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Search Results (1,297)

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16 pages, 4885 KiB  
Article
Germplasm Resource Status and Seed Adaptability of Nypa fruticans Wurmb, an Endangered Species in China
by Mengwen Zhang, Cairong Zhong, Xiaobo Lv, Zanshan Fang, Cheng Cheng and Jiewei Hao
Forests 2024, 15(8), 1396; https://doi.org/10.3390/f15081396 - 10 Aug 2024
Viewed by 271
Abstract
Nypa fruticans, commonly known as the Nipa palm, belongs to the true mangrove plants of the Arecaceae family. In China, it is naturally distributed only on Hainan Island and designated as a second-class National Key Protected Wild Plants List. Field research and [...] Read more.
Nypa fruticans, commonly known as the Nipa palm, belongs to the true mangrove plants of the Arecaceae family. In China, it is naturally distributed only on Hainan Island and designated as a second-class National Key Protected Wild Plants List. Field research and indoor simulation experiments were systematically employed to study the resource status of N. fruticans and the adaptation of seed germination to environmental factors. The results showed that: (1) Four natural populations of N. fruticans, approximately 9319 trees within a total area of 3.96 hm2, were distributed in Haikou, Wenchang, Qionghai, and Wanning on Hainan Island. Only the Wanning population was developed in small patches, while other populations were scattered sporadically. (2) A total of 23 mangrove species belonged to 19 genera in 13 families, which were recorded in all study sites, of which 18 were true mangroves and 5 were semi-mangrove species. The vertical structures of 4 N. fruticans communities exhibited the consistent pattern, characterized by distinct layers including the tree, shrub, and herb layers. However, notable differences in species composition and dominant species were observed among the layers of each community. (3) The population dynamics of N. fruticans in Haikou, Qionghai, and Wanning were declining, while the population in Wenchang was growing. (4) Seed germination of N. fruticans was not resistant to strong light and required some shade treatment with an optimal light intensity of 60%. The suitable salinity range for seed germination was 0‰ to 10‰. With the increase of salinity, the germination rate and seedling rate showed an increasing and then decreasing trend with maximum values of 63.3% and 50.0% at 5‰, which showed the sensitivity of seed germination to salinity, with low salinity promoting germination whereas high salinity inhibiting germination. Around 8 h/d of flooding time was most suitable for the seed germination, and 10 h/d was a critical flooding time. This study provides a theoretical basis for population recovery, resource utilization, and other further research of N. fruticans. Full article
(This article belongs to the Section Forest Ecophysiology and Biology)
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Figure 1
<p>Geographic distribution and resource status of <span class="html-italic">N. fruticans</span>.</p>
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<p>Age structure of the <span class="html-italic">N. fruticans</span> population.</p>
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<p>The effect of light intensity on germination parameters of <span class="html-italic">N. fruticans</span> seeds. (<b>A</b>) initial germination time (IGT), duration of germination (DG), initial emergence time (IET) and duration of emergence (DE); (<b>B</b>) germination percentage (GP) and emergence percentage (EP); (<b>C</b>) plant height (PH); (<b>D</b>) leaf number (LN). Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The effect of salinity on germination parameters of <span class="html-italic">N. fruticans</span> seeds. (<b>A</b>) initial germination time (IGT), duration of germination (DG), initial emergence time (IET) and duration of emergence (DE); (<b>B</b>) germination percentage (GP) and emergence percentage (EP); (<b>C</b>) plant height (PH); (<b>D</b>) leaf number (LN). Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The effect of flooding time on germination parameters of <span class="html-italic">N. fruticans</span> seeds. (<b>A</b>) initial germination time (IGT), duration of germination (DG), initial emergence time (IET) and duration of emergence (DE); (<b>B</b>) germination percentage (GP) and emergence percentage (EP); (<b>C</b>) plant height (PH); (<b>D</b>) leaf number (LN). Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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16 pages, 2677 KiB  
Article
Contribution of Mangrove Ecosystem Services to Local Livelihoods in the Indian Sundarbans
by Piyali Sarkar, Saon Banerjee, Saroni Biswas, Sarathi Saha, Dolgobinda Pal, Manish Kumar Naskar, Sanjeev K. Srivastava, Dhananjay Barman, Gouranga Kar and Sharif A. Mukul
Sustainability 2024, 16(16), 6804; https://doi.org/10.3390/su16166804 - 8 Aug 2024
Viewed by 833
Abstract
Mangrove forests, apart from their carbon sequestration and coastal protection benefits, provide a wide range of ecosystem services to people in tropical developing countries. Local people living in and around forests in the developing tropics also depend heavily on these mangrove ecosystem services [...] Read more.
Mangrove forests, apart from their carbon sequestration and coastal protection benefits, provide a wide range of ecosystem services to people in tropical developing countries. Local people living in and around forests in the developing tropics also depend heavily on these mangrove ecosystem services for their livelihoods. This study examines the impact of mangrove ecosystem services on the livelihoods of people in Indian part of the Sundarbans—the largest contagious mangrove forest on earth. To achieve this objective, a household survey was undertaken to gather data on the diverse range of provisioning and regulating ES local people derived from mangrove forests living near the Indian Sundarbans. Surveys were carried out in nine villages across the Kultali, Basanti, and Gosaba blocks, involving over one hundred respondents. Our study reveals the active participation of locals in gathering various ecosystem services, with fishing and crab collection being the most common in the area. Due to numerous challenges in the agricultural sector, such as soil salinity and frequent extreme weather events, people increasingly depend on non-farming incomes, particularly fishing. A questionnaire was used to assess the dependence of local people on different ecosystem services. Some villages, such as Amlamethi, Satyanarayanpur, Mathurakhand, Vivekananda Palli, and Second Scheme, demonstrated a higher reliance on forest ecosystem services compared to other villages. The study indicates that the contribution of ecosystem services sometimes surpasses traditional activities like farming and daily contractual work. River transportation emerged as the most crucial service, followed by freshwater, food, and fiber. While certain resources like fuel, natural medicine, and genetic resources may not be prioritized, they still hold significance within the community, contrasting with ornamental resources, which are considered the least important. Our findings underscore the importance of preserving natural services in the Sundarbans forest, highlighting the need to conserve the mangrove ecosystem services to ensure the long-term well-being of local communities. Full article
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<p>Location map of the study area: (<b>a</b>) India, (<b>b</b>) West Bengal and South 24 Parganas, and (<b>c</b>) Indian Sundarbans with the location of the three studied blocks.</p>
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<p>Dependency (%) of the people in the studied villages around the Indian Sundarbans on ecosystem services.</p>
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<p>Percentage income from different ecosystem services in our study villages in the Indian Sundarbans.</p>
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<p>Perceived weighted mean value of different provisioning ecosystem services, as collected in our study villages around the Indian Sundarbans.</p>
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<p>Utilization of provisioning ecosystem services among our surveyed villages in the Indian Sundarbans (bars with different letters indicate statistically significant differences, and bars with the same letters are not significantly different).</p>
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<p>Perceived weighted mean values of different regulatory ecosystem services collected in the study villages around the Indian Sundarbans.</p>
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<p>Utilization of regulating ecosystem services among our surveyed villages in the Indian Sundarbans (bars with different letters indicate statistically significant differences, and bars with the same letters are not significantly different).</p>
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10 pages, 1380 KiB  
Article
Polyketide Derivatives from the Mangrove-Derived Fungus Penicillium sp. HDN15-312
by Fuhao Liu, Wenxue Wang, Feifei Wang, Luning Zhou, Guangyuan Luo, Guojian Zhang, Tianjiao Zhu, Qian Che and Dehai Li
Mar. Drugs 2024, 22(8), 360; https://doi.org/10.3390/md22080360 - 8 Aug 2024
Viewed by 390
Abstract
Four new polyketides, namely furantides A–B (12), talamin E (3) and arugosinacid A (4), and two known polyketides were obtained from the mangrove-derived fungus Penicillium sp. HDN15-312 using the One Strain Many Compounds (OSMAC) strategy. [...] Read more.
Four new polyketides, namely furantides A–B (12), talamin E (3) and arugosinacid A (4), and two known polyketides were obtained from the mangrove-derived fungus Penicillium sp. HDN15-312 using the One Strain Many Compounds (OSMAC) strategy. Their chemical structures, including configurations, were elucidated by detailed analysis of extensive NMR spectra, HRESIMS and ECD. The DPPH radicals scavenging activity of 3, with an IC50 value of 6.79 µM, was better than vitamin C. Full article
(This article belongs to the Section Structural Studies on Marine Natural Products)
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Figure 1
<p>Structures of the isolated compounds <b>1</b>–<b>6</b>.</p>
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<p>The key HMBC and COSY correlations in <b>1</b>–<b>2</b>.</p>
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<p>ECD spectra of <b>1</b> and <b>2</b>.</p>
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<p>The key HMBC correlations of <b>3</b> and <b>4</b>.</p>
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<p>ECD spectra of <b>4</b>.</p>
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16 pages, 13502 KiB  
Article
Identification of Penexanthone A as a Novel Chemosensitizer to Induce Ferroptosis by Targeting Nrf2 in Human Colorectal Cancer Cells
by Genshi Zhao, Yanying Liu, Xia Wei, Chunxia Yang, Junfei Lu, Shihuan Yan, Xiaolin Ma, Xue Cheng, Zhengliang You, Yue Ding, Hongwei Guo, Zhiheng Su, Shangping Xing and Dan Zhu
Mar. Drugs 2024, 22(8), 357; https://doi.org/10.3390/md22080357 - 6 Aug 2024
Viewed by 447
Abstract
Ferroptosis has emerged as a potential mechanism for enhancing the efficacy of chemotherapy in cancer treatment. By suppressing nuclear factor erythroid 2-related factor 2 (Nrf2), cancer cells may lose their ability to counteract the oxidative stress induced by chemotherapy, thereby becoming more susceptible [...] Read more.
