
@article{Aerts.1999,
  title = {Plant-{{Mediated Controls}} on {{Nutrient Cycling}} in {{Temperate Fens}} and {{Bogs}}},
  author = {Aerts, R. and Verhoeven, J. T A. and Whigham, D. F.},
  year = {1999},
  month = oct,
  journal = {Ecology},
  volume = {80},
  number = {7},
  pages = {2170--2181},
  issn = {0012-9658},
  doi = {10.1890/0012-9658(1999)080[2170:PMCONC]2.0.CO;2},
  langid = {english}
}

@article{Agren.1996a,
  title = {Quality: {{A Bridge}} between {{Theory}} and {{Experiment}} in {{Soil Organic Matter Studies}}},
  shorttitle = {Quality},
  author = {{\AA}gren, G{\"o}ran I. and Bosatta, Ernesto and Agren, Goran I.},
  year = {1996},
  month = sep,
  journal = {Oikos},
  volume = {76},
  number = {3},
  pages = {522},
  issn = {00301299},
  doi = {10.2307/3546345},
  langid = {english}
}

@manual{Allaire.2020,
  type = {manual},
  title = {rticles: {{Article}} formats for r markdown},
  author = {Allaire, JJ and Xie, Yihui and {R Foundation} and Wickham, Hadley and {Journal of Statistical Software} and Vaidyanathan, Ramnath and {Association for Computing Machinery} and Boettiger, Carl and {Elsevier} and Broman, Karl and Mueller, Kirill and Quast, Bastiaan and Pruim, Randall and Marwick, Ben and Wickham, Charlotte and Keyes, Oliver and Yu, Miao and Emaasit, Daniel and Onkelinx, Thierry and Gasparini, Alessandro and Desautels, Marc-Andre and Leutnant, Dominik and {MDPI} and {Taylor and Francis} and {\"O}{\u g}reden, O{\u g}uzhan and Hance, Dalton and N{\"u}st, Daniel and Uvesten, Petter and Campitelli, Elio and Muschelli, John and Kamvar, Zhian N. and Ross, Noam and Cannoodt, Robrecht and Luguern, Duncan and Kaplan, David M.},
  year = {2020}
}

@article{Artz.2008,
  title = {{{FTIR}} spectroscopy can be used as a screening tool for organic matter quality in regenerating cutover peatlands},
  author = {Artz, Rebekka R.E. and Chapman, Stephen J. and Jean Robertson, A.H. and Potts, Jacqueline M. and {Laggoun-D{\'e}farge}, Fatima and Gogo, S{\'e}bastien and Comont, Laure and Disnar, Jean-Robert and Francez, Andre-Jean},
  year = {2008},
  month = feb,
  journal = {Soil Biology and Biochemistry},
  volume = {40},
  number = {2},
  pages = {515--527},
  issn = {00380717},
  doi = {10.1016/j.soilbio.2007.09.019},
  abstract = {Vegetational changes during the restoration of cutover peatlands leave a legacy in terms of the organic matter quality of the newly formed peat. Current efforts to restore peatlands at a large scale therefore require low cost and high throughput techniques to monitor the evolution of organic matter. In this study, we assessed the merits of using Fourier transform infrared (FTIR) spectra to predict the organic matter composition in peat samples at various stages of peatland regeneration from five European countries. Using predictive partial least squares (PLS) analyses, we were able to reconstruct peat C:N ratio and carbohydrate signatures with reasonable accuracy, but not the micromorphological composition of vegetation remains. Despite utilising different size fractions, both carbohydrate (o200 mm fraction) and FTIR (bulk soil) analyses report on the composition of plant cell wall constituents in the peat and therefore essentially reveal the composition of the parent vegetational material. The accuracy of the FTIR-based PLS models for C:N ratios and carbohydrate signatures was adequate to allow for their use as initial screening tools in the evaluation of the present and future organic matter composition of peat during monitoring of restoration efforts.},
  langid = {english}
}

@article{Bauer.2004,
  title = {Modelling effects of litter quality and environment on peat accumulation over different time-scales: {{{\emph{Peat}}}}{\emph{ accumulation over different time-scales}}},
  shorttitle = {Modelling effects of litter quality and environment on peat accumulation over different time-scales},
  author = {Bauer, Ilka E.},
  year = {2004},
  month = aug,
  journal = {Journal of Ecology},
  volume = {92},
  number = {4},
  pages = {661--674},
  issn = {00220477},
  doi = {10.1111/j.0022-0477.2004.00905.x},
  langid = {english}
}

@article{Baysinger.2022,
  title = {Compositional stability of peat in ecosystem-scale warming mesocosms},
  author = {Baysinger, Mackenzie R. and Wilson, Rachel M. and Hanson, Paul J. and Kostka, Joel E. and Chanton, Jeffrey P.},
  editor = {Hui, Dafeng},
  year = {2022},
  month = mar,
  journal = {PLOS ONE},
  volume = {17},
  number = {3},
  pages = {e0263994},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0263994},
  abstract = {Peatlands historically have acted as a C sink because C-fixation rates exceeded the rate of heterotrophic decomposition. Under future warmer conditions predicted for higher latitudes, however, that balance may shift towards higher rates of heterotrophic respiration leading to the release of previously stored C as CO               2               and CH               4               . The Spruce and Peatlands Response Under Changing Environments (SPRUCE) experiment is designed to test the response of peatlands to climate forcing using a series of warmed enclosures in combination with peat below-ground heating from 0 to +9\textdegree C above ambient conditions. This experimental design allowed a test of chemical changes occurring within peatland soils following five years of warming. We analyzed samples in the uppermost 2m of peat using Fourier Transform Infrared Spectroscopy (FT-IR) to quantify the relative abundance of carbohydrate and aromatic compounds in the peat. The peat soils were subjected to deep peat heating (DPH) beginning in June of 2014 followed by whole ecosystem warming (WEW) in August of 2015. We found that the relative amounts of labile and recalcitrant chemical compound groups across the full peat depth interval did not significantly change after five years of exposure to warming. This appears the case even though previous studies have shown that net C losses and loss of bulk peat mass to be instability over that time period. Results suggest that the current store of carbon in peatlands are largely compositionally stable leading to no changes the in the ratio of chemical moieties on the initial four-year timescale of this experiment.},
  langid = {english}
}

@article{Bengtsson.2018,
  title = {Biochemical determinants of litter quality in 15 species of {{Sphagnum}}},
  author = {Bengtsson, Fia and Rydin, H{\aa}kan and H{\'a}jek, Tom{\'a}{\v s}},
  year = {2018},
  month = apr,
  journal = {Plant and Soil},
  volume = {425},
  number = {1-2},
  pages = {161--176},
  issn = {0032-079X, 1573-5036},
  doi = {10.1007/s11104-018-3579-8},
  abstract = {Background and aims Sphagnum mosses are ecosystem engineers that create and maintain boreal peatlands. With unique biochemistry, waterlogging and acidifying capacities, they build up meters-thick layers of peat, reducing competition and impeding decomposition. We quantify within-genus differences in biochemical composition to make inferences about decay rates, related to hummock\textendash hollow and fen\textendash bog gradients and to phylogeny.},
  langid = {english}
}

@article{Benner.1984,
  title = {Anaerobic {{Biodegradation}} of the {{Lignin}} and {{Polysaccharide Components}} of {{Lignocellulose}} and {{Synthetic Lignin}} by {{Sediment Microflorat}}},
  author = {Benner, Ronald and Maccubbin, A E},
  year = {1984},
  journal = {Appl. Environ. Microbiol.},
  volume = {47},
  pages = {8},
  langid = {english}
}

@article{Biester.2014,
  title = {Comparison of different methods to determine the degree of peat decomposition in peat bogs},
  author = {Biester, H. and Knorr, K.-H. and Schellekens, J. and Basler, A. and Hermanns, Y.-M.},
  year = {2014},
  month = may,
  journal = {Biogeosciences},
  volume = {11},
  number = {10},
  pages = {2691--2707},
  issn = {1726-4189},
  doi = {10.5194/bg-11-2691-2014},
  abstract = {Abstract. Peat humification or decomposition is a frequently used proxy to extract past time changes in hydrology and climate from peat bogs. During the past century several methods to determine changes in peat decomposition have been introduced. Most of these methods are operationally defined only and the chemical changes underlying the decomposition process are often poorly understood and lack validation. Owing to the chemically undefined nature of many humification analyses the comparison of results obtained by different methods is difficult. In this study we compared changes in peat decomposition proxies in cores of two peat bogs (K\"onigsmoor, KK; Kleines Rotes Bruch, KRB) from the Harz Mountains (Germany) using C / N ratios, Fourier transform infrared spectra absorption (FTIR) intensities, Rock Eva\textregistered{} oxygen and hydrogen indices, {$\delta$}13C and {$\delta$}15N isotopic signatures and UV-absorption (UV-ABS) of NaOH peat extracts. In order to explain parallels and discrepancies between these methods, one of the cores was additionally analysed by pyrolysis gas chromatography mass spectrometry (pyrolysis-GC-MS). Pyrolysis-GC-MS data provide detailed information on a molecular level, which allows differentiation of both changes attributed to decomposition processes and changes in vegetation. Principal component analysis was used to identify and separate the effects of changes in vegetation pattern and decomposition processes because both may occur simultaneously upon changes in bog hydrology. Records of decomposition proxies show similar historical development at both sites, indicating external forcing such as climate as controlling the process. All decomposition proxies except UV-ABS and {$\delta$}15N isotopes show similar patterns in their records and reflect to different extents signals of decomposition. The molecular composition of the KK core reveals that these changes are mainly attributed to decomposition processes and to a lesser extent to changes in vegetation. Changes in the molecular composition indicate that peat decomposition in the KK bog is mainly characterized by preferential decomposition of phenols and polysaccharides and relative enrichment of aliphatics during drier periods. Enrichment of lignin and other aromatics during decomposition was also observed but showed less variation than polysaccharides or aliphatics, and presumably reflects changes in vegetation associated with changes in hydrology of the bogs. Significant correlations with polysaccharide and aliphatic pyrolysis products were found for C / N ratios, FTIR-band intensities and for hydrogen index values, supporting that these decomposition indices provide reasonable information. Correlations of polysaccharide and aliphatic pyrolysis products with oxygen index values and {$\delta$}13C was weaker, assumingly indicating carboxylation of the peat during drier periods and enrichment of isotopically lighter peat components during decomposition, respectively. FTIR, C / N ratio, pyrolysis-GC-MS analyses and Rock Eval hydrogen indices appear to reflect mass loss and related changes in the molecular peat composition during mineralization best. Pyrolysis-GC-MS allows disentangling the decomposition processes and vegetation changes. UV-ABS measurements of alkaline peat extracts show only weak correlation with other decomposition proxies and pyrolysis results as they mainly reflect the formation of humic acids through humification and to a lesser extent mass loss during mineralization.},
  langid = {english}
}

