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Physical principles and laws determine the set of possible organismal phenotypes. 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 ecological 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 this framework.
Linking ecomechanical models and functional
traits to understand phenotypic diversity
Timothy E. Higham,
*Lara A. Ferry,
Lars Schmitz,
Duncan J. Irschick,
Samuel Starko,
Philip S.L. Anderson,
Philip J. Bergmann,
Heather A. Jamniczky,
Leandro R. Monteiro,
Dina Navon,
Julie Messier,
Emily Carrington,
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,
Alejandro Rico-Guevara,
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 approachintegrates 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
this framework.
Using the ecomechanical approach to understand the rules of life
All forms of life must comply with physical laws, resulting in a series of universalor hard
constraints (see Glossary)[1,2]. Although these constraints limit the possible phenotypes,
localor softconstraints 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 [4].
Function interacts with ontogenetic and reproductive changes, and thus profoundly affects
survival and tness [5,6]. It also affects community and ecosystem-level processes, as well as
macroevolutionary patterns of diversity including biogeography, diversication rates, and specia-
tion [7]. 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 [813].
We aim to reinvigorate an integrative approach that incorporates physics as the basis for
organismal FTs [14]. FTs are morphological, phenological, and physiological characteristics
affecting an individualstness [15]. They are often measurements of convenience (i.e., dened
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 1
© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (
Trends in
Ecology & Evolution OPEN ACCESS
TREE 2866 No. of Pages 14
than any single FT taken in isolation [16]. However, understanding the limits to organismal
survival, which necessarily includes abiotic as well as biotic factors, requires a mechanistic
model that includes such factors [17]. 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 uid speed (wind or water), temperature, and habitat structure,
all of which have strong effects on organismal form and function.
gyEcology &
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 tness) 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
28013-602, Brazil
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
*Correspondence: (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 realtime,
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
formfunctiontness 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 [18]. 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 uid (air or water), such as velocity
and density.
Key to understanding survival is the determination of an organismssafety 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, bene-
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 [19].
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 [17]. 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 uids as model systems
There are two ways in which a uid (water, air, or both; Figure 3) can exert force on an organism:
pressure and friction. In turn, this force can reach sufcient 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
uid 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 individualstness.
Isometry: the maintenance of shape
with changes in size.
Ontogenetic change: changes
attending the growth and development
of an organism that can alter an
organisms 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 structures
ability to resist mechanical stresses, and
the maximum stress that it is likely to
experience over its lifetime in its
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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 ow speed (Box 1). In addition to the fact that organismal diversity has likely been shaped
signicantly by uid forces, uid (air and water) speeds are commonly projected to change as a
consequence of climate change [20], 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 dened 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 [23]). 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 [24]. That said, being able to deect 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
uid (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 classied into four key
categories: dispersal, selection, drift, and speciation [25]. 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 dened as the movement of individuals through space either by passive or active
transport (e.g., the wind dispersal of seeds and fruits, or the ight of insects and birds). The laws
of diffusion, for example, dene dispersaldistance curves [26], in which propagule concentration
is highest near the source [26,27]. Many organisms have evolved dispersal mechanisms that take
advantage of uid dynamics and moving air or water currents. Examples include the timing of
Ecology &
Figure 2. Framework for linking functional traits (FTs) to the environment and evolution. The relationships between FTs and tness 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 deneaspecic 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)[26] and corals (Cnidaria) [27], as well as the
elaborate winged fruits of maples (Acer palmatum)[28], and the ovulate scales of pinecones
(Pinus) creating airow patterns directing pollen toward receptive surfaces [29].
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 [34] or even across oceans [35]. In some corals,
and some terrestrial plants, asexual reproduction occurs through fragmentation and subsequent
vegetative growth of the fragments that are transported elsewhere [36], highlighting the role of
Environmental conditions affect the composition ofcommunities by ltering, 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)[3942]
and biotic factors (e.g., interspecic competition and preypredator 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 trees 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
σmax ¼4M=πr3;½I
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 coefcient. Thus, substituting Equations [II] and [III] into [I] yields the formula
σmax ¼ρabU2CdHe=2r3:½IV
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 coefcient of a prolate spheroi d subjected
to turbulent airow 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 aws, and that the wind speeds are steady. They also neglect
uprooting due to rootcrown oscillations, and ignore the additional loading resulting from rain and ying debris. Nevertheless, it provides an upper boundary condition
and reveals which biotic and abiotic factors inuence 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 dene 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 ight 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 [46] and crushing in coconut crab claws (Birgus latro)[47]to
high-speed strikes in snakes (Crotalus sp.)[48], aquatic bladderworts (Utricularia sp.)[49], and
mantis shrimp (Odontodactylus scyllarus)[45]. Identifying the rate of forceenergy 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
rates [5053].
