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Zuleta D, Arellano G, McMahon SM, Aguilar S, Bunyavejchewin S, Castaño N, Chang-Yang CH, Duque A, Mitre D, Nasardin M, Pérez R, Sun IF, Yao TL, Valencia R, Krishna Moorthy SM, Verbeeck H, Davies SJ. Damage to living trees contributes to almost half of the biomass losses in tropical forests. Glob Chang Biol 2023; 29:3409-3420. [PMID: 36938951 DOI: 10.1111/gcb.16687] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 02/11/2023] [Indexed: 05/16/2023]
Abstract
Accurate estimates of forest biomass stocks and fluxes are needed to quantify global carbon budgets and assess the response of forests to climate change. However, most forest inventories consider tree mortality as the only aboveground biomass (AGB) loss without accounting for losses via damage to living trees: branchfall, trunk breakage, and wood decay. Here, we use ~151,000 annual records of tree survival and structural completeness to compare AGB loss via damage to living trees to total AGB loss (mortality + damage) in seven tropical forests widely distributed across environmental conditions. We find that 42% (3.62 Mg ha-1 year-1 ; 95% confidence interval [CI] 2.36-5.25) of total AGB loss (8.72 Mg ha-1 year-1 ; CI 5.57-12.86) is due to damage to living trees. Total AGB loss was highly variable among forests, but these differences were mainly caused by site variability in damage-related AGB losses rather than by mortality-related AGB losses. We show that conventional forest inventories overestimate stand-level AGB stocks by 4% (1%-17% range across forests) because assume structurally complete trees, underestimate total AGB loss by 29% (6%-57% range across forests) due to overlooked damage-related AGB losses, and overestimate AGB loss via mortality by 22% (7%-80% range across forests) because of the assumption that trees are undamaged before dying. Our results indicate that forest carbon fluxes are higher than previously thought. Damage on living trees is an underappreciated component of the forest carbon cycle that is likely to become even more important as the frequency and severity of forest disturbances increase.
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Affiliation(s)
- Daniel Zuleta
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Washington, District of Columbia, USA
| | - Gabriel Arellano
- Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA
- Oikobit LLC, Albuquerque, New Mexico, USA
| | - Sean M McMahon
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Washington, District of Columbia, USA
- Smithsonian Environmental Research Center, Edgewater, Maryland, 21037, USA
| | - Salomón Aguilar
- Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, República de Panamá
| | - Sarayudh Bunyavejchewin
- Department of National Parks, Forest Research Office, Wildlife and Plant Conservation, Bangkok, 10900, Thailand
| | - Nicolas Castaño
- Herbario Amazónico Colombiano, Instituto Amazónico de Investigaciones Científicas Sinchi, Bogotá, Colombia
| | - Chia-Hao Chang-Yang
- Department of Biological Sciences, National Sun Yat-sen University, Kaohsiung, 80424, Taiwan
| | - Alvaro Duque
- Departamento de Ciencias Forestales, Universidad Nacional de Colombia Sede Medellín, Medellín, Colombia
| | - David Mitre
- Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, República de Panamá
| | - Musalmah Nasardin
- Forestry and Environment Division, Forest Research Institute Malaysia, 52109, Kepong, Selangor, Malaysia
| | - Rolando Pérez
- Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, República de Panamá
| | - I-Fang Sun
- Center for Interdisciplinary Research on Ecology and Sustainability, National Dong Hwa University, Hualien, 94701, Taiwan
| | - Tze Leong Yao
- Forestry and Environment Division, Forest Research Institute Malaysia, 52109, Kepong, Selangor, Malaysia
| | - Renato Valencia
- Escuela de Ciencias Biológicas, Pontificia Universidad Católica del Ecuador, Quito, Ecuador
| | - Sruthi M Krishna Moorthy
- Department of Geographical Sciences, University of Maryland, College Park, Maryland, USA
- Department of Environment, Ghent University, Ghent, Belgium
| | - Hans Verbeeck
- Department of Environment, Ghent University, Ghent, Belgium
| | - Stuart J Davies
- Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Washington, District of Columbia, USA
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Meunier F, Visser MD, Shiklomanov A, Dietze MC, Guzmán Q. JA, Sanchez‐Azofeifa GA, De Deurwaerder HPT, Krishna Moorthy SM, Schnitzer SA, Marvin DC, Longo M, Liu C, Broadbent EN, Almeyda Zambrano AM, Muller‐Landau HC, Detto M, Verbeeck H. Liana optical traits increase tropical forest albedo and reduce ecosystem productivity. Glob Chang Biol 2022; 28:227-244. [PMID: 34651375 PMCID: PMC9298317 DOI: 10.1111/gcb.15928] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/30/2021] [Accepted: 10/07/2021] [Indexed: 06/13/2023]
Abstract
Lianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated trees. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition, and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (-30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana-specific traits were also responsible for a significant reduction of tree (-19%) and ecosystem (-7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of lianas on forest functioning, and paves the way for the evaluation of the large-scale impacts of lianas on forest biogeochemical cycles.
