Genetic structure of the endemicDipterocarpus condorensisrevealed by microsatellite markers

Abstract

Anthropogenic disturbances in tropical forests often affect the genetic diversity of a species. Dipterocarpus condorensis is an endangered species in the tropical forests of southeastern Vietnam, both from its over-exploitation and habitat loss. Therefore, knowledge of population genetic diversity and population structure is essential for identifying the species conservation measures. In the present study, we evaluated genetic diversity and population structure using nine microsatellites for 183 individual trees from eight populations, representing the distribution range of D. condorensis in Vietnam. Two clustering approaches (Bayesian analysis and discriminant analysis of principal components) revealed that all studied individuals clustered into three genetic groups, which were related to gene flow across the range of D. condorensis in the lowland tropical forests of southeastern Vietnam. Limited gene flow was implicated in anthropogenic disturbance. Genetic differentiation among populations was relatively low (the Weir and Cockerham index of 0.122 and the Hedrick index of 0.149) and showed significant differentiation. The genetic variability of the populations was low (HO = 0.298 and HE = 0.324), which suggested the negative effects of habitat degradation and over-exploitation. Our studies also determined that D. condorensis populations can have undergone recent bottlenecks. We recommend conservation activities for this species based on these results.

Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective

“…Using DL for signal reconstruction (b) has, thus been successfully demonstrated in numerous domains [49,55,64,69,119,139]. The performance of such solutions not only matched, but also significantly exceeded the performance of the standard reconstruction approaches as additional signal structure can be captured by generative DL models [41,90,106,114]. The sampling matrix, too can be adapted to a problem at hand thanks to DL (c).…”

“…Variational autoencoder (VAE) is trained to ensure that the latent space exhibits suitable properties enabling generative decoder behavior. Thus, [106] proposes a VAE for the CS recovery problem, presenting it as "an intuitive choice". A novel Uncertainty autoencoder (UAE) structure is proposed by Grover and Ermon [41].…”