Harnessing Big Data to Support the Conservation and Rehabilitation of Mangrove Forests Globally

Characterising the distribution of mangroves along the southern coast of Vietnam using multi-spectral indices and a deep learning model

Mangroves are an ecologically and economically valuable ecosystem that provides a range of ecological services, including habitat for a diverse range of plant and animal species, protection of coastlines from erosion and storms, carbon sequestration, and improvement of water quality. Despite their significant ecological role, in many areas, including in Vietnam, large scale losses have occurred, although restoration efforts have been underway. Understanding the scale of the loss and the efficacy of restoration requires high resolution temporal monitoring of mangrove cover on large scales. We have produced a time series of 10-m-resolution mangrove cover maps using the Multispectral Instrument on the Sentinel 2 satellites and with this tool measured the changes in mangrove distribution on the Vietnamese Southern Coast (VSC). We extracted the annual mangrove cover ranging from 2016 to 2023 using a deep learning model with a U-Net architecture based on 17 spectral indices. Additionally, a comparison of misclassification by the model with global products was conducted, indicating that the U-Net architecture demonstrated superior performance when compared to experiments including multispectral bands of Sentinel-2 and time-series of Sentinel-1 data, as shown by the highest performing spectral indices. The generated performance metrics, including overall accuracy, precision, recall, and F1-score were above 90 % for entire years. Water indices were investigated as the most important variables for mangrove extraction. Our study revealed some misclassifications by global products such as World Cover and Global Mangrove Watch and highlighted the significance of our study for local analysis. While we did observe a loss of 34,778 ha (42.2 %) of mangrove area in the region, 47,688 ha (57.8 %) of new mangrove area appeared, resulting in a net gain of 12,910 ha (15.65 %) over the eight-year period of the study. The majority of new mangrove areas were concentrated in Ca Mau peninsulas and within estuaries undergoing recovery programs and natural recovery processes. Mangrove loss occurred in regions where industrial development, wind farm projects, reclaimed land, and shrimp pond expansion is occurring. Our study provides a theoretical framework as well as up-to-date data for mapping and monitoring mangrove cover change that can be readily applied at other sites.

Seabird nutrient subsidies enrich mangrove ecosystems and are exported to nearby coastal habitats

Eutrophication by human-derived nutrient enrichment is a major threat to mangroves, impacting productivity, ecological functions, resilience, and ecosystem services. Natural mangrove nutrient enrichment processes, however, remain largely uninvestigated. Mobile consumers such as seabirds are important vectors of cross-ecosystem nutrient subsidies to islands but how they influence mangrove ecosystems is poorly known. We assessed the contribution, uptake, cycling, and transfer of nutrients from seabird colonies in remote mangrove systems free of human stressors. We found that nutrients from seabird guano enrich mangrove plants, reduce nutrient limitations, enhance mangrove invertebrate food webs, and are exported to nearby coastal habitats through tidal flow. We show that seabird nutrient subsidies in mangroves can be substantial, improving the nutrient status and health of mangroves and adjacent coastal habitats. Conserving mobile consumers, such as seabirds, is therefore vital to preserve and enhance their role in mangrove productivity, resilience, and provision of diverse functions and services.

Uniting Experiments and Big Data to advance ecology and conservation

Many ecologists increasingly advocate for research frameworks centered on the use of 'big data' to address anthropogenic impacts on ecosystems. Yet, experiments are often considered essential for identifying mechanisms and informing conservation interventions. We highlight the complementarity of these research frameworks and expose largely untapped opportunities for combining them to speed advancements in ecology and conservation. With nascent but increasing application of model integration, we argue that there is an urgent need to unite experimental and big data frameworks throughout the scientific process. Such an integrated framework offers potential for capitalizing on the benefits of both frameworks to gain rapid and reliable answers to ecological challenges.

Assessment of long-term mangrove distribution using optimised machine learning algorithms and landscape pattern analysis

