Deer dietary responses to wildfire: optimal foraging, individual specialization, or opportunism?
Data files
Feb 29, 2024 version files 783.74 MB
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ASVs_counts.txt
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ASVs.fa
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ATR_A_S85_L001_R1_001.fastq.gz
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ATR_I_S860_L001_R1_001.fastq.gz
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Cutadapt_DADA2_script.docx
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DietComp.zip
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DietDiff.zip
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DietDiversity.zip
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NicheVar.zip
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README.md
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Abstract
Increasing impacts of wildfire on arid regions of the world fueled by climate change highlight the need to better understand how natural communities respond to fire. While plant communities have received much attention, less is known about responses of keystone herbivores, such as ungulates. Large herbivores may temporarily vacate burned areas as vegetation decreases, returning as vegetation recovers, and concentrating in burn areas as early successional herbaceous species begin to flourish. However, few data exist to assess herbivore trophic niches in burned and unburned habitats. We took advantage of a large (1,660-km2) wildfire that erupted in northern California during an in-progress study of black-tailed deer (Odocoileus hemionus columbianus) to investigate deer use of and diets within burned and unburned habitats before and after the fire. Depending on whether deer diet selection conformed better to optimal foraging models (including the niche variation hypothesis), or to that of a generalist forager, we expected that dietary breadth would either increase or decrease, respectively, in direct response to the reduction in forage associated with the fire and then decrease or increase again as the plant community recovered. We used fecal pellet counts and metabarcoding to document changes in habitat use and diet relative to the fire. Pellet counts supported predictions that deer increased use of unburned sites and reduced use of burn sites after the fire and began to return to burned sites in subsequent sampling years. Diet diversity did not differ significantly between control and burn sites before the fire, but was lower in burn than control sites post-fire (P < 0.001), when and where diet was dominated by oak (Quercus spp). In contrast, during subsequent years, diet diversity was higher (including more herbaceous plants) in burn than control sites (P < 0.05). In contrast to predictions of optimal foraging and niche variation hypotheses, individual deer foraged as generalists for which diversity of the plant community as determined by fire was the primary determinant of their dietary niche breadth.
README: Deer dietary responses to wildfire: optimal foraging, individual specialization, or opportunism?
This accession includes all of the raw sequencing reads for samples and controls used in the study, along with an ASV table(ASV_counts.txt) and ASV sequences (ASV.fa), output from DADA2. The sample names referenced in the ASV table correspond to the names of the fastq files, each of which corresponds to a single sample or control.
Description of the data and file structure
Fastq files (n = 865):
Each fastq file contains all of the raw forward sequencing reads for a single pellet group sample or control sample. The first portion of the filename contains the sample name, which can be cross-referenced with the ASV table and other data files listed below.
Conventions for names are
Samples: Sxx_xxxx, where x = a numeral, e.g., S20_1660
Replicated samples extracted for surface of pellet: SSxx_xxxx
Replicated samples extracted for interior of pellet: SIxx_xxxx
No-template controls: NTC
Positive control 1 (Salicornia): SAL
Positive control 2 (Atriplex): ATR
Cutadapt_DADA2_script.docx : Filtering and bioinformatic sorting into ASVs
ASV sequences and tabular summary of numbers of ASVs corresponding to each fecal DNA and control sample from DADA2:
ASV_counts.txt : This contains the raw read count corresponding to each ASV in each control and sample output from DADA2.
ASVs.FA : This contains the sequences clustered into ASVs in DADA2 that were used to BLAST and assign plant taxa.
Data analysis scripts and files included in 4 zipped folders:
Folder 1: DietDiff : describes procedure for Dietary Differences - PERMANOVA analysis.
