Home range and dynamic space use reveals age-related differences in risk exposure for reintroduced parrots

Abstract

Individual-level differences in animal spatial behaviour have been identified in numerous species, which can lead to differential exposure to risk for different groups, particularly for reintroduced species in fragmented landscapes. To assess the risk-exposure of a population of kākā (Nestor meridionalis) reintroduced into a fenced reserve, we GPS-tracked 10 individuals and estimated their home ranges and temporal space use variability (SUV). We compared home range area (within 95% contour of utilisation distribution - UD95) between individuals in relation to age, sex, and fledging origin, and assessed risk exposure by calculating the proportion of each individual's home range beyond the reserve's fence. To estimate temporal variability within each individual's space use, we used a sweeping window framework to estimate occurrence distributions of temporally overlapping snapshots of the movement trajectory. For each occurrence distribution, we calculated the proportion outside the reserve's fence to assess how risk exposure changed throughout time, and the UD95area and UD95centroid to assess the behavioural pattern of space use. Home range area declined significantly and consistently with age (up to 27-fold differences), and the space use of juvenile kākā was more temporally dynamic, particularly in relation to drift of the UD95centroid. The wider-ranging behaviour of younger kākā resulted in more time spent outside the reserve, which aligned with a higher number of incidental mortality observations of younger individuals. Quantifying both the home range and space use variability of populations that may encounter spatially dependent threats is an effective approach to assess risk exposure, which can provide guidance for management interventions. We also emphasise the temporal space use variability approach, which is a flexible approach that can provide numerous insights and has not been widely adopted in the literature. We provide code and data such that all the analyses in this study can be reproduced.

Predicting fine-scale distributions and emergent spatiotemporal patterns from temporally dynamic step selection simulations

“…Predicting how animals move and distribute across a landscape can provide critical information when planning spatially-dependent conservation management actions (Guisan & Thuiller, 2005;Guisan et al, 2013Guisan et al, , 2017Forrest et al, 2022), and incorporating temporal dynamics can lead to more accurate predictions.…”

“…Animals typically have daily rhythms that mediate foraging or hunting behaviours, resting, and finding water, and often have seasonal rhythms determined by the availability of resources and breeding behaviour (Tulloch, 1970; Ager et al, 2003; Rafiq et al, 2023). Predicting how animals move and distribute across a landscape can provide critical information when planning spatially-dependent conservation management actions (Guisan & Thuiller, 2005; Guisan et al, 2013; Guisan et al, 2017; Forrest et al, 2022), and incorporating temporal dynamics can lead to more accurate predictions.…”