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Der Artikel geht auf die entscheidende Rolle von Entscheidungshilfen bei der Wiederherstellung der Everglades in Florida ein, insbesondere im Zusammenhang mit dem Anstieg des Meeresspiegels (SLR). Es untersucht die Herausforderungen, vor denen Restaurationspartner bei der Integration von SLR-Projektionen in ihre Planungsprozesse stehen, und die Grenzen aktueller Modellierungswerkzeuge. Der Artikel unterstreicht die Notwendigkeit von Werkzeugen, die dazu beitragen können, ökologische Schwellenwerte zu ermitteln und die Widerstandsfähigkeit über räumliche und zeitliche Skalen hinweg zu bewerten. Außerdem wird die Bedeutung von Koproduktionsprozessen diskutiert, die die Endnutzer in die Entwicklung von DSTs einbeziehen, um ihre Relevanz und Benutzerfreundlichkeit sicherzustellen. Die Forschungsergebnisse deuten darauf hin, dass die Komponenten des Innovationsentscheidungsprozesses, wie relativer Vorteil, Kompatibilität, Komplexität, Testbarkeit und Beobachtbarkeit, wichtige Überlegungen für die Absicht der Nutzer sind, DSTs einzuführen. Der Artikel schließt mit der Betonung der Notwendigkeit starker Rahmenwerke und Werkzeuge zur Anpassung an den Klimawandel, um den Managern natürlicher Ressourcen zu helfen, sich in den sich wandelnden Realitäten des Klimawandels zurechtzufinden.
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Abstract
Although coastal ecosystems are impacted by climate change and sea-level rise, many ecological and hydrological models do not yet incorporate sea-level rise projections in their modeling outputs. Therefore, this research examined the various challenges that may prevent sea-level rise from being effectively incorporated in modeling and decision-support tools. We conducted semi-structured interviews with twenty-six professionals involved in Florida’s Everglades restoration. We applied the Diffusions of Innovations Theory to better understand factors that can impact practitioners’ adoption of newly designed decision-support tools that examine sea-level rise in the freshwater Everglades. The Diffusions of Innovations Theory provided insights into practitioners’ perceptions of these tools. We found that these practitioners have a strong interest in using dynamic decision-support tools to plan for sea-level rise impacts on Everglades restoration, particularly when they receive information at appropriate geographic and temporal scales and are given hands-on tools and training. However, challenges that prevent developing these tools include outdated data, limited organizational capacity and funding, limited use of long-term indicators, uncertainty about climate change impacts on local ecosystems, and lack of integration between hydrological and ecological models. Our research also highlights that greater availability of different types of tools can help to meet the needs of the scientific and non-scientific audiences involved in Everglades restoration.
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Introduction
Globally, coastal wetlands are susceptible to negative impacts of climate change because their position at the land-sea interface renders them vulnerable to sea-level rise (SLR) and intense storm events (Osland et al. 2016). This vulnerability compromises the ability of coastal freshwater wetlands to provide important ecosystem services, including carbon sequestration, water filtration and supply, flood control, coastal protection, and recreational opportunities (White et al. 2010). Therefore, decision-support tools (DSTs) are often used to assess the vulnerability of coastal and aquatic systems to climate change and manage these resources in changing socio-ecological conditions (Welch et al. 2020; Gibble et al. 2020).
Decision-support tools are intended to help restoration managers use the best available science to weigh their options and understand the potential consequences of their actions (Matthies et al. 2007; Palutikof et al. 2019). Decision-support tools can be grouped into three general categories: (1) processes and frameworks; (2) data and models; and (3) geospatial or web-based tools, which allow users to interact with data in ways that suit their own needs (Fanok et al. 2022; Palutikof et al. 2019). Although DSTs are often used for planning and decision-making in landscape-level conservation efforts (Gibble et al. 2020; Fanok et al. 2022), little is known about their effectiveness or how well they meet users’ needs (Palutikof et al. 2019). In addition, DSTs are sometimes developed without examining the behavioral, cultural, institutional, and cognitive context of users, which may lead to the tools being underutilized (Wardropper et al. 2021). In addition, there is often insufficient communication between tool developers and end-users, especially regarding how tools could be used to inform management actions (Schwartz et al. 2018; Fanok et al. 2022).
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Decision-support tools are critical for restoration of the Everglades in Florida, USA (Gibble et al. 2020), where changing climatic conditions and restoration activities are expected to significantly impact wildlife and plant populations and ultimately lead to novel ecosystems (Catano et al. 2015). The Greater Everglades (28,000 km2), which mainly comprises inland freshwater and estuarine coastal wetlands that span much of Central and South Florida, has been highly altered and severely degraded by development and other human activity (Light and Dineen, 1994). Efforts in the mid-20th century to drain large sections of the Everglades via a system of canals, dikes, and levees greatly restricted freshwater flow, destroying many Everglades habitats and significantly reducing wildlife populations (Light and Dineen, 1994). Today, the Everglades has been reduced to less than half its historic size (Davis et al. 1994).
In 2000, the United States Congress authorized the Comprehensive Everglades Restoration Plan (CERP; Water Resources Development Act of 2000, Public Law No. 106 − 541), a federally mandated Everglades restoration program that involves interested parties from federal, state, and local governments, tribal organizations, institutions of higher education, nongovernmental organizations, and nonprofit organizations (Gibble et al. 2020). Specifically, CERP oversight is a joint responsibility between the federal agency U.S. Army Corps of Engineers (USACE) and the state agency South Florida Water Management District (SFWMD); the former now requires SLR projections to be incorporated into CERP project evaluations (USACE 2013). Projected to cost more than USD $23 billion (Normand and Sheikh 2023), CERP is one of the most extensive and expensive wetland restoration projects in the world (LoSchiavo et al. 2013). CERP uses an iterative project development and planning approach, a process for continually improving natural resource management via ongoing monitoring and assessment (Canter and Atkinson, 2010; Fig. 1). This approach recognizes that uncertainties can be addressed through the iterative integration of new information (LoSchiavo et al. 2013; NASEM 2024).
