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Journal for Multicultural Education

ISSN : 2053-535X

Article publication date: 22 January 2021

Issue publication date: 4 June 2021

This paper aims to share responses from current literature, a small case study about perceptions and practices of the school of education faculty toward multicultural and educational issues concerning the rapid rise in online environments during coronavirus (COVID-19) experiences and just-in-time strategies for addressing digital equity and educational inclusion in K-16 online educational settings.

Design/methodology/approach

This is a conceptual paper that emerged from an action research case study. The study included four faculty in an urban school of education. The faculty participants were asked to provide examples of educational inclusion strategies used during transitioning their courses and advising to online environments in a Research I university. Faculty included one educational leadership, one sports management, one special education and one teacher education professor. Central issues explored practices related to language, technology access, curriculum design and technological competencies and assessment. A driving question was: How do institutions, schools or educators provide learning opportunities to support digital equity and inclusive education practice to maintain and strengthen relationships and core practices of multicultural education during a time of physical distancing during COVID-19? And what are the experiences, barriers, successes?

Research-based transformative knowledge, real situations and practical resources for considering inclusive education curriculum concepts were found that are connecting educators, teachers, learners and communities during this time of crisis.

Research limitations/implications

Methodological limitations that influenced the research design include conducting research in a totally virtual environment, small sample size, lack of diversity in curriculum content and one research site. The data collection was limited to written responses from the faculty participants. This action research study took place in a time frame limited by COVID-19 conditions during a four-month period.

Practical implications

In theory and practice, this new online movement suggests learners, teachers, educators and leaders are gaining experience and knowledge about resources and strategies for using new technologies, assessments and flexible curriculum as powerful tools for building language, curriculum and social-cultural communication bonds across generations and including special needs populations. Such new and emerging strategies could be used to bridge gaps in a time of distancing to support inclusive and equitable learning environments in education to minimize the effects of an emergent COVID-19 digital divide. Social learning culture as constructed, performed and captured in patterns of cooperation among faculties shows the world becoming more open and less restricted by borders. In conclusion, an emerging new conceptual framework is presented in Figure 2 to support action planning to bridge the digital equity access and learning gaps created by COVID-19.

Social implications

It is in times of strife and difficulty that problems and issues become exacerbated. While some educators easily adapted and took on the challenges of online learning, others needed time for learning and mourning (literally and figuratively). The issues of equity and access have become even more apparent as this paper takes inventory of intersections between multicultural education, special education, sports education and K-16 education overall. This is an excellent time to reflect on how education can address the cultural, economic and social barriers that impact student learning globally for all learners.

Originality/value

The brief collective case study reports educational experiences during a time of crisis that stimulates creative and innovative approaches to creating inclusive and equitable online learning environments to address diverse learning needs. The various and often contrasting educator responses from faculty facing digital and educational challenges present ideas that might be applicable in the global learning environment beyond the COVID-19 pandemic.

  • Online learning
  • New technologies
  • Transformative knowledge

Acknowledgements

World Council on Curriculum and Instruction -WCCI (UNESCO-NGO) Newsletter, Winter 2020 Content cited by Toh Swee-Hin (S.H.Toh) President, WCCI. Professor Emeritus, University of Alberta. Laureate, UNESCO Prize for Peace Education (2000).

Pittman, J. , Severino, L. , DeCarlo-Tecce, M.J. and Kiosoglous, C. (2021), "An action research case study: digital equity and educational inclusion during an emergent COVID-19 divide", Journal for Multicultural Education , Vol. 15 No. 1, pp. 68-84. https://doi.org/10.1108/JME-09-2020-0099

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Copyright © 2020, Emerald Publishing Limited

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Action Research vs. Case Study

What's the difference.

Action research and case study are both research methodologies used in social sciences to investigate and understand complex phenomena. However, they differ in their approach and purpose. Action research is a collaborative and participatory approach that involves researchers and practitioners working together to identify and solve practical problems in real-world settings. It aims to bring about positive change and improvement in the context being studied. On the other hand, case study is an in-depth and detailed examination of a particular individual, group, or situation. It focuses on understanding the unique characteristics and dynamics of the case being studied and often involves extensive data collection and analysis. While action research emphasizes practical application and problem-solving, case study emphasizes detailed exploration and understanding of a specific case.

Further Detail

Introduction.

Action research and case study are two widely used research methodologies in various fields. While both approaches aim to gain insights and understanding, they differ in their focus, design, and implementation. This article will explore the attributes of action research and case study, highlighting their similarities and differences.

Action Research

Action research is a participatory approach that involves collaboration between researchers and practitioners to address real-world problems. It emphasizes the active involvement of stakeholders in the research process, aiming to bring about practical change and improvement. Action research typically follows a cyclical process, consisting of planning, action, observation, and reflection.

One of the key attributes of action research is its focus on generating knowledge that is directly applicable to the context in which it is conducted. It aims to bridge the gap between theory and practice by actively involving practitioners in the research process. This participatory nature allows for a deeper understanding of the complexities and nuances of the problem being investigated.

Action research often involves multiple iterations, with each cycle building upon the insights gained from the previous one. This iterative approach allows for continuous learning and adaptation, enabling researchers to refine their interventions and strategies based on the feedback received. It also promotes a sense of ownership and empowerment among the participants, as they actively contribute to the research process.

Furthermore, action research is characterized by its emphasis on collaboration and co-learning. It encourages the exchange of ideas and knowledge between researchers and practitioners, fostering a sense of shared responsibility and collective action. This collaborative approach not only enhances the quality of the research but also increases the likelihood of successful implementation of the findings.

In summary, action research is a participatory and iterative approach that aims to generate practical knowledge through collaboration between researchers and practitioners. It focuses on addressing real-world problems and promoting positive change within specific contexts.

Case study, on the other hand, is an in-depth investigation of a particular phenomenon, event, or individual. It involves the detailed examination of a specific case or cases to gain a comprehensive understanding of the subject under study. Case studies can be conducted using various research methods, such as interviews, observations, and document analysis.

One of the key attributes of case study research is its ability to provide rich and detailed insights into complex phenomena. By focusing on a specific case, researchers can delve deep into the intricacies and unique aspects of the subject, uncovering valuable information that may not be easily captured through other research methods.

Case studies are often used to explore and understand real-life situations in their natural settings. They allow researchers to examine the context and dynamics surrounding the case, providing a holistic view of the phenomenon under investigation. This contextual understanding is crucial for gaining a comprehensive and nuanced understanding of the subject.

Furthermore, case studies are particularly useful when the boundaries between the phenomenon and its context are not clearly defined. They allow for the exploration of complex and multifaceted issues, enabling researchers to capture the interplay of various factors and variables. This holistic approach enhances the validity and reliability of the findings.

Moreover, case studies can be exploratory, descriptive, or explanatory in nature, depending on the research questions and objectives. They can be used to generate hypotheses, provide detailed descriptions, or test theoretical frameworks. This versatility makes case study research applicable in various fields, including psychology, sociology, business, and education.

In summary, case study research is an in-depth investigation of a specific phenomenon, providing rich and detailed insights into complex situations. It focuses on understanding the context and dynamics surrounding the case, allowing for a comprehensive exploration of multifaceted issues.

Similarities

While action research and case study differ in their focus and design, they also share some common attributes. Both approaches aim to gain insights and understanding, albeit through different means. They both involve the collection and analysis of data to inform decision-making and improve practice.

Furthermore, both action research and case study can be conducted in naturalistic settings, allowing for the examination of real-life situations. They both emphasize the importance of context and seek to understand the complexities and nuances of the phenomena under investigation.

Moreover, both methodologies can involve multiple data collection methods, such as interviews, observations, and document analysis. They both require careful planning and design to ensure the validity and reliability of the findings.

Additionally, both action research and case study can contribute to theory development. While action research focuses on generating practical knowledge, it can also inform and contribute to theoretical frameworks. Similarly, case studies can provide empirical evidence that can be used to refine and expand existing theories.

In summary, action research and case study share common attributes, including their aim to gain insights and understanding, their focus on real-life situations, their emphasis on context, their use of multiple data collection methods, and their potential contribution to theory development.

Action research and case study are two distinct research methodologies that offer unique approaches to gaining insights and understanding. Action research emphasizes collaboration, participation, and practical change, while case study focuses on in-depth investigation and contextual understanding. Despite their differences, both approaches contribute to knowledge generation and have the potential to inform theory and practice. Researchers should carefully consider the nature of their research questions and objectives to determine which approach is most suitable for their study.

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Home » Education » Difference Between Action Research and Case Study

Difference Between Action Research and Case Study

Main difference – action research vs case study.

Research is the careful study of a given field or problem in order to discover new facts or principles. Action research and case study are two types of research, which are mainly used in the field of social sciences and humanities. The main difference between action research and case study is their purpose; an action research study aims to solve an immediate problem whereas a case study aims to provide an in-depth analysis of a situation or case over a long period of time.

1. What is Action Research?      – Definition, Features, Purpose, Process

2. What is Case Study?      – Definition, Features, Purpose, Process

Difference Between Action Research and Case Study - Comparison Summary

What is Action Research

Action research is a type of a research study that is initiated to solve an immediate problem. It may involve a variety of analytical, investigative and evaluative research methods designed to diagnose and solve problems. It has been defined as “a disciplined process of inquiry conducted by and for those taking the action. The primary reason for engaging in action research is to assist the “actor” in improving and/or refining his or her actions” (Sagor, 2000). This type of research is typically used in the field of education. Action research studies are generally conductors by educators, who also act as participants.

Here, an individual researcher or a group of researchers identify a problem, examine its causes and try to arrive at a solution to the problem. The action research process is as follows.

Action Research Process

  • Identify a problem to research
  • Clarify theories
  • Identify research questions
  • Collect data on the problem
  • Organise, analyse, and interpret the data
  • Create a plan to address the problem
  • Implement the above-mentioned plan
  • Evaluate the results of the actions taken

The above process will keep repeating. Action research is also known as cycle of inquiry or cycle of action since it follows a specific process that is repeated over time.

Main Difference - Action Research vs Case Study

What is a Case Study

A case study is basically an in-depth examination of a particular event, situation or an individual. It is a type of research that is designed to explore and understand complex issues; however, it involves detailed contextual analysis of only a limited number of events or situations. It has been defined as “an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used.” (Yin, 1984)

Case studies are used in a variety of fields, but fields like sociology and education seem to use them the most. They can be used to probe into community-based problems such as illiteracy, unemployment, poverty, and drug addiction. 

Case studies involve both quantitative and qualitative data and allow the researchers to see beyond statistical results and understand human conditions. Furthermore, case studies can be classified into three categories, known as exploratory, descriptive and explanatory case studies.

However, case studies are also criticised since the study of a limited number of events or cases cannot easily establish generality or reliability of the findings. The process of a case study is generally as follows:

Case Study Process

  • Identifying and defining the research questions
  • Selecting the cases and deciding techniques for data collection and analysis
  • Collecting data in the field
  • Evaluating and analysing the data
  • Preparing the report

Action Research : Action research is a type of a research study that is initiated to solve an immediate problem.

Case Study : Case study is an in-depth analysis of a particular event or case over a long period of time.                         

Action Research : Action research involves solving a problem.

Case Study : Case studies involve observing and analysing a situation.

Action Research : Action research studies are mainly used in the field of education.

Case Study : Case studies are used in many fields; they can be specially used with community problems such as unemployment, poverty, etc.

Action Research : Action research always involve providing a solution to a problem.

Case Study : Case studies do not provide a solution to a problem.

Participants

Action Research : Researchers can also act as participants of the research.

Case Study : Researchers generally don’t take part in the research study.

Zainal, Zaidah.  Case study as a research method . N.p.: n.p., 7 June 2007. PDF.

 Soy, Susan K. (1997).  The case study as a research method . Unpublished paper, University of Texas at Austin.

Sagor, Richard.  Guiding school improvement with action research . Ascd, 2000.

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  • v.19(6); Nov-Dec 2021

Case Study With a Participatory Approach: Rethinking Pragmatics of Stakeholder Engagement for Implementation Research

Catherine hudon.

1 Department of Family Medicine and Emergency Medicine, University of Sherbrooke, Sherbrooke, Quebec, Canada

Maud-Christine Chouinard

2 Faculty of Nursing, University of Montreal, Montreal, Quebec, Canada

Mathieu Bisson

Alya danish, marlène karam, ariane girard, pierre-luc bossé, mireille lambert.

3 Integrated University Health and Social Services Centre, Chicoutimi, Quebec, Canada

Associated Data

The case study design is particularly useful for implementation analysis of complex health care innovations in primary care that can be influenced by the context of dynamic environments. Case studies may be combined with participatory approaches where academics conduct joint research with nonacademic stakeholders, to foster translation of findings results into practice. The aim of this article is to clarify epistemological and methodological considerations of case studies with a participatory approach. It also aims to propose best practice recommendations when using this case study approach. We distinguish between the participatory case study with full co-construction and co-governance, and the case study with a participatory approach whereby stakeholders are consulted in certain phases of the research. We then compare the epistemological posture of 3 prominent case study methodologists, Yin, Stake, and Merriam, to present the epistemological posture of case studies with a participatory approach. The relevance, applications, and procedures of a case study with a participatory approach methodology are illustrated through a concrete example of a primary care research program (PriCARE). We propose 12 steps for designing and conducting a case study with a participatory approach that may help guide researchers in the implementation analysis of complex health care innovations in primary care.

Over the last 40 years, case study research has become increasingly popular and has evolved rapidly in many disciplines. By allowing in-depth analysis of complex phenomena in real-world contexts, 1 the case study design is particularly useful in health services research, 2 for implementation analysis of complex interventions that can be influenced by the context of dynamic environments. 3 Public health and primary care research encourage a participatory approach because involvement of stakeholders fosters translation of research findings into practice. 4 This was the case of the PriCARE primary care research program. In this multijurisdictional Canadian study, the research team and stakeholders aimed to evaluate the implementation of a case management intervention in 10 primary care clinics, for frequent users of health care services with chronic diseases and complex care needs. 5 , 6 It is important to first distinguish the case study with a participatory approach from the participatory case study before proceeding with the example of the PriCARE program.

Participatory Research and the Case Study

Participatory research is a systematic inquiry whereby academics conduct joint research with nonacademic partners affected by the issue being studied, for purposes of education and taking action or promoting social change. 7 , 8 Participatory research conducted for empowerment or social change relies on the transformative/postmodern interpretative paradigm, in which knowledge is not neutral and reflects the power and social relationships within a society. The purpose of knowledge construction is to help people improve society. 9 Each phase of the research process is an opportunity to create knowledge through a collaborative effort to develop or refine the research questions, select the methodology, develop data collection methods and tools, choose outcome measures, interpret findings, craft the message, and disseminate the results, feasibility, and outcomes. 4 Rosemary C. Reilly, PhD, MEd 10 proposes that a case study may adopt a participatory focus with full co-governance where participants are fully involved as contributing researchers in all phases of the research process, from conceptualization of the study to write-up and dissemination of the findings.

Within the different participatory research approaches, the transformational intent of stakeholder involvement may, however, range from empowerment to more pragmatic considerations. The case study with a participatory approach may be adopted to facilitate knowledge translation and practice changes 4 in the implementation of a complex intervention such as case management, where several components interact with each other and with their context, and where there are multiple highly adaptable effects. 11 The intensity of stakeholder involvement will vary from full co-construction and involvement in all stages of the research to involvement or consultation in only certain phases of the research, balancing stakeholder engagement and availability. The participatory case study with a full co-governance structure relies on the transformative/postmodern interpretative paradigm, but what are the epistemological assumptions of the case study with a participatory approach? What steps should be taken to ensure the validity of this approach when applied to the case study? In this article, we aim to clarify epistemological and methodological considerations of case studies with a participatory approach. We also propose best practice recommendations when applying this approach to the case study.

EPISTEMOLOGICAL ASSUMPTIONS OF 3 PROMINENT CASE STUDY METHODOLOGISTS

Three prominent case study methodologists—Robert K. Yin, PhD, Robert E. Stake, PhD, and Sharan B. Merriam, MEd, EdD—brought differing perspectives to move case study knowledge forward in educational and social science research. All 3 provided definitions, designs, applications, and procedures to follow when conducting case study research. 12 Table 1 summarizes and compares their epistemological positions and assumptions, which we discuss in more detail below.

Comparison of Epistemological Assumptions of Yin, Stake, Merriam, and Reilly (Inspired by Patton 15 )

Yin: Postpositivism

Yin’s realist–postpositivist epistemological posture 1 , 13 defines a case study as “an empirical inquiry that investigates a contemporary phenomenon (the ‘case’) within its real-life context.” 14 Although reality cannot be entirely apprehended, the knowledge generated from the case study is the result of the combination of experimentations leading to a closer approximation of actual mechanisms. 15 Yin suggests combining quantitative and qualitative sources, viewing them as equally instrumental. He places considerable emphasis on preparing a detailed design at the outset of the research and advises that investigators make only minor changes in the design after they begin data collection. 16 Interaction with research participants therefore needs to be minimized and subjectivity managed to avoid biasing the results. 16

Stake: Constructivism

Stake’s epistemological commitment is to constructivism, which leads him to define the case study as the “study of the particularity and complexity of a single case, coming to understand its activity within important circumstances.” 17 Unlike Yin, Stake considers knowledge as a construction rather than the result of an empiric inquiry developed within a logical sequence. He argues that reality is multiple and subjective. 17 This assertion implies that human experiences can be known through every perspective of a given situation, all of which are equally valuable. While suggesting that every viewpoint of a situation be represented in the case study, he recommends minimal interaction between the researchers and the context of the case or the involved individuals. 18

Merriam: Constructivist Pragmatism

Merriam’s constructivist pragmatism appears similar to Stake’s at the outset. Reality is an intersubjective construction. 19 Where she diverges from Stake is mostly in the finality of knowledge, which is to address concrete problems and give answers or direction to progress. 15 In this perspective, the truth is what works in practice. 15 Merriam’s approach to case study design combines elements of Yin’s positivist standpoint with Stake’s constructivism. For her, a case study is essentially an in-depth description and analysis of a bounded system. 19 Merriam proposes a structured approach to designing research in a step-by-step process: conducting a literature review; constructing a theoretical framework; identifying a research problem; crafting and sharpening research questions; and selecting the sample (purposeful sampling). 19

Yet, Merriam recommends that the study design remain flexible to a certain degree, which means, for example, that sample selection may occur before or in conjunction with data collection. 16 As it is the unit of analysis that defines the case, other types of approaches can be combined with the case study. 19 The design will depend on the theoretical framework of the study, its purpose, and the research questions. 19 In Merriam’s constructivist pragmatism, participatory research is an approach to enhance internal validity. 16 This epistemological posture is compatible with a participatory approach to case study research.

THE WHY AND HOW OF USING A CASE STUDY WITH A PARTICIPATORY APPROACH IN IMPLEMENTATION RESEARCH

Which case study approach should be used in implementation research? The answer will depend on the epistemological assumptions on which the methods will rely. On one hand, a research team adopting a postpositivist standpoint (as proposed by Yin) will want to maintain independence from stakeholders and will conduct the implementation analysis from an external/objective point of view that precludes a participatory approach. On the other hand, a team adopting a constructivist perspective (as proposed by Stake) will plan qualitative methods to shed light on the multiple perspectives of stakeholders without involving them as co-researchers in the study. Then again, researchers who adopt a transformative posture (as proposed by Reilly) will work closely with community or organizational partners in the co-construction of the implementation using a participatory case study approach. Finally, a “middle ground” approach20 may be to adopt a pragmatic posture (as proposed by Merriam), where researchers use a case study with a participatory approach to conduct an implementation analysis of a health care innovation while consulting community or organizational stakeholders in certain phases of the research. Adopting this epistemological posture, we will present the example of the PriCARE program 5 , 6 in the next section.

TWELVE STEPS FOR CONDUCTING CASE STUDIES WITH A PARTICIPATORY APPROACH IN HEALTH CARE IMPLEMENTATION RESEARCH

Building on Merriam’s previously mentioned step-bystep process, 19 we propose 12 steps for conducting case studies with a participatory approach in health care implementation research. Figure 1 illustrates the proposed research process. Steps 1 through 10 are sequential and iterative, whereas steps 11 and 12 are concurrent and ongoing.

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Twelve steps to conduct a case study with a participatory approach.

(1) Think About What a Pragmatic Posture Means

Disagreements during the project within the academic research team, or between the academic research team and stakeholders, may be related to differences of epistemological posture or values. Being aware of and sharing this posture from the beginning of the project will help maintain the coherence of methodological choices throughout the project. For the PriCARE program, in accordance with the pragmatic posture of Merriam, the academic research team decided on consultation of varying intensity, rather than full partnership, depending on the category of stakeholders.

(2) Identify Stakeholders and Determine a Governance Structure for Consultation

To optimize the implementation process and practice changes, various stakeholders—decision makers, clinicians, and patient partners—may collaborate with the academic research team according to their interest, availability, and expertise. In PriCARE, decision makers and clinicians were consulted based on the relevance of their expertise to certain phases of the project, and to accommodate time constraints, whereas most patient partners were engaged as co-researchers in all steps of the project. Many stakeholders were involved before the grant was obtained and in a pragmatic context (people changing jobs or people expressing interest in being involved), whereas other stakeholders joined the team during the project (new patient partners, new case managers, etc). Supplemental Table 1 (available at https://www.AnnFamMed.org/lookup/suppl/doi:10.1370/afm.2717/-/DC1 ) identifies the committees and roles of stakeholders within the PriCARE program.

Four types of stakeholders were involved corresponding to the categories proposed by Damschroder et al21 in their Consolidated Framework for Implementation Research (Supplemental Figure 1, available at https://www.AnnFamMed.org/lookup/suppl/doi:10.1370/afm.2717/-/DC1 ). Their roles and contributions are detailed below.

