Despite its importance for forest regeneration, food webs, and human economies, changes in tree fecundity with tree size and age remain largely unknown. The allometric increase with tree diameter assumed in ecological models would substantially overestimate seed contributions from large trees if fecundity eventually declines with size. Current estimates are dominated by overrepresentation of small trees in regression models. We combined global fecundity data, including a substantial representation of large trees. We compared size–fecundity relationships against traditional allometric scaling with diameter and two models based on crown architecture. All allometric models fail to describe the declining rate of increase in fecundity with diameter found for 80% of 597 species in our analysis. The strong evidence of declining fecundity, beyond what can be explained by crown architectural change, is consistent with physiological decline. A downward revision of projected fecundity of large trees can improve the next generation of forest dynamic models.

Climate is one of the main factors driving species distributions and global biodiversity patterns.Obtaining accurate predictions of species' range shifts in response to ongoing climate change has thus become a key issue in ecology and conservation. Correlative species distribution models (cSDMs) have become a prominent tool to this aim in the last decade and have demonstrated good predictive abilities with current conditions, irrespective of the studied taxon. However, cSDMs rely on statistical association between species' presence and environmental conditions and have rarely been challenged on their actual capacity to reflect causal relationships between species and climate. In this study, we question whether cSDMs can accurately identify if climate and species distributions are causally linked, a prerequisite for accurate prediction of range shift in relation to climate change. We compared the performance of cSDMs in predicting the distributions of 132 European terrestrial species, chosen randomly within five taxonomic groups (three vertebrate groups and two plant groups), and of 1,320 virtual species whose distribution is causally fully independent from climate. We found that i) for real species, the performance of cSDMs varied principally with range size, rather than with taxonomic groups and ii) cSDMs did not predict the distributions of real species with a greater accuracy than the virtual ones. Our results unambiguously show that the high predictive power of cSDMs can be driven by spatial autocorrelation in climatic and distributional data and does not necessarily reflect causal relationships between climate and species distributions. Thus, high predictive performance of cSDMs does not ensure that they accurately depict the role of climate in shaping species distributions. Our findings therefore call for strong caution when using cSDMs to provide predictions on future range shifts in response to climate change.

Seed production in many plants is characterized by large interannual variation, which is synchronized at subcontinental scales in some species but local in others. The reproductive synchrony affects animal migrations, trophic responses to resource pulses and the planning of management and conservation. Spatial synchrony of reproduction is typically attributed to the Moran effect, but this alone is unable to explain interspecific differences in synchrony. We show that interspecific differences in the conservation of seed production-weather relationships combine with the Moran effect to explain variation in reproductive synchrony. Conservative timing of weather cues that trigger masting allows populations to be synchronized at distances >1000 km. Conversely, if populations respond to variable weather signals, synchrony cannot be achieved. Our study shows that species vary in the extent to which their weather cueing is spatiotemporally conserved, with important consequences, including an interspecific variation of masting vulnerability to climate change.

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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.

Lack of tree fecundity data across climatic gradients precludes the analysis of how seed supply contributes to global variation in forest regeneration and biotic interactions responsible for biodiversity. A global synthesis of raw seedproduction data shows a 250‐fold increase in seed abundance from cold‐dry to warm‐wet climates, driven primarily by a 100‐fold increase in seed production for a given tree size. The modest (threefold) increase in forest productivity across the same climate gradient cannot explain the magnitudes of these trends. The increase in seeds per tree can arise from adaptive evolution driven by intense species interactions or from the direct effects of a warm, moist climate on tree fecundity. Either way, the massive differences in seed supply ramify through food webs potentially explaining a disproportionate role for species interactions in the wet tropics.

The relationships that control seed production in trees are fundamental to understanding the evolution of forest species and their capacity to recover from increasing losses to drought, fire, and harvest. A synthesis of fecundity data from 714 species worldwide allowed us to examine hypotheses that are central to quantifying reproduction, a foundation for assessing fitness in forest trees. Four major findings emerged. First, seed production is not constrained by a strict trade-off between seed size and numbers. Instead, seed numbers vary over ten orders of magnitude, with species that invest in large seeds producing more seeds than expected from the 1:1 trade-off. Second, gymnosperms have lower seed production than angiosperms, potentially due to their extra investments in protective woody cones. Third, nutrient-demanding species, indicated by high foliar phosphorus concentrations, have low seed production. Finally, sensitivity of individual species to soil fertility varies widely, limiting the response of community seed production to fertility gradients. In combination, these findings can inform models of forest response that need to incorporate reproductive potential.

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.