Adaptive management is especially useful in situations where reference ecosystems do not exist, such as in novel ecosystems, which are defined as environments created by human activities and composed of new combinations of abiotic and biotic components (Hobbs et al. 2009). A city is one example of a novel ecosystem (Dearborn and Kark 2010). Because novel ecosystems have no natural analogues, recovery plans cannot be based on a historic reference system (Miller and Bestelmeyer 2016, Swartz 2018). However, they can still be incrementally improved through intervention projects designed under adaptive management principles.

Good adaptive management projects are designed as experiments in nature, because experiments show which interventions work, and why. Experimental design has three major components: 1) replication, managing multiple sites in the same way to estimate the variability in outcomes from a restoration; 2) repeatability, so others can recreate your work and also understand if any unique conditions have changed the outcome of the intervention; and 3) controls, where no intervention takes place, to disentangle the impact of the intervention from the effects of the background environment (Walters and Holling 1990, Brudvig et al. 2017). Ultimately, if interventions are designed as experiments in nature, then even failed interventions will generate knowledge to help animals in the future. Good adaptive management also acknowledges different sources of uncertainty and incorporates them into decision-making frameworks using structured-decision making, information-gap theory, looped learning, and other scenario-project tools (Figure 2; also reviewed by Regan et al. 2002, Halpern et al. 2006, Brudvig et al. 2017; for examples see Regan et al. 2005, Gerber et al. 2007, Hychka and Druschke 2017, Laidlaw et al. 2017, and Hof and Hjältén 2018). These tools further challenge the idea that uncertainty is an intractable problem.

The larger problem is that adaptive management is not always done in practice in ecological science because there is rarely sufficient funding for monitoring or adaptation (Bernhardt et al. 2007, Wortley et al. 2013, Hychka and Druschke 2017). By comparison, welfare biology is uniquely positioned to design every intervention to incorporate uncertainty into decision-making, monitor the effects of interventions, and adapt if we are not meeting our goals. With “challenging uncertainty” as a core value in welfare biology, I’m optimistic that our work will fill an important knowledge gap regarding how to cope with uncertainty in environmental sciences. This is a strength of the wild animal welfare movement, and a personal source of satisfaction for me as a researcher at Wild Animal Initiative.

Conclusion

Uncertainty is not an intractable problem because there are many tools that animal welfare advocates can adopt to mitigate uncertainty, even in cases where wild animal advocates and ecologists don’t share the same values. Reference ecosystems are one of the primary tools in restoration ecology for reducing uncertainty because they provide valuable baseline information that clarifies restoration objectives and demonstrates which dynamics occur in nature. To use reference systems to improve wild animal welfare, we could: 1) study a range of historical or existing ecosystems, 2) assess which system provides animals with the best lives, and 3) use that system as a reference ecosystem to provide a basis for improving the lives of animals in other areas. Another restoration tool which welfare biology can adopt when planning interventions is adaptive management, which emphasizes iterative learning through assessment, monitoring, and adaptation. Welfare biology can actually do better than conservation and restoration, as currently, ecological projects rarely have enough funding to monitor and adapt. With our strong focus on uncertainty, we can be sure to always design intervention projects with replication, repeatability, and controls, so that even failed interventions create knowledge to help animals in the future. Furthermore, we can contribute to the environmental sciences by illustrating how to incorporate uncertainty into all stages of projects by faithfully following best-practice standards in every project.

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