Prediction of potential mangrove distributions in the Beibu Gulf of Guangxi Zhuang Autonomous Region, China using the MaxEnt model

Prediction of potential mangrove distributions in the Beibu Gulf of Guangxi Zhuang Autonomous Region, China using the MaxEnt model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prediction of potential mangrove distributions in the Beibu Gulf of Guangxi Zhuang Autonomous Region, China using the MaxEnt model Li Lifeng, Wenai Liu, Hangqing Fan, Jingwen Ai, Shuangjiao Cai, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2203109/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Context The restoration of mangroves is an significant challenge within the protection of coastal habitats. Predicting the distribution of dominant species in mangrove communities is essential for the appropriate selection of species and spatial planning for restoration. Objectives We explored the spatial distribution of six mangrove species including their related environmental factors, thereby identifying potentially suitable habitats for mangrove protection and restoration. Methods Based on six dominant mangrove species that occur in the Beibu Gulf of Guangxi, we used linear correlation analysis to screen environmental factors. In addition, we used the maximum entropy model to analyze the spatial distribution of potentially suitable areas for mangrove afforestation. Based on spatial superposition analysis, we identified mangrove conservation and restoration hot spots. Results Our findings indicate that the main factors affecting the distribution of suitable mangrove habitat in the Beibu Gulf are topographic factors, followed by bioclimatic factors, land-use type, marine salinity, and substrate type. We identified 13,816 hm 2 of prime mangrove habitat in the Beibu Gulf, primarily distributed in protected areas. The protection rate for existing mangroves was approximately 42.62%. Conclusions We identified the dominant environmental factors and their thresholds for the distribution of six mangrove species and identified the spatial distribution of individual species and location of suitable rehabilitation sites. According to the predicted spatial distribution of mangrove plants, our findings suggest that mangrove restoration should be based on suitable species and sites. maximum entropy model Beibu Gulf of Guangxi mangrove suitable growth Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Mangroves are woody plant communities found in tropical and subtropical bays and estuaries and have important socioeconomic and environmental ecological functions (Li and Dai 2015 ; Lin et al. 2005 ). Mangroves have become a key focus for wetland ecology and biodiversity protection worldwide. From 1980 to 2000, over 35% of the global mangrove area was lost, exceeding the rate of habitat loss of other ecosystems such as rainforests (Valiela et al. 2001 ). The loss continues and the mangrove areas have disappeared at a rate of 1% per year ( Curnick et al. 2019 ). Therefore, mangrove restoration has, of late, received much research attention in ecological protection worldwide (Hai et al. 2020 ; Thomas et al. 2017 ). It is essential to predict the spatial distribution of potentially suitable mangrove habitats in Beibu Gulf to lay the foundation for restoration of mangrove wetlands. The coastal area of the Beibu Gulf in Guangxi Zhuang Autonomous Region is the most important mangrove swamp region in China. At the end of 2013, the mangrove wetland area in Guangxi had a size of 7243.15 hm 2 (Tao et al. 2017 ), which represents approximately one-third of the total mangrove area in China and plays an important role in the ecological balance of the coastal environment. The development of Beibu Gulf Economic Zone threatens the mangrove ecosystem in Guangxi. Seawall construction, shrimp pond enclosures, and mangrove cutting degrade the mangrove habitat, leading to mangrove damage and death (Fan and Mo 2018 ; Jia 2014 ). However, few studies have focused on predicting the potential distribution of mangroves in the Beibu Gulf. Identification of suitable mangrove restoration sites and the determination of suitable habitat conditions are key factors affecting the success of mangrove restoration (Balke and Friess 2016 ; Fan and Mo 2018 ; Lin 2003 ). Predicting the spatial distribution of potentially suitable habitats for species forms the basis of ecological restoration (Bell 2001 ). Classical niche models include the genetic algorithm for rule set production (GARP), maximum entropy model (MaxEnt), and species distribution models (SDMS) (Sobek-Swant et al. 2012 ; Townsend et al. 2017). Of these, MaxEnt is the most widely used model and has a high level of accuracy (Cobben et al. 2015 ; Liu et al. 2019 ; West et al. 2016 ). Based on the MaxEnt model, species records can be obtained and combined with the corresponding environmental variables to predict potential distribution areas (Bao et al. 2022 ; Dai et al. 2022 ). The MaxEnt model plays an important role in animal and plant protection as well as ecological research and is widely used in predicting the distribution of endangered animals and plants (Mukul et al. 2019 ; Volkan et al. 2021; Wan et al. 2019 ), suitable habitats for invasive organisms (Krauss et al. 2022; Liu et al. 2017 ), and determining the effects of global climate change on species distributions (Bai et al. 2021 ; Gao et al. 2016). The MaxEnt model has recently been applied to studies on potential restoration areas for coastal and intertidal organisms such as seagrass beds, Tachypleus tridentatus, Carcinoscorpius rotundicauda , and coral reefs (Jayathilake and Costello 2018 ; Yan et al. 2019 ; Zellmer et al. 2019 ). Predicting potential mangrove distributions has also garnered considerable research attention (Charrua et al. 2020 ; Rodríguez-Medina et al. 2020 ; Wang et al. 2021 ). Because mangroves lie at the junction of sea and land, the contrasting ecological and environmental characteristics of sea and land must be considered when identifying suitability factors. Therefore, predicting mangrove distributions is more complex than predicting the distributions of continental or marine organisms. Several researchers have incorporated mangrove communities while analyzing suitable mangrove restoration areas in the Guangdong and Fujian provinces (Chao et al. 2020 ; Hu et al. 2020 ). Considering the entire coast of China, mangroves can be divided into five groups according to location for adaptive factor analysis (Hu et al. 2020 ). However, existing research has not focused on the selection of specific mangrove species for restoration. For example, Hu et al. ( 2020 ) did not differentiate between mangrove species in the sampling process of their mangrove distribution prediction study. Existing mangrove influencing factors of distribution predictions primarily include SST, terrain factors, bioclimatic factors, sea surface salinity factors, and substrate type. Because mangrove patches are primarily distributed in wetlands, woodlands, and water bodies (Chao et al. 2021 ), we included land-use type in our study to appropriately limit the distribution of mangroves, thereby increasing the accuracy of predicted distribution results. We analyzed six dominant mangrove species to obtain their habitat condition thresholds, which are of practical significance for the selection of appropriate species and building the community structure in mangrove restoration work. In this study, we used the MaxEnt model to determine the potential distribution, area, and response interval of six mangrove species, namely Aricennia marina , Aegiceras corniculatum , Kandelia obovata , Bruguiera gymnorrhiza , Rhizophora stylosa , and Acanthus ilicifolius. In addition, all mangrove species were grouped as a whole for the estimation of kernel density to analyze mangrove restoration hot spots and unprotected mangrove areas. Our study provides a scientific basis for the restoration and protection of mangroves in the Beibu Gulf of Guangxi. 2. Methods 2.1 Study area The Beibu Gulf of Guangxi is located in southern China (107°56ʹ–109°47ʹE, 20°54ʹ–2l°24ʹN, Fig. 1 ). The continental coastline extends from the Ximi Estuary at Yingluo Port at the junction of Hepu and Lianjiang counties, Guangdong Province, in the east, to Beilun Estuary at the junction of Dongxing City and Vietnam in the west. The total length of the continental coastline is 1628.59 km. The coastline is tortuous and has many natural bays, including Tieshan, Lianzhou, Qinzhou, and Fangcheng Bays as well as Pearl Harbor (Li and Dai 2015 ). This area is characterized by a marginal tropical marine climate, with an annual rainfall of 1500 to 2000 mm and an annual average temperature of 22.0 to 23.4°C. The Beibu Gulf has a relatively extensive distribution of mangroves, with rich mangrove wetland resources. The area contains the following three mangrove reserves: Shankou Mangrove National Nature Reserve, Beilun Estuary Mangrove National Nature Reserve, Maoweihai MangroveNature Reserve. Beibu Gulf of Guangxi contains a National Wetland Park. Dominant mangrove species include Aricennia marina , Aegiceras corniculatum , Kandelia obovata , Bruguiera gymnorrhiza , Rhizophora stylosa , and Acanthus ilicifolius , with the top five mangrove communities in terms of area size consisting of Aricennia marina , Aegiceras corniculatum , Aricennia marina + Aegiceras corniculatum , Bruguiera gymnorrhiza + Aricennia marina , and Rhizophora stylosa . The Aricennia marina and Aegiceras corniculatum communities account for 41.74 and 32.91% of the total mangrove area, respectively (Tao et al. 2017 ). 2.2 Mangrove distribution To predict suitable distribution areas for mangrove species using the MaxEnt model, species distribution and environmental data are required. Mangrove distribution data for the Beibu Gulf were visually interpreted according to the 2020 Google Earth image (resolution 0.61 to 2.4 m) and verified by on-site investigations. Additional data were primarily obtained from the literature, and 908 data points from the mangrove forest survey were provided by the Guangxi Mangrove Research Center. In the study area, re-adoption work was conducted using the ArcGIS 10.4 (Environmental Systems Research Institute, Redlands, California, USA) fishing net tool supplemented by manual marking, and a 300 × 300 m grid was established. A total of 2306 sampling and coordinate data points were obtained for the study area. Longitude and latitude information regarding species distribution were extracted using the ArcGIS 10.4 “Calculate Geometry” tool, and data were stored as comma-separated values files to form a mangrove sample data set. We used an analysis framework to predict the potential distribution of mangrove species (Fig. 2 ). 2.3 Environmental data Environmental data can determine differences in growth factors between mangrove species. Environmental factors, such as temperature, salinity, and distance from coastline, are important indicators of potential mangrove growth and distribution (Peng et al. 2016 ). Because mangroves grow in the sea–land ecotone, their fitness factors are affected by both continental environmental and marine environmental factors. Thus, in our study, we combined environmental data from the sea and the land. To estimate the distribution of mangroves in Beibu Gulf, the boundary of the study area was fixed using the coastline as the reference. The estimated study area enclosed a 10 km buffer zone along the land from the coastline (Hu et al. 2020 ), and a 5 m isobath in the sea. Weizhou and Xieyang Islands were not included in this study. ArcGIS resampling was used to extract environmental data for existing mangrove distribution sample points, and a Kriging interpolation was conducted to expand the data to sea, ocean, or land and to integrate sea and land data. Correlation analysis is a statistical method that can screen environmental variables to analyze closeness. Mangrove distribution and environmental data were sampled using GIS. The environmental data included 19 environmental parameters, 2 terrain parameters, 3 SST data points, 3 sea salt data points, 1 substrate type data, and 1 land-use data. Pearson’s correlation was used to identify environmental variables and was performed for all 29 variables to calculate the correlation coefficient matrix. A correlation coefficient greater than 0.8 indicates a high correlation. Variables with small biological significance in the highly correlated variable group were selected (Chao et al. 2020 ; Ta et al. 2021 ; Wang et al. 2021 ; Xu et al. 2015), with a total of 16 environmental variables used to establish the model (Table 1 ). We calculated the wetland index (WTI), representing the spatial distribution of the runoff source area and groundwater level in the basin, using the following equation: $$WTI=\text{ln}\left(\frac{\alpha }{\text{tan}\beta }\right)$$ 1 where α equals (flow accumulation + 1) × pixel area (in m 2 ) and β represents the slope angle in radians (Hu et al. 2020 ). Table 1 Environmental variables used to predict mangrove distribution in Beibu Gulf of Guangxi,China. Data type Variable Description Unit Bioclimatic data Bio2 Mean diurnal range [mean of monthly (max. temp - min. temp)] °C×10 Bio3 Isothermality (BIO2 / BIO7) (× 100) % Bio5 Max. temp of warmest month °C×10 Bio6 Min. temp of the coldest month °C×10 Bio10 Mean temp of warmest quarter °C×10 Bio15 Precipitation seasonality (coefficient of variation) % Bio18 Precipitation in the warmest quarter mm Bio19 Precipitation in the coldest quarter mm Terrain Elevation Topographic elevation m WTI Wetland index -- Ocean salinity data C_sss Mean sea surface salinity in the coldest season ‰ W_sss Mean sea surface salinity in the warmest season ‰ Sea surface temperature data C_sst Mean sea surface temperature in the coldest season °C W_sst Mean SST in the warmest season °C Substrate type data Substrate Substrate type -- Land-use data Land-use Land-use type -- 2.4 Model parameter setting In this study, MaxEnt version 3.4.1 (Steven J. Phillips, Columbia University) was used for prediction analyses. To establish the model, 75% of the mangrove distribution data from the Beibu Gulf was used as training data and the remaining 25% used as test data (Hu et al. 2020 ). The mangrove distribution dataset established in this study included sufficient samples. To construct the MaxEnt model, the default feature combination was selected and sample data were randomly selected. To improve accuracy, the number of repeated calculations of the model was set to 10. Default values were used for other settings. Grid output results obtained after the operation were visually converted and analyzed using ArcGIS 10.4. The pixel value of each grid represents the distribution probability of mangroves in the grid. The pixel value ranged from 0 to 1. The larger the pixel value, the higher the potential mangrove distribution, indicating a high habitat suitability of the grid. In this study, we used the natural breakpoint method to grade fitness results based on Chao et al. ( 2020 ) and Hu et al. ( 2020 ) as follows: 0–0.2, non-fitness; 0.2–0.5, low fitness; 0.5–0.7, medium fitness; and > 0.7, optimal fitness. 