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The tree seed mycobiome has received little attention despite its potential role in forest regeneration and health. The aim of the present study was to analyze the processes shaping the composition of seed fungal communities in natural forests as seeds transition from the mother plant to the ground for establishment.We used metabarcoding approaches and confocal microscopy to analyze the fungal communities of seeds collected in the canopy and on the ground in four natural populations of sessile oak (Quercus petraea). Ecological processes shaping the seed mycobiome were inferred using joint species distribution models.Fungi were present in seed internal tissues, including the embryo. The seed mycobiome differed among oak populations and trees within the same population. Its composition was largely influenced by the mother, with weak significant environmental influences. The models also revealed several probable interactions among fungal pathogens and mycoparasites.Our results demonstrate that maternal effects, environmental filtering and biotic interactions all shape the seed mycobiome of sessile oak. They provide a starting point for future research aimed at understanding how maternal genes and environments interact to control the vertical transmission of fungal species that could then influence seed dispersal and germination, and seedling recruitment.

Background Cross-validation tools are used increasingly to validate and compare genetic evaluation methods but analytical properties of cross-validation methods are rarely described. There is also a lack of cross-validation tools for complex problems such as prediction of indirect effects (e.g. maternal effects) or for breeding schemes with small progeny group sizes. Results We derive the expected value of several quadratic forms by comparing genetic evaluations including “partial” and “whole” data. We propose statistics that compare genetic evaluations including “partial” and “whole” data based on differences in means, covariance, and correlation, and term the use of these statistics “method LR” (from linear regression). Contrary to common belief, the regression of true on estimated breeding values is (on expectation) lower than 1 for small or related validation sets, due to family structures. For validation sets that are sufficiently large, we show that these statistics yield estimators of bias, slope or dispersion, and population accuracy for estimated breeding values. Similar results hold for prediction of future phenotypes although we show that estimates of bias, slope or dispersion using prediction of future phenotypes are sensitive to incorrect heritabilities or precorrection for fixed effects. We present an example for a set of 2111 Brahman beef cattle for which, in repeated partitioning of the data into training and validation sets, there is very good agreement of statistics of method LR with prediction of future phenotypes. Conclusions Analytical properties of cross-validation measures are presented. We present a new method named LR for cross-validation that is automatic, easy to use, and which yields the quantities of interest. The method compares predictions based on partial and whole data, which results in estimates of accuracy and biases. Prediction of observed records may yield biased results due to precorrection or use of incorrect heritabilities.

Background Parental contributions to offspring phenotype extend beyond genetic inheritance, encompassing non-genetic factors that influence early development. However, the interplay between maternal and paternal effects remains poorly understood. This study investigates these contributions in Eurasian perch ( Perca fluviatilis ) — a valuable model to study parental effect in finfishes — by analyzing early life traits and transcriptomic profiles of larvae resulting from crosses between wild and domesticated parents. Results Maternal effects dominated key developmental traits, including hatching success, growth, and swim bladder inflation. Transcriptomic analysis revealed a complex regulatory interplay, with 573 genes under exclusive maternal control, while no genes were solely influenced by paternal input. Maternal-effect genes were primarily associated with metabolic, developmental, and stress-response pathways, shaping early larval physiology. Further analysis identified Eurasian-perch-specific candidate maternal-effect genes, such as crtac1 , slc16a7 , cox5b , kdr , cald1 , and bin2 , highlighting their potential role in early development. Although paternal contributions were limited, a subset of genes exhibited conditional paternal influence, suggesting a nuanced parental interplay in gene expression regulation. Conclusions These findings challenge the traditional perspective of strictly coordinated parental contributions, instead revealing a dynamic parental interplay over gene expression and offspring traits. The dominance of maternal effects suggests a primary role in shaping early development, while paternal factors may modulate expression patterns in a context-dependent manner. This study enhances our understanding of parental effects in fish, providing valuable insights for aquaculture breeding strategies and evolutionary biology research.

