Insights into subspecies classification and conservation priorities of Central Asian lynx populations revealed by morphometric and genetic analyses.

The Eurasian lynx (Lynx lynx) exhibits geographic variability and phylogenetic intraspecific relationships. Previous morphological studies have suggested the existence of multiple lynx subspecies, but recent genetic research has questioned this classification, particularly in Central Asia. In this study, we aimed to analyse the geographic and genetic variation in Central Asian lynx populations, particularly the Turkestan lynx and Altai lynx populations, using morphometric data and mtDNA sequences to contribute to their taxonomic classification. The comparative analysis of morphometric data revealed limited clinal variability between lynx samples from the Altai and Tien Shan regions. By examining mtDNA fragments (control region and cytochrome b) obtained from Kazakhstani lynx populations, two subspecies were identified: L. l. isabellinus (represented by a unique haplotype of the South clade, H46) and L. l. wrangeli (represented by haplotypes H36, H45, and H47 of the East clade). L. l. isabellinus was recognized only in Tien Shan Mountain, while Altai lynx was likely identical to L. l. wrangeli and found in northern Kazakhstan, Altai Mountain, Saur and Tarbagatai Mountains, and Tien Shan Mountain. The morphological and mtDNA evidence presented in this study, although limited in sample size and number of genetic markers, renders the differentiation of the two subspecies challenging. Further sampling and compilation of whole-genome sequencing data are necessary to confirm whether the proposed subspecies warrant taxonomic standing.

Expanded morphological description of the Drogons False Garden Lizard, Pseudocalotes drogon (Squamata: Agamidae) from Peninsular Malaysia.

Species in the genus Pseudocalotes are generally rare and known only from a few specimens. Pseudocalotes drogon was described based on a single male from Frasers Hill, Pahang, Peninsular Malaysia. During a recent field survey at Frasers Hill, a female specimen was collected and identified as P. drogon based on morphological characters and a molecular phylogenetic analysis. The morphological description of P. drogon is expanded here, based on the male holotype and the newly collected female specimen.

Advertisement calls from species of the Limnonectes hascheanus-limborgi complex (Anura: Dicroglossidae) from Peninsular Malaysia.

Limnonectes hascheanus and Limnonectes limborgi are two very similar-looking and closely related species and are sometimes referred to as the Limnonectes hascheanus-limborgi complex (Inger & Stuart 2010). Inger & Stuart (2010) tackled the systematics of the complex and confirmed the status of L. limborgi as a distinct species and not a junior synonym to L. hascheanus by providing molecular data and morphological characters of its distinctiveness. The geographic ranges of the two species were also reported to be distinct where L. hascheanus is mainly restricted to the southern part of the Thai-Malay Peninsula while L. limborgi is distributed from southern Myanmar north into northern Thailand and Laos before curving around into central Laos, northeastern Thailand, Cambodia, and southern Vietnam (Inger & Stuart 2010).

A SVM and SLIC Based Detection Method for Paddy Field Boundary Line.

Visual based route and boundary detection is a key technology in agricultural automatic navigation systems. The variable illumination and lack of training samples has a bad effect on visual route detection in unstructured farmland environments. In order to improve the robustness of the boundary detection under different illumination conditions, an image segmentation algorithm based on support vector machine was proposed. A superpixel segmentation algorithm was adopted to solve the lack of training samples for a support vector machine. A sufficient number of superpixel samples were selected for extraction of color and texture features, thus a 19-dimensional feature vector was formed. Then, the support vector machine model was trained and used to identify the paddy ridge field in the new picture. The recognition F1 score can reach 90.7%. Finally, Hough transform detection was used to extract the boundary of the ridge field. The total running time of the proposed algorithm is within 0.8 s and can meet the real-time requirements of agricultural machinery.