# Intraspecific variation in masting across climate gradients is inconsistent with the environmental stress hypothesis

Jessie J Foest, Thomas Caignard, Ian S Pearse, Michal Bogdziewicz, Andrew Hacket-pain

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Jessie J Foest, Thomas Caignard, Ian S Pearse, Michal Bogdziewicz, Andrew Hacket-pain. Intraspecific variation in masting across climate gradients is inconsistent with the environmental stress hypothesis. Ecology, 2025, 106 (4), pp.e70076. 10.1002/ecy.70076. hal-05168893

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# Intraspecific variation in masting across climate gradients is inconsistent with the environmental stress hypothesis

Jessie J. Foest<fnref n="1" />,<fnref n="2" /> ID | Thomas Caignard<fnref n="3" /> | Ian S. Pearse<fnref n="4" /> ID

Michał Bogdziewicz<fnref n="2" /> ID | Andrew Hacket-Pain<fnref n="1" /> ID

**Correspondence**

Jessie J. Foest

Email: j.j.foest@protonmail.com

**Funding information**

PHC Alliance programme, Grant/Award
Number: 814433447; European Research
Council, ForestFuture, Grant/Award
Number: 101039066; Natural
Environment Research Council,
Grant/Award Numbers: NE/S00713X/1,
NE/S007857/1; French National Research
Agency, University of Bordeaux
Experimental Forest project,
Grant/Award Number:
ANR-20-IDES-0001

Handling Editor: Lin Meng

**Abstract**

Year-to-year variation in seed crop size (i.e., masting) varies strongly among populations of the same species. Understanding what causes this variation is vital, as masting affects the ability of tree species to regenerate and determines the population dynamics of a wide variety of animals. It is commonly thought that environmental stress is a key driver of masting variability. The environmental stress hypothesis posits that more marginal conditions increase the strength of masting. Using 437 time series from 19 tree species, we find that this hypothesis fails to fully explain how masting varies across marginality gradients. We expected higher interannual variation and less frequent masting events at species margins but instead found that while mast years are indeed less frequent, the interannual variation was lower toward the margins. The observed patterns suggest that populations growing at the margins may invest more resources in low seed production years compared with their conspecifics, hedging their bets in these more challenging environments.

**KEYWORDS**

alternate bearing, climate gradients, climate marginality, intraspecific variation, mast seeding, masting, reproductive strategy

## INTRODUCTION

For many long-lived plant species from diverse taxa and ecosystems, seed production is highly variable between years, and individuals within populations synchronize their reproductive effort (Dale et al., 2021; Pearse

et al., 2016). The resulting population-level variability of seed production is known as masting (Kelly, 1994; Pearse et al., 2016). A widely held hypothesis to explain masting variability is that plants growing in resource-poor, stressful, or marginal environments show more pronounced masting (the “environmental stress,”, or “productivity

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Ecology* published by Wiley Periodicals LLC on behalf of The Ecological Society of America.

<footnote n="1">¹Geography and Planning, School of Environmental Sciences, University of Liverpool, Merseyside, Liverpool, UK</footnote>

<footnote n="2">²Forest Biology Center, Faculty of Biology, Adam Mickiewicz University, Poznań, Wielkopolska, Poland</footnote>

<footnote n="3">³INRAE, BIOGECO, Université de Bordeaux, Pessac, Gironde, France</footnote>

<footnote n="4">⁴U.S. Geological Survey, Fort Collins Science Center, Fort Collins, Colorado, USA</footnote>
gradient” hypothesis) (Allen et al., 2012; Kelly & Sork, 2002; Pearse et al., 2017; Roland et al., 2014; Satake & Bjørnstad, 2008; Wion et al., 2020, 2023). Here, we show that masting varies over environmental gradients, but that the environmental stress hypothesis does not explain this spatial variation.

We require a thorough understanding of what drives within-species variation in masting, given the important effects of masting on ecosystems. Masting increases reproductive efficiency through economies of scale that decrease seed predation rates and improve pollination efficiency (Kelly & Sork, 2002; Pearse et al., 2016). Understanding these reproductive patterns in plants is increasingly important in the context of climate change, because plant reproduction is a critical component of forest dynamics following climate-driven disturbance and environmental change (Sharma et al., 2022). Climate-driven changes to mast seeding can severely limit the viability of seeds (Bogdziewicz et al., 2023). Moreover, seeds represent a foundational food source, and masting affects animal populations throughout food webs (Clark et al., 2019).

The environmental stress hypothesis predicts that comparatively more stressful, lower productivity environments have less frequent large seed crops, resulting in higher interannual variability of seed production when compared with less limited populations (Allen et al., 2012; Kelly & Sork, 2002; Pearse et al., 2017; Satake & Bjørnstad, 2008). According to the hypothesis, such environments limit how quickly plants can accumulate sufficient resources to produce a bumper crop. With increasingly challenging growing conditions, we thus expect lower seed production in years following high seed crops (i.e., stronger resource depletion), causing the temporal autocorrelation at lag one year to become more negative (Koenig & Knops, 2000; Sork et al., 1993).

Until now, the environmental stress hypothesis has not been tested at a large scale. Testing is complicated by the fact that many factors can reduce plant productivity, and the stresses experienced by plants vary by species and location. This may help explain why the few studies that have examined the effects of different climate and productivity gradients on masting find contradicting responses, that is, positive, negative, or neutral responses (Table 1). We propose a way to find commonalities between species in their responses to stress gradients by using the concept of “climate marginality.”

