Abstract
One of the most critical challenges in species distribution modelling is testing and validating various digitally derived environmental predictors (e.g., remote-sensing variables, topographic variables) by field data. Therefore, here we aimed to explore the value of soil properties in the spatial distribution of four European indigenous crayfish species. A database with 473 presence and absence locations in Romania for Austropotamobius bihariensis, A. torrentium, Astacus astacus and Pontastacus leptodactylus was used in relation to eight digitalised soil properties. Using random forest modelling, we found a preference for dense soils with lower coarse fragments content together with deeper sediment cover and higher clay values for A. astacus and P. leptodactylus. These descriptors trigger the need for cohesive soil river banks as the microenvironment for building their burrows. Conversely, species that can use banks with higher coarse fragments content, the highland species A. bihariensis and A. torrentium, prefer soils with slightly thinner sediment cover and lower density while not influenced by clay/sand content. Of all species, A. astacus was found related with higher erosive soils. The value of these soil-related digital descriptors may reside in the improvement of approaches in crayfish species distribution modelling to gain adequate conservation measures.
Highlights
Under climate change, drought and flash flood episodes intensify in frequency and severity, invariable leading to disturbed aquatic fauna [1]
We expect that soil variables suggested as important for the spatial distribution modelling of crayfish species correlate better with CPUE than the other variables
The final distribution maps were assessed using the standard measures of overall accuracy (OA) and area under the curve (AUC), based on the 70%/30% rule
Summary
Drought and flash flood episodes intensify in frequency and severity, invariable leading to disturbed aquatic fauna [1]. Species distribution modelling relies on quality environmental data tested and validated as predictors by consistent field information [7,8,9]. Digitally derived environmental data (e.g., remote-sensing variables, topographic variables) are the most valuable because they can be computed and applied at large scales enlarging the perspective of scientific approaches [10,11]. In parallel with increasing the computation capabilities, the availability of remote-sensing based datasets is increasing [12,13,14]. Being a surrogate for describing the interactions between the species and ecosystem assemblage [15,16], digitally derived environmental data rely on acquiring accurate raw field distributional data because they are essential to validate the quality of a set of predictors for a given species
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
