Factors responsible for Ixodes ricinus presence and abundance across a natural-urban gradient

• Mode of collection: Field/Intervention experiment
• Description of the mode of collection: In 2017, ticks and field data were collected from 12 different sites in Stockholm County originally chosen as random controls for another study but was never used. In 2019, we collected ticks and field data at 35 randomly selected sites along the natural-urban gradient. To calculate and urbanization index, we used the proportion of artificial surfaces surrounding each site. All sampling sites were visited once with a total of 295 sampling plots inventoried for ticks and field data. For each sampling plot, we recorded date, time, temperature, weather conditions, number of ticks, vegetation height and tree stem density surrounding the inventory plot. To retrieve large landscape characteristics, we established 10 buffer zones ranging from 100m to 1000m around each sampling site in GIS using satellite land cover maps (retrieved from: https://www.naturvardsverket.se/verktyg-och-tjanster/kartor-och-karttjanster/nationella-marktackedata/ladda-ner-nationella-marktackedata/). These maps have a spatial resolution of 10m and include the following main categories 1) Forest and seminatural areas, 2) Open areas, 3) Arable land, 4) Wetlands, 5) Artificial surfaces and 6) Inland and marine water. These main categories are further divided into subcategories with detailed information regarding the different land cover classes. In the analyses, we used the main categories, with the exception of Forest and seminatural areas where we included eight individual forest types: Pine forest, Spruce forest, Mixed coniferous forest, Mixed forest, Broadleaved forest, Broadleaved hardwood forest, Broadleaved forest with hardwood forest and Temporarily non-forest. To calculate landscape configuration metrics at each sampling site, we used land cover data from the GIS buffers with a 1000m radius, exported to GeoTIFF format and analyzed them with FRAGSTATS version 4. For landscape heterogeneity we used Shannons’ diversity index (SHDI) and to measure the aggregation of landscape attributes we used Contagion (CONTAG). As measures of forest configuration, we used percent of forest cover (PLAND) and total forest edge length (TE). All statistical analyses were performed with R version 4.0.3. To analyze the effect of possible risk factors for tick abundance in different greenspaces across the natural-urban gradient, we used generalized linear mixed models assuming Poisson distributed residuals. As the data contained a larger proportion of zeros than would be expected according to a Poisson or a negative binomial distribution causing overdispersion, we fitted zero-inflated Poisson models using the package glmmTMB (generalized linear mixed models using Template Model Builder)
• Time period(s) for data collection: 2017 – 2019
• Source of the data: Biological samples, Other

Funding

• Funding agency: The Foundation for Baltic and East European Studies
• Funding agency's reference number: 52/2018_OSS
• Project name on the application: Human – environment interactions and the epidemiological periurban landscape of tick-borne diseases