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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Registration of the sorghum carbon–partitioning nested association mapping (CP–NAM) population

The sorghum [ Sorghum bicolor (L.) Moench] carbon-partitioning nested association mapping (CP_NAM) (Reg. no. MP-4, NSL 542189 MAP) population was developed at Clemson University, SC, using 11 diverse, male founder accessions, each crossed with a recurrent female parent ‘Grassl’. The male parents represent all five major botanical races and the four major agronomic types: cellulosic (5), sweet (3), grain (2) and forage (1). A set of 11 recombinant inbred line (RIL) families CP_NAM01 to CP_NAM011 were maintained, which consisted of 2,484 (F 6 ) individuals. Each RIL family contained a minimum of 193 individuals (CP_NAM01) and a maximum of 287 individuals (CP_NAM06). For the development of this population, the founder lines were judiciously selected from the sorghum Bioenergy Association Panel based on carbon-partitioning phenotypes that make this population an ideal genetic resource for dissecting a wide range of agronomic and compositional traits for basic and applied research. The founder accessions of the CP_NAM were phenotypically characterized for various traits, including agronomic, biomass and related components, and additional compositional components. Each of the 11 F 6 RIL families of the CP_NAM were genotyped using genotyping-by-sequencing analysis, and 144,087 single nucleotide polymorphisms were generated for each individual. Genotypic information along with phenotypic data were used for the characterization of this population and to explore the range of phenotypes that permits the understanding of carbon-partitioning dynamics. This population is a unique resource for researchers to study a wide range of contrasting carbon-partitioning characteristics in sorghum to understand the genetic architecture underlying whole-plant carbon partitioning and allocation.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

The evolution of transcriptional regulation in eukaryotes

Gene expression is central to the genotype-phenotype relationship in all organisms, and it is an important component of the genetic basis for evolutionary change in diverse aspects of phenotype. However, the evolution of transcriptional regulation remains understudied and poorly understood. Here we review the evolutionary dynamics of promoter, or cis-regulatory, sequences and the evolutionary mechanisms that shape them. Existing evidence indicates that populations harbor extensive genetic variation in promoter sequences, that a substantial fraction of this variation has consequences for both biochemical and organismal phenotype, and that some of this functional variation is sorted by selection. As with protein-coding sequences, rates and patterns of promoter sequence evolution differ considerably among loci and among clades for reasons that are not well understood. Studying the evolution of transcriptional regulation poses empirical and conceptual challenges beyond those typically encountered in analyses of coding sequence evolution: promoter organization is much less regular than that of coding sequences, and sequences required for the transcription of each locus reside at multiple other loci in the genome. Because of the strong context-dependence of transcriptional regulation, sequence inspection alone provides limited information about promoter function. Understanding the functional consequences of sequence differences among promoters generally requires biochemical and in vivo functional assays. Despite these challenges, important insights have already been gained into the evolution of transcriptional regulation, and the pace of discovery is accelerating.

Review, Academic↗
DOE OSTI2023

Genome-Wide Informative Microsatellite Markers and Population Structure of Fusarium virguliforme from Argentina and the USA

Soybean sudden death syndrome (SDS) is a destructive disease that causes substantial yield losses in South and North America. Whereas four Fusarium species were identified as the causal agents, F. virguliforme is the primary SDS-causing pathogen in North America and it also contributes substantially to SDS in Argentina. In this study, we comparatively analyzed genome assemblies of four F. virguliforme strains and identified 29 informative microsatellite markers. Sixteen of the 29 markers were used to investigate the genetic diversity and population structure of this pathogen in a collection of 90 strains from Argentina and the USA. A total of 37 multilocus genotypes (MLGs) were identified, including 10 MLGs in Argentina and 26 in the USA. Only MLG2, the most dominant MLG, was found in both countries. Analyses with three different approaches showed that these MLGs could be grouped into three clusters. Cluster IA consisting of four MLGs exclusively from the USA has much higher genetic diversity than the other two clusters, suggesting that it may be the ancestral cluster although additional data are necessary to support this hypothesis. Clusters IB and II consisted of 13 and 21 MLGs, respectively. MLGs belonging to these two clusters were present in all four sampled states in Argentina and all five sampled states in the USA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Virulence and Genetic Diversity of Puccinia spp., Causal Agents of Rust on Switchgrass (Panicum virgatum L.) in the USA

