A set of coupled logistic growth equations is used to simulate the temporal replacement of vegetation (species or groups). Simulation results approximately reproduce two time series of 415 to 585 years obtained from field investigations in the Swiss National Park (SNP). Although the shape and the fit of the simulated curves are convincing, the assumption that all species must be present at the beginning of the simulation and also the absence of movement in space are not realistic. To overcome this, the model is extended to include space and is used for simulating the succession in an abandoned pasture of the SNP. As long as only vegetation change in all individual quadrats is simulated separately, vegetation boundaries remain unchanged over the simulated period of 400 years. When species are allowed to move between quadrats, the spatial pattern changes over the simulated time, and field data can be taken as boundary conditions to realistically simulate change. It is concluded that spatial dynamics must be taken into account to model long-term succession.
We used space-for-time substitution to obtain a directed successional sequence for subalpine meadow vegetation in the Swiss National Park. Since human impacts (e.g., domestic animal grazing) ceased in 1914, the successional processes documented are assumed to be autogenic in nature. The data consist of 59 permanent plots spanning almost 90 years, and include many spatial replications. An initial inspection of the individual time series revealed the existence of a variety of response patterns, which are described in the literature as representing different successional types. However, a closer inspection suggested that many of these series can be superimposed, as they are part of a much longer deterministic series. Linking the individual time series proved to be challenging. A heuristic approach produced results that differed depending on initial starting conditions. We therefore derived a deterministic algorithm to produce a unique solution. The resulting sequence largely confirmed the heuristic interpretation, suggesting a trend from early successional (post-grazing) grassland to pine invasion spanning about 400 years. This timespan is valid only for the climatic conditions near the treeline, and for plant species specific to the study site. Our results suggest that the various species temporal response models described in the literature may be artifactual, representing portions of underlying Gaussian responses. The data also indicate that species assemblages may persist for several decades with only minor fluctuations, only to change suddenly for no apparent reason.
The identification of differentiating species, also termed indicator species, is a key issue in striving for group pattern in vegetation samples. With this objective in mind Jancey (1979) proposed species ranking on a variance ratio (F-ratio) criterion, the mechanism of analysis of variance. Almost 20 years later Dufrêne and Legendre (1997) presented their indicator value analysis with the same objective in mind but based on different reasoning. This raises the question if (i) the results of the two approaches are equivalent or (ii), if one of the measures of performance is superior in predicting specific properties of ecosystems, such as site conditions, biodiversity, succession or other. Because the outcome of both methods strictly relies on the strength of vegetation pattern reflected by the data sets used as well as the quality of classifications, we compare results from a small and also a large real-world data set and we evaluate the effect of the number of groups involved when clustering sites. In a subsequent step, the ranking of indicator species identified by either of the methods is compared with a ranking obtained by correlating species with measured environmental factors. The results confirm that the outcome of ranking by maximum indicator value (IndVal) according to Dufrêne and Legendre (1997) is very similar to the ranking devised by Jancey (1979). Rank correlation reaches a maximum of r > 0.95 when the data set is large and group number in clustering is low. In our examples Jancey’s method is generally more closely related to the environmental predictive power of species, outperforming IndVal when applied to continuous variables measured in the field. We conclude that the potential of Jancey’s method is generally underrated. As expected, the agreement of results between the two approaches depends on the strength of similarity patterns inherent in the data sets analysed. The method of Dufrêne and Legendre (1997) is well adapted to issues of phytosociology where classification is frequently based on expert knowledge. If the resolving power of species is used as a surrogate for patterns and processes of plant-environment systems, then ranking by variance ratio may be the more promising approach.
Authors:O. Wildi, E. Feldmeyer-Christe, S. Ghosh, and N.E. Zimmermann
We address issues of investigations in plant ecology with a time span exceeding the range of ordinary research projects. According to the findings in the papers of this special issue of Community Ecology and other related publications, long-term monitoring is faced with specific methodological challenges: 1. Since the system to be monitored as well as the objectives of monitoring may change over time, the spatial, temporal and thematic scales should be changed considerably. 2. Experience shows that monitoring results are often used for different purposes. However, there exists no sampling design that would offer a true multi-purpose application. The selection of the sampling design will therefore restrict the future use of data. 3. While the selection of sampling units is random and therefore statistical, the selection of variables is usually preferential. This may seriously hamper the results thematically and statistically. It is concluded that combining precise ground measures with remote sensing data by appropriate mathematical models will be a most promising approach in future monitoring projects.