2.5 Model test results The MaxEnt model was used to calculate the receiver operating characteristic (ROC) curve. The value of the diagnostic test was represented by the area under the curve (AUC) of the ROC curve, with AUC values ranging from 0 to 1. The closer to 1, the more accurate the prediction (Hu et al. 2020 ; Ta et al. 2021 ). The range of AUC values were interpreted using Hu et al. ( 2020 ) as follows: 1–0.9, excellent prediction; 0.8–0.9, good prediction; 0.7–0.8, average prediction; 0.6–0.7, poor prediction; and 0.5–0.6, prediction failure. 2.6 Vacancy analysis of mangrove protection and restoration Based on the results of mangrove habitat suitability, priority areas for mangrove protection and restoration (potential mangrove distribution hot spots) were calculated using kernel density estimation (KDE), which is a spatial hot spot analysis method (Cai et al. 2012 ). The distribution of discrete values in continuous space can be obtained by calculating the density of elements in their surrounding neighborhood (Hu et al. 2020 ). In this study, after KDE analysis in ArcGIS software, we used spatial superposition analysis of mangrove distribution status, priority area spatial distribution, and nature reserve distribution to further analyze the protection status of the mangrove ecosystem in Beibu Gulf. Furthermore, protected area information, such as the protection rate and protection and repair of vacant areas, was obtained from spatial superposition analysis. 3. Results 3.1 AUC value The AUC of the training and test sets of the distribution prediction model of the six mangrove species in Beibu Gulf ranged from 0.912 to 1 (Fig. 3 ). The AUC values of the training and test sets of the distribution prediction model for all mangrove species as a whole were 0.882, 0.869, respectively (Fig. 4 ). Based on the AUC of the test set, the simulation of the MaxEnt model for mangroves was deemed accurate. Thus, the model was highly reliable and could predict the distribution of the dominant mangrove species in Beibu Gulf of Guangxi. 3.2 Analysis of dominant environmental factors Factors affecting mangrove habitat included bioclimate, topography, marine salt data, SST, substrate type, and land-use type. The contribution of variables based on interactions between different environmental variables (Hu et al. 2020 ; Liu et al. 2019 ). We investigated the test results of different factors affecting mangrove distribution (Fig. 5 ) to ascertain the dominant factors affecting the distribution of specific species. The dominant factors affecting overall mangrove distribution in Beibu Gulf were elevation, wetland index, mean temperature of warmest quarter, and substrate type. (Fig. 5 ). Subsequently, we analyzed the factors affecting specific mangrove species distribution in Beibu Gulf. We investigated the importance of various environmental factors on the distribution probability of each of the six selected mangrove species. The three environmental factors imperative to the geographical distribution of Aricennia marina were elevation, mean sea surface salinity in the coldest season and maximum temperature of warmest month (Fig. 6 a), with their cumulative contribution rate accounting for 50.7% and the other 13 environmental factors accounting for 49.3% of its distribution (Fig. 7 ). Among the remaining factors, the total contribution of bioclimatic factors, topography, sea surface salinity, SST, substrate type, and land-use type accounted for 39.1, 34, 16.6, 1.2, 6.1, and 2.9%, respectively. Based on the contribution and importance of various environmental factors for predicting the distribution of Aricennia marina , elevation limited optimal planting areas, while the mean salinity of the sea surface in the coldest season reflected the salt preference of this species. The contributions of land-use type and SST to the distribution of Aricennia marina were relatively low. Regarding Aegiceras corniculatum , the three environmental factors imperative to its geographical distribution were elevation, wetland index, and substrate type (Fig. 6 b), with their cumulative contribution accounting for 41.7%, and the remaining 13 environmental factors accounting for 58.3% of its distribution. Among the remaining factors, the total contribution rate of bioclimate, topography, sea surface salinity, bioclimate, SST, substrate type, and land-use type accounted for 36.3, 39.6, 18.9, 0.1, 2.1, and 3.1%, respectively (Fig. 7 ). The contribution of marine salinity in predicting the distribution of Aegiceras corniculatum was greater than that for Aricennia marina , which indicates that Aegiceras corniculatum is more sensitive to salinity than Aricennia marina . The contribution of SST, substrate type, and land-use type to the distribution of Aegiceras corniculatum was relatively low. For Kandelia obovata , the three environmental factors most important to its geographical distribution were elevation, substrate type, and wetland index (Fig. 6 c), with a cumulative contribution percentage of 42.3%. Among the dominant influencing factors, elevation reflected the restrictions on the planting location of Kandelia obovata. In addition, possible this species were relatively sensitive to substrate type. Regarding Bruguiera gymnorrhiza , the three environmental factors with the largest effect on its geographical distribution were maximum temperature of the warmest month, precipitation in the warmest quarter, and substrate type (Fig. 6 d), accounting for 79.1% of the cumulative contribution. Among the influencing factors, the total contribution rate of bioclimate, topography, sea surface salinity, SST, substrate type, and land-use type accounted for 76.9, 4.1, 0.7, 0.4, 15.8, and 1.2%, respectively. These results show that Bruguiera gymnorrhiza was sensitive to bioclimate and substrate type. For Rhizophora stylosa , the three environmental factors that most affected its geographical distribution were precipitation of the warmest quarter, substrate type, and mean temperature of the warmest quarter (Fig. 6 c), together accounting for 82.9% of the cumulative contribution. Among the factors influencing the distribution of this species, the contribution rate of bioclimate, topography, sea surface salinity, SST, substrate types, and land-use type accounted for 78.3, 6.6, 2.9, 0.4, 11.9, and 0%, respectively. These results indicate that the distribution of Rhizophora stylosa is sensitive to bioclimate. Finally, for Acanthus ilicifolius , the three environmental factors with the greatest importance for its geographical distribution were substrate type, mean sea surface salinity in the warmest season, and wetland index (Fig. 6 f), with their cumulative contribution representing 72.5%. The significance of substrate type reflects the substrate preference of Acanthus ilicifolius , while the significance of wetland index reflects the limitations of topographic conditions on Acanthus ilicifolius . Furthermore, the importance of mean sea surface salinity in the warmest season reflects the sensitivity of Acanthus ilicifolius to salinity, with too high salinity levels possibly affecting the species growth. 3.3 Range of environmental factors affecting mangrove habitat suitability The variables representing the mangrove suitability factors in Beibu Gulf were selected based on the logistic mode of the MaxEnt model. Correlations between habitat suitability and environmental variables were analyzed using a probability distribution logic output value of 0.5 as the boundary. This enabled us to investigate the optimal thresholds for the main environmental variables affecting the six selected mangrove species (Table 2 ). The suitable elevation threshold ranges for the growth of mangrove plants were determined. The minimum suitable elevation values of the plants are ordered from lowest to highest: Aricennia marina , Aegiceras corniculatum , Rhizophora stylosa , Kandelia obovata , Bruguiera gymnorrhiza , and Acanthus ilicifolius. The lowest elevation value suitable for the growth of Aricennia marina was − 0.84 m, whereas the lowest elevation value suitable for the growth of Acanthus ilicifolius was relatively high (Fig. 8 ). Table 2 Thresholds of the dominant environmental factors affecting the distribution of six investigated mangrove species. Order Mangrove species Three dominant environmental factors Limitation 1 Aricennia marina Elevation -0.84–1.27 m Mean sea surface salinity in the coldest season 16.41–25.31‰ Max. temperature of warmest month 32.1–32.3°C 2 Aegiceras corniculatum Elevation -0.68–2.02 m Wetland index 4.11–9.81 Substrate type Mixed mudflat 3 Kandelia obovata Elevation -0.50–1.88 m Substrate type Mixed mudflat Wetland index 4.49–8.33 4 Bruguiera gymnorrhiza Max༎temperature of warmest month 32.3–32.4°C Precipitation in the warmest quarter 638–753 mm Substrate type Mixed mudflat 5 Rhizophora stylosa Precipitation in warmest quarter 723–746 mm Substrate type Mixed mudflat Mean temperature of warmest quarter 28.7–28.9°C 6 Acanthus ilicifolius Substrate type Mixed mudflat Mean sea surface salinity in the warmest season 3.39–7.37‰ Wetland index > 5.28 A number of trends were observed for the wetland index (Fig. 9 ). Compared to other mangrove species, Aricennia marina had the lowest minimum wetland index, as it can grow in the low tide zone. In contrast, Acanthus ilicifolius had the largest minimum wetland index indicating that it can grow in the high tide zone. Regarding the average salinity of the sea surface in the coldest season, Kandelia obovata had a relatively wide range (5.91–17.92‰; Fig. 10 ). According to the highest suitable value of mean sea surface salinity in the coldest season, the investigated species can be ordered from highest to lowest as follows: Rhizophora stylosa > Bruguiera gymnorrhiza > Aricennia marina > Aegiceras corniculatum > Kandelia obovata > Acanthus ilicifolius. Regarding the most suitable value of mean sea surface salinity in the warmest season, the species can be ordered from highest to the lowest as follows: Aricennia marina > Rhizophora stylosa > Kandelia obovata > Bruguiera gymnorrhiza > Aegiceras corniculatum > Acanthus ilicifolius . The most suitable range of mean sea surface salinity in the coldest season for Aricennia marina was 16.41–25.31‰, and the most suitable value was 24.27‰. The suitable range of the average sea surface salinity for Acanthus ilicifolius in the warmest season was 3.39–7.37‰, and the most suitable value was 4.07‰. 