Switchgrass (Panicum virgatum L.) is an important cellulosic biofuel grass native to North America. Rust, caused by Puccinia spp. is the most predominant disease of switchgrass and has the potential to impact biomass conversion. In this study, virulence patterns were determined on a set of 38 switchgrass genotypes for 14 single-spore rust isolates from 14 field samples collected in seven states. Single nucleotide polymorphism (SNP) variation was also assessed in 720 sequenced cloned amplicons representing 654 base pairs of the elongation factor 1-α gene from the field samples. Five major haplotypes were identified differing by 11 out of the 39 SNP positions identified. STRUCTURE, Principal Coordinate Analysis, and phylogenetic analyses divided the rust population into two genetic clusters. Virginia and Georgia had the highest and lowest rust genetic diversity, respectively. Only nine accessions showed a differential disease response between the 14 isolates, allowing the identification of eight races, differing by 1–3 virulence factors. Overall, the results suggested clonal reproduction of the pathogen and a North–South differentiation via local adaptation. However, similar haplotypes and races were also recovered from several states, suggesting migration events, and highlighting the need to further investigate the switchgrass rust population structure and evolution in the USA.

Bahri, Bochra A. (ORCID:0000000159055880)↗
DOE OSTI2023

Local-scale panmixia in the lichenized fungus Xanthoria parietina contrasts with substantial genetic structure in its Trebouxia photobionts

Microsatellite markers can provide valuable information about gene flow and population history. We developed and tested new microsatellites for the nitrophilic lichenized fungus Xanthoria parietina and studied its genetic diversity and structure within the urban area of Munich, Bavaria. We compared its local genetic pattern with that of its photobiont partner Trebouxia decolorans, for which existing microsatellites were applied. For comparison, a reference site with clean air was included in the sampling. We found support for three genetic clusters in the fungus X. parietina, which occurred intermingled in collecting sites. There was a high degree of admixture within fungal populations and individuals, and analysis of molecular variance revealed a lack of population structure in the mycobiont. The Trebouxia photobiont, in contrast, exhibited structured populations which grouped into two to five genetic clusters, and individuals showed less admixture than in the mycobiont. This indicates that the two lichen partners differ in their ability to move around in the landscape. The microsatellite markers we report are polymorphic and are suitable for population genetic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Local adaptation of both plant and pathogen: an arms‐race compromise in switchgrass rust

In coevolving species, parasites locally adapt to host populations as hosts locally adapt to resist parasites. Parasites often outpace host local adaptation since they have rapid life cycles, but host diversity, the strength of selection, and external environmental influence can result in complex outcomes. To better understand local adaptation in host–parasite systems, we examined locally adapted switchgrass (Panicum virgatum), and its leaf rust pathogen (Puccinia novopanici) across a latitudinal range in North America. We grew switchgrass genotypes in 10 replicated multiyear common gardens, measuring rust severity from natural infection in a ‘host reciprocal transplant’ framework for testing local adaptation. We conducted genome-wide association mapping to identify genetic loci associated with rust severity. Genetically differentiated rust populations were locally adapted to northern and southern switchgrass, despite host local adaptation to environmental conditions in the same regions. Rust resistance was polygenic, and distinct loci were associated with rust severity in the north and south. We narrowed a previously identified large-effect quantitative trait locus for rust severity to a candidate YELLOW STRIPE-LIKE gene and linked numerous other loci to defense-related genes. Overall, our results suggest that both hosts and parasites can be simultaneously locally adapted, especially when parasites impose less selection than other environmental factors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Enhanced Resistance Pines for Improved Renewable Biofuel and Chemical Production (Technical Report)

