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Michal Bogdziewicz

Marie-Claire Aravena Acuña

Robert Andrus

Davide Ascoli

Yves Bergeron

Daniel Berveiller

Thomas Boivin

Raul Bonal

Thomas Caignard

Maxime Cailleret

Marie-Claire Aravena Acuña

Yves Berg- Eron

Daniel Brveiller

Rafael Calama

Sergio Donoso Calderon

J Julio Camarero

Chia-Hao Chang-Yang

Jerome Chave

Francesco Chianucci

Natalie L Cleavitt

Benoit Courbaud

Andrea Cutini

Thomas Curt

Adrian J Das

Hendrik Davi

Nicolas Delpierre

Sylvain Delzon

Michael Dietze

Laurent Dormont

William Farfan-Rios

Catherine A Gehring

Gregory S Gilbert

Georg Gratzer

Cathryn H Greenberg

Arthur Guignabert

Qinfeng Guo

Andrew Hacket-Pain

Arndt Hampe

Qingmin Han

Kazuhiko Hoshizaki

Ines Ibanez

Jill F Johnstone

Valentin Journé

Thomas Kitzberger

Johannes M H Knops

Georges Kunstler

Richard Kobe

Jonathan G A Lageard

Jalene M Lamontagne

Mateusz Ledwon

Theodor Leininger

Jean-Marc Limousin

James A Lutz

Diana Macias

Anders Marell

Eliot J B Mcintire

Emily Moran

Renzo Motta

Jonathan A Myers

Thomas A Nagel

Shoji Naoe

Kyotaro Noguchi

Michio Oguro

Hiroko Kurokawa

Jean-Marc Ourcival

Robert Parmenter

Ignacio M Perez-Ramos

Lukasz Piechnik

Tomasz Podgórski

John Poulsen

Tong Qiu

Miranda D Redmond

Chantal D Reid

Kyle C Rodman

Pavel Šamonil

Jan Holik

C Lane Scher

Harald Schmidt Van Marle

Barbara Seget

Mitsue Shibata

Shubhi Sharma

Miles Silman

Michael A Steele

Jacob N Straub

I-Fang Sun

Samantha Sutton

Jennifer J Swenson

Peter A Thomas

Maria Uriarte

Giorgio Vacchiano

Thomas T Veblen

Boyd Wright

S Joseph Wright

Thomas G Whitham

Kai Zhu

Jess K Zimmerman

Magdalna Zywiec

James S Clark

Linking seed size and number to trait syndromes in trees

Keywords: fecundity, functional traits, life history strategies, size syndrome, leaf economics, tree recruitment

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Data availability statement

The data supporting the results are attached in the Online Supplement.

Summary

Aim : Understanding mechanisms that maintain forest diversity under changing climate can benefit from the knowledge of 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 if reproduction represents an independent dimension related to a seed size and number trade-off.

Location : The majority of the data comes from Europe, North and Central America, and East Asia. A majority of the data comes 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 PCA and Generalized Joint Attribute Modeling 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 the 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.

Introduction

Understanding the mechanisms that promote and maintain forest tree diversity under a warming climate can benefit from the knowledge of traits that are closely linked to fitness [START_REF] Adler | Functional traits explain variation in plant lifehistory strategies[END_REF][START_REF] Paine | Globally, functional traits are weak predictors of juvenile tree growth, and we do not know why[END_REF][START_REF] Yang | Why functional traits do not predict tree demographic rates[END_REF][START_REF] Kelly | Climatic and evolutionary contexts are required to infer plant life history strategies from functional traits at a global scale[END_REF]. Adaptive evolution operates on the variation that affects survival and reproduction. Leaf traits, wood density, and plant height are clearly important for trees, yet their connections to fitness are indirect [START_REF] Wright | The worldwide leaf economics spectrum[END_REF][START_REF] Violle | Let the concept of trait be functional![END_REF][START_REF] Chave | Towards a worldwide wood economics spectrum[END_REF][START_REF] Díaz | The global spectrum of plant form and function[END_REF]. For example, large, thin, short-lived leaves with high nitrogen content confer clear benefits in high-resource environments where long-lived, highly lignified leaves are less advantageous [START_REF] Shreve | Ecological aspects of the deserts of california[END_REF][START_REF] Field | On the Economy of Plant Form and Function[END_REF][START_REF] Reich | The world-wide 'fast-slow' plant economics spectrum: A traits manifesto[END_REF]. Fitness is the quantitative representation of individual reproductive success, an organism's ability to pass its genetic material to its offspring. Thus, interpreting the fitness implications of traits often requires broad extrapolation, such as ecophysiological measurements describing minute-scale responses of leaves, roots, or xylem elements that are integrated with many other responses to determine survival and/or reproduction over the lifetimes of whole plants. While no trait links directly to fitness in trees, many are so weakly tied to fitness that their utility for comparative studies remains uncertain. In that light, seedling recruitment at tropical Barro Colorado Island provides a more direct link to fitness [START_REF] Rüger | Beyond the fast-slow continuum: demographic dimensions structuring a tropical tree community[END_REF][START_REF] Rüger | Demographic trade-offs predict tropical forest dynamics[END_REF]. Nonetheless, given that recruitment varies for each species at each site, the species-level reproductive effort could be a valuable extension for trait understanding. Only recently have long-term and geographically extensive measurements of the number of seeds produced by trees needed for species-level synthesis become available [START_REF] Clark | Continent-wide tree fecundity driven by indirect climate effects[END_REF][START_REF] Journé | Globally, tree fecundity exceeds productivity gradients[END_REF][START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF][START_REF] Sharma | North american tree migration paced by climate in the west, lagging in the east[END_REF]. 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.

Principal components analysis (PCA) has been a primary tool for exploring combined trait variation, contributing to at least three interpretations for forest trees. One view sees the number of seeds produced and their size together with leaf traits as part of a "fast-slow" continuum of plant strategies [START_REF] Reich | The world-wide 'fast-slow' plant economics spectrum: A traits manifesto[END_REF]. That dimension represents the trade-off between resource acquisition and processing, and it could be linked to a growth-survival trade-off [START_REF] Poorter | Are functional traits good predictors of demographic rates? evidence from five neotropical forests[END_REF][START_REF] Wright | Functional traits and the growth-mortality trade-off in tropical trees[END_REF][START_REF] Rüger | Beyond the fast-slow continuum: demographic dimensions structuring a tropical tree community[END_REF]. Cheaply constructed leaves that assimilate carbon quickly, together with low wood density, characterize species that are resource-demanding, grow fast, and die young [START_REF] Westoby | Plant ecological strategies: Some leading dimensions of variation between species[END_REF][START_REF] Moles | Being john harper: Using evolutionary ideas to improve understanding of global patterns in plant traits[END_REF]. In such species, the production of a large number of seeds may offset mortality losses [START_REF] Muller-Landau | The tolerance-fecundity trade-off and the maintenance of diversity in seed size[END_REF][START_REF] Reich | The world-wide 'fast-slow' plant economics spectrum: A traits manifesto[END_REF]. Species with some or all of these traits might dominate early successional stages through effective colonization, and they might not persist under intense competition [START_REF] Poorter | Are functional traits good predictors of demographic rates? evidence from five neotropical forests[END_REF][START_REF] Wright | Functional traits and the growth-mortality trade-off in tropical trees[END_REF].

Alternatively, the size hypothesis suggests that the seed number and seed size is part of a stature-recruitment trade-off [START_REF] Kohyama | Size-structured tree populations in gap-dynamic forest-the forest architecture hypothesis for the stable coexistence of species[END_REF][START_REF] Díaz | The global spectrum of plant form and function[END_REF][START_REF] Rüger | Beyond the fast-slow continuum: demographic dimensions structuring a tropical tree community[END_REF][START_REF] Rüger | Demographic trade-offs predict tropical forest dynamics[END_REF].

