Characteristics of Mangrove Forests in São Tomé and Príncipe and The Potential for Restoration

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Here, we present a detailed analysis of the status and conditions of mangroves located in the Republic of São Tomé and Príncipe (STP). Mapping of mangroves was carried out using remotely sensed data and GIS. This was complemented by detailed ground truthing, GPS mapping, and community appraisals in all 19 mangrove sites in STP. Sampling was conducted in 24 square plots of 100 m 2 that were randomly distributed along belt transects established perpendicular to the waterline. Within each plot, all trees with a stem diameter ≥ 2.5 cm were identified, counted, and position marked. Data on tree height (m), stem diameter (cm), and canopy cover (%) were collected, from which basal area (m 2 ha − 1 ), stocking rates (stems ha − 1 ), and biomass carbon (Mg C ha − 1 ) were derived. Mangrove forests in STP are estimated to cover approximately 180 ha, with 97.1% of these occurring on São Tomé Island and the rest on the Island of Príncipe. There are six mangrove species in STP; dominated by Rhizophora racemosa G. Mey., R. mangle L., and Avicennia germinans (L.) L.. The stocking rates of mangroves in STP ranged from 400 to 2,880 stems ha⁻¹ (mean: 2,170.0 ± 366.0 stems ha⁻¹) with a basal area of 25.0 ± 3.9 m 2 ha⁻¹ (range: 1.5 to 30.2 m 2 ha⁻¹), and standing biomass of 240.1 ± 38.3 t ha⁻¹ (range: 11.1 to 315 t ha⁻¹). Together with below-ground biomass, mangroves in STP have a biomass carbon of 157.9 ± 24.9 Mg C ha⁻¹. Assuming a sediment carbon of 780.2 Mg C ha − 1 for Central African mangroves, the total ecosystem carbon of mangroves in STP is estimated at 938.1 Mg C ha − 1 (range: 701.1 to 1,123.2 Mg C ha − 1 ). Localized overexploitation of mangrove wood products for firewood and tannin extraction was witnessed in peri-urban mangrove sites at Praia das Conchas, Água Ize and Diogo Nunes, where natural regeneration was inadequate to support forest recovery. Other important threats to the mangroves were coastal development and waste disposal. These findings revealed spatial variation in mangrove distribution across STP, as well as identifying sites for targeted restorations. The study provides baseline data and information for exploring nature-based enterprises, including mangrove ecotourism and blue carbon. Mangrove forests blue carbon restoration potential and São Tomé and Príncipe Figures Figure 1 Figure 2 Figure 3 Introduction Mangroves are among the most productive and resilient coastal ecosystems on earth, providing habitat for fish and other wildlife, protecting shoreline from erosion, and regulating climate [ 1 – 3 ]. They form the interface between land and sea in tropical and subtropical regions [ 4 ] and are recognized for their capacity to deliver a wide range of ecosystem services that support local, national, and global economies [ 5 ]. Despite their relatively small global area, estimated at 13.7 million ha [ 6 ], mangroves capture and store huge stocks of carbon in both above- and below-ground components [ 7 ]. Their blue carbon values have drawn increased international attention, and mangrove countries have incorporated mangroves in their development and climate change agenda to the Paris Agreement [ 8 – 11 ]. In São Tomé and Príncipe (STP), mangroves are known to cover between 100 and 136 ha [ 12 ]. While they are relatively small in area and often found in the inner basins, mangroves in STP provide critical ecosystem services, including shoreline stabilization, fish spawning and feeding grounds, and climate regulation [ 13 ]. However, like elsewhere around the world, mangroves in STP are threatened by human and natural stressors, including over-exploitation of resources, conversion pressure, pollution effects, and climate change. Losses and degradation of mangroves adversely affect local biodiversity and fisheries, as well as the stability of shorelines. Protection and conservation of forests, such as mangroves, requires a clear understanding of what constitutes ecosystem health. Describing ecosystem health is fundamental in setting protection and conservation goals [ 14 ]. Until recently, there were limited field-based studies on the structure and distribution of mangrove forests in STP [ 12 ]. While resource mapping and coastal risk assessments were conducted under the West Africa Coastal Areas Management Program (WACA+), these lacked site-level ground-truthing and socio-ecological integration. Consequently, there was a need for empirical data to guide ecosystem restoration, support blue carbon initiatives, and design community-inclusive mangrove conservation programs. To our understanding, this was the first study to undertake a detailed account of mangrove forests in STP in terms of species composition, forest structure, environmental settings, degradation drivers, and socio-economic interactions. The results will inform the development plan for mangroves in STP and sites for targeted restoration. Materials and methods Study area São Tomé and Príncipe is a small island developing state (SIDS) located in the Gulf of Guinea, off the western equatorial coast of Central Africa (Fig. 1 ). It consists of two main islands: São Tomé and Príncipe, separated by approximately 182 km, along with several smaller islets such as the Tinhosa Islands, Ilhéu das Cabras, and Ilhéu das Rolas. The country lies across the equator and features a tropical climate characterized by high temperatures and humidity year-round. The annual rainfall in STP ranges from 2,000 mm to over 7,000 mm, with long rains occurring from September to May, followed by relatively dry seasons from June to August. These climatic conditions are conducive to mangroves; however, geomorphic constraints limit the expansion of these forests in the two islands. For instance, STP experiences semidiurnal micro-tidal conditions of 0.30–1.80 m that reduce the frequency and extension of tidal inundation, thus influencing coastal and estuarine environments, including mangrove systems. Mangrove cover To understand the occurrence and distribution of mangroves in São Tomé and Principe, an unsupervised classification of the area of interest was first performed to delineate distinct spectral classes, which guided initial field validation efforts. Sentinel imagery was accessed through Google Earth Engine (GEE), and key vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were calculated to highlight vegetated areas and distinguish mangroves from other plant types. Training samples from known mangrove and non-mangrove locations were collected, and supervised classification algorithms, specifically Random Forest and Support Vector Machine (SVM), were applied to categorize the imagery. The classification outputs were validated against detailed ground-truthing data, the Global Mangrove Watch (GMW) dataset, and high-resolution imagery from Airbus and Maxar Technologies, with overall accuracy and the kappa coefficient computed to quantify performance. To enhance map quality, spatial filters (majority filtering and edge smoothing) were applied to reduce noise. Subsequently, on-screen digitization of mangrove patch boundaries was conducted in ArcMap by visually tracing each patch on very-high-resolution imagery. Through photo-interpretation, individual polygons were manually delineated based on tonality, texture, shape, and proximity to water bodies. Mangroves typically exhibit a range of green hues, from pale to medium, depending on the image season, that contrast with the dark brown or mixed light green/brown tones of surrounding vegetation. Their characteristic sinuous perimeters with numerous inlets and proximity to water features further guided manual mapping. Potential areas of mangrove restoration were delineated based on a set of biophysical and ecological criteria. These included soil type, hydrological conditions (regular tidal flushing and water exchange), historical presence of mangroves as indicated by old stumps and local knowledge, and absence of current land-use conflicts such as settlements or infrastructure. Areas with degraded vegetation, open mangrove areas adjacent to healthy stands, and locations showing natural regeneration potential were prioritized. This combination of qualitative indicators ensured that only sites with favorable ecological conditions and restoration feasibility were classified as potentially restorable. Survey of mangrove forest structure Random plots of 100 m 2 were used for the study. The number of plots varied across sites and was dependent on the extent of mangroves in the area (Table 1 ). In each plot, all trees with stem diameter at breast height (DBH) ≥ 2.5 cm were identified, counted, and their positions marked. The following parameters were collected: tree height (m), stem diameter at breast height (DBH) (cm), number of live/dead trees, and quality of the lead stem (categorized as either straight, semi-straight or crooked). From these data, the following vegetation attributes were derived: basal area (m 2 ha − 1 ), stocking rates (stems ha − 1 ), and standing biomass (t ha − 1 ). Vegetation cover (%) was estimated from the area of the ground that one would see if flying above the tree canopy [ 15 ]. Tree heights were estimated using a graduated pole, while stem diameters were measured at 130 cm above ground, using a diameter tape. In the case of Rhizophora spp., stem diameter was taken 30 cm above the highest prop root [ 16 ]. For the forked stems below 130 cm, individual branches in a clump were treated as separate stems. Patterns and conditions of natural recruitment were assessed using linear regeneration sampling, whose applications could be found in Kairo [ 17 ]. Data analysis Graphical presentations and descriptive analysis of structural data helped to understand variations of mangroves across different sites in STP. The complexity index value for each district was determined using the approach in Holdridge et al. [ 18 ], which incorporates species composition, basal area, mean tree height, and stem density: Complexity Index (CI) = Number of species x basal area (m 2 ha − 1 ) x mean tree height (m) x stem density (stems ha − 1 ) x 10 − 5 Eq. 1 Biomass carbon was estimated using the general allometric equations of Komiyama et al. [ 16 , 19 ]: \(\:Aboveground\:Biomass\:\left(kg\right)=0.251\rho\:D\:2.46\:\) Eq. 2 \(\:Belowground\:Biomass\:\left(kg\right)=0.199{\rho\:}^{0.899}\times\:{D}^{2.22}\) Eq. 3 Where D = tree diameter at breast height (cm) and ⍴ = wood density (g cm − 3 ) Species-specific wood densities for mangroves, as reported by Howard et al. [ 20 ], were applied in this study. Biomass values were converted to carbon equivalents by multiplying with conversion factors of 0.50 and 0.39 for AGB and BGB, respectively, following procedures by Kauffman and Donato [ 21 ]. Results Mangrove distribution in São Tomé and Príncipe Mangroves in São Tomé and Príncipe