Spatial pattern of the vegetation, due to its complexity, cannot be described by ad hoc indices. It requires a solid methodological basis, which can describe different levels of patterns (patterns of populations, coalitions and whole community) in the same framework. There are two methods, which seem to be appropriate for this purpose: Juhász-Nagy's information theory functions and log-linear contingency table analysis. This paper shows that from mathematical point of view they are close relatives. The main advantage of Juhász-Nagy's approach that it is developed to describe spatial pattern of the vegetation, therefore biological meaning of the functions is a central part of the approach. Whereas log-linear contingency table analysis is a general statistical method without any special (biological) meaning of terms. On the other hand, it is a well-known statistical method, while most of the biologists are unfamiliar in information theory. The relationship between these two approaches makes it possible to hybridise their advantages.
Whenever we make any kind of ecological study it is obvious that a sample is analysed since we are not able to measure the whole statistic population. Numerical classification in general is a useful tool to explore the structure of different kinds of ecological data, but it reflects the structure of the studied dataset (the sample). However, we are interested in the structure of the statistical population from which the sample is derived. It is possible that among the clusters gained by the classification there are some, which are representative only for the sample and not for the whole statistical population, thus these clusters can be called “artificial”. This paper describes a method that helps us to avoid the interpretation of these “artificial” clusters, which are characteristic only for the sample, not for entire population. The method is called validation, because its steps are similar to validation used in other fields of numerical analysis. In case of cluster analysis the definitive characteristics of the particular clusters are unknown. This means that it is not possible to make testable hypothesis based on the results of the cluster analysis. Therefore, the method proposed here does not compare the clusters themselves, but the “meaning” of the clusters; i.e. their characteristics that are used for the interpretation of the results. Frequency of species was chosen as “meaning” of clusters here, but using other characteristics, e.g. mean or median for continuous variables is also possible. The new methods are applied to an artificial dataset to illustrate the procedure and to show its merits.
Previous overviews of plant invasion in Hungary were based on local case studies and the authors’ experience. The MÉTA survey provided an opportunity to outline a more exact picture based on the survey of the whole country. This paper summarises the basic statistics related to plant invasion: cover of invaded area estimated for the country, each geographical region and each distinguished (semi-)natural habitat category, and cover of the selected 15 alien species in each habitat category.
This study was motivated by the fact that although the plasticity of its above-ground organs is obvious in natural conditions and there are many data on the plasticity of Solidago’s rhizome system in glasshouse experiments, there are no data on below-ground plasticity under natural conditions. We compared the morphology of rhizomes in two, contrasting habitats. We found that rhizome system responded to environmental conditions: in the dry habitat, ramets developed more but shorter rhizomes compared to the wet habitat. The decrease in rhizome length can be explained by the decrease in the size of above-ground organs, but the increase of rhizome number cannot. The most important regulating factor of rhizome growth is probably its mechanical restriction by the root biomass of other species.
In community ecology, randomization tests with problem specific test statistics (e.g., nestedness, functional diversity, etc.) are often applied. Researchers in such studies may want not only to detect the significant departure from randomness, but also to measure the effect size (i.e., the magnitude of this departure). Measuring the effect size is necessary, for instance, when the roles of different assembly forces (e.g., environmental filtering, competition) are compared among sites. The standard method is to calculate standardized effect size (SES), i.e., to compute the departure from the mean of random communities divided by their standard deviations. Standardized effect size is a useful measure if the test statistic (e.g., nestedness index, phylogenetic or functional diversity) in the random communities follows a symmetric distribution. In this paper, I would like to call attention to the fact that SES may give us misleading information if the distribution is asymmetric (skewed). For symmetric distribution median and mean values are equal (i.e., SES = 0 indicates p = 0.5). However, this condition does not hold for skewed distributions. For symmetric distributions departure from the mean shows the extremity of the value, regardless of the sign of departure, while in asymmetric distributions the same deviation can be highly probable and extremely improbable, depending on its sign. To avoid these problems, I recommend checking symmetry of null-distribution before calculating the SES value. If the distribution is skewed, I recommend either log-transformation of the test statistic, or using probit-transformed p-value as effect size measure.