According to this hypothesis, large plant size maximizes canopy performance at the expense of recruitment, and vice versa. The analysis of 282 co-occurring tree species at tropical Barro Colorado Island (BCI) emphasized a leaf-trait axis and a size-recruitment axis, with species characterized by small stature, small leaves, and small seeds having high recruitment [START_REF] Rüger | Beyond the fast-slow continuum: demographic dimensions structuring a tropical tree community[END_REF]. Follow-up studies indicated that stature-recruitment trade-off extends to tropical forests more generally [START_REF] Kambach | Consistency of demographic trade-offs across 13 (sub)tropical forests[END_REF].

Finally, seed number and seed size may represent a third, largely independent, dimension of variation, as proposed by the twin-filter (TF) hypothesis [START_REF] Grime | The evolutionary strategies that shape ecosystems[END_REF]. According to the TF, primary strategies such as fast-slow determine persistence for the climate/habitat norms, while traits involved in episodic events, which might include reproduction, affect fitness independent of other traits [START_REF] Grime | The evolutionary strategies that shape ecosystems[END_REF][START_REF] Pierce | How well do seed production traits correlate with leaf traits, whole-plant traits and plant ecological strategies[END_REF]. The leaf-height-seed (LHS) scheme of [START_REF] Westoby | A leaf-height-seed (lhs) plant ecology strategy scheme[END_REF] hypothesizes that seed size plays a role similar to reproduction in the TF model. In both hypotheses, plants can produce either many or few seeds (TF) or small or large seeds (LHS), largely independent of other plant traits. All three of the foregoing hypotheses imply an important role of seed number and seed size, and they assume that all traits have some connection to fitness. The availability of species-level seed numbers can lend novel insight to trait analysis due to its close connection to recruitment, a major demographic and fitness indicator.

A limitation of summaries available from PCA comes from the fact that correlations include all the indirect ways that traits could be associated. For example, a correlation between seed size and wood density could occur if there was a need for high wood density in order to produce large seeds. If true, this would be a direct relationship. Alternatively, both variables might be driven by climate for reasons that do not depend on one another. In such a case, that would be an indirect relationship. Another indirect relationship is represented by phylogenic conservatism. Some species groups tend to produce larger seeds or denser wood than others, even if environments that might select for one or both traits change. The correlation structure exposed by PCA does not discriminate between direct (conditional) and indirect (marginal) relationships. If relationships are indirect, then conditional estimates offer the most transparent view of their connections [START_REF] Seyednasrollah | Where resource-acquisitive species are located: The role of habitat heterogeneity[END_REF]. To quantify direct links between traits, the traditional study with PCA is supplemented here with conditional relationships between traits using Generalized Joint Attribute Modeling (GJAM) [START_REF] Clark | Generalized joint attribute modeling for biodiversity analysis: median-zero, multivariate, multifarious data[END_REF]. Including environment as fixed effects and phylogenetic groups as random effects, GJAM decomposes trait relationships into direct and indirect relationships. While we believe GJAM is a valuable extension, we present results of PCA as well to facilitate comparison with past studies. In this study, we analyze trait syndromes in trees from a perspective that includes the number of seeds produced and seed size. The Masting Inference and Forecasting (MASTIF) network includes 12 million tree-year observations of the number of seeds produced by 775,991 trees from 784 species from a broad range of biomes [START_REF] Journé | Globally, tree fecundity exceeds productivity gradients[END_REF][START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF]. To control for variation within species and, thus, to sharpen our understanding of interspecific differences, we estimate seed numbers produced by trees with an analytical framework that includes trees' condition (species, size, shading), habitat (soils), and climate (temperature and moisture deficit), while accommodating dependence between and within trees across years [START_REF] Clark | Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet[END_REF]. This large sample size is important for the notoriously noisy seed production in trees [START_REF] Kelly | Climatic and evolutionary contexts are required to infer plant life history strategies from functional traits at a global scale[END_REF], where tree-to-tree and year-to-year variation in seed number span several orders of magnitude [START_REF] Clark | Fecundity of trees and the colonization-competition hypothesis[END_REF][START_REF] Journé | Globally, tree fecundity exceeds productivity gradients[END_REF]. By combining seed number with seed size into species seed productivity (seed size x seed number, SSP, developed by [START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF]), we show how reproductive traits relate to one another separately and in combination. Combining seed number and seed size into SSP brings more exhaustive information on reproductive investment because species that invest in large seeds are producing more seeds than expected from the 1:1 trade-off [START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF]. For this reason, SSP should be more strongly aligned with seed size than seed number. By standardizing these metrics for the tree size we account for the variation in size distribution within the data and facilitate comparisons. For example, the SSP is the average annual species seed productivity per m2 basal area at average environmental conditions across the species' range in the data (Qiu et al., 2021a[START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF]. If large seeds confer an advantage in competitive, shaded understories, while many small seeds allow colonization of distant sites, then SSP provides a direct link to fitness. The hypothesized relationships between seed number, seed size, SSP, and traits are summarized in Figure 1.

Methods

Seed number, species seed productivity (SSP) and MASTIF model Estimating the number of seeds produced in perennial plants suffers from extreme signal-to-noise problem, created by orders of magnitude variation from year to year and tree to tree [START_REF] Pesendorfer | The ecology and evolution of synchronized reproduction in long-lived plants[END_REF][START_REF] Pearse | Biogeography and phylogeny of masting: do global patterns fit functional hypotheses?[END_REF][START_REF] Clark | Fecundity of trees and the colonization-competition hypothesis[END_REF]) that can bury any trend [START_REF] Clark | Continent-wide tree fecundity driven by indirect climate effects[END_REF]. There are as many time series as there are trees that must be modeled together because there is dependence created by among-trees synchrony in masting variation [START_REF] Crone | What defines mast seeding? spatio-temporal patterns of cone production by whitebark pine[END_REF][START_REF] Bogdziewicz | Environmental variation drives continental-scale synchrony of european beech reproduction[END_REF]. Masting patterns are further complicated by the spatio-temporal variation in habitat and climate [START_REF] Pesendorfer | The ecology and evolution of synchronized reproduction in long-lived plants[END_REF][START_REF] Pearse | Biogeography and phylogeny of masting: do global patterns fit functional hypotheses?[END_REF]. The many sources of variation mean that estimation of a seed number produced by trees can only be achieved from broad coverage and large sample sizes while accounting for individual trees' condition, local habitat, and climate [START_REF] Clark | Continent-wide tree fecundity driven by indirect climate effects[END_REF]Qiu et al., 2021a;[START_REF] Sharma | North american tree migration paced by climate in the west, lagging in the east[END_REF]. This is here achieved with the MASTIF model [START_REF] Clark | Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet[END_REF].

