Potensi Kandungan Karbon Keragaman Spesies Lamun di Perairan Pesisir Selatan Lombok Timur

through the development of carbon zinc as an organic material produced from photosynthesis and stored and transported in the form of seagrass vegetation biomass. Seagrass is one of the aquatic vegetation that is able to absorb and store carbon. Seagrasses have the ability to absorb carbon through the process of photosynthesis. The purpose of this study was to describe the potential carbon content of seagrass species in the South Coastal Waters of East Lombok. This type of research is an expolarative descriptive research. The research method is a quadratic transect method. The population of this study were all seagrass species contained in 3 research stations. The collected data was then analyzed using analysis of seagrass species composition, seagrass cover, seagrass density, diversity, uniformity, dominance and analysis of carbon content through seagrass stand biomass (leaves, rhizomes/stems and roots). The result of this research is the discovery of 9 species of seagrass on Lungkak Beach and 5 species of seagrass on Gili Kere and Poton Bakau. The species density in the three study sites ranged from 0.09 to 56.91 stands/m2. Seagrass biomass values ranged from 1.47-261.9 gbk/m2 and total carbon content ranged from 295.91±202.88 gC. The value of this biomass and carbon content was dominated by seagrass species with large morphology such as Enhalus acroides, Thalasia hemprici, Cymodocea rotundata, and Cymodocea cerillata and high density and cover values of seagrass. The relationship between seagrass cover and seagrass carbon has a significant relationship where the higher the seagrass cover, the higher the carbon content of the seagrass.

Specialization of a mobile, apex predator affects trophic coupling among adjacent habitats

Mobile, apex predators are commonly assumed to stabilize food webs through trophic coupling across spatially distinct habitats. The assumption that trophic coupling is common remains largely untested, despite evidence that individual behaviors might limit trophic coupling. We used stable isotope data from common bottlenose dolphins across the Gulf of Mexico to determine if these apex predators coupled estuarine and adjacent, nearshore marine habitats. δ13C values differed among the sites, likely driven by environmental factors that varied at each site, such as freshwater input and seagrass cover. Within most sites, δ13C values differed such that dolphins sampled in the upper reaches of embayments had values indicative of estuarine habitats while those sampled outside or in lower reaches of embayments had values indicative of marine habitats. δ15N values were more similar among and within sites than δ13C values. Data from multiple tissues within individuals corroborated that most dolphins consistently used a narrow range of habitats but fed at similar trophic levels in estuarine and marine habitats. Because these dolphins exhibited individual habitat specialization, they likely do not contribute to trophic coupling between estuarine and adjacent marine habitats at a regional scale, suggesting that not all mobile, apex predators trophically couple adjacent habitats.

Seagrass Condition at Some Small Islands in The Taka Bonerate National Marine Park, South Sulawesi Indonesia

The assessment of seagrass bed condition in Indonesia still refers to the Decree of the State Minister for the Environment (KMNLH) no. 200 of 2004, which considers only one variable, namely the percentage of seagrass cover. To assess the seagrass beds condition to be more in-depth and meaningful, it is necessary to consider the addition of several variables, such as the biotic variables (seagrass species diversity including macroalgae and macro-benthos components) as well as the abiotic variables (reef flat areas and the substrate components). The purpose of this study is to determine the seagrass beds condition in several small islands in the Taka Bonerate National Marine Park by considering the additional analysis using both biotic and abiotic variables as mentioned above. The methodology used in this study is a combination of the use of the standard seagrass transect method, interpretation of satellite imagery related to the seagrass bottom habitat area, and its components on the reef flat of a particular island, as well as weighting and scoring based on those considered additional variables. By applying the criteria in the method, the seagrass bed conditions were then classified into three categories, namely seagrass in good, moderate, and unfavorable conditions, respectively. The results of the total score assessment on several small islands in Taka Bonerate Islands show that the seagrass bed in Latondu Besar Island is in good conditions with the highest score of (316) compared to Tarupa Besar, Jinato, Rajuni Kecil, and Tinabo Besar Islands with an average score of (173). The results of this study indicate that the assessment of seagrass conditions is more meaningful in terms of seagrass ecology than based on seagrass cover alone. However, this study requires a lot of validation for its application in assessing the condition of seagrass beds in other islands in Indonesia.

