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\begin{document}

\title{Signal detection theory applied to giant pandas: Do pandas go out of
their way to make sure their scent marks are found?}



\author[1]{Yue Wang}%
\author[2]{Ronald Swaisgood}%
\author[1]{Wei Wei}%
\author[1]{Hong Zhou}%
\author[3]{Feiyun Yuan}%
\author[1]{Mingsheng Hong}%
\author[4]{Han Han}%
\author[1]{zejun Zhang}%
\affil[1]{China West Normal University}%
\affil[2]{San Diego Zoo Institute for Conservation Research}%
\affil[3]{Sichuan Tibetan Area Expressway Co., Ltd.}%
\affil[4]{Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education)}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
The purpose of inter-animal communication is to allow signals released
by the animal to be perceived by others. Scent marking, with its
characteristics of specificity and delay, is thought to be the primary
mode of communication in giant pandas (Ailuropoda melanoleuca). The
``signal detection theory'' predicts that animals choose the substrate
and location of their scent marks so that the signals released are
transmitted more widely and remain longer. As an energetically marginal
species, the cost of generating and marking chemical signals is costly
for pandas, so they are predicted to make trade-offs in scent marking.
However, existing studies do not account for the selective preferences
of pandas for marking, as they are only explained by the density of
marks at a certain location. Our study wanted to investigate whether the
marking behavior of pandas is indeed consistent with signal detection
theory. For the first time, we propose to use fecal counts to reflect
the intensity of habitat use by pandas, combined with mark counts to
determine the selective preference for marking. Our findings show that
the scent marking behavior of pandas is consistent with signal detection
theory, and that they go out of their way to ensure that their marks are
detected. The results of the study will help us to further develop the
conservation of pandas and their habitats.%
\end{abstract}%



\sloppy


\textbf{Signal detection theory applied to giant pandas: Dopandas go out
of their way to make sure their scent marks are found?}

Yue Wang\textsuperscript{1,2}, Ronald R. Swaisgood\textsuperscript{3},
Wei Wei\textsuperscript{1,2}, Hong Zhou\textsuperscript{1,2}, Feiyun
Yuan\textsuperscript{4.5}, Mingsheng Hong\textsuperscript{1,2}, Han
Han\textsuperscript{1,2*}and Zejun Zhang\textsuperscript{1,2*}

\textsuperscript{1}Key Laboratory of Southwest China Wildlife Resources
Conservation (Ministry of Education), China West Normal University,
Nanchong, Sichuan, 637009, P.R. China

\textsuperscript{2}Liziping Giant Panda's Ecology and Conservation
Observation and Research Station of Sichuan Province, Nanchong, Sichuan,
637009, P.R. China

\textsuperscript{3} Conservation Science and Wildlife Health, San Diego
Zoo Wildlife Alliance, 15600 San Pasqual Valley Road, Escondido,
California, 92027, USA

\textsuperscript{4}Sichuan Tibetan Area Expressway Co., LTD, Chengdu,
Sichuan, 610041, P.R. China

\textsuperscript{5}Sichuan LuShi Expressway Co., LTD, Chengdu, Sichuan,
610041, P.R. China

\textbf{*} Correspondence: Han Han and Zejun Zhang, Key Laboratory of
Southwest China Wildlife Resources Conservation (Ministry of Education),
China West Normal University, Nanchong, China.

E-mail: hanghang739@hotmail.com and\emph{zhangzejun66@163.com}

Postal: 637009

Funding information

National Natural Science Foundation of China, Grant/Award Number:
32070524

\textbf{Abstract}

The purpose of inter-animal communication is to allow signals released
by the animal to be perceived by others. Scent marking, with its
characteristics of specificity and delay, is thought to be the primary
mode of communication in giant pandas (\emph{Ailuropoda melanoleuca} ).
The ''signal detection theory'' predicts that animals choose the
substrate and location of their scent marks so that the signals released
are transmitted more widely and remain longer. As an energetically
marginal species, the cost of generating and marking chemical signals is
costly for pandas, so they are predicted to make trade-offs in scent
marking. However, existing studies do not account for the selective
preferences of pandas for marking, as they are only explained by the
density of marks at a certain location. Our study wanted to investigate
whether the marking behavior of pandas is indeed consistent with signal
detection theory. For the first time, we propose to use fecal counts to
reflect the intensity of habitat use by pandas, combined with mark
counts to determine the selective preference for marking. Our findings
show that the scent marking behavior of pandas is consistent with signal
detection theory, and that they go out of their way to ensure that their
marks are detected. The results of the study will help us to further
develop the conservation of pandas and their habitats.

