Long-term monitoring data meet freshwater species distribution models: Lessons from an LTER-site

Abstract

Long-term monitoring datasets provide a solid framework for ecological research. Such a dataset from the German long-term ecological research (LTER) site Rhine-Main-Observatory was used to set up a species distribution model (SDM) for the Kinzig catchment. The extensive knowledge on the monitoring data provided by the LTER-site framework allowed to calibrate a robust model for 175 taxa of stream macroinvertebrates and to project their distributions on the Kinzig River stream network using bioclimatic, topographical, hydrological, land use and geological predictors. On average, model performance was good, with a TSS of 0.83 (±0.09 SD) and a ROC of 0.95 (±0.03 SD). The model delivered valuable insights on three sources of bias that plague SDMs in general: (a) level of taxonomic identification of the modeled organisms, (b) the spatial arrangement of sampling sites, and (c) the sampling intensity at each sampling site. Taxonomic resolution did not affect SDM performance. The distribution of high predicted probabilities of occurrence in the stream network coincided with those segments in the stream network most densely and frequently sampled, indicating both a spatial and temporal sampling bias. Species richness curves confirmed the temporal sampling bias. Next to spatial bias, sampling frequency also plays an important role in data collection, affecting further analysis and modeling procedures. Results indicate an underrepresentation of low order streams, an important aspect that should be addressed by both monitoring schemes and modeling approaches.