occur in small ‘discrete pockets’ distributed in estuaries of small rivers, creeks, and coastal lagoons of the two islands. Total mangrove area in São Tomé and Príncipe was estimated at 180.0 ha, with São Tomé constituting 174.801 ha (97.1%) and Príncipe 5.2 ha (2.9%) (Table 1 ; Supplementary Fig. 1–3). A total of 19 mangrove sites were identified on the ground, being 14 on São Tomé and 5 on Príncipe Island. Major mangrove forests on São Tomé Island were encountered at Malanza (126.6 ha), Angolares (19.1 ha), Praia Grande (11.3 ha), Água Izé (3.7 ha), and Praia de Morro Peixe (3.2 ha) (Fig. 1 ; Supplementary Table 1,2). At least 6 sites reported here had never been documented before, including Praia Grande, Água Izé, Praia do Morro Peixe, Morro Peixe Comunidade, Praia Melão, and Praia Francesa, all on São Tomé Island. On Príncipe Island, Praia Burra (1.1 ha) was specifically identified as a priority site for targeted future mangrove afforestation (Fig. 2 ). There are six mangrove species in São Tomé and Príncipe, dominated by Rhizophora racemosa G. Mey., Avicennia germinans (L.) L. , and Rhizophora mangle L. . Other species encountered were Rhizophora harrisonii Leechm, Conocarpus erectus L. , and the fern Acrostichum aureum L.. Although some national reports and previous studies (e.g.,[ 22 ]) indicated the presence of Laguncularia racemosa in São Tomé and Príncipe, its presence in all the sampled sites was not confirmed during this study. There was no obvious zonation of mangroves in STP. Several mangrove-associated tree species were also encountered in STP, including Cocos nucifera (coconut palm), Hibiscus tiliaceus, Phoenix reclinata (Senegal date palm), Terminalia catappa (tropical almond), and Erythrina fusca (coral bean), occurring in transitional ecotones. Table 1 Summary table of mangrove distributions on São Tomé and Príncipe Island District Mangrove Cover (ha) Potential Restoration Area (ha) Total Mangrove Area (ha) São Tomé Caué 144.4 12.6 157.0 Lobata 5.3 5.8 11.1 Cantagalo 2.7 2.9 5.6 Mé-Zóchi 0.1 0.9 1.0 Água Grande 0.001 0.1 0.101 Whole of São Tomé 152.501 22.3 174.801 Príncipe Pagué 2.0 3.2 5.2 Whole Country 154.501 25.5 180.001 Mangrove forest structure Structural attributes of mangroves in STP are presented in Table 2 . Stem density ranged from 400 ± 300 stems ha⁻¹ in the Mé-Zóchi and Agua Grande districts to 2,880 ± 743.2 stems ha⁻¹ in the Lobata district, with an overall mean of 2,169.6 ± 365.7 stems ha⁻¹. Structural complexity was highest in Lobata (CI = 31.7) and Cantagalo (24.2) districts, probably due to high stem densities combined with trees possessing large basal areas. Mé-Zóchi and Água Grande recorded low CI values, mostly due to human pressure. All structural attributes between the districts differed significantly except mean diameter (F (4, 23) , f = 0.22, p > 0.05, p = 0.92). The sources of these differences, based on Tukey’s post-hoc analysis, are provided in Table 2 . Table 2 Structural attributes (mean ± s.e) of mangroves in São Tomé and Príncipe District Species* Stem Density (Count ha − 1 ) Mean DBH (cm) Mean Height (m) Basal Area (m 2 ha − 1 ) Complexity Index (CI) Above-ground Biomass (t ha − 1 ) Below-ground Biomass (t ha − 1 ) Total Biomass (t ha − 1 ) Caué 4 1,740 ± 229.3 11.4 ± 1.5 11.3 ± 1.5 22.4 ± 3.7 17.6 227.2 ± 47.6 91.5 ± 17.5 318.7 ± 65.1 Lobata 4 2,880 ± 743.2 10.2 ± 1.1 9.1 ± 1.1 30.2 ± 7.4 31.7 266.1 ± 66.9 109.7 ± 26.4 375.8 ± 93.2 Cantagalo 3 1,850 ± 250 11.1 ± 3.3 16.7 ± 4 26.1 ± 14 24.2 315 ± 216.7 116.2 ± 71.4 431.2 ± 288.1 Mé-Zóchi & Água Grande 2 400 ± 300 10.1 ± 5.9 6.4 ± 0.7 1.5 ± 0.5 0.1 11.1 ± 5.2 5.1 ± 1.8 16.2 ± 7 Pagué 3 1,975 ± 579.3 11.4 ± 0.7 15.1 ± 1.9 26.3 ± 7.7 23.5 268.3 ± 78.1 109.1 ± 32 377.4 ± 110.1 Whole Country 5 2,169.6 ± 365.7 10.7 ± 0.7 11.0 ± 0.9 25 ± 3.9 29.8 240.1 ± 38.3 97.1 ± 14.8 337.2 ± 53.1 F-value - 2.79 0.22 10.46 3.31 3.04 3.28 3.10 p-value - 0.046 0.92 0.00002 0.02 0.03 0.02 0.03 Tukey’s HSD post-hoc grouping - Lob ≠ Me nsd Paq ≠ Lob, Paq ≠ Me, Can ≠ Cau, Can ≠ Lob Lob ≠ Me Can ≠ Me Paq ≠ Me, Can ≠ Me, Lob ≠ Me Can ≠ Me Complexity Index (CI) = Number of species x Basal area (m 2 ha − 1 ) x mean tree height (m) x stem density (stems ha − 1 ) x 10 − 5 . Note: nsd = not significant difference (p > 0.05). Paq = Paque, Can = Cantagalo, Cau = Caue, Lob = Lobata, and Me = Mé-Zóchi & Água Grande *Species included here are exclusive of the fern Acrostichum aureum Biomass carbon The standing biomass of mangroves in STP ranged from 7.5 to 202.8 Mg C ha⁻¹ (mean: 157.9 ± 24.9 Mg ha⁻¹), with the highest and lowest mean values recorded in Cantagalo (157.5 ± 108.4) and Mé-Zóchi & Água Grande (5.6 ± 2.6 Mg C ha ⁻¹ ) districts (Table 3 ). There were positive correlations between basal area and standing biomass carbon (r 2 = 0.97) as well as between stem density and biomass carbon (r 2 = 0.48), which is expected. Assuming a sediment carbon of 780.2 Mg C ha⁻¹ for Central African mangroves, the total ecosystem carbon (TEC) of mangroves in STP is estimated at 938.1 Mg C ha⁻¹ (range: 701.1 to 1,123.2 Mg C ha⁻¹). This translates to TEC stock of 168,858.9 Mg C for the entire mangrove system of STP (180.001 ha). Table 3 Biomass carbon stocks of mangrove forest in São Tomé and Príncipe District Above-ground Carbon (Mg C ha⁻¹) Below-ground Carbon (Mg C ha⁻¹) Total Biomass Carbon (Mg C ha⁻¹) Carbon dioxide Equivalent ha⁻¹ Cauê 113.6 ± 23.8 35.7 ± 6.8 149.3 ± 30.6 547.9 ± 112.4 Lobata 131.1 ± 33.5 42.8 ± 10.3 175.8 ± 43.7 645.3 ± 160.4 Cantagalo 157.5 ± 108.4 45.3 ± 27.8 202.8 ± 136.2 744.3 ± 499.9 Mé-Zóchi & Água Grande 5.6 ± 2.6 2.0 ± 0.7 7.6 ± 3.3 27.8 ± 12.2 Pagué 134.2 ± 39.1 42.6 ± 12.5 176.7 ± 51.5 648.6 ± 189.1 Whole of STP 120.1 ± 19.2 37.9 ± 5.8 157.9 ± 24.9 579.6 ± 91.4 F-value 3.04 3.28 3.09 3.09 P-value 0.03 0.02 0.03 0.03 Tukey’s HSD post-hoc grouping Can ≠ Me Paq ≠ Me, Can ≠ Me, Lob ≠ Me Can ≠ Me Can ≠ Me Carbon values were further expressed in terms of CO 2 equivalent by multiplying C stocks by 3.67, the molecular weight of C in CO 2 . All attributes are significantly different (p < 0.05) Harvesting pressure Exploitation of mangrove products for building, energy, and tannin is a common feature in both rural and peri-urban areas of STP. In all the sites sampled, a mean stump count of 621.7 ± 433.7 stumps ha⁻¹ was observed, with the highest cutting observed in the Lobata (1,050 ± 441.3 stumps ha⁻¹) and Cantagalo (850 ± 150 stumps ha⁻¹) districts (Fig. 3 ). The difference in stump density between the districts was not significantly different (F (4, 23) = 2.51, p > 0.05). Natural regeneration The density of juveniles ranged from 0 ± 0 juveniles ha⁻¹ in the Mé-Zóchi & Água Grande districts to 3,380 ± 2,374.3 juveniles ha⁻¹ in the Cauê district (mean ± s.e.: 1,587 ± 648.2 juveniles ha⁻¹). There was no significant difference in the density of juveniles between the districts (F (4, 23) = 1.47; p > 0.05; p = 0.24). The forest was dominated by Regeneration Class I juveniles (43%), followed by Regeneration Class II (41.6%)—Table 4 . Although C. erectus was represented among the adult trees, juveniles of this species were absent in the entire forest. Regeneration ratios (for RCI: RCII: RCIII) of whole STP mangroves was 2:2:1. Table 4 Juvenile densities (counts ha⁻¹) of mangroves in São Tomé and Príncipe District Regeneration Class Total (Juveniles ha − 1 ) RCI (< 40 cm) RCII (40–150 m) RCIII (150.1–300 cm) Cauê 920 ± 524.8 (27.2) 2,220 ± 1,743.6 (65.7) 260 ± 188.7 (7.7) 3,380 ± 2,374.3 Lobata 920 ± 681.5 (60.9) 260 ± 98 (17.2) 330 ± 143.8 (21.9) 1,510 ± 903.4 Cantagalo 450 ± 250 (39.1) 450 ± 50 (39.1) 250 ± 50 (21.7) 1,150 ± 150 Pagué 250 ± 104.1 (45.5) 175 ± 85.4 (31.8) 125 ± 25 (22.7) 550 ± 210.2 Mé-Zóchi & Água Grande 0 0 0 0 Whole of STP 682.6 ± 315 (43) 660.9 ± 315 (41.6) 243.5 ± 74.9 (15.3) 1,587 ± 648.2 F-value 2.72 2.11 1.40 1.47 P-value 0.58 0.10 0.26 0.24 Tukey’s HSD post-hoc grouping nsd nsd nsd nsd Values in parentheses denote percentages. nsd = not significant difference. (p > 0.05) Mangrove restoration potential in São Tomé and Príncipe Mangroves in STP are not pristine. They are facing a combination of human-induced and natural stressors. Direct removal of wood products and tannins remains the most significant threat, followed by pollution, land encroachment, water streams obstruction and climate change effects. Potential areas of mangrove restoration differ across sites, depending on drivers, site history, and accessibility. Total mangrove areas requiring targeted restoration in STP were estimated at 25.5 ha, with 89.8% of degraded areas occurring on São Tomé Island (Table 1 ). Sites like Praia das Conchas, Diogo Nunes, Praia Quinze, and Micolo, all on São Tomé Island, showed signs of historical disturbance, including altered hydrology, crab predation, and sand harvesting. These areas offer opportunities for enrichment planting with A. germinans and R. mangle . Sites threatened by increased sedimentation (like Praia da Conchas) require hydrological restoration to reconnect tidal flows and promote natural regeneration. Mangroves of Malanza, Praia Salgada, and Praia Caixão exhibited intact to moderately disturbed stands that could recover naturally. These sites are especially promising for nature-based enterprises such as ecotourism and blue carbon projects. Discussions Mangrove cover Total mangrove area in São Tomé and Príncipe was estimated at 180.0 ha, with São Tomé constituting 174.801 ha and Príncipe 5.2 ha. This value is lower than earlier reports, but represents the most accurate estimate of mangrove area in São Tomé and Príncipe (Table 6 ). Table 6 Mangrove area in São Tomé and Príncipe from different sources Island District Mangrove Area (ha) UNEP [ 23 ] Haroun et al. [ 22 ] Global Mangrove Watch Afonso [ 13 ] Afonso et al., [ 24 ] Machava-António et al. [ 12 ] This study São Tomé Caué - 68.6 46.1 165 200 - 157.0 Lobata - 0.8 2.3 1 1 - 11.1 Cantagalo - - - - - - 5.6 Mé-Zóchi - - - - - - 1.0 Água Grande - - - - - - 0.101 Whole of São Tomé - - 48.4 - 1-200 - 174.801 Príncipe Pagué - - - - - 1.6 5.2 Whole Country 140 - - - - 100–136 180.001 Detailed mangrove cover per site based on the different sources is provided in Supplementary Table 1. The observed differences in mangrove area of STP from different sources are attributed to differences in methodologies used, the periods data were taken, as well as the definition of mangrove areas. Forest structure The structural attributes, such as species composition, tree height, basal area, and standing biomass, are comparable to other mangroves in the Central and West Africa region [ 25 , 26 , 4 ]. Mangroves in STP are dominated by Rhizophora spp. and A. germinans , which is consistent with the mangroves of the region. Laguncularia racemosa has been reported in West and Central African mangroves, including Guinea-Bissau, Nigeria, Gabon, and Cameroon [ 4 , 27 ]. However, it was not recorded in STP during this study. Its absence may indicate historical occurrence followed