R语言 selectiongain包 syncsa()函数中文帮助文档(中英文对照)

loveR · 发表于 2012-9-30 00:27:59

syncsa(selectiongain)
syncsa()所属R语言包：selectiongain

 Syncsa
 Syncsa

 译者：生物统计家园网机器人LoveR

描述----------Description----------

This function integrates several steps for the analysis of phylogenetic assembly patterns and their links to traits and ecological processes in a metacommunity (Pillar et al. 2009, Pillar & Duarte 2010). It requires data organized into the following matrices: (1) the presences or abundances of species in a set of communities (W); (2) the phylogenetic pairwise dissimilarities of these species (DF, in the range 0 to 1); (3) a set of functional traits describing the species (B), which may be a mixture of binary and quantitative traits (ordinal, interval, ratio scales), but not nominal ones (these should be expanded into binary traits); and (4) the ecological gradient of interest, which may be one or more factors to which the communities respond or ecosystem effects of the communities (E). The function computes several matrix correlations that express trait-convergence assembly patterns (TCAP), trait-divergence asssembly patterns (TDAP), and phylogenetic signal in functional traits at the species poool level and at the metacomunity level. This function also generates P-values by permutation testing based on null models (Pillar et al. 2009, Pillar & Duarte 2010).
该功能集成了多种步骤的分析，的系统发育装配模式及其链接性状和生态过程的集合群落（柱等人，2009年，支柱和杜阿尔特2010年）。它需要的数据组织成如下矩阵：（1）存在或物种的丰度在社区一组（ W ），（2）这些物种的亲缘两两相异的（ DF ，范围在0到1），（3）一组的功能特征描述的物种（ B ），这可能是一个混合的二进制和数量性状（顺序，间隔，比率秤），但不是标称的（这些应扩大成二进制的特征）和（4）生态梯度的兴趣，这可能是一个或多个因素，社区响应的社区或生态系统的影响（Ë ）。函数计算数矩阵的相关性，表达的特质收敛装配模式（TCAP），的特质的分歧asssembly模式（TDAP），和功能性状的品种poool水平和在metacomunity的的系统发育信号。此功能还可以生成P-值基于空模型（柱等人，2009年，支柱和杜阿尔特2010）的排列测试。

The function implement methods that have been available in the SYNCSA application written in C++ (by Val茅rio Pillar, available at http://ecoqua.ecologia.ufrgs.br/ecoqua/SYNCSA.html).
功能实现的方法，已经在SYNCSA编写的应用程序C + +（瓦莱里奥支柱，可在http://ecoqua.ecologia.ufrgs.br/ecoqua/SYNCSA.html），。

用法----------Usage----------

syncsa(comm, traits, dist.spp, envir, method = "pearson", dist = "euclidean", scale = TRUE, scale.envir = TRUE, permutations = 999)

参数----------Arguments----------

参数：comm
Community data, with species as columns and sampling units as rows. This matrix can contain either presence/absence or abundance data.
社区数据，列和行的抽样单位的物种。这个矩阵可以包含存在/不存在或丰度数据。

参数：traits
Matrix data of species described by traits, with traits as columns and species as rows.
矩阵数据的物种所描述的特征，列和行的物种性状。

参数：dist.spp
Matrix containing phylogenetic distance between species. Must be a complete matrix (not a half diagonal matrix).
基质中含有亲缘物种之间的距离。必须是完整的矩阵（不是一个半对角矩阵）。

参数：envir
Environmental variables for each community, with variables as columns and sampling units as rows.
每个社区的环境变量，变量的列和行的抽样单位。

参数：method
Correlation method, as accepted by cor: "pearson", "spearman" or "kendall".
相关方法，接受相应：“培生”，“长枪兵”或“肯德尔”。

参数：dist
Dissimilarity index, as accepted by vegdist: "manhattan", "euclidean", "canberra", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup" , "binomial" or "chao". However, some of these will not make sense in this case.
相异指数，接受vegdist：“曼哈顿”，“欧几里得”，“堪培拉”，“布雷”中，“kulczynski”，“杰卡德”，“高尔”中，“altGower”，“ morisita“，”角“，”芒福德“，”劳普“，”二项式“或”炒“。然而，其中一些将不会使在这种情况下，感。

