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de Sales RP, da Silva LC, Neves AGDS, Fajardo CG, Pinheiro LG, Vieira FDA. Addressing Conservation Needs: Genetic Diversity and Population Ecology of the Endemic Tree Spondias tuberosa Arruda. SCIENTIFICA 2024; 2024:5023974. [PMID: 38938543 PMCID: PMC11208813 DOI: 10.1155/2024/5023974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 05/15/2024] [Accepted: 05/24/2024] [Indexed: 06/29/2024]
Abstract
Spondias tuberosa Arruda (Anacardiaceae), popularly known as umbuzeiro or imbuzeiro, is a fruit tree native to the semiarid region of Brazil. The extractive harvesting of its fruits contributes significantly to the economy, generating an annual revenue of approximately $4,2 million. The present study aimed to assess the spatial pattern, allometric variations, fruit measurements, and genetic diversity of trees within a remaining forest of the Caatinga biome, with a focus on intrapopulation analysis. We used intersimple repeated sequence markers and the second-order function density of neighbours to determine the genetic and spatial structure. The density of neighbours was highest within a 10-meter radius. Biometric analyses revealed average fruit lengths of 31.12 mm (±0.22), diameters of 28.68 mm (±0.25), and fresh masses of 15.56 g (±0.33). Diaspores exhibited an average length, diameter, and thickness of 19.27 mm, 13.95 mm, and 11.14 mm, respectively, with a fresh mass of 2.28 g. Notably, the fresh mass demonstrated the highest coefficient of variation. Ten molecular markers were selected, generating 103 highly polymorphic loci (99.03%) with an average informative content of 0.45. Nei's diversity index (0.37) and Shannon's index (0.55) indicated moderate genetic diversity. Furthermore, Bayesian analysis revealed a population structure with two distinct genetic groups. The Infinite Allele and Mutation Step Models suggested a significant historical decline in population size, indicative of a genetic bottleneck. As a result, proactive in situ conservation strategies, including establishing protected natural areas, become essential, considering the socioeconomic significance of the species. Additionally, it is recommended to establish germplasm banks for ex situ conservation and the development of managed cultivation initiatives to reduce the pressure on native populations of S. tuberosa caused by extraction.
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Affiliation(s)
- Raiane Pereira de Sales
- Federal University of Rio Grande do Norte, Forestry Engineering, Macaíba, Rio Grande do Norte, Brazil
| | - Luan Cavalcanti da Silva
- Federal University of Rio Grande do Norte, Forestry Engineering, Macaíba, Rio Grande do Norte, Brazil
| | | | - Cristiane Gouvêa Fajardo
- Federal University of Rio Grande do Norte, Forestry Engineering, Macaíba, Rio Grande do Norte, Brazil
| | - Luciana Gomes Pinheiro
- Federal University of Rio Grande do Norte, Forestry Engineering, Macaíba, Rio Grande do Norte, Brazil
| | - Fábio de Almeida Vieira
- Federal University of Rio Grande do Norte, Forestry Engineering, Macaíba, Rio Grande do Norte, Brazil
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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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A Revision of the Traditional Analysis Method of Allometry to Allow Extension of the Normality-Borne Complexity of Error Structure: Examining the Adequacy of a Normal-Mixture Distribution-Driven Error Term. BIOMED RESEARCH INTERNATIONAL 2022; 2022:8310213. [PMID: 36172489 PMCID: PMC9512611 DOI: 10.1155/2022/8310213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 11/17/2022]
Abstract
Huxley’s model of simple allometry provides a parsimonious scheme for examining scaling relationships in scientific research, resource management, and species conservation endeavors. Factors including biological error, analysis method, sample size, and overall data quality can undermine the reliability of a fit of Huxley’s model. Customary amendments enhance the complexity of the power function-conveyed systematic term while keeping the usual normality-borne error structure. The resulting protocols bear multiple-parameter complex allometry forms that could pose interpretative shortcomings and parameter estimation difficulties, and even being empirically pertinent, they could potentially bear overfitting. A subsequent heavy-tailed Q-Q normal spread often remains undetected since the adequacy of a normally distributed error term remains unexplored. Previously, we promoted the advantages of keeping Huxley’s model-driven systematic part while switching to a logistically distributed error term to improve fit quality. Here, we analyzed eelgrass leaf biomass and area data exhibiting a marked size-related heterogeneity, perhaps explaining a lack of systematization at data gathering. Overdispersion precluded adequacy of the logistically adapted protocol, thereby suggesting processing data through a median absolute deviation scheme aimed to remove unduly replicates. Nevertheless, achieving regularity to Huxley’s power function-like trend required the removal of many replicates, thereby questioning the integrity of a data cleaning approach. But, we managed to adapt the complexity of the error term to reliably identify Huxley’s model-like systematic part masked by variability in data. Achieving this relied on an error term conforming to a normal mixture distribution which successfully managed overdispersion in data. Compared to normal-complex allometry and data cleaning composites present arrangement delivered a coherent Q-Q normal mixture spread and a remarkable reproducibility strength of derived proxies. By keeping the analysis within Huxley’s original theory, the present approach enables substantiating nondestructive allometric proxies aimed at eelgrass conservation. The viewpoint endorsed here could also make data cleaning unnecessary.