\textbf{Keywords}

chemical communication, conservation implication, giant panda, scent
marking, signal detection theory

\textbf{1 INTRODUCTION}

Animal communication refers to the process by which an individual sends
signals to other individuals and the individual receiving the signals
responds to them. Chemical communication with characteristics such as
specificity and delay is considered to be one of the main modes of
communication in most mammals (Brown \& Macdonald, 1985), as it is
independent of light, can propagate even in dark conditions, and can
still propagate when the signal releaser is away from the chemical
signal (Wyatt, 2014). Scent marking, the deposition of signals by
individuals on objects in the environment, is a common form of chemical
signal in mammals (Gosling \& Roberts, 2001). The main vehicle for
transmitting information in mammalian chemical communication is the
chemical pheromone, which includes many complex information, such as
individual identity characteristics, sex, age, reproductive status,
social status and kinship (Brennan \& Kendrick, 2006; Ferrero \&
Liberles, 2010; Johansson \& Jones, 2007; Jojola et al., 2012; Kean et
al., 2011).

''Signal detection theory'' predicts that animals select scent-labeled
signal deposit substrates, resulting in a wider range and longer
retention of the released signal (Alberts, 1992). A growing number of
studies have demonstrated that this choice of signal deposit substrate
is more widespread in mammalian chemotaxis than we thought, such as the
spotted hyena (\emph{Crocuta crocuta} ) (Gorman, 1990) and the African
wild dog (\emph{Lycaon pictus} ) (Claase et al., 2022), which use urine
and feces, among other substances, for chemical communication. However,
we also need to be aware that both the chemical signal generation and
the marking process are very energy-consuming (Gosling et al., 2000);
and that after scent marking, the animal needs to be visited
periodically to observe and update the signal markers in order to
maintain the continued validity of the signal, a process that also
requires a significant investment of time and energy (Clapham et al.,
2014; Roberts \& Gosling, 2001). The economic constraints associated
with travel and time costs of chemical signals deployment across an
animal's entire home range preclude rangewide scent signal saturation,
forcing animals to be more strategic about selecting scent deposition
sites.

Giant pandas (\emph{Ailuropoda melanoleuca} ) are typically solitary
mammals that rarely come into direct contact with other individuals
except for the rutting season when they form gatherings (Nie et al.,
2012a; Schaller, 1985), and coupled with their long-term life in dense
bamboo forests, which has led to a relative degradation of vision (Wei
et al., 2015), information exchange between individuals in the wild
relies mainly on olfaction and hearing (Hu et al., 1985). Auditory
communication refers to communication between individuals through
acoustic signals and generally occurs during the mating aggregation
period in the breeding season (Charlton et al., 2009) or during casual
contact between individuals in the non-breeding season. Scent
marker-based chemical communication thus becomes a major mode of
communication for wild giant pandas (Swaisgood et al., 1999; Wei et al.,
2015). Giant pandas communicate chemically mainly by tagging anogenital
gland secretion (AGS) and urine to transmit individual information,
using the chemicals stored therein for inter-individual signal exchange
(Hu et al., 1985).

However, the cost of generating chemical signals is too high for an
energetically marginal species like the giant panda, which, unlike other
bear species, uses urine and AGS as chemical communication signals
rather than feces (Pan et al., 2001). Both urine and feces are metabolic
by-products and therefore consume less energy, whereas AGS is produced
by specialized glands and has a high fat content (Hagey \& Macdonald,
2003), further exacerbating energy expenditure.

