  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 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
paleo-fire reconstruction and extends existing methods to infer regional fire
history from multiple lake records with uncertainty following directly from
posterior distributions.