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Jucker T, Fischer FJ, Chave J, Coomes DA, Caspersen J, Ali A, Loubota Panzou GJ, Feldpausch TR, Falster D, Usoltsev VA, Adu‐Bredu S, Alves LF, Aminpour M, Angoboy IB, Anten NPR, Antin C, Askari Y, Muñoz R, Ayyappan N, Balvanera P, Banin L, Barbier N, Battles JJ, Beeckman H, Bocko YE, Bond‐Lamberty B, Bongers F, Bowers S, Brade T, van Breugel M, Chantrain A, Chaudhary R, Dai J, Dalponte M, Dimobe K, Domec J, Doucet J, Duursma RA, Enríquez M, van Ewijk KY, Farfán‐Rios W, Fayolle A, Forni E, Forrester DI, Gilani H, Godlee JL, Gourlet‐Fleury S, Haeni M, Hall JS, He J, Hemp A, Hernández‐Stefanoni JL, Higgins SI, Holdaway RJ, Hussain K, Hutley LB, Ichie T, Iida Y, Jiang H, Joshi PR, Kaboli H, Larsary MK, Kenzo T, Kloeppel BD, Kohyama T, Kunwar S, Kuyah S, Kvasnica J, Lin S, Lines ER, Liu H, Lorimer C, Loumeto J, Malhi Y, Marshall PL, Mattsson E, Matula R, Meave JA, Mensah S, Mi X, Momo S, Moncrieff GR, Mora F, Nissanka SP, O'Hara KL, Pearce S, Pelissier R, Peri PL, Ploton P, Poorter L, Pour MJ, Pourbabaei H, Dupuy‐Rada JM, Ribeiro SC, Ryan C, Sanaei A, Sanger J, Schlund M, Sellan G, Shenkin A, Sonké B, Sterck FJ, Svátek M, Takagi K, Trugman AT, Ullah F, Vadeboncoeur MA, Valipour A, Vanderwel MC, Vovides AG, Wang W, Wang L, Wirth C, Woods M, Xiang W, Ximenes FDA, Xu Y, Yamada T, Zavala MA. Tallo: A global tree allometry and crown architecture database. GLOBAL CHANGE BIOLOGY 2022; 28:5254-5268. [PMID: 35703577 PMCID: PMC9542605 DOI: 10.1111/gcb.16302] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/12/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.
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Affiliation(s)
- Tommaso Jucker
- School of Biological SciencesUniversity of BristolBristolUK
| | | | - Jérôme Chave
- Laboratoire Évolution et Diversité Biologique (EDB)UMR 5174 (CNRS/IRD/UPS)Toulouse Cedex 9France
- Université ToulouseToulouse Cedex 9France
| | - David A. Coomes
- Conservation Research InstituteUniversity of CambridgeCambridgeUK
| | - John Caspersen
- Institute of Forestry and ConservationUniversity of TorontoTorontoOntarioCanada
| | - Arshad Ali
- Forest Ecology Research Group, College of Life SciencesHebei UniversityBaodingHebeiChina
| | - Grace Jopaul Loubota Panzou
- Université de Liège, Gembloux Agro‐Bio TechGemblouxBelgium
- Laboratoire de Biodiversité, de Gestion des Ecosystèmes et de l'Environnement (LBGE), Faculté des Sciences et TechniquesUniversité Marien NgouabiBrazzavilleRepublic of Congo
| | - Ted R. Feldpausch
- College of Life and Environmental SciencesUniversity of ExeterExeterUK
| | - Daniel Falster
- Evolution & Ecology Research CentreUniversity of New South Wales SydneySydneyNew South WalesAustralia
| | - Vladimir A. Usoltsev
- Department of ForestryUral State Forest Engineering UniversityYekaterinburgRussia
- Department of Forest DynamicsBotanical Garden of the Ural Branch of Russian Academy of SciencesYekaterinburgRussia
| | - Stephen Adu‐Bredu
- Forestry Research Institute of Ghana, Council for Scientific and Industrial ResearchUniversityKumasiGhana
| | - Luciana F. Alves
- Center for Tropical Research, Institute of the Environment and SustainabilityUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Mohammad Aminpour
- Natural Recourses and Watershed Management Office, West Azerbaijan ProvinceUrmiaIran
| | - Ilondea B. Angoboy
- Institut National pour l'Etude et la Recherche AgronimiquesDemocratic Republic of the Congo
| | - Niels P. R. Anten
- Center for Crop Systems AnalysisWageningen UniversityWageningenThe Netherlands
| | - Cécile Antin
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | - Yousef Askari
- Research Division of Natural Resources, Kohgiluyeh and Boyerahmad Agriculture and Natural Resources Research and Education Center, AREEOYasoujIran
| | - Rodrigo Muñoz
- Departamento de Ecología y Recursos Naturales, Facultad de CienciasUniversidad Nacional Autónoma de México, CoyoacánCiudad de MéxicoMexico
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | | | - Patricia Balvanera
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de MéxicoMoreliaMichoacánMexico
