Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
Cite this dataset
Durighetto, Nicola; Bertassello, Leonardo Enrico; Botter, Gianluca (2022). Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset [Dataset]. Dryad. https://doi.org/10.5061/dryad.0zpc86709
Dynamic changes in the active portion of stream networks represent a phenomenon common to diverse climates and geologic settings. However, the ecological implications of river network expansions/retractions remain poorly understood owing to operational difficulties in mechanistically describing these processes at the relevant spatio-temporal scales. Here we present a novel Bayesian framework for the simulation of event-based channel network dynamics capitalizing on the concept of "hierarchical structuring of temporary streams" - a general principle to identify the activation/deactivation order of network nodes. The framework incorporates a dynamic version of a stochastic occupancy metapopulation model, and is used to analyze the impact of pulsing river networks on species persistence in different scenarios. Climate strongly controls temporal variations of the active length, influencing the preferential configuration of the active channels and the speed of network retraction during drying. We also identify a climate-dependent detrimental effect of network dynamics on species spread and persistence. This effect is enhanced by dry climates, where flashy expansions and retractions of the flowing channels induce metapopulation extinction. Survival probabilities are particularly reduced in settings where the spatial heterogeneity of network connectivity is pronounced. The proposed framework provides novel insight on the multi-faced ecological legacies of channel network dynamics.
The provided data were downloaded from the Veneto Region Geoportal. The dtm was processed with the Taudem toolbox for the calculation of the accumulated area and topographic wetness index.
The code should work as is, provided that it is run on Matlab 2021a (or newer), with the mapping and symbolic math toolbox are installed.
Additionally, the user may provide an alternative shapefile of the stream network and raster layer of contributing area / topographic wetness index, to run the code on a different study catchment.
European Commission, Award: 770999