By Timothy Kuiper, Nobesuthu Ngwenya, Blessing Kavhu, Roseline Mandisodza-Chikerema, and E. Milner-Gulland.
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Adaptive management is an approach that helps managers of natural systems (like protected areas or fisheries) to evaluate and learn from the success or failure of their past actions. By collecting data on key variables (like animal poaching rates or fish stocks), managers can see whether their actions are having the desired effect, and change/adapt them if not.
Although adaptive management makes a great deal of sense and is widely promoted, there are only a handful of cases where it is successfully implemented. Why? To answer this question, we thought that talking to on-the-ground managers (the ones ultimately responsible for adopting adaptive management) would be a good place to start. We interviewed protected area managers in Zimbabwe to understand how they use elephant poaching data to inform their anti-poaching actions. Rangers find and report carcasses and poacher footprints while on patrol).
We found that managers regularly used ranger-collected data to decide where to deploy rangers in the park (basic and reactive data use), but they tended not to systematically analyse longer-term trends in the data to help them evaluate their anti-poaching efforts. Why? Managers did not see how the approach was better than their current style of management based on intuition and experience (which they saw as more familiar and dependable) and felt that the costs of adoption (such as the time and skills requited for data analysis) outweighed the benefits. These findings lined up with previous research on technology adoption which suggest that people will not adopt a new approach if they feel it will not improve their performance, or if it requires too much effort.
Looking ahead, we developed a theory of change to promote more effective implementation of adaptive management, drawing on principles from human-centred design to ensure that solutions are sensitive to the priorities, perspectives, and challenges of managers.