Leaf habit affects the distribution of drought sensitivity but not water transport efficiency in the tropics

Vargas G., German1 ; Norbert, Kunert2 ; Hammond, William M.3; Berry, Z. Carter4; Werden, Leland K.5; Smith-Martin, Chris M.6; Wolfe, Brett T.7; Toro, Laura1; Mondragón-Botero, Ariadna1; Pinto-Ledezma, Jesús N.1; Schwartz, Naomi B.8; Uriarte, María6; Sack, Lawren9; Anderson-Texeira, Kristina J.10; Powers, Jennifer S.1

Published Nov 20, 2023 on Dryad. https://doi.org/10.5061/dryad.1ns1rn8x4

Data files

Nov 20, 2023 version files 1.35 MB

rainfall_niches_15_nin.csv

339.73 KB
README_VargasGetal2022

9.40 KB
README.md

10.85 KB
trait_data.csv

950.74 KB
Tree1084.tre

42.46 KB

Abstract

Considering the global intensification of aridity in tropical biomes due to climate change, we need to understand what shapes the distribution of drought sensitivity in tropical plants. We conducted a pantropical data synthesis representing 1117 species to test whether xylem-specific hydraulic conductivity (K_S), water potential at leaf turgor loss (Ψ_TLP), and water potential at 50% loss of K_S (ΨP50) varied along climate gradients. The Ψ_TLP and ΨP₅₀ increased with climatic moisture only for evergreen species, but K_S did not. Species with high Ψ_TLP and Ψ_P50 values were associated with both dry and wet environments. However, drought-deciduous species showed high Ψ_TLP and ΨP₅₀ values regardless of water availability whereas evergreen species only in wet environments. All three traits showed a weak phylogenetic signal and a short half-life. These results suggest that environmental controls on trait variance, which in turn is modulated by leaf habit along climatic moisture gradients in the tropics.

README: Leaf habit affects the distribution of drought sensitivity but not water transport efficiency in the tropics

https://doi.org/10.5061/dryad.1ns1rn8x4

This repository contains the data and associated Rstudio project to perform the analyses associated with the study: Vargas G., G., Kunert N., Hammond W.H., Berry Z.C., Werden L.K., Smith-Martin C.M., Wolfe B.T., Toro L., Mondragón-Botero A., Pinto-Ledezma J.N., Schwartz N.B., Uriarte M., Sack L., Anderson-Texeira K.J., & Powers J.S. 2022. Leaf habit affects the distribution of drought sensitivity but not water transport efficiency in the tropics. Ecology Letters, Volume25, Issue12, December 2022, Pages 2637-2650.

Description of the data and file structure

Data files used in the manuscript, and their descriptions:
- rainfall_niches_15_nin.csv: Climatic affiliation data obtained from species occurrence records. Each column is described below.

Column name	Description
scrubbed_species_binomial	Species name.
n	Number of occurrence records.
mar_med	Median annual rainfall (mm) across the geographical extent of the occurrence records.
mar_min	Minimum annual rainfall (mm) across the geographical extent of the occurrence records.
mar_max	Maximum annual rainfall (mm) across the geographical extent of the occurrence records.
mar_90	90th percentile annual rainfall (mm) across the geographical extent of the occurrence records.
mar_10	10th percentile annual rainfall (mm) across the geographical extent of the occurrence records.
mar_iqr	Interquartile range from the annual rainfall (mm) across the geographical extent of the occurrence records.
sea_med	Median seasonality index across the geographical extent of the occurrence records.
sea_max	Maximum seasonality index across the geographical extent of the occurrence records.
sea_min	Minimum seasonality index across the geographical extent of the occurrence records.
sea_90	90th percentile seasonality index across the geographical extent of the occurrence records.
sea_10	10th percentile seasonality index across the geographical extent of the occurrence records.
sea_iqr	Interquartile range from seasonality index across the geographical extent of the occurrence records.
mcw_med	Median maximum climatic water deficit across the geographical extent of the occurrence records.
mcw_max	Maximum maximum climatic water deficit across the geographical extent of the occurrence records.
mcw_min	Minimum maximum climatic water deficit across the geographical extent of the occurrence records.
mcw_90	90th percentile maximum climatic water deficit across the geographical extent of the occurrence records.
mcw_10	10th percentile maximum climatic water deficit across the geographical extent of the occurrence records.
mcw_iqr	Interquartile range from maximum climatic water deficit across the geographical extent of the occurrence records.
dsl_med	Median dry season length across the geographical extent of the occurrence records.
dsl_max	Maximum dry season length across the geographical extent of the occurrence records.
dsl_min	Minimum dry season length across the geographical extent of the occurrence records.
dsl_90	90th percentile dry season length across the geographical extent of the occurrence records.
dsl_10	10th percentile dry season length across the geographical extent of the occurrence records.
dsl_iqr	Interquartile range from the dry season length across the geographical extent of the occurrence records.
ai_med	median aridity index across the geographical extent of the occurrence records.
ai_min	minimum aridity index across the geographical extent of the occurrence records.
ai_max	maximum aridity index across the geographical extent of the occurrence records.
ai_90	90th percentile aridity index across the geographical extent of the occurrence records.
ai_10	10th percentile aridity index across the geographical extent of the occurrence records.
ai_igr	Interquartile range from aridity index across the geographical extent of the occurrence records.

trait_data.csv: Trait database compiled for the present study. NA values represent empty cells whenever for a given observation information on any given variable (column) was not available.

