Terrestrial Vegetation Growth Research Articles

Recent shift from dominant nitrogen to CO2 fertilization control on the growth of mature Qinghai spruce in China's Qilian Mountains

Terrestrial vegetation growth is stimulated by rising atmospheric CO2 concentration, a warmer climate, and increased soil nutrient availability. However, as plants age, progressive nutrient limitation is known to occur, especially in mature forests where soil nitrogen is deficient. Yet the long-term growth response of mature trees to rising CO2 accompanied by changing climate and nitrogen availability in semi-arid mountain regions is unclear. Here we used tree-ring widths and stable carbon (δ13C) and nitrogen (δ15N) isotopes to investigate the drivers of radial growth of mature Qinghai spruce (Picea crassifolia) in the central Qilian Mountains, northwest China, from 1840 through 2019. Tree growth benefited from improved nitrogen availability, chiefly via changes in bioavailable nitrogen pools modified by a favorable climate during 1930–1964. Enhanced intrinsic water-use efficiency (iWUE), driven by reduced stomatal conductance (gs) related to water deficit, lead to radial growth declines in 1985–2002. Recent acceleration of tree growth was largely attributed to a CO2 fertilization effect through enhanced iWUE during 2003–2019. Nitrogen availability was positively related to tree growth from the 1920s onward until greater CO2 fertilization ensued from 2000 onward. Hence, the negative effects of low nitrogen availability on growth could be mitigated or reversed by a high atmospheric CO2 concentration and warmer climate conditions. Our results suggest that mature spruce forests still harbor potential to increase ecosystem-level carbon sequestration and thereby partially mitigate climate warming. Such a nature-based solution in drought-prone forests would be achieved under warmer-wetter climate conditions in northwest China.

Accelerated vegetative growth measured by gross primary productivity in China from 1980 to 2018

Vegetation response to climate change can be characterized as long-term trends, but with periodic oscillations. Revealing vegetation response trends, with oscillations, is essential for formulating climate change mitigation and adaptation planning, as well as adopting ecological protection and restoration measures. Here, the long-term trend and periodic oscillations in vegetation changes were explored through a measure of gross primary production (GPP), an indicator of vegetation photosynthesis, using data of both high spatial resolution (1 km2) and a long-term of nearly 4 decades, from 1980 to 2018. The GPP data were estimated for China’s terrestrial ecosystem and validated against the eddy covariance-based observations of 70 site-year in China and compared to other GPP data products. The results showed the estimated GPP can explain 76% of spatial–temporal variance with the same data accuracy as other products, but with the advantage of both high spatial resolution and long time periods that other products do not have. From the estimated GPP data, Chinese terrestrial vegetation growth was found to have a periodic oscillation of close to 2.79 years and a long-term increasing trend of 59.8 Mt C per year over 92% of all vegetated land in China from 1980 to 2018. Moreover, it was found that vegetation growth accelerated significantly in the two recent decades over the previous two decades. The growth rate was 83.5 Mt C a−1 from 2000 to 2018, which was 2.31 times faster than the rate from 1980 to 2000. The underlying mechanism of the vegetation responses was analyzed by ridge regression, a method to decrease the effects of multicollinearity. Long-term variability and trends in vegetation could be attributed to a warming minimum air temperature when examined with precipitation, maximum air temperature, and solar shortwave radiation. Though more influences, such as those from freezing and thawing and human activities, should be explored in the future, this study offers insight that warming, with increasing daily minimum temperatures, is likely to be a vital contributor to ecosystem changes, and will be essential knowledge for mitigation of effects and adaptation to future climate.

