The results compiled by the residency() function include those of the explore() function. In this page, only changes/new outputs are documented.

status.df

The status.df object is a data frame that combines both the timetable data and your biometrics into a single, organised table (see "Compiling a timetable"). If you have stored any comments during the analysis process, they will show up in a reserved column in this table. The status.df also contains some more summary information for each animal. This table can be quite big, so it may be a good idea to use head(status.df) the first time you look at it.

section.movements

The section.movements list contains the section-level movement events created during the movement compressing process. Here is an example:

Section	Events	Detections	First.array	Last.array	First.time	Last.time	Time.travelling	Time.in.section
Down	1	1	Down1	Down1	...	...	11:45	0:00
Right	2	2	Right1	Right2	...	...	44:00	563:32
Left	2	2	Left4	Left4	...	...	56:27	295:59
Right	2	2	Right2	Right1	...	...	101:00	626:12
Down	1	1	Down2	Down2	...	...	462:47	0:00
Left	1	1	Left3	Left3	...	...	54:59	0:00

(Timestamps were trimmed so the table fits better in the page)

residency.list

This list contains a table for each of the target tags, detailing where it was since its first valid detection to its last. Here is an example of the residency list produced based on the section movements above:

Section	First.time	Last.time
Down	2019-06-06 00:00:15	2019-06-06 00:00:15
Down-Right	2019-06-06 00:00:15	2019-06-07 20:00:20
Right	2019-06-07 20:00:20	2019-07-01 07:33:00
Left-Right	2019-07-01 07:33:00	2019-07-03 16:00:53
Left	2019-07-03 16:00:53	2019-07-16 00:00:16
Left-Right	2019-07-16 00:00:16	2019-07-20 05:00:35
Right	2019-07-20 05:00:35	2019-08-15 07:13:00
Down-Right	2019-08-15 07:13:00	2019-09-03 14:00:10
Down	2019-09-03 14:00:10	2019-09-03 14:00:10
Down-Left	2019-09-03 14:00:10	2019-09-05 21:00:05
Left	2019-09-05 21:00:05	2019-09-05 21:00:05

array.times

This table contains information on the all arrival times for each tag, at each array. It is used to produce circular graphics in the report.

section.times

This list contains two tables. The first one has all the arrival times for each tag, at each section, and the second has all the departure times, sorted in the same fashion. These are used to produce circular graphics in the report.

time.ratios

This list contains detailed information on the time spent at each location, per day (or hour, if timestep = "hours"), for each of the target tags. These tables can be quite long. Here is an example:

Date	Down	pDown	Down-Right	pDown-Right	Right	pRight	...	Changes	Most.time
2019-06-06	0	0	86385	1.000	0	0.000	...	0	Down-Right
2019-06-07	0	0	72020	0.834	14380	0.166	...	1	Down-Right
2019-06-08	0	0	0	0.000	86400	1.000	...	0	Right
2019-06-09	0	0	0	0.000	86400	1.000	...	0	Right
2019-06-10	0	0	0	0.000	86400	1.000	...	0	Right
2019-06-11	0	0	0	0.000	86400	1.000	...	0	Right
...	...	...	...	...	...	...	...	...	...

(Columns and rows were hidden so the table fits better in the page)

time.locations

The data frame shows the place where each tag spent the most time during each day. It is a crucial middle step between the time.ratios and the global.ratios

global.ratios

The global ratios is a list containing two elements:

1. The absolute number of tags at each location in each day/hour:

   Date	Down	Down-Left	Down-Right	Left	Left-Right	Right	Total
   2019-06-05	0	0	0	2	0	0	2
   2019-06-06	0	0	1	2	0	0	3
   2019-06-07	1	0	2	1	0	0	4
   2019-06-08	3	0	1	1	0	1	6
   2019-06-09	3	0	1	1	0	1	6
   2019-06-10	2	1	1	1	0	1	6
   ...	...	...	...	...	...	...	...

