Multimodal Underwater Sensing of Octocoral Populations and Anthropogenic Impacts in a Conservation-Priority Area (NE Aegean Sea, Greece)

Abstract

Coralligenous assemblages are among the most diverse habitats of the Mediterranean Sea, yet those of the north-eastern basin remain understudied despite their vulnerability to human impacts and climate change. We applied a multimodal underwater sensing approach to map coralligenous formations, assess gorgonian populations and evaluate the effects of marine litter in a conservation-priority area (NE Aegean Sea, Greece). Side-scan sonar enabled seafloor mapping and guided targeted Remotely Operated Vehicle (ROV) surveys. ROV-based distance sampling and imagery provided quantitative data on Eunicella cavolini and Paramuricea clavata, including density, size structure, and injuries, alongside systematic documentation of marine litter. Gorgonians formed monospecific ecological facies, segregated by location—P. clavata occurred deeper than E. cavolini. Densities were low (E. cavolini: 0.35 colonies m⁻², P. clavata: 1.46 colonies m⁻²) and small colonies (<10 cm) were rare, suggesting limited recruitment. However, the presence of large colonies indicates stable environmental conditions that support long-term persistence, as reproductive output increases with colony size. Colony injuries were minor, but marine litter was abundant, dominated by fishing lines and ropes entangled with gorgonians and sponges. These findings highlight the value of acoustic–optical integration for non-destructive monitoring and provide essential baselines for conservation under EU directives.

1. Introduction

Mediterranean coralligenous formations are among the most ecologically valuable and structurally complex benthic ecosystems in the region [1,2]. They develop slowly at depths between 20–150 m, where the gradual accumulation of encrusting coralline algae and the calcareous remains of sessile invertebrates forms multi-layered bioconstructions [1,3]. Coralligenous formations typically occur either as banks on gently sloping detritic substrates or as rims along sublittoral rocky reefs [4,5]. The communities that develop on these formations are collectively termed “coralligenous assemblages”.

Coralligenous assemblages host around 10% of the known Mediterranean marine species, including numerous endemic, and protected taxa [6,7]. Among the most conspicuous and functionally important organisms are gorgonian octocorals. Three species, the yellow gorgonian Eunicella cavolini (Koch, 1887), the white gorgonian E. singularis (Esper, 1791), and the purple gorgonian Paramuricea clavata (Risso, 1827), commonly dominate the assemblages [1]. Their three-dimensional, tree-like colonies often form dense animal forests [8] which, much like terrestrial forests, enhance structural complexity, support biodiversity, and provide key ecological functions such as shelter, spawning grounds, and feeding areas for a wide range of marine species [9,10,11,12]. However, their growth form, along with their life history characteristics [13,14], renders them particularly vulnerable to anthropogenic pressures, emphasizing the need for systematic monitoring and effective protection [15,16].

Coralligenous assemblages are currently classified as Near Threatened in the European Red List of Habitats [17], while key species such as E. cavolini and P. clavata are listed as Near Threatened and Vulnerable, respectively, in the IUCN Red List of Species [18], underscoring the urgency of targeted monitoring and conservation measures. The UNEP-MAP Action Plan [2] and the Marine Strategy Framework Directive (MSFD; 2008/56/EC) both emphasize the importance of systematic mapping and status assessment of coralligenous habitats.

Despite their ecological importance and the multiple ecosystem services they provide—including productive fishing grounds for food provision, attractive seascapes for diving tourism, and raw materials such as bioactive compounds (e.g., [1,19,20,21,22])—coralligenous assemblages and their associated gorgonian populations are increasingly threatened across the Mediterranean by diverse anthropogenic pressures. This is particularly true in the NE Mediterranean Sea, an area that is exposed to climate-driven marine heat waves [23] and unregulated fishing [24], and where coralligenous assemblages remain largely unprotected [25].

Marine litter (ML)—particularly lost or discarded fishing gear—poses a major concern [26], which is acute in areas of intense small-scale fishing [27,28,29]. Lost or abandoned fishing ropes, lines, nets and hooks, often deployed near rocky reefs or transported there by currents, become easily entangled in the complex topography of coralligenous habitats [30,31,32,33]. The presence of erect or branching species with flexible skeletons, such as gorgonians, further increases their susceptibility to entanglement with ML [30,33]. Such entanglement has severe impacts on coralligenous assemblages, through smothering, breakage, and detachment, leading to the loss of key species and degradation of the overall habitat [30,32,34]. In gorgonians, the abrasive action of entangled ML causes tissue loss, heightening susceptibility to infections and epibiotic fouling, and ultimately leading to colony mortality [29,31,35,36].

