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

合作研究：海洋大型藻类的群落结构：基于性状的方法

• 批准号: 2146925
• 负责人: Brian Gaylord
• 金额: $ 19.18万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146925&HistoricalAwards=false
• 关键词: Collaborative; Research; Community; Structure; Marine

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.

大型藻类，通常称为海藻，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米高的巨型海带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，为众多物种提供食物和栖息地，其中包括许多对沿海渔业和娱乐至关重要的物种。与此同时，大型藻类物种和形式的多样性使得了解影响其丰度和分布的过程具有挑战性。过去根据总体形态来定义功能群的努力--例如，细分支与大叶片或直立与俯卧--在解释生态模式方面取得了一些成功，但关键信息丢失了，并且预测的定量测试往往会失败如此广泛的分组。或者，基于特征的建模是一种很有前途的方法，可以纳入更多的复杂性，并定义可量化的特征（如单位面积的叶片质量）与物种的分布和丰度之间的关系。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和验证沿海海洋物种分布模型。其结果将是一个大型藻类群落的框架，可用于预测群体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。教育推广工作将利用与圣巴巴拉沿海长期生态研究项目的合作，以接触K-12学生和教师，包括城市洛杉矶。其他外联活动包括与媒体、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。群落生态学往往陷入解释特定物种分布和丰度的案例历史中，但我们需要更全面地了解驱动海洋生态系统的力量，以预测气候和人类影响造成的变化。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超过几十年的总形态概括。基于特征的建模是一种很有前途的方法，可以将复杂性和可量化的特征与物种的分布和丰度之间的关系定义起来。研究人员将实地和实验室测量与建模相结合，以评估加州南部常见大型藻类物种的深度分布如何根据测量的功能特征进行预测。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性状; 2）创建一个基于性状的模拟模型，用于近岸大型藻类群落的组合作为深度的函数; 3）通过量化大型藻类物种、食草动物和圣巴巴拉海峡环境参数的深度分布来测试模型的预测;（4）利用个体模型检验种间竞争机制假说。研究人员通过测量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）与性能相关的一套功能性状来实现第一个目标，包括质量比光合速率，氮吸收速率，比叶片面积，树冠高度，树冠面积，叶片厚度，机械和材料特性，寿命和放牧阻力。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可行的大型藻类“物种”。初始化的物种将与彼此及其环境相互作用，进化成一个生态系统，在这个生态系统中，群落结构和多样性不是强加的，而是涌现的特性。第三个目标涉及一个广泛的实地项目，以量化分布的所有可识别的大型藻类物种在整个深度梯度从0米到终端的硬基板（高达40米的深度）在六个网站的圣巴巴拉通道。物理特性（光，温度，波浪力）也测量沿沿着每个跨深度样带。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添加种间竞争。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。