1
|
Qiu T, Clark JS, Kovach KR, Townsend PA, Swenson JJ. Remotely sensed crown nutrient concentrations modulate forest reproduction across the contiguous United States. Ecology 2024; 105:e4366. [PMID: 38961606 DOI: 10.1002/ecy.4366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 02/25/2024] [Accepted: 04/23/2024] [Indexed: 07/05/2024]
Abstract
Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with foliar nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the contiguous United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree-years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) across the continent. Within-species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first continental-scale study sheds light on the relationship between seed production and foliar nutrients, highlighting the potential of using combined Light Detection And Ranging (LiDAR) and hyperspectral remote sensing to evaluate forest regeneration. The fact that both ISP and CSP decline in the presence of high foliar P levels has immediate application in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales.
Collapse
Affiliation(s)
- Tong Qiu
- Department of Ecosystem Science and Management, The Pennsylvania State University, University Park, Pennsylvania, USA
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
| | - James S Clark
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
- Universite Grenoble Alpes, Institut National de Recherche pour Agriculture, Alimentation et Environnement (INRAE), Laboratoire EcoSystemes et Societes En Montagne (LESSEM), St. Martin-d'Heres, France
| | - Kyle R Kovach
- Department of Forest and Wildlife Ecology, University of Wisconsin Madison, Madison, Wisconsin, USA
| | - Philip A Townsend
- Department of Forest and Wildlife Ecology, University of Wisconsin Madison, Madison, Wisconsin, USA
| | - Jennifer J Swenson
- Center for Geospatial Analysis, The College of William and Mary, Williamsburg, Virginia, USA
| |
Collapse
|
2
|
Sardans J, Llusià J, Ogaya R, Vallicrosa H, Filella I, Gargallo-Garriga A, Peguero G, Van Langenhove L, Verryckt LT, Stahl C, Courtois EA, Bréchet LM, Tariq A, Zeng F, Alrefaei AF, Wang W, Janssens IA, Peñuelas J. Foliar elementome and functional traits relationships identify tree species niche in French Guiana rainforests. Ecology 2023; 104:e4118. [PMID: 37282712 DOI: 10.1002/ecy.4118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 03/28/2023] [Indexed: 06/08/2023]
Abstract
Biogeochemical niche (BN) hypothesis aims to relate species/genotype elemental composition with its niche based on the fact that different elements are involved differentially in distinct plant functions. We here test the BN hypothesis through the analysis of the 10 foliar elemental concentrations and 20 functional-morphological of 60 tree species in a French Guiana tropical forest. We observed strong legacy (phylogenic + species) signals in the species-specific foliar elemental composition (elementome) and, for the first time, provide empirical evidence for a relationship between species-specific foliar elementome and functional traits. Our study thus supports the BN hypothesis and confirms the general niche segregation process through which the species-specific use of bio-elements drives the high levels of α-diversity in this tropical forest. We show that the simple analysis of foliar elementomes may be used to test for BNs of co-occurring species in highly diverse ecosystems, such as tropical rainforests. Although cause and effect mechanisms of leaf functional and morphological traits in species-specific use of bio-elements require confirmation, we posit the hypothesis that divergences in functional-morphological niches and species-specific biogeochemical use are likely to have co-evolved.