The MASTIF model and data from the MASTIF network are summarized here, and extensively described in recent papers [START_REF] Clark | Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet[END_REF][START_REF] Clark | Continent-wide tree fecundity driven by indirect climate effects[END_REF]Qiu et al., 2021a;[START_REF] Sharma | North american tree migration paced by climate in the west, lagging in the east[END_REF][START_REF] Journé | Globally, tree fecundity exceeds productivity gradients[END_REF]. The tree-year observations of seed numbers in the network come from seed traps and from crop counts. Data include longitudinal (repeated) observations on individual trees (99%) and opportunistic observations that come through the iNaturalist project [START_REF] Clark | Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet[END_REF]. Seed trap data consists of numbers of seeds that accumulate annually in mapped seed traps on forest inventory plots. A fitted dispersal kernel relates seed counts to mapped trees, accounting for uncertainty in seed transport and Poisson seed counts [START_REF] Clark | Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet[END_REF]. Crop counts include counts of reproductive structures with estimates of the fraction of the crop observed, and beta-binomial distribution accounts for uncertainty in the crop-fraction estimates [START_REF] Clark | Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet[END_REF]. This study includes 12,008,722 tree-years from North America, South and Central America, Europe, Africa, Asia, and Oceania, which is gathered over 5,115 sites and 787,444 trees (Fig. 2). The MASTIF model jointly estimates the number of seeds produced based on all the observations. The seed number (SN) and species seed productivity (SSP) [START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF] used in the analysis are calculated based on 297,690 mature individuals and 3,730,381 tree-years. The MASTIF model uses the whole dataset to estimate seed numbers produced annually, but the SN and SSP are calculated based on a mature tree subset of these estimates. The list of species included in the analysis is given in the Online Supplement as a csv file. The MASTIF model, detailed in [START_REF] Clark | Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet[END_REF], is a dynamic biophysical model for year-to-year and tree-to-tree seed production. The MASTIF model is a Bayesian hierarchical, state-space model that allows for conditional independence in crop-count and seed-trap data through latent states. The model estimates the number of seeds produced with conditional fecundity, which depends on the probability that the tree is sexually mature, tree size, shading (five classes from full sun to full shade), local climate, and soil conditions. Random effects on individual and year allow for wide variation between trees and over time that is typical of seed production. The posterior covariance between trees and years can take any form, avoiding assumptions of standard time-series models, important due to the quasiperiodic variation in time and varying levels of synchronicity between individual trees [START_REF] Pesendorfer | The ecology and evolution of synchronized reproduction in long-lived plants[END_REF]. Model structure and methodology were implemented with R, version 4.0 (R Core Team, 2020) and the R package Mast Inference and Forecasting (MASTIF) [START_REF] Clark | Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet[END_REF].

Seed number and species seed productivity (SSP) at the tree and the species level 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 three-years of a genus are modeled jointly [START_REF] Clark | Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet[END_REF][START_REF] Clark | Continent-wide tree fecundity driven by indirect climate effects[END_REF]. For each tree 𝑖 of species 𝑠 at stand 𝑗 in year 𝑡, the expected seed number is the product of maturation probability ρ and conditional fecundity ψ,

𝐸 ( 𝑓 𝑖 𝑗 𝑠,𝑡 ) = f𝑖 𝑗 𝑠,𝑡 = ρ𝑖 𝑗 𝑠,𝑡 ψ𝑖 𝑗 𝑠,𝑡 (1)
Conditional fecundity depends on predictors, individual effects, year effects, and error,

𝑙𝑜𝑔( ψ𝑖 𝑗 𝑠,𝑡 ) = x ′ 𝑖 𝑗 𝑠,𝑡 𝜷 (𝑥) + 𝛽 (𝑤) 𝑖 𝑗 𝑠 + 𝛾 𝑔[𝑖 𝑗]𝑠,𝑡 + 𝜖 𝑖 𝑗 𝑠,𝑡 (2)
where x 𝑖𝑡 is a matrix holding individual attributes and environmental conditions (see Generalized joint attribute modeling below), and 𝜷 (𝑥) We estimated species investment into reproduction using two metrics, both scaled to the tree basal area: annual seed number (SN), and annual species seed productivity (SSP; seed number × seed mass) [START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF]. Estimation of both SN and SSP starts with the estimation of individual tree mean number of produced seeds that depends on each tree location that accounts for effects of the environment and includes uncertainty for each year. Individual trees' number of seeds produced over a species is then summarized as SN or SSP, as explained below. The tree-level estimate of seed number, i.e., individual seed production (ISP), is the product of seed size (its mass) 𝑚 𝑠 and seed number, scaled to tree basal area per year [START_REF] Journé | Globally, tree fecundity exceeds productivity gradients[END_REF].

We quantify ISP as the mass of a tree's seed production relative to its basal area to standardize for tree size (intermediate trees produce more seeds than smaller ones Qiu et al. (2021a)). All estimates are time averages across annual estimates, so we hereafter omit yr -1 from dimensions.

Therefore, ISP has the units of 𝑔/𝑚 2 . Following [START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF], species seed productivity (SSP) comes from the expectation of all ISP for a given species. The detailed calculations of ISP and SSP are provided in Online Supplement. Analyses of SSP are done on the proportionate (log) scale to avoid dominance of results by the few species that produce the highest seed number.

The seed number is estimated following the same steps, but the calculations omit seed size (mass of individual seed).

Traits

We selected six functional plant traits previously shown to capture plant life strategies well [START_REF] Díaz | The global spectrum of plant form and function[END_REF][START_REF] Carmona | Fine-root traits in the global spectrum of plant form and function[END_REF]: plant height (measured in m), leaf area (measured in mm 2 ), specific leaf area (SLA; measured in mm 2 /mg; the inverse of leaf mass per area), leaf nitrogen concentration (measured in mg/g), wood density (measured in g/m 3 ) and seed size (measured in g). The data was obtained from primary sources and supplemented with publicly

where the distribution of groups and environments [𝑃, 𝑋] is that which occurs in the data set.

To determine trait relationships we fit a joint model to the conditional distribution [T|𝑃, 𝑋],

which provides estimates of the phylogeny as random groups g[𝑠], 𝑔 = 1, . . . , 𝑀 for species 𝑠 and 𝑋 as a 𝑄 × 𝑀 matrix of coefficients B for 𝑄 predictors of 𝑀 traits. We then decompose the distribution into (conditional) effects of other traits and the environment [START_REF] Seyednasrollah | Where resource-acquisitive species are located: The role of habitat heterogeneity[END_REF][START_REF] Qiu | Niche shifts from trees to fecundity to recruitment that determine species response to climate change[END_REF]. The effect of trait 𝑚 on the remaining -𝑚 traits is the conditional

The first term is a conditional influence of 𝑚 as distinct from (𝑃, 𝑋).

Generalized joint attribute modeling

To incorporate the effects of environment and phylogeny on the distribution of traits, we use GJAM [START_REF] Clark | The impacts of increasing drought on forest dynamics, structure, and biodiversity in the united states[END_REF]. Environmental covariates include soil fertility (Cation Exchange Capacity), mean annual temperature, and annual accumulative moisture deficit (difference between potential evapotranspiration and precipitation)

averaged at the species level for the MASTIF data set. GJAM allowed us to accommodate the dependence between traits and phylogeny as random groups. A more detailed description of GJAM fitting is given in Online Supplement. GJAM fitting is open-access with R package GJAM on CRAN.

Results

Across the 784 species, foliar traits, wood density, and seed number and size are the dominant sources of variation. In the principal components analysis (PCA) of our data that include species seed productivity (SSP), 54.2% of variation is concentrated in two principal components of equal importance (Fig. 3A, Fig. S1). PCA1 is associated with leaf traits. At one end are species with thin, large, acquisitive leaves (large SLA, high area, high foliar nitrogen). Common examples include heaven lotus (Gustavia superba), Panama tree (Sterculia apetala), pawpaw (Asimina triloba), and eastern walnut (Juglans nigra). At the other end are species with low SLA, low foliar nitrogen, and low leaf area, including evergreen conifers like giant sequoia (Sequoiadendron giganteum), California redwood (Sequoia sempervirens), monkey puzzle tree (Araucaria araucana), Fraser fir (Abies fraseri), and white cedar (Thuja occidentalis). PCA2 is dominated by seed size, SSP, and wood density. Large seeds are associated with high SSP because species that produce large seeds tend to produce proportionally more of them than predicted the strict trade-off between seed size and number [START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF]. Dense wood is associated with both seed size and SSP, with examples including African crabwood (Carapa procera) and Fagales such as chestnuts (Castanea) and oaks (Quercus). At the opposite end with low-density wood and small seeds are willows (Salix), fuchsia (Fuchsia excorticata), and trumpet tree (Cecropia obtusa). Tree height is weakly associated with foliar attributes: small trees tend to have large, thin leaves.

