Explore the vast range of titles available.

Growth and yield (G&Y) of forest plantations can be significantly impacted by maladaptation resulting from climate change, and assisted migration has been proposed to mitigate these impacts by restoring populations to their historic climates. However, genecology models currently used for guiding assisted migration do not account for impacts of climate change on cumulative growth and assume that responses of forest population to climate do not change with age. Using provenance trial data for interior lodgepole pine (Pinus contorta subsp. latifolia Douglas) and white spruce (Picea glauca (Moench) Voss) in western Canada, we integrated Universal Response Functions, representing the relationship of population performance with their provenance and site climates, into top height curves in a G&Y model (Growth and Yield Projection System, GYPSY) to develop population-specific climate sensitive top height curves for both species. These new models can estimate the impact of climate change on top height of local populations and populations from a range of provenances to help guide assisted migration. Our findings reveal that climate change is expected to have varying effects on forest productivity across the landscape, with some areas projected to experience a slight increase in productivity by the 2050s, while the remainder are projected to face a significant decline in productivity for both species. Adoption of assisted migration, however, with the optimal populations selected was projected to maintain and even improve productivity at the provincial scale. The findings of this study provide a novel approach to incorporating assisted migration approaches into forest management to mitigate the negative impacts of climate change.

The widely used species-occurrence-based models that predict the realized climate niche of plants can be too restrictive and do not reflect among-population variation in assessing climate change impact and guiding assisted migration for adaptation to future climates. To mitigate this deficiency, this study built a fundamental climate niche model for lodgepole pine ( Pinus contorta Dougl. ex Loud.) based on 20-year tree height from wide-ranging provenance trials as a case study. The model was built through comparisons and optimizations of two candidate models, universal response function (URF) and universal transfer function (UTF), with linear and linear mixed-effect forms, against varying sample sizes based on the comprehensive provenance trials. We found that URF and UTF models had similar performances, while URF models were more straightforward in identifying optimal provenances for planting sites. Linear mixed-effect models did not show clear advantages over linear models in our case but prevented including additional predictors, which are often critical. We selected the linear model of URF and predicted the fundamental climate niche of lodgepole pine on a global scale and revealed a great potential of using this species for climate change adaptation beyond its native distribution, representing a significant step in forest genecology. Our study presented a new approach for assisted migration at the species and the population levels to optimize adaptation and productivity under a changing climate.

The most common tool to predict future changes in species range are species distribution models. These models do, however, often underestimate potential future habitat, as they do not account for phenotypic plasticity and local adaptation, although being the most important processes in the response of tree populations to rapid climate change. Here, we quantify the difference in the predictions of future range for Norway spruce, by (i) deriving a classic, occurrence-based species distribution model (OccurrenceSDM), and (ii) analysing the variation in juvenile tree height and translating this to species occurrence (TraitSDM). Making use of 32 site locations of the most comprehensive European trial series that includes 1,100 provenances of Norway spruce originating from its natural and further beyond from its largely extended, artificial distribution, we fit a universal response function to quantify growth as a function of site and provenance climate. Both the OccurrenceSDM and TraitSDM show a substantial retreat towards the northern latitudes and higher elevations (−55 and −43%, respectively, by the 2080s). However, thanks to the species’ particularly high phenotypic plasticity in juvenile height growth, the decline is delayed. The TraitSDM identifies increasing summer heat paired with decreasing water availability as the main climatic variable that restricts growth, while a prolonged frost-free period enables a longer period of active growth and therefore increasing growth potential within the restricted, remaining area. Clear signals of local adaptation to climatic clines spanning the entire range are barely detectable, as they are disguised by a latitudinal cline. This cline strongly reflects population differentiation for the Baltic domain, but fails to capture the high phenotypic variation associated to the geographic heterogeneity in the Central European mountain ranges paired with the species history of postglacial migration. Still the model is used to provide recommendations of optimal provenance choice for future climate conditions. In essence, assisted migration may not decrease the predicted range decline of Norway spruce, but may help to capitalize on potential opportunities for increased growth associated with warmer climates.

