10.5061/DRYAD.K6DJH9W4X
Calle, Leonardo
0000-0002-0269-6115
University of Montana
VTFT_Demography: global ageclass simulation data from the LPJ-wsl v2.0
Dynamic Global Vegetation Model
Dryad
dataset
2020
Ecosystem ecology, ecosystem modeling, DGVM, forest age, ecosystem
demography, age-class modeling
National Aeronautics and Space Administration
https://ror.org/027ka1x80
NNX16AP86H
2020-09-08T00:00:00Z
2020-09-08T00:00:00Z
en
535167057 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Forest ecosystem processes follow classic responses with age, peaking
production around canopy closure and declining thereafter. Although age
dynamics might be more dominant in certain regions over others,
demographic effects on net primary production (NPP) and heterotrophic
respiration (Rh) are bound to exist. Yet, explicit representation of
ecosystem demography is notably absent in most global ecosystem models.
This is concerning because the global community relies on these models to
regularly update our collective understanding of the global carbon cycle.
This paper aims to fill this gap in understanding by presenting the
technical developments of a computationally-efficient approach for
representing age-class dynamics within a global ecosystem model, the
LPJ-wsl v2.0 Dynamic Global Vegetation Model. The modeled age-classes are
initially created by fire feedbacks, wood harvesting, and abandonment of
managed land, otherwise aging naturally until a stand-clearing disturbance
is simulated or prescribed. In this paper, we show that the age-module can
capture classic demographic patterns in stem density and tree height
compared to inventory data, and that patterns of ecosystem function follow
classic responses with age. We also present a few scientific applications
of the model to assess the modeled age-class distribution over time and to
determine the demographic effect on ecosystem fluxes relative to climate.
Simulations show that, between 1860 and 2016, zonal age distribution on
Earth was driven predominately by fire, causing a ~45-year difference in
ages between boreal (50N-90N) and tropical (23S-23N) latitudes. Land use
change and land management was responsible for an additional decrease in
zonal age by -6 years in boreal and by -21 years in temperate (23N-50N)
and tropical latitudes, with the anthropogenic effect on zonal age
distribution increasing over time. A statistical model helped reduced
LPJ-wsl complexity by predicting per-grid-cell annual NPP and Rh fluxes by
three terms: precipitation, temperature and age-class; at global scales,
R2 was between 0.95 and 0.98. As determined by the statistical model, the
demographic effect on ecosystem function was often less than 0.10 kg C m-2
yr-1 but as high as 0.60 kg C m-2 yr-1 where the effect was greatest. In
eastern forests of North America, the demographic effect was of similar
magnitude, or greater than, the effects of climate; demographic effects
were similarly important in large regions of every vegetated continent.
Spatial datasets are provided for global ecosystem ages and the estimated
coefficients for effects of precipitation, temperature and demography on
ecosystem function. The discussion focuses on our finding of an increasing
role of demography in the global carbon cycle, the effect of demography on
relaxation times (resilience) following a disturbance event and its
implications at global scales, and a finding of a 40-Pg C increase in
turnover from age dynamics at global scales. Whereas time is the only
mechanism that increases ecosystem age, any additional disturbance not
explicitly modeled will decrease age. This LPJ-based age-module therefore
simulates the upper limit of age-class distributions on Earth and
represents another step forward towards understanding the role of
demography in global ecosystems.
These datasets contain raw data that were simulated by the LPJ-wsl v2.0
model <https://github.com/benpoulter/LPJ-wsl_v2.0> and R
scripts to reproduce analyses and figures in the associated publication
There is a general README text file in main folder of the archive. It
provides detail description of the folder/file structure and of every file
in the archive. For NetCDF files (*.nc), the pre-processing history is
self-contained in the metadata and can be accessed via the command-line
utility "ncdump -h {file.nc}", to print the history to screen.
The R scripts are heavily annotated and a main header describes the
contents and objectives of each script. The R scripts have been tested and
can be run from within the scripts folder. All R scripts reference
datasets in the data folder. The following R package libraries are
required to succesfully run the scripts: ggplot2, gridExtra, ncdf4,
plyr, raster, vioplot.