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Article

Nature-Based Solution for Climate Change Adaptation: Coastal Habitats Restoration in Xiamen Bay, China

by
Suzhen Yang
1,2,3,
Qinhua Fang
3,4,5,*,
Dian Zhang
3,4,
Lusita Meilana
6,
Harrison Odion Ikhumhen
3,4,5,
Xue Zhang
3,4,
Xiaoyan Jiang
3,4 and
Boding Lin
3,4,5
1
Fourth Institute of Oceanography, Ministry of Natural Resources, Beihai 536000, China
2
Guangxi Key Laboratory of Beibu Gulf Marine Resources, Environment and Sustainable Development, Fourth Institute of Oceanography, Ministry of Natural Resources, Beihai 536000, China
3
Key Laboratory of Ministry of Education for Coastal Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China
4
Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361102, China
5
Coastal and Ocean Management Institute (COMI), Xiamen University, Xiamen 361102, China
6
Center for Coastal and Marine Resources Studies (CCRMS), International Research Institute for Maritime, Ocean and Fisheries (i-MAR), IPB University Bogor, Bogor 16680, West Java, Indonesia
*
Author to whom correspondence should be addressed.
Forests 2024, 15(11), 1844; https://doi.org/10.3390/f15111844
Submission received: 27 September 2024 / Revised: 20 October 2024 / Accepted: 21 October 2024 / Published: 22 October 2024
(This article belongs to the Section Forest Ecology and Management)
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Abstract

:
Nature-based solutions (NbSs) of biodiversity conservation and ecosystem restoration have been paid increasing attention as an essential approach to slow down climate change. However, to what degree an NbS approach will contribute to the combined effects of human intervention and climate change has not been well studied. From a habitat quality perspective, we set four NbS scenarios to analyze whether the NbS—mangrove restoration in particular—will be enough for climate change in Xiamen Bay of Fujian Province, China. The habitat quality module of the InVEST model (InVEST-HQ) and the Sea Level Affecting Marshes Model (SLAMM) were used to simulate the spatial-temporal changes in habitat types and habitat quality. Results show that (1) rising sea levels will cause coastal squeeze effects, impacting habitat quality due to erosion and inundation in the study area; (2) mangrove restoration is an effective way to mitigate climate change effects and to increase habitat quality; and (3) further analysis of the effectiveness of mangrove restoration shows that the consideration of mangrove fragmentation effects and sea-use impacts are necessary. The findings in this study will enrich the international discussion of NbSs to climate change in coastal areas.

1. Introduction

Climate change, coupled with human activities, has brought about effects of habitat loss and degradation. Research found that effects of climate change, such as sea-level rise, would accelerate the effects of erosion and inundation [1]. Some methods of climate risk management include mitigation, adaptation, carbon sequestration, and solar radiation [2]. In recent years, the approach of nature-based solutions (NbSs) has been adopted as an essential tool for climate change adaptation and biodiversity conservation, especially for coastal areas [3,4].
NbSs refer to actions that are effective and adaptive in addressing social challenges, improving well-being, and restoring the biodiversity of both natural and human-altered ecosystems [3]. After its first use in the field of biodiversity [5], NbSs have shifted their focus towards addressing climate change, improving ecosystem services, fostering sustainable development, enhancing climate resilience, safeguarding biodiversity, and facilitating ecological restoration, etc. [6]. NbSs have gained widespread attention as essential strategies for confronting various environmental and social challenges [7]. Globally, there have been a number of NbS projects initiated which are relating to infrastructure, nature-based ecological restoration solutions, and NbS-related initiatives to address a number of global challenges. Simultaneously, NbSs consist of governmental decision-making processes in China [6,7], such as the ecological conservation red line, nature reserve network, biodiversity conservation priorities, and projects on the environmental protection and restoration of all ecosystems from hilltops to oceans [7,8].
NbS approaches support biodiversity conservation and the functioning of ecosystems [9], among which mangroves have significant ecological value and functions in climate change and in the protection of biodiversity [7,10]. Thus, the mangroves and wetlands are the most critical paths for NbSs [11]. Understanding whether NbSs are enough for climate change adaptation is essential for future biodiversity conservation policy formulation. Ecological rehabilitation, reconstruction, and replacement are the main NbS approaches [12]. Modeling and remote sensing are becoming the dominant methods for the effectiveness of NbSs; simultaneously, mangrove area and carbon storage are the leading effectiveness indicators. Studies combining remote sensing and big earth data and models examine whether mangrove afforestation and reforestation promotes climate change resilience [13], those using interviews and participant observation to evaluate the justice in trading ecosystem value [14], or those establishing the empirical modeling of the response of mangroves to sea-level rise and a Bayesian framework to assess the probability accretion of mangroves in low- and high-carbon emission scenarios [15] have all provided significant results. Other studies have focused on predicting how NbSs will work in the context of future climate change. In such cases, scenario comparison is a promising method for understanding whether NbSs will be enough for climate change adaptation. To examine both the negative and positive responses to mangroves in the future [10], local sea-level rise scenarios can be created by combining the available information [16]. Removing dike was proposed as a scenario, together with the propagation trajectory model to simulate the mangrove growth potential [17]. However, due to the complicated nature of climate change, most of the previous studies focus on a single scenario (of human intervention or climate change), where it is difficult to evaluate the habitat quality by considering both the human intervention and climate change; furthermore, there is little understanding about whether the NbS is enough for climate change. Meanwhile, mangrove spatial visualization with modeling is also a challenge for future sea-level rise monitoring.
Therefore, taking Xiamen Bay in China as an example, this study proposes to assess whether NbSs are enough for climate change adaptation using the InVEST-HQ model [18] and the SLAMM model [19] under four NbS scenarios, including Scenario #0, Scenario #1, Scenario #2, and Scenario #3, aiming to (1) assess and map the coastal habitat quality of Xiamen Bay, (2) analyze the spatiotemporal variation of habitat types and habitat quality, and (3) explore whether NbSs are enough for climate change adaptation by mangrove restoration.

