• Energy Research

Camptotheca acuminata is considered a natural medicinal plant with antitumor activity. The assessment of climate change impact on its suitable habitats is important for cultivation and conservation. In this study, we applied a novel approach to build ecological niche models with both climate and soil variables while the confounding effects between the variables in the two categories were avoided. We found that the degree-days below zero and mean annual precipitation were the most important climatic factors, while the basic soil saturation, soil gravel volume percentage, and clay content were the main soil factors, determining the suitable habitats of C. acuminata. We found that suitable habitats of this species would moderately increase in future climates under both the RCP4.5 and RCP8.5 climate change scenarios for the 2020s, 2050s, and 2080s. However, substantial shifts among levels of habitat suitability were projected. The dual high-suitable habitats would expand, which would be favorable for commercial plantations. Our findings contribute to a better understanding of the impact of climate change on this species and provide a scientific basis for the cultivation and conservation purposes.

Ensemble forecasting is advocated as a way of reducing uncertainty in species distribution modeling (SDM). This is because it is expected to balance accuracy and robustness of SDM models. However, there are little available data regarding the spatial similarity of the combined distribution maps generated by different consensus approaches. Here, using eight niche-based models, nine split-sample calibration bouts (or nine random model-training subsets), and nine climate change scenarios, the distributions of 32 forest tree species in China were simulated under current and future climate conditions. The forecasting ensembles were combined to determine final consensual prediction maps for target species using three simple consensus approaches (average, frequency, and median [PCA]). Species' geographic ranges changed (area change and shifting distance) in response to climate change, but the three consensual projections did not differ significantly with respect to how much or in which direction, but they did differ with respect to the spatial similarity of the three consensual predictions. Incongruent areas were observed primarily at the edges of species' ranges. Multiple stepwise regression models showed the three factors (niche marginality and specialization, and niche model accuracy) to be related to the observed variations in consensual prediction maps among consensus approaches. Spatial correspondence among prediction maps was the highest when niche model accuracy was high and marginality and specialization were low. The difference in spatial predictions suggested that more attention should be paid to the range of spatial uncertainty before any decisions regarding specialist species can be made based on map outputs. The niche properties and single-model predictive performance provide promising insights that may further understanding of uncertainties in SDM.

Identifying populations within tree species potentially adapted to future climatic conditions is an important requirement for reforestation and assisted migration programmes. Such populations can be identified either by empirical response functions based on correlations of quantitative traits with climate variables or by climate envelope models that compare the climate of seed sources and potential growing areas. In the present study, we analyzed the intraspecific variation in climate growth response of Douglas-fir planted within the non-analogous climate conditions of Central and continental Europe. With data from 50 common garden trials, we developed Universal Response Functions (URF) for tree height and mean basal area and compared the growth performance of the selected best performing populations with that of populations identified through a climate envelope approach. Climate variables of the trial location were found to be stronger predictors of growth performance than climate variables of the population origin. Although the precipitation regime of the population sources varied strongly none of the precipitation related climate variables of population origin was found to be significant within the models. Overall, the URFs explained more than 88% of variation in growth performance. Populations identified by the URF models originate from western Cascades and coastal areas of Washington and Oregon and show significantly higher growth performance than populations identified by the climate envelope approach under both current and climate change scenarios. The URFs predict decreasing growth performance at low and middle elevations of the case study area, but increasing growth performance on high elevation sites. Our analysis suggests that population recommendations based on empirical approaches should be preferred and population selections by climate envelope models without considering climatic constrains of growth performance should be carefully appraised before transferring populations to planting locations with novel or dissimilar climate.

Abstract Key message We developed a climatic response function using 20-year tree height observed from 45Ginkgo bilobaplantations in China and used it to predict the growth and habitat responses to anticipated climate change. We projected northward and upward shifts in the species habitat and productive areas, but a dramatic contraction of the species distribution is unlikely to occur at least during the present century. Context Ginkgo biloba is the only living species in the division Ginkgophyta. The species exists in small natural populations in southeastern China but is cultivated across China and the world. The species’ future under climate change is of concern. Aims This study was initiated to model the species’ growth response to climate change and to predict its range of suitable habitat under future climates. Methods Using height data from 45 20 years old plantations growing under a wide range of climatic conditions across China, we developed univariate and bivariate climatic response functions to identify the climate requirements of the species. Results According to the amount of variance explained (> 70%) and the high level of agreement (> 99%) with independent species occurrence coordinates, the developed climate response function was highly accurate and credible. Projections for future periods (2011–2040, 2041–2070, and 2071–2100) under the Representative Concentration Pathway 4.5 (RCP4.5) scenario indicated that the areas of potential suitable habitat would increase (25–67 million hectares). It would also be associated with northward (0.21–0.62° in latitude) and elevational (24–75 m) shifts. Conclusion Global climate change is projected to increase the area of potential suitable habitats for Ginkgo and shift its spatial distributions northward and upward.

