Ecological context influences scent‐marking behavior in

Ecological context influences scent-marking behavior in thegiant panda

W. Zhou1,2, Y. Nie1,3 , R. R. Swaisgood4, Y. Li5, D. Liu2 & F. Wei1,3

1 Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China2 Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences, Beijing Normal University,

Beijing, China3 CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China4 Institute for Conservation Research, San Diego Zoo Global, San Diego, CA, USA5 Wanglang National Nature Reserve, Mianyang, China

Keywords

behavioral plasticity; chemical communication;

habitat comparisons; scent marking; chemical

compounds; giant panda; chemosignaling.

Correspondence

Yonggang Nie, Key Laboratory of Animal Ecology

and Conservation Biology, Institute of Zoology,

Chinese Academy of Sciences, 1-5 Beichen Xilu,

Chaoyang District, Beijing, China.

Email: [email protected]

Editor: Matthew Hayward

Associate Editor: Graeme Shannon

Received 27 November 2018; revised 31 May

2019; accepted 7 June 2019

doi:10.1111/jzo.12711

Abstract

Signal detection theory predicts that animals should select scent-marking sites in away that maximizes their probability of detection by target receivers. Many studieshave been conducted with a focus on signaling behavior and function. Yet, the roleof the environment in structuring scent-mark patterns is poorly known. We studiedthe giant panda, a solitary species that relies heavily on chemosignaling for com-munication through depositing urine and anogential gland secretions (AGS) ontrees, to determine how environmental factors influence scent deposition strategies.Using a combination of sign surveys, camera trapping, and gas chromatographymass spectrometry analysis, we conducted a comparative study of giant panda scentmarking in two reserves, Wanglang and Foping, in China. The two reserves dif-fered with regard to prevalence of tree species that differed in bark roughness,diameter at breast height (DBH), and moss status. For urine-marking pandasselected large, mossy trees with very rough bark trees, whereas AGS marking wasused preferentially on rough-barked moss-free trees in both reserves. Both AGSand urine contained more volatile than nonvolatile constituents and urine hadhigher relative abundance of volatiles than AGS, indicating urine likely has a largersignal range and shorter signal persistence than AGS. Urine marking was signifi-cantly more prevalent in Wanglang, which contains more rough-barked, moss cov-ered, and higher DBH trees favored for urine marking, than in Foping. Thus,habitat features appear to constrain pandas’ use of different types of marking,potentially altering how the communication system functions in different ecologicalcontexts. Our study underscores the importance of future study evaluating environ-mental influences on chemosignaling behavior.

Introduction

The behavioral strategies used to make scent deposition moreeffective in communication have long been recognized asimportant on theoretical grounds (Wilson & Bossert, 1963;Alberts, 1992; Endler, 1992). Key to understanding scent depo-sition strategies is a fundamental issue underlying all commu-nication: signals must be detected to perform their function incommunication (Wiley, 2006). Many strategies are used tomaximize the likelihood that a receiver will detect a signal,including the production of signals with a high proportion ofvolatile constituents that increase signal range and hencedetectability (Alberts, 1992). Signal range and detectability canalso be mediated behaviorally by depositing scent on promi-nent objects in the environment (Barja, 2009), often trees

(Bowyer, Ballenberghe & Rock, 1994; Ramos et al., 2006).Signal detection theory predicts that animals should addition-ally select characteristics in marking platforms that influencedetectability through effects on signal persistence and range(Alberts, 1992). Animals place chemosignals along travelroutes and select marking sites based on variables such as treespecies and size, bark texture, and aromatic properties (Lazaro-Perea, Snowdon & de F�atima Arruda, 1999; Ramos et al.,2006; Barja, 2009; Pi~neiro & Barja, 2012; Clapham et al.,2013), and place scent in strategic locations to maximizeencounters by territorial rivals (Gosling, 1982). When consider-ing signal efficacy, it is important to recognize that chemosig-nals can be energetically expensive, both in their productionand in their deployment (Gorman, 1990; Hurst et al., 1998;Gosling, Roberts & Thornton, 2001), which often involves