| | | | - Nicolas Barbier
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | | | - Hans Beeckman
- Service of Wood BiologyRoyal Museum for Central AfricaTervurenBelgium
| | - Yannick E. Bocko
- Laboratoire de Biodiversité, de Gestion des Ecosystèmes et de l'Environnement (LBGE), Faculté des Sciences et TechniquesUniversité Marien NgouabiBrazzavilleRepublic of Congo
| | - Ben Bond‐Lamberty
- Pacific Northwest National LaboratoryJoint Global Change Research InstituteCollege ParkMarylandUSA
| | - Frans Bongers
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | - Samuel Bowers
- School of GeoSciencesUniversity of EdinburghEdinburghUK
| | - Thomas Brade
- School of GeoSciencesUniversity of EdinburghEdinburghUK
| | - Michiel van Breugel
- Yale‐NUS CollegeSingapore
- ForestGEOSmithsonian Tropical Research InstituteApartadoPanamaRepublic of Panama
- Department of GeographyNational University of SingaporeSingapore
| | | | - Rajeev Chaudhary
- Division Forest OfficeMinistry of ForestDhangadhiSudurpashchim ProvinceNepal
| | - Jingyu Dai
- College of Urban and Environmental Sciences and MOE Laboratory for Earth Surface ProcessesPeking UniversityBeijingChina
| | - Michele Dalponte
- Research and Innovation Centre, Fondazione Edmund MachSan Michele all'AdigeItaly
| | - Kangbéni Dimobe
- Institut des Sciences de l'Environnement et du Développement Rural (ISEDR)Université de DédougouDédougouBurkina Faso
| | - Jean‐Christophe Domec
- Bordeaux Sciences Agro‐UMR ISPA, INRAEBordeauxFrance
- Nicholas School of the EnvironmentDuke UniversityDurhamNCUSA
| | | | | | - Moisés Enríquez
- Departamento de Ecología y Recursos Naturales, Facultad de CienciasUniversidad Nacional Autónoma de México, CoyoacánCiudad de MéxicoMexico
| | - Karin Y. van Ewijk
- Department of Geography and Planning, Queen's UniversityKingstonOntarioCanada
| | | | | | - Eric Forni
- CIRAD, UPR Forêts et SociétésMontpellierFrance
| | | | - Hammad Gilani
- Institute of Space Technology, Islamabad HighwayIslamabadPakistan
| | | | | | - Matthias Haeni
- Swiss Federal Research Institute WSLBirmensdorfSwitzerland
| | - Jefferson S. Hall
- ForestGEOSmithsonian Tropical Research InstituteApartadoPanamaRepublic of Panama
| | - Jie‐Kun He
- Spatial Ecology Lab, School of Life SciencesSouth China Normal UniversityGuangzhouGuangdongChina
| | - Andreas Hemp
- Department of Plant SystematicsUniversity of BayreuthBayreuthGermany
| | | | | | | | - Kiramat Hussain
- Gilgit‐Baltistan Forest Wildlife and Environment DepartmentGilgitPakistan
| | - Lindsay B. Hutley
- Research Institute for the Environment & LivelihoodsCharles Darwin UniversityCasuarinaNorthern TerritoryAustralia
| | - Tomoaki Ichie
- Faculty of Agriculture and Marine ScienceKochi UniversityNankokuKochiJapan
| | - Yoshiko Iida
- Forestry and Forest Products Research InstituteTsukubaIbarakiJapan
| | - Hai‐sheng Jiang
- Spatial Ecology Lab, School of Life SciencesSouth China Normal UniversityGuangzhouGuangdongChina
| | | | - Hasan Kaboli
- Faculty of Desert Studies Semnan UniversitySemnanIran
| | | | - Tanaka Kenzo
- Japan International Research Center for Agricultural SciencesTsukubaIbarakiJapan
| | - Brian D. Kloeppel
- Department of Geosciences and Natural ResourcesWestern Carolina UniversityCullowheeNorth CarolinaUSA
- Graduate School and ResearchWestern Carolina UnversityCullowheeNorth CarolinaUSA
| | - Takashi Kohyama
- Faculty of Environmental Earth ScienceHokkaido UniversitySapporoJapan
| | - Suwash Kunwar
- Division Forest OfficeMinistry of ForestDhangadhiSudurpashchim ProvinceNepal
- Department of Forest Resources Management, College of ForestryNanjing Forestry UniversityNanjingJiangsuChina
| | - Shem Kuyah
- Jomo Kenyatta University of Agriculture and Technology (JKUAT)NairobiKenya
| | - Jakub Kvasnica
- Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood TechnologyMendel University in BrnoBrnoCzech Republic
| | - Siliang Lin
- Guangdong Provincial Key Laboratory of High Technology for Plant Protection, Plant Protection Research InstituteGuangdong Academy of Agricultural SciencesGuangzhouGuangdongChina
