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Complexity for Artificial Substrates (CASU): Software for Creating and Visualising Habitat Complexity
Loke, L.H.L.; Jachowski, N.R.; Bouma, T.J.; Ladle, R.J.; Todd, P.A. (2014). Complexity for Artificial Substrates (CASU): Software for Creating and Visualising Habitat Complexity. PLoS One 9(2): e87990. dx.doi.org/10.1371/journal.pone.0087990
In: PLoS One. Public Library of Science: San Francisco. ISSN 1932-6203, more
Peer reviewed article  

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  • Loke, L.H.L.
  • Jachowski, N.R.
  • Bouma, T.J., more
  • Ladle, R.J.
  • Todd, P.A.

Abstract
    Physical habitat complexity regulates the structure and function of biological communities, although the mechanisms underlying this relationship remain unclear. Urbanisation, pollution, unsustainable resource exploitation and climate change have resulted in the widespread simplification (and loss) of habitats worldwide. One way to restore physical complexity to anthropogenically simplified habitats is through the use of artificial substrates, which also offer excellent opportunities to explore the effects of different components (variables) of complexity on biodiversity and community structure that would be difficult to separate in natural systems. Here, we describe a software program (CASU) that enables users to visualise static, physical complexity. CASU also provides output files that can be used to create artificial substrates for experimental and/or restoration studies. It has two different operational modes: simple and advanced. In simple mode, users can adjust the five main variables of informational complexity (i.e. the number of object types, relative abundance of object types, density of objects, variability and range in the objects' dimensions, and their spatial arrangement) and visualise the changes as they do so. The advanced mode allows users to design artificial substrates by fine-tuning the complexity variables as well as alter object-specific parameters. We illustrate how CASU can be used to create tiles of different designs for application in a marine environment. Such an ability to systematically influence physical complexity could greatly facilitate ecological restoration by allowing conservationists to rebuild complexity in degraded and simplified habitats.

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