Nest Site Selection and Nestling Diet of the Texas Red-shouldered Hawk Buteo Lineatus
Texanus in South Texas
by
Bradley N. Strobel, B.S.
A Thesis
In
WILDLIFE SCIENCE
Submitted to the Graduate Faculty
of Texas Tech University in
Partial Fulfillment of
the Requirements for
the Degree of
MASTER OF SCIENCE
Approved
Dr. Clint W. Boal
Dr. Gad Perry
Dr. Mark C. Wallace
John Borrelli
Dean of the Graduate School
August, 2007
COPYRIGHT 2007, BRADLEY N. STROBEL
Texas Tech University, Strobel, August 2007
ACKNOWLEDGMENTS
The completion of this project was only possible through the help of several
entities. The Rob and Bessie Welder Wildlife Foundation provided indispensable
financial and logistical assistance. I thank the directors of the refuge, Dr. D. L. Drawe,
Dr. T. Blankenship and Dr. S. Glasscock, for council, support, suggestions, and
camaraderie. In addition, I thank the refuge staff, especially Mr. J. Cox, Mr. B. Martinez
and Mr. B. C. Glasscock, for their unfailing assistance with stuck trucks and “unique”
problems. Furthermore, I thank Mr. Jeff Rooke, owner of the twin oaks hunting resort,
for allowing us assess to his property.
I thank my advisor, Dr. Clint W. Boal, for providing me invaluable
encouragement and guidance. The dedication he shows his research and his students will
be the mark I use to measure my own achievements and career. I thank my committee
members; Dr. Gad Perry and Dr. Mark Wallace, their suggestions and involvement have
improved the project and shaped my thinking.
Many people have unknowingly contributed to the success of this project. My
fellow researchers, C. Huber, N. Mannan and D. Butler, have provided counsel on all
matters scientific, and otherwise. I thank my parents N. and D. Strobel for, aside from
providing my genesis; have offered endless support and understanding through an
arduous process. Finally, I thank my fiancée C. L. Haralson, who despite the trials of her
own research, has never failed to lend assistance, encouragement, or love.
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Texas Tech University, Strobel, August 2007
TABLE OF CONTENTS
ACKNOWLEDGMENTS
ii
LIST OF TABLES
v
LIST OF FIGURES
vii
CHAPTER
I.
II.
INTRODUCTION AND SUMMARY OF FINDINGS
Introduction
1
Summary of Findings
2
Literature Cited
3
NESTING HABITAT SELECTION OF
RED-SHOULDERED HAWKS IN SOUTH TEXAS
III.
1
4
Abstract
4
Introduction
5
Study Area
7
Methods
8
Results
12
Discussion
14
Literature Cited
19
TEMPORAL AND SPATIAL VARIATION IN PREY USE
BY BREEDING RED-SHOULDERED HAWKS
28
Abstract
28
Introduction
29
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Texas Tech University, Strobel, August 2007
Study Area
31
Methods
32
Results
35
Discussion
39
Literature Cited
46
IV. NESTLING DIET AND ADULT PROVISIONING RATES
OF TEXAS RED-SHOULDERED HAWKS IN SOUTH TEXAS
62
Abstract
62
Introduction
62
Study Area
64
Methods
65
Results
68
Discussion
70
Literature Cited
74
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Texas Tech University, Strobel, August 2007
LIST OF TABLES
2.1
Candidate models describing the probability of a tree being
selected as a red-shouldered hawk nest tree in south Texas
from 2005-2006.
2.2
23
Candidate models describing the probability of a 0.04 ha site
being selected as a red-shouldered hawk nest site in south
Texas from 2005-2006.
2.3
24
Candidate models describing characteristics of trees and 0.04ha
sites and their influence on the red-shouldered hawk nesting
habitat selection in south Texas from 2005-2006.
2.4
26
Variables and coefficients of the resource selection probability
functions for red-shouldered hawk nesting habitat selection at
two scales in south Texas.
3.1
27
Species list of prey items delivered to nestlings in 7 Redshouldered hawk nests in 2005-2006 in south Texas. Percent
of occurrence and biomass contributed to total nestling diet by
each prey item.
50
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Texas Tech University, Strobel, August 2007
3.2
Total number of each prey type used by breeding redshouldered hawks as reported in studies conducted across the
red-shouldered hawk breeding range.
4.1
56
Species list of prey items delivered to nestlings in 7 Redshouldered hawk nests in 2005-2006 in south Texas. Percent
of occurrence and biomass contributed to total nestling diet by
each prey item.
4.2
77
Occurrences of prey types in diets of nestling red-shouldered
hawks in south Texas. Data collected using video recorders at
7 nests in 2005 and 2 nests in 2006.
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Texas Tech University, Strobel, August 2007
LIST OF FIGURES
3.1
Daily temporal prey fluctuations in the mean number of each
taxonomic prey category delivered to 9 red-shouldered hawk nests in
south Texas in 2005-2006.
3.2
58
2004 NAIP infrared imagery of Refugio and San Patricio counties,
Texas. Locations of 9 red-shouldered hawk nests monitored using
time-lapse videography on the Welder Wildlife Refuge and Twin Oaks
59
Hunting Resort.
3.3
Dendrogram of Morisita’s similarity index calculated for diets of
nestling red-shouldered hawks in 7 nests in 2005 (02, 05, 07, 08, 09,
11, 12) and 2 in 2006 (14, 18) in south Texas.
3.4
60
Dendrogram of Morisita’s similarity index calculated for diets of
nestling red-shouldered hawks in 7 different studies across redshouldered hawk breeding range.
4.1
61
Mean percent (±95% CI) of occurrence and biomass contributed to the
diets of nestling Red-shouldered hawks by 5 prey categories. Data
collected at 7 nests in 2005-2006 in south Texas.
vii
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Texas Tech University, Strobel, August 2007
4.2
Nestling provisioning rates between different sized clutches by
adult Red-shouldered hawks in 2005-2006 in south Texas.
Mean (±95% CI) frequency of prey deliveries and mean (±95%
CI) number of grams of prey delivered per hour and per
nestling
85
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CHAPTER I
INTRODUCTION AND SUMMARY OF FINDINGS
Introduction
The collection of these data presented in this thesis was conducted during the
summers of 2005 and 2006 on the Rob and Bessie Welder Wildlife Refuge in San
Patricio County Texas and the Twin Oaks Hunting Resort in Refugio County Texas. The
study focused on the breeding ecology of the Texas Red-shouldered Hawk (Buteo
lineatus texanus) and included aspects of nesting habitat selection, nestling diet, adult
prey provisioning rates and productivity. Data presented here have been formatted into
discrete chapters to facilitate future publication of results. Each chapter has been written
as a stand-alone document and therefore, some redundancy exists in the description of the
introduction, justification, and study area. In addition, some similarities exist in the data
collection portions of the methodology sections, but analytical techniques used in each
chapter differ.
Beginning after the title page of each chapter (indicated by the table of contents)
each document is formatted to follow the guidelines required by the Wildlife Society for
publication in the Journal of Wildlife Management (Messmer and Morrison 2006).
Analyses, interpretation, and presentation of these data are the responsibility of the
author; however in publication each chapter will have more than1 author and hence I
have retained the plural form (we) through each document.
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Texas Tech University, Strobel, August 2007
Summary of findings
The following chapters of this thesis examine different aspects of the breeding
ecology of the Texas subspecies of red-shouldered hawk. In the second chapter, titled
“Nesting Habitat Selection of Red-shouldered Hawks In South Texas”, we create a
resource selection probability function for red-shouldered hawk nesting habitat selection
using a logistic regression model of characteristics measured at 15 active nest sites and 45
unused sites in 2005-2006 in south Texas. Vegetation characteristics were measured at
two scales (center trees and 0.04ha sites). Models from both scales were evaluated
independently with the best models from each scale then evaluated between scales. Tree
height and diameter appeared to be the most important characteristics determining nest
site selection at both scales, however the best model from the coarser 0.04ha site scale
received a much greater weight than the best model from the finer center tree scale. The
best site scale model used the parameters: canopy height, average tree diameter, and basal
area to correctly predict nest sites 93.3% of the time. The resource selection probability
function from this model can be applied to assess the suitably of forest stands as redshouldered hawk nesting habitat in south Texas.
In chapter 3, titled “Temporal and Spatial Variation in Prey useby Breeding Redshouldered Hawks”, we examine daily temporal patterns in the prey use by redshouldered hawks to determine if prey type used is influenced by time of day.
Additionally, we examine fine scale and large scale spatial variation in prey use by
calculating Morisita’s similarity indices across nests within our study as well as across
previously published studies. Through this we find significant temporal patterns in prey
type used by red-shouldered hawks breeding in south Texas. More amphibians were used
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during mid afternoon than during late evening, while more insects were used during late
evening than earlier in the day. Breeding pairs of red-shouldered hawks within our study
demonstrated differences in prey type used. Similarly, diets of red-shouldered hawks
varied significantly across much of their breeding range, and coincided with latitudinal
differences between study sites. These spatial patterns in prey use are likely caused by
spatial differences in prey availability and demonstrate the dietary flexibility of redshouldered hawks. Furthermore, these findings indicate the potential of regional
variations in prey availability to be an additional factor influencing reproductive success
and survival of red-shouldered hawks in North America.
In chapter 4, titled “Nestling Diet and Adult provisioning Rates of Texas Redshouldered Hawks in South Texas” we investigate the prey provisioning rates of adult
red-shouldered hawks to determine its potential effect on red-shouldered hawk
productivity. Through video surveillance we identified 1320 prey items delivered to
nestlings. We found, insect, mammalian and reptilian prey were used more frequently
than other prey types, however insect prey contributed less to the total biomass than all
other prey types except avian prey. In addition, we found that nestlings in broods of 2
and broods of 3 were provided prey in similar proportions. Adults rearing larger broods
delivered more prey per hour and more grams of prey per hour, but a similar number of
deliveries and grams of prey on a per nestling basis. We conclude that provisioning rates
and not the size of prey used are different between adults rearing different sized broods.
Literature cited
Messmer, T. A., and M. L. Morrison. Unified manuscript guidelines for The Wildlife
Society peer-reviewed publications. Journal of Wildlife Management 70:304-320.
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CHAPTER II
NESTING HABITAT SELECTION OF RED-SHOULDERED HAWKS IN SOUTH
TEXAS
Abstract
We created a resource selection probability function for red-shouldered hawk
nesting habitat selection using a logistic regression model of characteristics measured at
15 active nest sites and 45 unused sites in 2005-2006 in south Texas. Vegetation
characteristics were measured at two scales (center trees and 0.04ha sites). Models from
both scales were evaluated separately using Akaike Information Criterion. The best
models from each scale were again compared using Akaike Information Criterion to
evaluate the importance of spatial scale to nesting habitat selection. Center tree height
and average site canopy height appeared to be of greater importance to nesting habitat
selection than the respective center tree diameter or the site basal area. When we
compared models from each scale, the best model for the 0.04ha site scale received a
much greater weight than the best model from the center tree scale (0.62 and 0.05,
respectively). The best site scale model used the parameters: canopy height, average tree
diameter, and basal area to correctly predict nest sites 93.3% of the time. The resource
selection probability function from this model can be applied to assess the suitably of
forest stands as red-shouldered hawk nesting habitat in south Texas.
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Introduction
The red-shouldered hawk (Buteo lineatus, RSHA) was once regarded as the most
common hawk in moist woodlands of North America (Bent 1937), but is currently
considered to be rare or declining throughout much of its range (Gehring 2003).
Populations of the RSHA subspecies resident to south Texas (B. l. texanus) declined an
estimated 54% between 1950-1969 (Oberholser 1974), and may have continued to
decline from 1966-2004 (Sauer et al. 2005). Red-shouldered hawks may be particularly
sensitive to habitat fragmentation and alteration (Bednarz and Dinsmore 1982, Bryant
1986, Moorman and Chapman 1996), and many have suggested that their population
declines are due in part to the loss of wetland associated forests (Craighead and
Craighead 1956, Bednarz and Dinsmore 1981, Jacobs and Jacobs 2002, Gehring 2003).
Habitat fragmentation is detrimental to many species by increasing susceptibility to
predation, changing biotic communities, altering prey populations and increasing
extinction risk (Andren 1994, Herket 1994, Bayne and Hobson 1997, Fahrig 1997).
Furthermore, habitat fragmentation is detrimental to RSHAs by favoring more generalist
competitors such as great horned owls (Bubo virginianus) and red-tailed hawks (Buteo
jamaicensis, RTHA, Crocoll 1994).
By 1988 agricultural and urban development in south Texas had altered an
estimated 95% of the historic vegetative communities and 99% of the riparian areas
(Jahrsdoerfer and Leslie 1988), and the Texas Parks and Wildlife Department (TPWD)
forecasts an increasing rate of landscape fragmentation (TPWD 2005). With concerns
regarding the management of this ecoregion and its potential effect on RSHAs, TPWD
listed the gulf coast prairies and marshes as a Tier 1 – High Priority Ecoregion and the
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Texas Tech University, Strobel, August 2007
RSHA as a species of state concern (TPWD 2005). The high and increasing rates of
landscape fragmentation in south Texas raise concern of a possible long-term decline of
RSHA populations rooted in a lack of adequate nesting habitat and decreased
productivity.
