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Article

Conservation and Restoration of Mangroves in Response to Invasion of Spartina alterniflora Based on the MaxEnt Model: A Case Study in China

by
Lina Cui
1,2,
Uta Berger
2,
Minmin Cao
1,
Yaqi Zhang
1,
Junming He
2,3,
Lianghao Pan
4 and
Jiang Jiang
1,*
1
Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Nanjing Forestry University, Nanjing 210037, China
2
Institute of Forest Growth and Forest Computer Sciences, Faculty of Environmental Sciences, Technische Universität Dresden, 01062 Dresden, Germany
3
College of Forestry, Shenyang Agricultural University, Shenyang 110866, China
4
Guangxi Key Lab of Mangrove Conservation and Utilization, Guangxi Mangrove Research Center (GMRC), Guangxi Academy of Sciences, Nanning 536000, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(6), 1220; https://doi.org/10.3390/f14061220
Submission received: 29 April 2023 / Revised: 29 May 2023 / Accepted: 1 June 2023 / Published: 13 June 2023
(This article belongs to the Section Forest Hydrology)
Browse Figures
Versions Notes

Abstract

:
In China, the invasion of Spartina alterniflora is an important driver for the decrease of mangrove area and ecological service functions related to this habitat. In the past few decades, S. alterniflora clearing and mangrove restoration projects have mainly focused on the areas where it is already changed but ignored the potential distribution areas. This study suggested that implementation of mangrove protection prior to the areas with the threat of S. alterniflora invasion could greatly improve protection efficiency and save costs. Thus, using Maximum Entropy Modeling (MaxEnt), we estimated the potential spatial distribution of both mangroves and S. alterniflora in China, considering the current distribution data, topographical, sediments, sea surface temperature and bioclimatic variables. What’s more, we identified and calculated the potential distributed areas in each province. We aimed to explore (i) the key factors determining the distribution of mangrove and Spartina alterniflora along the coastline and (ii) the hotspots of their competitive occurrence, including S. alterniflora invasion areas and mangroves degradation areas, in order to support mangrove conservation. The model showed that the distance to the coastline and the topography play important roles in the distribution of S. alterniflora, while mangroves were more sensitive to the range of the annual sea surface temperature. Our results furthermore confirm that S. alterniflora has a wider potential distribution area (~10,585 km2) than mangroves (~9124 km2) at the coastline of China; and predict the provinces Zhangzhou, Quanzhou, Zhanjiang, Beihai and Wenzhou as hotspots for the competition between mangroves and S. alterniflora. We propose that priority should be given to the protection or restoration of mangrove plants in those areas which are co-suitable for mangroves and S. alterniflora. In these areas, management measures should be conducted that hinder S. alterniflora invasions or clear existing S. alterniflora plants, firstly. This study provides guidance for the management of native species by preventing biological invasion.

