Ecology

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Data collectionVirus-host data was collated from various sources. Major sources for the association databases included data shared by Olival et al4., Pandit et al.3, and Johnson et al.13. In data provided by Olival et al (assessed September 2019), host-virus associations have been assigned a score, based on detection methods and tests that are specific and more reliable. We used associations that have been identified as the most reliable (stringent data) from Olival et al4. In addition, a query in GenBank was run to parse out hosts reported for each GenBank submission for viruses presented in each of these three databases. Initially, for each virus name, taxonomic ID was identified using entrez.esearch function in biopython package. The taxonomic ID helped linked to the GenBank databases, identify the ICTV lineage and associated data in PubMed20,21. NCBI TaxID closely follows the ICTV database, but some recent changes in ICTV might not always be reflected in NCBI, so we manually checked names to ensure matching. This included virus genus and family information along with a standard virus name. Host data were aggregated based on the taxonomic ID and associated standard name. Finally, for each virus, a search was completed in PubMed to compile the number of hits related to the virus and their vertebrate hosts using the search terms below. The number of PubMed hits (PMH1) were used as a proxy for sampling bias3,13. The virus-host association data source is presented in supplementary code and data files (https://zenodo.org/record/5899054).$$ searchterm= (+virus_name+,[Title/Abstract])\ ANDleft(host,OR,hosts,OR,reservoir,OR,reservoirs,OR right.\ wild,OR,wildlife,OR,domestic,OR,animal,OR,animals,OR\ mammal,OR,bird,OR,birds,OR,aves,OR,avian,OR,avians\ left. OR,vertebrate,OR,vertebrates,OR,surveillance,OR,sylvaticright)$$Along with the PubMed terms we also queried the nucleotide database on PubMed using the taxonomic ID to find the number of GenBank entries for these viruses (PMH2). A correlation analysis between the PMH1 and PMH2 of well-recognized known viruses showed a high correlation with each other for us to safely use GenBank hits for novel viruses during the prediction stage of the model (Fig. S32).Development of ({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})
a. Centrality measures of observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}}))To test if centrality measures (degree centrality, betweenness centrality, eigenvector centrality, clustering coefficient) for viral nodes in the observed network (({G}_{c})) vary significantly between viral families, we firstly used the Kolmogorov-Smirnov (KS) test. KS test is routinely used to identify distances between cumulative distribution functions of two probability distributions and is largely used to compare degree distributions of networks22,23. For each viral family, distributions of centrality measures (degree centrality, betweenness centrality, and eigenvector centrality) and clustering coefficient within the observed network (({G}_{c})) were compared with the distribution of all nodes in the network using the two-tailed KS test. Secondly, a linear regression model with virus family as a categorical variable and the number of PubMed hits as a covariate to adjust for sampling bias were fitted to understand associations of viral families with centrality measures.$${centrality},{measure}={beta }_{0}{intercept}+{{beta }_{1}{Viral}{family}}_{{categorical}}+{beta }_{2}{PubMed},{hits}$$After fitting the model, node-level permutations were implemented. For each random permutation, the output variable was randomly assigned to covariate values and the model was re-fitted. Finally, a p-value was calculated by comparing the distribution of coefficients from permutations with the original model coefficient.Network topology feature selectionUsing the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})), multiple network topological features for all node (virus) pairs were calculated. The following are topological network features calculated. Features data type, definition and methods to calculate these features are presented in Table S3.1. The Jaccard coefficient: a commonly used similarity metric between nodes in information retrieval, is also called an intersection of over the union for two nodes in the network. In the unipartite network generated here, it represents the proportion of common neighbor viruses from the union of neighbor viruses for two nodes. Neighbor viruses are defined as viruses with which the virus shares at least a single host.2. Adamic/Adar (Frequency-Weighted Common Neighbors): Is the sum of inverse logarithmic degree centrality of the neighbors shared by two nodes in the network24. The concept of Adamic Adar index is a weighted common neighbors for viruses in the network. Within network prediction, the index assumes that viruses with large neighborhoods have a less significant impact while