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\begin{document}

\title{Genetic differentiation can be predicted from observational data for
reproductive but not vegetative traits in a widespread short-lived plant}



\author[1]{Jesus Villellas}%
\author[2]{Johan Ehrlén}%
\author[3]{Elizabeth Crone}%
\author[4]{Anna Csergő}%
\author[5]{Maria Garcia}%
\author[6]{Anna-Liisa Laine}%
\author[7]{Deborah Roach}%
\author[8]{Roberto Salguero-Gomez}%
\author[9]{Glenda Wardle}%
\author[10]{Dylan Childs}%
\author[11]{Bret Elderd}%
\author[12]{Alain Finn}%
\author[13]{Sergi Munne-Bosch}%
\author[14]{Benedicte Bachelot}%
\author[15]{Judit Bódis}%
\author[16]{Anna  Bucharova}%
\author[17]{Christina Caruso}%
\author[18]{Jane Catford}%
\author[19]{Matthew  Coghill}%
\author[20]{Aldo Compagnoni}%
\author[21]{Richard Duncan}%
\author[22]{John Dwyer}%
\author[23]{Aryana  Ferguson}%
\author[19]{Lauchlan Fraser}%
\author[24]{Emily  Griffoul}%
\author[25]{Ronny Groenteman}%
\author[26]{Liv Norunn  Hamre}%
\author[27]{Aveliina Helm}%
\author[12]{Ruth Kelly}%
\author[28]{Lauri Laanisto}%
\author[29]{Michele Lonati}%
\author[30]{Zuzana Munzbergova}%
\author[12]{Paloma Nuche}%
\author[31]{Siri Olsen}%
\author[32]{Adrian  Oprea}%
\author[27]{Meelis Partel}%
\author[33]{William Petry}%
\author[34]{Satu Ramula}%
\author[2]{Pil  Rasmussen}%
\author[29]{Simone  Enri}%
\author[35]{Anna  Roeder}%
\author[35]{Christiane Roscher}%
\author[36]{Cheryl Schultz}%
\author[37]{Olav Skarpaas}%
\author[12]{Annabel Smith}%
\author[2]{Ayco Tack}%
\author[31]{Joachim  Töpper}%
\author[38]{Peter Vesk}%
\author[39]{Gregory  Vose}%
\author[21]{Elizabeth  Wandrag}%
\author[40]{Astrid  Wingler}%
\author[12]{Yvonne Buckley}%
\affil[1]{Museo Nacional de Ciencias Naturales}%
\affil[2]{Stockholm University}%
\affil[3]{Tufts University}%
\affil[4]{Szent István University}%
\affil[5]{IPE-CSIC}%
\affil[6]{University of Zurich}%
\affil[7]{University of Virginia}%
\affil[8]{University of Oxford}%
\affil[9]{University of Sydney}%
\affil[10]{Sheffield University}%
\affil[11]{Louisiana State University}%
\affil[12]{Trinity College Dublin}%
\affil[13]{University of Barcelona}%
\affil[14]{Oklahoma State University}%
\affil[15]{University of Pannonia}%
\affil[16]{University of Münster}%
\affil[17]{University of Guelph}%
\affil[18]{King's College London}%
\affil[19]{Thompson Rivers University}%
\affil[20]{Martin Luther University Halle-Wittenberg}%
\affil[21]{University of Canberra}%
\affil[22]{The University of Queensland}%
\affil[23]{Madrona Stewardship}%
\affil[24]{Morton Arboretum}%
\affil[25]{Landcare Research}%
\affil[26]{Western Norway University of Applied Sciences}%
\affil[27]{University of Tartu}%
\affil[28]{Estonian University of Life Sciences}%
\affil[29]{University of Torino}%
\affil[30]{Charles University}%
\affil[31]{Norwegian Institute for Nature Research Oslo}%
\affil[32]{Alexandru Ioan Cuza University}%
\affil[33]{Princeton University}%
\affil[34]{University of Turku}%
\affil[35]{Helmholtz-Centre for Environmental Research - UFZ}%
\affil[36]{Washington State University}%
\affil[37]{University of Oslo}%
\affil[38]{University of Melbourne}%
\affil[39]{University of California Irvine}%
\affil[40]{University College Cork}%


\vspace{-1em}



  
  \date{May 12, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Phenotypic plasticity can mask population genetic differentiation,
reducing the predictability of trait-environment relationships. In
short-lived plants, reproductive traits may be more genetically
determined due to their direct impact on fitness, whereas vegetative
traits may show higher plasticity to buffer short-term perturbations.
Combining a multi-treatment greenhouse experiment with global field
observations for the short-lived Plantago lanceolata, we 1) disentangled
the genetic and plastic responses of functional traits to a set of
environmental drivers and 2) assessed the utility of trait-environment
relationshisps inferred from observational data for predicting genetic
differentiation. Reproductive traits showed distinct genetic
differentiation that was highly predictable from observational data, but
only when correcting traits for differences in their (labile) biomass
component. Vegetative traits showed higher plasticity and contrasting
genetic and plastic responses, leading to unpredictable trait patterns.
Our study suggests that genetic differentiation may be inferred from
observational data only for the traits most closely related with
fitness.%
\end{abstract}%



\sloppy


\textbf{Genetic differentiation can be predicted from observational data
for reproductive but not vegetative traits in a widespread short-lived
plant}

Jesus Villellas\textsuperscript{1,2}, Johan Ehrl\selectlanguage{ngerman}én\textsuperscript{3},
Elizabeth E. Crone\textsuperscript{4}, Anna Mária
Cserg\selectlanguage{polish}ő\selectlanguage{english}\textsuperscript{2,5}, Maria B. Garcia\textsuperscript{6},
Anna-Liisa Laine\textsuperscript{7,8}, Deborah A.
Roach\textsuperscript{9}, Roberto Salguero-G\selectlanguage{ngerman}ómez\textsuperscript{10-12},
Glenda M. Wardle\textsuperscript{13}, Dylan Z.
Childs\textsuperscript{14}, Bret D. Elderd\textsuperscript{15}, Alain
Finn\textsuperscript{2}, Sergi Munné-Bosch\textsuperscript{16,17},
Benedicte Bachelot\textsuperscript{18}, Judit Bódis\textsuperscript{19},
Anna Bucharova\textsuperscript{20,21}, Christina M.
Caruso\textsuperscript{22}, Jane A. Catford\textsuperscript{23,24},
Matthew Coghill\textsuperscript{25}, Aldo
Compagnoni\textsuperscript{26}, Richard P. Duncan\textsuperscript{27},
John M. Dwyer\textsuperscript{28,29}, Aryana
Ferguson\textsuperscript{30}, Lauchlan H. Fraser\textsuperscript{25},
Emily Griffoul\textsuperscript{31}, Ronny
Groenteman\textsuperscript{32}, Liv Norunn Hamre\textsuperscript{33},
Aveliina Helm\textsuperscript{34}, Ruth Kelly\textsuperscript{2,35},
Lauri Laanisto\textsuperscript{36}, Michele Lonati\textsuperscript{37},
Zuzana Münzbergová\textsuperscript{38,39}, Paloma
Nuche\textsuperscript{2}, Siri Lie Olsen\textsuperscript{40}, Adrian
Oprea\textsuperscript{41}, Meelis Pärtel\textsuperscript{34}, William K.
Petry\textsuperscript{42}, Satu Ramula\textsuperscript{43}, Pil U.
Rasmussen\textsuperscript{3,44}, Simone Ravetto
Enri\textsuperscript{37}, Anna Roeder\textsuperscript{45,46}, Christiane
Roscher\textsuperscript{45,46}, Cheryl Schultz\textsuperscript{47}, Olav
Skarpaas\textsuperscript{48}, Annabel L. Smith\textsuperscript{2,49},
Ayco J.M. Tack\textsuperscript{3}, Joachim Paul
Töpper\textsuperscript{40}, Peter A. Vesk\textsuperscript{50}, Gregory
E. Vose\textsuperscript{51}, Elizabeth Wandrag\textsuperscript{27,52},
Astrid Wingler\textsuperscript{53} and Yvonne M.
Buckley\textsuperscript{2,12}

