Next Article in Journal
Verification of Construction Method for Smart Liners to Prevent Oil Spill Spread in Onshore
Previous Article in Journal
Smart Electrical Planning, Roadmaps and Policies in Latin American Countries Through Electric Propulsion Systems: A Review
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Perspective

Rowing in the Same Direction Using MIX—A Tool to Initiate the Melding of Individual Disciplinary Experts into an Integrated Interdisciplinary Team

by
Martha E. Mather
1,*,
Jason S. Bergtold
2,
Marcellus M. Caldas
3,
Ethan M. Bernick
4,
Trisha L. Moore
5,
Gabriel Granco
6,
Aleksey Y. Sheshukov
5 and
Ignacio A. Ciampitti
7
1
U. S. Geological Survey, Kansas Cooperative Fish and Wildlife Research Unit, Kansas State University, Manhattan, KS 66506, USA
2
Department of Agricultural Economics, Kansas State University, Manhattan, KS 66506, USA
3
Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS 66506, USA
4
Department of Political Science, Kansas State University, Manhattan, KS 66506, USA
5
Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, USA
6
Department of Geography and Anthropology, Cal Poly Pomona, Pomona, CA 91768, USA
7
Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(23), 10625; https://doi.org/10.3390/su162310625
Submission received: 9 September 2024 / Revised: 11 November 2024 / Accepted: 13 November 2024 / Published: 4 December 2024
(This article belongs to the Section Resources and Sustainable Utilization)

Abstract

:
A common problem for interdisciplinary sustainability research is that scientists trained in different disciplines are often not rowing their boat effectively in the same direction. Sustainability tools can aid the implementation of this team-melding process. Here, our purpose is to illustrate our Multi-step Integrated graphical and structured discussion eXercise (MIX) tool that transforms diverse disciplinary experts into an interdisciplinary team. We use a visual puzzle-solving approach based on the blind men and the elephant metaphor (BMEM) because this story illustrates the shortcomings of siloed viewpoints and the need to integrate multiple perspectives. Our six-step MIX tool provides step-specific objectives, group activities, discussion questions, and learning outcomes. Activities promote experiential learning for team problem solving. The step-specific structured discussions are designed to get each individual to change their focus from their own discipline (i.e., an elephant trunk, tail, leg, or other isolated pieces of the whole animal) to the team’s interdisciplinary goal (i.e., the whole elephant or the entire multi-faceted problem). In our example proof of concept, we show that a narrow focus on only economic yield (trunk), ecological conservation (legs), or human values (tail) misrepresents the biologically involved sustainability problem (elephant) and blocks innovative solutions.

1. Introduction

Overview, Need, and Purpose. Many scientists want to work together on interdisciplinary and transdisciplinary teams to achieve sustainability, but transforming individual disciplinary experts into a single, effective team is difficult. A common unresolved problem for sustainability research is that scientists trained in different disciplines are often not rowing their boat effectively in the same direction. In fact, they often are not even in the same boat. Scientists who are interested in interdisciplinary sustainability problems likely will be more successful if they have access to practical step-by-step tools that help bond individual disciplinary experts together. Here, our purpose is to illustrate our Multi-step Integrated graphical and structured discussion eXercise (MIX) tool that blends diverse individual disciplinary experts into an interdisciplinary team. Our MIX tool is built around a puzzle-solving approach that is based on the blind men and the elephant metaphor (BMEM). The BMEM describes a group of visually impaired people, each of whom must define an elephant after examining only one part of the elephant body (Figure 1). We compare the BMEM to scientists of varying disciplines working in their own research silos without understanding how their discipline fits into a larger, more complex problem. We use a biologically involved sustainability (BIS) problem to demonstrate a proof of concept for the MIX tool. BIS is a family of applied research and resource management problems that grapple with the balance of use and conservation for natural–human systems in which a native, cultivated, or invasive biological organism (non-human animal or plant) plays a central role. BIS problems deserve special consideration because dynamic human–natural system tensions are embedded in every applied plant–animal–water–human sustainability problem and practitioners need to make science-driven decisions from multi-discipline, multi-scale, spatially complex datasets. Our MIX tool is directed toward professionals who have already decided to work together on interdisciplinary and transdisciplinary sustainability problems.

2. Literature Context

Scope. Our emphasis here is to illustrate a practical sustainability tool that can be used to address the common problem of melding individual expertise, perspectives, and work styles into a shared perspective. A substantial and relevant literature context exists on the need for interdisciplinary science, the value of interdisciplinary science, the challenges of interdisciplinary science, the science of team science, and challenging environmental problems. We provide a selective review of this relevant literature to establish the foundational context for our MIX tool. A tangential body of social psychology and organizational management literature also exists, which we do not review because of its limited connection to our MIX tool development.
Need for and Value of Interdisciplinary Science. Much has been written about the need for and value of interdisciplinary and transdisciplinary teams, e.g., [1,2,3,4,5,6,7,8]. Interdisciplinary and transdisciplinary research are critical for understanding interactions among natural ecosystems and society, achieving effective and lasting policy, and integrating social science with humanities. Sustainability science is already contributing to significant global policies (e.g., Paris Agreement; UN Agenda for 2030) [5,6]. Interdisciplinary research can advance transformative science, professional skills, and the development of technology [9]. Increasingly, granting agencies, e.g., the National Science Foundation (NSF) and National Institutes of Health (NIH), government agencies, and universities are promoting interdisciplinary and transdisciplinary solutions to complex problems [5,10].
Existing Guidelines for Interdisciplinary Teams. Guidelines exist for many aspects of interdisciplinary and transdisciplinary research, e.g., [11,12], and many professionals are using these approaches [13,14,15,16]. For example, an evidence-based framework can guide the implementation of interdisciplinary research after the shared research question and objectives have been determined [17]. A policy science approach with an emphasis on problem identification, via social and decision processes, can aid sustainable natural resource management for natural and physical scientists [18].
Challenges for Interdisciplinary Science. Yet, interdisciplinary skills can be challenging to master for several reasons [10,19,20,21,22,23,24,25,26]. First, interdisciplinary approaches require that researchers explore ideas and interactions outside of their disciplinary boundaries and comfort zones [9]. In so doing, unfamiliar differences can emerge in scientific vocabularies, methods, epistemology, interpersonal relationships, goals, and reward systems [25]. Second, individuals are often not addressing goals in the same way across disciplines [27,28]. This challenge may arise because of conflicting individual versus team goals, a lack of time, a lack of understanding of other disciplines, or a disconnect between disciplinary models [10]. A third major barrier that is especially acute for natural–social science collaborations is that individual researchers often lack the skillsets needed to effectively communicate and work on cross-disciplinary teams [18,22]. Two other major barriers to achieving interdisciplinary success are siloed training programs and rigid, out-of-date institutional structures that affect support and rewards [18,22]. Sixth, the lack of opportunities to practice working in teams across disciplines also slows the mastery of interdisciplinary skills [26]. Finally, other reviews of the challenges of interdisciplinary science have highlighted a lack of respect outside one’s own discipline, limited funding, and uncertainty amongst interdisciplinary researchers in knowing where to begin [18]. This foundational context, provided by the interdisciplinary literature, establishes that tools, like MIX, are needed to help researchers learn how to collaborate, communicate, and co-produce sustainability science [10,25,29].
Science of Team Science. The developing fields of team science (SciTS) and communication competence, e.g., [30,31,32], have emerged to understand and enhance the effectiveness of collaborative scientific efforts. SciTS focuses on what makes teams successful including group function, conflict resolution, team formation, effective communication, team composition, shared goals, and the impact of institutional settings [1,33,34,35,36,37,38,39,40]. Key studies have examined how teams are formed, the backgrounds and motivations of team members, and how teams learn to work together [41,42]. SciTS explores the social and psychological forces at play in teamwork including psychological safety, awareness, and adaptability. For example, because the discomfort that arises from working outside one’s discipline can drive researchers back into their disciplinary silos [10], research teams need to find an acceptable discomfort–comfort ratio to establish a learning zone where they learn and grow as a team [25]. SciTS research shows that collaborative research with diverse teams (e.g., expertise and background) tends to perform better and often produces higher-impact science compared to solo efforts [27,43]. Conceptual models in SciTS provide frameworks for understanding team dynamics, though they often lack practical steps for real-world application. For instance, models like the CFIR (Consolidated Framework for Implementation Research) and RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) offer templates for implementation [44], while the RISE framework builds on these foundations to offer practical applications [26]. Building on this foundational context, we propose that our MIX tool can advance SciTS by helping specialized experts break out of their disciplinary silos and see the natural–human-coupled world from a more holistic and connected interdisciplinary and transdisciplinary sustainability perspective.
Wicked BIS Problems. Many environmental issues are “wicked” problems [45,46,47,48,49] that require challenging trade-offs between use and conservation to solve complex societal and scientific problems. This is especially true for freshwater allocation and conservation across agricultural landscapes [50] where climate change and rising demand for food, feed, fiber, and fuel stress environments [51,52]. Examples of BIS problems include renewable resource conservation plans, species management (exploited, threatened, endangered, and invasive), land-use-related water allocations, climate-change strategies, protected area stewardship, green building, sustainable agriculture, and international sustainability projects. BIS problems require a shared set of interdisciplinary scientific expertise (e.g., ecology, hydrology, ecological engineering, land use, GIS, spatial data, economics, geography, social science, statistics, modeling, policy, and outreach–extension–engagement). BIS problems also involve a common suite of state, federal, and non-governmental agency practitioners with specialized and often varying missions, values, and goals (e.g., fish and wildlife conservation, land management, and water allocation). Consequently, BIS problems require interdisciplinary and transdisciplinary approaches [53,54]. Our MIX tool was developed to address BIS problems, but has many features that are generalizable to a wide range of environmental and sustainability problems.

