Tree diameter at breast height (dbh) and height are the most important variables used in forest inventory and management as well as forest carbon-stock estimation. In order to identify the key stand variables that influence the tree height-dbh relationship and to develop and validate a suit of models for predicting tree height, data from 5961 tree samples aged from 6 years to 53 years and collected from 80 Chinese-fir plantation plots were used to fit 39 models, including 33 nonlinear models and 6 linear models, were developed and evaluated into two groups. The results showed that composite models performed better in height estimate than one-independent-variable models. Nonlinear composite Model 34 and linear composite Model 6 were recommended for predicting tree height in Chinese fir plantations with a dbh range between 4 cm and 40 cm when the dbh data for each tree and the quadratic mean dbh of the stand (Dq) and mean height of the stand (Hm) were available. Moreover, Hm could be estimated by using the formula Hm=11.707×ln(Dq)-18.032. Clearly, Dq was the primary stand variable that influenced the height-dbh relationship. The parameters of the models varied according to stand age and site. The inappropriate application of provincial or regional height-dbh models for predicting small tree height at local scale may result in larger uncertainties. The method and the recommended models developed in this study were statistically reliable for applications in growth and yield estimation for even-aged Chinese-fir plantation in Huitong and Changsha. The models could be extended to other regions and to other tree species only after verification in subtropical China.

Quantifying transpiration and its response to meteorological factors at a stand scale and different canopy layers of forest is important for understanding the hydrological impact of nitrogen (N) deposition on local water balance. For this purpose, sap flows were measured in three upper (Schima superba, Castanea henryi, and Machilus chinensis) and three lower (Symplocos ramosissima, Ilex ficoidea, and Schefflera octophylla) canopy species based on a manipulative experimental platform with canopy (CN) and understory (UN) N additions in a low‐subtropical evergreen broadleaf forest. Each application included concentrations of 0, 25, and 50 kg·ha⁻¹·a⁻¹ N. Results showed that N addition obviously influenced the sap flux density and such influence varied among different species and between canopy layers. Upper canopy species contributed the most transpiration for the whole forest, and CN25 greatly affected the transpiration in both upper and lower canopy layers. Daily water use (Q_d) increased logarithmically with daytime average photosynthetically active radiation and daily average vapor pressure deficit. An appropriate canopy N addition (CN25) promoted the Q_d and its sensitivity to micrometeorological factors, whereas UN had a minimal effect. These results suggest a substantial difference in response of transpiration to the N addition ways. CN had more effective influence on the canopy transpiration. Thus, UN could not fully reflect the effects of increased N deposition on the canopy‐associated transpiration, and the response of forest transpiration to N deposition needs to be analyzed beyond species and upscaled to the canopy level in favor of meaningful assessments of N deposition.

The ratio between nitrogen and phosphorus (N/P) in plant leaves has been widely used to assess the availability of nutrients. However, it is challenging to rapidly and accurately estimate the leaf N/P ratio, especially for mixed forest. In this study, we collected 301 samples from nine typical karst areas in Guangxi Province during the growing season of 2018 to 2020. We then utilized five models (partial least squares regression (PLSR), backpropagation neural network (BPNN), general regression neural network (GRNN), PLSR+BPNN, and PLSR+GRNN) to estimate the leaf N/P ratio of plants based on these samples. We also applied the fractional differentiation to extract additional information from the original spectra of each sample. The results showed that the average leaf N/P ratio of plants was 17.97. Plant growth was primarily limited by phosphorus in these karst areas. The sensitive spectra to estimate leaf N/P ratio had wavelengths ranging from 400–730 nm. The prediction capabilities of these five models can be ranked in descending order as PLSR+GRNN, PLSR+BPNN, PLSR, GRNN, and BPNN when considering both accuracy and robustness. The PLSR+GRNN model yielded high R² and performance to deviation (RPD), and low root mean squared error (RMSE) with values of 0.91, 3.15, and 1.98, respectively, for the training test and 0.81, 2.25, and 2.46, respectively, for validation test. Compared with the PLSR model, both PLSR+BPNN and PLSR+GRNN models had higher accuracy and were more stable. Moreover, both PLSR+BPNN and PLSR+GRNN models overcame the issue of overfitting, which occurs when a single model is used to predict leaf N/P ratio. Therefore, both PLSR+BPNN and PLSR+GRNN models can be used to predict the leaf N/P ratio of plants in karst areas. Fractional differentiation is a promising spectral preprocessing technique that can improve the accuracy of models. We conclude that the leaf N/P ratio of mixed forest can be effectively estimated using combined models based on field spectroradiometer data in karst areas.

Evaluating the adaptive responses of different plant species to different types of Cd-contaminated soils and selecting the most effective hyperaccumulator are essential for improving remediation efficiency. In this study, we evaluated the Cd accumulation capacity and adaptive responses of five common (hyper)accumulator: Amaranthus hypochondriacus (A.H), Solanum nigrum (S.N), Celosia argentea (C.A), Phytolacca acinosa (P.A), and Sedum plumbizincicola (S.P), which were grown in two typical Cd-contaminated soils from Renhua (RH) and Maba (MB) in southern China. The results demonstrated that photosynthetic and rhizospheric responses were key factors in mediating plant adaptation to Cd stress. Leaf Cd levels were between 40.4 and 152.9 mg kg^-1 in MB soil and between 9.9 and 541.4 mg kg^-1 in RH soil, with S. plumbizincicola and P. acinosa accumulating the most and least Cd, respectively. Amaranthus hypochondriacus and S. nigrum exhibited the greatest above-ground biomass in MB and RH soils, respectively, whereas P. acinosa consistently demonstrated the highest root biomass in both soils. Phytolacca acinosa also showed the highest concentration of photosynthetic pigments, with 42.8 ∼ 220.4 % higher than those of other species grown in RH soil, likely due to higher Cu uptake. Conversely, S. plumbizincicola exhibited the lowest levels of photosynthetic pigments, suggesting an inverse correlation between leaf Cd concentration and the accumulation of photosynthetic pigments. Phytolacca acinosa demonstrated significantly higher rhizospheric microbes compared to other species, with increases in bacteria, actinomycetes, and fungi ranging from 20.9 % to 30.7 %, 10.6-30.2 %, and 78.4-182.9 %, respectively. In addition, root-secreted oxalic and malic acids were elevated in all species, particularly in RH soil, helping to mitigate the restrictive effects of near-neutral pH on nutrient availability. These findings underscore that hyperaccumulator species employ unique physiological and rhizospheric strategies to manage Cd stress, with these strategies differing based on Cd exposure level and soil types.