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Ustin SL, Middleton EM. Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications. SENSORS (BASEL, SWITZERLAND) 2024; 24:3488. [PMID: 38894281 PMCID: PMC11175343 DOI: 10.3390/s24113488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/05/2024] [Accepted: 05/13/2024] [Indexed: 06/21/2024]
Abstract
Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth's status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth's surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements.
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Affiliation(s)
- Susan L. Ustin
- Institute of the Environment, University of California, Davis, Davis, CA 95616, USA
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2
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Kim JE, Wang JA, Li Y, Czimczik CI, Randerson JT. Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America. GLOBAL CHANGE BIOLOGY 2024; 30:e17151. [PMID: 38273511 DOI: 10.1111/gcb.17151] [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: 06/28/2023] [Revised: 10/11/2023] [Accepted: 12/12/2023] [Indexed: 01/27/2024]
Abstract
Observations of the annual cycle of atmospheric CO2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape-level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat-derived land cover time series to characterize plant functional types (PFTs), and solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) as a proxy for photosynthesis. We analyze these datasets to characterize post-fire changes in plant succession and photosynthetic activity using a space-for-time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid-succession yield enhancements in SIF by 8-40% during June and July for 2- to 59-year stands relative to pre-fire controls. From the analysis of post-fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long-term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early- and mid-summer. Second, fire appears to facilitate a long-term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO2 exchange without a close coupling to long-term increases in carbon storage.
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Affiliation(s)
- Jinhyuk E Kim
- Department of Earth System Science, University of California, Irvine, California, USA
| | - Jonathan A Wang
- School of Biological Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Yue Li
- Department of Geography, University of California, Los Angeles, California, USA
| | - Claudia I Czimczik
- Department of Earth System Science, University of California, Irvine, California, USA
| | - James T Randerson
- Department of Earth System Science, University of California, Irvine, California, USA
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3
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Cushman KC, Albert LP, Norby RJ, Saatchi S. Innovations in plant science from integrative remote sensing research: an introduction to a Virtual Issue. THE NEW PHYTOLOGIST 2023; 240:1707-1711. [PMID: 37915249 DOI: 10.1111/nph.19237] [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: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 11/03/2023]
Abstract
This article is an Editorial to the Virtual issue on ‘Remote sensing’ that includes the following papers Chavana‐Bryant et al. (2017), Coupel‐Ledru et al. (2022), Cushman & Machado (2020), Disney (2019), D'Odorico et al. (2020), Dong et al. (2022), Fischer et al. (2019), Gamon et al. (2023), Gu et al. (2019), Guillemot et al. (2020), Jucker (2021), Koh et al. (2022), Konings et al. (2019), Kothari et al. (2023), Martini et al. (2022), Richardson (2019), Santini et al. (2021), Schimel et al. (2019), Serbin et al. (2019), Smith et al. (2019, 2020), Still et al. (2021), Stovall et al. (2021), Wang et al. (2020), Wong et al. (2020), Wu et al. (2021), Wu et al. (2017), Wu et al. (2018), Wu et al. (2019), Xu et al. (2021), Yan et al. (2021). Access the Virtual Issue at www.newphytologist.com/virtualissues.
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Affiliation(s)
- K C Cushman
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
| | - Loren P Albert
- College of Forestry, Oregon State University, Corvallis, OR, 97331, USA
| | - Richard J Norby
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, 37996, USA
| | - Sassan Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
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4
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Wang R, Springer KR, Gamon JA. Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study. GLOBAL CHANGE BIOLOGY 2023; 29:6120-6138. [PMID: 37589597 DOI: 10.1111/gcb.16916] [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: 01/19/2023] [Revised: 07/06/2023] [Accepted: 07/07/2023] [Indexed: 08/18/2023]
Abstract
Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.
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Affiliation(s)
- Ran Wang
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Kyle R Springer
- Department of Biological and Environmental Sciences, Concordia University of Edmonton, Edmonton, Alberta, Canada
| | - John A Gamon
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
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5
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Sun Y, Wen J, Gu L, Joiner J, Chang CY, van der Tol C, Porcar-Castell A, Magney T, Wang L, Hu L, Rascher U, Zarco-Tejada P, Barrett CB, Lai J, Han J, Luo Z. From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II-Harnessing data. GLOBAL CHANGE BIOLOGY 2023; 29:2893-2925. [PMID: 36802124 DOI: 10.1111/gcb.16646] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/09/2023] [Accepted: 02/14/2023] [Indexed: 05/03/2023]
Abstract
Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.
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Affiliation(s)
- Ying Sun
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Jiaming Wen
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Lianhong Gu
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Joanna Joiner
- National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Greenbelt, Maryland, USA
| | - Christine Y Chang
- US Department of Agriculture, Agricultural Research Service, Adaptive Cropping Systems Laboratory, Beltsville, Maryland, USA
| | - Christiaan van der Tol
- Affiliation Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
| | - Albert Porcar-Castell
- Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research (INAR)/Forest Sciences, Viikki Plant Science Center (ViPS), University of Helsinki, Helsinki, Finland
| | - Troy Magney
- Department of Plant Sciences, University of California, Davis, Davis, California, USA
| | - Lixin Wang
- Department of Earth Sciences, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, Indiana, USA
| | - Leiqiu Hu
- Department of Atmospheric and Earth Science, University of Alabama in Huntsville, Huntsville, Alabama, USA
| | - Uwe Rascher
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Pablo Zarco-Tejada
- School of Agriculture and Food (SAF-FVAS) and Faculty of Engineering and Information Technology (IE-FEIT), University of Melbourne, Melbourne, Victoria, Australia
| | - Christopher B Barrett
- Charles H. Dyson School of Applied Economics and Management, Cornell University, Ithaca, New York, USA
| | - Jiameng Lai
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Jimei Han
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
| | - Zhenqi Luo
- School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, New York, USA
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6
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Famiglietti CA, Worden M, Quetin GR, Smallman TL, Dayal U, Bloom AA, Williams M, Konings AG. Global net biome CO 2 exchange predicted comparably well using parameter-environment relationships and plant functional types. GLOBAL CHANGE BIOLOGY 2023; 29:2256-2273. [PMID: 36560840 DOI: 10.1111/gcb.16574] [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: 03/31/2022] [Accepted: 12/13/2022] [Indexed: 05/28/2023]
Abstract
Accurate estimation and forecasts of net biome CO2 exchange (NBE) are vital for understanding the role of terrestrial ecosystems in a changing climate. Prior efforts to improve NBE predictions have predominantly focused on increasing models' structural realism (and thus complexity), but parametric error and uncertainty are also key determinants of model skill. Here, we investigate how different parameterization assumptions propagate into NBE prediction errors across the globe, pitting the traditional plant functional type (PFT)-based approach against a novel top-down, machine learning-based "environmental filtering" (EF) approach. To do so, we simulate these contrasting methods for parameter assignment within a flexible model-data fusion framework of the terrestrial carbon cycle (CARDAMOM) at a global scale. In the PFT-based approach, model parameters from a small number of select locations are applied uniformly within regions sharing similar land cover characteristics. In the EF-based approach, a pixel's parameters are predicted based on underlying relationships with climate, soil, and canopy properties. To isolate the role of parametric from structural uncertainty in our analysis, we benchmark the resulting PFT-based and EF-based NBE predictions with estimates from CARDAMOM's Bayesian optimization approach (whereby "true" parameters consistent with a suite of data constraints are retrieved on a pixel-by-pixel basis). When considering the mean absolute error of NBE predictions across time, we find that the EF-based approach matches or outperforms the PFT-based approach at 55% of pixels-a narrow majority. However, NBE estimates from the EF-based approach are susceptible to compensation between errors in component flux predictions and predicted parameters can align poorly with the assumed "true" values. Overall, though, the EF-based approach is comparable to conventional approaches and merits further investigation to better understand and resolve these limitations. This work provides insight into the relationship between terrestrial biosphere model performance and parametric uncertainty, informing efforts to improve model parameterization via PFT-free and trait-based approaches.
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Affiliation(s)
| | - Matthew Worden
- Department of Earth System Science, Stanford University, Stanford, California, USA
| | - Gregory R Quetin
- Department of Geography, University of California at Santa Barbara, Santa Barbara, California, USA
| | - T Luke Smallman
- School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK
| | - Uma Dayal
- Department of Earth System Science, Stanford University, Stanford, California, USA
| | - A Anthony Bloom
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
| | - Mathew Williams
- School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK
| | - Alexandra G Konings
- Department of Earth System Science, Stanford University, Stanford, California, USA
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7
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Vernham G, Bailey JJ, Chase JM, Hjort J, Field R, Schrodt F. Understanding trait diversity: the role of geodiversity. Trends Ecol Evol 2023:S0169-5347(23)00039-3. [PMID: 37003934 DOI: 10.1016/j.tree.2023.02.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 04/03/2023]
Abstract
Geodiversity - the abiotic heterogeneity of Earth's (sub)surface - is gaining recognition for its ecological links to biodiversity. However, theoretical and conceptual knowledge of geodiversity-trait diversity relationships is currently lacking and can improve understanding of abiotic drivers of community assembly. Here we synthesise the state of knowledge of these relationships. We find that some components of geodiversity (e.g., topographic heterogeneity) elicit strong trait responses, whereas other components (e.g., substrate heterogeneity) have marginal effects in driving trait distributions. However, current knowledge is lacking in key aspects, including geodiversity's effect on trait-specific diversity and intraspecific variation. We call for the explicit inclusion of geodiversity when relating environmental drivers to trait diversity, taking advantage of the increasing availability of trait and geodiversity data.
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Affiliation(s)
| | - Joseph J Bailey
- School of Life Sciences, Anglia Ruskin University, East Road, Cambridge, CB1 1PT, UK
| | - Jonathan M Chase
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Jan Hjort
- Geography Research Unit, University of Oulu, Pentti Kaiteran katu 1, 90570 Oulu, Finland
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8
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Chen J, Shao Z, Huang X, Zhuang Q, Dang C, Cai B, Zheng X, Ding Q. Assessing the impact of drought-land cover change on global vegetation greenness and productivity. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 852:158499. [PMID: 36058327 DOI: 10.1016/j.scitotenv.2022.158499] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 06/15/2023]
Abstract
Drought-land cover change (D-LCC) is considered to be an important stress factor that affects vegetation greenness and productivity (VG&P) in global terrestrial ecosystems. Understanding the effects of D-LCC on VG&P benefits the development of terrestrial ecosystem models and the prediction of ecosystem evolution. However, till today, the mechanism remains underexploited. In this study, based on the Theil-Sen median estimator and Mann-Kendall test, Hurst exponent evaluation and rescaled range analysis (R/S), Pearson and Partial correlation coefficient analyses, we explore the spatiotemporal distribution characteristics and future trends of Leaf area index (LAI), Net primary productivity (NPP), Solar-induced chlorophyll fluorescence (SIF), Standardized precipitation evapotranspiration index (SPEI), Soil moisture (SM), Land cover type (LC), and the impact mechanism of D-LCC on global VG&P. Our results provide four major insights. First, three independent satellite observations consistently indicate that the world is experiencing an increasing trend of VG&P: LAI (17.69 %), NPP (20.32 %) and SIF (16.46 %). Nonetheless, productivity-reducing trends are unfolding in some tropical regions, notably the Amazon rainforest and the Congo basin. Second, from 2001 to 2020, the frequency, severity, duration, and scope of global droughts have been increasing. Third, the impact of land cover change on global VG&P is region-dependent. Finally, our results indicate that the continuous growth of VG&P in the global vegetation area is likely to become more difficult to maintain.
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Affiliation(s)
- Jinlong Chen
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
| | - Zhenfeng Shao
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China.
| | - Xiao Huang
- Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Qingwei Zhuang
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
| | - Chaoya Dang
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
| | - Bowen Cai
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
| | - Xueke Zheng
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
| | - Qing Ding
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
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9
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Hu Z, Dakos V, Rietkerk M. Using functional indicators to detect state changes in terrestrial ecosystems. Trends Ecol Evol 2022; 37:1036-1045. [PMID: 36008160 DOI: 10.1016/j.tree.2022.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 07/19/2022] [Accepted: 07/26/2022] [Indexed: 01/12/2023]
Abstract
Indicators to predict ecosystem state change are urgently needed to cope with the degradation of ecosystem services caused by global change. With the development of new technologies for measuring ecosystem function with fine spatiotemporal resolution over broad areas, we are in the era of 'big data'. However, it is unclear how large, emerging datasets can be used to anticipate ecosystem state change. We propose the construction of indicators based on functional variables (flows) and state variables (pools) to predict future ecosystem state changes. The indicators identified here may be useful signals for doing so. In addition, functional indicators have explicit ecological meanings that can identify the ecological mechanism that is causing state changes, and can thus be used to improve ecosystem models.
