New Zealand chironomids as proxies for human-induced and natural environmental change: Transfer functions for temperature and lake production (chlorophyll a)

The analysis of chironomid taxa and environmental datasets from 46 New Zealand lakes identified temperature (February mean air temperature) and lake production (chlorophyll a (Chl a)) as the main drivers of chironomid distribution. Temperature was the strongest driver of chironomid distribution and consequently produced the most robust inference models. We present two possible temperature transfer functions from this dataset. The most robust model (weighted averaging-partial least squares (WA-PLS), n = 36) was based on a dataset with the most productive (Chl a > 10 μg l⁻¹) lakes removed. This model produced a coefficient of determination () of 0.77, and a root mean squared error of prediction (RMSEP_jack) of 1.31°C. The Chl a transfer function (partial least squares (PLS), n = 37) was far less reliable, with an of 0.49 and an RMSEP_jack of 0.46 Log₁₀μg l⁻¹. Both of these transfer functions could be improved by a revision of the taxonomy for the New Zealand chironomid taxa, particularly the genus Chironomus. The Chironomus morphotype was common in high altitude, cool, oligotrophic lakes and lowland, warm, eutrophic lakes. This could reflect the widespread distribution of one eurythermic species, or the collective distribution of a number of different Chironomus species with more limited tolerances. The Chl a transfer function could also be improved by inputting mean Chl a values into the inference model rather than the spot measurements that were available for this study.     

Environmental controls on modern chironomid faunas from NW Iceland and implications for reconstructing climate change

Reconstructing climate change quantitatively over millennial timescales is crucial for understanding the processes that affect the climate system. One of the best methods for producing high resolution, low error, quantitative summer air temperature reconstructions is through chironomid analyses. We analysed over 50 lakes from NW and W Iceland covering a range of environmental gradients in order to test whether the distribution of the Icelandic chironomid fauna was driven by summer temperature, or whether other environmental factors were more dominant. A range of analyses showed the main environmental controls on chironomid communities to be substrate (identified through loss-on-ignition and carbon content) and mean July air temperature, although other factors such as lake depth and lake area were also important. The nature of the Icelandic landscape, with numerous volcanic centres (many of which are covered by ice caps) that produce large quantities of ash, means that relative lake carbon content and summer air temperature do not co-vary, as they often do in other chironomid datasets within the Arctic as well as more temperate environments. As the chironomid–environment relationships are thus different in Iceland compared to other chironomid training sets, we suggest that using an Icelandic model is most appropriate for reconstructing past environmental change from fossil Icelandic datasets. Analogue matching of Icelandic fossil chironomid datasets with the Icelandic training set and another European chironomid training set support this assertion. Analyses of a range of chironomid-inferred temperature transfer functions suggest the best to be a two component WA-PLS model with r ² _jack = 0.66 and RMSEP = 1.095°C. Using this model, chironomid-inferred temperature reconstructions of early Holocene Icelandic sequences show the magnitude of temperature change compared to contemporary temperatures to be similar to other NW European chironomid sequences, suggesting that the predictive power of the model is good.     

Chironomid-based inference models for estimating mean July air temperature and water depth from lakes in Yakutia,northeastern Russia

We investigated the subfossil chironomid fauna of 150 lakes situated in Yakutia, northeastern Russia. The objective of this study was to assess the relationship between chironomid assemblage composition and the environment and to develop chironomid inference models for quantifying past regional climate and environmental changes in this poorly investigated area of northern Russia. The environmental data and sediment samples for chironomid analysis were collected in 5 consecutive years, 2003–2007, from several regions of Yakutia. The lakes spanned wide latitudinal and longitudinal ranges and were distributed through several environmental zones (arctic tundra, typical tundra, steppe-tundra, boreal coniferous forest), but all were situated within the zone of continuous permafrost. Mean July temperature (T_July) varied from 3.4°C in the Laptev Sea region to 18.8°C in central Yakutia near Yakutsk. Water depth (WD) varied from 0.1 to 17.1 m. T_July and WD were identified as the strongest predictor variables explaining the chironomid communitiy composition and distribution of the taxa in our data set. Quantitative transfer functions were developed using two unimodal regression calibration techniques: simple weighted averaging (WA) and weighted averaging partial least squares (WA-PLS). The two-component T_July WA-PLS model had the best performance. It produced a strong coefficient of determination (r ² _boot = 0.87), root mean square error of prediction (RMSEP = 1.93), and max bias (max bias_boot = 2.17). For WD, the one-component WA-PLS model had the best performance (r ² _boot = 0.62, RMSEP = 0.35, max bias_boot = 0.47).     

