DOI: 10.5061/dryad.9s4mw6mqr
title: Impervious surface cover and number of restaurants shape diet variation in an urban carnivore

description: In the past decade, studies have demonstrated that urban and nonurban
 wildlife populations exhibit differences in foraging behavior and diet.
 However, little is known about how environmental heterogeneity shapes
 dietary variation of organisms within cities. We examined the vertebrate
 prey components of diets of coyotes (Canis latrans) in San Francisco to
 quantify territory- and individual-level dietary differences and determine
 how within-city variation in land cover and land use affect coyote diet.
 We genotyped fecal samples for individual coyote identification and used
 DNA metabarcoding to quantify diet composition and individual niche
 differentiation. The highest contributor to coyote diet overall was
 anthropogenic food followed by small mammals. The most frequently detected
 species were domestic chicken, pocket gopher (Thomomys bottae), domestic
 pig, and raccoon (Procyon lotor). Diet composition varied significantly
 across territories and among individuals, with territories explaining most
 of the variation. Within territories (i.e., family groups), the amount of
 dietary variation attributed to among-individual differences increased
 with green space and decreased with impervious surface cover. The quantity
 of anthropogenic food in scats also was positively correlated with
 impervious surface cover, suggesting that coyotes consumed more human food
 in more urbanized territories. The quantity of invasive, human-commensal
 rodents in the diet was positively correlated with the number of food
 services in a territory. Overall, our results revealed substantial
 intraspecific variation in coyote diet associated with urban landscape
 heterogeneity and point to a diversifying effect of urbanization on
 population diet.
description: # Impervious surface cover and number of restaurants shape diet variation
 in an urban carnivore ## SF-Coyote-Diet-Metabarcoding-main.zip The zip
 file contains raw data files and scripts used to generate results for the
 above manuscript, which has been accepted in *Ecosphere* (December 5th,
 2024). Below we provide descriptions of the files within each directory. 
 ### 1. Sequence-Processing **Trimming-Reads.Rmd**: script to trim raw
 fastq files with cutadapt. High performance computing required.
 **DADA2.Rmd**: script correct amplicon errors and infer amplicon sequence
 variants (ASVs) with DADA2 denoising algorithm. High performance computing
 required. ### 2. Assign-Taxonomy **BLAST.Rmd**: script to create a local
 BLAST database of 12SV5 sequences of vertebrate genera recovered from
 pilot studies. Use the *blastn* feature of BLAST+ to assign ASVs via the
 custom database and append or correct as needed with the nucleotide
 database of NCBI available online. **12S_reference_lib.fasta** is the
 custom reference library of 12SV5 sequences of all vertebrate genera
 recovered from pilot studies. ### 3. Filtering-and-QC
 **FilteringReads.Rmd:** script to manually filter denoised data. The input
 files **batch1_reads.csv** and **batch2_reads.csv ** are ASV count tables
 output by DADA2 during sequence processing with taxonomy assigned by
 BLAST+ (output of step 1). * *SampleID:* fecal sample identifier * *ASV:*
 amplicon sequence variant identifier * *Sequence:* amplicon sequence
 variant * *Kingdom/Phylum/Class/Order/Genus/Species:* taxonomy *
 *FinalName:* highest-resolution taxonomic classification for each ASV *
 *Flag:* amplicon sequence variants flagged for removal * *pident*: percent
 identity (from BLAST) * *qcovs:* query coverage (from BLAST)
 **All_Metadata.csv** provides the metadata associated with the samples. *
 *SampleID:* fecal sample identifier * *Replicate*: whether or not the
 sample is a PCR or extraction replicate * *Name*: field collection
 identifier * *Site*: location sample was collected * *Initials*: initials
 of the sample collector * *Year/Month/Day:* time of sample collection *
 *Lat/Long*: sample collection location * *Sus.sp*: suspected species
 origin as determined in the field * *Genotype*: whether or not genotyping
 was successful * *Geno.Sp*: species identified via genotyping *
 *Category*: whether or not the sample comes from a coyote in San Francisco
 * *Individual*: individual coyote identification * *Cytb_Sp*: species
 identified via Cyt b analysis **NoCanis_Scats.csv**: list of samples
 without *Canis* reads **Species_FunctionalGroups.csv**: list of diet items
 categorized into functional groups ### 4. Data Visualization and
 Statistical Analyses Creating figures or conducting statistical analyses
 requires that filtering and QC steps have been run as denoised and
 filtered data frames are required for visualizing and analyzing diet data.