Ecology &
Figure I. The evolution of safety factor among 37 extant tree species from Peru [101], 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 [102] to match our data, and visualized the estimated
safety factors through functions in the phytools [103] and ggtree [104] packages for R. The evolutionary traitgram is a projection of a phylogeny calibrated to time (x-axis)
in a space dened 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 [93]. 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 [96]. 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,fromaat surface [97].
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 [98]. 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(reecting self-similar scaling), adhesion increases to:
As noted by [99], the force of adhesion (F
) can be largely explained in both natural and synthetic systems through the fol-
lowing equation in which G
is dened as a measure of surface energy as dened by the material to which adhesion
occurs (see [99] for more details), Ais the area of the adhering pad, and Cis system compliance. In an analysis across
14 orders of magnitude, [99] 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 1.13.6% of the surface in the uppermost 30 μm is available for the
establishment of the adhesive bond [100]. By contrast, almost 100% of this same region is available on articially 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=1.67Pm)
600-grit sandpaper (Sq=4.85Pm)
Ecology &
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 Standings 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 [94].
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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 forceenergy transfer have been explored in high-speed puncture tests where the volume
of deformed material is inversely proportional to the strain rate [55], and higher speeds allow
for greater puncture depth before the macroscopic deformation of test materials [56]. 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 sh 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 [57] and even in some plants [58]. 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 ows in an intertidal zone. How FTs respond
Figure 3. The interaction between uid ow (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 ow 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 [59]. 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 uid 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, organismenvironment interactions are constantly in ux. 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). Organismenvironment
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 inuence performance in a changing environment.
Size inuences almost every aspect of an organisms biology, including biomechanical relationships
(e.g., [62]). As such, changes in size over time will affect organismenvironment interactions. In an
aquatic environment, Re, as discussed previously, is not only inuenced 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 sh invest in quick growth to avoid problems
associated with viscosity, rather than adapt to the viscous ow regime [63]. 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 [64].
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) [64]. Ecologically-relevant situations, such as food consumption, pregnancy, or carrying
young, will exacerbate this problem, potentially reducing the safety factor [65].
Many organismal structures exhibit changes in mechanical properties throughout development
[66,67]. A common driver of these changes is altered demand from the organisms 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 [68]. Thus, without con-
sidering the developmental stage of an organism and its ecology, it would be difcult 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 efcient locomotor systems [69]. Similarly, strength (i.e., maximum stress) commonly
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increases throughout ontogeny in plants and animals [70,71] due to microstructural changes
such as lignication or calcication/ossication. 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 organismenvironment
interactions, but the environment can also inuence the mechanical properties of organisms
directly. Ocean acidication, for example, can compromise the structural properties of calcied
organisms (e.g., coral) by reducing calcication or increasing dissolution [72]. Interestingly, early
developmental stages are often more negatively affected. In addition to calcication, ocean acidi-
cation can also inuence 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 [73].
Evolution: constraints, convergence, and ecomechanics
The basic laws governing the behavior of mass and energy are invariant, which establishes what
can be called hardor universalconstraints, boundary conditions that no form of life can
trespass. These establish what is physically possible and what is impossible [2]. 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 [1]. However, pendular mechanics
cannot dene all aspects of brachiation; transition between handholds involves a loss of mechanical
energy that requires input from the animal [74]. All forms of life also face softor localconstraints,
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 [7779].
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 ow, predation, and habitat structure
[80]. Those sh species that evolved in high ow, low predation, and open habitats, often
have more streamlined bodies and higher aspect ratio caudal ns for prolonged swimming.
Swimming performance (e.g., endurance) is then dependent on both ecology (e.g., ow) and
FTs (e.g., body and n shape). The idea of trade-offs arises here, where these morphological
traits are suboptimal in low ow, high predation, and/or highly structured habitats. This has
led to widespread convergent evolution in body form across shes [81], aquatic mammals
[82], and aquatic reptiles [105], emphasizing that ecomechanics strongly inuences 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, eld observations, and data processing are rate limiting.
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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 [84]. 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 reect the changes of performance and tness
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 dened
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 hardconstraints 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 specic function and phenotype may
not equal the tness 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 performancephenotype 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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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 exibility (e.g., in terms of eld
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,aswellasvideosetupsfor3Dintheeld
[87] 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 scientic community.
