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

2025-10-01

AI Summary

Traditional spatial Gaussian process models assume isotropy and stationarity, leading to substantial modeling distortions in marine environments featuring semi-permeable barriers—such as coastlines, islands, and steep bathymetric gradients. To address this, we propose a transparent barrier model embedded within the SPDE–INLA Bayesian framework: it explicitly represents geographic barriers as spatially varying permeability filters and introduces a spatially heterogeneous range parameter to flexibly capture partial barrier effects. Furthermore, the model integrates ecological–geographic priors to enhance interpretability and ecological plausibility. Evaluated on dugong distribution data from the Red Sea, our approach significantly improves predictive accuracy and biological realism for species distribution modeling in complex coastal zones. It overcomes the oversimplification inherent in conventional methods—which treat barriers as either fully permeable or completely impermeable—by enabling graded, ecologically informed barrier representation.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Kernel Methods

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

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.

Problem

• Modeling marine megafauna distribution with realistic permeable barriers
• Overcoming unrealistic assumptions in spatial Gaussian field models
• Incorporating ecological barriers with varying permeability levels

Innovation

• Introduces Transparent Barrier Model for spatial Gaussian fields
• Assigns varying permeability parameters to barrier regions
• Uses INLA and SPDEs for computational efficiency