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Analysis of Sensitivity and Uncertainty in an Individual-Based Model of a Threatened Wildlife Species
Marcot, B., P. Singleton, AND N. Schumaker. Analysis of Sensitivity and Uncertainty in an Individual-Based Model of a Threatened Wildlife Species. NATURAL RESOURCE MODELING. Wiley InterScience, Silver Spring, MD, 28(1):37-58, (2015).
Complex wildlife population models are increasingly used in the development of conservation and recovery strategies. But few studies have examined the sensitivity of such models to uncertainty in input parameter values. This study lends credibility to such modeling efforts by demonstrating that complex population models may be the most sensitive to the best known parameters.
We present a multi-faceted sensitivity analysis of a spatially explicit, individual-based model (IBM) (HexSim) of a threatened species, the Northern Spotted Owl (Strix occidentalis caurina) on a national forest in Washington, USA. Few sensitivity analyses have been conducted on IBMs but such analyses are very important to the interpretation of model output. To provide a sensitivity analysis, we first developed “normative” model settings parameterized from field studies of the owl’s biology and habitat use, and then we varied the values of ≥ 1 input parameter at a time by ±10% and ±50% of their normative values to determine their influence on response variables of population size and trend. We also tested sensitivity to general model architecture by determining the period of start-up bias, time to population equilibration, and influence of varying initial population size in free and forced response runs. We found that recovery time from small population size to a fixed carrying capacity greatly exceeded decay time from an overpopulated condition, suggesting the lag time required to repopulate newly-available habitat. We also found that response variables are most sensitive to input parameters of habitat quality which are well-studied for this species and controllable by management activities. HexSim thus seems useful for evaluating potential NSO population responses to landscape patterns because the NSO model is most sensitive to habitat quality and resource threshold settings for which we have good empirical information.
Record Details:Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL HEALTH AND ENVIRONMENTAL EFFECTS RESEARCH LABORATORY
WESTERN ECOLOGY DIVISION
ECOLOGICAL EFFECTS BRANCH