New methods in Palaeopalynology: Classification of pollen through pollen chemistry

Pollen grains are one of the primary tools of palaeoecologists to reconstruct vegetation changes in the past. The description, counting and analysis of pollen grains (palynology) has contributed to our understanding of establishment and dynamics of past and present plant communities. Advances in identification accuracy, precision and increased taxonomic resolution have greatly improved our understanding of biogeography and plant community interactions. Nevertheless, the techniques by which palynological studies are performed have not fundamentally changed. Taxonomic resolution and automation have been identified as some of the key challenges for palynology and palaeoecology. Chemical methods have been proposed as a potential alternative to morphological approaches and have demonstrated promising results in the classification of modern pollen grains and in the analysis of pollen chemical responses to UV-B radiation. The application of chemical methods for palynological needs have not been thoroughly explored, with analysis of (sub-)fossil pollen lagging behind their modern counterpart. Especially the application of infrared methods have gained popularity as an alternative to traditional morphological approaches. In this thesis, I explore the use of infrared methods for palynological applications, by exploring the chemical variation in modern pollen grains and in the analysis of fossil pollen grains with IR microscope approaches. The objectives of this thesis are formulated into three research objectives: * Collect modern pollen and explore the variation in chemical composition * Apply chemical methods to fossil material * Explore microscopy chemical methods on modern pollen The thesis is structured into four studies to study these objectives. Papers I and II explore variation and classification based on the chemical composition of modern *Quercus* pollen using two IR approaches, Fourier transform infrared spectroscopy (FTIR) and Fourier transform Raman spectroscopy (FT-Raman). After exploring modern chemical composition of pollen, paper III investigates FTIR methods for the analysis of fossil pollen, in spectra of Holocene *Pinus* pollen. Additionally, the effects of acetolysis and density separation on *Pinus* pollen is described. Paper IV addresses the challenge of scattering signals when measuring small pollen grains of four *Quercus* species with FTIR microscopy and ways to surpress or weaken the scattering signals. The results from paper I and II show classification success, surpassing traditional morphological approaches, at the *Quercus* section level and ~90% recall on species level with both IR approaches. Chemical bands most useful for classification are lipids, sporopollenin and proteins for both FT-Raman and FTIR. We observe differences in the importance of chemical functional groups for the classification. FT-Raman relies more on sporopollenin chemistry, while FTIR utilizes more variation in lipid bands. After finding considerable variation in sporopollenin chemistry in modern pollen samples, FTIR methods were applied to pollen from sediment cores spanning the Holocene. Paper III examines the differences between modern and sub-fossil pollen and reported large differences between them, mainly the removal of labile components, such as lipids and protein peaks from the sub-fossil spectra during diagenesis. Additionally, paper III finds changes to pollen chemistry caused by acetolysis in the 1200 - 1000 cm^-1^ region of the spectra, when comparing acetolysed spectra to non-acetolysed spectra. The paper concludes with findings of unwanted inorganic signals (BSi) and contamination from density separation media in the sediment pollen spectra. Paper IV demonstrates two successful methods of removing scattering signals from pollen spectra. Two approaches were examined, embedding and processing with signal correction algorithms. Spectra from embedded pollen have no scattering anomalies, but part of the spectra is unusable, because of absorbance of the embedding matrix (paraffin). The signal processing algorithm removes most of the scatter components and allows the scatter components to be extracted. Classification of the different data-sets (spectra without correction, embedded spectra, processed spectra, scatter parameters) reveals that scatter correction methods reduce classification success and that scatter parameters contain taxonomic information. This suggests that scatter corrections may not be the best approach for applications mainly focused on classification or identification, while reconstructions of, for example, UV-B radiation may benefit from scatter correction methods, when measuring single grain spectra. This thesis shows that the performance of IR methods surpasses traditional morphological methods for pollen classification and that a considerable amount of taxonomic information is stored in functional groups associated with sporopollenin (phenylpropanoids). In a study on fossil pollen, this thesis demonstrates that conventional chemical extraction methods, such as acetolysis, alter the chemical composition of pollen and may not be ideal for palaeochemical purposes. Additionally, the scatter correction methods show that IR can provide non-chemical information in the form of scatter parameters, which contain taxonomic information. These results are useful additions to the growing knowledge on chemical methods for palaeoecological and palynological analyses.Doktorgradsavhandlin

