Author Correction: Continent-wide tree fecundity driven by indirect climate effects

Clark, James S.; Andrus, Robert A.; Aubry‐Kientz, Mélaine; Bergeron, Yves; Bogdziewicz, Michał; Bragg, Don C.; Brockway, Dale G.; Cleavitt, Natalie L.; Cohen, Susan; Courbaud, Benoı̂t; Daley, Robert P.; Das, Adrian J.; Dietze, Michael C.; Fahey, Timothy J.; Fer, Istem; Franklin, Jerry F.; Gehring, Catherine A.; Gilbert, Gregory S.; Greenberg, Cathryn H.; Guo, Qinfeng; HilleRisLambers, Janneke; Ibáñez, Inés; Johnstone, Jill F.; Kilner, Christopher L; Knops, Johannes M. H.; Koenig, Walter D.; Künstler, Georges; LaMontagne, Jalene M.; Legg, Kristin; Luongo, Jordan; Lutz, James A.; Macias, Diana; McIntire, Eliot J. B.; Messaoud, Yassine; Moore, Christopher; Moran, Emily; Myers, Jonathan A.; Myers, Orrin; Núñez, Chase L.; Parmenter, Robert; Pearse, Sam; Pearson, Scott M.; Poulton-Kamakura, Renata; Ready, Ethan; Redmond, Miranda D.; Reid, Chantal D.; Rodman, Kyle C.; Scher, C. Lane; Schlesinger, William H.; Schwantes, Amanda M.; Shanahan, Erin; Sharma, Shubhi; Steele, Michael; Stephenson, Nathan L.; Sutton, Samantha; Swenson, Jennifer J.; Swift, Margaret; Veblen, Thomas T.; Whipple, Amy V.; Whitham, Thomas G.; Wion, Andreas P.; Zhu, Kai; Zlotin, Roman

doi:10.1038/s41467-021-22025-2

Cited by 1 publication

(7 citation statements)

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“…Estimation of the number of seeds produced in perennial plants suffers from an extreme signal‐to‐noise problem, created by orders of magnitude variation from year to year and tree to tree (Clark et al, 2004; Pearse et al, 2020; Pesendorfer et al, 2021) that can bury any trend (Clark et al, 2021). There are as many time series as there are trees that must be modelled together because there is dependence created by among‐tree synchrony in masting variation (Bogdziewicz et al, 2021; Crone et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

“…Masting patterns are complicated further by the spatio‐temporal variation in habitat and climate (Pearse et al, 2020; Pesendorfer et al, 2021). The many sources of variation mean that estimation of a seed number produced by trees can be achieved only from broad coverage and large sample sizes while accounting for the condition of individual trees, their local habitat and climate (Clark et al, 2021; Qiu, Aravena, et al, 2021; Sharma et al, 2022). This is achieved here with the MASTIF model (Clark et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…The MASTIF model and data from the MASTIF network are summarized here and have been described extensively in recent papers (Clark et al, 2019(Clark et al, , 2021Journé et al, 2022;Qiu, Aravena, et al, 2021;Sharma et al, 2022). The tree-year observations of seed numbers in the network come from seed traps and from crop counts.…”

Section: Seed Number Ssp and Mastif Modelmentioning

confidence: 99%

“…The MASTIF model incorporates the effects of tree attributes with the environment on maturation and conditional fecundity. To allow for an uncertain identification of seeds from trees of the same genus and for dependence within trees over time and between trees, all tree-years of a genus are modelled jointly (Clark et al, 2019(Clark et al, , 2021.…”

Section: Seed Number and Ssp At The Tree And Species Levelsmentioning

confidence: 99%

“…Nonetheless, given that recruitment varies for each species at each site, the species‐level reproductive effort could be a valuable extension for understanding traits. Only recently have long‐term and geographically extensive measurements of the number of seeds produced by trees needed for species‐level synthesis become available (Clark et al, 2021; Journé et al, 2022; Qiu et al, 2022; Sharma et al, 2022). Here, we re‐examine the hypotheses that describe the seed number and size as part of an omnibus syndrome that explains all traits (e.g., fast–slow plant economics spectrum) or, alternatively, as a separate axis of variation.…”

Section: Introductionmentioning

confidence: 99%

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Linking seed size and number to trait syndromes in trees

Bogdziewicz

Acuña

Andrus

et al. 2023

Global Ecol Biogeogr

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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.

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