(A) adaptive flow regimes: Tallapoosa River
For nearly a decade, the United States Geological Survey (USGS) implemented adaptive management on the Tallapoosa River to determine optimal flows under multiple competing stakeholder objectives. The iterative decision model included annual monitoring of ecosystem indicators to vary flow regimes optimally. Despite the adaptive decision framework, the best management strategies to provide both adequate hydrological and thermal habitats while sufficing recreational values remain a central controversy in the system [56].	objective: ensure the conservation of at-risk species and meet ecosystem service objectives
	learning: passive learning of ecosystem dynamics and the valuation of ecosystem services
	states: discrete set of ecosystem indicators, ecosystem values
	actions: four flow allocation strategies
	key challenges faced: multiple competing objectives, noisy observations, delayed feedbacks, stochasticity, high dimensionality
(B) adaptive harvest management: waterfowl
Since 1995, the United States Fish and Wildlife Service has used an adaptive management framework to regulate duck harvests. Harvest quotas rely on an iterative cycle of monitoring, assessment and decision-making. Based on monitoring data, managers continually refine models of the relationships between hunting regulation, harvests and waterfowl abundance. While significant updates to the model weights have occurred over the past 20 years, non-stationarity due to global change challenges the current methodology [57].	objectives: sustainable harvest of waterfowl
	learning: passive learning of population dynamics in response to harvest regimes
	states: waterfowl abundance
	actions: annual harvest quotas
	key challenges faced: non-stationarity, delayed feedbacks, stochasticity, multiple objectives
(C) adaptive harvest management: red knots (Calidris canutus rufas) and horseshoe crab​​s (Limulidae polyphemus)
Following increased harvest of horseshoe crabs in the Delaware Bay during the 1990s, migratory shorebird populations declined steeply. Recognizing this decline, the fisheries commission began regulating the horseshoe crab harvest. Proposed adaptive management frameworks focus on two competing models of red knot population dynamics and horseshoe crab harvests, seeking to iteratively improve harvest policies for both objectives [58].	objective: ensure the conservation of at-risk species while also meeting harvest objectives
	learning: passive learning of multi-species dynamics
	states: red knot abundance and fecundity, horseshoe crab abundance
	actions: harvest quota for horseshoe crabs
	key challenges faced: model set limitations, dimensionality, delayed feedbacks, multiple objectives