Styers Diane M, Schafer Jennifer L, Kolozsvary Mary Beth, Brubaker Kristen M, Scanga Sara E, Anderson Laurel J, Mitchell Jessica J, Barnett David
Western Carolina University Cullowhee NC USA.
Winthrop University Rock Hill SC USA.
Ecol Evol. 2021 Mar 21;11(9):3660-3671. doi: 10.1002/ece3.7385. eCollection 2021 May.
Biodiversity is a complex, yet essential, concept for undergraduate students in ecology and other natural sciences to grasp. As beginner scientists, students must learn to recognize, describe, and interpret patterns of biodiversity across various spatial scales and understand their relationships with ecological processes and human influences. It is also increasingly important for undergraduate programs in ecology and related disciplines to provide students with experiences working with large ecological datasets to develop students' data science skills and their ability to consider how ecological processes that operate at broader spatial scales (macroscale) affect local ecosystems. To support the goals of improving student understanding of macroscale ecology and biodiversity at multiple spatial scales, we formed an interdisciplinary team that included grant personnel, scientists, and faculty from ecology and spatial sciences to design a flexible learning activity to teach macroscale biodiversity concepts using large datasets from the National Ecological Observatory Network (NEON). We piloted this learning activity in six courses enrolling a total of 109 students, ranging from midlevel ecology and GIS/remote sensing courses, to upper-level conservation biology. Using our classroom experiences and a pre/postassessment framework, we evaluated whether our learning activity resulted in increased student understanding of macroscale ecology and biodiversity concepts and increased familiarity with analysis techniques, software programs, and large spatio-ecological datasets. Overall, results suggest that our learning activity improved student understanding of biological diversity, biodiversity metrics, and patterns of biodiversity across several spatial scales. Participating faculty reflected on what went well and what would benefit from changes, and we offer suggestions for implementation of the learning activity based on this feedback. This learning activity introduced students to macroscale ecology and built student skills in working with big data (i.e., large datasets) and performing basic quantitative analyses, skills that are essential for the next generation of ecologists.
生物多样性是生态学及其他自然科学专业本科生需要掌握的一个复杂但至关重要的概念。作为初涉科学领域的学生，他们必须学会识别、描述和解释不同空间尺度上的生物多样性模式，并理解其与生态过程及人类影响之间的关系。对于生态学及相关学科的本科课程而言，让学生参与处理大型生态数据集的实践，以培养他们的数据科学技能以及思考在更广泛空间尺度（宏观尺度）上运行的生态过程如何影响当地生态系统的能力，也变得越来越重要。为了支持在多个空间尺度上提高学生对宏观尺度生态学和生物多样性理解的目标，我们组建了一个跨学科团队，成员包括资助人员、科学家以及生态学和空间科学领域的教师，旨在设计一项灵活的学习活动，利用来自国家生态观测网络（NEON）的大型数据集教授宏观尺度生物多样性概念。我们在六门课程中试点了这项学习活动，这些课程共招收了109名学生，涵盖中级生态学和地理信息系统/遥感课程以及高级保护生物学课程。通过我们的课堂经验以及一个课前/课后评估框架，我们评估了我们的学习活动是否提高了学生对宏观尺度生态学和生物多样性概念的理解，以及是否增加了他们对分析技术、软件程序和大型空间生态数据集的熟悉程度。总体而言，结果表明我们的学习活动提高了学生对生物多样性、生物多样性指标以及多个空间尺度上生物多样性模式的理解。参与的教师反思了活动中进展顺利的方面以及哪些方面需要改进，我们根据这些反馈为学习活动的实施提供了建议。这项学习活动向学生介绍了宏观尺度生态学，并培养了学生处理大数据（即大型数据集）和进行基本定量分析的技能——这些技能对下一代生态学家来说至关重要。