Ferroptosis has emerged as a potential mechanism for enhancing the efficacy of chemotherapy in cancer treatment. By suppressing nuclear factor erythroid 2-related factor 2 (Nrf2), cancer cells may lose their ability to counteract the oxidative stress induced by chemotherapy, thereby becoming more susceptible to ferroptosis. In this study, we investigate the potential of penexanthone A (PXA), a xanthone dimer component derived from the endophytic fungus Diaporthe goulteri, obtained from mangrove plant Acanthus ilicifolius, to enhance the therapeutic effect of cisplatin (CDDP) on colorectal cancer (CRC) by inhibiting Nrf2. The present study reported that PXA significantly improved the ability of CDDP to inhibit the activity of and induce apoptosis in CRC cells. Moreover, PXA was found to increase the level of oxidative stress and DNA damage caused by CDDP. In addition, the overexpression of Nrf2 reversed the DNA damage and ferroptosis induced by the combination of PXA and CDDP. In vivo experiments using zebrafish xenograft models demonstrated that PXA enhanced the therapeutic effect of CDDP on CRC. These studies suggest that PXA enhanced the sensitivity of CRC to CDDP and induce ferroptosis by targeting Nrf2 inhibition, indicating that PXA might serve as a novel anticancer drug in combination chemotherapy. Full article
(This article belongs to the Special Issue Pharmacological Potential of Marine Natural Products, 2nd Edition)
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<p>PXA sensitizes CRC cells to CDDP-induced cytotoxicity and apoptosis. (<b>A</b>) Chemical structural of PXA. (<b>B</b>) CRC cells were co-treated with PXA and CDDP for 48 h, and the percentage of cell viability was determined by CCK-8 assay. (<b>C</b>) 3D visualization of synergy scores between PXA and CDDP obtained using the SynergyFinder tool; these calculated average synergy scores are 20.7 and 11.3 for these two panels of drug combinations (Synergy scores &gt; 10 are considered synergistic). (<b>D</b>) The percentage of apoptotic cells was analyzed and quantified using flow cytometry after Annexin V-FITC/PI staining. (<b>E</b>) The protein levels of Cleaved-PARP, Cleaved-caspase-3, BAX, and Bcl-2 in HCT116 and HT29 cells were detected by Western blot after 24 h treatment; β-acting was used as a loading control. Results are expressed as means ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 versus the control group, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 versus the CDDP-treatment group.</p>
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<p>PXA increased CDDP-induced ROS production. (<b>A</b>,<b>B</b>) HCT116 and HT29 cells treated with PXA and CDDP were incubated with the DCFH-DA probe for 20 min, and the ROS levels (DCF fluorescence) were observed and analyzed by fluorescence microscopy (<b>A</b>) and flow cytometry (<b>B</b>). Scale bars: 100 μm. Results are expressed as means ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 versus the control group, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 versus the CDDP-treatment group.</p>
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<p>PXA increased CDDP-induced DNA damage and oxidative stress. (<b>A</b>) DNA damage levels in HCT116 and HT29 cells treated with PXA and CDDP for 24 h were assessed using the comet assay. Scale bars: 100 μm. (<b>B</b>) After 24 h of treating HCT116 and HT29 cells with PXA and CDDP, the content of GSH, SOD, and HO-1 was measured using ELISA. Results are expressed as means ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 versus the control group, <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 versus the CDDP-treatment group.</p>
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<p>PXA inhibits Nrf2 protein expression. (<b>A</b>,<b>B</b>) Western blotting analyses of Nrf2 expression in HCT116 and HT29 cells treated with PXA for indicated concentrations (<b>A</b>) and time points (<b>B</b>). (<b>C</b>) CETSA was performed to confirm that PXA targets Nrf2 proteins. Results are expressed as means ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 versus the control group.</p>
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<p>Nrf2 overexpression reverses the chemosensitizing activity of PXA. (<b>A</b>) Protein levels of Nrf2 in stably overexpressing empty vector (EV) and Nrf2 HCT116 and HT29 cells were examined by Western blotting. (<b>B</b>) Western blot assay to detect the effect of PXA in combination with CDDP on Nrf2 protein in EV and Nrf2 stable overexpressing HCT116 and HT29 cells. (<b>C</b>) CCK-8 assay was performed on EV and Nrf2 stable overexpressing HCT116 and HT29 cells treated with PXA and CDDP for 48 h. (<b>D</b>) The DNA damage levels of EV and Nrf2 stable overexpressing HCT116 and HT29 cells treated with PXA and CDDP for 24 h were evaluated using the comet assay. Scale bars: 100 μm. Results are expressed as means ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 versus the EV group.</p>
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<p>PXA enhances CDDP-induced ferroptosis by inhibiting Nrf2 pathway. (<b>A</b>) HCT116 and HT29 cells were treated with PXA and CDDP with or without ferroptosis inhibitor (Fer-1) for 24h, and cell viability was measured using the CCK-8 assay. (<b>B</b>) Detection of lipid hydroperoxides by fluorescence imaging of Liperfluo in HCT116 and HT29 cells treated with PXA and CDDP with or without Fer-1. Scale bars: 100 μm. (<b>C</b>) EV and Nrf2 stable overexpressing HCT116 and HT29 cells were treated with PXA and CDDP for 24 h and the expression of SLC7A11 and GPX4 were examined by Western blotting. Results are expressed as means ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 versus the control group, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 versus the Fer-1 group, <sup>%</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>%%</sup> <span class="html-italic">p</span> &lt; 0.01 versus the EV group.</p>
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<p>PXA enhances the therapeutic efficacy of CDDP in CRC xenograft zebrafish model. (<b>A</b>) HCT116-CM-Dil cells were injected into zebrafish embryo. At the end of the experiments, phenotypic map of fluorescence of HCT116-CM-Dil cells in zebrafish were photographed. Scale bars: 250 μm (<b>B</b>) The fluorescence area and intensity of HCT116-CM-Dil cells in zebrafish were analyzed by Image J software (Version 1.54j). Results are expressed as means ± SD. ** <span class="html-italic">p</span> &lt; 0.01 versus the control group, <span class="html-italic"><sup>##</sup> p</span> &lt; 0.01 versus the CDDP-treatment group.</p>
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<p>Schematic representation of PXA enhancing the sensitivity of CRC cells to CDDP by inducing ferroptosis through the inhibition of Nrf2. “↓” indicates promotion; “⊥” indicates inhibition.</p>
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17 pages, 16804 KiB  
Article
Land Cover Mapping in a Mangrove Ecosystem Using Hybrid Selective Kernel-Based Convolutional Neural Networks and Multi-Temporal Sentinel-2 Imagery
by Seyd Teymoor Seydi, Seyed Ali Ahmadi, Arsalan Ghorbanian and Meisam Amani
Remote Sens. 2024, 16(15), 2849; https://doi.org/10.3390/rs16152849 - 3 Aug 2024
Viewed by 480
Abstract
Mangrove ecosystems provide numerous ecological services and serve as vital habitats for a wide range of flora and fauna. Thus, accurate mapping and monitoring of relevant land covers in mangrove ecosystems are crucial for effective conservation and management efforts. In this study, we [...] Read more.
Mangrove ecosystems provide numerous ecological services and serve as vital habitats for a wide range of flora and fauna. Thus, accurate mapping and monitoring of relevant land covers in mangrove ecosystems are crucial for effective conservation and management efforts. In this study, we proposed a novel approach for mangrove ecosystem mapping using a Hybrid Selective Kernel-based Convolutional Neural Network (HSK-CNN) framework and multi-temporal Sentinel-2 imagery. A time series of the Normalized Difference Vegetation Index (NDVI) products derived from Sentinel-2 imagery was produced to capture the temporal behavior of land cover types in the dynamic ecosystem of the study area. The proposed algorithm integrated Selective Kernel-based feature extraction techniques to facilitate the effective learning and classification of multiple land cover types within the dynamic mangrove ecosystems. The model demonstrated a high Overall Accuracy (OA) of 94% in classifying eight land cover classes, including mangrove, tidal zone, water, mudflat, urban, and vegetation. The HSK-CNN demonstrated superior performance compared to other algorithms, including random forest (OA = 85%), XGBoost (OA = 87%), Three-Dimensional (3D)-DenseNet (OA = 90%), Two-Dimensional (2D)-CNN (OA = 91%), Multi-Layer Perceptron (MLP)-Mixer (OA = 92%), and Swin Transformer (OA = 93%). Additionally, it was observed that the structure of the network, such as the types of convolutional layers and patch sizes, affected the classification accuracy using the proposed model and, thus, the optimum scenarios and values of these parameters should be determined to obtain the highest possible classification accuracy. Overall, it was observed that the produced map could offer valuable insights into the distribution of different land cover types in the mangrove ecosystem, facilitating informed decision-making for conservation and sustainable management efforts. Full article
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Figure 1
<p>(<b>A</b>) The study area and the distribution of all reference polygons within the study area, (<b>B</b>) the location of the study area within the Persian Gulf; (<b>C</b>) a zoomed image from the locations of mangroves; (<b>D</b>) the distribution of test samples; (<b>E</b>) the distribution of training samples; and (<b>F</b>) the distribution of validation samples.</p>
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<p>General framework of the proposed HSK-CNN model for mangrove ecosystem mapping. SK, GMP, GAP, FC, and NDVI stand for Selective Kernel-based, Global Max Pooling, Global Average Pooling, Fully Connected, and Normalized Difference Vegetation Index, respectively.</p>
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<p>The structure of the proposed 3D SK module.</p>
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<p>(<b>a</b>) Study area with three zoomed regions. (<b>b</b>) Mangrove ecosystem map produced using the proposed HSK-CNN model (see <a href="#remotesensing-16-02849-f001" class="html-fig">Figure 1</a> for the colors of different classes).</p>
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<p>The mangrove ecosystem maps produced using different classification algorithms from the study area (see <a href="#remotesensing-16-02849-f001" class="html-fig">Figure 1</a> for the colors of different classes).</p>
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<p>The confusion matrices of different models for mangrove ecosystem mapping: (<b>a</b>) Random Forest, (<b>b</b>) XGboost, (<b>c</b>) 2D-CNN, (<b>d</b>) MLP-Mixer, (<b>e</b>) Swin Transformer, (<b>f</b>) 3D-DeseNet, (<b>g</b>) Proposed HSK-CNN.</p>
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30 pages, 3685 KiB  
Review
A Pan-Asian Energy Transition? The New Rationale for Decarbonization Policies in the World’s Largest Energy Exporting Countries: A Case Study of Qatar and Other GCC Countries
by Ismail Abdallah, Hamed Alhosin, Mohamed Belarabi, Sanae Chaouki, Nousseiba Mahmoud and Jad Tayah
Energies 2024, 17(15), 3776; https://doi.org/10.3390/en17153776 - 31 Jul 2024
Viewed by 691
Abstract
Climate change has become a major agenda item in international relations and in national energy policy-making circles around the world. This review studies the surprising evolution of the energy policy, and more particularly the energy transition, currently happening in the Arabian Gulf region, [...] Read more.
