Accelerating advances in landscape connectivity modelling with the ConScape library. Issue 1 (27th April 2022) - Van Moorter, Bram; Kivimäki, Ilkka; Noack, Andreas; Devooght, Robin; Panzacchi, Manuela; Hall, Kimberly R.; Leleux, Pierre; Saerens, Marco
Abstract: Increasingly precise spatial data (e.g. high‐resolution imagery from remote sensing) allow for improved representations of the landscape network for assessing the combined effects of habitat loss and connectivity declines on biodiversity. However, evaluating large landscape networks presents a major computational challenge both in terms of working memory and computation time. We present the ConScape (i.e. "connected landscapes") software library implemented in the high‐performance open‐source Julia language to compute metrics for connected habitat and movement flow on high‐resolution landscapes. The combination of Julia's 'just‐in‐time' compiler, efficient algorithms and 'landmarks' to reduce the computational load allows ConScape to compute landscape ecological metrics—originally developed in metapopulation ecology (such as 'metapopulation capacity' and 'probability of connectivity')—for large landscapes. An additional major innovation in ConScape is the adoption of the randomized shortest paths framework to represent connectivity along the continuum from optimal to random movements, instead of only those extremes. We demonstrate ConScape 's potential for using large datasets in sustainable land planning by modelling landscape connectivity based on remote‐sensing data paired with GPS tracking of wild reindeer in Norway. To guide users, we discuss other applications, and provide a series of worked examples to showcase all ConScape 's functionalities in Supplementary Material. Built by a team of ecologists, network scientists and software developers, ConScape is able to efficiently compute landscape metrics for high‐resolution landscape representations to leverage the availability of large data for sustainable land use and biodiversity conservation. As a Julia implementation, ConScape combines computational efficiency with a transparent code base, which facilitates continued innovation through contributions from the rapidly growing community of landscape and connectivity modellers using Julia.