Mangrove resilience under tourism-driven land use change in a Caribbean SIDS: evidence from Samaná Bay, Dominican Republic

Mangrove resilience under tourism-driven land use change in a Caribbean SIDS: evidence from Samaná Bay, Dominican Republic | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mangrove resilience under tourism-driven land use change in a Caribbean SIDS: evidence from Samaná Bay, Dominican Republic Claudia Caballero Gonzalez, Victor Gomez-Valenzuela, Solhanlle Bonilla-Duarte, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9526838/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study examines Land Use and Land Cover (LULC) dynamics in the Samaná Bay socio-ecological system and asks what recent landscape changes reveal about mangrove resilience and vulnerability in a tourism coastal economy. Methods Remote sensing products, GIS analysis, and CA–Markov modeling were integrated to assess LULC transitions from 2000 to 2020, quantify mangrove extent change from 1996 to 2020, and construct exploratory mangrove scenarios to 2030. Results The wider landscape experienced substantial redistribution of cover classes, with built-up and cropland areas increasing by 45.37% and 39.12%, respectively, while tree cover declined by 9,220 ha. Mangroves showed a long-term net gain of 0.53% from 1996 to 2020, driven by expansion during 1996–2010 (+ 1.19%). However, 2010–2020 saw localized losses totaling 42.67 ha, attributed to agriculture, urban development, tourism infrastructure, erosion, and flooding. By 2030, scenarios range from continued decline to 6,443.08 ha under a loss trajectory to recovery up to 6,583.56 ha under a strong-gain trajectory. Conclusion Findings indicate conditional resilience because Samaná Bay mangroves can recover under favorable sedimentary and governance conditions, but continued tourism-driven development may push the system towards ecological thresholds. Integrated ridge-to-reef governance, coastal zoning, hydrology first restoration, and economic incentives are needed to reconcile local development with long-term coastal conservation. Mangrove conservation land use change CA-Markov modeling coastal ecosystem services Small Island Developing States ridge-to-reef governance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Coastal and marine ecosystems are central to livelihoods and welfare in Small Island Developing States (SIDS), where fisheries, tourism, coastal protection, and natural resource use are linked to local economies (Stephenson and Jones 2017 ), Zamboni, Noleto Filho et al. ( 2021 ). Among these ecosystems, mangroves contribute essential services, including flood protection, carbon sequestration, biodiversity conservation, water filtration, and fishery support (Avishek, Yu et al. 2012 , Failler, Pètre et al. 2015 , Moity, Delgado et al. 2019 , Blanco-Libreros and Ramírez-Ruiz 2021 , Zamboni, Noleto Filho et al. 2021 , Hakim, Lubis et al. 2022 , Kutir, Agblorti et al. 2022 , Chopade, Mahajan et al. 2023 ). Between 2002 and 2017, regional declines in global mangrove areas were observed (-22.06%), predominantly due to conversion for agriculture and urban development, although some local gains (+ 4.47%) occurred (Bunting, Rosenqvist et al. 2018 , Idris, Mustapha et al. 2021 ). These reductions have been exacerbated by escalating socioeconomic pressures and insufficient conservation policies, especially at the interface between land and sea (Soanes, Pike et al. 2021 , Potapov, Hansen et al. 2022 ). Despite these benefits, mangroves are highly exposed to land-use conversion because they occupy the interface between terrestrial and marine systems. Global assessment shows heterogeneous trends, including marked regional declines and localized gains with losses commonly linked to agriculture, urban development, infrastructure expansion, and weak conservation enforcement (Bunting, Rosenqvist et al. 2018 , Potapov, Hansen et al. 2022 ). In the Dominican Republic, mangroves cover 19,184 ha (Bunting, Rosenqvist et al. 2022 ) and provide significant socio-economic benefits. Annual fisheries yield US $ 24.88 million, with avoided damages valued at US $ 8.51 million, bringing total ecosystem services to US $ 69.92–95.27 million (Kauffman, Heider et al. 2014 , Chopade, Mahajan et al. 2023 , Ruiz de Gauna, Greño et al. 2024 ). This study analyzes the Bahía de Samaná socio-ecological system (BSE) through three complementary lenses: (1) Ridge-to-reef dynamics recognizes that land-based activities can alter sedimentation, hydrological connectivity, and nutrient flows, with direct consequences for coastal ecosystems (Carlson, Foo et al. 2019 , Kumari, Singh et al. 2020 ); (2) Ecosystem services trade-offs between short-term economic gains from real-estate and tourism development and long-term regulating services such as coastal protection, carbon storage, and fisheries support (Lau 2013 , Failler, Pètre et al. 2015 , Martino, Tett et al. 2019 ); and (3) Socio-ecological perspective focuses on thresholds, adaptive capacity, and governance constraints that may turn recoverable disturbance into long-term degradation (Blanco-Libreros and Ramírez-Ruiz 2021 , Soanes, Pike et al. 2021 ). The main research question is: what do LULC dynamics associated with mangrove ecosystems in the BSE during 2000–2020 reveal about spatial patterns, drivers, resilience, and vulnerability, and what scenarios can be projected for 2030 under different conservation and development trajectories? 1.1 The Samana Bay Context The Samaná Bay socio-ecological system (BSE) covers 5,174.92 km² terrestrial and 1,394.11 km² marine area across four provinces and twelve municipalities (Fig. 1 ). Its coastal municipalities include 299,163 inhabitants across 3,167.84 km² (ONE 2020 , IGN 2024 ). The BSE is strongly connected to the Yuna River, the country's highest-flow watercourse, which drains sediment-loaded waters from La Humeadora National Park through 197.2 km into BSE mangroves, creating critical ridge-to-reef connectivity linkage (Laba, Smith et al. 1997 , Bautista de los Santos 2014 ). Since the designation of Samaná as a tourist area in 1994 (Decree No. 91–94), hotel and real estate development have expanded along the coast, often with limited conservation measures (Francisco and Jaime 2014 ). The region holds 6,485.47 ha of mangroves, representing 25% of the country’s total, and has undergone major land use changes due to tourism, agriculture, and extreme weather (Delanoy, Díaz-Asencio et al. 2020, Bunting, Rosenqvist et al. 2022 ). Sedimentation rates increased by 1.78m per year between 2003–2019, and tropical storms caused 2.17 km² of ecosystem loss (Delanoy, Díaz-Asencio et al. 2019 ). Despite economic growth, household poverty remains at 17–25% (ONE 2023 ). This makes Samaná Bay a suitable case for examining how conservation and economic development interact in Caribbean SIDS, where evidence remains more limited than for larger estuarine systems in Asia and Latin America (Nguyen 2014 , Soanes, Pike et al. 2021 ). 2. Materials and methods 2.1. The BSE study area: a critical conservation space The BSE encompasses diverse terrestrial, coastal, and marine ecosystems with 14 notable protected areas covering around 34,689.82 km² (IGN 2024 ). The Marine Area of Strict Protection, "Sanctuary of the Banks of La Plata and La Navidad," comprises 96% of this region's protected area at 33,403.29 km², underscoring a strong commitment to marine conservation (Fig. 1 ). This sanctuary is crucial for humpback whales (Megaptera novangliae), which migrate annually from the North Atlantic to the Bay of Samaná for mating and reproduction during the boreal winter, supporting approximately 1,000 individuals (Barlow 1997 , Leslie F. New; Alisa J. Hall; Robert Harcourt 2015). 2.2. Data sources The GLAD satellite data (Potapov, Hansen et al. 2022 ) enabled the analysis of changes in land use and cover, such as forest height, cropland, urban areas, and wetlands, at a 30 m resolution in the BSE (Table 1 ). Mangrove coverage was assessed with the World Mangrove Atlas, which provides global ecosystem distribution at under 100 m per pixel. Data from 1996, 2010, and 2020 were used to compare mangrove coverage and estimate net changes. Intertemporal land use and cover analysis assessed 11 categories (Table 2 ). Area changes in mangrove and forest ecosystems were analyzed separately in hectares (Appendix A, Fig. 6 ). Table 1 Details of the geospatial datasets used. Satellite Product Source Year Period (years) Resolution (m) Global Land Analysis and Discovery (GLAD) GLAD (Potapov, Hansen et al. 2022 ) 2000 2020 20 30 World Atlas of Mangroves Ocean Data Viewer (Spalding M, Kainuma M et al. 2010) 1996 2010 2020 24 30 Global Forest Watch GFW (Potapov, Hansen et al. 2022 ) 2000 2020 20 30 Aster Elevation Model ASTGM V003 (NASA, METI et al. 2019 ) 2019 - Raster (.tiff) GEBCO V3 GEBCO (GEBCO 2024 ) 2024 - Raster (.tiff) Roads Open Street Map OSM 2024 - Vector (.shp) Provinces National Geographic Institute (IGN 2024 ) 2020 Vector (.shp) Municipalities National Geographic Institute (IGN 2024 ) 2020 Vector (.shp) Protected Areas (Marine and Terrestrial) Earth Map (Morales, Díaz et al.) 2022 - Vector (.shp) Source: own elaboration 2.3. Data processing Data processing followed the workflow summarized in Fig. 2 . ArcGIS Pro 3.1.0, QGIS 3.28.3, and MS Excel were used for spatial processing, area estimation, transition matrices, and scenario construction. All spatial layers were projected to WGS 84 / UTM Zone 19N (EPSG:32619), clipped to the BSE boundary, resampled to a common 30 m spatial resolution when required, and reclassified into eleven LULC categories before intertemporal comparison. TerrSet's Land Change Modeler and CA-Markov modules were used to model LULC transitions from 2000 to 2020 and to project potential patterns to 2030. CA–Markov modeling combines a transition-probability matrix with a spatial allocation rule. The Markov component estimates the probability that each land cover class i at time t will transition to class j at time t + 1 (Selmy, Kucher et al. 2023 , Aghababaei, Ebrahimi et al. 2024 , Farhan, Wu et al. 2024 , Madhusudhan, Shivapur et al. 2024 , Slamet, Nababan et al. 2024 ). Model performance was evaluated by comparing the simulated 2020 map against the observed 2020 map using K-standard, Kno, and Klocation statistics. The transition probability matrix is expressed as: $$\:\left[\begin{array}{cccc}P11&\:P12&\:P..&\:P1n\\\:P21&\:P22&\:P..&\:P2n\\\:..&\:..&\::&\:..\\\:Pn1&\:Pn2&\:..&\:Pnn\end{array}\right]\:\:\:\:\:\:\:\:\:\:\:\:\:\:1$$ where \(\:{p}_{ij}\) is the probability of transition from class \(\:i\) to class \(\:j\) , and each row sums to one: $$\:\begin{array}{cccc}&\:\sum\:_{j=1}^{n}{p}_{ij}=1\:\:\:\:\:&\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:&\:\:\:\:\:2\end{array}$$ The expected land-cover distribution at a future time can be estimated as: $$\:\begin{array}{cccc}&\:{A}_{t+1}={A}_{t}P&\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:3&\:\end{array}$$ where \(\:{A}_{t}\) is the vector of land cover areas at time \(\:t\) , \(\:P\) is the transition probability matrix, and \(\:{A}_{t+1}\) is the expected land cover distribution at the next time step. The cellular automata component then allocates these transitions spatially by considering the current state of each cell, the transition probabilities, and the configuration of neighboring cells: $$\:\begin{array}{cccc}&\:{S}_{t+1}\left(x\right)=f\left({S}_{t}\left(x\right),{N}_{t}\left(x\right),P\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:4&\:&\:\end{array}$$ where \(\:{S}_{t}\left(x\right)\) is the land-cover state of cell \(\:x\) at time \(\:t\) , \(\:{N}_{t}\left(x\right)\) represents the neighborhood configuration around cell \(\:x\) , \(\:P\) is the Markov transition matrix, and \(\:f\) is the spatial allocation function used to determine the future state of each cell. In this study, the CA–Markov procedure was implemented in TerrSet using the 2000 and 2020 LULC maps to estimate transition probabilities and generate the 2030 projection. The other equation used is that of the cellular automata (CA) model, where S (t,t+1) represents the current and future state of the system, and N refers to the state of the neighboring cells. The function indicates that the future state of each cell depends on both the current state of the pixel being analyzed and the states of the cells near that pixel \(\:f\) . 2.4. Analysis of mangroves Mangrove extent was assessed for 1996, 2010, and 2020 using global mangrove datasets. These products are suitable for intertemporal extent analysis but do not provide information on species composition, canopy height, forest structure, or ecological condition. Therefore, results are interpreted as changes in spatial extent rather than direct measures of ecosystem health. Future analysis should integrate NDVI or other biophysical indices, LiDAR-derived structure, socio-economic drivers, and field validation (Fig. 3 ). 2.4.1. Construction of restoration scenarios to 2030 Mangrove scenarios to 2030 were constructed from observed annual rates of change for 1996–2020, 1996–2010, and 2010–2020. Three exploratory scenarios were estimated: (1) Average Gain, assuming continuation of the long-term net recovery rate, (2) Strong Gain, assuming a recovery trajectory similar to 1996–2010, and (3) Continuous Loss, assuming persistence of the 2010–2020 degradation rate. These scenarios should be interpreted as plausible trajectories for conservation planning rather than deterministic forecasts (see Appendix A). 