PD Dr. Otto Wildi
What’s new in the third edition?
The introduction is extended by a short overview on the scientific conventions (paradigms) adopted in the analysis of ecosystems. Various new methods are included, such as divisive clustering and the reallocation of new observations. The use and interpretation of plant functional types and indicator values is now formally introduced and interpretations presented by example. All data sets used in the new examples are implemented in the updated version of the R package dave (2.0), available on CRAN.
In various chapters, sections helping to choose optimal methods are included, quantitatively as well as graphically:
Irrespective of whether our interest in the vegetation of the earth evolves from naive curiosity or from the endeavour to conserve this natural resource, the study of vegetation ecology inevitably follows the rules of epistemology and it is shaped by paradigms.
Patterns are recurring regularities, and pattern recognition is a target of data analysis in vegetation ecology. A proper sampling design is required to deliver data reflecting properties of the real system. Similarity patterns eventually occur in any sub-system, in resemblance space, in geographical space and in time.
Measurements in vegetation ecology take place at scales determined by measurement tools, conventions and traditions. Because subsequent analysis is considered evidence-based, data transformations must adapt the scale to the aim of the investigation and further statistical requirements.
Vegetation and the corresponding environment are described by a large number of variables such as species scores, plant traits and environmental measurements. Multivariate comparison of sampling units is therefore a key issue in any analysis.
Classification aims to find groups of similar sampling units or descriptive variables. It is a means of data reduction, serving ecological model building and it helps in the communication of the state of vegetation among scientists and practitioners.
Ordination is a means to visualize the similarity pattern of a sample, typically in two- or three-dimensional plots where the individual observations are points. Despite operating in reduced data space, ordination diagrams reveal dominant patterns like groups, gradients and outliers.
The similarity of patterns found in biological, environmental and geographical space is a hallmark of ecological interactions. Recognition of joint patterns requires vegetation data, ecological measurements and spatial coordinates to be amalgamated within the framework of analysis.
In large-scale and particularly global assessments of vegetation, species pools change in space and time. Descriptors other than species have to be found. Broad validity of results can be achieved when using indicator values, func- tional traits, life strategies or combinations of these as descriptors.
What is the most likely outcome of vegetation composition under a real or hypothetical set of environmental parameters? This is the question static modelling is attempting to answer by means of statistical inference.
Although just another factor determining vegetation pattern, time has some unique properties. It is one-dimensional, proceeds in one direction only and as processes usually last longer than observed, time series data tend to underestimate temporal variation.
Given the state of an ecosystem, mathematically formulated rules of change mimic dynamic system behaviour. Dynamic modelling is a perfect tool to evaluate the outcome of assumed or observed sets of mechanisms.
Classifications relate vegetation ecology to practical demands of society. Revising classifications aims to improve vegetation description for applied use while enhancing their power of predicting environmental conditions. This case study illustrates a path towards revision, performance testing and presentation.
Vegetation ecology performs best in the context of comprehensive statistical samples. In the present case study the sampling plan is a square grid con- strained to the forested area of Switzerland. It uses variable plot size for vegetation data and complements this with a set of environmental variables. This allows a stunning variety of questions to be answered.
Plant ecology is known for pioneering work in mathematical analysis. Many of the methods have been developed to handle the properties of vegetation data and sometimes to fulfil the prejudices of practitioners. Newly emerging free software may redirect these towards ever-growing all-purpose statistical methods.