Drivers of global mangrove loss and gain in social-ecological systems

Integrating socioeconomic and ecological data into restoration practice

Driven by the United Nations Decade on Restoration and international funding initiatives, such as the Mangrove Breakthrough, investment in mangrove restoration is expected to increase. Yet, mangrove restoration efforts frequently fail, usually because of ad hoc site-selection processes that do not consider mangrove ecology and the socioeconomic context. Using decision analysis, we developed an approach that accounts for socioeconomic and ecological data to identify sites with the highest likelihood of mangrove restoration success. We applied our approach in the Biosphere Reserve Marismas Nacionales Nayarit, Mexico, an area that recently received funding for implementing mangrove restoration actions. We identified 468 potential restoration sites, assessed their restorability potential based on socioeconomic and ecological metrics, and ranked sites for implementation with spatial optimization. The metrics we used included favorable conditions for propagules to establish and survive under sea-level rise, provision of ecosystem services, and community dynamics. Sites that were selected based on socioeconomic or ecological metrics alone had lower likelihood of mangrove restoration success than sites that were selected based on integrated socioeconomic and ecological metrics. For example, selecting sites based on only socioeconomic metrics captured 16% of the maximum attainable value of functioning mangroves able to provide propagules to potential restoration sites, whereas selecting sites based on ecological and socioeconomic metrics captured 46% of functioning mangroves. Our approach was developed as part of a collaboration between nongovernmental organizations, local government, and academics under rapid delivery time lines given preexisting mangrove restoration implementation commitments. The systematic decision process we used integrated socioeconomic and ecological considerations even under short delivery deadlines, and our approach can be adapted to help mangrove restoration site-selection decisions elsewhere.

Spatio-temporal variation and drivers of blue carbon sequestration in Hainan Island, China

Blue carbon ecosystems, such as mangrove, seagrass bed and salt marsh, have attracted increasing attention due to their remarkable capacity for efficient carbon sequestration. However, the current threat posed by human activities to these ecosystems necessitates the characterization of their changes and identification of the primary driving factors in order to facilitate the gradual restoration of blue carbon ecosystems. In this study, we present an analysis of the spatio-temporal characteristics and primary influencing factors governing carbon sequestration in mangrove and seagrass beds located in Hainan Island. The findings revealed a 40% decline in carbon sequestration by mangroves from 1976 to 2017, while seagrass beds exhibited a 13% decrease in carbon sequestering between 2009 and 2016. The decline in carbon sequestration was primarily concentrated in Wenchang city, with aquaculture and population growth identified as the primary driving factors. Despite the implementation of measures aimed at reducing aquaculture in Hainan Island to promote blue carbon sequestration over the past two decades, the resulting recovery remains insufficient in achieving macro-level goals for carbon sequestration. This study emphasizes the necessity of safeguarding blue carbon ecosystems in Hainan Island by effectively mitigating anthropogenic disturbances.

Microbial diversity and keystone species drive soil nutrient cycling and multifunctionality following mangrove restoration

Vegetation restoration exerts transformative effects on nutrient cycling, microbial communities, and ecosystem functions. While extensive research has been conducted on the significance of mangroves and their restoration efforts, the effectiveness of mangrove restoration in enhancing soil multifunctionality in degraded coastal wetlands remains unclear. Herein, we carried out a field experiment to explore the impacts of mangrove restoration and its chronosequence on soil microbial communities, keystone species, and soil multifunctionality, using unrestored aquaculture ponds as controls. The results revealed that mangrove restoration enhanced soil multifunctionality, with these positive effects progressively amplifying over the restoration chronosequence. Furthermore, mangrove restoration led to a substantial increase in microbial diversity and a reshaping of microbial community composition, increasing the relative abundance of dominant phyla such as Nitrospirae, Deferribacteres, and Fusobacteria. Soil multifunctionality exhibited positive correlations with microbial diversity, suggesting a link between variations in microbial diversity and soil multifunctionality. Metagenomic screening demonstrated that mangrove restoration resulted in a simultaneous increase in the abundance of nitrogen (N) related genes, such as N fixation (nirD/H/K), nitrification (pmoA-amoA/B/C), and denitrification (nirK, norB/C, narG/H, napA/B), as well as phosphorus (P)-related genes, including organic P mineralization (phnX/W, phoA/D/G, phnJ/N/P), inorganic P solubilization (gcd, ppx-gppA), and transporters (phnC/D/E, pstA/B/C/S)). The relationship between the abundance of keystone species (such as phnC/D/E) and restoration-induced changes in soil multifunctionality indicates that mangrove restoration enhances soil multifunctionality through an increase in the abundance of keystone species associated with N and P cycles. Additionally, it was observed that changes in microbial community and multifunctionality were largely associated with shifts in soil salinity. These findings demonstrate that mangrove restoration positively influences soil multifunctionality and shapes nutrient dynamics, microbial communities, and overall ecosystem resilience. As global efforts continue to focus on ecosystem restoration, understanding the complexity of mangrove-soil interactions is critical for effective nutrient management and mangrove conservation.

