Pfeiffer, J. M. | bioRxiv Science

bioRxiv2022

A tale of two paths: The evolution of mitochondrial recombination in bivalves with doubly uniparental inheritance

In most animals, mitochondrial DNA is strictly maternally inherited and non-recombining. One exception to these assumptions is called doubly uniparental inheritance (DUI): a phenomenon involving the independent transmission of female and male mitochondrial genomes. DUI is known only from the molluscan class Bivalvia. The phylogenetic distribution of male mitochondrial DNA in bivalves is consistent with several evolutionary scenarios, including multiple independent gains, losses, and varying degrees of recombination with female mitochondrial DNA. In this study, we use phylogenetic methods to test male mitochondrial DNA origination hypotheses and infer the prevalence of mitochondrial recombination in bivalves with DUI. Phylogenetic modeling using site concordance factors supported a single origin of male mitochondrial DNA in bivalves coupled with recombination acting over long evolutionary timescales. Ongoing mitochondrial recombination is present in Mytilida and Venerida, which results in a pattern of concerted evolution of female and male mitochondrial DNA. Mitochondrial recombination could be favored to offset the deleterious effects of asexual inheritance and maintain mitonuclear compatibility across tissues. Cardiida and Unionida have gone without recent recombination, possibly due to an extension of the COX2 gene in male mitochondrial DNA. The loss of recombination may be neutral but could be connected to the role of M mtDNA in sex determination or sexual development. Our results support recombination events in DUI species may occur throughout their genomes. Future investigations may reveal more complex patterns of inheritance of recombinants, which could explain the retention of signal for a single origination of male mitochondrial DNA in protein coding genes.

evolutionary biology↗

bioRxiv2022

Synthesis of natural history collections data reveals patterns of US freshwater mussel diversity and decline

Natural history collections are uniquely positioned to chronicle biodiversity changes across time and space and are a fundamental data source in taxon-based research and conservation. With over 90 species listed under the Endangered Species Act, freshwater mussels are one of the most imperiled animal assemblages in the United States and are the focus of considerable conservation efforts (e.g., species status assessments, listing decisions, and recovery plans). Unfortunately, natural history collections data is often underleveraged in such efforts, in part, because much of the data are decentralized and nonstandard, and thus, difficult to access and analyze. Our objective herein is to synthesize, standardize, and enrich digitized US freshwater mussel collections data to better suit the needs of conservation stakeholders. We aggregated specimen records from 45 US natural history collections and enriched these records by programmatically standardizing taxonomic information, flagging potentially problematic records, and joining records with freshwater-specific spatial frameworks (e.g., hydrological units and stream segments) and their associated metadata (e.g., area, stream order, discharge, velocity). The assembled dataset includes 410,665 records, 302 species, and 1,494 hydrological units (8 digit-level). Using these enriched records, we estimated ecological attributes for over 280 freshwater mussel species including aspects of range size (i.e., area of occupancy and change in area of occupancy) and habitat preferences (i.e., stream order size, discharge, slope, and velocity). Listed species had significantly fewer occurrences (p<0.001) and smaller area of occupancy (p<0.001) in comparison to non-listed listed species. Listed species also tended to have a higher stream order preference (p<0.001) and discharge preference than non-listed species (p<0.001). These important ecological attributes have not been incorporated into freshwater mussel conservation efforts in a quantitative way and our novel estimates can be used to make more data-driven ecological and conservation inferences.

ecology↗