3. MIX, a Tool to Transform Individuals into a Team

MIX Tool Goal. The goal of our practical MIX tool is to change the perspective of individuals interested in working together as a team from a focus on “my discipline” to a focus on “our overarching interdisciplinary question” (Figure 2A). We adapted the BMEM to guide a six-step series of thinking exercises, team problem-solving activities, and structured group discussions about sustainability problems (Figure 2B). The BMEM metaphor fits the MIX tool process well because it is often used to emphasize the difficulty of seeing a broader integrative picture and the value of developing a shared, holistic viewpoint. Each of the six steps in our MIX tool includes a specific objective, experiential group activities, possible questions for structured discussions, and learning objectives. One-time exposure to the MIX tool could provide an introduction to how concepts can be applied to interdisciplinary science, but repeated opportunities to practice broadening individual team member perspectives through the repeated use of BMEM exercises will likely be more beneficial. Many practical examples, beyond resource use and conservation, can be addressed using the MIX tool. Scientists, scholars, policymakers, and practitioners working to solve the wicked problems encountered in the world (e.g., homelessness, healthcare, poverty, or climate change), who work from within their own disciplinary silos, can learn from the BMEM and benefit from our MIX tool.
Step 1: Review Why Interdisciplinary Teams are Needed. The objective of the first step in our MIX tool is to review the need for, value of, and challenges related to working in interdisciplinary teams (Figure 2B). Two activities address this objective: (1) a review and discussion of the literature on interdisciplinary science and team science (see previous section), and (2) structured discussions of the disciplines needed to address unsolved, real-world, environmental problems. For the first activity, the literature review can include presentation and discussion formats to address both overall trends and critical analyses of specific papers that address interdisciplinary science. Structured discussion questions for this first activity in step 1 might include (1) When and why are multiple disciplines needed? What is the likely result if only a single discipline is included? (2) What are the challenges to interdisciplinary team projects? What tools and perspectives can make interdisciplinary teams more effective? (3) What are examples of projects that can only be addressed by interdisciplinary teams? For the second activity in step 1, individuals break down components, causes, and potential solutions to complex, unresolved, environmental sustainability problems that appear in the news. Examples could include water allocation (e.g., overuse of rivers or aquifers), conflicts over endangered species (fish, plants and wildlife), balancing competing objectives for protected land management (agriculture or development versus biodiversity), or whether protecting the environment hurts or helps the economy. Participants can identify these problems themselves, or a set of problem summaries can be provided as part of the activity assignment. Structured discussion questions for the second activity in step 1 might include (1) What is the nature of the conflict? What disciplines are involved? (2) What scientific evidence is needed for a solution and why? What other issues besides science are involved? (3) What might be desirable and undesirable outcomes? (4) What outcomes might result if only a single disciplinary perspective is considered? The learning outcome associated with step 1 is that many environmental sustainability problems demand input from multiple disciplines.
Step 2: Explore the Uses of the BMEM in Publications. The objective of the second step is to review and discuss the BMEM, assess how the BMEM has been used in scientific publications, and synthesize common insights of BMEM for scientists (Figure 2B). In this metaphor, a group of visually impaired people come upon an elephant (an unknown complex entity) that none had previously encountered. Each person examines a different part of the elephant in isolation. Based on the resulting fragmented information, each person proposes a different description of the elephant. In these individual narratives, each person misidentifies the elephant in a different way (snake, spear, tree, fan, rope, and wall) depending on which isolated body part of the elephant they examined (trunk, tusk, leg, ears, tail, and body). As a result, none of the people accurately described the whole elephant (i.e., the entire complex problem).
The BMEM is used in the scientific literature. As an example, 156 publications from the Web of Science use the keywords “blind,” “men,” and “elephant” in the title. These articles are consistently published over time, suggesting that the relevance of the BMEM to scientists persists (Appendix A Figure A1A). These articles are embedded in the general research domains of science technology, life sciences-biomedical research, social sciences, technology, arts and humanities, and physical sciences (Appendix A Figure A1B). More specifically, the BMEM is used to address challenges in the research areas of psychology, behavior, healthcare, neurosciences, biochemistry-molecular biology, cell biology, physiology, genetics, biomedical research–medical practice for nine disciplines-diseases, information science, science technology, business, law, life sciences, environmental sciences, and ecology. In spite of wide differences in these topic areas, a common theme from these BMEM publications is that partial descriptions of a complex entity (BMEM: whole elephant; authors: the solution to a scientific problem or cure for a difficult disease) that are provided by different pieces of incomplete information (BMEM: individual and isolated elephant body parts; authors: different individual disciplines, specialties, or isolated approaches) often do not advance our understanding of the larger issue of greatest interest.
The two activities that address the objective for step 2 are (1) reviewing a selection of the BMEM literature as individuals or small groups and (2) synthesizing common themes and recommendations. Depending on the specific group involved in the MIX tool training exercise, this literature review and discussion can be focused (e.g., specific issues, locations, and times), general, or designed by individual participants. Questions for a team-wide structured discussion of this BMEM literature could include (1) What are the individual elephant body parts (i.e., components of the larger problem)? What is the whole issue (i.e., elephant)? What are the connections and feedbacks? (2) Who are the specialists? What knowledge can each contribute? What are their knowledge gaps? (3) What is missed by looking only at the pieces? What do the authors recommend? The learning outcomes for step 2 are that (1) understanding one aspect of a complex problem does not accurately portray the whole complex entity or holistic problem and (2) pursuing isolated specialties may not always be the most effective approach to problem solving.
Step 3: Apply BMEM to an Unfamiliar Issue. The objectives of steps 3–5 consist of practicing the application of the BMEM puzzle approach to complex problem examples with which individuals have different levels of familiarity (step 3: a totally unfamiliar example; step 4: some familiarity by all but none are subject experts; and step 5: the research project for which the team has come together to address) (Figure 2B). A key requirement of steps 3–4 is that none of the participants have a preconceived notion of how the puzzle parts should fit together so they are not locked into the silos of their disciplinary expertise. The two activities for the third step ask small groups to work together to identify and connect pieces that create an elephant-shaped jigsaw puzzle related to the “assigned issue.” This activity would be repeated twice; once under conditions where participants can identify pieces of their choosing and once where they must use “required” puzzle pieces. The “assigned issue” for this step can take many forms as long as the larger entity has many diverse parts. Examples of unfamiliar “assigned issues” include describing a planet, characterizing how a common mechanical object works, listing key components of biodiversity, or assembling ingredients needed to create a river. Using the river example, in the first activity for this step, small groups might name plants, animals, or locations as individual puzzle pieces, such that the resulting complex entity is a geographically specific ecosystem with a narrow focus. In the second activity, when participants use “required” puzzle pieces such as jobs, fishing, or floods, the holistic puzzle would describe connections between a river and human society and would incorporate the disciplines of ecology, sociology, climatology, human impacts, and economics. The learning outcomes of this third step include (1) the understanding that actual experience and practice with a team is important, (2) the recognition that not all team members think the same way, and (3) realizing that shared, overarching principles for inclusion and connection are needed to create an interpretable multi-piece puzzle. This scientifically light discussion provides an opportunity for team members to start to refocus their perspectives in a minimal-stress environment. Engaging in this discussion on broad non-technical issues can establish a conversational process that will be useful later when the group engages in potentially divisive technical discussions.
Step 4: Apply the BMEM to a More Familiar Issue. The fourth step in the MIX tool is another elephant puzzle exercise and structured discussion with a similar objective as the previous step (Figure 2B). However, step 4 addresses a more familiar topic to environmental scientists, i.e., how to create a multi-piece elephant puzzle in which individual pieces and connections address a general environmental case study (e.g., ones published in Case Studies in the Environment (https://online.ucpress.edu/cse; accessed on 1 September 2024); and teaching modules from Project Environmental Data-Driven Inquiry and Exploration–EDDIE (https://serc.carleton.edu/eddie/enviro_data/index.html; https://qubeshub.org/publications/1753/1; accessed on 1 September 2024)). These activities and discussions will be closer to the scientific area in which the team is interested, but still abstract enough to avert narrow siloed discussions based on preconceived disciplinary mindsets [55]. Hands-on and learning-by-doing activities such as those that the MIX tool offers are critical for teaching interdisciplinary approaches [5]. Furthermore, case-based approaches that are experiential in nature also advance interdisciplinary learning [56]. Questions addressed in the structured discussions related to this fourth step include repeatedly querying (1) whether individual pieces (the topic, discipline, or question) are a trunk, tail, or leg (piece) or the whole elephant (the holistic multi-faceted problem to be solved), and (2) why or why not? Humorous conversations may develop at this stage that can redirect remarks that could be otherwise perceived as potentially critical or divisive. Together, the puzzle building in steps 3 and 4 initiates a concrete process for connecting small and large perspectives. This foundation can be used before individuals tackle the specific sustainability problem for which they have assembled. Learning objectives for step 4 include the recognition that (1) individual disciplinary experts do not have all of the information needed to address interdisciplinary problems, and (2) a structured methodology can help connect different perspectives. In these initial steps (1–4), time can also be made available to discuss other “team” issues. For example, interdisciplinary scientists can be rewarded (or at least not punished) for leaving their silos because their integrative research is valued by journals and the people evaluating tenure or promotion.
Step 5: Iterative Team Melding Using BMEM. The objective for Step 5 is to apply the BMEM jigsaw puzzle building process to the research topic for which the team has assembled (Step 2B). The desired endproducts of this iterative process are a shared team vision and overarching question (OQ) that utilizes all expertise of individual team members. First, for activity 5a, each disciplinary expert in the team will give a brief presentation in which they share information about their discipline. This is akin to the think–pair–share teaching technique [57]. This cooperative learning technique allows students to teach part of the curriculum to peers (in small groups) and cultivates student interdependence through the learning tasks [58]. Topics addressed by each team member include (1) questions of interest in their discipline, (2) strengths and weakness that their discipline brings to the technical issue for which they have assembled, (3) connections and feedbacks that could be important, and (4) suggestions for how these disciplinary issues help address relevant larger sustainability problems. Often, this step is the starting point for team meetings. However, introductory disciplinary discussions without some team-melding preparations can result in individual experts not fully listening to each other or not fully understanding the value of other disciplinary perspectives. The advantage of using the elephant-puzzle approach from the MIX tool before disciplinary reviews begin is that after steps 1–4, disciplinary experts are less likely to present their interests as the singular marquee issue of the interdisciplinary problem. Even so, the expectation is that at this stage, individual experts likely will still not be able to clearly and fully see a shared goal or how the disciplines fit together. Nevertheless, they can regularly ask and answer whether the issue under discussion is a truck, tail, leg (partial view of the problem), or the whole elephant (the entire complex problem).
The second activity in step 5 is to have small groups create an elephant jigsaw puzzle for the project on which they have agreed to collaborate. This process of identifying relevant pieces, important connections, and a compelling shared overarching question to which all puzzle pieces contribute follows the same process as steps 3 and 4. Examples of general categories to be included in the graphical framework (Figure 3) are (I) team directions (the head); (II) foundational pieces provided by disciplinary perspectives (legs); (III) the synthesis of existing disciplinary knowledge (ventral body trunk); (IV) innovative, new research questions and application needs (middle body); (V) an integrated project vision and unifying statement of overarching interdisciplinary questions that can drive the team (dorsal body); and (VI) practical tools for implementation (hind quarters and tail). As the small groups and larger teams coalesce, the size, shape, and placement of the body pieces can be modified as the group desires. Our example is presented as an illustration. In the third activity, 5c, each small group presents their vision to the whole team, fields questions, and receives feedback. In activities 5d–f, puzzle creation and presentation feedback are repeated, and suggestions and usable ideas from the other subgroups are incorporated into the subsequent puzzles. The initial expectation in the iterative steps of 5b–f is that individuals will repeatedly disagree with each other and even with the utility of this approach. The learning outcome for step 5 is that each presentation and revision can lead to future agreement in that each round of presentations and discussions can refine, merge, and build on previous ideas. Disagreements, as long as they are respectful, can advance the discussion and can even be included as testable alternate hypotheses.
Step 6. Document the Shared Team Vision. The objective of step 6 is to summarize the iterative team presentations to create a shared work plan from the melded team (Figure 2B). The learning outcome for step 6 is that the MIX tool can guide individuals to a final graphical product that can act as a shared roadmap for future team meetings and research directions. Following scientific integration, the team can repeat the exercise with select practitioners and stakeholders if appropriate. Young scientists can benefit from learning across multiple disciplines even if their organizational structure remains siloed. Such learning can be significantly enhanced if it starts early [10]. Our MIX tool can help to initiate this cross-discipline teaching within research teams and in classroom environments.

