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Vol.3, No.2, 109-115 (2013) Open Journal of Ecology
http://dx.doi.org/10.4236/oje.2013.32013
Predicting nesting habitat of Northern Goshawks in
mixed aspen-lodgepole pine forests in a
high-elevation shrub-steppe dominated landscape
Robert A. Miller1,2*, Jay D. Carlisle2, Marc J. Bechard1, Dena Santini3
1Raptor Research Center, Department of Biological Sciences, Boise State University, Boise, USA;
*Corresponding Author: RobertMiller7@u.boisestate.edu
2Idaho Bird Observatory, Department of Biological Sciences, Boise State University, Boise, USA
3Minidoka Ranger District, Sawtooth National Forest, USDA Forest Service, Burley, USA
Received 23 December 2012; revised 24 January 2013; accepted 12 February 2013
Copyright © 2013 Robert A. Miller et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
We developed a habitat suitability model for
predicting nest locations of breeding Northern
Goshawks (Accipiter gentilis) in the high-eleva-
tion mixed forest and shrub-steppe habitat of
south-central Idaho, USA. We used elevation,
slope, aspect, ruggedness, distance-to-water,
canopy cover, and individual bands of Landsat
imagery as predictors for known nest locations
with logistic regression. We found goshawks
prefer to nest in gently-sloping, east-facing,
non-rugged areas of dense aspen and lodgepole
pine forests with low reflectance in green (0.53 -
0.61 µm) wavelengths during the breeding sea-
son. We used the model results to classify our
43,169 hectare study area into nesting suitability
categories: well suited (8.8%), marginally suited
(5.1%), and poorly suited (86.1%). We evaluated
our model’s performance by comparing the
modeled results to a set of GPS locations of
known nests (n = 15) that were not used to de-
velop the model. Observed nest locations mat-
ched model results 93.3% of the time for well
suited habitat and fell within poorly suited areas
only 6.7% of the time. Our method improves on
goshawk nesting models developed previously
by others and may be applicable for surveying
goshawks in adjacent mountain ranges across
the northern Great Basin.
Keywords: Accipiter gentilis; Breeding Ecology;
Habitat; Idaho; Nest Model; Northern Goshawk
1. INTRODUCTION
Habitat sets the ultimate limit on the success and dis-
tribution of any wild species [1]. It follows that many
techniques have been developed to analyze relationships
between habitat features and species distributions. Spe-
cies habitat relationships are analyzed to predict the
range of a species [2], to predict a species response to
habitat change [3], to evaluate suitability of an environ-
ment to support species re-introduction [4], or to aid in
the search for presence of a species [5,6]. A habitat dis-
tribution model or habitat suitability model relates the
geographical distribution of species or communities to
their present environment [7]. Techniques for the devel-
opment of these models have ranged from using detailed
field measurements [8] to the use of Geographic Infor-
mation Systems (GIS) with remotely sensed data and
sophisticated statistical procedures [5,7].
The choice among various analysis techniques depend
upon the objectives of the work and the scale of the in-
ference required. In using habitat suitability models, a
mismatch between the scale of the data and the scale of
the inference can lead to significant bias in the result [9].
Habitat selection patterns at a large scale can often dis-
appear or change as the scale is reduced [10]. Addition-
ally, the application of habitat models generated from
data in one area may not readily apply to another area,
especially if the model must be extrapolated beyond the
range of data used to build the model [11,12].
The Northern Goshawk (Accipiter gentilis; hereafter
“goshawk”) is a generalist predator occupying boreal and
temperate forests of the Holarctic [13]. Studies have
shown that goshawks prefer to nest in mature dense
canopy cover with an open understory, often near water,
with a gentle north or east aspect [14-17]. In addition,
goshawks have been shown to nest in stands where het-
erogeneity is low [16,17] and nests are remote from hu-
man disturbance [18]. Lõhmus [19] found forest struc-
ture to be a more important influence than disturbance.