Collapse
Affiliation(s)
- Jordi Sardans
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
- CREAF, Cerdanyola del Vallès, Spain
| | - Joan Llusià
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
- CREAF, Cerdanyola del Vallès, Spain
| | - Romà Ogaya
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
- CREAF, Cerdanyola del Vallès, Spain
| | - Helen Vallicrosa
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
- CREAF, Cerdanyola del Vallès, Spain
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Iolanda Filella
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
- CREAF, Cerdanyola del Vallès, Spain
| | - Albert Gargallo-Garriga
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
- CREAF, Cerdanyola del Vallès, Spain
| | - Guille Peguero
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
- CREAF, Cerdanyola del Vallès, Spain
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Universitat de Barcelona, Barcelona, Spain
| | - Leandro Van Langenhove
- Research Group of Plants and Ecosystems (PLECO), Department of Biology, University of Antwerp, Antwerpen, Belgium
| | - Lore T Verryckt
- Research Group of Plants and Ecosystems (PLECO), Department of Biology, University of Antwerp, Antwerpen, Belgium
| | - Clément Stahl
- INRAE, UMR EcoFoG, CNRS, CIRAD, AgroParisTech, Université des Antilles, Université de Guyane, Kourou, France
| | - Elodie A Courtois
- INRAE, UMR EcoFoG, CNRS, CIRAD, AgroParisTech, Université des Antilles, Université de Guyane, Kourou, France
| | - Laëtitia M Bréchet
- Research Group of Plants and Ecosystems (PLECO), Department of Biology, University of Antwerp, Antwerpen, Belgium
- INRAE, UMR EcoFoG, CNRS, CIRAD, AgroParisTech, Université des Antilles, Université de Guyane, Kourou, France
| | - Akash Tariq
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
- Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele, China
| | - Fanjiang Zeng
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
- Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele, China
| | | | - Weiqi Wang
- Key Laboratory of Humid Subtropical Eco-Geographical Process, Ministry of Education, Fujian Normal University, Fuzhou, China
- College of Geographical Sciences, Fujian Normal University, Fuzhou, China
| | - Ivan A Janssens
- Research Group of Plants and Ecosystems (PLECO), Department of Biology, University of Antwerp, Antwerpen, Belgium
| | - Josep Peñuelas
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
- CREAF, Cerdanyola del Vallès, Spain
| |
Collapse
|
3
|
Liu Z, Yan Y, Pang J, Guo Q, Guan J, Gu J. Mini-drone assisted tree canopy sampling: a low-cost and high-precision solution. FRONTIERS IN PLANT SCIENCE 2023; 14:1272418. [PMID: 37929167 PMCID: PMC10622779 DOI: 10.3389/fpls.2023.1272418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 09/29/2023] [Indexed: 11/07/2023]
Abstract
The collection of tree canopy samples in forest ecosystems has been challenging for researchers and managers during the past decades. Various methods, including pole pruner, tree climber, shooter, throw-line launcher, hydraulic lift (e.g., tower crane) and UAV (unmanned aerial vehicle)-based devices, have been used, however, they are limited by sampling height restrictions, safety hazards to a climber, low retrieving accuracy, high equipment costs, and transportation inconvenience. This study proposed a novel method for collecting tree canopy samples using a portable mini-drone. The mini-drone is operated to pull a traction line across the target branch, drag the retrieving rope to the selected cutting point of the branch, and carry the equipped wire saw or chain saw to cut the canopy sample off. Through on-site testing and field trials, this method was feasible for lower- and middle-canopy sampling (up to 30 meters tall) across most temperate broad-leaved and coniferous tree species. This technique would have great potential in plantation and old-growth forests. Adopting this low-cost mini-drone technique, researchers can collect tree canopy samples safely and efficiently, leading to improvements in relevant physiological and ecological studies focusing on functional traits of branches, leaves, and seeds.
Collapse
Affiliation(s)
- Zhi Liu
- Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin, China
| | - Yuanyuan Yan
- Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin, China
| | - Jiayin Pang
- The UWA Institute of Agriculture and School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia
| | - Qi Guo
- Heilongjiang Institute of Forestry Science, Heilongjiang Academy of Forestry Science, Harbin, China
| | - Junze Guan
- Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin, China
| | - Jiacun Gu
- Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin, China
| |
Collapse
|
4
|
Dickman LT, Jonko AK, Linn RR, Altintas I, Atchley AL, Bär A, Collins AD, Dupuy J, Gallagher MR, Hiers JK, Hoffman CM, Hood SM, Hurteau MD, Jolly WM, Josephson A, Loudermilk EL, Ma W, Michaletz ST, Nolan RH, O'Brien JJ, Parsons RA, Partelli‐Feltrin R, Pimont F, Resco de Dios V, Restaino J, Robbins ZJ, Sartor KA, Schultz‐Fellenz E, Serbin SP, Sevanto S, Shuman JK, Sieg CH, Skowronski NS, Weise DR, Wright M, Xu C, Yebra M, Younes N. Integrating plant physiology into simulation of fire behavior and effects. THE NEW PHYTOLOGIST 2023; 238:952-970. [PMID: 36694296 PMCID: PMC10952334 DOI: 10.1111/nph.18770] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine-scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine-scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process-based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.