Fig. 1
A simplified representation of RECOVER’s project planning/decision process and how JEM assists in this process. RECOVER uses models and tools to evaluate the potential system-wide impacts of CERP restoration projects. During this process, hydrologic scenarios reflecting restoration plans are developed by an interagency hydrologic modeling team, which are then used as inputs into a suite of ecological models by JEM. JEM, in consultation with RECOVER, post-processes these model outputs into visualizations and data summaries. After project implementation, RECOVER assesses projects through field monitoring. The field monitoring is compared to results from the evaluation phase and combined with new science or emerging uncertainties; findings are integrated back into the models and tools used for the evaluation of projects
REstoration, COordination, and VERification (RECOVER), an interagency and interdisciplinary science team, is responsible for the coordination of CERP and its adaptive management program (RECOVER 2015, Fig. 1). At regular intervals, RECOVER provides a synthesis on the system-wide progress of CERP using information from this project evaluation and assessment cycle. The United States Geological Survey (USGS) Joint Ecosystem Modeling (JEM) team has co-developed a suite of models and DSTs with RECOVER since 2010 to assist in the evaluation of restoration plans. JEM works closely with RECOVER to provide ecological model output based on hydrologic scenarios, including visualizations and post-processed data to provide RECOVER with the information they need to complete their communication and integration tasks. Although RECOVER and independent scientists have long recognized that potential climate change impacts, such as SLR, should be formally incorporated into the evaluation phase (NASEM 2008; RECOVER 2014, RECOVER 2019; NASEM 2024), only the Biscayne Bay and Southeastern Everglades (BBSEER) project has attempted this thus far (USACE and SFWMD 2023).
JEM has been incorporating SLR scenarios into modeling tools for over a decade, most recently with the development of the Everglades Vulnerability Analysis (EVA), which assesses landscape-level, long-term vulnerabilities and health of the Everglades ecosystem (D’Acunto et al. 2023); and EverSparrow, a predictive model that estimates probability of presence for the endangered Cape Sable seaside sparrow (Ammospiza maritima mirabilis) under future conditions (Haider et al. 2021; Romañach et al. 2023). To make these and other ecological models more usable by members of RECOVER, JEM will tailor its model output visualizations so that SLR impacts can be considered in project evaluations.
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The Impacts of Climate Change on the Everglades Ecosystem
The Everglades is a biologically unique subtropical wetland ecosystem that supports various endemic, threatened, and endangered species (Lodge 2016). Much of the Everglades is coastal and low-lying, thereby making the freshwater wetland extremely vulnerable to flooding and saltwater intrusion from SLR (Pearlstine et al. 2010; Dessu et al. 2018). Sea-level rise and other climate change drivers are already altering coastal and nearshore habitats within the Everglades (Ross et al. 2024). Along the southeast Florida coast, average sea levels are expected to rise approximately 25 to 43 centimeters by 2040 and 53 to 137 centimeters by 2070, based on the baseline year 2000 (Regionally Unified Sea Level Rise Projection – Southeast Florida Regional Climate Compact n.d.). Saltwater intrusion and increased flooding can lead to soil subsidence or accretion (depending on the vegetation and the substrate; Charles et al. 2019). Climate change may also cause increased surface-water temperatures, acidity in marine and estuarine environments, and toxicity of pollutants (Pearlstine et al. 2010). Ecological consequences include habitat loss, increased inland migration of plant and animal communities, vegetation and wildlife range shifts, and phenological mismatches that may negatively affect wildlife populations (Pearlstine et al. 2010; Flower et al. 2017; Flower et al. 2019). Although there is uncertainty about the ecological consequences of future climate scenarios in the Everglades, it is generally accepted among decision-makers that increasing freshwater flow to the southern Everglades should help combat saltwater intrusion and give the ecosystem more time to adapt to increasing water depth and salinity (Catano et al. 2015; Dessu et al. 2021).
Challenges with Planning for Climate Change in the Everglades
In 2021, RECOVER contacted the USGS JEM team for assistance during the early project evaluation phase of BBSEER. BBSEER is the first CERP project to include SLR in the evaluation phase modeling as mandated by USACE regulations (USACE 2013; USACE 2021). Incorporating SLR into the evaluation modeling changes the assumptions within the models and requires adjustments to the visualizations already used to facilitate the ability for RECOVER to determine project effects from both restoration and SLR. Instead of an informal co-creation process that JEM historically employed, we engaged in a formal co-creation process using social science methodologies and theory. We used qualitative methods to examine the barriers that Everglades restoration partners encounter in integrating SLR into the evaluation phase processes, and to inform the development of DSTs that may help them overcome those barriers. Specifically, this research (1) examined the perceptions of restoration partners about the impacts of SLR on Everglades restoration outcomes, (2) identified both new and existing DSTs that restoration partners wanted to incorporate in SLR projections, (3) assessed which model outputs and DSTs would be most useful to restoration partners for evaluating projects in the context of SLR, and (4) engaged in a co-production process with restoration partners to develop DSTs that meet their needs and fit within CERP’s broader adaptive management framework.
Data Collection and Analysis
Our research examined the perceptions, decision-making needs, and preferences of RECOVER members around JEM’s new DSTs when using SLR scenarios. We incorporated a two-step approach to qualitative data collection, involving both focus groups and semi-structured individual interviews, which allowed participants to provide input on the SLR DSTs before and during JEM’s development process. The focus groups were conducted first (January 2023) and consisted of 33 participants. The focus group results were then used to inform the questions we asked in the individual semi-structured interviews (June – September 2023) as well as the prototype DSTs shown to participants. We used the focus groups to gather information on how RECOVER members believed SLR would broadly impact Everglades restoration, and the types of tools they needed to plan for those impacts. Our semi-structured interviews delved more deeply into these two themes and provided a key opportunity for participants to provide feedback on initial tool prototypes that the JEM team developed in response to RECOVER’s initial input.
Conducting both focus groups and interviews allows for a more detailed and nuanced understanding of the data (Natow 2020). This approach is particularly effective for eliciting diverse perspectives and gaining a comprehensive understanding of complex issues, as it leverages the strengths of both group and individual data collection methods to provide a more complete picture (Kaplowitz and Hoehn 2001). This paper primarily discusses the interview results.
