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Improving Marine Ecosystem Models with Biochemical Tracers
Annual Review of Marine Science ( IF 17.3 ) Pub Date : 2018-01-03 00:00:00 , DOI: 10.1146/annurev-marine-121916-063256
Heidi R. Pethybridge 1 , C. Anela Choy 2, 3 , Jeffrey J. Polovina 4 , Elizabeth A. Fulton 1
Affiliation  

Empirical data on food web dynamics and predator-prey interactions underpin ecosystem models, which are increasingly used to support strategic management of marine resources. These data have traditionally derived from stomach content analysis, but new and complementary forms of ecological data are increasingly available from biochemical tracer techniques. Extensive opportunities exist to improve the empirical robustness of ecosystem models through the incorporation of biochemical tracer data and derived indices, an area that is rapidly expanding because of advances in analytical developments and sophisticated statistical techniques. Here, we explore the trophic information required by ecosystem model frameworks (species, individual, and size based) and match them to the most commonly used biochemical tracers (bulk tissue and compound-specific stable isotopes, fatty acids, and trace elements). Key quantitative parameters derived from biochemical tracers include estimates of diet composition, niche width, and trophic position. Biochemical tracers also provide powerful insight into the spatial and temporal variability of food web structure and the characterization of dominant basal and microbial food web groups. A major challenge in incorporating biochemical tracer data into ecosystem models is scale and data type mismatches, which can be overcome with greater knowledge exchange and numerical approaches that transform, integrate, and visualize data.

中文翻译:


用生化示踪剂改善海洋生态系统模型

关于食物网动态和捕食者与猎物相互作用的经验数据为生态系统模型提供了基础,而生态系统模型已越来越多地用于支持海洋资源的战略管理。这些数据传统上是从胃内容物分析中得出的,但是越来越多的新的和补充形式的生态数据可以从生化示踪剂技术中获得。通过结合生化示踪剂数据和派生指标,存在广泛的机会来改善生态系统模型的经验稳健性,该领域由于分析技术的发展和先进的统计技术的发展而迅速扩大。在这里,我们探索了生态系统模型框架(物种,个体,并根据大小进行匹配),并将其与最常用的生化示踪剂(体组织和化合物特异性稳定同位素,脂肪酸和微量元素)进行匹配。从生化示踪剂得出的关键定量参数包括饮食组成,生态位宽度和营养位置的估计值。生化示踪剂还提供了对食物网结构的时空变化以及主要的基础和微生物食物网组特征的强大洞察力。将生化示踪剂数据纳入生态系统模型的主要挑战是规模和数据类型不匹配,可以通过更多的知识交流和转换,集成和可视化数据的数值方法来克服。从生化示踪剂得出的关键定量参数包括饮食组成,生态位宽度和营养位置的估计值。生化示踪剂还提供了对食物网结构的时空变化以及主要的基础和微生物食物网组特征的强大洞察力。将生化示踪剂数据纳入生态系统模型的主要挑战是规模和数据类型不匹配,可以通过更多的知识交流和转换,集成和可视化数据的数值方法来克服。从生化示踪剂得出的关键定量参数包括饮食组成,生态位宽度和营养位置的估计值。生化示踪剂还提供了对食物网结构的时空变化以及主要的基础和微生物食物网组特征的强大洞察力。将生化示踪剂数据纳入生态系统模型的主要挑战是规模和数据类型不匹配,可以通过更多的知识交流和转换,集成和可视化数据的数值方法来克服。

更新日期:2018-01-03
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