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Linking ecomechanical models and functional
traits to understand phenotypic diversity
Timothy E. Higham,
*Lara A. Ferry,
Duncan J. Irschick,
Philip S.L. Anderson,
Philip J. Bergmann,
Heather A. Jamniczky,
Leandro R. Monteiro,
Stacy C. Farina,
Kara L. Feilich,
L. Patricia Hernandez,
Michele A. Johnson,
Sandy M. Kawano,
Chris J. Law,
Sarah J. Longo,
Christopher H. Martin,
Patrick T. Martone,
Sharlene E. Santana,
and Karl J. Niklas
Physical principles and laws determine the set of possible organismal pheno-
types. Constraints arising from development, the environment, and evolutionary
history then yield workable, integrated phenotypes. We propose a theoretical
and practical framework that considers the role of changing environments. This
‘ecomechanical approach’integrates functional organismal traits with the eco-
logical variables. This approach informs our ability to predict species shifts in
survival and distribution and provides critical insights into phenotypic diversity.
We outline how to use the ecomechanical paradigm using drag-induced bending
in trees as an example. Our approach can be incorporated into existing research
and help build interdisciplinary bridges. Finally, we identify key factors needed
for mass data collection, analysis, and the dissemination of models relevant to
Using the ecomechanical approach to understand the rules of life
All forms of life must comply with physical laws, resulting in a series of ‘universal’or ‘hard’
constraints (see Glossary)[1,2]. Although these constraints limit the possible phenotypes,
‘local’or ‘soft’constraints emerge as a consequence of ecological, developmental, and
evolutionary processes that determine which phenotypes are adaptive. Thus, any realized pheno-
type is the result of: (i) physical principles and processes; (ii) the context in which the organism
performs the manifold tasks required for growth, survival, and reproduction (i.e., organism–
environment interactions); and (iii) its evolutionary history [1,3].
Function is a key concept at the intersection of developmental biology, ecology, and evolution .
Function interacts with ontogenetic and reproductive changes, and thus profoundly affects
survival and ﬁtness [5,6]. It also affects community and ecosystem-level processes, as well as
macroevolutionary patterns of diversity including biogeography, diversiﬁcation rates, and specia-
tion . Therefore, the concept of function bridges all levels of biological organization. Indeed,
there is growing momentum to connect functional traits (FTs) and mechanics of organisms
to their environments (i.e., ecomorphology and ecomechanics) in order to predict survival,
reproduction, and community structure [8–13].
We aim to reinvigorate an integrative approach that incorporates physics as the basis for
organismal FTs . FTs are morphological, phenological, and physiological characteristics
affecting an individual’sﬁtness . They are often measurements of convenience (i.e., deﬁned
aprioribased on ease of collection), but one way to formalize the function of a trait is to use
biophysical models to identify relevant traits and quantify how these traits contribute to overall
performance. These models can reveal integrated or compound FTs that provide greater insight
All organisms must comply with physical
laws, which place rigid or hard con-
straints on survival and reproduction.
Ecomechanics is the expression of that
interplay, and assumes a central role
when considering organismal develop-
ment, ecology, and evolution.
How organisms will respond to changes
in the environment, such as human-
mediated climate change, will depend
strongly on ecomechanics.
Functional traits are commonly used
to investigate the consequences of eco-
logical variation. Ecomechanical models
that incorporate functional traits and
environmental variables are key to
deciphering the rules of life and expand
upon functional trait studies.
The use of the ecomechanical framework
is illustrated using multiple examples
(e.g., wind-induced bending mechanics
in trees and gecko adhesion in the real
world). We emphasize safety factors as
a key metric when assessing the evolu-
tion of form and performance. Biologists
can apply our framework to many other
We offer suggestions for constr ucting
and tailoring the data pipeline for future
ecomechanical models to enhance
their availability and utility for various
Department of Evolution, Ecology, and
Organismal Biology, University of
California, Riverside, CA 92521, USA
School of Mathematical and Natural
Sciences, Arizona State University,
Glendale, AZ 85306, USA
Trends in Ecology & Evolution, Month 2021, Vol. xx, No. xx https://doi.org/10.1016/j.tree.2021.05.009 1
© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Ecology & Evolution OPEN ACCESS
TREE 2866 No. of Pages 14
than any single FT taken in isolation . However, understanding the limits to organismal
survival, which necessarily includes abiotic as well as biotic factors, requires a mechanistic
model that includes such factors . This differs from many approaches that are solely reliant
on intrinsic features of an individual (Figure 1), such as the Newtonian mechanics governing
animal motion. Our framework focuses on the former [i.e., models that include individual traits
and environmental variables (EVs) (Figure 1)], which we term ecomechanical models.
