post-baccalaureate experiences, public outreach, short-term internships, team training). Distinct from but complementary to training is the need for capacity building, which we define as increasing the ability and resources of the overall scientific endeavor to effectively and sustainably support research activities. The committee aimed to evaluate and create recommendations on training and capacity building for CSB for the research community, funders, and decision makers. To be able to inform responses to global environmental crises, the workforce must be able to perform research at multiple scales in the most effective way. The committee determined that training exists in these three core areas stated above, but they are rarely woven together in a way that would most robustly support CSB (Figure 5-1).

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¹ See https://www.ncbi.nlm.nih.gov/genbank/ (accessed February 7, 2024).

² See https://edirepository.org/ (accessed February 7, 2024).

entific workflow from data ingestion to data analysis and visualization. Many, but not all, subdomains of biology routinely teach undergraduates to program, and the standard languages differ across fields. The knowledge and agility one gains in one language can help them to operate in other languages that are less familiar (Videnovik et al. 2010). As analyses are scaled up, biologists also need to become more familiar with best practices in software development (e.g., versioning; Wilson et al. 2014), and become comfortable with online resources through which they can benefit from others helping create more effective code (e.g., Github). For example, analyses that scale up from a desktop computer to high-performance computing may benefit from parallelization, and large-scale data integrations or complex analyses may gain efficiencies from more formalized scientific workflows (Farley et al. 2018). Data visualization aids all stages of CSB, from checking the quality of data and analyses, to the final communication of results. Finally, it is clear that communication skills need to be gained alongside technical skills in working with data in order to be maximally effective. No one needs to be an expert in all the skills that underpin CSB; rather, individuals need to be comfortable working in a team, and prepared with the skills for effective communication that make teamwork successful (Cheruvelil and Soranno 2018, Cheruvelil et al. 2014, NRC 2015).

Enhancing data literacy means not only elevating the skills of those researchers working at the leading edges of CSB, for example, those funded by the NSF initiatives listed in Box 1-1, but also those currently lacking basic data skills of any kind, working

throughout the domains upon which CSB is built (some have referred to this principle as a “rising tide lifts all boats” or “raising the floor”). Basic data literacy is increasingly important across all domains and sectors, and in individuals’ lives as well, such that these skills are transferable both to the workplaces and to making informed decisions as citizens (UNESCO 2006). Some have argued that data literacy should be incorporated across the curriculum (e.g., Kjelvik and Schultheis 2019), rather than simply as standalone workshops and courses, in order for students to appreciate the authentic experiences of using data in the context of real-world questions and situations (Kjelvik and Schultheis 2019, Langen et al. 2014). “Data across the curriculum” could be modeled after “writing across the curriculum” widely implemented in universities since proposed in the 1970s; the idea is that writing is not only a useful skill set but also helps one think (Hampton et al. 2017). The same argument could be made for data skills (See Box 5-2).

In many ways, the focus on data necessitates new ways of thinking, and presents some unresolved questions about how science is done. First, scientists have been unac-

customed to thinking of data as a scientific product. Rather, it has been viewed as a “precursor to publication” (Elliot et al. 2016; Hampton et al. 2013). These attitudes are changing, evidenced by recent changes in funders’ policies, such as requiring data management plans and data sharing. This relatively new awareness ideally will spawn more robust data management approaches, cultivate wider data literacy, and the broader availability of and CARE (Collective benefit, Authority, Responsibility, Ethics) data that can be integrated in large-scale analyses. Perhaps more difficult to describe is the shift in mindset occurring about how “big data” are used in the scientific process, in which the gold standard is the testing of ideas based on a priori hypotheses (Sterner and Elliot 2022). Students are frequently confused about how and when to test a hypothesis with the data that are available (e.g., Langen et al. 2014). Are they allowed to look at the data to see if it is appropriate for their hypothesis testing? Is it wrong to change their hypotheses if they see that the dataset is not appropriate for testing? How much are they allowed to interrogate the data before they are “guilty” of “fishing” or “p-hacking”?