Collapse
Affiliation(s)
- L. Turin Dickman
- Earth & Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | - Alexandra K. Jonko
- Earth & Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | - Rodman R. Linn
- Earth & Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | - Ilkay Altintas
- San Diego Supercomputer Center and Halicioglu Data Science InstituteUniversity of California San DiegoLa JollaCA92093USA
| | - Adam L. Atchley
- Earth & Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | - Andreas Bär
- Department of BotanyUniversity of Innsbruck6020InnsbruckAustria
| | - Adam D. Collins
- Earth & Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | - Jean‐Luc Dupuy
- Ecologie des Forêts Méditerranéennes (URFM)INRAe84914AvignonFrance
| | | | | | - Chad M. Hoffman
- Department of Forest and Rangeland StewardshipColorado State UniversityFort CollinsCO80523USA
| | - Sharon M. Hood
- Rocky Mountain Research StationUSDA Forest ServiceMissoulaMT59801USA
| | | | - W. Matt Jolly
- Rocky Mountain Research StationUSDA Forest ServiceMissoulaMT59801USA
| | - Alexander Josephson
- Earth & Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | | | - Wu Ma
- Earth & Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | - Sean T. Michaletz
- Department of Botany and Biodiversity Research CentreThe University of British ColumbiaVancouverBCV6T 1Z4Canada
| | - Rachael H. Nolan
- Hawkesbury Institute for the EnvironmentWestern Sydney UniversityPenrithNSW2753Australia
- NSW Bushfire Risk Management Research HubWollongongNSW2522Australia
| | | | | | - Raquel Partelli‐Feltrin
- Department of Botany and Biodiversity Research CentreThe University of British ColumbiaVancouverBCV6T 1Z4Canada
| | - François Pimont
- Ecologie des Forêts Méditerranéennes (URFM)INRAe84914AvignonFrance
| | - Víctor Resco de Dios
- School of Life Sciences and EngineeringSouthwest University of Science and TechnologyMianyang621010China
- Department of Crop and Forest Sciences and JRU CTFC‐AGROTECNIOUniversitat de LleidaLleida25198Spain
| | - Joseph Restaino
- Fire and Resource Assessment ProgramCalifornia Department of Forestry and Fire ProtectionSouth Lake TahoeCA96155USA
| | - Zachary J. Robbins
- Earth & Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | - Karla A. Sartor
- Environmental Protection and Compliance DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | - Emily Schultz‐Fellenz
- Earth & Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | - Shawn P. Serbin
- Environmental and Climate Sciences DepartmentBrookhaven National LaboratoryUptonNY11973USA
| | - Sanna Sevanto
- Earth & Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | - Jacquelyn K. Shuman
- Climate and Global Dynamics Laboratory, Terrestrial Sciences SectionNational Center for Atmospheric ResearchBoulderCO80305USA
| | - Carolyn H. Sieg
- Rocky Mountain Research StationUSDA Forest ServiceFlagstaffAZ86001USA
| | | | - David R. Weise
- Pacific Southwest Research StationUSDA Forest ServiceRiversideCA92507USA
| | - Molly Wright
- Cibola National ForestUSDA Forest ServiceAlbuquerqueNM87113USA
| | - Chonggang Xu
- Earth & Environmental Sciences DivisionLos Alamos National LaboratoryLos AlamosNM87545USA
| | - Marta Yebra
- Fenner School of Environment and SocietyAustralian National UniversityCanberraACT2601Australia
- School of EngineeringAustralian National UniversityCanberraACT2601Australia
| | - Nicolas Younes
- Fenner School of Environment and SocietyAustralian National UniversityCanberraACT2601Australia
| |
Collapse
|
5
|
Atkins JW, Costanza J, Dahlin KM, Dannenberg MP, Elmore AJ, Fitzpatrick MC, Hakkenberg CR, Hardiman BS, Kamoske A, LaRue EA, Silva CA, Stovall AEL, Tielens EK. Scale dependency of lidar‐derived forest structural diversity. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.14040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Jeff W. Atkins
- Southern Research Station USDA Forest Service New Ellenton South Carolina USA
| | - Jennifer Costanza
- Southern Research Station USDA Forest Service Research Triangle Park North Carolina USA
| | - Kyla M. Dahlin
- Department of Geography, Environment & Spatial Sciences Michigan State University East Lansing Michigan USA
| | - Matthew P. Dannenberg
- Department of Geographical and Sustainability Sciences University of Iowa Iowa City Iowa USA
| | - Andrew J. Elmore
- National Socio‐Environmental Synthesis Center Annapolis Maryland USA
- Appalachian Laboratory University of Maryland Center for Environmental Science Frostburg Maryland USA