by local extinction, possibly due to limited habitat extent, small and vulnerable populations, or anthropogenic pressures such as land conversion and altered hydrology. Given the islands’ small and fragmented mangrove areas, the persistence of Laguncularia populations may have been ecologically constrained. Further surveys are needed to confirm whether the species is truly absent or persists in overlooked sites. The standing biomass of mangroves in STP is estimated at 337.2 Mg ha⁻¹, which is within the range of healthy mangroves of the Atlantic–East Pacific bioregion that includes the mangroves of the Central and West Africa region (Table 7 ). Table 7 Structural attributes of mangroves in São Tomé and Príncipe compared to other countries in Central Africa Country Structural Attribute Source No. of species Tree Density (stems ha⁻¹) Max. Height (m) Max. Diameter (cm) Mean Diameter (m) Mean Basal Area (m 2 ha − 1 ) Mean AGB (Mg ha − 1 ) Mean BGB (Mg ha − 1 ) Total mean Biomass (Mg ha − 1 ) São Tomé and Príncipe 6 2,169.6 30 44.6 10.7 25 240.1 97.1 337.2 This study Cameroon 4 3,255 52 102 4.6 25.1 505 306 811 Ajonina et al., 2014[ 27 ] Gabon 6 1,466 41 52 9.5 24.5 341 151 492 Ajonina et al., 2014[ 27 ] Congo 2 1,666 25 58 7.7 18.8 251 122 373 Ajonina et al., 2014[ 27 ] DRC 2 1,266 27 59 9.1 24.5 409 185 594 Ajonina et al., 2014[ 27 ] Biomass carbon The mangrove forests of STP exhibit moderate biomass carbon stocks, averaging 157.9 Mg C ha⁻¹, which is within the lower to mid-range compared to other regions in West and Central Africa [ 27 ]. The value is higher than the 147.5 Mg C ha − 1 average global mangrove biomass carbon [ 28 ] but lower than the global average of 190.2 Mg C ha − 1 [ 29 ]. Considerable spatial variability was observed, with Cantagalo supporting the highest biomass carbon (202.8 Mg C ha⁻¹), while Mé-Zóchi & Água Grande recorded severely depleted stocks (7.6 Mg C ha⁻¹), reflecting intense degradation and limited mangrove cover in urbanized districts. Districts such as Cantagalo, Lobata, and Pagué maintain relatively high carbon storage, making them priority areas for protection and integration into blue carbon initiatives. In contrast, the severely degraded sites of Mé-Zóchi and Água Grande offer opportunities for targeted restoration interventions aimed at enhancing biomass accumulation and carbon sequestration. As expected, there was a strong positive correlation between basal area and total biomass carbon (r² = 0.97), indicating that plots with larger tree sizes stored more biomass carbon. In contrast, a weak positive correlation was observed between stem density and total biomass carbon (r² = 0.48), suggesting that tree count alone was a less reliable predictor of carbon stocks compared to structural attributes like basal area. Natural regeneration Mangroves of STP were dominated by juvenile Rhizophora spp . However, the natural regeneration density, estimated at 1,587 ± 648.2 juveniles ha⁻¹, is considered insufficient to sustain forest recovery following disturbance. This value falls significantly below the minimum threshold of 2,500 seedlings ha⁻¹ recommended by FAO [ 30 ] as necessary for restocking a degraded mangrove stand without replanting. An ideal regeneration ratio of RCI:RCII:RCIII = 6:3:1 is considered adequate for effective mangrove recovery [ 31 ]. In STP, the observed ratio (2:2:1) was slightly below this benchmark, indicating limited natural regeneration potential. Although juvenile densities were not significantly different among districts, the predominance of Regeneration Class I and II individuals suggests that most stands are still in early successional stages. This skewed structure highlights ongoing regeneration but also indicates limited recruitment into advanced size classes, which may constrain long-term forest stability. The absence of C. erectus juveniles, despite the presence of adults, points to potential regeneration bottlenecks, possibly linked to hydrological conditions, propagule availability, or species-specific ecological constraints. Districts with complete absence of juveniles may reflect intense anthropogenic pressures or degraded environmental conditions that hinder natural recruitment. Restoration potential Mangrove ecosystems in São Tomé and Príncipe exhibit notable potential for restoration, though levels of degradation vary across sites. Highly disturbed areas such as Praia das Conchas, Diogo Nunes, Praia Quinze, and Micolo represent prime candidates for active interventions, including enrichment planting with native mangrove species. However, successful outcomes hinge on the restoration of hydrological connectivity, a foundational step emphasized in hydrological rehabilitation frameworks [ 32 , 33 ]. In contrast, relatively intact stands in Malanza, Praia Salgada, and Praia Caixão are well-suited for assisted natural regeneration (ANR) and expansion of ongoing afforestation. Studies from Southeast Asia and West Africa [ 34 , 35 ] have shown that ANR, when coupled with community stewardship, yields cost-effective and ecologically robust results. These latter sites exhibit high biomass and carbon sequestration capacity, suggesting strong potential for integration into blue carbon initiatives, including carbon credit markets and Nationally Determined Contributions (NDCs) under the Paris Agreement. Restoration strategies should adopt Community-Based Ecological Mangrove Restoration (CBEMR) principles, which emphasize community involvement, ecological integrity, and long-term monitoring [ 36 , 37 ]. Crab control should be considered as part of site-specific management plans, particularly in early-stage restoration plots where crab herbivory has been shown to significantly reduce seedling survival [ 38 , 39 ]. Conclusion This study provides the first comprehensive assessment of mangroves in São Tomé and Príncipe, documenting a total area of 180 ha across 19 sites, with São Tomé hosting 97.1%. Six species were recorded, dominated by Rhizophora racemosa, Avicennia germinans , and R. mangle , with smaller stands of R. harrisonii, Conocarpus erectus , and the fern Acrostichum aureum . Although limited in extent, these mangroves store an estimated 938.1 Mg C ha − 1 (168,858.9 Mg C), underscoring their ecological and climate value. Despite this importance, they face acute pressures from unsustainable harvesting, hydrological alteration, pollution, coastal infrastructure, and settlement encroachment, with about 25.5 ha viable for restoration. Weak governance, characterized by fragmented mandates and the absence of a national mangrove policy, further constrains management. Priority restoration and afforestation sites were identified on both islands, offering opportunities for Nature-based Solutions. Interventions should emphasize hydrological rehabilitation, Community-Based Ecological Mangrove Restoration (CBEMR), nursery development, crab control, long-term monitoring, and ecotourism to support livelihoods. To secure their future, São Tomé and Príncipe must adopt a unified National Mangrove Strategy, integrate mangroves into climate commitments (e.g., NDCs, blue carbon markets), strengthen institutional coordination, and promote community co-management. Addressing knowledge gaps in biomass and soil carbon, biodiversity, and hydrology will enhance adaptive management and open access to climate finance. With decisive action and collaboration, mangroves can become a cornerstone of coastal resilience and a model of blue carbon stewardship for small island states. Declarations Funding: Supported by the West Africa Coastal Areas Resilience Investment Project (WACA+), financed through the World Bank under Contract Nº: 23/C/WACA+/2024 and implemented under the management of the Agência Fiduciaria de Administração de Projetos (AFAP). Acknowledgement: The authors gratefully acknowledge the technical support provided by the Direção das Florestas e da Biodiversidade (DFB), the Direção do Ambiente e da Ação Climática (DAAC), and the Universidade de São Tomé e Príncipe (USTP). Special thanks are extended to the WACA+ team, particularly Eng. Arlindo Carvalho, Kassi Costa dos Santos, and Abnilde Lima, as well as the AFAP project staff led by Mr. Helio Silva Almeida, for their facilitation and guidance. The authors also sincerely appreciate the World Bank team, led by Juliana Castano Isaza and Juan Jose Miranda Montero, for technical support, and all stakeholders whose insights, data, and recommendations greatly enriched this study. Disclosure Statement: No potential conflict of interest was reported by the authors. Clinical Trial: Not applicable Clinical Trial Number: not applicable. Ethics, Consent to Participate, and Consent to Publish Declarations Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Data Availability: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Lovelock, C. E., & Duarte, C. M. (2019). Dimensions of blue carbon and emerging perspectives. 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Effect of canopy structure on crab damage in replanted mangroves in Kenya. Marine Ecology Progress Series, 299, 257–266. doi.org/10.3354/meps299257 Dahdouh-Guebas, F., Jayatissa, L. P., Di Nitto, D., Seen, D. L., & Koedam, N. (2005). How effective were mangroves as a defense against the recent tsunami? Current Biology, 15(12), R443–R447. doi.org/10.1016/j.cub.2005.06.008 Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx 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. 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1","display":"","copyAsset":false,"role":"figure","size":153557,"visible":true,"origin":"","legend":"\u003cp\u003eThe locational map of São Tomé and Príncipe\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7473521/v1/e8cac6ff0aca8909a58e5737.png"},{"id":93917380,"identity":"9399dc53-82b0-48c0-bce6-310658e08b60","added_by":"auto","created_at":"2025-10-20 09:03:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":455631,"visible":true,"origin":"","legend":"\u003cp\u003eMangrove sites in São Tomé and Príncipe\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7473521/v1/fbf553b0fc5d915d0b3500ac.png"},{"id":93917378,"identity":"63a2150c-9c81-4bcb-a1ff-480945c6ea49","added_by":"auto","created_at":"2025-10-20 09:03:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48331,"visible":true,"origin":"","legend":"\u003cp\u003eStump densities (counts ha⁻¹) in mangroves of São Tomé and Príncipe\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7473521/v1/668871aa6511ec90cc270cfe.png"},{"id":108113334,"identity":"2b58af69-84a3-4e78-8a45-277b0a2c5607","added_by":"auto","created_at":"2026-04-29 13:23:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1084303,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7473521/v1/e537390c-7a91-49fa-ae1c-5e00061eefd8.pdf"},{"id":93918628,"identity":"15473c33-0062-4f2c-863b-47d396746846","added_by":"auto","created_at":"2025-10-20 09:19:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1195629,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7473521/v1/1647efa5f6695471538c6313.