\textbf{Affiliations:}

\textsuperscript{1}Departamento Biogeografía y Cambio Global, Museo
Nacional de Ciencias Naturales (MNCN-CSIC), E-28006 Madrid,
Spain.\textsuperscript{2}School of Natural Sciences, Zoology, Trinity
College Dublin, Dublin 2, Ireland. \textsuperscript{3}Department of
Ecology, Environment and Plant Sciences, Stockholm University, SE-106 91
Stockholm, Sweden. \textsuperscript{4}Department of Biology, Tufts
University, Medford MA 02145, USA. \textsuperscript{5}Department of
Botany and Soroksár Botanical Garden, Szent István University, Budapest,
Hungary. \textsuperscript{6}Department of Biodiversity Conservation and
Ecosystem Restoration, Pyrenean Institute of Ecology (CSIC); Avda.
Montañana 1005, 50059-Zaragoza, Spain. \textsuperscript{7}Department of
Evolutionary Biology and Environmental Studies, University of Zurich,
Winterthurerstrasse 190, CH-8057 Zurich,
Switzerland.\textsuperscript{8}Organismal \& Evolutionary Biology
Research Program, Faculty of Biological \& Environmental Sciences, PO
Box 65 (Viikinkaari 1), FI-00014 University of Helsinki,
Finland.\textsuperscript{9}Department of Biology, University of
Virginia, Charlottesville, VA, USA. \textsuperscript{10}Department of
Zoology, University of Oxford, 11a Mansfield Rd, OX1 3SZ, Oxford,
UK.\textsuperscript{11}Max Planck Institute for Demographic Research,
Rostock 18057, Germany. \textsuperscript{12}Centre of Excellence in
Environmental Decisions, The University of Queensland, Goddard Building,
St Lucia, QLD 4104, Australia. \textsuperscript{13}School of Life and
Environmental Sciences, University of Sydney, Sydney, New South Wales
2006, Australia. \textsuperscript{14}Department of Animal and Plant
Sciences, University of Sheffield, Sheffield, S10 2TN,
UK.\textsuperscript{15}Department of Biological Sciences, Louisiana
State University, Baton Rouge, Louisiana, USA.
\textsuperscript{16}Department of Evolutionary Biology, Ecology and
Environmental Sciences, Faculty of Biology, University of Barcelona,
Barcelona, Spain.\textsuperscript{17}Institut de Recerca de la
Biodiversitat, Universitat de Barcelona, Barcelona, Spain.
\textsuperscript{18}Department of Plant Biology, Ecology and Evolution,
Oklahoma State University, 301 Physical Science, Stillwater, OK 74078,
USA. \textsuperscript{19}Department of Plant Sciences and Biotechnology,
Georgikon Faculty, University of Pannonia, Keszthely, Hungary.
\textsuperscript{20}Biodiversity and Ecosystem Research Group, Institut
of Landscape Ecology, University of Münster, Germany.
\textsuperscript{21}Plant Evolutionary Ecology, Institut of Evolution
and Ecology, University of Tübingen,
Germany.\textsuperscript{22}Department of Integrative Biology,
University of Guelph, Guelph, Ontario N1G 2W1 Canada.
\textsuperscript{23}Department of Geography, King's College London,
London, WC2B 4BG, UK.\textsuperscript{24}Biological Sciences, University
of Southampton, Southampton, SO17 1BJ, UK.
\textsuperscript{25}Department of Natural Resource Sciences, Thompson
Rivers University, British Columbia V2C 0C8, Canada.
\textsuperscript{26}Institute of Biology, Martin Luther University
Halle\selectlanguage{english}-Wittenberg, Halle (Saale), Germany.\textsuperscript{27}Institute
for Applied Ecology, University of Canberra, ACT 2617, Australia.
\textsuperscript{28}School of Biological Sciences, The University of
Queensland, St Lucia, Queensland, 4072 Australia.
\textsuperscript{29}CSIRO Land \& Water, EcoSciences Precinct, Dutton
Park Queensland, 4102 Australia.\textsuperscript{30}Madrona Stewardship,
Eugene, OR, 97405 USA.\textsuperscript{31}The Morton Arboretum, Lisle,
Illinois 60532 USA.\textsuperscript{32}Manaaki Whenua - Landcare
Research, 54 Gerald Street, Lincoln 7608, New Zealand.
\textsuperscript{33}Department of Environmental Sciences, Western Norway
University of Applied Sciences, Royrgata 6, N-6856 Sogndal, Norway.
\textsuperscript{34}Institute of Ecology and Earth Sciences, University
of Tartu, Lai 40, 51005 Tartu, Estonia. \textsuperscript{35}Agri-Food
and Biosciences Institute, 18a Newforge Lane, Belfast BT9 5PX, Northern
Ireland, UK.\textsuperscript{36}Institute of Agricultural and
Environmental Sciences, Estonian University of Life Sciences,
Kreutzwaldi 5, 51006, Estonia. \textsuperscript{37}Department of
Agriculture, Forest and Food Science, University of Torino, Largo
Braccini 2, Grugliasco (TO), Italy.\textsuperscript{38}Department of
Botany, Faculty of Science, Charles University, Prague, Czech Republic.
\textsuperscript{39}Department of Population Ecology, Institute of
Botany, Czech Academy of Sciences, Czech Republic.
\textsuperscript{40}Norwegian Institute for Nature Research, Sognsveien
68, N-0855 Oslo, Norway.\textsuperscript{41}Botanic Garden ''Anastasie
Fatu'', University ''Alexandru Ioan Cuza'' Ia\selectlanguage{polish}ş\selectlanguage{english}i, Romania.
\textsuperscript{42}Department of Ecology \& Evolutionary Biology,
Princeton University, Princeton, New Jersey 08544-2016 USA.
\textsuperscript{43}Department of Biology, University of Turku, 20014
Turku, Finland. \textsuperscript{44}The National Research Centre for the
Working Environment, Lers\selectlanguage{ngerman}ø Parkallé 105, DK-2100 Copenhagen, Denmark.
\textsuperscript{45}Department of Physiological Diversity, Helmholtz
Centre for Environmental Research (UFZ), Permoserstrasse 15, 04318
Leipzig, Germany.\textsuperscript{46}German Centre for Integrative
Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e,
04103 Leipzig, Germany.\textsuperscript{47}School of Biological
Sciences, Washington State University, Vancouver, WA 98686 USA.
\textsuperscript{48}Natural History Museum, University of Oslo, P.O. Box
1172 Blindern, NO-0318 Oslo, Norway. \textsuperscript{49}School of
Agriculture and Food Sciences, University of Queensland, Gatton, QLD
4343, Australia.\textsuperscript{50}School of BioSciences, University of
Melbourne, Melbourne 3010, VIC, Australia.
\textsuperscript{51}Department of Ecology and Evolutionary Biology,
University of California, Irvine, California 92697 USA.
\textsuperscript{52}Department of Biology, University of York, York
YO105DD, UK. \textsuperscript{53}School of Biological, Earth \&
Environmental Sciences and Environmental Research Institute, University
College Cork, Distillery Fields, North Mall, Cork, Ireland.