Opinion leaders. Decision makers, who are referred to as opinion leaders, 21 are in a good position to inform the team regarding the broad context of implementation and to play a role in disseminating results and applying new knowledge. In the PriCARE program, decision makers were health center chief executive officers, primary care services directors, and representatives of health ministries. The academic research team consulted decision makers from each participating province while writing the grant request to ensure consideration of the global context in which the project would be implemented. Decision makers were consulted for strategic decisions and for knowledge transfer activities.

Champions. As champions, 21 clinicians working on the ground are usually aware of the specific dynamics in their setting and can give useful advice to the research team regarding feasibility, potential challenges, or adaptation required before implementation. Champions can be helpful in convincing their colleagues to participate in the project and in encouraging them toward change. The academic research team was in contact, in person or by telephone, with family physicians as well as managers in the clinics to facilitate case management implementation.

Formally appointed internal implementation leaders. Individuals from within the organization who have been formally appointed with responsibility for implementing the intervention—as a part of their job—are called formally appointed internal implementation leaders. 21 In PriCARE, the case manager nurses were identified during recruitment of the participating clinics at the beginning of the project. In addition to doing fieldwork, they informed the academic research team about the challenges they were facing or about what helped them to carry out the intervention as the project was progressing. The academic research team organized formal training and a virtual community of practice bringing all case managers together to deploy co-development activities.

External change agents. Finally, patient partners may play the role of external change agents. 21 They bring an experiential perspective to research, provide valuable advice to the team about patient recruitment and data collection, and validate and interpret aspects of the analysis. In PriCARE, regular meetings with patient partners (not related to the clinics) and research assistants were organized to advise the team on different aspects of the project (questionnaires, patient recruitment, analysis, knowledge transfer plan, etc). Patient partners also contributed to training the case managers and met with them to advise them on approaching patients with complex needs.

Discussions with stakeholders addressed expectations and preferences regarding their contribution, as well as the management of interaction, engagement, and communication. A clear governance structure was proposed ( Supplemental Figure 1 ). Most communication was virtual and by e-mail to accommodate geographic realities. The way the team functioned always considered the various circumstances of different stakeholders, their level of involvement, and their ability to contribute during the project.

(3) Consult Stakeholders About the Research Problem

PriCARE decision makers and clinicians in each province helped the academic research team to understand their context of implementation and what was needed in that province in terms of adapting the intervention and training. We also consulted patient partners to develop a broader understanding of the problem.

(4) Conduct a Literature Review

The literature review determines the knowledge gap, which in turn allows the relevant research questions to be presented and specified for the project. In PriCARE, 2 literature reviews on case management were conducted: a systematic review by the academic research team22 and a realist synthesis by the research team engaging stakeholders in the steering committee, including decision makers, clinicians, and patient partners. 23 Both reviews were summarized and shared with stakeholders.

(5) Sharpen Research Questions or Objectives

The final research questions of the PriCARE program were formulated, after consultation with stakeholders, by the academic research team. They are as follows: what are the facilitators of and barriers to case management implementation in primary care clinics across Canada; what are the relationships between the actors, contextual factors, mechanisms, and outcomes of the case management intervention; and what are the next steps toward case management scale-up in primary care across Canada?

(6) Choose or Construct a Theoretical Framework

A theoretical framework emerging from the literature review helps elaborate research questions and points of emphasis. 24 It also often helps in the building of data collection tools (eg, interview guides and questionnaires) and in guiding the analysis process. Although stakeholders may contribute to this step, in PriCARE, it was the academic research team who decided to use a combination of the Consolidated Framework for Implementation Research 21 and the Rainbow Model of Integrated Care Framework, 25 combining the concepts of primary care and integrated care. 6 The academic research team took responsibility for explaining theoretical frameworks to stakeholders through brief, informal online meetings, to ensure a common comprehension and facilitate participation and engagement of all stakeholders throughout the research process.

(7) Define the Case and Its Boundaries

In implementation research, the case is often an innovation implemented in a specific primary care setting. To select the case and establish the research design, we recommend identifying the focus and refining the parameters of the case including the participants, location, and/or process to be explored, and also establishing the timeframe for investigating the case. 19 The focus and boundaries may also be influenced by the resources and time available to accomplish the research project. In PriCARE, stakeholders, especially opinion leaders and champions, helped delimit the cases. Each case was the case management intervention implemented in the individual clinic.

(8) Design the Methods and Collect the Data

We encourage researchers to use multiple methods of data collection to provide a more comprehensive view of the subject being studied. Data collection methods for case studies are usually qualitative but may also be quantitative. 1 Use of software is highly recommended for regrouping and managing all the data. 9 , 15 The complete design and data collection methods of PriCARE, which had a multiple-case, embedded, mixed methods design, are described elsewhere. 5 , 6 The research team designed the methods. Clinicians and case managers identified eligible patient participants registered to the clinic, who were contacted by the latter. Patient partners, well positioned to understand the situation of participants, contributed to explaining the research project in lay language, and to answering their questions to obtain their consent for participation. They were also involved in developing recruitment and data collection tools to adapt the scientific language to a lay audience.

Case study research with a participatory approach allowed the PriCARE academic research team to observe participants during meetings with stakeholders. The academic research team carefully planned interactions to manage key messages to be delivered to stakeholders and to record and document all interactions so that meetings were also opportunities for data collection, for promoting change, and for facilitating implementation. The impact of this approach on data collection and results must be rigorously documented, analyzed, and discussed. 9 , 26

(9) Do the Analysis

Although the various analytic strategies suggested by the 3 methodologists 13 , 19 , 27 remain relevant, the particulars of case studies with a participatory approach make it possible to involve partners in various steps, to better understand, to coanalyze, or to validate results. In PriCARE, patient partners participated in key steps of the analysis to ensure meaningful interpretation.

(10) Reflect on the Impact of the Participatory Approach on the Results

The case study with participatory approach should document the role of the research team during observation and consider it to be a contextual element in the analysis of each case. For example, positive relationships between the individuals involved in a case may promote implementation and improve impact. 28 This situation may differ with another group of individuals in another case. Although such facilitation is considered a desirable extra benefit of the participatory approach, its impact on the results still has to be made explicit and discussed. 8

In PriCARE, the research team used a logbook to document interactions and reflections to maintain a reflexive distancing. 9 , 19 We sought to involve all stakeholders in these reflections, to better understand the impacts of the participatory approach, both positive and negative, which were transparently discussed in reports or articles.

(11) Plan Strategies to Ensure Rigor

As a concurrent, ongoing step, the team has to plan strategies to ensure the rigor of the research. 29 In PriCARE, we ensured credibility through in-depth description and analysis of context using qualitative and quantitative data collection in each province. We kept an audit trail of all decisions and collected data to ensure dependability. We promoted triangulation of the expertise of team members (researchers of various backgrounds, diverse health care professionals, patient partners, decision makers) and reflexivity through team discussions and interactions. We made a thick description of each clinic’s context to promote transferability. We also respected rigor criteria when administering questionnaires. 30

(12) Elaborate and Apply a Knowledge Transfer Plan

Researcher and stakeholder collaboration throughout the research process is a strong predictor that research findings will be put into practice, 31 so stakeholders should be involved in the elaboration and the application of the knowledge transfer plan. In PriCARE, team members and stakeholders of each province representing each targeted audience (population, clinicians, decision makers, and researchers) helped to write the plan throughout the study, tailor messages, and disseminate case study findings. 31 All stakeholders mobilized within the case study contributed to knowledge transfer.

CONCLUSIONS

Engaging stakeholders in the design and conduct of case studies may enhance implementation analysis of complex health care interventions in primary care, whereby stakeholders are consulted to foster translation of findings results into practice. Ensuring transparency and rigor of the approach remains crucial as it lays the groundwork for critical evaluation of this strategy. The 12 steps we propose here constitute a major milestone toward attaining this goal. Future research could contribute to testing and refining these steps, and demonstrating the contribution of this approach to implementation in health care.

Supplementary Material

Acknowledgments.

We would like to acknowledge all team members and partners who were engaged in the PriCARE program.

Conflicts of interest: authors report none.

To read or post commentaries in response to this article, go to https://www.AnnFamMed.org/content/19/6/540/tab-e-letters .

Authors’ contributions: C.H. proposed a first draft of the manuscript. C.H., M-C.C., M.B., A.D., M.K., A.G., P-L.B., and M.L. substantially contributed to subsequent drafts. All authors read and approved the final manuscript.

Funding support: This work was supported by the Canadian Institutes of Health Research (CIHR).

Disclaimer: The views expressed are solely those of the authors and do not necessarily represent official views of the authors’ affiliated institutions or funder.

Supplemental materials: Available at https://www.AnnFamMed.org/lookup/suppl/doi:10.1370/afm.2717/-/DC1 .

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  • What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

  • Provide new or unexpected insights into the subject
  • Challenge or complicate existing assumptions and theories
  • Propose practical courses of action to resolve a problem
  • Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

  • Exemplify a theory by showing how it explains the case under investigation
  • Expand on a theory by uncovering new concepts and ideas that need to be incorporated
  • Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

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case study in action research

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

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Sustaining the collaborative chronic care model in outpatient mental health: a matrixed multiple case study

  • Bo Kim 1 , 2 ,
  • Jennifer L. Sullivan 3 , 4 ,
  • Madisen E. Brown 1 ,
  • Samantha L. Connolly 1 , 2 ,
  • Elizabeth G. Spitzer 1 , 5 ,
  • Hannah M. Bailey 1 ,
  • Lauren M. Sippel 6 , 7 ,
  • Kendra Weaver 8 &
  • Christopher J. Miller 1 , 2  

Implementation Science volume  19 , Article number:  16 ( 2024 ) Cite this article

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Sustaining evidence-based practices (EBPs) is crucial to ensuring care quality and addressing health disparities. Approaches to identifying factors related to sustainability are critically needed. One such approach is Matrixed Multiple Case Study (MMCS), which identifies factors and their combinations that influence implementation. We applied MMCS to identify factors related to the sustainability of the evidence-based Collaborative Chronic Care Model (CCM) at nine Department of Veterans Affairs (VA) outpatient mental health clinics, 3–4 years after implementation support had concluded.

We conducted a directed content analysis of 30 provider interviews, using 6 CCM elements and 4 Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) domains as codes. Based on CCM code summaries, we designated each site as high/medium/low sustainability. We used i-PARIHS code summaries to identify relevant factors for each site, the extent of their presence, and the type of influence they had on sustainability (enabling/neutral/hindering/unclear). We organized these data into a sortable matrix and assessed sustainability-related cross-site trends.

CCM sustainability status was distributed among the sites, with three sites each being high, medium, and low. Twenty-five factors were identified from the i-PARIHS code summaries, of which 3 exhibited strong trends by sustainability status (relevant i-PARIHS domain in square brackets): “Collaborativeness/Teamwork [Recipients],” “Staff/Leadership turnover [Recipients],” and “Having a consistent/strong internal facilitator [Facilitation]” during and after active implementation. At most high-sustainability sites only, (i) “Having a knowledgeable/helpful external facilitator [Facilitation]” was variably present and enabled sustainability when present, while (ii) “Clarity about what CCM comprises [Innovation],” “Interdisciplinary coordination [Recipients],” and “Adequate clinic space for CCM team members [Context]” were somewhat or less present with mixed influences on sustainability.

Conclusions

MMCS revealed that CCM sustainability in VA outpatient mental health clinics may be related most strongly to provider collaboration, knowledge retention during staff/leadership transitions, and availability of skilled internal facilitators. These findings have informed a subsequent CCM implementation trial that prospectively examines whether enhancing the above-mentioned factors within implementation facilitation improves sustainability. MMCS is a systematic approach to multi-site examination that can be used to investigate sustainability-related factors applicable to other EBPs and across multiple contexts.

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Contributions to the literature

We examined the ways in which the sustainability of the evidence-based Collaborative Chronic Care Model differed across nine outpatient mental health clinics where it was implemented.

This work demonstrates a unique application of the Matrixed Multiple Case Study (MMCS) method, originally developed to identify factors and their combinations that influence implementation, to investigate the long-term sustainability of a previously implemented evidence-based practice.

Contextual influences on sustainability identified through this work, as well as the systematic approach to multi-site examination offered by MMCS, can inform future efforts to sustainably implement and methodically evaluate an evidence-based practice’s uptake and continued use in routine care.

The sustainability of evidence-based practices (EBPs) over time is crucial to maximize the public health impact of EBPs implemented into routine care. Implementation evaluators focus on sustainability as a central implementation outcome, and funders of implementation efforts seek sustained long-term returns on their investment. Furthermore, practitioners and leadership at implementation sites face the task of sustaining an EBP’s usage even after implementation funding, support, and associated evaluation efforts conclude. The circumstances and influences contributing to EBP sustainability are therefore of high interest to the field of implementation science.

Sustainability depends on the specific EBP being implemented, the individuals undergoing the implementation, the contexts in which the implementation takes place, and the facilitation of (i.e., support for) the implementation. Hence, universal conditions that invariably lead to sustainability are challenging to establish. Even if a set of conditions could be identified as being associated with high sustainability “on average,” its usefulness is questionable when most real-world implementation contexts may deviate from “average” on key implementation-relevant metrics.

Thus, when seeking a better understanding of EBP sustainability, there is a critical need for methods that examine the ways in which sustainability varies in diverse contexts. One such method is Matrixed Multiple Case Study (MMCS) [ 1 ], which is beginning to be applied in implementation research to identify factors related to implementation [ 2 , 3 , 4 , 5 ]. MMCS capitalizes on the many contextual variations and heterogeneous outcomes that are expected when an EBP is implemented across multiple sites. Specifically, MMCS provides a formalized sequence of steps for cross-site analysis by arranging data into an array of matrices, which are sorted and filtered to test for expected factors and identify less expected factors influencing an implementation outcome of interest.

Although the MMCS represents a promising method for systematically exploring the “black box” of the ways in which implementation is more or less successful, it has not yet been applied to investigate the long-term sustainability of implemented EBPs. Therefore, we applied MMCS to identify factors related to the sustainability of the evidence-based Collaborative Chronic Care Model (CCM), previously implemented using implementation facilitation [ 6 , 7 , 8 ], at nine VA medical centers’ outpatient general mental health clinics. An earlier interview-based investigation of CCM provider perspectives had identified key determinants of CCM sustainability at the sites, yet characteristics related to the ways in which CCM sustainability differed at the sites are still not well understood. For this reason, our objective was to apply MMCS to examine the interview data to determine factors associated with CCM sustainability at each site.

Clinical and implementation contexts

CCM-based care aims to ensure that patients are treated in a coordinated, patient-centered, and anticipatory manner. This project’s nine outpatient general mental health clinics had participated in a hybrid CCM effectiveness-implementation trial 3 to 4 years prior, which had resulted in improved clinical outcomes that were not universally maintained post-implementation (i.e., after implementation funding and associated evaluation efforts concluded) [ 7 , 9 ]. This lack of aggregate sustainability across the nine clinics is what prompted the earlier interview-based investigation of CCM provider perspectives that identified key determinants of CCM sustainability at the trial sites [ 10 ].

These prior works were conducted in VA outpatient mental health teams, known as Behavioral Health Interdisciplinary Program (BHIP) teams. While there was variability in the exact composition of each BHIP team, all teams consisted of a multidisciplinary set of frontline clinicians (e.g., psychiatrists, psychologists, social workers, nurses) and support staff, serving a panel of about 1000 patients each.

This current project applied MMCS to examine the data from the earlier interviews [ 10 ] for the ways in which CCM sustainability differed at the sites and the factors related to sustainability. The project was determined to be non-research by the VA Boston Research and Development Service, and therefore did not require oversight by the Institutional Review Board (IRB). Details regarding the procedures undertaken for the completed hybrid CCM effectiveness-implementation trial, which serves as the context for this project, have been previously published [ 6 , 7 ]. Similarly, details regarding data collection for the follow-up provider interviews have also been previously published [ 10 ]. We provide a brief overview of the steps that we took for data collection and describe the steps that we took for applying MMCS to analyze the interview data. Additional file  1 outlines our use of the Consolidated Criteria for Reporting Qualitative Research (COREQ) Checklist [ 11 ].

Data collection

We recruited 30 outpatient mental health providers across the nine sites that had participated in the CCM implementation trial, including a multidisciplinary mix of mental health leaders and frontline staff. We recruited participants via email, and we obtained verbal informed consent from all participants. Each interview lasted between 30 and 60 min and focused on the degree to which the participant perceived care processes to have remained aligned to the CCM’s six core elements: work role redesign, patient self-management support, provider decision support, clinical information systems, linkages to community resources, and organizational/leadership support [ 12 , 13 , 14 ]. Interview questions also inquired about the participant’s perceived barriers and enablers influencing CCM sustainability, as well as about the latest status of CCM-based care practices. Interviews were digitally recorded and professionally transcribed. Additional details regarding data collection have been previously published [ 10 ].

Data analysis

We applied MMCS’ nine analytical steps [ 1 ] to the interview data. Each step described below was led by one designated member of the project team, with subsequent review by all project team members to reach a consensus on the examination conducted for each step.

We established the evaluation goal (step 1) to identify the ways in which sustainability differed across the sites and the factors related to sustainability, defining sustainability (step 2) as the continued existence of CCM-aligned care practices—namely, that care processes remained aligned with the six core CCM elements. Table  1 shows examples of care processes that align with each CCM element. As our prior works directly leading up to this project (i.e., design and evaluation of the CCM implementation trial that involved the very sites included in this project [ 6 , 15 , 16 ]) were guided by the Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework [ 17 ] and i-PARIHS positions facilitation (the implementation strategy that our trial was testing) as the core ingredient that drives implementation [ 17 ], we selected i-PARIHS’ four domains—innovation, recipients, context, and facilitation—as relevant domains under which to examine factors influencing sustainability (step 3). i-PARIHS posits that the successful implementation of an innovation and its sustained use by recipients in a context is enabled by facilitation (both the individuals doing the facilitation and the process used for facilitation). We examined the data on both sustainability and potentially relevant i-PARIHS domains (step 4) by conducting directed content analysis [ 18 ] of the recorded and professionally transcribed interview data. We used the six CCM elements and the four i-PARIHS domains as a priori codes.

Additional file  2 provides an overview of data input, tasks performed, and analysis output for MMCS steps 5 through 9 described below. We assessed sustainability per site (step 5) by generating CCM code summaries per site, and reached a consensus on whether each site exhibited high, medium, or low sustainability relative to other sites based on the summary data. We assigned a higher sustainability level for sites that exhibited more CCM-aligned care processes, had more participants consistently mention those processes, and considered those processes more as “just the way things are done” at the site. Namely, (i) high sustainability sites had concrete examples of CCM-aligned care processes (such as the ones shown in Table  1 ) for many of the six CCM elements, which multiple participants mentioned as central to how they deliver care, (ii) low sustainability sites had only a few concrete examples of CCM-aligned care processes, mentioned by only a small subset of participants and/or inconsistently practiced, and (iii) medium sustainability sites matched neither of the high nor low sustainability cases, having several concrete examples of CCM-aligned care process for some of the CCM elements, varying in whether they are mentioned by multiple participants or how consistently they are a part of delivering care. For the CCM code summaries per site, one project team member initially reviewed the coded data to draft the summaries including exemplar quotes. Each summary and relevant exemplar quotes were then reviewed by and refined with input from all six project team members during recurring team meetings to finalize the high, medium, or low sustainability designation to use in the subsequent MMCS steps. Reviewing and refining the summaries for the nine sites took approximately four 60-min meetings of the six project team members, with each site’s CCM code summary taking approximately 20–35 min to discuss and reach consensus on. We referred to lists of specific examples of how the six core CCM elements were operationalized in our CCM implementation trial [ 19 , 20 ]. Refinements occurred mostly around familiarizing the newer members of the project team (i.e., those who had not participated in our prior CCM-related work) with the examples and definitions. We aligned to established qualitative analysis methods for consensus-reaching discussions [ 18 , 21 ]. Recognizing the common challenge faced by such discussions in adequately accounting for everyone’s interpretations of the data [ 22 ], we drew on Bens’ meeting facilitation techniques [ 23 ] that include setting ground rules, ensuring balanced participation from all project team members, and accurately recording decisions and action items.

We then identified influencing factors per site (step 6), by generating i-PARIHS code summaries per site and identifying distinct factors under each domain of i-PARIHS (e.g., Collaborativeness and teamwork as a factor under the Recipients domain). For the i-PARIHS code summaries per site, one project team member initially reviewed the coded data to draft the summaries including exemplar quotes. They elaborated on each i-PARIHS domain-specific summary by noting distinct factors that they deemed relevant to the summary, proposing descriptive wording to refer to each factor (e.g., “team members share a commitment to their patients” under the Recipients domain). Each summary, associated factor descriptions, and relevant exemplar quotes were then reviewed and refined with input from all six project team members during recurring team meetings to finalize the relevant factors to use in the subsequent MMCS steps. Finalizing the factors included deciding which similar proposed factor descriptions from different sites to consolidate into one factor and which wording to use to refer to the consolidated factor (e.g., “team members share a commitment to their patients,” “team members collaborate well,” and “team members know each other’s styles and what to expect” were consolidated into the Collaborativeness and teamwork factor under the Recipients domain). It took approximately four 60-min meetings of the six project team members to review and refine the summaries and factors for the nine sites, with each site’s i-PARIHS code summary and factors taking approximately 20–35 min to discuss and reach consensus on. We referred to lists of explicit definitions of i-PARIHS constructs that our team members had previously developed and published [ 16 , 24 ]. We once again aligned to established qualitative analysis methods for consensus-reaching discussions [ 18 , 21 ], drawing on Bens’ meeting facilitation techniques [ 23 ] to adequately account for everyone’s interpretations of the data [ 22 ].