Climate change has become a major agenda item in international relations and in national energy policy-making circles around the world. This review studies the surprising evolution of the energy policy, and more particularly the energy transition, currently happening in the Arabian Gulf region, which features some of the world’s largest exporters of oil and gas. Qatar, Saudi Arabia, and other neighboring energy exporters plan to export blue and green hydrogen across Asia as well as towards Europe in the years and decades to come. Although poorly known and understood abroad, this recent strategy does not threaten the current exports of oil and gas (still needed for a few decades) but prepares the evolution of their national energy industries toward the future decarbonized energy demand of their main customers in East and South Asia, and beyond. The world’s largest exporter of Liquefied Natural Gas, Qatar, has established industrial policies and projects to upscale CCUS, which can enable blue hydrogen production, as well as natural carbon sinks domestically via afforestation projects. Full article
(This article belongs to the Special Issue Advances in Energy Transition to Achieve Carbon Neutrality)
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<p>The concentration of Asian imports of oil and gas products among all net importing countries of Asia in 2021, in billion USD. Source [<a href="#B7-energies-17-03776" class="html-bibr">7</a>]: Authors and the UN Comtrade database.</p>
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<p>The 2021 oil trade balance broken down by region in millions of tons. Source: Enerdata World Energy &amp; Climate Statistics—Yearbook 2022 [<a href="#B19-energies-17-03776" class="html-bibr">19</a>].</p>
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<p>Energy mixes of the six case countries in 2022. Source: The Energy Institute Statistical Review of World Energy (2023) [<a href="#B19-energies-17-03776" class="html-bibr">19</a>].</p>
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<p>GCC countries’ exports in 2021 by product. Source [<a href="#B36-energies-17-03776" class="html-bibr">36</a>]: OEC database.</p>
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<p>GCC countries’ exports in 2021 by destination. Source [<a href="#B36-energies-17-03776" class="html-bibr">36</a>]: OEC database.</p>
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<p>Qatar’s exports to the Asian countries under study. Source [<a href="#B36-energies-17-03776" class="html-bibr">36</a>]: OEC database.</p>
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<p>Green hydrogen production, domestic consumption, and export potential. Source: Strategy and (2020) Redrawn by the author Hamid AlHosin [<a href="#B62-energies-17-03776" class="html-bibr">62</a>].</p>
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<p>Projected global CO<sub>2</sub> captured in the IEA’s NZE. Source (IEA, 2021) [<a href="#B52-energies-17-03776" class="html-bibr">52</a>].</p>
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<p>Mangrove stands in Qatar. Source [<a href="#B87-energies-17-03776" class="html-bibr">87</a>]: Al-Khayat and Alatalo, 2021.</p>
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<p>Emission trajectory curves for the GCC countries. Source [<a href="#B14-energies-17-03776" class="html-bibr">14</a>]: IEA (2022).</p>
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<p>Total investment costs required by the GCC countries. Source: IEA (2022).</p>
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<p>Total current emissions and projected emissions reductions for the GCC countries. Source [<a href="#B14-energies-17-03776" class="html-bibr">14</a>]: IEA (2022).</p>
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22 pages, 16238 KiB  
Article
Spectroscopic Phenological Characterization of Mangrove Communities
by Christopher Small and Daniel Sousa
Remote Sens. 2024, 16(15), 2796; https://doi.org/10.3390/rs16152796 - 30 Jul 2024
Viewed by 341
Abstract
Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology [...] Read more.
Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology and response to environmental conditions. This analysis leverages both spectroscopic and phenological information to characterize vegetation communities in the Sundarban riverine mangrove forest of the Ganges–Brahmaputra delta. Parallel analyses of surface reflectance spectra from NASA’s EMIT imaging spectrometer and MODIS vegetation abundance time series (2000–2022) reveal the spectroscopic and phenological diversity of the Sundarban mangrove communities. A comparison of spectral and temporal feature spaces rendered with low-order principal components and 3D embeddings from Uniform Manifold Approximation and Projection (UMAP) reveals similar structures with multiple spectral and temporal endmembers and multiple internal amplitude continua for both EMIT reflectance and MODIS Enhanced Vegetation Index (EVI) phenology. The spectral and temporal feature spaces of the Sundarban represent independent observations sharing a common structure that is driven by the physical processes controlling tree canopy spectral properties and their temporal evolution. Spectral and phenological endmembers reside at the peripheries of the mangrove forest with multiple outward gradients in amplitude of reflectance and phenology within the forest. Longitudinal gradients of both phenology and reflectance amplitude coincide with LiDAR-derived gradients in tree canopy height and sub-canopy ground elevation, suggesting the influence of surface hydrology and sediment deposition. RGB composite maps of both linear (PC) and nonlinear (UMAP) 3D feature spaces reveal a strong contrast between the phenological and spectroscopic diversity of the eastern Sundarban and the less diverse western Sundarban. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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<p>Index map of the Sundarban mangrove forest at the mouths of the Ganges–Brahmaputra delta. The Sentinel 2 false color composite from 2018 shows river channel network and forest canopy cover variations. Note the contrast of the mangrove canopy with the dry season agriculture (bright green), fallow fields (tan), and aquaculture ponds (black) on embanked islands surrounding the forest. The contrast in mangrove reflectance between the eastern and western tiles is a BRDF effect due to the contrasting view geometries at the opposite edges of adjacent Sentinel 2 swaths. GPS tracks (white) show the extents of boat-based field surveys. The east–west scale is 185 km.</p>
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<p>Sundarban EMIT mosaic. Three swaths provide a full coverage of mangroves and surrounding agriculture and aquaculture, with significant swath overlap. Acquisition dates span nearly the full annual phenological cycle from post-monsoon in December to pre-monsoon in April. Solar zenith angles at times of acquisition range from 11.5° (04.24) to 50.5° (12.27). The white vector boundary shows the extent of the Bangladesh Sundarban, for which tree species maps are available. Acquisition times are UTC + 6 h offset.</p>
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<p>Spectral feature space of the vegetation-masked Sundarban EMIT mosaic. Orthogonal projections of three low-order principal components (PCs) reveal spectral endmembers (labeled) and distinct amplitude gradients (vectors) within three clusters. Varying amplitude reflectance spectra (right) correspond to vector continua of the same color (left). While the reflectance spectra of the clusters overlap at VNIR wavelengths, each is distinct in the SWIR. The feature space spans amplitude continua with both agricultural (Ag) and forest (F) components. The agricultural continuum arises from the varying abundance of photosynthetic (PV) and non-photosynthetic (NPV) vegetation, but the amplitude of mangrove reflectance is modulated primarily by the canopy structure and varying amounts of crown shadow. There is a considerable overlap in EVI range between adjacent gradients, but a negligible overlap in NDWI range.</p>
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<p>Complementary spectral feature spaces of the vegetation-masked Sundarban EMIT mosaic. Orthogonal projections of two 3D UMAP embeddings (nn: 50 and 100) reveal consistent spectral endmembers (labeled) and distinct reflectance amplitude gradients (vectors) within three clusters corresponding to those in <a href="#remotesensing-16-02796-f003" class="html-fig">Figure 3</a>. In both UMAP 3/2 projections (right), the western continuum (yellow) is distinct from the connected eastern and central continua (cyan and magenta). Note the bifurcation of the high-amplitude end of the eastern continuum into the northern (N) and southern (S) peripheries of the mangroves. As in the PC feature space, the surrounding agriculture forms a separate 2D continuum spanning photosynthetic and non-photosynthetic vegetation.</p>
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<p>PC and UMAP feature space composites. Reflectance amplitude gradients and inset spectra correspond to those in <a href="#remotesensing-16-02796-f003" class="html-fig">Figure 3</a> and <a href="#remotesensing-16-02796-f004" class="html-fig">Figure 4</a>. Gradient vectors indicate a direction of increasing NIR reflectance. Composite colors are determined by 3D feature space topology in PC and UMAP spaces. The swath edge discontinuity in the center contrasts post-monsoon (west) from dry season (east) reflectance and longitudinal gradients in species composition and environmental conditions.</p>
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<p>Bitemporal reflectance change in swath overlap. PCs of the bitemporal reflectance space show a continuum bounded by three endmember reflectance changes for mangrove forest and one for dry season agriculture (Ag). For each forest change endmember, SWIR liquid water absorptions are deeper in December, following the monsoon, but significantly reduced by the April dry season. In contrast, the chlorophyll absorptions in the visible change little. Coherent spatial patterns in bitemporal PC composite suggest aggregate responses to solar illumination (θ) and SWIR water absorption.</p>
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<p>Temporal feature space of MODIS EVI time series for the entire Sundarban mangrove forest, 2000–2022. The UMAP composite (<b>upper left</b>) shows both N–S and E–W gradients in seasonal phenology, as well as several abrupt transitions. The 1/3 projection of the 3D UMAP feature space (<b>upper center</b>) has a single root (11) corresponding to lower EVI mixtures of canopy, water, and shadow at riverbanks and narrow channels within the mangrove. As EVI increases with canopy closure, the root diverges (10) into mixing trends, terminating at 9 distinct temporal endmembers. EVI time series (<b>bottom</b>) increase abruptly during the summer monsoon, and then decrease gradually over the rest of the year. The 9 endmembers correspond to peripheral regions of the Sundarban (<b>upper left</b>) with the highest post-monsoon EVI. The correlation matrix (<b>upper right</b>) of all 11 endmembers shows the highest correlations between geographically adjacent endmembers. The 2 lowest- (10, 11) and 2 highest (1, 9)-amplitude endmembers are less intercorrelated than those at the southern and eastern peripheries (2–8). Also apparent in the UMAP composite is the distinction between the phenological diversity of the eastern Sundarban and the more homogeneous center in the west.</p>