3.4 Suitable areas for mangroves in the Beibu Gulf of Guangxi Based on the findings of Hu et al. ( 2020 ), we divided potential mangrove areas into four groups based on their suitability as follows: best (> 0.7), medium (0.7–0.5), low (0.2–0.5), and unsuitable (0–0.2). Based on the investigation of six mangrove species in Beibu Gulf as the overall input for the model (Fig. 11 g), we obtained an optimal suitable area of 13,816 hm 2 . The areas of high fitness are located primarily in the Dandou Sea on the west side of the Shatian Peninsula in the southeast of Hepu County, Guangxi; Tieshan Port, Qinzhou Bay, and the Dafeng River in the center of the Guangxi coast; Fangcheng Bay on the west of the Guangxi coast; and the Shankou Mangrove National Nature Reserve. The mangroves are concentrated in the Shankou Mangrove National Nature Reserve, Beilun Hekou Mangrove National Nature Reserve, and Maoweihai Mangrove Autonomous Region Nature Reserve (Fig. 11 g). With regard to individual species, the size of the optimum suitable area for Aricennia marina was 10,341 hm 2 (Fig. 11 a, Table 3 ), with areas of high fitness primarily distributed in Tie Shan Gang, the Beihai Golden Bay mangrove reserve, and along the open coasts in the south of the Beihai National Wetland Park and Beilun Estuary Mangrove National Nature Reserve. The size of the optimal suitable area for Aegiceras corniculatum was 13,154 hm 2 (Fig. 11 b), with highly suitable areas primarily distributed in estuaries, such as Lianzhou Bay, the Maoweihai Mangrove Autonomous Region Nature Reserve, and the Dafeng River. The optimal area for Kandelia obovata was 10,672 hm 2 (Fig. 11 c), with highly suitable areas distributed along Qinzhou Bay, the Dafeng River, and Beilun Estuary Mangrove National Nature Reserve. The size of the optimal area for Bruguiera gymnorrhiza was 2565 hm 2 (Fig. 11 d), with highest fitness areas for this species primarily distributed in the Dandou Sea area of the Shankou Mangrove Reserve, Yingluo Port, and Beilun Estuary Mangrove National Nature Reserve. The extent of the most suitable area for Rhizophora stylosa was 1158 hm 2 (Fig. 11 e), with optimal areas for this species distributed primarily in the Dandou Sea area and Yingluo Port of the Shankou Mangrove Reserve, but very few in other regions of the study area. The size of the most suitable area for Acanthus ilicifolius was 4054 hm 2 (Fig. 11 f), with areas with the highest fitness for this species primarily distributed in regions with low estuarine salinity in Lianzhou Bay and the Shankou Mangrove Reserve. Table 3 Suitable mangrove areas. Mangrove species Optimal suitable area (hm 2 ) Medium suitable area (hm 2 ) Aricennia marina 10,341 39,875 Aegiceras corniculatum 13,154 37,063 Kandelia obovata 10,672 20,682 Bruguiera gymnorrhiza 2565 4385 Rhizophora stylosa 1158 3226 Acanthus ilicifolius 4054 6949 Beibu Gulf contains three mangrove nature reserves, a National Wetland Park, with a total area of 15,794 hm 2 , and a mangrove area of 3977 hm 2 . The mangrove protection rate is 42.62%. Based on the analysis of the superposition of the distribution of protected areas and potentially suitable mangrove areas, 49.10% of the most suitable areas are included within protected areas (Fig. 12 ). Eight areas were identified as priority areas for mangrove protection and restoration. The vacant mangrove protection areas in Beibu Gulf are primarily distributed in Lianzhou Bay, along the Dafeng River, the East Bay of Fangchenggang, and Tieshan Harbor. 4. Discussion Our species distribution studies based on the MaxEnt model can be used for the restoration of mangroves. Based on 908 mangrove survey data and the visual interpretation of remote sensing images, the AUC of the training and test sets of the six mangrove species suitability distribution model for the Beibu Gulf, constructed with the MaxEnt model, ranged from 0.912 to 1. The values of the six were “extremely accurate,” indicating the good performance of the model based on survey data and remote sensing images. 4.1 Dominant environmental factors affecting mangrove suitability Through overall mangrove analysis, the dominant factors affecting the distribution of suitable mangrove habitat in Beibu Gulf were topographic factors, bioclimatic factors, land-use type, marine salinity, and substrate type. The effect of SST on the distribution of suitable habitats was relatively weak. In terms of topography, elevation was the dominant factor affecting the distribution of Aricennia marina , Aegiceras corniculatum , and Kandelia obovata . Within this context, Hu et al. ( 2020 ) showed that mangrove distribution was notably limited by topography on a small regional scale, while elevation exerted a significant influence over the distribution of species (Rick et al. 2017) and affected the habitat preference of mangrove species (Chen et al. 2021 ). Thus, in terms of elevation, our results correspond to those of other studies. In our study, the minimum critical value of the elevation of mangrove growth was negative or zero, indicating that mangrove plants could grow on the beach below the average sea level. Within this context, Liu et al. ( 2012 ) reported that large communities of Aegiceras corniculatum and Kandelia obovata can survive on beaches below average sea level. Aricennia marina had the lowest suitable elevation value, indicating that this species is a pioneer tree species for mangrove afforestation. This is consistent with the results from relevant research (Liao et al 2020 ), primarily because Aricennia marina has respiratory roots, is resistant to flooding and hypoxia stress, and has a relatively high salt tolerance (He et al 2007 ). The effect of bioclimatic factors was also a dominant factor in the distribution of potential mangrove habitats. These factors also played an important role in mangroves located in Fujian and Guangdong in China (Chao et al. 2020 ; Hu et al. 2020 ). The distributions of Rhizophora stylosa and Bruguiera gymnorrhiza were more sensitive to bioclimatic factors. Studies have shown that Rhizophora stylosa and Bruguiera gymnorrhiza are thermophilic widespread species (Mo 2002 ), which is consistent with the temperature preference of these two species in our study. Sea surface salinity is an important factor affecting the distribution of mangroves (Barik et al. 2018 ; Ken 2008; Meng et al. 2017 ; Sinsin et al. 2021 ). Although mangroves grow in an environment with a salinity of approximately 30‰ (Tang 2014), they can adapt to a wide salinity range. Under different salinity gradients, the physiological parameters of mangrove plants can change (Biber 2006 ). In our study, the maximum salinity of the sea surface favored by the six mangrove species was < 30‰, which corresponds with the results of previous research (Tang 2014). The model used in our study showed that sea surface salinity was one of the dominant factors affecting the predicted distribution of Aricennia marina and Acanthus ilicifolius . Salinity tolerance intervals of different mangrove species can differ (Jayatissa et al. 2008 ). Based on the most appropriate average sea surface salinity in the coldest and warmest seasons, the three mangrove species studied by Ye et al. ( 2004 ), can be ordered from highest to lowest salt tolerance as follows: Aricennia marina > Aegiceras corniculatum > Acanthus ilicifolius , which is consistent with the findings from our research. For Acanthus ilicifolius , the average salinity threshold of the sea surface in the warmest season ranged from 3.39–7.37‰. The suitable values for Aegiceras corniculatum varied from 3.78–11.81‰, and those of Aricennia marina ranged from 17.95–24.00‰. Therefore, within the threshold range, low-salinity beaches are suitable for Acanthus ilicifolius and medium-salinity beaches are suitable for Aegiceras corniculatum (Ye et al. 2004 ), while Aricennia marina can be planted on beaches with relatively high salinity levels. However, during the dry cold season, the salinity of the Dandou, Yingluo, and Tieshan ports in the Shankou Mangrove Reserve with high mangrove and Bruguiera gymnorrhiza distributions ranges from 26.8–28.0‰ (Lan et al. 2014 ). Therefore, the average salinity of the sea surface in the coldest season and the optimal salinity values of Rhizophora stylosa and Bruguiera gymnorrhiza were higher than those of Aricennia marina . Substrate type was one of the main factors affecting the growth of Aegiceras corniculatum , Kandelia obovata , Bruguiera gymnorrhiza , Rhizophora stylosa , and Acanthus ilicifolius . Previous research has shown that a mixed beach substrate is more suitable for mangrove growth than beach and mudflat substrates with single components (Sui et al. 1999; Zhang et al. 2001 ). Therefore, mixed beaches can be the index used for beach selection in the afforestation of the above mangrove species. 4.2 Recommendations for mangrove restoration As of 2013, the mangrove area in Beibu Gulf was 7243.15 hm 2 . In 2020, the mangrove area in the Beibu Gulf had increased to 9331.53 hm 2 , and is currently still increasing. This increase indicates that there is still space for ecological restoration and confirms that the Guangxi coast has much potential for mangrove restoration (Hu et al. 2020 ). Areas suitable for forests, distributed in Lianzhou Bay and Dafeng River in Qinzhou, and vacant areas in Beibu Gulf can be used for mangrove restoration and protection. The most suitable habitat in this area should be included in ecological mangrove restoration projects, based on the establishment of protected areas and wetland parks. Our study focuses specifically on predicting the distribution of dominant mangrove species and their suitable growth thresholds. Focusing on individual mangrove species provides more targeted results, which are conducive to the selection of species for mangrove restoration. In our study, bioclimatic and topographic factors, as well as sea surface salinity, substrate type, sea surface temperature, and land-use type were selected as environmental variables affecting the distribution of mangroves. In addition to the variable factors used in previous mangrove distribution prediction studies (Chao et al. 2020 ; Hu et al. 2020 ), we added land-use type as an extra factor. Although land-use type was not the most dominant factor affecting the distribution, it also had a certain impact on the distribution of mangroves. The contribution of land-use type to the predicted distribution of Kandelia obovata , Acanthus ilicifolius was 10.9 and 7.5%, respectively. We suggest that in future research, biological invasion factors and human interference factors (such as ports, waterways, and aquaculture) be added and discussed to expand the screening range of environmental variables of the model. 5. Conclusions The distribution prediction of main mangrove plants is of great significance to the selection and layout of mangrove restoration tree species. Our study used remote sensing images in combination with field survey data, sea surface temperature data, land use data, and other environmental data to predict and analyze the potential distribution of six mangrove species in the Beibu Gulf of Guangxi, China, based on the MaxEnt model. Specifically, we analyzed the dominant environmental impact factors of the predicted distribution of six mangrove species and the range of main environmental factors affecting mangrove growth. In addition, we explored potential locations for the six selected mangrove species, as well as hot spots for mangrove growth and protection. The most important factor that affected the overall distribution of mangroves in the Beibu Gulf was topology, followed by bioclimatic factors, land-use type, marine salinity, and substrate type. The SST had relatively weak effects. Among the mangrove species, Rhizophora stylosa and Bruguiera gymnorrhiza were more sensitive to bioclimatic factors than the remaining four species. The areas with potential for mangrove growth in the Beibu Gulf were located primarily in the Dandou Sea, Tieshangang, Qinzhou Bay, Dafeng River, and Fangcheng Harbor, together offering an optimal mangrove habitat of 13,816 hm 2 . Vacant mangrove protection areas in the Beibu Gulf were primarily distributed in Lianzhou Bay and along the Dafeng River in Quinzou. The areas with low estuarine salinity in Lianzhou Bay and the Maoweihai Mangrove Autonomous Region Nature Reserve were suitable for Aegiceras corniculatum and Acanthus ilicifolius habitats. In addition, Dandou and Yingluo ports of the Shankou Mangrove Reserve offered suitable Rhizophora stylosa and Bruguiera gymnorrhiza habitats. The Beilun Estuary National Nature Reserve is suitable for Aricennia marina , Kandelia obovata , and Bruguiera gymnorrhiza . In addition, Tieshan Harbor offers suitable Aricennia marina , Rhizophora stylosa , and Bruguiera gymnorrhiza habitats. In estuary areas, Aegiceras corniculatum and Acanthus ilicifolius are suitable species for the restoration and afforestation of mangroves. The distribution map of mangrove species can be used as the basis for mangrove afforestation and restoration in the Beibu Gulf of Guangxi. For mangrove restoration, the optimal growth areas should be selected, and land and trees should be adapted according to the suitable environmental threshold range for specific mangrove species. Thus, our study provides an important reference for the predicted distribution of six dominant mangrove species, as well as for the appropriate selection of species and the related spatial layout for successful mangrove restoration. Declarations Acknowledgments We gratefully acknowledge the support of the Guangxi Mangrove Research Center for the identification of mangrove species and for providing 908 mangrove survey data. Funding This work was supported by the National Natural Science Foundation of China [Grant number 32060282; U21A2022]. Author contributions All authors contributed to the design of the analyses and the writing of the manuscript. Meth-odology: JW; Formal analysis and investigation:SJ, LF, FQ; Writing (original draft preparation): LF; Writing (review and editing): WA, HQ, JW; Funding acquisition:WA. Data availability Bioclimatic factors were obtained from the World Climate Database archive at the following ink: https://www.worldclim.org/data/worldclim21.html. Terrain data were extracted from ETOP01 terrain elevation and ocean seabed terrain data released by the United States Geophysical Center archive at the following link: https://www.ngdc.noaa.gov/mgg/global/global.html. The SST data were obtained from the National Environmental Information Center of the Oceanic and Atmospheric Administration of the United States (1981–2020 SST data) archive at the following link: ftp://ftp.emc.ncep.noaa.gov/cmb/sst/oisst_v2/. Salinity data were obtained from the marine salinity products of the Institute of Atmospheric Physics, Chinese Academy of Sciences archive at the following link: http://159.226.119.60/cheng/, with the auxiliary data including the seawater salinity information (Lan et al. 2014; Wei et al. 2006) for the references of the study area. Substrate type data were obtained from the National Marine Science Data Center (nmdis.org.cn), and the auxiliary data included substrate classification types in the study area (Xiao et al. 2016). Land-use data were obtained from ESRI 10 m Cover (2020) in GEE archive at the following link: https://livingatlas.arcgis.com/landcover/. 