Aat Barendregt

Bio: Aat Barendregt is an academic researcher from Utrecht University. The author has contributed to research in topics: Wetland & Groundwater flow. The author has an hindex of 23, co-authored 71 publications receiving 2010 citations. Previous affiliations of Aat Barendregt include Tilburg University & HAN University of Applied Sciences.

Determining alternative models for vegetation response analysis: a non‐parametric approach

Abstract: . Vegetation models based on multiple logistic regression are of growing interest in environmental studies and decision making. The relatively simple sigmoid Gaussian optimum curves are most common in current vegetation models, although several different other response shapes are known. However, improvements in the technical means for handling statistical data now facilitate fast and interactive calculation of alternative complex, more data-related, non-parametric models. The aim in this study was to determine whether, and if so how often, a complex response shape could be more adequate than a linear or quadratic one. Using the framework of Generalized Additive Models, both parametric (linear and quadratic) and non-parametric (smoothed) stepwise multiple logistic regression techniques were applied to a large data set on wetlands and water plants and to six environmental variables: pH, chloride, orthophosphate, inorganic nitrogen, thickness of the sapropelium layer and depth of the water-body. All models were tested for their goodness-of-fit and significance. Of all 156 generalized additive models calculated, 77 % were found to contain at least one smoothed predictor variable, i.e. an environmental variable with a response better fitted by a complex, non-parametric, than by a linear or quadratic parametric curve. Chloride was the variable with the highest incidence of smoothed responses (48 %). Generally, a smoothed curve was preferable in 23 % of all species-variable correlations calculated, compared to 25 % and 18 % for sigmoid and Gaussian shaped curves, respectively. Regression models of two plant species are presented in detail to illustrate the potential of smoothers to produce good fitting and biologically sound response models in comparison to linear and polynomial regression models. We found Generalized Additive Modelling a useful and practical technique for improving current regression-based vegetation models by allowing for alternative, complex response shapes.

The relationship between fen vegetation gradients groundwater flow and flooding in an undrained valley mire at biebrza, Poland

Abstract: (1) Relationships between vegetation composition and nutrient and major ion concentrations in groundwater and peat were examined in 58 stands of fen vegetation in the Biebrza mire, Poland. The 58 stands included rich fens with a large standing biomass, rich fens with lower standing biomass and poor fens. (2) The highly productive rich fen (i.e. rich fen with large standing biomass) receives moderately eutrophic river water. The rheophilous part of the mire that is not, or only occasionally flooded contains gradients from quite productive rich fens to less productive rich fens and poor fens. In this part of the gradient, occasional river flooding appeared to be an important source of K and was probably responsible for the presence of quite productive rich fen vegetation. (3) Upward seepage of calcium-rich and phosphate-poor groundwater keeps phosphate concentration low in the superficial mire water and the peat from the lower productive rich fen. Phosphate concentration is higher in the mire water and the peat from the poor fen which is fed by downward-flowing rainwater. (4) Nitrogen concentration in mire water and peat is only poorly correlated with species composition and vegetation structure.

Groundwater chemistry and vegetation of gradients from rich fen to poor fen in the Naardermeer (the Netherlands)

Abstract: In the eastern part of the Naardermeer peatlands (the Netherlands) a regional calcium-rich groundwater flow discharges (here often called the seepage area), whereas in the western part infiltration takes place. The ecological consequence of this hydrological pattern is reflected by the pattern in reedland communities. In the seepage area, which is characterized by Thelypteris-reedlands including many rare and endangered species (Caricion davallianae, Calthion palustris), there is a complex gradient of water types. The lime potential in the peat soil is clearly influenced by the hydrological gradient. In the ombrotrophic (poor fen) part of the gradient (containing species of Caricion curto-nigrae) the lime potentials are low and the groundwater contains low amounts of dissolved ions. The rare and endangered species (Caricion davallianae) are restricted to a small area with high lime potentials which is nourished by regional calcium-rich groundwater. High lime potentials were also measured in eutrophic reedlands influenced by brackish groundwater. Several species which generally occur in wet meadows even show a preference for this brackish environment. In a part of the seepage area succession from rich fen to poor fen and Alnus wood has taken place over a period of 40 years. This development has been caused by the diminishing amount of fresh seepage due to a lowering of the water levels in the surrounding area. The characteristics of poikilotrophic zones (contact zones between water flows) are discussed in relation to their significance for the preservation of endangered marsh species.

Economic Reasons for Conserving Wild Nature

Abstract: On the eve of the World Summit on Sustainable Development, it is timely to assess progress over the 10 years since its predecessor in Rio de Janeiro. Loss and degradation of remaining natural habitats has continued largely unabated. However, evidence has been accumulating that such systems generate marked economic benefits, which the available data suggest exceed those obtained from continued habitat conversion. We estimate that the overall benefit:cost ratio of an effective global program for the conservation of remaining wild nature is at least 100:1.