AdehabitatHR: interpreting biased random bridge RD and ID output

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AdehabitatHR: interpreting biased random bridge RD and ID output

Rowena Hamer
Hi,

I am using the biased random bridges function within the adehabitatHR package to analyse home ranges, and am interested in breaking the utilisation distribution down into recursion and intensity of use, as outlined in the vignette. I have successfully run the analyses as per the vignette example (code below), and can happily plot the UD, ID and RD and pull out areas of high visitation using the isopleths etc.

What I can't understand is how the output of the BRB function relates to 'real' values of the number of visits per site, and the average length of time spent in these areas. The raw pixel values range between 0 and ~0.0000006 (as in example below): I can't find any explanation of the units used in these outputs, and whether there is a way to translate them into more readily interpreted values.

For example, in Benhamou and Riotte-Lambert 2012, they describe further analyses of the distributions:
"For instance, the minimum area accounting for 20% of the visits (red and orange area in the bottom right panel of Fig. 2) was visited 36 times (ignoring numerous short (<1 h) movement bouts inside or outside the area due to movements close to the area edges). The mean visit duration was about 5 h (this area thus was exploited for 27% of the total period whereas it encompassed only about 5% of the home range area), and the mean duration spent outside the area between one exit and the consecutive entry was about 13 h."

Is there a built in way to translate the ID and RD outputs into this kind of data, or do you have to calculate it separately (e.g. by overlaying the ID/ RD over the original data and summarising the number of visits manually?).

Thanks for any help you can give!

Rowena


Buffalo example from adehabitatHR vignette:

> data(buffalo)
> id <- BRB(buffalo$traj, D = 440/60, Tmax = 3*3600, Lmin = 50, type = "ID",
+           hmin=100, radius = 300, maxt = 2*3600, activity="act", filtershort=FALSE,
+           grid = 200, extent=0.1)
> rd <- BRB(buffalo$traj, D = 440/60, Tmax = 3*3600, Lmin = 50, type = "RD",
+           hmin=100, radius = 300, maxt = 2*3600, activity="act", filtershort=FALSE,
+           grid = 200, extent=0.1)
> ud <- BRB(buffalo$traj, D = 440/60, Tmax = 3*3600, Lmin = 50,
+           hmin=100, radius = 300, maxt = 2*3600, activity="act", filtershort=FALSE,
+           grid = 200, extent=0.1)
> range(ud@data)
[1] 0.000000e+00 5.660811e-07
> range(id@data)
[1] 0.000000e+00 6.552115e-07
> range(rd@data)
[1] 0.000000e+00 3.774331e-07



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Re: AdehabitatHR: interpreting biased random bridge RD and ID output

JLong

Hi Rowena,

 You can use the getverticeshr to compute the say 20% volume contour. But I know of no tools to automatically compute the number of visits or the visit time associated with different areas. The output values in UD, ID, and RD are typically very difficult to interpret, check that the entire UD sums to 1 for example. If so you can interpret the values as relative use (associated with each of UD, ID, RD).

Sorry not of more help,
Jed


-----Original Message-----
From: R-sig-Geo [mailto:[hidden email]] On Behalf Of Rowena Hamer
Sent: 23 October 2017 04:01
To: [hidden email]
Subject: [R-sig-Geo] AdehabitatHR: interpreting biased random bridge RD and ID output

Hi,

I am using the biased random bridges function within the adehabitatHR package to analyse home ranges, and am interested in breaking the utilisation distribution down into recursion and intensity of use, as outlined in the vignette. I have successfully run the analyses as per the vignette example (code below), and can happily plot the UD, ID and RD and pull out areas of high visitation using the isopleths etc.

What I can't understand is how the output of the BRB function relates to 'real' values of the number of visits per site, and the average length of time spent in these areas. The raw pixel values range between 0 and ~0.0000006 (as in example below): I can't find any explanation of the units used in these outputs, and whether there is a way to translate them into more readily interpreted values.

For example, in Benhamou and Riotte-Lambert 2012, they describe further analyses of the distributions:
"For instance, the minimum area accounting for 20% of the visits (red and orange area in the bottom right panel of Fig. 2) was visited 36 times (ignoring numerous short (<1 h) movement bouts inside or outside the area due to movements close to the area edges). The mean visit duration was about 5 h (this area thus was exploited for 27% of the total period whereas it encompassed only about 5% of the home range area), and the mean duration spent outside the area between one exit and the consecutive entry was about 13 h."

Is there a built in way to translate the ID and RD outputs into this kind of data, or do you have to calculate it separately (e.g. by overlaying the ID/ RD over the original data and summarising the number of visits manually?).

Thanks for any help you can give!

Rowena


Buffalo example from adehabitatHR vignette:

> data(buffalo)
> id <- BRB(buffalo$traj, D = 440/60, Tmax = 3*3600, Lmin = 50, type = "ID",
+           hmin=100, radius = 300, maxt = 2*3600, activity="act", filtershort=FALSE,
+           grid = 200, extent=0.1)
> rd <- BRB(buffalo$traj, D = 440/60, Tmax = 3*3600, Lmin = 50, type = "RD",
+           hmin=100, radius = 300, maxt = 2*3600, activity="act", filtershort=FALSE,
+           grid = 200, extent=0.1)
> ud <- BRB(buffalo$traj, D = 440/60, Tmax = 3*3600, Lmin = 50,
+           hmin=100, radius = 300, maxt = 2*3600, activity="act", filtershort=FALSE,
+           grid = 200, extent=0.1)
> range(ud@data)
[1] 0.000000e+00 5.660811e-07
> range(id@data)
[1] 0.000000e+00 6.552115e-07
> range(rd@data)
[1] 0.000000e+00 3.774331e-07



University of Tasmania Electronic Communications Policy (December, 2014).
This email is confidential, and is for the intended recipient only. Access, disclosure, copying, distribution, or reliance on any of it by anyone outside the intended recipient organisation is prohibited and may be a criminal offence. Please delete if obtained in error and email confirmation to the sender. The views expressed in this email are not necessarily the views of the University of Tasmania, unless clearly intended otherwise.

        [[alternative HTML version deleted]]

_______________________________________________
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[hidden email]
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Jed Long
Lecturer in GeoInformatics
Department of Geography & Sustainable Development
University of St Andrews, UK