| | - Emily R. Lines
- Department of GeographyUniversity of CambridgeCambridgeUK
| | - Hongyan Liu
- College of Urban and Environmental Sciences and MOE Laboratory for Earth Surface ProcessesPeking UniversityBeijingChina
| | - Craig Lorimer
- Department of Forest and Wildlife EcologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Jean‐Joël Loumeto
- Laboratoire de Biodiversité, de Gestion des Ecosystèmes et de l'Environnement (LBGE), Faculté des Sciences et TechniquesUniversité Marien NgouabiBrazzavilleRepublic of Congo
| | - Yadvinder Malhi
- Environmental Change Institute, School of Geography and the EnvironmentUniversity of OxfordOxfordUK
| | - Peter L. Marshall
- Faculty of ForestryUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Eskil Mattsson
- IVL Swedish Environmental Research InstituteGöteborgSweden
- Gothenburg Global Biodiversity Centre (GGBC), GothenburgSweden
| | - Radim Matula
- Faculty of Forestry and Wood SciencesCzech University of Life Sciences Prague, Prague 6SuchdolCzech Republic
| | - Jorge A. Meave
- Departamento de Ecología y Recursos Naturales, Facultad de CienciasUniversidad Nacional Autónoma de México, CoyoacánCiudad de MéxicoMexico
| | - Sylvanus Mensah
- Laboratoire de Biomathématiques et d'Estimations Forestières, Faculté des Sciences AgronomiquesUniversité d'Abomey CalaviCotonouBenin
| | - Xiangcheng Mi
- State Key Laboratory of Vegetation and Environmental Change, Institute of BotanyChinese Academy of SciencesBeijingChina
| | - Stéphane Momo
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
- Laboratoire de Botanique systématique et d'Ecologie, Département des Sciences Biologiques, Ecole Normale SupérieureUniversité de Yaoundé IYaoundéCameroon
| | - Glenn R. Moncrieff
- Fynbos Node, South African Environmental Observation NetworkClaremontSouth Africa
- Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical SciencesUniversity of Cape TownRondeboschSouth Africa
| | - Francisco Mora
- Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de MéxicoMoreliaMichoacánMexico
| | - Sarath P. Nissanka
- Department of Crop Science, Faculty of AgricultureUniversity of PeradeniyaPeradeniyaSri Lanka
| | | | | | - Raphaël Pelissier
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | - Pablo L. Peri
- Universidad Nacional de la Patagonia Austral (UNPA) ‐ Instituto Nacional de Tecnología Agropecuaria (INTA) ‐ CONICETRío GallegosSanta CruzArgentina
| | - Pierre Ploton
- AMAP LabMontpellier University, IRD, CIRAD, CNRS, INRAEMontpellierFrance
| | - Lourens Poorter
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | | | - Hassan Pourbabaei
- Department of Forestry, Faculty of Natural ResourcesUniversity of GuilanSomehsaraIran
| | - Juan Manuel Dupuy‐Rada
- Centro de Investigación Científica de Yucatán A.C., Unidad de Recursos NaturalesMéridaYucatánMexico
| | - Sabina C. Ribeiro
- Centro de Ciências Biológicas e da NaturezaUniversidade Federal do Acre, Campus UniversitárioRio BrancoBrazil
| | - Casey Ryan
- School of GeoSciencesUniversity of EdinburghEdinburghUK
| | - Anvar Sanaei
- Systematic Botany and Functional Biodiversity, Institute of BiologyLeipzig UniversityLeipzigGermany
| | | | - Michael Schlund
- Department of Natural Resources, Faculty of Geo‐information Science and Earth Observation (ITC)University of TwenteEnschedeThe Netherlands
| | - Giacomo Sellan
- UMR EcoFoG, CNRSKourouFrench Guiana
- Department of Natural SciencesManchester Metropolitan UniversityManchesterUK
| | - Alexander Shenkin
- Environmental Change Institute, School of Geography and the EnvironmentUniversity of OxfordOxfordUK
| | - Bonaventure Sonké
- Laboratoire de Botanique systématique et d'Ecologie, Département des Sciences Biologiques, Ecole Normale SupérieureUniversité de Yaoundé IYaoundéCameroon
| | - Frank J. Sterck
- Forest Ecology and Forest Management GroupWageningen UniversityWageningenThe Netherlands
| | - Martin Svátek
- Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood TechnologyMendel University in BrnoBrnoCzech Republic
| | - Kentaro Takagi
