Multi-millennial fire frequency and tree abundance differ between xeric and mesic boreal forests in central Canada

1. Macroscopic sedimentary charcoal and plant macroremains from two lakes, 50 km apart, in north-western Ontario, Canada, were analysed to investigate fire frequency and tree abundance in the central boreal forest. These records were used to examine the controls over the long-term fire regime, and vegetative dynamics associated with fire return intervals (FRIs). 2. There were 52 fire events at Lake Ben (surrounded by a xeric landscape) between 10 174 calibrated years before present (cal. year BP) and the present with an average FRI of 186 years with values oscillating between 40 and 820 years. Forty-three fire events were recorded at Lake Small (surrounded by a mesic landscape) between 9972 cal. year BP and the present with an average FRI of 229 years and a range of 60-660 years. FRIs at Lake Small decreased significantly after c. 4500 cal. year BP, whereas at Lake Ben FRIs remained similar throughout the Holocene. Different FRI distributions and independence in the occurrence of fire events were detected between 10 000 and 4500 cal. year BP for the two sites. Between 4500 cal. year BP and the present, similar FRIs were observed, but fires continued to occur independently. 3. Longer FRIs resulted in declining abundance of Larix laricina in both landscapes. Longer FRIs resulted in a decline in the abundance of Picea mariana in the xeric landscape, but a marginal increase in the mesic landscape. Abundances of Pinus banksiana, Pinus strobus and Betula papyrifera were unrelated to FRI, underlying that these species maintain their local abundance irrespective of fire frequency. 4. Synthesis. Our results show contrasting fire regime dynamics between a xeric and mesic landscape in central boreal forests, Canada. These results highlight the influence of local factors as important drivers of fire frequency at centennial to millennial scales. Local site factors, especially soil moisture, need to be incorporated into predictive models of vegetation response to climate change.