predicting a connection between two viruses compared with smaller neighborhoods.Both Jaccard and Adamic Adar coefficients have been routinely used for generalized network prediction and have shown high accuracy in predicting missing links in networks, specifically bipartite networks25, the information flowing through neighborhoods formed by two nodes might not always be enough to have similar predictive power in an unipartite network. This warrants use of other topology features along with neighborhood-based features.3. Resource allocation: Similarity score of two nodes defined by the weights of common neighbors of two nodes. Resource allocation is another measure to quantify the closeness of two nodes in the network and hence to understand the similarity of hosts they infect.4. Preferential attachment coefficients: The mechanism of preferential attachment can be used to generate evolving scale-free networks, where the probability that a new link is connected to node x is proportional to k26.5. Betweenness centrality: For a node in the network betweenness centrality is the sum of the fraction of all-pairs shortest paths that pass through it. The feature that we used for training the supervised learning model was the absolute difference between of betweenness centralities of two nodes. The difference between the betweenness centrality represents the difference in the sharing observed by two viruses in the pair.6. Degree centrality: The degree centrality for a node v is the fraction of nodes it is connected to. The feature that we used for training the supervised learning model was the absolute difference between degree centralities of two nodes. Unlike the difference in the betweenness centrality, the difference in degree centrality only looks at the difference in the number of observed host sharing.7. Network clustering: All nodes were classified into community clusters using Louvain methods27. A binary feature variable was generated to describe if both the nodes in the pair were part of the same cluster or not. If both viruses are from the same cluster, it represents a similar host predilection than when both viruses are not from the same cluster hence accounting for the evolutionary predilection of viruses (or virus families) to infect a certain type of host.These topological network characteristics come with certain limitations when it comes to the unipartite network of viruses with links formed due to shared hosts and might not truly represent the flow of information between nodes as compared to a bipartite network. Therefore, to account for these limitations, we use multiple network features as weak learners in our model building characteristics summarizing the network through the use of several quantitative metrics. In addition to this, we estimated the feature importance of these metrics in predicting missing links between viruses to quantify the information pasting through these links.Pearson’s correlation coefficients were calculated to identify highly correlated features and for choosing features for model training (Fig. S33). Virological features included in model training were categorical variables describing the virus family of both the nodes in the pair, followed by a binary variable if both the viruses belong to the same virus family. During the model development, PubMed hits generated three predictive features for each pair of viruses on which model training and predictions were conducted. These included two features representing PubMed hits for the two viruses in the pair (PubMedV1, PubMedV2) and the absolute difference between PubMedV1 and PubMedV2 to account for differences in sampling bias between the two viruses.Cross-validation and fitting generalized boosting machine (GBMs) modelsA nested-cross-validation was implemented for the binary model while simple cross-validation was implemented for the multiclass model (multiple output categories). The parameters of the binary model were first hyper-tuned using a cross-validated grid-search method. Values were tested using a grid search to find the best-performing model parameters that showed the highest sensitivity (recall). The parameters tested for hypertuning and their performance are provided in the supplementary material (supplementary results and Table S5). For further cross-validation of the overall binary model, all the viruses were randomly assigned to five groups. For each fold, the viruses assigned to a group were dropped from the data, and a temporary training network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{t}}}}}}}{{{{{boldsymbol{)}}}}}}) was constructed, assuming that this represented the current observed status of the virus-host community. For all possible pairs in ({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{t}}}}}}}) (both that sharing and not sharing any hosts) ten topological and viral characteristics were calculated as training features (Table S4). Categorical features were one-hot-encoded and numeric features were