The limit of a species' range is a manifestation of its ecological niche (Sexton et al., 2009). If the species range edges are near equilibrium (i.e., the species can be found throughout most of its potential distribution), climate marginality theory predicts that populations at the climatic periphery more frequently experience the effect of biotic and abiotic limiting factors compared with

populations living in core conditions and that demographic performance would consequently be impaired (Kunstler et al., 2021; Pironon et al., 2017; Sexton et al., 2009). Changes in reproduction patterns toward climatically marginal sites could result from the limited availability of resources, or from higher demands on these resources for maintenance or overcoming increased competition (Gaston, 2009; Roland et al., 2014), or because of more frequent extreme climate events that affect reproduction such as late spring frosts and summer droughts (Journé et al., 2021; Nussbaumer et al., 2020; Willi & Van Buskirk, 2022). All in all, we expect to observe changes in masting behavior across climate marginality gradients, with masting becoming stronger with increasing climate marginality.

Here, we present the first attempt to generalize, across 19 tree species, the effect of environmental stress on masting. While intraspecific variation in masting by populations is widely acknowledged (Crone et al., 2011; Greene & Johnson, 2004; Herrera et al., 1998; Satake & Bjørnstad, 2008), it has remained largely unclear how much intraspecific variation of masting exists within species across space, and what drives this variability. Thus far, studies designed to assess the effect of environmental stress on intraspecific masting variability have been constrained in terms of spatial and species coverage. Now, due to a recent synthesis of data on reproductive behavior in plants (Hackett-Pain et al., 2022), we are able to test the long-standing environmental stress hypothesis across species and extensive environmental gradients.

We expect to observe parabolic relationships between climate variables and masting metrics. Specifically, we expect (1) seed production to be more variable, (2) more negatively autocorrelated through time (indicative of a stronger resource limitation; Koenig et al., 2003), and (3) have less frequent large seed crops in populations growing in marginal climates. In contrast, range centers should be characterized by less variable, less autocorrelated, and more frequent large seed crops.

## MATERIALS AND METHODS

We describe the variability of masting for populations from 19 tree species occurring in mid-to-high latitudes using three masting metrics. Subsequently, we test whether these metrics vary along climate marginality gradients in the ways predicted by the environmental stress hypothesis. For the six best-represented species in terms of data coverage, we also fit models to gain insight into the species-specific responses behind the overall, across species, pattern. All models were fitted in R v. 4.3.1 (R Core Team, 2023).
<table><caption>**TABLE 1** Relationships between environmental gradients and metrics capturing temporal reproductive patterns per tree species reported in the literature.</caption><thead><tr><th rowspan="2">Metric</th><th rowspan="2">Gradient</th><th rowspan="2">Decrease</th><th colspan="2">Direction of effect</th><th rowspan="2">Species</th></tr><tr><th>Neutral</th><th>Increase</th></tr></thead><tbody><tr><td>CVp</td><td>Elevation</td><td>Masaki et al. (2020), Roland et al. (2014)</td><td></td><td></td><td>Fagus crenata, Swida controversa; Picea glauca</td></tr><tr><td></td><td></td><td></td><td>Lázaro et al. (2006), Masaki et al. (2020), Mencuccini et al. (1995), Mooney et al. (2011)</td><td></td><td>Buxus balearica; Quercus serrata, Castanea crenata, Prunus grayana; Picea abies; Pinus ponderosa</td></tr><tr><td></td><td></td><td></td><td></td><td>Buechling et al. (2016), Kelly et al. (2001), Kelly et al. (2008), Masaki et al. (2020), Sullivan and Kelly (2000), Webb and Kelly (1993)</td><td>Picea engelmannii; Nothofagus solandri; Chionochloa pallens; Chionochloa macra; Quercus crispula; Chionochloa rubra; Subalpine plants</td></tr><tr><td></td><td>Elevation & latitude</td><td></td><td>LaMontagne et al. (2021)</td><td></td><td>Abies, Picea, Pinus, Tsuga</td></tr><tr><td></td><td>Latitude</td><td></td><td>Pearse et al. (2020)</td><td></td><td>Global scale</td></tr><tr><td></td><td>Nitrogen</td><td>Tanentzap et al. (2012), Smaill et al. (2011)</td><td></td><td></td><td>Chionochloa spp.; Nothofagus solandri</td></tr><tr><td></td><td>Water availability</td><td>Espelta et al. (2008), Wion et al. (2020)</td><td></td><td></td><td>Quercus humilis; Pinus edulis</td></tr><tr><td></td><td></td><td></td><td>Bogdziewicz et al. (2020), Espelta et al. (2008), Fernández-Martínez et al. (2012), Le Roncé (2021)</td><td></td><td>Quercus ilex, Phillyrea latifolia, Arbutus unedo; Quercus ilex; Quercus ilex, Quercus pubescens; Quercus ilex</td></tr><tr><td>CVi & S</td><td>Water availability</td><td></td><td>Wion et al. (2023)</td><td></td><td>Pinus ponderosa</td></tr><tr><td>AR(1)</td><td>Water availability</td><td>Barringer et al. (2013)</td><td></td><td></td><td>Quercus lobata, Quercus agrifolia</td></tr><tr><td>Periodicity</td><td>Elevation</td><td>Allen et al. (2012)</td><td></td><td></td><td>Nothofagus solandri</td></tr><tr><td>Mast frequency</td><td>Elevation</td><td>Mencuccini et al. (1995)</td><td></td><td></td><td>Picea abies</td></tr><tr><td></td><td>Nitrogen</td><td></td><td>Tanentzap et al. (2012)</td><td></td><td>Chionochloa spp.</td></tr></tbody></table>

Note: Reference details in Appendix S1: Section S1.

Abbreviations: AR(1), 1-year lagged autocorrelation; CVi: coefficient of variation of tree-level seed crop size; CVp, coefficient of variation of population-level seed crop size; S, between-tree synchrony.