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characteristics of Mangrove Forests in São Tomé and Príncipe and The Potential for Restoration","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMangroves are among the most productive and resilient coastal ecosystems on earth, providing habitat for fish and other wildlife, protecting shoreline from erosion, and regulating climate [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. They form the interface between land and sea in tropical and subtropical regions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and are recognized for their capacity to deliver a wide range of ecosystem services that support local, national, and global economies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite their relatively small global area, estimated at 13.7\u0026nbsp;million ha [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], mangroves capture and store huge stocks of carbon in both above- and below-ground components [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Their blue carbon values have drawn increased international attention, and mangrove countries have incorporated mangroves in their development and climate change agenda to the Paris Agreement [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe (STP), mangroves are known to cover between 100 and 136 ha [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While they are relatively small in area and often found in the inner basins, mangroves in STP provide critical ecosystem services, including shoreline stabilization, fish spawning and feeding grounds, and climate regulation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, like elsewhere around the world, mangroves in STP are threatened by human and natural stressors, including over-exploitation of resources, conversion pressure, pollution effects, and climate change. Losses and degradation of mangroves adversely affect local biodiversity and fisheries, as well as the stability of shorelines. Protection and conservation of forests, such as mangroves, requires a clear understanding of what constitutes ecosystem health. Describing ecosystem health is fundamental in setting protection and conservation goals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUntil recently, there were limited field-based studies on the structure and distribution of mangrove forests in STP [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While resource mapping and coastal risk assessments were conducted under the West Africa Coastal Areas Management Program (WACA+), these lacked site-level ground-truthing and socio-ecological integration. Consequently, there was a need for empirical data to guide ecosystem restoration, support blue carbon initiatives, and design community-inclusive mangrove conservation programs. To our understanding, this was the first study to undertake a detailed account of mangrove forests in STP in terms of species composition, forest structure, environmental settings, degradation drivers, and socio-economic interactions. The results will inform the development plan for mangroves in STP and sites for targeted restoration.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eS\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe is a small island developing state (SIDS) located in the Gulf of Guinea, off the western equatorial coast of Central Africa (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It consists of two main islands: S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe, separated by approximately 182 km, along with several smaller islets such as the Tinhosa Islands, Ilh\u0026eacute;u das Cabras, and Ilh\u0026eacute;u das Rolas. The country lies across the equator and features a tropical climate characterized by high temperatures and humidity year-round. The annual rainfall in STP ranges from 2,000 mm to over 7,000 mm, with long rains occurring from September to May, followed by relatively dry seasons from June to August. These climatic conditions are conducive to mangroves; however, geomorphic constraints limit the expansion of these forests in the two islands. For instance, STP experiences semidiurnal micro-tidal conditions of 0.30\u0026ndash;1.80 m that reduce the frequency and extension of tidal inundation, thus influencing coastal and estuarine environments, including mangrove systems.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMangrove cover\u003c/h3\u003e\n\u003cp\u003eTo understand the occurrence and distribution of mangroves in S\u0026atilde;o Tom\u0026eacute; and Principe, an unsupervised classification of the area of interest was first performed to delineate distinct spectral classes, which guided initial field validation efforts. Sentinel imagery was accessed through Google Earth Engine (GEE), and key vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were calculated to highlight vegetated areas and distinguish mangroves from other plant types. Training samples from known mangrove and non-mangrove locations were collected, and supervised classification algorithms, specifically Random Forest and Support Vector Machine (SVM), were applied to categorize the imagery. The classification outputs were validated against detailed ground-truthing data, the Global Mangrove Watch (GMW) dataset, and high-resolution imagery from Airbus and Maxar Technologies, with overall accuracy and the kappa coefficient computed to quantify performance. To enhance map quality, spatial filters (majority filtering and edge smoothing) were applied to reduce noise. Subsequently, on-screen digitization of mangrove patch boundaries was conducted in ArcMap by visually tracing each patch on very-high-resolution imagery. Through photo-interpretation, individual polygons were manually delineated based on tonality, texture, shape, and proximity to water bodies. Mangroves typically exhibit a range of green hues, from pale to medium, depending on the image season, that contrast with the dark brown or mixed light green/brown tones of surrounding vegetation. Their characteristic sinuous perimeters with numerous inlets and proximity to water features further guided manual mapping.\u003c/p\u003e\u003cp\u003ePotential areas of mangrove restoration were delineated based on a set of biophysical and ecological criteria. These included soil type, hydrological conditions (regular tidal flushing and water exchange), historical presence of mangroves as indicated by old stumps and local knowledge, and absence of current land-use conflicts such as settlements or infrastructure. Areas with degraded vegetation, open mangrove areas adjacent to healthy stands, and locations showing natural regeneration potential were prioritized. This combination of qualitative indicators ensured that only sites with favorable ecological conditions and restoration feasibility were classified as potentially restorable.\u003c/p\u003e\n\u003ch3\u003eSurvey of mangrove forest structure\u003c/h3\u003e\n\u003cp\u003eRandom plots of 100 m\u003csup\u003e2\u003c/sup\u003e were used for the study. The number of plots varied across sites and was dependent on the extent of mangroves in the area (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In each plot, all trees with stem diameter at breast height (DBH)\u0026thinsp;\u0026ge;\u0026thinsp;2.5 cm were identified, counted, and their positions marked. The following parameters were collected: tree height (m), stem diameter at breast height (DBH) (cm), number of live/dead trees, and quality of the lead stem (categorized as either straight, semi-straight or crooked). From these data, the following vegetation attributes were derived: basal area (m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), stocking rates (stems ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and standing biomass (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Vegetation cover (%) was estimated from the area of the ground that one would see if flying above the tree canopy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Tree heights were estimated using a graduated pole, while stem diameters were measured at 130 cm above ground, using a diameter tape. In the case of \u003cem\u003eRhizophora\u003c/em\u003e spp., stem diameter was taken 30 cm above the highest prop root [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For the forked stems below 130 cm, individual branches in a clump were treated as separate stems. Patterns and conditions of natural recruitment were assessed using linear regeneration sampling, whose applications could be found in Kairo [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eGraphical presentations and descriptive analysis of structural data helped to understand variations of mangroves across different sites in STP. The complexity index value for each district was determined using the approach in Holdridge et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which incorporates species composition, basal area, mean tree height, and stem density:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003eComplexity Index (CI)\u0026thinsp;=\u0026thinsp;Number of species x basal area (m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eha\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) x mean tree height (m) x stem density (stems ha\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) x 10\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;5\u003c/em\u003e\u003c/sup\u003e Eq.\u0026nbsp;1\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBiomass carbon was estimated using the general allometric equations of Komiyama et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Aboveground\\:Biomass\\:\\left(kg\\right)=0.251\\rho\\:D\\:2.46\\:\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;2\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Belowground\\:Biomass\\:\\left(kg\\right)=0.199{\\rho\\:}^{0.899}\\times\\:{D}^{2.22}\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;3\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003eWhere D\u003c/em\u003e\u0026thinsp;=\u0026thinsp;tree diameter at breast height (cm) and \u003cem\u003e⍴\u003c/em\u003e = wood density (g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSpecies-specific wood densities for mangroves, as reported by Howard et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], were applied in this study. Biomass values were converted to carbon equivalents by multiplying with conversion factors of 0.50 and 0.39 for AGB and BGB, respectively, following procedures by Kauffman and Donato [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMangrove distribution in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe\u003c/h2\u003e\u003cp\u003eMangroves in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe occur in small \u0026lsquo;discrete pockets\u0026rsquo; distributed in estuaries of small rivers, creeks, and coastal lagoons of the two islands. Total mangrove area in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe was estimated at 180.0 ha, with S\u0026atilde;o Tom\u0026eacute; constituting 174.801 ha (97.1%) and Pr\u0026iacute;ncipe 5.2 ha (2.9%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Fig.