Abstract
Environmental risks are intensified by the growing interaction between human-induced pressures and climate change (CC), leading to biodiversity loss and ecosystem changes that threaten the resilience and health of marine and coastal ecosystems, along with their ability to provide services. The increasing risks posed by CC require appropriate adaptation actions that can boost biodiversity while also benefiting people. In this setting, nature-based solutions (NBSs) play a crucial role in coping with climate impacts by improving ecosystem resilience, thereby protecting and restoring natural ecosystems while providing benefits for human wellbeing. Current literature mainly focuses on NBSs in terrestrial environments, while knowledge on their implementation (and effectiveness) in marine and coastal environments remains limited. In particular, the analysis of suitable conditions for their successful implementation is lacking in marine and coastal areas when environmental, socio-economic, and governance aspects are considered, and the concept of ‘suitability’ itself is characterized by the absence of a consolidated and widely recognized definition. Through a systematic approach, this review work aims to reduce uncertainty surrounding the concept of suitability by providing clear guidance for its consistent interpretation and application in real-world NBS design and implementation. To this aim, we assess the state-of-the-art scientific literature on methodologies, modeling tools, analytical frameworks, and approaches used to evaluate NBS suitability in marine and coastal ecosystems. Based on a pool of 26 papers covering the 2011–2025 timeframe, a period selected to align with the emergence and formalization of the NBS concept, the review confirms that a unique and shared definition of suitability is missing. Within the framework of this work, the concept has, therefore, been extrapolated from a selection of key papers drawn from the literature. Furthermore, the review results reveal that environmental indicators (e.g., slope, depth, and sea surface temperature) are most commonly used to analyze suitability, followed by socio-economic indicators (e.g., land use and distance from the coast, which act as proxies for human activities or infrastructure). However, the socio-economic and governance dimensions remain poorly explored in the analyzed publications; instead, methodological approaches mainly focused on environmental aspects that define the optimal physical, ecological, and chemical conditions for NBS implementation. Overall, the literature suggests that suitability models represent an essential decision-making tool for guiding nature-based adaptation in marine and coastal areas as they are fundamental to ensuring the success of implemented NBSs and the restoration and preservation of ecosystems.
1 Introduction
Marine and coastal ecosystems are vital to millions of people around the world, directly through food, resources, or employment (Fao, 2022), and their provisioning relies on the health and quality of nature and biodiversity (IPBES, 2019; ; IPCC WGII AR6, 2022). These ecosystems (e.g., mangroves and seagrasses) are highly productive, sequester carbon at twice the rate of terrestrial forests (Mcleod et al., 2019), support habitats vital for commercial fishing (), underpin food supplies for millions of people (Selig et al., 2019), regulate pathogens, and provide protection to shorelines by reducing wave energy (Lamb et al., 2017; Ferrario et al., 2014). However, over the decades, coastal ecosystems have continued to decline, such that the global coverage of salt marshes, mangroves, seagrasses, oyster reefs, kelp beds, and coral reefs has decreased by 35%–85% (Waycott et al., 2009; Hughes et al., 2018; Lotze et al., 2006; Saunders et al., 2020). Urban sprawl along the coast, population growth, economic activities, fishing, and maritime tourism are all contributing to environmental degradation and cumulatively impacting marine and coastal habitats and ecosystems, particularly their functioning and state (IUCN, 2020; Firth et al., 2016; Heery et al., 2017). The acceleration of these processes, leading to biodiversity loss and climate change, represents two of the most critical challenges faced by humankind globally. However, these challenges would not be successfully resolved unless both are tackled together (Pörtner et al., 2021).
Effective solutions are urgently needed, requiring transdisciplinary approaches, national roadmaps, international frameworks, and political and financial commitments to address the global environmental crisis (Waltham et al., 2020). Nature-based solutions (NBSs) have emerged as an important component of the overall response to these challenges. The NBS concept is based on the principle that enhancing and protecting natural processes provides multiple benefits for societies, ensures a sustainable supply of ecosystem services (ESs), and mitigates the adverse impacts of climate change (Gómez Martín et al., 2020). Their implementation provides multiple benefits, generating various social, economic, and environmental co-benefits. NBSs bring together concepts of restoration, protection, and management actions, thereby holistically managing the impacts of human activities on marine and coastal ecosystems (IUCN, 2020). In this frame, different definitions have been formulated for the NBS concept (; ; Maes and Jacobs, 2017; ); particularly relevant among them are those provided by the International Union for the Conservation of Nature () and the European Commission () and a more recent definition provided by the United Nations Environmental Assembly (UNEA, 2022). These three well-recognized definitions emphasize the dual relationship between people and nature, highlighting the functional role of NBSs in addressing societal challenges and serving as an overall adaptation strategy to adapt to and mitigate the impacts of climate change (Gómez Martín et al., 2020). A distinction among the different definitions arises between those of the IUCN and the EC, with the former focusing on the protection, sustainable management, and restoration of ecosystems and the latter referring to actions that are “inspired” or “supported” by nature (). Although the different definitions give the central role to natural systems, argued that ecological wellbeing is an overlooked concept and that scientific literature emphasizes the positive impact of nature on human wellbeing but rarely the reverse. Accounting for how human and ecological wellbeing are inextricably related would instead favor conservation approaches, making them socially just and equitable. In addition, a better characterization of the connection between humans and their environment would facilitate the understanding of the socio-ecological processes shaping landscapes, including the dynamic interaction among ecological functions, land use, and human activities. This improved understanding would help anticipate how the connected processes respond to external pressures (e.g., climate change) and ensure that people can react or adapt early to changes in the system while also identifying appropriate NBS spatial opportunities for a system-oriented response. However, this requires selecting and using new resilience indicators that can guide the design of NBSs and support their effective integration into real-world conservation practices (). Despite the importance and novelty of NBSs in addressing climate impacts, limited knowledge is available on where and which criteria need to be adopted to ensure the successful implementation of such measures.
This knowledge gap is particularly evident among stakeholders and decision-makers, who are struggling to identify proper areas for NBS implementation that guarantee positive and successful results for both the environment and local communities. This is particularly relevant for avoiding cases of failed implementation, as highlighted by several studies (; ; La et al., 2015) in marine and coastal areas. Such studies have identified the factors that mainly affect the success of restorative or protective actions, focusing on inadequate site selection, unsuitable substrate conditions for transplantation actions, lack of management, and excessive predation that limits, for instance, seedling establishment in mangroves or seagrasses.
Therefore, drawing on the awareness of coastal ecological degradation, biodiversity loss, and climate change, the re-establishment of coastal and marine ecosystem functions and the implementation of NBSs will play a key role in reversing these trends, contributing to the achievement of human health and wellbeing and the UN Sustainable Development Goals (; Sandifer and Ariana, 2014). Given the critical role and potential of NBSs in addressing climate-related impacts, it is essential to assess the environmental conditions that determine their suitability for implementation in marine and coastal systems. This includes identifying and assessing the variables, states, and boundary conditions that can ensure the survival and development of ecosystems, i.e., the suitable conditions for NBS implementation that are able to leverage the benefits of the natural system. However, the concept of suitability in marine and coastal areas has not been widely explored; it remains an undefined concept encompassing multiple dimensions and therefore requires further investigation and in-depth analysis. Although existing literature has mainly focused on environmental factors shaping suitable conditions for marine ecosystems, this review broadens the perspective by understanding the concurrent elements (e.g., socio-economic) that contribute to determining the feasibility and effectiveness of NBS implementation. In this study, suitability is defined as a spatial and temporal attribute that integrates environmental, socio-economic, and governance parameters to identify suitable conditions under which the most appropriate ecosystem-based interventions can be successfully implemented. By reviewing literature collected within the 2011–2025 timeframe, a period selected to align with the emergence and formalization of the NBS concept, this review aims to i) evaluate the state-of-the-art of the scientific literature on the concepts of NBSs; ii) assess the tools and methods adopted for evaluating the suitability of NBSs in marine and coastal systems; iii) evaluate how suitability models can support and are used in scenario analysis to face the impacts of climate change along the marine and coastal areas.
The remainder of this study is structured into three sections: Section 1 outlines the methodological framework adopted for the data acquisition process underpinning the literature review; Section 2 presents the main results obtained from the screening and analysis of selected papers; and finally, Section 3 synthesizes the main findings, highlighting the main challenges and research gaps.
2 Materials and methods
The selection of the literature dealing with the concept of NBS suitability mapping and modeling in marine and coastal environments was achieved through a multi-phase review process. Specifically, the methodological approach involved four main steps (Figure 1), including i) data collection, ii) scientometric analysis, iii) systematic analysis, and iv) in-depth analysis of papers.