@article{Bockheim.2007,
  title = {Importance of {{Cryoturbation}} in {{Redistributing Organic Carbon}} in {{Permafrost-Affected Soils}}},
  author = {Bockheim, J. G.},
  year = {2007},
  month = jul,
  journal = {Soil Science Society of America Journal},
  volume = {71},
  number = {4},
  pages = {1335--1342},
  issn = {03615995},
  doi = {10.2136/sssaj2006.0414N},
  langid = {english}
}

@article{Broder.2012,
  title = {Peat decomposition records in three pristine ombrotrophic bogs in southern {{Patagonia}}},
  author = {Broder, T. and Blodau, C. and Biester, H. and Knorr, K. H.},
  year = {2012},
  month = apr,
  journal = {Biogeosciences},
  volume = {9},
  number = {4},
  pages = {1479--1491},
  issn = {1726-4189},
  doi = {10.5194/bg-9-1479-2012},
  abstract = {Abstract. Ombrotrophic bogs in southern Patagonia have been examined with regard to paleoclimatic and geochemical research questions but knowledge about organic matter decomposition in these bogs is limited. Therefore, we examined peat humification with depth by Fourier Transformed Infrared (FTIR) measurements of solid peat, C/N ratio, and {$\delta$}13C and {$\delta$}15N isotope measurements in three bog sites. Peat decomposition generally increased with depth but distinct small scale variation occurred, reflecting fluctuations in factors controlling decomposition. C/N ratios varied mostly between 40 and 120 and were significantly correlated (R2 {$>$} 0.55, p {$<$} 0.01) with FTIR-derived humification indices. The degree of decomposition was lowest at a site presently dominated by Sphagnum mosses. The peat was most strongly decomposed at the driest site, where currently peat-forming vegetation produced less refractory organic material, possibly due to fertilizing effects of high sea spray deposition. Decomposition of peat was also advanced near ash layers, suggesting a stimulation of decomposition by ash deposition. Values of {$\delta$}13C were 26.5 {$\pm$} 2\textperthousand{} in the peat and partly related to decomposition indices, while {$\delta$}15N in the peat varied around zero and did not consistently relate to any decomposition index. Concentrations of DOM partly related to C/N ratios, partly to FTIR derived indices. They were not conclusively linked to the decomposition degree of the peat. DOM was enriched in 13C and in 15N relative to the solid phase probably due to multiple microbial modifications and recycling of N in these N-poor environments. In summary, the depth profiles of C/N ratios, {$\delta$}13C values, and FTIR spectra seemed to reflect changes in environmental conditions affecting decomposition, such as bog wetness, but were dominated by site specific factors, and are further influenced by ash deposition and possibly by sea spray input.},
  langid = {english}
}

@article{Burkner.2017,
  title = {brms: {{An R Package}} for {{Bayesian Multilevel Models Using Stan}}},
  shorttitle = {{\textbf{brms}}},
  author = {B{\"u}rkner, Paul-Christian},
  year = {2017},
  journal = {Journal of Statistical Software},
  volume = {80},
  number = {1},
  issn = {1548-7660},
  doi = {10.18637/jss.v080.i01},
  abstract = {The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan. A wide range of distributions and link functions are supported, allowing users to fit \textendash{} among others \textendash{} linear, robust linear, binomial, Poisson, survival, ordinal, zero-inflated, hurdle, and even non-linear models all in a multilevel context. Further modeling options include autocorrelation of the response variable, user defined covariance structures, censored data, as well as meta-analytic standard errors. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. In addition, model fit can easily be assessed and compared with the Watanabe-Akaike information criterion and leave-one-out cross-validation.},
  langid = {english}
}

@article{Burkner.2018,
  title = {Advanced {{Bayesian Multilevel Modeling}} with the {{R Package}} brms},
  author = {B{\"u}rkner, Paul-Christian},
  year = {2018},
  journal = {The R Journal},
  volume = {10},
  number = {1},
  pages = {395},
  issn = {2073-4859},
  doi = {10.32614/RJ-2018-017},
  abstract = {The brms package allows R users to easily specify a wide range of Bayesian single-level and multilevel models which are fit with the probabilistic programming language Stan behind the scenes. Several response distributions are supported, of which all parameters (e.g., location, scale, and shape) can be predicted. Non-linear relationships may be specified using non-linear predictor terms or semi-parametric approaches such as splines or Gaussian processes. Multivariate models can be fit as well. To make all of these modeling options possible in a multilevel framework, brms provides an intuitive and powerful formula syntax, which extends the well known formula syntax of lme4. The purpose of the present paper is to introduce this syntax in detail and to demonstrate its usefulness with four examples, each showing relevant aspects of the syntax.},
  langid = {english}
}

@article{Chen.2016,
  title = {Cellulose (dissolving pulp) manufacturing processes and properties: {{A}} mini-review},
  shorttitle = {Cellulose (dissolving pulp) manufacturing processes and properties},
  author = {Chen, Chunxia and Duan, Chao and Li, Jianguo and Liu, Yishan and Ma, Xiaojuan and Zheng, Linqiang and Stavik, Jaroslav and Ni, Yonghao},
  year = {2016},
  month = mar,
  journal = {BioResources},
  volume = {11},
  number = {2},
  pages = {5553--5564},
  issn = {19302126, 19302126},
  doi = {10.15376/biores.11.2.Chen},
  abstract = {The increasing consumption of regenerated cellulose, in particular the viscose fiber, has led to a significant development of dissolving pulps in the last decade. In this review paper, the current status of dissolving pulp with respects to raw materials, manufacturing processes, and some key issues are discussed. Non-wood materials and the process concept of upgrading paper-grade pulp into dissolving pulp are also included. Some recent developments related to the analytical methods of the purity and molecular weight distribution based on the ion chromatography and gel permeation chromatography are discussed. Finally, further processing improvements of purification, such as mechanical, chemical, and enzymatic treatment, and their combinations during the manufacturing process of dissolving pulp, are included.},
  langid = {english}
}

@article{Cocozza.2003,
  title = {Characterization of solid and aqueous phases of a peat bog profile using molecular fluorescence spectroscopy, {{ESR}} and {{FT-IR}}, and comparison with physical properties},
  author = {Cocozza, C and D'Orazio, V and Miano, T M and Shotyk, W},
  year = {2003},
  journal = {Organic Geochemistry},
  pages = {12},
  abstract = {A peat core (10\^A10\^A100 cm) was removed from Etang de la Grue` re, an ombrotrophic peat bog in the Jura Mountains of Switzerland. The core was sliced into 3 cm sections and the samples characterized with respect to ash content, bulk density, moisture content, and concentrations of C and H. Solid peat residues were freeze-dried, milled very finely and analyzed using Fourier transform infrared (FT-IR) and electron spin resonance (ESR) spectroscopy. Pore waters expressed from the peat slices were collected, filtered (0.2 mm membrane filters) and analyzed using fluorescence emission, excitation and synchronous-scan spectroscopy, in addition to pH. Taken together, the physical properties of the core and the ESR measurements of solid peats appear to record the variation in humification of refractory organic matter (peat). The fluorescence and FT-IR data, on the other hand, appear to characterize the extent of recent decomposition of labile organic matter. In fact, these data indicate the presence of two distinct zones in the bog: (i) the acrotelm, or zone of active decay, which may be oxygenated, depending on the season and the depth to the water table, and (ii) the catotelm which is always anoxic, and where organic matter decomposition rates are negligible.},
  langid = {english}
}

@article{Cong.2020,
  title = {Stability of the permafrost peatlands carbon pool under climate change and wildfires during the last 150 years in the northern {{Great Khingan Mountains}}, {{China}}},
  author = {Cong, Jinxin and Gao, Chuanyu and Han, Dongxue and Li, Yunhui and Wang, Guoping},
  year = {2020},
  month = apr,
  journal = {Science of The Total Environment},
  volume = {712},
  pages = {136476},
  issn = {00489697},
  doi = {10.1016/j.scitotenv.2019.136476},
  abstract = {Peatlands store one-third of the total global soil carbon (C.) despite covering only 3\textendash 4\% of the global land surface. Most peatlands are distributed in mid-high latitude regions and are even in permafrost regions, are sensitive to climate change and are disturbed by wildfire. Although several studies have focused on the impact of historical climate change and regional human activities on the C. accumulation process in these peatlands, the impact of these factors on the stability of the C. pool remains poorly understood. Here, based on the 210Pb age-depth model, we investigated the historical variations of C. stability during the last 150 years for five typical peatlands in the northern Great Khingan Mountains (Northeast China), an area located in a permafrost region that is sensitive to climate change and to wildfires, which have clearly increased due to regional human activities. The results showed that low C. accumulation rates (CARs) and weakly C. stability in studied peatlands before 1900. While, the increasing anthropogenic wildfire frequency and the residual products (e.g. pyrogenic carbon) increased the CARs and C. stability in peatlands from 1900 to 1980. The mean July temperature is the most important climate factor for peatlands C. stability. After 1980, due to the low wildfire frequencies influenced by human policies, increasing temperatures and decreasing precipitation not only increased the CARs but also markedly increased the C. stability of the peatlands C. pool in the northern Great Khingan Mountains, especially after 2000. \textcopyright{} 2020 Elsevier B.V. All rights reserved.},
  langid = {english}
}

@article{Cong.2022,
  title = {Historical chemical stability of carbon pool in permafrost peatlands in northern {{Great Khingan Mountains}} ({{China}}) during the last millennium, and its paleoenvironmental implications},
  author = {Cong, Jinxin and Gao, Chuanyu and Xing, Wei and Han, Dongxue and Li, Yunhui and Wang, Guoping},
  year = {2022},
  month = feb,
  journal = {CATENA},
  volume = {209},
  pages = {105853},
  issn = {03418162},
  doi = {10.1016/j.catena.2021.105853},
  langid = {english}
}

@article{Cowling.1966,
  title = {Nitrogen in {{Wood}} and its {{Role}} in {{Wood Deterioration}}},
  author = {Cowling, Ellis B. and Merrill, William},
  year = {1966},
  month = nov,
  journal = {Canadian Journal of Botany},
  volume = {44},
  number = {11},
  pages = {1539--1554},
  issn = {0008-4026},
  doi = {10.1139/b66-167},
  abstract = {Based on present knowledge of the origin, amounts, chemical form, a n d distribution of nitrogen ( N ) in wood, hypotheses are proposed t o explain radial gradients in N content that exist across the xylem cylinder of tree stems: (1) N in the cytoplasn\textasciitilde of developing wood cells is diluted by apposition of cell wall substances; (2) after maturation of wood fiber cells, N in their cytoplasm is removed by elution into the transpiration stream; (3) death of xylem parenchyma cells during aging of sapwood and forination of heartwood is accompanied by reinoval of much of the X in their cytoplasm. Hypotheses 2 and 3 above suggest strongly that trees possess a n internal recycling mechanism for conservation a n d reuse of the N in the cytoplasm of xylary cells.},
  langid = {english}
}