## Intraspecific variation in reproductive patterns

### Reproduction time series

We obtained time series of annual reproduction from the open-access MASTREE+ database (Hacket-Pain

et al., 2022). This database collates geo-referenced reproductive time series from perennial plant species across the world. We subset MASTREE+ to seed, fruit, and cone production time series of species which had extensive temporal (≥10 years of observation) and spatial (≥10 sites) replication. From here on, seeds, fruit, and cones will be referred to as seeds, for simplicity. Quercus robur and
Quercus petraea time series were grouped together, as the often sympatric species have high hybridization rates (Abadie et al., 2012), and synchronize their seed production (Bogdziewicz et al., 2017). We only included series representing stand and patch scale populations (excluding [super-]regional scale records) with high-precision spatial references, to accurately link reproductive behavior and climate. Moreover, to ensure the masting metrics could be meaningfully calculated and compared, we excluded series which lacked a unit (i.e., ordinal data, index data) or which had synchrony-based units (i.e., "% of individuals reproducing"). Comparable collection methods were grouped together before conducting the analyses, and classified as either seed trap data, seed counts, or harvest records. This approach resulted in a dataset of 8082 annual records, forming 437 time series of the reproductive behavior of 19 plant species in 357 locations (Appendix S1: Table S1). On average, time series were 18.5 years long (median: 14, range: 10–62).

## Temporal reproductive patterns

Three metrics capturing temporal reproductive patterns were calculated for each time series to describe masting variability. Firstly, we calculated the coefficient of variation (CVp), the standard deviation divided by the mean annual seed crop size (i.e., number of seeds). Temporal autocorrelation was calculated with the 1-year lagged autocorrelation, AR(1), and obtained with the Acf function in R (Hyndman et al., 2022). Lastly, we calculated the proportion of high seed crop years, Psd. High seed crop years (“mast years”) were defined as years when the standardized annual deviate of reproductive effort exceeded the absolute magnitude of the largest deviate below the mean, as proposed by LaMontagne and Boutin (2007). Additionally, we calculated the recently introduced kCVp, a bounded alternative to CVp, which was found to increase statistical power when compared with CVp when testing for latitudinal patterns in masting (Lobry et al., 2023). This metric can be calculated from the CVp by dividing the squared CVp by 1 + the squared CVp and subsequently taking the square root.

## Environmental gradients

### Climate data and species' ranges

Climate data for each location were extracted from WorldClim v. 2.1 (Fick & Hijmans, 2017). Temperature variables were adjusted (lapse rate: 0.65°C/100 m) when the elevation of MASTREE+ time series was known and

deviated from the SRTM data used for WorldClim (Fick & Hijmans, 2017). To assess the climate gradients covered by our sampling, we extracted climate data for species distributions. Species distributions for all but two species were obtained from EUFORGEN (2022) and the Atlas of United States Trees (Petry, 2021). The range of *Fagus crenata* was digitized from Kobashi et al. (2006), and a digitized range of *Araucaria araucana* was provided by CONICET-UNS (M. Hadad, CONICET-UNS, unpublished data, 2023). For most species in the dataset, wide spatial and elevational gradients were sampled (Appendix S1: Figure S1 and Table S1), capturing a large part of the climatic envelope (Appendix S1: Figure S2 and Table S5).

## Linear models

### *Across-species patterns*

We tested for intraspecific effects of climate gradients on temporal reproductive patterns (i.e., CVp, AR1, and Psd) using the glmmTMB package (v. 1.1.7; Brooks et al., 2017). To allow for comparisons between CVp and kCVp, we also substituted kCVp in the final CVp models.

While the inclusion of the species as a predictor captures between-species differences in the dependent variable, this does not account for potential between-species differences in the other predictors (Pearse et al., 2020; Van de Pol & Wright, 2009). We therefore subject-centered all dependent and independent numeric variables by subtracting the species' (i.e., "subject") mean of each respective variable, except for climate variables (N.B. models using these data are referred to as "subject-centered models"). For climate variables, we subtracted the median value observed in the species' range, which allowed for explicit testing of our hypotheses, as the centered value captures distance from the core climate.

For each metric, we ran two models, namely (1) an annual average model, including mean annual temperature (MAT), annual precipitation (AP), the interaction between MAT and AP, as well the second order polynomials of MAT and AP as predictors, and (2) a seasonal model, using the temperatures and precipitation of the coldest (cq) and hottest (hq) quarter of the year, the temperature-precipitation interactions for both quarters (i.e., $T_{cq} \times P_{cq}$, $T_{hq} \times P_{hq}$) and the second order polynomials of both temperature and precipitation variables. Since all sites are situated in mid-to-high latitudes, the hottest and coldest quarter correspond to summer and winter, respectively. We accounted for methodological differences and time series properties by including as predictors (1) time series length, (2) a categorical classification of the measured reproductive variable
(i.e., “cone,” “fruit,” “seed”), and (3) the data collection method (i.e., “harvest record,” “seed trap,” “count-based method”). Since centering removes the average effect of species, no random factor was used to capture between-species differences. The AR(1) models showed a statistically significant latitudinal trend in the model residuals, and we therefore included subject-centered “Latitude” as a predictor in these models. All numeric predictor variables were scaled using the root mean square to ensure model convergence. We allowed the dispersion of residuals to vary as a function of species, collection method, and reproductive variable. Models were examined and validated with DHARMa (v. 0.4.6), by simulating model residuals and plotting these against observations and predictors to check for misfits, as well as testing for correct distribution (KS test), dispersion, and outliers (Hartig & Lohse, 2022). Additionally, we tested for collinearity with car (v. 3.1.2; Fox & Weisberg, 2019). To assess the significance of the linear terms in these across-species models, we conducted likelihood ratio tests. Standard *p*-values reflect conditional hypotheses and are not suitable for evaluating the independent contributions of predictors (*x*) due to the inclusion of quadratic terms (*I*(*x*²)). Specifically, for each linear climate predictor, we compared a “full” model—the model described above but excluding the quadratic and interaction terms involving the focal predictor—to a “reduced” model in which the linear term itself was also removed (see Appendix S1: Table S2).