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Affiliation(s)
- Zhongmin Hu
- Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, Hainan University, Haikou 570228, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong 519082, China.
| | - Vasilis Dakos
- Institut des Sciences de l'Evolution de Montpellier (ISEM), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD), Université de Montpellier, Ecole Pratique des Hautes Etudes (EPHE), Montpellier, France
| | - Max Rietkerk
- Copernicus Institute of Sustainable Development, Utrecht University, 3508, TC, Utrecht, The Netherlands
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Palmer PI, Woodwark AJP, Finch DP, Taylor TE, Butz A, Tamminen J, Bösch H, Eldering A, Vincent-Bonnieu S. Role of space station instruments for improving tropical carbon flux estimates using atmospheric data. NPJ Microgravity 2022; 8:51. [PMID: 36404345 PMCID: PMC9676185 DOI: 10.1038/s41526-022-00231-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 10/03/2022] [Indexed: 11/21/2022] Open
Abstract
The tropics is the nexus for many of the remaining gaps in our knowledge of environmental science, including the carbon cycle and atmospheric chemistry, with dire consequences for our ability to describe the Earth system response to a warming world. Difficulties associated with accessibility, coordinated funding models and economic instabilities preclude the establishment of a dense pan-tropical ground-based atmospheric measurement network that would otherwise help to describe the evolving state of tropical ecosystems and the associated biosphere-atmosphere fluxes on decadal timescales. The growing number of relevant sensors aboard sun-synchronous polar orbiters provide invaluable information over the remote tropics, but a large fraction of the data collected along their orbits is from higher latitudes. The International Space Station (ISS), which is in a low-inclination, precessing orbit, has already demonstrated value as a proving ground for Earth observing atmospheric sensors and as a testbed for new technology. Because low-inclination orbits spend more time collecting data over the tropics, we argue that the ISS and its successors, offer key opportunities to host new Earth-observing atmospheric sensors that can lead to a step change in our understanding of tropical carbon fluxes.
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Affiliation(s)
- Paul I Palmer
- School of GeoSciences, University of Edinburgh, Edinburgh, UK.
- National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK.
| | | | - Douglas P Finch
- School of GeoSciences, University of Edinburgh, Edinburgh, UK
| | - Thomas E Taylor
- Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
| | - André Butz
- Institute of Environmental Physics, Heidelberg University, Heidelberg, Germany
| | | | - Hartmut Bösch
- National Centre for Earth Observation, University of Leicester, Leicester, UK
- Earth Observation Science, School of Physics and Astronomy, University of Leicester, Leicester, UK
| | - Annmarie Eldering
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Sebastien Vincent-Bonnieu
- Directorate of Human and Robotic Exploration Programmes, European Space Agency - ESTEC, Noordwijk-ZH, The Netherlands
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11
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Kuai L, Parazoo NC, Shi M, Miller CE, Baker I, Bloom AA, Bowman K, Lee M, Zeng Z, Commane R, Montzka SA, Berry J, Sweeney C, Miller JB, Yung YL. Quantifying Northern High Latitude Gross Primary Productivity (GPP) Using Carbonyl Sulfide (OCS). GLOBAL BIOGEOCHEMICAL CYCLES 2022; 36:e2021GB007216. [PMID: 36590828 PMCID: PMC9787914 DOI: 10.1029/2021gb007216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 07/21/2022] [Accepted: 08/08/2022] [Indexed: 06/17/2023]
Abstract
The northern high latitude (NHL, 40°N to 90°N) is where the second peak region of gross primary productivity (GPP) other than the tropics. The summer NHL GPP is about 80% of the tropical peak, but both regions are still highly uncertain (Norton et al. 2019, https://doi.org/10.5194/bg-16-3069-2019). Carbonyl sulfide (OCS) provides an important proxy for photosynthetic carbon uptake. Here we optimize the OCS plant uptake fluxes across the NHL by fitting atmospheric concentration simulation with the GEOS-CHEM global transport model to the aircraft profiles acquired over Alaska during NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (2012-2015). We use the empirical biome-specific linear relationship between OCS plant uptake flux and GPP to derive the six plant uptake OCS fluxes from different GPP data. Such GPP-based fluxes are used to drive the concentration simulations. We evaluate the simulations against the independent observations at two ground sites of Alaska. The optimized OCS fluxes suggest the NHL plant uptake OCS flux of -247 Gg S year-1, about 25% stronger than the ensemble mean of the six GPP-based OCS fluxes. GPP-based OCS fluxes systematically underestimate the peak growing season across the NHL, while a subset of models predict early start of season in Alaska, consistent with previous studies of net ecosystem exchange. The OCS optimized GPP of 34 PgC yr-1 for NHL is also about 25% more than the ensembles mean from six GPP data. Further work is needed to fully understand the environmental and biotic drivers and quantify their rate of photosynthetic carbon uptake in Arctic ecosystems.
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Affiliation(s)
- Le Kuai
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | - Mingjie Shi
- Pacific Northwest National LaboratoryRichlandWAUSA
| | - Charles E. Miller
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Ian Baker
- Colorado State UniversityFort CollinsCOUSA
| | - Anthony A. Bloom
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Kevin Bowman
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Meemong Lee
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Zhao‐Cheng Zeng
- University of California Los AngelesJIFRESSELos AngelesCAUSA
| | - Roisin Commane
- Lamont‐Doherty Earth Observatory at Columbia UniversityPalisadesNYUSA
| | | | | | | | | | - Yuk L. Yung
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
- California Institute of TechnologyPasadenaCAUSA
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12
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da Rosa Ferraz Jardim AM, de Morais JEF, de Souza LSB, da Silva TGF. Understanding interactive processes: a review of CO 2 flux, evapotranspiration, and energy partitioning under stressful conditions in dry forest and agricultural environments. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:677. [PMID: 35974211 DOI: 10.1007/s10661-022-10339-7] [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: 06/07/2022] [Accepted: 08/06/2022] [Indexed: 06/15/2023]
Abstract
Arid and semiarid environments are characterized by low water availability (e.g., in soil and atmosphere), high air temperature, and irregularity in the spatio-temporal distribution of rainfall. In addition to the economic and environmental consequences, drought also causes physiological damage to crops and compromises their survival in ecosystems. The removal of vegetation is responsible for altering the energy exchange of heat and water in natural ecosystems and agricultural areas. The fluxes of CO2 are also changed, and environments with characteristics of sinks, which can be sources of CO2 after anthropic disturbances. These changes can be measured through methods such as sap flow, eddy covariance, remote sensing, and energy balance. Despite the relevance of each method mentioned above, there are limitations in their applications that must be respected. Thus, this review aims to quantify the processes and changes of energy fluxes, CO2, and their interactions with the surfaces of terrestrial ecosystems in dry environments. Studies report that the use of methods that integrate data from climate monitoring towers and remote sensing products helps to improve the accuracy of the determination of energy fluxes on a global scale, also helping to reduce the dissimilarity of results obtained individually. Through the collection of works in the literature, it is reported that several areas of the Brazilian Caatinga biome, which is a Seasonally Dry Tropical Forest have been suffering from changes in land use and land cover. Similar fluxes of sensible heat in areas with cacti and Caatinga can be observed in studies. On the other hand, one of the variables influenced mainly by air temperature is net radiation. In dry forest areas, woody species can store large amounts of carbon in their biomass above and belowground. The use of cacti can modify the local carbon budget when using tree crops together. Therefore, the study highlights the complexity and severity of land degradation and changes in CO2, water, and energy fluxes in dry environments with areas of forest, grassland, and cacti. Vegetation energy balance is also a critical factor, as these simulations are helpful for use in forecasting weather or climate change. We also highlight the need for more studies that address environmental conservation techniques and cactus in the conservation of degraded areas.
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Affiliation(s)
- Alexandre Maniçoba da Rosa Ferraz Jardim
- Department of Agricultural Engineering, Federal Rural University of Pernambuco, Dom Manoel de Medeiros avenue, s/n, Dois Irmãos, Recife, Pernambuco, 52171-900, Brazil.
- Academic Unit of Serra Talhada, Federal Rural University of Pernambuco, Gregório Ferraz Nogueira avenue, s/n, Serra Talhada, Pernambuco, 56909-535, Brazil.
| | - José Edson Florentino de Morais
- Academic Unit of Serra Talhada, Federal Rural University of Pernambuco, Gregório Ferraz Nogueira avenue, s/n, Serra Talhada, Pernambuco, 56909-535, Brazil
| | - Luciana Sandra Bastos de Souza
- Academic Unit of Serra Talhada, Federal Rural University of Pernambuco, Gregório Ferraz Nogueira avenue, s/n, Serra Talhada, Pernambuco, 56909-535, Brazil
| | - Thieres George Freire da Silva
- Academic Unit of Serra Talhada, Federal Rural University of Pernambuco, Gregório Ferraz Nogueira avenue, s/n, Serra Talhada, Pernambuco, 56909-535, Brazil
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Novick KA, Metzger S, Anderegg WRL, Barnes M, Cala DS, Guan K, Hemes KS, Hollinger DY, Kumar J, Litvak M, Lombardozzi D, Normile CP, Oikawa P, Runkle BRK, Torn M, Wiesner S. Informing Nature-based Climate Solutions for the United States with the best-available science. GLOBAL CHANGE BIOLOGY 2022; 28:3778-3794. [PMID: 35253952 DOI: 10.1111/gcb.16156] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.
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Affiliation(s)
- Kimberly A Novick
- O'Neill School of Public and Environmental Affairs, Indiana University-Bloomington, Bloomington, Indiana, USA
| | - Stefan Metzger
- Battelle, National Ecological Observatory Network, Boulder, Colorado, USA
| | | | - Mallory Barnes
- O'Neill School of Public and Environmental Affairs, Indiana University-Bloomington, Bloomington, Indiana, USA
| | - Daniela S Cala
- O'Neill School of Public and Environmental Affairs, Indiana University-Bloomington, Bloomington, Indiana, USA
| | - Kaiyu Guan
- College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Kyle S Hemes
- Woods Institute for the Environment, Stanford University, Stanford, California, USA
| | - David Y Hollinger
- USDA Forest Service, Northern Research Station, Durham, New Hampshire, USA
| | - Jitendra Kumar
- Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
| | - Marcy Litvak
- Department of Biology, University of New Mexico, Albuquerque, New Mexico, USA
| | | | | | - Patty Oikawa
- Department of Earth & Environmental Science, California State University-East Bay, Hayward, California, USA
| | - Benjamin R K Runkle
- Department of Biological and Agricultural Engineering, University of Arkansas, Fayetteville, Arkansas, USA
| | - Margaret Torn
- Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Susanne Wiesner
- Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
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14
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Beringer J, Moore CE, Cleverly J, Campbell DI, Cleugh H, De Kauwe MG, Kirschbaum MUF, Griebel A, Grover S, Huete A, Hutley LB, Laubach J, Van Niel T, Arndt SK, Bennett AC, Cernusak LA, Eamus D, Ewenz CM, Goodrich JP, Jiang M, Hinko‐Najera N, Isaac P, Hobeichi S, Knauer J, Koerber GR, Liddell M, Ma X, Macfarlane C, McHugh ID, Medlyn BE, Meyer WS, Norton AJ, Owens J, Pitman A, Pendall E, Prober SM, Ray RL, Restrepo‐Coupe N, Rifai SW, Rowlings D, Schipper L, Silberstein RP, Teckentrup L, Thompson SE, Ukkola AM, Wall A, Wang Y, Wardlaw TJ, Woodgate W. Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network. GLOBAL CHANGE BIOLOGY 2022; 28:3489-3514. [PMID: 35315565 PMCID: PMC9314624 DOI: 10.1111/gcb.16141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/30/2022] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.
