Projected vegetation redistribution (MaxClass): Australia - 9sec gridded projection to 2050, maximum probability class generalised pre-clearing patterns of Major Vegetation Sub-groups using kernel regression with GDM (VAS_v5_r11) (CMIP5: MIROC5 RCP 8.5)

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This collection contains a 9-second gridded dataset (ESRI binary float format in GDA94) showing the generalised projected future (2050-centred) potential pre-clearing vegetation patterns of 77 Major Vegetation Sub-groups (MVS classes) derived from the maximum of their respective predicted probabilities for each grid cell (V_85MIR50_MXC - MaxClass). Two additional datasets show the maximum probability in each gird cell that was used to assign that class (V_85MIR50_MXP - MaxProb), and the number of classes with non-zero probabilities with potential to represent their type in each grid cell (V_85MIR50_NMC - NumClasses). The predicted probabilities for each class were derived based on their 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. These details are provided with the data package “Potential 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)”. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. This dataset projects the generalised potential pre-clearing vegetation patterns based on 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 accuracy of projections is limited by the quality of the vegetation mapping used to train the models and the accuracy of environmental variables delimiting substrate boundaries and disturbance regimes. Uncertainty or errors in the underlying vegetation map and environmental data will be reproduced by the models. Furthermore, variables describing the relationship between extreme climatic events and ecological disturbance regimes, that have significant structural influences on vegetation, are not directly included in these models.

The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. 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

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这个数据集集合包含一个9秒分辨率的网格化数据集（采用GDA94坐标系的ESRI二进制浮点格式），展示了77个主要植被亚群（MVS类）的广义预测未来（以2050年为中心）潜在原始植被模式，该模式由每个网格单元各自预测概率的最大值推导而来（V_85MIR50_MXC - MaxClass）。另外两个数据集分别展示了用于分配该类别的每个网格单元中的最大概率（V_85MIR50_MXP - MaxProb），以及每个网格单元中具有非零概率且可能代表其类型的类别数量（V_85MIR50_NMC - NumClasses）。每个类别的预测概率基于其分布模式以及与基准生态环境（约1990年的气候、基质和地形）的相关性推导得出。原始植被模式和分类源自澳大利亚政府环境部及合作州机构开发的《澳大利亚——1750年前主要植被群估算——NVIS版本4.1（阿尔伯斯投影100米分析产品）》的4.1版本。针对77个MVS类，使用了核回归方法，其训练样本包含约155,000个位置，这些样本带有17个基于GDM缩放的维管植物环境预测因子，代表基准生态环境。这些详细信息随数据包《潜在植被再分布：澳大利亚——2050年9秒分辨率网格化投影，使用基于GDM缩放的维管植物环境因子的核回归方法得出的77个主要植被亚群原始范围（GDM：VAS_v5_r11；CMIP5：MIROC5 RCP 8.5）》提供。基于GDM缩放的环境预测因子可在"VAS_v5_r11"数据包中获取。该数据集基于以2050年为中心（30年平均值）的未来气候预测广义潜在原始植被模式，这些气候数据源自MIROC5全球气候模型，对应代表性浓度路径8.5（RCP8.5）定义的排放情景。

投影的准确性受限于用于训练模型的植被制图质量，以及界定基质边界和干扰机制的环境变量的准确性。基础植被图和环境数据中的不确定性或误差将被模型重现。此外，描述极端气候事件与生态干扰机制之间关系的变量（这些变量对植被有显著结构影响）未直接纳入这些模型。

数据以9秒分辨率（约250米）、采用GDA94坐标系的ESRI二进制浮点网格格式提供。该数据集系列及其使用方法在AdaptNRM指南《帮助生物多样性适应气候变化：群落层面的建模方法》中有详细描述，可在线获取：www.adaptnrm.org