Mangrove Extent Research Articles

Identifying toxic elements in water, sediments, and roots of mangrove forest (Avicennia marina) in Chabahar Bay, Sea of Oman

Mangroves play a crucial role in filtering pollutants from water and sediments. However, excessive accumulation of potentially toxic elements (PTEs) has harmful effects on marine organisms. This article investigates the concentration and distribution of PTEs in water, sediment, and the roots of endangered mangrove species in Chabahar Bay, a subtropical coastal wetland. The relationship between PTE absorption and accumulation rates with flow rate, mangrove extent, and sedimentation was also explored. Water, sediments, and aerial roots samples were taken at four stations along the wetland from upstream fresh water toward outfall. According to the results, Cd had more distribution in sediment and water samples and plants did not play as adsorbent in the study area. The lowest and highest PTEs concentrations were detected in water and sediment media, respectively. The average concentrations of PTEs in the sediments in the Chabahar Bay were Fe > Cr > Zn > Ni > Cu > Pb > Co > As > Cd while in aerial roots of the mangroves were Fe > Zn > Ni > Cr > Cu > Co > As > Pb > Cd. Except Zn, As, and Cd, there was a good correlation between increasing PTEs content in the sediments with decreasing flow velocity and increasing vegetation density along stations 3 to 4. In addition, the amount of PTEs uptake by the mangroves was less than that of global wetlands. The results also demonstrated a greater uptake in aerial roots in saline water for Cr, Ni and Co. Since the absorption rate of PTEs by the aerial roots of pneumatophores is slower than that in sediments, elevated concentrations of PTEs in the sediment can disrupt the entire ecosystem, leading to a potential decline in biodiversity. These toxins can enter the food chain, affecting not only organisms directly interacting with the sediment but also higher trophic levels, such as fish and birds.

A lifecycle model approach for predicting mangrove extent.

Tidal dynamics are a well-known driver of mangrove distribution, with most predictive measures using some form of tidal parameter (tidal plane or hydroperiod) to define mangrove extent. However, these methods often fail to consider the causative reason why mangroves thrive or perish at a specific elevation or how mangrove survivability thresholds can differ across a species' lifecycle. The lack of understanding of the drivers influencing mangrove establishment has resulted in poor success rates for mangrove restoration and creation projects worldwide.A novel mangrove lifecycle model that uses a multi-forcing threshold approach is proposed to simulate Avicennia marina viability across establishment and development phases. The lifecycle model includes critical threshold stages for reproduction, seed dispersal, seedling establishment and development, and mature tree survival. The model was validated at 37 sites in eastern Australia to predict mangrove extent across various estuary types and tidal dynamic conditions. The model accurately calculated the upper (RMSE = 0.0676, R2 = 0.8932) and lower (RMSE = 0.0899, R2 = 0.7417) mangrove surface elevations, providing physiological reasoning for establishment and development. Based on the various conditions tested, the model results highlight the highly dynamic spatial and temporal conditions where Avicennia forests thrive. It was found that stressors influencing mangrove establishment were the primary factor for mangrove extent across all sites. However, estuarine typology is important in forcing threshold limits and establishment opportunities. Estuaries with limited tidal decay (from the oceanic forcing) provide more opportunities for mangroves to establish than estuaries with significant tidal attenuation. Regardless of estuary typology, all sites tested had substantial spatial variability through time.Results from the lifecycle model suggest that mature Avicennia forests establish and thrive under a wide range of hydrologic conditions. This resilience suggests that mature mangroves may be able to withstand increases in climatic and hydrologic pressures via biophysical adaptations, although the upper thresholds and acceptable rates of change are difficult to predict. Overall, this study highlights the value of a new causal method for estimating mangrove extent across various lifecycle stages, locations, and time periods.

Unveiling complementarities between mangrove restoration and global sustainable development goals