| | - Matthew C. Fitzpatrick
- Appalachian Laboratory University of Maryland Center for Environmental Science Frostburg Maryland USA
| | | | - Brady S. Hardiman
- Department of Forestry and Natural Resources Purdue University West Lafayette Indiana USA
- Department of Civil and Environmental Engineering Purdue University West Lafayette Indiana USA
| | - Aaron Kamoske
- Ecosystem Management Coordination USDA Forest Service Saint Paul Minnesota USA
| | - Elizabeth A. LaRue
- Department of Biological Sciences The University of Texas at El Paso El Paso Texas USA
| | - Carlos Alberto Silva
- Forest Biometrics and Remote Sensing Lab, School of Forest, Fisheries, and Geomatics University of Florida Gainesville Florida USA
| | - Atticus E. L. Stovall
- Department of Geographical Sciences University of Maryland College Park Maryland USA
- NASA Goddard Space Flight Center Greenbelt Maryland USA
| | - Elske K. Tielens
- Corix Plains Institute University of Oklahoma Norman Oklahoma USA
| |
Collapse
|
6
|
SanClements MD, Record S, Rose KC, Donnelly A, Chong SS, Duffy K, Hallmark A, Heffernan JB, Liu J, Mitchell JJ, Moore DJP, Naithani K, O'Reilly CM, Sokol ER, Stack Whitney K, Weintraub‐Leff SR, Yang D. People, infrastructure, and data: A pathway to an inclusive and diverse ecological network of networks. Ecosphere 2022. [DOI: 10.1002/ecs2.4262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
| | - Sydne Record
- Department of Wildlife, Fisheries, and Conservation Biology University of Maine Orono Maine USA
| | - Kevin C. Rose
- Department of Biological Sciences Rensselaer Polytechnic Institute Troy New York USA
| | - Alison Donnelly
- Department of Geography University of Wisconsin‐Milwaukee Milwaukee Wisconsin USA
| | - Steven S. Chong
- University of California Berkeley Library University of California Berkeley California USA
| | - Katharyn Duffy
- School of Informatics, Computing and Cyber Systems Northern Arizona University Flagstaff Arizona USA
| | - Alesia Hallmark
- National Ecological Observatory Network Battelle Boulder Colorado USA
| | - James B. Heffernan
- Nicholas School of the Environment Duke University Durham North Carolina USA
| | - Jianguo Liu
- Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife Michigan State University East Lansing Michigan USA
| | | | - David J. P. Moore
- School of Natural Resources and the Environment University of Arizona Tucson Arizona USA
| | - Kusum Naithani
- Department of Biological Sciences University of Arkansas Fayetteville Arkansas USA
| | - Catherine M. O'Reilly
- Department of Geography, Geology, and the Environment Illinois State University Normal Illinois USA
| | - Eric R. Sokol
- National Ecological Observatory Network Battelle Boulder Colorado USA
| | - Kaitlin Stack Whitney
- Science, Technology & Society Department Rochester Institute of Technology Rochester New York USA
| | | | - Di Yang
- Wyoming Geographic Information Science Center University of Wyoming Laramie Wyoming USA
| |
Collapse
|
7
|
LaRue EA, Fahey R, Fuson TL, Foster JR, Matthes JH, Krause K, Hardiman BS. Evaluating the sensitivity of forest structural diversity characterization to
LiDAR
point density. Ecosphere 2022. [DOI: 10.1002/ecs2.4209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Affiliation(s)
- Elizabeth A. LaRue
- Department of Biological Sciences The University of Texas at El Paso El Paso Texas USA
| | - Robert Fahey
- Department of Natural Resources and the Environment and Center for Environmental Sciences and Engineering University of Connecticut Storrs Connecticut USA
| | - Tabatha L. Fuson
- Environmental Science and Engineering The University of Texas at El Paso El Paso Texas USA
| | - Jane R. Foster
- Rubenstein School of Environment and Natural Resources The University of Vermont Burlington Vermont USA
| | - Jaclyn Hatala Matthes
- Department of Biological Sciences Wellesley College Wellesley Massachusetts USA
- Harvard Forest Harvard University Petersham Massachusetts USA
| | - Keith Krause
- Battelle, National Ecological Observatory Network Boulder Colorado USA
| | - Brady S. Hardiman
- Forestry and Natural Resources Purdue University West Lafayette Indiana USA
- Environmental and Ecological Engineering Purdue University West Lafayette Indiana USA
| |
Collapse
|
8
|
Musinsky J, Goulden T, Wirth G, Leisso N, Krause K, Haynes M, Chapman C. Spanning scales: The airborne spatial and temporal sampling design of the National Ecological Observatory Network. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- John Musinsky
