urbioconnect

urbioconnect provides tools to quantify ecological connectivity for different urban wildlife species based on:

• wildlife habitat
• urban barrier
• the roaming range (in metres) of the wildlife
• A few other parameters

An example of this is raster grid data on blue tongued-lizard habitat, raster grid data on road and buildings (barriers), and how far their roaming range is (say, 100 metres).

urbioconnect computes metrics for assessing the connectedness of these habitat patches:

• effective mesh size
• probability of connectedness
• number of patches
• total patch area, and
• mean patch area

Note you can use vector (shapefile) or raster data for habitat and barrier formats.

This method is described in:

Kirk, H., Soanes, K., Amati, M., Bekessy, S., Harrison, L., Parris, K., Ramalho, C., van de Ree, R., & Threlfall, C. (2023). Ecological connectivity as a planning tool for the conservation of wildlife in cities. MethodsX, 10, 101989. https://doi.org/10.1016/j.mex.2022.101989

See the getting started vignette for more details.

Shiny app

We include a Shiny app for interactive analysis and report generation. You can launch the interactive Shiny app with:

library(urbioconnect)
run_connectivity_app()

A hosted version is available at: https://njtierney-urbioconnect.share.connect.posit.cloud/

You do not need the shiny app to perform analyses. All functions have been designed to work together in a pipeline - see example usage below.

Installation

Install from R-universe:

install.packages(
  "urbioconnect",
  repos = c("https://urbio-ecology.r-universe.dev", "https://cloud.r-project.org")
)

Or from GitHub:

# install.packages("pak")
pak::pak("urbio-ecology/urbioconnect")

Get started

library(urbioconnect)
library(terra)
#> terra 1.9.50

# load example habitat and barrier rasters
habitat <- example_habitat()
barrier <- example_barrier()

plot(habitat, col = "seagreen", legend = FALSE, main = "Lizard Habitat")
plot(barrier, col = c("grey", "white"), legend = FALSE, main = "Lizard Barriers")

plot(barrier, col = c("grey", "white"), legend = FALSE, main = "Lizard Barriers and Habitat")
plot(habitat, col = "seagreen", legend = FALSE, add = TRUE)

# run the full raster pipeline at a 10m interpatch distance
areas <- habitat_connectivity(
  habitat  = habitat,
  barrier  = barrier,
  species = "Blue-tongued Lizard",
  interpatch_distance = 10
)
#> ℹ Creating barrier mask
#> ✔ Creating barrier mask [40ms]
#> 
#> ℹ Removing habitat underneath barrier
#> ✔ Removing habitat underneath barrier [19ms]
#> 
#> ℹ Buffering habitat for an interpatch distance of 10m
#> Warning: `interpatch_distance` doesn't align with the raster resolution.
#> ✖ 10 m isn't a multiple of 4 m.
#> ℹ It snaps to 8 m.
#> ℹ Connectivity may shift for patches near the cut-off.
#> ℹ See `vignette(urbioconnect::interpatch-distance-and-resolution)`.
#> ✔ Buffering habitat for an interpatch distance of 10m [76ms]
#> 
#> ℹ Fragmenting habitat layer along barrier intersection
#> ✔ Fragmenting habitat layer along barrier intersection [15ms]
#> 
#> ℹ Assigning patches ID to fragments
#> ✔ Assigning patches ID to fragments [1.1s]
#> 
#> ℹ Summarising area in each patch
#> ✔ Summarising area in each patch [116ms]
#> 

areas
#> # A tibble: 1 × 9
#>   species     interpatch_distance n_patches effective_mesh_ha prob_connectedness
#>   <chr>                     <dbl>     <int>             <dbl>              <dbl>
#> 1 Blue-tongu…                  10       703              3.92              0.149
#> # ℹ 4 more variables: patch_area_mean <dbl>, patch_area_total_ha <dbl>,
#> #   data_resolution <chr>, patch_size <list>

areas already contains the landscape-level summary (effective mesh size, probability of connectedness, patch counts, and so on). The per-patch areas that summary is built from are stored in a list-column, and can be pulled out with patch_sizes():

patch_sizes(areas)[[1]]
#> # patch_size_tbl:      data.frame
#> # Species:             Blue-tongued Lizard
#> # Patches:             703
#> # Resolution:          2x2
#> # Interpatch Distance: 10 m
#>   patch_id    area
#>      <dbl>   <dbl>
#> 1        1    60.0
#> 2        2  1648. 
#> 3        7    12.0
#> 4        8  1200. 
#> 5       10 92034. 
#> # ℹ 698 more rows

Works with raster and vector (shapefile)

See the vignette, “raster-vs-vector” for a side-by-side comparison and guidance on when to use raster or vector. We mostly recommend using raster.

Example usage

We have an example pipeline using targets and geotarget, which includes generating reports as output at https://github.com/urbio-ecology/urbio-eco-targets. See the vignette “Using urbioconnect in a targets pipeline” for more detail.

Acknowledgements

This research was conducted on the unceded lands of the Wurundjeri Woi Wurrung and Bunurong Boon Wurrung peoples of the Eastern Kulin Nation, and the unceded lands of nipaluna, lutruwita of the muwinina people. We would like to thank Kylie Soanes, Marco Amati, Sarah Bekessy, Lee Harrison, Kirsten Parris, Cristina Ramalho, Rodney van de Ree, and Caragh Threlfall for their work on the original paper this work is based upon. We would also like to thank Hugh Stanford, Nadine Gaskell, Kerryn Kneebone, and Nicholas Golding for their comments and insight during the development of the methods and software.