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15
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GriddingMachine, a database and software for Earth system modeling at global and regional scales. Sci Data 2022; 9:258. [PMID: 35650204 PMCID: PMC9160223 DOI: 10.1038/s41597-022-01346-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 05/04/2022] [Indexed: 11/09/2022] Open
Abstract
Land and Earth system modeling is moving towards more explicit biophysical representations, requiring increasing variety of datasets for initialization and benchmarking. However, researchers often have difficulties in identifying and integrating non-standardized datasets from various sources. We aim towards a standardized database and one-stop distribution method of global datasets. Here, we present the GriddingMachine as (1) a database of global-scale datasets commonly used to parameterize or benchmark the models, from plant traits to vegetation indices and geophysical information and (2) a cross-platform open source software to download and request a subset of datasets with only a few lines of code. The GriddingMachine datasets can be accessed either manually through traditional HTTP, or automatically using modern programming languages including Julia, Matlab, Octave, Python, and R. The GriddingMachine collections can be used for any land and Earth modeling framework and ecological research at the regional and global scales, and the number of datasets will continue to grow to meet the increasing needs of research communities.
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16
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Variability of ecosystem carbon source from microbial respiration is controlled by rainfall dynamics. Proc Natl Acad Sci U S A 2021; 118:2115283118. [PMID: 34930848 DOI: 10.1073/pnas.2115283118] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2021] [Indexed: 11/18/2022] Open
Abstract
Soil heterotrophic respiration (R h) represents an important component of the terrestrial carbon cycle that affects whether ecosystems function as carbon sources or sinks. Due to the complex interactions between biological and physical factors controlling microbial growth, R h is uncertain and difficult to predict, limiting our ability to anticipate future climate trajectories. Here we analyze the global FLUXNET 2015 database aided by a probabilistic model of microbial growth to examine the ecosystem-scale dynamics of R h and identify primary predictors of its variability. We find that the temporal variability in R h is consistently distributed according to a Gamma distribution, with shape and scale parameters controlled only by rainfall characteristics and vegetation productivity. This distribution originates from the propagation of fast hydrologic fluctuations on the slower biological dynamics of microbial growth and is independent of biome, soil type, and microbial physiology. This finding allows us to readily provide accurate estimates of the mean R h and its variance, as confirmed by a comparison with an independent global dataset. Our results suggest that future changes in rainfall regime and net primary productivity will significantly alter the dynamics of R h and the global carbon budget. In regions that are becoming wetter, R h may increase faster than net primary productivity, thereby reducing the carbon storage capacity of terrestrial ecosystems.
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17
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Nagy RC, Balch JK, Bissell EK, Cattau ME, Glenn NF, Halpern BS, Ilangakoon N, Johnson B, Joseph MB, Marconi S, O’Riordan C, Sanovia J, Swetnam TL, Travis WR, Wasser LA, Woolner E, Zarnetske P, Abdulrahim M, Adler J, Barnes G, Bartowitz KJ, Blake RE, Bombaci SP, Brun J, Buchanan JD, Chadwick KD, Chapman MS, Chong SS, Chung YA, Corman JR, Couret J, Crispo E, Doak TG, Donnelly A, Duffy KA, Dunning KH, Duran SM, Edmonds JW, Fairbanks DE, Felton AJ, Florian CR, Gann D, Gebhardt M, Gill NS, Gram WK, Guo JS, Harvey BJ, Hayes KR, Helmus MR, Hensley RT, Hondula KL, Huang T, Hundertmark WJ, Iglesias V, Jacinthe P, Jansen LS, Jarzyna MA, Johnson TM, Jones KD, Jones MA, Just MG, Kaddoura YO, Kagawa‐Vivani AK, Kaushik A, Keller AB, King KBS, Kitzes J, Koontz MJ, Kouba PV, Kwan W, LaMontagne JM, LaRue EA, Li D, Li B, Lin Y, Liptzin D, Long WA, Mahood AL, Malloy SS, Malone SL, McGlinchy JM, Meier CL, Melbourne BA, Mietkiewicz N, Morisette JT, Moustapha M, Muscarella C, Musinsky J, Muthukrishnan R, Naithani K, Neely M, Norman K, Parker SM, Perez Rocha M, Petri L, Ramey CA, Record S, Rossi MW, SanClements M, Scholl VM, Schweiger AK, Seyednasrollah B, Sihi D, Smith KR, Sokol ER, Spaulding SA, Spiers AI, St. Denis LA, Staccone AP, Stack Whitney K, Stanitski DM, Stricker E, Surasinghe TD, Thomsen SK, Vasek PM, Xiaolu L, Yang D, Yu R, Yule KM, Zhu K. Harnessing the NEON data revolution to advance open environmental science with a diverse and data‐capable community. Ecosphere 2021. [DOI: 10.1002/ecs2.3833] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- R. Chelsea Nagy
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
| | - Jennifer K. Balch
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
- Department of Geography University of Colorado Boulder Boulder Colorado USA
| | - Erin K. Bissell
- Biology Department Metropolitan State University of Denver Denver Colorado USA
| | - Megan E. Cattau
- Human‐Environment Systems Boise State University Boise Idaho USA
| | - Nancy F. Glenn
- Human‐Environment Systems Boise State University Boise Idaho USA
- University of New South Wales Sydney Sydney New South Wales Australia
| | - Benjamin S. Halpern
- National Center for Ecological Analysis and Synthesis (NCEAS) Santa Barbara California USA
- University of California Santa Barbara Santa Barbara California USA
| | - Nayani Ilangakoon
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
| | - Brian Johnson
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
| | - Maxwell B. Joseph
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
| | - Sergio Marconi
- School of Natural Resources & Environment University of Florida Gainesville Florida USA
| | | | - James Sanovia
- Department of Math, Science, and Technology Oglala Lakota College Kyle South Dakota USA
| | | | - William R. Travis
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
- Department of Geography University of Colorado Boulder Boulder Colorado USA
| | - Leah A. Wasser
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
- Department of Geography University of Colorado Boulder Boulder Colorado USA
| | - Elizabeth Woolner
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
| | - Phoebe Zarnetske
- Department of Integrative Biology Michigan State University East Lansing Michigan USA
| | - Mujahid Abdulrahim
- Department of Civil and Mechanical Engineering University of Missouri Kansas City Kansas City Missouri USA
| | - John Adler
- Department of Geography University of Colorado Boulder Boulder Colorado USA
- CIRES University of Colorado Boulder Boulder Colorado USA
| | - Grenville Barnes
- Department of Forest, Fisheries and Geomatics Sciences University of Florida Gainesville Florida USA
| | - Kristina J. Bartowitz
- Department of Forest, Rangeland, and Fire Sciences University of Idaho Moscow Idaho USA
| | - Rachael E. Blake
- National Socio‐Environmental Synthesis Center University of Maryland Annapolis Maryland USA
| | - Sara P. Bombaci
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado USA
| | - Julien Brun
- National Center for Ecological Analysis and Synthesis (NCEAS) Santa Barbara California USA
- University of California Santa Barbara Santa Barbara California USA
| | - Jacob D. Buchanan
- Department of Biological Sciences Bowling Green State University Bowling Green Ohio USA
| | - K. Dana Chadwick
- Department of Geological Sciences University of Texas Austin Austin Texas USA
- Department of Integrative Biology University of Texas Austin Austin Texas USA
| | - Melissa S. Chapman
- Department of Environmental Science, Policy, and Management University of California Berkeley Berkeley California USA
| | - Steven S. Chong
- National Center for Ecological Analysis and Synthesis (NCEAS) Santa Barbara California USA
- University of California Santa Barbara Santa Barbara California USA
- University of California Berkeley Library University of California Berkeley Berkeley California USA
| | - Y. Anny Chung
- Departments of Plant Biology and Plant Pathology University of Georgia Athens Georgia USA
| | - Jessica R. Corman
- School of Natural Resources University of Nebraska Lincoln Lincoln Nebraska USA
| | - Jannelle Couret
- Department of Biological Sciences University of Rhode Island Kingston Rhode Island USA
| | - Erika Crispo
- Department of Biology Pace University New York City New York USA
| | - Thomas G. Doak
- Department of Biology Indiana University Bloomington Indiana USA
| | - Alison Donnelly
- Department of Geography University of Wisconsin‐Milwaukee Milwaukee Wisconsin USA
| | - Katharyn A. Duffy
- School of Informatics, Computing & Cyber Systems Northern Arizona University Flagstaff Arizona USA
| | - Kelly H. Dunning
- School of Forestry and Wildlife Auburn University Auburn Alabama USA
| | - Sandra M. Duran
- Department of Ecology and Evolutionary Biology University of Arizona Tucson Arizona USA
| | - Jennifer W. Edmonds
- Department of Physical and Life Sciences Nevada State College Henderson Nevada USA
| | - Dawson E. Fairbanks
- Department of Environmental Science University of Arizona Tucson Arizona USA
| | - Andrew J. Felton
- Department of Wildland Resources Utah State University Logan Utah USA
| | | | - Daniel Gann
- Department of Biological Sciences Florida International University Miami Florida USA
| | - Martha Gebhardt
- School of Natural Resources and the Environment University of Arizona Tucson Arizona USA
| | - Nathan S. Gill
- Department of Natural Resources Management Texas Tech University Lubbock Texas USA
| | - Wendy K. Gram
- University Corporation for Atmospheric Research Boulder Colorado USA
| | - Jessica S. Guo
- College of Agriculture and Life Sciences University of Arizona Tucson Arizona USA
| | - Brian J. Harvey
- School of Environmental and Forest Sciences University of Washington Seattle Washington USA
| | - Katherine R. Hayes
- Department of Integrative and Systems Biology University of Colorado Denver Denver Colorado USA
| | - Matthew R. Helmus
- Department of Biology Temple University Philadelphia Pennsylvania USA
| | - Robert T. Hensley
- Battelle National Ecological Observatory Network Boulder Colorado USA
| | - Kelly L. Hondula
- National Socio‐Environmental Synthesis Center University of Maryland Annapolis Maryland USA
| | - Tao Huang
- Human‐Environment Systems Boise State University Boise Idaho USA
- Cary Institute of Ecosystem Services Millbrook New York USA
| | | | - Virginia Iglesias
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
| | - Pierre‐Andre Jacinthe
- Department of Earth Sciences Indiana University Purdue University Indianapolis Indiana USA
| | - Lara S. Jansen
- Department of Environmental Science & Management Portland State University Portland Oregon USA
| | - Marta A. Jarzyna
- Department of Evolution, Ecology, and Organismal Biology The Ohio State University Columbus Ohio USA
- Translational Data Analytics Institute The Ohio State University Columbus Ohio USA
| | | | | | | | | | - Youssef O. Kaddoura
- Department of Forest, Fisheries and Geomatics Sciences University of Florida Gainesville Florida USA
| | | | - Aleya Kaushik
- National Oceanic and Atmospheric Administration Boulder Colorado USA
| | - Adrienne B. Keller
- Department of Ecology, Evolution, and Behavior University of Minnesota Twin Cities St. Paul Minnesota USA
| | - Katelyn B. S. King
- Department of Fisheries and Wildlife Michigan State University East Lansing Michigan USA
| | - Justin Kitzes
- Department of Biological Sciences University of Pittsburgh Pittsburgh Pennsylvania USA
| | - Michael J. Koontz
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
| | - Paige V. Kouba
- Department of Plant Sciences University of California Davis Davis California USA
| | - Wai‐Yin Kwan
- CALeDNA University of California Los Angeles Los Angeles California USA
| | | | - Elizabeth A. LaRue
- Department of Forestry and Natural Resources Purdue University West Lafayette Indiana USA
| | - Daijiang Li
- Department of Biological Sciences Louisiana State University Baton Rouge Louisiana USA
- Center for Computation & Technology Louisiana State University Baton Rouge Louisiana USA
| | - Bonan Li
- Department of Biological & Ecological Engineering Oregon State University Corvallis Oregon USA
| | - Yang Lin
- Soil and Water Sciences Department University of Florida Gainesville Florida USA
| | | | - William Alex Long
- Science and Technology Innovation Program Woodrow Wilson International Center for Scholars Washington D.C. USA
| | - Adam L. Mahood
- Department of Geography University of Colorado Boulder Boulder Colorado USA
| | - Samuel S. Malloy
- Battelle Center for Science, Engineering and Public Policy in the John Glenn College of Public Affairs Ohio State University Columbus Ohio USA
| | - Sparkle L. Malone
- Department of Biological Sciences Florida International University Miami Florida USA
| | | | - Courtney L. Meier
- Battelle National Ecological Observatory Network Boulder Colorado USA
| | - Brett A. Melbourne
- Department of Ecology and Evolutionary Biology University of Colorado Boulder Boulder Colorado USA
| | | | - Jeffery T. Morisette
- U.S. Department of Agriculture Forest Service Rocky Mountain Research Station Fort Collins Colorado USA
| | - Moussa Moustapha
- Department of Biological Science University of Ngaoundere Ngaoundere Adamawa Cameroon
| | - Chance Muscarella
- Department of Environmental Science University of Arizona Tucson Arizona USA
| | - John Musinsky
- Battelle National Ecological Observatory Network Boulder Colorado USA
| | | | - Kusum Naithani
- Department of Biological Sciences University of Arkansas‐Fayetteville Fayetteville Arkansas USA
| | - Merrie Neely
- GEO AquaWatch Clearwater Florida USA
- Global Science and Technology, Inc Greenbelt Maryland USA
| | - Kari Norman
- Department of Environmental Science, Policy, and Management University of California Berkeley Berkeley California USA
| | | | | | - Laís Petri
- School for Environment and Sustainability University of Michigan East Lansing Michigan USA
| | - Colette A. Ramey
- Biology Department Metropolitan State University of Denver Denver Colorado USA
| | - Sydne Record
- Department of Biology Bryn Mawr College Bryn Mawr Pennsylvania USA
| | - Matthew W. Rossi
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
| | | | - Victoria M. Scholl
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
- Department of Geography University of Colorado Boulder Boulder Colorado USA
| | - Anna K. Schweiger
- Remote Sensing Laboratories Department of Geography University of Zurich Zurich Switzerland