Throughout much of their breeding range, RSHA generally nest in taller trees
than randomly available and in areas with taller average canopy height than randomly
available (Morris and Lemon 1983, Parker 1986, Woodrey 1986, Preston et al. 1989,
McLeod et al. 2000). Red-shouldered hawk nest trees are also characterized by having
larger diameters at breast height (dbh) than randomly available trees (Parker 1986, Titus
and Mosher 1997, McLeod et al. 2000, Rottenborn 2000). Similarly, Woodrey (1986)
and Moorman and Chapman (1996) showed RSHA nest sites had more trees with “large”
dbh (>50cm and > 69cm; respectively) than randomly available sites. Red-shouldered
hawk nest sites in Minnesota had significantly higher basal areas than randomly available
sites (McLeod et al. 2000). Percent canopy closure was significantly higher at RSHA
nest sites than randomly available sites (Moorman and Chapman 1986, Parker 1986,
Woodrey 1986) or RTHA nest sites (Bednarz and Dinsmore 1982).
Larger scale factors also seem to be important in RSHA nest site selection.
Bednarz and Dinsmore (1982) found that RSHA nests were significantly further from
roads and buildings and in larger contiguous forest stands than RTHA nests.
Additionally, RSHAs generally nest nearer to permanent water sources than random or
than do RTHAs (Bednarz and Dinsmore 1982, Preston et al. 1989,McLeod et al. 2000).
These factors suggest an importance of wetland systems to RSHAs and the potential for
negative effects of landscape development or habitat degradation. Yet, despite research
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documenting deleterious effects of habitat fragmentation and development on RSHAs,
many apparently viable populations exist in highly developed human landscapes (e.g.,
Bloom et al. 1993, Dykstra et al. 2001). It is apparent that nest site selection is based on
some general but flexible criteria, and likely occurs at multiple spatial scales (Wiens and
Rotenberry 1981, Kotliar and Wiens 1990). Unfortunately, few data exist describing the
relative importance of landscape and habitat variables to RSHA habitat selection at
different scales.
Previous research has provided valuable information on characteristics unique to
RSHA nesting habitat, and thus apparently important in habitat selection. Many studies
have examined habitat selection of individual trees and 0.04ha sites (i.e., nest sites), as
suggested by James and Shugart (1970). However, these studies have been unable to
determine relative importance of individual characteristics within or across scales.
Understanding which characteristics increase the probability of a resource being selected
would provide further information on RSHA nest site selection, and better direction for
conservation and management efforts. This information may be identified using resource
selection probability functions (RSPF; Manly et al. 2002).
The objectives of this study were to (1) model RSHA nesting habitat selection at
two scales within forest stands, and (2) compare the relative importance of models within
and between these scales. Then using the best models from each scale (3) create a RSPF
to assess the probability of an area being RSHA nesting habitat.
Study area
This study was conducted on the Rob and Bessie Welder Wildlife Refuge and the
Twin Oaks Hunting Resort in San Patricio and Refugio Counties, Texas, respectively.
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Texas Tech University, Strobel, August 2007
These adjacent sites are separated by the Aransas River 10 km from its outlet into
Copano Bay in the Gulf of Mexico. The study site lies in the northern portion of the
Tamaulipan Biotic Province (Blair 1950) within the Gulf Coast Prairies and Marshes
Ecoregion (TPWD 2005). The Rob and Bessie Welder Wildlife Refuge (3,156 ha) and
the Twin Oaks Hunting Resort (2,857 ha) consist of a diverse mosaic of mesquite
(Prosopis spp.)-mixed grass communities, live oak (Quercus virginiana)-chaparral
communities and riparian woodlands dominated by pecan (Carya illinoinensis),
hackberry (Celtis spp.) and cedar elm (Ulmus crassifolia) (Drawe et al. 1978).
Woodlands are primarily small (< 2.5 ha) discontinuous patches interspersed with more
open communities. The growing season in this region is long, ranging from 275-320
days (Rappole and Blacklock 1985), but rainfall is often irregular, averaging 88 cm
annually (Drawe et al. 1978). Elevation is low, usually less than 30 meters, and summer
temperatures average 30 degrees C (Guckian and Garcia 1979). The diversity and types
of vegetation communities on the study site make this area unique compared to other
regions within the RSHAs range.
Methods
Field methods followed protocols approved by Texas Tech University’s animal
care and use committee (protocols: 03015-02 and 05067-11).
Nest searching
We located active RSHA nests on the study site during the 2005 and 2006
breeding seasons using broadcast survey methods as described by McLeod and Anderson
(1998). We systematically placed predefined survey points approximately 800m apart in
all forested areas. At each survey point we broadcast a 20 second RSHA alarm call
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followed by 40 seconds of silence. We made 6 such broadcasts, with each consecutive
broadcast directed 120 degrees clockwise from the prior broadcast. We concluded
surveying each point with a 4-minute listening period. Survey events were concluded at
each point when either a bird responded or after the 10 minute survey had been
completed. We conducted surveys between dawn and 1300 from April – June. We
recorded the direction, distance, and number of RSHA responses at each point surveyed.
Using the surveys to focus our efforts, we conducted nest search transects through all
potential nesting habitat. Once nests were discovered, we recorded their location using a
GPS unit and returned weekly to monitor breeding activity. We defined active nests as
those in which at least one egg was laid that year.
Vegetation Measurements
Within 2 weeks after nesting attempts ended by either fledging young or failing,
we measured vegetative characteristics at all active nests sites and at 3 random sites
associated with each nest. For comparative purposes, we adapted vegetation
measurements suggested by James and Shugart (1970). We defined nest sites and unused
random sites (collectively – sites) as a 0.04 ha circle (11.3 m radius) centered on the nest
tree or randomly selected unused tree (collectively – center trees). To ensure we were
comparing nest site selection within a forest stand we used stratified random distances
(between 75m and 200m) starting from the nest tree locate three unused trees and sites to
associate with each nest location. To ensure randomly selected trees were potential nest
trees, we constrained unused tree selection to living trees, greater than 3 m tall, that
appeared structurally capable of supporting a RSHA nest. Because of the size and
conspicuousness of RSHA nests, we are confident that all randomly selected trees were
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unused for nesting by RSHA during that breeding season. If random points fell outside
of the forest stand, in a markedly different vegetation type or age class, we used an
alternate random location.
Excluding nest-specific measurements, we measured the same vegetation
characteristics at all center trees and 0.04ha sites. We identified the tree species,
measured dbh (cm) with a diameter tape and height (m) with a clinometer, for all center
trees. To provide information on stand composition and calculate basal area, we recorded
the species and dbh of all trees with greater than 5cm dbh within each 0.04ha site. We
then divided the site into quarters with, 4, 11.3 meter transects oriented in the cardinal
directions. Each transect consisted of 11, 1 meter spaced points, resulting in 44 points at
each site. We estimated the percent canopy closure for each site by recording the
presence or absence of canopy cover at each of the 44 points, using a Geographic
Resource Solutions Densitometer. To estimate the average canopy height within each
site we measured the height of the four canopy trees nearest to the ending point of each
transect. We measured the distance from the center trees to permanent water using GIS.
Only data from active nests were included in analyses, and data from nests that were used
in both years were only included in the data set once.
Analyses
Model building and justification. We used 7 variables to build 17 a priori models, 4 at
the center tree scale and 13 at the 0.04ha site scale. We selected variables and
constructed models based on the findings of previous RSHA nesting habitat research.
We constructed models to examine both the importance of individual variables,
combinations of variables and interactions between some variables. Prior research has
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documented RSHA selection of taller and larger diameter trees than randomly available.
Therefore, those variables and interactions of those variables were included more often
than other potentially important variables. In addition, each model set included a global
model consisting of all parameters and interactions.
For models at the center tree scale, we included the parameters: dbh (CTDBH),
height (CTHT), and their interaction (CTDBH*CTHT) (Table 1). At the site scale we
included average dbh (ASDBH), basal area (BASAL), canopy cover (CANCOV),
average canopy height (CANHT), the distance to a permanent/semi permanent wetland
(H2ODIST) and the interaction between average dbh and average canopy height
(ASDBH*CANHT) (Table 2). We then used the variables from the best predicting
model of each scale, to build a set of 6 “mixed models” combining variables from each
scale (Table 3). This allowed us to determine the relative importance of the two scales to
RSHA nesting habitat selection and provide a best model regarding both of these scales.
Despite being constructed prior to analysis, these models should not be considered
genuinely a priori, because insight was drawn from modeling of each scale. However,
we believe this iterative modeling process does not compromise the merit of these mixed
models.
Model selection and evaluation. All analyses were done using Minitab 15.1 software
(Minitab Inc. State College, Pennsylvania). We conducted a logistic regression, with use
or non-use as the dependent variable criteria. Logistic regression was appropriate
because of its robustness to heteroscedasticity and no assumptions of normality. To
confirm that the models selected for a particular scale fit the data, we tested the global
model from that scale using the Hosmer-Lemeshow goodness-of-fit test (Hosmer and
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Lemeshow 2002). To facilitate model evaluation and alleviate some concerns stemming
from small sample sizes, we then calculated second order Akaike Information Criterion
(AICc) values, differences between AICc values of all models and the lowest scoring
model (∆i), and Akaike weights (ωi) for each model (Burnham and Anderson 2002). We
used the Wald statistic to confirm the significance of the coefficients of individual
variables in the best predicting models (Hosmer and Lemeshow 2002). We created a
RSPF for the best models at each scale using the estimated coefficients of each variable.
To compare the accuracy of the RSPFs we calculated the frequency of correct
classifications (>50 %= Use) of data in the original data set. Few nest site data limited
our ability to exclude a test set for evaluation of the RSPFs, instead we recognize the
limitations of testing the models against the original data and use this only as a means to
compare the accuracy between models.
Results
Center tree scale
The best model at the center tree scale included both CTDBH and CTHT (Table
1). The global model was less plausible, with only 23% of the AIC weight. However,
the Hosmer-Lemeshow test indicated that the global model was a good fit to the data (χ28
= 9.206, P = 0.325), suggesting that subsequent less parameterized models are also well
fit to the data. The single variable models, CTHT and CTDBH were less plausible
alternatives compared to the best model, with a combined AIC weight less than 0.09.
The Wald statistic indicated the coefficients of the parameters CTDBH and CTHT
were both significantly different from 0, and contributed significantly to the model (z =
2.18, P = 0.029 and z = 3.20, P = 0.001, respectively). Using the estimated coefficients
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for these variables (Table 4), the RSPF correctly distinguished nest trees from random
trees only 23.3% of time.
Site scale
Because the site scale model set contained 13 models, AIC weights were more
broadly distributed and the best model was less clearly defined. The three best models
combined had 71% of the AIC weight (Table 2). The best model was composed of 3
variables, ASDBH, BASAL and CANHT, and had 33% of the AIC weight. Because the
parameters: ASDBH, BASAL and CANHT, were in all of the top three models, they
likely accounted for the majority of each models importance, and were the only
parameters from this scale that we included in further analysis. The Hosmer-Lemeshow
test indicated the global model from the site scale also fit the data (χ28 = 6.434, P =
0.599).
Site scale variables (ASDBH, BASAL and CANHT) coefficients significantly
contributed to the model (z = 2.35, P = 0.019; z = 2.08, P = 0.038 and z = 2.91, P = 0.004,
respectively). Using the estimated coefficients for these parameters (Table 4), the RSPF
correctly identified nest sites and unused sites 93.3% of the time.
Both scales
The “mixed model” set included the best models from each scale, as well as
unique models incorporating parameters from both scales. The best model in this set was
also the best model from the site scale. In this set it earned an AICc of 42.06, and 62% of
the total AIC weight (Table 3). The next nearest model had a ∆i of 3.07. Additionally,
the model with site parameters: ASDBH, CANHT, was nearly 2 AICc values better than
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the similar model with center tree parameters: CTDBH, CTHT. The Hosmer-Lemeshow
test showed the global model in this set fit the data (χ2 = 10.752, df = 8, P = 0.216).
Discussion
Center tree models
The best predicting model for the center tree scale consisted of both CTDBH and
CTHT, showing that RSHA nest tree selection was influenced both by tree height and
dbh. Previous studies have documented the importance of tree height to nest site
selection of RSHAs (Morris and Lemon 1983, Woodrey 1986, Preston et al. 1989,
McLeod et al. 2000). Dijak et al. (1990) showed that nests used by RSHAs in multiple
years were in significantly taller trees than nests that were used once. Additionally,
successively used nests had higher success rates, suggesting that nesting in taller trees
provided a reproductive advantage over nesting in shorter trees (Dijak et al. 1990). Taller
nest trees may provide thermal conditions beneficial to nestling survival, or be less
accessible to potential predators of nestling.
