Long-term outdoor localisation with battery-powered de- vices remains an unsolved challenge, mainly due to the high energy consumption of GPS modules. The use of inertial sensors and short-range radio can reduce reliance on GPS to prolong the operational lifetime of tracking devices, but they only provide coarse-grained control over GPS activity. In this paper, we introduce our feature-rich lightweight Ca- mazotz platform as an enabler of Multimodal Activity-based Localisation (MAL), which detects activities of interest by combining multiple sensor streams for fine-grained control of GPS sampling times. Using the case study of long-term fly- ing fox tracking, we characterise the tracking, connectivity, energy, and activity recognition performance of our module under both static and 3-D mobile scenarios. We use Cama- zotz to collect empirical flying fox data and illustrate the utility of individual and composite sensor modalities in clas- sifying activity. We evaluate MAL for flying foxes through simulations based on retrospective empirical data. The re- sults show that multimodal activity-based localisation re- duces the power consumption over periodic GPS and single sensor-triggered GPS by up to 77% and 14% respectively, and provides a richer event type dissociation for fine-grained control of GPS sampling.