面向整合渔业依赖数据与独立数据的零膨胀时空模型及优势抽样问题
摘要
可持续管理海洋生态系统对维持健康的渔业资源至关重要， benefits from advanced scientific tools to accurately assess species distribution patterns。 In fisheries science, two primary data sources are used: fishery-independent data (FID), collected through systematic surveys, and fishery-dependent data (FDD), obtained from commercial fishing activities. While these sources provide complementary information, their distinct sampling schemes - systematic for FID and preferential for FDD - pose significant integration challenges. This study introduces a novel spatio-temporal model that integrates FID and FDD, addressing challenges associated with zero-inflation and preferential sampling (PS) common in ecological data. The model employs a six-layer structure to differentiate between presence-absence and biomass observations, offering a robust framework for ecological studies affected by PS biases. Simulation results demonstrate the model's accuracy in parameter estimation across diverse PS scenarios and its ability to detect preferential signals. Application to the study of the distribution patterns of the European sardine populations along the southern Portuguese continental shelf illustrates the model's effectiveness in integrating diverse data sources and incorporating environmental and vessel-specific covariates. The model reveals spatio-temporal variability in sardine presence and biomass, providing actionable insights for fisheries management. Beyond ecology, this framework offers broad applicability to data integration challenges in other disciplines.
引用
@article{arxiv.2509.09336,
title = {A Zero-Inflated Spatio-Temporal Model for Integrating Fishery-Dependent and Independent Data under Preferential Sampling},
author = {Daniela Silva and Raquel Menezes and Gonçalo Araújo and Ana Machado and Renato Rosa and Ana Moreno and Alexandra Silva and Susana Garrido},
journal= {arXiv preprint arXiv:2509.09336},
year = {2025}
}