<figure><img src="image_1.png" /></figure>

HAL
open science

# From organism traits to ecosystem processes: why size is so important

François Vasseur, Lucie Mahaut, Brian J. Enquist, Cyrille Violle

► To cite this version:

François Vasseur, Lucie Mahaut, Brian J. Enquist, Cyrille Violle. From organism traits to ecosystem processes: why size is so important. Annual Review of Ecology, Evolution, and Systematics, 2025, 56 (1), pp.145-170. 10.1146/annurev-ecolsys-102723-054525. hal-05381167

HAL Id: hal-05381167

https://hal.science/hal-05381167v1

Submitted on 25 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

# From Organism Traits to Ecosystem Processes: Why Size Is So Important

François Vasseur,<fnref n="1" /> Lucie Mahaut,<fnref n="2" />,<fnref n="3" />
Brian J. Enquist,<fnref n="4" />,<fnref n="5" /> and Cyrille Violle<fnref n="1" />

ANNUAL
REVIEWS CONNECT

www.annualreviews.org

• Download figures

• Navigate cited references

• Keyword search

• Explore related articles

• Share via email or social media

Annu. Rev. Ecol. Evol. Syst. 2025. 56:145-70

First published as a Review in Advance on
August 27, 2025

The *Annual Review of Ecology, Evolution, and Systematics* is online at ecolsys.annualreviews.org

https://doi.org/10.1146/annurev-ecolsys-102723-054525

Copyright © 2025 by the author(s). This work is
licensed under a Creative Commons Attribution 4.0
International License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are credited.
See credit lines of images or other third-party
material in this article for license information.

<figure><img src="image_4.png" /></figure>

## Keywords

allometry, body mass, ecosystem functioning, metabolism, population density, scaling up

## Abstract

Understanding the relationships between organism traits and ecosystem processes is crucial for advancing ecological theory and predicting ecosystem responses to environmental change. Fundamental biological functions—metabolism, growth, reproduction—scale predictably with organism size across diverse taxa. Moreover, scaling relationships are pervasive across organizational levels, governing population density, competitive interactions, and trophic food webs. This review synthesizes theoretical and empirical evidence linking organism size to ecosystem processes, highlighting the significance of allometric scaling laws, including the metabolic scaling theory and the energy equivalence rule. It discusses deviations from these predictions due to environmental heterogeneity, biotic interactions, and evolutionary dynamics. By exploring the role of size in modulating functional traits and ecosystem-level outcomes, we emphasize the need for integrated approaches to refine predictive models. This review underscores the centrality of size as a key driver of ecological processes and its implications for biodiversity conservation.

<footnote n="1">¹CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France; email: francois.vasseur@cefe.cnrs.fr</footnote>

<footnote n="2">²CEFE, Univ Montpellier, CNRS, EPHE, IRD, INRAE, Montpellier, France</footnote>

<footnote n="3">³FRB - CESAB, Montpellier, France</footnote>

<footnote n="4">⁴Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA</footnote>

<footnote n="5">⁵The Santa Fe Institute, Santa Fe, New Mexico, USA</footnote>
# 1. INTRODUCTION

Can we explain, and potentially predict, ecosystem processes from organismal features? To address this question, a key method is to establish quantitative relationships between organismal functions and ecosystem processes, which can be further used for predictions (Funk et al. 2017, Lavorel & Garnier 2002, Lavorel & Grigulis 2012). Decades of theoretical and empirical work have provided valuable insights, but considerable uncertainties remain (Duarte et al. 1995, Ehleringer & Field 1993, Reich 2012). One reason is the difficulty of assessing organismal functions directly or identifying relevant proxies for them (van der Plas et al. 2020).

The astonishing diversity of forms and functions found in animals, plants, and microorganisms has long fascinated biologists. A central challenge in ecology and evolutionary biology is to describe and understand this complexity. Over the past two decades, the rise of global species trait databases has enabled large-scale comparisons across different clades of the tree of life (Brosse et al. 2021, Díaz et al. 2022, Frimpong & Angermeier 2009, Herberstein et al. 2022, Kattge et al. 2011, Tobias et al. 2022). These analyses revealed a striking pattern: Across clades, size-related traits consistently emerge as the primary axis of variation (Carmona et al. 2021, Díaz et al. 2016, Loiseau et al. 2020). Accordingly, body size—an apparently easy trait to measure on organisms (but see the sidebar titled The Different Dimensions of Size)—is known to control most organismal functions in plants, animals, and microbes (Calder 1996, Guo et al. 2022, Niklas 1994). For instance, metabolic rate, growth patterns, and population density scale predictably with organismal size (Brown et al. 2004; Niklas & Enquist 2001; West et al. 1999a,b). The size dependence of organismal functions is rooted in physical and physiological considerations: Larger organisms allocate proportionally less of their resources to structural support and distribution networks, which influences resource acquisition and energy use (Peters 1986). These size-related effects are governed by first principles such as surface-area-to-volume ratios and energy allocation, which influence a myriad of processes from cellular efficiency to ecosystem dynamics (Enquist & Michaletz 2016, Enquist et al. 2003, Kerkhoff & Enquist 2006).

The size dependence of organism traits can be mathematically studied by modeling allometric relationships, which describe how various biological traits change in proportion to the size of an organism or its parts. These relationships often follow power laws of the form $Y = aM^b$, where

# THE DIFFERENT DIMENSIONS OF SIZE

Body size has long been used as an easily tractable metric in ecology and evolution. However, it is a relatively vague concept because it can be measured in several ways (length, weight, volume, surface area, or height) and because the different measures of size are not linearly related to each other. For instance, the first consequence of changes in the size of organisms is the modification of the proportional relationships between surface area and volume. Surface area, which enables exchanges between the internal environment and the outside world, increases according to a square function, while volume, a priori linked to the number of cells and therefore to energy requirements, increases according to a cubic function. As a result, the surface-area-to-volume ratio decreases with volume and mass at constant density. With a lower surface-area-to-volume ratio, larger organisms are assumed to use resources more efficiently. This principle underpins the idea that size is the main physiological response trait for the environment. Moreover, the different dimensions of body size are expected to differently constrain organismal physiology and resource use depending on the milieu and limiting resources. For instance, phytoplankton metabolic scaling follows volumetric size, whereas tree scaling is tied to linear dimensions, reflecting different physical constraints and resource allocation strategies (Perkins et al. 2019). By understanding these scaling nuances, researchers can better model global primary production and predict ecosystem responses to environmental changes.
$Y$ is the rate of change in one trait relative to the change in mass $M$, and $b$ is the scaling exponent. Taking the logarithm of both sides produces a straight line, $\log Y = \log a + b \log M$. However, it is important to note that while power laws may appear as straight lines in logarithmic space, these relationships are typically nonlinear when untransformed unless the exponent $b$ is exactly 1 (isometry). The log transformation serves a purpose beyond simply normalizing variance in a statistical sense (Kerkhoff & Enquist 2009). It is essential because most biological processes are cumulative and hierarchical, with organisms forming new structures through cell division. This multiplicative nature reflects the fact that biological growth occurs by repeated units of cellular addition, where each new cell or module builds on previous structures. Thus, metabolic rate or growth processes inherently reflect the sum of many small multiplicative events.

Allometric relationships provide essential insights into how forms and functions are optimized across diverse biological systems (White et al. 2022). For example, metabolic rate, life span, and growth often scale allometrically (i.e., $b \neq 1$) with body size, revealing the trade-offs that emerge between these traits and the constraints that shape organismal design (Brown et al. 2004, West et al. 1999a). Accordingly, studying allometric relationships is critical for understanding various physiological processes. For example, the heart mass of mammals scales allometrically with body mass, impacting cardiac output and metabolic efficiency (West & Brown 2005). Moreover, empirical studies demonstrated that allometric regularity emerges across organizational levels, e.g., with consistent size–metabolism ($b = 3/4$) and size–abundance ($b = -3/4$) relationships observed at both organismal and ecosystem levels, pointing to the existence of widespread and predictable scaling patterns (Allen et al. 2002, Damuth 1981, Kleiber 1947, Niklas 1994). Over the last two decades, theoretical models have been developed to explain this regularity at different organizational levels (Enquist 2002; Enquist et al. 1999, 2007; Price & Enquist 2007; Stark et al. 2024; West et al. 1999a,b, 2001). As a consequence, a set of allometric rules can be defined as size-dependent scaling relationships supported by established mathematical theory and extensive empirical testing. These expected rules offer a powerful framework to examine biological systems across scales, because they reveal the underlying mechanisms that link organismal traits to ecosystem processes (Enquist & Bentley 2012).

Does allometric regularity imply that the scaling exponent $b$ can reliably predict variation in ecosystem function? In theory, predictions based on allometric principles should be feasible, particularly for ecosystem-level markers of biogeochemical cycles, such as plant primary productivity (Enquist & Bentley 2012, Enquist et al. 2007). In practice, the predictive power of allometry differs depending on scale and process, including when explaining patterns in existing data or forecasting outcomes yet to be observed. The strength of macroecological patterns lies in their ability to provide general predictions that capture emergent properties while abstracting from the complexity of lower-level interactions (McGill 2019). However, deviations from theoretical expectations are common even for well-studied processes like primary productivity. This is largely because the theory often assumes that ecosystems are organized around ecological dynamics that approach equilibrium conditions with stable resource supply and optimal energy use, which are rarely met in natural systems (Kerkhoff & Enquist 2007). For instance, biotic interactions, environmental heterogeneity, and evolutionary changes can generate significant deviations from predicted values (Stark et al. 2024). Importantly, deviations from expected scaling relationships can serve as diagnostic tools at finer scales, helping to identify the unique effects of specific environmental drivers such as disturbances or competition.

Deviations from allometric rules can also occur because size-related traits do not fully capture the phenotypic variation among organisms. Despite the dominant role of size in structuring biodiversity, the analysis of species trait databases also shows that, across animals, plants, and microorganisms, multiple traits must be measured simultaneously to effectively characterize the
ecological strategies of organisms, which are inherently multidimensional (Laughlin 2014, Messier et al. 2017). So, what is the relative influence of size on the variation of ecosystem processes compared to other traits? For certain functions—particularly those involving higher trophic levels, such as pollination—the influence of body size is less straightforward, and scaling patterns (a fortiori, allometric rules) remain less well defined. The size–trait–function conundrum remains highly puzzling. Multi-trait analyses have uncovered several phenotypic dimensions that collectively reflect macro- and microevolutionary constraints shaping the diversity of life (Shoval et al. 2012). Consequently, comparing organisms within a shared, multidimensional phenotypic space is essential for a comprehensive understanding of biodiversity (Mouillot et al. 2021).

This review focuses on the theoretical foundations of allometric scaling, emphasizing the scaling of organism constraints through populations to communities. We discuss the sources of deviation from predicted allometric rules, notably the violation of the equilibrium assumption. To conclude, we highlight the complex linkage between organism size, functional traits, and ecosystem functions, as well as future avenues to disentangle it.