Modern influences on chironomid distribution in western Ireland: potential for palaeoenvironmental reconstruction

Ireland provides a unique setting for the study of past climates, as its climate is dominated by westerly airflow from the North Atlantic and readily responsive to changes in North Atlantic circulation patterns. Although there has been substantial research on Ireland’s past environments, quantitative palaeolimnological research, especially chironomid-based research, has been lacking. In order to further develop chironomid-based palaeolimnological investigations, a calibration set was constructed to determine the dominant environmental controls on modern chironomids in western Ireland. Chironomid subfossils were collected from surface sediments of 50 lakes. The lakes were characterised with 36 environmental variables, including physical attributes, lake water characteristics, lake sediment characteristics and land cover within each catchment. In this exploratory study, no specific environmental variable was targeted and lakes were chosen to span gradients of latitude, elevation, depth and trophic status. Redundancy analysis showed that six environmental variables—mean July air temperature, lake depth, dissolved organic carbon, and percentage catchment land cover of agriculture, peat bog and scrubland—captured a large and statistically significant portion of the variance in the chironomid data. July temperature and agricultural land cover were the most dominant environmental variables, with July temperature proving the most suitable for inference model development. A classical weighted-averaging model was developed to estimate July air temperature, with a coefficient of determination (r _jack ² ) of 0.60 and root mean square error of prediction (RMSEP) of 0.57 °C. Results suggest that summer temperature is the dominant influence on chironomid distribution across a wide variety of lake types, and the relatively small RMSEP should allow for more accurate reconstructions of Ireland’s relatively subdued Holocene temperature fluctuations.     

Development of a chironomid-based air temperature inference model for the central Canadian Arctic

Subfossil midge remains were identified in surface sediment recovered from 88 lakes in the central Canadian Arctic. These lakes spanned five vegetation zones, with the southern-most lakes located in boreal forest and the northern-most lakes located in mid-Arctic tundra. The lakes in the calibration are characterized by ranges in depth, summer surface-water temperature (SSWT), average July air temperature (AJAT) and pH of 15.5 m, 10.60°C, 8.40°C and 3.69, respectively. Redundancy analysis (RDA) indicated that maximum depth, pH, AJAT, total nitrogen-unfiltered (TN-UF), Cl and Al capture a large and statistically significant fraction of the overall variance in the midge data. Inference models relating midge abundances and AJAT were developed using different approaches including: weighted averaging (WA), weighted averaging-partial least squares (WA-PLS) and partial least squares (PLS). A chironomid-based inference model, based on a two-component WA-PLS approach, provided robust performance statistics with a high coefficient of determination (r ² = 0.77) and low root mean square error of prediction (RMSEP = 1.03°C) and low maximum bias. The use of a high-resolution gridded climate data set facilitated the development of the midge-based inference model for AJAT in a region with a paucity of meteorological stations and where previously only the development of a SSWT inference model was possible. David Porinchu and Nicolas Rolland contributed equally to the work.     

Midges (Chironomidae,Ceratopogonidae, Chaoboridae) as a temperature proxy: a training set from Tasmania,Australia

Chironomid and ceratopogonid head capsules, along with Chaoborus mandibles, were used to model mean temperature of the warmest quarter (TWARM) in Tasmania. Our transfer function is based on midge assemblages and 21 environmental variables sampled from 47 lakes. Canonical correspondence analysis (CCA) revealed seven variables that account for a significant (P ≤ 0.05) portion of the explainable variance. In order of explanatory power, these were pH, TWARM, annual radiation, magnesium, annual precipitation, SiO₂, and depth. TWARM was modeled using weighted averaging partial least squares (WA-PLS) and generated a model with and RMSEP = 0.94. Advances in chironomid paleoecology are progressing very quickly in the Southern Hemisphere. Chironomid identification guides and autecological data are available for many regions, highlighting the potential for developing midge-based quantitative models to address hemispheric and interhemispheric climate hypotheses.     