\textbf{E-mail addresses (in the same order as authors' names):}

jesus.villellas@gmail.com; Johan.Ehrlen@su.se;
elizabeth.crone@tufts.edu; Csergo.Anna.Maria@szie.hu;
mariab@ipe.csic.es; anna-liisa.laine@helsinki.fi; droach@virginia.edu;
rob.salguero@zoo.ox.ac.uk; glenda.wardle@sydney.edu.au;
d.childs@sheffield.ac.uk; elderd@lsu.edu; alfinn@tcd.ie; smunne@ub.edu;
bb32@rice.edu; sbj@georgikon.hu; anna.lampei-bucharova@uni-muenster.de;
carusoc@uoguelph.ca; jane.catford@kcl.ac.uk; matthewcoghill@live.ca;
aldo.compagnoni@idiv.de; richard.duncan@canberra.edu.au;
j.dwyer2@uq.edu.au; madronastewardship@gmail.com; lfraser@tru.ca;
emily.griffoul@gmail.com; groentemanr@landcareresearch.co.nz;
liv.hamre@hvl.no; aveliina.helm@ut.ee; kellyr44@tcd.ie; laanisto@ut.ee;
michele.lonati@unito.it; zuzmun@natur.cuni.cz; paloma.nuche@gmail.com;
siri.lie.olsen@nina.no; a\_aoprea@yahoo.co.uk; meelis.partel@ut.ee;
wpetry@princeton.edu; satu.ramula@utu.fi;
pil.uthaug.rasmussen@gmail.com; simone.ravettoenri@unito.it;
anna.roeder@ufz.de; christiane.roscher @ufz.de; schultzc@wsu.edu;
olav.skarpaas@nhm.uio.no; annabel.smith@uq.edu.au; aycotack@gmail.com;
joachim.topper@nina.no; pvesk@unimelb.edu.au; gvose@uci.edu;
elizabethwandrag@gmail.com; astrid.wingler@ucc.ie; buckleyy@tcd.ie

\textbf{Statement of authorship} : YMB coordinated the PlantPopNet
network. The founding steering committee (YMB, EEC, AMC, JE, MBG, A-LL,
DAR, RS-G and GMW) designed the PlantPopNet network and wrote the field
protocol. JV, JE and YMB designed the current study. All the authors
(except for A-LL, EG, GMW and PN) collected trait data on field
populations. AJMT, ALS, AMC, AR, BE, CR, JB, JD, JE, LHF, LNH, MC, PUR,
RK, RS-G, SM-B and YMB provided seeds for the common garden experiment.
JV carried out the common garden experiment with help from PN and JE,
and the statistical analyses with help from JE and YMB. JV wrote the
first draft of the manuscript with help from JE and YMB, and all authors
contributed substantially to revisions.

\textbf{Data accessibility statement} : Should the manuscript be
accepted, the data supporting the results will be archived in Dryad and
the data DOI will be included at the end of the article.

\textbf{Running title} : Predictability of genetic differentiation

\textbf{Keywords:} biomass; common garden experiment; countergradient
variation; fecundity; genotype by environment interaction; intraspecific
trait variation; introduced species; phenotypic plasticity; root:shoot
ratio; specific leaf area

\textbf{Type of article} : Letter

\textbf{No. words in abstract} : 150

\textbf{No. words in the main text} : 5146

\textbf{No. references} : 97

\textbf{No. figures} : 5

\textbf{No. tables} : 1

\textbf{No. text boxes} : 0

\textbf{Correspondence} : Jesus Villellas; email:
jesus.villellas@gmail.com; telephone: 0034914111328

\emph{Abstract}

Phenotypic plasticity can mask population genetic differentiation,
reducing the predictability of trait-environment relationships. In
short-lived plants, reproductive traits may be more genetically
determined due to their direct impact on fitness, whereas vegetative
traits may show higher plasticity to buffer short-term perturbations.
Combining a multi-treatment greenhouse experiment with global field
observations for the short-lived \emph{Plantago lanceolata} , we 1)
disentangled the genetic and plastic responses of functional traits to a
set of environmental drivers and 2) assessed the utility of
trait-environment relationshisps inferred from observational data for
predicting genetic differentiation. Reproductive traits showed distinct
genetic differentiation that was highly predictable from observational
data, but only when correcting traits for differences in their (labile)
biomass component. Vegetative traits showed higher plasticity and
contrasting genetic and plastic responses, leading to unpredictable
trait patterns. Our study suggests that genetic differentiation may be
inferred from observational data only for the traits most closely
related with fitness.

\emph{Introduction}

Functional traits are morphological, physiological or phenological
features of organisms that influence the components of fitness, i.e.
survival and reproduction (Reich \emph{et al} . 2003, Violle \emph{et
al} . 2007, Adler \emph{et al} . 2014). Intraspecific variation in
functional traits is widely documented and has important implications
for population dynamics (Hughes \emph{et al} . 2008, Villellas \& García
2017), evolutionary trajectories (Moran et el. 2016, Caruso \emph{et
al.} 2020), community assembly (Violle \emph{et al} . 2012, Des
Roches\emph{et al} . 2018), and ecosystem functioning (Crutsinger
\emph{et al} . 2006, Breza \emph{et al} . 2012). Disentangling the
environmental drivers of functional trait variation is thus of great
ecological and evolutionary interest (Liancourt et al. 2013, van de Pol
et al 2016, Bruelheide et al 2018), and can improve predictions of
species responses to global change (Benito Garzón \emph{et al} . 2011,
Violle \emph{et al} . 2014, Moran \emph{et al} . 2016).

The predominant approach to identify the drivers of functional trait
variation has relied upon assembling large databases of observed\emph{in
situ} trait variation (e.g., Enquist \emph{et al} . 2016, Moran\emph{et
al} . 2016, Iversen \emph{et al} . 2017, Kattge \emph{et al} . 2020) and
the association of these trait values with candidate environmental
drivers. However, interpreting trait-environment relationships inferred
from observational field datasets requires understanding the processes
underlying trait variation. Intraspecific trait variation observed
\emph{in situ} among populations may arise from genetic differentiation
and/or phenotypic plasticity (Chevin \emph{et al} . 2010, Merilä \&
Hendry 2014). Across large environmental gradients, genetic
differentiation among populations can result from adaptation to local
conditions (but see the role of neutral and historical processes in
Keller \emph{et al} . (2009), Santagelo \emph{et al} . (2018)).
Genetically determined traits are thus expected to show correlations
with the source environment. However, genetic differentiation might be
obscured by phenotypic plasticity (which can also be adaptive; see
Matesanz \emph{et al} . 2010, Palacio-López \emph{et al.} 2015),
reducing the consistency of trait-environment relationships across
environmental contexts.