We invited 62 individuals who were active members or collaborators with RECOVER to participate in the semi-structured interviews. We emailed interview requests and sent two follow-up emails within six weeks of the initial request if no response was received. Of the 62 invitations, 26 participants agreed to a virtual interview. The interviews were structured into four sections addressing participants’: (1) background and role in RECOVER; (2) perceived impacts of SLR on Everglades restoration; (3) challenges and opportunities for integrating SLR into restoration planning; and (4) perceptions of the modeling tools and outputs provided by JEM. During the fourth section of the interview, we showed participants three examples of prototype DSTs designed by JEM and asked a series of questions about those examples.
The interviews were recorded with the participants’ consent and lasted an average of sixty minutes. Interview transcripts were generated automatically by the virtual call software used and organized using NVivo 12 Plus software (Lumivero, Denver, CO). The coding process for our analysis began with inductive codes informed by the interview questions (Table 1). Organically, deductive codes emerged as the interviews were analyzed (Bernard 2011). The interview protocol was approved by the University of Florida Institutional Review Board (IRB Study No. UFIRB202300008).
Table 1
Inductive and Deductive Themes Explored in Interviews
Inductive Themes (arising from interview questions)
Deductive Themes (arising from interview responses)
Perceptions of sea-level rise impacts on Everglades restoration
Ecological impacts of sea-level rise
Importance of decision-support tools that account for climate change impacts beyond sea-level rise (such as changes in precipitation and temperature patterns)
Adjusting ecological monitoring to account for sea-level rise
Adjusting RECOVER performance measures to account for sea-level rise
How sea-level rise is changing the agency/institution’s work
Challenges and opportunities for integrating sea-level rise into restoration planning
Barriers to integrating sea-level rise projections into restoration planning
Perceptions of BBSEER (the first CERP project to incorporate sea-level rise projections into restoration planning)
Barriers to fully understanding the impacts of sea-level rise on restoration outcomes
Modeling tools or outputs currently used to assess the potential impacts of sea-level rise on restoration outcomes
Aspects or attributes of modeling tools that limit understanding of sea-level rise impacts on Everglades restoration
Desired information from ecological models
Perceptions of potential modeling tools and guidance provided by the USGS
Feedback on each example
Generalized feedback (applying to all examples)
Level of familiarity with similar tools
Questions that these tools could help answer
Anticipated ease of use
How well tools fit into current workflows
Time to experiment with tools
Potential barriers to using tools
Training or resources needed to adopt tools
Intention to adopt tools
Applying the Diffusion of Innovations Theory to Understand the Adoption of SLR DSTs
To explore RECOVER members’ perceptions of JEM’s DSTs, we integrated components of the Diffusion of Innovations Theory (Rogers 2003; Stern 2018) into the interview protocol. Diffusion of Innovations Theory examines how ideas or “innovations” are spread through social networks and adopted by target audiences (Rogers 2003). In our research, we categorized the newly developed SLR DSTs as the “innovation.” There are five characteristics of an innovation that influence human decisions to adopt it (i.e., the “innovation-decision process”): its relative advantage, compatibility, complexity, trialability, and observability (Rogers 2003). If the target audiences perceive an innovation to possess these characteristics, they are more likely to adopt it (Rogers, 2003). Therefore, our interview questions assessed participants’ perceptions that JEM’s new DSTs incorporated these five characteristics (refer to Table 2 for definitions of these five characteristics and the interview questions designed to measure them). Although the Diffusion of Innovations Theory has been frequently applied to the adoption of DSTs within agricultural research (i.e., Dissanayake et al. 2022; Kabir 2022), to our knowledge, the Diffusion of Innovations Theory and the innovation-decision process have not been applied to study the adoption of DSTs in wetlands restoration.
Table 2
Components of Diffusion of Innovation Theory Measured by Interview Questions
Theory Component
Definition
Associated Interview Questions
Prior Condition
One prior condition for adoption, or a characteristic of the adopter, is that they feel they have needs that are not being met
Q. What barriers, if any, prevent you or your agency from integrating sea-level rise into restoration planning?
Q. In your opinion, are there certain aspects or attributes of modeling tools that prevent you from fully considering the impacts of sea-level rise on Everglades restoration?
Q. If you could have any information from ecological models that you wanted (no constraints), what type of information would you like to have?
Relative Advantage
Perception that the benefits of a new practice outweigh the costs and exceed the value of current practices
Q. What questions could these types of visualizations help you answer in your job?
Compatibility
Perception that the new practice does not conflict with dominant social norms, personal norms, past experiences, or the needs of the adopter
Q. Do you anticipate any pushback or other barriers to using these tools? If so, please explain.
Complexity
Perception that the new practice is not overly difficult to understand or implement
Q. Do you anticipate these modeling outputs being easy to use and understand?
Trialability
Perception that there is an opportunity to experiment with the new practice on a trial basis at a low cost
Q. What training, resources, or additional information would you, your team, or agency need to implement these tools?
Q. Do you anticipate any pushback or other barriers to using these tools? If so, please explain.
Observability
Perception that there is an opportunity to easily observe the results of the practice, either through demonstration or observing neighbors
Q. What training, resources, or additional information would you, your team, or agency need to implement them?
An important “prior condition” that can also determine whether an innovation is adopted is the extent to which the target audience perceives that their needs are being met (Rogers 2003; Stern 2018). Because tools and methods for incorporating SLR into planning processes are still widely lacking within CERP, determining the prior condition would help us gauge how urgently SLR DSTs were needed by RECOVER members.
Results
A total of 26 individuals participated in the semi-structured interviews. Seventeen participants represented Federal agencies involved in CERP, seven represented State agencies, and two represented Tribal organizations (Table 3). Responses from all participants were organized into six key themes: (1) ecological modeling needs for SLR, (2) barriers to incorporating SLR projections into restoration planning, (3) perceived limitations of current modeling tools, (4) feedback on data visualizations, (5) perceived relative advantage of DSTs and (6) compatibility, complexity, trialability and observability of DSTs (refer to supplementary material (Castellano et al. 2025 for full descriptions). The remainder of this section will present and describe each key theme that emerged from the interviews.
