Bayesian analysis of lacustrine charcoal from Fuyun, northwestern China, records spatio-temporal variability in Altai Range fire history

Burning histories derived from charcoal preserved in sediment archives offer scope to
reconstruct past climate and landscape dynamics. The fault-bounded Aksay Pond in
northwestern China preserves an 80-year sediment sequence spanning 1931 to 2012 that
reveals undetected punctuated fire events within the last ca. 1000 years. We used Bayesian
inferential modelling of 24 macrocharcoals that have been directly 14C Accelerated Mass
Spectrometry dated to examine past phases of fire activity and compare these phases with other fire-proxy records from the Altai Ranges. That these charcoals formed, were stored in
the landscape and subsequently mobilised into the pond suggests that fires occurred at these
different times. This method for examining fire histories differs from more traditional
techniques and has some inherent uncertainties that are discussed. Importantly, our charcoal
record does not attempt to infer severity, intensity or number of fires but identifies undetected
periods of burning. Charcoal was dated to three statistically distinct phases spanning 95%
highest posterior density ranges of 1170 to 1290 CE (Phase 3), 1410 to 1650 CE (Phase 2)
and 1720 to 1900 CE (Phase 1) with some post-1950 CE charcoal. Bayesian modelling also
demonstrates that Phase 3 does not coincide with burning histories from elsewhere in the
Altai Ranges suggesting localised fires during the early to middle stages of the Medieval
Climate Anomaly. Phase 2 charcoals overlap with a significant period of burning from the
western Altai Range during the early stages of the Little Ice Age (LIA) indicating a larger
regional environment primed for fire. Phase 3 charcoals from Aksay Pond occurs during the
transition from peak LIA to Recent Warming and likely reflects regional increases in
anthropogenic burning. Our Bayesian analysis of the burning periods from the Aksay Pond
with other fire records from the Altai Ranges demonstrates that burning in the region is
spatio-temporally heterogeneous and that further sites need investigating to capture the true
history of burning from the region.Our novel approach also demonstrates the utility of shortlived
sedimentary archives as alternative proxy sources for long-term fire histories in datascarce
regions.