Key EVs in these models include ﬂuid speed (wind or water), temperature, and habitat structure,
all of which have strong effects on organismal form and function.
Figure 1. Three ways in which to use functional traits (FTs) in biology. The top example indicates that FTs, and
interactions among them, can be used to estimate performance (and ultimately ﬁtness) within a given ecological context
(green box). The middle example incorporates a biomechanical model that includes FTs as inputs. The output of the
biomechanical model is used to predict performance in a given ecological context. The bottom example, which we are
proposing as most useful in the study of organisms, incorporates an ecomechanical model. In this case, the inputs are
both FTs and environmental variables (EVs), and the output of the model is again used to predict performance in an
ecological context. Not only can FTs interact with one another, but EVs can also alter the properties of FTs (see text for
details). This integrative approach is ideal for understanding ecological performance.
W.M. Keck Science Department, 925 N.
Mills Avenue, Claremont McKenna,
Pitzer, and Scripps Colleges, Claremont,
CA, 91711, USA
Organismic and Evolutionary Biology
Program, University of Massachusetts
Amherst, Amherst, MA 01003, USA
Botany Department and Biodiversity
Research Centre, University of British
Columbia, Vancouver, BC V6T 1Z4,
Department of Biology, University of
Victoria, Victoria, BC V8W 2Y2, Canada
Department of Evolution, Ecology, and
Behavior, University of Illinois at Urbana-
Champaign, Urbana, IL 61801, USA
Biology Department, Clark University,
950 Main Street, Worcester, MA 01610,
Department of Cell Biology and
Anatomy, University of Calgary, Calgary,
T2N 1N4, Canada
Laboratório de Ciências Ambientais,
Universidade Estadual do Norte
Fluminense. Av. Alberto Lamego 2000,
Campos dos Goytacazes, RJ, cep
Human Genetics Institute of NJ,
Rutgers University, Piscataway,
NJ 08854, USA
Department of Biology, University of
Waterloo, 200 University Ave. W.,
Waterloo, Ontario, N2L 3G1, Canada
Department of Biology, University of
Washington, Seattle, WA 98195, USA
Department of Biology, Howard
University, 415 College Street NW,
Washington, DC 20059, USA
Departmentof Organismal Biology and
Anatomy, University of Chicago, 1027 E
57th Street, Chicago, IL 60637, USA
Department of Biological Sciences,
The George Washington University,
Washington, DC 20052, USA
Department of Biology, Trinity
University, San Antonio, TX 78212, USA
Department of Mammalogy and
Division of Paleontology, Richard Gilder
Graduate School, American Museum of
Natural History, 200 Central Park West,
New York, New York 10024, USA
Department of Biological Sciences,
Towson University, Towson, MD 21252,
Integrative Biology and Museum of
Vertebrate Zoology, University of
California, Berkeley, California 94720,
School of Integrative Plant Science,
Cornell University, Ithaca, NY, USA
email@example.com (T.E. Higham).
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2Trends in Ecology & Evolution, Month 2021, Vol. xx, No. xx
The general framework of our approach is outlined in Figure 2. A trait, or series of interacting
traits, will, based upon one or more biophysical laws, dictate the ecological perfor-
mance of an organism. The trait-to-performance link can occur rapidly, in ‘real’time,
ranging from nanoseconds to minutes. However, this framework extends to changes in
environmental conditions (representedbyenvironments1,2,and3inFigure 2)over
short (e.g., seasons) or long periods of time (e.g., millions of years). Additionally, our
framework accounts for developmental time, which can change the way in which the or-
ganism interacts with and within its habitat. This framework, therefore, builds upon the
form–function–ﬁtness paradigm by considering ecomechanical performance over relevant
timescales. Later, we highlight the novelty of ecomechanical models, and expand on
these timescales: rapid, ecological, evolutionary, and developmental. We illustrate the util-
ity of our framework using bending mechanics as an example, since it is broadly appli-
cable across nearly all organisms. This example illustrates the critical role of stochastic
EVs and FTs in ecomechanical models.
