Alpine treeline ecotones are harsh environment for tree establishment due to low temperature. Tree establishment at treelines requires favorable climate, suitable microsites, and viable seeds. But most researches have been addressed treeline microclimate and its effects on tree regeneration, the knowledge of seed quantity and quality and its controls on seedling recruitment were limited. We measured seed rain, soil seed bank, seed germination rate and seedling recruitment in natural forests in combination with seed transplanting manipulation to evaluate the controls of seed quantity and quality on seedling recruitment of Abies georgei var. smithii (smith fir) along altitudinal gradient, with focus on treeline ecotone in the Sygera Mountains, southeastern Tibetan Plateau. Both seed quantity and seed quality of smith fir decreased with increasing altitude and was thereby associated with decline in seed germination rate. Seed quantity and seedling recruitment were better in north-facing slope than in south slope. The treeline ecotone above 4200 m appeared as the threshold altitude to sharply decrease seed quality and seedling recruitment. The emergence and overwintering rates of transplanting seeds from 3600−3800 m also went down remarkably above 4200 m at north-facing slope. It also underpins the fact that treeline ecotone is the bottleneck of seedling recruitment. Our results suggest that seed quantity and quality are the principal limitation of treeline upward advance. This study also provides evidence to support stable treeline position in southeastern Tibetan Plateau.

With atmospheric reactive nitrogen (Nr) emissions increasing globally, research into Nr deposition has attracted increasing attention, especially in remote environments. These ecosystems are very sensitive to global change, especially enhanced Nr deposition. Forest environments, in particular, are highlighted because of their important ecological function. We quantified atmospheric Nr concentrations and deposition over four years of continuous monitoring in a southeast Tibetan boreal forest ecosystem, an ecosystem in which forest biomass and carbon density are high around the world. Average annual bulk Nr deposition was 3.00 kg N ha⁻¹ y⁻¹, with those of reduced and oxidized species estimated at 1.60 and 1.40 kg N ha⁻¹ y⁻¹, respectively. Bulk deposition of both NH₄⁺ and NO₃⁻ were controlled by precipitation amount: both Nr deposition and precipitation were highest in summer and lowest in winter. Dry deposition of NH₃ and NO₂ were 1.18 and 0.05 kg N ha⁻¹ y⁻¹, respectively. Atmospheric NH₃ concentrations were in the range 1.15–3.53 mg N L⁻¹, highest in summer and lowest in winter. In contrast, no clear trend in seasonal NO₂ concentrations was observed. Monthly NO₂ concentrations were 0.79–1.13 mg N L⁻¹. Total Nr deposition (bulk plus dry) was 4.23 (3.00 + 1.23) kg N ha⁻¹ y⁻¹ in the forest. Reduced nitrogen was the dominant species. In conclusion, Nr deposition was in the range at which forest net productivity and carbon sequestration are sensitive to any variation in nitrogen input, so quantification of Nr deposition should continue and with greater detail.

Climate warming is a major global challenge, and forests, essential carbon sinks, are critical in mitigating its effects. Forest carbon density is a key parameter in assessing the carbon sinks. Traditional estimating methods of forest carbon density are time-consuming, labor-intensive, and difficult to apply on a large scale. Combining multispectral data with machine learning offers a promising solution, but accurately estimating forest carbon density remains challenging due to the band limitations of multi-spectral data. This study proposes a novel approach to address this limitation gap. We utilized Landsat 8 data and 919 samples from Xizang, China, simultaneously constructed geographic (GEO) and environmental factors (GEF) for estimating forest carbon density for the first time, and adopted three models to evaluate the effectiveness. The results indicate that the extreme gradient boosting (XGB) model is significantly better, the average R² exceeds 0.77, especially in Rikaze exceeds 0.96. The total relative importance of GEF in the modelling exceeded 60%, Geo was the most critical variable, followed by CI. This study successfully used multi-spectral data to quantify the spatiotemporal distribution of forest carbon density and demonstrated that GEO and GEF are indispensable, which is expected to provide new perspectives and technical support for global carbon sink monitoring.