Combined with additional pressures—including unregulated fishing, diving and boating [37,38,39,40], as well as pollution, eutrophication, sedimentation, and biological invasions [41,42,43,44]—ML promotes the structural and functional homogenization of coralligenous assemblages [13,45,46], leading to simplified communities dominated by opportunistic, short-lived species, such as turf algae, encrusting sponges, tunicates, and boring organisms [42,45,46,47].

Several methodological approaches [48] and various ecological or ecosystem-based indices [49,50] have been developed to support the mapping, assessment, and monitoring of coralligenous habitats. Mapping commonly relies on hydroacoustic technology, such as side-scan sonar (SSS), multibeam sonar, and sub-bottom profilers [51,52,53], while at broader scales it is primarily based on bibliographic data compilations [25,54] and predictive modeling [55].

Ecological assessments typically employ visual methods, either through in situ observations or photographic and video surveys [49], which cause minimal disturbance. Shallow coralligenous habitats are usually surveyed by SCUBA divers, whereas deeper, more extensive, and less accessible areas are primarily explored using Remotely Operated Vehicles (ROVs) [49], or, in recent years, with Autonomous Underwater Vehicles (AUVs) that are often equipped with acoustic or optical technologies [56,57,58]. ROVs are also widely applied in ML assessments, especially in areas hosting sensitive species and habitats, as they allow targeted inspections and real-time decision making [28,34], whereas AUVs generally operate pre-programmed routes with limited capacity for opportunistic observations [56,57,58].

Nevertheless, single-sensor approaches remain insufficient to capture the structural complexity and spatial extent of these habitats. Acoustic mapping alone cannot reliably resolve biotic composition or discriminate substrates with similar acoustic responses, and robust habitat classification therefore requires in situ ground-truthing [59,60]. Conversely, ROV surveys provide high-resolution imagery that enables detailed assessments of benthic organisms and habitat condition, but their spatial coverage is limited and highly dependent on operational constraints [61,62]. Integrating these techniques through multimodal underwater sensing offers a powerful means of overcoming limitations of single-sensor surveys and achieving more holistic ecological monitoring [62].

The present study integrates SSS surveys with ROV-based distance sampling and imagery aiming to (i) map and characterize the coralligenous formations that occur within a marine protected area (MPA) in the NE Aegean Sea that remains largely understudied, (ii) evaluate the population status of the gorgonian species E. cavolini and P. clavata, and (iii) quantify the impact of marine litter on gorgonian colonies and other vulnerable benthic taxa. By combining complementary acoustic and optical datasets, this study demonstrates the value of mulitmodal underwater sensing for habitat mapping and impact assessment in mesophotic ecosystems. Such information is essential to inform conservation planning in line with EU directives, and to develop cost-efficient protocols for long-term ecological monitoring.

2. Materials and Methods

2.1. Study Area

The study site is situated off the northeastern coast of Lesvos Island in the Aegean Sea (Figure 1). It lies within a designated Special Area of Conservation (SAC), “GR4110015—Marine Area of Tokmakia Islets” of the Natura 2000 Network—the largest coordinated network of protected areas globally, established by the European Union (EU) to conserve its most valuable and threatened species and habitats. However, as is the case with most Natura 2000 sites, this area, although legally designated, lacks an operational management plan.

The marine area comprises a coastal plateau (the Tokmakia plateau) interspersed with several small islets and encompassing a mosaic of ecologically valuable habitats, including sand banks, Posidonia oceanica meadows down to 30 m depth, and rocky reefs. A central feature of this study is the rocky drop-off along the Tokmakia plateau, spanning nearly 2 km and descending from 25 to 60 m. This drop-off hosts diverse coralligenous assemblages and dense gorgonian populations [63,64], rendering it a focal site for biodiversity monitoring and ecological assessment. Despite its ecological importance, the area remains largely understudied, with the only known previous investigation being one related to its coralligenous formations conducted approximately 14 years ago [63,64]. Fieldwork for the present study was conducted between early spring and late summer 2025.

2.2. Seabed Mapping

Side-scan sonar (SSS) data acquisition took place in March 2025 to map the seabed morphology in the wider area, and to guide ROV dives. The SSS survey covered approximately 4.2 km², northeast of the Tokmakia Islets. The SSS system used was a CM2 (C-MAX) operating at 325 kHz, with a towing speed of 3–4 knots and an altitude of 10–25 m, depending on seabed morphology (seabed depth range: 15–65 m). The operating range of each channel was set at 200 m, giving a total swath of 400 m. A total of five parallel lines (~17 km in total) were surveyed with approximately 80% overlap to ensure full seabed coverage and seafloor mosaicking (Figure 2). For navigation and positioning, a TopCon Hiper HR RTK GNSS was used. SonarWiz v.8.0 software was used for SSS data processing and analysis, feature digitization, automated classification of seabed texture, and geo-referenced mosaicking. However, given the morphological complexity of the western SSS lines, the sloping seafloor, the high reflectivity return signal along the SSS nadir and the lack of underwater positioning, automated classification based on the gray level co-occurrence matrix (GLCM) texture analysis technique did not yield reliable results; therefore manual habitat mapping was performed instead (e.g., [65]). The SSS interpretation guided the selection of the ROV dive locations for ground-truth data to validate the observed SSS reflectivity types, and the ROV dives for ML and gorgonian assessment (Figure S1, Table S1).

