Linking TEK and adaptive environmental management: a new approach to science

There are several similarities between traditional or indigenous management systems and adaptive management. If the orderly and rational science of the Age of Enlightenment is replaced by a new paradigm along the lines of adaptive management, the chasm between indigenous knowledge and mainstream science essentially evaporates (Cajete 2000).

Many of the prescriptions of traditional knowledge and practice are consistent with AEM as an integrated method for resource and ecosystem management (Berkes et al. 2000). AEM, like many ТЕК systems, emphasizes processes that are part of ecological cycles of renewability and regards human use of the environment in terms of how well it fits these cycles. Like many ТЕК systems, AEM considers change as inevitable and assumes that nature cannot be controlled and yields cannot be predicted. In both AEM and ТЕК, uncertainty and unpredictability are considered to be characteristics of all ecosystems, including managed ones; in both, social learning appears to be the way in which societies respond to uncertainty. Often this involves social learning at the level of society or institutions (Ibid). The existence of (mainstream science) practices such as monitoring resource abundance, multiple species management and watershed-based management practices in some ТЕК systems is further evidence of the similarity between ТЕК and adaptive management. This makes a collaborative platform between mainstream science and ТЕК imperative.

A good example of this fluidity between mainstream science and ТЕК is rainwater harvesting, which has been found to be scientific and useful for rainfed areas. For instance, a validation comes from the Negev. Ancient stone mounds and water conduits are found on hillslopes over large areas of the Negev desert. Field and laboratory studies suggest that ancient farmers were very efficient in harvesting water. A comparison of the volume of stones in the mounds to the volume of surface stones from the surrounding areas indicates that the ancient farmers removed only stones that had rested on the soil surface and left the embedded stones untouched. According to results of simulated rainfall experiments, this selective removal increased the volume of runoff generated over one square meter by almost 250% for small rainfall events compared to natural untreated soil surfaces (Ghimire & Johnston 2015).

There is clearly much to be learned not only from a study of other cultural practices, but from other cultural ways of knowing as well (Fujitani et al. 2017). As Bateson once suggested with regard to contemporary ecological problems, it is possible that some of the most disparate epistemologies which human culture has generated may give us clues as to how we should proceed. Further, other attitudes and premises - other systems of human ‘values’ - have governed man's relation to his environment and his fellow man in other civilizations and at other times. In other words, our way is not the only possible human way. It is conceivably changeable (Bateson 2002).

AEM does not postpone actions until ‘enough’ is known about a managed ecosystem, but rather is designed to support action in the face of the limitations of scientific knowledge and the complexities and stochastic behaviour of large ecosystems. It aims to enhance scientific knowledge and thereby reduce uncertainties. Such uncertainties may stem from natural variability and stochastic behaviour of ecosystems and the interpretation of incomplete data, as well as social and economic changes and events (e.g., demographic shifts, changes in prices and consumer demands) that affect natural resources systems. AEM aims to create policies that can facilitate the response to deleterious ecological situations and challenges incidental to them, such as global pandemics. Instead of seeking precise predictions of future conditions, AEM recognizes the uncertainties associated with forecasting future outcomes, and calls for consideration of a range of possible future outcomes. Thus, policies formulated from AEM methods are designed to be flexible and are subject to adjustment in an iterative, social learning process (National Research Council 2004).

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