scaled. An XGBClassifier model with binary: logistic family was trained using the feature dataset to predict if virus pairs share hosts (1,0 encoded output). The cross-validation was also used to determine the optimum decision threshold for determining binary classification (Fig. S6) and a precision-recall curve was used to identify positive predictive value and sensitivity at the optimum threshold (Fig. S8).The multiclass model was implemented in the same way, creating an observed network (({G}_{c})) based on species-level sharing of hosts and randomly dropping viruses to generate a training network (({G}_{t})) to train the XGboost model. The output variables were generated based on the taxonomical orders of shared hosts. A pair of viruses can share multiple hosts, hence we trained a multioutput-multiclass model. Humans were considered an independent category of taxonomical order (label) and were given a separate label from primates. For fine-tuning the multiclass model, we started with the best performing parameters of the binary model and manually tested 5 combinations of model parameters by adjusting values of the learning rate, number of estimators, maximum depth, and minimum child weight (Supplementary code and results).We used three methods to estimate the importance of features for our binary model. Specifically, improvement in accuracy brought by branching based on the feature (gain), the percentage of times the feature appears in the XGboost tree model (weight), and the relative number of observations related to the specific feature (cover). Results for feature importance are shown in supplementary results (Fig. S10).Missing links for novel viruses, binary and multiclass predictionThe wildlife surveillance data represented a sampling of 99,379 animals (94,723 wildlife, 4656 domesticated animals) conducted in 34 countries around the world between 2009–2019 (Table S6)1. Specimens were tested using conventional Rt-PCR, Quantitative PCR, Sanger sequencing, and Next Generation Sequencing protocols to detect viruses from 28 virus families or taxonomic groups (Table S7). Testing resulted in 951 novel monophyletic clusters of virus sequences (referred to as novel viruses henceforth). Within 951 novel viruses, 944 novel viruses had vertebrate hosts that were identified with certainty based on barcoding methods and field identification. Host species identification was confirmed by cytochrome b (cytb) DNA barcoding using DNA extracted from the samples28. We predicted the shared host links between novel viruses and known viruses using binary and multiclass models in the following steps. Out of 944 novel viruses discovered in the last ten years, we were able to generate predictions for 531 novel viruses that were detected in species already classified as hosts within the network. The remaining 413 viruses were the first detection of any virus in that species and thus host associations could not be informed by the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{C}}}}}}})) data.1. A new node representing the novel virus was inserted in the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})). Using the list of species in which the novel virus was detected, new edges were created with known viruses that are also known to be found in those hosts. This generated a temporary network for the novel virus (({{{{{{boldsymbol{G}}}}}}}_{{temp}})). If the novel virus was not able to generate any edges with known viruses, meaning the host in which they have been found was never found positive for any known virus, predictions were not performed.2. Using ({{{{{{boldsymbol{G}}}}}}}_{{temp}}) feature values were calculated for the novel virus (betweenness centrality, clustering, and degree). For all possible pairs of the novel virus with known viruses that are not yet connected with each other through an edge in ({{{{{{boldsymbol{G}}}}}}}_{{temp}}) a feature dataset was generated (Jaccard coefficient(novel virus, known virus), the difference in betweenness centrality of the novel virus and known virus, if the novel virus and known virus were in the same cluster, the difference in degree centrality(novel virus, known virus), if the novel virus and known virus were from same virus family, the difference in PubMed hits(novel virus, known virus), PubMed hits for the novel virus, PubMed hits for the known virus). Studies and nucleotide sequences for novel viruses are expected to be published and shared on PubMed’s Nucleotide database and in various peer-reviewed publications. Data associated with GenBank accession numbers and nucleotide sequences for novel viruses are presented in Supplementary Data 3 and Supplementary Data 4 respectively. At the time of development of the model, data for all viruses was not shared in a format that would reflect on PubMed’s database, we decided to use the number of unique species the virus was detected in the last ten years of wildlife surveillance conducted by the USAID PREDICT project. These detections will be reflected in PubMed’s Nucleotide database and search term eventually, hence we considered them as a proxy for search terms conducted for known viruses. Currently, evaluation of the effects of this substitution of PubMed hits with the number of detections for novel viruses is not possible with limited data on novel viruses but needs to be reevaluated as more studies are published on these novel viruses. To further evaluate the association between PubMed hits through search term and Genbank hits, we ran a generalized linear regression model with PubMed hits as dependent variable and Genbank hits as intendent variable, accounting for virus families.