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R. A. Miller et al. / Open Journal of Ecology 3 (2013) 109-115
110
The goshawk exhibits regional variation with its habitat
use [5,14-16]. To better understand this variation and to
ensure the proper scale of inference, regional analyses
are warranted.
We developed habitat suitability models to quantify
the habitat needs of goshawks in the unique environment
of the South Hills and to aid in prioritizing areas to be
searched for occupancy by nesting goshawks. Searching
for nesting structures of goshawks is an expensive proc-
ess [20]. Significant time can be spent accessing and
searching low quality habitat. A high quality prediction
model can significantly aid the search effort by increas-
ing search efficiency. Our model will be useful for forest
management and future surveying activities within the
area and across the northern Great Basin where similar
habitat exists.
2. METHODS
2.1. Study Area
We conducted this study in the South Hills encompass-
ing the Cassia section of the Minidoka Ranger District of
the Sawtooth National Forest in south-central Idaho
(41.98˚ - 42.33˚N, 113.98˚ - 114.48˚W; Figure 1). The
section occupies portions of Twin Falls and Cassia coun-
ties. The Cassia section contains approximately 125,000
hectares and is bordered primarily by Bureau of Land
Management lands [21]. The naturally-fragmented forest
is dominated by grasslands and mountain big sagebrush
(Artemisia tridentata vaseyana; approximately 80%) [22].
The remaining forested landscape consists predominantly
of aspen (Populus tremuloides), lodgepole pine (Pinus
contorta), and sub-alpine fir (Abies lasiocarpa) [22].
2.2. Goshawk Nests
We discovered goshawk nests by searching historical
Figure 1. Cassia Section, Minidoka Ranger District, of
the Sawtooth National Forest in south-central Idaho with
known goshawk nest locations, 500-meter buffered mini-
mum convex polygon (MCP) around known nest locations,
and 200 randomly selected points constrained by MCP.
nesting territories provided by the Forest Service and
additional areas prioritized through geographic informa-
tion system analysis (Figure 1) [5,23]. We searched for
nests by first checking historical nesting structures for
occupancy, then searching on foot within a 300-meter
radius of historical nesting structures for new nests.
When no nests were found by these means, we then
broadcasted alarm calls every 300 meters out to 1370
meters (588-hectare area) from historical nest structures
to solicit a response [24]. We used an average male home
range of 588 hectares that was previously established in
the same study area [25]. Nest structures were discovered
during the formal search process in addition to accidental
discovery while we were in the area for other purposes.
We randomly reserved 15% of the nest locations for a
validation dataset and excluded these from model creation.
Nest locations were classified by nesting substrate—as-
pen or lodgepole pine—to enable a unified analysis in
addition to separate analyses by forest type.
We generated a minimum convex polygon encompass-
ing a 500-meter buffer around all discovered nest struc-
tures (Figure 1). We generated 200 random points within
this polygon to serve as control points for the habitat
analysis (Figure 1). We made no effort to check these
locations for nest structures or to limit their position rela-
tive to known nest locations. As a result our data set rep-
resented presence only data, with implied but not true
absence. Furthermore, we did not assign substrate values
to the random points, using the same control points for
the overall analysis, aspen only analysis and lodgepole
pine only analysis.
2.3. GIS Data
We acquired Digital Elevation Model (DEM) data
from Inside Idaho at a resolution of 30 meters [26]. We
calculated slope and aspect from the digital elevation
model. We transformed aspect into two variables, north-
ness and eastness, through trigonometric transformations
[27]. We generated a ruggedness index using the relative
position method [28]. Ruggedness is a measure of local
elevation differences within a 330-meter roving window.
Lower values represent nest trees located at or near the
local minimum elevation. We acquired canopy cover data
from the National Land Cover Database [29] and stream
location data from Inside Idaho [30].
We acquired Landsat 7 Enhanced Thematic Mapper
Plus (ETM+) imagery via ESRI’s Global Land Survey
2010 dataset [31]. The Landsat data included bands 1, 2,
3, 4, 5 and 7, was corrected for Scan Line Corrector
(SLC) errors, and was enhanced with radiometric correc-
tion and histogram stretching to make it more visually
appealing [31].