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Tristan Goulden
- National Ecological Observatory Network, Battelle Boulder CO USA
| | | | | | - Keith Krause
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Mitch Haynes
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Cameron Chapman
- National Ecological Observatory Network, Battelle Boulder CO USA
| |
Collapse
|
9
|
Rogers A, Serbin SP, Way DA. Reducing model uncertainty of climate change impacts on high latitude carbon assimilation. GLOBAL CHANGE BIOLOGY 2022; 28:1222-1247. [PMID: 34689389 DOI: 10.1111/gcb.15958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/17/2021] [Indexed: 06/13/2023]
Abstract
The Arctic-Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO2 . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.
Collapse
Affiliation(s)
- Alistair Rogers
- Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA
| | - Shawn P Serbin
- Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA
| | - Danielle A Way
- Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA
- Department of Biology, University of Western Ontario, London, Ontario, Canada
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
| |
Collapse
|
10
|
Design and Testing of a Novel Unoccupied Aircraft System for the Collection of Forest Canopy Samples. FORESTS 2022. [DOI: 10.3390/f13020153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Unoccupied Aircraft Systems (UAS) are beginning to replace conventional forest plot mensuration through their use as low-cost and powerful remote sensing tools for monitoring growth, estimating biomass, evaluating carbon stocks and detecting weeds; however, physical samples remain mostly collected through time-consuming, expensive and potentially dangerous conventional techniques. Such conventional techniques include the use of arborists to climb the trees to retrieve samples, shooting branches with firearms from the ground, canopy cranes or the use of pole-mounted saws to access lower branches. UAS hold much potential to improve the safety, efficiency, and reduce the cost of acquiring canopy samples. In this work, we describe and demonstrate four iterations of 3D printed canopy sampling UAS. This work includes detailed explanations of designs and how each iteration informed the design decisions in the subsequent iteration. The fourth iteration of the aircraft was tested for the collection of 30 canopy samples from three tree species: eucalyptus pulchella, eucalyptus globulus and acacia dealbata trees. The collection times ranged from 1 min and 23 s, up to 3 min and 41 s for more distant and challenging to capture samples. A vision for the next iteration of this design is also provided. Future work may explore the integration of advanced remote sensing techniques with UAS-based canopy sampling to progress towards a fully-automated and holistic forest information capture system.
Collapse
|
11
|
Pau S, Nippert JB, Slapikas R, Griffith D, Bachle S, Helliker BR, O'Connor RC, Riley WJ, Still CJ, Zaricor M. Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland. Ecology 2021; 103:e03590. [PMID: 34787909 DOI: 10.1002/ecy.3590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/20/2021] [Accepted: 09/03/2021] [Indexed: 11/12/2022]
Abstract
Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mapping. We tested the accuracy of NEON's readily available AOP derived data products - Leaf Area Index, Total biomass, Ecosystem structure (Canopy height model; CHM), and Canopy Nitrogen by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground-measured LAI (r = 0.32) and AOP Total biomass and ground-measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380-2500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression models. Only one of the eight traits examined, Nitrogen, had a validation R2 of more than 0.25. For all vegetation traits, validation R2 ranged from 0.08-0.29 and the root mean square error of prediction ranged from 14-64%. Our results suggest that currently available AOP derived data products should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field-based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogenous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time, yet the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in-situ field measurements across a diversity of sites.
