Philippa Kaur

In March, Brazil’s Ministry of Agriculture took an alarming step to boost trade of artisanal animal products across states (see go.nature.com/3by9). It added reptiles and amphibians — already the most threatened vertebrates on Earth — to the list permitting the capture of fishes, crustaceans and molluscs for human consumption.Brazil has the fastest rate of decline of amphibian populations in South America, owing to habitat loss and infectious diseases (B. C. Scheele et al. Science 363, 1459–1463; 2019). If the policy takes effect in its current form, trade of amphibians will increase — compounding the spread of lethal pathogens such as Batrachochytrium species and ranavirus.We urge the government to align its policy with the Convention on Biological Diversity and other international commitments that are backed by substantial scientific evidence. More

Soil properties in different rice farming systemsFive treatments were designed in the three selected rice fields, including (1) rice monoculture field (RM); (2) planting area in the 1st year of rice–fish field (OP); (3) aquaculture area in the 1st year of rice–fish field (OA); (4) planting area in the 5th year of rice–fish field (FP); (5) aquaculture area in the 5th year of rice–fish field (FA). The soil properties of the five treatments were shown in Table 1. The highest soil available nitrogen (AN) content was observed in FP and was significantly higher than that in RM, OP and OA. The highest soil available phosphorus (AP) content was observed in RM and was significantly higher than that in the other 4 treatments. The highest soil available potassium (AK) content was measured in the 1st year of rice–fish field (OP and OA), followed by RM and the 5th year of rice–fish field (FP and FA), and significant differences were observed among different rice fields. The highest soil organic matter (OM) content appeared in the 5th year of rice–fish field (FP and FA), and was only significantly higher than that in OA. In addition, the soil pH in the 1st year of rice–fish field (OP and OA) was significantly lower than that in RM and the 5th year of rice–fish field (FP and FA). In summary, significant differences of soil properties were observed among the different rice farming systems.Table 1 Soil properties in different rice systems and areas.Full size tableSoil bacterial community compositionA total of 1,346,468 sequences were obtained by 16S rRNA MiSeq sequencing analysis after basal quality control (reads containing ambiguous bases were discarded; only overlapping sequences longer than 10 bp were assembled; Operational taxonomic units (OTUs) were clustered with 97% similarity). These sequences were classified as 46 phyla, 800 genera and 5335 OTUs. As shown in Fig. 1, the dominant bacterial phyla across different treatments were Proteobacteria (26.06–29.41%) and Chloroflexi (20.07–27.99%), followed by Actinobacteria (7.22–20.87%), Acidobacteria (11.36–14.46%) and Nitrospirae (3.11–8.50%). Since the implementation of rice–fish farming regime, the soil bacterial community composition has greatly changed. For example, Actinobacteria abundance decreased from 20.87% in RM to 7.22% in FA, while Nitrospirae abundance greatly increased from 3.11% in RM to 8.50% in FA. Between different areas in a same rice–fish field (i.e. OP vs OA or FP vs FA), the bacterial community composition were similar. The PCoA analysis on OTU level also showed that different areas within the same rice–fish field had high similarity in bacterial community composition. In contrast, the bacterial community composition differed distinctly among different rice farming systems (Fig. 2). Bacterial alpha diversity indices, as evaluated by Shannon, Simpson, ACE and Chao1, were shown in Table 2. Student’s t-test was adopted to evaluate the difference among treatments. The results showed that the alpha indices of FP were significantly lower than other treatments, except for Simpson index.Figure 1The average relative abundances on phylum level of soil bacterial communities in different rice systems and areas.Full size imageFigure 2PCoA analysis on OTU level based on bray_curtis distance algorithm (significance among treatments were conducted with ANOSIM test, R = 0.4294, P = 0.0010).Full size imageTable 2 Alpha diversity indices of soil bacterial in different rice systems and areas.Full size tableBased on the Kruskal–Wallis test, the statistical differences among treatments were evaluated in the abundances of the top 15 phyla. The results showed that 5 phyla, including Actinobacteria, Nitrospirae, Bacteroidetes, Unclassified_k_norank and SBR1093 were observed significant differences among treatments, and the most significant phylum was Nitrospirae (Fig. 3). In order to trace the source of the significant differences, the Wilcoxon tests were conducted between every two rice cultivation patterns separately (Fig. 4). The results indicated that the significant differences were mainly derived from the comparison between RM and F_group (FP & FA), as well as the comparison between the O_group (OP & OA) and F_group. In the comparison between the RM and O_group, only the phylum Gemmatimonadetes was observed to have a significant difference. Furthermore, we also compared the differences of the top 15 phyla between planting area (P_group) and aquaculture area (A_group) within rice–fish fields, and the results showed no phyla observed with significant differences in the abundances.Figure 3The differences with significance of the top 15 phyla in different rice systems and areas (* indicates 0.01  More

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N. V. PatinPresent address: Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, Miami, FL, USAN. V. PatinPresent address: Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USAN. V. PatinPresent address: Stationed at Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, La Jolla, CA, USASchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USAN. V. Patin & F. J. StewartCenter for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USAN. V. Patin & F. J. StewartBowdoin College, Brunswick, ME, USAZ. A. DietrichHarbor Branch Oceanographic Institute, Florida Atlantic University, Ft. Pierce, FL, USAA. Stancil, M. Quinan & J. S. BecklerMote Marine Laboratory, Sarasota, FL, USAE. R. Hall & J. CulterU.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, USAC. G. SmithSchool of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USAM. TaillefertDepartment of Microbiology & Immunology, Montana State University, Bozeman, MT, USAF. J. Stewart More