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Beta diversity, species replacement and nestedness are often examined through pairwise comparisons of sites based on presence-absence data, and the relative importance of these ecological phenomena is evaluated by operations with dissimilarity coefficients. An example is the nestedness resultant dissimilarity (NRD) procedure recently proposed by Baselga (2010, Global Ecology andBiogeography 19: 134–143) to disentangle the nestedness fraction of beta diversity from species replacement. In our view, the component terms in this measure are not scaled uniformly and the nestedness fraction cannot be quantified properly without giving clear definitions for its measurement. We suggest to distinguish among three additive fractions of the species set of two sites: number of species shared (overlap), species replacement (=spatial turnover) and richness difference. Then, absolute beta diversity is obtained as a composite of the second two fractions (known as βWB), while nestedness is derived from the first and the third. To express beta diversity and nestedness in a relativized form, the respective sums are divided by the total number of species. These allow defining a new index to measure the fraction of beta diversity which is shared by nestedness as well, and is calculated as relativized richness difference with the condition that the two sites being compared have at least one species in common. It is called diversity-nestedness intersection coefficient (F). Baselga’s nestedness resultant dissimilarity and the diversity-nestedness intersection coefficient are compared graphically using artificial and actual examples. These functions follow a mathematical relationship for perfectly nested data, otherwise their results are divergent. Discrepancy increases when beta diversity is large, especially if richness differences override species replacement effects in shaping presence-absence data structures. An advantage of F is its compatibility with a general theoretical and methodological framework for revealing pattern in presence-absence data matrices.
The paper advocates a more extensive use of additive trees in community ecology. When the distance/dissimilarity coefficient is selected carefully, these trees can illuminate structural aspects that are not obvious otherwise. In particular, starting from squared distances based on presence/absence data, the resulting trees approximate relationships in species richness, a feature not available through other graphical techniques. The construction of additive trees is illustrated by three actual examples, representing different circumstances in the analysis of grassland community data.
The paper evaluates spatial autocorrelation structure in grassland vegetation at the community level. We address the following main issues: 1) How quadrat size affects the measurement of spatial autocorrelation for presence/ absence and cover data? 2) What is the relationship between spatial autocorrelation and classification? 3) Is there a temporal change of spatial autocorrelation in the vegetation studied? We found that multivariate variogram shape, variance explained and the sill are different for presence/absence data and cover data, whereas quadrat size increases apparently introduce a stabilizing effect for both. Spatial stationarity is detected for species presence, and non-stationarity for cover. A new graphical tool, the clusterogram is introduced to examine spatial dependence of classification at various numbers of clusters. We found that spatial autocorrelation plays a crucial role in the classification of vegetation and therefore we suggest that its effect should not be removed from clustering. Mutual interpretation of variogram and clusterogram shape may be informative on the number of meaningful clusters present in the data. Spatial autocorrelation structure did not change markedly after 23 years for presence/absence data, indicating that the vegetation of the study area is stationary in time as well. The present study demonstrates that traditional quadrat data are suitable for evaluating spatial autocorrelation, even though field coordinates are recorded several years after sampling is completed.
Two simulated coenoclines and a real data set were differently recoded with respect to the Braun-Blanquet coding (including presence/absence) and analysed through the most common multidimensional scaling methods. This way, we aim at contributing to the debate concerning the nature of the Braun-Blanquet coding and the consequent multidimensional scaling methods to be used. Procrustes, Pearson, and Spearman correlation matrices were computed to compare the resulting sets of coordinates and synthesized through their Principal Component Analyses (PCA). In general, both Procrustes and Pearson correlations showed high coherence of the obtained results, whereas Spearman correlation values were much lower. This proves that the main sources of variation are similarly identified by most of used methods/transformations, whereas less agreement results on the continuous variations along the detected gradients. The conclusion is that Correspondence Analysis on presence/absence data seems the most appropriate method to use. Indeed, presence/absence data are not affected by species cover estimation error and Simple Correspondence Analysis performs really well with this coding. As alternative, Multiple Correlation Analysis provides interesting information on the species distribution while showing a pattern of relevés very similar to that issued by PCA.
Alpine grasslands harbour species-rich communities of plants and invertebrates. We examined how environmental variables and anthropogenic impact shape species richness and community structure of terrestrial gastropods in alpine grasslands in the Val Müstair (Eastern Alps, Switzerland). Gastropods were sampled using a standardised method at 76 sites spanning an elevation range from 1430 m to 2770 m. A total of 4763 specimens representing 52 species were recorded. Correspondence analysis based on presence/absence data revealed that the grassland gastropod community was structured in a complex way with elevation, wetness, grazing intensity and inclination of the sites as key factors, while abundance-based analysis identified the importance of the elevation and wetness of sites. Generalized linear model showed that species richness decreased with increasing elevation and increased with increasing soil pH. The grassland gastropod communities were characterized by a high beta diversity, as indicated by the SDR-simplex analysis. Species-specific traits of gastropods showed sensitivity to the environmental characters of the sites, as shown by a fourth-corner analysis.
