Biodiversity is vital to the economy and future innovation opportunities, and we are currently witnessing an unprecedented loss of biodiversity. To better understand and hopefully mitigate this loss, networks of ground-level sensors, satellites, drones, and community scientists are deployed to collect natural-world data at unprecedented scales. There is valuable scientific information stored in these raw data, the vast majority of which are as-yet inaccessible due to the time and resources needed to process the data by small groups of relevant human experts. Computer vision (CV) will prove crucial to facilitate efficient extraction of scientific insights from quickly growing repositories of natural world imagery, but in order to realize the goal of global-scale, near-real-time biodiversity monitoring we must develop computer vision approaches keyed to challenges encountered in real world settings. This work formalizes and addresses cross-cutting limitations of current CV methods in the context of global-scale biodiversity monitoring, characterized in the following three research aims: (Aim 1) Robustly identify rare, visually similar, and even novel categories, all challenges separately for CV that co-occur in biodiversity data. We address this compounding challenge by augmenting limited training data and developing efficient active curation systems. (Aim 2) Adapt to new deployments and identify valuable data for specialized tasks. Biodiversity is non-uniformly distributed