\u0026nbsp;1\u0026ndash;3). A total of 19 mangrove sites were identified on the ground, being 14 on S\u0026atilde;o Tom\u0026eacute; and 5 on Pr\u0026iacute;ncipe Island. Major mangrove forests on S\u0026atilde;o Tom\u0026eacute; Island were encountered at Malanza (126.6 ha), Angolares (19.1 ha), Praia Grande (11.3 ha), \u0026Aacute;gua Iz\u0026eacute; (3.7 ha), and Praia de Morro Peixe (3.2 ha) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table\u0026nbsp;1,2). At least 6 sites reported here had never been documented before, including Praia Grande, \u0026Aacute;gua Iz\u0026eacute;, Praia do Morro Peixe, Morro Peixe Comunidade, Praia Mel\u0026atilde;o, and Praia Francesa, all on S\u0026atilde;o Tom\u0026eacute; Island. On Pr\u0026iacute;ncipe Island, Praia Burra (1.1 ha) was specifically identified as a priority site for targeted future mangrove afforestation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThere are six mangrove species in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe, dominated by \u003cem\u003eRhizophora racemosa G. Mey., Avicennia germinans (L.) L.\u003c/em\u003e, and \u003cem\u003eRhizophora mangle L.\u003c/em\u003e. Other species encountered were \u003cem\u003eRhizophora harrisonii Leechm, Conocarpus erectus L.\u003c/em\u003e, and the fern \u003cem\u003eAcrostichum aureum L..\u003c/em\u003e Although some national reports and previous studies (e.g.,[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]) indicated the presence of \u003cem\u003eLaguncularia racemosa\u003c/em\u003e in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe, its presence in all the sampled sites was not confirmed during this study. There was no obvious zonation of mangroves in STP. Several mangrove-associated tree species were also encountered in STP, including \u003cem\u003eCocos nucifera\u003c/em\u003e (coconut palm), \u003cem\u003eHibiscus tiliaceus, Phoenix reclinata\u003c/em\u003e (Senegal date palm), \u003cem\u003eTerminalia catappa\u003c/em\u003e (tropical almond), and \u003cem\u003eErythrina fusca\u003c/em\u003e (coral bean), occurring in transitional ecotones.\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\u003eSummary table of mangrove distributions on S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe\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\u003eIsland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMangrove Cover (ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePotential Restoration Area (ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal Mangrove Area (ha)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eS\u0026atilde;o Tom\u0026eacute;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCau\u0026eacute;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e144.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e157.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLobata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCantagalo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u0026eacute;-Z\u0026oacute;chi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026Aacute;gua Grande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eWhole of S\u0026atilde;o Tom\u0026eacute;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e152.501\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e22.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e174.801\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePr\u0026iacute;ncipe\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePagu\u0026eacute;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWhole Country\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e154.501\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e25.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e180.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMangrove forest structure\u003c/h3\u003e\n\u003cp\u003eStructural attributes of mangroves in STP are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Stem density ranged from 400\u0026thinsp;\u0026plusmn;\u0026thinsp;300 stems ha⁻\u0026sup1; in the M\u0026eacute;-Z\u0026oacute;chi and Agua Grande districts to 2,880\u0026thinsp;\u0026plusmn;\u0026thinsp;743.2 stems ha⁻\u0026sup1; in the Lobata district, with an overall mean of 2,169.6\u0026thinsp;\u0026plusmn;\u0026thinsp;365.7 stems ha⁻\u0026sup1;. Structural complexity was highest in Lobata (CI\u0026thinsp;=\u0026thinsp;31.7) and Cantagalo (24.2) districts, probably due to high stem densities combined with trees possessing large basal areas. M\u0026eacute;-Z\u0026oacute;chi and \u0026Aacute;gua Grande recorded low CI values, mostly due to human pressure. All structural attributes between the districts differed significantly except mean diameter (F\u003csub\u003e(4, 23)\u003c/sub\u003e, f\u0026thinsp;=\u0026thinsp;0.22, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, p\u0026thinsp;=\u0026thinsp;0.92). The sources of these differences, based on Tukey\u0026rsquo;s post-hoc analysis, are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eStructural attributes (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;s.e) of mangroves in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStem Density (Count ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean DBH (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean Height (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBasal Area (m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eComplexity Index (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAbove-ground Biomass (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eBelow-ground Biomass (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTotal Biomass (t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCau\u0026eacute;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,740\u0026thinsp;\u0026plusmn;\u0026thinsp;229.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e227.2\u0026thinsp;\u0026plusmn;\u0026thinsp;47.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e91.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e318.7\u0026thinsp;\u0026plusmn;\u0026thinsp;65.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,880\u0026thinsp;\u0026plusmn;\u0026thinsp;743.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e266.1\u0026thinsp;\u0026plusmn;\u0026thinsp;66.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e109.7\u0026thinsp;\u0026plusmn;\u0026thinsp;26.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e375.8\u0026thinsp;\u0026plusmn;\u0026thinsp;93.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCantagalo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,850\u0026thinsp;\u0026plusmn;\u0026thinsp;250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e315\u0026thinsp;\u0026plusmn;\u0026thinsp;216.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e116.2\u0026thinsp;\u0026plusmn;\u0026thinsp;71.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e431.2\u0026thinsp;\u0026plusmn;\u0026thinsp;288.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM\u0026eacute;-Z\u0026oacute;chi \u0026amp; \u0026Aacute;gua Grande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400\u0026thinsp;\u0026plusmn;\u0026thinsp;300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e16.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePagu\u0026eacute;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,975\u0026thinsp;\u0026plusmn;\u0026thinsp;579.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e268.3\u0026thinsp;\u0026plusmn;\u0026thinsp;78.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e109.1\u0026thinsp;\u0026plusmn;\u0026thinsp;32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e377.4\u0026thinsp;\u0026plusmn;\u0026thinsp;110.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWhole Country\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2,169.6\u0026thinsp;\u0026plusmn;\u0026thinsp;365.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e29.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e240.1\u0026thinsp;\u0026plusmn;\u0026thinsp;38.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e97.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e337.2\u0026thinsp;\u0026plusmn;\u0026thinsp;53.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eF-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e2.79\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.22\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e10.46\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e3.31\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e3.04\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e3.28\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e3.10\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e0.046\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.92\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.00002\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTukey\u0026rsquo;s HSD post-hoc grouping\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLob\u0026thinsp;\u0026ne;\u0026thinsp;Me\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ensd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePaq\u0026thinsp;\u0026ne;\u0026thinsp;Lob, Paq\u0026thinsp;\u0026ne;\u0026thinsp;Me, Can \u0026ne;\u0026thinsp;Cau, Can \u0026ne;\u0026thinsp;Lob\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLob\u0026thinsp;\u0026ne;\u0026thinsp;Me\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCan \u0026ne;\u0026thinsp;Me\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePaq\u0026thinsp;\u0026ne;\u0026thinsp;Me, Can \u0026ne;\u0026thinsp;Me, Lob\u0026thinsp;\u0026ne;\u0026thinsp;Me\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCan \u0026ne;\u0026thinsp;Me\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eComplexity Index (CI)\u0026thinsp;=\u0026thinsp;Number of species x Basal area (m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) x mean tree height (m) x stem density (stems ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNote: nsd\u0026thinsp;=\u0026thinsp;not significant difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Paq\u0026thinsp;=\u0026thinsp;Paque, Can =\u0026thinsp;Cantagalo, Cau\u0026thinsp;=\u0026thinsp;Caue, Lob\u0026thinsp;=\u0026thinsp;Lobata, and Me\u0026thinsp;=\u0026thinsp;M\u0026eacute;-Z\u0026oacute;chi \u0026amp; \u0026Aacute;gua Grande\u003c/p\u003e\u003cp\u003e*Species included here are exclusive of the fern \u003cem\u003eAcrostichum aureum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eBiomass carbon\u003c/h3\u003e\n\u003cp\u003eThe standing biomass of mangroves in STP ranged from 7.5 to 202.8 Mg C ha⁻\u0026sup1; (mean: 157.9\u0026thinsp;\u0026plusmn;\u0026thinsp;24.9 Mg ha⁻\u0026sup1;), with the highest and lowest mean values recorded in Cantagalo (157.5\u0026thinsp;\u0026plusmn;\u0026thinsp;108.4) and M\u0026eacute;-Z\u0026oacute;chi \u0026amp; \u0026Aacute;gua Grande (5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 Mg C ha\u003csup\u003e⁻\u0026sup1;\u003c/sup\u003e) districts (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). There were positive correlations between basal area and standing biomass carbon (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.97) as well as between stem density and biomass carbon (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.48), which is expected. Assuming a sediment carbon of 780.2 Mg C ha⁻\u0026sup1; for Central African mangroves, the total ecosystem carbon (TEC) of mangroves in STP is estimated at 938.1 Mg C ha⁻\u0026sup1; (range: 701.1 to 1,123.2 Mg C ha⁻\u0026sup1;). This translates to TEC stock of 168,858.9 Mg C for the entire mangrove system of STP (180.001 ha).