## 2. ALLOMETRIC RULES FROM ORGANISMS TO ECOSYSTEMS

### 2.1. Metabolic Scaling in Organisms: The 3/4 Rule

Metabolic rate follows a well-documented allometric relationship with body mass, known as the 3/4 rule. Formulated by Kleiber (1947), the 3/4 rule posits that the metabolic rate *B* (the rate at which an organism converts energy for biological functions) scales to the three-quarters power of body mass *M* (i.e., *B* = *aM*³/₄). This scaling indicates that larger organisms, while having higher absolute metabolic rates, tend to have lower metabolic rates per unit mass than smaller organisms. For instance, this scaling relationship explains why mice must eat ~15% of their mass daily while elephants need to eat only 1% of their mass daily. This phenomenon is partly attributed to differences in energy allocation and physiological constraints, since larger bodies require more energy for maintenance but benefit from greater efficiency in resource use (Huxley 1932). The significance of the 3/4 rule lies in its ability to provide a unified explanation for patterns of variation in metabolic rates across diverse taxa, including mammals, birds, reptiles, and plants (Figure 1a). However, over several decades, the 3/4 rule has mostly remained a phenomenological pattern, with strong implications for predicting the functioning of organisms and populations but without a comprehensive theoretical explanation of size–metabolism relationships.

Early explanations of the regularity of allometric relationships invoked surface-area-to-volume ratios and geometric constraints (Heusner 1982, Peters 1986, von Bertalanffy 1951). These earlier insights established that larger organisms benefit from greater resource-use efficiency because the ratio of surface area to volume decreases with size. However, they predict 2/3 scaling, contrasting with the observed ubiquity of 3/4 scaling (but see McMahon 1973). Theoretical work during the 1990s went a step further by linking metabolic rate to resource distribution networks, providing the first theory for the 3/4 rule (West et al. 1999b). Specifically, the metabolic scaling theory (MST) states that the fractal-like geometry of vascular networks is the primary determinant of several scaling relationships in both plants and animals (West et al. 1999a,b). For instance, organism metabolic rate *B* and growth rate *G* are predicted to scale with organism size with *b* = 3/4, as empirically observed. MST relies on two key tenets: (a) the 3/4 scaling arises because biological systems maximize efficiency in transporting energy and resources through fractal-like networks (e.g., blood vessels, xylem) and (b) MST applies universally, predicting relationships between body size and various traits, such as life span, growth rate, population density, and ecological dynamics. However, these tenets remain debated (see Sections 3 and 4).
<figure><img src="image_5.png" /><figcaption>Figure 1</figcaption></figure>

Allometric relationships at organism and population levels in eukaryotes. (a) Scaling relationships at the organism level. Basal metabolism versus body mass is shown for different taxa: protists (n = 95), ectotherms (n = 2,314), plants (n = 337), and mammals (n = 698). (b) Scaling relationships at the population level. The density of individuals versus body mass is shown for different taxa: protists (n = 301), ectotherms (n = 957), plants (n = 412), and mammals (n = 608). Lines represent SMA regressions (P < 0.001 for all). *b* is the scaling exponent (SMA slopes). Data are log₁₀ transformed. Figure adapted with permission from Hatton et al. (2019) (CC BY-NC-ND 4.0). Abbreviations: ind, individuals; SMA, standard major axis.

## 2.2. Density Scaling in Populations: The Energy Equivalence Rule

Within a limited area with fixed resources, organism size strongly influences population density, and vice versa. As larger organisms consume (in absolute terms) more resources per individual, such as food, water, or space, fewer large organisms can be supported in the same area compared to smaller organisms. Accordingly, smaller organisms tend to be more abundant than larger ones, a trend that can be observed across various taxa and habitats (E.P. White et al. 2007).

The size–abundance covariation is typically described by an inverse scaling relationship where population density (N) decreases with body size as N = N₀Mᵇ. This allometric relationship has been empirically supported, notably in mixed-species communities of plants and animals, with b = -3/4 across communities (Allen et al. 2002, Blackburn et al. 1993, Damuth 1981) (Figure 1b). Interestingly, this suggests that the scaling of density (N ∝ M⁻³/⁴) is the inverse of the scaling for individual metabolic rate (B ∝ M¹/³). As a consequence, the total energy consumed by a species per unit area is expected to be invariant with body size (N × B ∝ M⁻³/⁴ × M¹/³ ∝ M⁰), which is known as the energy equivalence rule (EER) (Damuth 1981). The EER implies that while larger organisms consume more energy individually, their lower population densities balance their total energy use. Empirical and theoretical works (Ernest et al. 2003, E.P. White et al. 2007) further clarified the mechanisms linking size, turnover rates, and equilibrium conditions. However, the mechanisms driving size–abundance relationships remain largely unclear. Distinct patterns can be expected depending on scales and organizational levels (E.P. White et al. 2007), and we are still lacking a unifying theory of the mechanisms modulating organism size and abundance.

Building on MST, a simple model was developed to explain how the EER emerges from plant allometry (Enquist et al. 1998). The model assumes that all individuals in a population compete for limited resources and that, at steady state, the rate of resource use balances the rate of re-source supply. Based on this assumption, the maximum number of individuals (Nmax) sustained per unit area is related to the average size of the plants (M), such that Nmax ∝ M⁻³/⁴. This has major
implications for ecosystem functioning, suggesting that energy and matter fluxes depend not on the size distribution of organisms but on their total biomass. For instance, net primary productivity is expected to scale allometrically (with *b* = 3/4) with total phytomass in plant communities (Kerkhoff & Enquist 2006). However, this relationship is often assumed to hold under equilibrium conditions, where resource turnover balances with organism birth and death rates. These conditions are rarely met in natural systems. For instance, tree communities often deviate due to differences in recruitment and disturbance (Kerkhoff & Enquist 2006), which suggests that some ecosystems may not achieve equilibrium conditions, leading to the violation of energy equivalence. Understanding the mechanisms behind these deviations is likely crucial for predicting how communities and ecosystems respond to environmental changes.

## 2.3. Allometric Rules Among Interacting Organisms and Populations

Despite having slower size-standardized metabolic rates, larger organisms often have disproportionate access to resources. This principle underpins asymmetric interactions and competitive hierarchies in plant communities, where taller plants outcompete shorter ones by limiting their access to light (Weiner 1990). Interestingly, empirical studies suggest that these competitive interactions are also determined by some allometric rules. For instance, Gaudet & Keddy (1988) found a strong negative relationship governing the response of a given plant species (focal plant) to competition with neighbor plant species of contrasting sizes (Figure 2a). This competitive advantage does not scale proportionally but follows an allometric relationship with neighbor size. A reanalysis of Gaudet & Keddy’s (1988) data on a log scale (i.e., from an allometric perspective) reveals that the relationship between focal plant mass and neighbor plant mass can be divided into two distinct phases (Figure 2b). In the first phase, focal and neighbor sizes are independent, as the neighbor is too small (less than 5% of the focal plant’s mass) to exert significant competitive pressure. As a result, the focal plant reaches its maximum size. The second phase corresponds to

<figure><img src="image_6.png" /><figcaption>Figure 2</figcaption></figure>

The scaling relationship governing asymmetric competition in plants. (a) The original relationship described by Gaudet & Keddy (1988). The focal plant is a single species, for which size ($M_{Focal}$) is measured in response to pairwise competition with different neighbor species of varying sizes ($M_{Neighbor}$; $n = 44$). (b) Reanalysis of the data from Gaudet & Keddy (1988). Data are $log_{10}$ transformed. When $M_{Neighbor} > 5\% M_{Focal}$, the focal plant size decreases as neighbor size increases, with a scaling exponent $b = -0.74$ (standard major axis regression, *P* < 0.001; solid line). Figure adapted with permission from Gaudet & Keddy (1988); copyright 1988 Springer Nature Limited.
a decrease in focal plant mass with increasing neighbor mass, indicating actual competition. This negative correlation mathematically binds focal and neighbor plant sizes, encapsulating an allo-metric relationship $M_{Focal} = aM_{Neighbor}^b$ [with $b = -0.74$ in the reanalyzed data from Gaudet & Keddy (1988)] (Figure 2b).

Plant height is a strong proxy for light depletion (Navas & Violle 2009), making it a critical predictor of competitive outcomes (Gaudet & Keddy 1988, Violle et al. 2009). This phenomenon is grounded in the Beer–Lambert law, which describes the exponential attenuation of light as it passes through a medium. Mathematically, it is expressed as $I = I_0 \times e^{-\kappa L}$, where I is the light intensity at a given depth in the canopy, $I_0$ is the incident light intensity, $\kappa$ is the light attenuation coefficient, and L is the cumulative leaf-area index above the measurement point. This scaling relationship highlights how size-dependent factors such as plant height and foliage density directly influence light availability. Taller plants with greater leaf area intercept more light, creating resource access asymmetries that cascade through the community. As a result, this allometric perspective reveals that competition-driven variation in resource acquisition emerges as a predictable consequence of size-dependent scaling principles (Coomes et al. 2011). Moreover, it highlights the importance of the metrics used to measure size (mass versus height versus volume versus area; see the sidebar titled The Different Dimensions of Size) when assessing metabolic scaling and energy equivalence.

Competitive hierarchy for light acquisition is a key mechanism that modulates the outcomes of plant–plant interactions on ecosystem functioning (Mahaut et al. 2023). As such, competitive hierarchy is considered a key driver of ecological succession (Huston & Smith 1987). This mechanism also has profound implications for the management of cultivated systems. For example, plant breeding has been conducted since the Green Revolution to introduce dwarf genes in modern crop cultivars as a means to prevent crop yield losses caused by the hierarchical competition for light. This has resulted in an unprecedented increase in yields (Hedden 2003). A comprehensive understanding of the allometric rules governing plant–plant interactions can further help design more efficient intercropping systems (Westgeest et al. 2024). Promisingly, interspecific interactions in cereal–legume intercropping systems significantly influence plant reproductive allometry, leading to a greater increase in yield for a given increase in plant size in intercropped systems (Gaudio et al. 2021). All these findings emphasize the importance of plant allometry in shaping community dynamics and ecosystem functioning.

Disproportionate resource appropriation by larger organisms is evident not only within trophic levels but also across them. Body size is a critical determinant of the strength and direction of trophic interactions, influencing who eats whom and how much energy is transferred through the food web (Trebilco et al. 2013). Strikingly, near-3/4 scaling has also been observed in the allometries of predator biomass and prey biomass with $M_{Predator} = aM_{Prey}^b$ (Figure 3). However, the origins of these scaling relationships remain poorly understood (Hatton et al. 2015). Similarly, body size plays a crucial role in shaping the structure and stability of trophic networks, as shown in studies by Durand-Bessart et al. (2023) and Perkins et al. (2022) that emphasize how size-based interactions define the architecture of food webs. For instance, trophic interactions did not adhere to a fixed allometric scaling but instead varied depending on the strength of prey limitation (Costa-Pereira et al. 2018).