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<p>Sundarban temporal endmember phenologies from the temporal feature space in <a href="#remotesensing-16-02796-f007" class="html-fig">Figure 7</a>. The mean EVI (white) shows the rapid post-monsoon greening and gradual dry season senescence, while mean-removed residuals (color) of individual endmembers (offset for clarity) show a diversity of periodic excursions from the mean. As seen in <a href="#remotesensing-16-02796-f007" class="html-fig">Figure 7</a>, all endmembers are phase-aligned and differ primarily in the rate and amplitude of dry season EVI decrease. Despite the considerable noise, distinct annual periodicity is apparent in all but the lowest-amplitude (e.g., 2, 4, 7) residuals—which are most similar to the mean. The largest-amplitude residuals are those from the root and branch (10, 11) of the feature space, corresponding to lower EVI associated with partial canopy cover on shorelines and small channels. Note the slight decadal increase in minimum EVI of the mean.</p>
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<p>EMIT reflectance mosaic UMAP composite and elevation maps for the Sundarban and surrounding delta. GEDI LiDAR maps (center) reveal that an ongoing sediment deposition in the mangrove forest results in 1–2 m higher ground elevation in the eastern Sundarban relative to the surrounding embanked islands, which have been sediment-starved for decades. The higher SRTM elevation of the eastern Sundarban is a result of both the higher ground elevation and the greater canopy height of the tree species. Mono-species epicenters, from a Bangladesh Forest Department species map (2002), are labeled by common (local) names of tree species. Bi-species gradients compose most of the eastern Sundarban. Arrows show two SRTM swath discontinuity artifacts, which are distinct from the numerous height discontinuities occurring across channels.</p>
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<p>Field photos illustrate the forest diversity of the Bangladesh Sundarban. The northeast Sundarban (<b>top</b>) reaches canopy heights of 25 m, in contrast with the surrounding embanked islands, which are often below sea level. The sand-dominant islands of the southeast (<b>upper center</b>) are intertidal only around their peripheries and contain different tree species from the rest of the Sundarban. The vegetation gradient of Bird Island on the Bay of Bengal (<b>lower center</b>) illustrates the succession of grasses, shrubs, and trees that colonize sand-dominant islands. River channel networks (<b>bottom</b>) continually deliver silt and mud to intertidal islands throughout the Sundarban. Photos © C. Small 2012–2022.</p>
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<p>Multiscale UMAP temporal feature space with 3D PC(UMAP<sub>10+50</sub>) composite for MODIS EVI phenology. The low-order PCs of two 3D UMAP embeddings with contrasting n neighbor scales (nn: 10 and 50) preserve both the global scale limb structure and the finer scale clusters that are both phenologically and geographically distinct—including anomalous tree species assemblages at Hiron Point (HP) and Shelar Char (SC) on the Bay of Bengal shorelines. Compare the map structure with the maps in <a href="#remotesensing-16-02796-f005" class="html-fig">Figure 5</a> and <a href="#remotesensing-16-02796-f007" class="html-fig">Figure 7</a>. Manifold density and UMAP color scale equivalent to those in <a href="#remotesensing-16-02796-f007" class="html-fig">Figure 7</a>.</p>
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<p>Variance partition and sparse component distribution for the MODIS EVI phenology of the Ganges–Brahmaputra delta. The singular values (top) of the low-rank component suggest that the temporal feature space is effectively 4D (&gt;1%) with 96% of the total variance, while the sparse component has a nearly uniform noise floor over all dimensions. The spatial standard deviation (σ) and range (ρ) of the sparse component peak during the monsoon as a result of a transient cloud cover.</p>
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<p>Coregistered overlap between 12.23 and 12.27 EMIT acquisitions. Natural color composites illustrate the difference in aerosol optical depth with a reduced dynamic range and a greater adjacency effect on 12.27.</p>
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<p>Coregistered overlap between 12.27 and 04.24 acquisitions. Natural color composites show the difference in aerosol optical depth with reduced dynamic range and greater adjacency effect on 12.27.</p>
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<p>Apparent change in reflectance for overlaps on 12.23, 12.27, and 04.24. The mean (white) ± 1 standard deviation (green) of all vegetation spectra in each swath overlap show the effects of residual atmospheric scattering on 12.27 and actual changes in illumination and leaf water content on 04.24. The December 23 and 27 difference suggests short wavelength-dependent scattering on 12.27 with increased visible and reduced NIR but negligible change in SWIR wavelengths. In contrast, a greater VNIR scatter on 12.27 is manifested as a reduced visible and increased NIR scatter relative to 04.24. The reduced leaf water content on 04.24 results in greater SWIR residual from reduced H<sub>2</sub>O absorption after the 4-month dry season. A higher solar elevation in April also contributes to higher NIR and SWIR reflectance.</p>
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20 pages, 4849 KiB  
Article
Bacterial Communities across Multiple Ecological Niches (Water, Sediment, Plastic, and Snail Gut) in Mangrove Habitats
by Muna Al-Tarshi, Sergey Dobretsov and Mohammed Al-Belushi
Microorganisms 2024, 12(8), 1561; https://doi.org/10.3390/microorganisms12081561 - 30 Jul 2024
Viewed by 367
Abstract
Microbial composition across substrates in mangroves, particularly in the Middle East, remains unclear. This study characterized bacterial communities in sediment, water, Terebralia palustris snail guts, and plastic associated with Avicennia marina mangrove forests in two coastal lagoons in the Sea of Oman using [...] Read more.
Microbial composition across substrates in mangroves, particularly in the Middle East, remains unclear. This study characterized bacterial communities in sediment, water, Terebralia palustris snail guts, and plastic associated with Avicennia marina mangrove forests in two coastal lagoons in the Sea of Oman using 16S rDNA gene MiSeq sequencing. The genus Vibrio dominated all substrates except water. In the gut of snails, Vibrio is composed of 80–99% of all bacterial genera. The water samples showed a different pattern, with the genus Sunxiuqinia being dominant in both Sawadi (50.80%) and Qurum (49.29%) lagoons. There were significant differences in bacterial communities on different substrata, in particular plastic. Snail guts harbored the highest number of unique Operational Taxonomic Units (OTUs) in both lagoons, accounting for 30.97% OTUs in Sawadi and 28.91% OTUs in Qurum, compared to other substrates. Plastic in the polluted Sawadi lagoon with low salinity harbored distinct genera such as Vibrio, Aestuariibacter, Zunongwangia, and Jeotgalibacillus, which were absent in the Qurum lagoon with higher salinity and lower pollution. Sawadi lagoon exhibited higher species diversity in sediment and plastic substrates, while Qurum lagoon demonstrated lower species diversity. The principal component analysis (PCA) indicates that environmental factors such as salinity, pH, and nutrient levels significantly influence bacterial community composition across substrates. Variations in organic matter and potential anthropogenic influences, particularly from plastics, further shape bacterial communities. This study highlights the complex microbial communities in mangrove ecosystems, emphasizing the importance of considering multiple substrates in mangrove microbial ecology studies. The understanding of microbial dynamics and anthropogenic impacts is crucial for shaping effective conservation and management strategies in mangrove ecosystems, particularly in the face of environmental changes. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Venn diagrams showing counts and percentages of common and unique OTUs in microbial communities across four substrates in Sawadi (<b>A</b>) and Qurum (<b>B</b>) lagoons.</p>
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<p>Mean abundance of OTUs in four substrates (sediment, gut, water, and plastic) from Sawadi and Qurum mangrove sites in the Sea of Oman. Data are the mean ± standard deviation.</p>
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<p>Relative abundance of bacterial phyla. The pie chart represents the relative abundance of phyla in percentage (<b>A</b>) and (<b>B</b>) the mean ± SD relative abundance of bacterial phyla in water, snail guts, sediments, and plastic.</p>
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<p>Mean relative abundance of classes across four substrates in two lagoons. Sediment (<b>A</b>), water (<b>B</b>), snail guts (<b>C</b>), and plastic (<b>D</b>).</p>
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<p>Mean relative abundance of genera across four substrates in two lagoons. Sediment (<b>A</b>), water (<b>B</b>), snail guts (<b>C</b>), and plastic (<b>D</b>).</p>
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<p>Principal component analysis (PCA) of OTUs at different substrates (water, sediments, snail gut, and plastics).</p>
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28 pages, 20130 KiB  
Article
A Novel Bacitracin-like Peptide from Mangrove-Isolated Bacillus paralicheniformis NNS4-3 against MRSA and Its Genomic Insights
by Namfa Sermkaew, Apichart Atipairin, Thamonwan Wanganuttara, Sucheewin Krobthong, Chanat Aonbangkhen, Yodying Yingchutrakul, Jumpei Uchiyama and Nuttapon Songnaka
Antibiotics 2024, 13(8), 716; https://doi.org/10.3390/antibiotics13080716 - 30 Jul 2024
Viewed by 563
Abstract
The global rise of antimicrobial resistance (AMR) presents a critical challenge necessitating the discovery of novel antimicrobial agents. Mangrove microbes are valuable sources of new antimicrobial compounds. This study reports the discovery of a potent antimicrobial peptide (AMP) from Bacillus paralicheniformis NNS4-3, isolated [...] Read more.