- Field Science Center for Northern BiosphereHokkaido UniversityHoronobeJapan
| | - Anna T. Trugman
- Department of GeographyUniversity of California Santa BarbaraSanta BarbaraCaliforniaUSA
| | - Farman Ullah
- Forest Ecology Research Group, College of Life SciencesHebei UniversityBaodingHebeiChina
- Department of Forest Resources Management, College of ForestryNanjing Forestry UniversityNanjingJiangsuChina
| | | | - Ahmad Valipour
- Department of Forestry and The Center for Research and Development of Northern Zagros ForestryUniversity of KurdistanErbilIran
| | | | - Alejandra G. Vovides
- School of Geographical and Earth SciencesUniversity of Glasgow, East QuadrangleGlasgowUK
| | - Weiwei Wang
- State Key Laboratory of Vegetation and Environmental Change, Institute of BotanyChinese Academy of SciencesBeijingChina
| | - Li‐Qiu Wang
- Department of Forest Resources Management, College of ForestryNanjing Forestry UniversityNanjingJiangsuChina
| | - Christian Wirth
- Systematic Botany and Functional Biodiversity, Institute of BiologyUniversity of LeipzigLeipzigGermany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
| | - Murray Woods
- Ontario Ministry of Natural ResourcesNorth BayOntarioCanada
| | - Wenhua Xiang
- Faculty of Life Science and TechnologyCentral South University of Forestry and TechnologyChangshaHunanChina
| | | | - Yaozhan Xu
- State Key Laboratory of Aquatic Botany and Watershed EcologyWuhan Botanical Garden, Chinese Academy of SciencesWuhanChina
- Center of Conservation Biology, Core Botanical GardensChinese Academy of SciencesWuhanChina
| | - Toshihiro Yamada
- Graduate School of Integrated Sciences of LifeHiroshima UniversityHiroshimaJapan
| | - Miguel A. Zavala
- Forest Ecology and Restoration Group (FORECO), Departamento de Ciencias de la VidaUniversidad de AlcaláMadridSpain
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Barber C, Graves SJ, Hall JS, Zuidema PA, Brandt J, Bohlman SA, Asner GP, Bailón M, Caughlin TT. Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2585. [PMID: 35333420 DOI: 10.1002/eap.2585] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 10/26/2021] [Accepted: 11/04/2021] [Indexed: 06/14/2023]
Abstract
Predicting forest recovery at landscape scales will aid forest restoration efforts. The first step in successful forest recovery is tree recruitment. Forecasts of tree recruit abundance, derived from the landscape-scale distribution of seed sources (i.e., adult trees), could assist efforts to identify sites with high potential for natural regeneration. However, previous work revealed wide variation in the effect of seed sources on seedling abundance, from positive to no effect. We quantified the relationship between adult tree seed sources and tree recruits and predicted where natural recruitment would occur in a fragmented, tropical, agricultural landscape. We integrated species-specific tree crown maps generated from hyperspectral imagery and property ownership data with field data on the spatial distribution of tree recruits from five species. We then developed hierarchical Bayesian models to predict landscape-scale recruit abundance. Our models revealed that species-specific maps of tree crowns improved recruit abundance predictions. Conspecific crown area had a much stronger impact on recruitment abundance (8.00% increase in recruit abundance when conspecific tree density increases from zero to one tree; 95% credible interval (CI): 0.80% to 11.57%) than heterospecific crown area (0.03% increase with the addition of a single heterospecific tree, 95% CI: -0.60% to 0.68%). Individual property ownership was also an important predictor of recruit abundance: The best performing model had varying effects of conspecific and heterospecific crown area on recruit abundance, depending on individual property ownership. We demonstrate how novel remote sensing approaches and cadastral data can be used to generate high-resolution and landscape-level maps of tree recruit abundance. Spatial models parameterized with field, cadastral, and remote sensing data are poised to assist decision support for forest landscape restoration.
