bluuebunny/crossref_metadata_2025_split huggingface dataset:
Vegetation type, not the legacy of warming, modifies the response of microbial functional genes and greenhouse gas fluxes to drought in Oro-Arctic and alpine regions
Authors: Fry Ellen L , Ashworth Deborah , Allen Kimberley A J , Chardon Nathalie Isabelle , Rixen Christian , Björkman Mats P , Björk Robert G , Stålhandske Thomas , Molau Mathias , Locke-King Brady , Cantillon Isabelle , McDonald Catriona , Liu Hongwei , De Vries Franciska T , Ostle Nick J , Singh Brajesh K , Bardgett Richard D 
Published: November 2023

Abstract
Climate warming and summer droughts alter soil microbial activity, affecting greenhouse gas (GHG) emissions in Arctic and alpine regions. However, the long-term effects of warming, and implications for future microbial resilience, are poorly understood. Using one alpine and three Arctic soils subjected to in situ long-term experimental warming, we simulated drought in laboratory incubations to test how microbial functional-gene abundance affects fluxes in three GHGs: carbon dioxide, methane, and nitrous oxide. We found that responses of functional gene abundances to drought and warming are strongly associated with vegetation type and soil carbon. Our sites ranged from a wet, forb dominated, soil carbon-rich systems to a drier, soil carbon-poor alpine site. Resilience of functional gene abundances, and in turn methane and carbon dioxide fluxes, was lower in the wetter, carbon-rich systems. However, we did not detect an effect of drought or warming on nitrous oxide fluxes. All gene–GHG relationships were modified by vegetation type, with stronger effects being observed in wetter, forb-rich soils. These results suggest that impacts of warming and drought on GHG emissions are linked to a complex set of microbial gene abundances and may be habitat-specific.
The impact of platform mounting systems used in rope parks on tree vitality
Authors: Szewczyk Grzegorz , Kraj Wojciech , Mitka Bartosz , Bożek Piotr , Tylek Paweł 
Published: November 2023

Abstract
Platforms mounted in rope parks cause injuries to trees. A new method for mounting platforms uses clamps, which are theoretically less damaging to trees. The aim of the study was to determine changes in the vitality of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) in response to both old and new platform mounting systems. In the old system, wooden brackets are embedded in the cut fragments of tree trunks. The new system utilizes clamps that hug the trunks. A vitality assessment was carried out for control trees and trees with platforms on the basis of changes in the assimilation apparatus and the degree of thinning of the outer parts of the tree crowns. The visual assessments differentiated between four classes 0–3. We conducted the visual assessment of tree vitality four years after the installation of the platforms. The level of trunk circumference damaged at the platform attachment point was determined using photogrammetric measurements and laser scanning technology. The obtained results were compared to the vitality recorded for the control trees. Trees that initially had better vitality showed a clear decrease in vitality of ~0.5 class. Trees that initially had worse vitality showed a clear increase in vitality, ranging from 0.25 class for pines to 1 class for beeches. Pine and beech trees reacted differently to injuries caused by platform installations. Pine trees responded with a slight increase in vitality, while beech trees experienced a decrease of over a hundred percent. The reactions were more pronounced for the new type of platform. Although the new method of platform installation caused more damage to the surface of tree trunks, its impact on tree vitality was not significantly greater than that of the old system. Since the new system allows, in the long term, to change the position on the tree and adjust the clamping force to the trunk, it can be recommended for use in practice despite the overall slightly worse state of the trees when compared to trees with the old mounting system.
A framework of moderators in social norm-based message persuasiveness based on a systematic review
Authors: Chen Junhan , Xia Shilin , Lin Tong 
Published: November 2023

Abstract
Social norm-based messages have been widely used for persuasion. However, the current trend that research focuses on using social norm theories rather than theorizing about social norms may hinder theory advancement. Although there are efforts theorizing moderators in norm–behavior relationship, the empirical studies testing the theories have yielded mixed findings, and the unclear focus on social norm-based messages versus perceived norms may impede theorizing the communication process regarding social norm-based persuasion. To bridge this gap, our study takes an inductive approach based on 85 studies to create a framework of moderators in social norm-based message persuasiveness. The framework identifies five factors moderating the effect of descriptive norm messages on behavioral intentions and two factors for injunctive norm messages. This framework lays a foundation for theorizing the mechanism of social norm-based message persuasiveness, highlights empirically supported conditions for message persuasiveness, and offers practical implications for designing targeted social norm-based messages.
Pulling the field out of a “One Variable, One Role” mindset: maximizing the theoretical value of interaction terms in communication’s mediation models
Authors: Holbert R Lance , Song Hyunjin , Ellithorpe Morgan E , LaMarre Heather L , Baik Elizabeth S , Tolan Colleen M 
Published: November 2023

