# Functional Traits 2.0: The power of the metabolome for ecology

Tom Walker, Jake Alexander, Pierre-marie Allard, Oliver Baines, Virginie Baldy, Richard Bardgett, Pol Capdevila, Phyllis Coley, Bruno David, Emmanuel Defossez, et al.

► To cite this version:

Tom Walker, Jake Alexander, Pierre-marie Allard, Oliver Baines, Virginie Baldy, et al. Functional Traits 2.0: The power of the metabolome for ecology. Journal of Ecology, 2022, 110 (1), pp.4-20. 10.1111/1365-2745.13826. hal-03812072

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# Functional Traits 2.0: The power of the metabolome for ecology

Tom W. N. Walker<fnref n="1" />,² ID | Jake M. Alexander<fnref n="1" /> ID | Pierre-Marie Allard³,⁴,⁵ ID |

Oliver Baines⁶ ID | Virginie Baldy⁷ | Richard D. Bardgett⁸ ID | Pol Capdevila⁹,¹⁰ ID |

Phyllis D. Coley¹¹ ID | Bruno David¹² | Emmanuel Defossez² ID | María-José Endara¹³ ID |

Madeleine Ernst¹⁴ ID | Catherine Fernandez⁷ ID | Dale Forrister¹¹ ID |

Albert Gargallo-Garriga¹⁵,¹⁶ | Vincent E. J. Jassey¹⁷ ID | Sue Marr¹⁸,¹⁹,²⁰ ID |

Steffen Neumann¹⁸,²⁰ ID | Loïc Pellissier<fnref n="1" />,²¹ ID | Josep Peñuelas¹⁵,¹⁶ ID |

Kristian Peters¹⁸,¹⁹,²⁰ ID | Sergio Rasmann² ID | Ute Roessner²² ID | Jordi Sardans¹⁵,¹⁶ ID |

Franziska Schrodt⁶ ID | Meredith C. Schuman²³ ID | Abrianna Soule¹¹,²⁴ ID |

Henriette Uthe¹⁸,²⁵ ID | Wolfram Weckwerth²⁶,²⁷ ID | Jean-Luc Wolfender³,⁴ ID |

Nicole M. van Dam¹⁸,²⁵ ID | Roberto Salguero-Gómez¹⁰ ID |

**Correspondence**

Tom W. N. Walker

Email: thomas.walker@unine.ch

**Funding information**

German Centre for Integrative
Biodiversity Research; Swiss National
Science Foundation, Grant/Award
Number: 31003A-176044 and
31003A_179481; UK Natural Environment
Research Council Independent Research
Fellowship, Grant/Award Number:
NE/M018458/1; European Research
Council, Grant/Award Number: 678841;
Ramón Areces Foundation Postdoctoral

**Abstract**

1. A major aim of ecology is to upscale attributes of individuals to understand processes at population, community and ecosystem scales. Such attributes are typically described using functional traits, that is, standardised characteristics that impact fitness *via* effects on survival, growth and/or reproduction. However, commonly used functional traits (e.g. wood density, SLA) are becoming increasingly criticised for not being truly mechanistic and for being questionable predictors of ecological processes.

<footnote n="1">¹Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland; ²Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland; ³School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland; ⁴Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland; ⁵Department of Biology, University of Fribourg, Fribourg, Switzerland; ⁶School of Geography, University of Nottingham, Nottingham, UK; ⁷Aix Marseille Université, Avignon Université, CNRS, IRD, IMBE, Marseille, France; ⁸Department of Earth & Environmental Sciences, The University of Manchester, Manchester, UK; ⁹School of Biological Sciences, University of Bristol, Bristol, UK; ¹⁰Department of Zoology, Oxford University, Oxford, UK; ¹¹School of Biological Sciences, University of Utah, Salt Lake City, Utah, USA; ¹²Green Mission Pierre Fabre, Institut de Recherche Pierre Fabre, Toulouse, France; ¹³Grupo de investigación en Biodiversidad, Medio Ambiente y Salud-BIOMAS, Universidad de las Américas, Quito, Ecuador; ¹⁴Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark; ¹⁵CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Catalonia, Spain; ¹⁶CREAF, Cerdanyola del Vallès, Catalonia, Spain; ¹⁷Laboratoire d'Ecologie Fonctionnelle et Environnement, Université de Toulouse, CNRS, Toulouse, France; ¹⁸German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany; ¹⁹Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle Wittenberg, Halle, Germany; ²⁰Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data, Halle, Germany; ²¹Swiss Federal Research Institute WSL, Birmensdorf, Switzerland; ²²School of BioSciences, University of Melbourne, Victoria, Australia; ²³Departments of Geography and Chemistry, University of Zurich, Zurich, Switzerland; ²⁴Department of Plant Biology, Michigan State University, East Lansing, Michigan, USA; ²⁵Institute of Biodiversity, Friedrich Schiller University Jena, Jena, Germany; ²⁶Molecular Systems Biology, Department of Functional and Evolutionary Ecology, 1010 University of Vienna, Vienna, Austria and ²⁷Vienna Metabolomics Center, 1010 University of Vienna, Vienna, Austria</footnote>
Scholarship; French Agence Nationale pour la Recherche, Grant/Award Number: ANR-12-BSV7-0016-01; BioDivMeX Mistrals programme; Aix-Marseille University 'Investissements d'Avenir' programme; Labex OT-Med programmes; US National Science Foundation, Grant/Award Number: DEB-1135733; US National Science Foundation Graduate Research Fellowships Program; French National Research Agency, Grant/Award Number: ANR-17-CE01-0007; iDiv Halle-Jena-Leipzig; German Research Foundation, Grant/Award Number: 202548816; DFG-FZT 118; German Network for Bioinformatics Infrastructure; BMBF, Grant/Award Number: 031L0107; Spanish Government, Grant/Award Number: PID2019-110521GB-I00; Catalan Government, Grant/Award Number: SGR 2017-1005; European Research Council Synergy Grant, Grant/Award Number: ERC-SyG-2013-610028; University of Nottingham Anne McLaren Fellowship; NOMIS Foundation; University of Zürich Research Priority Programme on Global Change and Biodiversity

Handling Editor: David Gibson

2. This Special Feature reviews and studies how the metabolome (i.e. the thousands of unique metabolites that underpin physiology) can enhance trait-based ecology and our understanding of plant and ecosystem functioning.

3. In this Editorial, we explore how the metabolome relates to plant functional traits, with reference to life-history trade-offs governing fitness between generations and plasticity shaping fitness within generations. We also identify solutions to challenges of acquiring, interpreting and contextualising metabolome data, and propose a roadmap for integrating the metabolome into ecology.

4. We next summarise the seven studies composing the Special Feature, which use the metabolome to examine mechanisms behind plant community assembly, plant-organismal interactions and effects of plants and soil micro-organisms on ecosystem processes.

5. **Synthesis.** We demonstrate the potential of the metabolome to improve mechanistic and predictive power in ecology by providing a high-resolution coupling between physiology and fitness. However, applying metabolomics to ecological questions is currently limited by a lack of conceptual, technical and data frameworks, which needs to be overcome to realise the full potential of the metabolome for ecology.

