A Systematic Review of Marine Habitat Mapping in the Central-Eastern Atlantic Archipelagos: Methodologies, Current Trends, and Knowledge Gaps

1. Introduction

Continental shelves provide a diverse network of habitats that support numerous species [1,2,3] and hold significant ecological, economic, and social values. Their importance is especially demonstrated through key ecosystem services, such as blue carbon sequestration [4,5,6], habitat formation [7,8,9], or preservation of fishing grounds and cultural activities [10,11,12,13,14]. These ecosystem services arise from various key habitats underpinned by “ecosystem engineers”, which not only provide resources and services, but also modify local conditions by creating, maintaining, or transforming habitats [15,16,17]. The complexity of these habitats, related to their three-dimensionality, diversity, and arrangement of physical elements (rocks, crevices, organisms, etc.), largely determines the increased diversity and density of species [18,19,20,21,22]. Consequently, habitat complexity is considered decisive in the structure and function of biological communities [23,24].

Key habitats include macrophyte beds and coral reefs [25,26,27,28,29]. Macrophyte beds encompass various habitats (e.g., macroalgal forests, rhodolith and seagrass seabeds), which function as submerged “forests”, offering protection and food for invertebrates and fish [30,31,32]. These habitats are known for their high primary production, biodiversity increases through “facilitation cascades” [14,16,33,34,35] and their role in carbon sequestration [3,16,17,36,37,38]. Particularly, rhodolith beds, consist of free-living, calcified red algae forming mobile nodules, creating unique habitats with high structural complexity over soft bare substrates. Seagrass meadows can also play a crucial role in sediment retention, juvenile fish habitat provision, and carbon fixation (e.g., blue carbon) [16,17,36]. Coral reefs, found in both shallow and deep waters [39,40,41,42,43], form biogenic structures that provide shelter and breeding grounds for invertebrates and fish, further enhancing biodiversity [16,17,44].

The high added value of these habitats, coupled with increasing pressures and threats, highlights the need to protect marine biodiversity while preserving essential ecosystem services [25,45,46,47,48,49]. Contemporary environmental management methodologies are shifting toward an ecosystem-based approach [50,51,52,53] that integrates all interactions related to the functioning of marine ecosystems -including anthropogenic- rather than addressing them in isolation [54,55,56]. The implementation of this marine spatial management framework requires understanding multiple factors, such as ecosystem functioning and former inter- and intraspecific interactions [57,58,59,60]. In this context, accurate data on the distribution and extent of marine habitats is essential [61,62,63,64]. Effective management and conservation strategies rely on baseline geographic information, including habitat distribution patterns, connectivity, associated biodiversity [46,65,66,67], and the impacts of different pressures [3,68].

Habitat mapping is a crucial first step in the sustainable management of the marine environments, a field that remains largely unexplored due to financial, logistical, and technical challenges [3,46,56]. Traditionally, habitat assessment relied on direct sampling and observations [69,70]. However, advancements in remote sensing technologies, such as acoustic, satellite, and optical methods [30,50,63,86,87], allow for the characterization of physical and biological features over large areas with reduced logistical and economic efforts [73,74,75,76,77,78]. Consequently, habitat mapping underpins essential baseline data for effective coastal management and conservation policies, though limited ecological knowledge of many marine habitats continues to hinder management effectiveness [29,59,66,69,79].

The Lusitanian biogeographical Province [80], known for its rich biodiversity, faces significant environmental pressures from climate change, tourism overdevelopment, coastal exploitation, and invasive species [81,82]. Specially, coastal economic and urban development intensifies anthropogenic pressures, as a large portion of the global population resides and/or works in these areas [83,84,85,86,87]. These pressures are even more pronounced in insular territories, where coastal zones constitute a substantial part of the total land area [83,88], such as the case of the Central-Eastern Atlantic volcanic archipelagos of the Azores, Madeira, Canary Islands, and Cabo Verde [44,89,90]. As insular systems, they exhibit high isolation shaping their marine biota [90,91]. These unique characteristics make them highly relevant in terms of their biogeography, ecology, evolution, and conservation biology, as they serve as natural laboratories for studying evolutionary and ecological processes [89,92,93]. However, their vulnerability underscores the importance of studying these islands to ensure their unique marine ecosystems, which requires detailed knowledge of marine habitat distribution and characteristics, highlighting the importance of advancements in seabed mapping [63,89,94,95,96].

This study aims to provide a comprehensive review of the current knowledge and gaps in mapping key habitats across the Central-Eastern Atlantic volcanic archipelagos. To achieve this, available methodologies mapping tools and methodologies were cataloged, their chronological use examined, and the challenges associated with their application across different habitats and depths analyzed. The review also identified knowledge gaps, such as understudied geographical areas and key habitats—both in terms of habitat type and taxonomy—highlighting regions with data scarcity that could hinder conservation efforts. Finally, recommendations were proposed to strengthen research initiatives and enhance the development of useful habitat mapping methodologies. In this sense, international cooperation will foster more effective decision-making processes in the framework of evolving Marine Spatial Planning activities, where marine conservation sector deserves a more intense focus.