$${{PubMed}}_{{Search}}left({log }right)={beta }_{0}{intercept}+{{beta }_{1}{Virus}{family}}_{{categorical}}+{beta }_{2}{Genbank},{hits},({log })$$The results indicated that Genbank hits had statistically significant predictive value in predicting PubMed hits (β = 0.72, p  More

All vertebrates examined in this study and histologically sampled (Supplementary Table 1) exhibit polyphyodonty and dentine growth lines (Figs. 2–4 and Supplementary Figs. 2–9) that are morphologically consistent with the incremental lines of von Ebner of extant mammalian and crocodilian teeth: alternating opaque zones, line trajectories paralleling the pulp cavity, and widths ranging between 1 and 30 mm18. All functional teeth were continuously replaced through the development of the replacement tooth, lingual to the functional tooth, resulting in resorption of its base and shedding.Fig. 2: Incremental lines of Mesenosaurus efremovi.a ROMVP 85502, lingual view of fragmented dentary with dashed red lines through the plane of the LL section of the functional and replacement teeth. b Whole view of tooth family LL section near crown apex. c Closeup view of functional tooth LL cross-section showing incremental lines, white arrows. d Closeup view of replacement tooth TR cross-section showing incremental lines, white arrows.Full size imageFig. 3: Incremental lines of Dimetrodon cf. D. limbatus.a Lateral view of Dimetrodon. b ROMVP 85510, maxillary tooth family, photographed in lingual view showing the plane of LL section through the functional tooth and replacement tooth. c Whole view of longitudinal LL section near the crown apex of functional and replacement tooth. d Closeup view of functional tooth LL cross-section showing incremental lines, white arrows. e Closeup view of replacement tooth LL cross-section showing incremental lines, white arrows. Skull drawing was modified from Reisz42 and Brink and Reisz43.Full size imageFig. 4: Incremental lines of Edaphosaurus sp.a Lateral view of Edaphosaurus. b USNM PAL 706602, maxillary tooth family, photographed in lingual view showing the plane of LL section through the functional tooth and replacement tooth. c Whole view of longitudinal LL section near crown apex of functional and replacement tooth. d Closeup view of functional tooth LL cross-section showing incremental lines, white arrows. Skull drawing was modified from Romer and Price41 and Modesto44.Full size imageReplacement pattern in Mesenosaurus efremovi
Replacement in the gracile predator Mesenosaurus efremovi from the Richards Spur locality (Fig. 1) appears to occur as a wave in alternating tooth positions, with every other functional tooth in a sequence undergoing replacement during one event. Gaps in the tooth row represent stages in the replacement cycle when the old tooth has been shed, but the replacement tooth has not yet become functional and is not ankylosed to the jawbone. Frequently, these small replacement teeth are lost during fossilization, but in the case of the Dolese Mesenosaurus, preservation is so exquisite that these unattached replacement teeth are preserved, often in place (Fig. 1e). We found that numerous specimens of M. efremovi have tooth families containing a functional tooth and a single replacement tooth lingual to it, but one maxilla (ROMVP 85456) was observed to have a tooth family containing a functional tooth and two successive replacement teeth (Fig. 1c).The replacement rate found in one tooth family within an M. efremovi dentary was 39 days (ROMVP 85502; Fig. 2), and 34 days for the left maxilla (ROMVP 85443; Supplementary Fig. 2). Replacement rates of three tooth families (mx10, mx12, and mx15) for ROMVP 85457 were estimated to be 46, 36, and 35 days. Thus, the replacement rate for M. efremovi does not appear to vary significantly in one specimen across tooth position, size, or ontogenetic age of tooth.Replacement pattern in other synapsidsIn contrast to the availability of many Mesenosaurus specimens for destructive sampling, other taxa are exceedingly rare, and few specimens were available for destructive analysis. Thus, only a single maxilla of the apex predator Dimetrodon with a replacement tooth in position was available (Fig. 3). The functional tooth had a total of 459 incremental lines, whereas the replacement tooth had a total of 354 lines, resulting in a replacement rate of 