Chemical variations in Quercus pollen as a tool for taxonomic identification: Implications for long-term ecological and biogeographical research

Aim Fossil pollen is an important tool for understanding biogeographical patterns in the past, but the taxonomic resolution of the fossil‐pollen record may be limited to genus or even family level. Chemical analysis of pollen grains has the potential to increase the taxonomic resolution of pollen analysis, but present‐day chemical variability is poorly understood. This study aims to investigate whether a phylogenetic signal is present in the chemical variations of Quercus L. pollen and to assess the prospects of chemical techniques for identification in biogeographical research. Location Portugal. Taxon Six taxa (five species, one subspecies) of Quercus L., Q. faginea, Q. robur, Q. robur ssp. estremadurensis, Q. coccifera, Q. rotundifolia and Q. suber belonging to three sections: Cerris, Ilex and Quercus (Denk, Grimm, Manos, Deng, & Hipp, 2017). Methods We collected pollen samples from 297 individual Quercus trees across a 4° (~450 km) latitudinal gradient and determined chemical differences using Fourier‐transform infrared spectroscopy (FTIR). We used canonical powered partial least squares regression (CPPLS) and discriminant analysis to describe within‐ and between‐species chemical variability. Results We find clear differences in the FTIR spectra from Quercus pollen at the section level (Cerris: ~98%; Ilex: ~100%; Quercus: ~97%). Successful discrimination is based on spectral signals related to lipids and sporopollenins. However, discrimination of species within individual Quercus sections is more challenging: overall, species recall is ~76% and species misidentifications within sections lie between 18% and 31% of the test set. Main Conclusions Our results demonstrate that subgenus level differentiation of Quercus pollen is possible using FTIR methods, with successful classification at the section level. This indicates that operator‐independent FTIR approaches can surpass traditional morphological techniques using light microscopy. Our results have implications both for providing new insights into past colonization pathways of Quercus, and likewise for forecasting future responses to climate change. However, before FTIR techniques can be applied more broadly across palaeoecology and biogeography, our results also highlight a number of research challenges that still need to be addressed, including developing sporopollenin‐specific taxonomic discriminators and determining a more complete understanding of the effects of environmental variation on pollen‐chemical signatures in Quercus.publishedVersio

Detection of ice core particles via deep neural networks

Insoluble particles in ice cores record signatures of past climate parameters like vegetation dynamics, volcanic activity, and aridity. For some of them, the analytical detection relies on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge, and often restrict sampling strategies. To help overcome these limitations, we present a framework based on flow imaging microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify seven commonly found classes, namely mineral dust, felsic and mafic (basaltic) volcanic ash grains (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber), and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system's potential and its limitations with respect to the detection of mineral dust, pollen grains, and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non-destructive, fully reproducible, and does not require any sample preparation procedures. The presented framework can bolster research in the field by cutting down processing time, supporting human-operated microscopy, and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented

New methods in Palaeopalynology: Classification of pollen through pollen chemistry

