TY - JOUR
T1 - Biometrical measurements as efficient indicators to assess wild boar body condition
AU - Risco, David
AU - Gonçalves, Pilar
AU - Mentaberre, Gregorio
AU - Navarro-González, Nora
AU - Casas-Díaz, Encarna
AU - Gassó, Diana
AU - Colom-Cadena, Andreu
AU - Fernández-Aguilar, Xavier
AU - Castillo-Contreras, Raquel
AU - Velarde, Roser
AU - Barquero-Pérez, Oscar
AU - Fernández-Llario, Pedro
AU - Lavín, Santiago
AU - Fonseca, Carlos
AU - Serrano, Emmanuel
PY - 2018/5/1
Y1 - 2018/5/1
N2 - © 2018 Elsevier Ltd Body condition (i.e., the amount of the energy stored in organs and tissues) is a key parameter that has been related with health, reproductive performance and density of wild ungulates including the wild boar (Sus scrofa). In this wild pig, a reference method to assess body condition has not yet been agreed and different procedures have been used in recent literature. The aim of this work was to generate an easy and reliable method based on biometrical measurements and with the ability to predict body fat in live or die boars. For this, a total of 207 hunted wild boar from three Spanish populations with contrasting food availability were included in this study. Sex, age, biometrical parameters (body weight, total length and chest girth) and body condition indicators (brisket and rump fat thickness, kidney fat index (KFI), ratio between chest girth-total length and scaled mass index) were assessed for each animal. A boosted regression trees (BRT) approach was carried out to find models based on age, sex and biometrical features that predicted brisket fat thickness in the studied animals. BRT models including sex, body weight, total length and chest girth as explanatory variables were able to predict brisket fat thickness in wild boar (68–73% of deviance explained). These models were not influenced by the location of sampling and their predictive values showed a good agreement with real brisket fat thickness (94.1–95.6). Predictive values obtained in BRT models from each area also agreed with food availability suggesting this is a valid indicator of body condition of wild boar in a broad range of environmental conditions.
AB - © 2018 Elsevier Ltd Body condition (i.e., the amount of the energy stored in organs and tissues) is a key parameter that has been related with health, reproductive performance and density of wild ungulates including the wild boar (Sus scrofa). In this wild pig, a reference method to assess body condition has not yet been agreed and different procedures have been used in recent literature. The aim of this work was to generate an easy and reliable method based on biometrical measurements and with the ability to predict body fat in live or die boars. For this, a total of 207 hunted wild boar from three Spanish populations with contrasting food availability were included in this study. Sex, age, biometrical parameters (body weight, total length and chest girth) and body condition indicators (brisket and rump fat thickness, kidney fat index (KFI), ratio between chest girth-total length and scaled mass index) were assessed for each animal. A boosted regression trees (BRT) approach was carried out to find models based on age, sex and biometrical features that predicted brisket fat thickness in the studied animals. BRT models including sex, body weight, total length and chest girth as explanatory variables were able to predict brisket fat thickness in wild boar (68–73% of deviance explained). These models were not influenced by the location of sampling and their predictive values showed a good agreement with real brisket fat thickness (94.1–95.6). Predictive values obtained in BRT models from each area also agreed with food availability suggesting this is a valid indicator of body condition of wild boar in a broad range of environmental conditions.
KW - Biometrical measurements
KW - Boosted regression trees
KW - Brisket fat thickness
KW - Sus scrofa
U2 - 10.1016/j.ecolind.2017.12.048
DO - 10.1016/j.ecolind.2017.12.048
M3 - Article
SN - 1470-160X
VL - 88
SP - 43
EP - 50
JO - Ecological Indicators
JF - Ecological Indicators
ER -