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Affiliation(s)
- Félicien Meunier
- CAVElab—Computational and Applied Vegetation EcologyDepartment of EnvironmentGhent UniversityGhentBelgium
- Department of Earth and EnvironmentBoston UniversityBostonMassachusettsUSA
| | - Marco D. Visser
- Department of Ecology and Evolutionary BiologyPrinceton UniversityPrincetonNew JerseyUSA
- Institute of Environmental SciencesLeiden UniversityLeidenThe Netherlands
| | | | - Michael C. Dietze
- Department of Earth and EnvironmentBoston UniversityBostonMassachusettsUSA
| | - J. Antonio Guzmán Q.
- Centre for Earth Observation Sciences (CEOS)Earth and Atmospheric Sciences DepartmentUniversity of AlbertaEdmontonAlbertaCanada
| | - G. Arturo Sanchez‐Azofeifa
- Centre for Earth Observation Sciences (CEOS)Earth and Atmospheric Sciences DepartmentUniversity of AlbertaEdmontonAlbertaCanada
- Smithsonian Tropical Research InstituteBalboaPanama
| | | | - Sruthi M. Krishna Moorthy
- CAVElab—Computational and Applied Vegetation EcologyDepartment of EnvironmentGhent UniversityGhentBelgium
| | - Stefan A. Schnitzer
- Smithsonian Tropical Research InstituteBalboaPanama
- Department of Biological SciencesMarquette UniversityMilwaukeeWisconsinUSA
| | | | - Marcos Longo
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCaliforniaUSA
| | - Chang Liu
- CAVElab—Computational and Applied Vegetation EcologyDepartment of EnvironmentGhent UniversityGhentBelgium
| | - Eben N. Broadbent
- Spatial Ecology and Conservation (SPEC) Lab, School of Forest, Fisheries, and Geomatics SciencesUniversity of FloridaGainesvilleFloridaUSA
- Spatial Ecology and Conservation (SPEC) Lab, Center for Latin American StudiesUniversity of FloridaGainesvilleFloridaUSA
| | - Angelica M. Almeyda Zambrano
- Spatial Ecology and Conservation (SPEC) Lab, Center for Latin American StudiesUniversity of FloridaGainesvilleFloridaUSA
| | | | - Matteo Detto
- Department of Ecology and Evolutionary BiologyPrinceton UniversityPrincetonNew JerseyUSA
- Smithsonian Tropical Research InstituteBalboaPanama
| | - Hans Verbeeck
- CAVElab—Computational and Applied Vegetation EcologyDepartment of EnvironmentGhent UniversityGhentBelgium
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3
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Van Langenhove L, Depaepe T, Verryckt LT, Fuchslueger L, Donald J, Leroy C, Krishna Moorthy SM, Gargallo-Garriga A, Ellwood MDF, Verbeeck H, Van Der Straeten D, Peñuelas J, Janssens IA. Comparable canopy and soil free-living nitrogen fixation rates in a lowland tropical forest. Sci Total Environ 2021; 754:142202. [PMID: 33254844 DOI: 10.1016/j.scitotenv.2020.142202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 09/01/2020] [Accepted: 09/02/2020] [Indexed: 06/12/2023]
Abstract
Biological nitrogen fixation (BNF) is a fundamental part of nitrogen cycling in tropical forests, yet little is known about the contribution made by free-living nitrogen fixers inhabiting the often-extensive forest canopy. We used the acetylene reduction assay, calibrated with 15N2, to measure free-living BNF on forest canopy leaves, vascular epiphytes, bryophytes and canopy soil, as well as on the forest floor in leaf litter and soil. We used a combination of calculated and published component densities to upscale free-living BNF rates to the forest level. We found that bryophytes and leaves situated in the canopy in particular displayed high mass-based rates of free-living BNF. Additionally, we calculated that nearly 2 kg of nitrogen enters the forest ecosystem through free-living BNF every year, 40% of which was fixed by the various canopy components. Our results reveal that in the studied tropical lowland forest a large part of the nitrogen input through free-living BNF stems from the canopy, but also that the total nitrogen inputs by free-living BNF are lower than previously thought and comparable to the inputs of reactive nitrogen by atmospheric deposition.