4. A Proof-of-Concept Example of a Final Shared Interdisciplinary Team Vision

A Sustainable Conservation Perspective. We illustrate a graphical outcome from the MIX tool using the interdisciplinary problem of sustaining aquatic biodiversity in agricultural watersheds. The fates of both human and natural systems are inextricably linked, e.g., [59,60,61,62]. Although sustainability remains the holy grail of environmental science [63,64], environmental report cards for resource conservation and sustainability are discouraging [65,66,67,68,69]. Melding disciplinary perspectives using the MIX tool can advance integrated interdisciplinary work plans for these complex renewable resource problems.
The Problem: Maintaining Biological Diversity in Agricultural Watersheds. A complex benefit–threat relationship exists among freshwater, freshwater biota, agriculture, and humans. Freshwater, with its fish and ecosystem components, provides valuable ecological goods and services that benefit society by contributing to the economy (i.e., $1.7 trillion a year for rivers and lakes) [70] and enhancing the quality of life [71,72,73]. Human health benefits also accrue from aquatic ecosystems through experiencing nature and biodiversity [74]. Fish and aquatic resources supply food, provide recreational opportunities, play an important role in maintaining aquatic food webs [75], and can act as indicators of ecosystem health [76]. However, native freshwater biodiversity has been reduced [67,68,69] because beneficial goods and services from freshwater ecosystems are adversely affected by anthropogenic impacts, e.g., [59,77,78,79].
Allocating water for multiple uses is challenging. A substantial amount of freshwater is used for agriculture (>70%) [73,80]. Agriculture provides jobs, but agriculture also adversely affects aquatic systems. Thus, the balance of the use and protection of water, through voluntary or mandatory regulations, is essential to maintain functioning natural ecosystems and the environmental goods and services they provide. However, watershed residents view the restrictions of environmental protection required for conservation with both support and skepticism. Therefore, attempting to simultaneously manage (a) freshwater, (b) native freshwater biodiversity, (c) land adjacent to freshwater, (d) consumptive and non-consumptive human uses of freshwater resources, and (e) freshwater and riparian biota use–allocation–conservation tradeoffs in agricultural watersheds results in a complex BIS problem.
What the MIX Tool Provides For This Complex BIS Problem. To meld different perspectives, we created a graphical summary using the MIX tool. We do not describe the steps in the MIX process again, but below we show and describe a final graphic that integrates the steps for this problem (Figure 4). First, strategic planning is needed [81] as was reviewed and discussed in MIX steps 1–2. Like the head of an elephant, identifying disciplinary components, specifying stakeholders, reviewing available data types, and listing agencies, laws, and other relevant practical constraints are important boundaries for melding a team perspective (puzzle pieces 1, 2; Figure 4).
Like the legs on which an elephant stands, puzzle pieces 3–6 establish the existing foundation provided by traditional disciplinary science and practitioner institutions and agencies (state, federal, and non-governmental partner institutions) (Figure 4). These pieces are direct products of the structured discussion steps that occur in MIX step 5a. Puzzle piece 7 (Figure 4) synthesizes disciplinary paradigms (e.g., ecology, social science, policy) to provide an integrated foundation for what is known. As a result, obvious gaps are identified and more nuanced gaps are revealed through iterative structured discussions.
Placing our BIS problem into socio-environmental [82] or social–ecological system (SES) frameworks [83,84,85] is an obvious gap to be filled (puzzle piece 8; Figure 4). Data are at the heart of most ecological, sociological, economic, and management problems, including ours. Because natural system data often do not fit the assumptions of traditional statistical designs, better quantitative tools for large-scale problems with high uncertainty are needed (puzzle piece 9; Figure 4) [86,87]. Relevant data on biodiversity, physical drivers, associated disturbances, human values, economic concerns, and policy options occur at multiple scales. Addressing spatial complexity, scale choice, and especially scale integration is a fundamental ecological and social science challenge for all complex environmental problems (puzzle piece 10; Figure 4), e.g., [88,89,90,91,92].
Scientific understanding and prediction have little to no conservation value if research findings cannot be translated into policy and action (puzzle piece 11; Figure 4) [93]. Translating data and practice into policy is critical [94]. Interdisciplinary social–ecological system research is expected to provide decision support for environmental practitioners and policymakers and connect general understanding to specific actions. Although the need to translate research into practice is well known, success is often limited by a failure to act [95]. This “know–do gap” exists because of a mismatch between simplistic ways in which research is often translated to policymakers (e.g., heuristics and simplified models) versus the actual complexity of policy subsystems [85,96,97,98,99,100,101,102]. The need to strengthen relationships between researchers, practitioners, and policymakers is a research gap that could be filled by providing research and decision support that balances the generality of ideas with specificity to individual systems. The team skills used in these discussions for puzzle pieces 7–11 were developed in steps 3–5 of the MIX process and iterative syntheses that occurred throughout Step 4.
Considered together, puzzle pieces 1–11 develop the following shared team vision statement that can lead to the identification of overarching project questions (puzzle piece 12; Figure 4). Physical, biological, or social science disciplines likely will not be successful in solving BIS problems (e.g., maintaining biological diversity in an agricultural watershed) unless scientists within these disciplines develop thoughtful and effective links between disciplines. For example, an ecologist, fisheries/wildlife biologist, and ecological engineer may be successful in understanding their specific biophysical systems, but they will likely be unable to implement an effective conservation/restoration plan without the buy-in from human stakeholders (e.g., links to economics, land use, social values, and culture). Conversely, social scientists likely will meet with limited success for economic or other policies within a community that supports conservation if they fail to successfully integrate natural/biophysical linkages. With the integrated vision and overarching questions that can emerge from our MIX tool (puzzle piece 12; Figure 4), interdisciplinary questions and work plans can be developed for associated applied activities (science communication, team skills, and practical tools; puzzle pieces 13–15; Figure 4).

5. Summary

Utility. MIX is useful for several reasons. The goal of this team-melding exercise is to get individuals thinking and talking about how different perspectives relate to a shared focus. Whether a group agrees with the metaphor, the puzzle pieces identified here, or the direction suggested is irrelevant, the BMEM is the vehicle for developing a new way of team thinking. In completing this series of structured discussions on a non-scientific concept, the team has something concrete on which to focus that is equally outside the disciplinary focus of all team members.
Who Can Benefit From Our MIX Tool. We describe the steps in our tool in detail and illustrate a proof of concept. A number of discussions amongst our authorship team contributed to the ideas associated with the development of the MIX tool: team meetings for an NSF Dynamics of Coupled Natural and Human Systems project, writing an NSF National Research Traineeship grant, a graduate seminar on Biologically Involved Sustainability in which we were all involved, and various presentations and publications [26,53,83,84,85]. Many ways exist to adapt our MIX tool to different audiences including multiple classes over a semester, a day-long workshop, a shorter series of exercises within a 1–2 h meeting, icebreakers at the beginning or end of regular group meetings, or special meetings designed to enhance collaboration. We did not quantitatively test our MIX tool because such an effort is beyond the scope of our tool development process. Many professionals are anecdotally aware, through personal experience, of the challenges of working with people with different backgrounds. We hope those professionals will try our MIX tool.
How Experts Can Break Out of Their Disciplinary Silos. Topical deficiencies created by professional silos of technical specialization that deter interdisciplinary creativity can be difficult to overcome because, often, established experts are not aware of the limitations of their knowledge. The increasing specialization that makes scientists more successful in their discipline often makes topical experts less able to see the value of other disciplinary perspectives, how disciplines might interact, and how their discipline fits with other disciplines [103,104]. If experts can sit down together with the MIX tool early in their collaboration, they can label different parts of their research puzzle, identify links among disciplinary components, and create a shared work plan and common series of interdisciplinary questions that can help guide the team out of their individual silos toward a jointly held vision. Because interdisciplinary approaches require that researchers integrate new, different, and, at times, unfamiliar ideas [105], our MIX process can initiate the paradigm shift from individual expertise to interdisciplinary cohesion, get members of the team in the same boat, and help them row in the same direction.

Author Contributions

Conceptualization, M.E.M., J.S.B., M.M.C., E.M.B., T.L.M., G.G., A.Y.S. and I.A.C.; methodology, M.E.M., M.M.C., J.S.B., E.M.B., T.L.M., G.G., A.Y.S. and I.A.C.; visualization, M.E.M.; roles/writing—original draft, M.E.M., J.S.B., M.M.C., E.M.B., T.L.M. and I.A.C.; writing—review and editing, M.E.M., J.S.B., M.M.C., E.M.B., T.L.M., G.G., A.Y.S. and I.A.C. All authors have read and agreed to the published version of the manuscript.

Funding

National Science Foundation, Dynamics of Coupled Natural and Human Systems Program, Award 1313815.

Informed Consent Statement

No humans or animals were used.

Data Availability Statement

No data were used.

Acknowledgments

This project was coordinated through the Kansas Cooperative Fish and Wildlife Research Unit, which involves cooperation between Kansas State University, the U.S. Geological Survey, the U.S. Fish and Wildlife Service, the Kansas Department of Wildlife and Parks and the Wildlife Management Institute. Some coauthors were supported in part by the National Science Foundation, Dynamics of Coupled Natural and Human Systems Program, Award 1313815. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. G.G. was partially funded by the USDA/AFRI#2022-67023-36150.

Conflicts of Interest

The authors have no conflicts of interest.

