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首页> 外文期刊>ICES Journal of Marine Science >Modelling the spatial-temporal distributions and associated determining factors of a keystone pelagic fish
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Modelling the spatial-temporal distributions and associated determining factors of a keystone pelagic fish

机译：建模空间分布及关联的梯形骨岩鱼类的确定因子

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摘要

Mobile pelagic species habitat is structured around dynamic oceanographic and ecological processes that operate and interact horizontally and vertically throughout the water column and change over time. Due to their extensive movements, pelagic species distributions are often poorly understood. We use the Maxent species distribution model to assess how changes in the relative importance of modelled oceanographic (e.g. temperature) and climatic variables (e.g. the North Atlantic Oscillation) over 17 years affect the monthly average horizontal and vertical distribution of a keystone pelagic forage species, Atlantic Canadian capelin (Mallotus villosus). We show that the range and distribution of capelin occurrence probabilities vary across horizontal and vertical axes over time, with binary presence/absence predictions indicating capelin occupy between 0.72% (April) and 3.45% (November) of the total modelled space. Furthermore, our analysis reveals that the importance of modelled oceanographic variables, such as temperature, varies between months (44% permutation importance in August to 2% in May). By capturing the spatial dynamics of capelin over horizontal, vertical, and temporal axes, our analysis builds on work that improves our understanding and predictive modelling ability of pelagic species distributions under current and future conditions for proactive ecosystem-based management.
机译：移动骨盆物种栖息地是在动态海洋学和生态过程中构建的，该过程在整个水柱中水平和垂直操作和互动并随时间变化。由于其广泛的运动，朴实物种分布往往明白很差。我们使用MaxEnt物种分布模型来评估建模的海洋（例如温度）和气候变量（例如北大洋振荡）超过17年的相对重要性的变化影响梯形骨质牧草物种的月平均水平和垂直分布，大西洋加拿大卡塞林（Mallotus Villosus）。我们表明，卡佩林的范围和分布在水平和垂直轴上随着时间的推移而变化，二元存在/缺失预测，表明Capelin占总建模空间的0.72％（4月）和3.45％（11月）。此外，我们的分析表明，建模的海洋变量（如温度）之间的重要性在月份之间变化（8月份的44％至2％至2％）。通过在水平，垂直和颞轴上捕获卡塞林的空间动态，我们的分析在工作中构建了我们在基于主动生态系统管理的当前和未来条件下提高了对皮拉格物种分布的理解和预测性建模能力。

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