Scientific tools capable of identifying distribution patterns of species are important as they contribute to improve knowledge about biodiversity and species dynamics. The present study aims to estimate the spatiotemporal distribution of sardine (Sardina pilchardus, Walbaum 1792) in the Portuguese continental waters, relating the spatiotemporal variability of biomass index with the environmental conditions. Acoustic data was collected during Portuguese spring acoustic surveys (PELAGO) over a total of 16,370 hauls from 2000 to 2020 (gap in 2012). We propose a spatiotemporal species distribution model that relies on a two-part model for species presence and biomass under presence, such that the biomass process is defined as the product of these two processes. Environmental information is incorporated with time lags, allowing a set of lags with associated weights to be suggested for each explanatory variable. This approach makes the model more complete and realistic, capable of reducing prediction bias and mitigating outliers in covariates caused by extreme events. In addition, based on the posterior predictive distributions obtained, we propose a method of classifying the occupancy areas by the target species within the study region. This classification provides a quite helpful tool for decision makers aiming at marine sustainability and conservation. Supplementary materials accompanying this paper appear on-line.

能够识别物种分布模式的科学工具非常重要，因为它们有助于提高有关生物多样性和物种动态的知识。本研究旨在估计葡萄牙大陆水域沙丁鱼（ Sardina pilchardus ，Walbaum 1792）的时空分布，将生物量指数的时空变化与环境条件联系起来。声学数据是在葡萄牙春季声学调查 (PELAGO) 期间收集的，从 2000 年到 2020 年（2012 年有间隔）总共 16,370 次运输。我们提出了一种时空物种分布模型，该模型依赖于物种存在和存在下的生物量的两部分模型，因此生物量过程被定义为这两个过程的产物。环境信息与时间滞后相结合，允许为每个解释变量建议一组具有相关权重的滞后。这种方法使模型更加完整和真实，能够减少预测偏差并减轻极端事件引起的协变量异常值。此外，根据获得的后验预测分布，我们提出了一种按研究区域内的目标物种对占用区域进行分类的方法。这种分类为旨在实现海洋可持续性和保护的决策者提供了非常有用的工具。本文附带的补充材料出现在网上。