An animal welfare assessment framework for helicopter darting: a case study with a newly developed method for feral horses

Abstract

Context Helicopter darting (chemical immobilisation) is a very useful technique for large wild herbivores, such as feral horses (Equus caballus). There is currently no reliable framework to report on the animal welfare impacts of helicopter darting methods. Aim The aim of this study was to develop an animal welfare assessment framework for helicopter darting methods, using quantifiable parameters, and to test it with a case study using a newly developed feral horse capture technique. Methods Quantifiable animal welfare parameters were recorded for 11 feral horses captured using a traditional helicopter darting method in north-western Australia in October 2014. Welfare parameters chosen focused on quantifying the duration of procedures and the frequency of adverse events. They included chase time (CT; min) before darting, induction time (IT; min) between darting and recumbency, recumbency time (RT; min), total time (TT; CT+IT+RT; min), repeat-darting rate (animals requiring >1 dart; %), target zone accuracy rate (darts striking the intended anatomical area; %) and mortality rate (at time of capture and 14 days post-capture; %). Results Median CT was 2 min, median IT was 19 min, median RT was 16 min and median TT was 38 min. Repeat-darting rate was 45%, target zone accuracy rate was 53% and mortality rates (time of capture and 14 days post-capture) were zero. Conclusions Animal welfare parameters can be quantified for helicopter darting through estimation of the duration of procedures and the frequency of adverse events. Use of this framework will allow the identification of parameters requiring refinement for newly developed helicopter darting techniques. Implications Animal welfare parameters are particularly important for helicopter-based darting methods. Pilot studies, using quantified parameters, should be performed for newly developed capture techniques before they are approved for large-scale programs.