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considerable time and energy involved in patrolling anddepositing scent in strategic locations (Roberts & Gosling,2001; Clapham et al., 2014).With the realization that scent-mark platforms, including

vegetation characteristics, play a fundamental role in chemosig-nal site selection and signaling efficacy, it is surprising thatfew studies have examined the role of the environment instructuring scent-mark patterns (Regnier & Goodwin, 1977;Endler, 1992; Mart�ın, Ortega & L�opez, 2015) in contrast tothe numerous studies demonstrating how the structure and rep-etition rate of acoustic signals are shaped by environmentalvariables such as habitat openness (Boncoraglio & Saino,2007; Ey & Fischer, 2009). Scent-mark deposition is governedby a variety of factors in the environment that influence signalrange and persistence, and hence the efficiency and effective-ness for scent signals to reach target receivers (Alberts, 1992).Here, we examined environmental influences on scent depo-

sition behavior in the giant panda (Ailuropoda melanoleuca).As a solitary species, the giant panda relies heavily on chemi-cal signals for coordinating many aspects of its social andreproductive life (Schaller et al., 1985; Swaisgood, Lindburg& Zhou, 1999; Swaisgood et al., 2000, 2004; White, Swais-good & Zhang, 2002, 2003, 2004; Nie et al., 2012a; Giladet al., 2016). Pandas deposit anogenital gland secretions (AGS,a waxy substance) and urine on trees, typically along ridges(‘scent ridges’ hereafter) in areas that serve as ‘community bul-letin boards’ where neighboring pandas can exchangechemosignals and convey information on competitive andreproductive status. Most studies on giant pandas have focusedon perception of chemosignals in captive environments whilefew have examined scent deposition in wild pandas (Schalleret al., 1985; Nie et al., 2012a).A number of characteristics of trees available for marking

may influence their suitability for marking by giant pandas(Nie et al., 2012a). Size, roughness, and the presence of mossmay influence how much scent is deposited, whether it remainson the tree, the size of the odor field and thus probability ofdetection by conspecifics, and signal persistence. Urine is avolatile liquid, is sprayed onto the marking surface with littleor no bodily contact, and is a short-lived signal that conveysmotivational messages related to reproductive readiness andcompetitive status (review in Swaisgood, Lindburg & Zhang,2002; Swaisgood et al., 2004; White et al., 2004). By contrast,AGS is thick, viscous substance that pandas deposit throughvigorous rubbing against the marking surface and functionsmore in individual identity and range marking. We thushypothesized that pandas would select marking surfaces on thebasis of tree characteristics and that selection would differ forurine marking vs. AGS marking, due to their different proper-ties and function. Further, we sought to evaluate the prevalenceof volatile chemical constituents in panda urine and AGSsecretions. Although AGS has been chemically characterizedpreviously (Zhang et al., 2008), a comparison of the volatilecomposition of urine and AGS has not yet been evaluated forthe panda, though urine has long been assumed to containmore volatiles. This knowledge gap limits our understandingof deployment of urine and AGS on different marking surfacesin different habitats.

Giant pandas also live in various environments, some withdistinct forest composition with the potential to influencescent-marking decisions. Specifically, we examined whetherpandas select scent-marking sites based on tree characteristicsthat have the capacity to affect signal range or persistence,including tree size, bark roughness, and presence of moss, andhow these preferences vary according to mark type (AGS vs.urine). We also examined whether pandas inhabiting environ-ments characterized by different forest types—and thereforedifferent tree characteristics that may influence scent-mark siteselection—employed urine and AGS marks with different fre-quencies. Empirical data indicating that animals’ environmentsinfluence the rates of different forms of marking to our knowl-edge have not been reported before in contrast with the numer-ous studies showing how acoustic and visual signal type andrates show adaptive plasticity in different environments (Bon-coraglio & Saino, 2007; Ey & Fischer, 2009; Ord, Stamps &Losos, 2010).