Appendix A

Figure A1. (A) Numbers of articles with the words “blind,” “men,” and “elephant,” in the title identified in a Web of Science search (Y axis) through time (X axis). (B) Research domains of these articles. Included in each colored square is the Web of Science category and the number of publications. Citation indices and years searched in the Web of Science database include: Science 1945–present; Social Sciences 1956–present; Arts and Humanities 1975–present; Emerging Sources: 2015.
Figure A1. (A) Numbers of articles with the words “blind,” “men,” and “elephant,” in the title identified in a Web of Science search (Y axis) through time (X axis). (B) Research domains of these articles. Included in each colored square is the Web of Science category and the number of publications. Citation indices and years searched in the Web of Science database include: Science 1945–present; Social Sciences 1956–present; Arts and Humanities 1975–present; Emerging Sources: 2015.
Sustainability 16 10625 g0a1

References

  1. NRC. National Research Council. Convergence: Facilitating Transdisciplinary Integration of Life Sciences, Physical Sciences, Engineering, and Beyond; The National Academies Press: Washington, DC, USA, 2014. [Google Scholar] [CrossRef]
  2. Ewel, K. Natural resource management: The need for interdisciplinary collaboration. Ecosystems 2001, 4, 716–722. [Google Scholar] [CrossRef]
  3. Frodeman, R.; Klein, J.T.; Pacheco, R. (Eds.) The Oxford Handbook of Interdisciplinarity; Oxford University Press: Oxford, UK, 2017. [Google Scholar]
  4. Bodmer, P.; Attermeyer, K.; Pastor, A.; Catalan, N. Collaborative projects: Unleashing early career scientists’ power. Trends Ecol. Evol. 2019, 34, 871–874. [Google Scholar] [CrossRef] [PubMed]
  5. Rocha, P.L.B.; Pardini, R.; Viana, B.F.; El-Hani, C.N. Fostering inter- and transdisciplinarity in discipline-oriented universities to improve sustainability science and practice. Sustain. Sci. 2020, 15, 717–728. [Google Scholar] [CrossRef]
  6. Shrivastava, P.; Smith, M.S.; O’Brien, K.; Zsolnai, L. Transforming sustainability science to generate positive social and environmental change globally. One Earth 2020, 2, 329–340. [Google Scholar] [CrossRef]
  7. Blue, G.; Davidson, D. Advancing a transformative social contract for the environmental sciences: From public engagement to justice. Environ. Res. Lett. 2020, 15, 115008. [Google Scholar] [CrossRef]
  8. Marcoci, A.; Thresher, A.C.; Martens, N.C.; Galison, P.; Doeleman, S.; Johnson, M. Big STEM collaborations should include humanities and social science. Nat. Hum. Behav. 2023, 7, 1229–1230. [Google Scholar] [CrossRef]
  9. Akkerman, S.F.; Bakker, A. Learning at the boundary: An introduction. Int. J. Educ. Res. 2011, 50, 1–5. [Google Scholar] [CrossRef]
  10. Fam, D.; Clarke, E.; Freeth, R.; Derwort, P.; Klaniecki, K.; Kater-Wettstädt, L.; Juarez-Bourke, S.; Hilser, S.; Peukert, D.; Meyer, E.; et al. Interdisciplinary and transdisciplinary research and practice: Balancing expectations of the ‘old’ academy with the future model of univeristies as ‘problem solvers’. High. Educ. Q. 2020, 74, 19–34. [Google Scholar] [CrossRef]
  11. Cheruvelil, K.S.; Soranno, P.A.; Weathers, K.C.; Hanson, P.C.; Goring, S.J.; Filstrup, C.T.; Read, E.K. Creating and maintaining high-performing collaborative research teams: The importance of diversity and interpersonal skills. Front. Ecol. Environ. 2014, 12, 31–38. [Google Scholar] [CrossRef]
  12. Gibert, A.; Tozer, W.C.; Westoby, M. Teamwork, soft skills and research training. Trends Ecol. Evol. 2017, 32, 81–84. [Google Scholar] [CrossRef]
  13. Guimarães, M.H.; Pohl, C.; Bina, O.; Varanda, M. Who is doing inter- and transdisciplinary research, and why? An empirical study of motivations, attitudes, skills, and behaviours. Futures 2019, 112, 102441. [Google Scholar] [CrossRef]
  14. Hou, D.; Bolan, N.S.; Tsang DC, W.; Kirkham, M.B.; O’Connor, D. Sustainable soil use and management: An interdisciplinary and systematic approach. Sci. Total Environ. 2020, 729, 138961. [Google Scholar] [CrossRef] [PubMed]
  15. Schoolman, E.D.; Guest, J.S.; Bush, K.F.; Bell, A.R. How interdisciplinary is sustainability research? Analyzing the structure of an emerging scientific field. Sustain. Sci. 2012, 7, 67–80. [Google Scholar] [CrossRef]
  16. Brelsford, C.; Dumas, M.; Schlager, E.; Dermody, B.J.; Aiuvalasit, M.; Allen-Dumas, M.R.; Beecher, J.; Bhatia, U.; D’Odorico, P.; Garcia, M.; et al. Developing a sustainability science approach for water systems. Ecol. Soc. 2020, 25, 23. [Google Scholar] [CrossRef]
  17. Tobi, H.; Kampen, J.K. Research design: The methodology for interdisciplinary research framework. Qual. Quant. 2018, 52, 1209–1225. [Google Scholar] [CrossRef]
  18. Clark, S.G.; Palis, F.; Trompf, G.W.; Terway, T.M.; Wallace, R.L. Interdisciplinary problem framing for sustainability: Challenges, a framework, case studies. J. Sustain. For. 2017, 36, 516–534. [Google Scholar] [CrossRef]
  19. Campbell, L.M. Overcoming obstacles to interdisciplinary research. Conserv. Biol. 2005, 19, 574–577. [Google Scholar] [CrossRef]
  20. Begg, M.D.; Vaughan, R.D. Are biostatistics students prepared to succeed in the era of interdisciplinary science? (And how will we know?). Am. Stat. 2011, 65, 71–79. [Google Scholar] [CrossRef]
  21. Morse, W.C.; Nielsen-Pincus, M.; Wulfhorst, J.D. Bridges and barriers to developing and conducting interdisciplinary graduate-student team research. Ecol. Soc. 2011, 12, 8. [Google Scholar] [CrossRef]
  22. Fischer, A.R.H.; Tobi, H.; Ronteltap, A. When natural met social: A review of collaboration between the natural and social sciences. Interdiscip. Sci. Rev. 2011, 36, 341–358. [Google Scholar] [CrossRef]
  23. Hall, T.E.; O’Rourke, M. (Eds.) Responding to communication challenges in transdisciplinary sustainability science. In Transdisciplinary Sustainability Studies; Routledge: London, UK, 2014. [Google Scholar]
  24. Goring, S.J.; Weathers, K.C.; Dodds, W.K.; Soranno, P.A.; Sweet, L.C.; Cheruvelil, K.S.; Kominoski, J.S.; Rüegg, J.; Thorn, A.M.; Utz, R.M. Improving the culture of interdisciplinary collaboration in ecology by expanding measures of success. Front. Ecol. Environ. 2014, 12, 39–47. [Google Scholar] [CrossRef]
  25. Freeth, R.; Caniglia, G. Learning to collaborate while collaborating: Advancing interdiscplinary sustainability research. Sustain. Sci. 2020, 15, 247–261. [Google Scholar] [CrossRef]
  26. Mather, M.E.; Granco, G.; Bergtold, J.; Caldas, M.; Heier-Stamm, J.; Sanderson, M.; Sheshukov, A.; Daniels, M. RISE to interdisciplinary success: A widely-implementable, iterative, multi-step structured process for mastering team skills. BioScience 2023, 73, 891–905. [Google Scholar] [CrossRef]
  27. Sievanen, L.; Campbell, L.M.; Leslie, H.M. Challenges to interdisciplinary research in ecosystem-based management. Conserv. Biol. 2022, 26, 315–323. [Google Scholar] [CrossRef]
