Henrique Corrêa Giacomini, Derrick T. de Kerckhove, Victoria Kopf, Cindy Chu
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Further progress in the field requires new methods that are flexible enough to combine multiple trophic groups and sampling gears into a single size spectrum estimate, while taking advantage of more accurate distributional approaches. The method we propose in this paper fills this gap by deriving the distribution of observed sizes explicitly from the underlying power-law spectrum and gear selectivity functions. It specifies likelihoods as a product of two components: (i) the probability of belonging to a given group and (ii) the probability distribution within the group. Using Bayesian estimation, we applied the method to surveys of phytoplankton, zooplankton, and fishes in lakes of Quetico Provincial Park, northwestern Ontario, using Van Dorn samplers, zooplankton nets, gillnets, and hydroacoustics. The results show that the spectra estimated from subsets of trophic groups or gears are weak predictors of more complete spectra, highlighting the importance of using more inclusive community data. 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引用次数: 0
摘要
体型光谱可用于评估全球海洋和淡水生态系统的状况和功能。其使用的理论基础是将营养相互作用的动态与生态群落中丰度随体型下降的幂律联系起来。近期有关经验性大小谱估计的论文认为,幂律概率分布的最大似然估计法(Maximum Likelihood Estimation)比传统的线性回归法更准确。目前使用的最大似然估计法尺寸谱估计的一个主要局限是,它们不能考虑多重取样方案的使用,也不能考虑渔具尺寸选择性造成的扭曲,因此它们仅限于相对狭窄的分类群和尺寸范围。该领域的进一步发展需要新的方法,这些方法必须足够灵活,能够将多个营养群组和取样渔具结合到单一的大小谱估计中,同时利用更精确的分布方法。我们在本文中提出的方法填补了这一空白,即从基本的幂律谱和渔具选择性函数中明确推导出观测到的大小分布。它将似然值指定为两个部分的乘积:(i) 属于某一群体的概率和 (ii) 群体内的概率分布。利用贝叶斯估计法,我们使用 Van Dorn 采样器、浮游动物网、刺网和水声对安大略省西北部 Quetico 省立公园湖泊中的浮游植物、浮游动物和鱼类进行了调查。结果表明,从营养群组或渔具子集估算出的光谱对更完整光谱的预测能力较弱,这突出了使用更具包容性的群落数据的重要性。对似然率的双分量划分也有助于证明组间谱系斜率的存在,总体上比组内斜率陡峭,这表明整个大小谱系中营养转移的异质性是构建这些生态系统的一个重要因素。
Statistical modelling of aquatic size spectra: integrating data from multiple taxa and sampling methods
Size spectra are used to assess the status and functioning of marine and freshwater ecosystems worldwide. Their use is underpinned by theory linking the dynamics of trophic interactions to a power-law decline of abundance with body size in ecological communities. Recent papers on empirical size spectrum estimation have argued for Maximum Likelihood Estimation of power-law probability distributions as a more accurate alternative to traditional linear regression approaches. One major limitation of currently used size spectrum estimators from Maximum Likelihood Estimation is that they cannot account for the use of multiple sampling protocols, nor the distortions caused by gear size selectivity, and therefore they become restricted to a relatively narrow taxonomic group and size range. Further progress in the field requires new methods that are flexible enough to combine multiple trophic groups and sampling gears into a single size spectrum estimate, while taking advantage of more accurate distributional approaches. The method we propose in this paper fills this gap by deriving the distribution of observed sizes explicitly from the underlying power-law spectrum and gear selectivity functions. It specifies likelihoods as a product of two components: (i) the probability of belonging to a given group and (ii) the probability distribution within the group. Using Bayesian estimation, we applied the method to surveys of phytoplankton, zooplankton, and fishes in lakes of Quetico Provincial Park, northwestern Ontario, using Van Dorn samplers, zooplankton nets, gillnets, and hydroacoustics. The results show that the spectra estimated from subsets of trophic groups or gears are weak predictors of more complete spectra, highlighting the importance of using more inclusive community data. The two-component partitioning of likelihoods also helped demonstrate the existence of between-group spectrum slopes that were overall steeper than within-group slopes, indicating that heterogeneity of trophic transfers across the size spectrum is an important factor structuring these ecosystems.
期刊介绍:
The journal publishes articles on the following themes and topics:
• Original articles focusing on ecosystem-based sciences, ecosystem health and management of marine and aquatic ecosystems
• Reviews, invited perspectives and keynote contributions from conferences
• Special issues on important emerging topics, themes, and ecosystems (climate change, invasive species, HABs, risk assessment, models)