Materials and methods

Study sites

We studied panda scent-marking behavior in two giant pandareserves, Wanglang National Nature Reserve (104°30E, 32°50N,~30 km2) and Foping National Nature Reserve (107°80E,33°80N, ~25 km2) located in the Minshan and Qinling moun-tain ranges, respectively. The elevation ranges from 980 to2904 m in Foping, whereas Wanglang is much higher withelevation ranging from 2400 to 4980 m. The population sizefor this research is not known with certainty but DNA censustechniques indicate c. 13–15 individuals lived in the study areawithin Foping and 11–14 individuals lived within the Wan-glang study area, with no significant sex bias in both areas(unpublished data). Because multiple pandas with overlappingranges deposit scents at the same location (‘community bulletinboards’) (Schaller et al., 1985; Nie et al., 2012a), we believethe transects we established at several known scent stationseffectively sampled most of the individuals living in the studyareas. In Foping, cameras placed on the same ridges where wecollected scent samples identified 11 individual pandas visitingthe area (Zheng et al., 2016).

Data collection and GC-MS analysis

Because it is known that pandas place scent marks primarilyon ridges (Schaller et al., 1985; Nie et al., 2012a), we estab-lished transects along ridges in the core area of these reserveswhere panda density is known to be high and known fromongoing monitoring programs to be favored scent-mark loca-tions and frequently visited by different individuals (Nie et al.,2012a; Zhou et al., 2019). In this study, the elevation of scentridges ranged from 1550 to 1750 m and 2750 to 3000 m inFoping and Wanglang, respectively. Deciduous broadleaf trees(76.2%) and evergreen coniferous trees (83.8%) were dominanton the scent ridges in Foping and Wanglang, respectively.Transect width was 3 m and the length varied from 300 m to

192 Journal of Zoology 309 (2019) 191–199 ª 2019 The Zoological Society of London

Scent-marking behavior in the giant panda W. Zhou et al.

1200 m, determined by length of the ridge. A total seven andnine transects were established in the study areas of the Fopingand Wanglang Reserve, respectively.We used a combination of sign survey and camera trapping

to evaluate scent-marking behavior. Data collection methodsfollowed Nie et al. (2012a), but we provide a brief overviewhere. All available trees with a diameter at breast height(DBH) >5 cm were included in the study, and several charac-teristics potentially influencing panda marking selectivity wererecorded, including degree of bark roughness, DBH and pres-ence/absence of moss. Bark roughness was categorized asRoughest (crevice depth >7 mm, width >14 mm), Rough (cre-vice depth 4–7 mm, width 10–14 mm), Medium (crevice depth<4 mm, width <10 mm), and Smooth (no crevice). Moss statuswas recorded as present/absent on the surface of tree below1 m, reflecting the range of height of scent deposition by pan-das using four standard scent-mark postures (Swaisgood et al.,2000; White et al., 2002). All available trees were monitoredfor signs of marking at circa weekly intervals. AGS markswere determined by characteristic signs such as removal ofbark flakes where pandas have rubbed and the presence of athick, waxy substance. We used a clean knife to remove asmall piece of bark from the mark site so that we could deter-mine whether a new AGS scent mark was deposited duringthe interval between two surveys. Urine marks were detectedby bark discoloration and a musky odor, and this odor is sodistinctive to the experienced nose that fresh urine marks couldbe easily detected even when not clearly visible. Urine marksare also typically placed in a handstand position, whereas AGSmarks are deposited in the reverse posture lower on the tree(Fig. 1), separating urine and AGS marks by height on a tree(White et al., 2002). In addition, observations indicate thatpandas deposit either AGS or urine, and rarely combine them(Swaisgood et al., 2004). Sign data were collected continu-ously from March 2016 to May 2017 in Wanglang and fromSeptember 2015 to November 2016 in Foping.An important issue with collection of sign data is detectabil-

ity and it is possible that one kind of sign (urine vs. AGS) inone environment (Foping vs. Wanglang) is more detectablethan another. Thus, we supplemented these sign data withinfrared motion-detector cameras positioned to capture pandavisits to a subset of the same trees where we collected scentsamples. From September 2012 to May 2017, in Foping weestablished 50 cameras along the same transects used for signsurveys, placing cameras oriented toward potential trees usedfor scent marks, maximizing data collection frequency. Here,we used the camera data from the same period of the sign datacollection of this study. In Wanglang, we also established 50cameras in the same way during the period of sign data collec-tion. Researchers visited cameras approximately every 1–2 months to retrieve data and replace batteries. Still imagesand videos were captured and reviewed to determine markingfrequencies using urine and AGS. Blinded methods were usedwhen all behavioral data were recorded and analyzed.To determine the volatility of the AGS and urine, we col-

