植物生态学报 ›› 2019, Vol. 43 ›› Issue (7): 611-623.DOI: 10.17521/cjpe.2019.0065
所属专题: 菌根真菌
收稿日期:
2019-03-25
接受日期:
2019-06-05
出版日期:
2019-07-20
发布日期:
2019-12-12
通讯作者:
马克明
基金资助:
Received:
2019-03-25
Accepted:
2019-06-05
Online:
2019-07-20
Published:
2019-12-12
Contact:
MA Ke-Ming
Supported by:
摘要:
零模型是判定网络嵌套性的重要依据, 菌根共生关系网络经常出现高度非对称性, 该文通过探究矩阵非对称变化对基于不同零模型构建方法的网络嵌套性的影响, 试图为非对称网络零模型的选择提供依据。结果表明: 不同零模型保守性不同, 增加限定条件减少零模型构建过程中的自由空间, 高度限定条件易导致第II类错误。高度非对称网络会增加基于完全随机(r00)零模型的矩阵温度(NT)偏离、降低配对重叠度(NODF)偏离, 标准化指数z-score值显示网络非对称增加后有助于NT和NODF显著性判定。行或列限定对非对称网络嵌套性判定的影响存在差异, 列限定(c0)的网络嵌套性判定对网络非对称性变化的响应规律与r00零模型的响应趋势基本一致, 具有更低的嵌套性偏离和标准差值。行限定(r0, 包括行列限定(backtrack))零模型NT值和NT偏移随矩阵非对称性的变化保持稳定, 较之c0零模型在高度非对称网络中呈现更低的NODF偏离值。选用完全随机和限定零模型相结合的方法, 有助于更加准确判断非对称网络是否具有嵌套结构。高度非对称网络嵌套性判定中对行属性特征比较敏感, 不同非对称性网络间嵌套性水平相比较时选用r0零模型要优于r00和c0零模型。
林力涛, 马克明. 菌根共生网络嵌套性判定的零模型选择. 植物生态学报, 2019, 43(7): 611-623. DOI: 10.17521/cjpe.2019.0065
LIN Li-Tao, MA Ke-Ming. Selection of null models in nestedness pattern detection of highly asymmetric mycorrhizal networks. Chinese Journal of Plant Ecology, 2019, 43(7): 611-623. DOI: 10.17521/cjpe.2019.0065
指标 Index | 反映特征 Underlying feature | 下限保守 Bottom boundary conservative | 上限保守 Top boundary conservative | 零模型保守 Null model conservative | 存在上限 Top limit |
---|---|---|---|---|---|
矩阵温度 matrix temperature (NT) | 矩阵中异常点数量、位置 Relative abundance and position of unexpected absences or presences in a matrix | 是 Yes | 是 Yes | 否 No | 是 Yes |
配对重叠度 nestedness metric based on overlap and decreasing fill (NODF) | 矩阵配对重叠中非子集行列的数量及位置 Percentage and position of columns (rows) overlaps with other columns (rows) | 是 Yes | 是 Yes | 是 Yes | 是 Yes |
嵌套性偏移 Nestedness deviation | 网络与零模型的距离 Distance deviates from null models | 是 Yes | 否 No | 是 Yes | 是 Yes |
标准化指数 Standardized effect size | 网络达到显著水平的难易程度 Significance of a network deviates from null models | 是 Yes | 否 No | 是 Yes | 否 No |
表1 嵌套性度量指标特征
Table 1 Characteristics of various nestedness metrics
指标 Index | 反映特征 Underlying feature | 下限保守 Bottom boundary conservative | 上限保守 Top boundary conservative | 零模型保守 Null model conservative | 存在上限 Top limit |
---|---|---|---|---|---|
矩阵温度 matrix temperature (NT) | 矩阵中异常点数量、位置 Relative abundance and position of unexpected absences or presences in a matrix | 是 Yes | 是 Yes | 否 No | 是 Yes |
配对重叠度 nestedness metric based on overlap and decreasing fill (NODF) | 矩阵配对重叠中非子集行列的数量及位置 Percentage and position of columns (rows) overlaps with other columns (rows) | 是 Yes | 是 Yes | 是 Yes | 是 Yes |
嵌套性偏移 Nestedness deviation | 网络与零模型的距离 Distance deviates from null models | 是 Yes | 否 No | 是 Yes | 是 Yes |
标准化指数 Standardized effect size | 网络达到显著水平的难易程度 Significance of a network deviates from null models | 是 Yes | 否 No | 是 Yes | 否 No |
图1 基于完全嵌套(A, B)和均匀分布(C, D)矩阵构建的不同零模型嵌套性特征(平均值±标准偏差)。Backtrack, 行列限定; c0, 列限定; IM, 初始矩阵; r0, 行限定; r00, 完全随机。***, 0.001水平显著。
Fig. 1 Absolute values of nestedness from null models constructed based on fully nested (A, B) and uniformly distributed matrices (C, D) (mean ± SD). Backtrack, fixed row-fixed column; c0, equiprobable row-fixed column; IM, initial matrix; r0, fixed row- equiprobable column; r00, equiprobable row-equiprobable column. ***, significant at 0.001 level.
