DOI: 10.5061/dryad.x0k6djhv8
title: Heterogeneous trait responses of Páramo plant species and community to experimental warming

description: Understanding climate change impact on the functional trait composition,
 and hence ecosystem functioning of tropical alpine regions is critical for
 predicting biodiversity responses. We tested the effects of a decade of
 warming on the morphological, chemical, and genomic traits of Páramo
 species using open-top-chambers (OTC). We conducted vegetation surveys and
 collected samples from individuals inside and outside the OTC plots to
 estimate differences between treatments (warming vs control). Vegetation
 cover decreased over time in both treatments suggesting a potential
 decline in soil moisture in our study area. Warming led to a
 reorganisation of the trait space and trait network structure.
 Nevertheless, we did not find significant differences in trait values or
 the direction of change between species whose % vegetation cover increased
 in OTC (or decreased less) compared to control over time.
 Community-weighted mean values of plant height, leaf area, leaf dry matter
 content, genome size, leaf C and P, significantly increased over time only
 in OTC plots– i.e. traits associated with carbon storage and
 decomposition. Whilst warming -and reduced soil moisture - lead to
 heterogeneous species responses without a clear winning trait strategy,
 changes at the community-level may have important implications for Páramo
 ecosystem functioning.
description: <strong>Study area and experimental
 design</strong> The study area is located in the
 Páramos of the Yanacocha Reserve, Ecuador, at 4,200 m a.s.l (0.13518°S,
 78.57458°W). The passive warming experiment was installed in 2012 and
 consists of five parallel monitoring blocks, each of them containing 40 1
 m<sup>2</sup> plots delimited for different treatments. Plots
 assigned to a warming treatment were surrounded by a 3 x 3 m hexagonal
 open-top chamber (OTC). Control plots and those under warming treatment
 (OTC plots) were randomly chosen across the five blocks. Our analyses are
 based on the data from 17 OTC plots and 20 control plots.
 For details on the climate in the study area refer to the
 published paper. <strong>Field data
 collection</strong> <strong>Vegetation
 surveys:</strong> Before installing the OTCs, a baseline survey was
 conducted in 2012, recording vegetation cover (%) of each seed plant
 species per plot. A further five vegetation surveys were conducted on the
 control and OTC plots in 2013, 2014, 2016 (a few plots for this survey
 were measured in December 2015), 2017, and 2019. The OTCs of five warming
 plots were blown away by the wind after the 2017 survey, therefore, for
 2019 we used the data from the remaining OTCs. A total of 71 taxa were
 recorded in the selected plots but we chose to work with the 40 most
 dominant species based on % vegetation cover.
 <strong>Collection of leaves for trait analysis:
 </strong>All trait data were collected in June 2022. To measure the
 morphological traits for each species whenever possible (i.e., we were
 limited by the number of individuals present in OTCs), we collected five
 leaves each from three individuals inside the OTC plots and from three
 individuals outside them. We also measured the vegetative plant height of
 1 to 10 individuals per treatment (OTC mean = 6, control mean = 8). For
 chemical traits, due to the small size of most species, collecting enough
 material for analysis was challenging and thus, for a given treatment
 (warming or control) and for each species, we pooled the leaves from more
 than one individual together. Lastly, for the genome size analyses, we
 collected leaf material from one individual per species and stored them in
 a plastic bag with a wet tissue to preserve them until analysed. Given the
 variable availability of plant material it was not possible to collect
 samples for all traits and all 40 species.
 <strong>Laboratory analyses</strong>
 Samples for morphological and chemical analyses were taken to the
 Universidad de las Américas in Ecuador, where the following morphological
 traits were measured: (i) leaf area (cm<sup>2</sup>), (ii)
 leaf blade thickness (mm), (iii) specific leaf area (SLA in
 cm<sup>2</sup>/g) which is the fresh leaf area divided by its
 dry mass, and (iv) leaf dry matter content (LDMC in mg/g) which is the dry
 leaf mass divided by the fresh leaf mass. Samples for
 characterising the chemical traits were processed to estimate: (i) the
 amounts of macro- (i.e., K, P, Mg, Ca in ppm) and (ii) micro-nutrients
 (i.e., Al, B, Fe, Na in ppm) in the leaves, and (iii) the leaf C and N
 content (% and C/N ratio). Finally, the genome size (number of base pairs
 in the DNA in gigabases per unreplicated gametic nucleus (i.e. the
 1C-value), Gb/1C) of each species was estimated at the Royal Botanic
 Gardens, Kew, by propidium iodide flow cytometry.
description: # "Dataset for paper Heterogeneous trait responses of Páramo plant
 species and community to experimental warming" # Summary of the
 study: Understanding climate change impact on the functional trait
 composition, and hence ecosystem functioning of tropical alpine regions is
 critical for predicting biodiversity responses. We tested the effects of a
 decade of warming on the morphological, chemical and genomic traits of
 Páramo species using open-top-chambers (OTC). We conducted vegetation
 surveys and collected samples from individuals inside and outside the OTC
 plots to estimate differences between treatments (warming vs control).
