Masting is shaped by tree-level attributes and stand structure, more than climate, in a Rocky Mountain conifer species

Wion, Andreas P.; Pearse, Ian S.; Rodman, Kyle C.; Veblen, Thomas T.; Redmond, Miranda D.

doi:10.1016/j.foreco.2023.120794

Cited by 13 publications

(15 citation statements)

References 62 publications

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“…The interannual variability and synchrony of seed production in plants show a significant degree of intraspecific variation, which is largely attributed to plant size and related mean fecundity (Minor & Kobe, 2017; Pesendorfer et al ., 2020; Bogdziewicz et al ., 2020c; Wion et al ., 2023). The size‐related variation in masting has several key implications, including the impact on the spatial dynamics of stand regeneration at both local and regional levels (Clark et al ., 2021; Gratzer et al ., 2022), and on the ontogeny of masting (Pesendorfer et al ., 2021).…”

Section: Improving Inference In Masting Studiesmentioning

confidence: 99%

How to measure mast seeding?

et al. 2023

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Summary 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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Section: Improving Inference In Masting Studiesmentioning

confidence: 99%

How to measure mast seeding?

et al. 2023

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“…the spatial scale of seed production synchrony, the importance of post-seeding resource depletion on next year's seed production) (Touzot et al, 2018;Tattoni et al, 2021), and the objective of the forecast. Further research into the physiology of resource depletion following seed production holds promise for improvement, including examining how stand age/mean size or local factors (soil type, competition) affect the limiting role of post-mast resource depletion on subsequent year's seed production (Pesendorfer et al, 2020;Wion et al, 2023), or whether extreme mast years can result in two consecutive mast failures.…”

Section: What Is Next?mentioning

confidence: 99%

Forecasting seed production in perennial plants: identifying challenges and charting a path forward

Journé

Hacket‐Pain

Oberklammer

et al. 2023

Preprint

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Interannual variability and spatial synchrony of seed production, known as masting, have far-reaching ecological impacts including effects on forest regeneration and the population dynamics of seed consumers, predators, parasites, and diseases. Because the relative timing of management and conservation efforts in ecosystems dominated by masting species often determines their success, there is an urgent need to study masting mechanisms and develop anticipatory forecasting tools for seed production. In this study, we aim to establish seed production forecasting ("mast casts") as a new branch of the discipline. To assess the current state of the ability to model intrinsic and extrinsic drivers of masting, we evaluate the predictive capabilities of three models -foreMast, ΔT, and a sequential model based on the developmental steps of seed production - designed to predict seed production in trees using a pan-European dataset of Fagus sylvatica seed production as a case study. Our hindcasting analysis indicates that the models are moderately successful in recreating seed production dynamics, with the best-performing model achieving R2 = 0.66. The availability of high-quality data on prior seed production significantly improved the model's predictive power, with R2 = 0.78 in the sequential model, suggesting that effective seed production monitoring methods are crucial for creating reliable forecasting tools. In terms of extreme events, the models were better at predicting crop failures than bumper crops, likely because the factors preventing seed production are better understood than the processes leading to large reproductive events. To create widely applicable tools, we need models that can make forecasts over large areas with readily available data. We summarize the current challenges and provide a roadmap to help advance the discipline and encourage the further development of mast forecasting.

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“…the spatial scale of seed production synchrony, the importance of post‐seeding resource depletion on next year's seed production; Touzot et al ., 2018; Tattoni et al ., 2021), and the objective of the forecast. Further research into the physiology of resource depletion following seed production holds promise for improvement, including examining how stand age/mean size or local factors (soil type, competition) affect the limiting role of post‐mast resource depletion on subsequent year's seed production (Pesendorfer et al ., 2020; Wion et al ., 2023), or whether extreme mast years can result in two consecutive mast failures.…”

Section: What Is Next?mentioning

confidence: 99%

Forecasting seed production in perennial plants: identifying challenges and charting a path forward

et al. 2023

View full text Add to dashboard Cite

Interannual variability of seed production, known as masting, has far-reaching ecological impacts including effects on forest regeneration and the population dynamics of seed consumers. Because the relative timing of management and conservation efforts in ecosystems dominated by masting species often determines their success, there is a need to study masting mechanisms and develop forecasting tools for seed production. Here, we aim to establish seed production forecasting as a new branch of the discipline. We evaluate the predictive capabilities of three models -foreMast, DT, and a sequential modeldesigned to predict seed production in trees using a pan-European dataset of Fagus sylvatica seed production. The models are moderately successful in recreating seed production dynamics. The availability of high-quality data on prior seed production improved the sequential model's predictive power, suggesting that effective seed production monitoring methods are crucial for creating forecasting tools. In terms of extreme events, the models are better at predicting crop failures than bumper crops, likely because the factors preventing seed production are better understood than the processes leading to large reproductive events. We summarize the current challenges and provide a roadmap to help advance the discipline and encourage the further development of mast forecasting.

show abstract

Masting is shaped by tree-level attributes and stand structure, more than climate, in a Rocky Mountain conifer species

Cited by 13 publications

References 62 publications

How to measure mast seeding?

How to measure mast seeding?

Forecasting seed production in perennial plants: identifying challenges and charting a path forward

Forecasting seed production in perennial plants: identifying challenges and charting a path forward

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