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Shrestha N, Kolarik NE, Brandt JS. Mesic vegetation persistence: A new approach for monitoring spatial and temporal changes in water availability in dryland regions using cloud computing and the sentinel and Landsat constellations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170491. [PMID: 38301786 DOI: 10.1016/j.scitotenv.2024.170491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/03/2024]
Abstract
Climate change and anthropogenic activity pose severe threats to water availability in drylands. A better understanding of water availability response to these threats could improve our ability to adapt and mitigate climate and anthropogenic effects. Here, we present a Mesic Vegetation Persistence (MVP) workflow that takes every usable image in the Sentinel (10-m) and Landsat (30-m) archives to generate a dense time-series of water availability that is continuously updated as new images become available in Google Earth Engine. MVP takes advantage of the fact that mesic vegetation can be used as a proxy of available water in drylands. Our MVP workflow combines a novel moisture-based index (moisture change index - MCI) with a vegetation index (Modified Chlorophyll Absorption Ratio Vegetation Index (MCARI2)). MCI is the difference in soil moisture condition between an individual pixel's state and the dry and wet reference reflectance in the image, derived using 5th and 95th percentiles of the visible and shortwave infra-red drought index (VSDI). We produced and validated our MVP products across drylands of the western U.S., covering a broad range of elevation, land use, and ecoregions. MVP outperforms NDVI, a commonly-employed index for mesic ecosystem health, in both rangeland and forested ecosystems, and in mesic habitats with particularly high and low vegetation cover. We applied our MVP product at case study sites and found that MVP more accurately characterizes differences in mesic persistence, late-season water availability, and restoration success compared to NDVI. MVP could be applied as an indicator of change in a variety of contexts to provide a greater understanding of how water availability changes as a result of climate and management. Our MVP product for the western U.S. is freely available within a Google Earth Engine Web App, and the MVP workflow is replicable for other dryland regions.
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Affiliation(s)
- Nawaraj Shrestha
- Human-Environment Systems, Boise State University, 1910 University Dr., Boise, ID 83725, USA; Conservation Survey Division, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.
| | - Nicholas E Kolarik
- Human-Environment Systems, Boise State University, 1910 University Dr., Boise, ID 83725, USA
| | - Jodi S Brandt
- Human-Environment Systems, Boise State University, 1910 University Dr., Boise, ID 83725, USA
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2
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Shekhar A, Hörtnagl L, Buchmann N, Gharun M. Long-term changes in forest response to extreme atmospheric dryness. GLOBAL CHANGE BIOLOGY 2023; 29:5379-5396. [PMID: 37381105 DOI: 10.1111/gcb.16846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/01/2023] [Indexed: 06/30/2023]
Abstract
Atmospheric dryness, as indicated by vapor pressure deficit (VPD), has a strong influence on forest greenhouse gas exchange with the atmosphere. In this study, we used long-term (10-30 years) net ecosystem productivity (NEP) measurements from 60 forest sites across the world (1003 site-years) to quantify long-term changes in forest NEP resistance and NEP recovery in response to extreme atmospheric dryness. We tested two hypotheses: first, across sites differences in NEP resistance and NEP recovery of forests will depend on both the biophysical characteristics (i.e., leaf area index [LAI] and forest type) of the forest as well as on the local meteorological conditions of the site (i.e., mean VPD of the site), and second, forests experiencing an increasing trend in frequency and intensity of extreme dryness will show an increasing trend in NEP resistance and NEP recovery over time due to emergence of long-term ecological stress memory. We used a data-driven statistical learning approach to quantify NEP resistance and NEP recovery over multiple years. Our results showed that forest types, LAI, and median local VPD conditions explained over 50% of variance in both NEP resistance and NEP recovery, with drier sites showing higher NEP resistance and NEP recovery compared to sites with less atmospheric dryness. The impact of extreme atmospheric dryness events on NEP lasted for up to 3 days following most severe extreme events in most forests, indicated by an NEP recovery of less than 100%. We rejected our second hypothesis as we found no consistent relationship between trends of extreme VPD with trends in NEP resistance and NEP recovery across different forest sites, thus an increase in atmospheric dryness as it is predicted might not increase the resistance or recovery of forests in terms of NEP.
