期刊
ECOLOGICAL INFORMATICS
卷 24, 期 -, 页码 238-247出版社
ELSEVIER
DOI: 10.1016/j.ecoinf.2013.06.002
关键词
GRASS GIS; Fish dispersal; Dispersal modelling; Raster GIS; Leptokurtic dispersal; Dispersal time lags
类别
资金
- German Federal Ministry for Education and Research [02WM1134]
Dispersal is a key process in community ecology and dynamics that comprise not only various movement processes, but also behavioural components and large-scale spatio-temporal patterns. Modelling fish dispersal further has to consider the species-specific dispersal abilities and the branching, dendritic nature of river networks. With FIDIMO (fish dispersal model) a tool is provided for predicting and simulating spatio-temporal patterns of fish dispersal in river networks with a real integration of GIS for the first time. The model has been written in Python programming language as open source add-on for GRASS GIS. The model has been developed to apply the characteristics of heterogeneous, species and size class specific fish movement on a rasterized river network including migration barriers. Fish dispersal is modelled as a leptokurtic diffusion process spreading from spatially predefined source populations consisting of stationary and mobile components each. The fish dispersal model FIDIMO links conceptual considerations on dispersal modelling with empirically observed fish movement patterns and the strengths of geographically explicit modelling in FOSS GIS. It can be used for different species and any river network meeting the input requirements. As main results FIDIMO yields probabilities of occurrence of fish species in river raster cells based on empirically derived dispersal kernels after the time step of interest modelled. The model output serves to understand and predict time lags and spatio-temporal patterns of recolonization events, the related success of river rehabilitation and also the spread of invasive species. The consequent use of open source programmes allows free access to the code and facilitates easy modifications, adoptions and improvements of the model. (C) 2013 Elsevier B.V. All rights reserved.
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