Projected vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: MIROC5 RCP 8.5)

***UPDATED*** This collection contains 9-second gridded datasets (ESRI binary float format in GDA94) showing the projected future (2050-centred) potential vegetation redistribution of 77 Major Vegetation Sub-groups (MVS classes) for continental Australia based on their pre-clearing distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. The training class data input to the kernel regression is provided with this package. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. Using the 1990 baseline training MVS class data, and without constraining the prediction to pre-existing map boundaries, the kernel regression projected to 2050 the distribution of the 77 Major Vegetation Sub-groups using 2050-centred (30 year average) future climates derived from the MIROC5 global climate model for the emission scenario defined by a representative concentration pathway of 8.5. The kernel regression generates unconstrained probabilities varying in the range from 0 and up to 1 for each of the 77 MVS classes. The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. Each class is denoted “UNCON###”, where the number refers to the code originally assigned to that MVS class by the supplier. A lookup table linking the MVS classes to the output codes and descriptive title is provided. Generalised representations of the vegetation classes derived from the individual class probabilities as the maximum probability in any grid cell are provided separately (see related information). There are three dataset packages in this series: 1) 1990 predictions of MVS classes; 2) 2050 CanESM2 RCP 8.5 predictions of MVS classes; 3) 2050 MIROC5 RCP 8.5 predictions of MVS classes. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org

**更新版** 本数据集集合包含以GDA94（Geocentric Datum of Australia 1994，澳大利亚大地基准1994）为坐标系的9秒分辨率栅格数据集（ESRI二进制浮点格式），展示了以2050年为中心的未来潜在植被再分布情况，涵盖澳大利亚大陆77个主要植被亚组（Major Vegetation Sub-groups，MVS）。该预测基于各植被亚组清理前的分布格局，以及其与基线生态环境（约1990年的气候、基质与地形）的相关性。
上述清理前植被格局与分类体系，源自澳大利亚联邦环境部及合作州级机构开发的《澳大利亚——1750年前估算主要植被组——NVIS版本4.1（Albers 100米分析产品）》（版本4.1）。
本研究针对77个MVS类别，采用核回归方法，以约15.5万个训练样本点位为基础，使用代表基线生态环境的17个GDM缩放环境预测因子开展建模。本数据包附带了输入至核回归的训练样本类别数据；GDM缩放的环境预测因子可通过"VAS_v5_r11"数据包获取。
基于1990年基线的MVS训练样本类别数据，且未将预测约束于现有地图边界内，本研究利用以2050年为中心的30年平均未来气候数据，通过MIROC5全球气候模式在典型浓度路径（Representative Concentration Pathway，RCP）8.5排放情景下的输出，对77个MVS的分布进行了2050年预测。核回归为每个MVS生成了取值范围为0至1的无约束概率值。
本数据集以9秒（约250米）分辨率、ESRI二进制浮点格式存储于GDA94坐标系中。每个类别的文件命名为"UNCON###"，其中###为供应商最初为该MVS分配的代码。本数据包附带了将MVS类别与输出代码、描述性标题关联的查找表。基于各栅格单元内最大概率值得到的植被类别通用表征结果已单独提供（详见相关信息）。
本系列共包含3个数据集包：1）MVS类别的1990年预测结果；2）基于CanESM2模式RCP8.5情景的2050年MVS类别预测结果；3）基于MIROC5模式RCP8.5情景的2050年MVS类别预测结果。
本数据集系列及其使用方法详见AdaptNRM指南《助力生物多样性适应气候变化：社区级建模方法》，可于以下网址在线获取：www.adaptnrm.org