  28. Daniel, K.L.; McConnell, M.; Schuchardt, A.; Peffer, M.E. Challenges facing interdisciplinary researchers: Findings from a professional development workshop. PLoS ONE 2022, 17, e0267234. [Google Scholar] [CrossRef]
  29. MacCrea, D.; Matuszewski, H. A framework for interdisciplinary research. Chapter. In Strange Bedfellows: An Experiment in Student-Directed Interdisciplinary Research; de Ruiter, N., Wittingslow, R., Chiu, R., Eds.; University of Groningen Press: Groningen, The Netherlands, 2023. [Google Scholar] [CrossRef]
  30. Hall, K.L.; Vogel, A.L.; Huang, G.C.; Serrano, K.J.; Rice, E.L.; Tsakraklides, S.P.; Fiore, S.M. The science of team science: A review of the empirical evidence and research gaps on collaboration in science. Am. Psychol. 2018, 73, 532–548. [Google Scholar] [CrossRef]
  31. Love, H.B.; Fosdick, B.K.; Cross, J.E.; Sutter, M.; Egan, D.; Tofany, E.; Fisher, E.R. Towards understanding the characteristics of successful and unsuccessful collaborations: A case-based team science study. Humanit. Soc. Sci. Commun. 2022, 9, 371. [Google Scholar] [CrossRef]
  32. Thompson, J.L. Building collective communication competence in interdisciplinary research teams. J. Appl. Commun. Res. 2009, 37, 278–297. [Google Scholar] [CrossRef]
  33. Salazar, M.R.; Lant, T.K.; Fiore, S.M.; Salas, E. Facilitating innovation in diverse science teams through integrative capacity. Small Group Res. 2012, 43, 527–558. [Google Scholar] [CrossRef]
  34. Begg, M.D.; Crumley, G.; Fair, A.M.; Martina, C.A.; McCormack, W.T.; Merchant, C.; Patino-Sutton, C.M.; Umans, J.G. Approaches to preparing young scholars for careers in interdisciplinary team science. J. Investig. Med. 2014, 62, 14–25. [Google Scholar] [CrossRef]
  35. Herz, N.; Dan, O.; Censor, N.; Bar-Haim, Y. Opinion: Authors overestimate their contribution to scientific work, demonstrating a strong bias. Proc. Natl. Acad. Sci. USA 2020, 117, 6282–6285. [Google Scholar] [CrossRef] [PubMed]
  36. Bisbey, T.M.; Wooten, K.C.; Campo, M.S.; Lant, T.K.; Salas, E. Implementing an evidence-based competency model for science team training and evaluation: TeamMAPPS. J. Clin. Transl. Sci. 2021, 5, e142. [Google Scholar] [CrossRef] [PubMed]
  37. Fiore, S.M.; Graesser, A.; Greiff, S. Collaborative problem-solving education for the twenty-first-century workforce. Nat. Hum. Behav. 2018, 2, 367–369. [Google Scholar] [CrossRef] [PubMed]
  38. National Research Council (NRC). Enhancing the Effectiveness of Team Science; The National Academies Press: Washington, DC, USA, 2015. [Google Scholar]
  39. Morton, L.W.; Eigenbrode, S.D.; Martin, T.A. Architectures of adaptive integration in large collaborative projects. Ecol. Soc. 2015, 20, 5. [Google Scholar] [CrossRef]
  40. Zaggl, M.A.; Pottbacker, J. Facilitators and inhibitors for integrating expertise diversity in innovation teams: The case of plasmid exchange in molecular biology. Res. Policy 2021, 50, 104313. [Google Scholar] [CrossRef]
  41. Pennington, D.D. Collaborative, cross-disciplinary learning and co-emergent innovation in eScience teams. Earth Sci. Inform. 2011, 4, 55–68. [Google Scholar] [CrossRef]
  42. Pennington, D.D.; Simpson, G.L.; McConnell, M.S.; Fair, J.M.; Baker, R.J. transdisciplinary research, transformative learning, and transformative science. BioScience 2013, 63, 564–573. [Google Scholar] [CrossRef]
  43. Martin, V.Y. Four common problems in environmental social research undertaken by natural scientists. BioScience 2019, 70, 13–16. [Google Scholar] [CrossRef]
  44. Rolland, B.; Resnik, F.; Hohl, S.D.; Johnson, L.J.; Saha-Muldowney, M.; Mahoney, J. Applying the lessons of implementation science to maximize feasibility and usability in team science intervention development. J. Clin. Transl. Sci. 2021, 5, e197. [Google Scholar] [CrossRef]
  45. Sun, J.Z.; Yang, K.Z. The wicked problem of climate change: A new approach based on social mess and fragmentation. Sustainability 2016, 8, 1312. [Google Scholar] [CrossRef]
  46. DeFries, R.; Nagendra, H. Ecosystem management as a wicked problem. Science 2017, 356, 265–270. [Google Scholar] [CrossRef] [PubMed]
  47. Walls, H.L. Wicked problems and a ‘wicked’ solution. Global Health 2018, 14, 34. [Google Scholar] [CrossRef] [PubMed]
  48. Wohlgezogen, F.; McCabe, A.; Osegowitsch, T.; Mol, J. The wicked problem of climate change and interdisciplinary research: Tracking management scholarship’s contribution. J. Manag. Organ. 2020, 26, 1048–1072. [Google Scholar] [CrossRef]
  49. Schipper, E.L.F.; Dubash, N.K.; Mulugetta, Y. Climate change research and the search for solutions: Rethinking interdisciplinarity. Clim. Chang. 2021, 168, 18. [Google Scholar] [CrossRef] [PubMed]
  50. Holden, J. Water Resources: An Integrated Approach, 2nd ed.; Routledge: London, UK, 2019. [Google Scholar]
  51. Falkenmark, M.; Lundqvist, J.; Klohn, W.; Postel, S.; Wallace, J.; Shuval, H.; Seckler, D.; Rockström, J. Water scarcity as a key factor behind global food insecurity: Round table discussion. Ambio 1998, 27, 148–154. [Google Scholar]
  52. Rosegrant, M.W.; Ringler, C.; Zhu, T. Water for agriculture: Maintaining food security under growing scarcity. Annu. Rev. Environ. Resour. 2009, 34, 205–222. [Google Scholar] [CrossRef]
  53. Caldas, M.; Mather, M.; Bergtold, J.; Daniels, M.; Granco, G.; Aistrup, J.; Haukos, D.; Sheshukov, A.; Sanderson, M.; Stamm, J.H. Understanding the Central Great Plains as a coupled climatic-hydrological-human system: Lessons learned in operationalizing interdisciplinary collaboration. In Collaboration Across Boundaries for Social-Ecological Systems Science–Experiences Around the World; Perz, S., Ed.; Palgrave MacMillan, University of Florida: Gainesville, FL, USA, 2019; pp. 265–294. [Google Scholar]
  54. Kim, M.K.; Douglas, M.M.; Pannell, D.; Setterfield, S.A.; Laborde, R.H.S.; Perrott, L.; Alvarez-Romera, J.G.; Beesley, L.; Canham, C.; Brecknell, A. When to use transdisciplinary approaches for environmental research. Front. Environ. Sci. 2022, 10, 840569. [Google Scholar] [CrossRef]
  55. Wei, C.A.; Burnside, W.R.; Che-Castaldo, J.P. Teaching socio-environmental synthesis with the case studies approach. J. Environ. Stud. Sci. 2015, 5, 42–49. [Google Scholar] [CrossRef]
  56. Sprain, L.; Thompson, W.M. Pedagogy for sustainability science: Case-based approaches for interdisciplinary instruction. Environ. Commun. 2012, 6, 532–550. [Google Scholar] [CrossRef]
  57. Kaddoura, M.A. Think pair share: A teaching learning strategy to enhance students’ critical thinking. Educ. Res. Q. 2013, 36, 3–24. [Google Scholar]
  58. Chang, W.L.; Benson, V. Jigsaw teaching method for collaboration on cloud platforms. Innov. Educ. Teach. Int. 2020, 59, 24–36. [Google Scholar] [CrossRef]
  59. Vorosmarty, C.J.; Mcintyre, P.B.; Gessner, M.O.; Dudgeon, D.; Prusevich, A.; Green, P.; Glidden, S.; Bunn, S.E.; Sullivan, C.A.; Liermann, C.R.; et al. Global threats to human water security and river biodiversity. Nature 2010, 467, 555–561. [Google Scholar] [CrossRef] [PubMed]