Collapse
Affiliation(s)
- Stephanie Pau
- Department of Geography, Florida State University, Tallahassee, FL, U.S.A
| | - Jesse B Nippert
- Division of Biology, Kansas State University, Manhattan, KS, U.S.A
| | - Ryan Slapikas
- Department of Geography, Florida State University, Tallahassee, FL, U.S.A
| | - Daniel Griffith
- US Geological Survey Western Geographic Science Center, Moffett Field, CA, 94035, U.S.A.,NASA Ames Research Center, Moffett Field, CA, 94035, U.S.A
| | - Seton Bachle
- Division of Biology, Kansas State University, Manhattan, KS, U.S.A
| | - Brent R Helliker
- Department of Biology, University of Pennsylvania, Philadelphia, PA, 19104, U.S.A
| | - Rory C O'Connor
- USDA-Agricultural Research Service, Eastern Oregon Agricultural Research Center, Burns, OR, U.S.A
| | - William J Riley
- Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, U.S.A
| | - Christopher J Still
- Forest Ecosystems and Society, Oregon State University, Corvallis, OR, 97331, U.S.A
| | - Marissa Zaricor
- Division of Biology, Kansas State University, Manhattan, KS, U.S.A
| |
Collapse
|
12
|
Burnett AC, Anderson J, Davidson KJ, Ely KS, Lamour J, Li Q, Morrison BD, Yang D, Rogers A, Serbin SP. A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:6175-6189. [PMID: 34131723 DOI: 10.1093/jxb/erab295] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 06/14/2021] [Indexed: 06/12/2023]
Abstract
Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. We provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.
Collapse
Affiliation(s)
- Angela C Burnett
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Jeremiah Anderson
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Kenneth J Davidson
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Kim S Ely
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Julien Lamour
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Qianyu Li
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Bailey D Morrison
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Dedi Yang
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Alistair Rogers
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Shawn P Serbin
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| |
Collapse
|
13
|
Burnett AC, Serbin SP, Lamour J, Anderson J, Davidson KJ, Yang D, Rogers A. Seasonal trends in photosynthesis and leaf traits in scarlet oak. TREE PHYSIOLOGY 2021; 41:1413-1424. [PMID: 33611562 DOI: 10.1093/treephys/tpab015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 01/22/2021] [Indexed: 06/12/2023]
Abstract
Understanding seasonal variation in photosynthesis is important for understanding and modeling plant productivity. Here, we used shotgun sampling to examine physiological, structural and spectral leaf traits of upper canopy, sun-exposed leaves in Quercus coccinea Münchh (scarlet oak) across the growing season in order to understand seasonal trends, explore the mechanisms underpinning physiological change and investigate the impact of extrapolating measurements from a single date to the whole season. We tested the hypothesis that photosynthetic rates and capacities would peak at the summer solstice, i.e., at the time of peak photoperiod. Contrary to expectations, our results reveal a late-season peak in both photosynthetic capacity and rate before the expected sharp decrease at the start of senescence. This late-season maximum occurred after the higher summer temperatures and vapor pressure deficit and was correlated with the recovery of leaf water content and increased stomatal conductance. We modeled photosynthesis at the top of the canopy and found that the simulated results closely tracked the maximum carboxylation capacity of Rubisco. For both photosynthetic capacity and modeled top-of-canopy photosynthesis, the maximum value was therefore not observed at the summer solstice. Rather, in each case, the measurements at and around the solstice were close to the overall seasonal mean, with values later in the season leading to deviations from the mean by up to 41 and 52%, respectively. Overall, we found that the expected Gaussian pattern of photosynthesis was not observed. We conclude that an understanding of species- and environment-specific changes in photosynthesis across the season is essential for correct estimation of seasonal photosynthetic capacity.
Collapse
Affiliation(s)
- Angela C Burnett
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Julien Lamour
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Jeremiah Anderson
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Kenneth J Davidson
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Dedi Yang
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Alistair Rogers
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
| |
Collapse
|