Warming from climate change and resulting increases in energy stored in the oceans is causing changes in the hydrodynamics and biogeochemistry of marine systems, exacerbating current challenges facing marine fisheries. Although studies have evaluated effects of rising temperatures on marine species, few have looked at these impacts along with other environmental drivers over long time periods. In this study, we associate long‐term density of blacklip abalone to changing oceanographic conditions in a climate change ‘hot‐spot’ off southeast Australia. We downscaled and hind‐casted existing hydrodynamic models to provide information on waves and currents over 25 yr and used this information to run biophysical connectivity models. We combined the connectivity models with 21 yr of data on abalone density, temperature, seafloor habitat, and the effects of a disease outbreak in an machine learning modeling approach to develop a spatio‐temporal model of abalone density. We found that the combination of temperature, connectivity, current speed, wave orbital velocity, fishery catch, depth, reef structure and a disease outbreak explain 70% of variation in abalone density and allowed us to create 30 m resolution predictive grids with 75% accuracy. An emerging hotspot analysis run on the individual predictive grids from each year detected a predominance of low‐density grids across the region, with 49.5% of cells classified as cold spots, 14.3% as hotspots and 36.2% with no significant patterns observed. This type of spatio‐temporal analysis provides important insights into how changing environmental conditions are impacting density in an important fishery species, allowing for better adaptive management in the face of future climate change.

中文翻译：

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利用物种分布模型评估海洋条件变化对澳大利亚东南部鲍鱼密度的长期影响

气候变化导致的变暖以及海洋中储存的能量增加，导致海洋系统的水动力和生物地球化学发生变化，加剧了海洋渔业目前面临的挑战。尽管研究已经评估了温度升高对海洋生物的影响，但很少有人与其他环境驱动因素一起长期研究这些影响。在这项研究中，我们将黑唇鲍鱼的长期密度与澳大利亚东南部气候变化“热点”的海洋学条件相联系。我们对现有的水动力模型进行了缩减和后播，以提供有关25年以上的波和流的信息，并使用此信息来运行生物物理连通性模型。我们将连通性模型与21年的鲍鱼密度，温度，海底栖息地，机器学习建模方法中疾病爆发的影响，以开发鲍鱼密度的时空模型。我们发现温度，连通性，当前速度，海浪轨道速度，渔业捕捞，深度，礁石结构和疾病暴发的综合解释了鲍​​鱼密度的70％变化，并允许我们创建7 m精度的30 m分辨率预测网格。每年对单个预测网格进行的新兴热点分析发现，该区域的低密度网格占主导地位，其中49.5％的细胞被归类为冷点，14.3％的细胞被归类为热点，36.2％的细胞未被观察到显着模式。这种类型的时空分析提供了重要的见解，以了解不断变化的环境条件如何影响重要渔业物种的密度，