Combining experimental and \emph{in situ} field data enables us to
assess the potential uses and misuses of observational trait datasets. A
common way to partition trait variation is through a common garden
experiment (Clausen \emph{et al.} 1940, MacColl 2011, Franks \emph{et
al} . 2014). Specifically, by growing offspring from multiple
provenances together in a set of controlled conditions, we can
disentangle the effects of source environments (leading to genetic
differentiation) from those of exposure environments (driving phenotypic
plasticity). Notably, by evaluating the different scenarios involving
genetic and plastic effects on traits, we can assess the utility of
observational data for predicting genetic differentiation (Fig. 1). For
example, a predominance of genetic over plastic effects decreases the
relative importance of genotype-by-environment interactions, and
increases the predictability of trait values from average environmental
conditions of source populations (Fig. 1a,f). In contrast, a high level
of plasticity causes traits to be more strongly determined by the
exposure environment, decreasing trait predictability from source
environment (Fig. 1b,g,h). Source and exposure environments can have
similar or opposing effects on traits (Fig. 1c-e), with opposing effects
known as countergradient variation (Conover \& Schultz 1995, Conover
\emph{et al} . 2009). Countergradient variation may lead to an apparent
absence of trait variation among populations in the field (Fig. 1d), or
even to patterns counter to those of genetic differentiation (Fig. 1e).

The role of genetic differentiation and phenotypic plasticity on
intraspecific variation differs among functional traits (Albert \emph{et
al} . 2010a, Funk \emph{et al} . 2017, Münzbergová \emph{et al} . 2017).
Species may show evolutionary conservation of the traits most directly
related to fitness through genetic differentiation (Scheiner 1993,
Stearns \& Kawecki 1994, Sih 2004), and instead display plasticity in
underlying morphological or physiological traits, to buffer
environmental perturbations (Sultan 1995, Richards \emph{et al} . 2006).
This view is also supported by demographic studies finding that the most
influential processes on population growth rate show relatively low
variability (Pfister 1998, Burns \emph{et al} . 2010, Hilde \emph{et al}
. 2020; but see McDonald \emph{et al} . 2017). In plants, vegetative
traits often show higher plasticity than reproductive traits (Bradshaw
1965, Schlichting \& Levin 1984, Frazee \& Marquis 1994). For example,
both biomass and reproductive investment per unit biomass determine
reproduction, but while biomass is expected to show high plasticity due
to its influence on several demographic parameters (Harper 1977),
reproductive investment per unit biomass may be more conserved. This
might be especially true for short-lived taxa, in which reproduction
usually has the highest influence on population growth
(Silvertown\emph{et al} . 1996, García \emph{et al} . 2008, Shefferson
and Roach 2012). Yet reproductive investment may appear to be strongly
driven by plasticity if evaluated at the whole plant level, due to the
inclusion of a more labile biomass-dependent component (Biere 1995,
Weiner\emph{et al} . 2009). It is important therefore to partition
reproductive traits into biomass dependent and independent components,
to better understand the role of genetic differentiation and plasticity.

Despite the abundance of studies analysing trait-environment
relationships at local or regional scales (e.g., Oleksyn \emph{et al} .
1998, Villellas \& García 2013, Preite \emph{et al} . 2015,
Münzbergová\emph{et al} . 2017), there is a critical gap in knowledge
about the drivers of intraspecific trait variation at global scales
(MacColl 2011). Environmental effects may be difficult to detect if
drivers are assessed independently from each other, or if studies omit
significant parts of a species' environmental niche (Matesanz \emph{et
al} . 2010, Hulme \& Barrett 2013, Shipley \emph{et al.} 2016).
Widespread plants offer a unique opportunity to unravel the multiple
drivers of trait variation from local to global scales. While some
studies have analysed trait genetic differentiation and plasticity
across species' ranges (e.g., Joshi \emph{et al} . 2001, Maron \emph{et
al} . 2004, Alexander\emph{et al} . 2012), we lack global assessments of
the responses of different types of traits to multiple environmental
drivers using the combined power of experimental and observational data.

Here we analyse responses of vegetative vs. reproductive traits of the
short-lived herb \emph{Plantago lanceolata} to a set of environmental
drivers, both in a common garden and in the field. By growing
individuals from multiple populations under several light and water
conditions, we tested 1) whether vegetative traits (plant biomass,
specific leaf area and root:shoot ratio) showed higher levels of
plasticity than reproductive traits (probability of flowering and
fecundity), and 2) whether reproductive traits showed more consistent
population genetic differentiation across exposure treatments than
vegetative traits, and higher consistency between genetic and plastic
responses. To account for the potential size-dependency of plant
reproductive investment, we examined reproductive traits by both
including and excluding plant biomass as a covariate in the analyses.
Finally, by comparing experimental results with trait-environment
relationships detected from a global-scale observational survey, we
evaluated 3) whether observational data provided a better prediction of
genetic differentiation for reproductive than vegetative traits.

\emph{Material and methods}

\emph{Study species}

\emph{Plantago lanceolata} L. (Plantaginaceae) is a short-lived
perennial herb with a typical lifespan of 2-5 yr (Lacey \emph{et al} .
2003, Roach 2003), although some individuals may exceed 12 yr
(Cavers\emph{et al} . 1980). Plants have one or more vegetative
rosettes. Inflorescences emerge in late spring or summer; flowers are
mostly self-incompatible and both wind- and insect-pollinated, (Clifford
1962, Sagar \& Harper 1964). \emph{Plantago lanceolata} is native to
Europe, Western Asia and North Africa, although it has been introduced
worldwide, mainly during the eighteenth and nineteenth centuries (Hooker
1867, Cavers \emph{et al} . 1980, Meyers \& Liston 2008). The species
occurs in a range of mostly open habitats, such as grasslands, sand
dunes or disturbed sites, showing a wide environmental niche (Fig. 2;
Sagar \& Harper 1964).

\emph{Field sampling of source populations}

Populations of \emph{P. lanceolata} included in this study were part of
the coordinated project PlantPopNet (Buckley \emph{et al.} 2019). In the
growing seasons of 2015 and 2016, we sampled 46 populations across the
species' range (29 native and 17 non-native populations; Fig. 2, Table
S1, S2 in Supporting Information), spanning a wide range of climatic,
management and plant community conditions, and a wide range of genoypes
(Smith \emph{et al.} 2020). For each population, we monitored all
individual plants within 0.25 m\textsuperscript{2} plots along 10 m
transects until we reached a minimum of 100 plants (Buckley \emph{et al}
. 2019). We recorded for each plant the number of rosettes and the
flowering status (flowering vs. non-flowering). For each rosette, we
recorded the number of leaves, the size of the longest leaf, the number
of flowering stems if any and the length of the most developed
inflorescence. We used these measurements to estimate biomass and total
inflorescence length at the whole plant level (see further details on
Appendix S1). In a subset of populations and outside the monitoring
plots, we collected leaves for the estimation of specific leaf area
(SLA; 25 populations) and seeds for the greenhouse experiment (15
populations; Table S1, Appendix S1).

\emph{Environmental conditions in source populations}

To analyse the effects of environmental conditions of source populations
on traits, we collected information on climate, land-use and vegetation
for each location (Table S2). Mean annual values and seasonality
(coefficient of variation in monthly values) for temperature,
precipitation and moisture index were obtained from the BioClim database
(Kriticos \emph{et al} . 2012, Fick \& Hijmans 2017). We used the
highest resolution available for temperature and precipitation (30 s)
and for moisture index (10 min). In the field, we recorded whether
populations were subject to mowing or not, and estimated the percentage
of vegetation cover and bare ground for four random plots per
population. In two opposite corners of the plots, we quantified
community vegetation height as the height at which a pole was completely
obscured by vegetation, looking from a distance of ca. 4 m.

