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RESEARCH ARTICLE
Habitat Use and Selection by Giant Pandas
Vanessa Hull1*, Jindong Zhang1,2, Jinyan Huang3, Shiqiang Zhou3, Andrés Viña1,
Ashton Shortridge4, Rengui Li3, Dian Liu3, Weihua Xu5, Zhiyun Ouyang5, Hemin Zhang3,
Jianguo Liu1
a11111
1 Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State
University, East Lansing, MI, United States of America, 2 Key Laboratory of Southwest China Wildlife
Resources Conservation, China West Normal University, Ministry of Education, Nanchong, China, 3 China
Conservation and Research Center for the Giant Panda (CCRCGP), Wolong Nature Reserve, Sichuan,
China, 4 Department of Geography, Michigan State University, East Lansing, MI, United States of America,
5 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco–environmental Sciences,
Chinese Academy of Sciences, Beijing, China
* [email protected]
Abstract
OPEN ACCESS
Citation: Hull V, Zhang J, Huang J, Zhou S, Viña A,
Shortridge A, et al. (2016) Habitat Use and Selection
by Giant Pandas. PLoS ONE 11(9): e0162266.
doi:10.1371/journal.pone.0162266
Editor: Bi-Song Yue, Sichuan University, CHINA
Received: December 21, 2015
Accepted: August 20, 2016
Published: September 14, 2016
Copyright: © 2016 Hull et al. This is an open access
article distributed under the terms of the Creative
Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: Data cannot be made
publicly available due to safety concerns for the
endangered species, but can be obtained upon
request from Vanessa Hull ([email protected]).
Funding: We gratefully acknowledge the following
funding sources: National Science Foundation (NSF),
the National Aeronautics and Space Administration
(NASA), Michigan AgBioResearch, the Michigan
State University Distinguished Fellowship Program,
the William W. and Evelyn M. Taylor International
Engagement Program, the National Natural Science
Foundation of China (40901289, 41571517), the
State Key Laboratory of Urban and Regional Ecology,
Research Center for Eco–Environmental Sciences,
Chinese Academy of Sciences (SKLURE2008–1),
Animals make choices about where to spend their time in complex and dynamic landscapes, choices that reveal information about their biology that in turn can be used to guide
their conservation. Using GPS collars, we conducted a novel individual-based analysis of
habitat use and selection by the elusive and endangered giant pandas (Ailuropoda melanoleuca). We constructed spatial autoregressive resource utilization functions (RUF) to model
the relationship between the pandas’ utilization distributions and various habitat characteristics over a continuous space across seasons. Results reveal several new insights, including
use of a broader range of habitat characteristics than previously understood for the species,
particularly steep slopes and non-forest areas. We also used compositional analysis to analyze habitat selection (use with respect to availability of habitat types) at two selection levels.
Pandas selected against low terrain position and against the highest clumped forest at the
at-home range level, but no significant factors were identified at the within-home range
level. Our results have implications for modeling and managing the habitat of this endangered species by illustrating how individual pandas relate to habitat and make choices that
differ from assumptions made in broad scale models. Our study also highlights the value of
using a spatial autoregressive RUF approach on animal species for which a complete picture of individual-level habitat use and selection across space is otherwise lacking.
Introduction
The relationship between animals and their habitats is a central component of wildlife ecology
[1]. One important area of research involves understanding the behavior of individual animals
as they use habitats distributed across heterogeneous space [2, 3]. Such studies often reveal
a wealth of information that cannot be obtained by population-level surveys, including information on fine-scale variation over space and time [2, 4]. Research has also been extended to
the study of habitat selection, or the choice of habitats relative to their availability on the
PLOS ONE | DOI:10.1371/journal.pone.0162266 September 14, 2016
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Habitat Use and Giant Pandas
and the Giant Panda International Collaboration Fund
(Grant SD0631; SD1113). The funders had no role in
study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
landscape [5, 6]. Such studies can inform conservation efforts of endangered species by revealing the full range of resources used that may be missed by population surveys alone, while also
pinpointing which types of habitats are selected in higher proportion to their availability, thus
potentially warranting increased conservation focus [7, 8].
