A multi-stage cluster sampling is proposed for quantifying and monitoring plant species richness at multiple spatial grains over large spatial extents. An unbiased estimator of average species richness at different grains and a conservative estimator of its sampling variance are obtained in a complete design-based framework, i.e., avoiding any assumption about the ecological community under study. An application to the Nature Reserve “ Lago di Montepulciano ” demonstrates that the proposed strategy may accomplish practical advantages and quite satisfactory levels of accuracy.
Species richness, the location of exotic species and heterogeneity (investigated via dissimilarity and via species-area relations) were investigated in relation to spatial scale, in an ecotone between Nothofagus forest and sub-alpine shrubland. The rate of change in ordination score as well as tree diameter and the dripline were used to locate the position of the ecotone. Patterns of species richness were largely scale-independent, with species richness lowest in the forest community, intermediate in the ecotone, highest a short distance into the shrubland, and lower again in the shrubland further from the ecotone. High richness just into the shrubland is attributed to the existence of a fine-scale spatial mosaic pattern of vegetation, though the spatial mass effect may have a role. Exotic species were absent in the forest, but occurred sparsely in the ecotone and in the shrubland, possibly with decreasing frequency away from the ecotone. Community pattern, expressed by species-based dissimilarity and species-area curves, also differed. The ecotone community was the most heterogeneous (indicated by higher dissimilarity values and a steeper species-area slope with higher Arrhenius z-values), with the forest the least heterogeneous and the shrubland intermediate. We conclude that z-values are inter-woven with both habitat and spatial scale, and that this argues against a universal relationship between species and area.
Due to the difficulties of field-based species data collection at wide spatial scales, remotely sensed spectral diversity has been advocated as one of the most effective proxies of ecosystem and species diversity. It is widely accepted that the relationship between species and spectral diversity is scale dependent. However, few studies have evaluated the impacts of scale on species diversity estimates from remote sensing data. In this paper we tested the species versus spectral relationship over very large scales (extents) with a varying spatial grain using floristic data of North America. Spectral diversity explained a low amount of variance while spatial extent of the sampling units (floras) explained a high amount of variance based on results from our variance partitioning analyses. This leads to the conclusion that spectral diversity must be carefully related to species diversity, explicitly taking into account potential area effects.
Alpha, beta, and gamma diversity are three fundamental biodiversity components in ecology, but most studies focus only on the scale issues of the alpha or gamma diversity component. The beta diversity component, which incorporates both alpha and gamma diversity components, is ideal for studying scale issues of diversity. We explore the scale dependency of beta diversity and scale relationship, both theoretically as well as by application to actual data sets. Our results showed that a power law exists for beta diversity-area (spatial grain or spatial extent) relationships, and that the parameters of the power law are dependent on the grain and extent for which the data are defined. Coarse grain size generates a steeper slope (scaling exponent z) with lower values of intercept (c), while a larger extent results in a reverse trend in both parameters. We also found that, for a given grain (with varying extent) or a given extent (with varying grain) the two parameters are themselves related by power laws. These findings are important because they are the first to simultaneously relate the various components of scale and diversity in a unified manner.
. 2007. Multi-stage cluster sampling for estimating average species richness at different spatial grains. Community Ecol . 8: 119–127. Chiarucci A. Multi-stage cluster sampling for
richness at different spatial grains. Community Ecol. 8: 119–127. Chiarucci A. Multi-stage cluster sampling for estimating average species richness at different spatial grains
Baffetta, F., G. Bacaro, L. Fattorini, D. Rocchini and A. Chiarucci. 2007. Multi-stage cluster sampling for estimating average species richness at different spatial grains. Community Ecol. 8: 119