1. Introduction

Mangroves, as the first line of protection for coastal ecosystems, contribute to ecological security and provide numerous ecosystem goods and services for the development of ecosystems, such as carbon storage, the conservation of global biodiversity coastline and protection [1,2]. Unfortunately, mangroves are sensitive to environmental change and are one of the most severely threatened ecosystems around the world [3,4]. China, for example, provides rich living conditions for mangroves and is one of the northernmost countries suitable for their occurrence; but the total mangrove areas have decreased by 70% from the 1950s to the 1990s [5]. Various factors have caused this degradation [6,7], among which species invasion has made a vital effect and is still an important obstacle to the restoration nowadays [8,9].
Spartina alterniflora Loisel (S. alterniflora) is native to North America but was introduced to many coastal and estuarine regions around the world for the purpose of ecological engineering [10,11]. Due to its high ability to tolerate harsh coastal conditions and reproductive capacity, it has successfully spread while displacing native species [12,13,14]. In China, S. alterniflora was first introduced to Fujian Province in 1979 and spread along the coastline rapidly [11]. It is now considered an invasive species with severe effects on the community structure and biogeochemical processes (e.g., primary productivity) of native ecosystems, including mangroves [15]. As a consequence, the distribution area of mangroves is decreasing, and ecological functions are seriously damaged [16], the carbon storage of S. alterniflora communities is lower than that in mangroves [17,18], and aquaculture is negatively affected [19]. The Environmental Protection Bureau of China listed S. as one of 16 invasive species in 2003 [19]. To mitigate the loss of mangroves and ecosystem functionality, numerous projects have been implemented to clear S. alterniflora and reforest mangroves in the past few years [3,20]. For example, 34 natural mangrove conservation areas have been established to protect mangroves [21] and take lots of measures to prevent the development of S. alterniflora, such as harvesting or freshwater flooding. However, S. alterniflora still continues to spread in the intertidal zone, although the distribution area of mangroves is increasing slightly in China [22]. There are even many areas of artificially restored mangrove forests that experience the threat of re-invasion by S. alterniflora. More scientific guidance is required to identify appropriate mangrove restoration sites and S. alterniflora clearing strategies to improve management efficiency.
A stable restoration and sustainable development of ecosystems can be realised with an adequate combination of species characteristics and environmental conditions [23]. Understanding how quickly the distribution of mangrove and S. alterniflora is changing in coastal wetlands is critical if we are to react appropriately to the challenges (for example, conservation or clear issues) and opportunities (for example, ecosystem services and stability) they may pose [24,25]. Species distribution models (SDMs) can support scientists and ecologists in understanding species distribution characteristics and the relationship between species and environmental factors [26,27,28]. SDMs have developed rapidly in recent decades, with the emergence of many methods, including CART, MARS, GARP, GLMs and GAMs, and machine learning algorithms such as RF, ANN and MaxEnt [29]. These models are extensively used for estimating the ecological effects of climate change [30], assessing the risk of invasive species [31], identifying priorities for conservation [32,33], simulating the distribution of scarce and threatened species, as well as assessing the effects of land-use change or sea-level rising on species distribution [34]. SDMs have become one of the most valuable quantitative tools in conservation biology [35,36]. The maximum entropy model (MaxEnt), based on the presence-only data, is still one of the common approaches for modelling species distributions [37,38]. Compared with other SDMs, MaxEnt is fairly easy to use due to its higher robust predictive ability and functionality than other methods [38,39]. It has been widely used in coastal wetlands for estimating the future range of a species under climate change, as well as assisting in reserve planning [34], as well as to explore potential mangrove and S. alterniflora distributions in China [40,41]. Previous studies have found that mangroves and S. alterniflora are mainly controlled by temperature, while S. alterniflora has a wider suitable range than mangroves [41,42,43]. These studies have contributed greatly to the promotion of stable development of coastal wetlands. However, most of the predictions ignored the contribution of tidal inundations. What’s more, Zheng et al. [44] established an invasive risk index to quantify the invasive risk of S. alterniflora to mangrove. However, based on the differences in the distribution characteristics of mangroves and S. alterniflora, no studies made targeted suggestions to improve the efficiency of mangrove restoration and S. alterniflora suppression.
In this study, we used the MaxEnt model to quantify the potential distribution and niche characteristics of mangrove and S. alterniflora along the coastline of China, respectively. Climates of air and sea surface, topography, and soil properties are taken into account. Besides, the distance to the coastline was established to reflect the tidal inundations. The objectives of this study were: 1. To explore the key factors and potential distribution areas of mangroves and S. alterniflora along the Chinese coastline; 2. to identify the hotspots of competitive co-occurrence of both vegetation types; and 3. The distinguish priority areas for mangrove protection that might be potentially vulnerable to S. alterniflora invasions. This study will provide theoretical support for the protection of mangroves against S. alterniflora invasions and for understanding the quantitative relationships between species distribution and environmental drivers.

2. Materials and Methods

2.1. Occurrence Data Set

Adequate species distribution points are an important prerequisite for building ecological niche models, and as the number of species distribution points decreases, the predictive power of models decreases. Due to varying levels of mapping accuracy and methodology, the coverage of the data is not uniform in space (vegetation surveys, citizen science, and floristic inventories). To eliminate the effect of unbalanced sampling intensity and maintain an even density of data points, we randomly generated 2000 distribution points for mangroves and S. alterniflora in China, based on a distribution map of mangrove and S. alterniflora. The distribution points were checked using Google Earth. To ensure model efficiency, we used ENMTools to control each grid to have only 1 distribution point for each species [45]. Basically, all records of the known mangrove and S. alterniflora ranges along the coast of China are covered. In the end, there were 1432 distribution points for mangroves and 1366 distribution points for S. alterniflora that were used for potential distribution predicting. Convert the distribution point data format to the CSV format required by the MaxEnt model for storage. Use the conversion tools in ArcGIS 10.4 software. The distribution map of mangrove and S. alterniflora comes from the National Earth System Science Data Center, National Science & Technology Infrastructure of China (http://www.geodata.cn/, accessed on 20 September 2022).

