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The gut microbiota of wild wintering great bustard (Otis tarda dybowskii): survey data from two consecutive years.

ABSTRACT: Background:The composition of the intestinal microbiota plays a significant role in modulating host health. It serves as a sensitive evaluation indicator and has substantial implications in protecting endangered species. Great Bustards are typical farmland-dependent wintering birds that are highly susceptible to the interference of human activities. However, information regarding their gut microbiota remains scarce. Methods:To ensure a comprehensive analysis of this crucial data, we collected fecal samples from wild Great Bustards at their wintering habitat for two consecutive years. High-throughput sequencing of the 16S rRNA gene was subsequently applied to characterize their core gut microbiota and determine whether the gut microbial composition was similar or varied interannually. Results:The gut microbiota of the Great Bustard was primarily comprised of four phyla: Firmicutes (82.87%), Bacteroidetes (7.98%), Proteobacteria (4.49%), and Actinobacteria (3.67%), accounting for 99.01% of the microbial community in all samples. Further analysis revealed 22 genera of core microbes and several pathogens. Notably, there were no significant differences in the alpha-diversity and beta-diversity between the two sample groups from different years. Conclusions:This study provides essential information for assessing the health and developing targeted protective measures of this threatened species.

SUBMITTER: Lu Z

PROVIDER: S-EPMC8641483 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The rapidly changing climate makes humans realize that there is a critical need to incorporate climate change adaptation into conservation planning. Whether the wintering habitats of Great Bustards (Otis tarda dybowskii), a globally endangered migratory subspecies whose population is approximately 1,500-2,200 individuals in China, would be still suitable in a changing climate environment, and where this could be found, is an important protection issue. In this study, we selected the most suitable species distribution model for bustards using climate envelopes from four machine learning models, combining two modelling approaches (TreeNet and Random Forest) with two sets of variables (correlated variables removed or not). We used common evaluation methods area under the receiver operating characteristic curves (AUC) and the True Skill Statistic (TSS) as well as independent test data to identify the most suitable model. As often found elsewhere, we found Random Forest with all environmental variables outperformed in all assessment methods. When we projected the best model to the latest IPCC-CMIP5 climate scenarios (Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 in three Global Circulation Models (GCMs)), and averaged the project results of the three models, we found that suitable wintering habitats in the current bustard distribution would increase during the 21st century. The Northeast Plain and the south of North China were projected to become two major wintering areas for bustards. However, the models suggest that some currently suitable habitats will experience a reduction, such as Dongting Lake and Poyang Lake in the Middle and Lower Yangtze River Basin. Although our results suggested that suitable habitats in China would widen with climate change, greater efforts should be undertaken to assess and mitigate unstudied human disturbance, such as pollution, hunting, agricultural development, infrastructure construction, habitat fragmentation, and oil and mine exploitation. All of these are negatively and intensely linked with global change.

Project description:Species distribution models (SDMs) have become important and essential tools in conservation and management. However, SDMs built with count data, referred to as species abundance models (SAMs), are still less commonly used to date, but increasingly receiving attention. Species occurrence and abundance do not frequently display similar patterns, and often they are not even well correlated. Therefore, only using information based on SDMs or SAMs leads to an insufficient or misleading conservation efforts. How to combine information from SDMs and SAMs and how to apply the combined information to achieve unified conservation remains a challenge. In this study, we introduce and propose a priority protection index (PI). The PI combines the prediction results of the occurrence and abundance models. As a case study, we used the best-available presence and count records for an endangered farmland species, the Great Bustard (Otis tarda dybowskii), in Bohai Bay, China. We then applied the Random Forest algorithm (Salford Systems Ltd. Implementation) with eleven predictor variables to forecast the spatial occurrence as well as the abundance distribution. The results show that the occurrence model had a decent performance (ROC: 0.77) and the abundance model had a RMSE of 26.54. It is noteworthy that environmental variables influenced bustard occurrence and abundance differently. The area of farmland, and the distance to residential areas were the top important variables influencing bustard occurrence. While the distance to national roads and to expressways were the most important influencing abundance. In addition, the occurrence and abundance models displayed different spatial distribution patterns. The regions with a high index of occurrence were concentrated in the south-central part of the study area; and the abundance distribution showed high populations occurrence in the central and northwestern parts of the study area. However, combining occurrence and abundance indices to produce a priority protection index (PI) to be used for conservation could guide the protection of the areas with high occurrence and high abundance (e.g., in Strategic Conservation Planning). Due to the widespread use of SDMs and the easy subsequent employment of SAMs, these findings have a wide relevance and applicability than just those only based on SDMs or SAMs. We promote and strongly encourage researchers to further test, apply and update the priority protection index (PI) elsewhere to explore the generality of these findings and methods that are now readily available.

Project description:The gut microbiota plays a key role in host health, and disruptions to gut bacterial homeostasis can cause disease. However, the effect of disease on gut microbiota assembly remains unclear and gut microbiota-based predictions of health status is a promising yet poorly established field. Using Illumina high-throughput sequencing technology, we compared the gut microbiota between healthy (HA and HB) and diarrhoeic (DS) Rana dybowskii groups and analyzed the functional profiles through a phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis. In addition, we estimated the correlation between gut microbiota structures and predicted the functional compositions. The results showed significant differences in the phylogenetic diversity (Pd), Shannon, and observed richness (Sobs) indices between the DS and HB groups, with significant differences observed in the gut microbiota composition between the DS group and the HA and HB groups. Linear discriminant analysis (LDA) effect size (LEfSe) results revealed that Proteobacteria were significantly enriched in the DS group; Bacteroidetes were significantly enriched in the HA and HB groups; and Aeromonas, Citrobacter, Enterococcus, Hafnia-Obesumbacterium, Morganella, Lactococcus, Providencia, Vagococcus, and Staphylococcus were significantly enriched in the DS group. Venn diagrams revealed that there were many more unique genera in the DS group than the HA and HB groups. Among 102 sensitive species selected using the indicator method, 33 indicated a healthy status and 69 (e.g., Acinetobacter, Aeromonas, Legionella, Morganella, Proteus, Providencia, Staphylococcus, and Vagococcus) indicated a diseased status. There was a significant and positive association between the composition and functional composition of the gut microbiota, thus indicating low functional redundancy of the frog gut bacterial community. Rana dybowskii disease was associated with changes in the gut microbiota, which subsequently disrupted bacterial-mediated functions. The results of this study can aid in revealing the effect of the R. dybowskii gut microbiota on host health and provide a basis for elucidating the mechanism of the occurrence of R. dybowskii disease.

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The gut microbiota of wild wintering great bustard (Otis tarda dybowskii): survey data from two consecutive years.

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