Data and code from:Pyrodiversity and phylodiversity; insights on post-fire forest communities
Data files
Sep 17, 2026 version files 267.90 KB
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1a_TaxonomicDiversity.R
4.44 KB
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1b_Phylogenetic_Diversity.R
5.73 KB
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2a_Regional_Analysis.R
7.80 KB
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2b_Local_Analysis.R
72.32 KB
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2c_Randomization_plots.R
16.73 KB
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Phylogenetic_Diversity_Metrics.csv
21.31 KB
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Plot_Taxon_Comp.csv
65.32 KB
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Plots.csv
28.68 KB
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README.md
6.16 KB
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Taxon_List.csv
30.69 KB
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Taxonomic_Diversity_Metrics.csv
8.70 KB
Abstract
Fire is a fundamental process that structures community assembly and biodiversity patterns; however, altered disturbance regimes associated with anthropogenic change present a major threat to fire adapted systems. Reintroducing pyrodiversity (spatiotemporal diversity in fire) has been identified as an important step in restoring fire adapted western temperate forests. Here, we utilize spatial phylogenetic (phylogenetic diversity, phylogenetic endemism, relative phylogenetic diversity, phylogenetic beta diversity) and taxonomic metrics (taxonomic richness, weighted endemism, taxonomic beta diversity) to assess how three different fire events (one lightning ignition managed fire, one planned prescribed wildfire, and one wildfire) influence plant community assembly dynamics and diversity patterns in the Central Sierra Nevada, California, USA. We found that taxonomic and phylogenetic diversity generally increased after fire. Further, after fire, community assembly mechanisms diversified and local phylogenetic endemism increased. Our findings suggest that fire is an important process driving landscape heterogeneity and creating opportunities for rarity and diverse community assembly mechanisms to occur, which in turn supports increased diversity. This demonstrates the importance of restoring mixed-severity fire into these systems and that phylogenetic tools provide a valuable way to monitor impacts of restoration.
Authors: Rebecca C. Wilcox, Becky L. Estes, Brent D. Mishler, Sarah J. Jacobs
Description of data and file structure, and code
Data and file structure:
INPUT DATA
File: Plots.csv
Description: data describing the plot sampling
Variables:
- Project (character): sample site, either: long fire site (Long) or caples creek site (Caples)
- ProjSubTimeID (numeric): sample ID
- PlotID (numeric): The plot number
- SampleName (character): The sampling time period: Pre fire (FireSuppress - same as fire exclusion), post-long fire (LongFire), post-caples fire (CaplesFire) or post-caldor fire (CaldorFire)
- SampDate (date): MM/DD/YYYY date when the plot was sampled
- UTM_E (numeric): UTM Easting
- UTM_N (numeric): UTM Northing
- VegCover (character): Dominated veg cover for the plot: tree (T), shrub (S), tree/shrub (T/S), or shrub/tree (S/T)
- Fire.Burn.Severity_Plot (numeric): The fire severity level recorded during the plot visit
- DEM (numeric): elevation (m)
- SolarRad (numeric): solar radiation
- tpi (character): topographic position index
- Buff50m (numeric): for post fire sample periods, the functional disparity for a 50m buffer around the plot (pyrodiversity)
- Buff100m (numeric): for post fire sample periods, the functional disparity for a 100m buffer around the plot (pyrodiversity)
- Buff150m (numeric): for post fire sample periods, the functional disparity for a 150m buffer around the plot (pyrodiversity)
- Buff200m (numeric): for post fire sample periods, the functional disparity for a 200m buffer around the plot (pyrodiversity)
- Buff250m (numeric): for post fire sample periods, the functional disparity for a 250m buffer around the plot (pyrodiversity)
File: Plot_Taxon_Comp.csv
Description: The species composition data for each plot level sample visit
Variables:
- ProjSubTimeID (numeric): sample ID
- USDACode (character): USDA taxa ID
