A chironomid-based calibration training set comprised of 100 lakes from
south-western China was established. Multivariate ordination analyses were
used to investigate the relationship between the distribution and abundance
of chironomid species and 18 environmental variables from these lakes.
Canonical correspondence analyses (CCAs) and partial CCAs showed that mean
July temperature is one of the independent and significant variables
explaining the second-largest amount of variance after potassium ions
(K
South-western China is an important region for examining changes in low- and mid-latitude atmospheric circulation in the Northern Hemisphere. It lies at the intersection of the influence of the northern hemispheric westerlies and two tropical monsoon systems, namely the Indian Ocean south-west monsoon (IOSM) and the East Asian monsoon (EAM), and should be able to inform us about changes in both the latitude and longitude of the influence of these respective systems through time. Reconstructing changes in circulation requires information about several climatic parameters, including past precipitation and temperature. While there are reasonable records of precipitation from this region (e.g. Wang et al., 2001, 2008; Dykoskia et al., 2005; Xiao et al., 2014), there is a paucity of information about temperature changes. In order to understand the extent and intensity of penetration of monsoonal air masses, robust summer temperature estimates are vital as this is the season that the monsoon penetrates south-western China.
Chironomid larvae are frequently the most abundant insects in freshwater ecosystems (Cranston, 1995) and subfossil chironomids are widely employed for palaeoenvironmental studies due to their sensitivity to environmental changes and ability of the head capsules to preserve well in lake sediments (Walker, 2001). A strong relationship between chironomid species assemblages and mean summer air temperature has been reported from many regions around the world and transfer functions were subsequently developed (e.g. Brooks and Birks, 2001; Larocque et al., 2001; Heiri et al., 2003; Gajewski et al., 2005; Barley et al., 2006; Woodward and Shulmeister, 2006; Langdon et al., 2008; Rees et al., 2008; Eggermont et al., 2010; Luoto, 2009; Holmes et al., 2011; Heiri et al., 2011; Chang et al., 2015a). The application of these transfer functions has provided quantitative temperature data since the last glacial period in many regions of the world (e.g. Woodward and Shulmeister, 2007; Rees and Cwynar, 2010; Samartin et al., 2012; Chang et al., 2015b; Muschitiello et al., 2015; Brooks et al., 2016). Consequently, subfossil chironomids have been the most widely applied proxy for past summer temperature reconstructions.
Merged regional chironomid training sets and combined inference models have been developed in Europe (Lotter et al., 1999; Holmes et al., 2011; Heiri et al., 2011; Luoto et al., 2014). These large data sets and models provide much more robust reconstructions than smaller local temperature inference models (Heiri et al., 2011; Luoto et al., 2014). However, the distribution of large regional inference models is limited to Europe and northern North America (e.g. Fortin et al., 2015). There is a need to build large training sets for other parts of the world where chironomids will likely be sensitive to temperature changes. Subfossil chironomids have been successfully used as paleoenvironmental indicators in China for over a decade. These included salinity studies on the Tibetan Plateau (Zhang et al., 2007) and the development of a nutrient-based inference model for eastern China and parts of Yunnan (Zhang et al., 2006, 2010, 2011, 2012). A large database of relatively undisturbed lakes, in which nutrient changes are minimal while temperature gradients are suitably large, is now available from south-western China and this provides the opportunity to develop a summer temperature inference model for this broad region.
Map of south-west China
In this study, a chironomid species assemblage training set and
chironomid-based mean July air temperature (MJT) inference models from 100 lakes
on the south-east margin of the Tibetan Plateau are developed. We test and
validate the selected transfer function models by applying them to a sediment
core collected from Tiancai Lake (26
The study area lies in the south-east margin of the Tibetan Plateau
including the south-west part of Qinghai Province, the western part of
Sichuan Province and the north-west part of Yunnan Province (Fig. 1). It is
situated between 26–34
The climate of the study area is dominated by the westerlies in winter and
by the IOSM in Yunnan and Tibet, but some of the easternmost lakes are
affected by the EAM. There is a wet season that extends from May (June) to
October that accounts for 85–90 % of total rainfall and a dry season from
November to April. Annual precipitation varies greatly according to altitude
and latitude. Most of the precipitation is derived from a strong south-west
summer monsoonal flow that emanates from the Bay of Bengal (Fig. 1).