Pollen grains are one of the primary tools of palaeoecologists to reconstruct vegetation changes in the past. The description, counting and analysis of pollen grains (palynology) has contributed to our understanding of establishment and dynamics of past and present plant communities. Advances in identification accuracy, precision and increased taxonomic resolution have greatly improved our understanding of biogeography and plant community interactions. Nevertheless, the techniques by which palynological studies are performed have not fundamentally changed. Taxonomic resolution and automation have been identified as some of the key challenges for palynology and palaeoecology. Chemical methods have been proposed as a potential alternative to morphological approaches and have demonstrated promising results in the classification of modern pollen grains and in the analysis of pollen chemical responses to UV-B radiation. The application of chemical methods for palynological needs have not been thoroughly explored, with analysis of (sub-)fossil pollen lagging behind their modern counterpart. Especially the application of infrared methods have gained popularity as an alternative to traditional morphological approaches. In this thesis, I explore the use of infrared methods for palynological applications, by exploring the chemical variation in modern pollen grains and in the analysis of fossil pollen grains with IR microscope approaches. The objectives of this thesis are formulated into three research objectives: * Collect modern pollen and explore the variation in chemical composition * Apply chemical methods to fossil material * Explore microscopy chemical methods on modern pollen The thesis is structured into four studies to study these objectives. Papers I and II explore variation and classification based on the chemical composition of modern *Quercus* pollen using two IR approaches, Fourier transform infrared spectroscopy (FTIR) and Fourier transform Raman spectroscopy (FT-Raman). After exploring modern chemical composition of pollen, paper III investigates FTIR methods for the analysis of fossil pollen, in spectra of Holocene *Pinus* pollen. Additionally, the effects of acetolysis and density separation on *Pinus* pollen is described. Paper IV addresses the challenge of scattering signals when measuring small pollen grains of four *Quercus* species with FTIR microscopy and ways to surpress or weaken the scattering signals. The results from paper I and II show classification success, surpassing traditional morphological approaches, at the *Quercus* section level and ~90% recall on species level with both IR approaches. Chemical bands most useful for classification are lipids, sporopollenin and proteins for both FT-Raman and FTIR. We observe differences in the importance of chemical functional groups for the classification. FT-Raman relies more on sporopollenin chemistry, while FTIR utilizes more variation in lipid bands. After finding considerable variation in sporopollenin chemistry in modern pollen samples, FTIR methods were applied to pollen from sediment cores spanning the Holocene. Paper III examines the differences between modern and sub-fossil pollen and reported large differences between them, mainly the removal of labile components, such as lipids and protein peaks from the sub-fossil spectra during diagenesis. Additionally, paper III finds changes to pollen chemistry caused by acetolysis in the 1200 - 1000 cm^-1^ region of the spectra, when comparing acetolysed spectra to non-acetolysed spectra. The paper concludes with findings of unwanted inorganic signals (BSi) and contamination from density separation media in the sediment pollen spectra. Paper IV demonstrates two successful methods of removing scattering signals from pollen spectra. Two approaches were examined, embedding and processing with signal correction algorithms. Spectra from embedded pollen have no scattering anomalies, but part of the spectra is unusable, because of absorbance of the embedding matrix (paraffin). The signal processing algorithm removes most of the scatter components and allows the scatter components to be extracted. Classification of the different data-sets (spectra without correction, embedded spectra, processed spectra, scatter parameters) reveals that scatter correction methods reduce classification success and that scatter parameters contain taxonomic information. This suggests that scatter corrections may not be the best approach for applications mainly focused on classification or identification, while reconstructions of, for example, UV-B radiation may benefit from scatter correction methods, when measuring single grain spectra. This thesis shows that the performance of IR methods surpasses traditional morphological methods for pollen classification and that a considerable amount of taxonomic information is stored in functional groups associated with sporopollenin (phenylpropanoids). In a study on fossil pollen, this thesis demonstrates that conventional chemical extraction methods, such as acetolysis, alter the chemical composition of pollen and may not be ideal for palaeochemical purposes. Additionally, the scatter correction methods show that IR can provide non-chemical information in the form of scatter parameters, which contain taxonomic information. These results are useful additions to the growing knowledge on chemical methods for palaeoecological and palynological analyses

Effect of historical land-use on lake-water carbon and geochemistry: : A multi-proxy study of two lake sediment profiles in Dalarna throughout the Holocene

This study examines changes in lake-water total organic carbon (LW-TOC) and lake sediment geochemistry in two lakes, Stångtjärnen and Holtjärnen in (Dalarna, Sweden), during the Holocene and the role of the historic forest grazing and farming (fäbod-system). The aims of the study were to: 1. Discern the effects of natural processes on the lake’s biogeochemistry in different position in the landscape. 2. Identify the effects and differences in intensity of historic land-use on the lakes. A multi-proxy study was conducted encompassing multi-element (15) geochemistry, biogenic silica, LW-TOC, chlorophyll a and published pollen records. The first lake, Stångtjärnen, is shaped and influenced by surrounding mires, which developed shortly after deglaciation and stabilized the LW-TOC at 19 mg L-1 throughout most of the Holocene, while Holtjärnen, a small upland lake, changed from a productive lake (BSi: 35 %), low humic (LW-TOC: 8 mg L-1) to a less productive (BSi: 4 %) more humic lake (LW-TOC: 12 mg L-1) in 7300 BP. The intensification of agricultural land-use (e.g. hay-making) in Stångtjärnen reduced the concentrations of organic associated elements (Br, Cl) and LW-TOC and increased lithogenic elements (K, Ti), while Holtjärnen showed less anthropogenic influence. The comparison between the two lakes displayed the intensive influence of land-use on the Stångtjärnen catchment, showcased by changes in the sediment geochemistry, vegetation composition and the extent of the forest-grazing system in a landscape perspective. In response to the changes of the Holocene, Stångtjärnen’s mires became the main influence, while Holtjärnen was more sensitive to changes.

