Journal of Ecology 2017, 105, 95–110
doi: 10.1111/1365-2745.12693
ESSAY REVIEW
Plant energetics and the synthesis of population and
ecosystem ecology
Jason D. Fridley*
Department of Biology, Syracuse University, Syracuse, NY 13244, USA
Summary
1. Historical conceptual divisions in ecology are dissolving as ecologists seek general currencies for
understanding plant function, population dynamics, species interactions and ecosystem processes. Foremost among such synthetic approaches are population-ecosystem models (PEMs), which treat the biomass dynamics of individuals as a foundation for coupled demographic and biogeochemical processes.
2. Because plant productivity and tissue composition drive terrestrial energy and nutrient cycling,
and plant growth itself is often used as a fitness proxy, PEMs predict that phenomena related to population dynamics, such as species coexistence, are associated with processes at the ecosystem level.
I argue that this line of thought reflects a persistent misunderstanding of the relationship of plant
growth—the creation of new structural biomass—and individual fitness.
3. A survey of measured energetic costs of a wide variety of plant functions suggests that the majority of assimilated energy for many plants is allocated to survival rather than growth processes. This
diversion of assimilate to survival—such as fighting infection, reducing potential tissue damage, or
storing reserves in the face of defoliation risk—contributes to individual fitness but may not impact
the flow of energy and nutrients in the environment or to other trophic levels.
4. Synthesis. Using simple models of plant energy budgets, I demonstrate how incorporating survival allocation into PEMs using a bet-hedging approach can decouple population and ecosystem
dynamics. In revisiting what little is known about how individual plants use energy, I suggest that
recasting plant strategy theory in an energetic-demographic framework may offer a useful way forward in the drive to unify population and ecosystem processes.
Key-words: demography, ecophysiology, ecosystem functioning, energetics, functional traits, plant
life-history traits, plant population and community dynamics
Introduction
If the plant is only the means by which a seed produces more seeds, the variations in efficiency of the
process are extraordinary.
Harper (1977), Population Biology of Plants, p. 647
Plant behaviour is shaped by forces operating on individuals. This insight, not fully articulated until well into the 20th
century (Harper 1967), is of great significance to modern
environmental science because it means that plants do not
directly evolve in ways that maximize the flow of energy and
materials in ecosystems—they evolve as a consequence of
converting energy and materials into offspring (Harper &
Ogden 1970; Brown, Marquet & Taper 1993). This is not to
deny the central role of plant productivity and tissue composition in biogeochemical cycles (De Deyn, Cornelissen &
*Correspondence author. E-mail: [email protected]
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society
Bardgett 2008) or trophic dynamics (Odum 1957; McNaughton et al. 1989), but to recognize that biomass production is
one of several means by which an individual is able to leave
more descendants than competing individuals. In disturbed
environments, for example, many plants allocate more assimilate to seed production than vegetative growth (Abrahamson
& Gadgil 1973; Harper 1977). In resource-poor environments,
most plants invest in mechanisms that promote survival
(Grime 1977; Chapin 1980; Canham 1985; Bellingham &
Sparrow 2000), which influence population growth rates more
than seed production or rapid growth to larger size classes
(Silvertown et al. 1993a; Kobe & Coates 1997; Adler et al.
2014). Plant growth rate is thus one potential component of
fitness, but not in itself a fitness proxy and often less favoured
by selection than mechanisms that enhance survival, particularly in long-lived species (Harper 1977; Silvertown, Franco
& McConway 1993b).
If plant growth rate is an insufficient measure of individual
fitness, then it follows that community assembly and
96 J. D. Fridley
coexistence processes cannot be described by the biomass
dynamics of populations alone; the persistence of individuals,
and their ability to produce offspring, also drive interactions
between species (Weldon & Slauson 1986; Aarssen & Keogh
2002; Freckleton, Watkinson & Rees 2009). Ecosystem processes, on the other hand, involve the flow of energy and
materials between plants, animals and microbes, and are thus
closely related to the biomass dynamics of those pools. The
contrasting currencies of fitness on the one hand, and biomass
dynamics on the other, poses a significant challenge to modelling ecosystem processes as a function of the behaviour of
individual organisms. Population-ecosystem models (PEMs),
an outgrowth of biodiversity-ecosystem functioning studies
(Tilman, Lehman & Thomson 1997; Loreau 1998, 2004),
were created in response to this challenge as an attempt to
unify ecological dynamics across organizational scales
through the mass and energy budgets of individuals (Loreau
2010). Although PEM studies to date have been largely theoretical (Tilman, Lehman & Thomson 1997; Cardinale, Nelson
& Palmer 2000; Mouquet, Moore & Loreau 2002; Loreau
2004, 2010; Gross & Cardinale 2005; Carroll, Cardinale &
Nisbet 2011; Cardinale 2013), they reflect much of what has
been learned empirically about plant interactions over the past
half century, including coexistence mechanisms (Chesson
2000; Carroll, Cardinale & Nisbet 2011) and how functional
differences among species contribute to ecosystem productivity and nutrient cycling (Dı́az & Cabido 2001). PEMs are
thus a leading example of attempts to better integrate demographic and functional approaches in plant ecology (Silvertown, Franco & McConway 1992; Poorter et al. 2008;
Wright et al. 2010; Adler et al. 2014; Iida et al. 2014; Visser
et al. 2016).
The most comprehensive population-ecosystem framework
to date, setting the stage for a wide array of new empirical
studies, is laid out in Michel Loreau’s book, From Populations to Ecosystems (2010). Loreau’s framework is an attempt
to reconcile long-standing theoretical perspectives of the
dynamics of communities—such as MacArthur’s (1970,
1972) consumer-resource theory—and fundamental ecosystem
approaches pioneered by Lindeman (1942) and Odum (1953).
Loreau’s premise (p. 9) is that the unification of population
and ecosystem approaches should be rooted in the ecophysiology of organisms, in particular, in the constraints that govern the acquisition, allocation, and disposal of materials and
energy. My goal in this paper is to, first, further advocate the
use of energy as a measure of ecological and evolutionary
plant responses to the environment, and, second, use Loreau’s
framework as a point of departure for creating PEMs that better reflect how plants use energy to produce offspring. In particular, I bring attention to Loreau’s omission of survival as a
dominant fitness component for most plants, of which we
know comparatively little in the context of plant energy or
material budgets.
Beyond specific application to PEMs, the approach I advocate in this paper was motivated by revisiting long-dormant
ideas of J. L. Harper and colleagues concerning plant energy
allocation and its relationship with offspring production
(Harper & Ogden 1970; Harper 1977). The legacy of the Darwinian revolution in plant ecology (Harper 1967) is the success of the demographic approach in understanding the
population dynamics of both single species (Silvertown,
Franco & Menges 1996) and communities (Chesson 2000),
and thus processes that govern the maintenance of plant biodiversity (Griffith et al. 2016). A challenge to this legacy is
the subsequent success of the functional approach to plant
community dynamics (Grime, Hodgson & Hunt 2007) and its
strong linkages to ecosystem processes, including mass fluxes
and storage (productivity and biomass), nutrient dynamics
(tissue stoichiometry), and trophic dynamics (tissue palatability), i.e. ‘functional traits’ (Reich 2014). Indeed, the rise of
plant functional ecology mirrors the growth of ecosystem
science (Fig. 1) and the need to better understand linkages
between plants and ecosystems in a changing environment
(Lavorel & Garnier 2002; Suding et al. 2008). In this global
change context, traditional plant demography, with indirect
and largely forgotten ties to plant energy and resource use
(Harper & Ogden 1970), is perceived to have less relevance
to ecosystem processes than plant functional approaches
(Fig. 1). Here I argue that plant behaviours and their impact
on ecosystems cannot be fully understood without a more
comprehensive accounting of how plants use energy for all
three fitness components of growth, survival and fecundity.
I first describe the PEM approach using Loreau’s (2010)
energetic model of population dynamics, and show how it
and other PEMs assume that offspring production is proportional to the net primary productivity (NPP) of an individual.