Precipitation declines from south-east to north-west. Mean summer
temperatures vary between about 6 and 22
Tiancai Lake (26
Surface sediment samples were collected from 100 lakes in the south-east
margin of the Tibetan Plateau via six field campaigns during autumn of
each year between 2006 and 2012. The lakes in this area are mainly
distributed at the top or upper slopes of the mountains and are primarily
glacial in origin. Most lakes were reached by hiking or with horses and the
lake investigation spanned several seasons. Small lakes (surface area
Surface sediments (0–1 cm) were collected from the deepest point in each
lake after a survey of the bathymetry using a portable echosounder. Surface
sediment samples were taken using a Kajak gravity corer (Renberg, 1991). The
samples were stored in plastic bags and kept in the refrigerators at 4
Water samples were collected for chemical analysis from 0.5 m below the lake
surface immediately before the sediment samples were obtained. Water samples
for chemical analysis were stored in acid-washed polythene bottles and kept
at 4
One hundred surface sediment samples from lakes of south-western China and 55 subsamples from the Tiancai Lake short core were analysed for chironomids
following standard methods (Brooks et al., 2007). The sediment was
deflocculated in 10 % potassium hydroxide (KOH) in a water bath at 75
List of all the 18 environmental and climate variables measured from 100 south-western Chinese lakes, with mean, minimum and maximum values.
A range of numerical methods were used to determine the relative influence
of the measured environmental parameters on the distribution and abundance
of chironomids in the surface sediments within the training set. A total of
18 environmental variables were considered in the initial statistical
analyses (Table 1). These measurements were normalized using a log
Chironomid-based transfer functions were developed for mean July
temperatures using C2 version 1.5 (Juggins, 2005) for the calibration data
set comprised of 100 lakes. The models were constructed using algorithms
based on weighted averaging (WA) and weighted-average
partial least squares (WA-PLS) (Birks, 1995). The bootstrap cross-validation
technique was tested for the data set because it was previously demonstrated
to be
more suitable for large data sets (Heiri et al., 2011) than the
jackknife technique. Transfer function models were evaluated based on the
performance of the coefficient of determination (
The transfer function models were then applied to the fossil chironomid data
from Tiancai Lake. MJTs were reconstructed from the
site and three types of reconstruction diagnostics suggested in Birks (1995)
were applied to assess the reliability of the results. These include
goodness-of-fit, modern analogue technique (MAT) and the percentage (%)
analysis of modern rare taxa in the fossil samples. For the goodness-of-fit
analysis, the squared residual length (SqRL) was calculated by passively
fitting fossil samples to the CCA ordination axis of the modern training set
data constrained to MJT in CANOCO version 4.5 (ter Braak and Smilauer,
2002). Fossil samples with a SqRL to axis 1 higher than the extreme 10 and
5 % of all residual distances in the modern calibration data set were
considered to have a “poor” and “very poor” fit with MJT respectively. The
chi-square distance to the closest modern assemblage data for each fossil
sample was calculated in C2 (Juggins, 2005) using the MAT. Fossil samples
with a chi-square distance to the closest modern sample larger than the
fifth percentile of all chi-square distances in the modern assemblage
data were identified as samples with “no good” analogue. The percentage of
rare taxa in the fossil samples was also calculated in C2 (Juggins, 2005),
where a rare taxon has a Hill's N
The top 28 cm of the sediment core recovered from Tiancai Lake were used for
A total of 85 non-rare taxa (Hill's N
CCA biplots of sample and species scores constrained to environmental
variables that individually explain a significant (
CCA summary of the seven significant variables (
The DCAs performed on the 100 lakes and 85 non-rare
chironomid taxa had an axis 1 gradient length of 3.033, indicating that a
CCA approach was appropriate for modelling the chironomid taxon response
(Birks, 1998). The 18 environmental variables were tested as in the
initial CCA and the results showed that total dissolved solids had the highest VIF. It was then
removed from the following CCAs. Seven of the remaining variables had
significant (
Partial canonical correspondence analysis (pCCA) result with