A second PCA in which SSP is replaced with seed number yields similar results (Fig. S2).

As with the PCA using SSP (fig. 3A), the first axis of this second PCA is associated with foliar traits with no contributions from seed numbers. The second PCA axis separates species according to seed size, seed number, and wood density. Tree height is again weakly associated with foliar attributes but also with reproduction: small trees tend to produce small seeds in large numbers (Fig. S2).

Using conditional prediction to control for the environment and taxonomic relatedness shows that seed size is positively related to SSP and negatively related to seed number (Fig. 3B).

Conditional prediction allows for uncertainty and the effects of the environment on all traits, but then isolates their direct (conditional) relationships to one another (see Methods). Nutrientdemanding species with high foliar nitrogen concentrations, high SLA, and low-density wood, produce small seeds in high numbers, a relationship that is not apparent in PCA. Large seeds are produced by trees with dense wood, few seeds, high leaf area, low foliar N, and low SLA (Fig. 4B-G). The relationship between high SSP and dense wood is suggested by PCA (Fig. S3). Extended PCA plots are available in supplement Fig. S2. SSP stands for species seed productivity and is the product of seed size × seed number [START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF]. Both SSP and seed number are standardized to a tree basal area. Thicker lines qualitatively separate main relationships from the minute correlations among some foliar and other traits. Each trait has a unique color to improve comparisons between A and B.

3A), but that relationship is not significant after accounting for environment and phylogeny (Fig. 3B). Rather, the PCA can be driven by indirect links between traits. Although the links between wood density and foliar traits are significant, they are weaker than the relationship between wood density and seed size (Fig. 4D).

Discussion

Across 784 species spanning tropical to boreal environments, estimation of the number of seeds produced by trees brings new insight to trait analysis with a strong connection to fitness. Seed size and number make a dominant contribution to trait syndromes in trees, but one that is not strictly consistent with the fast-slow or stature trade-offs. Controlling for common ancestry and environment with GJAM indicates that large seed size is weakly associated with high leaf area, low foliar nitrogen, low SLA, and dense wood. These associations were not detected by conventional PCA that does not condition on environmental dependencies. Thus, there is a weak, indirect link between these traits to SSP. Fast strategies, as captured by leaf traits, were not coupled with high seed numbers, even though nutrient-demanding trees show a tendency to produce small seeds. Seed size and number were also not associated with tree height as in the stature-recruitment hypothesis at the tropical BCI [START_REF] Rüger | Beyond the fast-slow continuum: demographic dimensions structuring a tropical tree community[END_REF][START_REF] Rüger | Demographic trade-offs predict tropical forest dynamics[END_REF]. Across all species and sites in this study, trees with dense wood, large leaves, and low nutrient demands produce large but few seeds. These species invest heavily in SSP. In contrast, a high seed number is associated with small seeds, most common in species with low-density wood, low leaf area, high foliar N, and high SLA.

Trait relationships identified here are consistent with some traditional trait concepts, including change of traits associated with species turnover through succession [START_REF] Bazzaz | The physiological ecology of plant succession[END_REF][START_REF] Falster | Alternative height strategies among 45 dicot rain forest species from tropical queensland, australia[END_REF][START_REF] Wilfahrt | Shifts in functional traits among tree communities across succession in eastern deciduous forests[END_REF]. Production of a large number of small seeds increases recruitment in distant, disturbed habitats (Muller-Landau, 2010). The r strategy of the r-K spectrum is associated with fast growth and high nutrient requirements [START_REF] Bazzaz | The physiological ecology of plant succession[END_REF][START_REF] Huston | Plant succession: Life history and competition[END_REF][START_REF] Henery | Seed mass and seed nutrient content as predictors of seed output variation between species[END_REF][START_REF] Muller-Landau | Interspecific variation in primary seed dispersal in a tropical forest[END_REF]. By including seed size and number, our analysis indicates that the traditional r strategy, which might include low-density wood that often comes with fast growth [START_REF] Chave | Towards a worldwide wood economics spectrum[END_REF], also includes the production of small seeds. High foliar nitrogen and cheap leaf construction (high SLA) align with high photosynthetic rates [START_REF] Reich | Global patterns of plant leaf n and p in relation to temperature and latitude[END_REF][START_REF] Reich | The world-wide 'fast-slow' plant economics spectrum: A traits manifesto[END_REF][START_REF] Moles | Being john harper: Using evolutionary ideas to improve understanding of global patterns in plant traits[END_REF]. On the K side are species with dense wood and slow growth [START_REF] Westoby | A leaf-height-seed (lhs) plant ecology strategy scheme[END_REF][START_REF] Poorter | A monocarpic tree species in a polycarpic world: How can tachigali vasquezii maintain itself so successfully in a tropical rain forest community[END_REF].

Low foliar nitrogen and low SLA can align with low foliar Rubisco content, low photosynthetic capacity, and, thus, low maintenance respiration in low light [START_REF] Reich | Relationships of leaf dark respiration to leaf nitrogen, specific leaf area and leaf life-span: a test across biomes and functional groups[END_REF][START_REF] Poorter | Are species adapted to their regeneration niche, adult niche[END_REF][START_REF] Moles | Being john harper: Using evolutionary ideas to improve understanding of global patterns in plant traits[END_REF]. Species with such conservative leaves are also selected for large seeds needed for seedling establishment in shade, at the expense of the many small seeds that would promote colonization of distant sites [START_REF] Westoby | Plant ecological strategies: Some leading dimensions of variation between species[END_REF][START_REF] Muller-Landau | The tolerance-fecundity trade-off and the maintenance of diversity in seed size[END_REF].

Species seed productivity (SSP) is more strongly driven by seed size than seed number, which follows from the observation that the size-numbers trade-off in trees is not 1:1 [START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF].

Instead, species that produce large seeds more than compensate (on a mass basis) for fewer of them, resulting in higher species seed productivity [START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF]). Therefore, the estimates of SSP for a given tree size, enrich the interpretation of plant reproductive strategies beyond the insights that come from seed size alone [START_REF] Westoby | Plant ecological strategies: Some leading dimensions of variation between species[END_REF][START_REF] Muller-Landau | The tolerance-fecundity trade-off and the maintenance of diversity in seed size[END_REF][START_REF] Lebrija-Trejos | Species with greater seed mass are more tolerant of conspecific neighbours: a key driver of early survival and future abundances in a tropical forest[END_REF]. On one hand, the production of small, copious seeds increases recruitment opportunities at the cost of limited investment per individual seed. Small seeds can mean low abiotic stress tolerance in competitive sites [START_REF] Westoby | Plant ecological strategies: Some leading dimensions of variation between species[END_REF][START_REF] Tilman | Competition and biodiversity in spatially structured habitats[END_REF][START_REF] Fricke | Linking intra-specific trait variation and plant function: seed size mediates performance tradeoffs within species[END_REF]. On the other hand, large seeds come with a cost of producing fewer of them [START_REF] Henery | Seed mass and seed nutrient content as predictors of seed output variation between species[END_REF][START_REF] Muller-Landau | Interspecific variation in primary seed dispersal in a tropical forest[END_REF][START_REF] Fricke | Linking intra-specific trait variation and plant function: seed size mediates performance tradeoffs within species[END_REF], each with a greater investment in seedling survival [START_REF] Fricke | Linking intra-specific trait variation and plant function: seed size mediates performance tradeoffs within species[END_REF][START_REF] Muller-Landau | Interspecific variation in primary seed dispersal in a tropical forest[END_REF]. However, the high SSP in species that produce large seeds means that such species are selected for proportionally high total seed biomass investment per individual to maintain populations in low light conditions [START_REF] Kohyama | Tree species differentiation in growth, recruitment and allometry in relation to maximum height in a bornean mixed dipterocarp forest[END_REF][START_REF] Falster | Alternative height strategies among 45 dicot rain forest species from tropical queensland, australia[END_REF]. In other words, the production of a large number of seeds appears to generate a generally higher cost of reproduction. Testing whether SSP is a better indicator of reproductive success than seed number or size alone appears a fruitful avenue for future research.

