DOI: 10.5061/dryad.rr4xgxdj0
title: Data from: Haemosporidian infection risk across an urban gradient in a songbird

description: Urbanization is a significant source of inter- and intra-city
 environmental variation and is associated with declining population sizes
 that are increasingly homogeneous. However, whether this shift extends to
 urban disease ecology and related parasite communities requires further
 examination. By comparing the prevalence and diversity of two related
 parasite genera (host-generalist Plasmodium and host-specialist
 Haemoproteus) in dark-eyed junco (Junco hyemalis) populations across an
 urbanization gradient in California, we can determine how broad
 urban-associated land use changes and localized habitat composition
 correlate with pathogen communities. Additionally, by examining vector
 abundance responses, we can begin to assess broader impacts on urban
 disease transmission and ecology. We report that Haemoproteus
 prevalence decreased in urban habitats, with a larger presence of
 host-generalist lineages, suggesting urbanization increases homogenization
 of host-specialist pathogens. Unsurprisingly, the host-generalist
 pathogen, Plasmodium, showed no correlation with urbanization, but
 prevalence increased with rainfall. Local habitat characteristics had
 limited effects on Plasmodium infection prevalence, but moderate shrub
 coverage and low human presence were associated with Plasmodium
 infections. Lastly, Culex tarsalis, an important vector for Plasmodium and
 zoonotic diseases, was the only vector to also increase in abundance in
 response to rainfall. Our results show that broad land use changes
 associated with urbanization decrease avian parasite biodiversity and
 highlight localized abiotic and biotic habitat characteristics that may
 reduce infection prevalence.
description: <strong>Field Sampling</strong>
 Bird capture was conducted during three consecutive breeding
 seasons (January–August) from 2021–2023. The study sites spanned four
 major metropolitan areas (Los Angeles, San Diego, Santa Barbara, and San
 Francisco, CA) and included two non-urban sites (Angeles Nation Forest,
 ANF; Santa Monica Mountains, SMM) and six urban sites (University of
 California, Los Angeles campus, UCLA; University of California, San Diego
 campus, UCSD; University of California, Santa Barbara, UCSB; Occidental
 College campus, Occ; San Francisco State University campus, SFSU; and
 parks throughout Los Angeles, LA). At each site, wild
 juncos were captured by targeted mist netting. Trapping efforts included
 audio lures recorded from juncos in Los Angeles. All captured individuals,
 including males, females, and juveniles, were used in this analysis. Upon
 capture, individuals were banded with metal federal aluminum bands and
 three plastic color bands to create unique band combinations for
 behavioral studies performed in tandem with this study. Body
 morphometrics, including weight, wing cord length, tail length, tarsus
 length, bill width, depth, and length, were recorded. Age is determined
 based on plumage characteristics, and sex is determined by a combination
 of plumage and brood patches or cloacal protuberances (Pyle et al. 1997).
 Blood samples, &gt;10% of the total mass with an average volume of 50
 µL, were collected from individuals via brachial venipuncture and a 30G
 needle following sterilization of the puncture site with alcohol pads.
 Blood was collected via heparinized capillary tubes. Parasitemia was not
 assessed for this study, but two blood smears were prepared per
 individual, and whole blood was then stored in lysis buffer (Valkiūnas
 2005). Samples were collected from recaptures if the capture date exceeded
 two weeks. <strong>Abiotic Environmental
 Assessment</strong> Habitats were characterized
 on the basis of local vegetation surveys and remote sensing data. For both
 the local and remote datasets, we focused on the 50 m radius surrounding a
 capture site. This space presents a reasonable approximation of the space
 used by small territorial passerines, including juncos (Chandler et al.