3. Results 3.1. Changes in land use and land cover (2000–2020) Between 2000 and 2020, the BSE landscape experienced substantial redistribution among LULC classes across 416,185 ha (Table 2 ). Tree Cover and Dense Short Vegetation remained the dominant classes, representing approximately 55%-57% and 25–26% of the area, respectively. However, Tree Cover declined by 9,220.20 ha (-3.86%), while Dense Short Vegetation increased by 4,114.40 ha (+ 3.88%), suggesting vegetation turnover and degradation or regeneration dynamics depending on location. Table 2 Inter-temporal analysis of land use and cover in the BSE (2000–2020) Cover class Value Description Area (ha) 2000 % 2000 Area (ha) 2020 % 2020 Net change (ha) 2020 − 2000 Variation (%) Semi-arid 1 51% vegetation cover 15.70 0.004% 18.84 0.005% 3.14 20.00% Dense short vegetation 2 91% vegetation cover 106,155.60 25.507% 110,270.00 26.495% 4,114.40 3.88% Tree cover 3 15m trees 238,901.90 57.403% 229,681.70 55.187% (9,220.20) -3.86% Salt pan 4 3% vegetation cover 10.05 0.0024% 18.84 0.005% 8.79 87.50% Wetland sparse vegetation 5 27% vegetation cover 10.05 0.0024% 25.12 0.006% 15.07 150.00% Wetland dense short vegetation 6 91% vegetation cover 32,035.52 7.6974% 27,190.88 6.533% (4,844.64) -15.12% Wetland tree cover 7 15m trees 13,320.08 3.2005% 13,311.29 3.198% (8.79) -0.07% Open surface water 8 60–69% of year 2,132.52 0.5124% 2,302.69 0.553% 170.17 7.98% Cropland 9 No info 11,442.51 2.7494% 15,918.54 3.825% 4,476.03 39.12% Built-up 10 No info 11,652.25 2.7998% 16,938.96 4.070% 5,286.71 45.37% Ocean 11 No info 508.64 0.1222% 508.64 0.122% - 0.00% Total 416,184.81 100.0000% 416,185.50 100.000% 0.69 0.000165% Source: Own elaboration Wetland Dense Short Vegetation declined by 4,844.64 ha (-15.12%), while Cropland and Built-up areas expanded by 4,476.03 ha (+ 39.12%) and 5,286.71 ha (+ 45.37%), respectively. These shifts indicate increasing pressure from agriculture, settlement expansion, and tourism-related infrastructure. Although the total mapped area remained constant, the internal redistribution of classes suggests growing fragmentation and potential loss of ecological connectivity and ecosystem services. 3.2. Prediction to 2030 The Markov Module estimated a transition probability matrix from 2000–2020 spatial data, which was then used in the Land Change Modeler to project transitions to 2030. Crosstab analysis produced the matrix (Table 3 ), while 11 conditional probability maps (one per class) show pixel transitions. The Markov Model assumes each state change depends only on the previous state (Takada, Miyamoto et al. 2010 ). Table 3 Transition Probability Matrix and Markov Module Details Class value 1 2 3 4 5 6 7 8 9 10 11 Total 1 0.5344 0.0567 0 0 0 0 0 0.2937 0.0298 0.0853 0 1.000 2 0.0001 0.8293 0.0489 0 0 0 0 0.0017 0.0575 0.0624 0 1.000 3 0.0001 0.1259 0.8306 0 0 0 0 0.0004 0.0026 0.0405 0 1.000 4 0 0 0 0.3573 0.0977 0 0.0388 0.5062 0 0 0 1.000 5 0 0 0 0.0511 0.4335 0.0833 0 0.432 0 0 0 1.000 6 0 0 0 0.0006 0.0007 0.7585 0.0172 0.0105 0.1994 0.0131 0 1.000 7 0 0 0 0 0 0.1155 0.8306 0.0028 0.0184 0.0326 0 1.000 8 0.0017 0.0138 0.0014 0.0017 0.0052 0.1145 0.0595 0.7973 0 0.0048 0 1.000 9 0 0.1161 0.0081 0.0001 0 0.1048 0.003 0.0016 0.743 0.0233 0 1.000 10 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 1.000 11 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 0.0909 1.000 Details of Markov Module First landcover image LULC 2000 Second landcover image LULC 2020 Time interval 1 20 Time interval 2 10 Proportional error 0.15 Source: Own elaboration based on the Markov module in Terrset 2020 Table 3 indicates that Tree Cover and Dense Short Vegetation are highly stable (83.06% and 82.93%), due to protected areas. In contrast, Salt pans and wetlands with scattered vegetation show lower stability (35.73% and 43.35%), reflecting greater change. Semi-arid vegetation has a 29.37% chance of turning into open surface waters, while wetlands with sparse vegetation have a 43.22% likelihood. These transitions are spatially consistent with areas undergoing agricultural expansion, settlement growth, and vegetation conversion. The 2030 projection indicates continued expansion of anthropogenic land uses and additional pressure on vegetation and wetland classes (Fig. 5 ). The model suggests further growth of cropland and built-up areas, while natural and semi-natural classes are expected to experience spatial redistribution. Because the projection is based on observed 2000–2020 transitions, it should be read as a business-as-usual scenario that does not explicitly include future policy shifts, climate shocks, or restoration interventions. The projected decline of natural vegetation classes would reduce carbon storage, habitat continuity, and regulating services, while projected gains in built-up and cropland areas would intensify pressure on wetlands and mangrove-adjacent landscapes. This reinforces the need to interpret LULC projections together with spatial planning and conservation policies rather than as purely ecological trends. Model validation showed acceptable performance for exploratory spatial planning. The comparison between the simulated and observed 2020 maps produced K-standard = 0.7226, Kno = 0.8443, and Klocation = 0.9572, indicating moderate-to-high overall agreement, strong quantity agreement, and high spatial allocation accuracy. Quantity disagreement was 12.61%, whereas location disagreement was 1.66%, suggesting that the model is more dependable for spatial allocation than for exact quantity estimates. 3.3. Mangrove-specific analysis (1996–2020) Mangrove cover increased by 0.53% from 1996 to 2020, with most growth (1.19%) occurring between 1996 and 2010 due to wetland recovery and sedimentation. From 2010 to 2020, mangroves declined by 42.67 ha, mostly because of agriculture, development, erosion, and flooding. Overall, there was a net annual gain of 1.42 ha (0.022%), with 563.21 ha gained and 529.08 ha lost (Fig. 6 ). Mangrove loss accelerated to -4.28 ha/year over the last decade, highlighting growing human impacts that need to be addressed in conservation policy. Bajo Yuna Mangrove National Park (Zone 1) saw the largest gains, due to reduced human pressure (Fig. 7). Note 1 indicates losses and 1 indicates gains Figure 7 Spatial distribution of mangrove gains and losses in the BSE between 1996 and 2020. Values indicate loss (-1), no change (0), and gain (1). Insets show areas with higher concentration of mangrove change across the study area. Source: Own elaboration from ArcGIS Pro Raster Calculator Miches, a municipality experiencing rapid tourism growth, has the largest mangrove loss in the study area, though its annual degradation rate is lower than prior estimates. Globally, 3.42% of mangroves were lost from 2000 to 2020, with an annual rate of 0.17% (Hamilton and Casey 2016 ). Myanmar, a deforestation hotspot, saw annual losses of over 3.6% between 1996 and 2016 (De Alban, Jamaludin et al. 2020 ). Malaysia’s Iskandar region reported 1.12% annual degradation due to urbanization and agriculture between 2000 and 2019 (Kanniah, Kang et al. 2021 ). On Vietnam's southern coast, efforts reduced average annual mangrove loss from 3.6% (1998–2011) to 1.5% (2011–2023) (Tran, Reef et al. 2024 ). Across the Caribbean, mangroves are declining by about 1% annually, with mainland areas seeing up to 1.7% per year (Cortés, Lorenzo-Trueba et al. 2024 ). The Tropical Northwestern Atlantic province experienced a 5.4% net decrease since 1996, with projections indicating further losses under climate change scenarios (Blanco-Libreros 2016 , Troche, Lugo et al. 2024 ). Some regional reports estimate recent declines exceeding 30% (Rull 2023 ), and under severe sea-level rise, up to 75.9% could be submerged by 2060 (Troche, Lugo et al. 2024 ). In Mexico’s Mahahual-Xcalak, annual degradation was 0.85% from 1995–2007, mainly due to urban and infrastructure expansion (Hirales-Cota, Espinoza-Avalos et al. 2010 ). 3.4. Drivers of mangrove degradation during the period 1996–2020 Despite a net growth rate of 0.53% over 24 years, approximately 43 hectares of mangrove were lost in the last decade, driven by shifting economic activities and policy gaps (Table 4 , Figs. 10 , 11 ). Table 4 Main drivers of mangroves degradation in BSE Period Net change (Ha) Description Anthropogenic drivers Natural drivers 1996–2010 + 76.81 Ha (1.19%) Mangroves expansion due favorable conditions Habitat restauration process Conservation of protected areas Increased sedimentation Favorable climate conditions 2010–2020 -42.67 Ha (-0.65%) Cover Loss due anthropogenic conditions Deforestation and Coastal Urbanization Mass tourism Agriculture Hurricanes Climate Change: Sea level rise Coastal erosion 1996–2020 + 34.14 Ha (0.53%) Balance between expansion and degradation Conservation Policies Growth of unregulated economic activities Climate change: Extreme events Marine pollution: Changes in salinity Source: own elaboration based on literature revision Despite a net increase in mangrove extent over 1996–2020, the loss of approximately 43 ha during 2010–2020 indicates that recent pressures have altered the trajectory of the system. The observed pattern suggests a period of conditional resilience followed by localized decline, shaped by the interaction between sediment dynamics, protected-area management, and expanding economic activities (Table 4 , Figs. 10 and 11 ). During 1996–2010, national environmental reforms, including Law 64 − 00 and the strengthening of the protected-area system, may have contributed to conservation outcomes in Bajo Yuna and Los Haitises, while favorable sedimentary conditions supported mangrove expansion. By contrast, the 2010–2020 period coincided with accelerated tourism development, urban expansion, and agricultural pressure, particularly in coastal municipalities experiencing land-market transformation. These dynamics reflect a broader governance challenge. Mangroves generate public benefits, including shoreline protection, carbon storage, biodiversity conservation, and fishery support, yet many of these benefits are not internalized in land-use and tourism decisions. As a result, short-term private returns from real-estate development, mass tourism, and agricultural conversion can be prioritized over long-term ecosystem services. This creates social and ecological costs, including increased exposure to coastal hazards, biodiversity loss, reduced water quality, and diminished climate-regulation benefits (Hirales-Cota, Espinoza-Avalos et al. 2010 , Garcés-Ordóñez, Ríos-Mármol et al. 2023 ). Addressing these trade-offs requires an integrated policy response. Enforceable coastal zoning should identify non-build areas in critical mangrove belts and link setbacks to hazard exposure and blue-carbon priorities. Tourism regulation should include density controls, cumulative-impact assessment, environmental licensing, and no-net-loss requirements for mangrove conversion. Economic instruments, including payments for ecosystem services and blue-carbon finance, could help align local development incentives with conservation outcomes (Lau 2013 ). Finally, restoration should prioritize hydrological reconnection, including tidal-channel reopening, barrier removal, and site-appropriate planting where natural regeneration is insufficient (Simpson, Mercer Clarke et al. 2012 , Moschetto, Ribeiro et al. 2021 ). 3.5. Restoration scenarios to 2030: mangroves of the Bay of Samana and their surroundings Following the methodology described, mangrove restoration scenarios were created using estimated annual recovery rates (see Appendix A). The results show that the three projected scenarios differ based on conservation and restoration trends. In the average gain scenario, mangroves would grow slowly to 6,499.75 ha by 2030 at 1.43 ha per year. The strong gain scenario projects faster restoration, reaching 6,583.56 ha with an annual increase of 9.81 ha. Conversely, continuous loss would shrink coverage to 6,443.08 ha, declining by 4.24 ha annually. 