Planning hydrological restoration of coastal wetlands: Key model considerations and solutions

The hydrological restoration of coastal wetlands is an emerging approach for mitigating and adapting to climate change and enhancing ecosystem services such as improved water quality and biodiversity. This paper synthesises current knowledge on selecting appropriate modelling approaches for hydrological restoration projects. The selection of a modelling approach is based on project-specific factors, such as costs, risks, and uncertainties, and aligns with the overall project objectives. We provide guidance on model selection, emphasising the use of simpler and less expensive modelling approaches when appropriate, and identifying situations when models may not be required for project managers to make informed decisions. This paper recognises and supports the widespread use of hydrological restoration in coastal wetlands by bridging the gap between hydrological science and restoration practices. It underscores the significance of project objectives, budget, and available data and offers decision-making frameworks, such as decision trees, to aid in matching modelling methods with specific project outcomes.

Mangrove ecosystems in Southeast Asia region: Mangrove extent, blue carbon potential and CO2 emissions in 1996-2020

This study aimed to analyze mangrove extent (ME), carbon stock, blue carbon potential, and CO2 emission from 1996 to 2020 in Southeast Asia region. The data was obtained through the Global Mangrove Alliance (GMA) on the platform www.globalmangrovewatch.org v.3. Furthermore, ME was analyzed descriptively and the triggers for mangrove land changes in each country were investigated through a relevant literature review. The spatial analysis was conducted for blue carbon potential, while CO2 emission was derived by multiplying net change by emission factor (EF) of mangrove ecosystem. The results showed that the total ME in Southeast Asia was 5.07 million hectares (Mha) in 1996, decreasing to 4.82 Mha by 2020 due to various land uses, primarily shrimp farming. The total carbon stock potential was 2367.68 MtC, while a blue carbon potential was 8682.32 MtCO2-e, consisting of 1304.33 MtCO2-e and 7377.99 MtCO2-e from above-ground and soil carbon. Indonesia contributed 5939.57 MtCO2-e to blue carbon potential, while Singapore and Timor-Leste had the lowest contributions of 1.05 MtCO2-e and 1.37 MtCO2-e, respectively. Carbon stock potential (AGC and SOC) in Southeast Asia was influenced by ME conditions. The relationship between ME and AGC was found to be exponential (AGC = 0.0307e0.8938x; R2 = 0.9331; rME-AGC = 0.9964, P < 0.01). Similarly, ME and SOC, or AGC and SOC showed a relationship where SOC = 0.2e0.8829x (R2 = 0.937, rME-SOC = 0.9965 and rAGC-SOC = 0.9989, P < 0.01). The average CO2-e emission in Southeast Asia reached 17.0760 MtCO2-e yr-1 and the largest were attributed to Indonesia at 16.3817 MtCO2-e yr-1. Meanwhile, Brunei and Timor Leste did not show CO2-e emission as mangrove in these countries absorbed more CO2 from the atmosphere at -0.034 MtCO2-e yr-1 and -0.0002 MtCO2-e yr-1, respectively.

Exploring the spatio-temporal patterns of global mangrove gross primary production and quantifying the factors affecting its estimation, 1996-2020

Mangrove ecosystems, as an important component of "Blue Carbon", play a curial role on global carbon cycling; however, the lack of the global estimates of mangrove ecosystem gross primary production (GPP) and the underlying environmental controls on its estimation remain a gap in knowledge. In this study, we utilized global mangrove eddy covariance data and applied Gaussian Process Regression (GPR) to estimate GPP for global mangrove ecosystems, aiming to elucidate the factors influencing these estimates. The optimal GPR achieved favorable estimation performance through cross-validation (R2 = 0.90, RMSE = 0.92 gC/m2/day, WI = 0.86). Over the study period, the globally annual averaged GPP was 2054.53 ± 38.51 gC/m2/yr (comparable to that of evergreen broadleaf forests and exceeds the GPP of most other plant function types), amounting to a total of 304.82 ± 7.71TgC/yr, hotspots exceeding 3000 gC/m2/yr observed near the equator. The analysis revealed a decline in global mangrove GPP during 1996-2020 of -0.89 TgC/yr. Human activities (changes in mangrove cover area) played a relatively consistent role in contributing to this decrease. Conversely, variations in external environmental conditions showed distinct inter-annual differences in their impact. The spatio-temporal distribution patterns of mangrove ecosystems GPP (e.g., the bimodal annual pattern, latitudinal gradients, etc.) demonstrated the regulatory influence of external environmental conditions on GPP estimates. The model ensemble attribution analysis indicated that the fraction of absorbed photosynthetically active radiation exerted the dominant control on GPP estimations, while temperature, salinity, and humidity acted as secondary constraints. The findings of this study provide valuable insights for monitoring, modeling, and managing mangrove ecosystems GPP; and underscore the critical role of mangroves in global carbon sequestration. By quantifying the influences of environmental factors, we enhance our understanding of mangrove carbon cycling estimates, thereby helping sustain of these disproportionately productive ecosystems.