105 days. In contrast, the maxillary tooth for the basal sphenacodont Haptodus, was calculated to have functional tooth longevity of approximately 152 days and since neither a replacement tooth nor a resorption pit was present, the minimum replacement rate is 152 days.Similarly, relatively little material was available for the larger varanopid predator Watongia meieri which is only known from the holotype material, with a resorption pit on one of the two teeth (mx19) on a maxillary fragment, but both teeth were missing the crown apex; thus, only a minimum age could be determined using the incremental line counts. The tooth with the resorption pit was determined to be a minimum of 81 days old, while the adjacent tooth not in the process of being replaced was approximately 68 days old. A second maxillary tooth with a resorption pit at mx18 was determined to be 145 days old. Additionally, one complete tooth with no resorption pit was longitudinally LL sectioned and estimated to be 108 days old.One maxilla of the small, very rare herbivorous caseid Oromycter was available for destructive sampling (Supplementary Fig. 3). The tooth with a resorption pit in position mx07 was determined to have a total of 506 incremental lines, whereas the tooth without a resorption pit (mx09) had a total of 426 incremental lines. For the mx09 tooth family, the missing replacement tooth was estimated to have 115 incremental lines, resulting in an approximate replacement rate of 391 days.The left dentary of the large herbivorous caseid Ennatosaurus, known only from five specimens, exhibited two posterior teeth with resorption pits on positions d08 and d07 (Supplementary Fig. 4). Tooth position d08 had a visibly larger and more developed resorption pit, with the functional tooth having a total of 628 incremental lines, whereas d07 had a smaller resorption pit and a total of 567 incremental lines. The missing replacement teeth for both d07 and d08 were estimated to have 136 and 169 incremental lines, resulting in a replacement rate of approximately 431 and 459 days, respectively.One maxilla of the herbivorous edaphosaurid Edaphosaurus had a resorption pit at tooth position mx09 (Fig. 4) and was estimated to have a total of 506 incremental lines. The adjacent tooth at position mx10 had no resorption pit and was determined to have a total of 429 lines. For the mx09 tooth family, the missing replacement tooth was estimated to have 131 incremental lines, resulting in a replacement rate of 381 days.Replacement pattern in early and extant reptilesFor the insectivorous parareptile Delorhynchus the functional tooth had a total of 147 incremental lines, while the replacement tooth had 43 lines (Supplementary Fig. 5), resulting in a replacement rate of 104 days. For the other parareptile Colobomycter the premaxillary functional tooth had a total of 157 incremental lines, whereas the replacement tooth had a total of 59 lines, resulting in a replacement rate of 98 days (Supplementary Fig. 6). For the omnivorous eureptile Captorhinus, the functional tooth was 146 days, and the replacement tooth was 69 days, resulting in a replacement rate of approximately 77 days. For the other eureptile, the highly specialized insectivore Opisthodontosaurus, the maximum tooth age for positions d04 to d07 was 151, 155, 206, and 258, respectively (Supplementary Fig. 7). Although no replacement teeth were present, it was possible to use the resorption pit heights to estimate the replacement rates of 182 and 193 days for d06 and d07, respectively. These rates, although different from Captorhinus are not unexpected since this small, close relative of Captorhinus has a very odd, unusual dentition, specialized for feeding on harder shelled invertebrates.In addition to the above Paleozoic amniotes, two skulls were examined for the extant varanid lizards, Varanus bengalensis and Varanus komodoensis, as well as shed teeth of the latter were also available for study and comparison. The maxillary bone of Varanus bengalensis carried dentition showing six replacement events, but only the mx04 tooth position was sectioned. The functional tooth was determined to have 188 incremental lines, and since a continuous record for the replacement tooth’s incremental lines was not visible, the replacement rate was estimated based on its entire dentine area divided by the functional tooth’s mean line width. The estimated replacement rate for V. bengalensis was approximately 110 days. Unlike M. efremovi, the base of the teeth is characterized by plicidentine, and neither tooth serrations (ziphodonty; Supplementary Fig. 8) nor resorption pits were observed for V. bengalensis.Similar to Mesenosaurus, Varanus komodoensis, a highly endangered varanid lizard, exhibits ziphodonty on both the mesial and distal tooth surfaces and provides a valuable comparison with the fossil taxon. Two isolated teeth of an adult individual that were in the process of attachment, but not yet ankylosed with the jawbone, were sectioned. The