The divergent results from this study and those suggesting a stature-recruitment trade-off at tropical forests [START_REF] Rüger | Beyond the fast-slow continuum: demographic dimensions structuring a tropical tree community[END_REF][START_REF] Rüger | Demographic trade-offs predict tropical forest dynamics[END_REF][START_REF] Guillemot | Small and slow is safe: On the drought tolerance of tropical tree species[END_REF][START_REF] Kambach | Consistency of demographic trade-offs across 13 (sub)tropical forests[END_REF] are not necessarily in conflict. The within-site covariation in traits, where short trees might be associated with small seeds and leaves in the shaded understory [START_REF] Rüger | Beyond the fast-slow continuum: demographic dimensions structuring a tropical tree community[END_REF], does not need to align with an among-site, species-level difference, which integrates over environments for each species at many sites. Moreover, [START_REF] Rüger | Beyond the fast-slow continuum: demographic dimensions structuring a tropical tree community[END_REF] measured the recruitment of saplings, whereas our analysis includes seed numbers. In turn, the lack of relationship between seed size and plant height reported by past studies [START_REF] Díaz | The global spectrum of plant form and function[END_REF] may follow from the fact that the GJAM models control for phylogeny, whereas PCA does not. This is supported by the fact that both PCA and GJAM models that do not include shared ancestry indicate a positive relationship between seed size and tree height. This, and other trait relationships, that are present only in phylogenetically-controlled GJAM indicate that conditional prediction to control for the environment and taxonomic relatedness may be a step forward for the subdiscipline.

Anticipating individual and combined effects of global change requires understanding the vulnerability not only of mature trees but also of seed number and recruitment [START_REF] Clark | Continent-wide tree fecundity driven by indirect climate effects[END_REF][START_REF] Sharma | North american tree migration paced by climate in the west, lagging in the east[END_REF][START_REF] Qiu | Niche shifts from trees to fecundity to recruitment that determine species response to climate change[END_REF][START_REF] Bogdziewicz | How will global change affect plant reproduction? a framework for mast seeding trends[END_REF][START_REF] Hanbury-Brown | Forest regeneration within earth system models: current process representations and ways forward[END_REF].

One major challenge, that exists in ecology more generally [START_REF] Nuñez | Making ecology really global[END_REF], is to increase the data coverage to underrepresented regions such as Africa or Southeast Asia in our case.

Earth system models currently assume that reproductive allocation does not differ among plant functional types [START_REF] Scholze | A climate-change risk analysis for world ecosystems[END_REF][START_REF] Hanbury-Brown | Forest regeneration within earth system models: current process representations and ways forward[END_REF]. There is area and promise for improvement using functional trait data. A recent study at the BCI predicted forest succession by replacing the hyper-diversity of tropical forests with just two trait axes associated with fastslow and size dimensions [START_REF] Rüger | Demographic trade-offs predict tropical forest dynamics[END_REF]. While the size of our seed production dataset is unprecedented, seed number is much more difficult to measure due to its variable nature, as compared to e.g. seed size. This could explain why we found stronger links between traits with seed size. The good news is that the easily measurable seed size is a strong predictor of species seed productivity (SSP) and seed number. The connection of SSP with the functional traits can form the first basis of improved fecundity prediction across global forests.

Figure 1 :

 1 Figure 1: Hypothetical associations between dimensions of plant life strategies represented by functional traits and the seed number, seed size, and species seed productivity (the product of seed number and seed size, SSP developed by Qiu et al. (2022)). Seed production can be associated with: A) fast life syndrome (slow-fast resource turnover axis); B) size syndrome; C) its own, largely independent axis of seed size-seed number trade-off. Both seed number and SSP are divided by tree basal area in our analyses.

Figure 2 :

 2 Figure 2: Map of raw data used to estimate the number of seeds produced by trees with the MASTIF model.

  available data from the latest version of the TRY Plant Trait Database TRY Plant Trait Database[START_REF] Kattge | Try plant trait database -enhanced coverage and open access[END_REF] extracted from the[START_REF] Carmona | Fine-root traits in the global spectrum of plant form and function[END_REF]. Missing values for the six traits were filled with genus-level means. Bivariate relationships are summarized in Fig.S4).Trait relationshipsPCA summarizes correlation in the joint distribution of traits, written in bracket notation as [T] = [𝑇 1 , . . . , 𝑇 𝑀 ]. If the relationship between traits depends on phylogeny, summarized by phylogenetic groups 𝑃 𝑔=1...𝐺 (taxonomic, e.g., genus or family), and on the environment 𝑋, then there is a joint distribution [T, 𝑃, 𝑋]. The indirect environment and phylogeny effects may dominate the relationships between some or many traits. An alternative approach uses conditional distribution, [T|𝑃, 𝑋] = [T, 𝑃, 𝑋] [𝑃, 𝑋]

Figure 3 :

 3 Figure 3: Seed size, seed number, and species seed productivity (SSP) on the spectrum of tree form. A) Biplot; arrow length indicates the loading of each considered trait onto the first two PCA axes. Points represent the position of species, coded blue for the needle, black for broad-deciduous, and yellow for broad evergreen leaf habit. Larger points indicate means for the groups. An extended version of that graph is given in Fig. S1. B) Summary of GJAM coefficients presented in Fig.4. Significant associations between traits are highlighted by lines, coded red for negative and blue for positive relationships. Dashed lines highlight associations that are significant only in the model without phylogenetical control (see Fig.S3). Extended PCA plots are available in supplement Fig.S2. SSP stands for species seed productivity and is the product of seed size × seed number[START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF]. Both SSP and seed number are standardized to a tree basal area. Thicker lines qualitatively separate main relationships from the minute correlations among some foliar and other traits. Each trait has a unique color to improve comparisons between A and B.

Figure 4 :

 4 Figure 4: Conditional relationships between traits after accounting for climate and phylogeny. Posterior distributions are shown as boxes that contain median vertical lines and are bounded by 68% credible intervals (CI), with 95% CI whiskers. Coefficients are evaluated on a standardized scale. The inset plots at A highlight the relationships between species seed productivity (SSP) and other traits after removing the effects of seed number and seed size that are part of SSP. Insets at B and C are analogous. Fig.3summarizes the significant relationships. See Fig.S3for conditional relationships derived from GJAM without the phylogenetical control. SSP stands for species seed productivity and is the product of seed size x seed number. Both SSP and seed number are standardized to a tree basal area.

Figure S1 :

 S1 Figure S1: PCA as presented on Fig. 3 extended with contributions and loadings of the three axes that explained the most variance. A) Species seed productivity (SSP) on the global spectrum of tree form. Arrow length indicates the loading of each considered functional trait onto PCA axes. Points represent the position of species. B) Explained variance for each principal component. Bar plots present the contribution (C,D,E) and loading (F,G,H) of each trait to each principal component. The large point shows the mean position for each group.

Figure S2 :

 S2 Figure S2: A) Seed number on the spectrum of tree form. Arrow length indicates the loading of each considered functional trait onto PCA axes. Points represent the position of species. B) Explained variance for each principal component. Bar plots present the contribution (C,D,E) and loading (F,G,H) of each trait to each principal component. The large point shows the mean position for each group.