The role of body size in trophic dynamics is so fundamental that allometric relationships have become indispensable tools for modeling ecological interactions. These models are applied to explore the effects of environmental gradients, such as temperature and resource availability, on predator–prey dynamics (Fussmann et al. 2014) and to understand energy fluxes in complex food webs (Barnes et al. 2018, Boit et al. 2012). For instance, Boit et al. (2012) used size-based models to predict energy transfer efficiencies and the stability of food webs under changing conditions,
<figure><img src="image_7.png" /><figcaption>**Figure 3**</figcaption></figure>

Scaling rules in trophic relationships. The scaling relationships between prey biomass and predator biomass are shown with *b* as the scaling exponent for different systems: terrestrial (*n* = 45 food webs), marine (*n* = 41), and freshwater (*n* = 30). Lines represent standard major axis regressions (*P* < 0.001 for all). Data are log₁₀ transformed. Figure adapted from Perkins et al. (2022) (CC BY 4.0).

revealing how variations in body-size distribution can alter ecosystem resilience. Moreover, integrating size-based models into broader ecological frameworks has advanced our understanding of multitrophic biodiversity and its contributions to ecosystem functioning. These advancements are supported by the development of computational tools and R libraries that enable the imputation and simulation of allometric trophic network models (Gauzens et al. 2023). These tools streamline the integration of body size into ecological research, offering standardized approaches to simulate and predict the cascading effects of size-based interactions across ecosystems. Together, these examples underscore the centrality of allometric rules in understanding and managing trophic interactions, emphasizing that size is not just a descriptor but a foundational driver of ecosystem structure and function.

**3. VARIATION AND DEVIATION IN ALLOMETRIC RULES**

Grounded on simple assumptions such as biological optimization and steady-state conditions, allometric theory proposes a mathematical toolbox to scale up from organismal traits to ecosystem processes. However, in the real world, many mechanisms can trigger departure from theoretical assumptions and deviation from predictions. At the organism level, key sources of deviation from allometric rules are within- and between-species variation, which can be explained by several factors (Figure 4a) (see Section 3.1). At the population and community levels, sources of variation include departures from equilibrium conditions, which are triggered by biotic interactions, as well as temporal and evolutionary dynamics (Figure 4b) (see Sections 3.2 and 3.3).

**3.1. Between- and Within-Species Variation**

While MST offers an elegant, mechanistically grounded explanation based on body size and energy use, critics have raised several concerns about its assumptions, mathematical foundations, and applicability across different biological contexts (Coomes 2006, Dodds et al. 2001, Glazier 2005, Kozłowski & Konarzewski 2004). First, variation in scaling exponents across taxa and ecological conditions, as observed in empirical data (Figure 1), challenges the universality of 3/4 scaling (Glazier 2005, Kozłowski et al. 2003, Sieg et al. 2009, Summers & Ord 2021). For instance,
<figure><img src="image_8.png" /><figcaption>Figure 4</figcaption></figure>

Sources of deviation from allometric rules at the (a) organismal and (b) population levels. Dots are illustrative (not plotted from empirical data) of putative trait–size relationships after log₁₀ transformation. Black lines represent scaling allometries predicted by MST (b = 0.75 in panel a, and b = -0.75 in panel b). Gray lines in the panel a inset represent allometric fitting within species. Abbreviations: Com, community; ind, individuals; MST, metabolic scaling theory; Sp, species; t, time.

empirical studies on plant allometry often find exponents different from 3/4, such as 2/3 for growth or 1 for certain resource-use traits (Mori et al. 2010, Reich et al. 2006, Russo et al. 2007). These discrepancies suggest that metabolic scaling may not be as rigidly fixed as often interpreted to be suggested by MST and that its fundamental assumptions can be violated. Some organisms, such as insects with open circulatory systems or microorganisms, do not rely on vascular networks
for resource transport, and alternative models can better explain their observed scaling patterns (Bestová et al. 2021, DeLong et al. 2010, Gallet et al. 2017). The debates around MST center on the trade-off between its simplicity and the complexity of real-world biological systems. Proponents argue that its broad applicability and predictive power outweigh the deviations, while critics emphasize the need for more flexible models incorporating ecological, evolutionary, and physiological diversity (Glazier 2022). Despite these debates, MST remains a solid foundation for investigating sources of departure from theoretically expected relationships (Brummer et al. 2017; Enquist et al. 2007; Kolokotrones et al. 2010; Price & Enquist 2006, 2007; Savage et al. 2008), while researchers refine its assumptions and broaden its scope (Enquist et al. 2015, Mori et al. 2010, Savage et al. 2010, Stark et al. 2024).

MST builds on the principle that natural selection has optimized vascular networks to minimize the energy required for resource distribution (Charnov 1993, West et al. 1999a). This would shape 3/4 allometric relationships for several trait-size relationships. However, there is a growing number of studies reporting intraspecific variation in allometry in both plants and animals (Bokma 2004; Coomes 2006; Glazier 2005, 2022; Huey & Kingsolver 2011; Linquist et al. 2016; Muir & Thomas-Huebner 2015; C.R. White et al. 2007). For instance, the study of intraspecific variability in *Arabidopsis thaliana* revealed that many ecotypes diverge from the canonical 3/4 value (Vasseur et al. 2018). Moreover, it demonstrated that this variability resulted from natural selection operating directly on the scaling exponent, suggesting that allometry is a key driver of plant evolution and adaptation. However, while natural selection favors traits that provide adaptive advantages, potentially generating phenotypes that deviate from allometric rules, another source of deviation is evolutionary drift or random genetic changes that occur over time. Genetic drift can cause traits, including size or scaling exponent, to diverge from their optimal values by chance, especially in small populations or under conditions of low selective pressure (Lewontin 1990). Over time, these random changes can lead to the evolution of traits that deviate from allometric expectations without being necessarily adaptive (Vasseur et al. 2022). Moreover, ecological constraints and trade-offs can influence the relationship between size and fitness. For example, organisms may not always reach their optimal size due to resource limitations or trade-offs between growth and reproduction, leading to a deviation from the expected allometric pattern (Bonser & Aarssen 2009, Engen & Saether 2017, Weiner et al. 2009).

At both inter- and intraspecific levels, phenotypic plasticity—the ability of organisms to modify their phenotype in response to environmental cues—can lead to significant deviations from allo-metric expectations (Figure 4a). Phenotypic plasticity allows organisms to adjust their size, shape, or metabolic rate in response to changing environmental conditions, sometimes leading to trait values that do not fit within traditional allometric models (Bonser & Aarssen 2001, Coomes et al. 2011). A recent analysis of total leaf area and plant growth rate in *A. thaliana* revealed that allo-metric relationships are weakly plastic, despite significant genetic variation within species (Vasseur et al. 2023). However, reproductive rates are critical physiological processes that scale with strong context dependency (Bonser & Aarssen 2001, 2009; Santos-del-Blanco et al. 2013). Quantifying their allometry (and plasticity) is still a grand challenge, but it is essential for understanding popu-lation growth and viability, particularly in the context of environmental changes that may impact reproductive success and survival rates.

Importantly, between- and within-species variability raise key questions about their implications for energy use at the population level. The EER predicts that the scaling of population density varies in inverse proportion to the scaling of organism traits. But is it still the case when allometric relationships deviate from MST predictions? In a recent study, Hatton et al. (2019) explored the relationships between body mass and four traits at organismal and population levels—metabolism, abundance, growth, and mortality—using an extensive dataset of over
22,000 observations in different taxa, including protists, plants, ectotherms, and mammals. The study reveals that metabolism and abundance scale with body size according to reciprocal exponents near ±3/4 within taxonomic groups. However, across all eukaryotes, these relationships shift to near ±1 but are still in accordance with the EER. Together, these findings suggest that while MST provides a valuable first approximation for understanding scaling relationships, deviations from its predictions might help to understand the adaptive strategies that organisms adopt to navigate trade-offs, competition, and resource limitations. Such deviations may reveal compensatory mechanisms that sustain ecosystem stability across scales. Unraveling the factors driving these deviations will enhance our understanding of the constraints shaping metabolic scaling in natural systems.

## 3.2. Nonadditivity in Biotic Interactions

Trait-based ecology is built upon the assumption of perfect physiological adaptation of organisms to their environment, encapsulated in the trait-environment matching hypothesis. This principle has been extended to studies examining how individual traits scale to influence ecosystem processes, particularly at the community level. By averaging species' trait values at the community level [with the average weighted by species' relative abundance to provide the community-weighted mean (CWM)] (Grime 1998), physiological optimality is expected to emerge at this level as well (Muscarella & Uriarte 2016). In this framework, allometric scaling relies on the assumption that size-dependent patterns observed at the organismal level can be directly extended to community and ecosystem scales by using the CWM of size as a predictive variable. However, empirical observations increasingly reveal significant variation around the CWM in most communities (Butler et al. 2017). These deviations highlight the importance of nonadditive processes, primarily driven by species interactions, which disrupt the direct application of allometric scaling to ecosystem processes.

In plant communities, biotic interactions can involve complementary resource use, facilitation, and competition, each altering how size influences ecosystem functioning. For instance, differences in plant height allow species to partition light resources more effectively (Barillot et al. 2014), enhancing productivity beyond what would be predicted based solely on average size. Larger plants can also facilitate the growth of smaller neighbors by creating favorable microclimatic conditions, such as shade or wind protection (Badano et al. 2006), while competitive interactions often suppress smaller species, reshaping community structure and its effects on ecosystem functions (Chesson 2000). These dynamics highlight the fact that ecosystem functioning often depends on how size interacts with other traits, such as leaf area or rooting depth, to drive processes like carbon sequestration or nutrient cycling. The emergent properties of communities, shaped by size variability, further amplify these processes. For example, structural diversity linked to differences in size among species can enhance biomass production and improve resource-use efficiency (Chen et al. 2024, Smith & Sperry 2014), challenging the predictive power of models based purely on size averaging.

The environmental context is critical in mediating the effects of size-based interactions on ecosystem processes. Resource availability, disturbance regimes, and abiotic conditions can all modify the strength and direction of these interactions, creating a complex, context-dependent relationship between size and ecosystem functioning. To improve the reliability of scaling frameworks, it is essential to integrate the effects of nonadditivity into allometric models (Coomes et al. 2011). This involves considering the size diversity within communities, as size variability often drives critical processes such as resource partitioning and facilitation (Enquist et al. 2015). Additionally, linking size with traits related to niche complementarity, such as leaf functional traits or
belowground resource acquisition strategies, can provide a more nuanced understanding of how organism traits scale to ecosystem-level functions (Chacón-Labella et al. 2023) (see also Section 4). In summary, while size remains a fundamental trait, its influence is mediated by nonadditive interactions and environmental context. Understanding these complexities is crucial for accurately linking organism traits to broader ecosystem dynamics.

