0.01 degree stack of climate layers for continental analysis of biodiversity pattern, version 1.0

These data provide rasterised layers of climatic variables hypothesised to explain spatial patterns in biological diversity at continental scales for use with statistical modelling tools. Specifically, these data were derived for modelling the compositional pattern of multiple species with environmental factors such as climate, soil and topography using the statistical technique Generalised Dissimilarity Modelling applied to continental Australia.Climate variables are monthly mean values for minimum temperature, maximum temperature, relative humidity, vapour pressure, precipitation, aridity, solar radiation, evaporation, wind and others. Some of these monthly variables were used to generate growth indices (using the GROCLIM module of ANUCLIM).These data for Generalised Dissimilarity Modelling (GDM) analysis were masked to consistently define data/nodata values and supplied in DIVA-GIS floating-grid format in the WGS84 geographic reference system.NOTE: Full details of the data, with a list of data sources and bibliography, are provided in a PDF file included as part of the data collection.Lineage: NOTE: Full details of the methods and data sources, as well as a bibliography, are provided in a PDF file included in the data collection.Monthly climatic layers were computed with the ESOCLIM module of ANUCLIM version 6.0 (beta) for each grid cell of a 0.01 degree resolution DEM derived by resampling the national 9 second DEM version 3 with the Arc/Info RESAMPLE function using bilinear interpolation. Annual growth indices were computed using the GROCLIM module of ANUCLIM, a derivative of GROWEST. These and other climatic variables are described below. The values of each layer were consistently expanded around the coastline using the focalmean function in Arc/INFO GRID with neighbourhood shape defined by a circle of radius two cells with DATA values. In each case, the original values of the grids were retained and values were extrapolated outwards from the coast, or to fill data voids where applicable. The data were expanded around the coast because of the coarseness of the 1km grid which results in removal of areas of coastline when viewed at finer scales. Data were masked to consistently define data/nodata values across all climatic and substrate/terrain layers (for the latter, see the Related Materials section). A coastline mask with resolution 0.01 degrees (OZMASK1K) was generated from the current extent of the Australian coastline and surrounding islands defined by the GEODATA Coast 1:100,000 topographic vector data series (Geoscience Australia 2004).This coastline incorporates some minor land and island locations that are not yet captured by the 9-second digital elevation model. It was created to mask model outputs to better reflect the GEODATA coastline, rather than the expanded coastline mask.

本数据集提供经栅格化处理的气候变量图层，这些变量被假设用于解释大陆尺度下生物多样性的空间分布格局，可配合统计建模工具使用。具体而言，本数据集是针对澳大利亚大陆开展研究时，借助广义相异建模（Generalised Dissimilarity Modelling）这一统计方法，以气候、土壤、地形等环境因子为自变量，构建多物种组成格局模型而生成的。

气候变量包含最低气温、最高气温、相对湿度、水汽压、降水量、干旱度、太阳辐射、蒸发量、风速等月度均值。部分月度变量被用于生成生长指数（通过ANUCLIM的GROCLIM模块实现）。

用于广义相异建模（Generalised Dissimilarity Modelling, GDM）分析的数据集已完成掩膜处理，以统一界定有效数据与无数据区域，并采用WGS84地理坐标系下的DIVA-GIS浮点栅格格式提供。

注：数据集完整说明（含数据源列表与参考文献）已收录于本次数据集中附带的PDF文件内。
Lineage: 数据谱系：数据集的方法、数据源及参考文献的完整细节，已收录于本次数据集中附带的PDF文件内。

月度气候图层基于国家9秒数字高程模型（Digital Elevation Model, DEM）第3版，通过Arc/Info的RESAMPLE工具以双线性插值法重采样得到分辨率为0.01度的DEM，随后利用ANUCLIM 6.0（测试版）的ESOCLIM模块为每个栅格单元计算月度气候图层。年度生长指数则通过ANUCLIM的GROCLIM模块生成，该模块是GROWEST的衍生工具。上述及其他气候变量的详细说明见下文。

所有图层的栅格值均通过Arc/INFO GRID的focalmean函数进行海岸线区域扩展：以有效数据栅格为邻域，定义半径为2个栅格单元的圆形邻域。所有操作均保留栅格原始数值，从海岸向外外推栅格值以填补数据空白。之所以需对海岸线区域进行栅格值扩展，是因为1km分辨率栅格较为粗糙，在更高分辨率下会出现海岸线区域缺失的情况。

所有气候图层与基质/地形图层均已完成掩膜处理，以统一界定有效数据与无数据区域（基质/地形图层的相关说明详见相关材料章节）。

本数据集采用分辨率为0.01度的海岸线掩膜（OZMASK1K），其基于澳大利亚当前海岸线及周边岛屿的范围生成，数据源为GEODATA Coast 1:100,000地形矢量数据集（澳大利亚地质科学局，2004年）。该海岸线图层包含了9秒DEM尚未覆盖的小型陆地与岛屿区域。生成该掩膜的目的是对模型输出结果进行掩膜处理，使其更贴合GEODATA海岸线数据，而非此前扩展得到的海岸线掩膜。