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

ABSTRACT Question: How may Landolt indicator values be re-calibrated to improve the performance of predictive models? Location: Mires Gross Moos Schwandital (1250 m a.s.l.) in the Prealps, Burgmoos (465 m. a.s.l.) on the Central Plateau and La Burtigniere (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 ...