A high resolution multi-proxy record of pronounced recent environmental change at Baker Lake,Nunavut

Arctic aquatic systems are considered to be especially sensitive to anthropogenic disturbance, which can have cascading effects on biological communities as aquatic food-web structure is altered. Bio-indicators that respond to major limnological changes can be used to detect and infer major environmental change, such as climate warming, with the use of paleolimnological techniques. A multi-proxy approach was used to quantify recent environmental changes at Baker Lake, Nunavut, Arctic Canada. Analyses of fossilized remains of chironomids and diatoms were conducted on a sediment core of 20 cm in length sampled at 0.5-cm intervals. A new surface sediment training set of subfossil chironomid assemblages from 65 lakes across the eastern Canadian Arctic generated a robust (r _jack² = 0.79) surface water paleotemperature transfer function. The transfer function was applied to stratigraphic intervals from the Baker Lake sediment core to generate a paleotemperature reconstruction of sub-decadal resolution. The surface water temperature reconstruction inferred a 2°C increase in mid-summer surface water temperature for Baker Lake over the last 60 years, which was corroborated by the local instrumental record spanning the period of 1950–2007 AD. The chironomid record shows a recent decline of several cold-water taxa and appearance of warm-water indicators. This shift in community structure began circa 1906 AD, and intensified after 1940 AD. The corresponding fossil diatom record showed an increase in small planktonic Cyclotella taxa over the past 60 years, intensifying in the last 5 years, which also suggests a warmer climate and longer ice-free periods. The shifts in the diatom assemblages began later than the shifts in the chironomid assemblages, and were of lower magnitude, reflecting differences in the mechanisms in which these two indicators respond to environmental change.     

Diatom habitats,species diversity and water-depth inference models across surface-sediment transects in Worth Lake,northwest Ontario,Canada

We analyzed surface-sediment samples collected along transects from three sub-basins of a relatively large (~115 ha), bathymetrically complex lake, in northwest Ontario, Canada, to assess the reproducibility of diatom species habitats and diversity along a water-depth gradient. Transects displayed different orientations with respect to prevailing wind direction and varied in complexity and degree of slope along the lake bottom. Each transect consisted of three replicate samples at a resolution of ~1 m water depth from ~1 to 30 m for the two deep-basin transects and from ~1 to 18 m in the shallower basin. Distinct diatom assemblages were identified in all transects: (1) a near-shore community composed largely of attached life-forms and some motile benthic taxa, (2) a mid-depth community composed largely of motile life-forms and other benthic taxa that are adapted to lower light conditions (e.g. Staurosirella pinnata), and (3) a deep-water community dominated by planktonic taxa. Species richness was highest in the benthic zones (<9 m), with greatest species evenness in the mid-depth zone (~3–9 m). Species richness and evenness were highly correlated across the three transects (r = 0.89–0.93, p < 0.01). Diatom-inferred depth models were developed from the individual transects to assess reproducibility and applicability for down-core analyses using modern analog (MAT) and weighted-averaging (WA-PLS) approaches. Coefficients of determination (r ²) for these models ranged from 0.80 to 0.98, and RMSEP ranged from 1.2 to 4.2 m. The models developed from the transect with the highest resolution sampling, gentlest non-complex slope and shallowest maximum depth were the strongest ( r_\textMAT² = 0.97 r_{\text{MAT}}^{2} = 0.97 ; r_{\textWA - PLS}² = 0.98 r_{\text{WA - PLS}}^{2} = 0.98 ) and had the lowest RMSEP (MAT = 1.2 m, WA-PLS = 1.3 m). These inference models can be used to infer past fluctuations in the depth of the benthic/planktonic boundary from cores retrieved near this ecotone and provide a sensitive record of the past change in location of the benthic zone. These types of data can be used to assess past variability in droughts and lake levels to better plan for potential future extremes. Such records incorporate more realistic estimates of natural variability than the ~100-year instrumental records currently used by water resource managers.     