参数：scale
Logical argument (TRUE or FALSE) to specify if the traits are measured on different scales (Default Scale = TRUE). Scale = TRUE if traits are measured on different scales, the matrix T is subjected to standardization within each trait. Scale = FALSE if traits are measured on the same scale, the matrix T is not subjected to standardization. Furthermore, if Scale = TRUE the matrix of traits is subjected to standardization within each trait, and Gower Index is used to calculate the degree of belonging to the species, and if Scale = FALSE the matrix of traits is not subjected to standardization, and Euclidean distance is calculated to determine the degree of belonging to the species.
逻辑参数（TRUE或FALSE）指定的特性测量不同尺度上（默认的比例= TRUE）。规模= TRUE，如果性状在不同的尺度衡量，矩阵T的标准化在每个特征。规模= FALSE，如果性状测量在相同的规模，矩阵T是不会受到标准化。此外，如果量程= TRUE性状的矩阵内各性状进行标准化，高尔指数是用来计算属于该物种的程度，并且如果量程= FALSE性状的矩阵不进行标准化，并欧几里德距离被计算，以确定属于该物种的程度。

参数：scale.envir
Logical argument (TRUE or FALSE) to specify if the environmental variables are measured on different scales (Default Scale = TRUE). If the enviromental variables are measured on different scales, the matrix is subjected to centralization and standardization within each variable.
逻辑参数（TRUE或FALSE），到指定的环境变量都在不同尺度上（默认的比例= TRUE）。如果对环境之变量在不同尺度计量，每个变量矩阵内进行集中化和标准化。

参数：permutations
Number of permutations in assessing significance.
号码的排列在评估的意义。

Details

详细信息----------Details----------

ro(TE)
 RO（TE）

This matrix correlation refers to trait-convergence assembly patterns related to the ecological gradient (TCAP, Pillar et al. 2009). For evaluating TCAP, by matrix multiplication we define T = B鈥橶, which with previous standardization of W to unit column totals will contain the trait averages in each community. The elements in T are community weighted mean values or community functional parameters (Violle et al. 2007). Standardization of the traits (rows) in T is needed if the trait set contains traits measured with different scales. By using matrix correlation, we evaluate how the trait patterns in T are associated to ecological gradients in E. For relating T to E, we define a distance matrix of the communities (DT) using T, and another distance matrix of the community sites (DE) using E. The matrix correlation ro(TE) = ro(DT;DE) measures the level of congruence between TCAP and E. A strong correlation ro(TE) indicates the factors directly or indirectly represented in E are involved in ecological filtering of species that, at least for the traits considered in the analysis, consistently produce trait-convergence assembly patterns along the gradient comprising the metacommunity.
此矩阵的相关性是指收敛特征的组装模式相关的生态梯度（TCAP，柱等，2009）。为了评估TCAP，矩阵乘法的定义 T = BW ，这与以前的标准化 W 单位列总计将包含性状平均每个社区。中的元素 T 是社会的加权平均值或社会功能参数（Violle等，2007）。标准化的特征（行） T 如果需要的特征集包含测量不同尺度的特征。通过使用矩阵的相关性，我们评估的特征模式 T 相关联的生态梯度 E 。有关 T 到 E ，我们定义了一个距离矩阵的社区（ DT ） T ，另一个距离矩阵的社区网站（ DE ）使用 E 。矩阵相关RO（ TE ）= RO（ DT DE>）测量水平的一致性TCAP和之间E 。很强的相关性RO（ TE ）的因素，直接或间接代表 E 参与生态物种过滤，至少为特征的分析认为，一贯沿渐变的特质收敛组件模式组成的集合群落。

ro(XE) and ro(XE.T)
 RO（XE）和ro（XE.T）的

These matrix correlations refer to trait-divergence assembly patterns related to the ecological gradient (TDAP, Pillar et al. 2009). For the identification of TDAP, in a first step the species pairwise similarities (in the range 0 to 1) in matrix SB based on traits in B are used to define matrix U with degrees of belonging of species to fuzzy sets. By matrix multiplication X = U鈥橶 will contain the species composition of the communities after fuzzy-weighting by their trait similarities (each row in X will refer to a species). Matrix X expresses both TCAP and TDAP (Pillar et al. 2009). By using matrix correlation, we evaluate how the trait patterns in X (TCAP and TDAP) are associated to ecological gradients in E. For relating X to E, we define a distance matrix of the communities (DX) using X, and another distance matrix of the community sites (DE) using E. The matrix correlation ro(XE) = ro(DX;DE) between X and E is defined. We then remove the trait-convergence component ro(TE) from ro(XE) by computing the partial matrix (Mantel) correlation ro(XE.T), which measures the level of congruence between TDAP and E. Trait-divergence assembly patterns (TDAP, Pillar et al. 2009) may result from community assembly processes related to biotic interactions (Stubbs & Wilson 2004; Wilson 2007).
这些矩阵的相关性是指性状分歧组件的相关模式的生态梯度（TDAP，柱等，2009）。 TDAP，在第一个步骤识别的物种成对的相似（范围0~1）矩阵 SB 基于性状的乙是用来定义矩阵 U 有度的物种，属于模糊集。通过矩阵乘法 X = UW 模糊加权后的社区包含的物种组成，其特征相似（中的每一行 X 是指一个物种）。矩阵 X 表示TCAP和TDAP（柱等人，2009）。通过使用矩阵的相关性，我们评估的特征模式 X （TCAP和TDAP）相关联的生态梯度 E 。有关 X 到 E ，我们定义了一个距离矩阵的社区（ DX ），使用 X ，另一个距离矩阵的社区网站（ DE ）使用 E 。矩阵相关RO（ XE ）= RO（ DX DE>）之间 X 和 E 的定义。然后，我们删除的特质收敛成分RO（ TE ）滚装（ XE ）的计算的部分矩阵（曼特尔）相关RO（ XE.T，），测量的TDAP及 E 的水平之间的一致性。特质发散组件模式（TDAP，柱等人，2009），可能会导致从社会装配过程相关的生物相互作用（拔威尔逊2004年威尔逊2007）。