In fact, new advances permit inexpensive high-speed video in combination with an automatic
trigger [88]. For data processing, there are already excellent open-source tools that facilitate
the extraction of mechanical data. For example, StereoMorph [89] is an open-source alternative
for manual tracking, and DeepLabCut [90] automates high-throughput video tracking. What is
needed is more coordination among all of these software and hardware elements into easy-to-
use, integrated workows.
This pipeline will only be effective if we use open-source tools with standardized, open le 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,
uid 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 [91],
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 questionsassociated
with the rules of life.
Concluding remarks
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., [92]), this approach has yet to be adopted on a
broad interdisciplinary scale to investigate organismenvironment 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
of life.
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.
Outstanding questions
How does development inuence the
ecomechanics of organisms?
How do rapid changes in environmental
conditions inuence functional traits?
How does the rate of loading indirectly
affect performance through changes in
functional traits?
How have safety factors evolved
across the tree of life, or within
individual lineages?
How do soft and hard constraints
affect phenotypic diversity?
Can ecomechanical models be used to
predict the future in the face of global
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12 Trends in Ecology & Evolution, Month 2021, Vol. xx, No. xx
Declaration of interests
No interests are declared.
Supplemental Information
Supplemental information associated with this article can be found online at
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... Under this repetitive loading, the shell material accumulates small cracks that eventually cause fatigue fracture (Boulding, 1984). Other examples of how predation success is affected by the time dependence of biomaterial properties are reviewed in Higham et al. (2021). ...
... Traditionally, ecology entered into evolutionary analyses because it determines how performance translates into fitness, but studies of mechanical ecology reveal that the environment also influences how morphology affects performance (Fig. 4). Two recent papers have proposed quantitative approaches for incorporating mechanical ecology into evolutionary biology (Kempes et al., 2019;Higham et al., 2021). Both recognize that physical laws set limits on biological form, that trade-offs between different functions affect overall performance, that different structures and mechanisms can accomplish a given function, that ecological conditions affecting performance and fitness vary in space and time, and that ontogenetic changes of organisms are important. ...
... Both recognize that physical laws set limits on biological form, that trade-offs between different functions affect overall performance, that different structures and mechanisms can accomplish a given function, that ecological conditions affecting performance and fitness vary in space and time, and that ontogenetic changes of organisms are important. Higham et al. (2021) use safety factor as a key metric in assessing the evolution of form and biomechanical performance, whereas Kempes et al. (2019) consider the interactions of multiple tasks in determining overall organism performance and physical limits (approach summarized in Fig. 5). ...
Organisms are subject to the laws of physics, so comparative biomechanics is a powerful approach for identifying basic principles that apply across taxa of how morphology affects performance of mechanical functions such as locomotion, feeding or resisting damage. Journal of Experimental Biology has been a leading journal for decades in publishing studies revealing such basic biomechanical principles. However, field studies of the physical environment, ecological interactions and life-history strategies of organisms reveal which aspects of their biomechanical performance are important to their success in different types of natural habitats, and thus enable us to design ecologically relevant laboratory experiments to understand biomechanical function. Because the fitness consequences of differences in morphology are affected by the biological and physical environment, biomechanics can be used to identify how physical constraints on the performance of organisms with different body plans in variable environments can affect evolution. I illustrate these points with examples from the literature that show how the biomechanical consequences of morphology depend on the ecology of the organisms. Knowledge of the temporal patterns of interactions of organisms with their physical and biological environments is essential for understanding their functional morphology as it changes during ontogeny, and it reveals constraints on their evolution.
... Climate imposes constraints on organismal trait diversity and evolution through its effects on physiological performance and fitness 1,2 . As climatic conditions vary across space and time, these constraints also change, leading to sometimes predictable variation in functional traits such as body size and shape, skin colour, physiological thermal tolerance and behaviour (ecogeographical rules 3,4 ). ...
... To address this complexity, behavioural thermoregulation can be modelled as a probabilistic process using the maximum entropy framework where the thermoregulatory constraint weights-but does not entirely determine-the probability of selection of each micro-environment in the repertoire 26 . Thus, the probability that the animal selects sun-exposed conditions depends on the difference between T pref and body temperature in the sun and a non-dimensional parameter, λ, that characterizes thermoregulatory ability: 1) and the probability of selecting the shade is 1 − P(T b,sun , λ, T pref ) 26 . The denominator, that is, the summation across all possible micro-environments (here, sun-exposed and shade conditions; subscript j is the index of summation across micro-environments), normalizes probabilities so that the sum of all probabilities is equal to 1. ...