Many scientists engaged in data-intensive analyses see big datasets as new lenses on the world. It has long been accepted that we can develop hypotheses about the distribution of trees or animals based on our visual observations of nature, before designing experiments and collecting quantitative data. If data provide the primary lens through which one can “see” the patterns of the wind or microbes, otherwise invisible, it is hard to argue against using such data to inspire new hypotheses. Perhaps a key principle is that such investigations are guided by sound theory, as discussed in Chapter 3.

Challenges That Limit Training and Capacity-Building Efforts to Connect Research Across Scales

In the pursuit of interdisciplinary collaboration and innovation, addressing the challenges that hinder training and capacity-building efforts is increasingly important. One such challenge is the saturation of existing curricula, which limits the incorporation of additional training modules focused on essential data skills necessary for connecting research across scales. Furthermore, instructors in the environmental sciences often face challenges in integrating data skills into their teaching, potentially inhibiting the dissemination of crucial knowledge. Moreover, the rapid pace of technological advancement outpaces researchers’ capacity to adapt and acquire the requisite data analysis skills, creating a gap between technological innovation and skill acquisition. These challenges underscore the need for targeted interventions and support mechanisms to overcome barriers and foster effective training and capacity-building initiatives in the scientific community.

Conclusions on Training and Capacity-Building Efforts to Connect Research Across Scales

To confront the challenges inherent in CSB, the committee presents conclusions surrounding training and capacity-building efforts aimed at equipping and connecting research across various scales. Emphasizing the need for refined training methodologies, the focus lies on elevating the efficacy of biological research endeavors. At the heart of this pursuit lies the cultivation of a collaborative research environment, where team members possess the agility to traverse diverse disciplines and harness multifaceted skill sets. As CSB ventures continue to transcend disciplinary boundaries, fostering a cohort of intersectional researchers capable of navigating these complexities emerges as imperative. Within this exploration, key areas of data literacy training are identified below, underscoring the pivotal role of interdisciplinary collaboration and adaptability in advancing biological inquiry.

Conclusion 5-1: Training aimed at individuals addressing CSB research. Individuals can actively seek opportunities for upskilling, such as workshops and online resources, and advocate for engagement in upskilling among students and colleagues. Additionally, they could integrate data projects into teaching, following examples such as Macrosystems EDDIE initiatives.

her specialized knowledge but must also be proficient in exchanging information across disciplinary boundaries. Effective teamwork in interdisciplinary projects demands a certain level of literacy in other fields and skill sets to facilitate connections and bridge the gaps between different areas of expertise. Even before the work starts, research networking tools can be used not only to assess any gaps among initial participants but also to foster connections and identify new ones among them (Vacca et al. 2015).

Team participation will likely span from undergraduate students embarking on their first research projects to senior researchers with extensive experience. Therefore, providing team training at all career stages is vital, with a particular emphasis on early-stage researchers who are just entering the collaborative research landscape (Read et al. 2016). Additionally, navigating the dynamics of a diverse team and having the ability to negotiate and resolve conflicts are crucial skills for all team members. To maximize team function in CSB—that is, defining roles and responsibilities, setting expectations, and developing policies for authorship and data sharing—team science training will be critical (Cheruvelil and Soranno 2018). Many of these traits are outlined in Chapter 4 as characteristics of successful networks, notably within the context of small-scale team science successfully growing into formal networks. Regardless of how a network forms, collaboration is key to successful network product formation, including data among many others.

This emphasis on team science training is echoed in a report from the National Academies of Sciences, Engineering, and Medicine (NRC 2015), which highlights the importance of cultivating collaboration and interdisciplinary skills in the scientific community. In the ecological field, researchers have also emphasized the need for team science training (e.g., Peterson et al. 2023, Read et al. 2016), underlining its relevance across scientific disciplines. This training could focus on professional competencies that ensure that team members possess a certain level of knowledge of the biological system, skills necessary to process and analyze associated biological and ecological data, and attributes necessary to make teamwork productive (Wiek et al. 2015). Training topics and methods can be geared to the team’s specific needs; however, team generic competencies include:

1. Basic literacy: training team members on the basic competencies—that is, disciplinary knowledge and skills—is necessary for optimal interactions. Such training will enable team members to understand the contributions of other team members (Stagl et al. 2007). Cross-training will improve each individual’s understanding of the team’s objectives (Fiore et al. 2005) and their contribution to those objectives. In this context that would include basic knowledge of:
   1. Disciplinary knowledge of the systems under study as well as data collection design and protocols. A basic understanding of the systems in question is critical for developing the analytical tools that best represent those systems. Exchange of interdisciplinary/interskills knowledge is commonly accomplished by close interactions between system and analytical experts, and a certain level of literacy in the other’s discipline will facilitate communication and help advance the project’s objectives.

1. 2. Data sciences. The rise of data-intensive science in biological studies across scales will require team members to have basic methodological knowledge of data processing, analysis, and visualization. Basic training in data sciences—including statistics, data management, data engineering, and computer languages—will be required not only to manage and use complex data (see section on Data Literacy Training and Capacity Building above), but also to improve team members’ ability to recognize linkages and assess possibilities.
   3. Modeling approaches. As with data sciences, basic knowledge of the models being used and developed will improve the capacity of team members to investigate possibilities, identify knowledge gaps, and design scientific advances (Pennington et al. 2020). Visual representations of the analytical methods may not only aid with development and interpretation, but it may also facilitate innovation by involving different conceptualizations and varying perspectives of the problem, experiment, and/or hypothesis at hand (Nersessian 1999).

      Most of this learning can take place during the collaboration (Pennington et al. 2013) and include developing the necessary vocabulary to communicate across disciplines and skill sets (Pennington et al. 2020).

2. Communication: Effective communication skills are vital for conveying ideas and information across disciplines and skill sets. Starting with creating a shared vision, information acquisition, collaboration, and dissemination all play an important role in team performance (Xia and Ya 2012). Language training and team management skills help team members transfer knowledge to colleagues outside their areas of expertise, facilitating productive collaboration (IOM 2005). Being able to draw, evaluate, modify, and integrate insights are skills that team members should have (Newell and Luckie 2019). As an example, the Employing Model-Based Reasoning in Socio-Environmental Synthesis (EMBeRS; Pennington 2016, Pennington et al. 2021) was developed to design and test collaborative knowledge integration via model-based reasoning. It includes modules on team composition and team processes and addresses disciplinary differences.
3. Team dynamics: Cooperative work requires a certain degree of “social intelligence” due to the complex social and intellectual processes involved. Training in team dynamics helps team members understand and navigate the intricacies of working together effectively (Fiore 2008). Furthermore, coordination of team tasks and frequent communication are critical in the production of new knowledge and tools and successful training of students (Cummings and Kiesler 2007). Leadership and project management skills training, across career stages, could also benefit the dynamics and productivity of the team. Mentorship training will also benefit team dynamics, and programs such as the Center for the Improvement of Mentored Experiences in Research (CIMER³) can provide resources for improving mentoring relationships.

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³ See https://cimerproject.org/ (accessed April 28, 2024).

4. Negotiation and conflict resolution: Conflict is a natural part of collaborative work, and the ability to negotiate and resolve conflicts constructively is essential for maintaining a harmonious and productive team environment. Trust among team members is crucial for having open discussions about the project that do not lead to conflict or misinterpretation (Bennett and Gadlin 2012). Thus, specific steps to establishing trust—e.g., conversations about tasks assignments, authorship, data sharing, and decision making—can be one of the first tasks during team training (Bennett and Gadlin 2012). Ensuring that all team members are well versed in conflict management strategies may be the key to a successful progression of work. Conflict among team members can be particularly damaging when there are career-associated power differences, for example, student–advisor (Brockman et al. 2010). Interest-based and cooperative approaches may work best in settings with a diversity of career stages (Klomparens et al. 2008).

In addition to these fundamental aspects of team science training, there are other areas of training that are intrinsically connected to research in CSB:

1. Connection to theory: Understanding the broader context of biological and ecological research, including the role of theory and the importance of advancing science, provides purpose and direction to interdisciplinary projects (see Chapter 3). Being able to assess what level of theoretical simplicity is sufficient to assess how scales relate will be key to developing and advancing CSB science.

