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From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance.
. 2019 12; 224(4):1557-1568.

Abstract

Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m-2 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R2 = 0.89; root mean square error (RMSE) = 15.45 g m-2). Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.

Authors+Show Affiliations

Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA.Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA. School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong.Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA.Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, 53706, USA.Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, 53706, USA.Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA. College of Natural Resources and Environment, Huazhong Agricultural University, Wuhan, Hubei, China.Smithsonian Tropical Research Institute, Apartado, 0843-03092, Balboa, Panama. School of Renewable Natural Resources, Louisiana State University, Baton Rouge, LA, USA.Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, 53706, USA.Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, 53706, USA.Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA.

Pub Type(s)

Journal Article
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

31418863

Citation

Serbin, Shawn P., et al. "From the Arctic to the Tropics: Multibiome Prediction of Leaf Mass Per Area Using Leaf Reflectance." The New Phytologist, vol. 224, no. 4, 2019, pp. 1557-1568.
Serbin SP, Wu J, Ely KS, et al. From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance. New Phytol. 2019;224(4):1557-1568.
Serbin, S. P., Wu, J., Ely, K. S., Kruger, E. L., Townsend, P. A., Meng, R., Wolfe, B. T., Chlus, A., Wang, Z., & Rogers, A. (2019). From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance. The New Phytologist, 224(4), 1557-1568. https://doi.org/10.1111/nph.16123
Serbin SP, et al. From the Arctic to the Tropics: Multibiome Prediction of Leaf Mass Per Area Using Leaf Reflectance. New Phytol. 2019;224(4):1557-1568. PubMed PMID: 31418863.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance. AU - Serbin,Shawn P, AU - Wu,Jin, AU - Ely,Kim S, AU - Kruger,Eric L, AU - Townsend,Philip A, AU - Meng,Ran, AU - Wolfe,Brett T, AU - Chlus,Adam, AU - Wang,Zhihui, AU - Rogers,Alistair, Y1 - 2019/09/17/ PY - 2019/03/20/received PY - 2019/07/28/accepted PY - 2019/8/17/pubmed PY - 2020/8/6/medline PY - 2019/8/17/entrez KW - leaf mass area KW - partial least-squares regression (PLSR) KW - plant traits KW - remote sensing KW - specific leaf area KW - spectroscopy SP - 1557 EP - 1568 JF - The New phytologist JO - New Phytol. VL - 224 IS - 4 N2 - Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m-2 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R2 = 0.89; root mean square error (RMSE) = 15.45 g m-2). Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes. SN - 1469-8137 UR - https://www.unboundmedicine.com/medline/citation/31418863/From_the_Arctic_to_the_tropics:_multibiome_prediction_of_leaf_mass_per_area_using_leaf_reflectance_ L2 - https://doi.org/10.1111/nph.16123 DB - PRIME DP - Unbound Medicine ER -