  60. Carlson, A.K.; Taylor, W.W.; Kinnison, M.T.; Sullivan, S.; Weber, M.J.; Melstrom, R.T.; Venturelli, P.A.; Wuellner, M.R.; Newman, R.M.; Hartman, K.J.; et al. Threats to freshwater fisheries in the United States: Perspectives and investments of state fisheries administrators and agricultural experiment station directors. Fisheries 2019, 44, 276–287. [Google Scholar] [CrossRef]
  61. He, F.Z.; Zarfl, C.; Bremerich, V.; David, J.N.; Hogan, Z.; Kalinkat, G.; Tockner, K.; Jaehnig, S. The global decline of freshwater megafauna. Glob. Chang. Biol. 2019, 25, 3883–3892. [Google Scholar] [CrossRef] [PubMed]
  62. Tickner, D.; Opperman, J.J.; Abell, R.; Acreman, M.; Arthington, A.A.; Bunn, S.E.; Cooke, S.J.; Dalton, J.; Darwall, W.; Edwards, G.; et al. Bending the curve of global freshwater biodiversity loss: An emergency recovery plan. BioScience 2020, 70, 330–342. [Google Scholar] [CrossRef] [PubMed]
  63. Ostrom, E. A general framework for analyzing sustainability of social-ecological systems. Science 2009, 325, 419–422. [Google Scholar] [CrossRef]
  64. Hart, D.D.; Bell, K.P.; Lindenfeld, L.A.; Jain, S.; Johnson, T.R.; Ranco, D.; McGill, B. Strengthening the role of universities in addressing sustainability challenges: The Mitchell Center for Sustainability Solutions as an institutional experiment. Ecol. Soc. 2015, 20, 4. [Google Scholar] [CrossRef]
  65. Butchart, S.H.M.; Walpole, M.; Collen, B.; van Strien, A.; Scharlemann, J.P.W.; Almond, R.E.A.; Baillie, J.E.M.; Bomhard, B.; Brown, C.; Bruno, J.; et al. Global biodiversity: Indicators of recent declines. Science 2010, 328, 1164–1168. [Google Scholar] [CrossRef]
  66. Hoekstra, A.Y.; Wiedmann, T.O. Humanity’s unsustainable environmental footprint. Science 2014, 344, 1114–1117. [Google Scholar] [CrossRef]
  67. Albert, J.S.; Destouni, G.; Duke-Sylvester, S.M.; Magurran, A.E.; Oberdorff, T.; Reis, R.E.; Winemiller, K.O.; Ripple, W.J. Scientists’ warning to humanity on the freshwater biodiversity crisis. Ambio 2021, 50, 85–94. [Google Scholar] [CrossRef]
  68. Su, G.; Logez, M.; Xu, J.; Tao, S.; Villéger, S.; Brosse, S. Human impacts on global freshwater fish biodiversity. Science 2021, 371, 835–838. [Google Scholar] [CrossRef] [PubMed]
  69. WWF. Living Planet Report. 2022. Available online: https://livingplanet.panda.org/en-US/ (accessed on 1 September 2024).
  70. Costanza, R.; d’Arge, R.; de Groot, R. The value of the world’s ecosystem services and natural capital. Nature 1997, 387, 253–260. [Google Scholar] [CrossRef]
  71. Baron, J.L.; Poff, N.L.; Angermeier, P.L.; Dahm, C.N.; Gleick, P.H.; Hairston, N.G., Jr.; Jackson, R.B.; Johnston, C.A.; Richter, B.D.; Steinman, A. Meeting Ecological and Societal Needs for Freshwater. Ecol. Appl. 2002, 12, 1247–1260. [Google Scholar] [CrossRef]
  72. Wilson, M.A.; Carpenter, S.R. Economic valuation of freshwater ecosystem services in the United States: 1971–1997. Ecol. Appl. 1999, 9, 772–783. [Google Scholar]
  73. Carpenter, S.R.; Stanley, E.H.; Vander Zanden, M.J. State of the World’s Freshwater Ecosystems: Physical, Chemical, and Biological Changes. Annu. Rev. Environ. Resour. 2011, 36, 75–99. [Google Scholar] [CrossRef]
  74. Sandifer, P.A.; Sutton-Grier, A.E.; Ward, B.P. Exploring connections among nature, biodiversity, ecosystem services, and human health and well-being: Opportunities to enhance health and biodiversity conservation. Ecosyst. Serv. 2015, 12, 1–15. [Google Scholar] [CrossRef]
  75. Moyle, P.B.; Leidy, R.A. Loss of biodiversity in aquatic ecosystems: Evidence from fish faunas. In Conservation Biology; Fiedler, P.L., Jain, S.K., Eds.; Springer: Boston, MA, USA, 1992. [Google Scholar] [CrossRef]
  76. Chovanec, A.; Hofer, R.; Schiemer, F. Chapter 18-Bioindicators. In Trace Metals and Other Contaminants in the Environment; Markert, B.A., Breure, A.M., Zechmeister, H.G., Eds.; Elsevier: Amsterdam, The Netherlands, 2003; Volume 6, pp. 639–676. [Google Scholar]
  77. Dudgeon, D.; Arthington, A.H.; Gessner, M.O.; Kawabata, Z.; Knowler, D.J.; Lévêque, C.; Naiman, R.J.; Prieur-Richard, A.-H.; Soto, D.; Stiassny, M.L.J.; et al. Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol. Rev. Camb. Philos. Soc. 2006, 81, 163–182. [Google Scholar] [CrossRef]
  78. Reid, A.; Carlson, A.K.; Creed, I.F.; Eliason, E.J.; Gell, P.A.; Johnson PT, J.; Kidd, K.A.; MacCormack, T.J.; Olden, J.D.; Ormerod, S.J.; et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 2019, 94, 849–873. [Google Scholar] [CrossRef]
  79. Ahmed, S.F.; Kumar, S.; Kabir, M.; Zuhara, F.T.; Mehjabin, A.; Tasannum, N.; Hoang, A.T.; Kabir, Z.; Mofijur, M. Threats, challenges and sustainable conservation strategies for freshwater biodiversity. Environ. Res. 2022, 214, 113808. [Google Scholar] [CrossRef]
  80. Perkin, S. Is agriculture sucking fresh water dry? Crops account for 92% of fresh water used each year, according to new global analysis. Science 2012. [Google Scholar] [CrossRef]
  81. Mather, M.E.; Dettmers, J.M. Adaptive problem maps (APM): Connecting data dots to build increasingly informed and defensible environmental conservation decisions. J. Environ. Manag. 2022, 312, 114826. [Google Scholar] [CrossRef]
  82. Pulver, S.; Ulibarri, N.; Sobocinski, K.L.; Alexander, S.M.; Johnson, M.L.; McCord, P.F.; Dell’Angelo, J. Frontiers in socio-environmental research: Components, connections, scale, and context. Ecol. Soc. 2018, 23, 23. [Google Scholar] [CrossRef]
  83. Granco, G.J.; Stamm, J.L.H.; Bergtold, J.S.; Daniels, M.; Sanderson, M.; Sheshukov, A.; Mather, M.; Caldas, M.; Ramsey, S.; Lehter, R.; et al. Evaluating environmental change and behavioral decision-making for sustainability policy using an agent-based model: A case study for the Smoky-Hill Watershed, Kansas. Sci. Total Environ. 2019, 695, 133769. [Google Scholar] [CrossRef] [PubMed]
  84. Granco, G.; Caldas, M.; Bergtold, J.S.; Stamm, J.L.H.; Mather, M.; Sanderson, M.; Daniels, M.; Sheshukov, A.; Haukos, D.; Ramsey, S. Local environment and individual’s beliefs: The dynamics shaping public support for sustainability policy in an agricultural landscape. J. Environ. Manag. 2022, 301, 113776. [Google Scholar] [CrossRef] [PubMed]
  85. Bergtold, J.S.; Caldas, M.M.; Ramsey, S.M.; Sanderson, M.R.; Granco, G.; Mather, M.E. The gap between experts, farmers and non-farmers on perceived environmental vulnerability and the influence of values and beliefs. J. Environ. Manag. 2022, 316, 115186. [Google Scholar] [CrossRef]
  86. Cressie, N.; Calder, C.A.; Clark, J.S.; Hoef, J.M.V.; Wikle, C.K. Accounting for uncertainty in ecological analysis: The strengths and limitations of hierarchical statistical modeling. Ecol. Appl. 2009, 19, 553–570. [Google Scholar] [CrossRef]
  87. Hampton, S.E.; Strasser, C.A.; Tewksbury, J.J.; Gram, W.K.; Budden, A.E.; Batcheller, A.L.; Duke, C.S.; Porter, J.H. Big data and the future of ecology. Front. Ecol. Environ. 2013, 11, 156–162. [Google Scholar] [CrossRef]
  88. Lee, K.N. Greed, scale mismatch, and learning. Ecol. Appl. 1993, 3, 560–564. [Google Scholar] [CrossRef]
