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Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area

Research output: Contribution to journal › Article › peer-review

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Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area. / Smart, Simon Mark; Glanville, Helen; del Carmen Blanes, Maria et al.
In: Functional Ecology, Vol. 31, No. 6, 06.2017, p. 1336-1344.

Research output: Contribution to journal › Article › peer-review

HarvardHarvard

Smart, SM, Glanville, H, del Carmen Blanes, M, Mercado, LM, Emmett, B, Cosby, B, Jones, D, Marrs, RH, Butler, A, Marshall, M, Reinsch, S, Herrero-Jauregui, C & Hodgson, JG 2017, 'Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area', Functional Ecology, vol. 31, no. 6, pp. 1336-1344. https://doi.org/10.1111/1365-2435.12832

APA

Smart, S. M., Glanville, H., del Carmen Blanes, M., Mercado, L. M., Emmett, B., Cosby, B., Jones, D., Marrs, R. H., Butler, A., Marshall, M., Reinsch, S., Herrero-Jauregui, C., & Hodgson, J. G. (2017). Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area. Functional Ecology, 31(6), 1336-1344. https://doi.org/10.1111/1365-2435.12832

CBE

Smart SM, Glanville H, del Carmen Blanes M, Mercado LM, Emmett B, Cosby B, Jones D, Marrs RH, Butler A, Marshall M, et al. 2017. Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area. Functional Ecology. 31(6):1336-1344. https://doi.org/10.1111/1365-2435.12832

MLA

Smart, Simon Mark et al. "Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area". Functional Ecology. 2017, 31(6). 1336-1344. https://doi.org/10.1111/1365-2435.12832

VancouverVancouver

Smart SM, Glanville H, del Carmen Blanes M, Mercado LM, Emmett B, Cosby B et al. Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area. Functional Ecology. 2017 Jun;31(6):1336-1344. Epub 2017 Jun 2. doi: 10.1111/1365-2435.12832

Author

Smart, Simon Mark ; Glanville, Helen ; del Carmen Blanes, Maria et al. / Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area. In: Functional Ecology. 2017 ; Vol. 31, No. 6. pp. 1336-1344.

RIS

TY - JOUR

T1 - Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area

AU - Smart, Simon Mark

AU - Glanville, Helen

AU - del Carmen Blanes, Maria

AU - Mercado, Lina Maria

AU - Emmett, Bridget

AU - Cosby, Bernard

AU - Jones, David

AU - Marrs, Robert Hunter

AU - Butler, Adam

AU - Marshall, Miles

AU - Reinsch, Sabine

AU - Herrero-Jauregui, Cristina

AU - Hodgson, John Gavin

PY - 2017/6

Y1 - 2017/6

N2 - Reliable modelling of above-ground net primary production (aNPP) at fine resolution is a significant challenge. A promising avenue for improving process models is to include response and effect trait relationships. However, uncertainties remain over which leaf traits are correlated most strongly with aNPP. We compared abundance-weighted values of two of the most widely used traits from the leaf economics spectrum (specific leaf area and leaf dry matter content) with measured aNPP across a temperate ecosystem gradient. We found that leaf dry matter content (LDMC) as opposed to specific leaf area (SLA) was the superior predictor of aNPP (R-2=055). Directly measured insitu trait values for the dominant species improved estimation of aNPP significantly. Introducing intraspecific trait variation by including the effect of replicated trait values from published databases did not improve the estimation of aNPP. Our results support the prospect of greater scientific understanding for less cost because LDMC is much easier to measure than SLA.

AB - Reliable modelling of above-ground net primary production (aNPP) at fine resolution is a significant challenge. A promising avenue for improving process models is to include response and effect trait relationships. However, uncertainties remain over which leaf traits are correlated most strongly with aNPP. We compared abundance-weighted values of two of the most widely used traits from the leaf economics spectrum (specific leaf area and leaf dry matter content) with measured aNPP across a temperate ecosystem gradient. We found that leaf dry matter content (LDMC) as opposed to specific leaf area (SLA) was the superior predictor of aNPP (R-2=055). Directly measured insitu trait values for the dominant species improved estimation of aNPP significantly. Introducing intraspecific trait variation by including the effect of replicated trait values from published databases did not improve the estimation of aNPP. Our results support the prospect of greater scientific understanding for less cost because LDMC is much easier to measure than SLA.

KW - Bayesian modelling; ecosystem function; global change; intraspecific variation; measurement error

U2 - 10.1111/1365-2435.12832

DO - 10.1111/1365-2435.12832

M3 - Article

VL - 31

SP - 1336

EP - 1344

JO - Functional Ecology

JF - Functional Ecology

SN - 0269-8463

IS - 6

ER -