2. Methods and Data

The following is a description of the research framework for this study (Figure 1):
Firstly, the habitat types were identified and classified using the Environment for Visualizing Images (ENVI) and ArcGIS. In this study, the data were preprocessed based on ENVI 5.3. Then, the habitat types were preliminarily classified through the maximum likelihood method based on ArcGIS 10.8.
Secondly, the threat factors were represented by sea uses and were identified with GIS tools. Combined with the study context, the threat factors in Xiamen Bay include the uses of ports and shipping, mineral, industrial construction, tourists, disposal, and special uses.
Thirdly, four NbS scenarios were set, including Scenario #0 (baseline scenario), Scenario #1 (climate change scenario), Scenario #2 (moderate mangrove restoration), and Scenario #3 (enhanced mangrove restoration).
Finally, changes in the habitat types and the habitat quality were simulated with the SLAMM model and the InVEST-HQ model, respectively.

2.1. Study Area

The Xiamen Bay was selected as the study area (Figure 2), which is located in Fujian Province, southeast China, with geographical coordinates of 24°20′–24°40′, 117°50′–118°30′, covering an area of 1093.4 km2. Xiamen Bay is governed by three coastal cities (Quanzhou, Xiamen, and Zhangzhou), making ecosystem-based modeling and area-based management tools (ABMTs) essential for addressing transboundary issues [20]. A variety of sea uses, including port and shipping, mineral uses, tourism uses, industrial construction uses, disposal and dumpling uses, etc., make the Xiamen seas crowded. The pollution sources in Xiamen Bay are complex, including the outfall, rivers, and industrial and domestic pollution.
Simultaneously, previous studies show that Xiamen Bay has lost 90% of its natural mangroves due to the coastal reclamation since the 1990s [21], with plans to afforest 244 ha and restore 180 ha of mangroves by 2025, thus further attracting concerns of whether NbSs will be enough for climate change. Additionally, the ecology of Xiamen Bay is sensitive due to rare species distribution, such as Chinese White Dolphins and amphioxus.
In addition, according to the Fujian Climate Bulletin [22], the average temperature in the year 2023 of study area was 20.5 °C, which is 0.7 °C higher than it was in 1960s, the precipitation was 1621.3 mm, and extreme weather and climate events occurred frequently. Researchers found that the decline in coastal ecosystems weakens the urban resilience to natural and climate change-induced disasters, including typhoons particularly [21]. On 15 September 2016, Typhoon Meranti landfall resulted in a reduction in the vegetation area, with approximately 95.1 ha being affected across the entirety of Xiamen Island [23]; the direct financial losses amounted to 10.2 billion RMB [24].
The complex human activities and sensitive ecological environment make the Xiamen Bay a typical study area. In order to conduct a more in-depth assessment of the habitat quality of Xiamen Bay, the Xiamen Bay was divided into subzones of Jiulong River Estuary (I), Western Sea (II), Southern Sea (III), Eastern Sea (IV), Tong’an Bay (V), Dadeng Sea (VI), Anhai Bay (VII), Weitou Bay (VIII), and Xiamen-Jinmen Sea (IX).