Table 3
Summary of RECOVER Participants and Affiliations
Type of Institution
Name of Institution
Federal
United States Geological Survey (USGS)
United States Fish and Wildlife Service (FWS)
National Oceanic and Atmospheric Administration (NOAA)
United States Army Corps of Engineers (USACE)
National Park Service (NPS)
State
Florida Fish and Wildlife Conservation Commission (FWC)
Florida Department of Agriculture and Consumer Services (FDACS)
South Florida Water Management District (SFWMD)
Tribal
Miccosukee Tribe of Indians
Seminole Tribe of Florida
Theme 1: Ecological Modeling Needs for SLR
When asked to describe the information needed from JEM ecological models, participants described a need to better understand projected changes in community composition in response to SLR, and ecological thresholds associated with salinity, water depth, accretion, and subsidence. Ten participants sought information on community composition responses to SLR over time at a landscape level. They also needed to know how SLR could drive changes in habitat types and how it impacts dependent species.
Specifically, nine participants requested to see how plant communities would change in response to SLR. For example, one participant said: “We need a good vegetation succession transition model that’s applicable to the entire Greater Everglades that incorporates both water quality and water quantity. The Everglades Landscape Vegetation Succession model (ELVeS) does provide this information,” this participant added, “but perhaps not at all the spatial and temporal scales needed.” Several participants requested that tools consider Everglades ecological indicator species and other important species and habitats under SLR scenarios, including the endangered Everglades snail kite (Rostrhamus sociabilis plumbeus) and native apple snail (Pomacea paludosa), marl prairie, seagrasses, oyster reefs, and tree islands. A few participants also desired modeling tools to help them understand how trophic structures would change under SLR and invasive species’ response to SLR.
Seventeen participants asked for ecological planning tools that cover a larger range of temporal and spatial scales.
Ten participants also wanted the models to address ecological resilience—whether they are looking at a particular population or the system. Understanding where species, sites, or habitats are on the scale from resilient to vulnerable could help restoration partners prioritize their efforts. Two participants also discussed the need to incorporate development and population growth into ecological models to understand how these factors might impact resilience.
Seventeen participants expressed a need to understand the thresholds associated with accretion, salinity, peat collapse, water depth, and subsidence under various SLR scenarios. Four participants stressed the importance of identifying ecological thresholds (also referred to as “tipping points,” beyond which ecological change is irreversible) for indicators. One participant said:
“To me, it’s at what point … will each of our indicators be excluded from certain places in the Everglades. How much area will become inhabitable, when? For each of our indicators. That’s the ultimate level of information. Getting there through [a Habitat Suitability Index] I think is possible, but we need the data to determine where these tipping points are”.
Six participants expressed concern about hydrological models and the extent to which SLR could counteract freshwater flows from CERP operations. For instance, there were questions about the possibility of increasing southbound freshwater flow to mitigate SLR. For example, as one participant described:
“That’s what I think about when I’m thinking about what kind of modeling outputs we would need, is how do we include the pacing of that sea level rise, those projections, with the required flow to maintain this freshwater ecosystem? Because I assume we’re underestimating the amount we will need”.
Six participants needed modeling information about where water was coming from, its level of salinity, and the effects on groundwater. According to some participants, the models used in CERP have yet to simulate SLR impacts on groundwater and still do not incorporate Florida Bay, the southwest coast, or the northern estuaries. Finally, several participants needed to know how other climatic factors beyond SLR — such as changes in precipitation — will affect the water budget.
Theme 2: Barriers to Incorporating SLR Projections into Restoration Planning
Fifteen participants explained that one of the most significant barriers to incorporating SLR projections into restoration planning was the lack of appropriate modeling tools. These participants acknowledged that uncertainty around climate change hindered project planning and prevented restoration partners from “fully embracing and using all the information available regarding sea level rise,” as one participant explained.
Ten participants acknowledged the difficulty—and yet the importance—of modeling climate change impacts beyond SLR, including the need to explore a wide range of potential precipitation and temperature scenarios. Future weather patterns were acknowledged as especially difficult to predict. Several participants also mentioned that climate change complicates efforts to provide an appropriate amount of water to support the social and ecological needs of the Everglades system. As noted by one participant, “We can’t answer with any certainty yet whether we’re going to have more water in the long run or less. That’s a big problem.” Participants discussed uncertainty embedded in other critical restoration questions, such as future salinity levels in freshwater ecosystems; when salinity thresholds for certain indicators will be reached; and accretion rates or peat collapse associated with SLR. Eight participants described the importance of being able to examine the influences of climate change and CERP activities on the system’s hydrology, separately and in conjunction. These comments identified additional gaps: (1) lacking reliable climate projections and (2) being unable to tease apart the effects of climate change on the natural system from the effects of restoration and water management activities.
Ten participants identified a lack of data as a barrier to integrating SLR projections into restoration planning. Participants acknowledged that restoration partners need more data to construct reliable models, especially at a smaller scale. For example, one participant stated: “I don’t think we have that [data] throughout, where we have these long-term data sets that we can actually use to kind of scale down those larger projections into something more meaningful for any given area within our footprint.”
These ten participants either explicitly mentioned the lack of data at smaller scales or mentioned data gaps in specific areas of the Everglades. For example, two participants mentioned a lack of hydrological data in the northern and inland parts of the Everglades compared to the southern and coastal portions. Another participant acknowledged observing changes in wildlife behaviors on the southwest side of Big Cypress National Preserve that could be due to SLR, but acknowledged that this was anecdotal. A few participants also described lacking data and a monitoring framework to capture potential thresholds relating to salinity and peat collapse. However, one participant believed that CERP already had a “plethora of data,” and suggested focusing more on data analysis to answer questions about Everglades restoration, specifically freshwater availability under current and future conditions. This participant also acknowledged lacking the workforce necessary for data analysis.
