Improving predictive mapping in Swiss mire ecosystems through re‐calibration of indicator values

Abstract

AbstractQuestion: How may Landolt indicator values be re‐calibrated to improve the performance of predictive models?Location: Mires Gross Moos Schwändital (1250 m a.s.l.) in the Prealps, Burgmoos (465 m. a.s.l.) on the Central Plateau and La Burtignière (1000 m a.s.l.) in the Jura, Switzerland.Methods: Habitat distribution models based on high resolution remotely sensed data and vegetation field data are applied to monitor 130 mires. Instead of plant species or communities we used mean indicator values of vegetation records as response variables. To improve the differential power of indicator values for wetland habitat conditions, we calibrated these values using field data. Different methods were tested with our predictive models in three mires to see which calibration method is best in enhancing model performance. To assess the effect of the uneven distribution of vegetation records along environmental gradients, calibrations based on random and evenly distributed samples were compared. As a test of the predictive power of the models we used r2 between ground truth and model prediction.This approach is illustrated through an application with nutrient indicator values in the mire La Burtignière. Results: Model performances were not the same for the three mires. The predictive power was better for the nutrient values, soil reaction and humus values than for light and moisture values. 2000 records were sufficient as basis for re‐calibration. Models based on original Landolt indicator values were overall the weakest compared with re‐calibrated values. By comparing the predictive power of Models based on randomly or evenly selected records were about equally predictive.Conclusions: 1. Ahabitat‐specific re‐calibration of the Landolt indicator values enhances the predictive mapping of the Swiss mire ecosystems. 2. The re‐calibration based on weighted averaging gives a better performance than the one based on Gaussian logistic regression. 3. The uneven distribution of indicator values due to the over‐representation of mire habitats does not hamper model performance. 4. 2000 vegetation records are a sufficient basis for an optimal re‐calibration of the vegetation types. An illustration of the method is given by using the soil fertility pattern of the mire La Burtignière.