Imelda Somodi

The effect of the expansion of the clonal grass Calamagrostis epigejos on the species turnover of a semi-arid grassland

ABSTRACT Question: How does the dominance of Calamagrostis epigejos influence species turnover of a grassland? Location: Loess grassland at the foothills of Bükk Mountains, Hungary (47°54′ N, 20°35′ E). Methods: Presence/absence of vascular plants and different performance attributes of C. epigejos were recorded in a plot-subplot system between 2002 and 2005. Appearance and disappearance rates of grassland species were calculated for pairs of consecutive years. 1. Mean appearance and disappearance rates were compared in grassland plots dominated by C. epigejos and in plots free from this species, based on Monte Carlo randomization. 2. Mean appearance rates were assessed for categories of C. epigejos performance and their confidence intervals were calculated via Monte Carlo randomization. For two performance variables (percentage cover and shoot number) analyses were performed at two spatial scales. Results: 1. C. epigejos-dominated plots differed from unaffected ones by significantly lower appearance rates. 2. Change in appearance rates was best explained by differences in percentage cover of C. epigejos. Coarse-scale C. epigejos performance had a closer correspondence with appearance rate change than fine-scale performance. Low level C. epigejos performance enhanced appearance rate compared to intact stands, while high level performance decreased it, regardless of the choice of performance measure. Conclusions: C. epigejos lowers species number by hindering reappearance of species of the original grassland. This is best explained by the increased shading effect at the coarse scale. The marked non-linear initial enhancement in appearance rate, however, can also be taken as an early sign of future species loss. Nomenclature: Tutin et al. (1964–1993).

Prevalence dependence in model goodness measures with special emphasis on true skill statistics

Abstract It has long been a concern that performance measures of species distribution models react to attributes of the modeled entity arising from the input data structure rather than to model performance. Thus, the study of Allouche et al. (Journal of Applied Ecology, 43, 1223, 2006) identifying the true skill statistics (TSS) as being independent of prevalence had a great impact. However, empirical experience questioned the validity of the statement. We searched for technical reasons behind these observations. We explored possible sources of prevalence dependence in TSS including sampling constraints and species characteristics, which influence the calculation of TSS. We also examined whether the widespread solution of using the maximum of TSS for comparison among species introduces a prevalence effect. We found that the design of Allouche et al. (Journal of Applied Ecology, 43, 1223, 2006) was flawed, but TSS is indeed independent of prevalence if model predictions are binary and under the strict set of assumptions methodological studies usually apply. However, if we take realistic sources of prevalence dependence, effects appear even in binary calculations. Furthermore, in the widespread approach of using maximum TSS for continuous predictions, the use of the maximum alone induces prevalence dependence for small, but realistic samples. Thus, prevalence differences need to be taken into account when model comparisons are carried out based on discrimination capacity. The sources we identified can serve as a checklist to safely control comparisons, so that true discrimination capacity is compared as opposed to artefacts arising from data structure, species characteristics, or the calculation of the comparison measure (here TSS).

Effect of slight vegetation degradation on soil properties in Brachypodium pinnatum grasslands

The interrelationship of soil and vegetation degradation is an emerging issue, where most studies have addressed severe degradation so far. We aimed at revealing changes in soil accompanying slight vegetation degradation in a case study involving xeromesophilous grasslands from Hungary. Slight degradation is of special interest here because the target community (Euphorbio pannonicae—Brachypodietum pinnati association) has great nature conservation value. Vegetation status was related to chemical and structural soil properties by principal component analysis and redundancy analysis. Vegetation conditions were assessed by species abundances and by fine-scale spatial structure, which is proposed here for soil-vegetation studies. Slight vegetation degradation clearly manifested itself in soil properties. Differences in vegetation status when assessed by species abundances were mirrored in chemical soil properties. When structural vegetation descriptors were used, a soil structure property (bulk density) was responsible for the segregation according to naturalness. Vegetation-soil relationships were more consistent over biogeographic regions, when vegetation structural descriptors were used. Differences in chemical soil properties reflected species abundance pattern, as was found in most non-grazing related degradation studies. However, changes to soil structure also accompanied slight degradation, and their importance was revealed when vegetation structure was taken into account.

