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Thin layers of phytoplankton have an important impact on coastal ocean ecology. The high spatial and temporal variability of such layers makes autonomous underwater vehicles (AUVs) ideal for their study. At the Monterey Bay Aquarium Research Institute (MBARI, Moss Landing, CA), the authors have used an AUV for obtaining repeated high-resolution surveys of thin layers in Monterey Bay, CA. The AUV is equipped with ten “gulpers” that can capture water samples when some feature is detected. In this paper, the authors present an adaptive triggering method for an AUV to capture water samples at chlorophyll fluorescence peaks in a thin layer. The algorithm keeps track of the fluorescence background level and the peaks' baseline in real time to ensure that detection is tuned to the ambient conditions. The algorithm crosschecks for concurrent high values of optical backscattering to ensure that sampling targets true particle peaks and not simply physiologically controlled fluorescence peaks. To let the AUV capture the thin layer's peak without delay, the algorithm takes advantage of the vehicle's sawtooth (i.e., yo-yo) trajectory: in one yo-yo cycle, the vehicle makes two crossings of the thin layer. On the first crossing, the vehicle detects the layer's fluorescence peak and saves the peak height; on the second crossing, as the fluorescence measurement reaches the saved peak height (plus meeting additional timing and depth conditions), a sampling is triggered. Based on the thin layer's vertical position in the vehicle's yo-yo profiles, the algorithm selects the pair of detection and triggering crossings so as to minimize the spacing between them. We use the algorithm to postprocess a data set of 20 AUV missions in the 2005 Layered Organization in the Coastal Ocean (LOCO) Experiment in Monterey Bay, CA, and compare its performance with that of a threshold triggering method. In October 2009, the presented method was field tested in an AUV mission in nort- - hern Monterey Bay, CA.