Ecomechanical models and organismal safety factor
Organismalperformance relies upon the coordinated response of multiple FTs in a given ecological
context. Importantly, an ecomechanical framework permits the prediction of survival in the face of
changing conditions using a quantitative framework . A prime example, which we highlight later
in our case study, is maximum breaking stress in plants (Box 1,andFigure 3). This model can be
applied to any cantilevered organism, such as coral (Figure 3), and includes morphological
traits (diameter, length, etc.) and characteristics of the ambient ﬂuid (air or water), such as velocity
Key to understanding survival is the determination of an organism’ssafety factor,both
within the bounds of current conditions and predicted future conditions. Safety factors
represent a margin of protection against failure; for example, a safety factor of 2 indi-
cates the maximum load that can be withstood without material failure is twice the
load actually experienced by the organism. Higher safety factors are, therefore, beneﬁ-
cial and may be more common in systems with unpredictable loading regimes; however,
they can be costly to maintain, as doing so often requires additional investment in
material. These periodic moments of excessive force have been considered potential
drivers of evolution. For example, amphisbaenians and skinks that burrow may occa-
sionally encounter sharp-edged objects that result in very high local stress, requiring a
reinforced skull to avoid failure .
Ecomechanical models provide an opportunity to explore safety factors under current and pre-
dicted environmental conditions. A classic example is the prediction of dislodgement of mussels
by wave-induced forces using a combination of time-varying hydrodynamic forces and mussel
attachment strength . Knowing the attachment ability of mussels and the magnitude of
wave forces then provides a critical tenacity that must be achieved to remain attached to the
substrate. Models of future changes in wave action can then be incorporated to determine the
biomechanical robustness of the system.
Bending and breaking: organisms in ﬂuids as model systems
There are two ways in which a ﬂuid (water, air, or both; Figure 3) can exert force on an organism:
pressure and friction. In turn, this force can reach sufﬁcient magnitude to cause an organism
attached to a substrate (e.g., sponges and trees) to bend or, as highlighted previously,
be dislodged from the substrate. A bending moment is the product of a distance or length
(e.g., tree trunk or branch), and an external force. On land, two predominant external mechanical
Bending mechanics: the behavior of a
slender structural element subjected to
an external load applied perpendicularly
to it longitudinal axis.
Biomecha nics: the study of the
mechanical design of organisms.
Biophysical models: simulations of
biological systems using mathematical
formalizations of the physical properties
of that system.
Constraint: anything, internal or
external to an organism, that limits the
production of new phenotypes.
Drag: the force exerted by a moving
ﬂuid on an organism.
Ecological performance: the ability to
execute an ecologically-relevant
Ecomechanics: the study of the
mechanisms underlying the interactions
of organisms with their biotic and abiotic
Ecomechanical models: models that
include individual traits and
environmental variables (EVs).
Ecomorphology: the study of the
relationship between the morphology of
an organism and its environment.
Force: mass multiplied by acceleration.
Functional traits (FTs): morphological,
phenological, and physiological traits
affecting an individual’sﬁtness.
Isometry: the maintenance of shape
with changes in size.
Ontogenetic change: changes
attending the growth and development
of an organism that can alter an
organism’s interactions with its
environment and how the environment
interacts with the organism.
Reynolds number (Re): a
dimensionless number that describes
the quotient of inertial and viscous
Safety factor: in biology, refers to the
dimensionless quotient of a structure’s
ability to resist mechanical stresses, and
the maximum stress that it is likely to
experience over its lifetime in its
Trends in Ecology & Evolution OPEN ACCESS
Trends in Ecology & Evolution, Month 2021, Vol. xx, No. xx 3
forces are gravity and drag. The acceleration of gravity is a constant force that organisms
respond to and accommodate as they grow in size. By contrast, the magnitude of drag changes
with ﬂow speed (Box 1). In addition to the fact that organismal diversity has likely been shaped
signiﬁcantly by ﬂuid forces, ﬂuid (air and water) speeds are commonly projected to change as a
consequence of climate change , leading to altered hydrodynamic and aerodynamic forces.
Be it a bone or a branch, bending is ubiquitous among plants and animals [21,22]. Bending can
be advantageous, as in elastic energy storage mechanisms and in drag reduction, or it can be
detrimental, resulting in breakage. The observed bending (or breakage) is deﬁned by functional
attributes, many of which are provided in online databases. For plants, resisting bending, or at
least failure, is important to maintain normal loads (e.g., the weight of a leaf lamina extending
from a petiole ). Excess force, as might occur in variable environments, presents a situation
that could result in breakage (safety factor <1), such as drag-induced bending moments due to
an extreme wind event . That said, being able to deﬂect energy is also critical for some plants,
leading to a reduction in drag by orienting the bulk of the structure parallel to the direction of the
ﬂuid (Figure 3). These examples are dynamic, which means that a model explaining the role of the
FT and the range of forces being experienced are necessary.