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Affiliation(s)
- Ankit Shekhar
- Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
| | - Lukas Hörtnagl
- Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
| | - Nina Buchmann
- Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
| | - Mana Gharun
- Institute of Landscape Ecology, Faculty of Geosciences, University of Münster, Münster, Germany
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Zhang Y, Zhang Y, Lian X, Zheng Z, Zhao G, Zhang T, Xu M, Huang K, Chen N, Li J, Piao S. Enhanced dominance of soil moisture stress on vegetation growth in Eurasian drylands. Natl Sci Rev 2023; 10:nwad108. [PMID: 37389136 PMCID: PMC10306363 DOI: 10.1093/nsr/nwad108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 04/16/2023] [Accepted: 04/19/2023] [Indexed: 07/01/2023] Open
Abstract
Despite the mounting attention being paid to vegetation growth and their driving forces for water-limited ecosystems, the relative contributions of atmospheric and soil moisture dryness stress on vegetation growth are an ongoing debate. Here we comprehensively compare the impacts of high vapor pressure deficit (VPD) and low soil water content (SWC) on vegetation growth in Eurasian drylands during 1982-2014. The analysis indicates a gradual decoupling between atmospheric dryness and soil dryness over this period, as the former has expanded faster than the latter. Moreover, the VPD-SWC relation and VPD-greenness relation are both non-linear, while the SWC-greenness relation is near-linear. The loosened coupling between VPD and SWC, the non-linear correlations among VPD-SWC-greenness and the expanded area extent in which SWC acts as the dominant stress factor all provide compelling evidence that SWC is a more influential stressor than VPD on vegetation growth in Eurasian drylands. In addition, a set of 11 Earth system models projected a continuously growing constraint of SWC stress on vegetation growth towards 2100. Our results are vital to dryland ecosystems management and drought mitigation in Eurasia.
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Affiliation(s)
- Yu Zhang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
| | | | - Xu Lian
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- Department of Earth and Environmental Engineering, Columbia University, New York, NY 10027, USA
| | - Zhoutao Zheng
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Guang Zhao
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Tao Zhang
- College of Agronomy, Shenyang Agricultural University, Shenyang 110866, China
| | - Minjie Xu
- College of Agronomy, Shenyang Agricultural University, Shenyang 110866, China
| | - Ke Huang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark
| | - Ning Chen
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Ji Li
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- Department of Geography, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
| | - Shilong Piao
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100085, China
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Chen L, Keski-Saari S, Kontunen-Soppela S, Zhu X, Zhou X, Hänninen H, Pumpanen J, Mola-Yudego B, Wu D, Berninger F. Immediate and carry-over effects of late-spring frost and growing season drought on forest gross primary productivity capacity in the Northern Hemisphere. GLOBAL CHANGE BIOLOGY 2023; 29:3924-3940. [PMID: 37165918 DOI: 10.1111/gcb.16751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/27/2023] [Indexed: 05/12/2023]
Abstract
Forests are increasingly exposed to extreme global warming-induced climatic events. However, the immediate and carry-over effects of extreme events on forests are still poorly understood. Gross primary productivity (GPP) capacity is regarded as a good proxy of the ecosystem's functional stability, reflecting its physiological response to its surroundings. Using eddy covariance data from 34 forest sites in the Northern Hemisphere, we analyzed the immediate and carry-over effects of late-spring frost (LSF) and growing season drought on needle-leaf and broadleaf forests. Path analysis was applied to reveal the plausible reasons behind the varied responses of forests to extreme events. The results show that LSF had clear immediate effects on the GPP capacity of both needle-leaf and broadleaf forests. However, GPP capacity in needle-leaf forests was more sensitive to drought than in broadleaf forests. There was no interaction between LSF and drought in either needle-leaf or broadleaf forests. Drought effects were still visible when LSF and drought coexisted in needle-leaf forests. Path analysis further showed that the response of GPP capacity to drought differed between needle-leaf and broadleaf forests, mainly due to the difference in the sensitivity of canopy conductance. Moreover, LSF had a more severe and long-lasting carry-over effect on forests than drought. These results enrich our understanding of the mechanisms of forest response to extreme events across forest types.