2.2. Environmental Data Set

In estuarine environments, factors such as climate, topography and soils are closely related to important variables that influence the geographical distribution of species [5,43]. Based on our ecological knowledge of mangroves and S. alterniflora, we selected 40 layers of environmental factors for the initial development of models (Table 1) that are potentially important in delimiting mangrove and S. alterniflora communities at regional scales. These variables correspond to a similar time period as the species occurrence data. The procedures by which these layers were obtained and processed are as follows:
Climate layers. Climatic variables represent annual trends, seasonality and environmental extremes. They have been widely used to model the ecological niche and potential distribution of species. These data layers were derived using 19 gridded temperature and precipitation data from WorldClim-Global Climate Data (averages for 1970–2000, https://www.worldclim.org), downscaled to a spatial resolution of 1 km2 (30 arc-seconds). The SST (sea surface temperature) data in these layers used the Microwave OI SST product (http://www.remss.com/measurements/sea-surface-temperature/, accessed on 20 September 2022). The SST data consisted of eleven calculated factors (Table 1, sst_01-sst_11) calculated by R packages (raster, rgdal, and dismo).
Topography layers. These variables include the elevation, slope and distance to the coastline. The elevation was derived from WorldClim-Global Climate Data (https://www.worldclim.org, accessed on 20 September 2022). The slope layer was established based on elevation by ArcMap 10.4. The distance to the coastline was created using Euclidean distances with the Spatial Analyst tool in ArcMap 10.4. We determined the coastline based on a vector layer of the political boundaries of China.
All layers were used at a spatial resolution of 30″ (~1 km2). We used a 10-km buffer on either side of the coastal line to trim these environmental data because mangrove distributions are only found in coastal regions [5,46]. ArcGIS 10.4 was used to process all the data. High collinearity is less of a concern for machine learning methods than it is for statistical approaches [47,48]. Thus, all the elements were incorporated into our models simultaneously.

2.3. MaxEnt Modeling Process

MaxEnt, one of the most important machine-learning modelling techniques, is popular in species distribution based on occurrence records and randomly generated background points, and it can generate robust and accurate predictions even using a small sample size [45,46]. In this study, the MaxEnt algorithm 3.4.1 was used to estimate environmental suitability and potential geographic distributions of mangroves and S. alterniflora in China. Utilising the MaxEnt bootstrap functionality, a final set of MaxEnt model parameters was created using 10,000 background points, ten replicates, the “logistic” output, and 75% training data and 25% evaluation data [37,49]. The test data proportion was defined using a random seed, and the replicated run type was specified as cross-validation. A maximum of 500 iterations, a convergence threshold of 0.00001, and a prevalence of 0.5 were set, and the generating response curves were used to analyse the influence of variables on the distribution [50]. Other settings were kept at their default values within the software. We converted the continuous suitability score (0–1) produced by the MaxEnt model into a habitat distribution plot in ArcGIS 10.4. We reclassified the model suitability into two classes: unsuitable habitat (<0.5) and suitable habitat (0.5–1.0) [41].
Best models were selected based on AUC, which provides a threshold-independent assessment of model performance using a web-based program and varies from 0.5 to 1. An AUC value of 0.5 represents a model with random predictions, while values close to one represent higher discrimination. An AUC value between 0.9 and 1.0 shows excellent model performance. Percentage contribution (PC) and permutation importance (PI) to were used to evaluate the contribution of each environmental variable to the best model.

3. Results

3.1. Performance of the Models

The prediction accuracy of MaxEnt models showed AUC values of 0.879 ± 0.001 and 0.922 ± 0.001 for mangrove and S. alterniflora, respectively, reaching a “good” level for mangrove and an “excellent” level for S. alterniflora.

