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 prompt increasing attention to identifying landscape features that support animal movement (corridors) or impede 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 spatio-temporally dynamic corridor-barrier continua connecting (separating) functional areas where individuals fulfill 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. Second, 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 multi-scale cognitive maps by which animals likely navigate real landscapes, and generalizes the most common algorithms for identifying corridors. Because sub-optimal, 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，使得人们愈发关注能够支持动物移动（廊道corridors）或阻碍动物移动（屏障barriers）的景观特征识别工作。当前多数用于预测动物移动廊道的算法，要么假设动物会以最优方式穿过偏好生境（如最小成本路径Least Cost Path），要么假设动物以随机游走方式移动（如电流模型Current Models），但这两种极端情形均不符合实际情况。
2. 我们提出，廊道与屏障实为同一事物的两面，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-屏障连续体。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移动数据，预测更具现实合理性的廊道-屏障连续体。
3. 我们的方法包含两项创新。其一，我们采用步长选择函数（Step Selection Functions, SSF）来预测摩擦图，用以量化连续定位点间战术性移动步长对应的廊道-屏障连续体。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：该算法基于摩擦图，可预测功能区域间战略性移动的廊道-屏障连续体。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能够弥合两种极端算法间的差距：当Ѳ趋近于无穷大时，RSP等价于最小成本路径（Least Cost Path）；当Ѳ趋近于0时，RSP则等价于电流模型（Current Models）。
4. 借助该方法，我们对挪威境内由GPS监测的野生驯鹿（Rangifer t. tarandus）开展了迁徙廊道识别工作。研究表明，当Ѳ取中间值时，驯鹿移动的预测效果最佳，这反映出其移动策略在优化与探索间存在权衡关系。模型校准可识别出与实证数据高度契合的廊道-屏障连续体，同时证明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用于在真实景观中导航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现实合理性的廊道-屏障连续体。