Fig. 1. Relative frequency of four two-word phrases (‘bigram’) in a
sample of books published in English from 1960 to 2008, according
to the Google Ngram Viewer (Michel et al. 2011). Occurrences are
case insensitive and reflect a 3-year moving average. Related bigrams
to those in the figure show the same qualitative patterns (e.g., ‘plant
functional type’ vs. ‘plant functional ecology’; ‘plant demography’
vs. ‘plant population biology’). The frequency of plant demography
increased from the 1970s to 1980s but has declined steadily since. In
its wake has been the emergence of plant functional ecology, following the rise of ecosystem ecology. Despite its origins nearly a century
ago, plant energetics remains relatively unstudied.
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
Plant and ecosystem energetics 97
I then propose an alternative model of plant function that
accounts for the fitness consequences of investing in growth,
survival or fecundity, using expressions of commonly measured plant traits. Finally, I ask whether revisiting an energetic perspective of fitness—popular in animal ecology but
nearly absent for plants—is likely to generate new insights
into plant behaviour, its evolution, and the impact of plants
on ecosystems.
My choice of energy as a unifying currency of population
and ecosystem dynamics stems from foundational ideas about
the evolution of life history strategies as a function of energy
allocation (Cody 1966; Harper & Ogden 1970). However, I
acknowledge that the use of energy as the primary currency
of allocation strategies in plants is controversial (Gibson
2015). Although plant resource economics theory suggests
that the fitness values of essential resources are exchangeable
(Bloom, Chapin & Mooney 1985; Reekie & Bazzaz 1987),
the allocation of other limiting resources such as phosphorus
may be more relevant to fitness in some circumstances (Chapin 1989; Elser et al. 2003). Nonetheless, nutrients once
acquired can be repurposed to support various plant functions
while energy cannot; there are also plant functions that incur
little to no direct nutrient costs (e.g., non-structural carbohydrates used as osmotic solutes). As I describe further below, a
more complete accounting of plant energy budgets in different
environmental (e.g., nutrient) contexts may help resolve this issue.
An energetic approach to plant population
dynamics
Mechanistic population growth models in ecology stem from
MacArthur’s (1970, 1972) consumer-resource theory, which
relates consumer abundance (N) to the density of resources
(Q) and vice-versa. A feature of consumer-resource models is
that the per-capita increase in abundance (birth rate) is
resource dependent (b = f(Q)) but loss (death rate, d) is not
(Fig. 2a; Leibold 1995):
dN/dt 1=N ¼ f ðQÞ d
eqn 1
The units of d vary according to the objectives of the
model; it can be expressed as individual mortality rate, as in
resource ratio theory (Tilman 1982), or as maintenance-related
resource needs (MacArthur 1972, p. 37) or biomass loss (Loreau 2010; Carroll, Cardinale & Nisbet 2011). In each case, d
is an efficiency parameter—it reduces the translation of
resource capture to the production of new individuals, and
has no relationship to f(Q). Loreau (2010, p. 11) adopts a
similar consumer-resource approach, but, following animal
metabolic models, treats population biomass (B) gains as a
function of energy consumed (E):
dB/dt ¼ e=cðE lBÞ dB
eqn 2
where B is in grams, E is energy intake in calories (note Loreau uses the symbol C rather than E), c converts between
caloric content and plant biomass, and e, l and d are constants: e describes gross production efficiency (fraction of
energy assimilated used to produce new biomass), l is mass-
(a)
(b)
Fig. 2. Energy loss in plants is a function of the environment. (a) In
consumer-resource models (MacArthur 1970, 1972; Tilman 1982),
potential growth and reproduction rates are functions of resources, but
loss rates (respiration, senescence) are constant. Net population
growth rate is zero when these parameters are equal (adapted from
Leibold 1995). (b) In plants, metabolic costs are also resource dependent: mass-based energy acquisition is more expensive as resource
density declines due to greater required investment in nutrient or
water foraging (1 q, where q is leaf investment), leaf function (respiration R) and longer leaf life span (T) associated with higher leaf
construction cost (C).
specific basal metabolic rate (maintenance respiration) and d
is mass-specific loss rate of biomass. In simple language, the
model states that the total biomass of a population changes
over time as the difference between energy acquired and
invested in new biomass, and biomass lost to both tissue
maintenance and death. If energy acquisition is expressed relative to biomass (E = eB), then eqn. 2 can be rearranged to:
dB/dt ¼ B½ðe=cÞðe lÞ d
eqn 3
As above, from the standpoint of plant fitness, e, c, l and
d are parameters representing efficiencies—the greater the e,
or lower the c, l or d, the less energy is ‘wasted’ in the conversion of energy to offspring. The allocation of energy or
materials between competing processes—for example, reducing the loss rate of biomass by investing in mechanisms that
reduce d at a cost to e—is not a feature of the model, nor are
the efficiency parameters functions of the environment.
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
98 J. D. Fridley
Although not included in Loreau’s model, an additional reproduction efficiency parameter could translate energy or biomass gains to births of individuals (MacArthur 1972), which
would represent a fourth process of energy loss in the conversion of available energy to offspring.
Equation 3 is a generalized expression of population energetics that offers a foundation for linking the behaviour of populations and ecosystems through biomass dynamics. As a model of
plant population dynamics, however, it includes untenable
assumptions about how plants gain and lose energy and omits
critical aspects of the strategies plants have adopted in response
to environmental stress and disturbance. To see this, I recast the
model in terms of specific plant traits without changing any of
its central assumptions. Plants acquire energy through photosynthesis, and in the process incur energetic costs of constructing and maintaining leaf tissue, which has a finite life span.
Leaf processes require materials that are costly to acquire, much
of which must be gathered by roots and transported through
stems to chloroplasts; structural tissue is also required for leaves
to collect solar energy without shading. Plant energy consumption (e) can thus be expressed as photosynthetic rate (A) in units
of carbon uptake per unit leaf mass (e.g., lmol CO2 g1 s1);
maintenance rate l as leaf dark respiration rate (R) in the same
units; and leaf death rate d as the inverse of leaf life span T.
Because A, R and T relate to leaf mass and not total biomass,
mass dynamics can be redefined as leaf mass L, with an efficiency parameter q describing the proportion of assimilated
energy invested in leaves, analogous to leaf mass fraction. Leaf
growth itself incurs a construction cost (C) in units of g CO2
per leaf mass. Note the ratio q/C is a gross production efficiency parameter analogous to Loreau’s e/c in the context of
leaf growth: returns on leaf investment go up with q (less nonleaf structure required) and down with C (greater leaf construction costs). Putting these terms together to describe the exponential growth of leaves, eqn. 3 becomes:
dL/dt ¼ L½ðq=CÞðA RÞ 1=T
eqn 4
which I have purposefully expressed as Givnish’s (2002)
model of leaf economics, equivalent to Loreau’s model with
the above parameter substitutions.
Givnish’s model (eqn. 4) is useful because it fully accounts
for energy capture—there are no other common processes by
which plants capture useable energy (ignoring carnivory and
the potential movement of sugars across mycorrhizal networks)—and it expresses in familiar terms the energetic costs
associated with growing. When varied independently, net carbon gain increases monotonically with increases in instantaneous photosynthetic rate (A), leaf allocation (q), and leaf life
span (T), and decreases as leaves become more costly to construct (C) or maintain (R). Givnish’s model is also useful
because it exposes three problems inherent in using a typical
consumer-resource framework for plant population growth.
First, the above leaf traits do not vary independently (Grime
et al. 1997; Wright et al. 2004). In particular, the independence of traits related to energy gains (A, q) and losses (1/T,
R, C) is inconsistent with leaf economics theory. For example, the quantity (A R)/C is a leaf’s ‘net return on
investment’ (N) (Williams, Field & Mooney 1989; Sims &
Pearcy 1991), which is roughly proportional to leaf life span
(T) (Williams, Field & Mooney 1989; Falster et al. 2012).
