Nutrient scarcity as a selective pressure for mast seeding


Mast seeding is one of the most intriguing reproductive traits in nature. Despite its potential drawbacks in terms of fitness, the widespread existence of this phenomenon suggests that it should have evolutionary advantages under certain circumstances. Using a global dataset of seed production time series for 219 plant species from all of the continents, we tested whether masting behaviour appears predominantly in species with low foliar nitrogen and phosphorus concentrations when controlling for local climate and productivity. Here, we show that masting intensity is higher in species with low foliar N and P concentrations, and especially in those with imbalanced N/P ratios, and that the evolutionary history of masting behaviour has been linked to that of nutrient economy. Our results support the hypothesis that masting is stronger in species growing under limiting conditions and suggest that this reproductive behaviour might have evolved as an adaptation to nutrient limitations and imbalances.

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Fig. 1: Masting behaviour intensity per species and its relationship with potential resource depletion (negative AR1) and temporal variability (PV) of reproductive effort.
Fig. 2: 3D graph showing the interaction between foliar N and P and masting intensity.
Fig. 3: Different optimum values of foliar N and P for subsets of masting and non-masting species.

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Data supporting the findings of this study can be found at and Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government.


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This research was supported by the Spanish Government project CGL2016-79835-P (FERTWARM), European Research Council Synergy Grant ERC-2013-726 SyG-610028 IMBALANCE-P and Catalan Government project SGR 2017-1005. M.F.-M. is a postdoctoral fellow of the Research Foundation – Flanders (FWO). M.B. was supported by (Polish) NSF grants Sonatina 2017/24/C/NZ8/00151 and Uwertura 2018/28/U/NZ8/00003. This research was also supported by NSF grants DEB-1745496 630 to J.M.L. and DEB-1256394 to W.D.K.

Author information

M.F.-M., I.P. and I.A.J. conceived the paper. M.F.-M and F.S. analysed the data. M.F.-M., J.S., J.P., I.P., W.D.K. and J.M.L. provided the data. All authors, including M.B., A.C., A.H.-P., G.V. and J.M.E. contributed substantially to the writing and discussion of the paper.

Correspondence to M. Fernández-Martínez.

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Peer review information Nature Plants thanks Shuli Niu, Ignacio Perez Ramos and the other, anonymous, reviewer for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Evolutionary relationship between potential resource depletion coefficient (AR1) and temporal variability (PV) in seed production.

Evolutionary relationship between potential resource depletion coefficient (AR1) and temporal variability (PV) in seed production shown in a continuous trait phylogenetic reconstruction (a) and a phylomorphospace plot (b). Phylogenetic signal was estimated using Pagel’s lambda (λ). Potential resource depletion and variability in seed production were not evolutionary correlated. Negative values of AR1 indicate that potential resource depletion may happen, see Methods. N=219 species. t- value of the Pearson’s correlation was 1.95 (218 DF).

Extended Data Fig. 2 Mean differences (ΔAICc, second-order Akaike information criterion) between each of the model’s AICc and the model with the lowest AICc.

Mean differences (ΔAICc, second-order Akaike information criterion) between each of the model’s AICc and the model with the lowest AICc. Evolutionary models were Brownian motion (BM1, BMS) and generalised Ornstein-Uhlenbeck-based Hansen (OU1, OUM, OUMV), fitting “masting” and “non-masting” species-state and foliar nutrient concentrations (N: nitrogen, P: phosphorus, N:P: ratio N-to-P and, N×P: N times P (overall nutrient availability). Average AICc values were calculated using the subset of models in which none of them presented negative eigenvalues (sound models, n column: samples, independent simulations). Non-masting and masting columns indicate the number of species used in each category depending on the percentile of masting intensity used to classify species as non-masting (that is, higher than for example, 33%) and masting (that is, lower than for example, 66%). Models with ΔAICc lower than 2 (indicating equal performance) were highlighted. See Methods for further information.

Extended Data Fig. 3 Phylogenetic tree including the subset of low (non-masting) and high masting intensity (masting) species used to perform the generalised Ornstein-Uhlenbeck model results.

Phylogenetic tree including the subset of low (non- masting) and high masting intensity (masting) species used to perform the generalised Ornstein-Uhlenbeck model results presented in the main text (20th – 80th percentile thresholds for non-masting and masting species, Fig. 3, Extended Data Fig. 2 and Extended Data Fig. 4). The phylogenetic tree includes the estimated probability that ancestor nodes were masting or non-masting species (large circles) as pie charts. Small circles indicate the current category of the species. The ancestral character reconstruction was performed using 1000 stochastic character-mapped trees (see Methods for further information).

Extended Data Fig. 4 Estimated foliar nitrogen (N) and phosphorus (P) concentrations, N:P and N×P (overall nutrient availability) optimal values for masting and non-masting species.

Estimated foliar nitrogen (N) and phosphorus (P) concentrations, N:P and N×P (overall nutrient availability) optimal values for masting and non-masting species using OUMV and OUM models (see Methods for further information about the models), chosen based on the lowest ΔAICc estimating different state means for masting and non-masting species (Extended Data Fig. 2). Masting and non-masting species were classified depending on the percentile of masting intensity (for example, masting for higher than 66%, non-masting for lower than 33%, see subheaders within the table). Columns 2.5%, 50 and 97.5% indicate, for masting and non-masting species, the percentiles of the optimal values based on the sound models (without negative eigenvalues, n column: samples, independent simulations) used. M>N% indicate the percentage of models in which masting species presented average higher N, P, N:P or N×P optimal values than non-masting species. ΔM-N, followed by s.e.m (standard error of the mean), indicate the paired (across simulations) difference between optimal values in masting and non-masting species. P (two-sided t-test) shows the P-value of the paired t-test testing for differences in the mean optimal values of masting and non-masting species. ΔM-N%, followed by s.e.m., indicates the average percentual difference (geometric, paired differences) in mean optimal values between masting and non-masting species.

Extended Data Fig. 5 Evolutionary relationship between foliar N and P shown in a continuous trait phylogenetic reconstruction (a) and a phylomorphospace plot (b).

Evolutionary relationship between foliar N and P shown in a continuous trait phylogenetic reconstruction (a) and a phylomorphospace plot (b). Phylogenetic signal was estimated using Pagel’s lambda (λ). Foliar N and P concentrations were evolutionary correlated. N=168 species. t-value of the Pearson’s correlation was 5.38 (166 DF).

Extended Data Fig. 6 Map showing interannual variability (PV index) in mean annual precipitation (MAP) and site of origin of our fruit production data (blue dots).

Map showing interannual variability (PV index) in mean annual precipitation (MAP) and site of origin of our fruit production data (blue dots).

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Fernández-Martínez, M., Pearse, I., Sardans, J. et al. Nutrient scarcity as a selective pressure for mast seeding. Nat. Plants 5, 1222–1228 (2019).

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