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A major challenge in ecology and evolution is to disentangle the mechanisms driving broad-scale variation in biological traits such as body size, colour, thermal physiology traits and behaviour. Climate has long been thought to drive trait evolution and abiotic filtering of trait variation in ectotherms because their thermal performance and fitness are closely related to environmental conditions. However, previous studies investigating climatic variables associated with trait variation have lacked a mechanistic description of the underpinning processes. Here, we use a mechanistic model to predict how climate affects thermal performance of ectotherms and thereby the direction and strength of the effect of selection on different functional traits. We show that climate drives macro-evolutionary patterns in body size, cold tolerance and preferred body temperatures among lizards, and that trait variation is more constrained in regions where selection is predicted to be stronger. These findings provide a mechanistic explanation for observations on how climate drives trait variation in ectotherms through its effect on thermal performance. By connecting physical, physiological and macro-evolutionary principles, the model and results provide an integrative, mechanistic framework for predicting organismal responses to present climates and climate change.
... According to the niche variation hypothesis [5], niche width variation at the population level, mainly resulting from the increased variation at the between-individual level, is accompanied by greater morphological and genetic variations [6][7][8][9]. Phenotypic variations in fish may result in fixed traits (i.e., traits that do not change as fast as the environment [10]), which arise from adaptation following natural selection or other evolutionary forces, or in plastic response to changing environments (phenotypic plasticity) [11] to the point that both populations of fish belonging to the same species or to different species may display both morphological and genetic differences reflecting the environmental conditions of the rivers they inhabit [12][13][14][15]. ...
... (a) Position of the 33 landmarks used for body shape analysis: (1) anterior tip of snout,(2,3) anterior and posterior end of the eye, (4) orthogonal projection on the dorsal profile of the eye center, (5) lateral projection of the eye center on the insertion of the operculum, (6) intersection of the operculum at the lateral profile,(7,8) ventral and dorsal end of gills, (9) anterior insertion of pectoral fin, (10, 11) orthogonal projections on the dorsal and ventral profile of the anterior insertion of pectoral fin,(12,13) anterior and posterior insertion of dorsal fin,(14) insertion of pelvic fin,(15,16) posterior and anterior insertion of anal fin, (17, 18) anterior attachment of dorsal and ventral membrane of caudal fin, (19) base of middle caudal rays, (20, 21) orthogonal projections on the dorsal and ventral profile of the base of middle caudal rays, (22) fork, (23, 24) orthogonal projections on the dorsal and ventral profile of fork, (25, 26) end of the upper and lower lobe of caudal fin, (27, 28) lateral projections of anterior attachment of dorsal and ventral membrane of caudal fin, (29) opening of mouth, (30) posterior end of jaw, (31) posterior-most edge of opercle, (32) supraoccipital, posteromedial tip (visible as an indentation in dorsal surface), (33) posterior nare, posterior margin. (b) The 25 morphometric traits (BLmax, maximum body length; BL, body (standard) length; ...
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Local adaptation and phenotypic plasticity can lead to environment-related morphological and genetic variations in freshwater fish. Studying the responses of fish to environmental changes is crucial to understand their vulnerability to human-induced changes. Here, we used a latitudinal gradient as a proxy for past and present environmental factors and tested its influences on both genetic and morphological patterns. We selected as a suitable biogeographic model, the barbels, which inhabit 17 Adriatic basins of the central-southern Italian Peninsula, and explored association among attributes from genetic, morphological, and environmental analyses. The analysis of the mitochondrial DNA control region evidenced a southward significant increase in the number of private haplotypes, supporting the isolation of the southernmost populations related to the Mio-Pleistocene events. In contrast, morphology was mainly affected by changes in the present environmental conditions. Particularly, the number of scales and fish coloration were clearly associated to latitude, and thus thermal and hydrological conditions. Other morphometric and functional traits varied under the selective pressure of other environmental factors like elevation and distance from headwater. These results highlight the sensitivity of barbels to climate changes, which can serve as a basis for future eco-evolutionary and conservation studies.
... The simple task of obtaining food presents a staggering diversity of mechanical solutions to the same problem, each with its own associated constraints on other functions. Form-function trade-offs enable examination of evolution in a mechanistic context, particularly for structures with great morphological variety and functional diversity (Arnold, 1983;Higham et al., 2021). In addition to biomimetic inspiration, the simple concepts of comparative biomechanics have transformed our inquiry and examination of the natural world. ...
