NDVI Changes Show Warming Increases the Length of the Green Season at Tundra Communities in Northern Alaska: A Fine-Scale Analysis

Diminishing warming effects on plant phenology over time

Plant phenology, the timing of recurrent biological events, shows key and complex response to climate warming, with consequences for ecosystem functions and services. A key challenge for predicting plant phenology under future climates is to determine whether the phenological changes will persist with more intensive and long-term warming. Here, we conducted a meta-analysis of 103 experimental warming studies around the globe to investigate the responses of four phenophases - leaf-out, first flowering, last flowering, and leaf coloring. We showed that warming advanced leaf-out and flowering but delayed leaf coloring across herbaceous and woody plants. As the magnitude of warming increased, the response of most plant phenophases gradually leveled off for herbaceous plants, while phenology responded in proportion to warming in woody plants. We also found that the experimental effects of warming on plant phenology diminished over time across all phenophases. Specifically, the rate of changes in first flowering for herbaceous species, as well as leaf-out and leaf coloring for woody species, decreased as the experimental duration extended. Together, these results suggest that the real-world impact of global warming on plant phenology will diminish over time as temperatures continue to increase.

Influence of climate variability and land cover dynamics on the spatio-temporal NDVI patterns in western hydrological regions of Bangladesh

Analyzing vegetation greenness considering climate and land cover changes is crucial for Bangladesh given the historically drier North-West and South-West regions of Bangladesh have shown prominent climatic and hydrological variations. Therefore, this study assessed the spatial and temporal variation of NDVI and its relationship with climate and land cover changes from 2000 to 2022 for these regions. In this study, Moran's I and Getis Ord Gi* were employed for spatial autocorrelation and Mann-Kendall, Sen's slope test along with Innovative Trend Analysis were deployed to identify temporal trends of NDVI. RMSE, MAE and R-squared values were assessed between computed and observed PET. Correlation of NDVI with climate variables were assessed through multivariate correlation analysis and correlation mapping. Additionally, Pearson product moment correlation was applied between different types of land cover and NDVI. Spatial autocorrelation outcomes showed that NDVI values have been clustered in distinct hotspots and cold-spots over the years. Temporal trend detection results indicate that NDVI values are rising significantly all over the regions. Multivariate correlation analysis identified no climate variable to be the limiting factor for NDVI changes. Similarly, the precipitation-NDVI correlation map displayed no significant correlation. Nonetheless, temperature-NDVI correlation map illustrated varying degrees of mostly moderate and strong positive correlations with distinct negative correlation results in the Sundarbans of South-West region. Land cover pattern analysis with NDVI showed a positive correlation to forest, cropland and vegetation area increasing and negative correlation to grassland and barren area decreasing. In this regard, Rangpur division exhibited stronger correlations than Rajshahi division in North-West. The findings indicate that NDVI changes in the regions are largely dependent on land cover changes in comparison to climate trends. This can instigate further research in other hydrological regions to explore the natural and man-made factors that can affect the greenery and vegetation density in specific regions.

Acclimation of subarctic vegetation to warming and increased cloudiness

Subarctic ecosystems are exposed to elevated temperatures and increased cloudiness in a changing climate with potentially important effects on vegetation structure, composition, and ecosystem functioning. We investigated the individual and combined effects of warming and increased cloudiness on vegetation greenness and cover in mesocosms from two tundra and one palsa mire ecosystems kept under strict environmental control in climate chambers. We also investigated leaf anatomical and biochemical traits of four dominant vascular plant species (Empetrum hermaphroditum, Vaccinium myrtillus, Vaccinium vitis-idaea, and Rubus chamaemorus). Vegetation greenness increased in response to warming in all sites and in response to increased cloudiness in the tundra sites but without associated increases in vegetation cover or biomass, except that E. hermaphroditum biomass increased under warming. The combined warming and increased cloudiness treatment had an additive effect on vegetation greenness in all sites. It also increased the cover of graminoids and forbs in one of the tundra sites. Warming increased leaf dry mass per area of V. myrtillus and R. chamaemorus, and glandular trichome density of V. myrtillus and decreased spongy intercellular space of E. hermaphroditum and V. vitis-idaea. Increased cloudiness decreased leaf dry mass per area of V. myrtillus, palisade thickness of E. hermaphroditum, and stomata density of E. hermaphroditum and V. vitis-idaea, and increased leaf area and epidermis thickness of V. myrtillus, leaf shape index and nitrogen of E. hermaphroditum, and palisade intercellular space of V. vitis-idaea. The combined treatment caused thinner leaves and decreased leaf carbon for V. myrtillus, and increased leaf chlorophyll of E. hermaphroditum. We show that under future warmer increased cloudiness conditions in the Subarctic (as simulated in our experiment), vegetation composition and distribution will change, mostly dominated by graminoids and forbs. These changes will depend on the responses of leaf anatomical and biochemical traits and will likely impact carbon gain and primary productivity and abiotic and biotic stress tolerance.