Eight participants identified CERP’s reliance on historic data to evaluate projects as a barrier to restoration planning because it creates outputs that do not capture potential changes in climate and SLR. This is a widely acknowledged problem within CERP (NASEM 2024). For instance, one participant said:
“[Project evaluations are] based on a system of models that look at the past 50 years of climate and project that 50 years in the future to see how the project will perform. And what’s very worrisome to many people is that we already pretty much know that the next 50 years is not going to look anything like the past 50 years in terms of climate”.
Model outputs are compared to performance measures (RECOVER-defined targets that reflect Everglades restoration performance, LoSchiavo et al. 2013); however, the current performance measures do not account for potential future shifts. Participants described the need to adjust the performance measures to incorporate SLR and climate change projections.
Six participants identified a lack of funding and political support as a barrier to integrating SLR into restoration planning, either for RECOVER or, more broadly, for CERP. Four participants said that current levels of funding are inadequate to support RECOVER monitoring efforts that would capture SLR impacts; another participant said that, within their agency, available funding does not support “holistic system-level monitoring,” which could better capture climate change impacts within CERP boundaries.
While all twenty-six participants acknowledged the need to consider SLR and climate change in restoration planning, many expressed uncertainties over how to do so—how to identify indicators that will be impacted by SLR, for example, and how to adjust performance measures that demonstrate associated shifts and thresholds for those indicators. Lack of understanding and agreement over which SLR projections to use can create additional barriers. While participants did not explicitly identify disagreement over SLR modeling tools and projections as a barrier, three participants discussed the need to either increase understanding of SLR scenarios among CERP restoration partners or to make sure everyone is aligned with the scenarios and models used. Finally, the following barriers to integrating SLR into restoration planning were identified by five or fewer participants: lack of time, bureaucracy, lack of understanding of how factors beyond CERP may impact restoration, lack of a dedicated team addressing SLR, and lack of cooperation between CERP partners despite shared goals. For various reasons, participants explained that information was not being shared effectively across these groups.
Theme 3: Perceived Limitations of Current Modeling Tools
We asked participants whether certain attributes of current modeling tools prevented them from understanding the impacts of SLR on Everglades restoration. Six participants acknowledged that the lack of succession modeling tools serves as a limitation. Many participants asked for more dynamic hydrologic models that would provide a more realistic representation of the progressive changes imposed by SLR over time. For example, within the BBSEER project, the total SLR was added to restoration scenarios for each year in a simulation, instead of changing incrementally over the years. Without hydrologic scenarios that change SLR incrementally, participants expressed concern that they may not be able to identify tipping points for certain indicators or species of concern that may occur between the sea levels and time periods analyzed in the current models. For example, one participant mentioned not being able to predict how freshwater availability in coastal islands, which is critical for species like the diamondback terrapin (Malaclemys terrapin), will be affected by SLR.
One participant explained the need to better understand vegetation succession under future conditions but acknowledged that there is no “across-the-board” tool to examine community-level changes as a function of SLR:
“What we’re lacking is, okay, if you have subsidence or accretion, how is that then feeding into an elevation for the next year and the year after that, and then how does that relate to the vegetation community? So a cool integrated tool like that I think would be fabulous”.
Nine participants said that the models used within CERP are either too limited in their geographic scope or function to be useful for their purposes. Three participants expressed concerns with current hydrologic models not extending beyond the coast, limiting understanding of how coral reefs may interact with SLR and subsequently affect onshore dynamics. Participants acknowledged that geographic scale and resolution can be problematic for CERP’s hydrologic and ecological models. One participant explained:
“You shouldn’t — ‘you’ — as in the collective scientific community, you should not use a general sea level rise scenario projection for a specific project area or target area. Those global-scale sea level rise projections scenarios are not meant to be targeted to a given location.”
Participants said ecological models also needed to function at smaller spatial scales ( < 400 m) as landscape-level analyses are good at providing a broader narrative of progress, but not for providing the sort of quantitative answers that smaller-scale models could provide.
Several participants discussed needing better data and modeling around elevation and changes in topography in response to SLR. Two mentioned that the modeling used to support the BBSEER project (which is the first to incorporate SLR projections) contains assumptions about accretion—whether sediment will accumulate with increased water depth but not determining whether the increased depth is due to more saltwater or more freshwater — an important distinction for determining which habitats can or cannot be supported. Some participants also discussed needing better salinity projections under SLR conditions.
Theme 4: Feedback on Data Visualization Tools to More Effectively Meet Needs
In the final section, participants were shown three examples of data visualizations— 1) animated maps, 2) static maps and bar charts, and 3) interactive maps—that reflect SLR projections in model outputs (refer to Fig. 1). The examples present information from the suite of ecological models that JEM runs for restoration planning, allowing users to better understand the model outputs under different water management and SLR scenarios. After viewing the examples, participants were asked a series of questions designed to measure their perceptions of the DSTs’ relative advantages, trialability, complexity, observability, and compatibility (Table 2).
Theme 5: Perceived Relative Advantages of Decision-Support Tools
Participants perceived the following advantages from using the data visualization tools, an important component of the Diffusion of Innovations Theory.
Almost all participants wanted the tools to help them see how SLR could drive landscape-level ecological changes across the Greater Everglades at smaller spatial and temporal scales. Many participants want to be able to measure spatial changes in habitat—for example, declines or expansion in acreage in mangrove ecotone, sawgrass marsh, and upland habitats like marl prairie or pine rocklands—under different SLR and water management scenarios. This would also allow them to see potential trends in land loss: where terrestrial habitats may, in the future, be converted to open water. They believe that these tools could help explore these potential changes. As one participant said:
“Yeah, these are helpful … in my case, if I’m looking at marl prairie, for example. And I want to see what that marl prairie habitat type is in the future, I can focus on marl prairie from 20 years ago or marl prairie today, and then see where it’s located on the landscape in the future and see what kind of habitat it’s changed into, did it go to sawgrass or something like that?”
