Plant community variability within potential natural vegetation units: a case study from the Bohemian Karst

Šamonil, Pavel; Polesná, K.; Unar, Pavel

doi:10.17221/111/2008-jfs

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Contributions to the literature reporting analysis of, for example, discordances among remnants and indicators are scarce (Šamonil et al. ; Muñoz & Raya ). Curiously, an inductive way to establish and characterize PNV units similar to the phytosociological tabulation procedure was proposed at the outset (Tüxen ; Rivas‐Martínez ) but it has rarely been put into practice since then, and hence has not been optimized.…”

Section: Critical Issues On Methodsmentioning

confidence: 99%

Potential natural vegetation: reburying or reboring?

Arregui

Fernández‐González

2012

J Vegetation Science

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This paper deals with the recent debate on the potential natural vegetation (PNV) concept. After reviewing its limitations with respect to understanding of the processes involved in secondary succession, spatial and temporal scaling and data processing, we still consider it a useful tool for summarizing knowledge about a territory in a way that can guide good practice in nature conservation, and for hypothesis generation. Mapping PNV has a descriptive aim and offers the possibility of depicting not only a 'natural' scenario according to the extant vegetation types and current environmental factors, but also an ecological description of the territory. It is not a commitment to build any ideal stage of nature but it can contribute to better management by providing targets for restoration and improving naturalness, ecosystem conservation and biodiversity preservation. Constant development of the concept, through discussion, improvement of methods and incorporation of new knowledge, is necessary, but we are reluctant to accept the idea of abandoning it because a part of what has been achieved to date in nature conservancy management using PNV could be lost, as arguments supporting naturalness would be weakened.… through the graves the wind is blowing … (L. Cohen)

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Section: Critical Issues On Methodsmentioning

confidence: 99%

Potential natural vegetation: reburying or reboring?

Arregui

Fernández‐González

2012

J Vegetation Science

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“…Although the EQ values in this instance range from 29-33, the 2 nd FVZ is prevalent in Hercynicum. However, due to the influence of mesoclimate, conditions for both the 1 st and 3 rd FVZ can emerge (Šamonil, Viewegh 2005;Šamonil et al 2009).…”

Section: Discussionmentioning

confidence: 99%

Assessment of the Ellenberg quotient as a practical tool for vertical vegetation zonation

Dujka¹,

Kusbach²

2023

J. For. Sci.

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The Ellenberg quotient (EQ) is a climate index defined as a ratio of the hottest month's temperature and the average annual precipitation sum. The quotient indirectly expresses the relationship between climate and vegetation, and its application is related to the ecological niche of Fagus sp. Although the quotient was curated on the grounds of field research primarily on German vegetation, the possibilities of its utilisation are not limited to the Central European region. The objective of this study is (i) to compare the EQ values calculated for the forest vegetation zones in the Czech Republic with the published data using the ecological niche of Fagus sylvatica; and (ii) to compare the new EQ-based vertical model with field empirical mapping. The study area is the Czech Republic, Central Europe. We used climate data from 1970-2000 and the data of the National Forest Inventory, 2 nd cycle (2011)(2012)(2013)(2014)(2015), representing an objective data design. Geospatial analytic methods, machine learning (boosting), and verification through statistical testing were performed. The results indicate higher EQ values between the two most substantial spatial frames -the Hercynicum and Carpaticum regions. By comparing empirical mapped units to their climatic potential (in the EQ), a match was found only within the Carpaticum region. The study presents a concretisation of the general climate index for a specific region, adds to the knowledge about the Fagus ecological niche in context with the Central European vegetation, and also points to the EQ's potential for evaluating the concept of vertical differentiation of forest communities, as well as a possible prediction tool for the vegetation migration in context with climate change.

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“…Therefore, in studies aiming at the description of maximum variation of vegetation across a landscape, stratified‐random sampling seems to be a promising alternative to preferential sampling (Austin & Heyligers 1989; Margules & Austin 1994; Bunce et al 1996). In this sampling method, a study area is stratified in advance according to different environmental variables that are thought to affect vegetation patterns, such as climate, altitude, topography, bedrock or soil types (Kenkel et al 1989; Margules & Austin 1994; Michaelsen et al 1994; Goedickemeier et al 1997; Stohlgren et al 1997b; Firbank et al 2003), according to land cover or vegetation maps (Ortega et al 2004; Acosta et al 2005; Holt et al 2008; Šamonil et al 2009) or combinations of these. After landscape stratification, random sampling is applied within each stratum.…”

Section: Introductionmentioning

confidence: 99%

Bias in vegetation databases? A comparison of stratified-random and preferential sampling

Michalcová

Lvončík

Chytrý

et al. 2011

Journal of Vegetation Science

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Aim: Vegetation plots collected since the early 20th century and stored in large vegetation databases are an important source of ecological information. These databases are used for analyses of vegetation diversity and estimation of vegetation parameters, however such analyses can be biased due to preferential sampling of the original data. In contrast, modern vegetation survey increasingly uses stratified‐random instead of preferential sampling. To explore how these two sampling schemes affect vegetation analyses, we compare parameters of vegetation diversity based on preferentially sampled plots from a large vegetation database with those based on stratified‐random sampling. Location: Moravian Karst and Silesia, Czech Republic. Methods: We compared two parallel analyses of forest vegetation, one based on preferentially sampled plots taken from a national vegetation database and the other on plots sampled in the field according to a stratified‐random design. We repeated this comparison for two different regions in the Czech Republic. We focussed on vegetation properties commonly analysed using data from large vegetation databases, including alpha (within‐plot) diversity, cover and participation of different species groups, such as endangered and alien species within plots, total species richness of data sets, beta diversity and ordination patterns. Results: The preferentially sampled data sets obtained from the database contained more endangered species and had higher beta diversity, whereas estimates of alpha diversity and representation of alien species were not consistently different between preferentially and stratified‐randomly sampled data sets. In ordinations, plots from the preferential samples tended to be more common at margins of plot scatters. Conclusions: Vegetation data stored in large databases are influenced by researcher subjectivity in plot positioning, but we demonstrated that not all of their properties necessarily differ from data sets obtained by stratified‐random sampling. This indicates the value of vegetation databases for use in biodiversity studies; however, some analyses based on these databases are clearly biased and their results must be interpreted with caution.

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Plant community variability within potential natural vegetation units: a case study from the Bohemian Karst

Cited by 4 publications

References 22 publications

Potential natural vegetation: reburying or reboring?

Potential natural vegetation: reburying or reboring?

Assessment of the Ellenberg quotient as a practical tool for vertical vegetation zonation

Bias in vegetation databases? A comparison of stratified-random and preferential sampling

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