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Affiliation(s)
- Leandro Van Langenhove
- Research group Plants and Ecosystem (PLECO), Department of Biology, University of Antwerp, Wilrijk, Belgium.
| | - Thomas Depaepe
- Laboratory of Functional Plant Biology, Department of Biology, Ghent University, Ghent, Belgium
| | - Lore T Verryckt
- Research group Plants and Ecosystem (PLECO), Department of Biology, University of Antwerp, Wilrijk, Belgium
| | - Lucia Fuchslueger
- Research group Plants and Ecosystem (PLECO), Department of Biology, University of Antwerp, Wilrijk, Belgium; Division of Terrestrial Ecosystem Research, Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
| | - Julian Donald
- CNRS, IRD, UMR 5174 Evolution et Diversité Biologique (EDB), Université Toulouse, 3 Paul Sabatier, Toulouse, France
| | - Celine Leroy
- AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France; UMR EcoFoG, CNRS, CIRAD, INRAE, AgroParisTech, Université des Antilles, Université de Guyane, 97310 Kourou, France
| | - Sruthi M Krishna Moorthy
- Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, 9000 Ghent, Belgium
| | - Albert Gargallo-Garriga
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, 08193 Bellaterra, Catalonia, Spain; CREAF, 08193 Cerdanyola del Vallès, Catalonia, Spain; Global Change Research Institute, The Czech Academy of Sciences, Belidla 986/4a, CZ-60300 Brno, Czech Republic
| | - M D Farnon Ellwood
- Centre for Research in Biosciences, University of the West of England, Coldharbour Lane, Bristol, BS16 1QY, UK
| | - Hans Verbeeck
- Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, 9000 Ghent, Belgium
| | | | - Josep Peñuelas
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, 08193 Bellaterra, Catalonia, Spain; CREAF, 08193 Cerdanyola del Vallès, Catalonia, Spain
| | - Ivan A Janssens
- Research group Plants and Ecosystem (PLECO), Department of Biology, University of Antwerp, Wilrijk, Belgium
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di Porcia e Brugnera M, Meunier F, Longo M, Krishna Moorthy SM, De Deurwaerder H, Schnitzer SA, Bonal D, Faybishenko B, Verbeeck H. Modeling the impact of liana infestation on the demography and carbon cycle of tropical forests. Glob Chang Biol 2019; 25:3767-3780. [PMID: 31310429 PMCID: PMC6856694 DOI: 10.1111/gcb.14769] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 07/02/2019] [Accepted: 07/04/2019] [Indexed: 05/21/2023]
Abstract
There is mounting empirical evidence that lianas affect the carbon cycle of tropical forests. However, no single vegetation model takes into account this growth form, although such efforts could greatly improve the predictions of carbon dynamics in tropical forests. In this study, we incorporated a novel mechanistic representation of lianas in a dynamic global vegetation model (the Ecosystem Demography Model). We developed a liana-specific plant functional type and mechanisms representing liana-tree interactions (such as light competition, liana-specific allometries, and attachment to host trees) and parameterized them according to a comprehensive literature meta-analysis. We tested the model for an old-growth forest (Paracou, French Guiana) and a secondary forest (Gigante Peninsula, Panama). The resulting model simulations captured many features of the two forests characterized by different levels of liana infestation as revealed by a systematic comparison of the model outputs with empirical data, including local census data from forest inventories, eddy flux tower data, and terrestrial laser scanner-derived forest vertical structure. The inclusion of lianas in the simulations reduced the secondary forest net productivity by up to 0.46 tC ha-1 year-1 , which corresponds to a limited relative reduction of 2.6% in comparison with a reference simulation without lianas. However, this resulted in significantly reduced accumulated above-ground biomass after 70 years of regrowth by up to 20 tC /ha (19% of the reference simulation). Ultimately, the simulated negative impact of lianas on the total biomass was almost completely cancelled out when the forest reached an old-growth successional stage. Our findings suggest that lianas negatively influence the forest potential carbon sink strength, especially for young, disturbed, liana-rich sites. In light of the critical role that lianas play in the profound changes currently experienced by tropical forests, this new model provides a robust numerical tool to forecast the impact of lianas on tropical forest carbon sinks.
