1Norwegian Institute for Nature Research, P.O. Box 5685 Sluppen, Trondheim, NO-7485, Norway2ICTEAM/UCL, Université catholique de Louvain, Louvain-la-Neuve, Belgium3Department of Biological Sciences, University of Alberta, Edmonton, T6G 2E9, Canada4Department of Biology, Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim, NO-7491, Norway5Department of Animal and Human Biology, Sapienza University, Viale dell'Università 32, Rome, 00185, Italy
Checking for direct PDF access through Ovid
Summary1. The loss, fragmentation and degradation of habitat everywhere on Earth prompts increasing attention to identifying landscape features that support animal movement (corridors) or impedes it (barriers). Most algorithms used to predict corridors assume that animals move through preferred habitat either optimally (e.g. least cost path) or as random walkers (e.g. current models), but neither extreme is realistic.2. We propose that corridors and barriers are two sides of the same coin and that animals experience landscapes as spatiotemporally dynamic corridor-barrier continua connecting (separating) functional areas where individuals fulfil specific ecological processes. Based on this conceptual framework, we propose a novel methodological approach that uses high-resolution individual-based movement data to predict corridor-barrier continua with increased realism.3. Our approach consists of two innovations. First, we use step selection functions (SSF) to predict friction maps quantifying corridor-barrier continua for tactical steps between consecutive locations. Secondly, we introduce to movement ecology the randomized shortest path algorithm (RSP) which operates on friction maps to predict the corridor-barrier continuum for strategic movements between functional areas. By modulating the parameter Symbol, which controls the trade-off between exploration and optimal exploitation of the environment, RSP bridges the gap between algorithms assuming optimal movements (when Symbol approaches infinity, RSP is equivalent to LCP) or random walk (when Symbol → 0, RSP → current models).4. Using this approach, we identify migration corridors for GPS-monitored wild reindeer (Rangifer t. tarandus) in Norway. We demonstrate that reindeer movement is best predicted by an intermediate value of Symbol, indicative of a movement trade-off between optimization and exploration. Model calibration allows identification of a corridor-barrier continuum that closely fits empirical data and demonstrates that RSP outperforms models that assume either optimality or random walk.5. The proposed approach models the multiscale cognitive maps by which animals likely navigate real landscapes and generalizes the most common algorithms for identifying corridors. Because suboptimal, but non-random, movement strategies are likely widespread, our approach has the potential to predict more realistic corridor-barrier continua for a wide range of species.Movement corridors and barriers are two sides of the same coin. The authors model the multi-scale cognitive maps by which animals likely navigate real landscapes, and identify corridor-barrier continua for animals adopting sub-optimal, but non-random, movement strategies. The approach generalizes the most common algorithms for identifying corridors, and allows predicting corridor-barrier continua with increased realism.