DOI: 10.5061/dryad.8sf7m0cjr
title: Shortgrass steppe and northern mixedgrass prairie plant species traits

description: 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. 
description: 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).
description: <b>Trait description
 and measurement details:    </b> 
 <b>Trait</b>
 
 <b>Description</b> 
 <b>Measurement</b> 
 Specific leaf area (SLA,
 cm<sup>2</sup> g<sup>-1</sup>)
 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 (<i>n</i>=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 (<i>n</i>=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 (<i>n</i>=5). 
 Stem specific density (SSD, mg
 mm<sup>-3</sup>) 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
 (<i>n</i>=10). 
 Leaf osmotic potential (π<sub>o,
 </sub>MPa) Leaf
 cell solute potential at full hydration Solute
 concentration of leaf cell water vapor (osmolality) measured via an
 osmometer, converted to osmotic potential
 (<i>n</i>=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
 (<i>n</i>=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 (<i>n</i>=5). 
 Leaf dry matter content
 (LDMC, g g<sup>-1</sup>) 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 (<i>n</i>=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 (<i>n</i>=5).
 Individual leaf area
 (cm<sup>2</sup>) 
 Projected area of an individual leaf See
 leaf area notes for SLA (<i>n</i>=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 (<i>n</i>=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 (<i>n</i>=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 (<i>n</i>=5). 
 Leaf lignin (%)
 
 Percentage of leaf dry mass that is lignin 
 Lignin
 content determined via wet chemistry analysis on ground oven-dry leaf
 tissue (<i>n</i>=5). 
                        
 <b>Data flag:</b> "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.