2.2. Scenario Setting

Four scenarios were set to study the effects of NbSs on the adaptation to climate change in Xiamen Bay, including Scenario #0, Scenario #1, Scenario #2, and Scenario #3. Scenario #0 illustrates the current status of the base year (2020). The setting principles and parameters of each scenario are shown in Table 1.
Scenario #1 is a climate change scenario in 2060 incorporating a predicted sea-level rise of 0.144 m. Compared to Scenario #1, Scenario #2 is an NbS restoration measure and considers an additional moderate mangrove restoration with the area of 0.33 km2 [25] (Figure 3a), while Scenario #3 is also an NbS scenario-enhanced mangrove restoration with a suitable area of 0.96 km2 (Figure 3b). Based on these four scenarios, the InVEST-HQ and SLAMM model were conducted to assess the habitat quality.

2.3. Simulation Models

In this study, changes in the habitat types and the habitat quality were simulated with the SLAMM model and the InVEST-HQ model, respectively.

2.3.1. SLAMM Model

Visualizing and simulating the sea-level rise process is critical in understanding climate change. This study utilized the SLAMM 6.7 model to simulate how habitat types respond to rising sea levels. The SLAMM model, being based on the sensitivity of coastal habitat types to tide and elevation [19,26] and being affected by slope, land cover, inundation, erosion, siltation, and salinity [27], has gained widespread recognition as a potent model for examining and forecasting wetland transformations [28,29]. A dependable database in this model is accessible, which was reviewed by both project teams and external advisors; thus, the main input data include habitat classification, elevation, and slope.
(1)
Habitat types
The Habitat type and its interaction with human activities are the main drivers of change in habitat quality [30]. This makes coastal areas a very important ecological feature and habitat for maintaining biodiversity [31]. The habitat type was obtained through the interpretation and classification of remote sensing images, which were acquired from Landsat 8-9 OLI/TIRS C2 L1, downloaded from the USGS website (www.usgs.gov, accessed on 9 January 2021), in the time range of 1 January 2020 to 1 January 2021 and which were preprocessed based on ENVI 5.3. The habitat types were preliminarily classified through the maximum likelihood method in ArcGIS 10.8. Finally, the habitat types and their distributions were checked via field investigation and images in the Bigemap GIS Office tool (www.bigemap.com, accessed on 9 January 2021).
According to the Technical Standards for Geological Surveys of the China Geological Survey (DD2012-04) [32] and the categories in the SLAMM, the habitat types of Xiamen Bay are classified into six categories, which are mangrove, saltmarsh, tidal flat, coastal beach, rocky coastline, and water.
(2)
Elevation and slope data
Elevation data were downloaded from the USGS website with a 30 m spatial resolution. The slope data were obtained by converting elevation data with the Raster surface-slope tool in ArcMap 10.8. To obtain the ASCII data for the SLAMM model, a Raster to ASCII tool was utilized for converting the elevation and slope data into ASCII format.
(3)
Sea-level rise data
According to the International report [33] and the China Sea Level Bulletin [34], the pace at which the sea level increases in Asia was around 1.700 mm/a during the year of 1900 to 2018. During the years from 1993 to 2018, the pace at which the sea level increases globally, in the Indo-Pacific, and in the Northwest Pacific was 3.250 mm/a, 3.650 mm/a, and 3.520 mm/a, respectively. Xiamen Bay is affected by both the Indo-Pacific and Northwest Pacific. Thus, we used the mean rate of sea-level rise in the Indo-Pacific and Northwest Pacific to represent the sea level in the study area, which was 3.59 mm/a; thus, we determined the sea-level rise from 2020 to 2060 as 0.144 m.