Abstract
Recent analytical work reveals the need to assess mediated interactions (independent variable-by-mediator multiplicative terms) in mediation models to ensure the proper reporting of indirect effects. Besides their analytical value, mediated interactions can aid theory development. This study adds a theoretical support structure to this emergent analytical imperative and provides a theory-driven decision tree for incorporating mediated interactions into communication models. More broadly, mediated interactions are used as a basis to encourage the field to move beyond a “one variable, one role” approach to model building. Monte Carlo simulations reflecting common communication research practices were constructed and 1,920,000 datasets were analyzed to reveal the relative upsides and minimal risk incurred from assessing mediated interactions. In addition, the analyses elucidate the downsides incurred from not exploring these relationships when they are present in a population. The implications of these findings for future research and theory development are explored.
117 Combining Large Broiler Populations into a Single Genomic Evaluation: Dealing with Genetic Divergence
Authors: Tabet Joe Menwer , Bussiman Fernando , Breen Vivian , Misztal Ignacy , Lourenco Daniela 
Published: November 2023

Abstract
Combining populations that diverged at some point is a common practice, especially in poultry, where generation intervals are short and genetic evaluations should be available often. This study assessed whether combining large, divergent broiler populations into one genomic evaluation is feasible and the best strategy for doing that in single-step GBLUP. The pedigree for populations 1 and 2 included 397,954 and 411,780 animals, of which 154,911 and 168,429 were genotyped. Phenotypes for body weight (g), carcass yield (%), mortality (0-1), and feet health (0-6) were available for 397,954 animals in population 1 and 411,780 in population 2. A four-trait model was used in the analyses, and the genetic differences between the two populations were modeled in different ways: M1) included an extra fixed effect that accounted for population origin; M2) added origin-specific fixed effects, where the animal origin was concatenated with all fixed effects in the model. The two models were compared against a conventional model without accounting for the animal origin as a fixed effect (M0). Unknown parent groups (UPG) were fit to account for the genetic differences in M0, M1, and M2; therefore, they were set based origin population of the animal and sex. The models were evaluated based on accuracy, bias, and dispersion from the linear regression (LR) method and predictive ability (PA), where GEBV in the reduced data was compared against phenotypes adjusted for fixed effects. Validations were done separately in single and multi-populations to better assess the benefits of combining the two populations. Overall, the accuracy obtained by PA was less than by LR. No apparent accuracy, PA, bias, and dispersion differences were observed among the multi-population models across all traits. This was also true when comparing validation parameters in a multi-population against the ones from a single population. However, we observed that animals from one population were always superior to the other. A multi-trait model was applied, treating the same trait in each population differently. Genetic correlations between the same trait in different populations were moderate to low, suggesting that the two lines should possibly be evaluated separately. Defining whether populations should be combined in a single evaluation may go beyond validation parameters. Identifying if traits are the same across populations is crucial for the feasibility of combined evaluations.
120 Using Simulated Data to Evaluate the Accuracy of Selection Signature Detection Methods in Varying Scenarios
Authors: Retief Anel , Brito Luiz F F , Visser Carina 
Published: November 2023

Abstract
The identification of molecular signatures of selection has become increasingly important in recent years. The detection of selection signatures can aid in both population characterization as well as the detection and identification of genes and advantageous mutations within populations. Various statistical methodologies have been developed to detect directional selection signatures based on different demographic or selection models. The detection of definite evidence of selection on genomes remains a challenge, as other events in the demographic history of a population under investigation may imitate the effects of selection on the genetic variation in the genome. To accurately detect selection signatures, researchers need to consider the large number of methodologies available as well as the factors influencing the detection of selection signatures. Using simulated data, it is possible to evaluate several methods and factors on a larger number of genotypes without the expense of genotyping several animals. The objective of this study was to simulate several sheep populations that have undergone different selection pressures, with varying parameters that may possibly influence the accuracy of detecting selection signatures and testing multiple methods of selection signature detection to find the most reliable method for each scenario. Sheep populations were simulated with varying numbers of QTL (5, 100, 2000) and h2 (0.15, 0.4). The populations were then expanded through either selection for high, low, or random genetic values. Thereafter the populations were selected for sample sizes of 20, 200 and 1000 individuals, resulting in a total of 54 populations for analysis. The populations were then subjected to ROH, iHS, Fst and XP-EHH analysis. After the most effective method for detection of selection signatures for each scenario was identified, the influence of effective population size, generations of animals included in the sample, relatedness, and sex of individuals on the accuracy of each method was tested. Preliminary results indicated that the accuracy of XP-EHH as well as ROH increased with larger population sizes, as well as greater numbers of QTL. Further results of this study will be presented at the conference.
121 Quantifying Genetic Relationships to Maintain Genetic Diversity in the Canadian Dairy Population
Authors: Obari Christiana O , Makanjuola Bayode O , Schenkel Flavio S , Miglior Filippo , Maltecca Christian , Baes Christine F 
Published: November 2023