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1","display":"","copyAsset":false,"role":"figure","size":305872,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area and sampling point locations.\u003c/p\u003e","description":"","filename":"Figure1.Studyareaandsamplingpointlocations..png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/e13a9bdefe0c70e6ea976182.png"},{"id":28374660,"identity":"0e041f4c-8343-4e9b-8c90-59672ba85a4d","added_by":"auto","created_at":"2022-10-28 15:15:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177526,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis framework used to predict the potential distribution of mangrove species for restoration purposes.\u003c/p\u003e","description":"","filename":"Figure2.Analysisframeworkusedtopredictthepotentialdistributionofmangrovespeciesforrestorationpurposes..png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/c7b9d05d6447bde423b185b1.png"},{"id":28374104,"identity":"eccf1de0-a7bc-4397-b803-8913766aa3f1","added_by":"auto","created_at":"2022-10-28 15:10:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1011892,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic\u003cstrong\u003e(\u003c/strong\u003eROC) curves of mangrove species used for verification of the MaxEnt model.\u003c/p\u003e","description":"","filename":"Figure3.ReceiveroperatingcharacteristicROCcurvesofmangrovespeciesusedforverificationoftheMaxEntmodel..png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/56e53ccdb5231815c362d8d2.png"},{"id":28374103,"identity":"b0c04ecb-c92c-4986-8377-beb31cd8074e","added_by":"auto","created_at":"2022-10-28 15:10:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":206981,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic\u003cstrong\u003e (\u003c/strong\u003eROC) curve of overall mangroves used for verification of the MaxEnt model.\u003c/p\u003e","description":"","filename":"Figure4.ThereceiveroperatingcharacteristicROCcurveofoverallmangrovesusedforverificationoftheMaxEntmodel..png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/145b3345f6fddde93a7e4dcc.png"},{"id":28374107,"identity":"cfe0d01f-626a-4942-9279-2d5370140d63","added_by":"auto","created_at":"2022-10-28 15:10:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":212523,"visible":true,"origin":"","legend":"\u003cp\u003eRegularized training gains of overall mangroves. Dark blue entries represent independent test results of each variable, light green entries represent test results excluding the variable, and red entries represent test results including all variables (Swets 1988). The length of the entry represents the size of the score; the longer the entry, the more important the variable.\u003c/p\u003e","description":"","filename":"Figure5.Regularizedtraininggainsofoverallmangroves..png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/902bf3bd4c1dedd608fa9782.png"},{"id":28374842,"identity":"7977f7a3-e98e-4eb9-be33-8c760598cf23","added_by":"auto","created_at":"2022-10-28 15:20:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":907779,"visible":true,"origin":"","legend":"\u003cp\u003eRegularized training gains for the six investigated mangrove species including (a) \u003cem\u003eAricennia marina\u003c/em\u003e, (b) \u003cem\u003eAegiceras corniculatum\u003c/em\u003e, (c) \u003cem\u003eKandelia obovata\u003c/em\u003e, (d)\u003cem\u003e Bruguiera gymnorrhiza\u003c/em\u003e, (e) \u003cem\u003eRhizophora stylosa\u003c/em\u003e, and (f)\u003cem\u003e Acanthus ilicifolius\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure6.Regularizedtraininggainsforthesixinvestigatedmangrovespecies.png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/53ec1b5f5531c194876c3897.png"},{"id":28374101,"identity":"ae48470d-5b35-4192-97a5-ee3f06e25d8b","added_by":"auto","created_at":"2022-10-28 15:10:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":15626,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of the six investigated environmental variables in predicting the distribution of mangrove species (\u003cem\u003eAricennia marina\u003c/em\u003e:\u003cem\u003e \u003c/em\u003eAM\u003cem\u003e, Aegiceras corniculatum\u003c/em\u003e:\u003cem\u003e \u003c/em\u003eAC\u003cem\u003e, Kandelia obovata\u003c/em\u003e:\u003cem\u003e \u003c/em\u003eKO\u003cem\u003e, Bruguiera gymnorrhiza\u003c/em\u003e:\u003cem\u003e \u003c/em\u003eBG\u003cem\u003e, Rhizophora stylosa\u003c/em\u003e:\u003cem\u003e \u003c/em\u003eRS\u003cem\u003e, \u003c/em\u003eand \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e: AI).\u003c/p\u003e","description":"","filename":"Figure7.Contributionofthesixinvestigatedenvironmentalvariablesinpredictingthedistributionofmangrovespecies.png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/257d9a5ff58a6d90335f2f5e.png"},{"id":28373000,"identity":"84808bb5-b973-4a80-bd04-55b61aa9f06a","added_by":"auto","created_at":"2022-10-28 15:05:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":39082,"visible":true,"origin":"","legend":"\u003cp\u003eMangrove elevation threshold for \u003cem\u003eAricennia marina \u003c/em\u003e(AM)\u003cem\u003e, Aegiceras corniculatum \u003c/em\u003e(AC)\u003cem\u003e, Kandelia obovata \u003c/em\u003e(KO)\u003cem\u003e, Bruguiera gymnorrhiza \u003c/em\u003e(BG)\u003cem\u003e, Rhizophora stylosa \u003c/em\u003e(RS)\u003cem\u003e, \u003c/em\u003eand \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e (AI).\u003c/p\u003e","description":"","filename":"Figure8.MangroveelevationthresholdforAricenniamarinaAMAegicerascorniculatumACKandeliaobovataKOBruguieragymnorrhizaBGRhizophorastylosaRSandAcanthusilicifoliusAI..png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/861ce9f4cd5b48c2f6ce7f72.png"},{"id":28373002,"identity":"0de8e35a-a672-4f2e-9363-3a1ade261fef","added_by":"auto","created_at":"2022-10-28 15:05:40","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":12260,"visible":true,"origin":"","legend":"\u003cp\u003eWetland index of selected mangrove species including \u003cem\u003eAricennia marina \u003c/em\u003e(AM)\u003cem\u003e, Aegiceras corniculatum \u003c/em\u003e(AC)\u003cem\u003e, Kandelia obovata \u003c/em\u003e(KO)\u003cem\u003e, Bruguiera gymnorrhiza \u003c/em\u003e(BG)\u003cem\u003e, Rhizophora stylosa \u003c/em\u003e(RS)\u003cem\u003e, \u003c/em\u003eand \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e (AI).\u003c/p\u003e","description":"","filename":"Figure9.WetlandindexofselectedmangrovespeciesincludingAricenniamarinaAMAegicerascorniculatumACKandeliaobovataKOBruguieragymnorrhizaBGRhizophorastylosaRSandAcanthu.png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/47a633225532467db8537dbb.png"},{"id":28373005,"identity":"c55cd636-cc78-4c17-8bab-562fc7167e8d","added_by":"auto","created_at":"2022-10-28 15:05:40","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":163377,"visible":true,"origin":"","legend":"\u003cp\u003eSalinity preferences of different mangrove species in the coldest and warmest seasons including (a) mean sea surface salinity in the coldest season and (b) mean sea surface salinity in the warmest season.\u003c/p\u003e","description":"","filename":"FIC0A91.png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/fd63fcb04514297440c5737d.png"},{"id":28373007,"identity":"4677a888-7bfa-4ad1-8d12-6d5f718c8668","added_by":"auto","created_at":"2022-10-28 15:05:40","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1363035,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of suitable habitat for mangroves in the Guangxi Beibu Gulf\u003c/p\u003e","description":"","filename":"Figure11.DistributionofsuitablehabitatformangrovesintheGuangxiBeibuGulf..png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/f321fc3f1d455ae59ec26b7b.png"},{"id":28373011,"identity":"3258a343-ab45-42b2-8649-5424f381975c","added_by":"auto","created_at":"2022-10-28 15:05:40","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":773270,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the hotspots for mangrove distribution and restoration in the Beibu Gulf.\u003c/p\u003e","description":"","filename":"Figure12.MapshowingthehotspotsformangrovedistributionandrestorationintheBeibuGulf..png","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/8e785f3535b1fb07d806ee5f.png"},{"id":28434179,"identity":"bb86c66e-465f-40d8-bc3a-1ac275c41895","added_by":"auto","created_at":"2022-10-31 04:29:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2972431,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2203109/v1/d3ed7c49-3f7a-41aa-a919-1b4f3aa4f24f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of potential mangrove distributions in the Beibu Gulf of Guangxi Zhuang Autonomous Region, China using the MaxEnt model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMangroves are woody plant communities found in tropical and subtropical bays and estuaries and have important socioeconomic and environmental ecological functions (Li and Dai \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Mangroves have become a key focus for wetland ecology and biodiversity protection worldwide. From 1980 to 2000, over 35% of the global mangrove area was lost, exceeding the rate of habitat loss of other ecosystems such as rainforests (Valiela et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The loss continues and the mangrove areas have disappeared at a rate of 1% per year ( Curnick et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, mangrove restoration has, of late, received much research attention in ecological protection worldwide (Hai et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thomas et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is essential to predict the spatial distribution of potentially suitable mangrove habitats in Beibu Gulf to lay the foundation for restoration of mangrove wetlands. The coastal area of the Beibu Gulf in Guangxi Zhuang Autonomous Region is the most important mangrove swamp region in China. At the end of 2013, the mangrove wetland area in Guangxi had a size of 7243.15 hm\u003csup\u003e2\u003c/sup\u003e (Tao et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which represents approximately one-third of the total mangrove area in China and plays an important role in the ecological balance of the coastal environment. The development of Beibu Gulf Economic Zone threatens the mangrove ecosystem in Guangxi. Seawall construction, shrimp pond enclosures, and mangrove cutting degrade the mangrove habitat, leading to mangrove damage and death (Fan and Mo \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jia \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, few studies have focused on predicting the potential distribution of mangroves in the Beibu Gulf. Identification of suitable mangrove restoration sites and the determination of suitable habitat conditions are key factors affecting the success of mangrove restoration (Balke and Friess \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fan and Mo \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lin \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePredicting the spatial distribution of potentially suitable habitats for species forms the basis of ecological restoration (Bell \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Classical niche models include the genetic algorithm for rule set production (GARP), maximum entropy model (MaxEnt), and species distribution models (SDMS) (Sobek-Swant et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Townsend et al. 2017). Of these, MaxEnt is the most widely used model and has a high level of accuracy (Cobben et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; West et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Based on the MaxEnt model, species records can be obtained and combined with the corresponding environmental variables to predict potential distribution areas (Bao et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dai et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The MaxEnt model plays an important role in animal and plant protection as well as ecological research and is widely used in predicting the distribution of endangered animals and plants (Mukul et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Volkan et al. 2021; Wan et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), suitable habitats for invasive organisms (Krauss et al. 2022; Liu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and determining the effects of global climate change on species distributions (Bai