The cultivation of hybrid poplar clones is increasing worldwide. Hundreds of hectares of plantations now occur across Europe and other continents such as North America, using tested clones and novel genotypes. Research effort aims are to develop fast growing disease- and pest-resistant clones to improve production quality and quantity. In this study the phenotypic plasticity of poplar clones was tested across environmental and temporal gradients. The growth performance of 49 hybrid poplar clones recorded between 1980 and 2021 was analysed using a mixed-effects model with climatic data as a predictor variable. Clones were aggregated into two groups according to their breeding protocol (i.e., standard clone, and improved material) and their growth modelled for future climate scenarios of RCPs 2.6 and 8.5 using a downscaled version of the variants 01 and 21 of UKCP18 climate projections dataset for three 30-year normal period time-slices: 2030s, 2040s, 2050s. The fitted growth models showed highly significant results, explaining more than 85% of the variance, with a mean relative absolute error of approximately 2%. Improved material showed more resistance to warmer and drier climates and less sensitivity to the changing climate. While no unique pattern was found when comparing growth performances, new improved clones were more productive than older clones (e.g., “I-214”) with an additional benefit of resistance to rust and pests. Spatial predictions confirmed the Po valley as the most important geographic area for poplar cultivation in Italy, but zones in Central and Southern Italy show potential. However, the Po Valley is also where poplars are predicted to be suitable in the next decades with large uncertainties. The analysis identified the need for more research on the topic of poplar breeding. For example, models using the most extreme (warm and dry) climate projection, variant 01 of RCP8.5 of the UKCP18, exceeded the historic climate threshold, and predictions used model extrapolation, with associated statistical uncertainty. Therefore, predictions should be considered with care and more research effort is required to test clones over wider environmental conditions.

Intraspecific assisted migration (ISAM) through seed transfer during artificial forest regeneration has been suggested as an adaptation strategy to enhance forest resilience and productivity under future climate. In this study, we assessed the risks and benefits of ISAM in white spruce based on long‐term and multilocation, rangewide provenance test data. Our results indicate that the adaptive capacity and growth potential of white spruce varied considerably among 245 range‐wide provenances sampled across North America; however, the results revealed that local populations could be outperformed by nonlocal ones. Provenances originating from south‐central Ontario and southwestern Québec, Canada, close to the southern edge of the species' natural distribution, demonstrated superior growth in more northerly environments compared with local populations and performed much better than populations from western Canada and Alaska, United States. During the 19–28 years between planting and measurement, the southern provenances have not been more susceptible to freezing damage compared with local populations, indicating they have the potential to be used now for the reforestation of more northerly planting sites; based on changing temperature, these seed sources potentially could maintain or increase white spruce productivity at or above historical levels at northern sites. A universal response function (URF), which uses climatic variables to predict provenance performance across field trials, indicated a relatively weak relationship between provenance performance and the climate at provenance origin. Consequently, the URF from this study did not provide information useful to ISAM. The ecological and economic importance of conserving white spruce genetic resources in south‐central Ontario and southwestern Québec for use in ISAM is discussed. This study reveals patterns of natural genetic variation in adaptation and growth among white spruce (Picea glauca [Moench] Voss) populations across North America. It also examines the response of populations of different geographic origins to varying climatic conditions and evaluates the potential risks and benefits of using intraspecific assisted migration to enhance forest adaptation to climate change.

We present a statistical model to predict the effects of climate change on the height growth of loblolly pine (Pinus taeda L.) families in the southeastern United States. Provenance-progeny trials were used for assessing the response of loblolly pine seed sources to environmental change. Ordinary least squares, ridge regression, and LASSO regression were used to develop height growth prediction models. The approach integrates both genetic and environmental effects and is meant to overcome the critical limitations of population response function and transfer function methods by making full use of data from provenance trials. Prediction models were tested using a hypothetical future climate scenario with 5% decrease in precipitation and 0.5° C increase in maximum and minimum temperatures, relative to historical average values. Under this scenario, local families from the coastal plains of Georgia, Florida, and South Carolina showed the highest performance relative to the current climate in their native environments. As these seed sources were moved to colder northern and inland regions from their origin, we observed declines in their height growth. Similarly, the climatic change scenario suggested that performance of northern seed sources declined significantly when they were moved to more southern warmer regions. The statistical model can be used as a quantitative tool to model the effect of climatic variables on the performance of loblolly pine seed sources and may help to develop sound breeding deployment strategies.

