Data from a flexible framework to assess patterns and drivers of beta diversity across spatial scales
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The patterns and underlying ecological (e.g., environmental filtering) and historical (e.g., priority effects) drivers of beta diversity are scale-dependent but generally difficult to distinguish and rarely explored with a sufficiently broad range of spatial scales. We propose a general scale-explicit framework to assess and contrast the patterns and drivers of beta diversity across hierarchical spatial scales ranging from within fine-scale ecoregion-scale to among broad-scale ecoregion-scale. By applying this framework to aquatic macroinvertebrate datasets, we show that beta diversity generally increases with spatial extent. With an increasing spatial extent, beta diversity shifts from being more influenced by environmental filtering to being more influenced by recent historical factors (i.e., past beta diversity). Such recent historical effects may result from past environmental variation rather than priority effects. We also found that the small-scale and large-scale environmental dr..., , Data are compiled in different files containing the following information: 1) Presence-absence macroinvertebrate taxon dataset in the previous survey, 2) Presence-absence macroinvertebrate taxon dataset in the following survey, 3) dataset of explanatory variables., # Macroinvertebrate taxon dataset assembled two different times (past and contemporary) and explanatory data Authors: Siwen He, Chunyan Qin, Janne Soininen; A flexible framework to assess patterns and drivers of beta diversity across spatial scales Correspondence: Siwen He; siwenhe@cqu.edu.cn ### Description of files: 1. nrsa0304\_past\_communities.csv: containing the following information: Presence-absence macroinvertebrate taxon dataset in previous (2000-2004) survey 2. nrsa0809\_contemporary\_communities.csv: containing the following information: Presence-absence macroinvertebrate taxon dataset in the following (2008-2009) survey 3. Explanatory\_variables.csv: containing nine physicochemical variables, and four landscape variables, and five spatial network variables, and four bioclimatic variables Physicochemical variables: * NH4 (mg/L) ammonia nitrogen * total.P (μg/L) total phosphorus * NO3 (mg N/L) nitrate * pH.lab pH * DOC (mg/L) dissolved organic carbon * LWD.reach (volu...
β多样性（beta diversity）的分布格局及其潜在的生态学驱动因子（如环境过滤）与历史学驱动因子（如优先效应）具有尺度依赖性，但通常难以区分，且极少在足够广泛的空间尺度范围内开展相关探索。本研究提出一个通用的尺度明晰化框架，用于评估并对比从精细尺度生态区域内部到大尺度生态区域之间的层级空间尺度下的β多样性分布格局及其驱动因子。通过将该框架应用于水生大型无脊椎动物数据集，本研究发现β多样性通常随空间幅度的增加而升高。随着空间幅度的扩大，β多样性的主导驱动因子从环境过滤逐渐转向近期历史因子（即历史β多样性）。这类近期历史效应可能源自过往的环境变异，而非优先效应。本研究同时发现，小尺度与大尺度的环境驱动因子……，相关数据整合于不同文件中，具体信息如下：1) 既往调查中的存在-缺失型大型无脊椎动物类群数据集；2) 后续调查中的存在-缺失型大型无脊椎动物类群数据集；3) 解释变量数据集。# 本数据集包含两次不同时段（历史时期与当代）采集的大型无脊椎动物类群数据及解释性数据 作者：何思雯、秦春燕、扬·索伊宁（Janne Soininen）；研究主题：跨空间尺度评估β多样性分布格局与驱动因子的灵活框架 通讯作者：何思雯；邮箱：siwenhe@cqu.edu.cn ### 文件说明： 1. nrsa0304_past_communities.csv：包含2000-2004年既往调查中的存在-缺失型大型无脊椎动物类群数据集 2. nrsa0809_contemporary_communities.csv：包含2008-2009年后续调查中的存在-缺失型大型无脊椎动物类群数据集 3. Explanatory_variables.csv：包含9项理化变量、4项景观变量、5项空间网络变量及4项生物气候变量 理化变量： * NH4(mg/L)：氨氮 * total.P(μg/L)：总磷 * NO3(mg N/L)：硝酸盐氮 * pH.lab：实验室测定pH值 * DOC(mg/L)：溶解性有机碳 * LWD.reach（体积……