We completed phenotyping constitutive and inducible oleoresin flow across two seasons, constitutive resin canal number and density and wood terpene content in our ADEPT2 and CCLONES populations. We completed genetic association between 19 oleoresin phenotypes and a total of 523,192 SNP markers from ADEPT2 and 13,883 SNP markers in CCLONES using four mixed linear models. A total of 293 significant SNPs (FDR = 0.20) were identified. We used the MENTOR tool to mine mechanistic connections from a multiplex network constructed from poplar multi-omic data to construct a conceptual model for a subset of these significant SNPs. Our model contains 6 transcriptional regulators in addition to 3 monoterpene synthases. To generate more lines of evidence for these significant SNPs, we completed a time course RNAseq experiment after inducing vascular zone cells to differentiate into new resin canals with a methyl jasmonate treatment, a single nuclei RNAseq that identified differentiating resin canal epithelial cells and are completing analysis for a QTL study in a hybrid pine population. The time course identified 4634 significantly down and 1890 significantly up regulated transcripts after treatment with methyl jasmonate, an inducer of new resin canal formation in the vascular cambial meristem. To analyze this large set of differentially regulated genes, we created a predictive expression network and analyzed it with random walk restart using 6 seed genes coding for transcription factors regulating xylem differentiation in poplar. Of the top ranked 200 transcripts, 119 transcripts were significant differentially expressed supporting these transcripts as potential candidates regulating resin canal formation. Analysis of single nuclei sequencing of shoot tips that contain differentiating resin canals, identified 10 clusters. One cluster was highly enriched in transcripts coding for 9 of the enzymes in the MEP pathway 3 prenyl synthetases, and 3 monoterpene synthases strongly suggesting that this cluster represents resin canal epithelial cells. We are mining the additional transcripts to create a trajectory analysis. In summary, we have identified > 10 novel genes that are strongly supported candidates for further analysis in breeding lines and for genetic engineering over- and under- expressing lines to increase wood terpene content to improve resistance to insect and fungal pathogens while simultaneously increasing terpene supplies for renewable chemicals and biofuels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Which Plant Traits Increase Soil Carbon Sequestration? Empirical Evidence From a Long‐Term Poplar Genetic Diversity Trial

Plants play a key role in mediating soil response to global change, and breeding or engineering crops to increase soil organic carbon (SOC) storage is a potential route to land-based carbon dioxide removal in agricultural systems. However, due to limited observational datasets plus shifting paradigms of SOC stabilization, it is unclear which plant traits are most important for enhancing different types of soil organic matter. Existing long-term common gardens of genetically diverse plant populations may provide an opportunity to evaluate biological controls on SOC, separate from environmental or management variability. Here we report on soil and root chemical data collected for 24 genotypes within a 13-year-old common garden in northwestern Oregon planted with a large natural variant population of Populus trichocarpa. Fractionating surface soil (0–15 cm) revealed substantial variation in stocks of mineral-associated organic matter (MAOM; 18–67 t C/ha) and particulate organic matter (POM; 2–22 t C/ha). Tree genotype explained 24% and 26% of the MAOM and POM stock variability, respectively, after controlling for background variability. We found minimal association between SOC concentration and either aboveground tree productivity or root biomass recalcitrance (C/N ratios and lignin content). In contrast, root elemental content appeared influential for MAOM-C concentration, which showed a strong positive association with root aluminum (Al) and a strong negative association with root boron (B) and magnesium (Mg). Furthermore, root concentrations of these elements were highly heritable (57%–78%) and not simply a reflection of background variation in soil elemental concentrations. We estimate that surface SOC stocks under these 24 genotypes have diverged at rates of up to 1.2–4.3 t C/ha/year. These results suggest that long-term genetic diversity trials have value for elucidating biological controls on soil organic matter dynamics, and that traits associated with root elemental content may be a useful target for enhancing biosequestration.

biomass recalcitrance↗
DOE OSTI2024

Utilizing digitized occurrence records of Midwestern feral Cannabis sativa to develop ecological niche models