Indonesia, renowned as the most mangrove-rich nation, has committed to extensive mangrove restoration policies, but the effects of these policies have yet to be systematically evaluated. Our study conducts a comprehensive network analysis to investigate the synergies between mangrove restoration policy and global Sustainable Development Goals (SDGs) achievements by exploring their interactions. This investigation follows the ‘product space’ method in economics and creates the ‘Mangrove-SDG space’ to assess each metric pair's co-occurrence and comparative advantages with validated stability. Our analysis unveils a tripartite structure, encompassing socio-economic and environmental clusters, each significantly contributing to global sustainability and a distinctive mangrove cluster tied to land attributes. At the Goal level, mangrove loss showcases robust synergies with SDGs 12 (Responsible Consumption and Production) and 13 (Climate Change), and mangrove metrics such as tropical storm frequency and mangrove change, indicating strong interdependences between mangrove forests and SDGs. The result indicates that improved performance of climate change and responsible consumption can greatly enhance mangrove forests' performance in alleviating mangrove loss and reducing tropical storms. Moreover, our analysis underscores the central roles played by ‘bridge’ Goals. Indicator-level space details how they warrant prioritization because of their cascade synergistic enhancements across widely interconnected indicators, triggering systematic positive improvements. Turning to Indonesia, we advocate a strategic shift from solely expanding mangrove extent to focusing on four critical policy priorities: effective nitrogen management, enhancing Ramsar site efficiency, optimizing logistic performance, and addressing urban population conditions. These priorities are pivotal to seeking complementarities between Indonesia's international sustainability commitment and fostering mangrove restoration success.

Optimising Marine Basic Spatial Units (MBSU) for Ocean Accounting using empirical data from Saleh Bay, Indonesia

Ocean Accounts, aligned with the UN System of Environmental Economic Accounting – Environmental Accounting (SEEA EA), bring together economic, social and environmental information in a coherent and standardised manner. Ecosystem extent is a structure to understand environmental assets and uses a basic spatial unit to facilitate the classification and measurement of ecosystems by type. This study tested the impact of grid size and method of designation per grid cell for Marine Basic Spatial Units (MBSU), using Saleh Bay, Indonesia as a case study. The extent of mangrove, seagrass and coral reefs were previously delineated in 2021 for ocean accounting activities. This study tested grids with two different cell sizes (10 x 10 m2 and 25 x 25 m2) and two different methods of designation, namely: (i) dominance (extent-based) and (ii) hierarchy (criteria-based) methods. The results indicated that a larger grid size is related to higher error in estimating both total area per ecosystem and spatial configuration within the study area. The dominance method produced more accurate results than the hierarchy method, although, when considering computational trade-offs, a larger grid size and the hierarchy method observed a much lower computational cost. These results demonstrate the need to carefully consider grid size and method when designating basic spatial units for accounting activities, as they impact linked accounting tables and, in turn, have implications when providing information for management and policy.

Extraction of lanceolate imagery for analysis of mangrove extent and shoreline changes

This study aims to determine the area of Topang Island's mangrove forest, an area experiencing extreme line changes and the magnitude of the coastline change rate from 1991 to 2021 on Topang Island, Kepulauan Meranti Regency, Riau Province. The method used in this research is remote sensing and field survey. The data obtained were discussed descriptively. The results found that the calculation of wave height prediction on the coast of Topang Island was included in the low category with an average height of 0.12 meters. The research area is dominated by abrasion, based on visual observations, Topang Island has a type of peat and muddy beach. The area of mangroves on Topang Island from 2001 to 2021 saw a big change, either adding or reducing the area of mangroves. Topang Island experienced the worst abrasion in the eastern part with an average abrasion rate of 6.13 m/year, and accretion in the western part with an average accretion rate of 0.62 m/year.

Spatial Distribution of Mangrove Density in Gili Gede, West Lombok Regency

Gili Gede hosts a vital mangrove ecosystem, essential for the community and protecting coastal areas from large waves. However, increasing development and human activities in and around the mangrove areas threaten their distribution and extent. This study aims to map the spatial distribution, density, and area of mangroves on Gili Gede. Sentinel-2A satellite imagery was utilized with the Normalized Difference Vegetation Index (NDVI) method, and the image data were validated using hemispherical photography. Environmental parameters measured included sediment salinity, temperature, pH, humidity, NPK nutrients, and substrate type. Additional data were gathered on slope, buffer distances, and through semi-structured interviews using purposive sampling. The findings revealed that the mangrove density on Gili Gede is predominantly sparse, covering 15.56 hectares or 77.07% of the total mangrove area. Medium-density mangroves span 3.45 hectares or 17.09%, while dense mangroves occupy the smallest area, 1.18 hectares or 5.84%. Mangroves are distributed across the northern, western, and eastern sides of Gili Gede. The study identified 22 species of mangroves, both true and associated, across 14 families. Environmental factors influencing the distribution, density, and extent of mangroves across three locations include sediment salinity, temperature, pH, humidity, and nutrient content (NPK), as well as substrate type, slope, and anthropogenic influences such as human activities and the proximity of settlements to the mangrove ecosystem.