The global rise of antimicrobial resistance (AMR) presents a critical challenge necessitating the discovery of novel antimicrobial agents. Mangrove microbes are valuable sources of new antimicrobial compounds. This study reports the discovery of a potent antimicrobial peptide (AMP) from Bacillus paralicheniformis NNS4-3, isolated from mangrove sediment, exhibiting significant activity against methicillin-resistant Staphylococcus aureus (MRSA). The AMP demonstrated a minimum inhibitory concentration ranging from 1 to 16 µg/mL in the tested bacteria and exhibited bactericidal effects at higher concentrations. Structural analysis revealed a bacitracin-like configuration and the peptide acted by disrupting bacterial membranes in a time- and concentration-dependent manner. The AMP maintained stability under heat, proteolytic enzymes, surfactants, and varying pH treatments. The ten biosynthetic gene clusters (BGCs) of secondary metabolites were found in the genome. Detailed sequence comparison of the predicted bacitracin BGC indicated distinct DNA sequences compared to previously reported strains. Although the antibiotic resistance genes were found, this strain was susceptible to antibiotics. Our findings demonstrated the potential of Bacillus paralicheniformis NNS4-3 and its AMP as a promising agent in combating AMR. The genetic information could be pivotal for future applications in the healthcare industry, emphasizing the need for continued exploration of marine microbial diversity in drug discovery. Full article
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<p>Growth curve and production kinetics of antibacterial components of NNS4-3.</p>
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<p>The purification of the AMP derived from NNS4-3 separated by RPC with gradient elution of mobile phase B (green solid line) showed that the major peak (arrow) was the active fraction (<b>a</b>). SDS-PAGE with 15% gel was performed for the characterization of protein bands stained with coomassie brilliant blue (<b>b</b>). The active fraction from each purification step, as followed by RPC (Lane 1), protein precipitation (Lane 2), and CFS collected from NNS4-3 culture (Lane 3), was electrophoresed and compared with the protein marker (Lane M). The protein bands exhibiting antibacterial activity were observed as an inhibition zone when overlaid with soft agar containing MRSA strain 2468 (<b>c</b>). The correlation between protein bands in the stained gel and the inhibition zone in the soft-agar overlaid gel in Lane 1 (solid arrow) and Lane 2 (dashed arrow) was found at a similar position.</p>
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<p>The mass spectrum of NNS4-3 AMP showed amino acid sequence by de novo sequencing with b-ion and mass detection with positive mode (<b>a</b>). The secondary structure of the determined amino acid sequence was analyzed by CD spectroscopy. The CD spectrum from 190 to 250 nm was observed when NNS4-3 AMP was dissolved in purified water or 50 mM SDS solution (<b>b</b>). The determination of AMP secondary structure components using CD spectra was analyzed by the BeStSel method via a web-based service (<b>c</b>). The amino acid arrangement of an alpha-helix structure of the peptide was predicted by HELIQUEST (the arrow indicates the hydrophobic face of the peptide) (<b>d</b>). The 3D model with the molecular surface was predicted using the web-based structure prediction from PEP-FOLD4. The top view and side view display the molecular surface area with the positive electrostatic potential area (indicated in blue), negative electrostatic potential area (indicated in red), and hydrophobic area (indicated in grey) (<b>e</b>).</p>
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<p>Photos captured using SEM at 20,000× magnification showed the morphological changes of <span class="html-italic">S. aureus</span> TISTR 517 and MRSA strain 2468 under antibiotics (vancomycin and cefoxitin) and 1× MIC of NNS4-3 AMP treatment. The non-treatment condition was used as a negative control. The arrow indicates the membrane disruption.</p>
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<p>Killing kinetics of NNS4-3 AMP. <span class="html-italic">S. aureus</span> TISTR 517 (<b>a</b>) and MRSA strain 2468 (<b>b</b>) were incubated with 1× (☐), 2× (◇), and 4× MIC (▽) of NNS4-3 AMP compared to the non-treatment group (◯). Significant differences in viable cell reduction at each time point (<span class="html-italic">p</span>-value &lt; 0.05) were analyzed by two-way ANOVA.</p>
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<p>The physical characteristics of NNS4-3. A single colony morphology displayed on ZM agar (<b>a</b>), Gram-stained vegetative cells (<b>b</b>), and malachite-green-stained endospores (<b>c</b>) were visualized under a light microscope at 1000× magnification. High-resolution scanning electron micrography revealed vegetative and endospore morphology at 20,000× magnification (<b>d</b>).</p>
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<p>The genome insight of <span class="html-italic">Bacillus paralicheniformis</span> NNS4-3 is displayed by a circular genome map visualized by Proksee (<b>a</b>). RAST provided the information on the cellular machinery that was predicted by the coding sequences used subsystem technology for functionalization and defined metabolic pathways of organisms (<b>b</b>). The GBDP method was performed for phylogenies determination and a query genome (arrow) was predicted against microorganism genomes in the TYGS database for the closest phylogenetic relation (<b>c</b>).</p>
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<p>The secondary metabolites produced by BGCs that were predicted by the similar orthologs in the MIBiG database. The similarity predictions of <span class="html-italic">Bacillus paralicheniformis</span> NNS4-3 BGCs are presented by the arrangement of colored genes, indicating the order and function of genes in the cluster that were responsible for secondary metabolite production. The color code indicates the functional type of the predicted domain following the AntiSMASH cluster visualization.</p>
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<p>The ortholog comparison of BGCs between <span class="html-italic">Bacillus paralicheniformis</span> NNS4-3 and the reference <span class="html-italic">Bacillus licheniformis</span> ATCC 10716. The prediction showed that the coding gene of NNS4-3 displayed 100% similarity to gene orthologs that were a cluster of bacitracin-like biosynthetic genes against the reference cluster. The function of predicted domain was represented by the color code in the AntiSMASH cluster visualization system (<b>a</b>). The encoded proteins from each gene that were responsible for AMP synthesis by modular enzymes (<b>b</b>). The differences in amino acid residues of each module enzyme (below) were compared to the reference (above), which is indicated by the red color along the amino acid sequences. The amino acid sequence similarity of each coding protein is presented with identity (%), coverage (%), and number of mismatches.</p>
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25 pages, 7511 KiB  
Article
Collembola Diversity across Vegetation Types of a Neotropical Island in a River Delta
by Maria Geovana de Mesquita Lima, Bruna Maria da Silva, Rudy Camilo Nunes, Alexandre de Oliveira Marques, Gleyce da Silva Medeiros, Fúlvio Aurélio de Morais Freire, Clécio Danilo Dias da Silva, Bruna Winck and Bruno Cavalcante Bellini
Diversity 2024, 16(8), 445; https://doi.org/10.3390/d16080445 - 27 Jul 2024
Viewed by 401
Abstract
Springtails, vital for ecosystem assessment, are often overshadowed by taxonomy-focused research, which mostly neglects their ecology and distribution, particularly in the Neotropical Region. The objective of this study was to identify how environmental factors, especially vegetation types, affect the availability of food resources [...] Read more.