### KEYWORDS

ecophysiology, fitness, functional traits, life history, metabolite, metabolomics, plasticity, trade-offs

# 1 | INTRODUCTION

A central tenet of functional ecology is the understanding that organisms display characteristics that go beyond taxonomy in explaining how they affect, and respond to, their environment (Calow, 1987). This understanding has led to the widespread adoption of trait-based approaches to link organismal functioning to ecological processes operating at population, community and ecosystem scales. Trait-based approaches are most famously embodied by the concept of functional traits—a suite of morphological, physiological, phenological or behavioural characteristics of an organism that impact fitness via their effects on survival, growth and/or reproduction (Violle et al., 2007). Functional traits are notable because they not only contain information about how organisms are adapted to and interact with their environment (Lavorel & Garnier, 2002; Violle et al., 2007), but also integrate processes across different levels of biological organisation, from organs, to species, to ecosystems (Gross et al., 2017; McGill et al., 2006). Functional traits have been extensively used to characterise organisms across the tree of life (Echeverri et al., 2020; Lundgren et al., 2021; Wong et al., 2019). However, their origin, and most common usage, is with reference to the plant kingdom (Calow, 1987). For instance, plant functional traits have been used to explain variation in plant life-history trade-offs (Adler et al., 2014; Díaz et al., 2016; Wright et al., 2004), community composition (Bruelheide et al., 2018), species interactions (Kunstler et al., 2016) and ecosystem

processes (Cornwell et al., 2008; De Deyn et al., 2008). Nevertheless, most commonly used plant functional traits (e.g. plant height, seed mass, leaf area, SLA and leaf carbon, nitrogen and phosphorus contents; Díaz et al., 2016) leave a large fraction of variation in ecological processes unexplained (Adler et al., 2014; Yang, Cao, et al., 2018), limiting the possibility of providing mechanistic insights into plant effects on ecosystem processes (Firn et al., 2019; Laughlin et al., 2020; van der Plas et al., 2020; Walker et al., 2019).

One reason for the low predictive power of functional traits is that they are aggregates of multiple physiological processes that can each be differently constrained by evolutionary history and environmental factors, and thus represent the combined product of homology, convergent evolution and plasticity. For instance, SLA, which describes a trade-off between investment in photosynthesis (i.e. leaf surface area) and protection against stressors (i.e. leaf mass), is controlled by evolutionary history and local environmental factors and to this day remains unpredictable in its response to global change (Dwyer et al., 2014; Firn et al., 2019; Leishman et al., 2007). Such complexity in interpreting the mechanistic basis of functional traits can mask the true nature of links between physiology and an ecological process of interest (Funk et al., 2017). As a consequence, while functional traits are central to characterising how plants function, they are imperfect proxies of physiology and so often cannot provide a mechanistic basis for plant functioning and its influence over populations, communities or ecosystems (Figure 1a,b).
<figure><img src="image_3.png" /><figcaption>FIGURE 1 Contextualising the plant metabolome. (a) A hypothetical relationship between elevation above sea-level and SLA, including its constituent traits (leaf mass and area), whereby one interpretation would be that leaves become less productive at higher elevation. (b) The same relationship captured through changes within the plant metabolome, namely concentrations of carbohydrates, amino acids, flavonoids and terpenoids, through which it is clear that reductions in SLA are influenced by increased investment in tissue defence (i.e. flavonoids, terpenoids) at higher elevation. (c) A schematic showing how the plant metabolome is the interface between genes and the environment</figcaption></figure>

The metabolome, that is, the collective name for the tens of
thousands of metabolites that are the substrates and products of
enzymatic reactions, offers a mechanistic bridge between plant
physiology and ecological processes (Figure 1c). Plants collectively
produce an astonishing diversity of metabolites—somewhere be-
tween 200,000 and 1,000,000 throughout the plant kingdom and
tens of thousands within an individual (Wang et al., 2019). For ex-
ample, plants create rigid structures from metabolites called lignans,
capture energy from sunlight and carbon dioxide in the metabo-
lite glucose and respond to drought, temperature and herbivore
stress with specialised metabolites, such as alkaloids and flavonoids
(Fernandez et al., 2016; Peters et al., 2019). While some families of
metabolites are restricted to particular taxa, such as glucosinolates
in Brassicales (Fahey et al., 2001), many are produced throughout the
plant kingdom (Wink, 2003). Thus, the metabolome varies among
organs, individuals, populations and species due to genetic and en-
vironmental factors that operate over multiple time-scales (Hilker,
2014; Schuman & Baldwin, 2016; Wink, 2003). In this sense, the
metabolome is regulated by the same suite of evolutionary and eco-
logical processes that limit the interpretation of classical functional
traits. However, the metabolome may provide better mechanistic
understanding in ecological and evolutionary contexts. The metab-
olome, as some functional traits, lies at the interface between genes

and the environment (Figure 1c), but is unique in encompassing the
physiological processes that act to maximise fitness both over an
individual's lifetime and between generations. Hence, by comprising
the composition, abundances and interplay of many thousands of
metabolites, the metabolome provides a direct and multidimensional
measure of the molecular mechanisms through which evolutionary
and ecological processes shape plant functioning—but does not ag-
gregate such mechanisms into a small number of variables (Box 1;
Weckwerth, 2003; van Dam & van der Meijden, 2018). From this, it
follows that the metabolome itself is a vast repository of functional
traits. Nevertheless, the metabolome remains on the periphery of
ecology, and few attempts have been made to integrate the metab-
olome into trait-based approaches.

Over recent decades, advances in analytical chemistry and mass
spectrometry have led to the advent of metabolomics, which al-
lows for the characterisation of the metabolome in a cost-effective
and fast manner (Weckwerth, 2003; Wishart, 2005). Plant metab-
olomics was developed by molecular biologists to understand bio-
chemical mechanisms behind phenotypes, and has found traction
throughout the life sciences, including biotechnology (Yang et al.,
2019), pharmacology (Newman & Cragg, 2016) and medicine (Clish,
2015). However, with notable exceptions (Inderjit et al., 2011), the
use of metabolomics in ecology is more recent (van Dam & van der
BOX 1 The resolution and explanatory power of the metabolome

At the most basic level, datasets describing classical functional traits and the metabolome share a similar structure (Figure 2a,b), albeit comprising tens of traits versus thousands of metabolites (commonly expressed as presences/absences or abundances; see Section 3.2). However, a unique facet of metabolomics data is the additional provision of background chemical information about detected metabolites (see Box 2). Such chemical information allows researchers to characterise not just the metabolites associated with a change among samples, but also the types of chemical structures and/or biochemical pathways involved (Djoumbou Feunang et al., 2016; da Silva et al., 2015). As such, measurements of the metabolome provide a large amount of molecular context with which to interpret each variable therein, which is not automatically the case for classical functional traits.

