Changes in large-scale patterns of plant biodiversity predicted from environmental economic scenarios

In the industrialized world large sums of money are spent on measures to preserve biodiversity by improving environmental quality. This creates a need to evaluate the effectiveness of such measures. In response we devel oped a model, NTM, that links plant biodiversity to abiotic variables that are under human control. These vari ables are: vegetation management, and the soil variables groundwater level, and nitrogen availability. We used species richness and the criteria of the Red Lists, i.e., the rarity and decline per species as measure for potential changes in biodiversity. NTM uses a statistical approach, and models potential plant biodiversity based on the above criteria as a non-linear function of the three soil variables. The regression model is calibrated on a data set consisting of 33,706 vegetation relevés. Because field data of vegetation combined with measurements of soil variables are insufficiently available, we used the average of Ellenberg's indicator values of the species in each relevé as a proxy. NTM was subjected to both validation and uncertainty analysis. The validation was car ried out by comparison with an independent data set. The uncertainty analysis showed that uncertainty in abso lute biodiversity values is large, but that comparative scenario studies can be carried out with an acceptable uncertainty. As an example we show the evaluation of the impact of three European economic scenarios on po tential plant biodiversity in the Netherlands. Although there were differences per vegetation type and per region, potential plant biodiversity had a tendency to increase, with the highest increase for the scenario with the highest reduction in atmospheric deposition of nitrogen and acidity.