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    Inter-annual variation patterns in the carbon footprint of farmland ecosystems in Guangdong Province, China

    Analysis of carbon sources in Guangdong farmland ecosystems under the “dual carbon” targetAnalysis of inter-annual variation in carbon emissions from farmland ecosystems in GuangdongGuangdong’s carbon emissions from farmland ecosystems showed an increasing trend year by year during 2001–2017 (Fig. 1a), with carbon emissions gradually reaching a peak of 4.153 million t a−1 in 2016 from 3.554 million t a−1 in 2001, but decreasing year by year from 2017 onwards. Eventually it’s decreasing to 3.533 million t a−1 by 2020. Showing that Guangdong’s farmland ecosystem carbon emissions have remained relatively flat over the past 20 years, with an average annual carbon emission of 3.7624 million t a−1. The carbon emissions per unit arable land area of Guangdong’s farmland ecosystems show an increasing trend year by year (Fig. 1b), from 1.12 t ha−1 in 2001 to 2.03 t ha−1 in 2020, an increase of 81.25% over 20 years, with an average annual carbon emission per unit arable land area of 1.43 t ha−1. While the carbon emissions per unit sown area show the opposite trend to the total carbon emissions, from 2001 to 2016, showing a decreasing trend year by year. The carbon emissions per unit of sown area decreased from 1.50 t ha−1 in 2001 to 1.01 t ha−1 in 2016 and then started to increase year by year from 2017 to 1.26 t ha−1 in 2020, with an overall decrease of 16% and an average annual carbon emission per unit of sown area of 1.19 t ha−1.Figure 1(a) Inter-annual variation of carbon emissions from farmland ecosystems in Guangdong; (b) inter-annual variation in carbon emissions per unit area of farmland ecosystems in Guangdong.Full size imageAnalysis of carbon sources in Guangdong farmland ecosystemsThe carbon emissions from agricultural production power (estimated by the total power of agricultural diesel and agricultural machinery) in Guangdong’s farmland ecosystems show an increasing trend year by year (Fig. 1a), from 411,000 t a−1 in 2001 to 513,000 t a−1 in 2020, an increase of nearly 25% in 20 years. Carbon emissions from tillage and irrigation inputs are relatively flat, from 116,000 t a−1 in 2001 to 109,000 t a−1 in 2020, with an average of 107,000 t a−1 over the last 20 years. Carbon emissions from chemicals in agricultural production (estimated by fertilizer, pesticide, and agricultural film inputs) have the greatest impact on the overall emissions, with carbon emissions from agricultural chemicals reaching 2.9097 million t a−1 in 2020, accounting for 82.36% of total carbon emissions from farmland ecosystems, but a relatively flat trend. Although the share of carbon emissions from agricultural production power is increasing year by year, the contribution of carbon emissions due to inputs of agricultural chemicals is still in an absolute position. The use of agricultural chemicals directly affects the carbon emissions of Guangdong’s farmland ecosystems. Therefore, a more detailed analysis of the carbon emissions of various agricultural chemicals is necessary in order to make carbon reduction proposals.Depending on Fig. 2a, although the proportion of carbon emissions caused by agricultural films has been increasing year by year, chemical fertilizers still occupy an absolute position, with their carbon emissions accounting for 78.45% of agricultural chemicals on average in the past 20 years. Which the average proportions of carbon emissions caused by pesticides and agricultural films are 15.17% and 6.38% respectively. Among the carbon emissions from various fertilizers (Fig. 2b), the annual average share of carbon emissions in the past 20 years is distributed from the largest to the smallest: 81.63% from nitrogen fertilizers, 9.57% from compound fertilizers, 5.60% from phosphate fertilizers and 3.20% from potash fertilizers. From the trend of carbon emissions of various types of fertilizers, we can learn that the carbon emissions of nitrogen fertilizers have been decreasing year by year, from 85.63% in 2001 to 78.10% in 2020, and the emissions have slowly risen from 2.061 million t a−1 in 2001 to a peak of 2.1276 million t a−1 in 2016, then gradually decreased to 1.7797 million t a−1 in 2020. Compound fertilizers, on the other hand, rose from 6.17% in 2001 to 11.40% in 2020, an increase of nearly 85%, and their carbon emissions rose year by year from 148,600 t a−1 to a peak of 305,200 t a−1 in 2016 and then gradually fell to 259,900 t a−1 in 2020, an increase of 74.90%. The share of carbon emissions from potash is relatively stable, rising from 2.89% to 3.29%, reaching a peak of 91,800 t a−1 in 2016 and then gradually decreasing to 75,500 t a−1 in 2020. The share of carbon emissions from phosphate fertilizers is also on a year-on-year rise, from 5.31% to 7.20%, an increase of 37.47%. However, the carbon emissions from phosphate fertilizers do not produce a peak in 2016 but keep increasing in a relatively stable trend, with its carbon emissions rising from 127,800 t a−1 in 2001 to 164,100 t a−1 in 2020, an increase