Up to the present study, we found that giant pandas have a greater
number of scent markers at the ridge and will select the appropriate
substrate material depending on the marks (Nie et al., 2012a). However,
it is not rigorous to equate the number of markers with selection
preference. Because pandas mark more in a certain place may be because
they spend more time in that place, but not because they prefer to mark
there. We propose, for the first time, that there is a need to
demonstrate that giant pandas mark scent more frequently than expected
at a given location as an indication of selective preference. The
easiest way to do this is to collect feces during the sample line survey
and use fecal density to indicate the intensity of use of the site by
pandas; since pandas defecate nearly 50 times per day and do not use
feces for communication (Nie et al., 2012a), their choice of defecation
location is random, depending only on where they are located when they
want to defecate, so this method is rigorous.

We have innovated on previous studies by proposing for the first time to
use panda feces counts to laterally reflect the intensity of habitat use
as a way to assess the selection preferences of giant pandas when
scent-marking. This study allows us to investigate whether the scent
marking behavior of pandas is consistent with signal detection theory;
and whether pandas go out of their way to deposit markers in places
where they are most likely to be found. Based on the results of the
study, we can protect the habitats that pandas prefer to use with
precision and enhance communication among individual pandas and their
populations.

\textbf{2 MATERIALS AND METHODS}

\textbf{2.1 Study sites}

We conducted this study in Shaanxi Foping National Nature Reserve
(Figure 1), which is located in the Qinling Mountains, with a
geographical range between 107\selectlanguage{ngerman}°41\selectlanguage{english}' and 107deg55\selectlanguage{english}' E and 33deg33\selectlanguage{english}' and 33deg46\selectlanguage{english}'
N. The total area is about 293 km\textsuperscript{2} and the elevation
range is from 980 to 2904 m. In The Fourth National Survey Report on the
Giant Panda, there were about 67 wild pandas in the reserve. There are
six conservation stations in the reserve, namely Liangfengya,
Sanguanmiao, Xihe, Daguping, Longtanzi and Yueba. Among them,
Sanguanmiao and Xihe are the core areas of the reserve, which are rich
in flora and fauna. In addition to the giant panda, there are also a
wide range of national key protected animals such as the takin
(\emph{Budorcas taxicolor} ), forest musk deer (\emph{Moschus
berezovskii} ), leopard (\emph{Panthera pardus} ) and so on.

\textbf{2.2 Field data collection}

We established a sample line for the study near Sanguanmiao, the core
area of the reserve, where the density of giant pandas is known to be
high and where pandas are often scent marked (Nie et al., 2012a). We
established six types of sample lines along three types of terrain
(Table 1): 12, 7, and 3 each of ridge, valley, and slope samples with
animal trails, and 11, 7, and 4 each without animal trails, the former
as experimental samples and the latter as control samples, corresponding
to roughly equal sample line lengths. Animal trails are identifiable
trails traveled by wildlife on repeated trips, evident by worn
appearance, compacted soil, and lack of vegetation.

The width of the sample line was 4 m, i.e. 2 m on each side of the road,
and within this range we could easily detect marked trees and feces.
Data collection methods followed Nie et al., (2012a), but differed (Nie
et al., 2012a). Walking along the sample line, the number of giant panda
faeces found along the line was recorded, as well as the species and
location of all trees with DBH greater than 5 cm and several
characteristics that may affect the selectivity of giant panda markers
(Table 2), and they were marked as ''available trees''. For the trees
with scent marks, additional characteristics such as mark type and
orientation were recorded as ''marked trees''. Urine marks causing a
greener bark with a musky smell, and AGS marks causing the bark to turn
brown. To determine if the marked trees were repeatedly marked, we
monitored each marked tree on the sample line every other week. The
musky smell of the urine marker disappeared after about 5 days, which
allowed us to determine if the same area was repeatedly marked. For AGS
marking, we cut a small piece of bark in the middle of the marked area,
leaving out the lighter colored bark underneath, and monitored it to see
if it was again covered with AGS marks to determine if it was repeatedly
marked.

For each scent-marked tree, where feasible, we set up a 5*5m sample
square centered on them and measured the variables of vegetation
density, vegetation coverage, slope and slope aspect (Table 2). After
that, a control sample was set up by walking 200m along the sample line
and the same indicators were measured for it. Note that if there are
marked trees that happen to be located within the control sample, we
need to reposition the sample to the nearest area that does not contain
the marked trees in order to form a better control.

