Simple null model analysis subsumes a new species co‐occurrence index: A comment on Mainali et al. (2022)
Simple null model analysis subsumes a new species co‐occurrence index: A comment on Mainali et al. (2022)
复制标题
简单的零模型分析包含一个新物种共现指数：对 Mainali 等人的评论。
DOI：
10.1111/jbi.14486
复制
发表时间：
2022
影响因子：
3.9
通讯作者：
Gotelli, Nicholas J.
中科院分区：
文献类型：
--
作者：
Ulrich, Werner;Sfenthourakis, Spyros;Strona, Giovanni;Gotelli, Nicholas J.
最近，Mainali et al. (2022 MSSF）提出了一种新的基于条件发生概率对数比值比α的物种成对共现指数。在假设两个物种的总出现次数在一些地点是固定的情况下，他们证明了成对出现的相关随机分布遵循非中心超几何分布，α是单个未知参数。MSSF称α的最大似然估计为两个物种的“亲和力”，并建议它可以作为物种共现的指标。请注意，在大多数生物多样性文献中，α是指当地（地点内）物种丰富度。亲和指数α以零为中心（随机关联），正指数值表明联合物种出现的数量增加。对于共享物种的网站对，它的行为类似于Jaccard的指数，与正值表示相对较大数量的共享物种（物种聚集）和负值表示相对较小数量的共享物种（物种分离）。正如MSSF所承认的那样，Veech（2013）在近十年前提出了类似的概率方法。最近，Carmona和Pärtel（2020）独立地引入了超几何分布来估计暗多样性，Zhou等人也引入了超几何分布。（2022）医学文献计量学。Arita（2016）首先指出，Veech（2013）的概率方法与Fisher的精确检验对于2× 2的物种共现发生率矩阵是相同的。重要的是，亲和力指数仅针对物种或位点对的个体对定义。但是共现数据通常被组织成一个矩阵，在多个地点（=列）记录多个物种（=行）的出现。对于这种矩阵，物种共现指数量化了β多样性水平（地点之间物种组成的差异）。目前还不清楚如何将亲和指数与这样一个矩阵一起使用，其中包含许多对物种和许多对位点。在所有可能的物种或位点对上取平均亲和力得分可能会模糊来自多个物种（和位点）相互作用的模式（Chao等人，2008年），并失去了网站之间的物种有序损失的信息（嵌套程度，巴塞尔加，2010年）。与其他成对指数（如Jaccard、Sørensen和Simpson）一样，在基于多个地点的地理和生态背景下，亲和力指数可能难以使用或解释。MSSF认为，成对共现的常见指数对出现次数的已知敏感性使这些传统指数的使用无效。他们还大胆地宣称，“共现分析的半个世纪的发展已经被失败所破坏”，这个新的指数将“解决所有上述挑战”。然而，这些笼统的主张受到三个问题的破坏：
Recently, Mainali et al.(2022, termed MSSF henceforth) proposed a new index of pair-wise species co-occurrence based on the log odds ratio α of conditional occurrence probability. Under the assumption that the total numbers of occurrences of both species are fixed in a number of sites, they demonstrate that the associated random distribution of paired occurrences follows a non-central hypergeometric distribution, with α being the single unknown parameter. MSSF call the maximum likelihood estimator of α the ‘affinity’of both species and suggest that it might serve as an index of species co-occurrence. Note that in most of the biodiversity literature, α refers to local (within-site) species richness. The affinity index α is centred around zero (random association), with positive index values indicating increased numbers of joint species occurrences. For pairs of sites that share species, it behaves similarly to Jaccard's index, with positive values indicating a relatively large number of shared species (species aggregation) and negative values indicating a relatively small number shared species (species segregation). As recognized by MSSF, an analogous probabilistic approach was proposed almost a decade ago by Veech (2013). Recently and independently, the hypergeometric distribution was introduced by Carmona and Pärtel (2020) to estimate dark diversity and by Zhou et al.(2022) in medical bibliometrics. Arita (2016) first noted that Veech's (2013) probabilistic approach is identical to Fisher's exact test for a 2× 2 matrix of species co-occurrence incidences. Importantly, the affinity index is defined only for individual pairs of species or pairs of sites. But co-occurrence data are usually organized as a matrix, with the occurrence of multiple species (= rows) recorded at multiple sites (= columns). For such matrices, indices of species co-occurrences quantify the level of β-diversity (betweensite differences in species composition). It is unclear how the affinity index would be used with such a matrix, which contains many pairs of species and many pairs of sites. Taking average affinity scores across all possible pairs of species or sites potentially obscures patterns from multiple species (and site) interactions (Chao et al., 2008) and loses information on the ordered loss of species among sites (the degree of nestedness, Baselga, 2010). Like other pairwise indices (eg Jaccard, Sørensen and Simpson), the affinity index may be difficult to use or interpret within a biogeographic and ecological context based on multiple sites.MSSF argue that the known sensitivity of common indices of pair-wise co-occurrence to the number of occurrences invalidates the use of these traditional indices. They also make the bold claim that ‘half a century of development in analyses of co-occurrence has been marred by failures’ and that this new index will ‘resolve all the aforementioned challenges’. However, these sweeping claims are undermined by three problems:
影响因子：
4.6
作者：
Chiarucci A;Fattorini S;Foggi B;Landi S;Lazzaro L;Podani J;Simberloff D
通讯作者：
Simberloff D
装配规则的空模型：杰克霍纳效应比水仙效应更阴险
DOI：
--
发表时间：
1995
期刊：
影响因子：
--
作者：
J. Wilson
通讯作者：
J. Wilson