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Affiliation(s)
- Cristina Barber
- Biological Sciences, Boise State University, Boise, Idaho, USA
| | - Sarah J Graves
- Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jefferson S Hall
- Smithsonian Tropical Research Institute, ForestGEO, Panama City, Panama
| | - Pieter A Zuidema
- Forest Ecology and Forest Management group, Wageningen University, Wageningen, The Netherlands
| | - Jodi Brandt
- Human-Environment Systems, Boise State University, Boise, Idaho, USA
| | - Stephanie A Bohlman
- School of Forest Resources and Conservation, University of Florida, Gainesville, Florida, USA
- Smithsonian Tropical Research Institute, Panama City, Panama
| | - Gregory P Asner
- Center for Global Discovery and Conservation Science, Arizona State University, Tempe, Arizona, USA
| | - Mario Bailón
- Smithsonian Tropical Research Institute, Panama City, Panama
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Rau EP, Fischer F, Joetzjer É, Maréchaux I, Sun IF, Chave J. Transferability of an individual- and trait-based forest dynamics model: A test case across the tropics. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2021.109801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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7
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Jucker T. Deciphering the fingerprint of disturbance on the three-dimensional structure of the world's forests. THE NEW PHYTOLOGIST 2022; 233:612-617. [PMID: 34506641 DOI: 10.1111/nph.17729] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
Abstract
Canopy gaps and the processes that generate them play an integral role in shaping the structure and dynamics of forests. However, it is only with recent advances in remote sensing technologies such as airborne laser scanning that studying canopy gaps at scale has become a reality. Consequently, we still lack an understanding of how the size distribution and spatial organization of canopy gaps varies among forests ecosystems, nor have we determined whether these emergent properties can be reconciled with existing theories of forest dynamics. Here, I outline a roadmap for integrating remote sensing with field data and individual-based models to build a comprehensive picture of how environmental constraints and disturbance regimes shape the three-dimensional structure of the world's forests.
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Affiliation(s)
- Tommaso Jucker
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK
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8
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Maréchaux I, Langerwisch F, Huth A, Bugmann H, Morin X, Reyer CP, Seidl R, Collalti A, Dantas de Paula M, Fischer R, Gutsch M, Lexer MJ, Lischke H, Rammig A, Rödig E, Sakschewski B, Taubert F, Thonicke K, Vacchiano G, Bohn FJ. Tackling unresolved questions in forest ecology: The past and future role of simulation models. Ecol Evol 2021; 11:3746-3770. [PMID: 33976773 PMCID: PMC8093733 DOI: 10.1002/ece3.7391] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 02/04/2021] [Accepted: 02/20/2021] [Indexed: 12/13/2022] Open
Abstract
Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio-temporal scales unreachable by most empirical investigations.We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.Using three widely applied but contrasting approaches - species distribution models, individual-based forest models, and dynamic global vegetation models - as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change.
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Affiliation(s)
| | - Fanny Langerwisch
- Department of Ecology and Environmental SciencesPalacký University OlomoucOlomoucCzech Republic
- Department of Water Resources and Environmental ModelingCzech University of Life SciencesPragueCzech Republic
| | - Andreas Huth
- Helmholtz Centre for Environmental Research ‐ UFZLeipzigGermany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Institute of Environmental Systems ResearchOsnabrück UniversityOsnabrückGermany
| | - Harald Bugmann
- Forest EcologyInstitute of Terrestrial EcosystemsETH ZürichZurichSwitzerland
| | - Xavier Morin
- EPHECEFECNRSUniv MontpellierUniv Paul Valéry MontpellierIRDMontpellierFrance
| | - Christopher P.O. Reyer
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
| | - Rupert Seidl
- Institute of SilvicultureUniversity of Natural Resources and Life Sciences (BOKU)ViennaAustria
- TUM School of Life SciencesTechnical University of MunichFreisingGermany
| | - Alessio Collalti
- Forest Modelling LabInstitute for Agriculture and Forestry Systems in the MediterraneanNational Research Council of Italy (CNR‐ISAFOM)Perugia (PG)Italy
- Department of Innovation in Biological, Agro‐food and Forest SystemsUniversity of TusciaViterboItaly
| | | | - Rico Fischer
- Helmholtz Centre for Environmental Research ‐ UFZLeipzigGermany
| | - Martin Gutsch
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
| | | | - Heike Lischke
- Dynamic MacroecologyLand Change ScienceSwiss Federal Institute for Forest, Snow and Landscape Research WSLBirmensdorfSwitzerland
| | - Anja Rammig
- TUM School of Life SciencesTechnical University of MunichFreisingGermany
| | - Edna Rödig
- Helmholtz Centre for Environmental Research ‐ UFZLeipzigGermany
| | - Boris Sakschewski
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
| | | | - Kirsten Thonicke
- Potsdam Institute for Climate Impact Research (PIK)Member of the Leibniz AssociationPotsdamGermany
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9
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Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing. REMOTE SENSING 2021. [DOI: 10.3390/rs13081592] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.