Hemp (Cannabis sativa L.) has historically played a vital role in agriculture across the globe. Feral and wild populations have served as genetic resources for breeding, conservation, and adaptation to changing environmental conditions. However, feral populations of Cannabis, specifically in the Midwestern United States, remain poorly understood. This study aims to characterize the abiotic tolerances of these populations, estimate suitable areas, identify regions at risk of abiotic suitability change, and highlight the utility of ecological niche models (ENMs) in germplasm conservation. The Maxent algorithm was used to construct a series of ENMs. Validation metrics and MOP (Mobility-oriented Parity) analysis were used to assess extrapolation risk and model performance. We also projected the final projected under current and future climate scenarios (2021–2040 and 2061–2080) to assess how abiotic suitability changes with time. Climate change scenarios indicated an expansion of suitable habitat, with priority areas for germplasm collection in Indiana, Illinois, Kansas, Missouri, and Nebraska. This study demonstrates the application of ENMs for characterizing feral Cannabis populations and highlights their value in germplasm conservation and breeding efforts. Populations of feral C. sativa in the Midwest are of high interest, and future research should focus on utilizing tools to aid the collection of materials for the characterization of genetic diversity and adaptation to a changing climate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Detect and correct bias in multi-site neuroimaging datasets

The desire to train complex machine learning algorithms and to increase the statistical power in association studies drives neuroimaging research to use ever-larger datasets. The most obvious way to increase sample size is by pooling scans from independent studies. However, simple pooling is often ill-advised as selection, measurement, and confounding biases may creep in and yield spurious correlations. In this work, we combine 35,320 magnetic resonance images of the brain from 17 studies to examine bias in neuroimaging. In the first experiment, Name That Dataset, we provide empirical evidence for the presence of bias by showing that scans can be correctly assigned to their respective dataset with 71.5% accuracy. Given such evidence, we take a closer look at confounding bias, which is often viewed as the main shortcoming in observational studies. In practice, we neither know all potential confounders nor do we have data on them. Hence, we model confounders as unknown, latent variables. Kolmogorov complexity is then used to decide whether the confounded or the causal model provides the simplest factorization of the graphical model. Finally, we present methods for dataset harmonization and study their ability to remove bias in imaging features. In particular, we propose an extension of the recently introduced ComBat algorithm to control for global variation across image features, inspired by adjusting for unknown population stratification in genetics. Overall, our results demonstrate that harmonization can reduce dataset-specific information in image features. Further, confounding bias can be reduced and even turned into a causal relationship. However, harmonization also requires caution as it can easily remove relevant subject-specific information. Code is available at https://github.com/ai-med/Dataset-Bias.

42 ENGINEERING↗
DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Steelhead ( Oncorhynchus mykiss ) lineages and sexes show variable patterns of association of adult migration timing and age‐at‐maturity traits with two genomic regions

Abstract As life history diversity plays a critical role in supporting the resilience of exploited populations, understanding the genetic basis of those life history variations is important for conservation management. However, effective application requires a robust understanding of the strength and universality of genetic associations. Here, we examine genetic variation of single nucleotide polymorphisms in genomic regions previously associated with migration phenology and age‐at‐maturity in steelhead ( Oncorhynchus mykiss ) from the Columbia River. We found chromosome 28 markers (GREB1L, ROCK1 genes) explained significant variance in migration timing in both coastal and inland steelhead. However, strength of association was much greater in coastal than inland steelhead ( R 2 0.51 vs. 0.08), suggesting that genomic background and challenging inland migration pathways may act to moderate effects of this region. Further, we found that chromosome 25 candidate markers (SIX6 gene) were significantly associated with age and size at first return migration for inland steelhead, and this pattern was mediated by sex in a predictable pattern (males R 2 = 0.139–0.170; females R 2 = 0.096–0.111). While this encourages using these candidate regions in predicting life history characteristics, we suggest that stock‐specific associations and haplotype frequencies will be useful in guiding implementation of genetic assays to inform management.