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Affiliation(s)
- Liang Chen
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Sarita Keski-Saari
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
- Department of Geographical and Historical Studies, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Sari Kontunen-Soppela
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Xudan Zhu
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Xuan Zhou
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Heikki Hänninen
- State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou, China
| | - Jukka Pumpanen
- Department of Environmental and Biological Sciences, Kuopio Campus, University of Eastern Finland, Kuopio, Finland
| | - Blas Mola-Yudego
- School of Forest Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Di Wu
- Department of Environmental and Biological Sciences, Kuopio Campus, University of Eastern Finland, Kuopio, Finland
| | - Frank Berninger
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
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Yang K, Huang Y, Yang J, Lv C, Hu Z, Yu L, Sun W. Effects of three patterns of elevated CO2 in single and multiple generations on photosynthesis and stomatal features in rice. ANNALS OF BOTANY 2023; 131:463-473. [PMID: 36708194 PMCID: PMC10072110 DOI: 10.1093/aob/mcad021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND AIMS Effects of elevated CO2 (E) within a generation on photosynthesis and stomatal features have been well documented in crops; however, long-term responses to gradually elevated CO2 (Eg) and abruptly elevated CO2 (Ea) over multiple generations remain scarce. METHODS Japonica rice plants grown in open-top chambers were tested in the first generation (F1) under Ea and in the fifth generation (F5) under Eg and Ea, as follows: Ea in F1: ambient CO2 (A) + 200 μmol mol-1; Eg in F5: an increase of A + 40 μmol mol-1 year-1 until A + 200 μmol mol-1 from 2016 to 2020; Ea in F5: A + 200 μmol mol-1 from 2016 to 2020. For multigenerational tests, the harvested seeds were grown continuously in the following year in the respective CO2 environments. KEY RESULTS The responses to Ea in F1 were consistent with the previous consensus, such as the occurrence of photosynthetic acclimation, stimulation of photosynthesis, and downregulation of photosynthetic physiological parameters and stomatal area. In contrast, multigenerational exposure to both Eg and Ea did not induce photosynthetic acclimation, but stimulated greater photosynthesis and had little effect on the photosynthetic physiology and stomatal traits. This suggests that E retained intergenerational effects on photosynthesis and stomatal features and that there were no multigenerational differences in the effects of Eg and Ea. CONCLUSIONS The present study demonstrated that projecting future changes induced by E based on the physiological responses of contemporary plants could be misleading. Thus, responses of plants to large and rapid environmental changes within a generation cannot predict the long-term response of plants to natural environmental changes over multiple generations, especially in annual herbs with short life cycles.
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Affiliation(s)
- Kai Yang
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yao Huang
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingrui Yang
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Chunhua Lv
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhenghua Hu
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing, China
| | - Lingfei Yu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China
| | - Wenjuan Sun
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China
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Zhang X, Cao Q, Chen H, Quan Q, Li C, Dong J, Chang M, Yan S, Liu J. Effect of Vegetation Carryover and Climate Variability on the Seasonal Growth of Vegetation in the Upper and Middle Reaches of the Yellow River Basin. REMOTE SENSING 2022; 14:5011. [DOI: 10.3390/rs14195011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
Vegetation dynamics are often affected by climate variability, but the past state of vegetation has a non-negligible impact on current vegetation growth. However, seasonal differences in the effects of these drivers on vegetation growth remain unclear, particularly in ecologically fragile areas. We used the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and leaf area index (LAI) to describe the vegetation dynamic in the upper and middle reaches of the Yellow River basin (YRB). Three active vegetation growing seasons (early, peak, and late) were defined based on phenological metrics. In light of three vegetation indicators and the climatic data, we identified the correlation between the inter-annual variation of vegetation growth in the three sub-seasons. Then, we quantified the contributions of climate variability and the vegetation growth carryover (VGC) effect on seasonal vegetation greening between 2000–2019. Results showed that both the vegetation coverage and productivity in the study area increased over a 20-year period. The VGC effect dominated vegetation growth during the three active growing seasons, and the effect increased from early to late growing season. Vegetation in drought regions was found to generally have a stronger vegetation carryover ability, implying that negative disturbances might have severer effects on vegetation in these areas. The concurrent seasonal precipitation was another positive driving factor of vegetation greening. However, sunshine duration, including its immediate and lagged impacts, had a negative effect on vegetation growth. In addition, the VGC effect can sustain into the second year. The VGC effect showed that initial ecological restoration and sustainable conservation would promote vegetation growth and increase vegetation productivity. This study provides a comprehensive perspective on understanding the climate–vegetation interactions on a seasonal scale, which helps to accurately predict future vegetation dynamics over time in ecologically fragile areas.