Community ecology and biomechanics
In 2010, Vellend proposed that all community level processes can be classiﬁed into four key
categories: dispersal, selection, drift, and speciation . With the exception of drift, each of
the remaining processes is strongly tied to biophysics and organismal function. Thus, our
ecomechanical framework can be applied to almost all community-level processes. Dispersal
may be broadly deﬁned as the movement of individuals through space either by passive or active
transport (e.g., the wind dispersal of seeds and fruits, or the ﬂight of insects and birds). The laws
of diffusion, for example, deﬁne dispersal–distance curves , in which propagule concentration
is highest near the source [26,27]. Many organisms have evolved dispersal mechanisms that take
advantage of ﬂuid dynamics and moving air or water currents. Examples include the timing of
Figure 2. Framework for linking functional traits (FTs) to the environment and evolution. The relationships between FTs and ﬁtness depend strongly on the
environment (shown as Environment 1, 2, and 3 at each developmental stage) and developmental stage of the organism. In the case of development, it mightbe
that the organism interacts differently with a similar environment as it grows, or the environment itself might change as an animal grows (e.g., habitat shifts). Traits
not only combine, in some cases, to deﬁneaspeciﬁc level of performance, but traits can also act indirectly through another trait (shown as broken yellow and
white lines on left panel).
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spawning events in intertidal mussels (Mytilus) and corals (Cnidaria) , as well as the
elaborate winged fruits of maples (Acer palmatum), and the ovulate scales of pinecones
(Pinus) creating airﬂow patterns directing pollen toward receptive surfaces .
Migration is another important (passive or active) component of dispersal. In the ocean, long-
distance dispersal is commonly linked to buoyancy [30,31]. In habitat-forming brown algae
(e.g., kelps), buoyancy is a convergent trait shared by multiple lineages [32,33]. Buoyant algae
can form large rafts known to travel across reefs  or even across oceans . In some corals,
and some terrestrial plants, asexual reproduction occurs through fragmentation and subsequent
vegetative growth of the fragments that are transported elsewhere , highlighting the role of
Environmental conditions affect the composition ofcommunities by ﬁltering, or limiting the survival
and presence of, organisms adapted to their local environments [37,38]. This selective process
has two components, abiotic gradients (e.g.,temperature,precipitation,andlight)[39–42]
and biotic factors (e.g., interspeciﬁc competition and prey–predator interactions) [43,44].
Ecomechanical models connect abiotic and biotic factors and provide the ability to predict
community composition in the present, past, and future.
Box 1. The evolution of drag-induced bending mechanics and safety factors in trees
The use of ecomechanical models is illustrated by assessing the ability of a tree to resist the bending moments resulting fromthe drag forces induced by oncoming wind
(Figure I). For simplicity, the geometry of a tree’s canopy is modeled as a vertical prolate spheroid with a projected sail area, S, equal to πab/4, where aand bare canopy
height and canopy width, respectively (Figure 3A). The maximum bending stress, σ
, at the base of a trunk is given by the formula
where Mis the bending moment and ris the radius of the trunk at its base. The bending moment is equal to the product of the drag force, F
, exerted by the oncoming
wind and the effective height of the canopy, H
, for example,
and the drag force is given by the formula
where ρis the density of air, Uis wind speed, and C
is the drag coefﬁcient. Thus, substituting Equations [II] and [III] into [I] yields the formula
The safety factor, SF, against wind-throw equals the quotient of the critical breaking stress, σ
(i.e., the maximum stress that the wood at the base of the tree can
sustain before breaking) and the maximum bending stress at the base of the trunk. Thus,
SF ¼σcrit=σmax ¼2r3σcrit =ρabU2CdHe
Three of the parameters in Equation [V] can be a sserted apriori(i.e., the density of air at 15°C is 1.225 kg/m
, the drag coefﬁcient of a prolate spheroi d subjected
to turbulent airﬂow is 0.20 (unitless), and the average critical breaking stress of greenwood across a broad spectrum of eudicot trees is 9.7 GN/m
). Specifying the
remaining vari ables in Equation [V] clearly depends on the dimensions of the tree and the ambient wind speed. Our estim ates of safety factor may be considered
exceptionally high because they assume that the trunk has a uniform radius, that the wood has no ﬂaws, and that the wind speeds are steady. They also neglect
uprooting due to root–crown oscillations, and ignore the additional loading resulting from rain and ﬂying debris. Nevertheless, it provides an upper boundary condition
and reveals which biotic and abiotic factors inﬂuence windthrow and safety factors.
In addition to this ecomechanical model (drag-induced bending in trees), there are numerous models that could be leveraged to explore developmental, ecological
and/or evolutionary questions. For many of the existing models, trait inputs are available in online databases, as are historical and current environmental variables
(EVs). These models can be used to deﬁne which functional traits (FTs) should be measured moving forward, along with the relevant ecological variables.