Uncertainty of Partial Dependence Relationship between Climate and Vegetation Growth Calculated by Machine Learning Models

As more machine learning and deep learning models are applied in studying the quantitative relationship between the climate and terrestrial vegetation growth, the uncertainty of these advanced models requires clarification. Partial dependence plots (PDPs) are one of the most widely used methods to estimate the marginal effect of independent variables on the predicted outcome of a machine learning model, and it is regarded as the main basis for conclusions in relevant research. As more controversies regarding the reliability of the results of the PDPs emerge, the uncertainty of the PDPs remains unclear. In this paper, we experiment with real, remote sensing data to systematically analyze the uncertainty of partial dependence relationships between four climate variables (temperature, rainfall, radiation, and windspeed) and vegetation growth, with one conventional linear model and six machine learning models. We tested the uncertainty of the PDP curves across different machine learning models from three aspects: variation, whole linear trends, and the trait of change points. Results show that the PDP of the dominant climate factor (mean air temperature) and vegetation growth parameter (indicated by the normalized difference vegetation index, NDVI) has the smallest relative variation and the whole linear trend of the PDP was comparatively stable across the different models. The mean relative variation of change points across the partial dependence curves of the non-dominant climate factors (i.e., radiation, windspeed, and rainfall) and vegetation growth ranged from 8.96% to 23.8%, respectively, which was much higher than those of the dominant climate factor and vegetation growth. Lastly, the model used for creating the PDP, rather than the relative importance of these climate factors, determines the fluctuation of the PDP output of these climate variables and vegetation growth. These findings have significant implications for using remote sensing data and machine learning models to investigate the quantitative relationships between the climate and terrestrial vegetation.

Trend of Vegetation and Environmental Factors and Their Feedback in the Karst Regions of Southwestern China

Vegetation plays a vital role in the terrestrial ecosystem. Vegetation variations not only result from climatic and environmental change but also feed back to the climate through biogeochemical and biogeophysical processes. Previous studies have mainly focused on the influence of environmental factors on vegetation changes, but the interactions between vegetation and the prevalent environmental factors in the karst areas of southwestern China have been poorly understood. Based on remote sensing data, this study used trend analysis and Granger causality analysis to investigate vegetation trends, the driving factors, and their interactions during the period 2001 to 2021. In summary, we explored how these factors influenced vegetation growth and how vegetation changes fed back to produce ecosystems and environmental variations in southwestern China during this period. The results showed improvements in water conditions as well as a vegetation greening trend in most of the regions of southwestern China, together with a weakening trend of rocky desertification and an increasing trend of vegetation growth during the period. Both terrestrial water storage and vegetation growth in the energy-limited alpine regions are sensitive to temperature. Natural restoration is suggested in this area. Vegetation growth in the karst areas is sensitive to water stress-related variables due to the particular geological and soil characteristics. The bidirectional causality relationship between vegetation greening and the environment factors in many of these areas indicates that the vegetation changes can also significantly affect water balance and conditions. Ecological engineering projects are suggested in this area. The vegetation growth in the SC Basin, HN, HB, GD, and eastern GX is also sensitive to water stress, while these regions are vulnerable to waterlogging. This study helps to improve our understanding of ecosystem management and promote sustainable development in southwestern China.

Phenology-based seasonal terrestrial vegetation growth response to climate variability with consideration of cumulative effect and biological carryover

Vegetation growth is influenced not only by climate variability but also by its past states. However, the differences in the degree of the climate variability and past states affecting vegetation growth over seasons are still poorly understood, particularly given the cumulative climate effects. Relying on the Normalized Difference Vegetation Index (NDVI) data from 1982 to 2014, the vegetation growing season was decomposed into three periods (sub-seasons) - green-up (GSgp), maturity (GSmp), and senescence (GSsp) - following a phenology-based definition. A distributed lag model was then utilized to analyze the time-lag effect of vegetation growth response to climatic factors including precipitation, temperature, and solar radiation during each sub-season. On this basis, the relative importance of climatic factors and vegetation growth carryover (VGC) effect on vegetation growth was quantified at the phenology-based seasonal scale. Results showed that the longest peak lag of precipitation, temperature, and solar radiation occurred in the GSmp, GSsp, and GSgp, with 1.27 (1.13 SD), 0.89 (1.02 SD), and 0.80 (1.04 SD) months, respectively. The influence of climate variability was strongest in the GSgp, and diminished over the season, while the opposite for the VGC effect. The relative influence of each climatic factor also varied between sub-seasons. Vegetation in more than 58% of areas was more affected by temperature in the GSgp, and the proportion decreased to 34.00% and 31.78% in the GSmp and GSsp, respectively. Precipitation and solar radiation acted as the dominant climatic factors in only 28.80% and 20.88% of vegetation areas in the GSgp, but they increased to 35.21%, 32.61% in the GSmp, and 38.20%, 30.02% in the GSsp, respectively. The increased regions influenced by precipitation were mainly in dry areas especially for the boreal and cool temperate climate zones, while increased regions influenced by solar radiation were primarily located in moist areas of mid-high latitudes of the Northern Hemisphere. By introducing the cumulative climate effect, our findings highlight seasonal patterns of vegetation growth affected by climate variability and the VGC effect. The results provide a more comprehensive perspective on climate-vegetation interactions, which may help us to accurately forecast future vegetation growth under accelerating global warming.