Substituting N into Givnish’s model for (A R)/C and as
some proportion (c) of leaf life span yields:
dL/dt 1=L ¼ qN 1=cN
eqn 5
which indicates both gain and loss rates at the leaf level are a
function of leaf chemical and structural traits associated with
the leaf economics spectrum (Falster et al. 2012).
Second, expression of carbon gains and losses in terms of well
explored leaf traits reveals that plant energy loss, like energy capture, is not constant but a function of the environment (Fig. 2b).
Leaf dark respiration rate (R), for example, generally increases
with site temperature and aridity (Atkin et al. 2015). Leaf
construction costs (C) are generally higher in evergreen, stresstolerant species (Villar & Merino 2001; Fridley et al. 2016), particularly those of high lipid concentration (Villar & Merino
2001). Allocation of assimilate to non-leaf processes that support
growth (1 q), including stems and nutrient foraging (root
mass, carbon loss to mycorrhizal fungi), display a range of sensitivities to environmental gradients (K€
orner 1991; Givnish 2002).
For example, below-ground dry matter partitioning generally
decreases along soil nitrogen gradients (Reich 2002) and carbon
investments in mycorrhizae and root exudates generally increase
as phosphorus becomes less available (Peng et al. 1993; Lambers et al. 2008). Stem allocation increases with plant stature,
and often increases as resource availability promotes crowding
and a greater competitive ability for taller plants (Givnish 1995;
Gibert et al. 2016). Thus, a model of plant growth that assumes
constant loss rates, whether expressed in terms of maintenance
respiration, costs of new biomass synthesis, support costs of root
and stem production, or the rate of leaf loss, cannot account for
energetic losses that stem from a dynamic environment.
Third, and most importantly in the context of plant demography, both the Givnish leaf economics model and the Loreau
energy-biomass model, like any model of plant fitness based
on growth, assume that fitness gains come only from activities that generate the fastest net return on energy investments.
Loreau (2010, p. 9) states this explicitly as ‘growth and reproduction are the two processes at the individual level that are
responsible for population growth’; in the models thus far
described, fitness losses are incurred only by metabolic activities related to growth itself: synthesis of new biomass, maintenance of tissue and its finite life span, and transport of
materials required for photosynthesis and nutrient uptake. But
plants do much more than grow (Mooney 1972)—to what
extent do non-growth activities contribute to fitness?
Growth is not a fitness proxy
The idea that plants do better if they grow faster is deeply
engrained in ecological thought. Arguably, it was not until
the foundational ideas of Grime (1974; Grime & Hunt 1975)
and Chapin (1980) that it became widely appreciated that
most plants are not selected for maximizing potential growth
rate (Lambers & Poorter 1992). More recently, the leaf
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
Plant and ecosystem energetics 99
Menges 1996; Franco & Silvertown 2004; Adler et al. 2014).
For example, forest demography studies have repeatedly
demonstrated that sapling survival is more important than
growth rate to population dynamics (Kobe & Coates 1997).
Aside from the obvious consideration that individual growth
rate is easy to measure, one reason it remains a common surrogate for plant fitness in ecological studies stems from the original development of ecological energetics and its close ties to
animal metabolism (Phillipson 1966). Consider the basic ‘Sankey diagram’ of energy transfer efficiencies as described in
many undergraduate ecology texts (Fig. 3a). An organism
captures a certain proportion of food energy (consumption
efficiency, EC) and assimilates some of it into its metabolism
(assimilation efficiency, EA), which is stored as energy in biomass (production efficiency, EP). Some of the stored energy is
then used to produce offspring (reproductive efficiency, ER).
The ability of an organism to convert food energy into biomass is called gross production efficiency; the gross rate of
energy conversion into offspring has been called reproductive
power (Brown, Marquet & Taper 1993). For plants, Penning
De Vries (1975) coined the term ‘production value’ as the proportion of energy in gross carbon assimilated over the growing
season that is stored in grain or other plant commodity; Harper
& Ogden (1970) considered a plant’s ‘net reproduction efficiency’ as the proportion of stored energy of production (NPP)
economics spectrum (Wright et al. 2004; Reich 2014) has
helped to unify a panoply of leaf traits related to carbon and
nutrient allocation that differentiate ‘fast and leaky’ versus
‘slow and tight’ growth strategies. Leaf economics, however,
concerns the rate of energy return (carbon gain) across
resource gradients (Givnish 2002; Falster et al. 2012), and
bears no necessary relation to whole-plant survival and fecundity strategies (Wright et al. 2010; Grubb 2015; Visser et al.
2016). Even in models of plant community dynamics where
survival is a dominant fitness component, such as the SORTIE forest simulation model (Pacala et al. 1996), growth rates
rather than survival are used to parameterize the response of
individuals to resource gradients (Kobe et al. 1995).
Demographic studies of plant populations, however, show
that individual growth rate—the rate at which individuals progress to larger size classes—is rarely the most important behaviour that contributes to population growth. Werner & Caswell
(1977), for example, showed that the individual growth rate of
Dipsacus sylvestris was unrelated to its population growth rate,
which de Kroon et al. (1986) showed through elasticity analysis to be the result of selection for slow growth coupled with
higher fecundity later in life. Subsequent comparative analyses
suggested that population growth is most often a function of
survival, except for populations in particularly fertile and disturbed habitats (Silvertown et al. 1993a; Silvertown, Franco &
(a)
Traditional plant energetics
EA
Light
absorbed
NPP
ER
Seeds
Heat, litter,
consumption
Heat
Heat, ET
(b)
EP
GPP
Demographic plant energetics
θ
Fecundity
φ
EA
Light
absorbed
σ
GPP
(1-ER)
Survival
ms
(1-σ-φ)
α
Growth
Heat, ET
F
β
A,ρ
R,C,1/T,(1-ρ)
G
S
sf
ss
sg
λ
Projection matrix A
Heat, litter,
consumption
Fig. 3. Traditional versus demographic approaches to plant energy budgets. (a) The traditional approach is described by energy transfer efficiencies as presented in ecology textbooks in the context of ecosystem energetics (Lindeman 1942; Odum 1957). Of the energy absorbed by a leaf,
only a small fraction (EA) is assimilated into organic carbon (gross primary productivity, GPP), which is used to produce biomass at production
efficiency EP due to respiratory losses associated with growth. The energy stored in biomass (net primary productivity, NPP) is used to produce
offspring at net reproductive efficiency ER (Harper & Ogden 1970). From the standpoint of gross reproductive economy (EA 9 EP 9 ER), respiration, evapotranspiration (ET), and consumed or residual biomass (litter) represent wasted energy (gray arrows), because it is not present in an
individual’s offspring. (b) A demographic model of plant energetics accommodates energy-demanding processes that may lead to greater fitness
(k) through enhanced individual survival and fecundity in addition to growth. Symbols refer to the fitness-energy model of eqn. 14, consisting of
allocation (/, r), growth (A, q), and loss parameters (R, C, 1/T, 1 q, ms, 1 ER). Growth (G), survival (S), and fecundity (F) investments
contribute to their associated transitions (aij) in a population projection matrix A according to the parameters a, b and h; those transitions in turn
drive fitness (k) according to their demographic sensitivities (s), which are largely determined by the environment (Silvertown et al. 1993a).
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
100 J. D. Fridley
contained in seeds. Implicit in these treatments of plant energy
budgets, however, is the treatment of non-growth expenditures
as a constant loss rate. That is, gross production is assumed
‘lost’ due to the respiratory demands associated with maintaining and synthesizing biomass (protein turnover, ion transport,
membrane potential; Lynch & Ho 2005).
Energy that does not go into production, however, often does
work for the plant that contributes to its fitness (Mooney 1972).
Table 1 lists a sample of plant activities, many of which are not
reflected in biomass dynamics, particularly the structural biomass that constitutes long-term dry mass increase in plants.
