Interspecific variation in growth responses to tree size, competition and climate of western Canadian boreal mixed forests

Forest Age Drives the Resource Utilization Indicators of Trees in Planted and Natural Forests in China

Specific leaf area (SLA) and leaf dry matter content (LDMC) are key leaf functional traits commonly used to reflect tree resource utilization strategies and predict forest ecosystem responses to environmental changes. Previous research on tree resource utilization strategies (SLA and LDMC) primarily focused on the species level within limited spatial scales, making it crucial to quantify the spatial variability and driving factors of these strategies. Whether there are discrepancies in resource utilization strategies between trees in planted and natural forests, and the dominant factors and mechanisms influencing them, remain unclear. This study, based on field surveys and the literature from 2008 to 2020 covering 263 planted and 434 natural forests in China, using generalized additive models (GAMs) and structural equation models (SEMs), analyzes the spatial differences and dominant factors in tree resource utilization strategies between planted and natural forests. The results show that the SLA of planted forests is significantly higher than that of natural forests (p < 0.01), and LDMC is significantly lower (p < 0.0001), indicating a "faster investment-return" resource utilization strategy. As the mean annual high temperature (MAHT) and mean annual precipitation (MAP) steadily rise, trees have adapted their resource utilization strategies, transitioning from a "conservative" survival tactic to a "rapid investment-return" model. Compared to natural forests, planted forest trees exhibit stronger environmental plasticity and greater variability with forest age in their resource utilization strategies. Overall, forest age is the dominant factor influencing resource utilization strategies in both planted and natural forests, having a far greater direct impact than climatic factors (temperature, precipitation, and sunlight) and soil nutrient factors. Additionally, as forest age increases, both planted and natural forests show an increase in SLA and a decrease in LDMC, indicating a gradual shift towards more efficient resource utilization strategies.

Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe

To enhance our understanding of forest carbon sequestration, climate change mitigation and drought impact on forest ecosystems, the availability of high-resolution annual forest growth maps based on tree-ring width (TRW) would provide a significant advancement to the field. Site-specific characteristics, which can be approximated by high-resolution Earth observation by satellites (EOS), emerge as crucial drivers of forest growth, influencing how climate translates into tree growth. EOS provides information on surface reflectance related to forest characteristics and thus can potentially improve the accuracy of forest growth models based on TRW. Through the modelling of TRW using EOS, climate and topography data, we showed that species-specific models can explain up to 52 % of model variance (Quercus petraea), while combining different species results in relatively poor model performance (R2 = 13 %). The integration of EOS into models based solely on climate and elevation data improved the explained variance by 6 % on average. Leveraging these insights, we successfully generated a map of annual TRW for the year 2021. We employed the area of applicability (AOA) approach to delineate the range in which our models are deemed valid. The calculated AOA for the established forest-type models was 73 % of the study region, indicating robust spatial applicability. Notably, unreliable predictions predominantly occurred in the climate margins of our dataset. In conclusion, our large-scale assessment underscores the efficacy of combining climate, EOS and topographic data to develop robust models for mapping annual TRW. This research not only fills a critical void in the current understanding of forest growth dynamics but also highlights the potential of integrated data sources for comprehensive ecosystem assessments.

Species mixing enhances the resistance of Robinia pseudoacacia L. to drought events in semi-arid regions: Evidence from China's Loess Plateau

As a potential planting strategy, species mixing increases biomass production, improves ecosystem service functions, and mitigates climate change. However, the effect of species mixing on tree growth and drought resilience in semi-arid areas remains unclear. Hence, we established tree-ring chronologies of Robinia pseudoacacia L. in pure Robinia pseudoacacia L. plantation (RP) and mixed plantations with Hippophae rhamnoides L. and Populus simonii Carr. at different proportions of 8:2 and 5:5 (RH 8:2, RH 5:5, RC 8:2, RC 5:5) in the typical semi-arid region of the Loess Plateau (LP), China. The mean annual growth, climate-growth relationships, and tree resilience (Rs) to drought, including resistance (Rt) and recovery (Rc), were analyzed using dendrochronological methods. The results showed that the growth of R. pseudoacacia L. in mixed plantations was lower when Palmer Drought Severity Index (PDSI) >0, but much higher than that in monoculture under drought stress (PDSI <0 or after drought event). Meanwhile, the relationship between PDSI and tree growth was significantly positive in the pure plantation, but weakened in the mixed plantations, indicating that species mixing alleviated drought stress to some extent. The resilience results showed that, although the Rc was higher in monoculture after drought events, species mixing could enhance Rt and mitigate the growth decline of R. pseudoacacia L. during drought events. Moreover, the Rt varied significantly among mixing species and proportions and was also affected by the magnitude and timing of drought. The RC 5:5 and RH 8:2 had higher resistance to moderate and severe drought stress. However, RC 8:2 and RH 5:5 could cope better with mild drought stress. These results indicate that species mixing can alleviate drought stress and improve the drought resistance. Therefore, it is necessary to expand species mixing to maximize plantation functions and minimize the potential impacts of warming and drought in semi-arid regions.