- LayerCode (character): vegetation layer
- PercentCover (numeric): Plot level abundance estimate
File: Taxon_List.csv
Description: Study species list
Variables:
- Order (character): Order name
- Family (character): Family name
- Genus (character): Genus name
- Species (character): Species name
- Sub_Variety (character): Subspecies or variety name
- SpeciesName (character): The Latin name of the taxa
- SpeciesName_rank_type (character): The taxonomic rank for the species name: genus, species, ssp, var
- USDACode (character): USDA taxa ID
- OTU (character): Operational taxonomic unit for the phylogenetic tree created by Thornhill et al. 2016. The 15 nonnative taxa do not have an OTU listed (NA)
OUTPUT DATA
File: TaxonomicDiversity.csv
Description: taxonomic diversity metric by plot
Variables:
- Project (character): sample site, either: long fire site (Long) or caples creek site (Caples)
- ProjSubTimeID (numeric): sample ID
- tr_obs (numeric): taxonomic richness
- we_obs (numeric): weighted endemism
- tbd_region_period (numeric): beta diversity by time period
File: Phylogenetic_Diversity_Metrics.csv
Description: phylogenetic diversity metric by plot
Variables:
- ProjSubTimeID (numeric): sample ID
- pd_obs (numeric): phylogenetic diversity
- pd_signif (character): significance level for phylogenetic diversity (PD)
- pe_obs (numeric): phylogenetic endemism
- pe_signif (character): significance level for phylogenetic endemism (PE)
- rpd_obs (numeric): relative phylogenetic diversity
- rpd_signif (character): significance level for relative phylogenetic diversity (RPD)
- pbd_region_period (numeric): beta diversity by time period
CODE
The code is broken into two parts, code used to calculate diversity metrics (1a-b) and code used to conduct the analyses and graph (create figures; 2a-c). 1a_TaxonomicDiversity and 1b_Phylogenetic_Diversity allow you to calculate the diversity metrics used to conduct the analyses. 2a_Regional_Analysis and 2b_Local_Analysis allow you to run the analyses and 2c_Randomization_plots.R allows you to graph the statistical tests of alpha phylogenetic metrics.
File: 1a_TaxonomicDiversity.R
Description: Calculating diversity metrics. Run this file to calculate taxonomic alpha and beta diversity.
- Input files: Plot_Taxon_Comp.csv
- Output files: Taxonomic_Diversity_Metrics.csv
File: 1b_Phylogenetic_Diversity.R
Description: Calculating diversity metrics. Run this file to calculate phylogenetic alpha and beta diversity metrics. Before running this code, download the phylogenetic tree file (Californian_clades_tree_final.nex) from: Thornhill et al. 2017 [Dataset]. Dryad. https://doi.org/10.6078/D1VD4P
- Input files: Plot_Taxon_Comp.csv, Taxon_List.csv
- Output files: Phylogenetic_Diversity_Metrics.csv
File: 2a_Regional_Analysis.R
Description: Analysis Regional analysis.Run this file to perform regional scale analysis to assess if metrics differed between the four time periods
- Input files: Phylogenetic_Diversity_Metrics.csv, Taxonomic_Diversity_Metrics.csv, Plots.csv
- Output files:
- Table S3 content: TableS3_region_level_betaTable.csv
- Table S4 content: TableS4_region_level_rsquaredTable.csv
- Figure 2: Figure_2_region_level.pdf
File: 2b_Local_Analysis.R
Description: Analysis: Local analysis. Run this file to perform local scale analysis to assess if metrics differed based on fire severity, fire event, and/or local context
- Input files: Phylogenetic_Diversity_Metrics.csv, Taxonomic_Diversity_Metrics.csv, Plots.csv
- Output files:
- Table S5 content: TableS5_local_level_AICcTable.csv
- Figure 3: Figure_3_local_level.pdf
File: 2c_Randomization_plots.R
Description: Graphing: Statistical tests of alpha phylogenetic metrics: Run this file to perform local scale analysis to assess if metrics differed based on fire severity, fire event, and/or local context
- Input files: Phylogenetic_Diversity_Metrics.csv, Plots.csv
- Output files:
- Figure 4: Figure_4_statistical_tests.pdf
In our analysis we used the phylogenetic tree created by Thornhill et al. 2017. Before running the code, download a copy of their tree from:
Thornhill, Andrew; Baldwin, Bruce; Freyman, William et al. (2017). Sequence matrix and tree files for Spatial phylogenetics of the native California flora (Thornhill et al. BMC Biology) [Dataset]. Dryad. https://doi.org/10.6078/D1VD4P