Implementation and application of multiple potential natural vegetation models – a case study of Hungary

Questions Multiple potential natural vegetation (MPNV) is a framework for the probabilistic and multilayer representation of potential vegetation in an area. How can an MPNV model be implemented and synthesized for the full range of vegetation types across a large spatial domain such as a country? What additional ecological and practical information can be gained compared to traditional potential natural vegetation (PNV) estimates? Location Hungary. Methods MPNV was estimated by modelling the occurrence probabilities of individual vegetation types using gradient boosting models (GBM). Vegetation data from the Hungarian Actual Habitat Database (META) and information on the abiotic background (climatic data, soil characteristics, hydrology) were used as inputs to the models. To facilitate MPNV interpretation a new technique for model synthesis (re-scaling) enabling comprehensive visual presentation (synthetic maps) was developed which allows for a comparative view of the potential distribution of individual vegetation types. Results The main result of MPNV modelling is a series of raw and re-scaled probability maps of individual vegetation types for Hungary. Raw probabilities best suit within-type analyses, while re-scaled estimations can also be compared across vegetation types. The latter create a synthetic overview of a locations PNV as a ranked list of vegetation types, and make the comparison of actual and potential landscape composition possible. For example, a representation of forest vs grasslands in MPNV revealed a high level of overlap of the potential range of the two formations in Hungary. Conclusion The MPNV approach allows viewing the potential vegetation composition of locations in far more detail than the PNV approach. Re-scaling the probabilities estimated by the models allows easy access to the results by making potential presence of vegetation types with different data structure comparable for queries and synthetic maps. The wide range of applications identified for MPNV (conservation and restoration prioritization, landscape evaluation) suggests that the PNV concept with the extension towards vegetation distributions is useful both for research and application.

Transferability of a predictive Robinia pseudacacia distribution model in northeast Slovenia

The main goal of this study is to assess the transferability of a species distribution model (SDM) for Robinia pseudacacia (black locust) to two testing sites in the Prekmurje region in northeast Slovenia. The predictive performance of the SDM at the testing sites was measured by 1) visual evaluation, 2) confusion matrix, 3) true positive rate (TPR), 4) the maximum of the true skill statistics (TSS) over possible cutoffs, and 5) paired-sample ANOVA. We show that the model adequately predicted potential distribution of the species in the region, which ensures that extension of the prediction at this scale will be a reliable base for nature conservation decisions. This also serves as a positive example for within-region transfer and extension of SDMs.

A Bayesian MCMC approach to reconstruct spatial vegetation dynamics from sparse vegetation maps

In studies of vegetation dynamics, data points describing the changes are often sparse, because changes were not recognized in early stages or investigations were part of different projects. The snapshots at hand often leave the nature of the dynamics unrevealed and only give a rough estimation of the directions of changes. Extrapolation of the dynamics with traditional cellular automaton modeling is also complicated in such cases, because rules often cannot be deduced from field data for each interaction. We developed a Bayesian MCMC method, using a discrete time stochastic cellular automaton model to reconstruct vegetation dynamics between vegetation maps available and provide estimation of vegetation pattern in years not surveyed. Spread capability of each vegetation type was characterized by a lateral spread parameter and another for establishment from species pool. The method was applied to a series of three vegetation maps depicting vegetation change at a grassland site following abandonment of grazing in north-eastern Hungary. The Markov chain explored the missing data space (missing maps) as well as the parameter space. Transitions by lateral expansion had a greater importance than the appearance of new vegetation types without spatial constraints at our site. We estimated the trajectory of change for each vegetation type, which bore a considerable non-linear element in most cases. To our best knowledge, this is the first work that tries to estimate vegetation transition parameters in a stochastic cellular automaton based on field measurements and provides a tool to reconstruct past dynamics from observed pattern.