Springtails, vital for ecosystem assessment, are often overshadowed by taxonomy-focused research, which mostly neglects their ecology and distribution, particularly in the Neotropical Region. The objective of this study was to identify how environmental factors, especially vegetation types, affect the availability of food resources for epiedaphic Collembola and influence their diversity patterns in three vegetation types (riparian forest, mangrove, and restinga) in the Canárias Island, in Delta do Parnaíba Environmental Protection Area, Brazil (APADP). We collected samples along 200 m transects in each vegetation type during the dry and rainy seasons. After, specimens were sorted, counted and identified. Alpha (species richness, Shannon, Simpson, and Pielou indices) and beta diversity (Whittaker index) were analyzed, along with environmental factors’ influence through Redundancy Analysis (RDA). We sampled a total of 5346 specimens, belonging to three orders, eight families, 23 genera, 31 morphospecies, and one nominal species. Species abundance was positively influenced by soil moisture, plant richness, and leaf litter. The riparian forest sheltered a higher species richness and diversity, and its biotic and abiotic factors likely enhanced the food resource availability, including vegetal organic matter, fungi, and bacteria. These results provide the first taxonomic and ecological data on the Collembola fauna in the APADP. Full article
(This article belongs to the Section Animal Diversity)
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<p>Sampling sites and their distribution throughout the Canárias Island: (<b>A</b>) mangrove; (<b>B</b>) restinga; (<b>C</b>) riparian forest.</p>
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<p>Accumulation curve with the interpolated (continuous line) and extrapolated (dashed line) species richness as a function of the number of individuals collected in different study areas.</p>
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<p>Morphospecies composition of Collembola assemblage between: (<b>a</b>) vegetation types (mangrove, restinga, and riparian Forest); (<b>b</b>) seasons (rainy and dry); and (<b>c</b>) the interaction of these factors. * Indicates statistically significant data (<span class="html-italic">p</span> &lt; 0.05). Legends for morphospecies are detailed in <a href="#diversity-16-00445-t0A1" class="html-table">Table A1</a>.</p>
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<p>Environmental characterization of each area (through the measured variables) in this study, during the dry and rainy seasons through PCA: (<b>a</b>) comparison between riparian forest, mangrove, and restinga; (<b>b</b>) comparison between dry and rainy periods; and (<b>c</b>) interaction of variables with each other. * Indicates statistical significance of the presented data. Descriptive data of the abiotic and biotic factors of each vegetation types are available in <a href="#diversity-16-00445-t0A2" class="html-table">Table A2</a>.</p>
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<p>Comparison of Collembola abundance between vegetation types and seasons: (<b>a</b>) riparian forest, mangrove, and restinga; (<b>b</b>) dry and rainy seasons; and (<b>c</b>) interaction between spatiotemporal factors. Asterisk indicates statistical differences (<span class="html-italic">p</span> ≤ 0.05) within compared groups. For three-factor groups (vegetation type), different letters indicate statistical differences (<span class="html-italic">p</span> ≤ 0.05) between each compared couple, while the lack of letters indicates no statistical differences. For comparing two factors (period), letters were not used due to the limited number of comparisons. * Indicates statistical significance of the presented data.</p>
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<p>Comparison of Collembola morphospecies richness in the studied areas: (<b>a</b>) riparian forest, mangrove, and restinga; (<b>b</b>) in dry and rainy seasons; and (<b>c</b>) interaction between spatiotemporal factors. Asterisk indicates statistical differences (<span class="html-italic">p</span> ≤ 0.05) within compared groups. For three-factor groups (vegetation type), different letters indicate statistical differences (<span class="html-italic">p</span> ≤ 0.05) between each compared couple, while the lack of letters indicates no statistical differences. For comparing two factors (period), letters were not used due to the limited number of comparisons. * Indicates statistical significance of the presented data.</p>
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<p>Comparison of Shannon diversity (H′) in the study areas and periods: (<b>a</b>) riparian forest, mangrove, and restinga; (<b>b</b>) in dry and rainy seasons; and (<b>c</b>) interaction between spatiotemporal factors. Asterisk indicates statistical differences (<span class="html-italic">p</span> ≤ 0.05) within compared groups. For three-factor groups (vegetation type), different letters indicate statistical differences (<span class="html-italic">p</span> ≤ 0.05) between each compared couple. For comparing two factors (period), letters were not used due to the limited number of comparisons. * Indicates statistical significance of the presented data.</p>
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<p>Simpson diversity (D) in the study areas and periods: (<b>a</b>) riparian forest, mangrove, and restinga; (<b>b</b>) in dry and rainy seasons; and (<b>c</b>) interaction between spatiotemporal factors. Asterisk indicates statistical differences (<span class="html-italic">p</span> ≤ 0.05) within compared groups. For three-factor groups (vegetation type), different letters indicate statistical differences (<span class="html-italic">p</span> ≤ 0.05) between each compared couple, while the lack of letters indicates no statistical differences. For comparing two factors (period), letters were not used due to the limited number of comparisons. * Indicates statistical significance of the presented data.</p>
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<p>Comparison of Pielou’s evenness (J′) in the study areas and periods: (<b>a</b>) riparian forest, mangrove, and restinga; (<b>b</b>) in dry and rainy seasons; and (<b>c</b>) interaction between factors. Asterisk indicates statistical differences (<span class="html-italic">p</span> ≤ 0.05) within compared groups. For three-factor groups (vegetation type), different letters indicate statistical differences (<span class="html-italic">p</span> ≤ 0.05) between each compared couple, while the lack of letters indicates no statistical differences. For comparing two factors (period), letters were not used due to the limited number of comparisons. * Indicates statistical significance of the presented data.</p>
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<p>Comparative boxplots of beta diversity between vegetations types and periods expressed by the Whittaker index: (<b>a</b>) Vegetation types; (<b>b</b>) periods; (<b>c</b>) influence of spatiotemporal factors. Asterisk indicates statistical differences (<span class="html-italic">p</span> ≤ 0.05) within compared groups. For three-factor groups (vegetation type), different letters indicate statistical differences (<span class="html-italic">p</span> ≤ 0.05) between each compared couple. For comparing two factors (period), letters were not used due to the limited number of comparisons. * Indicates statistical significance of the presented data.</p>
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<p>Results of the redundancy analysis indicating the environmental variables that most influenced the abundance of Collembola species observed in this study. * Indicates statistical significance of the axis (<span class="html-italic">p</span> ≤ 0.05). Supplemental information available in <a href="#diversity-16-00445-t0A3" class="html-table">Table A3</a>.</p>
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16 pages, 3305 KiB  
Article
Extraction of 10 m Resolution Global Mangrove in 2022
by Xiangyu Liu, Jingjuan Liao, Guozhuang Shen, Li Zhang and Bowei Chen
Remote Sens. 2024, 16(15), 2723; https://doi.org/10.3390/rs16152723 - 25 Jul 2024
Viewed by 372
Abstract
With the intensification of global climate change, there is an increasing emphasis on protecting natural resources. Mangrove forests, critical to tropical and subtropical intertidal ecosystems, have garnered considerable attention in recent years for their strong carbon sink capacity, rich species diversity, and abundant [...] Read more.
With the intensification of global climate change, there is an increasing emphasis on protecting natural resources. Mangrove forests, critical to tropical and subtropical intertidal ecosystems, have garnered considerable attention in recent years for their strong carbon sink capacity, rich species diversity, and abundant natural resources. This study utilizes the 2020 global mangrove vector data as a baseline to construct a reasonable buffer zone by calculating the increase in mangrove crown width. The Google Earth Engine (GEE) platform and its Sentinel-2 data from 2022 are employed to acquire synthetic images across all regions using the mosaic algorithm. Then, mangrove forests are extracted using the Otsu algorithm, and a map depicting the global spatial distribution of mangrove forests in 2022 is obtained. The average overall accuracy of the extracted mangrove forests in this study reaches 92.4%, and it is determined that the global mangrove forest area expanded by 4920.6 km2 between 2020 and 2022, This study provides crucial data support for the global monitoring of mangrove changes and holds significant importance for protecting and restoring mangrove ecosystems. Full article
(This article belongs to the Section Remote Sensing and Geo-Spatial Science)
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<p>Comparison of mangrove buffer zones in some areas. The left and right figures, respectively, show two different areas of mangroves (green-filled areas) and their original buffer zones (orange boundary). It can be seen that the buffer zones in (<b>a</b>) and their vicinity are dominated by mangroves, mudflats, and rivers, while (<b>b</b>) and its vicinity are dominated by mangroves, aquaculture ponds, and mudflats. In addition, the green and yellow dots are sample points for mangrove and non-mangrove forests, respectively.</p>
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<p>The 10 m resolution global mangrove vector map in 2022.</p>
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<p>The classification results from four different mangrove national parks or their surrounding areas are compared with Google’s submeter satellite images, which are (<b>a</b>) Abu Dhabi National Park in the United Arab Emirates, (<b>b</b>) Sian Ka’an Biological Reserve in Mexico, (<b>c</b>) Sundarbans National Park in Bangladesh, and (<b>d</b>) near Cape Orange National Park in Brazil. The horizontal lines in (<b>b</b>) were left behind when dividing the global mangrove vector map and will not affect the subsequent statistics and analysis. For the convenience of viewing satellite images inside the vector for comparison, the vectors in the figure only retain the boundaries, and the filled values inside are empty.</p>
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<p>Comparison between mangroves in Sundarbans Park and its surrounding environment. The red border represents the boundary of the mangrove forest vector map in the Sundarbans Park area in 2020. For the convenience of viewing satellite images inside the vector for comparison, the vectors in the figure only retain the boundaries, and the filled values inside are empty. It can be clearly seen from satellite images that the side with dense vegetation on the boundary is a concentrated distribution area of mangroves, while the other side is other types of land such as rivers, farmland, and bare land. (<b>a</b>) Boundary between mangroves and bare land, (<b>b</b>) Boundary between mangroves and rivers, (<b>c</b>) Boundary between mangroves and buildings, (<b>d</b>) Boundary between mangroves and cultivated land.</p>
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<p>The proportion of mangrove forest area in each continent and the bar chart of mangrove area ranking in the top 7 countries.</p>
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<p>The proportion of mangrove forest area in each continent and the bar chart of mangrove area ranking in the top 7 countries.</p>
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<p>The schematic diagram of the misclassification of the global mangrove dataset in 2020, with the red area roughly reflecting the misclassification of rivers (<b>a</b>) and bare land (<b>b</b>) into mangroves in the 2020 dataset. For ease of identification, the submeter-level satellite images provided by GEE were compared with vector images. The red border represents the boundary of the global mangrove forests vector map for 2020, and the green part represents the mangrove forests’ grid extracted in 2022.</p>
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<p>A schematic diagram of the transformation of mangrove forests to other types of land features in 2022. The parts included in the red line in the diagram are all mangrove forests in 2020. However, it is evident that in the satellite images of 2022, some mangrove forests have been transformed into bare land (<b>a</b>) or buildings (<b>b</b>), which are not covered by green areas in the figure. As mentioned above, the red border represents the mangrove vector map of the region in 2020, and the green part represents the mangrove extracted in 2022.</p>
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13 pages, 3049 KiB  
Communication
Induction of Three New Secondary Metabolites by the Co-Culture of Endophytic Fungi Phomopsis asparagi DHS-48 and Phomopsis sp. DHS-11 Isolated from the Chinese Mangrove Plant Rhizophora mangle
by Jingwan Wu, Jingjing Ye, Juren Cen, Yuanjie Chen and Jing Xu
Mar. Drugs 2024, 22(8), 332; https://doi.org/10.3390/md22080332 - 24 Jul 2024
Viewed by 458
Abstract
Co-cultivation is a powerful emerging tool for awakening biosynthetic gene clusters (BGCs) that remain transcriptionally silent under artificial culture conditions. It has recently been used increasingly extensively to study natural interactions and discover new bioactive metabolites. As a part of our project aiming [...] Read more.