This constitutes an important research topic for endangered species such as the giant panda
(Ailuropoda melanoleuca). Endemic to mountainous forests found in southwestern China, giant
pandas are the most endangered ursid on earth. Currently limited to a mere 25,000 km2 of estimated suitable habitat [9], the estimated 1,864 remaining giant pandas are facing human threats
including road construction, timber harvesting, tourism, and livestock grazing [9, 10]. The
remaining panda habitat is defined by the existence of bamboo, their main food source, which
makes up over 99% of their diet [11]. Bamboo occurs in mixed deciduous and coniferous forests
in areas that are often rugged, with steep mountainsides and rapidly changing elevation [12, 13].
Aside from bamboo, panda habitat suitability is most commonly defined by three variablesforest, slope, and elevation [14]. Pandas use forests located at the mid-elevations that provide
suitable conditions for bamboo growth [11, 15]. Slope is also one of the most important habitat
characteristics for pandas, as pandas use areas of low or moderate slope for energetically efficient traveling [11, 16]. Pandas also use areas with high solar radiation, choosing warmer topographic aspects [17] in addition to areas farther from focal areas of human activity such as
villages [18] and in areas not recently subjected to timber harvesting [13, 19]. Pandas also select
many of these same characteristics at a higher proportion than what is available (e.g. higher
bamboo cover and higher solar radiation) or at a lower proportion than what is available (e.g.
steep slopes) [20].
Despite this information, many gaps remain in our understanding of the habitat use and
selection by this elusive species [20, 21]. Previous research was mainly conducted at the population level, with little known about habitat use and selection of individual giant pandas. This is
in large part due to a government moratorium on all giant panda telemetry from 1995–2006
[22, 23], which limited the information available on the behavior of individual pandas. As a
result, information on habitat use and selection was derived from panda signs (e.g., fecal droppings) detected along transects sampled throughout their habitat areas. Such an approach,
while valuable, leaves little appreciation for variation in intensity of habitat use and selection
because it is derived as a binary (presence/non-presence) variable and cannot be ascribed to
individual animals. This limitation started to be overcome in a recent study by Zhang et al. [24]
involving habitat use of GPS-collared giant pandas which found significant effects of several
geophysical factors (including elevation and slope).
But several questions remain to be answered, including the relationship between habitat use
and both existing habitat suitability models and habitat selection. For instance, existing habitat
suitability models were built from wildlife sign data, which does not capture the complete picture of areas that pandas use less frequently but may still be valuable. Such population level
data, while valuable, may also be biased if collected in a non-random manner, such as on easily
accessible trails, as is common in panda research [20]. The proportion of individual panda
home ranges made up of different habitat suitability classes is unknown, as is whether some
variables may be less important when combined with other variables in a multivariate model of
habitat use. In addition, there has only been one individual-level assessment of habitat selection
in the species [17, 25]. The individual-level analysis is important because it sheds light on how
each panda is making choices on the landscape in relationship to what type of resources are
available to it. This was a valuable work, but was derived from pairing radio telemetry locations
(with limited spatial accuracy and temporal resolution) to other locations in a reserve outside
of the home ranges, leaving remaining questions about variation in habitat use across the home
range and definition of available habitats.
PLOS ONE | DOI:10.1371/journal.pone.0162266 September 14, 2016
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Habitat Use and Giant Pandas
To begin filling these knowledge gaps, in this study we examined the habitat use and selection by individual, GPS-collared giant pandas across space and time. Our objectives were to (1)
analyze the biogeophysical factors related to continuous predictions of habitat use via a multivariate spatial autoregressive modeling approach, (2) relate habitat use to existing habitat suitability models for giant pandas via spatial overlay techniques, and (3) investigate habitat
selection (use/availability) by individual giant pandas at within-home range and at-home range
selection levels via a modified compositional analysis. This study generates new information
on the ecology of this endangered species, specifically by providing necessary individual context for understanding how pandas relate to their complex environments, in turn informing
conservation by prioritizing specific areas of remaining panda habitat that need to be managed.
