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Decreasing diversity and plant cover, as well as increasing variability of these characteristics with increasing aridity are expected in grasslands due to climate change. These predictions were tested in perennial sand grasslands in Hungary. Two sites were chosen in different positions on an aridity gradient and two stands in each site were monitored for 9 years. Presence of plant species were recorded along 52 m long circular belt transects of 1040 units of 5 cm x 5 cm contiguous microquadrats. This sampling procedure – a version of line-intercept methods – enabled us to monitor diversity and total abundance in a sensitive, precise and non-destructive way. We found no trend but fluctuation in most community level attributes and in species composition. Contrary to fluctuations, between-site differences in diversity did not change and diversity remained lower in the more arid site during our 9-year-study. Compositional diversity performed better than species diversity because allowed us to detect vegetation changes that would have remained hidden if monitoring would be based only on the species richness. Comparing the magnitudes of fluctuations, five times higher relative interannual variability (CV%) was found for compositional diversity at the more arid site, while the relative temporal variability of total abundance and species richness did not show consistent patterns. We conclude that a 9 year-long study was too short to identify trends caused by the changing climate. However, the larger temporal variability of species combinations found in the more arid site suggests larger vulnerability and highlights the importance of non-linear dynamics during climate changes.

Decreasing diversity and plant cover, as well as increasing variability of these characteristics with increasing aridity are expected in grasslands due to climate change. These predictions were tested in perennial sand grasslands in Hungary. Two sites were chosen in different positions on an aridity gradient and two stands in each site were monitored for 9 years. Presence of plant species were recorded along 52 m long circular belt transects of 1040 units of 5 cm × 5 cm contiguous microquadrats. This sampling procedure — a version of line-intercept methods — enabled us to monitor diversity and total abundance in a sensitive, precise and non-destructive way. We found no trend but fluctuation in most community level attributes and in species composition. Contrary to fluctuations, between-site differences in diversity did not change and diversity remained lower in the more arid site during our 9-year-study. Compositional diversity performed better than species diversity because allowed us to detect vegetation changes that would have remained hidden if monitoring would be based only on the species richness. Comparing the magnitudes of fluctuations, five times higher relative interannual variability (CV%) was found for compositional diversity at the more arid site, while the relative temporal variability of total abundance and species richness did not show consistent patterns. We conclude that a 9 year-long study was too short to identify trends caused by the changing climate. However, the larger temporal variability of species combinations found in the more arid site suggests larger vulnerability and highlights the importance of non-linear dynamics during climate changes.

Long term vegetation monitoring provides valuable information on spatio-temporal patterns in plant communities that could be analysed to detect spatial relationship changes among species and to interpret dynamic tendencies and assembly rules in non-equilibrium phytocoenoses. In studies of this kind, one should take into account recent ecological theories emphasizing the scale dependence of vegetation; in particular, fine-scale spatial patterns of vegetation are important constraints in the genesis and maintenance of diversity. The information theory models of Juhász-Nagy offer an appropriate tool for describing the relationship between diversity and multispecies spatial dependence in vegetation. Diversity (florula diversity) and spatial dependence (associatum) are calculated for a series of increasing plot sizes (spatial scaling). The plot sizes at which the two coenostate descriptors reach the maximum information represent the characteristic scales that should be considered as optimal plot sizes in monitoring data collection. Moreover, this methodology enables us to study non-equilibrium dynamics and assembly rules in a more effective way. Diversity and spatial dependence are related, but the power and direction of this relationship change according to environmental characteristics, vegetation type and successional context. The demonstrated correspondence between dominant pattern-generating mechanisms and the related trajectories in abstract coenostate spaces (florula diversity and associatum maximum values), obtained by exploratory simulation studies, can improve interpretation of dynamic state and vegetation tendencies and can support a better inference about the relative role of different background mechanisms. We present some results obtained using this methodology with field data from the forest of Bialowieza National Park (Poland). In particular, we compared the herb layer spatial patterns of dynamically contiguous regeneration phases of the same phytocoenosis. Sampling was performed by recording the presence of plant species in 10 cm x 10cm contiguous microquadrats arranged in 150 m long circular transects. Field data were analysed with the same information theory methods as the ones applied to simulated data. Results show that assemblages of plant individuals are less diverse and more associated in primary than in regenerating stands, suggesting, in both situations, competitive dominance and disturbance as the main ecological mechanisms. Thus, the method was proven effective in distinguishing slightly different dynamical processes.

Long term vegetation monitoring provides valuable information on spatio-temporal patterns in plant communities that could be analysed to detect spatial relationship changes among species and to interpret dynamic tendencies and assembly rules in non-equilibrium phytocoenoses. In studies of this kind, one should take into account recent ecological theories emphasizing the scale dependence of vegetation; in particular, fine-scale spatial patterns of vegetation are important constraints in the genesis and maintenance of diversity. The information theory models of Juhász-Nagy offer an appropriate tool for describing the relationship between diversity and multispecies spatial dependence in vegetation. Diversity (florula diversity) and spatial dependence (associatum) are calculated for a series of increasing plot sizes (spatial scaling). The plot sizes at which the two coenostate descriptors reach the maximum information represent the characteristic scales that should be considered as optimal plot sizes in monitoring data collection. Moreover, this methodology enables us to study non-equilibrium dynamics and assembly rules in a more effective way. Diversity and spatial dependence are related, but the power and direction of this relationship change according to environmental characteristics, vegetation type and successional context. The demonstrated correspondence between dominant pattern-generating mechanisms and the related trajectories in abstract coenostate spaces (florula diversity and associatum maximum values), obtained by exploratory simulation studies, can improve interpretation of dynamic state and vegetation tendencies and can support a better inference about the relative role of different background mechanisms. We present some results obtained using this methodology with field data from the forest of Bialowieza National Park (Poland). In particular, we compared the herb layer spatial patterns of dynamically contiguous regeneration phases of the same phytocoenosis. Sampling was performed by recording the presence of plant species in 10 cm x 10cm contiguous microquadrats arranged in 150 m long circular transects. Field data were analysed with the same information theory methods as the ones applied to simulated data. Results show that assemblages of plant individuals are less diverse and more associated in primary than in regenerating stands, suggesting, in both situations, competitive dominance and disturbance as the main ecological mechanisms. Thus, the method was proven effective in distinguishing slightly different dynamical processes.

The scale dependence of vegetation patterns and processes is generally accepted, but few long-term studies have directly and explicitly considered scaling aspects in their sampling designs. In order to optimise data collection, particular emphasis must be given to the spatial scales at which maximum diversity, maximum heterogeneity, and maximum spatial dependence appear. The spatial pattern of the herb layer of woodland vegetation (old Quercus cerris coppice) in the central Apennines (Italy) was studied using information theory models. Inside a permanent plot, ground layers of vegetation were sampled along three circular transects. Data were analysed along a series of scales between 0.1 and 60 m. Detected patterns were tested against neutral models generated by Monte-Carlo simulations. The maximum diversity of species combinations was found between 1 and 4 m, while the maximum heterogeneity and the highest degree of spatial dependence of populations appeared around 10 m. Complete randomisation detected much smaller scales for randomised communities with the same abundances of species, suggesting significant aggregations of individuals. However, no significant associations between patches of species were found by applying random shift algorithms. Our results suggest that vegetation is a multiscale phenomenon i.e. there is no single optimum plot size for permanent plot studies.