Two instances
We will use tracking data from Fishers from New York State, USA.
leroy <- amt_fisher |> filter(name == "Leroy")
lupe <- amt_fisher |> filter(name == "Lupe")
Create a template raster for the KDE
trast <- make_trast(amt_fisher |> filter(name %in% c("Leroy", "Lupe")), res = 50)
And estimate home-ranges for both fishers
hr_leroy <- hr_kde(leroy, trast = trast, levels = c(0.5, 0.9))
hr_lupe <- hr_kde(lupe, trast = trast, levels = c(0.5, 0.9))
hr and phr are directional, this means the
order matters. For all other overlap measures the order does not
matter.
hr_overlap(hr_leroy, hr_lupe, type = "hr")
## # A tibble: 2 × 2
## levels overlap
## <dbl> <dbl>
## 1 0.9 0.309
## 2 0.5 0.191
hr_overlap(hr_lupe, hr_leroy, type = "hr")
## # A tibble: 2 × 2
## levels overlap
## <dbl> <dbl>
## 1 0.9 0.986
## 2 0.5 0.574
By default conditional = FALSE and the full UD is
used.
hr_overlap(hr_leroy, hr_lupe, type = "phr", conditional = FALSE)
## # A tibble: 1 × 2
## levels overlap
## <dbl> <dbl>
## 1 1 1.00
hr_overlap(hr_lupe, hr_leroy, type = "phr", conditional = FALSE)
## # A tibble: 1 × 2
## levels overlap
## <dbl> <dbl>
## 1 1 0.759
If we set conditional = TRUE, the overlap is measured at
home-range levels that were specified during estimation.
hr_overlap(hr_leroy, hr_lupe, type = "phr", conditional = TRUE)
## # A tibble: 2 × 2
## levels overlap
## <dbl> <dbl>
## 1 0.5 0.580
## 2 0.9 0.992
hr_overlap(hr_lupe, hr_leroy, type = "phr", conditional = TRUE)
## # A tibble: 2 × 2
## levels overlap
## <dbl> <dbl>
## 1 0.5 0.221
## 2 0.9 0.401
Note, for the remaining overlap measures the order does not matter.
Below we show this for the volumnic intersection
(type = "vi") as an example.
hr_overlap(hr_lupe, hr_leroy, type = "vi", conditional = FALSE)
## # A tibble: 1 × 2
## levels overlap
## <dbl> <dbl>
## 1 1 0.439
hr_overlap(hr_leroy, hr_lupe, type = "vi", conditional = FALSE)
## # A tibble: 1 × 2
## levels overlap
## <dbl> <dbl>
## 1 1 0.439
\(> 2\) instances
Lets calculate daily ranges for Lupe and then and then see how
different ranges overlap with each other.
We have to use the same template raster in order to make ranges
comparable.
trast <- make_trast(lupe, res = 50)
Then we add a new column with day and calculate for each day a
KDE home range.
dat <- lupe |>
mutate(week = lubridate::floor_date(t_, "week")) |>
nest(data = -week) |>
mutate(kde = map(data, hr_kde, trast = trast, levels = c(0.5, 0.95, 0.99)))
Now we can use the list column with the home-range estimates to
calculate overlap between the different home-ranges. By default
which = "consecutive", this means for each list entry (=
home-range estimate) the overlap to the next entry will be
calculated.
hr_overlap(dat$kde, type = "vi")
## # A tibble: 3 × 4
## from to levels overlap
## <int> <int> <dbl> <dbl>
## 1 1 2 1 0.0431
## 2 2 3 1 0.551
## 3 3 4 1 0.612
This works as well, if we set conditional = TRUE:
hr_overlap(dat$kde, type = "vi", conditional = TRUE)
## # A tibble: 9 × 4
## from to levels overlap
## <int> <int> <dbl> <dbl>
## 1 1 2 0.5 0
## 2 1 2 0.95 0.0264
## 3 1 2 0.99 0.0357
## 4 2 3 0.5 0.264
## 5 2 3 0.95 0.528
## 6 2 3 0.99 0.547
## 7 3 4 0.5 0.318
## 8 3 4 0.95 0.592
## 9 3 4 0.99 0.608
Sometimes it can be useful to provide meaningful labels. We can do
this with the labels argument.
hr_overlap(dat$kde, type = "vi", labels = dat$week)
## # A tibble: 3 × 4
## from to levels overlap
## <chr> <chr> <dbl> <dbl>
## 1 2010-12-12 2010-12-19 1 0.0431
## 2 2010-12-19 2010-12-26 1 0.551
## 3 2010-12-26 2011-01-02 1 0.612
Different options exist for the argument which. For
example, which = "one_to_all" calculates the overlap
between the first and all other home ranges.
