Modelling the distribution of large branchiopods (Crustacea: Notostraca, Anostraca & Spinicaudata) for predicting occurrences in poorly sampled regions

Large branchiopods are one of the most iconic species groups in temporary pools. Due to their specific biology and massive habitat degradation, they are one of the most threatened freshwater species groups. In this study, we used a unique dataset on the distribution of large branchiopods on a national scale (Austria), data on the distribution of temporary pools, and environmental variables to model the potential distribution of seven large branchiopod species. For species distribution modeling, we used six predictors which were assumed to be potentially relevant for explaining large branchiopod distribution. We used three modeling techniques (Generalized Linear Models, General Boosted Models, Random Forests) for distribution modeling. While some predictors (e.g. mean annual temperature, mean annual precipitation and the proportion of arable land at the temporary pool) were important for all target species, the importance of the other variables (size of the temporary pool, connectivity, flooding probability) was variable among species, and partly also between the three model techniques used. The resulting ensemble distribution maps, which are based on ensemble models of the above-mentioned modeling techniques, show the distribution of occurrence probabilities for the modeled species. These maps can serve as a baseline for future surveying regions that have been under-sampled so far. Furthermore, as these maps identify regions where large branchiopods likely do occur, conservation measures can be prioritized towards these regions. Finally, we discuss conservation measures which should improve land use by farmers to appropriately protect large branchiopods and their habitats, temporary pools.

Abstract licence: CC BY-NC

40 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.