@article{DelaCruz.2016,
  title = {Determination of {{Sources}} of {{Organic Matter}} in {{Solid Waste}} by {{Analysis}} of {{Phenolic Copper Oxide Oxidation Products}} of {{Lignin}}},
  author = {{De la Cruz}, Florentino B. and Osborne, Jason and Barlaz, Morton A.},
  year = {2016},
  month = feb,
  journal = {Journal of Environmental Engineering},
  volume = {142},
  number = {2},
  pages = {04015076},
  issn = {0733-9372, 1943-7870},
  doi = {10.1061/(ASCE)EE.1943-7870.0001038},
  abstract = {The decomposition behavior of lignocellulosic materials is related to the taxonomic classes of the parent plant tissues. Thus, methods to characterize the composition of mixtures of lignocellulosic materials such as municipal solid waste (MSW) according to different taxonomic classes of plants are useful. The copper oxide (CuO) oxidation products of lignin provide molecular markers that allow for characterization of the initial plants. The objective of this study was to determine if the chemical composition of the monomers present after CuO oxidation can be used to infer information on the content of woody angiosperms (hardwood\textemdash HW), non-woody angiosperms (leaves and grasses\textemdash LG), woody gymnosperms (softwood\textemdash SW), and non-woody gymnosperms (needles\textemdash GN). Results showed that the composition of HW and SW in synthetic mixtures can be estimated from CuO oxidation products of lignin within an error of \AE 60\% of the expected values. The high uncertainty in the estimate of composition in synthetic mixtures can be attributed to the fact that the components selected for the synthetic mixtures had different CuO oxidation products compared to the average which was used in developing the mixing model. Application of the mixing model to estimate the composition of samples from a Kentucky (KY) landfill showed that the majority of the samples are from HW and SW with little to no non-woody tissues (LG, GN). However, the mixing model is not sufficiently precise to infer more detailed information on MSW composition. DOI: 10.1061/(ASCE)EE.1943-7870.0001038. \textcopyright{} 2015 American Society of Civil Engineers.},
  langid = {english}
}

@article{Eglin.2010,
  title = {Historical and future perspectives of global soil carbon response to climate and land-use changes},
  author = {Eglin, T. and Ciais, P. and Piao, S.L. and Barre, P. and Bellassen, V. and Cadule, P. and Chenu, C. and Gasser, T. and Koven, C. and Reichstein, M. and Smith, P.},
  year = {2010},
  month = jan,
  journal = {Tellus B: Chemical and Physical Meteorology},
  volume = {62},
  number = {5},
  pages = {700--718},
  issn = {1600-0889},
  doi = {10.1111/j.1600-0889.2010.00499.x},
  abstract = {In this paper, we attempt to analyse the respective influences of land-use and climate changes on the global and regional balances of soil organic carbon (SOC) stocks. Two time periods are analysed: the historical period 1901\textendash 2000 and the period 2000\textendash 2100. The historical period is analysed using a synthesis of published data as well as new global and regional model simulations, and the future is analysed using models only. Historical land cover changes have resulted globally in SOC release into the atmosphere. This human induced SOC decrease was nearly balanced by the net SOC increase due to higher CO2 and rainfall. Mechanization of agriculture after the 1950s has accelerated SOC losses in croplands, whereas development of carbon-sequestering practices over the past decades may have limited SOC loss from arable soils. In some regions (Europe, China and USA), croplands are currently estimated to be either a small C sink or a small source, but not a large source of CO2 to the atmosphere.},
  langid = {english}
}

@article{Elle.2019,
  title = {Fine root lignin content is well predictable with near-infrared spectroscopy},
  author = {Elle, Oliver and Richter, Ronny and Vohland, Michael and Weigelt, Alexandra},
  year = {2019},
  month = dec,
  journal = {Scientific Reports},
  volume = {9},
  number = {1},
  pages = {6396},
  issn = {2045-2322},
  doi = {10.1038/s41598-019-42837-z},
  langid = {english}
}

@article{Fenner.2011,
  title = {Drought-induced carbon loss in peatlands},
  author = {Fenner, Nathalie and Freeman, Chris},
  year = {2011},
  month = dec,
  journal = {Nature Geoscience},
  volume = {4},
  number = {12},
  pages = {895--900},
  issn = {1752-0894, 1752-0908},
  doi = {10.1038/ngeo1323},
  langid = {english}
}

@article{Ferragina.2015,
  title = {Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data},
  author = {Ferragina, A. and {de los Campos}, G. and Vazquez, A.I. and Cecchinato, A. and Bittante, G.},
  year = {2015},
  month = nov,
  journal = {Journal of Dairy Science},
  volume = {98},
  number = {11},
  pages = {8133--8151},
  issn = {00220302},
  doi = {10.3168/jds.2014-9143},
  abstract = {The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict ``difficult-to-predict'' dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fouriertransform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm-1 were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/ BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure.},
  langid = {english}
}

@misc{Gabry.2020,
  title = {bayesplot: {{Plotting}} for {{Bayesian Models}}},
  author = {Gabry, J and Mahr, T},
  year = {2020}
}

@article{Gelman.2020,
  title = {Bayesian {{Workflow}}},
  author = {Gelman, Andrew and Vehtari, Aki and Simpson, Daniel and Margossian, Charles C. and Carpenter, Bob and Yao, Yuling and Kennedy, Lauren and Gabry, Jonah and B{\"u}rkner, Paul-Christian and Modr{\'a}k, Martin},
  year = {2020},
  month = nov,
  journal = {arXiv:2011.01808 [stat]},
  eprint = {2011.01808},
  eprinttype = {arxiv},
  primaryclass = {stat},
  abstract = {The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. Using Bayesian inference to solve real-world problems requires not only statistical skills, subject matter knowledge, and programming, but also awareness of the decisions made in the process of data analysis. All of these aspects can be understood as part of a tangled workflow of applied Bayesian statistics. Beyond inference, the workflow also includes iterative model building, model checking, validation and troubleshooting of computational problems, model understanding, and model comparison. We review all these aspects of workflow in the context of several examples, keeping in mind that in practice we will be fitting many models for any given problem, even if only a subset of them will ultimately be relevant for our conclusions.},
  archiveprefix = {arXiv},
  langid = {english}
}

@misc{Goodrich.2020,
  title = {rstanarm: {{Bayesian}} applied regression modeling via {{Stan}}},
  author = {Goodrich, Ben and Gabry, Jonah and Ali, I. and Brilleman, S.},
  year = {2020}
}

@article{Harris.2020,
  title = {Limited effect of drainage on peat properties, porewater chemistry, and peat decomposition proxies in a boreal peatland},
  author = {Harris, Lorna I. and Moore, Tim R. and Roulet, Nigel T. and Pinsonneault, Andrew J.},
  year = {2020},
  month = nov,
  journal = {Biogeochemistry},
  volume = {151},
  number = {1},
  pages = {43--62},
  issn = {0168-2563, 1573-515X},
  doi = {10.1007/s10533-020-00707-1},
  langid = {english}
}

@article{Hayes.2012,
  title = {Development of near infrared spectroscopy models for the quantitative prediction of the lignocellulosic components of wet {{Miscanthus}} samples},
  author = {Hayes, Daniel J.M.},
  year = {2012},
  month = sep,
  journal = {Bioresource Technology},
  volume = {119},
  pages = {393--405},
  issn = {09608524},
  doi = {10.1016/j.biortech.2012.05.137},
  abstract = {Miscanthus samples were scanned over the visible and near infrared wavelengths at several stages of processing (wet-chopped, air-dried, dried and ground, and dried and sieved). Models were developed to predict lignocellulosic and elemental constituents based on these spectra. The dry and sieved scans gave the most accurate models; however the wet-chopped models for glucose, xylose, and Klason lignin provided excellent accuracies with root mean square error of predictions of 1.27\%, 0.54\%, and 0.93\%, respectively. These models can be suitable for most applications. The wet models for arabinose, Klason lignin, acid soluble lignin, ash, extractives, rhamnose, acid insoluble residue, and nitrogen tended to have lower R2 values (0.80+) for the validation sets and the wet models for galactose, mannose, and acid insoluble ash were less accurate, only having value for rough sample screening. This research shows the potential for online analysis at biorefineries for the major lignocellulosic constituents of interest.},
  langid = {english}
}

@article{Hayes.2015,
  title = {Analysis of the lignocellulosic components of peat samples with development of near infrared spectroscopy models for rapid quantitative predictions},
  author = {Hayes, D.J.M. and Hayes, M.H.B. and Leahy, J.J.},
  year = {2015},
  month = jun,
  journal = {Fuel},
  volume = {150},
  pages = {261--268},
  issn = {00162361},
  doi = {10.1016/j.fuel.2015.01.094},
  abstract = {Analytical data and quantitative near infrared (NIR) spectroscopy models for various lignocellulosic components (including Klason lignin and the constituent sugars glucose, xylose, mannose, arabinose, galactose, and rhamnose), moisture, and ash were obtained for 53 peat samples. These included samples with high, medium, and low degrees of humification. Klason lignin was the main constituent and was greatest in the samples classified as being highly humified, with structural sugars the lowest in this class. The total sugars contents of all samples were considered to be insufficient to allow for their use in biorefining hydrolysis processes for the production of chemicals and biofuels. NIR models were developed for spectral datasets obtained from the samples in their unprocessed (wet), dry and unground, and dry and ground states. Typically the most accurate models were based on the spectra of dry and ground samples. However the NIR models for the wet samples still offered reasonable predictive capabilities. All models were suitable at least for sample screening, with the models for total sugars, glucose, xylose, galactose, and moisture suitable for quantitative analyses.},
  langid = {english}
}

@article{Hodgkins.2018,
  title = {Tropical peatland carbon storage linked to global latitudinal trends in peat recalcitrance},
  author = {Hodgkins, Suzanne B. and Richardson, Curtis J. and Dommain, Ren{\'e} and Wang, Hongjun and Glaser, Paul H. and Verbeke, Brittany and Winkler, B. Rose and Cobb, Alexander R. and Rich, Virginia I. and Missilmani, Malak and Flanagan, Neal and Ho, Mengchi and Hoyt, Alison M. and Harvey, Charles F. and Vining, S. Rose and Hough, Moira A. and Moore, Tim R. and Richard, Pierre J. H. and De La Cruz, Florentino B. and Toufaily, Joumana and Hamdan, Rasha and Cooper, William T. and Chanton, Jeffrey P.},
  year = {2018},
  month = dec,
  journal = {Nature Communications},
  volume = {9},
  number = {1},
  pages = {3640},
  issn = {2041-1723},
  doi = {10.1038/s41467-018-06050-2},
  abstract = {Peatlands represent large terrestrial carbon banks. Given that most peat accumulates in boreal regions, where low temperatures and water saturation preserve organic matter, the existence of peat in (sub)tropical regions remains enigmatic. Here we examined peat and plant chemistry across a latitudinal transect from the Arctic to the tropics. Near-surface low-latitude peat has lower carbohydrate and greater aromatic content than near-surface high-latitude peat, creating a reduced oxidation state and resulting recalcitrance. This recalcitrance allows peat to persist in the (sub)tropics despite warm temperatures. Because we observed similar declines in carbohydrate content with depth in high-latitude peat, our data explain recent field-scale deep peat warming experiments in which catotelm (deeper) peat remained stable despite temperature increases up to 9\,\textdegree C. We suggest that high-latitude deep peat reservoirs may be stabilized in the face of climate change by their ultimately lower carbohydrate and higher aromatic composition, similar to tropical peats.},
  langid = {english}
}