Abstract
Intensive selection pressure coupled with the use of a limited number of high genetic merit sires has resulted in reduced effective population size and increased levels of inbreeding in dairy cattle populations. The increased rate of inbreeding amplifies the frequency of recessive deleterious alleles, possibly leading to inbreeding depression in economically important traits, such as disease resistance, fertility and production. To better understand the relationship of available and active breeding sires to Canadian herds, quantifying genetic relationships between these groups could prove insightful. The genetic relationship value (R-value) represents the average number of alleles identical by descent shared between an animal and a reference population and is currently estimated based on pedigree information. Furthermore, the average R-value indicates the relationship of a sire to the rest of the population. Estimating the R-value between sires and individual herds may offer a more refined tool for producers to select sires that are less related to their specific herds. The aim of this study was to quantify and characterize R-values between individual sires and individual herds within the Canadian Holstein population. To quantify R-values, a dataset comprised of 11,914 sires born between 1953 and 2020 and 584,740 active cows born between 1997 and 2022 from 5,592 herds was considered. Active cows were defined as those currently alive, on milk recording, and actively contributing to the national milk inventory. Active sires were considered those used to breed the active cows. All data were provided by Lactanet Canada and analyses were carried out on PEDIG using the par2 program for relationship estimation. Results indicate that herd-level R-values (i.e., R-values estimated using individual sires and individual herds) showed variation. The R-value of individual sires to individual herds containing active cows ranged from 0.43% to 32.38. This result indicates that outbred sires are available to individual farmers when the population is considered at the herd level, and that the development of herd-level R-values may provide a selection tool which promotes the use of less related sires. Next steps include developing and estimating genomic R-values and comparing them with the pedigree-based R-values for the genotyped Canadian Holstein population. By better understanding of the relationship of available breeding sires to individual Canadian herds, a simple tool for producers to identify sires which are less related to their herds can be developed.
122 Comparison of Algorithms for Approximation of Accuracies for Single-Step Genomic Best Linear Unbiased Predictor Models
Authors: Ramos Pedro Vital Brasil , Garcia Andre , Retallick Kelli J , Bermann Matias , Misztal Ignacy , Lourenco Daniela 
Published: November 2023

Abstract
The theoretical accuracy of breeding values can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side of the mixed model equations. However, inverting the coefficient matrix is not computationally feasible for an extensive system of equations, especially if genomic information is available. Thus, different algorithms to approximate accuracies have been proposed. We aimed to compare the efficiency of two algorithms implemented in the accf90GS and accf90GS2 software from the BLUPF90 family. The accf90GS algorithm approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, the accf90GS2 uses block sparse inversion of G-1. The data for this study were provided by the American Angus Association (Saint Joseph, MO) and included phenotypes of three growth traits and eleven carcass traits, 1,235,930 genotyped animals, and pedigree ranging from 4,315,913 to 12,492,581 animals. Three analyses were performed: 1) comparison of the approximated accuracies between algorithms; 2) comparison between approximated and exact accuracies; and 3) evaluation of the impact of adding genotyped animals, with and without phenotypes, in the approximated accuracies. The results were obtained for genotyped animals (group 1) and genotyped animals without phenotypes (group 2). The complete datasets under multi-trait models with ssGBLUP and the algorithm for proven and young were used for analysis 1. A subset of data under single-trait models was used for analyses 2 and 3, so it was possible to calculate the exact accuracies. In analysis 1, the correlations of groups 1 and 2 ranged from 0.55 to 0.74 and 0.38 to 0.65, respectively. The growth traits presented the lowest correlations. In addition, the slope was under 0.75 for both groups. These results suggest that the approximated accuracies are considerably different, especially for growth traits and genotyped animals without phenotype. The accf90GS2 algorithm presented the highest correlations with exact accuracies, which indicates that it is more efficient than the accg90GS algorithm. The intercept was almost zero for both approximation methods, whereas the slope ranged from 0.937 to 1.234 and 0.819 to 0.949 for accf90GS and accf90GS2 accuracies, respectively. This suggests a minor overestimation and underestimation by the accf90GS2 and accf90GS algorithms, respectively. The increase in mean accuracy due to adding genotyped animals with phenotypes was higher for accf90GS2. According to the results, the algorithm implemented in accf90GS2 is recommended for genetic evaluations because of its efficiency and precision.
402 Awardee Talk: Stress-Induced Dna Methylation Changes and Their Implications in Livestock Production
Authors: Baker Emilie C , Earnhardt-San Audrey L , Cilkiz Kubra Z , Littlejohn Brittni P , Cardoso Rodolfo C , Ghaffari Noushin , Long Charles R , Riggs Penny K , Randel Ronald D , Welsh Thomas H , Riley David G 
Published: November 2023