### Species-specific patterns

Since mean trends may obscure ecologically relevant processes, we also fitted species-specific linear models with glmmTMB for CVp, AR(1), and Psd for six species with ≥30 observations. We used the same predictors as those used in the subject-centered models, but since only a single species was examined in each model, variables other than the climate variables were not species-centered. Variable scaling was not required for convergence. If there was no variation in reproductive variable or collection method for a species, the variable could not be included in the model. The collection method predictor was removed from the *Picea glauca* models because it exceeded the collinearity threshold ($GVIF_{2 \times df}^{1/2} > \sqrt{10}$).

# RESULTS

## Intraspecific variation

Reproductive patterns varied widely within species (Appendix S1: Table S1; Figures 1 and 2). Notably, for 14 out of 19 species, the observed intraspecific range of

CVp values was greater than the interspecific variation in mean CVp (0.89–1.94, Appendix S1: Table S1; Figure 1). Similarly, the intraspecific variation in AR(1) of 16 species exceeded the observed interspecific variation in mean AR(1) (–0.39–0.01, Appendix S1: Table S1; Figure 1). The majority of time series, both within and across species, had negative AR(1) values. All intraspecific ranges in Psd exceeded the interspecific range in mean Psd (0.11–0.21, Appendix S1: Table S1; Figure 1).

The species-specific metric space plots in Figure 2 highlight how intraspecific variation in CVp and AR(1) in some species covers large portions of the across-species metric space (e.g., *Alnus incana*: 49.5%, *Picea abies*: 32.9%). In contrast, some species have high intraspecific variability in only one metric (e.g., CVp of *Picea engelmannii*, AR(1) of *Pinus sylvestris*), or relatively low intraspecific variability in both CVp and AR(1) (e.g., *Abies amabilis*: 2.9%, *Quercus douglasii*: 3.7%).

## Climate marginality

### Across-species patterns

Large seeding years were less frequent toward more marginal climates, which is consistent with predictions of the environmental stress hypothesis. In the annual model, Psd responded to within-species change in MAT² (Figure 3, climate effects: Table 2, full model: Appendix S1: Table S3). Sites with a climate closely matching the species' range median MAT displayed more frequent masting. It should be noted that while the concave relationship between Psd and AP best fitted the observations, this relationship was not statistically significant. Squared winter temperatures predicted Psd in the seasonal model, with the lowest masting frequencies occurring at cold and warm sites. Thus, annual climate patterns appear to be more closely related to climatic conditions in the winter rather than in the summer.

Yet, these changes in the frequency of large seeding years did not result in a higher CVp toward the margins, as predicted by the environmental stress hypothesis. CVp varied significantly with temperature, with the annual model predicting lower CVp in the warmest sites, and no effect of precipitation was observed (Figure 3, climate effects: Table 2, full model: Appendix S1: Table S3). The seasonal model shows that the effect of winter temperature depends on winter precipitation. For populations growing in sites close to the species' median winter precipitation level, the CVp declined with increasing winter temperatures. Combined, these findings show that the relationships between CVp and marginality gradients are more complex than anticipated under the environmental
<figure><img src="image_2.png" /><figcaption>**FIGURE 1** Intraspecific variation in masting metrics often exceeds interspecific variation. The shaded gray rectangle indicates the interspecific range in metric values. The colors of the violin plots indicate plant families, with blue-greens reserved for gymnosperms and orange-reds for angiosperms. AR(1), 1-year lagged autocorrelation; CVp, coefficient of variation of population-level seed crop size; Psd, the proportion of large seed production years. See Figure 2 for genus names.</figcaption></figure>

stress hypothesis. The simulated kCVp model residuals showed deviations from normality (Kolmogorov-Smirnov; annual model: *D* = 0.08, *p* < 0.05; seasonal model: *D* = 0.09, *p* < 0.01), but the model results are presented in Appendix S1: Figure S3 and Table S3 to allow for comparisons with the CVp models. The results from the annual average kCVp model imply that year-to-year variability in seed production decreases toward cold and warm margins, not just toward the warmer sites as in the CVp model.

These findings directly contrast with the predictions under the environmental stress hypothesis. While the detected climate patterns differ from the annual average CVp model, the same methodological variables were found to have effects in both the annual average CVp and kCVp models (i.e., reproductive variable and data collection method). In the seasonal model, a similarly nuanced story emerges, and the observed relationships between climate and the temporal variability of seed production also
<figure><img src="image_3.png" /><figcaption>**FIGURE 2** Within-species variation in masting metrics covers a large part of across-species masting metric space for some, but not all tree species. The top-left plot shows how three metrics co-vary. (AR(1), 1-year lagged autocorrelation; CVp, coefficient of variation of population-level seed crop size; Psd, the proportion of large seed production years.) Dashed-line convex hull: Variation of CVp and AR(1) across all analyzed species. Solid-line convex hull: the CVp and AR(1) metric space of all 378 species (1018 time series) in MASTREE+ with time series matching sub-setting criteria, irrespective of whether they had adequate spatial replication for inclusion in our analyses. Species-specific metric space is shown in surrounding plots. Percentages in the top-right corner of plots show the ratio of the solid to dashed convex hull area. Outlined time series in the top-left plot are plotted in the “Subsample” plots (3 random time series [1960–2020] per region; regions marked with 1–3 and A–C) to illustrate example time series. Example time series are scaled by their maximum value.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**FIGURE 3** Climate marginality explains variation in masting, but patterns diverge from predictions under the environmental stress hypothesis. The three columns on the left show response surfaces for the subject-centered models of masting metrics CVp, AR(1), and Psd (AR(1), 1-year lagged autocorrelation; CVp, coefficient of variation of population-level seed crop size; Psd, the proportion of large seed production years) at baseline levels of the centered and scaled predictor variables. The annual average model is found in the first column, and the results of the seasonal model (cq, coldest quarter; hq, hottest quarter; P, precipitation; T, temperature) are found in columns 2–3. The boundaries of the response surfaces in columns 1–3 were determined by a convex hull of climate data of the time series (open circles). The right-most column shows an alternative representation of the annual model found in the first column. Specifically, it shows the marginal effects (and associated 95% CIs, shaded) for mean annual temperature (MAT) at different levels of annual precipitation (AP; blue colors) in the annual CVp, AR(1), and Psd models, alongside partial residuals. Spp., species.</figcaption></figure>

differed. A concave relationship was found between squared winter temperatures and kCVp, rather than the significant interaction between winter temperature and precipitation detected in the CVp model. Both models show that kCVp is not simply a more powerful variant of CVp. Instead, different relationships may be detected.

