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. 全球各地生境的丧失、破碎化与退化，使得学界愈发关注识别能够支撑动物移动的景观特征（廊道，corridor），或是阻碍其移动的景观特征（障碍，barrier）。当前多数用于预测动物移动廊道的算法，要么假设动物会最优地穿行于偏好生境中（如最小成本路径法），要么将其视为随机游走者（如现有模型），但这两种极端情形均不符合实际情况。
2. 我们提出，廊道与障碍实为一体两面，动物所感知的景观是连接（分隔）个体完成特定生态过程的功能区域的时空动态廊道-障碍连续体。基于这一概念框架，我们提出一种全新的方法论路径：利用高分辨率的个体水平移动数据，以更高的真实度预测廊道-障碍连续体。
3. 我们的方法包含两项创新。其一，我们采用步选择函数（step selection functions, SSF）生成摩擦图，以量化连续定位点间战术移动步的廊道-障碍连续体。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，该算法可基于摩擦图预测功能区域间战略移动的廊道-障碍连续体。通过调节参数Ѳ（该参数控制环境探索与最优利用间的权衡关系），RSP在两类极端算法之间搭建了桥梁：一类假设动物进行最优移动（当Ѳ趋近于无穷大时，RSP等价于最小成本路径（least cost path, LCP）），另一类假设动物进行随机游走（当Ѳ→0时，RSP趋近于现有模型）。
4. 我们利用该方法对挪威境内受GPS监测的野生驯鹿（Rangifer t. tarandus）的迁徙廊道进行了识别。研究表明，采用Ѳ的中间值可最佳拟合驯鹿的移动模式，这体现了优化与探索间的移动权衡关系。通过模型校准可识别出与实测数据高度吻合的廊道-障碍连续体，且证实RSP的表现优于仅假设最优移动或随机游走的模型。
5. 所提出的方法可对动物导航真实景观时可能采用的多尺度认知地图进行建模，同时推广了当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，我们的方法有望为众多物种预测出更具真实度的廊道-障碍连续体。