Mangrove ecosystems provide numerous benefits, including carbon storage, coastal protection and food for marine organisms. However, mapping and monitoring of mangrove status in some regions, such as the Red Sea area, has been hindered by a lack of data, accurate and precise maps and technical expertise. In this study, an advanced machine learning algorithm was proposed to produce an accurate and precise high-resolution land use map that includes mangroves in the Al Wajh Bank habitat in northeastern Saudi Arabia. To achieve this, high-resolution multispectral images were generated using an image fusion technique, and machine learning algorithms were applied, including artificial neural networks, random forests and support vector machine algorithms. The performance of the models was evaluated using various matrices, and changes in mangrove distribution and connectivity were assessed using the landscape fragmentation model and Getis-Ord statistics. The research gap that this study aims to address is the lack of accurate and precise mapping and assessment of mangrove status in the Red Sea area, particularly in data-scarce regions. Our study produced high-resolution mobile laser scanning (MLS) imagery of 15-m length for 2014 and 2022, and trained 5, 6 and 9 models for artificial neural networks, support vector machines and random forests (RF) to predict land use and land cover maps using 15-m and 30-m resolution MLS images. The best models were identified using error matrices, and it was found that RF outperformed other models. According to the 15-m resolution map of 2022 and the best models of RF, the mangrove cover in the Al Wajh Bank is 27.6 km², which increased to 34.99 km² in the case of the 30-m resolution image of 2022, and was 11.94 km² in 2014, indicating a doubling of the mangrove area. Landscape structure analysis revealed an increase in small core and hotspot areas, which were converted into medium core and very large hotspot areas in 2014. New mangrove areas were identified in the form of patches, edges, potholes and coldspots. The connectivity model showed an increase in connectivity over time, promoting biodiversity. Our study contributes to the promotion of the protection, conservation and planting of mangroves in the Red Sea area.

Mapping global distribution of mangrove forests at 10-m resolution

Mangrove forests deliver incredible ecosystem goods and services and are enormously relevant to sustainable living. An accurate assessment of the global status of mangrove forests warrants the necessity of datasets with sufficient information on spatial distributions and patch patterns. However, existing datasets were mostly derived from ∼30 m resolution satellite imagery and used pixel-based image classification methods, which lacked spatial details and reasonable geo-information. Here, based on Sentinel-2 imagery, we created a global mangrove forest dataset at 10-m resolution, namely, High-resolution Global Mangrove Forests (HGMF_2020), using object-based image analysis and random forest classification. We then analyzed the status of global mangrove forests from the perspectives of conservation, threats, and resistance to ocean disasters. We concluded the following: (1) globally, there were 145,068 km² mangrove forests in 2020, among which Asia contained the largest coverage (39.2%); at the country level, Indonesia had the largest amount of mangrove forests, followed by Brazil and Australia. (2) Mangrove forests in South Asia were estimated to be in the better status due to the higher proportion of conservation and larger individual patch size; in contrast, mangrove forests in East and Southeast Asia were facing intensive threats. (3) Nearly, 99% of mangrove forest areas had a patch width greater than 100 m, suggesting that nearly all mangrove forests were efficient in reducing coastal wave energy and impacts. This study reports an innovative and up-to-date dataset and comprehensive information on mangrove forests status to contribute to related research and policy implementation, especially for supporting sustainable development.

Multi-spatiotemporal analysis of changes in mangrove forests in Palawan, Philippines: predicting future trends using a support vector machine algorithm and the Markov chain model

Multi-temporal remote sensing imagery can be used to explore how mangrove assemblages are changing over time and facilitate critical interventions for ecological sustainability and effective management. This study aims to explore the spatial dynamics of mangrove extents in Palawan, Philippines, specifically in Puerto Princesa City, Taytay and Aborlan, and facilitate future predictions for Palawan using the Markov Chain model. The multi-date Landsat imageries during the period 1988-2020 were used for this research. The support vector machine algorithm was sufficiently effective for mangrove feature extraction to generate satisfactory accuracy results (>70% kappa coefficient values; 91% average overall accuracies). In Palawan, a 5.2% (2693 ha) decrease was recorded during 1988-1998 and an 8.6% increase in 2013-2020 to 4371 ha. In Puerto Princesa City, a 95.9% (2758 ha) increase was observed during 1988-1998 and 2.0% (136 ha) decrease during 2013-2020. The mangroves in Taytay and Aborlan both gained an additional 2138 ha (55.3%) and 228 ha (16.8%) during 1988-1998 but also decreased from 2013 to 2020 by 3.4% (247 ha) and 0.2% (3 ha), respectively. However, projected results suggest that the mangrove areas in Palawan will likely increase in 2030 (to 64,946 ha) and 2050 (to 66,972 ha). This study demonstrated the capability of the Markov chain model in the context of ecological sustainability involving policy intervention. However, as this research did not capture the environmental factors that may have influenced the changes in mangrove patterns, it is suggested adding cellular automata in future Markovian mangrove modelling.