Lastly, how AR(1) varies along climate marginality gradients is not well explained by the predictions under the environmental stress hypothesis. Intraspecific variation in AR(1) varied with MAT in the annual climate model, where sites with higher MATs had lower AR(1) values (Figure 3, climate effects: Table 2, full
<table><caption>**TABLE 2** Relationships between climate variables and masting metrics CVp, AR(1), and Psd found using subject-centered annual average and seasonal models.</caption><thead><tr><th rowspan="2">Term</th><th colspan="3">CVp</th><th colspan="3">AR(1)</th><th colspan="3">Psd</th></tr><tr><th>Estimate</th><th>z</th><th>p-value</th><th>Estimate</th><th>z</th><th>p-value</th><th>Estimate</th><th>z</th><th>p-value</th></tr></thead><tbody><tr><td colspan="10"><strong>Annual</strong></td></tr><tr><td>AP</td><td>9.73 x 10<sup>-3</sup><br>(3.07 x 10<sup>-2</sup>)</td><td>0.32</td><td>0.75</td><td>1.49 x 10<sup>-3</sup><br>(1.29 x 10<sup>-2</sup>)</td><td>0.12</td><td>0.91</td><td><strong>9.67 x 10<sup>-3</sup></strong><br>(<strong>4.51 x 10<sup>-3</sup></strong>)</td><td><strong>2.14</strong></td><td><strong>0.03</strong></td></tr><tr><td>AP<sup>2</sup></td><td>-1.40 x 10<sup>-3</sup><br>(1.38 x 10<sup>-2</sup>)</td><td>-0.10</td><td>0.92</td><td>-2.86 x 10<sup>-3</sup><br>(5.32 x 10<sup>-3</sup>)</td><td>-0.54</td><td>0.59</td><td>-3.75 x 10<sup>-3</sup><br>(2.03 x 10<sup>-3</sup>)</td><td>-1.84</td><td>0.07</td></tr><tr><td>MAT</td><td><strong>-6.21 x 10<sup>-2</sup></strong><br>(<strong>2.69 x 10<sup>-2</sup></strong>)</td><td><strong>-2.31</strong></td><td><strong>0.02</strong></td><td><strong>-3.45 x 10<sup>-2</sup></strong><br>(<strong>1.10 x 10<sup>-2</sup></strong>)</td><td><strong>-3.13</strong></td><td><strong>&lt;0.01</strong></td><td>-1.31 x 10<sup>-3</sup><br>(4.08 x 10<sup>-3</sup>)</td><td>-0.32</td><td>0.75</td></tr><tr><td>MAT x AP</td><td>1.43 x 10<sup>-2</sup><br>(1.95 x 10<sup>-2</sup>)</td><td>0.73</td><td>0.47</td><td>-1.51 x 10<sup>-2</sup><br>(9.75 x 10<sup>-3</sup>)</td><td>-1.55</td><td>0.12</td><td>2.37 x 10<sup>-3</sup><br>(3.55 x 10<sup>-3</sup>)</td><td>0.67</td><td>0.50</td></tr><tr><td>MAT<sup>2</sup></td><td>-8.66 x 10<sup>-3</sup><br>(8.77 x 10<sup>-3</sup>)</td><td>-0.99</td><td>0.32</td><td>9.95 x 10<sup>-3</sup><br>(5.95 x 10<sup>-3</sup>)</td><td>1.67</td><td>0.09</td><td><strong>-4.23 x 10<sup>-3</sup></strong><br>(<strong>2.06 x 10<sup>-3</sup></strong>)</td><td><strong>-2.05</strong></td><td><strong>0.04</strong></td></tr><tr><td colspan="10"><strong>Seasonal</strong></td></tr><tr><td>hq P</td><td>-4.08 x 10<sup>-3</sup><br>(2.77 x 10<sup>-2</sup>)</td><td>-0.15</td><td>0.88</td><td>2.29 x 10<sup>-3</sup><br>(1.09 x 10<sup>-2</sup>)</td><td>0.21</td><td>0.83</td><td>-1.74 x 10<sup>-3</sup><br>(3.81 x 10<sup>-3</sup>)</td><td>-0.46</td><td>0.65</td></tr><tr><td>hq P<sup>2</sup></td><td>6.14 x 10<sup>-3</sup><br>(1.22 x 10<sup>-2</sup>)</td><td>0.50</td><td>0.61</td><td><strong>-1.14 x 10<sup>-2</sup></strong><br>(<strong>3.98 x 10<sup>-3</sup></strong>)</td><td><strong>-2.87</strong></td><td><strong>&lt;0.01</strong></td><td>1.35 x 10<sup>-3</sup><br>(1.60 x 10<sup>-3</sup>)</td><td>0.84</td><td>0.40</td></tr><tr><td>hq T</td><td>5.15 x 10<sup>-3</sup><br>(2.44 x 10<sup>-2</sup>)</td><td>0.21</td><td>0.83</td><td>2.32 x 10<sup>-4</sup><br>(8.66 x 10<sup>-3</sup>)</td><td>0.03</td><td>0.98</td><td>-1.07 x 10<sup>-3</sup><br>(3.21 x 10<sup>-3</sup>)</td><td>-0.33</td><td>0.74</td></tr><tr><td>hq T x hq P</td><td>-4.49 x 10<sup>-3</sup><br>(1.83 x 10<sup>-2</sup>)</td><td>-0.25</td><td>0.81</td><td><strong>-2.18 x 10<sup>-2</sup></strong><br>(<strong>7.17 x 10<sup>-3</sup></strong>)</td><td><strong>-3.05</strong></td><td><strong>&lt;0.01</strong></td><td>-2.91 x 10<sup>-3</sup><br>(2.70 x 10<sup>-3</sup>)</td><td>-1.08</td><td>0.28</td></tr><tr><td>hq T<sup>2</sup></td><td>-5.03 x 10<sup>-3</sup><br>(1.33 x 10<sup>-2</sup>)</td><td>-0.38</td><td>0.70</td><td>-1.79 x 10<sup>-3</sup><br>(5.07 x 10<sup>-3</sup>)</td><td>-0.35</td><td>0.72</td><td>1.07 x 10<sup>-3</sup><br>(1.91 x 10<sup>-3</sup>)</td><td>0.56</td><td>0.58</td></tr><tr><td>cq P</td><td>1.96 x 10<sup>-2</sup><br>(3.15 x 10<sup>-2</sup>)</td><td>0.62</td><td>0.54</td><td>1.78 x 10<sup>-2</sup><br>(1.25 x 10<sup>-2</sup>)</td><td>1.43</td><td>0.15</td><td>6.74 x 10<sup>-3</sup><br>(4.87 x 10<sup>-3</sup>)</td><td>1.39</td><td>0.17</td></tr><tr><td>cq P<sup>2</sup></td><td>-1.36 x 10<sup>-2</sup><br>(1.01 x 10<sup>-2</sup>)</td><td>-1.35</td><td>0.18</td><td><strong>-8.32 x 10<sup>-3</sup></strong><br>(<strong>3.79 x 10<sup>-3</sup></strong>)</td><td><strong>-2.20</strong></td><td><strong>0.03</strong></td><td>-2.23 x 10<sup>-3</sup><br>(1.53 x 10<sup>-3</sup>)</td><td>-1.46</td><td>0.14</td></tr><tr><td>cq T</td><td><strong>-1.02 x 10<sup>-1</sup></strong><br>(<strong>4.45 x 10<sup>-2</sup></strong>)</td><td><strong>-2.30</strong></td><td><strong>0.02</strong></td><td><strong>-5.96 x 10<sup>-2</sup></strong><br>(<strong>1.92 x 10<sup>-2</sup></strong>)</td><td><strong>-3.10</strong></td><td><strong>&lt;0.01</strong></td><td>8.26 x 10<sup>-3</sup><br>(5.98 x 10<sup>-3</sup>)</td><td>1.38</td><td>0.17</td></tr><tr><td>cq T x cq P</td><td><strong>4.46 x 10<sup>-2</sup></strong><br>(<strong>2.11 x 10<sup>-2</sup></strong>)</td><td><strong>2.11</strong></td><td><strong>0.03</strong></td><td>8.31 x 10<sup>-3</sup><br>(1.40 x 10<sup>-2</sup>)</td><td>0.59</td><td>0.55</td><td>3.42 x 10<sup>-3</sup><br>(4.53 x 10<sup>-3</sup>)</td><td>0.75</td><td>0.45</td></tr><tr><td>cq T<sup>2</sup></td><td>-4.68 x 10<sup>-3</sup><br>(1.45 x 10<sup>-2</sup>)</td><td>-0.32</td><td>0.75</td><td>7.67 x 10<sup>-3</sup><br>(8.76 x 10<sup>-3</sup>)</td><td>0.88</td><td>0.38</td><td><strong>-8.13 x 10<sup>-3</sup></strong><br>(<strong>2.53 x 10<sup>-3</sup></strong>)</td><td><strong>-3.21</strong></td><td><strong>&lt;0.01</strong></td></tr></tbody></table>

