The ¹⁵N tracer technique is pivotal for quantifying nitrogen (N) dynamics in intercropping, but the assumption that results are independent of the tracer’s chemical form remains untested. We demonstrated that the choice of tracer form (NO₃^- vs. NH₄⁺) systematically affected quantitative estimates of both symbiotic N₂ fixation (SNF) and interspecific N transfer. In a pot experiment with maize and three leguminous green manures, the ¹⁵N-dilution technique revealed that the use of ¹⁵NH₄⁺ as a tracer resulted in higher estimates of the proportion of N derived from the atmosphere (%Ndfa) by an average of 18.0% compared to ¹⁵NO₃^-. Concurrently, a ¹⁵N foliar labeling experiment showed that the tracer form assimilated by the donor plant strongly altered the observed interspecific transfer pattern: legumes transferred 2.2 times more N derived from ¹⁵NO₃^- than from ¹⁵NH₄⁺ to maize, while maize transferred 1.6 times more N derived from ¹⁵NH₄⁺ than from ¹⁵NO₃^- to legumes. This bidirectional transfer pattern can be best explained by the distinct biogeochemical behaviors of the two N forms and their divergent metabolic assimilation pathways within plants. Our findings exposed a critical, yet previously unquantified, methodological effect. We contend that the chemical identity of the tracer should be reported as a mandatory methodological parameter, as estimates are not absolute but represent methodology-dependent perspectives. This necessitates a critical reevaluation of data across studies, cautioning against direct comparisons of results obtained with different tracer forms.

Exotic invasions by a single plant species have been well researched, but multi-species invasions are also common and are studied much less. In a greenhouse experiment, we studied co-invasions with two native species and three invasive species which co-occur in grasslands in the northern Rocky Mountains in a greenhouse experiment. Without competition from native neighbors and for all species combined, the biomass of two-invader mixtures was greater than the monoculture means when different species pairs were combined, generating a synergistic non-additive positive effect of co-invasions on total biomass. However, this increase in biomass with co-invasion did not correspond with a greater cumulative competitive effect, across all species combined, on native species or a cumulative competitive response to native species. However, when explored as separate species combinations, two pairs of the invasive species increased their competitive effects relative to their monocultures and one pair increased biomass and also increased their competitive response, or tolerance, to natives. Different combinations of co-invading species had strikingly different effects. Potentilla recta facilitated the growth of both of the other invasive species it was paired with, whereas Centaurea stoebe suppressed the growth of both species it was paired with. Our results suggest that even weak invaders might amplify synergistic effects when paired with stronger invasive species. This indicates that early management of emerging invaders prior to intense co-invasion scenarios may be important.

As global warming continues, non-uniform diel warming is widely affecting terrestrial ecosystems. Because plants undergo distinct physiological processes during the day and night, daytime and nighttime warming can exert contrasting effects on ecosystem carbon uptake and release. Numerous studies have shown that alpine ecosystems are more sensitive to temperature change than low-elevation ecosystems. Owing to low-temperature constraints, alpine ecosystems may exhibit distinct responses to asymmetric diel warming compared to lowland ecosystems. However, little is known about how the differential warming intensity between day and night would affect alpine ecosystems. Here, we integrated a four-year field warming experiment (daytime warming: +1.11 ℃; nighttime warming: +2.08 ℃) with landscape-scale observations to investigate the effects of asymmetric diurnal warming on carbon cycling in alpine grasslands. We found that daytime warming reduced net ecosystem productivity (NEP) by 32.4%, whereas nighttime warming increased NEP by 14.0%. Soil moisture, the coefficient of variation of soil temperature (CV_ST), precipitation during phenological periods, and aboveground biomass jointly mediated these responses. Daytime warming suppressed carbon uptake primarily by intensifying soil moisture loss, while nighttime warming enhanced carbon uptake by reducing CV_ST and creating more favorable thermal conditions. Moreover, precipitation during phenophases modulated the effects of warming-induced phenological shifts on ecosystem carbon exchange. Our results suggest that daytime and nighttime warming influence carbon cycling in alpine grasslands through distinct ecological mechanisms, underscoring the importance of explicitly considering the regulatory roles of soil moisture and temperature variability when assessing ecosystem carbon responses to diurnally asymmetric warming.

Afforestation is a key strategy to mitigate climate warming, yet its direct biophysical impacts on microclimate remain contentious in temperate regions. To address this, we conducted a 5-year in situ study (2019–2023) in paired catchments (afforested vs. natural grassland) on the Chinese Loess Plateau, monitoring temperature-moisture dynamics from 1 m belowground to 2 m above ground. The results indicate that afforestation induces divergent microclimate regulation across vertical strata. At 2 m aboveground, afforestation resulted in a significant increase in mean annual air temperature by 0.10℃ (P = 0.041) and a decrease in mean annual relative humidity by 1.69% (P = 0.003), whereas at 1 m it led to a significant decrease in air temperature by 0.35℃ (P = 0.004) and an increase in relative humidity by 0.59% (P = 0.038). In soils, afforestation overall resulted in a decrease in temperature by 0.54℃ (P < 0.001) and an increase in moisture by 0.74% (P = 0.019). Seasonally, the strongest effects of afforestation were observed in summer and winter, with effects particularly pronounced in winter, when afforestation resulted in a significant increase in air temperature at 2 m (0.157℃, P = 0.003) but a pronounced decrease in soil temperature, especially in the 10–40 cm layers (1.3–1.5℃, P < 0.01), highlighting contrasting responses between air and soil. Analysis of stand attributes indicated that leaf area index was the strongest regulator across vertical layers, exerting pronounced cooling effects on air and soil temperatures; canopy gap fraction was consistently associated with the afforestation-induced warming effect, particularly affecting soil minimum temperature. Synthetically, our results demonstrate pronounced vertical and seasonal variability in afforestation-induced biophysical climate regulation. Notably, under ongoing global warming, the microclimate regulation effect of afforestation via biophysical processes may weaken in semi-arid and arid ecosystems.

The spatiotemporal distribution of individual tree age within a forest community is important for understanding ecological processes, such as competition, succession, and function, at different scales. However, traditional methods are expensive and inefficient, particularly at large scales. This study proposes a novel conceptual framework to obtain the Forest Community Age Spectrum (FCAS) and evaluates its feasibility by integrating an explainable machine learning model with high-resolution hyperspectral remote sensing of leaves. Focusing on Larix gmelinii, we used hyperspectral data to rapidly estimate tree age. The results showed that the hyperspectral model of mature leaves could accurately estimate tree age (best model performance: R² = 0.78, RMSE = 6.13, RPD = 2.12). The model performed best in the 400–1000 nm wavelength band because of leaf structure-sensitive wavelength (near 644.88 nm) band and Photosynthetic pigment wavelength bands (701–724 nm), and captured the entire age gradient within the 900–1700 nm wavelength band due to the presence of phenolic aldehyde and other secondary metabolite-sensitive wavelength bands (1460–1517 nm and 1600–1700 nm). Overall, this study successfully established a key methodological foundation for estimating tree age and, ultimately, constructing the FCAS. The framework provides a potential pathway for future FCAS-based research to quantify spatial age patterns and investigate mechanisms driving competition, succession, functional optimization, and carbon sequestration. These findings offer both an empirical basis and an operational tool for quantitatively linking forest age structure with core ecological processes through FCAS, representing a critical first step toward its realization.