Inducible defenses, for example, can increase leaf respiration by
over 20% (Zangerl, Arntz & Berenbaum 1997). Microbial interactions represent significant energy expenditures, including the
supply of carbohydrates to mycorrhizal fungi (Peng et al. 1993)
or symbiotic bacteria (Ryle, Powell & Gordon 1979), or the exudation of organic compounds to facilitate nutrient mineralization
and mobilization (Nguyen 2003). The maintenance and turnover
of fine roots alone can be as much as a third of annual plant production (Janssens et al. 2002), which goes unaccounted for in
plant yield or root:shoot ratios. Survival in response to flooding,
salt, or heavy metals induces ATP-dependent stress response
pathways (e.g., Lambers, Chapin & Pons 1998; Gibbs & Greenway 2003). Still other environmental hazards like defoliation
require energy storage in the form of starch and other non-structural carbohydrates, often in considerable amounts and subject
to strong herbivore pressure (Chapin, Schulze & Mooney 1990;
Iwasa & Kubo 1997). Reproduction is also expensive; in addition to structural tissue supporting flowers and fruits that is
included in dry mass increase, the loss of energy associated with
attracting pollinators and dispersers can be significant, such as a
15% cost to NPP associated with nectar production in Medicago
sativa (Southwick 1984). Although I am unaware of any attempt
to integrate the energy demands of the activities listed in
Table 1 to establish a full plant energy budget, even for shortlived plants, non-growth expenditures may often be the largest
component of plant energy investments. Thus, energy that does
not become structural biomass is far from wasted—it can mean
the difference between contributing some or no offspring to the
next generation.
It has been argued that annual plants are a special case where
fitness and growth rate are closely related (Loreau 2004, 2010).
Genetic studies of annual crops and Arabidopsis thaliana, however, suggest that many genotypes bear significant energy
expenditures that do not involve growth or fecundity but are
nonetheless adaptive. For example, A. thaliana plants expressing a cold tolerance-related gene exhibit dwarf, late-flowering
phenotypes of significantly reduced final biomass and seed
mass, associated with elevated production of freezing-protective proteins and soluble sugars (Gilmour et al. 2000; Zhen &
Ungerer 2008). Other stress-related pathways induce similar
growth-survival trade-offs, including responses associated with
shading (Wagner et al. 2008) and disease (Tian et al. 2003). It
seems that selection has favoured a wide variety of energetic
investments in A. thaliana, and that neither production
efficiency nor reproductive efficiency are optimized across populations. For example, Aarssen & Clauss (1992) compared
size-fecundity relationships for 10 lines of A. thaliana under
greenhouse conditions and documented an eightfold difference
in the number of seeds produced relative to final vegetative biomass. Furthermore, in annual crops genetic breeding for feedstock or cellulosic ethanol production (vegetative biomass) is
distinct from grain yield selection (e.g., Jahn et al. 2011 for
rice; Lorenz et al. 2009 for maize). Thus, annual plants do not
meet the assumption that fitness is proportional to biomass production; rather, environmental conditions and biotic interactions associated with non-growth energetic investments
determine the relative contribution of growth to fitness. To
explore this quantitatively, energy investments in survival must
be represented in a demographic framework.
Modelling plant population growth: beyond
biomass
In principle, it should be possible to trace demography
back to the mass and energy budgets of the individual
organisms that make up the population.
Loreau 2010
Can ecosystem currencies of mass and energy budgets be
reconciled with empirical models of plant population growth
that are based on births and deaths of individuals? Rather
than assume vital rates are proportional to biomass dynamics,
I outline a demographic approach based on population projection models (Caswell 2001) and explore how fitness components of growth, survival, and fecundity can be expressed in
energetic terms. Plant fitness at the population level is
described by the finite rate of increase k, which is the dominant eigenvalue of a projection matrix that contains elements
aij representing life history size or stage transitions (Silvertown, Franco & Menges 1996). For example, a12 could indicate the annual per capita probability of a seed (stage 1)
developing into seedling (stage 2). Following Silvertown
et al. (1993a) and Silvertown, Franco & Menges (1996)
Franco & Silvertown 2004), I consider three classes of transition probabilities that separate the three major fitness components: growth (G) includes all aij that describe an increase to
a larger size class; survival (S) includes stasis (no change in
size class) or retrogression (size loss); and fecundity (F)
includes the production of new seeds. One of the principal
features of projection models is that relative contributions of
G, S and F to fitness can be quantified as elasticities that
measure the relative effect of a small change in aij on k.
Because elasticities of all matrix transitions sum to unity, de
Kroon et al. (1986) showed that k can be decomposed into
separate fitness contributions of each transition:
k¼
X X
i
a s
j ij ij
eqn 6
where sensitivity sij is the absolute rather than relative influence of small changes in aij on k. If G, S and F represent all
transitions associated with growth, survival and fecundity (of
element lengths g, s, and f, respectively), then fitness can be
expressed as:
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
Tissue repair
Reproduction
Allelopathy
[Interference]
Storage
Environmental
tolerance
Biotic defence
Water foraging
Nutrient
foraging
Carbon
assimilation
Function
Fecundity
Survival
Growth
Fitness
component
Storage organs (e.g. lignotuber)
Starch or lipid formation/breakdown +
translocation
Xylem repair (embolism response)
Nectar, pollen and volatile attractants
Flowers, peduncle production
Seed production (incl. fruit, pappus, seed coat,
etc.)
Fruit production
Leaf or root allelochemicals (e.g., juglone,
catechins)
Constitutive defence compounds
Hypersensitive response (HR) to pathogens
Anti-herbivore structure (thorns, winged stems,
trichomes)
Volatile production to attract beneficial
predators
Heat/drought protection (leaf cuticle, hairs,
anthocyanins)
Cold acclimation or freezing tolerance
Salt tolerance (e.g., ion export)
Anoxia (flooding)
Toxic minerals (e.g., Al, Mn)
Photon harvest (thylakoid production)
Carboxylation (stroma production)
Leaf chloroplast structure and accessory tissue
(epidermis, guard cells, vascular bundles)
Leaf support (stem structure)
Fine root production and maintenance
respiration
Active ion transport
C supply to mycorrhizae
C exudation (priming)
Nutrient reduction
Nutrient resorption
C supply to nitrogen-fixing bacteria
Xylem and root production
Solute production
Inducible defence compounds
Activity
Stems are generally cheaper to produce than leaves (Griffin 1994)
≤31% of NPP (Janssens et al. 2002)
≤50% of total root respiration (Lambers, Chapin & Pons 1998)
37% increase in root C allocation (Peng et al. 1993)
On average 17% of NPP (Nguyen 2003)
Varies by reduction state of nutrient source (Lambers, Chapin & Pons 1998)
Unknown (cf. Wright & Westoby 2003)
13% of GPP during nodule N fixation (Ryle, Powell & Gordon 1979)
Costs vary with root size, nutrient concentration and life span (Eissenstat et al. 2000)
Cost of osmotic adjustment in response to water stress may be small (Chimenti, Pearson & Hall 2002)
22% increase in leaf respiration from induced defences (furanocoumarins; Zangerl, Arntz &
Berenbaum 1997)
15% increase in whole-plant energy demand (glucosinolates; Bekaert et al. 2012)
11% decrease in NPP due to presence of HR gene (anti-bacterial; Tian et al. 2003)
Unknown energy cost; 58% fruit set reduction from thorn production in the absence of herbivores
(Gomez & Zamora 2002)
Unknown; some volatiles (e.g., terpenoids) have high biosynthesis costs (Gershenzon 1994)
Yes
Some/No
No
No
No
No
No
No
Yes
No
Some
Yes
No
No
No
Yes
Yes
Unknown, but depends on production of osmotic solutes (McDowell 2011)
Nectar: 15% of NPP (alfalfa; Southwick 1984)
3–5% of NPP (Chaplin & Walker 1982)
25–60% loss associated with seed energy storage depending on lipid content (Penning de Vries, van
Laar & Chardon 1983); 21% of NPP in the annual Senecio vulgaris (Harper & Ogden 1970)
17% energy loss when stored in kiwifruit (Walton, De Jong & Loomis 1990)
Unknown
Reduced RGR and fecundity due to cold tolerance transgene expression (Gilmour et al. 2000)
Root and xylem exclusion of sodium requires ATP (Lambers, Chapin & Pons 1998)
ATP for synthesis of proteins associated with anaerobic metabolism (Gibbs & Greenway 2003)
ATP for synthesis and membrane transport of chelates at the root apex or in the cytosol (Kochian,
Hoekenga & Pi~neros 2004)
20% energy loss in tuber storage (Penning de Vries, van Laar & Chardon 1983)
5% energy loss in starch; ≤40% energy loss in lipids (Chapin 1989)
No
No
No
No
Some
No
Cuticles and other lipid-rich structures have high construction cost (Villar & Merino 2001)
Some
No
Yes
No
Yes
Twofold variation (11–2) in leaf construction cost (Griffin 1994)
Energetic cost example (various units)
Some
Some
Yes
Accounted for in
long-term biomass
increase?