We organized the examined data (i.e., the assessed sustainability and identified factors per site) into a sortable matrix (step 7) using Microsoft Excel [ 25 ], laid out by influencing factor (row), sustainability (column), and site (sheet). We conducted within-site analysis of the matrixed data (step 8), examining the data on each influencing factor and designating whether the factor (i) was present, somewhat present, or minimally present [based on aggregate reports from the site’s participants; used “minimally present” when, considering all available data from a site regarding a factor, the factor was predominantly weak (e.g., predominantly weak Ability to continue patient care during COVID at a medium sustainability site); used “somewhat present” when, considering all available data from a site regarding a factor, the factor was neither predominantly strong nor predominantly weak (e.g., neither predominantly strong nor predominantly weak Collaborativeness and teamwork at a low sustainability site)], and (ii) had an enabling, hindering, or neutral/unclear influence on sustainability (designated as “neutral” when, considering all available data from a site regarding a factor, the factor had neither a predominantly enabling nor a predominantly hindering influence on sustainability). These designations of factors’ presence and influence are conceptually representative of what is commonly referred to as magnitude and valence, respectively, by other efforts that construct scoring for qualitative data (e.g., [ 26 , 27 ]). Like the team-based consensus approach of earlier MMCS steps, factors’ presence and type of influence per site were initially proposed by one project team member after reviewing the matrix’s site-specific data, then refined with input from all project team members during recurring team meetings that reviewed the matrix. Accordingly, similar to the earlier MMCS steps, we aligned to established qualitative methods [ 18 , 21 ] and meeting facilitation techniques [ 23 ] for these consensus-reaching discussions.

We then conducted a cross-site analysis of the matrixed data (step 9), assessing whether factors and their combinations were (i) present across multiple sites, (ii) consistently associated with higher or lower sustainability, and (iii) emphasized at some sites more than others. We noted that any factor may have not come up during interviews with a site because either it is not pertinent or it is pertinent but still did not come up, although we asked an open-ended question at the end of each interview about whether there was anything else that the participant wanted to share regarding sustainability. To adequately account for these possibilities, we decided as a team to regard a factor or a combination of factors as being associated with high/medium/low sustainability if it was identified at a majority (i.e., even if not all) of the sites designated as high/medium/low sustainability (e.g., if the Collaborativeness and teamwork factor is identified at a majority, even if not all, of the high sustainability sites, we would find it to be associated with high sustainability). Like the team-based consensus approach of earlier MMCS steps, cross-site patterns were initially proposed by one project team member after reviewing the matrix’s cross-site data, then refined with input from all project team members during recurring team meetings that reviewed the matrix. Accordingly, similar to the earlier MMCS steps, we aligned to established qualitative methods [ 18 , 21 ] and meeting facilitation techniques [ 23 ] for these consensus-reaching discussions. We acknowledged the potential existence of additional factors influencing sustainability that may not have emerged during our interviews and also may vary substantially between sites. For example, adaptation of the CCM, characteristics of the patient population, and availability of continued funding, which are factors that extant literature reports as being relevant to sustainability [ 28 , 29 ], were not seen in our interview data. To maintain our analytic focus on the factors seen in our data, we did not add these factors to our analysis.

For the nine sites included in this project, we found the degree of CCM sustainability to be split evenly across the sites—three high-, three medium-, and three low-sustainability. Twenty-five total influencing factors were identified under the i-PARIHS domains of Innovation (6), Recipients (6), Context (8), and Facilitation (5). Table  2 shows these identified influencing factors by domain. Figure  1 shows 11 influencing factors that were identified for at least two sites within a group of high/medium/low sustainability sites—e.g., the factor “consistent and strong internal facilitator” is shown as being present at high sustainability sites with an enabling influence on sustainability, because it was identified as such at two or more of the high sustainability sites. Of these 11 influencing factors, four were identified only for sites with high CCM sustainability and two were identified only for sites with medium or low CCM sustainability.

figure 1

Influencing factors that were identified for at least two sites within a group of high/medium/low sustainability sites

Key trends in influencing factors associated with high, medium, and/or low CCM sustainability

Three factors across two i-PARIHS domains exhibited strong trends by sustainability status. They were the Collaborativeness and teamwork and Turnover of clinic staff and leadership factors under the Recipients domain, and the Having a consistent and strong internal facilitator factor under the Facilitation domain.

Recipients-related factors

Collaborativeness and teamwork was present with an enabling influence on CCM sustainability at most high and medium sustainability sites, while it was only somewhat present with a neutral influence on CCM sustainability at most low sustainability sites. When asked what had made their BHIP team work well, a participant from a high sustainability site said,

“Just a collaborative spirit.” (Participant 604)

A participant from a medium sustainability site said,

“We joke that [the BHIP teams] are even family, that the teams really do function pretty tightly and they each have their own personality.” (Participant 201)

At the low sustainability sites, willingness to work as a team varied across team members; a participant from a low sustainability site said,

“… I think it has to be the commitment of the people who are on the team. So those that are regularly attending, we get a lot more out of it than those that probably don't ever come [to team meetings].” (Participant 904)

Collaborativeness and teamwork of BHIP team members were often perceived as the highlight of pursuing interdisciplinary care.

Turnover of clinic staff and leadership was present with a hindering influence on CCM sustainability at most high, medium, and low sustainability sites.

“We’ve lost a lot of really, really good providers here in the time I’ve been here …,” (Participant 102)

said a participant from a low-sustainability site that had to reconfigure its BHIP teams due to clinic staff shortages. Turnover of mental health clinic leadership made it difficult to maintain CCM practices, especially beyond the teams that participated in the original CCM implementation trial. A participant from a medium sustainability site said,

“Probably about 90 percent of the things that we came up with have fallen by the wayside. Within our team, many of those remain but again, that hand off towards the other teams that I think partly is due to the turnover rate with program managers, supervisors, didn’t get fully implemented.” (Participant 703)

Although turnover was an issue for high sustainability sites as well, there was also indication of the situation improving in recent years; a participant from a high sustainability site said,

“… our attrition rollover rate has dropped quite a bit and I would really attribute that to [the CCM being] more functional and more sustainable and tolerable for the providers.” (Participant 502)

As such, staff and leadership turnover was deemed a major challenge for CCM sustainability for all sites regardless of the overall level of sustainability.

Facilitation-related factor

Having a consistent and strong internal facilitator was present with an enabling influence on CCM sustainability at high sustainability sites, not identified as an influencing factor at most of the medium sustainability sites, and variably present with a hindering, neutral, or unclear influence on CCM sustainability at low sustainability sites. Participants from a high sustainability site perceived that it was important for the internal facilitator to understand different BHIP team members’ personalities and know the clinic’s history. A participant from another high sustainability site shared that, as an internal facilitator themselves, they focused on recognizing and reinforcing the progress of team members:

“… I'm often the person who kind of [starts] off with, ‘Hey, look at what we've done in this location,’ ‘Hey look at what the team's done this month.’” (Participant 402)

A participant from a low sustainability site had also served as an internal facilitator and recounted the difficulty and importance of readying the BHIP team to function in the long run without their assistance:

“I should have been able to get out sooner, I think, to get it to have them running this themselves. And that was just a really difficult process.” (Participant 301)

Participants, especially from the high and low sustainability sites, attributed their BHIP teams’ successes and challenges to the skills of the internal facilitator.

Influencing factors identified only for sites with high CCM sustainability

Four factors across four i-PARIHS domains were identified for high sustainability sites and not for medium or low sustainability sites. They were the factors Details about the CCM being well understood (Innovation domain), Interdisciplinary coordination (Recipients domain), Having adequate clinic space for CCM team members (Context domain), and Having a knowledgeable and helpful external facilitator (Facilitation domain).

Innovation-related factor

Details about the CCM being well understood was minimal to somewhat present with an unclear influence on CCM sustainability.

“We’ve … been trying to help our providers see the benefit of team-based care and the episodes-of-care idea, and I would say that is something our folks really have continued to struggle with as well,” (Participant 401)

said a participant from a high sustainability site. “What is considered CCM-based care?” continued to be a question on providers’ minds. A participant from a high sustainability site asked during the interview,

“Is there kind of a clearing house of some of the best practices for [CCM] that you guys have … or some other collection of resources that we could draw from?” (Participant 601)

Although such references are indeed accessible online organization-wide, participants were not always aware of those resources or what exactly CCM entails.

Recipients-related factor

Interdisciplinary coordination was somewhat present with a hindering, neutral, or unclear influence on CCM sustainability. Coordination between psychotherapy and psychiatry providers was deemed difficult by participants from high-sustainability sites. A participant said,

“We were initially kind of top heavy on the psychiatry so just making sure we have … therapy staff balancing that out [has been important].” (Participant 501)

Another participant perceived that BHIP teams were helpful in managing.

… ‘sibling rivalry’ between different disciplines … because [CCM] puts us all in one team and we communicate.” (Participant 505)

Interdisciplinary coordination was understood by the participants as being necessary for effective CCM-based care yet difficult to achieve.

Context-related factor

Having adequate clinic space for CCM team members was minimal to somewhat present with a hindering, neutral, or unclear influence on CCM sustainability. COVID-19 led to changes in how clinic space was used/assigned. A participant from a high sustainability site remarked,

“Pre-COVID everything was in a room instead of online. And now all our meetings are online and so it's actually really easy for the supervisors to be able to rotate through them and then, you know, they can answer programmatic questions ….” (Participant 402)

Participants from another high sustainability site found that issues regarding limited clinic space were both exacerbated and alleviated by COVID, with the mental health service losing space to vaccine clinics but more mental health clinicians teleworking and in less need of clinic space. Virtual connections were seen to alleviate some physical workspace-related concerns.

Having a knowledgeable and helpful external facilitator was variably present; when present, it had an enabling influence on CCM sustainability. Participants from a high sustainability site noted how many of the external facilitator’s efforts to change the BHIP team’s work processes very much remained over time. An example of a change was to have team meetings be structured to meet evolving patient needs. Team members came to meetings with the shared knowledge and expectation that,

“… we need to touch on folks who are coming out of the hospital, we need to touch on folks with higher acuity needs.” (Participant 402)

Implementation support that sites received from their external facilitator mostly occurred during the time period of the original CCM implementation trial; correspondence with the external facilitator after that trial time period was not common for sites. Participants still largely found the external facilitator to provide helpful guidance and advice on delivering CCM-based care.

Influencing factors identified only for sites with medium or low CCM sustainability

Two factors were identified for medium or low sustainability sites and not for high sustainability sites. They were the factors Ability to continue patient care during COVID and Adequate resources/capacity for care delivery . These factors were both under i-PARIHS’ Context domain, unlike the influencing factors above that were identified only for high sustainability sites, which spanned all four i-PARIHS domains.

Context-related factors

Ability to continue patient care during COVID had a hindering influence on CCM sustainability when minimally present. Participants felt that their CCM work was challenged when delivering care through telehealth was made difficult—e.g., at a medium sustainability site, site policies during the pandemic required a higher number of in-person services than the BHIP team providers expected or desired to deliver. On the other hand, this factor had an enabling influence on CCM sustainability when present. A participant at a low sustainability site mentioned the effect of telehealth on being able to follow up more easily with patients who did not show up for their appointments:

“… my no-show rate has dropped dramatically because if people don’t log on after a couple minutes, I call them. They're like ‘oh, I forgot, let me pop right on,’ whereas, you know, in the face-to-face space, you know, you wait 15 minutes, you call them, it’s too late for them to come in so then they're no shows.” (Participant 102)

The advantages of virtual care delivery, as well as the challenges of getting approvals to pursue it to varying extents, were well recognized by the participants.

Adequate resources/capacity for care delivery was minimally present at medium sustainability sites with a hindering influence on CCM sustainability. At a medium sustainability site, although leadership was supportive of CCM, resources were being used to keep clinics operational (especially during COVID) rather than investing in building new CCM-based care delivery processes.

“I think that if my boss came to me, [and asked] what could I do for [the clinics] … I would say even more staff,” (Participant 202)

said a participant from a medium sustainability site. At the same time, the participant, as many others we interviewed, understood and emphasized the need for BHIP teams to proceed with care delivery even when resources were limited:

“… when you’re already dealing with a very busy clinic, short staff and then you’re hit with a pandemic you handle it the best that you can.” (Participant 202)

Participants felt the need for basic resource requirements to be met in order for CCM-based care to be feasible.

In this project, we examined factors influencing the sustainability of CCM-aligned care practices at general mental health clinics within nine VA medical centers that previously participated in a CCM implementation trial. Guided by the core CCM elements and i-PARIHS domains, we conducted and analyzed CCM provider interviews. Using MMCS, we found CCM sustainability to be split evenly across the nine sites (three high, three medium, and three low), and that sustainability may be related most strongly to provider collaboration, knowledge retention during staff/leadership transitions, and availability of skilled internal facilitators.

In comparison to most high sustainability sites, participants from most medium or low sustainability sites did not mention a knowledgeable and helpful external facilitator who enabled sustainability. Participants at the high sustainability sites also emphasized the need for clarity about what CCM-based care comprises, interdisciplinary coordination in delivering CCM-aligned care, and adequate clinic space for BHIP team members to connect and collaborate. In contrast, in comparison to participants at most high sustainability sites, participants at most medium or low sustainability sites emphasized the need for better continuity of patient-facing activities during the COVID-19 pandemic and more resources/capacity for care delivery. A notable difference between these two groups of influencing factors is that the ones emphasized at most high sustainability sites are more CCM-specific (e.g., external facilitator with CCM expertise, knowledge, and structures to support delivery of CCM-aligned care), while the ones emphasized at most medium or low sustainability sites are factors that certainly relate to CCM sustainability but are focused on care delivery operations beyond CCM-aligned care (e.g., COVID’s widespread impacts, limited staff availability). In short, an emphasis on immediate, short-term clinical needs in the face of the COVID-19 pandemic and staffing challenges appeared to sap sites’ enthusiasm for sustaining more collaborative, CCM-consistent care processes.

Our previous qualitative analysis of these interview data suggested that in order to achieve sustainability, it is important to establish appropriate infrastructure, organizational readiness, and mental health service- or department-wide coordination for CCM implementation [ 10 ]. The findings from the current project augment these previous findings by highlighting the specific factors associated with higher and lower CCM sustainability across the project sites. This additional knowledge provides two important insights into what CCM implementation efforts should prioritize with regard to the previously recommended appropriate infrastructure, readiness, and coordination. First, for knowledge retention and coordination during personnel changes (including any changes in internal facilitators through and following implementation), care processes and their specific procedures should be established and documented in order to bring new personnel up to speed on those care processes. Management sciences, as applied to health care and other fields, suggest that such organizational knowledge retention can be maximized when there are (i) structures set up to formally recognize/praise staff when they share key knowledge, (ii) succession plans to be applied in the event of staff turnover, (iii) opportunities for mentoring and shadowing, and (iv) after action reviews of conducted care processes, which allow staff to learn about and shape the processes themselves [ 30 , 31 , 32 , 33 ]. Future CCM implementation efforts may thus benefit from enacting these suggestions alongside establishing and documenting CCM-based care processes and associated procedures.

Second, efforts to implement CCM-aligned practices into routine care should account for the extent to which sites’ more fundamental operational needs are met or being addressed. That information can be used to appropriately scope the plan, expectations, and timeline for implementation. For instance, ongoing critical staffing shortages or high turnover [ 34 ] at a site are unlikely to be resolved through a few months of CCM implementation. In fact, in that situation, it is possible that CCM implementation efforts could lead to reduced team effectiveness in the short term, given the effort required to establish more collaborative and coordinated care processes [ 35 ]. Should CCM implementation move forward at a given site, implementation goals ought to be set on making progress in realms that are within the implementation effort’s control (e.g., designing CCM-aligned practices that take staffing challenges into consideration) [ 36 , 37 ] rather than on factors outside of the effort’s control (e.g., staffing shortages). As healthcare systems determine how to deploy support (e.g., facilitators) to sites for CCM implementation, they would benefit from considering whether it is primarily CCM expertise that the site needs at the moment, or more foundational organizational resources (e.g., mental health staffing, clinical space, leadership enhancement) [ 38 ] to first reach an operational state that can most benefit from CCM implementation efforts at a later point in time. There is growing consensus across the field that the readiness of a healthcare organization to innovate is a prerequisite to successful innovation (e.g., CCM implementation) regardless of the specific innovation [ 39 , 40 ]. Several promising strategies specifically target these organizational considerations for implementing evidence-based practices (e.g., [ 41 , 42 ]). Further, recent works have begun to more clearly delineate leadership-related, climate-related, and other contextual factors that contribute to organizations’ innovation readiness [ 43 ], which can inform healthcare systems’ future decisions regarding preparatory work leading to, and timing of, CCM implementation at their sites.

These considerations informed by MMCS may have useful implications for implementation strategy selection and tailoring for future CCM implementation efforts, especially in delineating the target level (e.g., system, organizational, clinic, individual) and timeline of implementation strategies to be deployed. For instance, of the three factors found to most notably trend with CCM sustainability, Collaborativeness and teamwork may be strengthened through shorter-term team-building interventions at the organizational and/or clinic levels [ 38 ], Turnover of clinic staff and leadership may be mitigated by aiming for longer-term culture/climate change at the system and/or organizational levels [ 44 , 45 , 46 ], and Having a consistent and strong internal facilitator may be ensured more immediately by selecting an individual with fitting expertise/characteristics to serve in the role [ 15 ] and imparting innovation/facilitation knowledge to them [ 47 ]. Which of these factors to focus on, and through what specific strategies, can be decided in partnership with an implementation site—for instance, candidate strategies can be identified based on ones that literature points to for addressing these factors [ 48 ], systematic selection of the strategies to move forward can happen with close input from site personnel [ 49 ], and explicit further specification of those strategies [ 50 ] can also happen in collaboration with site personnel to amply account for site-specific contexts [ 51 ].

As is common for implementation projects, the findings of this project are highly context-dependent. It involves the implementation of a specific evidence-based practice (the CCM) using a specific implementation strategy (implementation facilitation) at specific sites (BHIP teams within general mental health clinics at nine VA medical centers). For such context-dependent findings to be transferable [ 52 , 53 ] to meaningfully inform future implementation efforts, sources of variation in the findings and how the findings were reached must be documented and traceable. This means being explicit about each step and decision that led up to cross-site analysis, as MMCS encourages, so that future implementation efforts can accurately view and consider why and how findings might be transferable to their own work. For instance, beyond the finding that Turnover of clinic staff and leadership was a factor present at most of the examined sites, MMCS’ traceable documentation of qualitative data associated with this factor at high sustainability sites also allowed highlighting the perception that CCM implementation is contributing to mitigating turnover of providers in the clinic over time, which may be a crucial piece of information that fuels future CCM implementation efforts.

Furthermore, to compare findings and interpretations across projects, consistent procedures for setting up and conducting these multi-site investigations are indispensable [ 54 , 55 , 56 ]. Although many projects involve multiple sites and assess variations across the sites, it is less common to have clearly delineated protocols for conducting such assessments. MMCS is meant to target this very gap, by offering a formalized sequence of steps that prompt specification of analytical procedures and decisions that are often interpretive and left less specified. MMCS uses a concrete data structure (the matrix) to traceably organize information and knowledge gained from a project, and the matrix can accommodate various data sources and conceptual groundings (e.g., guiding theories, models, and frameworks) that may differ from project to project – for instance, although our application of MMCS aligned to i-PARIHS, other projects applying MMCS [ 2 , 5 ] use different conceptual guides (e.g., Consolidated Framework for Implementation Research [ 57 ], Theoretical Domains Framework [ 58 ]). Therefore, as more projects align to the MMCS steps [ 1 ] to identify factors related to implementation and sustainability, better comparisons, consolidations, and transfers of knowledge between projects may become possible.

This project has several limitations. First, the high, medium, and low sustainability assigned to the sites were based on the sites’ CCM sustainability relative to one another, rather than based on an external metric of sustainability. As measures of sustainability such as the Program Sustainability Assessment Tool [ 59 , 60 ] and the Sustainment Measurement System Scale [ 61 ] become increasingly developed and tested, future projects may consider the feasibility of incorporating such measures to assess each site’s sustainability. In our case, we worked on addressing this limitation by using a consensus approach within our project team to assign sustainability levels to sites, as well as by confirming that the sites that we designated as high sustainability exhibited CCM elements that we had previously observed at the end of their participation in the original CCM implementation trial [ 19 ]. Second, we did not assign strict thresholds above/below which the counts or proportions of data regarding a factor would automatically indicate whether the factor (i) was present, somewhat present, or minimally present and (ii) had an enabling, hindering, or neutral/unclear influence on sustainability. This follows widely accepted qualitative analytical guidance that discourages characterizing findings solely based on the frequency with which a notion is mentioned by participants [ 62 , 63 , 64 ], in order to prevent unsubstantiated inferences or conclusions. We sought to address this limitation in two ways: We carefully documented the project team’s rationale for each consensus reached, and we reviewed all consensuses reached in their entirety to ensure that any two factors with the same designation (e.g., “minimally present”) do not have associated rationale that conflict across those factors. These endeavors we undertook closely adhere to established case study research methods [ 65 ], which MMCS builds on, that emphasize strengthening the validity and reliability of findings through documenting a detailed analytic protocol, as well as reviewing data to ensure that patterns match across analytic units (e.g., factors, interviewees, sites). Third, our findings are based on three sites each for high/medium/low sustainability, and although we identified single factors associated with sustainability, we found no specific combinations of factors’ presence and influence that were repeatedly existent at a majority of the sites designated as high/medium/low sustainability. Examining additional sites on the factors identified through this work (as we will for our subsequent CCM implementation trial described below) will allow more opportunities for repeated combinations and other factors to emerge, making possible firmer conclusions regarding the extent to which the currently identified factors and absence of identified combinations are applicable beyond the sites included in this study. Fourth, the identified influencing factor “leadership support for CCM” (under the Context domain of the i-PARIHS framework) substantially overlaps in concept with the core “organizational/leadership support” element of the CCM. To avoid circular reasoning, we used leadership support-related data to inform our assignment of sites’ high, medium, or low CCM sustainability, rather than as a reason for the sites’ CCM sustainability. In reality, strong leadership support may both result from and contribute to implementation and sustainability [ 16 , 66 ], and thus causal relationships between the i-PARIHS-aligned influencing factors and the CCM elements (possibly with feedback loops) warrant further examination to most appropriately use leadership support-related data in future analyses of CCM sustainability. Fifth, findings may be subject to both social desirability bias in participants providing more positive than negative evidence of sustainability (especially participants who are responsible for implementing and sustaining CCM-aligned care at their site) and the project team members’ bias in interpreting the findings to align to their expectations of further effort being necessary to sustainably implement the CCM. To help mitigate this challenge, the project interviewers strove to elicit from participants both positive and negative perceptions and experiences related to CCM-based care delivery, both of which were present in the examined interview data.

