Too good to be true: pitfalls of usingmean Ellenberg indicator values in vegetation analyses

Question: Mean Ellenberg indicator values (EIVs) inherit information about compositional similarity, because during their calculation species abundances (or presence–absences) are used as weights. Can this similarity issue actually be demonstrated, does it bias results of vegetation analyses correlating mean EIVs with other aspects of species composition and how often are biased studies published? Methods: In order to separate information on compositional similarity possibly present in mean EIVs, a new variable was introduced, calculated as a weighted average of randomized species EIVs. The performance of these mean randomized EIVs was compared with that of the mean real EIVs on the one hand and random values (randomized mean EIVs) on the other. To demonstrate the similarity issue, differences between samples were correlated with dissimilarity matrices based on various indices. Next, the three mean EIV variables were tested in canonical correspondence analysis (CCA), detrended correspondence analysis (DCA), analysis of variance (ANOVA) between vegetation clusters, and in regression on species richness. Subsequently, a modified permutation test of significance was proposed, taking the similarity issue into account. In addition, an inventory was made of studies published in the Journal of Vegetation Science and Applied Vegetation Science between 2000 and 2010 likely reporting biased results due to the similarity issue. Results: Using mean randomized EIVs, it is shown that compositional similarity is inherited into mean EIVs and most resembles the inter-sample distances in correspondence analysis, which itself is based on iterative weighted averaging. The use of mean EIVs produced biased results in all four analysis types examined: unrealistic (too high) explained variances in CCA, too many significant correlations with ordination axes in DCA, too many significant differences between cluster analysis groups and too high coefficients of determination in regressions on species richness. Modified permutation tests provided ecologically better interpretable results. From 95 studies using Ellenberg indicator values, 36 reported potentially biased results. Conclusions: No statistical inferences should bemade in analyses relatingmean EIVs with other variables derived from the species composition as this can produce highly biased results, leading to misinterpretation. Alternatively, a modified permutation test using mean randomized EIVs can sometimes be used

Effectafstand van stikstof uit verkeersemissies op de vegetatie - een inventarisatie van de literatuur

Stikstof afkomstig van het wegverkeer kan in natuurgebieden leiden tot een afname van de natuurkwaliteit en kan het behalen van de natuurdoelen in Natura 2000-gebieden in de weg staan. Het is echter niet eenvoudig om op één bepaalde plaats vast te stellen welk deel van de stikstofbelasting aan het wegverkeer op een nabij gelegen weg toe te schrijven is en welk deel aan andere bronnen. Als het gaat om daadwerkelijk aangetoonde effecten is echter maar weinig kennis aanwezig. Daarom is vanuit Rijkswaterstaat de volgende vraag gedefinieerd: Welke onderzoeksgegevens bestaan er m.b.t. dosis - effect relaties van stikstof (NH3 en NOx) geproduceerd door het verkeer, op langs de weg gelegen natuurgebieden. Daarbij staat vooral de vraag centraal tot op welke afstand deze effecten zich nog doen gelden

Co-correspondence analysis: a new ordination method to relate two community compositions

A new ordination method, called co-correspondence analysis, is developed to relate two types of communities (e.g., a plant community and an animal community) sampled at a common set of sites in a direct way. The method improves the simple, indirect approach of applying correspondence analysis (reciprocal averaging) to the separate species data sets and correlating the resulting ordination axes. Co-correspondence analysis maximizes the weighted covariance between weighted averaged species scores of one community and weighted averaged species scores of the other community. It thus attempts to identify the patterns that are common to both communities. Both a symmetric descriptive and an asymmetric predictive form are developed. The symmetric form relates to co-inertia analysis and the asymmetric, predictive form to partial least-squares regression. In two examples the predictive power of co-correspondence analysis is compared with that of canonical correspondence analyses on syntaxonomic and environmental data. In the first example, carabid beetles in roadside verges are shown to be more closely related to plant species composition than to vegetation structure (biomass, height, roughness, among others), and, in the second example, bryophytes in spring meadows are shown to be more closely related to the species composition of the vascular plants than to the measured water chemistry

Wegberm biedt hulp tegen bestuivingscrisis

De achteruitgang van bloemzoekende insecten is een bedrieging voor een scala aan ecologische processen en diensten die deze dieren verzorgen. Wegbermen zijn vaak rijk aan bloeiende kruiden en kunnen daardoor van groot belang zijn voor deze dieren. Maar hoe kunnen deze bermen het beste beheerd worden? Wageningen Universiteit deed een experiment in een grazige berm, waarbij bloembezoek bekeken werd in relatie to vijf maairegime