 1994, Blair 2001). Local vegetation was assessed
 between 2022 and 2023 via a modified version of the combined Rapid
 Assessment Protocol (CNPS-RAP) developed by the California Native Plant
 Society and California Department of Fish and Wildlife (California Native
 Plant Society (CNPS), 2022). These modifications included conducting
 surveys at a junco capture location, regardless of whether they fell
 within the CNPS-RAP definition of a vegetation stand. Rapid assessments
 were conducted in circular plots with a 50-meter radius around each
 individual’s capture location. Finally, we counted the number of trash
 cans and tables within the assessment plot as a proxy for anthropogenic
 waste availability (Mazué et al., 2023). Broader
 habitat conditions included Built-Up Index (BU) as a metric for
 urbanization and monthly precipitation as a metric for water availability.
 To calculate the BU values, the difference between the normalized
 difference built-up index (NDBI) and the normalized difference vegetation
 index (NDVI) was calculated (He et al. 2010). The raster files for both
 the NDBI and the NDVI were from the U.S. Geological Survey Earth Resources
 Observation and Science (EROS) Science Processing Architecture (ESPA)
 Collection 2 Level 2 Landsat Surface Reflectance-Derived Spectral Indices
 and had a resolution of 30 m. The BU values corresponded to the average
 within a 50 m radius of a banding site. The monthly precipitation values
 were obtained from the PRISM Climate Group via
 the <em>prism</em> 0.2.0 R package. The total rainfall for the
 month of the capture date was obtained.
 <strong>Laboratory analysis</strong>
 DNA was extracted from whole blood lysis solution via a Qiagen
 DNeasy Blood and Tissue Extraction Kit (San Diego, CA, USA) or a Wizard®
 SV Blood and Tissue Extraction Kit (Madison, WI, USA) following the
 manufacturer’s instructions. A total of 10 µL of DNA was then used to
 screen for the presence of
 <em>Haemoproteus</em>/<em>Plasmodium</em> via the
 previously described nested PCR protocol (Waldenström et al. 2004). In
 brief, an initial 25-µL reaction was performed with the primers HaemNF and
 HaemNR2, followed by HaemF and HaemR2 for nested PCR (Waldenström et al.
 2004). All reactions were conducted via ThermoFisher DreamTaq MasterMix
 (Hanover Park, IL, USA). Positive samples were submitted for Sanger
 sequencing (Azenta US Inc., La Jolla, CA). No coinfections were detected
 via chromatography; however, this is likely due to PCR bias, which
 preferentially amplifies <em>Haemoproteus</em> over
 <em>Plasmodium</em> (Ciloglu et al. 2019).
 Sanger sequences were used to identify parasite genera and
 lineages. All sequences were able to be identified to the genus level;
 however, only sequences with &gt;80% high-quality reads were used to
 assign lineages. All sequences were initially aligned via the MUSCLE
 alignment feature available in Geneious Prime 2023.2.1, with all sequences
 that exceeded 1% (4 bp) dissimilarity classified as a unique lineage
 (Bensch et al. 2009). Each unique lineage was compared via BLAST with
 sequences available in the GenBank and MalAvi databases to determine if it
 was previously observed. Host-parasite specificity was
 reported as S<sub>TD</sub> and calculated via TAXOBIODIV2
 http://www.otago.ac.nz/parasitegroup/downloads.html.While other metrics of host-parasite specificity exist (Svensson-Coelho et al. 2013), S<sub>TD</sub> is a comparable method and is broadly used (Poulin and Mouillot 2005). The data on hosts were based on reported observations available from the MalAvi data for each parasite lineage. Host-parasite specificity was reported via S<sub>TD</sub> values ranging from 1--4, with higher values suggesting more generalist parasites. Lineages that were unique from those previously published were given a default S<sub>TD</sub> value of 1.