age of the first tooth was determined to have 106 lines, and the second tooth had approximately 135 lines. A third isolated shed tooth (due to resorption from replacement tooth or from the processing of food)29 provided by the Toronto Zoo was determined to have approximately 227 incremental lines. Thus, from the age of initial tooth attachment to the age of shedding, a tooth appears to be functional for an average of 107 days. Additionally, as in Mesenosaurus, the adult skull of V. komodoensis (ROM R7565) showed that each tooth position exhibited multiple replacement teeth for both the dentary and the maxilla, also confirmed by the data from Auffenberg30.Replacement pattern in a stem amnioteFor the representative carnivorous stem amniote Seymouria (Supplementary Fig. 9) the functional tooth was determined to have a maximum of 171 incremental lines, while the missing replacement tooth was estimated to have had approximately 36 lines. Thus, the estimated replacement rate for Seymouria was calculated to be 135 days.Replacement rate and body massThere seems to be no significant relationship between replacement rate and body mass (kg) for the taxa examined (Supplementary Fig. 10). Although the largest body sized taxon Ennatosaurus had the longest replacement rate, but the other large species had varying rates, while the smallest taxa (Captorhinus, Delorhynchus, Colobomycter, and Opisthodontosaurus) all have varying replacement rates. Instead, replacement rates appear to be related to feeding behaviour since the herbivorous synapsids all exhibited long replacement rates and great tooth longevities (Fig. 5).Fig. 5: Rates of tooth replacement and age across a range of taxa.a Relationship between the total number of incremental lines of von Ebner (age) for the functional tooth and the tooth families replacement rate or period (days). The symbols indicate the type of feeding behaviour, with circles representing carnivory, triangles representing herbivory, square representing insectivory, and diamond representing omnivory. b Phylogenetic tree of all taxa (n = 11) used in the analyses, displaying the age in millions of years ago (length of bars) and tooth longevity (gradient in branch colours). c Phylogenetic tree of all taxa (n = 9) used in the analyses, displaying the age in millions of years ago (mya) (length of bars) and tooth replacement rate (gradient in branch colours). Reconstructed using the ‘contMap’ function in the ‘phytools’ R package. The tree was modified from Maddin, Evans, and Reisz45 and Reisz and Sues12. Source data are provided as a Source Data file.Full size image More

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Influence of independent variables on the extent of stereotyped behaviourThe overall level of stereotypy we observed was low, suggesting that the pandas in our study were not seriously stressed. The variables that we found to be correlated with stereotypy are consistent with what we know of pandas’ natural history. Our study reports that variables like logs on the ground, nest, sociality, zoo, tree density, age and tree height used by pandas are the driving force for stereotypy in captive pandas involved in the study.Making the captive environment more naturalistic by integrating enrichment into the enclosure seems to be a promising way of alleviating stress and improving both welfare and reintroduction success41. It also helps to improve reproductive rate and overall health39. Improved health reduces stress and gives greater control over the environment increasing the chances of survival and longevity both in captivity and following release into the wild5. It is generally accepted that enrichment of the captive environment increases animals’ ability to cope with challenges and positive use of the environment reduces or eliminates aberrant behaviour23. Lack of enclosure enrichments and less complex enclosures can cause stereotypy and other atypical behaviours24, while providing enrichment increases the frequency of natural behaviours25 and thereby reduces stress, which in turn decreases stereotypy27. But enrichment needs to be appropriate for the species of animal concerned. Abnormal behaviours are often associated with captive conditions that deviate greatly from the species’ natural environment. Consistent with this argument we found that though dead and fallen logs on the ground are one of the important characteristics of the panda habitats in the wild42,43,44,45, merely providing them in captivity does not ensure the species’ welfare: in fact, stereotypy increased with log density in our study subjects. This could be due to the fact that four individuals that showed more stereotypy were housed in the small barren enclosures with no trees but more logs as a part of enrichment. Without those four individuals, the linear relation between stereotypy and log density was not statistically significant. This clearly suggested that merely providing logs in the small enclosures does not maintain welfare.