AbstractPredicting the geographic range of species and their response to climatic variation and change are entwined goals in conservation and evolutionary ecology. Species distribution models (SDMs) are foundational in this effort and used to visualize the geographic range of species as the spatial representation of its realized niche. SDMs are also used to forecast range shifts under climate change, but often in the absence of empirical evidence that climate limits population growth. We explored the influence of climate on demography, seasonal migration, and the extent of the geographic range in song sparrows (Melospiza melodia), a species thought to display marked local adaptation to regional climate. To do so, we developed SDMs to predict the demographic and climate niches of migratory and resident song sparrows across our study area in western North America from California to Alaska, using 48 years of demographic data from a focal population in British Columbia and 1.2 million continental‐scale citizen science observations. Spatial agreement of our demographic and climate niche models in the region of our focal population was strong (76%), supporting the hypothesis that demographic performance and the occurrence of seasonal migration varied predictably with climatic conditions. In contrast, agreement at the northern (58%) and southern (40%) extents of our study area was lower, as expected if the factors limiting population growth vary regionally. Our results support the hypothesis that local climate drives spatial variation in the occurrence of seasonal migration in song sparrows by limiting the fitness of year‐round residents, and suggest that climate warming has favored range expansions and facilitated an upward shift in elevational range song sparrows that forgo seasonal migration. Our work highlights the potential role of seasonal migration in climate adaptation and limits on the reliability of climate niche models not validated with demographic data.

SummaryThe nature of selection responsible for the maintenance of the economically and ecologically importantPicea glauca × Picea engelmanniihybrid zone was investigated. Genomic, phenotypic and climatic data were used to test assumptions of hybrid zone maintenance and to model future scenarios under climate change.Genome‐wide estimates of admixture based on a panel of 86 candidate gene single nucleotide polymorphisms were combined with long‐term quantitative data on growth and survival (over 20 yr), as well as one‐time assessments of bud burst and bud set phenology, and cold hardiness traits. A total of 15 498 individuals were phenotyped for growth and survival.Our results suggest that theP. glauca × P. engelmanniihybrid zone is maintained by local adaptation to growing season length and snowpack (exogenous selection). Hybrids appeared to be fitter than pure species in intermediate environments, which fits expectations of the bounded hybrid superiority model of hybrid zone maintenance. Adaptive introgression from parental species has probably contributed to increased hybrid fitness in intermediate habitats.WhileP. engelmanniiancestry is higher thanP. glaucaancestry in hybrid populations, on average, selective breeding in managed hybrid populations is shifting genomic composition towardsP. glauca, potentially pre‐adapting managed populations to warmer climates.

AbstractAs most regions of the earth transition to altered climatic conditions, new methods are needed to identify refugia and other areas whose conservation would facilitate persistence of biodiversity under climate change. We compared several common approaches to conservation planning focused on climate resilience over a broad range of ecological settings across North America and evaluated how commonalities in the priority areas identified by different methods varied with regional context and spatial scale. Our results indicate that priority areas based on different environmental diversity metrics differed substantially from each other and from priorities based on spatiotemporal metrics such as climatic velocity. Refugia identified by diversity or velocity metrics were not strongly associated with the current protected area system, suggesting the need for additional conservation measures including protection of refugia. Despite the inherent uncertainties in predicting future climate, we found that variation among climatic velocities derived from different general circulation models and emissions pathways was less than the variation among the suite of environmental diversity metrics. To address uncertainty created by this variation, planners can combine priorities identified by alternative metrics at a single resolution and downweight areas of high variation between metrics. Alternately, coarse‐resolution velocity metrics can be combined with fine‐resolution diversity metrics in order to leverage the respective strengths of the two groups of metrics as tools for identification of potential macro‐ and microrefugia that in combination maximize both transient and long‐term resilience to climate change. Planners should compare and integrate approaches that span a range of model complexity and spatial scale to match the range of ecological and physical processes influencing persistence of biodiversity and identify a conservation network resilient to threats operating at multiple scales.