FIGURE 1
2.1 Data collection
The peer-reviewed literature regarding NBS suitability mapping and modeling was searched through a global citation database, i.e., Scopus. Scopus is considered the largest curated bibliographic abstract and citation database (Moher et al., 2009) and was chosen as the main source of literature underpinning this review work. Scopus provides advanced functions to export structured data including citations, bibliographical information, abstracts, keywords, and references, and it includes articles in press and multiple journals (Heilig and Vob, 2014).
Five different queries were developed in Scopus, each focusing on a particular aspect of the research. Specifically, each query aimed to narrow down the list of articles by addressing specific parameters in order to obtain only publications closely related to the topic of interest. The keywords utilized in the queries were selected as ‘umbrella terms’ capable of capturing a broad spectrum of related concepts. For example, the term NBS was used alongside associated terminology (e.g., green infrastructure) as it inherently encompasses multiple dimensions of ecosystem-based interventions, including restoration, protection, and sustainable management. This approach ensured that the search string systematically retrieved a sound range of relevant concepts and practices without the need to exhaustively include each term individually. The multiple queries provided different results in terms of the amount and types of publications available (Table 1). The fifth query was the wider one, providing 1,149 papers. The keywords considered were as follows: ‘NBS, ecosystems, suitability mapping, suitability modeling, and marine and coastal environment,’ spaced out by appropriate Boolean operators (i.e., “AND” and “OR”), which allowed correlating these words and including articles dealing with more than one of the search parameters. Moreover, no restrictions on time (e.g., publishing years), nationality, language, journal, or field of study were applied during the data collection phase in the developed queries, in order to assess the concept and its evolution from a global perspective. The first part of the query string allowed for the identification of publications related to “NBS and suitability mapping” and associated keywords. The overall list of papers and their associated records (e.g., information on titles, authors, abstracts, keywords, affiliations, and journals) was exported as BibTeX files for qualitative and quantitative analysis using the Bibliometrix R-package, with the analysis performed in RStudio (). Papers relevant to this work were screened through the five developed queries, from which potential duplicates were removed.
TABLE 1
| Queries | Keywords of the query | Papers |
|---|---|---|
| First query | TITLE-ABS-KEY ((“NbS” OR “Nature-based Solution*” OR “green infrastructure*”) AND (“Suitability mapping” OR “suitability model”)) | 7 |
| Second query | TITLE-ABS-KEY ((“NbS” OR “Nature-based Solution*” OR “green infrastructure*” OR “marine and coastal ecosystem*” OR “marine environment*”) AND (“Suitability mapping” OR “suitability model”)) | 24 |
| Third query | TITLE-ABS-KEY ((“marine environment*” OR “marine and coastal environment*” OR “coastal environment*”) AND (“suitability mapping” OR “suitability map*” OR “suitability model*” OR “suitability modelling”)) | 34 |
| Fourth query | TITLE-ABS-KEY ((“marine environment*” OR “marine and coastal environment*” OR “coastal environment*” OR “NbS” OR “Nature-based solution*”) AND (“suitability mapping” OR “suitability map*” OR “suitability model*” OR “suitability modelling”)) | 40 |
| Fifth query | TITLE-ABS-KEY ((“NbS” OR “Nature-based Solutions” OR “ecosystem*” OR “marine environment*” OR “coastal area*” OR “marine and coastal environment*”) AND (“suitability model*” OR “suitability modelling” OR “suitability mapping” OR “suitability map*” OR “marine ecosystem model*”)) | 1,149 |
List of queries and the related keywords developed for selecting the papers to review (n = 1,254).
Notably, the queries reflect NBS concepts and definitions that emerged in the preceding years. Even though suitability modeling is a well-known concept and process, particularly used in ecological studies (e.g., habitat suitability and species suitability), its application in relation to NBSs remains limited.
2.2 Scientometric analysis
The scientometric analysis aims to analyze and explore a particular research field in order to identify its components and theoretical frameworks. A further purpose of this type of analysis is to reduce the effort required to analyze a large number of peer-reviewed papers (Heilig and Vob, 2014). This quantitative analysis starts from the metadata of each publication, considering citation information, bibliographic information, abstracts, and keywords. The information gathered in Scopus was processed using the open-source Bibliometrix package, designed for the statistical RStudio software program (), and used for mapping the scientific literature and analyzing keyword network patterns. The tool supports the bibliometric and co-citation analysis by performing several routine actions to facilitate the overall understanding of network patterns and the identification of focal topics ().
2.3 Systematic review
The resulting preliminary list of articles, collected from the five queries developed (Section 2.1) for a total of 1,254 papers, was analyzed in-depth through a systematic analysis. This step of the analysis consists of a rigorous methodological examination of the selected literature, differentiating studies that are insignificant or out of the scope of the review from those that are more critical and prominent and therefore deserving of further investigation (Mulrow, 1994). The systematic review was completed using the PRISMA approach, an iterative and dynamic process that guides authors through a stepwise methodology and meta-analysis to improve efficiency and increase the transparency of the analysis (Moher et al., 2009; Liberati et al., 2009).
This process led to the assessment of the relevance of the different articles to the study, resulting in the selection of studies that focus on suitability mapping and modeling for the implementation of NBSs in marine and coastal environments. Articles were screened following a set of eligibility criteria, including a primary focus on marine and coastal areas, the presentation of methodological approaches addressing suitability assessment and modeling, and, preferably, the inclusion of NBSs. As represented in Figure 1, the screening process follows different phases, thereby allowing the reduction of the overall list of publications and including (i) the selection of articles according to title and keywords’ pertinence to the review topic and scope; (ii) screening by reading the abstract and methodological sections of the publications selected from the earlier step; (iii) final screening by reading the full paper; (iv) selection of the most relevant publications and identification of the key papers, which are representative of the topic of concern; and (v) finally, comparison and discussion of the list of relevant papers according to a set of comparison criteria.
The purpose of the comparison criteria is to depict the main features of the review, specifically focusing on i) the objectives of the study; ii) the geographical scope of the case-study area; iii) the methodological framework developed by the authors to assess NBS suitability; iv) the indicators used to cover different suitability dimensions (e.g., environmental and socio-economic); v) the types of data used to build up the suitability mapping/model; vi) the approach/tool adopted for evaluating and mapping suitability (e.g., GIS-based multi-criteria decision analysis (MCDA) and machine learning (ML)); vii) the strengths and benefits related to the methodology developed and the weaknesses and drawbacks of the implemented methodology; if included, viii) the type of NBSs or ESs assessed; and finally, ix) the integration of climate projections within suitability scenario analysis.
The overall methodological workflow, including the criteria applied for the identification and selection of relevant studies addressing NBS suitability mapping in marine and coastal areas, is presented in Figure 1.
From a total pool of 1,254 papers, collected from the five queries developed, only 26 papers were included in the analysis as relevant to the review’s objective. The table with the full list of 26 papers resulting from the systematic review is provided in Appendix A. Among the 26 papers, 4 were identified as “key papers” (, Syhaid et al. (2020), Mubeen et al. (2021), and ) and were particularly relevant for this review work as they fulfill the majority of the eligibility criteria.
3 Results
3.1 Insights from the scientometric analysis
The results presented in this section consider the literature collected through all five queries (Section 2.1), as displayed in Table 1, and from which duplicates of papers have been removed. The selected literature spans the 2011–2025 timeframe, during which the NBS concept emerged. However, despite the applied query strings specifically addressing marine and coastal environments, a large pool of papers outside the scope of our study was detected. In particular, although our queries were constructed to focus on marine and coastal areas using terms such as marine environment, marine and coastal environment, and coastal areas, they also incorporated references situated within terrestrial environments and environments with a terrestrial/coastal interplay due to spatial mapping being at the core of the studies.
Papers focusing on marine and coastal areas constitute approximately 70% of the assessed case studies among the selected literature, and a smaller number of studies also evaluate NBS applications in rivers and coastal wetlands, which are frequently linked to coastal areas through watersheds, while studies focusing only on terrestrial environments were discarded.
According to the geographical patterns, different ecosystems are suitable in marine and coastal areas, with the most studied ecosystems being seagrasses, corals, mangroves, oyster reefs, and algae (e.g., Cystoseira canopy) (Figure 2). Among the papers analyzed, seagrasses are the most represented ecosystems, reflecting their wide distribution and the extensive literature devoted to this habitat. In particular, most studies assessing the suitability of seagrass meadows focus on Mediterranean coastal areas, where seagrass beds are widespread and ecologically relevant. In contrast, although macroalgae play a key role in ecosystem protection, management, and restoration, contributing to addressing societal challenges, only one study in this review assessed algal distribution at the Mediterranean scale.