2.3. Marine Litter and Gorgonian Population Assessment

The marine litter (ML) and gorgonian assessments took place in March, May, and July 2025 (Figure S1, Table S1). Dedicated surveys were conducted using an ROV (FIFISH E-MASTER) with a maximum operating depth of 200 m. Its main camera has a field of view of 120° and supports still images up to 3840 × 2160 px resolution and videos up to 4K UHD at 30 fps, stabilized by electronic image stabilization. A secondary rear-facing camera was employed to monitor the tether and prevent obstructions and tangling. Illumination was provided by dual 5000-lumen lights with a 160° beam angle. The ROV was also equipped with a red laser scaler (660 nm) with a fixed width of 10 cm, used to calibrate measurements in metric units. The vessel’s position was recorded via GPS at the start and end of each ROV dive.

2.3.1. Marine Litter

Information on the presence of ML was obtained from visual analysis of video footage collected during dedicated ROV dives across the rocky drop-off. During each dive, the ROV was kept at a constant distance of ~1.5 m from the seabed and moved at a steady slow speed following a random path. The ROV dive ID, depth range, and total video duration were logged. In the absence of an underwater GPS, dive distances were estimated based on the vessel’s geographic coordinates at deployment and retrieval.

Video footage was then manually analyzed using standard video playback software to characterize and quantify ML occurrence. At each ML encounter, the video was paused and the following information recorded: ML type, abundance (number of items), depth (m), type of impact, level of ML biofouling, interaction with vulnerable taxa, and feasibility of removal. ML type was defined according to the categories adopted for ML monitoring under the Marine Framework Strategy Directive in the Mediterranean [66].

The type of ML impact was classified into four categories of interaction with benthic habitats and organisms [29]: (a) covering/entanglement: ML covering or entangled with substrate and/or organisms; (b) abrasion: visible scars or injuries on substrate or organisms caused by ML; (c) hanging: ML suspended between obstacles but causing no apparent damage, (d) lying: ML resting on the substrate causing no apparent harm on habitats or species.

The biofouling level of each ML item was also recorded as it provides information on putative residence time and potential ecological effects [29,32]. Specifically four biofouling stages were defined (adopted from [32]): Stage 0—no biofouling present, residence time <1 month; Stage 1—mainly characterized by the presence of few small filamentous algae and cambanularid hydrozoans, indicative of residence time of approximately 1–4 months; Stage 2—presence of larger macroalgal and hydrozoan taxa, as well as small carbonatic encrusting organisms (e.g., serpulids and coralline algae), or taxa of limited vertical growth (e.g., bryozoans), indicative of residence time between 4–12 months; and Stage 3—well developed communities of multiple conspicuous taxa, including macroalgae, hydrozoans, encrusting and erect carbonatic taxa (e.g., mollusks, bryozoans, etc.), indicative of a residence time of >1 year.

The feasibility of ML removal was assessed based on the accessibility, degree of entanglement, and the level of biofouling, using a 0–3 scale: (0) 0% removal possibility; (1) <50% removable; (2) ≥50<90% removable; and (3) fully or almost fully removable (>90%). In addition, the number of individuals/colonies of vulnerable, erect species in contact with ML was recorded. To assess the ML density in the absence of an underwater navigation system, the surveyed area per ROV dive was calculated from the estimated ROV dive length (i.e., derived from the vessel GPS coordinates at the ROV entry and exit points) and an estimated field of view of 2.5 m.

2.3.2. Gorgonian Population Density

Using previous knowledge of the area [63,64], and the information collected during the ROV dives for ground-truthing and ML assessment, two gorgonian populations were located and investigated, one population of Eunicella cavolini and one of Paramuricea clavata. ROV-based distance sampling was performed to estimate population density. During the specific assessment, the ROV followed vertical linear transects, placed perpendicular to the bathymetric contours. The ROV was positioned approximately 0.5–1 m above the seabed, and moved at a steady, slow speed. The camera faced downwards, at 90° degrees angle from the substrate, with an offset of no more than 25° degrees at times, to enable avoidance of obstacles (i.e., due to substrate rugosity and the presence of ML). The two points of the laser scaler (calibrated to show a 10 cm distance) were always visible during the video recording to enable estimation of the perpendicular distances between each colony and the transect line (i.e., the theoretical line running along the center of the field of view), the transect length (i.e., the length of the theoretical straight line along the center of the field of view that connects consecutive landmarks on the seafloor), and the maximum visible straight-line segment of each colony (i.e., a surrogate of colony size). Measurements were performed using the image processing ImageJ/Fiji 1.54p software [67], which supports sequential calibrations within the same image stack. The ROV video files (.mp4 format) were imported using the FFMPEG plugin, allowing frame-by-frame analysis. Following calibration of each frame, measurement lines were drawn and labeled accordingly (transect line/perp. distance/colony size) in the ROI Manager (Region of Interest), while measurement data were exported into a spreadsheet using a custom Java macro that preserved the associated labels. Transect lines, in particular, were drawn using stable, distinguishable landmarks on the seafloor visible across frames. The ROV’s forward movement was tracked by advancing to subsequent frames where the end-point landmark of the previous linear segment served as the starting point of the next. Calibration was repeated as necessary to ensure measurement accuracy. The total transect length was calculated as the cumulative sum of all measured linear segments across frames.

