Data from: Predicting the continuum between corridors and barriers to animal movements using Step Selection Functions and Randomized Shortest Paths

1. 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 Ѳ, which controls the trade-off between exploration and optimal exploitation of the environment, RSP bridges the gap between algorithms assuming optimal movements (when Ѳ approaches infinity, RSP is equivalent to LCP) or random walk (when Ѳ → 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 Ѳ, 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.1. 全球范围内的栖息地丧失、破碎化与退化，使得学界愈发关注识别助力动物移动的景观要素（廊道（corridors））与阻碍动物移动的景观要素（阻隔物（barriers））。当前用于预测动物移动廊道的多数算法，均假设动物会以最优路径（最小成本路径（least cost path））或随机游走（如现有模型）的方式穿越偏好栖息地，但这两种极端假设均不符合实际情况。 2. 我们提出，廊道与阻隔物实为一体两面；动物感知的景观是一类时空动态的廊道-阻隔连续体，该连续体连接（分隔）了动物个体完成特定生态过程的功能区域。基于此概念框架，我们提出一种创新性方法：借助高分辨率的个体级移动数据，以更高的现实性预测廊道-阻隔连续体。 3. 本方法包含两项创新。其一，我们利用步长选择函数（step selection functions, SSF）预测阻力图，以量化连续定位点间战术性移动步对应的廊道-阻隔连续体。其二，我们将随机最短路径算法（randomized shortest path algorithm, RSP）引入移动生态学领域：该算法基于阻力图，可预测功能区域间战略性移动对应的廊道-阻隔连续体。通过调控参数Ѳ（该参数控制探索环境与最优利用环境间的权衡关系），RSP可弥合两类算法的鸿沟：一类假设动物最优移动（当Ѳ趋近于无穷大时，RSP等价于最小成本路径（least cost path, LCP）），另一类假设动物随机游走（当Ѳ→0时，RSP等价于现有模型）。 4. 我们利用该方法，识别了挪威境内受GPS监测的野生驯鹿（Rangifer t. tarandus）的迁徙廊道。研究表明，采用Ѳ的中间值可最优地预测驯鹿的移动模式，这体现了移动策略在最优化与探索间的权衡。模型校准可得到与实测数据高度契合的廊道-阻隔连续体，同时证实RSP的表现优于仅假设最优移动或随机游走的模型。 5. 本方法对动物可能用于导航真实景观的多尺度认知地图进行建模，同时推广了目前最常用的廊道识别算法。由于次优但非随机的移动策略可能广泛存在，本方法有望为众多物种预测出更具现实性的廊道-阻隔连续体。