4. Conclusions This study examined Land Use and Land Cover (LULC) changes in the Samaná Bay socio-ecological system from 2000 to 2020 and projected potential trajectories to 2030, with a specific focus on mangrove ecosystems. The findings provide three main conclusions for coastal conservation and planning in Caribbean SIDS. First, the BSE exhibits conditional mangrove resilience with a recent localized decline. Mangroves showed a long-term net increase of + 0.53% between 1996 and 2020, with the strongest expansion during 1996–2010 (+ 1.19%), linked to favorable sedimentation and conservation policies. However, 2010–2020 recorded a loss of -42.67 ha, driven by agriculture, hotel development, urbanization, erosion, and flooding. This shows that mangrove resilience is not indefinite; once anthropogenic pressure exceeds ecological and governance thresholds, recovery becomes less certain. Second, the 2030 scenarios reveal divergent futures shaped by trade-offs. Continued development without conservation measures would sustain mangrove decline, whereas targeted interventions, including coastal zoning, enforcement of setbacks, environmental licensing, and hydrology-first restoration, could stabilize or increase mangrove cover. The trade-off is clear: short-term private benefits from tourism and real-estate development can undermine long-term public benefits such as shoreline protection, carbon storage, fisheries productivity, and cultural values. Third, land-sea interdependence is a central governance challenge. A ridge-to-reef perspective shows how upland agriculture, deforestation, sediment dynamics, and coastal construction cascade into mangrove degradation. Mangrove conservation in Samaná Bay, therefore, cannot be addressed as an isolated issue within a protected area. It requires integrated governance that manages terrestrial, coastal, and marine pressures simultaneously. Four policy implications follow from these results. Tourism and land-use regulation should strengthen coastal zoning, restrict construction in mangrove belts, enforce setbacks, and require cumulative-impact assessment for hotel and real-estate projects. Restoration strategies should prioritize community participation and hydrological reconnection through tidal-channel reopening and barrier removal. Monitoring systems should combine remote sensing, NDVI or other biophysical indicators, LiDAR where available, and ground validation to move beyond area-based assessments. Finally, economic incentives such as payments for ecosystem services and blue-carbon finance under Law 44 − 18 should be expanded to align local development incentives with ecosystem stewardship. Overall, the results suggest that future mangrove trajectories in Samaná Bay will be strongly shaped by governance choices, particularly the capacity to regulate tourism-led land conversion, restore hydrological connectivity, and align local economic incentives with conservation. For the Dominican Republic and other Caribbean SIDS, reconciling tourism-led economic growth with coastal ecosystem conservation is urgent, feasible, and essential for long-term sustainability. 4.1. Limitations This study has methodological and data limitations. The LULC analysis relies on available global datasets and selected temporal snapshots, which may affect transition probability estimates and reduce sensitivity to small changes or highly heterogeneous coastal areas. The CA-Markov model simplifies complex processes by estimating future change from previous states and does not explicitly incorporate future climate shocks, policy interventions, market shifts, or abrupt infrastructure development. Mangrove analysis was based on changes in spatial extent and did not assess ecosystem condition, species composition, canopy height, biomass, or forest health. Therefore, apparent gains in area should not be interpreted automatically as ecological recovery. Future research should integrate field validation, higher-resolution imagery, LiDAR, NDVI or other vegetation indices, socio-economic drivers, and ecosystem service indicators such as fisheries, recreation, flood protection, and blue carbon. These improvements would reduce uncertainty and strengthen the use of spatial models for coastal zoning and mangrove conservation in the BSE. Declarations Supplementary Materials: Appendix A provides data analysis supporting mangrove change and scenario calculations. Appendix B provides the CA–Markov model validation outputs. Funding This research was supported by the National Fund for Scientific and Technological Research (FONDOCYT) of the Ministry of Higher Education, Science and Technology of the Dominican Republic, grant number 2022-2B5-162. Author Contribution C.C and V.G.V : conceptualization, methodology, formal analysis, investigation, data curation, visualization, writing—original draft, writing—review and editing. K.R and S.B.D: writing—review and editing. Data Availability The geospatial datasets used in this study are publicly available from the sources listed in Table 1 in the manuscript. The processed database, transition matrices, and generated spatial layers are available from the corresponding author upon reasonable request and will be deposited in an appropriate repository upon acceptance. References Aghababaei M, Ebrahimi A, Naghipour AA, Asadi E, Verrelst J (2024) Monitoring of Plant Ecological Units Cover Dynamics in a Semiarid Landscape from Past to Future Using Multi-Layer Perceptron and Markov Chain Model. Remote Sens 16(9) Avishek K, Yu X, Liu J (2012) Ecosystem management in Asia Pacific: Bridging science–policy gap. Environ Dev 3:77–90 Barlow J (1997) A report of Cetacean Acoustic Detection and Dive Interval Studies (CADDIS) conducted in the southern Gulf of California, 1995 Bautista de los Santos Q, Melina (2014) Determinación de los Caudales Ambientales de la Cuenca del río Yuna. República Dominicana Tecnología y Ciencia del Agua 5(6):33–40 Blanco-Libreros J (2016) Cambios globales en los manglares del golfo de Urabá (Colombia): entre la cambiante línea costera y la frontera agropecuaria en expansión. Actualidades Biológicas 38 Blanco-Libreros JF, Ramírez-Ruiz K (2021) Threatened Mangroves in the Anthropocene: Habitat Fragmentation in Urban Coastalscapes of Pelliciera spp. (Tetrameristaceae) in Northern South America. Front Mar Sci 8 Bunting P, Rosenqvist A, Hilarides L, Lucas RM, Thomas N, Tadono T, Worthington TA, Spalding M, Murray NJ, Rebelo L-M (2022) Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0. Remote Sensing 14(15) Bunting P, Rosenqvist A, Lucas RM, Rebelo L-M, Hilarides L, Thomas N, Hardy A, Itoh T, Shimada M, Finlayson CM (2018) The Global Mangrove Watch—A New 2010 Global Baseline of Mangrove Extent. Remote Sens 10. 10.3390/rs10101669 Carlson RR, Foo SA, Asner GP (2019) Land Use Impacts on Coral Reef Health: A Ridge-to-Reef Perspective. Front Mar Sci 6 Chopade MR, Mahajan S, Chaube N (2023) Assessment of land use, land cover change in the mangrove forest of Ghogha area, Gulf of Khambhat. Gujarat Expert Syst Appl 212:118839 Cortés IM, Lorenzo-Trueba J, Rovai AS, Twilley RR, Chopping M, Fatoyinbo T (2024) Net evaporation-induced mangrove area loss across low-lying Caribbean islands. Environ Research: Clim 3(4):045004 De Alban JDT, Jamaludin J, Wong De Wen D, Than MM, Webb EL (2020) Improved estimates of mangrove cover and change reveal catastrophic deforestation in Myanmar. Environ Res Lett 15(3) Delanoy, R., M. Díaz-Asencio and R. Méndez-Tejeda (2020). Sedimentation in the Bay of Samaná, Dominican Republic (1900–2016). AIMS Geosciences 6(3): 298–315 Delanoy RA, Díaz-Asencio M, Méndez-Tejeda R (2019) Effect of Extreme Weather Events on the Sedimentation of the Bay of Samaná, Dominican Republic (1900–2016). J Geogr Geol 11(3) Failler P, Pètre É, Binet T, Maréchal JP (2015) Valuation of marine and coastal ecosystem services as a tool for conservation: The case of Martinique in the Caribbean. Ecosyst Serv 11:67–75 Farhan M, Wu T, Anwar S, Yang J, Naqvi SAA, Soufan W, Tariq A (2024) Predicting Land Use Land Cover Dynamics and Land Surface Temperature Changes Using CA-Markov-Chain Models in Islamabad, Pakistan (1992–2042). IEEE J Sel Top Appl Earth Observations Remote Sensing:1–18 Francisco P, Pino; and, Jaime N (2014) Barber Turismo y desarrollo sostenible en la provincia de Samaná, República Dominicana. Investigación Turísticas, Centro Ecuatoriano de Derecho Ambiental (CEDA), Universitat Politécnica de València Garcés-Ordóñez O, Ríos-Mármol M, Vivas-Aguas LJ, Espinosa-Díaz LF, Romero-D’Achiardi D, Canals M (2023) Degradation factors and their environmental impacts on the mangrove ecosystem of the Mallorquin Lagoon, Colombian Caribbean. Wetlands 43(7) GEBCO (2024) Compilation Group Hakim MA, Lubis DP, Harefa MS, Damanik MRS, Suciani A (2022) Analysis Changes in Mangrove Forest Cover Using Multi-Sensor Image in North Luwu District South Sulawesi 2015–2020. Tunas Geografi 11(2) Hamilton SE, Casey D (2016) Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC‐21). Glob Ecol Biogeogr 25(6):729–738 Hirales-Cota M, Espinoza-Avalos J, Schmook B, Ruiz-Luna A, Ramos-Reyes R (2010) Drivers of mangrove deforestation in Mahahual-Xcalak, Quintana Roo, southeast Mexico. Ciencias Marinas 36(2):147–159 Idris NS, Mustapha MA, Sulaiman N, Khamis S, Husin SM, Darbis NDA (2021) The dynamics of landscape changes surrounding a firefly ecotourism area. Global Ecol Conserv 29:e01741 IGN (2024) Datos Geográficos Fundamentales. from https://ign.gob.do/ Kanniah KD, Kang CS, Sharma S, Aldrie Amir A (2021) Remote sensing to study mangrove fragmentation and its impacts on leaf area index and gross primary productivity in the south of peninsular malaysia. Remote Sens 13(8) Kauffman JB, Heider C, Norfolk J, Payton F (2014) Carbon stocks of intact mangroves and carbon emissions arising from their conversion in the Dominican Republic. Ecol Appl 24(3):518–527 Kumari P, Singh JK, Pathak B Chapter 1 - Potential contribution of multifunctional mangrove resources and its conservation. Biotechnological Utilization of Mangrove Resources., Patra JK, Mishra RR, Thatoi H (2020) Academic Press: 1–26 Kutir C, Agblorti SKM, Campion BB (2022) Migration and Estuarine Land Use/Land Cover (LULC) Change along Ghana’s Coast. Reg Stud Mar Sci 54:102488 Laba M, Smith SD, Degloria SD (1997) Landsat-based land cover mapping in the lower Yuna River watershed in the Dominican Republic. Int J Remote Sens 18(14):3011–3025 Lau WWY (2013) Beyond carbon: Conceptualizing payments for ecosystem services in blue forests on carbon and other marine and coastal ecosystem services. Ocean Coastal Manage 83:5–14 Leslie F, New; Alisa J, Hall S (2015) The modelling and assessment of whale-watching impacts. Ocean & Coastal Management 115: 16 Madhusudhan MS, Shivapur AV, Surendra JH (2024) Forecasting Land Use and Land Cover Changes in the Malaprabha Right Bank Canal Command Area through Cellular Automata and Markov Chain Modeling. Ecol Eng Environ Technol 25(6):54–65 Martino S, Tett P, Kenter J (2019) The interplay between economics, legislative power and social influence examined through a social-ecological framework for marine ecosystems services. Sci Total Environ 651:1388–1404 Moity N, Delgado B, Salinas-De-León P (2019) Mangroves in the Galapagos islands: Distribution and dynamics. PLoS ONE 14(1) Morales C, Díaz AS-P, Dionisio D, Guarnieri L, Marchi G, Maniatis D and D. Mollicone Earth Map: A Novel Tool for Fast Performance of Advanced Land Monitoring and Climate Assessment. J Remote Sens 3: 0003 Moschetto FA, Ribeiro RB, De Freitas DM (2021) Urban expansion, regeneration and socioenvironmental vulnerability in a mangrove ecosystem at the southeast coastal of São Paulo. Brazil Ocean Coastal Manage 200:105418 NASA METI, Japan Spacesystems AIST and U.S and, Team JAS (2019) ASTER Global Digital Elevation Model V003. N. E. L. P. D. A. A. C. A. 2024-11-05 Nguyen H-H (2014) The relation of coastal mangrove changes and adjacent land-use: A review in Southeast Asia and Kien Giang. Vietnam Ocean Coastal Manage 90:1–10 ONE (2020) Estimaciones y Proyecciones Nacionales de Poblacion 1950–2100, 2014. Oficina Nacional de Estadística (ONE) ONE A (2023) MITUR Cuadro 3.09-03 REPÚBLICA DOMINICANA: Establecimientos y habitaciones de alojamiento turístico por provincia y municipio según años,2004–2023* Potapov P, Hansen MC, Pickens A, Hernandez-Serna A, Tyukavina A, Turubanova S, Zalles V, Li X, Khan A, Stolle F, Harris N, Song X-P, Baggett A, Kommareddy I, Kommareddy A (2022) The Global 2000–2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results. Frontiers in Remote Sensing 3 Ruiz de Gauna I, Greño F, Torres X, Galarraga I (2024) Economic valuation of ecosystem services provided by blue economy ecosystems in the Dominican Republic Rull V (2023) Rise and Fall of Caribbean Mangroves Selmy SA, H. DE, Kucher G, Mozgeris ARA, Moursy R, Jimenez-Ballesta OD, Kucher ME, Fadl, Mustafa ARA (2023) Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques. Remote Sens 15(23). Simpson M, Mercer Clarke CSL, Clarke JD, Scott D, Clarke AJ (2012) Coastal Setbacks in Latin America and the Caribbean: A Study of Emerging Issues and Trends that Inform. Guidelines for Coastal Planning and Development Slamet B, Nababan AM, Anggraini N (2024) Prediction of land cover change in the Belawan watershed using the cellular automata-markov chain model. IOP Conference Series: Earth and Environmental Science 1352(1): 012050 Soanes LM, Pike S, Armstrong S, Creque K, Norris-Gumbs R, Zaluski S, Medcalf K (2021) Reducing the vulnerability of coastal communities in the Caribbean through sustainable mangrove management. Ocean Coastal Manage 210:105702 Spalding M, Kainuma M (2010) and C. L World Atlas of Mangroves (version 3.1). A collaborative project of ITTO, ISME, FAO, UNEP-WCMC, UNESCO-MAB, UNU-INWEH and TNC Stephenson TS, Jones JJ (2017) Impacts of climate change on extreme events in the coastal and marine environments of Caribbean Small Island Developing States (SIDS). Science Review, Caribbean Climate Change Report Card, pp 10–22 Takada T, Miyamoto A, Hasegawa SF (2010) Derivation of a yearly transition probability matrix for land-use dynamics and its applications. Landscape Ecol 25:561–572 Tran TV, Reef R, Zhu X (2024) Long-term changes of mangrove distribution and its response to anthropogenic impacts in the Vietnamese Southern Coastal Region. J Environ Manage 370 Troche C, Lugo A, Heartsill-Scalley T, López-Portillo J, Velázquez-Salazar S, Fraiz-Toma A, Alcántara JA, Maya E, Villeda-Chávez B, Vazquez L, Valderrama M, Rodríguez-Zúñiga J, Blanco-Libreros Y, Cruz O, Reyes Dominguez L, Corrales A, Lara D, Castillo J, Rangel, Suarez (2024) IUCN Red List of Ecosystems, Mangroves of the Tropical Northwestern Atlantic Zamboni NS, Noleto Filho EM, Carvalho AR (2021) Unfolding differences in the distribution of coastal marine ecosystem services values among developed and developing countries. Ecological Economics 189 Additional Declarations No competing interests