FIGURE 2
3.2 Systematic review
3.2.1 Suitability modeling: a concept with multiple interpretations
In the screening of the papers, “habitat suitability mapping” was embedded within the framework of “habitat suitability modeling” or used interchangeably in similarly structured studies. The selection of the literature highlighted an incongruity between suitability mapping in terrestrial environments and that in marine and coastal areas. Although there is extensive literature assessing NBS suitability in the terrestrial system, research on suitability mapping in coastal and marine areas is scarce. We attribute this to a lack of (i) a thorough definition of “marine suitability mapping” and (ii) field studies utilizing suitability mapping in the marine environment. Although suitability modeling is weakly defined in the marine environment, it is intended as a tool to identify the best areas for rehabilitating, restoring, and managing interventions. However, in the assessed literature, the suitability concept has been referred to by different designations and used interchangeably (e.g., spatial suitability mapping, land suitability, and ecosystem suitability), all referring to the conditions for the presence/absence of habitats and species or for the implementation of solutions. Specific features depicting suitability can be extrapolated from the literature identified.
While focusing on marine and coastal areas, the suitability concept has been developed differently by authors. developed a habitat suitability model to identify potential sites for seagrass species restoration through the incorporation of environmental variables such as depth, slope, and sediment yield. Syahid et al. (2020) determined the optimal location for mangrove planting to favor their rehabilitation and restoration, given the decreasing areas suitable for their growth, considering both present and future climate scenarios. Specifically, suitable areas are assessed while taking into account both environmental (i.e., hydrodynamic, geomorphological, and climatic) and socio-economic parameters of the study area, along with three representative concentration pathway (RCP) scenarios. On the other hand, Mubeen et al. (2021) determined spatial suitability mapping for the implementation of NBSs at a large scale by considering multiple factors (e.g., environmental input data) and context conditions (i.e., planning and governance dimensions). Finally, addressed the vulnerability of seagrass meadows, given the crucial ecological role they play in the Mediterranean basin, using a habitat suitability model. In that study, both environmental and anthropogenic variables were considered, with the former derived from in situ measurements (e.g., depth and pH) and the latter being modeled (e.g., human impact and population pressure on marine ecosystems). In this study, the suitability concept was intended as the presence or absence of target species and was, therefore, indicative of suitable conditions for the growth and survival of seagrass meadows.
Drawing on these four key papers (, Syhaid et al. (2020), Mubeen et al. (2021), and ), suitability is addressed as the identification of areas for allocating species or for defining and implementing restoration and management measures. These resulting features are interlinked with one another according to the scope of the suitability analysis. Figure 3 summarizes the key features characterizing the suitability concept as identified in these papers.
FIGURE 3
3.2.2 Methodological approaches and tools applied for suitability analysis and mapping in marine and coastal ecosystems
Although NBS applications are context-specific (), such solutions can be applied at multiple scales, from global and regional to landscape and local or urban levels. Because the scale of analysis determines both the appropriate location for the targeted solution and its feasibility, it directly affects the choice of the model, the indicators to consider, and the data used to develop the suitability model itself (Mubeen et al., 2021). Reflecting this scale dependence, most of the reviewed papers focused on basins, estuaries, or coastline areas in which NBSs have been implemented or ecosystems have been restored (Kane et al., 2015; Mubeen et al., 2021; Prybutok et al., 2021). Only Stankovic et al. (2019) and Antognelli and Vizzari (2021) conducted fine-scale analyses at the landscape level, highlighting the correlations between NBSs and the natural environment to collect data on the ecology of the habitat and consider locally specific socio-economic and political features. Local-scale analyses are better suited to disentangling the relationships among ecosystem functioning, environmental conditions, and societal benefits and needs, thereby helping target management actions that incorporate specific socio-economic and governance features; however, they require detailed data and information that often limit such studies (). In line with this, developed a gulf–bay-scale seagrass habitat suitability model using an MCDA-GIS approach that integrates expert knowledge with environmental variables. The data used allowed us to characterize the study area and identify the main factors determining the environmental suitability of Posidonia oceanica growth. Furthermore, the local-scale analysis also favored the development of plans for seagrass restoration and transplantation.
In this review, we found that studies operating at broader spatial scales tend to rely on environmental indicators that are consistently available and comparable (e.g., hydrodynamic, chemical, geomorphological, and lithological variables). Accordingly, these account for approximately 80% of all indicators reported across the three main categories (environmental, socio-economic, and governance), allowing us to evaluate NBS suitability. In contrast, socio-economic indicators, considered proxies for human activities and infrastructures, were frequently simplified to land-use/land-cover variables, with governance indicators often omitted altogether, leaving a gap in the understanding of the factors that contribute to defining suitable areas and conditions for the implementation of NBSs. Among the environmental variables, slope, land use, distance, depth, salinity, and temperature were the most recurrent indicators in the literature (Figure 4). The literature also diverges on how many indicators to include in the assessment: some authors (Stankovic et al., 2019; ) advocate enlarging the set to account for all variables contributing to suitability, whereas others (Valle et al., 2011; Kane et al., 2015; Linhoss, Camacho, and Ashby, 2016) recommend focusing on fewer indicators. This is a relevant aspect, since the number of indicators considered affects the model type to be used, the input data, and, to a lesser extent, the scale of analysis. Overall, the suggestion to advance toward multi-scale suitability approaches is recurrent, transitioning from large-scale environmental screening to more detailed local-scale analyses that allow the integration of context-specific socio-economic and governance information.
FIGURE 4
Among the multitude of indicators assessed by the authors, environmental indicators are the most recurrent and most frequently evaluated. Environmental conditions are complex, interrelated, and site-specific, and some key indicators are consistently identified in the analyzed literature as fundamental for defining suitability across different ecosystems. Climatic parameters, such as temperature and rainfall rate, are fundamental for mangrove transplantation (; Syahid et al., 2020), while underwater light availability and depth are essential factors influencing seagrass growth (; ). For coral ecosystems, Lauria et al. (2021), Yesson et al. (2017), and Silveira et al. (2021) highlighted the importance of bathymetry and seafloor slope as fundamental determinants of coral growth. Similarly, identified topography and climatic site factors (e.g., precipitation and land surface temperature) as crucial for modeling suitable sites for mangrove afforestation. In addition, Sahana et al. (2022) demonstrated that bioclimatic variables and disturbance indicators (e.g., distance from settlement, population density, and distance from crop field) significantly contribute to habitat suitability for most species.
Several modeling approaches have been applied by the research community to identify suitable areas for the implementation and restoration of ecosystems in marine and coastal areas. GIS-based mapping, multi-criteria decision analysis, indicators/indexes, ML, ecological models (e.g., MaxEnt), statistical and numerical models, and expert-based ranking are some of the most applied methods and tools to analyze and model suitability from small- to large-scale, while providing support for sustainable marine ecosystem management.
Many studies (Linhoss, et al., 2016; Lauria et al., 2021; Yesson et al., 2017; Silva et al., 2019; Silveira et al., 2021; Shafiezadeh et al., 2019) build on ecological models, primarily maximum entropy (MaxEnt), a modeling approach widely used to model the geographic distribution of species, particularly when processing presence-only data. GIS-based tools are used to spatially visualize data through maps, whereas multi-criteria decision analysis combines environmental variables with expert opinions, providing a method to integrate quantitative data with qualitative, expert-based evaluation, which can then support planners and decision-makers in framing management strategies. Frequently, the GIS and MCDA are combined to improve spatial decision-making by integrating multiple criteria and helping decision-makers achieve effective solutions (Kazemi and Akinci, 2018). On the other hand, ML techniques are widely applied to address complex environmental issues by modeling interconnected phenomena and producing suitability models, as demonstrated by and Stankovic et al. (2019). They both used ML approaches to develop a habitat suitability model for modeling the habitat suitability of seagrasses over coastal waters, which were applicable as management and decision support tools. More recently, studies () have adopted remote-sensing techniques to identify optimal sites for restoration actions and collect input data, such as for the application of the MaxEnt modeling approach. The ArcGIS tool is then utilized to spatially visualize the best locations for siting actions.
A different approach for site selection was adopted by Pittman et al. (2022), who framed a seven-step site selection framework, co-designed with stakeholders, to identify and evaluate criteria, review the outcomes from the site selection model (i.e., InVEST), and select a subset of seascapes for NBS interventions. Local knowledge is used here as a foundation for deciding on preferred or unsuitable sites where implementing NBSs would be feasible and accepted, while criteria-weighted methods are applied to prioritize areas.