environmental variables that showed a significant correlation (
A biplot of the CCA species scores indicated that taxa such as
Performance of the weighted-average models with inverse deshrinking
(WAinv) and partial least squares (WA-PLS) models using the 100 lakes
calibration data sets:
The transfer functions were developed for MJT. We
acknowledge that MJT is not the sole independent variable on CCA axis 2 in
the data set but transfer functions based on this large regional data set are
created and applied to reconstruct MJT because it is a more useful parameter
compared to K
A total of 55 subsamples were analysed for chironomid taxa throughout the
top 28 cm of the core recovered from Tiancai Lake. There were 41 non-rare
(Hill's N
The
The age and depth model for
Obtaining reliable estimates of the modern climate data has been challenging
in south-western China. There are very few meteorological stations and
climate monitoring in the high mountains of our study area is virtually
non-existent. Climate parameters including mean July temperatures and mean
annual precipitation used in this study are interpolated from climate
surfaces derived from a mathematical climate surface model based on the
limited meteorological data and a digital terrain model (DTM) applied to the
whole of the wider Tibetan region (400
We examined the chironomid taxa that appeared as temperature indicators in
the calibration set. A number of taxa, namely
Well-known warm stenotherms that are distributed along the MJT gradient of
the CCA species biplot (Fig. 3a) include
This 100-lake training set covers a temperature gradient ranging from 4.2 to 20.8
The second CCA axis is co-dominated by MJT and Cl
We selected the WA-PLS-based transfer function models over the WAinv-based
approach for both training sets because the addition of PLS components can
reduce the prediction error in data sets with moderate to large noise (ter
Braak and Juggins, 1993). The training set has a MJT gradient of 16.6
All three types of applied diagnostic techniques (Fig. 6b–d) suggest that a
reliable MJT reconstruction was provided by the two-component WA-PLS model
based on this 100-lake data set overall. We highlight that the eight samples
from the years between 2000 and 2007 AD have “poor” and “very poor” fit to
MJT, which may suggest that it is possible a second gradient other than MJT
influenced the chironomid species distribution and abundance in the most
recent fossil samples of Tiancai Lake. In the comparison of the MJT
reconstruction results with the instrumental record from Lijiang weather
station (Fig. 6a), we do not expect the absolute MJT values to be identical
because Lijiang is located
The reconstruction results are well matched with the expected outcomes as
the transfer function models based on 100 lakes for the broad area of
south-western China reconstructs MJT broadly match the trend recorded by the
instrument. By applying the environmental lapse rate, we observe a
temperature depression from Lijiang to Tiancai Lake of about 9.3
Chironomid-based summer temperature transfer functions using 100 lakes from south-western China have been constructed and applied to Yunnan region in the south-east margin of the Tibetan Plateau. Both the ordination and transfer function statistics show that the chironomid-based transfer function is reliable. This large regional training set allowed insight into the regional chironomid distribution and species abundance despite having many more independent environmental gradients. The test of the transfer function models against the modern data suggests that the two-component WA-PLS model provided reconstructions that match the trend of the local instrumental record for the last 50 years. As also demonstrated from pan-European chironomid-based transfer functions (e.g. Brooks and Birks, 2001; Heiri et al., 2011), this broadly based Chinese 100-lake training set is likely robust and is appropriate for use in reconstructing long-term summer temperature changes of south-western China.
The data used for this study can be downloaded at
The authors declare that they have no conflict of interest.
We thank X. Chen, E. F. Liu, M. Ji, R. Chen, Y. L. Li, X. Y. Xiao, J. J. Wang, Q. Lin and B. Y. Zheng (Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences) for field assistance, Jürgen Böhner (Georg-August University Göttingen, Germany) for help with climate data. This research was supported by the Program of Global Change and Mitigation (2016YFA0600502) of the National Natural Science Foundation of China (nos. 41272380, 41572337). Edited by: L. Zhou Reviewed by: two anonymous referees