Willis, Stuart C.↗
DOE OSTI2021

Nonparallel transcriptional divergence during parallel adaptation

Abstract How underlying mechanisms bias evolution toward predictable outcomes remains an area of active debate. In this study, we leveraged phenotypic plasticity and parallel adaptation across independent lineages of Trinidadian guppies ( Poecilia reticulata ) to assess the predictability of gene expression evolution during parallel adaptation. Trinidadian guppies have repeatedly and independently adapted to high‐ and low‐predation environments in the wild. We combined this natural experiment with a laboratory breeding design to attribute transcriptional variation to the genetic influences of population of origin and developmental plasticity in response to rearing with or without predators. We observed substantial gene expression plasticity, as well as the evolution of expression plasticity itself, across populations. Genes exhibiting expression plasticity within populations were more likely to also differ in expression between populations, with the direction of population differences more likely to be opposite those of plasticity. While we found more overlap than expected by chance in genes differentially expressed between high‐ and low‐predation populations from distinct evolutionary lineages, the majority of differentially expressed genes were not shared between lineages. Our data suggest alternative transcriptional configurations associated with shared phenotypes, highlighting a role for transcriptional flexibility in the parallel phenotypic evolution of a species known for rapid adaptation.

Fischer, Eva K.↗
DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

Varela, Sebastian↗
DOE OSTI2021

Molecular phylogenetic analysis of the Paguristes tortugae Schmitt, 1933 complex and selected other Paguroidea (Crustacea: Decapoda: Anomura)

Morphological characters, as presently applied to describe members of the Paguristes tortugae Schmitt, 1933 species complex, appear to be of limited value in inferring phylogenetic relationships within the genus, and may have similarly misinformed understanding of relationships between members of this complex and those presently assigned to the related genera Areopaguristes Rahayu & McLaughlin, 2010 and Pseudopaguristes McLaughlin, 2002. Previously undocumented observations of similarities and differences in color patterns among populations additionally suggest genetic divergences within some species, or alternatively seem to support phylogenetic groupings of some species. In the present study, a Maximum Likelihood (ML) phylogenetic analysis was undertaken based on the H3, 12S mtDNA, and 16S mtDNA sequences of 148 individuals, primarily representatives of paguroid species from the western Atlantic. This molecular analysis supported a polyphyletic Diogenidae Ortmann, 1892, although incomplete taxonomic sampling among the genera of Diogenidae limits the utility of this finding for resolving family level relationships. Several hypotheses regarding the evolutionary relationships among hermit crab genera were refuted by the Kishino-Hasegawa (KH). ShimodairaHasegawa (SH) and Approximately Unbiased (AU) tree topology tests, among them the hypothesis that Areopaguristes is monophyletic. A lack of support for the monophyly of Areopaguristes calls into question the phylogenetic validity of gill number for the differentiation of Paguristes, Areopaguristes, and Pseudopaguristes. The study was inconclusive with regard to the relationships among these three genera, but previously unknown diversity within both Paguristes and Areopaguristes was demonstrated. Existence of an undescribed species confounded under the name Paguristes tortugae Schmitt, 1933 was supported by genetics, morphology, and coloration. A second undescribed species with remarkable similarity to Areopaguristes hummi Wass, 1955 was discovered based on genetics and coloration.

12S↗
DOE OSTI2023

Reverse-zoonoses of 2009 H1N1 pandemic influenza A viruses and evolution in United States swine results in viruses with zoonotic potential