## 3.3. Ecological and Evolutionary Dynamics

Ecosystems are inherently dynamic, shaped by disturbances, environmental variability, and species interactions. In such fluctuating contexts, nonequilibrium is the law, and deviations from allometric predictions, such as energy equivalence, are frequently observed. For example, in nonclosed systems, the movement of individuals across space and time through dispersal may lead to a mismatch between the traits observed at the local scale and the expected optimal traits based on environmental conditions (Negrín Dastis et al. 2019). Introducing new individuals with different trait values can increase phenotypic variation, especially when species are distributed across heterogeneous environments (Guisan & Thuiller 2005). Historical contingencies also play a role in generating deviations from allometric relationships. Past environmental conditions, such as resource availability, habitat complexity, and disturbances, shape the traits that dominate a given population or community (Chevin & Bridle 2025). These historical factors can create patterns of trait distribution that do not align with the current optimal relationship predicted by allometric theory (Simini et al. 2010, van Kleunen et al. 2010, Zaoli et al. 2017). For instance, young or recently disturbed ecosystems often deviate more from the EER than mature, late succession communities (Kerkhoff & Enquist 2007). This suggests that the EER represents an energetic optimum reached only under stable conditions. During ecological succession, for example, the interplay between size, abundance, and energy use evolves as communities transition from early colonization stages to more stable states (Romera et al. 2024). These dynamics highlight the importance of reevaluating allometric principles across ecological contexts over time, particularly in the face of rapid environmental change. Accordingly, extensions of MST have been proposed to assess how departures from equilibrium conditions lead to predictable deviation and variation in scaling relationships (Stark et al. 2024).

In addition to ecological variability, evolutionary dynamics contribute to deviations from allometric predictions through eco-evolutionary feedback. This occurs when organisms influence ecosystem processes, which modify the selective pressures acting on the organisms themselves (Day et al. 2003). For instance, plant traits such as size and leaf nitrogen content can shape soil microbial communities, which then impact plant biomass in reciprocal interactions (Van Nuland et al. 2019). Moreover, feedback effects can be transgenerational, as shown in *A. thaliana*, where biotic soil conditioning by plants with one genotype has a positive effect on the biomass of the next generation of plants with the same genotype but a null or negative effect on plants with other genotypes (Przybylska et al. 2024). Such feedback illustrates the interconnectedness of ecological and evolutionary processes, with the potential to either amplify or stabilize trait-environment relationships (Hendry 2016). Understanding how metabolic rates vary under different ecological and evolutionary pressures requires integrating ecological and evolutionary perspectives. Experimental evolution, leveraging short-generation organisms like microbes, insects, or annual plants, offers a powerful tool to explore how size and related traits respond to changing conditions (Kawecki et al. 2012). By tracking trait dynamics across generations, researchers can address critical questions, such as whether populations reach demographic equilibrium, how metabolism and other traits scale under different selective pressures, and the extent to which eco-evolutionary feedbacks influence ecosystem processes.
Advancing allometric research is essential for improving models that predict ecosystem processes, particularly by incorporating eco-evolutionary feedback, demographic shifts, and deviations from equilibrium assumptions. By combining traditional allometric scaling with ecological and evolutionary dynamics, models can better capture the complex interplay between size, metabolism, and ecosystem processes, improving forecasts of biodiversity responses to climate change and anthropogenic pressures (Stark et al. 2024). This integrated approach not only refines our understanding of size-abundance relationships but also underscores the adaptive, dynamic interplay between organisms and their environments, driving ecological resilience and evolutionary innovation.

# 4. SIZE: JUST ONE TRAIT AMONG MANY MODULATING ECOSYSTEM PROCESSES?

## 4.1. Size: The First Dimension of Cross-Species Phenotypic Space

Among animals, plants, and even microorganisms, body size varies enormously, spanning several orders of magnitude within each group. But what is the relative influence of size on total phenotypic variation compared to other functional traits? Over the last decade, the emergence and analysis of large-scale species trait databases allowed this question to be addressed. Analysis revealed that across clades and taxa, size-related traits consistently dominate the first axis of phenotypic variation (Carmona et al. 2021, Díaz et al. 2016, Loiseau et al. 2020). In other words, the diversity of life is primarily structured by differences in size (e.g., mass, volume, height, and area, as size is itself multidimensional; see the sidebar titled Measuring Size in Plants: Not So Easy). Even within a single species, size variation can be substantial. Recent studies have shown that, similar to interspecific patterns, size represents the primary axis of variation within the intraspecific phenotypic space (Garnier et al. 2025; Lemoine et al. 2023; Vasseur et al. 2012, 2014; Vile et al. 2012). However, while size variation within a clade or species can be substantial, it is often discontinuous. This is one of the reasons why size is often log transformed in ecological studies. There are many very small organisms but only a limited number of very large ones. The causes behind this distribution are multifaceted, and there is no consensus yet on an explanation of this asymmetry

### MEASURING SIZE IN PLANTS: NOT SO EASY

In plants, determining individual size is critical for understanding ecological interactions, growth patterns, and resource allocation within ecosystems. However, accurately measuring plant size presents unique challenges due to various factors intrinsic to plant biology. One primary difficulty arises from the diverse forms and growth habits of plants, which can lead to variability in how size is defined and measured. For instance, while height and diameter are commonly used metrics, they may not fully capture the structural complexity of multi-stemmed species or those with sprawling growth forms, resulting in under- or overestimations of individual mass (Wang et al. 2024). Moreover, how (and when) should the biomass of a perennial plant that grows continuously be estimated? Clonally reproducing plants present unique challenges for accurately determining individual size and understanding their ecological roles. Clonal species propagate through vegetative means, leading to the formation of genetically identical individuals or ramets that can share resources and spatially overlap. This interconnectedness complicates size assessments, as the traditional metrics of height, diameter, or biomass may not reflect the true ecological individuality of a plant but rather the collective traits of a clone or clone group. Understanding these complexities is essential for accurately modeling population dynamics, assessing biodiversity, and predicting the impacts of environmental changes on plant communities.
(Dombroskie & Aarssen 2010). For a long time, ecologists have sought to mitigate this asymmetry through mathematical transformations. Today, research into functional rarity has led to a more focused examination of phenotypic extremes, exploring the reasons behind their persistence within populations and communities (Munoz et al. 2023), their implications for ecosystem functioning, and their relevance to biodiversity conservation and the management of both natural and cultivated ecosystems (Leitão et al. 2016, Mouillot et al. 2013, Violle et al. 2017). However, the principles of allometric theory have yet to be fully integrated into this approach. In other words, while we know that size is not evenly distributed across communities and that larger organisms disproportionately influence ecosystem processes (Norkko et al. 2013), the implications of the asymmetric size distribution on physiological processes—and vice versa—have not yet been explicitly and thoroughly incorporated into research on the role of biodiversity (and functional rarity, in particular) in regulating ecosystem functioning.

## 4.2. Beyond Size: What Do Other Traits Contribute?

Despite the dominant role of size in explaining major dimensions of biodiversity, many other axes contribute to the construction of phenotypic spaces, and their variance still accounts for a substantial proportion of phenotypic variability. This again reflects the fact that an organism must be described by multiple axes of ecological specialization, which are mathematically independent by design in principal component analyses (Messier et al. 2017). How many axes (or independent traits) are necessary to describe a phenotype? This is far from a trivial question, and researchers from different fields may provide different answers. To address broader ecological questions, Mouillot et al. (2021) analyzed the phenotypic space structure of numerous taxonomic groups and estimated the minimum number of dimensions required to describe them. This number ranges from three to five, depending on the group. While this is relatively low, it demonstrates that a single size dimension cannot capture an organism's full ecological profile.

In plants, Laughlin (2014) suggests characterizing seven dimensions (e.g., traits related to competition for light or dispersal) to gain the most comprehensive understanding of their functioning and life history, which requires measuring traits across all plant organs, including aboveground parts. This work builds on seminal studies of plant comparative strategies by Westoby (1998) and Grime (1974). Westoby's ecological strategy model (the leaf-height-seed scheme) proposes that size, reproductive, and leaf functional traits are independent. Grime also suggested that vegetative-phase traits are independent of those related to the reproductive phase (Grime 2006). Expanding these ideas through analyzing the TRY plant trait database (Kattge et al. 2020) has supported some of these assumptions. While the first principal component axis is associated with plant size (although plant size is itself a complex trait; see the sidebar titled Measuring Size in Plants: Not So Easy), the second axis reflects leaf functioning variations, particularly in carbon acquisition and utilization (known as the leaf economics spectrum) (Díaz et al. 2016). The traits associated with this second axis have historically been the focus of scaling up organism traits to ecosystem processes, as they provide a direct link to how plants acquire and use carbon. In a seminal paper, Garnier et al. (2004) demonstrated that specific leaf area (SLA), averaged at the ecosystem level (CWM), was strongly correlated with the productivity of herbaceous communities along a Mediterranean secondary succession. More recently, Enquist et al. (2015), through the trait driver theory, established the theoretical foundation for this relationship. By combining multiple major empirical approaches into a single modeling framework, this theory based on size distribution within communities enhances our ability to predict how ecosystems will respond to future environmental changes, including climate change, habitat fragmentation, and pollution.
A paradox seems to arise in how ecosystem functioning is understood through traits, at least in plants: Size is the trait that most strongly structures phenotypic diversity, yet traits seemingly independent of size are more closely associated with ecosystem functioning or, at least, are the most studied in this context (van der Plas et al. 2020). However, terrestrial ecosystem productivity appears to be controlled by both plant size and physiology (Saugier et al. 2001). Several factors could explain the apparently low power of size for explaining variation in ecosystem processes. The first is that, within an ecosystem or between ecosystems of the same biome, plant size variation may be reduced due to various ecological processes (e.g., abiotic filtering, competitive arms races). Consequently, size effects may not be observed in analyses due to the lack of size variation. The second reason stems from the EER hypothesis mentioned earlier: If this hypothesis holds, the process will be independent of size. The final reason is partly epistemological. In recent years, comparative plant ecology has focused mainly on measuring and analyzing organ traits rather than whole-plant traits (Díaz 2025). Yet, it is at the whole-plant scale that selection pressures act and where variations in carbon acquisition can be interpreted (Shipley et al. 2016). Increasingly, the focus is shifting toward examining whole-plant traits and allocation traits and interpreting organ traits relative to the size (or biomass) of each individual (Freschet et al. 2018, He et al. 2018, Laurans et al. 2024). Given the theoretical implications of organism size for metabolism, it is crucial to systematically integrate size when studying the regulatory effects of other traits on multiple ecosystem processes.

4.3. Are Traits of Secondary Dimensions Truly Independent of Size?

Could global trait variation analyses be misleading? To what extent can we take relationships identified at the global scale for granted and extrapolate them to the local scale? The case of size is particularly telling. At the global scale, plant size variation clearly distinguishes herbaceous species from woody species (the two density groups along the size axis in **Figure 5a**). But does this tell us anything about the physiological and macroevolutionary constraints that shape phenotypic variation in life forms? To what extent do these analyses provide insights into the (in)dependence between size and other traits?

