The Landscape Ecology of Rivers: from Patch-Based to Spatial Network Analyses

  • Erős T
  • Lowe W
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Abstract

We synthesize recent methodological and conceptual advances in the field of riverscape ecology, emphasizing areas of synergy with current research in landscape ecology. Recent advances in riverscape ecology highlight the need for spatially explicit examinations of how network structure influences ecological pattern and process, instead of the simple linear (upstream-downstream) view. Developments in GIS, remote sensing, and computer technologies already offer powerful tools for the application of patch- and gradient-based models for characterizing abiotic and biotic heterogeneity across a range of spatial and temporal scales. Along with graph-based analyses and spatial statistical stream network models (i.e., geostatistical modelling), these approaches offer improved capabilities for quantifying spatial and temporal heterogeneity and connectivity relationships, thereby allowing for rigorous and high-resolution analyses of pattern, process, and scale relationships. Spatially explicit network approaches are able to quantify and predict biogeochemical, hydromorphological, and ecological patterns and processes more precisely than models based on longitudinal or lateral riverine gradients alone. Currently, local habitat characteristics appear to be more important than spatial effects in determining population and community dynamics, but this conclusion may change with direct quantification of the movement of materials, energy, and organisms along channels and across ecosystem boundaries—a key to improving riverscape ecology. Coupling spatially explicit riverscape models with optimization approaches will improve land protection and water management efforts, and help to resolve the land sharing vs. land sparing debate.

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Erős, T., & Lowe, W. H. (2019). The Landscape Ecology of Rivers: from Patch-Based to Spatial Network Analyses. Current Landscape Ecology Reports, 4(4), 103–112. https://doi.org/10.1007/s40823-019-00044-6

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