The application of acoustic monitoring in ecological sciences has grown exponentially in the last two decades. It has been used to answer many questions, including detecting the presence or absence of animal species in an environment, evaluating animal behavior, and identifying ecological stressors and illegal activities. However, current uses are limited to the coverage of relatively small geographic areas with a fixed number of sensors. Animal-borne GPS-based location trackers paired with other sensors are another widely used tool in aiding wildlife conservation and ecosystem monitoring. Since capturing and collaring wild animals is a traumatic event for them, as well as being expensive and resource-intensive, multiyear deployments are required. There are severely limited opportunities to recharge batteries making relatively power-hungry sensing, such as acoustic monitoring, out of reach for existing tracking collars. The aim of the A3EM project is to devise an animal-borne adaptive acoustic monitoring system to enable long-term, real-time observation of the environment and behavior of wildlife. Animal-borne acoustic monitoring will be a novel tool that may provide new insights into biodiversity loss, a severe but underappreciated problem of our time. Combining acoustic monitoring with location tracking collars will enable entirely new applications that will facilitate census gathering and monitoring of threatened and endangered species, detecting poachers of elephants in Africa or caribou in Alaska, and evaluating the effects of mining and logging on wildlife, among many others. All data, hardware designs, and software source code will be released to the public domain, enabling tracking collar manufacturers to include the technology within their products. A3EM constitutes a complex cyber-physical architecture involving humans, animals, distributed sensing devices, intelligent environmental monitoring agents, and limited power and network connectivity. This intermittently connected CPS, with a power budget an order of magnitude lower than typical, calls for novel approaches with a high level of autonomy and adaptation to the physical environment. A3EM will employ a unique combination of supervised and semi-supervised embedded machine learning to identify new and unexplored event classes in a given environment, dynamically control and adjust parameters related to data acquisition and storage, opportunistically share knowledge and data between distributed sensing devices, and optimize the management of storage and communication to minimize resource needs. These methods will be evaluated through the creation of a wearable acoustic monitoring system used to support ecological applications such as enhanced wildlife protection, rare species identification, and human impact studies on animal behavior.