Assessment of vegetation growth and drought conditions using satellite-based vegetation health indices in Jing-Jin-Ji region of China

Terrestrial vegetation growth activity plays pivotal roles on regional development, which has attracted wide attention especially in water resources shortage areas. The paper investigated the spatiotemporal change characteristics of vegetation growth activity using satellite-based Vegetation Health Indices (VHIs) including smoothed Normalized Difference Vegetation Index (SMN), smoothed Brightness Temperature (SMT), Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and VHI, based on 7-day composite temporal resolution and 16 km spatial resolution gridded data, and also estimated the drought conditions for the period of 1982–2016 in Jing-Jin-Ji region of China. The Niño 3.4 was used as a substitution of El Niño Southern Oscillation (ENSO) to reveal vegetation sensitivity to ENSO using correlation and wavelet analysis. Results indicated that monthly SMN has increased throughout the year especially during growing season, starts at approximate April and ends at about October. The correlation analysis between SMN and SMT, SMN and precipitation indicated that the vegetation growth was affected by joint effects of temperature and precipitation. The VCI during growing season was positive trends dominated and vice versa for TCI. The relationships between VHIs and drought make it possible to identify and quantify drought intensity, duration and affected area using different ranges of VHIs. Generally, the intensity and affected area of drought had mainly decreased, but the trends varied for different drought intensities, regions and time periods. Large-scale global climate anomalies such as Niño 3.4 exerted obvious impacts on the VHIs. The Niño 3.4 was mainly negatively correlated to VCI and positively correlated to TCI, and the spatial distributions of areas with positive (negative) correlation coefficients were mainly opposite. The linear relationships between Niño 3.4 and VHIs were in accordance with results of nonlinear relationships revealed using wavelet analysis. The results are of great importance to assess the vegetation growth activity, to monitor and quantify drought using satellite-based VHIs in Jing-Jin-Ji region.

Modulation of Compound Extremes of Low Soil Moisture and High Vapor Pressure Deficit by Irrigation in India

AbstractCompound extremes of low soil moisture and high vapor pressure deficit (VPD) have implications on terrestrial carbon sequestration, vegetation growth, and net primary productivity (NPP). Yet, the role of irrigation on the occurrence of compound extremes remains unexplored in India. Here, we use satellite observations, reanalysis data sets, and high‐resolution simulations from the Weather Research and Forecasting (WRF) model to show that irrigation significantly reduces the frequency of compound extremes of low soil moisture and high VPD in India. The Indo‐Gangetic plain witnessed a significant (p < 0.05) increase in mean annual soil moisture while a substantial reduction in VPD during 1979–2018. Moreover, irrigation caused considerable cooling over the Indo‐Gangetic plain during 1982–2018. The increase in soil moisture and the decline in VPD partly contributed to a substantial rise in Normalized Difference Vegetation Index (NDVI) and NPP. We conducted high‐resolution simulations using the WRF model to examine the role of irrigation in modulating the frequency of compound extremes during 1979–2018. The WRF simulations under the irrigation‐on show a considerably reduced frequency of the compound extremes compared to the irrigation‐off (control) simulations. Irrigation modulates compound extremes by changing the land surface energy budget and planetary boundary layer height. Therefore, there is a need to consider irrigation for reliable projections of compound extremes under a warming climate in intensively irrigated regions.