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Wood DJA, Stoy PC, Powell SL, Beever EA. Antecedent climatic conditions spanning several years influence multiple land-surface phenology events in semi-arid environments. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.1007010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Ecological processes are complex, often exhibiting non-linear, interactive, or hierarchical relationships. Furthermore, models identifying drivers of phenology are constrained by uncertainty regarding predictors, interactions across scales, and legacy impacts of prior climate conditions. Nonetheless, measuring and modeling ecosystem processes such as phenology remains critical for management of ecological systems and the social systems they support. We used random forest models to assess which combination of climate, location, edaphic, vegetation composition, and disturbance variables best predict several phenological responses in three dominant land cover types in the U.S. Northwestern Great Plains (NWP). We derived phenological measures from the 25-year series of AVHRR satellite data and characterized climatic predictors (i.e., multiple moisture and/or temperature based variables) over seasonal and annual timeframes within the current year and up to 4 years prior. We found that antecedent conditions, from seasons to years before the current, were strongly associated with phenological measures, apparently mediating the responses of communities to current-year conditions. For example, at least one measure of antecedent-moisture availability [precipitation or vapor pressure deficit (VPD)] over multiple years was a key predictor of all productivity measures. Variables including longer-term lags or prior year sums, such as multi-year-cumulative moisture conditions of maximum VPD, were top predictors for start of season. Productivity measures were also associated with contextual variables such as soil characteristics and vegetation composition. Phenology is a key process that profoundly affects organism-environment relationships, spatio-temporal patterns in ecosystem structure and function, and other ecosystem dynamics. Phenology, however, is complex, and is mediated by lagged effects, interactions, and a diversity of potential drivers; nonetheless, the incorporation of antecedent conditions and contextual variables can improve models of phenology.
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8
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Tree growth sensitivity to climate varies across a seasonal precipitation gradient. Oecologia 2022; 198:933-946. [DOI: 10.1007/s00442-022-05156-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 03/28/2022] [Indexed: 10/18/2022]
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Yuan Y, Bao A, Jiapaer G, Jiang L, De Maeyer P. Phenology-based seasonal terrestrial vegetation growth response to climate variability with consideration of cumulative effect and biological carryover. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 817:152805. [PMID: 34982988 DOI: 10.1016/j.scitotenv.2021.152805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 12/06/2021] [Accepted: 12/27/2021] [Indexed: 06/14/2023]
Abstract
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.
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Affiliation(s)
- Ye Yuan
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Department of Geography, Ghent University, Ghent 9000, Belgium
| | - Anming Bao
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; China-Pakistan Joint Research Center on Earth Sciences, CAS-HEC, Islamabad 45320, Pakistan; Sino-Belgian Laboratory for Geo-Information, Urumqi 83011, China.
| | - Guli Jiapaer
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
| | - Liangliang Jiang
- School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China
| | - Philippe De Maeyer
- Department of Geography, Ghent University, Ghent 9000, Belgium; Sino-Belgian Laboratory for Geo-Information, Ghent 9000, Belgium
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Peltier DMP, Guo J, Nguyen P, Bangs M, Wilson M, Samuels-Crow K, Yocom LL, Liu Y, Fell MK, Shaw JD, Auty D, Schwalm C, Anderegg WRL, Koch GW, Litvak ME, Ogle K. Temperature memory and non-structural carbohydrates mediate legacies of a hot drought in trees across the southwestern USA. TREE PHYSIOLOGY 2022; 42:71-85. [PMID: 34302167 DOI: 10.1093/treephys/tpab091] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 07/16/2021] [Indexed: 06/13/2023]
Abstract
Trees are long-lived organisms that integrate climate conditions across years or decades to produce secondary growth. This integration process is sometimes referred to as 'climatic memory.' While widely perceived, the physiological processes underlying this temporal integration, such as the storage and remobilization of non-structural carbohydrates (NSC), are rarely explicitly studied. This is perhaps most apparent when considering drought legacies (perturbed post-drought growth responses to climate), and the physiological mechanisms underlying these lagged responses to climatic extremes. Yet, drought legacies are likely to become more common if warming climate brings more frequent drought. To quantify the linkages between drought legacies, climate memory and NSC, we measured tree growth (via tree ring widths) and NSC concentrations in three dominant species across the southwestern USA. We analyzed these data with a hierarchical mixed effects model to evaluate the time-scales of influence of past climate (memory) on tree growth. We then evaluated the role of climate memory and the degree to which variation in NSC concentrations were related to forward-predicted growth during the hot 2011-2012 drought and subsequent 4-year recovery period. Populus tremuloides exhibited longer climatic memory compared to either Pinus edulis or Juniperus osteosperma, but following the 2011-2012 drought, P. tremuloides trees with relatively longer memory of temperature conditions showed larger (more negative) drought legacies. Conversely, Pinus edulis trees with longer temperature memory had smaller (less negative) drought legacies. For both species, higher NSC concentrations followed more negative (larger) drought legacies, though the relevant NSC fraction differed between P. tremuloides and P. edulis. Our results suggest that differences in tree NSC are also imprinted upon tree growth responses to climate across long time scales, which also underlie tree resilience to increasingly frequent drought events under climate change.
