Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling
International scientific fishery survey programmes systematically collect samples of target stocks’ biomass and abundance and use them as the basis to estimate stock status in the framework of stock assessment models. The research surveys can also inform decision makers about Essential Fish Habitat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.919339/full |
| _version_ | 1811225462843637760 |
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| author | Gianpaolo Coro Pasquale Bove Enrico Nicola Armelloni Enrico Nicola Armelloni Francesco Masnadi Francesco Masnadi Martina Scanu Martina Scanu Giuseppe Scarcella |
| author_facet | Gianpaolo Coro Pasquale Bove Enrico Nicola Armelloni Enrico Nicola Armelloni Francesco Masnadi Francesco Masnadi Martina Scanu Martina Scanu Giuseppe Scarcella |
| author_sort | Gianpaolo Coro |
| collection | DOAJ |
| description | International scientific fishery survey programmes systematically collect samples of target stocks’ biomass and abundance and use them as the basis to estimate stock status in the framework of stock assessment models. The research surveys can also inform decision makers about Essential Fish Habitat conservation and help define harvest control rules based on direct observation of biomass at the sea. However, missed survey locations over the survey years are common in long-term programme data. Currently, modelling approaches to filling gaps in spatiotemporal survey data range from quickly applicable solutions to complex modelling. Most models require setting prior statistical assumptions on spatial distributions, assuming short-term temporal dependency between the data, and scarcely considering the environmental aspects that might have influenced stock presence in the missed locations. This paper proposes a statistical and machine learning based model to fill spatiotemporal gaps in survey data and produce robust estimates for stock assessment experts, decision makers, and regional fisheries management organizations. We apply our model to the SoleMon survey data in North-Central Adriatic Sea (Mediterranean Sea) for 4 stocks: Sepia officinalis, Solea solea, Squilla mantis, and Pecten jacobaeus. We reconstruct the biomass-index (i.e., biomass over the swept area) of 10 locations missed in 2020 (out of the 67 planned) because of several factors, including COVID-19 pandemic related restrictions. We evaluate model performance on 2019 data with respect to an alternative index that assumes biomass proportion consistency over time. Our model’s novelty is that it combines three complementary components. A spatial component estimates stock biomass-index in the missed locations in one year, given the surveyed location’s biomass-index distribution in the same year. A temporal component forecasts, for each missed survey location, biomass-index given the data history of that haul. An environmental component estimates a biomass-index weighting factor based on the environmental suitability of the haul area to species presence. Combining these components allows understanding the interplay between environmental-change drivers, stock presence, and fisheries. Our model formulation is general enough to be applied to other survey data with lower spatial homogeneity and more temporal gaps than the SoleMon dataset. |
| first_indexed | 2024-04-12T09:07:38Z |
| format | Article |
| id | doaj.art-d2e35e31175f47688c6196138d911e69 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-04-12T09:07:38Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-d2e35e31175f47688c6196138d911e692022-12-22T03:39:04ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452022-07-01910.3389/fmars.2022.919339919339Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental ModellingGianpaolo Coro0Pasquale Bove1Enrico Nicola Armelloni2Enrico Nicola Armelloni3Francesco Masnadi4Francesco Masnadi5Martina Scanu6Martina Scanu7Giuseppe Scarcella8Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), Pisa, ItalyInstitute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), Pisa, ItalyInstitute for Biological Resources and Marine Biotechnology (IRBIM), National Research Council of Italy (CNR), Ancona, ItalyDepartment of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Bologna, ItalyInstitute for Biological Resources and Marine Biotechnology (IRBIM), National Research Council of Italy (CNR), Ancona, ItalyDepartment of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Bologna, ItalyInstitute for Biological Resources and Marine Biotechnology (IRBIM), National Research Council of Italy (CNR), Ancona, ItalyDepartment of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Bologna, ItalyInstitute for Biological Resources and Marine Biotechnology (IRBIM), National Research Council of Italy (CNR), Ancona, ItalyInternational scientific fishery survey programmes systematically collect samples of target stocks’ biomass and abundance and use them as the basis to estimate stock status in the framework of stock assessment models. The research surveys can also inform decision makers about Essential Fish Habitat conservation and help define harvest control rules based on direct observation of biomass at the sea. However, missed survey locations over the survey years are common in long-term programme data. Currently, modelling approaches to filling gaps in spatiotemporal survey data range from quickly applicable solutions to complex modelling. Most models require setting prior statistical assumptions on spatial distributions, assuming short-term temporal dependency between the data, and scarcely considering the environmental aspects that might have influenced stock presence in the missed locations. This paper proposes a statistical and machine learning based model to fill spatiotemporal gaps in survey data and produce robust estimates for stock assessment experts, decision makers, and regional fisheries management organizations. We apply our model to the SoleMon survey data in North-Central Adriatic Sea (Mediterranean Sea) for 4 stocks: Sepia officinalis, Solea solea, Squilla mantis, and Pecten jacobaeus. We reconstruct the biomass-index (i.e., biomass over the swept area) of 10 locations missed in 2020 (out of the 67 planned) because of several factors, including COVID-19 pandemic related restrictions. We evaluate model performance on 2019 data with respect to an alternative index that assumes biomass proportion consistency over time. Our model’s novelty is that it combines three complementary components. A spatial component estimates stock biomass-index in the missed locations in one year, given the surveyed location’s biomass-index distribution in the same year. A temporal component forecasts, for each missed survey location, biomass-index given the data history of that haul. An environmental component estimates a biomass-index weighting factor based on the environmental suitability of the haul area to species presence. Combining these components allows understanding the interplay between environmental-change drivers, stock presence, and fisheries. Our model formulation is general enough to be applied to other survey data with lower spatial homogeneity and more temporal gaps than the SoleMon dataset.https://www.frontiersin.org/articles/10.3389/fmars.2022.919339/fulltime series analysisspatial interpolationecological niche modellingscientific surveysAdriatic seamarine surveys |
| spellingShingle | Gianpaolo Coro Pasquale Bove Enrico Nicola Armelloni Enrico Nicola Armelloni Francesco Masnadi Francesco Masnadi Martina Scanu Martina Scanu Giuseppe Scarcella Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling Frontiers in Marine Science time series analysis spatial interpolation ecological niche modelling scientific surveys Adriatic sea marine surveys |
| title | Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling |
| title_full | Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling |
| title_fullStr | Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling |
| title_full_unstemmed | Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling |
| title_short | Filling Gaps in Trawl Surveys at Sea through Spatiotemporal and Environmental Modelling |
| title_sort | filling gaps in trawl surveys at sea through spatiotemporal and environmental modelling |
| topic | time series analysis spatial interpolation ecological niche modelling scientific surveys Adriatic sea marine surveys |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2022.919339/full |
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