\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\u003eBiomass carbon stocks of mangrove forest in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe\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\u003eDistrict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove-ground Carbon (Mg C ha⁻\u0026sup1;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBelow-ground Carbon (Mg C ha⁻\u0026sup1;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal Biomass Carbon (Mg C ha⁻\u0026sup1;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarbon dioxide Equivalent ha⁻\u0026sup1;\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCau\u0026ecirc;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113.6\u0026thinsp;\u0026plusmn;\u0026thinsp;23.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e149.3\u0026thinsp;\u0026plusmn;\u0026thinsp;30.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e547.9\u0026thinsp;\u0026plusmn;\u0026thinsp;112.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131.1\u0026thinsp;\u0026plusmn;\u0026thinsp;33.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e175.8\u0026thinsp;\u0026plusmn;\u0026thinsp;43.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e645.3\u0026thinsp;\u0026plusmn;\u0026thinsp;160.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCantagalo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157.5\u0026thinsp;\u0026plusmn;\u0026thinsp;108.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.3\u0026thinsp;\u0026plusmn;\u0026thinsp;27.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e202.8\u0026thinsp;\u0026plusmn;\u0026thinsp;136.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e744.3\u0026thinsp;\u0026plusmn;\u0026thinsp;499.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM\u0026eacute;-Z\u0026oacute;chi \u0026amp; \u0026Aacute;gua Grande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePagu\u0026eacute;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134.2\u0026thinsp;\u0026plusmn;\u0026thinsp;39.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e176.7\u0026thinsp;\u0026plusmn;\u0026thinsp;51.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e648.6\u0026thinsp;\u0026plusmn;\u0026thinsp;189.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWhole of STP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e120.1\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e37.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e157.9\u0026thinsp;\u0026plusmn;\u0026thinsp;24.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e579.6\u0026thinsp;\u0026plusmn;\u0026thinsp;91.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e3.04\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e3.28\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e3.09\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e3.09\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTukey\u0026rsquo;s HSD post-hoc grouping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCan \u0026ne;\u0026thinsp;Me\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePaq\u0026thinsp;\u0026ne;\u0026thinsp;Me, Can \u0026ne;\u0026thinsp;Me, Lob\u0026thinsp;\u0026ne;\u0026thinsp;Me\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCan \u0026ne;\u0026thinsp;Me\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCan \u0026ne;\u0026thinsp;Me\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eCarbon values were further expressed in terms of CO\u003csub\u003e2\u003c/sub\u003e equivalent by multiplying C stocks by 3.67, the molecular weight of C in CO\u003csub\u003e2\u003c/sub\u003e. All attributes are significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eHarvesting pressure\u003c/h2\u003e\u003cp\u003eExploitation of mangrove products for building, energy, and tannin is a common feature in both rural and peri-urban areas of STP. In all the sites sampled, a mean stump count of 621.7\u0026thinsp;\u0026plusmn;\u0026thinsp;433.7 stumps ha⁻\u0026sup1; was observed, with the highest cutting observed in the Lobata (1,050\u0026thinsp;\u0026plusmn;\u0026thinsp;441.3 stumps ha⁻\u0026sup1;) and Cantagalo (850\u0026thinsp;\u0026plusmn;\u0026thinsp;150 stumps ha⁻\u0026sup1;) districts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The difference in stump density between the districts was not significantly different (F\u003csub\u003e(4, 23)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.51, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eNatural regeneration\u003c/h2\u003e\u003cp\u003eThe density of juveniles ranged from 0\u0026thinsp;\u0026plusmn;\u0026thinsp;0 juveniles ha⁻\u0026sup1; in the M\u0026eacute;-Z\u0026oacute;chi \u0026amp; \u0026Aacute;gua Grande districts to 3,380\u0026thinsp;\u0026plusmn;\u0026thinsp;2,374.3 juveniles ha⁻\u0026sup1; in the Cau\u0026ecirc; district (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;s.e.: 1,587\u0026thinsp;\u0026plusmn;\u0026thinsp;648.2 juveniles ha⁻\u0026sup1;). There was no significant difference in the density of juveniles between the districts (F\u003csub\u003e(4, 23)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.47; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; p\u0026thinsp;=\u0026thinsp;0.24). The forest was dominated by Regeneration Class I juveniles (43%), followed by Regeneration Class II (41.6%)\u0026mdash;Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Although \u003cem\u003eC. erectus\u003c/em\u003e was represented among the adult trees, juveniles of this species were absent in the entire forest. Regeneration ratios (for RCI: RCII: RCIII) of whole STP mangroves was 2:2:1.\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\u003eJuvenile densities (counts ha⁻\u0026sup1;) of mangroves in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eRegeneration Class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(Juveniles ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRCI (\u0026lt;\u0026thinsp;40 cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRCII (40\u0026ndash;150 m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRCIII (150.1\u0026ndash;300 cm)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCau\u0026ecirc;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e920\u0026thinsp;\u0026plusmn;\u0026thinsp;524.8 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,220\u0026thinsp;\u0026plusmn;\u0026thinsp;1,743.6 (65.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e260\u0026thinsp;\u0026plusmn;\u0026thinsp;188.7 (7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3,380\u0026thinsp;\u0026plusmn;\u0026thinsp;2,374.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e920\u0026thinsp;\u0026plusmn;\u0026thinsp;681.5 (60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e260\u0026thinsp;\u0026plusmn;\u0026thinsp;98 (17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e330\u0026thinsp;\u0026plusmn;\u0026thinsp;143.8 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,510\u0026thinsp;\u0026plusmn;\u0026thinsp;903.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCantagalo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e450\u0026thinsp;\u0026plusmn;\u0026thinsp;250 (39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e450\u0026thinsp;\u0026plusmn;\u0026thinsp;50 (39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e250\u0026thinsp;\u0026plusmn;\u0026thinsp;50 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,150\u0026thinsp;\u0026plusmn;\u0026thinsp;150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePagu\u0026eacute;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e250\u0026thinsp;\u0026plusmn;\u0026thinsp;104.1 (45.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175\u0026thinsp;\u0026plusmn;\u0026thinsp;85.4 (31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125\u0026thinsp;\u0026plusmn;\u0026thinsp;25 (22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e550\u0026thinsp;\u0026plusmn;\u0026thinsp;210.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM\u0026eacute;-Z\u0026oacute;chi \u0026amp; \u0026Aacute;gua Grande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWhole of STP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e682.6\u0026thinsp;\u0026plusmn;\u0026thinsp;315 (43)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e660.9\u0026thinsp;\u0026plusmn;\u0026thinsp;315 (41.6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e243.5\u0026thinsp;\u0026plusmn;\u0026thinsp;74.9 (15.3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1,587\u0026thinsp;\u0026plusmn;\u0026thinsp;648.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eF-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e2.72\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e2.11\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e1.40\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e1.47\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e0.58\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e0.10\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.26\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.24\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTukey\u0026rsquo;s HSD post-hoc grouping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ensd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ensd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ensd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ensd\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eValues in parentheses denote percentages. nsd\u0026thinsp;=\u0026thinsp;not significant difference. (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMangrove restoration potential in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe\u003c/h2\u003e\u003cp\u003eMangroves in STP are not pristine. They are facing a combination of human-induced and natural stressors. Direct removal of wood products and tannins remains the most significant threat, followed by pollution, land encroachment, water streams obstruction and climate change effects. Potential areas of mangrove restoration differ across sites, depending on drivers, site history, and accessibility. Total mangrove areas requiring targeted restoration in STP were estimated at 25.5 ha, with 89.8% of degraded areas occurring on S\u0026atilde;o Tom\u0026eacute; Island (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Sites like Praia das Conchas, Diogo Nunes, Praia Quinze, and Micolo, all on S\u0026atilde;o Tom\u0026eacute; Island, showed signs of historical disturbance, including altered hydrology, crab predation, and sand harvesting. These areas offer opportunities for enrichment planting with \u003cem\u003eA. germinans\u003c/em\u003e and \u003cem\u003eR. mangle\u003c/em\u003e. Sites threatened by increased sedimentation (like Praia da Conchas) require hydrological restoration to reconnect tidal flows and promote natural regeneration. Mangroves of Malanza, Praia Salgada, and Praia Caix\u0026atilde;o exhibited intact to moderately disturbed stands that could recover naturally. These sites are especially promising for nature-based enterprises such as ecotourism and blue carbon projects.