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Predicting abundance indices in areas without coverage with a latent spatio-temporal Gaussian model
ICES Journal of Marine Science ( IF 3.3 ) Pub Date : 2021-04-07 , DOI: 10.1093/icesjms/fsab073
Olav Nikolai Breivik 1 , Fredrik Aanes 1 , Guldborg Søvik 2 , Asgeir Aglen 2 , Sigbjørn Mehl 2 , Espen Johnsen 2
Affiliation  

A general spatio-temporal abundance index model is introduced and applied on a case study for North East Arctic cod in the Barents Sea. We demonstrate that the model can predict abundance indices by length and identify a significant population density shift in northeast direction for North East Arctic cod. Varying survey coverage is a general concern when constructing standardized time series of abundance indices, which is challenging in ecosystems impacted by climate change and spatial variable population distributions. The applied model provides an objective framework that accommodates for missing data by predicting abundance indices in areas with poor or no survey coverage using latent spatio-temporal Gaussian random fields. The model is validated, and no violations are observed.

中文翻译:

使用潜在时空高斯模型预测无覆盖区域的丰度指数

介绍了一个通用的时空丰度指数模型,并将其应用于巴伦支海东北北极鳕鱼的案例研究。我们证明该模型可以通过长度预测丰度指数,并确定东北北极鳕鱼在东北方向的显着种群密度变化。在构建标准化的丰度指数时间序列时,不同的调查覆盖范围是一个普遍关注的问题,这在受气候变化和空间可变人口分布影响的生态系统中具有挑战性。所应用的模型提供了一个客观的框架,通过使用潜在的时空高斯随机场预测调查覆盖率较差或没有调查覆盖率的地区的丰度指数来适应缺失数据。该模型经过验证,没有观察到违规行为。
更新日期:2021-04-07
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