Subfossil Chironomidae (Insecta: Diptera) along a latitudinal gradient in Finland: development of a new temperature inference model

Luoto, Tomi P.

doi:10.1002/jqs.1191

Cited by 74 publications

(113 citation statements)

References 24 publications

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“…Of these, mean July air temperature is the most significant variable affecting chironomid distribution. The close relationship between chironomid assemblages and mean air temperature of the warmest month is well documented and has been used to develop chironomid-inferred temperature transfer functions in northern Eurasia (Lotter et al, 1997;Olander et al, 1999;Larocque et al, 2001;Luoto, 2008). The July air temperature gradient (8.8e19.0 C) in the Russian data-set is comparable in length to the Swiss data-set (6.6e17.3 C) (Lotter et al, 1997), but includes warmer temperatures than the Swedish (Larocque et al, 2001) or Norwegian data-sets (7.0e14.7 C and 3.5e16.0 C respectively).…”

Section: Discussionmentioning

confidence: 99%

The distribution and abundance of chironomids in high-latitude Eurasian lakes with respect to temperature and continentality: development and application of new chironomid-based climate-inference models in northern Russia

Self

Brooks

Birks

et al. 2011

Quaternary Science Reviews

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a b s t r a c tThe large landmass of northern Russia has the potential to influence global climate through amplification of climate change. Reconstructing climate in this region over millennial timescales is crucial for understanding the processes that affect the global climate system. Chironomids, preserved in lake sediments, have the potential to produce high resolution, low error, quantitative summer air temperature reconstructions. Canonical correspondence analysis of modern surface sediments from high-latitude lakes, located in northern European Russia and central Siberia, suggests that mean July air temperature is the most significant variable explaining chironomid distribution and abundance. This strong relationship enabled the development of a chironomid-based mean July air temperature-inference model based on 81 lakes and 89 taxa which has a r jack 2 ¼ 0.92 and RMSEP ¼ 0.89 C. Comparison of taxon responses to July temperature between this Russian and existing Norwegian data-sets shows that the temperature optima of individual taxa were between 1 and 3 C higher in the Russian data regardless of modelling technique. Reconstructions based on fossil assemblages from a Russian tundra lake core (VORK5) using a Norwegian chironomid-based inference model provide mean July air temperature estimates that are 1.0e2.7 C colder than from the 81-lake Russian model and are also lower than the instrumental record from a nearby meteorological station. The Norwegian model also did not reconstruct decadal-scale fluctuations in temperature seen in the instrumental record. These observations suggest that chironomid-based inference models should only be applied to sediment cores which have similar climate regimes to the geographic area of the training set. In addition a 149 lake, 120 taxa chironomid-based continentality inference model was also developed from the modern Norwegian and Russian training sets. A 2-component WA-PLS model was the minimal adequate model with r jack 2 ¼ 0.73 and RMSEP ¼ 9.9 using the Gorczynski continentality index. Comparison of reconstructed continentality indices from the tundra lake, VORK5, show close agreement with local instrumental records over the past 70 years and suggest that the model is reliable. Recent warming in the Arctic has been spatially and seasonally heterogeneous; in many areas warming is more pronounced in the spring and autumn leading to a lengthening of the summer, while summer temperatures have remained relatively stable. A continentality inference model has the potential to detect these seasonal changes in climate.

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Section: Discussionmentioning

confidence: 99%

The distribution and abundance of chironomids in high-latitude Eurasian lakes with respect to temperature and continentality: development and application of new chironomid-based climate-inference models in northern Russia

Self

Brooks

Birks

et al. 2011

Quaternary Science Reviews

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“…These strong relationships also appear to persist over the wider geographical area of north-west Eurasia (i.e. Norway and north-west Russia), are well documented and have been used to develop chironomid-inferred temperature transfer functions in northern Eurasia (Olander et al, 1999;Brooks and Birks, 2001;Larocque et al, 2001;Luoto, 2008) as well as in northern North America (Palmer et al, 2002;Walker and Cwynar, 2006).…”

Section: Environmental Parametersmentioning

confidence: 96%

Northern Russian chironomid-based modern summer temperature data set and inference models

Nazarova

Self

Brooks

et al. 2015

Global and Planetary Change

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West and East Siberian data sets and 55 new sites were merged based on the high taxonomic similarity, and the strong relationship between mean July air temperature and the distribution of chironomid taxa in both data sets compared with other environmental parameters. Multivariate statistical analysis of chironomid and environmental data from the combined data set consisting of 268 lakes, located in northern Russia, suggests that mean July air temperature explains the greatest amount of variance in chironomid distribution compared with other measured variables (latitude, longitude, altitude, water depth, lake surface area, pH, conductivity, mean January air temperature, mean July air temperature, and continentality). We established two robust inference models to reconstruct mean summer air temperatures from subfossil chironomids based on ecological and geographical approaches. The North Russian 2-component WA-PLS model (RMSEP Jack = 1.35°C, r Jack 2 = 0.87) can be recommended for application in palaeoclimatic studies in northern Russia. Based on distinctive chironomid fauna and climatic regimes of Kamchatka the Far East 2-component WAPLS model (RMSEP Jack = 1.3°C, r Jack 2 = 0.81) has potentially better applicability in Kamchatka.

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“…Most available chironomid-based transfer functions for temperature are based on weighted averaging (WA) or weighted averaging-partial least squares regression (WA-PLS) (e.g., Walker et al 1991;Lotter et al 1997;Brooks and Birks 2001;Larocque et al 2001;Luoto 2009). To assess the influence of taxonomic resolution on transfer-function performance, we calculated both inference models based on simple WA with inverse deshrinking as well as on WA-PLS, with the number of useful components assessed following Birks (1998), and this approach was applied to each level of taxonomic resolution.…”

Section: Inference Modelsmentioning

confidence: 99%

How does taxonomic resolution affect chironomid-based temperature reconstruction?

Heiri

Lotter

2010

J Paleolimnol

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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.

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Subfossil Chironomidae (Insecta: Diptera) along a latitudinal gradient in Finland: development of a new temperature inference model

Cited by 74 publications

References 24 publications

The distribution and abundance of chironomids in high-latitude Eurasian lakes with respect to temperature and continentality: development and application of new chironomid-based climate-inference models in northern Russia

The distribution and abundance of chironomids in high-latitude Eurasian lakes with respect to temperature and continentality: development and application of new chironomid-based climate-inference models in northern Russia

Northern Russian chironomid-based modern summer temperature data set and inference models

How does taxonomic resolution affect chironomid-based temperature reconstruction?

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