2.3.2. InVEST-HQ Model

Habitat quality means the ecosystem’s capacity to supply persistence circumstances for the ongoing survival of individuals and communities alike [18]. In this study, the InVEST-HQ was chosen for simulating and predicting the habitat quality, which is considered as a variable continuum spanning from low to medium to high levels. The region characterized by a high habitat quality sustains a structure and functionality that is consistent with historical variability. The habitat quality in the InVEST-HQ model is represented by the habitat quality index (HQI) and is mediated by four factors, which are the main processes of parameter setting, including (1) the threat and its relative impact, (2) the closeness of habitats to sources of threats, (3) habitat suitability, and (4) habitat sensitivity; further model specifications can be explored in the InVEST 3.12.0 user’s manual [18].
(1)
Threat and its relative impact
The ocean and coastal ecosystems face various human-induced pressures [35]; the threats to habitat quality include climate change, human activities, and other natural disasters, whereas climate change and sea uses represent threats to habitats in this study, and the magnitude of each threat’s impact is closely associated with the sea-use intensity. The greater the sea-use intensity, the more serious the impacts. Within the InVEST-HQ model, the relative impact of the threat factor varies between 0 and 1, where a relative impact closer to 1 indicates greater damage caused by the threat.
In this study, we used sea use as the threat. Five types of sea use with high sea-use intensity were selected as the threat factors, including industrial construction, port and shipping, mineral exploitation, tourism, and disposal uses [20]. The threat and its relative impact were referred to in existing studies [36,37]. The specific rules for determining the relative influence of the threat factors are as follows: the relative impact is 1 if the threat occupies the sea area permanently; the relative impact is 0.70~0.90 if the threat produces pollution or destroys habitats; the relative impact is 0.50~0.70 if the threat factor includes structural aspects, such as port and shipping; and the relative impact is 0~0.5 if the threat produces a short-term impact and can be repaired. Based on these principles, the threat factors and their impacts in this study are provided in Table 2.
(2)
The closeness of habitats to sources of threats
The second factor affecting habitat quality is the closeness of the habitat type to the threat source. In general, the intensity of the threat factor decreases with spatial distance, which depends on the decay rate in the space, usually including linear decay or exponential decay, which are shown in Equation (1) and Equation (2), respectively.
i r x y = 1 − d x y d r   m a x                 i f   l i n e a r
i r x y = exp ⁡ − 2.99 d r   m a x d x y          i f   e x p o n e n t i a l
where r represents threats, i r x y represents the proximity between the habitat and threats, d x y represents the linear distance separating two cells (x and y), and d r   m a x represents the furthest reach of impact caused by each threat.
(3)
Habitat suitability
The third factor affecting habitat quality is the level of legal, social, and physical protection, where the value ranges from 0 to 1. In the InVEST-HQ model, suitability increases as the value approaches 1.
In this study, the habitat suitability is determined according to their disturbance to the marine environment. The habitat suitability is 0 if the activity occupies the sea area permanently; the habitat suitability is between 0 and 0.30 if the activity produces pollution or destroys the habitat; the habitat suitability is between 0.40 and 0.60 if the activity is a structural form of sea use or extracts materials from the sea; and the habitat suitability is 0.70~1.00 if the activity is an ecological use or the habitat is natural. Based on this principle, the suitability of each habitat type or sea use was obtained, as shown in Table 3.
(4)
Habitat sensitivity
The fourth factor affecting habitat quality is the habitat sensitivity to threats, which ranges from 0 to 1 and was decided in accordance with the principle of biodiversity conservation, combining threat disturbance, water quality change, and hydrodynamic conditions with sea-use conflicts [36,37], where the sensitivity is greater if the value is closer to 1. According to the previous study, the conflict between activities is between 0 and 3 [20]. Thus, in this study, the relative sensitivity is 0 if the conflict is 0; the relative sensitivity is between 0.30 and 0.40 if the conflict value is 1; the relative sensitivity is between 0.60 and 0.80 if the conflict value is 2; and the relative sensitivity is between 0.70 and 1.00 if the conflict value is 3. The sensitivity of each type can be found in Table 3.

3. Results

Results of this research include the changes in the habitat types and habitat quality in scenarios in Xiamen Bay and its zones.

3.1. Habitat Distribution and Change

3.1.1. Habitat Types in Scenario #0 (Current Scenario)

The habitat types of Xiamen Bay are divided into six categories: mangrove, saltmarsh, tidal flat, coastal beach, rocky coastline, and coastal water (Figure 4a). The results show that coastal water is the largest habitat type in Xiamen Bay, which accounts for 88.75% of the habitat types. The tidal flat is second, accounting for 10.04% and being distributed in zones I, II, IV, VII, and VIII. The mangrove accounts for 0.54% and is distributed in zones I, V, and VII. The saltmarsh accounts for 0.47% and is distributed in zones I, VI, and VII. The coastal beach and rocky coastline account for 0.19% and 0.01%, respectively, and the distribution is scattered.

