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20 pages, 8904 KiB  
Article
Habitat Loss in the IUCN Extent: Climate Change-Induced Threat on the Red Goral (Naemorhedus baileyi) in the Temperate Mountains of South Asia
by Imon Abedin, Tanoy Mukherjee, Joynal Abedin, Hyun-Woo Kim and Shantanu Kundu
Biology 2024, 13(9), 667; https://doi.org/10.3390/biology13090667 - 27 Aug 2024
Cited by 2 | Viewed by 1450
Abstract
Climate change has severely impacted many species, causing rapid declines or extinctions within their essential ecological niches. This deterioration is expected to worsen, particularly in remote high-altitude regions like the Himalayas, which are home to diverse flora and fauna, including many mountainous ungulates. [...] Read more.
Climate change has severely impacted many species, causing rapid declines or extinctions within their essential ecological niches. This deterioration is expected to worsen, particularly in remote high-altitude regions like the Himalayas, which are home to diverse flora and fauna, including many mountainous ungulates. Unfortunately, many of these species lack adaptive strategies to cope with novel climatic conditions. The Red Goral (Naemorhedus baileyi) is a cliff-dwelling species classified as “Vulnerable” by the IUCN due to its small population and restricted range extent. This species has the most restricted range of all goral species, residing in the temperate mountains of northeastern India, northern Myanmar, and China. Given its restricted range and small population, this species is highly threatened by climate change and habitat disruptions, making habitat mapping and modeling crucial for effective conservation. This study employs an ensemble approach (BRT, GLM, MARS, and MaxEnt) in species distribution modeling to assess the distribution, habitat suitability, and connectivity of this species, addressing critical gaps in its understanding. The findings reveal deeply concerning trends, as the model identified only 21,363 km2 (13.01%) of the total IUCN extent as suitable habitat under current conditions. This limited extent is alarming, as it leaves the species with very little refuge to thrive. Furthermore, this situation is compounded by the fact that only around 22.29% of this identified suitable habitat falls within protected areas (PAs), further constraining the species’ ability to survive in a protected landscape. The future projections paint even degraded scenarios, with a predicted decline of over 34% and excessive fragmentation in suitable habitat extent. In addition, the present study identifies precipitation seasonality and elevation as the primary contributing predictors to the distribution of this species. Furthermore, the study identifies nine designated transboundary PAs within the IUCN extent of the Red Goral and the connectivity among them to highlight the crucial role in supporting the species’ survival over time. Moreover, the Dibang Wildlife Sanctuary (DWLS) and Hkakaborazi National Park are revealed as the PAs with the largest extent of suitable habitat in the present scenario. Furthermore, the highest mean connectivity was found between DWLS and Mehao Wildlife Sanctuary (0.0583), while the lowest connectivity was observed between Kamlang Wildlife Sanctuary and Namdapha National Park (0.0172). The study also suggests strategic management planning that is a vital foundation for future research and conservation initiatives, aiming to ensure the long-term survival of the species in its natural habitat. Full article
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<p>Map showing the study area for the present study along with the IUCN extent of Red Goral (<span class="html-italic">N. baileyi</span>). The figure also highlights the location points acquired from primary and secondary sources used for training the model. The photograph of the Red Goral was taken by Mr. Ravi Mekola in Dibang Valley, Arunachal Pradesh, India. Protected areas are represented by blue lines: 1. YardiRabe Supse Wildlife Sanctuary; 2. Mouling National Park; 3. Dibang Wildlife Sanctuary; 4. Mehao Wildlife Sanctuary; 5. Kamlang Wildlife Sanctuary; 6. Namdapha National Park; 7. Hponkanrazi Wildlife Sanctuary; 8. Hkakaborazi National Park; 9. Three Parallel Rivers of Yunnan Protected Areas.</p>
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<p>Model evaluation plot showing the average training ROC of both training and cross-validation (CV) and the predictors chosen by the model for the replicate runs under four models of Red Goral: (<b>A</b>) showing ROC plot of Boosted Regression Tree (BRT), (<b>B</b>) Generalized Linear Model (GLM), (<b>C</b>) Multivariate Adaptive Regression Spines (MARS), and (<b>D</b>) Maximum Entropy (MaxEnt).</p>
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<p>(<b>A</b>) This figure shows the present suitable habitats for <span class="html-italic">N. baileyi</span> in the study area. The four classes (1–4) defined in the map show the four model arguments used in the present study. Class “0” of habitat suitability is not indicated in the map as it represents no suitability and zero model agreement. (<b>B</b>) Map representing the habitat connectivity in the IUCN extent in the present scenario.</p>
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<p>Maps representing the two-time frames of the SSP245 scenario for <span class="html-italic">N. baileyi</span>: (<b>A</b>,<b>B</b>) determine the habitat suitable in the IUCN extent, whereas (<b>C</b>,<b>D</b>) determine the connectivity in the landscape in these scenarios.</p>
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<p>Maps representing the two-time frames of the SSP585 scenario for <span class="html-italic">N. baileyi</span>: (<b>A</b>,<b>B</b>) determine the habitat suitable in the IUCN extent, whereas (<b>C</b>,<b>D</b>) determine the connectivity in the landscape in these scenarios.</p>
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17 pages, 352 KiB  
Article
Bringing Animals in-to Wildlife Tourism
by Siobhan I. M. Speiran and Alice J. Hovorka
Sustainability 2024, 16(16), 7155; https://doi.org/10.3390/su16167155 - 20 Aug 2024
Viewed by 2638
Abstract
The objective of this paper is to highlight animal stakeholders, evidenced-based best practices, care ethics, and compassion as essential components of sustainable wildlife tourism. These tenets stem from an animal geography lens, which is well-positioned for studies of animal-based tourism and transspecies caregiving. [...] Read more.
The objective of this paper is to highlight animal stakeholders, evidenced-based best practices, care ethics, and compassion as essential components of sustainable wildlife tourism. These tenets stem from an animal geography lens, which is well-positioned for studies of animal-based tourism and transspecies caregiving. As a conceptual contribution, this paper presents a theory synthesis that ‘stays with the trouble’ of wildlife tourism and identifies ways to ‘bring animals in’. Our approach could be described as multispecies, critical, and socio-ecological. We argue that the trouble with wildlife tourism writ large includes nonhuman suffering and biodiversity loss, unethical and unevidenced practices, gaps in the knowledge of wildlife welfare, and limited engagement with animals as stakeholders. We then present four ways to ‘bring animals in’ as co-participants in wildlife tourism research and practice. This involves enfranchising animals as stakeholders in wildlife tourism, buttressed by ethics of care, best practices, and a commitment to improved outcomes along the conservation-welfare nexus. Finally, we consider the extent to which wildlife sanctuary tourism serves as a further problem or panacea that balances the conservation and welfare of wild animals. The result of our theory synthesis is the promotion of a more care-full and compassionate paradigm for wildlife tourism, which draws from diverse scholarships that contribute, conceptually and practically, to the underserved niches of wildlife welfare, rehabilitation, and sanctuary research. Full article
17 pages, 16421 KiB  
Article
Distribution Model Reveals Rapid Decline in Habitat Extent for Endangered Hispid Hare: Implications for Wildlife Management and Conservation Planning in Future Climate Change Scenarios
by Imon Abedin, Tanoy Mukherjee, Ah Ran Kim, Hyun-Woo Kim, Hye-Eun Kang and Shantanu Kundu
Biology 2024, 13(3), 198; https://doi.org/10.3390/biology13030198 - 20 Mar 2024
Cited by 7 | Viewed by 2059
Abstract
The hispid hare, Caprolagus hispidus, belonging to the family Leporidae is a small grassland mammal found in the southern foothills of the Himalayas, in India, Nepal, and Bhutan. Despite having an endangered status according to the IUCN Red List, it lacks studies [...] Read more.
The hispid hare, Caprolagus hispidus, belonging to the family Leporidae is a small grassland mammal found in the southern foothills of the Himalayas, in India, Nepal, and Bhutan. Despite having an endangered status according to the IUCN Red List, it lacks studies on its distribution and is threatened by habitat loss and land cover changes. Thus, the present study attempted to assess the habitat suitability using the species distribution model approach for the first time and projected its future in response to climate change, habitat, and urbanization factors. The results revealed that out of the total geographical extent of 188,316 km2, only 11,374 km2 (6.03%) were identified as suitable habitat for this species. The results also revealed that habitat significantly declined across its range (>60%) under certain climate change scenarios. Moreover, in the present climate scenario protected areas such as Shuklaphanta National Park (0.837) in Nepal exhibited the highest mean extent of habitat whereas, in India, Dibru-Saikhowa National Park (0.631) is found to be the most suitable habitat. Notably, two protected areas in Uttarakhand, India, specifically Corbett National Park (0.530) and Sonanandi Wildlife Sanctuary (0.423), have also demonstrated suitable habitats for C. hispidus. Given that protected areas showing a future rise in habitat suitability might also be regarded as potential sites for species translocation, this study underscores the importance of implementing proactive conservation strategies to mitigate the adverse impacts of climate change on this species. It is essential to prioritize habitat restoration, focused protection measures, and further species-level ecological exploration to address these challenges effectively. Furthermore, fostering transboundary collaboration and coordinated conservation actions between nations is crucial to safeguarding the long-term survival of the species throughout its distribution range. Full article
(This article belongs to the Section Conservation Biology and Biodiversity)
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<p>The map illustrates the global range distribution and observed locations of endangered hispid hare, <span class="html-italic">C. hispidus</span>. Color code represents the elevation gradient in the study landscape. The original image of hispid hare reproduced with permission through direct communication with the original copyright holder Bhaskar Choudhury.</p>
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<p>Showing model evaluation along with variable influence. (<b>A</b>) The average training ROC (Receiver Operating Characteristics) for the model. (<b>B</b>) Percentage contribution and permutation importance of covariates. (<b>C</b>) Jackknife test for all the selected variables, where the blue bar shows the importance of each variable in explaining the data variation when used separately. The green bar shows the loss in overall gain after the particular variable was dropped. Red bar = total model gain. (<b>D</b>) The response curves of the major contributing predictors governing the habitat suitability of <span class="html-italic">C. hispidus</span>.</p>
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<p>Map representing the present suitable habitat for hispid hare, <span class="html-italic">C. hispidus</span> in the distribution range and protected areas. The subfigures (<b>A</b>–<b>D</b>) illustrate the partial enlargements of above map, intended to demonstrate the habitat quality of the Protected Areas (PAs). 1. Shuklaphanta National Park, 2. Dibru-Saikhowa National Park, 3. Orang National Park, 4. Corbett National Park, 5. D’Ering Memorial Wildlife Sanctuary, 6. Dudhwa National Park, 7. Kaziranga National Park, 8. Chitawan National Park, 9. Burachapori Wildlife Sanctuary, 10. Sonanandi Wildlife Sanctuary, 11. Bardia National Park, 12. Nameri National Park, 13. Laokhowa Wildlife Sanctuary, 14. Pani-Dihing Wildlife Sanctuary, 15. Sonai-Rupai Wildlife Sanctuary, 16. Manas Tiger Reserve, 17. Valmiki National Park, 18. Katerniaghat Wildlife Sanctuary, 19. Kishanpur Wildlife Sanctuary, and 20. Borail Wildlife Sanctuary.</p>
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<p>The habitat suitability for <span class="html-italic">C. hispidus</span> in future climatic projection scenarios of ssp126, ssp245, and ssp585 for the years 2041–2060 and 2061–2080. The projection for the years: (<b>A</b>) 2041–2060-SSP-126, (<b>B</b>) 2061–2080-SSP-126, (<b>C</b>) 2041–2060-SSP-245, and (<b>D</b>) 2061–2080-SSP-245, (<b>E</b>) 2041–2060-SSP-585, (<b>F</b>) 2061–2080-SSP-585.</p>
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<p>Habitat quality assessment for <span class="html-italic">C. hispidus</span> in present and future scenarios: Area (km<sup>2</sup>) change trend from present to future scenarios.</p>
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17 pages, 12012 KiB  
Article
Regional Sustainability through Dispersal and Corridor Use of Asiatic Lion Panthera leo persica in the Eastern Greater Gir Landscape
by Abhinav Mehta, Shrey Rakholia, Reuven Yosef, Alap Bhatt and Shital Shukla
Sustainability 2024, 16(6), 2554; https://doi.org/10.3390/su16062554 - 20 Mar 2024
Viewed by 1638
Abstract
Despite previous concerns regarding the survival of Asiatic Lions confined to the Gir Protected Area, their dispersal into surrounding landscapes has become a subject of considerable research and discussion. This study employs species distribution modeling, corridor analysis, and additional landscape assessment using satellite-based [...] Read more.
Despite previous concerns regarding the survival of Asiatic Lions confined to the Gir Protected Area, their dispersal into surrounding landscapes has become a subject of considerable research and discussion. This study employs species distribution modeling, corridor analysis, and additional landscape assessment using satellite-based temperatures and Land Cover statistics to investigate this dispersal and identify potential corridors based on extensive field data. The results reveal the identification of a potential corridor from Gir Wildlife Sanctuary towards Velavadar Blackbuck National Park, indicating the expansion of the Asiatic Lion’s range in the Eastern Greater Gir Landscape. These findings highlight the significance of resilience in Lion dispersal and corridor expansion, with implications for conservation and potential regional benefits, including ecosystem services and eco-tourism for sustainable development of the region. Full article
(This article belongs to the Special Issue Biodiversity, Biologic Conservation and Ecological Sustainability)
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<p>Study area map of Eastern Greater Gir Landscape and the subdistricts (talukas) it encompasses.</p>
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<p>Receiver operating characteristic (ROC) curve showing average sensitivity vs. specificity for the Asiatic lion <span class="html-italic">P. leo persica</span>.</p>
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<p>Habitat Suitability Map of Asiatic Lion based on the species distribution modeling in MaxEnt.</p>
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<p>Jackknife plot for the test of variable importance. Darker blue shades show the gain in isolation from other variables.</p>
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<p>The Corridor Centrality Linkages map showing multiple possible linkages.</p>
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<p>The Potential Corridor least-cost paths map showing all potential paths.</p>
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<p>Main Potential Corridor Map showing major corridors.</p>
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<p>Marginal response curve of bio2 Mean Diurnal Temperature Range with a predicted probability of presence (cloglog output; in blue). Note: The Bio2 values (in °C) are scaled by a factor of 10.</p>
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<p>Ridge Plot of Mean LSTs (2018–2019) with respective Land Cover class in lion occurrence locations. Note: the Black jitter data points are shown as ‘|’, LST ranges (in °C) are shown in the density plots in each land cover class and Red line shows overall mean (42.4 ± 2.7 °C).</p>
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11 pages, 1785 KiB  
Article
Assessment of Nutrients in Natural Saltlicks, Artificial Saltlicks, and General Soils Used by Wild Asian Elephants (Elephas maximus) in the Western Forests of Thailand
by Rattanawat Chaiyarat, Salisa Kanthachompoo, Nikorn Thongtip and Monthira Yuttitham
Resources 2024, 13(1), 6; https://doi.org/10.3390/resources13010006 - 29 Dec 2023
Cited by 1 | Viewed by 2240
Abstract
Saltlicks are fundamental resources for wild Asian elephants (Elephas maximus). This study aimed to assess the nutrients found in natural saltlicks (NSs) and artificial saltlicks (ASs), as well as general soils (GS) in the natural forest of Salakphra Wildlife Sanctuary (SWS) [...] Read more.
Saltlicks are fundamental resources for wild Asian elephants (Elephas maximus). This study aimed to assess the nutrients found in natural saltlicks (NSs) and artificial saltlicks (ASs), as well as general soils (GS) in the natural forest of Salakphra Wildlife Sanctuary (SWS) and a restoration area of Kui Buri National Park (KNP), a which is a forest in Western Thailand. We monitored 33 NSs, 35 ASs, and 20 GSs used by wild Asian elephants. In both areas, the K, Mg, Fe, and Cu in NSs were significantly higher than in ASs. The Ca and Zn in NSs of KNP were lower than the ASs of SWS. The salinity of ASs was the highest, making it significantly higher than that of the NSs in both areas. The ASs can supplement Na, thereby increasing salinity in both areas. The Ca, K, Mg, Fe, and Cu in NSs were significantly higher than in ASs, making them a primary target for elephants. These findings have consequences for conserving elephants and other large herbivores by supplementing essential macro- and micro-nutrients in ASs. Full article
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<p>Salakphra Wildlife Sanctuary in Kanchanaburi province and Kui Buri National Park in Prachuap Khiri Khan Province.</p>
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<p>Soil pH (<b>a</b>) and salinity (<b>b</b>) (mean ± SD) of general soils (Soil); natural saltlicks (Natural) and artificial saltlicks (Artificial) from Salakphra Wildlife Sanctuary (purple); and Kui Buri National Park (green); open circle is outside the inner fence.</p>
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<p>Macronutrients: (<b>a</b>) Magnesium (Mg), (<b>b</b>) Calcium (Ca), (<b>c</b>) Potassium (K), (<b>d</b>) Sodium (Na), and (<b>e</b>) phosphorus (P) (mean ± SD) of general soils (Soil), natural saltlicks (Natural), and artificial saltlicks (Artificial) from Salakphra Wildlife Sanctuary (purple) and Kui Buri National Park (green); star is outside the outer fence, and open circle is outside the inner fence.</p>
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<p>Micronutrients: (<b>a</b>) Zinc (Zn); (<b>b</b>) Manganese (Mn); (<b>c</b>) Selenium (Se); (<b>d</b>) Ion (Fe); and (<b>e</b>) Copper (Cu) (mean ± SD) of general soils (Soil), natural saltlicks (Natural), and artificial saltlicks (Artificial) from Salakphra Wildlife Sanctuary (purple) and Kui Buri National Park (green); star is outside the outer fence, and open circle is outside the inner fence.</p>
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<p>Relationships of 10 minerals and environmental parameters (pH and salinity) (black triangle) among general soil, natural saltlicks, and artificial saltlicks in Salakphra Wildlife Sanctuary and Kui Buri National Park (GSSWS = general soil in Salakphra Wildlife Sanctuary; ASSWS = artificial saltlicks in Salakphra Wildlife Sanctuary; NSSWS = natural saltlicks in Salakphra Wildlife Sanctuary; GSKNP = general soil in Kui Buri National Park; ASKNP = artificial saltlicks in Kui Buri National Park; and NSKNP = natural saltlicks in Kui Buri National Park; black circle).</p>
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14 pages, 2738 KiB  
Article
Heavy Metals in Wetland Ecosystem: Investigating Metal Contamination in Waterbirds via Primary Feathers and Its Effect on Population and Diversity
by Jeganathan Pandiyan, Radjassegarin Arumugam, Khalid A. Al-Ghanim, Nadezhda Sachivkina, Marcello Nicoletti and Marimuthu Govindarajan
Soil Syst. 2023, 7(4), 104; https://doi.org/10.3390/soilsystems7040104 - 16 Nov 2023
Cited by 4 | Viewed by 2735
Abstract
Wetlands are dynamic ecosystems that provide feeding and nesting grounds for diverse species of waterbirds. The quality of wetland habitat may have an impact on the density, diversity, and species richness of waterbirds. Toxic metal contamination is one of the most significant threats [...] Read more.
Wetlands are dynamic ecosystems that provide feeding and nesting grounds for diverse species of waterbirds. The quality of wetland habitat may have an impact on the density, diversity, and species richness of waterbirds. Toxic metal contamination is one of the most significant threats to wetland habitats. Feathers are a key indicator of heavy metal contamination in avian communities as a non-invasive method. We examined the levels of Arsenic (As), Cadmium (Cd), Cobalt (Co), Chromium (Cr), Copper (Cu), Lead (Pb), Nickel (Ni), and Zinc (Zn) using ICP-AAS and standards of digestion procedure from the primary feathers of 10 distinct species of waterbirds. The study was conducted at four wetlands, viz., Point Calimere Wildlife Sanctuary (Ramsar site); Pallikaranai Marshland (Ramsar site); Perunthottam freshwater lake (unprotected wetland), Tamil Nadu and the Pulicat Lake, Andhra Pradesh, (Ramsar site), India. The Large crested tern had higher concentrations of As, Co, Cr, and Ni. Cu was greater in the Indian pond heron, and Zn was higher in the Grey heron. The accumulation of metals differed among the waterbirds (p < 0.05), and the inter-correlation of metals found positive influences between the tested metals, i.e., Co was positively associated with As, Cr had a positive correlation with As and Co, and Ni was positively correlated with As, Co, Cr, and Cu. In contrast, Pb had a positive association with Cu and Ni. The Zn was associated with Co, Cr and Cu. The level of metals in waterbirds was Zn > Cu > Cr > Ni > Pb > Co > Cd > As. The results showed that metal levels in the primary feathers of waterbirds were greater than the other species of waterbirds examined across the world. Thus, the study emphasizes that managing wetlands and controlling pollution is crucial to saving waterbirds; otherwise, the population and diversity of waterbirds will decline and become a significant threat to waterbird communities. Full article
(This article belongs to the Special Issue Research on Heavy Metals in Soils and Sediments)
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<p>Map showing the locations of the four different wetlands, Tamil Nadu, and Andhra Pradesh, India.</p>
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<p>Level of Arsenic (As) accumulated in the feathers of the different waterbirds studied in various wetlands in India. [PH = Purple heron; NS = Northern Shoveler; LE = Large egret; PoH = Pond Heron; GH = Grey heron; SH = Striated heron; SPB = Spot-billed pelican; HG = Heuglin’s gull; BHG = Brown-headed gull; LCT = Large-crested tern]. (Bar indicates the mean value and the line indicates the standard error values).</p>
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<p>Level of Cadmium (Cd) accumulated in the feathers of the different waterbirds studied in diverse wetlands in India. (Bar indicates the mean value and the line indicates the standard error values).</p>
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<p>Level of Cobalt (Co) accumulated in the feathers of the different waterbirds studied in different wetlands in India. (Bar indicates the mean value and the line indicates the standard error values).</p>
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<p>Level of Chromium (Cr) accumulated in the feathers of the different waterbirds studied in diverse wetlands in India. (Bar indicates the mean value and the line indicates the standard error values).</p>
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<p>Level of Copper (Cu) accumulated in the feathers of the different waterbirds studied in diverse wetlands in India. (Bar indicates the mean value and the line indicates the standard error values).</p>
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<p>Level of Lead (Pb) accumulated in the feathers of the different waterbirds studied in diverse wetlands in India. (Bar indicates the mean value and the line indicates the standard error values).</p>
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<p>Level of Nickel (Ni) accumulated in the feathers of the different waterbirds studied in diverse wetlands in India. (Bar indicates the mean value and the line indicates the standard error values).</p>
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<p>Level of Zinc (Zn) accumulated in the feathers of the different waterbirds studied in diverse wetlands in India. (Bar indicates the mean value and the line indicates the standard error values).</p>
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20 pages, 10640 KiB  
Article
Near Real-Time Mapping of Tropical Forest Disturbance Using SAR and Semantic Segmentation in Google Earth Engine
by John Burns Kilbride, Ate Poortinga, Biplov Bhandari, Nyein Soe Thwal, Nguyen Hanh Quyen, Jeff Silverman, Karis Tenneson, David Bell, Matthew Gregory, Robert Kennedy and David Saah
Remote Sens. 2023, 15(21), 5223; https://doi.org/10.3390/rs15215223 - 3 Nov 2023
Cited by 7 | Viewed by 2908
Abstract
Satellite-based forest alert systems are an important tool for ecosystem monitoring, planning conservation, and increasing public awareness of forest cover change. Continuous monitoring in tropical regions, such as those experiencing pronounced monsoon seasons, can be complicated by spatially extensive and persistent cloud cover. [...] Read more.
Satellite-based forest alert systems are an important tool for ecosystem monitoring, planning conservation, and increasing public awareness of forest cover change. Continuous monitoring in tropical regions, such as those experiencing pronounced monsoon seasons, can be complicated by spatially extensive and persistent cloud cover. One solution is to use Synthetic Aperture Radar (SAR) imagery acquired by the European Space Agency’s Sentinel-1A and B satellites. The Sentinel 1A and B satellites acquire C-band radar data that penetrates cloud cover and can be acquired during the day or night. One challenge associated with operational use of radar imagery is that the speckle associated with the backscatter values can complicate traditional pixel-based analysis approaches. A potential solution is to use deep learning semantic segmentation models that can capture predictive features that are more robust to pixel-level noise. In this analysis, we present a prototype SAR-based forest alert system that utilizes deep learning classifiers, deployed using the Google Earth Engine cloud computing platform, to identify forest cover change with near real-time classification over two Cambodian wildlife sanctuaries. By leveraging a pre-existing forest cover change dataset derived from multispectral Landsat imagery, we present a method for efficiently developing a SAR-based semantic segmentation dataset. In practice, the proposed framework achieved good performance comparable to an existing forest alert system while offering more flexibility and ease of development from an operational standpoint. Full article
(This article belongs to the Special Issue Deep Learning Techniques Applied in Remote Sensing)
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<p>The proposed framework uses Google Earth Engine (GEE) to pre-process the SAR imagery and build the deep learning training datasets. The training datasets are exported from GEE and are used to train models on the Google AI Platform. The final models were then hosted on the AI Platform and accessed in GEE to classify forest cover change in new SAR acquisitions as they appear in the GEE Sentinel-1 cloud storage buckets. A validation dataset was developed using the Collect Earth Online photo interpretation tool.</p>
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<p>The study area of this analysis two wildlife sanctuaries. Prey Lang and Beng Per (<b>A</b>). However, to aggregate sufficient training data, all of Cambodia was considered (black outline). The region experiences significant cloud cover during the monsoon season. The % of Landsat 7 ETM+ and Landsat 8 OLI observations that were flagged as occluded in 2020 (<b>B</b>).</p>
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<p>The MobileNetV3-UNet model used to map forest cover change events in this analysis.</p>
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<p>The point locations indicate plots where photointerpreters visually assessed the timing of SAR-based forest alert system. Locations were selected using a stratified random sampling approach in 2018, 2019, and 2020. The forest/non-forest layer base map was derived from the Global Forest Watch dataset [<a href="#B51-remotesensing-15-05223" class="html-bibr">51</a>].</p>
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<p>The cumulative distribution of forest alerts issued by the ascending, descending, and combined alert systems over the course of 2018, 2019, and 2020. Darker colors indicate a lower inclusion threshold that results in a greater rate of mapped disturbance. The red horizontal line indicates the count of pixels in the reference FCC product (at a 10 m scale to match the SAR systems) that was used to create the neural network training dataset.</p>
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<p>The accuracy of the SAR classifiers plotted against the classifier’s precision and recall scores. The red vertical line indicates the threshold selected for each classifier.</p>
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<p>Three 2.5 km-by-2.5 km subsets within the Prey Lang wildlife sanctuary (bold black outline) displaying the forest alerts issued by the descending SAR deep learning classifier for 2018, 2019, and 2020. The forest alerts are rendered over a false-color Sentinel-2 image.</p>
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<p>The total number of alerts issued by the descending SAR classifier using an inclusion threshold of 0.25 and the GLAD alert system. Each GLAD alert was counted 9 times as the GLAD alert system maps FCC at a 30 m spatial resolution while the SAR-based system maps FCC at a 10 m resolution.</p>
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26 pages, 10263 KiB  
Article
Tham Chiang Dao: A Hotspot of Subterranean Biodiversity in Northern Thailand
by Louis Deharveng, Martin Ellis, Anne Bedos and Sopark Jantarit
Diversity 2023, 15(10), 1076; https://doi.org/10.3390/d15101076 - 11 Oct 2023
Cited by 5 | Viewed by 2566
Abstract
The Doi Chiang Dao massif, which became a UNESCO Biosphere Reserve in 2021, is the highest karst mountain in Thailand. Tham Chiang Dao cave is located at the foot of this massif and is among the best-known caves in Thailand, having been visited [...] Read more.
The Doi Chiang Dao massif, which became a UNESCO Biosphere Reserve in 2021, is the highest karst mountain in Thailand. Tham Chiang Dao cave is located at the foot of this massif and is among the best-known caves in Thailand, having been visited since prehistoric times, and being a sacred place for the local Shan and Thai people. The cave consists of five main interconnected passages with a total length of 5342 m which ranks it as the 11th longest cave in Thailand. Tham Chiang Dao is the best studied cave in Thailand with a long series of explorations, investigations and zoological collecting. Here, we summarize the 110 years of biological exploration and investigation devoted to this cave. A total of 149 taxa have been recognized in Tham Chiang Dao, of which 61 have been identified to species level. The cave is the type locality for 14 species. The obligate subterranean fauna includes 37 species, of which 33 are troglobionts and 4 are stygobionts. Conservation issues are addressed in the discussion. This work is intended to provide a reference for the knowledge of cave fauna of the Chiang Dao Wildlife Sanctuary and a tool for its management by the local cave management committee, the National Cave Management Policy Committee, and the Department of Mineral Resources. It also documents the biological importance of Tham Chiang Dao in the Doi Chiang Dao UNESCO Biosphere Reserve. Full article
(This article belongs to the Special Issue Hotspots of Subterranean Biodiversity—2nd Volume)
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<p>(<b>A</b>) Aerial view of Doi Chiang Dao. Red dot indicates cave entrance at the base of the mountain (from Google Earth Pro); (<b>B</b>) Geological map of Doi Chiang Dao and Tham Chiang Dao.</p>
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<p>(<b>A</b>) Map of Tham Chiang Dao system, modified from Deharveng and Brouquisse (1986); (<b>B</b>) Tham Chiang Dao system with nearby cave entrances overlaid on Doi Chiang Dao (from Google Earth Pro).</p>
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<p>Gastropoda. A troglobiotic microsnail <span class="html-italic">Acmella</span> sp., photo by R. Promdam with permission.</p>
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<p>Acari. A troglobiotic Leeuwenhoekiidae, photos by S. Jantarit.</p>
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<p>Pseudoscorpiones. A troglobiotic <span class="html-italic">Tyrannochthonius</span> sp., photos by S. Jantarit.</p>
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<p>Diplopoda. (<b>Left</b>) <span class="html-italic">Eutrichodesmus gremialis</span> (Hoffman, 1982); undescribed species of Opisotretidae sp. (<b>Right</b>), photos by S. Jantarit.</p>
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<p>Isopoda: Oniscidea. (<b>Left</b>) Philosciidae sp.; <span class="html-italic">Cubaris</span> sp. (<b>Right</b>), photos by S. Jantarit.</p>
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<p>Blattodea. Troglomorphic cave cockroaches: (<b>above left</b>) <span class="html-italic">Spelaeoblatta</span> sp. and <span class="html-italic">Helmablatta</span> sp. (<b>above right</b>); photos by S. Jantarit and guanobiotic cockroach <span class="html-italic">Blattella</span> cf. <span class="html-italic">cavernicola</span> (<b>below</b>), photos by T. Jeenthong with permission.</p>
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<p>Orthoptera. (<b>A</b>) A troglomorphic ant-cricket <span class="html-italic">Myrmecophilus</span> sp. (photo by T. Jeenthong with permission); two troglophilic crickets (<b>B</b>) <span class="html-italic">Rhaphidophora</span> sp. and <span class="html-italic">Paradiestrammena</span> sp. (<b>C</b>), photos by S. Jantarit.</p>
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<p>(<b>Left</b>) undescribed species of staphylinid beetle (Oxytelinae); a possible troglobiotic ant species <span class="html-italic">Brachyponera</span> sp. (<b>right</b>), photos by S. Jantarit.</p>
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<p>Non-glowing sticky worm, <span class="html-italic">Chetoneura</span> sp. (<b>A</b>) its sticky threads and (<b>B</b>) its larva, photos by R. Promdam with permission.</p>
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<p>The proliferation of lampenflora (<b>A</b>–<b>C</b>) and little towers by piling up stones in Tham Chiang Dao (<b>D</b>), photos by P. Chananin with permission.</p>
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<p>Temperature and carbon dioxide in Tham Chiang Dao. Black indicates measurements done in July 1985 [<a href="#B84-diversity-15-01076" class="html-bibr">84</a>], blue, those done in January 2023 [<a href="#B14-diversity-15-01076" class="html-bibr">14</a>], and red, those done in June 2023 [<a href="#B14-diversity-15-01076" class="html-bibr">14</a>].</p>
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22 pages, 3416 KiB  
Article
Phenotypic, Geological, and Climatic Spatio-Temporal Analyses of an Exotic Grevillea robusta in the Northwestern Himalayas
by Aman Dabral, Rajeev Shankhwar, Marco Antonio Caçador Martins-Ferreira, Shailesh Pandey, Rama Kant, Rajendra K. Meena, Girish Chandra, Harish S. Ginwal, Pawan Kumar Thakur, Maneesh S. Bhandari, Netrananda Sahu and Sridhara Nayak
Sustainability 2023, 15(16), 12292; https://doi.org/10.3390/su151612292 - 11 Aug 2023
Viewed by 1416
Abstract
The last five decades (since 1980) have witnessed the introduction of exotic trees as a popular practice in India to fulfill the demand of forest-based products for utilization in afforestation programmes. This study examines the distribution and habitat suitability of exotic Grevillea robusta [...] Read more.
The last five decades (since 1980) have witnessed the introduction of exotic trees as a popular practice in India to fulfill the demand of forest-based products for utilization in afforestation programmes. This study examines the distribution and habitat suitability of exotic Grevillea robusta trees in the northwestern Himalayas (state: Uttarakhand), focusing on the interaction between G. robusta and abiotic factors, such as climate, soil, and habitat suitability. This multipurpose agroforestry species is mainly grown by farmers as a boundary tree, windbreak, or shelterbelt and among intercrops on small farms in agroforestry systems worldwide. The results indicate that phenotypic plasticity is determined by tree height and diameter, indicating a higher frequency of young and adult trees. The study also highlights spatio-temporal modeling coupled with geological analysis to address the current distribution pattern and future habitat suitability range through MaxEnt modeling. The AUC ranged from 0.793 ± 3.6 (RCP 6.0_70) to 0.836 ± 0.008 (current) with statistical measures, such as K (0.216), NMI (0.240), and TSS (0.686), revealing the high accuracy of the model output. The variables, which include the minimum temperature of the coldest month (Bio 6), the slope (Slo), the mean temperature of the driest quarter (Bio 9), and the precipitation of the driest quarter (Bio 17), contribute significantly to the prediction of the distribution of the species in the Himalayan state. The model predicts a significant habitat suitability range for G. robusta based on bio-climatic variables, covering an area of approximately ~1641 km2 with maximal occurrence in Pauri (~321 km2) and Almora (~317 km2). Notably, the future prediction scenario corroborates with the regions of Tons (Upper Yamuna, Uttarkashi), Kalsi (Mussoorie, Dehradun), the Kedarnath Wildlife Sanctuary, and the Badrinath Forest Division for the potentially suitable areas. The climate was found to have a strong influence on the species’ distribution, as evidenced by its correlation with the Köppen–Geiger climate classification (KGCC) map. While the species demonstrated adaptability, its occurrence showed a high correlation with bedrocks containing an elevated iron content. Furthermore, the study also provides the first trees outside forests (TOF) map of G. robusta in the region, as well as insight into its future habitat suitability. Full article
(This article belongs to the Special Issue Urban Sprawl and Sustainable Land Use Planning)
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<p>Tree form of <span class="html-italic">Grevillea robusta</span>. (<b>a</b>) roadside plantation, (<b>b</b>) trunk representing bark, (<b>c</b>) leaves, (<b>d</b>) flowering trees, (<b>e</b>) inflorescences showing florets, (<b>f</b>) immature pods, and (<b>g</b>) mature seeds.</p>
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<p>The SENTINEL showing the current distribution of <span class="html-italic">G. robusta</span> in northwestern Himalayas.</p>
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<p>Stand structure and morphological attributes—frequency distribution comparative analysis along the altitudinal gradient. (<b>a</b>) Tree height (TH), (<b>b</b>) girth at breast height (GBH), (<b>c</b>) diameter at breast height (DBH), (<b>d</b>) clear bole height (CBH), (<b>e</b>) branch angle (BA), (<b>f</b>) crown width (CW), (<b>g</b>) disease incidences (DI), and (<b>h</b>) insect/pest incidences (IPI). Red = juvenile seedlings/trees, blue = young trees, green = adult trees, and yellow = mature trees.</p>
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<p>Plot of current MaxEnt results over Köppen–Geiger climate classification (KGCC) map. Cwa—warm, temperate winter and dry, hot summer; Cwb—warm, temperate winter and dry, warm summer; Dfc—snowy winter and humid, cool summer; Dwb—snowy winter and dry, warm summer; Dwc—snowy winter and dry, cool summer; and ET—polar tundra.</p>
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<p>Representation of future habitat suitability range of <span class="html-italic">G. robusta</span> in northwestern Himalayas. Note: (<b>a</b>) RCP 8.5–2050 and (<b>b</b>) RCP 8.5–2070, for 500 m buffer; (<b>c</b>) RCP 6.0–2050 and (<b>d</b>) RCP 8.5–2070, for 1000 m buffer. (1) Netwar near Tons River, (2) Kalsi &amp; Mussoorie, (3) Kuthnaur near Barkot, (4) Karn prayag, Kedarnath Wildlife Sanctuary &amp; Badrinath Forest Division, and (5) Dhanya in Almora Forest Division.</p>
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<p>Plot of MaxEnt results for current distribution and future habitat suitability prediction (RCP 8.5 in 2050, 1000 m buffer) of <span class="html-italic">G. robusta</span> over the geological map of northwestern Himalayas (1:2M scale, geological survey of India). See text in <a href="#sec3dot4-sustainability-15-12292" class="html-sec">Section 3.4</a> for description of habitat suitability prediction sites. (1) Netwar near Tons River, (2) Kalsi &amp; Mussoorie, (3) Kuthnaur near Barkot, (4) Karn prayag, Kedarnath Wildlife Sanctuary &amp; Badrinath Forest Division, and (5) Dhanya in Almora Forest Division.</p>
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29 pages, 7446 KiB  
Article
Potentiality of Actinomycetia Prevalent in Selected Forest Ecosystems in Assam, India to Combat Multi-Drug-Resistant Microbial Pathogens
by Rajkumari Mazumdar, Kangkon Saikia and Debajit Thakur
Metabolites 2023, 13(8), 911; https://doi.org/10.3390/metabo13080911 - 3 Aug 2023
Cited by 1 | Viewed by 2182
Abstract
Actinomycetia are known for their ability to produce a wide range of bioactive secondary metabolites having significant therapeutic importance. This study aimed to explore the potential of actinomycetia as a source of bioactive compounds with antimicrobial properties against multi-drug-resistant (MDR) clinical pathogens. A [...] Read more.
Actinomycetia are known for their ability to produce a wide range of bioactive secondary metabolites having significant therapeutic importance. This study aimed to explore the potential of actinomycetia as a source of bioactive compounds with antimicrobial properties against multi-drug-resistant (MDR) clinical pathogens. A total of 65 actinomycetia were isolated from two unexplored forest ecosystems, namely the Pobitora Wildlife Sanctuary (PWS) and the Deepor Beel Wildlife Sanctuary (DBWS), located in the Indo-Burma mega-biodiversity hotspots of northeast India, out of which 19 isolates exhibited significant antimicrobial activity. 16S rRNA gene sequencing was used for the identification and phylogenetic analysis of the 19 potent actinomycetia isolates. The results reveal that the most dominant genus among the isolates was Streptomyces (84.21%), followed by rare actinomycetia genera such as Nocardia, Actinomadura, and Nonomuraea. Furthermore, seventeen of the isolates tested positive for at least one antibiotic biosynthetic gene, specifically type II polyketide synthase (PKS-II) and nonribosomal peptide synthetases (NRPSs). These genes are associated with the production of bioactive compounds with antimicrobial properties. Among the isolated strains, three actinomycetia strains, namely Streptomyces sp. PBR1, Streptomyces sp. PBR36, and Streptomyces sp. DBR11, demonstrated the most potent antimicrobial activity against seven test pathogens. This was determined through in vitro antimicrobial bioassays and the minimum inhibitory concentration (MIC) values of ethyl acetate extracts. Gas chromatography–mass spectrometry (GS-MS) and whole-genome sequencing (WGS) of the three strains revealed a diverse group of bioactive compounds and secondary metabolite biosynthetic gene clusters (smBGCs), respectively, indicating their high therapeutic potential. These findings highlight the potential of these microorganisms to serve as a valuable resource for the discovery and development of novel antibiotics and other therapeutics with high therapeutic potential. Full article
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<p>Soil sample collection site: soil samples were collected from two poorly explored forest ecosystems of Assam, India; (1) PWS. (2) DBWS.</p>
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<p>Isolation of pathogens from urine: (<b>A</b>) Most abundant pathogens isolated from culture plate, such as <span class="html-italic">Escherichia coli</span> (GNR19) on UTI Hi-chrome agar media. (<b>B</b>) In vitro screening of pathogens against antibiotics based on sensitivity disk (HiMedia), such as conducted for (I) <span class="html-italic">Escherichia coli</span> (GNR19); (II) <span class="html-italic">Enterococcus faecalis</span> (GNR7); and (III) <span class="html-italic">Pseudomonas aeruginosa</span> (GNR18).</p>
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<p>Isolation of actinomycetia from soil samples: (<b>A</b>) Pure culture plates of actinomycetia strains in GLM media. (I) <span class="html-italic">Streptomyces</span> sp. DBR11. (II) <span class="html-italic">Streptomyces</span> sp. PBR21. (III) <span class="html-italic">Actinomadura</span> sp. DBR17. (IV) <span class="html-italic">Streptomyces</span> sp. PBR30. (V) <span class="html-italic">Streptomyces</span> sp. DBR3. (VI) <span class="html-italic">Streptomyces</span> sp. PBR1. (VII); <span class="html-italic">Streptomyces</span> sp. DBR5. (VIII); <span class="html-italic">Streptomyces</span> sp. PBR11. (IX) <span class="html-italic">Streptomyces</span> sp. PBR19. (X) <span class="html-italic">Streptomyces</span> sp. PBR4. (XI) <span class="html-italic">Streptomyces</span> sp. DBR16. (XII) <span class="html-italic">Streptomyces</span> sp. PBR36. (XIII); <span class="html-italic">Streptomyces</span> sp. DBR1, (XIV); <span class="html-italic">Streptomyces</span> sp. PBR19, (XV) <span class="html-italic">Streptomyces</span> sp. DBR10. (XVI) <span class="html-italic">Nonomuraea</span> sp. DBR25. (XVII) <span class="html-italic">Streptomyces</span> sp. DBR4. (XVIII) <span class="html-italic">Streptomyces</span> sp. PBR35. (<b>B</b>) SEM image showing rectiflexibiles-type spore chains. (I) <span class="html-italic">Streptomyces</span> sp. DBR11. (II) <span class="html-italic">Streptomyces</span> sp. PBR1. (III) <span class="html-italic">Streptomyces</span> sp. PBR36.</p>
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<p>Isolation of actinomycetia from soil samples: (<b>A</b>) Pure culture plates of actinomycetia strains in GLM media. (I) <span class="html-italic">Streptomyces</span> sp. DBR11. (II) <span class="html-italic">Streptomyces</span> sp. PBR21. (III) <span class="html-italic">Actinomadura</span> sp. DBR17. (IV) <span class="html-italic">Streptomyces</span> sp. PBR30. (V) <span class="html-italic">Streptomyces</span> sp. DBR3. (VI) <span class="html-italic">Streptomyces</span> sp. PBR1. (VII); <span class="html-italic">Streptomyces</span> sp. DBR5. (VIII); <span class="html-italic">Streptomyces</span> sp. PBR11. (IX) <span class="html-italic">Streptomyces</span> sp. PBR19. (X) <span class="html-italic">Streptomyces</span> sp. PBR4. (XI) <span class="html-italic">Streptomyces</span> sp. DBR16. (XII) <span class="html-italic">Streptomyces</span> sp. PBR36. (XIII); <span class="html-italic">Streptomyces</span> sp. DBR1, (XIV); <span class="html-italic">Streptomyces</span> sp. PBR19, (XV) <span class="html-italic">Streptomyces</span> sp. DBR10. (XVI) <span class="html-italic">Nonomuraea</span> sp. DBR25. (XVII) <span class="html-italic">Streptomyces</span> sp. DBR4. (XVIII) <span class="html-italic">Streptomyces</span> sp. PBR35. (<b>B</b>) SEM image showing rectiflexibiles-type spore chains. (I) <span class="html-italic">Streptomyces</span> sp. DBR11. (II) <span class="html-italic">Streptomyces</span> sp. PBR1. (III) <span class="html-italic">Streptomyces</span> sp. PBR36.</p>
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<p>In vitro antimicrobial bioassay of actinomycetia extracts against model pathogens and MDR pathogens: (<b>A</b>) Antimicrobial activity by spot inoculation (I,II) and well diffusion method (III–V) against model pathogens: (I) <span class="html-italic">Candida albicans</span> (MTCC 227). (II,III) MRSA (ATCC 43300). (IV) <span class="html-italic">Klebsiella pneumonia</span> (MTCC 3384); (V) <span class="html-italic">Pseudomonas aeruginosa</span> (MTCC 741). (<b>B</b>) Bioactivity of fermentation broth of actinomycetia isolates against model pathogens, based on triplicate experiments. (<b>C</b>) Antimicrobial activity by well diffusion method against MDR pathogens: (I,II) <span class="html-italic">Pseudomonas aeruginosa</span>. (III–V) <span class="html-italic">Enterococcus faecalis</span>. (VI–VIII) <span class="html-italic">Escherichia coli</span>. (<b>D</b>) Bioactivity of fermentation broth of actinomycetia isolates against 3 MDR pathogens, based on triplicate experiments, against: GNR7; <span class="html-italic">Enterococcus faecalis</span>; GNR18; <span class="html-italic">Pseudomonas aeruginosa</span>; and GNR19; <span class="html-italic">Escherichia coli</span>.</p>
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<p>SEM image showing the effects of ethyl acetate extract of PBR36: (<b>I</b>,<b>III</b>) without treatment; (<b>II</b>,<b>IV</b>) treatment against <span class="html-italic">Candida albicans</span> MTCC 227 and <span class="html-italic">Escherichia coli</span> (GNR19), respectively, with 1 × MIC EtAc-PBR36. Arrow line denotes ruptured cells after treatment.</p>
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<p>Phylogenetic tree of actinomycetia isolated from forest ecosystems and the closest type strains based on the 16S rRNA sequences: Sequences were aligned using MUSCLE and subjected to phylogenetic analysis by maximum likelihood method using MEGA X with 1000 bootstrap steps and Tamura–Nei distance model.</p>
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<p>Agarose gel electrophoresis of PCR amplified products of actinobacteria: (<b>A</b>) Amplification of NRPS gene using A3F/A7R specific primers. (<b>B</b>) Amplification of PKS-II gene using degenerate primers KSαF/KSβR.</p>
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<p>Genome mining for identifying smBGCs: (<b>A</b>) Distribution of antiSMASH hits of major classes of smBGCs identified in the genome of three actinomycetia strains; <span class="html-italic">Streptomyces</span> sp. DBR11, <span class="html-italic">Streptomyces</span> sp. PBR1, and <span class="html-italic">Streptomyces</span> sp. PBR36. The clusters of the “Hybrid” type that were predicted to belong to more than one type of BGCs were combined. BGCs predicted to belong to the hopene, ectoine, germicidin, sapB, melanin, geosmin, spore pigment keywimysin, carotenoid, 2-methylisoborneol, or flaviolin types were grouped under “Other”. (<b>B</b>) Comparison of the number and types of secondary metabolites found in the genomes of the three actinomycetia strains. (<b>C</b>) Similarity percentage of BGCs identified in the genome of three actinomycetia strains to known homologous gene clusters. No homology shown by the gene clusters revealed no similarity shared by the BGCs to known gene clusters.</p>
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<p>Genome mining for identifying smBGCs: (<b>A</b>) Distribution of antiSMASH hits of major classes of smBGCs identified in the genome of three actinomycetia strains; <span class="html-italic">Streptomyces</span> sp. DBR11, <span class="html-italic">Streptomyces</span> sp. PBR1, and <span class="html-italic">Streptomyces</span> sp. PBR36. The clusters of the “Hybrid” type that were predicted to belong to more than one type of BGCs were combined. BGCs predicted to belong to the hopene, ectoine, germicidin, sapB, melanin, geosmin, spore pigment keywimysin, carotenoid, 2-methylisoborneol, or flaviolin types were grouped under “Other”. (<b>B</b>) Comparison of the number and types of secondary metabolites found in the genomes of the three actinomycetia strains. (<b>C</b>) Similarity percentage of BGCs identified in the genome of three actinomycetia strains to known homologous gene clusters. No homology shown by the gene clusters revealed no similarity shared by the BGCs to known gene clusters.</p>
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<p>An overview of the subsystem category of the annotated whole genomes of three actinomycetia strains showing virulence or antibiotic resistance encoding genes using RAST pipeline: (<b>A</b>) <span class="html-italic">Streptomyces</span> sp. PBR36. (<b>B</b>) <span class="html-italic">Streptomyces</span> sp. PBR36. (<b>C</b>) <span class="html-italic">Streptomyces</span> sp. DBR11. Green color in the color bar represents features that are found in the RAST subsystem. The blue color represents features not assigned to a subsystem.</p>
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<p>An overview of the subsystem category of the annotated whole genomes of three actinomycetia strains showing virulence or antibiotic resistance encoding genes using RAST pipeline: (<b>A</b>) <span class="html-italic">Streptomyces</span> sp. PBR36. (<b>B</b>) <span class="html-italic">Streptomyces</span> sp. PBR36. (<b>C</b>) <span class="html-italic">Streptomyces</span> sp. DBR11. Green color in the color bar represents features that are found in the RAST subsystem. The blue color represents features not assigned to a subsystem.</p>
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16 pages, 5956 KiB  
Article
The Preferred Habitat of Reintroduced Banteng (Bos javanicus) at the Core and the Edge of Salakphra Wildlife Sanctuary, Thailand
by Rattanawat Chaiyarat, Passorn Ingudomnukul, Nattanicha Yimphrai, Seree Nakbun and Namphung Youngpoy
Animals 2023, 13(14), 2293; https://doi.org/10.3390/ani13142293 - 13 Jul 2023
Viewed by 1991
Abstract
Monitoring of banteng (Bos javanicus) after reintroduction is important for their management. This study aimed to monitor the preferred habitat and area of use of reintroduced banteng at the core (13 banteng) and the edge (three banteng) of Salakphra Wildlife Sanctuary [...] Read more.
Monitoring of banteng (Bos javanicus) after reintroduction is important for their management. This study aimed to monitor the preferred habitat and area of use of reintroduced banteng at the core (13 banteng) and the edge (three banteng) of Salakphra Wildlife Sanctuary between 2019 and 2021 and compared the finding with previous studies conducted from 2014 to 2019. The Binary Logistic Regression (BLR) showed the most preferred, moderately preferred, and least preferred areas were 44.7 km2, 1.2 km2, and 54.1 km2 in the dry season, and 25.9 km2, 1.0 km2, and 9.3 km2 in the wet season, respectively. Maximum Entropy (MaxEnt) showed the most preferred, moderately preferred, and least preferred areas as 12.1 km2, 17.3 km2, and 65.9 km2, respectively. Banteng have previously been found close to ponds and salt licks. The area of use size, as determined by Minimum Convex Polygon (MCP) and Kernel Density Estimation (KDE), was 20.3 km2 and 6.5 km2, respectively. Three banteng were reintroduced to the edge area in 2020. The edge area was temporarily utilized by these individuals. In the core area, the area of use in this study decreased compared to the previous studies from 2014 to 2019, indicating they were able to find their preferred habitat. This study suggested that, if the area is managed appropriately, banteng will be able to live in a smaller habitat, and we will be able to restore the banteng population in the future. Full article
(This article belongs to the Section Wildlife)
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<p>Camera trap locations in Salakphra Wildlife Sanctuary.</p>
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<p>Location of camera traps that captured the reintroduced banteng: (<b>a</b>) the dry season and the whole year and (<b>b</b>) the wet season at the core area of Salakphra Wildlife Sanctuary, Thailand.</p>
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<p>Location of camera traps that captured the reintroduced banteng at an edge area of Salakphra Wildlife Sanctuary in the dry season and the whole year.</p>
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<p>The receiver-operating characteristic (ROC) curve plot with evenly spaced thresholds marked along the ROC curves and area under the ROC (AUC = 0.904 in both the core and edge areas), calculated by MaxEnt, and the jackknife test for contributions of the variables of the reintroduced banteng habitat models (<b>a</b>) in the core area and (<b>b</b>) in the edge area of Salakphra Wildlife Sanctuary, Thailand. Jackknife test of regularized training gain. Dark blue columns show how the model gain would be using each variable in isolation. Light blue columns show the change in the model gain if the variable was excluded. The longest dark blue column indicates the variable with the most useful information by itself. The shortest light blue column indicates the variable which has the most information that was not present in other variables. Aspect; elevation was given in m; distance from salt lick (m); lu2562 = land use types in 2019; distance from artificial pond (m); distance from forest road (m); slope (%); distance from Salakphra Wildlife Sanctuary Guard Station (m); distance from stream (m) and distance from village (m).</p>
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<p>The receiver-operating characteristic (ROC) curve plot with evenly spaced thresholds marked along the ROC curves and area under the ROC (AUC = 0.904 in both the core and edge areas), calculated by MaxEnt, and the jackknife test for contributions of the variables of the reintroduced banteng habitat models (<b>a</b>) in the core area and (<b>b</b>) in the edge area of Salakphra Wildlife Sanctuary, Thailand. Jackknife test of regularized training gain. Dark blue columns show how the model gain would be using each variable in isolation. Light blue columns show the change in the model gain if the variable was excluded. The longest dark blue column indicates the variable with the most useful information by itself. The shortest light blue column indicates the variable which has the most information that was not present in other variables. Aspect; elevation was given in m; distance from salt lick (m); lu2562 = land use types in 2019; distance from artificial pond (m); distance from forest road (m); slope (%); distance from Salakphra Wildlife Sanctuary Guard Station (m); distance from stream (m) and distance from village (m).</p>
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<p>Location of camera traps that captured the photographs of reintroduced banteng by used MaxEnt: (<b>a</b>) at the core area and (<b>b</b>) at the edge of Salakphra Wildlife Sanctuary, Thailand.</p>
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<p>Location of camera traps that captured photographs of reintroduced banteng: (<b>a</b>) in the dry season and (<b>b</b>) in the wet season in the core area of Salakphra Wildlife Sanctuary, Thailand. The 1.9 km<sup>2</sup> area resulted from the small sample size and the poor configuration of the three points.</p>
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10 pages, 965 KiB  
Article
Occurrence of Leishmaniasis in Iberian Wolves in Northwestern Spain
by Javier Merino Goyenechea, Verónica Castilla Gómez de Agüero, Jesús Palacios Alberti, Rafael Balaña Fouce and María Martínez Valladares
Microorganisms 2023, 11(5), 1179; https://doi.org/10.3390/microorganisms11051179 - 30 Apr 2023
Cited by 1 | Viewed by 2594
Abstract
Canine leishmaniasis is an important vector-borne protozoan disease in dogs that is responsible for serious deterioration in their health. In the Iberian Peninsula, as in most countries surrounding the Mediterranean Sea, canine leishmaniasis is caused by Leishmania infantum (zymodeme MON-1), a digenetic trypanosomatid [...] Read more.
Canine leishmaniasis is an important vector-borne protozoan disease in dogs that is responsible for serious deterioration in their health. In the Iberian Peninsula, as in most countries surrounding the Mediterranean Sea, canine leishmaniasis is caused by Leishmania infantum (zymodeme MON-1), a digenetic trypanosomatid that harbors in the parasitophorous vacuoles of host macrophages, causing severe lesions that can lead to death if the animals do not receive adequate treatment. Canine leishmaniasis is highly prevalent in Spain, especially in the Mediterranean coastal regions (Levante, Andalusia and the Balearic Islands), where the population of domestic dogs is very high. However, the presence of this disease has been spreading to other rural and sparsely populated latitudes, and cases of leishmaniasis have been reported for years in wildlife in northwestern Spain. This work describes for the first time the presence of wolves that tested positive for leishmaniasis in the vicinity of the Sierra de la Culebra (Zamora province, northwestern Spain), a protected sanctuary of this canid species, using PCR amplification of L. infantum DNA from different non-invasive samples such as buccal mucosa and those from both ears and hair. In addition to live animals (21), samples from carcasses of mainly roadkill animals (18) were also included and analyzed using the same technique, obtaining a positivity rate of 18 of the 39 wolves sampled (46.1%) regardless of their origin. Full article
(This article belongs to the Special Issue Vector-Borne Infections in Wildlife)
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<p>Distribution map of sampled and PCR-positive wolves (green areas) studied in Zamora (northwestern Spain) for the detection of <span class="html-italic">Leishmania</span> DNA from 2017 to 2022.</p>
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<p>Detection of kinetoplast Leishmania DNA in hair of wolves infected with <span class="html-italic">L. infantum</span>. M: molecular weight ladder. PCR for the NCX1 or <span class="html-italic">L. infantum</span> (Li) amplification with 5, 10 or 20 hairs. In addition, positive (NCX_+ and Li_+) and negative (NCX_− and Li_−) controls were included in the reaction.</p>
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10 pages, 617 KiB  
Article
COVID-19’s Impact on the Pan African Sanctuary Alliance: Challenging Times and Resilience from Its Members
by Nora Bennamoun, Marco Campera, Gregg Tully and K.A.I. Nekaris
Animals 2023, 13(9), 1486; https://doi.org/10.3390/ani13091486 - 27 Apr 2023
Viewed by 1987
Abstract
The worldwide pandemic caused by SARS-CoV-2 challenged conservation organizations. The lack of tourism has benefited or negatively affected wildlife organizations in various ways, with several primate sanctuaries struggling to cope with the COVID-19 crisis and to keep providing for their inhabitants. In addition, [...] Read more.
The worldwide pandemic caused by SARS-CoV-2 challenged conservation organizations. The lack of tourism has benefited or negatively affected wildlife organizations in various ways, with several primate sanctuaries struggling to cope with the COVID-19 crisis and to keep providing for their inhabitants. In addition, the genetic similarity between great apes and humans puts them at higher risk than any other species for the transmission of COVID-19. PASA is a non-profit organization comprising 23 sanctuaries, and cares for many species of primate, including African great apes. In light of the pandemic, we aimed to understand the direct effects of COVID-19 on PASA management throughout three time periods: before (2018–2019), at the start of (2019–2020), and during (2020–2021) the pandemic. We collected data via annual surveys for PASA members and ran Generalized Linear Mixed Models to highlight any significant differences in their management that could be linked to COVID-19. Our findings demonstrated no particular impact on the number of primates rescued, employees, or expenses. However, revenues have been decreasing post-COVID-19 due to the lack of income from tourism and volunteer programs. Nonetheless, our results reveal a form of resilience regarding the sanctuaries and the strategy applied to maintain their management. Consequently, we emphasize the specific impacts of the COVID-19 outbreak and its repercussions for conservation work. We discuss the difficulties that sanctuaries have faced throughout the crisis and present the best measures to prevent future outbreaks and protect biodiversity. Full article
(This article belongs to the Special Issue Importance of Sanctuaries and Rehabilitation Centres for Wildlife)
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<p>Areas voted for by sanctuaries as percentages in response to the question “If additional funding became available to you, how would you use it?”, according to the three COVID-19 periods: before (2018–2019), at the start of (2019–2020), and during (2020–2021).</p>
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14 pages, 2044 KiB  
Article
Spatial Distribution, Diversity Mapping, and Gap Analysis of Wild Vigna Species Conserved in India’s National Genebank
by Thendral Uma Shankar, Dinesh Prasad Semwal, Veena Gupta, Sunil Archak, Ramakrishnan M. Nair and Kuldeep Tripathi
Diversity 2023, 15(4), 552; https://doi.org/10.3390/d15040552 - 13 Apr 2023
Cited by 1 | Viewed by 2163
Abstract
The genus Vigna has several crop species that could be used to feasibly address nutritional security challenges in the subtropical and tropical regions of the world, particularly in climate-changing scenarios. Wild taxa of Vigna are a source of economically important traits and need [...] Read more.
The genus Vigna has several crop species that could be used to feasibly address nutritional security challenges in the subtropical and tropical regions of the world, particularly in climate-changing scenarios. Wild taxa of Vigna are a source of economically important traits and need to be studied. Out of the 34 wild Vigna species reported in India, 928 indigenous accessions belonging to 19 wild Vigna are conserved in India’s National Genebank (INGB) housed at the National Bureau of Plant Genetic Resources, New Delhi. Geospatial mapping has identified diversity-rich areas and the Western Ghats region exhibits the highest Shannon diversity values (H = 1.65–3.0). Using the complementarity procedure, six diversity hotspots were identified for the 34 wild Vigna, and these require utmost priority for exploration and germplasm collection. Due to the meagre amount of information available for wild Vigna, the BioClim model was used to successfully predict the Idukki district of Kerala as a suitable site for germplasm-collecting expeditions. Coastal areas identified as rich in twelve wild taxa, V. bourneae, V. dalzelliana, V. marina, V. sublobata, V. subramaniana, V. vexillata, V. stipulacea, V. trilobata, and V. trinervia, require immediate attention to protect hotspots as well as to collect accessions from these areas for ex situ conservation. A hotspot in the protected forest of Anshi National Park and Bhagwan Mahavira Wildlife Sanctuary was identified as an ideal spot for possible in situ conservation of V. konkanensis, V silvestris, and V. sublobata. The 15 wild Vigna species do not have representation in the INGB, and 11 Vigna species have been identified as endemic species to India. Priority needs to be given to these species for focussed exploration and germplasm collection. This paper discusses the future focus on explorations to be carried out for the collection of the germplasm of wild Vigna species. Full article
(This article belongs to the Section Plant Diversity)
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<p>Georeferenced and diversity richness map of wild <span class="html-italic">Vigna</span> germplasm.</p>
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<p>Wild taxa of the <span class="html-italic">Vigna</span> germplasm collected and conserved in INGB (1976–2021) based on a log scale with a base of 10. 1—<span class="html-italic">Vigna angularis</span> var. <span class="html-italic">nipponensis</span>, 2—<span class="html-italic">V</span>. <span class="html-italic">adenantha</span>, 3—<span class="html-italic">V</span>. <span class="html-italic">bourneae</span>, 4—<span class="html-italic">V</span>. <span class="html-italic">dalzelliana</span>, 5—<span class="html-italic">V</span>. <span class="html-italic">hainiana</span>, 6—<span class="html-italic">V</span>. <span class="html-italic">glabrescens</span>, 7—<span class="html-italic">V</span>. <span class="html-italic">khandalensis</span>, 8—<span class="html-italic">V</span>. <span class="html-italic">marina</span>, 9—<span class="html-italic">V</span>. <span class="html-italic">sublobata</span>, 10—<span class="html-italic">V</span>. <span class="html-italic">minima</span>, 11—<span class="html-italic">V</span>. <span class="html-italic">luteola</span>, 12—<span class="html-italic">V</span>. <span class="html-italic">nepalensis</span>, 13—<span class="html-italic">V</span>. <span class="html-italic">trilobata</span>, 14—<span class="html-italic">V</span>. <span class="html-italic">reflexopilosa</span>, 15—<span class="html-italic">V. silvestris</span>, 16—<span class="html-italic">V</span>. <span class="html-italic">stipulacea</span>, 17—<span class="html-italic">V</span>. <span class="html-italic">trinervia</span>, 18—<span class="html-italic">V</span>. <span class="html-italic">vexillata</span>, 19—<span class="html-italic">V</span>. <span class="html-italic">wightii</span>.</p>
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<p>Diversity map for 19 taxa of the wild <span class="html-italic">Vigna</span> germplasm in 80 × 80 km grid cells using the Shannon Diversity Index (H).</p>
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<p>Species richness of 34 wild taxa of <span class="html-italic">Vigna</span> in 70 × 70 km grid cells.</p>
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<p>Diversity-rich hotspots for 34 wild taxa of <span class="html-italic">Vigna</span> identified using the complementarity technique.</p>
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<p>Species richness for 19 taxa of wild <span class="html-italic">Vigna</span> in 70 × 70 km grid cells.</p>
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<p>Diversity-rich hotspots for 19 taxa of wild <span class="html-italic">Vigna</span> identified using a complementarity analysis.</p>
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<p>Predicted areas for additional ex-situ conservation of the priority <span class="html-italic">Vigna</span> species.</p>
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11 pages, 1401 KiB  
Article
A Camera-Trap Survey of Mammals in Thung Yai Naresuan (East) Wildlife Sanctuary in Western Thailand
by Supagit Vinitpornsawan and Todd K. Fuller
Animals 2023, 13(8), 1286; https://doi.org/10.3390/ani13081286 - 9 Apr 2023
Cited by 3 | Viewed by 3626
Abstract
The Thung Yai Naresuan (East) Wildlife Sanctuary (TYNE), in the core area of the Western Forest Complex of Thailand, harbors a diverse assemblage of wildlife, and the region has become globally significant for mammal conservation. From April 2010 to January 2012, 106 camera [...] Read more.
The Thung Yai Naresuan (East) Wildlife Sanctuary (TYNE), in the core area of the Western Forest Complex of Thailand, harbors a diverse assemblage of wildlife, and the region has become globally significant for mammal conservation. From April 2010 to January 2012, 106 camera traps were set, and, in 1817 trap-nights, registered 1821 independent records of 32 mammal species. Of the 17 IUCN-listed (from Near Threatened to Critically Endangered) mammal species recorded, 5 species listed as endangered or critically endangered included the Asiatic elephant (Elephas maximus), tiger (Panthera tigris), Malayan tapir (Tapirus indicus), dhole (Cuon alpinus), and Sunda pangolin (Manis javanica). The northern red muntjac (Muntiacus vaginalis), large Indian civet (Viverra zibetha), Malayan porcupine (Hystrix brachyuran), and sambar deer (Cervus unicolor) were the most frequently recorded species (10–22 photos/100 trap-nights), representing 62% of all independent records, while the golden jackal (Canis aureus), clouded leopard (Neofelis nebulosa), marbled cat (Pardofelis marmorata), and Sunda pangolin were the least photographed (<0.1/100 trap-nights). Species accumulation curves indicated that the number of camera trap locations needed to record 90% of taxa recorded varied from 26 sites for herbivores to 67 sites for all mammals. TYNE holds a rich community of mammals, but some differences in photo-rates from an adjacent sanctuary and comparisons with other research on local mammals suggest that some species are rare and some are missed because of the limitations of our technique. We also conclude that the management and conservation plan, which involves the exclusion of human activities from some protected areas and strict protection efforts in the sanctuaries, is still suitable for providing key habitats for endangered wildlife populations, and that augmented and regular survey efforts will help in this endeavor. Full article
(This article belongs to the Topic Ecology, Management and Conservation of Vertebrates)
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<p>Location of camera trapping array (outlined in yellow) in the Thung Yai Naresuan (East) Wildlife Sanctuary within the Western Forest Complex of Thailand.</p>
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<p>Species accumulation curves for large mammal taxa in Thung Yai Naresuan (East) Wildlife Sanctuary; vertical dash lines demonstrate the number of camera trap locations needed for 90% detection of all large mammal, carnivore, herbivore, and omnivore species.</p>
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