图2 矩阵形状变动对矩阵嵌套性指数影响。矩阵形状, 矩阵列数/矩阵行数。Backtrack, 行列限定; c0, 列限定; r0, 行限定; r00, 完全随机。
Fig. 2 Effects of changes in matrix shape on nestedness metrics. Matrix shape, number of columns/number of rows. Backtrack, fixed row-fixed column; c0, equiprobable row-fixed column; r0, fixed row-equiprobable column; r00, equiprobable row-equiprobable column.
图3 非对称性变化对构建二分零模型矩阵温度(NT)和NT偏移的影响(平均值±标准偏差)。Backtrack, 行列限定; c0, 列限定; r0, 行限定; r00, 完全随机。IM, 初始矩阵; 5 times, 5倍列合并网络; 10 times, 10倍列合并网络。
Fig. 3 Effects of matrix asymmetric variations on matrix temperature (NT) and NT deviation based on different binary matrix construction methods (mean ± SD). Backtrack, fixed row-fixed column; c0, equiprobable row-fixed column; r0, fixed row-equiprobable column; r00, equiprobable row-equiprobable column. IM, Initial matrix; 5 times, 5 times column merge network; 10 times, 10 times column merge network.
图4 非对称性变化对矩阵温度标准化指数的影响。Backtrack, 行列限定; c0, 列限定; r0, 行限定; r00, 完全随机。IM, 初始矩阵; 5 times, 5倍列合并网络; 10 times, 10倍列合并网络。
Fig. 4 Effects of matrix asymmetric variations on the standardized effect size (z-score) of matrix temperature (NT). Backtrack, fixed row-fixed column; c0, equiprobable row-fixed column; r0, fixed row-equiprobable column; r00, equiprobable row-equiprobable column. IM, initial matrix; 5 times, 5 times column merge network; 10 times, 10 times column merge network.
图5 非对称性变化对构建二分零模型配对重叠度(NODF)和NODF偏移影响(平均值±标准偏差)。Backtrack, 行列限定; c0, 列限定; r0, 行限定; r00, 完全随机。IM, 初始矩阵; 5 times, 5倍列合并网络; 10 times, 10倍列合并网络。
Fig. 5 Effects of matrix asymmetric variations on nestedness metric based on overlap and decreasing fill (NODF) and NODF deviation based on different binary matrix construction methods (mean ± SD). Backtrack, fixed row-fixed column; c0, equiprobable row-fixed column; r0, fixed row-equiprobable column; r00, equiprobable row-equiprobable column. IM, Initial matrix; 5 times, 5 times column merge network; 10 times, 10 times column merge network.
图6 非对称性变化对矩阵配对重叠度标准化指数的影响。Backtrack, 行列限定; c0, 列限定; r0, 行限定; r00, 完全随机。IM, 初始矩阵; 5 times, 5倍列合并网络; 10 times, 10倍列合并网络。
Fig. 6 Effects of matrix asymmetric variations o z-scores of on the standardized effect size (z-score) of nestedness metric based on overlap and decreasing fill (NODF). Backtrack, fixed row-fixed column; c0, equiprobable row-fixed column; r0, fixed row-equiprobable column; r00, equiprobable row- equiprobable column. IM, Initial matrix; 5 times, 5 times column merge network; 10 times, 10 times column merge network.
图7 矩阵随机化程度对嵌套性度量指标的影响。Square, 对称网络, 行列数30/30; Asy, 非对称网络, 行列数5/55。随机化程度, 网络中无序单元格的数量/单元格总数量。随机程度包含4:100 (%)共计97水平, 每水平50个重复, 共计4 850矩阵。
Fig. 7 Effects of the level of matrix randomness on nestedness metrics. Square, symmetric networks with rows and columns 30/30; Asy, asymmetric networks with rows and columns 5/55. Randomness, percentage of disordered cells deviates from fully nested matrix. Data contains a total of 4 850 matrices, with 4:100 (%) randomness degree 97 levels of, 50 repetitions per level.
图8 矩阵温度(NT)与配对重叠度(NODF)间的相关性。Square, 对称网络, 行列数30/30; Asy, 非对称网络, 行列数5/55。随机化程度, 网络中无序单元格的数量/单元格总数量。随机程度包含4:100 (%)共计97水平, 每水平50个重复, 共计4 850矩阵。
Fig. 8 Correlation between matrix temperature (NT) and nestedness metric based on overlap and decreasing fill (NODF). Square, symmetric networks with rows and columns 30/30; Asy, asymmetric networks with rows and columns 5/55. Randomness, percentage of disordered cells deviates from fully nested matrix. Data contains a total of 4 850 matrices, with 4:100 (%) randomness degree 97 levels of, 50 repetitions per level.
图9 矩阵随机化程度对嵌套标准化指标影响。Square, 对称网络, 行列数30/30; Asy, 非对称网络, 行列数5/55。随机化程度, 网络中无序单元格的数量/单元格总数量。随机程度包含4:100 (%)共计97水平, 每水平50个重复, 共计4 850矩阵。NT, 矩阵温度; NODF, 配对重叠度。
Fig. 9 Effects of the level of matrix randomness on the standardized effect size of nestedness. Square, symmetric networks with rows and columns 30/30; Asy, asymmetric networks with rows and columns 5/55. Randomness, percentage of disordered cells deviates from fully nested matrix. Data contains a total of 4 850 matrices, with 4:100 (%) random-ness degree 97 levels of, 50 repetitions per level. NT, matrix temperature; NODF, nestedness metric based on overlap and decreasing fill.
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