To further illustrate the explanatory power of the metabolome, let us imagine that a set of measured functional traits is suffi-
cient to capture all variation in plant fitness or function among samples. In this situation, measuring the metabolomes of the same
samples would provide two further interpretative tools. First, by identifying which metabolites are responsible for changes to traits
(Figure 2a,b: grey shading), it would be possible to determine the molecular mechanisms behind changes among samples (e.g. Walker
et al., 2019). Second, most functional traits are the aggregation of many interacting physiological processes (Funk et al., 2017), making
it challenging to isolate effects of external factors, such as evolutionary history, climate or organismal interactions, on plant fitness
(Figure 2a, white box; Koricheva, 2002; Leishman et al., 2007; Dwyer et al., 2014; Hu et al., 2018; Walker et al., 2019, 2020; Firn et al.,
2019). The metabolome provides a direct measure of such physiological processes (van Dam & van der Meijden, 2018; Weckwerth,
2003), making it easier to pinpoint which external factors affect which aspects of plant fitness or function (Figure 2b, white box).
In short, by measuring the metabolome alongside classical functional traits, researchers would gain greater mechanistic insight and
finer resolution than provided by functional traits alone.

In reality, any set of measured functional traits is unlikely to encompass all variation in plant fitness or function (see Section 3; Adler et al., 2014; Díaz et al., 2016). Measurements of the metabolome thus provide a vast repository of molecular information through which to capture variation missed by classical functional traits. For example, by interrogating links between external factors and those metabolites unrelated to existing functional traits, it may be possible to identify novel 'molecular functional traits' that characterise variation among apparently similar samples (Figure 2b). The amount of biological information captured by the metab- olome should also allow for much greater separation of species along established life-history trade-offs (e.g Díaz et al., 2016) than classical functional traits (Figure 2c,d), although this remains to be empirically tested. Importantly, the roles of most metabolites in shaping plant fitness and function remain unexplored (Figure 2b, white box), making the metabolome a frontier for improving explana- tory power in trait-based ecology.

Meijden, 2018). Early studies in chemical ecology used analytical
standards to determine the presence or abundance of a limited set
of pre-determined metabolites in a sample (Everette et al., 2010).
The field has now shifted towards untargeted metabolomics, which
instead attempts to detect and identify all of the thousands of me-
tabolites in a sample (Schrimpe-Rutledge et al., 2016). Untargeted
metabolomics is a powerful holistic tool that requires only small
amounts of material and uses simple laboratory extractions relative
to other ‘omics’ approaches (Ryan & Robards, 2006). However, it
employs a complex analytical pipeline and yields many thousands
of often-unknown metabolites, both of which create major chal-
lenges for interpretation (Allard et al., 2017). Accordingly, most
ecological metabolomics studies are restricted to plants, in partic-
ular model species (Nagler et al., 2018) or within-species treatment
comparisons (Wiggins et al., 2016), with few studies attempting
multi-species comparisons (Endara et al., 2018; Forrister et al., 2019;
Gargallo-Garriga et al., 2020; Ristok et al., 2019; Rivas-Ubach et al.,
2012; Sedio, Parker, et al., 2018; Sedio et al., 2017). Recent pushes
towards ‘big data’ ecology (e.g. Díaz et al., 2016; Kattge et al., 2020;
Katz et al., 2021; Maldonado et al., 2015), coupled with decreasing

analytical costs and improved data analysis tools (Allard et al., 2017; Wang et al., 2016), make it increasingly possible to undertake the multi-species studies necessary to explore the relevance of the metabolome for trait-based assessments of plant and ecosystem functioning. Nevertheless, no coherent framework yet exists for harmonising data acquisition, standardisation and analysis across such studies.

This Special Feature represents a coordinated effort to bring
focus to the potential of the metabolome to provide deeper mech-
anistic insight and predictive power to a variety of ecological ques-
tions. In this Editorial, we focus on the link between the metabolome
and plant functional traits, which are established and commonly
used metrics of plant functioning. We consider the capacity of the
metabolome to shed light on the mechanisms behind plant func-
tional traits and to unlock a new generation of molecular functional
traits that underpin links between physiology and ecological pro-
cesses (Sardans et al., 2020; Sedio et al., 2017), placing particular
emphasis on life-history trade-offs that govern fitness over multiple
generations and plasticity that shapes fitness within an individual's
lifetime. We then examine and propose solutions to major challenges
CLASSICAL FUNCTIONAL TRAITS

THE METABOLOME

<figure><img src="image_4.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_8.png" /></figure>

**FIGURE 2** The dimensionality and resolution of the metabolome versus functional traits. (a, b) Tables illustrating the dimensionality of data describing (a) classical functional traits (green) or (b) the metabolome (red) of *n* species (rows). Columns represent a small selection of traits (*t*<sub>x</sub>) or metabolites (*m*<sub>x</sub>), and estimates are given for the total number of traits/metabolites attainable from one sampling effort. Hypothetical relationships are also shown between traits/metabolites and some drivers of plant fitness or function (white boxes, coloured dots), as well as plausible coupling between some functional traits and some metabolites (grey shading). (c, d) Hypothetical separations of species from (a, b) into distinct groups using ordinations based on (c) classical functional traits (following Díaz et al., 2016) or (d) the metabolome. See Box 1

of acquiring, interpreting and contextualising metabolomics data in ecological studies. Following this, we showcase the seven papers that comprise the Special Feature, which reveal the potential for metabolomics to help resolve mechanisms behind plant community assembly, plant-organismal interactions and the effects of plants and soil micro-organisms on ecosystem processes, as well to improve predictions of them. Finally, we synthesise these insights to build a roadmap for integrating metabolomics into ecology, including key questions for future research.

## 2 | THE METABOLOME IN PLANT ECOLOGY

### 2.1 | The metabolome and plant life history

A key contribution of trait-based ecology has been the description of broad trade-offs in plant functioning that occur throughout the plant kingdom (Díaz et al., 2016). A notable example is the leaf economics spectrum (Wright et al., 2004), which describes correlations among leaf traits along an axis from small, long-lived, unproductive leaves (e.g. low SLA, low photosynthetic rates) to large, short-lived, productive leaves (e.g. high SLA, high photosynthetic rates). These traits also correlate with the relative importance of survival, growth

and reproduction for plant population performance (Adler et al., 2014; Pistón et al., 2019), demonstrating their relevance to life history. Other schemes have sought to classify plants into life-history strategies using functional traits, for example by distinguishing competitive species from ruderal and stress-tolerating species (Grime et al., 1997). Regardless of the conceptual system used, trait-based life-history trade-offs reflect relatively conserved characteristics of species generated by selection and phylogenetic constraints that operate over millennia—although it should be noted that local adaptation over several generations can also drive life-history variation at the population level (Halbritter et al., 2018).

