Blunier, T. and Brook, E. J.: Timing of millennial-scale climate change in Antarctica and Greenland during the Last Glacial period, Science, 291, 109–112, https://doi.org/10.1126/science.291.5501.109, 2001.
Bond, G., Broecker, W., Johnsen, S., McManus, J., Labeyrie, L., Jouzel, J., and Bonani, G.: Correlations between climate records from North Atlantic sediments and Greenland ice, Nature, 365, 143–147, https://doi.org/10.1038/365143a0, 1993.
Brooks, S. J. and Birks, H. J. B.: Chironomid-inferred air temperatures from Lateglacial and Holocene sites in north-west Europe: progress and problems, Quat. Sci. Rev., 20, 1723–1741, https://doi.org/10.1016/S0277-3791(01)00038-5, 2001.
Bush, M. B., Correa-Metrio, A., van Woesik, R., Collins, A., Hanselman, J., Martinez, P., and McMichael, C. N. H.: Modern pollen assemblages of the Neotropics, J. Biogeogr., 48, 231–241, https://doi.org/10.1111/jbi.13960, 2021.
Christin, S., Hervet, É., and Lecomte, N.: Applications for deep learning in ecology, Methods Ecol. Evol., 10, 1632–1644, https://doi.org/10.1111/2041-210X.13256, 2019.
Clapperton. C. M: Maximum extent of the late Wisconsin glaciation in the Ecuadorian Andes Quaternary of South America and Antarctic Peninsula, Balkema, Rotterdam, 165–180, ISBN 9781003079323, 1987.
Cleator, S. F., Harrison, S. P., Nichols, N. K., Prentice, I. C., and Roulstone, I.: A new multivariable benchmark for Last Glacial Maximum climate simulations, Clim. Past, 16, 699–712, https://doi.org/10.5194/cp-16-699-2020, 2020.
Colinvaux, P. A., Olson, K., and Liu, K. B.: Late-glacial and Holocene pollen diagrams from two endorheic lakes of the inter-Andean plateau of Ecuador, Rev. Palaeobot. Palynol., 55, 83–99, https://doi.org/10.1016/0034-6667(88)90055-3, 1988.
Colinvaux, P. A., Bush, M. B., Steinitz-Kannan, M., and Miller, M. C.: Glacial and Postglacial Pollen Records from the Ecuadorian Andes and Amazon, Quat. Res., 48, 69–78, https://doi.org/10.1006/qres.1997.1908, 1997.
de la Houssaye, B., Flaming, P. L., Nixon, Q., and Acton, G. D.: Machine learning and deep learning applications for international ocean discovery program geoscience research, SMU Data Sci
. Rev., 2, 9,
https://scholar.smu.edu/datasciencereview/vol2/iss3/9 (last access: 17 October 2024), 2019.
Friedman J. H.: Greedy function approximation: A gradient boosting machine, Ann. Statist, 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2011.
Geurts, P., Ernst, D., and Wehenkel, L.: Extremely randomized trees, Mach. Learn., 63, 3–42, https://doi.org/10.1007/s10994-006-6226-1, 2006.
Ghosh, S., Liao, Q. V., Ramamurthy, K. N., Navratil, J., Sattigeri, P., Varshney, K. R., and Zhang, Y.: Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI, https://doi.org/10.48550/arXiv.2106.01410, 2021.
Hais, M., Komprdová, K., Ermakov, N., and Chytrý, M.: Modelling the Last Glacial Maximum environments for a refugium of Pleistocene biota in the Russian Altai Mountains, Siberia, Palaeogeogr. Palaeoclimatol. Palaeoecol., 438, 135–145, https://doi.org/10.1016/j.palaeo.2015.07.037, 2015.
Harrison, S. P: Modern pollen data for climate reconstructions, version 1 (SMPDS), University of Reading, https://doi.org/10.17864/1947.194, 2019.
Harrison, S. P.: Climate reconstructions for the SMPDSv1 modern pollen data set, Zenodo [data set], https://doi.org/10.5281/zenodo.3605003, 2020.
