Mapping the footprint of untracked small-scale fisheries using multicriteria spatial analysis and optical satellite imagery

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Yet, this remains challenging due to limited vessel tracking requirements for small boats under 12 m. This study presents the first attempt to combine Multi-Criteria Spatial Analysis (MCSA) and Sentinel-2 remote sensing approaches for mapping small-scale fisheries (SSF) footprint, using Sicily as a case study. The MCSA combined a habitat suitability index, informed by interviews with small-scale fishers across main Sicilian ports, with an activity index based on fleet’s characteristics and distance from the nearest ports. Sentinel-2 remote sensing data provided direct vessel detections processed by Global Fishing Watch using optical imagery, used for the first time in this study to map SSF vessels. The direct comparison between MCSA and Sentinel-2 data revealed a significant positive correlation (Spearman’s ρ: 0.43, p < 0.001). Both approaches consistently identified fishing hotspots and coldspots and showed stronger agreement for fishing hotspots (62% concordance) than for coldspots and neutral areas (34% and 47% concordance respectively). This result may be explained by MCSA's more conservative approach, which identified twice as many cold spots compared to Sentinel-2. The robustness of our results reveals the importance of combining two approaches when estimating fisheries footprint, especially for SSF where tracking remains highly limited. By integrating MCSA with remote sensing, our approach enables large-scale SSF monitoring with considerably reduced logistical demands compared to extensive participatory mapping, offering a practical and replicable framework with critical implications for sustainable fisheries management and marine spatial planning globally. fishing multicriteria analysis hotspots concordance Mediterranean distribution Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Small-scale fisheries (SSF) play a vital socio-economic role globally, by securing nutrition, livelihoods, and preserving cultural heritage for millions of people (Schuhbauer and Sumaila, 2016 ; FAO, 2023 ; Basurto et al. 2025 ). SSFs employ 90% of professional fishers and extract 37 million tonnes of fish from the ocean annually, significantly contributing to the food requirement of coastal populations (FAO, 2019 ; 2023 ; Basurto et al. 2025 ). The definition of SSF varies across geographical regions and contexts, but it is generally associated with low-technology, low-capital, labour-intensive and traditional practices (Smith 2019 and Basurto, 2019; Smith et al. 2021; Basurto et al. 2025 ). The European Maritime and Fisheries Fund defines SSF as “fishing carried out by fishing vessels of an overall length of less than 12 m and not using towed fishing gear” (Regulation (UE) 508/2014; Tzanatos et al. 2006 ; Battaglia et al. 2010 ; Lloret et al. 2018 ; Grati et al., 2022 ). SSF activity is known to exert lower impacts on benthic habitats and generate fewer discards than large-scale fisheries (LSF) (Catanese et al. 2018 ; Selgrath et al. 2018 ; Zeller et al. 2018 ). However, recent studies have pointed out overlooked aspects, showing that even small-scale fisheries can significantly harm marine ecosystems. These impacts include the depletion of target species stocks, incidental bycatch, habitat degradation and the accumulation of lost or abandoned fishing gears, all of which can undermine ecosystem integrity and sustainable fisheries management (Lloret et al. 2020 ; Silvestrini et al. 2024 , 2025 ). Evidence suggests that, despite harvesting a broad spectrum of species, SSF often depends on a limited number of species, substantially contributing to both catches and revenues (Calò et al. 2022 ). As a consequence of this unbalanced exploitation of fishing resources, SSFs insistently exploit habitats that are essential for species spawning, as well as nursery grounds (Stergiou et al. 2006 ). Such practices may cause significant impacts on fish stocks and seafloor integrity, affecting ecosystem functioning and services (Sbragaglia et al. 2021 ; Silvestrini et al. 2024 ; 2025 ). Although the impact of SSF on marine ecosystems has received increasing attention, a lack of knowledge persists, particularly with respect to their spatial footprint, defined here as the fishing spatial patterns (Kroodsma et al. 2018 ),. These data are essential for assessing their ecological impacts, including identifying areas where fishing activity is most concentrated, and properly evaluating the status of the targeted resources. While LSF spatial distribution is known through remote sensing systems, such as the Vessel Monitoring System (VMS, mandatory with Electronic logbook for all European fishing vessels exceeding 12 m in length since 1 January 2012), and the Automatic Identification System (AIS, required to be carried on board of EU fishing vessels longer than 15 m) (European Commission, 2009), analogous vessel tracking devices and electronic monitoring systems are currently not required on SSF vessels. This gap reflects a combination of historical, legislative, and socio-economic factors: SSF have traditionally been neglected or marginalized for a long time compared with other fishery sectors (Guyader et al. 2013 ; Smith et al. 2021) and financial as well as technical constraints discourage widespread adoption of such systems that are heavy, costly, and require external power sources (Orofino et al. 2023 ; Tassetti et al. 2022 ). As a result, the spatial footprint of SSF remains difficult to capture and monitor (Johnson et al. 2017 ). To fill the knowledge gap about SSF footprint in a cost-effective manner, participatory mapping has been used, where fishers are asked to identify their fishing grounds on provided maps (e.g., Grati et al. 2022 ). Yet, to generate reliable fishing spatial footprint estimates, participatory mapping must rely on very large samples of interviewed fishers, a suitable sampling design, which is often difficult to achieve due to logistical issues (Thiault et al. 2017 ). Besides logistics, it is challenging to obtain the complete trust of fishers in releasing information of interest to their activity and thus the data obtained may be biased. Other direct approaches include boat-based visual surveys, which involve observing fishing vessels along transects that cover the target area and recording their activities (Horta e Costa et al. 2013 ; Rufino et al. 2023 ; Blight et al. 2023 ). However, this approach is time-consuming and can only be applied over relatively small spatial scales. Additionally, various tracking systems have been developed as alternative monitoring tools for SSF. For instance, Behivoke et al. ( 2021 ) deployed GPS trackers on small-scale vessels and applied a random forest model to distinguish between non-fishing and fishing vessels. Tassetti et al. ( 2022 ) developed an architecture to collect real-time positional data sent over LoRaWAN technology or 2G/3G/4G connections by small-scale vessels. However, these approaches remain largely experimental and have only been tested at local scales, limiting their current applicability for comprehensive SSF assessment. When spatially explicit data were not available, modelling approaches utilising proxies for mapping fishing have been developed (Chollett et al. 2022 ). These models often combine different predictors relevant for fishing activities, such as the proximity to fishing grounds (Hamel et al. 2018 ), human pressure (Ban et al. 2009 ), weather conditions (Zu Ermgassen et al. 2020 ) and environmental drivers (Harborne et al. 2018 ). While those proxies are relevant factors for fishing, they should be adapted to the specific region of study (Chollett et al. 2022 ). An effective example of an indirect method to predict the SSF footprint without vessel tracking data at the regional level is the Multi-Criteria Decision Analysis (MCDA) developed by Kavadas et al. ( 2015 ). This method combines environmental and economic proxies and fleet capacity data that can both influence the footprint of fisheries. However, the reliability of such indirect methods remains largely untested, as to the best of our knowledge no previous studies combined indirect methods with direct detections of vessels presence in SSF. Drawing on the methodological approaches described above and considering their respective limitations, this study proposes a combination of different methods to obtain validated large-scale data on SSF spatial footprint, using the waters surrounding Sicily, the largest Mediterranean island and hotspot of fishing activity, as a case study. The SSF footprint was assessed by combining two independent approaches: a) a Multicriteria Spatial Analysis (MCSA) framework, integrating key environmental and socio-economic components relevant for fishers, affecting the footprint of their fishing activity and b) a remote sensing analysis using Global Fishing Watch vessel detection data derived from Sentinel-2 optical imagery. Vessel detections from satellite imagery are a recent addition to the set of approaches that can provide valuable information on fishing activity distribution in areas where vessel tracking data is unavailable (Paolo et al. 2024 ; Marsaglia et al. 2024). Our study represents the first attempt to combine MCSA methodology with satellite-derived vessel detection data for mapping SSF, thereby offering a novel framework that integrates direct information from fishers with remote sensing capacities. This approach is scalable and transferable to other marine regions where there is a lack of tracking data for SSF vessels. 2. Materials and methods 2.1 Study area As the largest island in the Mediterranean, Sicily hosts the highest number of fishing vessels and fishers in Italy (Andaloro et al. 2015 ). For the majority of fish stocks, fishing pressure exceeds sustainable levels (EU STECF 2023; Sinerchia et al. 2025 ). At the same time, the region is recognised as a marine biodiversity hotspot, owing to its geographical position and the variety of ecosystems (Médail and Quézel 1999; Di Lorenzo et al. 2018 ). This makes assessing human impacts on its vulnerable ecosystems essential. The boundaries selected for the study encompass the marine waters surrounding Sicily, extending between 11 and 16 °E and between 35 and 39 °N (Fig. 1 ). This area was chosen to include both coastal and continental shelf habitats adjacent to the islands, thereby ensuring a comprehensive coverage of the Sicilian marine coastline. The study area also includes seven nationally-established Marine Protected Areas (MPAs). The bathymetric range considered spans from 1 m above sea level to 600 m of depth, based on both fishers' documented preference for fishing up to 500 m of depth and empirical data from our previous field campaigns indicating 600 m as the maximum depth reached by local SSF operations (authors’ unpublished data). 