Genes of the major histocompatibility complex (MHC) code for cell surface proteins essential for adaptive immunity. They show the most outstanding genetic diversity in vertebrates, which has been connected with various fitness traits and thus with the long-term persistence of populations. In this study, polymorphism of the MHC class II DRB locus was investigated in chamois with Single-Strand Conformation Polymorphism (SSCP)/Sanger genotyping and Ion Torrent S5 next-generation sequencing (NGS). From eight identified DRB variants in 28 individuals, five had already been described, and three were new, undescribed alleles. With conventional SSCP/Sanger sequencing, we were able to detect seven alleles, all of which were also detected with NGS. We found inconsistencies in the individual genotypes between the two methods, which were mainly caused by allelic dropout in the SSCP/Sanger method. Six out of 28 individuals were falsely classified as homozygous with SSCP/Sanger analysis. Overall, 25% of the individuals were identified as genotyping discrepancies between the two methods. Our results show that NGS technologies are better performing in sequencing highly variable regions such as the MHC, and they also have a higher detection capacity, thus allowing a more accurate description of the genetic composition, which is crucial for evolutionary and population genetic studies.
Aim Understanding the drivers of species distribution ranges and population genetic structure can help predict species' responses to global change, while mitigating threats to biodiversity through effective conservation measures. Here, we combined species habitat suitability through time with process‐based models and genomic data to investigate the role of landscape features and functional connectivity in shaping the population genetic structure of Northern chamois. Location European Alps. Taxon Northern chamois (Rupicapra rupicapra). Methods Using a model that simulates dispersal and tracks the functional connectivity of populations over dynamic landscapes, we modelled the response of the chamois to climate change from the last glaciation (20,000 years ago) to the present. We reconstructed species habitat suitability and landscape connectivity over time and simulated cumulative divergence of populations as a proxy for genetic differentiation. We then compared simulated divergence with the actual population structure of 449 chamois (with >20 k SNPs) sampled across the Alps. Results We found that Alpine populations of chamois are structured into two main clades, located in the south‐western and the eastern Alps. The contact zone between the two lineages is located near the Rhone valley in Switzerland. Simulations reproduced the geographic differentiation of populations observed in the genomic data, and limited dispersal ability and landscape connectivity co‐determined the fit of the simulations to data. Main conclusions The contemporary genetic structure of the chamois across the Alps is explained by limited functional connectivity in combination with large rivers or valleys acting as dispersal barriers. The results of our analysis combining simulations with population genomics highlight how biological characteristics, habitat preference and landscapes shape population genetic structure over time and in responses to climate change. We conclude that spatial simulations could be used to improve our understanding of how landscape dynamics, shaped by geological or climatic forces, impact intra‐ and interspecific diversity.
Genetic monitoring of populations currently attracts interest in the context of the Convention on Biological Diversity but needs long-term planning and investments. Genetic diversity has been largely neglected in biodiversity monitoring, and when addressed is treated separately, detached from other conservation issues, such as habitat alteration due to climate change. Genetic monitoring supports the conservation and management of fisheries, game, and threatened populations. It also can contribute to the assessment of predicted and realized impacts of climate change, and their management. We report the first accounting of genetic monitoring efforts among countries in Europe (their genetic monitoring capacity, GMC) to determine where GMC suggests the combination of national infrastructure, political support and resources for continued and expanded monitoring. Overlaying GMC with areas where species ranges approach current and future climate niche limits (i.e., niche marginality) helps identify whether GMC coincides with anticipated climate change effects on biodiversity. Our analysis suggests that country area extent, financial resources, and conservation policy influence GMC, high values of which inconsistently match joint species patterns of climate niche marginality. Populations at niche margins likely hold genetic diversity that is important to adaptation to changing climate, and our results illuminate the need in Europe for expanded genetic monitoring across the climate gradients occupied by species, a need arguably greatest in southeastern European countries.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.