How to measure mast seeding?

Author:

Bogdziewicz Michał12ORCID,Calama Rafael3ORCID,Courbaud Benoit2,Espelta Josep M.4ORCID,Hacket‐Pain Andrew5ORCID,Journé Valentin1ORCID,Kunstler Georges2,Steele Michael6,Qiu Tong7ORCID,Zywiec Magdalena8,Clark James S.29ORCID

Affiliation:

1. Forest Biology Centre, Institute of Environmental Biology, Faculty of Biology Adam Mickiewicz University Uniwersytetu Poznanskiego 6 Poznan 61‐614 Poland

2. Institut National de Recherche Pour Agriculture, Alimentation et Environnement (IN23‐RAE), Laboratoire EcoSystemes et Societes En Montagne (LESSEM) Université Grenoble Alpes St Martin‐d'Hères 38402 France

3. Instituto de Ciencias Forestales (INIA‐CSIC) Madrid 28040 Spain

4. Centre de Recerca Ecologica i Aplicacions Forestals (CREAF) Bellaterra Catalonia 08193 Spain

5. Department of Geography and Planning, School of Environmental Sciences University of Liverpool Liverpool UK

6. Department of Biology Wilkes University 84 West South Street Wilkes‐Barre PA 18766 USA

7. Department of Ecosystem Science and Management Pennsylvania State University University Park State College PA 16802 USA

8. W. Szafer Institute of Botany, Polish Academy of Sciences Lubicz 46 Kraków 31‐512 Poland

9. Nicholas School of the Environment Duke University Durham NC 27708 USA

Abstract

SummaryThe 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.

Funder

Narodowa Agencja Wymiany Akademickiej

Narodowe Centrum Nauki

Publisher

Wiley

Subject

Plant Science,Physiology

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