DOI: 10.5061/dryad.8sf7m0d2g
title: Faecal pathogens and ectoparasites associated with small mammals in forest fringes around Sydney, Australia

description: This dataset contains curated and Hellinger-tranformed sequencing data
 obtained from DNA extracted from small mammal scats collected in
 Ku-ring-gai Chase National Park, and surrounding urban reserves. We
 present the csv files neccesary to obtained the published results of our
 study, in which we aimed to analyse the influence of host species
 identity and traits (i.e. sex, body mass index; BMI), and seasonality on
 the presence of faecal pathogenic fungi and bacteria as well as the
 ectoparasites associated with small mammals inhabiting forest reserves
 near urban areas in the Sydney region, New South Wales (NSW),
 Australia. Across samples, we identified 12 pathogenic fungi,
 nine bacterial pathogens, and 15 ectoparasite taxa. The most abundant
 representatives of each group were Malassezia japonica (fungi),
 Escherichia coli (bacteria), and Siphonaptera (fleas). Host traits
 influenced pathogen and ectoparasite occurrence in distinct ways. Host sex
 affected flea prevalence, with males more frequently infested than
 females. Host body mass index had no detectable effect on pathogen or
 ectoparasite presence. Host species was a strong predictor with Rattus
 fuscipes being more likely to carry fleas and mites, whereas Antechinus
 stuartii had a higher likelihood of harbouring fungal and bacterial
 pathogens in their scats. Seasonality also shaped pathogen and
 ectoparasite dynamics. Pathogenic fungi, bacteria, and ticks were more
 common in the autumn (wet season), whereas flea prevalence was highest in
 spring. Overall, our findings underscore the importance of broad-scale
 assessments of pathogen communities in wildlife species that live near
 humans, as such work is critical for identifying potential vectors and
 emerging zoonoses.
description: We live-trapped small mammals at five sites in Ku-ring-gai Chase
 National Park, each paired with one surrounding urban reserve. The urban
 reserves were located at least 100 m outside the KNP boundary and
 separated from one another by at least 1.5 km. At each site, we installed
 50 Elliot traps in a sampling grid spaced at 10 m intervals. At the forest
 sites, the sampling grids were 5 × 10 trap rectangles. At the urban
 reserves, this grid arrangement was not always feasible due to the
 irregular shape of some sites. We sampled a pair of sites
 simultaneously. Each trap was baited with a mixture of peanut butter,
 vanilla essence, and oats. We sampled the sites during both spring
 (September 2023) and autumn (April 2024). Upon capture, any small mammals
 showing signs of stress (16% of captures) were immediately released. The
 remaining individuals were taxonomically identified. After processing the
 captured individuals, any fresh scats present were collected in 1.5 ml
 Eppendorf tubes from inside the Elliot traps. We did not collect scats
 from recaptured individuals. The scat samples were immediately placed in a
 cooler box with ice packs following collection and then transferred to a
 -80 °C freezer upon arriving back in the laboratory. We extracted DNA from
 the scat samples after each sampling season using the DNeasy PowerSoil Pro
 kit from Qiagen®. We processed 108 scat samples that were sequenced with
 the ITS 1F and ITS 2R and used the DADA2 pipeline to process the
 sequencing data and identify the fungal species using the UNITE database
 for fungi version 10.0. These data were then assigned to functional guilds
 using the package FunguildR. Sixty of these samples were also sequenced
 using the 16s primers 16s V3-V4 primers 341F and 806R. The DADA2 pipeline
 was used to curate this data, where the taxonomical assignment was done
 using the reference database SILVA taxonomic training data version 138.2.
 The identified bacterial taxa were classified into ecological functional
 groups using the FAPROTAX and the NJC19 databases in
 <em>microeco</em> R package, and only human pathogenic
 bacteria were filtered for further analyses. For the ectoparasites, each
 captured small mammal was inspected for the presence of ectoparasites for
 two minutes. Upon collection, the ectoparasites from each mammal were
 submerged in 100% ethanol in an Eppendorf tube. Individual ectoparasites
 were examined under a stereomicroscope for identification. 