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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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11
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Blanchet S, Prunier JG, Paz‐Vinas I, Saint‐Pé K, Rey O, Raffard A, Mathieu‐Bégné E, Loot G, Fourtune L, Dubut V. A river runs through it: The causes, consequences, and management of intraspecific diversity in river networks. Evol Appl 2020; 13:1195-1213. [PMID: 32684955 PMCID: PMC7359825 DOI: 10.1111/eva.12941] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 02/14/2020] [Accepted: 02/19/2020] [Indexed: 01/01/2023] Open
Abstract
Rivers are fascinating ecosystems in which the eco-evolutionary dynamics of organisms are constrained by particular features, and biologists have developed a wealth of knowledge about freshwater biodiversity patterns. Over the last 10 years, our group used a holistic approach to contribute to this knowledge by focusing on the causes and consequences of intraspecific diversity in rivers. We conducted empirical works on temperate permanent rivers from southern France, and we broadened the scope of our findings using experiments, meta-analyses, and simulations. We demonstrated that intraspecific (genetic) diversity follows a spatial pattern (downstream increase in diversity) that is repeatable across taxa (from plants to vertebrates) and river systems. This pattern can result from interactive processes that we teased apart using appropriate simulation approaches. We further experimentally showed that intraspecific diversity matters for the functioning of river ecosystems. It indeed affects not only community dynamics, but also key ecosystem functions such as litter degradation. This means that losing intraspecific diversity in rivers can yield major ecological effects. Our work on the impact of multiple human stressors on intraspecific diversity revealed that-in the studied river systems-stocking of domestic (fish) strains strongly and consistently alters natural spatial patterns of diversity. It also highlighted the need for specific analytical tools to tease apart spurious from actual relationships in the wild. Finally, we developed original conservation strategies at the basin scale based on the systematic conservation planning framework that appeared pertinent for preserving intraspecific diversity in rivers. We identified several important research avenues that should further facilitate our understanding of patterns of local adaptation in rivers, the identification of processes sustaining intraspecific biodiversity-ecosystem function relationships, and the setting of reliable conservation plans.
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Affiliation(s)
- Simon Blanchet
- Centre National pour la Recherche ScientifiqueStation d'Écologie Théorique et Expérimentale du CNRS à MoulisUniversité Toulouse III Paul SabatierUMR‐5321MoulisFrance
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
| | - Jérôme G. Prunier
- Centre National pour la Recherche ScientifiqueStation d'Écologie Théorique et Expérimentale du CNRS à MoulisUniversité Toulouse III Paul SabatierUMR‐5321MoulisFrance
| | - Ivan Paz‐Vinas
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
- Laboratoire Ecologie Fonctionnelle et EnvironnementUniversité de ToulouseUPSCNRSINPUMR‐5245 ECOLABToulouseFrance
| | - Keoni Saint‐Pé
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
| | - Olivier Rey
- IHPEUniv. MontpellierCNRSIfremerUniv. Perpignan Via DomitiaPerpignanFrance
| | - Allan Raffard
- Centre National pour la Recherche ScientifiqueStation d'Écologie Théorique et Expérimentale du CNRS à MoulisUniversité Toulouse III Paul SabatierUMR‐5321MoulisFrance
| | - Eglantine Mathieu‐Bégné
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
- IHPEUniv. MontpellierCNRSIfremerUniv. Perpignan Via DomitiaPerpignanFrance
| | - Géraldine Loot
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
| | - Lisa Fourtune
- Centre National pour la Recherche ScientifiqueLaboratoire Evolution & Diversité BiologiqueInstitut de Recherche pour le DéveloppementUniversité Toulouse III Paul SabatierUMR‐5174 EDBToulouseFrance
- PEIRENEEA 7500Université de LimogesLimogesFrance
| | - Vincent Dubut
- Aix Marseille UniversitéCNRSIRDAvignon UniversitéIMBEMarseilleFrance
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12
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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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13
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Sequential PCA-based Classification of Mediterranean Forest Plants using Airborne Hyperspectral Remote Sensing. REMOTE SENSING 2019. [DOI: 10.3390/rs11232800] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In recent years, hyperspectral remote sensing (HRS) has become common practice for remote analyses of the physiognomy and composition of forests. Supervised classification is often used for this purpose, but demands intensive sampling and analyses, whereas unsupervised classification often requires information retrieval out of the large HRS datasets, thereby not realizing the full potential of the technology. An improved principal component analysis-based classification (PCABC) scheme is presented and intended to provide accurate and sequential image-based unsupervised classification of Mediterranean forest species. In this study, unsupervised classification and reduction of data size are performed simultaneously by applying binary sequential thresholding to principal components, each time on a spatially reduced subscene that includes the entire spectral range. The methodology was tested on HRS data acquired by the airborne AisaFENIX HRS sensor over a Mediterranean forest in Mount Horshan, Israel. A comprehensive field-validation survey was performed, sampling 257 randomly selected individual plants. The PCABC provided highly improved results compared to the traditional unsupervised classification methodologies, reaching an overall accuracy of 91%. The presented approach may contribute to improved monitoring, management, and conservation of Mediterranean and similar forests.