When animals are housed in enclosures designed to resemble their natural habitat by considering their natural history (provision of vegetation, shelter, pool, etc.), there is a reduction or elimination of abnormal patterns of behaviour such as stereotypies, increased fitness and improved health, all of which may influence reproduction25,46,47,48. For many species, nests, shelter or burrows in enclosures will serve as retreat and hiding places, which are essential to cope with environmental stressors10. Gerbils, mice and rabbits have all shown less stereotyped behaviour when retreats are provided9,49,50,51. Such retreats can mitigate the effects of zoo visitors, who can serve as a source of stress for species that rarely interact with humans in the wild. Consistent with these previous results, we found that with provision of nests, the extent of stereotypy decreased in captive pandas. Many species prefer nests both for rearing the young as well as for resting and shelter, and pandas follow this pattern, so providing nests in adequate numbers will supports their natural behaviour as well as provide relief from environmental stressors. Zidar recommends providing one more nest than there are individuals in an enclosure52.Although pandas are an asocial species, our study showed that pandas show more stereotyped behaviour when housed alone than when with another individual or in group. Being a solitary species in the wild might encourage management to house them singly in captivity, but not every activity and habit of species in the wild can be used in captivity. For example, polar bears are also a solitary species, and it was at one time thought best to manage them alone, but it was found that managing them in a social setting reduces stereotypic pacing behaviour53, consistent with this study. Importantly, managers of zoo should note that living in group is greatly influenced by the individuals’ compatibility and hence this should be kept in mind while pairing.Similarly, we found that the presence of trees, and greater mean tree height use by pandas, reduced stereotypy. Pandas’ preferred high elevation habitat is favourable for taller trees20, and Shrestha et al. found that canopy cover was an important factor in habitats for pandas in the wild54. In European zoos, pandas spend 90% of their time off the ground37. Consistent with these previous findings, our study reveals that more and taller trees support natural behaviours in panda. The Central Zoo Authority (CZA) of India enrichment manual recommends taller tree provision in panda enclosures, and again we provide empirical support for its recommendation.We found that with increasing age stereotypy increased in pandas. The older the individuals the more time spent in captivity with its associated risks of stereotypic behaviour. The same trend has been observed in other species: for example in captive bears stereotypic behaviour increased with age55. In another study Asiatic black bear and sun bear showed more stereotypy with age56.Influence of independent variables on behavioural diversityAs noted in the “Introduction” section, in a species like the panda, high daytime behavioural diversity is not necessarily a positive indication of good welfare. However, our comparison of behavioural diversity with stereotypy showed a negative trend (though not significant), suggesting that low behavioural diversity might be associated with poorer welfare.Nonetheless, we found some results that suggested that lower diversity might in fact be associated with a more natural lifestyle. Because of the amount of time that wild pandas spend foraging57 and sleeping or inactive, they cannot show much behavioural diversity, and in our sample of captive individuals, they showed the same trend. For example, behavioural diversity was lower when pandas were provided with more trees in the enclosure. This suggests that when appropriate conditions are maintained in captivity, panda prefer to be inactive during the day, as is consistent with their natural history57. As pandas are essentially arboreal mammals, naturally they also spend most of the time inactive (e.g. sleeping) on the trees57. Indeed, providing larger trees would promote inactive behaviours and hence lower behaviour diversity in captivity, this captures their natural behaviour. This is consistent with our results where increased tree height used by pandas decreased behavioural diversity.We found behavioural diversity was greater when there are more logs in the enclosure. In the Yele Reserve in Sichuan, China, Wei et al. found 107 of 185 panda dropping sites (57.8%) on shrub branches, 49 (26.5%) on fallen logs, and only 29 (15.7%) on the forest floor44. Droppings were found mostly on elevated structures ranging from 1 to 3 m above the forest floor and occasionally on trees over 12 m. Moreover, microhabitats selected by pandas were also characterized by fallen logs and tree stumps42,45. Wei and Zhang mention that to access bamboo leaves easily, pandas usually use some elevated objects, such as shrub branches, fallen logs, or tree stumps to lift their