Table 1. Plant activities requiring energy from photosynthate grouped by fitness component (growth, survival, and fecundity), and whether they are reflected in biomass dynamics
Plant and ecosystem energetics 101
102 J. D. Fridley
k¼
X
a s
g g g
þ
X
as
s s s
þ
X
f
af sf
eqn 7
This equation expresses a population’s growth rate in
terms of the sum of the three fitness components, each of
which are the product of two different quantities. Transition
probabilities (aij) are determined by the biology of the plant
in the context of its environment: how many seeds are produced? how many seeds germinate? how many individuals
of a certain size die, survive, grow or shrink? All of these
transitions depend on the assimilation and allocation of
energy and nutrients at the individual level (Caswell & Werner 1978), and can in principle be modelled as a function of
energy or resource use. Sensitivities (sij), however, are largely a function of the environment, because they describe
the link between a particular life history or functional strategy and the rate of population growth. In general, disturbance increases the importance of fecundity over survival
and growth, and resource availability increases the importance of growth and fecundity over survival (Silvertown
et al. 1993a).
The fitness model of eqn. 7 implicitly accounts for how
individuals differ in the assimilation and allocation of energy
(ag, as, af), and, critically, how those behaviours translate to
fitness (sg, ss, sf). What remains is to express ag, as, and af in
terms of plant energy budgets. Assume, for example, that the
transition probability of moving from one size class to
another (stage growth) is proportional to an individual’s rate
of leaf mass increase (carbon gain), defined in terms of Givnish’s leaf trait model above (eqn. 5):
ag ¼ ag dL/dt ¼ ag L½qN 1=T
eqn 8
where a converts between leaf mass and size class, which
assumes different values for different growth transitions (g). The
contribution of growth to fitness is therefore (from eqn. 7):
kg ¼
X
g
ag L½qN 1=Tsg
eqn 9
Before adding to this model the fitness contributions of survival and fecundity, it is worth considering how eqn. 9 relates
to consumer-resource models. As noted above, N is a net
assimilation parameter that can be expressed in terms of
energy or resource use, and depends on assimilation (A), leaf
maintenance (R) and costs of leaf construction (C); 1/T is the
rate of leaf loss; and q is an efficiency parameter relating
energy gains to production of new leaf mass. These parameters are all represented in one form or another in consumerresource models and show up in Loreau’s (2010) population
model as e, l, c, d and e, respectively. (Note Loreau
described mass-specific energy consumption in terms of the
energetic content of resources R, which he expressed as cƒ
(R) and I have simplified to e.) The parameter a converts
between biomass and demographic stages and is analogous to
a mass-individual conversion parameter when consumerresource models are expressed as individual rather than biomass dynamics (MacArthur 1972; Carroll, Cardinale & Nisbet
2011). The sensitivity parameter sg, however, has no equivalent in non-demographic models, because it is an empirical
Box 1. Plant survival allocation as a bet-hedging strategy
The allocation of an individual’s acquired energy away from growth and towards survival is a ‘bet-hedging’ strategy—energy
(capital) is stored rather than invested to reduce potential capital loss. Bet-hedging strategies are favoured when risks are
unpredictable and the probability of loss is high (Philippi & Seger 1989). Energy allocation to growth versus survival can thus be
expressed in a bet-hedging framework, where an individual’s energy budget changes as a function of the net energy return from
growth investment—i.e., eqn. 5—and the accumulation of stored energy used for whole-plant survival:
d(energy)/dt ¼ net energy return on growth investment þ stored energy for survival
d(energy)/dt ¼ ð1 rÞL½qN 1=T þ rL½1 ms eqn 10
The model looks complex but has easily interpreted components. For simplicity, energy dynamics are described in units of
CO2, such that the quantity L[qN 1/T] is the total CO2 yield from photosynthesis (eqn. 5). The parameter r represents the
proportion of energy allocated to survival rather than growth; growth allocation in a model that does not consider reproduction is thus 1 r. Like growth, the energy allocated to survival has potential costs in terms of maintenance, translocation or
tissue construction (e.g., lignotuber), represented by ms. I have expressed a plant’s energy budget in this form because it mirrors the original formulation of Cohen’s (1966) bet-hedging model in relation to seed dormancy:
dS/dt ¼ S½YG þ ð1 GÞð1 DÞ
eqn 11
where dS/dt is the population growth of seeds (S), Y is per capita seed production (equivalent to net yield, or [qN 1/T]),
G is the germination frequency of seeds (growth allocation, equivalent to 1 r) and D is the rate of seed loss in the seed
bank (analogous to ms). Just as the evolution of seed dormancy in unpredictable environments increases a species’ long-term
population growth rate by reducing the impact of ‘bad years’ on population size (increasing the geometric mean growth rate;
Philippi & Seger 1989), allocation of energy to survival mechanisms by an individual plant can increase its lifetime energy
budget, in addition to promoting whole-plant survival.
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
Plant and ecosystem energetics 103
estimate of the importance of growth to fitness. Adler et al.
(2014) placed individual functional traits in a fitness context;
eqn. 9 shows how these traits could be integrated mechanistically and related to growth sensitivities.
For a model of fitness to account for all plant energy
expenditures, however, fitness contributions of survival (ks)
and fecundity (kf) must also be expressed in terms of energy.
From the perspective of the energy budget of an individual
plant, investments in survival or fecundity are doubly expensive because they do not provide energy returns. That is,
investment in growth compounds a plant’s energy budget, but
investing in survival or (non-photosynthetic) reproductive tissue represents a potentially large opportunity cost of foregone
energy accumulation (Chapin, Schulze & Mooney 1990). In
this context, the allocation of energy away from growth and
towards survival is a ‘bet-hedging’ strategy (Cohen 1966; Philippi & Seger 1989). Bet-hedging models are widely used in
life history theory to describe optimal resource partitioning in
risky environments; their application to plant survival allocation is described in Box 1.
Energetic expressions of growth (eqn. 9) and survival
(eqn. 10, Box 1) can be integrated into the fitness model as:
X
X
kg þ ks ¼
a
s
þ
as
g
g
g
s s s
X
X
¼
s a ð1 rÞL½qN 1=T þ
s b rL½1 ms g g g
s s s
eqn 12
Just as a converts between biomass and demographic
stages, b links the energy invested in survival to empirical
measurements of mortality rates; in other words, b is the efficacy of investment in survival to reduced mortality of size
class s. As described in Box 1, r represents energetic tradeoffs associated with an individual’s investment in survival
versus growth. Investment in growth (1 r) is further partitioned into leaf and non-leaf investment (q), which may be
independent of survival (r) allocation. Growth rate influences
fitness according to sg, while survival influences fitness
according to ss. Note that leaf mass (L) appears in both
growth and survival components, because the greater the leaf
mass, the greater the whole-plant assimilation rate.