Future work stemming from this project is twofold. Regarding CCM implementation, we will conduct a subsequent CCM implementation trial involving eight new sites to prospectively examine how implementation facilitation with an enhanced focus on these findings affects CCM sustainability. We started planning for sustainability prior to implementation, looking to this work for indicators of specific modifications needed to the previous way in which we used implementation facilitation to promote the uptake of CCM-based care [ 67 ]. Findings from this work suggest that sustainability may be related most strongly to (i) provider collaboration, (ii) knowledge retention during staff/leadership transitions, and (iii) availability of skilled internal facilitators. Hence, we will accordingly prioritize developing procedures for (i) regular CCM-related information exchange amongst BHIP team members, as well as between the BHIP team and clinic leadership, (ii) both translating knowledge to and keeping knowledge documented at the site, and (iii) supporting the sites’ own personnel to take the lead in driving CCM implementation.

Regarding MMCS, we will continuously refine and improve the method by learning from other projects applying, testing, and critiquing MMCS. Outside of our CCM-related projects, examinations of implementation data using MMCS are actively underway for various implementation efforts including that of a data dashboard for decision support on transitioning psychiatrically stable patients from specialty mental health to primary care [ 2 ], a peer-led healthy lifestyle intervention for individuals with serious mental illness [ 3 ], screening programs for intimate partner violence [ 4 ], and a policy- and organization-based health system strengthening intervention to improve health systems in sub-Saharan Africa [ 5 ]. As MMCS is used by more projects that differ from one another in their specific outcome of interest, and especially in light of our MMCS application that examines factors related to sustainability, we are curious whether certain proximal to distal outcomes are more subject to heterogeneity in influencing factors than other outcomes. For instance, sustainability outcomes, which are tracked following a longer passage of time than some other outcomes, may be subject to more contextual variations that occur over time and thus could particularly benefit from being examined using MMCS. We will also explore MMCS’ complementarity with coincidence analysis and other configurational analytical approaches [ 68 ] for examining implementation phenomena. We are excited about both the step-by-step traceability that MMCS can bring to such methods and those methods’ computational algorithms that can be beneficial to incorporate into MMCS for projects with larger numbers of sites. For example, Salvati and colleagues [ 69 ] described both the inspiration that MMCS provided in structuring their data as well as how they addressed MMCS’ visualization shortcomings through their innovative data matrix heat mapping, which led to their selection of specific factors to include in their subsequent coincidence analysis. Coincidence analysis is an enhancement to qualitative comparative analysis and other configurational analytical methods, in that it is formulated specifically for causal inference [ 70 ]. Thus, in considering improved reformulations of MMCS’ steps to better characterize examined factors as explicit causes to the outcomes of interest, we are inspired by and can draw on coincidence analysis’ approach to building and evaluating causal chains that link factors to outcomes. Relatedly, we have begun to actively consider the potential contribution that MMCS can make to hypothesis generation and theory development for implementation science. As efforts to understand the mechanisms through which implementation strategies work are gaining momentum [ 71 , 72 , 73 ], there is an increased need for methods that help decompose our understanding of factors that influence the mechanistic pathways from strategies to outcomes [ 74 ]. Implementation science is facing the need to develop theories, beyond frameworks, which delineate hypotheses for observed implementation phenomena that can be subsequently tested [ 75 ]. The methodical approach that MMCS offers can aid this important endeavor, by enabling data curation and examination of pertinent factors in a consistent way that allows meaningful synthesis of findings across sites and studies. We see these future directions as concrete steps toward elucidating the factors related to sustainable implementation of EBPs, especially leveraging data from projects where the number of sites is much smaller than the number of factors that may matter—which is indeed the case for most implementation projects.

Using MMCS, we found that provider collaboration, knowledge retention during staff/leadership transitions, and availability of skilled internal facilitators may be most strongly related to CCM sustainability in VA outpatient mental health clinics. Informed by these findings, we have a subsequent CCM implementation trial underway to prospectively test whether increasing the aforementioned factors within implementation facilitation enhances sustainability. The MMCS steps used here for systematic multi-site examination can also be applied to determining sustainability-related factors relevant to various other EBPs and implementation contexts.

Availability of data and materials

The data analyzed during the current project are not publicly available because participant privacy could be compromised.

Abbreviations

Behavioral Health Interdisciplinary Program

Collaborative Chronic Care Model

Consolidated Criteria for Reporting Qualitative Research

coronavirus disease

evidence-based practice

Institutional Review Board

Integrated Promoting Action on Research Implementation in Health Services

Matrixed Multiple Case Study

United States Department of Veterans Affairs

Kim B, Sullivan JL, Ritchie MJ, Connolly SL, Drummond KL, Miller CJ, et al. Comparing variations in implementation processes and influences across multiple sites: What works, for whom, and how? Psychiatry Res. 2020;283:112520.

Article   PubMed   Google Scholar  

Hundt NE, Yusuf ZI, Amspoker AB, Nagamoto HT, Kim B, Boykin DM, et al. Improving the transition of patients with mental health disorders back to primary care: A protocol for a partnered, mixed-methods, stepped-wedge implementation trial. Contemp Clin Trials. 2021;105:106398.

Tuda D, Bochicchio L, Stefancic A, Hawes M, Chen J-H, Powell BJ, et al. Using the matrixed multiple case study methodology to understand site differences in the outcomes of a Hybrid Type 1 trial of a peer-led healthy lifestyle intervention for people with serious mental illness. Transl Behav Med. 2023;13(12):919–27.

Adjognon OL, Brady JE, Iverson KM, Stolzmann K, Dichter ME, Lew RA, et al. Using the Matrixed Multiple Case Study approach to identify factors affecting the uptake of IPV screening programs following the use of implementation facilitation. Implement Sci Commun. 2023;4(1):145.

Article   PubMed   PubMed Central   Google Scholar  

Seward N, Murdoch J, Hanlon C, Araya R, Gao W, Harding R, et al. Implementation science protocol for a participatory, theory-informed implementation research programme in the context of health system strengthening in sub-Saharan Africa (ASSET-ImplementER). BMJ Open. 2021;11(7):e048742.

Bauer MS, Miller C, Kim B, Lew R, Weaver K, Coldwell C, et al. Partnering with health system operations leadership to develop a controlled implementation trial. Implement Sci. 2016;11:22.

Bauer MS, Miller CJ, Kim B, Lew R, Stolzmann K, Sullivan J, et al. Effectiveness of implementing a Collaborative Chronic Care Model for clinician teams on patient outcomes and health status in mental health: a randomized clinical trial. JAMA Netw Open. 2019;2(3):e190230.

Ritchie MJ, Dollar KM, Miller CJ, Smith JL, Oliver KA, Kim B, et al. Using Implementation Facilitation to Improve Healthcare (Version 3): Veterans Health Administration, Behavioral Health Quality Enhancement Research Initiative (QUERI). 2020.

Google Scholar  

Bauer MS, Stolzmann K, Miller CJ, Kim B, Connolly SL, Lew R. Implementing the Collaborative Chronic Care Model in mental health clinics: achieving and sustaining clinical effects. Psychiatr Serv. 2021;72(5):586–9.

Miller CJ, Kim B, Connolly SL, Spitzer EG, Brown M, Bailey HM, et al. Sustainability of the Collaborative Chronic Care Model in outpatient mental health teams three years post-implementation: a qualitative analysis. Adm Policy Ment Health. 2023;50(1):151–9.

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57.

Von Korff M, Gruman J, Schaefer J, Curry SJ, Wagner EH. Collaborative management of chronic illness. Ann Intern Med. 1997;127(12):1097–102.

Article   Google Scholar  

Wagner EH, Austin BT, Von Korff M. Organizing care for patients with chronic illness. Milbank Q. 1996;74(4):511–44.

Article   CAS   PubMed   Google Scholar  

Coleman K, Austin BT, Brach C, Wagner EH. Evidence on the chronic care model in the new millennium. Health Aff (Millwood). 2009;28(1):75–85.

Connolly SL, Sullivan JL, Ritchie MJ, Kim B, Miller CJ, Bauer MS. External facilitators’ perceptions of internal facilitation skills during implementation of collaborative care for mental health teams: a qualitative analysis informed by the i-PARIHS framework. BMC Health Serv Res. 2020;20(1):165.

Kim B, Sullivan JL, Drummond KL, Connolly SL, Miller CJ, Weaver K, et al. Interdisciplinary behavioral health provider perceptions of implementing the Collaborative Chronic Care Model: an i-PARIHS-guided qualitative study. Implement Sci Commun. 2023;4(1):35.

Harvey G, Kitson A. PARIHS revisited: from heuristic to integrated framework for the successful implementation of knowledge into practice. Implement Sci. 2016;11:33.

Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277–88.

Sullivan JL, Kim B, Miller CJ, Elwy AR, Drummond KL, Connolly SL, et al. Collaborative Chronic Care Model implementation within outpatient behavioral health care teams: qualitative results from a multisite trial using implementation facilitation. Implement Sci Commun. 2021;2(1):33.

Miller CJ, Sullivan JL, Kim B, Elwy AR, Drummond KL, Connolly S, et al. Assessing collaborative care in mental health teams: qualitative analysis to guide future implementation. Adm Policy Ment Health. 2019;46(2):154–66.

Miles MB, Huberman AM. Qualitative data analysis: an expanded sourcebook: sage. 1994.

Jones J, Hunter D. Consensus methods for medical and health services research. BMJ. 1995;311(7001):376–80.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bens I. Facilitating with Ease!: core skills for facilitators, team leaders and members, managers, consultants, and trainers. Hoboken: John Wiley & Sons; 2017.

Ritchie MJ, Drummond KL, Smith BN, Sullivan JL, Landes SJ. Development of a qualitative data analysis codebook informed by the i-PARIHS framework. Implement Sci Commun. 2022;3(1):98.

Excel: Microsoft. Available from: https://www.microsoft.com/en-us/microsoft-365/excel . Accessed 15 Feb 2024.

Madrigal L, Manders OC, Kegler M, Haardörfer R, Piper S, Blais LM, et al. Inner and outer setting factors that influence the implementation of the National Diabetes Prevention Program (National DPP) using the Consolidated Framework for Implementation Research (CFIR): a qualitative study. Implement Sci Commun. 2022;3(1):104.

Wilson HK, Wieler C, Bell DL, Bhattarai AP, Castillo-Hernandez IM, Williams ER, et al. Implementation of the Diabetes Prevention Program in Georgia Cooperative Extension According to RE-AIM and the Consolidated Framework for Implementation Research. Prev Sci. 2023;Epub ahead of print.

Proctor E, Luke D, Calhoun A, McMillen C, Brownson R, McCrary S, et al. Sustainability of evidence-based healthcare: research agenda, methodological advances, and infrastructure support. Implement Sci. 2015;10:88.

Fathi LI, Walker J, Dix CF, Cartwright JR, Joubert S, Carmichael KA, et al. Applying the Integrated Sustainability Framework to explore the long-term sustainability of nutrition education programmes in schools: a systematic review. Public Health Nutr. 2023;26(10):2165–79.

Guptill J. Knowledge management in health care. J Health Care Finance. 2005;31(3):10–4.

PubMed   Google Scholar  

Gammelgaard J. Why not use incentives to encourage knowledge sharing. J Knowledge Manage Pract. 2007;8(1):115–23.

Liebowitz J. Knowledge retention: strategies and solutions. Boca Raton: CRC Press; 2008.

Ensslin L, CarneiroMussi C, RolimEnsslin S, Dutra A, Pereira Bez Fontana L. Organizational knowledge retention management using a constructivist multi-criteria model. J Knowledge Manage. 2020;24(5):985–1004.

Peterson AE, Bond GR, Drake RE, McHugo GJ, Jones AM, Williams JR. Predicting the long-term sustainability of evidence-based practices in mental health care: an 8-year longitudinal analysis. J Behav Health Serv Res. 2014;41(3):337–46.

Miller CJ, Griffith KN, Stolzmann K, Kim B, Connolly SL, Bauer MS. An economic analysis of the implementation of team-based collaborative care in outpatient general mental health clinics. Med Care. 2020;58(10):874–80.

Silver SA, Harel Z, McQuillan R, Weizman AV, Thomas A, Chertow GM, et al. How to begin a quality improvement project. Clin J Am Soc Nephrol. 2016;11(5):893–900.

Dixon-Woods M. How to improve healthcare improvement-an essay by Mary Dixon-Woods. BMJ. 2019;367:l5514.

Miller CJ, Kim B, Silverman A, Bauer MS. A systematic review of team-building interventions in non-acute healthcare settings. BMC Health Serv Res. 2018;18(1):146.

Robert G, Greenhalgh T, MacFarlane F, Peacock R. Organisational factors influencing technology adoption and assimilation in the NHS: a systematic literature review. Report for the National Institute for Health Research Service Delivery and Organisation programme. London; 2009.

Kelly CJ, Young AJ. Promoting innovation in healthcare. Future Healthc J. 2017;4(2):121–5.

PubMed   PubMed Central   Google Scholar  

Aarons GA, Ehrhart MG, Farahnak LR, Hurlburt MS. Leadership and organizational change for implementation (LOCI): a randomized mixed method pilot study of a leadership and organization development intervention for evidence-based practice implementation. Implement Sci. 2015;10:11.

Ritchie MJ, Parker LE, Kirchner JE. Facilitating implementation of primary care mental health over time and across organizational contexts: a qualitative study of role and process. BMC Health Serv Res. 2023;23(1):565.

van den Hoed MW, Backhaus R, de Vries E, Hamers JPH, Daniëls R. Factors contributing to innovation readiness in health care organizations: a scoping review. BMC Health Serv Res. 2022;22(1):997.

Melnyk BM, Hsieh AP, Messinger J, Thomas B, Connor L, Gallagher-Ford L. Budgetary investment in evidence-based practice by chief nurses and stronger EBP cultures are associated with less turnover and better patient outcomes. Worldviews Evid Based Nurs. 2023;20(2):162–71.

Jacob RR, Parks RG, Allen P, Mazzucca S, Yan Y, Kang S, et al. How to “start small and just keep moving forward”: mixed methods results from a stepped-wedge trial to support evidence-based processes in local health departments. Front Public Health. 2022;10:853791.

Aarons GA, Conover KL, Ehrhart MG, Torres EM, Reeder K. Leader-member exchange and organizational climate effects on clinician turnover intentions. J Health Organ Manag. 2020;35(1):68–87.

Kirchner JE, Ritchie MJ, Pitcock JA, Parker LE, Curran GM, Fortney JC. Outcomes of a partnered facilitation strategy to implement primary care-mental health. J Gen Intern Med. 2014;29 Suppl 4(Suppl 4):904–12.

Strategy Design: CFIR research team-center for clinical management research. Available from: https://cfirguide.org/choosing-strategies/ . Accessed 15 Feb 2024.

Kim B, Wilson SM, Mosher TM, Breland JY. Systematic decision-making for using technological strategies to implement evidence-based interventions: an illustrated case study. Front Psychiatry. 2021;12:640240.

Proctor EK, Powell BJ, McMillen JC. Implementation strategies: recommendations for specifying and reporting. Implement Sci. 2013;8:139.

Lewis CC, Scott K, Marriott BR. A methodology for generating a tailored implementation blueprint: an exemplar from a youth residential setting. Implement Sci. 2018;13(1):68.

Maher C, Hadfield M, Hutchings M, de Eyto A. Ensuring rigor in qualitative data analysis: a design research approach to coding combining NVivo with traditional material methods. Int J Qual Methods. 2018;17(1):1609406918786362.

Holloway I. A-Z of qualitative research in healthcare. 2nd ed. Oxford: Wiley-Blackwell; 2008.

Reproducibility and Replicability in Research: National Academies. Available from: https://www.nationalacademies.org/news/2019/09/reproducibility-and-replicability-in-research . Accessed 15 Feb 2024.

Chinman M, Acosta J, Ebener P, Shearer A. “What we have here, is a failure to [Replicate]”: ways to solve a replication crisis in implementation science. Prev Sci. 2022;23(5):739–50.

Vicente-Saez R, Martinez-Fuentes C. Open Science now: a systematic literature review for an integrated definition. J Bus Res. 2018;88:428–36.

Consolidated Framework for Implementation Research: CFIR Research Team-Center for Clinical Management Research. Available from: https://cfirguide.org/ . Accessed 15 Feb 2024.

Atkins L, Francis J, Islam R, O’Connor D, Patey A, Ivers N, et al. A guide to using the Theoretical Domains Framework of behaviour change to investigate implementation problems. Implement Sci. 2017;12(1):77.

Luke DA, Calhoun A, Robichaux CB, Elliott MB, Moreland-Russell S. The Program Sustainability Assessment Tool: a new instrument for public health programs. Prev Chronic Dis. 2014;11:130184.

Calhoun A, Mainor A, Moreland-Russell S, Maier RC, Brossart L, Luke DA. Using the Program Sustainability Assessment Tool to assess and plan for sustainability. Prev Chronic Dis. 2014;11:130185.

Palinkas LA, Chou CP, Spear SE, Mendon SJ, Villamar J, Brown CH. Measurement of sustainment of prevention programs and initiatives: the sustainment measurement system scale. Implement Sci. 2020;15(1):71.

Sandelowski M. Real qualitative researchers do not count: the use of numbers in qualitative research. Res Nurs Health. 2001;24(3):230–40.

Wood M, Christy R. Sampling for Possibilities. Qual Quant. 1999;33(2):185–202.

Chang Y, Voils CI, Sandelowski M, Hasselblad V, Crandell JL. Transforming verbal counts in reports of qualitative descriptive studies into numbers. West J Nurs Res. 2009;31(7):837–52.

Yin RK. Case study research and applications. Los Angeles: Sage; 2018.

Bauer MS, Weaver K, Kim B, Miller C, Lew R, Stolzmann K, et al. The Collaborative Chronic Care Model for mental health conditions: from evidence synthesis to policy impact to scale-up and spread. Med Care. 2019;57 Suppl 10 Suppl 3(10 Suppl 3):S221-s7.

Miller CJ, Sullivan JL, Connolly SL, Richardson EJ, Stolzmann K, Brown ME, et al. Adaptation for sustainability in an implementation trial of team-based collaborative care. Implement Res Pract. 2024;5:26334895231226197.

Curran GM, Smith JD, Landsverk J, Vermeer W, Miech EJ, Kim B, et al. Design and analysis in dissemination and implementation research. In: Brownson RC, Colditz GA, Proctor EK, editors. Dissemination and Implementation Research in Health: Translating Science to Practice. 3 ed. New York: Oxford University Press; In press.

Salvati ZM, Rahm AK, Williams MS, Ladd I, Schlieder V, Atondo J, et al. A picture is worth a thousand words: advancing the use of visualization tools in implementation science through process mapping and matrix heat mapping. Implement Sci Commun. 2023;4(1):43.

Whitaker RG, Sperber N, Baumgartner M, Thiem A, Cragun D, Damschroder L, et al. Coincidence analysis: a new method for causal inference in implementation science. Implement Sci. 2020;15(1):108.

Lewis CC, Powell BJ, Brewer SK, Nguyen AM, Schriger SH, Vejnoska SF, et al. Advancing mechanisms of implementation to accelerate sustainable evidence-based practice integration: protocol for generating a research agenda. BMJ Open. 2021;11(10):e053474.

Kilbourne AM, Geng E, Eshun-Wilson I, Sweeney S, Shelley D, Cohen DJ, et al. How does facilitation in healthcare work? Using mechanism mapping to illuminate the black box of a meta-implementation strategy. Implement Sci Commun. 2023;4(1):53.

Kim B, Cruden G, Crable EL, Quanbeck A, Mittman BS, Wagner AD. A structured approach to applying systems analysis methods for examining implementation mechanisms. Implementation Sci Commun. 2023;4(1):127.

Geng EH, Baumann AA, Powell BJ. Mechanism mapping to advance research on implementation strategies. PLoS Med. 2022;19(2):e1003918.

Luke DA, Powell BJ, Paniagua-Avila A. Bridges and mechanisms: integrating systems science thinking into implementation research. Annu Rev Public Health. In press.

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Acknowledgements

The authors sincerely thank the project participants for their time, as well as the project team members for their guidance and support. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

This project was funded by VA grant QUE 20–026 and was designed and conducted in partnership with the VA Office of Mental Health and Suicide Prevention.

