[AniMov] Irregular time lag

Clément Calenge clement.calenge at oncfs.gouv.fr
Fri Dec 18 10:08:18 CET 2009


Hello Martin,


> I'm working on Ring-Billed Gull habitat selection with GPS data logger technology. I read about "adehabitat" and I'm really interesting to work with this package. But there's my problem: we have some kind of irregular or "hierarchical" time lag. We get relocation every second during 1 min (60 relocations), each 4 min, 24 hours a day for 3 days. Is there a way to work with all our relocations or we must work with a 4 min time lag and loss some information? I would prefer to work with a 20-30 sec regular time lag, but it was not possible with our technology last spring. Besides, for next spring fieldwork, we are thinking about turning off our data loggers during the night. Is there a problem again with this kind of design, time lag and analysis with 

I think that you have first to define precisely the aim of your study. 
The best approach depends on many different things, including your issue 
(including your preliminary scientific hypotheses), the spatial and 
temporal scale at which you want to draw your conclusions, the 
resolution of your habitat information, whether you want to perform an 
exploratory or confirmatory analysis, etc. You did not give enough 
information. There are many many ways to study habitat selection (which 
is a rather vague objective: habitat selection occurs at multiple 
spatial scales, and your data are also collected at multiple temporal 
scales; etc.). For example:

First, you may choose to ignore the time dependence of the successive 
relocations, and to compare the habitat use (e.g. measured by the 
proportion of relocations in each habitat type) and habitat availability 
(e.g. habitat proportion in the home range). Of course, this is only 
exploratory (the unaccounted time dependence between successive 
relocations prevents any inference relying on the independence between 
successive relocations), but can return interesting results (and again, 
there are many different ways to carry out such an exploratory analysis, 
depending on what you do want to see).

Or/and you may want to take into account the trajectory structure. For 
example, if your aim is to study the small scale moving behavior of the 
gull, that you have habitat maps with a very fine resolution, it may be 
sensible to define regular "bursts" of relocations, each burst covering 
one minute with one relocation every second, and then to relate e.g. the 
speed of the animal, the turning angles between successive moves, etc. 
with environmental variables. Or, if there is temporal dependence 
between successive moves of 1 second, you may also partition each 
trajectory (i.e. each burst of relocations) into "types of behaviour" 
(e.g. using modpartltraj, or any partitioning method existing in the 
literature), and relating these types of behaviors to the habitat (with 
some kind of discriminant analysis).

Or, if you want to study the movements of the animals at the scale of 
the day and if you do not want to consider the timing of the 
relocations, but just the shape of the trajectory of the animal, one 
solution could be to define an "ltraj" object with each burst covering  
24 hours, and then to rediscretize the trajectory into regular steps 
(see the function redisltraj), and finally to analyse how the turning 
angles and rediscretized relocations are related to the habitat. Or, if 
you want to take into account the time, you may consider the first 
relocation of each burst of 1 min in defining bursts of 24 hours with 
one relocation every 4 min, to consider the timing of your relocations.

Or, you may...

What I am trying to say by giving these untidy examples is that there 
are many many possible different ways to analyse habitat selection with 
your GPS data, and that the best depends on your issue. But, above all, 
that the most sensible analysis is not necessarily the method that will 
allow you to take into account all your relocations. The analysis 
approach depends on your question. You can use all your data or not, but 
this is not, IMHO, the most important point. If you delete some 
relocations, it will result into a loss of information, but this 
information may not be relevant for your question. First define your 
question, and then, given your data, define the most sensible analysis 
approach. The chosen approach may imply the loss of 80% of the original 
information to build a relevant dataset. Keeping information not 
relevant to the question adds noise to the data and should be avoided.

HTH,

Clément Calenge

-- 
Clément CALENGE
Cellule d'appui à l'analyse de données
Office national de la chasse et de la faune sauvage
Saint Benoist - 78610 Auffargis
tel. (33) 01.30.46.54.14



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