Aims Classification of vegetation is an essential tool to describe, understand, predict and manage biodiversity. Given the multiplicity of approaches to classify vegetation, it is important to develop international consensus around a set of general guidelines and purpose‐specific standard protocols. Before these goals can be achieved, however, it is necessary to identify and understand the different choices that are made during the process of classifying vegetation. This paper presents a framework to facilitate comparisons between broad‐scale plot‐based classification approaches. Results Our framework is based on the distinction of four structural elements (plot record, vegetation type, consistent classification section and classification system) and two procedural elements (classification protocol and classification approach). For each element we describe essential properties that can be used for comparisons. We also review alternative choices regarding critical decisions of classification approaches; with a special focus on the procedures used to define vegetation types from plot records. We illustrate our comparative framework by applying it to different broad‐scale classification approaches. Conclusions Our framework will be useful for understanding and comparing plot‐based vegetation classification approaches, as well as for integrating classification systems and their sections.

Using empirical data (ca. 7500 phytosociological releves), a simple, probabilistic ‘vegetation‐site’ model was developed, to simulate geographical distribution of 71 forest community types, representing the potential natural vegetation (PNV) of Switzerland. The model was interfaced to a geographic information system (GIS) and used to generate a numerical vegetation map, on the basis of digital maps of 12 environmental variables including climatic conditions (temperature and precipitation), topography (elevation, slope, aspect), and soil parameters (soil pH and physical soil parameters). The predicted distribution of forest communities was compared with several vegetation maps, prepared for some subregions of Switzerland by means of traditional field methods. Similarity ranged from 50 to 80 %, depending on the community type, level of vegetational hierarchy and the geographical region. The current resolution and accuracy of the simulated vegetation map allows us to study the vegetational patterns on the level of the entire country or its major geographical and climatic regions. The simulated vegetation map is potentially an important tool in ecological risk assessment studies concerning the possible impacts of climate change on the ecological potential of forest sites and biological diversity of forest communities.

Abstract. A spatially explicit, climate‐sensitive vegetation model is presented to simulate both present and future distribution of potential natural vegetation types in Switzerland at the level of zonal forest communities. The model has two versions: (1) a ‘basic’ version using geographical region, aspect, bedrock (represented by soil pH), and elevation, and (2) a ‘climate‐sensitive’ version obtained by replacing elevation (complex environmental gradient) with temperature (climatic factor). Version 2 is used to predict vegetation response under different (today's and projected) climatic conditions. Two regional climate scenarios are applied: (1) assuming an annual mean temperature increase of 1.1 — 1.4 °C, and (2) assuming an increase of 2.2 — 2.75 °C. Both scenarios result in significant changes of the spatial vegetation patterns as compared with today's climatic conditions. In scenario 1, ca. 33 % of the sample points remain unchanged in terms of the simulated zonal forest community; in scenario 2, virtually all sample points change. The most noticeable changes occur on the Swiss Plateau with Carpinion forests (zonal vegetation of present colline belt) expanding to areas that are occupied today by submontane and low‐montane Fagus forests. To estimate the reliability of the simulation, quantitative (comparison with field mapping) and qualitative (comparison with climate types in the Alpine region) tests are performed and the main limitations of the approach are evaluated.

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