Effect of terrestrial vegetation growth on climate change in China

Globally, some vegetation has grown significantly over the past decades, but the climate benefits remain unclear, especially in the temperate regions. Understanding the biophysical effects and identifying the potential of vegetation will help to mitigate climate change. Here, we propose a vegetation-adjusted temperature index to understand how terrestrial vegetation growth in China affects the air temperature for 2001–2013, based on satellite-derived normalized difference vegetation index, near-surface air temperature (Ta) and the land surface temperature. Grassland growth and cropland growth are found to cool the Ta by −0.08 ± 0.32 °C (mean ± one standard deviation) and −0.06 ± 0.28 °C, respectively. Forest growth results in net climate warming by 0.05 ± 0.29 °C. Biophysical effects, elevation and background climate are used to explain the climate impacts of vegetation. Results show that the biophysical effects dominate the climate impact. More specifically, evapotranspiration (ET) controls the daytime climate impact, and at night, an indirect effect of albedo (the release of daytime heat storage) dominates it. Lower precipitation, temperature and elevation reinforce the warming potential. Moreover, the effects of albedo and ET on climate are nonlinear. During the day, although lower albedo absorbs more incoming radiation, it releases more extra heat per unit ET that can compensate for the increased incoming radiation. At night, the warming effect reflects the release of daytime heat storage. Overall, tropical vegetation growth (−0.04 ± 0.10 °C) and warm temperate vegetation growth (−0.08 ± 0.15 °C) achieve the climatic benefits. Overall, the method proposed contributes to quantitatively evaluate the role of afforestation plan on regional climate cooling, and provides some policy/practical implications for future afforestation projects. Future afforestation projects should carefully consider the biophysical process and background climate to mitigate climate change.

Impact of 2004 co-seismic coastal uplift on the mangrove cover along the North Andaman Islands

The December 2004 earthquake and the subsequent tsunami have incurred devastating impacts on vital habitats along the Indian coastal region, where few of them are irreversible. The earthquake has uplifted the northern Andaman coast, resulting in a drastic reduction of tidal water influx into the adjoining mangrove-laden mudflats. Satellite images of the period from 2003 to 2019 were used to assess the impact of coastal upliftment on the northern Andaman mangroves. Through satellite data analysis, we report a loss of 6500 ha of northern Andaman mangroves. Tidal hydrodynamics simulation under the post-earthquake scenario showed a vast area of mudflats deprived of tidal influx. This reduction in the tidal inundation has incurred a gradual and permanent mangrove area degradation. The earthquake impact on the mangroves was not spontaneous, but a gradual process, where the mangroves were intact until Mach 2006. The earthquake and the resultant coastal uplift have significantly altered the coastal geomorphology as well as the plant demography of the study area. Field visit revealed the terrestrial vegetation growth along the erstwhile mudflats, and vast areas of mangrove peat are observed, surrounded by successions of marshy and terrestrial plants. As mangroves are preferred habitat for diverse marine and terrestrial organisms and act as a vital food source and juvenile shelters for various fish species, the extensive loss of mangrove area may adversely affect a large scale biological ecosystem.

