标题： 复杂区域的空间高斯场及其在海洋大型动物保护中的应用 显示英文标题

标题： Spatial Gaussian fields for complex areas with application to marine megafauna conservation

摘要： 空间高斯场（SGFs）被广泛用于建模海洋大型动物的分布，但传统上依赖于各向同性和平稳性的假设，在具有海岸线、岛屿和深度梯度等部分移动障碍的复杂生态环境中，这些条件往往不现实。 现有的空间模型通常将这些障碍视为完全不可渗透，完全阻止物种的移动和扩散，或者完全不存在，这不能充分代表大多数现实情况。 为解决这一限制，我们引入了透明障碍模型，这是空间高斯场的一个扩展，明确地结合了不同渗透水平的障碍。 该模型为不同的障碍区域分配空间变化的范围参数，使关于障碍渗透性的生态和地理知识可以直接指导模型规范。 通过使用集成嵌套拉普拉斯近似（INLA）框架结合随机偏微分方程（SPDEs），这种方法保持了计算效率，确保在大型复杂空间域中仍能可行应用。我们通过在红海儒艮（Dugong dugon）分布数据上的应用，展示了透明障碍模型的实际效用和灵活性。 显示英文摘要

摘要： Spatial Gaussian fields (SGFs) are widely employed in modeling the distributions of marine megafauna, yet they traditionally rely on assumptions of isotropy and stationarity, conditions that often prove unrealistic in complex ecological environments featuring coastlines, islands, and depth gradients acting as partial movement barriers. Existing spatial models typically treat these barriers as either fully impermeable, completely blocking species movement and dispersal, or entirely absent, which inadequately represents most real-world scenarios. To address this limitation, we introduce the Transparent Barrier Model, an extension of spatial Gaussian fields that explicitly incorporates barriers with varying levels of permeability. The model assigns spatially varying range parameters to distinct barrier regions, allowing ecological and geographical knowledge about barrier permeability to directly inform model specifications. This approach maintains computational efficiency by utilizing the integrated nested Laplace approximation (INLA) framework combined with stochastic partial differential equations (SPDEs), ensuring feasible application even in large, complex spatial domains.We demonstrate the practical utility and flexibility of the Transparent Barrier Model through its application to dugong (Dugong dugon) distribution data from the Red Sea.