# Home ranges Functions to calculate animal home ranges from a `track_xy*`. `hr_mcp`, `hr_kde`, and `hr_locoh` calculate the minimum convex polygon, kernel density, and local convex hull home range respectively. ```r hr_akde(x, ...) ## S3 method for class 'track_xyt' hr_akde( x, model = fit_ctmm(x, "iid"), keep.data = TRUE, trast = make_trast(x), levels = 0.95, wrap = FALSE, ... ) hr_kde(x, ...) ## S3 method for class 'track_xy' hr_kde( x, h = hr_kde_ref(x), trast = make_trast(x), levels = 0.95, keep.data = TRUE, wrap = FALSE, ... ) hr_locoh(x, ...) ## S3 method for class 'track_xy' hr_locoh( x, n = 10, type = "k", levels = 0.95, keep.data = TRUE, rand_buffer = 1e-05, ... ) hr_mcp(x, ...) hr_od(x, ...) ``` ## Arguments - `x`: `[track_xy, track_xyt]` A track created with `make_track`. - `...`: Further arguments, none implemented. - `model`: A continuous time movement model. This can be fitted either with `ctmm::ctmm.fit` or `fit_ctmm`. - `keep.data`: `[logic(1)]` Should the original tracking data be included in the estimate? - `trast`: `[SpatRast]` A template raster for kernel density home-ranges. - `levels`: `[numeric]` The isopleth levels used for calculating home ranges. Should be `0 < level < 1`. - `wrap`: `[logical(1)]` If `TRUE` the UD is wrapped (see `terra::wrap()`). - `h`: `[numeric(2)]` The bandwidth for kernel density estimation. - `n`: `[integer(1)]` The number of neighbors used when calculating local convex hulls. - `type`: `k`, `r` or `a`. Type of LoCoH. - `rand_buffer`: `[numeric(1)]` Random buffer to avoid polygons with area 0 (if coordinates are numerically identical). ## Returns A `hr`-estimate. ## Examples ```r data(deer) mini_deer <- deer[1:100, ] # MCP --------------------------------------------------------------------- mcp1 <- hr_mcp(mini_deer) hr_area(mcp1) # calculated MCP at different levels mcp1 <- hr_mcp(mini_deer, levels = seq(0.3, 1, 0.1)) hr_area(mcp1) # CRS are inherited get_crs(mini_deer) mcps <- hr_mcp(mini_deer, levels = c(0.5, 0.95, 1)) has_crs(mcps) # Kernel density estimaiton (KDE) ----------------------------------------- kde1 <- hr_kde(mini_deer) hr_area(kde1) get_crs(kde1) # akde data(deer) mini_deer <- deer[1:20, ] ud1 <- hr_akde(mini_deer) # uses an iid ctmm ud2 <- hr_akde(mini_deer, model = fit_ctmm(deer, "ou")) # uses an OU ctmm # od data(deer) ud1 <- hr_od(deer) # uses an iid ctmm ud2 <- hr_akde(deer, model = fit_ctmm(deer, "ou")) # uses an OU ctmm ``` ## References Worton, B. J. (1989). Kernel methods for estimating the utilization distribution in home-range studies. **Ecology, 70(1)**, 164-168. C. H. Fleming, W. F. Fagan, T. Mueller, K. A. Olson, P. Leimgruber, J. M. Calabrese, “Rigorous home-range estimation with movement data: A new autocorrelated kernel-density estimator”, Ecology, 96:5, 1182-1188 (2015). Fleming, C. H., Fagan, W. F., Mueller, T., Olson, K. A., Leimgruber, P., & Calabrese, J. M. (2016). Estimating where and how animals travel: an optimal framework for path reconstruction from autocorrelated tracking data. Ecology, 97(3), 576-582.

hrest function