For completeness, I include a fecundity fitness component
that accounts for the energetic costs of producing reproductive
biomass, the amount of stored energy (as carbon) present in
seeds, and the demographic translation of seed allocation to
total seeds produced (fecundity):
X
kf ¼
s hL/ER
eqn 13
f f
where / is the proportion of energy allocated to reproduction,
and ER is net reproduction efficiency, or the proportion of
energy allocated to reproduction (L/) that ends up in seeds
(accounting for construction and respiration costs of non-seed
reproductive tissue: flowers, fruits, stems, nectar, pollen, etc.).
Like a and b, h coverts between an energy pool and a demographic parameter: in this case, the number of seeds produced
per gram of seed, which is unlikely to vary by the size class
(f) of reproductively mature individuals. The per capita
impact of fecundity on population growth rate (k) is sf.
Putting growth, survival and fecundity components
together, an energetic expression of plant fitness becomes:
P
k ¼ g sg ag ð1 r /ÞL½ðq=CÞðA RÞ 1=T ðgrowthÞ
P
þ s ss bs rL½1 ms ðsurvivalÞ
P
þ f sf hL/ER
ðfecundityÞ
eqn 14
Figure 3b illustrates these relationships between plant
energy use and demographic transitions in a comparative
context with traditional plant energy models. Note that the fitness components are energetically linked in two ways. First,
energy expenditures must be equal to the total amount of
energy assimilated, so growth investment is constrained by
survival (r) and fecundity (/) allocation: i.e., 1 (r + /).
Second, investment in growth increases the total energy budget, here represented by leaf mass (L), such that the total
energy available for survival and fecundity could be impacted
positively or negatively by r and /. Because growth capacity
will depend on resource availability and other environmental
qualities, the fitness costs and benefits associated with nongrowth allocation should interact strongly with a species’ life
history (e.g., life span, age at maturity) and disturbance rates.
In other words, optimal values of growth parameters
associated with leaf functional traits (A, R, C, T) do not contribute to fitness independently of plant life history strategies,
including processes associated with allocation (q, r, /) and
the efficacy (b, h) or efficiency (ms, ER) of survival and
reproduction.
Population-ecosystem linkages decay with
increasing survival allocation
The energetic-demographic model of eqn. 14 can, in principle, account for all activities that contribute to an individual
plant’s fitness, whether present as stored energy in biomass or
shed or respired for the purposes of tissue protection or
whole-plant survival. Because it involves a full accounting of
biomass (growth) dynamics in addition to energy and offspring, it is well suited for exploring the impact of adaptive
plant behaviours, and particularly survival allocation, on
ecosystem properties such as carbon dynamics. In what follows I focus on two questions. First, I complete the analogy
of growth-survival allocation in plants to bet-hedging models
of population growth: what is the optimal allocation of energy
to survival mechanisms in response to resource availability
and disturbance frequency (Box 2)? Second, I explore the
ramifications of survival allocation to carbon fluxes at the
ecosystem level: if assimilates are diverted away from growth
as an adaptive response to disturbance risk, what is the relationship between fitness and ecosystem-level productivity?
To address both questions, I consider a simple scenario that
focuses on growth and survivorship components of eqn. 14,
where a semelparous plant produces offspring at the end of a
fixed life span as a proportion of its acquired energy. In other
words, I treat the quantity h/ΕR (eqn. 13) implicitly as a constant that translates a plant’s stored energy into per capita
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
104 J. D. Fridley
Box 2. Plant survival strategies in an unpredictable environment
A plant’s investment in survival mechanisms in light of disturbance risk is analogous to the evolution of seed dormancy in
unpredictable germination environments (Cohen 1966). Consider an environment in which a perennial plant can experience one
of two different growing seasons. In a ‘good year’, a plant grows according to its maximum annual yield, defined by the Givnish
(2002) model (eqn. 4):
Yg ¼ dL/dtð1=LÞ ¼ ðq=CÞðA RÞ 1=T
eqn 15
In principle, each of the growth parameters A, R, C, T and q is a function of resources; here only Yg is of interest and particular values of these parameters do not have a qualitative impact on the outcome, so for simplicity I follow Givnish (2002)
and treat dark respiration rate (R) as a fixed proportion of assimilation rate (R = 7%(A)) and construction cost as a constant
(C = 2). For leaf life span, I consider a deciduous plant with a single annual leaf cohort such that all leaves represent spent
capital for one year (T = 1). I allow photosynthetic rate A to vary as a saturating function of resource levels (Q) with maximum rate 015 lmol CO2 g1 s1, and leaf growth allocation (q) to vary as a simple logistic function of Q between 01 and
09. Across a generic resource gradient (Q) from 1 to 100, this produces a net annual yield in a good year (Yg) between 0
and 15. R code showing all calculations is presented in Appendix S1 (Supporting Information).
In this scenario plants can also experience a ‘bad year’, where all energy invested in growth—represented as leaf biomass L
in the model—is lost and annual yield (Yb) is zero. This could occur due to, for example, total defoliation by disease, herbivory, fire, freezing, cyclones or other disturbance event. Survival and regrowth in this scenario depends on a pool of labile
stored energy, such as starch or other non-structural carbohydrates present in perennating tissue (e.g., tuber, rhizome, wood).
The pool of stored energy accumulates according to the survival allocation parameter r and the loss rate of stored energy ms,
as described in eqn. 10. As in Cohen’s (1966) study of seed dormancy, I allow a disturbance (bad year) to occur with probability Pb, from 0 to 100%.
Across a full range of resource availabilities (Q) and disturbance probabilities (Pb), the optimal allocation of energy to survival can be determined by maximizing an individual’s fitness in response to varying r; fitness here is proportional to total
acquired energy. In the case of only two (good and bad) year types, Cohen (1966) showed that expected fitness (W) is:
W ¼ ½rð1 ms Þ þ Yb ð1 rÞPb ½rð1 ms Þ þ Yg ð1 rÞ1Pb
eqn 16
Using eqn. 16 I calculated the expected energy gain for all possible values of survival allocation (r; 0-1) across factorial gradients of disturbance frequency (Pb; 0-1) and resource availability (Q; 1-100). Values of r that maximize fitness are shown in
the upper left panel of Fig. 4.
offspring production. In this way the full energy-demography
model of eqn. 14 reduces to a growth-survival model (eqn. 12),
where lifetime carbon gain increases exponentially with annual
carbon gain unless there is risk of biomass loss (Iwasa & Kubo
1997). If biomass loss can be minimized by investing assimilates in tissue protection, or the impact of a disturbance lessened by storing assimilates that allow rapid regrowth, then an
individual’s fitness increases by minimizing interannual variation in performance, in addition to increasing growth in an average year—that is, long-term carbon gain is equal to the
geometric mean of annual carbon gain. In this scenario, a plant
faces an optimization problem of allocating energy to both high
growth rate between disturbances, and mechanisms that reduce
the risk of mortality if a disturbance occurs (Iwasa & Kubo
1997). The solution to this problem involves an application of
bet-hedging theory, and is described in Box 2.
For plants growing in risky environments across a resource
gradient, bet-hedging theory predicts that survival allocation
depends on both maximum growth rate and the rate of disturbance (upper left panel, Fig. 4). As a plant’s maximum growth
rate (Yg) increases, optimal survival allocation converges on
the expected rate of disturbance (Pb)—this is a central feature
of bet-hedging models of seed dormancy (Cohen 1966;
MacArthur 1972; Philippi 1993). As resource availability and
maximum growth rate decline, however, optimal survival allocation increases even as disturbance frequency is constant, due
to the reduced opportunity cost associated with storing energy
rather than investing in new growth (Chapin, Schulze &
Mooney 1990). Thus, the model suggests that plants achieve
higher fitness in a risky environment by allocating energy
investments away from growth and toward survival, and
increasingly so as the availability of resources decreases.
Although the scenario I’ve outlined here involves stored
energy as the mechanism of survival, analogous scenarios
might involve energetic investments that reduce the probability
of disturbance to leaves or other growth organs themselves
(e.g., thorns, defensive chemistry, protective mutualisms).