2. Materials and Methods

This systematic review was conducted following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), to ensure transparency, reproducibility, and comprehensiveness at each stage of the process, from sourcing and selection to the analysis of the selected articles [97,98].

2.1. Study Area

The study focuses on the four volcanic archipelagos located in the Central-Eastern Atlantic Ocean: Azores, Madeira, Canary Islands, and Cabo Verde. A section of the Mid-Atlantic Ridge within the Azores exclusive economic zone (EEZ) was also considered, as it plays a crucial role in ocean current dynamics and ecological connectivity among the four archipelagos (Figure 1; [99]).

The marine study area covers the so-called European Macaronesian region [100]; the term Macaronesia (composes by two Greek words: makarion [fortunate] and neosi [islands], have being used in the biogeographic literature to include the four archipelagos located in the Central-East North Atlantic Ocean: Azores, Madeira (including the Selvagens Isles), the Canary Islands and Cabo Verde. These oceanic archipelagos have a volcanic origin, as geological demonstrations of hotspots off Northwestern Africa with large depths found between adjacent islands. The biogeographic position of these European archipelagos encompasses a large macroclimatic gradient among them of almost 2500 km of latitudinal extension, from hyperoceanic temperate conditions in the Azores to Mediterranean to subtropical ecosystems in the case of Madeira, Selvagens, and the Canaries [101,102]. Their marine environments are mainly connected through the Canary Current, a branch of the Gulf Stream in the Eastern Atlantic moving down from the Azores through Madeira to the Canary Islands and further south to the Cape Verde Islands [103]. Recent biogeographical studies have shown a more isolated biogeographical position in the case of the Azores, whereas the marine biota of the three central archipelagos: Madeira, Selvagens and the Canary Islands showed stronger affinities among them [104,105]; as consequence, these three archipelagos defined a new biogeographical unit (= Webbnesia) and the marine biota of the southernmost archipelago of Cabo Verde is considered as part of the West African Transition province [106].

Aside from their common volcanic origin, these archipelagos exhibit a high degree of endemism in the terrestrial realm, underscoring their worldwide biogeographic significance [99,107], and including marked differences in both terrestrial and coastal habitats. The Azores, Madeira and Canary Islands, located at more northerly latitudes, display temperate and subtropical oceanic climates alongside rugged coastlines featuring rocky reefs that support a range of benthic communities. Their coasts range from volcanic cliffs to sandy beaches and emergent rock outcrops, which foster highly diverse marine habitats encompassing seagrass beds, macroalgal assemblages on rocky reefs and cold-water coral reefs (CWC) [99,108,109]. Conversely, Cabo Verde is situated in a dry subtropical zone, characterized by a warmer and more arid climate, where marine ecosystems are dominated by sedimentary seabeds (sandy and muddy), followed by rocky and coral reefs, and whose biota differs in composition from that of the more northerly islands [110].

2.2. Data Sources and Search Strategies

The literature collection was conducted in two primary databases, SciVerse Scopus and ISI Web of Science, with Google Scholar as a supplementary source. These platforms were selected for their extensive coverage and ability to capture relevant publications on cartography and mapping in the different oceanic archipelagos of the Central-Eastern Atlantic Ocean.

The search strategy was carefully designed and executed in December 2024. A Boolean sequence of search terms was developed, using synonyms and related terms aligned with the main objective of the study. As shown in Table 1, each column represented a set of similar terms combined using the “OR” logic, while different columns were linked using the “AND” logic. Wildcard characters (*) were applied to account for variations in term usage (e.g., “canar*” retrieves for canaries, canary, or canarias). The complete search formula is available in the supplementary material. In the Web of Science, the TS= operator was used in the search equation, while in Scopus, a similar logic was applied by adapting the TS= to TITLE-ABS-KEY=. In both, filters were applied to exclude non-relevant subject areas, such as Medicine, Arts, and Physical-Chemical-Mathematical Sciences.

2.3. Inclusion and Exclusion Criteria

To ensure the quality and relevance of the selected studies, rigorous “inclusion” and “exclusion” criteria were established. Studies addressing cartography and providing spatial data were included, even if explicit maps were absent. Reviews containing maps or spatial data, as well as studies focused on one or more Central-Eastern Atlantic archipelagos or the Mid-Atlantic Ridge, when linked to the concerned archipelagos, were also considered. No language or date restrictions were applied, but only peer-reviewed journal articles were included. Conversely, studies that only reported species presence/absence without spatial data, articles focused solely on methodologies without data or maps, and grey literature or non-peer-reviewed works were excluded.

2.4. Screening and Selection Process of Articles

The screening process followed the PRISMA guidelines, and it was conducted in two phases (Figure 2). First, the results were imported into Rayyan software [111], where duplicates were automatically removed and manually verified. At least two authors independently screened titles and abstracts to exclude non-relevant articles [90]. In the second phase, the preselected articles underwent a thorough review to confirm their relevance, with discrepancies resolved by a third reviewer.

2.5. Data Extraction and Coding

The information from each article was first consolidated into an Excel template, creating two separate databases (Table 2): one for studies and another for species observations. The first database compiled methodological and contextual features, including mapping techniques, study area, research period, and other relevant factors. The second database focuses on taxonomic details, tracking the species recorded, the number of studies in which they appeared, and their presence or absence in each archipelago.