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Kim, B., Sullivan, J.L., Brown, M.E. et al. Sustaining the collaborative chronic care model in outpatient mental health: a matrixed multiple case study. Implementation Sci 19 , 16 (2024). https://doi.org/10.1186/s13012-024-01342-2

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  • Published: 14 February 2024

Critical transitions in the Amazon forest system

  • Bernardo M. Flores   ORCID: orcid.org/0000-0003-4555-5598 1 ,
  • Encarni Montoya   ORCID: orcid.org/0000-0002-4690-190X 2 ,
  • Boris Sakschewski   ORCID: orcid.org/0000-0002-7230-9723 3 ,
  • Nathália Nascimento   ORCID: orcid.org/0000-0003-4819-0811 4 ,
  • Arie Staal   ORCID: orcid.org/0000-0001-5409-1436 5 ,
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  • David M. Lapola 8 ,
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  • Niklas Boers   ORCID: orcid.org/0000-0002-1239-9034 3 , 14 ,
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  • Marina Hirota   ORCID: orcid.org/0000-0002-1958-3651 1 , 12 , 25  

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  • Climate and Earth system modelling
  • Ecosystem ecology
  • Ecosystem services
  • Sustainability

The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern 1 , 2 , 3 . For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system 1 . Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.

The Amazon forest is a complex system of interconnected species, ecosystems and human cultures that contributes to the well-being of people globally 1 . The Amazon forest holds more than 10% of Earth’s terrestrial biodiversity, stores an amount of carbon equivalent to 15–20 years of global CO 2 emissions (150–200 Pg C), and has a net cooling effect (from evapotranspiration) that helps to stabilize the Earth’s climate 1 , 2 , 3 . The forest contributes up to 50% of rainfall in the region and is crucial for moisture supply across South America 4 , allowing other biomes and economic activities to thrive in regions that would otherwise be more arid, such as the Pantanal wetlands and the La Plata river basin 1 . Large parts of the Amazon forest, however, are projected to experience mass mortality events due to climatic and land use-related disturbances in the coming decades 5 , 6 , potentially accelerating climate change through carbon emissions and feedbacks with the climate system 2 , 3 . These impacts would also involve irreversible loss of biodiversity, socioeconomic and cultural values 1 , 7 , 8 , 9 . The Amazon is home to more than 40 million people, including 2.2 million Indigenous peoples of more than 300 ethnicities, as well as afrodescendent and local traditional communities 1 . Indigenous peoples and local communities (IPLCs) would be harmed by forest loss in terms of their livelihoods, lifeways and knowledge systems that inspire societies globally 1 , 7 , 9 .

Understanding the risk of such catastrophic behaviour requires addressing complex factors that shape ecosystem resilience 10 . A major question is whether a large-scale collapse of the Amazon forest system could actually happen within the twenty-first century, and if this would be associated with a particular tipping point. Here we synthesize evidence from paleorecords, observational data and modelling studies of critical drivers of stress on the system. We assess potential thresholds of those drivers and the main feedbacks that could push the Amazon forest towards a tipping point. From examples of disturbed forests across the Amazon, we analyse the most plausible ecosystem trajectories that may lead to alternative stable states 10 . Moreover, inspired by the framework of ‘planetary boundaries’ 11 , we identify climatic and land use boundaries that reveal a safe operating space for the Amazon forest system in the Anthropocene epoch 12 .

Theory and concepts

Over time, environmental conditions fluctuate and may cause stress on ecosystems (for example, lack of water for plants). When stressing conditions intensify, some ecosystems may change their equilibrium state gradually, whereas others may shift abruptly between alternative stable states 10 . A ‘tipping point’ is the critical threshold value of an environmental stressing condition at which a small disturbance may cause an abrupt shift in the ecosystem state 2 , 3 , 13 , 14 , accelerated by positive feedbacks 15 (see Extended Data Table 1 ). This type of behaviour in which the system gets into a phase of self-reinforcing (runaway) change is often referred to as ‘critical transition’ 16 . As ecosystems approach a tipping point, they often lose resilience while still remaining close to equilibrium 17 . Thus, monitoring changes in ecosystem resilience and in key environmental conditions may enable societies to manage and avoid critical transitions. We adopt the concept of ‘ecological resilience’ 18 (hereafter ‘resilience’), which refers to the ability of an ecosystem to persist with similar structure, functioning and interactions, despite disturbances that push it to an alternative stable state. The possibility that alternative stable states (or bistability) may exist in a system has important implications, because the crossing of tipping points may be irreversible for the time scales that matter to societies 10 . Tropical terrestrial ecosystems are a well-known case in which critical transitions between alternative stable states may occur (Extended Data Fig. 1 ).

Past dynamics

The Amazon system has been mostly covered by forest throughout the Cenozoic era 19 (for 65 million years). Seven million years ago, the Amazon river began to drain the massive wetlands that covered most of the western Amazon, allowing forests to expand over grasslands in that region. More recently, during the drier and cooler conditions of the Last Glacial Maximum 20 (LGM) (around 21,000 years ago) and of the mid-Holocene epoch 21 (around 6,000 years ago), forests persisted even when humans were already present in the landscape 22 . Nonetheless, savannas expanded in peripheral parts of the southern Amazon basin during the LGM and mid-Holocene 23 , as well as in the northeastern Amazon during the early Holocene (around 11,000 years ago), probably influenced by drier climatic conditions and fires ignited by humans 24 , 25 . Throughout the core of the Amazon forest biome, patches of white-sand savanna also expanded in the past 20,000–7,000 years, driven by sediment deposition along ancient rivers 26 , and more recently (around 800 years ago) owing to Indigenous fires 27 . However, during the past 3,000 years, forests have been mostly expanding over savanna in the southern Amazon driven by increasingly wet conditions 28 .

Although palaeorecords suggest that a large-scale Amazon forest collapse did not occur within the past 65 million years 19 , they indicate that savannas expanded locally, particularly in the more seasonal peripheral regions when fires ignited by humans were frequent 23 , 24 . Patches of white-sand savanna also expanded within the Amazon forest owing to geomorphological dynamics and fires 26 , 27 . Past drought periods were usually associated with much lower atmospheric CO 2 concentrations, which may have reduced water-use efficiency of trees 29 (that is, trees assimilated less carbon during transpiration). However, these periods also coincided with cooler temperatures 20 , 21 , which probably reduced water demand by trees 30 . Past drier climatic conditions were therefore very different from the current climatic conditions, in which observed warming trends may exacerbate drought impacts on the forest by exposing trees to unprecedented levels of water stress 31 , 32 .

Global change impacts on forest resilience

Satellite observations from across the Amazon suggest that forest resilience has been decreasing since the early 2000s 33 , possibly as a result of global changes. In this section, we synthesize three global change impacts that vary spatially and temporally across the Amazon system, affecting forest resilience and the risk of critical transitions.

Regional climatic conditions

Within the twenty-first century, global warming may cause long-term changes in Amazonian climatic conditions 2 . Human greenhouse gas emissions continue to intensify global warming, but the warming rate also depends on feedbacks in the climate system that remain uncertain 2 , 3 . Recent climate models of the 6th phase of the Coupled Model Intercomparison Project (CMIP6) agree that in the coming decades, rainfall conditions will become more seasonal in the eastern and southern Amazonian regions, and temperatures will become higher across the entire Amazon 1 , 2 . By 2050, models project that a significant increase in the number of consecutive dry days by 10−30 days and in annual maximum temperatures by 2–4 °C, depending on the greenhouse gas emission scenario 2 . These climatic conditions could expose the forest to unprecedented levels of vapour pressure deficit 31 and consequently water stress 30 .

Satellite observations of climatic variability 31 confirm model projections 2 , showing that since the early 1980s, the Amazonian region has been warming significantly at an average rate of 0.27 °C per decade during the dry season, with the highest rates of up to 0.6 °C per decade in the centre and southeast of the biome (Fig. 1a ). Only a few small areas in the west of the biome are significantly cooling by around 0.1 °C per decade (Fig. 1a ). Dry season mean temperature is now more than 2 °C higher than it was 40 years ago in large parts of the central and southeastern Amazon. If trends continue, these areas could potentially warm by over 4 °C by 2050. Maximum temperatures during the dry season follow a similar trend, rising across most of the biome (Extended Data Fig. 2 ), exposing the forest 34 and local peoples 35 to potentially unbearable heat. Rising temperatures will increase thermal stress, potentially reducing forest productivity and carbon storage capacity 36 and causing widespread leaf damage 34 .

figure 1

a , Changes in the dry season (July–October) mean temperature reveal widespread warming, estimated using simple regressions between time and temperature observed between 1981 and 2020 (with P  < 0.1). b , Potential ecosystem stability classes estimated for year 2050, adapted from current stability classes (Extended Data Fig. 1b ) by considering only areas with significant regression slopes between time and annual rainfall observed from 1981 through 2020 (with P  < 0.1) (see Extended Data Fig. 3 for areas with significant changes). c , Repeated extreme drought events between 2001–2018 (adapted from ref. 39 ). d , Road network from where illegal deforestation and degradation may spread. e , Protected areas and Indigenous territories reduce deforestation and fire disturbances. f , Ecosystem transition potential (the possibility of forest shifting into an alternative structural or compositional state) across the Amazon biome by year 2050 inferred from compounding disturbances ( a – d ) and high-governance areas ( e ). We excluded accumulated deforestation until 2020 and savannas. Transition potential rises with compounding disturbances and varies as follows: less than 0 (in blue) as low; between 1 and 2 as moderate (in yellow); more than 2 as high (orange–red). Transition potential represents the sum of: (1) slopes of dry season mean temperature (as in a , multiplied by 10); (2) ecosystem stability classes estimated for year 2050 (as in b ), with 0 for stable forest, 1 for bistable and 2 for stable savanna; (3) accumulated impacts from extreme drought events, with 0.2 for each event; (4) road proximity as proxy for degrading activities, with 1 for pixels within 10 km from a road; (5) areas with higher governance within protected areas and Indigenous territories, with −1 for pixels inside these areas. For more details, see  Methods .

Since the early 1980s, rainfall conditions have also changed 31 . Peripheral and central parts of the Amazon forest are drying significantly, such as in the southern Bolivian Amazon, where annual rainfall reduced by up to 20 mm yr −1 (Extended Data Fig. 3a ). By contrast, parts of the western and eastern Amazon forest are becoming wetter, with annual rainfall increasing by up to 20 mm yr −1 . If these trends continue, ecosystem stability (as in Extended Data Fig. 1 ) will probably change in parts of the Amazon by 2050, reshaping forest resilience to disturbances (Fig. 1b and Extended Data Fig. 3b ). For example, 6% of the biome may change from stable forest to a bistable regime in parts of the southern and central Amazon. Another 3% of the biome may pass the critical threshold in annual rainfall into stable savanna in the southern Bolivian Amazon. Bistable areas covering 8% of the biome may turn into stable forest in the western Amazon (Peru and Bolivia), thus becoming more resilient to disturbances. For comparison with satellite observations, we used projections of ecosystem stability by 2050 based on CMIP6 model ensembles for a low (SSP2–4.5) and a high (SSP5–8.5) greenhouse gas emission scenario (Extended Data Fig. 4 and Supplementary Table 1 ). An ensemble with the 5 coupled models that include a dynamic vegetation module indicates that 18–27% of the biome may transition from stable forest to bistable and that 2–6% may transition to stable savanna (depending on the scenario), mostly in the northeastern Amazon. However, an ensemble with all 33 models suggests that 35–41% of the biome could become bistable, including large areas of the southern Amazon. The difference between both ensembles is possibly related to the forest–rainfall feedback included in the five coupled models, which increases total annual rainfall and therefore the stable forest area along the southern Amazon, but only when deforestation is not included in the simulations 4 , 37 . Nonetheless, both model ensembles agree that bistable regions will expand deeper into the Amazon, increasing the risk of critical transitions due to disturbances (as implied by the existence of alternative stable states; Extended Data Fig. 1 ).

Disturbance regimes

Within the remaining Amazon forest area, 17% has been degraded by human disturbances 38 , such as logging, edge effects and understory fires, but if we consider also the impacts from repeated extreme drought events in the past decades, 38% of the Amazon could be degraded 39 . Increasing rainfall variability is causing extreme drought events to become more widespread and frequent across the Amazon (Fig. 1c ), together with extreme wet events and convective storms that result in more windthrow disturbances 40 . Drought regimes are intensifying across the region 41 , possibly due to deforestation 42 that continues to expand within the system (Extended Data Fig. 5 ). As a result, new fire regimes are burning larger forest areas 43 , emitting more carbon to the atmosphere 44 and forcing IPLCs to readapt 45 . Road networks (Fig. 1d ) facilitate illegal activities, promoting more deforestation, logging and fire spread throughout the core of the Amazon forest 38 , 39 . The impacts of these pervasive disturbances on biodiversity and on IPLCs will probably affect ecosystem adaptability (Box 1 ), and consequently forest resilience to global changes.

Currently, 86% of the Amazon biome may be in a stable forest state (Extended Data Fig. 1b ), but some of these stable forests are showing signs of fragility 33 . For instance, field evidence from long-term monitoring sites across the Amazon shows that tree mortality rates are increasing in most sites, reducing carbon storage 46 , while favouring the replacement by drought-affiliated species 47 . Aircraft measurements of vertical carbon flux between the forest and atmosphere reveal how southeastern forests are already emitting more carbon than they absorb, probably because of deforestation and fire 48 .

As bistable forests expand deeper into the system (Fig. 1b and Extended Data Fig. 4 ), the distribution of compounding disturbances may indicate where ecosystem transitions are more likely to occur in the coming decades (Fig. 1f ). For this, we combined spatial information on warming and drying trends, repeated extreme drought events, together with road networks, as proxy for future deforestation and degradation 38 , 39 . We also included protected areas and Indigenous territories as areas with high forest governance, where deforestation and fire regimes are among the lowest within the Amazon 49 (Fig. 1e ). This simple additive approach does not consider synergies between compounding disturbances that could trigger unexpected ecosystem transitions. However, by exploring only these factors affecting forest resilience and simplifying the enormous Amazonian complexity, we aimed to produce a simple and comprehensive map that can be useful for guiding future governance. We found that 10% of the Amazon forest biome has a relatively high transition potential (more than 2 disturbance types; Fig. 1f ), including bistable forests that could transition into a low tree cover state near savannas of Guyana, Venezuela, Colombia and Peru, as well as stable forests that could transition into alternative compositional states within the central Amazon, such as along the BR319 and Trans-Amazonian highways. Smaller areas with high transition potential were found scattered within deforestation frontiers, where most forests have been carved by roads 50 , 51 . Moreover, 47% of the biome has a moderate transition potential (more than 1 disturbance type; Fig. 1f ), including relatively remote parts of the central Amazon where warming trends and repeated extreme drought events overlap (Fig. 1a,c ). By contrast, large remote areas covering 53% of the biome have low transition potential, mostly reflecting the distribution of protected areas and Indigenous territories (Fig. 1e ). If these estimates, however, considered projections from CMIP6 models and their relatively broader areas of bistability (Extended Data Fig. 4 ), the proportion of the Amazon forest that could transition into a low tree cover state would be much larger.

Box 1 Ecosystem adaptability

We define ‘ecosystem adaptability’ as the capacity of an ecosystem to reorganize and persist in the face of environmental changes. In the past, many internal mechanisms have probably contributed to ecosystem adaptability, allowing Amazonian forests to persist during times of climate change. In this section we synthesize two of these internal mechanisms, which are now being undermined by global change.

Biodiversity

Amazonian forests are home to more than 15,000 tree species, of which 1% are dominant and the other 99% are mostly rare 107 . A single forest hectare in the central and northwestern Amazon can contain more than 300 tree species (Extended Data Fig. 7a ). Such tremendous tree species diversity can increase forest resilience by different mechanisms. Tree species complementarity increases carbon storage, accelerating forest recovery after disturbances 108 . Tree functional diversity increases forest adaptability to climate chance by offering various possibilities of functioning 99 . Rare species provide ‘ecological redundancy’, increasing opportunities for replacement of lost functions when dominant species disappear 109 . Diverse forests are also more likely to resist severe disturbances owing to ‘response diversity’ 110 —that is, some species may die, while others persist. For instance, in the rainy western Amazon, drought-resistant species are rare but present within tree communities 111 , implying that they could replace the dominant drought-sensitive species in a drier future. Diversity of other organisms, such as frugivores and pollinators, also increases forest resilience by stabilizing ecological networks 15 , 112 . Considering that half of Amazonian tree species are estimated to become threatened (IUCN Red list) by 2050 owing to climate change, deforestation and degradation 8 , biodiversity losses could contribute to further reducing forest resilience.

Indigenous peoples and local communities

Globally, Indigenous peoples and local communities (IPLCs) have a key role in maintaining ecosystems resilient to global change 113 . Humans have been present in the Amazon for at least 12,000 years 114 and extensively managing landscapes for 6,000 years 22 . Through diverse ecosystem management practices, humans built thousands of earthworks and ‘Amazon Dark Earth’ sites, and domesticated plants and landscapes across the Amazon forest 115 , 116 . By creating new cultural niches, humans partly modified the Amazonian flora 117 , 118 , increasing their food security even during times of past climate change 119 , 120 without the need for large-scale deforestation 117 . Today, IPLCs have diverse ecological knowledge about Amazonian plants, animals and landscapes, which allows them to quickly identify and respond to environmental changes with mitigation and adaptation practices 68 , 69 . IPLCs defend their territories against illegal deforestation and land use disturbances 49 , 113 , and they also promote forest restoration by expanding diverse agroforestry systems 121 , 122 . Amazonian regions with the highest linguistic diversity (a proxy for ecological knowledge diversity 123 ) are found in peripheral parts of the system, particularly in the north-west (Extended Data Fig. 7b ). However, consistent loss of Amazonian languages is causing an irreversible disruption of ecological knowledge systems, mostly driven by road construction 7 . Continued loss of ecological knowledge will undermine the capacity of IPLCs to manage and protect Amazonian forests, further reducing their resilience to global changes 9 .

CO 2 fertilization

Rising atmospheric CO 2 concentrations are expected to increase the photosynthetic rates of trees, accelerating forest growth and biomass accumulation on a global scale 52 . In addition, CO 2 may reduce water stress by increasing tree water-use efficiency 29 . As result, a ‘CO 2 fertilization effect’ could increase forest resilience to climatic variability 53 , 54 . However, observations from across the Amazon 46 suggest that CO 2 -driven accelerations of tree growth may have contributed to increasing tree mortality rates (trees grow faster but also die earlier), which could eventually neutralize the forest carbon sink in the coming decades 55 . Moreover, increases in tree water-use efficiency may reduce forest transpiration and consequently atmospheric moisture flow across the Amazon 53 , 56 , potentially reducing forest resilience in the southwest of the biome 4 , 37 . Experimental evidence suggests that CO 2 fertilization also depends on soil nutrient availability, particularly nitrogen and phosphorus 57 , 58 . Thus, it is possible that in the fertile soils of the western Amazon and Várzea floodplains, forests may gain resilience from increasing atmospheric CO 2 (depending on how it affects tree mortality rates), whereas on the weathered (nutrient-poor) soils across most of the Amazon basin 59 , forests might not respond to atmospheric CO 2 increase, particularly on eroded soils within deforestation frontiers 60 . In sum, owing to multiple interacting factors, potential responses of Amazonian forests to CO 2 fertilization are still poorly understood. Forest responses depend on scale, with resilience possibly increasing at the local scale on relatively more fertile soils, but decreasing at the regional scale due to reduced atmospheric moisture flow.

Local versus systemic transition

Environmental heterogeneity.

Environmental heterogeneity can reduce the risk of systemic transition (large-scale forest collapse) because when stressing conditions intensify (for example, rainfall declines), heterogeneous forests may transition gradually (first the less resilient forest patches, followed by the more resilient ones), compared to homogeneous forests that may transition more abruptly 17 (all forests transition in synchrony). Amazonian forests are heterogeneous in their resilience to disturbances, which may have contributed to buffering large-scale transitions in the past 37 , 61 , 62 . At the regional scale, a fundamental heterogeneity factor is rainfall and how it translates into water stress. Northwestern forests rarely experience water stress, which makes them relatively more resilient than southeastern forests that may experience water stress in the dry season, and therefore are more likely to shift into a low tree cover state. As a result of low exposure to water deficit, most northwestern forests have trees with low drought resistance and could suffer massive mortality if suddenly exposed to severe water stress 32 . However, this scenario seems unlikely to occur in the near future (Fig. 1 ). By contrast, most seasonal forest trees have various strategies to cope with water deficit owing to evolutionary and adaptive responses to historical drought events 32 , 63 . These strategies may allow seasonal forests to resist current levels of rainfall fluctuations 32 , but seasonal forests are also closer to the critical rainfall thresholds (Extended Data Fig. 1 ) and may experience unprecedented water stress in the coming decades (Fig. 1 ).

Other key heterogeneity factors (Extended Data Fig. 6 ) include topography, which determines plant access to groundwater 64 , and seasonal flooding, which increases forest vulnerability to wildfires 65 . Future changes in rainfall regimes will probably affect hydrological regimes 66 , exposing plateau (hilltop) forests to unprecedented water stress, and floodplain forests to extended floods, droughts and wildfires. Soil fertility is another heterogeneity factor that may affect forest resilience 59 , and which may be undermined by disturbances that cause topsoil erosion 60 . Moreover, as human disturbances intensify throughout the Amazon (Fig. 1 ), the spread of invasive grasses and fires can make the system increasingly homogeneous. Effects of heterogeneity on Amazon forest resilience have been poorly investigated so far (but see refs. 37 , 61 , 62 ) and many questions remain open, such as how much heterogeneity exists in the system and whether it can mitigate a systemic transition.