To promote discovery and better address environmental challenges, data analyses should be guided by specific goals developed in accordance with current understanding of the system and knowledge needs. For that, substantial theoretical knowledge by team members that work closely with other members will be critical. Forecasting trends and predicting thresholds will require CSB research to complement pattern recognition with mechanistic processes and synergies underlying those patterns. Training team members to move beyond data exploration and to be able to formulate knowledge-based research goals will be essential for the development of CSB science. Given the complexity of CSB science, linking theory and going back and forth between theory and empirical work are vital. Particularly, early-career researchers will greatly benefit from learning to develop theoretical frameworks, relate frameworks, test frameworks with data, and communicate conclusions to other CSB members. To effectively assess ecosystem functioning—its resilience and vulnerability, and ultimately the sustainability of the ecosystem services provided—will require a conceptualization that links processes and drivers acting across scales.

2. Systems thinking: The nature of CSB makes training on systems thinking a potential asset for its success. Understanding the many biological processes involved, their differences and relationships, connections and feedbacks, their synthesis as a whole, are all critical concepts in CSB sciences. Training

1. researchers from a modular mindset to high-conductivity thinking will also benefit cross-scale research (Elmqvist et al. 2021). Systems thinking training provides tools that facilitate this integrated conceptualization of a system. Furthermore, in many research contexts, collaborating with stakeholders or partners from industries, agencies, or local governments and communities is essential. Educational programs that integrate such collaborations—such as those within NEON and Google Earth, for example—can be valuable resources for interdisciplinary research teams.

Interdisciplinary work and team science are essential for addressing complex research challenges associated with working at multiple scales. Effective training in communication, literacy, team dynamics, negotiation, and conflict resolution is crucial for the success of collaborative research projects. Additionally, training to use and develop theoretical knowledge will ensure that research is led by the goal of advancing biology, and systems-thinking training that incorporates the intricacies of CSB in engaging with coupled human–natural systems can further enrich interdisciplinary teams and contribute to the advancement of science.

CHALLENGES THAT LIMIT EFFECTIVE RESEARCH ACROSS SCALES AND TRAINING APPROACHES TO OVERCOME THEM

Identifying and addressing challenges in CSB associated with training will be relevant to improving research efforts. The committee identified three major challenges associated with team science training. First, effective collaboration practices are crucial for advancing research in CSB. Encouraging collaborative efforts and breaking down silos are essential to foster teamwork and ensure a collective approach toward research objectives. Second, communication across three dimensions is imperative in CSB endeavors. This includes improving communication across disciplines, skill levels, and career stages to facilitate the productive exchange of ideas and expertise. Last, coordinating disjointed teams scattered across different institutions and geographical locations presents a significant challenge. Overcoming these obstacles requires establishing efficient coordination mechanisms to bridge gaps and streamline efforts for more effective research outcomes.

Conclusions on Interdisciplinary Team Science Training to Connect Research Across Scales

Creating a more adaptable and collaborative research landscape where team members can navigate between different disciplines and skill sets will be essential to the success of CSB. Areas of team science training that will enhance the overall effectiveness of CBS research projects are:

tives and backgrounds are incorporated, fostering a richer understanding of biological systems across vast geographical areas.

Additionally, accessibility plays a critical role, focusing on the design and provision of facilities and information to enable all individuals, including those with disabilities, to fully participate (NASEM 2024). Despite legal protections, such as the Americans with Disabilities Act, which guarantee access to education and employment in STEMM fields, people with disabilities remain underrepresented. The 2023 NASEM report emphasizes the need for antiracism and comprehensive diversity, equity, and inclusion (DEI) initiatives in STEMM to dismantle systemic barriers and promote equitable opportunities, outlining specific recommendations for policy changes, institutional practices, and leadership strategies to foster inclusivity and accessibility in scientific and educational environments. The emphasis on accessibility, which is expressed in further detail below, is particularly critical in the context of CSB, ensuring that individuals with diverse abilities have equal opportunities to contribute to and benefit from the understanding of vast ecosystems and biodiversity across continents.

