TY - JOUR
T1 - Resilience in Environmental Risk and Impact Assessment
T2 - Concepts and Measurement
AU - Angeler, David G.
AU - Allen, Craig R.
AU - Garmestani, Ahjond
AU - Pope, Kevin L.
AU - Twidwell, Dirac
AU - Bundschuh, Mirco
N1 - Publisher Copyright:
© 2018, The Author(s).
PY - 2018/11/1
Y1 - 2018/11/1
N2 - Different resilience concepts have different assumptions about system dynamics, which has implications for resilience-based environmental risk and impact assessment. Engineering resilience (recovery) dominates in the risk assessment literature but this definition does not account for the possibility of ecosystems to exist in multiple regimes. In this paper we discuss resilience concepts and quantification methods. Specifically, we discuss when a system fails to show engineering resilience after disturbances, indicating a shift to a potentially undesired regime. We show quantification methods that can assess the stability of this new regime to inform managers about possibilities to transform the system to a more desired regime. We point out the usefulness of an adaptive inference, modelling and management approach that is based on reiterative testing of hypothesis. This process facilitates learning about, and reduces uncertainty arising from risk and impact.
AB - Different resilience concepts have different assumptions about system dynamics, which has implications for resilience-based environmental risk and impact assessment. Engineering resilience (recovery) dominates in the risk assessment literature but this definition does not account for the possibility of ecosystems to exist in multiple regimes. In this paper we discuss resilience concepts and quantification methods. Specifically, we discuss when a system fails to show engineering resilience after disturbances, indicating a shift to a potentially undesired regime. We show quantification methods that can assess the stability of this new regime to inform managers about possibilities to transform the system to a more desired regime. We point out the usefulness of an adaptive inference, modelling and management approach that is based on reiterative testing of hypothesis. This process facilitates learning about, and reduces uncertainty arising from risk and impact.
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U2 - 10.1007/s00128-018-2467-5
DO - 10.1007/s00128-018-2467-5
M3 - Article
C2 - 30357430
AN - SCOPUS:85055718407
SN - 0007-4861
VL - 101
SP - 543
EP - 548
JO - Bulletin of Environmental Contamination and Toxicology
JF - Bulletin of Environmental Contamination and Toxicology
IS - 5
ER -