2.6. Methodological Quality Assessment and Data Analysis

Methodological quality was assessed using an ad hoc strategy that evaluated the clarity of the technique descriptions, transparency in identifying limitations, and the relevance of the methodologies employed. Three of the authors conducted the literature review, resolving any discrepancies through consensus [90].

The data analysis incorporates qualitative descriptions and graphical representations to evaluate the temporal distribution and application of mapping techniques. Statistical tools, such as SPSS [112] and PAST [113], were used for descriptive analyses and to assess correlations between different variables. Additionally, methods and patterns of habitat representation were examined to identify optimal combinations for mapping complex ecosystems. Studies with a high risk of bias were excluded to enhance the validity of the conclusions. In the graphical and tabular summaries, individual publications may be represented multiple times, as a single article can report multiple mapping methodologies and/or assess multiple habitat types across archipelagos. Consequently, cumulative frequencies and percentage distributions in the figures do not sum to 100%, reflecting the overlapping nature of methodological and habitat classifications rather than data omission.

3. Results

3.1. Research Effort and Objectives

From a total of 12,011 articles identified across three databases, the initial screening narrowed the sample to 2,965, of which 224 were analyzed in detail. The review focused on 69 studies, providing key insights into the mapping of marine ecosystem-engineers in the concerned archipelagos. Additionally, data on 506 species was collected and compiled into a separate database as part of the taxonomic analysis derived from the reviewed articles.

Geographical and temporal patterns revealed that 30 studies were recorded in the Canary Islands, 29 in Azores, 8 in Madeira, and 3 in Cabo Verde. Research effort has exhibited temporal and spatial variations, with differences in intensity across each archipelago (Figure 3). Azores experienced a peak in research activity between the late 1990s and early 2000s, followed by a resurgence and stabilization between 2005 and 2015, with a relative increase from 2020 onwards. In Madeira, research progressively increased starting in 2020. In the Canary Islands, research levels remained relatively constant since the early 2010s, with more intense activity peaks observed from 2020 onwards, persisting to the present day. Finally, in Cabo Verde, all recorded studies were conducted between 2023 and 2024. Regarding the study objectives, 41% (28 studies) focused on mapping, 38% (26 studies) on explorations, and 21% (15 studies) on monitoring (Table A1).

3.2. Methodologies and Habitats Analysis

The analyzed studies covered three main types of marine habitats: macrophyte beds, rhodolith beds, and coral reefs. Various methodologies were employed, including direct, remote, acoustic, satellite and/or aerial observation, sampling, and bibliographic review.

The methodologies have exhibit different usage patterns over time (Figure 3). Direct observation has been documented since the beginning, with higher recurrence during 2020 and 2021. Remote observation first appeared in 2000 and showed notable occurrences in 2013 and 2021. Aerial methodologies emerged in 2019, with additional occurrences in 2021 and 2024. Satellite observation first appeared in 2015 and was documented in isolated studies until 2024. Acoustics was not reported until 2012 and became more prevalent from 2020 onwards. Sampling has been used intermittently since 1992, with increased application during the 2010s. Finally, bibliographic reviews were initially recorded in the early 2000s and reappeared consistently from 2020 to 2024.

Comparative analysis among (Figure 4) and within (Figure 5) archipelagos revealed similar variations in the application of observation and data collection methodologies. In Azores, remote observation was the predominant method, followed to a lesser extent by direct observation and sampling, with similar usage. Acoustic methods, aerial techniques, and bibliographic review were employed less frequently. In contrast, although all methodologies—except remote observation—were relatively more common in the Canary Islands, direct observation emerged as the primary method, complemented by sampling. The use of the remaining methodologies (remote observation, acoustic methods, and bibliographic review) was identical, whereas satellite observation was incorporated in a moderate and exclusive manner in the Canary Islands. In Madeira, direct observation, acoustic methods, and sampling constituted the most employed techniques, whereas remote sensing, aerial observation, and bibliographic review were applied to a lesser extent. The index of aerial observation usage was similar to that used in Canary Islands and Azores. Finally, in Cabo Verde, the only methodologies implemented were remote observation—which proved to be the most used—followed by direct observation and acoustic methods, with equivalent usage. Bibliographic review was the only additional methodology recorded in all studies conducted in Cabo Verde.

3.3. Biodiversity Analysis

The greatest research effort was carried out on macroalgal beds (42 studies), followed by coral reefs (33 studies) and, to a lesser extent, by rhodolith beds (11 studies). Similarly, habitats comparisons revealed differences in the research efforts on these habitats among (Figure 4) and within (Figure 5) the archipelagos, where Azores exhibited the higher percentage of studies related to coral reefs. Conversely, the Canary Islands focused its research efforts toward macroalgal beds, followed by Azores and Madeira. Rhodolith beds had a similar percentage of studies across all the archipelagos, except for Cabo Verde, where no studies targeting this habitat were observed.