 Vegetation cover decreased over time in both treatments suggesting a
 potential decline in soil moisture in our study area. Warming led to a
 reorganisation of the trait space and trait network structure. Species
 showed a wide range of responses to warming, with significant changes
 across different trait combinations. Nevertheless, we did not find
 significant differences in trait values or the direction of change between
 species whose % vegetation cover increased in OTC (or decreased less)
 compared to control over time. Community-weighted mean values of plant
 height, leaf area, leaf dry matter content, genome size, leaf C and P,
 significantly increased over time only in OTC plots– i.e. traits
 associated with carbon storage and decomposition. Whilst warming -and
 reduced soil moisture - lead to heterogeneous species responses without a
 clear winning trait strategy, changes at the community-level may have
 important implications for Páramo ecosystem functioning.
 \================================================================================== * Article's DOI: 10.1098/rspb.2025.0245 * Dataset's DOI: 10.5061/dryad.x0k6djhv8 * Suggested citation: Tovar C, Bellot S, Llerena-Zambrano M, Leitch IJ, Carpio-Cordero P, Granda-Albuja MG, Rondal J, Duchiceal S, Bernardi A, Salazar E, Mian S, Tejera E, Cuesta F. Dataset for "Heterogeneous trait responses of Páramo plant species and community to experimental warming". DOI: 10.5061/dryad.x0k6djhv8. * Overall description: This README file contains descriptions for the datasets used in our study and the R scritps. # Data and File Overview \====================== ## Summary Metrics * File count: 13 * Total file size: 1.87 MB * Range of individual file sizes: 6-849 KB * File formats: .csv, .R, .new ## Table of Contents CSV files * plots_species_cover.csv * plots_total_cover.csv * species_present_OTC_and_control_contributing_to_95perc_cover.csv * trait_all_individual_records.csv * trait_all_mean_x_sp_group.csv * All_together_ConcITSmatKrbcLtrnLF_IQtreeBP.new * species_name_phylogeny.csv R scripts * 01_PCA trait space_intraspecific variability_trait network.R * 02_species fitness responses_mean cover trends mixed models beta distribution.R * 03_species trait responses to warming.R * 04_species responses_figures.R * 05_community responses to warming.R * 06_community responses_figures.R # Details for each file \====================== ## Details for: plots\_species\_cover.csv * Description: a comma-delimited file that should contain vegetation cover per species in a given plot (1m2) per survey. 20 plots are control plots and 17 plots are OTC plots (warming experiment). There are 6 surveys (2012, 2013, 2014, 2016, 2017, 2019) * Format(s): .csv * Size(s): 270 KB * Dimensions: 3018 rows x 10 columns * Variables: * survey: Number of survey (from S1 to S6) * year: Survey's year * plot: plot ID * data_period: surveys for which vegetation cover was collected * treatment_per_survey: treatment assigned to the plot. Note that all OTC plots surveyed in S1 (2012) were considered as control (baseline) as the OTC were installed after this first survey. * treatment: treatment assigned to the plot (Control vs OTC or warming) * family: family * species_name: original species recorded in plots * species_name_analysis: species name (taxa name) used for the analyses (some species were grouped due to identification issues, see methods) * cover: vegetation cover per species per plot per survey (%/m2) ## Details for: plots\_total\_cover.csv * Description: a comma-delimited file that should contain total vegetation cover of seeded plants pero plot (1m2) per survey (2012, 2013, 2014, 2016, 2017, 2019) * Format(s): .csv * Size(s): 10 KB * Dimensions: 217 rows x 6 columns * Variables: * plot: plot ID * survey: Number of survey (from S1 to S6) * year: Survey's year * treatment: treatment assigned to the plot (Control vs OTC or warming) * data_period: surveys for which vegetation cover was collected * cover: total vegetation cover of seeded plants per plot per survey (%/m2) ## Details for: species\_present\_OTC\_and\_control\_contributing\_to\_95perc\_cover.csv * Description: a comma-delimited file that should contain list of species which cumulative reprsent at least 95% of vegetation cover across plots and surveys and information of whether trait data collection was possible * Format(s): .csv * Size(s): 4 KB * Dimensions: 45 rows x 7 columns * Variables: * species_name_analysis: species name (taxa name) used for the analyses (some species were grouped due to identification issues, see methods) * family: family * n_surv_Control: number of surveys in which the species was recorded at least in one control plot * n_surv_OTC: number