The BioDeepTime Project seeks to address one of the central challenges in biodiversity science by compiling and harmonizing ecological time series from modern and fossil sources to investigate how biological dynamics and drivers vary across timescales ranging from months to millions of years. The project was launched as Paleosynthesis Center working group in 2020, resulting in the BioDeepTime database v1.0, and continues to pursue questions related to temporal dynamics of biodiversity.
Our core motivation is the need to understand the normal rate and magnitude of biodiversity change to disentangle to effect of humanity on natural communities from natural drivers. Achieving this understanding requires multiple advances including
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the development of a comprehensive database of ecological and paleontological time series of assemblages across biomes, habitats, clades, time, and time scales (grain, resolution, duration)
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accounting for differences in signal preservation and bias, temporal resolution and grain, spatial resolution and grain, and ecological and evolutionary drivers
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theory
Stephen J. Gould fought to include paleontology at the high table of evolutionary biology as the fossil record captures information that cannot be inferred from modern taxa alone. Paleoecological records have a similar role to play in advancing our understanding of ecology. By bringing paleontologists and ecologists together with an ever-expanding set of empirical records, the BioDeepTime Project aims to remake the ecological high table with insights garnered across temporal scales.
The vast scope of this project means that we welcome new members and the initiatives of all team members from driving new inquiries to leading papers and proposals. Our project was made possible by the decades of work in databases like Neotoma, BioTime, Neptune, Triton, Paleobiology Database, the Geobiodiversity Database and the many theoretical, analytical, and synthetic advances of our colleagues through the years.
The next phase of BioDeepTime, sTime, has just been funded by sDiv to advance the theoretical and model-based synthesis needed to infer drivers across temporal scales, and to expanded BioDeepTime v1.0 to BioDeepTime v2.0.