Several studies have shown that RSHAs select to nest in trees with larger dbh than
randomly available (Parker 1986, Titus and Mosher 1997, McLeod et al. 2000). It is
unlikely that the dbh directly influences nest tree selection, but instead is correlated to
characteristics of the tree that are important to RSHA productivity. Bednarz and
Dinsmore (1982) showed that despite RSHAs being markedly smaller than RTHAs,
branches supporting RSHA nests had significantly larger diameters than those supporting
RTHA nests. Furthermore, RSHAs, and the closely related broad winged hawk (Buteo
platypterus), select to nest low within the canopy of nest trees (Bednarz and Dinsmore
1982, Keran 1978). Nests placed low within larger dbh trees may be more resistant to
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windy conditions (Dijak et al. 1990), such as those which can occur during the RSHA
breeding season in south Texas. Furthermore, the evidence ratio of the model CTHT
versus the model CTDBH is 56.12 for our data. This shows that there is a considerably
greater chance that the CTHT represents more important patterns in the data than
CTDBH. Tree height may be more critical in RSHA nest tree selection than tree
diameter. However, the AIC weights strongly suggest that the presence of the parameter
CTDBH contributes substantially to the predictive power of the best model.
The RSPF for the center tree scale correctly identified only 23.3% of the nest
trees and random trees. The criterion we used to select potential nest locations may have
hampered the models ability to detect differences in our data. However, the poor
accuracy of this RSPF may also be caused by a lack of selection by RSHAs at this scale.
Janes (1985) suggested that in most cases, suitable nest locations are not a limiting factor
within a raptors homerange. If availability of nest trees is not limiting RSHA
productivity, the patterns of tree selection found in other studies may be artifacts of
habitat selection at larger scales.
Site models
The best model for the site scale included the parameters ASDBH, BASAL and
CANHT. This shows that similar to the best nest tree selection model, RSHA nest site
selection was influenced by both the height of the trees and their dbh. Additionally, the
parameter BASAL indicates that the number of large dbh trees present is also important
to RSHA nest site selection. Because the top three models each included all the
parameters in the best predicting model, it was apparent that those three parameters
contributed heavily to the explanatory power of the second and third best models.
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Previous studies have documented RSHAs selecting nest sites with taller canopies
and larger dbh trees than randomly available (Woodrey 1986, Preston et al. 1989,
McLeod at al. 2000). We found that similar to nest trees, RSHAs selected nest sites that
consisted of taller and larger dbh trees than random sites. Sites with taller and larger
diameter trees may provide security against inclement weather, or a canopy structure
facilitating nestlings to evade predators or fledge safely. Additionally, we found that
sites with higher basal area were more likely to be selected as a nest site by RSHAs.
Previous studies have documented the importance of high basal area to nest site selection
of RSHAs (Morris and Lemon 1983, Parker 1986, Belleman 1998, McLeod et al. 2000).
RSHAs that nest in sites with higher basal area may incur a greater reproductive
advantage through a lower risk of nest failure to wind events.
Models not including a measure of tree diameter either through average diameter
or basal area were not ranked as highly as models that included such parameters. In
addition, the evidence ratio of the model ASDBH+CANHT versus the model
CANHT+BASAL showed that the model ASDBH+CANHT was 5 times more likely to
be the best model in the set. This suggests that presence of large diameter trees is more
important to RSHA nest site selection than the basal area. The RSPF from the best
predicting model at the site scale, correctly predicted nest sites and random sites far more
often (93.3% accuracy) than the RSPF for the center tree scale. This suggests that the
RSPF from the site scale is better able to identify RSHA nesting habitat than the RSPF
from the center tree scale.
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Texas Tech University, Strobel, August 2007
Mixed models
When models from both scales were compared, the best model at the site scale
received 62% of the potential AIC weight, while the best model from the center tree scale
only received 5%. This indicates that site variables are more important to Texas RSHAs
in nesting habitat selection than the center tree variables. Greater variability between
sites than between center trees may allow the RSPF to better predict the differences
between nest sites and unused sites. Since trees and sites within the same stand are
inherently similar, limiting the sample of unused center trees and sites may have caused a
bias to center trees and sites with characteristics similar to the nest tree or nest site.
However, the difference in the accuracy of the RSPFs from each scale could also indicate
that nesting habitat selection is stronger at the site scale. Meaning that the vegetation
characteristics at the site scale are more important to RSHA nesting habitat selection that
characteristics of the center trees. Further analysis of characteristics of used and unused
stands would provide a more complete RSPF.
After the completion of the mixed model analysis, we conducted a best subsets
logistic regression to compare the predictive power of our a priori models against that of
all other possible models. We found that our best predicting model
(ASDBH+BASAL+CANHT) had the second best AICc score compared to all possible
models. It was only surpassed by the model (BASAL+CANHT+CTDBH) with a ∆i of
0.296. These two models accounted for 33.6% of the total AICc weights available in the
set and both included the canopy height and basal area of the site scale. Interestingly, the
best model of all possible models included the parameter ASDBH instead of the
parameter CTDBH. The average dbh of the trees within the site weighted more heavily
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Texas Tech University, Strobel, August 2007
in the nest habitat selection than the diameter of the center tree. This model could be
interpreted hierarchically as: RSHAs select nesting habitat first as sites with many tall
trees of large diameters, and then select nest trees within the site based on dbh, or more
likely a beneficial characteristic correlated to dbh.
We conclude that RSHA selection of an appropriate nesting habitat at the site
scale has primacy over the individual tree scale. Therefore, available and apparently
suitable trees may not be used as nest trees because of inadequacies of the surrounding
site. By comparing the RSPF for the center tree and the site scale we found that patterns
of univariate differences between characteristics of used and unused resources, such as
those apparent in our data set and previously published RSHA habitat selection studies,
do not necessarily confer important habitat selection processes. This forces us to look
more deeply into previous habitat selection studies and examine whether significant
differences between used and available resources reflect a biological process or are
simply an artifact of the scale examined.
We recognize that both the center tree scale and the 0.04ha site scale are thirdorder selection scales as described by Johnson (1980). Unfortunately, during this study
we were unable to examine the importance of lower-orders of selection. Studying habitat
selection across homeranges (second-order, Johnson 1980) could likely provide important
information regarding forest stand selection by RSHA in south Texas. Additionally, a
second-order RSPF would better facilitate forest management and species conservation at
the landscape scale.
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Texas Tech University, Strobel, August 2007
Management implications
This study indicates the importance of forest structure to RSHA nesting habitat
selection, and its applicability to forest stand management in south Texas. Our results
show that the height and the dbh of trees are important characteristics to RSHA nesting
habitat selection. Additionally, we suggest that RSHAs are more selective of nesting
habitat at the 0.04ha site scale than at the center tree scale. This reinforces the
importance of forest management in providing RSHA nesting habitat. The RSPF from
the best model includes parameters often measured in traditional forest management
(canopy height, basal area, and average diameter) and therefore can be easily
incorporated into current forest management efforts. However, vegetation types and
structure in south Texas were atypical of those found in many portions of RSHA breeding
range. Therefore, nesting habitat selection criterion likely differ and the RSPF created
here, though useful, should be applied cautiously.
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Herket, J. R. 1994. The effects of habitat fragmentation on midwestern grassland bird
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Table 2.1 Candidate models describing the probability of a tree being selected as a redshouldered hawk nest tree in south Texas from 2005-2006. The -2 * log-likelihood (2LL), number of parameters (K), AICc, difference between AICc and the lowest AICc
(∆i), and model probabilities (ωi).
Modela
K
-2LL
AICc
∆i
ωi
CTDBH + CTHT
3
40.56
46.99
0.00
0.70
4
40.52
49.25
2.25
0.23
CTHT
2
47.17
51.38
4.39
0.08
CTDBH
2
55.23
59.44
12.44
0.00
CTDBH + CTHT +
CTDBH*CTHT
a
CTDBH = center tree dbh; CTHT = center tree height
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Texas Tech University, Strobel, August 2007
Table 2.2 Candidate models describing the probability of a 0.04 ha site being selected as
a red-shouldered hawk nest site in south Texas from 2005-2006. The -2 * log-likelihood
(-2LL), number of parameters (K), AICc, difference between AICc and the lowest AICc
(∆i), and model probabilities (ωi).
Modela
K
-2LL
AICc
∆i
ωi
ASDBH + BASAL + CANHT
4
33.33
42.06
0.00
0.33
6
29.07
42.65
0.59
0.24
5
32.68
43.79
1.73
0.14
7
28.88
45.03
2.97
0.07
ASDBH + CANHT
3
38.62
45.05
2.99
0.07
BASAL + CANCOV + CANHT
4
38.25
46.98
4.92
0.03
4
38.32
47.04
4.98
0.03
ASDBH + CANHT + H2ODIST
4
38.44
47.17
5.11
0.03
ASDBH + CANCOV + CANHT
4
38.56
47.29
5.23
0.02
BASAL + CANHT
3
41.49
47.92
5.85
0.02
CANHT
2
44.47
48.68
6.62
0.01
ASDBH + BASAL + CANCOV +
CANHT + H2ODIST
ASDBH + BASAL + CANHT +
ASDBH*CANHT
ASDBH + BASAL + CANCOV +
CANHT + H2ODIST +
ASDBH*CANHT
ASDBH + CANHT +
ASDBH*CANHT
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Texas Tech University, Strobel, August 2007
Table 2.2 Continued
Modela
K
-2LL
AICc
∆i
ωi
5
38.20
49.31
7.25
0.01
3
43.85
50.28
8.22
0.01
ASDBH + CANHT + H2ODIST +
ASDBH*CANHT
CANCOV + CANHT
a
ASDBH = average tree dbh; BASAL = basal area; CANCOV = percent canopy closure;
CANHT = canopy height; H2ODIST = distance to permanent wetland
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Texas Tech University, Strobel, August 2007
Table 2.3 Candidate models describing characteristics of trees and 0.04ha sites and their
influence on the red-shouldered hawk nesting habitat selection in south Texas from 20052006. The -2 * log-likelihood (-2LL), number of parameters (K), AICc, difference
between AICc and the lowest AICc (∆i), and model probabilities (ωi).
Modela
K
-2LL
AICc
∆i
ωi
ASDBH + BASAL + CANHT
4
33.33
42.06
0.00
0.62
ASDBH + CANHT
3
38.62
45.05
3.07
0.13
CTDBH + CTHT
6
31.80
45.38
3.15
0.13
CTDBH + CTHT
3
40.56
46.99
5.01
0.05
BASAL + CTDBH + CTHT
4
38.28
47.01
4.95
0.05
BASAL + CANHT + CTHT
4
41.46
50.19
8.13
0.01
ASDBH + BASAL + CANHT +
a
ASDBH = average tree dbh; BASAL = basal area; CANHT = average canopy height;
CTDBH = center tree dbh; CTHT = center tree height
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Texas Tech University, Strobel, August 2007
Table 2.4 Variables and coefficients of the resource selection probability functions for
red-shouldered hawk nesting habitat selection at two scales in south Texas.
Center Tree Scale
Variable Namea
Site Scale (0.04ha)
Coefficient (±SE)
Variable Nameb
Coefficient (±SE)
-12.8958 (3.6848)
Constant
-9.0709
(2.3453)
Contstant
CTDBH
0.0070
(0.0032)
ASDBH
0.0102
(0.0043)
CTHT
0.3429
(0.1071)
BASAL
0.1275
(0.0613)
CANHT
0.5379
(0.1849)
a
CTDBH = center tree dbh (cm); CTHT = center tree height (m)
b
ASDBH = average tree dbh; BASAL = basal area (m2/ha); CANHT = average canopy
height (m)
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Texas Tech University, Strobel, August 2007
CHAPTER III
TEMPORAL AND SPATIAL VARIATION IN PREY USE BY BREEDING REDSHOULDERED HAWKS
Abstract
Patterns in resource use by animals provide important information regarding
species ecology. We examine daily temporal patterns in the prey use by red-shouldered
hawks to determine if prey type used is influenced by time of day. Additionally, we
examine fine scale and large scale spatial variation in prey use by calculating Morisita’s
similarity indices across nests within our study as well as across previously published
studies. There are significant temporal patterns in prey type used by red-shouldered
hawks breeding in south Texas. More amphibians were used during mid afternoon than
during late evening, while more insects were used during late evening than earlier in the
day. Breeding pairs of red-shouldered hawks within our study demonstrated differences
in prey type used, likely based on spatial variations in prey abundances. Similarly, diets
of red-shouldered hawks varied significantly across much of their breeding range, and
coincided with latitudinal differences between study sites. Our study demonstrates the
dietary flexibility of red-shouldered hawks in south Texas and suggests regional
variations in prey availability as an additional factor influencing reproductive success and
survival of red-shouldered hawks in North America.
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Texas Tech University, Strobel, August 2007
Introduction
The Red-shouldered hawk (Buteo lineatus) was once considered to be the most
abundant raptor in moist woodlands of North America (Bent 1932). However, recent
declines in red-shouldered hawk populations have led to the species being listed as
threatened or endangered by many states in the northern portions of its range (Jacobs and
Jacobs 2002). Red-shouldered hawks (RSHA) are often associated with large tracts of
moist forests and swamps (Bent 1932, Craighead and Craighead 1956). Because of this,
recent declines in RSHA populations throughout the Midwestern and Northeastern
United States have been attributed to forest fragmentation and resource loss (Bednarz and
Dinsmore 1981, Jacobs and Jacobs 2002).