Comparative ecological metabolomics studies are relatively rare, and those that do exist are mostly limited to single growth forms or ecosystems (Endara et al., 2018; Forrister et al., 2019; Gargallo-Garriga et al., 2020; Ristok et al., 2019; Rivas-Ubach et al., 2012; Sedio, Parker, et al., 2018; Sedio et al., 2017). However, there is emerging evidence that the metabolome plays a central role in plant life history. This is particularly true when considering the role of the metabolome in specialisation towards herbivory and climate, which are important factors governing plant fitness (Bruelheide et al., 2018). Plants generate a plethora of specialised metabolites to defend themselves against biotic stressors, such as herbivory, pathogens and competitors, as well as abiotic stressors, such as extreme temperatures or drought (Sardans et al., 2020).
## BOX 2 Annotating mass spectra with chemical structures

Mass spectrometry allows users to detect thousands of mass spectra in a single analysis. However, resolving such spectra into distinct metabolites with associated chemical structures is challenging. In contrast to DNA, RNA and proteins, which are mostly polymeric materials (bases or amino acids respectively), metabolites can possess one of many thousands of possible chemical structures and so cannot be systematically deconstructed into their component parts (Lipinski & Hopkins, 2004). As such, typically only 2% to 5% of features detected during an untargeted mass spectrometry analysis are matched to known metabolites in public libraries (da Silva et al., 2015). Assigning chemical structures to spectra, that is, metabolite annotation, is a major research objective and the subject of recent developments in computational metabolomics. Here, we present some of the most promising approaches emerging from such developments.

It is now possible to propagate chemical information from known metabolites in a sample to unknown features using molecular networks (see Section 3.2; Wang et al., 2016). At the same time, the coverage of chemical structures in existing public spectral libraries is being improved using approaches such as *in silico* spectral calculations (Allard et al., 2016; da Silva et al., 2018), spectral fingerprint matching to structural databases (Dührkop et al., 2019) and unsupervised sub-structure discovery (van der Hooft et al., 2016). Metabolite annotation approaches have been further improved by including taxonomic information (e.g. up to sevenfold improvements; Rutz et al., 2019) or combining them (e.g. 60% of features annotated; Ernst et al., 2019). Moreover, researchers can increase the number of features annotated by aggregating at broader organisational levels of a chemical taxonomy (e.g. chemical subclass, to class, to superclass; Djoumbou Feunang et al., 2016) or propagating chemical classifications to unannotated compounds that are spectrally similar (e.g. among clusters of a molecular network). However, doing so involves a trade-off between precision and confidence. A loss of precision in metabolite annotation is acceptable, in that it allows users to eliminate instrument and protocol biases in raw data and standardise data between analytical platforms (see Section 3.2). However, once spectral clusters have been generated, merging chemical information into broader categories sacrifices precision for confidence in interpretation. For instance, in a dataset describing the metabolomes of 472 tropical plant species (unpublished data), we identified a total of 515 annotated metabolites from 7649 spectral clusters, but this could be collapsed into 146 metabolite classes or 15 metabolite superclasses. Such information loss is not unique to metabolomics—in metagenomics, which has become a powerful tool in soil microbial ecology, it is common to map less than 1% of genes to a function (Bahram et al., 2018). Ultimately, while metabolite annotation is under development, we argue that it is already sufficiently developed to be useful in ecology. Indeed, given both ongoing developments in metabolite annotation and the growing usage of metabolomics in ecology (Figure 3), we are on the cusp of a revolution in ecological metabolomics akin to that seen in ecological genomics over the past decade (Baldwin, 2012).

Such metabolites can be produced throughout a plant's growth and development (i.e. constitutively) or in response to a specific event (i.e. facultatively; see Section 2.2; Feeny, 1976). Recent studies examining the metabolomes of temperate and tropical plant species suggest that herbivory generally selects for genotypes possessing more diverse metabolomes (Becerra, 2015; Kursar et al., 2009; Salazar et al., 2018; Sedio, Parker, et al., 2018). Moreover, the selection pressure exerted by herbivores appears to drive metabolic variation that reflects a trade-off between investment in specialised metabolism (e.g. flavonoids, anthocyanins) versus central metabolism (e.g. amino sugars, nucleotide sugars; Gargallo-Garriga et al., 2020). Studies examining the impact of climate on the plant metabolome also suggest a coupling between plant metabolic variation and life history. In a comparative study of temperate plant species (Defossez et al., 2021), it was found that alpine species possess metabolites that are not present in low elevation species, suggesting that cold temperatures may select for unique metabolites not required under a warmer climate. Moreover, a recent study on the sedge *Eriophorum vaginatum* L. demonstrated that populations genetically adapted to alpine climates invest more in cold-stress

metabolites than populations adapted to low elevation climates, and that this is associated with slower plant growth (Walker et al., 2019).

In general, species with functional trait values at the conservative end of the leaf economics spectrum (Wright et al., 2004) or competitor-stress-ruderal axes (Grime et al., 1997) are more resistant to herbivores and better adapted to cold climates. We thus propose that the metabolome not only underpins plant life-history trade-offs, but also has the potential to provide further mechanistic insight into such trade-offs by describing finer scale variation nested within them (Box 1; Forrister et al., 2019; Schuman et al., 2016). While further work is necessary to explore the full interplay between the metabolome and plant life history, the examples discussed here provide clues about what to expect. For instance, we might expect metabolites that associate with life-history trade-offs to display strong phylogenetic signals that reflect known constraints on ecological strategies (e.g. gymnosperms vs. angiosperms; Díaz et al., 2016). We might also suppose that such metabolites provide life-long fitness benefits and so will be present throughout an individual's lifetime or among
<figure><img src="image_9.png" /></figure>

FIGURE 3 Overlap between metabolomics and ecology. (a) The estimated geographical distribution of all plant species matched from metabolomics data in the Dictionary of Natural Products (DNP; N = 18,456 species; Chassagne et al., 2019) to cleaned occurrence records in GBIF (www.gbif.org). Each point denotes the presence of at least one species in a 0.25° grid cell, with colour intensity increasing as a function of the number of species present (maximum 854 species, natural log-scale). (b) The per cent matches between plant species in the DNP and plant species in the GBIF, TRY (Kattge et al., 2020) and BIEN (Maitner et al., 2018) databases. (c) The percent coverage of different plant growth forms present in the DNP, considering species for which data are available (N = 8111; other: bryophytes, ferns, herb/shrub, shrub/tree, herb/shrub/tree). (d) The cumulative number of manuscripts published on ecological metabolomics (from a Web of Science search for the keywords 'ecolog* AND metabolom*'). Annotated points highlight the rapid change within the last 10 years

cohorts of leaves, in spite of diurnal or seasonal fluctuations in other parts of the metabolome. Finally, given that all trait-based life-history trade-offs make a distinction between acquisitive (i.e. fast-growing) and conservative (i.e. slow-growing) species (Díaz et al., 2016; Grime et al., 1997; Wright et al., 2004), it is likely that such a distinction also exists in the metabolome. This could, for example, manifest as a varying investment in central versus specialised metabolism or changes in the abundances of metabolites associated with fast rates of growth. While the idea of a metabolic basis to life history is not new (Coley et al., 1985; Feeny, 1976), it is only now becoming possible to go beyond isolated examples and examine how the metabolome supports, and expands upon, established life-history trade-offs widely across the plant kingdom.