DietDiff.R : Includes multi-way permanovas starting will all variables and working down to the final PERMANOVAs used in the paper, betadisper analysis with ANOVA and TukeyHSD -- Includes one line explanation of how to repeat with FOO data (for supplementary table)
Data Files
1 test, 4 variables (site, visit, sex, habitat):
R_genus4.csv\
R_sample_lookup.csv2 tests, 2 variables (site, visit):
shrub_domRRA.csv\
shrub_dom_samplelookup.csv\
tree_domRRA.csv\
tree_dom_samplelookup.csv4 tests, 1 variable (visit):
shrub_burn_RRA.csv\
shrub_burn_lookup.csv\
shrub_control_RRA.csv\
shrub_control_lookup.csv\
tree_burn_RRA.csv\
tree_burn_lookup.csv\
tree_control_RRA.csv\
tree_control_lookup.csv
Variables
- sample: Unique sample ID for each sample collected
- lat: Latitude where the sample was collected in decimal degrees, WGS84
- lon: Longitude where the sample was collected in decimal degrees, WGS84
- day: Day of the month the sample was collected on
- month: Month the sample was collected in, ranging 4-7 for April-July
- year: Year the sample was collected in, ranging from 2017 - 2021
- plate: Unique ID for each plate DNA samples were processed on
- success: Success of determining the genotype of the individual deer, 2 variables. Yes: genotyping was successful, fail: genotyping was unsuccessful and individual ID or sex were not identified
- individual: Individual ID of the deer determined by genotyping
- sex: Sex of the deer determiened by genotyping, 3 variables. M: male, F: female, NA: unknown
- plot: Unique ID for each sampled transect
- pre_treecover: Percent of tree cover estimated in the field before the Ranch Fire burned, 7 variables. 1: <1%, 2: 1-5%, 3: 6-15%, 4: 16-25%, 5: 26-50%, 6: 51-75%, 7: >75%
- pre_shrubcover: Percent of shrub cover estimated in the field before the Ranch Fire burned, 7 variables. 1: <1%, 2: 1-5%, 3: 6-15%, 4: 16-25%, 5: 26-50%, 6: 51-75%, 7: >75%
- dom_TvS: Dominant habitat type, determied by percent cover codes in pre_treecover and pre_shrubcover, 2 variables. tree_dom: tree dominated, percent cover of trees was higher than percent cover of shrubs prefire. shrub_dom: shrub dominated, percent cover of shrubs was higher than that of trees prefire
- dom_TSO: Dominant habitat type, determied by percent cover codes in pre_treecover and pre_shrubcover and tree composition, 3 variables. tree_dom: tree dominated, percent cover of trees was higher than percent cover of shrubs prefire. shrub_dom: shrub dominated, percent cover of shrubs was higher than that of trees prefire. open_tree: tree dominated habitat primarily open oak woodland
- site: Sampling site, 2 variables. control: sampling sites that never burned, burn: sampling sites that burned in the Ranch Fire
- visit3: Sampling period, 3 variables. prefire: 2017-2018, before the Ranch fire burned. postfire: 2019, first year after the Ranch fire burned. recovery: 2020-2021, 2-3 years after the Ranch fire burned
- visit: Sampling period, 4 variables. A: 2017-2018, pre-Ranch fire. B: 2019, 1 year postfire. C: 2020, 2 years post-Ranch fire. D: 2021, 3 years post-Ranch fire.
- visit_burned_r: Site burned in the Ranch Fire relative to sampling date, three variables. unburned: site never burned, prefire: site burned at a later date after sample collection, postfire: site burned before sample collection
- twoclass_severity_r: Ranch Fire soil burn severity, 0: unburned, 1: Very Low-Low, 2: Moderate-Severe
- twoclass_severity_a: August Complex Fire soil burn severity, 0: unburned, 1: Very Low-Low, 2: Moderate-Severe
- severity_r: Ranch Fire soil burn severity, 0: unburned, 1: Very Low, 2: Low, 3: Moderate, 4: Severe
- severity_a: August Complex Fire soil burn severity, 0: unburned, 1: Very Low, 2: Low, 3: Moderate, 4: Severe
- ranch: Site burned in the Ranch Fire, TRUE or FALSE
- august: Site burned in the August Complex Fire, TRUE or FALSE
- abies...vitis: plant genus detected in the sample and corresponding Relative Read Abundance
Folder 2: DietDiversity
Dietary Diversity and ANOVA analysis.
DietDiv.R
Includes fully factorial ANOVA model with all 3 variables, 3 one-way ANOVAs to test effect of treatment on visit
Includes one line explanation of how to repeat with FOO data (for supplementary table)
Files
All samples pooled, RRA:
R_genus4.csv
R_sample_lookup.csv (included in DietDiff folder)
Three one ways, effect of site on visit:
prefireRRA.csv, prefireLookup.csv
postfireRRA.csv, postfireLookup.csv
recoveryRRA.csv, recoveryLookup.csv
Variables
same as above, see "Folder 1: DietDiff"
Folder 3: NicheVar
Niche Variation RinSp analysis.