Similar to other portions of the RSHAs range, agricultural and urban development
in south Texas had altered an estimated 95% of the historic vegetative communities and
99% of the riparian areas by 1988 (Jahrsdoerfer and Leslie 1988). Despite few data
regarding RSHAs in south Texas, these changes have certainly led to differences in
resource availability and, likely, resource use by RSHAs. However, Breeding Bird
Survey data suggest modest increases in RSHA populations in the south Texas brush
lands (Sauer et al. 2005). Likewise, despite forecasting increasing rates of landscape
fragmentation, the Texas Parks and Wildlife Department (TPWD), considers Texas’s
population of RSHA to be stable (TPWD 2005). Recent studies have documented
apparently sustainable RSHA populations in highly human altered landscapes elsewhere
(e.g. Bloom et al. 1993, Rottenborn 2000, Dykstra et al. 2001). This indicates that factors
other than habitat alteration, such as prey availability, may be contributing to the
inconsistencies in RSHA population trends.
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Texas Tech University, Strobel, August 2007
Bloom (1989) suggested that habitat use by RSHAs is greatly influenced by the
presence of attainable prey. Therefore, the ability of RSHAs to cope with changes in
land use is facilitated by its diverse diet. Previous studies of breeding season diet indicate
that RSHAs use a variety of prey items across their range. Snyder and Wiley (1976)
reported that invertebrates composed over 40% of the diet of RSHAs nesting in
California. In Missouri, Parker (1986) documented that nearly 75% of the diet of
breeding RSHAs consisted of herpetiles. Diets of RSHAs nesting in Maryland were
made up of over 70% mammals, nearly 50% of which were eastern chipmunks (Tamias
striatus; Portnoy and Dodge 1979). In addition to these substantive regional diet
variations, Bednarz and Dinsmore (1985) documented a dietary shift between two
breeding seasons in Iowa, and found similar productivity of RSHA under both conditions.
Clearly, RSHAs are capable of producing young under many habitat and prey conditions.
Red-shouldered hawk population trends vary considerably throughout their range.
While the apparent abundance of some populations in the southern U.S. has increased
since the 1990s, more northern populations of RSHAs have not responded similarly
(Sauer et al. 2005). Regional variation may be caused by inherent ecological differences
in vegetation communities or prey types and abundances. However, anthropogenic
changes in land use, such as forest conversion to agriculture, urbanization and pesticide
use have also influenced the population trends of RSHA populations in North America
(Brown 1971, Henny et al. 1973, Peterson and Crocoll 1992, Jacobs and Jacobs 2002).
The ability of RSHAs to use various vegetation and prey types may support their
successful reproduction under changing conditions. Yet with increasing rates of human
induced landscape alteration it is critical to understand the degree to which RSHAs can
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Texas Tech University, Strobel, August 2007
vary their diet within spatial and temporal scales. Further understanding of the
flexibility of RSHA diet will provide insight into the potential influence of future
landscape changes, as well as shed light on region differences in RSHA population
trends.
Study area
This study was conducted on the Rob and Bessie Welder Wildlife Refuge and the
Twin Oaks Hunting Resort in San Patricio and Refugio Counties, Texas (respectively).
The adjacent sites are separated by the Aransas River 8 miles from its outlet into Copano
Bay in the Gulf of Mexico. The study site lies in the northern portion of the Tamaulipan
biotic province (Blair 1950) within the gulf coast prairies and marshes ecoregion (TPWD
2005). The Rob and Bessie Welder Wildlife Refuge (3,156 ha) and the Twin Oaks
Hunting Resort (2,857 ha) consist of a diverse mosaic of mesquite-mixed grass
communities, live oak-chaparral communities dominated by live oak (Quercus
virginiana) and riparian woodlands dominated by pecan (Carya illinoinensis), hackberry
(Celtis spp.) and cedar elm (Ulmus crassifolia) (Drawe et al. 1978). Woodlands are
primarily small (< 2.5 ha) discontinuous patches interspersed with more open
communities. The growing season in this region is long, ranging from 275-320 days
(Rappole and Blacklock 1985), but rainfall is often irregular, averaging 88 cm annually
(Drawe et al. 1978). Elevation is low, usually less than 30 meters, and summer
temperatures average 30 degrees C (Guckian and Garcia 1979). The diversity and types
of vegetation communities on the study site made this area unique compared to other
regions within the RSHAs range.
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Texas Tech University, Strobel, August 2007
Methods
Field methods used followed protocols approved by Texas Tech University’s
animal care and use committee (protocols: 03015-02 and 05067-11).
Data collection
We located active RSHA nests on the study site during the 2005 and 2006
breeding seasons using broadcast survey methods as described by McLeod and Anderson
(1998). We systematically placed predefined survey points approximately 800m apart in
all wooded areas adequate for RSHA nesting. We conducted surveys from April – June
between dawn and 1300. We recorded the direction, distance, and number of RSHA
responses at each point surveyed. Using the surveys to focus our efforts, we conducted
nest searching transects through all potential nesting habitat. Once nests were
discovered, we recorded their location using a GPS unit and returned weekly to monitor
breeding activity and determine if the eggs successfully hatched.
After eggs hatched and young were approximately 1 week old, we installed color
video surveillance cameras (Model OC-225, Clover Electronics®, Los Alamitos, CA
U.S.A.) < 1 m above the nests. We recorded video feed from each camera using timelapse VHS recorders (Piczel video security products® and Security Labs®, Noblesville,
IN U.S.A.). We set record speeds to 48Hr time-lapse mode, resulting in approximately
45 images recorded per minute. We programmed the time-lapse VCRs to record daily,
beginning before sunrise and ending after sunset. Each recording system was powered by
12-volt deep cycle marine batteries and stored in latching plastic containers at least 25
meters away from the nest tree to avoid disturbing hawks while replacing batteries and
VHS tapes. We replaced batteries and VHS tapes in each recording system every third
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day. We recorded activities in the nest until after the young had fledged the nest or the
nesting attempt had failed. However, to ensure an unbiased account of prey items
delivered here we only analyze prey items delivered prior to fledging and for nests with
more than 30 identified prey items.
After retrieving VHS tapes, we reviewed each tape using a stop action VCR to
allow scrutiny of still images of prey items delivered. We recorded all prey items
delivered to the nest by the adult RSHAs as well as the respective date, time, and nest
identification number. We used regional field guides, museum specimens and refuge
staff biologist’s expertise to identify prey items to the lowest taxon possible. The
advantages provided through video documentation of nestling diet yield far more detailed
and accurate data than other methods such as prey remain, pellet analysis and direct
observation (Marti 1987, Redpath et al. 2001). Despite the advantages of video
documentation, we were not able to precisely identify all prey items. We pooled prey
items that we could not identify to genus or species into categories based on general
taxon and size (e.g. small mammal, large snake, etc.). We classified prey items that were
delivered to the nest but were never visible on film as unidentified and did not include
them in analyses.
To compare diets of RSHA nestlings in south Texas to other RSHA populations,
we conducted a literature search to find previously published studies of RSHA breeding
season diet. There are no other published studies of RSHA diet using video recording
techniques, therefore, we could only use studies that estimated diet using direct
observations, pellet analysis and prey remains identification techniques. We pooled prey
types into 4 categories: birds, mammals, herpetile and invertebrates. In addition, when
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Texas Tech University, Strobel, August 2007
studies used multiple methods to assess diet we pooled results found using different
methods. However, when studies published diet data for more than 1 year we analyzed
each year independently to allow for temporal variation within published studies.
Analysis
Temporal variation. To test for temporal differences in types of prey delivered to
nests in our study area, we pooled the prey items into 6 discrete taxonomic categories
(birds, amphibians, insects, mammals, lizards and snakes). In addition, we pooled all
prey delivery times to the hour in which prey items were delivered (0600 – 2000). We
used a MANOVA to test for differences in the mean number of deliveries per hour for
each of the six prey categories delivered to all nests. We then used a Tukey’s HSD test to
determine where differences in means occurred for each prey category considered.
Spatial variation. To examine spatial patterns of diet among nests within our
study, we calculated a Morisita’s similarity index (Morisita 1959), based on the
abundances of prey species delivered to each nest. We conducted analysis with the
program MATLAB (Mathworks 1993), and a function written for this purpose (Strauss
2007). Similarly, we calculated a Morisita’s similarity index for our data and those from
previously published studies, based on 4 prey categories: birds, herpetile, insects, and
mammals. The values of the Morisita’s index range from 0 to 1, indicating no
community overlap to complete community overlap. This index has been shown to be
robust against bias incurred through differences in sample size, and analyzing data sets
with many unique members in each community (Smith and Zaret 1982, Krebs 1989).
When examining spatial patterns in diets among nests within our study, as well as
across studies, we used the Morisita’s similarity index as the distance function within an
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Texas Tech University, Strobel, August 2007
unweighted pair-group method using arithmetic means (UPGMA) cluster analysis. Using
the UPGMA cluster analysis, we generated a dendrogram illustrating the similarity
between diets of nestlings in different nests within our study, as well as RSHA diets in
our study and other published studies. To determine which clusters in the dendrogram
were significant (alpha < 0.05), we ran 10,000 bootstrap iterations of the distance matrix
across variables (prey items) to create a normal distribution of the dataset (Nemec and
Brinkhurst 1988, Krebs 1989). By comparing the Morisita’s similarity index calculated
from the data to the generated distribution we assigned alpha levels to each cluster.
Upon identifying which clusters were significantly different, we used a post hoc
test to identify which variables contributed to the differences between clusters. Prior to
post hoc analysis, we compositionally transformed the data to allow traditional analysis
of the respective proportion of each prey category independent of differences in sample
size and sampling effort (Aitchison 2003). We then used a T2 test to determine which
prey categories contributed to the differences in the diets of nestlings among significant
clusters (Manly 1994). All analyses other than the calculation of the Morisita’s similarity
indices and the bootstrap procedures were conducted using STATISTICA version 6.0
(Statsoft Tulsa, Oklahoma).
Results
We installed cameras at 7 active RSHA nests in 2005 and 3 active RSHA nests in
2006 with which we documented 1495 prey deliveries. In 2005 we were able to collect
over 1300 hours of footage from which we identified over 1100 of the prey items
delivered. In 2006, dramatically lower nesting density and nest success rates allowed us
to only document 145 hours of footage during which we identified 149 of the prey items
35
Texas Tech University, Strobel, August 2007
delivered. In total we identified 1320 unique prey items during 1457 hours of video
monitoring (Table 1). Of the 1320 prey items identified, 41% were insects, primarily
June bugs (Scarabaeidae), leaf footed beetles (Coreid spp.) and cicadas (Tibicen spp.).
Snakes, most of which were rough green snakes (Opheodrys aestivus), garter snakes
(Thamnophis spp.) and yellowbelly racers (Coluber constrictor), comprised 24% of the
prey items delivered to nestlings. Mammals, such as deer and white-footed mice
(Peromyscus spp.) and cotton rats (Sigmodon hispidus), comprised 15% of the prey items
delivered to RSHA nestlings. In addition, lizards, primarily green anoles (Anolis
carolinensis), and amphibians, primarily Rio Grande leopard frogs (Rana berlandieri)
and bullfrogs (R. catesbeiana), each made up 8% of prey items in nestling diets. Birds
were least often found in nestling diets (5%), and were most often songbirds or juvenile
water birds such as common moorhen (Gallinula chloropus,).
Temporal variation
We found the time of delivery influenced the average number of deliveries of
prey types (F84 = 2.009, P < 0.001). The mean number of snakes delivered to nests was
lower at 0600 than it was at 1700 (F14 = 3.557, P < 0.001) but showed a bimodal pattern
of use throughout the day (Figure 1). The average number of amphibians delivered to
RSHA nests rose steadily from 0600 until it peaked at 1400; when it was higher than
anytime after 1700 (F14 = 2.647, P = 0.002; Figure 1). The mean number of insects
delivered to nests remain relatively low throughout most of the day until it sharply
increased from 1700 until 1900 when there were significantly more delivered than at any
period prior to 1700 (F14 = 2.901, P < 0.001; Figure 1). We were unable to detect a
36
Texas Tech University, Strobel, August 2007
difference in the mean number of birds (F14 = 0.669, P = 0.799), lizards (F14 = 1.687, P =
0.068), or mammals (F14 = 0.988, P = 0.469) in regards to the time of delivery.
Spatial variation within our study
Nest locations within our study site were located along an 11km section of the
riparian corridor of the Aransas River (Figure 2). Using a cluster analysis of the
Morisita’s similarity index we produced a dendrogram illustrating the similarity of the
diets of nestlings based on all prey items delivered to each nest (Figure 3). The results of
the bootstrap procedure indicated that the nests could be simplified into 7 significantly
different clusters. The diets of nestlings in nest 14 and nest 18 were not found to be
different (P = 0.071) from each other, nor were the diets of nestlings in Nest 09 and Nest
11 (P = 0.085). Two primary clusters grouped nests with more similar diets within the
cluster than between clusters. Cluster 1 consisted of nests: 02, 08, 12, 14, 18, while
cluster 2 included nests: 05, 07, 09, 11 (Figure 3).