Column name	Description
XFT_uniqueID	Xylem functional trait database unique ID. (https://xylemfunctionaltraits.org/).
old_XFT_uniqueID	Previous compilation of the xylem functional trait database unique ID.
obs_id	Observation ID in the present database.
Lat	Latitude coordinates in decimal degrees of each observation.
Long	Longitude coordinates in decimal degrees of each observation.
BIOME_NAME	Biome name associated to each observation.
REALM	Biological realm name associated with each trait observatio.
ECO_NAME	Ecoregion name associated with each trait observation.
Country	Country in which each trait observation was collected.
Location	Location in the country for each trait observation.
Sampling_season	Whether traits were sampled in the rainy or dry season as reported by the source article.
Sampling_month	Month in which traits were collected as reported by the original source.
Ks_method_dif	Hydraulic conductivity method as reported by the original source. CAV (cavitron), FLM (flushed accounting for maximum vessel length), FLM_short (flushed not accounting for maximum vessel length), FLM_short_roots (Flushed not accounting for maximum vessel length in roots, NM (native accounting for maximum vessel length), NM_roots (native accounting for maximum vessel length in roots), NM_short (native conductivity not accounting for maximum vessel length).
TLP_method	Turgor loss point method as reported by the original source. PVC (pressure-volume curve), OSM (osmometer method).
P50_method	Xylem vulnerability to embolism method as reported by the original source in the same coding as the xylem functional trait database (https://xylemfunctionaltraits.org/), where: DH (bench dehydration), CE (Centrifuge), CA (Cavitron), AD (air-injection double end), AS (air-injection single end), AE (acoustic emissions), OV (optical vulnerability), Pn (pneumatic method)
Treatment	Treatment during data collection if any as reported in the original source.
Family	Taxonomic family.
Accepted.Family	Accepted family as of June 2022.
Binomial	Species name as reported by the original source.
corrected_genus	Accepted species genus as of June 2022.
corrected_sp	Accepted species epithet as of June 2022.
Species	Accepted species binomial name as of June 2022.
sp_code	Six letter species code.
Accepted_author	Accepted species name authorship as of June 2022.
plant_organ_ks	Organ in which hydraulic conductivity measurements were done. Where: L (leaves), P (petiole), R (root), S (stems).
plant_organ_p50	Organ in which xylem vulnerability measurements were done. Where: L (leaves), P (petiole), R (root), S (stems).
growth_form	Type of plant growth form.
Resource_growthform	Reference for growth form if it was not specified in the original source.
leaf_habit_1	Leaf habit as reported in the original source.
leaf_habit	Leaf habit as evergreen , deciduous, or not reported (this study).
is_drought	If leaf habit reported by the original source is related to water availability in the dry season. Where, Y (yes) and N (no).
Resource_leafhabit	Reference for leaf habit if it was not specified in the original source.
Ks	xylem specific hydraulic conductivity (kg m^-1 s^-1 MPa^-1).
TLP_MPa	Leaf water potential at turgor loss point (MPa).
P50_MPa	The water potential at which 50 % loss of conductivity or accumulation of embolisms (MPa).
WD_gcm3	Wood density (g cm^-3).
SLA_g.1cm2	Specific leaf area (cm² g^-1).
short_reference	Original source of the data if any.
DOI	Digital object identifier of original source if any.
Contributor	Name of participant who contributed the data.
evapot	Observation locality mean annual evapotranspiration (mm).
pet	Observation locality mean annual potential evapotranspiration (mm).
alpha	Observation locality precipitation alpha.
MAR	Observation locality mean annual rainfall (mm).
S	Observation locality seasonality index.
MCWD	Observation locality maximum climatic water deficit (mm).
DSL	Observation locality dry season length (months).

Tree1084.tre: phylogenetic tree

Alternative access information

The hydraulic trais' data can also be accessed through the xylem functional trait database: https://xylemfunctionaltraits.org/

Code/Software

The compressed folder contains. Compressed file containing the R studio project associated with the analyses in the manuscript with the scripts associated with each statistical procedure in the manuscript:

File name	Description
01_data_management.R	Script associated with the preparation of the data for phylogenetic multilevel models (BMLM).
02_univariate_analyses_all.R	Script to run BMLM for each hydraulic trait as a function of each climatic predictor.
03_univariate_analyses_stem.R	Script to run BMLM for hydraulic conductivity and xylem vulnerability to embolism as a function of each climatic predictor using data from observations made in stems only.
04_multivariate_analyses.R	Script to run BMLM for each hydraulic trait as a function of aridity and seasonality, this also includes the code to run each model with observations made on stems only.
05_phylogenetic_signal.R	Script to obtain the phylogenetic signal from the multivariate BMLMs and the calculation of the phylogenetic half-life with the Ornstein-Uhlenbeck models.
06_quantile_regression.R	Script to run quantile BMLM for each hydraulic trait as a function of each aridity and seasonality, this also includes the code to run each model with observations made on stems only.
07_sampling_climatic_biases.R	Script to quantify to what extent climatic predictors obtained from trait sampling locations explain variation in species climatic affiliations.
08_climate_affinities_leaf_habit.R	Script to test whether species for each leaf habit differ in their climatic affiliations.

Citation

Subject keywords

Funding

University of Minnesota: Doctoral Dissertation Fellowship to GVG
Interdisciplinary Center for the Study of Global Change, University of Minnesota: ICGC scholar fellowship to GVG
National Science Foundation: DEB-1753810
United States Department of Energy: DE-SC0020344