We used the stream data to create a 30-meter resolu-
tion raster file representing the distance of each pixel
Copyright © 2013 SciRes. OPEN ACCESS
R. A. Miller et al. / Open Journal of Ecology 3 (2013) 109-115 111
from the nearest water source. The distance-to-water
layer and each of the Landsat image layers were resam-
pled using bilinear interpolation to match the alignment
of the digital elevation model. We did not include a
measure of disturbance as not all of our nest structures
were discovered using a random sample process and thus
our dataset may be spatially biased toward human access.
All raster layers were placed into a “raster stack” to aid
in data management [32].
2.4. Statistical Analysis
We extracted data from each raster layer for each of
our nests and random points. We used logistic regression
to generate a model using elevation, slope, northness,
eastness, ruggedness, canopy cover, distance-to-water,
and each of the six Landsat image layers as predictors for
nest presence. We selected the final predictor variables
by using both backwards and forwards stepwise selection
via AIC [33]. We verified the absence of multicollinear-
ity in the final model using a Pearson correlation test
with a threshold of 0.70. The resulting top model was
recombined with the raster data block to generate a pre-
diction layer for the study area.
We evaluated spatial autocorrelation of nest structures
and occupied nest structures using the habitat model re-
siduals and nest coordinates to calculate the Geary’s C
statistic [34]. A Geary’s C value < 1 indicates clustered
resources, whereas a value > 1 implies spatial regularity,
and values near one imply a random distribution with no
auto-correlation [34].
We reclassified the prediction layer into three catego-
ries—poorly suited, marginally suited and well suited—
by first using a quantitative breakpoint that maximized
the difference between the proportion of the study area
considered poorly suited and the proportion of known
nests located in marginally or well suited habitat [5]. We
separated marginally and well suited habitat qualitatively
where a logical breakpoint was present as evidenced by
plotting the difference between the two empirical cumu-
lative distribution functions.
We validated the habitat models by extracting the
habitat suitability values on the basis of the nest locations
we had previously reserved for this purpose and checked
for omission errors [35]. Commission errors cannot be
evaluated using presence only data [35].
We produced substrate specific models by repeating
the model creation procedure for nests located in aspen
and separately for nests located in lodgepole pine. We
used the union of these two substrate-specific models for
comparison against the global model. The substrate-spe-
cific models were considered “more useful” if the union
of the two models produced a smaller area of well suited
habitat or if a higher proportion of the validation nests
fell in well suited habitat.
We used an alpha value of 0.05 to measure signify-
cance in all frequentist statistical tests (i.e. Geary’s C).
We conducted all statistical analyses in R [36]. We per-
formed most raster processing using the R package
“raster” [32]. We generated raster slope and aspect layers
using the R library “SDMTools” [37]. We calculated the
Geary’s C statistic using the R library “spdep” [38]. Map
exploration and visualization was performed in ArcMap
10.1 [39].
3. RESULTS
We discovered 95 nest structures that were occupied
by or were not occupied but appeared to have been built
by goshawks. Of these, 62 nests were located in aspen
trees and 33 nests were located in lodgepole pine trees.
We randomly selected 15 nests to withhold for vali-
dation purposes, 12 in aspen trees and three in lodge-
pole pine trees.
The nest structures were distributed randomly with
respect to each other within suitable habitat (Geary’s C
statistic = 1.31, p = 0.89). Of the total 95 nest structures
observed, 19 were occupied by goshawks in 2012. The
occupied nests were distributed randomly with respect
to each other as well (Geary’s C statistic = 1.14, p =
0.85).
The top habitat model included elevation, slope,
eastness, ruggedness, canopy cover, and Landsat band 2.
The nests of goshawks were more often associated with
lower elevations within the study area, with gentle or no
slope, eastern facing aspect, in non-rugged terrain, with
dense canopy cover, and low relative reflectance in the
green spectrum (0.53 - 0.61 µm wavelength; Figure 2)
[31].
