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

To better understand the spatial distribution of the common tick Ixodes ricinus, we investigated how local site factors and landscape characteristics influence tick presence and abundance in different greenspaces along the natural-urban gradient in Stockholm County, Sweden. Ticks and field data were collected in 2017 and 2019 and analyzed in relation to habitat type distributions estimated from land cover maps using geographical information system (GIS). 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)为更深入理解蓖麻硬蜱（Ixodes ricinus）的空间分布格局，我们探究了瑞典斯德哥尔摩县沿自然-城市梯度的不同绿地中，局地生境因子与景观特征如何影响蜱虫的存在与否及种群丰度。本研究于2017年与2019年采集蜱虫与野外数据，并结合地理信息系统（GIS）解译的土地覆盖图估算的生境类型分布开展关联分析。2017年，我们从斯德哥尔摩县的12个样点采集了蜱虫与野外数据，这些样点原本是另一项研究预留的随机对照样地，但未被该研究使用。2019年，我们沿自然-城市梯度选取了35个随机样点采集蜱虫与野外数据。为计算城市化指数，我们以每个样点周边人工地表的占比作为计算依据。所有采样点均仅访问一次，共计完成295个采样样方的蜱虫与野外数据调查记录。针对每个采样样方，我们均记录了调查日期、时间、气温、天气状况、蜱虫数量、植被高度以及样方周边的树干密度。为获取大尺度景观特征，我们基于卫星土地覆盖数据（获取自：https://www.naturvardsverket.se/verktyg-och-tjanster/kartor-och-karttjanster/nationella-marktackedata/ladda-ner-nationella-marktackedata/），在GIS中为每个采样点构建了覆盖100m至1000m梯度的10个缓冲区。该数据空间分辨率为10m，包含以下六大土地覆盖类别：1）森林与半自然区域；2）开阔地带；3）耕地；4）湿地；5）人工地表；6）内陆与海洋水域。上述大类可进一步细分为多个子类别，涵盖各类土地覆盖类型的详细信息。本研究分析中仅使用上述大类地物，唯森林与半自然区域除外，我们纳入了8种具体森林类型：松林、云杉林、混交针叶林、混交林、阔叶林、阔叶硬木林、带硬木林的阔叶林以及临时非林地。为计算每个采样点的景观格局指数，我们提取了1000m半径缓冲区的土地覆盖数据，导出为GeoTIFF格式后，使用FRAGSTATS 4版本开展分析。景观异质性量化采用香农多样性指数（Shannons’ Diversity Index, SHDI）；景观属性的聚集程度则采用蔓延度指数（Contagion, CONTAG）。森林格局的量化指标包括森林覆盖率（PLAND）与森林总边缘长度（TE）。所有统计分析均基于R 4.0.3版本完成。为探究自然-城市梯度下不同绿地中蜱虫丰度的潜在风险因子影响效应，我们采用了残差服从泊松分布的广义线性混合模型。鉴于本数据中零值占比高于泊松分布或负二项分布的预期值，存在过离散问题，故使用glmmTMB包（即基于模板模型构建器的广义线性混合模型）拟合零膨胀泊松模型。