3.2. Environmental Variable for Mangroves and Spartina Alterniflora

Estimates of the relative contributions of the environmental variables to the MaxEnt model indicated that there are 11 factors (total per cent contribution > 90%) that correlate most with the distribution of mangroves and S. alterniflora (Table 2). The impact of each environmental variable on the model prediction is demonstrated by the linkages between the predictor and response variables. By looking at marginal response curves, or the dependency of the probability from the model prediction on one variable while the other variables are set to the averages, we were able to further investigate how the anticipated species occurrence probability changes with each significant variable. The marginal response curves for the most significant environmental factors in the MaxEnt model are shown in Figure 1.
The model found that some factors are critical for both mangrove and S. alterniflora. For example, elevation is the most important topographical variable in the distribution of mangroves (PC = 20.6, PI = 36.4) and ranked third in the S. alterniflora model (PC = 13.4, PI = 21). The response curves showed that the presence of both mangroves and S. alterniflora decreases with increasing elevation, but mangroves have a wider elevation suitable range than S. alterniflora (Figure 1, elevation). The most important factor for S. alterniflora is the distance to the coastline (PC = 39.6, PI = 15.5), and the PC for mangroves is 18.8. Furthermore, the response curves of mean sea surface temperature of the wettest quarter (sst_08) displayed a negative relationship with the presence of species distribution, and it is important for both mangrove (PC = 4.7, PI = 2.4) and S. alterniflora (PC = 17.9, PI = 11.8). But the different rates of decrease indicated that mangrove has a higher tolerance for sst_08 than S. alterniflora. Temperature seasonality (bio_04) is another important climatic factor that plays a key role in the distribution of mangroves (PC = 1.6, PI = 25.9) and S. alterniflora (PC = 2.2, PI = 11.7). The relationships of present probability and bio_04 are different for them, which is positive for S. alterniflora and humped for mangrove. The contribution of annual mean sea surface temperature (sst_01) and ECE for mangroves is larger than that for S. alterniflora. The response curves showed that the increased sst_01 and ECE effectively promote the presence of mangroves but not S. alterniflora. Conversely, the contribution of the mean temperature of the wettest quarter (bio_08) is more important for S. alterniflora and negatively correlated with the presence of S. alterniflora. The mean sea surface temperature of the warmest quarter (sst_10) for mangrove and S. alterniflora are important, and their contributions are comparable.
Differently, there are some factors that make great contributions only on mangrove or S. alterniflora. The key climate variable for mangroves is the annual sea surface temperature range (sst_07), which is not important in the S. alterniflora model. The increasing mean temperature of the warmest quarter (bio10) contributes to the presence of mangroves but humped for S. alterniflora. Moreover, the slope is important for mangroves but not for S. alterniflora. In addition, the mean sea surface temperature of the coldest quarter (sst_11), max sea surface temperature of the warmest month (sst_05) and mean diurnal range (bio_02) showed a humped relationship with mangrove and S. alterniflora, and it is more important for S. alterniflora than mangrove.

3.3. Habitat Suitability of Mangroves and Spartina alterniflora

Figure 2 shows the geographical distribution of mangrove and S. alterniflora in China under the current climate conditions predicted by MaxEnt.
S. alterniflora is suitable in the most coastal province of China, and the total potential area is 10,585 km2. The highly suitable areas were mainly located in most coastlines of Zhejiang, Shandong, Jiangsu, Tianjin, the northern coastline of Fujian, southern Guangxi, and little in southern Liaoning, Hebei and Guangdong. Moreover, the largest area for S. alterniflora was in Zhejiang (4079 km2). Besides, the model shows that Hainan and Taiwan are not suitable for the presence of S. alterniflora.
Mangrove is mainly distributed in tropical and subtropical areas, and there are five provinces in China suitable for mangroves, including Fujian, Guangdong, Guangxi, Hainan, as well as Taiwan. Guangdong province has the largest mangrove forests (5169 km2), especially in the south of the province, followed by Guangxi province, and there are about 1514 km2 of mangroves. Taiwan province has the smallest area for mangroves, about 509 km2. Fujian is the northmost province suitable for mangroves, especially in the south of the province.
The potential distribution maps of mangrove and S. alterniflora based on the MaxEnt model were similar in Fujian, Guangdong and Guangxi (Figure 3). The biggest area is Fujian, especially in Zhangzhou, Quanzhou, Fuzhou and south of Ningde. There are also some areas in Guangxi suitable for both S. alterniflora and mangroves, which are mainly distributed in Beihai. The smallest area suitable for both mangroves and S. alterniflora is in Guangdong (114 km2), mainly distributed in the Leizhou peninsula.