reported. Supplementary Files AppendixA.DataAnalysis.xlsx AppendixBMarkovModelvalidation.txt Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9526838","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635405257,"identity":"125db96f-d561-4744-ac89-f998760a7812","order_by":0,"name":"Claudia Caballero Gonzalez","email":"data:image/png;base64,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","orcid":"","institution":"Instituto Tecnológico de Santo Domingo","correspondingAuthor":true,"prefix":"","firstName":"Claudia","middleName":"Caballero","lastName":"Gonzalez","suffix":""},{"id":635405259,"identity":"2576b8be-2e7c-401d-bc51-8a90c8a01679","order_by":1,"name":"Victor Gomez-Valenzuela","email":"","orcid":"","institution":"Instituto Tecnológico de Santo Domingo","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Gomez-Valenzuela","suffix":""},{"id":635405261,"identity":"1932884a-9e88-4885-88c5-f789f847e17f","order_by":2,"name":"Solhanlle Bonilla-Duarte","email":"","orcid":"","institution":"Instituto Tecnológico de Santo Domingo","correspondingAuthor":false,"prefix":"","firstName":"Solhanlle","middleName":"","lastName":"Bonilla-Duarte","suffix":""},{"id":635405262,"identity":"5032467d-3ceb-47e5-89d2-17a8a7d16c37","order_by":3,"name":"Katerin Ramirez","email":"","orcid":"","institution":"Instituto Tecnológico de Santo Domingo","correspondingAuthor":false,"prefix":"","firstName":"Katerin","middleName":"","lastName":"Ramirez","suffix":""}],"badges":[],"createdAt":"2026-04-25 15:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9526838/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9526838/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108972468,"identity":"19272ebe-323a-41cd-a46c-453acc37a1ef","added_by":"auto","created_at":"2026-05-11 10:36:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148494,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area of the Samaná Bay socio-ecological system (BSE), Dominican Republic, showing the terrestrial and marine study boundaries, protected areas, and elevation context. Elevation data are based on ASTER GDEM, and protected area boundaries were obtained from national and global spatial datasets. Source: Own elaboration.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/524fe95856ac202b59775699.jpg"},{"id":108972473,"identity":"d1898407-42e7-46e9-aa01-9fdc15e1c45b","added_by":"auto","created_at":"2026-05-11 10:36:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":163791,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow used for land-use and land-cover change analysis and CA–Markov projection in the Samaná Bay socio-ecological system. The process included data acquisition, spatial harmonization, reclassification, intertemporal change detection, transition-probability estimation, model validation, and projection to 2030. Source: Own elaboration.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/4e2d361497a39a83aa536497.jpg"},{"id":108972470,"identity":"be4cccda-fd43-4d55-ab3c-3b5d5c588c54","added_by":"auto","created_at":"2026-05-11 10:36:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73796,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow used for mangrove extent analysis in the BSE. Mangrove layers for 1996, 2010, and 2020 were clipped to the study boundary, spatially standardized, compared through intertemporal matrices, and processed with raster algebra to estimate gains, losses, and net change. Source: Own elaboration.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/757fb05a0ceb0295def60bb7.jpg"},{"id":108972472,"identity":"d31de1ad-6c4c-4862-9175-f578450deafb","added_by":"auto","created_at":"2026-05-11 10:36:26","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":175539,"visible":true,"origin":"","legend":"\u003cp\u003eLand-use and land-cover distribution in the BSE in (a) 2000 and (b) 2020, and (c) net gains and losses by LULC class between 2000 and 2020. LULC data are based on the GLAD global land-cover product (Potapov et al. 2022). Source: Own elaboration.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/cf73791944b08e0bbc48602d.jpg"},{"id":108972476,"identity":"ebe526c2-d424-49bc-acd6-b089e2d89ca3","added_by":"auto","created_at":"2026-05-11 10:36:26","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":161435,"visible":true,"origin":"","legend":"\u003cp\u003eObserved LULC in 2020 and projected LULC in 2030 under a business-as-usual CA–Markov scenario. Panel (c) shows projected net gains and losses by class between 2020 and 2030. The projection is based on transition probabilities estimated from 2000–2020 LULC data. Source: Own elaboration.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/9b0376f9521d1835d69c81ca.jpg"},{"id":108972477,"identity":"c92af935-4817-470f-9658-95086872669b","added_by":"auto","created_at":"2026-05-11 10:36:26","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":90675,"visible":true,"origin":"","legend":"\u003cp\u003eMangrove extent in the BSE in (a) 1996, (b) 2010, and (c) 2020, with (d) total mangrove area by year. Value 1 indicates mangrove presence. Mangrove extent data are based on the global mangrove dataset used in this study. Source: Own elaboration.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/5ee61b82478595cd7f6132f2.jpg"},{"id":108972480,"identity":"11f866d4-4767-4f2a-8eea-adb66ca5d675","added_by":"auto","created_at":"2026-05-11 10:36:26","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":101775,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of mangrove gains and losses in the BSE between 1996 and 2020. Values indicate loss (-1), no change (0), and gain (1). Insets show areas with higher concentration of mangrove change across the study area. Source: Own elaboration from ArcGIS Pro Raster Calculator\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/fb53737270fb56adcaa5b0e0.jpg"},{"id":108972474,"identity":"85d0aaca-469b-4255-aab8-181b093459a5","added_by":"auto","created_at":"2026-05-11 10:36:26","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":45860,"visible":true,"origin":"","legend":"\u003cp\u003eVariation and annual rate of mangrove change by period. Source: Own elaboration\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/c2e5c5160063b3430154bb82.jpg"},{"id":108977441,"identity":"7d863bc1-3912-472f-90be-3915bf098a7d","added_by":"auto","created_at":"2026-05-11 11:31:46","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":37806,"visible":true,"origin":"","legend":"\u003cp\u003eVariation and annual rate of mangrove change by period. Source: Own elaboration\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/8c9cb5ee234eccdd143c644a.jpg"},{"id":108977915,"identity":"d15a1930-ec37-43c2-9660-9c4f39ebfd69","added_by":"auto","created_at":"2026-05-11 11:33:26","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":85662,"visible":true,"origin":"","legend":"\u003cp\u003eMangrove restoration/conservation scenarios to 2030. Source: Own elaboration\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/111181755f0f3f2066974926.jpg"},{"id":108972479,"identity":"1021255d-c1bb-415c-a1c6-3ba9f0bdf0f8","added_by":"auto","created_at":"2026-05-11 10:36:26","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":79783,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual trajectories by scenario. Source: Own elaboration\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/4868b7431920bcb8089804a5.jpg"},{"id":108979818,"identity":"f89472a0-f5af-4b1c-bd2a-67a4e3b820a8","added_by":"auto","created_at":"2026-05-11 12:01:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1664722,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/616b67d1-d4c4-456b-9f95-6ef977c763ef.pdf"},{"id":108977902,"identity":"ca46abf3-1344-4f16-ade3-b4abacd946b9","added_by":"auto","created_at":"2026-05-11 11:33:24","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":90791,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.DataAnalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/223964b05e16c6397ad01484.xlsx"},{"id":108972469,"identity":"758d9cb7-92ab-424f-bc14-03dd53dfa84f","added_by":"auto","created_at":"2026-05-11 10:36:26","extension":"txt","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1917,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixBMarkovModelvalidation.txt","url":"https://assets-eu.researchsquare.com/files/rs-9526838/v1/401674024d9cb9a885938f9a.txt"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mangrove resilience under tourism-driven land use change in a Caribbean SIDS: evidence from Samaná Bay, Dominican Republic","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoastal and marine ecosystems are central to livelihoods and welfare in Small Island Developing States (SIDS), where fisheries, tourism, coastal protection, and natural resource use are linked to local economies (Stephenson and Jones \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Zamboni, Noleto Filho et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among these ecosystems, mangroves contribute essential services, including flood protection, carbon sequestration, biodiversity conservation, water filtration, and fishery support (Avishek, Yu et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Failler, P\u0026egrave;tre et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Moity, Delgado et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Blanco-Libreros and Ram\u0026iacute;rez-Ruiz \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Zamboni, Noleto Filho et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Hakim, Lubis et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Kutir, Agblorti et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Chopade, Mahajan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Between 2002 and 2017, regional declines in global mangrove areas were observed (-22.06%), predominantly due to conversion for agriculture and urban development, although some local gains (+\u0026thinsp;4.47%) occurred (Bunting, Rosenqvist et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Idris, Mustapha et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These reductions have been exacerbated by escalating socioeconomic pressures and insufficient conservation policies, especially at the interface between land and sea (Soanes, Pike et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Potapov, Hansen et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these benefits, mangroves are highly exposed to land-use conversion because they occupy the interface between terrestrial and marine systems. Global assessment shows heterogeneous trends, including marked regional declines and localized gains with losses commonly linked to agriculture, urban development, infrastructure expansion, and weak conservation enforcement (Bunting, Rosenqvist et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Potapov, Hansen et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the Dominican Republic, mangroves cover 19,184 ha (Bunting, Rosenqvist et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and provide significant socio-economic benefits. Annual fisheries yield US\u003cspan\u003e$\u003c/span\u003e24.88\u0026nbsp;million, with avoided damages valued at US\u003cspan\u003e$\u003c/span\u003e8.51\u0026nbsp;million, bringing total ecosystem services to US\u003cspan\u003e$\u003c/span\u003e69.92\u0026ndash;95.27\u0026nbsp;million (Kauffman, Heider et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Chopade, Mahajan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Ruiz de Gauna, Gre\u0026ntilde;o et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study analyzes the Bah\u0026iacute;a de Saman\u0026aacute; socio-ecological system (BSE) through three complementary lenses: (1) Ridge-to-reef dynamics recognizes that land-based activities can alter sedimentation, hydrological connectivity, and nutrient flows, with direct consequences for coastal ecosystems (Carlson, Foo et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Kumari, Singh et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); (2) Ecosystem services trade-offs between short-term economic gains from real-estate and tourism development and long-term regulating services such as coastal protection, carbon storage, and fisheries support (Lau \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Failler, P\u0026egrave;tre et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Martino, Tett et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); and (3) Socio-ecological perspective focuses on thresholds, adaptive capacity, and governance constraints that may turn recoverable disturbance into long-term degradation (Blanco-Libreros and Ram\u0026iacute;rez-Ruiz \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Soanes, Pike et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The main research question is: what do LULC dynamics associated with mangrove ecosystems in the BSE during 2000\u0026ndash;2020 reveal about spatial patterns, drivers, resilience, and vulnerability, and what scenarios can be projected for 2030 under different conservation and development trajectories?