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Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.
The Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia
(2019)
The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites.
Question: The majority of studies investigating the impact of climate change on local plant communities ignores changes in regional processes, such as immigration from the regional seed pool. Here we explore: (i) the potential impact of climate change on composition of the regional seed pool, (ii) the influence of changes in climate and in the regional seed pool on local community structure, and (iii) the combinations of life history traits, i.e. plant functional types (PFTs), that are most affected by environmental changes. Location: Fire-prone, Mediterranean-type shrublands in southwestern Australia. Methods: Spatially explicit simulation experiments were conducted at the population level under different rainfall and fire regime scenarios to determine the effect of environmental change on the regional seed pool for 38 PFTs. The effects of environmental and seed immigration changes on local community dynamics were then derived from community-level experiments. Classification tree analyses were used to investigate PFT- specific vulnerabilities to climate change. Results: The classification tree analyses revealed that responses of PFTs to climate change are determined by specific trait characteristics. PFT-specific seed production and community patterns responded in a complex manner to climate change. For example, an increase in annual rainfall caused an increase in numbers of dispersed seeds for some PFTs, but decreased PFT diversity in the community. Conversely, a simulated decrease in rainfall reduced the number of dispersed seeds and diversity of PFTs. Conclusions: PFT interactions and regional processes must be considered when assessing how local community structure will be affected by environmental change.
Modelling and empirical studies have shown that input from the regional seed pool is essential to maintain local species diversity. However, most of these studies have concentrated on simplified, if not neutral, model systems, and focus on a limited subset of species or on aggregated measures of diversity only (e.g., species richness or Shannon diversity). Thus they ignore more complex species interactions and important differences between species. To gain a better understanding of how seed immigration affects community structure at the local scale in real communities we conducted computer simulation experiments based on plant functional types (PFTs) for a species-rich, fire-prone Mediterranean-type shrubland in Western Australia. We developed a spatially explicit simulation model to explore the community dynamics of 38 PFTs, defined by seven traits - regeneration mode, seed production, seed size, maximum crown diameter, drought tolerance, dispersal mode and seed bank type - representing 78 woody species. Model parameterisation is based on published and unpublished data on the population dynamics of shrub species collected over 18 years. Simulation experiments are based on two contrasting seed immigration scenarios: (1) the 'equal seed input number' scenario, where the number of immigrant seeds is the same for all PFTs, and (2) the 'equal seed input mass' scenario, where the cumulative mass of migrating seeds is the same for all PFTs. Both scenarios were systematically tested and compared for different overall seed input values. Without immigration the local community drifts towards a state with only 13 coexisting PFTs. With increasing immigration rates in terms of overall mass of seeds the simulated number of coexisting PFTs and Shannon diversity quickly approaches values observed in the field. The equal seed mass scenario resulted in a more diverse community than did the seed number scenario. The model successfully approximates the frequency distributions (relative densities) of all individual plant traits except seed size for scenarios associated with equal seed input mass and high immigration rate. However, no scenario satisfactorily approximated the frequency distribution for all traits in combination. Our results show that regional seed input can explain the more aggregated measures of local community structure, and some, but not all, aspects of community composition. This points to the possible importance of other (untested) processes and traits (e.g., dispersal vectors) operating at the local scale. Our modelling framework can readily allow new factors to be systematically investigated, which is a major advantage compared to previous simulation studies, as it allows us to find structurally realistic models, which can address questions pertinent to ecological theory and to conservation management.
Low-dimensional trade-offs fail to explain richness and structure in species-rich plant communities
(2011)
Mathematical models and ecological theory suggest that low-dimensional life history trade-offs (i.e. negative correlation between two life history traits such as competition vs. colonisation) may potentially explain the maintenance of species diversity and community structure. In the absence of trade-offs, we would expect communities to be dominated by 'super-types' characterised by mainly positive trait expressions. However, it has proven difficult to find strong empirical evidence for such trade-offs in species-rich communities. We developed a spatially explicit, rule-based and individual-based stochastic model to explore the importance of low-dimensional trade-offs. This model simulates the community dynamics of 288 virtual plant functional types (PFTs), each of which is described by seven life history traits. We consider trait combinations that fit into the trade-off concept, as well as super-types with little or no energy constraints or resource limitations, and weak PFTs, which do not exploit resources efficiently. The model is parameterised using data from a fire-prone, species-rich Mediterranean-type shrubland in southwestern Australia. We performed an exclusion experiment, where we sequentially removed the strongest PFT in the simulation and studied the remaining communities. We analysed the impact of traits on performance of PFTs in the exclusion experiment with standard and boosted regression trees. Regression tree analysis of the simulation results showed that the trade-off concept is necessary for PFT viability in the case of weak trait expression combinations such as low seed production or small seeds. However, species richness and diversity can be high despite the presence of super-types. Furthermore, the exclusion of super-types does not necessarily lead to a large increase in PFT richness and diversity. We conclude that low-dimensional trade-offs do not provide explanations for multi-species co-existence contrary to the prediction of many conceptual models.