Identifying plant wax inputs in lake sediments using machine learning

Peaple, Mark D.; Tierney, Jessica E.; McGee, David; Lowenstein, Tim K.; Bhattacharya, Tripti; Feakins, Sarah J.

doi:10.1016/j.orggeochem.2021.104222

Cited by 22 publications

(38 citation statements)

References 62 publications

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“…Specifically, this study provides evidence that submerged aquatic plants are the primary sources of mid‐chain waxes to QPT lake sediments, and thus can be used in paleoclimate studies to reconstruct changes in lake water δ 2 H over time (Gorbey et al., 2021). While we were unable to quantify the proportion of terrestrial and aquatic plant contribution to long‐chain waxes in QPT lake sediments, future studies may be able to achieve this using a machine learning or multi‐source mixing model approach (Peaple et al., 2021; Yang & Bowen, 2022).…”

Section: Discussionmentioning

confidence: 93%

“…n ‐alkanes are less prone to degradation than n ‐alkanoic acids in sediments (Cranwell, 1981), and their distributions are additional tools for assessing the source of plant waxes to lake sediments (Ficken et al., 2000; Gao et al., 2011; Peaple et al., 2021). Due to their resistance to degradation in high‐latitude environments like the QPT catchment, however, terrestrial plant n ‐alkanes may be prone to prolonged storage in soils prior to erosion and deposition in the lake sediment (Drenzek et al., 2007; Kusch et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies found that mixing model approaches using plant wax chain length distributions (Peaple et al., 2021), dual isotopes (Freimuth et al., 2019), and chain length distributions paired with isotopes (Yang & Bowen, 2022) can be useful for reconstructing vegetation communities. These techniques may be best suited to work with multiple compound classes (e.g., n ‐alkanoic acids and n ‐alkanes), and with vegetation groups with distinct chain length distributions (Inglis et al., 2022; Peaple et al., 2021), which may be a limiting factor in applying these techniques to settings like the QPT catchment where multiple clades produce similar chain length distributions. In settings like QPT, including compound‐specific isotope data would be useful to distinguish sources (Yang & Bowen, 2022).…”

Section: Discussionmentioning

confidence: 99%

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Aquatic and Terrestrial Plant Contributions to Sedimentary Plant Waxes in a Modern Arctic Lake Setting

et al. 2022

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The Arctic is the fastest-warming region on Earth, with mean annual temperatures expected to increase 10°C by the end of the century under the most extreme scenarios (IPCC, 2021). Arctic precipitation is also expected to increase as local evaporation from newly ice-free seas and poleward moisture transport increase (Bintanja & Andry, 2017) (Singh et al., 2017). This rapid climate change is exacerbated by positive feedbacks primarily linked to decreasing albedo (Holland & Bitz, 2003). Records of past Arctic climate change, including water cycle change, can provide crucial long-term perspectives to understand how this region will respond to future climate change.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Aquatic and Terrestrial Plant Contributions to Sedimentary Plant Waxes in a Modern Arctic Lake Setting

et al. 2022

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“…This allows δ 13 C to be used to constrain the uncertainty associated with n-alkane chain length distributions, which have previously been used in isolation to reconstruct vegetation (Gao et al, 2011;Jansen et al, 2010;Peaple et al, 2021). The model also addresses a common assumption in the interpretation of lipid δ 13 C data via linear mixing relationships, in which n-alkane production for a selected chain is assumed to be the same among all sources (Bush and Mcinerney, 2013;Garcin et al, 2014).…”

Section: Model Achievementsmentioning

confidence: 99%

Vegetation reconstruction using plant wax n-alkane chain length distribution and δ¹³C of multiple chains: A multi-source mixing model using Bayesian framework

Yang

Bowen

2022

Preprint

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Abstract. Plant wax n-alkane chain length distribution and δ13C have been studied in modern ecosystems as proxies to reconstruct vegetation and climate of the past. Studies on modern plants often report both chain-specific n-alkane concentrations and δ13C values. However, studies on geological archives interpret only one proxy, while both carry crucial information on the mixing sources. We propose a multi-source mixing model in a Bayesian framework that evaluates both proxies simultaneously. The model consists of priors that include user-defined source groups and their associated parametric distributions of n-alkane concentration and δ13C with well characterized uncertainties. The mixing process involves newly defined mixing fractions such as fractional leaf mass contribution (FLMC) that can be used in vegetation reconstruction, and fractional source contribution to a specific n-alkane homologue (FSCn). Markov Chain Monte Carlo is used to generate samples from the posterior distribution conditioned on both proxies. We present two case studies with distinct sets of priors. One involves n-C27, n-C29 and n-C31 alkanes in lake surface sediments of Lake Qinghai, China. The model provides more specific interpretations on the n-alkane input from aquatic sources than the conventional Paq proxy. The other involves n-C29, n-C31 and n-C33 alkanes in lake surface sediments in Cameroon, western Africa. The model produces mixing fractions of forest C3, savanna C3, and C4 plants, offering additional information on the dominant biomes compared to the traditional two-endmember mixing regime. FSCn can be used to assess the interpretation of associated n-alkane δ2H values, and future versions of the model incorporating lipid H isotope systematics could support integration of this proxy with C isotope and chain length distribution data. Despite the achievements, processes associated with n-alkane integration into sedimentary archives have not been incorporated, and the model could be further improved by adding components such as n-alkane turnover and transportation. Future studies on modern plants and catchment systems will be critical to develop calibration datasets that advance the strength and utility of the framework.

