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Zhang, et al, 2011

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http://rsbl.royalsocietypublishing.org/content/early/2011/01/03/rsbl.2010.1081.full.pdf+html?sid=071cacd7-49d1-414f-927f-b556dcbcf198

[1]Giant pandas (Ailuropoda melanoleuca) are an
iconic conservation species, but despite significant
research effort, do we understand what they really
need? Estimating and mapping suitable habitat
play a critical role in conservation planning and
policy. [2]But if assumptions about ecological needs
are wrong, maps with misidentified suitable habitat
willmisguide conservation action. Here, we use
an information-theoretic approach to analyse the
largest, landscape-level dataset on panda habitat
use to date, and challenge the prevailing wisdom
about panda habitat needs. [3]We show that pandas
are associated with old-growth forest more than
with any ecological variable other than bamboo.
Other factors traditionally used in panda habitat
models, such as topographic slope, are less important.
We suggest that our findings are disparate
from previous research in part because our
research was conducted over a larger ecological
scale than previous research conducted over
more circumscribed areas within individual
reserves. Thus, extrapolating from habitat studies
on small scales to conservation planning on large
scales may entail some risk. [4]As the Chinese government
is considering the renewal of its logging
ban, it should take heed of the panda’s dependency on old growth.

1. INTRODUCTION
The giant panda is at a high risk of extinction and
will require informed management for recovery.
Panda conservation science has come a long way in
recent years; now it is time to apply our increasingly
sophisticated knowledge of pandas to conservation
management strategies [1]. Although the giant
panda genome has now been sequenced [2], we are
still struggling to fully understand the panda’s
ecological requirements.
Estimating and mapping suitable habitat play a critical
role in conservation planning and policy, but if
assumptions about habitat suitability are wrong, conservation
action will be misguided [3,4]. Ecological scale,
perhaps the most fundamental concept in all of ecology
[5], intersects with conservation when research conducted
on one scale is used for conservation planning
on another scale [3]. For the iconic endangered giant
panda, models of habitat suitability have been used to
infer rates of habitat loss, estimate amount of suitable
habitat remaining inside and outside reserves, and
design reserve systems linked by ecologically relevant
corridors [6–8]. But maps and measures of habitat suitability
are only as good as the underlying biological
assumptions, which are sometimes influenced by the
scale over which data are obtained. Modellers of
panda habitat have not ignored the available ecological
data, but have been forced to rely on data collected
over limited temporal and spatial scales. What happens
if the resulting habitat models are wrong?
Here, we analyse the largest, landscape-level dataset
on panda habitat use, challenge prevailing wisdom
about panda habitat needs, and provide direction to
conservation policy and planning at the national
level. We use data collected by dozens of field teams
throughout Sichuan during the Chinese State Forestry
Administration’s Third National Survey on the giant
panda, representing one of the most intensive efforts
to survey any endangered species. This massive undertaking
produced by far the largest dataset—in scope,
sample size and range—of habitat use by giant pandas.
2. MATERIAL AND METHODS
From 1999 to 2003, field observers recorded ecological variables
associated with panda signs, matched with control plots where panda
signs were not observed. The data included in our analysis were collected
across the giant panda range in Sichuan province, including 19
reserves in the four primary mountain ranges: Minshan, Qionglai,
Liangshan and Xiaoxiangling (figure 1; electronic supplementary
material). This multi-year survey—across seasons and much of the
landscape available to pandas today—avoids the potential limitations
arising from data collected over small spatial and temporal scales.
Details of the Third National Survey are available elsewhere [9].
The known and potential range of giant pandas was divided into sections
measuring 2–6 km2 and field teams established transects along
altitudinal gradients, ensuring that each transect sampled all representative
habitat types. The presence of panda along transects was
determined by signs (primarily faeces, but also foraging sites and
dens) and vegetation was sampled in 20m 20 m plots. A sign
more than 200 m from another sign merited establishing another
plot. Data collected for each plot included topography, altitude,
slope, forest type, forest age, tree diameter at breast height (DBH),
canopy coverage, shrub cover, shrub height and bamboo presence.
Control plots were established at every 200 m change in elevation
along the transect and following transitions between forest types, to
ensure that all habitat types encountered were sampled. After excluding
marginal habitats, such as monocultured forests and data from
nine reserves with inadequate records, we had 4908 plots for analysis
(1116 panda sign plots and 3792 control plots).