Svalbard reindeer winter diets: Long-term dietary shifts to graminoids in response to a changing climate

Arctic ecosystems are changing dramatically with warmer and wetter conditions resulting in complex interactions between herbivores and their forage. We investigated how Svalbard reindeer (Rangifer tarandus platyrhynchus) modify their late winter diets in response to long-term trends and interannual variation in forage availability and accessibility. By reconstructing their diets and foraging niches over a 17-year period (1995-2012) using serum δ¹³ C and δ¹⁵ N values, we found strong support for a temporal increase in the proportions of graminoids in the diets with a concurrent decline in the contributions of mosses. This dietary shift corresponds with graminoid abundance increases in the region and was associated with increases in population density, warmer summer temperatures and more frequent rain-on-snow (ROS) in winter. In addition, the variance in isotopic niche positions, breadths, and overlaps also supported a temporal shift in the foraging niche and a dietary response to extreme ROS events. Our long-term study highlights the mechanisms by which winter and summer climate changes cascade through vegetation shifts and herbivore population dynamics to alter the foraging niche of Svalbard reindeer. Although it has been anticipated that climate changes in the Svalbard region of the Arctic would be detrimental to this unique ungulate, our study suggests that environmental change is in a phase where conditions are improving for this subspecies at the northernmost edge of the Rangifer distribution.

Seasonal climate drivers of peak NDVI in a series of Arctic peatlands

Changes in plant cover and productivity are important in driving Arctic soil carbon dynamics and sequestration, especially in peatlands. Warming trends in the Arctic are known to have resulted in changes in plant productivity, extent and community composition, but more data are still needed to improve understanding of the complex controls and processes involved. Here we assess plant productivity response to climate variability between 1985 and 2020 by comparing peak growing season NDVI (Normalised Difference Vegetation Index data from Landsat 5 and 7), to seasonal-average weather data (temperature, precipitation and snow-melt timing) in nine locations containing peatlands in high- and low-Arctic regions in Europe and Canada. We find that spring (correlation 0.36 for peat dominant and 0.39 for mosaic; MLR coefficient 0.20 for peat, 0.29 for mosaic), summer (0.47, 0.42; 0.18, 0.17) and preceding-autumn (0.35, 0.25; 0.33, 0.27) temperature are linked to peak growing season NDVI at our sites between 1985 and 2020, whilst spring snow melt timing (0.42, 0.45; 0.25, 0.32) is also important, and growing season water availability is likely site-specific. According to regression trees, a warm preceding autumn (September-October-November) is more important than a warm summer (June-July-August) in predicting the highest peak season productivity in the peat-dominated areas. Mechanisms linked to soil processes may explain the importance of previous-Autumn conditions on productivity. We further find that peak productivity increases in these Arctic peatlands are comparable to those in the surrounding non-peatland-dominant vegetation. Increased productivity in and around Arctic peatlands suggests a potential to increased soil carbon sequestration with future warming, but further work is needed to test whether this is evident in observations of recent peat accumulation and extent.