Heiri, O., Lotter, A. F., Hausmann, S., and Kienast, F.: A chironomid-based Holocene summer air temperature reconstruction from the Swiss Alps, Holocene, 13, 477–484, https://doi.org/10.1191/0959683603hl640ft, 2003.
Helama, S., Makarenko, N. G., Karimova, L. M., Kruglun, O. A., Timonen, M., Holopainen, J., Meriläinen, J., and Eronen, M.: Dendroclimatic transfer functions revisited: Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms, Ann. Geophys., 27, 1097–1111, https://doi.org/10.5194/angeo-27-1097-2009, 2009.
Holden, P. B., Birks, H. J. B., Brooks, S. J., Bush, M. B., Hwang, G. M., Matthews-Bird, F., Valencia, B. G., and van Woesik, R.: BUMPER v1.0: a Bayesian user-friendly model for palaeo-environmental reconstruction, Geosci. Model Dev., 10, 483–498, https://doi.org/10.5194/gmd-10-483-2017, 2017.
Huang, Y., Yang, L., and Fu, Z.: Reconstructing coupled time series in climate systems using three kinds of machine-learning methods, Earth Syst. Dynam., 11, 835–853, https://doi.org/10.5194/esd-11-835-2020, 2020.
Jones, V. J., Stevenson, A. C., and Battarbee, R. W.: Acidification of lakes in Galloway, south-west Scotland: a diatom and pollen study of the post-glacial history of the Round Loch of Glenhead, J. Ecol., 77, 1–22, https://doi.org/10.2307/2260912, 1989.
Jordan, G. J., Harrison, P. A., Worth, J. R. P., Williamson, G. J., and Kirkpatrick, J. B.: Palaeoendemic plants provide evidence for persistence of open, well-watered vegetation since the Cretaceous, Glob. Ecol. Biogeogr., 25, 127–140, https://doi.org/10.1111/geb.12389, 2016.
Juggins, S.: Rioja: analysis of Quaternary science data, CRAN [code], R package version (0.9–15.1),
https://github.com/nsj3/rioja (last access: 17 October 2024), 2017.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y.: LightGBM: A highly efficient gradient boosting decision tree, in: Advances in Neural Information Processing Systems, edited by: Guyon, I., Von Luxburg, U., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., 30,
https://papers.nips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf (last access: 17 October 2024), 2017.
Liaw, A. and Wiener, M.: Classification and regression by randomForest, R news, 2, 18–22,
https://journal.r-project.org/articles/RN-2002-022/RN-2002-022.pdf (last access: 17 October 2024), 2002.
Liu, M., Prentice, I. C., ter Braak, C. J. F., and Harrison, S. P.: An improved statistical approach for reconstructing past climates from biotic assemblages, Proc. R. Soc. A, 476, 20200346, https://doi.org/10.1098/rspa.2020.0346, 2020.
Ovaskainen, O., Tikhonov, G., Norberg, A., Blanchet, F. G., Duan, L., Dunson, D., Roslin, T., and Abrego, N.: How to make more out of community data? A conceptual framework and its implementation as models and software, Ecol. Lett., 20, 561–576,
https://onlinelibrary.wiley.com/doi/10.1111/ele.12757 (last access: 23 October 2024), 2017.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L. and Desmaison, A., Andreas K., Edward Z. Y., Zachary D., Martin R., Alykhan T., Sasank C., Benoit S., Lu F., Junjie B., and Soumith C.: Pytorch: An imperative style, high-performance deep learning library, Adv. Neur. In., 32, 8024–8035, arXiv:1912.01703, 2019.
Pennington, J., Socher, R., and Manning, C.: GloVe: Global Vectors for Word Representation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543, https://doi.org/10.3115/v1/D14-1162, 2014.
Schulte, P. J. and Hinckley, T. M.: A comparison of pressure-volume curve data-analysis techniques, J. Exp. Bot., 36, 1590– 1602, https://doi.org/10.1093/jxb/36.10.1590, 1985.