## 2.2 | The metabolome and plant phenotypic plasticity

As well as describing broad trade-offs in plant life history, functional traits are commonly used to characterise plant phenotypic plasticity in response to changing environmental conditions. However, with notable exceptions (Jung et al., 2014; Siefert et al., 2015), classical functional traits are usually not measured at sufficient frequency to capture changes in plant functioning over short temporal scales. Even when they have been, commonly used functional traits, such as leaf carbon and nitrogen concentrations and SLA, show strong, mixed or no responses to short-term environmental change (Dwyer et al., 2014; Firn et al., 2019; Hu et al., 2018; Koricheva, 2002; Leishman et al., 2007; Walker et al., 2019, 2020). As we discuss
below, the documented lack of consistent functional trait plasticity might not be because changes to plant functioning do not occur, but because classical functional traits are either too coarse to detect them (Figure 1a,b) or do not capture aspects of physiology that are responsive to environmental changes (Box 1). Here, we propose that measurements of the metabolome offer potential to expand the mechanistic insight provided by existing functional traits.

Comprehensive reviews already exist on the effects of temperature stress, drought, nitrogen availability and biotic interactions on the plant metabolome (Bundy et al., 2009; Rivas-Ubach et al., 2012; Sardans et al., 2011, 2020; Schuman & Baldwin, 2016). In general, temperature or drought stress rapidly stimulates production of simple sugars and amino acids (Sardans et al., 2020), as well as phenolic compounds, such as flavonoids (Yang, Wen, et al., 2018). By contrast, mild to moderate warming, which can occur over hours to days in temperate ecosystems and last for days to weeks during heat waves, has been shown to lower concentrations of phenolic compounds in a number of vascular plant species and bryophytes (Jassey et al., 2011; Pellissier et al., 2014). The onset of herbivory, as well as pathogen attack, is also known to increase production of phenolic compounds, as well as alkaloids, terpenoids and other specialised molecules, in many plant species (Peters et al., 2019; Schuman & Baldwin, 2016).

The production of specialised metabolites in response to biotic or abiotic stress acts to maximise fitness by prioritising immediate defence over growth or reproduction (Herms & Mattson, 1992). However, although dynamic coupling between the plant metabolome and leaf traits has been found (Gargallo-Garriga et al., 2015; Monson et al., 2021; Rivas-Ubach et al., 2012), it does not follow that such plasticity in the metabolome is always evident in classical functional traits, for two reasons. First, while specialised metabolites are relatively rich in carbon or nitrogen (Züst & Agrawal, 2017), the number of carbon and nitrogen atoms allocated to such metabolites is several orders of magnitude lower than that allocated to morphological structures. This means that diverting nitrogen into amino acids to resist drought may occur without affecting leaf nitrogen concentration and converting sugars into phenolic compounds during a herbivore attack may not alter leaf carbon concentration (Campbell & Vallano, 2018). Coupled with this, the metabolites involved in a plastic response usually compose only a small fraction of total tissue mass (Heiling et al., 2010) and can thus impact fitness without affecting mass-based functional traits (e.g. leaf mass, SLA, plant height, seed mass). Second, the production of specialised metabolites normally induces a downregulation of growth processes via signalling, even if energy or nutrients are not limiting (Monson et al., 2021), which can impact fitness without affecting functional traits (van Dam & Baldwin, 2001; Guo et al., 2018; Strauss et al., 2002). Indeed, it is now known that investment in plant growth versus defence is coordinated at the cellular scale by two transcription factors (jaz, fitness; Monson et al., 2021) and is widely detectable in the metabolome of affected plants (Schuman & Baldwin, 2016; Strauss et al., 2002), whereas associated changes to functional traits may only occur in subsequent generations (Van Dam & Baldwin, 2001), if at all (Guo et al., 2018; Strauss et al., 2002). More generally, changes to the metabolome occur within seconds to hours of an environmental stimulus

(Schuman & Baldwin, 2016), whereas changes to functional traits can take weeks or years to become apparent (Jassey & Signarbieux, 2019). As such, while effects of ephemeral stressors (e.g. drought) and fluctuating environments on plant fitness are detectable in the metabolome, they may be missed entirely by classical functional traits.

In short, environmental variation over the lifetime of an individual plant causes alterations to the plant metabolome that directly impact plant functioning and fitness. As such, considering the metabolome will likely improve the power of functional traits to explain mechanisms behind plant and ecosystem functioning. For instance, metabolomics studies have convincingly shown how neighbouring plant identity and diversity affect plant performance (Chiapusio et al., 2018; Scherling et al., 2010) and have provided a mechanistic basis to root competition (Latif et al., 2017), plant-soil feedback effects (Ristok et al., 2019) and facilitation (Rasmann & Turlings, 2016). More broadly, we might expect that metabolites that mediate plasticity in response to environmental factors are facultative (i.e. induced only when needed) and vary in concentration, for example over the lifetime of a plant or among cohorts of leaves. Such metabolites may thus be distinct from constitutive (i.e. always present) metabolites associated with long-lived strategies (see Section 2.1). We may also hypothesise that, with the exception of metabolites restricted to specific taxa (e.g. glucosinolates in Brassica; Fahey et al., 2001), such plastic metabolites display a weaker phylogenetic signal, although the capacity to modify their concentrations in response to stressors may still be under strong selection (Kergunteil et al., 2019).

## 3 | MEASURING, INTERPRETING AND CONTEXTUALISING THE PLANT METABOLOME

Ecological research is increasingly gravitating towards big data studies that employ large sampling campaigns or draw from vast data repositories to make inferences beyond site-specific factors and context dependency (Dietze & Lynch, 2019; Schrodt et al., 2019). Moving forwards, ecological metabolomics studies will also need to involve multi-species and multi-biome comparisons to explore the full relevance of the metabolome to ecology. To achieve this, researchers will need to overcome known challenges relating to the design of ecological metabolomics studies and the acquisition and analysis of metabolomics data. Researchers will also need to identify the limitations of applying metabolomics to ecological questions, both generally and compared to existing trait-based approaches, which are currently unknown. In this section, we summarise those known challenges and propose solutions to help ecological metabolomics enter the era of big data ecology.

### 3.1 | Challenge 1: Designing ecological metabolomics studies

The recent development of data science approaches specifically adapted to large and complex datasets yields exciting possibilities
for ecologists to interrogate links between the plant metabolome and functional trait theory over large spatial, environmental or taxonomic scales. At the same time, movements to make scientific data openly available are providing motivation to share raw metabolomics data on digital platforms, such as GNPS (Wang et al., 2016), the Metabolomics Workbench (https://www.metabolomicsworkbench.org/) and MetaboLights (Haug et al., 2013). However, inferring links between the plant metabolome and variation in classical functional traits requires that there is sufficient overlap between metabolomics and ecological databases (e.g. TRY, Kattge et al., 2020; GBIF, www gibif.org). This is currently not the case. Molecular biologists apply metabolomics mostly to small numbers of model plant species and often to specific organs or tissues. Phytochemists typically screen ecosystems for metabolites of pharmaceutical value or focus on the metabolomes of species from extreme environments or certain taxonomic groups. Chemical ecologists have chiefly employed metabolomics to identify metabolites involved in organismal interactions and mostly for individual species, families or growth forms. As such, metabolomics data are currently restricted to subsets of targeted species, which is of limited use in large-scale ecological studies.