Pemetaan Lamun Mengunakan Machine Learning Dengan Citra Planetscope Di Nusa Lembongan

Seagrass is one community in benthic habitat that has tremendous benefits for the ecosystem, however the existence of seagrass has been frequently marginalized in recent decades. Seagrass beds functions as a blue carbon ecosystem which are able to absorb carbon higher than terrestrial vegetation. Therefore, it is important to detect and map the seagrass beds distribution to calculate the potential carbon uptake from seagrass. The seagrass mapping can be employed efficiently by using remote sensing imagery and the use of machine learning technology. This research aims to examine the utilization of PlanetScope imagery (3.7 m spatial resolution) for seagrass mapping and to subsequently examine, the effect of atmospheric corrections, sun-glint, and the water column corrections on the accuracy of seagrass mapping. In addition, this study also identified the cover changes in seagrass area from 2016 to 2021 in Nusa Lembongan. The study utilized the tree-based machine learning methods such as decision tree and random forest. The results showed that the best model accuracy was generated by using raw PlanetScope data the best model accuracy of 98% and classification accuracy of 94% from decision tree method. Based on the decision tree mapping using PlanetScope data for 2016 and 2021, there was a decline in the seagrass cover from 100.53 hectares to 97.31 hectares. Lamun merupakan salah satu dari ekosistem habitat bentik yang memiliki manfaat yang sangat besar namun sebagai ekosistem, kehadiran lamun sering dikesampingkan beberapa dekade terakhir. Fungsi padang lamun sebagai ekosistem karbon biru mampu menyerap karbon lebih tinggi dibandingkan vegetasi daratan. Karena itu, penting untuk mendeteksi dan memetakan informasi padang lamun untuk memperhitungkan serapan karbon oleh lamun. Pemanfaatan lamun dapat dilakukan secara cepat dan efisien dengan mengunakan teknologi penginderaan jauh dan pemenfaatan teknologi machine learning. Penelitian bertujuan untuk mengkaji pemanfaatan citra PlanetScope untuk memetakan lamun dan selanjutnya menganalisis pengaruh kalibrasi atmosferik, sun-glint, dan kolom air terhadap akurasi pemetaan padang lamun. Selain itu, perubahan tutupan lamun tahun 2016 – 2021 di Nusa Lembongan juga dipetakan. Penelitian ini menggunakan metode machine learning berbasis pohon seperti decision tree dan random forest. Hasil penelitian menunjukkan akurasi model terbaik dihasilkan dengan menggunakan data mentah dengan akurasi model 98% dan akurasi klasifikasi 94% dari metode decision tree. Berdasarkan data PlanetScope tahun 2016 dan 2021 dengan mengunakan metode decision tree terjadi penurunan luasan lamun dari 100,53 Ha menjadi 97,31 Ha.

Seagrass Vegetation Analysis in Jhembengan and Pasir Putih Beach, Bawean Island, East Java

Seagrass is a flowering plant that lives in coastal areas In Indonesia there are 12 species, where seagrasses are able to live at a depth of 1-90 meters, seagrass growth is influenced by several factors including the intensity of sunlight. The purpose of this study was to observe the cover, distribution, Importance Value Index (IVI) and correlation of abiotic factors with seagrass in Jhembangan Beach and White Sand on Bawean Island, East Java. The quadratic transect with 50 m length was used. Each station equipped with 3 transects with a distance of 25 m. The data collected includes the parameter of type, stand, and water quality. The data analyze use Past Program 3.15 systems. Three species of seagrass plant were collected. The total seagrass cover value was 32.6 percent at Jhembangan Beach, and 38 percent at Pasir Putih Beach. Clumped and uniform types were included in the distribution of seagrass at Jhembangan and Pasir Putih beach. The highest of important value index In Jhembangan and Pasir Putih was Thallasia hemprichii, while the association to abiotic factor such temperature, pH, salinity and DO variables are included in the good or perfect correlation with values range from 0.7 to 0.9.