Using a T2 test on the compositionally transformed diet data we found 4 prey
items contributed differently to the diets of nestlings in nests in cluster 1 and nests in
cluster 2. Nests in cluster 1 had more yellowbelly racers and more Texas patchnose
snakes (Salvadora grahamiae) than nests in cluster 2 (t7 = 4.751, P = 0.002 and t7 =
3.434, P = 0.010, respectively). In addition, nests in cluster 1 had more insects delivered
than nests in cluster 2 (t7 = 5.709 P < 0.001). Conversely, nestlings from cluster 2 had
more rough green snakes delivered than nests in cluster 1 (t7 = -2.812, P = 0.026).
Spatial variation across studies
We used our data and those reported by 7 published studies on RSHA (Table 2) to
assess breeding season diet similarities across the species distribution. Craighead and
37
Texas Tech University, Strobel, August 2007
Craighead (1956) analyzed pellets and prey remains to identify prey items consumed at
RSHA nests in Michigan in 1942 and 1948. Portnoy and Dodge (1979) used visual
observation and pellet analysis to document prey items delivered to 7 RSHA nests in
central Massachusetts in 1974. Bednarz and Dinsmore (1985) documented RHSA diets
in eastern Iowa in the summers of 1977 and 1978. Janik and Mosher (1982) summarized
RSHA diets in the Appalachians of western Maryland in 1978 and 1979 through direct
observation methods. Similarly, Penak (1982) used direct observation at 3 nests in 1979
and 5 nests in 1980 near the northern extent of RSHA breeding range in southwestern
Quebec. Diet of RSHA nestlings in the Georgia piedmont in 1994, were documented at 8
nests by direct observation (Howell and Chapman 1998). Snyder and Wiley (1976)
reported the diets of breeding RSHA through direct observation and prey remains from
nests in California and Florida.
The UPGMA cluster analysis of the Morisita’s similarity index for the 13 unique
study years recognized 12 clusters because differences between data reported in
Maryland and Massachusetts were slight. The cluster analysis created 2 primary clusters
correlating with the latitudinal differences across the study sites. The high latitude cluster
(HLC) included data from 1977 in Iowa (Bednarz and Dinsmore 1985), Maryland (Janik
and Mosher 1982), Massachusetts (Portnoy and Dodge 1979), Michigan (Craighead and
Craighead 1956), and Quebec (Penak 1982). The low latitude cluster (LLC) included
data from 1978 in Iowa (Bednarz and Dinsmore 1985), Georgia (Howell and Chapman
1998), California and Florida (Snyder and Wiley 1976) and our study in Texas. Out of
all potential clusters, 10,000 bootstrap iterations found several clusters to be significantly
different (P < 0.05, Figure 3). Diet data reported in Michigan and Quebec were not found
38
Texas Tech University, Strobel, August 2007
to be different, nor were diets reported for Maryland, Massachusetts and the 1979 data
from Iowa. In addition, the 2005 data from our study was clustered with data from Iowa
in 1978, California, Florida and Georgia and, as a cluster, was different from the diet of
RSHA nestlings in our study in 2006.
The T2 test used on the compositionally transformed data confirmed that
differences existed between diets reported for nests in the HLC and LLC (F4,6 = 10.742,
P < 0.006). Nests in the HLC had significantly higher proportions of mammalian prey
than nests in the LLC (t11 = 7.022, P < 0.001). Although not statistically different, nests
in the LLC appear to have used proportionally more herpetiles than nests in the HLC (X̄
HLC = 23%, X̄ LLC = 40%; t11 = -2.011, P = 0.069). Birds and insects were used in
similar proportions by nests in both clusters (X̄ HLC = 6%, X̄ LLC = 3% and X̄ HLC =
34%, X̄ LLC = 38%, respectively).
Discussion
Temporal patterns
Many previous studies have documented temporal patterns in resource use by
predators (see Korpimäki and Krebs 1996). Fluctuations in prey use have been
documented for several European raptors and are often attributed to differences in prey
abundance (Korpimäki and Norrdahl 1991). Patterns of prey availability are also evident
at shorter temporal scales such as bimodal activity patterns in small birds (McNamara et
al. 1994) and temperature correlates to activity of reptiles (Adolph and Porter 1993).
Indeed, Janik and Mosher (1982) noted that increased small mammal activity on their
study site correlated with increases of small mammal occurrences in the diets of breeding
RSHAs in Maryland. In addition, Roth and Lima (2007) documented increased Cooper’s
39
Texas Tech University, Strobel, August 2007
hawk (Accipiter cooperii) activity in early morning and late evening to be in conjunction
with increased activity of prey species.
Analyses of temporal patterns in prey use by RSHA in our study indicate that
different prey types are not used randomly through the day with respect to time. The
average number of amphibians delivered hourly slowly rose from sunrise until late
afternoon, then declined until sunset. This trend could be explained by activity levels of
amphibians increasing as ambient temperature increases throughout the day (Bridges and
Dorcas 2000). Although no significant differences were discernable, these patterns are
echoed by the average number of lizard and snake prey items delivered to RSHA
nestlings per hour. In addition, the sharp increase in the average number of insects
delivered to nests per hour coincides with crepuscular emergences patterns of large insect
prey. In contrast, the patterns of use of birds by RSHAs showed no apparent temporal
pattern. This may be caused by the relatively low predation rate on birds due to their
presumably greater difficulty of capture. The lack of a trend in bird predation could also
be caused by the patterns of activity of endothermic prey item being more evenly
dispersed throughout the day than that of poikliothermic prey items. In support of this,
the average number of mammalian prey items delivered to RSHA nestlings also remained
relatively consistent throughout the day, though slightly increasing during the morning
and evening hours. Increased use of mammals during mornings and evenings coincides
with crepuscular activity patterns of many small mammal species (Killduff and Dube
1979).
The temporal patterns in prey use across all nests in our study raise questions
regarding the behavior and resource selection processes used by hunting adult RSHAs.
40
Texas Tech University, Strobel, August 2007
The distinctness of nestling diets in 2006 from those in 2005 suggests that RSHAs in
south Texas may exhibit climate influenced differences in prey use similar to those found
by Bednarz and Dinsmore (1985) in Iowa. Abundant winter rains in 2005 were
contrasted by drought conditions in 2006 and coincided with significant differences in
RSHA nestling diets, nesting densities and productivity (Strobel and Boal, unpublished
data). In addition to climate driven prey fluctuations causing differences in predator
diets, Price (1987) suggested variations in diet might be caused by factors external to the
prey type, such as specific foraging areas facilitating vigilance or territorial behaviors.
Further implications of temporal patterns in prey use by RSHAs may include temporal
variation in foraging habitat selection, hunting techniques, behavior and predation risk
incurred by RSHAs.
Spatial patterns within our study
Cluster analysis revealed that the diets of nestlings at most of the RSHA nests in
our study had lower overlap than would be expected by chance, with nests divided into
two primary clusters (Figure 3). The average number of nestlings per nest in cluster 1
(2.4 ± 0.5) was similar to cluster 2 (2.3 ± 0.5), suggesting that nutritional demand did not
cause differences in prey use. It is possible that patterns in prey use were caused by
individual preference of specific resources (Price 1987). However, the inability of the
cluster analysis to discriminate the data recorded at nests during the dry conditions of
2006 (nest 14 and nest 18) suggest that prey availability is the most likely explanation for
spatial variations in prey type used.
The diets of nestlings in cluster 1 were comprised of a higher proportion of
yellowbelly racers, Texas patchnose snakes and insects, while those in cluster 2 consisted
41
Texas Tech University, Strobel, August 2007
of proportionally more rough green snakes. Yellow bellied racers and Texas patchnose
snakes both favor dry grassland or mixed grassland forest communities (Tennant 1998).
In contrast, the arboreal rough green snakes are more commonly found in areas of dense
foliage often around wetlands (Plummer 1990). Although we did not quantitatively
assess landscape composition around nests, our qualitative impression is that nests in
cluster 1 have markedly fewer wetlands in close proximity compared to nests in cluster 2.
The correlation between the differences in prey use and apparent differences in habitat
types indicate that spatial differences in prey availability across RSHA homeranges likely
influence spatial patterns found in prey use.
The high diversity of plant and animal communities in south Texas clearly
influence the availability of RSHA prey items. Therefore, it is not surprising that diets
between RSHA broods within our study were often significantly different despite their
proximity. Assuming that spatial variation in RSHA diet is due in part to spatial variation
in prey abundances, it could be assumed that such variation in raptor breeding season
diets occur only in areas of high biodiversity. However, in a presumably more
contiguous forest community in Minnesota, Smithers et al. (2005) documented spatial
patterns in prey use by breeding Northern Goshawks (Accipiter gentiles). In addition to
nesting in more homogeneous forest communities, diets of Northern Goshawks are
usually less diverse than RSHAs (Squires and Reynolds 1997). This indicates that spatial
variation in raptor diets may be prevalent in many raptor species, potentially due to
spatial variation in prey abundance. Furthermore, much of the information regarding
diets of raptors may be misleading due to the high spatial variation, paucity of research,
and differences in accuracy of prey identification techniques.
42
Texas Tech University, Strobel, August 2007
Spatial patterns across studies
We found several significant patterns when comparing the diets reported for
breeding RSHAs in many areas of their breeding range. Although biases in methodology
used by some studies may hinder our analyses, we suspect that patterns present in the
data are valid for evaluation and interpretation of geographic patterns in RHSA breeding
season diets. The cluster analysis identified 2 primary clusters. Interestingly, the clusters
grouped the studies nearly perfectly by the latitude of their study site (Figure 4). Diets of
breeding RSHAs in the northernmost studies (Quebec, Michigan, Massachusetts,
Maryland and Iowa 1977) were pooled into one significant cluster, while diets in the
southernmost studies (California, Florida, Georgia, Texas and Iowa 1978) were pooled
into another significant cluster. The distinctness of RSHAs diets Bednarz and Dinsmore
(1985) found in Iowa in 1977 and 1978, likely caused the two years of the study to be
separated into significantly different clusters in our analysis. They reported the majority
of prey items in 1977 were mammalian (56.9%) while in 1978 most of the prey items
were amphibians and invertebrates (62%). The posthoc compositional analysis indicated
that diets reported for nestlings in the HLC contained significantly higher proportions of
mammalian prey than nestlings in the LLC. Not surprisingly, the diet reported by
Bednarz and Dinsmore (19865) from 1977 was pooled with the studies conducted at
more northern latitudes, while the diet reported from 1978 was pooled with the studies
conducted on more southerly RSHA populations.
Patterns in abundance of different species in accordance with latitude have long
been recognized (see Pianka 1966). These patterns are echoed by our results indicating
differences in mammalian prey use between studies conducted at different latitudes. This
43
Texas Tech University, Strobel, August 2007
may be caused by proportional differences in the abundance of mammalian prey
throughout the RSHA breeding range or lower abundances of alternative prey types such
as herpetile. Although not significantly different, the data indicate that RSHAs in the
northern studies used fewer herpetile than those in the southern studies. Higher
abundance of herpetile at southern study sites probably leads to proportional differences
in the diets of RSHAs there. This suggests that RSHAs breeding in northern latitudes
may have a less diverse prey base to select from during the breeding season.
Nationwide RSHA populations declined precipitously (Henny et al. 1973, Crocoll
1994, Jacobs and Jacobs 2002). More recently, however, many populations of RSHAs,
especially in the southern U.S., have shown steady increases in breeding abundance
(Sauer et al. 2005, TPWD 2005). Although declines may have been induced by habitat
fragmentation benefiting competitors of RSHAs such as red-tailed hawks (Buteo
jamaeciensis) or great-horned owls (Bubo virginianus; Crocoll 1994), recent increases in
some RSHA populations appear to be occurring independent of rates of landscape
fragmentation and alteration. The results of our cluster analysis indicate spatial patterns
in prey use, which may provide some alternative explanations for the dissimilarity of
RSHA population trends nation wide.
The higher use of mammalian prey in northern breeding populations may put the
northern birds at greater risk of food stress and hence reduce survival and productivity.
Small mammal populations are heavily predated by a plethora of predators and often
demonstrate cyclic patterns (Korpimäki and Krebs 1996). In combination with natural
prey cycles, landscape alteration resulting in a more open and heterogeneous environment
may directly effect small mammal populations or benefit species competing for
44
Texas Tech University, Strobel, August 2007
mammalian prey with RSHA. The uncertainty of small mammal populations may have
deleterious effects that are more visible in RSHA populations breeding in northern
latitudes where alternative prey types such as reptiles, amphibians, and large insects are
less abundant.
Lower diversity of prey types available in northern latitudes compared to southern
latitudes may make it more difficult for breeding RSHAs to adjust to landscape changes.
The dietary flexibility provided by higher prey diversity might help explain why RSHA
populations in Texas appear to be stable in the face of high and increasing rates of habitat
fragmentation. This indicates that similar land management practices applied throughout
the RSHAs range could result in markedly different population responses. This also
demonstrates the potential importance of regional variation in RSHA breeding ecology,
and illustrates the need for local data prior to making decisions regarding land use and
wildlife population management.
Management Implications
Temporal patterns in RSHA diet may indicate temporal patterns in RSHA
foraging habitat selection. Most raptor habitat selection studies have been limited to the
breeding season. Our results suggest that temporal patterns in resource use likely occur
at several scales and further research should be conducted prior to management decisions.