Data were divided into two strata representing the two populations. Population density was estimated using distance sampling modeling, which is well suited to account for imperfect detectability in population estimates [68]. This is done by modeling the detection function $g\left(y\right)$, which describes the relationship between the probability of detection $P_a$and the perpendicular distance $\left(y\right)$ of the colonies from the transect line. The probability of detection $P_a$ along the transects can be estimated as:

$${\widehat{P}}_a={\displaystyle \frac{{\int }_0^w\widehat{g}\left(y\right)dy}{w}}$$

(1)

Population density is then estimated as:

$$\widehat{D}={\displaystyle \frac{n}{a\widehat{P_a}}}$$

(2)

where $w$ is the half-width of the transect (defined by the maximum detection distance), $n$ is the number of recorded colonies, $a$ is the surveyed area $(a=2wL)$, and $L$ is the total length of the transects.

The detection function was modeled using the DISTANCE 7.5 Release 2 software [69] following [68] with the general form:

$$g\left(y\right)={\displaystyle \frac{key\left(y\right)[1+series\left(y\right)]}{key\left(0\right)[1+series\left(0\right)]}}$$

(3)

where $key\left(y\right)$is a key function and a $series\left(y\right)$ expansion used to improve the model fit.

In total, 32 models were fitted using a set of combinations among: (i) two key functions, Half-normal $key\left(y\right)=exp\left({\displaystyle \frac{{-y}^2}{{2\sigma }^2}}\right)$ and Hazard-rate $key\left(y\right)=1-exp[-{\left({\displaystyle \frac{y}{\sigma }}\right)}^{-b}]$, where $\sigma$ is a scale parameter and $b$ a shape parameter, (ii) a series expansion among cosine, simple, and hermite polynomial, and (iii) two covariates: the colony size (i.e., estimated using the maximum visible straight-line segment of each colony), and the species (E. cavolini or P. clavata). The covariates were incorporated into the models using the multiple-covariate distance sampling (MCDS) approach [70].

The Akaike Information Criterion (AIC) was used for model selection [71,72], and a multi-model inference approach was followed, weighting parameter estimates from all models by their Akaike weights [72]. A right truncation was applied to the data, to ensure detectability did not fall below 0.15, as recommended by [68]. The overall goodness-of-fit of the selected models was examined using cosine-weighted and un-weighted Cramér-von Mises tests.

2.3.3. Population Structure and Health Status

Additional ROV dives were conducted to obtain high-resolution video footage of gorgonian colonies. Starting from the deepest sections and moving towards the shallowest areas of each population, the ROV was slowly maneuvered along a non-linear random path to capture close-up video footage of individual colonies. At each gorgonian encounter, particular attention was given to maintaining the camera perpendicular to the colony’s main axis and keeping the ROV laser points visible on each colony targeted for measurement, thereby ensuring proper calibration and measurement accuracy during image analysis.

ROV video footage was analyzed to extract high-quality image frames, targeting a minimum of 100 gorgonian colonies per population [73]. Frame selection followed a stratified semi-random approach (i.e., random images were obtained from different parts of each population) to ensure representative sampling across the entire spatial extent of each population. The ImageJ software (https://imagej.net/ij/index.html, accessed on 20 May 2025) was used to (a) calibrate each image using the fixed 10 cm distance between the two ROV laser points as a scale reference, and (b) to measure the maximum colony height, as the straight-line distance from the base of the colony to the tip of its furthest branch.

Using the same images, the health status of individual colonies was assessed visually according to three key indicators: (i) extent of injury, (ii) type of injury, and (iii) percentage of unaffected colonies (i.e., <10% injured surface area) [74,75]. Joint analysis of injury type and extent provides valuable information about the nature and timing of past disturbances [39,76]. The extent of injury was visually estimated as the proportion of colony surface devoid of coenenchyma tissue or colonized by epibionts, using the following categories: [0–10%), [10–50%), [50–100%), and 100% (dead colonies). Injury type was defined based on the colonization stage of epibionts [76]: Type A—exposed colony axis, indicating a recent injury (≤1 month); Type B—overgrowth by early colonizers such as filamentous algae and hydrozoans, suggesting a disturbance within the past 1–12 months; Type C—dominance of bryozoans, sponges, or well-developed and encrusting algae, indicating injuries >12 months.