Co-cultivation is a powerful emerging tool for awakening biosynthetic gene clusters (BGCs) that remain transcriptionally silent under artificial culture conditions. It has recently been used increasingly extensively to study natural interactions and discover new bioactive metabolites. As a part of our project aiming at the discovery of structurally novel and biologically active natural products from mangrove endophytic fungi, an established co-culture of a strain of Phomopsis asparagi DHS-48 with another Phomopsis genus fungus DHS-11, both endophytes in mangrove Rhizophora mangle, proved to be very efficient to induce the production of new metabolites as well as to increase the yields of respective target metabolites. A detailed chemical investigation of the minor metabolites produced by the co-culture of these two titled fungal strains led to the isolation of six alkaloids (16), two sterols (7, 8), and six polyketides (914). In addition, all the compounds except 8 and 10, as well as three new metabolites phomopyrazine (1), phomosterol C (7), and phomopyrone E (9), were not present in discrete fungal cultures and only detected in the co-cultures. The structures were elucidated on the basis of spectroscopic analysis, and the absolute configurations were assumed by electronic circular dichroism (ECD) calculations. Subsequently, the cytotoxic, immunosuppressive, and acetylcholinesterase inhibitory properties of all the isolated metabolites were determined in vitro. Compound 8 exhibited moderate inhibitory activity against ConA-induced T and LPS-induced B murine splenic lymphocytes, with IC50 values of 35.75 ± 1.09 and 47.65 ± 1.21 µM, respectively. Full article
(This article belongs to the Section Marine Pharmacology)
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<p>Structures of the isolated compounds <b>1</b>–<b>14</b>.</p>
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<p>Unrooted neighbor-joining phylogenetic tree based on the ITS gene sequences showing the taxonomic positions of DHS-48, DHS-11, and type strains of closely related <span class="html-italic">Phomopsis</span> taxa. The values at each node represent the bootstrap values from 1000 replicates, and the scale bar represents 0.05 substitutions per nucleotide. <span class="html-italic">Bacillus toyonensis</span> BCT-7112T served as an outgroup.</p>
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<p>Key COSY and HMBC correlations of compounds <b>1</b>, <b>7</b>, and <b>9</b>.</p>
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<p>Key NOESY correlations of compounds <b>7</b> and <b>9</b>.</p>
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<p>Experimental and calculated electronic circular dichroism (ECD) spectra of <b>7</b> and <b>9</b>.</p>
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24 pages, 18430 KiB  
Article
Climate Change Threatens Barringtonia racemosa: Conservation Insights from a MaxEnt Model
by Yanfang Tan, Xiaohui Tan, Yanping Yu, Xiaping Zeng, Xinquan Xie, Zeting Dong, Yilan Wei, Jinyun Song, Wanxing Li and Fang Liang
Diversity 2024, 16(7), 429; https://doi.org/10.3390/d16070429 - 22 Jul 2024
Viewed by 835
Abstract
Barringtonia racemosa (L.) Spreng. (Lecythidaceae), a crucial species in mangrove ecosystems, is facing endangerment primarily due to habitat loss. To address this issue, research is imperative to identify suitable conservation habitats for the endangered B. racemosa within mangrove ecosystems. The utilization of the [...] Read more.
Barringtonia racemosa (L.) Spreng. (Lecythidaceae), a crucial species in mangrove ecosystems, is facing endangerment primarily due to habitat loss. To address this issue, research is imperative to identify suitable conservation habitats for the endangered B. racemosa within mangrove ecosystems. The utilization of the optimized Maximum Entropy (MaxEnt) model has been instrumental in predicting potential suitable regions based on global distribution points and environmental variables under current and future climates conditions. The study revealed that the potential distribution area of B. racemosa closely aligns with its existing range with an Area Under the Curve (AUC) greater than 0.95. The Jackknife, AUC, percent contribution (PC), and permutation importance (PI) tests were employed alongside the optimized MaxEnt model to examine the influence of environmental variables on the distribution of B. racemosa. The primary factors identified as significant predictors of B. racemosa distribution included the average temperature of the ocean surface (Temperature), average salinity of the ocean surface (Salinity), precipitation of the warmest quarter (Bio18), precipitation of the driest month (Bio14), seasonal variation coefficient of temperature (Bio4), and isothermality (Bio3). Currently, the habitat range of B. racemosa is predominantly found in tropical and subtropical coastal regions near the equator. The total suitable habitat area measures 246.03 km2, with high, medium, low, and unsuitable areas covering 3.90 km2, 8.57 km2, 16.94 km2, and 216.63 km2, respectively. These areas represent 1.58%, 3.48%, 6.88%, and 88.05% of the total habitat area, respectively. The potential distribution area of B. racemosa demonstrated significant variations under three climate scenarios (SSP126, SSP245, and SSP585), particularly in Asia, Africa, and Oceania. Both low and high suitable areas experienced a slight increase in distribution. In summary, the research suggests that B. racemosa primarily flourishes in coastal regions of tropical and subtropical areas near the equator, with temperature and precipitation playing a significant role in determining its natural range. This study offers important implications for the preservation and control of B. racemosa amidst habitat degradation and climate change threats. Through a comprehensive understanding of the specific habitat needs of B. racemosa and the implementation of focused conservation measures, efforts can be made to stabilize and rejuvenate its populations in their natural environment. Full article
(This article belongs to the Special Issue Climate Change: Vegetation Diversity Monitoring)
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<p><span class="html-italic">Barringtonia racemosa</span> (L.) Spreng. (Lecythidaceae): global distribution, local population, and floral details. (<b>A</b>) The global distribution of <span class="html-italic">B. racemosa</span>; (<b>B</b>) the natural population of <span class="html-italic">B. racemosa</span> in Suixi County, Leizhou City, Guangdong Province; (<b>C</b>) the inflorescence of <span class="html-italic">B. racemosa</span>; (<b>D</b>) magnification of <span class="html-italic">B. racemosa</span> flowers.</p>
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<p>Visualization chart of correlation analysis of <span class="html-italic">Barringtonia racemosa</span> (L.) Spreng. (Lecythidaceae) environmental variables by the Pearson test. The correlation coefficient R indicates the strength of the relationship, with values above 0.7 being very close, from 0.4 to 0.7 being close, and from 0.2 to 0.4 being general. Blue represents negative correlation, red represents positive correlation, and darker colors indicate higher correlation values. * Significant correlation at 0.05 level; ** significant correlation at 0.01 level; *** was significantly correlated at 0.001 level.</p>
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<p>Optimal model parameter combination selection.</p>
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<p>AUC value of <span class="html-italic">Barringtonia racemosa</span> (L.) Spreng. (Lecythidaceae) predicted by MaxEnt model. (<b>A</b>) The current period ROC curve; (<b>B</b>) the future (2040s, SSP126) period ROC curve; (<b>C</b>) the future (2040s, SSP245) period ROC curve; (<b>D</b>) the future (2040s, SSP585) period ROC curve.</p>
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<p>Jackknife test of the environment variables. (<b>A</b>–<b>C</b>) The contribution of each environmental factor to each scenario using the Jacknife test in test gain, regularized training gain, and AUC, respectively.</p>
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<p>Response of main environmental elements to the probability of suitable growth of <span class="html-italic">Barringtonia racemosa</span> (L.) Spreng. (Lecythidaceae). (<b>A</b>) Average temperature of ocean surface (Temperature), (<b>B</b>) average salinity of ocean surface (Salinity); (<b>C</b>) precipitation of warmest quarter (Bio18); (<b>D</b>) precipitation of the driest month (Bio14); (<b>E</b>) seasonal variation coefficient of temperature (Bio4); (<b>F</b>) isothermality (Bio3).</p>
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<p>Potential geographical distribution of <span class="html-italic">Barringtonia racemosa</span> (L.) Spreng. (Lecythidaceae) <span class="html-italic">i</span>n the current climatic environment (1970–2000, global suitable growth area).</p>
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<p>Potential geographical distribution of <span class="html-italic">Barringtonia racemosa</span> (L.) Spreng. (Lecythidaceae) <span class="html-italic">i</span>n the current climatic environment (1970–2000). (<b>A</b>) Suitable growth area of <span class="html-italic">B. racemosa</span> in Asia and Oceania; (<b>B</b>) suitable growth area of <span class="html-italic">B. racemosa</span> in Africa. The red dotted frame delineates the research area.</p>
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<p>Prediction of potential suitable distribution of <span class="html-italic">Barringtonia racemosa</span> (L.) Spreng. (Lecythidaceae) under future climate scenarios (2040s, SSP126): (<b>A</b>), SSP126 scenarios in Asia and Oceania; (<b>B</b>) SSP126 scenarios in Africa. The red dotted frame delineates the research area.</p>
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<p>Prediction of potential suitable distribution of <span class="html-italic">Barringtonia racemosa</span> (L.) Spreng. (Lecythidaceae) under future climate scenarios (2040s, SSP245): (<b>A</b>) SSP245 scenarios in Asia and Oceania; (<b>B</b>) SSP245 scenarios in Africa. The red dotted frame delineates the research area.</p>
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<p>Prediction of potential suitable distribution of <span class="html-italic">Barringtonia racemosa</span> (L.) Spreng. (Lecythidaceae) under future climate scenarios (2040s, SSP585): (<b>A</b>) SSP585 scenarios in Asia and Oceania; (<b>B</b>) SSP585 scenarios in Africa. The red dotted frame delineates the research area.</p>
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27 pages, 1930 KiB  
Review
Mangrove Biodiversity and Conservation: Setting Key Functional Groups and Risks of Climate-Induced Functional Disruption
by Alexander C. Ferreira, Elizabeth C. Ashton, Raymond D. Ward, Ian Hendy and Luiz D. Lacerda
Diversity 2024, 16(7), 423; https://doi.org/10.3390/d16070423 - 19 Jul 2024
Viewed by 876
Abstract
Climate change (CC) represents an increasing threat to mangroves worldwide and can amplify impacts caused by local anthropogenic activities. The direct effects of CC on mangrove forests have been extensively discussed, but indirect impacts such as the alteration of ecological processes driven by [...] Read more.
