General view of the home range

We first obtain a general representation using 1-ha cells:

grid_hr(
  macaques,
  cell_area = 10000,
  fill_by = "locations",
  title = "Macaques"
)

#> 
#> ----------------------------------------
#> Grid-based home-range analysis: Macaques 
#> ----------------------------------------
#> Locations:               16478 
#> Cell shape:              hex 
#> Cell area:               10000 square meters
#> Fill method:             locations 
#> Used cells:              441 
#> Home-range area:         4410000 square meters
#> Home-range area:         441 ha
#> ----------------------------------------

This provides an initial view of the spatial extent of the home range and the distribution of locations among grid cells.

At this stage, the objective is not necessarily to select the final cell size, but to explore the spatial structure of the data.

Depending on the movement ecology of the species and the scale of the study, some occupied cells may appear as isolated areas separated from the main home range. These may represent genuine, repeatedly used peripheral or salient areas, or they may result from the spatial resolution being too fine relative to the movement scale of the animal.

Importantly, a GPS location should not necessarily be interpreted as an isolated point in space. A location represents an observation of an animal whose use of the surrounding landscape extends beyond the exact recorded coordinate. Increasing the cell size can therefore provide a more spatially coherent representation of the area used by the animal.

This motivates evaluating connectivity as a function of grid-cell size.

Identify a spatially coherent cell size

min_cell(
  macaques,
  min = 5000,
  max = 40000,
  interval = 10000,
  cell_shape = "hex"
)

The function identifies the smallest tested cell area at which all occupied cells form a single connected component. This is, it identifies the smallest tested cell area at which the occupied cells form a spatially connected home range. To do this, it identifies groups of neighbouring occupied cells that touch each other and determines the largest contiguous group (or connected component). The proportion of occupied cells belonging to this largest group provides a measure of how spatially coherent the home range is at each grid resolution.

This provides a practical way of identifying a grid resolution that retains as much spatial detail as possible while producing a spatially coherent representation of the observed home range. For the macaque example, the analysis indicates that approximately 2.5-ha hexagons provide a connected representation of the home range.

This does not imply that 2.5 ha is a universally appropriate resolution. The appropriate resolution depends on the movement scale, sampling design and ecological characteristics of the study species.

Examine the effect of cell size on home-range size

Connectivity provides one criterion for selecting a resolution. We can also examine how the estimated home-range size changes as cell size increases:

hr_cell_size(
  macaques,
  min = 100,
  max = 50000,
  interval = 10000,
  cell_shape = "hex",
  title = "Grid-based home-range across cell sizes"
)

For these macaque data, the relationship shows a noticeable change around 2.5 ha. For these macaque data, the relationship shows a noticeable change around 2.5 ha. Note also that larger cell sizes tend to overestimate total home-range area, particularly along the periphery, because occupied border cells include areas extending beyond the outermost locations. Thus, the choice of cell size can have an important effect on grid-based home-range estimates.

Together with the connectivity analysis, this provides support for selecting a 2.5-ha hexagonal grid as a reasonable resolution for the subsequent analysis.

The objective is not to identify a universally optimal cell size, but to make the choice explicit and empirically informed.

Check whether there are enough locations

Even with an appropriate spatial resolution, a home-range estimate may be poorly characterized if too few locations have been collected.

We can use hr_rare() to examine how estimated home-range size changes as locations are progressively added:

hr_rare(
  macaques,
  cell_area = 25000,
  cell_shape = "hex",
  n_reps = 99,
  step = 100
)

In this example, the home-range estimate approaches an asymptote at approximately 5,000 locations.

Because the dataset contains substantially more locations than this, the available sample appears sufficient to characterize the home range at the selected 2.5-ha resolution.

This step is particularly important because cell size and sampling effort are related: a finer grid requires sufficient observations to characterize the spatial structure at that resolution.

Estimate the final grid-based home range

Having selected a 2.5-ha hexagonal grid, we can now estimate the final home range and visualize space-use intensity:

grid_hr(
  macaques,
  cell_area = 25000,
  fill_by = "radial",
  title = "Macaques",
  low_colour = "grey90",
  high_colour = "grey10"
)

#> 
#> ----------------------------------------
#> Grid-based home-range analysis: Macaques 
#> ----------------------------------------
#> Locations:               16478 
#> Cell shape:              hex 
#> Cell area:               25000 square meters
#> Fill method:             radial 
#> Used cells:              221 
#> Home-range area:         5525000 square meters
#> Home-range area:         552.5 ha
#> ----------------------------------------

Here, the hexagons are shaded according to normalized space-use intensity, with lighter cells representing lower use and darker cells representing higher use.

With fill_by = “radial”, the spatial distribution of intensity is also considered in relation to the distance of each cell from the centre of the observed locations.

In this example, the map suggests greater space-use intensity near the centre of the home range, with intensity generally declining towards the periphery.

Describe the radial space-use pattern

The same pattern can be represented explicitly as a radial profile:

radial_plot(
  macaques,
  cell_area = 25000
)

The radial plot summarizes the relationship between distance from the centre of the home range and space-use intensity.

Thus, the map and radial plot provide complementary views of the same spatial structure:

• the map shows where space-use intensity occurs within the home range;
• the radial plot summarizes how space-use intensity changes with distance from the centre.

This radial representation is one of the distinctive features of gridHR and provides a way of comparing the internal organization of home ranges independently of their absolute size or shape.