Examples include gecko adhesion (Box 2), running on water in lizards, bite force in mammals and other groups, and aerodynamics of ﬂight in birds and bats
(Figure S1 in the supplemental information online).
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Ecomechanics through the lens of time
Rapid organismal-level events
The expression of a FT, which is important for ecomechanical models, is often rate-dependent.
Thus, considering how FTs respond to varying loading rates will be of prime importance when
predicting how organisms function in their environment. For example, force and energy are linked
to prey capture and feeding through their transfer from the predator to its prey via an attack
[45,46]. However, the time frame over which force (or energy) is transferred to a target differs
widely, from chewing in mammals  and crushing in coconut crab claws (Birgus latro)to
high-speed strikes in snakes (Crotalus sp.), aquatic bladderworts (Utricularia sp.), and
mantis shrimp (Odontodactylus scyllarus). Identifying the rate of force–energy transfer
between predators and prey is essential to evaluating traits such as bite force or strike energy
because materials, especially biological materials, respond differently when loaded at different
Figure I. The evolution of safety factor among 37 extant tree species from Peru , illustrated through an evolutionary traitgram on the left and a
mapping of ancestral states on the right. We pruned a time-calibrated molecular phylogeny for angiosperms  to match our data, and visualized the estimated
safety factors through functions in the phytools  and ggtree  packages for R. The evolutionary traitgram is a projection of a phylogeny calibrated to time (x-axis)
in a space deﬁned by safety factor (y-axis). Based on data on living species alone, the traitgram suggests an increase in maximum safety factor over the last 100 million
years. A safety factor of 0 (top blue broken line) is the point at which a tree is considered susceptible to damage, and only very few species have safety factorsless than
50 (lower broken line). Most species appear to be overbuilt. The mapping of ancestral states underscores this pattern and suggests that the traits underlying large safety
factors (>400) evolved several times independently.
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Box 2. Real world gecko adhesion as a future ecomechanical model
Dry adhesion, an attachment mechanism found in a variety of invertebrates and squamate reptiles, has a rich history in
engineering and biological sciences . Despite the widespread appreciation of the adhesive apparatus in geckos
(Figure II), few attempts have been made to incorporate ecological aspects, such as humidity and substrate roughness,
with a few exceptions [94,95]. However, these facto rs are likely determinants of both the origin, evolutio n, and function
of the system . Several models have been use d to describe adhesion, especially in geckos. The Johnson, Kendall,
Roberts (JKR) model describes the force Frequired to pull an elastic sphere, with a radius R,fromaﬂat surface .
Predicted adhesion is then calculated by:
where γis the adhesion energy between the sphere and the surface. Expanding on this, Arzt et al. utilized the JKR model to
examine the role of setal density in adhesion from insects to geckos . They note that adhesion force is relative to a linear
dimension of the contact. Thus, dividing the contact area into a number of nsubcontacts (in this case, setae), each with a
radius of R=ﬃﬃﬃ
p(reﬂecting self-similar scaling), adhesion increases to:
As noted by , the force of adhesion (F
) can be largely explained in both natural and synthetic systems through the fol-
lowing equation in which G
is deﬁned as a measure of surface energy as deﬁned by the material to which adhesion
occurs (see  for more details), Ais the area of the adhering pad, and Cis system compliance. In an analysis across
14 orders of magnitude,  showed that stiffer materials produce more powerful adhesion.
Natural surface topography will alt er the Ain the previous equation, with rough surfaces reducing A, thereby reducing F
example, on rough sandstone surfaces, only 1.1–3.6% of the surface in the uppermost 30 μm is available for the
establishment of the adhesive bond . By contrast, almost 100% of this same region is available on artiﬁcially smooth
surfaces. As a validation of this reduction in force, geckos from the genus Phelsuma exhibit a reduction in adhesion with
increasing roughness (Figure II). Future work could incorporate these basic modelsin tests of adhesion across spatiotemporal
gradients. This ecomechanical approach will be critical when trying to understand the evolution of adhesion.
P. standingi P. klemmeri P. madagascariensis
Clinging force relative to Acrylic (%)
Acrylic (perfectly smooth)
Sansevieria plant (Sq=1.67Pm)
600-grit sandpaper (Sq=4.85Pm)
Figure II. Gecko adhesion on different surfaces. Presented here is clinging perform ance for three species of day
geckos (genus Phelsuma) on surfaces varying in roughness. Shown are data for Standing’s day gecko (P. standingi),
the yellow-headed day gecko (P. klemmeri), and the Madagascar day gecko (P. madagascariensis). Sq represents area
roughness. Clinging force measurements are from .