2. The percentage of tags at each location in each day/hour:

   Date	Down	Down-Left	Down-Right	Left	Left-Right	Right	Total
   2019-06-05	0.000	0.000	0.000	1.000	0	0.000	1
   2019-06-06	0.000	0.000	0.333	0.667	0	0.000	1
   2019-06-07	0.250	0.000	0.500	0.250	0	0.000	1
   2019-06-08	0.500	0.000	0.167	0.167	0	0.167	1
   2019-06-09	0.500	0.000	0.167	0.167	0	0.167	1
   2019-06-10	0.333	0.167	0.167	0.167	0	0.167	1
   ...	...	...	...	...	...	...	...

group.ratios

The group ratios are lists similar to the `global.ratios`, but where the data has been split by the groups you defined in the biometrics. This allows you to compare the residency patterns of your different groups!

last.seen

This summary table shows how many tags from each group were last seen at each section, with an extra column for tags that were never detected:

	Disap. in Down	Disap. in Left	Disap. in Right	Disap. at Release
Hatchery	2	1	0	0
Wild	2	2	3	0

efficiency

The efficiency is a list containing three elements.

1. A table of absolute events used to calculate the efficiency
2. The maximum efficiency estimates for each array, using the data in the absolutes table.
3. The minimum efficiency estimates, using the same table.

You can find more about how efficiency estimations are made in the residency analysis in this manual page.

intra.array.CJS

If you provided intra-array estimates in the replicates argument, actel will estimate intra-array efficiencies for the target arrays. These results are stored in the object intra.array.CJS, and the combined efficiency estimate is used to complement the overall efficiency results.

The residency function saves [outputs similar to those saved by explore(). The main differences are in the two elements listed below.

actel_residency_results.RData

Would you like to save a copy of the results to actel_residency_results.RData?(y/N) 

To make sure that you don't accidentally lose your results, you can save them right away in the current directory. The results present in this file are the same as the ones you obtain directly in your R console (see above). It differs from the explore() output both in name and content.

actel_residency_report.html (if report = TRUE)

This is the main non-R output. If you activated the report option, actel will compile an html report for you. The migration report contains the same sections as the explore report, plus the following:

1. Array efficiency

   Here you can see how efficient your receiver arrays were at detecting the tags that moved past them. These results can also be found in the efficiency object, which is in your results in R. If you supplied replicates, the results of the intra-array estimations will also show up here.

2. Last seen

   The sections where the tags were last detected is displayed both as a table and a figure, both of which use the content of the last.seen object, which is in your results in R.

3. Average array arrival times

   For each of your study area's arrays, a circular plot will be drawn with all the arrival times of each tag. The tags are grouped by the groups listed in the biometrics.csv file. These plots are saved in .svg format in the Report folder, so you can easily use them elsewhere, if needed.

4. Section progression

   This section has a graphic representation of the data present in the global.ratios object. It shows how the tags have distributed across your study area as the days pass by.

5. Individual residency plots

   Here you can find a graphic representation of the data present in the daily.ratios object. It shows how much time each tag has spent at each location for each day/hour. All plots start and end in the same day, so it is easy to compare the behaviour of different animals.

6. Graphics of detected tags

   These graphics contain more information than those created by the explore function: the vertical grey dashed lines show the assigned moments of entry and exit for each study area section, and the assigned fate of the animal is shown in the header.

   It is a good idea to have a good look at all of these graphics and verify that everything is looking correct (i.e. actel has assigned fates and entry/exit points correctly). This is also a good chance to have a closer look at the tags you flagged as suspicious during the analysis.

Tags with strange behaviour can be complicated to analyse. While actel's functions have been designed to deal with this as best as possible, you can always enter manual mode if you find out that actel has miss-assigned something. I.e. has a movement event been invalidated that seems valid, or vice-versa? Are part of the movements detected characteristic of predator behaviour?