\textbf{2.3 Data analysis}

We used the Mann-Whitney U test and one-way ANOVA to determine whether
the presence or absence of a sample line of animal trails influenced the
degree of preference for marking by pandas, based on whether the
independent variables conformed to a normal distribution and the
chi-squaredness of the variance; for the degree of preference for
different terrain we used the Kruskal Wallis test. In addition, we used
the Mann-Whitney U test as well as the chi-square test to determine
whether individual variables were significantly different between the
marker and control samples, and between the marked and control trees.
Afterwards, we used Pearson Correlation Analysis to determine the
correlation among the variables, and after eliminating the significantly
correlated variables, we used stepwise regression to build the best
model to determine which variable had the greatest effect on the
selection of marking sites by giant pandas. In this study, we determined
that the differences were statistically significant at
\emph{P}\textless{} 0.05 and all tests were two tailed. All data
analysis was carried out in IBM SPSS Statistics 27.0.1.

\textbf{3 RESULT}

The total length of the 22 experimental lines was 18.02 km and the total
length of the 22 control lines was 16.37 km. 152 marked trees were found
in the former and 5 in the latter. A total of 790 giant panda faeces
were found in the former and 498 in the latter (Table S1).

\textbf{3.1 Preferred terrain for marking by giant pandas}

There was a significant difference in the degree of marking preference
between experimental and control lines, with pandas preferring to scent
mark on lines with animal trails (Mann-Whitney \emph{U} = 33.00,\emph{P}
\textless{} 0.001, Figure 2). The preference for terrain also differed
significantly, with giant pandas preferring to scent mark on ridges,
followed by valleys, and finally slopes (Kruskal Wallis
test,\selectlanguage{greek}\emph{χ\textsuperscript{2}}\selectlanguage{english} = 9.74, \emph{P} = 0.008, Figure 3).
The preferred location for scent marking by giant pandas was ridges with
animal trails, while no selection was made for slopes without animal
trails (Table S1).

\textbf{3.2 The tree species preferred by pandas for marking}

We counted the species of giant panda marked trees and classified their
marking frequency into 3 categories, low frequency (i.e., the species
had only 1 to 5 marking records in total), medium frequency (i.e., the
species had 6 to 10 marking records in total) and high frequency (i.e.,
the species had more than 10 marking records in total). There were 16
species of low frequency, 4 species of medium frequency and 3 species of
high frequency. The three species with high frequency are Badung oak
(\emph{Quercus engleriana} ), Yaupon pine (\emph{Pinus tabulaeformis} ),
and Huashan pine (\emph{Pinus armandii} ) (Table S2). These three trees
are all locally dominant species with a wide distribution (Ming et al.,
1999).

\textbf{3.3 Microhabitat differences between marked and control sites}

We found a total of 157 scent marked trees in the sample line survey,
but due to topography and slope, etc., not every location of the marked
trees was suitable for sample squares, so we made a total of 144 marked
samples, corresponding to 114 control samples. Compared to the control
sample, bamboo density (\emph{U} = 5474.00, \emph{P} \textless{} 0.001),
bamboo cover (\emph{U} = 5905.50, \emph{P} \textless{} 0.001), and slope
(\emph{U} = 6328.50, \emph{P} \textless{} 0.001) were lower in the
marked sample; however, tree density (\emph{U} = 4731.00,
\emph{P}\textless{} 0.001) and tree cover (\emph{U} = 5729.00,
\emph{P}\textless{} 0.001) were higher; shrub density (\emph{U}
=7523.50,\emph{P} = 0.226) and shrub cover (\emph{U} = 7500.00, \emph{P}
= 0.130) were not significantly different between both (Table~S3). In
order to detect which variables had the greatest influence on the
selection of this sample site by pandas, we performed stepwise logistic
regression analysis on the remaining variables after Pearson Correlation
Analysis to remove the significantly correlated variables. Finally, only
tree density, bamboo density, slope, tree cover and shrub cover entered
into the stepwise logistic regression equation. where tree cover was the
most explained variable for whether giant pandas chose the location for
scent marking or not (Table 3), and giant pandas preferred to scent mark
at sites with higher tree cover.