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Affiliation(s)
| | - Félicien Meunier
- CAVElab – Computational and Applied Vegetation EcologyGhent UniversityGhentBelgium
- Ecological Forecasting LabDepartment of Earth and EnvironmentBoston UniversityBostonMAUSA
| | - Marcos Longo
- Embrapa Agricultural InformaticsCampinasSPBrazil
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | | | - Stefan A. Schnitzer
- Smithsonian Tropical Research InstituteBalboaAnconPanama
- Department of Biological SciencesMarquette UniversityMilwaukeeWIUSA
| | - Damien Bonal
- UMR SilvaUniversité de Lorraine, AgroParisTech, INRANancyFrance
| | - Boris Faybishenko
- Earth and Environmental Science AreaLawrence Berkeley National LaboratoryBerkeleyCAUSA
| | - Hans Verbeeck
- CAVElab – Computational and Applied Vegetation EcologyGhent UniversityGhentBelgium
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5
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Krishna Moorthy SM, Bao Y, Calders K, Schnitzer SA, Verbeeck H. Semi-automatic extraction of liana stems from terrestrial LiDAR point clouds of tropical rainforests. ISPRS J Photogramm Remote Sens 2019; 154:114-126. [PMID: 31417229 PMCID: PMC6686632 DOI: 10.1016/j.isprsjprs.2019.05.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 05/22/2019] [Accepted: 05/27/2019] [Indexed: 06/10/2023]
Abstract
Lianas are key structural elements of tropical forests having a large impact on the global carbon cycle by reducing tree growth and increasing tree mortality. Despite the reported increasing abundance of lianas across neotropics, very few studies have attempted to quantify the impact of lianas on tree and forest structure. Recent advances in high resolution terrestrial laser scanning (TLS) systems have enabled us to quantify the forest structure, in an unprecedented detail. However, the uptake of TLS technology to study lianas has not kept up with the same pace as it has for trees. The slower technological adoption of TLS to study lianas is due to the lack of methods to study these complex growth forms. In this study, we present a semi-automatic method to extract liana woody components from plot-level TLS data of a tropical rainforest. We tested the method in eight plots from two different tropical rainforest sites (two in Gigante Peninsula, Panama and six in Nouragues, French Guiana) along an increasing gradient of liana infestation (from plots with low liana density to plots with very high liana density). Our method uses a machine learning model based on the Random Forest (RF) algorithm. The RF algorithm is trained on the eigen features extracted from the points in 3D at multiple spatial scales. The RF based liana stem extraction method successfully extracts on average 58% of liana woody points in our dataset with a high precision of 88%. We also present simple post-processing steps that increase the percentage of extracted liana stems from 54% to 90% in Nouragues and 65% to 70% in Gigante Peninsula without compromising on the precision. We provide the entire processing pipeline as an open source python package. Our method will facilitate new research to study lianas as it enables the monitoring of liana abundance, growth and biomass in forest plots. In addition, the method facilitates the easier processing of 3D data to study tree structure from a liana-infested forest.
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Affiliation(s)
| | - Yunfei Bao
- Beijing Institute of Space Mechanics and Electricity, No. 104, Road Youyi, Beijing 100094, China
| | - Kim Calders
- CAVElab – Computational and Applied Vegetation Ecology, Ghent University, 9000 Ghent, Belgium
| | - Stefan A. Schnitzer
- Department of Biological Sciences, Marquette University, Milwaukee, WI 53201-1881, USA
- Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, Ancon, Panama
| | - Hans Verbeeck
- CAVElab – Computational and Applied Vegetation Ecology, Ghent University, 9000 Ghent, Belgium
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