| | - Bijan Seyednasrollah
- School of Informatics, Computing & Cyber Systems Northern Arizona University Flagstaff Arizona USA
| | - Debjani Sihi
- Department of Environmental Sciences Emory University Atlanta Georgia USA
| | - Kathleen R. Smith
- Biology Department Metropolitan State University of Denver Denver Colorado USA
| | - Eric R. Sokol
- Battelle National Ecological Observatory Network Boulder Colorado USA
- INSTAAR University of Colorado Boulder Boulder Colorado USA
| | | | - Anna I. Spiers
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
- Department of Ecology and Evolutionary Biology University of Colorado Boulder Boulder Colorado USA
| | - Lise A. St. Denis
- Earth Lab, CIRES University of Colorado Boulder Boulder Colorado USA
| | - Anika P. Staccone
- Department of Ecology, Evolution, & Environmental Biology Columbia University New York New York USA
| | - Kaitlin Stack Whitney
- Department of Science, Technology, and Society Rochester Institute of Technology Henrietta New York USA
| | | | - Eva Stricker
- Department of Biology University of New Mexico Albuquerque New Mexico USA
| | - Thilina D. Surasinghe
- Department of Biological Sciences Bridgewater State University Bridgewater Massachusetts USA
| | - Sarah K. Thomsen
- Department of Integrative Biology Oregon State University Corvallis Oregon USA
| | - Patrisse M. Vasek
- Department of Math, Science, and Technology Oglala Lakota College Kyle South Dakota USA
| | - Li Xiaolu
- Department of Earth and Atmospheric Sciences Cornell University Ithaca New York USA
| | - Di Yang
- Wyoming GIS Center University of Wyoming Laramie Wyoming USA
| | - Rong Yu
- Department of Geography University of Wisconsin‐Milwaukee Milwaukee Wisconsin USA
| | - Kelsey M. Yule
- Biodiversity Knowledge Integration Center Arizona State University Tempe Arizona USA
| | - Kai Zhu
- Department of Environmental Studies University of California, Santa Cruz Santa Cruz California USA
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18
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Townsend PA, Clare JDJ, Liu N, Stenglein JL, Anhalt‐Depies C, Van Deelen TR, Gilbert NA, Singh A, Martin KJ, Zuckerberg B. Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02436. [PMID: 34374154 PMCID: PMC9286556 DOI: 10.1002/eap.2436] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/25/2021] [Accepted: 04/21/2021] [Indexed: 06/13/2023]
Abstract
Biological data collection is entering a new era. Community science, satellite remote sensing (SRS), and local forms of remote sensing (e.g., camera traps and acoustic recordings) have enabled biological data to be collected at unprecedented spatial and temporal scales and resolution. There is growing interest in developing observation networks to collect and synthesize data to improve broad-scale ecological monitoring, but no examples of such networks have emerged to inform decision-making by agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin, which links synoptic environmental data derived from SRS to biodiversity observations collected continuously from a trail camera network to support management decision-making. We use several examples to illustrate that Snapshot Wisconsin improves the spatial, temporal, and biological resolution and extent of information available to support management, filling gaps associated with traditional monitoring and enabling consideration of new management strategies. JONs like Snapshot Wisconsin further strengthen monitoring inference by contributing novel lines of evidence useful for corroboration or integration. SRS provides environmental context that facilitates inference, prediction, and forecasting, and ultimately helps managers formulate, test, and refine conceptual models for the monitored systems. Although these approaches pose challenges, Snapshot Wisconsin demonstrates that expansive observation networks can be tractably managed by agencies to support decision making, providing a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.
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Affiliation(s)
- Philip A. Townsend
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | - John D. J. Clare
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | - Nanfeng Liu
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | | | - Christine Anhalt‐Depies
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
- Wisconsin Department of Natural ResourcesMadisonWisconsin53707USA
| | - Timothy R. Van Deelen
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | - Neil A. Gilbert
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | - Aditya Singh
- Department of Agricultural and Biological EngineeringUniversity of FloridaGainesvilleFlorida32603USA
| | - Karl J. Martin
- Division of ExtensionUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
| | - Benjamin Zuckerberg
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsin53706USA
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19
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Yan Z, Guo Z, Serbin SP, Song G, Zhao Y, Chen Y, Wu S, Wang J, Wang X, Li J, Wang B, Wu Y, Su Y, Wang H, Rogers A, Liu L, Wu J. Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types. THE NEW PHYTOLOGIST 2021; 232:134-147. [PMID: 34165791 DOI: 10.1111/nph.17579] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 06/20/2021] [Indexed: 06/13/2023]
Abstract
Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (Vc,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-Vc,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (Vc,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between Vc,max and the four morphological and biochemical traits (R2 ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-Vc,max relationships across three forest biomes into a single robust model for Vc,max (R2 = 0.77), and also accurately estimated the four traits (R2 = 0.75-0.94). These findings challenge the traditional use of the empirical trait-Vc,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising Vc,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.
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Affiliation(s)
- Zhengbing Yan
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Zhengfei Guo
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Shawn P Serbin
- Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Guangqin Song
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Yingyi Zhao
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Yang Chen
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Shengbiao Wu
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Jing Wang
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Xin Wang
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
| | - Jing Li
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Bin Wang
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Yuntao Wu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Yanjun Su
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Han Wang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
- Joint Centre for Global Change Studies, Tsinghua University, Beijing, 100084, China
| | - Alistair Rogers
- Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Lingli Liu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Jin Wu
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
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20
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Lin M, Simons AL, Harrigan RJ, Curd EE, Schneider FD, Ruiz-Ramos DV, Gold Z, Osborne MG, Shirazi S, Schweizer TM, Moore TN, Fox EA, Turba R, Garcia-Vedrenne AE, Helman SK, Rutledge K, Mejia MP, Marwayana O, Munguia Ramos MN, Wetzer R, Pentcheff ND, McTavish EJ, Dawson MN, Shapiro B, Wayne RK, Meyer RS. Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02379. [PMID: 34013632 PMCID: PMC9297316 DOI: 10.1002/eap.2379] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/23/2020] [Accepted: 02/04/2021] [Indexed: 05/15/2023]
Abstract
Ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system-level patterns and that can be applied rapidly across space and time. Using modern ecological models and community science, we integrate environmental DNA and Earth observations to produce a time snapshot of regional biodiversity patterns and provide multi-scalar community-level characterization. We collected 278 samples in spring 2017 from coastal, shrub, and lowland forest sites in California, a complex ecosystem and biodiversity hotspot. We recovered 16,118 taxonomic entries from eDNA analyses and compiled associated traditional observations and environmental data to assess how well they predicted alpha, beta, and zeta diversity. We found that local habitat classification was diagnostic of community composition and distinct communities and organisms in different kingdoms are predicted by different environmental variables. Nonetheless, gradient forest models of 915 families recovered by eDNA analysis and using BIOCLIM variables, Sentinel-2 satellite data, human impact, and topographical features as predictors, explained 35% of the variance in community turnover. Elevation, sand percentage, and photosynthetic activities (NDVI32) were the top predictors. In addition to this signal of environmental filtering, we found a positive relationship between environmentally predicted families and their numbers of biotic interactions, suggesting environmental change could have a disproportionate effect on community networks. Together, these analyses show that coupling eDNA with environmental predictors including remote sensing data has capacity to test proposed Essential Biodiversity Variables and create new landscape biodiversity baselines that span the tree of life.
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Affiliation(s)
- Meixi Lin
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Ariel Levi Simons
- Department of Marine and Environmental Biology, University of Southern California, Los Angeles, California 90089 USA
- Institute of the Environment and Sustainability, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Ryan J. Harrigan
- Center for Tropical Research, Institute of the Environment and Sustainability, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Emily E. Curd
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Fabian D. Schneider
- Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, California 91009 USA
| | - Dannise V. Ruiz-Ramos
- Columbia Environmental Research Center, U.S. Geological Survey, Columbia, Missouri 65201 USA
- Department of Life & Environmental Sciences, University of California-Merced, Merced, California 95343 USA
| | - Zack Gold
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Melisa G. Osborne
- Department of Molecular and Computational Biology, University of Southern California, Los Angeles, California 90089 USA
| | - Sabrina Shirazi
- Department of Ecology and Evolutionary Biology, University of California-Santa Cruz, Santa Cruz, California 95064 USA
| | - Teia M. Schweizer
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
- Department of Biology, Colorado State University, Fort Collins, Colorado 80523 USA
| | - Tiara N. Moore
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
- School of Environmental and Forestry Sciences, University of Washington, Seattle, Washington 98195 USA
| | - Emma A. Fox
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Rachel Turba
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Ana E. Garcia-Vedrenne
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Sarah K. Helman
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Kelsi Rutledge
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Maura Palacios Mejia
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Onny Marwayana
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Sciences (LIPI), Cibinong, Bogor 16911 Indonesia
| | - Miroslava N. Munguia Ramos
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Regina Wetzer
- Research and Collections, Natural History Museum of Los Angeles County, Los Angeles, California 90007 USA
- Biological Sciences, University of Southern California, Los Angeles, California 90089 USA
| | - N. Dean Pentcheff
- Research and Collections, Natural History Museum of Los Angeles County, Los Angeles, California 90007 USA
| | - Emily Jane McTavish
- Department of Life & Environmental Sciences, University of California-Merced, Merced, California 95343 USA
| | - Michael N. Dawson
- Department of Life & Environmental Sciences, University of California-Merced, Merced, California 95343 USA
| | - Beth Shapiro
- Department of Ecology and Evolutionary Biology, University of California-Santa Cruz, Santa Cruz, California 95064 USA
- Howard Hughes Medical Institute, University of California-Santa Cruz, Santa Cruz, California 95064 USA
| | - Robert K. Wayne
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
| | - Rachel S. Meyer
- Department of Ecology and Evolutionary Biology, University of California-Los Angeles, Los Angeles, California 90095 USA
- Department of Ecology and Evolutionary Biology, University of California-Santa Cruz, Santa Cruz, California 95064 USA
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21
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Xu X, Konings AG, Longo M, Feldman A, Xu L, Saatchi S, Wu D, Wu J, Moorcroft P. Leaf surface water, not plant water stress, drives diurnal variation in tropical forest canopy water content. THE NEW PHYTOLOGIST 2021; 231:122-136. [PMID: 33539544 DOI: 10.1111/nph.17254] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 01/27/2021] [Indexed: 05/25/2023]
Abstract
Variation in canopy water content (CWC) that can be detected from microwave remote sensing of vegetation optical depth (VOD) has been proposed as an important measure of vegetation water stress. However, the contribution of leaf surface water (LWs ), arising from dew formation and rainfall interception, to CWC is largely unknown, particularly in tropical forests and other high-humidity ecosystems. We compared VOD data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and CWC predicted by a plant hydrodynamics model at four tropical sites in Brazil spanning a rainfall gradient. We assessed how LWs influenced the relationship between VOD and CWC. The analysis indicates that while CWC is strongly correlated with VOD (R2 = 0.62 across all sites), LWs accounts for 61-76% of the diurnal variation in CWC despite being < 10% of CWC. Ignoring LWs weakens the near-linear relationship between CWC and VOD and reduces the consistency in diurnal variation. The contribution of LWs to CWC variation, however, decreases at longer, seasonal to inter-annual, time scales. Our results demonstrate that diurnal patterns of dew formation and rainfall interception can be an important driver of diurnal variation in CWC and VOD over tropical ecosystems and therefore should be accounted for when inferring plant diurnal water stress from VOD measurements.
