10.5061/DRYAD.QNK98SFGJ
Matthaeus, William
0000-0002-0117-4059
Baylor University
Macarewich, Sophia I.
University of Michigan-Ann Arbor
Richey, Jon D.
0000-0001-9332-3375
Baylor University
Wilson, Jonathan P.
0000-0002-8586-171X
Haverford College
McElwin, Jennifer C.
Trinity College Dublin
Montañez, Isabel P.
University of California, Davis
DiMichele, William A.
Smithsonian Institution
Hren, Michael T.
University of Connecticut
Poulsen, Christopher J.
University of Michigan-Ann Arbor
White, Joseph D.
Baylor University
Data for: Freeze tolerance influenced forest cover and hydrology during
the Pennsylvanian
Dryad
dataset
2021
FOS: Earth and related environmental sciences
Paleobiology
Computational biology
earth system science
Plant physiology
Division of Earth Sciences
https://ror.org/05v01mk25
EAR-1338247
Division of Earth Sciences
https://ror.org/05v01mk25
EAR-1338281
Division of Earth Sciences
https://ror.org/05v01mk25
EAR-1338200
Division of Earth Sciences
https://ror.org/05v01mk25
EAR-1338256
Division of Earth Sciences
https://ror.org/05v01mk25
EAR-1338256
European Research Council
https://ror.org/0472cxd90
ERC-2011-StG
European Research Council
https://ror.org/0472cxd90
279962-OXYEVOL
2021-12-02T00:00:00Z
2021-12-02T00:00:00Z
en
https://github.com/wjmatthaeus/bgc_utils
https://github.com/josephdwhite/paleo-bgc
https://doi.org/10.1073/pnas.2025227118
292409358797 bytes
6
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Global forest cover affects the Earth system by altering surface mass and
energy exchange. Physiology determines plant environmental limits and
influences geographical vegetation distribution. Ancient plant physiology,
therefore, likely affected vegetation-climate feedbacks. We combine
climate modeling and ecosystem-process modeling to simulate arboreal
vegetation in the late Paleozoic ice age. Using GENESIS V3 GCM
simulations, varying pCO2, pO2, and ice extent for the Pennsylvanian, and
fossil-derived leaf C:N, maximum stomatal conductance, and specific
conductivity for several major Carboniferous plant groups, we simulated
global ecosystem processes at a 2-degree (longitude, latitude) resolution
with Paleo-BGC. Based on leaf water constraints, Pangaea could have
supported widespread arboreal plant growth and forest cover. However,
these models do not account for the impacts of freezing on plants.
According to our interpretation, freezing would have affected plants in
89% of unglaciated land during peak glacial periods, and 65% during the
warmer interglacials. Comparing forest cover, minimum temperatures, and
paleo-locations of Pennsylvanian-aged plant fossils from the Paleobiology
Database supports restriction of global forest extent due to freezing.
Many genera were limited to 25% of unglaciated land where temperatures
remained above −4°C. Freeze-intolerance of Pennsylvanian arboreal
vegetation had the potential to alter surface runoff, silicate weathering,
CO2 levels, and climate forcing. As a bounding case, we assume total
plant mortality at −4°C and estimate that contracting forest cover
increased net global surface runoff by up to 6.1%. Repeated freezing
likely influenced freeze- and drought-tolerance evolution in lineages like
the coniferophytes, which became increasingly dominant in the Permian and
early Mesozoic.
Forest cover in the Pennsylvanian was simulated for a global grid
consisting of 2° × 2° cells using the ecosystem process model Paleo-BGC
(1), a modified version of the ecosystem model BIOME-BGC (2, 3), driven by
daily data derived from GENESIS V3 (4, 5) Forest cover in the
Pennsylvanian was simulated for a global grid consisting of 2° × 2° cells
using the ecosystem process model Paleo-BGC (1), which is a modified
version of the ecosystem model BIOME-BGC (2, 3) driven by daily-resolution
output derived from GENESIS V3 (4, 5)( Global Environmental and Ecological
Simulation of Interactive Systems; Thompson and Pollard 1997, Alder et al.
2011). The Paleo-BGC model (1) includes variable atmospheric pO2,
mesophyll conductance important for plants with thickened leaves, and leaf
hydraulic conductivity to account for simple and complex vascular
pathways. Measurements of leaf carbon to nitrogen ratio (C:N; kg kg-1)
were derived from preserved cuticular material. Maximum stomatal
conductance (gsmax; mol m-2s-1) was determined from stomatal density and
size of leaf fossil impressions. Fossil measurements were collected for
Pennsylvanian representative plant-fossil taxa (described in (6) including
lycopsids, sphenophytes, pteridosperms, stem-group marattialean tree ferns
(tree ferns), and early-diverging coniferophytes (cordaitaleans; (6, 7).
Specific leaf area (SLA; m2 kg C-1) and related leaf attributes,
important for converting carbon allocated to leaves to leaf area, were
estimated using measured leaf C:N to SLA relationships from extant
relatives (8, 9). Boundary layer conductance (gb; μmol s-1Pa-1m-2) was
estimated from the mean leaf or leaflet width (10). For leaf mesophyll
conductance (gm; μmol s-1Pa-1m-2), a single, mean value of 0.273 mol H2O
m-2s-1 was used as a basis for measurements of leaf air space, leaf
thickness, mean mesophyll cell width, and cell-wall thickness of leaf
fossil cross-sections of Pennsylvanian taxa (1). Finally, leaf hydraulic
conductivity values (Kleaf; mmol m-2s-1MPa-1) were calculated from an
empirical relationship (11) using minimum and maximum leaf mesophyll
pathlength derived from leaf fossil cross-sections (11). Data was
generated by GENESIS V3 as described in (12–14). Minimum temperatures were
averaged over terrestrial locations that were not covered by glacial ice.