Despite the limited overlap between existing ecological and metabolomics data, comparative ecological metabolomics studies are now beginning to emerge (Endara et al., 2018; Forrister et al., 2019; Gargallo-Garriga et al., 2020; Ristok et al., 2019; Rivas-Ubach et al., 2012; Sedio, Parker, et al., 2018; Sedio et al., 2017), demonstrating efforts from ecologists to overcome these issues (Figure 3d). Moreover, we estimate that there is already sufficient overlap between metabolomics and ecological trait databases to make a start towards a global comparative ecological metabolomics study (Figure 3a–c). Such an effort would strongly depend on compatibility between analyses (see Section 3.2) and would require active information exchange between two distinct fields. On the metabolomics side, the inclusion of raw data and metadata will play a crucial role in allowing researchers to reprocess data within the context of their study. On the ecology side, there is a need to engage metabolomics researchers early on in a project cycle, preferably before sampling species or ecosystems of interest (Uthe et al., 2021). Despite the challenges ahead, the success of the ecological genomics revolution (Baldwin, 2012) is a clear demonstration that combining distinct fields is possible and has the capacity to yield a powerful tool for ecology.

## 3.2 | Challenge 2: Metabolomics data acquisition

Even after samples have been selected, the data they yield are sensitive to a number of decisions made during collection and analysis. This used to be true for classical functional traits, but has been effectively eliminated due to huge effort from ecologists to establish and share best practice for acquiring standardised trait data (Dawson et al., 2018; Pérez-Harguindeguy et al., 2013). The search for best practice in ecological metabolomics has now begun (Sedio, Boya P., et al., 2018; Uthe et al., 2021), although the field is still missing a

handbook that sets out standardised approaches for acquiring and analysing metabolomics data in ecological applications. Developing such a handbook will be an essential step for bringing metabolomics approaches to ecology.

During fieldwork, researchers must decide whether to cool or freeze samples immediately or leave them to air-dry. Flash-freezing samples in liquid nitrogen provides the most holistic view of the metabolome, in that it prevents less stable metabolites from decaying before analysis (but see Sedio, Boya P., et al., 2018). Freezing (or at least cooling) samples on collection may be important in situations where the metabolome shifts subtly under manipulation (e.g. plasticity; see Section 2.2), but is practically challenging—especially in remote locations. For this reason, air-drying samples is common practice in some disciplines (Chassagne et al., 2019). In deciding to air-dry samples, researchers sacrifice the ability to detect less stable metabolites in favour of collecting more samples overall. Such an approach may thus be suited to comparative metabolomics studies, such as detecting persistent differences in metabolomes among a large number of species (e.g. life-history variation; see Section 2.1).

On return to the laboratory, samples must be extracted and analysed. Numerous extraction methods exist, all of which vary in their capacity to extract different aspects of the metabolome (e.g. polar vs. non-polar metabolites). Settling on one appropriate extraction method for ecological metabolomics studies is an ongoing task for ecologists and metabolomics researchers. Further work is also needed to harmonise the analytical side of metabolomics, which varies greatly between studies due to intrinsic variability in mass spectrometry (and see Richards et al. (2018) for other approaches). Briefly, gas and liquid chromatography, when coupled with mass spectroscopy, characterise samples by quantifying a unique set of spectral features. Each feature is described based on its relative signal intensity, retention time (i.e. physicochemical properties) and mass spectrum (i.e. mass-to-charge ratio of the intact—and in tandem mass spectrometry, fragmented—feature). While relative signal intensity is used as a proxy for relative abundance, it is also a function of a feature's ionisation response, which can vary due to a feature's chemical structure, a sample's overall composition or an instrument's design or protocol (De Vijlder et al., 2018). Moreover, retention time, which is used to align features between samples, is variable between runs on the same instrument, let alone between laboratories (Watrous et al., 2017). As such, while synthesising datasets from multiple sources is a common practice in ecology, it is challenging to do so with untargeted metabolomics datasets.

An immediate solution for synthesising metabolomics data is to treat separate datasets as independent replicates, but a longer term solution is to focus on mass spectra. Recent advances in computational metabolomics make it now possible to do this, and specifically use similarities between mass spectra to build molecular networks of related features and group them into spectral clusters (Wang et al., 2016). Such an approach can deliberately ignore retention time and signal intensity and can thus be applied to multiple datasets simultaneously. Moreover, spectral information relating to each cluster can be cross-referenced against public libraries to annotate it with
a putative chemical structure or metabolite name, which is help-
ful for interpretation. However, while generating spectral clusters
is reasonably robust, assigning chemical structures to them is not.
Metabolite annotation remains a major bottleneck in metabolomics
and is far from being a routine operation (Box 2; Peters et al., 2018).
Nevertheless, by treating assigned chemical information as putative,
it is still possible to gain ecological insight into the metabolomes of
multiple species, and the annotation step can be repeated as new
approaches are developed.

3.3 | **Challenge 3: Statistics for ecological**
**metabolomics**

The amount of information present in untargeted metabolomics
data provides a vast resource for examining links between the me-
tabolome and ecological processes. However, the nuances and com-
plexity of these data create three common traps for statisticians.

First, it is not meaningful to compare the abundances of differ-
ent metabolic features within a sample, because the abundance of a
feature is also dependent on its chemical structure (see Section 3.2).
This precludes the use of common ecological diversity indices (e.g.
Shannon, Simpson), which compare abundances both between and
within metabolic features. Metabolic diversity can thus currently
only confidently be expressed as metabolic feature richness.

Second, metabolomics datasets contain a large number of zeros,
owing to many features being unique to individual samples, while
also possessing many features that do not covary. This is import-
ant because it is common to reduce the complexity of metabolomics
data by performing ordinations (e.g. principal component analysis,
principal coordinate analysis and non-metric multidimensional scal-
ing), which can be sensitive to both of these factors. Methods like
sparse Partial Least Squares analysis, Random Forest or Support
Vector Machines are increasingly being applied to metabolomics
data (e.g. Defossez et al., 2021) because they are less biased to zero-
inflated data than classical ordinations.

Finally, most commonly used distance matrices assume that all
metabolic features are independent and do not take into account the
varying degrees of chemical similarity or relatedness among them.
For this reason, developing statistical approaches that accurately
reflect the chemical space of metabolomics data is the subject of ac-
tive research. For instance, several research groups are developing
chemically informed metrics that take into account spectral related-
ness across individual metabolic features (Endara et al., 2018; Salazar
et al., 2018; Sedio et al., 2017). Other groups are using chemically
informed tree structures to constrain tree-based distance metrics
(e.g. UniFrac; Junker, 2018). Until a consensus emerges, ordinations
of metabolomics data should be performed carefully and interpreted
with caution. Assuming these traps are avoided, metabolomics data
are similar to other multivariate data, with the exception that they
contain thousands of metabolic features (Box 1) and give users the
flexibility to choose how precise or confident their interpretation
should be (Box 2).