2.2 Multi-Criteria Spatial Analysis (MCSA) To assess the spatial footprint of SSF along the Sicilian coastal and continental shelf habitats adjacent to the islands, a Multi-Criteria Spatial Analysis (MCSA) was carried out, building upon the MCDA framework developed by Kavadas et al. ( 2015 ). While multicriteria approaches were traditionally developed for decision-making contexts (Tatham et al. 2013 ; Malczewski 2006 ), they are increasingly applied in spatially explicit frameworks to assess and map pressures of human activities on coastal and marine regions (Halpern et al., 2008 ; Wu et al. 2016 ; Malczewski and Jankowski 2020 ). In our study, the MCSA was employed to predict the footprint of SSF in Sicilian waters through an adapted version of the Fishing Footprint index (FF c ) developed by Kavadas et al. ( 2015 ). The FF c index is calculated as the product of two components: a Suitability Index (S c ) and a Vessel Activity Index (A c ). S c defines the suitability of a marine area for SSF based on a selection of environmental criteria, management criteria, and information on competition with other fishery sectors. A c captures information about the number of SSF vessels in each port, their characteristics and the distance from the port to each potential fishing site. 2.2.1 Suitability Index (S c ) The relevant criteria for S c were initially based on those used by Kavadas et al. ( 2015 ) and were: legislation, no-take zones, bathymetry, distance from coast, bottom-trawl activity, purse-seine activity and marine traffic. Data related to each criterion were retrieved from available platforms (Copernicus, Global Fishing Watch, GEBCO) and authors’ unpublished data. For each criterion, data were then divided into value intervals, to which ranking values (hereafter referred to as “grades”) were assigned. Grades range from 4 (indicating the most favourable condition for SSF) to 1 or 0 (indicating the least favourable condition). A minimum grade of 0 is assigned when the specific level of environmental parameters prevents SSF activities from taking place, whereas a minimum grade of 1 is applied to criteria where conditions are suboptimal but remain within the operational range of SSF vessels. The grades for each criterion were determined using frequency plots of data related to the selected variable (e.g., distance from port) and subdividing them into intervals using the Jenks Natural Breaks classification method (through the getJenkBreaks function in the R package “BAMMtools”). The method minimises the average deviation within each class from its mean while maximising the differences between the means of the classes. A relative weight (1 = lowest, 7 = highest) was assigned to each criterion to reflect its importance for SSF. Criteria and grades were initially defined based on the framework of Kavadas et al. ( 2015 ) and the expertise of the authors, and subsequently validated through a stakeholder engagement process that involved a total of 27 interviews with fishers or their representatives from the main Sicilian ports (see Fig. 1 ). The interviews were conducted in person by the same researcher between August and September 2024. Grades were requested for criteria that respondents could directly evaluate based on their practical experience (e.g., depth, distance from coast, and distance from port). Participants answered two targeted questionnaires: a) a questionnaire for fisher representatives aimed at defining criteria and assigning them weights and grades and b) a more straightforward version for fishers, focusing solely on defining criteria and assigning weights. Information on vessel length, tonnage, and gear type used was also collected. Respondents were asked to rank the seven factors by order of importance from 7 (highest importance) to 1 (lowest importance) for weights, and from 4 (most favorable condition) to 1 or 0 (least favorable condition) for grades. Equal rankings could be assigned when factors or grade levels were considered equivalent. After interviews were carried out, the final selected criteria based on fishers’ responses were: bathymetry, distance from coast, distance from port, weather conditions (e.g. wind speed), trawlers fleet activity, purse seine fleet activity, and marine traffic. A description of the final criteria and details of their grading and weighting are provided in the Online Resource (see also Tab. S1 in Online Resource). Fishers' rankings of weights and grades were then normalised between 0 and 1, by assigning 0 to the lowest grade and 1 to the highest. Normalised grades were calculated for each cell of the study area, which was gridded at a spatial resolution of 0.02°. The grid resolution was selected based on the approximate mean length of SSF nets (1.5 km) recorded in the area during previous field observations by the authors. This ensures that one 0.02° cell (≈ 2 km at Sicily’s latitude) can accommodate an average net in its entirety, thus providing an appropriate scale for mapping the footprint of fishing pressure. Weights were also standardised between 0 and 1. Standardised grades and weights were subsequently utilised to compute the suitability index in each cell using the formula: where Σ i w i x i is the weighted sum of all grades, with w i representing the standardised weight and x i the standardised grade for criterion i, and c represents a spatial restriction factor that assumes values of 0 (areas where fishing is prohibited, e.g., no-take zones), 0.5 (zones where fishing is regulated, i.e., MPAs’ partially protected areas) and 1 (areas with no spatial restrictions). 2.2.2 Activity Index (A c ) As a first step in calculating A c, data of Sicilian ports and their vessels were retrieved from the Fleet Register (the official register of fishing vessels in the EU, https://webgate.ec.europa.eu/fleet-europa/index_en ), filtering for vessels with length < 12 m. While SSF is technically defined by both vessel size and passive gear use, the 12 m threshold was used as a proxy given the constraints of available data in the Fleet Register, which does not record the use of passive gears. A final dataset of 1776 vessels distributed across 39 ports was obtained. The next step consisted of calculating the Activity Index by port (VAI p ), using the following formula, modified from Kavadas et al. ( 2015 ): Where VAI p is the sum of the products of vessel gross tonnage (GT) and engine power (P) for all vessels (v, from 1 to n) registered in that port. This index provides a comprehensive measure of fishing capacity by combining key vessel characteristics that strongly influence the potential for fishing effort. We decided to replace vessel length in the original formula with engine power, as tonnage and engine power are the most common indicator used to determine fishing capacity (see Communication from the Commission to the Council and the European Parliament of 5 February 2007 on improving fishing capacity and effort indicators under the Common Fisheries Policy [COM(2007) 39 final – not published in the Official Journal]). Where not available (20% of the fleet), P was estimated using a linear regression of the available length values (from Fleet Register). After assigning the VAI value to each Sicilian port, the final A c index was obtained by applying the VAI p to a modified formula of gravity, meant as distance of the nearest ports to each potential fishing site (Cinner et al. 2018 ; Bosch et al. 2022 ): Where A c represents the activity index for cell c, VAI p is the vessel activity index for port p, δ(c,p) is the distance decay function, d(c,p) is the Euclidean distance (in km) between cell c and port p, and n represents all ports within a 70 km buffer from cell c. The decay coefficient (-0.078) was derived by fitting an exponential curve to empirical data of maximum fishing distances travelled by SSF vessels during fishing trips (across 420 trips previously assessed by the authors). A 1 km radius buffer was applied around each port to exclude the immediate harbour area from A c calculations, as vessels cannot fish in the confining port waters. 2.2.3 Fishing Footprint index (FF c ) The overall Fishing Footprint index (FF c ) was calculated as: FF c = S c × A c , where FF c is the product of the Suitability Index S c and the normalised Activity Index A c , i.e., the suitability of an area for SSF and fishing activity intensity. The resulting values were then min–max normalised to a 0–1 scale, so that 1 identifies cells with the highest SSF footprint, whereas 0 corresponds to cells with the lowest SSF footprint within the study area. 2.3 Remote sensing - Sentinel-2 optical Imagery A vessel detection dataset produced by Global Fishing Watch (GFW) was used for the years from 2019 to 2023. The dataset is derived from multispectral Sentinel-2 satellite imagery provided by the European Space Agency and accessed through the Google Public Cloud Data program (Global Fishing Watch, 2025 ). GFW’s methodology involves a deep-learning object detection model trained to identify vessels and estimate their length, speed, and orientation from 10 m resolution multispectral images, combined with a secondary classification stage to reduce false positives (Global Fishing Watch, 2025 ). Detected vessels are also probabilistically matched to Automatic Identification System (AIS) signals by estimating the likely vessel positions before and after AIS timestamps and comparing predicted vessel lengths, enabling improved identification and validation of fishing and other vessel activities (Global Fishing Watch, 2025 ). Each detection is subsequently classified as fishing or non-fishing by a neural network model developed by Paolo et al. ( 2024 ), which integrates vessel characteristics with environmental and operational context, including vessel density, bathymetry, sea surface temperature, and chlorophyll within a 100×100 km area centred on the detection (Global Fishing Watch, 2025 ). In our study, the dataset was filtered to include only detections of vessels that are not matched to AIS and with inferred lengths below 30 m, which represents the upper limit for reliable satellite-based detection. The AIS filtering choice was based on the fact that the SSF fleet considered in this study does not use AIS, as only vessels over 15 m in length within the target area must be equipped with AIS under EU regulations. This dual filtering ensures that resulting detections predominantly correspond to SSF vessels, defined in this study as those shorter than 12 m. 2.4 Statistical analysis Moran’s test was applied to Sentinel-2 data to verify spatial autocorrelation. To compare the spatial patterns between the MCSA and Sentinel-2 approaches, Spearman's rank correlation was first calculated to assess the overall relationship between the two datasets across all spatial cells. Subsequently, hot spot and cold spot analyses were conducted using the Getis-Ord Gi* statistic (Getis and Ord 1992, “sfdep” package in R). The analysis classified each cell unit into seven categories based on the Gi* statistic and significance levels: "Very hot" (Gi* > 0, p ≤ 0.01), "Hot" (Gi* > 0, p ≤ 0.05), "Somewhat hot" (Gi* > 0, p ≤ 0.1), "Insignificant" (p > 0.1), "Somewhat cold" (Gi* < 0, p ≤ 0.1), "Cold" (Gi* < 0, p ≤ 0.05), and "Very cold" (Gi* < 0, p ≤ 0.01). The spatial concordance between hot spots and cold spots identified by both methodologies was then assessed using Cohen's Kappa test to quantify the level of agreement between the MCSA and Sentinel-2 approaches. For the spatial concordance analysis using Cohen's Kappa test, the categories of the the Getis-Ord Gi* analysis were simplified into three groups: "Hot" (combining "Very hot" and "Hot"), "Cold" (combining "Very cold" and "Cold"), and "Neutral" (including "Somewhat hot", "Somewhat cold", and "Insignificant" categories). The hotspot analysis was repeated using Sentinel-2 dataset filtered to exclude the summer months (June, July, August). This additional analysis allowed for the assessment of whether the concordance between the two approaches might be affected by the presence of private/touristic vessels, which are much more abundant during summer months. 3. RESULTS 3.1 MCSA 3.1.1 Fishers’ interviews and criteria validation All the 27 interviewed fishers and representatives confirmed the previously identified MCSA criteria, without suggesting new ones. The participants showed strong agreement on the criteria with the highest and lowest importance (i.e., depth and marine traffic respectively, see Online Resource Table S2). Similarly, there was high agreement among responses on criteria grading (Table S3 in Online Resource). 3.1.2 Suitability Index (S c ) spatial patterns The Suitability Index ranged from 0 to 0.99 (Fig. 2 ), and the highest suitable areas appear to be along the northern and southwestern coast of Sicily, as well as the southeastern coast (values > 0.7), while the lowest suitability is found in offshore waters. 3.1.3 Activity Index (A c ) spatial patterns The A c revealed various patterns of potential fishing footprint based on port characteristics and vessel accessibility around the Sicilian coast (Fig. 3 ). The highest activity concentrations (purple areas, > 0.75) were observed around major fishing ports, particularly in the northwestern coast, the southeastern coast and the northeastern Aeolian Islands. 3.1.4 Fishing Footprint (FF c ) index spatial patterns The areas with the highest overall FF (> 0.8, dark red) were located along the northwestern coast and the southeastern coast (Fig. 4 a and b). Areas with intermediate fishing spatial footprint (0.4–0.8, red in Fig. 4 ) extended along the northeastern coast (including the northeastern islands) and the southern coast. Areas with low SSF spatial footprint (< 0.4, yellow in Fig. 4 ) were the ones distant from major ports, in offshore waters and in zones with other constraints (such as the presence of MPAs). 3.2 Optical imagery Sentinel 2 data Remote-sensing vessel detections showed significant spatial autocorrelation (Moran’s I test: 0.76, p < 0.001), revealing the tendency of fishing activities to cluster in specific areas (Fig. 5 ). The total detections from 2019 to 2023 were 200,556, after applying the AIS matching filter the detections were 87,670. The filtered detections per year were 18,477 in 2019, 19,510 in 2020, 16,217 in 2021,17,382 in 2022, and 16,084 in 2023. In Sicily, the averaged detections seem to concentrate closer to coastal areas with hotspots scattered along. Similarly to MCSA, the detections appear to be clustered also around Lampedusa Island. 