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Affiliation(s)
- Drew M P Peltier
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Jessica Guo
- Communications and Cyber Technologies, University of Arizona, Tucson, AZ 85721, USA
| | - Phiyen Nguyen
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Michael Bangs
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Michelle Wilson
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Kimberly Samuels-Crow
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Larissa L Yocom
- Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT 84322, USA
| | - Yao Liu
- Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Michael K Fell
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - John D Shaw
- USDA Forest Service, Rocky Mountain Research Station, Ogden, UT 84401, USA
| | - David Auty
- School of Forestry, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Christopher Schwalm
- Woods Hole Research Center, Falmouth, MA 02540, USA
- Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - William R L Anderegg
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - George W Koch
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA
- Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Marcy E Litvak
- Department of Biology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Kiona Ogle
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA
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11
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Migliavacca M, Musavi T, Mahecha MD, Nelson JA, Knauer J, Baldocchi DD, Perez-Priego O, Christiansen R, Peters J, Anderson K, Bahn M, Black TA, Blanken PD, Bonal D, Buchmann N, Caldararu S, Carrara A, Carvalhais N, Cescatti A, Chen J, Cleverly J, Cremonese E, Desai AR, El-Madany TS, Farella MM, Fernández-Martínez M, Filippa G, Forkel M, Galvagno M, Gomarasca U, Gough CM, Göckede M, Ibrom A, Ikawa H, Janssens IA, Jung M, Kattge J, Keenan TF, Knohl A, Kobayashi H, Kraemer G, Law BE, Liddell MJ, Ma X, Mammarella I, Martini D, Macfarlane C, Matteucci G, Montagnani L, Pabon-Moreno DE, Panigada C, Papale D, Pendall E, Penuelas J, Phillips RP, Reich PB, Rossini M, Rotenberg E, Scott RL, Stahl C, Weber U, Wohlfahrt G, Wolf S, Wright IJ, Yakir D, Zaehle S, Reichstein M. The three major axes of terrestrial ecosystem function. Nature 2021; 598:468-472. [PMID: 34552242 PMCID: PMC8528706 DOI: 10.1038/s41586-021-03939-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 08/20/2021] [Indexed: 02/08/2023]
Abstract
The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.
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Affiliation(s)
- Mirco Migliavacca
- Max Planck Institute for Biogeochemistry, Jena, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany.
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
| | - Talie Musavi
- Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Miguel D Mahecha
- Max Planck Institute for Biogeochemistry, Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany
- Remote Sensing Center for Earth System Research, Leipzig University, Leipzig, Germany
- Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
| | | | - Jürgen Knauer
- CSIRO Oceans and Atmosphere, Canberra, Australian Capital Territory, Australia
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
| | - Dennis D Baldocchi
- Department of Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA, USA
| | - Oscar Perez-Priego
- Department of Forest Engineering, ERSAF Research Group, University of Cordoba, Cordoba, Spain
| | - Rune Christiansen
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jonas Peters
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Karen Anderson
- Environment and Sustainability Institute, University of Exeter, Penryn, UK
| | - Michael Bahn
- Department of Ecology, University of Innsbruck, Innsbruck, Austria
| | - T Andrew Black
- Faculty of Land and Food Systems, Vancouver, British Columbia, Canada
| | - Peter D Blanken
- Department of Geography, University of Colorado, Boulder, CO, USA
| | - Damien Bonal
- Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy, France
| | - Nina Buchmann
- Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | | | - Arnaud Carrara
- Fundación Centro de Estudios Ambientales del Mediterráneo (CEAM), Paterna, Spain
| | - Nuno Carvalhais
- Max Planck Institute for Biogeochemistry, Jena, Germany
- Departamento de Ciências e Engenharia do Ambiente, Universidade Nova de Lisboa, Caparica, Portugal
| | | | - Jiquan Chen
- Landscape Ecology & Ecosystem Science (LEES) Lab, Center for Global Change and Earth Observations, and Department of Geography, Environmental and Spatial Science, Michigan State University, East Lansing, MI, USA
| | - Jamie Cleverly
- School of Life Sciences, University of Technology Sydney, Ultimo, New South Wales, Australia
- Terrestrial Ecosystem Research Network, College of Science and Engineering, James Cook University, Cairns, Queensland, Australia
| | - Edoardo Cremonese
- Climate Change Unit, Environmental Protection Agency of Aosta Valley, Aosta, Italy
| | - Ankur R Desai
- Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Martha M Farella
- O'Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN, USA
| | - Marcos Fernández-Martínez
- Research Group Plant and Ecosystems (PLECO), Department of Biology, University of Antwerp, Wilrijk, Belgium
| | - Gianluca Filippa
- Climate Change Unit, Environmental Protection Agency of Aosta Valley, Aosta, Italy
| | - Matthias Forkel
- Institute of Photogrammetry and Remote Sensing, TU Dresden, Dresden, Germany
| | - Marta Galvagno
- Climate Change Unit, Environmental Protection Agency of Aosta Valley, Aosta, Italy
| | | | | | | | - Andreas Ibrom
- Department of Environmental Engineering, Technical University of Denmark (DTU), Kongens Lyngby, Denmark
| | - Hiroki Ikawa
- Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Ivan A Janssens