description: # Data from: Haemosporidian infection risk across an urban gradient in a
 songbird
 [https://doi.org/10.5061/dryad.rr4xgxdj0](https://doi.org/10.5061/dryad.rr4xgxdj0) ## Description of the data and file structure Data was collected from dark-eyed juncos from San Francisco, Santa Barbara, Los Angeles, and San Diego. Sites are listed based on broad categorization (urban vs non-urban) but also include GPS coordinates at each capture site. Per-individual data is listed, including morphometric data, infection status, and habitat conditions (including vegetation data within 50 m of a capture site). Local vegetation is based on a modified survey by combining the California Native Plant Society and California Department of Fish and Wildlife surveys. Both precipitation and urbanization values (calculated as NDBI-NDVI) are based on remote sensing data. Lineage data is based onthe  closest similarity to published sequence data available on MalAvi and verified in NCBI GenBank. The MalAvi dataset was also used to determine known susceptible hosts to calculate host-specificity.  NOTE: Data in tables listed as "NULL" indicates measurements/relevant information were not able to be obtained atthe  time of sampling. "NA" denotes "not applicable".  ### Files and variables #### File: BU_Precip_Cleaned.csv **Description:** Complete, per-individual dataset ##### Variables * Sample.Number: Corresponds to a specific blood sample * USGS.band: Federal band number * Specificity: Parasite–host specificity (STD value based on host relatedness) * Species: Species and lineage of parasites * Haemoproteus: Infection status (presence/absence) * Plasmodium: Infection status (presence/absence) * HP: Infection status for either *Haemoproteus* or *Plasmodium*, including non-specific lineages * Year: Year of capture * Month: Month of capture * Day: Day of month of capture * Date: Full date of capture (YYYY-MM-DD) * Julian_Date: Julian date (days) * Time: Time of capture (local time) * Status: New or recapture * BandCombo: Color combination of plastic markers (see USGS for full details) * Min_Age: Lowest possible age based on aging criteria (years, categorical) * Age: Age of the individual (L = Local, HY = Hatch Year [flying], AHY = After Hatch Year, SY = Second Year [based on molt limits], ASY = After Second Year) * Sex: Numeric sex categorization (coded) * Sex..apprt.: Apparent sex (M = male, F = female, U = unknown) * Reproductive_stage: Ranking for cloacal protuberance (M) or brood patch (F/U) * Tarsus: Tarsus length (mm) * Wing: Wing cord at rest (mm) * Mass: Total body mass (g) * Fat: Visible fat score along the breast (ordinal score) * Bill_length: Bill length from nares to tip (mm) * Bill_depth: Bill depth at widest point at the nares (mm) * Bill_width: Bill width at widest point at the nares (mm) * Tail: Tail length (mm) * General.location: Description of capture site for banders * Notes: Comments on significant observations * Lat: Latitude of capture site (decimal degrees) * Long: Longitude of capture site (decimal degrees) * GPS.coordinates: Combined latitude/longitude (decimal degrees) * LA_Sites: Indicates sites located within Los Angeles (binary/categorical) * URB: Broad site categorization (urban or non-urban) * Site: Broader region and/or county * Name: Name of bird * bled.: Blood collected or not (yes/no) * Who measured: Bander initials * Cloacal.sample: Indicates whether a cloacal swab was collected in RNAlater (Y = yes, N = no) * Fecal.Sample: Indicates whether fecal samples were collected (none collected in this study) * Tail.Pictures: Indicates whether tail photographs were collected (yes/no) * Feathers.: Indicates whether feathers were collected (contour feathers from dorsal or ventral region) * NestWatchID: NestWatch identifier (no nest data available for these individuals) * Proportion.of.White.on.Tail: Proportion of tail feathers that are white (unitless proportion; NA if unavailable) * Bill_SA: Bill surface area (mm²) * Bill_Volume: Bill volume (mm³) * BillSAV: Bill surface area-to-volume ratio (unitless) * Veg.Survey: Indicates whether a vegetation survey was conducted within the year (yes/no) * Who.Collected..Veg.Survey..Comments: Collector initials and/or comments * Cloacal.Swab.DNA.Yield..ng.uL.: DNA yield from cloacal swab (ng/µL; NA if unavailable) * Total.CS.DNA.Yield: Total cloacal swab DNA yield (ng; NA if unavailable) * lat_v: Latitude for vegetation survey location (decimal degrees) * long_v: Longitude for vegetation survey location (decimal