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Hierarchical and dynamic seascapes: A quantitative framework for scaling pelagic biogeochemistry and ecology

机译:分层和动态海景:缩放中上层生物地球化学和生态学的定量框架

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摘要

Comparative analyses of oceanic ecosystems require an objective framework to define coherent study regions and scale the patterns and processes observed within them. We applied the hierarchical patch mosaic paradigm of landscape ecology to the study of the seasonal variability of the North Pacific to facilitate comparative analysis between pelagic ecosystems and provide spatiotemporal context for Eulerian time-series studies. Using 13-year climatologies of sea surface temperature (SST), photosyn-thetically active radiation (PAR), and chlorophyll a (chl-a), we classified seascapes in environmental space that were monthly-resolved, dynamic and nested in space and time. To test the assumption that seascapes represent coherent regions with unique biogeochemical function and to determine the hierarchical scale that best characterized variance in biogeochemical parameters, independent data sets were analyzed across seascapes using analysis of variance (ANOVA), nested-ANOVA and multiple linear regression (MLR) analyses. We also compared the classification efficiency (as defined by the ANOVA F-statistic) of resultant dynamic seascapes to a commonly-used static classification system. Variance of nutrients and net primary productivity (NPP) were well characterized in the first two levels of hierarchy of eight seascapes nested within three superseascapes (R~2 = 0.5-0.7). Dynamic boundaries at this level resulted in a nearly 2-fold increase in classification efficiency over static boundaries. MLR analyses revealed differential forcing on pCO_2 across seascapes and hierarchical levels and a 33% reduction in mean model error with increased partitioning (from 18.5 μatm to 12.0 μatm pCO_2). Importantly, the empirical influence of seasonality was minor across seascapes at all hierarchical levels, suggesting that seascape partitioning minimizes the effect of non-hydrographic variables. As part of the emerging field of pelagic seascape ecology, this effort provides an improved means of monitoring and comparing oceanographic biophysical dynamics and an objective, quantitative basis by which to scale data from local experiments and observations to regional and global biogeochemical cycles.
机译:海洋生态系统的比较分析需要一个客观的框架来定义一致的研究区域,并缩放在其中的观测模式和过程。我们将景观生态学的分层斑块镶嵌范例应用于北太平洋的季节变化研究,以促进中上层生态系统之间的比较分析,并为欧拉时间序列研究提供时空背景。使用海表温度(SST),光合有效辐射(PAR)和叶绿素a(chl-a)的13年气候,我们对环境空间中的海景进行了每月解析,动态分析并嵌套在空间和时间中。为了检验假设海景代表具有独特生物地球化学功能的相干区域并确定能最好地描述生物地球化学参数差异的层次结构规模,使用方差分析(ANOVA),嵌套ANOVA和多元线性回归分析了独立的数据集。 MLR)分析。我们还将最终动态海景的分类效率(由ANOVA F统计量定义)与常用的静态分类系统进行了比较。在嵌套在三个超级海景中的八个海景的前两个层次中,营养成分和净初级生产力(NPP)的差异得到了很好的表征(R〜2 = 0.5-0.7)。在此级别上,动态边界导致分类效率比静态边界提高了近2倍。 MLR分析显示,跨海景和分层级别对pCO_2的强迫作用不同,并且随着分区的增加(从18.5μatm到12.0μatmpCO_2),平均模型误差降低了33%。重要的是,季节性因素的经验影响在所有分层级别的海景中都是很小的,这表明海景分区最大程度地减少了非水文变量的影响。作为远洋海洋生态学新兴领域的一部分,这项工作为监测和比较海洋生物物理动力学提供了一种改进的手段,并为客观,定量的基础提供了依据,从而可以将数据从本地实验和观测数据扩展到区域和全球生物地球化学循环。

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  • 来源
    《Progress in Oceanography》 |2014年第1期|291-304|共14页
  • 作者单位

    Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA, USA,College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 104 CEOAS Administration Building, Corvallis, OR, USA;

    College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 104 CEOAS Administration Building, Corvallis, OR, USA;

    Departamento de Ciencias de las Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina,Centro de Investigaciones del Mary la Atmosfera (CIMA/C0N1CET-UBA), UMI1FAECI/CNRS, Buenos Aires, Argentina;

    College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 104 CEOAS Administration Building, Corvallis, OR, USA;

    College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 104 CEOAS Administration Building, Corvallis, OR, USA;

    College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 104 CEOAS Administration Building, Corvallis, OR, USA;

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