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“…The large uncertainties associated with characteristic n-alkane chain length distributions have limited their application in quantitative vegetation reconstruction (Bush and McInerney, 2013). Several models have been developed to reconstruct vegetation composition from n-alkyl lipid chain length distribution (Jansen et al, 2010;Gao et al, 2011;Peaple et al, 2021), but they all require a wide spectrum of chains to be analyzed, which may not be feasible depending on the type of sediment analyzed.…”

Section: Introductionmentioning

confidence: 99%

Integrating plant wax abundance and isotopes for paleo-vegetation and paleoclimate reconstructions: a multi-source mixing model using a Bayesian framework

Yang

Bowen

2022

Clim. Past

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Abstract. Plant wax n-alkane chain length distribution and isotopes have been studied in modern ecosystems as proxies to reconstruct vegetation and climate of the past. However, most paleo-proxies focus on either concentrations or isotopes, whereas both carry complementary information on the mixing sources. We propose a multi-source mixing model in a Bayesian framework that evaluates both chain length distributions and isotopes simultaneously. The model consists of priors that include user-defined source groups and their associated parametric distributions of n-alkane concentration and δ13C. The mixing process involves newly defined mixing fractions such as fractional leaf mass contribution (FLMC) that can be used in vegetation reconstruction. Markov Chain Monte Carlo is used to generate samples from the posterior distribution of these parameters conditioned on both data types. We present three case studies from distinct settings. The first involves n-C27, n-C29, and n-C31 alkanes in lake surface sediments of Lake Qinghai, China. The model provides more specific interpretations on the n-alkane input from aquatic sources than the conventional Paq proxy. The second involves n-C29, n-C31, and n-C33 alkanes in lake surface sediments in Cameroon, western Africa. The model produces mixing fractions of forest C3, savanna C3, and C4 plants, offering additional information on the dominant biomes compared to the traditional two-end-member mixing regime. The third couples the vegetation source model to a hydrogen isotope model component, using biome-specific apparent fractionation factors (εa) to estimate the δ2H of mean annual precipitation. By leveraging chain length distribution, δ13C, and δ2H data of four n-alkane chains, the model produces estimated precipitation δ2H with relatively small uncertainty limits. The new framework shows promise for interpretation of paleo-data but could be further improved by including processes associated with n-alkane turnover in plants, transport, and integration into sedimentary archives. Future studies on modern plants and catchment systems will be critical to develop calibration datasets that advance the strength and utility of the framework.

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Identifying plant wax inputs in lake sediments using machine learning

Cited by 22 publications

References 62 publications

Aquatic and Terrestrial Plant Contributions to Sedimentary Plant Waxes in a Modern Arctic Lake Setting

Aquatic and Terrestrial Plant Contributions to Sedimentary Plant Waxes in a Modern Arctic Lake Setting

Vegetation reconstruction using plant wax n-alkane chain length distribution and δ¹³C of multiple chains: A multi-source mixing model using Bayesian framework

Integrating plant wax abundance and isotopes for paleo-vegetation and paleoclimate reconstructions: a multi-source mixing model using a Bayesian framework

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Identifying plant wax inputs in lake sediments using machine learning

Cited by 22 publications

References 62 publications

Aquatic and Terrestrial Plant Contributions to Sedimentary Plant Waxes in a Modern Arctic Lake Setting

Aquatic and Terrestrial Plant Contributions to Sedimentary Plant Waxes in a Modern Arctic Lake Setting

Vegetation reconstruction using plant wax n-alkane chain length distribution and δ13C of multiple chains: A multi-source mixing model using Bayesian framework

Integrating plant wax abundance and isotopes for paleo-vegetation and paleoclimate reconstructions: a multi-source mixing model using a Bayesian framework

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Vegetation reconstruction using plant wax n-alkane chain length distribution and δ¹³C of multiple chains: A multi-source mixing model using Bayesian framework