 <em>Data analyses.</em> We calculated the overall
 prevalence of fungal and bacterial pathogens, as well as ectoparasites for
 each host species and season. We then developed binomial generalised
 linear models to predict the probability of presence for each pathogen
 group, and per ectoparasite order (i.e., Ixodida, Mesostigmata and
 Siphonaptera). We generated a global model in <em>MuMIn</em>
 to assess the influence of host BMI and species, as well as season on the
 presence/absence of each of the above mentioned pathogens and ectoparasite
 groups.
description: # Faecal pathogens and ectoparasites associated with small mammals in
 forest fringes around Sydney, Australia Dataset DOI:
 [10.5061/dryad.8sf7m0d2g](10.5061/dryad.8sf7m0d2g) ## Description of the
 data and file structure The provided data were retrieved from sequencing
 data of DNA extracted from live-trapped small mammal scats using the
 primers ITS1F-ITS2R and 16S V3-V4 primers 341F and 806R. The DADA2
 pipeline was used to curate this data, where the taxonomic assignment was
 done using the reference database SILVA taxonomic training data version
 138.2. The data on ectoparasite counts were obtained by direct inspection
 of small mammals and morphological identification. ### Files and variables
 #### File: 20241123_sp80_40r_hellinger.csv **Description:** Taxonomical
 identification of fungi with a bootstrap confidence above 80 % found in
 small mammal scat samples, and those fungal species with more than 50
 reads. The sequencing depth was rarefied to 40,000 reads. The resulting
 sequencing depth was transformed using the Hellinger transformation. #####
 Variables * sp_ID: Consecutive ID of the fungi species * Kingdom to
 Species: Taxonomic classification of each identified fungi * Taxonomy:
 Fungi species * boot.Genus.80: Bootstrap confidence value above 80 % for
 genus (yes/no) * boot.Species.80: Bootstrap confidence value above 80 %
 for species (yes/no) * reads.50: Whether the taxon had more than 50
 reads (yes/no) * K.1.1 to U.5.E2: Small mammal scat samples #### File:
 20250306_human_pathogen_fungi_sm.csv **Description:** Human pathogenic
 fungi identified with a bootstrap confidence above 80 % found in small
 mammal scat samples, and those fungal species with more than 50 reads. The
 sequencing depth was transformed using the Hellinger transformation. Data
 on small mammal individuals is included. ##### Variables * ID: Consecutive
 ID  * sample_ID: Sample ID * site: Site ID (Nine different sites) * date:
 Date of sample collection * season: Season of sample collection *
 vector.genus: Small mammal individual genus * vector: Small mammal
 individual species * site.type: Site type (forest/urban) * sex: Small
 mammal sex * weight: Small mammal weight * body_length: Small mammal body
 length * bmi: Small mammal Body Mass Index * pathogen_pa: Presence/absence
 of fungal pathogens * Exophiala.xenobiotica to Malassezia.restricta:
 Fungal pathogens identified in small mammal scat samples. The row numbers
 represent the Hellinger transformed reads per sample of each fungal
 species. #### File: 20250226_biom_16s.csv **Description:** Taxonomic
 identification of all the bacteria identified in small mammal scat
 samples. ##### Variables * sequence: Identified DNA sequenced of the ASV.
 * tax.Kingdom to tax.Species: Taxonomic classification of the identified
 ASV. * taxonomy: ASV species * taxonomy_level_80: Taxonomic level of the
 identification with a confidence of &gt; 80 %. *
 boot_80_taxonomy: Bootstrap confidence value above 80 % for the specified
 taxonomic level (yes/no) * boot.Kingdom to boot.Species: Bootstrap
 confidence value per each Taxonomic level * reads: Number of reads of the
 identified species considering all the small mammal scat samples. *
 K-1-12-16S to U-5-1-16S: Sample IDS. The row numbers represent the
 Hellinger transformed reads per sample of each identified taxon per
 sample. #### File: 20250304_sample_depth_16s.csv **Description:** Small
 mammal scat sample general data ##### Variables * sample: Sample ID *
 depth: Sequencing depth of the sample * site_type: Site type where the
 sample was collected (forest/urban) * site: Site identifier #### File:
 20250304_asv_taxmat_16s.csv **Description:** Taxonomic identity matrix for
 the bacteria identified in small mammal scat samples ##### Variables *
 ASV_ID: ASV identifier * Kingdom: * Phylum to Genus: Taxonomic
 classification of the bacteria * Taxonomy: Taxa of bacteria identified in
 the sample with a bootstrap confidence level &gt; 80 % *
 taxonomy_level_80: corresponding taxonomic level of identification with