lected the very fresh samples of using sterilized capillary tubesduring the sign surveying, transferred the material to sealed2 mL glass headspace vials, and stored at �20°C until

laboratory analysis. For AGS sample extraction, we transferredthe secretion into a new glass vial, added 600 lL dichloro-methane and vortexed for 15 s. Extracted constituents wereheld 12 h in �4°C and centrifuged for 3 min at 3000 g. Forurine samples, we transferred 200 lL urine into a new glassvial, added 200 lL dichloromethane and vortexed for 15 s.The resulting supernatants were subjected to GC-MS analysisusing an Agilent Technologies Network 6890N gas chro-matograph system equipped with a 30 m HP5-MS glass capil-lary column coupled with 5973 Mass Selective Detector.Compounds were tentatively identified by matching their reten-tion time and mass spectra with structures available in theNIST 2002 library (Agilent Technologies 2002). Compoundswith a molecular weight >300 were considered nonvolatile andthose below 300 were considered volatile (sensu Wilson, 1963;Bradbury & Vehrencamp, 1998; Yuan et al., 2004).

Statistical analysis

We used generalized linear models (GLMs) with gamma dis-tributed errors (the errors were non-normally distributed andvariance were nonconstant) and logistic regression to analyzethe influence of DBH, bark roughness, and moss on the urineand AGS marking preferences of giant pandas. Scent-markingtype and the bark characteristics of available and marked treesin Wanglang and Foping were analyzed by comparing propor-tions with a chi-square test. We used the Mann–Whitney Utest to compare the DBH of available trees between Wanglangand Foping reserve. We used a paired-sample T test to com-pare both the composition and relative abundance of volatileand nonvolatile compounds, and an independent samples T test

(a) (b)

Figure 1 The frequency of urine and anogenital gland secretions

(AGS) marks deposited by giant pandas in Wanglang (WL) and Foping

(FP) reserve. a, Urine mark; b, AGS mark; C, camera data; S, sign

data.*P < 0.05. [Colour figure can be viewed at zslpublications.online

library.wiley.com].


W. Zhou et al. Scent-marking behavior in the giant panda

was used for the relative abundance of volatile compoundsbetween AGS and urine. Differences were considered signifi-cant at P < 0.05 and all tests were two tailed.

Results

Our analysis included 105 available trees in Wanglang (17broadleaf trees, 88 coniferous trees) and 417 (318 broadleaftrees, 99 coniferous trees) in Foping. Specifically, we foundthat Abies faxoniana Rehd. comprised 78.1% of the trees inour ridge sample in Wanglang and Quercus aliena BI.var.acuteserrata Maxim ex Wenz. comprised 68.8% of our sampletrees in Foping. We recorded 52 scent marks by sign and 40by camera survey in Foping, and 62 and 18 scent marks bysign and camera survey, respectively, in Wanglang.

Scent marking by pandas and treecharacteristics in different environments

From our sign survey data, we found that pandas in Foping useAGS marks more than urine marks; in fact, AGS markingoccurred more than three times more often than urine marking.By contrast, in Wanglang pandas urine marked about three timesmore often than they AGS marked (Fig. 1). The same patternheld for scent-marking data collected from cameras. These dif-ferences were significant for both sign data (v21 = 21.70,P < 0.001) and camera data (v21 = 5.91, P = 0.015).Several characteristics of available trees varied significantly

between the coniferous-dominant habitat of Wanglang and thebroadleaf-dominant habitat of Foping (Fig. 2). Available treesin Wanglang had rougher bark (v23 = 22.89, P < 0.001;Fig. 2a), larger DBH (Mann–Whitney U: z = �11.28, N1 =105, N2 = 417, P < 0.001; Fig. 2b), and a larger proportion oftrees had moss than in Foping (v21 = 11.76, P = 0.001;Fig. 2c).

