Inferring fine scale wild species distribution from spatially aggregated data
| Type | Article | ||||||||
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| Acceptance Date | 2023-01 IN PRESS | ||||||||
| Language | English | ||||||||
| Author(s) | Alglave Baptiste1, Kristensen Kasper2, Rivot Etienne1, Woillez Mathieu 3, Vermard Youen4, Etienne Marie-Pierre5 |
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| Affiliation(s) | 1 : DECOD (Ecosystem Dynamics and Sustainability), IFREMER, Institut Agro, INRAE, Nantes, France 2 : Institute for Aquatic Resources, Section for Marine Living Resources, Technical University of Denmark, Kemitorvet, Kongens Lyngby, Denmark 3 : DECOD (Ecosystem Dynamics and Sustainability), IFREMER, Institut Agro, INRAE, Brest, France 4 : DECOD (Ecosystem Dynamics and Sustainability), IFREMER, Institut Agro, INRAE, Nantes, France 5 : Mathematical Research Institute of Rennes IRMAR, Rennes University, Rennes, France |
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| Source | Journal of the Royal Statistical Society: Series C (Applied Statistics) (0035-9254) (Wiley / Blackwell) In Press | ||||||||
| Keyword(s) | spatial statistics, change of support, integrated hierarchical model, species distribution model, fisheries data. | ||||||||
| Abstract | In spatial ecology, huge amount of aggregated and non-aggregated spatial data offer possibilities to map wild species distribution. However, this requires to properly handle the difference in spatial resolution between the different data sources. Such issue is often referred as the change of support (COS) problem. In this paper, we develop a hierarchical approach that allows (1) to handle COS for a mixture of zero-inflated positive continuous data and (2) to combine fine scale data and aggregated data. We assess the framework through simulations and apply it on real data for the common sole of the Bay of Biscay. |
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