Accurately identifying spatial patterns of species distribution is crucial for scientific insight and societal benefit, aiding our understanding of species fluctuations. The increasing quantity and quality of ecological datasets present heightened statistical challenges, complicating spatial species dynamics comprehension. Addressing the complex task of integrating multiple data sources to enhance spatial fish distribution understanding in marine ecology, this study introduces a pioneering five-layer Joint model. The model adeptly integrates fishery-independent and fishery-dependent data, accommodating zero-inflated data and distinct sampling processes. A comprehensive simulation study evaluates the model performance across various preferential sampling scenarios and sample sizes, elucidating its advantages and challenges. Our findings highlight the model's robustness in estimating preferential parameters, emphasizing differentiation between presence-absence and biomass observations. Evaluation of estimation of spatial covariance and prediction performance underscores the model's reliability. Augmenting sample sizes reduces parameter estimation variability, aligning with the principle that increased information enhances certainty. Assessing the contribution of each data source reveals successful integration, providing a comprehensive representation of biomass patterns. Empirical validation within a real-world context further solidifies the model's efficacy in capturing species' spatial distribution. This research advances methodologies for integrating diverse datasets with different sampling natures further contributing to a more informed understanding of spatial dynamics of marine species.

翻译：准确识别物种分布的空间格局对于科学认知与社会效益至关重要，有助于理解物种数量波动。生态数据集数量与质量的不断提升带来了更高的统计挑战，使空间物种动态的理解复杂化。针对整合多源数据以提升海洋生态中鱼类空间分布认知这一复杂任务，本研究提出了一种创新的五层联合模型。该模型巧妙整合了渔业独立性与渔业依赖性数据，能够处理零膨胀数据及不同的采样过程。通过全面的模拟研究，评估了模型在不同偏好性采样情景与样本量下的性能，阐明了其优势与挑战。研究结果表明，该模型在估计偏好性参数方面具有稳健性，尤其强调区分存在-缺失观测与生物量观测。对空间协方差估计与预测性能的评估印证了模型的可靠性。增加样本量可降低参数估计的变异性，符合信息量增加提升确定性的原则。通过评估各数据源的贡献，证实了模型成功实现了数据整合，提供了生物量格局的综合表征。在真实场景中的实证验证进一步巩固了模型在捕捉物种空间分布方面的有效性。本研究推进了整合具有不同采样性质的多源数据集的方法论，为更深入理解海洋物种空间动态提供了支持。