3. Results

3.1. Results on Seabed Mapping

Analysis and interpretation of the individual SSS images and the sonar mosaic identified four reflectivity types (RT; Figure S2) that correspond to different substrates or habitat types (Figure 3), which were ground-truthed by ROV observations. RT1 exhibits relatively uniform medium-toned backscatter intensity, indicating an almost featureless seafloor covered by loose sediments (sands to muddy sands), and covers 72% of the surveyed area. RT2 displays a mixed (high to low) reflectivity pattern with acoustic shadows, indicating a highly uneven relief corresponding to exposed hard substrates (rocky outcrops). It occupies 11% of the study area and was detected in multiple locations, particularly along steeply sloping seafloor sections, where it forms two large, elongated outcrops with a northwestern-southeastern orientation (Figure 3).

The two elongated outcrops are separated by a ~200 m-wide corridor with moderately steep walls up to 10 m in height, resembling a channel-like feature (paleo-river), apparently covered by loose sediments. In the central part of the surveyed area, east of the elongated rocky outcrops, nearly circular patches of higher reflectivity within a medium-toned backscatter (RT3), few tens of centimeters in height, appear on the seabed, likely corresponding to small biogenic build-ups. These occupy approximately 7% of the study area. Similar formations, shaping a mounded relief of ~0.8 m in height, were observed by [77] further south, along the thalweg of a paleo-channel. RT4 exhibits a variable, medium-toned backscatter with small acoustic shadows, attributed to seagrass (P. oceanica meadows). This type occurs in the shallowest parts of the surveyed area and covers around 10% of the seabed.

Anthropogenic features were also detected, mainly within RT1. Specifically, the SSS survey revealed (i) a prominent shipwreck (Figure 3 and Figure S3b), ~60 m long and ~10 m wide, rising more than 8 m above the seabed, with clearly visible crane masts on one side, and (ii) occasional linear scars, up to 250 m long, likely resulting from anchoring or bottom trawling (Figure 3).

3.2. Results on Marine Litter and Gorgonian Population Assessment

3.2.1. Results on Marine Litter

The ML assessment was conducted during 15 ROV dives (Figure 4, Table S2), covering a total area of 4798 m² (range: 45–777 km²), across a depth range of 20–60 m. In total, 6 hr 53 min (range: 7–60 min) of video footage were analyzed. ML items were recorded between 24 and 59 m depth, with a mean occurrence depth of 43.7 ± 8.0 m (± standard deviation).

The total number of ML items observed in contact with benthic habitats and associated species was 1626, with an estimated density of 0.6 ± 0.7 items m⁻². ML was present in all ROV dives, with the highest observed density during dive 10 (2.8 m⁻²) (Figure 5). The vast majority were fishing lines (88.3%), followed by fishing ropes (9.3%), other fisheries-related items (nets, traps, and anchors; collectively 2.0%), and miscellaneous other ML (<1%) (Figure 5,

Table 1. Data summary of marine litter (ML) type, depth range, number of items (N), type of interaction (based on [29]), ML fouling stage (based on [32]), and removal feasibility.

Type	Depth Range (m)	N	ML Interaction with Substrate				ML Fouling Stage				Removal Feasibility (%)
			Covering/Entangled	Abrasion	Hanging	Lying	0	1	2	3	0	<50	>50–90	100
Fishing lines	20–59	1435	507	30	736	162	3	86	424	922	190	422	485	338
Fishing ropes	20–59	151	26	0	46	79	0	22	86	43	11	53	25	62
Artisanal nets	41–57	12	3	0	5	4	0	5	3	4	5	2	0	5
Fishing traps	31–47	9	2	0	0	7	0	0	4	5	0	0	0	9
Trawl nets	39–52	8	4	1	3	0	0	0	3	5	0	0	2	6
Anchors	38–48	3	2	0	0	1	0	1	0	2	0	1	2	3
Plastic bottle	44–53	3	0	0	0	3	0	0	1	2	0	0	0	3
Plastic bag	58	1	0	0	0	1	0	0	1	0	0	0	0	1
Ceramic jar	33	1	0	0	0	1	0	0	0	1	0	0	0	1
Glove	56	1	0	0	0	1	0	1	0	0	0	0	0	1
Metal ring	48.5	1	0	0	0	1	0	1	0	0	0	0	0	1
Tube	35	1	0	0	0	1	0	0	1	0	0	0	0	1
Total		1626

).

