DeepDive: estimating global biodiversity patterns through time using deep learning – Nature.com
Sepkoski, J. J. A factor analytic description of the phanerozoic marine fossil record. Paleobiology 7, 3653 (1981).
Article Google Scholar
Quental, T. B. & Marshall, C. R. Diversity dynamics: molecular phylogenies need the fossil record. Trends Ecol. Evol. 25, 434441 (2010).
Article PubMed Google Scholar
Ezard, T. H., Aze, T., Pearson, P. N. & Purvis, A. Interplay between changing climate and species ecology drives macroevolutionary dynamics. Science 332, 349351 (2011).
Article ADS CAS PubMed Google Scholar
Benton, M. J. Exploring macroevolution using modern and fossil data. Proc. R. Soc. B: Biol. Sci. 282, 20150569 (2015).
Article Google Scholar
Niklas, K. J. Measuring the tempo of plant death and birth. N. Phytol. 207, 254256 (2015).
Article Google Scholar
Rabosky, D. L. & Hurlbert, A. H. Species richness at continental scales is dominated by ecological limits. Am. Nat. 185, 572583 (2015).
Article PubMed Google Scholar
Harmon, L. J. & Harrison, S. Species diversity is dynamic and unbounded at local and continental scales. Am. Nat. 185, 584593 (2015).
Article PubMed Google Scholar
Sepkoski Jr, J. Phanerozoic overview of mass extinction. In Patterns and Processes in the History of Life: Report of the Dahlem Workshop on Patterns and Processes in the History of Life Berlin 1985, June 1621, 277295 (Springer, 1986).
Benton, M. J. & Emerson, B. C. How did life become so diverse? the dynamics of diversification according to the fossil record and molecular phylogenetics. Palaeontology 50, 2340 (2007).
Article Google Scholar
Alroy, J. Geographical, environmental and intrinsic biotic controls on phanerozoic marine diversification. Palaeontology 53, 12111235 (2010).
Article Google Scholar
Weber, M. G., Wagner, C. E., Best, R. J., Harmon, L. J. & Matthews, B. Evolution in a community context: on integrating ecological interactions and macroevolution. Trends Ecol. Evol. 32, 291304 (2017).
Article PubMed Google Scholar
Niklas, K. J., Tiffney, B. H. & Knoll, A. H. Patterns in vascular land plant diversification. Nature 303, 614 616 (1983).
Article Google Scholar
Foote, M., Miller, A., Raup, D. & Stanley, S.Principles of Paleontology (W. H. Freeman, 2007). https://books.google.ch/books?id=8TsDC2OOvbYC
Close, R., Benson, R., Saupe, E., Clapham, M. & Butler, R. The spatial structure of phanerozoic marine animal diversity. Science 368, 420424 (2020).
Article ADS CAS PubMed Google Scholar
Raja, N. B. et al. Colonial history and global economics distort our understanding of deep-time biodiversity. Nat. Ecol. Evol. 6, 145154 (2022).
Article PubMed Google Scholar
Smith, A. B. & McGowan, A. J. The ties linking rock and fossil records and why they are important for palaeobiodiversity studies. Geol. Soc. Lond. Spec. Publ. 358, 17 (2011).
Article ADS Google Scholar
Benson, R., Butler, R., Close, R., Saupe, E. & Rabosky, D. Biodiversity across space and time in the fossil record. Curr. Biol. 31, R1225R1236 (2021).
Article CAS PubMed Google Scholar
Smith, A. B. Largescale heterogeneity of the fossil record: implications for phanerozoic biodiversity studies. Philos. Trans. R. Soc. Lond. Ser. B: Biol. Sci. 356, 351367 (2001).
Article CAS Google Scholar
Alroy, J. Fair sampling of taxonomic richness and unbiased estimation of origination and extinction rates. Paleontol. Soc. Pap. 16, 5580 (2010).
Article Google Scholar
Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 25332547 (2012).
Article PubMed Google Scholar
Raup, D. Taxonomic diversity estimation using rarefaction. Paleobiology 1, 333342 (1975).
Article Google Scholar
Alroy, J. et al. Effects of sampling standardization on estimates of phanerozoic marine diversification. Proc. Natl Acad. Sci. 98, 62616266 (2001).
Article ADS CAS PubMed PubMed Central Google Scholar
Starrfelt, J. & Liow, L. H. How many dinosaur species were there? fossil bias and true richness estimated using a poisson sampling model. Philos. Trans. R. Soc. B: Biol. Sci. 371, 20150219 (2016).
Article Google Scholar
Flannery-Sutherland, J. T., Silvestro, D. & Benton, M. J. Global diversity dynamics in the fossil record are regionally heterogeneous. Nat. Commun. 13, 117 (2022).
Article Google Scholar
Chao, A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics 43, 783791 (1987).
Alroy, J. Limits to species richness in terrestrial communities. Ecol. Lett. 21, 17811789 (2018).
Article PubMed Google Scholar
Alroy, J. On four measures of taxonomic richness. Paleobiology 46, 158175 (2020).
Article Google Scholar
Close, R., Evers, S., Alroy, J. & Butler, R. How should we estimate diversity in the fossil record? testing richness estimators using sampling-standardised discovery curves. Methods Ecol. Evol. 9, 13861400 (2018).