@article{Huang.2010,
  title = {The {{Rapid Estimation}} of {{Cellulose}}, {{Hemicellulose}}, and {{Lignin Contents}} in {{Rice Straw}} by {{Near Infrared Spectroscopy}}},
  author = {Huang, C. and Han, L. and Liu, X. and Ma, L.},
  year = {2010},
  month = nov,
  journal = {Energy Sources, Part A: Recovery, Utilization, and Environmental Effects},
  volume = {33},
  number = {2},
  pages = {114--120},
  issn = {1556-7036, 1556-7230},
  doi = {10.1080/15567030902937127},
  abstract = {Rice straw can be used for biochemical conversion for energy production. The use of near infrared spectroscopy was explored to estimate the contents of cellulose, hemicellulose, and lignin in this study. A total of 172 samples from 17 provinces of China were collected and scanned using NIRSystem 6500 spectrophotometer. Different spectra pretreatments and regression methods were adopted to optimize the models. When evaluated on independent validations, the standard errors of validation and determination coefficient of validation were 1.49\%, 1.59\%, 0.65\% and 0.82, 0.71, 0.78, respectively. It was concluded that near infrared spectroscopy can be used to roughly analyze the contents of cellulose and lignin, and can only distinguish high contents and low contents of hemicellulose in rice straw.},
  langid = {english}
}

@article{Karlstrom.1994,
  title = {Spectroscopic {{Peat Classification}} and {{Calibration Using Electron Spin Resonance}} and {{Multivariate Data Analysis}}},
  shorttitle = {Spectroscopic {{Peat Classification}} and {{Calibration Using Electron Resonance}} and {{Multivariate Dtaa Analysis}}},
  author = {Karlstr{\"o}m, Hans and Nord{\'e}n, B O and Wikander, G{\"o}ran},
  year = {1994},
  month = may,
  journal = {Soil Science},
  volume = {157},
  number = {5},
  pages = {300--311},
  issn = {0038-075X},
  doi = {10.1097/00010694-199405000-00004},
  langid = {english}
}

@article{Koven.2009,
  title = {On the formation of high-latitude soil carbon stocks: {{Effects}} of cryoturbation and insulation by organic matter in a land surface model},
  shorttitle = {On the formation of high-latitude soil carbon stocks},
  author = {Koven, C. and Friedlingstein, P. and Ciais, P. and Khvorostyanov, D. and Krinner, G. and Tarnocai, C.},
  year = {2009},
  month = nov,
  journal = {Geophysical Research Letters},
  volume = {36},
  number = {21},
  pages = {L21501},
  issn = {0094-8276},
  doi = {10.1029/2009GL040150},
  langid = {english}
}

@article{Kubo.2005,
  title = {Hydrogen {{Bonding}} in {{Lignin}}: {{A Fourier Transform Infrared Model Compound Study}}},
  shorttitle = {Hydrogen {{Bonding}} in {{Lignin}}},
  author = {Kubo, Satoshi and Kadla, John F.},
  year = {2005},
  month = sep,
  journal = {Biomacromolecules},
  volume = {6},
  number = {5},
  pages = {2815--2821},
  issn = {1525-7797, 1526-4602},
  doi = {10.1021/bm050288q},
  langid = {english}
}

@article{Lehmann.2015,
  title = {The contentious nature of soil organic matter},
  author = {Lehmann, Johannes and Kleber, Markus},
  year = {2015},
  month = dec,
  journal = {Nature},
  volume = {528},
  number = {7580},
  pages = {60--68},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/nature16069},
  langid = {english}
}

@article{Leifeld.2012,
  title = {Sensitivity of peatland carbon loss to organic matter quality},
  shorttitle = {Sensitivity of peatland carbon loss to organic matter quality},
  author = {Leifeld, Jens and Steffens, Markus and {Galego-Sala}, Angela},
  year = {2012},
  month = jul,
  journal = {Geophysical Research Letters},
  volume = {39},
  number = {14},
  pages = {n/a-n/a},
  issn = {00948276},
  doi = {10.1029/2012GL051856},
  langid = {english}
}

@article{Lemoine.2019,
  title = {Moving beyond noninformative priors: why and how to choose weakly informative priors in {{Bayesian}} analyses},
  shorttitle = {Moving beyond noninformative priors},
  author = {Lemoine, Nathan P.},
  year = {2019},
  month = jul,
  journal = {Oikos},
  volume = {128},
  number = {7},
  pages = {912--928},
  issn = {0030-1299, 1600-0706},
  doi = {10.1111/oik.05985},
  abstract = {Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods. However, protocols for incorporating prior information, the defining characteristic of Bayesian philosophy, are nearly nonexistent in the ecological literature. Here, I hope to encourage the use of weakly informative priors in ecology and evolution by providing a `consumer's guide' to weakly informative priors. The first section outlines three reasons why ecologists should abandon noninformative priors: 1) common flat priors are not always noninformative, 2) noninformative priors provide the same result as simpler frequentist methods, and 3) noninformative priors suffer from the same high type I and type M error rates as frequentist methods. The second section provides a guide for implementing informative priors, wherein I detail convenient `reference' prior distributions for common statistical models (i.e. regression, ANOVA, hierarchical models). I then use simulations to visually demonstrate how informative priors influence posterior parameter estimates. With the guidelines provided here, I hope to encourage the use of weakly informative priors for Bayesian analyses in ecology. Ecologists can and should debate the appropriate form of prior information, but should consider weakly informative priors as the new `default' prior for any Bayesian model.},
  langid = {english}
}

@article{Loisel.2014,
  title = {A database and synthesis of northern peatland soil properties and {{Holocene}} carbon and nitrogen accumulation},
  author = {Loisel, Julie and Yu, Zicheng and Beilman, David W and Camill, Philip and Alm, Jukka and Amesbury, Matthew J and Anderson, David and Andersson, Sofia and Bochicchio, Christopher and Barber, Keith and Belyea, Lisa R and Bunbury, Joan and Chambers, Frank M and Charman, Daniel J and De Vleeschouwer, Fran{\c c}ois and {Fia{\l}kiewicz-Kozie{\l}}, Barbara and Finkelstein, Sarah A and Ga{\l}ka, Mariusz and Garneau, Michelle and Hammarlund, Dan and Hinchcliffe, William and Holmquist, James and Hughes, Paul and Jones, Miriam C and Klein, Eric S and Kokfelt, Ulla and Korhola, Atte and Kuhry, Peter and Lamarre, Alexandre and Lamentowicz, Mariusz and Large, David and Lavoie, Martin and MacDonald, Glen and Magnan, Gabriel and M{\"a}kil{\"a}, Markku and Mallon, Gunnar and Mathijssen, Paul and Mauquoy, Dmitri and McCarroll, Julia and Moore, Tim R and Nichols, Jonathan and O'Reilly, Benjamin and Oksanen, Pirita and Packalen, Maara and Peteet, Dorothy and Richard, Pierre JH and Robinson, Stephen and Ronkainen, Tiina and Rundgren, Mats and Sannel, A Britta K and Tarnocai, Charles and Thom, Tim and Tuittila, Eeva-Stiina and Turetsky, Merritt and V{\"a}liranta, Minna and {van der Linden}, Marjolein and {van Geel}, Bas and {van Bellen}, Simon and Vitt, Dale and Zhao, Yan and Zhou, Weijian},
  year = {2014},
  month = sep,
  journal = {The Holocene},
  volume = {24},
  number = {9},
  pages = {1028--1042},
  issn = {0959-6836, 1477-0911},
  doi = {10.1177/0959683614538073},
  abstract = {Here, we present results from the most comprehensive compilation of Holocene peat soil properties with associated carbon and nitrogen accumulation rates for northern peatlands. Our database consists of 268 peat cores from 215 sites located north of 45\textdegree N. It encompasses regions within which peat carbon data have only recently become available, such as the West Siberia Lowlands, the Hudson Bay Lowlands, Kamchatka in Far East Russia, and the Tibetan Plateau. For all northern peatlands, carbon content in organic matter was estimated at 42 {$\pm$} 3\% (standard deviation) for Sphagnum peat, 51 {$\pm$} 2\% for non- Sphagnum peat, and at 49 {$\pm$} 2\% overall. Dry bulk density averaged 0.12 {$\pm$} 0.07 g/cm               3               , organic matter bulk density averaged 0.11 {$\pm$} 0.05 g/cm               3               , and total carbon content in peat averaged 47 {$\pm$} 6\%. In general, large differences were found between Sphagnum and non- Sphagnum peat types in terms of peat properties. Time-weighted peat carbon accumulation rates averaged 23 {$\pm$} 2 (standard error of mean) g C/m               2               /yr during the Holocene on the basis of 151 peat cores from 127 sites, with the highest rates of carbon accumulation (25\textendash 28 g C/m               2               /yr) recorded during the early Holocene when the climate was warmer than the present. Furthermore, we estimate the northern peatland carbon and nitrogen pools at 436 and 10 gigatons, respectively. The database is publicly available at https://peatlands.lehigh.edu .},
  langid = {english}
}

@article{Marchessault.1960,
  title = {Infrared spectra of crystalline polysaccharides. {{III}}. {{Mercerized}} cellulose},
  author = {Marchessault, R. H. and Liang, C. Y.},
  year = {1960},
  month = mar,
  journal = {Journal of Polymer Science},
  volume = {43},
  number = {141},
  pages = {71--84},
  issn = {00223832, 15426238},
  doi = {10.1002/pol.1960.1204314107},
  langid = {english}
}

@article{Marwick.2018,
  title = {Packaging {{Data Analytical Work Reproducibly Using R}} (and {{Friends}})},
  author = {Marwick, Ben and Boettiger, Carl and Mullen, Lincoln},
  year = {2018},
  month = jan,
  journal = {The American Statistician},
  volume = {72},
  number = {1},
  pages = {80--88},
  issn = {0003-1305, 1537-2731},
  doi = {10.1080/00031305.2017.1375986},
  abstract = {Computers are a central tool in the research process, enabling complex and large-scale data analysis. As computer-based research has increased in complexity, so have the challenges of ensuring that this research is reproducible. To address this challenge, we review the concept of the research compendium as a solution for providing a standard and easily recognizable way for organizing the digital materials of a research project to enable other researchers to inspect, reproduce, and extend the research. We investigate how the structure and tooling of software packages of the R programming language are being used to produce research compendia in a variety of disciplines. We also describe how software engineering tools and services are being used by researchers to streamline working with research compendia. Using real-world examples, we show how researchers can improve the reproducibility of their work using research compendia based on R packages and related tools.},
  langid = {english}
}

@misc{Marwick.2019,
  title = {rrtools: {{Creates}} a reproducible research compendium},
  author = {Marwick, Ben},
  year = {2019}
}