Previous NASEM consensus studies have examined different components of increasing inclusivity in STEMM (e.g., women of color in tech, research at minority-serving institutions), but the 2023 Beyond Broadening Participation report is the first and most comprehensive consensus study to examine antiracism and DEI holistically across STEMM. NASEM (2023) reviewed bias and racism in STEMM workplaces, proposed strategies to enhance DEI, and emphasized antiracism as active measures against systemic racism. The report’s recommendations include requests for increased support for minority-serving institutions, evidence-based programs to connect minoritized individuals, and leadership responsibilities for advancing DEI.

DEI Themes Particularly Relevant to Continental-Scale Biology

In addition to the general report outlined above, the committee noted that the interdisciplinary nature of CSB necessitates inclusivity, diversity, and evidence-based best practices in team science. At broad spatial and temporal scales, humans are undeniably a major driver of pattern and process, and inequities in socioeconomic systems are reflected in natural systems. Executive Order 14096 encourages federal activities across the whole of government—including those related to science, data, and research—to advance environmental justice, the just treatment and equal involvement of everyone, regardless of income, race, color, national origin, Tribal affiliation, or disability—regarding environmental protections and benefits, as well as meaningful involvement in the policies that shape their communities. OSTP recently released the first Environmental Justice Science, Data, and Research Plan which charges the scientific community with providing critical evidence that federal agencies can use to develop environmental justice policies and decisions, ensuring that actions are informed, targeted, and effective (National Science and Technology Council 2024). Research and training in CSB are central to achieving the science, data, and research goals that play important roles in the achievement of environmental justice. Indeed, the CSB research community is increasingly calling for incorporating environmental justice into telecoupling research to

better address issues surrounding socioecological inequities with common governance across distances (Boillat et al. 2020).

Given CSB’s interdisciplinary nature, embracing inclusivity and diverse perspectives is essential to foster innovation and productivity. However, interdisciplinary research is not always valued by traditional academic systems of career advancement (e.g., promotion and tenure). It can be risky for pretenure faculty to engage in research that addresses socioecological systems if they were hired to conduct primarily research (and vice versa) or to attempt work at larger spatial or temporal scales. This risk is particularly high for faculty at moderate research activity (i.e., R3) or primarily undergraduate institutions who are more often “the only” in their discipline or for faculty from minoritized or underrepresented groups. Funding programs that focus on principal investigators at institutions such as the Building Research Capacity of New Faculty in Biology (BRC-BIO) program support pretenure faculty by demonstrating to their universities that they are engaging in cutting-edge, funding-worthy research. Furthermore, meetings that bring CSB researchers together and elevate the work of early-career scientists (e.g., ESIIL Innovation Summit) help grow CSB research networks and build critical mass among researchers, which lends validity to the work by tenure and promotion committees and tenure letter writers. As much as possible, these meetings could be open and accessible to all current and interested CSB research community stakeholders.

Three additional themes, described as (1) Accessibility, (2) Indigenous and Traditional Ecological Knowledge, and (3) Systemic and Cultural Change, were found to be particularly relevant to CSB and are described below:

Accessibility

Accessibility has emerged as a significant concern highlighted by experts in DEI within STEMM. These concerns pertained to access for both individuals with disabilities and people belonging to other historically minoritized groups in STEM. The interdisciplinary nature of CSB means that students and trainees enter CSB through a variety of pathways given their varied disciplines. Internships, research assistantships, or other traineeships may be in field experiences, data science, museum collections, socioeconomic research, and more. Field experiences (e.g., seasonal internships with NEON) may not be accessible to individuals with disabilities or for whom identity poses safety concerns in certain locations due to race/ethnicity, sexual orientation, gender identity, and/or religion (Demery and Pipkin 2021). On the data science side, experiences are not always accessible to people with certain disabilities because websites and data standards (i.e., computer languages, data visualization software, etc.) vary in their accessibility and adherence to universal design. Solutions exist that make training and employment opportunities across CSB more accessible for individuals from a variety of marginalized backgrounds. For example, clear community guidelines that utilize gender-inclusive language, established medical/emergency response systems, and developed plans for travel and accommodation create an inclusive environment for members of the LGBTQ+ community and demonstrate a commitment from project leadership (Lundin and Bombaci 2022). For individuals with disabilities, accommoda-

tions such as flexible schedules, remote work options, 508 compliance, and a supportive work culture support access to CSB training and employment. During the Covid-19 pandemic, many individuals benefited from and relied on technological advances championed by disability advocates; continuing to accommodate people with disabilities in STEM benefits us all (Daehn and Croxson 2021).