Selection of marking trees as a function oftree characteristics

Foping pandas selected trees with the roughest bark for urinemarking with greater frequency than expected based on avail-ability and showed a preference to AGS-mark Rough-barkedtrees (v23 = 9.50, P = 0.023; Fig. 3a). Pandas selected largertrees for urine marking than AGS marking (Z = 2.80,P = 0.005; Table 1; Fig. 3c). Also, pandas appeared to avoidmoss-covered trees when using AGS marks, selecting treeswith moss in only 16.2% instances, whereas they selectedmoss-covered trees 53.3% of the time when using urine marks(z = �2.07, P = 0.038; Table 1; Fig. 3e).Similarly, giant pandas in Wanglang showed a strong prefer-

ence for urine-marking trees with the Roughest bark available,while they AGS-marked Rough- and Medium rough-barkedtrees (v23 = 13.40, P = 0.004; Fig. 3b). Smooth-barked treeswere avoided for marking of either type. Pandas also showedevidence of selection based on tree DBH: urine-marked treeswere significantly larger than AGS-marked trees z = 2.91,P = 0.003; Table 1; Fig. 3d). Pandas AGS-marked moss-

covered trees only 35.3% of the time, but urine-marked moss-covered trees more than moss-free trees (73.3% of the time;z = �1.90, P = 0.057 Table 1; Fig. 3f).Since tree size and bark texture may influence moss growth,

we examined the correlations between these variables to deter-mine whether underlying tree characteristics may be partiallyresponsible for panda marking preferences for moss. GLMsanalysis indicated no such correlations in Wanglang, but wefound that DBH (z = �4.496, P < 0.001) and the roughness of

Figure 2 Characteristics of available trees in Wanglang (WL) and

Foping (FP) reserve as a function of (a) bark roughness, (b) tree

diameter at breast height (DBH, �x + SD), and (c) presence of

moss. *P < 0.05.



FP

FP

FP

WL

WL

WL

(a) (b)

(c) (d)

(c) (d)

Figure 3 Characteristics of trees marked with urine and anogenital gland secretions (AGS) of giant pandas in Foping and Wanglang reserve as a

function of (a/b) bark roughness, (c/d) tree diameter at breast height (DBH, �x + SD), and (e/f) presence of moss. *P < 0.05.

Table 1 Effect of tree diameter at breast height (DBH), bark roughness and presence of moss on the urine- and AGS-marking preferences of

giant pandas in two reserves (Foping and Wanglang) based on the generalized linear models (gamma family, binomial)

Predictor variables Level

Foping Wanglang

Estimate �SE z value Pr(>|z|) Estimate �SE z value Pr(>|z|)

AGS marks vs. Urine marks (Intercept) �4.397 1.959 �2.244 0.024 �3.092 1.356 �2.280 0.022

DBH DBH 0.248 0.088 2.802 0.005 0.130 0.044 2.908 0.003

Roughness Rough �0.546 1.169 �0.467 0.640 1.720 1.506 1.142 0.253

Roughest 0.926 1.346 0.688 0.491 1.325 1.485 0.892 0.372

Smooth 1.233 1.808 0.682 0.495 0.909 1.378 0.660 0.660

Moss Moss-free �2.149 1.039 �2.068 0.038 �2.158 1.136 �1.900 0.057

*The bold values mean P < 0.05.



the tree bark (z = 2.744, P = 0.006) had a negative influenceon moss on trees in Foping (Table 2). This result runs contraryto results for panda marking preferences based on tree charac-teristics: pandas selected larger trees for urine marking, but inFoping moss was more prevalent on smaller trees and pandasurine marked more on rough-barked trees but these trees hadless moss than smooth-barked trees. Thus, we conclude thatpanda behavioral selection for tree size, bark texture, and mosswere independent, not the result of inter-correlation amongvariables.