Early embryonic exposure to maternal glucocorticoids can broadly impact physiology and behaviour across phylogenetically diverse taxa. The transfer of maternal glucocorticoids to offspring may be an inevitable cost associated with poor environmental conditions, or serve as a maternal effect that alters offspring phenotype in preparation for a stressful environment. Regardless, maternal glucocorticoids are likely to have both costs and benefits that are paid and collected over different developmental time periods. We manipulated yolk corticosterone (cort) in domestic chickens (Gallus domesticus) to examine the potential impacts of embryonic exposure to maternal stress on the juvenile stress response and cellular ageing. Here, we report that juveniles exposed to experimentally increased cort in ovo had a protracted decline in cort during the recovery phase of the stress response. All birds, regardless of treatment group, shifted to oxidative stress during an acute stress response. In addition, embryonic exposure to cort resulted in higher levels of reactive oxygen metabolites and an over-representation of short telomeres compared with the control birds. In many species, individuals with higher levels of oxidative stress and shorter telomeres have the poorest survival prospects. Given this, long-term costs of glucocorticoid-induced phenotypes may include accelerated ageing and increased mortality.

Summary For most organisms, early life‐history stages are the most sensitive to environmental stress and so transgenerational phenotypic plasticity, whereby the parental environment and offspring environment interact to alter the phenotype of the offspring, is viewed as key to promoting persistence in the face of environmental change. While there has been long‐standing interest in the role of transgenerational plasticity via the maternal line (traditionally the field of maternal effects), increasingly it appears that paternal effects can also play a role. Despite the emerging role of paternal effects in studies of global change, key knowledge gaps remain: first, whether paternal effects act to increase or decrease offspring performance remains largely unexplored; second, the relative roles of maternal and paternal effects are rarely disentangled; and third, the role of environmental variation, a key determinant of the benefits of transgenerational plasticity, has not been explored with regard to paternal effects. Here, we explore all three issues using the marine tubeworm Galeolaria caespitosa, an important habitat‐forming species in southern Australia. We found that both paternal and maternal experiences affected key stages of offspring performance (fertilization and larval development) and, surprisingly, paternal effects were often stronger than maternal effects. Furthermore, we found that paternal effects often reduced offspring performance, especially when environments varied compared with when environments were stable. Our results suggest that, while transgenerational plasticity may play an important role in modifying the impacts of global change, these effects are not uniformly positive. Importantly, paternal effects can be as strong, or stronger, than maternal effects and environmental variability strongly alters the impacts of paternal effects. Lay Summary

Environmental conditions can have a lasting epigenetic impact on development, and there is increasing evidence that these effects can be transmitted across generations. Evidence for parental transmission of epigenetic variation to offspring has been primarily focused on paternal epigenetic influences induced by a male's experience of nutritional, social and toxicological exposures. There is an assumption in the literature that paternal influence on offspring in non‐biparental species is mediated exclusively through epigenetic transmission via the germline. However, integration of concepts from behavioural ecology into the study of parental transmission of environmental effects reveals the importance of mating tactics and maternal–paternal interplay in shaping resource allocation towards offspring in considering the mechanism(s) of epigenetic transmission. This paper describes the current state of knowledge regarding paternal epigenetic germline effects, the interplay between maternal and paternal influences and the importance of considering the complex nature of reproduction when predicting the transmission of phenotype across generations. Further, this paper highlights how incorporating concepts from behavioural ecology into the study of epigenetic transmission can refine predictions of phenotypes that emerge and create a more integrated notion of development and inheritance. It is proposed that theoretical and methodological approaches that consider the impact of reproductive context, which include mating dynamics, fertility, variation in parental life history and assessment of maternal effects, could improve the predictions made within studies of paternal epigenetic effects on offspring development. A free Plain Language Summary can be found within the Supporting Information of this article. A free Plain Language Summary can be found within the Supporting Information of this article.