The composition, distribution, and socio-economic dimensions of Ghana's mangrove ecosystems

Mangrove ecosystems are recognised as one of the nature-based solutions to a changing climate. Notwithstanding the socio-ecological benefits of mangrove ecosystems, they are increasingly being destructed in some regions of the world. In Ghana, several studies have reported on the status, use, and management strategies of mangrove ecosystems in different sites of the country. However, these studies do not make it possible to appreciate the broader picture of Ghana's mangrove ecosystems since they are not synthesized into a single comprehensive report. This study uses the ROSES method for systematic reviews to report on Ghana's mangrove ecosystem distribution and species composition, as well as their socio-economic benefits, the anthropogenic and natural impacts on Ghana's mangrove ecosystems, and the management strategies and/or practices on Ghana's mangrove ecosystems. The study reveals there is no existing management strategy for Ghana's mangrove ecosystems, and therefore recommends the need to develop and implement policies and regulations that specifically target the protection and sustainable use of mangrove ecosystems in Ghana.

Sustainably developing global blue carbon for climate change mitigation and economic benefits through international cooperation

Blue carbon is the carbon storage in vegetated coastal ecosystems such as mangroves, salt marshes, and seagrass. It is gaining global attention as its role in climate change mitigation and local welfare growth. However, a global assessment on the long-term spatiotemporal sustainable development status of blue carbon has not been conducted, and the relations among blue carbon ecosystems, driving forces for climate change mitigation, and socioeconomic interventions for development capacity on a global scale are still unclear. Here, we constructed a blue carbon development index (BCDI), comprising three subsystems: driving force, resource endowment, and development capacity, to assess the sustainable development level of 136 coastal countries' blue carbon over 24 consecutive years and explore the relationship among subsystems. We further propose a cooperation model to explore the feasibility of global blue carbon cooperation and quantify benefit allocation to specific countries. The results showed an upward trend in BCDI scores with variations in regional performance over the past two decades, and we found a positive correlation between development capacity and blue carbon resource endowment. Based on the scenario simulations of global cooperation, we found that coastal countries could improve the global average BCDI score, add 2.96 Mt of annual carbon sequestration, and generate $136.34 million in 2030 under Global Deep Cooperation scenario compared with the Business-As-Usual scenario.

A novel coupled framework for detecting hotspots of methane emission from the vulnerable Indian Sundarban mangrove ecosystem using data-driven models

Coastal mangroves have been lost to deforestation for anthropogenic activities such as agriculture over the past two decades. The genesis of methane (CH₄), a significant greenhouse gas (GHG) with a high potential for global warming, occurs through these mangrove beds. The mangrove forests in the Indian Sundarban deltaic region were studied for pre-monsoonal and post-monsoonal variations of CH₄ emission. Considering the importance of CH₄ emission, a process-based spatiotemporal (PBS) and an analytical neural network (ANN) model were proposed and used to estimate the amount of CH₄ emission from different land use land cover classes (LULC) of mangroves. The field work was performed in 2020, and gas samples of various LULC were directly collected from the mangrove bed using the enclosed box chamber method. Historical climatic data (1960-1989) were used to predict future climate scenarios and associated CH₄ emissions. The analysis and estimation activities were carried out utilizing satellite images from the pre-monsoonal and post-monsoonal seasons of the same year. The study revealed that pre-monsoonal CH₄ emission was higher in the south-west and northern parts of the deforested mangrove of the Indian Sundarban. A sensitivity study of the anticipated models was conducted using a variety of environmental input parameters and related main field observations. The measured precision area under curve of receiver operating characteristics was 0.753 for PBS and 0.718 for ANN models, respectively. The temperature factor (T_f) was the most crucial variable for CH₄ emissions. Based on the PBS model with coupled model intercomparison project-6 temperature data, a global circulation model was run to predict increasing CH₄ emissions up to 2100. The model revealed that the agricultural lands were the prime emitters of CH₄ in the Sundarban mangrove ecosystem.