Linking seed size and number to trait syndromes in trees

Abstract

Aim Our understanding of the mechanisms that maintain forest diversity under changing climate can benefit from knowledge about traits that are closely linked to fitness. We tested whether the link between traits and seed number and seed size is consistent with two hypotheses, termed the leaf economics spectrum and the plant size syndrome, or whether reproduction represents an independent dimension related to a seed size–seed number trade‐off. Location Most of the data come from Europe, North and Central America and East Asia. A minority of the data come from South America, Africa and Australia. Time period 1960–2022. Major taxa studied Trees. Methods We gathered 12 million observations of the number of seeds produced in 784 tree species. We estimated the number of seeds produced by individual trees and scaled it up to the species level. Next, we used principal components analysis and generalized joint attribute modelling (GJAM) to map seed number and size on the tree traits spectrum. Results Incorporating seed size and number into trait analysis while controlling for environment and phylogeny with GJAM exposes relationships in trees that might otherwise remain hidden. Production of the large total biomass of seeds [product of seed number and seed size; hereafter, species seed productivity (SSP)] is associated with high leaf area, low foliar nitrogen, low specific leaf area (SLA) and dense wood. Production of high seed numbers is associated with small seeds produced by nutrient‐demanding species with softwood, small leaves and high SLA. Trait covariation is consistent with opposing strategies: one fast‐growing, early successional, with high dispersal, and the other slow‐growing, stress‐tolerant, that recruit in shaded conditions. Main conclusions Earth system models currently assume that reproductive allocation is indifferent among plant functional types. Easily measurable seed size is a strong predictor of the seed number and species seed productivity. The connection of SSP with the functional traits can form the first basis of improved fecundity prediction across global forests.

How to measure mast seeding?

“…Volatility can also be used in comparative species-or populationlevel studies, but in those cases, it will require the estimation of absolute values of annual seed production (Qiu et al, 2023). This is now possible using the mastif model that accommodates both seed trap and crop count data to estimate absolute seed production of individual trees (Clark et al, 2019;Journé et al, 2022;Bogdziewicz et al, 2023).…”

“…In other words, we tested whether processes (such as pollination or seed consumption) are related to masting patterns (such as volatility or synchrony) once temporal patterns in the latter were extracted. Further details on the GJAM fitting and conditional predictions can be found in Qiu et al (2021) and Bogdziewicz et al (2023).…”

Evolution of masting in plants is linked to investment in low tissue mortality

“…In summary, our analysis supports the idea that the extent of year-to-year variation in masting is regulated by a species’ phylogeny, location (climate), and life history (plant form). The effects of climate and phylogeny on mast seeding and functional traits necessitated conditional predictions that extracted direct associations 27 , 28 . A PCA analysis that combined all the ways in which variables can be linked suggested that masting created a third, mostly independent dimension of variation in plant traits.…”

“…The GJAM allowed us to accommodate the dependence between traits and phylogeny as random groups. To this end, we followed past studies that used a similar approach 27 , 67 , and grouped species according to genus or family (when the genus had <10 species). We used the ’multiple’ category for families with <5 species.…”

“…Certain taxa may exhibit large interannual variations in seed production and high stem tissue density even if environmental conditions that select one or both traits change. Traditional summaries such as principal component analysis (PCA) summarize correlations that include all the indirect ways traits could be associated 26 , 27 . To address this issue, novel methods such as joint attribute modeling enable the decomposition of relationships into direct and indirect, driven by either climate or phylogeny 26 , 28 .…”