Feral horse cause-specific mortality relative to mustering (gathering) and individual demographic attributes in the USA

Abstract

Abstract ContextMustering (gathering) feral horses (Equus ferus caballus) often cause mortalities, yet cause-specific details are lacking. AimsGiven the need to optimise horse welfare, we analysed public horse muster data from the USA to understand specific causes of mortalities. MethodsWe coded 393 individual horse mortality reports for 92 cause-specific mortality terms (keywords informing the deciphering of specific causes of mortality classified as anatomical, causal or conditional) and demographic details (age, sex, and body condition). Data were derived from 50 musters across seven states with at least one horse mortality. Musters were coded for type (helicopter or bait), emergency or regular planned efforts, and number of horses mustered and shipped daily. Key ResultsMore horses were euthanased than died naturally (330 (84.0%) and 39 (9.9%) respectively), and more horses had chronic than acute conditions (317 (80.7%) and 76 (19.3%) respectively), with both trends holding for both sexes and across ages. Body condition scores (BCS) for female horses were skewed low, whereas male horse BCS data were more normally distributed. Female horses had lower BCS than did male horses (P < 0.001). On average, each horse mortality had two cause-specific mortality terms, ranging from 1 to 7. Only 57 horses (14.5%) had terms describing anatomy, cause and condition, concurrently. Phi coefficients (φ; indicators of fidelity and constancy) for cause-specific terms were related to demographic or muster attributes and were analysed with post hoc ANOVA tests of estimated marginal means to allow for ranking. Female horses were most often described as emaciated, weak, and starving, whereas male horses were described as lame, arthritic, blind or dangerous. Bait trapping and emergency musters included horses that were starving, dehydrated and weak. ConclusionsGenerally, disorders associated with legs and feet, eyes, necks and nutrition were the most prevalent cause-specific mortality issues. Using a machine learning approach, validation and test accuracy were high for predicting euthanasia versus natural mortalities, but low for predicting acute versus chronic mortalities. Individual horse demographics or daily muster features had a greater relative influence than did capture type or emergency status in both comparisons. ImplicationsThese results provide practical insight for potential cause-specific mortalities relative to demographics and muster techniques.