@article{Moore.2019,
  title = {The effect of long-term fertilization on peat in an ombrotrophic bog},
  author = {Moore, Tim R. and Knorr, Klaus-Holger and Thompson, Lauren and Roy, Cameron and Bubier, Jill L.},
  year = {2019},
  month = jun,
  journal = {Geoderma},
  volume = {343},
  pages = {176--186},
  issn = {00167061},
  doi = {10.1016/j.geoderma.2019.02.034},
  abstract = {Peat accumulation makes nutrient-limited ombrotrophic bogs important carbon sinks, while enhanced atmospheric nitrogen (N) deposition threatens this capacity. Here we show that the stoichiometry of peat and porewater, a control of plant growth and decomposition, has been altered in response to long-term N, phosphorus (P), and potassium (K) fertilization at Mer Bleue ombrotrophic bog in eastern Ontario, Canada. In Nfertilized peat, receiving up to an additional 6.4 g N m-2 y-1 above ambient deposition, we found N significantly (p {$<$} 0.05) greater, P both significantly greater and smaller dependent on treatment concentration, calcium (Ca) and K concentrations significantly lower, relative to control plots. In NPK-fertilized peat, P was greater than control while Ca was significantly smaller. In the porewater of N- and NPK-fertilized peat, we observed significantly more ammonium, nitrate, dissolved organic nitrogen, phosphate, and dissolved organic phosphorus than in the controls. Fourier-transform infrared (FTIR) spectroscopic analysis demonstrated that fertilization in all treatments increased decomposition, as indicated by residual enrichment of refractory moieties such as aromatics, carboxylates, and lignin, albeit only in the uppermost depths. Changes in peat quality may, however, not only relate to advanced decomposition but also to changes in litter input, as predominant vascular vegetation in the fertilized plots shared several features in FTIR spectra with samples of uppermost peat layers. Our results demonstrate that the peat in this ombrotrophic bog has responded to long-term fertilization with loss of Sphagnum moss and its filter function, increased shrub cover and enhanced decomposition.},
  langid = {english}
}

@article{Morris.2015,
  title = {Untangling climate signals from autogenic changes in long-term peatland development},
  shorttitle = {Untangling climate signals from autogenic changes in long-term peatland development},
  author = {Morris, Paul J. and Baird, Andy J. and Young, Dylan M. and Swindles, Graeme T.},
  year = {2015},
  month = dec,
  journal = {Geophysical Research Letters},
  volume = {42},
  number = {24},
  pages = {10,788--10,797},
  issn = {00948276},
  doi = {10.1002/2015GL066824},
  abstract = {Peatlands represent important archives of Holocene paleoclimatic information. However, autogenic processes may disconnect peatland hydrological behavior from climate and overwrite climatic signals in peat records. We use a simulation model of peatland development driven by a range of Holocene climate reconstructions to investigate climate signal preservation in peat records. Simulated water-table depths and peat decomposition profiles exhibit homeostatic recovery from prescribed changes in rainfall, whereas changes in temperature cause lasting alterations to peatland structure and function. Autogenic ecohydrological feedbacks provide both high- and low-pass filters for climatic information, particularly rainfall. Large-magnitude climatic changes of an intermediate temporal scale (i.e., multidecadal to centennial) are most readily preserved in our simulated peat records. Simulated decomposition signals are offset from the climatic changes that generate them due to a phenomenon known as secondary decomposition. Our study provides the mechanistic foundations for a framework to separate climatic and autogenic signals in peat records.},
  langid = {english}
}

@article{Park.2004,
  title = {Bayesian {{Multilevel Estimation}} with {{Poststratification}}: {{State-Level Estimates}} from {{National Polls}}},
  shorttitle = {Bayesian {{Multilevel Estimation}} with {{Poststratification}}},
  author = {Park, David K. and Gelman, Andrew and Bafumi, Joseph},
  year = {2004},
  journal = {Political Analysis},
  volume = {12},
  number = {4},
  pages = {375--385},
  issn = {1047-1987, 1476-4989},
  doi = {10.1093/pan/mph024},
  abstract = {We fit a multilevel logistic regression model for the mean of a binary response variable conditional on poststratification cells. This approach combines the modeling approach often used in small-area estimation with the population information used in poststratification (see Gelman and Little 1997,               Survey Methodology               23:127\textendash 135). To validate the method, we apply it to U.S. preelection polls for 1988 and 1992, poststratified by state, region, and the usual demographic variables. We evaluate the model by comparing it to state-level election outcomes. The multilevel model outperforms more commonly used models in political science. We envision the most important usage of this method to be not forecasting elections but estimating public opinion on a variety of issues at the state level.},
  langid = {english}
}

@article{Peltre.2011,
  title = {Near infrared reflectance spectroscopy: {{A}} tool to characterize the composition of different types of exogenous organic matter and their behaviour in soil},
  shorttitle = {Near infrared reflectance spectroscopy},
  author = {Peltre, C. and Thuri{\`e}s, L. and Barth{\`e}s, B. and Brunet, D. and Morvan, T. and Nicolardot, B. and Parnaudeau, V. and Houot, S.},
  year = {2011},
  month = jan,
  journal = {Soil Biology and Biochemistry},
  volume = {43},
  number = {1},
  pages = {197--205},
  issn = {00380717},
  doi = {10.1016/j.soilbio.2010.09.036},
  abstract = {In addition to total organic carbon and nitrogen, potential organic carbon mineralization under controlled laboratory conditions and indicators such as the indicator of remaining organic carbon in soil (IROC), based on Van Soest biochemical fractionation and short-term carbon mineralization in soil, are used to predict the evolution of exogenous organic matter (EOM) after its application to soils. The purpose of this study was to develop near infrared reflectance spectroscopy (NIRS) calibration models that could predict these characteristics in a large dataset including 300 EOMs representative of the broad range of such materials applied to cultivated soils (plant materials, animal manures, composts, sludges, etc.). The NIRS predictions of total organic matter and total organic carbon were satisfactory (R2P {$\frac{1}{4}$} 0.80 and 0.85, ratio of performance to deviation, RPDP {$\frac{1}{4}$} 2.2 and 2.6, respectively), and prediction of the Van Soest soluble, cellulose and holocellulose fractions were acceptable (R2P {$\frac{1}{4}$} 0.82, 0.73 and 0.70, RPDP {$\frac{1}{4}$} 2.3, 1.9 and 1.8, respectively) with coefficients of variation close to those of the reference methods. The NIRS prediction of carbon mineralization during incubation was satisfactory and indeed better regarding the short-term results of mineralization (R2P {$\frac{1}{4}$} 0.78 and 0.78, and RPDP {$\frac{1}{4}$} 2.1 and 2.0 for 3 and 7 days of incubation, respectively). The IROC indicator was predicted with fairly good accuracy (R2P {$\frac{1}{4}$} 0.79, RPDP {$\frac{1}{4}$} 2.2). Variables related to the long-term C mineralization of EOM in soil were not predicted accurately, except for IROC which was based on analytical and well-identified characteristics, probably because of the increasing interactions and complexity of the factors governing EOM mineralization in soil as a function of incubation time. This study demonstrated the possibility of developing NIRS predictive models for EOM characteristics in heterogeneous datasets of EOMs. However, specific NIRS predictive models still remain necessary for sludges, organo-mineral fertilizers and liquid manures. \'O 2010 Elsevier Ltd. All rights reserved.},
  langid = {english}
}

@article{Piironen.2017,
  title = {Comparison of {{Bayesian}} predictive methods for model selection},
  author = {Piironen, Juho and Vehtari, Aki},
  year = {2017},
  month = may,
  journal = {Statistics and Computing},
  volume = {27},
  number = {3},
  pages = {711--735},
  issn = {0960-3174, 1573-1375},
  doi = {10.1007/s11222-016-9649-y},
  abstract = {The goal of this paper is to compare several widely used Bayesian model selection methods in practical model selection problems, highlight their differences and give recommendations about the preferred approaches. We focus on the variable subset selection for regression and classification and perform several numerical experiments using both simulated and real world data. The results show that the optimization of a utility estimate such as the cross-validation (CV) score is liable to finding overfitted models due to relatively high variance in the utility estimates when the data is scarce. This can also lead to substantial selection induced bias and optimism in the performance evaluation for the selected model. From a predictive viewpoint, best results are obtained by accounting for model uncertainty by forming the full encompassing model, such as the Bayesian model averaging solution over the candidate models. If the encompassing model is too complex, it can be robustly simplified by the projection method, in which the information of the full model is projected onto the submodels. This approach is substantially less prone to overfitting than selection based on CV-score. Overall, the projection method appears to outperform also the maximum a posteriori model and the selection of the most probable variables. The study also demonstrates that the model selection can greatly benefit from using crossvalidation outside the searching process both for guiding the model size selection and assessing the predictive performance of the finally selected model.},
  langid = {english}
}

@article{Piironen.2017a,
  title = {Sparsity information and regularization in the horseshoe and other shrinkage priors},
  author = {Piironen, Juho and Vehtari, Aki},
  year = {2017},
  month = jan,
  journal = {Electronic Journal of Statistics},
  volume = {11},
  number = {2},
  issn = {1935-7524},
  doi = {10.1214/17-EJS1337SI},
  abstract = {The horseshoe prior has proven to be a noteworthy alternative for sparse Bayesian estimation, but has previously suffered from two problems. First, there has been no systematic way of specifying a prior for the global shrinkage hyperparameter based on the prior information about the degree of sparsity in the parameter vector. Second, the horseshoe prior has the undesired property that there is no possibility of specifying separately information about sparsity and the amount of regularization for the largest coefficients, which can be problematic with weakly identified parameters, such as the logistic regression coefficients in the case of data separation. This paper proposes solutions to both of these problems. We introduce a concept of effective number of nonzero parameters, show an intuitive way of formulating the prior for the global hyperparameter based on the sparsity assumptions, and argue that the previous default choices are dubious based on their tendency to favor solutions with more unshrunk parameters than we typically expect a priori. Moreover, we introduce a generalization to the horseshoe prior, called the regularized horseshoe, that allows us to specify a minimum level of regularization to the largest values. We show that the new prior can be considered as the continuous counterpart of the spike-and-slab prior with a finite slab width, whereas the original horseshoe resembles the spike-and-slab with an infinitely wide slab. Numerical experiments on synthetic and real world data illustrate the benefit of both of these theoretical advances.},
  langid = {english}
}

@article{Piironen.2017b,
  title = {On the {{Hyperprior Choice}} for the {{Global Shrinkage Parameter}} in the {{Horseshoe Prior}}},
  author = {Piironen, Juho and Vehtari, Aki},
  year = {2017},
  month = apr,
  journal = {arXiv:1610.05559 [stat]},
  eprint = {1610.05559},
  eprinttype = {arxiv},
  primaryclass = {stat},
  abstract = {The horseshoe prior has proven to be a noteworthy alternative for sparse Bayesian estimation, but as shown in this paper, the results can be sensitive to the prior choice for the global shrinkage hyperparameter. We argue that the previous default choices are dubious due to their tendency to favor solutions with more unshrunk coefficients than we typically expect a priori. This can lead to bad results if this parameter is not strongly identified by data. We derive the relationship between the global parameter and the effective number of nonzeros in the coefficient vector, and show an easy and intuitive way of setting up the prior for the global parameter based on our prior beliefs about the number of nonzero coefficients in the model. The results on real world data show that one can benefit greatly \textendash{} in terms of improved parameter estimates, prediction accuracy, and reduced computation time \textendash{} from transforming even a crude guess for the number of nonzero coefficients into the prior for the global parameter using our framework.},
  archiveprefix = {arXiv},
  langid = {english}
}