of surveys in which the species was recorded at least in one OTC plot * in_both_treat: whether the species was recorded in both control and OTC plots * cover95: whether the species was among those that cumulative reached 95% of the vegetation cover * traits_collected: whether traits were collected in the field * growth_form_group: growth form ## Details for: trait\_all\_individual\_records.csv * Description: a comma-delimited file that should contain trait data for leave for each individual for a treatment (control vs OTC) for a given species in the case of leaf traits (SLA, LDMC, leaf thickness, leaf area). For plant height data, the measurements are per individual per treatment. In the case of the chemical traits (Al, B, C, C/N, Ca, Fe, K, Mg, N, Na, P) samples from different individuals wer pull together to reach the minimum weight needed for the analysis. Thus, there is only one row per species per treatment per chemical trait, except for Calamagrostis group which has some repetitions as there was enough material. For genome size we only have one measurement per species as this trait does not change with warming, however to have values for both treatments (Control and OTC) we duplicated the values. * Format(s): .csv * Size(s): 849 KB * Dimensions: 5863 rows x 12 columns * Variables: * species_name_analysis: species name (taxa name) used for the analyses (some species were grouped due to identification issues, see methods) * species: original species name for which trait data was collected * individual: number of individual for which a given species for which the trait was recorded. Only for morphological traits as for chemical traits we pulled samples together from different individuals and for genome size only one sample was collected per species. * leave_number: leaf id of a given individual of a give species per treatment for leaf traits. Only for morphological traits as for chemical traits we pulled samples together from different individuals and for genome size only one sample was collected per species. * treatment: whether the sample was collected inside an OTC plot or outside (control) * variable_full_name: trait full name * variable: trait name: trait acronym and units * value: trait value * value_type: * variable_type: whether the trait measured was a morphological, chemical or genomic one * variable_units: variable units * growth_form_group: growth form * family: family ## Details for: trait\_all\_mean\_x\_sp\_group.csv * Description: a comma-delimited file that should contain summarised trait data (mean values, sd and CV) per species (field species_name_analysis) per treatment. In case there is no SD and CV value it means that we only had 1 value per species. * Format(s): .csv * Size(s): 108 KB * Dimensions: 964 rows x 10 columns * Variables: * family: family * species_name_analysis: species name (taxa name) used for the analyses (some species were grouped due to identification issues, see methods) * growth_form_group: growth form * treatment: whether the sample was collected inside an OTC plot or outside (control) * variable: trait name * variable_units: variable units * variable_type: whether the trait measured was a morphological, chemical or genomic one * mean_value: mean trait value per species (species_name_analysis) per treatment when there was more than one sample per species, if not, it is the value of the unique sample collected for that species. * sd_value: standard deviation of trait value of all samples collected per species (species_name_analysis) per treatment. When there was more than one sample per species, if not, it is the value of the unique sample collected for that species. NA indicates there were no repetitions that allowed SD estimation * cv_value: coefficient of variation of trait values of all samples collected per species (species_name_analysis) per treatment. When there was more than one sample per species, if not, it is the value of the unique sample collected for that species. NA indicates there were no repetitions that allowed CV estimation ## Details for: All\_together\_ConcITSmatKrbcLtrnLF\_IQtreeBP.new * Description: A text file containing a phylogenetic tree in the newick format * Format(s): .new * Size(s): 9 KB * Dimensions: NA * Variables: NA ## Details for: species\_name\_phylogeny.csv * Description: a comma-delimited file that should contain the names of the species used in the phylogeny and how the matched with the species used for this study (species_name_analysis) * Format(s): .csv * Size(s): 6 KB * Dimensions: 68 rows x 6 columns * Variables: * id: species id * Family: family * species_name: original species name recorded in the plots * name_tree: species used to build the phylonetic