\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 The Samana Bay Context\u003c/h2\u003e \u003cp\u003eThe Saman\u0026aacute; Bay socio-ecological system (BSE) covers 5,174.92 km\u0026sup2; terrestrial and 1,394.11 km\u0026sup2; marine area across four provinces and twelve municipalities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Its coastal municipalities include 299,163 inhabitants across 3,167.84 km\u0026sup2; (ONE \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, IGN \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The BSE is strongly connected to the Yuna River, the country's highest-flow watercourse, which drains sediment-loaded waters from La Humeadora National Park through 197.2 km into BSE mangroves, creating critical ridge-to-reef connectivity linkage (Laba, Smith et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, Bautista de los Santos \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince the designation of Saman\u0026aacute; as a tourist area in 1994 (Decree No. 91\u0026ndash;94), hotel and real estate development have expanded along the coast, often with limited conservation measures (Francisco and Jaime \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The region holds 6,485.47 ha of mangroves, representing 25% of the country\u0026rsquo;s total, and has undergone major land use changes due to tourism, agriculture, and extreme weather (Delanoy, D\u0026iacute;az-Asencio et al. 2020, Bunting, Rosenqvist et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Sedimentation rates increased by 1.78m per year between 2003\u0026ndash;2019, and tropical storms caused 2.17 km\u0026sup2; of ecosystem loss (Delanoy, D\u0026iacute;az-Asencio et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite economic growth, household poverty remains at 17\u0026ndash;25% (ONE \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This makes Saman\u0026aacute; Bay a suitable case for examining how conservation and economic development interact in Caribbean SIDS, where evidence remains more limited than for larger estuarine systems in Asia and Latin America (Nguyen \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Soanes, Pike et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1. The BSE study area: a critical conservation space\u003c/h2\u003e \u003cp\u003eThe BSE encompasses diverse terrestrial, coastal, and marine ecosystems with 14 notable protected areas covering around 34,689.82 km\u0026sup2; (IGN \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Marine Area of Strict Protection, \"Sanctuary of the Banks of La Plata and La Navidad,\" comprises 96% of this region's protected area at 33,403.29 km\u0026sup2;, underscoring a strong commitment to marine conservation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This sanctuary is crucial for humpback whales (Megaptera novangliae), which migrate annually from the North Atlantic to the Bay of Saman\u0026aacute; for mating and reproduction during the boreal winter, supporting approximately 1,000 individuals (Barlow \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, Leslie F. New; Alisa J. Hall; Robert Harcourt 2015).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data sources\u003c/h2\u003e \u003cp\u003eThe GLAD satellite data (Potapov, Hansen et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) enabled the analysis of changes in land use and cover, such as forest height, cropland, urban areas, and wetlands, at a 30 m resolution in the BSE (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMangrove coverage was assessed with the World Mangrove Atlas, which provides global ecosystem distribution at under 100 m per pixel. Data from 1996, 2010, and 2020 were used to compare mangrove coverage and estimate net changes. Intertemporal land use and cover analysis assessed 11 categories (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Area changes in mangrove and forest ecosystems were analyzed separately in hectares (Appendix A, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\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\u003eDetails of the geospatial datasets used.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatellite Product\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003cp\u003e(years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResolution (m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal Land Analysis and Discovery (GLAD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLAD\u003c/p\u003e \u003cp\u003e(Potapov, Hansen et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorld Atlas of Mangroves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOcean Data Viewer\u003c/p\u003e \u003cp\u003e(Spalding M, Kainuma M et al. 2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003cp\u003e2010\u003c/p\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal Forest Watch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFW\u003c/p\u003e \u003cp\u003e(Potapov, Hansen et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAster Elevation Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASTGM V003\u003c/p\u003e \u003cp\u003e(NASA, METI et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaster\u003c/p\u003e \u003cp\u003e(.tiff)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEBCO V3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGEBCO\u003c/p\u003e \u003cp\u003e(GEBCO \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaster (.tiff)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpen Street Map\u003c/p\u003e \u003cp\u003eOSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVector (.shp)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvinces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational Geographic Institute\u003c/p\u003e \u003cp\u003e(IGN \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVector (.shp)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMunicipalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational Geographic Institute\u003c/p\u003e \u003cp\u003e(IGN \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVector (.shp)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtected Areas\u003c/p\u003e \u003cp\u003e(Marine and Terrestrial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarth Map\u003c/p\u003e \u003cp\u003e(Morales, D\u0026iacute;az et al.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVector (.shp)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: own elaboration\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data processing\u003c/h2\u003e \u003cp\u003eData processing followed the workflow summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. ArcGIS Pro 3.1.0, QGIS 3.28.3, and MS Excel were used for spatial processing, area estimation, transition matrices, and scenario construction. All spatial layers were projected to WGS 84 / UTM Zone 19N (EPSG:32619), clipped to the BSE boundary, resampled to a common 30 m spatial resolution when required, and reclassified into eleven LULC categories before intertemporal comparison. TerrSet's Land Change Modeler and CA-Markov modules were used to model LULC transitions from 2000 to 2020 and to project potential patterns to 2030.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCA\u0026ndash;Markov modeling combines a transition-probability matrix with a spatial allocation rule. The Markov component estimates the probability that each land cover class \u003cem\u003ei\u003c/em\u003e at time \u003cem\u003et\u003c/em\u003e will transition to class \u003cem\u003ej\u003c/em\u003e at time \u003cem\u003et\u0026thinsp;+\u0026thinsp;1\u003c/em\u003e (Selmy, Kucher et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Aghababaei, Ebrahimi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Farhan, Wu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Madhusudhan, Shivapur et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Slamet, Nababan et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Model performance was evaluated by comparing the simulated 2020 map against the observed 2020 map using K-standard, Kno, and Klocation statistics. The transition probability matrix is expressed as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\left[\\begin{array}{cccc}P11\u0026amp;\\:P12\u0026amp;\\:P..\u0026amp;\\:P1n\\\\\\:P21\u0026amp;\\:P22\u0026amp;\\:P..\u0026amp;\\:P2n\\\\\\:..\u0026amp;\\:..\u0026amp;\\::\u0026amp;\\:..\\\\\\:Pn1\u0026amp;\\:Pn2\u0026amp;\\:..\u0026amp;\\:Pnn\\end{array}\\right]\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:1$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{ij}\\)\u003c/span\u003e\u003c/span\u003eis the probability of transition from class \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003eto class \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e, and each row sums to one:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{cccc}\u0026amp;\\:\\sum\\:_{j=1}^{n}{p}_{ij}=1\\:\\:\\:\\:\\:\u0026amp;\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\u0026amp;\\:\\:\\:\\:\\:2\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe expected land-cover distribution at a future time can be estimated as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{cccc}\u0026amp;\\:{A}_{t+1}={A}_{t}P\u0026amp;\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:3\u0026amp;\\:\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{t}\\)\u003c/span\u003e\u003c/span\u003eis the vector of land cover areas at time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\)\u003c/span\u003e\u003c/span\u003eis the transition probability matrix, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{t+1}\\)\u003c/span\u003e\u003c/span\u003eis the expected land cover distribution at the next time step. The cellular automata component then allocates these transitions spatially by considering the current state of each cell, the transition probabilities, and the configuration of neighboring cells:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{cccc}\u0026amp;\\:{S}_{t+1}\\left(x\\right)=f\\left({S}_{t}\\left(x\\right),{N}_{t}\\left(x\\right),P\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:4\u0026amp;\\:\u0026amp;\\:\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{t}\\left(x\\right)\\)\u003c/span\u003e\u003c/span\u003eis the land-cover state of cell \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003eat time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{t}\\left(x\\right)\\)\u003c/span\u003e\u003c/span\u003erepresents the neighborhood configuration around cell \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\)\u003c/span\u003e\u003c/span\u003eis the Markov transition matrix, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\)\u003c/span\u003e\u003c/span\u003eis the spatial allocation function used to determine the future state of each cell. In this study, the CA\u0026ndash;Markov procedure was implemented in TerrSet using the 2000 and 2020 LULC maps to estimate transition probabilities and generate the 2030 projection.\u003c/p\u003e \u003cp\u003eThe other equation used is that of the cellular automata (CA) model, where S\u003csub\u003e(t,t+1)\u003c/sub\u003e represents the current and future state of the system, and N refers to the state of the neighboring cells. The function indicates that the future state of each cell depends on both the current state of the pixel being analyzed and the states of the cells near that pixel \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Analysis of mangroves\u003c/h2\u003e \u003cp\u003eMangrove extent was assessed for 1996, 2010, and 2020 using global mangrove datasets. These products are suitable for intertemporal extent analysis but do not provide information on species composition, canopy height, forest structure, or ecological condition. Therefore, results are interpreted as changes in spatial extent rather than direct measures of ecosystem health. Future analysis should integrate NDVI or other biophysical indices, LiDAR-derived structure, socio-economic drivers, and field validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Construction of restoration scenarios to 2030\u003c/h2\u003e \u003cp\u003eMangrove scenarios to 2030 were constructed from observed annual rates of change for 1996\u0026ndash;2020, 1996\u0026ndash;2010, and 2010\u0026ndash;2020. Three exploratory scenarios were estimated: (1) Average Gain, assuming continuation of the long-term net recovery rate, (2) Strong Gain, assuming a recovery trajectory similar to 1996\u0026ndash;2010, and (3) Continuous Loss, assuming persistence of the 2010\u0026ndash;2020 degradation rate. These scenarios should be interpreted as plausible trajectories for conservation planning rather than deterministic forecasts (see Appendix A).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Changes in land use and land cover (2000\u0026ndash;2020)\u003c/h2\u003e \u003cp\u003eBetween 2000 and 2020, the BSE landscape experienced substantial redistribution among LULC classes across 416,185 ha (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Tree Cover and Dense Short Vegetation remained the dominant classes, representing approximately 55%-57% and 25\u0026ndash;26% of the area, respectively. However, Tree Cover declined by 9,220.20 ha (-3.86%), while Dense Short Vegetation increased by 4,114.40 ha (+\u0026thinsp;3.88%), suggesting vegetation turnover and degradation or regeneration dynamics depending on location.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInter-temporal analysis of land use and cover in the BSE (2000\u0026ndash;2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCover class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNet change (ha)\u003c/p\u003e \u003cp\u003e2020\u0026thinsp;\u0026minus;\u0026thinsp;2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eVariation\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSemi-arid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51% vegetation cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDense short vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91% vegetation cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106,155.