3.3 Comparison of spatial patterns from MCSA and Sentinel-2 3.3.1 Overall spatial correlation The direct comparison between MCSA and Sentinel-2 data revealed a significant positive correlation (Spearman’s ρ: 0.43, p < 0.001) across 14,641 analysed cells, suggesting consistency between areas with high predicted fishing pressure and areas with high vessel frequency. When summer months were excluded from Sentinel-2 observations (cells analysed:15,717) the Spearman correlation was 0.40 (p < 0.001), nearly identical to that of the full dataset. 3.3.2 Hotspot analysis and spatial concordance Getis-Ord Gi hotspot analysis applied to the aligned rasters of MCSA and Sentinel-2 data (cells analysed: 14,568) showed high spatial clustering in both datasets (Global G: MCSA = 194.24, Sentinel-2 = 161.92, p < 0.001), with concordant hotspots primarily distributed along the northwestern coast, northeastern waters and southeastern coast. The two approaches resulted in a similar percentage of cells as SSF hotspots: 13.5% (very hot: 7.9% and hot: 5.6%) and 11.8% (very hot: 7.3% and hot: 4.5%) cells were identified as hotspots respectively for the MCSA and for Sentinel-2 data. The two approaches showed different footprints in the cold and neutral cells, with the MCSA classifying 45.1% cells as cold spots (very cold: 28.3% and cold: 16.8%) and 41.4% as neutral and Sentinel-2 only 23.4% as cold spots (very cold: 8.1% and cold: 15.4%) and 64.8% as neutral spots. Cohen’s Kappa analysis for spatial concordance revealed significant concordance between the categories (“hot”, “neutral” and “cold”, see Table 1 ): the hotspots showed the highest concordance (κ_hot = 0.61, p < 0.001), while coldspots and neutral areas showed weaker agreement (κ_cold = 0.19 and k_neutral = 0.15, p < 0.001) (Fig. 6 ). The overall Cohen's Kappa (κ = 0.25, p < 0.001) reflected this mixed pattern of concordance across all spatial categories. When summer months were excluded from Sentinel-2 observations (cells analysed:12,919) the spatial concordance measures remained the same or slightly decreased (k_hot = 0.61, p < 0.001; k_cold = 0.16, p < 0.001 and k_neutral = 0.13, p < 0.001) (Table 1 ). Table 1 Cell counts and spatial concordance between MCSA and Sentinel-2 data. The table shows the number of cells classified by each method, concordant cells, and agreement percentages for hot spots, cold spots, and neutral areas across complete and summer-filtered datasets. Category Dataset MCSA Cells Sentinel-2 Cells Concordant Cells Cohen's κ p-value Concordance (%) Hot All seasons 1967 1712 1220 0.61 < 0.001 62.0 Filtered 1770 1544 1101 0.61 < 0.001 62.2 Cold All seasons 6571 3416 2231 0.19 < 0.001 34.0 Filtered 5468 2804 1655 0.15 < 0.001 30.3 Neutral All seasons 6030 9440 4493 0.15 < 0.001 47.6 Filtered 5681 8571 4196 0.12 < 0.001 49.0 Overall All seasons 14568 14658 7944 0.25 < 0.001 54.5 Filtered 12919 12919 6952 0.23 < 0.001 53.8 4. DISCUSSION Herein we demonstrated how two complementary methodological approaches can be combined to map the SSF spatial footprint in regions where small-scale vessels are not equipped with tracking systems, using Sicily as a case study. The Multi-Criteria Spatial Analysis (MCSA) framework integrates fishers' knowledge with environmental and socio-economic criteria to predict suitable fishing areas, while Sentinel-2 optical imagery, here used for the first time to map SSF vessels, provides direct observations of vessel activity from satellite-derived detection data. Our results indicate that both approaches show high spatial clustering along the north-western and south-eastern Sicilian coast and its islands: MCSA identified clustered areas of high suitability for SSF activity, while Sentinel-2 revealed clustered patterns of actual vessel detections. The two approaches reveal strong concordance for hotspots of SSF footprint. The combined methodology used in our study has the potential to overcome individual limitations of each approach, the absence of direct vessel detection in MCSA and the vessel size constraints and environmental dependencies of satellite detections. The MCSA approach adopted herein represents a significant methodological advancement compared to previous participatory studies by integrating multiple empirical data sources beyond questionnaires alone. We incorporated data from previous datasets (e.g., fishing distances from ports) and from the interviews with fishers and representatives across different ports to capture variability in fishing habits and preferences. This approach primarily aims at identifying fishers’ preferences for their most suitable fishing ground, without directly identifying specific sites of interest. Therefore, it avoids asking directly to fishers information about their fishing ground (Scholz et al. 2011 , Yates and Schoeman 2013 ), which in turn fosters greater trust and enhances willingness to provide critical information, as also observed by Thiault et al. ( 2017 ). By limiting the number of interviews, this method significantly differs from direct participatory mapping (Grati et al. 2024; Zampardi et al. 2024). The methodology used in our study combined MCSA and Weighted Linear Combination, following Kavadas et al. ( 2015 ). The original framework employs AHP (Analytic Hierarchy Process, a structured decision-making method using pairwise comparisons) for criteria weighting through pair-wise comparisons and Fuzzy Membership functions for value standardisation. However, given the homogeneity of responses across our geographically distributed interviews, we applied direct standardisation to a scale of 0 to 1. This simplified approach maintained analytical rigour while reducing computational complexity and potential uncertainty propagation inherent in Fuzzy Membership functions. Furthermore, we implemented the Suitability Index developed by Kavadas et al. ( 2015 ), incorporating additional relevant components such as weather conditions and distance to ports. The fishing footprint expressed by the FF index revealed concentrated major SSF activity below three nautical miles, as also observed by previous studies (Kavadas et al. 2015 ; Thiault et al. 2017 ; Grati et al. 2022 ), in shallow areas (< 100 meters depth), characterised by a low number of windy days (< 130) and low presence of other users such as trawlers and purse seiners. While the methodological approaches to quantify vessel activity varied across the above-mentioned studies—from vessel characteristics (Kavadas et al. 2015 ; this study) to socioeconomic indicators (Thiault et al. 2017 )— all three demonstrate the novelty of incorporating characteristics of the population or fishing fleet into the activity index, which can highly affect the final prediction of footprint. In our study, the highest SSF footprint was detected along the northwestern and southeastern coastal zones, with notable high activity hotspots emerging around Sicily's smaller islands, e.g., the Aeolian and Lampedusa Islands, whose inhabitants historically rely on fishing activity (Lentini and Romeo 2000 ; Orsini 2015). Further studies implementing MCSA could also include seasonal variations in fishing patterns and gears’ specificity, which are not captured in our static model and might represent important criteria to include. Nevertheless, this methodology obviously lacks information about vessels’ actual position and does not include ground-truthing, while having some approximations of the criteria weights and grades, which are only based on experts' opinions. These weaknesses can be addressed by coupling MCSA predictions with empirical vessel detection data from advanced remote sensing platforms, such as the Sentinel-2 optical imagery utilised in this study, thereby providing ground-truth validation and enhancing the reliability of fishing pressure estimates. In general, the remote-sensing results showed high fishing vessel presence aligned with those of the MCSA approach, with particularly high presence in the northwestern and southeastern areas. However, unlike MCSA predictions, remote-sensing detections show notably lower vessel presence around the smaller islands (i.e., the Aeolian archipelago and Lampedusa), possibly a result of the average smaller vessel size common in islands, which points out the detection limits of satellite-based systems. The detection density map shows a clear coastal preference with sparse offshore activity, consistent with typical small-scale fishing operational patterns that prefer nearshore waters. As for MCSA, remote-sensing vessel detections showed significant spatial clustering, confirming fishers’ preference for specific areas. Remote-sensing detection offers several advantages for fisheries monitoring, including broad spatial coverage and regular temporal resolution (Paolo et al. 2024 ; Marsaglia et al. 2024). The Global Fishing Watch methodology combines neural network classification to distinguish between fishing and non-fishing vessels (Global Fishing Watch 2025 ; Paolo et al. 2024 ), and can reveal potential hotspots of fishing vessel presence. However, the method faces limitations in a) estimating vessel sizes, b) distinguishing between different fishing activities, and c) addressing the presence of cloud cover. Additionally, the concentration of detected vessels in nearshore waters may reflect genuine small-scale fishing patterns but could also include coastal small trawlers (length < 15 m) operating along the continental shelf edge or vessels engaged in illegal activities. These findings emphasise the need for contextual interpretation when applying satellite-based vessel detection to fisheries assessment. The significant positive correlation between the results of the MCSA and remote-sensing approaches could be considered as a cross-methodological validation and stresses areas where uncertainty is higher (areas of disagreement indicated by Cohen's kappa analysis). MCSA identified twice as many cold spots (areas with low suitability for SSF activity) as Sentinel-2 (45.5% vs 23.4%), which could suggest a higher reliability of this approach in predicting areas where environmental, operational, or competitive conditions discourage fishing activity but also an overestimation of cold spots. Hence, MCSA framework systematically excludes areas where conditions fall below fisher-defined suitability thresholds (e.g., depth > 500 meters, distance from coast > 6 nm, high competitors’ presence). In contrast, Sentinel-2 detections represent vessel presence rather than confirmed fishing activity, and may include false positives from recreational vessels or small or medium size trawlers misclassified as SSF, potentially explaining the lower proportion of areas identified as unsuitable for small-scale fishing operations. When excluding summer months, the correlation between the two approaches remained almost unchanged, suggesting that seasonal recreational vessel activity has limited impact on the overall spatial concordance patterns in our study area. This finding contrasts with expectations of strong seasonal tourism effects and may reflect either the year-round presence of recreational vessels in Sicilian waters, or the persistence of local recreational fishing activities beyond peak tourist periods. Our approach represents a methodological advancement for mapping SSF activity by explicitly accounting for seasonal variations in tourism-intensive regions (Lloret and Font 2013 ; Font and Lloret 2014 ). The complementary nature of both approaches suggests potential for developing standardised calibration protocols, where MCSA serves as a consistent baseline for validating remote sensing data across diverse Mediterranean and global environments. MCSA outputs could be used to inform and calibrate satellite detection algorithms, potentially improving the accuracy and reliability of satellite-derived fishing activity maps. Our study offers a baseline for systematic, large-scale monitoring that can be consistently applied across different regions and repeated over time. Such a framework would enable the development of more robust methodologies for small-scale fisheries monitoring in data-poor environments, maintaining local relevance and fishers' engagement. It could also be adapted to improve the monitoring of recreational fisheries, whose activity and impact are currently neglected due to data accessibility restrictions and the absence of recreational vessels from official European fishing registries such as the Fleet Register. Better commercial and recreational fisheries’ footprint information would not only improve our knowledge about the impacts of fishing on marine biodiversity in coastal areas but also allow us to use this information for improved marine spatial planning (e.g., Markantonatou et al. 2021 ). 