398 Monitoring Threatened Species and Ecological Communities
nevertheless produced some important learnings about the establishment and
maintenance of AM experiments per se. These include the need to: (1) consider
whether an AM framework is the best option for monitoring a given threatened
species and determine if other kinds of investigations (e.g. observational studies or
quasi-experiments) might be better for delivering the conservation outcome; (2)
build AM studies on existing resource management operations to limit the costs of
establishing and then maintaining them; (3) be aware of the risks of major natural
environmental disturbances undermining the integrity of the AM design, while
maintaining f lexibly to redirect research focus to take advantage of new
opportunities that may arise from such disturbances; (4) report the results of AM
studies in the scientific literature; and (5) consider the broader ecological and
policy context before embarking on an AM experiment.
Introduction
Adaptive management (AM) is a widely promoted way to manage natural
resources. The concept was first discussed in the 1970s (Holling 1978), with a
definitive textbook penned on the subject in the mid-1980s: Adaptive Management
for Renewable Resources (Walters 1986). The topic has been so widely written about
and discussed that a simple Google search on the words ‘adaptive management’ at
the time of writing this chapter (January 2017) produced more than 10.3 million
hits. There are many different definitions of AM (Westgate et al. 2013), but it can
be broadly defined as: ‘... a systematic approach for improving resource
management by learning from management outcomes’ (Williams et al. 2009).
The concept of AM means different things to different people and different
organisations, and there is a continuum of definitions and forms of the approach that
spans active AM through to passive AM. In active AM, there is a well-considered
conceptual model of a target population or ecosystem, strong experimental design
contrasting different treatments that test the response to plausible (but competing)
kinds of management intervention, a priori predictions of potential response based
on the conceptual model of the system being targeted for experimental testing, and a
strong commitment to ongoing monitoring of continuously updated management
practices identified through experimentation (e.g. see Chapter 31 for an example of
an active AM program for the malleefowl Leipoa ocellata). Passive AM lacks many of
the integrated design, experimentation and monitoring features that characterise
active AM and is generally characterised by a broad commitment to change
management practices when new information is accumulated (as is active AM but in
a more formalised way). Across the continuum of kinds of AM, Westgate et al. (2013)
argued that AM has at least 10 key steps (Fig. 32.1).
Despite the intuitive appeal of AM and enormous literature on its various
forms in ecology and conservation, the paucity of formally published studies in