body43. Hence, providing tree logs in the vicinity supports their natural behaviour. But at the same time management should keep in mind that merely providing logs in the enclosure would not guarantee species welfare, as discussed in previous section with respect to stereotypy.Temperature is an important element of microclimate for animals, and influences the activity level of captive animals10. When temperature rises, many species show distress in captivity10. The red panda inhabits low-temperature areas20, so it is unlikely that higher temperatures would support natural behaviours. We found that with increased temperature behavioural diversity decreased in captive pandas. Similarly, we found that pandas showed higher behavioural diversity in the winter season, where temperatures are low as compared to summer season.Studies that have tried to relate behavioural diversity and stereotypy in captive animals have varied in their interpretation; many have found significantly inverse relationships between the two19. In this study our multivariate model suggested that behavioural diversity is negatively influenced by stereotypy in captive pandas, confirming previous research.Other factors associated with variations in behavioural diversity are less easy to identify with welfare, positive or negative. Behavioural diversity also decreases with age of pandas and increases with distance to cage mate, number of visitors and quantum of bamboo provided, though these effects were not significant in the REVS model.Taken together, these results suggest that higher behavioural diversity is not a straightforward indicator of better welfare in all captive animals. The overall non-significant relationship between stereotyped behaviour and diversity we observed could well be the result of a mixture of factors operating in opposite directions. To interpret diversity correctly, it would be helpful to know what level of diversity the species shows in the wild, and such data are rarely available—a limitation of our study as of many others. Although there are dissenting voices58, arguably what matters most both in terms of welfare and in terms of potential reintroduction to the wild, is that a captive animal’s time budget approximates as closely as possible that of a wild animal. It is not diversity as such that is important, but the behaviours that the animal exhibits.Differences between zoosOur study showed that both the extent of stereotyped behaviour and behavioural diversity varied significantly among zoos. However, Zoo 2, an important breeding centre, housed only a female and her two cubs; this may lead to many factors being confounded and is thus a limitation to our study. Captive animals rely on the zoo environment, its routine and husbandry practices to limit their stress levels, and any failure to provide suitable resources will certainly disturb them and lead to distress10. Controlling such variables appropriately will help reduce stress among captive animals, and we can rely to some extent on our knowledge of the species’ natural history to guide us through this challenge. Our study was able to identify some of the factors that are associated with better welfare, but even with these factors taken into account, significant differences among the three zoos remained. These are presumably due to subtler variations in the zoos’ environment or management regimes. Since the panda is endemic to high elevations, we considered whether differences between the elevations of the zoos might be relevant, but the biggest differences were between Zoos 1 and 3, which are at essentially the same elevation.In Zoo 1 pandas showed lower stereotypy and higher behavioural diversity then the other two zoos. Again, these differences may be due to subtle differences between the management regimes in the three zoos; possibilities include keepers’ attitudes and the zoo’s experience in managing pandas. It is notable that Zoo 1 has longer and wider experience in the management of red pandas than the other two zoos, which have joined the captive breeding programme more recently and have fewer animals. Other notable differences were that in Zoo 1, pandas are fed twice a day as compared to the other two zoos where feed is given all at one time (both bamboo and supplementary diet); and that in Zoo 1 the enclosures were of a good size for a small mammal like the red panda, and were well maintained with much natural vegetation. The other two zoos had a large enclosure with poor vegetation (trees and grass), or a small enclosure with a barren floor and no trees at all. Location of the enclosure also needs to be considered: in two of the enclosures at Zoo 3 the sun shone directly on the animals with no shade as such, keeping the temperature higher than would be natural for pandas. Any of these factors could be the reason the pandas performed comparatively well in Zoo 1, and it would be necessary to study a wider (and, therefore, cross-national) sample of zoos holding pandas to identify which of them are the most important. More

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