- Research Group Plant and Ecosystems (PLECO), Department of Biology, University of Antwerp, Wilrijk, Belgium
| | - Martin Jung
- Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Jens Kattge
- Max Planck Institute for Biogeochemistry, Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany
| | - Trevor F Keenan
- Department of Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA, USA
- Earth and Environmental Science Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Alexander Knohl
- Bioclimatology, Faculty of Forest Sciences and Forest Ecology, University of Goettingen, Goettingen, Germany
- Centre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Goettingen, Germany
| | - Hideki Kobayashi
- Research Institute for Global Change, Institute of Arctic Climate and Environment Research, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
| | - Guido Kraemer
- Remote Sensing Center for Earth System Research, Leipzig University, Leipzig, Germany
- Image Processing Laboratory (IPL), Universitat de València, València, Spain
| | - Beverly E Law
- Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, USA
| | - Michael J Liddell
- Centre for Tropical, Environmental, and Sustainability Sciences, James Cook University, Cairns, Queensland, Australia
| | - Xuanlong Ma
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
| | - Ivan Mammarella
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
| | - David Martini
- Max Planck Institute for Biogeochemistry, Jena, Germany
| | | | - Giorgio Matteucci
- Consiglio Nazionale delle Ricerche, Istituto per la BioEconomia (CNR - IBE), Sesto Fiorentino, Italy
| | - Leonardo Montagnani
- Facoltà di Scienze e Tecnologie, Libera Universita' di Bolzano, Bolzano, Italy
- Forest Services of the Autonomous Province of Bozen-Bolzano, Bolzano, Italy
| | | | - Cinzia Panigada
- Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Milan, Italy
| | - Dario Papale
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Viterbo, Italy
| | - Elise Pendall
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
| | - Josep Penuelas
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain
- CREAF, Barcelona, Spain
| | | | - Peter B Reich
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
- Department of Forest Resources, University of Minnesota, Saint Paul, MN, USA
- Institute for Global Change Biology and School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA
| | - Micol Rossini
- Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Milan, Italy
| | - Eyal Rotenberg
- Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Russell L Scott
- Southwest Watershed Research Center, USDA Agricultural Research Service, Tucson, AZ, USA
| | - Clement Stahl
- INRAE, UMR EcoFoG, CNRS, Cirad, AgroParisTech, Université des Antilles, Université de Guyane, Kourou, France
| | - Ulrich Weber
- Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Georg Wohlfahrt
- Department of Ecology, University of Innsbruck, Innsbruck, Austria
| | - Sebastian Wolf
- Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | - Ian J Wright
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
- Department of Biological Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Dan Yakir
- Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Sönke Zaehle
- Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Markus Reichstein
- Max Planck Institute for Biogeochemistry, Jena, Germany.
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany.
- Michael-Stifel-Center Jena for Data-driven and Simulation Science, Friedrich-Schiller-Universität Jena, Jena, Germany.
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12
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Ukkola AM, De Kauwe MG, Roderick ML, Burrell A, Lehmann P, Pitman AJ. Annual precipitation explains variability in dryland vegetation greenness globally but not locally. GLOBAL CHANGE BIOLOGY 2021; 27:4367-4380. [PMID: 34091984 DOI: 10.1111/gcb.15729] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/20/2021] [Accepted: 05/23/2021] [Indexed: 06/12/2023]
Abstract
Dryland vegetation productivity is strongly modulated by water availability. As precipitation patterns and variability are altered by climate change, there is a pressing need to better understand vegetation responses to precipitation variability in these ecologically fragile regions. Here we present a global analysis of dryland sensitivity to annual precipitation variations using long-term records of normalized difference vegetation index (NDVI). We show that while precipitation explains 66% of spatial gradients in NDVI across dryland regions, precipitation only accounts for <26% of temporal NDVI variability over most (>75%) dryland regions. We observed this weaker temporal relative to spatial relationship between NDVI and precipitation across all global drylands. We confirmed this result using three alternative water availability metrics that account for water loss to evaporation, and growing season and precipitation timing. This suggests that predicting vegetation responses to future rainfall using space-for-time substitution will strongly overestimate precipitation control on interannual variability in aboveground growth. We explore multiple mechanisms to explain the discrepancy between spatial and temporal responses and find contributions from multiple factors including local-scale vegetation characteristics, climate and soil properties. Earth system models (ESMs) from the latest Coupled Model Intercomparison Project overestimate the observed vegetation sensitivity to precipitation variability up to threefold, particularly during dry years. Given projections of increasing meteorological drought, ESMs are likely to overestimate the impacts of future drought on dryland vegetation with observations suggesting that dryland vegetation is more resistant to annual precipitation variations than ESMs project.