et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gao et al. 2016). The MaxEnt model has recently been applied to studies on potential restoration areas for coastal and intertidal organisms such as seagrass beds, \u003cem\u003eTachypleus tridentatus, Carcinoscorpius rotundicauda\u003c/em\u003e, and coral reefs (Jayathilake and Costello \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yan et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zellmer et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Predicting potential mangrove distributions has also garnered considerable research attention (Charrua et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rodr\u0026iacute;guez-Medina et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Because mangroves lie at the junction of sea and land, the contrasting ecological and environmental characteristics of sea and land must be considered when identifying suitability factors. Therefore, predicting mangrove distributions is more complex than predicting the distributions of continental or marine organisms. Several researchers have incorporated mangrove communities while analyzing suitable mangrove restoration areas in the Guangdong and Fujian provinces (Chao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Considering the entire coast of China, mangroves can be divided into five groups according to location for adaptive factor analysis (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, existing research has not focused on the selection of specific mangrove species for restoration. For example, Hu et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) did not differentiate between mangrove species in the sampling process of their mangrove distribution prediction study. Existing mangrove influencing factors of distribution predictions primarily include SST, terrain factors, bioclimatic factors, sea surface salinity factors, and substrate type. Because mangrove patches are primarily distributed in wetlands, woodlands, and water bodies (Chao et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we included land-use type in our study to appropriately limit the distribution of mangroves, thereby increasing the accuracy of predicted distribution results.\u003c/p\u003e \u003cp\u003eWe analyzed six dominant mangrove species to obtain their habitat condition thresholds, which are of practical significance for the selection of appropriate species and building the community structure in mangrove restoration work. In this study, we used the MaxEnt model to determine the potential distribution, area, and response interval of six mangrove species, namely \u003cem\u003eAricennia marina\u003c/em\u003e, \u003cem\u003eAegiceras corniculatum\u003c/em\u003e, \u003cem\u003eKandelia obovata\u003c/em\u003e, \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e, \u003cem\u003eRhizophora stylosa\u003c/em\u003e, and \u003cem\u003eAcanthus ilicifolius.\u003c/em\u003e In addition, all mangrove species were grouped as a whole for the estimation of kernel density to analyze mangrove restoration hot spots and unprotected mangrove areas. Our study provides a scientific basis for the restoration and protection of mangroves in the Beibu Gulf of Guangxi.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe Beibu Gulf of Guangxi is located in southern China (107\u0026deg;56ʹ\u0026ndash;109\u0026deg;47ʹE, 20\u0026deg;54ʹ\u0026ndash;2l\u0026deg;24ʹN, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The continental coastline extends from the Ximi Estuary at Yingluo Port at the junction of Hepu and Lianjiang counties, Guangdong Province, in the east, to Beilun Estuary at the junction of Dongxing City and Vietnam in the west. The total length of the continental coastline is 1628.59 km. The coastline is tortuous and has many natural bays, including Tieshan, Lianzhou, Qinzhou, and Fangcheng Bays as well as Pearl Harbor (Li and Dai \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This area is characterized by a marginal tropical marine climate, with an annual rainfall of 1500 to 2000 mm and an annual average temperature of 22.0 to 23.4\u0026deg;C. The Beibu Gulf has a relatively extensive distribution of mangroves, with rich mangrove wetland resources. The area contains the following three mangrove reserves: Shankou Mangrove National Nature Reserve, Beilun Estuary Mangrove National Nature Reserve, Maoweihai MangroveNature Reserve. Beibu Gulf of Guangxi contains a National Wetland Park. Dominant mangrove species include \u003cem\u003eAricennia marina\u003c/em\u003e, \u003cem\u003eAegiceras corniculatum\u003c/em\u003e, \u003cem\u003eKandelia obovata\u003c/em\u003e, \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e, \u003cem\u003eRhizophora stylosa\u003c/em\u003e, and \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e, with the top five mangrove communities in terms of area size consisting of \u003cem\u003eAricennia marina\u003c/em\u003e, \u003cem\u003eAegiceras corniculatum\u003c/em\u003e, \u003cem\u003eAricennia marina\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eAegiceras corniculatum\u003c/em\u003e, \u003cem\u003eBruguiera gymnorrhiza\u0026thinsp;+\u0026thinsp;Aricennia marina\u003c/em\u003e, and \u003cem\u003eRhizophora stylosa\u003c/em\u003e. The \u003cem\u003eAricennia marina\u003c/em\u003e and \u003cem\u003eAegiceras corniculatum\u003c/em\u003e communities account for 41.74 and 32.91% of the total mangrove area, respectively (Tao et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Mangrove distribution\u003c/h2\u003e \u003cp\u003eTo predict suitable distribution areas for mangrove species using the MaxEnt model, species distribution and environmental data are required. Mangrove distribution data for the Beibu Gulf were visually interpreted according to the 2020 Google Earth image (resolution 0.61 to 2.4 m) and verified by on-site investigations. Additional data were primarily obtained from the literature, and 908 data points from the mangrove forest survey were provided by the Guangxi Mangrove Research Center. In the study area, re-adoption work was conducted using the ArcGIS 10.4 (Environmental Systems Research Institute, Redlands, California, USA) fishing net tool supplemented by manual marking, and a 300 \u0026times; 300 m grid was established. A total of 2306 sampling and coordinate data points were obtained for the study area. Longitude and latitude information regarding species distribution were extracted using the ArcGIS 10.4 \u0026ldquo;Calculate Geometry\u0026rdquo; tool, and data were stored as comma-separated values files to form a mangrove sample data set. We used an analysis framework to predict the potential distribution of mangrove species (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Environmental data\u003c/h2\u003e \u003cp\u003eEnvironmental data can determine differences in growth factors between mangrove species. Environmental factors, such as temperature, salinity, and distance from coastline, are important indicators of potential mangrove growth and distribution (Peng et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Because mangroves grow in the sea\u0026ndash;land ecotone, their fitness factors are affected by both continental environmental and marine environmental factors. Thus, in our study, we combined environmental data from the sea and the land. To estimate the distribution of mangroves in Beibu Gulf, the boundary of the study area was fixed using the coastline as the reference. The estimated study area enclosed a 10 km buffer zone along the land from the coastline (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and a 5 m isobath in the sea. Weizhou and Xieyang Islands were not included in this study. ArcGIS resampling was used to extract environmental data for existing mangrove distribution sample points, and a Kriging interpolation was conducted to expand the data to sea, ocean, or land and to integrate sea and land data.\u003c/p\u003e \u003cp\u003eCorrelation analysis is a statistical method that can screen environmental variables to analyze closeness. Mangrove distribution and environmental data were sampled using GIS. The environmental data included 19 environmental parameters, 2 terrain parameters, 3 SST data points, 3 sea salt data points, 1 substrate type data, and 1 land-use data. Pearson\u0026rsquo;s correlation was used to identify environmental variables and was performed for all 29 variables to calculate the correlation coefficient matrix. A correlation coefficient greater than 0.8 indicates a high correlation. Variables with small biological significance in the highly correlated variable group were selected (Chao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ta et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xu et al. 2015), with a total of 16 environmental variables used to establish the model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We calculated the wetland index (WTI), representing the spatial distribution of the runoff source area and groundwater level in the basin, using the following equation:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$WTI=\\text{ln}\\left(\\frac{\\alpha }{\\text{tan}\\beta }\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere α equals (flow accumulation\u0026thinsp;+\u0026thinsp;1) \u0026times; pixel area (in m\u003csup\u003e2\u003c/sup\u003e) and β represents the slope angle in radians (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnvironmental variables used to predict mangrove distribution in Beibu Gulf of Guangxi,China.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnit\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\u003eBioclimatic data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean diurnal range [mean of monthly (max. temp - min. temp)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u0026times;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsothermality (BIO2 / BIO7) (\u0026times; 100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax. temp of warmest month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u0026times;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin. temp of the coldest month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u0026times;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean temp of warmest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u0026times;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation seasonality (coefficient of variation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation in the warmest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation in the coldest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTerrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTopographic elevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWetland index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOcean salinity data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC_sss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean sea surface salinity in the coldest season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026permil;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW_sss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean sea surface salinity in the warmest season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026permil;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSea surface temperature data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC_sst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean sea surface temperature in the coldest season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW_sst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean SST in the warmest season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubstrate type data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubstrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubstrate type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand-use data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand-use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLand-use type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\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=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Model parameter setting\u003c/h2\u003e \u003cp\u003eIn this study, MaxEnt version 3.4.1 (Steven J. Phillips, Columbia University) was used for prediction analyses. To establish the model, 75% of the mangrove distribution data from the Beibu Gulf was used as training data and the remaining 25% used as test data (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The mangrove distribution dataset established in this study included sufficient samples. To construct the MaxEnt model, the default feature combination was selected and sample data were randomly selected. To improve accuracy, the number of repeated calculations of the model was set to 10. Default values were used for other settings. Grid output results obtained after the operation were visually converted and analyzed using ArcGIS 10.4. The pixel value of each grid represents the distribution probability of mangroves in the grid. The pixel value ranged from 0 to 1. The larger the pixel value, the higher the potential mangrove distribution, indicating a high habitat suitability of the grid. In this study, we used the natural breakpoint method to grade fitness results based on Chao et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Hu