DOI: 10.5061/dryad.wpzgmsbw7
title: Extending Grime’s CSR model to predict plant demographic responses across resource availability gradients: evidence from the Patagonian steppes

description: Sexual reproduction, growth, and survival are crucial demographic
 strategies for plant population viability. Here, we propose a conceptual
 model predicting demographic responses of species based on their
 ecological strategy and the heterogeneity of environmental conditions
 within a biogeographical unit and then applied it to a case study from a
 5-degree latitudinal gradient in the Patagonian steppes. We also aim to
 disentangle genetic from environmental effects on demographic responses.
 We performed in-situ and common garden experiments with two species from
 six local populations of the Occidental Phytogeographical District of the
 Patagonian steppes. Species differ in key ecological traits, and thus fit
 into Grime´s model for evolutionary strategies in plants: one as
 competitive species and the other as stress-tolerant species. We
 calculated population growth rate (λ) and performed elasticity analyses to
 compare the contribution of each demographic strategy to population
 fitness between species and among local populations distributed along 600
 km latitudinal gradient with differences in mean annual precipitation
 (MAP). We highlight four results. First, the competitive species change
 from sexual reproduction to growth as MAP increases. Second, the
 stress-tolerant species relied on growth and survival along the MAP
 gradient. Third, interannual variation in resource availability modulated
 demographic responses for both strategies. Fourth, based on the comparison
 of the in-situ and common garden experiments, we submit that demographic
 responses were genetically driven. Our study shows that demographic
 responses can be roughly predicted by the ecological strategy across
 environmental gradients. We show that differences arise not only between
 species, but also were genetically driven differences within species among
 local populations. Scaling up plant-level responses to population-level
 dynamics allows for a process-based understanding of current and future
 biogeographical species organization. Furthermore, conservation and
 restoration efforts should be guided by demographic strategies underlying
 population viability.
description: # Nasta et al., 2024 - Extending Grime’s CSR model to predict plant
 demographic responses across resource availability gradients: evidence
 from the Patagonian steppes (Oikos)
 [https://doi.org/10.5061/dryad.wpzgmsbw7](https://doi.org/10.5061/dryad.wpzgmsbw7) We have submitted our raw data for the in situ experiment (**in_situ.xlsx**) and for the common garden experiment (**common_garden.xlsx**). We also have submitted the elasticity values resulting from the population analysis described in the methods of the paper (**elasticity.csv**), and the R script to reproduce the statistical analyses (**bromus_poa_script.R**) ## Description of the data and file structure **File: in_situ.xlxs** *Sheet: Year_1* Contains the raw data corresponding to year 1, referred to as the wet year in the paper. * Site: the name of the original site of the populations, referred to as habitat in Table 1 of the paper * Species: currently valid scientific name of the studied grass species * 2005: stage of the tiller marked in the first census (V=vegetative; F=reproductive; M=dead) * 2006: stage of the tiller marked in the first census (V=vegetative; F=reproductive; M=dead) Note: the rows that do not contain data in the 2005 column but do contain data in the 2006 column indicate newly recruited tillers *Sheet Year_2* Contains the raw data corresponding to year 2, referred to as the near average year in the paper. * Site: the name of the original site of the populations, referred to as habitat in Table 1 of the paper * Species: currently valid scientific name of the studied grass species * 2006: stage of the tiller marked in the first census (V=vegetative; F=reproductive; M=dead) * 2007: stage of the tiller marked in the first census (V=vegetative; F=reproductive; M=dead) Note: the rows that do not contain data in the 2006 column but do contain data in the 2007 column indicate newly recruited tillers **File: common_garden.xlxs** * Site: the name of the original site of the populations, referred to as habitat in Table 1 of the paper * Species: currently valid scientific name of the studied grass species * 2005: stage of the tiller marked in the first census (V=vegetative; F=reproductive; M=dead) * 2006: stage of the tiller marked in the first census (V=vegetative; F=reproductive; M=dead) Note: the rows that do not contain data in the 2005 column but do contain data in the 2006 column indicate newly recruited tillers **File: Elasticity.csv** * Species: currently valid scientific name of the studied grass species * Site: the name of the original site of the populations, referred to as habitat in Table 1 of the paper * Condition: the experiment (i.e., in situ or common garden) * MAP: mean annual precipitation of each site (mm.yr-1) * MAT: mean annual temperature of each site (°C) * Year: the year of the experiment (1 or 2 for the in situ experiment, NA for the common garden experiment) * Transition: the demographic transitions depicted in Figure 1 and Figure 2 of the paper (reproduction, growth or survival) * Elasticity: the value of the elasticity (i.e., the relative contribution to the population growth rate) of each transition at each site ## Code/Software **File: bromus_poa_script.R** R is required to run bromus_poa_script.R; the script was created using version 4.3.1. Annotations are provided throughout the script through 1) library loading, 2) dataset loading and cleaning, 3) analyses of the in situ experiment, and 4) analyses of the common garden experiment. Note: it is necessary to install the libraries library(car), library(lme4), and library(ggeffects).