| Popis: |
Wildland research, management, and policy in western democracies have long relied on concepts of equilibrium: succession, sustained yield, stable age or species compositions, fire return intervals, and historical range of variability critically depend on equilibrium assumptions. Not surprisingly, these largely static concepts form the basis for societal expectations, dominant management paradigms, and environmental legislation. Knowledge generation has also assumed high levels of stasis, concentrating on correlational patterns with the expectation that these patterns would be reliably transferrable. Changes in climate, the introduction of large numbers of exotic organisms, and anthropogenic land conversion are leading to unprecedented changes in disturbance regimes and landscape composition. Importantly, these changes are largely non-reversable; once introduced exotic species are seldom eradicated, climates will continue to warm for the foreseeable future, and many types of land conversion cannot be easily undone. Due to their effects on extant infrastructure and expectations for ecosystem services, these changes are, and will be, viewed by western societies as overwhelmingly negative. The continued acceleration of change will generate increasingly novel systems for which the transferability of correlational relationships will prove unreliable. Our abilities to predict system trajectories will therefore necessarily decrease. In this environment, top-down, expert dominated approaches to environmental decision making are unlikely to produce results that meet broader societal expectations. To be successful we need to embrace a more inclusive paradigm of collaborative governance and multiple forms of knowledge for adapting to constant change, including indigenous epistemological systems. By increasing public and stakeholder participation, we can encourage collaborative social learning allowing all parties to more fully understand the complexities and tradeoffs associated with wildland management and the technical limits of models that seek to quantify those tradeoffs. System novelty will necessarily make forecasting more dependent on predictive modeling and will require better models. Data collection should therefore be strongly influenced by model input requirements and validation; research will need to focus on fundamental and causal relationships to a much greater degree than is done currently. |