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Generally, when strain rates increase, biological materials become stiffer [50,54] as a conse-
quence of their viscoelasticity. Thus, a material that undergoes deformation under a static load
will deform much less under the same force applied at higher speeds. The consequences of strain
rate on force–energy transfer have been explored in high-speed puncture tests where the volume
of deformed material is inversely proportional to the strain rate , and higher speeds allow
for greater puncture depth before the macroscopic deformation of test materials . From a
biological perspective, it is less clear how the bite force of an animal measured under static
conditions might change with different jaw closing speeds. It is certainly the case that animals
will close their jaws at different speeds given varying external conditions affecting the feeding
interaction. Jaw-opening in ﬁsh is another example of rate-dependent function, as it is fundamen-
tally different when performed at different speeds. However, only sudden, high-speed gape
opening and cavity expansion leads to suction production, which is essential for prey capture
in many vertebrates  and even in some plants . These examples illustrate the need to
properly parameterize ecomechanical models with realistic EVs and FT values.
The environment in which an organism lives often changes through time (Figure 2), which causes
it to continually experience varying rates of applied forces. Examples of this include varying wind
conditions at the edge of a forest and varying water ﬂows in an intertidal zone. How FTs respond
Figure 3. The interaction between ﬂuid ﬂow (air or water) and organisms. Trees will bend as wind speeds increase
(upper panel), coral will resist bending (lower left), and bull kelp will bend and align with the direction of ﬂow in water (lower
right). Our model is superimposed on the tree in the upper left, where F
is the drag force, S is the area of the canopy, ais
the height of the crown, and bis the width of the crown.
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to varying loading rates will therefore impact responses to global change . The environment
(e.g., wind speed) not only affects how a FT will contribute to performance, but also potentially
alters the properties of the FT itself (Figure 1). For example, higher wind speeds will increase
drag forces on a bending tree, but may also increase the stiffness of the tree through rate-
dependent material properties discussed previously.
The response of an organism to changes in the environment can lead to performance thresholds
being crossed, such that an organism shifts to a different micro-environment where performance
is enhanced. For example, the rate of ﬂuid movement affects the interplay between inertial and
viscous forces [designated by the Reynolds number (Re)]. Faster speeds can allow small
organisms to transition to higher Re regimes, allowing them to overcome viscous forces in a
new microenvironment [60,61].
Development/ontogeny (individual-level timescale)
As noted, organism–environment interactions are constantly in ﬂux. This not only arises from a
variable environment, but also a variable organism (i.e., organisms experience their environment
differently as they develop, and most organismal traits depend on size). Organism–environment
interactions should be considered relative to directionality: both the effect of the environment
on the organism and the capacity of the organism to perform in the environment may be altered
in response to ontogenetic change. Ecomechanical models provide the framework for investi-
gating how developmental changes will inﬂuence performance in a changing environment.
Size inﬂuences almost every aspect of an organism’s biology, including biomechanical relationships
(e.g., ). As such, changes in size over time will affect organism–environment interactions. In an
aquatic environment, Re, as discussed previously, is not only inﬂuenced by speed, but also size.
Therefore, escaping the viscous regime can be accomplished by increasing swimming velocity
and/or by increasing size. In fact, it appears that many ﬁsh invest in quick growth to avoid problems
associated with viscosity, rather than adapt to the viscous ﬂow regime . This example highlights
just one way in which development can play a critical role in determining the relationships between
biomechanics, behavior, and responses to changing ecological conditions.
Maintaining geometric similarity (similar shape) is referred to as isometry, whereas the change in
one or more aspects of shape relative to body size indicates allometry. Isometric growth can
have negative consequences, which will ultimately place constraints on the biomechanics of an
organism. A common example is stress on support elements in a terrestrial environment .
The forces applied to skeletal elements are directly proportional to body mass. However, cross-
sectional area of the element increases as the 2/3 power of body mass. This scaling relationship
increases the risk of mechanical failure in larger organisms, although mammals circumvent this
issue by larger species exhibiting a more upright posture (increased effective mechanical advan-
tage) . Ecologically-relevant situations, such as food consumption, pregnancy, or carrying
young, will exacerbate this problem, potentially reducing the safety factor .
Many organismal structures exhibit changes in mechanical properties throughout development
[66,67]. A common driver of these changes is altered demand from the organism’s environment.