4. Discussion

Machine learning algorithms, as complex modelling techniques, can closely match occurrence data to produce accurate results for predicting species’ potential distributions [29]. MaxEnt was used in this study to explore the potential distribution and ecological characteristics of mangroves and S. alterniflora in China, which is the northmost area suitable for mangroves and has been invaded by S. alterniflora in the past few years. The AUC of the MaxEnt model for both mangrove and S. alterniflora are >0.8. Thus, the results of the models are believable to reflect their distribution characteristics along the Chinese coastline.
Prior to the current work, studies have focused on the distribution of mangrove and S. alterniflora by both ecology modelling and field experiments [5,51,52]. S. alterniflora, one of the salt marsh species, is widely distributed around the world [53]; its strong adaptability and reproductive capacity allow the alternative vegetation to expand rapidly, forming a high, dense community with a variety of negative effects on the native coastal ecosystem. The distribution of S. alterniflora is contributed by climates, topography, as well as sea levels. In our study, the distance to the coastline makes the greatest contribution to S. alterniflora, followed by elevation and the mean sea surface temperature of the wettest quarter (sst_08). And the result is not entirely consistent with previous studies. For example, Liu, Qi, Gong, Li, Zhang, Li and Lin [40] has reported that S. alterniflora can adapt to a wide range of climatic conditions, while topographical factors, such as elevation, are the most limiting factor. What is more, Zheng, Wei, Chen, Liu, Wang and Gu [44] combined chlorophyll concentration into models to explore the effect of nutrient concentration on the distribution of mangrove and S. alterniflora, and they found that chlorophyll concentration makes the greatest contribution in the distribution of S. alterniflora. The reason is that most of the previous studies did not take the distance to the coastline into consideration, which directly reflects the topography, flooding, salinity, and nutrient concentrations. Therefore, the results from this study indicate that, in addition to climatic factors, other environmental variables have a strong influence on the distribution of plants in coastal wetlands, especially for S. alterniflora. In China, it was reported that the total distribution area of S. alterniflora is in China reached 554 km2 [54], while in this study, the potential distribution area of S. alterniflora was bigger than the real distribution area. The results demonstrate the enormous invasive potential of S. alterniflora in China while stressing the necessity and importance of implementing S. alterniflora clearing and restraint policies in China for the protection of coastal wetland ecosystems. S. alterniflora is mainly distributed in the middle coastal wetland of China, such as Zhejiang, southern Shandong, Jiangsu, Tianjin, and Shanghai. In these areas, some native salt marsh plants dominated the marshlands, such as Phragmites australis, S. mariqueter, and Kandelia candel. Moreover, there are still some S. alterniflora found along the coasts of southern Liaoning, Hebei, northern Fujian, Guangdong and Guangxi. The results indicated that these provinces also provide suitable areas for S. alterniflora [55]. In addition, some of these areas have been invaded by S. alterniflora, and others may become the main invasion area of S. alterniflora in the future.
The invasion of S. alterniflora may break the stabilisation of the ecosystem and leads to the degradation of native communities [56]. For example, salt marsh, such as Phragmites communis in the north and mangroves in the south of China [12,18,35,57]. Studies have reported that the invasion of S. alterniflora to salt marsh plants contributes to carbon storage [58], while that of mangroves decreases ecosystem services, especially carbon sequestration [59]. Thus, stopping S. alterniflora from invading mangroves plays a key role in carbon storage. Mangrove is one of the most productive and sensitive ecosystems in the world. Our study confirmed that temperature is the most critical factor for mangrove distribution, which is consistent with the results of previous studies [4,5,60,61]. For example, the minimum temperature for mangrove growth is 0–6 °C [5], and the annual mean temperature for mangroves is suitable from 18.7 to 25.7 °C [44]. Thus, mangroves are distributed along the coastline in tropical and subtropical areas. China is the global northern distribution margin of mangroves and whose richness shows a decreasing trend from south to north [5]. There are five provinces in the south of China which have suitable environments for mangroves. Guangdong has the most extensive distribution areas, with mangroves spreading along most of