of 28.40%.Figure 2(a) Proportion of carbon emissions from various types of agricultural chemicals in Guangdong farmland ecosystems; (b) proportion of carbon emissions from different fertilizer types in Guangdong farmland ecosystems.Full size imageAnalysis of carbon sequestration in Guangdong farmland ecosystems under the “dual carbon” targetAnalysis of inter-annual variation in the carbon sequestration function of Guangdong farmland ecosystemsIn the inter-annual variation of carbon sequestration function of farmland ecosystems in Guangdong (Fig. 3a), although there are fluctuations in the variation of total carbon sequestration in farmland ecosystems, the overall decrease is not significant. With the total carbon sequestration decreasing from 21.3176 million t a−1 in 2001 to 19.1178 million t a−1 in 2020, a decrease of 10.32% in the last 20 years, and the average annual carbon sequestration is 19.0363 million t a−1, among which the total carbon sequestration in 2008 is the lowest, only 17.2033 million t a−1.Figure 3(a) Carbon sequestration function of farmland ecosystems in Guangdong; (b) inter-annual variation of carbon sequestration function per unit area of farmland ecosystems in Guangdong.Full size imageThe total carbon sequestered in 2008 was the lowest at 17.2033 million t a−1. The inter-annual variation of carbon sequestration by food crops (paddy, wheat, corn, legumes, yams, and other food crops) is similar to that of farmland ecosystems, decreasing from 13.9742 million t a−1 to 10.209 million t a−1, a decrease of 27%. The inter-annual variation of carbon sequestration by cash crops (sugarcane, peanuts, Canola, and tobacco) and vegetables generally shows a stable upward trend, with carbon sequestration increasing by 15.54% and 55.54% respectively over the past 20 years. Meanwhile, the amount of carbon sequestered per unit sown area in Guangdong’s farmland ecosystems was generally flat (Fig. 3b), with an average annual carbon sequestration per unit sown area of 4.31 t ha−1. While the amount of carbon sequestered per unit arable land area showed an increasing trend, especially in 2017, when it started to rise rapidly, from 6.82 t ha−1 per unit arable land area in 2001 to 10.97 t ha−1 per unit arable land area in 2020, an increase of 60.85%. The average annual carbon sequestration per arable area is 7.25 t ha−1, an increase of 56.71% in the 4 years from 2017 to 2020.Analysis of the role of crop carbon sequestrations in Guangdong’s farmland ecosystemsAs can be seen from Fig. 4a, food crops play the largest role in carbon sequestration in Guangdong’s farmland ecosystems, with an average share of 56.95% of the total carbon sequestration in the past 20 years. Its share tends to decline over time, but the amount of carbon sequestered by food crops in Guangdong still reaches 10.209 million t a−1 in 2020. The carbon sequestration role of cash crops is next, rising from 29.43% in 2001 to 37.92% in 2020, with an average share of 36.17%, an increase of 28.85%, and average annual carbon sequestration of 6.8863 million t a−1. The inter-year variation of vegetables carbon sequestration also shows an increasing trend, rising from 5.02 to 8.73%, with an increase of 73.90%, and average annual carbon sequestration of 1.13112 million t a−1.Figure 4(a) Proportion of carbon sequestered by various crops in Guangdong farmland ecosystems. (b) Proportion of carbon sequestered by various food crops in Guangdong farmland ecosystems; (c) proportion of carbon sequestered by various cash crops in Guangdong farmland ecosystems.Full size imageWhen the carbon sequestration capacity of food (Fig. 4b) and cash crops (Fig. 4c) in Guangdong’s farmland ecosystems is broken down, it is easy to see that paddy is in an absolute position in terms of carbon sequestration among food crops, with an average share of 83.81% over the past 20 years and average annual carbon sequestration of 8.8946 million t a−1. Especially since 2017, the carbon sequestration share of paddy has risen to over 87% and will remain until 2020. Also, sugarcane’s share of carbon sequestration in cash crops is absolute, with average annual share of 86.73% and an average annual carbon sequestration of 5.9712 million t a−1. while, peanut’s share of carbon sequestration in cash crops is also not small, with average annual share of 12.47% and an average annual carbon sequestration of 0.8606 million t a−1.An analysis of the inter-annual variation in carbon sequestration of various crops (Fig. 5) shows that paddy and sugar cane play the largest role in carbon sequestration in Guangdong’s farmland ecosystems. Their combined annual average carbon sequestration amounting to 14.8658 million t a−1, accounting for 78.09% of the total annual average carbon sequestration in Guangdong’s farmland ecosystems. Vegetables, peanuts, and yams also play a significant role in carbon sequestration, with the combined annual average carbon sequestration of the three species being 2.9936 million t a−1, accounting for 15.73% of the total annual average carbon sequestration.Figure 5Comparison of carbon sequestration by various crops in Guangdong farmland