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 these issues, using MAFs. MAFs were originally developed for noise removal in hyperspectral multivariate data and are obtained using a specific double principal components analysis. Here, MAFs were used to extract the most continuous spatial components that are consistent in time, together with the time series of their amplitudes. MAFs formed an empirical isofactorial model of the data, which served for kriging in each year using all available information across the data series. The approach was applied on the spawning distributions of sardine in the Bay of Biscay from 2000 to 2017. A multivariate approach for dealing with space–time data was adapted here, because the evolution in time was highly variable. Maps were classified using the amplitudes of the MAFs, and groups of typical distributions were identified, which showed different occurrence probabilities in different periods.

中文翻译：

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使用最小-最大自相关因子分析空间分布中的时间变异：比斯开湾的沙丁鱼蛋

本文介绍了称为最小-最大自相关因子（MAF）的地统计多元方法在时空背景下分析渔业调查数据的新应用。该方法用于绘制基本的鱼类栖息地，并评估其生存时间的变异性。几十年来，已经对海洋鱼类种群进行了海上研究调查。数据是鱼类密度的年度图的时间序列，从而有可能分析鱼类空间分布的时空变化。时空模型是解决保护问题的关键，因为保护问题需要表征栖息地图随时间的变化。在这里，渔业调查数据系列的变异性在空间和时间上被分解，以利用MAF解决这些问题。MAF最初是为消除高光谱多元数据中的噪声而开发的，并使用特定的双主成分分析获得。在此，MAF用于提取时间上一致的最连续的空间成分及其幅度的时间序列。MAF形成了数据的经验等因模型，使用数据系列中的所有可用信息，每年都可用于克里金法。该方法应用于2000年至2017年比斯开湾沙丁鱼的产卵分布。此处对时空数据进行了多变量处理，因为时间的变化是高度可变的。使用MAF的幅度对地图进行分类，并识别出典型分布的组，