为adehabitatHR中的多个动物创建内核密度估计

Question

I am trying to create kernel density estimates (KDEs) to estimate the home ranges for multiple pairs of long-tailed tits. Despite filtering out pairs ("nest_id" variable) that have less than five GPS locations, I still get the error message "At least 5 relocations are required to fit an home range" when trying to create kernelUDs for all individuals. I'm not sure exactly what the problem is/what I'm doing wrong. I am very new to home range analyses in general, as well as in R and adehabitatHR. Below is my code.

library(dplyr)
# reading in location data
locations <- read.csv("./data/GPS_LOCATIONS_MASTER.csv")

## making nest_id a factor
locations$nest_id <- as.factor(locations$nest_id)

## filtering for points I want to include
location_sub <- filter(locations, start_at_nest == "no")

## taking out pairs (via nest_id) with less than 5 GPS locations
xorig <- subset(location_sub,
                with(location_sub,
                     nest_id %in% names(which(table(nest_id) >= 5))))

glimpse(xorig)


Observations: 257
Variables: 10
$ point_id          <int> 293, 294, 299, 300, 303, 306, 307, 312, 315, 318, 324…
$ nest_id           <fct> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,…
$ female_id         <fct> RPR, RPR, RPR, RPR, RPR, RPR, RPR, RPR, RPR, RPR, RPR…
$ male_id           <fct> OWL, OWL, OWL, OWL, OWL, OWL, OWL, OWL, OWL, OWL, OWL…
$ lat               <dbl> 53.38293, 53.38285, 53.38282, 53.38302, 53.38300, 53.…
$ lon               <dbl> -1.554700, -1.554000, -1.553967, -1.554067, -1.553867…
$ start_at_nest     <fct> no, no, no, no, no, no, no, no, no, no, no, no, no, n…
$ place_in_sequence <int> 3, 1, 3, 4, 3, 3, 4, 3, 3, 3, 3, 4, 5, 1, 2, 3, 4, 5,…
$ date_time         <fct> 2019-02-27T13:26:31Z, 2019-02-27T13:28:46Z, 2019-02-2…
$ notes             <fct> , , , , , , , , , , , , , , , , , , , , , , , , , , 


library(sp)

## projecting so that my x & y (in coords.x1 & coords.x2) are in metres
xorig_s <- SpatialPointsDataFrame(xorig,
                                  coords = cbind(xorig$lon, xorig$lat),
                                  proj4string =  CRS("+proj=longlat +datum=WGS84 +no_defs"))
xorig_utm <- spTransform(xorig_s, CRS("+proj=utm +zone=30 +ellps=WGS84 
                                      +datum=WGS84 +units=m +no_defs"))
glimpse(as.data.frame(xorig_utm))

Observations: 257
Variables: 12
$ point_id          <int> 293, 294, 299, 300, 303, 306, 307, 312, 315, 318, 324…
$ nest_id           <fct> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,…
$ female_id         <fct> RPR, RPR, RPR, RPR, RPR, RPR, RPR, RPR, RPR, RPR, RPR…
$ male_id           <fct> OWL, OWL, OWL, OWL, OWL, OWL, OWL, OWL, OWL, OWL, OWL…
$ lat               <dbl> 53.38293, 53.38285, 53.38282, 53.38302, 53.38300, 53.…
$ lon               <dbl> -1.554700, -1.554000, -1.553967, -1.554067, -1.553867…
$ start_at_nest     <fct> no, no, no, no, no, no, no, no, no, no, no, no, no, n…
$ place_in_sequence <int> 3, 1, 3, 4, 3, 3, 4, 3, 3, 3, 3, 4, 5, 1, 2, 3, 4, 5,…
$ date_time         <fct> 2019-02-27T13:26:31Z, 2019-02-27T13:28:46Z, 2019-02-2…
$ notes             <fct> , , , , , , , , , , , , , , , , , , , , , , , , , , 
$ coords.x1         <dbl> 596131.3, 596178.1, 596180.4, 596173.3, 596186.6, 596…
$ coords.x2         <dbl> 5915843, 5915835, 5915831, 5915853, 5915852, 5915848,…

library(adehabitatHR)

## trying to create :
kud <- kernelUD(xorig_utm[,1], h = "href")
## this procures the error message:
Error in kernelUD(xorig_utm[, 1], h = "href") : 
  At least 5 relocations are required to fit an home range

Answer 1

Updated to point to the current version of kernelUD() function documentation:

help docs for kernelUD

Usage

 kernelUD(xy, h = "href", grid = 60, same4all = FALSE, hlim = c(0.1, 1.5), kern = c("bivnorm", "epa"), extent = 1, boundary = NULL) 

...

Arguments

 xy An object inheriting the class SpatialPoints containing the x and y relocations of the animal. If xy inherits the class SpatialPointsDataFrame, it should contain only one column (factor) corresponding to the identity of the animals for each relocation. 

So using above, you might also use the Vignette where there is an example you may already be following:

I give below a short example of the use of kernelUD, using the puechabonsp dataset. Remember that the first column of the component relocs of this dataset contains the identity of the animals:

 > data(puechabonsp) > kud <- kernelUD(puechabonsp$relocs[,1], h="href") > kud 

One way to understand what you need for above xy is to print out the example data included in the package example:

data("puechabonsp")
head( puechabonsp$relocs[,1] )


            coordinates  Name
    1 (699889, 3161560) Brock
    2 (700046, 3161540) Brock
    3 (698840, 3161030) Brock
    4 (699809, 3161500) Brock
    5 (698627, 3160940) Brock
    6 (698719, 3160990) Brock
    Coordinate Reference System (CRS) arguments: NA 

Note above, puechabonsp$relocs[,1] is a SpatialPointsDataFrame object containing both the coordinates and the ID (which is 'Name' above).

Moving on from here, look at the ?SpatialPointsDataFrame help to understand that function and the object it creates:

Usage

 SpatialPointsDataFrame(coords, data, coords.nrs = numeric(0), proj4string = CRS(as.character(NA)), match.ID, bbox = NULL) 

Arguments

 coords numeric matrix or data.frame with coordinates (each row is a point); in case of SpatialPointsDataFrame an object of class SpatialPoints-class is also allowed. 

"Your mission, should you choose to accept..." is to properly create coords in your SpatialPointsDataFrame where coords includes both the coordinates and the animal id.

From here it may be helpful to raise a new question like "How to create a SpatialPointsDataFrame that includes animal ids?" If you do raise that question be sure to include a reproducible data set as that will make a big difference to people helping.