Abstract
It is well known that pre- and post-natal stressors can impact livestock phenotype and performance. However, less is known about the molecular mechanisms responsible for the changes observed in individuals exposed to stress. A proposed mechanism is epigenetic modifications, such as DNA methylation (DNAm). Epigenetic marks are considered a bridge between the environment and genetics because the marks shift in response to environmental stimuli. Stress induced alterations of DNAm can result in changes in gene expression which could be the driver behind altered phenotype. “Omic” technologies have allowed the study of how DNAm patterns change in response to stressors in livestock species. Stressors such as weaning, transportation, and heat stress have altered DNAm patterns. These changes in methylation patterns have been associated with changes in immune function, growth, and behavior. Prenatal stressors have also modified DNAm patterns within the offspring, with some differences persisting later in life. These stress induced DNAm alterations can be transgenerational, potentially affecting livestock productivity and health over multiple generations. Not all alterations are negative; certain stress-induced modifications could allow animals to adapt better to stressors and changing conditions. These marks may indicate exposure of the animal to stress and the possible long-term effects on productivity. There is potential for using methylation patterns as a noninvasive way to monitor stress levels and assess animal welfare. Understanding how the methylome responds to stressors and how it contributes to performance will be beneficial for future selection decisions. More research is needed to identify management practices that might mitigate the negative impacts of stress induced DNAm changes and how the industry can take advantage of the role DNAm has in adapting to stressors and new environments. The epigenome will likely play a critical role in the livestock industry's future as the climate shifts and consumer demands focus more on animal welfare.
149 Marker Effect P-Value for Large Genotype Populations with the Algorithm for Proven and Young
Authors: Leite Natalia , Bermann Matias , Tsuruta Shogo , Misztal Ignacy , Lourenco Daniela 
Published: November 2023

Abstract
The single-step method (ssGBLUP) allows for simultaneous evaluations of populations composed of genotyped and non-genotyped animals. As a GBLUP-based method, the output of ssGBLUP is breeding values. However, interest might rely on the estimation of SNP effects and on how genome segments are associated with the phenotype of interest. When that is the case, SNP effects can be obtained from a linear transformation of breeding values, and P-values can be used as a measure of estimation certainty. P-values are derived from the prediction error variance of SNP effects, which relies on the prediction error (co)variance matrix of breeding values obtained from the inverse of the left-hand side of the mixed model equations (LHS). However, inverting the LHS becomes unfeasible with large, genotyped populations. Therefore, alternative strategies to overcome this problem and obtain p-values with increasing genotyped populations might be evaluated. In this study, we aimed to estimate marker effect p-values when the algorithm for proven and young (APY) was used to approximate the predictor error (co)variance for animals in a large, genotyped population. We estimate P-values of SNP marker effects for PWG in an Angus cattle population. The dataset was composed of around 844K phenotyped animals, 450K genotypes, and 1.8M pedigree records. Analyses were split into two sets. In the first set, a reduced genotype data of 50K was used, so p-values obtained from a direct-inverse of LHS using the regular G-1 (Exact_G) or APY G-1 (Exact_GAPY) were compared with APY G-1 and an approximation of the prediction error variance that did not require the inversion of the LHS (Approx_GAPY). In the second set of analyses, P-values with Approx_GAPY were estimated with the full genomic set of 450K (Approx_GAPY_450K), and computational requirements were recorded. All analyses were performed in three replicates. In the first set of analyses, genome-wide association with Exact_G uncovered two significant regions in chromosomes 7 and 20. Along all replicates, the same regions were also identified with Exact_GAPY and Approx_GAPY, indicating that similar resolutions are obtained with exact and approximated p-values. Results from Approx_GAPY_450K showed that, with a complete genome set, besides the two regions in chromosomes 7 and 20, two new regions in chromosomes 6 and 14 were identified. On average, the entire procedure for obtaining P-values with 450K genotyped animals had an elapsed wall clock time of 24h with a maximum memory usage of 87.6 GB. Altogether, our results suggest that with APY and the approximation of the prediction error variance, current computational boundaries for obtaining marker effect P-values for large genotyped populations should be lifted.