Carl Walters and CS Holling, and originally termed Adaptive Environmental Assessment and Management (AEAM), this approach emphasizes identifying critical uncertainties regarding natural resource dynamics and the design of diagnostic management experiments to reduce these uncertainties (Walters 2007). The AM process is a learning cycle that can be distilled down to six stages (Fig. 1). Aside from these six stages, Walters and Holling also emphasized those’s participation outside the management institution in the process in order to manage conflict and increase contributions’ pool to potential management solutions ( Holling 1978, Walters 1986). This emphasis acknowledged the broader social structure within which management is embedded and is an element of adaptive management that has continued to evolve producing related concepts that emphasize this focus (Table 1). nevertheless, ecological uncertainty’s reduction continues to be the key objective of AM specifically (Walters 2007), and it is this original meaning that is this paper’s focus.
Four decades after its first formal articulation, adaptive management (AM) continues to have broad appeal (Holling 1978, Walters 1986). Yet despite its conceptual simplicity confusion persists about exactly what the approach entails, in which management contexts its use is appropriate, its application feasible, and the extent to which it has been applied successfully (Rist et al. 2013). Some have reported success where the management context is large, complex, and messy, while others claim the approach is most feasible in small-scale applications that deal with relatively simple management questions (Walters and Holling 1990, McConnaha and Paquet 1996, Johnson 1999, Simberloff 2009). ‘appropriateness’, ‘efficacy’, and ‘success’ are just a few of the terms used, often interchangeably, when AM is evaluated (Gregory et al. 2006, McFadden et al. 2011). Additionally, what is considered to constitute ‘success’ differs with some authors referring to adherence to the cyclical AM process and others to reduced uncertainty. Thus, while there is little overall clarity one consistent message nevertheless emerges; AM is challenging to implement and appropriate in only a subset of natural resource management problems (Allen and Gunderson 2011).