To avoid collinearity in environmental predictor variables (climate,
land-use and vegetation data), we performed a Principal Component
Analysis (\emph{psych} package in R; R Core Team 2017, Revelle 2018). We
performed a second, orthogonal rotation that improved the interpretation
of the components (Quinn \& Keough 2002). The first three rotated
components explained 70.4\% of the variance (Fig. S1, Table S1). The
first component (hereafter ``Aridity'') was positively associated with
low mean and high seasonality in precipitation and mean moisture index.
The second component (``Temperature'') was positively associated with
high mean and low seasonality in temperature. The third component
(``Vegetation cover'') was positively associated with high percent
vegetation cover, greater height of vegetation and low percent bare
ground cover. We used these rotated components and the binary factor
Mowing to test the effects of source climate (Aridity, Temperature),
vegetation (Vegetation cover) and land-use (Mowing) on trait variation.
We used t-tests to analyse differences between native and non-native
populations in the rotated components and the underlying variables
(effects of native/non-native range on Mowing were tested with a
Generalized Linear Model using Binomial errors; \emph{stats} package, R
Core Team 2017).

\emph{Greenhouse experiment}

We performed a common garden experiment in a greenhouse with a subset of
15 populations (Fig. 2, Table S1). The experiment spanned almost the
entire geographical and environmental native range, and included three
non-native populations to increase the breadth of source environmental
conditions (Appendix S2). We pooled all the collected seeds at the
population level. We sowed 2,728 seeds (180-200 per population) and
obtained 1485 seedlings in individual pots after 25 days. Seedlings were
then exposed to treatments with two levels of water supply crossed with
three levels of light availability (one block with six treatment
combinations). We used 18 seedlings per treatment combination for each
population (except for BG, RO and TW populations with, respectively, 14,
10 and 8 seedlings per treatment combination; Table S1). The treatments
were chosen to compare their effects with those of two source
environmental drivers: Aridity (related to water availability) and
Vegetation cover (related to light availability). These treatments also
represent parameters likely affected by climate and land-use change. For
the water treatments, half of the plants were watered every three days
(``wet'' treatment), and the other half every nine days (``dry''
treatment), by flooding the supporting trays until soil was soaked with
water. Each water treatment level was divided into three light levels:
1) 100\% light, 2) 64\% light and 3) 33\% light (Appendix S2). Watering
and light levels were designed to span a wide environmental range,
characteristic of cosmopolitan plants.

To collect trait data in the greenhouse, we measured plant leaves,
flowering status and inflorescences 2.5 months after the onset of
treatments in the same way as in field populations. To account for
possible maternal effects, usually more manifest in early life stages
(Roach \& Wulff 1987), control leaf measurements were also taken 1 month
after the onset of treatments. At the end of the experiment, the longest
healthy leaf was collected from each of 10 individuals per population
and treatment combination. Leaves were scanned to estimate leaf area,
oven-dried (60ºC), and weighed to calculate SLA. Root:shoot ratio (RSR)
was also calculated in the individuals used for SLA measurements, but
only for eight populations (Table S1) and excluding the intermediate
light treatment due to logistical constraints. To measure RSR, the
remaining leaves and the roots were collected, roots were washed, and
both leaves and roots were oven-dried.

\emph{Analyses of trait variation in greenhouse and field conditions}

We used data from three vegetative and two reproductive traits to
analyse the drivers of intraspecific variation in greenhouse and field
conditions. Vegetative traits were biomass, SLA and RSR (the latter only
measured in greenhouse conditions), and reproductive traits were
probability of flowering and fecundity. Biomass was estimated for all
greenhouse and field individuals using leaf measurements and an equation
obtained for a subset of plants (Appendix S3). Probability of flowering
was modelled as a binary variable with data from the flowering vs.
non-flowering plant status. Total inflorescence length was used as a
proxy for fecundity, as we found a strong correlation between total
inflorescence length and seed production (conditional
R\textsuperscript{2} = 0.77; Appendix S3). In a preliminary analysis of
field data, we found generally weak correlations among traits (Appendix
S3). Thus we did not systematically consider trait covariation when
analysing the sources of trait variation. However, the correlation
between biomass and fecundity was moderately strong, so reproductive
traits were analysed by controlling for biomass. This allowed us to
assess size-independent reproductive investment (see below).

To analyse the effects of source and exposure environment on traits in
the greenhouse, we applied 1) Linear Mixed Models (LMM) to plant
biomass, SLA, fecundity and RSR and 2) Generalized Linear Mixed Models
(GLMM) with a binomial error for probability of flowering (see details
on Appendix S3). For each trait, we constructed a full model with four
source environmental drivers (rotated components for Aridity,
Temperature and Vegetation cover, and the binary variable Mowing), Water
and Light treatments, interactions between environmental drivers and
treatments, and Population as a random effect (Table S3). Full models
for biomass, probability of flowering and fecundity included control
biomass as a covariate. For a comparison of the role of genetic
differentiation vs. plasticity, we assessed whether the effects of two
source environmental drivers (Aridity and Vegetation Cover) were higher,
similar to, or lower than the effects from their corresponding exposure
treatments (Water and Light) and their interactions.

To test for the effects of environmental drivers on traits in field
populations, we applied 1) LMMs to biomass, SLA and fecundity, and 2)
GLMM with a binomial error distribution for probability of flowering
(see details on Appendix S3). We constructed full models including the
four source environmental drivers. To account for the possible influence
of range (native vs. non-native), the models included the effect of
range and its interaction with each environmental driver (Table S4). We
added Population and Plot nested within Population as random effects.
For probability of flowering and fecundity, we included biomass as a
covariate.

Full models of the analyses with either greenhouse or field data were
compared with all possible model subsets using the Akaike Information
Criterion corrected for finite sample sizes (AIC\textsubscript{c}) and
the AIC\textsubscript{c} weights (Burnham \& Anderson 2002, Johnson \&
Omland 2004). We focused on the best AIC\textsubscript{c} models, since
they had high support and parameter values were overall consistent
across competing models (see Appendix S3, Table S5, S6). Finally, we
evaluated the utility of observational datasets to predict genetic
differentiation. Genetic differentiation was considered predictable if
the presence and direction of source environment effects on traits were
the same in greenhouse and field conditions, and unpredictable
otherwise. For probability of flowering and fecundity, we also assessed
whether excluding the covariate biomass from the original analyses
modified our evaluation of the predictability of genetic
differentiation.

\emph{Results}

\emph{Effects of source and exposure environment in the greenhouse}

In the analyses of drivers of trait variation in the greenhouse, the
best models always included effects of at least one source environmental
driver and both light and water exposure treatments (see blue lines in
Fig. 3; Table S3; Fig. S2-S6), but results differed between vegetative
and reproductive traits. For vegetative traits (biomass, SLA and RSR),
light or water treatments showed the strongest effect sizes when
compared with source drivers (Fig. 4c,f,i). The effect of source drivers
on biomass and SLA frequently changed between positive and negative
directions depending on the treatment (see interactions in Fig. 3a,b;
Fig. 4a,b,e). For biomass, the effects of Aridity, Vegetation cover and
Mowing differed among treatments, although effect sizes were low and had
95\% confidence intervals (CI) that mostly overlapped with zero (Fig.
3a). For SLA, all source drivers were selected in the best model except
for Mowing. SLA showed two contrasting effects between source and
exposure environments: 1) SLA was lower in the Dry treatment, but higher
in plants from the most arid populations (Fig. 4d); 2) SLA was higher in
the treatment with lowest light, but also higher in populations with
lowest source vegetation cover and thus highest light availability (this
took place in treatment L\textsubscript{33}; Fig. 4e). RSR increased
with source Aridity, although the effect was smaller than the effect of
the Dry treatment (Fig. 4g,i).

