The residency() function builds on the same principles as the explore function and, in addition, computes metrics aiming to show the animal behaviour over time.
residency() has the same arguments as explore(), and includes a set of new arguments aimed to control the residency-related metrics. Like with explore, you do not need to start working with all arguments right away. Defining the
tz is enough to get you going!
The arguments within residency() allow you thoroughly analyse the animal behaviour.
residency(tz, section.order = NULL, success.arrays = NULL, max.interval = 60, minimum.detections = 2, start.time = NULL, stop.time = NULL, speed.method = c("last to first", "first to first"), speed.warning = NULL, speed.error = NULL, jump.warning = 2, jump.error = 3, inactive.warning = NULL, inactive.error = NULL, exclude.tags = NULL, override = NULL, report = FALSE, auto.open = TRUE, discard.orphans = FALSE, save.detections = FALSE, section.minimum = 2, timestep = c("days", "hours"), replicates = NULL, GUI = c("needed", "always", "never"), save.tables.locally = FALSE, detections.y.axis = c("stations", "arrays"))
Below are listed the new arguments, which do not exist in explore. If you cannot find a description of an argument here, have a look at the explore() page.
This argument allows you to specify by which order you would like the sections to appear in plots and tables. By default, actel will order the sections by the order in which they appear in the spatial.csv file. However, you can force a specific order using the
section.order argument. To learn more about how to organise your study area in an actel-friendly way, have a look at this manual page.
This argument controls how many times a tag must be registered at a section for the event to be considered reliable. As part of the analysis, residency() will crunch the array level movement events into section level events. Should there be section events with less overall detections than those listed in
section.minimum, a warning is raised, and you are given the chance to render events invalid.
You can find more about the creation and validation of section movements here.
By default, residency metrics are calculated on a daily basis (e.g. where did my animal spent the most time this day?). However, you may be interested in extracting results at a finer scale. By setting
timestep = "hours", actel will calculate all residency-related values on an hourly basis, rather than on a daily basis.
timestepto hours will increase computing times.
Estimating inter-array efficiency can be tricky in study areas with multiple channels and pathways. However, if these “isolated” arrays are composed by two lines of receivers, one line can be used as a replicate of the other, which in turn allows for the estimation of intra-array efficiency. If this is your case, then you should make use of the
You must use the stations standard names (i.e. St.10, St.12) when referring to replicates. If you are not sure what the standard names of your stations are, you can run
loadSpatial() in the same folder as your spatial.csv file or
loadSpatial(file = "path/to/spatial.csv") to check them out (look at the last column).
Once you know which replicates you want to list for your array(s), you can include them in the analysis. Lets imagine you want to use stations St.1 and St.2 as replicates for the array River1, and St.3 as a replicate for array River3. This is how they should be listed:
Where […] stands for other arguments you want to specify in the function.
It is important that you list replicates using the
list(Array = "Replicate") construction. Keep this in mind while you prepare the code.
Now that you know how to run the residency analysis, you may want to:
Learn more about what residency() is doing
Learn more about the results of residency()