Among the selected papers, ML techniques account for 34% of the applied methods (Figure 5). Within this macro category, we grouped specific techniques observed in the literature, such as random forest, artificial neural networks, classification tree analysis, bootstrap, and generalized linear models. However, these techniques were generally applied in single studies, whereas MaxEnt and MCDA methods were observed in multiple selected studies, representing 14% of the overall papers, followed by GIS (13.6%) tools. Therefore, the majority of the studies utilized ML, MaxEnt, or MCDA methods to model suitability and assess variable importance, followed by the use of GIS tools to create maps representing suitable areas.
FIGURE 5
3.2.3 Capturing evolving suitability under scenario analysis
The suitable conditions in which NBSs are implemented or restored might vary under climate change scenarios, leading to spatial changes in habitats and environmental variables in the future. Exploring and predicting changes in environmental and climate conditions before they occur can be a crucial task to support policymakers in designing protective and restorative management actions and, particularly, in identifying high-risk areas. Consequently, researchers have begun applying methodologies that integrate scenario analysis to identify changes in suitable areas for the implementation of measures. However, this approach is still in its early stages, with most methodologies applied in the 26 selected papers focusing on a snapshot of current and recent conditions. Only five papers (i.e., ; Silva et al., 2019; Syahid et al., 2020; Kane et al., 2015; and ) evaluated changes in suitable areas against different climate scenarios. Specifically, the baseline models are forced by RCP scenarios of projected changes in environmental conditions (e.g., temperature, pH, and chlorophyll-a) obtained from global climate models developed by the IPCC. Within these five papers, all studies explore variations in the suitable geographical distribution of ecosystems against different climate scenarios based on projections from numerical models of the IPCC. The scenarios are built by considering single years of future scenarios (e.g., 2050, 2070, and 2100) and not timeframes of decades.
determined the spatial changes in the habitat suitability potential for 2050 and 2070 under two RCPs, 2.6 and 8.5. They tested the spatial coverage change of habitat suitability potential under current and future scenarios, observing significant changes in the high suitability potential of current conditions and an increase in the number of no potential zones in the projected future time period. A similar approach was adopted by Silva et al. (2019), who forced the baseline scenario with the four RCP scenarios specified in the AR5 report of the IPCC to simulate the suitable habitat distribution of anchovy for the 2015–2050 time period. They altered the baseline environmental variables (i.e., SST, Chl-a, and upwelling index), which are representative of ocean conditions for the future mid-century period. The vulnerability of coastal wetlands to sea-level rise was assessed by Kane et al. (2015) under the worst scenario RCP 8.5 for 2 years, 2057 and 2100. ArcGIS was then applied to map the cumulative vulnerability and used to identify adaptive management actions. These methodologies classified and ranked suitable conditions supporting ecosystems growth into different categories, thereby identifying and prioritizing areas for intervention in response to their projected decline under climate scenarios. Finally, Syahid et al. (2020) determined the land suitable for planting mangroves today and in the future under RCPs 2.6, 4.5, and 8.5 with two global climate models. They analyzed the differences in each RCP scenario and their effects on mangrove land suitability, assessing variations in hydrodynamic, geomorphological, climatic, and socio-economic parameters. The future mangrove suitability assessment was also conducted by , who considered 14 parameters to generate future scenarios of mangrove growth under RCPs 4.5, 6.0, and 8.5. A different perspective on the integration of future climate change was adopted by Pittman et al. (2022), who identified and prioritized coastal seascape areas for NBSs based on their potential to contribute to climate change mitigation. In this approach, climate scenarios are not explicitly considered; instead, specific metrics associated with climate change mitigation impacts (e.g., blue carbon) are evaluated.
Examining the set of papers applying scenario analysis, authors primarily analyzed exogenic pressures to simulate future changes. Temperature is the variable considered by almost all assessed studies (; Syahid et al., 2020; ; Kane et al., 2015), followed by precipitation (; Syahid et al., 2020; ), sea surface temperature, and chlorophyll-a (Silva et al., 2019). Beyond the climatic and geomorphological variables (slope and elevation), also integrated socio-economic factors (e.g., population and land use) to simulate the changes in the distribution of mangroves under the scenario analysis.
4 Discussion
4.1 From concept to action: unpacking suitability for effective environmental management
The concept of suitability has been defined in various ways in the literature. Steiner (1983) described suitability analysis as “the process of determining the fitness, or the appropriateness, of a given tract of land for a specified use.” Thuiller and Münkemüller (2010) further refined this definition, highlighting suitability models as tools to identify key indicators influencing species occurrence and abundance, along with the optimal relationships between species distribution and environmental factors (e.g., climate conditions, landscape structure, resource availability, and biotic interactions). These definitions emphasize the multidimensional nature of NBS suitability, which depends on a combination of environmental, socio-economic, and governance factors. However, the review of the SoA literature revealed that these dimensions are rarely considered together in multi-criteria evaluations (MCEs) (; Mubeen et al., 2021; Syahid et al., 2020).
The definitions applied in marine and coastal domains contrast with those commonly used for terrestrial environments. In this setting, to refine the suitability concept, terrestrial studies, initially excluded due to their geographic scope, were reconsidered. Among these, some studies (Saha and Paul, 2021; ; Firmansyah et al., 2019; ) focused on modeling NBS suitability for green infrastructure (GI) placement. For instance, suitability modeling was utilized to assess water quality and enhance urban planning strategies for cleaner water access in densely populated and industrialized cities or to guide urban GI planning, identifying optimal locations for interventions to maximize benefits for both ecosystems and people. These studies demonstrate how, in terrestrial settings, suitability mapping already integrates environmental, socio-economic, and governance factors to identify optimal areas for ecosystem implementation, particularly in urbanized regions. Such broader approaches can assist decision-makers by informing sustainable land-use strategies while maximizing societal benefits (; Saha and Paul, 2021). Typically conducted at fine spatial scales, terrestrial NBS suitability assessments conceptualize GI as interconnected networks of green spaces, rather than isolated interventions, thereby promoting holistic solutions and a more systemic approach to adaptation planning (; ). In contrast, NBS suitability studies in marine and coastal areas are generally conducted at broader spatial scales and primarily focus on spatial and temporal attributes that synthesize complex environmental parameters, such as slope, distance, and salinity. Environmental conditions form the baseline for ecosystem survival and service provision, yet effective NBS implementation also requires the consideration of system dynamics, multi-scale changes, and the potential disconnection between short-term actions and long-term outcomes (Kabisch et al., 2016; Raymond et al., 2017; Ozkiper et al., 2024). However, assessing and monitoring NBS effectiveness is crucial for their successful adoption, ensuring that they address targeted challenges and deliver intended benefits (Raymond et al., 2017).
To define proper management actions, the scale of analysis affects the type and choice of indicators to consider. This is particularly relevant in complex socio-ecological systems, such as marine and coastal environments, which require careful planning and location-specific analysis, along with the characterization of baseline conditions and the safe operating space in which ecosystem growth and wellness are ensured. The concept of the “safe operating space” has emerged to set the boundaries for humanity and the planet’s biophysical subsystems and processes (Rockström et al., 2009). This idea is particularly valuable in the management of NBSs as it establishes a range of “safe” values within which we can operate without compromising the stability and resilience of the ecosystem or NBSs (Scheffer et al., 2015; Rockström et al., 2009). The conditions defining the safe operating space are frequently species-specific, as seen from the literature review, such as seagrass meadows requiring oxygenated, oligotrophic, and transparent water characteristics and gentle slopes (; ), while slope and bathymetry are relevant factors influencing corals’ growth (Lauria et al., 2021; Silveira et al., 2021). Beyond this comfort zone, stress-induced changes, such as those driven by climate change, may trigger ecosystems collapse, from which recovery is difficult. Transgressing boundary conditions is expected to undermine the resilience of ecosystems, driving large-scale responses with aggregate consequences that could lead to increased risks to one or more aspects of human wellbeing (Mace et al., 2014). This involves managing cumulative impacts by evaluating the combined effects of endogenic (i.e., managed pressures or those emanating from within the systems, such as biological disturbances or chemical pollution) and exogenic pressures (i.e., unmanaged pressures emanating from outside the system, such as sea surface temperature and precipitation) to support integrated management that enhances ecosystem resilience to environmental changes and drives decision-makers in conserving and restoring ecosystems (Simeoni et al., 2023; Scheffer et al., 2015).