5. Conclusions Maps of SSF footprint are crucial for designing effective management strategies of marine resources, providing essential baseline data for monitoring fishing pressure and supporting the design of resource management measures and new marine protected areas, while minimising possible conflicts with fishing communities. Our combined MCSA-optical imagery methodology demonstrates strong scalability potential, from fine-scale port-level management to regional Mediterranean-level applications. This multi-scale suitability makes the framework particularly valuable for implementing the EU Maritime Spatial Planning Directive and addressing critical knowledge gaps in data-poor regions globally, which are increasingly subjected to coastal urbanisation and climate-driven changes in species distributions. Statements and Declarations Acknowledgements We sincerely thank the fishers and their representatives from the communities of Isola delle Femmine, Pantelleria, Lampedusa, Milazzo, and Trapani for their valuable participation in this study. Their insights were essential for developing and validating our Multi-Criteria Spatial Analysis framework. The research was supported by PRIN 2022 project Reconnect “Reconciling conservation and exploitation of a keystone species through networks of Marine Protected Areas” (CUP C53D23003550006), funded by European Union-Next Generation EU and the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union– NextGenerationEU; Award Number: Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP C63C22000520001, Project title “National Biodiversity Future Center - NBFC”. Competing Interests The authors declare that they have no competing interests to declare. Funding The research was supported by PRIN 2022 project Reconnect "Reconciling conservation and exploitation of a keystone species through networks of Marine Protected Areas" (CUP C53D23003550006), funded by European Union-Next Generation EU and the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 under the call for tender No. 3138 of 16 December 2021. Additional support was provided by the National Biodiversity Future Center - NBFC (Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022). Author Contributions Claudia Bommarito: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Visualization (equal); Writing – original draft (equal); Writing – review and editing (equal). Luca Marsaglia: Conceptualization, Formal analysis, Writing – review and editing. Mariagrazia Graziano: Conceptualization; Writing – review and editing (equal); Silvia María Bellù: Formal analysis (equal); Writing – review and editing (equal). Paco Melià: Formal analysis (equal); Writing – review and editing (equal). Giacomo Milisenda: Formal analysis. Antonio Di Franco: Conceptualization (equal); Investigation (equal); Writing – review and editing (equal). Giorgio Aglieri, Antonio Calò, Carlo Cattano, Elena Desiderà, Manfredi Di Lorenzo, Giulio Franzitta, Sylvaine Giakoumi, Paolo Guidetti, Marco Milazzo, Chiara Papetti, Federico Quattrocchi, Stefania Russo, Claudia Scianna, Emanuele Somma, Davide Spatafora, Federica Stranci: Writing – review and editing. Data Availability Statement The Sentinel-2 vessel detection data are publicly available from Global Fishing Watch (https://globalfishingwatch.org/). The multicriteria spatial analysis outputs are available upon request from the corresponding authors. 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10:08:31","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173942,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8211723/v1/3d55bc9a515f89a87469d220.html"},{"id":98764162,"identity":"06bd54cd-4439-4a84-887e-52eaffd4d31b","added_by":"auto","created_at":"2025-12-22 10:08:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":292288,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area showing all Sicilian fishing ports included in the study (light blue circles). The five ports where fisher interviews were conducted are highlighted in yellow (Isola delle Femmine, Milazzo, Trapani, Pantelleria, and Lampedusa). Blue polygons indicate no-take zones and zones where fishing is regulated.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8211723/v1/d981cc6bd4ea6af07e43c776.png"},{"id":98764200,"identity":"416c2fa2-c286-4257-a28f-9fb0bc8fb06f","added_by":"auto","created_at":"2025-12-22 10:08:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177033,"visible":true,"origin":"","legend":"\u003cp\u003eSuitability Index (S\u003csub\u003ec \u003c/sub\u003e) across Sicilian waters resulting from the weighted combination of environmental and socio-economic criteria (bathymetry, distance from coast and ports, weather conditions, competition with other vessels), with values ranging from 0 (lowest suitability, light green) to 0.99 (highest suitability, dark green). The island of Malta is also represented because included in the area considered (less than 600 m bathymetry), but the representation is for graphic purposes only.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8211723/v1/488f7546f772ed16b5b083b9.png"},{"id":98764195,"identity":"f50c4dc0-a1b3-4003-831b-48a825e32e6b","added_by":"auto","created_at":"2025-12-22 10:08:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":112827,"visible":true,"origin":"","legend":"\u003cp\u003eActivity Index (A\u003csub\u003ec\u003c/sub\u003e) representing the gravitational attraction of vessels to fishing sites based on vessel characteristics and distance decay from each Sicilian port (blue dots), and subsequently min-max normalised, with values ranging from 0 (lowest activity, light pink) to 1 (highest activity, dark pink).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8211723/v1/9519d3bfa686e52c1d2c0c28.png"},{"id":98764194,"identity":"47c49289-787c-4528-95b8-9c9266f7655a","added_by":"auto","created_at":"2025-12-22 10:08:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":223323,"visible":true,"origin":"","legend":"\u003cp\u003eFishing Footprint (FF\u003csub\u003ec\u003c/sub\u003e) index obtained as the product of Suitability and Activity indices, subsequently min-max normalised, with values ranging from 0 (lowest fishing footprint, yellow) to 1 (highest fishing footprint, dark red). Panel (a) shows the overall footprint across Sicilian waters, with panels (b) and (c) providing detailed views of the northwestern and southeastern coastal areas, respectively, where the highest fishing footprint is found. Grey rectangles in all panels indicate the ports, masked with a 1 km buffer.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8211723/v1/6702c25f963cdc694007fb75.png"},{"id":98764209,"identity":"e633af56-132e-4db4-8e51-727a86fc76b5","added_by":"auto","created_at":"2025-12-22 10:08:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":255094,"visible":true,"origin":"","legend":"\u003cp\u003eSmall-scale fishing activity detection density around Sicily from Sentinel-2 data (2019-2023). Distribution of fishing vessels \u0026lt;30m, unmatched with AIS, identified through neural network classification. Marine cells without detections meeting the above-mentioned criteria were assigned zero values.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8211723/v1/207661031a16df3d00712f1a.png"},{"id":98764213,"identity":"bb154357-c4f3-4df3-8eb2-3d2a9923aad5","added_by":"auto","created_at":"2025-12-22 10:08:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":452358,"visible":true,"origin":"","legend":"\u003cp\u003eBivariate map (a) of spatial concordance between MCSA fishing footprint and Sentinel-2 vessel detection hotspots. Colours represent nine combinations of hotspot classifications (Cold, Neutral, Hot) for both methods. Dark red areas indicate high concordance for fishing hotspots (Hot-Hot), blue areas show agreement on low activity zones (Cold-Cold), and mixed colours represent areas of disagreement between approaches. The barplot (b) shows spatial concordance percentages by category.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8211723/v1/42b012db68cfb6f0f0620c64.png"},{"id":98785440,"identity":"b6dee01b-6fea-4476-80dd-0b8370a1ec38","added_by":"auto","created_at":"2025-12-22 12:43:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2659242,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8211723/v1/389ff9d0-95fa-4884-bbf1-bb54d218c63c.pdf"},{"id":98764186,"identity":"11178122-91f3-4d48-b6f0-b57164fb196e","added_by":"auto","created_at":"2025-12-22 10:08:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":302125,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8211723/v1/a10afba24cb3dedc8028cc96.pdf"},{"id":98778251,"identity":"b9fcdc90-2315-4c02-aa15-9d58a108c726","added_by":"auto","created_at":"2025-12-22 12:29:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":210232,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-8211723/v1/dc2d2bff49b4e9e6445ff24c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping the footprint of untracked small-scale fisheries using multicriteria spatial analysis and optical satellite imagery","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSmall-scale fisheries (SSF) play a vital socio-economic role globally, by securing nutrition, livelihoods, and preserving cultural heritage for millions of people (Schuhbauer and Sumaila, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; FAO, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Basurto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). SSFs employ 90% of professional fishers and extract 37\u0026nbsp;million tonnes of fish from the ocean annually, significantly contributing to the food requirement of coastal populations (FAO, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Basurto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The definition of SSF varies across geographical regions and contexts, but it is generally associated with low-technology, low-capital, labour-intensive and traditional practices (Smith \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e and Basurto, 2019; Smith et al. 2021; Basurto et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The European Maritime and Fisheries Fund defines SSF as \u0026ldquo;fishing carried out by fishing vessels of an overall length of less than 12 m and not using towed fishing gear\u0026rdquo; (Regulation (UE) 508/2014; Tzanatos et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Battaglia et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lloret et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Grati et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSSF activity is known to exert lower impacts on benthic habitats and generate fewer discards than large-scale fisheries (LSF) (Catanese et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Selgrath et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zeller et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, recent studies have pointed out overlooked aspects, showing that even small-scale fisheries can significantly harm marine ecosystems. These impacts include the depletion of target species stocks, incidental bycatch, habitat degradation and the accumulation of lost or abandoned fishing gears, all of which can undermine ecosystem integrity and sustainable fisheries management (Lloret et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Silvestrini et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Evidence suggests that, despite harvesting a broad spectrum of species, SSF often depends on a limited number of species, substantially contributing to both catches and revenues (Cal\u0026ograve; et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a consequence of this unbalanced exploitation of fishing resources, SSFs insistently exploit habitats that are essential for species spawning, as well as nursery grounds (Stergiou et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Such practices may cause significant impacts on fish stocks and seafloor integrity, affecting ecosystem functioning and services (Sbragaglia et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Silvestrini et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although the impact of SSF on marine ecosystems has received increasing attention, a lack of knowledge persists, particularly with respect to their spatial footprint, defined here as the fishing spatial patterns (Kroodsma