Sources of connectivity

Connectivity across Amazonian landscapes and regions can contribute to synchronize forest dynamics, causing different forests to behave more similarly 17 . Depending on the processes involved, connectivity can either increase or decrease the risk of systemic transition 17 . For instance, connectivity may facilitate forest recovery after disturbances through seed dispersal, but also it may spread disturbances, such as fire. In the Amazon, an important source of connectivity enhancing forest resilience is atmospheric moisture flow westward (Fig. 2 ), partly maintained by forest evapotranspiration 4 , 37 , 67 . Another example of connectivity that may increase social-ecological resilience is knowledge exchange among IPLCs about how to adapt to global change 68 , 69 (see Box 1 ). However, complex systems such as the Amazon can be particularly vulnerable to sources of connectivity that spread disturbances and increase the risk of systemic transition 70 . For instance, roads carving through the forest are well-known sources of illegal activities, such as logging and burning, which increase forest flammability 38 , 39 .

figure 2

Brazil holds 60% of the Amazon forest biome and has a major responsibility towards its neighbouring countries in the west. Brazil is the largest supplier of rainfall to western Amazonian countries. Up to one-third of the total annual rainfall in Amazonian territories of Bolivia, Peru, Colombia and Ecuador depends on water originating from Brazil’s portion of the Amazon forest. This international connectivity illustrates how policies related to deforestation, especially in the Brazilian Amazon, will affect the climate in other countries. Arrow widths are proportional to the percentage of the annual rainfall received by each country within their Amazonian areas. We only show flows with percentages higher than 10% (see  Methods for details).

Five critical drivers of water stress

Global warming.

Most CMIP6 models agree that a large-scale dieback of the Amazon is unlikely in response to global warming above pre-industrial levels 2 , but this ecosystem response is based on certain assumptions, such as a large CO 2 -fertilization effect 53 . Forests across the Amazon are already responding with increasing tree mortality rates that are not simulated by these models 46 , possibly because of compounding disturbance regimes (Fig. 1 ). Nonetheless, a few global climate models 3 , 14 , 71 , 72 , 73 , 74 indicate a broad range for a potential critical threshold in global warming between 2 and 6 °C (Fig. 3a ). These contrasting results can be explained by general differences between numerical models and their representation of the complex Amazonian system. While some models with dynamic vegetation indicate local-scale tipping events in peripheral parts of the Amazon 5 , 6 , other models suggest an increase in biomass and forest cover (for example, in refs. 53 , 54 ). For instance, a study found that when considering only climatic variability, a large-scale Amazon forest dieback is unlikely, even under a high greenhouse gas emission scenario 75 . However, most updated CMIP6 models agree that droughts in the Amazon region will increase in length and intensity, and that exceptionally hot droughts will become more common 2 , creating conditions that will probably boost other types of disturbances, such as large and destructive forest fires 76 , 77 . To avoid broad-scale ecosystem transitions due to synergies between climatic and land use disturbances (Fig. 3b ), we suggest a safe boundary for the Amazon forest at 1.5 °C for global warming above pre-industrial levels, in concert with the Paris Agreement goals.

figure 3

a , Five critical drivers of water stress on Amazonian forests affect (directly or indirectly) the underlying tipping point of the system. For each driver, we indicate potential critical thresholds and safe boundaries that define a safe operating space for keeping the Amazon forest resilient 11 , 12 . We followed the precautionary principle and considered the most conservative thresholds within the ranges, when confidence was low. b , Conceptual model showing how the five drivers may interact (arrows indicate positive effects) and how these interactions may strengthen a positive feedback between water stress and forest loss. These emerging positive feedback loops could accelerate a systemic transition of the Amazon forest 15 . At global scales, driver 1 (global warming) intensifies with greenhouse gas emissions, including emissions from deforestation. At local scales, driver 5 (accumulated deforestation) intensifies with land use changes. Drivers 2 to 4 (regional rainfall conditions) intensify in response to drivers 1 and 5. The intensification of these drivers may cause widespread tree mortality for instance because of extreme droughts and fires 76 . Water stress affects vegetation resilience globally 79 , 104 , but other stressors, such as heat stress 34 , 36 , may also have a role. In the coming decades, these five drivers could change at different rates, with some approaching a critical threshold faster than others. Therefore, monitoring them separately can provide vital information to guide mitigation and adaptation strategies.

Annual rainfall

Satellite observations of tree cover distributions across tropical South America suggest a critical threshold between 1,000 and 1,250 mm of annual rainfall 78 , 79 . On the basis of our reanalysis using tree cover data from the Amazon basin (Extended Data Fig. 1a ), we confirm a potential threshold at 1,000 mm of annual rainfall (Fig. 3a ), below which forests become rare and unstable. Between 1,000 and 1,800 mm of annual rainfall, high and low tree cover ecosystems exist in the Amazon as two alternative stable states (see Extended Data Table 2 for uncertainty ranges). Within the bistability range in annual rainfall conditions, forests are relatively more likely to collapse when severely disturbed, when compared to forests in areas with annual rainfall above 1,800 mm (Extended Data Fig. 1a ). For floodplain ecosystems covering 14% of the forest biome, a different critical threshold has been estimated at 1,500 mm of annual rainfall 65 , implying that floodplain forests may be the first to collapse in a drier future. To avoid local-scale ecosystem transitions due to compounding disturbances, we suggest a safe boundary in annual rainfall conditions at 1,800 mm.

Rainfall seasonality intensity

Satellite observations of tree cover distributions across tropical South America suggest a critical threshold in rainfall seasonality intensity at −400 mm of the maximum cumulative water deficit 37 , 80 (MCWD). Our reanalysis of the Amazon basin (Extended Data Fig. 1c ) confirms the critical threshold at approximately −450 mm in the MCWD (Fig. 3a ), and suggests a bistability range between approximately −350 and −450 mm (see Extended Data Table 2 for uncertainty ranges), in which forests are more likely to collapse when severely disturbed than forests in areas with MCWD below −350 mm. To avoid local-scale ecosystem transitions due to compounding disturbances, we suggest a safe boundary of MCWD at −350 mm.

Dry season length

Satellite observations of tree cover distributions across tropical South America suggest a critical threshold at 7 months of dry season length 79 (DSL). Our reanalysis of the Amazon basin (Extended Data Fig. 1d ) suggests a critical threshold at eight months of DSL (Fig. 3a ), with a bistability range between approximately five and eight months (see Extended Data Table 2 for uncertainty ranges), in which forests are more likely to collapse when severely disturbed than forests in areas with DSL below five months. To avoid local-scale ecosystem transitions due to compounding disturbances, we suggest a safe boundary of DSL at five months.

Accumulated deforestation

A potential vegetation model 81 found a critical threshold at 20% of accumulated deforestation (Fig. 3a ) by simulating Amazon forest responses to different scenarios of accumulated deforestation (with associated fire events) and of greenhouse gas emissions, and by considering a CO 2 fertilization effect of 25% of the maximum photosynthetic assimilation rate. Beyond 20% deforestation, forest mortality accelerated, causing large reductions in regional rainfall and consequently an ecosystem transition of 50−60% of the Amazon, depending on the emissions scenario. Another study using a climate-vegetation model found that with accumulated deforestation of 30−50%, rainfall in non-deforested areas downwind would decline 67 by 40% (ref.  67 ), potentially causing more forest loss 4 , 37 . Other more recent models incorporating fire disturbances support a potential broad-scale transition of the Amazon forest, simulating a biomass loss of 30–40% under a high-emission scenario 5 , 82 (SSP5–8.5 at 4 °C). The Amazon biome has already lost 13% of its original forest area due to deforestation 83 (or 15% of the biome if we consider also young secondary forests 83 that provide limited contribution to moisture flow 84 ). Among the remaining old-growth forests, at least 38% have been degraded by land use disturbances and repeated extreme droughts 39 , with impacts on moisture recycling that are still uncertain. Therefore, to avoid broad-scale ecosystem transitions due to runaway forest loss (Fig. 3b ), we suggest a safe boundary of accumulated deforestation of 10% of the original forest biome cover, which requires ending large-scale deforestation and restoring at least 5% of the biome.

Three alternative ecosystem trajectories

Degraded forest.

In stable forest regions of the Amazon with annual rainfall above 1,800 mm (Extended Data Fig. 1b ), forest cover usually recovers within a few years or decades after disturbances, yet forest composition and functioning may remain degraded for decades or centuries 84 , 85 , 86 , 87 . Estimates from across the Amazon indicate that approximately 30% of areas previously deforested are in a secondary forest state 83 (covering 4% of the biome). An additional 38% of the forest biome has been damaged by extreme droughts, fires, logging and edge effects 38 , 39 . These forests may naturally regrow through forest succession, yet because of feedbacks 15 , succession can become arrested, keeping forests persistently degraded (Fig. 4 ). Different types of degraded forests have been identified in the Amazon, each one associated with a particular group of dominant opportunistic plants. For instance, Vismia forests are common in old abandoned pastures managed with fire 85 , and are relatively stable, because Vismia trees favour recruitment of Vismia seedlings in detriment of other tree species 88 , 89 . Liana forests can also be relatively stable, because lianas self-perpetuate by causing physical damage to trees, allowing lianas to remain at high density 90 , 91 . Liana forests are expected to expand with increasing aridity, disturbance regimes and CO 2 fertilization 90 . Guadua bamboo forests are common in the southwestern Amazon 92 , 93 . Similar to lianas, bamboos self-perpetuate by causing physical damage to trees and have been expanding over burnt forests in the region 92 . Degraded forests are usually dominated by native opportunistic species, and their increasing expansion over disturbed forests could affect Amazonian functioning and resilience in the future.

figure 4

From examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories related to the types of disturbances, feedbacks and local environmental conditions. These alternative trajectories may be irreversible or transient depending on the strength of the novel interactions 15 . Particular combinations of interactions (arrows show positive effects described in the literature) may form feedback loops 15 that propel the ecosystem through these trajectories. In the ‘degraded forest’ trajectory, feedbacks often involve competition between trees and other opportunistic plants 85 , 90 , 92 , as well as interactions between deforestation, fire and seed limitation 84 , 87 , 105 . At the landscape scale, secondary forests are more likely to be cleared than mature forests, thus keeping forests persistently young and landscapes fragmented 83 . In the ‘degraded open-canopy ecosystem’ trajectory, feedbacks involve interactions among low tree cover and fire 97 , soil erosion 60 , seed limitation 105 , invasive grasses and opportunistic plants 96 . At the regional scale, a self-reinforcing feedback between forest loss and reduced atmospheric moisture flow may increase the resilience of these open-canopy degraded ecosystems 42 . In the ‘white-sand savanna’ trajectory, the main feedbacks result from interactions among low tree cover and fire, soil erosion, and seed limitation 106 . Bottom left, floodplain forest transition to white-sand savanna after repeated fires (photo credit: Bernardo Flores); bottom centre, forest transition to degraded open-canopy ecosystem after repeated fires (photo credit: Paulo Brando); bottom right, forest transition to Vismia degraded forest after slash-and-burn agriculture (photo credit: Catarina Jakovac).

White-sand savanna

White-sand savannas are ancient ecosystems that occur in patches within the Amazon forest biome, particularly in seasonally waterlogged or flooded areas 94 . Their origin has been attributed to geomorphological dynamics and past Indigenous fires 26 , 27 , 94 . In a remote landscape far from large agricultural frontiers, within a stable forest region of the Amazon (Extended Data Fig. 1b ), satellite and field evidence revealed that white-sand savannas are expanding where floodplain forests were repeatedly disturbed by fires 95 . After fire, the topsoil of burnt forests changes from clayey to sandy, favouring the establishment of savanna trees and native herbaceous plants 95 . Shifts from forest to white-sand savanna (Fig. 4 ) are probably stable (that is, the ecosystem is unlikely to recover back to forest within centuries), based on the relatively long persistence of these savannas in the landscape 94 . Although these ecosystem transitions have been confirmed only in the Negro river basin (central Amazon), floodplain forests in other parts of the Amazon were shown to be particularly vulnerable to collapse 45 , 64 , 65 .

Degraded open-canopy ecosystem

In bistable regions of the Amazon forest with annual rainfall below 1,800 mm (Extended Data Fig. 1b ), shifts to degraded open-canopy ecosystems are relatively common after repeated disturbances by fire 45 , 96 . The ecosystem often becomes dominated by fire-tolerant tree and palm species, together with alien invasive grasses and opportunistic herbaceous plants 96 , 97 , such as vines and ferns. Estimates from the southern Amazon indicate that 5−6% of the landscape has already shifted into degraded open-canopy ecosystems due to deforestation and fires 45 , 96 . It is still unclear, however, whether degraded open-canopy ecosystems are stable or transient (Fig. 4 ). Palaeorecords from the northern Amazon 98 show that burnt forests may spend centuries in a degraded open-canopy state before they eventually shift into a savanna. Today, invasion by alien flammable grasses is a novel stabilizing mechanism 96 , 97 , but the long-term persistence of these grasses in the ecosystem is also uncertain.

Prospects for modelling Amazon forest dynamics

Several aspects of the Amazon forest system may help improve earth system models (ESMs) to more accurately simulate ecosystem dynamics and feedbacks with the climate system. Simulating individual trees can improve the representation of growth and mortality dynamics, which ultimately affect forest dynamics (for example, refs. 61 , 62 , 99 ). Significant effects on simulation results may emerge from increasing plant functional diversity, representation of key physiological trade-offs and other features that determine water stress on plants, and also allowing for community adjustment to environmental heterogeneity and global change 32 , 55 , 62 , 99 . For now, most ESMs do not simulate a dynamic vegetation cover (Supplementary Table 1 ) and biomes are represented based on few plant functional types, basically simulating monocultures on the biome level. In reality, tree community adaptation to a heterogenous and dynamic environment feeds into the whole-system dynamics, and not covering such aspects makes a true Amazon tipping assessment more challenging.

Our findings also indicate that Amazon forest resilience is affected by compounding disturbances (Fig. 1 ). ESMs need to include different disturbance scenarios and potential synergies for creating more realistic patterns of disturbance regimes. For instance, logging and edge effects can make a forest patch more flammable 39 , but these disturbances are often not captured by ESMs. Improvements in the ability of ESMs to predict future climatic conditions are also required. One way is to identify emergent constraints 100 , lowering ESMs variations in their projections of the Amazonian climate. Also, fully coupled ESMs simulations are needed to allow estimates of land-atmosphere feedbacks, which may adjust climatic and ecosystem responses. Another way to improve our understanding of the critical thresholds for Amazonian resilience and how these link to climatic conditions and to greenhouse gas concentrations is through factorial simulations with ESMs. In sum, although our study may not deliver a set of reliable and comprehensive equations to parameterize processes impacting Amazon forest dynamics, required for implementation in ESMs, we highlight many of the missing modelled processes.

Implications for governance

Forest resilience is changing across the Amazon as disturbance regimes intensify (Fig. 1 ). Although most recent models agree that a large-scale collapse of the Amazon forest is unlikely within the twenty-first century 2 , our findings suggest that interactions and synergies among different disturbances (for example, frequent extreme hot droughts and forest fires) could trigger unexpected ecosystem transitions even in remote and central parts of the system 101 . In 2012, Davidson et al. 102 demonstrated how the Amazon basin was experiencing a transition to a ‘disturbance-dominated regime’ related to climatic and land use changes, even though at the time, annual deforestation rates were declining owing to new forms of governance 103 . Recent policy and approaches to Amazon development, however, accelerated deforestation that reached 13,000 km 2 in the Brazilian Amazon in 2021 ( http://terrabrasilis.dpi.inpe.br ). The southeastern region has already turned into a source of greenhouse gases to the atmosphere 48 . The consequences of losing the Amazon forest, or even parts of it, imply that we must follow a precautionary approach—that is, we must take actions that contribute to maintain the Amazon forest within safe boundaries 12 . Keeping the Amazon forest resilient depends firstly on humanity’s ability to stop greenhouse gas emissions, mitigating the impacts of global warming on regional climatic conditions 2 . At the local scale, two practical and effective actions need to be addressed to reinforce forest–rainfall feedbacks that are crucial for the resilience of the Amazon forest 4 , 37 : (1) ending deforestation and forest degradation; and (2) promoting forest restoration in degraded areas. Expanding protected areas and Indigenous territories can largely contribute to these actions. Our findings suggest a list of thresholds, disturbances and feedbacks that, if well managed, can help maintain the Amazon forest within a safe operating space for future generations.

Our study site was the area of the Amazon basin, considering large areas of tropical savanna biome along the northern portion of the Brazilian Cerrado, the Gran Savana in Venezuela and the Llanos de Moxos in Bolivia, as well as the Orinoco basin to the north, and eastern parts of the Andes to the west. The area includes also high Andean landscapes with puna and paramo ecosystems. We chose this contour to allow better communication with the MapBiomas Amazonian Project (2022; https://amazonia.mapbiomas.org ). For specific interpretation of our results, we considered the contour of the current extension of the Amazon forest biome, which excludes surrounding tropical savanna biomes.

We used the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) data (MOD44B version 6; https://lpdaac.usgs.gov/products/mod44bv006/ ) for the year 2001 at 250-m resolution 124 to reanalyse tree cover distributions within the Amazon basin, refining estimates of bistability ranges and critical thresholds in rainfall conditions from previous studies. Although MODIS VCF can contain errors within lower tree cover ranges and should not be used to test for bistability between grasslands and savannas 125 , the dataset is relatively robust for assessing bistability within the tree cover range of forests and savannas 126 , as also shown by low uncertainty (standard deviation of tree cover estimates) across the Amazon (Extended Data Fig. 8 ).

We used the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS; https://www.chc.ucsb.edu/data/chirps ) 127 to estimate mean annual rainfall and rainfall seasonality for the present across the Amazon basin, based on monthly means from 1981 to 2020, at a 0.05° spatial resolution.

We used the Climatic Research Unit (CRU; https://www.uea.ac.uk/groups-and-centres/climatic-research-unit ) 128 to estimate mean annual temperature for the present across the Amazon basin, based on monthly means from 1981 to 2020, at a 0.5° spatial resolution.

To mask deforested areas until 2020, we used information from the MapBiomas Amazonia Project (2022), collection 3, of Amazonian Annual Land Cover and Land Use Map Series ( https://amazonia.mapbiomas.org ).

To assess forest fire distribution across the Amazon forest biome and in relation to road networks, we used burnt area fire data obtained from the AQUA sensor onboard the MODIS satellite. Only active fires with a confidence level of 80% or higher were selected. The data are derived from MODIS MCD14ML (collection 6) 129 , available in Fire Information for Resource Management System (FIRMS). The data were adjusted to a spatial resolution of 1 km.

Potential analysis

Using potential analysis 130 , an empirical stability landscape was constructed based on spatial distributions of tree cover (excluding areas deforested until 2020; https://amazonia.mapbiomas.org ) against mean annual precipitation, MCWD and DSL. Here we followed the methodology of Hirota et al. 104 . For bins of each of the variables, the probability density of tree cover was determined using the MATLAB function ksdensity. Local maxima of the resulting probability density function are considered to be stable equilibria, in which local maxima below a threshold value of 0.005 were ignored. Based on sensitivity tests (see below), we chose the intermediate values of the sensitivity parameter for each analysis, which resulted in the critical thresholds most similar to the ones previously published in the literature.

Sensitivity tests of the potential analysis

We smoothed the densities of tree cover with the MATLAB kernel smoothing function ksdensity. Following Hirota et al. 104 , we used a flexible bandwidth ( h ) according to Silverman’s rule of thumb 131 : h  = 1.06 σn 1/5 , where σ is the standard deviation of the tree cover distribution and n is the number of points. To ignore small bumps in the frequency distributions, we used a dimensionless sensitivity parameter. This parameter filters out weak modes in the distributions such that a higher value implies a stricter criterion to detect a significant mode. In the manuscript, we used a value of 0.005. For different values of this sensitivity parameter, we here test the estimated critical thresholds and bistability ranges (Extended Data Table 2 ). We inferred stable and unstable states of tree cover (minima and maxima in the potentials) for moving windows of the climatic variables. For mean annual precipitation, we used increments of 10 mm yr −1 between 0 and 3500 mm yr −1 . For dry season length, we used increments of 0.1 months between 0 and 12 months. For MCWD, we used increments of 10 mm between −800 mm and 0 mm.

Transition potential

We quantified a relative ecosystem transition potential across the Amazon forest biome (excluding accumulated deforestation; https://amazonia.mapbiomas.org ) to produce a simple spatial measure that can be useful for governance. For this, we combined information per pixel, at 5 km resolution, about different disturbances related to climatic and human disturbances, as well as high-governance areas within protected areas and Indigenous territories. We used values of significant slopes of the dry season (July–October) mean temperature between 1981 and 2020 ( P  < 0.1), estimated using simple linear regressions (at 0.5° resolution from CRU) (Fig. 1a ). Ecosystem stability classes (stable forest, bistable and stable savanna as in Extended Data Fig. 1 ) were estimated using simple linear regression slopes of annual rainfall between 1981 and 2020 ( P  < 0.1) (at 0.05° resolution from CHIRPS), which we extrapolated to 2050 (Fig. 1b and Extended Data Fig. 3 ). Distribution of areas affected by repeated extreme drought events (Fig. 1c ) were defined when the time series (2001–2018) of the MCWD reached two standard deviation anomalies from historical mean. Extreme droughts were obtained from Lapola et al. 39 , based on Climatic Research Unit gridded Time Series (CRU TS 4.0) datasets for precipitation and evapotranspiration. The network of roads (paved and unpaved) across the Amazon forest biome (Fig. 1d ) was obtained from the Amazon Network of Georeferenced Socio-Environmental Information (RAISG; https://geo2.socioambiental.org/raisg ). Protected areas (PAs) and Indigenous territories (Fig. 1e ) were also obtained from RAISG, and include both sustainable-use and restricted-use protected areas managed by national or sub-national governments, together with officially recognized and proposed Indigenous territories. We combined these different disturbance layers by adding a value for each layer in the following way: (1) slopes of dry season temperature change (as in Fig. 1a , multiplied by 10, thus between −0.1 and +0.6); (2) ecosystem stability classes estimated for year 2050 (as in Fig. 1b ), with 0 for stable forest, +1 for bistable and +2 for stable savanna; (3) accumulated impacts from repeated extreme drought events (from 0 to 5 events), with +0.2 for each event; (4) road-related human impacts, with +1 for pixels within 10 km from a road; and (5) protected areas and Indigenous territories as areas with lower exposure to human (land use) disturbances, such as deforestation and forest fires, with −1 for pixels inside these areas. The sum of these layers revealed relative spatial variation in ecosystem transition potential by 2050 across the Amazon (Fig. 1f ), ranging from −1 (low potential) to 4 (very high potential).

