Generation and application of river network analogues for use in ecology and evolution. Issue 14 (30th June 2020)

• Record Type: Journal Article
• Authors: Carraro, Luca; Bertuzzo, Enrico; Fronhofer, Emanuel A.; Furrer, Reinhard; Gounand, Isabelle; Rinaldo, Andrea; Altermatt, Florian
• Is Part Of: Ecology and evolution. Volume 10:Issue 14(2020)
• Page Start: 7537
• Page End: 7550
• Publication Date: 2020-06-30
• Subjects: biodiversity -- dispersal -- ecological modeling -- landscape -- metacommunity -- optimal channel network -- river networks -- spanning trees; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.6479
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: Several key processes in freshwater ecology are governed by the connectivity inherent to dendritic river networks. These have extensively been analyzed from a geomorphological and hydrological viewpoint, yet structures classically used in ecological modeling have been poorly representative of the structure of real river basins, often failing to capture well‐known scaling features of natural rivers. Pioneering work identified optimal channel networks (OCNs) as spanning trees reproducing all scaling features characteristic of natural stream networks worldwide. While OCNs have been used to create landscapes for studies on metapopulations, biodiversity, and epidemiology, their generation has not been generally accessible. Given the increasing interest in dendritic riverine networks by ecologists and evolutionary biologists, we here present a method to generate OCNs and, to facilitate its application, we provide the R‐package OCNet . Owing to the stochastic process generating OCNs, multiple network replicas spanning the same surface can be built; this allows performing computational experiments whose results are irrespective of the particular shape of a single river network. The OCN construct also enables the generation of elevational gradients derived from the optimal network configuration, which can constitute three‐dimensional landscapes for spatial studies in both terrestrial and freshwater realms. Moreover, the package provides functions that aggregate OCNs into an arbitrary number of nodes, calculate several descriptors of river networks, and draw relevant network features. We describe the main functionalities of the package and its integration with other R‐packages commonly used in spatial ecology. Moreover, we exemplify the generation of OCNs and discuss an application to a metapopulation model for an invasive riverine species. In conclusion, OCNet provides a powerful tool to generate realistic river network analogues for various applications. It thereby allows the design of spatially realistic studies in increasingly impacted ecosystems and enhances our knowledge on spatial processes in freshwater ecology in general. Abstract : Recently, research on spatial dynamics in ecology and evolution has bloomed. However, spatial structures used in theoretical and empirical ecological studies are often not representative of realistic landscapes. This is particularly true for river networks, which are of paramount interest to ecologists owing to their wide (but currently severely declining) biodiversity. Indeed, most of ecological work has been neglecting the scaling character of real river networks, despite well‐established knowledge in the fields of geomorphology and hydrology. Here, we present a method to create optimal channel networks (OCNs, i.e., river network analogues reproducing all topographic and scaling features of natural rivers), and the respective R‐package allowing their generation and analysis. We review the theoretical background underlying the OCN concept, present the main package functionalities, discuss possible applications in the realm of ecology and evolution, and detail how the package can be integrated with other popular R‐packages in spatial ecology.