The field of comparative biomechanics examines how form, mechanical properties and environmental interactions shape the function of biological structures. Biomechanics has advanced by leaps and bounds as rapid technological progress opens up new research horizons. In this Review, I describe how our understanding of the avian bill, a morphologically diverse multifunctional appendage, has been transformed by employing a biomechanical perspective. Across functions from feeding to excavating hollows in trees and as a vocal apparatus, the study of the bill spans both solid and fluid biomechanics, rendering it useful to understand general principles across disciplines. The different shapes of the bill across bird species result in functional and mechanical trade-offs, thus representing a microcosm of many broader form-function questions. Using examples from diverse studies, I discuss how research into bird bills has been shaped over recent decades, and its influence on our understanding of avian ecology and evolution. Next, I examine how bill material properties and geometry influence performance in dietary and non-dietary contexts, simultaneously imposing trade-offs on other functions. Following an examination of the interactions of bills with fluids and their role as part of the vocal apparatus, I end with a discussion of the sensory biomechanics of the bill, focusing specifically on the bill-tip mechanosensory organ. With these case studies, I highlight how this burgeoning and consequential field represents a roadmap for our understanding of the function and evolution of biological structures.
... On the other hand, the heat loss surface indicates that increasing wing loading and aspect ratio concurrently would increase the cost of maneuverable flight. These boundaries might be related to "hard" constraints (Higham et al. 2021), but the nature of this evolutionary trend is yet to be determined. ...
Studies on functional performance are important to understand the processes responsible for the evolution of diversity. Morphological trait variation within species influences the energetic cost of locomotion and impacts life history traits, with ecological and evolutionary consequences. This study examined wing morphology correlates of flight performance measured by energetic expenditure in the Seba's short‐tailed bat, Carollia perspicillata. In the flight experiments, nature caught bats (59 females, 57 males) were allowed to fly for three minutes in a room. After each flight, thermographic images were taken to measure body temperature, and biophysical models were used to calculate sensible heat loss as a measure of energetic expenditure. Wing morphological traits were measured for each individual and associated with heat loss and power required to fly on performance surfaces. Wing morphological traits explained 7–10% of flight energetic cost, and morphologies with the best performance would save the energy equivalent to 9–30% of total daily requirements. The optimal performance areas within the C. perspicillata morphospace were consistent with predicted selection trends from the literature. A trade‐off between demands for flight speed and maneuverability was observed. Wing loading and camber presented sexual dimorphism. These morphological differences are likely associated with more economical but less maneuverable flight in females, leading them to fly more often in open areas along the forest edge. Our findings demonstrate how small scale changes in wing morphology can affect life history strategies and fitness. This article is protected by copyright. All rights reserved
... Essentially, they focus on diversity and are thus closely related to taxonomy, systematics, or biodiversity research. They act as a promising remedy to a lingering conundrum in ecology (how does biodiversity shape ecosystems?) by translating taxonomic biodiversity into quantifiable trait diversity [8,25,38,[44][45][46][47][48][49][50] (Figure 2A). Thus, in this philosophy, traits promise a mathematical window onto the potential ecological effects of biodiversity that go beyond quantifying taxonomic diversity itself. ...
Traits are measurable features of organisms. Functional traits aspire to more. They quantify an organism’s ecology and, ultimately, predict ecosystem functions based on local communities. Such predictions are useful, but only if ‘functional’ really means ‘ecologically relevant’. Unfortunately, many ‘functional’ traits seem to be characterized primarily by availability and implied importance – not by their ecological information content. Better traits are needed, but a prevailing trend is to ‘functionalize’ existing traits. The key may be to invert the process, that is, to identify functions of interest first and then identify traits as quantifiable proxies. We propose two distinct, yet complementary, perspectives on traits and provide a ‘taxonomy of traits’, a conceptual compass to navigate the diverse applications of traits in ecology.
... Future field observations that determine the potential for ontogenetic habitat shifts are needed. Additionally, an ecomechanical model [55], incorporating contact area information from surfaces in nature, would help to understand the mechanisms underlying any shifts. ...