\textbf{3.4 Microhabitat differences between marked and control trees}

Our survey identified 157 marked trees, corresponding to 441 unmarked
control trees, with significant differences in bark roughness between
both (\selectlanguage{greek}\emph{χ\textsuperscript{2}}\selectlanguage{english} = 19.31, \emph{P} \textless{} 0.001);
DBH was greater in scent-marked trees compared to control trees, but the
difference was not significant (\emph{U} = 34473.00, \emph{P} = 0.079);
SDT was significantly shorter in marked trees than control trees
(\emph{U} = 11791.50, \emph{P} \textless{} 0.001) (Table~S4). To detect
which variables had the greatest influence on the selection of the tree
by pandas, we performed stepwise logistic regression analysis on the
remaining variables after Pearson Correlation Analysis to eliminate
those that were significantly correlated. Finally, only roughness, DBH
and SDT entered into the stepwise logistic regression equation, where
roughness was the variable that explained the highest degree of whether
pandas selected the tree species for scent marking (Table 4), and pandas
preferred scent marking on trees with higher roughness.

\textbf{3.5 Microhabitat differences between repeatedly and singly
marked trees}

We found a total of 157 scent marked trees, of which 139 were marked
multiple times, accounting for 88.3\% of all marked trees, and 18 were
marked only once, accounting for 11.7\% of all marked trees (Figure~S1).
One-way ANOVA was performed on the factors that might affect the
frequency of marking, and we found no significant differences between
roughness (\emph{P} =0.65), DBH (\emph{P} =0.82) and SDT (\emph{P}
=0.70) (Table S5).

\textbf{3.6 Giant panda mark types and orientations}

We recorded the type of marks used for scent marking in pandas and the
orientation of the marks. The most frequently found marker was the AGS
mark, which contained 144 of the 157 scent marked trees, accounting for
91.7\% of all marked trees. The least frequent mark was Scratch mark,
which contained 4 out of 157 scent marked trees, accounting for 2.5\% of
all marked trees, and Scratch mark was always accompanied by AGS mark
(Figure~S2). The most marker orientation is face to trail, which
contains 128 out of 157 scent marked trees, accounting for 81.5\%, while
the least mark orientation is back to trail, with 6 trees, accounting
for 3.8\%, and multiple directional marks were accompanied with face to
trail mark (Figure~S3).

\textbf{4 DISCUSSION}

By monitoring a total of 44 sample lines, we found that the
scent-marking behavior of giant pandas is indeed consistent with signal
detection theory (Alberts, 1992), and that giant pandas do select and
modify their marking sites to maximize their signaling function at both
micro- and macro-habitat scales. Giant pandas are an energetically
marginal species, their diet is bamboo and their nutritional phase is
low, which affects aspects of movement, communication and reproduction
(Hu et al., 1985; Nie et al., 2012b; Nie et al., 2015). The selection of
marking sites will minimize energy expenditure while increasing the
effectiveness of inter-individual communication.

Our findings show that pandas prefer to scent mark on ridges with animal
trails, and we know by the density of faeces on the sample line that
this is indeed the area with the highest intensity of use by pandas as
well (Table S1). The presence of many animal trails on the ridges also
makes them important paths for the movement of giant pandas (Bai et al.,
2020). However, we likewise noted that slopes without animal trails as
well as ridges had high fecal densities, demonstrating that pandas spend
a significant amount of time in these areas, yet the preference index
for these areas was significantly lower than the corresponding terrain
with animal trails. From this we learn that pandas choose these
locations for scent marking not because they happen to be there, but
because they really need to choose such terrain to maximize signal
propagation efficiency.