et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as follows: 0\u0026ndash;0.2, non-fitness; 0.2\u0026ndash;0.5, low fitness; 0.5\u0026ndash;0.7, medium fitness; and \u0026gt;\u0026thinsp;0.7, optimal fitness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Model test results\u003c/h2\u003e \u003cp\u003eThe MaxEnt model was used to calculate the receiver operating characteristic (ROC) curve. The value of the diagnostic test was represented by the area under the curve (AUC) of the ROC curve, with AUC values ranging from 0 to 1. The closer to 1, the more accurate the prediction (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ta et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The range of AUC values were interpreted using Hu et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as follows: 1\u0026ndash;0.9, excellent prediction; 0.8\u0026ndash;0.9, good prediction; 0.7\u0026ndash;0.8, average prediction; 0.6\u0026ndash;0.7, poor prediction; and 0.5\u0026ndash;0.6, prediction failure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Vacancy analysis of mangrove protection and restoration\u003c/h2\u003e \u003cp\u003eBased on the results of mangrove habitat suitability, priority areas for mangrove protection and restoration (potential mangrove distribution hot spots) were calculated using kernel density estimation (KDE), which is a spatial hot spot analysis method (Cai et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The distribution of discrete values in continuous space can be obtained by calculating the density of elements in their surrounding neighborhood (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, after KDE analysis in ArcGIS software, we used spatial superposition analysis of mangrove distribution status, priority area spatial distribution, and nature reserve distribution to further analyze the protection status of the mangrove ecosystem in Beibu Gulf. Furthermore, protected area information, such as the protection rate and protection and repair of vacant areas, was obtained from spatial superposition analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":" \u003ch2\u003e3.1 AUC value\u003c/h2\u003e \u003cp\u003eThe AUC of the training and test sets of the distribution prediction model of the six mangrove species in Beibu Gulf ranged from 0.912 to 1 (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The AUC values of the training and test sets of the distribution prediction model for all mangrove species as a whole were 0.882, 0.869, respectively (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Based on the AUC of the test set, the simulation of the MaxEnt model for mangroves was deemed accurate. Thus, the model was highly reliable and could predict the distribution of the dominant mangrove species in Beibu Gulf of Guangxi.\u003c/p\u003e \u003ch2\u003e3.2 Analysis of dominant environmental factors\u003c/h2\u003e \u003cp\u003eFactors affecting mangrove habitat included bioclimate, topography, marine salt data, SST, substrate type, and land-use type. The contribution of variables based on interactions between different environmental variables (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe investigated the test results of different factors affecting mangrove distribution (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) to ascertain the dominant factors affecting the distribution of specific species. The dominant factors affecting overall mangrove distribution in Beibu Gulf were elevation, wetland index, mean temperature of warmest quarter, and substrate type. (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Subsequently, we analyzed the factors affecting specific mangrove species distribution in Beibu Gulf.\u003c/p\u003e \u003cp\u003eWe investigated the importance of various environmental factors on the distribution probability of each of the six selected mangrove species. The three environmental factors imperative to the geographical distribution of \u003cem\u003eAricennia marina\u003c/em\u003e were elevation, mean sea surface salinity in the coldest season and maximum temperature of warmest month (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), with their cumulative contribution rate accounting for 50.7% and the other 13 environmental factors accounting for 49.3% of its distribution (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Among the remaining factors, the total contribution of bioclimatic factors, topography, sea surface salinity, SST, substrate type, and land-use type accounted for 39.1, 34, 16.6, 1.2, 6.1, and 2.9%, respectively. Based on the contribution and importance of various environmental factors for predicting the distribution of \u003cem\u003eAricennia marina\u003c/em\u003e, elevation limited optimal planting areas, while the mean salinity of the sea surface in the coldest season reflected the salt preference of this species. The contributions of land-use type and SST to the distribution of \u003cem\u003eAricennia marina\u003c/em\u003e were relatively low.\u003c/p\u003e \u003cp\u003eRegarding \u003cem\u003eAegiceras corniculatum\u003c/em\u003e, the three environmental factors imperative to its geographical distribution were elevation, wetland index, and substrate type (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), with their cumulative contribution accounting for 41.7%, and the remaining 13 environmental factors accounting for 58.3% of its distribution. Among the remaining factors, the total contribution rate of bioclimate, topography, sea surface salinity, bioclimate, SST, substrate type, and land-use type accounted for 36.3, 39.6, 18.9, 0.1, 2.1, and 3.1%, respectively (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The contribution of marine salinity in predicting the distribution of \u003cem\u003eAegiceras corniculatum\u003c/em\u003e was greater than that for \u003cem\u003eAricennia marina\u003c/em\u003e, which indicates that \u003cem\u003eAegiceras corniculatum\u003c/em\u003e is more sensitive to salinity than \u003cem\u003eAricennia marina\u003c/em\u003e. The contribution of SST, substrate type, and land-use type to the distribution of \u003cem\u003eAegiceras corniculatum\u003c/em\u003e was relatively low.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eKandelia obovata\u003c/em\u003e, the three environmental factors most important to its geographical distribution were elevation, substrate type, and wetland index (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), with a cumulative contribution percentage of 42.3%. Among the dominant influencing factors, elevation reflected the restrictions on the planting location of \u003cem\u003eKandelia obovata.\u003c/em\u003e In addition, possible this species were relatively sensitive to substrate type.\u003c/p\u003e \u003cp\u003eRegarding \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e, the three environmental factors with the largest effect on its geographical distribution were maximum temperature of the warmest month, precipitation in the warmest quarter, and substrate type (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed), accounting for 79.1% of the cumulative contribution. Among the influencing factors,\u003c/p\u003e \u003cp\u003ethe total contribution rate of bioclimate, topography, sea surface salinity, SST, substrate type, and land-use type accounted for 76.9, 4.1, 0.7, 0.4, 15.8, and 1.2%, respectively. These results show that \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e was sensitive to bioclimate and substrate type.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eRhizophora stylosa\u003c/em\u003e, the three environmental factors that most affected its geographical distribution were precipitation of the warmest quarter, substrate type, and mean temperature of the warmest quarter (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), together accounting for 82.9% of the cumulative contribution. Among the factors influencing the distribution of this species, the contribution rate of bioclimate, topography, sea surface salinity, SST, substrate types, and land-use type accounted for 78.3, 6.6, 2.9, 0.4, 11.9, and 0%, respectively. These results indicate that the distribution of \u003cem\u003eRhizophora stylosa\u003c/em\u003e is sensitive to bioclimate.\u003c/p\u003e \u003cp\u003eFinally, for \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e, the three environmental factors with the greatest importance for its geographical distribution were substrate type, mean sea surface salinity in the warmest season, and wetland index (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef), with their cumulative contribution representing 72.5%. The significance of substrate type reflects the substrate preference of \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e, while the significance of wetland index reflects the limitations of topographic conditions on \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e. Furthermore, the importance of mean sea surface salinity in the warmest season reflects the sensitivity of \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e to salinity, with too high salinity levels possibly affecting the species growth.\u003c/p\u003e \u003ch2\u003e3.3 Range of environmental factors affecting mangrove habitat suitability\u003c/h2\u003e \u003cp\u003eThe variables representing the mangrove suitability factors in Beibu Gulf were selected based on the logistic mode of the MaxEnt model. Correlations between habitat suitability and environmental variables were analyzed using a probability distribution logic output value of 0.5 as the boundary. This enabled us to investigate the optimal thresholds for the main environmental variables affecting the six selected mangrove species (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe suitable elevation threshold ranges for the growth of mangrove plants were determined. The minimum suitable elevation values of the plants are ordered from lowest to highest: \u003cem\u003eAricennia marina\u003c/em\u003e, \u003cem\u003eAegiceras corniculatum\u003c/em\u003e, \u003cem\u003eRhizophora stylosa\u003c/em\u003e, \u003cem\u003eKandelia obovata\u003c/em\u003e, \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e, and \u003cem\u003eAcanthus ilicifolius.\u003c/em\u003e The lowest elevation value suitable for the growth of \u003cem\u003eAricennia marina\u003c/em\u003e was \u0026minus;\u0026thinsp;0.84 m, whereas the lowest elevation value suitable for the growth of \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e was relatively high (Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e Table 2 \u003cp\u003eThresholds of the dominant environmental factors affecting the distribution of six investigated mangrove species.\u003c/p\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth colname=\"c1\"\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c2\"\u003e \u003cp\u003eMangrove species\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c3\"\u003e \u003cp\u003eThree dominant environmental factors\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c4\"\u003e \u003cp\u003eLimitation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eAricennia marina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e-0.84\u0026ndash;1.27 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eMean sea surface salinity in the coldest season\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e16.41\u0026ndash;25.31\u0026permil;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eMax. temperature of warmest month\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e32.1\u0026ndash;32.3\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eAegiceras corniculatum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e-0.68\u0026ndash;2.02 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eWetland index\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e4.11\u0026ndash;9.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eSubstrate type\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003eMixed mudflat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eKandelia obovata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e-0.50\u0026ndash;1.88 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eSubstrate type\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003eMixed mudflat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eWetland index\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e4.49\u0026ndash;8.