Methods
Study area and panda subjects
The study area is located in Wolong Nature Reserve (102°52’– 103°24’E, 30°45’– 31°25’N),
Sichuan, China. Home to around 10% of the total wild giant panda population [26], the reserve
contains ample forest stretching across mountains with steep slopes (above 50°, [11]). Our
team has been doing research in the reserve since 1996 [27–34]. Many of the results and methods generated in the reserve have also been applied to regional, national, and global levels [35–
44]. It is our hope that this study will also be useful for other species in other parts of the world.
The study was conducted in the northeastern portion of the reserve in an area known as
Hetaoping (Fig 1). Roughly 40 km2 in size and spanning an elevational range of 1,800 to 3,100
m, the study area includes mixed deciduous and coniferous forests and subalpine coniferous
forests with bamboo dominating their understories. Common tree species include Chinese
walnut (Juglans cathayensis), mono maple (Acer mono), hemlock (Tsuga longibracteata) and
spruce (Picea asperata), while the main bamboo species are arrow (Bashania fangiana),
umbrella (Fargesia robusta) and Yushan (Yushania bravipaniculata) bamboo.
Camera trapping and genetic testing of DNA extracted from field-collected feces suggest
that Hetaoping supports a local population of 16–25 pandas [45]. Camera trapping also
revealed the presence of other large mammals, including the tufted deer (Elaphodus cephalophus), serow (Capricornis milneedwardsii), and sambar (Rusa unicolor), although none are
believed to be direct competitors with pandas for space and food [11]. Hetaoping is geographically bounded by human activity zones: it is located between two villages to the southwest and
northeast and a provincial highway to the west (Fig 1).
Five giant panda individuals were captured in 2010 and 2011 at Hetaoping, fitted with GPS
collars, and released (Table 1). Capture was accomplished using anesthetization dart guns
loaded with weight-dependent doses of ketamine. Animals were handled for short (~30 minute) periods. Staff members at the China Conservation and Research Center for the Giant
Panda (CCRCGP) were responsible for animal safety. Research was approved (via granting a
waiver) by the Michigan State University Institutional Animal Care and Use Committee
(IACUC). The study pandas included 4 females and 1 male, all adults except for a sub-adult
female.
Pandas were fitted with Lotek GPS_4400 M collars (Lotek Engineering Inc., Newmarket,
Ont., Canada). The collars weighed about 1.2 kg and recorded longitude, latitude, and elevation
once every four hours. Collars also measured activity (movement of pandas’ heads along the X
and Y axes every 5 minutes). Number of days of monitoring varied across individuals (Table 1)
due to either the collar falling off of the animal or collar damage. Data collected within one
week of an individual’s release were excluded to minimize bias introduced by the capture/
release event. Static testing on collars in various habitat locations prior to deployment on
PLOS ONE | DOI:10.1371/journal.pone.0162266 September 14, 2016
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Habitat Use and Giant Pandas
Fig 1. Hetaoping study area for giant panda GPS collar research in Wolong Nature Reserve, China. Forest layer is derived from supervised
classification of Landsat TM imagery [43]. Chinese names refer to individual pandas.
doi:10.1371/journal.pone.0162266.g001
Table 1. Summary of study pandas and GPS collar performance over the one year period included in this study.
Pan
Long
Mei
Zhong
Chuan
Sex
female
female
female
female
male
Age
adult
sub-adult
adult
adult
adult
4/11/2011
Start date
4/18/2010
4/11/2011
4/18/2010
4/11/2011
Days monitored
219
184
365
365
351
Total fixes recorded
507
458
961
458
1473
Fix acquisition rate
0.39
0.41
0.47
0.30
0.70
doi:10.1371/journal.pone.0162266.t001
PLOS ONE | DOI:10.1371/journal.pone.0162266 September 14, 2016
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Habitat Use and Giant Pandas
pandas revealed a fix acquisition rate of over 90% for each collar (n = 30 habitat locations). Fix
acquisition was not correlated to habitat characteristics (e.g., slope or forest cover). Fix acquisition rate of collars while worn by pandas was lower (30–70%, Table 1). This is likely due to animal behavior (e.g. antenna obstruction while the panda was sleeping or eating). A successful fix
occurred at least once every 10 days (and usually at least once every 3 days) for all pandas
except the male, whose collar malfunctioned and did not record data for two longer periods
(11/14/2011-12/25/2011 and 3/27/2012-4/11/2012). While the overall higher fix acquisition
rate of the male panda compared to the others may influence model results and performance,
we did not see consistent patterns that differentiated this individual panda’s habitat use and
selection data from that of the others. Positional errors of collars compared to a differentially
corrected GPS unit averaged 16 to 23 m across individuals (n = 30 locations per collar). Since
pre-deployment testing showed no significant difference in location error of 2-D (3 satellites)
versus 3-D (4 or more satellites) locations, we included both in the analysis. However, we
excluded data for which the elevation estimate was inaccurate (measuring below 1,000 m,
n = 11% of all observations), as these fixes appeared to also have inaccurate longitude and latitude measurements.