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussions","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMangrove cover\u003c/h2\u003e\u003cp\u003eTotal mangrove area in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe was estimated at 180.0 ha, with S\u0026atilde;o Tom\u0026eacute; constituting 174.801 ha and Pr\u0026iacute;ncipe 5.2 ha. This value is lower than earlier reports, but represents the most accurate estimate of mangrove area in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMangrove area in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe from different sources\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIsland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e\u003cp\u003eMangrove Area\u003c/p\u003e\u003cp\u003e(ha)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUNEP\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHaroun et al.\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlobal Mangrove Watch\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAfonso\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAfonso et al.,\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMachava-Ant\u0026oacute;nio et al.\u003c/p\u003e\u003cp\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eThis study\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eS\u0026atilde;o Tom\u0026eacute;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCau\u0026eacute;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e200\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\u003e157.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLobata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\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\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCantagalo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\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\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u0026eacute;-Z\u0026oacute;chi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\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\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026Aacute;gua Grande\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\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\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eWhole of S\u0026atilde;o Tom\u0026eacute;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e48.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1-200\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e174.801\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePr\u0026iacute;ncipe\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePagu\u0026eacute;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWhole Country\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e140\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e100\u0026ndash;136\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e180.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eDetailed mangrove cover per site based on the different sources is provided in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe observed differences in mangrove area of STP from different sources are attributed to differences in methodologies used, the periods data were taken, as well as the definition of mangrove areas.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eForest structure\u003c/h2\u003e\u003cp\u003eThe structural attributes, such as species composition, tree height, basal area, and standing biomass, are comparable to other mangroves in the Central and West Africa region [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Mangroves in STP are dominated by \u003cem\u003eRhizophora spp.\u003c/em\u003e and \u003cem\u003eA. germinans\u003c/em\u003e, which is consistent with the mangroves of the region. \u003cem\u003eLaguncularia racemosa\u003c/em\u003e has been reported in West and Central African mangroves, including Guinea-Bissau, Nigeria, Gabon, and Cameroon [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, it was not recorded in STP during this study. Its absence may indicate historical occurrence followed by local extinction, possibly due to limited habitat extent, small and vulnerable populations, or anthropogenic pressures such as land conversion and altered hydrology. Given the islands\u0026rsquo; small and fragmented mangrove areas, the persistence of Laguncularia populations may have been ecologically constrained. Further surveys are needed to confirm whether the species is truly absent or persists in overlooked sites.\u003c/p\u003e\u003cp\u003eThe standing biomass of mangroves in STP is estimated at 337.2 Mg ha⁻\u0026sup1;, which is within the range of healthy mangroves of the Atlantic\u0026ndash;East Pacific bioregion that includes the mangroves of the Central and West Africa region (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStructural attributes of mangroves in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe compared to other countries in Central Africa\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e\u003cp\u003eStructural Attribute\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. of species\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTree Density (stems ha⁻\u0026sup1;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax. Height (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax. Diameter (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean Diameter (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMean Basal Area\u003c/p\u003e\u003cp\u003e(m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMean AGB (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMean BGB (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTotal mean Biomass (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eS\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,169.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e240.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e97.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e337.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eThis study\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCameroon\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eAjonina et al., 2014[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGabon\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eAjonina et al., 2014[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCongo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eAjonina et al., 2014[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDRC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eAjonina et al., 2014[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eBiomass carbon\u003c/h2\u003e\u003cp\u003eThe mangrove forests of STP exhibit moderate biomass carbon stocks, averaging 157.9 Mg C ha⁻\u0026sup1;, which is within the lower to mid-range compared to other regions in West and Central Africa [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The value is higher than the 147.5 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e average global mangrove biomass carbon [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] but lower than the global average of 190.2 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Considerable spatial variability was observed, with Cantagalo supporting the highest biomass carbon (202.8 Mg C ha⁻\u0026sup1;), while M\u0026eacute;-Z\u0026oacute;chi \u0026amp; \u0026Aacute;gua Grande recorded severely depleted stocks (7.6 Mg C ha⁻\u0026sup1;), reflecting intense degradation and limited mangrove cover in urbanized districts. Districts such as Cantagalo, Lobata, and Pagu\u0026eacute; maintain relatively high carbon storage, making them priority areas for protection and integration into blue carbon initiatives. In contrast, the severely degraded sites of M\u0026eacute;-Z\u0026oacute;chi and \u0026Aacute;gua Grande offer opportunities for targeted restoration interventions aimed at enhancing biomass accumulation and carbon sequestration. As expected, there was a strong positive correlation between basal area and total biomass carbon (r\u0026sup2; = 0.97), indicating that plots with larger tree sizes stored more biomass carbon. In contrast, a weak positive correlation was observed between stem density and total biomass carbon (r\u0026sup2; = 0.48), suggesting that tree count alone was a less reliable predictor of carbon stocks compared to structural attributes like basal area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eNatural regeneration\u003c/h2\u003e\u003cp\u003eMangroves of STP were dominated by juvenile \u003cem\u003eRhizophora spp\u003c/em\u003e. However, the natural regeneration density, estimated at 1,587\u0026thinsp;\u0026plusmn;\u0026thinsp;648.2 juveniles ha⁻\u0026sup1;, is considered insufficient to sustain forest recovery following disturbance. This value falls significantly below the minimum threshold of 2,500 seedlings ha⁻\u0026sup1; recommended by FAO [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] as necessary for restocking a degraded mangrove stand without replanting. An ideal regeneration ratio of RCI:RCII:RCIII\u0026thinsp;=\u0026thinsp;6:3:1 is considered adequate for effective mangrove recovery [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In STP, the observed ratio (2:2:1) was slightly below this benchmark, indicating limited natural regeneration potential. Although juvenile densities were not significantly different among districts, the predominance of Regeneration Class I and II individuals suggests that most stands are still in early successional stages. This skewed structure highlights ongoing regeneration but also indicates limited recruitment into advanced size classes, which may constrain long-term forest stability. The absence of \u003cem\u003eC. erectus\u003c/em\u003e