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Water availability dictates how plant traits predict demographic rates

Citation

Stears, Alice et al. (2022), Water availability dictates how plant traits predict demographic rates, Dryad, Dataset, https://doi.org/10.5061/dryad.31zcrjdp5

Abstract

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.

Methods

Demographic Data  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). 

Trait Data 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).

Usage Notes

Demographic Data 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. 

Trait Data 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. 

Funding

University of Wyoming