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eMax༎temperature of warmest month\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e32.3\u0026ndash;32.4\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003ePrecipitation in the warmest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e638\u0026ndash;753 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eSubstrate type\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003eMixed mudflat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eRhizophora stylosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003ePrecipitation in warmest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e723\u0026ndash;746 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eSubstrate type\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003eMixed mudflat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eMean temperature of warmest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e28.7\u0026ndash;28.9\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eAcanthus ilicifolius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eSubstrate type\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003eMixed mudflat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eMean sea surface salinity in the warmest season\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e3.39\u0026ndash;7.37\u0026permil;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c3\"\u003e \u003cp\u003eWetland index\u003c/p\u003e \u003c/td\u003e \u003ctd colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e \u003c/p\u003e \u003cp\u003eA number of trends were observed for the wetland index (Fig. \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Compared to other mangrove species, \u003cem\u003eAricennia marina\u003c/em\u003e had the lowest minimum wetland index, as it can grow in the low tide zone. In contrast, \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e had the largest minimum wetland index indicating that it can grow in the high tide zone.\u003c/p\u003e \u003cp\u003eRegarding the average salinity of the sea surface in the coldest season, \u003cem\u003eKandelia obovata\u003c/em\u003e had a relatively wide range (5.91\u0026ndash;17.92\u0026permil;; Fig. \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). According to the highest suitable value of mean sea surface salinity in the coldest season, the investigated species can be ordered from highest to lowest as follows: \u003cem\u003eRhizophora stylosa\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eAricennia marina\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eAegiceras corniculatum\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eKandelia obovata\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eAcanthus ilicifolius.\u003c/em\u003e Regarding the most suitable value of mean sea surface salinity in the warmest season, the species can be ordered from highest to the lowest as follows: \u003cem\u003eAricennia marina\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eRhizophora stylosa\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eKandelia obovata\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eAegiceras corniculatum\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eAcanthus ilicifolius\u003c/em\u003e. The most suitable range of mean sea surface salinity in the coldest season for \u003cem\u003eAricennia marina\u003c/em\u003e was 16.41\u0026ndash;25.31\u0026permil;, and the most suitable value was 24.27\u0026permil;. The suitable range of the average sea surface salinity for \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e in the warmest season was 3.39\u0026ndash;7.37\u0026permil;, and the most suitable value was 4.07\u0026permil;.\u003c/p\u003e \u003ch2\u003e3.4 Suitable areas for mangroves in the Beibu Gulf of Guangxi\u003c/h2\u003e \u003cp\u003eBased on the findings of Hu et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we divided potential mangrove areas into four groups based on their suitability as follows: best (\u0026gt;\u0026thinsp;0.7), medium (0.7\u0026ndash;0.5), low (0.2\u0026ndash;0.5), and unsuitable (0\u0026ndash;0.2). Based on the investigation of six mangrove species in Beibu Gulf as the overall input for the model (Fig. \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eg), we obtained an optimal suitable area of 13,816 hm\u003csup\u003e2\u003c/sup\u003e. The areas of high fitness are located primarily in the Dandou Sea on the west side of the Shatian Peninsula in the southeast of Hepu County, Guangxi; Tieshan Port, Qinzhou Bay, and the Dafeng River in the center of the Guangxi coast; Fangcheng Bay on the west of the Guangxi coast; and the Shankou Mangrove National Nature Reserve. The mangroves are concentrated in the Shankou Mangrove National Nature Reserve, Beilun Hekou Mangrove National Nature Reserve, and Maoweihai Mangrove Autonomous Region Nature Reserve (Fig. \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003eWith regard to individual species, the size of the optimum suitable area for \u003cem\u003eAricennia marina\u003c/em\u003e was 10,341 hm\u003csup\u003e2\u003c/sup\u003e (Fig. \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with areas of high fitness primarily distributed in Tie Shan Gang, the Beihai Golden Bay mangrove reserve, and along the open coasts in the south of the Beihai National Wetland Park and Beilun Estuary Mangrove National Nature Reserve. The size of the optimal suitable area for \u003cem\u003eAegiceras corniculatum\u003c/em\u003e was 13,154 hm\u003csup\u003e2\u003c/sup\u003e (Fig. \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eb), with highly suitable areas primarily distributed in estuaries, such as Lianzhou Bay, the Maoweihai Mangrove Autonomous Region Nature Reserve, and the Dafeng River.\u003c/p\u003e \u003cp\u003eThe optimal area for \u003cem\u003eKandelia obovata\u003c/em\u003e was 10,672 hm\u003csup\u003e2\u003c/sup\u003e (Fig. \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ec), with highly suitable areas distributed along Qinzhou Bay, the Dafeng River, and Beilun Estuary Mangrove National Nature Reserve. The size of the optimal area for \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e was 2565 hm\u003csup\u003e2\u003c/sup\u003e (Fig. \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ed), with highest fitness areas for this species primarily distributed in the Dandou Sea area of the Shankou Mangrove Reserve, Yingluo Port, and Beilun Estuary Mangrove National Nature Reserve. The extent of the most suitable area for \u003cem\u003eRhizophora stylosa\u003c/em\u003e was 1158 hm\u003csup\u003e2\u003c/sup\u003e (Fig. \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ee), with optimal areas for this species distributed primarily in the Dandou Sea area and Yingluo Port of the Shankou Mangrove Reserve, but very few in other regions of the study area. The size of the most suitable area for \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e was 4054 hm\u003csup\u003e2\u003c/sup\u003e (Fig. \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ef), with areas with the highest fitness for this species primarily distributed in regions with low estuarine salinity in Lianzhou Bay and the Shankou Mangrove Reserve.\u003c/p\u003e \u003cp\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e Table 3 \u003cp\u003eSuitable mangrove areas.\u003c/p\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth colname=\"c1\"\u003e \u003cp\u003eMangrove species\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c2\"\u003e \u003cp\u003eOptimal suitable area (hm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth colname=\"c3\"\u003e \u003cp\u003eMedium suitable area (hm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAricennia marina\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39,875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAegiceras corniculatum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37,063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKandelia obovata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20,682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRhizophora stylosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAcanthus ilicifolius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e \u003c/p\u003e \u003cp\u003eBeibu Gulf contains three mangrove nature reserves, a National Wetland Park, with a total area of 15,794 hm\u003csup\u003e2\u003c/sup\u003e, and a mangrove area of 3977 hm\u003csup\u003e2\u003c/sup\u003e. The mangrove protection rate is 42.62%. Based on the analysis of the superposition of the distribution of protected areas and potentially suitable mangrove areas, 49.10% of the most suitable areas are included within protected areas (Fig. \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Eight areas were identified as priority areas for mangrove protection and restoration. The vacant mangrove protection areas in Beibu Gulf are primarily distributed in Lianzhou Bay, along the Dafeng River, the East Bay of Fangchenggang, and Tieshan Harbor.\u003c/p\u003e "},{"header":"4. Discussion","content":"\u003cp\u003eOur species distribution studies based on the MaxEnt model can be used for the restoration of mangroves. Based on 908 mangrove survey data and the visual interpretation of remote sensing images, the AUC of the training and test sets of the six mangrove species suitability distribution model for the Beibu Gulf, constructed with the MaxEnt model, ranged from 0.912 to 1. The values of the six were \u0026ldquo;extremely accurate,\u0026rdquo; indicating the good performance of the model based on survey data and remote sensing images.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Dominant environmental factors affecting mangrove suitability\u003c/h2\u003e \u003cp\u003eThrough overall mangrove analysis, the dominant factors affecting the distribution of suitable mangrove habitat in Beibu Gulf were topographic factors, bioclimatic factors, land-use type, marine salinity, and substrate type. The effect of SST on the distribution of suitable habitats was relatively weak.\u003c/p\u003e \u003cp\u003eIn terms of topography, elevation was the dominant factor affecting the distribution of \u003cem\u003eAricennia marina\u003c/em\u003e, \u003cem\u003eAegiceras corniculatum\u003c/em\u003e, and \u003cem\u003eKandelia obovata\u003c/em\u003e. Within this context, Hu et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed that mangrove distribution was notably limited by topography on a small regional scale, while elevation exerted a significant influence over the distribution of species (Rick et al. 2017) and affected the habitat preference of mangrove species (Chen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, in terms of elevation, our results correspond to those of other studies. In our study, the minimum critical value of the elevation of mangrove growth was negative or zero, indicating that mangrove plants could grow on the beach below the average sea level. Within this context, Liu et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported that large communities of \u003cem\u003eAegiceras corniculatum\u003c/em\u003e and \u003cem\u003eKandelia obovata\u003c/em\u003e can survive on beaches below average sea level.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAricennia marina\u003c/em\u003e had the lowest suitable elevation value, indicating that this species is a pioneer tree species for mangrove afforestation. This is consistent with the results from relevant research (Liao et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), primarily because \u003cem\u003eAricennia marina\u003c/em\u003e has respiratory roots, is resistant to flooding and hypoxia stress, and has a relatively high salt tolerance (He et al \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe effect of bioclimatic factors was also a dominant factor in the distribution of potential mangrove habitats. These factors also played an important role in mangroves located in Fujian and Guangdong in China (Chao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The distributions of \u003cem\u003eRhizophora stylosa\u003c/em\u003e and \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e were more sensitive to bioclimatic factors. Studies have shown that \u003cem\u003eRhizophora stylosa\u003c/em\u003e and \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e are thermophilic widespread species (Mo \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), which is consistent with the temperature preference of these two species in our study.\u003c/p\u003e \u003cp\u003eSea surface salinity is an important factor affecting the distribution of mangroves (Barik et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ken 2008; Meng et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sinsin et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although mangroves grow in an environment with a salinity of approximately 30\u0026permil; (Tang 2014), they can adapt to a wide salinity range. Under different salinity gradients, the physiological parameters of mangrove plants can change (Biber \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In our study, the maximum salinity of the sea surface favored by the six mangrove species was \u0026lt;\u0026thinsp;30\u0026permil;, which corresponds with the results of previous research (Tang 2014). The model used in our study showed that sea surface salinity was one of the dominant factors affecting the predicted distribution of \u003cem\u003eAricennia marina and Acanthus ilicifolius\u003c/em\u003e. Salinity tolerance intervals of different mangrove species can differ (Jayatissa et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Based on the most appropriate average sea surface salinity in the coldest and warmest seasons, the three mangrove species studied by Ye et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), can be ordered from highest to lowest salt tolerance as follows: \u003cem\u003eAricennia marina\u0026thinsp;\u0026gt;\u0026thinsp;Aegiceras corniculatum\u0026thinsp;\u0026gt;\u0026thinsp;Acanthus ilicifolius\u003c/em\u003e, which is consistent with the findings from our research.