Data sets and R scripts used for the publication "Species' competitive ability and phenology niche affects the flowering intensity in herbaceous species"

These data sets and R scripts were used for the analysis of the publication: Species’ competitive ability and phenology niche affects the flowering intensity in herbaceous species. Phenological data for this was collected between 2019 and 2023 in 14 botanical gardens. Data collection and analysis were carried out as part of the PhenObs project (https://www.idiv.de/research/projects/phenobs/). Abstract:Climate change may increase phenological mismatches in biotic interactions due to shifts in flowering times of plants. Most studies focussed on first flowering, the timing of entire flowering periods have hardly been studied though this information is important when evaluating phenological mismatches. Here, we explore variations in flowering curves across 263 perennial herbaceous species spanning the entire flowering period, and determine whether species-specific patterns are linked to species’ functional properties. The results clearly suggest that competitive species, early-flowering and late-flowering species tended to invest resources in single but intensive flowering events, with shorter flowering durations and more left-skewed curves (‘all-in-one’ strategy). In contrast, stress-tolerant species distributed resources over several flowering peaks (‘bet-hedging’ strategy). We conclude that information on species properties can be used to extract information on different flowering strategies, that can be used to evaluated impacts of climate change not only on flowering times but also on biotic interactions. Readme: To work with the data and scripts, please download all files and save them in one folder.To extract the properties of the flowering curves, first use the scripts 1_Flowering_Curves_NumberPeak_FloweringIntensity.R and 1_Flowering_Curves_Skewness.R.The analysis steps performed can then be found in script 2_Flowering_Curves_Analyses.R.To create a graph in the appendix of the article, you can use 3_Analyse_Flowering_Curves_ExampleCurves.R. rawdata_PhenObs_20xx.csv: These files contain the raw data used to create the flowering curves. This data can also be accessed via https://www.idiv.de/research/projects/phenobs/data-access/. CS_values.csv: The data from this file is used as predictors in the models (competition and stress-tolerating score per species). Phenology_stages_2019_2023: The data from this file is used as response variable (FlDu = Flowering duration) and as predictor variable (FlOn = Flowering onset) Example curves.csv: The data is necessary to create Figure S5. Metadata.xlsx: This file contains the metadata for CS_values.csv, rawdata_PhenObs_20xx.csv, phenology_stages_2019-2023.csv and Example curves.csv in separate data sheets.