Our literature review recorded a total of 505 species, with the highest diversity observed in the Azores (393 species), followed by the Canary Islands (131), Madeira (69), and Cabo Verde (10) (Figure 6, 7; Table A1). The Azores concentrated the highest percentage of cnidarian species, exhibiting a similar diversity in both Hexacorallia and Octocorallia classes, accounting for ca. 86.4% of the cnidarian species observed. In particular, the Mid-Atlantic Ridge predominantly documented cnidarians (ca. 99%: Table A1). Likewise, the Azores included the greatest diversity of marine plants (Figure 7; Table A2), with the exception of Tracheophyta, which were not represented. Rhodophyta were predominant, followed by Chlorophyta and a similar percentage of Ochrophyta (Figure 7; Table A2). In the other archipelagos, a similar pattern was observed regarding cnidarians and macroalgae. However, Madeira documented a similarity in the diversity of cnidarians between Hexacorallia and Hydrozoa, presenting a lower representation of Octocorallia and a slight representation of Tracheophyta. The Canary Islands exhibited the highest diversity of Tracheophyta (Figure 7; Table A2). With respect to cnidarians in the Canary Islands, a higher percentage of Hexacorallia was observed, followed by Octocorallia and Hydrozoa. Finally, in Cabo Verde, the diversity was concentrated in cnidarians, with a greater diversity of Octocorallia than of Hexacorallia, and no macroalgae were documented, except for Tracheophyta (Figure 7; Table A2).

Regarding to singular species (Table A2), it was evidenced that the greatest research effort was focused on seagrass meadows, specifically on Cymodocea nodosa (Ucria) Ascherson, 1870, which was the subject of 14 studies. Subsequently, the effort was directed toward brown macroalgae, in particular Dictyota spp (J.V. Lamouroux, 1809) and Padina pavonica (Linnaeus, Thivy, 1960), which were the subject of 12 and 10 studies, respectively. Among corals, the species that were studied most intensively were the octocoral Viminella flagellum (Johnson, 1863) and two hexacorals, Desmophyllum pertusum (Linnaeus, 1758) and Antipathella wollastoni (Gray, 1857), each appearing in 8 studies.

Methodological approaches to studying marine benthic communities varied according to habitat and region (Figure 8). Throughout the geographical range considered, macroalgae were studied mainly by direct observation and sampling, while remote sensing and acoustic methods were used less frequently. In rhodolith beds, direct observation (7 cases) and acoustic methods (5 cases) were the most common approaches. Corals, which were found at depths of up to 4,000 m, were investigated primarily by remote sensing (25 cases) and acoustic methods (13 cases), although sampling and direct observation were also applied. Acoustic methodologies, in particular, were used more frequently in the Canary Islands (8 cases) and Azores (6 cases). Overall, the Azores and the Canary Islands represented the majority of the studies, demonstrating a diverse methodological approach.

3.4. Postprocessing Data Analyses

Of the total studies analyzed, 62% incorporated some form of mapping, with 25% generating geological cartography. The remaining studies provided cartographic data as coverage or visual percentages, supplementing spatial analyses. Computational modeling appeared in ca. 49% of the studies, showing a ca. 750% increase between 2010 and 2020, followed by an additional 100% rise between 2020 and 2024 (Table A1).

This growth coincided with the integration of Geographic Information Systems (GIS), described in ca. 47% of the studies, combined often with image analysis tools (18 studies) or remote sensing data (15 studies). GIS was primarily used to predict coral and macrophyte beds distributions based on remote, acoustic, and direct observation data (Figure 9).

The Azores and Canary Islands showed a strong preference for GIS, while Madeira relied more on taxonomic and morphological tools. Rhodolith beds were linked to GIS and acoustic/bathymetric analysis, macrophytes to GIS and statistical analysis, and corals to GIS and image analysis (Figure 9).

4. Discussion

Although marine research in some of the Central-Eastern Atlantic archipelagos dates back to the 19th century, these studies have predominantly focused on taxonomy and species cataloguing, relegating habitat mapping to a secondary position [114]. During the last 30 years, research efforts have been concentrated, particularly in the Azores and the Canary Islands archipelagos, while in Cabo Verde, such efforts are scarce or nearly non-existent (Figure 10).

Transnational cooperation projects focus on monitoring, conservation, cataloguing, and mapping of marine biotopes dominated by habitat-forming megafauna, considered Vulnerable Marine Ecosystems (VMEs), through projects such as MISTICSEAS, MIMAR, POPCORN, and MOVE ON (https:misticseas3.com/es; https:www.mare-centre.pt/en/proj/mimar; https:ww.ecoaqua.eu/es/habitats-popcorn.html; https://www.moveon-project.eu /homepage/). However, updated information on the current progress in comprehensive seabed mapping remains limited, a situation comparable to studies conducted on the Mid-Atlantic Ridge, where much accumulated knowledge from multiple multinational expeditions since 1954 remains unpublished in indexed scientific journals, primarily existing as technical reports or grey literature [115,116].

4.1. Methodologies and Technological Advances

4.1.1. Direct and Remote Observation Techniques

The most cost-effective methodology for characterizing marine habitats is direct observation, generally complemented by in situ sampling, as it provides detailed descriptions and generates thematic maps based on biota distribution, facilitating the identification of spatial patterns and ecological processes [96,117,118,119,120].