The tree species most frequently scent-tagged by giant pandas were
Badung oak (\emph{Quercus engleriana} ), Yaupon pine (\emph{Pinus
tabulaeformis} ), and Huashan pine (\emph{Pinus armandii} ), which are
dominant species with a wide local distribution (Ming et al., 1999). We
speculate that they choose dominant species for marking because they
take less time and energy to be found. In addition to their wide
distribution, their high marking frequency may also be due to the fact
that marked trees are well located for signal transmission and over time
become scent stations (Hu et al., 1985), and they are often located
along animal trails on mountain ridges (Nie et al., 2012a). Marking on
these scent stations commonly used by giant pandas would also increase
the likelihood of their information being detected by signal receivers.
Zhou et al. (2019) have demonstrated that both volatile and non-volatile
compounds in AGS marks were not significantly degraded within two weeks,
suggesting that pandas visit and update marks frequently (Zhou et al.,
2019). Thus depositing marks in better located scent stations would also
reduce the energetic cost of returning and updating markers. Future work
could increase research on scent stations and explore their
microhabitats and conditions such as light that may affect marker
persistence and dispersal to increase conservation of habitats where
potential scent stations exist.

The roughness and SDT of the marked tree of giant pandas differed
significantly from the control tree, similar to the results of previous
study (Nie et al., 2012a), where giant pandas generally do not scent
mark on smooth trunks, which would reduce the persistence of marking;
and the SDT of the marked tree was significantly shorter than that of
the control tree because the closer the distance to the animal trail,
the more likely it is to be detected by the signal recipient, and
because when marking here, giant pandas can travel shorter distances and
save energy.

For both marked and control samples, bamboo density, bamboo cover and
slope were lower and tree density and cover were higher in the marked
sample. Higher bamboo density and cover make it more difficult for giant
pandas to walk among bamboo forests, while relatively low bamboo density
will allow giant pandas to obtain sufficient nutrition while reducing
energy expenditure to traverse bamboo forests (Wei et al., 2015). As for
the lower slope of the marked samples, the reason is that the gentle
slope is a suitable habitat preferred by pandas (Hu et al., 1985), and
the lower slope ensures that pandas complete scent marking in a
relatively stable position (Hou et al., 2021), and walking in a gentle
slope reduces energy expenditure. The higher tree density and tree cover
in the marked sample is mainly due to the fact that the more trees there
are, the greater the chance of trees with larger DBH appearing around
them, and trees with larger DBH are more likely to form tree dens for
giant pandas to breed and nurse their cubs (Wei et al., 2018; Zhang et
al., 2011). In addition, the higher the density of arborvitae, the more
concealed the habitat is, and thus the less likely to be detected by
natural predators (Hu et al., 1985).

We found that the number of marked trees that were reused was much
higher than those that were used only once, but did not detect
significant differences between the variables of either. We hypothesize
that the location of the repeated marked trees may be more prominent and
have a wider field of view, where pandas often pass by, which may also
increase the chance of other similar species finding the signal. The
most used mark type in giant pandas is the AGS marks, which often remain
in the natural environment for more than 3 months because of their low
volatility (Hagey \& Macdonald, 2003; Swaisgood et al., 2004), in order
to better convey individual information. In contrast, urine markers
contain more volatile substances than AGS marks and have a shorter
retention time in the environment, and are often used to convey the
estrus status of individuals (Zhou et al., 2019) and the competitive
ability of males (White et al., 2002), among others.

The appearance of scratch mark is always accompanied by AGS mark, so we
believe that scratch mark itself does not convey information, but is a
by-product left behind when AGS marking is performed. An interesting
finding is that some marked trees have only bite mark and are more
numerous than those with only urinary mark, which was not found in
previous studies, and we predict that its role is also correlated with
mark height, and if the mark is higher, it indicates that the individual
is larger and more competitive, similar to AGS mark and urinary mark
(White et al., 2002), but specific role needs to be further
investigated. McGuire et al. found that body size affects the frequency
of marking in a study of domestic dogs (\emph{Canis lupus familiaris} )
(Mcguire \& Bemis, 2017). However, in the study of giant pandas, we only
know that body size reflects their competitive ability (Nie et al.,
2012b), but whether it affects the frequency of marking deserves further
investigation.

