000131678 001__ 131678
000131678 005__ 20241125101141.0
000131678 0247_ $$2doi$$a10.1007/s10651-023-00583-6
000131678 0248_ $$2sideral$$a137188
000131678 037__ $$aART-2023-137188
000131678 041__ $$aeng
000131678 100__ $$0(orcid)0000-0003-3859-0248$$aCastillo-Mateo, Jorge$$uUniversidad de Zaragoza
000131678 245__ $$aSpace-time multi-level modeling for zooplankton abundance employing double data fusion and calibration
000131678 260__ $$c2023
000131678 5060_ $$aAccess copy available to the general public$$fUnrestricted
000131678 5203_ $$aAn 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.
000131678 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000131678 590__ $$a3.0$$b2023
000131678 592__ $$a0.605$$b2023
000131678 591__ $$aSTATISTICS & PROBABILITY$$b16 / 168 = 0.095$$c2023$$dQ1$$eT1
000131678 593__ $$aEnvironmental Science (miscellaneous)$$c2023$$dQ2
000131678 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b22 / 135 = 0.163$$c2023$$dQ1$$eT1
000131678 593__ $$aStatistics, Probability and Uncertainty$$c2023$$dQ2
000131678 591__ $$aENVIRONMENTAL SCIENCES$$b169 / 358 = 0.472$$c2023$$dQ2$$eT2
000131678 593__ $$aStatistics and Probability$$c2023$$dQ2
000131678 594__ $$a5.9$$b2023
000131678 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000131678 700__ $$aGelfand, Alan E.
000131678 700__ $$aHudak, Christine A.
000131678 700__ $$aMayo, Charles A.
000131678 700__ $$aSchick, Robert S.
000131678 7102_ $$12007$$2265$$aUniversidad de Zaragoza$$bDpto. Métodos Estadísticos$$cÁrea Estadís. Investig. Opera.
000131678 773__ $$g30, 4 (2023), 769-795$$pEnviron. ecol. stat.$$tENVIRONMENTAL AND ECOLOGICAL STATISTICS$$x1352-8505
000131678 8564_ $$s3718769$$uhttps://zaguan.unizar.es/record/131678/files/texto_completo.pdf$$yVersión publicada
000131678 8564_ $$s1336483$$uhttps://zaguan.unizar.es/record/131678/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000131678 909CO $$ooai:zaguan.unizar.es:131678$$particulos$$pdriver
000131678 951__ $$a2024-11-22-12:02:35
000131678 980__ $$aARTICLE