Distribution Pattern of Mangrove Fish Communities in China

Mangroves are among the most productive marine and coastal ecosystems and play an important role in maintaining the stability and diversity of fish communities. To explore the structure of mangrove fish communities in China, we compiled previous studies, monographs, and two databases on 54 mangrove areas published in the past 30 years. Mangrove fish communities in China comprised Osteichthys (597 species) and Chondrichthyes (14 species), representing 611 species in 344 genera, 117 families, and 28 orders. Perciformes were the predominant taxon, with 350 species in 52 families, accounting for 57% of the total species richness. Reef fish accounted for 29.62%. With regard to feeding groups, there were 328 carnivorous species (53.68%), 214 omnivorous species (35.02%), 41 herbivorous species (6.71%), and 28 detritivores species (4.58%). Classified by body size, 57.61% were small-sized, 24.22% medium-sized, and 18.17% were large-sized fishes. A total of 5.23% (32 species) of these mangrove fish are currently on IUCN red lists, i.e., 2 species are critically endangered, 4 are endangered, 12 are vulnerable, and 14 are near threatened. Cluster analyses shows that Chinese mangroves fish were divided into two categories, i.e., coastal mangrove and island mangrove type. This is closely related to the distribution of reef fish. Moreover, the number of fish species showed a strong positive correlation with mangrove area, but not with latitude. The main reasons may be the subtropical and tropical geographic locations, as well as the characteristics of the South China Sea and the Taiwan Warm Current. The size and integrity of mangrove area are crucial to the local ecosystems; thus, protecting and restoring mangroves is of great significance to large-scale ecosystem-stability and local biodiversity.

Improving the Use of Blockchain Technology in Stroke Care Information Management Systems

Blockchain is a new and popular technology in the digital age. Blockchain technology is referred to as decentralised and distributed digital ledgers, which are called blocks. These blocks are linked together with the cryptographic hashes and are used to record transactions between many computers. No single block can be altered without altering the related blocks. Modification of individual block data is impossible because each block contains information from the previous block. This is the unique strength of blockchain. Timestamps and hashes are some of the important terms when blockchains are considered. Data security is guaranteed with this advanced technology. Blockchain technology finds its application in the healthcare industry with many advantages in a queue. Medical data can be transferred safely and securely for fool-proof management of the medicine supply chain, which helps in healthcare research. Blockchains are used to securely encrypt a patient's information in the event of an outbreak of a pandemic disease. A stroke is referred to as a brain attack, also called cerebral infarction. A cerebral infarction is a sudden stoppage of blood flow in the blood vessels connected to the brain. This study focused on evaluating the application of blockchain technology in Stroke Nursing Information Management Systems. This emerging technology is already in use in the healthcare industry. The patient's data is kept decentralized, transparent, and mainly incorruptible, thus keeping it secured and sharing of data is quick.

High-resolution global maps of tidal flat ecosystems from 1984 to 2019

Assessments of the status of tidal flats, one of the most extensive coastal ecosystems, have been hampered by a lack of data on their global distribution and change. Here we present globally consistent, spatially-explicit data of the occurrence of tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation. More than 1.3 million Landsat images were processed to 54 composite metrics for twelve 3-year periods, spanning four decades (1984–1986 to 2017–2019). The composite metrics were used as predictor variables in a machine-learning classification trained with more than 10,000 globally distributed training samples. We assessed accuracy of the classification with 1,348 stratified random samples across the mapped area, which indicated overall map accuracies of 82.2% (80.0–84.3%, 95% confidence interval) and 86.1% (84.2–86.8%, 95% CI) for version 1.1 and 1.2 of the data, respectively. We expect these maps will provide a means to measure and monitor a range of processes that are affecting coastal ecosystems, including the impacts of human population growth and sea level rise.

Measurement(s)	ecosystem occurrence
Technology Type(s)	earth observation
Sample Characteristic - Environment	tidal flats • coastal wetlands
Sample Characteristic - Location	global

Novel coexisting mangrove-coral habitats: Extensive coral communities located deep within mangrove canopies of Panama, a global classification system and predicted distributions