Statistics on mark orientation revealed that the most marks were toward
the animal path, which is because facing the animal trail also increases
the chance of the mark being detected by its own species; for those
marks that were backward toward the trail, we predicted possible scent
counter-marking behavior, which is one of the main responses taken by
animals when they encounter the scent of other competitors of the same
species (Johnson, 1973). But before counter-marking they evaluate
individuals that are already marked, probably because they are not as
competitive as the former and want to mark in a superior position on the
marker tree, so they mark on the back (M\selectlanguage{ngerman}üller \& Manser, 2008). However,
the exact reason needs to be confirmed by a large number of future
studies.

Footpad scent communication has been found in fellow bear species brown
bear (\emph{Ursus arctos} ) (Sergiel et al., 2017) and polar bear
(\emph{Ursus maritimus} ) (Owen et al., 2015), and most bear species
have large home ranges and are non-dominant, and releasing scent while
walking is a very effective form of intraspecific communication. An
interesting question is whether giant pandas use footpad scent similarly
for communication? We monitored some sites in this study where giant
panda tracks were pitted (Figure S4). Unfortunately, we did not find
other pandas observing and sniffing the tracks in our follow-up
observations. This may be due to our small sample size or the fact that
we did not specifically focus on this direction, and we hope to continue
this study in the future to confirm our suspicions.

It has been shown that the frequency of marking in giant pandas (Nie et
al., 2012a) and the compound content of AGS marks (Zhou et al., 2019)
differ significantly between the sexes. Zhou et al. detected large
differences in the composition and content of compounds in AGS marks
between captive and wild giant pandas (Zhou et al., 2019), which may
also be one of the reasons for the low reproductive ability of captive
giant pandas, and future research on scent marks of giant pandas in the
field environment should be strengthened to clarify which compounds
affect the estrus and reproduction of giant pandas, and applied to
captive giant pandas to improve their reproductive success. This is a
tremendous step forward for the survival and reproduction of the panda
populations.

However, current research on chemical communication has still not kept
up with acoustic communication (Wiley, 2006). Due to the construction of
Giant Panda National Park, some human facilities will inevitably be
built in the living environment of giant pandas in the future. It has
been shown that human facilities can affect the scent marking behaviors
of domesticated felines (Krofel et al., 2017), and scent marking of
pandas should be continuously studied to detect whether the marking
behaviors of pandas will be changed due to the influence of human
facilities, so that more precise conservation of panda habitats can be
carried out. For example, assessing the impact range size of human
facilities prior to their construction and then locating them as far
away from core giant panda habitat as possible.

\textbf{5 CONCLUSION}

Our findings suggest that pandas prefer to scent-mark ridges with animal
trails in order to maximise the probability of their information being
found by their companions. We can therefore strengthen the protection of
such habitats as a matter of policy. In addition, we can take these
habitats into account when building ecological corridors to maximise
communication between pandas. In addition, there are significant
differences in scent marking behaviour between captive and wild pandas,
so scent marking learning for captive individuals should be enhanced
during wild release, habitats with moderate bamboo density and high tree
cover should be provided within the release enclosure, and commonly used
marked tree species for giant pandas should be planted in them to
maximise the survival probability of released individuals in the wild.

\textbf{AUTHOR CONTRIBUTIONS}

Yue Wang: Data curation (equal); formal analysis (equal); writing --
original draft (equal). Ronald R. Swaisgood: Writing -- review and
editing (equal). Wei Wei: Writing -- review and editing (equal);
resources (equal). Hong Zhou: Writing -- review and editing (equal).
Feiyun Yuan: Funding acquisition (equal). Mingsheng Hong: Writing --
review and editing (equal). Han Han: Supervision (equal); writing --
review and editing (equal). Zenjun Zhang: Supervision (equal); writing
-- review and editing (equal).

\textbf{ACKNOWLEDGMENTS}

This study was supported by the National Natural Science Foundation of
China (32070524, 32270552), Science and Technology Department of Sichuan
Province (2022JDJQ0060, 2022JDR0033), Technology of Tibetan Area
Expressway CO., LTD (2021-09), and the Second Qinghai-Tibet Plateau
Comprehensive Scientific Survey (2019QZKK05010502).