degrees) * GPS_device: Device used to collect GPS coordinates for vegetation surveys * elev_ft: Elevation (feet) * Elevation: Elevation (m) * soil.texture: Soil texture code * habitat_type: Habitat type code * macro..relative.topography.: Broad-scale topographic classification * micro: Local habitat pattern classification * Impervious_Cover: Percent of total impervious cover (%, non-vegetated surfaces) * lg_rock....: Percent cover of large rocks (%) * sm_rock: Percent cover of small rocks (%) * bare_fine: Percent cover that is bare ground or fine substrate (%) * litter....: Percent leaf litter cover (%) * X..BaseArea_stems: Percent cover of stem base area (%) * Water_Cover: Percent water cover (%) * impervious: Percent impervious surface cover (%) * Vegetation_Cover: Percent total vegetation cover (%) * slope_exposure: Slope aspect or direction (categorical) * slope_steep: Slope steepness (degrees, categorical bins) * stand_size: Broad stand size category * X01_development: Degree of human development (ordinal/categorical) * X05_exotics: Presence of exotic plants (yes/no) * X15_road: Presence of road cover (yes/no) * X20_trampling: Presence of human or animal trampling (yes/no) * X29_recreation: Presence of human recreational activity (yes/no) * DBH: Diameter at breast height of trees (cm) * overstory_spp1: Most prominent overstory species * overstory_spp2: Second most prominent overstory species * overstory_spp3: Third most prominent overstory species * shurb_cat: Shrub categorization * herbs_cat: Herbaceous plant categorization * Desert_Palms: Percent cover of desert palms (%) * Nonvascular: Percent cover of nonvascular plants (%) * veg_cover: Percent vegetation cover (%) * OS_Conifers: Percent cover of overstory conifers (%) * os_conifer_height_meters: Overstory conifer height categories (meters) * OS_Hardwoods: Percent cover of overstory hardwoods (%) * os_hardwood_height: Overstory hardwood height categories (meters) * Understory: Percent cover of understory plants (%) * understory_height: Understory height categories (meters) * Shrubs_Cover: Percent shrub cover (%) * shrub_height: Shrub height categories (meters) * Herbaceous: Percent cover of herbaceous plants (%) * herb_height: Herbaceous height categories (meters) * Buildings: Percent cover of buildings (%) * building_height: Building height categories (meters) * Built_features: Percent cover of other built features (%) * Trash_counts: Number of trash cans within the region (count) * Tables_Count: Number of tables within the region (count) * built_feat_height: Height categories of other built features (meters) * multiple_juncos: Presence of multiple juncos within a territory (yes/no) * X.4: Indicates sites located within Los Angeles (binary) * BU_50m: Built-up Index value within 50 m of capture sites (unitless index) * ppt: Total precipitation within the capture month/year (mm) ## Code/software All script was conducted in R version 4.2.1. Vector_Data_Analysis.R: Prepares mosquito vector data by merging trapping records with precipitation and built-up land cover metrics. Runs species-level abundance models and generates plots linking vectors to rainfall and urbanization. SiteComparisson_Models.R: Summarizes and compares haemosporidian infection prevalence across sites, years, and habitat types. Uses Fisher’s tests and visualizations to test urban vs montane differences among parasites. Random_Forest_for_Haemosporidians.R: Builds random forest and classification tree models to predict parasite infection from environmental and host variables. Evaluates variable importance and model accuracy, focusing mainly on Plasmodium infections. Linear_Models_for_Infection_Across_Habitats.R: Fits generalized linear mixed models to test effects of precipitation and urbanization on infection probability. Produces effect plots for Plasmodium and Haemoproteus across habitats and environmental gradients. ## Access information Other publicly accessible locations of the data: * None Data was derived from the following sources: * Vector data was requested from California Vector Borne Disease Surveillance System (CalSurv). [https://vectorsurv.org/](https://vectorsurv.org/) * NDVI and NDBI data was identified and request from the US Geological Survey Earth Resources Observation and Science using reference obtained from [https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/) * Climate data was requested from the Oregon PRISM Climate Group [https://prism.oregonstate.edu/recent/](https://prism.oregonstate.edu/recent/)