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Affiliation(s)
- Xiangtao Xu
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, 14850, USA
| | - Alexandra G Konings
- Department of Earth System Science, Stanford University, Stanford, CA, 94305, USA
| | - Marcos Longo
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
| | - Andrew Feldman
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Liang Xu
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
| | - Sassan Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
- Institute of Environment and Sustainability, University of California, Los Angeles, CA, 90024, USA
| | - Donghai Wu
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, 14850, USA
| | - Jin Wu
- School of Biological Sciences, University of Hong Kong, Hong Kong, China
| | - Paul Moorcroft
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
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22
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Raczka B, Hoar TJ, Duarte HF, Fox AM, Anderson JL, Bowling DR, Lin JC. Improving CLM5.0 Biomass and Carbon Exchange Across the Western United States Using a Data Assimilation System. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2021; 13:e2020MS002421. [PMID: 34434490 PMCID: PMC8365651 DOI: 10.1029/2020ms002421] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 05/25/2021] [Accepted: 06/11/2021] [Indexed: 06/13/2023]
Abstract
The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite-derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above-ground biomass. The adjusted simulation significantly reduced the above-ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation-constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.
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Affiliation(s)
- Brett Raczka
- School of Biological SciencesUniversity of UtahSalt Lake CityUTUSA
- Now at National Center for Atmospheric ResearchBoulderCOUSA
| | | | - Henrique F. Duarte
- Department of Atmospheric SciencesUniversity of UtahSalt Lake CityUTUSA
- Now at Earth System Science CenterNational Institute for Space ResearchSão José dos CamposBrazil
| | - Andrew M. Fox
- Joint Center for Satellite Data AssimilationBoulderCOUSA
| | | | - David R. Bowling
- School of Biological SciencesUniversity of UtahSalt Lake CityUTUSA
- Department of Atmospheric SciencesUniversity of UtahSalt Lake CityUTUSA
| | - John C. Lin
- Department of Atmospheric SciencesUniversity of UtahSalt Lake CityUTUSA
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23
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Rocchini D, Thouverai E, Marcantonio M, Iannacito M, Da Re D, Torresani M, Bacaro G, Bazzichetto M, Bernardi A, Foody GM, Furrer R, Kleijn D, Larsen S, Lenoir J, Malavasi M, Marchetto E, Messori F, Montaghi A, Moudrý V, Naimi B, Ricotta C, Rossini M, Santi F, Santos MJ, Schaepman ME, Schneider FD, Schuh L, Silvestri S, Ŝímová P, Skidmore AK, Tattoni C, Tordoni E, Vicario S, Zannini P, Wegmann M. rasterdiv-An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back. Methods Ecol Evol 2021; 12:1093-1102. [PMID: 34262682 PMCID: PMC8252722 DOI: 10.1111/2041-210x.13583] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 02/08/2021] [Indexed: 11/28/2022]
Abstract
Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow.In this paper, we present a new R package-rasterdiv-to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms.
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Affiliation(s)
- Duccio Rocchini
- BIOME Lab, Department of Biological, Geological and Environmental SciencesAlma Mater Studiorum University of BolognaBolognaItaly
- Department of Spatial Sciences, Faculty of Environmental SciencesCzech University of Life Sciences PraguePraha ‐ SuchdolCzech Republic
| | - Elisa Thouverai
- BIOME Lab, Department of Biological, Geological and Environmental SciencesAlma Mater Studiorum University of BolognaBolognaItaly
| | - Matteo Marcantonio
- Department of Pathology, Microbiology, and ImmunologySchool of Veterinary MedicineUniversity of CaliforniaDavisCAUSA
| | | | - Daniele Da Re
- Georges Lemaître Center for Earth and Climate ResearchEarth and Life InstituteUCLouvainLouvain‐la‐NeuveBelgium
| | - Michele Torresani
- Faculty of Science and TechnologyFree University of Bolzano/BozenPiazza Universitá/Universitätsplatz 1BolzanoItaly
| | - Giovanni Bacaro
- Department of Life SciencesUniversity of TriesteTriesteItaly
| | - Manuele Bazzichetto
- EcoBio (Ecosystèmes, Biodiversité, Évolution) ‐ UMR 6553Université de RennesCNRSRennesFrance
| | | | | | - Reinhard Furrer
- Department of MathematicsUniversity of ZurichZurichSwitzerland
- Department of Computational ScienceUniversity of ZurichZurichSwitzerland
| | - David Kleijn
- Plant Ecology and Nature Conservation GroupWageningen UniversityWageningenThe Netherlands
| | - Stefano Larsen
- Unit of Computational BiologyResearch and Innovation CenterFondazione Edmund MachSan Michele all'AdigeItaly
- Department of CivilEnvironmental and Mechanical EngineeringUniversity of TrentoTrentoItaly
| | - Jonathan Lenoir
- UR “Ecologie et Dynamique des Systèmes Anthropisés” (EDYSAN, UMR 7058 CNRS‐UPJV)Université de Picardie Jules VerneAmiensFrance
| | - Marco Malavasi
- Department of Spatial Sciences, Faculty of Environmental SciencesCzech University of Life Sciences PraguePraha ‐ SuchdolCzech Republic
| | - Elisa Marchetto
- BIOME Lab, Department of Biological, Geological and Environmental SciencesAlma Mater Studiorum University of BolognaBolognaItaly
| | - Filippo Messori
- BIOME Lab, Department of Biological, Geological and Environmental SciencesAlma Mater Studiorum University of BolognaBolognaItaly
| | - Alessandro Montaghi
- DAGRI Department of Agriculture, Food, Environment and ForestryUniversity of FlorenceFirenzeItaly
| | - Vítězslav Moudrý
- Department of Spatial Sciences, Faculty of Environmental SciencesCzech University of Life Sciences PraguePraha ‐ SuchdolCzech Republic
| | - Babak Naimi
- Department of Geosciences and GeographyUniversity of HelsinkiHelsinkiFinland
| | - Carlo Ricotta
- Department of Environmental BiologyUniversity of Rome “La Sapienza'”RomeItaly
| | - Micol Rossini
- Remote Sensing of Environmental Dynamics LaboratoryDISATUniversitá degli Studi Milano‐BicoccaMilanoItaly
| | - Francesco Santi
- BIOME Lab, Department of Biological, Geological and Environmental SciencesAlma Mater Studiorum University of BolognaBolognaItaly
| | - Maria J. Santos
- Department of Geography, Earth System ScienceUniversity of ZurichZurichSwitzerland
| | - Michael E. Schaepman
- Department of GeographyRemote Sensing LaboratoriesUniversity of ZurichZurichSwitzerland
| | | | - Leila Schuh
- Department of MathematicsUniversity of ZurichZurichSwitzerland
| | - Sonia Silvestri
- BIOME Lab, Department of Biological, Geological and Environmental SciencesAlma Mater Studiorum University of BolognaBolognaItaly
| | - Petra Ŝímová
- Department of Spatial Sciences, Faculty of Environmental SciencesCzech University of Life Sciences PraguePraha ‐ SuchdolCzech Republic
| | - Andrew K. Skidmore
- Faculty of Geo‐Information Science and Earth Observation (ITC)University of TwenteEnschedeThe Netherlands
- Department of Environmental ScienceMacquarie UniversitySydneyNSWAustralia
| | - Clara Tattoni
- Department of Agriculture, Food, Environment and Forestry (DAGRI)University of FlorenceFirenzeItaly
| | - Enrico Tordoni
- Department of Life SciencesUniversity of TriesteTriesteItaly
| | - Saverio Vicario
- CNR‐IIA C/O Physics Department “M. Merlin” University of BariBariItaly
| | - Piero Zannini
- BIOME Lab, Department of Biological, Geological and Environmental SciencesAlma Mater Studiorum University of BolognaBolognaItaly
| | - Martin Wegmann
- Department of Remote SensingUniversity of WuerzburgWürzburgGermany
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24
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Thouverai E, Marcantonio M, Bacaro G, Re DD, Iannacito M, Marchetto E, Ricotta C, Tattoni C, Vicario S, Rocchini D. Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective. COMMUNITY ECOL 2021. [DOI: 10.1007/s42974-021-00042-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractThe variation of species diversity over space and time has been widely recognised as a key challenge in ecology. However, measuring species diversity over large areas might be difficult for logistic reasons related to both time and cost savings for sampling, as well as accessibility of remote ecosystems. In this paper, we present a new package - - to calculate diversity indices based on remotely sensed data, by discussing the theory behind the developed algorithms. Obviously, measures of diversity from space should not be viewed as a replacement of in situ data on biological diversity, but they are rather complementary to existing data and approaches. In practice, they integrate available information of Earth surface properties, including aspects of functional (structural, biophysical and biochemical), taxonomic, phylogenetic and genetic diversity. Making use of the package can result useful in making multiple calculations based on reproducible open source algorithms, robustly rooted in Information Theory.
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25
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Hemes KS, Runkle BRK, Novick KA, Baldocchi DD, Field CB. An Ecosystem-Scale Flux Measurement Strategy to Assess Natural Climate Solutions. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:3494-3504. [PMID: 33660506 DOI: 10.1021/acs.est.0c06421] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Eddy covariance measurement systems provide direct observation of the exchange of greenhouse gases between ecosystems and the atmosphere, but have only occasionally been intentionally applied to quantify the carbon dynamics associated with specific climate mitigation strategies. Natural climate solutions (NCS) harness the photosynthetic power of ecosystems to avoid emissions and remove atmospheric carbon dioxide (CO2), sequestering it in biological carbon pools. In this perspective, we aim to determine which kinds of NCS strategies are most suitable for ecosystem-scale flux measurements and how these measurements should be deployed for diverse NCS scales and goals. We find that ecosystem-scale flux measurements bring unique value when assessing NCS strategies characterized by inaccessible and hard-to-observe carbon pool changes, important non-CO2 greenhouse gas fluxes, the potential for biophysical impacts, or dynamic successional changes. We propose three deployment types for ecosystem-scale flux measurements at various NCS scales to constrain wide uncertainties and chart a workable path forward: "pilot", "upscale", and "monitor". Together, the integration of ecosystem-scale flux measurements by the NCS community and the prioritization of NCS measurements by the flux community, have the potential to improve accounting in ways that capture the net impacts, unintended feedbacks, and on-the-ground specifics of a wide range of emerging NCS strategies.
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Affiliation(s)
- Kyle S Hemes
- Stanford Woods Institute for the Environment, Stanford University, Stanford, California 94305, United States
| | - Benjamin R K Runkle
- Department of Biological and Agricultural Engineering, University of Arkansas, Fayetteville, Arkansas 72701, United States
| | - Kimberly A Novick
- O'Neill School of Public and Environmental Affairs, Indiana University - Bloomington, Bloomington, Indiana 47405-7000, United States
| | - Dennis D Baldocchi
- Environmental Science, Policy & Management Department, University of California, Berkeley, California 94720, United States
| | - Christopher B Field
- Stanford Woods Institute for the Environment, Stanford University, Stanford, California 94305, United States
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26
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Muller-Landau HC, Cushman KC, Arroyo EE, Martinez Cano I, Anderson-Teixeira KJ, Backiel B. Patterns and mechanisms of spatial variation in tropical forest productivity, woody residence time, and biomass. THE NEW PHYTOLOGIST 2021; 229:3065-3087. [PMID: 33207007 DOI: 10.1111/nph.17084] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 10/12/2020] [Indexed: 05/25/2023]
Abstract
Tropical forests vary widely in biomass carbon (C) stocks and fluxes even after controlling for forest age. A mechanistic understanding of this variation is critical to accurately predicting responses to global change. We review empirical studies of spatial variation in tropical forest biomass, productivity and woody residence time, focusing on mature forests. Woody productivity and biomass decrease from wet to dry forests and with elevation. Within lowland forests, productivity and biomass increase with temperature in wet forests, but decrease with temperature where water becomes limiting. Woody productivity increases with soil fertility, whereas residence time decreases, and biomass responses are variable, consistent with an overall unimodal relationship. Areas with higher disturbance rates and intensities have lower woody residence time and biomass. These environmental gradients all involve both direct effects of changing environments on forest C fluxes and shifts in functional composition - including changing abundances of lianas - that substantially mitigate or exacerbate direct effects. Biogeographic realms differ significantly and importantly in productivity and biomass, even after controlling for climate and biogeochemistry, further demonstrating the importance of plant species composition. Capturing these patterns in global vegetation models requires better mechanistic representation of water and nutrient limitation, plant compositional shifts and tree mortality.