ro(PE)
 RO（PE）

This matrix correlation refers to the phylogenetic structure related to the ecological gradient comprising the metacommunity. The phylogenetic pairwise dissimilarities in DF are transformed into similarities and used to define degrees of belonging qij to fuzzy sets. This is analogous to the definition of functional fuzzy sets (Pillar & Orl贸ci 1991; Pillar et al. 2009). Based on the phylogenetic similarities, every species i among s species in the metacommunity specifies a fuzzy set to which every species j (j = 1 to s species, including species i) belongs with a certain degree of belonging in the interval [0, 1]. In our definition, each row in matrix Q with the degrees of belonging must add to unit, i.e., the degrees of belonging of a given species across the fuzzy sets are standardized to unit total. By matrix multiplication P = Q'W will contain the composition of the communities after fuzzy-weighting of species presences or abundances by the species' phylogenetic similarities. Each column in matrix P holds the phylogenetic structure of a community. The standardization of Q is essential for the community totals in each column in W remaining the same in P. Further, matrix W is adjusted to unit column totals prior to multiplication, so that the total richness or abundance within each community in W will be standardized. Matrix correlation ro(PE) = ro(DP;DE) measures the strength of the association between community distances based on their phylogenetic structure in DP and distances based on their ecological conditions (DE). Further, P can be explored for phylogenetic patterns at the metacommunity level by using, e.g., ordination techniques.
这个矩阵的相关性是指包括集合群落的生态梯度的谱系结构。的亲缘两两相异的 DF 转化为的相似之处和使用的Qij属于模糊集定义度。这是类似的功能定义模糊集（支柱和Orlóci的1991年，支柱等，2009）。我在集合群落的物种之间的系统发育上的相似性，每一个物种基于指定一个模糊集合，每个物种j（j = 1至i）属于具有一定程度的属于在区间[0，1的种类，包括物种。在我们的定义，矩阵的程度属于Q 中的每一行必须添加到单位，也就是说，属于一个特定物种的模糊集合之间的度是标准化的单元总数。通过矩阵乘法 P = QW 将包含组成的社区模糊加权后的物种存在或丰度物种的系统发育的相似性。一个社会中的每一列矩阵 P 持有的谱系结构。标准化是必不可少的社区总数的 Q 在每列 W 保持不变 P 。此外，矩阵 W。调整为单位列总计乘法之前，因此，总在每个社区内的丰富度和丰富 W 将被标准化。相关矩阵（RO PE ）= RO（ DP DE ）测量社会的距离，根据他们的谱系结构之间的关联强度 DP 和距离的基础上的生态条件（ DE ）。此外， P 在集合群落的进化模式可以探索使用，例如，协调技术。

ro(PT) and ro(PX.T)
 RO（PT）和ro（PX.T）的

These matrix correlations measure phylogenetic signal at the metacommunity level related to TCAP and to TDAP. We define phylogenetic signal at the metacommunity level related to TCAP (PSMT) as the correlation between the phylogenetic structure described in matrix P and the trait-convergence structure described in matrix T. For this, a proper distance matrix (e.g. Euclidean distances) of communities (DP) is computed using P and another distance matrix of the same communities (DT) is computed using T. Then matrix correlation ro(PT) = ro(DP;DT) will measure the level of congruence between variation in P and T, which is a measure of PSMT. A strong phylogenetic signal at the metacommunity level is expected when communities that are more similar in terms of phylogenetic structure are also similar regarding their average trait values. We also define phylogenetic signal at the metacommunity level related to TDAP (PSMX.T) as the partial matrix correlation ro(PX.T) = ro(DP;DX.DT) between community distances DP computed on phylogenetic structure and community distances DX computed on species composition after fuzzy-weighting by the species鈥毭劽

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