Biometrical measurements as efficient indicators to assess wild boar body condition. (May 2018)

• Record Type: Journal Article
• Authors: Risco, David; Gonçalves, Pilar; Mentaberre, Gregorio; Navarro-González, Nora; Casas-Díaz, Encarna; Gassó, Diana; Colom-Cadena, Andreu; Fernández-Aguilar, Xavier; Castillo-Contreras, Raquel; Velarde, Roser; Barquero-Pérez, Oscar; Fernández-Llario, Pedro; Lavín, Santiago; Fonseca, Carlos; Serrano, Emmanuel
• Is Part Of: Ecological indicators. Volume 88(2018)
• Page Start: 43
• Page End: 50
• Publication Date: 2018-05
• Subjects: Biometrical measurements -- Boosted regression trees -- Brisket fat thickness -- Sus scrofa; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2017.12.048
• Languages: English
• ISSNs: 1470-160X

Abstract

Abstract: 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.