Does the adaptive allocation of plant assimilate to survival
influence a population’s ecosystem impact? If we assume that
growth-survival strategies are not density-dependent—that is,
if we consider only the short-term impact of plant behaviours
on population growth, and ignore the possibility that optimal
growth-survival allocation strategies may change as individuals experience more crowded conditions—then we can contrast an individual’s ecosystem contribution in the form of its
NPP to its fitness across resource and disturbance gradients.
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
Plant and ecosystem energetics 105
Fig. 4. Optimal allocation of energy to
survival mechanisms across disturbance and
resource gradients, and implications for
fitness, production efficiency, and the
relationship between individual fitness and
net primary productivity (NPP). Top left:
survival allocation (r) that maximizes fitness
in relation to potential growth rate (Yg) and
disturbance frequency (Pb), calculated from
the bet-hedging model of eqn. 16. Top right:
per capita offspring production at optimal
values of r across disturbance (Pb) and
resource (Q) gradients. Bottom left:
production efficiency (NPP/GPP) at optimal
values of r; see text for details. Bottom
right: Pearson’s correlation of NPP and
fitness at each level of Pb and Q; correlations
are positive in the grey region and negative
elsewhere. For R code calculations see
Appendix S1.
In the scenario described in Box 2, fitness is proportional to
expected energy gain from eqn. 16. If acquired energy is
translated into per capita number of offspring by an arbitrary
constant of 05 (=h/ΕR), the predicted number of offspring
for an individual with optimal survival allocation (r) is
shown in the top right panel of Fig. 4. Unsurprisingly, fewer
resources beget fewer offspring, as rates of assimilation
decrease and costs of resource harvesting increase. Disturbances further erode plant fitness by removing stored energy
and shifting optimal energy allocation toward survival and
away from assimilation; such costs are greater at high
resource levels because individuals have more to lose by
investing in survival versus growth. One way to quantify
these costs is production efficiency (EP)—the ratio of carbon
gain as NPP (potential yield [Yg] 9 growth allocation
[1 r]) and total energy assimilated annually in photosynthesis (GPP). Here I again follow Givnish (2002) and assume
for simplicity that carbon uptake occurs over a daily period of
12 hours across a 6-month growing season, which results in
production efficiency values shown in the lower left panel of
Fig. 4; these approach empirically reported values of EP
(Transeau 1926; Odum 1957).
Finally, to determine the relationship of NPP and fitness
across resource and disturbance gradients, I calculated their correlation (Pearson’s r) across all values of survival allocation (r)
for all levels of disturbance probability (Pb) and resource availability (Q). The resulting correlations are shown in the lower
right panel of Fig. 4. When disturbance frequency is low and
resources are abundant, individuals invest little in survival,
growth rate is high, and NPP and fitness increase together as
more assimilated energy makes its way into offspring. As
disturbance increases, plant fitness is maximized by allocating
energy to survival and NPP in turn decreases, eventually to a
point where high rates of NPP are strongly maladaptive and the
fitness-NPP correlation becomes negative. Comparison of the
upper left and lower right panels of Fig. 4 demonstrates the
contribution of survival allocation to the NPP-fitness relationship—energy is either used to produce structural biomass and
contribute to NPP, or becomes part of the survival pool and
does not contribute to ecosystem-level biomass increase.
The scenario illustrated in Fig. 4 concerns only a single
population responding to one resource, and ignores changes
in growth vs. survival allocation that may come from crowding and resource shortage as a population approaches its carrying capacity. It also ignores life history variation that is of
obvious importance in a disturbance context (Harper 1977).
This simple scenario nevertheless illustrates the principle that
material fluxes in ecosystems depend in part on the degree to
which dry matter production influences plant fitness, which
occurs at the individual level as a result of the assimilation of
energy and its allocation to all three fitness components of
growth, survival and fecundity. Although the relative importance of these components depends on ecological context (Silvertown et al. 1993a), an energetic representation of all three
is necessary for building models of plant population dynamics
that accurately reflect the adaptive behaviours of individuals
across environments.
Time to revisit plant energy budgets?
There is no probability that we shall have in the near
future an experimental determination of the energy
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
106 J. D. Fridley
budget of a complete plant such as we have for a great
variety of animals.
Transeau 1926
While considerable research is being done on the consumption and expenditure of energy in certain animal
species. . . little or no research is being done on the
energy dynamics of individual species of plants.
Kieckhefer 1962
[Dynamic energy budget] models for plants have been
proposed. . . but they need to be tested against data,
which has not been easy because detailed studies in
plant biology are not available.
Sousa et al. 2010
In this paper, I have focused on the fate of plant assimilates
that are not accounted for in biomass dynamics (Table 1)—
energy that supports offspring production, and is thus subject
to natural selection, but which may not play a direct role in
the cycling of materials or trophic dynamics at the ecosystem
level. Unlike nutrients, which can be repurposed within plants
for a variety of metabolic, foraging, defense and reproductive
activities (Chapin, Schulze & Mooney 1990), energy stored in
the form of carbohydrates must be continually reacquired
from the environment to support plant function. To a large
extent, plant fitness, like that of all organisms, can be
described as the acquisition of energy and its conversion into
offspring (Brown, Marquet & Taper 1993). In this context, it
is remarkable that nearly a century after the first attempt at a
plant energy budget—for Zea mays, shortly after the advent
of techniques for measuring solar radiation, respiration, and
tissue caloric content (Transeau 1926)—our understanding of
energy allocation at the whole-plant level remains in its
infancy. Why should this be, and why does it matter?
Interestingly, ecological energetics is a thriving field in animal ecology (Tomlinson et al. 2014), with both strong theoretical foundations [e.g., dynamic energy budget (DEB)
theory; Kooijman 2010] and increasing applications to animal
conservation and global change (Kearney et al. 2013). Indeed,
Shertzer & Ellner (2002) parameterized a DEB model for a
rotifer that corroborates the key finding of Fig. 4 (above) that
increased survival allocation, associated with reduced biomass
production, promotes fitness in a variable environment. At the
ecosystem level, the pioneering studies of Lindeman (1942)
and Odum (1957) are today a foundation for the study of the
global carbon cycle, where, for example, understanding carbon fluxes is a critical component to forecasting climate
change (Wehr et al. 2016) and bioenergy development (Fargione et al. 2008). These studies concern energy use by vegetation, however, not the fitness consequences of energy use
by individual plants (Phillipson 1966; Currie 2003).
Efforts to consider individual plant function from an energetic standpoint have been limited since Transeau (1926), and
all have been restricted to particular allocation pathways,
mostly involving biomass. Harper & Ogden (1970) set out an
ambitious agenda for recasting the nascent field of plant
demography from an energetics perspective, but settled on an
approach based on biomass dynamics due to logistical difficulties in separating GPP and NPP (Ogden 1974). Production
efficiency in crops (Loomis & Williams 1963; Penning de
Vries, van Laar & Chardon 1983) and wild plants (Mooney,
Ehleringer & Berry 1976; Patten 1978) received empirical
interest by physiologists in the wake of ecosystem studies, but
largely in the context of assimilation. Griffin (1994) suggested
that interest in plant energetics from a biosynthesis perspective
flagged when it was discovered that most plant tissues converge on similar construction cost values (Chapin 1989). There
is an array of more recent approaches to modelling wholeplant function in which energy plays a supporting role, including function-structural models (Fourcaud et al. 2008), the tree
seedling adaptive designs model of Marks & Lechowicz
(2006), and even one example of a dynamic energy budget
model applied to plants (Bijlsma & Lambers 2000; Bijlsma,
Lambers & Kooijman 2000). Although these approaches could
be adapted to address energy allocation to fitness components,
plant energy use per se has not been their focus.