Although direct observation of keystone species and habitats has been systematically employed over time, it is now complemented by emerging remote technologies, such as optical remote observation, or its combination with acoustic and satellite methods, enabling three-dimensional analysis (horizontal and vertical dimensions) [96,121,122]. Currently, marine habitats can be effectively mapped using various stationery and mobile cameras, including remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), providing high-resolution imagery for species identification with expert analysis [123,124]. These tools can explore challenging areas, enabling long-term monitoring and broad data collection [125,126,127,128]. However, they face limitations in covering very extensive areas and dealing with the “canopy effect,” which obscures deeper layers, necessitating complementary mapping methods [120,129]. Specifically, in the Canary Islands, camera-based methods — either standalone or combined with other methods — has proven particularly effective for subtidal habitats and deep-water ecological studies, more so than for intertidal zones [120,130]. Furthermore, this approach aligns well with other methodologies, such as the MNCR biotope method employed in the Azores, facilitating the identification and monitoring of structural changes within algal communities [131].

4.1.2. Acoustic Methodologies

Technological advancements have transformed the way information is acquired and interpreted [132,133]. The development of acoustic technology in the 1940s revolutionized seabed exploration, enabling the interpretation of seabed textures over extensive areas and the creation of more realistic images [134,135]. Over time, technological improvements have significantly enhanced data acquisition and image resolution by refining acoustic signal properties and advancing digital tools for acoustic data analysis [135,136]. Among the various acoustic systems, wide-beam or multibeam systems (Side-Scan Sonar or SSS) [134,135,137], single-beam echo sounders or fish-finders [135,138], and multibeam bathymetric systems (MBES) [135,139] are widely utilized. Within the concerned oceanic archipelagos, this review highlighted SSS and MBES as the most versatile acoustic technologies employed by regional marine research, particularly for habitat characterization along the Mid-Atlantic Ridge [14,56,140,141].

Our review suggests that SSS was particularly effective in identifying substrate textures, highly efficient in habitats with sedimentary and rocky bottoms, as well as detecting coral reefs and seagrass beds in the Azores and Canary Islands [14,141,142]. Conversely, MBES was more precise in measuring bathymetry and seabed relief, making it more suitable for characterizing deep-water habitats, such as coral reefs along the Mid-Atlantic Ridge [56,143,144,145]. Furthermore, advancements in MBES post-processing have facilitated the implementation of new Multi-Detection (MD) techniques capable of estimating the presence of CWC gardens in the Azores and Canary Islands (e.g., black corals, [46,56,146], undetectable directly via SSS or MBES due to their proteinaceous composition. Such techniques lay the groundwork for enhancing future distribution maps of other key habitats [146].

4.1.3. Aerial and Satellite Methodologies

Aerial remote sensing is transforming marine mapping by providing high-resolution data on habitat coverage and coastal morphological changes using optical, thermal, and hyperspectral sensors [147]. Its capacity for operating at high spatial resolution makes it ideal for monitoring coastal ecosystems and intertidal zones, facilitating vegetation cover analysis [4,147,148,149,150]. Aerial platforms equipped with LIDAR (Light Detection and Ranging) enable precise bathymetric mapping in shallow waters [96], generating detailed digital models of sandy bottoms and reefs [4,148,149,150,151]. Specifically, in the Azores, Madeira and the Canary Islands, drones (Unmanned Aircraft Systems or UAS) have effectively monitored shallow waters and algal blooms; however, highly heterogeneous habitats present methodological challenges due to environmental and meteorological variables limiting their spatio-temporal applicability [149,150,151]. Additionally, they cannot accurately differentiate benthic communities without ground-truthing support [149,150]. To overcome these limitations, Monteiro et al. (2021) proposes an integrated approach, by combining in situ data— specific benthic composition —with physiographic mapping derived from UAS imagery.

Satellite platforms (Sentinel, Landsat, WorldView) employ multispectral and hyperspectral sensors, enabling large-scale monitoring of shallow marine ecosystems [152,153,154,155,156,157,158]. They offer extensive temporal and spatial coverage, providing characterizable images alongside abiotic and oceanographic variables such as temperature, pH, or chlorophyll [147,159,160,161,162,163,164,165]. Despite their effectiveness in marine habitat mapping, satellite studies have been conducted almost exclusively in the Canary Islands, frequently adopting a single-methodological approach and facing limitations due to reliance on reflectance and/or spectral profiles without integrating in situ biological data [149,164]. For example, multitemporal satellite image analysis was applied to evaluate the impact of the Granadilla port construction on the Special Conservation Area (ZEC) of “Sebadales del Sur” in Tenerife (Canary Islands) [166]. Cosme De Esteban et al. (2023) underscored the importance of ground-truthing regarding satellite approach, as for example comparisons carried out in Príncipe Island (Gulf of Guinea) that revealed discrepancies in delineating marine habitats of high ecological value, such as rhodolith beds, where the acoustic methodology supported by direct observations was more precise compared to satellite methods. Furthermore, Eugenio et al. (2023) highlights the efficacy of these multitemporal approaches integrating bathymetric and benthic studies to detect variations through qualitative and quantitative analysis, producing high-resolution maps.