et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e),. These data are essential for assessing their ecological impacts, including identifying areas where fishing activity is most concentrated, and properly evaluating the status of the targeted resources. While LSF spatial distribution is known through remote sensing systems, such as the Vessel Monitoring System (VMS, mandatory with Electronic logbook for all European fishing vessels exceeding 12 m in length since 1 January 2012), and the Automatic Identification System (AIS, required to be carried on board of EU fishing vessels longer than 15 m) (European Commission, 2009), analogous vessel tracking devices and electronic monitoring systems are currently not required on SSF vessels. This gap reflects a combination of historical, legislative, and socio-economic factors: SSF have traditionally been neglected or marginalized for a long time compared with other fishery sectors (Guyader et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Smith et al. 2021) and financial as well as technical constraints discourage widespread adoption of such systems that are heavy, costly, and require external power sources (Orofino et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tassetti et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, the spatial footprint of SSF remains difficult to capture and monitor (Johnson et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo fill the knowledge gap about SSF footprint in a cost-effective manner, participatory mapping has been used, where fishers are asked to identify their fishing grounds on provided maps (e.g., Grati et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Yet, to generate reliable fishing spatial footprint estimates, participatory mapping must rely on very large samples of interviewed fishers, a suitable sampling design, which is often difficult to achieve due to logistical issues (Thiault et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Besides logistics, it is challenging to obtain the complete trust of fishers in releasing information of interest to their activity and thus the data obtained may be biased. Other direct approaches include boat-based visual surveys, which involve observing fishing vessels along transects that cover the target area and recording their activities (Horta e Costa et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rufino et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Blight et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, this approach is time-consuming and can only be applied over relatively small spatial scales. Additionally, various tracking systems have been developed as alternative monitoring tools for SSF. For instance, Behivoke et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) deployed GPS trackers on small-scale vessels and applied a random forest model to distinguish between non-fishing and fishing vessels. Tassetti et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) developed an architecture to collect real-time positional data sent over LoRaWAN technology or 2G/3G/4G connections by small-scale vessels. However, these approaches remain largely experimental and have only been tested at local scales, limiting their current applicability for comprehensive SSF assessment. When spatially explicit data were not available, modelling approaches utilising proxies for mapping fishing have been developed (Chollett et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These models often combine different predictors relevant for fishing activities, such as the proximity to fishing grounds (Hamel et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), human pressure (Ban et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), weather conditions (Zu Ermgassen et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and environmental drivers (Harborne et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While those proxies are relevant factors for fishing, they should be adapted to the specific region of study (Chollett et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). An effective example of an indirect method to predict the SSF footprint without vessel tracking data at the regional level is the Multi-Criteria Decision Analysis (MCDA) developed by Kavadas et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This method combines environmental and economic proxies and fleet capacity data that can both influence the footprint of fisheries. However, the reliability of such indirect methods remains largely untested, as to the best of our knowledge no previous studies combined indirect methods with direct detections of vessels presence in SSF.\u003c/p\u003e \u003cp\u003eDrawing on the methodological approaches described above and considering their respective limitations, this study proposes a combination of different methods to obtain validated large-scale data on SSF spatial footprint, using the waters surrounding Sicily, the largest Mediterranean island and hotspot of fishing activity, as a case study. The SSF footprint was assessed by combining two independent approaches: a) a Multicriteria Spatial Analysis (MCSA) framework, integrating key environmental and socio-economic components relevant for fishers, affecting the footprint of their fishing activity and b) a remote sensing analysis using Global Fishing Watch vessel detection data derived from Sentinel-2 optical imagery. Vessel detections from satellite imagery are a recent addition to the set of approaches that can provide valuable information on fishing activity distribution in areas where vessel tracking data is unavailable (Paolo et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Marsaglia et al. 2024). Our study represents the first attempt to combine MCSA methodology with satellite-derived vessel detection data for mapping SSF, thereby offering a novel framework that integrates direct information from fishers with remote sensing capacities. This approach is scalable and transferable to other marine regions where there is a lack of tracking data for SSF vessels.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study area\u003c/h2\u003e\n \u003cp\u003eAs the largest island in the Mediterranean, Sicily hosts the highest number of fishing vessels and fishers in Italy (Andaloro et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). For the majority of fish stocks, fishing pressure exceeds sustainable levels (EU STECF 2023; Sinerchia et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). At the same time, the region is recognised as a marine biodiversity hotspot, owing to its geographical position and the variety of ecosystems (M\u0026eacute;dail and Qu\u0026eacute;zel 1999; Di Lorenzo et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). This makes assessing human impacts on its vulnerable ecosystems essential.\u003c/p\u003e\n \u003cp\u003eThe boundaries selected for the study encompass the marine waters surrounding Sicily, extending between 11 and 16 \u0026deg;E and between 35 and 39 \u0026deg;N (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This area was chosen to include both coastal and continental shelf habitats adjacent to the islands, thereby ensuring a comprehensive coverage of the Sicilian marine coastline. The study area also includes seven nationally-established Marine Protected Areas (MPAs). The bathymetric range considered spans from 1 m above sea level to 600 m of depth, based on both fishers\u0026apos; documented preference for fishing up to 500 m of depth and empirical data from our previous field campaigns indicating 600 m as the maximum depth reached by local SSF operations (authors\u0026rsquo; unpublished data).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Multi-Criteria Spatial Analysis (MCSA)\u003c/h2\u003e\n \u003cp\u003eTo assess the spatial footprint of SSF along the Sicilian coastal and continental shelf habitats adjacent to the islands, a Multi-Criteria Spatial Analysis (MCSA) was carried out, building upon the MCDA framework developed by Kavadas et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). While multicriteria approaches were traditionally developed for decision-making contexts (Tatham et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Malczewski \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), they are increasingly applied in spatially explicit frameworks to assess and map pressures of human activities on coastal and marine regions (Halpern et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wu et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Malczewski and Jankowski \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). In our study, the MCSA was employed to predict the footprint of SSF in Sicilian waters through an adapted version of the Fishing Footprint index (FF\u003csub\u003ec\u003c/sub\u003e) developed by Kavadas et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). The FF\u003csub\u003ec\u003c/sub\u003e index is calculated as the product of two components: a Suitability Index (S\u003csub\u003ec\u003c/sub\u003e) and a Vessel Activity Index (A\u003csub\u003ec\u003c/sub\u003e). S\u003csub\u003ec\u003c/sub\u003e defines the suitability of a marine area for SSF based on a selection of environmental criteria, management criteria, and information on competition with other fishery sectors. A\u003csub\u003ec\u003c/sub\u003e captures information about the number of SSF vessels in each port, their characteristics and the distance from the port to each potential fishing site.\u003c/p\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Suitability Index (S\u003csub\u003ec\u003c/sub\u003e)\u003c/h2\u003e\n \u003cp\u003eThe relevant criteria for S\u003csub\u003ec\u003c/sub\u003e were initially based on those used by Kavadas et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) and were: legislation, no-take zones, bathymetry, distance from coast, bottom-trawl activity, purse-seine activity and marine traffic. Data related to each criterion were retrieved from available platforms (Copernicus, Global Fishing Watch, GEBCO) and authors\u0026rsquo; unpublished data. For each criterion, data were then divided into value intervals, to which ranking values (hereafter referred to as \u0026ldquo;grades\u0026rdquo;) were assigned. Grades range from 4 (indicating the most favourable condition for SSF) to 1 or 0 (indicating the least favourable condition). A minimum grade of 0 is assigned when the specific level of environmental parameters prevents SSF activities from taking place, whereas a minimum grade of 1 is applied to criteria where conditions are suboptimal but remain within the operational range of SSF vessels. The grades for each criterion were determined using frequency plots of data related to the selected variable (e.g., distance from port) and subdividing them into intervals using the Jenks Natural Breaks classification method (through the \u003cem\u003egetJenkBreaks\u003c/em\u003e function in the R package \u0026ldquo;BAMMtools\u0026rdquo;). The method minimises the average deviation within each class from its mean while maximising the differences between the means of the classes. A relative weight (1\u0026thinsp;=\u0026thinsp;lowest, 7\u0026thinsp;=\u0026thinsp;highest) was assigned to each criterion to reflect its importance for SSF. Criteria and grades were initially defined based on the framework of Kavadas et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) and the expertise of the authors, and subsequently validated through a stakeholder engagement process that involved a total of 27 interviews with fishers or their representatives from the main Sicilian ports (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The interviews were conducted in person by the same researcher between August and September 2024. Grades were requested for criteria that respondents could directly evaluate based on their practical experience (e.g., depth, distance from coast, and distance from port). Participants answered two targeted questionnaires: a) a questionnaire for fisher representatives aimed at defining criteria and assigning them weights and grades and b) a more straightforward version for fishers, focusing solely on defining criteria and assigning weights. Information on vessel length, tonnage, and gear type used was also collected. Respondents were asked to rank the seven factors by order of importance from 7 (highest importance) to 1 (lowest importance) for weights, and from 4 (most favorable condition) to 1 or 0 (least favorable condition) for grades. Equal rankings could be assigned when factors or grade levels were considered equivalent. After interviews were carried out, the final selected criteria based on fishers\u0026rsquo; responses were: bathymetry, distance from coast, distance from port, weather conditions (e.g. wind speed), trawlers fleet activity, purse seine fleet activity, and marine traffic. A description of the final criteria and details of their grading and weighting are provided in the Online Resource (see also Tab. S1 in Online Resource).