Collaborative Research: Community Structure of Marine Macroalgae: A Trait-Based Approach

大型藻类，俗称海藻，是沿海海洋生态系统中最重要和最多样化的初级生产者之一。大型藻类的形式从30米高的巨藻到毫米级的丝状草坪。它们塑造了全世界的水生生态系统，为大量物种提供食物和栖息地，其中包括许多对沿海渔业和娱乐很重要的物种。与此同时，巨藻种类和形态的多样性使得了解影响其丰度和分布的过程具有挑战性。过去基于大体形态来定义功能群的努力——例如，细分支与大叶片，直立与匍匐——在解释生态模式方面取得了一些成功，但关键信息丢失了，预测的定量测试往往在如此广泛的分类中失败。另外，基于特征的建模是一种很有前途的方法，可以整合更多的复杂性，并定义可量化特征（如每面积叶片质量）与物种分布和丰度之间的关系。该项目结合了野外、实验室和建模的组成部分来测量大型藻类的特征，并验证沿海海洋物种分布的模型。结果将是一个大型藻类群落的框架，可以用来预测群体和物种的分布如何在空间和时间上变化。该项目将为本科生和研究生提供实地和实验室研究方面的培训。教育推广工作将利用与圣巴巴拉海岸长期生态研究项目的合作，覆盖包括洛杉矶城市在内的K-12学生和教师。其他外展活动包括与媒体、圣巴巴拉海洋中心以及圣巴巴拉海峡国家海洋保护区和海峡群岛国家公园等机构的互动。群落生态学经常陷入解释特定物种分布和丰富程度的历史案例中，然而我们需要更全面地了解驱动海洋生态系统的力量，以预测由于气候和人类影响而导致的变化。在温带海洋生态系统中，大型藻类作为食物网的基础并提供栖息地，但我们缺乏一个框架来理解大型藻类生态学，而不是几十年来的总体形态概括。基于特征的建模是一种很有前途的方法，可以将复杂性和定义可量化特征与物种分布和丰度之间的关系。研究人员将实地和实验室测量与建模相结合，以评估基于测量的功能特征，南加州常见大型藻类物种的深度分布是如何预测的。项目目标包括：1)定义和测量大型藻类内部和跨关键物种的关键功能特征；2)建立了近岸大型藻群落群落随深度变化的特征模拟模型；3)通过量化圣巴巴拉海峡大型藻类物种、食草动物和环境参数的深度分布来验证模型的预测；4)利用基于个体的模型检验种间竞争的机制假设。研究人员通过测量一系列与圣巴巴拉海峡中至少20种常见大型藻类在多个地点（和深度）的性能相关的功能特征来实现第一个目标，包括质量比光合速率、氮吸收率、比叶片面积、冠层高度、冠层面积、叶片厚度、机械和材料特性、寿命和抗放牧性。对于目标2，研究人员正在创建一个生态系统模型，该模型代表了大量具有随机确定的生理特征的潜在存活的大型藻类“物种”。初始化的物种将与其他物种及其环境相互作用，进化成一个生态系统，其中群落结构和多样性不是强加的，而是自然出现的特性。第三个目标涉及一个广泛的实地计划，量化圣巴巴拉海峡六个地点从0米到硬基质末端（高达40米深度）的深度梯度中所有可识别的大型藻类物种的分布。物理性质（光、温度、波浪力）也沿着每个跨深度样条测量。在第四个目标中，研究人员正在使用结果来改进他们的模型，在基于个体的框架中加入种间竞争。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Macroalgae, commonly known as seaweeds, are among the most important and diverse primary producers in coastal marine ecosystems. Macroalgae range in form from 30-m high giant kelp to mm-scale filamentous turfs. They shape aquatic ecosystems worldwide, providing food and habitat to legions of species including many important to coastal fisheries and recreation. At the same time, the diversity of macroalgal species and forms makes it challenging to understand the processes affecting their abundance and distribution. Past efforts to define functional groups based on gross morphology – for example, finely branching vs. large-bladed or erect vs. prostrate - have had some success in interpreting ecological patterns, yet key information is lost and quantitative tests of predictions often fail with such broad groupings. Alternatively, trait-based modeling is a promising approach to incorporate more complexity and define relationships between quantifiable characteristics, such as blade mass per area, and the distribution and abundance of species. This project combines field, laboratory, and modeling components to measure macroalgal traits and validate models of species distribution in the coastal ocean. The outcome will be a framework for macroalgal communities that can be used to predict how the distribution of groups and species change across space and time. The project will provide training for undergraduate and graduate students in field and laboratory research. Educational outreach efforts will leverage collaboration with the Santa Barbara Coastal Long Term Ecological Research project to reach K-12 students and teachers, including urban Los Angeles. Other outreach includes interaction with the media, the Santa Barbara Sea Center, and agencies such as the Santa Barbara Channel National Marine Sanctuary and Channel Islands National Park. Community ecology is often mired in case histories explaining distribution and abundance of select species, yet we need a more holistic understanding of the forces driving marine ecosystems to predict change due to climate and human impacts. In temperate marine ecosystems, macroalgae serve as the base of food webs and provide habitat, but we lack a framework for understanding macroalgal ecology beyond decades-old gross morphological generalizations. Trait-based modeling is a promising approach to incorporate complexity and define relationships between quantifiable traits and the distribution and abundance of species. The investigators are combining field and laboratory measurements with modeling to assess how well the depth distribution of common macroalgal species in southern California is predictable based on measured functional traits. The project objectives include: 1) define and measure key functional traits within and across key species of macroalgae; 2) create a trait-based simulation model for the assemblage of nearshore macroalgal communities as a function of depth; 3) test the model’s predictions by quantifying the depth distribution of macroalgal species, grazers, and environmental parameters in the Santa Barbara Channel; and 4) use individual-based models to test mechanistic hypotheses for interspecific competition. The investigators are accomplishing the first objective by measuring a suite of functional traits related to performance at multiple sites (and depths) for at least 20 species of common macroalgae in the Santa Barbara Channel, including mass-specific photosynthetic rate, nitrogen uptake rate, specific blade area, canopy height, crown area, blade thickness, mechanical and material properties, lifespan, and grazing resistance. For objective 2 the investigators are creating an ecosystem model that represents a large number of potentially viable macroalgal “species” with stochastically determined physiological characteristics. Initialized species will interact with one another and their environment, evolving into an ecosystem where community structure and diversity are not imposed, but are emergent properties. The third objective involves an extensive field program to quantify the distribution of all identifiable macroalgal species across a depth gradient from 0 m to the terminus of hard substrate (up to 40m depth) at six sites in the Santa Barbara Channel. Physical properties (light, temperature, wave forces) are also measured along each cross-depth transect. In the fourth objective, the investigators are using results to improve their model, adding interspecific competition in an individual-based framework.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.