A model‐based approach to wildland fire reconstruction using sediment charcoal records. Issue 7 (20th June 2017)

• Record Type: Journal Article
• Authors: Itter, Malcolm S.; Finley, Andrew O.; Hooten, Mevin B.; Higuera, Philip E.; Marlon, Jennifer R.; Kelly, Ryan; McLachlan, Jason S.
• Is Part Of: Environmetrics. Volume 28:Issue 7(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-06-20
• Subjects: Bayesian hierarchical model -- fire history -- fire return interval -- paleoecology -- Poisson point process; Environmental sciences -- Statistical methods -- Periodicals; 550.72
• DOI: 10.1002/env.2450
• Languages: English
• ISSNs: 1180-4009

Abstract

Abstract : Lake sediment charcoal records are used in paleoecological analyses to reconstruct fire history, including the identification of past wildland fires. One challenge of applying sediment charcoal records to infer fire history is the separation of charcoal associated with local fire occurrence and charcoal originating from regional fire activity. Despite a variety of methods to identify local fires from sediment charcoal records, an integrated statistical framework for fire reconstruction is lacking. We develop a Bayesian point process model to estimate the probability of fire associated with charcoal counts from individual‐lake sediments and estimate mean fire return intervals. A multivariate extension of the model combines records from multiple lakes to reduce uncertainty in local fire identification and estimate a regional mean fire return interval. The univariate and multivariate models are applied to 13 lakes in the Yukon Flats region of Alaska. Both models resulted in similar mean fire return intervals (100–350 years) with reduced uncertainty under the multivariate model due to improved estimation of regional charcoal deposition. The point process model offers an integrated statistical framework for paleofire reconstruction and extends existing methods to infer regional fire history from multiple lake records with uncertainty following directly from posterior distributions.