The long-term protection of plant species is impossible without a comprehensive and thorough knowledge of their biology. This paper deals with some of the important aspects of the biology of two strictly protected species,
The changes of the leaf area of plants were measured with a non-destructive method and the growth pattern was investigated during the vegetation period. It was also studied how the plant size of individuals changed from year to year, and what kind of connection there was between the plant condition and the flowering. Large size plants of both species were characterised by intensive growth rates in autumn and spring, their growth stopped in winter. Often they lost some of their leaf areas because of damage from insects (mainly in the case of
, and frost (almost exclusively in the case of
). In some years the medium and small size plants were characterised by similar growth pattern to those of large size ones, but more often their growth did not stop in winter, though their growth was not so fast. The medium and small size
individuals showed an intense autumn growth only in one vegetation period, which characterised both large size individuals of
and all individuals of
The constant annual rate of growth, which may have been slowed meaning that the leaf number did not grow, but it did not stop either, was detected in hardly more than 10% of individuals with both species. In the case of
the critical size for flowering seemed to be 50 cm
, which was usually reached in the four-leaf stage of the rosettes. The same value was 100 cm
in the case of
and it needed at least a six-leaf basal rosette. The leaf number and the leaf area of reproductive plants had been larger for already two years before flowering took place, than those of the plants that remained vegetative.
Effective conservation of (semi-)natural grasslands requires an understanding of the factors affecting naturalness (i.e. the actual quality of a habitat or vegetation patch) and the importance of the particular factors. Both local or patch and landscape or matrix variables affect habitat quality, and the proportions of the effects need to be identified. Therefore, we performed a hypothesis generating and testing analysis with generalised linear models on three typical grassland habitat types (forest steppe meadows,
alkali steppes, and lowland wet meadows), differing in their fragmentation, ecology and history, and representing characteristic types of grassland habitats with the use of the national database of the vegetation of Hungary (MÉTA). Our results, in general, show that naturalness depends upon both intra-habitat and matrix attributes: presence or proportion of other habitat types in the surrounding landscape, threatening factors and landscape ecological attributes. Higher number of habitat types and higher proportions of (semi-)natural habitats in the landscape have significant effects: presence of other grassland types similar in ecological demands to the model habitat positively affect the naturalness, while non-characteristic, secondary or disturbed habitats and invasive alien species have negative effects. However, there are clear differences among the three habitat types, indicating that for effective conservation, good knowledge of conserved habitat types is essential. Landscape or matrix factors, both compositional and structural, affecting habitat patch quality have significant effects that cannot be overlooked. In the case of fragmented grasslands, matrix factors might be even more important than patch or local factors.
We studied biomass and species composition changes of open perennial sand grassland (
) as response to different levels of simulated grazing pressures. We conducted a factorial micro-plot field experiment on previously grazed grassland that has been abandoned for a long time. In a two-way factorial design of 12 treatments × 8 repeats, we performed clipping (twice a year for three years) and litter treatments (removing and adding litter once at the beginning of the experiment) to simulate components of grazing, namely the biomass removal and the reduction of the litter accumulation. We used field spectroscopy and visual canopy cover estimation to measure the effects on the amount of the above-ground green biomass and on the vegetation composition.
Phytosociological databases are important data sources for a broad scale of ecological investigations. Vegetation samples are traditionally managed and published in tabular format, allowing for handling of the vegetation data in various combinations. Such tables usually comprise relevés originated from the same locality, vegetation type and collected by the same investigator. Nevertheless, these relevés are usually affected by the same bias. In this paper, we demonstrate the importance of the effects acting at the level of the table (i.e., ‘locally’), using the example of species removals from groups of relevés. We examine the effect of the removal of infrequent species on community classification in relation with several data set properties using simulated plot data sampled from simulated coenoclines. A data set comprised groups of relevés (‘tables’), within which relevés are sampled from the same point of the coenocline. Classifications obtained after the removal or permutation of infrequent species occurrences from these tables, after the removal of rare species from randomised tables and without any treatment were compared to a reference classification based on gradient positions of the relevés. The results show that the removal of locally infrequent species helps to recognise the gradient pattern incorporated in the tabular arrangement of relevés if the arrangement of relevés among tables is in accordance with their gradient position. In cases when the grouping of relevés is irrelevant regarding the real underlying pattern, the species removal is disadvantageous. Testing between-table heterogeneity within a data set is an especially successful way of examination of biological relevance of the arrangement of relevés. We conclude that influence of table-level effects is mainly dependent on the pattern which is in accordance with the grouping of plots.
Canopy height, leaf area (LA), specific leaf area (SLA) and leaf dry matter content (LDMC) data of 210 species of the Hungarian flora resulting from our field sampling are presented in this data paper.