  89. Cumming, G.S.; Cumming, D.H.M.; Redman, C.L. Scale mismatches in social-ecological systems: Causes, consequences, and solutions. Ecol. Soc. 2006, 11, 14. Available online: https://www.jstor.org/stable/26267802 (accessed on 1 September 2024). [CrossRef]
  90. Stevens, C.J.; Fraser, I.; Mitchley, J.; Thomas, M.B. Making ecological science policy-relevant: Issues of scale and disciplinary integration. Landsc. Ecol. 2007, 22, 799–809. [Google Scholar] [CrossRef]
  91. Nelson, K.S.; Burchfield, E.K. Landscape complexity and US crop production. Nat. Food 2021, 2, 330–338. [Google Scholar] [CrossRef] [PubMed]
  92. Stoffers, T.; Buijse, A.D.; Geerling, G.W.; Jans, L.H.; Schoor, M.M.; Poos, J.J.; Nagelkerke, L.A.J. Freshwater fish biodiversity restoration in floodplain rivers requires connectivity and habitat heterogeneity at multiple spatial scales. Sci. Total Environ. 2022, 838, 156509. [Google Scholar] [CrossRef] [PubMed]
  93. Wall, T.U.; McNie, E.; Garfin, G.M. Use-inspired science: Making science usable by and useful to decision makers. Front. Ecol. Environ. 2017, 15, 551–559. [Google Scholar] [CrossRef]
  94. Cobourn, K.M.; Carey, C.C.; Boyle, K.J.; Duffy, C.; Dugan, H.A.; Farrell, K.J.; Fitchett, L.; Hanson, P.C.; Hart, J.A.; Henson, V.R.; et al. From concept to practice to policy: Modeling coupled natural and human systems in lake catchments. Ecosphere 2018, 9, e02209. [Google Scholar] [CrossRef]
  95. Hunter, D.J. Meeting the challenge of the “know-do” gap: Comment on CIHR Health System Impact Fellows: Reflections on ‘driving change’ within the health system. Int. J. Health Policy Manag. 2019, 8, 498. [Google Scholar] [CrossRef]
  96. Hjern, B.; Porter, D. Implementation structures: A new unit of administrative analysis. Organ. Stud. 1981, 2, 211–227. [Google Scholar] [CrossRef]
  97. Ostrom, E. A public service industry approach to the study of local government structure and reform. Policy Politics 1983, 11, 313–341. [Google Scholar] [CrossRef]
  98. Sabatier, P. Top-down and bottom-up models of policy implementation: A critical and suggested synthesis. J. Public Policy 1986, 6, 21–48. [Google Scholar] [CrossRef]
  99. Rhodes, R.A.W. Beyond Westminster and Whitehall; Unwin & Hyman: London, UK, 1988. [Google Scholar]
  100. Jordan, A.G. Sub-governments, policy communities, and networks. J. Theor. Politics 1990, 2, 319–338. [Google Scholar] [CrossRef]
  101. Kahneman, D. Thinking, Fast and Slow; Macmillan: New York, NY, USA, 2011. [Google Scholar]
  102. Gentry, S.; Milden, L.; Kelly, M.P. Why is translating research into policy so hard? How theory can help public health researchers achieve impact? Public Health 2020, 178, 90–96. [Google Scholar] [CrossRef]
  103. Macleod, M. What makes interdisciplinarity difficult? Some consequences of domain specificity in interdisciplinary practice. Syntheses 2018, 195, 697–720. [Google Scholar] [CrossRef]
  104. Mather, M.E.; Smith, J.M.; Gerber, K.M.; Taylor, R.B.; Kennedy, C.G.; Hitchman, S.M.; Fencl, J.S.; Frank, H.M. Merging scientific silos: Integrating specialized approaches for thinking about and using spatial data that can provide new directions for persistent fisheries problems. Fisheries 2021, 46, 485–494. [Google Scholar] [CrossRef]
  105. Ivanov, V.Y.; Ungar, P.S.; Ziker, J.P.; Abdulmanova, S.; Celis, G.; Dixon, A.; Ehrich, D.; Fufachev, I.; Gilg, O.; Heskel, M.; et al. A Convergence Science Approach to Understanding the Changing Arctic. Earth’s Future 2024, 12, e2023EF004157. [Google Scholar] [CrossRef]
Figure 1. An interdisciplinary jigsaw puzzle can help meld many disciplinary experts into a single interdisciplinary team through the six steps of the MIX process. The elephant puzzle approach is inspired by the blind men and the elephant metaphor (BMEM). This puzzle is used repeatedly throughout the MIX tool. Each use incrementally builds on the previous step.
Figure 1. An interdisciplinary jigsaw puzzle can help meld many disciplinary experts into a single interdisciplinary team through the six steps of the MIX process. The elephant puzzle approach is inspired by the blind men and the elephant metaphor (BMEM). This puzzle is used repeatedly throughout the MIX tool. Each use incrementally builds on the previous step.
Sustainability 16 10625 g001
Figure 2. (A) The goal of the MIX process. (B) A six-step flow chart associated with the MIX tool including the name of the step and associated activities. BMEM refers to the blind men and the elephant metaphor. Step-specific objectives, more details on activities, questions to guide the structured discussions, and expected learning outcomes are provided in the text.
Figure 2. (A) The goal of the MIX process. (B) A six-step flow chart associated with the MIX tool including the name of the step and associated activities. BMEM refers to the blind men and the elephant metaphor. Step-specific objectives, more details on activities, questions to guide the structured discussions, and expected learning outcomes are provided in the text.
Sustainability 16 10625 g002
Figure 3. In using the elephant puzzle example for MIX steps 5b–f, we recommend that puzzles and structured discussions focus on (I) directions for the team (head and trunk); (II) disciplinary synthesis on which the team project builds (legs); (III) integration of existing knowledge (lower body); (IV) innovative research and applications (upper body); (V) overarching interdisciplinary vision for the project (dorsal saddle region); (VI) practical tools (tail).
Figure 3. In using the elephant puzzle example for MIX steps 5b–f, we recommend that puzzles and structured discussions focus on (I) directions for the team (head and trunk); (II) disciplinary synthesis on which the team project builds (legs); (III) integration of existing knowledge (lower body); (IV) innovative research and applications (upper body); (V) overarching interdisciplinary vision for the project (dorsal saddle region); (VI) practical tools (tail).
Sustainability 16 10625 g003
Figure 4. How a team interested in sustaining aquatic biodiversity in an agricultural watershed can create an overarching team vision and connected interdisciplinary work plan. The 15 individual puzzle pieces, related connections, and resulting overarching team direction are described in the text.
Figure 4. How a team interested in sustaining aquatic biodiversity in an agricultural watershed can create an overarching team vision and connected interdisciplinary work plan. The 15 individual puzzle pieces, related connections, and resulting overarching team direction are described in the text.
Sustainability 16 10625 g004
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Mather, M.E.; Bergtold, J.S.; Caldas, M.M.; Bernick, E.M.; Moore, T.L.; Granco, G.; Sheshukov, A.Y.; Ciampitti, I.A. Rowing in the Same Direction Using MIX—A Tool to Initiate the Melding of Individual Disciplinary Experts into an Integrated Interdisciplinary Team. Sustainability 2024, 16, 10625. https://doi.org/10.3390/su162310625

