Author(s): Silva, Daniela ; Menezes, Raquel ; Araújo, Gonçalo ; Rosa, Renato ; Moreno, Ana ; Silva, Alexandra ; Garrido, Susana
Date: 2025
Persistent ID: http://hdl.handle.net/10362/188816
Origin: Repositório Institucional da UNL
Subject(s): Fish data; Geostatiscal modeling; Integrating data sources; Preferential sampling; Species distribution model; Statistics and Probability; Computers in Earth Sciences; Management, Monitoring, Policy and Law; SDG 14 - Life Below Water
Funding Information: This study received support from Portuguese funding provided through the Centre for Mathematics via the following projects: DOI 10.54499/UIDP/00013/2020 , DOI 10.54499/UIDB/00013/2020 , and the Portuguese Foundation for Science and Technology (FCT), Portugal through the Individual PhD Scholarship PD/BD/150535/2019 , the research grant UIDB / 04292/2020 , and the project PTDC/MAT-STA/28243/2017. Additionally, support was provided by the SARDINHA2030 project ( MAR-111.4.1-FEAMPA-00001 ). Publisher Copyright: © 2025 The Authors
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 application 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.