The relationship between plant size and SLA—or its inverse, leaf mass per area (LMA)—which is a reliable proxy for leaf gas exchange, is a prime example for addressing these questions. On the one hand, global analyses (Díaz et al. 2016, Price et al. 2014) suggest an independence between these two traits, as they are associated with two orthogonal dimensions (Figure 5a). On the other hand, theoretical expectations in plant physiology (McDowell 2011, Ryan & Yoder 1997) emphasize the interdependence between plant size (at least height) and leaf physiology in the context of hydraulic constraints. At the local scale, within Mediterranean herbaceous communities, it was very recently shown that traits related to water use and gas exchange measured at the leaf scale, including LMA and leaf nitrogen content, were indeed dependent on plant size (height) in a comparative analysis of 60 species (Garnier et al. 2025) (Figure 5b). The context dependence of the presence or absence of such relationships cannot be overlooked if we aim to use traits more effectively and predictably in scaling up from organism functioning to ecosystem processes. It also seems necessary to more systematically incorporate theoretical expectations to understand the potentially indirect role of size in the variation of other traits and their consequences for ecosystem functioning.

In this regard, Sack et al. (2002) laid the theoretical groundwork for a potential relationship
between variation in SLA and plant biomass (with a negative allometric exponent of -1/4) dur-
ing ontogeny, specifically. It is particularly interesting to note that the analysis of intergenotypic
variability of these two traits in the model species *A. thaliana* precisely follows this expectation
a 2,214 species worldwide

<figure><img src="image_9.png" /></figure>

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

<figure><img src="image_11.png" /><figcaption>Figure 5</figcaption></figure>

Trait-size correlations depend on scale. (a) The global spectrum of plant form and function, illustrating two main phenotypic dimensions among six functional traits: one related to size (size axis, 49% of total phenotypic variation) and one related to resource-use traits at the leaf level, i.e., traits of the LES (LES axis, 25%). Principal component analysis was performed using the PhenoSpace application (Segrestin et al. 2021). (b) SMA regression between plant height and leaf nitrogen (N) content across 60 Mediterranean species (*P* < 0.001; *b* is the slope). Data are log₁₀ transformed. (c) SMA regression between aboveground plant dry mass and SLA across 120 genotypes of *Arabidopsis thaliana* (*P* < 0.001; *b* is the slope). Data are log₁₀ transformed. *R*² is the Pearson coefficient of correlation (*** indicates a *P* value <0.001). Panel *b* adapted from Garnier et al. (2025) (CC BY-NC 3.0); data in panel *c* from Vasseur et al. (2012). Abbreviations: LES, leaf economics spectrum; SLA, specific leaf area; SMA, standard major axis.

(Figure 5c). Intraspecific trait covariation analysis is both interesting and complementary to interspecific analysis (Anderegg et al. 2018, Messier et al. 2018, Siefert et al. 2015). It allows us to abstract from phylogenetic constraints that may either enforce or attenuate certain relationships. If an expectation is grounded in physiological principles, we would expect to observe it
primarily within a species (Calow 1987). The nonindependence of many physiological traits from size within a model species (and likely among many herbaceous annual species) is compelling evidence (Vasseur et al. 2012). Therefore, it is crucial to study the (non)dependence of traits on organism size in relation to theory in order to assess if, and how, size should be integrated into scaling up from organisms to ecosystems.

# 5. CONCLUSION: THE CENTRAL ROLE OF SIZE AND ALLOMETRIC PRINCIPLES IN ECOLOGY AND BEYOND

As a fundamental trait, size shapes organisms' physiology, life history, and ecological roles, serving as a cornerstone of ecological and evolutionary theory. The allometric relationships linking size to metabolism, abundance, and ecosystem processes provide a unifying framework for studying biodiversity and its functional implications. Yet, our understanding of these relationships remains incomplete, particularly when contextualized within dynamic and human-modified ecosystems. Addressing this knowledge gap is essential for advancing both ecological science and its practical applications.

Predicting ecosystem functioning is a central challenge in ecology, particularly in the face of rapid environmental change. The high explanatory power of allometric relationships across taxa and scales suggests that allometry could provide a strong predictive framework. In many respects, it does—especially for constraints such as the energy equivalence rule, which imposes fundamental limits on biological processes. However, the predictive power of allometric theories is not absolute and depends on both conceptual distinctions in how predictions are framed and the inherent variability of ecological systems.

From a theoretical perspective, metabolic scaling theory and other allometric frameworks establish strong expectations that can be empirically tested. Deviations from predicted relationships—such as departures from the expected 3/4 slope—serve as powerful diagnostic tools. In the context of global change, allometric relationships can help identify instability, past or ongoing disturbances, and shifts toward new functional equilibria (Stark et al. 2024). Where allometric relationships have been well characterized, they enable extrapolations: The metabolism or demographic trajectory of an unstudied species or ecosystem can often be inferred from size alone. At the population level, the scaling exponent of demographic changes can also reveal potential declines (Chisholm et al. 2014), particularly in response to shifting environmental conditions.

Like other macroecological frameworks, allometric models excel at capturing broad patterns and relative differences rather than providing precise estimates. This is partly because they focus on slopes rather than intercepts in trait-function relationships. Yet, for quantitative predictions (e.g., estimating biomass accumulation in grams per second), both slope and intercept must be known. This is where functional trait-based approaches offer a critical complement: By integrating organismal traits, they refine intercept estimates and enhance predictive accuracy (Enquist et al. 2007).

Combining functional trait knowledge with macroecological and allometric principles holds great promise for improving predictions of ecosystem processes. However, these approaches often assume a stable biological composition—requiring prior knowledge of the size distribution of organisms within an ecosystem. In reality, global change is profoundly altering biodiversity at local and global scales, necessitating predictions not only of ecosystem functioning but also of future biodiversity composition (Violle et al. 2014). A promising direction in this regard is the trait-driver theory (Enquist et al. 2015), which integrates trait-based ecology, adaptive dynamics, and allometric scaling (Webb et al. 2010). This framework provides a way to predict both how communities will respond to environmental change and how these shifts will influence ecosystem processes,
particularly carbon capture. While its large-scale implementation remains a challenge, this approach fosters a much-needed synthesis between ecological and evolutionary theories (Loreau et al. 2023, McGill et al. 2019), linking biodiversity dynamics to metabolic principles governing energy and material fluxes.

In conclusion, while ecological forecasting has not yet reached the predictive precision of climate science, allometric approaches—beyond their explanatory power—can generate robust predictions when integrated with complementary frameworks. Moreover, they provide valuable constraints for process-based models, such as dynamic global vegetation models (Cramer et al. 2001, Sitch et al. 2003, van Bodegom et al. 2014), by informing key parameters and incorporating physiological constraints that are often overlooked.

One critical application lies in the framework of nature's contributions to people (NCP). Size-mediated traits directly influence ecosystem services, from primary production and carbon sequestration to nutrient cycling and habitat provision (Guo et al. 2022). Understanding the scaling of size-related traits is thus critical to explain, and potentially predict, how ecosystems deliver services and how these contributions are affected by anthropogenic pressures. For instance, metabolic scaling principles can inform the management of fisheries (Andersen & Beyer 2006, Jennings et al. 2008), providing a framework for sustainable practices that account for the energetic demands of organisms at different scales. Research has further linked metabolic scaling to patterns of size and oxygen constraints in marine ecosystems (Cheung et al. 2013) and the structure of size pyramids in ecological communities (Trebilco et al. 2013), highlighting the broader ecological relevance of these scaling principles. Moreover, understanding the allometric principles governing physiological rates is essential for advancing our knowledge of biodiversity, species interactions, and ecosystem dynamics. For instance, Antunes et al. (2024) linked multi-trophic size distributions to NCP, illustrating how trophic interactions drive essential services such as pest control and nutrient cycling. Moreover, in human-modified ecosystems, particularly agroecosystems, incorporating size and allometric principles into management strategies offers opportunities to guide breeding and enhance sustainability (Westgeest et al. 2024). For instance, optimizing crop species based on size-related traits could improve water and nutrient use while minimizing environmental impacts (Singh et al. 2024). Similarly, promoting a range of organism sizes among pollinators, soil organisms, and pest predators can stabilize agroecosystem functions under changing conditions (de la Riva et al. 2023). Recognizing these size-related dynamics is crucial for transitioning toward sustainable agricultural practices.

A persistent challenge in ecology is the skewed academic focus toward larger species, leading to significant knowledge gaps for smaller organisms. This disparity is reflected in the correlation between organism size and the extent of scientific knowledge (Mouquet et al. 2024), leaving vast portions of biodiversity poorly studied (Tensen 2018). Small organisms, including microbes, invertebrates, and understudied plant species, are critical for ecosystem functioning but remain underrepresented in ecological research. Addressing this imbalance is key to resolving the size–trait–function conundrum and developing a more inclusive understanding of biodiversity. Moreover, conservation efforts often prioritize large, charismatic species, leaving smaller, less conspicuous organisms underprotected, despite their critical ecological roles (Macdonald et al. 2015). Expanding conservation priorities to encompass a broader range of sizes, while protecting habitats that sustain diverse size distributions, would better reflect the full spectrum of biodiversity and its functional importance. This holistic approach would also mitigate biases in species selection for protection, ensuring that ecosystems maintain their functional integrity across scales.

In addition, advancing our knowledge of the size–trait–function nexus requires interdisciplinary approaches that integrate ecological, evolutionary, and ecophysiology perspectives. The dynamic interplay between size, traits, and ecosystem processes—mediated by environmental and
evolutionary feedbacks—underscores the complexity of ecological systems. Investigating these re-
lationships in diverse contexts, from pristine ecosystems to anthropized landscapes, will provide
deeper insights into how size shapes ecological and evolutionary outcomes. In a rapidly changing
world, where ecosystems are increasingly subject to anthropogenic pressures, the study of size and
its scaling relationships is more important than ever. By bridging theoretical models with practical
applications, we can better understand and manage ecosystems, conserve biodiversity, and sustain
the critical services upon which humanity depends.

DISCLOSURE STATEMENT

The authors are not aware of any affiliations, memberships, funding, or financial holdings that
might be perceived as affecting the objectivity of this review.

ACKNOWLEDGMENTS

This work was supported by Agence Nationale de la Recherche (ANR; https://anr.fr) and Deutsche Forschungsgemeinschaft (DFG; https://www.dfg.de) funding as part of the AraBreed project (grants ANR-17-CE02-0018-01 and SCHE 1899/2-1) and the REFRAME project (grant ANR-24-CE02-7222). It was also supported by the European Research Council (ERC) Starting Grant project PHENOVIGOUR (grant ERC-StG-2020-949843), by the University of Arizona–CNRS PhD Joint Program in the context of the project Beyond the Mean: Plant Functional Diversity and the Maintenance of Rare Phenotypes in Plant Communities, and by the Foundation for Biodiversity Research (FRB) and Électricité de France (EDF) in the context of the Centre for the Synthesis and Analysis of Biodiversity (CESAB) working group FREE 2 (Towards a Change of Paradigm in Biological Conservation: From Species to Functions, From Individuals to Ecosystems, From Local to Global Scales).