 #### Diet-Plots **Diet-Plots.Rmd:** generate Figures 1 and 2 in the main
 text to visualize relative amounts of diet items in the population diet
 and among biological seasons, territories, and
 individuals. **12S_species_categories.csv** categorizes diet items into
 general categories of species. **IDs.csv** assigns individual coyotes in
 family groups and sexes.  #### Regression-Analyses
 **Regression-Analyses.Rmd:** script to generate (1) correlation matrix of
 land cover and land use covariates; (2) beta regression for RRA and
 quasibinomial GLM for FOO to test the effect of percent cover of
 impervious surfaces on the proportion and frequency of anthropogenic food
 in each coyote territory and the number of food services on the proportion
 and frequency of nuisance rodents in the diet in each coyote territory;
 and (3) generate the figure for the manuscript showing the correlation
 between land cover/land use and diet items.  Territory_All_Covariates.csv
 provides territory-level covariates * *Territory:* territory name *
 *ISA:* mean percent impervious surface cover in 1km buffer * *Urban:* mean
 percent urban cover in 1km buffer * *Food.Services*: number of food
 services in 1km buffer * *Pop.Den.2020*: mean human population density in
 1km buffer * *Housing.Den.2020*: mean housing density in 1km buffer ####
 iNEXT **iNEXT.Rmd**: script to generate rarefaction curve plots and
 calculating diversity metrics and sample coverage for coyote territories
 and individuals. #### nMDS **nMDS.Rmd:** script to construct dissimilarity
 matrices and ordinate with non-metric multidimensional scaling to
 visualize dietary differences among biological seasons, territories, and
 individuals. To save time, model output is provided
 (sp.FOO.ind.rds, sp.FOO.rds, sp.RRA.ind.rds, sp.RRA.rds). #### PERMANOVA
 **PERMANOVA.Rmd**: script to conduct permutation-based multivariate
 analysis of variance tests to investigate differences in diet as a
 function of biological season and territory as well as among individuals
 and family groups. #### SIMPER **SIMPER.Rmd**: script to conduct
 similarity percentage analysis to assess which diet items contributed the
 most to observed differences in coyote diets among territories. ####
 Replication-Analyses **Replication-Analyses.Rmd**: script to calculate
 correlations between extraction replicate and PCR replicate sample pairs
 to assess the repeatability of results. **Replicates_set1.csv** and
 **Replicates_set7.csv** provide sequence reads for PCR replicates from
 sets 1 and 7 and **sp.RRA.clean.replicates.csv **provides filtered and
 cleaned diet data containing duplicate samples from extraction and PCR
 replication. #### RInSp **Diet-Specialization.Rmd**: script to calculate
 BIC/TNW and PSi metrics with the R package RInSp. ## Model Output Files
 **PERMANOVA_sample_FOO.rds** and **PERMANOVA_sample_RRA.rds** are model
 output from the **PERMANOVA.Rmd** script in the zip file described above.
 Because the model can take a long time to run, the model output is
 provided. Both files contains the results from 1,000 PERMANOVA trials
 following random subsampling of scats down to one sample per individual,
 for both frequency of occurrence (FOO) and relative read abundance (RRA)
 data, respectively. See the description in **PERMANOVA.Rmd** for further
 details. ## Data Tables ### Data\_Table\_1.csv Organized results from
 1,000 PERMANOVA trials following random subsampling of scats down to one
 sample per individual based on frequency of occurrence data (csv file of
 model output provided in **PERMANOVA_sample_FOO.rds**).** ** * *Trial:*
 nth trial of 1,000 * *Covariate:* biological season or territory *
 *F:* Pseudo-F statistic * *R2:* R2 value * *Pr(&gt;F):* p-value ###
 Data\_Table\_2.csv Organized results from 1,000 PERMANOVA trials following
 random subsampling of scats down to one sample per individual based on
 relative read abundance data (csv file of model output provided
 in **PERMANOVA_sample_RRA.rds**).** ** * *Trial:* nth trial of 1,000 *
 *Covariate:* biological season or territory * *F:* Pseudo-F statistic *
 *R2:* R2 value * *Pr(&gt;F):* p-value ### Data\_Table\_3.xlsx Contribution
 of dietary functional groups to coyote diet composition across territories
 in San Francisco, CA, summarized with relative read abundance and
 frequency of occurrence. Functional groups include: anthropogenic food,
 small mammals, medium-sized mammals, birds, herptiles, and marine mammals.
 Sheet 1 presents the data and sheet 2 lists the diet items included in
 each functional group. ### Data\_Table\_4.xlsx Contribution of diet items
 sorted into 26 categories to coyote diet composition across territories in
 San Francisco, CA, summarized with relative read abundance and frequency
 of occurrence.  Sheet 1 presents the data and sheet 2 lists the diet items
 included in each category. ### Data\_Table\_5.xlsx Contribution of dietary
 functional groups to coyote diet composition across individual coyotes in
 San Francisco, CA, summarized with relative read abundance and frequency
 of occurrence. Functional groups include: anthropogenic food, small
 mammals, medium-sized mammals, birds, herptiles, and marine mammals. Sheet
 1 presents the data and sheet 2 lists the diet items included in each
 functional group. ### Data\_Table\_6.xlsx Contribution of diet items
 sorted into 26 categories to coyote diet composition across individual
 coyotes in San Francisco, CA, summarized with relative read abundance and
 frequency of occurrence.  Sheet 1 presents the data and sheet 2 lists the
 diet items included in each category. ## Code/Software R is required to
 run all Rmd scripts, except for **Trimming-Reads.Rmd**, which requires
 cutadapt, and **BLAST.Rmd**, which requires BLAST+. Scripts were created
 using R version 4.2.1 and BLAST+ version 2.13.0. Microsoft Excel** **can
 be used to view all data tables.