Most fishing lines and ropes were hanging (49.3%), covering/entangled (33.6%), or lying passively (15.2%). All ML showed some level of fouling, mostly Stage 3 (60.5%), followed by Stage 2 (32.2%) and Stage 1 (7.1%). Considering ML accessibility, interaction with substrate and associated organisms, and fouling stage, 32.0% of ML items had 50–99% removal potential, 29.4% had <50% removal potential, 26.5% had 100% removal potential, and 13% had 0% removal potential (Table 1).

A total of seven vulnerable or protected taxa (Table S3) were recorded in contact with ML: four Porifera—Aplysina spp., Axinella cannabina (Esper, 1794), Axinella spp., and Sarcotragus spp.—and three Cnidaria—Eunicella cavolini (Koch, 1887), Paramuricea clavata (Risso, 1827), and Savalia savaglia (Bertoloni, 1819)—(Figure 6). Out of 517 individual/colony interactions, Aplysina spp. accounted for 34.2%, followed by P. clavata (29.2%), E. cavolini (17.4%), A. cannabina (10.1%), Axinella spp. (including A. polypoides; 7.2%), Sarcotragus spp. (1.7%), and a single interaction with S. savaglia. Interactions were mainly covering/entanglement (47.4%), followed by hanging (44.3%), abrasion (7.0%), and lying (1.4%) (Figure 6).

3.2.2. Results on Gorgonian Population Density

Information obtained from the ROV dives for ground-truthing and ML assessment revealed the presence of two gorgonian populations, located ~1500 m apart (Figure 7 and Figure S4). The core of the E. cavolini population occurs at the northern rocky shoal at 30–45 m depth, although scattered individual colonies were also detected elsewhere on the rocky shoals, whereas the P. clavata is more spatially restricted, occurring at the southernmost end of the southern shoal at 45–56 m depth (Figure 7 and Figure S4).

Distance sampling surveys were carried out along six transects for E. cavolini and ten transects for P. clavata. The surveyed area for E. cavolini covered 731 m², with transects positioned between 29 and 43 m depth. For P. clavata, a total area of 1038 m² was surveyed across transects between 47 and 57 m depth. Density estimates were based on 104 detections for E. cavolini and 615 for P. clavata, following right truncation of data. Based on AIC model selection, the best-supported model among the 32 candidate models (Table S4) was the one with a half-normal as key function, no series expansion, and species as a covariate. Four additional models had ΔAIC < 2 and were therefore considered in multi-model inference: (i) half-normal key function with no series expansion and both species and size as covariates (ΔAIC = 1.91); (ii) half-normal with one cosine adjustment term and species as a covariate (ΔAIC = 1.92); (iii) half-normal with one simple polynomial adjustment term and species as covariate (ΔAIC = 1.93); and (iv) half-normal with one hermite polynomial adjustment term and species as a covariate (ΔAIC = 1.96). All supported models showed a good fit to the data, as indicated by both cosine-weighted and unweighted Cramér-von Mises tests (all p values > 0.05).

The estimated density of E. cavolini was 0.35 colonies m⁻² (95% confidence interval (CI): 0.14–0.57) and for P. clavata 1.47 colonies m⁻² (95% CI: 0.94–1.99). The coefficient of variation (CV) indicated greater relative variability in the density estimate of E. cavolini (31.0%) than in P. clavata (18.4%), suggesting lower precision for the former.

3.2.3. Results on Population Structure and Health Status

The height frequency distribution for E. cavolini ranged between 4.3 and 65.5 cm, with a mean of 29.7 ± 12.5 cm (SD). Skewness indicated a roughly symmetrical distribution, while kurtosis suggested no prevalent peakedness (Figure 8,

Table 2. Characteristics of the studied Eunicella cavolini and Paramuricea clavata populations, including depth range (D), number of gorgonians (N), density, minimum (Min) and maximum (Max) height, mean height, and standard deviation (SD). Skewness (g1) and kurtosis (g2) are considered significant if the absolute value of coefficient/standard error (SEg1 and SEg2) is greater than 2.

Species	N	Depth Range (m)	Height
			Min	Max	Mean	SD	g1	SEg1	Sig g1	g2	SEg2	Sig g2
E. cavolini	120	30–45	4.3	65.5	29.7	12.5	0.3	0.2	1.4	−0.0	0.4	−0.0
P. clavata	104	45–56	10.3	86.9	50.6	16.3	−0.1	0.3	−0.5	−0.4	0.5	−0.8

). The height frequency distribution for P. clavata ranged from 10.3 to 86.9 cm, with a mean of 50.6 ± 16.3 cm. It also appeared approximately symmetrical lacking prevalent peakedness (Figure 8, Table 2).

