DOI: 10.5061/dryad.31zcrjdp5
title: Water availability dictates how plant traits predict demographic rates

description: A major goal in ecology is to make generalizable predictions of organism
 responses to environmental variation based on their traits. However,
 straightforward relationships between traits and fitness are rare and
 likely vary with environmental context. Characterizing how traits mediate
 demographic responses to the environment may enhance predictions of
 organism responses to global change. We synthesized 15 years of
 demographic data and species-level traits in a shortgrass steppe to
 determine whether the effects of leaf and root traits on growth and
 survival depend on seasonal water availability. We predicted that (1)
 species with drought-tolerant traits, such as lower leaf turgor loss point
 (TLP) and higher leaf and root dry matter content (LDMC and RDMC), would
 be more likely to survive and grow in drier years due to higher wilting
 resistance, (2) these traits would not predict fitness in wetter years,
 and (3) traits that more directly measure physiological mechanisms of
 water use such as TLP would best predict demographic responses. We found
 that graminoids with more negative TLP and higher LDMC and RDMC had higher
 survival rates in drier years. Forbs demonstrated similar yet more
 variable responses. Graminoids grew larger in wetter years, regardless of
 traits. However, in both wet and dry years, graminoids with more negative
 TLP and higher LDMC and RDMC grew larger than less negative TLP and low
 LDMC and RDMC species. Traits significantly mediated the impact of drought
 on survival, but not growth, suggesting survival could be a stronger
 driver of species’ drought response in this system. TLP predicted survival
 in drier years, but easier-to-measure LDMC and RDMC were equal or better
 predictors. These results advance our understanding of the mechanisms by
 which drought drives population dynamics, and show that abiotic context
 determines how traits drive fitness.
description: <strong>Demographic Data </strong> We monitored
 growth and survival for eight graminoid and eight forb species in 24,
 1-m° chart-quadrats from 1997 to 2010 at the Central Plains Experimental
 Research location (CPER) in Nunn, Colorado, USA (40.8 °N/110.8 °W). This
 North American shortgrass steppe is at 1650 m elevation and is dominated
 by Bouteloua gracilis and Bouteloua dactyloides. It receives an annual
 average of 340 mm of precipitation, and has a mean annual temperature of 8
 °C (Chu et al. 2014) (Appendix S2: Section S1). The chart-quadrat method
 maps each plant in each year, but does not uniquely identify each
 individual. Plants with a sizeable basal area are mapped as polygons,
 while grasses and forbs with few stems are mapped as points. Graminoids in
 this analysis were measured as polygons, and forbs as points, so we use
 these functional groups in place of “polygon” or “point.” Points
 representing forbs do not indicate plant size, so we can only measure
 growth for graminoids. We extracted growth and survival from a digitized
 version of this map dataset using "tracking algorithms" in R
 (version 4.0.3) (Lauenroth and Adler 2008, R Core Team 2021). These
 algorithms loop through the annual maps for a given quadrat, and assume
 that individuals of the same species growing in the same location in
 consecutive years are the same individual. In this way, the algorithms
 generate records of survival for all individuals, and records of growth or
 shrinkage for individuals measured as polygons. Individual plants can go
 dormant for up to several years, such that no above-ground material is
 visible. Individuals were allowed to be ‘dormant’ for up to one year.
 These tracking scripts allow for a user-defined period of dormancy, which
 we set to the conservative limit of one year. This means that if an
 individual is present in year one, absent in year two, but present in year
 three at the same coordinates as year one, this plant is considered the
 same individual. We also allowed a “buffer” of 5 cm in observations of the
 same individual from year to year, which accounts for measurement error as
 well as true variation in re-sprouting location across years (Fair et al.
 1999, Lauenroth and Adler 2008, Chu et al. 2014). 
 <strong>Trait Data</strong> We measured leaf and root
 traits for the 16 species in the demographic dataset. Five to ten mature,
 healthy individuals of each species were sampled for each trait. A
 majority of the values used in this analysis were collected at the CPER.
 However, several additional species were measured at the USDA-ARS High
 Plains Grasslands Research Station (HPGRS), a northern mixed-grass prairie
 60 km from the CPER. In most cases, trait samples were collected from CPER
 and HPGRS between 2014 and 2018, and the associated data has been
 published (Blumenthal et al. 2020). For species without trait data from
 CPER or HPGRS, we used species-level trait values measured in 2018 and
 2019 at Hays, KS, Miles City, MT and Dubois, ID. We calculated species
 mean values for seven traits: specific leaf area (SLA), leaf and root dry
 matter content (LDMC and RDMC), leaf turgor loss point (TLP), specific
 root length (SRL), average root diameter (RDiam), and root tissue density
 (RTD). SLA and LDMC were measured using standard methods
 (Pérez-Harguindeguy et al. 2013). TLP was calculated from measurements of
 leaf osmotic potential at full turgor made using a Vapro 5600 osmometer
 (Bartlett et al. 2012). TLP was derived from osmotic potential using the
 following equations: graminoid leaf turgor loss point = 0.944*(leaf
 osmotic potential) – 0.622, forb leaf turgor loss point = 0.80*(leaf
 osmotic potential) – 0.845 (Griffin-Nolan et al. 2019). Below ground
 traits were measured for fine, absorptive root tissue (typically 1st-3rd
 order roots) (McCormack et al. 2015). Intact absorptive root branches were
 extracted from soil monoliths that were sampled to 20 cm beneath the soil
 surface (Blumenthal et al., unpublished). Root length, average diameter,
 and root volume were measured using WinRhizo software (Regent Instruments,
 Inc., Ville de Québec, QC Canada). Further details of leaf trait sampling
 and measurement protocols can be found in (Blumenthal et al.
 2020).
description: <strong>Demographic Data</strong> The demographic
 data is in two files: one for that has data for species measured as points
 (forbs), and another that has data for species measured as polygons
 (graminoids). The points data file does not have information for
 "area_t" or "area_tplus1" because size was not
 measured for species measured as points. 
 <strong>Trait Data</strong> This file shows mean
 trait values for each species for each trait. Mean values for leaf traits
 were calculated from at least five replicates for each species.