Oxygen isotope fractionation in the ostracod <Emphasis Type="Italic">Eucypris mareotica</Emphasis>: results from a culture experiment and implications for paleoclimate reconstruction

Oxygen isotope analysis of the adult ostracod Eucypris mareotica cultured at controlled temperatures (10, 15, and 19°C) was used to measure isotopic fractionation during shell calcification. The ostracod shells that precipitated at experimental temperatures are almost in isotopic equilibrium with the culture water as compared to the oxygen isotope fractionation of inorganic carbonates. Moreover, they had almost constant offsets from equilibrium based on the oxygen isotope fractionation of inorganic carbonates. The δ¹⁸O values of ostracod shells from the 10°C cultures were higher than those of the 15 and 19°C cultures by 1.6 and 2.7‰, respectively. The observed fractionations are shown by the regression equations: *20c 10^° \textC:d ^{1 8} \textO_{\textostracod} = 1. 1 7+ 0. 5 7d ^{1 8} \textO_\textwater 1 5^° \textC:d ^{1 8} \textO_{\textostracod} = - 0. 4 8+ 0. 6d ^{1 8} \textO_\textwater 1 9^° \textC:d ^{1 8} \textO_{\textostracod} = - 1. 6+ 0. 6d ^{1 8} \textO_\textwater \begin{array}{*{20}c} { 10^\circ {\text{C}}:\delta^{ 1 8} {\text{O}}_{\text{ostracod}} = 1. 1 7+ 0. 5 7\delta^{ 1 8} {\text{O}}_{\text{water}} } \\ { 1 5^\circ {\text{C}}:\delta^{ 1 8} {\text{O}}_{\text{ostracod}} = - 0. 4 8+ 0. 6\delta^{ 1 8} {\text{O}}_{\text{water}} } \\ { 1 9^\circ {\text{C}}:\delta^{ 1 8} {\text{O}}_{\text{ostracod}} = - 1. 6+ 0. 6\delta^{ 1 8} {\text{O}}_{\text{water}} } \\ \end{array} The fractionation factors (α) are slightly lower for the 15 and 19°C cultures, but slightly higher for the 10°C culture, as compared to inorganic carbonates (O’Neil et al. in J Chem Phys 51:5547–5558, 1969). The oxygen fractionation factors of E. mareotica are very close to those of synthetic calcite formed in isotopic equilibrium. The ‘vital offsets’ of valve-δ¹⁸O for E. mareotica is so small that we can neglect its effect when using the δ¹⁸O of E. mareotica living in lake waters with high pH and salinity to reconstruct the paleoenvironment. The paleotemperature or paleoisotopic composition of lake water interpreted from a core of lacustrine sediment may be closer to the true values when the δ¹⁸O data for E. mareotica are used.     

Influence of industrial activity and pollution on the paleoclimate reconstruction from a eutrophic lake in lowland England,UK

Reliable estimates of Holocene temperatures are important for understanding past climate dynamics, the response of biota to climate change, and validating climate models. Chironomids in lake sediment cores are used widely to quantify past summer temperatures, for which high-latitude and/or high-altitude lakes, remote from human influence, are usually considered appropriate. Temperature inferences from lowland lakes are likely influenced by other variables, specifically eutrophication and industrial pollution, but their reliability has never been tested. We used a Norwegian chironomid-based transfer function (r ² = 0.91; RMSEP = 1.01 °C) to infer mean July air temperature over the last 200 years, using chironomid assemblages in a core collected from a polluted, nutrient-enriched lake at Speke Hall, Liverpool, England. The chironomid-inferred temperatures correlate significantly with the local instrumental temperature record and follow long-term national temperature trends. These results show that chironomids can be used to produce reliable estimates of past mean July air temperature, even when other variables have also influenced the composition of the chironomid community. These findings underline the value of chironomids as sensitive and reliable quantitative proxies for summer temperature.     