Assessing mangrove cover change in Madagascar (1972–2019): Widespread mangrove deforestation is slowing down

Between the 1970s and early 2000s, Madagascar witnessed significant mangrove extent and condition declines due to deforestation and degradation. Data on mangrove extent prior to the early 1990s is scarce, contributing to uncertainties in long-term trend analyses. Recent estimates (since 2010) have reported widely varying mangrove extents from 2000 km² to 3400 km², indicating large uncertainties in current trends. Leveraging five decades of Landsat satellite data, this study offers a methodologically consistent approach to classify Madagascar's mangrove extent, providing a comprehensive dataset from 1972, 1989, 1999, 2009, to 2019. Our findings reveal an overall decrease of 8 % from 2935 km² in 1972–2699 km² in 2019, with loss rates decelerating from 0.4 % per year in the initial period to 0.2 % per year post-1999. This decline primarily reflects anthropogenic pressures, notably in the northern regions. Conversely, the last decade has witnessed a 5 % increase in mangrove coverage, primarily in the central and southern regions, thanks to concerted conservation and reforestation efforts, highlighting a positive shift in mangrove management and protection strategies. This study's long-term perspective is crucial for understanding the dynamics of mangrove coverage and guiding effective intervention strategies in Madagascar.

Mangrove extent reflects estuarine typology and lifecycle events

Ecosystem services provided by mangrove forests are increasingly recognised worldwide. These values have led to numerous ecosystem protection and restoration measures, including recent blue carbon economic incentives to re-establish mangroves at scale. However, our current ability to predict mangrove extent is based mainly on inundation averages (e.g. hydroperiod) or tidal planes of mature mangrove forests. This study investigates the validity of existing methods to accurately predict mangrove extent by comparing the location of existing mangroves at 27 sites (in 23 estuaries in southeast Australia) against commonly used approaches. To this aim, the downslope and upslope surface elevation limits of the mangrove species Avicenna marina were measured on-site using high-resolution RTK-GPS equipment across different estuarine typologies, including drowned river valleys, strong and weakly tidal estuaries, tidal lakes, and intermittently open/closed estuaries. These measurements were analysed against long-term (8–30+ years) tidal records to estimate inundation characteristics within the estuary. Study results indicate that the commonly used tidal plane method for predicting mangrove extent can incorrectly predict vertical extent by over 40 per cent. Likewise, hydroperiod is a poor predictor of extent as similar hydroperiods can be calculated across significantly different estuarine inundation patterns.Interestingly, the results indicate that mangrove elevations and inundation patterns are not uniform within or across estuaries, although there is uniformity across related estuarine typologies. For instance, inundation patterns in drowned river valley estuaries are more consistent (less extreme inundation and exposure events) than in estuaries with a variable tidal range, such as tidal lakes. Ultimately, this study highlights that predictions of mangrove extent should consider estuarine typology, the lifecycle of mangroves, and potential physio-adaptations to changing inundation patterns. Importantly, future tidal conditions may encourage biophysical adaptations in mangroves which may confound the extent of existing predictive techniques.

Enhanced mangrove index: A spectral index for discrimination understorey, nypa, and mangrove trees

Unsupervised classification using vegetation indices has been extensively employed to map mangrove forests using medium-resolution satellite images. However, its capability is restricted to determining the extent of mangroves only. This study introduces a new spectral index called the enhanced mangrove index (EMI) for accurately mapping different components of mangrove vegetation, including mangrove trees, nypa, and understorey. An immediate effort is required to monitor the invasion of nypa and understorey in the mangrove forest of Segara Anakan Lagoon, located in Central Java, Indonesia. This issue may also be prevalent in other mangrove areas worldwide. The development of EMI involved: 1). the analysis of the reflectance exhibited by different types of mangrove vegetation, and 2). The performance of EMI was evaluated by comparing it with spectral indices such as automated mangrove map and index (AMMI), as well as supervised classification models like random forest (RF). The accuracy assessment indicates that the overall accuracy and Kappa coefficient achieved values of 0.87 and 0.84, respectively, surpassing other spectral indices and supervised classification models. AMMI and RF exhibited high overall accuracy, with values of 0.82 and 0.73, respectively. Additionally, they demonstrated a Kappa coefficient of 0.77 and 0.66, respectively.