LITERATURE CITED

Allen AP, Brown JH, Gillooly JF. 2002. Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. *Science* 297(5586):1545–48

Anderegg LDL, Berner LT, Badgley G, Sethi ML, Law BE, HilleRisLambers J. 2018. Within-species patterns challenge our understanding of the leaf economics spectrum. *Ecol. Lett.* 21(5):734–44

Andersen KH, Beyer JE. 2006. Asymptotic size determines species abundance in the marine size spectrum. *Am. Nat.* 168(1):54–61

Antunes AC, Berti E, Brose U, Hirt MR, Karger DN, et al. 2024. Linking biodiversity, ecosystem function, and Nature's contributions to people: a macroecological energy flux perspective. *Trends Ecol. Evol.* 39(5):427–34

Badano E, Jones C, Cavieres L, Wright J. 2006. Assessing impacts of ecosystem engineers on community organization: a general approach illustrated by effects of a high-Andean cushion plant. *Oikos* 115(2):369–85

Barillot R, Escobar-Gutiérrez AJ, Fournier C, Huynh P, Combes D. 2014. Assessing the effects of architectural variations on light partitioning within virtual wheat-pea mixtures. *Ann. Bot.* 114(4):725–37

Barnes AD, Jochum M, Lefcheck JS, Eisenhauer N, Scherber C, et al. 2018. Energy flux: the link between multitrophic biodiversity and ecosystem functioning. *Trends Ecol. Evol.* 33(3):186–97

Bestová H, Segrestin J, von Schwartzenberg K, Škaloud P, Lenormand T, Violle C. 2021. Biological scaling in green algae: the role of cell size and geometry. *Sci. Rep.* 11(1):14425

Blackburn TM, Brown VK, Doube BM, Greenwood JJD, Lawton JH, Stork NE. 1993. The relationship between abundance and body size in natural animal assemblages. *J. Anim. Ecol.* 62(3):519

Boit A, Martinez ND, Williams RJ, Gaedke U. 2012. Mechanistic theory and modelling of complex food-web dynamics in Lake Constance. *Ecol. Lett.* 15(6):594–602

Bokma F. 2004. Evidence against universal metabolic allometry. *Funct. Ecol.* 18(2):184–87
Bonser SP, Aarssen LW. 2001. Allometry and plasticity of meristem allocation throughout development in *Arabidopsis thaliana*. *J. Ecol.* 89(1):72–79

Bonser SP, Aarssen LW. 2009. Interpreting reproductive allometry: Individual strategies of allocation explain size-dependent reproduction in plant populations. *Perspect. Plant Ecol. Evol. Syst.* 11:31–40

Brosse S, Charpin N, Su G, Toussaint A, Herrera-R GA, et al. 2021. FISHMORPH: a global database on morphological traits of freshwater fishes. *Glob. Ecol. Biogeogr.* 30(12):2330–36

Brown JH, Gillooly JF, Allen AP, Savage VM, West GB. 2004. Toward a metabolic theory of ecology. *Ecology* 85(7):1771–89

Brummer AB, Savage VM, Enquist BJ. 2017. A general model for metabolic scaling in self-similar asymmetric networks. *PLOS Comput. Biol.* 13(3):e1005394

Butler EE, Datta A, Flores-Moreno H, Chen M, Wythers KR, et al. 2017. Mapping local and global variability in plant trait distributions. *PNAS* 114(51):E10937–46

Calder WA. 1996. *Size, Function, and Life History*. Courier Corp.

Calow P. 1987. Towards a definition of functional ecology. *Funct. Ecol.* 1(1):57–61

Carmona CP, Tamme R, Pärtel M, de Bello F, Brosse S, et al. 2021. Erosion of global functional diversity across the tree of life. *Sci. Adv.* 7(13):eabf2675

Chacón-Labela J, Hinojo-Hinojo C, Bohner T, Castorena M, Violle C, et al. 2023. How to improve scaling from traits to ecosystem processes. *Trends Ecol. Evol.* 38(3):228–37

Charnov EL. 1993. *Life History Invariants: Some Explorations of Symmetry in Evolutionary Ecology*. Oxford Univ. Press

Chen B, Liu K, Wang C, Guo J, Lu J, et al. 2024. Tree allometry responses to competition and complementarity in mixed-species plantations of *Betula alnoides*. *For. Ecosyst.* 11:100207

Chesson P. 2000. Mechanisms of maintenance of species diversity. *Annu. Rev. Ecol. Syst.* 31:343–66

Cheung WWL, Sarmiento JL, Dunne J, Frölicher TL, Lam VWY, et al. 2013. Shrinking of fishes exacerbates impacts of global ocean changes on marine ecosystems. *Nat. Clim. Chang.* 3(3):254–58

Chevin L-M, Bridle J. 2025. Impacts of limits to adaptation on population and community persistence in a changing environment. *Philos. Trans. R. Soc. B* 380:20230322

Chisholm RA, Condit R, Rahman KA, Baker PJ, Bunyavejchewin S, et al. 2014. Temporal variability of forest communities: empirical estimates of population change in 4000 tree species. *Ecol. Lett.* 17(7):855–65

Coomes DA. 2006. Challenges to the generality of WBE theory. *Trends Ecol. Evol.* 21(11):593–96

Coomes DA, Lines ER, Allen RB. 2011. Moving on from Metabolic Scaling Theory: hierarchical models of tree growth and asymmetric competition for light. *J. Ecol.* 99(3):748–56

Costa-Pereira R, Araújo MS, Olivier RDS, Souza FL, Rudolf VHW. 2018. Prey limitation drives variation in allometric scaling of predator-prey interactions. *Am. Nat.* 192(4):E139–49

Cramer W, Bondeau A, Woodward FI, Prentice IC, Betts RA, et al. 2001. Global response of terrestrial ecosystem structure and function to CO₂ and climate change: results from six dynamic global vegetation models. *Glob. Chang. Biol.* 7(4):357–73

Damuth J. 1981. Population density and body size in mammals. *Nature* 290(5808):699–700

Day RL, Laland KN, Odling-Smee FJ. 2003. Rethinking adaptation: the niche-construction perspective. *Perspect. Biol. Med.* 46(1):80–95

de la Riva EG, Ulrich W, Batáry P, Baudry J, Beaumelle L, et al. 2023. From functional diversity to human well-being: a conceptual framework for agroecosystem sustainability. *Agric. Syst.* 208(103659):103659

DeLong JP, Okie JG, Moses ME, Sibly RM, Brown JH. 2010. Shifts in metabolic scaling, production, and efficiency across major evolutionary transitions of life. *PNAS* 107(29):12941–45

Díaz S. 2025. Plant functional traits and the entangled phenotype. *Funct. Ecol.* 39(5):1144–59

Díaz S, Kattge J, Cornelissen JHC, Wright IJ, Lavorel S, et al. 2016. The global spectrum of plant form and function. *Nature* 529(7585):167–71

Díaz S, Kattge J, Cornelissen JHC, Wright IJ, Lavorel S, et al. 2022. The global spectrum of plant form and function: enhanced species-level trait dataset. *Sci. Data* 9:755

Dodds PS, Rothman DH, Weitz JS. 2001. Re-examination of the “3/4-law” of metabolism. *J. Theor. Biol.* 209(1):9–27

Dombroskie SL, Aarssen LW. 2010. Within-genus size distributions in angiosperms: Small is better. *Perspect. Plant Ecol. Evol. Syst.* 12(4):283–93
Duarte CM, Sand-Jensen K, Nielsen SL, Enriquez S, Agustí S. 1995. Comparative functional plant ecology: rationale and potentials. *Trends Ecol. Evol.* 10(10):418–21

Durand-Bessart C, Cordeiro NJ, Chapman CA, Abernethy K, Forget P-M, et al. 2023. Trait matching and sampling effort shape the structure of the frugivory network in Afrotropical forests. *New Phytol.* 237(4):1446–62

Ehleringer JR, Field CB. 1993. *Scaling Physiological Processes: Leaf to Globe*. Academic

Engen S, Saether B-E. 2017. *r*- and *K*-selection in fluctuating populations is determined by the evolutionary trade-off between two fitness measures: growth rate and lifetime reproductive success. *Evolution* 71(1):167–73

Enquist BJ. 2002. Universal scaling in tree and vascular plant allometry: toward a general quantitative theory linking plant form and function from cells to ecosystems. *Tree Physiol.* 22(15–16):1045–64

Enquist BJ, Bentley LP. 2012. Land plants: new theoretical directions and empirical prospects. In *Metabolic Ecology*, ed. RM Sibly, JH Brown, A Kodric-Brown, pp. 164–87. John Wiley & Sons

Enquist BJ, Brown JH, West GB. 1998. Allometric scaling of plant energetics and population density. *Nature* 395(6698):163–65

Enquist BJ, Economo EP, Huxman TE, Allen AP, Ignace DD, Gillooly JF. 2003. Scaling metabolism from organisms to ecosystems. *Nature* 423(6940):639–42

Enquist BJ, Kerkhoff AJ, Stark SC, Swenson NG, McCarthy MC, Price CA. 2007. A general integrative model for scaling plant growth, carbon flux, and functional trait spectra. *Nature* 449(7159):218–22

Enquist BJ, Michaletz ST. 2016. Toward a general scaling theory for linking traits, stoichiometry, and body size to ecosystem function. In *A Biogeoscience Approach to Ecosystems*, ed. EA Johnson, YE Martin, pp. 9–46. Cambridge Univ. Press

Enquist BJ, Norberg J, Bonser SP, Violle C, Webb CT, et al. 2015. Scaling from traits to ecosystems: developing a general trait driver theory via integrating trait-based and metabolic scaling theories. In *Advances in Ecological Research*, Vol. 52, ed. S Pawar, G Woodward, AI Dell, pp. 249–318. Academic

Enquist BJ, West GB, Charnov EL, Brown JH. 1999. Allometric scaling of production and life-history variation in vascular plants. *Nature* 401:907–11

Ernest SKM, Enquist BJ, Brown JH, Charnov EL, Gillooly JF, et al. 2003. Thermodynamic and metabolic effects on the scaling of production and population energy use. *Ecol. Lett.* 6(11):990–95

Freschet GT, Violle C, Bourget MY, Scherer-Lorenzen M, Fort F. 2018. Allocation, morphology, physiology, architecture: the multiple facets of plant above- and below-ground responses to resource stress. *New Phytol.* 219(4):1338–52

Frimpong EA, Angermeier PL. 2009. Fish traits: a database of ecological and life-history traits of freshwater fishes of the United States. *Fisheries* 34(10):487–95

Funk JL, Larson JE, Ames GM, Butterfield BJ, Cavender-Bares J, et al. 2017. Revisiting the Holy Grail: using plant functional traits to understand ecological processes. *Biol. Rev. Camb. Philos. Soc.* 92(2):1156–73

Fussmann KE, Schwarzmüller F, Brose U, Jousset A, Rall BC. 2014. Ecological stability in response to warming. *Nat. Clim. Chang.* 4(3):206–10

Gallet R, Violle C, Fromin N, Jabbour-Zahab R, Enquist BJ, Lenormand T. 2017. The evolution of bacterial cell size: the internal diffusion-constraint hypothesis. *ISME J.* 11(7):1559–68