Forests strongly affect Earth's carbon cycles, making our ability to forecast forest-productivity changes associated with rising temperatures and changes in precipitation increasingly critical. In this study, we model the influence of climate on annual radial growth using lodgepole pine ( Pinus contorta ) trees grown for 34 years in a large provenance experiment in western Canada. We use a random-coefficient modeling approach to build universal growth-trend response functions that simultaneously incorporate the impacts of different provenance and site climates on radial growth trends under present and future annual (growth-year), summer, and winter climate regimes. This approach provides new depth to traditional quantitative genetics population response functions by illustrating potential changes in population dominance over time, as well as indicating the age and size at which annual growth begins declining for any population growing in any location under any present or future climate scenario within reason, given the ages and climatic conditions sampled. Our models indicate that lodgepole pine radial-growth levels maximize between 3.9°° and 5.1°°C mean growth-year temperature. This translates to productivity declining by the mid-21st century in southern and central British Columbia (BC), while increasing beyond the 2080s in northern BC and Yukon, as temperatures rise. Relative to summer climate indices, productivity is predicted to decline continuously through the 2080s in all locations, while relative to winter climate variables, the opposite trend occurs, with the growth increases caused by warmer winters potentially offsetting the summer losses. Trees from warmer provenances, i.e., from the center of the species range, perform best in nearly all of our present and future climate-scenario models. We recommend that similar models be used to analyze population growth trends relative to annual and intra-annual climate in other large-scale provenance trials worldwide. An open-access growth-trend data set encompassing numerous biomes, species, and provenances would contribute substantially to predicting forest productivity under future-climate scenarios.

Background Effective matching of genotypes and environments is required for the species to reach optimal productivity and act effectively for carbon sequestration. A common garden experiment across five different environments was undertaken to assess genotype x environment interaction (GxE) of coast redwood in order to understand the performance of genotypes across environments. Results The quantitative genetic analysis discovered no GxE between investigated environments for diameter at breast height (DBH). However, no genetic component was detected at one environment possibly due to stressful conditions. The implementation of universal response function allowed for the identification of important environmental factors affecting species productivity. Additionally, this approach enabled us to predict the performance of species across the New Zealand environmental conditions. Conclusions In combination with quantitative genetic analysis which identified genetically superior material, the URF model can directly identify the optimal geographical regions to maximize productivity. However, the finding of ideally uncorrelated climatic variables for species with narrow ecological amplitude is rather challenging, which complicates construction of informative URF model. This, along with a small number of tested environments, tended to overfit a prediction model which resulted in extreme predictions in untested environments.

1. Large differences exist in the predictions of plant responses to climate change among models that consider population variation and those that do not. Models that treat species as homogeneous entities typically predict net positive impacts of climate change on temperate forest productivity, while most studies that consider adaptive genetic variation within species conclude that the impacts of climate change on forest productivity will be negative. 2. We present a modelling approach that predicts plant responses to climate change using both ecological and genetic variables. The model uses growth data from multi-site provenance trials together with climate data for provenance source locations and test sites to predict distribution and productivity of tree populations under climate change. We used an extensive lodgepole pine Pinus contorta provenance data set to illustrate the model. 3. Spatially explicit predictions of the impacts of climate change on production were developed and suggested that different populations of lodgepole pine will respond very differently to climate change. Large production losses will be seen in many areas, although modest production increases may occur in some areas by 2085. The model further projects a significant redistribution of the species' potential habitat northwards and upwards in altitude over the next several decades. 4. Synthesis and applications. This study points to the need to consider population differences when modelling biotic responses to climate change, and suggests that climate change will render populations maladapted in many areas. The model also provides a key tool potentially to mitigate climate change impacts by identifying populations expected to be best adapted throughout the next generation of forests. Finally, the study highlights the value of wide-ranging long-term provenance tests in addressing key issues in ecology and climate change.

Salmonella Typhimurium (ST) is a zoonotic pathogen that can cause gastroenteritis in humans when they consume contaminated food or water. When exposed to various stressors, both from living organisms (biotic) and the environment (abiotic), Salmonella Typhimurium produces Universal Stress Proteins (USPs). These proteins are gaining recognition for their crucial role in bacterial stress resistance and the ability to enter a prolonged state of growth arrest. Additionally, USPs exhibit diverse structures due to the fusion of the USP domain with different catalytic motifs, enabling them to participate in various reactions and cellular activities during stressful conditions. In this particular study, researchers cloned and analyzed the uspA gene obtained from poultry-derived strains of Salmonella Typhimurium. The gene comprises 435 base pairs, encoding a USP family protein consisting of 144 amino acids. Phylogenetic analysis demonstrated a close relationship between the uspA genes of Salmonella Typhimurium and those found in other bacterial species. We used molecular dynamics simulations and 3D structure prediction to ensure that the USPA protein was stable. Furthermore, we also carried out motif search and network analysis of protein–protein interactions. The findings from this study offer valuable insights for the development of inhibitors targeted against Salmonella Typhimurium.