We used an information-theoretic approach [10] to determine the
suite of factors that best predicted the presence of panda signs. Pearson’s
(for continuous variables) or Kendall’s (for discrete variables)
correlation analysis was first conducted to test independence
between variables. For those variables with a correlation coefficient
above 0.5, we only kept the variable with clear biological meaning
in the subsequent analysis in order to weaken multi-collinearity.
We constructed a full model set, including global models with all
meaningful explanatory variables, and calculated the Akaike information
criterion (AIC) to evaluate model fit. Using the differences
in AIC values (Di) between the lowest scoring model and each candidate
model, we calculated the Akaike weights and evidence ratios1. INTRODUCTION
The giant panda is at a high risk of extinction and
will require informed management for recovery.
Panda conservation science has come a long way in
recent years; now it is time to apply our increasingly
sophisticated knowledge of pandas to conservation
management strategies [1]. Although the giant
panda genome has now been sequenced [2], we are
still struggling to fully understand the panda’s
ecological requirements.
Estimating and mapping suitable habitat play a critical
role in conservation planning and policy, but if
assumptions about habitat suitability are wrong, conservation
action will be misguided [3,4]. Ecological scale,
perhaps the most fundamental concept in all of ecology
[5], intersects with conservation when research conducted
on one scale is used for conservation planning
on another scale [3]. For the iconic endangered giant
panda, models of habitat suitability have been used to
infer rates of habitat loss, estimate amount of suitable
habitat remaining inside and outside reserves, and
design reserve systems linked by ecologically relevant
corridors [6–8]. But maps and measures of habitat suitability
are only as good as the underlying biological
assumptions, which are sometimes influenced by the
scale over which data are obtained. Modellers of
panda habitat have not ignored the available ecological
data, but have been forced to rely on data collected
over limited temporal and spatial scales. What happens
if the resulting habitat models are wrong?
Here, we analyse the largest, landscape-level dataset
on panda habitat use, challenge prevailing wisdom
about panda habitat needs, and provide direction to
conservation policy and planning at the national
level. We use data collected by dozens of field teams
throughout Sichuan during the Chinese State Forestry
Administration’s Third National Survey on the giant
panda, representing one of the most intensive efforts
to survey any endangered species. This massive undertaking
produced by far the largest dataset—in scope,
sample size and range—of habitat use by giant pandas.
2. MATERIAL AND METHODS
From 1999 to 2003, field observers recorded ecological variables
associated with panda signs, matched with control plots where panda
signs were not observed. The data included in our analysis were collected
across the giant panda range in Sichuan province, including 19
reserves in the four primary mountain ranges: Minshan, Qionglai,
Liangshan and Xiaoxiangling (figure 1; electronic supplementary
material). This multi-year survey—across seasons and much of the
landscape available to pandas today—avoids the potential limitations
arising from data collected over small spatial and temporal scales.
Details of the Third National Survey are available elsewhere [9].
The known and potential range of giant pandas was divided into sections
measuring 2–6 km2 and field teams established transects along
altitudinal gradients, ensuring that each transect sampled all representative
habitat types. The presence of panda along transects was
determined by signs (primarily faeces, but also foraging sites and
dens) and vegetation was sampled in 20m 20 m plots. A sign
more than 200 m from another sign merited establishing another
plot. Data collected for each plot included topography, altitude,
slope, forest type, forest age, tree diameter at breast height (DBH),
canopy coverage, shrub cover, shrub height and bamboo presence.
Control plots were established at every 200 m change in elevation
along the transect and following transitions between forest types, to
ensure that all habitat types encountered were sampled. After excluding
marginal habitats, such as monocultured forests and data from
nine reserves with inadequate records, we had 4908 plots for analysis
(1116 panda sign plots and 3792 control plots).
We used an information-theoretic approach [10] to determine the
suite of factors that best predicted the presence of panda signs. Pearson’s
(for continuous variables) or Kendall’s (for discrete variables)
correlation analysis was first conducted to test independence
between variables. For those variables with a correlation coefficient
above 0.5, we only kept the variable with clear biological meaning
in the subsequent analysis in order to weaken multi-collinearity.