Article Google Scholar
Close, R. et al. The apparent exponential radiation of phanerozoic land vertebrates is an artefact of spatial sampling biases. Proc. R. Soc. B 287, 20200372 (2020).
Article PubMed PubMed Central Google Scholar
Antell, G. T., Benson, R. B. & Saupe, E. E. Spatial standardization of taxon occurrence dataa call to action. Paleobiology https://doi.org/10.1017/pab.2023.36 (2024).
Dunne, E. M., Thompson, S. E., Butler, R. J., Rosindell, J. & Close, R. A. Mechanistic neutral models show that sampling biases drive the apparent explosion of early tetrapod diversity. Nat. Ecol. Evol. 7, 14801489 (2023).
Article PubMed PubMed Central Google Scholar
Hauffe, T., Pires, M. M., Quental, T. B., Wilke, T. & Silvestro, D. A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils. Methods Ecol. Evol. 13, 12011213 (2022).
Article Google Scholar
Cermeo, P. et al. Post-extinction recovery of the phanerozoic oceans and biodiversity hotspots. Nature 607, 507511 (2022).
Article ADS PubMed PubMed Central Google Scholar
Hagen, O. et al. gen3sis: a general engine for eco-evolutionary simulations of the processes that shape earths biodiversity. PLoS Biol. 19, e3001340 (2021).
Article CAS PubMed PubMed Central Google Scholar
Hagen, O., Skeels, A., Onstein, R. E., Jetz, W. & Pellissier, L. Earth history events shaped the evolution of uneven biodiversity across tropical moist forests. Proc. Natl Acad. Sci. 118, e2026347118 (2021).
Article CAS PubMed PubMed Central Google Scholar
Vilhena, D. A. & Smith, A. B. Spatial bias in the marine fossil record. PLoS One 8, e74470 (2013).
Article ADS CAS PubMed PubMed Central Google Scholar
Raup, D. M. Taxonomic diversity during the phanerozoic: the increase in the number of marine species since the paleozoic may be more apparent than real. Science 177, 10651071 (1972).
Article ADS CAS PubMed Google Scholar
Raup, D. M. Species diversity in the phanerozoic: a tabulation. Paleobiology 2, 279288 (1976).
Article Google Scholar
Foote, M., Crampton, J. S., Beu, A. G. & Nelson, C. S. Aragonite bias, and lack of bias, in the fossil record: lithological, environmental, and ecological controls. Paleobiology 41, 245265 (2015).
Article Google Scholar
Silvestro, D., Salamin, N. & Schnitzler, J. Pyrate: a new program to estimate speciation and extinction rates from incomplete fossil data. Methods Ecol. Evol. 5, 11261131 (2014).
Article Google Scholar
Cantalapiedra, J. L. et al. The rise and fall of proboscidean ecological diversity. Nat. Ecol. Evol. 5, 12661272 (2021).
Article PubMed Google Scholar
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533536 (1986).
Article ADS Google Scholar
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 17351780 (1997).
Article CAS PubMed Google Scholar
Gers, F., Schmidhuber, J. & Cummins, F. Learning to forget: continual prediction with lstm. Neural Comput. 12, 24512471 (2000).
Article CAS PubMed Google Scholar
Gal, Y. & Ghahramani, Z. A theoretically grounded application of dropout in recurrent neural networks. Adv. Neural Inform. Process. Syst. 29, 19 (2016).
Gal, Y. & Ghahramani, Z. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In International Conference on Machine Learning 48, 10501059 (PMLR, 2016).
Silvestro, D. & Andermann, T. Prior choice affects ability of bayesian neural networks to identify unknowns. arXiv preprint arXiv:2005.04987 (2020).
Brusatte, S. L. et al. The extinction of the dinosaurs. Biol. Rev. 90, 628642 (2015).
Article PubMed Google Scholar
Dunne, E. M., Farnsworth, A., Greene, S. E., Lunt, D. J. & Butler, R. J. Climatic drivers of latitudinal variation in late triassic tetrapod diversity. Palaeontology 64, 101117 (2021).
Article Google Scholar
De Celis, A., Narvez, I., Arcucci, A. & Ortega, F. Lagersttte effect drives notosuchian palaeodiversity (crocodyliformes, notosuchia). Historical Biol. 33, 30313040 (2021).
Article Google Scholar
Cleary, T. J., Benson, R. B., Holroyd, P. A. & Barrett, P. M. Tracing the patterns of non-marine turtle richness from the triassic to the palaeogene: from origin to global spread. Palaeontology 63, 753774 (2020).
Article Google Scholar
Silvestro, D. et al. Fossil data support a pre-Cretaceous origin of flowering plants. Nat. Ecol. Evol. 5, 449457 (2021).
Leuenberger, C. & Wegmann, D. Bayesian computation and model selection without likelihoods. Genetics 184, 243252 (2010).
Article PubMed PubMed Central Google Scholar
Marjoram, P., Molitor, J., Plagnol, V. & Tavar, S. Markov chain monte carlo without likelihoods. Proc. Natl Acad. Sci. 100, 1532415328 (2003).
Article ADS CAS PubMed PubMed Central Google Scholar
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DeepDive: estimating global biodiversity patterns through time using deep learning - Nature.com
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