Climate change (CC) represents an increasing threat to mangroves worldwide and can amplify impacts caused by local anthropogenic activities. The direct effects of CC on mangrove forests have been extensively discussed, but indirect impacts such as the alteration of ecological processes driven by specific functional groups of the biota are poorly investigated. Ecological roles of key functional groups (FGs) in mangroves from the Atlantic–Caribbean–East Pacific (ACEP) and Indo-West Pacific (IWP) regions are reviewed, and impacts from CC mediated by these FGs are explored. Disruption by CC of ecological processes, driven by key FGs, can reinforce direct effects and amplify the loss of ecological functionality and further degradation of mangrove forests. Biogeochemistry mediator microbiotas of the soil, bioturbators, especially semiterrestrial crabs (Ocypodoids and Grapsoids) and herbivores (crustaceans and Insects), would be the most affected FG in both regions. Effects of climate change can vary regionally in the function of the combination of direct and indirect drivers, further eroding biodiversity and mangrove resilience, and impairing the predictability of ecosystem behaviour. This means that public policies to manage and conserve mangroves, as well as rehabilitation/restoration programs, should take into consideration the pressures of CC in specific regions and the response of key FGs to these pressures. Full article
(This article belongs to the Special Issue Biodiversity and Conservation of Mangroves)
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<p>Specific diversity of worldwide mangroves. Colours indicate the number of catalogued regional species, but considering that local stands have only limited sets of regional species. The Americas–Caribbean and West Africa are the ACEP region, and from East Africa towards the East, this is the IWP region [Adapted from ‘World Atlas of Mangroves’, [<a href="#B9-diversity-16-00423" class="html-bibr">9</a>]].</p>
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<p>Fauna of mangroves. (<b>A</b>) Langurs (<span class="html-italic">Presbytis</span> sp.); (<b>B</b>) <span class="html-italic">Episesarma versicolor</span>; (<b>C</b>) mudskipper (in their burrow); (<b>D</b>) <span class="html-italic">Goniopsis cruentata</span> (eating a <span class="html-italic">Rhizophora</span> propagule); (<b>E</b>) teredinid (<span class="html-italic">Neoteredo</span> sp.) (circle shows the anterior portion of an exposed individual, surrounded by calcified galleries); (<b>F</b>) fiddler crab (<span class="html-italic">Tubuca</span> sp.); (<b>G</b>) <span class="html-italic">Dysphania</span> sp. (herbivore, larva feeds on <span class="html-italic">Kandelia candel</span>). All images from IWP, except (<b>D</b>,<b>E</b>) from ACEP. [Credits: E. Ashton (<b>A</b>,<b>B</b>,<b>F</b>,<b>G</b>); M. Zimmer (<b>C</b>); C.E. Alencar (<b>D</b>); A. Ferreira (<b>E</b>)].</p>
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<p>Effects of climate change (in green box) on FGs and the direct effects of FGs on the forest (blue boxes). The numbers of the effects do not necessarily express event order and are (1) disruption to soil biogeochemical processes; (2) decreased nutrient availability &gt; impact on forest productivity; (3) changing forest structure and biomass/C stock; (4) changes in propagule recruitment patterns; (5) changing existing forest zonation patterns; (6) decrease in forest structural resistance; (7) decrease/increase sediment aeration by sediment reworking; (8) mass defoliation; (9) disruption to tree development; (10) disruption to pollination and reproductive output; (11) decrease in inputs of OM, litter and deadwood processing, and nutrient cycling reduction. [Note: For more (indirect) effects see <a href="#diversity-16-00423-t002" class="html-table">Table 2</a>. The herbivore FG includes the several mobile Grapsoids (<span class="html-italic">Sesarmids</span> in IWP and <span class="html-italic">G. cruentata</span> (Grapsidae) and a few <span class="html-italic">Sesarmids</span> in ACEP) that live in forest soil and climb trunks and roots, also with omnivore and preying habits (in red circles). Some of these crabs are simultaneously bioturbator/burrowers and major Herbivores (in blue circles)].</p>
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25 pages, 14290 KiB  
Article
Land Use Change Impacts over the Indus Delta: A Case Study of Sindh Province, Pakistan
by Maira Masood, Chunguang He, Shoukat Ali Shah and Syed Aziz Ur Rehman
Land 2024, 13(7), 1080; https://doi.org/10.3390/land13071080 - 17 Jul 2024
Viewed by 555
Abstract
Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans and nature, particularly in diverse and evolving landscapes. This study employs remote sensing (RS) data and machine learning algorithms (MLAs) to investigate LULCC dynamics within the [...] Read more.
Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans and nature, particularly in diverse and evolving landscapes. This study employs remote sensing (RS) data and machine learning algorithms (MLAs) to investigate LULCC dynamics within the Indus River Delta region of Sindh, Pakistan. The focus is on tracking the trajectories of land use changes within mangrove forests and associated ecosystem services over twenty years. Our findings reveal a modest improvement in mangrove forest cover in specific areas, with an increase from 0.28% to 0.4%, alongside a slight expansion of wetland areas from 2.95% to 3.19%. However, significant increases in cropland, increasing from 22.76% to 28.14%, and built-up areas, increasing from 0.71% to 1.66%, pose risks such as altered sedimentation and runoff patterns as well as habitat degradation. Additionally, decreases in barren land from 57.10% to 52.7% and a reduction in rangeland from 16.16% to 13.92% indicate intensified land use conversion and logging activities. This study highlights the vulnerability of mangrove ecosystems in the Indus Delta to agricultural expansion, urbanization, resource exploitation, and land mismanagement. Recommendations include harmonizing developmental ambitions with ecological conservation, prioritizing integrated coastal area management, reinforcing mangrove protection measures, and implementing sustainable land use planning practices. These actions are essential for ensuring the long-term sustainability of the region’s ecosystems and human communities. Full article
(This article belongs to the Section Landscape Ecology)
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<p>Map (<b>a</b>) shows the study area in Sindh province, Pakistan, and (<b>b</b>) shows the mangrove area in Sindh.</p>
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<p>(<b>a</b>–<b>e</b>) LULC maps of the Indus Delta from 2000 to 2020.</p>
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<p>Categorized vegetation cover over Sindh province: (<b>a</b>) NDVI-2000; (<b>b</b>) NDVI-2005; (<b>c</b>) NDVI-2010; (<b>d</b>) NDVI-2015; (<b>e</b>) NDVI-2020.</p>
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<p>Categorized wetlands and no-water cover over Sindh province: (<b>a</b>) 2000; (<b>b</b>) 2005; (<b>c</b>) 2010; (<b>d</b>) 2015; (<b>e</b>) 2020.</p>
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<p>Categorized wetlands, built-up, and no-built-up areas over Sindh province: (<b>a</b>) 2000; (<b>b</b>) 2005; (<b>c</b>) 2010; (<b>d</b>) 2015; (<b>e</b>) 2020.</p>
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<p>(<b>a</b>) First-order thematic conversion change map; (<b>b</b>) second-order thematic conversion change map; (<b>c</b>) the total-order thematic conversion change map.</p>
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<p>(<b>a</b>) Elevation; (<b>b</b>) river distance; (<b>c</b>) road distance; (<b>d</b>) slope; (<b>e</b>) avg. temperature; (<b>f</b>) avg. rainfall.</p>
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<p>LULC prediction: (<b>a</b>) LULC-2020; (<b>b</b>) predicted LULC-2020; (<b>c</b>) predicted LULC-2025; (<b>d</b>) predicted LULC-2030.</p>
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<p>Cross-validation of the classified LULC map and simulated LULC map of 2020.</p>
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<p>LULC change the trend pattern.</p>
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<p>Heatmap illustrating the change analysis for LULC types from 2000 to 2030.</p>
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