In one of the committee’s public information-gathering sessions on inclusive training and workforce development, expert panelist Dr. Sara Bombaci also identified limited income opportunities as a significant financial barrier to diversifying and retaining a diverse workforce for CSB. In a nationwide survey of undergraduate students interested in environmental and natural sciences jobs, 43 percent of respondents agreed or strongly agreed that income was a barrier to accepting an internship (Jensen et al. 2021). Students reported that, with additional support for housing and transportation, a position would need to pay $8.68/hour in order for them to accept it, but only 65 percent of jobs (according to job board surveys) pay $8.68 or more. Students who identified as racial and/or ethnic minorities reported that they needed $10.80/hour but only 56 percent of jobs paid $10.80/hour or more. This could be because many racially or ethnically minoritized students lack a financial “safety net” provided by family members or because they themselves financially support family members. An hourly rate of $20.00/hour was required to retain 90 percent of all students, but only 3 percent of jobs in the environmental and natural sciences, including CSB, paid $20.00/hour or more. Researchers also identified other key barriers to recruiting and retaining a diverse workforce including conflicts with work and school, lack of transportation and housing, and mental health or physical concerns about being able to carry out the work (Jensen et al. 2021). Finally, the seasonal nature of certain positions emphasized in CSB is a barrier for applicants who do not want or cannot afford short-term work.

Indigenous and Traditional Ecological Knowledge

At the outset of its formation, the committee recognized the key role that Indigenous and Traditional ecological knowledge (ITEK) plays in CSB. ITEK is a body of observations, oral and written knowledge, practices, and beliefs that promotes environmental sustainability and the responsible stewardship of natural resources through relationships between humans and environmental systems. It is applied to phenomena across biological, physical, cultural, and spiritual systems. As conceptualized, CSB addresses questions about biological processes and patterns that emerge at broad organizational, spatial, and/or temporal scales, which is inherently tied to land and its historic legacy of use and contemporary management by Indigenous peoples. As Dr. Gillian Bowser stated in her presentation to the committee, “Data is cultural, economic, and place-based.” Today, Indigenous peoples remain stewards of a considerable amount of land and biodiversity on Earth; globally, Indigenous lands intersect with 40 percent of protected areas and overall cover roughly one-fourth of Earth’s surface (Grantham 2022). The White House Office of Science and Technology Policy (OSTP) released a 2021 memorandum committing to elevating ITEK in federal scientific and policy processes in the United States (White House 2021b). Currently an ad hoc National Academies Committee on the

Co-Production of Environmental Knowledge, Methods, and Approaches⁴ is writing a report on the nature and sociocultural dimensions of bridging and integrating Indigenous and local knowledge systems with professional scientific ones.