Estimating panda utilization distributions
A utilization distribution (UD) is a probability density function representing a continuous prediction of an animal’s frequency of use across space [46]. We estimated the UD of each individual by applying the biased random bridge (BRB) movement model to the GPS locations [47].
We chose this model over another commonly used method, the Brownian bridge movement
model (BBMM), because a comparison of the two approaches using the area under the receivers operating characteristic curve (AUC) approach (as in [48]) showed the BRB to outperform
the BBMM model. The BRB model is a is a stochastic model composed of a biased random
walk in which the probability of being found at a particular location is dependent on the starting and ending locations and the time elapsed between them. An advection-diffusion component accounts for animals having a higher probability of drifting toward certain directions. We
estimated the probability density function using a circular bivariate normal distribution. The
diagonal of the variance-covariance matrix of this model was the diffusion coefficient (D) [47].
Parameters Tmax (maximum step duration), hmin (location uncertainty parameter), and Lmin
(minimum distance between successive locations) were set as 36 hours, 10 m, and 20 m, respectively. We used a standard diffusion parameter (D) chosen using the plug–in method by taking
the average across all animals (D = 0.85 m2/s, [47]). We also used the collars’ activity data to
correct for “active” time since the previous location (i.e. only the proportion of time with nonzero activity measured using the collars’ activity sensors, [47]). We defined the extent of space
use by each animal at the 95% UD boundary (commonly defined as the animal’s home range
[49]). For the purposes of this study and to standardize across individuals, we limited the data
range to one year for those pandas with more than one year of data (Table 1; the second year of
data was used for the adult female panda named Mei Mei due to a pregnancy in the first year).
Home ranges of the individuals studied ranged from 1.2 to 6 km2 during this time period [23].
Habitat characteristics
We examined 6 habitat characteristics relevant for habitat use of giant pandas. These included
slope, elevation, terrain position, solar radiation, forest presence, and forest clumpiness. Slope,
elevation, and forest presence are commonly used in giant panda habitat suitability mapping
for the species. Pandas are believed to use gentle slopes due to ease of travel and mid-elevation
forested areas due to suitability for bamboo growing conditions [14, 15]. Topographic position
PLOS ONE | DOI:10.1371/journal.pone.0162266 September 14, 2016
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Habitat Use and Giant Pandas
has been hypothesized to be an important predictor of panda use in the past, with pandas
using ridges intensively for travel and scent communication [50, 51]. Solar radiation has been
hypothesized to be an important predictor of panda use, with pandas using warmer areas more
intensively than cooler areas [17].
Slope, elevation, topographic position, and solar radiation were derived from a Digital Elevation Model (DEM) released in 2011 by the National Aeronautics and Space Administration’s
(NASA) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER GDEM
v2, 29 m resolution). Topographic position was calculated using the topographic position index
(TPI), a measure of the difference between the elevation in a pixel and the average elevation in
the surrounding pixels (we chose a 9-pixel neighborhood area) calculated using the Land Facet
Corridor Designer in ArcGIS [52]. Higher values represent mountain ridges and lower values
represent valleys. Solar radiation was estimated using the Area Solar Radiation tool in ArcGIS
(with a 200 m sky size and a year-long estimation using monthly intervals). The forest/non-forest layer was derived from a supervised classification (with an 82.6% accuracy) of Landsat TM
imagery (30 x 30 m resolution) acquired in 2007 [53]. The measure of forest clumpiness we
used was calculated on the Landsat forest cover layer using the Clumpiness Index in Fragstats.
The index equals the “proportional deviation of the proportio …
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