Landmasses and glacial extents were specified after the description of
Ziegler, Hulver, and Rowley (15) for ~290 Ma (earliest Artinskian).
Vegetated land area was mapped following a two-part categorization process
for each un-glaciated terrestrial grid cell. Inputs taken for Paleo-BGC
included daily maximum, minimum, and average temperature (Tmin, Tmax, and
Tave), precipitation, and shortwave radiation for ten years. Vapor
pressure deficit (VPD), a required input for Paleo-BGC, was calculated
using Tmin and Tave in the modified Tetens Equation (16) for each grid
cell. Finally, daily daylength was derived for each grid cell based on
latitude and year day (10). Two climate scenarios were evaluated,
intended to provide a characterization of the glacial and interglacial
intervals. One climate scenario with glacial ice and CO2 of 182 ppm is
referred to as the glacial, and one with reduced glacial ice and CO2 of
546 ppm is referred to as the interglacial. For both scenarios,
atmospheric oxygen was set to 28%, which is considered a maximum pO2 for
this period based on recent modeling (17). 1. J. D. White, et
al., A Process-Based Ecosystem Model (Paleo-BGC) to Simulate the Dynamic
Response of Late Carboniferous Plants to Elevated O2 And Aridification.
American Journal of Science In Press (2020). 2. M. A. White, P. E.
Thornton, S. W. Running, R. R. Nemani, Parameterization and sensitivity
analysis of the BIOME-BGC terrestrial ecosystem model: net primary
production controls. Earth interactions 4, 1–85 (2000). 3. J. S.
Golinkoff, “Estimation and modeling of forest attributes across large
spatial scales using BiomeBGC, high-resolution imagery, LiDAR data, and
inventory data.,” University of Montana. (2013). 4. S. L.
Thompson, D. Pollard, Greenland and Antarctic Mass Balances for Present
and Doubled Atmospheric CO 2 from the GENESIS Version-2 Global Climate
Model. Journal of Climate 10, 871–900 (1997). 5. J. R. Alder, S.
W. Hostetler, D. Pollard, A. Schmittner, Evaluation of a present-day
climate simulation with a new coupled atmosphere-ocean model GENMOM.
Geoscientific Model Development 4, 69–83 (2011). 6. I. P.
Montañez, et al., Climate, pCO2 and terrestrial carbon cycle linkages
during late Palaeozoic glacial–interglacial cycles. Nature Geoscience 9,
824–828 (2016). 7. J. D. Richey, et al., Influence of temporally
varying weatherability on CO2-climate coupling and ecosystem change in the
late Paleozoic. Clim. Past 16, 1759–1775 (2020). 8. M. T. van
Wijk, M. Williams, G. R. Shaver, Tight coupling between leaf area index
and foliage N content in arctic plant communities. Oecologia 142, 421–427
(2005). 9. Joseph. D. White, Neal. A. Scott, Specific leaf area
and nitrogen distribution in New Zealand forests: Species independently
respond to intercepted light. Forest Ecology and Management 226, 319–329
(2006). 10. G. s. Campbell, J. M. Norman, An introduction to
environmental biophysics (Springer, 1998). 11. T. J. Brodribb, T. S.
Feild, G. J. Jordan, Leaf Maximum Photosynthetic Rate and Venation Are
Linked by Hydraulics. PLANT PHYSIOLOGY 144, 1890–1898 (2007). 12. D.
E. Horton, C. J. Poulsen, I. P. Montañez, W. A. DiMichele,
Eccentricity-paced late Paleozoic climate change. Palaeogeography,
Palaeoclimatology, Palaeoecology 331–332, 150–161 (2012). 13. D. E.
Horton, C. J. Poulsen, D. Pollard, Influence of high-latitude vegetation
feedbacks on late Palaeozoic glacial cycles. Nature Geoscience 3, 572–577
(2010). 14. D. E. Horton, C. J. Poulsen, Paradox of late Paleozoic
glacioeustasy. Geology 37, 715–718 (2009). 15. I. P. Martini, Ed.,
Late glacial and postglacial environmental changes : Quaternary,
Carboniferous-Permian, and Proterozoic (Oxford University Press, 1997).
16. F. W. Murray, On the Computation of Saturation Vapor Pressure.
Journal of Applied Meteorology 6, 203–204 (1967). 17. A. J. Krause,
et al., Stepwise oxygenation of the Paleozoic atmosphere. Nature
Communications 9 (2018).
Scripts for porting GENESIS V3 data to Paleo-BGC input can be found
at github.com/wjmatthaeus/bgc_utils. Paleo-BGC and its associated
parameterizations can be found at github.com/josephdwhite/paleo-bgc.
GENESIS V3 files are named for their history variable, CO2 and O2 levels,
simulation year, and resolution (i.e. PRECIP_546.28_41.2x2.nc). Paleo-BGC
output is named for the glacial scenario (GLAC/IGLAC), CO2 and O2 levels,
longitude and latitude, plant type, and fossil parameter values. Filenames
were used to parse Paleo-BGC output into summary data structures in R for
analysis. Please note: Paleo-BGC output is in a file structure that
associates each model run with its parameterization an initialization
files. This output is best processed using unix based command line
utilities provided on github above.