Defining ecosystems’ safe operating space and, in turn, the suitable conditions for NBS planning and implementation is a key step for effectively contributing to sustainable growth while supporting the transformative change needed to tackle the plethora of crisis facing ocean spaces (i.e., climate change, pollution, and biodiversity loss) (UNFCCC News, 13). To achieve that, documenting and identifying synergies and trade-offs between ES and stakeholders’ expectations is fundamental for successfully restoring and protecting ecosystems, accounting for future environmental changes and potential impacts through time and space (). This is particularly relevant to avoid the mismatch that might exist between ES goals and governance needs, which can otherwise hinder the achievement of effective on-the-ground outcomes (Primmer and Furman, 2012). Therefore, for the successful deployment of NBS measures, they need to be embedded within a broader and coherent strategy aligned with research and policy agendas. This helps reduce the risk of misinterpretation, misapplication, or non-acceptance of solutions () and is essential for engaging stakeholders who must navigate the diverse positive and negative consequences arising from both action and inaction (Simpson et al., 2021).
At the same time, the multi-dimensional nature of the NBS concept, evident in the review findings and the NBS definitions, highlights the need for enabling conditions that facilitate and accelerate transformative approaches and make changes toward climate resilience possible. Under such conditions, the Mission Adaptation to Climate Change () set four enabling domains, namely, Knowledge and Data, Governance and Engagement, Behavioral Change, and Finances and Resources, that can support and mainstream NBS implementation. These domains should align with the dimensions of suitability identified in this review, acting as tools to adapt to changing conditions, which are continuously evolving and require the management of multiple challenges inherent to NBS deployment.
4.2 Indicators and modeling approaches for suitability assessment
The results of this review have highlighted the main methodologies and tools used to assess NBS suitability across different marine and coastal ecosystems. A variety of methodologies are applied, ranging from ecological and habitat suitability models (e.g., MaxEnt) to ML-based models (e.g., random forest, artificial neural networks, classification tree analysis, and bootstrap) and multi-decision criteria methods (e.g., AHP and MCDA). These methodologies are frequently combined with GIS tools to spatially visualize suitable areas for intervention. However, selecting appropriate methodologies for evaluating areas suitable for NBS implementation requires the use of proper, context-specific indicators. These indicators serve as proxies for complex phenomena, reflect changes occurring over time, and are useful for supporting the definition of management activities and model development (Hattam et al., 2015), as in the case of suitability assessment. Identifying variables to address societal challenges and support environmental decision-making is necessary to capture specific dimensions of the phenomena and is critical for detecting changes across space and time ().
As the typology of indicators and variables is instrumental in determining the type of method or tool to adopt, this review revealed a two-fold use of them. On one hand, indicators are fundamental for defining the suitability assessment and, hence, the model type, as some methods (e.g., ML) require large amounts of data and variables; on the other hand, indicators are adopted to monitor the effectiveness of the solutions in place. In the majority of the studies assessed in this review, effectiveness is poorly considered, and is not the focus of this work, but it can be viewed as a means to monitor, evaluate, and learn from the actions adopted (). Evaluating the effectiveness of NBSs implemented in suitable areas is a key aspect for ensuring the long-term success of activities, avoiding poor or failed practices, and testing the efficacy of previously identified suitable areas. Moreover, given the evidence-based nature of NBSs, understanding their performance hinges on spatio-temporal indicator evaluations, which is why marine–coastal NBSs are frequently compared against physically similar habitat types (Gittman et al., 2016; McGlathery et al., 2012). Evidence-based guidelines are essential for improving the understanding of marine NBSs and their suitability while also advancing methodologies and enhancing marine spatial planning overall (O’Leary et al., 2023). As found in the review, standardized evaluation frameworks should integrate multiple indicators, including environmental performance, stakeholder engagement, transferability, and wellbeing impacts (Kabisch et al., 2016). However, knowledge gaps regarding NBS effectiveness for climate adaptation and their broader socio-environmental impacts remain. Successful NBS implementation largely depends on stakeholder engagement, clear communication of best practices, and regulatory frameworks that align with context-specific needs and objectives (Hansen and Pauleit, 2014; Kabisch et al., 2016). Suitability mapping is a valuable tool for integrating this knowledge, aiding decision-makers in identifying effective short- and long-term strategies. Without coordinated, large-scale approaches, site-specific implementation may result in fragmented and inconsistent measures, limiting the achievement of multiple goals (such as biodiversity conservation, human wellbeing, and climate resilience) and potentially undermining effective governance ().
4.3 Moving on NBS scaling up processes: from scenarios to implementation schemes
NBS are well-recognized priorities in national climate and biodiversity policies; however, they continue to be implemented on small scales and on an ad hoc basis (). A lack of awareness and gaps in technical capacity have hindered their application at larger scales, and major obstacles persist in demonstrating and quantifying NBS benefits and performance over time, without which they tend to be outperformed by alternative solutions (OECD, 2021). The upscaling process of NBSs is asserted in one of the principles (i.e., principle 6) defined by the IUCN on its NBS definitional framework, which guides the operationalization of the NBSs to address global societal challenges while promoting interaction across multiple scales ().
However, the scale considered for planning and implementing NBSs substantially affects their ability to deliver expected outcomes, and it might be dependent on processes occurring at larger scales (). Therefore, large-scale planning processes are needed to build connectivity among interventions and to enhance system resilience and the effectiveness of proposed actions. This also relates to the temporal dimension of actions as ecosystems evolve over time in response to external pressures and as part of intrinsic natural processes (). Nonetheless, the NBS implementation requires principles and guidelines that drive interventions in an integrated manner and support the policy design. Integrated management activities, aligned with marine-scale planning and policy, are central to the successful improvement of ecosystems and human wellbeing. Some NBSs produce additional co-benefits when upscaled, strengthening institutions and stakeholder confidence, while also requiring multi-actor partnerships (Raymond et al., 2017). NBSs can integrate with other types of solutions to support a broader range of social and environmental benefits through targeted and developed through targeted and connected interventions (). Integration with the landscape context is critical for the success of management interventions, alongside consideration of the causes leading to environmental degradation. In addition, planning at the landscape level allows consideration of ecological interactions among ecosystems located within the same landscape, which might enhance or hinder management successes ().
Given the multiple approaches that the NBSs concept embraces in addressing specific or multiple challenges, their large-scale implementation is particularly desirable in marine–coastal systems to achieve socially and ecologically meaningful outcomes without compromising the ecological integrity of the area (Lovelock and Brown, 2019). Cross-sectoral and cross-governmental approaches are needed to facilitate the collaboration between multiple actors to create synergies and avoid trade-offs between policy objectives, leading to fostering accountability for NBSs and to promoting their integration into spatial planning programs (OECD, 2021).
5 Conclusion
In this study, we aimed to investigate the scientific literature on approaches, methodologies, tools, and frameworks for NBS suitability modeling and mapping in marine and coastal areas. Specifically, an iterative scientometric and systematic literature review of relevant studies was carried out to identify gaps and trends in this research field. More than 1,000 articles were initially identified, from which a set of 22 papers was selected as representative of the NBS suitability modeling in marine and coastal areas.
The first finding from the screening of the papers shows that the concept of suitability mapping is closely associated with habitat suitability modeling and is referred to by different designations. In particular, three key features were identified as contributors to the definition of suitability analysis, namely, study area, indicators and models, and future scenarios, which guide the choice of solutions and the most suitable actions to be implemented. The performed review highlighted that to define the suitability, not only environmental features but also socio-economic and governance factors play a pivotal role. However, these conditions are rarely assessed concurrently, instead favoring the environmental dimension to characterize suitable areas. Environmental conditions are crucial for the suitability assessment, particularly through key indicators such as slope and land use, while socio-economic and governance dimensions remain poorly integrated into the investigated suitability modeling approaches. Most of the revised papers focus on ecological and environmental conditions of suitability, highlighting the environmental benefits provided by NBSs.
A key feature of suitability assessment is the scale of analysis, which influences the choice of indicators considered when establishing appropriate management actions. This is particularly relevant in complex socio-ecological systems, such as marine and coastal environments, which require careful planning and locally specific analysis, along with the identification and framing of baseline conditions and safe operating space boundaries within which we can establish states for the growth and achievement of good environmental status of ecosystems.
The analysis emphasized the integration of climate scenarios (against different RCPs) to examine temporal and spatial changes in NBS suitability, providing useful insights for more robust decision-making processes in the selection of adaptive solutions.