Chironomid-based water depth reconstructions: an independent evaluation of site-specific and local inference models

Water depth is an important environmental variable that explains a significant portion of the variation in the chironomid fauna of shallow lakes. We developed site-specific and local chironomid water-depth inference models using 26 and 104 surface-sediment samples, respectively, from seven kettlehole lakes in the Plymouth Aquifer, southeast Massachusetts, USA. Our site-specific model spans a depth gradient of 5.6?m, has an $ {\text{r}}_{\text{jack}}^{2} $ of 0.90, root mean square error of prediction (RMSEP) of 0.5?m and maximum bias of 0.7?m. Our local model has a depth gradient of 11.7?m, an $ {\text{r}}_{\text{jack}}^{2} $ of 0.71, RMSEP of 1.6?m and maximum bias of 2.9?m. Principal coordinates of neighbourhood matrices (PCNM) analysis showed that there is no influence of spatial autocorrelation on the site-specific model, but PCNM variables explained a significant amount of variance (4.8%) in the local model. This variance, however, is unique from the variance explained by water depth. We applied the inference models to a Holocene chironomid record from Crooked Pond, a site for which multiple, independent palaeohydrological reconstructions are available. The chironomid-based reconstructions are remarkably similar and show stable water depths of ~5?m, interrupted by a 2-m decrease between 4,200 and 3,200?cal a BP. Sedimentological evidence of water level fluctuations at Crooked Pond, obtained using the so-called Digerfeldt approach, also shows a drop in water depths around that time. The period of reconstructed lower water levels coincides with the abrupt decline in moisture-dependent hemlock in this region, providing further evidence for this major palaeohydrological event. The site-specific model has the best performance statistics, but the high percent abundance of fossil taxa from the long core that are absent or rare in the training set makes the site-specific reconstruction unreliable for the period before 4,400?cal a BP. The fossil taxa are well represented in the local model, making it the preferred inference model. The strong similarity between the chironomid-based reconstructions and the independent palaeohydrological records highlights the potential for using chironomid-based inference models to determine past lake depths at sites where temperature was not an influencing factor.     

The potential of site-specific and local chironomid-based inference models for reconstructing past lake levels

We examined the relationship between three key environmental variables (water depth, loss-on-ignition, and bottom-water temperature) and fossil chironomid distributions sampled from within-lake gradients in three small, moderately deep (18–35 m), maar lakes on St Michael Island, western Alaska. Site-specific (one lake, 29 samples) and local (three lakes, 87 samples) inference models for reconstructing water depth were developed using partial least squares regression and calibration. These models and a previously published regional model (136 lakes, one central-lake sample from each) are used to infer water depths from 78 fossil samples spanning the last ~30,000 ¹⁴C years B.P. at Zagoskin Lake. Although the site-specific [r ² _boot = 0.90, root mean square error of prediction (RMSEP) = 1.76] and local (r _boot² = 0.68, RMSEP = 4.36) inference models have better performance statistics than the regional model, few clear trends among all three models exist in the lake-level reconstruction. We propose that multiple, within-lake sampling of gradients can be used to improve the performance statistics of water-depth transfer functions and ultimately reconstruct paleohydrology in regions known to exhibit large fluctuations in moisture balance through time given that: (1) adequate analogs are established and (2) taphonomic processes important to benthic invertebrate remains are more fully understood.     

Palaeolimnological validation of estimated reference values for a lake profundal macroinvertebrate metric (Benthic Quality Index)