The volatility of AGS and urine

Panda chemosignals are comprised of more volatile than non-volatile compounds, both for AGS (65.04% vs. 34.96%,t10 = 12.97, P < 0.001) and urine marks (73.27% vs. 26.73%,t5 = 17.25, P < 0.001; Fig. 4a). Urine and AGS marks did notdiffer in their volatile vs. nonvolatile composition. By contrast,the relative abundance of volatile compounds was significantlyhigher in urine than in AGS marks (59.23% vs. 34.60%, t15 =10.36, P < 0.001; Fig. 4b–d). Volatile compounds had higherrelative abundance than nonvolatile compounds in urine (59.23%vs. 40.05%, t10 = 8.89, P < 0.001), whereas AGS marks con-tained a greater relative abundance of the nonvolatile compounds(58.46% vs. 34.60%, t5 = 3.76, P = 0.013; Fig. 4b–d).

Discussion

Our findings are consistent with predictions from signal detec-tion theory, which predicts that animals should deploy scentsignals in a way that maximizes the chances they are detectedby receivers, balancing competing goals of signal range(through short-lived volatiles) and signal persistence (throughlong-lived volatiles or deposition on surfaces that extend signalpersistence) (Alberts, 1992). Our results also confirm andextend earlier findings (Nie et al., 2012a) indicating that pan-das select marking surfaces based on features that maximizesignal efficacy by extending their longevity or odor field. Giantpandas may be among the most energy-limited species due totheir relatively low-nutrient diet which influences many aspectsof their life history, such as movement ecology, reproductivestrategies, and communication (Schaller et al., 1985; Nie et al.,2012b, 2015).By depositing chemosignals on rougher bark, pandas may

extend chemosignal life by protecting it against rainfall,

especially in the wet, rainy environments that pandas occupy.Marking on rougher bark may also expand the odor field byincreasing the evaporative surface of a set quantity of secre-tions (Alberts, 1992) and, in the case of urine, may reduce thequantity of urine that trickles down the trunk below nose levelof receivers. Lower-positioned odors also have reduced odorfield, extending the signal over shorter distances due to disper-sal patterns of volatile molecules (Wilson & Bossert, 1963;Alberts, 1992). Similarly, pandas appear to avoid AGS mark-ing on moss-covered trees, which is probably too soft toremove much glandular secretion (given the vigorous rubbingassociated with AGS deposition) and prone to falling to theground when rubbed too vigorously, consequences that wouldreduce signal range and detection. By contrast, moss is anexcellent signaling platform for urine, as more urine wouldadhere to the moss and remain at nose height of receivers andgenerate a larger odor field. The fact that pandas deployedurine more on very rough bark and moss-covered trees mayalso reflect the need to extend signal persistence in a signalwith higher relative abundance of volatile compounds. Thesefindings are consistent with a priori predictions that pandaswould select marking sites based on tree characteristics thatshould promote signal range and persistence, which wehypothesize enhances signal detection. We conclude that natu-ral selection has likely shaped these scent-mark depositionstrategies to maximize detection and therefore facilitate signalfunction.We also report novel findings showing that a mammal reli-

ant on scent marking for effective communication is influencedto use one type of chemosignal over another type of chemosig-nal by the features in the environment important for marking.Although studies have addressed how animals adaptively shiftdisplay intensity to contend with background ‘noise’ to opti-mize signal detection (Ord et al., 2010), research of the typereported here is poorly represented, and large-scale habitat-level effects on chemosignaling are virtually unknown. Wefound that Wanglang pandas more frequently deployed urinemarks than did Foping pandas, a somewhat unexpected resultsthat require for explanation. The most parsimonious explana-tion is that pandas in both reserves are using the same scent-marking strategies in that they selected trees with the samecharacteristics—governed by bark roughness, moss, and DBH—for urine marking and AGS marking in both reserves. Thepredominant forest type in Wanglang is coniferous forest,which is comprised of trees with rougher bark, more moss,

Table 2 Effect of diameter at breast height (DBH) and bark roughness on moss attachment on trees based on the generalized linear models

(gamma family, binomial)

Predictor variables Level

Foping (n = 417) Wanglang (n = 107)

Estimate �SE z value Pr(>|z|) Estimate �SE z value Pr(>|z|)