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e, the average salinity threshold of the sea surface in the warmest season ranged from 3.39\u0026ndash;7.37\u0026permil;. The suitable values for \u003cem\u003eAegiceras corniculatum\u003c/em\u003e varied from 3.78\u0026ndash;11.81\u0026permil;, and those of \u003cem\u003eAricennia marina\u003c/em\u003e ranged from 17.95\u0026ndash;24.00\u0026permil;. Therefore, within the threshold range, low-salinity beaches are suitable for \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e and medium-salinity beaches are suitable for \u003cem\u003eAegiceras corniculatum\u003c/em\u003e (Ye et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), while \u003cem\u003eAricennia marina\u003c/em\u003e can be planted on beaches with relatively high salinity levels. However, during the dry cold season, the salinity of the Dandou, Yingluo, and Tieshan ports in the Shankou Mangrove Reserve with high mangrove and \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e distributions ranges from 26.8\u0026ndash;28.0\u0026permil; (Lan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, the average salinity of the sea surface in the coldest season and the optimal salinity values of \u003cem\u003eRhizophora stylosa\u003c/em\u003e and \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e were higher than those of \u003cem\u003eAricennia marina\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eSubstrate type was one of the main factors affecting the growth of \u003cem\u003eAegiceras corniculatum\u003c/em\u003e, \u003cem\u003eKandelia obovata\u003c/em\u003e, \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e, \u003cem\u003eRhizophora stylosa\u003c/em\u003e, and \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e. Previous research has shown that a mixed beach substrate is more suitable for mangrove growth than beach and mudflat substrates with single components (Sui et al. 1999; Zhang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Therefore, mixed beaches can be the index used for beach selection in the afforestation of the above mangrove species.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Recommendations for mangrove restoration\u003c/h2\u003e \u003cp\u003eAs of 2013, the mangrove area in Beibu Gulf was 7243.15 hm\u003csup\u003e2\u003c/sup\u003e. In 2020, the mangrove area in the Beibu Gulf had increased to 9331.53 hm\u003csup\u003e2\u003c/sup\u003e, and is currently still increasing. This increase indicates that there is still space for ecological restoration and confirms that the Guangxi coast has much potential for mangrove restoration (Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Areas suitable for forests, distributed in Lianzhou Bay and Dafeng River in Qinzhou, and vacant areas in Beibu Gulf can be used for mangrove restoration and protection. The most suitable habitat in this area should be included in ecological mangrove restoration projects, based on the establishment of protected areas and wetland parks.\u003c/p\u003e \u003cp\u003eOur study focuses specifically on predicting the distribution of dominant mangrove species and their suitable growth thresholds. Focusing on individual mangrove species provides more targeted results, which are conducive to the selection of species for mangrove restoration.\u003c/p\u003e \u003cp\u003eIn our study, bioclimatic and topographic factors, as well as sea surface salinity, substrate type, sea surface temperature, and land-use type were selected as environmental variables affecting the distribution of mangroves. In addition to the variable factors used in previous mangrove distribution prediction studies (Chao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we added land-use type as an extra factor. Although land-use type was not the most dominant factor affecting the distribution, it also had a certain impact on the distribution of mangroves. The contribution of land-use type to the predicted distribution of \u003cem\u003eKandelia obovata\u003c/em\u003e, \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e was 10.9 and 7.5%, respectively.\u003c/p\u003e \u003cp\u003eWe suggest that in future research, biological invasion factors and human interference factors (such as ports, waterways, and aquaculture) be added and discussed to expand the screening range of environmental variables of the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe distribution prediction of main mangrove plants is of great significance to the selection and layout of mangrove restoration tree species. Our study used remote sensing images in combination with field survey data, sea surface temperature data, land use data, and other environmental data to predict and analyze the potential distribution of six mangrove species in the Beibu Gulf of Guangxi, China, based on the MaxEnt model. Specifically, we analyzed the dominant environmental impact factors of the predicted distribution of six mangrove species and the range of main environmental factors affecting mangrove growth. In addition, we explored potential locations for the six selected mangrove species, as well as hot spots for mangrove growth and protection.\u003c/p\u003e \u003cp\u003eThe most important factor that affected the overall distribution of mangroves in the Beibu Gulf was topology, followed by bioclimatic factors, land-use type, marine salinity, and substrate type. The SST had relatively weak effects. Among the mangrove species, \u003cem\u003eRhizophora stylosa\u003c/em\u003e and \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e were more sensitive to bioclimatic factors than the remaining four species.\u003c/p\u003e \u003cp\u003eThe areas with potential for mangrove growth in the Beibu Gulf were located primarily in the Dandou Sea, Tieshangang, Qinzhou Bay, Dafeng River, and Fangcheng Harbor, together offering an optimal mangrove habitat of 13,816 hm\u003csup\u003e2\u003c/sup\u003e. Vacant mangrove protection areas in the Beibu Gulf were primarily distributed in Lianzhou Bay and along the Dafeng River in Quinzou. The areas with low estuarine salinity in Lianzhou Bay and the Maoweihai Mangrove Autonomous Region Nature Reserve were suitable for \u003cem\u003eAegiceras corniculatum\u003c/em\u003e and \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e habitats. In addition, Dandou and Yingluo ports of the Shankou Mangrove Reserve offered suitable \u003cem\u003eRhizophora stylosa\u003c/em\u003e and \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e habitats. The Beilun Estuary National Nature Reserve is suitable for \u003cem\u003eAricennia marina\u003c/em\u003e, \u003cem\u003eKandelia obovata\u003c/em\u003e, and \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e. In addition, Tieshan Harbor offers suitable \u003cem\u003eAricennia marina\u003c/em\u003e, \u003cem\u003eRhizophora stylosa\u003c/em\u003e, and \u003cem\u003eBruguiera gymnorrhiza\u003c/em\u003e habitats. In estuary areas, \u003cem\u003eAegiceras corniculatum\u003c/em\u003e and \u003cem\u003eAcanthus ilicifolius\u003c/em\u003e are suitable species for the restoration and afforestation of mangroves. The distribution map of mangrove species can be used as the basis for mangrove afforestation and restoration in the Beibu Gulf of Guangxi. For mangrove restoration, the optimal growth areas should be selected, and land and trees should be adapted according to the suitable environmental threshold range for specific mangrove species. Thus, our study provides an important reference for the predicted distribution of six dominant mangrove species, as well as for the appropriate selection of species and the related spatial layout for successful mangrove restoration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the support of the Guangxi Mangrove Research Center for the identification of mangrove species and for providing 908 mangrove survey data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [Grant number 32060282; U21A2022].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e \u0026nbsp;All authors contributed to the design\u0026nbsp;of the analyses and the writing of the manuscript. Meth-odology:\u0026nbsp;JW; Formal analysis and investigation:SJ, LF, FQ; Writing (original draft preparation):\u0026nbsp;LF; Writing (review\u0026nbsp;and editing):\u0026nbsp;WA,\u0026nbsp;HQ,\u0026nbsp;JW; Funding acquisition:WA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eBioclimatic factors were obtained from the World Climate Database archive at the following ink: https://www.worldclim.org/data/worldclim21.html. Terrain data were extracted from ETOP01 terrain elevation and ocean seabed terrain data released by the United States Geophysical Center archive at the following link: https://www.ngdc.noaa.gov/mgg/global/global.html. The SST data were obtained from the National Environmental Information Center of the Oceanic and Atmospheric Administration of the United States (1981\u0026ndash;2020 SST data) archive at the following link: ftp://ftp.emc.ncep.noaa.gov/cmb/sst/oisst_v2/. Salinity data were obtained from the marine salinity products of the Institute of Atmospheric Physics, Chinese Academy of Sciences archive at the following link: http://159.226.119.60/cheng/,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ewith the auxiliary data including the seawater salinity information (Lan et al. 2014; Wei et al. 2006) for the references of the study area. Substrate type data were obtained from the National Marine Science Data Center (nmdis.org.cn), and the auxiliary data included substrate classification types in the study area (Xiao et al. 2016). Land-use data were obtained from ESRI 10 m Cover (2020) in GEE archive at the following\u0026nbsp;\u003c/p\u003e\n\u003cp\u003elink:\u0026nbsp;https://livingatlas.arcgis.com/landcover/. The accuracy of the above-mentioned data was normalized to 30\u0026Prime; using a geographic information system (GIS), and the graph was saved in ASCII format.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare no ethical concerns.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBai R, Li N, Liu ShJ, Tong JH, Chen XM, Chu HP (2021) Prediction of global suitable area for white root disease of rubber tree under climate change. Plant Prot 47:66\u0026ndash;72. https://doi.org/10.16688/j.zwbh.2020151\u003c/li\u003e\n\u003cli\u003eBalke T, Friess DA (2016) Geomorphic knowledge for mangrove restoration: A pan-tropical categorization. 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Acta Ecol Sin 35:557\u0026ndash;567. https://doi.org/10.5846/stxb201304030600\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":"maximum entropy model, Beibu Gulf of Guangxi, mangrove, suitable growth","lastPublishedDoi":"10.21203/rs.3.rs-2203109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2203109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eContext\u003c/h2\u003e \u003cp\u003eThe restoration of mangroves is an significant challenge within the protection of coastal habitats. Predicting the distribution of dominant species in mangrove communities is essential for the appropriate selection of species and spatial planning for restoration.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eWe explored the spatial distribution of six mangrove species including their related environmental factors, thereby identifying potentially suitable habitats for mangrove protection and restoration.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBased on six dominant mangrove species that occur in the Beibu Gulf of Guangxi, we used linear correlation analysis to screen environmental factors. In addition, we used the maximum entropy model to analyze the spatial distribution of potentially suitable areas for mangrove afforestation. Based on spatial superposition analysis, we identified mangrove conservation and restoration hot spots.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur findings indicate that the main factors affecting the distribution of suitable mangrove habitat in the Beibu Gulf are topographic factors, followed by bioclimatic factors, land-use type, marine salinity, and substrate type. We identified 13,816 hm\u003csup\u003e2\u003c/sup\u003e of prime mangrove habitat in the Beibu Gulf, primarily distributed in protected areas. The protection rate for existing mangroves was approximately 42.62%.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe identified the dominant environmental factors and their thresholds for the distribution of six mangrove species and identified the spatial distribution of individual species and location of suitable rehabilitation sites. According to the predicted spatial distribution of mangrove plants, our findings suggest that mangrove restoration should be based on suitable species and sites.\u003c/p\u003e","manuscriptTitle":"Prediction of potential mangrove distributions in the Beibu Gulf of Guangxi Zhuang Autonomous Region, China using the MaxEnt model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-28 15:05:38","doi":"10.21203/rs.3.rs-2203109/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":"8ad746cc-3abb-42e1-bf13-3102e18146a5","owner":[],"postedDate":"October 28th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-10-31T04:29:17+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-28 15:05:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2203109","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2203109","identity":"rs-2203109","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}