@article{Piironen.2020,
  title = {Projective inference in high-dimensional problems: {{Prediction}} and feature selection},
  shorttitle = {Projective inference in high-dimensional problems},
  author = {Piironen, Juho and Paasiniemi, Markus and Vehtari, Aki},
  year = {2020},
  month = jan,
  journal = {Electronic Journal of Statistics},
  volume = {14},
  number = {1},
  issn = {1935-7524},
  doi = {10.1214/20-EJS1711},
  abstract = {This paper reviews predictive inference and feature selection for generalized linear models with scarce but high-dimensional data. We demonstrate that in many cases one can benefit from a decision theoretically justified two-stage approach: first, construct a possibly non-sparse model that predicts well, and then find a minimal subset of features that characterize the predictions. The model built in the first step is referred to as the reference model and the operation during the latter step as predictive projection. The key characteristic of this approach is that it finds an excellent tradeoff between sparsity and predictive accuracy, and the gain comes from utilizing all available information including prior and that coming from the left out features. We review several methods that follow this principle and provide novel methodological contributions. We present a new projection technique that unifies two existing techniques and is both accurate and fast to compute. We also propose a way of evaluating the feature selection process using fast leave-one-out cross-validation that allows for easy and intuitive model size selection. Furthermore, we prove a theorem that helps to understand the conditions under which the projective approach could be beneficial. The key ideas are illustrated via several experiments using simulated and real world data.},
  langid = {english}
}

@misc{Piironen.2020a,
  title = {projpred: {{Projection}} predictive feature selection},
  author = {Piironen, Juho and Paasiniemi, Markus and Catalina, Alejandro and Vehtari, Aki},
  year = {2020},
  collaborator = {Gabry, Jonah and Colombo, Marco and B{\"u}rkner, Paul-Christian}
}

@article{Pineiro.2008,
  title = {How to evaluate models: {{Observed}} vs. predicted or predicted vs. observed?},
  shorttitle = {How to evaluate models},
  author = {Pi{\~n}eiro, Gervasio and Perelman, Susana and Guerschman, Juan P. and Paruelo, Jos{\'e} M.},
  year = {2008},
  month = sep,
  journal = {Ecological Modelling},
  volume = {216},
  number = {3-4},
  pages = {316--322},
  issn = {03043800},
  doi = {10.1016/j.ecolmodel.2008.05.006},
  abstract = {A common and simple approach to evaluate models is to regress predicted vs. observed values (or vice versa) and compare slope and intercept parameters against the 1:1 line. However, based on a review of the literature it seems to be no consensus on which variable (predicted or observed) should be placed in each axis. Although some researchers think that it is identical, probably because r2 is the same for both regressions, the intercept and the slope of each regression differ and, in turn, may change the result of the model evaluation. We present mathematical evidence showing that the regression of predicted (in the y-axis) vs. observed data (in the x-axis) (PO) to evaluate models is incorrect and should lead to an erroneous estimate of the slope and intercept. In other words, a spurious effect is added to the regression parameters when regressing PO values and comparing them against the 1:1 line. Observed (in the y-axis) vs. predicted (in the x-axis) (OP) regressions should be used instead. We also show in an example from the literature that both approaches produce significantly different results that may change the conclusions of the model evaluation.},
  langid = {english}
}

@article{Raftery.2003,
  title = {Discussion: {{Performance}} of {{Bayesian Model Averaging}}},
  shorttitle = {Discussion},
  author = {Raftery, Adrian E and Zheng, Yingye},
  year = {2003},
  month = dec,
  journal = {Journal of the American Statistical Association},
  volume = {98},
  number = {464},
  pages = {931--938},
  issn = {0162-1459, 1537-274X},
  doi = {10.1198/016214503000000891},
  langid = {english}
}

@article{Rambo.2013,
  title = {Potential of visible-near infrared spectroscopy combined with chemometrics for analysis of some constituents of coffee and banana residues},
  author = {Rambo, M.K.D. and Amorim, E.P. and Ferreira, M.M.C.},
  year = {2013},
  month = may,
  journal = {Analytica Chimica Acta},
  volume = {775},
  pages = {41--49},
  issn = {00032670},
  doi = {10.1016/j.aca.2013.03.015},
  langid = {english}
}

@manual{RCoreTeam.2020,
  type = {manual},
  title = {R: {{A}} language and environment for statistical computing},
  author = {{R Core Team}},
  year = {2020},
  address = {{Vienna, Austria}},
  organization = {{R Foundation for Statistical Computing}}
}

@article{Reich.2004,
  title = {Global patterns of plant leaf {{N}} and {{P}} in relation to temperature and latitude},
  author = {Reich, P. B. and Oleksyn, J.},
  year = {2004},
  month = jul,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {101},
  number = {30},
  pages = {11001--11006},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.0403588101},
  langid = {english}
}

@article{Scharlemann.2014,
  title = {Global soil carbon: understanding and managing the largest terrestrial carbon pool},
  shorttitle = {Global soil carbon},
  author = {Scharlemann, J{\"o}rn PW and Tanner, Edmund VJ and Hiederer, Roland and Kapos, Valerie},
  year = {2014},
  month = feb,
  journal = {Carbon Management},
  volume = {5},
  number = {1},
  pages = {81--91},
  issn = {1758-3004, 1758-3012},
  doi = {10.4155/cmt.13.77},
  langid = {english}
}

@article{Schellekens.2015,
  title = {Preferential degradation of polyphenols from {{Sphagnum}} \textendash{} 4-{{Isopropenylphenol}} as a proxy for past hydrological conditions in {{Sphagnum-dominated}} peat},
  author = {Schellekens, Judith and Bindler, Richard and {Mart{\'i}nez-Cortizas}, Antonio and McClymont, Erin L. and Abbott, Geoffrey D. and Biester, Harald and {Pontevedra-Pombal}, Xabier and Buurman, Peter},
  year = {2015},
  month = feb,
  journal = {Geochimica et Cosmochimica Acta},
  volume = {150},
  pages = {74--89},
  issn = {00167037},
  doi = {10.1016/j.gca.2014.12.003},
  langid = {english}
}

@article{Shipley.2021,
  title = {Causal hypotheses accounting for correlations between decomposition rates of different mass fractions of leaf litter},
  author = {Shipley, Bill and Tardif, Antoine},
  year = {2021},
  month = jan,
  journal = {Ecology},
  volume = {102},
  number = {1},
  issn = {0012-9658, 1939-9170},
  doi = {10.1002/ecy.3196},
  abstract = {Whole-leaf decomposition rates are the sum of the decomposition rates of each chemical fraction (water-soluble, cellulose, hemicellulose, lignin), but the decomposition rates of each fraction show complicated patterns of covariation. What explains these patterns of covariation? After measuring the k values of each fraction in 42 different mixtures of tree leaf litters from five species, we tested three alternative causal hypotheses that have been proposed in the literature concerning these mixture interactions using structura equations modeling. All three hypotheses were rejected by the data. We then proposed a new hypothesis, in which rapid decomposition of the labile (water-soluble) fraction stimulates the decomposition of lignin by white-rot fungi and the decomposition of hemicellulose by brown-rot fungi. A more rapid decomposition of hemicellulose then stimulates the decomposition of cellulose. This hypothesis is both consistent with known biology and with our data and is proposed as the most viable current hypothesis.},
  langid = {english}
}

@book{Smith.2002,
  title = {Quantitative spectroscopy: theory and practice},
  shorttitle = {Quantitative spectroscopy},
  author = {Smith, Brian C.},
  year = {2002},
  publisher = {{Academic Press}},
  address = {{Amsterdam}},
  isbn = {978-0-12-650358-6},
  langid = {english},
  annotation = {OCLC: 255358110}
}

@misc{StanDevelopmentTeam.2020,
  title = {{{RStan}}: the {{R}} interface to {{Stan}}},
  author = {{Stan Development Team}},
  year = {2020}
}

@misc{StanDevelopmentTeam.2021,
  title = {Stan {{Modeling Language Users Guide}} and {{Reference Manual}}},
  author = {{Stan Development Team}},
  year = {2021}
}

@article{Strakova.2010,
  title = {Litter quality and its response to water level drawdown in boreal peatlands at plant species and community level},
  author = {Strakov{\'a}, Petra and Anttila, Jani and Spetz, Peter and Kitunen, Veikko and Tapanila, Tarja and Laiho, Raija},
  year = {2010},
  month = oct,
  journal = {Plant and Soil},
  volume = {335},
  number = {1-2},
  pages = {501--520},
  issn = {0032-079X, 1573-5036},
  doi = {10.1007/s11104-010-0447-6},
  langid = {english}
}

@book{Stuart.2004,
  title = {Infrared {{Spectroscopy}}: {{Fundamentals}} and {{Applications}}},
  shorttitle = {Infrared {{Spectroscopy}}},
  author = {Stuart, Barbara H.},
  year = {2004},
  month = jun,
  series = {Analytical {{Techniques}} in the {{Sciences}}},
  publisher = {{John Wiley \& Sons, Ltd}},
  address = {{Chichester, UK}},
  doi = {10.1002/0470011149},
  isbn = {978-0-470-01114-0 978-0-470-85428-0},
  langid = {english}
}

@article{Sun.2011,
  title = {Application of {{FT-NIR-DR}} and {{FT-IR-ATR}} spectroscopy to estimate the chemical composition of bamboo ({{Neosinocalamus}} affinis {{Keng}})},
  author = {Sun, Bailing and Liu, Junliang and Liu, Shujun and Yang, Qing},
  year = {2011},
  month = aug,
  journal = {Holzforschung},
  volume = {65},
  number = {5},
  issn = {1437-434X, 0018-3830},
  doi = {10.1515/hf.2011.075},
  abstract = {Neosinocalamus affinis Keng is widely grown in south-western China for pulp and paper production. Rapid assessment of the chemical properties of N. affinis is necessary for both bamboo breeding and industrial utilization. This study was performed to investigate the abilities of Fourier transform near-infrared spectroscopy in the diffuse reflectance mode (FT-NIR-DR) and Fourier transform infrared attenuated total reflectance (FT-IR-ATR) spectroscopy to predict the contents of holocellulose, a-cellulose, Klason lignin, and NaOH extractives in N. affinis. Partial least squares regression models based on the raw and preprocessed spectra, including multiplicative scatter correction (MSC) and Savitzky-Golay 1st and 2nd derivative spectra, were developed for the chemical components of bamboo. The NIR-based calibrations displayed better performance than those using FT-IR-ATR spectra. The best calibrations developed by both methods for properties all had satisfactory correlations, with coefficient of determination (R2c) values ranging from 0.81 (Klason lignin by FT-IR and MSC) to 0.98 (a-cellulose by FT-NIR and 2nd derivative), and root mean standard error of calibration between 0.50 and 1.47\%. When applied to prediction sets, the correlations were good, with R2p above 0.68. The results demonstrate that both spectroscopic methods, combined with chemometric strategies, could rapidly predict the chemical composition of bamboo.},
  langid = {english}
}