\textbf{FUNDING INFORMATION}

National Natural Science Foundation of China, Grant/Award Number:
32070524

\textbf{CONFLICT OF INTEREST}

The authors declare no conflict of interest.

\textbf{PERMISSION TO REPRODUCE MATERIALS FROM OTHER SOURCES}

None.

\textbf{DATA AVAILABILITY STATEMENT}

Raw dataset is available at the online version.

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https://doi.org/10.1111/jzo.12711\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1 Diagram of the Foping National Nature Reserve.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2 Comparison of marking density on transects between those with
trails and without (Mann-Whitney \emph{U} = 33.00, \emph{P} \textless{}
0.001)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3 Comparison of Preference index on different topographies which
with trails (Kruskal Wallis test,\selectlanguage{greek}\emph{χ\textsuperscript{2}}\selectlanguage{english} = 9.74,
\emph{P} = 0.008)

Table 1 Definition of the topography where the transects are located\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Topography & Definition\tabularnewline
\midrule
\endhead
Ridge & Narrow raised area, typically 2-5 meters wide, with steep slopes
on either side\tabularnewline
Valley & Low and narrow area between two hills, often with a water
source\tabularnewline
Slope & A surface that is higher at one end or side than at the other; a
rising or falling level\tabularnewline
\bottomrule
\end{longtable}

Table 2 Main variables to be measured for marked/control trees and sites\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Variables & Definition and description\tabularnewline
\midrule
\endhead
Marking type & Including anogenital gland secretion mark, bite mark,
urinary mark and scratch mark.\tabularnewline
Marking orientation & Whether the mark is face to, parallel to or back
to the trail.\tabularnewline
DBH (cm) & Tree trunk diameter at 1.5 m above ground
level.\tabularnewline
Roughness & Divided into four categories: (1) smooth, (2) relatively
smooth, (3) relatively rough and (4) rough.\tabularnewline
SDT (cm) & Straight line distance from the tree to the center of the
trail.\tabularnewline
Topography & Whether the site located in ridge, valley or
slope.\tabularnewline
Slope & Slope of the mark tree or the tree in the middle of the control
sites.\tabularnewline
Slope aspect & Aspect of the slope of the mark tree or the tree in the
middle of the control sites.\tabularnewline
Vegetation density & Including tree density, shrub density and bamboo
density in the sites.\tabularnewline
Vegetation coverage & Including tree cover, shrub cover and bamboo cover
in the sites.\tabularnewline
\bottomrule
\end{longtable}

Table 3 Variables entered stepwise logistic regression equation between
marked sites and control ones\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Variables & Non-Standard regression coefficient & Non-Standard
regression coefficient & Standard regression coefficient &
&\tabularnewline
\midrule
\endhead
& B & SE & Beta & t & Sig.\tabularnewline
Tree density & 0.048 & 0.018 & 0.204 & 2.614 & \emph{P} =
0.009\tabularnewline
Bamboo density & -0.015 & 0.004 & -0.166 & -3.366 & \emph{P} \textless{}
0.001\tabularnewline
Slope & -0.097 & 0.030 & -0.234 & -3.192 & \emph{P} =
0.002\tabularnewline
Tree cover & 0.169 & 0.027 & 0.739 & 6.145 & \emph{P} \textless{}
0.001\tabularnewline
Shrub cover & 0.085 & 0.037 & 0.176 & 2.272 & \emph{P} =
0.024\tabularnewline
\bottomrule
\end{longtable}

Table 4 Variables entered logistic regression equation between marked
trees and unmarked ones\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Variables & Non-Standard regression coefficient & Non-Standard
regression coefficient & Standard regression coefficient &
&\tabularnewline
\midrule
\endhead
& B & SE & Beta & t & Sig.\tabularnewline
Roughness & 0.132 & 0.012 & 0.673 & 10.637 & \emph{P} \textless{}
0.001\tabularnewline
DBH (cm) & 0.004 & 0.001 & 0.156 & 2.977 & \emph{P} =
0.003\tabularnewline
SDT (cm) & -0.002 & \textless{}0.001 & -0.407 & -7.807 & \emph{P}
\textless{} 0.001\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
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