Note: SEs within parentheses. *p*-values were calculated with Wald tests via the summary() function. For linear predictors, these *p*-values reflect conditional hypotheses and should not be interpreted as evidence of independent contributions due to the inclusion of quadratic terms as *I(x²)*. Significant effects are in bold, and underlined values indicate that the independent linear predictor was also significant in likelihood ratio tests comparing a model with the predictor (excluding interactions and quadratic terms) to one without it, to test the independent contributions. These likelihood ratio results can be found in Appendix S1: Table S2.

Abbreviations: AP, annual precipitation; AR(1), 1-year lagged autocorrelation; cq, coldest quarter; CVp, coefficient of variation of population-level seed crop size; hq, hottest quarter; MAT, mean annual temperature; P, precipitation; Psd, the proportion of large seed production years; T, temperature.

model: Appendix S1: Table S3). Similarly to the annual model, the seasonal model indicates AR(1) was lower when sites experienced warmer temperatures in winter. AR(1) decreased toward both winter precipitation margins, that is, toward dry and wet sites. The effect of the summer temperatures depended on precipitation; lower AR(1) occurred in drier and colder sites, as well as in

warmer and wetter sites. Summer precipitation also showed a parabolic relationship with AR(1), where AR(1) was lowest at the wettest and driest sites. Thus, we only found limited evidence for concave relationships in the seasonal model.

Overall, we find that masting patterns vary across marginality gradients. However, this variation differs from
predictions under the environmental stress hypothesis (Table 3). In addition to these diverging relationships, we also find that there is substantial, and potentially ecologically meaningful, variation around these predictions (see partial residuals, Figure 3). This indicates that factors other than climate marginality or methodological differences play an important role in driving masting variability.