Modern assessment and monitoring of aquatic ecosystems is increasingly based on biota and the “reference condition” approach, in which the observed values (O) of biological variables are compared to those expected in the absence of human disturbance (E). To use this approach, correct estimation and validation of reference conditions are critical. Because appropriate modern or historical data are never available for this approach, palaeolimnological data offer an alternative. We used a calibration data set from 73 profundal sites in semi-pristine Finnish lakes to construct a regression model for estimating expected values for the chironomid Benthic Quality Index (BQI)—a macroinvertebrate metric widely used in bioassessment—from environmental variables that are insensitive to human disturbance. For comparison, reference values were estimated using the European legislative rationale based on a priori lake typology. Performance of the alternative approaches was assessed by internal ‘leave-one-out’ cross-validation using the calibration set and by external cross-validation using independent palaeolimnological data on BQI values representing the historical pristine status of 24 lake basins. Additionally, for 19 of these sites, which vary in their degree of human impact, the ratio of present BQI to that in pristine condition, which shows the degree of actual change, if any, was calculated from palaeolimnological data and compared with the O/E ratios based on the present chironomid data and estimated E. A linear regression model with mean depth and mean/maximum depth ratio as independent variables estimated the reference values of BQI much closer to the observed ones (r ² = 0.58, RMSEP = 0.65 and r ² = 0.71 RMSEP = 0.55; for internal and external cross-validation, respectively) than did the typology approach (r ² = 0.28, RMSEP = 0.86; r ² = 0.10, RMSEP = 0.97). The regression approach also yielded O/E ratios more similar to the actual ones (r ² = 0.79, RMSEP = 0.09) than did the typology approach (r ² = 0.62, RMSEP = 0.23). Our results strongly support the use of lake morphometric variables and modelling instead of categorical lake typology for the establishment of reference conditions for profundal macroinvertebrate communities and demonstrate the utility of palaeolimnological data in the validation of reference values and assessment methods.     

Sub-fossil chironomids are significant indicators of turbidity in shallow lakes of northeastern USA

Small, shallow, temperate lakes are predominant landscape features in North America, however, little is known about their long-term ecosystem dynamics, and few data exist on the chironomid fauna they harbor. Using multivariate analyses, we defined relationships between sub-fossil chironomid assemblage composition and environmental variables in 26 shallow lakes of northeastern USA and quantified how differences in taxonomic resolution affect transfer function model performance. Using redundancy analysis, we found that chironomid assemblages are best explained by turbidity, dissolved inorganic carbon and drainage basin/lake area ratio. Turbidity explained the greatest proportion of variance found in the chironomid assemblage (10.4%), followed by total nitrogen. Through ordination analyses and an analysis of similarity, we found that macrophyte density was also a significant predictor of chironomid assemblages. We used partial least squares analysis to develop a robust model for quantitative reconstruction of turbidity, with r _jack² = 0.62. When using a more coarsely resolved taxonomic dataset, we found that model performance statistics were weaker, suggesting the need for fine-resolution taxonomy. Overall, our findings highlight the importance of variables related to lake trophic state in structuring chironomid assemblages in shallow, temperate lakes and provide tools for inferring past ecological changes in these ecosystems.     

How does taxonomic resolution affect chironomid-based temperature reconstruction?

The resolution achievable for chironomid identifications has increased in recent years because of significant improvements in taxonomic literature. However, high taxonomic resolution requires more training for analysts. Furthermore, with greater taxonomic resolution, misidentifications and the number of rare, poorly represented taxa in chironomid calibration datasets may increase. We assessed the effects of various levels of taxonomic resolution on the performance of chironomid-based temperature inference models (transfer functions) and temperature reconstruction. A calibration dataset consisting of chironomid assemblage and temperature data from 100 lakes was examined at four levels of taxonomic detail. The coarsest taxonomic resolution primarily represented identifications to genus or suprageneric level. At the highest level of taxonomic resolution, identification to genus level was possible for 37% of taxa, and identification below genus was possible for 60% of taxa. Transfer functions were obtained using Weighted Averaging (WA) and Weighted Averaging-Partial Least Squares (WA-PLS) regression. Cross-validated performance statistics, such as the root mean square error of prediction (RMSEP) and the coefficient of determination (r ²) between inferred and observed values improved considerably from the lowest taxonomic resolution level (WA: RMSEP 1.91°C, r ² 0.78; WA-PLS: RMSEP 1.59°C, r ² 0.86) to the highest taxonomic resolution level (WA: RMSEP 1.66°C, r ² 0.84; WA-PLS: RMSEP 1.41°C, r ² 0.89). Reconstructed July air temperatures during the Lateglacial period based on fossil chironomid assemblages from Hijkermeer (The Netherlands) were similar for all levels of taxonomic resolution, except the coarsest level. At the coarsest taxonomic level, reconstruction failed to infer one of the known Lateglacial cold episodes in the record. Also, the difference in reconstructed values based on lowest and highest taxonomic resolutions exceeded sample-specific estimated standard errors of prediction in several instances. Our results suggest that chironomid-based transfer functions at the highest taxonomic resolution outperform models based on lower-resolution calibration data. However, transfer functions of intermediate taxonomic resolution produced results very similar to models based on high-resolution taxonomic data. In studies that include analysts with different levels of expertise, inference models based on intermediate taxonomic resolution, therefore, might provide an alternative to transfer functions of maximum taxonomic detail in order to ensure taxonomic consistency between calibration datasets and down-core records produced by different analysts.     