For reproductive traits (biomass-corrected probability of flowering and
fecundity), exposure treatments exerted equivalent or smaller effects
than source drivers (Fig. 4l,o) and the effects of source drivers were
consistent in direction across treatments (Fig. 3d,e, 4k,m). Probability
of flowering was negatively affected by source Vegetation cover and
positively affected by Mowing, and exposure treatments changed the
magnitude of these source effects but not their sign (Fig. 3d, 4k).
Fecundity was positively affected by source Aridity and Temperature, and
showed no interactions between source and exposure environments (Fig.
3e, 4m). When biomass was excluded as a covariate from the analyses, 1)
source effects decreased in magnitude and exposure effects generally
increased for probability of flowering, and 2) source effects were not
included in the best model of fecundity (Table S7).

\emph{Effects of environmental drivers in field populations}

Trait variation for \emph{in situ} field populations was associated with
both environmental drivers and biogeographic range (native vs.
non-native) in the best models, although their effects did not interact
in most cases (see blue lines in Fig. 5; Table S4; Fig. S7-S9). Biomass
was positively correlated with Vegetation Cover and Mowing, and was
higher in non-native populations (Fig. 5a). SLA did not differ between
ranges, although we found an interaction between Temperature and
biogeographic range, whereby SLA decreased with increasing Temperature
in the native but not the non-native range (Fig. 5b). Biomass-corrected
probability of flowering was affected negatively by Vegetation Cover and
positively by Mowing, and was lower in non-native populations (Fig. 5c).
Biomass-corrected fecundity was positively affected by Aridity and
Temperature, and the effect of Aridity was stronger in the native than
the non-native range (Fig. 5d). When biomass was excluded as a covariate
from the analyses, the best model of probability of flowering lost the
effects of Vegetation Cover and Range, and the best model of fecundity
incorporated the effects of Mowing and the interaction between Range and
several source drivers (Table S8). Non-native populations showed
significantly higher temperature and seasonality of moisture index than
native populations, and lower values in moisture index (Table S9).

\emph{Utility of observational datasets to predict genetic
differentiation}

Biomass corrected reproductive traits, compared to vegetative traits,
showed a better match between observational trait-environment
relationships and response to source environmental gradients in the
greenhouse (Table 1). For vegetative traits, there were two cases of
predictable genetic differentiation out of eight. In both cases, field
data correctly predicted not the presence but the absence of genetic
differentiation. The low predictability in vegetative traits originated
in some cases from interacting (e.g., Fig. 4b) or opposing (Fig. 4d,e)
effects of source and exposure environments. In other cases, field
patterns did not match with those expected from the combined source and
exposure greenhouse effects (Fig. 4a). For biomass-corrected
reproductive traits, observational data predicted the presence and
direction of all seven source environment effects in the greenhouse.
However, when reproductive traits were analysed without biomass as a
covariate, observational data only predicted the presence and direction
of three out of seven source environment effects (Table S10).

\emph{Discussion}

Combining a multi-treatment greenhouse experiment with global-scale
field observations, we disentangled the main sources of intraspecific
trait variation for the short-lived herb \emph{Plantago lanceolata} .
Expression of functional traits along environmental gradients in the
field was retained to some extent in the greenhouse, indicative of
population genetic differentiation. However, while reproductive traits
(biomass-corrected probability of flowering and fecundity) showed
similar effects of source environment across exposure treatments,
vegetative traits (biomass, specific leaf area and root:shoot ratio)
showed more plastic responses and some contrasting effects of source and
exposure environments. These results imply a higher predictability of
genetic differentiation from field observations in reproductive than
vegetative traits.

\emph{Effects of source and exposure environment on vegetative and
reproductive traits}

Vegetative and reproductive traits responded differently to source and
exposure environments in the greenhouse, as expected based on the
fundamental relationship of each type of trait with overall fitness.
Reproductive traits corrected for biomass showed stronger source effects
than vegetative traits, and less variability across environmental
treatments. According to evolutionary theory, traits with the strongest
impact on fitness should show evolutionary conservation (Scheiner 1993,
Stearns \& Kawecki 1994, Sih 2004). In parallel, the demographic
buffering theory predicts that the most influential processes in species
life cycles should be maintained relatively constant around local
optimal values, to reduce variation in population growth rates (Pfister
1998, Burns \emph{et al} . 2010, Hilde \emph{et al} . 2020). For
short-lived plants like \emph{P. lanceolata} , reproduction has been
identified as the most influential fitness component (Silvertown\emph{et
al} . 1996, García \emph{et al} . 2008, Shefferson and Roach 2012),
which may explain the smaller role of plasticity and the higher
consistency in genetic differentiation found for biomass corrected
reproductive traits.

Stronger genetic differentiation in reproductive investment seemed to be
facilitated by a higher plasticity in vegetative traits, buffering
short-term environmental perturbations (Scheiner 1993, Alpert \& Simms
2002, Sih 2004). This phenomenon, known as fitness homeostasis, has been
highlighted before as a mechanism for maintaining high individual
performance across a range of environments (Sultan 1995,
Richards\emph{et al} . 2006). The adjustment of vegetative traits to
environmental conditions was manifest in our greenhouse experiment in
several ways, and is best exemplified by SLA patterns. SLA increased in
the shade treatment to optimize light capture and decreased in dry
conditions to reduce water loss through leaf surface, common plastic
responses in herbaceous plants (Poorter \emph{et al} . 2009,
Dwyer\emph{et al} . 2014). Remarkably, some effects of exposure
treatments on SLA were opposed by source environment effects suggesting
countergradient variation (sensu Conover \& Schultz 1995), such as the
positive effect of source Aridity combined with the negative effect of
the dry treatment. This apparent contradiction possibly arises because
water scarcity in populations from dry sites is compensated through
selection for higher RSR and/or stomatal function.

The complex interplay between plasticity and genetic differentiation,
and the trait-specific nature of environmental effects found in our
study highlight the variety of strategies for plant response to local
conditions (see also Albert \emph{et al} . 2010b, Le
Bagousse-Pinguet\emph{et al} . 2015, Roybal \& Butterfield 2019), but
also the difficulty of assessing the mechanisms and drivers of trait
variation. The trait patterns found in \emph{P. lanceolata} , including
countergradient variation, could be partly explained by the influence of
additional drivers not considered in the analyses, such as nutrient
availability or biotic interactions (Chevin \& Lande 2015).
Additionally, further research could be undertaken to disentangle
genetic differentiation from unaccounted maternal environment effects.