Marine ecosystems are structured by coexisting species occurring in adjacent or nested assemblages. Mangroves and corals are typically observed in adjacent assemblages (i.e., mangrove forests and coral reefs) but are increasingly reported in nested mangrove-coral assemblages with corals living within mangrove habitats. Here we define these nested assemblages as “coexisting mangrove-coral” (CMC) habitats and review the scientific literature to date to formalize a baseline understanding of these ecosystems and create a foundation for future studies. We identify 130 species of corals living within mangrove habitats across 12 locations spanning the Caribbean Sea, Red Sea, Indian Ocean, and South Pacific. We then provide the first description, to our knowledge, of a canopy CMC habitat type located in Bocas del Toro, Panama. This canopy CMC habitat is one of the most coral rich CMC habitats reported in the world, with 34 species of corals growing on and/or among submerged red mangrove aerial roots. Based on our literature review and field data, we identify biotic and abiotic characteristics common to CMC systems to create a classification framework of CMC habitat categories: (1) Lagoon, (2) Inlet, (3) Edge, and (4) Canopy. We then use the compiled data to create a GIS model to suggest where additional CMC habitats may occur globally. In a time where many ecosystems are at risk of disappearing, discovery and description of alternative habitats for species of critical concern are of utmost importance for their conservation and management.

Development and Structural Organization of Mexico’s Mangrove Monitoring System (SMMM) as a Foundation for Conservation and Restoration Initiatives: A Hierarchical Approach

Mangroves provide ecosystem services worth billions of dollars worldwide. Although countries with extensive mangrove areas implemented management and conservation programs since the 1980s, the global area is still decreasing. To recuperate this lost area, both restoration and rehabilitation (R/R) projects have been implemented but with limited success, especially at spatial scales needed to restore functional properties. Monitoring mangroves at different spatial scales in the long term (decades) is critical to detect potential threats and select cost-effective management criteria and performance measures to improve R/R program success. Here, we analyze the origin, development, implementation, and outcomes of a country-level mangrove monitoring system in the Neotropics covering >9000 km2 over 15 years. The Mexico’s Mangrove Monitoring System (SMMM) considers a spatiotemporal hierarchical approach as a conceptual framework where remote sensing is a key component. We analyze the role of the SMMM’s remote sensing products as a “hub” of multi- and interdisciplinary ecological and social-ecological studies to develop national priorities and inform local and regional mangrove management decisions. We propose that the SMMM products, outcomes, and lessons learned can be used as a blueprint in other developing countries where cost-effective R/R projects are planned as part of mangrove protection, conservation, and management programs.

Ambitious global targets for mangrove and seagrass recovery

There is an urgent need to halt and reverse loss of mangroves and seagrass to protect and increase the ecosystem services they provide to coastal communities, such as enhancing coastal resilience and contributing to climate stability.^1,2 Ambitious targets for their recovery can inspire public and private investment in conservation,³ but the expected outcomes of different protection and restoration strategies are unclear. We estimated potential recovery of mangroves and seagrass through gains in ecosystem extent to the year 2070 under a range of protection and restoration strategies implemented until the year 2050. Under a protection-only scenario, the current trajectories of net mangrove loss slowed, and a minor net gain in global seagrass extent (∼1%) was estimated. Protection alone is therefore unlikely to drive sufficient recovery. However, if action is taken to both protect and restore, net gains of up to 5% and 35% of mangroves and seagrasses, respectively, could be achieved by 2050. Further, protection and restoration can be complementary, as protection prevents losses that would otherwise occur post-2050, highlighting the importance of implementing protection measures. Our findings provide the scientific evidence required for setting strategic and ambitious targets to inspire significant global investment and effort in mangrove and seagrass conservation.

Global Mangrove Deforestation and Its Interacting Social-Ecological Drivers: A Systematic Review and Synthesis

Globally, mangrove forests are substantially declining, and a globally synthesized database containing the drivers of deforestation and drivers’ interactions is scarce. Here, we synthesized the key social-ecological drivers of global mangrove deforestation by reviewing about two hundred published scientific studies over the last four decades (from 1980 to 2021). Our focus was on both natural and anthropogenic drivers with their gradual and abrupt impacts and on their geographic coverage of effects, and how these drivers interact. We also summarized the patterns of global mangrove coverage decline between 1990 and 2020 and identified the threatened mangrove species. Our consolidated studies reported an 8600 km2 decline in the global mangrove coverage between 1990 and 2020, with the highest decline occurring in South and Southeast Asia (3870 km2). We could identify 11 threatened mangrove species, two of which are critically endangered (Sonneratia griffithii and Bruguiera hainseii). Our reviewed studies pointed to aquaculture and agriculture as the predominant driver of global mangrove deforestation though their impacts varied across global regions. Gradual climate variations, i.e., sea-level rise, long-term precipitation, and temperature changes and driven coastline erosion, salinity intrusion and acidity at coasts, constitute the second major group of drivers. Our findings underline a strong interaction across natural and anthropogenic drivers, with the strongest interaction between the driver groups aquaculture and agriculture and industrialization and pollution. Our results suggest prioritizing globally coordinated empirical studies linking drivers and mangrove deforestation and global development of policies for mangrove conservation.