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Affiliation(s)
- Helene C Muller-Landau
- Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, PO Box 0843-03092, Balboa, Ancón, Panama
| | - K C Cushman
- Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, PO Box 0843-03092, Balboa, Ancón, Panama
| | - Eva E Arroyo
- Department of Ecology, Evolution and Environmental Biology, Columbia University, 1200 Amsterdam Avenue, New York, NY, 10027, USA
| | - Isabel Martinez Cano
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Kristina J Anderson-Teixeira
- Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, PO Box 0843-03092, Balboa, Ancón, Panama
- Conservation Ecology Center, Smithsonian Conservation Biology Institute and National Zoological Park, 1500 Remount Rd, Front Royal, VA, 22630, USA
| | - Bogumila Backiel
- Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, PO Box 0843-03092, Balboa, Ancón, Panama
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Kamoske AG, Dahlin KM, Serbin SP, Stark SC. Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02230. [PMID: 33015908 DOI: 10.1002/eap.2230] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/26/2020] [Accepted: 08/16/2020] [Indexed: 06/11/2023]
Abstract
Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top of canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the three -dimensional spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top-of-canopy percent N and total canopy N (i.e., the total amount of N [g/m2 ] within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Our resulting maps suggest that, in contrast to top of canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.
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Affiliation(s)
- Aaron G Kamoske
- Department of Geography, Environment, & Spatial Sciences, Michigan State University, 673 Auditorium Road #116, East Lansing, Michigan, 48824, USA
| | - Kyla M Dahlin
- Department of Geography, Environment, & Spatial Sciences, Michigan State University, 673 Auditorium Road #116, East Lansing, Michigan, 48824, USA
- Program in Ecology, Evolutionary Biology, & Behavior, Michigan State University, 103 Giltner Hall, 293 Farm Lane #103, East Lansing, Michigan, 48824, USA
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, 98 Rochester Street, Upton, New York, 11973, USA
| | - Scott C Stark
- Department of Forestry, Michigan State University, 480 Wilson Road #126, East Lansing, Michigan, 48824, USA
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28
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Walker AP, De Kauwe MG, Bastos A, Belmecheri S, Georgiou K, Keeling RF, McMahon SM, Medlyn BE, Moore DJP, Norby RJ, Zaehle S, Anderson-Teixeira KJ, Battipaglia G, Brienen RJW, Cabugao KG, Cailleret M, Campbell E, Canadell JG, Ciais P, Craig ME, Ellsworth DS, Farquhar GD, Fatichi S, Fisher JB, Frank DC, Graven H, Gu L, Haverd V, Heilman K, Heimann M, Hungate BA, Iversen CM, Joos F, Jiang M, Keenan TF, Knauer J, Körner C, Leshyk VO, Leuzinger S, Liu Y, MacBean N, Malhi Y, McVicar TR, Penuelas J, Pongratz J, Powell AS, Riutta T, Sabot MEB, Schleucher J, Sitch S, Smith WK, Sulman B, Taylor B, Terrer C, Torn MS, Treseder KK, Trugman AT, Trumbore SE, van Mantgem PJ, Voelker SL, Whelan ME, Zuidema PA. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO 2. THE NEW PHYTOLOGIST 2021; 229:2413-2445. [PMID: 32789857 DOI: 10.1111/nph.16866] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 07/06/2020] [Indexed: 05/22/2023]
Abstract
Atmospheric carbon dioxide concentration ([CO2 ]) is increasing, which increases leaf-scale photosynthesis and intrinsic water-use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO2 ] increase and thus climate change. However, ecosystem CO2 responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO2 ]-driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO2 ] (iCO2 ) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre-industrial times. Established theory, supported by experiments, indicates that iCO2 is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO2 responses are high in comparison to experiments and predictions from theory. Plant mortality and soil carbon iCO2 responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO2 , albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.
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Affiliation(s)
- Anthony P Walker
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Martin G De Kauwe
- ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, 2052, Australia
- Climate Change Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia
- Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Ana Bastos
- Ludwig Maximilians University of Munich, Luisenstr. 37, Munich, 80333, Germany
| | - Soumaya Belmecheri
- Laboratory of Tree Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ, 85721, USA
| | - Katerina Georgiou
- Department of Earth System Science, Stanford University, Stanford, CA, 94305, USA
| | - Ralph F Keeling
- Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, 92093, USA
| | - Sean M McMahon
- Smithsonian Environmental Research Center, Edgewater, MD, 21037, USA
| | - Belinda E Medlyn
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - David J P Moore
- School of Natural Resources and the Environment, 1064 East Lowell Street, Tucson, AZ, 85721, USA
| | - Richard J Norby
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Sönke Zaehle
- Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745, Germany
| | - Kristina J Anderson-Teixeira
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, MRC 5535, Front Royal, VA, 22630, USA
- Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Panama City, Panama
| | - Giovanna Battipaglia
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Università della Campania, Caserta, 81100, Italy
| | | | - Kristine G Cabugao
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Maxime Cailleret
- INRAE, UMR RECOVER, Aix-Marseille Université, 3275 route de Cézanne, Aix-en-Provence Cedex 5, 13182, France
- Swiss Federal Institute for Forest Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
| | - Elliott Campbell
- Department of Geography, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Josep G Canadell
- CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, F-91191, France
| | - Matthew E Craig
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - David S Ellsworth
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Graham D Farquhar
- Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT, 2601, Australia
| | - Simone Fatichi
- Department of Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore, 117576, Singapore
- Institute of Environmental Engineering, ETH Zurich, Stefano-Franscini Platz 5, Zurich, 8093, Switzerland
| | - Joshua B Fisher
- Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA, 91109, USA
| | - David C Frank
- Laboratory of Tree Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ, 85721, USA
| | - Heather Graven
- Department of Physics, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Lianhong Gu
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Vanessa Haverd
- CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - Kelly Heilman
- Laboratory of Tree Ring Research, University of Arizona, 1215 E Lowell St, Tucson, AZ, 85721, USA
| | - Martin Heimann
- Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745, Germany
| | - Bruce A Hungate
- Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Colleen M Iversen
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Fortunat Joos
- Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Sidlerstr. 5, Bern, CH-3012, Switzerland
| | - Mingkai Jiang
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Trevor F Keenan
- Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, 94720, USA
- Earth and Environmental Sciences Area, Lawrence Berkeley National Lab., Berkeley, CA, 94720, USA
| | - Jürgen Knauer
- CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT, 2601, Australia
| | - Christian Körner
- Department of Environmental Sciences, Botany, University of Basel, Basel, 4056, Switzerland
| | - Victor O Leshyk
- Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Sebastian Leuzinger
- School of Science, Auckland University of Technology, Auckland, 1142, New Zealand
| | - Yao Liu
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Natasha MacBean
- Department of Geography, Indiana University, Bloomington, IN, 47405, USA
| | - Yadvinder Malhi
- School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, UK
| | - Tim R McVicar
- CSIRO Land and Water, GPO Box 1700, Canberra, ACT, 2601, Australia
- Australian Research Council Centre of Excellence for Climate Extremes, 142 Mills Rd, Australian National University, Canberra, ACT, 2601, Australia
| | - Josep Penuelas
- CSIC, Global Ecology CREAF-CSIC-UAB, Bellaterra, Barcelona, Catalonia, 08193, Spain
- CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, 08193, Spain
| | - Julia Pongratz
- Ludwig Maximilians University of Munich, Luisenstr. 37, Munich, 80333, Germany
- Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
| | - A Shafer Powell
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Terhi Riutta
- School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, UK
| | - Manon E B Sabot
- ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, 2052, Australia
- Climate Change Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia
- Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Juergen Schleucher
- Department of Medical Biochemistry & Biophysics, Umeå University, Umea, 901 87, Sweden
| | - Stephen Sitch
- College of Life and Environmental Sciences, University of Exeter, Exeter, Laver Building, EX4 4QF, UK
| | - William K Smith
- School of Natural Resources and the Environment, 1064 East Lowell Street, Tucson, AZ, 85721, USA
| | - Benjamin Sulman
- Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Benton Taylor
- Smithsonian Environmental Research Center, Edgewater, MD, 21037, USA
| | - César Terrer
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
| | - Margaret S Torn
- Earth and Environmental Sciences Area, Lawrence Berkeley National Lab., Berkeley, CA, 94720, USA
| | - Kathleen K Treseder
- Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA, 92697, USA
| | - Anna T Trugman
- Department of Geography, 1832 Ellison Hall, Santa Barbara, CA, 93016, USA
| | - Susan E Trumbore
- Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, 07745, Germany
| | | | - Steve L Voelker
- Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, NY, 13210, USA
| | - Mary E Whelan
- Department of Environmental Sciences, Rutgers University, 14 College Farm Road, New Brunswick, NJ, 08901, USA
| | - Pieter A Zuidema
- Forest Ecology and Forest Management group, Wageningen University, PO Box 47, Wageningen, 6700 AA, the Netherlands
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29
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Modeling Transpiration with Sun-Induced Chlorophyll Fluorescence Observations via Carbon-Water Coupling Methods. REMOTE SENSING 2021. [DOI: 10.3390/rs13040804] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Successfully applied in the carbon research area, sun-induced chlorophyll fluorescence (SIF) has raised the interest of researchers from the water research domain. However, current works focused on the empirical relationship between SIF and plant transpiration (T), while the mechanistic linkage between them has not been fully explored. Two mechanism methods were developed to estimate T via SIF, namely the water-use efficiency (WUE) method and conductance method based on the carbon–water coupling framework. The T estimated by these two methods was compared with T partitioned from eddy covariance instrument measured evapotranspiration at four different sites. Both methods showed good performance at the hourly (R2 = 0.57 for the WUE method and 0.67 for the conductance method) and daily scales (R2 = 0.67 for the WUE method and 0.78 for the conductance method). The developed mechanism methods provide theoretical support and have a great potential basis for deriving ecosystem T by satellite SIF observations.
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30
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Cavender-Bares J, Reich P, Townsend P, Banerjee A, Butler E, Desai A, Gevens A, Hobbie S, Isbell F, Laliberté E, Meireles JE, Menninger H, Pavlick R, Pinto-Ledezma J, Potter C, Schuman M, Springer N, Stefanski A, Trivedi P, Trowbridge A, Williams L, Willis C, Yang Y. BII-Implementation: The causes and consequences of plant biodiversity across scales in a rapidly changing world. RESEARCH IDEAS AND OUTCOMES 2021. [DOI: 10.3897/rio.7.e63850] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The proposed Biology Integration Institute will bring together two major research institutions in the Upper Midwest—the University of Minnesota (UMN) and University of Wisconsin-Madison (UW)—to investigate the causes and consequences of plant biodiversity across scales in a rapidly changing world—from genes and molecules within cells and tissues to communities, ecosystems, landscapes and the biosphere. The Institute focuses on plant biodiversity, defined broadly to encompass the heterogeneity within life that occurs from the smallest to the largest biological scales. A premise of the Institute is that life is envisioned as occurring at different scales nested within several contrasting conceptions of biological hierarchies, defined by the separate but related fields of physiology, evolutionary biology and ecology. The Institute will emphasize the use of ‘spectral biology’—detection of biological properties based on the interaction of light energy with matter—and process-oriented predictive models to investigate the processes by which biological components at one scale give rise to emergent properties at higher scales. Through an iterative process that harnesses cutting edge technologies to observe a suite of carefully designed empirical systems—including the National Ecological Observatory Network (NEON) and some of the world’s longest running and state-of-the-art global change experiments—the Institute will advance biological understanding and theory of the causes and consequences of changes in biodiversity and at the interface of plant physiology, ecology and evolution.