3.1.2. Habitat Types in Scenario #1 (Climate Change Scenario)

Results show that due to sea-level rise, the area of coastal water will expand by 8.60% in Scenario #1, while the area of mangroves, saltmarshes, tidal flats, rocky coastlines, and coastal beaches will show a decrease in the range of 20%~71% (Table 4 and Figure 4e). Simultaneously, the spatial distribution shows that most of the area of mangroves, saltmarshes, and tidal flats will change to coastal water area in Scenario #1 (Figure 4b). For instance, the mangroves and saltmarshes in zone I will decrease obviously, and the tidal flat area will move landward in the zones of II, V, VI, VII, and VIII, where the width of landward area is from around 0.1 km to 0.3 km.

3.1.3. Habitat Types in Scenario #2 (Moderate Mangrove Restoration)

The results show that, in Scenario #2, the loss proportion of the mangrove area will decrease from 43.51% in Scenario #1 to 39.94% (Table 4), while the loss proportion of tidal flats, saltmarshes, rocky coastlines, and coastal beaches will increase by 0.1% to 2.0%. However, there is no obvious change in spatial distribution when compared with Scenario #1 (Figure 4c).

3.1.4. Habitat Types in Scenario #3 (Enhanced Mangrove Restoration)

The results show the same trend as Scenario #2: the loss proportion of the mangrove area will decrease by 1.5% and the loss proportion of other habitat types will increase by between 0.1% and 0.3%. The spatial distribution of habitat types is not obviously changed when compared with Scenario #2 (Figure 4d).

3.2. Habitat Quality Prediction

Results show that the HQI of Xiamen Bay in Scenario #0, Scenario #1, Scenario #2, and Scenario #3 are 0.55, 0.53, 0.54, and 0.54, respectively.

3.2.1. Habitat Quality of Scenario #0

Results show that the HQI in Xiamen Bay is 0.55 in Scenario #0 (Figure 5). The HQIs of zones I, II, III, IV, V, VI, VII, VIII, and IX is 0.56, 0.45, 0.50, 0.79, 0.44, 0.33, 0.51, 0.40, and 0.65, respectively (Figure 5a). The highest HQI is distributed in zone IV, and the minimal HQI is distributed in zone VI. The HQIs in zones I, IV, and IX are higher than the average index in Xiamen Bay, while the HQIs in II, III, V, VI, VII, and VIII are lower than the average index in Xiamen Bay (Figure 5b).
The spatial distribution (Figure 6a) shows that high HQIs are distributed in zones I, IV, and IX, while the low HQIs are distributed in zones VI, VII, and VIII.

3.2.2. Habitat Quality of Scenario #1

The results show that the HQI in the Scenario #1 will decrease by 4.2% compared with Scenario #0 (Figure 5a,c), and the HQI in zones I, III, IV, and IX will decrease by 1.26%, 5.85%, 38.22%, and 36.28%, respectively; while the HQI in zones II, V, VI, VII, and VIII will migrate landward from the coastal water area, which will increase by 8.05%, 19.09%, 55.29%, 1.57%, and 12.63%, respectively. The biggest HQI change will be observed in zone VI, with an increase of 55.29%.
The spatial distribution shows that there is almost no change in the highest value of the HQI in Xiamen Bay of Scenario #1 compared with Scenario #0, while the low HQI area in zone VI is less than Scenario #0 (Figure 6b).

3.2.3. Habitat Quality of Scenario #2

The results show that both the value and spatial distribution of the HQI will increase in Scenario #2 (Figure 5a,d and Figure 6c). When compared with Scenario #1, the HQI in Scenario #2 will increase by 1.5%. This indicates that mangrove restoration and protected-area extension will increase habitat quality and slow down climate change. Simultaneously, the HQI in zones III, IV, V, VIII, and IX will increase by 0.49% to 14.85% (Figure 5a,d), the biggest change will be distributed in zone IV (14.85%), and the lowest change will be distributed in zone IX. However, the HQI in zones I, II, VI, and VII will decrease by 2.51% to 16.98%; the greatest reduction will be distributed in zone II (16.98%).