Garnier E, Cortez J, Billès G, Navas M-L, Roumet C, et al. 2004. Plant functional markers capture ecosystem properties during secondary succession. *Ecology* 85(9):2630–37

Garnier E, Vile D, Debain S, Bottin L, Laurent G, Roumet C. 2025. Photosynthesis, water-use and nitrogen relate to both plant height and leaf structure in 60 species from the Mediterranean. *Funct. Ecol.* 39(2):567–82

Gaudet CL, Keddy PA. 1988. A comparative approach to predicting competitive ability from plant traits. *Nature* 334:242–43

Gaudio N, Violle C, Gendre X, Fort F, Mahmoud R, et al. 2021. Interspecific interactions regulate plant reproductive allometry in cereal–legume intercropping systems. *J. Appl. Ecol.* 58(11):2579–89

Gauzens B, Brose U, Delmas E, Berti E. 2023. *ATNr*: Allometric Trophic Network models in R. *Methods Ecol. Evol.* 14(11):2766–73

Glazier DS. 2005. Beyond the “3/4-power law”: variation in the intra- and interspecific scaling of metabolic rate in animals. *Biol. Rev.* 80:611–62
Glazier DS. 2022. Variable metabolic scaling breaks the law: from “Newtonian” to “Darwinian” approaches. *Proc. R. Soc. B* 2891985:20221605

Grime JP. 1974. Vegetation classification by reference to strategies. *Nature* 250:26-31

Grime JP. 1998. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. *J. Ecol.* 86(6):902-10

Grime JP. 2006. Trait convergence and trait divergence in herbaceous plant communities: mechanisms and consequences. *J. Veg. Sci.* 17(2):255-60

Guisan A, Thuiller W. 2005. Predicting species distribution: offering more than simple habitat models. *Ecol. Lett.* 8(9):993-1009

Guo C, Yan E-R, Cornelissen JHC. 2022. Size matters for linking traits to ecosystem multifunctionality. *Trends Ecol. Evol.* 37(9):803-13

Hatton IA, Dobson AP, Storch D, Galbraith ED, Loreau M. 2019. Linking scaling laws across eukaryotes. *PNAS* 116(43):21616-22

Hatton IA, McCann KS, Fryxell JM, Davies TJ, Smerlak M, et al. 2015. The predator-prey power law: biomass scaling across terrestrial and aquatic biomes. *Science* 349(6252):aac6284

He N, Liu C, Piao S, Sack L, Xu L, et al. 2018. Ecosystem traits linking functional traits to macroecology. *Trends Ecol. Evol.* 34(3):200-10

Hedden P. 2003. The genes of the Green Revolution. *Trends Genet.* 19(1):5-9

Hendry AP. 2016. *Eco-Evolutionary Dynamics*. Princeton Univ. Press.

Herberstein M, McLean D, Lowe E, Wolff JO, Khan MK, et al. 2022. Animal Traits – a curated animal trait database for body mass, metabolic rate and brain size. *Sci. Data* 9:265

Heusner A. 1982. Energy metabolism and body size. II. Dimensional analysis and energetic non-similarity. *Respir. Physiol.* 48(1):13-25

Huey RB, Kingsolver JG. 2011. Variation in universal temperature dependence of biological rates. *PNAS* 108(26):10377-78

Huston M, Smith T. 1987. Plant succession: life history and competition. *Am. Nat.* 130(2):168-98

Huxley JS. 1932. *Problems of Relative Growth*. Methuen & Co.

Jennings S, Mélin F, Blanchard JL, Forster RM, Dulvy NK, Wilson RW. 2008. Global-scale predictions of community and ecosystem properties from simple ecological theory. *Proc. R. Soc. B* 275(1641):1375-83

Kattge J, Bönisch G, Díaz S, Lavorel S. 2020. TRY plant trait database – enhanced coverage and open access. *Glob. Chang. Biol.* 26(1):119-88

Kattge J, Díaz S, Lavorel S, Prentice IC, Leadley P, et al. 2011. TRY – a global database of plant traits. *Glob. Chang. Biol.* 17(9):2905-35

Kawecki TJ, Lenski RE, Ebert D, Hollis B, Olivieri I, Whitlock MC. 2012. Experimental evolution. *Trends Ecol. Evol.* 27(10):547-60

Kerkhoff AJ, Enquist BJ. 2006. Ecosystem allometry: the scaling of nutrient stocks and primary productivity across plant communities. *Ecol. Lett.* 9(4):419-27

Kerkhoff AJ, Enquist BJ. 2007. The implications of scaling approaches for understanding resilience and reorganization in ecosystems. *Bioscience* 57(6):489-99

Kerkhoff AJ, Enquist BJ. 2009. Multiplicative by nature: why logarithmic transformation is necessary in allometry. *J. Theor. Biol.* 257(3):519-21

Kleiber M. 1947. Body size and metabolic rate. *Physiol. Rev.* 27(4):511-41

Kolokotrones T, Van Savage Deeds EJ, Fontana W. 2010. Curvature in metabolic scaling. *Nature* 464(7289):753-56

Kozłowski J, Konarzewski M. 2004. Is West, Brown and Enquist's model of allometric scaling mathematically correct and biologically relevant? *Funct. Ecol.* 18(2):283-89

Kozłowski J, Konarzewski M, Gawelczyk AT. 2003. Intraspecific body size optimization produces interspecific allometries. In *Macroecology: Concepts and Consequences*, ed. TM Blackburn, KJ Gaston, pp. 299-320. Blackwell

Laughlin DC. 2014. The intrinsic dimensionality of plant traits and its relevance to community assembly. *J. Ecol.* 102(1):186-93

Laurans M, Munoz F, Charles-Dominique T, Heuret P, Fortunel C, et al. 2024. Why incorporate plant architecture into trait-based ecology? *Trends Ecol. Evol.* 39(6):524-36
Lavorel S, Garnier E. 2002. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. *Funct. Ecol.* 16(5):545–56

Lavorel S, Grigulis K. 2012. How fundamental plant functional trait relationships scale-up to trade-offs and synergies in ecosystem services: Plant traits scale to ecosystem services trade-offs. *J. Ecol.* 100(1):128–40

Leitão RP, Zuanon J, Villéger S, Williams SE, Baraloto C, et al. 2016. Rare species contribute disproportion-
ately to the functional structure of species assemblages. *Proc. R. Soc. B* 283(1828):20160084

Lemoine T, Violle C, Montazeaud G, Isaac ME, Rocher A, et al. 2023. Plant trait relationships are main-
tained within a major crop species: lack of artificial selection signal and potential for improved agronomic
performance. *New Phytol.* 240(6):2227–38

Lewontin RC. 1990. *Genetic Basis of Evolutionary Change*. Columbia Univ. Press

Linquist S, Gregory TR, Elliott TA. 2016. Yes! There are resilient generalizations (or “laws”) in ecology. *Q. Rev. Biol.* 91(2):119–31

Loiseau N, Mouquet N, Casajus N, Grenié M, Guéguen M, et al. 2020. Global distribution and conservation status of ecologically rare mammal and bird species. *Nat. Commun.* 11(1):5071

Loreau M, Jarne P, Martiny JBH. 2023. Opportunities to advance the synthesis of ecology and evolution. *Ecol. Lett.* 26(Suppl. 1):S11–15

Macdonald EA, Burnham D, Hinks AE, Dickman AJ, Malhi Y, Macdonald DW. 2015. Conservation inequality and the charismatic cat: Felis felicis. *Glob. Ecol. Conserv.* 3:851–66

Mahaut L, Violle C, Shihan A, Pélissier R, Morel J-B, et al. 2023. Beyond trait distances: Functional distinctiveness captures the outcome of plant competition. *Funct. Ecol.* 37(9):2399–412

McDowell NG. 2011. Mechanisms linking drought, hydraulics, carbon metabolism, and vegetation mortality. *Plant Physiol.* 155:1051–59

McGill BJ. 2019. The what, how and why of doing macroecology. *Glob. Ecol. Biogeogr.* 28(1):6–17

McGill BJ, Chase JM, Hortal J, Overcast I, Rominger AJ, et al. 2019. Unifying macroecology and macroevolution to answer fundamental questions about biodiversity. *Glob. Ecol. Biogeogr.* 28(12):1925–36

McMahon T. 1973. Size and shape in biology. *Science* 179(4079):1201–4

Messier J, Lehowicz MJ, McGill BJ, Violle C, Enquist BJ. 2017. Interspecific integration of trait dimensions at local scales: the plant phenotype as an integrated network. *J. Ecol.* 105(6):1775–90

Messier J, Violle C, Enquist BJ, Lehowicz MJ, McGill BJ. 2018. Similarities and differences in intrapopulation trait correlations of co-occurring tree species: consistent water-use relationships amid widely different correlation patterns. *Am. J. Bot.* 105(9):1477–90

Mori S, Yamaji K, Ishida A, Prokushkin SG, Masyagina OV, et al. 2010. Mixed-power scaling of whole-plant respiration from seedlings to giant trees. *PNAS* 107(4):1447–51

Mouillot D, Bellwood DR, Baraloto C, Chave J, Galzin R, et al. 2013. Rare species support vulnerable functions in high-diversity ecosystems. *PLOS Biol.* 11(5):e1001569

Mouillot D, Loiseau N, Grenié M, Algar AC, Allegra M, et al. 2021. The dimensionality and structure of species trait spaces. *Ecol. Lett.* 24(9):1988–2009

Mouquet N, Langlois J, Casajus N, Auber A, Flandrin U, et al. 2024. Low human interest for the most at-risk reef fishes worldwide. *Sci. Adv.* 10(29):eadj9510

Muir CD, Thomas-Huebner M. 2015. Constraint around quarter-power allometric scaling in wild tomatoes (*Solanum sect. Lycopersicon*; Solanaceae). *Am. Nat.* 186(3):421–33

Munoz F, Klausmeier CA, Gaüzère P, Kandlikar G, Litchman E, et al. 2023. The ecological causes of functional distinctiveness in communities. *Ecol. Lett.* 26(8):1452–65

Muscarella R, Uriarte M. 2016. Do community-weighted mean functional traits reflect optimal strategies? *Proc. R. Soc. B* 283(1827):20152434

Navas ML, Violle C. 2009. Plant traits related to competition: How do they shape the functional diversity of communities? *Commun. Ecol.* 10:131–37

Negrín Dastis JO, Milne R, Guichard F, Derry AM. 2019. Phenotype–environment mismatch in metapopulations—implications for the maintenance of maladaptation at the regional scale. *Evol. Appl.* 12(7):1475–86

Niklas KJ. 1994. *Plant Allometry: The Scaling of Form and Process*. Univ. Chicago Press

Niklas KJ, Enquist BJ. 2001. Invariant scaling relationships for interspecific plant biomass production rates and body size. *PNAS* 98(5):2922–27
Norkko A, Villnäs A, Norkko J, Valanko S, Pilditch C. 2013. Size matters: implications of the loss of large individuals for ecosystem function. *Sci. Rep.* 3:2646