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Affiliation(s)
- Anna M Ukkola
- ARC Centre of Excellence for Climate Extremes and Climate Change Research Centre, UNSW Sydney, Sydney, NSW, Australia
- ARC Centre of Excellence for Climate Extremes and Research School of Earth Sciences, Australian National University, Canberra, ACT, Australia
| | - Martin G De Kauwe
- ARC Centre of Excellence for Climate Extremes and Climate Change Research Centre, UNSW Sydney, Sydney, NSW, Australia
| | - Michael L Roderick
- ARC Centre of Excellence for Climate Extremes and Research School of Earth Sciences, Australian National University, Canberra, ACT, Australia
| | | | - Peter Lehmann
- Soil and Terrestrial Environmental Physics, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
| | - Andy J Pitman
- ARC Centre of Excellence for Climate Extremes and Climate Change Research Centre, UNSW Sydney, Sydney, NSW, Australia
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13
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Wood DJA, Powell S, Stoy PC, Thurman LL, Beever EA. Is the grass always greener? Land surface phenology reveals differences in peak and season-long vegetation productivity responses to climate and management. Ecol Evol 2021; 11:11168-11199. [PMID: 34429910 PMCID: PMC8366863 DOI: 10.1002/ece3.7904] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 06/15/2021] [Accepted: 06/25/2021] [Indexed: 11/23/2022] Open
Abstract
Vegetation phenology-the seasonal timing and duration of vegetative phases-is controlled by spatiotemporally variable contributions of climatic and environmental factors plus additional potential influence from human management. We used land surface phenology derived from the Advanced Very High Resolution Radiometer and climate data to examine variability in vegetation productivity and phenological dates from 1989 to 2014 in the U.S. Northwestern Plains, a region with notable spatial heterogeneity in climate, vegetation, and land use. We first analyzed interannual trends in six phenological measures as a baseline. We then demonstrated how including annual-resolution predictors can provide more nuanced insights into measures of phenology between plant communities and across the ecoregion. Across the study area, higher annual precipitation increased both peak and season-long productivity. In contrast, higher mean annual temperatures tended to increase peak productivity but for the majority of the study area decreased season-long productivity. Annual precipitation and temperature had strong explanatory power for productivity-related phenology measures but predicted date-based measures poorly. We found that relationships between climate and phenology varied across the region and among plant communities and that factors such as recovery from disturbance and anthropogenic management also contributed in certain regions. In sum, phenological measures did not respond ubiquitously nor covary in their responses. Nonclimatic dynamics can decouple phenology from climate; therefore, analyses including only interannual trends should not assume climate alone drives patterns. For example, models of areas exhibiting greening or browning should account for climate, anthropogenic influence, and natural disturbances. Investigating multiple aspects of phenology to describe growing-season dynamics provides a richer understanding of spatiotemporal patterns that can be used for predicting ecosystem responses to future climates and land-use change. Such understanding allows for clearer interpretation of results for conservation, wildlife, and land management.
