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首页> 外文期刊>Journal of great lakes research >Modeling spatiotemporal variabilities of length-at-age growth characteristics for slow-growing subarctic populations of Lake Whitefish, using hierarchical Bayesian statistics
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Modeling spatiotemporal variabilities of length-at-age growth characteristics for slow-growing subarctic populations of Lake Whitefish, using hierarchical Bayesian statistics

机译：使用分层贝叶斯统计模型为怀特菲什湖缓慢增长的亚北极种群的成年生长特征时空变化建模

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摘要

Though Lake Whitefish are ecologically, culturally and economically important to aboriginal communities in the Northwest Territories, Canada, growth characteristics of the fish populations have not received extensive interpretations, resulting in a lack of quantitative information to support fisheries management efforts in subarctic great lake systems. The overall objective of this study is to investigate spatiotemporal variations of growth characteristics of Lake Whitefish populations in Great Slave Lake (GSL) from 1972-2009. Using hierarchical Bayesian statistics, we structured four candidate growth models: generalized (GGM), logistic (LGM), Gompertz (PGM), and von Bertalanffy (VBM), with four parameterization scenarios combining all possible options of varying or constant L-infinity and K. In terms of deviance information criterion (DIC) and multimodel inference (MMI), the plausibility of the candidate models was evaluated to select the best combinations of growth models and the parameter scenarios. The GGM with varying L-infinity and K best delineated the fish growth characteristics in almost all areas of GSL, while the fish growth model parameterized with constant L-infinity and varying K performed best in the shallow western basin. The VGM where L-infinity and K were varied partially described fish growth in the shallow waters. Applying the MMI-based growth analysis, we found that smaller and slower-growing fish were mainly distributed in deep waters, while larger and faster-growing fish inhabited shallow waters. These spatiotemporal variations of fish growth characteristics have been attributed to the presence of coupled impacts derived from both climate-driven and anthropogenic events. Crown Copyright (C) 2015 Published by Elsevier B.V. on behalf of International Association for Great Lakes Research. All rights reserved.
机译：尽管怀特菲什湖对加拿大西北地区的原住民社区在生态，文化和经济上都很重要，但是鱼类种群的增长特征尚未得到广泛的解释，导致缺乏定量信息来支持北极大湖系统中的渔业管理工作。这项研究的总体目标是调查1972-2009年大奴湖（GSL）怀特菲什湖种群生长特征的时空变化。使用分层贝叶斯统计数据，我们构建了四个候选增长模型：广义（GGM），逻辑（LGM），贡普兹（PGM）和冯·贝塔兰菲（VBM），以及四个参数化方案，结合了可变或恒定L无限和恒定的所有可能选项。 K.根据偏差信息标准（DIC）和多模型推断（MMI），评估了候选模型的合理性，以选择增长模型和参数方案的最佳组合。改变L无限和K的GGM最好地描绘了GSL几乎所有区域的鱼类生长特征，而参数L恒定且K改变的鱼类生长模型在浅西部盆地表现最好。 L-无穷大和K发生变化的VGM部分描述了浅水鱼类的生长。应用基于MMI的生长分析，我们发现较小和生长较慢的鱼主要分布在深水区，而较大和生长较快的鱼则栖息在浅水区。鱼类生长特征的这些时空变化归因于气候驱动和人为事件引起的耦合影响。官方版权（C）2015，由Elsevier B.V.代表国际大湖研究协会出版。版权所有。

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