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. 全球范围内的生境丧失、破碎化与退化，使得学界愈发关注能够支撑动物移动的景观特征——即廊道（corridors），以及阻碍动物移动的景观特征——即障碍（barriers）。当前多数用于预测廊道的算法，均假设动物会以最优方式（如最小成本路径（least cost path）），或以随机游走的方式（如现有模型）穿行于偏好生境中，但这两种极端假设均不符合实际情况。
2. 本研究提出，廊道与障碍实为同一事物的两面，动物所感知的景观是一类时空动态的廊道-障碍连续体，该连续体连接（分隔）了个体可完成特定生态过程的功能区。基于这一概念框架，我们提出一种全新的方法论路径：利用高分辨率的基于个体的移动数据，以更高的现实合理性预测廊道-障碍连续体。
3. 本研究的方法包含两项创新。其一，我们采用步选择函数（SSF），针对连续定位间的战术性移动步，预测可量化廊道-障碍连续体的阻力图（friction maps）。其二，我们将随机最短路径算法（RSP）引入移动生态学领域：该算法基于阻力图，可预测功能区间战略性移动对应的廊道-障碍连续体。通过调节控制环境探索与最优利用间权衡的参数Ѳ，RSP能够弥合两类算法的鸿沟——一类假设动物以最优方式移动（当Ѳ趋近于无穷大时，RSP等价于最小成本路径（LCP）），另一类假设动物以随机游走方式移动（当Ѳ→0时，RSP趋近于现有模型）。
4. 利用该方法，我们为挪威境内受GPS监测的野生驯鹿（*Rangifer t. tarandus*）识别出了迁徙廊道。研究表明，当Ѳ取中间值时，对驯鹿移动的预测效果最佳，这体现了优化与探索间的移动权衡。通过模型校准，我们可识别出与实证数据高度契合的廊道-障碍连续体，并证实RSP的表现优于仅假设最优移动或随机游走的模型。
5. 本研究提出的方法，对动物可能用于导航真实景观的多尺度认知地图进行了建模，同时对最常用的廊道识别算法进行了泛化推广。由于次优但非随机的移动策略可能广泛存在，因此本方法有望为众多物种预测出更具现实合理性的廊道-障碍连续体。