4 | SPECIAL FEATURE CONTENT

Thus far, we have provided evidence that measurements of the me-
tabolome, once properly incorporated into classical functional trait
theory, may improve our understanding of the coupling between
physiology and fitness in trait-based studies. The papers in this
Special Feature build on this foundation to explore how the metabo-
lome can help characterise the mechanisms behind plant community
assembly, plant-organismal interactions and the effects of plants and
soil micro-organisms on ecosystem processes, as well to improve
predictions of them.

Beginning with plant community assembly, Endara et al. (2021) couple phylogenetic and metabolomics analyses on the neotropical genus of trees *Inga* across five locations in the Amazon to show that individuals with dissimilar metabolomes are more likely to co-occur, but that regional variation in metabolites related to herbivore defence occurs independently of phylogenetic relatedness. These findings suggest that the metacommunity for any regional community in the Amazon is potentially the whole Amazon itself, and that community assembly in this region likely depends on how herbivores interact with plant metabolic diversity. Fernandez-Conradi et al. (2021) examine such interactions directly by assessing rates of herbivory on experimental plant communities possessing high vs. low community-wide metabolic diversity in the Swiss Alps. While the expectation was that plant communities with high metabolic diversity would be more resistant to herbivores than communities with low metabolic diversity, this effect only occurred at low elevation, whereas communities with high metabolic diversity were less resistant to herbivores at high elevation.

One explanation for the findings of Fernandez-Conradi et al. (2021) is that metabolite richness represents only one aspect of plant metabolic diversity that has consequences for plant-herbivore interactions. Indeed, Philbin et al. (2021) perform metabolomics analyses on two *Piper* shrub species to show that herbivores interact with two distinct aspects of plant metabolic diversity, namely metabolic composition and metabolite structural complexity. This distinction explains how closely related species support diverse herbivore assemblages, and confirms that plants are able to draw on multiple aspects of metabolic diversity in response to herbivore pressure. Another explanation for the findings of Fernandez-Conradi et al. (2021) could be that the metabolome is not static and represents the collective outcome of many interacting biotic and abiotic factors. In this regard, Weinhold et al. (2021) exploit a biodiversity ecosystem functioning experiment in China to show that tree diversity is an important determinant of the leaf metabolome in four subtropical tree species. At the same time, Rawlings et al. (2021) use controlled experiments on wood-decay fungi in UK beech woodlands to show that generalist pioneer and stress-tolerant species display contrasting metabolic responses to constant versus fluctuating temperature regimes. Finally, the studies by Fernandez-Conradi et al. (2021) and Philbin et al. (2021) suggest that the metabolome responds not only directly to abiotic shifts (in both studies induced by elevation change), but also indirectly via interactions between
the metabolome and herbivores, which alter a plant's capacity to respond to abiotic change itself.

The above studies collectively demonstrate that the metabolome provides deeper mechanistic insight into plant community assembly and plant-herbivore interactions. However, they also suggest that the metabolome is an important regulator of ecosystem processes. Indeed, Fernandez-Conradi et al. (2021) and Philbin et al. (2021) both show that the plant metabolome shapes herbivore community dynamics, and Endara et al. (2021) suggest that the metabolome dictates plant community assembly processes. Similarly, Rawlings et al. (2021) show the potential for wood-decay fungi with different ecological strategies to display different metabolic responses to fluctuating temperature, with consequences for wood decomposition—an important biogeochemical process in forest ecosystems.

The remaining papers in this Special Feature examine the metabolome as the foundation of plant-soil interactions. Specifically, Williams et al. (2021) characterise root traits and the rates and metabolic composition of root exudates in experiments involving 18 European grassland species. They show that the root exudate metabolome is a key component of root functioning, and posit that the root exudate metabolome characterises an aspect of root functional space not currently described by existing functional trait frameworks. Finally, Sytiuk et al. (2021) undertake a comparative study across five European peatland ecosystems on a latitudinal gradient to determine how climate, edaphic characteristics and the phylogeny, functional traits and metabolites of peat-forming *Sphagnum* mosses affect the composition and functioning of peatland soil microbial communities. They find that *Sphagnum* metabolites have the strongest bearing over the soil microbial food web, revealing that the *Sphagnum* metabolome is an important regulator of peatland soil processes.

## 5 | CONCLUSIONS AND FUTURE DIRECTIONS

In this review, we have shown that the metabolome comprises the pool of molecular mechanisms that underpins physiology and represents a highly resolved lens through which to examine plant fitness, both between and within generations. By focussing on the metabolome as the biochemical basis of organismal functioning and the language of organismal interactions, the studies that form this Special Feature illustrate how metabolomics can provide deeper mechanistic insight into open ecological questions, and show how using the metabolome within a functional ecology framework holds great promise for improving predictive power in ecology. Yet, applying metabolomics approaches to ecological questions requires careful experimental design, as well as the development of standardised approaches for acquiring and analysing ecological metabolomics data. In our opinion, ecologists and metabolomics researchers must now work together to develop three key frameworks necessary for integrating the plant metabolome into ecology (Figure 4): (a) a conceptual framework, placing the metabolome within the context of established trait-based approaches and theory; (b) a technical

<figure><img src="image_10.png" /></figure>

**FIGURE 4** Research needs for integrating the metabolome into functional ecology. The conceptual (green), technical (yellow) and data (red) frameworks needed to establish measurements of the metabolome into a functional trait framework. Expanded boxes correspond to research directions proposed in Box 3
BOX 3 Nine key questions for the future of ecological metabolomics

**Conceptual framework**

Q1. How does the metabolome vary across the plant kingdom? Which aspects are strongly phylogenetically constrained, and which relate to established life-history trade-offs? Does measuring the metabolome capture additional axes of specialisation not apparent from classical functional traits?

Q2. How does global environmental variation (e.g. climate) shape the plant metabolome? Do species with larger geographical ranges possess more diverse, or less specialised, metabolisms? Do species from distinct environmental contexts also possess distinct metabolomes?

Q3. How does the metabolome fluctuate over time? Which aspects remain static (i.e. providing a lifetime fitness benefit; see Section 2.1), and which aspects are dynamic (i.e. providing a short-lived fitness benefit; see Section 2.2)? Can this knowledge be used to disentangle the mechanisms behind the (lack of) classical functional trait variation under short-term environmental change?

**Technical framework**

Q4. Can insights from the conceptual framework, above, as well as from the growing number of ecological metabolomics studies (Figure 3d), be combined to derive a suite of candidate molecular functional traits that can be measured in the metabolome?

Q5. What are the limitations of using metabolomics approaches in ecology? For which applications, and in which situations, does the metabolome not provide additional value to existing trait-based approaches?

Q6. What are the most appropriate sample collection and extraction methods for ecological metabolomics? Is there a 'one size fits all' solution, or should researchers pick from a number of standardised options, each with their own benefits and limitations?

Q7. Following the lead of functional trait ecology (Dawson et al., 2018; Pérez-Harguindeguy et al., 2013) and building on early momentum (Sedio, Boya P., et al., 2018; Uthe et al., 2021), can we develop a handbook for the standardised collection, acquisition and analysis of ecological metabolomics data?

**Data framework**

Q8. To what extent can existing metabolomics data be used in comparative ecological studies? Which comparisons are valid (i.e. what are the limits to inferences)? For which species and ecosystems is there already sufficient coverage, and where do we need to focus future effort?