Classifying the model output for the area within the
minimum convex polygon surrounding known nest lo-
cations resulted in 86.1% of the area rated as poorly
suited habitat, 5.1% as marginally suited habitat, and
8.8% as well suited habitat (Figures 3 and 4). Vali-
dating the model with the reserved set of nests found
that 14 of 15 nests (93.3%) were located in habitat clas-
sified as well suited, 0 of 15 in habitat categorized as
marginally suited, and 1 of 15 (6.7%) in habitat catego-
rized as poorly suited. The nest located in poorly suited
habitat was not occupied in 2012.
Repeating the process separately by nesting substrate,
the top model for nests located in aspen included eleva-
tion, slope, canopy cover, and Landsat bands 4 & 5. The
nests of goshawks in aspen were more often located at
lower elevations, with low slope, in high canopy cover
and forest structure with high reflectance in near-in-
frared (0.75 - 0.9 µm wavelength) and low reflectance
in the short-wave infrared spectrums (1.55 - 1.75 µm
wavelength) [31].
Copyright © 2013 SciRes. OPEN ACCESS
R. A. Miller et al. / Open Journal of Ecology 3 (2013) 109-115
Copyright © 2013 SciRes.
112
Figure 2. Characterization of habitat classified as well suited as compared to all habitat for all forest types
constrained by minimum convex polygon buffered by 500m around known goshawk nest locations within the
Cassia Section, Minidoka Ranger District, Sawtooth National Forest in south-central Idaho USA. Boxplots
represent median (line), quartiles (box), 1.5 times inter-quartile range (whiskers), and outliers (points).
OPEN ACCESS
Figure 3. Habitat model results for full study area as
compared to known goshawk nest locations illustrating quan-
titative and qualitative breakpoints for habitat suitability
classification. Data include the cumulative distribution of
model results for all 30-meter pixels within the minimum
convex polygon encompassing all know goshawk nest loca-
tions, cumulative distribution of model results for each of the
80 known nest locations in the model building data set, the
difference between these two distributions, the breakpoint for
marginal habitat (chosen quantitatively) and the breakpoint
for suitable habitat (chosen qualitatively).
For nests located in lodgepole pine the top model in-
cluded elevation, slope, eastness, ruggedness, canopy
cover and Landsat band 4. The nests of goshawks lo-
cated in lodgepole pine were more often located at
lower elevation, with low slope, with east-facing aspect,
low ruggedness, high canopy cover, and forest structure
with low reflectance of near-infrared (0.75 - 0.9 µm
wavelength) [31].
The aspen model classified 83.0% of the total avail-
able habitat as poorly suited, 10.5% as marginally
suited, and 6.5% as well suited. The lodgepole pine
model classified 95.9% of the total habitat as poorly
suited, 0.6% of habitat as marginally suited, and 3.5%
as well suited.
In validating the models with the reserved set of tests,
83.3% of 12 aspen nests were located in habitat classi-
fied as well suited by the aspen model, 8.3% in habitat
classified as marginally suited and 8.3% in habitat clas-
sified as poorly suited. For lodgepole pine two of the
three validation nests were located in habitat classified
as well suited by the lodgepole pine model and one of
the three in habitat classified as marginally suited. The
union of the aspen and lodgepole pine models classified
habitat as well suited covers 8.6% of the minimum
convex polygon encompassing the known nest locations.
This model covers nearly the same amount of territory
as the combined model, yet does not perform as well
against the validation nests.
4. DISCUSSION
Habitat suitability models can be an effective way to
R. A. Miller et al. / Open Journal of Ecology 3 (2013) 109-115 113
Figure 4. Resulting habitat model for all forest types con-
strained by minimum convex polygon buffered by 500 meters
around known goshawk nest locations within the Cassia
Section, Minidoka Ranger District, Sawtooth National Forest in
south-central Idaho USA.
quantify the habitat requirements of a species. The habi-
tat variables represented in our model are fairly consis-
tent with other studies. Many studies, including ours,
have found that goshawks prefer dense canopy cover on
relatively gentle slopes with east facing aspect [5,14-17].