\u003c/p\u003e\n \u003cp\u003eFishers\u0026apos; rankings of weights and grades were then normalised between 0 and 1, by assigning 0 to the lowest grade and 1 to the highest. Normalised grades were calculated for each cell of the study area, which was gridded at a spatial resolution of 0.02\u0026deg;. The grid resolution was selected based on the approximate mean length of SSF nets (1.5 km) recorded in the area during previous field observations by the authors. This ensures that one 0.02\u0026deg; cell (\u0026asymp; 2 km at Sicily\u0026rsquo;s latitude) can accommodate an average net in its entirety, thus providing an appropriate scale for mapping the footprint of fishing pressure. Weights were also standardised between 0 and 1. Standardised grades and weights were subsequently utilised to compute the suitability index in each cell using the formula:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere \u0026Sigma;\u003csub\u003ei\u003c/sub\u003ew\u003csub\u003ei\u003c/sub\u003ex\u003csub\u003ei\u003c/sub\u003e is the weighted sum of all grades, with w\u003csub\u003ei\u003c/sub\u003e representing the standardised weight and x\u003csub\u003ei\u003c/sub\u003e the standardised grade for criterion i, and c represents a spatial restriction factor that assumes values of 0 (areas where fishing is prohibited, e.g., no-take zones), 0.5 (zones where fishing is regulated, i.e., MPAs\u0026rsquo; partially protected areas) and 1 (areas with no spatial restrictions).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 Activity Index (A\u003csub\u003ec\u003c/sub\u003e)\u003c/h2\u003e\n \u003cp\u003eAs a first step in calculating A\u003csub\u003ec,\u003c/sub\u003e data of Sicilian ports and their vessels were retrieved from the Fleet Register (the official register of fishing vessels in the EU, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://webgate.ec.europa.eu/fleet-europa/index_en\u003c/span\u003e\u003c/span\u003e), filtering for vessels with length\u0026thinsp;\u0026lt;\u0026thinsp;12 m. While SSF is technically defined by both vessel size and passive gear use, the 12 m threshold was used as a proxy given the constraints of available data in the Fleet Register, which does not record the use of passive gears. A final dataset of 1776 vessels distributed across 39 ports was obtained. The next step consisted of calculating the Activity Index by port (VAI\u003csub\u003ep\u003c/sub\u003e), using the following formula, modified from Kavadas et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere VAI\u003csub\u003ep\u003c/sub\u003e is the sum of the products of vessel gross tonnage (GT) and engine power (P) for all vessels (v, from 1 to n) registered in that port. This index provides a comprehensive measure of fishing capacity by combining key vessel characteristics that strongly influence the potential for fishing effort. We decided to replace vessel length in the original formula with engine power, as tonnage and engine power are the most common indicator used to determine fishing capacity (see Communication from the Commission to the Council and the European Parliament of 5 February 2007 on improving fishing capacity and effort indicators under the Common Fisheries Policy [COM(2007) 39 final \u0026ndash; not published in the Official Journal]). Where not available (20% of the fleet), P was estimated using a linear regression of the available length values (from Fleet Register). After assigning the VAI value to each Sicilian port, the final A\u003csub\u003ec\u003c/sub\u003e index was obtained by applying the VAI\u003csub\u003ep\u003c/sub\u003e to a modified formula of gravity, meant as distance of the nearest ports to each potential fishing site (Cinner et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bosch et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e):\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eWhere A\u003csub\u003ec\u003c/sub\u003e represents the activity index for cell c, VAI\u003csub\u003ep\u003c/sub\u003e is the vessel activity index for port p, \u0026delta;(c,p) is the distance decay function, d(c,p) is the Euclidean distance (in km) between cell c and port p, and n represents all ports within a 70 km buffer from cell c. The decay coefficient (-0.078) was derived by fitting an exponential curve to empirical data of maximum fishing distances travelled by SSF vessels during fishing trips (across 420 trips previously assessed by the authors). A 1 km radius buffer was applied around each port to exclude the immediate harbour area from A\u003csub\u003ec\u003c/sub\u003e calculations, as vessels cannot fish in the confining port waters.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.3 Fishing Footprint index (FF\u003csub\u003ec\u003c/sub\u003e)\u003c/h2\u003e\n \u003cp\u003eThe overall Fishing Footprint index (FF\u003csub\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/sub\u003e) was calculated as: \u003cem\u003eFF\u003c/em\u003e\u003csub\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/sub\u003e \u003cem\u003e= S\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e\u0026times; A\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e, where FF\u003csub\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/sub\u003e is the product of the Suitability Index S\u003csub\u003ec\u003c/sub\u003e and the normalised Activity Index A\u003csub\u003ec\u003c/sub\u003e, i.e., the suitability of an area for SSF and fishing activity intensity. The resulting values were then min\u0026ndash;max normalised to a 0\u0026ndash;1 scale, so that 1 identifies cells with the highest SSF footprint, whereas 0 corresponds to cells with the lowest SSF footprint within the study area.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Remote sensing - Sentinel-2 optical Imagery\u003c/h2\u003e\n \u003cp\u003eA vessel detection dataset produced by Global Fishing Watch (GFW) was used for the years from 2019 to 2023. The dataset is derived from multispectral Sentinel-2 satellite imagery provided by the European Space Agency and accessed through the Google Public Cloud Data program (Global Fishing Watch, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). GFW\u0026rsquo;s methodology involves a deep-learning object detection model trained to identify vessels and estimate their length, speed, and orientation from 10 m resolution multispectral images, combined with a secondary classification stage to reduce false positives (Global Fishing Watch, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Detected vessels are also probabilistically matched to Automatic Identification System (AIS) signals by estimating the likely vessel positions before and after AIS timestamps and comparing predicted vessel lengths, enabling improved identification and validation of fishing and other vessel activities (Global Fishing Watch, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Each detection is subsequently classified as fishing or non-fishing by a neural network model developed by Paolo et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), which integrates vessel characteristics with environmental and operational context, including vessel density, bathymetry, sea surface temperature, and chlorophyll within a 100\u0026times;100 km area centred on the detection (Global Fishing Watch, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). In our study, the dataset was filtered to include only detections of vessels that are not matched to AIS and with inferred lengths below 30 m, which represents the upper limit for reliable satellite-based detection. The AIS filtering choice was based on the fact that the SSF fleet considered in this study does not use AIS, as only vessels over 15 m in length within the target area must be equipped with AIS under EU regulations. This dual filtering ensures that resulting detections predominantly correspond to SSF vessels, defined in this study as those shorter than 12 m.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eMoran\u0026rsquo;s test was applied to Sentinel-2 data to verify spatial autocorrelation. To compare the spatial patterns between the MCSA and Sentinel-2 approaches, Spearman\u0026apos;s rank correlation was first calculated to assess the overall relationship between the two datasets across all spatial cells. Subsequently, hot spot and cold spot analyses were conducted using the Getis-Ord Gi* statistic (Getis and Ord 1992, \u0026ldquo;sfdep\u0026rdquo; package in R). The analysis classified each cell unit into seven categories based on the Gi* statistic and significance levels: \u0026quot;Very hot\u0026quot; (Gi* \u0026gt; 0, p\u0026thinsp;\u0026le;\u0026thinsp;0.01), \u0026quot;Hot\u0026quot; (Gi* \u0026gt; 0, p\u0026thinsp;\u0026le;\u0026thinsp;0.05), \u0026quot;Somewhat hot\u0026quot; (Gi* \u0026gt; 0, p\u0026thinsp;\u0026le;\u0026thinsp;0.1), \u0026quot;Insignificant\u0026quot; (p\u0026thinsp;\u0026gt;\u0026thinsp;0.1), \u0026quot;Somewhat cold\u0026quot; (Gi* \u0026lt; 0, p\u0026thinsp;\u0026le;\u0026thinsp;0.1), \u0026quot;Cold\u0026quot; (Gi* \u0026lt; 0, p\u0026thinsp;\u0026le;\u0026thinsp;0.05), and \u0026quot;Very cold\u0026quot; (Gi* \u0026lt; 0, p\u0026thinsp;\u0026le;\u0026thinsp;0.01). The spatial concordance between hot spots and cold spots identified by both methodologies was then assessed using Cohen\u0026apos;s Kappa test to quantify the level of agreement between the MCSA and Sentinel-2 approaches. For the spatial concordance analysis using Cohen\u0026apos;s Kappa test, the categories of the the Getis-Ord Gi* analysis were simplified into three groups: \u0026quot;Hot\u0026quot; (combining \u0026quot;Very hot\u0026quot; and \u0026quot;Hot\u0026quot;), \u0026quot;Cold\u0026quot; (combining \u0026quot;Very cold\u0026quot; and \u0026quot;Cold\u0026quot;), and \u0026quot;Neutral\u0026quot; (including \u0026quot;Somewhat hot\u0026quot;, \u0026quot;Somewhat cold\u0026quot;, and \u0026quot;Insignificant\u0026quot; categories). The hotspot analysis was repeated using Sentinel-2 dataset filtered to exclude the summer months (June, July, August). This additional analysis allowed for the assessment of whether the concordance between the two approaches might be affected by the presence of private/touristic vessels, which are much more abundant during summer months.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 MCSA\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Fishers\u0026rsquo; interviews and criteria validation\u003c/h2\u003e \u003cp\u003eAll the 27 interviewed fishers and representatives confirmed the previously identified MCSA criteria, without suggesting new ones. The participants showed strong agreement on the criteria with the highest and lowest importance (i.e., depth and marine traffic respectively, see Online Resource Table S2). Similarly, there was high agreement among responses on criteria grading (Table S3 in Online Resource).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Suitability Index (S\u003csub\u003ec\u003c/sub\u003e) spatial patterns\u003c/h2\u003e \u003cp\u003eThe Suitability Index ranged from 0 to 0.99 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and the highest suitable areas appear to be along the northern and southwestern coast of Sicily, as well as the southeastern coast (values\u0026thinsp;\u0026gt;\u0026thinsp;0.7), while the lowest suitability is found in offshore waters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Activity Index (A\u003csub\u003ec\u003c/sub\u003e) spatial patterns\u003c/h2\u003e \u003cp\u003eThe A\u003csub\u003ec\u003c/sub\u003e revealed various patterns of potential fishing footprint based on port characteristics and vessel accessibility around the Sicilian coast (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The highest activity concentrations (purple areas, \u0026gt;\u0026thinsp;0.75) were observed around major fishing ports, particularly in the northwestern coast, the southeastern coast and the northeastern Aeolian Islands.