In-field genetic stock identification of overwintering coho salmon in the Gulf of Alaska: Evaluation of Nanopore sequencing for remote real-time deployment.

Genetic stock identification (GSI) from genotyping-by-sequencing of single nucleotide polymorphism (SNP) loci has become the gold standard for stock of origin identification in Pacific salmon. The sequencing platforms currently applied require large batch sizes and multiday processing in specialized facilities to perform genotyping by the thousands. However, recent advances in third-generation single-molecule sequencing platforms, such as the Oxford Nanopore minION, provide base calling on portable, pocket-sized sequencers and promise real-time, in-field stock identification of variable batch sizes. Here we evaluate utility and comparability to established GSI platforms of at-sea stock identification of coho salmon (Oncorhynchus kisutch) using targeted SNP amplicon sequencing on the minION platform during a high-sea winter expedition to the Gulf of Alaska. As long read sequencers are not optimized for short amplicons, we concatenate amplicons to increase coverage and throughput. Nanopore sequencing at-sea yielded data sufficient for stock assignment for 50 out of 80 individuals. Nanopore-based SNP calls agreed with Ion Torrent-based genotypes in 83.25%, but assignment of individuals to stock of origin only agreed in 61.5% of individuals, highlighting inherent challenges of Nanopore sequencing, such as resolution of homopolymer tracts and indels. However, poor representation of assayed salmon in the queried baseline data set contributed to poor assignment confidence on both platforms. Future improvements will focus on lowering turnaround time and cost, increasing accuracy and throughput, as well as augmentation of the existing baselines. If successfully implemented, Nanopore sequencing will provide an alternative method to the large-scale laboratory approach by providing mobile small batch genotyping to diverse stakeholders.

Whole-organism responses to constant temperatures do not predict responses to variable temperatures in the ecosystem engineer Mytilus trossulus.

Understanding and predicting responses of ectothermic animals to temperature are essential for decision-making and management. The thermal performance curve (TPC), which quantifies the thermal sensitivity of traits such as metabolism, growth and feeding rates in laboratory conditions, is often used to predict responses of wild populations. However, central assumptions of this approach are that TPCs are relatively static between populations and that curves measured under stable temperature conditions can predict performance under variable conditions. We test these assumptions using two latitudinally matched populations of the ecosystem engineer Mytilus trossulus that differ in their experienced temperature variability regime. We acclimated each population in a range of constant or fluctuating temperatures for six weeks and measured a series of both short term (feeding rate, byssal thread production) and long-term (growth, survival) metrics to test the hypothesis that performance in fluctuating temperatures can be predicted from constant temperatures. We find that this was not true for any metric, and that there were important interactions with the population of origin. Our results emphasize that responses to fluctuating conditions are still poorly understood and suggest caution must be taken in the use of TPCs generated under constant temperature conditions for the prediction of wild population responses.