the coastlines, particularly in the south. The model displayed that mangroves are sparse in Fujian, which is the northmost province suitable for mangroves in China. The predicted suitable locations were mostly consistent with known locations for the species. Differently, Kandelia obovate was successfully established in Zhejiang province in 1957 [21], and the mangrove distribution area was up to 268 hm2 in 2013 [62]. In addition, Hu, Wang, Dong, Zhang, Yu, Ma, Chen, Liu, Du, Chen, and Lei [5] reported that theoretically suitable areas for mangroves in Zhejiang extracted from GARP and MaxEnt models were 2800, 1100 hectares, respectively. We inferred that was because reliable mangrove presence records were not available in this study, a factor that is critical for predictive performance in the MaxEnt model. Furthermore, the resolution of the environmental dataset, which obscures the variety of the fine-scale habitat, may have an impact on the model. Consequently, the model frequently suggests broad areas as appropriate despite the ecosystem really being varied. In total, the results indicated that there are about 9124 km2 in China is suitable for mangrove growth, while the total area of mangroves in China was 345 km2 according to the National Wetland Resources Survey in 2013 [3]. The gap between potential areas and actual areas indicated that China has a huge potential for the development of mangroves.
To date, China has carried out a large number of mangrove restoration projects and S. alterniflora controlling projects with remarkable achievements [12,63]. However, there are still issues with mangrove restoration planning due to competition by S. alterniflora [64]. Based on the findings of this study, we identified the following challenges and made the following recommendations for future actions to mangrove and S. alterniflora. Firstly, mangrove potential distribution areas should be selected for restoration and management. Most of the coastline in Fujian, Zhejiang Guangdong, Guangxi, Hainan, Taiwan, and the south coastline of Zhejiang are suitable for mangroves. Thus, the tidal flat along these coastlines could be used to develop mangroves. Besides, seafood aquaculture is a vital socio-economic source for coastal regions, which has a serious impact on coastal wetlands occupying the vegetation growth environment and even polluting the surrounding environments. Thus, reducing shrimp and crab farming not only promotes healthy mangrove development but also brings up more land for mangrove restoration. In addition, it is important to note that suitable mangrove species should be chosen during the mangrove development process. Kandelia obovate, for example, is the most resistant to cold temperatures, and Avicennia germinans is much more salt-tolerant [65]. Secondly, keeping S. alterniflora from invading and clearing the existing S. alterniflora in potential distribution areas of mangroves are both critically important for mangrove restoration. In past projects, actions mainly operated in the areas in which S. alterniflora existed, ignoring the areas suitable for both mangroves and S. alterniflora. Prior to the invasion of S. alterniflora, areas suited for its growth should be strengthened in terms of restoration and management. This will increase management effectiveness and lower input costs. In this study, we found that mangrove and S. alterniflora are both suitable to establish in Fujian Province, Guangdong Province and Guangxi Province. Therefore, we should pay more attention to the prevention and restoration in these regions, especially in the hotspots. For example, Zhangzhou, Quanzhou, Zhanjiang, as well as Beihai. In addition, the mangroves in Zhejiang Province should make great efforts in the management. Since the mangroves in Zhejiang were artificially established, the distribution area is much smaller than the S. alterniflora distribution range, making it more likely to be invaded by S. alterniflora. Moreover, species diversity is one of the most important factors influencing community stability, and the mangroves in Zhejiang only have Kandelia obovate, exacerbating the instability of the community.
This study proves that a combined assessment of the potential distribution of mangroves and S. alterniflora is suitable to identify hotspots of conflicts of both vegetation types and areas to be particularly considered for mangrove protection or restoration considering S. alterniflora invasion. Therefore, this research is of great significance for the planning and design of mangrove restoration and S. alterniflora prevention projects in China or other regions. Nevertheless, due to the resolution of the model being 1 km, the results of the present study provide only general guidance on a large scale. At a local scale, environmental and biotic factors should be taken into careful consideration to refine the particular measures.

