Estimation of pollen productivity and dispersal: How pollen assemblages in small lakes represent vegetation

Liu, Yao, Ogle, Kiona, Lichstein, Jeremy W. and Jackson, Stephen T. (2022) Estimation of pollen productivity and dispersal: How pollen assemblages in small lakes represent vegetation. Ecological Monographs. ISSN 0012-9615 (In Press)

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Despite ongoing advances, quantitative understanding of vegetation dynamics over timespans beyond a century remains limited. In this regard, pollen-based reconstruction of past vegetation enables unique research opportunities by quantifying changes in plant community compositions over hundreds to thousands of years. Critically, the methodological basis for most reconstruction approaches rests upon estimates of pollen productivity and dispersal. However, previous studies have reached contrasting conclusions concerning these estimates, which may be perceived to challenge the applicability and reliability of pollen-based reconstruction. Here we show that conflicting estimates of pollen production and dispersal are, at least in part, artifacts of fixed assumptions of pollen dispersal and insufficient spatial resolution of vegetation data surrounding the pollen-collecting lake. We implemented a Bayesian statistical model that relates pollen assemblages in surface sediments of 33 small lakes (< 2 ha) in the northeastern United States, with surrounding vegetation ranging from 101 to >105 m from the lake margin. Our analysis reveals three key insights. First, pollen productivity is largely conserved within taxa and across forest types. Second, when local (within 1-km radius) vegetation abundances are not considered, pollen-source areas may be overestimated for a number of common taxa (Cupressaceae, Pinus, Quercus, and Tsuga). Third, pollen dispersal mechanisms may differ between local and regional scales, which is missed by pollen-dispersal models used in previous studies. These findings highlight the complex interactions between vegetation heterogeneity on the landscape and pollen dispersal. We suggest that, when estimating pollen productivity and dispersal, both detailed local and extended regional vegetation must be accounted for. Also, both deductive (mechanistic models) and inductive (statistical models) approaches are needed to better understand the emergent properties of pollen dispersal in heterogeneous landscapes.

Item Type: Article
Additional Information: Funding information: This work is funded by National Science Foundation grant EAR-1003848. ORNL is managed by UT-Battelle,LLC, for the DOE under contract DE-AC05-00OR22725. We thank Andria Dawson, Chris Paciorek, Dunbar Car-penter, Jack Williams, Marie-José Gaillard, John Calder,Simon Goring, and Colleen Iversen for discussion andcomment. Major computational tasks were run on North-ern Arizona University’s Monsoon computing cluster,funded by Arizona’s Technology and Research Initiative Fund.
Uncontrolled Keywords: Bayesian statistical model, pollen dispersal, pollen productivity, pollen-based vegetation reconstruction, pollen-vegetation relationship
Subjects: F800 Physical and Terrestrial Geographical and Environmental Sciences
Department: Faculties > Engineering and Environment > Geography and Environmental Sciences
Depositing User: John Coen
Date Deposited: 24 Feb 2022 14:57
Last Modified: 19 Apr 2022 14:57

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