Uncovering mangrove range limits using very high resolution satellite imagery to detect fine‐scale mangrove and saltmarsh habitats in dynamic coastal ecotones

AbstractMangroves are important ecosystems for coastal biodiversity, resilience and carbon dynamics that are being threatened globally by human pressures and the impacts of climate change. Yet, at several geographic range limits in tropical–temperate transition zones, mangrove ecosystems are expanding poleward in response to changing macroclimatic drivers. Mangroves near range limits often grow to smaller statures and form dynamic, patchy distributions with other coastal habitats, which are difficult to map using moderate‐resolution (30‐m) satellite imagery. As a result, many of these mangrove areas are missing in global distribution maps. To better map small, scrub mangroves, we tested Landsat (30‐m) and Sentinel (10‐m) against very high resolution (VHR) Planet (3‐m) and WorldView (1.8‐m) imagery and assessed the accuracy of machine learning classification approaches in discerning current (2022) mangrove and saltmarsh from other coastal habitats in a rapidly changing ecotone along the east coast of Florida, USA. Our aim is to (1) quantify the mappable differences in landscape composition and complexity, class dominance and spatial properties of mangrove and saltmarsh patches due to image resolution; and (2) to resolve mapping uncertainties in the region. We found that the ability of Landsat to map mangrove distributions at the leading range edge was hampered by the size and extent of mangrove stands being too small for detection (50% accuracy). WorldView was the most successful in discerning mangroves from other wetland habitats (84% accuracy), closely followed by Planet (82%) and Sentinel (81%). With WorldView, we detected 800 ha of mangroves within the Florida range‐limit study area, 35% more mangroves than were detected with Planet, 114% more than Sentinel and 537% more than Landsat. Higher‐resolution imagery helped reveal additional variability in landscape metrics quantifying diversity, spatial configuration and connectedness among mangrove and saltmarsh habitats at the landscape, class and patch scales. Overall, VHR satellite imagery improved our ability to map mangroves at range limits and can help supplement moderate‐resolution global distributions and outdated regional maps.

Subtropical mangroves poleward shift to the Yangtze Estuary under different carbon emission scenarios

Mangroves are crucial plant communities in the coastal intertidal zones, providing various ecological services and supporting biodiversity. To identify the primary factors affecting the poleward shift of subtropical mangroves and the northernmost extent of subtropical mangroves under various carbon emission scenarios, the MaxEnt model was utilized with presence data of subtropical mangrove and environmental data under different carbon emission scenarios. Kandelia obovata, Avicennia marina, and Aegiceras corniculatum in China were included for modeling, utilizing 22 bioclimate variables to predict mangrove distributions, and kernel density analysis was further employed to identify conservation and afforestation hotspots for mangroves. The results indicated that the number of days below 10 °C per year is the major climatic factor shaping the distribution of subtropical native mangroves, while precipitation exerts a relatively lower contribution. Under the low and medium emissions scenarios (SSP126 and SSP245), the northern boundary of the distribution of K. obovata remains stable, while A. marina and A. corniculatum are projected to shift northward to Fuzhou, Fujian Province (26.1°N) from 24.9°N by 2100. Under higher emissions scenarios (SSP370 and SSP585), the suitable habitat of K. obovata is expected to shift north to the mouth of the Yangtze River (31.9°N) from 26.8°N, and A. marina and A. corniculatum are projected to move northward to the coastal area of Ningde, Fujian Province (26.8°N) by 2100. In conclusion, with the increase in carbon emissions, there is a significant northward migration observed in subtropical native mangroves (p < 0.01), and suitable areas for restoration in China have been identified, with the northernmost region located in Zhejiang Province. The spatial and temporal patterns of range change predicted in this study provide valuable information for conservation and afforestation strategies for these ecologically important species.

Multidecadal mangrove forest change in Macajalar Bay, Northern Mindanao, Philippines (1950-2020) using remote sensing and geographic information systems.