Drawing on these findings, this review identified key challenges that need to be addressed in future suitability analyses to provide more accurate guidance and support to policymakers in managing sustainable coastal ecosystem management. This integrates with the future developments of this work, which will address the concept of safe operating space and unravel and deepen the knowledge on socio-economic and political factors that contribute to identifying suitable and coherent solutions to implement in marine and coastal areas. This will address NBS suitability analysis in marine and coastal areas, supporting decision-makers in implementing coastal management measures and adapting and mitigating climate change impacts through nature-based approaches. A further challenge for the scientific community is to develop and test advanced models that capture the complex interrelations among multiple pressures affecting marine and coastal areas, which increase the complexity and uncertainty of suitability models. Based on these analyses, future works should incorporate future climate scenarios, governance scenarios, and socio-economic constraints and the role of NBSs in addressing them.
Statements
Author contributions
EA: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft. EM: Conceptualization, Data curation, Investigation, Methodology, Writing – original draft. EF: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – review and editing. JB: Writing – review and editing. RS: Conceptualization, Writing – review and editing. AM: Supervision, Writing – review and editing. AC: Conceptualization, Supervision, Validation, Writing – review and editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. The research leading to these results has been partially funded under the PhD program in Science and Management of Climate Change of Ca' Foscari University of Venice (PhD research grant), from the EU's Horizon 2020 research and innovation program under Grant Agreement No 869710 (EU-MaCoBioS https://macobios.eu/), the Horizon Europe program under Grant Agreement No 101112972 (EU-DesirMED https://www.desirmed.eu/), and from the Interreg IT-HR AcquaGuard (Project ID: ITHR0200497).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The handling editor PF declared a past co-authorship with the author EA.
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The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2026.1591204/full#supplementary-material
References
1
AlbertC.SchröterB.HaaseD.BrillingerM.HenzeJ.HerrmannS.et al (2019). Addressing societal challenges through nature-based solutions: how can landscape planning and governance research contribute?Landsc. Urban Plan.182 (October 2018), 12–21. 10.1016/j.landurbplan.2018.10.003
2
AnandV.OinamB.SinghI. H. (2021). Predicting the current and future potential spatial distribution of endangered Rucervus Eldii Eldii (Sangai) using MaxEnt model. Environ. Monit. Assess.193 (3), 147. 10.1007/s10661-021-08950-1
3
AnderssonE.BorgströmS.McPhearsonT. (2017). “Double insurance in dealing with extremes: ecological and social factors for making nature-based solutions last,” in Nature-based solutions to climate change adaptation in urban areas: linkages between science, policy and practice. Editors KabischN.KornH.StadlerJ.BonnA. (Cham: Springer International Publishing), 51–64. 10.1007/978-3-319-56091-5_4
4
AntognelliS.VizzariM. (2021). Assessing ecosystem and urban services for landscape suitability mapping. Appl. Sci11 (17), 8232. 10.3390/app11178232
5
ApudA.FaggianR.SpositoV.MartinoD. (2020). Suitability analysis and planning of green infrastructure in Montevideo, Uruguay. Available online at: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096405003&doi=10.3390%2Fsu12229683&partnerID=40&md5=172ab850ad16b51733b88892dd596d05 (Accessed April 5, 2022).
6
AriaM.CuccurulloC. (2017). “Bibliometrix: an R-Tool for comprehensive science mapping analysis.” J. Inf.11 (4): 959–975. 10.1016/j.joi.2017.08.007
7
BakirmanT.GumusayM. U. (2020). A novel GIS-MCDA-Based spatial habitat suitability model for Posidonia Oceanica in the Mediterranean. Environ. Monit. Assess.192 (4), 231. 10.1007/s10661-020-8198-1
8
BalvaneraP.BraumanK. A.CordA. F.DrakouE. G.GeijzendorfferI. R.KarpD. S.et al (2022). Essential ecosystem service variables for monitoring progress towards sustainability. Curr. Opin. Environ. Sustain.54. 10.1016/j.cosust.2022.101152
9
BarnuevoA.AsaedaT.SanjayaK.KanesakaY.FortesM. (2017). Drawbacks of mangrove rehabilitation schemes: lessons learned from the large-scale mangrove plantations. Estuar. Coast. Shelf Sci.198 (B), 432–437. 10.1016/j.ecss.2017.02.015
10
BayraktarovE.SaundersM. I.AbdullahS.MillsM.BeherJ.PossinghamH. P.et al (2016). The cost and feasibility of marine coastal restoration. Ecol. Appl.26 (4), 1055–1074. 10.1890/15-1077
11
BellamyC. C.van der JagtA. P. N.BarbourS.SmithM.MoseleyD. (2017). A spatial framework for targeting urban planning for pollinators and people with local stakeholders: a route to healthy, blossoming communities?Environ. Res.158 (June), 255–268. 10.1016/j.envres.2017.06.023
12
BrownC. J.BroadleyA.AdameM. F.BranchT. A.TurschwellM. P.ConnollyR. M. (2019). The assessment of fishery status depends on fish habitats. Fish Fish.20 (1), 1–14. 10.1111/faf.12318
13
CaillonS.CullmanG.VerschuurenB.SterlingE. J. (2017). Moving beyond the human - nature dichotomy through biocultural approaches. Ecol. Soc.22 (4).
14
CalliariE.StaccioneA.MysiakJ. (2019). An assessment framework for climate-proof nature-based solutions. Sci. Total Environ.656, 691–700. 10.1016/j.scitotenv.2018.11.341
15
CatucciE.ScardiM. (2020). A machine learning approach to the assessment of the vulnerability of Posidonia Oceanica meadows. Ecol. Indic.108 (June 2019), 105744. 10.1016/j.ecolind.2019.105744
16
ChakrabortyS.SahooS.MajumdarD.SahaS.RoyS. (2019). Future mangrove suitability assessment of andaman to strengthen sustainable development. J. Clean. Prod.234, 597–614. 10.1016/j.jclepro.2019.06.257
17
ClaudetJ.BoppL.CheungW. W. L.DevillersR.Escobar-BrionesE.HauganP.et al (2020). A roadmap for using the UN decade of Ocean science for sustainable development in support of science, policy, and action. One Earth2 (1), 34–42. 10.1016/j.oneear.2019.10.012
18
Cohen-ShachamE.WaltersG.JanzenC.MaginnisS. (2016). Nature-based solutions to address global societal challenges. Gland, Switzerland. 10.2305/iucn.ch.2016.13.en
19
Cohen-ShachamE.AndradeA.DaltonJ.DudleyN.JonesM.KumarC.et al (2019). Core principles for successfully implementing and upscaling nature-based solutions. Environ. Sci. Policy98 (June 2018), 20–29. 10.1016/j.envsci.2019.04.014
20
DarkhalH.KabiriK.ShokriM. R. (2025). Optimizing coral reef restoration: a GIS-based site selection for coral nurseries in Kish Island, the Persian Gulf. J. Nat. Conservation86. 10.1016/j.jnc.2025.126898
21
DuffyJ. E.LefcheckJ. S.Stuart-SmithR. D.NavarreteS. A.EdgarG. J. (2016). Biodiversity enhances Reef fish biomass and resistance to climate change. Proc. Natl. Acad. Sci. U. S. A.113 (22), 6230–6235. 10.1073/pnas.1524465113
22
DumitruA.FrantzeskakiN.CollierM. (2020). Identifying principles for the design of robust impact evaluation frameworks for nature-based solutions in cities. Environ. Sci. Policy112 (May), 107–116. 10.1016/j.envsci.2020.05.02
23
Dutta RoyA.MohanM.HendyI.AlMeallaR.WattM. S.BurtJ. A.et al (2025). Optimizing mangrove afforestation site selection in gulf cooperation council nations using remote sensing and machine learning. Sci. Total Environ.988. 10.1016/j.scitotenv.2025.179805
24
EEA (2021). Nature-based solutions in Europe. Available online at: https://www.eea.europa.eu/publications/nature-based-solutions-in-europe (Accessed April 7, 2022).
25
EEA (2023). Scaling nature-based solutions for climate resilience and nature restoration. Available online at: https://www.eea.europa.eu/publications/scaling-nature-based-solutions? (Accessed September 13, 2024).
26
EggermontH.BalianE.AzevedoJ. M. N.BeumerV.BrodinT.ClaudetJ.et al (2015). Nature-based solutions: new influence for environmental management and research in Europe. GAIA - Ecol. Perspect. Sci. Soc.24 (4), 243–248. 10.14512/gaia.24.4.9
27
European Commision (2024). Climate mission implementation plan. Available online at: https://research-and-innovation.ec.europa.eu/system/files/2021-09/climat_mission_implementation_plan_final_for_publication.pdf (Accessed August 17, 2025).
