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| Petitgas, Pierre; Renard, Didier; Desassis, Nicolas; Huret, Martin; Romagnan, Jean-baptiste; Doray, Mathieu; Woillez, Mathieu; Rivoirard, Jacques. |
| This paper presents a novel application of the geostatistical multivariate method known as min–max autocorrelation factors (MAFs) for analysing fisheries survey data in a space–time context. The method was used to map essential fish habitats and evaluate the variability in time of their occupancy. Research surveys at sea on marine fish stocks have been undertaken for several decades now. The data are time series of yearly maps of fish density, making it possible to analyse the space–time variability in fish spatial distributions. Space–time models are key to addressing conservation issues requiring the characterization of variability in habitat maps over time. Here, the variability in fisheries survey data series is decomposed in space and time to address... |
| Tipo: Text |
Palavras-chave: MAF; Space-time; Habitat; Mapping; Sardine; Bay of Biscay. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00599/71157/69915.pdf |
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| Petitgas, Pierre; Woillez, Mathieu; Doray, Mathieu; Rivoirard, Jacques. |
| Marine research survey data on fish stocks often show a small proportion of very high-density values, as for many environmental data. This makes the estimation of second-order statistics, such as the variance and the variogram, non-robust. The high fish density values are generated by fish aggregative behaviour, which may vary greatly at small scale in time and space. The high values are thus imprecisely known, both in their spatial occurrence and order of magnitude. To map such data, three indicator-based geostatistical methods were considered, the top-cut model, min–max autocorrelation factors (MAF) of indicators, and multiple indicator kriging. In the top-cut and MAF approaches, the variable is decomposed into components and the most continuous ones... |
| Tipo: Text |
Palavras-chave: Top-cut; MAF; Indicators; Co-kriging; Skewed distribution; Anchovy; Bay of Biscay; Fisheries survey data; Aggregation. |
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00429/54021/55339.pdf |
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