Perkins DM, Hatton IA, Gauzens B, Barnes AD, Ott D, et al. 2022. Consistent predator-prey biomass scaling in complex food webs. *Nat. Commun.* 13(1):4990

Perkins DM, Perna A, Adrian R, Cermeño P, Gaedke U, et al. 2019. Energetic equivalence underpins the size structure of tree and phytoplankton communities. *Nat. Commun.* 10(1):255

Peters RH. 1986. *Cambridge Studies in Ecology: The Ecological Implications of Body Size*. Cambridge Univ. Press

Price CA, Enquist BJ. 2006. Scaling of mass and morphology in plants with minimal branching: an extension of the WBE model. *Funct. Ecol.* 20(1):11–20

Price CA, Enquist BJ. 2007. Scaling mass and morphology in leaves: an extension of the WBE model. *Ecology* 88(5):1132–41

Price CA, Wright IJ, Ackerly DD, Niinemets Ü, Reich PB, Veneklaas EJ. 2014. Are leaf functional traits 'invariant' with plant size and what is 'invariance' anyway? *Funct. Ecol.* 28(6):1330–43

Przybylska MS, Violle C, Vile D, Scheepens JF, Munoz F, et al. 2024. Can plants build their niche through modulation of soil microbial activities linked with nitrogen cycling? A test with *Arabidopsis thaliana*. *New Phytol.* 243(2):620–35

Reich PB. 2012. Key canopy traits drive forest productivity. *Proc. R. Soc. B* 279(1736):2128–34

Reich PB, Tjoelker MG, Machado J-L, Oleksyn J. 2006. Universal scaling of respiratory metabolism, size and nitrogen in plants. *Nature* 439(7075):457–61

Romera PJ, Wallace KJ, McCarthy JK, Rai B, Mitchell G, et al. 2024. Ecological succession shapes size-density scaling relationships of trees and soil invertebrates. *Funct. Ecol.* 38(10):2156–68

Russo SE, Wiser SK, Coomes DA. 2007. Growth-size scaling relationships of woody plant species differ from predictions of the Metabolic Ecology Model. *Ecol. Lett.* 10(10):889–901

Ryan MG, Yoder BJ. 1997. Hydraulic limits to tree height and tree growth. *BioScience* 47(4):235–42

Sack L, Marañón T, Grubb PJ. 2002. Global allocation rules for patterns of biomass partitioning. *Science* 296(5575):1923

Santos-del-Blanco L, Bonser SP, Valladares F, Chambel MR, Climent J. 2013. Plasticity in reproduction and growth among 52 range-wide populations of a Mediterranean conifer: adaptive responses to environmental stress. *J. Evol. Biol.* 26(9):1912–24

Saugier B, Roy J, Mooney HA. 2001. Estimations of global terrestrial productivity: converging toward a single number? In *Terrestrial Global Productivity*, ed. J Roy, B Saugier, HA Mooney, pp. 543–57. Academic

Savage VM, Bentley LP, Enquist BJ, Sperry JS, Smith DD, et al. 2010. Hydraulic trade-offs and space filling enable better predictions of vascular structure and function in plants. *PNAS* 107(52):22722–27

Savage VM, Deeds EJ, Fontana W. 2008. Sizing up allometric scaling theory. *PLOS Comput. Biol.* 4(9):e1000171

Segrestin J, Sartori K, Navas M, Kattge J, Díaz S, Garnier E. 2021. PhenoSpace: a Shiny application to visualize trait data in the phenotypic space of the global spectrum of plant form and function. *Ecol. Evol.* 11(4):1526–34

Shipley B, De Bello F, Cornelissen JHC, Laliberté E, Laughlin DC, Reich PB. 2016. Reinforcing loose foundation stones in trait-based plant ecology. *Oecologia* 180(4):923–31

Shoval O, Sheftel H, Shinar G, Hart Y, Ramote O, et al. 2012. Evolutionary trade-offs, Pareto optimality, and the geometry of phenotype space. *Science* 336(6085):1157–60

Siefert A, Violle C, Chalmandrier L, Albert CH, Taudiere A, et al. 2015. A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. *Ecol. Lett.* 18(12):1406–19

Sieg AE, O'Connor MP, McNair JN, Grant BW, Agosta SJ, Dunham AE. 2009. Mammalian metabolic allometry: Do intraspecific variation, phylogeny, and regression models matter? *Am. Nat.* 174(5):720–33

Simini F, Anfodillo T, Carrer M, Banavar JR, Maritan A. 2010. Self-similarity and scaling in forest communities. *PNAS* 107(17):7658–62

Singh H, Kumar N, Kumar A. 2024. Enhancing resource use efficiency in crops through plant functional traits. In *Plant Functional Traits for Improving Productivity*, ed. N Kumar, H Singh, pp. 97–117. Springer

Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, et al. 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. *Glob. Chang. Biol.* 9(2):161–85
Smith DD, Sperry JS. 2014. Coordination between water transport capacity, biomass growth, metabolic scaling and species stature in co-occurring shrub and tree species: coordinating water transport and growth. *Plant Cell Environ.* 37(12):2679–90

Stark KA, Clegg T, Bernhardt JR, Grainger TN, Kempes CP, et al. 2024. Towards a more dynamic metabolic theory of ecology to predict climate change effects on biological systems. *Am. Nat.* 205(3):285–305

Summers TC, Ord TJ. 2021. The stabilising impact of natural selection on the allometry of sexual ornaments: Fish that escape locomotor constraints exhibit extravagant ornamentation. *Funct. Ecol.* 36(2):500–11

Tensen L. 2018. Biases in wildlife and conservation research, using felids and canids as a case study. *Glob. Ecol. Conserv.* 15(e00423):e00423

Tobias JA, Sheard C, Pigot AL, Devenish AJM, Yang J, et al. 2022. AVONET: morphological, ecological and geographical data for all birds. *Ecol. Lett.* 25(3):581–97

Trebilco R, Baum JK, Salomon AK, Dulvy NK. 2013. Ecosystem ecology: size-based constraints on the pyramids of life. *Trends Ecol. Evol.* 28(7):423–31

Van Bodegom PM, Douma JC, Verheijen LM. 2014. A fully traits-based approach to modeling global vegetation distribution. *PNAS* 111(38):13733–38

Van der Plas F, Schröder-Georgi T, Weigelt A, Barry K, Meyer S, et al. 2020. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. *Nat. Ecol. Evol.* 4(12):1602–11

Van Kleunen M, Weber E, Fischer M. 2010. A meta-analysis of trait differences between invasive and non-invasive plant species. *Ecol. Lett.* 13(2):235–45

Van Nuland ME, Ware IM, Bailey JK, Schweitzer JA. 2019. Ecosystem feedbacks contribute to geographic variation in plant-soil eco-evolutionary dynamics across a fertility gradient. *Funct. Ecol.* 33(1):95–106

Vasseur F, Bontpart T, Dauzat M, Granier C, Vile D. 2014. Multivariate genetic analysis of plant responses to water deficit and high temperature revealed contrasting adaptive strategies. *J. Exp. Bot.* 65(22):6457–69

Vasseur F, Exposito-Alonso M, Ayala-Garay OJ, Wang G, Enquist BJ, et al. 2018. Adaptive diversification of growth allometry in the plant *Arabidopsis thaliana*. *PNAS* 115(13):3416–21

Vasseur F, Violle C, Enquist BJ, Granier C, Vile D. 2012. A common genetic basis to the origin of the leaf economics spectrum and metabolic scaling allometry. *Ecol. Lett.* 15(10):1149–57

Vasseur F, Violle C, Enquist BJ, Vile D. 2023. Genetic variability and plasticity of plant allometry. *Funct. Ecol.* 37(4):1095–105

Vasseur F, Westgeest AJ, Vile D, Violle C. 2022. Solving the grand challenge of phenotypic integration: allometry across scales. *Genetica.* 150(3–4):161–69

Vile D, Pervent M, Belluau M, Vasseur F, Bresson J, et al. 2012. *Arabidopsis* growth under prolonged high temperature and water deficit: independent or interactive effects? *Plant Cell Environ.* 35(4):702–18

Violle C, Garnier E, Lecoeur J, Roumet C, Podeur C, et al. 2009. Competition, traits and resource depletion in plant communities. *Oecologia* 160(4):747–55

Violle C, Reich PB, Pacala SW, Enquist BJ, Kattge J. 2014. The emergence and promise of functional biogeography. *PNAS* 111:13690–96

Violle C, Thuiller W, Mouquet N, Munoz F, Kraft NJB, et al. 2017. Functional rarity: the ecology of outliers. *Trends Ecol. Evol.* 32(5):356–67

von Bertalanffy L. 1951. Metabolic types and growth types. *Am. Nat.* 85(821):111–17

Wang Y, Kershaw JA, Ducey MJ, Sun Y, McCarter JB. 2024. What diameter? What height? Influence of measures of average tree size on area-based allometric volume relationships. *For: Ecosyst.* 11:100171

Webb CT, Hoeting JA, Ames GM, Pyne MI, Poff NL. 2010. A structured and dynamic framework to advance traits-based theory and prediction in ecology. *Ecol. Lett.* 13(3):267–83

Weiner J. 1990. Asymmetric competition in plant populations. *Trends Ecol. Evol.* 5(11):360–64

Weiner J, Campbell LG, Pino J, Echarte L. 2009. The allometry of reproduction within plant populations. *J. Ecol.* 97(6):1220–33

West GB, Brown JH. 2005. The origin of allometric scaling laws in biology from genomes to ecosystems: towards a quantitative unifying theory of biological structure and organization. *J. Exp. Biol.* 208(Part 9):1575–92

West GB, Brown JH, Enquist BJ. 1999a. The fourth dimension of life: fractal geometry and allometric scaling of organisms. *Science* 284(5420):1677–79
West GB, Brown JH, Enquist BJ. 1999b. A general model for the structure and allometry of plant vascular systems. *Nature* 400(6745):664–67

West GB, Brown JH, Enquist BJ. 2001. A general model for ontogenetic growth. *Nature* 413(6856):628–31

Westgeest AJ, Vasseur F, Enquist BJ, Milla R, Gómez-Fernández A, et al. 2024. An allometry perspective on crops. *New Phytol.* 244(4):1223–37

Westoby M. 1998. A leaf-height-seed (LHS) plant ecology strategy scheme. *Plant Soil* 199(2):213–27

White CR, Alton LA, Bywater CL, Lombardi EJ, Marshall DJ. 2022. Metabolic scaling is the product of life-history optimization. *Science* 377(6608):834–39

White CR, Cassey P, Blackburn TM. 2007. Allometric exponents do not support a universal metabolic allometry. *Ecology* 88(2):315–23

White EP, Ernest SKM, Kerkhoff AJ, Enquist BJ. 2007. Relationships between body size and abundance in ecology. *Trends Ecol. Evol.* 22(6):323–30

Zaoli S, Giometto A, Maritan A, Rinaldo A. 2017. Covariations in ecological scaling laws fostered by community dynamics. *PNAS* 114(40):10672–77