 bootstrap confidence level &gt; 80 % confirmation *
 boot_80_taxonomy: bootstrap confidence level &gt; 80 % confirmation (yes)
 #### File: 20250304_asv_mat_16s.csv **Description:** ASV matrix with reads
 data for small mammal scat samples ##### Variables * ASV_ID: ASV
 identifier * K.1.12.16S to U.5.1.16S: Sample identifier #### File:
 20250304_asv_mat_30r_hellinger.csv **Description:** ASV matrix with reads
 data for small mammal scat samples with rarefaction to 30,000 reads, and
 Hellinger transformation applied to the reads. ##### Variables * X: ASV
 identifier * K.1.12.16S to U.5.1.16S: Sample identifier. Row numbers
 represent the number of Hellinger transformed reads per ASV. #### File:
 20250304_asv_taxmat_30r_hellinger.csv **Description:** Taxonomy matrix of
 the ASV found small mammal scat samples with rarefaction to 30,000 reads,
 and Hellinger transformation applied to the reads. ##### Variables * X:
 ASV identifier * Kingdom to Genus: Taxonomic identification of the
 bacteria * Taxonomy: Taxa of bacteria identified in the sample with a
 bootstrap confidence level &gt; 80 % * taxonomy_level_80: corresponding
 taxonomic level of identification with bootstrap confidence level &gt; 80
 % confirmation * boot_80_taxonomy: bootstrap confidence level &gt; 80 %
 confirmation (yes) #### File: 20250305_pathogens_16s_sm.csv
 **Description:** Bacterial human pathogens identified with a bootstrap
 confidence above 80% found in small mammal scat samples, and those fungal
 species with more than 50 reads. The sequencing depth was transformed
 using the Hellinger transformation. Data on small mammal individuals is
 included. ##### Variables * site_type: site type where the samples were
 collected (urban/forest) * site: site identifier * sample_ID: sample ID *
 date: Date of sample collection * season: Season of sample collection *
 sex: Small mammal sex * vector.genus: small mammal genus * vector: small
 mammal species * weight: small mammal weight * body_length: small mammal
 body length * bmi: body mass index of small mammal individual *
 pathogen_pa: presence/absence of bacterial pathogens in the sample *
 Escherichia to Mycobacterium.fortuitum: Pathogenic bacteria taxa
 identified in the samples. The row values are the Hellinger transformed
 reads of each bacteria per sample. #### File:
 20250409_ectoparasite_counts.csv **Description:** Data on the counts of
 ectoparasites collected from small mammals.  ##### Variables * site.type:
 site type where the ectoparasites were collected * site: site identifier *
 ID: sample identifier * date: date of sample collection * season: season
 of collection * trap.ID: ID of the trap where the inspected small mammal
 was captured * weight: small mammal host weight * body.length: small
 mammal host body length * bmi: small mammal host body mass index * sex:
 small mammal host sex * vector.genus: small mammal host genus * vector:
 small mammal host species * ectoparasite_load: total ectoparasite load of
 small mammal host, i.e., total number of ectoparasites collected from the
 inspected individual. * ixodida_load: number of ticks collected from the
 host * mesostigmata_load: number of mites collected from the host *
 siphonaptera_load: number of fleas collected from the host *
 ectoparasite_pa: presence/absence of ectoparasites on the small mammal
 inspected * ixodida_pa: presence/absence of ticks on the small mammal
 inspected * mesostigmata_pa: presence/absence of mites on the small mammal
 inspected * siphonaptera_pa: presence/absence of fleas on the small mammal
 inspected #### File: 20250804_small_mammal_pathogens_DRYAD.R
 **Description:** Code used to analyse the collected data on bacterial and
 fungal pathogens identified on small mammal scats, and small mammal
 ectoparasites found in urban reserves and forest in Sydney, NSW,
 Australia. ## Code/software \#####Small mammal pathogen prevalence and
 ectoparasite load################### \#By Margarita Gil-Fernández The code
 used to run the analyses can be found in the files under the name
 of 20250804_small_mammal_pathogens_DRYAD.R This code was created and ran
 under RStudio version R-4.4.1. We gathered all the files needed to run the
 code in one single folder, which was used as the working directory. ##
 Access information Other publicly accessible locations of the data: * The
 fungal sequencing data were also used to analyse the diversity of
 ectomycorrhizal fungi in small mammal scats. Data was derived from the
 following sources: * Sequencing data and complementary processing
 information for the fungal sequences can be found
 in: DOI:10.5061/dryad.jwstqjqns