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10 results for “Cuon”
Fig. 2 in First detection and molecular identification of Babesia gibsoni and Hepatozoon canis in an Asiatic wild dog (Cuon alpinus) from Thailand
Fig. 2. Neighbor-joining (NJ) tree of the Hepatozoon partial 18S ribosomal RNA (18S rRNA) gene sequence. Hepatozoon canis (MK144332) was amplified from an Asiatic wild dog in Thailand and analyzed for comparison with other Hepatozoon spp. from the GenBank database. The numbers on branches indicate percent bootstrap support based on 1000 bootstrap replications and only bootstrap values ≥ 50% are shown.
Fig. 1 in First detection and molecular identification of Babesia gibsoni and Hepatozoon canis in an Asiatic wild dog (Cuon alpinus) from Thailand
Fig. 1. Neighbor-joining (NJ) tree of the Babesia partial 18S ribosomal RNA (18S rRNA) gene sequence. Babesia gibsoni (MK144331) was amplified from an Asiatic wild dog in Thailand and analyzed for comparison with other Babesia spp. from the GenBank database. The numbers on branches indicate percent bootstrap support based on 1000 bootstrap replications and only bootstrap values ≥ 50% are shown.
Fig. 6 in Preliminary assessment of abundance and distribution of Dholes Cuon alpinus in Rimbang Baling and Tesso Nilo landscapes, Sumatra
Fig. 6. The land cover chart above shows the mean response of the 100 replicate Maxent runs (front) and the mean +/– one standard deviation (two shades for categorical variables). Number 5 is the forest cover variable (details of variables can be seen in Table 1).
Fig. 3 in Preliminary assessment of abundance and distribution of Dholes Cuon alpinus in Rimbang Baling and Tesso Nilo landscapes, Sumatra
Fig. 3. Activity pattern graph of dholes in each sampling block based on density estimates of the daily activity patterns by using kernel density estimation following Linkie & Ridout (2011). RB2012 is northeastern Rimbang Baling (n=41), RB2014 is northwestern Rimbang Baling (n=106), RB2015 is southern Rimbang Baling (n=18), TN2013 is Tesso Nilo (n=35), CA2012 is Bukit Bungkuk (n=70), and HL2013 is Bukit Betabuh (n=5). Black-dashed lines indicate the approximate edge of night and dusk or dawn. Red-dashed lines indicate the approximate edge of both dusk or dawn with nights and day. The solid line is the kernel density of dholes. X-axis indicates the time of individual photographs and Y-axis indicates the kernel density.
Fig. 2 in Preliminary assessment of abundance and distribution of Dholes Cuon alpinus in Rimbang Baling and Tesso Nilo landscapes, Sumatra
Fig. 2. Activity pattern graph of dholes (n=275) in all sampling blocks in Sumatra based on density estimates of the daily activity patterns by using kernel density estimation following Linkie & Ridout (2011). Black-dashed lines indicate the approximate edge of night and dusk or dawn. Red-dashed lines indicate the approximate edge of both dusk or dawn with nights and day. The solid black line is the kernel density of dholes. X-axis indicates the time of individual photographs and Y-axis indicates the kernel density.
Fig. 5 in Preliminary assessment of abundance and distribution of Dholes Cuon alpinus in Rimbang Baling and Tesso Nilo landscapes, Sumatra
Fig. 5. Map of predicted distribution model of dholes generated by MaxEnt, with median summary grids and percent contributions of variables which were 83.3% for land cover, 8.7% for road, 6.5% for river, and 1.5% for elevation.
Fig. 4 in Preliminary assessment of abundance and distribution of Dholes Cuon alpinus in Rimbang Baling and Tesso Nilo landscapes, Sumatra
Fig. 4. Curve of the receiver operating characteristic (ROC). The average test AUC for the replicate runs is 0.903 and the standard deviation is 0.025. This graph was generated by modelling in MaxEnt from 30 dhole locations with four habitat variables: land cover, road, elevation, and river. The random test percentage was 25 with 100 replicates and 1000 maximum iterations. The AUC result of the study was closer to 1 which indicates better model performance. The best AUC has an AUC of 1. The maximum AUC is therefore less than one and is smaller for wider-ranging species (Phillips et al., 2004).
Fig. 1 in Preliminary assessment of abundance and distribution of Dholes Cuon alpinus in Rimbang Baling and Tesso Nilo landscapes, Sumatra
Fig. 1. Map of sampling blocks and camera stations in Bukit Rimbang Bukit Baling Wildlife Reserve, Bukit Betabuh Protected Forest, Bukit Bungkuk Nature Reserve, and Tesso Nilo National Park.
Data from: Standardization and validation of a panel of cross-species microsatellites to individually identify the Asiatic wild dog (Cuon alpinus)
The Asiatic wild dog or dhole (Cuon alpinus) is a highly elusive, monophyletic, forest dwelling, social canid distributed across south and Southeast Asia. Severe pressures from habitat loss, prey depletion, disease, human persecution and interspecific competition resulted in global population decline in dholes. Despite a declining population trend, detailed information on population size, ecology, demography and genetics is lacking. Generating reliable information at landscape level for dholes is challenging due to their secretive behaviour and monomorphic physical features. Recent advances in non-invasive DNA-based tools can be used to monitor populations and individuals across large landscapes. In this paper, we describe standardization and validation of faecal DNA-based methods for individual identification of dholes. We tested this method on 249 field-collected dhole faeces from five protected areas of the central Indian landscape in the state of Maharashtra, India. Results We tested a total of 18 cross-species markers and developed a panel of 12 markers for unambiguous individual identification of dholes. This marker panel identified 101 unique individuals from faecal samples collected across our pilot field study area. These loci showed varied level of amplification success (57-88%), polymorphism (3-9 alleles), heterozygosity (0.23-0.63) and produced a cumulative misidentification rate or PID(unbiased) and PID(sibs) value of 4.7x10-10 and 1.5x10-4, respectively, indicating a high statistical power in individual discrimination from poor quality samples. Conclusion Our results demonstrated that the selected panel of 12 microsatellite loci can conclusively identify dholes from poor quality, non-invasive biological samples and help in exploring various population parameters. This genetic approach would be useful in dhole population estimation across its range and will help in assessing population trends and other genetic parameters for this elusive, social carnivore.
Data from: Standardization and validation of a panel of cross-species microsatellites to individually identify the Asiatic wild dog (Cuon alpinus)
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