Q9. Can further momentum be generated for sharing and reusing metabolomics data hosted on online repositories? What tools are necessary for integrating and synthesising existing and future metabolomics and ecological data (e.g. knowledge graph exploitation, R packages, new statistical methods, cross-talk between database owners, adequate metadata)?

framework, equipping ecologists with the tools and expertise for measuring the metabolome in a wide range of species; and (c) a data framework, providing the digital infrastructure and data processing pipelines needed for the use of existing and future high throughput metabolomics data in comparative ecological studies. In Box 3, we outline nine key research directions that we believe will help to achieve this vision.

ACKNOWLEDGEMENTS

This study was part funded by a grant from the Synthesis Centre (sDiv) of the German Centre for Integrative Biodiversity Research (iDiv), awarded to T.W.N.W., F.S. and N.M.v.D.; J.M.A. and T.W.N.W. are funded by the Swiss National Science Foundation (grant no. 31003A-176044). R.S.-G. is funded by a UK Natural Environment Research Council Independent Research Fellowship (grant no. NE/M018458/1). J.M.A. is additionally funded by the European Research Council (grant no. 678841). P.C. is supported by a Ramón Areces Foundation Postdoctoral Scholarship. V.B. is supported by the French Agence Nationale pour la Recherche (grant no. ANR-12-BSV7-0016-01: SecPriMe2), the BioDivMeX Mistrals programme,

the Aix-Marseille University 'Investissements d'Avenir' programme and the Labex OT-Med programmes. P.D.C. is funded by the US National Science Foundation (grant no. DEB-1135733). D.L.F. is supported by the US National Science Foundation Graduate Research Fellowships Program. V.E.J.J. is funded by the French National Research Agency (grant no. ANR-17-CE01-0007: Mixxopeat). N.M.v.D., S.M., K.P. and H.U. are supported by iDiv Halle-Jena-Leipzig, which is funded by the German Research Foundation (grant nos DFG-FZT 118 and 202548816). S.N. and K.P. are additionally funded by the German Network for Bioinformatics Infrastructure (de.NBI) and acknowledge BMBF funding (grant no. 031L0107). J.P. and J.S. are funded by the Spanish Government (grant no. PID2019-110521GB-100), Catalan Government (grant no. SGR 2017-1005) and a European Research Council Synergy Grant (grant no. ERC-SyG-2013-610028: IMBALANCE-P). S.R. is funded by the Swiss National Science Foundation (grant no. 31003A_179481). F.S. acknowledges funding by the University of Nottingham Anne McLaren Fellowship. M.C.S. is supported by a NOMIS Foundation project ('Remotely Sensing Ecological Genomics') and by the University of Zürich Research Priority Programme on Global Change and Biodiversity.
## CONFLICT OF INTEREST

The authors declare no conflict of interest.

## AUTHORS' CONTRIBUTIONS

Special Feature Guest Editors and Editorial text: M.M.v.D., R.S.-G. and T.W.N.W.; Review conceptualisation: T.W.N.W. with support from J.M.A., P.-M.A., O.B., V.B., R.D.B., P.C., N.M.v.D., E.D., C.F., A.G.-G., V.E.J.J., S.N., J.P., K.P., S.R., R.S.-G., J.S., F.S., M.C.S., W.W. and J.-L.W.; Review data curation: P.-M.A., O.B., P.C. and T.W.N.W.; Review formal analysis: T.W.N.W.; Review data visualisation: O.B., P.C., V.E.J.J. and T.W.N.W.; Review writing original draft: J.M.A., P.-M.A., P.C., P.D.C., N.M.v.D., E.D., M.E., M.-J.E., D.F., V.E.J.J., S.M., S.N., K.P., S.R., U.R., R.S.-G., F.S., M.C.S., A.S. and T.W.N.W.; Review editing: T.W.N.W., J.M.A., P.-M.A., O.B., V.B., R.D.B., P.C., P.D.C., N.M.v.D., B.D., E.D., M.-J.E., M.E., C.F., D.F., A.G.-G., V.E.J.J., S.M., S.N., L.P., J.P., K.P., S.R., U.R., R.S.-G., J.S., F.S., M.C.S., A.S., H.U., W.W. and J.-L.W.

## PEER REVIEW

The peer review history for this article is available at https://publo ns.com/publon/10.1111/1365-2745.13826.

## DATA AVAILABILITY STATEMENT

This paper does not use data.

## ORCID

Tom W. N. Walker https://orcid.org/0000-0001-8095-6026

Jake M. Alexander https://orcid.org/0000-0003-2226-7913

Pierre-Marie Allard https://orcid.org/0000-0003-3389-2191

Oliver Baines https://orcid.org/0000-0002-2415-8502

Richard D. Bardgett https://orcid.org/0000-0002-5131-0127

Pol Capdevila https://orcid.org/0000-0002-2842-4302

Phyllis D. Coley https://orcid.org/0000-0001-7718-597X

Emmanuel Defossez https://orcid.org/0000-0002-3279-9190

María-José Endara https://orcid.org/0000-0002-8805-1456

Madeleine Ernst https://orcid.org/0000-0001-9530-3837

Catherine Fernandez https://orcid.org/0000-0002-6868-4774

Dale Forrister https://orcid.org/0000-0001-8170-7187

Vincent E. J. Jassey https://orcid.org/0000-0002-1450-2437

Steffen Neumann https://orcid.org/0000-0002-7899-7192

Loïc Pellissier https://orcid.org/0000-0002-2289-8259

Josep Peñuelas https://orcid.org/0000-0002-7215-0150

Kristian Peters https://orcid.org/0000-0002-4321-0257

Sergio Rasmann https://orcid.org/0000-0002-3120-6226

Ute Roessner https://orcid.org/0000-0002-6482-2615

Jordi Sardans https://orcid.org/0000-0003-2478-0219

Franziska Schrodt https://orcid.org/0000-0001-9053-8872

Meredith C. Schuman https://orcid.org/0000-0003-3159-3534

Abrianna Soule https://orcid.org/0000-0001-9164-7317

Henriette Uthe https://orcid.org/0000-0002-2523-196X

Wolfram Weckwerth https://orcid.org/0000-0002-9719-6358

Jean-Luc Wolfender https://orcid.org/0000-0002-0125-952X

Nicole M. van Dam https://orcid.org/0000-0003-2622-5446

Roberto Salguero-Gómez https://orcid.org/0000-0002-6085-4433

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**How to cite this article:** Walker, T. W. N., Alexander, J. M., Allard, P.-M., Baines, O., Baldy, V., Bardgett, R. D., Capdevila, P., Coley, P. D., David, B., Defossez, E., Endara, M.-J., Ernst, M., Fernandez, C., Forrister, D., Gargallo-Garriga, A., Jassey, V. E. J., Marr, S., Neumann, S., Pellissier, L., ... Salguero-Gómez, R. (2022). *Functional Traits 2.0: The power of the metabolome for ecology*. *Journal of Ecology*, **110**, 4–20. https://doi.org/10.1111/1365-2745.13826