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.507%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e110,270.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.495%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,114.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15m trees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e238,901.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.403%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e229,681.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.187%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(9,220.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.86%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalt pan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3% vegetation cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0024%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland sparse vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27% vegetation cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0024%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e150.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland dense short vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91% vegetation cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32,035.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.6974%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27,190.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.533%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(4,844.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-15.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland tree cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15m trees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,320.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2005%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13,311.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.198%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(8.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen surface water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u0026ndash;69% of year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,132.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5124%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,302.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.553%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e170.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo info\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,442.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7494%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15,918.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.825%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,476.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo info\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,652.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7998%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16,938.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.070%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5,286.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.37%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo info\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e508.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1222%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e508.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.122%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e416,184.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100.0000%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e416,185.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e100.000%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.000165%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eSource: Own elaboration\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWetland Dense Short Vegetation declined by 4,844.64 ha (-15.12%), while Cropland and Built-up areas expanded by 4,476.03 ha (+\u0026thinsp;39.12%) and 5,286.71 ha (+\u0026thinsp;45.37%), respectively. These shifts indicate increasing pressure from agriculture, settlement expansion, and tourism-related infrastructure. Although the total mapped area remained constant, the internal redistribution of classes suggests growing fragmentation and potential loss of ecological connectivity and ecosystem services.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Prediction to 2030\u003c/h2\u003e \u003cp\u003eThe Markov Module estimated a transition probability matrix from 2000\u0026ndash;2020 spatial data, which was then used in the Land Change Modeler to project transitions to 2030. Crosstab analysis produced the matrix (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), while 11 conditional probability maps (one per class) show pixel transitions. The Markov Model assumes each state change depends only on the previous state (Takada, Miyamoto et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTransition Probability Matrix and Markov Module Details\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.0567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.2937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.0853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.8293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.0624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.1259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.0405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.3573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.5062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.1994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.0326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.0138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.7973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.1161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.0233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDetails of Markov Module\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c15\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFirst landcover image\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eLULC 2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c15\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSecond landcover image\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eLULC 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c15\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTime interval 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c15\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTime interval 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c15\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eProportional error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c15\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Own elaboration based on the Markov module in Terrset 2020\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicates that Tree Cover and Dense Short Vegetation are highly stable (83.06% and 82.93%), due to protected areas. In contrast, Salt pans and wetlands with scattered vegetation show lower stability (35.73% and 43.35%), reflecting greater change. Semi-arid vegetation has a 29.37% chance of turning into open surface waters, while wetlands with sparse vegetation have a 43.22% likelihood. These transitions are spatially consistent with areas undergoing agricultural expansion, settlement growth, and vegetation conversion.\u003c/p\u003e \u003cp\u003eThe 2030 projection indicates continued expansion of anthropogenic land uses and additional pressure on vegetation and wetland classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The model suggests further growth of cropland and built-up areas, while natural and semi-natural classes are expected to experience spatial redistribution. Because the projection is based on observed 2000\u0026ndash;2020 transitions, it should be read as a business-as-usual scenario that does not explicitly include future policy shifts, climate shocks, or restoration interventions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe projected decline of natural vegetation classes would reduce carbon storage, habitat continuity, and regulating services, while projected gains in built-up and cropland areas would intensify pressure on wetlands and mangrove-adjacent landscapes. This reinforces the need to interpret LULC projections together with spatial planning and conservation policies rather than as purely ecological trends.\u003c/p\u003e \u003cp\u003eModel validation showed acceptable performance for exploratory spatial planning. The comparison between the simulated and observed 2020 maps produced K-standard\u0026thinsp;=\u0026thinsp;0.7226, Kno\u0026thinsp;=\u0026thinsp;0.8443, and Klocation\u0026thinsp;=\u0026thinsp;0.9572, indicating moderate-to-high overall agreement, strong quantity agreement, and high spatial allocation accuracy. Quantity disagreement was 12.61%, whereas location disagreement was 1.66%, suggesting that the model is more dependable for spatial allocation than for exact quantity estimates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Mangrove-specific analysis (1996\u0026ndash;2020)\u003c/h2\u003e \u003cp\u003eMangrove cover increased by 0.53% from 1996 to 2020, with most growth (1.19%) occurring between 1996 and 2010 due to wetland recovery and sedimentation. From 2010 to 2020, mangroves declined by 42.67 ha, mostly because of agriculture, development, erosion, and flooding. Overall, there was a net annual gain of 1.42 ha (0.022%), with 563.21 ha gained and 529.08 ha lost (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMangrove loss accelerated to -4.28 ha/year over the last decade, highlighting growing human impacts that need to be addressed in conservation policy. Bajo Yuna Mangrove National Park (Zone 1) saw the largest gains, due to reduced human pressure (Fig.\u0026nbsp;7).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e \u003cem\u003e1 indicates losses and 1 indicates gains\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;7\u003c/b\u003e Spatial distribution of mangrove gains and losses in the BSE between 1996 and 2020. Values indicate loss (-1), no change (0), and gain (1). Insets show areas with higher concentration of mangrove change across the study area. Source: Own elaboration from ArcGIS Pro Raster Calculator\u003c/p\u003e \u003cp\u003eMiches, a municipality experiencing rapid tourism growth, has the largest mangrove loss in the study area, though its annual degradation rate is lower than prior estimates. Globally, 3.42% of mangroves were lost from 2000 to 2020, with an annual rate of 0.17% (Hamilton and Casey \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Myanmar, a deforestation hotspot, saw annual losses of over 3.6% between 1996 and 2016 (De Alban, Jamaludin et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Malaysia\u0026rsquo;s Iskandar region reported 1.12% annual degradation due to urbanization and agriculture between 2000 and 2019 (Kanniah, Kang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On Vietnam's southern coast, efforts reduced average annual mangrove loss from 3.6% (1998\u0026ndash;2011) to 1.5% (2011\u0026ndash;2023) (Tran, Reef et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross the Caribbean, mangroves are declining by about 1% annually, with mainland areas seeing up to 1.7% per year (Cort\u0026eacute;s, Lorenzo-Trueba et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Tropical Northwestern Atlantic province experienced a 5.4% net decrease since 1996, with projections indicating further losses under climate change scenarios (Blanco-Libreros \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Troche, Lugo et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Some regional reports estimate recent declines exceeding 30% (Rull \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and under severe sea-level rise, up to 75.9% could be submerged by 2060 (Troche, Lugo et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Mexico\u0026rsquo;s Mahahual-Xcalak, annual degradation was 0.85% from 1995\u0026ndash;2007, mainly due to urban and infrastructure expansion (Hirales-Cota, Espinoza-Avalos et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Drivers of mangrove degradation during the period 1996\u0026ndash;2020\u003c/h2\u003e \u003cp\u003eDespite a net growth rate of 0.53% over 24 years, approximately 43 hectares of mangrove were lost in the last decade, driven by shifting economic activities and policy gaps (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e10\u003c/span\u003e, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain drivers of mangroves degradation in BSE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNet change (Ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnthropogenic drivers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNatural drivers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1996\u0026ndash;2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;76.81 Ha (1.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMangroves expansion due favorable conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHabitat restauration process\u003c/p\u003e \u003cp\u003eConservation of protected areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncreased sedimentation\u003c/p\u003e \u003cp\u003eFavorable climate conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-42.67 Ha\u003c/p\u003e \u003cp\u003e(-0.