NicheVar.R : Includes examination of TNW, WID, AID in each period for burn and control sites
Files
- BurnCounts.csv
- ControlCounts.csv
Variables
o sample: Unique sample ID for each sample collected
o lat: Latitude where the sample was collected in decimal degrees, WGS84
o lon: Longitude where the sample was collected in decimal degrees, WGS84
o day: Day of the month the sample was collected on
o month: Month the sample was collected in, ranging 4-7 for April-July
o year: Year the sample was collected in, ranging from 2017 - 2021
o plate: Unique ID for each plate DNA samples were processed on
o success: Success of determining the genotype of the individual deer, 2 variables. Yes: genotyping was successful, fail: genotyping was unsuccessful and individual ID or sex were not identified
o individual: Individual ID of the deer determined by genotyping
o sex: Sex of the deer determiened by genotyping, 3 variables. M: male, F: female, NA: unknown
o plot: Unique ID for each sampled transect
o site: Sampling site, 2 variables. control: sampling sites that never burned, burn: sampling sites that burned in the Ranch Fire
o visit: Sampling period, 4 variables. A: 2017-2018, pre-Ranch fire. B: 2019, 1 year postfire. C: 2020, 2 years post-Ranch fire. D: 2021, 3 years post-Ranch fire.
o visit3: Sampling period, 3 variables. prefire: 2017-2018, before the Ranch fire burned. postfire: 2019, first year after the Ranch fire burned. recovery: 2020-2021, 2-3 years after the Ranch fire burned
o dom_TvS: Dominant habitat type, determied by percent cover codes in pre_treecover and pre_shrubcover, 2 variables. tree_dom: tree dominated, percent cover of trees was higher than percent cover of shrubs prefire. shrub_dom: shrub dominated, percent cover of shrubs was higher than that of trees prefire
o twoclass_severity_r: Ranch Fire soil burn severity, 0: unburned, 1: Very Low-Low, 2: Moderate-Severe
o twoclass_severity_a: August Complex Fire soil burn severity, 0: unburned, 1: Very Low-Low, 2: Moderate-Severe
o severity_r: Ranch Fire soil burn severity, 0: unburned, 1: Very Low, 2: Low, 3: Moderate, 4: Severe
o severity_a: August Complex Fire soil burn severity, 0: unburned, 1: Very Low, 2: Low, 3: Moderate, 4: Severe
o ranch: Site burned in the Ranch Fire, TRUE or FALSE
o august: Site burned in the August Complex Fire, TRUE or FALSE
o abies...vitis: plant genus detected in the sample and corresponding Frequency of Occurance, the count of reads detected in each sample
Folder 4: DietComp
Diet Composition K-W test.
DietComp.R: Includes tests of significance for differences in proportions of plant form in the diet between treatments in each period, and for differences in mean RRA of taxon between treatments in each period
Files
Change in proportion of plant forms in diet
FormVisitA_both.csv
FormVisitA_Lookup.csv
FormVisitB_both.csv (no longer need lookup)
FormVisitC_both.csv
Ashrub.csv
Atree.csv
Bshrub.csv
Btree.csv
Cshrub.csv
Ctree.csv
Variables
o sample: Unique sample ID for each sample collected
o site: Sampling site, 2 variables. control: sampling sites that never burned, burn: sampling sites that burned in the Ranch Fire
o visit3: Sampling period, 3 variables. prefire: 2017-2018, before the Ranch fire burned. postfire: 2019, first year after the Ranch fire burned. recovery: 2020-2021, 2-3 years after the Ranch fire burned
o tree: Relative read abundance of all tree genera in each sample
o shrub: Relative read abundance of all shrub genera in each sample
o grass: Relative read abundance of all grass genera in each sample
o herb: Relative read abundance of all herbacious genera in each sample
o undetermined: Relative read abundance of all unknown genera in each sample
o abies...vitis: plant genus detected in the sample and corresponding Relative Read Abundance
Sharing/Access information
Data was derived from the following sources: DNA extracted from fecal pellets from black-tailed deer.
Code/Software
Code is all listed above. Software included R and DADA2, both open source.
Usage notes
All data can be accessed with any text editor or spreadsheet software. Analysis scripts are for use in open source software, including DAD2 and R.