Dissolved organic matter in the deep TALDICE ice core: A nano-UPLC-nano-ESI-HRMS method

Trace organic compounds in deep ice cores supply important paleoclimatic information. Untargeted analyses of dissolved organic matter provide an overview of molecular species in ice samples however, sample volumes usually required for these analyses are generally not available from deep ice cores. Here, we developed an analytical method using a nano-UPLC-nano-ESI-HRMS to detect major molecular species in ice cores. Samples (4 µL) from the TALos Dome Ice CorE (TALDICE), allowed investigating molecular species across a range of depths including during glacial and interglacial periods. We detected 317 chemical species that were tentatively assigned to fatty acids, hydroxy fatty acids and their degradation products (oxo-fatty acids and dicarboxylic acids), as well as oxidation byproducts of isoprene and monoterpenes. These compounds indicate that the main sources of the organic fraction are microbes as well as primary and secondary aerosols. Interglacial samples encompass a wide range of species including compounds from the oxidation of isoprene and monoterpenes as well as unsaturated fatty acids, while the glacial samples contained less diverse species. This difference may be due to decreased temperatures during the glacial period inhibiting terrestrial vegetation growth and increasing the sea ice extent, thereby weakening the emission sources.

Late Quaternary environmental change record in biomarker lipid compositions of Lake Ebinur sediments, Northwestern China

Late Quaternary paleoclimate records from arid regions help us understand the response of ecological systems to natural climate change to place recent changes in a longer-term perspective. In this research, a biogeochemical analysis of a sediment core from Lake Ebinur, Xinjiang, allowed us to infer the climate and environmental changes in the late Quaternary in arid northwestern China. The combined lipid biomarkers and bulk biogeochemical properties (TOC, δ13Corg) revealed moderately wet conditions from 34 to 28 ka, favoring the growth of terrestrial vegetation. The subsequent period from 28 to 13 ka was dominated by aquatic plants, algae and microbes, indicating cold and dry conditions. Ameliorated regional moisture conditions were deduced from the higher carbon preference index (CPI) and from δ13Corg values from 13 to 3.6 ka. The optimum climate conditions occurred from 9 to 5 ka. Several climatic events also exist in the Lake Ebinur records with a wide distribution into other regions. Our biogeochemical-based paleoclimate and paleoenvironmental reconstruction is generally synchronous with sedimentary records from mid-latitude Eurasia and the NGRIP ice core record, which probably has a certain linkage with the 45°N summer insolation. We propose that summer insolation conditions in the northern hemisphere might have an impact on the climate and environmental changes in arid northwestern China in the late Quaternary.

Modeling biodiversity benchmarks in variable environments.

Effective environmental assessment and management requires quantifiable biodiversity targets. Biodiversity benchmarks define these targets by focusing on specific biodiversity metrics, such as species richness. However, setting fixed targets can be challenging because many biodiversity metrics are highly variable, both spatially and temporally. We present a multivariate, hierarchical Bayesian method to estimate biodiversity benchmarks based on the species richness and cover of native terrestrial vegetation growth forms. This approach uses existing data to quantify the empirical distributions of species richness and cover within growth forms, and we use the upper quantiles of these distributions to estimate contemporary, “best‐on‐offer” biodiversity benchmarks. Importantly, we allow benchmarks to differ among vegetation types, regions, and seasons, and with changes in recent rainfall. We apply our method to data collected over 30 yr at ~35,000 floristic plots in southeastern Australia. Our estimated benchmarks were broadly consistent with existing expert‐elicited benchmarks, available for a small subset of vegetation types. However, in comparison with expert‐elicited benchmarks, our data‐driven approach is transparent, repeatable, and updatable; accommodates important spatial and temporal variation; aligns modeled benchmarks directly with field data and the concept of best‐on‐offer benchmarks; and, where many benchmarks are required, is likely to be more efficient. Our approach is general and could be used broadly to estimate biodiversity targets from existing data in highly variable environments, which is especially relevant given rapid changes in global environmental conditions.