The 2009 H1N1 pandemic (pdm09) lineage of influenza A virus (IAV) crosses interspecies barriers with frequent human-to-swine spillovers each year. These spillovers reassort and drift within swine populations, leading to genetically and antigenically novel IAV that represent a zoonotic threat. We quantified interspecies transmission of the pdm09 lineage, persistence in swine, and identified how evolution in swine impacted zoonotic risk. Human and swine pdm09 case counts between 2010 and 2020 were correlated and human pdm09 burden and circulation directly impacted the detection of pdm09 in pigs. However, there was a relative absence of pdm09 circulation in humans during the 2020–21 season that was not reflected in swine. During the 2020–21 season, most swine pdm09 detections originated from human-to-swine spillovers from the 2018–19 and 2019–20 seasons that persisted in swine. We identified contemporary swine pdm09 representatives of each persistent spillover and quantified cross-reactivity between human seasonal H1 vaccine strains and the swine strains using a panel of monovalent ferret antisera in hemagglutination inhibition (HI) assays. The swine pdm09s had variable antigenic reactivity to vaccine antisera, but each swine pdm09 clade exhibited significant reduction in cross-reactivity to one or more of the human seasonal vaccine strains. Further supporting zoonotic risk, we showed phylogenetic evidence for 17 swine-to-human transmission events of pdm09 from 2010 to 2021, 11 of which were not previously classified as variants, with each of the zoonotic cases associated with persistent circulation of pdm09 in pigs. These data demonstrate that reverse-zoonoses and evolution of pdm09 in swine results in viruses that are capable of zoonotic transmission and represent a potential pandemic threat.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2004

A Novel, Real-Valued Genetic Algorithm for Optimizing Radar Absorbing Materials

A novel, real-valued Genetic Algorithm (GA) was designed and implemented to minimize the reflectivity and/or transmissivity of an arbitrary number of homogeneous, lossy dielectric or magnetic layers of arbitrary thickness positioned at either the center of an infinitely long rectangular waveguide, or adjacent to the perfectly conducting backplate of a semi-infinite, shorted-out rectangular waveguide. Evolutionary processes extract the optimal physioelectric constants falling within specified constraints which minimize reflection and/or transmission over the frequency band of interest. This GA extracted the unphysical dielectric and magnetic constants of three layers of fictitious material placed adjacent to the conducting backplate of a shorted-out waveguide such that the reflectivity of the configuration was 55 dB or less over the entire X-band. Examples of the optimization of realistic multi-layer absorbers are also presented. Although typical Genetic Algorithms require populations of many thousands in order to function properly and obtain correct results, verified correct results were obtained for all test cases using this GA with a population of only four.

Hall, John Michael↗
DOE OSTI2024

Population assignment of migratory Westslope Cutthroat Trout (WCT) in the Clark Fork–Pend Oreille River basin

Abstract Objective The Clark Fork–Pend Oreille River basin of northeastern Washington and the Idaho Panhandle historically supported a robust metapopulation of the Westslope Cutthroat Trout (WCT) Oncorhynchus lewisi, a western native salmonid of high cultural and economic value. The construction of impassible hydroelectric dams and smaller instream barriers has prevented the return of migratory WCT to spawning tributaries, leading to the fragmentation of this metapopulation over the past 100 years. One such impassible barrier is Albeni Falls Dam (AFD) near Newport, Washington, which was completed without fish passage capabilities in 1955. We sought to examine large-scale genetic patterns in the study area and determine the most likely spawning tributary of origin for migratory WCT captured below AFD. Methods We created a genetic baseline representative of populations within the Clark Fork–Pend Oreille River basin from upstream and downstream of the dam using 191 biallelic single-nucleotide polymorphism genetic markers. Our data set included 124 collections, which allowed for an examination of population structure and hatchery influence across the study area and provided a robust tool for population assignment. Population assignment tests were conducted using the program RUBIAS. Result Population assignment tests were successful for all pure WCT of unknown origin despite potential influence from hatchery lineages across the study area. Of 83 migratory WCT captured below AFD, approximately 80% were assigned to tributaries upstream of AFD with a posterior assignment probability of at least 90%. Only one fish was assigned to a tributary downstream of AFD. Conclusion Our results indicate that AFD disrupts the natural metapopulation dynamics of WCT populations in the basin. Passage for WCT at this barrier would reestablish metapopulation connectivity within the basin by allowing migratory individuals to make genetic contributions to populations upstream of the dam.

Wells, Craig D. (ORCID:0000000258513250)↗