Additionally, the spatial variation in RSHA diet illustrates the importance of local
information to sound land management decisions. We suggest that reduced prey
diversity in northern areas of the RSHAs breeding range may make those populations
more susceptible to lower survival and productivity caused by landscape alteration and
fragmentation.
45
Texas Tech University, Strobel, August 2007
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Table 3.1 Species list of prey items delivered to nestlings in 7 Red-shouldered hawk
nests in 2005-2006 in south Texas. Percent of occurrence and biomass contributed to
total nestling diet by each prey item. N = number of individuals, %N = percent
occurrence of prey, Mass = estimated biomass (g), and % Mass = percent of total
biomass contributed by prey type.
N
%N
Mass (g)
% Mass
Cicada (Tibicen spp.)
38
1.81
3a
0.15
Leaf-footed bug (Coreidae spp.)
2
0.16
3a
0.01
Unidentified beetles (Scarabaeidae spp.)
6
0.71
1a
0.02
Unidentifed insects
500
37.64
2a
2.11
Subtotal
536
40.61
Gulf cost toad (Bufo valliceps)
2
0.16
20b
0.09
Bullfrog (Rana catesbieana)
12
0.82
500b
11.49
Rio Grande leopard frog (Rana
1
0.11
30b
0.09
Unidentified frog (Rana spp.)
93
6.78
30b
5.70
Subtotal
108
8.18
Insectaa
2.37
Amphibiab
berlandieri)
50
19.13
Texas Tech University, Strobel, August 2007
Table 3.1 Continued
N
%N
Mass (g)
% Mass
1
0.05
10b
0.02
Green anole (Anolis carolinensis)
71
4.10
3.34c
0.38
Slender glass lizard (Ophisaurus
3
0.27
109b
0.84
21
1.86
17b
0.89
Unidentified lizard
10
0.66
17b
0.31
Checkered garter snake (Thamnophis
4
0.38
109b
1.17
1
0.05
62d
0.09
3
0.22
109b
0.67
16
0.88
178d
4.36
1
0.16
68.75e
0.32
Reptilia
Common ground skink (Scincella
lateralis)
attenuatus)
Unidentified tree lizard
(Sceloporus spp.)
marcianus)
Diamondback watersnake (Nerodia
rhombifer)
Easter garter snake (Thamnophis
sirtalis)
Eastern coachwhip (Masticophis
flagellum)
Eastern hog-nosed snake (Heterodon
platirhinos)
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Texas Tech University, Strobel, August 2007
Table 3.1 Continued
N
%N
Mass (g)
% Mass
Great plains rat snake (Elaphe gutatta)
4
0.22
120d
0.74
Gulf coast ribbon snake (Thamnophis
5
0.49
109b
1.50
1
0.05
40d
0.06
2
0.11
9b
0.03
164
16.03
30f
13.47
12
1.09
109b
3.34
Texas rat snake (Elaphe obsoleta)
5
0.27
135d
1.03
Yellow belly racer (Coluber constrictor)
26
2.02
77b
4.37
Yellow-bellied watersnake (Nerodia
1
0.05
62d
0.09
Unidentified watersnake (Nerodia spp.)
1
0.05
62d
0.09
Unidentified garter snake (Thamnophis
6
0.44
109b
1.34
Unidentified small snake
16
0.98
9b
0.25
Unidentified medium snake
38
3.12
109b
9.52
Reptilia (continued)
proximus)
Prairie king snake (Lampropeltis
calligaster)
Rough earth snake (Virignia striatula)
Rough green snake (Opheodrys
aestivus)
Texas patch nose snake (Salvadora
grahamiae)
erythrogaster)
spp.)
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Texas Tech University, Strobel, August 2007
Table 3.1 Continued
N
%N
Mass (g)
% Mass
4
0.44
111b
1.36
416
31.52
6
0.33
100g
0.92
European starling (Sturnus vulgaris)
1
0.05
82g
0.13
Hooded warbler (Wilsonia citrina)
2
0.11
10.5g
0.03
Indigo bunting (Passerina cyanea)
1
0.05
15.5g
0.02
Mourning warbler (Oporornis
1
0.05
12.5g
0.02
Northern cardinal (Cardnalis cardinalis)
2
0.11
45g
0.14
Plain chachalaca (Ortalis vetula)
1
0.05
100g
0.15
Prothonotary warbler (Protonotaria
1
0.05
16g
0.02
Red-eyed vireo (Vireo olivaceus)
1
0.05
17g
0.03
Sora (Porzana carolina)
1
0.05
75g
0.11
Yellow billed cuckoo (Coccyzus
1
0.05
52g
0.08
2i / 2
0.33
40 / 85g
0.51
Reptilia (continued)
Unidentified reptile
Subtotal
43.92
Aves
Common moorhen (Gallinula
cholorpus)
philadelphia)
citrea)
americanus)
Unidentified rail (Rallus spp.)
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Texas Tech University, Strobel, August 2007
Table 3.1 Continued
N
%N
Mass (g)
% Mass
Unidentified small bird
39
2.19
10g
0.61
Unidentified medium bird
1
0.05
25g
0.04
Unidentified large bird
1
0.05
50g
0.08
Subtotal
63
4.77
1i
0.22
750h
4.60
4
0.22
18h
0.11
2
0.11
52h
0.16
Hispid cotton rat (Sigmodon hisipidus)
63
4.43
115h
14.27
Least shrew (Cryptotis parva)
4
0.27
5.75h
0.04
Marsh rice rat (Oryzymus palustris)
4
0.27
51h
0.39
No. grasshopper mouse (Onychomys
1
0.05
36.5h
0.06
Pocket gopher (Geomys spp.)
9
0.55
212h
3.25
Southern plains wood rat (Neotoma
1
0.05
257h
0.39
Aves
3.88
Mammalia
Eastern cottontail (Sylvilagus
floridanus)
Fulvous harvest mouse
(Reithrodontomys fulvescens)
Gulf coast kangaroo rat (Dipodomys
compactus)
leucogaster)
micropus)
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Texas Tech University, Strobel, August 2007
Table 3.1 Continued
N
%N
Mass (g)
% Mass
Unidentified mouse (Peromyscus spp.)
57
3.67
22.75h
2.34
Unidentified shrew (Scoricidae)
3
0.22
5.2h
0.03
Unidentified small mammal
8
0.66
13h
0.24
Unidentified medium mammal
36
2.90
50h
4.06
Unidentified large mammal
4
0.33
100h
0.92
Subtotal
197
14.92
30.70
TOTAL
1320
100
100
a
– estimated mass from video images
b
– mass from nearest related species reported by Steenhof (1983)
c
– average mass reported by Jenssen et al. (1995)
d
– calculated from equation reported by Kaufman and Gibbons (1975) and estimated
lengths
e
– median value from 160 individuals reported by Platt (1969)
f
– calculated from equation reported by Plummer (1985) with estimated lengths
g
– mass reported by Sibley (2003)
h
– mass reported by Davis and Schmidly (1994)
i
– represent subadult animals
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Texas Tech University, Strobel, August 2007
Table 3.2 Total number of each prey type used by breeding red-shouldered hawks as
reported in studies conducted across the red-shouldered hawk breeding range.
Location
Year
Mamm. Bird
Herp.
Invert. Source
California
1976
6
2
11
14
Snyder and Wiley 1976
Florida
1976
3
2
50
0
Snyder and Wiley 1976
Georgia
1994
34
15
89
43
Howell and Chapman 1998b
Bednarz and Dinsmore
Iowa
1977
33
1
2
0
1985b
Bednarz and Dinsmore
Iowa
Maryland
Massachusetts
1978
8
2
39
30
1985b
1978/79
23
1
6
0
Janik and Mosher 1982b
1974
33
2
11
0
Portnoy and Dodge 1979b
Craighead and Craighead
Michigan
1942
332
64
190
104
1956a
Craighead and Craighead
Michigan
1948
223
138
148
93
1956a
Quebec
1979
44
5
52
0
Penak 1982b
Quebec
1980
76
6
48
3
Penak 1982b
Texas
2005
182
61
481
443
this studyc
Texas
2006
15
2
38
93
this studyc
56
Texas Tech University, Strobel, August 2007
Table 3.2 Continued.
a
- diet determined through pellet and prey remain analysis
b
- diet determined through direct observations
c
- diet determined through remote video surveillance
57
0.0
2.5
2.0
1.5
1.0
Mammals (n = 198)
28
2.5
24
1.5
1.0
0.5
4
0.0
0
Snakes (n = 311)
3.5
2.0
Lizards (n = 103)
2.0
1.5
1.5
1.0
0.5
0.0
58
0.0
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
2.0
mean # delivered
3.0
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
4.0
mean # delivered
0.5
mean # delivered
Birds (n = 63)
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
mean # delivered
1.0
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
3.0
mean # delivered
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
0.0
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
mean # delivered
Texas Tech University, Strobel, August 2007
Inse cts (n = 538)
20
16
12
8
Amphibians (n = 107)
1.0
0.5
0.5
Figure 3.1 Daily temporal prey fluctuations in the mean number of each taxonomic prey
category delivered to 9 red-shouldered hawk nests in south Texas in 2005-2006.
59
nests monitored using time-lapse videography on the Welder Wildlife Refuge and Twin Oaks Hunting Resort.
Figure 3.2 2004 NAIP infrared imagery of Refugio and San Patricio counties, Texas. Locations of 9 red-shouldered hawk
Texas Tech University, Strobel, August 2007
Texas Tech University, Strobel, August 2007
Figure 3.3 Dendrogram of Morisita’s similarity index calculated for diets of nestling
red-shouldered hawks in 7 nests in 2005 (02, 05, 07, 08, 09, 11, 12) and 2 in 2006 (14,
18) in south Texas. * - indicates separation into significantly (p < 0.05) different
groups based on 10,000 bootstrap iterations of the original topology matrix of an
UPGMA cluster analysis.
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Texas Tech University, Strobel, August 2007
Figure 3.4 Dendrogram of Morisita’s similarity index calculated for diets of nestling
red-shouldered hawks in 7 studies across red-shouldered hawk breeding range. * indicates separation into significantly (p < 0.05) different groups based on 10,000
bootstrap iterations of the original topology matrix of an UPGMA cluster analysis.
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Texas Tech University, Strobel, August 2007
CHAPTER IV
NESTLING DIET AND ADULT PROVISIONING RATES OF TEXAS REDSHOULDERED HAWKS IN SOUTH TEXAS
Abstract
Prey provisioning rates are known to affect avian productivity. To determine the
effects diet has on red-shouldered hawk productivity, we monitored the diets of nestlings
in south Texas in 2005 and 2006. We used video surveillance cameras to identified 1320
prey items delivered to nestlings, and examined patterns in prey types used and
provisioning rates of adult red-shouldered hawks. Insect, mammalian and reptilian prey
were used more often than other prey types, however insect prey contributed less to the
total biomass than all other prey types except avian prey. Nestlings in broods of 2 and
broods of 3 were provided prey in similar proportions. Adults rearing larger broods
delivered more prey per hour and more grams of prey per hour, but a similar number of
deliveries and grams of prey on a per nestling basis. We conclude that to meet higher
caloric demands of larger broods, red-shouldered hawks increase their provisioning rate.
Introduction
Prey use has frequently been investigated as a factor limiting avian reproduction
(Lack 1954, Martin 1987). Many studies have examined the effects of prey provisioning
on reproductive output and reproductive success (Boutin 1990 and references therein).
Results primarily show that increased prey abundance lead to earlier nest initiation, larger
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Texas Tech University, Strobel, August 2007
clutch sizes, higher nest success and greater nestling survival (e.g. Yom-Tov 1974,
Newton and Marquiss 1981, Simons and Martin 1990). These studies have provided
valuable information on the breeding ecology of, and population limitation in, birds.
However, few studies have examined the relationship between natural provisioning rates
and productivity of raptors in North America.
Once considered the most abundant raptor in moist woodlands of North America
(Bent 1937), red-shouldered hawk (RSHA) populations decreased drastically from 19501970, with declines estimated from 35-65% in the eastern U.S., 85-94% in the Midwest,
and 54% in Texas (Brown 1971, Oberholser 1974). With the ban of DDT and changing
land management practices, many RSHA populations have begun to rebound. However,
RSHA trends vary considerably throughout their range. While populations in the
southern U.S. have apparently increased since the 1990s, more northern portions of
RSHAs have continued to decline (Sauer et al. 2005). Regional variation may be caused
by inherent ecological differences in vegetation communities or prey types and
abundances. Prey availability has been shown to have dramatic effects on productivity of
many species of bird (Lack 1954). The ability of the RSHA to use various prey types
make it an ideal species through which to understand the effects of prey use on
productivity.
Previous studies of breeding season diet of RSHAs suggest they use a variety of
prey items across their range. Snyder and Wiley (1976) reported that invertebrates
composed over 40% of the diet of RSHAs nesting in California. Parker (1986)
documented nearly 75% of the diet of breeding RSHAs in Missouri consisted of
herpetiles. In contrast, diets of RSHAs nesting in Maryland consisited of over 70%
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Texas Tech University, Strobel, August 2007
mammals, nearly 50% of which were eastern chipmunks (Tamias striatus, Portnoy and
Dodge 1979). Bednarz and Dinsmore (1985) documented dietary shifts between
breeding seasons, but found similar productivity of RSHA under both conditions.
