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: CanESM2 RCP 8.5)

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 CanESM2 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

本数据集集合包含采用GDA94（澳大利亚地心大地坐标系1994）下的ESRI二进制浮点格式的9秒栅格数据集（约合250米分辨率），展示了澳大利亚大陆77个主要植被亚组（Major Vegetation Sub-groups，以下简称MVS），基于其开垦前分布格局以及与基准生态环境（约1990年的气候、基质与地貌）的相关性，所预测的以2050年为中心的未来潜在植被再分布情况。

本次研究使用的开垦前植被分布格局与分类体系，源自澳大利亚环境部及协作州级机构开发的《澳大利亚——1750年前估算主要植被群系——国家植被信息系统（National Vegetation Information System，简称NVIS）第4.1版（Albers 100米分析产品）》。

研究采用核回归（Kernel Regression）方法，以77个MVS类别的约15.5万个训练样本点位为基础，结合代表基准生态环境的17个经广义相异性模型（Generalized Dissimilarity Modeling，简称GDM）标准化的维管植物环境预测因子开展建模。本次核回归模型所用的训练样本类别数据，已随本数据集包一同提供。经GDM标准化的环境预测因子可通过“VAS_v5_r11”数据集包获取。

本研究以1990年基准的MVS类别训练数据为基础，且未将预测结果约束于现有地图边界，采用以2050年为中心的30年平均未来气候数据（来自加拿大地球系统模式2（Canadian Earth System Model 2，简称CanESM2）全球气候模式，对应典型浓度路径8.5（Representative Concentration Pathway 8.5，简称RCP8.5）排放情景），通过核回归模型预测了77个MVS类别的2050年分布格局。核回归模型会为77个MVS类别分别生成介于0至1之间的无约束概率值。

本数据集采用GDA94坐标系下的ESRI二进制浮点格式，分辨率为9秒（约合250米）。每个MVS类别以“UNCON###”命名，其中“###”为供应商最初为该MVS类别分配的代码。本数据集附带一张对照表，用于关联MVS类别、输出代码与描述性标题。此外，本数据集还单独提供了基于每个栅格单元内最大概率值得到的植被类别综合表征结果（详见相关资料）。

本系列数据集共包含三个数据包：1）MVS类别的1990年预测结果；2）基于CanESM2模式RCP8.5情景的MVS类别2050年预测结果；3）基于MIROC5全球气候模式RCP8.5情景的MVS类别2050年预测结果。

本数据集系列及其使用方法可参阅AdaptNRM指南《助力生物多样性适应气候变化：一种社区级建模方法》，该指南可通过以下网址在线获取：www.adaptnrm.org