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCover Loss due anthropogenic conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeforestation and Coastal Urbanization\u003c/p\u003e \u003cp\u003eMass tourism\u003c/p\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHurricanes\u003c/p\u003e \u003cp\u003eClimate Change: Sea level rise\u003c/p\u003e \u003cp\u003eCoastal erosion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1996\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;34.14 Ha (0.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBalance between expansion and degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConservation Policies\u003c/p\u003e \u003cp\u003eGrowth of unregulated economic activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClimate change: Extreme events\u003c/p\u003e \u003cp\u003eMarine pollution: Changes in salinity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: own elaboration based on literature revision\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDespite a net increase in mangrove extent over 1996\u0026ndash;2020, the loss of approximately 43 ha during 2010\u0026ndash;2020 indicates that recent pressures have altered the trajectory of the system. The observed pattern suggests a period of conditional resilience followed by localized decline, shaped by the interaction between sediment dynamics, protected-area management, and expanding economic activities (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e10\u003c/span\u003e and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e11\u003c/span\u003e). During 1996\u0026ndash;2010, national environmental reforms, including Law 64\u0026thinsp;\u0026minus;\u0026thinsp;00 and the strengthening of the protected-area system, may have contributed to conservation outcomes in Bajo Yuna and Los Haitises, while favorable sedimentary conditions supported mangrove expansion. By contrast, the 2010\u0026ndash;2020 period coincided with accelerated tourism development, urban expansion, and agricultural pressure, particularly in coastal municipalities experiencing land-market transformation.\u003c/p\u003e \u003cp\u003eThese dynamics reflect a broader governance challenge. Mangroves generate public benefits, including shoreline protection, carbon storage, biodiversity conservation, and fishery support, yet many of these benefits are not internalized in land-use and tourism decisions. As a result, short-term private returns from real-estate development, mass tourism, and agricultural conversion can be prioritized over long-term ecosystem services. This creates social and ecological costs, including increased exposure to coastal hazards, biodiversity loss, reduced water quality, and diminished climate-regulation benefits (Hirales-Cota, Espinoza-Avalos et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Garc\u0026eacute;s-Ord\u0026oacute;\u0026ntilde;ez, R\u0026iacute;os-M\u0026aacute;rmol et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAddressing these trade-offs requires an integrated policy response. Enforceable coastal zoning should identify non-build areas in critical mangrove belts and link setbacks to hazard exposure and blue-carbon priorities. Tourism regulation should include density controls, cumulative-impact assessment, environmental licensing, and no-net-loss requirements for mangrove conversion. Economic instruments, including payments for ecosystem services and blue-carbon finance, could help align local development incentives with conservation outcomes (Lau \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Finally, restoration should prioritize hydrological reconnection, including tidal-channel reopening, barrier removal, and site-appropriate planting where natural regeneration is insufficient (Simpson, Mercer Clarke et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Moschetto, Ribeiro et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Restoration scenarios to 2030: mangroves of the Bay of Samana and their surroundings\u003c/h2\u003e \u003cp\u003eFollowing the methodology described, mangrove restoration scenarios were created using estimated annual recovery rates (see Appendix A). The results show that the three projected scenarios differ based on conservation and restoration trends.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the average gain scenario, mangroves would grow slowly to 6,499.75 ha by 2030 at 1.43 ha per year. The strong gain scenario projects faster restoration, reaching 6,583.56 ha with an annual increase of 9.81 ha. Conversely, continuous loss would shrink coverage to 6,443.08 ha, declining by 4.24 ha annually.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study examined Land Use and Land Cover (LULC) changes in the Saman\u0026aacute; Bay socio-ecological system from 2000 to 2020 and projected potential trajectories to 2030, with a specific focus on mangrove ecosystems. The findings provide three main conclusions for coastal conservation and planning in Caribbean SIDS.\u003c/p\u003e \u003cp\u003eFirst, the BSE exhibits conditional mangrove resilience with a recent localized decline. Mangroves showed a long-term net increase of +\u0026thinsp;0.53% between 1996 and 2020, with the strongest expansion during 1996\u0026ndash;2010 (+\u0026thinsp;1.19%), linked to favorable sedimentation and conservation policies. However, 2010\u0026ndash;2020 recorded a loss of -42.67 ha, driven by agriculture, hotel development, urbanization, erosion, and flooding. This shows that mangrove resilience is not indefinite; once anthropogenic pressure exceeds ecological and governance thresholds, recovery becomes less certain.\u003c/p\u003e \u003cp\u003eSecond, the 2030 scenarios reveal divergent futures shaped by trade-offs. Continued development without conservation measures would sustain mangrove decline, whereas targeted interventions, including coastal zoning, enforcement of setbacks, environmental licensing, and hydrology-first restoration, could stabilize or increase mangrove cover. The trade-off is clear: short-term private benefits from tourism and real-estate development can undermine long-term public benefits such as shoreline protection, carbon storage, fisheries productivity, and cultural values.\u003c/p\u003e \u003cp\u003eThird, land-sea interdependence is a central governance challenge. A ridge-to-reef perspective shows how upland agriculture, deforestation, sediment dynamics, and coastal construction cascade into mangrove degradation. Mangrove conservation in Saman\u0026aacute; Bay, therefore, cannot be addressed as an isolated issue within a protected area. It requires integrated governance that manages terrestrial, coastal, and marine pressures simultaneously.\u003c/p\u003e \u003cp\u003eFour policy implications follow from these results. Tourism and land-use regulation should strengthen coastal zoning, restrict construction in mangrove belts, enforce setbacks, and require cumulative-impact assessment for hotel and real-estate projects. Restoration strategies should prioritize community participation and hydrological reconnection through tidal-channel reopening and barrier removal. Monitoring systems should combine remote sensing, NDVI or other biophysical indicators, LiDAR where available, and ground validation to move beyond area-based assessments. Finally, economic incentives such as payments for ecosystem services and blue-carbon finance under Law 44\u0026thinsp;\u0026minus;\u0026thinsp;18 should be expanded to align local development incentives with ecosystem stewardship.\u003c/p\u003e \u003cp\u003eOverall, the results suggest that future mangrove trajectories in Saman\u0026aacute; Bay will be strongly shaped by governance choices, particularly the capacity to regulate tourism-led land conversion, restore hydrological connectivity, and align local economic incentives with conservation. For the Dominican Republic and other Caribbean SIDS, reconciling tourism-led economic growth with coastal ecosystem conservation is urgent, feasible, and essential for long-term sustainability.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Limitations\u003c/h2\u003e \u003cp\u003eThis study has methodological and data limitations. The LULC analysis relies on available global datasets and selected temporal snapshots, which may affect transition probability estimates and reduce sensitivity to small changes or highly heterogeneous coastal areas. The CA-Markov model simplifies complex processes by estimating future change from previous states and does not explicitly incorporate future climate shocks, policy interventions, market shifts, or abrupt infrastructure development.\u003c/p\u003e \u003cp\u003eMangrove analysis was based on changes in spatial extent and did not assess ecosystem condition, species composition, canopy height, biomass, or forest health. Therefore, apparent gains in area should not be interpreted automatically as ecological recovery. Future research should integrate field validation, higher-resolution imagery, LiDAR, NDVI or other vegetation indices, socio-economic drivers, and ecosystem service indicators such as fisheries, recreation, flood protection, and blue carbon. These improvements would reduce uncertainty and strengthen the use of spatial models for coastal zoning and mangrove conservation in the BSE.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eSupplementary Materials:\u003c/h2\u003e \u003cp\u003eAppendix A provides data analysis supporting mangrove change and scenario calculations. Appendix B provides the CA\u0026ndash;Markov model validation outputs.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by the National Fund for Scientific and Technological Research (FONDOCYT) of the Ministry of Higher Education, Science and Technology of the Dominican Republic, grant number 2022-2B5-162.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.C and V.G.V : conceptualization, methodology, formal analysis, investigation, data curation, visualization, writing\u0026mdash;original draft, writing\u0026mdash;review and editing. K.R and S.B.D: writing\u0026mdash;review and editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe geospatial datasets used in this study are publicly available from the sources listed in Table 1 in the manuscript. The processed database, transition matrices, and generated spatial layers are available from the corresponding author upon reasonable request and will be deposited in an appropriate repository upon acceptance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAghababaei M, Ebrahimi A, Naghipour AA, Asadi E, Verrelst J (2024) Monitoring of Plant Ecological Units Cover Dynamics in a Semiarid Landscape from Past to Future Using Multi-Layer Perceptron and Markov Chain Model. Remote Sens 16(9)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvishek K, Yu X, Liu J (2012) Ecosystem management in Asia Pacific: Bridging science\u0026ndash;policy gap. Environ Dev 3:77\u0026ndash;90\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarlow J (1997) A report of Cetacean Acoustic Detection and Dive Interval Studies (CADDIS) conducted in the southern Gulf of California, 1995\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBautista de los Santos Q, Melina (2014) Determinaci\u0026oacute;n de los Caudales Ambientales de la Cuenca del r\u0026iacute;o Yuna. Rep\u0026uacute;blica Dominicana Tecnolog\u0026iacute;a y Ciencia del Agua 5(6):33\u0026ndash;40\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanco-Libreros J (2016) Cambios globales en los manglares del golfo de Urab\u0026aacute; (Colombia): entre la cambiante l\u0026iacute;nea costera y la frontera agropecuaria en expansi\u0026oacute;n. Actualidades Biol\u0026oacute;gicas 38\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanco-Libreros JF, Ram\u0026iacute;rez-Ruiz K (2021) Threatened Mangroves in the Anthropocene: Habitat Fragmentation in Urban Coastalscapes of Pelliciera spp. (Tetrameristaceae) in Northern South America. Front Mar Sci 8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBunting P, Rosenqvist A, Hilarides L, Lucas RM, Thomas N, Tadono T, Worthington TA, Spalding M, Murray NJ, Rebelo L-M (2022) Global Mangrove Extent Change 1996\u0026ndash;2020: Global Mangrove Watch Version 3.0. Remote Sensing 14(15)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBunting P, Rosenqvist A, Lucas RM, Rebelo L-M, Hilarides L, Thomas N, Hardy A, Itoh T, Shimada M, Finlayson CM (2018) The Global Mangrove Watch\u0026mdash;A New 2010 Global Baseline of Mangrove Extent. Remote Sens 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/rs10101669\u003c/span\u003e\u003cspan address=\"10.3390/rs10101669\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlson RR, Foo SA, Asner GP (2019) Land Use Impacts on Coral Reef Health: A Ridge-to-Reef Perspective. Front Mar Sci 6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChopade MR, Mahajan S, Chaube N (2023) Assessment of land use, land cover change in the mangrove forest of Ghogha area, Gulf of Khambhat. Gujarat Expert Syst Appl 212:118839\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCort\u0026eacute;s IM, Lorenzo-Trueba J, Rovai AS, Twilley RR, Chopping M, Fatoyinbo T (2024) Net evaporation-induced mangrove area loss across low-lying Caribbean islands. Environ Research: Clim 