In un messaggio del Tuesday 10 February 2009, Paolo Cavallini ha scritto: > Hi all. > In the process of building up the randomHR QGIS plugin[1], we stumbled > across a more general question: which is the most appropriate metric for > measuring overlap among HRs? > Should it be: > > 1) area of overlapping / area occupied by at least one HR > 2) area of overlapping / sum of areas of all HRs > 3) N x area of overlapping / sum of areas of all HRs > (where N = number of overlapped HRs in given place) > 4) something else
We usually evaluate overlap on a "per animal/per HR" basis, i.e. we start with
a set of homeranges, either belonging to each individual, or to each valid
cartesian product of animal x 'season'.
next we evaluate overlap as follows:
- pick homerange of animal i (HRi)
- for each homerange j <> i calculate 'HRi/HRj'
'HRi/HRj' is just an oversimplification, see [1] for the gory details.
The result is a non-symmetric distance matrix, which eventually can be
aggregated to have, say, average overlaps between sexes, seasons, etc..
In practice, all that is done with a slightly modified version of Clément's
kerneloverlap. the modification consists in commenting put the first rows
that call kernelUD, and making function kerneloverlap work on already
calculated UDs.
This raises another point, i.e. a suggestion to Clément: since often the 40x40
cells approach gives undesirable results, we prefer to prepare as a
preliminary step, a raster (of course using exclusively ESRI software, duh!)
with cells of suitable size, that encompassess an extent slightly wider than
the study area, an then calculate once all the UDs.
This 'single point of homerange truth' [2] guarantees a consistency between
subsequant calculations, i.e. 'getvoumes + polygon extraction', compana,
kerneloverlapd etc.
This also saves lots of time, since UD calculation, moreover with a 'fine'
grid, takes some time: UD processing can be done on a fast number crunching
machine [3], and the result can be saved (as part of a sahrlocs structure)
and retrieved on a less performant host to do other postprocessing.
Well... my 2 eurocents, of course!
[1] Fieberg, J. and Kochanny, C.O. (2005) Quantifying home-range overlap: the
importance of the utilization distribution. Journal of Wildlife Management,
69, 1346–1359.
[2] Credits to Anne Ghisla for the name :)
[3] About 8 min for 119 snowshoe hare homeranges ('Hadj' method, 3 calls to
kernelUD for each animal) on a Core Duo T8100, 2GHz, 3GB RAM.
--
La realta' e' brutta quanto basta, perche' dovrei dire la verita'?
-- Patrick Sky
-----------------------------------------------------------
Damiano G. Preatoni, PhD
Unità di Analisi e Gestione delle Risorse Ambientali
Dipartimento Ambiente-Salute-Sicurezza
Università degli Studi dell'Insubria
Via J.H. Dunant, 3 - 21100 Varese (ITALY)
tel +39 0332421538 fax +39 0332421446
http://biocenosi.dipbsf.uninsubria.it/
ICQ: 78690321 jabber: [email protected] skype: prea.net
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