Why is the lack of empirical data on plant energy allocation
a detriment to our understanding of the evolution and function
of plant communities? One reason is the underlying theme of
this paper—that further integration of demographic and ecosystem approaches in plant ecology will require a unified currency
of fitness and ecosystem functioning (Loreau 2010), of which
energy rather than biomass allows for the inclusion of a greater
array of plant behaviours in an adaptive context. Even outside
an ecosystem context, it remains unclear whether biomass
dynamics is an appropriate general currency for measuring
plant community assembly processes or evolutionary responses
of plants to biotic and abiotic factors. For example, the interaction of plants with soil microbes can determine competitive
outcomes (Bever et al. 2010), and those interactions incur
energetic costs in the form of root respiration and carbohydrate
transfer that may not be reflected in plant biomass dynamics
(Wright, Read & Scholes 1998; Lynch & Ho 2005). Similarly,
energetic costs of leaf inducible defenses may be apparent only
in increased rates of leaf respiration (Zangerl, Arntz & Berenbaum 1997), and yet control plant-plant interactions through
altered survival rates. In both cases it is an individual’s allocation of energy to mutualists or defense that determines community dynamics—the best competitor needn’t be the species that
grows the fastest. Lacking an energy budget for any plant in a
demographic or competitive context, questions about ‘what to
measure’ (Aarssen & Keogh 2002) will remain a thorny issue
in field ecology studies.
It is also interesting to consider the evolutionary implications of plant energy use in a manner that parallels biogeographic studies of animal metabolism, such as the suggestion
by Brown, Marquet & Taper (1993) that there exists an optimal body size for mammals (100 g) that is approached on
islands as populations are freed from biotic constraints of
competition and predation. Their hypothesis stems from purported size-related efficiencies of acquiring energy from the
environment and converting it into offspring, quantities that
should also be maximized in plants. But are they? As noted
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
Plant and ecosystem energetics 107
by Transeau (1926), even a short-lived plant like maize converts less than 1% of its incident solar energy into grain
energy—mostly as a result of extremely low assimilation efficiency (Mooney, Ehleringer & Berry 1976). Plants on islands
have both lower rates of assimilation (Heberling & Fridley
2012) and NPP (Leigh, Vermeij & Wikelski 2009)—does this
suggest, as Leigh, Vermeij & Wikelski (2009) argue, that
plant assimilation rate and growth allocation are ultimately
determined by the selective influence of competition and herbivory? If so, would such dependence of high growth allocation on species interactions explain why most successful plant
invaders are from diverse mainland ecosystems (Fridley &
Sax 2014) and exhibit high growth rates even in relatively
stressful habitats (Heberling & Fridley 2016)? Studies of plant
energy budgets in mainland versus island ecosystems, or of
successful invaders in the context of co-occurring native species, would be interesting places to start in the effort to determine whether plant energetics can provide new insights into
our understanding of plant community dynamics.
Conclusion: hope for a population-ecosystem
synthesis?
The promise of PEMs, exemplified by Loreau’s (2010) suggestion that both demographic and ecosystem models can be
placed in an energetics framework, is that they transcend historical disciplinary boundaries and further the conceptual
exchange between plant evolutionary biology, ecology and
biogeochemistry. The pitfalls of this approach include an
oversimplification of what plants have evolved to do—a situation not unlike early studies in comparative plant ecology that
changed the paradigm of plant function from one based on
field crop agronomy to that of wild plants in an array of harsh
and disturbed environments (Grime 1974; Chapin 1980). My
aim in this paper has been to recast Loreau’s (2010) model in
a form that better reflects how plants have evolved in such
environments (Fig. 3b), and evaluate its implications for linking population and ecosystem dynamics. Although the refined
model can accommodate an array of plant behaviours, I
focused on survival allocation and showed that, all else equal,
the greater the allocation of GPP to survival mechanisms, the
greater the separation between population and ecosystem
dynamics (Fig. 4d). In other words, despite the critical role
that plant growth processes play in ecosystem dynamics (Dı́az
& Cabido 2001; De Deyn, Cornelissen & Bardgett 2008),
many plant behaviours under selection do not directly impact
ecosystems. This has important ramifications for studies of
plant functional ecology at the community level: in particular,
mechanisms that promote species coexistence impact ecosystem functioning only insofar as they relate to biomass dynamics, and it is important that this link be demonstrated rather
than assumed (cf. Tilman, Lehman & Thomson 1997; Loreau
2010; Carroll, Cardinale & Nisbet 2011).
Different uses of the energetic-demographic model depicted
in eqn. 14 and Fig. 3b could alter these conclusions. I have
ignored density dependence in the form of resource consumption, and have considered ecosystem mass and energy fluxes
only from the standpoint of a single population responding to a
single resource. Density dependence is likely to alter the contribution of growth (G) to fitness, and in a manner dependent on a
species’ life history. Compared with energy allocation to
growth and survival under density-independent disturbance
(Fig. 4), crowded conditions may promote higher growth allocation (1 r /) under asymmetric competition in a relatively short-lived plant (i.e., a high competitive effect;
Goldberg 1990). Conversely, higher survival allocation (r)
coupled with high fecundity late in life may increase fitness in
high-density stands in long-lived species (i.e., high competitive
response). These scenarios could be examined by treating the
sensitivities (s) in eqn. 14 as a function of population size or
total biomass. Species interactions can also be examined using
eqn. 14 for multiple populations in a consumer-resource framework (MacArthur 1972; Tilman, Lehman & Thomson 1997;
Loreau 2010; Carroll, Cardinale & Nisbet 2011), where an
energetic-demographic context allows for stable coexistence
beyond multiple resource dynamics. For example, the temporal
storage effect (Chesson & Warner 1981; Adler et al. 2006) is a
mechanism of coexistence in variable environments where
potentially long-lived species vary in their response to the environment and experience high competitive intensities in different
years. The storage effect depends on both growth—resource
consumption driving intraspecific competition in favourable
years—and survival in unfavourable years. Because a simple
growth-survival trade-off in a variable environment can lead to
stable coexistence under a single limiting resource (e.g., rainfall; Angert et al. 2009), eqn. 14 can accommodate a wider
array of species interactions than consumer-resource models
based only on biomass (Tilman, Lehman & Thomson 1997;
Loreau 2010). It is likely that coexistence mechanisms involving survival decouple community and ecosystem dynamics in a
way similar to that shown for one population in Fig. 4,
although this remains to be explored.
Whether or not an energetics perspective is the key to linking
ecological processes across organizational scales, there are
undoubtedly new insights into plant behaviour waiting to be
discovered from renewing efforts to measure plant energy budgets in a demographic context. Inherent barriers to the separation of gross and net production have all but dissolved since
Harper & Ogden (1970) with the development of carbon flux
studies based on gas exchange (e.g., Peng et al. 1993; Zangerl,
Arntz & Berenbaum 1997) and isotopic tracers (e.g., Eissenstat
et al. 1993; Nguyen 2003). Even the lack of such techniques
did not prevent Transeau’s (1926) effort with maize, which is a
landmark in bioenergy research despite it being largely ignored
in ecology (cf. Harper & Ogden 1970; Colinvaux 1979; Allen
& Hoekstra 1992). Had Transeau’s study been more widely
appreciated by ecologists over the past century, how much closer might we be today in deconstructing plants as ‘seed producing machines’ (Harper & Ogden 1970)?
Acknowledgements
I am indebted to many colleagues who provided advice, discussion and manuscript comments, including L. Aarssen, P. Bellingham, R. Duncan, D.
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110
108 J. D. Fridley
Goldberg, P. Grime, D. Frank, D. Gibson, M. Heberling, E. Hinman, P. Hulme,
M. Lechowicz, D. Peltzer, T. Philippi, S. Richardson, M. Ritchie, J. Silvertown,
M. Vellend and two anonymous reviewers. This study was supported in part by
a U.S.-New Zealand Fulbright Scholarship.
Data accessibility
This paper does not use data.
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Received 22 July 2016; accepted 27 October 2016
Handling Editor: David Gibson
Supporting Information
Details of electronic Supporting Information are provided below.
Appendix S1. R code for bet-hedging model illustrated in Fig. 4.
© 2016 The Author. Journal of Ecology © 2016 British Ecological Society, Journal of Ecology, 105, 95–110