\emph{Accounting for size effects and life history to refine analyses of
trait variation}

The effect of plasticity on reproductive traits increased in our
greenhouse experiment when plant biomass was not accounted for in the
models. We thus show here that variance in reproductive effort at the
individual scale has a ``biomass'' component that is strongly driven by
plasticity, and an ``investment per unit biomass'' component that is
more genetically determined. Our results emphasize the importance of
dissecting reproduction into size dependent and independent components.
These dependencies among traits have implications for the expectations
of demographic buffering and may explain some of the cases contradicting
this theory (see McDonald \emph{et al} . 2017, Hilde \emph{et al} .
2020), e.g. when reproductive traits are strongly driven by underlying
individual biomass.

Our study organism is a short-lived plant, with reproduction having a
strong influence on population performance. However, in species with
different life histories, other demographic rates and their underlying
traits might exert the largest effects on fitness. For example,
longer-lived taxa usually depend more on survival rates for population
persistence (Silvertown \emph{et al} . 1996, Morris \& Doak 2005), and
may display low variance in survival-related traits. In fact,
Preite\emph{et al} . (2015) found stronger genetic differentiation for
survival than reproduction in a long-lived herb. Environmental drivers
of trait variation for various taxa with different life histories and
ecological strategies should be analysed in order to better generalise
the results presented here.

Plant size or biomass is likely to structure the most relevant
demographic rates (Harper 1977, Easterling \emph{et al} . 2000, Biere
1995), and decomposition of trait variability into size dependent and
independent components will also help to shed light on the drivers of
trait variability, as shown in our study. Accounting for biomass
dependency in trait variation across different life histories may refine
previous findings of stronger local adaptation in reproduction than in
survival across plant and animal species (Hereford 2009), of higher
levels of plasticity vs. local adaptation in reproductive traits of
invasive plants (Liao \emph{et al} . 2016), and of an absent
relationship between trait plasticity and its proximity to fitness
(Acasuso-Rivero\emph{et al} . 2019). The detection of more common
genotype-by-environment interactions in short-lived than long-lived
plants (Matesanz and Ramírez-Valiente 2019) could also be evaluated for
different trait categories separately. These additional interpretations
from functional and demographic perspectives may advance our
understanding of trait-environment relationships and improve our
predictions of species responses to climate change.

\emph{Observational datasets and their utility for predicting genetic
differentiation}

Our global observational dataset revealed that different combinations of
biotic and abiotic factors drove variation on each trait. This
trait-specificity would have remained hidden had the environmental and
geographical scale of the study been smaller, since we could have not
analysed together such a variety of environmental conditions and
drivers. In addition, the combination of large-scale field and
experimental studies, rarely implemented in evolutionary ecology (but
see, e.g., Winn \& Gross 1993, Woods \emph{et al} . 2012), allowed us to
assess the potential uses and misuses of observational datasets. In
particular, trait-environment relationships inferred from \emph{in
situ}populations correctly predicted genetic differentiation for
reproductive but not vegetative traits. For vegetative traits, the
predictability diminished as the presence of plasticity led to
interacting or opposing effects of source and exposure environments, as
initially forecasted (Fig. 1). The predictability of genetic
differentiation was also low for reproductive traits when analysed
without accounting for their size-dependence. Therefore, observational
data may reliably inform about the current drivers of selection and the
adaptive capacity of species only for the traits most closely related
with fitness. This might be important for species- and community-level
predictive models that rely on trait-environment relationships, and for
conservation programs focusing on intraspecific genetic diversity.

Evaluating trait-environment relationships can also be useful for
predicting plant performance in populations introduced outside native
ranges (Alexander \emph{et al} . 2012, Hulme \& Barrett 2013).
In\emph{P. lanceolata} , traits showed broadly similar correlations with
environmental factors in both native and non-native ranges, in agreement
with previous work in other taxa (Maron \emph{et al} . 2004,
Montague\emph{et al} . 2008, Rosche \emph{et al} . 2019; but see Keller
\emph{et al} . 2009). Notably, the similarities in trait patterns
between ranges held despite the location of non-native populations in
warmer and more arid conditions. This suggests that the
trait-environment correlations largely persist for some species even if
they occupy more extreme areas of environmental space, facilitating
ecological predictions in a context of global change. Yet some
trait-environment correlations observed in\emph{P. lanceolata} were
weaker in the non-native range (see also Alexander \emph{et al} . 2012).
This finding highlights that genetic differentiation may be less
predictable for non-native populations and that a total equivalence in
trait patterns between ranges cannot be taken for granted due to
potential evolutionary divergence. The presence of weaker
trait-environment relationships in non-native populations may be due to
a higher role of plasticity (although the latter is not clearly
supported by a recent meta-analyses across species; see Palacio-López \&
Gianoli 2011), or may instead result from repeated introductions in the
non-native range (Smith \emph{et al} . 2020). Further sudies on
widespread species might help to clarify the processes and patterns
resulting from ecological and evolutionary divergence at large spatial
scales. In particular, our observational network can form the basis for
future experimental work.

\emph{Conclusions}

Our study improves the understanding of intraspecific trait variation
along environmental gradients, showing that the underlying ecological
and evolutionary mechanisms differ between reproductive and vegetative
traits of \emph{P. lanceolata} . The environmental structuring of
variation in biomass-corrected reproductive traits was retained in
common greenhouse conditions, indicative of genetic differentiation. In
contrast, vegetative traits showed strong plastic responses to buffer
short-term environmental variation, sometimes in opposition to genetic
differentiation. Differences between vegetative and reproductive traits
seem to arise due to the different relationship between each type of
trait and overall fitness. These results provide a crucial insight into
the potential uses and limitations of observational data, which is
readily available for a considerable number of species and traits, but
which may provide more uncertain information than common-garden
experiments. While genetic differentiation was accurately predicted from
observational trait-environment relationships for biomass-corrected
reproductive traits, it was sometimes masked for vegetative traits by
phenotypic plasticity and countergradient variation. Thus, evaluating
evolutionary responses to environment from observational data may lead,
in the case of vegetative traits or traits not closely related with
fitness, to underestimate the capacity of plants to adapt to new
environmental conditions. We also advocate for considering biomass
dependency in trait variation analyses, as well as the implications of
species life histories on trait-fitness relationships. In view of the
general call for including intraspecific trait variation in ecological
models (Moran \emph{et al} . 2016, Funk \emph{et al} . 2017), these
considerations are important for a more informed prediction of species
responses to global change.

\emph{Acknowledgements}

This publication emanated from research conducted with the financial
support of Science Foundation Ireland (SFI) under Grant Number
15/ERCD/2803 (YMB). Financial support was also provided by the Spanish
Ministry of Science, Innovation and Universities to JV
(IJCI-2017-32039), by the Natural Sciences and Engineering Research
Council of Canada to LHF, by the Academy of Finland to SR, by the
Estonian Research Council (grants PRG609 to MP and PUT1409 to LL) and by
the European Regional Development Fund (funding for the Centre of
Excellence EcolChange to MP). S. P. Blomberg and J. van Groenendael
helped design the PlantPopNet network. We thank A. Ehrlén and A.
Pettersson for their help in the greenhouse experiment and S. Coutts for
help with Figure 1. We are also grateful to the Archipelago Research
Institute, and to numerous field assistants and landowners of sampled
sites.

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\emph{Supporting information}

Additional Supporting Information may be downloaded via the online
version of this article at Wiley Online Library
(www.ecologyletters.com).