INTELLECTUAL MERIT
The Institute brings together a diverse, gender-balanced and highly productive team with significant leadership experience that spans biological disciplines and career stages and is poised to integrate biology in new ways. Together, the team will harness the potential of spectral biology, experiments, observations and synthetic modeling in a manner never before possible to transform understanding of how variation within and among biological scales drives plant and ecosystem responses to global change over diurnal, seasonal and millennial time scales. In doing so, it will use and advance state-of-the-art theory. The institute team posits that the designed projects will unearth transformative understanding and biological rules at each of the various scales that will enable an unprecedented capacity to discern the linkages between physiological, ecological and evolutionary processes in relation to the multi-dimensional nature of biodiversity in this time of massive planetary change. A strength of the proposed Institute is that it leverages prior federal investments in research and formalizes partnerships with foreign institutions heavily invested in related biodiversity research. Most of the planned projects leverage existing research initiatives, infrastructure, working groups, experiments, training programs, and public outreach infrastructure, all of which are already highly synergistic and collaborative, and will bring together members of the overall research and training team.
BROADER IMPACTS
A central goal of the proposed Institute is to train the next generation of diverse integrative biologists. Post-doctoral, graduate student and undergraduate trainees, recruited from non-traditional and underrepresented groups, including through formal engagement with Native American communities, will receive a range of mentoring and training opportunities. Annual summer training workshops will be offered at UMN and UW as well as training experiences with the Global Change and Biodiversity Research Priority Program (URPP-GCB) at the University of Zurich (UZH) and through the Canadian Airborne Biodiversity Observatory (CABO). The Institute will engage diverse K-12 audiences, the general public and Native American communities through Market Science modules, Minute Earth videos, a museum exhibit and public engagement and educational activities through the Bell Museum of Natural History, the Cedar Creek Ecosystem Science Reserve (CCESR) and the Wisconsin Tribal Conservation Association.
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31
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Fer I, Gardella AK, Shiklomanov AN, Campbell EE, Cowdery EM, De Kauwe MG, Desai A, Duveneck MJ, Fisher JB, Haynes KD, Hoffman FM, Johnston MR, Kooper R, LeBauer DS, Mantooth J, Parton WJ, Poulter B, Quaife T, Raiho A, Schaefer K, Serbin SP, Simkins J, Wilcox KR, Viskari T, Dietze MC. Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration. GLOBAL CHANGE BIOLOGY 2021; 27:13-26. [PMID: 33075199 PMCID: PMC7756391 DOI: 10.1111/gcb.15409] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 09/16/2020] [Indexed: 05/10/2023]
Abstract
In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is a key to meeting the pressing needs of science and society in the 21st century.
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Affiliation(s)
- Istem Fer
- Finnish Meteorological InstituteHelsinkiFinland
| | - Anthony K. Gardella
- Department of Earth and EnvironmentBoston UniversityBostonMAUSA
- School for Environment and SustainabilityUniversity of MichiganAnn ArborMIUSA
| | | | | | | | - Martin G. De Kauwe
- ARC Centre of Excellence for Climate ExtremesSydneyNSWAustralia
- Climate Change Research CentreUniversity of New South WalesSydneyNSWAustralia
- Evolution & Ecology Research CentreUniversity of New South WalesSydneyNSWAustralia
| | - Ankur Desai
- Department of Atmospheric and Oceanic SciencesUniversity of Wisconsin‐MadisonMadisonWIUSA
| | | | - Joshua B. Fisher
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | - Forrest M. Hoffman
- Computational Earth Sciences Group and Climate Change Science InstituteOak Ridge National LaboratoryOak RidgeTNUSA
- Department of Civil and Environmental EngineeringUniversity of TennesseeKnoxvilleTNUSA
| | - Miriam R. Johnston
- Department of Organismic and Evolutionary BiologyHarvard UniversityCambridgeMAUSA
| | - Rob Kooper
- NCSA (National Center for Supercomputing Applications)University of Illinois at Urbana ChampaignUrbanaILUSA
| | - David S. LeBauer
- College of Agriculture and Life SciencesUniversity of ArizonaTucsonAZUSA
| | | | - William J. Parton
- Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsCOUSA
| | - Benjamin Poulter
- Biospheric Sciences Laboratory (618)NASA Goddard Space Flight CenterGreenbeltMDUSA
| | - Tristan Quaife
- UK National Centre for Earth Observation and Department of MeteorologyUniversity of ReadingReadingUK
| | - Ann Raiho
- Fish, Wildlife, and Conservation Biology DepartmentColorado State UniversityFort CollinsCOUSA
| | - Kevin Schaefer
- National Snow and Ice Data CenterCooperative Institute for Research in Environmental SciencesUniversity of ColoradoBoulderCOUSA
| | - Shawn P. Serbin
- Brookhaven National LaboratoryEnvironmental and Climate Sciences DepartmentUptonNYUSA
| | | | - Kevin R. Wilcox
- Ecosystem Science and ManagementUniversity of WyomingLaramieWYUSA
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32
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Affiliation(s)
- Kai Zhu
- Environmental Studies at the University of California, Santa Cruz, USA
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33
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Williams LJ, Cavender-Bares J, Townsend PA, Couture JJ, Wang Z, Stefanski A, Messier C, Reich PB. Remote spectral detection of biodiversity effects on forest biomass. Nat Ecol Evol 2020; 5:46-54. [PMID: 33139920 DOI: 10.1038/s41559-020-01329-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 09/09/2020] [Indexed: 11/10/2022]
Abstract
Quantifying how biodiversity affects ecosystem functions through time over large spatial extents is needed for meeting global biodiversity goals yet is infeasible with field-based approaches alone. Imaging spectroscopy is a tool with potential to help address this challenge. Here, we demonstrate a spectral approach to assess biodiversity effects in young forests that provides insight into its underlying drivers. Using airborne imaging of a tree-diversity experiment, spectral differences among stands enabled us to quantify net biodiversity effects on stem biomass and canopy nitrogen. By subsequently partitioning these effects, we reveal how distinct processes contribute to diversity-induced differences in stand-level spectra, chemistry and biomass. Across stands, biomass overyielding was best explained by species with greater leaf nitrogen dominating upper canopies in mixtures, rather than intraspecific shifts in canopy structure or chemistry. Remote imaging spectroscopy may help to detect the form and drivers of biodiversity-ecosystem function relationships across space and time, advancing the capacity to monitor and manage Earth's ecosystems.
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Affiliation(s)
- Laura J Williams
- Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, MN, USA. .,Department of Forest Resources, University of Minnesota, St Paul, MN, USA.
| | | | - Philip A Townsend
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA
| | - John J Couture
- Department of Entomology, Purdue University, West Lafayette, IN, USA.,Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA
| | - Zhihui Wang
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA
| | - Artur Stefanski
- Department of Forest Resources, University of Minnesota, St Paul, MN, USA
| | - Christian Messier
- Centre for Forest Research, Université du Québec à Montréal, Montréal, Quebec, Canada.,Institut des sciences de la forêt tempérée, Université du Québec en Outaouais, Ripon, Quebec, Canada
| | - Peter B Reich
- Department of Forest Resources, University of Minnesota, St Paul, MN, USA.,Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
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34
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Xia J, Wang J, Niu S. Research challenges and opportunities for using big data in global change biology. GLOBAL CHANGE BIOLOGY 2020; 26:6040-6061. [PMID: 32799353 DOI: 10.1111/gcb.15317] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 07/13/2020] [Indexed: 06/11/2023]
Abstract
Global change biology has been entering a big data era due to the vast increase in availability of both environmental and biological data. Big data refers to large data volume, complex data sets, and multiple data sources. The recent use of such big data is improving our understanding of interactions between biological systems and global environmental changes. In this review, we first explore how big data has been analyzed to identify the general patterns of biological responses to global changes at scales from gene to ecosystem. After that, we investigate how observational networks and space-based big data have facilitated the discovery of emergent mechanisms and phenomena on the regional and global scales. Then, we evaluate the predictions of terrestrial biosphere under global changes by big modeling data. Finally, we introduce some methods to extract knowledge from big data, such as meta-analysis, machine learning, traceability analysis, and data assimilation. The big data has opened new research opportunities, especially for developing new data-driven theories for improving biological predictions in Earth system models, tracing global change impacts across different organismic levels, and constructing cyberinfrastructure tools to accelerate the pace of model-data integrations. These efforts will uncork the bottleneck of using big data to understand biological responses and adaptations to future global changes.
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Affiliation(s)
- Jianyang Xia
- Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, Research Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China
| | - Jing Wang
- Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, Research Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai, China
| | - Shuli Niu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
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35
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Gholizadeh H, Gamon JA, Helzer CJ, Cavender-Bares J. Multi-temporal assessment of grassland α- and β-diversity using hyperspectral imaging. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2020; 30:e02145. [PMID: 32338798 DOI: 10.1002/eap.2145] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 01/27/2020] [Accepted: 02/24/2020] [Indexed: 06/11/2023]
Abstract
While more and more studies are exploring the application of remote sensing in assessing biodiversity for different ecosystems, most consider biodiversity at one point in time. Using several remote-sensing-based metrics, we asked how well remote sensing can detect biodiversity (both α- and β-diversity) in a prairie grassland across time using airborne hyperspectral data collected in two successive years (2017 and 2018) and at different periods in the growing season (2018). The ability to detect biodiversity using "spectral diversity" and "spectral species" types indeed varied significantly over a 2-yr timespan. Toward the end of the growing season in 2018, the relationship between field- and remote-sensing-based α- and β-diversity weakened compared to data collected from the same season in the previous year. This contrasting pattern between the two years was likely influenced by prescribed fire, altered weather, and the resulting shifting species composition and phenology. These findings indicate that direct detection of α- and β-diversity in grasslands should be multi-temporal when possible and should consider the effect of disturbances, climate variables, and phenology. We demonstrate an essential role for airborne platforms in developing a global biodiversity monitoring system involving forthcoming space-borne hyperspectral sensors.
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Affiliation(s)
- Hamed Gholizadeh
- Center for Applications of Remote Sensing, Department of Geography, Oklahoma State University, Stillwater, Oklahoma, 74078, USA
- Center for Advanced Land Management Information Technologies, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska, 68583, USA
| | - John A Gamon
- Center for Advanced Land Management Information Technologies, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska, 68583, USA
- Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, T6G 2E3, Canada
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, T6G 2R3, Canada
| | - Christopher J Helzer
- Nebraska Director of Science, The Nature Conservancy, Aurora, Nebraska, 68818, USA
| | - Jeannine Cavender-Bares
- Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, Minnesota, 55108, USA
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Wang Z, Chlus A, Geygan R, Ye Z, Zheng T, Singh A, Couture JJ, Cavender-Bares J, Kruger EL, Townsend PA. Foliar functional traits from imaging spectroscopy across biomes in eastern North America. THE NEW PHYTOLOGIST 2020; 228:494-511. [PMID: 32463927 DOI: 10.1111/nph.16711] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 05/15/2020] [Indexed: 05/28/2023]
Abstract
Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes. With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America. Model validation accuracy varied among traits (normalized root mean squared error, 9.1-19.4%; coefficient of determination, 0.28-0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28-81% provided high confidence for multiple traits concurrently. Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental-scale functional diversity.
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Affiliation(s)
- Zhihui Wang
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, USA
| | - Adam Chlus
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, USA
| | - Ryan Geygan
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, USA
| | - Zhiwei Ye
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, USA
| | - Ting Zheng
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, USA
| | - Aditya Singh
- Department of Agricultural and Biological Engineering, University of Florida, 1741 Museum Rd, Gainesville, FL, 32611, USA
| | - John J Couture
- Departments of Entomology and Forestry and Natural Resources and Center for Plant Biology, Purdue University, 901 W. State St, West Lafayette, IN, 47907, USA
| | - Jeannine Cavender-Bares
- Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Eric L Kruger
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, USA
| | - Philip A Townsend
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, USA
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Ferraz A, Saatchi SS, Longo M, Clark DB. Tropical tree size-frequency distributions from airborne lidar. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2020; 30:e02154. [PMID: 32347996 DOI: 10.1002/eap.2154] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 03/26/2020] [Accepted: 03/30/2020] [Indexed: 06/11/2023]
Abstract
In tropical rainforests, tree size and number density are influenced by disturbance history, soil, topography, climate, and biological factors that are difficult to predict without detailed and widespread forest inventory data. Here, we quantify tree size-frequency distributions over an old-growth wet tropical forest at the La Selva Biological Station in Costa Rica by using an individual tree crown (ITC) algorithm on airborne lidar measurements. The ITC provided tree height, crown area, the number of trees >10 m height and, predicted tree diameter, and aboveground biomass from field allometry. The number density showed strong agreement with field observations at the plot- (97.4%; 3% bias) and tree-height-classes level (97.4%; 3% bias). The lidar trees size spectra of tree diameter and height closely follow the distributions measured on the ground but showed less agreement with crown area observations. The model to convert lidar-derived tree height and crown area to tree diameter produced unbiased (0.8%) estimates of plot-level basal area and with low uncertainty (6%). Predictions on basal area for tree height classes were also unbiased (1.3%) but with larger uncertainties (22%). The biomass estimates had no significant bias at the plot- and tree-height-classes level (-5.2% and 2.1%). Our ITC method provides a powerful tool for tree- to landscape-level tropical forest inventory and biomass estimation by overcoming the limitations of lidar area-based approaches that require local calibration using a large number of inventory plots.
