Prepare Data for RSF
Random Points
Before fitting a RSF we have to do some data preparation. We have to generate random points, points that we think the animal could have used. The random points define the availability domain. In amt the function random_points is designed to do just that. The function can be used in 3 different ways, depending to the type of object that is passed to the function call.
- A
track_*(such as thedeerobject) can be passed to the functionrandom_points. The function then calculates a home range (the home-range estimator can be controlled with argumenthr). Within this home rangenrandom points are generated. The default value ofnis ten times the number of present points. - If a
hr-object (i.e., the result of a home-range estimation inamt) is passed torandom_points, points are generated within the home range. This allows to generate random points within any home range that was previously estimated inamt. Note, that this could be a home range of multiple animals. In this case, the functionrandom_pointshas one additional argument calledpresence. This argument takes atrk_*with the presence points and adds these points for convenience to the random points. - A
SpatialPolygons*-object orsf-object. The latter must containPOLYGONs orMULTIPOLYGONs as features. This can be useful in situation where a home range needs to be buffered, or when other geographical features are considered as the availability domain. As before, this method forrandom_pointsalso takes the argumentpresenceto optionally add the observed points to the output.
Lets now illustrate the three different situations. First we take random points from a track_xy
r1 <- random_points(deer)
plot(r1)
With the argument n we can control the number of random points (remember that the default is ten times as many points as we observed points).
r1 <- random_points(deer, n = 100)
plot(r1)
Next, we can create random point within a home range, that we estimated before.
hr <- hr_mcp(deer)
r1 <- random_points(hr, n = 500)
plot(r1)
Here, we can also add the observed points:
hr <- hr_mcp(deer)
r1 <- random_points(hr, n = 500, presence = deer)
plot(r1)
Finally, we can work with the home range and for example a buffer and then generate random points within the this new polygon.
hr <- hr_mcp(deer) %>% hr_isopleths() %>%
sf::st_buffer(dist =3e4) # add a 30km buffer
r1 <- random_points(hr, n = 500)
plot(r1)
And we can also add the observed points.
hr <- hr_mcp(deer) %>% hr_isopleths() %>%
sf::st_buffer(dist =3e4) # add a 30km buffer
r1 <- random_points(hr, n = 500, presence = deer)
plot(r1)
Of course we are not restricted to the sf::st_buffer function. All geometric operations from the sf package can be used to generate arbitrarily complex availability domains.
Extract covariates
As the next step we have to extract the covariates at point. We can do this with extract_covariates.
rsf1 <- deer %>% random_points() %>%
extract_covariates(sh_forest) %>%
mutate(forest = sh.forest == 1)