Most participants imagined using these tools to explore different abiotic conditions associated with SLR, such as increased salinity, and how that might drive shifts in vegetation communities or wildlife and endangered populations across the Everglades. Several participants said these tools could help them understand projections of the suitability of habitat indicators under different SLR and water management scenarios. One participant imagined these tools being used for this purpose in both the evaluation and assessment phases of CERP projects. For instance, one participant said:
“I think the kind of progression of habitat condition or habitat deterioration per indicator would be a useful tool to be able to project out change that our indicators might experience and then actually compare that to what we’re measuring in the field with our monitoring and assessment plan. And so I see that being an important part to forecast and then actually verify how our indicators are responding to CERP combined with sea level rise going forward”.
Six participants identified using the tools for restoration or land management decisions, such as invasive species treatments, plantings, endangered species, and prescribed fire on public lands in the Everglades. A few participants described using these tools to help them understand which areas to prioritize for restoration or conservation, based on their vulnerability to SLR. As one participant said:
“What parts of the South Florida system are most vulnerable to specific aspects of climate change? … We never have enough money to do everything and focus on every problem. So I want to know where the problems are going to be the most severe and where I might have the greatest impact [in] solving this problem”.
When shown the first example of the three data visualization tools (refer to Fig. 2a), which was an animated map depicting species or vegetation responses (such as probability of presence) to hydrological conditions over time, most participants believed the map was useful for communicating trends to a non-scientific audience and policymakers but was inappropriate for scientific analysis. For example, one participant commented:
Fig. 2
Example data visualizations shown to interview participants. a Animated maps: participants were shown an animated map of ecological responses through time; b Maps with charts: participants were shown static maps at points in time with accompanying charts; c Interactive maps: participants were shown maps where layers showing different information could be toggled on and off
“Those kinds of things are actually good for communications, to give presentations to Congress or to policymakers at the state level. That’s a big impact and shows a lot of things in a more compact automation, which I think is really useful and important. It might not be as useful in terms of the data, ability to get more data and manipulate it and analyze it”.
However, other participants explained that this kind of visualization would be very helpful for understanding landscape-level changes across the Everglades in response to hydrological conditions, such as the encroachment of mangroves into freshwater habitats.
The second example shared with participants (refer to Fig. 2b) depicted a series of successional outputs of species or vegetation responses (such as probability of presence) to hydrological conditions over time, through both maps and bar graphs of the outputs at fixed time points (5- and 10-year increments). Several participants said the second example seemed more usable for the scientific community because it is more readily quantifiable than an animation. However, some participants had questions or comments about the time stamps depicted, including that five- or ten-year increments may not capture certain thresholds or tipping points in ecological communities. One participant asked for the ability to forecast changes 100 years into the future to capture potential larger-scale changes driven by SLR, such as changes in hectares or habitats.
The third example (refer to Fig. 2c) featured a dynamic map that participants could interact with via a web-based platform. In this example, users could add and remove layers of both hydrological metric inputs and species or vegetation responses (such as probability of presence). Participants were enthusiastic about the interactive potential of the third example, such as the functionality of adding and removing map layers and zoom capabilities. One participant imagined using this tool to simultaneously compare indicator responses to varying levels of SLR. Another participant said they would use it to pull acreage for certain habitats and show changes over time. A few participants commented that Example 3 would be more useful for someone in a more technical role, or someone who produces technical reports, but less so for someone in a management or executive role, who may prefer outputs. These observations suggest that they perceived limited compatibility of the tool for those in higher-level roles. It may be worth noting that while most participants seemed to assume that this tool would be easy to use without modeling or geographic information system (GIS) skills, others believed it would require basic familiarity and comfort with GIS software and geographic modeling.
While we did not explicitly ask participants to state their preference for one of the three examples in our interview protocol, most expressed a stronger preference for Example 2 (static maps and bar charts) and Example 3 (interactive maps) than Example 1 (animated maps). However, many participants expressed a desire for multiple types of visualizations, including a mix of numbers, visuals, and animations. Different visualizations could also appeal to different audiences or learning styles — “so the more the merrier,” one participant said. Some participants in management roles said that although they would not personally work with the animation tools, the tools can be used for visual communication to people in higher-level roles, such as members of Congress, or to other stakeholders with less technical knowledge of the Everglades and CERP. Several participants also wanted to understand the data input for the tools, the scenarios being used, and the assumptions underlying those scenarios. When looking at probability modeling, participants expressed interest in learning how the probability is calculated and interpreted. A few participants commented on certain functionalities they would like to see in these tools; for example, a slider bar that allows them to control factors like sea level, rainfall, or date range. These observations help to establish again that participants see relative advantages to the tools, as well as areas where the tools may fall short of their expectations.
Theme 6: Perceived Compatibility, Complexity, Trialability, and Observability of DSTs
As a proxy to identify any potential negative perceptions of the tools, we asked participants whether they anticipated any barriers to using them. Although eleven participants did not identify any barriers to using these tools, the other participants mostly focused on the time and effort required to learn to operate new tools, suggesting that they perceived barriers related to the tools’ complexity, trialability, and observability. Again, according to the Diffusion of Innovations Theory, complexity refers to the perception that an innovation is not overly difficult to understand or implement; trialability refers to the perception that there is opportunity to experiment with the innovation on a trial basis at a low cost; and observability refers to the perception that there is opportunity to easily observe the results of using the innovation, either through demonstration or observing neighbors. For example, participants might be deterred from using the tools if they believed it would take a significant amount of time to learn the tools or process the results, if the spatial or temporal scales do not match what they need, or if they could not get training support. Some participants also said that any lack of clarity on how the tools were developed, such as the data sources used or how probability is calculated and interpreted by the models, may represent a barrier.
Although twelve participants commented on their limited familiarity with data visualization tools, they did not perceive this as a barrier to using the tools independently, based on the examples they were shown. They stipulated that they would like technical support when using the tools for the first time. For example, one participant said: “The more we can make tools usable directly by people that don’t have to be modeling experts…The modeling skills are built into the tool. That’s really helpful.” Although most participants did not perceive the tools as overly complex, five participants preferred to work with the tools themselves before making a final decision. However, two participants indicated that Example 3 could be challenging to use because of their limited experience with GIS. Despite these barriers, however, none of the participants expected significant difficulty in using and understanding the tools.