Clearly, RSHAs are capable of producing young under many habitat and prey conditions,
but additional research would aid in developing a better understanding of the effects of
prey on RSHA productivity (Crocoll 1994) and their ecological role as top-trophic
predators. We examine the composition of diets of nestling red-shouldered hawks (Buteo
lineatus, RSHAs), in south Texas, based on frequency of occurrence and biomass
contributed by unique prey categories of RSHAs.
Study area
This study was conducted on the Rob and Bessie Welder Wildlife Refuge and the
Twin Oaks Hunting Resort in San Patricio and Refugio Counties, Texas (respectively).
The adjacent study sites are separated by the Aransas River 8 miles from its outlet into
Copano Bay in the Gulf of Mexico. The study site lies in the northern portion of the
Tamaulipan biotic province (Blair 1950) within the gulf coast prairies and marshes
ecoregion (TPWD 2005). The Rob and Bessie Welder Wildlife Refuge (3,156 ha) and
the Twin Oaks Hunting Resort (2,857 ha) consist of a diverse mosaic of mesquite-mixed
grass communities, live oak-chaparral communities dominated by live oak (Quercus
virginiana) and riparian woodlands dominated by pecan (Carya illinoinensis), hackberry
(Celtis spp.) and cedar elm (Ulmus crassifolia) (Drawe et al. 1978). Woodlands are
primarily small (< 2.5 ha) discontinuous patches interspersed with more open
communities. The growing season in this region is long, ranging from 275-320 days
(Rappole and Blacklock 1985), but rainfall is often irregular, averaging 88 cm annually
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Texas Tech University, Strobel, August 2007
(Drawe et al. 1978). Elevation is low, usually less than 30 meters, and summer
temperatures average 30 degrees C (Guckian and Garcia 1979). The diversity and types
of vegetation communities on the study site made this area unique compared to other
regions within the RSHAs range.
Methods
Data animal handling methods followed protocols approved by Texas Tech
University’s animal care and use committee (protocols: 03015-02 and 05067-11).
Data collection
We located active RSHA nests on the study site during the 2005 and 2006
breeding seasons using broadcast survey methods as described by McLeod and Anderson
(1998). We systematically placed predefined survey points approximately 800m apart in
all forested areas of the study area. At each survey point we broadcast a 20 second
RSHA alarm call followed by 40 seconds of silence. We made 6 such broadcasts with
each consecutive broadcast directed 120 degrees clockwise from the prior broadcast. We
concluded surveying each point with a 4-minute listening period. Survey events were
concluded at each point when either a bird responded or after the 10 minute survey had
been completed. We conducted surveys from April – June between dawn and 1300. We
recorded the direction, distance, and number of RSHA responses at each point surveyed.
Using the surveys to focus our efforts, we conducted nest search transects through all
potential nesting habitat. Once nests were discovered, we recorded their location using a
GPS unit and returned weekly to monitor breeding activity. We defined active nests as
those in which at least one egg was laid that year.
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Texas Tech University, Strobel, August 2007
After eggs hatched and young were approximately 1 week old, we installed color
video surveillance cameras (Model OC-225, Clover Electronics®, Los Alamitos, CA
U.S.A.) < 1 m above active RSHA nests. We recorded video feed from each camera
using time-lapse VHS recorders (Piczel video security products® and Security Labs®,
Noblesville, IN U.S.A.). We set the recorders to record in the 48Hr time-lapse mode,
resulting in approximately 45 images recorded per minute. We programmed the timelapse VCRs to record daily, beginning before sunrise and ending after sunset. Each
recording system was powered by 12-volt deep cycle marine batteries, and stored least 25
meters away from the nest tree in latching plastic containers. We replaced batteries and
VHS tapes in each recording system every third day. We recorded all prey deliveries to
the nest until after the young had fledged or the nesting attempt had failed.
We reviewed VHS tapes using a stop action VCR to allow scrutiny of still images
of prey items delivered. We recorded all prey items delivered to the nest by the adult
RSHAs as well as the respective date, time, and nest identification number. We used
regional field guides, museum specimens and refuge staff biologist’s expertise to identify
prey items to the lowest possible taxonomic level.
We estimated the mass of prey items delivered using those reported in several
published sources (Table 1). However, despite the advantages of video documentation,
we were not able to precisely identify all prey items. We categorized prey items that we
could not identify to at least genus into groups based on general physical features and
size (i.e. small mammal, large snake, etc.) and used the average biomass of similar
identified prey items. To facilitate analysis we pooled prey items into 5 taxonomic
groups: amphibians, birds, insects, mammals and reptiles. We identified prey items that
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Texas Tech University, Strobel, August 2007
were not fully consumed, cached and subsequently delivered and only included the
original delivery in analyses. In addition, we excluded from analyses, all prey items that
were unidentifiable to at least a taxon group or were unseen (Table 1).
Analysis
To ensure an unbiased estimate of delivery rates and proportions of prey types
delivered to nestlings we restricted our analysis to data collected prior to fledging. We
included data collected at nests that failed if the mortalities were likely independent of
provisioning rates (i.e., predation) and more than 30 prey items had been identified. We
conducted all analyses using STATISTICA version 6.0 (Statsoft Tulsa, Oklahoma).
We used compositionally transformed data (Aitchison 2003) in ANOVA, and
Tukey’s HSD test to determine if all prey categories were used similarly across all nests.
We examined the frequency of occurrence and proportion of biomass for each prey
category. In addition, we used brood size as the independent variable in an ANOVA to
test for differences in prey categories used between brood sizes. Furthermore, we used a
t-test to compared average prey size delivered by adults rearing broods of 2 nestlings and
broods of 3 nestlings.
To assess provisioning rates of adult RSHAs we analyzed the average daily
frequency of occurrence and the average biomass delivery rates of each prey category for
each nest. We define the frequency of occurrence as the total number of prey items in
each category delivered per hour each day. Similarly, we define the biomass delivery
rate as the total number of estimated grams of prey in each category delivered per hour
each day. To assess the relationships of brood size and prey provisioning rates we also
analyzed the frequency of occurrence and the biomass delivery rates of each prey
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Texas Tech University, Strobel, August 2007
category on a per-nestling basis. To control for potential bias caused by the age of
nestlings, we estimated hatch date by back dating from the estimated age of nestlings
when first found, and included nestling age as a covariate in an ANCOVA. We tested the
effects of brood size as the independent variable on the daily delivery rates using
dependent variables: occurrences/hour, grams/hour, occurrences/hour/nestling and
grams/hour/nestling.
Results
We installed cameras at 7 active RSHA nests in 2005 and 3 active RSHA nests in
2006; all nests contained clutches of 2 or 3 young. In 2005 we were able to collect over
1300 hours of footage yielding over 1100 identified prey items. In 2006, due to
dramatically lower nesting density and nest success rates we were only able to document
145 hours of footage during which we identified 149 prey items delivered. In total we
identified 1320 of the 1495 (88.3%) prey items delivered during 1457 hours of video
monitoring (Tables 1 & 2). Adults delivered an average of 11.8 ± 7.7(X̄ ± SD) prey
items and 433 ± 340 of prey per day. On average each nestling received 5 ± 4 prey items
and 183 ± 131g of prey each day. Assuming a 35-day nestling period (Crocoll 1994) we
estimate that adult RSHAs provide an average of 183 prey items and 6391g of prey to
fledge one nestling. The large standard deviations around these means indicate the
variable nature of nestling diets and reflect differences in prey availability across nests
and between study years. For example, nest 12 was initiated 28 days later than other
nests monitored in 2005. Adults from nest 12 delivered more prey items (22 ± 10) but
fewer grams of prey (295 ± 187) per day than adults from other nests.
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Texas Tech University, Strobel, August 2007
Composition of diet
Using ANOVA, we found significant differences between the occurrence and
biomass contributed by prey categories to RSHA nestling diets (F2,78 = 16.142, P <
0.001). Further examination with univariate tests indicated that prey category
significantly influenced both the relative proportion of occurrences and biomass
contributed to RSHA nestling diets (F4,40 = 12.423, P < 0.001 and F4,40 = 6.268, P <
0.001; respectively). Using Tukey’s HSD test, we found that insect and reptilian prey
were delivered more often than bird prey, but at rates similar to amphibian and
mammalian prey items (Figure 1). We found that insects and birds contributed less
biomass to nestling diets than all other prey categories. In addition, mammalian and
reptilian prey items contributed more biomass than all other prey categories other than
amphibians.
We were unable to detect a difference in frequency of occurrence of prey
categories between brood sizes (F5,3 = 2.66, P = 0.224). Furthermore, we found that the
biomass contributed by prey category was similar between brood sizes (F5,3 = 0.608, P =
0.670). When considering the average size of prey items delivered by adults at all nests,
we initially found that adults raising 3 nestlings delivered larger prey items (30.4 ± 67.4g)
than those with 2 nestlings (44.5 ± 67.3g; t1822 = 4.330, P < 0.001). However, because of
the much later initiation date, we were concerned about the potential bias caused by the
data collected at nest 12. To evaluate the possible influence of data collected at nest 12,
we again compared mean prey size between all nests excluding nest 12. Results then
showed no detectable difference in mean prey size delivered to the different clutches (2
nestlings = 41.9 ± 77.2g, 3 nestlings = 44.5 ± 67.3g; t1336 = 0.655, P = 0.512). Research
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Texas Tech University, Strobel, August 2007
as early as Lack (1954) has shown differences in brood size and survival of late initiated
nests. Because of this, we conclude that the data recorded at nest 12 is likely not
representative of those found during the normal breeding season, and hence they were
excluded from analyses of prey provisioning rates.
Provisioning rates and productivity
Using ANCOVA, we found that clutch size was associated with both the number
of prey items delivered per hour as well as the amount of biomass delivered per hour
(F4,106 = 47.439, P < 0.001; Figure 2). Adults with larger broods delivered more prey per
hour than adults with smaller broods (F1,109 = 5.544, P = 0.020), but similar number of
prey per hour per nestling (F1,109 = 0.700, P = 0.404). In addition, adults provisioning
broods of 3 delivered more biomass per hour than those providing for 2 nestlings (F1,109 =
8.544, P = 0.004), but did not provide significantly different amounts of biomass per
nestling (F1,109 = 0.099, P = 0.752).
Discussion
Composition of diet
Red-shouldered hawks in south Texas did not use all prey categories in similar
frequencies. Mammalian, reptilian, and insect prey items were used more often than any
other type of prey but the majority of biomass came from mammalian and reptilian prey.
This pattern can be explained through optimal foraging theory wherein predators attempt
to maximize the nutritional gain while minimizing the energetic cost incurred through
travel, prey pursuit, capture, and handling time (Schoener 1979). Red-shouldered hawk
productivity may be most befitted by utilizing prey in proportion to some function of the
ease with which it can be obtained and the energy provided by it. The low frequency and
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Texas Tech University, Strobel, August 2007
biomass avian prey contributed to the diets of RSHAs may be explained by a presumably
greater difficulty of capturing comparatively quick and agile prey. Grieco (2001) found
that smaller prey items correlated with higher prey delivery rates in nesting blue tits
(Parus caeruleus). This is likely caused by the necessity of adults to meet a minimum
caloric value to maintain the clutch. Red-shouldered hawks demonstrated similar
patterns while providing nestlings with small prey items such as insects. Optimal
foraging theory would suggest that during periods when RSHAs used insect prey items,
the energetic costs in procuring them was likely low.
To successfully meet higher prey demands of larger clutch sizes adults must
increase the total caloric values of food provided to young. Adults birds can compensate
heightened demands in two conceivable ways: first adults may select prey in accordance
with their nutritional value (i.e. biomass), or second, adults may deliver prey to nestlings
at a higher rate. Grieco (2001) showed that decreased rates of food begging by nestling
blue tits resulted in lower prey delivery rates but larger prey items delivered. This
suggests that provided less demand adults blue tits changed their foraging efforts by
being more selective in prey types delivered to nestlings. Similarly, nesting American
kestrels (Falco sparverious) minimized travel costs by delivering larger prey items to
nestlings while consuming smaller prey items (Rudolph 1982). Further, Golet et al.
(2000) have shown that adult pigeon guillemots (Cepphus clumba) that specialized in
larger and higher-energy prey species fledged more young than those that used a variety
of prey. In order to successfully fledge larger broods, adults must meet the greater
caloric demands of more young. To meet these demands adults could be more selective
regarding the type or size of prey used. However, in our study, clutch size did not appear
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Texas Tech University, Strobel, August 2007
to influence the types of prey adult RSHAs delivered to nestlings, nor the average size of
prey used. Thus as our results show, in order to fledge broods of 3 nestlings, RSHAs
provided prey items at higher rates than adults rearing 2 young.