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14
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Rödig E, Knapp N, Fischer R, Bohn FJ, Dubayah R, Tang H, Huth A. From small-scale forest structure to Amazon-wide carbon estimates. Nat Commun 2019; 10:5088. [PMID: 31704933 PMCID: PMC6841659 DOI: 10.1038/s41467-019-13063-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 09/27/2019] [Indexed: 11/30/2022] Open
Abstract
Tropical forests play an important role in the global carbon cycle. High-resolution remote sensing techniques, e.g., spaceborne lidar, can measure complex tropical forest structures, but it remains a challenge how to interpret such information for the assessment of forest biomass and productivity. Here, we develop an approach to estimate basal area, aboveground biomass and productivity within Amazonia by matching 770,000 GLAS lidar (ICESat) profiles with forest simulations considering spatial heterogeneous environmental and ecological conditions. This allows for deriving frequency distributions of key forest attributes for the entire Amazon. This detailed interpretation of remote sensing data improves estimates of forest attributes by 20–43% as compared to (conventional) estimates using mean canopy height. The inclusion of forest modeling has a high potential to close a missing link between remote sensing measurements and the 3D structure of forests, and may thereby improve continent-wide estimates of biomass and productivity. Improving estimates of forest biomass based on remote sensing data is important to assess global carbon cycling. Here the authors develop an approach to use forest gap models to simulate lidar waveforms and compare the outputs with ICESAT-1 GLAS profiles, showing improved estimates across the Amazon basin.
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Affiliation(s)
- Edna Rödig
- Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany. .,Department of Computational Hydrosystems, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany.
| | - Nikolai Knapp
- Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany
| | - Rico Fischer
- Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany
| | - Friedrich J Bohn
- Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany
| | - Ralph Dubayah
- Department of Geographical Sciences, University of Maryland, College Park, 2120 Lefrak Hall, College Park, MD, 20742, USA
| | - Hao Tang
- Department of Geographical Sciences, University of Maryland, College Park, 2120 Lefrak Hall, College Park, MD, 20742, USA
| | - Andreas Huth
- Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany.,University of Osnabrück, Barbarastraße 12, 49076, Osnabrück, Germany.,German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany
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15
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Schimel D, Schneider FD. Flux towers in the sky: global ecology from space. THE NEW PHYTOLOGIST 2019; 224:570-584. [PMID: 31112309 DOI: 10.1111/nph.15934] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Accepted: 04/29/2019] [Indexed: 05/25/2023]
Abstract
Global ecology - the study of the interactions among the Earth's ecosystems, land, atmosphere and oceans - depends crucially on global observations: this paper focuses on space-based observations of global terrestrial ecosystems. Early global ecology relied on an extrapolation of detailed site-level observations, using models of increasing complexity. Modern global ecology has been enabled largely by vegetation indices (greenness) from operational space-based imagery but current capabilities greatly expand scientific possibilities. New observations from spacecraft in orbit allowed an estimation of gross carbon fluxes, photosynthesis, biomass burning, evapotranspiration and biomass, to create virtual eddy covariance sites in the sky. Planned missions will reveal the dimensions of the diversity of life itself. These observations will improve our understanding of the global productivity and carbon storage, land use, carbon cycle-climate feedback, diversity-productivity relationships and enable improved climate forecasts. Advances in remote sensing challenge ecologists to relate information organised by biome and species to new data arrayed by pixels and develop theory to address previously unobserved scales.
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Affiliation(s)
- David Schimel
- Jet Propulsion Lab, California Institute of Technology, Pasadena, CA, 91101, USA
| | - Fabian D Schneider
- Jet Propulsion Lab, California Institute of Technology, Pasadena, CA, 91101, USA
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