Aim: To propose a modification of the TWINSPAN algorithm that enables production of divisive classifications that better respect the structure of the data. Methods: The proposed modification combines the classical TWINSPAN algorithm with analysis of heterogeneity of the clusters prior to each division. Four different heterogeneity measures are involved: Whittaker's beta, total inertia, average Sørensen dissimilarity and average Jaccard dissimilarity. Their performance was evaluated using empirical vegetation datasets with different numbers of plots and different levels of heterogeneity. Results: While the classical TWINSPAN algorithm divides each cluster coming from the previous division step, the modified algorithm divides only the most heterogeneous cluster in each step. The four tested heterogeneity measures may produce identical or very similar results. However, average Jaccard and Sørensen dissimilarities may reach extreme values in clusters of small size and may produce classifications with a highly unbalanced cluster size. Conclusions: The proposed modification does not alter the logic of the TWINSPAN classification, but it may change the hierarchy of divisions in the final classification. Thus, unsubstantiated divisions of homogeneous clusters are prevented, and classifications with any number of terminal clusters can be created, which increases the flexibility of TWINSPAN.
Aim The development of metacommunity theory inspired a series of studies exploring the importance of environmental and spatial effects on the composition of biotic assemblages. However, the comparison of different groups of organisms has been hampered by differences in sampling design, spatial scales or the environmental variables involved. Our aim was to test how dispersal ability affects metacommunity structure and associated species distributions by sampling different species groups in the same plots to avoid these problems. Location Western Carpathian Mountains (Europe).Methods In 191 fens we sampled the composition of diatom, bryophyte, vascular plant and mollusc assemblages, water chemistry, and macroclimatic data. We then generated spatial variables covering all relevant spatial scales using analysis of principal coordinates of neighbour matrices (PCNM). We applied the adjusted variation partitioning algorithm to quantify the effects of environment and space.Results Pure effects of water chemistry and space were highly significant for all groups of organisms. Spatial effects were stronger for groups with larger propagules (vascular plants, molluscs) than for those with smaller propagules (diatoms, bryophytes). Assemblages of macroscopic bryophytes were structured slightly less by geography and much more by environment than were those of microscopic diatoms. Vascular plant and mollusc assemblages turned out to be more spatially structured (as compared to diatom and bryophyte assemblages), with small differences between the two groups. Coarse-scale spatial effects dominated in the bryophyte metacommunity, while in the other groups, including diatoms, finer-scale effects were also important.Main conclusions Given that our analyses are based on a standardized sampling and analytical framework, our findings provide strong support for the hypothesis that both environmental and spatial variables structure metacommunities of organisms with very different dispersal abilities, including microscopic diatoms. In addition, we show for the first time that the strengths of these effects and their scale dependence may be predicted using important trait differences between organisms, for example differences in propagule size.
Aims Classification of vegetation is an essential tool to describe, understand, predict and manage biodiversity. Given the multiplicity of approaches to classify vegetation, it is important to develop international consensus around a set of general guidelines and purpose‐specific standard protocols. Before these goals can be achieved, however, it is necessary to identify and understand the different choices that are made during the process of classifying vegetation. This paper presents a framework to facilitate comparisons between broad‐scale plot‐based classification approaches. Results Our framework is based on the distinction of four structural elements (plot record, vegetation type, consistent classification section and classification system) and two procedural elements (classification protocol and classification approach). For each element we describe essential properties that can be used for comparisons. We also review alternative choices regarding critical decisions of classification approaches; with a special focus on the procedures used to define vegetation types from plot records. We illustrate our comparative framework by applying it to different broad‐scale classification approaches. Conclusions Our framework will be useful for understanding and comparing plot‐based vegetation classification approaches, as well as for integrating classification systems and their sections.
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