In similar habitat (high-elevation shrub-steppe with
fragmented forest stands), Younk and Bechard [15] ch-
aracterized nest trees with slope aspects north or east
facing and a close proximity to water. Distance to water
was dropped via model selection from all models that we
considered, possibly due to the fairly ubiquitous access
to small streams within our study area. The emphasis of
lower elevation in our model was no surprise as the
higher elevations of our study area have increasing con-
centration of sub-alpine fir, a species largely insufficient
as a structure for the nests of goshawks.
With this application, we have successfully paired
down the available habitat in the study area to those ar-
eas with a higher likelihood of hosting nests of breeding
goshawks. Over 90% of the study area has been elimi-
nated from consideration if we chose to search only the
well suited areas. The approach we used in our study
classified more area as poor habitat (86.1%), yet per-
formed better on validation (93.3% of reserved nests
located in habitat classified as marginal or suitable) as
compared with Reich et al. [5] and Mathieu et al. [6].
The success of our model may be the result of a more
highly fragmented landscape of our study area as com-
pared to other studies.
The direct use of Landsat imagery in the model se-
lection is a unique approach for our study. Others have
introduced an intermediate step of first translating Land-
sat data into vegetation classes which are then used in
model selection [6]. The vegetation class approach is
preferred when the goal is to quantify the habitat into
easily interpretable forms. However, the lost resolution
resulting from the dual processing step, and the general
poor performance of classification analysis in mixed
sagebrush-aspen habitats, decrease the value of this ap-
proach in our area [40].
Our model has substantiated what features may limit
nesting by goshawks within the unique environment in
southern Idaho. However, it should be noted that this
predictive model should be used with care beyond the
immediate area. The model is based on the range of val-
ues within the study area as sampled with the set of ran-
dom points. Evaluation of habitats with characteristics
beyond the range of values used in our model will likely
result in distortion of the predictions [11].
In conclusion, we have generated a strong predictive
model for the habitat suitable for hosting breeding gos-
hawks that has performed well against our validation data
and compares well with other studies and approaches.
This work will assist future surveyors of goshawks in our
area and in adjacent areas within the northern Great Ba-
sin with similar characteristics.
5. ACKNOWLEDGEMENTS
We thank the U.S.D.A. Forest Service Minidoka Ranger District for
their financial, consulting, and equipment support for the completion of
this project, particularly wildlife biologist Dena Santini. We also thank
Natural Research Ltd. for awarding us the 2011 Mike Madder’s Field
Copyright © 2013 SciRes. OPEN ACCESS
R. A. Miller et al. / Open Journal of Ecology 3 (2013) 109-115
114
Research Award and the Butler family for awarding us the 2012 Mi-
chael W. Butler Ecological Research Award.
We received logistic and equipment support from Boise State Uni-
versity’s Raptor Research Center, Idaho Bird Observatory, the Depart-
ment of Biological Sciences, Inovus Solar, David L. Anderson, and
Doug Guillory. We received general consulting from Dr. Jennifer For-
bey, from previous goshawk researchers Kristin Hasselblad, Greg Kal-
tenecker and Susan Patla, and from Boise State University statistician
Laura Bond. Thank you to Skye Cooley and David L. Anderson for
reviewing this manuscript.
Much of this work was completed using volunteer time from field
volunteers including: Jeri Albro, David L. Anderson, Alexis Billings,
Karyn deKramer, Michelle Jeffries, Ayla Kaltenecker, Cathy Lapinel,
Lauren Lapinel, Kraig Laskowski, Michelle Laskowski, Dusty Perkins,
Thurman Pratt, Kerry Rogers, Nicole Rogers, Uri Rogers, Emmy Tyr-
rell, Cristen Walker, Heidi Ware, Carol Wike, Dave Wike, and Mike
Zinn. We would like to thank them all for their hard work and dedica-
tion.
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