Khan, K. A. | Search bioRxiv

bioRxiv2024

Understanding genetic diversity and phylogeography of Common Teal and its phylogenetic relationship with other water bird species in the wetlands of Kashmir Himalayas.

Understanding the genetic diversity and phylogeography of migratory species is critical for biodiversity conservation and the effective management of wetland ecosystems. The Kashmir Himalayas, an integral part of the Central Asian Flyway, host several key wetlands that provide critical wintering grounds for a variety of migratory birds. This study focuses on assessing the genetic characteristics and phylogenetic relationships of the Common Teal (Anas crecca) in comparison to other species within the families Anatidae and Rallidae. We analysed 149 blood samples, including 71 from A. crecca and 78 from other species in the two families, collected from wetlands in the Kashmir region. Using four mitochondrial markers--cytochrome oxidase subunit I (COI), cytochrome b (Cyt b), 16S rRNA, and the control region--we evaluated the genetic diversity and lineage connectivity of these species. Our findings reveal that the mitochondrial DNA haplotypes of A. crecca in the Kashmir Himalayas are shared with European populations, indicating strong maternal gene flow and connectivity between distant populations. A minimum spanning haplotype network analysis showed minimal nucleotide differences among haplotypes, particularly in the Cyt b and control regions, suggesting low genetic differentiation and a high degree of similarity among individuals. Notably, we identified at least four distinct maternal lineages of A. crecca in the Kashmir wetlands, reflecting diverse migratory sources. Our results also highlight that DNA barcoding using COI exhibited both high and low species resolution, with significant intraspecific variation, making it a valuable tool for further phylogeographic studies. The observed genetic diversity and haplotype sharing with distant populations underscore the ecological importance of Kashmirs wetlands as crucial habitats for migratory species. Our study emphasizes the need for targeted conservation and management strategies to preserve these vital ecosystems and the biodiversity they support.

genetics↗

bioRxiv2022

Deep-Worm-Tracker: Deep Learning Methods for Accurate Detection and Tracking for Behavioral Studies in C. elegans

Accurate detection and tracking of model organisms such as C. elegans worms remains a fundamental task in behavioral studies. Traditional Machine Learning (ML) and Computer Vision (CV) methods produce poor detection results and suffer from repeated ID switches during tracking under occlusions and noisy backgrounds. Using Deep Learning (DL) methods, the task of animal tracking from video recordings, like those in camera trap experiments, has become much more viable. The large amount of data generated in ethological studies, makes such models suitable for real world scenarios in the wild. We propose Deep-Worm-Tracker, an end to end DL model, which is a combination of You Only Look Once (YOLOv5) object detection model and Strong Simple Online Real Time Tracking (Strong SORT) tracking backbone that is highly accurate and provides tracking results in real time inference speeds. Present literature has few solutions to track animals under occlusions and even fewer publicly available large scale animal re-ID datasets. Thus, we also provide a worm re-ID dataset to minimize worm ID switches, which, to the best of our knowledge, is first-of-its-kind for C. elegans. We are able to track worms at a mean Average Precision (mAP@0.5) > 98% within just 9 minutes of training time with inference speeds of 9-15 ms for worm detection and on average 27 ms for worm tracking. Our tracking results show that Deep-Worm-Tracker is well suited for ethological studies involving C. elegans.

animal behavior and cognition↗