\emph{Tables}

\textbf{Table 1.} Assessment of the utility of observational field data
to predict genetic differentiation in \emph{Plantago lanceolata} .
Vegetative traits are biomass and specific leaf area (SLA), and biomass
corrected reproductive traits are probability of flowering (Flw Prob)
and fecundity. The effects of environmental drivers (Aridity,
Temperature, Vegetation Cover and Mowing) on each trait are compared for
greenhouse (Grh) vs. field (Fld) conditions, based on effects from best
models shown in Figures 3 and 5. The comparison (Comp) determines
genetic differentiation to be 1) predictable if effects in greenhouse
and field conditions share presence or absence, and direction if present
(green) or 2) unpredictable otherwise (black; cf. Fig. 1). Signs of
effects are ``+'' (positive), ``-'' (negative), ``\textasciitilde{}''
(inconsistent direction due to interactions; note some interactions do
not lead to inconsistent direction), ``abs'' (absent) and ``na'' (not
analysed; no comparison is made).\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
\textbf{Trait} & \textbf{Aridity} & \textbf{Aridity} & \textbf{Aridity}
& \textbf{Temperature} & \textbf{Temperature} & \textbf{Temperature} &
\textbf{Cover} & \textbf{Cover} & \textbf{Cover} & \textbf{Mowing} &
\textbf{Mowing} & \textbf{Mowing}\tabularnewline
\midrule
\endhead
& Grh & Fld & \textbf{Comp} & Grh & Fld & \textbf{Comp} & Grh & Fld &
\textbf{Comp} & Grh & Fld & \textbf{Comp}\tabularnewline
\emph{(Vegetative)} & & & & & & & & & & & &\tabularnewline
Biomass & \textasciitilde{} & abs & & abs & abs & & \textasciitilde{} &
+ & & \textasciitilde{} & + &\tabularnewline
SLA & + & abs & & \textasciitilde{} & \textasciitilde{} & &
\textasciitilde{} & abs & & abs & abs &\tabularnewline
\emph{(Reproductive)} & & & & & & & & & & & &\tabularnewline
Flw Prob & abs & abs & & abs & abs & & - & - & & + & + &\tabularnewline
Fecundity & + & + & & + & + & & abs & abs & & na & abs &\tabularnewline
\bottomrule
\end{longtable}



\emph{Figure legends}

\textbf{Figure 1.} Predictability of trait genetic differentiation among
populations after comparison of experimental data and observational
field data. In a common garden experiment with individuals from multiple
provenances (``source'' environments) growing in a set of treatments
(``exposure'' environments), one can partition the independent (a-e) or
interacting (f-h) effects of genetic differentiation and phenotypic
plasticity. Population genetic differentiation is identified as trait
variation along source environments (blue lines), and plasticity is
detected by comparing trait values between low (light blue) and high
(dark blue) levels of the exposure environment. Note that source and
exposure environments are driven here by the same underlying
environmental factor. The resulting pattern expected to be observed
across field populations is shown with red dashed lines, linking two
extreme populations along the environmental gradient in the treatment
closest to their corresponding source conditions (from low to high
treatments). Observational field data will provide a reliable prediction
of genetic differentiation (green squares) in the presence of (a) only
source effects, (c) source and exposure effects with the same direction,
or (f) source effects with consistent direction but inconsistent slope
across exposure environments. In contrast, genetic differentiation will
not be predictable from field patterns (black squares) in the presence
of (b) only exposure effects, (d,e) source and exposure effects with
opposite directions (``countergradient variation''), or (g,h) source
effects with inconsistent direction across exposure environments. Note
that the x-axis contains source rather than exposure environment (this
differs thus from classical displays of plasticity in reaction norms, as
the exposure environment is best envisaged as a set of treatments with
observations over a continuous environmental gradient comprising the
source environments).

\textbf{Figure 2.} Location of native (black) and non-native (grey)
study populations of \emph{Plantago lanceolata} in geographical (a) and
environmental (b) space. Circles indicate populations studied in the
field; triangles indicate populations studied in the field and included
in the greenhouse experiment. Colours filling the world map in a)
correspond to mean annual temperature and precipitation as shown in b).
In b), small black and grey background points correspond to the
environmental niche occupied by the species in the native and non-native
range, respectively, according to occurrence data from GBIF and BIEN
databases.

\textbf{Fig. 3.} Effects from the best model (blue) and competing models
(grey; [?]AIC\textsubscript{c} \textless{} 2) for each trait
of\emph{Plantago lanceolata} in the greenhouse, with 95\% confidence
intervals. The effects correspond to source environmental drivers (A =
Aridity; T = Temperature; C = Vegetation Cover; M = Mowing),
experimental treatments of Water (W\textsubscript{d} = dry) and Light
(L\textsubscript{64} and L\textsubscript{33}), and the interactions
between them. Vegetative traits (a-c) are biomass, specific leaf area
(SLA) and root:shoot ratio (RSR), and reproductive traits (d-e) are
probability of flowering (``Flw Prob'') and fecundity (``Fecund''). For
simplicity, we omit the effects of control biomass. The effects of
L\textsubscript{64} treatment and Mowing were not tested in RSR and
fecundity, respectively (absent labels; see \emph{Material and
methods}for details).

\textbf{Figure 4.} Effects of two source environmental drivers (A =
Aridity; C = Vegetation Cover) and their corresponding exposure
treatments (Water and Light) on \emph{Plantago lanceolata} traits in the
greenhouse. Vegetative traits are biomass (a-c), specific leaf area
(SLA; d-f) and root:shoot ratio (RSR; g-i), and reproductive traits are
probability of flowering (Flw Prob; j-l) and fecundity (Fecund; m-o).
Results are presented with 95\% confidence intervals (CI), and
correspond to the best model according to Akaike Information Criterion
(empty subpanels or bars indicate no effect in the best model). In the
left and middle columns, trait values are shown for wet
(W\textsubscript{w}) and dry (W\textsubscript{d}) water treatments, and
for L\textsubscript{100}, L\textsubscript{64} and
L\textsubscript{33}light treatments. All traits are mean centred and
scaled by the standard deviation, except for probability of flowering
(Y-axis in logit scale). Source drivers are mean centred and scaled by
two times the standard deviation (see Appendix S3). The distribution of
populations along source environment values is shown by rug marks on the
inside of the x axis. In the right column, the effects of source
environment (genetic differentiation; yellow), exposure environment
(plasticity; orange) and their interaction (red) are compared. Note that
effect sizes are given as absolute values for comparison, and only the
CI upper limit is shown.

\textbf{Fig. 5.} Effects from the best model (blue) and competing models
(grey; [?]AIC\textsubscript{c} \textless{} 2) for each trait
of\emph{Plantago lanceolata} in the field, with 95\% confidence
intervals. The effects correspond to environmental factors (A = Aridity;
T = Temperature; C = Vegetation Cover; M = Mowing), non-native range
(R\textsubscript{nnat}), and the interactions between them. Vegetative
traits (a-b) are biomass and specific leaf area (SLA), and reproductive
traits (c-d) are probability of flowering (``Flw Prob'') and fecundity
(``Fecund''). The effects of environmental factors alone correspond to
native populations; the effects of environmental factors on non-native
populations can be deduced by summing environmental effects alone and
the effects of range \selectlanguage{ngerman}× environment interactions. For simplicity, we omit
the effect of biomass in models of probability of flowering and
fecundity.

\emph{Figures}

\textbf{Fig. 1}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig. 2}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}



\textbf{Fig. 3}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Fig. 4}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Fig. 5}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\selectlanguage{english}
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