Quantitative summer-temperature reconstructions for the last 2000 years based on pollen-stratigraphical data from northern Fennoscandia

Quantitative reconstructions of mean July temperatures (T _jul) based on new and previously published pollen-stratigraphical data covering the last 2000 years from 11 lakes in northern Fennoscandia and the Kola Peninsula are presented. T _jul values are based on a previously published pollen-climate transfer function for the region with a root-mean-square error of prediction (RMSEP) of 0.99°C. The most obvious trend in the inferred temperatures from all sites is the general decrease in T _jul during the last 2000 years. Pollen-inferred T _jul values on average 0.18 ± 0.56°C (n = 91) higher than present (where “present” refers to the last three decades based on pollen-inferred T _jul in core-top samples) are indicated between 0 and 1100 AD (2000–850 cal year BP), and temperatures −0.2 ± 0.47°C (n = 78) below present are inferred between 1100 and 1900 AD (850–50 cal year BP). No consistent temperature peak is observed during the ‘Medieval Warm Period’, ca. 900–1200 AD (1100–750 cal year BP), but the cooler period between 1100 and 1900 AD (850–50 cal year BP) corresponds in general with the ‘Little Ice Age’ (LIA). Consistently with independent stable isotopic data, the composite pollen-based record suggests that the coldest periods of the LIA date to 1500–1600 AD (450–350 cal year BP) and 1800–1850 AD (150–100 cal year BP). An abrupt warming occurred at about 1900 AD and the twentieth century is the warmest century since about 1000 AD (950 cal year BP).

The modern distribution of chironomid sub-fossils (Insecta: Diptera) in the Sierra Nevada, California: Potential for paleoclimatic reconstructions

Surface lake sediment was recovered from 57 lakes along an elevation gradient in the central, eastern Sierra Nevada of California. The surface sediment was analysed for subfossil chironomid remains in order to assess the modern distribution of chironomids in the region. The lakes sampled for the calibration dataset were between 2.0 and 40.0 m in depth, spanned an altitudinal gradient of 1360 m and a surface water temperature gradient of approximately 14 °C. Redundancy analysis (RDA) identified that five of the measured environmental variables – surface water temperature, elevation, depth, strontium, particulate organic carbon – accounted for a statistically significant amount of the variance in chironomid community composition. Quantitative transfer functions, based on weighted-averaging (WA), partial least squares (PLS) and weighted-averaging partial least squares (WA-PLS), were developed to estimate surface water temperature from the chironomid assemblages. The best model was a WA model with classical deshrinking, which had a relatively high coefficient of determination (r² = 0.73), low root mean square error of prediction (RMSEP = 1.2 °C) and a low maximum bias (0.90 °C). The results from this study suggest that robust quantitative estimates of past surface water temperature can be derived from the application of these models to fossil chironomid assemblages preserved in late-Quaternary lake sediment in this region.     