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Fishing Footprint (FF\u003csub\u003ec\u003c/sub\u003e) index spatial patterns\u003c/h2\u003e \u003cp\u003eThe areas with the highest overall FF (\u0026gt;\u0026thinsp;0.8, dark red) were located along the northwestern coast and the southeastern coast (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and b). Areas with intermediate fishing spatial footprint (0.4\u0026ndash;0.8, red in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) extended along the northeastern coast (including the northeastern islands) and the southern coast. Areas with low SSF spatial footprint (\u0026lt;\u0026thinsp;0.4, yellow in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) were the ones distant from major ports, in offshore waters and in zones with other constraints (such as the presence of MPAs).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Optical imagery Sentinel 2 data\u003c/h2\u003e \u003cp\u003eRemote-sensing vessel detections showed significant spatial autocorrelation (Moran\u0026rsquo;s I test: 0.76, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), revealing the tendency of fishing activities to cluster in specific areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The total detections from 2019 to 2023 were 200,556, after applying the AIS matching filter the detections were 87,670. The filtered detections per year were 18,477 in 2019, 19,510 in 2020, 16,217 in 2021,17,382 in 2022, and 16,084 in 2023. In Sicily, the averaged detections seem to concentrate closer to coastal areas with hotspots scattered along. Similarly to MCSA, the detections appear to be clustered also around Lampedusa Island.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Comparison of spatial patterns from MCSA and Sentinel-2\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Overall spatial correlation\u003c/h2\u003e \u003cp\u003eThe direct comparison between MCSA and Sentinel-2 data revealed a significant positive correlation (Spearman\u0026rsquo;s ρ: 0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) across 14,641 analysed cells, suggesting consistency between areas with high predicted fishing pressure and areas with high vessel frequency. When summer months were excluded from Sentinel-2 observations (cells analysed:15,717) the Spearman correlation was 0.40 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), nearly identical to that of the full dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Hotspot analysis and spatial concordance\u003c/h2\u003e \u003cp\u003eGetis-Ord Gi hotspot analysis applied to the aligned rasters of MCSA and Sentinel-2 data (cells analysed: 14,568) showed high spatial clustering in both datasets (Global G: MCSA\u0026thinsp;=\u0026thinsp;194.24, Sentinel-2\u0026thinsp;=\u0026thinsp;161.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with concordant hotspots primarily distributed along the northwestern coast, northeastern waters and southeastern coast. The two approaches resulted in a similar percentage of cells as SSF hotspots: 13.5% (very hot: 7.9% and hot: 5.6%) and 11.8% (very hot: 7.3% and hot: 4.5%) cells were identified as hotspots respectively for the MCSA and for Sentinel-2 data. The two approaches showed different footprints in the cold and neutral cells, with the MCSA classifying 45.1% cells as cold spots (very cold: 28.3% and cold: 16.8%) and 41.4% as neutral and Sentinel-2 only 23.4% as cold spots (very cold: 8.1% and cold: 15.4%) and 64.8% as neutral spots. Cohen\u0026rsquo;s Kappa analysis for spatial concordance revealed significant concordance between the categories (\u0026ldquo;hot\u0026rdquo;, \u0026ldquo;neutral\u0026rdquo; and \u0026ldquo;cold\u0026rdquo;, see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): the hotspots showed the highest concordance (κ_hot\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while coldspots and neutral areas showed weaker agreement (κ_cold\u0026thinsp;=\u0026thinsp;0.19 and k_neutral\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The overall Cohen's Kappa (κ\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) reflected this mixed pattern of concordance across all spatial categories.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen summer months were excluded from Sentinel-2 observations (cells analysed:12,919) the spatial concordance measures remained the same or slightly decreased (k_hot\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; k_cold\u0026thinsp;=\u0026thinsp;0.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and k_neutral\u0026thinsp;=\u0026thinsp;0.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCell counts and spatial concordance between MCSA and Sentinel-2 data. The table shows the number of cells classified by each method, concordant cells, and agreement percentages for hot spots, cold spots, and neutral areas across complete and summer-filtered datasets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMCSA Cells\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSentinel-2 Cells\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConcordant Cells\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCohen's κ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eConcordance (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHot\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll seasons\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFiltered\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCold\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll seasons\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFiltered\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll seasons\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFiltered\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll seasons\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFiltered\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eHerein we demonstrated how two complementary methodological approaches can be combined to map the SSF spatial footprint in regions where small-scale vessels are not equipped with tracking systems, using Sicily as a case study. The Multi-Criteria Spatial Analysis (MCSA) framework integrates fishers' knowledge with environmental and socio-economic criteria to predict suitable fishing areas, while Sentinel-2 optical imagery, here used for the first time to map SSF vessels, provides direct observations of vessel activity from satellite-derived detection data. Our results indicate that both approaches show high spatial clustering along the north-western and south-eastern Sicilian coast and its islands: MCSA identified clustered areas of high suitability for SSF activity, while Sentinel-2 revealed clustered patterns of actual vessel detections. The two approaches reveal strong concordance for hotspots of SSF footprint. The combined methodology used in our study has the potential to overcome individual limitations of each approach, the absence of direct vessel detection in MCSA and the vessel size constraints and environmental dependencies of satellite detections.\u003c/p\u003e \u003cp\u003eThe MCSA approach adopted herein represents a significant methodological advancement compared to previous participatory studies by integrating multiple empirical data sources beyond questionnaires alone. We incorporated data from previous datasets (e.g., fishing distances from ports) and from the interviews with fishers and representatives across different ports to capture variability in fishing habits and preferences. This approach primarily aims at identifying fishers\u0026rsquo; preferences for their most suitable fishing ground, without directly identifying specific sites of interest. Therefore, it avoids asking directly to fishers information about their fishing ground (Scholz et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Yates and Schoeman \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which in turn fosters greater trust and enhances willingness to provide critical information, as also observed by Thiault et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By limiting the number of interviews, this method significantly differs from direct participatory mapping (Grati et al. 2024; Zampardi et al. 2024).\u003c/p\u003e \u003cp\u003eThe methodology used in our study combined MCSA and Weighted Linear Combination, following Kavadas et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The original framework employs AHP (Analytic Hierarchy Process, a structured decision-making method using pairwise comparisons) for criteria weighting through pair-wise comparisons and Fuzzy Membership functions for value standardisation. However, given the homogeneity of responses across our geographically distributed interviews, we applied direct standardisation to a scale of 0 to 1. This simplified approach maintained analytical rigour while reducing computational complexity and potential uncertainty propagation inherent in Fuzzy Membership functions. Furthermore, we implemented the Suitability Index developed by Kavadas et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), incorporating additional relevant components such as weather conditions and distance to ports. The fishing footprint expressed by the FF index revealed concentrated major SSF activity below three nautical miles, as also observed by previous studies (Kavadas et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thiault et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Grati et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), in shallow areas (\u0026lt;\u0026thinsp;100 meters depth), characterised by a low number of windy days (\u0026lt;\u0026thinsp;130) and low presence of other users such as trawlers and purse seiners. While the methodological approaches to quantify vessel activity varied across the above-mentioned studies\u0026mdash;from vessel characteristics (Kavadas et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; this study) to socioeconomic indicators (Thiault et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u0026mdash; all three demonstrate the novelty of incorporating characteristics of the population or fishing fleet into the activity index, which can highly affect the final prediction of footprint. In our study, the highest SSF footprint was detected along the northwestern and southeastern coastal zones, with notable high activity hotspots emerging around Sicily's smaller islands, e.g., the Aeolian and Lampedusa Islands, whose inhabitants historically rely on fishing activity (Lentini and Romeo \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Orsini 2015).\u003c/p\u003e \u003cp\u003eFurther studies implementing MCSA could also include seasonal variations in fishing patterns and gears\u0026rsquo; specificity, which are not captured in our static model and might represent important criteria to include. Nevertheless, this methodology obviously lacks information about vessels\u0026rsquo; actual position and does not include ground-truthing, while having some approximations of the criteria weights and grades, which are only based on experts' opinions. These weaknesses can be addressed by coupling MCSA predictions with empirical vessel detection data from advanced remote sensing platforms, such as the Sentinel-2 optical imagery utilised in this study, thereby providing ground-truth validation and enhancing the reliability of fishing pressure estimates.\u003c/p\u003e \u003cp\u003eIn general, the remote-sensing results showed high fishing vessel presence aligned with those of the MCSA approach, with particularly high presence in the northwestern and southeastern areas. However, unlike MCSA predictions, remote-sensing detections show notably lower vessel presence around the smaller islands (i.e., the Aeolian archipelago and Lampedusa), possibly a result of the average smaller vessel size common in islands, which points out the detection limits of satellite-based systems. The detection density map shows a clear coastal preference with sparse offshore activity, consistent with typical small-scale fishing operational patterns that prefer nearshore waters. As for MCSA, remote-sensing vessel detections showed significant spatial clustering, confirming fishers\u0026rsquo; preference for specific areas.\u003c/p\u003e \u003cp\u003eRemote-sensing detection offers several advantages for fisheries monitoring, including broad spatial coverage and regular temporal resolution (Paolo et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Marsaglia et al. 2024). The Global Fishing Watch methodology combines neural network classification to distinguish between fishing and non-fishing vessels (Global Fishing Watch \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Paolo et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and can reveal potential hotspots of fishing vessel presence. However, the method faces limitations in a) estimating vessel sizes, b) distinguishing between different fishing activities, and c) addressing the presence of cloud cover. Additionally, the concentration of detected vessels in nearshore waters may reflect genuine small-scale fishing patterns but could also include coastal small trawlers (length\u0026thinsp;\u0026lt;\u0026thinsp;15 m) operating along the continental shelf edge or vessels engaged in illegal activities. These findings emphasise the need for contextual interpretation when applying satellite-based vessel detection to fisheries assessment.