In both populations, most colonies exhibited low injury levels, with 83% of E. cavolini and 84% of P. clavata showing <10% injured surface area. Moderate injury levels (10–50%) were recorded in 17% of E. cavolini and 13% of P. clavata colonies. Complete tissue loss (100%—dead colonies) was rare, occurring in only 1% of E. cavolini and 4% of P. clavata colonies (

Table 3. Summary of data on the injury characteristics of the studied Eunicella cavolini and Paramuricea clavata populations. Only colonies presenting ≥10% injuries were used to assess the type of injury following [76]. Type A—exposed colony axis, indicating a recent injury (≤1 month); Type B—overgrowth by early colonizers such as filamentous algae and hydrozoans, suggesting a disturbance within the past 1–12 months; Type C—dominance of bryozoans, sponges, or well-developed and encrusting algae, indicating injuries >12 months.

Species	N	Proportion of Uninjured, Injured and Dead Colonies According to the Proportion of Injured Surface Area (%)			Proportion of Colonies per Type of Injury (%)
		[0–10%)	[10–99%)	100%	A	B	C
E. cavolini	120	83	17	1	0	38.1	61.9
P. clavata	104	84	13	4	0	35.3	64.7

). Most injured or dead colonies exhibited type C injuries (E. cavolini: 61.9%; P. clavata: 64.7%), fewer displayed type B injuries (E. cavolini: 38.1%; P. clavata: 35.3%), while none exhibited type A injuries (i.e., <1 month old) (Table 3).

4. Discussion

The combined use of SSS and targeted ROV surveys enabled the collection of quantitative data on the distribution of coralligenous formations and gorgonian populations within a marine area originally designated as a Natura 2000 site (SAC GR4110015) only for Posidonia oceanica beds. This approach facilitated the detailed mapping of the Tokmakia drop-off, a heterogeneous seascape where coralligenous assemblages occur primarily along steep rocky shoals, but also as small patches on adjacent soft bottoms. These findings are consistent with previous studies highlighting the mosaic nature and structural complexity of coralligenous systems both across the Mediterranean [1,2,3] and within the Aegean Sea (e.g., [51,53,64]).

Within this diverse seascape, E. cavolini and P. clavata formed distinct monospecific ecological facies (i.e., areas where gorgonians dominate the assemblages), occurring at different sites along the rocky drop-off and across different depth ranges, with P. clavata occupying slightly deeper zones than E. cavolini. Although co-occurrence of the two species is common (e.g., [78,79]), spatial segregation is also frequently observed [79,80], reflecting differences in ecological preferences and tolerance to environmental and anthropogenic stressors [81,82,83,84].

The two species also displayed clear differences in their density. P. clavata exhibited a significantly higher density (1.46 colonies m⁻²) than E. cavolini (0.35 colonies m⁻²). These values are notably lower than those reported for comparable depths in the western Mediterranean, where densities often exceed 20 colonies m⁻² [63]. In the northern Aegean, reported mean densities of E. cavolini range from 4.5 to 34.8 colonies m⁻² [63,80]. The population documented here has a density about six times lower than that previously reported for the same site (6 colonies m⁻² at “Palios” [63]). In contrast, the mean density of P. clavata, though relatively low, falls within the range reported for other northern Aegean sites (1–14.5 colonies m⁻²; [80]).

The lower density of E. cavolini may reflect local environmental conditions, habitat extent, or disturbance history, but methodological differences must also be considered. Previous studies relied on quadrat or transect sampling by SCUBA diving [63,80], whereas the present estimates were derived from ROV-based distance sampling along extended transects. Quadrat sampling captures fine-scale patchiness, whereas ROV transects integrate broader-scale patterns, potentially smoothing local variation. Comparative studies applying both methods simultaneously would be critical to disentangle ecological variation from methodological effects.

The height structure of E. cavolini is consistent with patterns reported for both shallow [63,80] and deep populations [84], although small colonies (<10 cm) were scarce. P. clavata colonies were generally larger, matching sizes reported elsewhere in the Aegean Sea [80] and the wider Mediterranean [74], and also showed a notable scarcity of juveniles. While smaller colonies may be under-detected by ROV, similar patterns reported in quadrat-based surveys [63] in this area suggest genuinely low recruitment. Nevertheless, the presence of large colonies in both populations indicates stable environmental conditions allowing long-term persistence. Such stability may offset low recruitment, as larger colonies contribute disproportionately to sustain populations, given that gamete production and reproductive output scale exponentially with colony size [13,85,86].

Injury levels were generally low in both populations, with most colonies showing <10% injured surface area. Injuries may result from abrasion, predation, or tissue necrosis under thermal stress, potentially leading to partial necrosis or total colony mortality [87]. Both gorgonian species are highly vulnerable to thermal stress and have suffered repeated mass mortalities linked to marine heatwaves in the northwestern Mediterranean (e.g., [75,88,89]. The relative isolation and rather deep distribution (i.e., >30 m) of the Tokmakia populations [63] may buffer them from anthropogenic pressures such as pollution, sedimentation, eutrophication, and ocean warming [23,42,43]. The low injury levels observed in E. cavolini, along with the prevalence of large colonies, suggest no major mortality events in the past 14 years, in agreement with earlier reports [63]. For P. clavata, the absence of historical data precludes similar conclusions.