Atmospheric moisture tracking

To determine the atmospheric moisture flows between the Amazonian countries, we use the Lagrangian atmospheric moisture tracking model UTrack 132 . The model tracks the atmospheric trajectories of parcels of moisture, updates their coordinates at each time step of 0.1 h and allocates moisture to a target location in case of precipitation. For each millimetre of evapotranspiration, 100 parcels are released into the atmosphere. Their trajectories are forced with evaporation, precipitation, and wind speed estimates from the ERA5 reanalysis product at 0.25° horizontal resolution for 25 atmospheric layers 133 . Here we use the runs from Tuinenburg et al. 134 , who published monthly climatological mean (2008–2017) moisture flows between each pair of 0.5° grid cells on Earth. We aggregated these monthly flows, resulting in mean annual moisture flows between all Amazonian countries during 2008–2017. For more details of the model runs, we refer to Tuinenburg and Staal 132 and Tuinenburg et al. 134 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All data supporting the findings of this study are openly available and their sources are presented in the Methods.

Science Panel for the Amazon. Amazon Assessment Report 2021 (2021); www.theamazonwewant.org/amazon-assessment-report-2021/ .

IPCC. Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) https://www.ipcc.ch/report/ar6/wg1/#FullReport (Cambridge Univ. Press, 2021).

Armstrong McKay, D. et al. Exceeding 1.5 °C global warming could trigger multiple climate tipping points. Science 377 , abn7950 (2022).

Article   Google Scholar  

Staal, A. et al. Forest-rainfall cascades buffer against drought across the Amazon. Nat. Clim. Change 8 , 539–543 (2018).

Article   ADS   Google Scholar  

Cano, I. M. et al. Abrupt loss and uncertain recovery from fires of Amazon forests under low climate mitigation scenarios. Proc. Natl Acad. Sci. USA 119 , e2203200119 (2022).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Parry, I. M., Ritchie, P. D. L. & Cox, P. M. Evidence of localised Amazon rainforest dieback in CMIP6 models. Earth Syst. Dynam. 13 , 1667–1675 (2022).

Bromham, L. et al. Global predictors of language endangerment and the future of linguistic diversity. Nat. Ecol. Evol. 6 , 163–173 (2022).

Article   PubMed   Google Scholar  

Gomes, V. H. F., Vieira, I. C. G., Salomão, R. P. & ter Steege, H. Amazonian tree species threatened by deforestation and climate change. Nat. Clim. Change 9 , 547–553 (2019).

Cámara-Leret, R., Fortuna, M. A. & Bascompte, J. Indigenous knowledge networks in the face of global change. Proc. Natl Acad. Sci. USA 116 , 9913–9918 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413 , 591–596 (2001).

Article   ADS   CAS   PubMed   Google Scholar  

Rockstrom, J. et al. A safe operating space for humanity. Nature 461 , 472–475 (2009).

Article   ADS   PubMed   Google Scholar  

Scheffer, M. et al. Creating a safe operating space for iconic ecosystems. Science 347 , 1317–1319 (2015).

van Nes, E. H. et al. What do you mean, ‘tipping point’? Trends Ecol. Evol. 31 , 902–904 (2016).

Lenton, T. M. et al. Tipping elements in the Earth’s climate system. Proc. Natl Acad. Sci. USA 105 , 1786–1793 (2008).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Flores, B. M. & Staal, A. Feedback in tropical forests of the Anthropocene. Global Change Biol. 28 , 5041–5061 (2022).

Article   CAS   Google Scholar  

Scheffer, M. Critical Transitions in Nature and Society (Princeton Univ. Press, 2009).

Scheffer, M. et al. Anticipating critical transitions. Science 338 , 344–348 (2012).

Holling, C. S. Engineering Resilience versus Ecological Resilience (National Academy Press, 1996).

Hoorn, C. et al. Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity. Science 330 , 927–931 (2010).

Wang, X. et al. Hydroclimate changes across the Amazon lowlands over the past 45,000 years. Nature 541 , 204–207 (2017).

Kukla, T. et al. The resilience of Amazon tree cover to past and present drying. Global Planet. Change 202 , 103520 (2021).

Clement, C. R. et al. Disentangling domestication from food production systems in the neotropics. Quaternary 4 , 4 (2021).

Mayle, F. E. & Power, M. J. Impact of a drier Early–Mid-Holocene climate upon Amazonian forests. Phil. Trans. R. Soc. B 363 , 1829–1838 (2008).

Article   PubMed   PubMed Central   Google Scholar  

Montoya, E. & Rull, V. Gran Sabana fires (SE Venezuela): a paleoecological perspective. Quat. Sci. Rev. 30 , 3430–3444 (2011).

Rull, V., Montoya, E., Vegas-Vilarrúbia, T. & Ballesteros, T. New insights on palaeofires and savannisation in northern South America. Quat. Sci. Rev. 122 , 158–165 (2015).

Rossetti, D. F. et al. Unfolding long-term Late Pleistocene-Holocene disturbances of forest communities in the southwestern Amazonian lowlands. Ecosphere 9 , e02457 (2018).

Prance, G. T. & Schubart, H. O. R. Notes on the vegetation of Amazonia I. A preliminary note on the origin of the open white sand campinas of the lower Rio Negro. Brittonia 30 , 60 (1978).

Wright, J. L. et al. Sixteen hundred years of increasing tree cover prior to modern deforestation in Southern Amazon and central Brazilian savannas. Glob. Change Biol. 27 , 136–150 (2021).

Article   ADS   CAS   Google Scholar  

van der Sleen, P. et al. No growth stimulation of tropical trees by 150 years of CO 2 fertilization but water-use efficiency increased. Nat. Geosci. 8 , 24–28 (2015).

Smith, M. N. et al. Empirical evidence for resilience of tropical forest photosynthesis in a warmer world. Nat. Plants 6 , 1225–1230 (2020).

Article   CAS   PubMed   Google Scholar  

Marengo, J. A., Jimenez, J. C., Espinoza, J.-C., Cunha, A. P. & Aragão, L. E. O. Increased climate pressure on the agricultural frontier in the Eastern Amazonia–Cerrado transition zone. Sci. Rep. 12 , 457 (2022).

Tavares, J. V. et al. Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests. Nature 617 , 111–117 (2023).

Boulton, C. A., Lenton, T. M. & Boers, N. Pronounced loss of Amazon rainforest resilience since the early 2000s. Nat. Clim. Change 12 , 271–278 (2022).

Doughty, C. E. et al. Tropical forests are approaching critical temperature thresholds. Nature 621 , 105–111 (2023).

Xu, C., Kohler, T. A., Lenton, T. M., Svenning, J.-C. & Scheffer, M. Future of the human climate niche. Proc. Natl Acad. Sci. USA 117 , 11350–11355 (2020).

Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368 , 869–874 (2020).

Zemp, D. C. et al. Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks. Nat. Commun. 8 , 14681 (2017).

Bullock, E. L., Woodcock, C. E., Souza, C. Jr & Olofsson, P. Satellite-based estimates reveal widespread forest degradation in the Amazon. Global Change Biol. 26 , 2956–2969 (2020).

Lapola, D. M. et al. The drivers and impacts of Amazon forest degradation. Science 379 , eabp8622 (2023).

Feng, Y., Negrón-Juárez, R. I., Romps, D. M. & Chambers, J. Q. Amazon windthrow disturbances are likely to increase with storm frequency under global warming. Nat. Commun. 14 , 101 (2023).

Anderson, L. O. et al. Vulnerability of Amazonian forests to repeated droughts. Phil. Trans. R. Soc. B 373 , 20170411 (2018).

Staal, A. et al. Feedback between drought and deforestation in the Amazon. Environ. Res. Lett. 15 , 044024 (2020).

Alencar, A. A., Brando, P. M., Asner, G. P. & Putz, F. E. Landscape fragmentation, severe drought, and the new Amazon forest fire regime. Ecol. Appl. 25 , 1493–1505 (2015).

Aragão, L. E. O. C. et al. 21st century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 9 , 536 (2018).

Silvério, D. V. et al. Intensification of fire regimes and forest loss in the Território Indígena do Xingu. Environ. Res. Lett. 17 , 045012 (2022).

Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519 , 344–348 (2015).

Esquivel‐Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Change Biol. 25 , 39–56 (2019).

Gatti, L. V. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 595 , 388–393 (2021).

Nepstad, D. et al. Inhibition of Amazon deforestation and fire by parks and Indigenous lands: inhibition of Amazon deforestation and fire. Conserv. Biol. 20 , 65–73 (2006).

Botelho, J., Costa, S. C. P., Ribeiro, J. G. & Souza, C. M. Mapping roads in the Brazilian Amazon with artificial intelligence and Sentinel-2. Remote Sensing 14 , 3625 (2022).

Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369 , 1378–1382 (2020).

Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free‐air CO 2 enrichment (FACE)? A meta‐analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO 2 . New Phytol. 165 , 351–372 (2005).

Kooperman, G. J. et al. Forest response to rising CO 2 drives zonally asymmetric rainfall change over tropical land. Nat. Clim. Change 8 , 434–440 (2018).

Lapola, D. M., Oyama, M. D. & Nobre, C. A. Exploring the range of climate biome projections for tropical South America: the role of CO 2 fertilization and seasonality: future biome distribution in South America. Global Biogeochem. Cycles 23 , https://doi.org/10.1029/2008GB003357 (2009).

Brienen, R. J. W. et al. Forest carbon sink neutralized by pervasive growth-lifespan trade-offs. Nat. Commun. 11 , 4241 (2020).

Lammertsma, E. I. et al. Global CO 2 rise leads to reduced maximum stomatal conductance in Florida vegetation. Proc. Natl Acad. Sci. USA 108 , 4035–4040 (2011).

Terrer, C. et al. Nitrogen and phosphorus constrain the CO 2 fertilization of global plant biomass. Nat. Clim. Change 9 , 684–689 (2019).

Ellsworth, D. S. et al. Elevated CO 2 does not increase eucalypt forest productivity on a low-phosphorus soil. Nat. Clim. Change 7 , 279–282 (2017).

Quesada, C. A. et al. Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate. Biogeosciences 9 , 2203–2246 (2012).

Flores, B. M. et al. Soil erosion as a resilience drain in disturbed tropical forests. Plant Soil https://doi.org/10.1007/s11104-019-04097-8 (2020).

Longo, M. et al. Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts. New Phytol. 219 , 914–931 (2018).

Levine, N. M. et al. Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change. Proc. Natl Acad. Sci. USA 113 , 793–797 (2016).

Staver, A. C. et al. Thinner bark increases sensitivity of wetter Amazonian tropical forests to fire. Ecol. Lett. 23 , 99–106 (2020).

Mattos, C. R. C. et al. Double stress of waterlogging and drought drives forest–savanna coexistence. Proc. Natl Acad. Sci. USA 120 , e2301255120 (2023).

Flores, B. M. et al. Floodplains as an Achilles’ heel of Amazonian forest resilience. Proc. Natl Acad. Sci. USA 114 , 4442–4446 (2017).

Marengo, J. A. & Espinoza, J. C. Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts. Int. J. Climatol. 36 , 1033–1050 (2016).

Boers, N., Marwan, N., Barbosa, H. M. J. & Kurths, J. A deforestation-induced tipping point for the South American monsoon system. Sci. Rep. 7 , 41489 (2017).

Alexander, C. et al. Linking Indigenous and scientific knowledge of climate change. BioScience 61 , 477–484 (2011).

Ford, J. D. et al. The resilience of Indigenous peoples to environmental change. One Earth 2 , 532–543 (2020).

Cooper, G. S., Willcock, S. & Dearing, J. A. Regime shifts occur disproportionately faster in larger ecosystems. Nat. Commun. 11 , 1175 (2020).

Drijfhout, S. et al. Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models. Proc. Natl Acad. Sci. USA 112 , E5777–E5786 (2015).

Salazar, L. F. & Nobre, C. A. Climate change and thresholds of biome shifts in Amazonia: climate change and Amazon biome shift. Geophys. Res. Lett. 37 , https://doi.org/10.1029/2010GL043538 (2010).

Jones, C., Lowe, J., Liddicoat, S. & Betts, R. Committed terrestrial ecosystem changes due to climate change. Nat. Geosci. 2 , 484–487 (2009).

Schellnhuber, H. J., Rahmstorf, S. & Winkelmann, R. Why the right climate target was agreed in Paris. Nat. Clim. Change 6 , 649–653 (2016).

Chai, Y. et al. Constraining Amazonian land surface temperature sensitivity to precipitation and the probability of forest dieback. npj Clim. Atmos. Sci. 4 , 6 (2021).

Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought-fire interactions. Proc. Natl Acad. Sci. USA 111 , 6347–6352 (2014).

Berenguer, E. et al. Tracking the impacts of El Niño drought and fire in human-modified Amazonian forests. Proc. Natl Acad. Sci. USA 118 , e2019377118 (2021).

Staal, A. et al. Hysteresis of tropical forests in the 21st century. Nat. Commun. 11 , 4978 (2020).

Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334 , 230–232 (2011).

Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106 , 20610–20615 (2009).

Nobre, C. A. et al. Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proc. Natl Acad. Sci. USA 113 , 10759–10768 (2016).

Burton, C. et al. South American fires and their impacts on ecosystems increase with continued emissions. Clim. Resil. Sustain. 1 , e8 (2022).

Google Scholar  

Smith, C. C. et al. Old-growth forest loss and secondary forest recovery across Amazonian countries. Environ. Res. Lett. 16 , 085009 (2021).

Brando, P. M. et al. Prolonged tropical forest degradation due to compounding disturbances: Implications for CO 2 and H 2 O fluxes. Glob. Change Biol. 25 , 2855–2868 (2019).

Mesquita, R. C. G., Ickes, K., Ganade, G. & Williamson, G. B. Alternative successional pathways in the Amazon Basin: successional pathways in the Amazon. J. Ecol. 89 , 528–537 (2001).

Jakovac, C. C., Peña-Claros, M., Kuyper, T. W. & Bongers, F. Loss of secondary-forest resilience by land-use intensification in the Amazon. J. Ecol. 103 , 67–77 (2015).

Barlow, J. & Peres, C. A. Fire-mediated dieback and compositional cascade in an Amazonian forest. Phil. Trans. R. Soc. B 363 , 1787–1794 (2008).

Jakovac, A. C. C., Bentos, T. V., Mesquita, R. C. G. & Williamson, G. B. Age and light effects on seedling growth in two alternative secondary successions in central Amazonia. Plant Ecol. Divers. 7 , 349–358 (2014).

Mazzochini, G. G. & Camargo, J. L. C. Understory plant interactions along a successional gradient in Central Amazon. Plant Soil https://doi.org/10.1007/s11104-019-04100-2 (2020).

Schnitzer, S. A. & Bongers, F. Increasing liana abundance and biomass in tropical forests: emerging patterns and putative mechanisms: Increasing lianas in tropical forests. Ecology Letters 14 , 397–406 (2011).

Tymen, B. et al. Evidence for arrested succession in a liana-infested Amazonian forest. J Ecol 104 , 149–159 (2016).

da Silva, S. S. et al. Increasing bamboo dominance in southwestern Amazon forests following intensification of drought-mediated fires. For. Ecol. Manag. 490 , 119139 (2021).

Carvalho, A. Lde et al. Bamboo-dominated forests of the southwest Amazon: detection, spatial extent, life cycle length and flowering waves. PLoS ONE 8 , e54852 (2013).

Adeney, J. M., Christensen, N. L., Vicentini, A. & Cohn‐Haft, M. White‐sand ecosystems in Amazonia. Biotropica 48 , 7–23 (2016).

Flores, B. M. & Holmgren, M. White-sand savannas expand at the core of the Amazon after forest wildfires. Ecosystems 24 , 1624–1637 (2021).

Veldman, J. W. & Putz, F. E. Grass-dominated vegetation, not species-diverse natural savanna, replaces degraded tropical forests on the southern edge of the Amazon Basin. Biol. Conserv. 144 , 1419–1429 (2011).

Silvério, D. V. et al. Testing the Amazon savannization hypothesis: fire effects on invasion of a neotropical forest by native cerrado and exotic pasture grasses. Phil. Trans. R. Soc. B 368 , 20120427 (2013).

Rull, V. A palynological record of a secondary succession after fire in the Gran Sabana, Venezuela. J. Quat. Sci. 14 , 137–152 (1999).

Sakschewski, B. et al. Resilience of Amazon forests emerges from plant trait diversity. Nat. Clim. Change 6 , 1032–1036 (2016).

Hall, A., Cox, P., Huntingford, C. & Klein, S. Progressing emergent constraints on future climate change. Nat. Clim. Change 9 , 269–278 (2019).

Willcock, S., Cooper, G. S., Addy, J. & Dearing, J. A. Earlier collapse of Anthropocene ecosystems driven by multiple faster and noisier drivers. Nat. Sustain 6 , 1331–1342 (2023).

Davidson, E. A. et al. The Amazon basin in transition. Nature 481 , 321–328 (2012).

Hecht, S. B. From eco-catastrophe to zero deforestation? Interdisciplinarities, politics, environmentalisms and reduced clearing in Amazonia. Envir. Conserv. 39 , 4–19 (2012).

Hirota, M., Holmgren, M., Van Nes, E. H. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334 , 232–235 (2011).

Hawes, J. E. et al. A large‐scale assessment of plant dispersal mode and seed traits across human‐modified Amazonian forests. J. Ecol. 108 , 1373–1385 (2020).

Flores, B. M. & Holmgren, M. Why forest fails to recover after repeated wildfires in Amazonian floodplains? Experimental evidence on tree recruitment limitation. J. Ecol. 109 , 3473–3486 (2021).

ter Steege, H. et al. Biased-corrected richness estimates for the Amazonian tree flora. Sci. Rep. 10 , 10130 (2020).

Poorter, L. et al. Diversity enhances carbon storage in tropical forests: Carbon storage in tropical forests. Global Ecol. Biogeogr. 24 , 1314–1328 (2015).

Walker, B., Kinzig, A. & Langridge, J. Plant attribute diversity, resilience, and ecosystem function: the nature and significance of dominant and minor species. Ecosystems 2 , 95–113 (1999).

Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1 , 488–494 (2003).

Esquivel-Muelbert, A. et al. Seasonal drought limits tree species across the Neotropics. Ecography 40 , 618–629 (2017).

Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333 , 301–306 (2011).

Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1 , 369–374 (2018).

Morcote-Ríos, G., Aceituno, F. J., Iriarte, J., Robinson, M. & Chaparro-Cárdenas, J. L. Colonisation and early peopling of the Colombian Amazon during the Late Pleistocene and the Early Holocene: new evidence from La Serranía La Lindosa. Quat. Int. 578 , 5–19 (2021).

Levis, C. et al. How people domesticated Amazonian forests. Front. Ecol. Evol. 5 , 171 (2018).

Clement, C. R. et al. The domestication of Amazonia before European conquest. Proc. R. Soc. B. 282 , 20150813 (2015).

Levis, C. et al. Persistent effects of pre-Columbian plant domestication on Amazonian forest composition. Science 355 , 925–931 (2017).

Coelho, S. D. et al. Eighty-four per cent of all Amazonian arboreal plant individuals are useful to humans. PLoS ONE 16 , e0257875 (2021).

de Souza, J. G. et al. Climate change and cultural resilience in late pre-Columbian Amazonia. Nat. Ecol. Evol. 3 , 1007–1017 (2019).

Furquim, L. P. et al. Facing change through diversity: resilience and diversification of plant management strategies during the Mid to Late Holocene Transition at the Monte Castelo shellmound, SW Amazonia. Quaternary 4 , 8 (2021).

Schmidt, M. V. C. et al. Indigenous knowledge and forest succession management in the Brazilian Amazon: contributions to reforestation of degraded areas. Front. For. Glob. Change 4 , 605925 (2021).

Tomioka Nilsson, M. S. & Fearnside, P. M. Yanomami mobility and its effects on the forest landscape. Hum. Ecol. 39 , 235–256 (2011).

Cámara-Leret, R. & Bascompte, J. Language extinction triggers the loss of unique medicinal knowledge. Proc. Natl Acad. Sci. USA 118 , e2103683118 (2021).

DiMiceli, C. et al. MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250 m SIN Grid V006. https://doi.org/10.5067/MODIS/MOD44B.006 (2015).

Sexton, J. O. et al. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int. J. Digital Earth 6 , 427–448 (2013).

Staver, A. C. & Hansen, M. C. Analysis of stable states in global savannas: is the CART pulling the horse? – a comment. Global Ecol. Biogeogr. 24 , 985–987 (2015).

Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2 , 150066 (2015).

Mitchell, T. D. & Jones, P. D. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. 25 , 693–712 (2005).

Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178 , 31–41 (2016).

Livina, V. N., Kwasniok, F. & Lenton, T. M. Potential analysis reveals changing number of climate states during the last 60 kyr. Clim. Past 6 , 77–82 (2010).

Silverman, B. W. Density Estimation for Statistics and Data Analysis (Chapman & Hall/CRC Taylor & Francis Group, 1998).

Tuinenburg, O. A. & Staal, A. Tracking the global flows of atmospheric moisture and associated uncertainties. Hydrol. Earth Syst. Sci. 24 , 2419–2435 (2020).

Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146 , 1999–2049 (2020).

Tuinenburg, O. A., Theeuwen, J. J. E. & Staal, A. High-resolution global atmospheric moisture connections from evaporation to precipitation. Earth Syst. Sci. Data 12 , 3177–3188 (2020).

Oliveira, R. S. et al. Embolism resistance drives the distribution of Amazonian rainforest tree species along hydro‐topographic gradients. New Phytol. 221 , 1457–1465 (2019).

Mattos, C. R. C. et al. Rainfall and topographic position determine tree embolism resistance in Amazônia and Cerrado sites. Environ. Res. Lett. 18 , 114009 (2023).

NASA JPL. NASA Shuttle Radar Topography Mission Global 1 arc second. https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003 (2013).

Hess, L. L. et al. Wetlands of the Lowland Amazon Basin: Extent, Vegetative Cover, and Dual-season Inundated Area as Mapped with JERS-1 Synthetic Aperture Radar. Wetlands 35 , 745–756 (2015).

Eberhard, D. M., Simons, G. F. & Fennig, C. D. Ethnologue: Languages of the World . (SIL International, 2021).

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Acknowledgements

This work was inspired by the Science Panel for the Amazon (SPA) initiative ( https://www.theamazonwewant.org/ ) that produced the first Amazon Assessment Report (2021). The authors thank C. Smith for providing deforestation rates data used in Extended Data Fig. 5b . B.M.F. and M.H. were supported by Instituto Serrapilheira (Serra-1709-18983) and C.J. (R-2111-40341). A.S. acknowledges funding from the Dutch Research Council (NWO) under the Talent Program Grant VI.Veni.202.170. R.A.B. and D.M.L. were supported by the AmazonFACE programme funded by the UK Foreign, Commonwealth and Development Office (FCDO) and Brazilian Ministry of Science, Technology and Innovation (MCTI). R.A.B. was additionally supported by the Met Office Climate Science for Service Partnership (CSSP) Brazil project funded by the UK Department for Science, Innovation and Technology (DSIT), and D.M.L. was additionally supported by FAPESP (grant no. 2020/08940-6) and CNPq (grant no. 309074/2021-5). C.L. thanks CNPq (proc. 159440/2018-1 and 400369/2021-4) and Brazil LAB (Princeton University) for postdoctoral fellowships. A.E.-M. is supported by the UKRI TreeScapes MEMBRA (NE/V021346/1), the Royal Society (RGS\R1\221115), the ERC TreeMort project (758873) and the CESAB Syntreesys project. R.S.O. received a CNPq productivity scholarship and funding from NERC-FAPESP 2019/07773-1. S.B.H. is supported by the Geneva Graduate Institute research funds, and UCLA’s committee on research. J.A.M. is supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq grant 465501/2014-1; FAPESP grants 2014/50848-9, the National Coordination for Higher Education and Training (CAPES) grant 88887.136402-00INCT. L.S.B. received FAPESP grant 2013/50531-0. D.N. and N.B. acknowledge funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 820970. N.B. has received further funding from the Volkswagen foundation, the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 956170, as well as from the German Federal Ministry of Education and Research under grant no. 01LS2001A.

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Graduate Program in Ecology, Federal University of Santa Catarina, Florianopolis, Brazil

Bernardo M. Flores, Carolina Levis & Marina Hirota

Geosciences Barcelona, Spanish National Research Council, Barcelona, Spain

Encarni Montoya

Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany

Boris Sakschewski, Da Nian & Niklas Boers

Institute of Advanced Studies, University of São Paulo, São Paulo, Brazil

Nathália Nascimento & Carlos A. Nobre

Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands

Met Office Hadley Centre, Exeter, UK

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Adriane Esquível-Muelbert

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Catarina Jakovac

Department of Plant Biology, University of Campinas, Campinas, Brazil

Rafael S. Oliveira & Marina Hirota

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Luskin School for Public Affairs and Institute of the Environment, University of California, Los Angeles, CA, USA

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Contributions

B.M.F. and M.H. conceived the study. B.M.F. reviewed the literature, with inputs from all authors. B.M.F., M.H., N.N., A.S., C.L., D.N, H.t.S. and C.R.C.M. assembled datasets. M.H. analysed temperature and rainfall trends. B.M.F. and N.N. produced the maps in main figures and calculated transition potential. A.S. performed potential analysis and atmospheric moisture tracking. B.M.F. produced the figures and wrote the manuscript, with substantial inputs from all authors. B.S. wrote the first version of the ‘Prospects for modelling Amazon forest dynamics’ section, with inputs from B.M.F and M.H.

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Correspondence to Bernardo M. Flores or Marina Hirota .

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Extended data figures and tables

Extended data fig. 1 alternative stable states in amazonian tree cover relative to rainfall conditions..

Potential analysis of tree cover distributions across rainfall gradients in the Amazon basin suggest the existence of critical thresholds and alternative stable states in the system. For this, we excluded accumulated deforestation until 2020 and included large areas of tropical savanna biome in the periphery of the Amazon basin (see  Methods ). Solid black lines indicate two stable equilibria. Small grey arrows indicate the direction towards equilibrium. (a) The overlap between ~ 1,000 and 1,800 mm of annual rainfall suggests that two alternative stable states may exist (bistability): a high tree cover state ~ 80 % (forests), and a low tree cover state ~ 20% (savannas). Tree cover around 50 % is rare, indicating an unstable state. Below 1,000 mm of annual rainfall, forests are rare, indicating a potential critical threshold for abrupt forest transition into a low tree cover state 79 , 104 (arrow 1). Between 1,000 and 1,800 mm of annual rainfall, the existence of alternative stable states implies that forests can shift to a low tree cover stable state in response to disturbances (arrow 2). Above 1,800 mm of annual rainfall, low tree cover becomes rare, indicating a potential critical threshold for an abrupt transition into a high tree cover state. In this stable forest state, forests are expected to always recover after disturbances (arrow 3), although composition may change 47 , 85 . (b) Currently, the stable savanna state covers 1 % of the Amazon forest biome, bistable areas cover 13 % of the biome (less than previous analysis using broader geographical ranges 78 ) and the stable forest state covers 86 % of the biome. Similar analyses using the maximum cumulative water deficit (c) and the dry season length (d) also suggest the existence of critical thresholds and alternative stable states. When combined, these critical thresholds in rainfall conditions could result in a tipping point of the Amazon forest in terms of water stress, but other factors may play a role, such as groundwater availability 64 . MODIS VCF may contain some level of uncertainty for low tree cover values, as shown by the standard deviation of tree cover estimates across the Amazon (Extended Data Fig. 8 ). However, the dataset is relatively robust for assessing bistability within the tree cover range between forest and savanna 126 .

Extended Data Fig. 2 Changes in dry-season temperatures across the Amazon basin.

(a) Dry season temperature averaged from mean annual data observed between 1981 and 2010. (b) Changes in dry season mean temperature based on the difference between the projected future (2021−2050) and observed historical (1981−2010) climatologies. Future climatology was obtained from the estimated slopes using historical CRU data 128 (shown in Fig. 1a ). (c, d) Changes in the distributions of dry season mean and maximum temperatures for the Amazon basin. (e) Correlation between dry-season mean and maximum temperatures observed (1981–2010) across the Amazon basin ( r  = 0.95).

Extended Data Fig. 3 Changes in annual precipitation and ecosystem stability across the Amazon forest biome.

(a) Slopes of annual rainfall change between 1981 and 2020 estimated using simple regressions (only areas with significant slopes, p  < 0.1). (b) Changes in ecosystem stability classes projected for year 2050, based on significant slopes in (a) and critical thresholds in annual rainfall conditions estimated in Extended Data Fig. 1 . Data obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), at 0.05° spatial resolution 127 .

Extended Data Fig. 4 Changes in ecosystem stability by 2050 across the Amazon based on annual rainfall projected by CMIP6 models.

(a) Changes in stability classes estimated using an ensemble with the five CMIP6 models that include vegetation modules (coupled for climate-vegetation feedbacks) for two emission scenarios (Shared Socio-economic Pathways - SSPs). (b) Changes in stability classes estimated using an ensemble with all 33 CMIP6 models for the same emission scenarios. Stability changes may occur between stable forest (F), stable savanna (S) and bistable (B) classes, based on the bistability range of 1,000 – 1,800 mm in annual rainfall, estimated from current rainfall conditions (see Extended Data Fig. 1 ). Projections are based on climate models from the 6 th Phase of the Coupled Model Intercomparison Project (CMIP6). SSP2-4.5 is a low-emission scenario of future global warming and SSP5-8.5 is a high-emission scenario. The five coupled models analysed separately in (a) were: EC-Earth3-Veg, GFDL-ESM4, MPI-ESM1-2-LR, TaiESM1 and UKESM1-0-LL (Supplementary Information Table 1 ).

Extended Data Fig. 5 Deforestation continues to expand within the Amazon forest system.

(a) Map highlighting deforestation and fire activity between 2012 and 2021, a period when environmental governance began to weaken again, as indicated by increasing rates of annual deforestation in (b). In (b), annual deforestation rates for the entire Amazon biome were adapted with permission from Smith et al. 83 .

Extended Data Fig. 6 Environmental heterogeneity in the Amazon forest system.

Heterogeneity involves myriad factors, but two in particular, related to water availability, were shown to contribute to landscape-scale heterogeneity in forest resilience; topography shapes fine-scale variations of forest drought-tolerance 135 , 136 , and floodplains may reduce forest resilience by increasing vulnerability to wildfires 65 . Datasets: topography is shown by the Shuttle Radar Topography Mission (SRTM; https://earthexplorer.usgs.gov/ ) 137 at 90 m resolution; floodplains and uplands are separated with the Amazon wetlands mask 138 at 90 m resolution.

Extended Data Fig. 7 The Amazon is biologically and culturally diverse.

(a) Tree species richness and (b) language richness illustrate how biological and cultural diversity varies across the Amazon. Diverse tree communities and human cultures contribute to increasing forest resilience in various ways that are being undermined by land-use and climatic changes. Datasets: (a) Amazon Tree Diversity Network (ATDN, https://atdn.myspecies.info ). (b) World Language Mapping System (WLMS) obtained under license from Ethnologue 139 .

Extended Data Fig. 8 Uncertainty of the MODIS VCF dataset across the Amazon basin.

Map shows standard deviation (SD) of tree cover estimates from MODIS VCF 124 . We masked deforested areas until 2020 using the MapBiomas Amazonia Project (2022; https://amazonia.mapbiomas.org ).

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Flores, B.M., Montoya, E., Sakschewski, B. et al. Critical transitions in the Amazon forest system. Nature 626 , 555–564 (2024). https://doi.org/10.1038/s41586-023-06970-0

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High Operating Costs and Legacy Liabilities Among Bankruptcy Drivers for US Airlines

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Related Content: Airline and Transportation Bankruptcy Enterprise Values and Creditor Recoveries (2024 Fitch Case Studies) Fitch Ratings-New York/Toronto-22 February 2024: Bankruptcies for airlines were historically driven by operational costs; however, some bankruptcies, including Western Global who filed in 2023, filed to focus on debt reduction, according to a new Fitch Ratings report. “Most airline bankruptcy filings tend to be focused on mitigating operational costs. This includes lowering labor costs associated with collective bargaining contracts, increasing worker productivity or contract rejections, revision of unprofitable customer supply and equipment financing contracts, and elimination of overcapacity,” said Judah Gross, Senior Director. However, there are some airline filers who enter Chapter 11 to address capital structure challenges in addition to operational issues. Notwithstanding the specific bankruptcy trigger, the vast majority of airline filers emerge as going concerns. Twenty one out of 23 airline and transportation sector bankruptcy cases were resolved as going concerns, although several of these companies sold all assets during bankruptcy to another operator. The median multiple of reorganization enterprise value/forward EBITDA forecast for the cases analyzed was 6.1x, with a range of 3.0x-12.5x. The multiples for the 11 going concern airlines were 3.0x to 9.0x. The median debt reduction was 44% for the airline and transportation cases, compared with 76% in Fitch’s cross-sector corporate bankruptcy database. The average ultimate recovery rate on the 53 first-lien issue claims compiled from previous editions of this study was 86%. New addition to this study, Western Global, was an outlier with 37% recoveries for revolver lenders and 54% recoveries for first-lien term loan lenders. The company is a small independent cargo operator that experienced operational weakness from high pilot attrition rates and an aged fleet. For more information, a special report titled “Airline and Transportation Bankruptcy Enterprise Values and Creditor Recoveries” is available on the Fitch website at ‘ www.fitchratings.com ’ or by clicking on the link above. Contact: Judah Gross Senior Director +1 212 908-0884 [email protected] Fitch Ratings, Inc. 33 Whitehall Street New York, NY 10004 Media Relations: Eleis Brennan, New York, Tel: +1 646 582 3666, Email: [email protected] Additional information is available on www.fitchratings.com All Fitch Ratings (Fitch) credit ratings are subject to certain limitations and disclaimers. Please read these limitations and disclaimers by following this link: https://www.fitchratings.com/understandingcreditratings . 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case study in action research

IMAGES

  1. Difference Between Action Research and Case Study

    case study in action research

  2. Action Research Case Study Part 1 Case Study Sample

    case study in action research

  3. research methods vs case study

    case study in action research

  4. (PDF) An Action Research Case Study on Students' Diversity in the

    case study in action research

  5. (PDF) Researcher-practitioner partnerships and crime analysis: A case

    case study in action research

  6. (PDF) The Researcher-Participant Relationship In Action Research: A

    case study in action research

VIDEO

  1. Developing a Research Question

  2. Case Studies

  3. Research based case history and protocols

  4. How to design action research?

  5. Action Research Workshop 09-01-2024 Day1 Part1

  6. ACTION RESEARCH FILE/2023-2024/B.ED/#knowledge #practical

COMMENTS

  1. Action research in education: a set of case studies?

    A case study, described as an umbrella term, focuses on understanding classroom situations in real contexts. Although it seems that the defining characteristics of the case study are common among different authors, there are numerous classifications, which sometimes overlap.

  2. What Is Action Research?

    Action research is a research method that aims to simultaneously investigate and solve an issue. In other words, as its name suggests, action research conducts research and takes action at the same time.

  3. An action research case study: digital equity and educational inclusion

    An action research case study: digital equity and educational inclusion during an emergent COVID-19 divide Joyce Pittman, Lori Severino, Mary Jean DeCarlo-Tecce, Cameron Kiosoglous Journal for Multicultural Education ISSN: 2053-535X Article publication date: 22 January 2021 Permissions Issue publication date: 4 June 2021 Downloads 5607 Abstract

  4. Action Research by Practitioners: A Case Study of a High School's

    Action Research by Practitioners: A Case Study of a High School's Attempt to Create Transformational Change Jeffrey Glanz Yeshiva University and Michlalah-Jerusalem College, [email protected] Follow this and additional works at: https://digitalcommons.usf.edu/jpr Part of the Curriculum and Instruction Commons, and the Educational Methods Commons

  5. Action research in education: a set of case studies?

    We define this study as a multi-case study approach based on two rationales, namely that (1) the nature of action research's reflective cycles is not present in our study, and (2) the...

  6. Action Research and Systematic, Intentional Change in Teaching Practice

    Action researchers engage in "systematic and intentional inquiry" (Cochran-Smith & Lytle, 2009, p. 142) or "systematic, self-critical enquiry" (Stenhouse, 1985).The focus is on bringing about change in practice, improving student outcomes, and empowering teachers (Mills, 2017).Following a cycle of inquiry and reflection, action researchers collect and analyze data related to an issue(s ...

  7. JME Anactionresearchcasestudy: digitalequityandeducational

    content and one research site. The data collection was limited to written responses from the faculty participants. This action research study took place in a time frame limited by COVID-19 conditions during a four-monthperiod. Practical implications - In theory and practice, this new online movement suggests learners, teachers,

  8. Doing Participatory Action Research in a Multicase Study

    Case Study and Multicase Study Research. Case study research has been defined as an intensive study of one case to better understand a population or larger class of cases (Gerring, 2007). Case studies can examine individual. Department of Sociology and Social Studies, University of Regina, Regina, Saskatchewan, Canada.

  9. A Case Study of Participatory Action Research in a Public New England

    Action research is recognized as an empowering process that allows educators to assess their practices, examine how they might improve and develop their skills, and problem solve classroom issues. However, it appeared in this case that the action research requirement was perceived, at least in part, by the teachers, as another means of ...

  10. Participatory Action Research: a case study on the school

    The present case study of a rural school in the Valencian Community (Spain) takes an ethnographic approach to analyse how PAR strategies and processes facilitate the democratisation process and the educational community's perceptions of the transformations in the school culture.

  11. Action Research vs. Case Study

    Introduction Action research and case study are two widely used research methodologies in various fields. While both approaches aim to gain insights and understanding, they differ in their focus, design, and implementation. This article will explore the attributes of action research and case study, highlighting their similarities and differences.

  12. Case Study Methodology of Qualitative Research: Key Attributes and

    1. Case study is a research strategy, and not just a method/technique/process of data collection. 2. A case study involves a detailed study of the concerned unit of analysis within its natural setting.

  13. What's the difference between action research and a case study?

    Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon. Frequently asked questions: Methodology What is differential attrition? What is the main purpose of action research?

  14. How School Teachers Can Benefit from Action Research: A Case Study

    involved with action research for conducting their study. In that case action research can involve a single teacher investigation, a group of teachers working on a common problem, a team of teachers working on a school- or district-wide issue (IGNOU-MHRD project, New Delhi). Now we present a comparative study on types of action research according

  15. A scoping review of action research in higher education: implications

    ABSTRACT Several scholars argue for a closer association between research and teaching in higher education, but it is unclear how research-based teaching can be actualized. Action research (AR) offers designs that position students as actors of the research processes, for example by doing research themselves or co-researching.

  16. (PDF) Action case study

    Action case study, initially referred to as action case (Braa et al. 1994), stem s from the research of information systems in the mid 90s (Braa & Vidgen 1 995). Today, the term refers to a hybrid ...

  17. Difference Between Action Research and Case Study

    The main difference between action research and case study is their purpose; an action research study aims to solve an immediate problem whereas a case study aims to provide an in-depth analysis of a situation or case over a long period of time. 1. What is Action Research? - Definition, Features, Purpose, Process 2. What is Case Study?

  18. Case Study With a Participatory Approach: Rethinking Pragmatics of

    BACKGROUND. Over the last 40 years, case study research has become increasingly popular and has evolved rapidly in many disciplines. By allowing in-depth analysis of complex phenomena in real-world contexts, 1 the case study design is particularly useful in health services research, 2 for implementation analysis of complex interventions that can be influenced by the context of dynamic ...

  19. View of Creating a Wider Audience for Action Research: Learning from

    Both case-study research and action research are concerned with the researcher's gaining an in-depth understanding of particular phenomena in real-world settings. The two types of research seem quite similar in their focus on the field or the world of action, while embracing considerable diversity in theory and practice. ...

  20. The action research case study approach: A methodology for complex

    Action research was chosen to investigate the interface between economic and environmental factors in the aviation sector. A variant of the methodology was developed which combined the ethos of action research with the prescriptive mechanism of case study analysis.

  21. What Is a Case Study?

    Step 1: Select a case Once you have developed your problem statement and research questions, you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to: Provide new or unexpected insights into the subject Challenge or complicate existing assumptions and theories

  22. Difference between Case Study and Action research

    1. Action Research : Action Research is a type of qualitative research. As the name suggests it is more action oriented in order to solve an immediate problem. Action research helps the researcher to improvise its current practices and is applied for researching into issues.

  23. Sustaining the collaborative chronic care model in outpatient mental

    Sustaining evidence-based practices (EBPs) is crucial to ensuring care quality and addressing health disparities. Approaches to identifying factors related to sustainability are critically needed. One such approach is Matrixed Multiple Case Study (MMCS), which identifies factors and their combinations that influence implementation. We applied MMCS to identify factors related to the ...

  24. Critical transitions in the Amazon forest system

    An ensemble with the 5 coupled models that include a dynamic vegetation module indicates that 18-27% of the biome may transition from stable forest to bistable and that 2-6% may transition to ...

  25. Case Study Method: A Step-by-Step Guide for Business Researchers

    Case study method is the most widely used method in academia for researchers interested in qualitative research ( Baskarada, 2014 ). Research students select the case study as a method without understanding array of factors that can affect the outcome of their research.

  26. High Operating Costs and Legacy Liabilities Among Bankruptcy Drivers

    Related Content: Airline and Transportation Bankruptcy Enterprise Values and Creditor Recoveries (2024 Fitch Case Studies) Fitch Ratings-New York/Toronto-22 February 2024: Bankruptcies for airlines were historically driven by operational costs; however, some bankruptcies, including Western Global who filed in 2023, filed to focus on debt reduction, according to a new Fitch Ratings report.

  27. Water

    The action of freeze-thaw (F-T) cycles of claystone exerts a profound impact on the slope stability of open-pit mines in water-rich regions. Microstructural changes are observed as a crucial factor in determining the hydraulic characteristics and mechanical behaviors of claystone. The present work integrates a micro-X-ray computed tomography (Micro-CT) scanner, equipped with image ...