NGD 452.62 931.49 1.91 0.05 13 age + date + AVIAN date*year CN*NGD 452.68 931.61 2.04 0.05 13 age + date + MESO date*year CN*NGD 452.68 931.61 2.04 0.05 13 age + date + SNAKES date*year CN*NGD 452.68 931.61 2.04 0.05 13 age*PLVOCV year CN*NGD454.03 932.27 2.70 0.02 12 age date CN 459.22932.51 2.94 0.02 7 age year CN*NGD457.84 933.80 4.22 0.01 9 constant date CN*NGD459.01 934.12 4.54 0.00 8 age + AVIAN date CN*NGD 457.06 934.28 4.70 0.00 10 age*BGS date CN*NGD455.08 934.37 4.80 0.00 12 age + BGS date CN*NGD 457.20 934.56 4.99 0.00 10 age year*CN CN*NGD456.31 934.80 5.23 0.00 11 age*MESO date CN*NGD455.48 935.18 5.61 0.00 12 constant year CN 461.61935.28 5.71 0.00 6 157
Appendix A continued age + NLVO year CN*NGD 457.65 935.46 5.89 0.00 10 constant constant CN 462.73935.50 5.93 0.00 5 Appendix A continued constant constant CN*NGD460.75 935.58 6.00 0.00 7 year date CN*NGD459.01 936.14 6.56 0.00 9 age*CN year CN*NGD457.46 937.10 7.52 0.00 11 age date year 462.33938.73 9.15 0.00 7 age constant NGD 463.97940.00 10.42 0.00 6 age*NLVO year NGD^2 461.05940.23 10.66 0.00 9 constant constant constant 466.49941.01 11.44 0.00 4 age + BGS + SNAKES + AVIAN + MESO year CN*NGD457.39 941.03 11.46 0.00 13 constant constant NGD 465.76941.55 11.98 0.00 5 a Model structure for the three parameters shown included: age = age of nest, AVIAN = encounter rate for avian predators, BGS = Belding's ground squirrel abundance, CN = number of livestock, constant = a constant daily failure rate, date = day of breeding season, MESOPREDATORS = mesopredator activity index, NGD = number of days cattle were present before a nest was initiated, NLVO = visual obstruction at the nest-site, PLVOCV = coefficient of variation of paddock-level visual obstruction, SNAKES = encounter rate for snakes, year = 2007 or 2008.
158 159
Appendix B. Values of paddock-level covariates used in analysis of daily nest failure rates at the Zumwalt Prairie Preserve, northeastern Oregon, USA.
Covariatea MESO- Treatment Block Year PLVOCV BGS SNAKES AVIAN PREDATORS controlA20070.580.750.000.070.19 20080.740.401.410.320.13 B20070.540.330.000.090.50 20080.760.200.000.450.00 C20070.480.280.000.090.06 20080.570.281.410.590.00 D20070.520.001.690.160.00 20080.530.000.820.340.25 lowA20070.671.430.000.190.19 20080.772.250.000.640.25 B20070.730.150.520.090.31 20080.770.131.300.270.13 C20070.680.151.050.070.19 20080.630.451.890.240.13 D20070.580.031.090.110.13 20080.690.000.770.160.13 moderateA20070.690.201.000.100.19 20080.770.402.760.290.25 B20070.890.750.000.090.06 20080.691.401.960.520.13 C20070.530.000.510.130.31 20080.870.030.000.120.13 D20070.900.000.550.120.06 20080.910.004.000.190.00 highA20070.830.050.000.120.00 20080.850.350.640.290.00 B20070.811.630.000.070.31 20081.085.231.350.650.00 C20070.700.230.550.090.13 20080.800.432.090.260.00 D20070.770.001.700.050.06 20080.860.000.000.220.13 a Covariates were estimated seperately for each paddock: PLVOCV = coefficient of variation of paddock-level visual obstruction; BGS = Belding's ground squirrel density (estimated using distance-sampling); SNAKES = snakes encountered per 24 hrs of survey effort; AVIAN = avian predators encountered per survey; MESOPREDATORS = mesopredator activity index.
160
Appendix C. Estimation of standard error of daily failure rates in McNestimate.
McNestimate uses the delta method to estimate standard errors. For clarity, the following is an example with only one failure cause, modeled as a function of a single quantitative covariate (X, with specific values of X denoted x). For this model there will be two betas:
βI = Intercept of the linear model, and
βs = Slope of the linear model.
The covariance matrix will be a 2*2 matrix:
⎡⎤vc()βII(ββ, s ) COV()β = ⎢⎥, where: ⎣⎦cv()()ββI , ss β
v(βI) = sampling variance of the intercept to the linear portion of the failure model,
v(βs) = sampling variance of the slope of the linear portion of the failure model, and
c(βI, βs) = sampling covariance between the slope and intercept.
Note that the bold beta (β) is intended to represent a vector of betas. So the variances of the betas are given by the diagonal of the covariance matrix (which is generated by the optimization procedure). But for the daily probabilities we require the variances of a function of the betas (the daily probabilities). To estimate daily probabilities, the logit transformation is required. Below is that transformation for our simple model and an arbitrary value of the covariate (x).
e(ββIs+ *x) m f = 1e+ ()ββIs+ *x
Above, mf is a function of the betas. Its variance is estimated as:
Equation (1)
161
T ⎡⎤⎡⎤∂∂mmff ∂∂ mmff ⎢⎥⎢⎥,COV,()β ∂∂ββ ∂∂ ββ ⎣⎦⎣⎦Is Is
Where:
∂m f =−mmf (1 f ), and βI
∂m f =−mmff()1 x βs
Equation (1) can be written as:
2 T ()mmff()11,COV1,− [] x()β [] x
Or:
Equation 2:
2 2 ()mmvff()12,−++()()β I xcxv (ββ Iss ) () β
Equation 2 can be represented as the product of two terms:
2 Term 1 = mmff()1− ()
2 Term 2 = vxcxv()βII++2, (ββs ) ( βs)
To see how these terms change as a function of x, we can take the derivatives of each with respect to x:
∂ ()Term 1 2 2 =−21mmf ()(fsβ 12 − m f) ∂x
∂ ()Term 2 =+2,()cxv()(βIsββ s) ∂x