131678 20241125101141.0 doi 10.1007/s10651-023-00583-6 sideral 137188 ART-2023-137188 eng Castillo-Mateo, Jorge Universidad de Zaragoza (orcid)0000-0003-3859-0248 Space-time multi-level modeling for zooplankton abundance employing double data fusion and calibration 2023 Access copy available to the general public Unrestricted An important objective for marine biologists is to forecast the distribution and abundance of planktivorous marine predators. To do so, it is critically important to understand the spatiotemporal dynamics of their prey. Here, the prey we study are zooplankton and we build a novel space-time hierarchical fusion model to describe the distribution and abundance of zooplankton species in Cape Cod Bay (CCB), MA, USA. The data were collected irregularly in space and time at sites within the first half of the year over a 17 year period, using two different sampling methods. We focus on sea surface zooplankton abundance and incorporate sea surface temperature as a primary driver, also collected with two different sampling methods. So, with two sources for each, we observe true abundance or true sea surface temperature with measurement error. To account for such error, we apply calibrations to align the data sources and use the fusion model to develop a prediction of daily spatial zooplankton abundance surfaces throughout CCB. To infer average abundance on a given day within a given year in CCB, we present a marginalization of the zooplankton abundance surface. We extend the inference to consider abundance averaged to a bi-weekly or annual scale as well as to make an annual comparison of abundance. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.0 2023 STATISTICS & PROBABILITY 16 / 168 = 0.095 2023 Q1 T1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS 22 / 135 = 0.163 2023 Q1 T1 ENVIRONMENTAL SCIENCES 169 / 358 = 0.472 2023 Q2 T2 0.605 2023 Environmental Science (miscellaneous) 2023 Q2 Statistics, Probability and Uncertainty 2023 Q2 Statistics and Probability 2023 Q2 5.9 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Gelfand, Alan E. Hudak, Christine A. Mayo, Charles A. Schick, Robert S. 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 30, 4 (2023), 769-795 Environ. ecol. stat. ENVIRONMENTAL AND ECOLOGICAL STATISTICS 1352-8505 3718769 http://zaguan.unizar.es/record/131678/files/texto_completo.pdf Versión publicada 1336483 http://zaguan.unizar.es/record/131678/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:131678 articulos driver 2024-11-22-12:02:35 ARTICLE