An automated approach for fringe frequency estimation and removal in infrared spectroscopy and hyperspectral imaging of biological samples

In infrared spectroscopy of thin film samples, interference introduces distortions in spectra, commonly referred to as fringes. Fringes may alter absorbance peak ratios, which hampers the spectral analysis. We have previously introduced extended multiplicative signal correction (EMSC) for fringes correction. In the current article, we provide a robust open-source algorithm for fringe correction in infrared spectroscopy and propose several improvements to the Fringe EMSC model. The suggested algorithm achieves a more precise fringe frequency estimation by mean centering of the measured spectrum and applying a window function prior to the Fourier transform. It selects two frequencies from a user defined number of maxima in the Fourier domain. The improved Fringe EMSC algorithm is validated on two experimental datasets, one of them being a hyperspectral image. Techniques for separating sample spectra from background spectra in hyperspectral images, and techniques to identify spectra affected by fringes are also provided.publishedVersio

Chemical variations in Quercus pollen as a tool for taxonomic identification: Implications for long-term ecological and biogeographical research

Aim Fossil pollen is an important tool for understanding biogeographical patterns in the past, but the taxonomic resolution of the fossil‐pollen record may be limited to genus or even family level. Chemical analysis of pollen grains has the potential to increase the taxonomic resolution of pollen analysis, but present‐day chemical variability is poorly understood. This study aims to investigate whether a phylogenetic signal is present in the chemical variations of Quercus L. pollen and to assess the prospects of chemical techniques for identification in biogeographical research. Location Portugal. Taxon Six taxa (five species, one subspecies) of Quercus L., Q. faginea, Q. robur, Q. robur ssp. estremadurensis, Q. coccifera, Q. rotundifolia and Q. suber belonging to three sections: Cerris, Ilex and Quercus (Denk, Grimm, Manos, Deng, & Hipp, 2017). Methods We collected pollen samples from 297 individual Quercus trees across a 4° (~450 km) latitudinal gradient and determined chemical differences using Fourier‐transform infrared spectroscopy (FTIR). We used canonical powered partial least squares regression (CPPLS) and discriminant analysis to describe within‐ and between‐species chemical variability. Results We find clear differences in the FTIR spectra from Quercus pollen at the section level (Cerris: ~98%; Ilex: ~100%; Quercus: ~97%). Successful discrimination is based on spectral signals related to lipids and sporopollenins. However, discrimination of species within individual Quercus sections is more challenging: overall, species recall is ~76% and species misidentifications within sections lie between 18% and 31% of the test set. Main Conclusions Our results demonstrate that subgenus level differentiation of Quercus pollen is possible using FTIR methods, with successful classification at the section level. This indicates that operator‐independent FTIR approaches can surpass traditional morphological techniques using light microscopy. Our results have implications both for providing new insights into past colonization pathways of Quercus, and likewise for forecasting future responses to climate change. However, before FTIR techniques can be applied more broadly across palaeoecology and biogeography, our results also highlight a number of research challenges that still need to be addressed, including developing sporopollenin‐specific taxonomic discriminators and determining a more complete understanding of the effects of environmental variation on pollen‐chemical signatures in Quercus

Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra

Infrared spectroscopy delivers abundant information about the chemical composition, as well as the structural and optical properties of intact samples in a non-destructive manner. We present a deep convolutional neural network which exploits all of this information and solves full-wave inverse scattering problems and thereby obtains the 3D optical, structural and chemical properties from infrared spectroscopic measurements of intact micro-samples. The proposed model encodes scatter-distorted infrared spectra and infers the distribution of the complex refractive index function of concentrically spherical samples, such as many biological cells. The approach delivers simultaneously the molecular absorption, sample morphology and effective refractive index in both the cell wall and interior from a single measured spectrum. The model is trained on simulated scatter-distorted spectra, where absorption in the distinct layers is simulated and the scatter-distorted spectra are estimated by analytic solutions of Maxwell’s equations for samples of different sizes. This allows for essentially real-time deep learning-enabled infrared diffraction micro-tomography, for a large subset of biological cells.publishedVersio