Globally, aerial methods provide higher spatial resolution and greater flexibility for selecting optimal environmental conditions, whereas satellite imagery analysis reduces exploratory sampling needs, improving efficiency in time, cost, and reducing sampling bias [149,150,151]. Despite proven efficacy, aerial and satellite remote sensing methodologies remain underrepresented, predominantly applied in the Canary Islands and the Azores, where usage has increased since 2021. In the Canary Islands, image processing and automatic classification algorithms based on artificial intelligence and deep learning have achieved up to ca. 96% effectiveness in discriminating species, such as the genera Cymodocea and Gongolaria [151], highlighting the increased analytical precision made possible by these advancements; however, clearly defined training areas and ground-truthing validation remain essential [160,164,167].

4.1.4. Data Postprocessing

Once cartographic data is collected, integrating them into a unified reference framework becomes essential. This integration is facilitated by geographic information systems (GIS), which aid in spatial organization and analysis [168,169,170]. GIS effectively consolidate datasets that differ in spatial, temporal, and thematic scales, thereby addressing informational heterogeneity [171,172]. Literature demonstrates extensive use of specialized software for image analysis [114,120] and GIS programs (e.g., ESRI’s ArcGIS or QGIS) for the integration of georeferenced vector and raster data [4,115,148,173]. Additionally, analytical interfaces have been optimized through additional tools that facilitate visualization and map overlays, allowing the combination of habitat information and physicochemical variables. Such integration is pivotal for conducting statistical analyses and creating predictive maps through modeling [141,142].

Following database integration, habitat mapping advances by modeling habitat conditions, emphasizing the biological integrity index and anthropogenic stress factors [72,174].

Identifying new biotopes and extensive data collection on taxonomic diversity significantly enhances predictive statistical modeling at multiple spatial scales [175,176]. This modeling facilitates an integrated assessment of biodiversity and generates predictive maps incorporating anthropogenic risks and impacts [177,178,179,180]. According to Braga-Henriques et al. (2013), accurate modeling and data analysis require standardized data formats. However, heterogenous data collection from different periods, campaigns, and multinational organizations complicates direct standardization, requiring the use of probability mapping or prior data refinement according to established standards [114]. Refinement involves standardizing classifications and terminologies to ensure coherent mapping and effective environmental data storage [114]. Consequently, the hierarchical EUNIS habitat classification system, extensively applied across Europe, can support large-scale habitat mapping by integrating comprehensive ecological knowledge and spatial data on seabed characteristics [166]. Accordingly, in the Canary Islands, a study provided the first comprehensive spatial assessment of benthic ecosystem service supply (until 50 m depth; [181]), generating maps through a flexible, updatable methodological approach based on scientific literature, directly applicable to marine spatial planning.

One of the initial steps in Mapping and Assessment of Ecosystems and their Services (MAES) involves ecological niche and food web characterization using specialized models and software (e.g., ECOPATH, ECOSIM, ENFA, or MAXENT). These tools are crucial for analyzing energy flows and predicting species distribution changes due to environmental disturbances and anthropogenic activities [96,147]. Their application has been pivotal in predictive analyses of species distribution, such as CWC, in studies conducted in the Azores and Cabo Verde [176,182]. When information is limited or historically incomplete, implementing ensemble models has proven effective. In Cabo Verde and the Canary Islands, combining these models with AI and deep learning classification techniques, predicted (i) new CWC presence areas on seamounts [182], (ii) identified the potential distribution of algae (both essential and invasive), and (iii) assessed seagrass meadow conditions following port infrastructure construction [130,183]. In the Azores, integrating data on anthropogenic activities (e.g., fisheries) and protected areas has facilitated evaluations of interactions between key habitats, such as macroalgae and CWC, and resources [129,142,176]. This information provides critical insights into ecosystem functioning, niche interactions and influencing variables, contributing to the development of distribution maps that inform spatial planning and conservation strategies of these essential habitats [4,142,162,176]. Finally, in the Canary Islands, visual analyses and the first ecological model of an MPA have demonstrated the potential of this conservation tool and the responses of communities to natural events [162,184], thereby facilitating adjustments in MPA design and management policies. Collectively, these data support improvements in design and management policies to ensure the conservation and recovery of ecosystems [4,162,184].

4.2. Habitat Mapping

Marine habitat maps are developed using a variety of methodologies that integrate multiple sources of data (see section 4.1). Biological data (e.g., species and community distributions), geophysical variables (e.g., bathymetry, geomorphology, physicochemical parameters), and habitat-related factors (e.g., ecosystem services and local and global threats) collectively determine the precision, spatial coverage, quality, and overall utility of the final habitat map.