Figure S3 :

 S3 Figure S3: Conditional relationships between traits after accounting for climate but not shared ancestry.Marginal posterior distributions are shown as boxes that contain median vertical lines and are bounded by 68% credible intervals (CI), with 95% CI whiskers. Fig.3summarizes the significant relationships.

Figure S4 :

 S4 Figure S4: Summary of bivariate relationships between considered traits. Points are species, lines are loess regression and associated 95% CI. Coefficients are Pearson correlations. Traits are log-transformed. Significance levels are * < 0.05; ** < 0.01; *** p < 0.001.

  Group membership for year effects (𝑔[𝑖 𝑗]𝑠) is defined by species-ecoRegion[START_REF] Clark | Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet[END_REF]. There is a noise term 𝜖 𝑖 𝑗 𝑠,𝑡 . Maturation probability ρ𝑖 𝑗 𝑠,𝑡 accounts for the immature state (for small trees) and failed crop in larger trees. The model implementation is open-access with R package MASTIF, with algorithm details provided in[START_REF] Clark | Foodwebs based on unreliable foundations: spatiotemporal masting merged with consumer movement, storage, and diet[END_REF].

are fixed-effects coefficients. 𝛽 (𝑤) 𝑖 𝑗 𝑠 is the random effect for tree 𝑖 of species 𝑠 at stand 𝑗. 𝛾 𝑔[𝑖 𝑗]𝑠,𝑡 are year effects that are random across groups 𝑔 and fixed for the year 𝑡 to account for interannual variation that is not fully captured by climate anomalies.

  𝐸 (T -𝑚 |𝑇 𝑚 , 𝑃, 𝑋) = 𝐸 (T -𝑚 |𝑇 𝑚 ) + 𝐸 (T -𝑚 |𝑃, 𝑋)

distribution [T -𝑚 |𝑇 𝑚 , 𝑃, 𝑋]. Using the fitted model in GJAM (see below), we decompose the conditional effect of 𝑚 on other traits as,

Acknowledgments

We thank the National Ecological Observatory Network (NEON) for access to sites and vegetation structure data. F. Lefèvre (INRAE, France), William Schlesinger (the Comstock Foundation), and W. Koenig provided additional data. The project has been funded by grants to JSC from the National Science Foundation, most recently DEB-1754443, and by the Belmont Forum (1854976), NASA (AIST16-0052, AIST18-0063), and the Programme d'Investissement d'Avenir under project FORBIC (18-MPGA-0004) (Make Our Planet Great Again). Jerry Franklin's data remain accessible through NSF LTER DEB-1440409. Data from Hubbard Brook (New Hampshire) were funded through NSF-LTER. Puerto Rico data were funded by NSF grants, most recently, DEB 0963447 and LTREB 11222325. Data from the Andes Biodiversity and Ecosystem Research Group were funded by the Gordon and Betty Moore Foundation and NSF 727 LTREB 1754647. MB was supported by grant no. 2019/35/D/NZ8/00050 from the (Polish) National Science Centre, and Polish National Agency for Academic Exchange Bekker programme PPN/BEK/2020/1/00009/U/00001. Research by the USDA Forest Service and the USGS was funded by these agencies. Any use of trade, firm, or product names does not imply endorsement by the U.S. Government.

Supplementary material

This Supplement provides additional Methods descriptions and additional data summaries as tables and figures. Full summaries of the MASTIF network are available at these links for sites and species.

Materials and Methods

Individual seed productivity (ISP) and species seed productivity (SSP) Calculation of ISP combines posterior mean values with their uncertainties, as an expectation over the variations across years (tree 𝑖 of species 𝑠 at stand 𝑗 in year 𝑡):

where 𝑚 𝑠 is seed size (g), 𝑏 𝑖 𝑗 is basal area (𝑚 2 ), and weight 𝑤 𝑖 𝑗 𝑠,𝑡 is the inverse of the coefficient of variation (CV),

𝑠 𝑖 𝑗 𝑠,𝑡 is the standard error of the estimate. The 𝐶𝑉 -1 is used instead of the inverse of variance, because the mean tends to scale with variance. Low values for f𝑖 𝑗 𝑠,𝑡 are noisy and less important than high values, which are emphasized by the 𝐶𝑉.

Following [START_REF] Qiu | Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery[END_REF], species seed productivity (SSP) comes from the expectation of all ISP for a given species 𝑠:

where 𝑤 𝑖 𝑗 𝑠 is defined the same way as 𝑤 𝑖 𝑗 𝑠,𝑡 , i.e., root mean predictive variance divided by the mean ISP for tree 𝑖 𝑗 𝑠.

Generalized joint attribute modeling (GJAM)

To partition the expected effects that one trait can have on the observations of other traits (eq. ( 4)), we start with the joint distribution of 𝑀 traits from a species 𝑠 fitted with GJAM, all the traits were log-transformed before entering the model,

where B 𝑚 holds column 𝑚 and B -𝑚 holds the other 𝑀 -1 columns of B. The covariance matrix is also partitioned as

This joint distribution allows us to isolate the contributions of trait 𝑚 as a conditional distribution.

Subtracting the random effect for species 𝑠 gives the trait vector T𝑠 = T 𝑠g

There are now two sets of coefficients, a length 𝑀 -1 vector for effects of 𝑚, A = Σ -𝑚,𝑚 Σ -1 𝑚,𝑚 , and another 𝑀 -1 × 𝑄 matrix for effects of x, C = B -𝑚 -AB 𝑚 . The elements of matrix A are arrows in figure fig. 3. Matrix A were obtained with the conditionalParameters function in the GJAM package [START_REF] Qiu | Niche shifts from trees to fecundity to recruitment that determine species response to climate change[END_REF].


Charlotte Vemisse

Edouard Tuallori

Delphine Gras7

Anne Sophie Bedin4

Jeremy Charriot

Isabelle Viachier

Pascal Chanez7

Professor Arnaud Bourdin
email: .a-bourdin@chu-montpellier.fr

Airway epithelial type-2 alarmin profiles: Blood eosinophil counts remain in memory

Keywords: Alarmins . Blood eosinophil counts . Epithelïal cells . lL-25 . TSLP ÀIJ: air-liquid interface . COPD: chronic obstructive, pulmonary disease . FDR: false discovery rate. HBEC: human, bronchial epithelial cell . TSLP: thymic stromal lymphopoietin

Epithelial cytokines are involved in the orchestration of Tlq2 inflammatory patterns. We question the persistence of this trait in air-liquid interface (ALD epithelial cultures and whether this local orientation can be related to systemic patterns (e.g., blood eosinophil counts [BECs]). We investigated alarmin release related to high versus low T2 phenotypes associated with chronic airway diseases. ALIs were reconstituted from 32 control. 40 chronic obstructive pulmonary disease, and 20 asthmatic patients, Interleukin-8 OEL-8; a T1-cytokine), IL-25, IL-33, and thymic stromal lymphopoietin (T22-alarmins) concentrations were assessed in subnatants at steady state and used to explain blood neutrophil and eosinophil counts. IL-25 and IL-8 levels were highest in asthma AI.I-subnatants, whereas lL-33 was sparsely detected. Thymic stromal lymphopoietin levels were similar among groups. All asthma cell cultures were T1-high/T2-high, while chronic obstructive pulmonary disease and controls tended to be mixed. BECs were independently explained by both disease and in-culture T2-alamin levels, irrespective of the T2-alazTnin considered. The epithelial ALI-T2 signature `x/as more frequently high in patients with a BE:C: > 300/mm3. Despite removal from an in vivo environment for >/=2 months, ALIs release disease-specific cytokine "cocktails" into their subnatants, suggesting continued persistence of alarmin orientation in differentiated cell line environments.