Long-Term Effects of Climate and Competition on Radial Growth, Recovery, and Resistance in Mongolian Pines

Understanding the response of tree growth and drought vulnerability to climate and competition is critical for managing plantation forests. We analyzed the growth of Mongolian pines in six forests planted by the Three-North Shelter Forest Program with tree-ring data and stand structures. A retroactive reconstruction method was used to depict the growth-competition relationships of Mongolian pines during the growth period and their climatic responses under different competition levels. Drought vulnerability was analyzed by measuring the basal area increment (BAI) of different competition indices (CIs). In young trees, differences in BAIs in stands with different CIs were not statistically significant. After 15-20 years, medium- and high-CI stands had significantly lower tree-ring widths (TWs) and BAIs than the low-CI stands (p < 0.05). The standardized precipitation evapotranspiration index (SPEI), precipitation, relative humidity, and vapor pressure deficit were major factors affecting tree growth. On a regional scale, climate outweighed competition in determining radial growth. The relative contribution of climatic factors increased with the gap in SPEI between plantation sites and the native range, while the reverse pattern of the competition-growth relationship was observed. Drought reduced TWs and BAIs at all sites. Stands of different CIs exhibited similar resistance, but, compared with low-CI stands, high- and medium-CI stands had significantly lower recovery, resilience, and relative resilience, indicating they were more susceptible to drought stresses. Modeled CI was significantly negatively related to resistance, resilience, and relative resilience, indicating a density-dependence of tree response to drought. After exposure to multiple sequential drought events, the relative resilience of high-CI stands decreased to almost zero; this failure to fully recover to pre-drought growth rates suggests increased mortality in the future. In contrast, low-CI stands are more likely to survive in hotter, more arid climates. These results provide a better understanding of the roles of competition and climate on the growth of Mongolian pines and offer a new perspective for investigating the density-dependent recovery and resilience of these forests.

Common Species Maintain a Large Root Radial Extent and a Stable Resource Use Status in Soil-Limited Environments: A Case Study in Subtropical China

Coarse root systems provide a framework for water and nutrient absorption from the soil and play an important role in plant survival in harsh environments. However, the adaptions of plant roots in soil-limited environments with low water storage capacity and nutrient content needs to be better understood. The adaptation strategies of two common plant species (a deciduous tree Platycarya longipes and an evergreen shrub Tirpitzia ovoidea) from two contrasting habitats (a shallow rocky soil and a nearby deep soil) in a karst region of subtropical China were compared and analyzed. Foliar nutrient concentrations, stoichiometry, stable carbon, and oxygen isotopes were used to determine plant nutrient and water use status across these two habitats. Six indexes, including maximum root depth, maximum root radial extent, number of major roots and secondary roots, and tapering rate and curvature, were all investigated to characterize coarse root systems. Results show that both species exhibited similar nutrient and water use status in the two habitats that had contrasting water holding capacity and available nutrient content. On the other hand, although maximum root depths of the individual plants were not deeper than 33 cm, maximum radial extents were much larger when compared to rooting depths. Specifically, the ratio of radial extent to depth in the soil-limited habitat was approximately 6 and 1.5 times higher than that in the deep-soil habitat for the tree and shrub, respectively. Additionally, especially for the tree, a larger root radial extent was further accompanied by lower root tapering rate and bending levels. Our results provided evidence that plants growing in soil-limited environments maintain a stable resource use status along with large radially extended coarse root systems in humid karst regions like southwest China.

Taxonomy, together with ontogeny and growing conditions, drives needleleaf species' sensitivity to climate in boreal North America

Currently, there is no consensus regarding the way that changes in climate will affect boreal forest growth, where warming is occurring faster than in other biomes. Some studies suggest negative effects due to drought-induced stresses, while others provide evidence of increased growth rates due to a longer growing season. Studies focusing on the effects of environmental conditions on growth-climate relationships are usually limited to small sampling areas that do not encompass the full range of environmental conditions; therefore, they only provide a limited understanding of the processes at play. Here, we studied how environmental conditions and ontogeny modulated growth trends and growth-climate relationships of black spruce (Picea mariana) and jack pine (Pinus banksiana) using an extensive dataset from a forest inventory network. We quantified the long-term growth trends at the stand scale, based on analysis of the absolutely dated ring-width measurements of 2,266 trees. We assessed the relationship between annual growth rates and seasonal climate variables and evaluated the effects of various explanatory variables on long-term growth trends and growth-climate relationships. Both growth trends and growth-climate relationships were species-specific and spatially heterogeneous. While the growth of jack pine barely increased during the study period, we observed a growth decline for black spruce which was more pronounced for older stands. This decline was likely due to a negative balance between direct growth gains induced by improved photosynthesis during hotter-than-average growing conditions in early summers and the loss of growth occurring the following year due to the indirect effects of late-summer heat waves on accumulation of carbon reserves. For stands at the high end of our elevational gradient, frost damage during milder-than-average springs could act as an additional growth stressor. Competition and soil conditions also modified climate sensitivity, which suggests that effects of climate change will be highly heterogeneous across the boreal biome.

Development of a Tree Growth Difference Equation and Its Application in Forecasting the Biomass Carbon Stocks of Chinese Forests in 2050

Global climate change has raised concerns about the relationship between ecosystems and forests, which is a core component of the carbon cycle and a critical factor in understanding and mitigating the effects of climate change. Forest models and sufficient information for predictions are important for ensuring efficient afforestation activities and sustainable forest development. Based on the theory of difference equations and the general rules of tree growth, this study established a difference equation for the relationship between the ratio of tree diameter at breast height (DBH) to the tree height and age of age of China’s main arbor species. A comparison with equations that represent the traditional tree growth models, i.e., Logistic and Richards equations, showed that the difference equations exhibited higher precision for both fitting and verification data. Moreover, the biomass carbon stocks (BCS) of Chinese forests from 2013 to 2050 were predicted by combining the 8th Chinese Ministry of Forestry and partial continuous forest inventory (CFI) data sets. The results showed that the BCS of Chinese forests would increase from 7342 to 11,030 terra grams of carbon (Tg C) in 2013–2050, with an annual biomass C (carbon) sink of 99.68 Tg C year−1, and they indicated that the Chinese land-surface forest vegetation has an important carbon sequestration capability.

Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms

Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and nonlinear machine learning methods, such as random forest (RF), boosted regression tree (BRT), cubist (Cubist) and multivariate adaptive regression splines (MARS), provides a new way for predicting individual tree growth. However, the application of these approaches to individual tree growth modelling is still limited and short of a comparison of their performance. The objectives of this study were to compare and evaluate the performance of the RF, BRT, Cubist and MARS models for modelling the individual tree diameter growth based on tree size, competition, site condition and climate factors for larch–spruce–fir mixed forests in northeast China. Totally, 16,619 observations from long-term sample plots were used. Based on tenfold cross-validation, we found that the RF, BRT and Cubist models had a distinct advantage over the MARS model in predicting individual tree diameter growth. The Cubist model ranked the highest in terms of model performance (RMSEcv [0.1351 cm], MAEcv [0.0972 cm] and R2cv [0.5734]), followed by BRT and RF models, whereas the MARS ranked the lowest (RMSEcv [0.1462 cm], MAEcv [0.1086 cm] and R2cv [0.4993]). Relative importance of predictors determined from the RF and BRT models demonstrated that the competition and tree size were the main drivers to diameter growth, and climate had limited capacity in explaining the variation in tree diameter growth at local scale. In general, the RF, BRT and Cubist models are effective and powerful modelling methods for predicting the individual tree diameter growth.