4.2.1. Methodologies

Marine habitat mapping remains operationally challenging and costly despite technological advancements since the late 1990s, which have enhanced spatial coverage and data accuracy [114,185]. While studies often rely on either direct imaging or remote observation methods individually [149,186], integrating multiple methodologies significantly enhance accuracy by offsetting each technique’s inherent limitations [120]. For instance, in the Canary Islands and Azores, studies relying exclusively on remote sensing techniques effectively describe facies and community structure. However, these approaches can be limited by the “canopy effect”, where dense algae cover or CWC gardens may obscure underlying strata, hindering the observation of all present taxa [120]. Furthermore, although most benthic areas provide valuable data, not all provide sufficient information to produce comprehensive habitat mapping. Therefore, it is essential to clearly define the types of thematic maps that can be generated from such data [31,144]. No single approach exists for their development, given the multidisciplinary complexity and the variability associated with the specific objectives of each project. Consequently, multidisciplinary campaigns are conducted—such as the one carried out in the Azores by Somoza et al. (2020)—in which both biological and abiotic data (e.g., geophysical, hydrographic, geological, oceanographic, etc.) were collected, enabling the description of new hard and soft coral gardens and the analysis of the factors that determined their location and distribution.

4.2.2. Biotic-Abiotic Data

Marine habitat maps integrate multiple data sources, including biological (e.g., species and community distributions) and geophysical variables (e.g., bathymetry, geomorphology and physicochemical parameters). According to Brown et al. (2011), habitat maps can be classified based on mapped variables, distinguishing between biological and abiotic data [45,187,188]. Adopting a hierarchical, multiscale approach at species, community, and ecoregion levels enhances maps comprehensiveness [123,189,190]. Additionally, discovering new biotopes and detecting structural or distributional changes through repeat mapping or predictive modelling improve habitat-classification accuracy [149,162,167,183]. However, maps focused solely on geophysical features remain incomplete without integrating biological information [169,191,192,193]. In these volcanic archipelagos, geological studies commonly regard biological analyses as secondary, especially in deep-water habitats such as the Mid-Atlantic Ridge and hydrothermal areas of the Azores [141,143,185,194]. Since 2013, however, multidisciplinary research —particularly in the Azores and the Canary Islands— has significantly expanded, driven by technological innovations, advanced methodologies, integrated management plans, and improved infrastructure and funding availability (Figure 10; [178]).

Other mapping investigations treat spatial information as complementary or secondary to the primary objective, which is to collect biotic or abiotic data from marine habitats or facies. For example, research on hydrothermal habitats, in metal-rich areas—such as those hosting Rimicaris shrimp populations along the Mid-Atlantic Ridge (Azores)—enable the generation of more accurate predictive models for species distributions [186]. Conversely, investigations that specifically map and characterize valuable habitats, such as the seagrass meadows of C. nodosa in the Canary Islands, facilitate the prediction of dynamics in related communities, including fishery populations [164,195].

4.2.3. Habitat-Related Factors

Marine habitats are increasingly threatened by global and local threats (e.g., climate change, overfishing, pollution and coastal development), underscoring the need for up-to-date habitat mapping to manage these ecosystems effectively [149]. In the concerned oceanic archipelagos, several studies have already used direct and remote-sensing techniques to detect and map local threats (e.g., marine litter, trawling, long line fisheries, etc.), showing how cumulative stressors erode functional diversity and community structure over time, particularly in morphologically complex taxa such as CWC [136,140,185,196]. Additionally, marine litter data has been used in advanced predictive modelling to pinpoint plastic accumulation hotspots and compare them with potential key habitat areas [197]. Documenting spatial links between habitats and both historical and contemporary local threats is therefore essential for evidence-based conservation and management [116,130,148,164]. Thus, Martín-García et al. (2022) established cause and effect relationships between human and environmental drivers that influence the distribution of brown macroalgae (e.g., Gongolaria abies-marina) in the western Canary waters. Equally, studies performed in Cabo Verde on abiotic factors (e.g., topography and hydrodynamics) are essential for characterizing habitats and supporting predictive models. This approach has effectively identified food supply mechanisms for suspension feeders and potential areas for key species such as the coral Enallopsammia rostrata [184].

Understanding how the distribution and persistence of key habitats have changed in response to local threats is also critical for contextualizing recent observations. In Madeira, a southward regression of Laminaria ochroleuca has been documented [140], along with a decline in several Sargassaceae species [130]. Nonetheless, high resolution imagery of adult L. ochroleuca specimens (ca. 5–7 years) suggests that the archipelago may serve as a climate refuge against ocean warming [140]. Meanwhile, the first exhaustive mapping of rhodolith beds in Madeira revealed a widespread distribution across the archipelago, extending to depths of ~100 m—considerably deeper than those typically reported in the northeastern Atlantic—and comparable to the Azores [175]. Finally, regarding seagrass beds, Cymodocea nodosa and Avrainvillea canariensis patches have decreased in southern areas within a Marine Protected Area (Parque Natural Marino de Cabo Girão) [114,173,198]. In the Azores, regressions have also been observed in several species of the Sargassaceae family [130]. However, while the mesophotic niche occupied by algae in the Azores benefits from some protection against natural and anthropogenic stressors, it remains vulnerable to climate change, with predictions indicating a decrease of between 23% and 85% in the thermal niche of L. ochroleuca by 2100 [199]. Finally, in the Canary Islands, intensified maritime traffic favors invasive species introduction, notably populations of scleractinian corals (Savalia savaglia, Tubastraea sp., Oculina sp., Culicia sp.), necessitating continuous monitoring of these species [136,200]. Brown macroalgae forest of Gongolaria abies-marina transitioned from extensive to fragmented forests in the upper sublittoral zone, almost disappearing in areas such as Canary Islands [130,148,151]. This coincides temporally with the increase in marine heatwaves (MHW), which displace species toward cooler waters [201], and with increased coastal urbanization. These activities can also negatively impact seagrass seabeds, causing reduction or disappearance of C. nodosa meadows and rhodolith beds, frequently replaced by invasive species (Caulerpa racemosa, Caulerpa prolifera) accompanied by cyanobacterial proliferations (Lyngbya spp.) [163,195,196,202].

