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Fig. 6 in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 6. Light and scanning electron micrograph of cyprids of: A–H, Amphibalanus variegatus; and I–L, Euraphia withersi. Details of specific carapace sculpturing patterns in each species are shown at higher magnification. 6I, E. withersi has reddish pigments around the carapace (arrows) and a dark rounded pigmentation spot (circled).

Fig. 5 in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 5. Light and scanning electron micrograph of cyprids of: A–D, Fistulobalanus sp.; and E–J, Fistulobalanus patellaris. Details of specific carapace sculpturing patterns in each species are shown at higher magnification.

Fig. 2 in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 2. Lateral view of cyris larvae of barnacle showing measurements used for morphometric analysis. CL: carapace length; CH: carapace height; A: posterior carapace angle. Ratio of CL/CH was also calculated.

Fig. 1 in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 1. Map of sampling locations at Matang Mangrove Forest Reserve (MMFR) in Perak, Malaysia. Sampling was carried out in April 2011 at sites 1–8 and in June 2012 at sites 9–14.

Fig. 4 in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 4. Histogram showing variations of pair-wise genetic distances computed from 12S-rRNA gene fragment sequences using Kimura 2-parameter model. Note the distribution of within-species variations does not overlap with that of inter-species variation.

Fig. 3. Neighbour-joining tree contructed from partial 12S in A combined morphological and molecular approach in identifying barnacle cyprids from the Matang Mangrove Forest Reserve in Malaysia: essentials for larval ecology studies

Fig. 3. Neighbour-joining tree contructed from partial 12S-rRNA gene fragment sequences of cyprids and adults of barnacle. The sequences were clustered into eight clades, and species name were labelled at the clades containing sequence(s) of identified adult of barnacle. Clades with no sequence of identified barnacle adult clustered within were designated as OTU (Operational Taxonomic Unit). Number of sequences in each clade were also shown. Scale bar denotes 0.02 base substituition per site.

opencc-by-4.0Sep 2021View details →

Fig. 1 Insect alpha-diversity across tropical forest habitats. a in Mangroves are an overlooked hotspot of insect diversity despite low plant diversity

Fig. 1 Insect alpha-diversity across tropical forest habitats. a Mangroves treated as one habitat; b Comparison of mangrove sites: Pulau Ubin (PU), Sungei Buloh (SB), Pulau Semakau old-growth (SMO), Pulau Semakau new-growth (SMN), other smaller mangrove fragments (see Additional File 1: Table S13); solid lines = rarefaction; dotted = extrapolations. The arrow on the x-axis indicates the point of rarefaction where species richness comparisons were made (see bar charts for absolute numbers with 95% confidence intervals)

Figure 7 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 7. Onchidium typhae on the wooden hole of mangrove.

Figure 9. Omobranchus zebra, a in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 9. Omobranchus zebra, a blennid fish.

Figure 6 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 6. Europila withersi on the leaf of mangroves.

Figure 4 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 4. Coenobita cavipes climbing on Exocearia aggolacha, mangrove.

Figure 5 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 5. Balanus amhritrite and Nerita articulata on the mangrove.

Figure 2 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 2. Mangrove area of Subarnarekha estuary at Bichitrapur.

Figure 1 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 1. Satellite Map showing the mangrove area of Bichitrapur (Courtsey: Google earth.com).

opencc-zeroDec 2021View details →

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Data from: Establishing community-wide DNA barcode references for conserving mangrove forests in China

Background: Mangrove ecosystems have been the focus of global attention for their crucial role in sheltering coastal communities and retarding global climate change by sequestering 'blue carbon'. China is relatively rich in mangrove diversity, with one-third of the ca. 70 true mangrove species and a number of mangrove associate species occurring naturally along the country's coasts. Mangrove ecosystems, however, are widely threatened by intensifying human disturbances and rising sea levels. The urgent need to protect mangrove ecosystems could be assisted by using barcoding technology, which provides rapid species identification. Results: To investigate this potential, 898 plant specimens were collected from 33 of the major mangrove sites in China. Based on the morphologic diagnosis, the specimens were assigned to 72 species, including all 28 true mangrove species and all 12 mangrove associate species recorded in China. Three chloroplast DNA markers rbcL, trnH-psbA, matK, and one nuclear marker ITS2 were chosen to investigate the utility of using barcoding to identify these species. According to the criteria of barcoding gaps in genetic distance, sequence similarity and phylogenetic monophyly, we propose that a single marker, ITS2, is sufficient to barcode the species of mangroves and their associates in China. Furthermore, rbcL or trnH-psbA can also be used to gather supplement confirming data. In using these barcodes, we revealed a very low level of genetic variation among geographic locations in the mangrove species, which is an alert to their vulnerability to climate and anthropogenic disturbances. Conclusion: We suggest to use ITS2 to barcode mangrove species and terrestrial coastal plants in South China. The DNA barcode sequences we obtained would be valuable in monitoring biodiversity and the restoration of ecosystems, which are essential for mangrove conservation.

Figure 3 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 3. Sea anemone Diadumene lineata on the mangrove trunk.

Figure 8 in Animal diversity in the mangrove forest at Bichitrapur of Balasore district, Odisha, India- A case study

Figure 8. Sphaeroma terebrans and its juveniles colonize in wooden holes.

opencc-by-4.0Sep 2024View details →

Mapping Soil Organic Carbon in the World's Largest Arid Mangrove Forest (Indus Delta, Pakistan): A Multi-Sensor Remote Sensing and Machine Learning Approach

Mangrove forests play a crucial role in carbon sequestration, especially in arid regions where their ability to store carbon in soil is vital for mitigating climate change. The Indus Delta in Pakistan, the world’s largest arid mangrove forest system, lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (0-10 cm) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 65.88 Mg C ha⁻¹ with substantial spatial variability, ranging from 15.06 Mg C ha⁻¹ to 138.03 Mg C ha⁻¹ with a total of 0.91 Pg C. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 Mg C ha⁻¹. However, the region faces challenges such as seawater intrusion and salinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan’s mangroves