AMA Style

Mather ME, Bergtold JS, Caldas MM, Bernick EM, Moore TL, Granco G, Sheshukov AY, Ciampitti IA. Rowing in the Same Direction Using MIX—A Tool to Initiate the Melding of Individual Disciplinary Experts into an Integrated Interdisciplinary Team. Sustainability. 2024; 16(23):10625. https://doi.org/10.3390/su162310625

Chicago/Turabian Style

Mather, Martha E., Jason S. Bergtold, Marcellus M. Caldas, Ethan M. Bernick, Trisha L. Moore, Gabriel Granco, Aleksey Y. Sheshukov, and Ignacio A. Ciampitti. 2024. "Rowing in the Same Direction Using MIX—A Tool to Initiate the Melding of Individual Disciplinary Experts into an Integrated Interdisciplinary Team" Sustainability 16, no. 23: 10625. https://doi.org/10.3390/su162310625

APA Style

Mather, M. E., Bergtold, J. S., Caldas, M. M., Bernick, E. M., Moore, T. L., Granco, G., Sheshukov, A. Y., & Ciampitti, I. A. (2024). Rowing in the Same Direction Using MIX—A Tool to Initiate the Melding of Individual Disciplinary Experts into an Integrated Interdisciplinary Team. Sustainability, 16(23), 10625. https://doi.org/10.3390/su162310625

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop