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16 pages, 6827 KiB  
Article
Habitat Suitability of Danaus genutia Based on the Optimized MaxEnt Model
by Jun Yao, Chengli Zhou, Wenquan Wang, Yangyang Li, Ting Du and Lei Shi
Insects 2024, 15(12), 971; https://doi.org/10.3390/insects15120971 - 5 Dec 2024
Viewed by 507
Abstract
Danaus genutia, commonly known as the tiger butterfly, is a visually appealing species in the Danaidae family. As it is not currently classified as endangered, it is excluded from key protected species lists at national and local levels, limiting focus on its [...] Read more.
Danaus genutia, commonly known as the tiger butterfly, is a visually appealing species in the Danaidae family. As it is not currently classified as endangered, it is excluded from key protected species lists at national and local levels, limiting focus on its population and habitat status, which may result in it being overlooked in local butterfly conservation initiatives. Yunnan, characterized by high butterfly diversity, presents an ideal region for studying habitat suitability for D. genutia, which may support the conservation of regional biodiversity. This study employs the MaxEnt ecological niche model, predictions regarding suitable habitat distribution, and trends for D. genutia and identifying primary environmental factors influencing their distribution. The results indicate that the niche model that includes interspecies relationships provides a distribution prediction closely aligned with the observed range of D. genutia. Under current climatic conditions, highly suitable habitats for both D. genutia and its host plant, Cynanchun annularium, are located predominantly in the Yuanjiang River Valley. Optimal conditions occur at average annual temperatures of 19.80–22 °C for D. genutia and 22–24 °C for C. annularium. The distribution range of C. annularium is a vital biological factor limiting D. genutia’s habitat. By 2040, projections under four future climate scenarios indicate a potential increase in the total area of suitable habitats for D. genutia, with a general trend of northward expansion. Full article
(This article belongs to the Section Insect Ecology, Diversity and Conservation)
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<p>The distribution records of <span class="html-italic">Danaus genutia</span> and <span class="html-italic">Cynanchum annularium</span> in Yunnan.</p>
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<p>Optimization of MaxEnt model parameters using the ENMeval package in R.</p>
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<p>Optimization results for the model by ENMeval (H—Hinge, L—Linear, Q—Quadratic, P—Product, T—Threshold).</p>
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<p>ROC evaluation curve of MaxEnt.</p>
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<p>Prediction of potential suitable habitat distribution of <span class="html-italic">C. annularium</span> and <span class="html-italic">D. genutia</span> with different model structures under current climate conditions in Yunnan.</p>
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<p>Evaluation of the importance of different environmental factors based on the Jackknife test.</p>
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<p>Response curves of existence probability for <span class="html-italic">C. annularium</span> and <span class="html-italic">D. genutia</span> to dominant climatic factors.</p>
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<p>Habitat suitability for <span class="html-italic">D. genutia</span> under future climate scenarios.</p>
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<p>Habitat suitability for <span class="html-italic">D. genutia</span> under future climate change scenarios.</p>
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17 pages, 15638 KiB  
Article
Unveiling the Secrets: How Landscape Patterns Shape Habitat Quality in Northeast China Tiger and Leopard National Park
by Xishihui Du, Ying Chen and Zhaoguo Wang
Forests 2024, 15(11), 1889; https://doi.org/10.3390/f15111889 - 26 Oct 2024
Viewed by 984
Abstract
The Northeast China Tiger and Leopard National Park (NCTLNP) is a critical habitat for the endangered Amur tiger and Amur leopard, making it a global biodiversity hotspot. This study explores how changes in landscape patterns have influenced habitat quality in the park, aiming [...] Read more.
The Northeast China Tiger and Leopard National Park (NCTLNP) is a critical habitat for the endangered Amur tiger and Amur leopard, making it a global biodiversity hotspot. This study explores how changes in landscape patterns have influenced habitat quality in the park, aiming to develop strategies for enhancing biodiversity conservation and ensuring the park’s long-term sustainability. From 2012 to 2017, habitat quality in the NCTLNP experienced a significant decline; however, the launch of the national park pilot program in 2017 resulted in improvements, particularly in core protected areas, where habitat quality increased and landscape fragmentation decreased. These findings indicate that the national park initiative reduced the degradation of habitat quality. Key landscape metrics, especially the Shannon Diversity Index (SHDI), were found to significantly affect habitat quality. Additionally, the interaction between SHDI and landscape contagion (CONTAG) played a pivotal role in shaping habitat quality over time. Areas with high SHDI and low CONTAG showed declines in habitat quality, pointing to the need for focused conservation efforts. This study offers valuable insights for policymakers seeking to improve habitat quality through targeted landscape management practices. Full article
(This article belongs to the Special Issue Forest Management: Planning, Decision Making and Implementation)
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<p>Spatial context of the study area: (<b>a</b>) location of the NCTLNP in China; (<b>b</b>) three management units of the NCTLNP; (<b>c</b>) topography of the NCTLNP.</p>
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<p>Framework of this study.</p>
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<p>Variations in habitat quality from 2012 to 2022. (Note: CP = core protection zone; GC = general control zone; PA = population aggregation zone; Total = total area).</p>
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<p>Variations in landscapes pattern from 2012 to 2022. (Note: CP = core protection zone; GC = general control zone; PA = population aggregation zone; Total = total area).</p>
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<p>LISA clustering maps of habitat quality.</p>
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<p>Single-factor detection results for the spatial heterogeneity of habitat quality. (Note: CP = core protection zone; GC = general control zone; PA = population aggregation zone; Total = total area).</p>
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<p>Interaction detection results for the spatial heterogeneity of habitat quality. (Note: CP = core protection zone; GC = general control zone; PA = population aggregation zone; Total = total area).</p>
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16 pages, 4988 KiB  
Article
Leg Attachment Devices of Tiger Beetles (Coleoptera, Cicindelidae) and Their Relationship to Their Habitat Preferences
by Zheng Liu, Stanislav N. Gorb, Hongbin Liang, Ming Bai and Yuanyuan Lu
Insects 2024, 15(9), 650; https://doi.org/10.3390/insects15090650 - 29 Aug 2024
Viewed by 996
Abstract
The ability of many insects to adhere vertically or even upside down to smooth substrates is closely related to the morphology and distribution of the adhesive structures on their legs. During locomotion, the legs are in direct contact with different substrates, and it [...] Read more.
The ability of many insects to adhere vertically or even upside down to smooth substrates is closely related to the morphology and distribution of the adhesive structures on their legs. During locomotion, the legs are in direct contact with different substrates, and it is hypothesized that the adhesive structures have been evolved as an adaption to smooth substrates in specific environments. To investigate whether there is a relationship between the presence of adhesive structures and the combined effects of different environments and mating behavior, we compared five species of tiger beetles belonging to two tribes living in arboreal and non-arboreal environments, respectively. In three non-arboreal species, we found a specific type of adhesive structure consisting of elongated spoon-like setae present on the protarsi of males but absent on the male meso- and metatarsi and on females. In Tricondyla pulchripes, an arboreal species living on stems, we found three types of adhesive setae on male protarsi, while only two types of setae were found on male meso- and metatarsi and on females. In Neocollyris linearis, an arboreal species living on leaves, we found three types of adhesive setae on male pro-, meso- and meta-tarsi but only two types of adhesive setae on females. The adaptive evolution of these adhesive structures was probably driven by the selective pressures of both mating behavior and the presence of smooth substrates in the respective environments. It is discussed that the adhesive structures in tiger beetles may be an adaptive evolutionary response to the plant surfaces and may play an important role in species differentiation. Full article
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Graphical abstract

Graphical abstract
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<p>Arboreal and non-arboreal tiger beetle groups inhabiting different environments.</p>
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<p>The ventral view of the tarsi and adhesive setae in non-arboreal species of Cicindelidae. (<b>A</b>–<b>F</b>). <span class="html-italic">Cicindela sachalinensis</span>. (<b>A</b>). Male protarsus. (<b>B</b>). Elongated spoon-like setae on the male protarsus. (<b>C</b>). Male mesotarsus. (<b>D</b>). Male metatarsus. (<b>E</b>). Female protarsus. (<b>F</b>). Female meso- and metatarsus. (<b>G</b>–<b>L</b>). <span class="html-italic">Cosmodela separata</span>. (<b>G</b>). Male protarsus. (<b>H</b>). Elongated spoon-like setae on the male protarsus. (<b>I</b>). Male mesotarsus. (<b>J</b>). Female protarsus. (<b>K</b>). Female mesotarsus. (<b>L</b>). Female metatarsus. (<b>M</b>–<b>R</b>). <span class="html-italic">Cylindera kaleea</span>. (<b>M</b>). Male protarsus. (<b>N</b>). Elongated spoon-like setae on the male protarsus. (<b>O</b>). Male mesotarsus. (<b>P</b>). Male metatarsus. (<b>Q</b>). Female protarsus. R. Female meso- and metatarsus. Abbreviations: UN, unguis (claw); Tar I, the 1st tarsomere; Tar II, the 2nd tarsomere; Tar III, the 3rd tarsomere; Tar IV, the 4th tarsomere; Tar V, the 5th tarsomere; sh, setal shaft; tp, terminal plate.</p>
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<p>The tarsi and adhesive setae of <span class="html-italic">Tricondyla pulchripes</span> (arboreal species). (<b>A</b>–<b>H</b>). Male. (<b>A</b>). Protarsus, ventral view. (<b>B</b>). Unguis (claw) of protarsus. (<b>C</b>). Elliptical setae on protarsus. (<b>D</b>). Branched setae on protarsus. (<b>E</b>). Mesotarsus, ventral view. (<b>F</b>). Branched setae on mesotarsus. (<b>G</b>). Filament-like setae on mesotarsus. (<b>H</b>). Metatarsus, ventral view. (<b>I</b>–<b>P</b>). Female. (<b>I</b>). Protarsus, ventral view. (<b>J</b>). Unguis (claw) of protarsus. (<b>K</b>). Branched setae on protarsus. (<b>L</b>). Filament-like setae on protarsus. (<b>M</b>). Metatarsus, ventral view. (<b>N</b>). Unguis (claw) of metatarsus. (<b>O</b>). Branched setae on metatarsus. (<b>P</b>). Filament-like setae on metatarsus. Abbreviations: UN, unguis (claw); Tar I, the 1st tarsomere; Tar II, the 2nd tarsomere; Tar III, the 3rd tarsomere; Tar IV, the 4th tarsomere; Tar V, the 5th tarsomere; sh, setal shaft; tp, terminal plate.</p>
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<p>The tarsi and adhesive setae of <span class="html-italic">Neocollyris linearis</span> (arboreal species). (<b>A</b>–<b>H</b>). Male. (<b>A</b>). Protarsus, ventral view. (<b>B</b>). Unguis (claw) of protarsus. (<b>C</b>). Discoidal setae on protarsus. (<b>D</b>). Spatulate setae on protarsus. (<b>E</b>). Mesotarsus, ventral view. (<b>F</b>). Spatulate setae on mesotarsus. (<b>G</b>). Metatarsus, ventral view. (<b>H</b>). Discoidal setae on metatarsus. (<b>I</b>–<b>P</b>). Female. (<b>I</b>). Protarsus, ventral view. (<b>J</b>). Unguis (claw) of protarsus. (<b>K</b>). Spatulate setae on protarsus. (<b>L</b>). Tapered setae on protarsus. (<b>M</b>). Spatulate setae on mesotarsus. (N). Tapered setae on mesotarsus. (<b>O</b>). Metatarsus, ventral view. (<b>P</b>). Spatulate setae on metatarsus. Abbreviations: UN, unguis (claw); Tar I, the 1st tarsomere; Tar II, the 2nd tarsomere; Tar III, the 3rd tarsomere; Tar IV, the 4th tarsomere; Tar V, the 5th tarsomere; sh, setal shaft; tp, terminal plate.</p>
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<p>The contact position of the legs during mating. (<b>A</b>). Mating of <span class="html-italic">Ropaloteres desgodinsii</span> (Fairmaire, 1887) (Sichuan, photographed by Li He). (<b>B</b>). Mating of <span class="html-italic">Tricondyla pulchripes</span> (Hong Kong, photographed by Siuyeung Ho). (<b>C</b>). Mating of <span class="html-italic">Tricondyla pulchripes</span> (Hong Kong, photographed by Alfred Cheung). (<b>D</b>). Mating of <span class="html-italic">Neocollyris parvula</span> (Chaudoir, 1848) (Nagla Block, Palghar, Maharashtra, India, photographed by Dinesh Sharma). (<b>E</b>,<b>F</b>). Mating of <span class="html-italic">Neocollyris</span> sp. (Yeoor Hills, Thane West, Thane, Maharashtra, India, photographed by Anil Kumar Verma). (<b>G</b>–<b>I</b>). Screenshot from a video of the mating process of <span class="html-italic">Cosmodela juxtata</span> (Acciavatti and Pearson, 1989). (<b>G</b>). At 15 s (red arrows show that the protarsi of the male does not contact the body of the female). (<b>H</b>). At 20 s (red arrows show the protarsi of the male trying to contact the body of the female, and the male clasps the female with his mandibles). (<b>I</b>). At 22 s (red arrows show the same as in (<b>H</b>), the protarsi of the male contact with the body of the female more tightly).</p>
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<p>Microstructure of the elytra and metasternum in female tiger beetles. (<b>A</b>). Elytra of <span class="html-italic">Ci. sachalinensis</span>. (<b>B</b>). Elytra of <span class="html-italic">Co. separata</span>. (<b>C</b>). Elytra of <span class="html-italic">Co. separata</span>. (<b>D</b>). Metasternum of <span class="html-italic">Ci. sachalinensis</span>. (<b>E</b>). Metasternum of <span class="html-italic">Co. separata</span>. (<b>F</b>). Metasternum of <span class="html-italic">Co. separata</span>. (<b>G</b>). Elytra of <span class="html-italic">T. pulchripes</span>. (<b>H</b>). Elytra of <span class="html-italic">N. linearis</span>. (<b>I</b>). Elytra of <span class="html-italic">N. linearis</span>. (<b>J</b>). Metasternum of <span class="html-italic">T. pulchripes</span>. (<b>K</b>). Metasternum of <span class="html-italic">N. linearis</span>. (<b>L</b>). Metasternum of <span class="html-italic">N. linearis</span>. The yellowish regions in (<b>A</b>–<b>C</b>) indicate the raised parts.</p>
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<p>Scheme of possible correlations between the adhesive structures of the legs in both genders and the different environments where they live. The vertical axis represents the two genders: female (white) and male (pink), and the horizontal axis represents the smoothness of the habitat environments: ground (white), tree stem (light blue), and leaves (blue). ESs, elongated spoon-like setae; Es, elliptical setae; Fs, filament-like setae; Bs, branched setae; Ds, discoidal setae; Ts, tapered setae; Ss, spatulate setae.</p>
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16 pages, 8070 KiB  
Article
Predicting the Impact of Climate Change on the Selection of Reintroduction Sites for the South China Tiger (Panthera tigris amoyensis) in China
by Yueqing Luo, Jin Xu, Xinyi Zhang and Yulin Hou
Animals 2024, 14(17), 2477; https://doi.org/10.3390/ani14172477 - 26 Aug 2024
Viewed by 1077
Abstract
The South China tiger (Panthera tigris amoyensis) is a tiger subspecies unique to China and one of the top ten endangered species in the world. It used to play an important role in the overall function of the ecosystem. This study [...] Read more.
The South China tiger (Panthera tigris amoyensis) is a tiger subspecies unique to China and one of the top ten endangered species in the world. It used to play an important role in the overall function of the ecosystem. This study rationally screened out key prey species of the South China tiger—the Chinese serow, Chinese goral, tufted deer, water deer, Chinese muntjac, red muntjac, sambar deer, and wild boar. Candidate sites for the rewilding and reintroduction of the South China tiger were derived by exploring changes in suitable habitats for the prey using the MaxEnt model. The results show that: (1) by 2070, except for the high-suitability areas of water deer and Chinese muntjac, the areas of suitable habitats for the other six prey species would all have decreased significantly; (2) the location of the high-suitability area of the South China tiger obtained by superimposing the suitable areas of the eight prey species would be almost stable in 2050 and 2070, but the habitat index of some high- and medium-suitability areas would decrease and turn into low-suitability areas; (3) the core candidate sites were 83,415 km2 in total, of which 25,630 km2 overlapped with existing protected areas, accounting for 30.7% of the core candidate sites, and the remaining 69.3% of the core candidate sites were mostly distributed around the protected areas; (4) the maximum core candidate site area was projected to be 10,000 km2 by 2070, which could support a small population of 23 male tigers and 66 female tigers to survive and reproduce in the wild. This study revealed the core candidate sites for the rewilding of South China tigers and estimated the number of tigers that could be reintroduced to these areas, providing a preliminary research basis for promoting the rewilding of South China tigers in China. Full article
(This article belongs to the Section Wildlife)
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<p>Distribution of selected sites of eight key prey species in China.</p>
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<p>Prediction of the distribution of potential habitats of South China tigers. (<b>a</b>) Current habitat distribution of South China tigers; (<b>b</b>) habitat distribution of South China tigers in 2050; (<b>c</b>) habitat distribution of South China tigers in 2070; (<b>d</b>) the relatively stable habitat distribution of South China tigers after superposition.</p>
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<p>Relationship between core candidate sites, human footprints, and protected areas.</p>
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25 pages, 83944 KiB  
Article
Integrating Entropy Weight and MaxEnt Models for Ecotourism Suitability Assessment in Northeast China Tiger and Leopard National Park
by Qianhong Quan and Yijin Wu
Land 2024, 13(8), 1269; https://doi.org/10.3390/land13081269 - 12 Aug 2024
Viewed by 1065
Abstract
The development of ecotourism in protected areas faces the challenge of balancing conservation and ecotourism. Ecotourism suitability assessments are essential tools for managing tourism in these areas. However, current assessments often overlook biological factors, leading to adverse effects on wildlife. This study uses [...] Read more.
The development of ecotourism in protected areas faces the challenge of balancing conservation and ecotourism. Ecotourism suitability assessments are essential tools for managing tourism in these areas. However, current assessments often overlook biological factors, leading to adverse effects on wildlife. This study uses the Northeast China Tiger and Leopard National Park as a case study to establish a comprehensive assessment system that integrates ecotourism suitability with tiger and leopard habitat suitability, thereby linking ecotourism with wildlife conservation. The primary research methods include ecotourism suitability analysis based on the entropy weight method and habitat suitability analysis using the MaxEnt model. Based on the zoning results of ecotourism and habitat suitability, a comprehensive ecotourism suitability zoning map was produced. This map indicates that areas of very high suitability account for 45.62% of the total area, covering approximately 6152.563 km2, and are primarily located on the edges of village clusters. These areas can be prioritized for developing tourism infrastructure. The comprehensive ecotourism assessment system can balance the development of ecotourism with wildlife conservation, contributing significantly to the coordinated development of economic, social, and environmental objectives. Full article
(This article belongs to the Special Issue Landscape-Scale Sustainable Tourism Development)
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<p>Location map of the study area.</p>
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<p>The overall workflow of the research methodology.</p>
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<p>Criteria maps of the study area: (<b>a</b>) NDVI; (<b>b</b>) Land Use Classes; (<b>c</b>) Soil Erosion; (<b>d</b>) Temperature; (<b>e</b>) Precipitation; (<b>f</b>) Distance to Rivers; (<b>g</b>) Elevation; (<b>h</b>) Slope; (<b>i</b>) Aspect; (<b>j</b>) Distance to Roads; (<b>k</b>) Distance to Villages; (<b>l</b>) Distance to Tourist Attractions.</p>
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<p>Density maps of the prey species: (<b>a</b>) line transect sampling distribution (2017–2019); (<b>b</b>) Density of Wild Boars; (<b>c</b>) Density of Roe Deer.</p>
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<p>Comprehensive suitability zoning criteria of ecotourism based on TLH suitability and ecotourism suitability.</p>
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<p>Ecotourism suitability map for the NCTLNP.</p>
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<p>The ROC curve of the MaxEnt prediction.</p>
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<p>Jackknife test of the variable importance.</p>
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<p>The response curves of the major variables: (<b>a</b>) Density of Wild Boars; (<b>b</b>) Temperature; (<b>c</b>) Density of Roe Deer.</p>
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<p>Tiger and leopard habitat suitability map for the NCTLNP: (<b>a</b>) logistic prediction and (<b>b</b>) the Natural Breaks classification.</p>
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<p>Comprehensive suitability map for the NCTLNP.</p>
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12 pages, 3191 KiB  
Article
The Bear Truth: Analyzing Genetic Variability and Population Structure in Sloth Bear across the Vidarbha Landscape Using Microsatellite Markers
by Lynette Gomes, Shrushti Modi, Parag Nigam and Bilal Habib
Diversity 2024, 16(2), 74; https://doi.org/10.3390/d16020074 - 24 Jan 2024
Cited by 1 | Viewed by 1713
Abstract
Endemic to the Indian subcontinent, the sloth bear (Melursus ursinus) is a threatened species, present in fragmented habitats across India. Field techniques such as direct observation and camera trapping alone are not sufficient and may not be explicit enough to understand [...] Read more.
Endemic to the Indian subcontinent, the sloth bear (Melursus ursinus) is a threatened species, present in fragmented habitats across India. Field techniques such as direct observation and camera trapping alone are not sufficient and may not be explicit enough to understand a monomorphic species like the sloth bear at larger spatial scales. In this study, we looked into the genetic structure, variability and population demographics amongst the extant sloth bear populations in the highly fragmented Vidarbha landscape, using a panel of 13 microsatellite markers with a cumulative PID value of 1.48 × 10−5 PIDsibs. Our results revealed genetic clustering (K = 5) and moderate structuring amongst the study populations. Despite being geographically distant and placed in two different genetic clusters, sloth bears from the Melghat Tiger Reserve and Sahyadri Tiger Reserve shared genetic signatures, indicating connectivity, while migration was detected amongst other study areas as well. The findings from this study can serve as baseline assessment for future genetic monitoring of the species in the human-dominated landscape and assist in managerial decisions to step up protection of fragmented forest patches and reduce human–bear conflicts without compromising on the genetic connectivity. Full article
(This article belongs to the Section Biodiversity Conservation)
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<p>Study area map indicating sampling location used (NNTR n = 82, STR n = 268, PTR n = 35, MTR n = 83, TATR n = 69, UKWLS n = 28, BTR n = 1).</p>
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<p>TESS assignment plot of sloth bears across the Vidarbha landscape, India, assigning the samples to 5 distinct populations.</p>
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<p>Circus plot visualization of migration rates between different protected areas of the Vidarbha landscape, India.</p>
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23 pages, 9001 KiB  
Article
Tiger Habitat Quality Modelling in Malaysia with Sentinel-2 and InVEST
by Valentin Louis, Susan E. Page, Kevin J. Tansey, Laurence Jones, Konstantina Bika and Heiko Balzter
Remote Sens. 2024, 16(2), 284; https://doi.org/10.3390/rs16020284 - 10 Jan 2024
Cited by 4 | Viewed by 2334
Abstract
Deforestation is a threat to habitat quality and biodiversity. In intact forests, even small levels of deforestation can have profound consequences for vertebrate biodiversity. The risk hotspots are Borneo, the Central Amazon, and the Congo Basin. Earth observation (EO) now provides regular, high-resolution [...] Read more.
Deforestation is a threat to habitat quality and biodiversity. In intact forests, even small levels of deforestation can have profound consequences for vertebrate biodiversity. The risk hotspots are Borneo, the Central Amazon, and the Congo Basin. Earth observation (EO) now provides regular, high-resolution satellite images from the Copernicus Sentinel missions and other platforms. To assess the impact of forest conversion and forest loss on biodiversity and habitat quality, forest loss in a tiger conservation landscape in Malaysia is analysed using Sentinel-2 imagery and the InVEST habitat quality model. Forest losses are identified from satellites using the random forest classification and validated with PlanetScope imagery at 3–5 m resolution for a test area. Two scenarios are simulated using InVEST, one with and one without the forest loss maps. The outputs of the InVEST model are maps of tiger habitat quality and habitat degradation in northeast Peninsular Malaysia. In addition to forest loss, OpenStreetMap road vectors and the GLC2000 land-cover map are used to model habitat sensitivity to threats from roads, railways, water bodies, and urban areas. The landscape biodiversity score simulation results fall sharply from ~0.8 to ~0.2 for tree-covered land cover when forest loss is included in the habitat quality model. InVEST makes a reasonable assumption that species richness is higher in pristine tropical forests than in agricultural landscapes. The landscape biodiversity score is used to compare habitat quality between administrative areas. The coupled EO/InVEST modelling framework presented here can support decision makers in reaching the targets of the Kunming-Montreal Global Biodiversity Framework. Forest loss information is essential for the quantification of habitat quality and biodiversity in tropical forests. Next generation ecosystem service models should be co-developed alongside EO products to ensure interoperability. Full article
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Graphical abstract

Graphical abstract
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<p>Land cover from the GLC2000 dataset [<a href="#B36-remotesensing-16-00284" class="html-bibr">36</a>], forest loss derived from Sentinel-2 imagery as well as the Tx2 tiger conservation landscape (TCL).</p>
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<p>Conceptual diagram of the forest cover-change detection algorithm. The red box provides a legend of the symbols.</p>
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<p>Locations of the validation points in the province of Terengganu.</p>
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<p>Forest cover loss events between 2017 and 2018 were observed from Sentinel-2 overlaid with land-cover data (GLC2000, JRC/EU) and the extent of the Tx2 tiger conservation landscapes. The GLC2000 land-cover classes were summarized and renamed to the classes in the legend.</p>
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<p>Bar plot (blue) and cumulative line plot (orange) showing the area distribution of detected tree cover loss cover per GLC2000 land-cover class in the study area. The <span class="html-italic">x</span>-axis shows the land-cover classes, which are: (1) mosaic (crops, tree, natural vegetation), (2) cultivated and managed areas, (3) tree cover, (4) mosaic (tree cover, natural vegetation), (5) sparse and herbaceous and shrubs, (6) artificial surfaces, (7) water.</p>
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<p>(<b>a</b>) The map on the left shows the resulting habitat quality index distribution of scenario 1, which includes infrastructure (roads, rail roads), human settlements and agricultural data. (<b>b</b>) The map on the right shows the results for scenario 2, where forest cover loss is added as an additional driver of habitat degradation.</p>
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<p>Bar plot of mean (i.e., landscape biodiversity score) and standard deviation (sd) of the habitat quality index (HQI) by land-cover class for the two scenarios described in the text. Error bars are ±1 sd. 0 is low habitat quality and 1 high habitat quality.</p>
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13 pages, 2752 KiB  
Article
Habitat Characteristics, Distribution, and Abundance of Cicindelidia haemorrhagica (LeConte) (Coleoptera: Cicindelidae) in Yellowstone National Park
by Kelly A. Willemssens, John L. Bowley, Laissa Cavallini, Erik Oberg, Robert K. D. Peterson and Leon G. Higley
Insects 2024, 15(1), 15; https://doi.org/10.3390/insects15010015 - 29 Dec 2023
Cited by 1 | Viewed by 1213
Abstract
We observed the tiger beetle species, Cicindelidia haemorrhagica (LeConte), foraging in and reproducing near the thermal pools of Yellowstone National Park (YNP). Although this species was recorded in YNP more than 130 years ago, its distribution, ecology, and association with thermal features are [...] Read more.
We observed the tiger beetle species, Cicindelidia haemorrhagica (LeConte), foraging in and reproducing near the thermal pools of Yellowstone National Park (YNP). Although this species was recorded in YNP more than 130 years ago, its distribution, ecology, and association with thermal features are unknown. Therefore, we examined the distribution and habitat characteristics of C. haemorrhagica and evaluated methods for studying its abundance. Given the extreme environments in which these beetles live, typical methods to estimate abundance are challenging. We used a series of presence/absence studies and observations to assess distribution and recorded temperature and pH measurements to determine habitat characteristics. We also conducted visual counts, light trapping, and mark/recapture experiments to assess abundance. The inability to capture C. haemorrhagica with lights led to a phototaxis experiment, which showed minimal attraction to light. Cicindelidia haemorrhagica was found throughout YNP, but it was exclusively associated with thermal springs. The thermal springs ranged from pH 2.7 to 9.0 with temperatures from 29.1 to 75.0 °C and had varying metal concentrations in soil and water. However, all thermal springs with C. haemorrhagica had barren soil with a gradual slope toward the thermal water. Specifically, habitats were thermal pools with gradual margins (a less than five-degree slope) and thermal (i.e., heated) soils for larval burrows by thermal springs or pools. Population sizes of C. haemorrhagica ranged between 500 and 1500 individuals based on visual counts. Full article
(This article belongs to the Section Insect Ecology, Diversity and Conservation)
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<p>Light-trapping set-up showing portable light and battery, a collection box, and white cloth at a 1 m distance from the thermal spring at Angel Terrace.</p>
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<p>Active burrows of <span class="html-italic">Cicindelidia haemorrhagica</span> in Yellowstone National Park. The burrow has a round shape and the area surrounding it is cleared. Photograph by John Bowley at Dragon–Beowulf Springs in 2019.</p>
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<p>The locations of <span class="html-italic">Cicindelidia haemorrhagica</span> within Yellowstone National Park. The yellow stars represent the sites where a thriving <span class="html-italic">C. haemorrhagica</span> population was found, the red circles represent the sites containing a thriving population that were chosen as research sites.</p>
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<p>Phototaxis results showing in which tube the beetle was located after 10 min (0 = beetle did not choose the lightened tube, 1 = beetle was found in lightened tube, 2 = beetle was found in darkened tube.) A = Light or dark choice for <span class="html-italic">C. haemorrhagica</span>, B = Light or dark choice for <span class="html-italic">Cicindelidia punctulata</span>, C = Light or not light (dark or no choice) for <span class="html-italic">Cicindelidia haemorrhagica</span>, D = Light or not light (dark or no choice) for <span class="html-italic">C. punctulata</span>.</p>
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14 pages, 5074 KiB  
Article
Transboundary Cooperation in the Tumen River Basin Is the Key to Amur Leopard (Panthera pardus) Population Recovery in the Korean Peninsula
by Hailong Li, Puneet Pandey, Ying Li, Tianming Wang, Randeep Singh, Yuxi Peng, Hang Lee, Woo-Shin Lee, Weihong Zhu and Chang-Yong Choi
Animals 2024, 14(1), 59; https://doi.org/10.3390/ani14010059 - 22 Dec 2023
Cited by 2 | Viewed by 2420
Abstract
The interconnected forest regions along the lower Tumen River, at the Sino-North Korean border, provide critical habitats and corridors for the critically endangered Amur Leopard (Panthera pardus orientalis). In this region, there are two promising corridors for leopard movement between China and North [...] Read more.
The interconnected forest regions along the lower Tumen River, at the Sino-North Korean border, provide critical habitats and corridors for the critically endangered Amur Leopard (Panthera pardus orientalis). In this region, there are two promising corridors for leopard movement between China and North Korea: the Jingxin–Dapanling (JD) and Mijiang (MJ) corridors. Past studies have confirmed the functionality of the JD corridor, but leopards’ utilization of the MJ corridor has not yet been established or confirmed. In this study, we assessed the functionality of the MJ corridor. The study area was monitored using camera traps between May 2019 and July 2021. We also analyzed 33 environmental and vegetation factors affecting leopard survival and analyzed leopard movement. In the Mijiang area, the Amur leopard was mainly active in the region adjacent to the Northeast China Tiger and Leopard National Park and did not venture into area near the North Korean border. The complex forest structure allowed leopards to move into the Mijiang area. However, the high intensity of human disturbance and manufactured physical barriers restricted further southward movement. Therefore, human-induced disturbances such as grazing, mining, farming, logging, and infrastructure development must be halted and reversed to make the Mijiang region a functional corridor for the Amur leopard to reach the North Korean forest. This necessitates inter-governmental and international cooperation and is essential for the long-term survival of the Amur leopard. Full article
(This article belongs to the Special Issue Ecology and Conservation of Large Carnivores)
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<p>Distribution of the Amur leopard in Far East Asia (CHN—China, RUS—Russia, DPRK—North Korea). Note: Gray animals represent dispersed Amur leopards [<a href="#B7-animals-14-00059" class="html-bibr">7</a>]. The solid black line and arrows depict the current dispersal path, while the dotted lines indicate possible dispersal paths for leopards [<a href="#B19-animals-14-00059" class="html-bibr">19</a>,<a href="#B20-animals-14-00059" class="html-bibr">20</a>].</p>
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<p>The study area’s map displaying camera trap locations, physical barriers, and human settlements. Note: All layers have been processed using ArcMap 10.8 (desktop.arcgis.com, accessed on 2 July 2023; ESRI, Redlands, USA).</p>
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<p>Monthly variations in the intensity of the grazing and human activity. Note: The axis at the bottom of the figure represents the months, while the left axis represents the RAI value over 100 days. The values displayed at the top of the figure represent the RAI values over a total of 100 days during the monitoring period in areas A, B, and C. The black line indicates changes in area A, the red line indicates changes in area B, and the green line indicates changes in area C.</p>
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<p>Monthly variations in the relative abundance of four primary prey species of the Amur leopard. Note: The axis at the bottom of Figure represents the months, while the left axis represents the RAI value over 100 days. The values displayed at the top of the figure represent the RAI values over a total of 100 days during the monitoring period in areas A, B, and C. The black line indicates changes in area A, the red line indicates changes in area B, and the green line indicates changes in area C.</p>
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<p>Schematic diagram of the classification of 22 factors with significant differences across the three Zones (A, B, and C) of the MJ tunnel corridor.</p>
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15 pages, 5749 KiB  
Article
An Evaluation of Suitable Habitats for Amur Tigers (Panthera tigris altaica) in Northeastern China Based on the Random Forest Model
by Chunyu Gao, Yang Hong, Shiquan Sun, Ning Zhang, Xinxin Liu, Zheyu Wang, Shaochun Zhou and Minghai Zhang
Biology 2023, 12(11), 1444; https://doi.org/10.3390/biology12111444 - 17 Nov 2023
Cited by 1 | Viewed by 2190
Abstract
Amur tigers are at the top of the food chain and play an important role in maintaining the health of forest ecosystems. Scientific and detailed assessment of the habitat quality of Amur tigers in China is the key to maintaining the forest ecosystem [...] Read more.
Amur tigers are at the top of the food chain and play an important role in maintaining the health of forest ecosystems. Scientific and detailed assessment of the habitat quality of Amur tigers in China is the key to maintaining the forest ecosystem and also addressing the urgent need to protect and restore the wild population of Amur tigers in China. This study used the random forest method to predict the potential habitat of Amur tigers in Heilongjiang and Jilin provinces using animal occurrence sites and a variety of environmental variables. Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. The results showed that the AUC value of the test set was 0.955. The true skill statistic (TSS) value is 0.5924, indicating that the model had good prediction accuracy. Using the optimal threshold determined by the Youden index as the cutoff value, we found that the suitable habitat for Amur tigers in the field was approximately 107,600 km2, accounting for 16.3% of the total study areas. It was mainly distributed in the Sino-Russian border areas in the south of the Laoyeling Mountains at the junction of Jilin and Heilongjiang provinces, the Sino-Russian border areas of Hulin–Raohe in the eastern part of the Wanda Mountains, and the Lesser Khingan Mountain forest region. The habitat suitability of the Greater Khingan Mountain and the plain areas connecting Harbin and Changchun was relatively low. Prey potential richness was the most critical factor driving the distribution of Amur tigers. Compared with their prey, the potential habitats for Amur tigers in Heilongjiang and Jilin provinces were small in total areas, sporadically distributed, and had low continuity and a lack of connectivity between patches. This indicates that some factors may restrict the diffusion of the Amur tiger, whereas the diffusion of ungulates is less restricted. The Amur tigers in this area face a serious threat of habitat fragmentation, suggesting that habitat protection, restoration, and ecological corridor construction should be strengthened to increase population dispersal and exchange. We provide a reference for future population conservation, habitat restoration, construction of ecological migration corridors, and population exchange of Amur tigers. Full article
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<p>Geographical location of the study areas. Source: GEBCO Compilation Group (2023) GEBCO 2023 Grid (<a href="https://doi.org/10.5285/f98b053b-0cbc-6c23-e053-6c86abc0af7b" target="_blank">https://doi.org/10.5285/f98b053b-0cbc-6c23-e053-6c86abc0af7b</a>, accessed on 5 January 2023).</p>
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<p>The occurrence sites of the Amur tigers and four ungulates.</p>
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<p>(<b>a</b>) Generalization error rate of the prey model for the presence/absence situation. The black, red, and green lines represent all observations, occurrences, and random non-occurrences that vary with the number of decision trees, respectively. (<b>b</b>) Cross-validation curve of variables in the Amur tiger prey potential richness model. (<b>c</b>) Dot chart of random forest variables importance of prey.</p>
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<p>Prediction of prey potential richness in the Amur tigers.</p>
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<p>(<b>a</b>) Generalization error rate of the Amur tiger model for presence/absence situations. (<b>b</b>) Importance ranking of variables in the analysis of suitable habitat selection for Amur tigers. (<b>c</b>) Variables with the top six importance rankings. (<b>d</b>) The importance of cross-validation curves in the analysis of suitable habitat selection for Amur tigers. (<b>e</b>) AUC under ROC curve.</p>
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<p>Prediction of habitat suitability of Amur tigers.</p>
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<p>(<b>a</b>) Prediction of Amur tiger habitat selection. (<b>b</b>) Prediction of Amur tiger prey occupancy selection. (<b>c</b>) Overlap of habitat selection zone.</p>
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18 pages, 6366 KiB  
Article
A Computational Model for Determining Tiger Dispersal and Related Patterns in a Landscape Complex
by Saurabh Shanu and Alok Agarwal
Sustainability 2023, 15(11), 8539; https://doi.org/10.3390/su15118539 - 24 May 2023
Viewed by 1351
Abstract
Species dispersal from one territorial zone to another is a complex process. The reasons for species dispersal are determined by both natural and human factors. The purpose of this study is to develop a cost surface for a hypothetical landscape that accounts for [...] Read more.
Species dispersal from one territorial zone to another is a complex process. The reasons for species dispersal are determined by both natural and human factors. The purpose of this study is to develop a cost surface for a hypothetical landscape that accounts for various species dispersion features. With tigers (Panthera tigris tigris) as the focal species, a computational model for a landscape has been proposed to predict the dispersion patterns of the species’ individuals from one habitat patch to another. Knowing how tigers disperse is very crucial because it improves the likelihood of successful conservation. The likelihood is raised because it strengthens conservation efforts in the targeted regions identified by the proposed model and encourages landscape continuity for tiger dispersal. Initially, four major factors influencing tiger dispersal are explored. Following that, grids are overlaid over the tiger-carrying landscape map. Further, game theory assigns a score to each grid in the landscape matrix based on the landscape features in the focal landscape. Specific predefined ratings are also utilized for scenarios that are very complex and may change depending on variables, such as the interaction of the dispersing tiger with co-predators. The two scores mentioned above are combined to create a cost matrix that is shown across a landscape complex to estimate the impact of each landscape component on tiger dispersal. This approach helps wildlife managers develop conservation plans by recognizing important characteristics in the landscape. The results of the model described in this work might be beneficial for a wide range of wildlife management activities, such as corridor management, smart patrols, and so on. A cost surface over any focal landscape may serve as a basis for policy and purpose design based on current landscape conditions. Full article
(This article belongs to the Special Issue Ecological Sustainability and Landscape Ecology)
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<p>Hypothetical landscape for the study.</p>
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<p>Region extracted from the landscape matrix with all defined components for modeling.</p>
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<p>Presence/absence of co-predators with F3 as the source grid, where the red color with 1 indicates the presence of a stronger predator than the dispersing individual, green with −1 represents the presence of a weaker predator than the dispersing individual, and white with 0 represents the absence of any co-predators. The grid with black color represents the source grid from which the tiger dispersal begins.</p>
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<p>Presence/absence of water body (WB), where blue and 1 represents the presence of WB and white with 0 represents the absence of WB.</p>
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<p>Presence/absence of forest area (FA), where green and 1 represents the presence of FA and white with 0 represents the absence of FA.</p>
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<p>Presence/absence of humans (HP), where yellow and 1 represents the presence of HP and white with 0 represents the absence of HP.</p>
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<p>Presence/absence of prey base (PB), where brown with 1 represents the presence of PB and white with 0 represents the absence of PB.</p>
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<p>Presence/absence of non-forested area (NF), where pink with 1 represents the presence of NF and white with 0 represents the absence of NF.</p>
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<p>Dispersion coefficients for various landscape parameters according to <a href="#sustainability-15-08539-t003" class="html-table">Table 3</a> for the event of “dispersion away from home” for tigers.</p>
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<p>Cost of each grid for the life event of “dispersion away from home” for tigers.</p>
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<p>Dispersion network for the dispersing tiger for the event of “dispersion away from home” from the grid F3. The grid shown in black is the source grid for tiger dispersal, the green color represents the forested area, the brown color represents the prey base presence, yellow color represents the human presence, blue color represents the water base and white color represents the non-forested area.</p>
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<p>Cost surface over the landscape obtained for dispersion for dominance. The grid shown in black is the source grid for tiger dispersal, the green color represents the forested area, the brown color represents the prey base presence, yellow color represents the human presence, blue color represents the water base and white color represents the non-forested area.</p>
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<p>Cost surface over the landscape obtained for dispersion away from home. The grid shown in black is the source grid for tiger dispersal, the green color represents the forested area, the brown color represents the prey base presence, yellow color represents the human presence, blue color represents the water base and white color represents the non-forested area.</p>
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<p>Cost surface over the landscape obtained for dispersion for food. The grid shown in black is the source grid for tiger dispersal, the green color represents the forested area, the brown color represents the prey base presence, yellow color represents the human presence, blue color represents the water base and white color represents the non-forested area.</p>
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<p>Cost surface over the landscape obtained for dispersion for breeding. The grid shown in black is the source grid for tiger dispersal, the green color represents the forested area, the brown color represents the prey base presence, yellow color represents the human presence, blue color represents the water base and white color represents the non-forested area.</p>
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<p>Analysis of results for all four categories of movement.</p>
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<p>Cost surface with dispersion network for tigers, dispersing for various reasons within the landscape complex. The grid shown in black is the source grid for tiger dispersal, the green color represents the forested area, the brown color represents the prey base presence, yellow color represents the human presence, blue color represents the water base and white color represents the non-forested area.</p>
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19 pages, 9758 KiB  
Article
Machine Learning Modeling of Aedes albopictus Habitat Suitability in the 21st Century
by Pantelis Georgiades, Yiannis Proestos, Jos Lelieveld and Kamil Erguler
Insects 2023, 14(5), 447; https://doi.org/10.3390/insects14050447 - 9 May 2023
Cited by 4 | Viewed by 2728
Abstract
The Asian tiger mosquito, Aedes albopictus, is an important vector of arboviruses that cause diseases such as dengue, chikungunya, and zika. The vector is highly invasive and adapted to survive in temperate northern territories outside its native tropical and sub-tropical range. Climate [...] Read more.
The Asian tiger mosquito, Aedes albopictus, is an important vector of arboviruses that cause diseases such as dengue, chikungunya, and zika. The vector is highly invasive and adapted to survive in temperate northern territories outside its native tropical and sub-tropical range. Climate and socio-economic change are expected to facilitate its range expansion and exacerbate the global vector-borne disease burden. To project shifts in the global habitat suitability of the vector, we developed an ensemble machine learning model, incorporating a combination of a Random Forest and XGBoost binary classifiers, trained with a global collection of vector surveillance data and an extensive set of climate and environmental constraints. We demonstrate the reliable performance and wide applicability of the ensemble model in comparison to the known global presence of the vector, and project that suitable habitats will expand globally, most significantly in the northern hemisphere, putting at least an additional billion people at risk of vector-borne diseases by the middle of the 21st century. We project several highly populated areas of the world will be suitable for Ae. albopictus populations, such as the northern parts of the USA, Europe, and India by the end of the century, which highlights the need for coordinated preventive surveillance efforts of potential entry points by local authorities and stakeholders. Full article
(This article belongs to the Special Issue Climate Sensitive Ecological and Dynamical Models of Insects)
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<p>Distribution of the number of examples in the training set for each month of the year (<b>top</b> panel) and geographical distribution of the dataset used to train and evaluate the model’s performance (<b>bottom</b> panel). The colour bar shows the number of examples for each grid cell.</p>
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<p>The schematic, high-level overview of the procedures followed in this study to train the machine learning model and project <span class="html-italic">Ae. albopictus</span> habitat suitability.</p>
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<p>The ROC curves obtained on the test dataset (<b>left</b> panel) and precision-recall curves (<b>middle</b> panel) for the Random Forest, XGBoost and, ensemble models. On the (<b>right</b> panel), the sensitivity of the ensemble model compared to the known presence of <span class="html-italic">Ae. albopictus</span> as a function of the number of months set as a threshold for habitat suitability. The inset in the (<b>left</b> panel) shows a zoomed view of the ROC curves.</p>
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<p>Predicted <span class="html-italic">Ae. albopictus</span> habitat suitability in terms of months predicted as suitable by the ML model for the early part of the century (2020–2025). The normalization scenario (SSP245) is presented on the top panel, whereas the “business as usual” (SSP585) scenario is presented on the bottom panel. The colorbar shows the average number of months predicted as suitable by the machine learning model for each grid cell.</p>
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<p>Comparison of the latitude profiles for the SSP245 climate scenario (<b>left</b> panel) and SSP585 (<b>right</b> panel), for the early, mid, and end of century time periods. Solid lines and the shaded areas represent the median and the 95% range, respectively.</p>
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<p>Latitude profiles for the total number of months predicted as suitable by the ML model (<b>top</b> panels) and the transitional changes between the early to mid, end of century, and mid to end of century periods (<b>bottom</b> panels). The latitude profiles obtained for the SSP245 scenario are shown in red and the corresponding profiles for the SSP585 scenario are shown in blue, whereas the difference between the two is shown in gray. Solid lines and the shaded areas represent the median and the 95% range, respectively.</p>
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<p>Suitable months normalised to 100 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math> (<b>left</b> panel), the total area over which habitat suitability is projected (<b>middle</b> panel) and the total number of months projected as suitable for <span class="html-italic">Ae. albopictus</span> (<b>right</b> panel), for the two IPCC scenarios. The blue and red lines, which correspond to the SSP245 and SSP585 scenarios, respectively, show the ensemble average for the nine climate models used for each of the two scenarios. Solid lines and the shaded areas represent the median and the 95% range, respectively.</p>
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<p>Comparison between the tropical (<b>top</b> row of panels) and extratropical (<b>bottom</b> row of panels) regions of the world for habitat suitability normalised to 100 km<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math> (<b>left</b> column), total area covered (<b>middle</b> column) and total number of months predicted for each year (<b>right</b> column). Solid lines and the shaded areas represent the median and the 95% range, respectively.</p>
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<p>Population at risk of <span class="html-italic">Ae. albopictus</span>-borne diseases per year for the two scenarios examined in this study (<b>left</b> panel). In the inset, the total population projected until the end of the century for the scenarios is presented. In addition, the increase in population at risk of <span class="html-italic">Ae. albopictus</span>-borne diseases with respect to the start of the projection window (2020) for the SSP245 scenario (blue) and SSP585 scenario (red) is shown on the right panel. The three time periods presented here correspond to the early, mid, and end of the 21st century. The median of the output from the nine climate scenarios is presented here and the shaded area (<b>left</b> panel) and lines (<b>right</b> panel) represent the 95% range.</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 2 | Viewed by 3571
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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13 pages, 9903 KiB  
Article
Animal Imagery in Eastern Han Tomb Reliefs from Shanbei 陝北
by Leslie V. Wallace
Arts 2023, 12(1), 26; https://doi.org/10.3390/arts12010026 - 30 Jan 2023
Viewed by 2446
Abstract
Wild and fantastical animals climb, fly, scamper, and prance across pictorial stone carvings decorating Eastern Han tomb doors in northern Shaanxi. Alongside dragons and other mythical animals, bears felicitously dance, tigers grin opening their mouths to roar, and other wild animals frolic in [...] Read more.
Wild and fantastical animals climb, fly, scamper, and prance across pictorial stone carvings decorating Eastern Han tomb doors in northern Shaanxi. Alongside dragons and other mythical animals, bears felicitously dance, tigers grin opening their mouths to roar, and other wild animals frolic in swirling cloudscapes. While the same animals can be found in Eastern Han tomb reliefs and mortuary art in other regions, their frequency, emphasis on plasticity and movement, and combination with the yunqi 雲氣 motif are unique to the region. Originating in a hybrid style of art that was created during the Mid-Western Han Dynasty (206 BCE–9 CE), their significance was dependent not so much on any individual creature but on their display as an assemblage of shared forms, behaviors, and habitats. This paper explores how Eastern Han patrons and artists in Shanbei reinvigorated such imagery. It argues that on tomb doors through the region, these same wild and fantastical animals have become a key element of compositions meant to pacify the potentially dangerous realms that awaited the deceased in their postmortem ascension to Heaven (tian 天). Full article
(This article belongs to the Special Issue The Zoomorphic Arts of Ancient Central Eurasia)
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<p>Major locations where Eastern Han tombs with stone reliefs have been found in Shaanxi (After <a href="#B44-arts-12-00026" class="html-bibr">Wallace 2011b, Figure 1</a>).</p>
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<p>Plan of a multi-chambered tomb excavated at Yanjiacha 延家岔, Suide 綏德, and Shaanxi. The central chamber has a domed ceiling while the other chambers have barrel-vaulted ceilings. Carved reliefs decorate the tomb doorway, and the areas around the entranceway to the two side and rear chambers (After <a href="#B4-arts-12-00026" class="html-bibr">Dai and Li 1983, Figure 1</a>).</p>
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<p>Rubbing of a tomb doorway, Hejiagou 賀家溝, Qingjian 清澗, Shaanxi. Eastern Han dynasty (After <a href="#B17-arts-12-00026" class="html-bibr">Li et al. 1995, p. 216</a>).</p>
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<p>Artist’s rendering of a panel from an inlaid bronze chariot ornament from Sanpanshan, Hebei. Western Han dynasty, ca. 90 BCE (After <a href="#B58-arts-12-00026" class="html-bibr">Zhonghua renmin gonghe guo chutu wenwu zhanlan zhanpin xuanji 1973, p. 85</a>).</p>
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<p>Bronze <span class="html-italic">boshanlu</span> from the tomb of Lady Dou Wan (ca. 113 BCE) (After <a href="#B57-arts-12-00026" class="html-bibr">Zhongguo shehui kexue kaogu yanjiusuo 1978, Figure 17</a>).</p>
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<p>Top: Immortal frolicking with animals in an undulating landscape/cloudscape. Mizhi, Shaanxi. Eastern Han dynasty (After <span class="html-italic">Zhonguo huaxiang shi quanji</span> vol. 5, Figure 63). Bottom: Line drawing of the top two registers on the Sanpanshan <span class="html-italic">bini</span>.</p>
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<p>Side panels on the west wall of the Yanjiacha tomb (After <a href="#B4-arts-12-00026" class="html-bibr">Dai and Li 1983, Figure 2.3</a>).</p>
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<p>Tomb door lintel from Dangjiagou 黨家溝 Mizhi, Shaanxi (After <a href="#B44-arts-12-00026" class="html-bibr">Wallace 2011b, Figure 8a</a>).</p>
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34 pages, 10056 KiB  
Article
Is the Lesser Khingan Suitable for the Amur Tiger Restoration? Perspectives with the Current State of the Habitat and Prey Base
by Anna Yachmennikova, Shibing Zhu, Ivan Kotlov, Robert Sandlersky, Qu Yi and Viatcheslav Rozhnov
Animals 2023, 13(1), 155; https://doi.org/10.3390/ani13010155 - 30 Dec 2022
Cited by 1 | Viewed by 3234
Abstract
The Amur tiger (Panthera tigris) has a status of being endangered on the world’s IUCN red list. The northwestern part of its range is situated in Russia and China, where tigers were exterminated by humans in the 1950–1970s. To restore tiger [...] Read more.
The Amur tiger (Panthera tigris) has a status of being endangered on the world’s IUCN red list. The northwestern part of its range is situated in Russia and China, where tigers were exterminated by humans in the 1950–1970s. To restore tiger population within a historical range, an estimation of the habitat suitability is firstly needed. The Lesser Khingan mountains (Heilongjiang) was analyzed. Habitat types were mapped by satellite images analysis and field proven. The potential habitats of the main tiger’s prey species (wild boar (Sus scrofa), roe deer (Capreolus pygargus), and red deer (Cervus elaphus xanthopygus) were also assessed. Maximum entropy and linear discriminant analysis methods were applied and compared for species distribution modeling (SDM). Species distribution maps were used to design an ecological network. The fragmentation of habitat patches was evaluated by spatial ecological metrics. The habitat patches with the best metrics were assigned as cores for the ecological network, which were connected by calculated corridors. The least cost distance method (based on distance to roads and settlements) was used. The recovery of the Amur tiger in habitats of China’s Lesser Khingan is shown to be possible. Types of habitats were calculated as natural corridors for moving tigers. They are mainly located at the forests’ edges and characterized with various canopy structures and high variability in the tree species composition. Three potential transboundary corridors are described: (a) foothills and low mountains of the northern Lesser Khingan; (b) connection between the southeast Lesser Khingan and the western part of the Wandashan mountain system; and (c) corridor within foothills and low mountains of the eastern part of Lesser Khingan. It is recommended to establish protected areas for the important tiger core habitats, and the main optimal ways for their migrations are described during the current investigation. Moreover, it is necessary to implement habitat recovery activities for key areas. Full article
(This article belongs to the Special Issue Animals in 2023)
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Figure 1

Figure 1
<p>(<bold>a</bold>) Map of the study area. The Lesser Khingan mountain system is located in the north central part of Heilongjiang Province, China and borders Russia in the northeast, rectangle – study area; (<bold>b</bold>) map indicating field collection sites.</p>
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<p>Map of habitat types of the Lesser Khingan obtained by supervised classification of environmental variables. Scale 1:1,500,000. White color corresponds to clouds and shadows in a multispectral image.</p>
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<p>Response curves of three ungulates model prediction to environmental variables. Cloglog is the default model output, which is the simplest to understand: it gives a probability of species occurrence estimate between 0 and 1.</p>
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<p>Prediction of potential habitats using MaxEnt modeling for (<bold>a</bold>) wild boar, (<bold>b</bold>) roe deer, (<bold>c</bold>) red deer, and (<bold>d</bold>) an integrated habitat map for the three tiger prey species.</p>
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<p>Habitat suitability index (HSI) for 3 species of ungulates according to linear discriminant analysis (LDA): (<bold>a</bold>) wild boar, (<bold>b</bold>) roe deer, (<bold>c</bold>) red deer, and (<bold>d</bold>) integrated suitability index.</p>
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<p>Habitat suitability index (HSI) for 3 species of ungulates according to linear discriminant analysis (LDA): (<bold>a</bold>) wild boar, (<bold>b</bold>) roe deer, (<bold>c</bold>) red deer, and (<bold>d</bold>) integrated suitability index.</p>
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<p>Box–whisker plot of cross-tabulation between MaxEnt and Linear Discriminant Analysis LDA integrated models.</p>
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<p>(<bold>a</bold>) Potential ecological network of the Lesser Khingan; (<bold>b</bold>) fragmentation of potential ecological network along the Amur River valley overlaid with track of tiger Kuzya [<xref ref-type="bibr" rid="B3-animals-13-00155">3</xref>]. Protected areas: 1—Liangshui, 2—Wuyiling, 3—Youhao, 4—Xinqing, 5—Hongxing, 6—Dazhanhe, 7—Maolangou, 8—Taipinggou, 9—Shuangbaoshan, 10—Wumahe, 11—Zhayinhe, 12—Jiayin, 13—Duerbinhe, 14—Langxiang, 15—Shankou, 16—Shuangchahe, 17—Xinlinhe, 18—Heyuantou, 19—Bishui, 20—Pingdingshan, 21—Longkou, 22—Pingyanghe, 23—Kuerbinhe, 24—Fenglin, 25—Lichun, 26—Zhuravliny.</p>
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<p>Three integrated prey-based habitat suitability models: (<bold>A</bold>) MaxEnt modeling based on the Lesser Khingan winter track census survey, (<bold>B</bold>) discriminant analysis based on the Lesser Khingan winter track census survey, (<bold>C</bold>) discriminant analysis based on Taippingou winter track census survey.</p>
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<p>(<bold>A</bold>) Integrated prey-based MaxEnt model based on the Lesser Khingan winter track census survey, (<bold>B</bold>) MaxEnt model based on data from GPS-collars on tigers.</p>
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<p>Combining the results and regional zoning schemes. (<bold>A</bold>) Orographic diagram of the basin of Amur River (Heilongjiang) [<xref ref-type="bibr" rid="B55-animals-13-00155">55</xref>] and potential corridors calculated using the least cost method. Symbols on the map A: 1—mountain ranges and systems of ridges, 2—plains. (<bold>B</bold>) Floristic areas of Northeast China [<xref ref-type="bibr" rid="B85-animals-13-00155">85</xref>] and tiger GPS tracks (lines of different colors marked with the tigers’ names Ilona, Svetlaya, Kuzya, Borya, Ustin [<xref ref-type="bibr" rid="B3-animals-13-00155">3</xref>]); designations on map B: 1—Manchurian mixed forests, 2—Ussuri broadleaved and mixed forests, 3—Amur wetland and wet steppes, 4—Ussuri wetlands and forest-steppes. Green circles indicate regions with potentially important green corridors of level 1.</p>
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<p>Tiger geographic range [<xref ref-type="bibr" rid="B1-animals-13-00155">1</xref>], integrated with our resulting prey-based HSI model and with pure GPS tracks received from reintroduced tigers’ collars.</p>
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<p>Map of human disturbance and anthropogenic activity of Lesser Khingan.</p>
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