Home > Research > Publications & Outputs > A method for reconstructing temporal changes in...

Links

Text available via DOI:

View graph of relations

A method for reconstructing temporal changes in vegetation functional trait composition using Holocene pollen assemblages

Research output: Contribution to journalJournal article

Published

Standard

A method for reconstructing temporal changes in vegetation functional trait composition using Holocene pollen assemblages. / Carvalho, Fabio; Brown, Kerry A.; Waller, Martyn P.; Bunting, M. Jane; Boom, Arnoud; Leng, Melanie J.

In: PLoS ONE, Vol. 14, No. 5, E0216698, 29.05.2019.

Research output: Contribution to journalJournal article

Harvard

APA

Vancouver

Author

Carvalho, Fabio ; Brown, Kerry A. ; Waller, Martyn P. ; Bunting, M. Jane ; Boom, Arnoud ; Leng, Melanie J. / A method for reconstructing temporal changes in vegetation functional trait composition using Holocene pollen assemblages. In: PLoS ONE. 2019 ; Vol. 14, No. 5.

Bibtex

@article{c08235be73c14c9d94807be4e944e08d,
title = "A method for reconstructing temporal changes in vegetation functional trait composition using Holocene pollen assemblages",
abstract = "Methods of reconstructing changes in plant traits over long time scales are needed to understand the impact of changing environmental conditions on ecosystem processes and services. Although Holocene pollen have been extensively used to provide records of vegetation history, few studies have adopted a functional trait approach that is pertinent to changes in ecosystem processes. Here, for woody and herbaceous fen peatland communities, we use modern pollen and vegetation data combined with pollen records from Holocene deposits to reconstruct vegetation functional dynamics. The six traits chosen (measures of leaf area-to-mass ratio and leaf nutrient content) are known to modulate species{\textquoteright} fitness and to vary with changes in ecosystem processes. We fitted linear mixed effects models between community weighted mean (CWM) trait values of the modern pollen and vegetation to determine whether traits assigned to pollen types could be used to reconstruct traits found in the vegetation from pollen assemblages. We used relative pollen productivity (RPP) correction factors in an attempt to improve this relationship. For traits showing the best fit between modern pollen and vegetation, we applied the model to dated Holocene pollen sequences from Fenland and Romney Marsh in eastern and southern England and reconstructed temporal changes in trait composition. RPP adjustment did not improve the linear relationship between modern pollen and vegetation. Leaf nutrient traits (leaf C and N) were generally more predictable from pollen data than mass-area traits. We show that inferences about biomass accumulation and decomposition rates can be made using Holocene trait reconstructions. While it is possible to reconstruct community-level trends for some leaf traits from pollen assemblages preserved in sedimentary archives in wetlands, we show the importance of testing methods in modern systems first and encourage further development of this approach to address issues concerning the pollen-plant abundance relationship and pollen source area.",
author = "Fabio Carvalho and Brown, {Kerry A.} and Waller, {Martyn P.} and Bunting, {M. Jane} and Arnoud Boom and Leng, {Melanie J.}",
year = "2019",
month = may
day = "29",
doi = "10.1371/journal.pone.0216698",
language = "English",
volume = "14",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "5",

}

RIS

TY - JOUR

T1 - A method for reconstructing temporal changes in vegetation functional trait composition using Holocene pollen assemblages

AU - Carvalho, Fabio

AU - Brown, Kerry A.

AU - Waller, Martyn P.

AU - Bunting, M. Jane

AU - Boom, Arnoud

AU - Leng, Melanie J.

PY - 2019/5/29

Y1 - 2019/5/29

N2 - Methods of reconstructing changes in plant traits over long time scales are needed to understand the impact of changing environmental conditions on ecosystem processes and services. Although Holocene pollen have been extensively used to provide records of vegetation history, few studies have adopted a functional trait approach that is pertinent to changes in ecosystem processes. Here, for woody and herbaceous fen peatland communities, we use modern pollen and vegetation data combined with pollen records from Holocene deposits to reconstruct vegetation functional dynamics. The six traits chosen (measures of leaf area-to-mass ratio and leaf nutrient content) are known to modulate species’ fitness and to vary with changes in ecosystem processes. We fitted linear mixed effects models between community weighted mean (CWM) trait values of the modern pollen and vegetation to determine whether traits assigned to pollen types could be used to reconstruct traits found in the vegetation from pollen assemblages. We used relative pollen productivity (RPP) correction factors in an attempt to improve this relationship. For traits showing the best fit between modern pollen and vegetation, we applied the model to dated Holocene pollen sequences from Fenland and Romney Marsh in eastern and southern England and reconstructed temporal changes in trait composition. RPP adjustment did not improve the linear relationship between modern pollen and vegetation. Leaf nutrient traits (leaf C and N) were generally more predictable from pollen data than mass-area traits. We show that inferences about biomass accumulation and decomposition rates can be made using Holocene trait reconstructions. While it is possible to reconstruct community-level trends for some leaf traits from pollen assemblages preserved in sedimentary archives in wetlands, we show the importance of testing methods in modern systems first and encourage further development of this approach to address issues concerning the pollen-plant abundance relationship and pollen source area.

AB - Methods of reconstructing changes in plant traits over long time scales are needed to understand the impact of changing environmental conditions on ecosystem processes and services. Although Holocene pollen have been extensively used to provide records of vegetation history, few studies have adopted a functional trait approach that is pertinent to changes in ecosystem processes. Here, for woody and herbaceous fen peatland communities, we use modern pollen and vegetation data combined with pollen records from Holocene deposits to reconstruct vegetation functional dynamics. The six traits chosen (measures of leaf area-to-mass ratio and leaf nutrient content) are known to modulate species’ fitness and to vary with changes in ecosystem processes. We fitted linear mixed effects models between community weighted mean (CWM) trait values of the modern pollen and vegetation to determine whether traits assigned to pollen types could be used to reconstruct traits found in the vegetation from pollen assemblages. We used relative pollen productivity (RPP) correction factors in an attempt to improve this relationship. For traits showing the best fit between modern pollen and vegetation, we applied the model to dated Holocene pollen sequences from Fenland and Romney Marsh in eastern and southern England and reconstructed temporal changes in trait composition. RPP adjustment did not improve the linear relationship between modern pollen and vegetation. Leaf nutrient traits (leaf C and N) were generally more predictable from pollen data than mass-area traits. We show that inferences about biomass accumulation and decomposition rates can be made using Holocene trait reconstructions. While it is possible to reconstruct community-level trends for some leaf traits from pollen assemblages preserved in sedimentary archives in wetlands, we show the importance of testing methods in modern systems first and encourage further development of this approach to address issues concerning the pollen-plant abundance relationship and pollen source area.

U2 - 10.1371/journal.pone.0216698

DO - 10.1371/journal.pone.0216698

M3 - Journal article

VL - 14

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 5

M1 - E0216698

ER -