Integrating Optimization and Resilience Thinking in Conservation
Resilience thinking and optimization are often viewed as opposites, but resilience thinking is more critical of how optimization is frequently applied rather than the technique per-se. A new paper in TREE Integrating resilience thinking and optimisation for conservation (doi:10.1016/j.tree.2009.03.020) by Joern Fischer and others, including myself, attempt to integrate resilience thinking and optimization. We propose that by actively embedding optimisation analyses within a resilience-thinking framework ecosystem management could draw on the complementary strengths of both, thereby promoting cost-effective and enduring conservation outcomes.
The paper’s Table 1 provides an overview of the strengths and weaknesses of optimization for conservation and resilience thinking:
|
|
Optimisation for conservation
|
Resilience thinking
|
|---|---|---|
| Strengths (inherent) | Recognises resource scarcity | Recognises system complexity |
| Encourages transparency in resource allocation | Recognises interdependence of social and biophysical systems | |
| Strengths (in practice) | Can provide specific answers to a well-defined problem | Encourages anticipation of undesirable surprises or thresholds |
| Fits well with how business and governments operate | Encourages reflection on how a system works | |
| Weaknesses (inherent) | Sensitive to accuracy of underlying assumptions and system model | Potentially difficult to apply to systems without identifiable alternate states |
| Weaknesses (in practice) | Targets or budget constraints are often informed by politics rather than an in-depth understanding of underlying system dynamics | Reliant on tools from other disciplines to be operational to inform policy |
| The term ‘optimal’ can sound absolute to policymakers and the general public | The term ‘resilience’ can appear vague to policymakers and the general public |
And we discuss three themes that both approaches need to address (i) dealing with social issues; (ii) dealing with uncertainties and the limited extent to which they can be controlled; and (iii) avoiding undesirable states that constrain reversibility.
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