Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees.

Climate is widely recognised as an important determinant of the latitudinal diversity gradient. However, most existing studies make no distinction between direct and indirect effects of climate, which substantially hinders our understanding of how climate constrains biodiversity globally. Using data from 35 large forest plots, we test hypothesised relationships amongst climate, topography, forest structural attributes (stem abundance, tree size variation and stand basal area) and tree species richness to better understand drivers of latitudinal tree diversity patterns. Climate influences tree richness both directly, with more species in warm, moist, aseasonal climates and indirectly, with more species at higher stem abundance. These results imply direct limitation of species diversity by climatic stress and more rapid (co-)evolution and narrower niche partitioning in warm climates. They also support the idea that increased numbers of individuals associated with high primary productivity are partitioned to support a greater number of species.

The use of R in photosynthesis research.

R is one of the most commonly used analytical tools in the plant sciences. To identify key trends in general reported R use and patterns in photosynthesis research, we explored the frequency of R use in 2966 articles published in the 377 journals with 'photosynthesis' in the title from 2010 to 2019 using the Web of Science search. Solutions provided by each R package cited in the articles or online sources was recorded and classified. The percentage of research articles reporting R use increased linearly from 3.6% in 2010 to 12.5% in 2019. The three main categories of R package solutions were 'general statistical calculations and graph packages' (G); 'photosynthesis special-purpose packages' (S); and 'genetic and evolutionary packages' (E). The top five R packages cited were nlme (G), lme4 (G), multcomp (G), plantecophys (S), and ape (E). The increasing popularity of R use in photosynthesis research is due to its user-friendly and abundant open-source codes online for handling specific issues, particularly in fitting photosynthesis models. These findings are limited by the number of articles and online sources, but they reveal a significant increase in usage in photosynthesis research over the past decade and have a bright prospect in the future.

[Characteristic of soil fungal community and the influencing factors of Pinus massoniana forests in the Three Gorges Reservoir Region]. / ä¸‰å³¡åº“åŒºé©¬å°¾æ¾æž—åœŸå£¤çœŸèŒç¾¤è½ç‰¹å¾åŠå½±å“å› ç´ .

Soil fungi are important components of belowground biodiversity and play important roles in soil carbon and nutrient cycling. We investigated fungal communities in the top soil (0-10 cm) of 22 Pinus massoniana forests in the Three Gorges Reservoir Region using high-throughput sequencing technique. We found that Ascomycota and Basidiomycota were the dominant fungi phyla, and Eurotiales, Russulales, and Tremellales were the most abundant fungi orders. The dominant functional groups in P. massoniana forests were saprophytic fungi, ectomycorrhizal fungi, and ericoid mycorrhizal fungi. Results of redundancy analysis showed that environmental variables but not spatial variables were the main drivers of soil fungal community structure across the 22 P. massoniana forests, which suggested that habitat filtering rather than dispersal limitation shaped soil fungal community structure. Aboveground biomass, soil conductivity, available phosphorus, soil bulk density, carbon to nitrogen ratio, nitrate concentration, and proportion of slit were the main factors explaining the variation in soil fungal community structure. It should be noted that the key factors influencing different fungal functional groups differed across forests.

Drivers of tree carbon storage in subtropical forests.

Tropical and subtropical forest ecosystems play an important role in the global carbon regulation. Despite increasing evidence for effects of biodiversity (species diversity, functional diversity and functional dominance), stand structural attributes, stand age and environmental conditions (climate and topography) on tree carbon storage, the relative importance of these drivers at large scale is poorly understood. It is also still unclear whether biodiversity effects on tree carbon storage work through niche complementarity (i.e. increased tree carbon storage due to interspecific resource partitioning) or through the mass-ratio effect (tree carbon storage regulated by dominant traits within communities). Here we analyze tree carbon storage and its drivers using data of 480 plots sampled across subtropical forests in China. We use multiple regression models to test the relative effects of biodiversity, stand structural attributes, stand age and environmental conditions on tree carbon storage, and use a partial least squares path model to test how these variables directly and/or indirectly affect tree carbon storage. Our results show that tree carbon storage is most strongly affected by stand age, followed by climate, biodiversity and stand structural attributes. Stand age and climate had both direct and indirect (through species diversity, functional dominance and stand structural attributes) effects. We find that tree carbon storage correlates with both species diversity and functional dominance after stand age and environmental drivers are accounted for. Our results suggest that niche complementarity and the mass-ratio effect, not necessarily mutually exclusive, both play a role in maintaining ecosystem functioning. Our results further indicate that biodiversity conservation might be an effective way for enhancing tree carbon storage in natural, species-rich forest ecosystems.

The allometry of coarse root biomass: log-transformed linear regression or nonlinear regression?

Precise estimation of root biomass is important for understanding carbon stocks and dynamics in forests. Traditionally, biomass estimates are based on allometric scaling relationships between stem diameter and coarse root biomass calculated using linear regression (LR) on log-transformed data. Recently, it has been suggested that nonlinear regression (NLR) is a preferable fitting method for scaling relationships. But while this claim has been contested on both theoretical and empirical grounds, and statistical methods have been developed to aid in choosing between the two methods in particular cases, few studies have examined the ramifications of erroneously applying NLR. Here, we use direct measurements of 159 trees belonging to three locally dominant species in east China to compare the LR and NLR models of diameter-root biomass allometry. We then contrast model predictions by estimating stand coarse root biomass based on census data from the nearby 24-ha Gutianshan forest plot and by testing the ability of the models to predict known root biomass values measured on multiple tropical species at the Pasoh Forest Reserve in Malaysia. Based on likelihood estimates for model error distributions, as well as the accuracy of extrapolative predictions, we find that LR on log-transformed data is superior to NLR for fitting diameter-root biomass scaling models. More importantly, inappropriately using NLR leads to grossly inaccurate stand biomass estimates, especially for stands dominated by smaller trees.

Topographic variation in aboveground biomass in a subtropical evergreen broad-leaved forest in China.

The subtropical forest biome occupies about 25% of China, with species diversity only next to tropical forests. Despite the recognized importance of subtropical forest in regional carbon storage and cycling, uncertainties remain regarding the carbon storage of subtropical forests, and few studies have quantified within-site variation of biomass, making it difficult to evaluate the role of these forests in the global and regional carbon cycles. Using data for a 24-ha census plot in east China, we quantify aboveground biomass, characterize its spatial variation among different habitats, and analyse species relative contribution to the total aboveground biomass of different habitats. The average aboveground biomass was 223.0 Mg ha(-1) (bootstrapped 95% confidence intervals [217.6, 228.5]) and varied substantially among four topographically defined habitats, from 180.6 Mg ha(-1) (bootstrapped 95% CI [167.1, 195.0]) in the upper ridge to 245.9 Mg ha(-1) (bootstrapped 95% CI [238.3, 253.8]) in the lower ridge, with upper and lower valley intermediate. In consistent with our expectation, individual species contributed differently to the total aboveground biomass of different habitats, reflecting significant species habitat associations. Different species show differently in habitat preference in terms of biomass contribution. These patterns may be the consequences of ecological strategies difference among different species. Results from this study enhance our ability to evaluate the role of subtropical forests in the regional carbon cycle and provide valuable information to guide the protection and management of subtropical broad-leaved forest for carbon sequestration and carbon storage.