Stevenson, A. C, Juggins, S., Birks, H. J. B., Anderson, D. S., Anderson, N. J., Battarbee, R. W., Berge, F., Davis, R. B, Flower, R. J., and Haworth, E. Y.: The Surface Waters Acidification Project Palaeolimnology Programme: Modern Diatom/Lake-Water Chemistry Data-Set, UCL Environmental Change Research Centre, https://doi.org/10.1098/rstb.1990.0056, 1991.
Sun, P.: Schimasuperbra/MEMLM: Multi Ensemble Machine Learning Model (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.13138593, 2024.
Syam, N. and Kaul, R.: Overfitting and Regularization in Machine Learning Models in Machine Learning and Artificial Intelligence in Marketing and Sales, Emerald Publishing Limited, Bingley, 65–84, https://doi.org/10.1108/978-1-80043-880-420211004, 2021.
Telford, R. J. and Birks, H. J. B.: A novel method for assessing the statistical significance of quantitative reconstructions inferred from biotic assemblages, Quat. Sci. Rev., 30, 1272–1278, https://doi.org/10.1016/j.quascirev.2011.03.002, 2011.
Telford, R. J. and Trachsel, M.: palaeoSig: Significance Tests for Palaeoenvironmental Reconstructions, R Package Version 1.1-3, University of Bergen [code], Bergen,
https://github.com/richardjtelford/palaeoSig (last access: 17 October 2024), 2015.
ter Braak, C. J. F.: Partial canonical correspondence analysis, in: Classification and Related Methods of Data Analysis, edited by: Bock, H. H., 551–558, Elsevier Science Publishers B.V. (North-Holland),
http://edepot.wur.nl/241165 (last access: 17 October 2024), 1988.
ter Braak, C. J. F. and Barendregt, L. G.: Weighted averaging of species indicator values: Its efficiency in environmental calibration, Math Biosci., 78, 57–72, https://doi.org/10.1016/0025-5564(86)90031-3, 1986.
ter Braak, C. J. F. and te Beest, D. E.: Testing environmental effects on taxonomic composition with canonical correspondence analysis: alternative permutation tests are not equal, Environ. Ecol. Stats, 29, 849–868, https://doi.org/10.1007/s10651-022-00545-4, 2022.
ter Braak, C. J. F. and Juggins, S.: Weighted averaging partial least squares regression (WA-PLS): an improved method for reconstructing environmental variables from species assemblages, Hydrobiologia, 269, 485–502, https://doi.org/10.1007/BF00028046, 1993.
Turner, M. G., Wei, D., Prentice, I. C., and Harrison, S. P.: The impact of methodological decisions on climate reconstructions using WA-PLS, Quat. Res., 99, 341–356, https://doi.org/10.1017/qua.2020.44, 2020.
Urrego, D. H., Bush, M. B., and Silman, M. R.: A long history of cloud and forest migration from Lake Consuelo, Peru, Quat. Res., 73, 364–373, https://doi.org/10.1016/j.yqres.2009.10.005, 2010.
Wei, D., González-Sampériz, P., Gil-Romera, G., Harrison, S. P., and Prentice, I. C.: Seasonal temperature and moisture changes in interior semi-arid Spain from the last interglacial to the Late Holocene, Quat. Res., 101, 143–155, https://doi.org/10.1017/qua.2020.108, 2021a.
Wei, G., Peng, C., Zhu, Q., Zhou, X., and Yang, B.: Application of machine learning methods for paleoclimatic reconstructions from leaf traits, Int. J. Clim., 41, E3249–E3262, https://doi.org/10.1002/joc.6921, 2021b.
Yates, L. A., Aandahl, Z., Richards, S. A., and Brook, B. W.: Cross validation for model selection: A review with examples from ecology, Ecol. Monogr., 93, e1557, https://doi.org/10.1002/ecm.1557, 2023.
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S.: Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting, 2018 IEEE 31st Computer Security Foundations Symposium (CSF), 268–282, https://doi.org/10.1109/CSF.2018.00027, 2018.
Zhou, Z. H.: Ensemble Methods: Foundations and Algorithms, 236, CRC Press, New York, ISBN 9780429151095, 2012.
