A northwest North American training set: distribution of freshwater midges in relation to air temperature and lake depth

Barley, Erin M.; Walker, Ian R.; Kurek, Joshua; Cwynar, Les C.; Mathewes, Rolf W.; Gajewski, Konrad; Finney, Bruce P.

doi:10.1007/s10933-006-0014-6

Cited by 183 publications

(206 citation statements)

References 56 publications

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“…In our study all samples were analyzed by the same analyst. Increasingly, chironomid-based temperature inference models are applied to downcore records that have not been identified by the same analyst (Heiri and Millet 2005;Larocque-Tobler et al 2009), or regional calibration datasets by various analysts are combined to cover a broader environmental range (Barley et al 2006). At very high taxonomic resolution, the possibility of taxonomic inconsistencies among analysts increases.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

How does taxonomic resolution affect chironomid-based temperature reconstruction?

Heiri

Lotter

2010

J Paleolimnol

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“…To the extent that changes in precipitation regime affect the depth of a lake, and the pH and nutrient supply, these can be used (Medeiros et al, 2015). However, most work has emphasized the reconstruction of temperature, and probably there would need to be very large changes in depth to have a noticeable effect on the chironomid 25 community (e.g., Barley et al, 2006;Fortin et al, 2015).…”

Section: Extracting Hydroclimatic Information From Arctic Lakesmentioning

confidence: 99%

Arctic hydroclimate variability during the last 2000 years – current understanding and research challenges

Linderholm¹,

Nicolle²,

Francus³

et al. 2017

Preprint

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Abstract. Along with Arctic amplification, changes in Arctic hydroclimate have become increasingly apparent. Reanalysis data show increasing trends in Arctic temperature and precipitation over the 20 th century, but changes are not homogenous 30 across seasons or space. The observed hydroclimate changes are expected to continue, and possibly accelerate, in the coming century, not only affecting pan-Arctic natural ecosystems and human activities, but also lower latitudes through changes in atmospheric and oceanic circulation. However, a lack of spatiotemporal observational data makes reliable quantification of Arctic hydroclimate change difficult, especially in a long-term context. To understand hydroclimate variability and the mechanisms driving observed changes, beyond the instrumental record, climate proxies are needed. Here we bring together 35 the current understanding of Arctic hydroclimate during the past 2000 years, as inferred from natural archives and proxies and palaeoclimate model simulations. Inadequate proxy data coverage is apparent, with distinct data gaps in most of Eurasia and parts of North America, which makes robust assessments for the whole Arctic currently impossible. Hydroclimate proxies and climate models indicate that the Medieval Climate Anomaly (MCA) was anomalously wet, while conditions Clim. Past Discuss., doi:10.5194/cp-2017Discuss., doi:10.5194/cp- -34, 2017 Manuscript under review for journal Clim. Past Discussion started: 17 March 2017 c Author(s) 2017. CC-BY 3.0 License. 2 were in general drier during the Little Ice Age (LIA), relative to the last 2000 years. However, it is clear that there are large regional differences, which are especially evident during the LIA. Due to the spatiotemporal differences in Arctic hydroclimate, we recommend detailed regional studies, e.g. including field reconstructions, to disentangle spatial patterns and potential forcing factors. At present, it is only possible to carry out regional syntheses for a few areas of the Arctic, e.g. Fennoscandia, Greenland and western North America. To fully assess pan-Arctic hydroclimate variability for the last two 5 millennia additional proxy records are required.

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“…Quantitative transfer functions were developed using weighted averaging (WA) and weighted averaging partial least squares (WA-PLS) methods (Barley et al, 2006;ter Braak and Looman, 1986;ter Braak and Juggins, 1993). The performance of the models and optimal number of components in the transfer function were assessed using leave-oneout cross validation.…”

Section: Multivariate Statistics For Climate Reconstructionmentioning

confidence: 99%

“…Here, we report on the first high-resolution (20-50 years) mid-to late Holocene pollen records from Lake Teletskoye, obtained from the underwater Sofia Lepneva Ridge in 2006(core Tel 2006 and from the deepest part of the lake in 2001(combined core Tel 2001-2004. The uppermost part of the Tel 2001-2004 core was palynologically examined by Andreev et al (2007), but was never used for quantitative reconstructions.…”

mentioning

confidence: 99%

Quantitative reconstructions of mid- to late holocene climate and vegetation in the north-eastern altai mountains recorded in lake teletskoye

Rudaya

Nazarova

Novenko

et al. 2016

Global and Planetary Change

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A northwest North American training set: distribution of freshwater midges in relation to air temperature and lake depth

Cited by 183 publications

References 56 publications

How does taxonomic resolution affect chironomid-based temperature reconstruction?

How does taxonomic resolution affect chironomid-based temperature reconstruction?

Arctic hydroclimate variability during the last 2000 years – current understanding and research challenges

Quantitative reconstructions of mid- to late holocene climate and vegetation in the north-eastern altai mountains recorded in lake teletskoye

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