Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386885458> ?p ?o ?g. }
- W4386885458 abstract "Branched and isoprenoidal glycerol dialkyl glycerol tetraethers (br- and isoGDGTs) are membrane lipids produced by bacteria and archaea, respectively. These lipids form the basis of several frequently used paleoclimatic proxies. For example, the degree of methylation of brGDGTs (MBT’5Me) preserved in mineral soils (as well as peats and lakes) is one of the most important terrestrial paleothermometers, but features substantial variability that is so far insufficiently constrained. The distribution of isoGDGTs in mineral soils has received less attention and applications have focused on the use of the relative abundance of the isoGDGT crenarchaeol versus brGDGTs (BIT index) as an indicator of aridity. To expand our knowledge of the factors that can impact the br- and isoGDGT distribution in mineral soils, including the MBT’5Me index, and to improve isoGDGT-based precipitation reconstructions, we surveyed the GDGT distribution in a large collection of mineral surface soils (n=229) and soil profiles (n=22) across tropical South America. We find that the MBT’5Me index is significantly higher in grassland compared to forest soils, even among sites with the same mean annual air temperature. This is likely a result of a lack of shading in grasslands, leading to warmer soils. We also find a relationship between MBT’5Me and soil pH in tropical soils. Together with existing data from arid areas in mid-latitudes, we confirm the relationship between the BIT-index and aridity, but also find that the isoGDGT distribution alone is aridity dependent. The combined use of the BIT-index and isoGDGTs can strengthen reconstructions of past precipitation in terrestrial archives. In terms of site-specific variations, we find that the variability in BIT and MBT’5Me is larger at sites that show on average lower BIT and MBT’5Me values. In combination with modelling results, we suggest that this pattern arises from the mathematical formulation of these proxies that amplifies variability for intermediate values and mutes it for values close to saturation (value of 1). Soil profiles show relatively little variation with depth for the brGDGT indices. On the other hand, the isoGDGT distribution changes significantly with depth as does the relative abundance of br- versus isoGDGTs. This pattern is especially pronounced for the isoGDGTIsomerIndex where deeper soil horizons show a near absence of isoGDGT isomers. This might be driven by archaeal community changes in different soil horizons, potentially driven by the difference between aerobic and anaerobic archaeal communities. Finally, we use our extensive new dataset and Bayesian neural networks (BNNs) to establish new brGDGT-based temperature models. We provide a tropical soil calibration that removes the pH dependence of tropical soils (n=404; RMSE=2.0 °C) and global peat and soil models calibrated against the temperature of the months above freezing (n=1740; RMSE=2.4) and mean annual air temperature (n=1740; RMSE=3.6). All models correct for the bias found in arid samples and also adjust the cold bias found in tropical river sediments. We also successfully test the new calibrations on Chinese loess records. Overall, the new calibrations provide improved temperature reconstructions for terrestrial archives." @default.
- W4386885458 created "2023-09-21" @default.
- W4386885458 creator A5001348132 @default.
- W4386885458 creator A5008464911 @default.
- W4386885458 creator A5010657520 @default.
- W4386885458 creator A5011959039 @default.
- W4386885458 creator A5019982427 @default.
- W4386885458 creator A5039548856 @default.
- W4386885458 creator A5055249308 @default.
- W4386885458 creator A5064672057 @default.
- W4386885458 creator A5068585319 @default.
- W4386885458 creator A5081196261 @default.
- W4386885458 date "2023-09-01" @default.
- W4386885458 modified "2023-10-03" @default.
- W4386885458 title "GDGT distribution in tropical soils and its potential as a terrestrial paleothermometer revealed by Bayesian deep-learning models" @default.
- W4386885458 cites W1885905980 @default.
- W4386885458 cites W1964616350 @default.
- W4386885458 cites W1966592018 @default.
- W4386885458 cites W2000470427 @default.
- W4386885458 cites W2002715778 @default.
- W4386885458 cites W2005832186 @default.
- W4386885458 cites W2007628197 @default.
- W4386885458 cites W2017888569 @default.
- W4386885458 cites W2023338126 @default.
- W4386885458 cites W2028837171 @default.
- W4386885458 cites W2032834744 @default.
- W4386885458 cites W2037768795 @default.
- W4386885458 cites W2043882198 @default.
- W4386885458 cites W2044355487 @default.
- W4386885458 cites W2044420278 @default.
- W4386885458 cites W2054508070 @default.
- W4386885458 cites W2057554980 @default.
- W4386885458 cites W2059751070 @default.
- W4386885458 cites W2061374328 @default.
- W4386885458 cites W2069403683 @default.
- W4386885458 cites W2073889844 @default.
- W4386885458 cites W2088514110 @default.
- W4386885458 cites W2094576127 @default.
- W4386885458 cites W2099440591 @default.
- W4386885458 cites W2104208100 @default.
- W4386885458 cites W2113387025 @default.
- W4386885458 cites W2117456204 @default.
- W4386885458 cites W2127785256 @default.
- W4386885458 cites W2131661856 @default.
- W4386885458 cites W2134425065 @default.
- W4386885458 cites W2138755654 @default.
- W4386885458 cites W2147199223 @default.
- W4386885458 cites W2157506923 @default.
- W4386885458 cites W2163048106 @default.
- W4386885458 cites W2163708731 @default.
- W4386885458 cites W2221717039 @default.
- W4386885458 cites W2272473773 @default.
- W4386885458 cites W2278086497 @default.
- W4386885458 cites W2333645461 @default.
- W4386885458 cites W2342633710 @default.
- W4386885458 cites W2409860713 @default.
- W4386885458 cites W2417209869 @default.
- W4386885458 cites W2496833846 @default.
- W4386885458 cites W2520642359 @default.
- W4386885458 cites W2575838082 @default.
- W4386885458 cites W2586326828 @default.
- W4386885458 cites W2618071004 @default.
- W4386885458 cites W2625288750 @default.
- W4386885458 cites W2762034515 @default.
- W4386885458 cites W2773954523 @default.
- W4386885458 cites W2781887754 @default.
- W4386885458 cites W2796147816 @default.
- W4386885458 cites W2797190677 @default.
- W4386885458 cites W2808232822 @default.
- W4386885458 cites W2883269897 @default.
- W4386885458 cites W2887255624 @default.
- W4386885458 cites W2888503013 @default.
- W4386885458 cites W2891920714 @default.
- W4386885458 cites W2895992460 @default.
- W4386885458 cites W2897174293 @default.
- W4386885458 cites W2899349157 @default.
- W4386885458 cites W2905956552 @default.
- W4386885458 cites W2918511501 @default.
- W4386885458 cites W2943295550 @default.
- W4386885458 cites W2946396287 @default.
- W4386885458 cites W2955349119 @default.
- W4386885458 cites W2957165050 @default.
- W4386885458 cites W2957265326 @default.
- W4386885458 cites W2968041531 @default.
- W4386885458 cites W2972140006 @default.
- W4386885458 cites W2979680525 @default.
- W4386885458 cites W2990591399 @default.
- W4386885458 cites W2995235331 @default.
- W4386885458 cites W2995290646 @default.
- W4386885458 cites W3003554433 @default.
- W4386885458 cites W3011735238 @default.
- W4386885458 cites W3017043543 @default.
- W4386885458 cites W3025905002 @default.
- W4386885458 cites W3041963743 @default.
- W4386885458 cites W3042389420 @default.
- W4386885458 cites W3091934181 @default.
- W4386885458 cites W3104895181 @default.
- W4386885458 cites W3106326848 @default.
- W4386885458 cites W3120584402 @default.
- W4386885458 cites W3126431274 @default.