Impact of climate change-induced alterations in peatland vegetation phenology and composition on carbon balance

Global climate is changing faster than humankind has ever experienced. Model-based predictions of future climate are becoming more complex and precise, but they still lack crucial information about the reaction of some important ecosystems, such as peatlands. Peatlands belong to one of the largest carbon stores on the Earth. They are mostly distributed in high latitudes, where the temperature rises faster than in the other parts of the planet. Warmer climate and changes in precipitation patterns cause changes in the composition and phenology of peatland vegetation. Peat mosses are becoming less abundant, vascular plants cover is increasing, and the vegetation season and phenophases of vascular plants start sooner. The alterations in vegetation cause changes in the carbon assimilation and release of greenhouse gases. Therefore, this article reviews the impact of climate change-induced alterations in peatland vegetation phenology and composition on future climate and the uncertainties that need to be addressed for more accurate climate prediction.

Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

Grasslands are crucial components of ecosystems. In recent years, owing to certain natural and socio-economic factors, alpine grassland ecosystems have experienced significant degradation. This study integrated the frequency ratio model (FR) and Bayesian belief networks (BBN) for grassland degradation risk assessment to mitigate several issues found in previous studies. Firstly, the identification of non-encroached degraded grasslands and shrub-encroached grasslands could help stakeholders more accurately understand the status of different types of alpine grassland degradation. In addition, the index discretization method based on the FR model can more accurately ascertain the relationship between grassland degradation and driving factors to improve the accuracy of results. On this basis, the application of BBN not only effectively expresses the complex causal relationships among various variables in the process of grassland degradation, but also solves the problem of identifying key factors and assessing grassland degradation risks under uncertain conditions caused by a lack of information. The obtained result showed that the accuracies based on the confusion matrix of the slope of NDVI change (NDVIs), shrub-encroached grasslands, and grassland degradation indicators in the BBN model were 85.27, 88.99, and 74.37%, respectively. The areas under the curve based on the ROC curve of NDVIs, shrub-encroached grasslands, and grassland degradation were 75.39% (P < 0.05), 66.57% (P < 0.05), and 66.11% (P < 0.05), respectively. Therefore, this model could be used to infer the probability of grassland degradation risk. The results obtained using the model showed that the area with a higher probability of degradation (P > 30%) was 2.22 million ha (15.94%), with 1.742 million ha (78.46%) based on NDVIs and 0.478 million ha (21.54%) based on shrub-encroached grasslands. Moreover, the higher probability of grassland degradation risk was mainly distributed in regions with lower vegetation coverage, lower temperatures, less potential evapotranspiration, and higher soil sand content. Our research can provide guidance for decision-makers when formulating scientific measures for alpine grassland restoration.

Winter snow and spring temperature have differential effects on vegetation phenology and productivity across Arctic plant communities

Tundra dominates two-thirds of the unglaciated, terrestrial Arctic. Although this region has experienced rapid and widespread changes in vegetation phenology and productivity over the last several decades, the specific climatic drivers responsible for this change remain poorly understood. Here we quantified the effect of winter snowpack and early spring temperature conditions on growing season vegetation phenology (timing of the start, peak, and end of the growing season) and productivity of the dominant tundra vegetation communities of Arctic Alaska. We used daily remotely sensed normalized difference vegetation index (NDVI), and daily snowpack and temperature variables produced by SnowModel and MicroMet, coupled physically based snow and meteorological modeling tools, to (1) determine the most important snowpack and thermal controls on tundra vegetation phenology and productivity and (2) describe the direction of these relationships within each vegetation community. Our results show that soil temperature under the snowpack, snowmelt timing, and air temperature following snowmelt are the most important drivers of growing season timing and productivity among Arctic vegetation communities. Air temperature after snowmelt was the most important control on timing of season start and end, with warmer conditions contributing to earlier phenology in all vegetation communities. In contrast, the controls on the timing of peak season and productivity also included snowmelt timing and soil temperature under the snowpack, dictated in part by the snow insulating capacity. The results of this novel analysis suggest that while future warming effects on phenology may be consistent across communities of the tundra biome, warming may result in divergent, community-specific productivity responses if coupled with reduced snow insulating capacity lowers winter soil temperature and potential nutrient cycling in the soil.