The use of GPS data to assess behaviour of red deer hinds over calving: hinds farmed in extensive high-country environments

Abstract

ABSTRACT Global Positioning System (GPS) technology was used to track movements of extensively farmed red deer hinds over the calving period on two New Zealand high-country farms, during the summers of 2006/07, 2007/08 (Haycocks Station) and 2008/09 (White Rock Station). The goal was to analyse positional data around the expected birthing period to define patterns of hind movement that are potentially descriptive of parturient behaviour. A Hidden Markov model (HMM) was used to identify the time and site of calving based on four pre-defined stages of behaviour described for red deer hinds. These stages are: Late pregnancy (State 1), birth-site searching (State 2), birthing (State 3) and post-birthing (State 4). Previous research on red deer hinds over calving on an intensive lowland farm showed a close alignment between actual calving behaviour and those behaviours inferred from the algorithms developed in the HMM. Hinds in the high-country study conformed to the four-stage model of positional movement patterns. Inferred birthing dates ranged from 8 Nov to 3 Dec. The length of time hinds spent searching for a birthing site (State 2) was highly variable and averaged 16 (SE 3.9) hours, with an average 4-fold increase in distance between consecutive GPS recordings relative to that occurring earlier in late pregnancy (State 1), while in the birthing stage (State 3) there was a 40% reduction in distance between recordings compared with State 1. Although actual incidences of birthing were not observable in this study, it is concluded that definable behavioural characteristics of red deer hinds over the calving period can be discerned from GPS positional data. A key observation of this study was the between-hind variability of duration of these innate behaviours that indicates behavioural plasticity in the face of intraspecific competition for resources or perception of threat within extensive farm environments.