Author Correction: Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The Status of Seagrass Health: Supporting Sustainable Small-Scale Fisheries in Misool Marine Protected Area, Raja Ampat, Indonesia

Seagrass plays an important role in aquatic resources, such as to support the sustainable management of small-scale fisheries, ensuring the availability of seagrass stocks for generations of local communities to cultivate in a sustainable manner. The purpose of this study is to provide information on the seagrass health status to support sustainable small-scale fisheries in the South Misool Regional Waters Conservation Areas which is located within the Raja Ampat Marine Protected Area of West Papua. The research was conducted in January 2019 in the Yefgag, Yellu and Harapan Jaya island. A total of ten quadratic transects measuring 1x1 m were laid perpendicularly to the coastline adapted from the seagrass watch method to collect the seagrass data, i.e. the species and the frequency of seagrass found, the dominance and the percentage of seagrass cover. Additional data on fish species were collected by interviewing the local fishermen directly. The relationship between seagrass cover and the number of fish species was analyzed. Th results showed that there were eight species of seagrass found in three observation stations, i.e. Halophila ovalis, Halodule uninervis, Halodule pinifolia, Halophila minor, Syringodium isoetifolium, Cymodocea serrulata, Cymodocea rotundata and Enhalus acoroides. According to the standard criteria for the health status of seagrass beds, the three locations are classified as less rich/less healthy. It because the seagrass coverage was in the range of 30-59%. The relationship between the percentage of seagrass cover and the number of fish species resulted equation of Y = 15,923x + 0,3174 with R2 = 0,763. It means that the percentage of seagrass cover affects the abundance of fish species by 76,3% with the remaining being influenced by other variables, such as water quality.

Impact of Extreme Disturbances on Suspended Sediment in Western Florida Bay: Implications for Seagrass Resilience

Seagrasses are threatened worldwide due to anthropogenic and natural disturbances disrupting the multiple feedbacks needed to maintain these ecosystems. If the disturbance is severe enough, seagrass systems may undergo a regime shift to a degraded system state that is resistant to recovery. In Florida Bay, Florida, United States, two recent, large-scale disturbances (a drought-induced seagrass die-off in 2015 and Hurricane Irma in 2017) have caused 8,777 ha of seagrass beds to degrade into a turbid, unvegetated state, causing a large sediment plume. Using satellite imagery digitization and long-term seagrass cover data, we investigate the expansion of this sediment plume between 2008 and 2020 and the potential interaction of this sediment plume with seagrass recovery in two focal basins in Florida Bay affected by the die-off, Johnson and Rankin. The average size of the sediment plume increased by 37% due to the die-off and Hurricane Irma, increasing from an average of 163.5 km2 before the disturbances to an average of 223.5 km2. The expansion of the plume was basin-specific, expanding into Johnson after the 2015 seagrass die-off with expansive and long-lasting effects, but only expanding into Rankin after Hurricane Irma with less severe and short-term effects. Furthermore, the sediment plume was negatively correlated with seagrass cover in Johnson, but held no relationship with seagrass cover in Rankin. Thus, different disturbances can act upon seagrass ecosystems at varying scales with varying consequences. This study illustrates the advantage of combining satellite imagery with field data to monitor disturbances as well as highlights the importance of investigating disturbances of seagrass ecosystems at various scales to comprehend seagrass resilience in the context of future extreme events.

Coastal Reef and Seagrass Monitoring for Coastal Ecosystem Management

The growth of human occupations in coastal areas and climate change impact have changed the dynamics of seagrass cover and accelerated the damage to coral reefs globally. For these reasons, coastal management measures need to be developed and renewed to preserve the state of seagrass beds and coral reefs. An example includes the improvement of spatial and multitemporal analyses. This study sought to analyze changes in seagrass cover and damages to coral reefs in Gili Sumber Kima, Buleleng Regency, Bali based on multitemporal Sentinel 2A-MSI imagery. The algorithms of a machine learning, Random Forest (RF), and a Support Vector Machine (SVM) were used to classify the benthic habitats (seagrass beds and coral reefs). Also, a change detection analysis was performed to identify the pattern and the extent to which seagrass beds had changed. The multispectral classification of, particularly, coral reefs was used to explain the condition of this benthic habitat. The results showed +-70% to +-83% accuracies of estimated seagrass cover, and the change detection analysis revealed three directions of change, namely an increase of 27.9 ha, a decrease by 86 ha, and a preserved state in 157 ha of seagrass cover. The product of coral reefs mapping had an accuracy of 42%, and the coral reefs in Gili Sumber Kima were split almost equally between the good (1505 ha) and damaged ones (1397 ha). With the spatial information on seagrass beds and coral reefs in every region, the ecological functions of the coast can be assessed more straightforwardly and appropriately incorporated as the basis for monitoring the dynamics of resources and coastal area management.