juveniles, despite the presence of adults, points to potential regeneration bottlenecks, possibly linked to hydrological conditions, propagule availability, or species-specific ecological constraints. Districts with complete absence of juveniles may reflect intense anthropogenic pressures or degraded environmental conditions that hinder natural recruitment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eRestoration potential\u003c/h2\u003e\u003cp\u003eMangrove ecosystems in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe exhibit notable potential for restoration, though levels of degradation vary across sites. Highly disturbed areas such as Praia das Conchas, Diogo Nunes, Praia Quinze, and Micolo represent prime candidates for active interventions, including enrichment planting with native mangrove species. However, successful outcomes hinge on the restoration of hydrological connectivity, a foundational step emphasized in hydrological rehabilitation frameworks [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In contrast, relatively intact stands in Malanza, Praia Salgada, and Praia Caix\u0026atilde;o are well-suited for assisted natural regeneration (ANR) and expansion of ongoing afforestation. Studies from Southeast Asia and West Africa [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] have shown that ANR, when coupled with community stewardship, yields cost-effective and ecologically robust results. These latter sites exhibit high biomass and carbon sequestration capacity, suggesting strong potential for integration into blue carbon initiatives, including carbon credit markets and Nationally Determined Contributions (NDCs) under the Paris Agreement. Restoration strategies should adopt Community-Based Ecological Mangrove Restoration (CBEMR) principles, which emphasize community involvement, ecological integrity, and long-term monitoring [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Crab control should be considered as part of site-specific management plans, particularly in early-stage restoration plots where crab herbivory has been shown to significantly reduce seedling survival [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first comprehensive assessment of mangroves in S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe, documenting a total area of 180 ha across 19 sites, with S\u0026atilde;o Tom\u0026eacute; hosting 97.1%. Six species were recorded, dominated by \u003cem\u003eRhizophora racemosa, Avicennia germinans\u003c/em\u003e, and \u003cem\u003eR. mangle\u003c/em\u003e, with smaller stands of \u003cem\u003eR. harrisonii, Conocarpus erectus\u003c/em\u003e, and the fern \u003cem\u003eAcrostichum aureum\u003c/em\u003e. Although limited in extent, these mangroves store an estimated 938.1 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (168,858.9 Mg C), underscoring their ecological and climate value. Despite this importance, they face acute pressures from unsustainable harvesting, hydrological alteration, pollution, coastal infrastructure, and settlement encroachment, with about 25.5 ha viable for restoration. Weak governance, characterized by fragmented mandates and the absence of a national mangrove policy, further constrains management. Priority restoration and afforestation sites were identified on both islands, offering opportunities for Nature-based Solutions. Interventions should emphasize hydrological rehabilitation, Community-Based Ecological Mangrove Restoration (CBEMR), nursery development, crab control, long-term monitoring, and ecotourism to support livelihoods. To secure their future, S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe must adopt a unified National Mangrove Strategy, integrate mangroves into climate commitments (e.g., NDCs, blue carbon markets), strengthen institutional coordination, and promote community co-management. Addressing knowledge gaps in biomass and soil carbon, biodiversity, and hydrology will enhance adaptive management and open access to climate finance. With decisive action and collaboration, mangroves can become a cornerstone of coastal resilience and a model of blue carbon stewardship for small island states.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eSupported by the West Africa Coastal Areas Resilience Investment Project (WACA+), financed through the World Bank under Contract N\u0026ordm;: 23/C/WACA+/2024 and implemented under the management of the Ag\u0026ecirc;ncia Fiduciaria de Administra\u0026ccedil;\u0026atilde;o de Projetos (AFAP).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e The authors gratefully acknowledge the technical support provided by the Dire\u0026ccedil;\u0026atilde;o das Florestas e da Biodiversidade (DFB), the Dire\u0026ccedil;\u0026atilde;o do Ambiente e da A\u0026ccedil;\u0026atilde;o Clim\u0026aacute;tica (DAAC), and the Universidade de S\u0026atilde;o Tom\u0026eacute; e Pr\u0026iacute;ncipe (USTP). Special thanks are extended to the WACA+ team, particularly Eng. Arlindo Carvalho, Kassi Costa dos Santos, and Abnilde Lima, as well as the AFAP project staff led by Mr. Helio Silva Almeida, for their facilitation and guidance. The authors also sincerely appreciate the World Bank team, led by Juliana Castano Isaza and Juan Jose Miranda Montero, for technical support, and all stakeholders whose insights, data, and recommendations greatly enriched this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure Statement:\u003c/strong\u003e No potential conflict of interest was reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate: Not applicable.\u003cbr\u003e\u0026nbsp;Consent for publication: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n\u003cli\u003eLovelock, C. 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Ecological restoration of mangroves: A global synthesis. Nature Reviews Earth \u0026amp; Environment, 1(1), 1\u0026ndash;13. doi.org/10.1038/s43017-019-0001-7\u003c/li\u003e\n\u003cli\u003eEllison, J. C. (2000). Mangrove restoration: Do we know enough? Restoration Ecology, 8(3), 219\u0026ndash;229. doi.org/10.1046/j.1526-100x.2000.80033.x\u003c/li\u003e\n\u003cli\u003eBrown, B., Fadillah, R., Nurdin, Y., Soulsby, I., \u0026amp; Ahmad, R. (2014). Community-based ecological mangrove rehabilitation in Indonesia: A case study of integrated planning and implementation in Lampung, Sumatra. Sustainability, 6(11), 6853\u0026ndash;6872. doi.org/10.3390/su6116853\u003c/li\u003e\n\u003cli\u003eBosire, J. O., Dahdouh-Guebas, F., Kairo, J. G., Kazungu, J., Dehairs, F., \u0026amp; Koedam, N. (2005). Effect of canopy structure on crab damage in replanted mangroves in Kenya. Marine Ecology Progress Series, 299, 257\u0026ndash;266. doi.org/10.3354/meps299257\u003c/li\u003e\n\u003cli\u003eDahdouh-Guebas, F., Jayatissa, L. P., Di Nitto, D., Seen, D. L., \u0026amp; Koedam, N. (2005). How effective were mangroves as a defense against the recent tsunami? Current Biology, 15(12), R443\u0026ndash;R447. doi.org/10.1016/j.cub.2005.06.008\u003c/li\u003e\n\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 forests, blue carbon, restoration potential, and São Tomé and Príncipe","lastPublishedDoi":"10.21203/rs.3.rs-7473521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7473521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMangrove forests in Small Island States (SIDs) have not been fully incorporated into the global mangrove atlas. Here, we present a detailed analysis of the status and conditions of mangroves located in the Republic of S\u0026atilde;o Tom\u0026eacute; and Pr\u0026iacute;ncipe (STP). Mapping of mangroves was carried out using remotely sensed data and GIS. This was complemented by detailed ground truthing, GPS mapping, and community appraisals in all 19 mangrove sites in STP. Sampling was conducted in 24 square plots of 100 m\u003csup\u003e2\u003c/sup\u003e that were randomly distributed along belt transects established perpendicular to the waterline. Within each plot, all trees with a stem diameter\u0026thinsp;\u0026ge;\u0026thinsp;2.5 cm were identified, counted, and position marked. Data on tree height (m), stem diameter (cm), and canopy cover (%) were collected, from which basal area (m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), stocking rates (stems ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and biomass carbon (Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were derived. Mangrove forests in STP are estimated to cover approximately 180 ha, with 97.1% of these occurring on S\u0026atilde;o Tom\u0026eacute; Island and the rest on the Island of Pr\u0026iacute;ncipe. There are six mangrove species in STP; dominated by \u003cem\u003eRhizophora racemosa G. Mey., R. mangle L., and Avicennia germinans (L.) L..\u003c/em\u003e The stocking rates of mangroves in STP ranged from 400 to 2,880 stems ha⁻\u0026sup1; (mean: 2,170.0\u0026thinsp;\u0026plusmn;\u0026thinsp;366.0 stems ha⁻\u0026sup1;) with a basal area of 25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 m\u003csup\u003e2\u003c/sup\u003e ha⁻\u0026sup1; (range: 1.5 to 30.2 m\u003csup\u003e2\u003c/sup\u003e ha⁻\u0026sup1;), and standing biomass of 240.1\u0026thinsp;\u0026plusmn;\u0026thinsp;38.3 t ha⁻\u0026sup1; (range: 11.1 to 315 t ha⁻\u0026sup1;). Together with below-ground biomass, mangroves in STP have a biomass carbon of 157.9\u0026thinsp;\u0026plusmn;\u0026thinsp;24.9 Mg C ha⁻\u0026sup1;. Assuming a sediment carbon of 780.2 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for Central African mangroves, the total ecosystem carbon of mangroves in STP is estimated at 938.1 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (range: 701.1 to 1,123.2 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Localized overexploitation of mangrove wood products for firewood and tannin extraction was witnessed in peri-urban mangrove sites at Praia das Conchas, \u0026Aacute;gua Ize and Diogo Nunes, where natural regeneration was inadequate to support forest recovery. Other important threats to the mangroves were coastal development and waste disposal. These findings revealed spatial variation in mangrove distribution across STP, as well as identifying sites for targeted restorations. The study provides baseline data and information for exploring nature-based enterprises, including mangrove ecotourism and blue carbon.\u003c/p\u003e","manuscriptTitle":"Characteristics of Mangrove Forests in São Tomé and Príncipe and The Potential for Restoration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 09:03:12","doi":"10.21203/rs.3.rs-7473521/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":"b6e7c34a-23e2-4f2a-a626-e871304e1d5f","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T13:23:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 09:03:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7473521","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7473521","identity":"rs-7473521","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}