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Many geckos have the remarkable ability to reversibly adhere to surfaces using a hierarchical system that includes both internal and external elements. The vast majority of studies have examined the performance of the adhesive system using adults and engineered materials and substrates (e.g., acrylic glass). Almost nothing is known about how the system changes with body size, nor how these changes would influence the ability to adhere to surfaces in nature. Using Tokay geckos ( Gekko gecko ), we examined the post-hatching scaling of morphology and frictional adhesive performance in animals ranging from 5 to 125 grams in body mass. We quantified setal density, setal length, and toepad area using SEM. This was then used to estimate the theoretical maximum adhesive force. We tested performance with 14 live geckos on eight surfaces ranging from extremely smooth (acrylic glass) to relatively rough (100-grit sandpaper). Surfaces were attached to a force transducer, and multiple trials were conducted for each individual. We found that setal length scaled with negatively allometry, but toepad area scaled with isometry. Setal density remained constant across the wide range in body size. The relationship between body mass and adhesive performance was generally similar across all surfaces, but rough surfaces had much lower values than smooth surfaces. The safety factor went down with body mass and with surface roughness, suggesting that smaller animals may be more likely to occupy rough substrates in their natural habitat.
Within the field of evolutionary biology, divergence and convergence are two major phenomena that have helped shape the diversity and disparity of the Earth’s biota throughout the history of life. Exploration of them has contributed to the interpretation of dissimilarities (divergence) and similarities (convergence) in organismic form, function and behaviour at various hierarchical levels and how they favour, in some fashion, the emergence of optimal traits via natural and/or sexual selection across the full spectrum of occupied environments.
Ornithischian dinosaurs were primary consumers in Mesozoic ecosystems, their evolution intricately linked to challenges of a plant-heavy diet. Whether phenotypic similarities among different ornithischian lineages imply a common functional solution to herbivory is unclear. New research suggests that they evolved herbivory via multiple biomechanical pathways.
Lineages with independent evolutionary histories often differ in both their morphology and diet. Experimental work has improved our understanding of the links between the biomechanics of morphological traits and foraging performance (trait utility). However, because the expression of foraging-relevant traits and their utility can be highly context-specific, it is often unclear how dietary divergence arises from evolved phenotypic differences. Here, we explore the phenotypic causes of dietary divergence between two genetically and phenotypically divergent lineages of threespine stickleback (Gasterosteus aculeatus) with independent evolutionary histories of freshwater colonization and adaptation. First, using individuals from a line-cross breeding design, we conducted 150 common-garden foraging trials with a community of multiple prey species and performed morphological and behavioral analyses to test for prey-specific trait utility. Second, we tested if the traits that explain variation in foraging performance among all individuals could also explain the dietary divergence between the lineages. Overall, we found evidence for the utility of several foraging traits, but these traits did not explain the observed dietary divergence between the lineages in a common garden. This work suggests that evolved dietary divergence results not only from differences in morphology but also from divergence in behaviors that underlie prey capture success in species-rich prey communities.
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Tropical cyclones are a major hazard for numerous countries surrounding the tropical-to-subtropical North Atlantic sub-basin including the Caribbean Sea and Gulf of Mexico. Their intense winds, which can exceed 300 km.h-1 , can cause serious damage, particularly along coastlines where the combined action of waves, currents and low atmospheric pressure leads to storm surge and coastal flooding. This work presents future projections of North Atlantic tropical cyclone-related wave climate. A new configuration of the ARPEGE-Climat global atmospheric model on a stretched grid reaching ~14 km resolution to the northeast of the eastern Caribbean is able to reproduce the distribution of tropical cyclone winds, including Category 5 hurricanes. Historical (1984-2013, 5 members) and future (2051-2080, 5 members) simulations with the IPCC RCP8.5 scenario are used to drive the MFWAM (Météo-France Wave Action Model) spectral wave model over the Atlantic basin during the hurricane season. An intermediate 50-km resolution grid is used to propagate mid-latitude swells into a higher 10-km resolution grid over the tropical cyclone main development region. Wave model performance is evaluated over the historical period with the ERA5 reanalysis and satellite altimetry data. Future projections exhibit a modest but widespread reduction in seasonal mean wave heights in response to weakening subtropical anticyclone, yet marked increases in tropical cyclone-related wind sea and extreme wave heights within a large region extending from the African coasts to the North American continent.