Cumulative Effects of Climatic Factors on Terrestrial Vegetation Growth

AbstractExtensive studies have focused on instantaneous and time‐lag impacts of climatic factors on vegetation growth; however, the chronical and accumulative indirect impacts of antecedent climatic factors carrying over for a period of time on vegetation growth, defined as cumulative effects, are less investigated. Here we aimed to disentangle the cumulative effects of climatic factors on vegetation growth by using vegetation indexes and accumulated meteorological data. First, we investigated the explanation and fit of climate changes on vegetation variations by applying stepwise multiple linear regression with Akaike information criterion. Then, we obtained the correlation coefficients and lagged time of climatic factors on vegetation growth whereby partial correlation and time‐lag effect analyses. Results showed that (i) consideration of cumulative climate effects increased the explanation and fit of climate changes on vegetation dynamics for more than 77% of vegetated surface with an average global explanation of 68.33%, which was approximately 3.35% higher than the scenario when only time‐lag effects were considered; (ii) big differences exhibited in the correlation coefficients and lagged times under the scenarios with cumulative climate effects considered or not; and (iii) positive accumulated temperature (accumulated solar radiation) effects with zero (three‐month) time lag dominates most mid‐high latitude ecosystems, and negative accumulated temperature effects with three‐month delay dominates the temperate arid and semiarid regions and tropical dry ecosystems. By comparison, accumulated precipitation had relatively complex cumulative effects on vegetation growth. We concluded that climatic factors had significant cumulative effects on vegetation growth; consideration of the cumulative effects helps us better understand the climate‐vegetation interactions.

NDVI indicated inter-seasonal non-uniform time-lag responses of terrestrial vegetation growth to daily maximum and minimum temperature

Abstract Climate warming exhibits asymmetric patterns over a diel cycle, with the trend of daily minimum temperature (Tmin) exceeds that of daily maximum temperature (Tmax), which is expected to significantly affect terrestrial vegetation growth. To date, many studies have documented the instantaneously asymmetric impacts of Tmax and/or Tmin on terrestrial ecosystems, but little is known about the non-uniform time-lag effects of Tmax and/or Tmin on vegetation growth. Using monthly datasets of normalized difference vegetation index (NDVI) as the proxy for vegetation vigor and the accumulated Tmax and Tmin on representatives of asymmetric warming, we quantitatively investigated the inter-seasonal non-uniform time-lag effects of Tmax and/or Tmin on terrestrial vegetation growth from 1982 to 2014 by applying partial correlation and time-lag analyses. Our results indicate that both Tmax and Tmin have significant time-lag effects on vegetation growth that exhibit spatial heterogeneities; the same vegetation type exhibits distinguished asymmetric responses to Tmax vs. Tmin; and different vegetation types respond to Tmax and/or Tmin with diverse time-lag effects; the overall average lagged times of Tmax and Tmin on vegetation growth were 1.68 ± 1.05 months and 1.45 ± 0.96 months, respectively; terrestrial vegetation growth correlates with Tmax reversely against Tmin over most regions and vegetation types. This study provided new insights into the non-uniform responses of vegetation to asymmetric warming. Moreover, our findings highlight the urgency of the currently used climate-vegetation models to consider the non-uniform time-lag effects found in this study so as to improve their performances in future investigation of vegetation-climate interactions.

Global NDVI Patterns in Response to Atmospheric Water Vapor Anomalies over the Indo-Pacific Warm Pool during April–June

Vertically integrated atmospheric water vapor (VIWV) over the Indo-Pacific warm pool (IPWP) indirectly affects terrestrial vegetation growth (TVG) patterns through atmospheric water vapor transmission. However, their linkages and mechanisms are poorly understood. This study intends to understand the contributions of VIWVIPWP to TVG and the mechanisms by which VIWVIPWP impacts TVG. Combining monthly SST, VIWV, and NDVI data from 1982 to 2015, the linkage between VIWVIPWP and NDVI is investigated during April–June (AMJ). A strong correlation between VIWVIPWP and NDVI suggests that VIWVIPWP is an important factor affecting TVG. A composite analysis of VIWVIPWP anomalies and their relation to NDVI patterns shows that VIWVIPWP positively influences the NDVI of 68.1% of global green land during high-VIWVIPWP years but negatively influences 74.7% in low years. Corresponding to these results, during high-VIWVIPWP years, the warm and humid terrestrial climate conditions improved TVG by 9% and 2% in the Northern and Southern Hemispheres, respectively, but cold and dry conditions inhibited TVG for both hemispheres during the low years. Additionally, strong spatial correlations between VIWVIPWP and precipitation imply that VIWVIPWP affects the spatial–temporal pattern of precipitation. There is a stronger interaction between the Pacific north–south ridge and the two land troughs during high-VIWVIPWP years than during low-VIWVIPWP years. The zonally averaged wind at 850 hPa and VIWV results indicate that, during high-VIWVIPWP years, the enhanced wind from the ocean brings more atmospheric water vapor to land, increasing the probability of precipitation and resulting in moist climate conditions that promote AMJ vegetation growth. In brief, VIWVIPWP indirectly induces vegetation growth by affecting the distributions of terrestrial VIWV and precipitation.