## Species-specific patterns

There is always a risk that ecologically meaningful relationships go undetected if trends are only tested at a general level. Or, phrased differently, there is a chance that the hypothesis holds, but only for a subset of species. Consequently, we tested how masting metrics varied across annual climate marginality gradients for the six best-sampled species (Appendix S1: Table S4). Here, we reveal, where applicable, evidence of species-specific responses matching the predictions of the environmental stress hypothesis.

Evidence in line with our prediction that Psd would be lower in the climatic margins was found for *P. abies* and *P. sylvestris*. Specifically, we observed concave effects on Psd of MAT for *P. abies* and of AP for *P. sylvestris*, although highest Psd values were found in sites with higher-than-median precipitation in this species.

The relationship between CVp and climate did not generally match the predicted convex pattern of highest variability in more marginal climates, with the exceptions of the effect of MAT on *F. sylvatica* and the effect of AP on *Pinus edulis* (although this later effect was not statistically significant [p = 0.06]).

For AR(1), the evidence in line with predictions is the concave relationship between AR(1) and AP in *Q. robur/ petraea*, and between AP and temperature and AR(1) in *P. abies*.

It is worth noting that these species-specific models have relatively small sample sizes (Appendix S1: Table S1), and therefore have lower power than the subject-centered models. Yet, these models reveal that species responses are diverse.

## DISCUSSION

The environmental stress hypothesis fails to explain intraspecific masting variation when evaluated across climate marginality gradients (Table 3). Under the environmental stress hypothesis, we would predict higher interannual variation and, simultaneously, less frequent large seeding events at species' margins (Allen et al., 2012; Kelly & Sork, 2002; Pearse et al., 2017; Roland et al., 2014; Satake & Bjørnstad, 2008; Wion et al., 2020, 2023). Instead, we found that while high seeding years indeed became less frequent toward the margins, the interannual variation becomes lower instead of higher toward the warm margin (or toward warm and cold margins for the alternative kCVp metric). Combined, these patterns signal more constant seed production toward the margins, not stronger masting. The patterns in the temporal autocorrelation are also indicative of more constant seed production. When there are few large seeding events (which happens toward the warm margins), this average relationship is determined mostly by pairs of subsequent low reproduction years. If

<table><caption>TABLE 3 Summary of main findings, showing whether the relationship between climate and masting metrics matched predictions under the environmental stress hypothesis.</caption><thead><tr><th colspan="6">Environmental stress hypothesis</th></tr><tr><th>Metric</th><th>Prediction</th><th>Climate variable</th><th>Supported</th><th>Results</th><th></th></tr></thead><tbody><tr><td rowspan="2">Psd</td><td rowspan="2">Less frequent masting in more marginal climates</td><td>MAT</td><td>Yes</td><td>Less frequent masting in cool and warm sites</td><td></td></tr><tr><td>AP</td><td>Partially</td><td>Less frequent masting in dry and wet sites, but not significant</td><td></td></tr><tr><td rowspan="2">AR(1)</td><td rowspan="2">More negative autocorrelation in more marginal climates</td><td>MAT</td><td>No</td><td>More negative autocorrelation in warm sites</td><td></td></tr><tr><td>AP</td><td>No</td><td>No significant effects</td><td></td></tr><tr><td rowspan="2">CVp</td><td rowspan="2">Higher variability in more marginal climates</td><td>MAT</td><td>No</td><td>Lower variability in warm sites</td><td></td></tr><tr><td>AP</td><td>No</td><td>No significant effects</td><td></td></tr><tr><td rowspan="2">kCVp</td><td rowspan="2">Higher variability in more marginal climates</td><td>MAT</td><td>No</td><td>Lower variability in cold & warm sites</td><td></td></tr><tr><td>AP</td><td>No</td><td>No significant effects</td><td></td></tr></tbody></table>

Abbreviations: AP, annual precipitation; AR(1), 1-year lagged autocorrelation; CVp, coefficient of variation of population-level seed crop size; MAT, mean annual temperature; Psd, the proportion of large seed production years.
populations shift from zero or extremely low seed output
in low seed crop years to increased seed production in
some of these years, the AR(1) would decrease, but the
overall seed crop would become more constant. Overall,
our results provide solid support that masting changes
across climate gradients, but not in the way expected
under the environmental stress hypothesis.

Our work helps to develop a clearer picture of how
reproduction varies across climate space. Various studies
have found that the number of flowers, fruits, and seeds
produced at the edge of the geographic distribution range
was comparable to that produced at the geographic cen-
ter (Abeli et al., 2014; Pironon et al., 2017). However, in
masting populations, reproductive outcomes depend not
only on the number of seeds produced but also on the
temporal variability of these seed crops (Bogdziewicz
et al., 2023). The more constant reproduction observed
toward climate margins has many potential conse-
quences for ecosystem dynamics. For example, less fre-
quent mast years of large-seeded species like beech and
oak lead to declines in seed consumer populations
(Touzot et al., 2020). In addition to the general, across
species, trends in masting variability across space, we
find evidence that species-specific responses to climate
gradients are diverse. This implies that the masting of
some species is more responsive to climatic variation
than others.

In this analysis, the spatial sampling was limited by
the availability of long-term reproductive data. Future
efforts which deliberately sample marginal populations
over decades would be highly valuable, and would help
to detect species-specific signals of stress on masting.
However, such data are currently still limited.
Regardless, our data were sampled across strong gradi-
ents, which, for the best-sampled species, cover the
reported climatic envelope of the species (see
Appendix S1: Section S2).