We constructed a full model set, including global models with all
meaningful explanatory variables, and calculated the Akaike information
criterion (AIC) to evaluate model fit. Using the differences
in AIC values (Di) between the lowest scoring model and each candidate
model, we calculated the Akaike weights and evidence ratiosfor each of the models.
3. RESULTS
Multiplemodels (i.e. 11 of the 256) had an evidence ratio
less than 10 and differences between candidate models
tended to be small (table 1). However, the first three
models in table 1, including bamboo presence, slope,
forest age, tree DBH and understory height, were
approximately 1.5, 2.1 and 2.6 times more likely to
explain the presence of panda signs than the models
that included those same variables plus shrub cover,
tree canopy and elevation, respectively.
Our analyses reveal presence of bamboo and forest
age (old growth versus secondary growth) as the ecological
variables best predicting panda habitat use. Of the
top models (evidence ratios less than 10), bamboo and
forest age are the only variables common to all models.
Following model selection, we estimated the relative
importance of each of the contributing variables by calculating
the sum of the Akaike weights for all models
that included the variable under consideration.
Although the presence of bamboo ranked first,
old-growth forest was essentially equivalent (Akaike
weights ¼ 1.010 and 1.009, respectively), followed
closely by treeDBH(0.958), ameasure that is undoubtedly
related to forest age (as well as site productivity).
Slope, a variable frequently used in habitat models
for pandas [7,8], ranked much lower (0.844), no more
important than understory height (0.824), which rarely
factors into panda habitat suitability models. The
remaining variables—shrub cover, elevation and tree
canopy—had poor predictive power (less than 0.500).
4. DISCUSSION
As an obligate bamboo specialist, it is no surprise that
bamboo predicts panda habitat use. But it is surprising
that old-growth forest should attain the same level ofimportance as bamboo. Our data do not allow us to discern
what draws pandas to advanced stages of forest
succession. One possibility is that the bamboo that
grows underneath old growth is more nutritious [11].
Another intriguing possibility is that only old-growth
trees grow large enough to form cavities suitable for
maternity dens [12]. This raises the question: are birth
dens a factor-limiting panda population size in reserves
with a history of logging [1]?
Despite numerous studies based on data collected
over a limited scale—typically a portion of a single
reserve [6,13–15]—previous research has not clearly
identified this strong association with old-growth forests.
Although one single-reserve study indicated a
positive relationship between forest age and panda presence
[13], old growth did not assume the primacy it did
in our findings. Lack of robust range-wide data and conflicting
evidence among several studies over smaller
areas has meant that old growth has not figured prominently
in habitat mapping, policy and planning. By
contrast, our dataset represents more than 70 per cent
of the panda’s current range, which allows us to make
inferences across the entire panda landscape. This
underscores the importance of ecological scale in inferring
habitat selection: old growth emerges as an
important factor influencing panda habitat preferences
only when data are collected over a large enough scale.
Our fine-grained study over large extent captured habitat
relations that were not fully understood previously. Now,
maps and models of suitable panda habitat should be
revised to give priority to old-growth forest.
Our data point to a need for habitat modelling to be
more sensitive to the issue of ecological scale,
especially when scaling up from data collected on localized
scales to plan conservation practice over much
larger scales. Appropriate conservation planningoften requires this type of range-wide landscape scale
approach. We recognize that scale-appropriate ecological
research will not always be easy and may require
substantial effort over time and space. However,
there are at times no good substitutes to real effort.
While such endeavours in ecology are increasingly
rare in an era when quick empirical studies trump
investment in long-term ecological research, the
pay-off can be substantial and irreplaceable [16].
More than a decade ago the Chinese government
implemented a ban on all logging throughout the
panda’s range, an action that curtailed significant
losses to the forest ecosystem on which giant pandas
and other wildlife depend [17]. This logging moratorium
expires in 2010 and the government must decide
whether to extend the ban [17,18]. It is critical for
the long-term future of the panda to identify the ecological
variables important for their persistence now,when the government still has time to consider these
variables while devising its timber-harvest policy.
As the Chinese government decides whether or not
to lift the ban on logging, it should consider this: itmay be more cost-effective to protect the existing old
growth than to open it up to logging while protectingan equivalent area of secondary growth forest.
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