3(4):045004\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Alban JDT, Jamaludin J, Wong De Wen D, Than MM, Webb EL (2020) Improved estimates of mangrove cover and change reveal catastrophic deforestation in Myanmar. Environ Res Lett 15(3)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelanoy, R., M. D\u0026iacute;az-Asencio and R. M\u0026eacute;ndez-Tejeda (2020). Sedimentation in the Bay of Saman\u0026aacute;, Dominican Republic (1900\u0026ndash;2016). AIMS Geosciences 6(3): 298\u0026ndash;315\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelanoy RA, D\u0026iacute;az-Asencio M, M\u0026eacute;ndez-Tejeda R (2019) Effect of Extreme Weather Events on the Sedimentation of the Bay of Saman\u0026aacute;, Dominican Republic (1900\u0026ndash;2016). J Geogr Geol 11(3)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFailler P, P\u0026egrave;tre \u0026Eacute;, Binet T, Mar\u0026eacute;chal JP (2015) Valuation of marine and coastal ecosystem services as a tool for conservation: The case of Martinique in the Caribbean. Ecosyst Serv 11:67\u0026ndash;75\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarhan M, Wu T, Anwar S, Yang J, Naqvi SAA, Soufan W, Tariq A (2024) Predicting Land Use Land Cover Dynamics and Land Surface Temperature Changes Using CA-Markov-Chain Models in Islamabad, Pakistan (1992\u0026ndash;2042). IEEE J Sel Top Appl Earth Observations Remote Sensing:1\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrancisco P, Pino; and, Jaime N (2014) Barber Turismo y desarrollo sostenible en la provincia de Saman\u0026aacute;, Rep\u0026uacute;blica Dominicana. Investigaci\u0026oacute;n Tur\u0026iacute;sticas, Centro Ecuatoriano de Derecho Ambiental (CEDA), Universitat Polit\u0026eacute;cnica de Val\u0026egrave;ncia\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026eacute;s-Ord\u0026oacute;\u0026ntilde;ez O, R\u0026iacute;os-M\u0026aacute;rmol M, Vivas-Aguas LJ, Espinosa-D\u0026iacute;az LF, Romero-D\u0026rsquo;Achiardi D, Canals M (2023) Degradation factors and their environmental impacts on the mangrove ecosystem of the Mallorquin Lagoon, Colombian Caribbean. Wetlands 43(7)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGEBCO (2024) Compilation Group\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHakim MA, Lubis DP, Harefa MS, Damanik MRS, Suciani A (2022) Analysis Changes in Mangrove Forest Cover Using Multi-Sensor Image in North Luwu District South Sulawesi 2015\u0026ndash;2020. Tunas Geografi 11(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton SE, Casey D (2016) Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC‐21). Glob Ecol Biogeogr 25(6):729\u0026ndash;738\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirales-Cota M, Espinoza-Avalos J, Schmook B, Ruiz-Luna A, Ramos-Reyes R (2010) Drivers of mangrove deforestation in Mahahual-Xcalak, Quintana Roo, southeast Mexico. Ciencias Marinas 36(2):147\u0026ndash;159\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIdris NS, Mustapha MA, Sulaiman N, Khamis S, Husin SM, Darbis NDA (2021) The dynamics of landscape changes surrounding a firefly ecotourism area. Global Ecol Conserv 29:e01741\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIGN (2024) Datos Geogr\u0026aacute;ficos Fundamentales. from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ign.gob.do/\u003c/span\u003e\u003cspan address=\"https://ign.gob.do/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanniah KD, Kang CS, Sharma S, Aldrie Amir A (2021) Remote sensing to study mangrove fragmentation and its impacts on leaf area index and gross primary productivity in the south of peninsular malaysia. Remote Sens 13(8)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKauffman JB, Heider C, Norfolk J, Payton F (2014) Carbon stocks of intact mangroves and carbon emissions arising from their conversion in the Dominican Republic. Ecol Appl 24(3):518\u0026ndash;527\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumari P, Singh JK, Pathak B Chapter 1 - Potential contribution of multifunctional mangrove resources and its conservation. Biotechnological Utilization of Mangrove Resources., Patra JK, Mishra RR, Thatoi H (2020) Academic Press: 1\u0026ndash;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKutir C, Agblorti SKM, Campion BB (2022) Migration and Estuarine Land Use/Land Cover (LULC) Change along Ghana\u0026rsquo;s Coast. Reg Stud Mar Sci 54:102488\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaba M, Smith SD, Degloria SD (1997) Landsat-based land cover mapping in the lower Yuna River watershed in the Dominican Republic. Int J Remote Sens 18(14):3011\u0026ndash;3025\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLau WWY (2013) Beyond carbon: Conceptualizing payments for ecosystem services in blue forests on carbon and other marine and coastal ecosystem services. Ocean Coastal Manage 83:5\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeslie F, New; Alisa J, Hall S (2015) The modelling and assessment of whale-watching impacts. Ocean \u0026amp; Coastal Management 115: 16\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadhusudhan MS, Shivapur AV, Surendra JH (2024) Forecasting Land Use and Land Cover Changes in the Malaprabha Right Bank Canal Command Area through Cellular Automata and Markov Chain Modeling. Ecol Eng Environ Technol 25(6):54\u0026ndash;65\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartino S, Tett P, Kenter J (2019) The interplay between economics, legislative power and social influence examined through a social-ecological framework for marine ecosystems services. Sci Total Environ 651:1388\u0026ndash;1404\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoity N, Delgado B, Salinas-De-Le\u0026oacute;n P (2019) Mangroves in the Galapagos islands: Distribution and dynamics. PLoS ONE 14(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorales C, D\u0026iacute;az AS-P, Dionisio D, Guarnieri L, Marchi G, Maniatis D and D. Mollicone Earth Map: A Novel Tool for Fast Performance of Advanced Land Monitoring and Climate Assessment. J Remote Sens 3: 0003\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoschetto FA, Ribeiro RB, De Freitas DM (2021) Urban expansion, regeneration and socioenvironmental vulnerability in a mangrove ecosystem at the southeast coastal of S\u0026atilde;o Paulo. Brazil Ocean Coastal Manage 200:105418\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNASA METI, Japan Spacesystems AIST and U.S and, Team JAS (2019) ASTER Global Digital Elevation Model V003. N. E. L. P. D. A. A. C. A. 2024-11-05\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen H-H (2014) The relation of coastal mangrove changes and adjacent land-use: A review in Southeast Asia and Kien Giang. Vietnam Ocean Coastal Manage 90:1\u0026ndash;10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eONE (2020) Estimaciones y Proyecciones Nacionales de Poblacion 1950\u0026ndash;2100, 2014. Oficina Nacional de Estad\u0026iacute;stica (ONE)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eONE A (2023) MITUR Cuadro 3.09-03 REP\u0026Uacute;BLICA DOMINICANA: Establecimientos y habitaciones de alojamiento tur\u0026iacute;stico por provincia y municipio seg\u0026uacute;n a\u0026ntilde;os,2004\u0026ndash;2023*\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePotapov P, Hansen MC, Pickens A, Hernandez-Serna A, Tyukavina A, Turubanova S, Zalles V, Li X, Khan A, Stolle F, Harris N, Song X-P, Baggett A, Kommareddy I, Kommareddy A (2022) The Global 2000\u0026ndash;2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results. Frontiers in Remote Sensing 3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuiz de Gauna I, Gre\u0026ntilde;o F, Torres X, Galarraga I (2024) Economic valuation of ecosystem services provided by blue economy ecosystems in the Dominican Republic\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRull V (2023) Rise and Fall of Caribbean Mangroves\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelmy SA, H. DE, Kucher G, Mozgeris ARA, Moursy R, Jimenez-Ballesta OD, Kucher ME, Fadl, Mustafa ARA (2023) Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques. Remote Sens 15(23).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimpson M, Mercer Clarke CSL, Clarke JD, Scott D, Clarke AJ (2012) Coastal Setbacks in Latin America and the Caribbean: A Study of Emerging Issues and Trends that Inform. Guidelines for Coastal Planning and Development\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlamet B, Nababan AM, Anggraini N (2024) Prediction of land cover change in the Belawan watershed using the cellular automata-markov chain model. IOP Conference Series: Earth and Environmental Science 1352(1): 012050\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoanes LM, Pike S, Armstrong S, Creque K, Norris-Gumbs R, Zaluski S, Medcalf K (2021) Reducing the vulnerability of coastal communities in the Caribbean through sustainable mangrove management. Ocean Coastal Manage 210:105702\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpalding M, Kainuma M (2010) and C. L World Atlas of Mangroves (version 3.1). A collaborative project of ITTO, ISME, FAO, UNEP-WCMC, UNESCO-MAB, UNU-INWEH and TNC\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStephenson TS, Jones JJ (2017) Impacts of climate change on extreme events in the coastal and marine environments of Caribbean Small Island Developing States (SIDS). Science Review, Caribbean Climate Change Report Card, pp 10\u0026ndash;22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakada T, Miyamoto A, Hasegawa SF (2010) Derivation of a yearly transition probability matrix for land-use dynamics and its applications. Landscape Ecol 25:561\u0026ndash;572\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTran TV, Reef R, Zhu X (2024) Long-term changes of mangrove distribution and its response to anthropogenic impacts in the Vietnamese Southern Coastal Region. J Environ Manage 370\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTroche C, Lugo A, Heartsill-Scalley T, L\u0026oacute;pez-Portillo J, Vel\u0026aacute;zquez-Salazar S, Fraiz-Toma A, Alc\u0026aacute;ntara JA, Maya E, Villeda-Ch\u0026aacute;vez B, Vazquez L, Valderrama M, Rodr\u0026iacute;guez-Z\u0026uacute;\u0026ntilde;iga J, Blanco-Libreros Y, Cruz O, Reyes Dominguez L, Corrales A, Lara D, Castillo J, Rangel, Suarez (2024) IUCN Red List of Ecosystems, Mangroves of the Tropical Northwestern Atlantic\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZamboni NS, Noleto Filho EM, Carvalho AR (2021) Unfolding differences in the distribution of coastal marine ecosystem services values among developed and developing countries. Ecological Economics 189\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mangrove conservation, land use change, CA-Markov modeling, coastal ecosystem services, Small Island Developing States, ridge-to-reef governance","lastPublishedDoi":"10.21203/rs.3.rs-9526838/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9526838/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study examines Land Use and Land Cover (LULC) dynamics in the Saman\u0026aacute; Bay socio-ecological system and asks what recent landscape changes reveal about mangrove resilience and vulnerability in a tourism coastal economy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRemote sensing products, GIS analysis, and CA\u0026ndash;Markov modeling were integrated to assess LULC transitions from 2000 to 2020, quantify mangrove extent change from 1996 to 2020, and construct exploratory mangrove scenarios to 2030.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe wider landscape experienced substantial redistribution of cover classes, with built-up and cropland areas increasing by 45.37% and 39.12%, respectively, while tree cover declined by 9,220 ha. Mangroves showed a long-term net gain of 0.53% from 1996 to 2020, driven by expansion during 1996\u0026ndash;2010 (+\u0026thinsp;1.19%). However, 2010\u0026ndash;2020 saw localized losses totaling 42.67 ha, attributed to agriculture, urban development, tourism infrastructure, erosion, and flooding. By 2030, scenarios range from continued decline to 6,443.08 ha under a loss trajectory to recovery up to 6,583.56 ha under a strong-gain trajectory.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eFindings indicate conditional resilience because Saman\u0026aacute; Bay mangroves can recover under favorable sedimentary and governance conditions, but continued tourism-driven development may push the system towards ecological thresholds. Integrated ridge-to-reef governance, coastal zoning, hydrology first restoration, and economic incentives are needed to reconcile local development with long-term coastal conservation.\u003c/p\u003e","manuscriptTitle":"Mangrove resilience under tourism-driven land use change in a Caribbean SIDS: evidence from Samaná Bay, Dominican Republic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 10:36:14","doi":"10.21203/rs.3.rs-9526838/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a2ce459-2c66-464e-963e-6277d81dbedf","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"14","date":"2026-05-01T07:00:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-01T05:39:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-01T05:39:12+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T10:36:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 10:36:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9526838","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9526838","identity":"rs-9526838","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}