Participants acknowledged the tools’ compatibility with their work habits and generally viewed the tools as user-friendly. No responses indicated that participants thought the tools might be incompatible with their workflows, goals, or technical constraints (such as agency firewalls); however, one potential barrier acknowledged by several participants was the time and effort that may be required to learn how to use the tools.
Finally, we also measured the tools’ trialability and observability by asking participants what types of training or other support relating to the tools would benefit them. A workshop for RECOVER members (delivered by JEM) was the most requested type of support by participants. Many participants desired the chance to try the tools in a group setting where their questions could be answered, and they could provide feedback in real-time. Some participants commented on the value of being part of the learning process and participating in question exchanges with their colleagues. Several participants also requested a user guide.
In addition, a few participants expressed that it was helpful for them to have opportunities to provide feedback throughout the tool development process. In the future, they want to be more actively involved as co-developers (as they were for this project), instead of merely as end-users who are given the finalized tool without their input. As noted by one participant: “Just to say generically what would make tools more useful is … more conversations with the user groups through the entire development process.” To highlight a tool that truly meets the users’ needs, some participants referred to the collaboration between JEM and the U.S. Fish and Wildlife Service (FWS) to develop the Cape Sable Seaside Sparrow (CSSS) Viewer.
Discussion
Decision Support Tools used in climate adaptation efforts are often developed without due consideration of factors that may influence their adoption by end-users (Wardropper et al. 2021). Sometimes this can be attributed to insufficient communication with the end-users before, during, and after the tool development process (Fanok et al. 2022). Although there is little precedent for applying the innovation-decision process to DST development and usage, we found that it is a highly useful and practical framework for addressing this critical gap. Our efforts suggest this approach can be replicated in other conservation or natural resource management contexts where DSTs are used to navigate complexity and uncertainty related to climate change. Below, we discuss specific considerations for measuring components of the innovation-decision process. Our study also provided insights into the broader challenges and potential solutions to planning for SLR impacts in various ecosystems, particularly in the Everglades. We highlight two of these challenges here for their implications for other coastal wetland systems in the face of climate change.
Hydrological and ecological processes are tightly linked in freshwater and coastal wetland systems (Wu et al. 2017; Xin et al. 2022), but models exploring these dynamics can be prone to both oversimplification and excessive parameterization (Ganju et al. 2016). Our research found that in the context of Everglades restoration, there is limited understanding of systemic hydrological changes due to perceived limitations in both hydrological and ecological modeling. Our participants acknowledged that changes in water depth, hydroperiod (i.e., duration of inundation), and salinity could be due to several factors: SLR (which affects both groundwater and surface water), changes in precipitation, temperature-driven changes in evapotranspiration, or water operations and restoration projects. As of now, there is no comprehensive tool that allows restoration partners to parse out the reasons behind the hydrological changes they are observing. Tracking the drivers of hydrological changes could inform restoration and water management decisions in the short and long term in the Everglades and other wetland systems. This may be particularly relevant where water flow and dispersal are controlled. A better understanding of the drivers of hydrological changes could enable better decision-making around how to release stored freshwater to combat saltwater intrusion (Sklar et al. 2019). As wetlands are increasingly managed to mitigate the effects of climate change, the ability to make science-based decisions around “rewetting” drained wetlands, for example, may become more relevant (Zou et al. 2022).
Identifying ecological thresholds (also referred to as tipping points or trigger points) has proven a challenge in the Everglades ecosystem due to the complexity of the system (NASEM 2024). Identification of ecological thresholds requires dynamic models that can explore feedback mechanisms among various environmental drivers (Estenoz and Bush 2015). However, more robust models could establish “early warning” indicators in coastal wetland monitoring programs to detect critical tipping points (Catano et al. 2015). They could also support the development of decision-support tools facilitating the incorporation of thresholds in restoration planning. For example, the EVA model is a landscape-level risk assessment tool that identifies thresholds for certain indicator species beyond which ecological change is likely irreversible. It is designed to inform management actions that take these thresholds into account and mitigate or minimize negative impacts to the ecosystem (D’Acunto et al. 2023).
Our results suggest a strong desire for additional ecological thresholds to be built into DSTs used for managing coastal wetland systems like the Everglades, particularly in the context of SLR. For example, freshwater deliveries into the southern coastal part of the Everglades are likely to push back saltwater intrusion from SLR and prevent sudden vegetation shifts (Sklar et al. 2019). Incorporating an ecological threshold at the point vegetation will shift beyond the desired restoration state into a DST could inform Everglades decision-makers; however, developing and using these tools has been challenging within CERP (LoSchiavo et al. 2013).
Applying the Innovation-Decision Framework to Decision-Support Tool Development
Exploring the “prior condition” component of the innovation-decision process allowed us to establish participants’ need for decision-support tools to (1) evaluate potential ecological impacts of SLR on the Everglades, and (2) proceed with restoration actions that increase ecosystem resilience despite facing scientific uncertainty. Additionally, exploring relative advantage, compatibility, and complexity allowed us to gather feedback that directly informed the development of these tools (refer to Fig. 1; Table 2). We had several group and individual organized sessions to gather feedback, which was summarized and then used to edit the tools as they were developed. This iterative co-production process was instrumental in building end-user support.
We found that complexity is an especially useful component of the innovation-decision process for determining the likelihood of users to adopt decision-support tools (or any innovation). Perceptions of high complexity can be a barrier to adopting an innovation (Rogers, 2003); therefore, it is important to establish whether this perception exists among users. In our case, we found that participants did not perceive the DSTs to be overly complex; however, some participants acknowledged that the time and effort required to learn to use the tools may serve as a barrier. This may seem like a contradiction, but we interpret this as participants' confidence in their abilities to learn and use the tools and understand their outputs. This interpretation is supported by our finding that lack of time, due to heavy workloads, is considered a barrier to addressing sea-level rise in restoration efforts.
Our results suggest that users’ workloads are considered when designing and developing decision-support tools, as well as users’ perceptions of the tools’ complexity. Participants explained that if they have high workloads and perceive that the tools are too complex, they may not have the time to learn how to apply the tools to their work. This could lead to lower rates of adoption, even if participants believed the tools presented an advantage or addressed a need. This phenomenon could be an example of how the elements of the innovation-decision process differently depending on their needs or context. Additionally, this could be the basis of a future case study: examining how user workloads combine with perceptions of complexity to influence user adoption rates of DSTs.
