Quantitative reasoning is fundamental to ecology, and its importance will only continue to grow over time. The abundance of quantitative methods and applications that are now in practice is a profound achievement for the discipline, yet navigating through this labyrinth of options can be overwhelming for beginning graduate students, especially those with limited prior training in mathematics, statistics, or programming. Advisors and other faculty naturally have great expectations for incoming graduate students to quickly become proficient in multiple quantitative approaches, correctly apply them to imperfect data, and eloquently justify their decisions. However, this level of quantitative aptitude is often far beyond what is typically acquired from an introductory undergraduate‐level statistics course, which may be the only experience that some incoming students have. This leaves students underequipped and ill‐prepared to conduct a basic tenet of the scientific process. Consequently, improved quantitative standards and scaffolding at the graduate level will be needed to better guide new students through the myriad of choices available.

In December 2020, the National Institute for Mathematical and Biological Synthesis (NIMBioS; nimbios.org) hosted an international group of educators, researchers, and students for an investigative workshop on quantitative education in life‐science graduate programs. Participants solicited evidence for effective pedagogical approaches, and professional ecologists shared their unique insights. Because considerable work has already focused on the undergraduate level, this workshop concentrated exclusively on the graduate experience.

Like their peers in other sciences, graduate students in ecology have multiple concurrent obligations, including taking courses, conducting research, and serving as teaching assistants. However, for ecologists, extensive field seasons consume considerable time, which is already limited for Master’s students, many of whom are enrolled in two‐year programs. This “rushed” experience frequently leaves students scrambling to analyze their data to meet graduation‐dependent deadlines, making their work more susceptible to errors and markedly reducing the confidence necessary to conduct subsequent independent inquiry.

Nearly all ecological research relies in some fashion on quantitative reasoning. Instructors should emphasize the importance of quantitative techniques to the discipline beyond their use as tools, and that anyone can master them. Even so, a dichotomy exists in ecology, where we label ourselves as quantitatively minded or not. When responsibilities in a collaborative research project are partitioned so that all quantitative duties fall to a single person, this existing divide may be exacerbated. In contrast, our research would benefit if we prepared all ecologists to sufficiently critique methodological choices in collaborative projects.

A balance must be found in teaching foundational principles and providing exposure to the breadth of choices available. Requiring coursework in basic descriptive and inferential statistics before students select applications that align with their individual research projects would represent an early step in the right direction. Likewise, because the popularity of specific models is likely to change over the course of one’s career, prioritization should be given to teaching the skills and concepts likely to be common across many applications, perhaps through establishing and adhering to an international set of standards. In this manner, students would be able to scrutinize multiple forms of inference with greater confidence and graduate knowing the appropriate circumstances in which to deploy certain techniques.

College graduates arrive in graduate‐level ecology programs with degrees in various subjects, with differing exposure to statistics or data science. Similarly, science departments vary in their ability to host quantitative classes, often forcing students to take courses in other departments, which may have different learning priorities. Thus, it is the shared responsibility of the department, advisor, and student to co‐create an individual development plan at the beginning of the student’s graduate experience to assemble various modes of instruction – including but not limited to formal courses, short courses, workshops, online tutorials, bootcamps, and peer groups – that offer the desired competencies. Everyone would benefit if students carefully defined their quantitative goals, advisors were flexible and eager to facilitate learning opportunities, and departments were responsive to the evolving needs of students. Whether as peers, collaborators, mentors, or formal educators, we all have a role to play in properly preparing the next generation of ecologists.

The short‐term goal of ensuring that students can correctly select and apply the appropriate quantitative methods in their research is paramount. However, this experience should not come at the expense of students’ self‐confidence. We are all lifelong learners and, as ecologists, we have the responsibility to keep up with recent advancements. The graduate experience should prepare us to wholly assume that duty. The world needs competent ecologists. Let us seek and create more opportunities for quantitative success.

定量推理是生态学的基础，其重要性将随着时间的推移而不断增长。如今，实践中大量的定量方法和应用对于该学科而言是一项巨大的成就，但是对于初学者，尤其是那些在数学，统计学或程序设计方面受过较少培训的研究生来说，在这种迷宫般的选择中进行导航可能会不胜枚举。顾问和其他教师自然对新来的研究生抱有很高的期望，他们很快会精通多种定量方法，正确地将其应用于不完善的数据，并雄辩地证明自己的决定合理。但是，这种定量的才能水平通常远远超出通常从入门级的本科统计学课程所获得的水平，这可能是一些新生的唯一经历。这使学生在进行科学过程的基本原则方面装备不足，准备不足。因此，将需要改进研究生水平的定量标准和脚手架，以更好地指导新生通过各种各样的选择。

2020年12月，美国国家数学和生物合成研究所（NIMBioS; nimbios.org）接待了一个国际教育家，研究人员和学生团体，参加有关生命科学研究生课程中定量教育的调查研讨会。与会者征集了有效的教学方法的证据，专业的生态学家分享了他们独特的见解。由于大量的工作已经集中在本科层次上，因此该研讨会完全集中于研究生的经验。

与其他科学领域的同行一样，生态学的研究生也承担着多项同时的义务，包括参加课程，进行研究和担任助教。但是，对于生态学家而言，漫长的野外季节会占用大量时间，这已经限制了硕士生的学习，其中许多人都参加了为期两年的课程。这种“匆忙”的经历经常使学生争先恐后地分析其数据，以符合毕业相关的截止日期，这使他们的工作更容易出错，并显着降低了进行后续独立询问所必需的信心。

几乎所有的生态研究都以某种方式依赖于定量推理。教师应强调定量技术对学科的重要性，超越其用作工具的范围，任何人都可以掌握它们。即便如此，生态学中仍然存在二分法，我们将自己标记为定量思维或不定量思维。如果将合作研究项目中的职责进行划分，以使所有定量职责落在一个人身上，则这种现有的鸿沟可能会加剧。相反，如果我们让所有生态学家准备好在合作项目中充分批评方法论的选择，我们的研究将受益。

必须在教授基本原理和提供可供选择的多种选择之间找到平衡。在此之前需要基本的描述性和推论统计学课程学生选择适合其个人研究项目的应用程序将代表朝正确方向迈出的第一步。同样，由于特定模型的流行性可能会随着职业的发展而发生变化，因此应该优先考虑讲授可能在许多应用程序中通用的技能和概念，方法可能是建立并遵守一套国际标准。通过这种方式，学生将能够以更大的信心仔细检查多种形式的推理，并让毕业生知道部署某些技术的适当情况。

大学毕业生进入具有不同学科学位的研究生水平的生态课程，对统计或数据科学的了解也有所不同。同样，科学系主持定量课程的能力也各不相同，这常常迫使学生在其他系中上课，而这些系可能具有不同的学习重点。因此，部门，顾问和学生的共同责任是在学生的毕业经历开始之初共同制定一份个人发展计划，以汇编各种教学模式，包括但不限于正规课程，短期课程，讲习班，在线教程，训练营和同龄人小组-提供所需的能力。如果学生认真定义自己的量化目标，每个人都会从中受益，顾问非常灵活，渴望提供学习机会，各系对学生不断变化的需求做出了回应。无论是作为同龄人，合作者，导师还是正式的教育家，我们都应在适当地培养下一代生态学家方面发挥作用。

确保学生能够在研究中正确选择和应用适当的定量方法的短期目标至关重要。但是，这种经历不应以牺牲学生的自信心为代价。我们都是终身学习者，作为生态学家，我们有责任跟上最新进展。毕业生的经验应使我们准备完全承担起这项职责。世界需要合格的生态学家。让我们寻找并创造更多的定量成功机会。