Quantitative Analysis of Methodological and Environmental Influences on Survival of Planted Mangroves in Restoration and Afforestation

Mangrove planting has been employed for decades to achieve aims associated with restoration and afforestation. Often, survival of planted mangroves is low. Improving survival might be aided by augmenting the understanding of which planting methods and environmental variables most influence plant survival across a range of contexts. The aim of this study was to provide a global synthesis of the influence of planting methods and background environment on mangrove survival. This was achieved through a global meta-analysis, which compiled published survival rates for the period 1979–2021 and analyzed the influence of decisions about minimum spacing and which life stage to plant, and environmental contexts such as climate, tidal range and coastal setting on the reported survival of planted individuals, classified by species and root morphology. Generalized Additive Mixed Modeling (GAMM) revealed that planting larger mangrove saplings was associated with increased survival for pencil-rooted species such as Avicennia spp. and Sonneratia spp. (17% increase cf. seedlings), while greater plant spacing was associated with higher survival of stilt-rooted species in the family Rhizophoraceae (39% increase when doubling plant spacing from 1.5 to 3.0 m). Tidal range showed a nonlinear positive correlation with survival for pencil-rooted species, and the coastal environmental setting was associated with significant variation in survival for both pencil- and stilt-rooted species. The results suggest that improving decisions about which species to plant in different contexts, and intensive care after planting, is likely to improve the survival of planted mangroves.

Unique functional responses of fungal communities to various environments in the mangroves of the Maowei Sea in Guangxi, China

Fungi are important compartments of microbial communities of mangroves. Their diversity might be influenced by their habitat environment. This study analyzed the distribution and function of fungal communities in the sediments and plant samples from mangrove ecosystem of the Maowei Sea area in Guangxi, China. The results showed that phytopathogenic fungi Cladosporium (17.00%) was mainly observed in the sediments from the protected zone, while endophytic fungi Alternaria (9.22%) and Acremonium (6.09%) were only observed in the sediments from wharf. The fungi in the sediments from village and park were mainly consisted of high-activity endophytes and fungi related to lignin-degrading, respectively. Acaulospora and Aspergillus with higher relative abundance discovered in plant tissues could help plant growth. Cirrenalia (37.66%) and Lignincola (26.73%) with high-activity for lignin-degrading were discovered in decayed leaves. The distribution and function of fungi were highly dependent on the environment settings, thus the fungi can be used as indicators for monitoring the environmental change of mangrove ecosystems.

Design and Application of Electronic Rehabilitation Medical Record (ERMR) Sharing Scheme Based on Blockchain Technology

As the value of blockchain has been widely recognized, more and more industries are proposing their blockchain solutions, including the rehabilitation medical industry. Blockchain can play a powerful role in the field of rehabilitation medicine, bringing a new research idea to the management of rehabilitation medical data. The electronic rehabilitation medical record (ERMR) contains rich data dimensions, which can provide comprehensive and accurate information for assessing the health of patients, thereby enhancing the effect of rehabilitation treatment. This paper analyzed the data characteristics of ERMR and the application requirements of blockchain in rehabilitation medicine. Based on the basic principles of blockchain, the technical advantages of blockchain used in ERMR sharing have been studied. In addition, this paper designed a blockchain-based ERMR sharing scheme in detail, using the specific technologies of blockchain such as hybrid P2P network, block-chain data structure, asymmetric encryption algorithm, digital signature, and Raft consensus algorithm to achieve distributed storage, data security, privacy protection, data consistency, data traceability, and data ownership in the process of ERMR sharing. The research results of this paper have important practical significance for realizing the safe and efficient sharing of ERMR, and can provide important technical references for the management of rehabilitation medical data with broad application prospects.