A model for inferring dissolved organic carbon (DOC) in lakewater from visible-near-infrared spectroscopy (VNIRS) measures in lake sediment

We developed an inference model to infer dissolved organic carbon (DOC) in lakewater from lake sediments using visible-near-infrared spectroscopy (VNIRS). The inference model used surface sediment samples collected from 160 Arctic Canada lakes, covering broad latitudinal (60–83°N), longitudinal (71–138°W) and environmental gradients, with a DOC range of 0.6–39.6 mg L⁻¹. The model was applied to Holocene lake sediment cores from Sweden and Canada and our inferences are compared to results from previous multiproxy paleolimnological investigations at these two sites. The inferred Swedish and Canadian DOC profiles are compared, respectively, to inferences from a Swedish-based VNIRS-total organic carbon (TOC) model and a Canadian-based diatom-inferred (Di-DOC) model from the same sediment records. The 5-component Partial Least Squares (PLS) model yields a cross-validated (CV) R_CV² R_{CV}^{2}  = 0.61 and a root mean squared error of prediction (RMSEP _CV ) = 4.4 mg L⁻¹ (11% of DOC gradient). The trends inferred for the two lakes were remarkably similar to the VNIRS-TOC and the Di-DOC inferred profiles and consistent with the other paleolimnological proxies, although absolute values differed. Differences in the calibration set gradients and lack of analogous VNIRS signatures in the modern datasets may explain this discrepancy. Our results corroborate previous geographically independent studies on the potential of using VNIRS to reconstruct past trends in lakewater DOC concentrations rapidly.     

Climate of the past millennium inferred from varved proglacial lake sediments on northeast Baffin Island,Arctic Canada

This study uses ^239+240Pu-dated varved sediments from Big Round Lake, a proglacial lake on northeast Baffin Island, Arctic Canada to generate a 1000-year-long, annual-resolution record of past climate. Varve thickness is positively correlated with July–August–September temperature measured at Clyde River, 70 km to the north of the lake (r = 0.46, p < 0.001). We therefore interpret the variability and trends in varve thickness to partially represent summer temperature. The coolest Little Ice Age temperatures occurred in this record from 1575 to 1760 AD and were approximately 1.5°C cooler than today (average from 1995 to 2005 AD) and 0.2°C cooler than the last millennium (average from 1000 to 2000 AD). Pre-twentieth-century warmth occurred during two intervals, 970–1150 AD and 1375–1575 AD; temperatures were approximately 1.2°C cooler than today, but 0.1°C warmer than the last millennium. The Big Round Lake varve-thickness record contains features similar to that reconstructed elsewhere in the eastern Canadian Arctic. This high-resolution quantitative record expands our understanding of arctic climate during the past millennium.

Diatom-inference models for surface-water temperature and salinity developed from a 57-lake calibration set from the Sierra Nevada, California, USA

Physical, chemical, and biological data were collected from a suite of 57 lakes that span an elevational gradient of 1360 m (2115 to 3475 m a.s.l.) in the eastern Sierra Nevada, California, USA as part of a multiproxy study aimed at developing transfer functions from which to infer past drought events. Multivariate statistical techniques, including canonical correspondence analysis (CCA), were used to determine the main environmental variables influencing diatom distributions in the study lakes. Lakewater depth, surface-water temperature, salinity, total Kjeldahl nitrogen, and total phosphorus were important variables in explaining variance in the diatom distributions. Weighted-averaging (WA) and weighted-averaging partial least squares (WA-PLS) were used to develop diatom-based surface-water temperature and salinity inference models. The two best diatom-inference models for surface-water temperature were developed using simple WA and inverse deshrinking. One model covered a larger surface-water temperature gradient (13.7 °C) and performed slightly poorer (r² = 0.72, RMSE = 1.4 °C, RMSEP_jack = 2.1 °C) than a second model, which covered a smaller gradient (9.5 °C) and performed slightly better (r² = 0.89, RMSE = 0.7 °C, RMSEP_jack = 1.5 °C). The best diatom-inference model for salinity was developed using WA-PLS with three components (r² = 0.96, RMSE = 4.06 mg L^–1, RMSEP_jack = 11.13 mg L^–1). These are presently the only diatom-based inference models for surface-water temperature and salinity developed for the southwestern United States. Application of these models to fossil-diatom assemblages preserved in Sierra Nevada lake sediments offers great potential for reconstructing a high-resolution time-series of Holocene and late Pleistocene climate and drought for California.