Our first large-scale assessment of within-species vari-
ation in masting shows that it can exceed the
among-species variation. *P. glauca*, for example, is a spe-
cies that is typically associated with masting (i.e., high
variability of seed production). Yet, we found that the
CVp of populations of this species ranged from very high
(3.34) to moderately low (0.93). This range is more than
double the among-species range in mean CVp
(0.89–1.94). Such high intraspecific variability provides
further evidence of the pitfalls of classifying species as
“masting” or “non-masting” (Kelly, 2023), and illum-
nates the need for caution when working with
species-level averages: such models implicitly assume
that species means are meaningful and that the intraspe-
cific variation in the trait of interest is lower than the
interspecific variation (Garamszegi, 2014; Westerband

et al., 2021). Moreover, the striking extent of intraspecific
variation in masting suggests that the downstream conse-
quences of masting may be more spatially variable than
assumed. Given the important effects of masting on wider
ecosystems, it is crucial that we develop a thorough
understanding of what causes masting to vary so much
between populations.

Masting at species climate margins was characterized
by less frequent large seeding years and lower
interannual variation in seed production. The observed
patterns might be explained by larger reproductive
investment in small seed production years toward species
margins, as has been observed for European beech
(Müller-Haubold et al., 2015). Theory predicts that the
costs of masting, such as the risk of dying before produc-
ing bumper crops, increase with decreased adult survi-
vorship and that longer periods between large seeding
years elevate these costs (Journé et al., 2023;
Waller, 1979). Therefore, lower survival rates at marginal
populations together with lower frequencies of producing
large seeding years may translate into higher reproduc-
tive investment in low seed production years, a form of
bet hedging in these more challenging environments.
Moreover, lower benefits to masting may also occur in
these sites if seed predator pressures are lower
(Alexander et al., 2007; Vaupel & Matthies, 2012). Future
work considering masting variability alongside factors
determining the cost–balance equation of masting bene-
fits is therefore required.

The observed variance in masting patterns around the
effects of climate marginality suggests that factors beyond
climate contribute to intraspecific masting variability.
The importance of local environmental effects was dem-
onstrated in *Pinus ponderosa*, where intraspecific varia-
tion in masting was associated with stand characteristics
including stand density, tree size, and age (Wion
et al., 2023). Plant ontogeny was also found to be an
important determinant of masting in some temperate for-
est trees (Pesendorfer et al., 2020). If these stand charac-
teristics explain a large proportion of variability in
masting, then the effects of climate change on masting
and the availability of viable seeds at the climatic margins
might be alleviated through appropriate management
strategies (Bogdziewicz et al., 2023). A limited number of
studies indicate that additional reproductive variability is
explained by provenance (Caignard et al., 2021;
Mark, 1965), but data from reciprocal transplant or com-
mon gardens established along climate gradients are still
needed.

Due to the rapid accumulation of data on reproduc-
tive behaviors (Hacket-Pain et al., 2022; Pearse
et al., 2020), we have been able to test the long-standing
environmental stress hypothesis and unveil the
remarkable within-species variation in masting. We find masting variation across climate space is inconsistent with the environmental stress hypothesis. High seed crop years are less frequent at the climatic periphery, but, importantly, the year-to-year variability of seed crops is lower toward the warm margin (or warm and cold margins for the alternative kCVp metric). As climate change may drive more populations to the climatic periphery, it is urgent that we establish if the spatial patterns observed here translate into temporal patterns. Recent work shows that this is not the case in European beech (Foest et al., 2024). Moreover, more work is required to establish whether changes in masting result in lower reproductive efficiency. Lower interannual variation at marginal populations may translate into less effective predator saturation, higher losses to seed predators, and lower reproductive efficiency (Zwolak et al., 2022). The large unexplained variation around the effects of climate not only sets important challenges, but also offers management potential (e.g., managing stand age and structure). If the source of this variability results from stand characteristics, we may be able to develop appropriate management strategies to optimize reproductive efficiency.

## AUTHOR CONTRIBUTIONS

Jessie J. Foest, Andrew Hacket-Pain, and Thomas Caignard conceptualized the study, and Jessie J. Foest led the development of the methodology, with support from Andrew Hacket-Pain, Ian S. Pearse, and Thomas Caignard. Investigation, formal analysis, and data visualization were performed by Jessie J. Foest. Jessie J. Foest wrote the original draft, and all authors contributed critical feedback on the manuscript. Andrew Hacket-Pain provided supervision.

## ACKNOWLEDGMENTS

The authors would like to acknowledge Akiko Satake and Martin Hadad for species distribution maps, Jalene LaMontagne, and Iris Oberklammer for discussions about the initial results. At the time of publication, *Araucaria* range data were not available from CONICET-UNS. This work was supported by a PhD studentship to Jessie J. Foest under Natural Environment Research Council grant number NE/S00713X/1, by the Natural Environment Research Council grant number NE/S007857/1 to Andrew Hacket-Pain, and by the “PHC Alliance” program, funded by the UK Department for Business, Energy & Industrial Strategy, the French Ministry for Europe and Foreign Affairs, and the French Ministry of Higher Education, Research and Innovation (grant number 814433447). Thomas Caignard was supported by the French National Research Agency, University of Bordeaux Experimental Forest project

(ANR-20-IDES-0001, ACT project). Michał Bogdziewicz was supported by the European Union (ERC, ForestFuture, 101039066). The views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US government.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

## DATA AVAILABILITY STATEMENT

Data and code (Foest et al., 2025) are available in Figshare at https://doi.org/10.6084/m9.f figshare.26355463.v1.

## ORCID

Jessie J. Foest https://orcid.org/0000-0002-8236-4646

Ian S. Pearse https://orcid.org/0000-0001-7098-0495

Michał Bogdziewicz https://orcid.org/0000-0002-6777-9034

Andrew Hacket-Pain https://orcid.org/0000-0003-3676-1568

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Foest, Jessie J., Thomas Caignard, Ian S. Pearse, Michał Bogdziewicz, and Andrew Hacket-Pain. 2025. "Intraspecific Variation in Masting across Climate Gradients is Inconsistent with the Environmental Stress Hypothesis." *Ecology* 106(4): e70076. https://doi.org/10.1002/ecy.70076