3.2.4. Habitat Quality of Scenario #3

The results show that the HQI in Scenario #3 is almost the same as Scenario #2. However, the HQI in zones I, II, V, VI, and VII will increase by 0.57% to 24.19% (Figure 5a,e); the biggest change will be distributed in zone II (24.19%). However, the HQI in zones III, IV, VIII, and IX will decrease by 0.48% to 12.93%; the greatest reduction will be distributed in zone IV (12.93%).
The spatial distribution shows that the highest value of the HQI is lower than Scenario #2 (Figure 6d), and the HQI in each zone is not obviously changed when compared to Scenario #1.

4. Discussion

The main results in this study are further discussed in this section. We found that NbSs will be an effective way to slow down climate change and to increase habitat quality, while this may be offset by the conflicting effects of human sea-use activities.

4.1. Sea-Level Rise Will Bring Effects to Habitat Quality in the Study Area

The rising sea levels in response to climate change have adverse effects on coastal regions worldwide [38]. Comparing Scenario #1 with Scenario #0 in this study, the area of coastal habitats will decrease by a range of 20%~71% (Figure 4a,b and Figure 5b), indicating that the inundation effect will decrease the area of coastal habitats. Simultaneously, the results show that the area of tidal flats will move to the landside with a range of 0.1~0.3 km, indicating the sea-level rise will bring a coastal squeeze effect due to erosion and inundation [39]. The finding of coastal squeeze effects on coastal habitats occurring from sea-level rise, i.e., a landward migration trend, is also confirmed in other studies [40,41]; meanwhile, sea-level rise poses an urgent threat to habitat quality by inundation and erosion [42].
In addition, the HQI in Scenario #1 will decrease by 4.2% compared with Scenario #0. This finding is consistent with the conclusions that sea-level rise may pose a risk of disproportionately affecting the marine habitat quality and lead to the deterioration of human foundations [26,43,44].

4.2. Mangrove Restoration as a Key NbS Is Effective in Mitigating Climate Change Impact and Improving Habitat Quality

This study shows that mangrove restoration as a key NbS is an effective way to slow down the climate change impact and increase habitat quality; the HQI increases by 1.5% in Scenario #2 compared to Scenario #1. Other studies, including IUCN [3], also show that NbSs are considered crucial components of strategies for climate change mitigation and that there are many policies and plans that encourage using NbSs for climate change [45]. However, NbSs for climate change require the consideration of socioeconomic and governance characteristics [46,47,48,49], the sustainability of NbSs such as the SDGs [50,51,52], and the maintenance and management actions in subsequent phases [53]. Thus, with increases in the scope and magnitude of biodiversity conservation and ecological restoration, further NbS studies are needed.

4.3. The Effectiveness of Mangrove Restoration in This Study Shows the Necessary Consideration of the Mangrove Fragmentation and the Sea-Use Impacts

Comparing Scenario #2 with Scenario #3, both the numerical variation and spatial distribution of the HQI in Xiamen Bay have very little change (Figure 5 and Figure 6), indicating that more restoration does not necessarily lead to higher habitat quality.
On the one hand, the habitat types, structure, and function characteristics are the main factors in habitat quality [54,55]. The mangrove fragmentation is mainly due to the changes in human activities, such as land uses. And mangrove fragments may result in a severe reduction in mangrove biodiversity and ecosystem functions [56]. Research found that there was a 60% reduction in mangrove areas since the 1960s [55,56] and that the total density and habitats of individuals are becoming destroyed at an alarming rate with effects on biodiversity [57]. The study result shows that the zones with mangroves, tourism, and MPA have high habitat quality (Figure 6), such as zones of I, V, IV, and IX. However, with mangrove restoration in Scenario #2, the HQI in zones with mangrove restoration are lower than those of Scenario #1, such as zones I and II, indicating that the mangrove fragmentation may increase the habitat vulnerability and the habitat heterogeneity [27,53,58,59] and that it is necessary pay attention to this in mangrove restoration.
On the other hand, results of Scenario #0 show that high HQIs can be found in zones I, V, and IV, where sea-use intensity is low, while low HQIs are in zones VI, VII, and VIII where the sea-use intensity is high. Simultaneously, the HQI in each zone of different scenarios shows that zone VI with high sea-use intensity is easily affected by the erosion effect of sea-level rise, while zone IX with low sea-use intensity is less affected by sea-level rise. These findings indicate that the sea-use impacts must be considered in habitat quality [60]; how to coordinate the sea-use impacts and the coastal habitat quality has become a key issue in the development of coastal zones [20,61].

