The survival of ectothermic species is heavily dependent on environmental conditions, such as temperature and water balance. Understanding their survival responses to abiotic factors could help predict impacts of climate change on their population dynamics and human health. However, making a statistical inference and formulating a predictive model for mortality rates can be challenging when the observation numbers are limited. This study proposed an expert opinion elicitation framework that integrates expert opinions as prior distributions for the effects of continuous explanatory variables, through a Bayesian Parametric Survival Model (B-PSM). A historical survival dataset of female Ixodes ricinus ticks (Acari: Ixodidae) with small sample size was used. A total of 6 acarologists were recruited as experts for interactive online interview sessions to provide their opinions on average survival time under 4 different temperature and humidity scenarios. Most experts shared similar opinions on the effects of abiotic variables, and none of the experts was confident in the interaction effect. The variation of the opinions across multiple experts was handled by two approaches: 1) pooling and 2) averaging methods. The results showed that the pooling approach retains the variations of expert opinions, it may also disregard some irrelevant opinions to the observed data. While the averaging approach forms a numerical consensus across all the experts, but it may be less informative when the opinions distinctly diverge. The survival time of I. ricinus was found to be best described by the Weibull distribution, suggesting the mortality rate of ticks increases over time (aging effects). Also, the posterior predictions revealed that I. ricinus ticks were susceptible to desiccation conditions, with an interaction effect with the temperature. Therefore, our results suggested that relative humidity is an important factor in the survival of I. ricinus that should not be disregarded when evaluating the impacts of climate change on their population dynamics. Finally, this study provided a guideline for implementing the B-PSM framework to incorporate expert opinions and develop predictive survival models that can be applied in other ecological contexts.