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Affiliation(s)
- David J. A. Wood
- U.S. Geological SurveyNorthern Rocky Mountain Science CenterBozemanMontanaUSA
- Department of Land Resources and Environmental SciencesMontana State UniversityBozemanMontanaUSA
| | - Scott Powell
- Department of Land Resources and Environmental SciencesMontana State UniversityBozemanMontanaUSA
| | - Paul C. Stoy
- Department of Land Resources and Environmental SciencesMontana State UniversityBozemanMontanaUSA
- Department of Biological Systems EngineeringUniversity of Wisconsin–MadisonMadisonWisconsinUSA
| | - Lindsey L. Thurman
- U.S. Geological SurveyNorthern Rocky Mountain Science CenterBozemanMontanaUSA
- U.S. Geological SurveyNorthwest Climate Adaptation Science CenterCorvallisOregonUSA
| | - Erik A. Beever
- U.S. Geological SurveyNorthern Rocky Mountain Science CenterBozemanMontanaUSA
- Department of EcologyMontana State UniversityBozemanMontanaUSA
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14
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Lian X, Piao S, Chen A, Wang K, Li X, Buermann W, Huntingford C, Peñuelas J, Xu H, Myneni RB. Seasonal biological carryover dominates northern vegetation growth. Nat Commun 2021; 12:983. [PMID: 33579949 PMCID: PMC7881040 DOI: 10.1038/s41467-021-21223-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 01/19/2021] [Indexed: 11/17/2022] Open
Abstract
The state of ecosystems is influenced strongly by their past, and describing this carryover effect is important to accurately forecast their future behaviors. However, the strength and persistence of this carryover effect on ecosystem dynamics in comparison to that of simultaneous environmental drivers are still poorly understood. Here, we show that vegetation growth carryover (VGC), defined as the effect of present states of vegetation on subsequent growth, exerts strong positive impacts on seasonal vegetation growth over the Northern Hemisphere. In particular, this VGC of early growing-season vegetation growth is even stronger than past and co-occurring climate on determining peak-to-late season vegetation growth, and is the primary contributor to the recently observed annual greening trend. The effect of seasonal VGC persists into the subsequent year but not further. Current process-based ecosystem models greatly underestimate the VGC effect, and may therefore underestimate the CO2 sequestration potential of northern vegetation under future warming.
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Affiliation(s)
- Xu Lian
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Shilong Piao
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
- Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.
- Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing, China.
| | - Anping Chen
- Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA
| | - Kai Wang
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Xiangyi Li
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Wolfgang Buermann
- Institute of Geography, Augsburg University, Augsburg, Germany
- Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Josep Peñuelas
- CREAF, Cerdanyola del Valles, Barcelona, Catalonia, Spain
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Barcelona, Catalonia, Spain
| | - Hao Xu
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Ranga B Myneni
- Department of Earth and Environment, Boston University, Boston, MA, USA
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15
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Jiang C, Ryu Y, Wang H, Keenan TF. An optimality-based model explains seasonal variation in C3 plant photosynthetic capacity. GLOBAL CHANGE BIOLOGY 2020; 26:6493-6510. [PMID: 32654330 DOI: 10.1111/gcb.15276] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 05/18/2020] [Indexed: 06/11/2023]
Abstract
The maximum rate of carboxylation (Vcmax ) is an essential leaf trait determining the photosynthetic capacity of plants. Existing approaches for estimating Vcmax at large scale mainly rely on empirical relationships with proxies such as leaf nitrogen/chlorophyll content or hyperspectral reflectance, or on complicated inverse models from gross primary production or solar-induced fluorescence. A novel mechanistic approach based on the assumption that plants optimize resource investment coordinating with environment and growth has been shown to accurately predict C3 plant Vcmax based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation in Vcmax has not been fully investigated. Here, we adapt an optimality-based model to simulate daily Vcmax,25C (Vcmax at a standardized temperature of 25°C) by incorporating the effects of antecedent environment, which affects current plant functioning, and dynamic light absorption, which coordinates with plant functioning. We then use seasonal Vcmax,25C field measurements from 10 sites across diverse ecosystems to evaluate model performance. Overall, the model explains about 83% of the seasonal variation in C3 plant Vcmax,25C across the 10 sites, with a medium root mean square error of 12.3 μmol m-2 s-1 , which suggests that seasonal changes in Vcmax,25C are consistent with optimal plant function. We show that failing to account for acclimation to antecedent environment or coordination with dynamic light absorption dramatically decreases estimation accuracy. Our results show that optimality-based approach can accurately reproduce seasonal variation in canopy photosynthetic potential, and suggest that incorporating such theory into next-generation trait-based terrestrial biosphere models would improve predictions of global photosynthesis.
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Affiliation(s)
- Chongya Jiang
- Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, Korea
| | - Youngryel Ryu
- Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, Korea
| | - Han Wang
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Trevor F Keenan
- Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, USA
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16
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Peltier DMP, Ogle K. Tree growth sensitivity to climate is temporally variable. Ecol Lett 2020; 23:1561-1572. [DOI: 10.1111/ele.13575] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 04/14/2020] [Accepted: 06/25/2020] [Indexed: 02/06/2023]
Affiliation(s)
- Drew M. P. Peltier
- Center for Ecosystem Science and Society Northern Arizona University Flagstaff Arizona USA
- School of Informatics, Computing, and Cyber Systems Northern Arizona University Flagstaff Arizona USA
| | - Kiona Ogle
- Center for Ecosystem Science and Society Northern Arizona University Flagstaff Arizona USA
- School of Informatics, Computing, and Cyber Systems Northern Arizona University Flagstaff Arizona USA
- Department of Biological Sciences Northern Arizona University Flagstaff Arizona USA
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