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Suction feeding has evolved independently in two highly disparate animal and plant systems, aquatic vertebrates and carnivorous bladderworts. We review the suction performance of animal and plant suction feeders to explore biomechanical performance limits for aquatic feeders based on morphology and kinematics, in the context of current knowledge of suction feeding. While vertebrates have the greatest diversity and size range of suction feeders, bladderworts are the smallest and fastest known suction feeders. Body size has profound effects on aquatic organismal function, including suction feeding, particularly in the intermediate flow regime that tiny organisms can experience. A minority of tiny organisms suction feed, consistent with model predictions that generating effective suction flow is less energetically efficient and also requires more flow-rate specific power at small size. Although the speed of suction flows generally increases with body and gape size, some specialized tiny plant and animal predators generate suction flows greater than those of suction feeders 100 times larger. Bladderworts generate rapid flow via high-energy and high-power elastic recoil and suction feed for nutrients (relying on photosynthesis for energy). Small animals may be limited by available muscle energy and power, although mouth protrusion can offset the performance cost of not generating high suction pressure. We hypothesize that both the high energetic costs and high power requirements of generating rapid suction flow shape the biomechanics of small suction feeders, and that plants and animals have arrived at different solutions due in part to their different energy budgets.
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Nematocysts are stinging organelles used by members of the phylum Cnidaria (e.g., jellyfish, anemones, hydrozoans) for a variety of important functions including capturing prey and defense. Nematocysts are the fastest-known accelerating structures in the animal world. The small scale (microns) coupled with rapid acceleration (in excess of 5 million g) present significant challenges in imaging that prevent detailed descriptions of their kinematics. The immersed boundary method was used to numerically simulate the dynamics of a barb-like structure accelerating a short distance across Reynolds numbers ranging from 0.9–900 towards a passive elastic target in two dimensions. Results indicate that acceleration followed by coasting at lower Reynolds numbers is not sufficient for a nematocyst to reach its target. The nematocyst’s barb-like projectile requires high accelerations in order to transition to the inertial regime and overcome the viscous damping effects normally encountered at small cellular scales. The longer the barb is in the inertial regime, the higher the final velocity of the projectile when it touches its target. We find the size of the target prey does not dramatically affect the barb’s approach for large enough values of the Reynolds number, however longer barbs are able to accelerate a larger amount of surrounding fluid, which in turn allows the barb to remain in the inertial regime for a longer period of time. Since the final velocity is proportional to the force available for piercing the membrane of the prey, high accelerations that allow the system to persist in the inertial regime have implications for the nematocyst’s ability to puncture surfaces such as cellular membranes or even crustacean cuticle.
Because of the importance of specimen identification, and for establishing protocols for new species boundaries, novel methods and tools for identifying and sharing specimen data for vertebrate organisms, particularly amphibians and reptiles, is an important aim for taxonomists (Dayrat 2005; McDiarmid et al. 2011). In general, the gold standard for specimen collection and identification for reptiles and amphibians is euthanization with appropriate preservation and deposition as vouchered material in natural history holdings (Allmon 1994; Davis 1996; Reynolds and McDiarmid 2011; Shaffer et al. 1998; Simmons 2015; Suarez and Tsutsui 2004). This important approach will rightfully remain the gold standard for collecting and identifying most reptile and amphibian specimens (McDiarmid et al. 2011). However, there is also value in establishing other methods to gain specimen identification as a complement to this method. Several methods already exist (Simmons 2015), including photographs, audio recordings, and scientific illustrations, among others. Photographs have proven to be a useful resource for specimen identification and are widely used in online resources such as amphibiaweb ( The collection of audio recordings is especially valuable for recording of vocalizations, such as from frogs (e.g., Kohler et al. 2017). Scientific illustrations can be a valuable tool for effective recreation of specimens, especially for emphasizing key elements of scalation and color that might be challenging to document in a photograph. Here, we describe novel tools and techniques for the creation of 3D models of live reptiles and amphibians, both in wild settings in the field and in the laboratory.
Aquatic bladderworts (Utricularia gibba and U. australis) capture zooplankton in mechanically triggered underwater traps. With characteristic dimensions less than 1 mm, the trapping structures are among the smallest known to capture prey by suction—a mechanism that is not effective in the creeping‐flow regime where viscous forces prevent the generation of fast and energy‐efficient suction flows. To understand what makes suction feeding possible on the small scale of bladderwort traps, we characterized their suction flows experimentally (using particle image velocimetry) and mathematically (using computational fluid dynamics and analytical mathematical models). We show that bladderwort traps avoid the adverse effects of creeping flow by generating strong, fast‐onset suction pressures. Our findings suggest that traps use three morphological adaptions: the trap walls’ fast release of elastic energy ensures strong and constant suction pressure; the trap door’s fast opening ensures effectively instantaneous onset of suction; the short channel leading into the trap ensures undeveloped flow, which maintains a wide effective channel diameter. Bladderwort traps generate much stronger suction flows than larval fish with similar gape sizes because of the traps’ considerably stronger suction pressures. However, bladderworts’ ability to generate strong suction flows comes at considerable energetic expense.
