Constrained multivariate analysis is a common tool for linking ecological communities to environment. The follow-up is the development of the double-constrained correspondence analysis (dc-CA), integrating traits as species-related predictors. Further, methods have been proposed to integrate information on phylogenetic relationships and space variability. We expand this framework, proposing a dc-CA-based algorithm for decomposing variation in community structure and testing the simple and conditional effects of four sets of predictors: environment characteristics and space configuration as predictors related to sites, while traits and niche (dis)similarities as species-related predictors. In our approach, ecological niches differ from traits in that the latter are distinguished by and characterize the individual level, while niches are measured on the species level, and when compared, they are characteristics of communities and should be used as separate predictors. The novelties of this approach are the introduction of new niche parameters, niche dissimilarities, synthetic niche-based diversity which we related to environmental features, the development of an algorithm for the full variation decomposition and testing of the community–environment–niche–traits–space (CENTS) space by dc-CAs with and without covariates, and new types of diagrams for the results. Applying these methods to a dataset on freshwater mollusks, we learned that niche predictors may be as important as traits in explaining community structure and are not redundant, overweighting the environmental and spatial predictors. Our algorithm opens new pathways for developing integrative methods linking life, environment, and other predictors, both in theoretical and practical applications, including assessment of human impact on habitats and ecological systems.

约束多元分析是将生态群落与环境联系起来的常用工具。后续是双约束对应分析（dc-CA）的发展，将性状整合为与物种相关的预测因子。此外，已经提出了整合系统发育关系和空间变异性信息的方法。我们扩展了这个框架，提出了一种基于 dc-CA 的算法，用于分解群落结构的变化，并测试四组预测变量的简单和条件效应：环境特征和空间配置作为与站点相关的预测变量，而特征和生态位 (dis)作为与物种相关的预测因子的相似性。在我们的方法中，生态位与特征不同，后者由个体水平区分和表征，虽然生态位是在物种水平上衡量的，但在比较时，它们是群落的特征，应用作单独的预测指标。这种方法的新颖之处在于引入了新的生态位参数、生态位差异、我们与环境特征相关的基于生态位的合成多样性、开发用于完全变异分解和测试群落-环境-生态位-特征的算法-带有和不带有协变量的 dc-CA 的空间 (CENTS) 空间，以及用于结果的新型图表。将这些方法应用于淡水软体动物的数据集，我们了解到生态位预测因子在解释群落结构方面可能与特征一样重要，并且不是多余的，过度重视环境和空间预测因子。