28
FAO (2022). “The state of world fisheries and aquaculture 2022,” in In brief to the state of world fisheries and aquaculture 2022. 10.4060/cc0463en
29
FerrarioF.BeckM. W.StorlazziC. D.MicheliF.ShepardC. C.AiroldiL. (2014). The effectiveness of coral reefs for coastal hazard risk reduction and adaptation. Nat. Commun.5 (May), 1–9. 10.1038/ncomms4794
30
FirmansyahF.YusufM.SetiawanP. R.SusetyoC. (2019). Ecosystem services-based land use suitability analysis in East Java province. IOP Conf. Ser. Earth Environ. Sci.340 (1), 012020. 10.1088/1755-1315/340/1/012020
31
FirthL. B.KnightsA. M.BridgerD.EvansA. J.MieszkowskaN.MooreP. J.et al (2016). Ocean sprawl: challenges and opportunities for biodiversity management in a changing world. Oceanogr. Mar. Biol. An Annual Review54, 189–262. 10.1201/9781315368597
32
GittmanR. K.PetersonC. H.CurrinC. A.Joel FodrieF.PiehlerM. F.BrunoJ. F. (2016). Living shorelines can enhance the nursery role of threatened estuarine habitats. Ecol. Appl.26 (1), 249–263. 10.1890/14-0716.1/suppinfo
33
Gómez MartínE.CostaM. M.Schwerdtner MáñezK. (2020). An operationalized classification of nature based solutions for water-related hazards: from theory to practice. Ecol. Econ. 167, 106460. 10.1016/j.ecolecon.2019.106460
34
HansenR.PauleitS. (2014). From multifunctionality to multiple ecosystem services? A conceptual framework for multifunctionality in green infrastructure planning for urban areas. AMBIO43 (4), 516–529. 10.1007/s13280-014-0510-2
35
HattamC.AtkinsJ. P.BeaumontN.BorgerT.Bohnke-HenrichsA.BurdonD.et al (2015). Marine ecosystem services: linking indicators to their classification. Ecol. Indic.49, 61–75. 10.1016/j.ecolind.2014.09.026
36
HeeryE. C.BishopM. J.CritchleyL. P.BugnotA. B.AiroldiL.Mayer-PintoM.et al (2017). Identifying the consequences of ocean sprawl for sedimentary habitats. J. Exp. Mar. Biol. Ecol.492, 31–48. 10.1016/j.jembe.2017.01.020
37
HeiligL.VobS. (2014). A scientometric analysis of cloud computing literature. IEEE Trans. Cloud Comput.2 (3), 266–278. 10.1109/TCC.2014.2321168
38
HughesT. P.AndersonK. D.ConnollyS. R.HeronS. F.KerryJ. T.LoughJ. M.et al (2018). Spatial and temporal patterns of mass bleaching of corals in the anthropocene. Science359 (6371), 80–83. 10.1126/science.aan8048
39
IPBES (2019). Global assessment report of the intergovernmental science-policy platform on biodiversity and ecosystem services. Editors E. S. Brondízio, J. Settele, S. Díaz, H. T. Ngo (Bonn, Germany: IPBES secretariat). Available online at: https://ipbes.net/global-assessment%0Ahttps://ipbes.net/global-assessment-report-biodiversity-ecosystem-services (Accessed October 21, 2023).
40
IPCC WGII AR6 (2022). Climate change 2022 - impacts, adaptation and vulnerability summary for policymakers. Camb. Univ. Press21 (1), 1–9. Available online at: http://journal.um-surabaya.ac.id/index.php/JKM/article/view/2203 (Accessed October 21, 2023).
41
IUCN (2020). Guidance for using the IUCN global standard for nature-based solutions: first editions. Guid. Using IUCN Glob. Stand. Nature-Based Solutions First Ed.10.2305/iucn.ch.2020.09.en
42
KabischN.FrantzeskakiN.PauleitS.NaumannS.DavisM. K.ArtmannM.et al (2016). Nature-based solutions to climate change mitigation and adaptation in urban areas: perspectives on indicators, knowledge gaps, barriers, and opportunities for action. Ecol. Soc.21 (2), art39. 10.5751/ES-08373-210239
43
KaneH. H.FletcherC. H.Neil FrazerL.AndersonT. R.BarbeeM. M. (2015). Modeling sea-level rise vulnerability of coastal environments using ranked management concerns. Clim. Change131 (2), 349–361. 10.1007/s10584-015-1377-3
44
KazemiH.AkinciH. (2018). A land use suitability model for rainfed farming by multi-criteria decision-making analysis (MCDA) and geographic information system (GIS). Ecol. Eng.116 (February), 1–6. 10.1016/j.ecoleng.2018.02.021
45
LaP. M. K.SerraK.JoynerT. A.HumphriesA. (2015). Assessing shoreline exposure and oyster habitat suitability maximizes potential success for sustainable shoreline protection using restored oyster reefs. PeerJ3, e1317. 10.7717/peerj.1317
46
LambJ. B.van de WaterJ. A. J. M.BourneD. G.CraigA.HeinM. Y.FiorenzaE. A.et al (2017). Seagrass ecosystems reduce exposure to bacterial pathogens of humans, fishes, and invertebrates. Science355 (6326), 731–733. 10.1126/science.aal1956
47
LauriaV.MassiD.FiorentinoF.MilisendaG.CillariT. (2021). Habitat suitability mapping of the black coral Leiopathes glaberrima to support conservation of vulnerable marine ecosystems. Sci. Rep.11 (1), 1–12. 10.1038/s41598-021-95256-4
48
LiberatiA.AltmanD. G.TetzlaffJ.MulrowC.GøtzscheP. C.IoannidisJ. P. A.et al (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ Clin. Res. Ed.339, b2700. 10.1136/bmj.b2700
49
LinhossA. C.CamachoR.AshbyS. (2016). Oyster habitat suitability in the Northern Gulf of Mexico. J. Shellfish Res.35 (4), 841–849. 10.2983/035.035.0412
50
LotzeH. K.LenihanH. S.BourqueB. J.BradburyR. H.CookeR. G.KayM. C.et al (2006). And Coastal seas. Science312 (June), 1806–1809. 10.1126/science.1128035
51
LovelockC. E.BrownB. M. (2019). Land tenure considerations are key to successful mangrove restoration. Nat. Ecol. Evol.3, 1135. 10.1038/s41559-019-0942-y
52
MaceG. M.ReyersB.AlkemadeR.BiggsR.ChapinI. I. I. F. S.CornellS. E.et al (2014). Approaches to defining a planetary boundary for biodiversity. Glob. Environ. Change28, 289–297. 10.1016/j.gloenvcha.2014.07.009
53
MaesJ.JacobsS. (2017). Nature-based solutions for europe’s sustainable development. Conserv. Lett.10 (1), 121–124. 10.1111/conl.12216
54
McGlatheryK. J.ReynoldsL. K.ColeL. W.OrthR. J.MarionS. R.SchwarzschildA. (2012). Recovery trajectories during state change from bare sediment to eelgrass dominance. Mar. Ecol. Prog. Ser.448, 209–221. 10.3354/meps09574
55
McleodE.Bruton-AdamsM.FörsterJ.FrancoC.GainesG.GorongB.et al (2019). Lessons from the Pacific Islands - adapting to climate change by supporting social and ecological resilience. Available online at: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068625165&doi=10.3389%2Ffmars.2019.00289&partnerID=40&md5=6886d2a8439a45e3950c8566d505c2ae.
56
MoherD.LiberatiA.TetzlaffJ.AltmanD. G.AltmanD.AntesG.et al (2009). Preferred reporting items for systematic reviews and Meta-analyses: the PRISMA statement. PLoS Med.6 (7), e1000097. 10.1371/journal.pmed.1000097
57
MubeenA.RuangpanL.VojinovicZ.Sanchez TorrezA.PlavšićJ. (2021). Planning and suitability assessment of large-scale nature-based solutions for flood-risk reduction. Available online at: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110342303&doi=10.1007%2Fs11269-021-02848-w&partnerID=40&md5=1d178ce71cf00409a7445ac6d407ba5d (Accessed April 5, 2022).
58
MulrowC. D. (1994). Rationale for systematic reviews. Br. Med. J.309, 597–599.