Systemic and Cultural Change

During the committee’s third information-gathering session, 30-year STEMM equity expert and scholar Dr. John Matsui stated that programs devoted to promoting DEI in STEMM produce significant knowledge around identifying evidence-based, inclusive, and cost-effective best practices in closing STEMM equity gaps. He goes on, however, to state that programs themselves are not the solution; programs address symptoms of a broken system (e.g., equity gaps, underrepresentation) and direct services to students are “institutional workarounds.” Instead, programs should be viewed as “labs” or “incubators” to produce knowledge around ways to fix institutions, not the students, and to develop talent instead of skim talent. Successful models that address systemic and cultural change, or “Inclusive Excellence,” in STEMM include NSF ADVANCE (Organizational Change for Gender Equity in STEM Academic Professions), National Institutes of Health BUILD (BUilding Infrastructure Leading to Diversity), and Howard Hughes Medical Institute (HHMI) Driving Change. HHMI Driving Change is a multiyear funding initiative designed to encourage a comprehensive approach to institutional culture change using three elements: creation of a multi-institutional learning community, an institution-centered program designed to promote inclusivity in a university’s STEM learning environment, and student-centered programs where faculty assume responsibility for the success of all students. Phase I of the program is a deep self-study of the university’s systems and culture that contribute to equity gaps and underrepresentation. Only after a thorough self-study that demonstrates knowledge of a university’s own unique challenges are universities eligible for Phase II funding that supports program implementation. The HHMI Driving Change program embodies what Dr. Matsui and many other STEMM equity experts suggest: that top-down programs are helpful, but true systemic and cultural change is local and happens at the institutional level. This is a challenge because the DEI in STEMM landscape in the United States is becoming increasingly patchy due to varied investment at state and institutional levels, funding agencies have a significant opportunity and play a key role in incentivizing systemic and cultural change and supporting the necessary staffing to do this work.

Another gap exists around training of faculty and leadership to learn equitable advising, mentoring, and teaching practices. Researchers in CSB who supervise others (e.g., faculty, team leaders, program directors) need regular training in evidence-based best practices. For example, CIMER at the University of Wisconsin–Madison aims to improve research mentoring relationships for mentees and mentors at all career stages through the development, implementation, and study of evidence-based and culturally responsive interventions. CIMER offers numerous curricula, provides training, and has

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⁴ See https://www.nationalacademies.org/our-work/co-production-of-environmental-knowledge-methods-and-approaches (accessed February 7, 2024).

trained almost 1,000 facilitators who are part of a network to support mentorship across institutions. Participation in training such as CIMER’s by CSB researchers and team leaders can be incentivized to grow an inclusive team environment where time spent learning skills to effectively manage mentoring relationships is valued by all stakeholders. Participation can be encouraged by building it into existing tenure and promotion requirements or other departmental career advancement and reward structures.

Addressing diversity, equity, inclusion, and accessibility (DEIA) in CSB necessitates multifaceted approaches. Initiatives such as BRC-BIO and ESIIL can offer targeted support to tackle specific challenges hindering DEIA in CSB. Moreover, addressing financial barriers by appropriately funding CSB training and professions, particularly within the biotechnology workforce, can help foster inclusivity. Beyond mere land acknowledgments, efforts could focus on training CSB leadership to effectively collaborate with Indigenous communities, with examples such as NEON Tribal Liaison positions serving as models for such endeavors. Finally, supporting training programs that promote systemic and cultural shifts can help develop supportive environments conducive to fostering DEIA in CSB.

A PATH FORWARD FOR TRAINING AND CAPACITY BUILDING IN CONTINENTAL-SCALE BIOLOGY

Focusing on data literacy, interdisciplinary team science, and promoting DEI is paramount for the effective development of CSB. The necessity of data literacy, ranging from basic to advanced expertise, is emphasized given the reliance on large-scale spatial and temporal data in CSB research. Additionally, the interdisciplinary nature of CSB underscores the importance of effective communication and collaboration across diverse backgrounds to ensure successful outcomes.

Furthermore, inclusivity and diverse perspectives are vital for maximizing creativity and productivity in CSB team science. Although training efforts exist in these areas, a more cohesive approach could weave these areas together effectively. This can be achieved through enhanced capacity building to aid in the sustainable overall support for research activities in CSB.

Moving forward, the following recommendation aims to guide not only the research community but also funders and decision makers in fostering a well-trained workforce equipped to address the multifaceted challenges of CSB. By prioritizing comprehensive training and capacity-building initiatives, we can better prepare a task force capable of conducting impactful research at various scales to respond effectively to global environmental crises.

Recommendation 5-1: The three key areas of training that funders, researchers, and educators should prioritize for developing a scientific workforce with the knowledge and skills necessary to address future challenges in CSB are data literacy, interdisciplinary team science, and promoting diversity, equity, inclusion, and accessibility.