4.4. Limitation and Outlook

The main climate change effects include sea-level rise, warming, and acidification, each one being interconnected, and all of these are accelerated and increase the vulnerability of coastal habitat quality [62,63]. Where the sea-level rise may result in a risk of flooding or erosion [64], ocean warming and acidification would undergo significant changes in physical processes, chemical processes, and ecological processes [65]. Since coral reefs are particularly sensitive to pH changes in oceans, in regions with coral reefs, it is imperative to incorporate the impacts of acidification. However, acidification was not studied in this study, mainly due to the absence of coral reefs in Xiamen Bay. In addition, the SLAMM model also focuses on sea-level rise without any consideration of warming and acidification. Thus, future studies should incorporate more models that accurately simulate extreme weather events and their consequences on marine environments.
The InVEST-HQ model offers flexibility in configuring parameters, and it is a challenge to choose the threat factors and their effects on the habitat. To address this problem, we innovatively used the sea-use impacts as threats in the InVEST-HQ model; however, it was still determined based on empirical data and expert insight. Thus, future studies should also consider these elements, combining more field observations and laboratory observations, especially for the factors of pollution, sedimentation, and changes in hydrodynamics. Furthermore, more models and methods should be used to reduce subjectivity.
Finally, the methodology in this study could be used to explore mangrove restoration in a larger area in response to climate change and anthropogenic activities.

5. Conclusions

Based on the four NbS scenarios, we tried to answer whether NbSs will be enough for climate change adaptation by evaluating the coastal habitat quality with the InVEST-HQ model and the SLAMM model. Our study finds that sea-level rise will bring about effects on habitat quality, and the coastal habitats will have a trend of migrating landward, causing a coastal squeeze effect due to the erosion and inundation on sea-level rise. Mangrove restoration, as a key NbS, is an effective way to mitigate climate change effects and to improve habitat quality; however, the effectiveness of mangrove restoration in this study shows the necessary attention that should be paid to mangrove fragmentation and sea-use impacts. The innovations of this study include the exploration of whether the NbS is enough for climate change adaptation through the modeling of multiple scenarios and the consideration of sea-use impacts as threat factors quantitatively. The information in this study will inform the formulation of biodiversity protection policies and supply technical support for the future NbS restoration of Xiamen Bay, giving reference for other similar coastal areas in the context of climate change.