ecosystems.Full size imageAnalysis of the carbon footprint of Guangdong’s farmland ecosystems under the “dual carbon” targetThe carbon footprint of Guangdong’s farmland ecosystem ((CEF)) is 531,100 ha a−1 per year, showing a general decrease (Fig. 6), with a 59.65% decrease from 513,900 ha a−1 in 2001 to 321,900 ha a−1 in 2020. The carbon footprint of Guangdong’s farmland ecosystems in the past 20 years (the peak value is 611,500 in 2008 ha a−1) is smaller than the ecological carrying capacity (i.e. the arable land area, the lowest value is 1.7421 million ha a−1 in 2020), and is in a state of carbon ecological surplus. Guangdong’s farmland carbon surplus ((CS)) shows a decreasing trend year by year (Fig. 6), from 2.1611 million ha a−1 in 2001 to 1.4202 million ha a−1 in 2020, a decrease of 45.61%. Although the carbon footprint and the inter-annual variation of the carbon surplus both show a decreasing trend, the productive area required to absorb the carbon emissions from farmland (i.e. the carbon footprint) rises from 16.44 to 18.48% of the arable land area in the same period.Figure 6Inter-annual variation of carbon footprint and ecological surplus of farmland ecosystems in Guangdong.Full size imageAn overview of the interannual variability of carbon emissions, sequestrations and footprints of farmland ecosystems in GuangdongIn the above analysis of the inter-annual variation of carbon emissions, sequestration, and footprint of Guangdong’s farmland ecosystems, it was found that 2017 was a special year. After 2017, which the total carbon emissions from Guangdong’s farmland ecosystems and carbon emissions due to agricultural chemicals (Fig. 1a), carbon emissions per unit of arable land area and sown area (Fig. 1b), carbon sequestration per unit of arable land area (Fig. 3b) and carbon footprint and carbon surplus (Fig. 6) all show a large turnaround. Based on the analysis of the factors after 2017 in Table 3, it can be seen that the number of various fertilizers using is gradually decreasing after 2017, especially the number of nitrogen fertilizers decreased by 149,600 t a−1 in 2018 compared with the amount of the previous year, a decrease of 14.44% in a single year, and the carbon emission decreased by 316,600 t a−1. The arable land area in Guangdong is decreasing after 2017, from 2017 to 2019, it decreased by 697,800 ha, a decrease of 26.84%, but the total carbon sequestration still remains above 19 million t a−1 (Fig. 3a), and while the area of arable land in Guangdong is decreasing, the area sown is climbing. The ratio of sown area to arable land area is used as the number of tillage per unit of arable land area in the paper, and the number of tillage per unit of the arable land area rises from 1.63 ha ha−1 in 2017 to 2.56 ha ha−1 in 2020.Table 3 Inter-annual variation of selected factors in Guangdong agro-ecosystems, 2017–2020.Full size tableBased on the conclusions obtained, the author looked up the agriculture-related policies of Guangdong Province in 2016 and 2017. And found that on 30 December 2016, the Guangdong Provincial People’s Government, in response to the soil prevention and control plan of the Central Government, formulated and issued to the cities and counties under its jurisdiction the Implementation Plan of the Guangdong Provincial Soil Pollution Prevention and Control Action Plan (here in after referred to as the “Plan”). The Plan encourages farmers in all areas to reduce the number of chemical fertilizers and apply pesticides scientifically. The effectiveness of the implementation of the Plan in Guangdong Province is remarkable as seen through the changes in the application of various fertilizers, which in the aspect of reducing fertilizer application alone resulted in a 344,900 t ha−1 reduction in carbon emissions from fertilizer inputs in 2018 compared to 2017. At the same time, the number of farmland tillage has increased, and the area of arable land has been reduced, but the total sown area of crops has remained relatively constant. In 2019, while the area of arable land in Guangdong (actual data on arable land in 2020 is missing, and the forecast alone may cause too much error, so 2019 is used as an example) is 69.78 ha less than that in 2017, the total sown area has increased by 22.43 ha, and the total agricultural output value still increased by RMB 64 billion. Which shows that the utilization rate of arable land and the output value per unit of arable land in Guangdong have both increased. More

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    Predicting the potential for zoonotic transmission and host associations for novel viruses

    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

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    Dogs suppress a pivotal function in the food webs of sandy beaches

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    Predictors of psychological stress and behavioural diversity among captive red panda in Indian zoos and their implications for global captive management

    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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