How to measure mast seeding?
2023
Bogdziewicz, Michał | Calama, Rafael | Courbaud, Benoit | Espelta, Josep | Hacket-Pain, Andrew | Journé, Valentin | Kunstler, Georges | Steele, Michael | Qiu, Tong | Zywiec, Magdalena | Clark, James | Uniwersytet im. Adama Mickiewicza w Poznaniu = Adam Mickiewicz University in Poznań (UAM) | Laboratoire des EcoSystèmes et des Sociétés en Montagne (UR LESSEM) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Observatoire des Sciences de l'Univers de Grenoble (Fédération OSUG)-Université Grenoble Alpes (UGA) | Instituto de Ciencias Forestales (ICIFOR) ; Consejo Superior de Investigaciones Cientificas [España] = Spanish National Research Council [Spain] (CSIC) | Centre for Ecological Research and Applied Forestries = Centre de Recerca Ecologica i Aplicacions Forestals (CREAF) ; Consejo Superior de Investigaciones Cientificas [España] = Spanish National Research Council [Spain] (CSIC) | University of Liverpool | Wilkes University | Pennsylvania State University (Penn State) ; Penn State System | W. Szafer Institute of Botany ; Polska Akademia Nauk = Polish Academy of Sciences = Académie polonaise des sciences (PAN) | Duke University [Durham] | Polish National Agency for Academic Exchange (NAWA) PPN/BEK/2020/1/00009/U/00001Polish National Science Centre 2019/35/D/NZ8/00050 OLDPINE AGL-2017-83828-C2.1R. | FORESEE-SHS
International audience
Afficher plus [+] Moins [-]anglais. The periodic production of large seed crops, or masting, is a widespread phenomenon in perennial plants. This behavior can enhance the reproductive efficiency of plants, leading to increased fitness, and produce ripple effects on food webs. While variability from year to year is a defining characteristic of masting, the methods used to quantify this variability are highly debated. The commonly used coefficient of variation lacks the ability to account for the serial dependence in mast data and can be influenced by zeros, making it a less suitable choice for various applications based on individual‐level observations, such as phenotypic selection, heritability, and climate change studies, which rely on individual‐plant‐level datasets that often contain numerous zeros. To address these limitations, we present three case studies and introduce volatility and periodicity, which account for the variance in the frequency domain by emphasizing the significance of long intervals in masting. By utilizing examples of Sorbus aucuparia , Pinus pinea , Quercus robur , Quercus pubescens , and Fagus sylvatica , we demonstrate how volatility captures the effects of variance at both high and low frequencies, even in the presence of zeros, leading to improved ecological interpretations of the results. The growing availability of long‐term, individual‐plant datasets promises significant advancements in the field, but requires appropriate tools for analysis, which the new metrics provide.
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