\u003c/p\u003e \u003cp\u003eThe significant positive correlation between the results of the MCSA and remote-sensing approaches could be considered as a cross-methodological validation and stresses areas where uncertainty is higher (areas of disagreement indicated by Cohen's kappa analysis). MCSA identified twice as many cold spots (areas with low suitability for SSF activity) as Sentinel-2 (45.5% vs 23.4%), which could suggest a higher reliability of this approach in predicting areas where environmental, operational, or competitive conditions discourage fishing activity but also an overestimation of cold spots. Hence, MCSA framework systematically excludes areas where conditions fall below fisher-defined suitability thresholds (e.g., depth\u0026thinsp;\u0026gt;\u0026thinsp;500 meters, distance from coast\u0026thinsp;\u0026gt;\u0026thinsp;6 nm, high competitors\u0026rsquo; presence). In contrast, Sentinel-2 detections represent vessel presence rather than confirmed fishing activity, and may include false positives from recreational vessels or small or medium size trawlers misclassified as SSF, potentially explaining the lower proportion of areas identified as unsuitable for small-scale fishing operations.\u003c/p\u003e \u003cp\u003eWhen excluding summer months, the correlation between the two approaches remained almost unchanged, suggesting that seasonal recreational vessel activity has limited impact on the overall spatial concordance patterns in our study area. This finding contrasts with expectations of strong seasonal tourism effects and may reflect either the year-round presence of recreational vessels in Sicilian waters, or the persistence of local recreational fishing activities beyond peak tourist periods. Our approach represents a methodological advancement for mapping SSF activity by explicitly accounting for seasonal variations in tourism-intensive regions (Lloret and Font \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Font and Lloret \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The complementary nature of both approaches suggests potential for developing standardised calibration protocols, where MCSA serves as a consistent baseline for validating remote sensing data across diverse Mediterranean and global environments. MCSA outputs could be used to inform and calibrate satellite detection algorithms, potentially improving the accuracy and reliability of satellite-derived fishing activity maps. Our study offers a baseline for systematic, large-scale monitoring that can be consistently applied across different regions and repeated over time. Such a framework would enable the development of more robust methodologies for small-scale fisheries monitoring in data-poor environments, maintaining local relevance and fishers' engagement. It could also be adapted to improve the monitoring of recreational fisheries, whose activity and impact are currently neglected due to data accessibility restrictions and the absence of recreational vessels from official European fishing registries such as the Fleet Register. Better commercial and recreational fisheries\u0026rsquo; footprint information would not only improve our knowledge about the impacts of fishing on marine biodiversity in coastal areas but also allow us to use this information for improved marine spatial planning (e.g., Markantonatou et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eMaps of SSF footprint are crucial for designing effective management strategies of marine resources, providing essential baseline data for monitoring fishing pressure and supporting the design of resource management measures and new marine protected areas, while minimising possible conflicts with fishing communities. Our combined MCSA-optical imagery methodology demonstrates strong scalability potential, from fine-scale port-level management to regional Mediterranean-level applications. This multi-scale suitability makes the framework particularly valuable for implementing the EU Maritime Spatial Planning Directive and addressing critical knowledge gaps in data-poor regions globally, which are increasingly subjected to coastal urbanisation and climate-driven changes in species distributions.\u003c/p\u003e"},{"header":" Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the fishers and their representatives from the communities of Isola delle Femmine, Pantelleria, Lampedusa, Milazzo, and Trapani for their valuable participation in this study. Their insights were essential for developing and validating our Multi-Criteria Spatial Analysis framework.\u003c/p\u003e\n\u003cp\u003eThe research was supported by PRIN 2022 project Reconnect “Reconciling conservation and exploitation of a keystone species through networks of Marine Protected Areas” (CUP C53D23003550006), funded by European Union-Next Generation EU and the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union– NextGenerationEU; Award Number: Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP C63C22000520001, Project title “National Biodiversity Future Center - NBFC”.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was supported by PRIN 2022 project Reconnect \"Reconciling conservation and exploitation of a keystone species through networks of Marine Protected Areas\" (CUP C53D23003550006), funded by European Union-Next Generation EU and the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 under the call for tender No. 3138 of 16 December 2021. Additional support was provided by the National\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBiodiversity Future Center - NBFC (Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClaudia Bommarito: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Visualization (equal); Writing – original draft (equal); Writing – review and editing (equal). Luca Marsaglia: Conceptualization, Formal analysis, Writing – review and editing. Mariagrazia Graziano: Conceptualization; Writing – review and editing (equal); Silvia María Bellù: Formal analysis (equal); Writing – review and editing (equal). Paco Melià: Formal analysis (equal); Writing – review and editing (equal). Giacomo Milisenda: Formal analysis. \u0026nbsp;Antonio Di Franco: Conceptualization (equal); Investigation (equal); Writing – review and editing (equal). Giorgio Aglieri, Antonio Calò, Carlo Cattano, Elena Desiderà, Manfredi Di Lorenzo, Giulio Franzitta, Sylvaine Giakoumi, Paolo Guidetti, Marco Milazzo, Chiara Papetti, Federico Quattrocchi, Stefania Russo, Claudia Scianna, Emanuele Somma, Davide Spatafora, Federica Stranci: Writing – review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Sentinel-2 vessel detection data are publicly available from Global Fishing Watch (https://globalfishingwatch.org/). The multicriteria spatial analysis outputs are available upon request from the corresponding authors. Interview data are available upon request subject to\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eprivacy protections for participating fishers. All data and code supporting this publication are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interviews with fishers and their representatives were conducted between August and September 2024 with explicit informed consent from all participants.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndaloro F, Battaglia P, Giovanardi O et al (2015) Pesca e acquacoltura. Annuario dei dati ambientali. ISPRA. Accessed 10 Jan 2016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBan, N. C., Hansen, G. J., Jones, M., \u0026amp; Vincent, A. C. (2009). Systematic marine conservation planning in data-poor regions: socioeconomic data is essential. 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Estuarine, Coastal and Shelf Science, 247, 106975.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"reviews-in-fish-biology-and-fisheries","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Reviews in Fish Biology and Fisheries](https://link.springer.com/journal/11160)","snPcode":"11160","submissionUrl":"https://submission.nature.com/new-submission/11160/3","title":"Reviews in Fish Biology and Fisheries","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"fishing, multicriteria analysis, hotspots, concordance, Mediterranean, distribution","lastPublishedDoi":"10.21203/rs.3.rs-8211723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8211723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The effective monitoring of small-scale fisheries footprint is critical for fisheries management and marine conservation. Yet, this remains challenging due to limited vessel tracking requirements for small boats under 12 m. This study presents the first attempt to combine Multi-Criteria Spatial Analysis (MCSA) and Sentinel-2 remote sensing approaches for mapping small-scale fisheries (SSF) footprint, using Sicily as a case study. The MCSA combined a habitat suitability index, informed by interviews with small-scale fishers across main Sicilian ports, with an activity index based on fleet\u0026rsquo;s characteristics and distance from the nearest ports. Sentinel-2 remote sensing data provided direct vessel detections processed by Global Fishing Watch using optical imagery, used for the first time in this study to map SSF vessels. The direct comparison between MCSA and Sentinel-2 data revealed a significant positive correlation (Spearman\u0026rsquo;s ρ: 0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both approaches consistently identified fishing hotspots and coldspots and showed stronger agreement for fishing hotspots (62% concordance) than for coldspots and neutral areas (34% and 47% concordance respectively). This result may be explained by MCSA's more conservative approach, which identified twice as many cold spots compared to Sentinel-2. The robustness of our results reveals the importance of combining two approaches when estimating fisheries footprint, especially for SSF where tracking remains highly limited. By integrating MCSA with remote sensing, our approach enables large-scale SSF monitoring with considerably reduced logistical demands compared to extensive participatory mapping, offering a practical and replicable framework with critical implications for sustainable fisheries management and marine spatial planning globally.","manuscriptTitle":"Mapping the footprint of untracked small-scale fisheries using multicriteria spatial analysis and optical satellite imagery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:08:06","doi":"10.21203/rs.3.rs-8211723/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-09T12:30:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T09:09:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T18:54:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-16T01:48:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64858826036280436882716752978999356705","date":"2026-01-08T08:02:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25045452681188712593953944924624503498","date":"2026-01-07T16:25:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50167470528214411732450631249082534345","date":"2025-12-17T21:57:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T13:22:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-28T09:12:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-28T09:11:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Reviews in Fish Biology and Fisheries","date":"2025-11-26T10:07:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"reviews-in-fish-biology-and-fisheries","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Reviews in Fish Biology and Fisheries](https://link.springer.com/journal/11160)","snPcode":"11160","submissionUrl":"https://submission.nature.com/new-submission/11160/3","title":"Reviews in Fish Biology and Fisheries","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a62d8d84-a03e-4f77-a8f2-c5dc6e769c18","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T13:54:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:08:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8211723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8211723","identity":"rs-8211723","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}