Introduction

The inflammatory cascades found in the airways of both asthma and chronic obstructive pulmonary disease (COPD) patients share a classic type 1 (T1)/type 2 (T2) inflammation paradigm [START_REF] Agusti | Treatable traits toward precision medicine of chronic airway diseases[END_REF][START_REF] Carr | Eosinophilic and noneosinophilic asthma[END_REF].

Accordingly, as a potential surrogate for the epithelial orchestration of the latter, blood eosinophil counts became the peripheral biomarker of T2 inflammation [START_REF] Brusselle | Eosinophils in the spotlight eosinophilic airway inflammation in nonallergic asthma[END_REF][START_REF] Green | Asthma cxaccrbation3 and sputum eosinophil counts a randomised controlled trial[END_REF]. Conversely, neutrophil counts are thought to be more related to low T2 status. This epithelial origin/orchestration is purported to act through the release of epithelial derived cytokines, such as IL-33, IL-25, and thymic stromal lymphopoietin (TSLP) for high T2 phenotypes and IL-8 for low T2. though the latter is less firmly established [START_REF] Barnes | The cytokine network in asthma and chronic obstructive pulmonary disease[END_REF][START_REF] Barnes | Targeting cytokines to treat asthma and chronic obstructive pulmonary disease[END_REF].

Airway epithelial alarmins may be sufficiently strong to drive eosinophil counts in the blood. The relationships between localized and circulating eosinophil population § (i.e., which one is driving the other), especially when considering blood versus sputum eosinophilia, is therefore open [o debate [7]. In therapeutic studies, the biologics targeting IL-5, lL-13, and/or IL-4 were found efficacious in controlling eosinophilic asthma (and to a lesser extent eosinophilic COPD), with further evidence forthcoming [START_REF] Liu | Mepolizumab treatment in patients with severe eosinophilic asthma[END_REF][START_REF] Castro | Dupilumab efficacy and safety in moderate-to-severe uncontrolled asthma[END_REF][START_REF] Bleecker | Efficacy and safety of benralizumab for patients with severe asthma uncontrolled with high-dosage inhaled corticosteroids and long-acting P2-agonists (SIROCCO)-a randomised. multicentre. placebo-controlled phase 3 trial[END_REF][START_REF] Castro | Reslizumab for poorly controlled. eosinophilic asthma a randomized, placebo-controlled study[END_REF][START_REF] Pavord | Mepolizumab for eosinophilic chronic obstructive pulmonary disease[END_REF][START_REF] Criner | Benralizumab for the prevention of COPD exacerbations[END_REF]. IL-8-targeting was found (o have significant (but limited) benefits in patients not selected according to inflammatory traits [START_REF] Nair | Safety and efficacy of a CXCR2 antagonist in patients with severe asthma and sputum neutrophils, a randomized, placebo controlled clinical trial[END_REF][START_REF] Fôrster | The safety and tolerability of oral AZD5069, a selective CXCR2 antagonist, in patients with moderate-to-severe COPD[END_REF].

Because cultured epithelia are reconstituted outside the physiological environment and its associated inflammatory cocktail, the persistence of a T1/T2 orientation during cell culture is considered a sign of a constitutive nature that will persist in the absence of local triggers. This is a distinct possibility as air-liquid interface (ALI) cultures have been shown to reliably reflect donor-specific characteristics [START_REF] Grag | An ex vivo model of severe asthma using reconstituted human bronchial epithelium[END_REF]17]. Assessing alarmin release in ALI culture subnatants can also be used to elucidate whether an expressed T2 orientation is dominated by one such alarmin or is potentially the result of a cock[ail phenomenon. Different alarmin cascades may be observed for different patients but result in similar T1/I`2 orientations (or not). Cytokine functions are multiple and redundant, and their up-or down-regulation may correspond to responses other than just eosinophil attraction, further entangling signaling pathways. We therefore hypothesized that alarmin levels at steady state were related to blood eosinophil counts irrespective of the underlying chronic airway disease. For testing this hypothesis, IL-33, IL-25, and TSIP for the T2 high inflammatory pattern and IL-8 for the low T2 pattern were assessed in ALI cultures derived from a large group of patents (with asthma or COPD) and healthy controls and compared to baseline clinical characteristics, including white blood ce]l counts.

Results and discussion

Patient characteristics

Descriptive statistics for clinical characteristics are given in Table 1 and are largely in line with the classical features of the studied disease groups. Most asthma/COPD patients were considered as "severe" and required high treatment doses. Controls were subjects who required bronchoscopy (as confirmed by a senior pulmonologist) for foreign body removal, exploration of a peripheral nodule or other abnormal thoracic imaging findings (see the Supporting information for more details).

Cytokine profile § in chronic airway diseases

As demonstrated in Fig. 1, despite removal from an in vivo environment for over 2 months, ALI-cultured human bronchial epithelial cells (HBECs) release patient-specific and potentially disease-specific cytokine "cocktails" into their basal-state subnatants, suggesting persistent alarmin orientation in differentiated airway epithelia. The control, asthma, and COPD-derived cytokine profiles differed as follows: (i) significantly different IL-25 concentrations between all three groups (asthma > control COPD; Fig. 1A), (ii) sparse IL-33 signaling detectable in only a few isolated individuals in the COPD group {FLg.18), (iii) similar levels of TSIP for all groups (Fig. 1C), and (iv) higher IL-8 concentrations in the severe asthma group as compared to the other two (Fig. 1D). As concerns this last observation concerning IL8, which may appear unexpected, there are now many reports indicating that the view that eosinophils are restricted to asthma and neutrophils to COPD is a matter of debate [START_REF] Carr | Eosinophilic and noneosinophilic asthma[END_REF][START_REF] Pavord | Mepolizumab for eosinophilic chronic obstructive pulmonary disease[END_REF]. Whether low or non-T2 asthma exists is difficult to conclude [START_REF] Kuo | T-helper cell type 2 (Th2) and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in U-BIOPRED[END_REF]. Potentially, these patients may have hidden T2 phenotypes, in particular due to steroid exposure.

As concerns combined "eT2" profiles (the average of scaled values for IL-25, lL-33, and TSIP as in [START_REF] Peters | Measures of gene expression in sputum cells can identify TH2-high and TH2-low subtypes of asthma[END_REF]), there was a great deal of overlap between the three groups, while the "eT1"/lL8 profile singled out the asthma group (Fig. 1E). Among patients with asthma, all but one were characterized as being both eT1 and eT2 high (Fig. 1F). The distributions of TIAE2 high/low categories for patients with asthma tended to be different from those of the control or COPD groups (false discovery rate [FDR] corrected Fisher's exact tests, p = 0.085, 0.092). The T1:T2 high/low classification distributions were similar for the latter two groups (Fig. 1G-H), with a variety of T1/T2 high/low combinations found. Whether these persistent orientations are related to polymorphisms [START_REF] Moffatt | A large-scale. consortium-based genomewide association study of asthma[END_REF] affecting the expression of these cytokines would be ideally assisted by performing complimentary analyses, but unlikely able (o provide a definitive answer given the limited sample sizes. Although relevant polymorphisms were found in 17q21 affecting TSLP and lL-33 and the IL-33 receptor ST2 in asthma, this finding was not reproduced in COPD [START_REF] Ragland | Genet]c advances m chronic obstructive pulmonary disease insights from COPDGene[END_REF].