5. Conclusions

MaxEnt modelling is a powerful approach to exploring the potential distribution of species. In the present study, it was adopted to predict the distribution of mangroves and S. alterniflora in China. The good accuracy in statistical tests reflected the power of the method to identify and predict the distribution characteristics of both vegetation types. The study identifies the key factors for mangrove and S. alterniflora on a regional scale. The distance to the coastline and the topography play important roles in the distribution of S. alterniflora, while mangroves were more sensitive to the range of the annual sea surface temperature. S. alterniflora has a wider potential distribution area (~10,585 km2) than mangroves (~9124 km2) at the coastline of China. In addition, the outcome of this study indicates that mangroves are mainly distributed in Fujian, Guangdong, Guangxi, Hainan, Taiwan, and little in Zhejiang, while S. alterniflora is potentially available to invade most of the coastline. Priority in developing and implementing mitigation measures should be given to the hotspots with great competition potential between mangroves and S. alterniflora in order to minimise the S. alterniflora invasion and the subsequent replacement of mangroves in China.
Despite the usefulness of the proposed method in preventing the invasion of S. alterniflora and promoting mangrove restoration, it still has some limitations that could be improved. First, we did not compare the performance of MaxEnt with other traditional presence-absence methods (e.g., logistic regression). We would have accomplished this task if a consensus had been reached on the best method for selecting pseudo-absence samples. Secondly, the theory of machine learning is derived from mathematics and statistics and does not reflect ecological processes. Although multiple iterations of the run can increase the accuracy of the results, differences in algorithms, data, etc., can also lead to different results to some extent. Therefore, in later studies, attempts can be made to combine species distribution models with ecological process-based models to obtain potential distribution results that are more consistent with the characteristics of the ecosystem. Third, the data resolution affected the visualisation of the results to some extent. Last, in future studies, the MaxEnt model could consider more relevant spatial influences, such as human activities. All these limitations are the focus of our attention in the next research.

Author Contributions

Conceptualisation, L.C. and J.J.; methodology, Y.Z., L.C. and J.J.; software, L.C.; resources, M.C. and L.P.; writing—original draft preparation, L.C.; writing—review and editing, L.C., U.B. and J.J.; visualisation, L.C.; supervision, J.H.; project administration, L.C. and J.J. All authors have read and agreed to the published version of the manuscript.

Funding

Jiangsu Postgraduate Research and Practice Innovation Program (KYCX21_0856), Zhejiang Forestry Science and Technology Project (2022SY06), National Natural Science Foundation of China under Grant (U21A2022).

Data Availability Statement

Data available on request due to restrictions eg privacy or ethical. The data presented in this study are available on request from the corresponding author.

Acknowledgments

We thank all the authors whose works are included in the review. Acknowledgement for the data support from “National Earth System Science Data Center, National Science & Technology Infrastructure of China. (http://www.geodata.cn, accessed on 20 September 2022)”. This research was supported by the Jiangsu Postgraduate Research and Practice Innovation Program (KYCX21_0856), Zhejiang Forestry Science and Technology Project (2022SY06), National Natural Science Foundation of China under Grant (U21A2022).