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Affiliation(s)
- António Ferraz
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, 91109, USA
- Institute of Environment and Sustainability, University of California, Los Angeles, California, 90024, USA
| | - Sassan S Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, 91109, USA
- Institute of Environment and Sustainability, University of California, Los Angeles, California, 90024, USA
| | - Marcos Longo
- NASA Postdoctoral fellow, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, 91109, USA
| | - David B Clark
- Department of Biology, University of Missouri-St. Louis, St. Louis, Missouri, 63121, USA
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Longo M, Saatchi S, Keller M, Bowman K, Ferraz A, Moorcroft PR, Morton DC, Bonal D, Brando P, Burban B, Derroire G, dos‐Santos MN, Meyer V, Saleska S, Trumbore S, Vincent G. Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests. JOURNAL OF GEOPHYSICAL RESEARCH. BIOGEOSCIENCES 2020; 125:e2020JG005677. [PMID: 32999796 PMCID: PMC7507752 DOI: 10.1029/2020jg005677] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 05/28/2020] [Accepted: 06/02/2020] [Indexed: 05/31/2023]
Abstract
Selective logging, fragmentation, and understory fires directly degrade forest structure and composition. However, studies addressing the effects of forest degradation on carbon, water, and energy cycles are scarce. Here, we integrate field observations and high-resolution remote sensing from airborne lidar to provide realistic initial conditions to the Ecosystem Demography Model (ED-2.2) and investigate how disturbances from forest degradation affect gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H). We used forest structural information retrieved from airborne lidar samples (13,500 ha) and calibrated with 817 inventory plots (0.25 ha) across precipitation and degradation gradients in the eastern Amazon as initial conditions to ED-2.2 model. Our results show that the magnitude and seasonality of fluxes were modulated by changes in forest structure caused by degradation. During the dry season and under typical conditions, severely degraded forests (biomass loss ≥66%) experienced water stress with declines in ET (up to 34%) and GPP (up to 35%) and increases of H (up to 43%) and daily mean ground temperatures (up to 6.5°C) relative to intact forests. In contrast, the relative impact of forest degradation on energy, water, and carbon cycles markedly diminishes under extreme, multiyear droughts, as a consequence of severe stress experienced by intact forests. Our results highlight that the water and energy cycles in the Amazon are driven by not only climate and deforestation but also the past disturbance and changes of forest structure from degradation, suggesting a much broader influence of human land use activities on the tropical ecosystems.
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Affiliation(s)
- Marcos Longo
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Sassan Saatchi
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
- Institute of Environment and SustainabilityUniversity of CaliforniaLos AngelesCAUSA
| | - Michael Keller
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
- International Institute of Tropical ForestryUSDA Forest ServiceRio PiedrasPuerto Rico
- Embrapa Informática AgropecuáriaCampinasBrazil
| | - Kevin Bowman
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - António Ferraz
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
- Institute of Environment and SustainabilityUniversity of CaliforniaLos AngelesCAUSA
| | - Paul R. Moorcroft
- Department of Organismic and Evolutionary BiologyHarvard UniversityCambridgeMAUSA
| | | | - Damien Bonal
- Université de Lorraine, INRAE, AgroParisTech, UMR SilvaNancyFrance
| | - Paulo Brando
- Department of Earth System ScienceUniversity of CaliforniaIrvineCAUSA
- Woods Hole Research CenterWoods HoleMAUSA
- Instituto de Pesquisa Ambiental da AmazôniaBrasíliaBrazil
| | - Benoît Burban
- Institut National de Recherche en Agriculture, Alimentation et Environnement (INRAE), UMR 0745 EcoFoG, Campus AgronomiqueKourouFrance
| | - Géraldine Derroire
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR EcoFoG (Agroparistech, CNRS, INRAE, Université des Antilles, Université de Guyane), Campus AgronomiqueKourouFrance
| | | | - Victoria Meyer
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Scott Saleska
- Ecology and Evolutionary BiologyUniversity of ArizonaTucsonAZUSA
| | | | - Grégoire Vincent
- AMAP, Univ Montpellier, IRD, CIRAD, CNRS, INRAEMontpellierFrance
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Anderegg WRL, Trugman AT, Badgley G, Anderson CM, Bartuska A, Ciais P, Cullenward D, Field CB, Freeman J, Goetz SJ, Hicke JA, Huntzinger D, Jackson RB, Nickerson J, Pacala S, Randerson JT. Climate-driven risks to the climate mitigation potential of forests. Science 2020; 368:368/6497/eaaz7005. [DOI: 10.1126/science.aaz7005] [Citation(s) in RCA: 175] [Impact Index Per Article: 43.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
| | - Anna T. Trugman
- Department of Geography, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Grayson Badgley
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84113, USA
| | | | - Ann Bartuska
- Resources for the Future, Washington, DC 20036, USA
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre Simon Laplace CNRS CEA UVSQ Gif sur Yvette, 91191, France
| | | | - Christopher B. Field
- Woods Institute for the Environment, Stanford University, Stanford, CA 94305, USA
| | | | - Scott J. Goetz
- School of Informatics and Computing, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Jeffrey A. Hicke
- Department of Geography, University of Idaho, Moscow, ID 83844, USA
| | - Deborah Huntzinger
- School of Earth and Sustainability, Northern Arizona University, Flagstaff, AZ 86011, USA
| | - Robert B. Jackson
- Woods Institute for the Environment, Stanford University, Stanford, CA 94305, USA
- Department of Earth System Science and Precourt Institute, Stanford University, Stanford, CA 94305, USA
| | | | - Stephen Pacala
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA
| | - James T. Randerson
- Department of Earth System Science, University of California Irvine, Irvine, CA 92697, USA
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40
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Vaglio Laurin G, Vittucci C, Tramontana G, Ferrazzoli P, Guerriero L, Papale D. Monitoring tropical forests under a functional perspective with satellite-based vegetation optical depth. GLOBAL CHANGE BIOLOGY 2020; 26:3402-3416. [PMID: 32150768 DOI: 10.1111/gcb.15072] [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: 05/07/2019] [Revised: 02/06/2020] [Accepted: 02/21/2020] [Indexed: 06/10/2023]
Abstract
Monitoring ecosystem functions in forests is a priority in a climate change scenario, as climate-induced events may initially alter the functions more than slow-changing attributes, such as biomass. The ecosystem functional properties (EFPs) are quantities that characterize key ecosystem processes. They can be derived by point observations of gas and energy exchanges between the ecosystems and the atmosphere that are collected globally at FLUXNET flux tower sites and upscaled at ecosystem level. The properties here considered describe the ability of ecosystems to optimize the use of resources for carbon uptake. They represent functional forest information, are dependent on environmental drivers, linked to leaf traits and forest structure, and influenced by climate change effects. The ability of vegetation optical depth (VOD) to provide forest functional information is investigated using 2011-2014 satellite data collected by the Soil Moisture and Ocean Salinity mission and using the EFPs as reference dataset. Tropical forests in Africa and South America were analyzed, also according to ecological homogeneous units. VOD jointly with water deficit information explained 93% and 87% of the yearly variability in both flux upscaled maximum gross primary productivity and light use efficiency functional properties, in Africa and South America forests respectively. Maps of the retrieved properties evidenced changes in forest functional responses linked to anomalous climate-induced events during the study period. The findings indicate that VOD can support the flux upscaling process in the tropical range, affected by high uncertainty, and the detection of forest anomalous functional responses. Preliminary temporal analysis of VOD and EFP signals showed fine-grained variability in periodicity, in signal dephasing, and in the strength of the relationships. In selected drier forest types, these satellite data could also support the monitoring of functional dynamics.
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Affiliation(s)
| | | | - Gianluca Tramontana
- DIBAF, Tuscia University, Viterbo, Italy
- Image Processing Laboratory (ERI-IPL), Universitat De Valencia, Valencia, Spain
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41
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Houghton RA. Terrestrial fluxes of carbon in GCP carbon budgets. GLOBAL CHANGE BIOLOGY 2020; 26:3006-3014. [PMID: 32100912 DOI: 10.1111/gcb.15050] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 02/12/2020] [Indexed: 06/10/2023]
Abstract
The Global Carbon Project (GCP) has published global carbon budgets annually since 2007 (Canadell et al. [2007], Proc Natl Acad Sci USA, 104, 18866-18870; Raupach et al. [2007], Proc Natl Acad Sci USA, 104, 10288-10293). There are many scientists involved, but the terrestrial fluxes that appear in the budgets are not well understood by ecologists and biogeochemists outside of that community. The purpose of this paper is to make the terrestrial fluxes of carbon in those budgets more accessible to a broader community. The GCP budget is composed of annual perturbations from pre-industrial conditions, driven by addition of carbon to the system from combustion of fossil fuels and by transfers of carbon from land to the atmosphere as a result of land use. The budget includes a term for each of the major fluxes of carbon (fossil fuels, oceans, land) as well as the rate of carbon accumulation in the atmosphere. Land is represented by two terms: one resulting from direct anthropogenic effects (Land Use, Land-Use Change, and Forestry or land management) and one resulting from indirect anthropogenic (e.g., CO2 , climate change) and natural effects. Each of these two net terrestrial fluxes of carbon, in turn, is composed of opposing gross emissions and removals (e.g., deforestation and forest regrowth). Although the GCP budgets have focused on the two net terrestrial fluxes, they have paid little attention to the gross components, which are important for a number of reasons, including understanding the potential for land management to remove CO2 from the atmosphere and understanding the processes responsible for the sink for carbon on land. In contrast to the net fluxes of carbon, which are constrained by the global carbon budget, the gross fluxes are largely unconstrained, suggesting that there is more uncertainty than commonly believed about how terrestrial carbon emissions will respond to future fossil fuel emissions and a changing climate.
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42
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O'Connor B, Bojinski S, Röösli C, Schaepman ME. Monitoring global changes in biodiversity and climate essential as ecological crisis intensifies. ECOL INFORM 2020. [DOI: 10.1016/j.ecoinf.2019.101033] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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43
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Smith WK, Fox AM, MacBean N, Moore DJP, Parazoo NC. Constraining estimates of terrestrial carbon uptake: new opportunities using long-term satellite observations and data assimilation. THE NEW PHYTOLOGIST 2020; 225:105-112. [PMID: 31299099 DOI: 10.1111/nph.16055] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 06/25/2019] [Indexed: 06/10/2023]
Abstract
The response of terrestrial carbon uptake to increasing atmospheric [CO2 ], that is the CO2 fertilization effect (CFE), remains a key area of uncertainty in carbon cycle science. Here we provide a perspective on how satellite observations could be better used to understand and constrain CFE. We then highlight data assimilation (DA) as an effective way to reconcile different satellite datasets and systematically constrain carbon uptake trends in Earth System Models. As a proof-of-concept, we show that joint DA of multiple independent satellite datasets reduced model ensemble error by better constraining unobservable processes and variables, including those directly impacted by CFE. DA of multiple satellite datasets offers a powerful technique that could improve understanding of CFE and enable more accurate forecasts of terrestrial carbon uptake.
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Affiliation(s)
- William K Smith
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85719, USA
| | - Andrew M Fox
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85719, USA
| | - Natasha MacBean
- Department of Geography, Indiana University, Bloomington, IN, 47405, USA
| | - David J P Moore
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85719, USA
| | - Nicholas C Parazoo
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
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