The behavioral basis for habitat selection has been intensively studied, but comparatively little attention has been paid to how the resultant species assemblages are formed or affected. Further, how habitat quality interacts with behavior during habitat selection needs greater exploration. We sought to identify some of the behavioral interactions influencing the development of bird assemblages in agricultural habitats, which we consider a structurally simple model system. We performed point counts in non-cultivated meadows, intensive agriculture, and non-intensive agriculture areas in the 2011 and 2012 breeding seasons in which we particularly focussed on Bobolink (Dolichonyx oryzivorus), Eastern Meadowlark (Sturnella magna), Field Sparrow (Spizella pusilla), Grasshopper Sparrow (Ammodramus savannarum), Savannah Sparrow (Passerculus sandwichensis), and Vesper Sparrow (Pooecetus gramineus). Using presence-absence matrices and EcoSim software on 2011 census data, we determined where competition was likely to occur, and which species were competing. In 2012, we experimentally tested these relationships by introducing artificial competitors onto sites. We implemented a before-after control-impact study by comparing presence-absence data from 2011 to 2012 and using multinomial logistic regression. We found grassland bird assemblages are structured by interspecific competition or attraction. The experimental introduction of Grasshopper Sparrows resulted in several presence/absence changes, which differed based on habitat quality, by conspecifics and four heterospecifics (especially Bobolinks). We speculate that the response to competitors is actually determined by the relative quality of each habitat type for each species.
Bryophyte vegetation on volcanic rock outcrops and dead wood is studied in a near-natural montane beech stand in northern Hungary. Substrate specificity of the species and the existing interspecific relationships are described. The most important species combinations and their diversity are evaluated using information theoretical functions and Monte-Carlo simulations. All analyses are based on presence/absence data of 33 species in 1508 100 cm2 microplots. Most species exhibit strong substrate specificity. Of the species that occurred with frequencies higher than 10, 8 are associated to rock, 5 to dead wood and 5 to both substrate types. Analyses of interspecific associations and agglomerative classification reveal that frequent species of species-poor bare rocks are separated from species-rich assemblages of humus-rich outcrops and coarse woody debris. Monte Carlo simulations reveal that many species combinations are significantly more frequent than expected under the assumption of random combining of species. Observed number, diversity and evenness of species combinations are significantly lower, whereas interspecific constraint (expressed as associatum) is significantly higher than under the neutral models even when data are stratified according to substrate type. The presence of coarse woody debris, not only provides habitat for wood inhabiting bryophytes, but also results in diverse rupicolous bryophyte assemblages on humus-rich outcrops.
Fish assemblages along marine artificial reefs have been the objects of numerous studies. Most of them distinguish resident from transient species according to their habitat association level. Despite its wide use, this distinction presents practical complications for two main reasons: first, the method used for assessing habitat association level may cause mistakes, and second, no objective method has been proposed thus far to determine the habitat association level that should be considered as a boundary between resident and transient species. This paper aims at overcoming these two problems. In order to improve and standardize assessment of habitat association level, we developed a habitat association index (HAI) whose calculation requires only presence-absence data. By taking into account both species occurrence rate and occurrence patterns, our index minimizes the risk of erroneously perceiving species as being equally associated to a habitat. In order to distinguish between resident and transient species objectively, we propose to plot HAI against occurrence rate and then seek the combination providing the most significantly different groups with reference to the relation between these two variables. Using two different datasets collected along Japanese artificial reefs and comparing the results with the ones obtained through an alternate method consisting of plotting persistence against maximum abundance and looking for a major gap along the persistence axis, we demonstrate the effectiveness of this two-step method.
A detailed analysis of the relationship between woody types and environmental variables (pedological and topographical) was carried out inside the city of Rome. Twenty-three sample sites 100 m2 each were selected according to the principle that they were inside woody vegetation patches greater than 2.3 ha. Presence-absence data were analysed through hierarchical classification and principal coordinates analysis in order to detect woody vegetation types. The six groups identified were then analysed according to thirty-four variables: a spatial discriminant analysis was performed using soil physical and chemical variables measured in the A1 and A2 horizons, topographical variables (altitude, slope and aspect), and annual potential irradiation. This procedure was able to quantify the contribution of the spatial distribution of the samples with respect to that of the environmental variables, thus improving the discriminant model. The combination of three variables: aspect, organic matter A2 and exchangeable cations A2 is the most effective in discriminating the woody types allowing a hypothesis for the planning and management of these communities.
A detailed analysis of the relationship between woody types and environmental variables (pedological and topographical) was carried out inside the city of Rome. Twenty-three sample sites 100 m2 each were selected according to the principle that they were inside woody vegetation patches greater than 2.3 ha. Presence-absence data were analysed through hierarchical classification and principal coordinates analysis in order to detect woody vegetation types. The six groups identified were then analysed according to thirty-four variables: a spatial discriminant analysis was performed using soil physical and chemical variables measured in the A1 and A2 horizons, topographical variables (altitude, slope and aspect), and annual potential irradiation. This procedure was able to quantify the contribution of the spatial distribution of the samples with respect to that of the environmental variables, thus improving the discriminant model. The combination of three variables: aspect, organic matter A2 and exchangeable cations A2 is the most effective in discriminating the woody types allowing a hypothesis for the planning and management of these communities.