Individual differences in the energy cost of self-maintenance (resting metabolic rate, RMR) are substantial and the focus of an emerging research area. These differences may influence fitness because self-maintenance is considered as a life-history component along with growth and reproduction. In this review, we ask why do some individuals have two to three times the ‘maintenance costs’ of conspecifics, and what are the fitness consequences? Using evidence from a range of species, we demonstrate that diverse factors, such as genotypes, maternal effects, early developmental conditions and personality differences contribute to variation in individual RMR. We review evidence that RMR is linked with fitness, showing correlations with traits such as growth and survival. However, these relationships are modulated by environmental conditions (e.g. food supply), suggesting that the fitness consequences of a given RMR may be context-dependent. Then, using empirical examples, we discuss broad-scale reasons why variation in RMR might persist in natural populations, including the role of both spatial and temporal variation in selection pressures and trans-generational effects. To conclude, we discuss experimental approaches that will enable more rigorous examination of the causes and consequences of individual variation in this key physiological trait.

Maternal genetic effects can be defined as the effect of a mother’s genotype on the phenotype of her offspring, independent of the offspring’s genotype. Maternal genetic effects can act via the intrauterine environment during pregnancy and/or via the postnatal environment. In this manuscript, we present a simple extension to the basic adoption design that uses structural equation modelling (SEM) to partition maternal genetic effects into prenatal and postnatal effects. We examine the power, utility and type I error rate of our model using simulations and asymptotic power calculations. We apply our model to polygenic scores of educational attainment and birth weight associated variants, in up to 5,178 adopted singletons, 943 trios, 2687 mother-offspring pairs, 712 father-offspring pairs and 347,980 singletons from the UK Biobank. Our results show the expected pattern of maternal genetic effects on offspring birth weight, but unexpectedly large prenatal maternal genetic effects on offspring educational attainment. Sensitivity and simulation analyses suggest this result may be at least partially due to adopted individuals in the UK Biobank being raised by their biological relatives. We show that accurate modelling of these sorts of cryptic relationships is sufficient to bring type I error rate under control and produce asymptotically unbiased estimates of prenatal and postnatal maternal genetic effects. We conclude that there would be considerable value in following up adopted individuals in the UK Biobank to determine whether they were raised by their biological relatives, and if so, to precisely ascertain the nature of these relationships. These adopted individuals could then be incorporated into informative statistical genetics models like the one described in our manuscript to further elucidate the genetic architecture of complex traits and diseases.

As is the case with any metaphor, parental effects mean different things to different biologists-from developmental induction of novel phenotypic variation to an evolved adaptation, and from epigenetic transference of essential developmental resources to a stage of inheritance and ecological succession. Such a diversity of perspectives illustrates the composite nature of parental effects that, depending on the stage of their expression and whether they are considered a pattern or a process, combine the elements of developmental induction, homeostasis, natural selection, epigenetic inheritance and historical persistence. Here, we suggest that by emphasizing the complexity of causes and influences in developmental systems and by making explicit the links between development, natural selection and inheritance, the study of parental effects enables deeper understanding of developmental dynamics of life cycles and provides a unique opportunity to explicitly integrate development and evolution. We highlight these perspectives by placing parental effects in a wider evolutionary framework and suggest that far from being only an evolved static outcome of natural selection, a distinct channel of transmission between parents and offspring, or a statistical abstraction, parental effects on development enable evolution by natural selection by reliably transferring developmental resources needed to reconstruct, maintain and modify genetically inherited components of the phenotype. The view of parental effects as an essential and dynamic part of an evolutionary continuum unifies mechanisms behind the origination, modification and historical persistence of organismal form and function, and thus brings us closer to a more realistic understanding of life's complexity and diversity.

Maternal effects in cattle genetics are defined as the causal influence of the phenotype or maternal genotype on the offspring’s phenotype by effects occurring when the genetic and environmental characteristics of the mother influence the phenotype of the offspring beyond the direct inheritance of genes. Its relevance has been strongly described in genetic models focused on the genetic improvement of preweaning traits in cow-calf beef cattle production systems. Here, basic concepts and the importance of maternal effects when using linear and animal model procedures for genetic evaluations of growth and live-weight traits in beef cattle are reviewed and discussed. A brief history of estimation methods from classical studies to recent studies used for the development of animal models for studying maternal effects is also provided. Some important biometric concepts for maternal effect estimation are described, and the antagonism between direct genetic effects and maternal effects, its biological basis, and sources of error in the estimation of direct genetic and maternal covariance are discussed. Finally, some genomic perspectives are presented.