Thigmomorphogenesis in plants is a prime example, whereby plants sense and respond to
mechanical stimuli, in some cases leading to strengthening of the tissue . Thus, without con-
sidering the developmental stage of an organism and its ecology, it would be difﬁcult to interpret
biomechanical and morphological phenomena. It is common for organisms to exhibit an increase
in structural stiffness through development, which leads to less deformable structures, but also
more efﬁcient locomotor systems . Similarly, strength (i.e., maximum stress) commonly
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Trends in Ecology & Evolution, Month 2021, Vol. xx, No. xx 9
increases throughout ontogeny in plants and animals [70,71] due to microstructural changes
such as ligniﬁcation or calciﬁcation/ossiﬁcation. Like the rate-dependent shifts in FT properties,
development must be considered when parameterizing ecomechanical models.
Structural changes can occur as organisms grow and deal with changes in organism–environment
interactions, but the environment can also inﬂuence the mechanical properties of organisms
directly. Ocean acidiﬁcation, for example, can compromise the structural properties of calciﬁed
organisms (e.g., coral) by reducing calciﬁcation or increasing dissolution . Interestingly, early
developmental stages are often more negatively affected. In addition to calciﬁcation, ocean acidiﬁ-
cation can also inﬂuence attachment mechanisms in marine invertebrates. For example, the pro-
teinaceous byssal threads of mytilid mussels are negatively affected, reducing the extensibility,
force to break, and tenacity of attachment to hard substrates .
Evolution: constraints, convergence, and ecomechanics
The basic laws governing the behavior of mass and energy are invariant, which establishes what
can be called ‘hard’or ‘universal’constraints, boundary conditions that no form of life can
trespass. These establish what is physically possible and what is impossible . An excellent
example of a universal constraint is that of arm swinging (i.e., brachiation) in gibbons, which
generally follows the constraints imposed by a pendulum model . However, pendular mechanics
cannot deﬁne all aspects of brachiation; transition between handholds involves a loss of mechanical
energy that requires input from the animal . All forms of life also face ‘soft’or ‘local’constraints,
the trade-offs that emerge as organisms perform multiple functions to grow, survive, and reproduce.
In turn, how these trade-offs are accomplished (phenotypic ‘solutions’) help inform how biodiversity
has evolved [75,76]. When cast in the context of theoretical morphology, hard constraints can be
thought of as prohibited regions in a morphospace, whereas soft constraints can be thought of
as the roads that lead to adaptive morphologies provided that organisms can evolve ways to
navigate them. Unlike hard constraints, soft constraints can change over the lifespan of an organism,
or over ecological or evolutionary time just as they can differ in space (local to global) (Figure 4). Most
tasks cannot be maximized simultaneously, and must trade off with other performance tasks.
However, there are different solutions for achieving the same set of functions, a principle known
as many-to-one mapping [77–79].
The direction of evolution is often governed by ecological conditions, thus highlighting the
importance of ecomechanical models. Fishes are a prime example, in which body form has
frequently diverged along ecological gradients including ﬂow, predation, and habitat structure
. Those ﬁsh species that evolved in high ﬂow, low predation, and open habitats, often
have more streamlined bodies and higher aspect ratio caudal ﬁns for prolonged swimming.
Swimming performance (e.g., endurance) is then dependent on both ecology (e.g., ﬂow) and
FTs (e.g., body and ﬁn shape). The idea of trade-offs arises here, where these morphological
traits are suboptimal in low ﬂow, high predation, and/or highly structured habitats. This has
led to widespread convergent evolution in body form across ﬁshes , aquatic mammals
, and aquatic reptiles , emphasizing that ecomechanics strongly inﬂuences the evolu-
tion of phenotypic diversity. Convergence, trade-offs, and many-to-one mapping are prevalent
across the tree of life in various environmental scenarios, but ecomechanical models provide
the tool to understand them.
Data pipeline and open trait networks
To implement ecomechanical models on a wide scale, we note that databases must be expanded
and coordinated, and their accessibility increased. However, the nature of data collection, at
present, is inherently slow. Experiments, ﬁeld observations, and data processing are rate limiting.
Trends in Ecology & Evolution
10 Trends in Ecology & Evolution, Month 2021, Vol. xx, No. xx
As a result, sharing data needs to become a priority such that the working life of any single
datapoint is prolonged beyond a single study . To do this, we must standardize data acquisi-
tion, reporting, and archiving, to ultimately ensure that data collection and statistical analyses
remain comparable and reproducible across researchers. Given the range of ecomechanical
models and organismal systems, it is not possible to detail every aspect here. However, we outline
guidelines that should be considered.