Exposures to temperature beyond threshold disproportionately reduce vegetation growth in the northern hemisphere.

In recent decades, terrestrial vegetation in the northern hemisphere (NH) has been exposed to warming and more extremely high temperatures. However, the consequences of these changes for terrestrial vegetation growth remain poorly quantified and understood. By examining a satellite-based vegetation index, tree-ring measurements and land-surface model simulations, we discovered a consistent convex pattern in the responses of vegetation growth to temperature exposure (TE) for forest, shrub and grass in both the temperate (30°−50° N) and boreal (50°−70° N) NH during the period of 1982−2012. The response of vegetation growth to TE for the three vegetation types in both the temperate and boreal NH increased convergently with increasing temperature, until vegetation type-dependent temperature thresholds were reached. A TE beyond these temperature thresholds resulted in disproportionately weak positive or even strong negative responses. Vegetation growth in the boreal NH was more vulnerable to extremely high-temperature events than vegetation growth in the temporal NH. The non-linear responses discovered here provide new insights into the dynamics of northern terrestrial ecosystems in a warmer world.

Extended Water-Level Drawdowns in Dammed Rivers Enhance Fish Habitat: Environmental Pool Management in the Upper Mississippi River.

Environmental Pool Management (EPM) can improve ecosystem function in rivers by restoring aspects of the natural flow regime lost to dam construction. EPM recreates summer baseflow conditions and promotes the growth of terrestrial vegetation which is inundated in the fall, thereby improving habitat heterogeneity for many aquatic taxa. A three-year experiment was conducted wherein terrestrial floodplain areas were dewatered through EPM water-level reductions and the resulting terrestrial vegetation was (1) allowed to remain or (2) removed in paired plots in Mississippi River pool 25. Fish assemblage and abundance were quantified in paired plots after inundation. Abundances of many fish species were greater in vegetated plots, especially for species that utilize vegetation during portions of their life history. Fish assemblages varied more between plot types when the magnitude of EPM water-level drawdowns was greater, which produced greater vegetation growth. Young-of-year individuals, especially from small, early maturing species and/or species reliant on vegetation for refuge, feeding, or life history, utilized vegetated plots more than devegetated plots. Vegetation growth produced under EPM was heavily used by river fishes, including young-of-year individuals, which may ultimately positively influence recruitment. Increased habitat heterogeneity may mitigate some of the negative impacts of dam construction and water-level regulation on river fishes. Annual variability in vegetation responses that occurs under EPM enhances natural environmental variability which could ultimately contribute to increased fish diversity. Low-cost programs like EPM can be implemented as a part of adaptive management plans to help maintain biodiversity and ecosystem health in anthropogenically altered rivers.

Enhanced peak growth of global vegetation and its key mechanisms.

