Robotic sampling is attractive in many field robotics applications that require persistent collection of physical samples for ex-situ analysis. Examples abound in the earth sciences in studies involving the collection of rock, soil, and water samples for lab analysis. The desirability of samples in these domains can be expressed as a property that cannot be determined in-situ, but can be predicted by covariates measurable in real-time using sensors carried aboard a robot. In our test domain, marine ecosystem monitoring, accurate measurement of plankton abundance requires lab analysis of water samples, but predictions using physical and chemical properties measured in real-time by sensors carried aboard an autonomous underwater vehicle (AUV) can guide sample collection decisions. We present a principled approach to minimize cumulative regret of plankton samples acquired by an AUV over multiple surveys in batches of k water samples per survey. Samples are labeled at the end of each survey, and used to update a probabilistic model that guides sampling in subsequent surveys. The problem is formulated in an online setting: given a predetermined survey duration and a probabilistic model learned from earlier surveys, the AUV makes irrevocable sample collection decisions on a sequential stream of candidates, with no knowledge of the future. Our experimental results are based on extensive retrospective studies emulating 100 campaigns, each composed of 17 surveys. The campaigns were emulated by mining historical field data collected by an AUV operating at depths of up to 100 m over a 40 sq. km area in an 8 day period. These studies establish the efficacy of the approach – beginning with no prior, successive surveys by the AUV result in samples that are progressively higher-abundance in a pre-specified type of plankton. Additionally we carried out a one-day field trial with an AUV operating at depths of up to 30 m over a 1 sq. km area. Beginning with a prior learned from data collected and labeled in an earlier campaign, the AUV field survey resulted in samples with a high-abundance of a pre-specified type of plankton – a potentially toxinogenic alga of interest to marine ecologists. This is the first time such a field experiment has been carried out in its entirety in a data-driven fashion, in effect ‘closing the loop’ on a significant and relevant ecosystem monitoring problem. Although the experimental context for work is marine ecosystem monitoring, it is well-suited for autonomous and persistent robotic observation of any property that cannot be measured in-situ, but possesses observable covariates, thus opening up the potential for advanced autonomous robotic exploration of unstructured environments that are inaccessible to humans.