Detecting indicator species: Some extensions of the IndVal measure

Macrobenthic community for assessment of estuarine health in tropical areas (Northeast, Brazil): Review of macrofauna classification in ecological groups and application of AZTI Marine Biotic Index

“…Still, the high demand for a relatively simple and easily interpretable coefficient had led to the wide application of the IndVal method in the marine sciences, ranging from bacteria and diatoms (Soininen et al, 2004;Wetzel et al, 2002) to macroinvertebrates (Podani and Csányi, 2010;Yeung and McConnaughey, 2006) and fish (Mouillot et al, 2002). Despite the limitations of this coefficient added to the number of sites used in the present study, significant indicator values enabled the objective re-assignment of important taxa for tropical macrobenthic communities (nematodes, Streblospio sp.…”

Building relevant ecological indicators with basic data: Species and community specialization indices derived from atlas data

“…In practice, however, large-scale monitoring data for national or regional surveys are currently being collected for only a few groups (mostly birds, butterflies, and mammals) and are based on presence-absence data. Moreover, measuring species specialization is often impaired by the lack of high enough resolution data on habitat requirements or by the difficulty of defining habitat selection accurately (Podani and Csányi, 2010). Consequently, two main approaches have been used to quantify change in community composition following landscape disturbance: (i) at global or national scales, some authors have used crude classifications of species into specialist versus generalist groups.…”

Toward a framework of integrated knowledge of terrestrial vegetation system: The role of databases of phytosociological relevés

“…For each syntaxon, diagnostic species are defined with the same criteria used for defining association, namely constancy and fidelity. Measures of constancy and fidelity can be defined on the basis of many statistical criteria that today are applied and discussed under the term of indicator species analysis, using only binary data, or using cover data and/or both of them (Podani & Csányi 2010;Terzi et al 2010 and reference therein). In this respect, we think that methods based on probability theory such as information theory and/or on Bayes's theorem and, alternatively, methods based on fuzzy set theory should be further explored for finding diagnostic species and/or diagnostic species groups corresponding to the four hierarchical levels of the BB syntaxonomy.…”