Eosinophilia association with disease and T2 cytokine levels

Multiple regression indicated no associations between blood neutrophil levels, disease groups or IL-8, suggesting that airway epithelia are not a critical factor determining this trai[. However, blood eosinophilia levels were significantly associated with group (asthma [coefficient ± SD with significance level]: 65.61 ± 25.S7, p = 0.0110; COPD: 50.60 ± 2].50, p = 0.0195) with a tendency for association with cytokine levels (17.30 ± 9.06, p = 0.0575). However, the type of T2 cytokine considered was insignificant, suggesting that high levels of any T2 cytokine were associated with high eosinophilia levels. A summary figure demonstrating the relationship between high eosinophil status (here defined as those above a threshold of 300 celvmm3) and T2 markers is presented in Fig. 2. Logistic regression similarly found two factors that independently predicted high eosinophil status: asthma status (1.26 ± 0.43, p = 0.004) and the scaled value of any of the three T2 cytokines (0.36 ± 0.14, p=0.011).

Study limitations

The limitations of this study include its small size and observational nature. In addition, the relationship found between the eT2 signatures and the blood eosinophil count deserves multiple comments: (i) this result should be taken cautiously as the correlation was far from perfect and was no[ consistent in all groups and [he sample sizes were limited; (ii) we used the scaled values of the three classical T2 alarmins, whereas the biological reality behind this is unknown, (iii) highly sensitive IL-33 immunoassays based on a digital F.LISA methods (such as the kit recently launched by Quanterix) may improve detection of this cytokine in future ALI studies. lf we accept these limitations, this relationship may indicate first that the airway epithelium is a major contributor to a systemic trait, thus suggesting that alarmin release is not restricted to the airway submucosa and diffuses beyond the nose and lungs in a way that affects au organs (at least for patients with respiratory diseases [START_REF] George | Blood eosinophil count and airway epithelial transcriptome relationships in COPD versus asthma[END_REF] ). Second, it would be interesting to assess experimental epithelial injury and repair (though reproducible scratch challenges are difficult to reliably achieve in so many ALI cultures). Third, the fact that there was no single alarmin dependence may suggest that alarmin targeting may require personalization.

Concluding remarks

Despite over 2 months in similar culture conditions, we observe continued persistence of alarmin orientation in differentiated lung epithelial cell lines from asthma, COPD, and control populations. Beyond group differences, the heterogeneity present in alarmin orientation suggests that different cytokines may be affecting different people.

Materials and methods

Primary HBECs were obtained from biopsies specimens collected during fibreoptic bronchoscopy. Subjects (32 controls, 40 COPD patients, and 20 asthmatics) were recruited at two public hospitals in France, and HBECs were cultured as previously reported [START_REF] Grag | An ex vivo model of severe asthma using reconstituted human bronchial epithelium[END_REF] (see Supporting information for more details). Clinical and demographic characteristics, including blood eosinophil and neutrophil counts assessed on the day of bronchoscopy, were recorded for all subjects.

Cytokine detection

AI.I epitheliums were considered as fully differentiated and representing a basal state on day 28. The concentrations of IL-25 (EK1179, Bioocean), lL-33 (435908. Biolegend), TSLP (434204, Biolegend), and IL-8 (851530005, Diaclone) in ALI culture mediums were determined by duplicate measurements on day 28 as previously reported [START_REF] Grag | An ex vivo model of severe asthma using reconstituted human bronchial epithelium[END_REF], using sandwich enzyme-linked immunosorbent assays (ELISAs) according to the manufacturer's instructions. Color changes were spectrophotometrically detected at a wavelength of 450 nm, and sample concentrations were interpolated from standard curves.

Statistics

Statistics and graphics generation were performed using the R programming environment. Epithelial Tl profiles (eT1) were determined by scaled IL-8 concentrations. Epithelial T2 profiles (eT2) were determined by the average of the scaled concentrations of IL-25, IL-33, and TSLP (as in [START_REF] Peters | Measures of gene expression in sputum cells can identify TH2-high and TH2-low subtypes of asthma[END_REF]). Medians were used to separate `high" versus "low" eT1 or eT2 profiles. "High eosinophil" status was determined according to a 300 cells/mm3 threshold [START_REF] Buhl | Severe eosinophilic asthma, a roadmap to consensus[END_REF].

Cytokine concentrations were compared among disease groups using Conover's nonparametric all-pairs rank comparison tests with FDR corrections for multiple testing.

Linear models were used to predict (i) blood neutrophil counts as a function of group (control, asthma, or COPD) and ALI subnatant cytokine scaled value; (ii) blood eosinophil counts as a function of group, type-2 cytokine identity (IL-2S, IL-33, or TSLP) and ALI subnatant cytokine scaled value; and (iii) blood eosinophil counts as a function of group and the number of above-median type-2 cytokines in ALI subnatants. Final]y, a logistic regression model was used to predict high eosinophil status as a function of disease group, type-2 cytokine identiq/, and (ype-2 cytokine scaled value.

The distributions of T1 -high-T2-high, T1 -high-T2-low, T1 -low-T2-high, and T1-low-T2low patients were compared between groups using Fishers exact tests with a FDR correction. The distributions of patients with 0,1, or 2 high-T2 cytokine status were compared between groups in a similar fashion.

Wagener, A. H., de Nijs, S. B.. Lutter. R., Sousa, A. R" Weersink, E. J. M., Bel, £. H. and Sterk, P. J. Extemal validation of blood eosinophils, FE(NO) and serum periostin as surrogates for sputum eosinophils in asthma . Thorax 2015 70.115-120

Acknowledgment : This research is part of the PhD studies of Dr. Charlotte Vernisse, whose salary was provided via an industrial research training scholarship (CIFRE 2016/1000 between Medicine Biology Mediterranean [Isabelle Vachier], Montpellier, France and the PhyMedExp Laboratory, lNSERM, CNRS, Université de Montpellier, France). Further support was provided by the Montpellier University Hospitals (No. 2013-A01405-40 [Principal Investigator (PI): Arnaud Bourdin] and No. 2018-A01530-S5 [PI: Arnaud Bourdin]), an inter-regional French grant (Direction Générale de l'Offre de Soins, PHRC (IR 2013-14) No. 2013-A00553-42 [PI: Pascal Chanez]), a French National Research Agency grant (ANR-13-BSV5-0015-02 -Mucocil [PI: Pascal Chanez]). the COBRA cohort (French National lnstitute of Health and Medical Research, INSERM, IDRGB 2008-A00284-51). and the Apard association (Adène Group). -

Data availability statement :

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Conflict of interest

CV ET, DG, ASB, LK, rv and IP report no conflicts of interest. CMS reports grants or persona] fees from Astra Zeneca, GSK. JC has sewed as consultant, or lecturer, and has previously received honoraria from Astrazeneca, Chiesi, GSK, Sanofi. PC has served as an advisory board member, consultant, or lecturer. and has previously received honoraria or grants from ALK. Boehringer lngelheim, Almirall, Centocor, GSK. MSD, Astrazeneca, Novartis, Teva, Chiesi, SNCF, and AMU. Arnaud Bourdin reports grants, personal fees, nonfinancial support, and/or other from GSK, As[ra Zeneca, Boeringher lngelheim, Novartis, Chiesi Farma, Teva, Sanofi Regeneron, Actelion/Jansen, United Therapeutics, Pulsar, and Roche.

Ethic approval

Subjects were recruited under protocols approved by independent ethics committees (the COBRA cohort [START_REF] Pretolani | COBRA Study Group and COBRA cohort Study Group. Clinical and biological characteristics of the French C0BRA cohort or adult subjects with asthma[END_REF] for asthma patients [Comité de Protection des Personnes (CPP) Ile-de-France 1, 09-11962]) and the RhéMuc protocol for others (NCT03187860; CPP Es[-II,18/07/23/59436); au patients gave written informed consent prior to enrolment.