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。大型藻类的形状从30米高的巨型海藻到毫米级的丝状草皮。它们塑造了世界各地的水生生态系统，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。同时，大型藻类物种和形态的多样性使得理解影响其丰度和分布的过程具有挑战性。过去基于大体形态定义功能基团的努力--例如，细枝与大叶或直立与平卧--在解释生态模式方面取得了一些成功，但关键信息丢失了，对于如此广泛的类群，预测的定量测试往往失败。或者，基于特征的建模是一种很有前途的方法，可以结合更多的复杂性，并定义可量化特征之间的关系，如单位面积的叶片质量，以及物种的分布和丰富程度。该项目结合了现场、实验室和建模组件，以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，可以用来预测种群和物种的分布如何在空间和时间上发生变化。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。教育外展工作将利用与圣巴巴拉海岸长期生态研究项目的合作，接触到包括洛杉矶市区在内的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。群落生态学经常深陷在解释特定物种的分布和丰富的案例中，但我们需要更全面地了解推动海洋生态系统预测气候和人类影响变化的力量。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，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，可以结合复杂性并定义可量化特征与物种分布和丰度之间的关系。研究人员正在将现场和实验室测量与建模相结合，以评估根据测量的功能特征预测南加州常见大型藻类物种深度分布的程度。项目目标包括：1)定义和测量大型藻类关键物种内部和之间的关键功能特征；2)为近岸大型藻类群落的组合创建一个基于特征的模拟模型，作为深度的函数；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测试模型的预测；以及4)使用基于个体的模型来测试物种间竞争的机械假说。研究人员正在通过测量圣巴巴拉海峡至少20种常见大型藻类在多个地点(和深度)的表现来实现第一个目标，包括质量比光合作用速率、氮素吸收速率、比叶片面积、树冠高度、树冠面积、叶片厚度、机械和材料特性、寿命和耐牧性。对于目标2，研究人员正在创建一个生态系统模型，该模型代表了具有随机确定的生理特征的大量具有潜在生存能力的大型藻类“物种”。被初始化的物种将彼此及其环境相互作用，演化成一个生态系统，在那里群落结构和多样性不是强加的，而是新兴的属性。第三个目标涉及一个广泛的实地方案，以量化所有可识别的大型藻类在圣巴巴拉海峡六个地点从0米到硬底物末端(最多40米深)的深度梯度上的分布。物理特性(光、温度、波浪力)也沿每个深度横断面进行测量。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，在基于个人的框架中增加物种间的竞争。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.