(Intercept) 0.428 0.237 1.806 0.071 4.468 1.808 2.471 0.014

DBH DBH �0.039 0.009 �4.496 6.93e-06 �0.053 0.039 �1.366 0.172

Roughness Rough 0.296 0.257 1.151 0.250 1.333 1.969 0.677 0.498

Roughest 0.401 0.287 1.401 0.161 20.658 3774.325 0.005 0.096

Smooth 0.884 0.322 2.744 0.006 18.342 6591.225 0.003 0.998

aThe bold values mean P < 0.05.



and larger DBH than those found in the broadleaf-dominatedFoping. Thus, the availability of good trees for urine markingappears to constrain the behavior of individuals employing thesame general strategy such that the outcome is different, withpandas in one location urine marking more than in the otherlocation. This shift does not appear to be strategic, rather thatpandas are stimulated to urine mark more often by the avail-ability of suitable urine-marking trees.It is important to recognize that urine and AGS marks are

not redundant communication systems, instead each function toconvey different kinds of information over different temporalscales (reviewed in: Swaisgood et al., 2004). Urine marking isa short-lived signal (remaining bioactive for about 2 weeks)and carries a more transient motivational message includingaggressive intent, competitive ability, and reproductive status(Swaisgood et al., 2004). By contrast, AGS marks are morelong-lived (bioactive for ~4 months) and carry more staticinformation on individual identity and range use. Recentresearch demonstrated that AGS chemistry is very stable overa period of at least 2 weeks, with no change in the number ofchemical compounds or the relative abundance with time(Zhou et al., 2019). Our chemical analysis here supports thesebehavioral bioassays, demonstrating that both AGS and urinewere dominated by compounds with low molecular weight, animportant factor contributing to volatility, which should

function to increase signal range and detectability. Further,panda urine was characterized by a higher relative abundanceof volatiles than AGS, indicating that urine has a larger signalrange and increased detectability compared to AGS, but is alsomore ephemeral.We conclude that pandas living in Wanglang may have a

communication system dominated by more transient signalsand pandas living in Foping may have a communication sys-tem dominated by more long-lasting range marks believed toemphasize information content related to identity (Alberts,1992; Swaisgood et al., 2004). However, pandas may partiallyoffset any disadvantage of employing short-lived urine signalsin their deployment of scent signals on surfaces that retardevaporation and extend signal persistence (Nie et al., 2012a;see also below).With increasingly sophisticated studies of chemical signaling

in mammals in the wild, we are beginning to gain moreinsights into how animals deploy chemosignals in space andtime to maximize signal efficacy and, most recently, how envi-ronmental variables influence these scent deposition strategies.These data can be difficult to obtain for rare species, yet theinsights provided are worth the effort. Chemical communica-tion research also has not kept pace with research applyingsignal detection theory to acoustic communication (Wiley,2006), thus many aspects of chemosignal design and

Figure 4 Comparative analysis of scent chemistry for anogenital gland secretions (AGS) and urine for (a) chemical composition and (b) relative

abundance (�x + SD) of volatile and nonvolatile chemical constituents, and representative ion chromatograms in (c) AGS and (d) urine. The arrow

shows the retention time of a chemical constituents with a molecular weight of 300, demarcating volatile (<300) from nonvolatile (>300)

compounds. *P < 0.05. [Colour figure can be viewed at zslpublications.onlinelibrary.wiley.com].



deployment remain unexplored. The behavior governing wheresignalers decide to leave scent in the environment, and envi-ronmental constraints on marking behavior, is a rich area forfuture study. Further study of the behavioral and fitness effectsof chemosignals on receivers will also yield important insightsregarding the evolution of chemosignal site selection. Valuablefuture research includes experimentation in captive environ-ments that manipulates some of the tree characteristics wefound influence panda marking behavior—tree size, barkroughness, and moss coverage—to determine how they influ-ence marking behavior, odor fields, signal persistence, anddetection.

Acknowledgements

We gratefully acknowledge the support of Foping National Nat-ure Reserve and Wanglang National Nature Reserve, and thefield staff, including XL. Wang, YW. He, ZW. Yuan, Y. Zheng,and HL Zhou. This work was funded by the Chinese Academyof Sciences (QYZDB-SSW-SMC047), National Natural ScienceFoundation of China Grant (31622012), and the Ministry ofScience and Technology of China (2016YFC0503200).

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