The annual peak growth of vegetation is critical in characterizing the capacity of terrestrial ecosystem productivity and shaping the seasonality of atmospheric CO2 concentrations. The recent greening of global lands suggests an increasing trend of terrestrial vegetation growth, but whether or not the peak growth has been globally enhanced still remains unclear. Here, we use two global datasets of gross primary productivity (GPP) and a satellite-derived Normalized Difference Vegetation Index (NDVI) to characterize recent changes in annual peak vegetation growth (that is, GPPmax and NDVImax). We demonstrate that the peak in the growth of global vegetation has been linearly increasing during the past three decades. About 65% of the NDVImax variation is evenly explained by expanding croplands (21%), rising CO2 (22%) and intensifying nitrogen deposition (22%). The contribution of expanding croplands to the peak growth trend is substantiated by measurements from eddy-flux towers, sun-induced chlorophyll fluorescence and a global database of plant traits, all of which demonstrate that croplands have a higher photosynthetic capacity than other vegetation types. The large contribution of CO2 is also supported by a meta-analysis of 466 manipulative experiments and 15 terrestrial biosphere models. Furthermore, we show that the contribution of GPPmax to the change in annual GPP is less in the tropics than in other regions. These multiple lines of evidence reveal an increasing trend in the peak growth of global vegetation. The findings highlight the important roles of agricultural intensification and atmospheric changes in reshaping the seasonality of global vegetation growth.

Nonuniform Time-Lag Effects of Asymmetric Warming on Net Primary Productivity across Global Terrestrial Biomes

Abstract Climate warming exhibits asymmetric patterns over a diel time, with the trend of nighttime warming exceeding that of daytime warming, a phenomenon commonly known as asymmetric warming. Recently, increasing studies have documented the significant instantaneous impacts of asymmetric warming on terrestrial vegetation growth, but the indirect effects of asymmetric warming carrying over vegetation growth (referred to here as time-lag effects) remain unknown. Here, we quantitatively studied the time-lag effects (within 1 year) of asymmetric warming on global plant biomes by using terrestrial vegetation net primary production (NPP) derived by the Carnegie–Ames–Stanford Approach (CASA) model and accumulated daytime and nighttime temperature (ATmax and ATmin) from 1982 to 2013. Partial correlation and time-lag analyses were conducted at a monthly scale to obtain the partial correlation coefficients between NPP and ATmax/ATmin and the lagged durations of NPP responses to ATmax/ATmin. The results showed that (i) asymmetric warming has nonuniform time-lag effects on single plant biomes, and distinguishing correlations exist in different vegetation biomes’ associations to asymmetric warming; (ii) terrestrial biomes respond to ATmax (4.63 ± 3.92 months) with a shorter protracted duration than to ATmin (6.06 ± 4.27 months); (iii) forest biomes exhibit longer prolonged duration in responding to asymmetric warming than nonforest biomes do; (iv) mosses and lichens (Mosses), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), and mixed forests (MF) tend to positively correlate with ATmax, whereas the other biomes associate with ATmax with near-equal splits of positive and negative correlation; and (v) ATmin has a predominantly positive influence on terrestrial biomes, except for Mosses and DNF. This study provides a new perspective on terrestrial ecosystem responses to asymmetric warming and highlights the importance of including such nonuniform time-lag effects into currently used terrestrial ecosystem models during future investigations of vegetation–climate interactions.

Weakening Relationship Between Vegetation Growth Over the Tibetan Plateau and Large‐Scale Climate Variability

AbstractVegetation growth on the Tibetan Plateau is strongly affected by large‐scale climate variability, particularly the Pacific Decadal Oscillation (PDO) and the North Atlantic Oscillation (NAO). However, potential temporal changes in both the direction and strength of relationships between regional vegetation growth and large‐scale climate variability remain poorly understood. Here we quantify temporal changes in these relationships during 1982–2012, using satellite‐derived normalized difference vegetation index, global ecosystem model simulations of net primary productivity, regional tree ring chronologies, and PDO and NAO indexes. We found consistent weakening relationships between mean growing‐season (April–October) normalized difference vegetation index and both PDO and NAO. A similar pattern was also found in the temporal relationship between net primary productivity and PDO. Such weakening relationships were partly attributable to weakening regional summer atmospheric circulation and its causal effects on changes in hydrothermal conditions over the Tibetan Plateau. These findings highlight a varying coupling of regional vegetation growth to large‐scale climate variability at the interannual/decadal scale during past decades, and this feature should be considered in future prediction of terrestrial vegetation growth in response to shifting climate regime.