59
OECD (2021). “Scaling up nature-based solutions to tackle water-related climate risks,” in Scaling up nature-based solutions to tackle water-related climate risks. 10.1787/736638c8-en
60
OzkiperO.AllegriE.BianconiA.PhamH. V.FurlanE.SimideR.et al (2024). A GIS-MCDA approach to map environmental suitability of Posidonia oceanica meadows as blue nature-based solutions in the Mediterranean eco-region. Sci. Total Environ.955, 176803. 10.1016/j.scitotenv.2024.176803
61
O’LearyB. C.FonsecaC.CornetC. C.de VriesM. B.Karima DegiaA.FaillerP.et al (2023). Embracing nature-based solutions to promote resilient marine and coastal ecosystems. Nature-Based Solutions3 (December 2022), 100044. 10.1016/j.nbsj.2022.100044
62
PittmanS. J.StamoulisK. A.AntonopoulouM.DasH. S.ShahidM.DelevauxJ. M.et al (2022). Rapid site selection to prioritize coastal seascapes for nature-based solutions with multiple benefits. Front. Mar. Sci.9, 832480. 10.3389/fmars.2022.832480
63
PörtnerH. O.ScholesR. J.AgardJ.ArcherE.ArnethA.BaiX.et al (2021). IPBES-IPCC co-sponsored workshop report on biodiversity and climate change. IPBES and IPCC. 10.5281/zenodo.4782538
64
PrimmerE.FurmanE. (2012). Operationalising ecosystem service approaches for governance: do measuring, mapping and valuing integrate sector-specific knowledge systems?Ecosyst. Serv.1 (1), 85–92. 10.1016/j.ecoser.2012.07.008
65
PrybutokS.NewmanG.AtobaK.SansomG.TaoZ. (2021). Combining co$ting Nature and suitability modeling to identify high flood risk areas in need of nature-based services. Land10 (8), 853. 10.3390/land10080853
66
RaymondC. M.FrantzeskakiN.KabischN.BerryP.BreilM.NitaM. R.et al (2017). A framework for assessing and implementing the Co-Benefits of nature-based solutions in urban areas. Available online at: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025591068&doi=10.1016%2Fj.envsci.2017.07.008&partnerID=40&md5=a4b2136c49e24ad8f09bfeb7b761e6b7 (Accessed October 21, 2023).
67
RockströmJ.SteffenW.NooneK.PerssonÅ.ChapinF. S.LambinE. F.et al (2009). A safe operation space for humanity. Nature461 (September), 472–475. 10.1038/461472a
68
SahaP.PaulB. (2021). Identification of potential strategic sites for city planning based on water quality through GIS-AHP-Integrated model. Environ. Sci. Pollut. Res.28 (18), 23073–23086. 10.1007/s11356-020-12292-9
69
SahanaM.AreendranG.SajjadH. (2022). Assessment of suitable habitat of mangrove species for prioritizing restoration in coastal ecosystem of Sundarban biosphere reserve, India. Sci. Rep.12, 10.1038/s41598-022-24953-5
70
SandiferP. A.ArianaE.S.-G. (2014). Connecting stressors, ocean ecosystem services, and human health. Nat. Resour. Forum38 (3), 157–167. 10.1111/1477-8947.12047
71
SaundersM. I.DoropoulosC.BayraktarovE.BabcockR. C.GormanD.EgerA. M.et al (2020). Bright spots in coastal marine ecosystem restoration. Available online at: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098073327&doi=10.1016%2Fj.cub.2020.10.056&partnerID=40&md5=8c6799d8ddebd38090460cfd536401f6 (Accessed October 21, 2023).
72
SchefferM.BarrettS.CarpenterS. R.FolkeC.GreenA. J.HolmgrenM.et al (2015). Creating a safe operating space for iconic ecosystems. Science347 (6228), 1317–1319. 10.1126/science.aaa3769
73
SeligE. R.HoleD. G.AllisonE. H.ArkemaK. K.McKinnonM. C.ChuJ.et al (2019). Mapping global human dependence on marine ecosystems. Conserv. Lett.12 (2), e12617. 10.1111/conl.12617
74
ShafiezadehM.MoradiH.FakheranS.PourmanafiS.SennJ. (2019). Integrated coastal-terrestrial conservation planning for landscape-scale reserve design in southeastern Iran. Ecol. Indic.103 (February), 756–765. 10.1016/j.ecolind.2019.03.006
75
SilvaC.LeivaF.LastraJ. (2019). Predicting the current and future suitable habitat distributions of the anchovy (Engraulis Ringens) using the maxent model in the coastal areas off central-northern Chile. Fish. Oceanogr.28 (2), 171–182. 10.1111/fog.12400
76
SilveiraC. B. L. daStrenzelG. M. R.MaidaM.GasparA. L. B.FerreiraB. P. (2021). Coral Reef mapping with remote sensing and machine learning: a nurture and nature analysis in marine protected areas. Remote Sens.13 (15). 10.3390/rs13152907
77
SimeoniC.FurlanE.PhamH. V.CrittoA.de JuanS.TrégarotE.et al (2023). Evaluating the combined effect of climate and anthropogenic stressors on marine and coastal ecosystems: insights from a systematic review of cumulative impact assessment approaches. Sci. Total Environ.861. 10.1016/j.scitotenv.2022.160687
78
SimpsonN. P.MachK. J.ConstableA.HessJ.HogarthR.HowdenM.et al (2021). A framework for complex climate change risk assessment. One Earth4 (4), 489–501. 10.1016/j.oneear.2021.03.005
79
StankovicM.KaewsrikhawR.RattanachotE.PrathepA. (2019). Modeling of suitable habitat for small-scale seagrass restoration in tropical ecosystems. Estuar. Coast. Shelf Sci.231 (November), 106465. 10.1016/j.ecss.2019.106465
80
SteinerF. (1983). Resource suitability: methods for analyses. Environ. Manag.7 (5), 401–420.
81
SyahidL. N.SaktiA. D.VirtrianaR.WikantikaK.WindupranataW.TsuyukiS.et al (2020). Determining optimal location for mangrove planting using remote sensing and climate model projection in Southeast Asia. Remote Sens.12 (22), 1–29. 10.3390/rs12223734
82
ThuillerW.MünkemüllerT. (2010). Habitat suitability modeling. Effects of climate change on birds, 75–85. Available online at: https://www.researchgate.net/publication/285828765
83
UNEA (2022). Available online at: https://wwf.panda.org/wwf_news/?5226891/nature-based-solutions-UNEA (Accessed October 21, 2023).
84
UNFCCC News (2022). What is the triple planetary crisis?Available online at: https://unfccc.int/news/what-is-the-triple-planetary-crisis? (Accessed October 21, 2023).
85
ValleM.BorjaÁ.ChustG.GalparsoroI.GarmendiaJ. M. (2011). Modelling suitable estuarine habitats for Zostera Noltii, using ecological niche factor analysis and bathymetric LiDAR. Estuar. Coast. Shelf Sci.94 (2), 144–154. 10.1016/j.ecss.2011.05.031
86
WalthamN. J.ElliottM.LeeS. Y.LovelockC.DuarteC. M.BuelowC.et al (2020). UN decade on ecosystem restoration 2021–2030—What chance for success in restoring coastal ecosystems?Front. Mar. Sci.7 (February 2020), 1–5. 10.3389/fmars.2020.00071
87
WaycottM.DuarteC. M.CarruthersT. J. B.OrthR. J.DennisonW. C.OlyarnikS.et al (2009). Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc. Natl. Acad. Sci. U. S. A.106 (30), 12377–12381. 10.1073/pnas.0905620106
88
YessonC.BedfordF.RogersA. D.TaylorM. L. (2017). The global distribution of deep-water Antipatharia habitat. Deep-Sea Res. Part II Top. Stud. Oceanogr.145, 79–86. 10.1016/j.dsr2.2015.12.004
Summary
Keywords
suitability mapping, suitability models, marine and coastal areas, nature-based solutions, ecosystems, climate change
Citation
Allegri E, Miglio EC, Furlan E, Bishop J, Simide R, Marcomini A and Critto A (2026) Suitability mapping for the implementation of nature-based solutions in marine and coastal areas. Front. Environ. Sci. 14:1591204. doi: 10.3389/fenvs.2026.1591204
Received
10 March 2025
Revised
14 January 2026
Accepted
27 January 2026
Published
06 March 2026
Volume
14 - 2026
Edited by
Pierre Failler, University of Portsmouth, United Kingdom
Reviewed by
Arantza Murillas Maza, AZTI - Sustainable Fisheries Management, Spain
Simon James Pittman, University of Oxford, United Kingdom
Updates
Copyright
© 2026 Allegri, Miglio, Furlan, Bishop, Simide, Marcomini and Critto.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Elisa Furlan, elisa.furlan@cmcc.it
ORCID: Antonio Marcomini, orcid.org/0000-0002-3363-7820
Disclaimer
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