Paper No. 192-12
Presentation Time: 4:40 PM
SCALE DEPENDENCY OF TEMPORAL TURNOVER IN COMMUNITY COMPOSITION IN THE FOSSIL RECORD
TOMASOVYCH, Adam1, DORNELAS, Maria2, FASTOVICH, David3, FINNEGAN, Seth4, HUANG, Huai-Hsuan5, HULL, Pincelli M.6, KIESSLING, Wolfgang7, KOCSIS, Ádám8, LIOW, Lee Hsiang9, MARGULIS-OHNUMA, Miranda10, MEYERS, Stephen11, PENNY, Amelia M.12, PIPPENGER, Katherine6, RENAUDIE, Johan13, RILLO, Marina14, SAUPE, Erin15, SMITH, Jansen16, STEINBAUER, Manuel J.17, SUGAWARA, Mauro18, YASUHARA, Moriaki19 and WILLIAMS, John20, (1)Earth Science Institute, Slovak Academy of Sciences, Bratislava, 84005, Slovakia, (2)University of St Andrews, St Andrews, KY16 9TH, United Kingdom, (3)Department of Geography, University of Wisconsin-Madison, 550 N. Park Street, Madison, WI 53706, (4)Department of Integrative Biology & Museum of Paleontology, University of California, Berkeley, Valley Life Sciences Building, Berkeley, CA 94720-4780, (5)School of Biological Sciences, The University of Hong Kong, Hong Kong, SAR, China, (6)Department of Earth and Planetary Sciences, Yale University, 210 Whitney Ave, New Haven, CT 06511, (7)GeoZentrum Nordbayern, Friedrich-Alexander-Universität Erlangen-Nürnberg, Loewenichstrasse 28, Erlangen, 91054, Germany, (8)Friedrich-Alexander-UniversitätGeoZentrum Nordbayern, Loewenichstr. 28, Erlangen, D-91054, GERMANY, (9)Natural History Museum, University of Oslo, Oslo, 0562, Norway, (10)Earth and Planetary Sciences, Yale University, Kline Geology Laboratory, 210 Whitney Avenue, New Haven, CT 06511, (11)Department of Geoscience, University of Wisconsin – Madison, Madison, WI 53706, (12)Finnish Museum of Natural History, University of Helsinki, PO Box 44 (Jyrängöntie 2), Helsinki, FI-00014, Finland, (13)Museum für Naturkunde, Invalidenstraße 43, Berlin, D-10115, Germany, (14)University of OldenburgICBM, Schleusenstrasse 1, Wilhelmshaven, 26382, GERMANY, (15)Department of Earth Sciences, University of Oxford, South Parks Road, Oxford, OX1 3AN, United Kingdom, (16)Department of Biology, University of New Mexico, Albuquerque, NM 87131, (17)Bayreuth Center of Ecology and Environmental Research, University of Bayreuth, Dr. Hans-Frisch-Straße 1-3, Bayreuth, 95440, Germany, (18)University of British Columbia, Vancouver, BC V6T1Z4, Canada, (19)School of Biological Sciences, Area of Ecology and Biodiversity, Swire Institute of Marine Science, Institute for Climate and Carbon Neutrality, Musketeers Foundation Institute of Data Science, and State Key Laboratory of Marine Pollution, The University of Hong Kong, Kadoorie Biological Sciences Building, Pokfulam Road, Hong Kong, SAR, China, (20)Department of Geography, University of Wisconsin-Madison, 550 N Park St, Madison, WI 53706
Understanding and quantifying how the rate and magnitude of temporal change in community composition depend on temporal scale (temporal grain, among-sample temporal separation, and temporal duration of time series) is vital in assessments of the importance of present-day ecosystem perturbations induced by anthropogenic impacts, and in comparative analyses of variability in turnover rates in the deep-time fossil record. However, this scale dependency remains poorly explored in paleoecological time series that span several orders of magnitude in duration (from decadal-centennial to tens of millions of years). Here, in the BioDeepTime project, we explore this scale dependency using a compilation of more than 1,000 marine and terrestrial paleoecological time series from several global databases (Neptune, Triton, and Neotoma), supplemented by a new MarBen database devoted to benthic foraminifers, molluscs and ostracods from shelf and deep-sea environments. Specifically, we assess (1) the functional form of temporal decay in compositional similarity (power-law, exponential, linear, or no change), (2) the scale dependency of the temporal rate of change in composition, and (3) the extent to which neutral models predict the observed scaling.We find that the majority of distance-decay relations follow a power-law or exponential model, leading to rates of change that systematically decline with increased duration of time series. Time series with larger temporal separation between samples and longer total durations tend to support power-law models whereas time series with higher resolution support exponential models, in accord with scaling predictions from neutral models. Across datasets, the relationship between pairwise compositional change and among-sample temporal separation (and core duration) is positively rank-correlated. The mean rate of change in community composition per time series scales negatively with between-sample temporal separation. This negative scaling indicates that high magnitudes turnover events observed in time series with high stratigrahic resolution are quickly dampened and lost as between-sample temporal intervals increase.
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