Despite this apparent resilience, fisheries-related marine litter was abundant (Figure 9). Fishing lines and ropes dominated marine litter, consistent with reports from other Mediterranean reefs where small-scale and recreational fishing are widespread (e.g., [29,30,32,90,91,92]). Such litter mainly affects arborescent taxa, like gorgonians [92,93,94]. The destructive potential of such interactions is well documented, with Costa et al. [93] reporting the mortality of over 60 P. clavata colonies entangled in a single abandoned trammel net.

Most litter exhibited advanced fouling, confirming long residence times and persistence impacts [29,32]. Entanglement and hanging were the most frequent interaction types, primarily affecting structural taxa, such as gorgonians and sponges, in agreement with [27,28,32,33]. As fouled gear becomes heavier, it imposes increased mechanical stress on benthic organisms, leading to tissue abrasion, breakage, and detachment [29,30,31,32]. Over time, these chronic impacts reduce the fitness and survival potential of affected organisms, leading to habitat degradation and loss of structural complexity, with cascading effects on the associated biodiversity [36,90,93].

Regarding ML management, removal feasibility varied substantially. A risk-based clean-up strategy, focused on retrieving lightly fouled, hanging fishing gear with minimal entanglement, offers a practical approach to minimize further harm, whereas heavily fouled or tightly entangled items should generally be left in place to avoid collateral damage [32,34,94]. These findings carry important implications for the future management of Marine Protected Areas (MPAs), where conservation efforts must carefully balance litter removal with the protection of sensitive benthic habitats and species. Targeted, low-impact removal strategies, informed by knowledge of litter distribution and persistence, as the information provided herein, are essential for the effective intervention, and long-term preservation of structural complexity and biodiversity within MPAs.

Our methodological approach, which combined acoustic backscatter classification with visual ROV observations, proved effective in overcoming single-sensor limitations. Acoustics offer broad spatial coverage but limited biological resolution, whereas ROVs provide fine-scale ecological detail over restricted areas [59,60,61,62]. Integrating these complementary datasets improved habitat discrimination, as merging large-scale acoustic context with fine-scale imagery enhanced both operational breadth and ecological detail. Our workflow supports UNEP-MAP and MSFD priorities for systematic mapping and status assessment of coralligenous habitats ([2]; MSFD 2008/56/EC), while contributing to advances of multimodal underwater sensing for marine ecology and conservation [49,62].

Interpretation, however, must consider methodological constraints. Automatic SSS classification underperformed in heterogeneous terrain, requiring manual operator-based mapping [51,52,53]. ROV surveys, although non-destructive and high-resolution [34,49], were spatially limited and operationally constrained by currents and navigation uncertainty. Vessel GPS recorded positions only at deployment and retrieval, while cable length and drift introduced positional errors, affecting mapping accuracy. Distance sampling assumptions of perfect detection near the transect line and accurate measurements of perpendicular distances [68,69,70] may be compromised by camera angle and ROV altitude. Laser scaling and standardized survey protocols mitigated, but did not eliminate, these issues.

To enhance the robustness and precision of future assessments, several methodological refinements are recommended. Combining acoustic outputs with video imagery through machine-learning approaches would improve automated habitat mapping in complex seabed environments (e.g., [95]), supporting objective habitat classification in heterogeneous terrains. More advanced platforms—such as multi-beam echo sounders and AUVs equipped with multiple sensors, could further capture high-resolution 3D seafloor morphology (e.g., [96]), thereby increasing the spatial coverage, albeit at higher operational costs. Integrating ROVs with ultra-short baseline (USBL) positioning, inertial navigation systems, or acoustic tracking, and synchronized navigation, attitude, altitude and depth data would improve spatial accuracy and distance-sampling reliability, particularly when coupled with stable vehicle altitude and motion control. Finally, standardizing protocols, increasing temporal replication, and enhancing environmental metadata collection (e.g., temperature, currents, turbidity) would promote more rigorous and comparable management-oriented assessments.

Despite these limitations, our approach demonstrates the value of acoustic–optical integration for monitoring complex benthic systems and provides a cost-effective protocol which, with few improvements in tracking and geo-referencing, could advance systematic coralligenous mapping and monitoring. Most importantly, it provides quantitative baselines critical for MPA management including: (i) refined no-take and no-trawl zones along the drop-off; (ii) evidence-based marine litter mitigation, targeting accumulation corridors and entanglement hotspots, and (iii) population metrics that can be used to establish site-specific thresholds and indicators for MSFD and Habitats Directive reporting [17,18,49,50].