The mangrove forest in Macajalar Bay is regarded as an important coastal ecosystem since it provides numerous ecosystem services. Despite their importance, the clearing of mangroves has been rampant and has reached critical rates. Addressing this problem and further advancing its conservation require accurate mangrove mapping. However, current spatial information related to mangroves is sparse and insufficient to understand the historical change dynamics. In this study, the synergy of 1950 vegetation maps and Landsat images was explored to provide multidecadal monitoring of mangrove forest change dynamics in Macajalar Bay, Philippines. Vegetation maps containing the 1950 mangrove extent and Landsat images were used as input data to monitor the rates of loss over 70years. In 2020, the mangrove forest cover was estimated to be 201.73ha, equivalent to only 61.99% of the 325.43ha that was estimated in 1950. Between 1950 and 2020, net mangrove loss in Macajalar Bay totaled 324.29ha. The highest clearing rates occurred between 1950 and 1990 when it recorded a total of 258.51ha, averaging 6.46ha/year. The original mangrove forest that existed in 1950 only represents 8.56% of the 2020 extent, suggesting that much of the old-growth mangrove had been cleared before 2000 and the existing mangrove forest is mainly composed of secondary mangrove forest stands. Across Macajalar Bay, intensified clearing that happened between 1950 and 1990 has been driven by large-scale aquaculture developments. Mangrove gains on the other hand were evident and have increased the total extent by 79.84ha since 2000 as a result of several afforestation programs. However, approximately half of these gains that were observed since 2010 exhibited low canopy cover. As of writing, approximately 85% of the 2020 mangrove forest stands fall outside the 1950 original mangrove extent. Examining the viability of the original mangrove forest for mangrove reforestation together with promoting site-species matching, and biophysical assessment are necessary undertakings to advance current mangrove conservation initiatives in Macajalar Bay.

Preliminary Study on Floral Distribution, Abundance and Diversity of Mangroves in Vankalai, North-west Coast of Sri Lanka

Sri Lanka harbors a remarkable amount of mangrove habitats. Northern Province shares about 16% of the total mangrove extent of the country. Vankalai consists of several ecosystems which range from arid-zone thorn scrubland, arid-zone pastures and maritime grasslands, sand dunes, mangroves, salt marshes, lagoons, tidal flats, sea-grass beds and shallow marine areas. Due to the integrated nature of shallow wetland and terrestrial coastal habitats, this sanctuary is highly productive, supporting high ecosystem and species diversity. The research was aimed to identify the distribution, abundance and diversity of mangroves in Vankalai of the Northern Province, Sri Lanka. Ten 25m wide belt transects with 10m intervals were randomly arranged in the small patch of mangrove vegetation perpendicular to the 350m of shoreline across the water-land gradient. Each transect was divided into five 5mx5m subplots for convenience of sampling. True mangrove species in each transect were identified and counted. The quantitative data has been used to compute the Shannon diversity index (H), Simpson Index (D) and Shannon Evenness (E), which were used to compare the diversity of the study sites. Ten different mangrove species including Avicennia marina (n=115), Avicennia officinalis (n=73), Rhizophora apiculate (n=33), Rhizophora mucronata (n=102), Sonneratia alba (n=3), Pemphis acidula (n=4), Excoecaria agallocha (n=40), Lumnitzera racemose (n=91), Bruguiera cylindrical (n=97), Ceriops tagal (n=31) were identified and they were belonging to five families; Avicenniaceae (n=2), Rhizophoraceae (n=5), Lythraceae (n=1), Euphorbiaceae (n=1) and Combretaceae (n=1). Further, based on the IUCN status, two mangrove species (Sonneratia alba and Bruguiera cylindrical) were identified as endangered species. The highest Shannon diversity index (H=2.21) and the lowest Simpson index value (D=0.14) were observed in the transects of point 1 due to minimal disturbances. The least Shannon diversity index and evenness were recorded in the point 5 as 1.65 and 0.85 respectively at the point 5 since fishing and tourism activities take place in the proximity of this place. The Shannon diversity index varied at the points of 1 (H=2.21)&gt;2 (H=2.10)&gt;3 (H=1.89)&gt;4 (H=1.84)&gt;5 (H=1.65) whereas Simpson index value were varied at the points of 1 (D=0.14)&lt;2 (D=0.17)&lt;3,4 (D=0.18)&lt;5 (D=0.19) respectively. Pollution and human interaction would have degraded the environmental quality of the mangrove habitats. Based on the observations it was found that the extent of disturbances was minimum at the points of 1 and 2 whereas anthropogenic influence was increased at the points of 3&lt;4&lt;5 respectively. Further, use of mangrove poles and sticks to build fish baits and fishing rods by the anglers could possibly destroy these small patches of mangroves. Hence, actions should be taken for conservation of existing patch and replenishment of new mangroves. &#x0D; Keywords: Mangroves, Diversity, Shannon Diversity Index (H), Simpson Index (D) and Shannon Evenness (E)

Ecological and archaeological consequences of ongoing, largely human-mediated colonisation of endemic soft, shelly habitats by mangroves in a northern New Zealand embayment

ABSTRACT Areal extent of native mangroves, Avicennia marina, has doubled in Bay of Islands since 1951. This expansion is widely attributed to deforestation and poor land-use practices beginning in the late 1800s to early 1900s, and led to greatly transformed intertidal and supratidal habitats. One example has been the ecological isolation and crowding (sometimes, colonisation) by mangroves of shelly beaches, together with their associated ‘chenier-like’ spits – themselves now among the rarest of New Zealand’s inshore marine habitats, with only six still relatively intact ecologically out of at least 44 in the Bay of Islands. These shores were also where pre Contact Māori often gathered to harvest intertidal cockles Austrovenus stutchburyi and to fish, archaeological signatures typically becoming obscured once mangroves colonised upper shores. Although recent seaward intrusion by mangroves into the cockle habitat of the Parekura Bay study site appears to have been proportionately small, there may be potential for more as elevated levels of sedimentation persist. With wholesale mangrove removals demonstrably ineffective in re-establishing original communities, the ongoing ecological and archaeological disruption of endemic soft, shelly contexts through largely human-mediated mangrove colonisation might be curbed in certain situations through strategic, proactive removal of new mangrove recruits, particularly on expanding edges.

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.

Restoration promotes ecological functioning through greater complementarity

Restoration projects are increasingly widespread and many promote habitat succession and the diversity and abundance of faunal communities. These positive effects on biodiversity and abundance may extend to enhancing the ecological functioning and resilience of previously degraded ecosystems, but this is rarely quantified. This study surveyed a 200‐ha restoring coastal wetland and three control wetlands in the Maroochy River, eastern Australia to compare the effects of wetland restoration on the consumption of carrion and the biodiversity, abundance, and functional diversity of functionally important fish and crustaceans. Carrion consumption by fish and crustaceans was measured every 6 months from spring 2017 until spring 2021 for nine events using a combination of baited cameras and scavenging assays. We found restoration improved rates of carrion consumption and the biodiversity, functional diversity, and abundance of scavenger species. Despite positive effects on the diversity of scavengers and carrion consumption, the abundance of two species, longfin eels (Anguilla reinhardtii) and mud crabs (Scylla serrata), was the most important predictors of carrion consumption rates. The spatial distribution of carrion consumption was concentrated in areas with high saltmarsh extent, moderate to high mangrove extent, and high salinity, which also resembled the distribution of both longfin eels and mud crabs. We show that restoration can promote the rates of key ecological functions but that increases to functions are likely to be characterized by low functional redundancy and greater complementarity. Therefore, maintaining or increasing the abundance of functionally important species should become a key objective in future restoration projects.

Assessing the conservation status of mangroves in Rakhine, Myanmar

Abstract Ecosystem degradation is a key challenge that human society faces, as ecosystems provide services that are tied to human well‐being. Particularly, mangrove ecosystems provide important services to communities but are suffering heavy degradation, loss and potential collapse due to anthropogenic activities. The IUCN Red List of Ecosystems is a transparent and consistent framework for assessing ecosystems' risk of collapse and is increasingly used to inform legislation and ecosystem management globally. Satellite data have become increasingly common in environmental monitoring due to their extensive spatial and temporal coverage. Here, recent advances in analyses using satellite‐derived data were implemented to reassess the conservation status of the ‘Rakhine mangrove forest on mud’, an important intertidal ecosystem in Myanmar, extending a previous national Red List assessment that assessed the ecosystem as Critically Endangered. By incorporating additional data sources and analyses, the extended assessment produced more robust results and reduced the uncertainty in the previous assessment. Overall, the ecosystem was assessed as Critically Endangered (range: Vulnerable to Critically Endangered) as a result of historical mangrove extent loss. Recent losses and biotic disruptions were also observed, which would have led to the ecosystem being assessed as Vulnerable. While the final outcome of the Red List assessment remained at Critically Endangered due to the historical state of the mangroves pre‐dating the temporal coverage from satellite data, the uncertainty of the ecosystem's status was reduced, and the reassessment highlighted the recent areal changes and mangrove degradation that has occurred. The importance of conducting reassessments when new data become available is discussed, and a template for future mangrove Red List assessments that use satellite data as their primary source of information to improve the robustness of their results is presented.

ESTIMATION OF MANGROVE FRACTIONAL COVER FROM MULTISPECTRAL AND HYPERSPECTRAL DATA USING MIXTURE TUNED MATCHED FILTERING

Abstract. Mangroves provide various ecosystem services and contribute to climate change adaptation being one of the blue carbon ecosystems. The extents of mangroves are mapped and monitored using commonly available multispectral images, such as Landsat and Sentinel-2, to detect and assess gains and losses. However, this presence or absence per pixel based on crisp classification or index thresholding offers limited information on the dynamics of mangrove growth or decline. In this paper, we evaluated the use of Mixture Tuned Matched Filtering (MTMF) in estimating mangrove fractional cover (MFC) from multispectral (Landsat-8) and hyperspectral (PRISMA) satellite images. We also examined the utility of the mangrove vegetation index (MVI) and enhanced MVI (EMVI) for this purpose. The images were first denoised using Minimum Noise Fraction (MNF). MTMF was then separately applied to the sets of MNF bands, excluding noise bands, to generate Matched Filtering (MF) Score and Infeasibility layers. The endmember (mangrove) spectrum was extracted from a pixel identified using pixel purity index (PPI) and examination of high-resolution Google Earth base image, from which detailed mangrove extents were also delineated. A 30-m vector grid file was created and populated with MFC, MF Score, and Infeasibility values using zonal analysis. Correlation analysis, exploratory regression, and ordinary least squares (OLS) regression were performed. MF Score is moderately and positively correlated with MFC. In contrast, Infeasibility, MVI, and EMVI are uncorrelated or very weakly correlated with MFC. MFC can be estimated using an OLS model with MF Score and Infeasibility as explanatory variables. The performance of the PRISMA-based model (R2Adj=0.30, AIC=98643.20) was found to be better than the Landsat-8-based model (R2Adj=0.36, AIC=97428.89).

Spatio-temporal dynamics of mangrove extent in coastal Bekasi Regency, Indonesia

One of West Java’s largest mangroves is found alongside the shore of Bekasi Regency. Nevertheless, the primary issues are the high degree of waste contamination, extraction of mangrove products, and conversion of mangrove forests into aquaculture ponds. To determine the present state of mangroves on the coast of Bekasi Regency, research on the dynamics of the mangrove extent spatio-temporally must be conducted. Mangrove mapping has made extensive use of machine learning and satellite images. This study aims to calculate the mangrove area in the villages of Pantaibahagia and Pantaibakti along the coast of Bekasi Regency using Random Forest (RF) classification based on the Google Earth Engine (GEE) Platform. The RF classification results showed a significant loss in mangrove extent over a short period of time (seven years). In 2017, the tidal zone was primarily covered by mangroves. From the linear trend line, it is known that from to 2017-2023, the mangrove area tended to decrease, while in 2023, there was a decrease in the mangrove area, especially in the north coast area. In 2017, the total mangrove area was 305.03 ha. Until 2019, there has been a slight increment, reaching 366.41 ha of mangrove are. The most significant loss was found in 2020, in which the total loss reached 85.58 ha in one year. After 2020, the mangrove area has slightly improved, but it is not significant. We tested the produced map of the RF classification using a confusion matrix and kappa coefficient, which resulted in an Overall Accuracy (OA) of 90.50% and a Kappa coefficient of 0.8105.

Global mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning.

This dataset presents global soil organic carbon stocks in mangrove forests at 30 m resolution, predicted for 2020. We used spatiotemporal ensemble machine learning to produce predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks. This was done by using training data points of both SOC (%) and BD in mangroves from a global dataset and from recently published studies, and globally consistent predictive covariate layers. A total of 10,331 soil samples were validated to have SOC (%) measurements and were used for predictive soil mapping. We used time-series remote sensing data specific to time periods when the training data were sampled, as well as long-term (static) layers to train an ensemble of machine learning model. Ensemble models were used to improve performance, robustness and unbiasedness as opposed to just using one learner. In addition, we performed spatial cross-validation by using spatial blocking of training data points to assess model performance. We predicted SOC stocks for the 2020 time period and applied them to a 2020 mangrove extent map, presenting both mean predictions and prediction intervals to represent the uncertainty around our predictions. Predictions are available for download under CC-BY license from 10.5281/zenodo.7729491 and also as Cloud-Optimized GeoTIFFs (global mosaics).