4.2.4. Final Habitat Map in the Central-Eastern Atlantic Archipelagos

Most conservation projects are undertaken within MPAs, whose design has progressively shifted—driven by technological advances and the adoption of ecosystem-based policies—from a focus on single emblematic species to an integrated view of ecosystem functioning. Consistent, high resolution habitat mapping is therefore indispensable for redefining existing MPAs, proposing new ones, and ensuring comparability at the regional scale [3,56,140,203]. For example, Cosme De Esteban et al. (2024) recommended revising an Azorean MPA originally delimited to protect a seabird colony; the absence of a marine component resulted in a buffer zone dominated by sedimentary substrates and excluded reef habitats characterized by cold water corals. Likewise, cartographic and taxonomic surveys along the Mid Atlantic Ridge—sites initially designated as Natura 2000 Sites of Community Importance for cetacean conservation—have revealed extensive mesophotic and deep-water habitats of high ecological value, warranting an expansion of the protection boundaries [140].

4.3. Challenges and Future Implications

Globally, the review of the current status of marine habitat mapping in the oceanic and volcanic archipelagos highlighted two fundamental and interdependent methodological stages: exploration and monitoring. The exploratory stage aims to address initial questions about habitat presence, distribution, and ecological importance, laying the groundwork for subsequent, detailed studies. Clear examples from the Azores and the Canary Islands demonstrate how initial exploration identified essential habitats, such as cold-water coral gardens and rhodolith beds, which might otherwise have been overlooked in marine protected area planning. Conversely, the monitoring stage assesses temporal habitat changes and evaluates the effectiveness of implemented conservation measures, as exemplified in Azores, Madeira and the Canary Islands, where inadequate monitoring has limited effective management of key habitats.

A comprehensive analysis of methodologies and outcomes revealed significant progress in advanced technological applications, particularly in the Azores and Canary Islands, where a wide array of sophisticated tools—such as remote sensing, acoustic, and satellite sensors (e.g., Side-Scan Sonar, MBES)—have been successfully implemented. However, this contrasts sharply with Madeira and especially Cabo Verde, archipelagos constrained by limited technological infrastructure and marine research funding. This regional disparity directly impacts the quality and comprehensiveness of the bionomic maps produced, hindering the necessary interregional comparisons and integrations required for effective integrated management. To address this, regional initiatives promoting technological cooperation and specialized training of local researchers are crucial, as partially achieved by the Azores and Canary Islands through European-funded programs.

Furthermore, integrating and standardizing data generated from diverse methodological techniques remained a significant challenge. The involvement of various stakeholders employing different methodologies resulted in heterogeneous databases, complicating their coherent integration into thematic maps. Studies conducted in the different archipelagos, notably in the Azores, emphasized the imperative need for standardized reference frameworks, systematic field validation (ground-truthing), and robust data validation protocols. Adopting these measures will enhance data reliability and accuracy thereby strengthening predictive modeling capabilities essential for effective marine ecosystem management.

Advanced techniques such as computational modeling, GIS systems, and artificial intelligence have also revolutionized marine mapping in the region. Nevertheless, these technological innovations introduced specific challenges related to processing, analyzing, and interpreting large volumes of data, especially in contexts with limited technological infrastructure. In areas such as Cabo Verde, where technological adoption remains constrained, it is essential to strengthen analytical capabilities and provide specialized training in spatial data processing. Such improvements are crucial for accurate data interpretation and scientifically informed decision-making.

Effective and sustainable management of marine habitats in the Central-Eastern Atlantic archipelagos require an integrated approach that combines biological and geophysical data to produce detailed bionomic maps. In the Azores, the initial lack of such integration resulted in the exclusion of keystone habitats, including CWC, from established MPAs. Therefore, regular updates and continuous refinement of habitat maps, supported by GIS tools and predictive modeling, are essential for accurate ecological assessments of anthropogenic pressures, ensuring the effectiveness of ecosystem-based conservation strategies.

At present time, stronger scientific cooperation about marine habitat mapping will foster more effective decision-making processes in the framework of evolving Marine Spatial Planning activities, where marine conservation sector usually is not so much represented and deserves a more intense focus.

In conclusion, overcoming the challenges identified in this review requires coordinated efforts integrating methodological standardization, investment in advanced technologies, and robust interregional collaboration. The successful implementation of these strategies will substantially enhance the precision of marine habitat mapping in the region, providing a solid foundation for the effective management and conservation on marine ecosystems in face of current and future environmental and anthropogenic pressures.