Interdisciplinary research can have strong and surprising synergistic effects, leading to rapid knowledge gains. Equally important, it can help to reintegrate fragmented fields across increasingly isolated specialist sub-disciplines. However, the lack of a common identifier for research 'in between fields' can make it difficult to find relevant research outputs, and network effectively. We illustrate and address this issue for the emerging interdisciplinary hotspot of 'mechanical ecology', which we define here as the intersection of quantitative biomechanics and field ecology at the organism level. We show that an integrative approach crucially advances our understanding in both disciplines by (1) putting biomechanical mechanisms into a biologically meaningful ecological context and (2) addressing the largely neglected influence of mechanical factors in organismal and behavioural ecology. We call for the foundation of knowledge exchange platforms such as meeting symposia, special issues in journals, and focus groups dedicated to mechanical ecology.
Long distance dispersal plays a key role in evolution, facilitating allopatric divergence, range expansions, and species movement in response to environmental change. Even species that seem poorly suited to dispersal can sometimes travel long distances, for example via hitchhiking with other, more intrinsically dispersive species. In marine macroalgae, buoyancy can enable adults – and diverse hitchhikers – to drift long distances, but the evolution and role of this trait is poorly understood. The southern bull kelp genus Durvillaea includes several non‐buoyant and buoyant species, including some that have only recently been recognized. In revising the genus, we not only provide updated identification tools and describe two new species (D. incurvata comb. nov. from Chile, and D. fenestrata sp. nov. from the Antipodes Islands), but also carry out biogeographic analyses to determine the evolutionary history of buoyancy in the genus. Although the ancestral state was resolved as non‐buoyant, the distribution of species suggests that this trait has been both gained and lost, possibly more than once. We conclude that although buoyancy is a trait that can be useful for dispersal (creating evolutionary pressure for its gain) there is also evolutionary pressure for its loss as it restricts species to narrow environmental ranges (i.e., shallow depths).
This paper presents a study on the material behaviors and constitutive models of lead at high strain rates. Quasi-static compressive tests and split Hopkinson pressure bar (SHPB) tests were conducted at room temperature. The results of the SHPB tests were verified by high-speed photography, and the error of strain is less than 3% at high strain rate (5000/s). Our results show that the yield stress and flow stress increase at high strain rates. This result indicates that lead is sensitive to the strain rate at high strain rates, but the dependence is not linear during 3000-5000/s. The strain-rate dependence of lead was fitted by a quadratic polynomial curve. To describe the nonlinear strain-rate relationship of lead, modified Johnson–Cook and Cowper–Symonds material models were used to fit the experimental stress–strain curves. The modified Cowper–Symonds model agrees better with the experimental results and can better describe the dynamic mechanical behavior of lead under high strain rates.
The study of gecko adhesion is necessarily interdisciplinary due to the hierarchical nature of the adhesive system and the complexity of interactions between the animals and their habitats. In nature, geckos move on a wide range of surfaces including soft sand dunes, trees, and rocks, but much of the research over the past two decades has focused on their adhesive performance on artificial surfaces. Exploring the complex interactions between geckos and their natural habitats will reveal aspects of the adhesive system that can be applied to biomimetic research, such as the factors that facilitate movement on dirty and rough surfaces with varying microtopography. Additionally, contrasting suites of constraints and topographies are found on rocks and plants, likely driving differences in locomotion and morphology. Our overarching goals are to bring to light several aspects of ecology that are important for gecko-habitat interactions, and to propose a framework for how they can inspire material scientists and functional ecologists. We also present new data on surface roughness and topography of a variety of surfaces, and adhesive performance of Phelsuma geckos on surfaces of varying roughness. We address the following key questions: (1) why and how should ecology be incorporated into the study of gecko adhesion? (2) What topographical features of rocks and plants likely drive adhesive performance? (3) How can ecological studies inform material science research? Recent advances in surface replication techniques that eliminate confounding factors among surface types facilitate the ability to address some of these questions. We pinpoint gaps in our understanding and identify key initiatives that should be adopted as we move forward. Most importantly, fine details of locomotor microhabitat use of both diurnal and nocturnal geckos are needed.