tree * species_name_analysis: species name (taxa name) used for the analyses (some species were grouped due to identification issues, see methods) * keep: 1 for species that were used in the phylogenetic tree and 0 for those that were excluded ## Details for: 01\_PCA trait space\_intraspecific variability\_trait network.R * Description: a R script that estimates 1) the environmental trait space (PCA biplot) of Páramo species using morphological, chemical and genomic traits, 2) PERMANOVA, 3) intraspecific variability for morphological traits using a coefficient of variability, and 4) trait networks using all traits. * Format(s): .R * Size(s): 20 KB * Input csv: * trait_all_mean_x_sp_group.csv * Output svg: * correlation_chemical_traits.svg * traits_PCA_results.svg * traits_intraspecific_varibility.svg * trait_network_otc.svg * trait_network_control.svg ## Details for: 02\_species fitness responses\_mean cover trends mixed models beta distribution.R * Description: a R script that estimates fitness responses to warming for each species where fitness is measured as changes in vegetation cover over time. For this we used two separately models for each species, one for control and another for OTC plots where a model was fitted for the vegetation cover per plot per survey against the survey year using a mixed model effect with a beta distribution. * Format(s): .R * Size(s): 10KB * Input csv: * plots_species_cover.csv * species_present_OTC_and_control_contributing_to_95perc_cover.csv * Output csv: * species_veg_cover_regression_results.csv * species_veg_cover_regression_slopes_fitness_response_category.csv * Output svg: * plots of regression models for each species (svg) ## Details for: 03\_species trait responses to warming.R * Description: a R script that estimates differences between traits estimations of morphological traits from samples collected inside OTCs and outside (control). First, we estimated differences per trait per species between treatment. Secondly we clustered species based on the trait response hey had (whether similar traits either significantly increased or decreased with warming), Thirdly we compare trait values between species with positive, negative and neutral fitness response to warming accounting for phylogenetic relationships. * Format(s): .R * Size(s): 8 KB * Input csv: * trait_all_individual_records.csv * species_veg_cover_regression_slopes_fitness_response_category.csv * All_together_ConcITSmatKrbcLtrnLF_IQtreeBP.new * species_name_phylogeny.csv * trait_all_mean_x_sp_group.csv * Output csv: * species_trait_comparison_OTC_control_x_sp_wilcox_test.csv * species_trait_comparison_OTC_control_x_sp_summary_counts.csv * species_phylo_anova_control_results.csv * species_phylo_anova_otc_results.csv ## Details for: 04\_species responses\_figures.R * Description: a R script that plots the main figures related to species responses to warming for both fitness and trait responses * Format(s): .R * Size(s): 16KB * Input csv: * species_veg_cover_regression_slopes_fitness_response_category.csv * species_present_OTC_and_control_contributing_to_95perc_cover.csv * species_trait_comparison_OTC_control_x_sp_wilcox_test.csv * All_together_ConcITSmatKrbcLtrnLF_IQtreeBP.new * species_name_phylogeny.csv * plots_species_cover.csv * Output svg: * species_paired_t_test_slope_regressions.svg * cluster_sp.svg * cluster_sp_traits.svg * phylo_tree_heatmap.svg * phylo_tree_growth_form.svg * phylo_tree_fitness_response.svg * species_traits_comparing_fitness_responses.svg ## Details for: 05\_community responses to warming.R * Description: a R script that estimates community responses to warming. First we analysed changes in vegetation cover over time summarising all plots. Secondly we estimated community weighted means (and variance) using a bootstrap approach, the contribution of each species to the CWM (based on their vegetation cover) and trait coverage across plots. Thirdly we tested differences in CWM bewteen treatments. * Format(s): .R * Size(s): 6KB * Input csv: * plots_species_cover.csv * trait_all_mean_x_sp_group.csv * Output csv: * plot_trait_data_coverage.csv * cwm_all_traits_x_plot_x_survey.csv ## Details for: 06\_community responses\_figures.R * Description: a R script that plots the figures related to community responses to warming. * Format(s): .R * Size(s): 13 KB * Input csv: * plots_total_cover.csv * plots_species_cover.csv * species_veg_cover_regression_slopes_fitness_response_category.csv * plot_trait_data_coverage.csv * cwm_all_traits_x_plot_x_survey.csv * Output svg: * sup_mat_cover_change_over_time_per_treatment.svg * species composition plots * cwm_morpho_chem_traits_2012_2019.svg * sup_mat_sp_prop_seed_plants_contribution_cwm