Impacts of climate change on tick-borne diseases in livestock : case study of Theileria in cattle in New Zealand : a thesis submitted for the partial fulfilment of the requirements for the degree of Master of Science in Agricultural Science, Massey University, Palmerston North, Manawatu Campus, New Zealand

Abstract

Aims: This paper aimed to investigate the potential influence of climate change variables on the distribution of Theileria orientalis in cattle species across New Zealand from 2012 to 2024. The research paper is divided into two main components: a systematic review and a temporal spatial analysis of Theileria case counts. The systematic review aims to identify and synthesise published evidence on the relationship between climate-driven factors and Theileria orientalis Ikeda from 2017 to 2025. The analytical component aimed to explore temporal trends of Theileria orientalis cases at the district level to assess spatial variation in New Zealand from 2012 to 2024. Methods: The systematic review was conducted using two electronic databases: Scopus and PubMed—a total of 40 abstracts qualified for inclusion after the implementation of predefined scoring criteria for qualitative synthesis. The review identified key climatic factors, including rainfall, temperature, humidity and seasonal variation, as associated with tick-borne diseases, such as Theileria orientalis. The research findings from the systematic review were used to identify the gaps in knowledge such as — limited temporal analyses, lack of exposure intensity data, and underrepresentation of extreme weather events—justify the subsequent longitudinal analyses in this thesis. By using laboratory reports for Theileria case data sourced from the Ministry for Primary Industries (MPI) broader passive surveillance system. The climatic data was sourced from the National Institute of Water and Atmospheric Research (NIWA), from which the pre-processed monthly and zonal mean formatted data was used for analysis in this study. A total of 47 climatic, demographic and spatial variables were analysed by fitting univariable negative binomial models to assess individual association with Theileria case counts. Results: From the analysis, five climatic predictors are observed to be strongly associated with Theileria orientalis cases in cattle. The lagged months going back three months were also included to capture delayed effects. Higher wind speeds (m/s) was positively associated with 78% (RR: 1.780, 95% CI: 1.609–1.969, p < 0.001) in the current month and remained elevated at 1-month 51% ( (RR: 1.508, 95% CI: 1.366–1.665, p < 0.001) and 2-month lags 35% (RR: 1.345, 95% CI: 1.214–1.490, p < 0.001) risk increase in Theileria cases. Median vapour pressure from February to April was also positively associated with 45% (RR: 1.446, 95% CI: 1.351–1.548, p < 0.001) risk increase of Theileria cases. Potential evapotranspiration showed a temporal gradient: in the current month, 15% (RR: 1.149, 95% CI: 1.103–1.197, p < 0.001), increased disease risk, whereas lagged values suggested a protective effect. Temperature exhibited strong lag effects a showing significant negative association with Theileria cases. The mean annual temperature was statistically significant with a positive association contributing to 27% (RR: 1.272, 95%CI: 1.094, 1.479, p< 0.001) and at a 3-month lags with 5% (RR: 1.049, 95% CI: 1.026 1.073, p < 0.001) were positively associated with increased risk of Theileria cases. Similarly, relative humidity at a 2-month lag was 3% (RR: 1.030, 95%CI: 1.021, 1.040, p< 0.001) and at a 3-month lag was 6% (RR: 1.055, 95% CI: 1.045–1.064, p < 0.001), indicating a positive associated with an increase of Theileria cases. Apart from climatic variables, demographic and spatial predictor variables are also observed to be statistically associated with Theileria cases from the analysis. Conclusion: In conclusion, cases of Theileria orientalis in cattle across New Zealand from 2012 to 2024 were strongly associated with specific climate variables. These included wind speed, median vapour pressure (February–April), potential evapotranspiration, temperature, rainfall, and relative humidity. These patterns suggest that climate may play an influential role in disease dynamics. However, univariable analysis cannot account for complex interactions between variables or establish causal relationships, particularly given the ecological nature of the data. The results from this research, based on these associations, serve as a foundation for future multivariable modelling. Further analysis can be done to better understand the interplay of climatic and ecological factors influencing Theileria orientalis transmission in New Zealand.