We also found that trialability and observability are highly important considerations for participants decisions on whether to implement the tools. Participants seemed to be willing to invest time in attending such a workshop if they could learn how to apply the tools directly to their work and exchange ideas and advice with peers. JEM frequently holds workshops to train end-users on its DSTs, and our findings suggest that it is a critical step in the tool development process and end-user adoption of the tools.
Study Limitations and Opportunities for Future Research
While participants described many ways that JEM’s DSTs could be advantageous in their RECOVER roles, the tools’ relative advantage could have been further established by asking more specific questions about which management decisions the tools could support. JEM designs DSTs to support a variety of decisions across multiple temporal and spatial scales. Therefore, participants’ responses to this question could have more directly informed JEM’s tool development process. This could be helpful to ask in any context where DSTs are being developed for natural resource managers, as management decisions are often tied directly to tool outputs (LoSchiavo et al. 2013).
In addition, our proxy for measuring compatibility, which was to ask about barriers to using the tools, yielded necessary and important information, but compatibility may also have been established through asking additional questions. Future researchers may incorporate brainstorming sessions with RECOVER (or other restoration groups) to develop interview questions and determine whether they are truly designed to improve the match between tool outputs and RECOVER’s information needs. Future research can also investigate the perceptions of restoration planning tools held by a wider range of stakeholders, including members of the public and other organizations involved in Everglades restoration.
Scientists and restoration managers can use scenario planning to compare ecological responses to future climate conditions (Peterson et al. 2003). The USACE provides three SLR scenarios—low, intermediate, and high—that should be applied by decision-makers when evaluating project alternatives (USACE 2021). However, USACE has faced criticism for only considering SLR and not prioritizing other facets of climate change, such as variations in temperature and precipitation, in project evaluations (NASEM 2016). This is not unique to the Everglades: climate change-related vulnerability assessments for coastal wetlands generally focus on SLR and fail to consider other macroclimatic drivers (Osland et al. 2016). Therefore, future research can explore the assessments of vulnerability of other macroclimatic drivers on coastal ecosystems facing climate change.
A major limitation of SLR modeling within CERP is that it applies a step-change approach, which simply projects a future time “step” increase in sea level but does not consider incremental sea-level changes or ecosystem responses to changes in the rate of sea-level rise or other environmental drivers (NASEM 2016). Understanding the complexity and nonlinearity of these drivers, which operate over multiple spatiotemporal scales, and how they interact to affect ecological changes, requires a more dynamic modeling approach (Wu et al. 2017). The development of DSTs that integrate SLR and predicted ecological responses through time can help restoration planners understand potential outcomes of restoration actions.
Conclusion
Everglades restoration partners face challenges as they seek to restore an ecosystem susceptible to the impacts of climate change. Over the years, restoration goals have shifted from restoring the Everglades to its historic conditions to creating a more resilient ecosystem that can withstand those pressures (Catano et al. 2015; Pearlstine et al. 2010). This project was conducted as CERP is beginning to formally address climate change impacts in its planning processes and design new measures of success.
We aimed to gain contextual knowledge of the broader challenges restoration partners face in planning for climate change. Some of these challenges are deeply rooted in CERP. The interviews highlight many issues that are already widely acknowledged among Everglades restoration partners (for example, a reliance on historic data for building models to project future climate scenarios, and a lack of monitoring programs that would capture SLR impacts). However, our research provided a unique opportunity to gain insights into the types of practical tools and guidance that can be used to overcome some of those challenges.
In exploring what types of SLR modeling and DSTs RECOVER would find most useful, participants expressed a need for tools to help them consider tradeoffs across the landscape, identify ecological thresholds, minimize uncertainty, and assess resilience across spatial and temporal scales. Tradeoffs in a system as complex as the Everglades, where the demands of a growing urban population are bound to conflict with those of native wildlife populations, are inevitable (Sklar et al. 2005). CERP restoration efforts seek to minimize tradeoffs where possible, and restoration partners can use new DSTs to help them do that. Participants requested tools that offer a landscape-level view of indicator responses to both SLR and water management scenarios, which could help them understand where ecological tradeoffs may occur, as well as identify areas, species, or ecological communities with high vulnerability to SLR. They prefer these tools to be both interactive and user-friendly to prevent a lack of modeling expertise from barring them from using and interpreting model outputs. Restoration and natural resource management professionals also requested decision-support tools that can quantify uncertainty and help them compare potential outcomes of various courses of action.
Our research has shown the innovation-decision process is a useful framework for understanding users’ perceptions of such tools and suggests how likely they are to adopt them. Our results suggest that the components of the innovation-decision process that we measured—users’ “prior conditions” and their perceptions of an innovation’s relative advantage, compatibility, complexity, trialability, and observability—are important considerations of users’ intention to adopt DSTs.
Climate change is continually moving the goalposts for restoration and conservation efforts around the world. Historically desirable and feasible outcomes may no longer be appropriate or possible—particularly in coastal ecosystems facing SLR. Natural resource managers are increasingly relying on strong adaptive management frameworks and tools to help them choose the best course forward, monitor the results, and re-adjust, as necessary. Co-development processes rooted in social science research methods, such as structured interviews or participatory approaches with end-users, can lead to DSTs that help natural resource managers achieve the best possible outcomes, even as they navigate the new realities of our changing climate.
Acknowledgements
We are grateful to the members of REstoration COordination & VERification (RECOVER), who shared their time and expertise with us throughout our study. We are also thankful for the current and former lab members at the University of Florida’s Human Dimensions Lab for their help co-facilitating the focus groups: Ricardo Platero, Charles Wallace, Lillian Dinkins, and Shiala Morales. This research was funded by Cooperative Agreement No. G22AC00433-00 from the U.S. Geological Survey Southeast Climate Adaptation Science Center. The interviews described in this information product were organized and implemented by the University of Florida. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Compliance with Ethical Standards
Competing interests
The authors declare no competing interests.
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