10.5061/DRYAD.8SF7M0CJR
Blumenthal, Dana
0000-0001-7496-0766
Agricultural Research Service
Kray, Julie
United States Department of Agriculture
Mueller, Kevin
0000-0002-0739-7472
Cleveland State University
Ocheltree, Troy
Colorado State University
Shortgrass steppe and northern mixedgrass prairie plant species traits
Dryad
dataset
2020
2020-07-21T00:00:00Z
2020-07-21T00:00:00Z
en
1321850 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Despite progress in trait-based ecology, there is limited understanding of
the plant traits that structure semiarid grasslands. In particular, it
remains unclear how traits that enable plants to cope with water
limitation are related to traits that influence other key functions such
as herbivore defense and growth. The hypothesis that drought and herbivory
exert convergent selection pressures is supported for morphological
traits, but largely untested for structural, physiological, and
phenological traits. Drought and economic traits can also covary, but
where and to what degree remains uncertain. Here we address these
uncertainties in semiarid shortgrass steppe and mixedgrass prairie, the
largest remaining grasslands in North America. Using a broad selection of
traits for 37 of the most common plant species in each ecosystem, we ask
whether traits that confer drought tolerance, avoidance and escape covary
with herbivore resistance traits and economic traits. Results reveal that
both drought tolerance and escape are coordinated with other functions,
but in opposite fashion. Drought tolerant species (low leaf osmotic
potential and high leaf dry matter content, LDMC) were also herbivore
resistant (high leaf toughness and cellulose) and at the ‘slow’ end of the
economic spectrum (low leaf nitrogen, leaf phosphorus, and high stem
density). Conversely, drought escape via early senescence was associated
with lower drought tolerance, lower herbivore resistance, and ‘fast’
economic traits. Drought avoidance, as indicated by thick leaves, may also
be associated with lower drought tolerance (LDMC). Senescence date and
LDMC appear to be key traits in these semiarid grasslands, differentiating
species along multiple axes of function. Synthesis – Covariation between
drought, herbivory and economic traits means that, of the many potential
trait combinations, few actually exist within these grasslands.
Consequently, changes in land management and climate should have
predictable effects on drought resistance, forage quality and productivity
in the western Great Plains.
Sampling methods-general: We sampled 5-10 replicate individual plants per
species, depending upon the trait. For most traits, we sampled during
flowering, thereby standardizing each measurement by plant developmental
stage. For leaf osmotic potential, which can vary as water availability
changes within a growing season, we constrained our sampling campaigns to
3-4 week periods of favorable soil moisture conditions when species
diversity was at its seasonal maximum. The majority of traits were
measured in mixedgrass prairie in 2013 and shortgrass steppe in 2014.
Exceptions were leaf senescence date, leaf pubescence, plant height
(measured in 2015 at both sites), and leaf osmotic potential (measured in
mixedgrass in 2015 and shortgrass in 2017).
Trait description and measurement details: Trait Description
Measurement Specific leaf area (SLA, cm2 g-1) Area of an individual leaf
divided by the leaf’s dry mass Area of fully hydrated leaves measured on
multiple leaves from each replicate plant and divided by oven-dry mass of
the same leaves; mean SLA determined for each replicate (n=10). Leaf
nitrogen (N, %) Percentage of leaf dry mass that is N N content determined
from ground oven-dry leaf tissue run through an elemental analyzer (n=10).
Leaf phosphorus (P, %) Percentage of leaf dry mass that is P P content
determined via wet chemistry analysis of ground oven-dry leaf tissue
(n=5). Stem specific density (SSD, mg mm-3) Stem mass per unit volume of
stem tissue Volume calculated from dimensions measured via calipers near
base of hydrated stem, divided by oven-dry mass of the same stem section
(n=10). Leaf osmotic potential (πo, MPa) Leaf cell solute potential at
full hydration Solute concentration of leaf cell water vapor (osmolality)
measured via an osmometer, converted to osmotic potential (n=5). Leaf
thickness (mm) Thickness of leaf lamina Thickness measured perpendicular
to primary axis of extension from the stem, at a central location along
the leaf lamina, using calipers (n=10). Leaf pubescence (%) Percent of
leaf surface covered by hairs Mean percent cover of hairs determined using
a grid of sample points placed over 40X magnified digital images of upper
& lower surfaces of each leaf (n=5). Leaf dry matter content
(LDMC, g g-1) Ratio of dry leaf mass to leaf mass at full hydration Mass
of fully hydrated leaves measured on multiple leaves from each replicate
plant paired with oven-dry mass of the same leaves; mean LDMC determined
for each replicate (n=10). Senescence date (DOY) Day of year on which leaf
senescence began Mean day of year on which leaf canopy greenness of
observed plants declined from 75-100% to 50-75% green (n=5). Individual
leaf area (cm2) Projected area of an individual leaf See leaf area notes
for SLA (n=10). Plant height (mm) Height of uppermost leaf above ground
surface Height of uppermost leaves (i.e. top of canopy), excluding
reproductive structures, above ground surface (n=25). Leaf toughness (N)
Force required to punch a hole through the leaf lamina Force-to-punch
measured on fully hydrated leaves by a LF-Plus materials testing machine
(n=10). Leaf cellulose (%) Percentage of leaf dry mass that is cellulose
Cellulose content, calculated from lignin and acid-detergent fiber content
determined via wet chemistry analysis on ground oven-dry leaf tissue
(n=5). Leaf lignin (%) Percentage of leaf dry mass that is lignin Lignin
content determined via wet chemistry analysis on ground oven-dry leaf
tissue (n=5). Data flag: "P" indicates
three instances where senescence date was not recorded for a species
during the observation year. In these cases, we predicted senescence date
from regression models constructed using other phenology information
across species, sites and years.