@techreport{Teickner.2019,
  title = {Elevated {{Nitrogen Deposition Resulted}} in {{Enhanced Peat Decomposition Across Europe During}} the 20th {{Century}}},
  author = {Teickner, Henning and {Estop-Aragon{\'e}s}, Cristian and Zaj{\k{a}}c, Katarzyna},
  year = {2019},
  pages = {1}
}

@misc{Teickner.2020,
  title = {ir: {{A}} simple package to handle and preprocess infrared spectra},
  author = {Teickner, Henning},
  year = {2020}
}

@misc{Teickner.2020a,
  title = {irpeat: {{Simple}} functions to analyse mid infrared spectra of peat samples},
  author = {Teickner, Henning and Hodgkins, Suzanne B.},
  year = {2020}
}

@article{Teickner.2022,
  title = {Electrochemical {{Properties}} of {{Peat Particulate Organic Matter}} on a {{Global Scale}}: {{Relation}} to {{Peat Chemistry}} and {{Degree}} of {{Decomposition}}},
  shorttitle = {Electrochemical {{Properties}} of {{Peat Particulate Organic Matter}} on a {{Global Scale}}},
  author = {Teickner, Henning and Gao, Chuanyu and Knorr, Klaus-Holger},
  year = {2022},
  month = feb,
  journal = {Global Biogeochemical Cycles},
  issn = {0886-6236, 1944-9224},
  doi = {10.1029/2021GB007160},
  langid = {english}
}

@article{Tfaily.2014,
  title = {Organic matter transformation in the peat column at {{Marcell Experimental Forest}}: {{Humification}} and vertical stratification: {{Organic}} matter dynamics},
  shorttitle = {Organic matter transformation in the peat column at {{Marcell Experimental Forest}}},
  author = {Tfaily, Malak M. and Cooper, William T. and Kostka, Joel E. and Chanton, Patrick R. and Schadt, Christopher W. and Hanson, Paul J. and Iversen, Colleen M. and Chanton, Jeffrey P.},
  year = {2014},
  month = apr,
  journal = {Journal of Geophysical Research: Biogeosciences},
  volume = {119},
  number = {4},
  pages = {661--675},
  issn = {21698953},
  doi = {10.1002/2013JG002492},
  abstract = {We characterized peat decomposition at the Marcell Experimental Forest (MEF), Minnesota, USA, to a depth of 2 m to ascertain the underlying chemical changes using Fourier transform infrared (FT IR) and 13C nuclear magnetic resonance (NMR) spectroscopy) and related these changes to decomposition proxies C:N ratio, {$\delta$}13C and {$\delta$}15N, bulk density, and water content. FT IR determined that peat humification increased rapidly between 30 and 75 cm, indicating a highly reactive intermediate-depth zone consistent with changes in C:N ratio, {$\delta$}13C and {$\delta$}15N, bulk density, and water content. Peat decomposition at the MEF, especially in the intermediate-depth zone, is mainly characterized by preferential utilization of O-alkyl-C, carboxyl-C, and other oxygenated functionalities with a concomitant increase in the abundance of alkyl- and nitrogen-containing compounds. Below 75 cm, less change was observed but aromatic functionalities and lignin accumulated with depth. Significant correlations with humification indices, identified by FT IR spectroscopy, were found for C:N ratios. Incubation studies at 22\textdegree C revealed the highest methane production rates, greatest CH4:CO2 production ratios, and significant O-alkyl-C utilization within this 30 and 75 cm zone. Oxygen-containing functionalities, especially O-alkyl-C, appear to serve as excellent proxies for soil decomposition rate and should be a sensitive indicator of the response of the solid phase peat to increased temperatures caused by climate change and the field study manipulations that are planned to occur at this site. Radiocarbon signatures of microbial respiration products in deeper pore waters at the MEF resembled the signatures of more modern dissolved organic carbon rather than solid phase peat, indicating that recently photosynthesized organic matter fueled the bulk of subsurface microbial respiration. These results indicate that carbon cycling at depth at the MEF is not isolated from surface processes.},
  langid = {english}
}

@article{Tsuchikawa.,
  title = {A {{Review}} of {{Recent Near-Infrared Research}} for {{Wood}} and {{Paper}} ({{Part}} 2)},
  author = {Tsuchikawa, Satoru and Schwanninger, Manfred},
  pages = {29},
  abstract = {This review article introduces recent technical and scientific reports on nearinfrared (NIR) spectroscopy in the wood and paper industry, which have increased during the last decade. Many researchers have reported that the NIR technique is useful for detection of both chemical and physical properties of wood materials and has been widely used in cases where the characteristic cellular structure of the material is retained. With regard to application of NIR spectroscopy to pulp and paper, many publications have reported its potential as an on-line measurement technique during paper-making process control. NIR spectroscopy is considered fundamental in applied research on wood and paper. Utilization of NIR spectroscopy in the wood and paper industry should take into account its applicability and limitations as a nondestructive technique.},
  langid = {english}
}

@article{Turetsky.2008,
  title = {Trade-offs in resource allocation among moss species control decomposition in boreal peatlands},
  author = {Turetsky, Merritt R. and Crow, Susan E. and Evans, Robert J. and Vitt, Dale H. and Wieder, R. Kelman},
  year = {2008},
  month = nov,
  journal = {Journal of Ecology},
  volume = {96},
  number = {6},
  pages = {1297--1305},
  issn = {00220477, 13652745},
  doi = {10.1111/j.1365-2745.2008.01438.x},
  langid = {english}
}

@article{vanBreemen.1995,
  title = {How {{Sphagnum}} bogs down other plants},
  author = {{van Breemen}, Nico},
  year = {1995},
  pages = {6},
  langid = {english}
}

@article{Vehtari.2017,
  title = {Practical {{Bayesian}} model evaluation using leave-one-out cross-validation and {{WAIC}}},
  author = {Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
  year = {2017},
  month = sep,
  journal = {Statistics and Computing},
  volume = {27},
  number = {5},
  pages = {1413--1432},
  issn = {0960-3174, 1573-1375},
  doi = {10.1007/s11222-016-9696-4},
  abstract = {Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values. LOO and WAIC have various advantages over simpler estimates of predictive error such as AIC and DIC but are less used in practice because they involve additional computational steps. Here we lay out fast and stable computations for LOO and WAIC that can be performed using existing simulation draws. We introduce an efficient computation of LOO using Pareto-smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. Although WAIC is asymptotically equal to LOO, we demonstrate that PSIS-LOO is more robust in the finite case with weak priors or influential observations. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors and for comparison of predictive errors between two models. We implement the computations in an R package called loo and demonstrate using models fit with the Bayesian inference package Stan.},
  langid = {english}
}

@misc{Vehtari.2019,
  title = {loo: {{Efficient}} leave-one-out cross-validation and {{WAIC}} for bayesian models},
  author = {Vehtari, Aki and Gabry, Jonah and Magnusson, Mans and Yao, Yuling and B{\"u}rkner, Paul-Christian and Paananen, Topi and Gelman, Andrew and Goodrich, Ben and Piironen, Juho},
  year = {2019}
}

@article{Verbeke.2022,
  title = {Latitude, elevation, and mean annual temperature predict peat organic matter chemistry at a global scale},
  author = {Verbeke, Brittany A. and Lamit, Louis J. and Lilleskov, Erik A. and Hodgkins, Suzanne B. and Basiliko, Nate and Kane, Evan S. and Andersen, Roxane and Artz, Rebekka R. E. and Benavides, Juan C. and Benscoter, Brian W. and Borken, Werner and Bragazza, Luca and Brandt, Stefani M. and Br{\"a}uer, Suzanna L. and Carson, Michael A. and Charman, Dan and Chen, Xin and Clarkson, Beverley R. and Cobb, Alexander R. and Convey, Peter and {\'A}guila Pasquel, Jhon and Enriquez, Andrea S. and Griffiths, Howard and Grover, Samantha P. and Harvey, Charles F. and Harris, Lorna and Hazard, Christina and Hodgson, Dominic and Hoyt, Alison M. and Hribljan, John and Jauhiainen, Jyrki and Juutinen, Sari and Knorr, Klaus-Holger and Kolka, Randall K. and K{\"o}n{\"o}nen, Mari T. and Larmola, Tuula and McCalley, Carmody K. and McLaughlin, James and Moore, Tim R. and Mykytczuk, Nadia and Normand, Anna E. and Rich, Virginia and Roulet, Nigel and Royles, Jessica and Rutherford, Jasmine and Smith, David S. and Svenning, Mette M. and Tedersoo, Leho and Thu, Pham Q. and Trettin, Carl C. and Tuittila, Eeva-Stiina and Urbanov{\'a}, Zuzana and Varner, Ruth K. and Wang, Meng and Wang, Zheng and Warren, Matt and Wiedermann, Magdalena M. and Williams, Shanay and Yavitt, Joseph B. and Yu, Zhi-Guo and Yu, Zicheng and Chanton, Jeffrey P.},
  year = {2022},
  month = jan,
  journal = {Global Biogeochemical Cycles},
  issn = {0886-6236, 1944-9224},
  doi = {10.1029/2021GB007057},
  abstract = {Peatlands contain a significant fraction of global soil carbon, but how these reservoirs will respond to the changing climate is still relatively unknown. A global picture of the variations in peat organic matter chemistry will aid our ability to gauge peatland soil response to climate. The goal of this research is to test the hypotheses that 1) peat carbohydrate content, an indicator of soil organic matter reactivity, will increase with latitude and decrease with mean annual temperatures (MAT), 2) while peat aromatic content, an indicator of recalcitrance, will vary inversely, and 3) elevation will have a similar effect to latitude. We used Fourier Transform Infrared Spectroscopy (FTIR) to examine variations in the organic matter functional groups of 1034 peat samples collected from 10-20, 30-40, and 60-70 cm depths at 165 individual sites across a latitudinal gradient of 79\r{}N to 65\r{}S and from elevations of 0 to 4773 meters. Carbohydrate contents of high latitude peat were significantly greater than peat originating near the equator, while aromatic content showed the opposite trend. For peat from similar latitudes but different elevations, the carbohydrate content was greater and aromatic content was lower at higher elevations. Higher carbohydrate content at higher latitudes indicates a greater potential for mineralization, whereas the chemical composition of low latitude peat is consistent with their apparent relative stability in the face of warmer temperatures. The combination of low carbohydrates and high aromatics at warmer locations near the equator suggests the mineralization of high latitude peat until reaching recalcitrance under a new temperature regime.},
  langid = {english}
}