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 14 01220 g001 550
Figure 1. Response curves of important factors influencing both mangrove (red) and Spartina alterniflora (green) distribution. (A): the factors important to both mangrove and Spartina alterniflora; (B): the factors important to mangrove than Spartina alterniflora; (C): the factors important to Spartina alterniflora than mangrove.
Figure 1. Response curves of important factors influencing both mangrove (red) and Spartina alterniflora (green) distribution. (A): the factors important to both mangrove and Spartina alterniflora; (B): the factors important to mangrove than Spartina alterniflora; (C): the factors important to Spartina alterniflora than mangrove.
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Figure 2. Potential distribution map of mangrove (left) and Spartina alterniflora (right) in China. mg_only: the area is only suitable to mangrove; sp_only: the area is only suitable to Spartina alterniflora; mg&sp: the area is both suitable to mangrove and Spartina alterniflora.
Figure 2. Potential distribution map of mangrove (left) and Spartina alterniflora (right) in China. mg_only: the area is only suitable to mangrove; sp_only: the area is only suitable to Spartina alterniflora; mg&sp: the area is both suitable to mangrove and Spartina alterniflora.
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Forests 14 01220 g003 550
Figure 3. Distribution map of hotspots with competition between mangrove and Spartina alterniflora. mg_only: the area is only suitable to mangrove; sp_only: the area is only suitable to Spartina alterniflora; mg&sp: the area is both suitable to mangrove and Spartina alterniflora.
Figure 3. Distribution map of hotspots with competition between mangrove and Spartina alterniflora. mg_only: the area is only suitable to mangrove; sp_only: the area is only suitable to Spartina alterniflora; mg&sp: the area is both suitable to mangrove and Spartina alterniflora.
Forests 14 01220 g003
Table
Table 1. Bioclimatic and other environmental factors used for MaxEnt models.
Table 1. Bioclimatic and other environmental factors used for MaxEnt models.
No.Data Type Variable Description Units
1Bioclimaticbio_01Annual mean temperature°C
2Bioclimaticbio_02Mean diurnal range°C
3Bioclimaticbio_03Isothermality (BIO2/BIO7) × 100)unitless
4Bioclimaticbio_04Temperature seasonalityunitless
5Bioclimaticbio_05Max temperature of the warmest month°C
6Bioclimaticbio_06Min temperature of the coldest month°C
7Bioclimaticbio_07Annual temperature range°C
8Bioclimaticbio_08Mean temperature of the wettest quarter°C
9Bioclimaticbio_09Mean temperature of the driest quarter°C
10Bioclimaticbio_10Mean temperature of the warmest quarter°C
11Bioclimaticbio_11Mean temperature of the coldest quarter°C
12Bioclimaticbio_12Annual precipitationmm
13Bioclimaticbio_13Precipitation of the wettest month mm
14Bioclimaticbio_14Precipitation of the driest monthmm
15Bioclimaticbio_15Precipitation seasonalityunitless
16Bioclimaticbio_16Precipitation of the wettest quartermm
17Bioclimaticbio_17Precipitation of the driest quartermm
18Bioclimaticbio_18Precipitation of the warmest quartermm
19Bioclimaticbio_19Precipitation of the coldest quartermm
20TopographicelevationElevation m
21TopographicslopeSlope
22TopographicdistanceDistance to coastline
23sedimentsTOCTotal organic carbon
24sedimentsSAND
25sedimentsSILT
26sedimentsCLAT
27sedimentspH
28sedimentsREBULK
29sedimentsECE
30SSTsst_01Annual mean sea surface temperature°C
31SSTsst_02Mean diurnal range of SST°C
32SSTsst_03Isothermality of SSTunitless
33SSTsst_04Sea surface temperature seasonalityunitless
34SSTsst_05Max sea surface temperature of the warmest month°C
35SSTsst_06Min sea surface temperature of the coldest month°C
36SSTsst_07Annual sea surface temperature range°C
37SSTsst_08Mean sea surface temperature of the wettest quarter°C
38SSTsst_09Mean sea surface temperature of the driest quarter°C
39SSTsst_10Mean sea surface temperature of the warmest quarter°C
40SSTsst_11Mean sea surface temperature of the coldest quarter°C
Table
Table 2. Per cent contribution of environmental factors. (cumulative contribution rate > 90%).
Table 2. Per cent contribution of environmental factors. (cumulative contribution rate > 90%).
ParametersMangroveS. alterniflora
Elevation 20.613.4
Distance to coastline18.8139.6
Mean sea surface temperature of the wettest quarter (sst_08)4.717.9
ECE3.91.2
Annual mean sea surface temperature (sst_01)3.51.2
Mean sea surface temperature of the warmest quarter (sst_10)1.81.4
Mean temperature of the wettest quarter (bio_08)1.44.9
Temperature seasonality (bio_04)1.62.2
Annual sea surface temperature range (sst_07)31.1----
Mean temperature of the warmest quarter (bio10)2----
Slope 1.5----
Mean sea surface temperature of the coldest quarter (sst_11)----3.6
Max sea surface temperature of the warmest month (sst_05)----3.1
Mean diurnal range (bio_02)----1.7
Others 9.019.8
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Cui, L.; Berger, U.; Cao, M.; Zhang, Y.; He, J.; Pan, L.; Jiang, J. Conservation and Restoration of Mangroves in Response to Invasion of Spartina alterniflora Based on the MaxEnt Model: A Case Study in China. Forests 2023, 14, 1220. https://doi.org/10.3390/f14061220

AMA Style

Cui L, Berger U, Cao M, Zhang Y, He J, Pan L, Jiang J. Conservation and Restoration of Mangroves in Response to Invasion of Spartina alterniflora Based on the MaxEnt Model: A Case Study in China. Forests. 2023; 14(6):1220. https://doi.org/10.3390/f14061220

Chicago/Turabian Style

Cui, Lina, Uta Berger, Minmin Cao, Yaqi Zhang, Junming He, Lianghao Pan, and Jiang Jiang. 2023. "Conservation and Restoration of Mangroves in Response to Invasion of Spartina alterniflora Based on the MaxEnt Model: A Case Study in China" Forests 14, no. 6: 1220. https://doi.org/10.3390/f14061220

APA Style

Cui, L., Berger, U., Cao, M., Zhang, Y., He, J., Pan, L., & Jiang, J. (2023). Conservation and Restoration of Mangroves in Response to Invasion of Spartina alterniflora Based on the MaxEnt Model: A Case Study in China. Forests, 14(6), 1220. https://doi.org/10.3390/f14061220

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