Inferring late-Holocene climate in the Ecuadorian Andes using a chironomid-based temperature inference model

摘要

Presented here is the first chironomid calibration data set for tropical South America. Surface sediments were collected from 59 lakes across Bolivia (15 lakes), Peru (32 lakes), and Ecuador (12 lakes) between 2004 and 2013 over an altitudinal gradient from 150mabovesealevel (a.s.l) to 4655ma.s.l, between 0–17°S and 64–78°W. The study sites cover a mean annual temperature (MAT) gradient of 25°C. In total, 55 chironomid taxa were identified in the 59 calibration data set lakes. When used as a single explanatory variable, MAT explains 12.9% of the variance (λ1/λ2?=?1.431). Two inference models were developed using weighted averaging (WA) and Bayesian methods. The best-performing model using conventional statistical methods was a WA (inverse) model (R2jack?=?0.890; RMSEPjack?=?2.404°C, RMSEP – root mean squared error of prediction; mean biasjack?=??0.017°C; max biasjack?=?4.665°C). The Bayesian method produced a model with R2jack?=?0.909, RMSEPjack?=?2.373°C, mean biasjack?=?0.598°C, and max biasjack?=?3.158°C. Both models were used to infer past temperatures from a ca. 3000-year record from the tropical Andes of Ecuador, Laguna Pindo. Inferred temperatures fluctuated around modern-day conditions but showed significant departures at certain intervals (ca. 1600calyrBP; ca. 3000–2500calyrBP). Both methods (WA and Bayesian) showed similar patterns of temperature variability; however, the magnitude of fluctuations differed. In general the WA method was more variable and often underestimated Holocene temperatures (by ca. ?7±2.5°C relative to the modern period). The Bayesian method provided temperature anomaly estimates for cool periods that lay within the expected range of the Holocene (ca. ?3±3.4°C). The error associated with both reconstructions is consistent with a constant temperature of 20°C for the past 3000 years. We would caution, however, against an over-interpretation at this stage. The reconstruction can only currently be deemed qualitative and requires more research before quantitative estimates can be generated with confidence. Increasing the number, and spread, of lakes in the calibration data set would enable the detection of smaller climate signals.