Carbon response of tundra ecosystems to advancing greenup and snowmelt in Alaska

Unraveling the drivers and impacts of leaf phenological diversity in a subtropical forest: A fine-scale analysis using PlanetScope CubeSats

Leaf phenology variations within plant communities shape community assemblages and influence ecosystem properties and services. However, questions remain regarding quantification, drivers, and productivity impacts of intra-site leaf phenological diversity. With a 50-ha subtropical forest plot in China's Heishiding Provincial Nature Reserve (part of the global ForestGEO network) as a testbed, we gathered a unique dataset combining ground-derived abiotic (topography, soil) and biotic (taxonomic diversity, functional diversity, functional traits) factors. We investigated drivers underlying leaf phenological diversity extracted from high-resolution PlanetScope data, and its influence on aboveground biomass (AGB) using structural equation modeling (SEM). Our results reveal considerable fine-scale leaf phenological diversity across the subtropical forest landscape. This diversity is directly and indirectly influenced by abiotic and biotic factors (e.g. slope, soil, traits, taxonomic diversity; r2 = 0.43). While a notable bivariate relationship between AGB and leaf phenological diversity was identified (r = -0.24, P < 0.05), this relationship did not hold in SEM analysis after considering interactions with other biotic and abiotic factors (P > 0.05). These findings unveil the underlying mechanism regulating intra-site leaf phenological diversity. While leaf phenology is known to be associated with ecosystem properties, our findings confirm that AGB is primarily influenced by functional trait composition and taxonomic diversity rather than leaf phenological diversity.

Alpine plant species converge towards adopting elevation-specific resource-acquisition strategy in response to experimental early snow-melting

Snow-melt is one of the important factors limiting growth and survival of alpine plants. Changes in snow-melt timing have profound effects on eco-physiological characteristics of alpine plant species through alterations in growing season length. Here, we conducted a field experiment and studied species response to experimentally induced early snow-melting (ES) (natural vs. early) at an alpine site (Rohtang) in the western Himalaya region. Eco-physiological response of eight snow-bed restricted alpine plant species from different elevations (lower: 3850 m and upper: 4150 m amsl) and belonging to contrasting resource acquisition strategies (conservative and acquisitive) were studied after 2-years (2019 & 2020) of initiating ES field experiment. We estimated the functional traits related to leaf economic spectrum and physiological performance and assessed their pattern of phenotypic plasticity. Analysis by linear mixed effect model showed that both the 'conservative' and 'acquisitive' species had responded to ES with significant effects on species specific leaf area, leaf dry matter content, leaf thickness, leaf water content and sugar content. Our results also revealed that ES treatment induced significant increase in leaf C/N ratio (10.57 % to 13.65 %) and protein content (15.85 % to 20.76 %) at both the elevations, irrespective of species groups. The phenotypic plasticity was found to be low and was essentially species-specific. However, for leaf protein content, the upper elevation species exhibited a higher phenotypic plasticity (0.43 ± 0.18) than the lower elevation species (0.31 ± 0.21). Interestingly, we found that irrespective of species unique functional strategy, species adapt to perform more conservative at lower elevation and more acquisitive at upper elevation, in response to ES. We conclude that plants occurring at contrasting elevations respond differentially to ES. However, species showed capacity for short-term acclimation to future environmental conditions, but may be vulnerable, if their niche is occupied by new species with greater phenotypic plasticity and a superior competitive ability.

Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications

Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.

Summer temperature—but not growing season length—influences radial growth of Salix arctica in coastal Arctic tundra

Arctic climate change is leading to an advance of plant phenology (the timing of life history events) with uncertain impacts on tundra ecosystems. Although the lengthening of the growing season is thought to lead to increased plant growth, we have few studies of how plant phenology change is altering tundra plant productivity. Here, we test the correspondence between 14 years of Salix arctica phenology data and radial growth on Qikiqtaruk–Herschel Island, Yukon Territory, Canada. We analysed stems from 28 individuals using dendroecology and linear mixed-effect models to test the statistical power of growing season length and climate variables to individually predict radial growth. We found that summer temperature best explained annual variation in radial growth. We found no strong evidence that leaf emergence date, earlier leaf senescence date, or total growing season length had any direct or lagged effects on radial growth. Radial growth was also not explained by interannual variation in precipitation, MODIS surface greenness (NDVI), or sea ice concentration. Our results demonstrate that at this site, for the widely distributed species S. arctica, temperature—but not growing season length—influences radial growth. These findings challenge the assumption that advancing phenology and longer growing seasons will increase the productivity of all plant species in Arctic tundra ecosystems.

Classification of Snow Cover Persistence across China

In this study, we classified the variability in snow cover persistence across China by using a novel method; continuous snow cover days and variability of snow cover were used as the evaluation indicators based on a long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product. The product has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. There were obvious differences in snow cover classification in three snow cover areas (northern Xinjiang, northeast China, and the Tibetan Plateau): northern Xinjiang was dominated by persistent snow cover, most regions of northeast China were covered by persistent and periodic variable snow cover. There was the most abundant snow cover classification in the Tibetan Plateau. The extents of persistent and periodic variable snow cover were gradually shrinking due to rising temperatures and decreasing snowfall during 1981–2019. In contrast, non-periodic variable snow cover areas increased significantly. This method takes into account the stability, continuity, and variability of snow cover, and better captures the characteristics and changes of snow cover across China. Based on our research, we found that snow disasters in ephemeral-type (belong to non-periodic variable snow cover) regions cannot be well prevented because of the unfixed snow cover timing. Therefore, we recommend that monitoring and forecasting of snow cover in these snow cover regions should be strengthened.