Data di Pubblicazione:
2012
Citazione:
The cadaver dog: phenotypical analysis of behaviour / S. Marelli, J. Riva, C. Lazzari, M. Longeri, M. Polli. ((Intervento presentato al convegno European veterinary behaviour meeting tenutosi a Martigny nel 2012.
Abstract:
Introduction:
The aim of the present research was to evaluate the behavioural patterns in working cadaver dogs.
Materials and methods:
7 specifically trained dogs (6 Labrador Retrievers; 1 Italian Wolfdog) for human remains search
(HRS) activity were singularly video-recorded during 21 HRS tests in standardied conditions (3
sessions). 3 different odorous samples were used (decomposing teeth, decomposing soft-tissues,
decomposing blood). Frequency(N) and lasting (s) of the scanned behaviours were calculated. Dog
posture and handler behaviour were considered to verify handler dog interaction and response. An
anamnestic-behavioural report was filled for each dog. SAS ® statistic package was applied to data
analysis: MEAN and NPAR1WAY procedure were applied; the analysis of variance was carried out
using a Kruscal-Wallis one-way ANOVA. Dogs sex and breed, Odorous sample, wind speed and
direction and test order were considered sources of variance.
Results:
The obtained results list an objective sequence of naturally expressed quantitative behaviours during
HRS activity. A strong influence of genetics on trained dogs was recorded, thus breed and sex
showed the most significant effect on dogs HRS behaviours.
Discussion:
Objective studies of breed and sex specific behaviours in HRS, such as the preliminary one we are
presenting, could improve training success and limit training costs and results variability.
Furthermore the constant measurements of environmental and atmospheric variables should become
a common practice before every HRS activity.
According to our results; the handlers could be able to modify through the comprehension of dog’s
behaviour a better way to carry on the HRS.
Tipologia IRIS:
14 - Intervento a convegno non pubblicato
Elenco autori:
S. Marelli, J. Riva, C. Lazzari, M. Longeri, M. Polli
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