@article{ViscarraRossel.2006,
  title = {Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties},
  author = {Viscarra Rossel, R.A. and Walvoort, D.J.J. and McBratney, A.B. and Janik, L.J. and Skjemstad, J.O.},
  year = {2006},
  month = mar,
  journal = {Geoderma},
  volume = {131},
  number = {1-2},
  pages = {59--75},
  issn = {00167061},
  doi = {10.1016/j.geoderma.2005.03.007},
  langid = {english}
}

@book{Wadoux.2021,
  title = {Soil {{Spectral Inference}} with {{R}}: {{Analysing Digital Soil Spectra}} using the {{R Programming Environment}}},
  shorttitle = {Soil {{Spectral Inference}} with {{R}}},
  author = {Wadoux, Alexandre M.J.-C. and Malone, Brendan and Minasny, Budiman and Fajardo, Mario and McBratney, Alex B.},
  year = {2021},
  series = {Progress in {{Soil Science}}},
  publisher = {{Springer International Publishing}},
  address = {{Cham}},
  doi = {10.1007/978-3-030-64896-1},
  isbn = {978-3-030-64895-4 978-3-030-64896-1},
  langid = {english}
}

@article{Wagai.2013,
  title = {Linking temperature sensitivity of soil organic matter decomposition to its molecular structure, accessibility, and microbial physiology},
  author = {Wagai, Rota and {Kishimoto-Mo}, Ayaka W. and Yonemura, Seiichiro and Shirato, Yasuhito and Hiradate, Syuntaro and Yagasaki, Yasumi},
  year = {2013},
  month = apr,
  journal = {Global Change Biology},
  volume = {19},
  number = {4},
  pages = {1114--1125},
  issn = {13541013},
  doi = {10.1111/gcb.12112},
  abstract = {Temperature sensitivity of soil organic matter (SOM) decomposition may have a significant impact on global warming. Enzyme-kinetic hypothesis suggests that decomposition of low-quality substrate (recalcitrant molecular structure) requires higher activation energy and thus has greater temperature sensitivity than that of high-quality, labile substrate. Supporting evidence, however, relies largely on indirect indices of substrate quality. Furthermore, the enzyme-substrate reactions that drive decomposition may be regulated by microbial physiology and/or constrained by protective effects of soil mineral matrix. We thus tested the kinetic hypothesis by directly assessing the carbon molecular structure of low-density fraction (LF) which represents readily accessible, mineral-free SOM pool. Using five mineral soil samples of contrasting SOM concentrations, we conducted 30-days incubations (15, 25, and 35 \textdegree C) to measure microbial respiration and quantified easily soluble C as well as microbial biomass C pools before and after the incubations. Carbon structure of LFs ({$<$}1.6 and 1.6\textendash 1.8 g cm\`A3) and bulk soil was measured by solid-state 13C-NMR. Decomposition Q10 was significantly correlated with the abundance of aromatic plus alkyl-C relative to O-alkyl-C groups in LFs but not in bulk soil fraction or with the indirect C quality indices based on microbial respiration or biomass. The warming did not significantly change the concentration of biomass C or the three types of soluble C despite two- to three-fold increase in respiration. Thus, enhanced microbial maintenance respiration (reduced C-use efficiency) especially in the soils rich in recalcitrant LF might lead to the apparent equilibrium between SOM solubilization and microbial C uptake. Our results showed physical fractionation coupled with direct assessment of molecular structure as an effective approach and supported the enzyme-kinetic interpretation of widely observed C quality-temperature relationship for short-term decomposition. Factors controlling long-term decomposition Q10 are more complex due to protective effect of mineral matrix and thus remain as a central question.},
  langid = {english}
}

@article{Wang.2015,
  title = {Dual controls on carbon loss during drought in peatlands},
  author = {Wang, Hongjun and Richardson, Curtis J. and Ho, Mengchi},
  year = {2015},
  month = jun,
  journal = {Nature Climate Change},
  volume = {5},
  number = {6},
  pages = {584--587},
  issn = {1758-678X, 1758-6798},
  doi = {10.1038/nclimate2643},
  langid = {english}
}

@article{Williams.1998,
  title = {Cupric oxide oxidation products of northern peat and peat-forming plants},
  author = {Williams, Christopher J and Yavitt, Joseph B and Wieder, R Kelman and Cleavitt, Natalie L},
  year = {1998},
  month = jan,
  journal = {Canadian Journal of Botany},
  volume = {76},
  number = {1},
  pages = {51--62},
  issn = {0008-4026},
  doi = {10.1139/b97-150},
  abstract = {Alkaline cupric oxide oxidation and proximate analysis were used to investigate the sources and diagenetic state of organic matter in six Sphagnum-dominated peatlands located between Alberta, Canada, and Ohio, U.S.A. Cupric oxide oxidation was also used to characterize vascular and nonvascular wetland plant species to provide a specific biological fingerprint of these plant tissues. Oxidation of 15 species of Sphagnum moss released large quantities of unsubstituted p-hydroxyl phenolic compounds as well as the species specific sphagnum acid (p-hydroxy-{$\beta$}-[carboxymethyl]-cinnamic acid). By contrast, vascular plant tissues released large amounts of lignin oxidation products. Cupric oxide oxidation of Sphagnum peat from more northerly sites produced mainly p-hydroxyl phenolic monomers with lesser amounts of vascular lignin derived phenols. In contrast, southern sites and those dominated by woody vegetation produced oxidation products characteristic of vascular plant lignin. A distinct relationship exists between the amount of acid-insoluble Klason lignin and both the diagenetically sensitive phenolic acid to aldehyde ratios as well as the total yield of vanillyl phenolic oxidation products. We found evidence of selective decay of phenolic lignin precursors. These relationships indicate the lignin component in surficial layers of Sphagnum-dominated peat is influenced by both Sphagnum and vascular plant lignin, and the structure of lignin appears to undergo diagenetic changes in these layers. Application of an end-member mixing model revealed that lignin oxidation products poorly predicted vegetational composition of the lignin in more decomposed peat, probably as a result of selective decay of lignin structural phenols.},
  langid = {english}
}

@article{Williams.2003,
  title = {Botanical composition of peat and degree of peat decomposition in three temperate peatlands},
  author = {Williams, Christopher J. and Yavitt, Joseph B.},
  year = {2003},
  month = jan,
  journal = {\'Ecoscience},
  volume = {10},
  number = {1},
  pages = {85--95},
  issn = {1195-6860, 2376-7626},
  doi = {10.1080/11956860.2003.11682755},
  abstract = {Models of peat accumulation assume that peat decomposition occ decomposition once peat enters the deeper, saturated, anoxic zone. Howeve exist. In this paper we quantified differences in degree of decomposit composition of peat from three temperate peatlands with different water peat, capped by a layer of Sphagnum peat. The lignin content and degree o index, generally increased with depth. Analysis of the lignin with cupric differences between peat deposits occurred in the upper, aerated peat laye was characterized by high /7-hydroxyl phenolic yield, whereas Sphagnum of humification and high yield of vanillyl oxidation products despite bein herbaceous peat below the water table was similar in terms of degree o results confirm that peat decomposition occurs mostly above the water hydrology interact to create multiple trajectories in peat chemistry.},
  langid = {english}
}

@article{Worrall.2017,
  title = {The flux of organic matter through a peatland ecosystem: {{The}} role of cellulose, lignin, and their control of the ecosystem oxidation state: {{Flux}} of {{Organic Matter Through}} a {{Peat}}},
  shorttitle = {The flux of organic matter through a peatland ecosystem},
  author = {Worrall, Fred and Moody, Catherine S. and Clay, Gareth D. and Burt, Tim P. and Rose, Rob},
  year = {2017},
  month = jul,
  journal = {Journal of Geophysical Research: Biogeosciences},
  volume = {122},
  number = {7},
  pages = {1655--1671},
  issn = {21698953},
  doi = {10.1002/2016JG003697},
  abstract = {This study used thermogravimetric analysis (TGA) to study the transit of organic C through a peatland ecosystem. The biomass, litter, peat soil profile, particulate organic matter (POM), and dissolved organic matter (DOM) fluxes were sampled from the Moor House National Nature Reserve, a peat-covered catchment in northern England where both the dry matter and carbon budget for the catchment were known. The study showed that although TGA traces showed distinct differences between organic matter reservoirs and fluxes, the traces could not readily be associated with particular functionalities or elemental properties. The TGA trace shows that polysaccharides are preferentially removed by humification and degradation with residual peat being dominated by lignin compositions. The DOM is derived from the degradation of lignin while the POM is derived from erosion of the peat profile. The carbon lost as gases (CO2 and CH4) was estimated to be composed of 92 to 95\% polysaccharide carbon. The composition of the organic matter lost from the peat ecosystem means that the oxidative ratio (OR) of the ecosystem experienced by the atmosphere was between 0.96 and 0.99: currently, the Intergovernmental Panel on Climate Change uses an OR value of 1.1 for all ecosystems.},
  langid = {english}
}

@article{Yavitt.2005,
  title = {Soil chemistry versus environmental controls on production of {{CH4}} and {{CO2}} in northern peatlands},
  author = {Yavitt, J. B. and Williams, C. J. and Wieder, R. K.},
  year = {2005},
  month = apr,
  journal = {European Journal of Soil Science},
  volume = {56},
  number = {2},
  pages = {169--178},
  issn = {1351-0754, 1365-2389},
  doi = {10.1111/j.1365-2389.2004.00657.x},
  abstract = {Rates of organic carbon mineralization (to CO2 and CH4) vary widely in peat soil. We transplanted four peat soils with different chemical composition into six sites with different environmental conditions to help resolve the debate about control of organic carbon mineralization by resource availability (e.g. carbon and nutrient chemistry) versus environmental conditions (e.g. temperature, moisture, pH). The four peat soils were derived from Sphagnum (bog moss). Two transplant sites were in mid-boreal Alberta, Canada, two were in low-boreal Ontario, Canada, and two were in the temperate United States. After 3 years in the field, CH4 production varied significantly as a function of peat type, transplant site, and the type\textendash site interaction. All four peat soils had very small rates of CH4 production ({$<$} 20 nmol g\`A1 day\`A1) after transplant into two sites, presumably caused by acid site conditions (pH {$<$} 4.0). One peat soil had small CH4 production rates regardless of transplant site. A canonical discriminant analysis revealed that large rates of CH4 production (4000 nmol g\`A1 day\`A1) correlated with large holocellulose content, a large concentration of p-hydroxyl phenolic compounds in the Klason lignin, and small concentrations of N, Ca and Mn in peat. Significant variation in rates of CO2 production correlated positively with holocellulose content and negatively with N concentrations, regardless of transplant site. The temperature response for CO2 production varied as a function of climate, being greater for peat formed in a cold climate, but did not apply to transplanted peat. Although we succeeded in elucidating some aspects of peat chemistry controlling production of CH4 and CO2 in Sphagnum-derived peat soils, we also revealed idiosyncratic combinations of peat chemistry and site conditions that will complicate forecasting rates of peat carbon mineralization into the future.},
  langid = {english}
}