Figure 4. The realized phenotype of organisms is expected to reﬂect the changes of performance and ﬁtness
landscapes through evolutionary time. In contrast to the ecological and developmental time scales in Figure 2, we now
consider time points that may be separated by many millions of years. The evolutionary traitgram on the left illustrates
phenotypic changes throughout the history of a hypothetical clade, with the phenotype represented by the area deﬁned
by x- and y-axis, and time represented by the z-axis. The time slices t1 and t2 represent two different times in history of
the clade. The broken blue ellipses represent the limits of realizable phenotypes set by ‘hard’constraints which are
invariant over time. For each time slice, one can model performance as a function of the phenotype, resulting in
performance landscapes that are illustrated in the right panel. The position an d number of performance peaks that arise
from phenotypes can change through time. Note that the p erformance peaks for a speciﬁc function and phenotype may
not equal the ﬁtness peak for the whole organism. As landscapes change, so do th e relationships outlined in Figure 2.In
other words, the environment might have been dramatica lly different at t1 than t2, and an ecomechanical model could be
used to predict the performance–phenotype relationships across time. Red dots on the left panel indicate the end of a
branch, such that lower red dots represent extinct species.
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Trends in Ecology & Evolution, Month 2021, Vol. xx, No. xx 11
A standardized data pipeline would allow us to quickly and easily record data from focal taxa,
especially those in natural settings, automatically process those data to return all salient variables,
and archive the data in a publicly accessible format. That level of ﬂexibility (e.g., in terms of ﬁeld
recording) and automation for functional data does not currently exist within a single framework.
However, aspects of such a pipeline are beginning to take shape, with data acquisition, process-
ing, and archiving tools being added every day. In terms of data acquisition and the ability to
capture video in nature, rigs like BeastCam [85,86] permit full 3D point cloud data acquisition.
Relatively affordable drone setups for 2D data,aswellasvideosetupsfor3Dintheﬁeld
 are available. Further, there is enormous potential to engage in community-led science by
encouraging those with pit traps and camera traps to share videos with the scientiﬁc community.
In fact, new advances permit inexpensive high-speed video in combination with an automatic
trigger . For data processing, there are already excellent open-source tools that facilitate
the extraction of mechanical data. For example, StereoMorph  is an open-source alternative
for manual tracking, and DeepLabCut  automates high-throughput video tracking. What is
needed is more coordination among all of these software and hardware elements into easy-to-
use, integrated workﬂows.
This pipeline will only be effective if we use open-source tools with standardized, open ﬁle for-
mats and implement best-practices for the inclusion of salient metadata. This has been done
extensively for FT data, but has not been extended to higher-level biomechanical traits. For
video data, we recommend establishing a standard for reporting camera position, scale,
frame rate, and resolution of videos, whether those videos are original works or mined from
other sources. Additionally, environmental factors such as physical location, temperature,
ﬂuid speed, humidity, size, and date should follow minimum-acceptable metadata standards.
This information should be included in video archives and in data reporting, facilitating the inte-
gration of FTs and EVs for ecomechanical models. We encourage readers to reference ,
which provides a rubric for data management practices, as well as Darwin Core (DwC) meta-
data standards. Ultimately, we see that improving access to affordable, high-quality, portable
methods of data acquisition, combined with methodological standardization of data collection
and analysis, will have a profound impact on our ability to answer the ‘big questions’associated
with the rules of life.
The ecomechanical approach advocated here is critical for understanding patterns of species
distributions and interactions, developmental patterns, and evolutionary processes. Although
ecomechanical modeling is not new (e.g., ), this approach has yet to be adopted on a
broad interdisciplinary scale to investigate organism–environment interactions. Here, we high-
light how and why such models should be adopted across diverse systems. To facilitate the
applicability of ecomechanical models in the broadest context, we must expand FT databases
to include biomechanically-meaningful traits, standardize the collection of these biomechanical
traits, and increase the access to models using these traits via freely-available online platforms.
By doing so, we can start addressing key questions about the phenotypic diversity and the
interplay between ecology and biomechanics (see Outstanding questions). We are at a turning
point where we can leverage technological advances and big data to further explain the rules
This paper resulted from an NSF-funded working group (Rules of Life IOS 1839786) to T.E.H. and L.F. Alex
Boersma provided the illustrations for all Figures other than Figure 1. Pierre Couteron helped us select the data
set for trees in Peru.
How does development inﬂuence the
ecomechanics of organisms?
How do rapid changes in environmental
conditions inﬂuence functional traits?
How does the rate of loading indirectly
affect performance through changes in
How have safety factors evolved
across the tree of life, or within
How do soft and hard constraints
affect phenotypic diversity?
Can ecomechanical models be used to
predict the future in the face of global
Trends in Ecology & Evolution
12 Trends in Ecology & Evolution, Month 2021, Vol. xx, No. xx
Declaration of interests
No interests are declared.
Supplemental information associated with this article can be found online at https://doi.org/10.1016/j.tree.2021.05.009.
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