Provisioning rates and productivity
Experiments on the effects of prey availability and brood sizes have documented
significant responses of prey provisioning by adult birds. Wright et al. (1998) altered
brood sizes of European starlings (Sturnus vulgaris) to find that larger brood sizes
resulted in higher delivery rates and changes in prey types used by adults. Similarly,
female Eurasian kestrels (Falco tinnunculus) demonstrated lower hunting effort and prey
delivery rates when their broods were provided a supplementary food source (Wiehn and
Korpimäki 1997). Cooper’s hawks (Accipiter cooperii), however, did not show any
increase in prey provisioning rates as brood sizes were manipulated (Snyder and Snyder
1973). Similar to results found for starlings and Eurasian kestrels, we found that under
natural circumstances adult RSHAs rearing broods of 3 made more deliveries than adults
rearing broods of 2 nestlings. However, these higher delivery rates did not result in more
deliveries per nestling. Similarly, adult RSHAs rearing broods of 3 delivered
significantly more biomass per hour than adults raising only 2 nestlings, yet on a per
nestling basis, nestlings in broods of 3 were provided a similar biomass of prey as those
in broods of 2.
The apparent compensation for brood size through delivery rates has often been
suggested to be a life history trait to maximize adult productivity under variable
conditions (Lack 1954). Lack (1954) argued that the availability of resources such as
prey determine avian clutch sizes and hence productivity. Similarly, studies have shown
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Texas Tech University, Strobel, August 2007
that supplemental feeding often induces earlier nest initiation, larger clutches and higher
productivity in raptors (Newton and Marquiss 1981, Dijkstra et al. 1982). The increased
productivity of RSHAs rearing larger broods appears to result from higher delivery rates
of prey items and biomass of prey. Higher delivery rates may be indicative of higher
quality habitat or greater hunting proficiency of adults (Newton 1979). However, larger
brood sizes may not directly relate to higher productivity. Wright et al. (1998), found
that starlings originating form larger broods fledged at lower body masses and likely had
lower survival than those of smaller clutches. Further research should be conducted to
examine the survival rates and productivity of individuals reared in different sized
broods.
Management Implications
Red-shouldered hawks in south Texas use mammalian and reptilian prey items
more often than most other prey types. Mammalian and reptilian prey may be selected
more often because their higher biomass and abundance make them a more energetically
economic. In addition RSHAs with higher delivery rates succeeded in fledging more
nestlings than adults with lower delivery rates. This suggests that breeding habitat
containing high abundances of mammalian and reptilian prey items is likely of higher
value to RSHA and will result in higher productivity. Therefore, management practices
intended to enhance RSHA productivity should include management benefiting small
mammal and herpetile populations.
73
Texas Tech University, Strobel, August 2007
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of the red-shouldered hawk, Buteo lineatus, in Iowa. The Canadian Field-Naturalist
99:262-264.
Bent, A. C. 1937. Life histories of American birds of prey. U.S. National Museum
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Blair, W. F. 1950. The biotic provinces of Texas. Texas Journal of Science 2:93-116.
Boutin, S. 1990. Food supplementation experiments with terrestrial vertebrates:
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in blue tits, Parus caeruleus. Animal Behaviour 62:107-116.
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Kaufman, G. A. and J. W. Gibbons. 1975. Weight-length relationships in thirteen
species of snake in the southeastern United States. Herpetologica 31:31-37.
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Martin, T. E. 1987. Food as a limit on breeding birds: a life history perspective. Annual
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McLeod, M. A., and D. E. Andersen. 1998. Red-shouldered Hawk broadcast surveys:
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Newton, I. 1979. Population ecology in raptors. T. & A. D. Poyser Limited,
Hertfordshire, England.
Newton, I. and M. Marquiss. 1981. Effects of additional food on laying dates and clutch
sizes of Sparrowhawk. Ornis Scandinavica 12:224-229.
Oberholser, H. C. 1974. The bird life of Texas. Volume 1. University of Texas press,
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Hawks Buteo lineatus in southeastern Missouri. Thesis, University of Missouri,
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Platt, D. R. 1969. Natural history of the hognose snakes Heterodon platyrhinos and
Heterodon nasicus. University of Kansas Publications 18:253-420.
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Herpetologica 41:28-33.
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Schoener, T. W. 1979. Generality of the size-distance relation in models of optimal
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Table 4.1 Species list of prey items delivered to nestlings in 7 Red-shouldered hawk
nests in 2005-2006 in south Texas. Percent of occurrence and biomass contributed to
total nestling diet by each prey item. N = number of individuals, %N = percent
occurrence of prey, Mass = estimated biomass (g), and % Mass = percent of total
biomass contributed by prey type.
N
%N
Mass (g)
% Mass
Cicada (Tibicen spp.)
38
1.81
3a
0.15
Leaf-footed bug (Coreidae spp.)
2
0.16
3a
0.01
Unidentified beetles (Scarabaeidae spp.)
6
0.71
1a
0.02
Unidentifed insects
500
37.64
2a
2.11
Subtotal
536
40.61
Gulf cost toad (Bufo valliceps)
2
0.16
20b
0.09
Bullfrog (Rana catesbieana)
12
0.82
500b
11.49
Rio Grande leopard frog (Rana
1
0.11
30b
0.09
Unidentified frog (Rana spp.)
93
6.78
30b
5.70
Subtotal
108
8.18
Insectaa
2.37
Amphibiab
berlandieri)
77
19.13
Texas Tech University, Strobel, August 2007
Table 4.1 Continued
N
%N
Mass (g)
% Mass
1
0.05
10b
0.02
Green anole (Anolis carolinensis)
71
4.10
3.34c
0.38
Slender glass lizard (Ophisaurus
3
0.27
109b
0.84
21
1.86
17b
0.89
Unidentified lizard
10
0.66
17b
0.31
Checkered garter snake (Thamnophis
4
0.38
109b
1.17
1
0.05
62d
0.09
3
0.22
109b
0.67
16
0.88
178d
4.36
1
0.16
68.75e
0.32
Reptilia
Common ground skink (Scincella
lateralis)
attenuatus)
Unidentified tree lizard
(Sceloporus spp.)
marcianus)
Diamondback watersnake (Nerodia
rhombifer)
Easter garter snake (Thamnophis
sirtalis)
Eastern coachwhip (Masticophis
flagellum)
Eastern hog-nosed snake (Heterodon
platirhinos)
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Texas Tech University, Strobel, August 2007
Table 4.1 Continued
N
%N
Mass (g)
% Mass
Great plains rat snake (Elaphe gutatta)
4
0.22
120d
0.74
Gulf coast ribbon snake (Thamnophis
5
0.49
109b
1.50
1
0.05
40d
0.06
2
0.11
9b
0.03
164
16.03
30f
13.47
12
1.09
109b
3.34
Texas rat snake (Elaphe obsoleta)
5
0.27
135d
1.03
Yellow belly racer (Coluber constrictor)
26
2.02
77b
4.37
Yellow-bellied watersnake (Nerodia
1
0.05
62d
0.09
Unidentified watersnake (Nerodia spp.)
1
0.05
62d
0.09
Unidentified garter snake (Thamnophis
6
0.44
109b
1.34
Unidentified small snake
16
0.98
9b
0.25
Unidentified medium snake
38
3.12
109b
9.52
Reptilia (continued)
proximus)
Prairie king snake (Lampropeltis
calligaster)
Rough earth snake (Virignia striatula)
Rough green snake (Opheodrys
aestivus)
Texas patch nose snake (Salvadora
grahamiae)
erythrogaster)
spp.)
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Texas Tech University, Strobel, August 2007
Table 4.1 Continued
N
%N
Mass (g)
% Mass
4
0.44
111b
1.36
416
31.52
6
0.33
100g
0.92
European starling (Sturnus vulgaris)
1
0.05
82g
0.13
Hooded warbler (Wilsonia citrina)
2
0.11
10.5g
0.03
Indigo bunting (Passerina cyanea)
1
0.05
15.5g
0.02
Mourning warbler (Oporornis
1
0.05
12.5g
0.02
Northern cardinal (Cardnalis cardinalis)
2
0.11
45g
0.14
Plain chachalaca (Ortalis vetula)
1
0.05
100g
0.15
Prothonotary warbler (Protonotaria
1
0.05
16g
0.02
Red-eyed vireo (Vireo olivaceus)
1
0.05
17g
0.03
Sora (Porzana carolina)
1
0.05
75g
0.11
Yellow billed cuckoo (Coccyzus
1
0.05
52g
0.08
2i / 2
0.33
40 / 85g
0.51
Reptilia (continued)
Unidentified reptile
Subtotal
43.92
Aves
Common moorhen (Gallinula
cholorpus)
philadelphia)
citrea)
americanus)
Unidentified rail (Rallus spp.)
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Texas Tech University, Strobel, August 2007
Table 4.1 Continued
N
%N
Mass (g)
% Mass
Unidentified small bird
39
2.19
10g
0.61
Unidentified medium bird
1
0.05
25g
0.04
Unidentified large bird
1
0.05
50g
0.08
Subtotal
63
4.77
1i
0.22
750h
4.60
4
0.22
18h
0.11
2
0.11
52h
0.16
Hispid cotton rat (Sigmodon hisipidus)
63
4.43
115h
14.27
Least shrew (Cryptotis parva)
4
0.27
5.75h
0.04
Marsh rice rat (Oryzymus palustris)
4
0.27
51h
0.39
No. grasshopper mouse (Onychomys
1
0.05
36.5h
0.06
Pocket gopher (Geomys spp.)
9
0.55
212h
3.25
Southern plains wood rat (Neotoma
1
0.05
257h
0.39
Aves
3.88
Mammalia
Eastern cottontail (Sylvilagus
floridanus)
Fulvous harvest mouse
(Reithrodontomys fulvescens)
Gulf coast kangaroo rat (Dipodomys
compactus)
leucogaster)
micropus)
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Texas Tech University, Strobel, August 2007
Table 4.1 Continued
N
%N
Mass (g)
% Mass
Unidentified mouse (Peromyscus spp.)
57
3.67
22.75h
2.34
Unidentified shrew (Scoricidae)
3
0.22
5.2h
0.03
Unidentified small mammal
8
0.66
13h
0.24
Unidentified medium mammal
36
2.90
50h
4.06
Unidentified large mammal
4
0.33
100h
0.92
Subtotal
197
14.92
30.70
TOTAL
1320
100
100
a
– estimated mass from video images
b
– mass from nearest related species reported by Steenhof (1983)
c
– average mass reported by Jenssen et al. (1995)
d
– calculated from equation reported by Kaufman and Gibbons (1975) and estimated
lengths
e
– median value from 160 individuals reported by Platt (1969)
f
– calculated from equation reported by Plummer (1985) with estimated lengths
g
– mass reported by Sibley (2003)
h
– mass reported by Davis and Schmidly (1994)
i
– represent subadult animals
82
28
2
9
69
3
13
319
71
22
536
Nest 02
Nest 05
Nest 05
Nest 08
Nest 09
Nest 11
Nest 12
Nest 14
Nest 18
Subtotal
N
35.85
37.29
57.72
74.01
9.35
2.16
29.36
5.52
2.78
20.90
%
Insecta
108
3
3
18
9
13
6
26
8
22
N
7.22
5.08
2.44
4.18
6.47
9.35
2.55
15.95
11.11
16.42
%
Amphibia
416
11
21
33
60
66
110
58
27
30
27.83
18.64
17.07
7.66
43.17
47.48
46.81
35.58
37.50
22.39
%
Reptilia
N
recorders at 7 nests in 2005 and 2 nests in 2006.
83
63
0
2
0
18
10
2
16
7
8
N
%
4.21
0.00
1.63
0.00
12.95
7.19
0.85
9.82
9.72
5.97
Aves
197
12
3
19
25
24
28
43
19
24
N
13.18
20.34
2.44
4.41
17.99
17.27
11.91
26.38
26.39
17.91
%
Mammalia
175
11
23
42
14
23
20
11
9
22
N
11.71
18.64
18.70
9.74
10.07
16.55
8.51
6.75
12.50
16.42
%
Unknown
1495
Total
59
123
431
139
139
235
163
72
134
Subtotal
Table 4.2 Occurrence of prey types in diets of nestling red-shouldered hawks in south Texas. Data collected using video
Texas Tech University, Strobel, August 2007
Texas Tech University, Strobel, August 2007
60%
50%
40%
30%
20%
10%
0%
Insecta
Amphibia
Reptilia
prey category
percent occurence
Aves
Mammalia
percent biomass
Figure 4.1 Mean percent (±95% CI) of occurrence and estimated biomass contributed by
5 prey categories to the diets of nestling red-shouldered hawks. Data collected at 7
RSHA nests in 2005-2006 in south Texas.
84
1.25
50
1.00
40
0.75
30
0.50
20
0.25
10
0.00
2 nestlings
3 nestlings
grams of prey delivered
prey items delivered
Texas Tech University, Strobel, August 2007
0
Clutch Size
deliveries / hr
deliveries / hr / nestling
biomass / hr
biomass / hr / nestling
Figure 4.2 Comparison of nestling provisioning rates between different sized clutches
by adult Red-shouldered hawks in 2005-2006 in south Texas. Mean (±95% CI)
frequency of prey deliveries and mean (±95% CI) estimated grams of prey delivered per
hour and per nestling. Data collected at 7 RSHA nests in 2005-2006 in south Texas.
85
Texas Tech University, Strobel, August 2007
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