Author Contributions

Conceptualization, Q.F.; methodology, S.Y., D.Z., X.Z., X.J. and B.L.; software, S.Y., D.Z. and X.Z.; validation, S.Y., Q.F. and D.Z.; formal analysis, S.Y.; investigation, S.Y.; resources, S.Y., D.Z. and X.Z.; data curation, S.Y. and D.Z.; writing—original draft preparation, S.Y.; writing—review and editing, S.Y., Q.F., L.M. and H.O.I.; visualization, S.Y., L.M., H.O.I., X.Z., X.J. and B.L.; supervision, Q.F.; project administration, S.Y. and Q.F.; funding acquisition, S.Y. and Q.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (Grant No. 2022YFF0802203), the Scientific Research Fund of the Fourth Institute of Oceanography, MNR, China (Grant No. JKF202304) and the National Natural Science Foundation of China (Grant No. 41877515).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The funders are gratefully acknowledged. We would like to express our gratitude to the anonymous reviewers for their valuable and thoughtful comments.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Figure 1. The research framework.
Figure 1. The research framework.
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Figure 2. Study area and the sea uses in Xiamen Bay.
Figure 2. Study area and the sea uses in Xiamen Bay.
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Figure 3. The mangrove restoration area of Scenarios #2 and #3. The area with the red circle is the main difference between Scenarios #2 and #3.
Figure 3. The mangrove restoration area of Scenarios #2 and #3. The area with the red circle is the main difference between Scenarios #2 and #3.
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Figure 4. The habitat distribution and area changed under different scenarios.
Figure 4. The habitat distribution and area changed under different scenarios.
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Figure 5. The numerical variation of the HQI at Xiamen Bay and its zones under four scenarios.
Figure 5. The numerical variation of the HQI at Xiamen Bay and its zones under four scenarios.
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Figure 6. The spatial distribution of HQI in Xiamen Bay and its zones under different scenarios. Color red indicates high HQI, blue indicates low HQI, and yellow indicates medium HQI.
Figure 6. The spatial distribution of HQI in Xiamen Bay and its zones under different scenarios. Color red indicates high HQI, blue indicates low HQI, and yellow indicates medium HQI.
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Table 1. The setting principles and parameters of each scenario.
Table 1. The setting principles and parameters of each scenario.
ScenariosPrinciplesParameters
Scenario #0The current situationThe current situation in 2020
Scenario #1Sea-level rise in 2060 as a climate change scenarioSea-level rise 0.144 m
Scenario #2Moderate mangrove restoration as an NbS based on Scenario #1Sea-level rise 0.144 m,
0.33 km2 mangroves to restore
Scenario #3Enhanced mangrove restoration based on Scenario #2Sea-level rise of 0.144 m,
0.96 km2 mangroves to restore
Table 2. The habitat threat factors and their relative impact on Xiamen Bay.
Table 2. The habitat threat factors and their relative impact on Xiamen Bay.
Maximum Effect
Distance (km)		WeightThreatDecayDescribePath
81.000ICexponentialIndustrial constructionIC_c.tif
30.900DispexponentialdisposalDisp_c.tif
30.600PortlinearPort and shippingPort_c.tif
30.600MineralexponentialMineral explorationMineral_c.tif
30.400TourexponentialtourismTour_c.tif
Note on abbreviations: IC means the industrial construction area, Disp means the disposal area, Port means the Port and shipping area, Mineral means the mineral exploration area, Tour means the tourism area.
Table 3. The sensitivity of habitat types to threat factors.
Table 3. The sensitivity of habitat types to threat factors.
Habitat Types or Sea UsesSuitabilitySensitivity
Industrial ConstructionPort and ShippingMineral ExplorationTourismDisposal
Coastal water0.800 1.000 0.600 0.600 0.4000.900
Saltmarsh0.8001.000 0.350 0.350 0.3500.800
Mangrove1.000 1.000 0.750 0.750 0.3000.900
Tidal flat0.750 1.000 0.750 0.750 0.5000.900
Coastal beach0.900 1.000 0.850 0.850 0.3000.900
Rocky coastline0.9000.0000.0000.0000.0000.900
MPA1.000 1.000 1.000 1.000 0.9000.900
Industrial construction0.0000.0000.0000.0000.0000.300
Port and shipping0.5001.0000.0000.8500.7500.000
Tourism0.750 1.000 0.750 0.600 0.0000.800
Mineral exploration0.3001.0000.8500.0000.6000.800
Disposal0.0000.3000.0000.8000.8000.000
Table 4. The change in each habitat type compared to the current scenario.
Table 4. The change in each habitat type compared to the current scenario.
Habitat TypesScenario #1Scenario #2Scenario #3
Mangrove−43.51%−39.94%−38.39%
Salt marsh−65.76%−65.89%−66.02%
Tidal flat−70.37%−70.55%−70.61%
Coastal beach−20.43%−20.53%−20.75%
Rocky coastline−32.65%−34.22%−32.33%
Coastal water+8.55%+8.54%+8.54%
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Yang, S.; Fang, Q.; Zhang, D.; Meilana, L.; Ikhumhen, H.O.; Zhang, X.; Jiang, X.; Lin, B. Nature-Based Solution for Climate Change Adaptation: Coastal Habitats Restoration in Xiamen Bay, China. Forests 2024, 15, 1844. https://doi.org/10.3390/f15111844

AMA Style

Yang S, Fang Q, Zhang D, Meilana L, Ikhumhen HO, Zhang X, Jiang X, Lin B. Nature-Based Solution for Climate Change Adaptation: Coastal Habitats Restoration in Xiamen Bay, China. Forests. 2024; 15(11):1844. https://doi.org/10.3390/f15111844

Chicago/Turabian Style

Yang, Suzhen, Qinhua Fang, Dian Zhang, Lusita Meilana, Harrison Odion Ikhumhen, Xue Zhang, Xiaoyan Jiang, and Boding Lin. 2024. "Nature-Based Solution for Climate Change Adaptation: Coastal Habitats Restoration in Xiamen Bay, China" Forests 15, no. 11: 1844. https://doi.org/10.3390/f15111844

APA Style

Yang, S., Fang, Q., Zhang, D., Meilana, L., Ikhumhen, H. O., Zhang, X., Jiang, X., & Lin, B. (2024). Nature-Based Solution for Climate Change Adaptation: Coastal Habitats Restoration in Xiamen Bay, China. Forests, 15(11), 1844. https://doi.org/10.3390/f15111844

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