Future carbon emissions from global mangrove forest loss

Mangroves have among the highest carbon densities of any tropical forest. These 'blue carbon' ecosystems can store large amounts of carbon for long periods, and their protection reduces greenhouse gas emissions and supports climate change mitigation. Incorporating mangroves into Nationally Determined Contributions to the Paris Agreement and their valuation on carbon markets requires predicting how the management of different land-uses can prevent future greenhouse gas emissions and increase CO₂ sequestration. We integrated comprehensive global datasets for carbon stocks, mangrove distribution, deforestation rates, and land-use change drivers into a predictive model of mangrove carbon emissions. We project emissions and foregone soil carbon sequestration potential under 'business as usual' rates of mangrove loss. Emissions from mangrove loss could reach 2391 Tg CO_2 eq by the end of the century, or 3392 Tg CO_2 eq when considering foregone soil carbon sequestration. The highest emissions were predicted in southeast and south Asia (West Coral Triangle, Sunda Shelf, and the Bay of Bengal) due to conversion to aquaculture or agriculture, followed by the Caribbean (Tropical Northwest Atlantic) due to clearing and erosion, and the Andaman coast (West Myanmar) and north Brazil due to erosion. Together, these six regions accounted for 90% of the total potential CO_2 eq future emissions. Mangrove loss has been slowing, and global emissions could be more than halved if reduced loss rates remain in the future. Notably, the location of global emission hotspots was consistent with every dataset used to calculate deforestation rates or with alternative assumptions about carbon storage and emissions. Our results indicate the regions in need of policy actions to address emissions arising from mangrove loss and the drivers that could be managed to prevent them.

The Benefits of Combining Global and Local Data—A Showcase for Valuation and Mapping of Mangrove Climate Regulation and Food Provisioning Services within a Protected Area in Pará, North Brazil

Mangrove forests provide a large variety of ecosystem services (ES) to coastal societies. Using a case study focusing on the Ajuruteua peninsula in Northern Brazil and two ES, food provisioning (ES1) and global climate regulation (ES2), this paper proposes a new framework for quantifying and valuing mangrove ES and allow for their small-scale mapping. We modelled and spatialised the two ES from different perspectives, the demand (ES1) and the supply (ES2) side respectively. This was performed by combining worldwide databases related to the global human population (ES1) or mangrove distribution and canopy height (ES2) with locally derived parameters, such as crab catches (ES1) or species-specific allometric equations based on local estimates of tree structural parameters (ES2). Based on this approach, we could estimate that the area delivers the basic nutrition of about 1400 households, which equals 2.7 million USD, and that the mangrove biomass in the area contains 2.1 million Mg C, amounting to 50.9 million USD, if it were paid as certificates. In addition to those figures, we provide high-resolution maps showing which areas are more valuable for the two respective ES, information that could help inform management strategies in the future.

Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

Context Dependence: A Conceptual Approach for Understanding the Habitat Relationships of Coastal Marine Fauna

Coastal habitats, such as seagrasses, mangroves, rocky and coral reefs, salt marshes, and kelp forests, sustain many key fish and invertebrate populations around the globe. Our understanding of how animals use these broadly defined habitat types is typically derived from a few well-studied regions and is often extrapolated to similar habitats elsewhere. As a result, a working understanding of their habitat importance is often based on information derived from other regions and environmental contexts. Contexts such as tidal range, rainfall, and local geomorphology may fundamentally alter animal–habitat relationships, and there is growing evidence that broadly defined habitat types such as “mangroves” or “salt marsh” may show predictable spatial and temporal variation in habitat function in relation to these environmental drivers. In the present article, we develop a framework for systematically examining contextual predictability to define the geographic transferability of animal–habitat relationships, to guide ongoing research, conservation, and management actions in these systems.

Global declines in human-driven mangrove loss

Global mangrove loss has been attributed primarily to human activity. Anthropogenic loss hotspots across Southeast Asia and around the world have characterized the ecosystem as highly threatened, though natural processes such as erosion can also play a significant role in forest vulnerability. However, the extent of human and natural threats has not been fully quantified at the global scale. Here, using a Random Forest-based analysis of over one million Landsat images, we present the first 30 m resolution global maps of the drivers of mangrove loss from 2000 to 2016, capturing both human-driven and natural stressors. We estimate that 62% of global losses between 2000 and 2016 resulted from land-use change, primarily through conversion to aquaculture and agriculture. Up to 80% of these human-driven losses occurred within six Southeast Asian nations, reflecting the regional emphasis on enhancing aquaculture for export to support economic development. Both anthropogenic and natural losses declined between 2000 and 2016, though slower declines in natural loss caused an increase in their relative contribution to total global loss area. We attribute the decline in anthropogenic losses to the regionally dependent combination of increased emphasis on conservation efforts and a lack of remaining mangroves viable for conversion. While efforts to restore and protect mangroves appear to be effective over decadal timescales, the emergence of natural drivers of loss presents an immediate challenge for coastal adaptation. We anticipate that our results will inform decision-making within conservation and restoration initiatives by providing a locally relevant understanding of the causes of mangrove loss.