Soil and Landscape Grid National Soil Attribute Maps - Soil Bacteria and Fungi Beta Diversity (3" resolution) - Release 1

This is Version 1 of the Soil Bacteria and Fungi Beta Diversity product of the Soil and Landscape Grid of Australia. \n\nThe Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. These products provide estimates of the Beta Diversity of soil fungi and bacteria. The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels). \n\nThese maps are generated using Digital Soil Mapping methods\n\nAttribute Definition: Soil Bacteria and Fungi Beta Diversity\nUnits: NA; \nPeriod (temporal coverage; approximately): 1950-2022; \nSpatial resolution: 3 arc seconds (approx 90m); \nTotal number of gridded maps for this attribute: 6; \nNumber of pixels with coverage per layer: 2007M (49200 * 40800); \nTotal size before compression: about 8GB; \nTotal size after compression: about 4GB; \nData license : Creative Commons Attribution 4.0 (CC BY); \nFormat: Cloud Optimised GeoTIFF.\n\nLineage: Soil microorganisms mediate a wide range of key processes and ecosystem services on which humans depend. In this study, we report on the biogeography and spatial pattern of soil biota for the Australian continent. We used as basis the DNA sequences from the Biome of Australia Soil Environments (BASE) which were collected over a range of different sites across Australia. \n\nWe calculated the beta diversity of abundant taxa of soil bacteria and fungi, treating representative sequence data (OTUs) as individual taxa. Two ordination methods were applied to investigate the dissimilarities in microbial community composition, non-metric multidimensional scaling (NMDS) and Uniform Manifold Approximation and Projection (UMAP) for dimension reduction. The NMDS and UMAP used the weighted UniFrac distance for bacteria and Bray-Curtis dissimilarity for fungi on taxa relative abundance. The results of the NMDS for bacteria indicated that the structure of the data was captured fairly well, with a stress of 0.09. However, the stress of the fungi NMDS was 0.16, indicating that the fungi community composition was moderately well explained. \n\nWe further collected a large set of environmental covariates that control the biogeography of soil biota, such as soil properties terrain attributes of vegetation indices, and of which maps are available. We fitted a quantile regression forest machine learning model to exploit the quantitative relationship between point-estimated values of beta diversity and environmental covariates, and used to model to predict beta diversity across Australia along with an estimate of uncertainty. \n\nSoil property and vegetation are the dominant controls of soil biota. The resulting maps also reveal the pattern of soil biota which can further be used for regional assessment of soil biodiversity and from which degradation induced by global changes can be monitored. \n\nCode - https://github.com/AusSoilsDSM/SLGA\nObservation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html\nCovariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html本数据集为澳大利亚土壤与景观网格（Soil and Landscape Grid of Australia）推出的土壤细菌与真菌β多样性（Beta Diversity）产品的V1版本。 澳大利亚土壤与景观网格已推出一系列数字化土壤属性产品，本系列产品可对土壤真菌与细菌的β多样性进行估算。本次数字化土壤属性地图采用栅格格式，空间分辨率为3角秒（约90×90米像素）。 此类地图通过数字土壤制图（Digital Soil Mapping）方法生成。 属性定义：土壤细菌与真菌β多样性 单位：无（NA）； 时间覆盖范围（近似）：1950年—2022年； 空间分辨率：3角秒（约90米）； 该属性的栅格地图总数：6幅； 单图层覆盖像素数：2007M（49200 × 40800）； 压缩前总大小：约8GB； 压缩后总大小：约4GB； 数据许可协议：知识共享署名4.0（Creative Commons Attribution 4.0, CC BY）； 数据格式：云优化地理TIFF（Cloud Optimised GeoTIFF）。 数据集溯源： 土壤微生物介导了人类赖以生存的诸多关键过程与生态系统服务。本研究针对澳大利亚大陆的土壤生物群生物地理学特征与空间分布模式展开分析，以澳大利亚土壤环境生物群（Biome of Australia Soil Environments, BASE）项目采集的全澳多站点DNA序列作为研究基础。 我们以代表性序列数据（操作分类单元，Operational Taxonomic Unit, OTU）作为独立分类单元，计算了土壤细菌与真菌的优势类群β多样性。为探究微生物群落组成的差异，我们采用了两种排序方法进行降维：非度量多维尺度分析（non-metric multidimensional scaling, NMDS）与均匀流形近似与投影（Uniform Manifold Approximation and Projection, UMAP）。其中，细菌分析采用加权UniFrac距离，真菌分析则基于类群相对丰度计算Bray-Curtis相异度。细菌NMDS分析结果显示数据结构得到较好拟合，拟合应力为0.09；而真菌NMDS的拟合应力为0.16，表明群落组成得到中等程度的解释。 我们还收集了一系列调控土壤生物群生物地理学分布的环境协变量，包括土壤属性、地形属性与植被指数，且均已生成对应栅格地图。我们构建了分位数回归森林机器学习模型，以挖掘β多样性点估计值与环境协变量间的定量关系，并利用该模型对全澳范围内的β多样性进行预测，同时输出不确定性估计结果。 土壤属性与植被是调控土壤生物群分布的核心因子。最终生成的栅格地图揭示了土壤生物群的空间分布模式，可进一步用于土壤生物多样性的区域评估，以及监测全球变化引发的土壤退化过程。 代码仓库：https://github.com/AusSoilsDSM/SLGA 观测数据集：https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html 协变量栅格数据：https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html