Third order selection
We ran integrated Step Selection Analysis (iSSA; Avgar et al. 2016) to determine drivers of within-home range fine-scale resource selection while accounting for individual movement using the broad and fine thematic resolution landcover layers. The movement from 59 deer was modeled using broad thematic resolution. Additionally, the movement from a subset of deer (n = 27) whose location data spanned all six landcover types was modeled using the fine thematic resolution layer during the deer breeding/adult I. scapularis feeding seasons (2016 – 2020).
To prepare the data for iSSA, we used the amt (Signer et al. 2019) R package to estimate step lengths and turning angles between successive steps to fit tentative gamma and von Mises distributions, respectively. From these distributions, ten random steps were generated for each observed step (Supporting Information). Habitat attributes were extracted at the beginning and end of observed and random steps using the two landcover layers. We fit four iSSA models to data from each deer using the ‘fit_issf’ function in amt which uses a conditional logistic regression stratified by start step ID. We included three covariates expected to influence the movement process in the core model and modeled habitat selection for all deer using broad thematic resolution and the subset of 27 deer using fine thematic resolution (Table 1, model 1). We expected time-of-day to vary deer’s strength of selection for features within their HR, thus we included day or night as an interactive term with habitat selection (model 2). Movement differences driven by the starting habitat were assessed through an interaction between starting step habitat and movement covariates (model 3). To assess whether deer moved differently depending on habitat and time-of-day, we included interactions between the ending step landcover, movement covariates, and time-of-day (model 4).
We assessed model fit for 160 deer-season models by bootstrapping each individual’s four models independently (n= 1000) to acquire mean coefficient estimates with a 95% confidence interval and used the ΔAIC (Burnham & Anderson 2002) to determine the individual’s best fit model between the null model and model 1 and between models 1 - 4 (Table 1). We tallied how many deer showed the strongest support for each model by season and sex to determine the top model (Table 2). The coefficient estimates from the best fit model for each sex were bootstrapped (n = 1000) for each individual and the coefficients’ standard errors were estimated. We used individual movement parameter estimates from model 4 (where habitat selection was accounted for) to update the tentative gamma and von Mises distributions and estimated movement rates and directionality for individual deer over natural and urban landcovers. These were summarized using boxplots to show deer’s average and individual movement rates by sex and landcover, faceted by season.
Lastly, to showcase how individual variation in movement and resource use affects the future probability of vector dispersal, we simulated spatially-explicit dispersal kernels informed by movement and selection coefficients estimated from fitted iSSA models for three individuals that varied in the habitat diversity within their HR and their strength of selection for fine resolution landcover types using amt(Signer et al. 2017). We utilized the same initialization points for the simulations across individuals in an area of SI that exemplifies the juxtaposition of habitat types in residential areas, but where no individuals were observed occupying. The simulation was informed by the movement and habitat selection coefficients from model 4 that used a fine resolution landcover. Rasterized dispersal kernels were constructed for the first 100 locations for three individuals. The individual dispersal kernels were mosaicked using the maximum cell value from overlapping kernel layers to create a single raster mosaic per deer (Supplemental Information). Because hosts’ movement characteristics may translate to vector distribution patterns; simulating deer space use informed by observed movement and habitat selection over a landscape provides a visualization of this connection.