Genetic Variation of the Endangered Neotropical Catfish Steindachneridion scriptum (Siluriformes: Pimelodidae)

Abstract

Steindachneridion scriptum is an important species as a resource for fisheries and aquaculture; it is currently threatened and has a reduced occurrence in South America. The damming of rivers, overfishing, and contamination of freshwater environments are the main impacts on the maintenance of this species. We accessed the genetic diversity and structure of S. scriptum using the DNA barcode and control region (D-loop) sequences of 43 individuals from the Upper Uruguay River Basin (UUR) and 10 sequences from the Upper Paraná River Basin (UPR), which were obtained from GenBank. S. scriptum from the UUR and the UPR were assigned in two distinct molecular operational taxonomic units (MOTUs) with higher inter-specific K2P distance than the optimum threshold (OT = 0.0079). The COI Intra-MOTU distances of S. scriptum specimens from the UUR ranged from 0.0000 to 0.0100. The control region indicated a high number of haplotypes and low nucleotide diversity, compatible with a new population in recent expansion process. Genetic structure was observed, with high differentiation between UUR and UPR basins, identified by BAPS, haplotype network, AMOVA (FST = 0.78, p < 0.05) and Mantel test. S. scriptum from the UUR showed a slight differentiation (FST = 0.068, p < 0.05), but not isolation-by-distance. Negative values of Tajima’s D and Fu’s Fs suggest recent demographic oscillations. The Bayesian skyline plot analysis indicated possible population expansion from beginning 2,500 years ago and a recent reduction in the population size. Low nucleotide diversity, spatial population structure, and the reduction of effective population size should be considered for the planning of strategies aimed at the conservation and rehabilitation of this important fisheries resource.

DNA barcoding for identification of fish species from freshwater in Enugu and Anambra States of Nigeria

“…Previous reports yielded 33 haplotypes and 149 polymorphic sites in 83 specimens from four extant Pacifastacus species (Larson et al 2016); 36 haplotypes and 56 variable sites in 43 sequences of Steindachneridion scriptum (Paixão et al 2018); and, 44 haplotypes and 76 variable sites in 74 fish (Pappalardo et al 2015). In this study, the genetic variability (Hd = 0.999) is similar, but nucleotide diversity (Pi = 0.73721) is higher than the ones (Hd = 0.959; Pi = 0.007) reported by Paixão et al (2018) from Steindachneridion scriptum; and in 68 species of Sicyopus zosterophorum (Hd = 0.885; Pi = 0.0039) by Taillebois et al (2013). It has been reported that Tajima's D-neutrality tests are applied to detect evidence of strong selective pressures, while Fu's Fs-tests are used specifically to identify population expansion (Tajima 1989;Fu 1997) D* = 2.17427, P < 0.02; Fu and Li's F* 1.7450, p < 0.005) for all individuals.…”

Phylogeographic Structure of Freshwater Tor sp. in River Basins of Sabah, Malaysia

“…Following the stream hierarchy model (SHM) [78], obligate freshwater fish populations are often genetically structured and differentiated (freshwater fish, mean Fst = 0.222) [79] at both among and within river basin scales as the results of gene-flow restriction and dispersal limitation among populations [80][81][82][83][84]. Isolation by distance (IBD) [85], isolation by barriers (IBB) (e.g., dams, culvert, waterfalls, rapids) [86], and isolation by resistance (IBR) (e.g., temperature, stream gradient, number of confluences, drainage basin, seasonal precipitation, seasonal water flow, and high flow events) [87,88] are reported to influence genetic variation in riverine freshwater fishes [89].…”

Applications of Next-Generation Sequencing Technologies and Computational Tools in Molecular Evolution and Aquatic Animals Conservation Studies: A Short Review

“…To date, computational methods and NGS have been employed by conservation and evolutionary geneticists to improve conservation management of marine populations, especially for endangered species. [46][47][48] Despite the technological advancements, analyzing genome-wide molecular data poses a major challenge. Handling large-scale and complex data requires high competency in bioinformatics and the ability to analyze and interpret the vast amount of data and translate them into biological applications.…”