This study addresses the need to provide comprehensive historical climate data and climate change projections at a scale suitable for, and readily accessible to, researchers and resource managers. This database for western North America (WNA) includes over 20 000 surfaces of monthly, seasonal, and annual climate variables from 1901 to 2009; several climate normal periods; and multimodel climate projections for the 2020s, 2050s, and 2080s. A software package, ClimateWNA, allows users to access the database and query point locations, obtain time series, or generate custom climate surfaces at any resolution. The software uses partial derivative functions of temperature change along elevation gradients to improve medium-resolution baseline climate estimates and calculates biologically relevant climate variables such as growing degree-days, number of frost-free days, extreme temperatures, and dryness indices. Historical and projected future climates are obtained by using monthly temperature and precipitation anomalies to adjust the interpolated baseline data for the location of interest. All algorithms used in the software package are described and evaluated against observations from weather stations across WNA. The downscaling algorithms substantially improve the accuracy of temperature variables over the medium-resolution baseline climate surfaces. Climate variables that are usually calculated from daily data are estimated from monthly climate variables with high statistical accuracy.
Summary1. 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.
Questions: Can probability of occurrence and dominance be accurately estimated for six important conifer species with varying range sizes? Does range size impact the accuracy of species probability of occurrence models? Is species predicted probability of occurrence significantly related to observed dominance? Location: Pacific Northwest region, North America (60°–40°N, 140°–110°W). Methods: This study develops near range‐wide predictive distribution maps for six important conifer species (Pseudotsuga menziesii, Tsuga heterophylla, Pinus contorta, Thuja plicata, Larix occidentalis, and Picea glauca) using forest inventory data collected across the United States and Canada. Species model accuracies are compared with range size using a rank scoring system. A suite of climate and topographic predictor variables are used to investigate environmental constraints that limit species range and quantify relationships between species predicted probability of occurrence and dominance at both plot and landscape scales. Results: Evaluation statistics revealed significant and accurate probability of occurrence models were developed for all six species. Based on ranked evaluation statistics, Tsuga heterophylla had highest overall model accuracy (statistic rank score=5) and Pinus contorta the lowest (statistic rank score=17). Across species, ranked evaluation statistics also revealed a pattern of decreasing model accuracy with increasing range size. At plot level, correlations between dominance and probability of occurrence were weakly positive for all species with only half of the species having statistically significant correlations. Pseudotsuga menziesii had the highest correlation (r=0.36, P<0.001) and Thuja plicata lowest (r=0.038, P=0.799). At the 50‐km scale, correlations between dominance and probability of occurrence improved for all species except Pinus contorta. Pseudotsuga menziesii displayed the highest correlation (r=0.68, P<0.001) and Thuja plicata the lowest (r=0.07, P>0.709). Conclusions: Species probability of occurrence model accuracy decreased with increasing range size. The strength and significance of correlations between probability of occurrence and dominance varied considerably by species and across spatial scales. Apart from Pseudotsuga menziesii and L. occidentalis, the results suggest that probability of occurrence is not a consistently reliable surrogate for species dominance in Pacific Northwest forests. We demonstrate how the degree of correlation between species occurrence and dominance can be used as an indicator of how well predictions of occurrence characterize the optimal niche of a species.
MicroRNAs (miRNAs) are short noncoding RNAs (20–25 nucleotides) that regulate gene expression posttranscriptionally. However, identification and characterization of miRNAs remain limited for conifer species. In this study, we applied transcriptome-wide miRNAs sequencing to a conifer species Platycladus orientalis, which is highly adaptable to a wide range of environmental adversities, including drought, barren soil, and mild salinity. A total of 17,181,542 raw reads were obtained from the Illumina sequencing platform; 31 conserved and 91 novel miRNAs were identified, and their unique characteristics were further analyzed. Ten randomly selected miRNAs were validated by quantificational real-time polymerase chain reaction. Through miRNA target predictions based on psRNATarget, 2331 unique mRNAs were predicted to be targets of P. orientalis miRNAs that involved in 187 metabolic pathways in KEGG database. These targets included not only important transcription factors (e.g., class III homeodomain leucine zipper targeted by por-miR166d) but also indispensable nontranscriptional factor proteins (i.e., por-miR482a-3p regulated nucleotide-binding site leucine-rich repeat protein). Interestingly, six miRNAs (por-miR16, -miR44, -miR60-5p, -miR69–3p, -miR166b-5p, and -miR395c) were found in adaptation-related pathways (e.g., drought), indicating their possible involved in this species’ stress-tolerance characteristics. The present study provided essential information for understanding the regulatory role of miRNAs in P. orientalis and sheds light on their possible use in tree improvement for stress tolerance.
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